diff --git "a/4229.jsonl" "b/4229.jsonl"
new file mode 100644--- /dev/null
+++ "b/4229.jsonl"
@@ -0,0 +1,1402 @@
+{"seq_id":"14318466025","text":"from flask import Flask, request, jsonify\n\napi = Flask(__name__)\nservices = dict()\n\n\ndef next_service(service_name):\n    global services\n    if service_name in services:\n        if services[service_name]['counter'] < len(services[service_name]['services']):\n            services[service_name]['counter'] += 1\n            counter_index = services[service_name]['counter'] - 1\n            return services[service_name]['services'][counter_index]\n        else:\n            services[service_name]['counter'] = 1\n            counter_index = services[service_name]['counter'] - 1\n            return services[service_name]['services'][counter_index]\n    else:\n        return None\n\n\n@api.route('/register', methods = ['POST'])\ndef add_service():\n    data = request.get_json()\n    service_type = data['service_type']\n    service_name = data['service_name']\n    service_url = data['service_address']\n    if service_type not in services:\n        services[service_type] = dict()\n        services[service_type]['counter'] = 1\n        services[service_type]['services'] = []\n        service_info = {\n            'service_name': service_name,\n            'service_address': service_url\n        }\n        services[service_type]['services'].append(service_info)\n        response = jsonify({'message': 'Service registered'})\n        return response, 200\n    else:\n        service_info = {\n            'service_name': service_name,\n            'service_address': service_url\n        }\n        if service_info not in services[service_type]['services']:\n            services[service_type]['services'].append(service_info)\n            response = jsonify({'message': 'Service registered'})\n            return response, 200\n        else:\n            response = jsonify({'message': 'Service already registered'})\n            return response, 409\n\n\n@api.route('/get_service', methods = ['GET'])\ndef get_service():\n    service_type = request.args.get('service_name')\n    service = next_service(service_type)\n    if service is not None:\n        response = jsonify(service)\n        return response, 200\n    else:\n        response = jsonify({'message': 'Service not found'})\n        return response, 404\n\n\n@api.route('/get_all_services', methods = ['GET'])\ndef get_all_services():\n    response = jsonify(services)\n    return response, 200\n","repo_name":"ThatSalbert/PAD_LabWorks","sub_path":"service-discovery/service_discovery.py","file_name":"service_discovery.py","file_ext":"py","file_size_in_byte":2309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"42806650094","text":"# -*- coding: utf-8 -*-\n'''\n멀쩡한 사각형 : https://programmers.co.kr/learn/courses/30/lessons/62048?language=python3\n풀이 : https://leedakyeong.tistory.com/entry/%ED%94%84%EB%A1%9C%EA%B7%B8%EB%9E%98%EB%A8%B8%EC%8A%A4-%EB%A9%80%EC%A9%A1%ED%95%9C-%EC%82%AC%EA%B0%81%ED%98%95-in-python\n'''\n\n\ndef solution(w,h):\n    from math import gcd\n    answer = w * h - w - h + gcd(w, h)\n    return answer\n\n\nif __name__ == '__main__':\n    result = solution(8, 12)\n    print(f'result = {result}')\n    assert result == 80","repo_name":"junjongwook/programmers","sub_path":"Skill Check/Level2/s62048.py","file_name":"s62048.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"19820944338","text":"# pylint: skip-file\n\"\"\"add_additional_fields\n\nRevision ID: 5823cf21ee2e\nRevises: 271f00b3ca42\nCreate Date: 2021-10-20 15:29:59.707190\n\n\"\"\"\nimport sqlalchemy as sa\nfrom alembic import op\n\n# revision identifiers, used by Alembic.\nrevision = \"5823cf21ee2e\"\ndown_revision = \"271f00b3ca42\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column(\n        \"etl_clients\",\n        sa.Column(\"next_recommended_home_visit_date\", sa.Date(), nullable=True),\n    )\n    op.add_column(\n        \"etl_clients\",\n        sa.Column(\n            \"most_recent_treatment_collateral_contact_date\", sa.Date(), nullable=True\n        ),\n    )\n    op.add_column(\n        \"etl_clients\",\n        sa.Column(\n            \"next_recommended_treatment_collateral_contact_date\",\n            sa.Date(),\n            nullable=True,\n        ),\n    )\n    # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column(\"etl_clients\", \"next_recommended_treatment_collateral_contact_date\")\n    op.drop_column(\"etl_clients\", \"most_recent_treatment_collateral_contact_date\")\n    op.drop_column(\"etl_clients\", \"next_recommended_home_visit_date\")\n    # ### end Alembic commands ###\n","repo_name":"Recidiviz/pulse-data","sub_path":"recidiviz/persistence/database/migrations/case_triage/versions/2021_10_20_1529_5823cf21ee2e_add_additional_fields.py","file_name":"2021_10_20_1529_5823cf21ee2e_add_additional_fields.py","file_ext":"py","file_size_in_byte":1306,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"85"}
+{"seq_id":"39372378133","text":"from abc import abstractmethod\nfrom typing import Any, Callable, Dict, Protocol, Union\n\nfrom httpx import AsyncClient, Client\nfrom pydantic import BaseModel\nfrom pytest import mark\nfrom typing_extensions import Annotated\n\nfrom combadge.core.interfaces import SupportsService\nfrom combadge.support.http.markers import CustomHeader, FormData, FormField, QueryParam, http_method, path\nfrom combadge.support.httpx.backends.async_ import HttpxBackend as AsyncHttpxBackend\nfrom combadge.support.httpx.backends.sync import HttpxBackend as SyncHttpxBackend\n\n\n@mark.vcr\ndef test_form_data() -> None:\n    class Data(BaseModel):\n        foo: int\n\n    class Response(BaseModel):\n        form: Dict[str, Any]\n\n    class SupportsHttpbin(SupportsService, Protocol):\n        @http_method(\"POST\")\n        @path(\"/anything\")\n        @abstractmethod\n        def post_anything(\n            self,\n            data: FormData[Data],\n            bar: Annotated[int, FormField(\"barqux\")],\n            qux: Annotated[int, FormField(\"barqux\")],\n        ) -> Response:\n            ...\n\n    service = SupportsHttpbin.bind(SyncHttpxBackend(Client(base_url=\"https://httpbin.org\")))\n    response = service.post_anything(data=Data(foo=42), bar=100500, qux=100501)\n\n    assert response == Response(form={\"foo\": \"42\", \"barqux\": [\"100500\", \"100501\"]})\n\n\n@mark.vcr\ndef test_query_params() -> None:\n    class Response(BaseModel):\n        args: Dict[str, Any]\n\n    class SupportsHttpbin(SupportsService, Protocol):\n        @http_method(\"GET\")\n        @path(\"/anything\")\n        @abstractmethod\n        def get_anything(\n            self,\n            foo: Annotated[int, QueryParam(\"foobar\")],\n            bar: Annotated[int, QueryParam(\"foobar\")],\n        ) -> Response:\n            ...\n\n    service = SupportsHttpbin.bind(SyncHttpxBackend(Client(base_url=\"https://httpbin.org\")))\n    response = service.get_anything(foo=100500, bar=100501)\n\n    assert response == Response(args={\"foobar\": [\"100500\", \"100501\"]})\n\n\n@mark.vcr\ndef test_headers_sync() -> None:\n    class Response(BaseModel):\n        headers: Dict[str, Any]\n\n    class SupportsHttpbin(SupportsService, Protocol):\n        @http_method(\"GET\")\n        @path(\"/headers\")\n        @abstractmethod\n        def get_headers(\n            self,\n            foo: Annotated[str, CustomHeader(\"x-foo\")],\n            bar: Annotated[str, CustomHeader(\"x-bar\")] = \"barval\",\n            baz: Annotated[Union[str, Callable[[], str]], CustomHeader(\"x-baz\")] = lambda: \"bazval\",\n        ) -> Response:\n            ...\n\n    service = SupportsHttpbin.bind(SyncHttpxBackend(Client(base_url=\"https://httpbin.org\")))\n    response = service.get_headers(foo=\"fooval\")\n    assert response.headers[\"X-Foo\"] == \"fooval\"\n    assert response.headers[\"X-Bar\"] == \"barval\"\n    assert response.headers[\"X-Baz\"] == \"bazval\"\n\n\n@mark.vcr\nasync def test_headers_async() -> None:\n    class Response(BaseModel):\n        headers: Dict[str, Any]\n\n    class SupportsHttpbin(SupportsService, Protocol):\n        @http_method(\"GET\")\n        @path(\"/headers\")\n        @abstractmethod\n        async def get_headers(\n            self,\n            foo: Annotated[str, CustomHeader(\"x-foo\")],\n            bar: Annotated[str, CustomHeader(\"x-bar\")] = \"barval\",\n            baz: Annotated[Union[str, Callable[[], str]], CustomHeader(\"x-baz\")] = lambda: \"bazval\",\n        ) -> Response:\n            ...\n\n    service = SupportsHttpbin.bind(AsyncHttpxBackend(AsyncClient(base_url=\"https://httpbin.org\")))\n    response = await service.get_headers(foo=\"fooval\")\n    assert response.headers[\"X-Foo\"] == \"fooval\"\n    assert response.headers[\"X-Bar\"] == \"barval\"\n    assert response.headers[\"X-Baz\"] == \"bazval\"\n","repo_name":"kpn/combadge","sub_path":"tests/integration/test_httpbin.py","file_name":"test_httpbin.py","file_ext":"py","file_size_in_byte":3673,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"85"}
+{"seq_id":"6546566541","text":"###\r\n# Copyright 2021 New H3C Technologies Co., Ltd.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n#     http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n###\r\n\r\n# -*- coding: utf-8 -*-\r\n\r\n\r\nimport os\r\nfrom exception.ToolException import FailException\r\nfrom utils.model import BaseModule\r\nfrom utils.client import RestfulClient\r\nfrom utils.common import Constant\r\nfrom utils.tools import init_args\r\nfrom utils.predo import AllowCommand\r\n\r\n\r\nclass ImportSshKey(BaseModule):\r\n\r\n    def __init__(self):\r\n\r\n        super().__init__()\r\n        self.args_lst = [\"file_uri\", \"u_name\"]\r\n\r\n    @AllowCommand()\r\n    def run(self, args):\r\n\r\n        init_args(args, self.args_lst)\r\n        if args.u_name is None:\r\n            args.u_name = args.username\r\n        config_file = args.file_uri\r\n\r\n        if not os.path.isfile(config_file):\r\n            err_info = (\"Failure: import SSH key failed, reason: \"\r\n                        \"file does not exist\")\r\n            self.err_list.append(err_info)\r\n            raise FailException(*self.err_list)\r\n        try:\r\n            with open(config_file, \"rb\") as config_file_data:\r\n                file_read = config_file_data.read()\r\n        except (TypeError, ValueError, KeyError, IOError, Exception) as err:\r\n            self.err_list.append(str(err))\r\n            raise FailException(*self.err_list)\r\n        else:\r\n            _, file_name = os.path.split(config_file)\r\n            fields = {\r\n                \"upload_ssh_key\": (file_name, file_read,\r\n                                   \"application/octet-stream\")\r\n            }\r\n        client = RestfulClient(args)\r\n        try:\r\n\r\n            url = \"/api/settings/users\"\r\n            resp = client.send_request(\"get\", url)\r\n            if isinstance(resp, list):\r\n                for info in resp:\r\n                    if info.get(\"name\") == args.u_name:\r\n                        user_id = info.get(\"id\")\r\n\r\n                        url = \"/api/settings/user/ssh-key-upload/%s\" % user_id\r\n                        import_resp = client.upload_request(\"POST\", url,\r\n                                                            fields=fields)\r\n                        if (isinstance(import_resp, dict) and import_resp.get(\r\n                                Constant.COMPLETE_CODE)\r\n                                == Constant.SUCCESS_0):\r\n                            url = \"/api/settings/users/%s\" % user_id\r\n                            tmp_resp = client.send_request(\"get\", url)\r\n                            if isinstance(tmp_resp, list) and tmp_resp:\r\n                                data = build_payload(tmp_resp[0])\r\n                            else:\r\n                                err = (\"Failure: failed to get user \"\r\n                                       \"configuration information: %s\" %\r\n                                       args.u_name)\r\n                                self.err_list.append(err)\r\n                                raise FailException(*self.err_list)\r\n                            config_resp = client.send_request(\"put\", url, data)\r\n                            if (isinstance(config_resp, dict) and\r\n                                    config_resp.get(Constant.COMPLETE_CODE)\r\n                                    == Constant.SUCCESS_0):\r\n                                suc_info = (\"Success: import SSH \"\r\n                                            \"key successfully\")\r\n                                self.suc_list.append(suc_info)\r\n                            else:\r\n                                err_info = \"Failure: SSH enabled failed\"\r\n                                self.err_list.append(err_info)\r\n                                raise FailException(*self.err_list)\r\n                        else:\r\n                            err_info = \"Failure: SSH key import failed\"\r\n                            self.err_list.append(err_info)\r\n                            raise FailException(*self.err_list)\r\n                        break\r\n                else:\r\n                    err = \"Failure: the user does not exist: %s\" % args.u_name\r\n                    self.err_list.append(err)\r\n                    raise FailException(*self.err_list)\r\n            else:\r\n                err = \"Failure: filed to get user list\"\r\n                self.err_list.append(err)\r\n                raise FailException(*self.err_list)\r\n        finally:\r\n            if client.cookie:\r\n                client.delete_session()\r\n        return self.suc_list\r\n\r\n\r\ndef build_payload(detail):\r\n\r\n    res = dict()\r\n    res[\"id\"] = detail.get(\"id\")\r\n    res[\"name\"] = detail.get(\"name\")\r\n    res[\"email_id\"] = \"\"\r\n    res[\"user_operation\"] = 1\r\n    res[\"web\"] = detail.get(\"web\")\r\n    res[\"ipmi\"] = detail.get(\"ipmi\")\r\n    res[\"network_privilege\"] = detail.get(\"network_privilege\")\r\n    res[\"snmp\"] = detail.get(\"snmp\")\r\n    res[\"access\"] = detail.get(\"access\")\r\n    res[\"confirm_password\"] = \"\"\r\n    res[\"password\"] = \"\"\r\n    res[\"password_size\"] = \"\"\r\n    res[\"snmp_access\"] = detail.get(\"snmp_access\")\r\n    res[\"snmp_authentication_protocol\"] = detail.get(\r\n        \"snmp_authentication_protocol\")\r\n    res[\"snmp_privacy_protocol\"] = detail.get(\"snmp_privacy_protocol\")\r\n    return res\r\n","repo_name":"H3C/hdm-redfish-script","sub_path":"model/import_ssh_key.py","file_name":"import_ssh_key.py","file_ext":"py","file_size_in_byte":5634,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"85"}
+{"seq_id":"14073759343","text":"#!/usr/bin/env python3\n\n#===========================================================================\n#\n# Produce plots for HCR vel corrections\n#\n#===========================================================================\n\nimport os\nimport sys\nimport subprocess\nfrom optparse import OptionParser\nimport numpy as np\nfrom numpy import convolve\nfrom numpy import linalg, array, ones\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom matplotlib import dates\nimport math\nimport datetime\nimport contextlib\nimport pathlib\n\ndef main():\n\n    # globals\n\n    global options\n    global debug\n    global startTime\n    global endTime\n    global timeLimitsSet\n    timeLimitsSet = False\n    global figNum\n    figNum = 0\n    \n    # parse the command line\n\n    usage = \"usage: %prog [options]\"\n    parser = OptionParser(usage)\n    parser.add_option('--debug',\n                      dest='debug', default=False,\n                      action=\"store_true\",\n                      help='Set debugging on')\n    parser.add_option('--verbose',\n                      dest='verbose', default=False,\n                      action=\"store_true\",\n                      help='Set verbose debugging on')\n    parser.add_option('--file',\n                      dest='filePath',\n                      default='/tmp/HcrVelCorrect.txt',\n                      help='File path for correction results')\n    parser.add_option('--widthMain',\n                      dest='mainWidthMm',\n                      default=400,\n                      help='Width of main figure in mm')\n    parser.add_option('--heightMain',\n                      dest='mainHeightMm',\n                      default=300,\n                      help='Height of main figure in mm')\n    parser.add_option('--filtLen',\n                      dest='filtLen',\n                      default=1,\n                      help='Len of moving mean filter')\n    parser.add_option('--start',\n                      dest='startTime',\n                      default='2018 01 16 00 20 00',\n                      help='Start time for XY plot')\n    parser.add_option('--end',\n                      dest='endTime',\n                      default='2018 01 16 0 30 00',\n                      help='End time for XY plot')\n    parser.add_option('--figDir',\n                      dest='figureDir',\n                      default='/figs/',\n                      help='Directory for output figures')\n    \n    \n    (options, args) = parser.parse_args()\n    \n    if (options.verbose == True):\n        options.debug = True\n\n    # time limits\n\n    year, month, day, hour, minute, sec = options.startTime.split()\n    startTime = datetime.datetime(int(year), int(month), int(day),\n                                  int(hour), int(minute), int(sec))\n\n    year, month, day, hour, minute, sec = options.endTime.split()\n    endTime = datetime.datetime(int(year), int(month), int(day),\n                                int(hour), int(minute), int(sec))\n\n    Jan1970 = datetime.datetime(1970, 1, 1, 0, 0, 0)\n    if ((startTime != Jan1970) and (endTime != Jan1970)):\n        timeLimitsSet = True\n\n    if (options.debug == True):\n        print(\"Running %prog\", file=sys.stderr)\n        print(\"  compFilePath: \", options.compFilePath, file=sys.stderr)\n        if (timeLimitsSet):\n            print(\"  startTime: \", startTime, file=sys.stderr)\n            print(\"  endTime: \", endTime, file=sys.stderr)\n\n\n    # read in column headers for correction results\n    \n    iret, corrHdrs, corrData = readColumnHeaders(options.filePath)\n    if (iret != 0):\n        sys.exit(-1)\n\n    # read in data for corr results\n\n    corrData, corrTimes = readInputData(options.filePath, corrHdrs, corrData)\n\n    # load up the data arrays\n\n    loadDataArrays(corrData, corrTimes)\n    \n    # make output figure name string\n    \n    if options.figureDir == '/figs/':\n        options.figureDir=os.path.split(options.filePath)[0] + '/figs/'\n        \n    outFile=options.figureDir + os.path.splitext(os.path.split(options.filePath)[1])[0]\n    \n    # create figure directory if necessary\n    \n    pathlib.Path(options.figureDir).mkdir(parents=False, exist_ok=True)\n    \n    # close all existing figures\n    \n    mpl.pyplot.close(\"all\")\n\n    # render the plots\n    \n    doPlotRawAndFilt()\n    #doPlotFiltAndGeoref()\n\n    # If you want to show the plots, uncomment the following line\n    # Showing the plots will stop the script so it does not work when run as script\n    \n    plt.show()\n   \n    sys.exit()\n   \n    \n########################################################################\n# Read columm headers for the data\n# this is in the first line\n\ndef readColumnHeaders(filePath):\n\n    colHeaders = []\n    colData = {}\n\n    fp = open(filePath, 'r')\n    line = fp.readline()\n    fp.close()\n    \n    commentIndex = line.find(\"#\")\n    if (commentIndex == 0):\n        # header\n        colHeaders = line.lstrip(\"# \").rstrip(\"\\n\").split()\n        if (options.debug == True):\n            print(\"colHeaders: \", colHeaders, file=sys.stderr)\n    else:\n        print(\"ERROR - readColumnHeaders\", file=sys.stderr)\n        print(\"  First line does not start with #\", file=sys.stderr)\n        return -1, colHeaders, colData\n    \n    for index, var in enumerate(colHeaders, start=0):\n        colData[var] = []\n        \n    return 0, colHeaders, colData\n\n########################################################################\n# Read in the data\n\ndef readInputData(filePath, colHeaders, colData):\n\n    # open file\n\n    fp = open(filePath, 'r')\n    lines = fp.readlines()\n\n    # read in a line at a time, set colData\n    for line in lines:\n        \n        commentIndex = line.find(\"#\")\n        if (commentIndex >= 0):\n            continue\n            \n        # data\n        \n        data = line.strip().split()\n        if (len(data) != len(colHeaders)):\n            if (options.debug == True):\n                print(\"skipping line: \", line, file=sys.stderr)\n            continue;\n\n        for index, var in enumerate(colHeaders, start=0):\n            if (var == 'count' or \\\n                var == 'year' or var == 'month' or var == 'day' or \\\n                var == 'hour' or var == 'min' or var == 'sec' or \\\n                var == 'unix_time'):\n                colData[var].append(int(data[index]))\n            else:\n                colData[var].append(float(data[index]))\n\n    fp.close()\n\n    # load observation times array\n\n    year = colData['year']\n    month = colData['month']\n    day = colData['day']\n    hour = colData['hour']\n    minute = colData['min']\n    sec = colData['sec']\n\n    obsTimes = []\n    for ii, var in enumerate(year, start=0):\n        thisTime = datetime.datetime(year[ii], month[ii], day[ii],\n                                     hour[ii], minute[ii], sec[ii])\n        obsTimes.append(thisTime)\n\n    return colData, obsTimes\n\n########################################################################\n# Moving average filter\n\ndef movingAverage(values, window):\n\n    if (window < 2):\n        return values\n\n    weights = np.repeat(1.0, window)/window\n    sma = np.convolve(values, weights, 'same')\n    return sma\n\n########################################################################\n# Set up arrays for plotting\n\ndef loadDataArrays(corrData, corrTimes):\n\n    # set up arrays\n\n    global ctimes\n\n    ctimes = np.array(corrTimes).astype(datetime.datetime)\n\n    global VelSurf\n    global DbzSurf\n    global RangeToSurf\n    global VelStage1\n    global VelSpike\n    global VelCond\n    global VelFilt\n    global VelCorr\n    global Altitude\n    global VertVel\n    global Roll\n    global Pitch\n    global Rotation\n    global Tilt\n    global Elevation\n    global DriveAngle1\n    global DriveAngle2\n\n    VelSurf = np.array(corrData[\"VelSurf\"]).astype(np.double)\n    DbzSurf = np.array(corrData[\"DbzSurf\"]).astype(np.double)\n    RangeToSurf = np.array(corrData[\"RangeToSurf\"]).astype(np.double)\n    VelStage1 = np.array(corrData[\"VelStage1\"]).astype(np.double)\n    VelSpike = np.array(corrData[\"VelSpike\"]).astype(np.double)\n    VelCond = np.array(corrData[\"VelCond\"]).astype(np.double)\n    VelFilt = np.array(corrData[\"VelFilt\"]).astype(np.double)\n    VelCorr = np.array(corrData[\"VelCorr\"]).astype(np.double)\n    Altitude = np.array(corrData[\"Altitude\"]).astype(np.double)\n    VertVel = np.array(corrData[\"VertVel\"]).astype(np.double)\n    Roll = np.array(corrData[\"Roll\"]).astype(np.double)\n    Pitch = np.array(corrData[\"Pitch\"]).astype(np.double)\n    Rotation = np.array(corrData[\"Rotation\"]).astype(np.double)\n    Tilt = np.array(corrData[\"Tilt\"]).astype(np.double)\n    Elevation = np.array(corrData[\"Elevation\"]).astype(np.double)\n    DriveAngle1 = np.array(corrData[\"DriveAngle1\"]).astype(np.double)\n    DriveAngle2 = np.array(corrData[\"DriveAngle2\"]).astype(np.double)\n\n########################################################################\n# Plot raw data plus filtered\n\ndef doPlotRawAndFilt():\n\n    # set up plots\n\n    widthIn = float(options.mainWidthMm) / 25.4\n    htIn = float(options.mainHeightMm) / 25.4\n\n    global figNum\n    fig = plt.figure(figNum, (widthIn, htIn))\n    figNum = figNum + 1\n    \n    ax1 = fig.add_subplot(2,1,1,xmargin=0.0)\n    ax2 = fig.add_subplot(2,1,2,xmargin=0.0)\n    \n    ax1.plot(ctimes, VelSurf, \\\n             label='VelSurf', color='gray', linewidth=1)\n    \n    ax1.plot(ctimes, VelStage1, \\\n             label='VelStage1', color='red', linewidth=1)\n    \n    ax1.plot(ctimes, VelSpike, \\\n             label='VelSpike', color='orange', linewidth=1)\n    \n    ax1.plot(ctimes, VelCond, \\\n             label='VelCond', color='blue', linewidth=2)\n    \n    ax2.plot(ctimes, VelCorr, \\\n             label='VelCorr', color='blue', linewidth=1)\n    \n    ax2.plot(ctimes, VertVel, \\\n             label='VertVel', color='cyan', linewidth=1)\n\n    configTimeAxis(ax1, -2, 2, \"Surf velocity raw (m/s)\", 'upper center')\n    configTimeAxis(ax2, -2, 2, \"Surf velocity filtered (m/s)\", 'upper center')\n\n    fig.autofmt_xdate()\n    fig.tight_layout()\n    fig.subplots_adjust(bottom=0.08, left=0.06, right=0.97, top=0.96)\n\n    fig.suptitle(\"RAW VELOCITY WITH FILTERING - file \" + os.path.basename(options.filePath))\n\n    return\n\n########################################################################\n# Plot filtered data plus georef data\n\ndef doPlotFiltAndGeoref():\n\n    # set up plots\n\n    widthIn = float(options.mainWidthMm) / 25.4\n    htIn = float(options.mainHeightMm) / 25.4\n\n    global figNum\n    fig = plt.figure(figNum, (widthIn, htIn))\n    figNum = figNum + 1\n    \n    ax1 = fig.add_subplot(4,1,1,xmargin=0.0)\n    ax2 = fig.add_subplot(4,1,2,xmargin=0.0)\n    ax3 = fig.add_subplot(4,1,3,xmargin=0.0)\n    ax4 = fig.add_subplot(4,1,4,xmargin=0.0)\n    \n    ax1.plot(ctimes, VelNoiseFilt, \\\n             label='VelNoiseFilt', color='gray', linewidth=1)\n    \n    ax1.plot(ctimes, VelWaveFiltMedian, \\\n             label='VelWaveFiltMedian', color='blue', linewidth=1)\n    \n    ax1.plot(ctimes, VelWaveFiltMean, \\\n             label='VelWaveFiltMean', color='green', linewidth=1)\n    \n    ax1.plot(ctimes, VelWaveFiltPoly, \\\n             label='VelWaveFiltPoly', color='red', linewidth=2)\n    \n    ax1.plot(ctimes, VertVel, \\\n             label='VertVel', color='cyan', linewidth=2)\n    \n    ax2.plot(ctimes, Pitch, label='Pitch', color='red', linewidth=1)\n    ax2.plot(ctimes, Elevation + 90.0, label='Elev+90', color='blue', linewidth=1)\n    ax2.plot(ctimes, Tilt * -1, label='Tilt*-1', color='green', linewidth=1)\n\n    ax3.plot(ctimes, Roll, label='Roll', color='red', linewidth=1)\n    ax3.plot(ctimes, Rotation * -1.0 + 180.0, label='-Rotation+180', color='blue', linewidth=1)\n    \n    ax4.plot(ctimes, Altitude, label='Altitude', color='blue', linewidth=1)\n\n    configTimeAxis(ax1, -2, 2, \"Surface velocity (m/s)\", 'upper center')\n    configTimeAxis(ax2, -3, 3, \"Pitch and Radar Elevation (deg)\", 'upper center')\n    configTimeAxis(ax3, -9999, -9999, \"Roll and Radar Rotation (deg)\", 'upper center')\n    configTimeAxis(ax4, -9999, -9999, \"Altitude (km)\", 'upper center')\n\n    fig.autofmt_xdate()\n    fig.tight_layout()\n    fig.subplots_adjust(bottom=0.08, left=0.06, right=0.97, top=0.96)\n\n    fig.suptitle(\"FILTERING with GEOREF - file \" + os.path.basename(options.filePath))\n\n    return\n\n########################################################################\n# Configure axes, legends etc\n\ndef configTimeAxis(ax, miny, maxy, ylabel, legendLoc):\n    \n    legend = ax.legend(loc=legendLoc, ncol=8)\n    for label in legend.get_texts():\n        label.set_fontsize('x-small')\n        ax.set_xlabel(\"Time\")\n    ax.set_ylabel(ylabel)\n    ax.grid(True)\n    if (miny > -9990 and maxy > -9990):\n        ax.set_ylim([miny, maxy])\n    hfmt = dates.DateFormatter('%H:%M:%S')\n    ax.xaxis.set_major_locator(dates.AutoDateLocator())\n    ax.xaxis.set_major_formatter(hfmt)\n    for tick in ax.xaxis.get_major_ticks():\n        tick.label.set_fontsize(8) \n\n    if (timeLimitsSet):\n        ax.set_xlim(startTime, endTime)\n\n########################################################################\n# Run a command in a shell, wait for it to complete\n\ndef runCommand(cmd):\n\n    if (options.debug == True):\n        print(\"running cmd:\",cmd, file=sys.stderr)\n    \n    try:\n        retcode = subprocess.call(cmd, shell=True)\n        if retcode < 0:\n            print(\"Child was terminated by signal: \", -retcode, file=sys.stderr)\n        else:\n            if (options.debug == True):\n                print(\"Child returned code: \", retcode, file=sys.stderr)\n    except OSError as e:\n        print(\"Execution failed:\", e, file=sys.stderr)\n\n########################################################################\n# Run - entry point\n\nif __name__ == \"__main__\":\n   main()\n\n","repo_name":"tianshuiwuqing/lorse-core","sub_path":"codebase/apps/radar/src/HcrVelCorrect/testing/PlotHcrCorr.fir.py","file_name":"PlotHcrCorr.fir.py","file_ext":"py","file_size_in_byte":13649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"11774933926","text":"# 파이썬 함수의 구조\n# def 함수명(매개변수):\n#     <수행할 문장1>\n#     <수행할 문장2>\n#     ...\n# 함수는 시스템에 내장되어 있는 내장 함수, 사용자가 만든 사용자 정의 함수, 라이브러리 형태로 모듈화시킨 외장 함\n\ndef gugu(a,b):        #a,b는 매개변수 : 함수에 입력으로 전달된 값을 받는 변수\n\n    return a+b\nprint(gugu(2,5))      #2,5는 인수 : 함수를 호출할 때 전달하는 입력값\n\n# 여러개의 입력값을 받는 함수\n# def 함수이름(*매개변수):\n#     <수행할 문장>\n#     ...\n\ndef many_add(*num):     #매개변수 이름 앞에 *을 붙이면 입력값을 전부 모아서 튜플로 만들어 주\n    sum=0\n    for i in num :\n        sum=sum+i\n    return sum\n\nprint(many_add(1,2,3,))\n\n\n\ndef add_mul(choice, *num) :\n    if choice=='add':\n        result=0\n        for i in num:\n            result=result+i\n    elif choice=='mul':\n        result=1\n        for i in num:\n            result=result*i\n    else: result='오류'\n    return result\n\nprint(add_mul('add', 1,2,3,4,5,6,7,8,9,10,))\nprint(add_mul('mul', 1,2,3,4,5,6,7,8,9,10,))\nprint(add_mul('mul'))       #인수와 매개변수의 수가 일치하지 않아도 함수 실행\n#print(add_mul())            #인수가 없으면 오류\nprint(add_mul('??'))\n\n\n#함수의 결과값은 1개이다\ndef add_and_mul(a,b):\n     return a+b, a*b\n\nprint(add_and_mul(3,5))     # 튜플로 결과 값을 갖게됨\n\nresult1,result2=add_and_mul(3,5)    #튜플로 된 값을 분리\nprint(result1)\nprint(result2)\n\n\n\n#매개 변수에 초깃값 설정하기\ndef say_myself(name, old, man=True):\n    print(\"나의 이름은 %s 입니다.\" % name)\n    print(\"나이는 %d살입니다.\" % old)\n    if man:\n        print(\"남자입니다.\")\n    else:\n        print(\"여자입니다.\")\n\n\nsay_myself('홍길동',27)\nsay_myself('영심이',24,False)\nsay_myself('고길동',30, True)\n\n#초기화시키고 싶은 매개변수를 항상 뒤쪽에 놓는 것을 잊지 말자. IndentationError: unexpected indent\n# def say_myself(name, man=True, old):\n#     print(\"나의 이름은 %s 입니다.\" % name)\n#     print(\"나이는 %d살입니다.\" % old)\n#     if man:\n#         print(\"남자입니다.\")\n#     else:\n#         print(\"여자입니다.\")\n#\n# say_myself('김길',27)\n\n\n#함수 안에서 함수 밖의 변수를 변경하는 방법\n# 1. return 사용하기\na = 1\ndef vartest(a):\n    a = a +1\n    return a\n\na = vartest(a)\nprint('return1',a)\na = vartest(a)\nprint('return2',a)\na = vartest(a)\nprint('return3',a)\n\n# 2. global 명령어 사용하기\na = 10\ndef vartest():\n    global a    # 함수 안에서 함수 밖의 a 변수를 직접 사용하겠다는 뜻(좋은 방법은 아니다)\n    a = a+1\n\nvartest()\nprint('global1',a)\nvartest()\nprint('global2',a)\nvartest()\nprint('global3',a)\n\n#lambda\ndef a(a,b):\n    return a+b\nadd=lambda a,b:a+b\nprint(add(3,5))\nprint(add)\n# 내장함수\n\n# format()\n\n# enumerate() - 순서가 있는 자료형(리스트,튜플, 문자열) 입력하면 인덱스를 포함한 요솟값을 반환\n\nnum=(10,20,30,40,50)\nfor i in num :\n    print(i)\n\nprint(enumerate(num))\nfor i in enumerate(num) :\n    print(i)\nfor i,v in enumerate(num) :\n    print('index:%d value:%d' %(i,v))\n\n# str() - 입력으로 들어온 데이터를 문자열 객체로 반환\n\nprint(type(20))\nprint(type(str(20)))\na=[1,2,3]\nprint(str(a))   # 리스트 [1,2,3]을 -> '[1,2,3]' 으로 변환하여 반환\n\n# join() - 리스트의 요소들을 지정한 구분자로 구분해 문자열로 반환(리스트 내 요소들을 문자열로 합칠 때 많이 사용)\n# split() - 문자열을 특정 구분자를 기준으로 분리해 리스트로 반환\n\nv=['cat', 'dog','monkey','tiger']\nv1='/'.join(v)\nprint(v1)\nprint(type(v1))\n\nv2=v1.split('/')\nprint(v2)\nprint(type(v2))\n\n# id() - 객체를 입력받아 객체의 고유 주솟값(레퍼런스)을 반환\n\nprint(id('str'))\n\n# find() - 특정 문자열을 찾기 위해 사용 / 그 문자열의 시작 위치를 반환하고 찾지 못하면 -1을 반환\nstr='hello'\nprint(str.find('l'))\nprint(str.find('a'))\n\n# strip() - 주어진 문자열 양쪽 끝의 공백을 제거\n\nstr='  hello my name is gildong  '\nprint(str)\nprint(str.strip())\n\n\n# filter() - 개별 요소를 반복적으로 셀 수 있는 객체(iterable object)를 입력받아 각 요소를 함수로 수행한 후 결과가 True인것만 묶어서 반환\n\ndef func(num) :\n    list=[]\n    for i in num:\n        if(i%2==0) : list.append(i)\n        else : pass\n    return list\nnumber=(1,2,3,4,5,6,7,8,9,10)\nprint(func(number))\n\ndef ev(num) :\n    return num%2==0 #짝수면 true 반환\n\n\nprint(filter(ev,number))\nprint(list(filter(ev,number)))      #list() -> 인자로 들어온 데이터(객체)를 리스트로 반환\n\n# lambda - 함수를 생성할 때 사용 - 익명 함수\n\ndef t2(num):\n    return num*2\nt=lambda x: x*2\n\nprint(t2(10))\nprint(t(10))\n\nprint(list(filter(lambda num :num%2==0, number)))\n\n# map - 개별 요소를 반복적으로 셀 수 있는 객체를 입력받아 각 요소를 함수로 수행한 후 결과를 묶어서 반환\n\nnumber=[1,2,3,4,5,6]\nprint(list(map(lambda x:x**3, number)))\n\n\n","repo_name":"cozynye/python-study","sub_path":"function_2week.py","file_name":"function_2week.py","file_ext":"py","file_size_in_byte":5176,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"37766878528","text":"import os\nimport pygame\nimport math\nfrom sys import exit\n\n# Classes from other files\nimport Board\nimport Timer\nimport Button\nimport ClearButton\nimport CheckPuzzleButton\nimport MuteMusicButton\nimport Tutorial\nimport LevelSelect\nimport GamePause\nimport ThemeMgr\n\n# Functions from other files\nimport load_image\nimport blink_anim\nimport get_path\n\ndef main():\n    themeMgr = ThemeMgr.ThemeMgr(False) # starts in light mode\n    clock = pygame.time.Clock()\n    screen = pygame.init()\n    surface = pygame.display.set_mode((900,900))\n    pygame.display.set_caption(\"Pynogram\")\n    surface.fill(themeMgr.getBgColor())\n    level = LevelSelect.LevelSelect()\n    timer = Timer.Timer()\n    board = Board.Board()\n    tutorial = Tutorial.Tutorial()\n    pause = GamePause.Pause()\n\n    # Determines whether user can edit grid (including clear grid)\n    # Check solution is also disabled; since user can't modify grid, result of check won't change\n    # This is false when:\n    # (1) User has checked their solution and is correct (completed the puzzle)\n    # (2) User has checked their solution and is wrong, but chooses to see solution\n    # (3) User is not on the board (ex. popup is covering it, different menu)\n    canEditGrid = True\n    solCorrect = False\n    #Used when puzzle incorrect, prevents game from continuing until user selects to try again or give up\n\n    # Set up clear button, check button, mute music button\n    clearButton = ClearButton.ClearButton(725, 120, \"images/clear.png\")\n    checkPuzzleButton = CheckPuzzleButton.CheckPuzzleButton(325, 800, \"images/check.png\")\n    muteMusicButton = MuteMusicButton.MuteMusicButton(790,800,\"images/music_on.png\")\n\n    # Tutorial Button\n    tut = Button.Button(250,410, \"images/Tutorial-Button.png\")\n\n    # Pause Button\n    pauseB = Button.Button(30,30, \"images/Pause-Button.png\")\n\n    # Theme Toggle button\n    themeToggle = Button.Button(35,780,\"images/theme_toggle.png\")\n\n    # Popup buttons\n    puzzleComplete = Button.Button(50,115, \"images/puzzleComplete.png\")\n    pcMainMenu = Button.Button(275,495, \"images/mainmenu.png\")\n    puzzleIncorrect = Button.Button(50,115, \"images/incorrect.png\")\n    tryAgain = Button.Button(300,420, \"images/tryAgain.png\")\n    showSolution = Button.Button(300, 510, \"images/showSolution.png\")\n\n    # Main menu buttons\n    quitGame = Button.Button(300, 590, \"images/quit.png\")\n    startGame = Button.Button (170, 200, \"images/startGame.png\")\n\n    blinkSoln = False # animation when showing solution\n\n    # Load song\n    pygame.mixer.music.load(get_path.get_path(\"assets/music/Arpent.wav\"))\n    pygame.mixer.music.play(-1) # loop indefinitely\n\n    #Used to prevent interaction with puzzle while popup is active\n    gameState = 0\n\n    page = \"Main Menu\" # FIXME - transition testing\n\n    notNew = True\n\n    while True:\n        clock.tick(60) # 60 fps\n\n        surface.fill(themeMgr.getBgColor()) # background, needed for all pages\n\n        if page == \"Main Menu\":\n            muteMusicButton.draw(surface,themeMgr) # mute button\n            tut.draw(surface,themeMgr) # Tutorial Button\n            themeToggle.draw(surface,themeMgr) # Theme Toggle Button\n\n            # Title text\n            font = pygame.font.Font(get_path.get_path('assets/font/freesansbold.ttf'), 70)\n            text = font.render(\"Pynogram\", True, themeMgr.getFontColor())\n            surface.blit(text, [280, 40])\n\n            # buttons\n            startGame.draw(surface,themeMgr)\n            quitGame.draw(surface,themeMgr)\n\n        elif page == \"Difficulty Selection\":\n            if level.difficulty == \"\":\n                level.Difficulty(surface,muteMusicButton,themeMgr)\n            if level.difficulty != \"\":\n                level.lvlSelect(surface,muteMusicButton,themeMgr)\n\n        elif page == \"Tutorial\":\n            page = tutorial.tutScreen(surface,muteMusicButton,themeMgr)\n\n\n        elif page == \"Pause\":\n            page = pause.pauseScreen(surface,muteMusicButton,themeToggle,themeMgr)\n            themeToggle.draw(surface,themeMgr) # Theme Toggle Button\n\n        elif page == \"Board\":\n            # All the code to display things on the screen goes here\n            clearButton.draw(surface,themeMgr) # clear button\n            checkPuzzleButton.draw(surface,themeMgr) # check puzzle button\n            muteMusicButton.draw(surface,themeMgr) # mute button\n            timer.displayTime(surface, themeMgr) # show timer\n            pauseB.draw(surface,themeMgr) # Pause button\n\n            #FIXME - comments from Pedro on gameState\n            if gameState == 0:\n                board.displayBoard(surface,themeMgr) # grid and numbers\n                board.displayBoxes(surface, themeMgr) # boxes in the grid\n            elif gameState == 1:\n                if solCorrect:\n                    puzzleComplete.draw(surface,themeMgr)\n                    pcMainMenu.draw(surface,themeMgr)\n                else:\n                    puzzleIncorrect.draw(surface,themeMgr)\n                    tryAgain.draw(surface,themeMgr)\n                    showSolution.draw(surface,themeMgr)\n\n            if blinkSoln:\n                blinkSoln = blink_anim.blink_anim(timer, board, surface, themeMgr, startTime) # very slow blinking animation to show solution briefly\n\n        if level.solnName != \"\" and notNew:\n            page = \"Board\"\n            notNew = False\n            board.setUpPuzzle(int(level.difficulty), level.solnName)\n            timer.resetTimer()\n            timer.setRunning(True)\n\n        pygame.display.update() # Update the display only one per loop (otherwise get flickering)\n\n        if page == \"Pause Main\":\n            page = \"Main Menu\"\n            gameState = 0\n            canEditGrid = True\n            notNew = True\n            level.difficulty = \"\"\n            level.solnName = \"\"\n\n        elif page == \"Resume\":\n            timer.setRunning(True)\n            page = \"Board\"\n\n        for e in pygame.event.get():\n            if e.type == pygame.QUIT:\n                pygame.quit()\n                exit() # Prevents error message when quitting\n\n            if e.type == pygame.MOUSEBUTTONDOWN:\n                x,y = pygame.mouse.get_pos()\n\n                if e.button == 1 and muteMusicButton.rect.collidepoint(x, y): # left click on mute music button\n                    muteMusicButton.toggleMusic(themeMgr) # mute/unmute music\n\n                elif e.button == 1 and page == \"Main Menu\" and quitGame.rect.collidepoint(x, y): # quit game button\n                    pygame.quit()\n                    exit() # Prevents error message when qutting\n\n                elif e.button == 1 and page == \"Main Menu\" and startGame.rect.collidepoint(x, y): # start game button\n                    page = \"Difficulty Selection\"\n\n                elif e.button == 1 and page == \"Main Menu\" and themeToggle.rect.collidepoint(x, y): # theme toggle button\n                    themeMgr.toggleDarkMode()\n\n                elif e.button == 1 and page == \"Main Menu\" and tut.rect.collidepoint(x, y):\n                    page = \"Tutorial\"\n\n                elif e.button == 1 and page == \"Board\" and pauseB.rect.collidepoint(x,y):\n                    timer.setRunning(False)\n                    page = \"Pause\"\n\n                elif page == \"Board\" and gameState == 0:\n                    if e.button == 1 and clearButton.rect.collidepoint(x, y) and canEditGrid and (not blinkSoln): # left click on clear button\n                        board.clearGrid() # clear grid\n                        timer.resetTimer()\n\n                    if e.button == 1 and checkPuzzleButton.rect.collidepoint(x, y) and canEditGrid and (not blinkSoln): # left click on check button\n                        # Check if user completed puzzle successfully\n                        # If they did or they choose to see solution, prevent them from editing the grid\n                        canEditGrid, solCorrect = checkPuzzleButton.checkPuzzle(board)\n                        #gameState switches to 1, so popup appears and prevents interaction with board\n                        gameState = 1\n                        if solCorrect:\n                            timer.setRunning(False) # stop the timer\n                            canEditGrid = False\n\n                    if (e.button == 1 or e.button == 3) and canEditGrid and (not blinkSoln): # click on box in grid\n                        board.clickBox(x,y,e.button)\n\n                elif page == \"Board\" and gameState == 1:\n                    if solCorrect == False:\n                        if e.button == 1 and tryAgain.rect.collidepoint(x, y):\n                            gameState = 0\n\n                        if e.button == 1 and showSolution.rect.collidepoint(x, y):\n                            board.showSolution()\n                            startTime = pygame.time.get_ticks() #keep track of time for blink animation\n                            blinkSoln = True\n                            gameState = 0\n\n                    # Return to main menu\n                    if solCorrect == True and e.button == 1 and pcMainMenu.rect.collidepoint(x, y) and canEditGrid == False:\n                        page = \"Main Menu\"\n                        gameState = 0\n                        canEditGrid = True\n                        notNew = True\n                        level.difficulty = \"\"\n                        level.solnName = \"\"\n\n# Run in command prompt (otherwise closes instantly)\nmain()\n","repo_name":"Cone-Virus/Pynogram","sub_path":"Pynogram.py","file_name":"Pynogram.py","file_ext":"py","file_size_in_byte":9374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"10851798387","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Date    : 2015-12-07 10:32:36\n# @Author  : jiong (447991103@qq.com)\n# @Version : $Id$\n\nimport sys\nreload(sys)\nsys.setdefaultencoding('utf-8')\n\nimport MySQLdb\nimport subprocess\nimport os\nos.system('net start MySql')\nsubprocess.Popen(['mysql','-uroot',])\n# subprocess.Popen(['CREATE DATABASE `test2` DEFAULT CHARACTER SET utf8 COLLATE utf8_general_ci'])\nconn= MySQLdb.connect(\n        host='localhost',\n        port = 3306,\n        user='root',\n        passwd='',\n        db ='jiong_test',\n        )\ncur = conn.cursor()\n\n#创建数据表\n# cur.execute(\"CREATE DATABASE test1 DEFAULT CHARACTER SET utf8 COLLATE utf8_general_ci\")\n\ncur.execute(\"DROP TABLE IF EXISTS api_info\")\ncur.execute(\"DROP TABLE IF EXISTS api_followers\")\ncur.execute(\"DROP TABLE IF EXISTS api_mushup\")\ncur.execute(\"create table api_info(API_Name varchar(200) ,\\\n\t\t\t\t\t\t\t\t\tAPI_ID varchar(20),\\\n\t\t\t\t\t\t\t\t\tDescription varchar(3000),\\\n\t\t\t\t\t\t\t\t\tPrimary_Category varchar(100),\\\n\t\t\t\t\t\t\t\t\tSecondary_Categories varchar(300),\\\n\t\t\t\t\t\t\t\t\tFollowers_Number varchar(30),\\\n\t\t\t\t\t\t\t\t\tAPI_Homepage varchar(300),\\\n\t\t\t\t\t\t\t\t\tAPI_Provider varchar(300))\")\ncur.execute(\"create table api_followers(API_ID varchar(20) ,\\\n\t\t\t\t\t\t\t\t\tFollowers_Name varchar(3000) )\")\ncur.execute(\"create table api_mushup(API_ID varchar(20) ,\\\n\t\t\t\t\t\t\t\t\t  mushup_name varchar(3000))\")\n\n\ncur.close()\nconn.commit()\nconn.close()\n\n","repo_name":"strange-jiong/programmableweb","sub_path":"pro2/create_db.py","file_name":"create_db.py","file_ext":"py","file_size_in_byte":1391,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"85"}
+{"seq_id":"1250427199","text":"import re\n\ndef clauses(knowledge_base):\n    \"\"\"Takes the string of a knowledge base; returns an iterator for pairs\n    of (head, body) for propositional definite clauses in the\n    knowledge base. Atoms are returned as strings. The head is an atom\n    and the body is a (possibly empty) list of atoms.\n\n    -- Kourosh Neshatian - 2 Aug 2021\n\n    \"\"\"\n    ATOM   = r\"[a-z][a-zA-Z\\d_]*\"\n    HEAD   = rf\"\\s*(?P<HEAD>{ATOM})\\s*\"\n    BODY   = rf\"\\s*(?P<BODY>{ATOM}\\s*(,\\s*{ATOM}\\s*)*)\\s*\"\n    CLAUSE = rf\"{HEAD}(:-{BODY})?\\.\"\n    KB     = rf\"^({CLAUSE})*\\s*$\"\n\n    assert re.match(KB, knowledge_base)\n\n    for mo in re.finditer(CLAUSE, knowledge_base):\n        yield mo.group('HEAD'), re.findall(ATOM, mo.group('BODY') or \"\")\n\ndef forward_deduce(kb):\n    C = set()\n    prev_c = None\n    \n    while prev_c != C:\n        prev_c = C\n        for clause in list(clauses(kb)):\n            if len(clause[1]) == 0:\n                C = C.union([clause[0]])\n            else:\n                if set(clause[1]).issubset(C):\n                    C = C.union([clause[0]])\n                else:\n                    continue\n    return C\n    \n        \n\ndef main():\n    kb = \"\"\"\n    a :- b.\n    b.\n    \"\"\"\n    print(\", \".join(sorted(forward_deduce(kb))))\n    \n    print()\n    \n    kb = \"\"\"\n    good_programmer :- correct_code.\n    correct_code :- good_programmer.\n    \"\"\"\n    print(\", \".join(sorted(forward_deduce(kb))))\n    \n    print()\n    \n    kb = \"\"\"\n    a :- b, c.\n    b :- d, e.\n    b :- g, e.\n    c :- e.\n    d.\n    e.\n    f :- a,\n         g.\n    \"\"\"\n    print(\", \".join(sorted(forward_deduce(kb)))) \n    \n    print()\n    \n    kb = \"\"\"\n    wet :- is_raining.\n    wet :- sprinkler_is_going.\n    wet.\n    \"\"\"\n    print(len(forward_deduce(kb)))\n    #print(forward_deduce(kb))\n    \n    \nif __name__ == \"__main__\":\n    main()","repo_name":"Loquaxious/COSC367","sub_path":"Quiz3/q2_foward_deduce.py","file_name":"q2_foward_deduce.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"14942583777","text":"import turtle as t\nimport random\n\ntim = t.Turtle()\n\n# tim.shape(\"turtle\")\n# Timothy.color(\"red\")\n\n# draws a dashed line\n# for _ in range(16):\n#     tim.pendown()\n#     tim.forward(10)\n#     tim.penup()\n#     tim.forward(10)\n# tim.color(\"red\")\n\n# colours = [\"CornflowerBlue\", \"DarkOrchid\", \"IndianRed\", \"DeepSkyBlue\", \"LightSeaGreen\",\n#            \"wheat\", \"SlateGray\", \"SeaGreen\"]\n\n\n# draws shapes like triangle square hexagon etc.\n# def shapes(sides):\n#     angle = 360 / sides\n#     for _ in range(sides):\n#         tim.forward(100)\n#         tim.right(angle)\n#\n#\n# for side in range(3, 11):\n#     colors = random.choice(colours)\n#     tim.color(colors)\n#     shapes(side)\n\n# Draw random walk\n# direction = [0, 90, 180, 270]\n# t.colormode(255)\n# tim.pensize(15)\n# tim.speed(0)\n#\ndef random_color():\n    r = random.randint(0, 255)\n    g = random.randint(0, 255)\n    b = random.randint(0, 255)\n    random_colour = (r, g, b)\n    return random_colour\n\n# for _ in range(200):\n#     tim.color(random_color())\n#     tim.forward(30)\n#     tim.setheading(random.choice(direction))\n\n# Draw a spirograph\nt.colormode(255)\ntim.speed(0)\n\ndef draw_spirograph(size):\n    for _ in range(int(360 / size)):\n        tim.color(random_color())\n        tim.circle(100)\n        tim.setheading(tim.heading() + size)\n\n\ndraw_spirograph(5)\n\n\nscreen = t.Screen()\nscreen.exitonclick()\n","repo_name":"sanketdev05/HirstPainting","sub_path":"turtle_practice.py","file_name":"turtle_practice.py","file_ext":"py","file_size_in_byte":1357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"19352854917","text":"import xbmcplugin, string, sys, os, traceback, xbmcaddon, logging\r\n\r\n__plugin__ =  'RomLauncher'\r\n__author__ = 'PW <peteward44@gmail.com>'\r\n__date__ = '13-05-2013'\r\n__version__ = '1.0.0'\r\n\r\nADDON = xbmcaddon.Addon()\r\nREMOTE_DBG = False\r\n\r\nsys.path.append( os.path.join( os.path.join( ADDON.getAddonInfo('path'), \"resources\" ), \"lib\" ) )\r\n\r\n# append pydev remote debugger\r\nif REMOTE_DBG:\r\n\t# Make pydev debugger works for auto reload.\r\n\t# Note pydevd module need to be copied in XBMC\\system\\python\\Lib\\pysrc\r\n\ttry:\r\n\t\timport pysrc.pydevd as pydevd\r\n\t\t# stdoutToServer and stderrToServer redirect stdout and stderr to eclipse console\r\n\t\tpydevd.settrace('localhost', stdoutToServer=True, stderrToServer=True)\r\n\texcept ImportError:\r\n\t\tsys.stderr.write(\"Error: You must add org.python.pydev.debug.pysrc to your PYTHONPATH.\")\r\n\t\tsys.exit(1)\r\n\r\nimport EntryPoint\r\n\r\ntry:\r\n\tEntryPoint.StartApp( ADDON )\r\nexcept Exception as e:\r\n\tlogging.exception(e)\r\n\r\n","repo_name":"peteward44/xbmc-romulator","sub_path":"plugin.program.romulator/default.py","file_name":"default.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"28695902704","text":"from collections.abc import Iterator\n\nfrom pytest import MonkeyPatch, fixture, mark\nfrom starlette.testclient import TestClient\n\nfrom api.app import create_app\nfrom api.config import AppConfig\n\n\n# see https://github.com/pytest-dev/pytest/issues/363#issuecomment-406536200\n@fixture(scope=\"module\")\ndef real_monkeypatch() -> Iterator[MonkeyPatch]:\n    monkeypatch = MonkeyPatch()\n    monkeypatch.setenv(\"CACHE_MONGO_DATABASE\", \"datasets_server_cache_test\")\n    monkeypatch.setenv(\"QUEUE_MONGO_DATABASE\", \"datasets_server_queue_test\")\n    monkeypatch.setenv(\"COMMON_HF_ENDPOINT\", \"https://huggingface.co\")\n    monkeypatch.setenv(\"COMMON_HF_TOKEN\", \"\")\n    yield monkeypatch\n    monkeypatch.undo()\n\n\n@fixture(scope=\"module\")\ndef real_client(real_monkeypatch: MonkeyPatch) -> TestClient:\n    return TestClient(create_app())\n\n\n@fixture(scope=\"module\")\ndef real_app_config(real_monkeypatch: MonkeyPatch) -> AppConfig:\n    app_config = AppConfig.from_env()\n    if \"test\" not in app_config.cache.mongo_database or \"test\" not in app_config.queue.mongo_database:\n        raise ValueError(\"Test must be launched on a test mongo database\")\n    if app_config.common.hf_endpoint != \"https://huggingface.co\":\n        raise ValueError(\"Test must be launched on the production hub\")\n    return app_config\n\n\n@mark.real_dataset\ndef test_webhook(\n    real_client: TestClient,\n) -> None:\n    dataset = \"glue\"\n    payload = {\"event\": \"add\", \"repo\": {\"type\": \"dataset\", \"name\": dataset, \"gitalyUid\": \"123\", \"headSha\": \"revision\"}}\n    response = real_client.post(\"/webhook\", json=payload)\n    assert response.status_code == 200, response.text\n","repo_name":"huggingface/datasets-server","sub_path":"services/api/tests/test_app_real.py","file_name":"test_app_real.py","file_ext":"py","file_size_in_byte":1619,"program_lang":"python","lang":"en","doc_type":"code","stars":549,"dataset":"github-code","pt":"85"}
+{"seq_id":"33302966176","text":"from aws_cdk import core\nimport aws_cdk.aws_s3 as s3\n\n\nclass AwsCdkTestStack(core.Stack):\n\n    def __init__(self, scope: core.Construct, id: str, **kwargs) -> None:\n        super().__init__(scope, id, **kwargs)\n\n        bucket = s3.Bucket(self,\n    \t\t\"BucketCDK\", \n    \t\tversioned=True,\n    \t\tbucket_name='cdk-managed-bucket-s843971',\n            removal_policy=core.RemovalPolicy.DESTROY)\n        \n","repo_name":"abdul-pfg/aws-cdk-test01","sub_path":"aws_cdk_test/aws_cdk_test_stack.py","file_name":"aws_cdk_test_stack.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"38988915939","text":"#!/usr/bin/python\n\n#\n# Woźniak Marcin\n# 434812\n#\n\nimport pandas as pd\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom sklearn import model_selection\n\n# Dane pochodzą ze strony\n# https://archive.ics.uci.edu/ml/datasets/banknote+authentication\ndata = pd.read_csv(\"data.csv\")\nx = data.iloc[:, 0:-1]\ny = data.iloc[:, -1]\n\nx_train, x_test, y_train, y_test = model_selection.train_test_split(x, y, test_size=0.2)\n\nmodel = keras.Sequential(\n    [\n        layers.Dense(15, activation=\"relu\"),\n        layers.Dense(9, activation=\"relu\"),\n        layers.Dense(1, activation=\"sigmoid\"),\n    ]\n)\n\nmodel.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\nmodel.fit(x_train, y_train, epochs=1600, batch_size=200, validation_split=0.2)\n\nscore = model.evaluate(x_test, y_test, verbose=0)\nprint(\"Accuracy:\", score[1])\nprint(\"Loss:\", score[0])\n\n# Accuracy: 1.0 Loss: 1.3936358300270513e-05\n# Accuracy: 1.0 Loss: 1.3100921023578849e-05\n# Accuracy: 1.0 Loss: 1.039111793943448e-05\n# Accuracy: 1.0 Loss: 2.8891838155686855e-05\n","repo_name":"aleksandrazb/Machine_learning","sub_path":"Project/zad.py","file_name":"zad.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"8488324213","text":"\nimport serial\nimport threading\nimport signal\nimport re\nimport time\n\n# A simple serial console.\n# Type a line and press enter\n# and it goes to serial port.\n# Chars get read on another thread.\n\n# debug\ntesta = bytearray(256)\nfor b in range(256):\n    testa[b] = b\ntestf = open('b256.bin', 'wb')\ntestf.write(testa)\ntestf.close()\n\nser = serial.Serial(\"COM5\", 57600, timeout=1)\n\ndef local_upload(filen, offset):\n    print(\"-> reading data\")\n    file = open(filen,\"rb\")\n    membytes = file.read()\n    file.close()\n    print(f\"-> file size {len(membytes)} bytes\")\n    file_offset = int(offset, 16)\n\n    dataa = bytearray(64)\n\n    # write 256 bytes to serial\n    # in 64 byte blocks\n    for o1 in range(4):\n        print(f'-> writing block ${o1}')\n        for o2 in range(64):\n            # load the byte array from the file data\n            dataa[o2] = membytes[file_offset + (o1 * 64) + o2];\n        # wait a sec...\n        print(\"-> writing 64 bytes\")\n        ser.write(dataa)\n        time.sleep(0.5)\n    print(\"-> done\")\n\ndef handler(signum, frame):\n    ser.close()\n    exit(1)\n\nsignal.signal(signal.SIGINT, handler)\n\ndef thread_func(name):\n    while True:\n        try:\n            if ser.inWaiting():\n                c = ser.read()\n                print(chr(c[0]),end=\"\")\n            if ser.closed:\n                break\n        except:\n            break\n\nx = threading.Thread(target=thread_func, args=(1,))\nx.start()\n\np = re.compile(\"^local upload (.*) ([0-9A-Za-z]{4})$\");\n\nwhile True:\n    aline = input()\n\n    # check for local 'upload' command\n    m = p.match(aline)\n    if m == None:\n        aline += '\\n'\n        ser.write(aline.encode())\n    else:\n        # load file\n        print(\"-> file = \" + m.group(1))\n        print(\"-> ofst = \" + m.group(2))\n        local_upload(m.group(1), m.group(2))\n","repo_name":"stharlan/e6502","sub_path":"python/sercon/sercon.py","file_name":"sercon.py","file_ext":"py","file_size_in_byte":1799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"38234529821","text":"\"\"\"\nTests for discussion pages\n\"\"\"\n\nimport datetime\nfrom pytz import UTC\nfrom uuid import uuid4\nfrom nose.plugins.attrib import attr\n\nfrom .helpers import BaseDiscussionTestCase\nfrom ..helpers import UniqueCourseTest\nfrom ...pages.lms.auto_auth import AutoAuthPage\nfrom ...pages.lms.courseware import CoursewarePage\nfrom ...pages.lms.discussion import (\n    DiscussionTabSingleThreadPage,\n    InlineDiscussionPage,\n    InlineDiscussionThreadPage,\n    DiscussionUserProfilePage,\n    DiscussionTabHomePage,\n    DiscussionSortPreferencePage,\n)\nfrom ...pages.lms.learner_profile import LearnerProfilePage\n\nfrom ...fixtures.course import CourseFixture, XBlockFixtureDesc\nfrom ...fixtures.discussion import (\n    SingleThreadViewFixture,\n    UserProfileViewFixture,\n    SearchResultFixture,\n    Thread,\n    Response,\n    Comment,\n    SearchResult,\n    MultipleThreadFixture)\n\nfrom .helpers import BaseDiscussionMixin\n\n\nTHREAD_CONTENT_WITH_LATEX = \"\"\"Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt\n                               ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation\n                               ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in\n                               reprehenderit in voluptate velit sse cillum dolore eu fugiat nulla pariatur.\n                               \\n\\n----------\\n\\nLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt\n                               ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation\n                               ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in\n                               reprehenderit in voluptate velit sse cillum dolore eu fugiat nulla pariatur. (b).\\n\\n\n                               **(a)** $H_1(e^{j\\\\omega}) = \\\\sum_{n=-\\\\infty}^{\\\\infty}h_1[n]e^{-j\\\\omega n} =\n                               \\\\sum_{n=-\\\\infty} ^{\\\\infty}h[n]e^{-j\\\\omega n}+\\\\delta_2e^{-j\\\\omega n_0}$\n                               $= H(e^{j\\\\omega})+\\\\delta_2e^{-j\\\\omega n_0}=A_e (e^{j\\\\omega}) e^{-j\\\\omega n_0}\n                               +\\\\delta_2e^{-j\\\\omega n_0}=e^{-j\\\\omega n_0} (A_e(e^{j\\\\omega})+\\\\delta_2)\n                               $H_3(e^{j\\\\omega})=A_e(e^{j\\\\omega})+\\\\delta_2$. Dummy $A_e(e^{j\\\\omega})$ dummy post $.\n                               $A_e(e^{j\\\\omega}) \\\\ge -\\\\delta_2$, it follows that $H_3(e^{j\\\\omega})$ is real and\n                               $H_3(e^{j\\\\omega})\\\\ge 0$.\\n\\n**(b)** Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt\n                               ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation\n                               ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in\n                               reprehenderit in voluptate velit sse cillum dolore eu fugiat nulla pariatur.\\n\\n\n                               **Case 1:** If $re^{j\\\\theta}$ is a Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt\n                               ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation\n                               ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in\n                               reprehenderit in voluptate velit sse cillum dolore eu fugiat nulla pariatur.\n                               \\n\\n**Case 3:** Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt\n                               ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation\n                               ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in\n                               reprehenderit in voluptate velit sse cillum dolore eu fugiat nulla pariatur.\n                               Lorem $H_3(e^{j\\\\omega}) = P(cos\\\\omega)(cos\\\\omega - cos\\\\theta)^k$,\n                               Lorem Lorem Lorem Lorem Lorem Lorem $P(cos\\\\omega)$ has no\n                               $(cos\\\\omega - cos\\\\theta)$ factor.\n                               Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt\n                               ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation\n                               ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in\n                               reprehenderit in voluptate velit sse cillum dolore eu fugiat nulla pariatur.\n                               $P(cos\\\\theta) \\\\neq 0$. Since $P(cos\\\\omega)$ this is a dummy data post $\\\\omega$,\n                               dummy $\\\\delta > 0$ such that for all $\\\\omega$ dummy $|\\\\omega - \\\\theta|\n                               < \\\\delta$, $P(cos\\\\omega)$ Lorem ipsum dolor sit amet, consectetur adipiscing elit,\n                               sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim\n                               veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo\n                               consequat. Duis aute irure dolor in reprehenderit in voluptate velit sse cillum dolore\n                               Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt\n                               ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation\n                               ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in\n                               reprehenderit in voluptate velit sse cillum dolore eu fugiat nulla pariatur.\n                               Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt\n                               ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation\n                               ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in\n                               reprehenderit in voluptate velit sse cillum dolore eu fugiat nulla pariatur.\n                               Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt\n                               ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation\n                               ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in\n                               reprehenderit in voluptate velit sse cillum dolore eu fugiat nulla pariatur.\n                               Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt\n                               ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation\n                               ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in\n                               reprehenderit in voluptate velit sse cillum dolore eu fugiat nulla pariatur.\n                               Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt\n                               ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation\n                               ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in\n                               reprehenderit in voluptate velit sse cillum dolore eu fugiat nulla pariatur.\n                               \"\"\"\n\n\nclass DiscussionResponsePaginationTestMixin(BaseDiscussionMixin):\n    \"\"\"\n    A mixin containing tests for response pagination for use by both inline\n    discussion and the discussion tab\n    \"\"\"\n    def assert_response_display_correct(self, response_total, displayed_responses):\n        \"\"\"\n        Assert that various aspects of the display of responses are all correct:\n        * Text indicating total number of responses\n        * Presence of \"Add a response\" button\n        * Number of responses actually displayed\n        * Presence and text of indicator of how many responses are shown\n        * Presence and text of button to load more responses\n        \"\"\"\n        self.assertEqual(\n            self.thread_page.get_response_total_text(),\n            str(response_total) + \" responses\"\n        )\n        self.assertEqual(self.thread_page.has_add_response_button(), response_total != 0)\n        self.assertEqual(self.thread_page.get_num_displayed_responses(), displayed_responses)\n        self.assertEqual(\n            self.thread_page.get_shown_responses_text(),\n            (\n                None if response_total == 0 else\n                \"Showing all responses\" if response_total == displayed_responses else\n                \"Showing first {} responses\".format(displayed_responses)\n            )\n        )\n        self.assertEqual(\n            self.thread_page.get_load_responses_button_text(),\n            (\n                None if response_total == displayed_responses else\n                \"Load all responses\" if response_total - displayed_responses < 100 else\n                \"Load next 100 responses\"\n            )\n        )\n\n    def test_pagination_no_responses(self):\n        self.setup_thread(0)\n        self.assert_response_display_correct(0, 0)\n\n    def test_pagination_few_responses(self):\n        self.setup_thread(5)\n        self.assert_response_display_correct(5, 5)\n\n    def test_pagination_two_response_pages(self):\n        self.setup_thread(50)\n        self.assert_response_display_correct(50, 25)\n\n        self.thread_page.load_more_responses()\n        self.assert_response_display_correct(50, 50)\n\n    def test_pagination_exactly_two_response_pages(self):\n        self.setup_thread(125)\n        self.assert_response_display_correct(125, 25)\n\n        self.thread_page.load_more_responses()\n        self.assert_response_display_correct(125, 125)\n\n    def test_pagination_three_response_pages(self):\n        self.setup_thread(150)\n        self.assert_response_display_correct(150, 25)\n\n        self.thread_page.load_more_responses()\n        self.assert_response_display_correct(150, 125)\n\n        self.thread_page.load_more_responses()\n        self.assert_response_display_correct(150, 150)\n\n    def test_add_response_button(self):\n        self.setup_thread(5)\n        self.assertTrue(self.thread_page.has_add_response_button())\n        self.thread_page.click_add_response_button()\n\n    def test_add_response_button_closed_thread(self):\n        self.setup_thread(5, closed=True)\n        self.assertFalse(self.thread_page.has_add_response_button())\n\n\n@attr('shard_2')\nclass DiscussionHomePageTest(UniqueCourseTest):\n    \"\"\"\n    Tests for the discussion home page.\n    \"\"\"\n\n    SEARCHED_USERNAME = \"gizmo\"\n\n    def setUp(self):\n        super(DiscussionHomePageTest, self).setUp()\n        CourseFixture(**self.course_info).install()\n        AutoAuthPage(self.browser, course_id=self.course_id).visit()\n        self.page = DiscussionTabHomePage(self.browser, self.course_id)\n        self.page.visit()\n\n    def test_new_post_button(self):\n        \"\"\"\n        Scenario: I can create new posts from the Discussion home page.\n            Given that I am on the Discussion home page\n            When I click on the 'New Post' button\n            Then I should be shown the new post form\n        \"\"\"\n        self.assertIsNotNone(self.page.new_post_button)\n        self.page.click_new_post_button()\n        self.assertIsNotNone(self.page.new_post_form)\n\n\n@attr('shard_2')\nclass DiscussionTabSingleThreadTest(BaseDiscussionTestCase, DiscussionResponsePaginationTestMixin):\n    \"\"\"\n    Tests for the discussion page displaying a single thread\n    \"\"\"\n\n    def setUp(self):\n        super(DiscussionTabSingleThreadTest, self).setUp()\n        AutoAuthPage(self.browser, course_id=self.course_id).visit()\n\n    def setup_thread_page(self, thread_id):\n        self.thread_page = self.create_single_thread_page(thread_id)  # pylint: disable=attribute-defined-outside-init\n        self.thread_page.visit()\n\n    def test_mathjax_rendering(self):\n        thread_id = \"test_thread_{}\".format(uuid4().hex)\n\n        thread_fixture = SingleThreadViewFixture(\n            Thread(\n                id=thread_id,\n                body=THREAD_CONTENT_WITH_LATEX,\n                commentable_id=self.discussion_id,\n                thread_type=\"discussion\"\n            )\n        )\n        thread_fixture.push()\n        self.setup_thread_page(thread_id)\n        self.assertTrue(self.thread_page.is_discussion_body_visible())\n        self.thread_page.verify_mathjax_preview_available()\n        self.thread_page.verify_mathjax_rendered()\n\n    def test_markdown_reference_link(self):\n        \"\"\"\n        Check markdown editor renders reference link correctly\n        and colon(:) in reference link is not converted to %3a\n        \"\"\"\n        sample_link = \"http://example.com/colon:test\"\n        thread_content = \"\"\"[enter link description here][1]\\n[1]: http://example.com/colon:test\"\"\"\n        thread_id = \"test_thread_{}\".format(uuid4().hex)\n        thread_fixture = SingleThreadViewFixture(\n            Thread(\n                id=thread_id,\n                body=thread_content,\n                commentable_id=self.discussion_id,\n                thread_type=\"discussion\"\n            )\n        )\n        thread_fixture.push()\n        self.setup_thread_page(thread_id)\n        self.assertEqual(self.thread_page.get_link_href(), sample_link)\n\n    def test_marked_answer_comments(self):\n        thread_id = \"test_thread_{}\".format(uuid4().hex)\n        response_id = \"test_response_{}\".format(uuid4().hex)\n        comment_id = \"test_comment_{}\".format(uuid4().hex)\n        thread_fixture = SingleThreadViewFixture(\n            Thread(id=thread_id, commentable_id=self.discussion_id, thread_type=\"question\")\n        )\n        thread_fixture.addResponse(\n            Response(id=response_id, endorsed=True),\n            [Comment(id=comment_id)]\n        )\n        thread_fixture.push()\n        self.setup_thread_page(thread_id)\n        self.assertFalse(self.thread_page.is_comment_visible(comment_id))\n        self.assertFalse(self.thread_page.is_add_comment_visible(response_id))\n        self.assertTrue(self.thread_page.is_show_comments_visible(response_id))\n        self.thread_page.show_comments(response_id)\n        self.assertTrue(self.thread_page.is_comment_visible(comment_id))\n        self.assertTrue(self.thread_page.is_add_comment_visible(response_id))\n        self.assertFalse(self.thread_page.is_show_comments_visible(response_id))\n\n\n@attr('shard_2')\nclass DiscussionTabMultipleThreadTest(BaseDiscussionTestCase):\n    \"\"\"\n    Tests for the discussion page with multiple threads\n    \"\"\"\n    def setUp(self):\n        super(DiscussionTabMultipleThreadTest, self).setUp()\n        AutoAuthPage(self.browser, course_id=self.course_id).visit()\n        self.thread_count = 2\n        self.thread_ids = []\n        self.setup_multiple_threads(thread_count=self.thread_count)\n\n        self.thread_page_1 = DiscussionTabSingleThreadPage(\n            self.browser,\n            self.course_id,\n            self.discussion_id,\n            self.thread_ids[0]\n        )\n        self.thread_page_2 = DiscussionTabSingleThreadPage(\n            self.browser,\n            self.course_id,\n            self.discussion_id,\n            self.thread_ids[1]\n        )\n        self.thread_page_1.visit()\n\n    def setup_multiple_threads(self, thread_count):\n        threads = []\n        for i in range(thread_count):\n            thread_id = \"test_thread_{}_{}\".format(i, uuid4().hex)\n            thread_body = \"Dummy Long text body.\" * 50\n            threads.append(\n                Thread(id=thread_id, commentable_id=self.discussion_id, body=thread_body),\n            )\n            self.thread_ids.append(thread_id)\n        view = MultipleThreadFixture(threads)\n        view.push()\n\n    def test_page_scroll_on_thread_change_view(self):\n        \"\"\"\n        Check switching between threads changes the page focus\n        \"\"\"\n        # verify threads are rendered on the page\n        self.assertTrue(\n            self.thread_page_1.check_threads_rendered_successfully(thread_count=self.thread_count)\n        )\n\n        # From the thread_page_1 open & verify next thread\n        self.thread_page_1.click_and_open_thread(thread_id=self.thread_ids[1])\n        self.assertTrue(self.thread_page_2.is_browser_on_page())\n\n        # Verify that the focus is changed\n        self.thread_page_2.check_focus_is_set(selector=\".discussion-article\")\n\n\n@attr('shard_2')\nclass DiscussionOpenClosedThreadTest(BaseDiscussionTestCase):\n    \"\"\"\n    Tests for checking the display of attributes on open and closed threads\n    \"\"\"\n\n    def setUp(self):\n        super(DiscussionOpenClosedThreadTest, self).setUp()\n\n        self.thread_id = \"test_thread_{}\".format(uuid4().hex)\n\n    def setup_user(self, roles=[]):\n        roles_str = ','.join(roles)\n        self.user_id = AutoAuthPage(self.browser, course_id=self.course_id, roles=roles_str).visit().get_user_id()\n\n    def setup_view(self, **thread_kwargs):\n        thread_kwargs.update({'commentable_id': self.discussion_id})\n        view = SingleThreadViewFixture(\n            Thread(id=self.thread_id, **thread_kwargs)\n        )\n        view.addResponse(Response(id=\"response1\"))\n        view.push()\n\n    def setup_openclosed_thread_page(self, closed=False):\n        self.setup_user(roles=['Moderator'])\n        if closed:\n            self.setup_view(closed=True)\n        else:\n            self.setup_view()\n        page = self.create_single_thread_page(self.thread_id)\n        page.visit()\n        page.close_open_thread()\n        return page\n\n    def test_originally_open_thread_vote_display(self):\n        page = self.setup_openclosed_thread_page()\n        self.assertFalse(page._is_element_visible('.forum-thread-main-wrapper .action-vote'))\n        self.assertTrue(page._is_element_visible('.forum-thread-main-wrapper .display-vote'))\n        self.assertFalse(page._is_element_visible('.response_response1 .action-vote'))\n        self.assertTrue(page._is_element_visible('.response_response1 .display-vote'))\n\n    def test_originally_closed_thread_vote_display(self):\n        page = self.setup_openclosed_thread_page(True)\n        self.assertTrue(page._is_element_visible('.forum-thread-main-wrapper .action-vote'))\n        self.assertFalse(page._is_element_visible('.forum-thread-main-wrapper .display-vote'))\n        self.assertTrue(page._is_element_visible('.response_response1 .action-vote'))\n        self.assertFalse(page._is_element_visible('.response_response1 .display-vote'))\n\n\n@attr('shard_2')\nclass DiscussionCommentDeletionTest(BaseDiscussionTestCase):\n    \"\"\"\n    Tests for deleting comments displayed beneath responses in the single thread view.\n    \"\"\"\n    def setup_user(self, roles=[]):\n        roles_str = ','.join(roles)\n        self.user_id = AutoAuthPage(self.browser, course_id=self.course_id, roles=roles_str).visit().get_user_id()\n\n    def setup_view(self):\n        view = SingleThreadViewFixture(Thread(id=\"comment_deletion_test_thread\", commentable_id=self.discussion_id))\n        view.addResponse(\n            Response(id=\"response1\"), [\n                Comment(id=\"comment_other_author\"),\n                Comment(id=\"comment_self_author\", user_id=self.user_id, thread_id=\"comment_deletion_test_thread\")\n            ]\n        )\n        view.push()\n\n    def test_comment_deletion_as_student(self):\n        self.setup_user()\n        self.setup_view()\n        page = self.create_single_thread_page(\"comment_deletion_test_thread\")\n        page.visit()\n        self.assertTrue(page.is_comment_deletable(\"comment_self_author\"))\n        self.assertTrue(page.is_comment_visible(\"comment_other_author\"))\n        self.assertFalse(page.is_comment_deletable(\"comment_other_author\"))\n        page.delete_comment(\"comment_self_author\")\n\n    def test_comment_deletion_as_moderator(self):\n        self.setup_user(roles=['Moderator'])\n        self.setup_view()\n        page = self.create_single_thread_page(\"comment_deletion_test_thread\")\n        page.visit()\n        self.assertTrue(page.is_comment_deletable(\"comment_self_author\"))\n        self.assertTrue(page.is_comment_deletable(\"comment_other_author\"))\n        page.delete_comment(\"comment_self_author\")\n        page.delete_comment(\"comment_other_author\")\n\n\n@attr('shard_2')\nclass DiscussionResponseEditTest(BaseDiscussionTestCase):\n    \"\"\"\n    Tests for editing responses displayed beneath thread in the single thread view.\n    \"\"\"\n    def setup_user(self, roles=[]):\n        roles_str = ','.join(roles)\n        self.user_id = AutoAuthPage(self.browser, course_id=self.course_id, roles=roles_str).visit().get_user_id()\n\n    def setup_view(self):\n        view = SingleThreadViewFixture(Thread(id=\"response_edit_test_thread\", commentable_id=self.discussion_id))\n        view.addResponse(\n            Response(id=\"response_other_author\", user_id=\"other\", thread_id=\"response_edit_test_thread\"),\n        )\n        view.addResponse(\n            Response(id=\"response_self_author\", user_id=self.user_id, thread_id=\"response_edit_test_thread\"),\n        )\n        view.push()\n\n    def edit_response(self, page, response_id):\n        self.assertTrue(page.is_response_editable(response_id))\n        page.start_response_edit(response_id)\n        new_response = \"edited body\"\n        page.set_response_editor_value(response_id, new_response)\n        page.submit_response_edit(response_id, new_response)\n\n    def test_edit_response_add_link(self):\n        \"\"\"\n        Scenario: User submits valid input to the 'add link' form\n            Given I am editing a response on a discussion page\n            When I click the 'add link' icon in the editor toolbar\n            And enter a valid url to the URL input field\n            And enter a valid string in the Description input field\n            And click the 'OK' button\n            Then the edited response should contain the new link\n        \"\"\"\n        self.setup_user()\n        self.setup_view()\n        page = self.create_single_thread_page(\"response_edit_test_thread\")\n        page.visit()\n\n        response_id = \"response_self_author\"\n        url = \"http://example.com\"\n        description = \"example\"\n\n        page.start_response_edit(response_id)\n        page.set_response_editor_value(response_id, \"\")\n        page.add_content_via_editor_button(\n            \"link\", response_id, url, description)\n        page.submit_response_edit(response_id, description)\n\n        expected_response_html = (\n            '<p><a href=\"{}\">{}</a></p>'.format(url, description)\n        )\n        actual_response_html = page.q(\n            css=\".response_{} .response-body\".format(response_id)\n        ).html[0]\n        self.assertEqual(expected_response_html, actual_response_html)\n\n    def test_edit_response_add_image(self):\n        \"\"\"\n        Scenario: User submits valid input to the 'add image' form\n            Given I am editing a response on a discussion page\n            When I click the 'add image' icon in the editor toolbar\n            And enter a valid url to the URL input field\n            And enter a valid string in the Description input field\n            And click the 'OK' button\n            Then the edited response should contain the new image\n        \"\"\"\n        self.setup_user()\n        self.setup_view()\n        page = self.create_single_thread_page(\"response_edit_test_thread\")\n        page.visit()\n\n        response_id = \"response_self_author\"\n        url = \"http://www.example.com/something.png\"\n        description = \"image from example.com\"\n\n        page.start_response_edit(response_id)\n        page.set_response_editor_value(response_id, \"\")\n        page.add_content_via_editor_button(\n            \"image\", response_id, url, description)\n        page.submit_response_edit(response_id, '')\n\n        expected_response_html = (\n            '<p><img src=\"{}\" alt=\"{}\" title=\"\"></p>'.format(url, description)\n        )\n        actual_response_html = page.q(\n            css=\".response_{} .response-body\".format(response_id)\n        ).html[0]\n        self.assertEqual(expected_response_html, actual_response_html)\n\n    def test_edit_response_add_image_error_msg(self):\n        \"\"\"\n        Scenario: User submits invalid input to the 'add image' form\n            Given I am editing a response on a discussion page\n            When I click the 'add image' icon in the editor toolbar\n            And enter an invalid url to the URL input field\n            And enter an empty string in the Description input field\n            And click the 'OK' button\n            Then I should be shown 2 error messages\n        \"\"\"\n        self.setup_user()\n        self.setup_view()\n        page = self.create_single_thread_page(\"response_edit_test_thread\")\n        page.visit()\n        page.start_response_edit(\"response_self_author\")\n        page.add_content_via_editor_button(\n            \"image\", \"response_self_author\", '', '')\n        page.verify_link_editor_error_messages_shown()\n\n    def test_edit_response_add_decorative_image(self):\n        \"\"\"\n        Scenario: User submits invalid input to the 'add image' form\n            Given I am editing a response on a discussion page\n            When I click the 'add image' icon in the editor toolbar\n            And enter a valid url to the URL input field\n            And enter an empty string in the Description input field\n            And I check the 'image is decorative' checkbox\n            And click the 'OK' button\n            Then the edited response should contain the new image\n        \"\"\"\n        self.setup_user()\n        self.setup_view()\n        page = self.create_single_thread_page(\"response_edit_test_thread\")\n        page.visit()\n\n        response_id = \"response_self_author\"\n        url = \"http://www.example.com/something.png\"\n        description = \"\"\n\n        page.start_response_edit(response_id)\n        page.set_response_editor_value(response_id, \"Some content\")\n        page.add_content_via_editor_button(\n            \"image\", response_id, url, description, is_decorative=True)\n        page.submit_response_edit(response_id, \"Some content\")\n\n        expected_response_html = (\n            '<p>Some content<img src=\"{}\" alt=\"{}\" title=\"\"></p>'.format(\n                url, description)\n        )\n        actual_response_html = page.q(\n            css=\".response_{} .response-body\".format(response_id)\n        ).html[0]\n        self.assertEqual(expected_response_html, actual_response_html)\n\n    def test_edit_response_add_link_error_msg(self):\n        \"\"\"\n        Scenario: User submits invalid input to the 'add link' form\n            Given I am editing a response on a discussion page\n            When I click the 'add link' icon in the editor toolbar\n            And enter an invalid url to the URL input field\n            And enter an empty string in the Description input field\n            And click the 'OK' button\n            Then I should be shown 2 error messages\n        \"\"\"\n        self.setup_user()\n        self.setup_view()\n        page = self.create_single_thread_page(\"response_edit_test_thread\")\n        page.visit()\n        page.start_response_edit(\"response_self_author\")\n        page.add_content_via_editor_button(\n            \"link\", \"response_self_author\", '', '')\n        page.verify_link_editor_error_messages_shown()\n\n    def test_edit_response_as_student(self):\n        \"\"\"\n        Scenario: Students should be able to edit the response they created not responses of other users\n            Given that I am on discussion page with student logged in\n            When I try to edit the response created by student\n            Then the response should be edited and rendered successfully\n            And responses from other users should be shown over there\n            And the student should be able to edit the response of other people\n        \"\"\"\n        self.setup_user()\n        self.setup_view()\n        page = self.create_single_thread_page(\"response_edit_test_thread\")\n        page.visit()\n        self.assertTrue(page.is_response_visible(\"response_other_author\"))\n        self.assertFalse(page.is_response_editable(\"response_other_author\"))\n        self.edit_response(page, \"response_self_author\")\n\n    def test_edit_response_as_moderator(self):\n        \"\"\"\n        Scenario: Moderator should be able to edit the response they created and responses of other users\n            Given that I am on discussion page with moderator logged in\n            When I try to edit the response created by moderator\n            Then the response should be edited and rendered successfully\n            And I try to edit the response created by other users\n            Then the response should be edited and rendered successfully\n        \"\"\"\n        self.setup_user(roles=[\"Moderator\"])\n        self.setup_view()\n        page = self.create_single_thread_page(\"response_edit_test_thread\")\n        page.visit()\n        self.edit_response(page, \"response_self_author\")\n        self.edit_response(page, \"response_other_author\")\n\n    def test_vote_report_endorse_after_edit(self):\n        \"\"\"\n        Scenario: Moderator should be able to vote, report or endorse after editing the response.\n            Given that I am on discussion page with moderator logged in\n            When I try to edit the response created by moderator\n            Then the response should be edited and rendered successfully\n            And I try to edit the response created by other users\n            Then the response should be edited and rendered successfully\n            And I try to vote the response created by moderator\n            Then the response should be voted successfully\n            And I try to vote the response created by other users\n            Then the response should be voted successfully\n            And I try to report the response created by moderator\n            Then the response should be reported successfully\n            And I try to report the response created by other users\n            Then the response should be reported successfully\n            And I try to endorse the response created by moderator\n            Then the response should be endorsed successfully\n            And I try to endorse the response created by other users\n            Then the response should be endorsed successfully\n        \"\"\"\n        self.setup_user(roles=[\"Moderator\"])\n        self.setup_view()\n        page = self.create_single_thread_page(\"response_edit_test_thread\")\n        page.visit()\n        self.edit_response(page, \"response_self_author\")\n        self.edit_response(page, \"response_other_author\")\n        page.vote_response('response_self_author')\n        page.vote_response('response_other_author')\n        page.report_response('response_self_author')\n        page.report_response('response_other_author')\n        page.endorse_response('response_self_author')\n        page.endorse_response('response_other_author')\n\n\n@attr('shard_2')\nclass DiscussionCommentEditTest(BaseDiscussionTestCase):\n    \"\"\"\n    Tests for editing comments displayed beneath responses in the single thread view.\n    \"\"\"\n    def setup_user(self, roles=[]):\n        roles_str = ','.join(roles)\n        self.user_id = AutoAuthPage(self.browser, course_id=self.course_id, roles=roles_str).visit().get_user_id()\n\n    def setup_view(self):\n        view = SingleThreadViewFixture(Thread(id=\"comment_edit_test_thread\", commentable_id=self.discussion_id))\n        view.addResponse(\n            Response(id=\"response1\"),\n            [Comment(id=\"comment_other_author\", user_id=\"other\"), Comment(id=\"comment_self_author\", user_id=self.user_id)])\n        view.push()\n\n    def edit_comment(self, page, comment_id):\n        page.start_comment_edit(comment_id)\n        new_comment = \"edited body\"\n        page.set_comment_editor_value(comment_id, new_comment)\n        page.submit_comment_edit(comment_id, new_comment)\n\n    def test_edit_comment_as_student(self):\n        self.setup_user()\n        self.setup_view()\n        page = self.create_single_thread_page(\"comment_edit_test_thread\")\n        page.visit()\n        self.assertTrue(page.is_comment_editable(\"comment_self_author\"))\n        self.assertTrue(page.is_comment_visible(\"comment_other_author\"))\n        self.assertFalse(page.is_comment_editable(\"comment_other_author\"))\n        self.edit_comment(page, \"comment_self_author\")\n\n    def test_edit_comment_as_moderator(self):\n        self.setup_user(roles=[\"Moderator\"])\n        self.setup_view()\n        page = self.create_single_thread_page(\"comment_edit_test_thread\")\n        page.visit()\n        self.assertTrue(page.is_comment_editable(\"comment_self_author\"))\n        self.assertTrue(page.is_comment_editable(\"comment_other_author\"))\n        self.edit_comment(page, \"comment_self_author\")\n        self.edit_comment(page, \"comment_other_author\")\n\n    def test_cancel_comment_edit(self):\n        self.setup_user()\n        self.setup_view()\n        page = self.create_single_thread_page(\"comment_edit_test_thread\")\n        page.visit()\n        self.assertTrue(page.is_comment_editable(\"comment_self_author\"))\n        original_body = page.get_comment_body(\"comment_self_author\")\n        page.start_comment_edit(\"comment_self_author\")\n        page.set_comment_editor_value(\"comment_self_author\", \"edited body\")\n        page.cancel_comment_edit(\"comment_self_author\", original_body)\n\n    def test_editor_visibility(self):\n        \"\"\"Only one editor should be visible at a time within a single response\"\"\"\n        self.setup_user(roles=[\"Moderator\"])\n        self.setup_view()\n        page = self.create_single_thread_page(\"comment_edit_test_thread\")\n        page.visit()\n        self.assertTrue(page.is_comment_editable(\"comment_self_author\"))\n        self.assertTrue(page.is_comment_editable(\"comment_other_author\"))\n        self.assertTrue(page.is_add_comment_visible(\"response1\"))\n        original_body = page.get_comment_body(\"comment_self_author\")\n        page.start_comment_edit(\"comment_self_author\")\n        self.assertFalse(page.is_add_comment_visible(\"response1\"))\n        self.assertTrue(page.is_comment_editor_visible(\"comment_self_author\"))\n        page.set_comment_editor_value(\"comment_self_author\", \"edited body\")\n        page.start_comment_edit(\"comment_other_author\")\n        self.assertFalse(page.is_comment_editor_visible(\"comment_self_author\"))\n        self.assertTrue(page.is_comment_editor_visible(\"comment_other_author\"))\n        self.assertEqual(page.get_comment_body(\"comment_self_author\"), original_body)\n        page.start_response_edit(\"response1\")\n        self.assertFalse(page.is_comment_editor_visible(\"comment_other_author\"))\n        self.assertTrue(page.is_response_editor_visible(\"response1\"))\n        original_body = page.get_comment_body(\"comment_self_author\")\n        page.start_comment_edit(\"comment_self_author\")\n        self.assertFalse(page.is_response_editor_visible(\"response1\"))\n        self.assertTrue(page.is_comment_editor_visible(\"comment_self_author\"))\n        page.cancel_comment_edit(\"comment_self_author\", original_body)\n        self.assertFalse(page.is_comment_editor_visible(\"comment_self_author\"))\n        self.assertTrue(page.is_add_comment_visible(\"response1\"))\n\n\n@attr('shard_2')\nclass InlineDiscussionTest(UniqueCourseTest, DiscussionResponsePaginationTestMixin):\n    \"\"\"\n    Tests for inline discussions\n    \"\"\"\n\n    def setUp(self):\n        super(InlineDiscussionTest, self).setUp()\n        self.thread_ids = []\n        self.discussion_id = \"test_discussion_{}\".format(uuid4().hex)\n        self.additional_discussion_id = \"test_discussion_{}\".format(uuid4().hex)\n        self.course_fix = CourseFixture(**self.course_info).add_children(\n            XBlockFixtureDesc(\"chapter\", \"Test Section\").add_children(\n                XBlockFixtureDesc(\"sequential\", \"Test Subsection\").add_children(\n                    XBlockFixtureDesc(\"vertical\", \"Test Unit\").add_children(\n                        XBlockFixtureDesc(\n                            \"discussion\",\n                            \"Test Discussion\",\n                            metadata={\"discussion_id\": self.discussion_id}\n                        ),\n                        XBlockFixtureDesc(\n                            \"discussion\",\n                            \"Test Discussion 1\",\n                            metadata={\"discussion_id\": self.additional_discussion_id}\n                        )\n                    )\n                )\n            )\n        ).install()\n\n        self.user_id = AutoAuthPage(self.browser, course_id=self.course_id).visit().get_user_id()\n\n        self.courseware_page = CoursewarePage(self.browser, self.course_id)\n        self.courseware_page.visit()\n        self.discussion_page = InlineDiscussionPage(self.browser, self.discussion_id)\n        self.additional_discussion_page = InlineDiscussionPage(self.browser, self.additional_discussion_id)\n\n    def setup_thread_page(self, thread_id):\n        self.discussion_page.expand_discussion()\n        self.assertEqual(self.discussion_page.get_num_displayed_threads(), 1)\n        self.thread_page = InlineDiscussionThreadPage(self.browser, thread_id)  # pylint: disable=attribute-defined-outside-init\n        self.thread_page.expand()\n\n    def setup_multiple_inline_threads(self, thread_count):\n        \"\"\"\n        Set up multiple treads on the page by passing 'thread_count'\n        \"\"\"\n        threads = []\n        for i in range(thread_count):\n            thread_id = \"test_thread_{}_{}\".format(i, uuid4().hex)\n            threads.append(\n                Thread(id=thread_id, commentable_id=self.discussion_id),\n            )\n            self.thread_ids.append(thread_id)\n        thread_fixture = MultipleThreadFixture(threads)\n        thread_fixture.add_response(\n            Response(id=\"response1\"),\n            [Comment(id=\"comment1\", user_id=\"other\"), Comment(id=\"comment2\", user_id=self.user_id)],\n            threads[0]\n        )\n        thread_fixture.push()\n\n    def test_page_while_expanding_inline_discussion(self):\n        \"\"\"\n        Tests for the Inline Discussion page with multiple treads. Page should not focus 'thread-wrapper'\n        after loading responses.\n        \"\"\"\n        self.setup_multiple_inline_threads(thread_count=3)\n        self.discussion_page.expand_discussion()\n        thread_page = InlineDiscussionThreadPage(self.browser, self.thread_ids[0])\n        thread_page.expand()\n\n        # Check if 'thread-wrapper' is focused after expanding thread\n        self.assertFalse(thread_page.check_if_selector_is_focused(selector='.thread-wrapper'))\n\n    def test_initial_render(self):\n        self.assertFalse(self.discussion_page.is_discussion_expanded())\n\n    def test_expand_discussion_empty(self):\n        self.discussion_page.expand_discussion()\n        self.assertEqual(self.discussion_page.get_num_displayed_threads(), 0)\n\n    def check_anonymous_to_peers(self, is_staff):\n        thread = Thread(id=uuid4().hex, anonymous_to_peers=True, commentable_id=self.discussion_id)\n        thread_fixture = SingleThreadViewFixture(thread)\n        thread_fixture.push()\n        self.setup_thread_page(thread.get(\"id\"))\n        self.assertEqual(self.thread_page.is_thread_anonymous(), not is_staff)\n\n    def test_anonymous_to_peers_threads_as_staff(self):\n        AutoAuthPage(self.browser, course_id=self.course_id, roles=\"Administrator\").visit()\n        self.courseware_page.visit()\n        self.check_anonymous_to_peers(True)\n\n    def test_anonymous_to_peers_threads_as_peer(self):\n        self.check_anonymous_to_peers(False)\n\n    def test_discussion_blackout_period(self):\n        now = datetime.datetime.now(UTC)\n        self.course_fix.add_advanced_settings(\n            {\n                u\"discussion_blackouts\": {\n                    \"value\": [\n                        [\n                            (now - datetime.timedelta(days=14)).isoformat(),\n                            (now + datetime.timedelta(days=2)).isoformat()\n                        ]\n                    ]\n                }\n            }\n        )\n        self.course_fix._add_advanced_settings()\n        self.browser.refresh()\n        thread = Thread(id=uuid4().hex, commentable_id=self.discussion_id)\n        thread_fixture = SingleThreadViewFixture(thread)\n        thread_fixture.addResponse(\n            Response(id=\"response1\"),\n            [Comment(id=\"comment1\", user_id=\"other\"), Comment(id=\"comment2\", user_id=self.user_id)])\n        thread_fixture.push()\n        self.setup_thread_page(thread.get(\"id\"))\n        self.assertFalse(self.discussion_page.element_exists(\".new-post-btn\"))\n        self.assertFalse(self.thread_page.has_add_response_button())\n        self.assertFalse(self.thread_page.is_response_editable(\"response1\"))\n        self.assertFalse(self.thread_page.is_add_comment_visible(\"response1\"))\n        self.assertFalse(self.thread_page.is_comment_editable(\"comment1\"))\n        self.assertFalse(self.thread_page.is_comment_editable(\"comment2\"))\n        self.assertFalse(self.thread_page.is_comment_deletable(\"comment1\"))\n        self.assertFalse(self.thread_page.is_comment_deletable(\"comment2\"))\n\n    def test_dual_discussion_module(self):\n        \"\"\"\n        Scenario: Two discussion module in one unit shouldn't override their actions\n        Given that I'm on courseware page where there are two inline discussion\n        When I click on one discussion module new post button\n        Then it should add new post form of that module in DOM\n        And I should be shown new post form of that module\n        And I shouldn't be shown second discussion module new post form\n        And I click on second discussion module new post button\n        Then it should add new post form of second module in DOM\n        And I should be shown second discussion new post form\n        And I shouldn't be shown first discussion module new post form\n        And I have two new post form in the DOM\n        When I click back on first module new post button\n        And I should be shown new post form of that module\n        And I shouldn't be shown second discussion module new post form\n        \"\"\"\n        self.discussion_page.wait_for_page()\n        self.additional_discussion_page.wait_for_page()\n        self.discussion_page.click_new_post_button()\n        with self.discussion_page.handle_alert():\n            self.discussion_page.click_cancel_new_post()\n        self.additional_discussion_page.click_new_post_button()\n        self.assertFalse(self.discussion_page._is_element_visible(\".new-post-article\"))\n        with self.additional_discussion_page.handle_alert():\n            self.additional_discussion_page.click_cancel_new_post()\n        self.discussion_page.click_new_post_button()\n        self.assertFalse(self.additional_discussion_page._is_element_visible(\".new-post-article\"))\n\n\n@attr('shard_2')\nclass DiscussionUserProfileTest(UniqueCourseTest):\n    \"\"\"\n    Tests for user profile page in discussion tab.\n    \"\"\"\n\n    PAGE_SIZE = 20  # django_comment_client.forum.views.THREADS_PER_PAGE\n    PROFILED_USERNAME = \"profiled-user\"\n\n    def setUp(self):\n        super(DiscussionUserProfileTest, self).setUp()\n        CourseFixture(**self.course_info).install()\n        # The following line creates a user enrolled in our course, whose\n        # threads will be viewed, but not the one who will view the page.\n        # It isn't necessary to log them in, but using the AutoAuthPage\n        # saves a lot of code.\n        self.profiled_user_id = AutoAuthPage(\n            self.browser,\n            username=self.PROFILED_USERNAME,\n            course_id=self.course_id\n        ).visit().get_user_id()\n        # now create a second user who will view the profile.\n        self.user_id = AutoAuthPage(\n            self.browser,\n            course_id=self.course_id\n        ).visit().get_user_id()\n\n    def check_pages(self, num_threads):\n        # set up the stub server to return the desired amount of thread results\n        threads = [Thread(id=uuid4().hex) for _ in range(num_threads)]\n        UserProfileViewFixture(threads).push()\n        # navigate to default view (page 1)\n        page = DiscussionUserProfilePage(\n            self.browser,\n            self.course_id,\n            self.profiled_user_id,\n            self.PROFILED_USERNAME\n        )\n        page.visit()\n\n        current_page = 1\n        total_pages = max(num_threads - 1, 1) / self.PAGE_SIZE + 1\n        all_pages = range(1, total_pages + 1)\n        return page\n\n        def _check_page():\n            # ensure the page being displayed as \"current\" is the expected one\n            self.assertEqual(page.get_current_page(), current_page)\n            # ensure the expected threads are being shown in the right order\n            threads_expected = threads[(current_page - 1) * self.PAGE_SIZE:current_page * self.PAGE_SIZE]\n            self.assertEqual(page.get_shown_thread_ids(), [t[\"id\"] for t in threads_expected])\n            # ensure the clickable page numbers are the expected ones\n            self.assertEqual(page.get_clickable_pages(), [\n                p for p in all_pages\n                if p != current_page\n                and p - 2 <= current_page <= p + 2\n                or (current_page > 2 and p == 1)\n                or (current_page < total_pages and p == total_pages)\n            ])\n            # ensure the previous button is shown, but only if it should be.\n            # when it is shown, make sure it works.\n            if current_page > 1:\n                self.assertTrue(page.is_prev_button_shown(current_page - 1))\n                page.click_prev_page()\n                self.assertEqual(page.get_current_page(), current_page - 1)\n                page.click_next_page()\n                self.assertEqual(page.get_current_page(), current_page)\n            else:\n                self.assertFalse(page.is_prev_button_shown())\n            # ensure the next button is shown, but only if it should be.\n            if current_page < total_pages:\n                self.assertTrue(page.is_next_button_shown(current_page + 1))\n            else:\n                self.assertFalse(page.is_next_button_shown())\n\n        # click all the way up through each page\n        for i in range(current_page, total_pages):\n            _check_page()\n            if current_page < total_pages:\n                page.click_on_page(current_page + 1)\n                current_page += 1\n\n        # click all the way back down\n        for i in range(current_page, 0, -1):\n            _check_page()\n            if current_page > 1:\n                page.click_on_page(current_page - 1)\n                current_page -= 1\n\n    def test_0_threads(self):\n        self.check_pages(0)\n\n    def test_1_thread(self):\n        self.check_pages(1)\n\n    def test_20_threads(self):\n        self.check_pages(20)\n\n    def test_21_threads(self):\n        self.check_pages(21)\n\n    def test_151_threads(self):\n        self.check_pages(151)\n\n    def test_pagination_window_reposition(self):\n        page = self.check_pages(50)\n        page.click_next_page()\n        page.wait_for_ajax()\n        self.assertTrue(page.is_window_on_top())\n\n    def test_redirects_to_learner_profile(self):\n        \"\"\"\n        Scenario: Verify that learner-profile link is present on forum discussions page and we can navigate to it.\n\n        Given that I am on discussion forum user's profile page.\n        And I can see a username on left sidebar\n        When I click on my username.\n        Then I will be navigated to Learner Profile page.\n        And I can my username on Learner Profile page\n        \"\"\"\n        learner_profile_page = LearnerProfilePage(self.browser, self.PROFILED_USERNAME)\n\n        page = self.check_pages(1)\n        page.click_on_sidebar_username()\n\n        learner_profile_page.wait_for_page()\n        self.assertTrue(learner_profile_page.field_is_visible('username'))\n\n\n@attr('shard_2')\nclass DiscussionSearchAlertTest(UniqueCourseTest):\n    \"\"\"\n    Tests for spawning and dismissing alerts related to user search actions and their results.\n    \"\"\"\n\n    SEARCHED_USERNAME = \"gizmo\"\n\n    def setUp(self):\n        super(DiscussionSearchAlertTest, self).setUp()\n        CourseFixture(**self.course_info).install()\n        # first auto auth call sets up a user that we will search for in some tests\n        self.searched_user_id = AutoAuthPage(\n            self.browser,\n            username=self.SEARCHED_USERNAME,\n            course_id=self.course_id\n        ).visit().get_user_id()\n        # this auto auth call creates the actual session user\n        AutoAuthPage(self.browser, course_id=self.course_id).visit()\n        self.page = DiscussionTabHomePage(self.browser, self.course_id)\n        self.page.visit()\n\n    def setup_corrected_text(self, text):\n        SearchResultFixture(SearchResult(corrected_text=text)).push()\n\n    def check_search_alert_messages(self, expected):\n        actual = self.page.get_search_alert_messages()\n        self.assertTrue(all(map(lambda msg, sub: msg.lower().find(sub.lower()) >= 0, actual, expected)))\n\n    def test_no_rewrite(self):\n        self.setup_corrected_text(None)\n        self.page.perform_search()\n        self.check_search_alert_messages([\"no threads\"])\n\n    def test_rewrite_dismiss(self):\n        self.setup_corrected_text(\"foo\")\n        self.page.perform_search()\n        self.check_search_alert_messages([\"foo\"])\n        self.page.dismiss_alert_message(\"foo\")\n        self.check_search_alert_messages([])\n\n    def test_new_search(self):\n        self.setup_corrected_text(\"foo\")\n        self.page.perform_search()\n        self.check_search_alert_messages([\"foo\"])\n\n        self.setup_corrected_text(\"bar\")\n        self.page.perform_search()\n        self.check_search_alert_messages([\"bar\"])\n\n        self.setup_corrected_text(None)\n        self.page.perform_search()\n        self.check_search_alert_messages([\"no threads\"])\n\n    def test_rewrite_and_user(self):\n        self.setup_corrected_text(\"foo\")\n        self.page.perform_search(self.SEARCHED_USERNAME)\n        self.check_search_alert_messages([\"foo\", self.SEARCHED_USERNAME])\n\n    def test_user_only(self):\n        self.setup_corrected_text(None)\n        self.page.perform_search(self.SEARCHED_USERNAME)\n        self.check_search_alert_messages([\"no threads\", self.SEARCHED_USERNAME])\n        # make sure clicking the link leads to the user profile page\n        UserProfileViewFixture([]).push()\n        self.page.get_search_alert_links().first.click()\n        DiscussionUserProfilePage(\n            self.browser,\n            self.course_id,\n            self.searched_user_id,\n            self.SEARCHED_USERNAME\n        ).wait_for_page()\n\n\n@attr('shard_2')\nclass DiscussionSortPreferenceTest(UniqueCourseTest):\n    \"\"\"\n    Tests for the discussion page displaying a single thread.\n    \"\"\"\n\n    def setUp(self):\n        super(DiscussionSortPreferenceTest, self).setUp()\n\n        # Create a course to register for.\n        CourseFixture(**self.course_info).install()\n\n        AutoAuthPage(self.browser, course_id=self.course_id).visit()\n\n        self.sort_page = DiscussionSortPreferencePage(self.browser, self.course_id)\n        self.sort_page.visit()\n\n    def test_default_sort_preference(self):\n        \"\"\"\n        Test to check the default sorting preference of user. (Default = date )\n        \"\"\"\n        selected_sort = self.sort_page.get_selected_sort_preference()\n        self.assertEqual(selected_sort, \"date\")\n\n    def test_change_sort_preference(self):\n        \"\"\"\n        Test that if user sorting preference is changing properly.\n        \"\"\"\n        selected_sort = \"\"\n        for sort_type in [\"votes\", \"comments\", \"date\"]:\n            self.assertNotEqual(selected_sort, sort_type)\n            self.sort_page.change_sort_preference(sort_type)\n            selected_sort = self.sort_page.get_selected_sort_preference()\n            self.assertEqual(selected_sort, sort_type)\n\n    def test_last_preference_saved(self):\n        \"\"\"\n        Test that user last preference is saved.\n        \"\"\"\n        selected_sort = \"\"\n        for sort_type in [\"votes\", \"comments\", \"date\"]:\n            self.assertNotEqual(selected_sort, sort_type)\n            self.sort_page.change_sort_preference(sort_type)\n            selected_sort = self.sort_page.get_selected_sort_preference()\n            self.assertEqual(selected_sort, sort_type)\n            self.sort_page.refresh_page()\n            selected_sort = self.sort_page.get_selected_sort_preference()\n            self.assertEqual(selected_sort, sort_type)\n","repo_name":"Edraak/edx-platform","sub_path":"common/test/acceptance/tests/discussion/test_discussion.py","file_name":"test_discussion.py","file_ext":"py","file_size_in_byte":52649,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"85"}
+{"seq_id":"25978545333","text":"# My_Dictionary={ \"car\":\"fast\",\"truck\":\"big\"}\n#\n# print(\"car\"in My_Dictionary.keys())\n\n# My_String =\"Being HEADER text/html sender: bob\"\\\n#         \"***** Hi, welcome to python class! ***** 1/1 message END FOOTER\"\n# Header =\"*****\"\n# My_Start_Index = My_String.find(Header) + len(Header)\n# print(My_Start_Index)\n# My_End_Index = My_String.rfind(\"*****\")\n# My_Real_Message =My_String[My_Start_Index:My_End_Index]\n# print(My_Real_Message)\n\n# while True :\n#     User_selection = input(\"what would you like  to do\\n\"\n#                            \" .Display student\\n\"\n#                            \"3.Export Student List\\n\"\n#                            \"4.Exit\\n\")\n#     if User_selection== \"1\":\n#             for student in student_List:\n#                 print(student)\n#             input(\"press enter to continue\")\n#     if User_selection ==\"2\":\n#         student_To_Add = input(\"what is the student's name?\")\n#         student_List.append(student_To_Add)\n#         print(\"student added\")\n#     elif User_selection ==\"3\":\n#         Out_File = open(Export_File_Location,'w')\n\n## you need to read the file ,load it into your 'program some how'\n\n##once you load it in, provide a user interface,that allows the user to search\n\n#for a word, and display the full article(s) that it is contained In.\n\n#run until a user types the word \"EXITPROGRAM\"\n\nfrom collections import OrderedDict\nEXITPROGRAM =False\nMy_File = open('UNDHR.txt', 'r').read()\nCounter= 1\nArticles ={}\nloop =True\nwhile loop :\n\n    while \"Article\" in My_File:\n        start =My_File.find(\"Article \" +str(Counter))\n        End =My_File.find(\"Article \" +str(Counter+1))\n        Articles[Counter] = My_File[start:End]\n        Counter +=1\n        My_File= My_File[End:]\n\n    search_a=input(\"search word Article\")\n\n    for v in Articles.values():\n        if search_a in v:\n            print(v)\n        else:\n            continue\n         # search_a = My_File.find(\"Article \" + str(search_word))\n         # My_Dictionary = {search_a: My_File}\n\n\n\n\n\n","repo_name":"swpheus/Python_Study","sub_path":"DayOne/DayFive.py","file_name":"DayFive.py","file_ext":"py","file_size_in_byte":1999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"13885790286","text":"from caso import Caso  # type: ignore\nfrom graficos import grafico_cmo_subsistema  # type: ignore\nfrom graficos import grafico_deficit_subsistema\nfrom graficos import grafico_earm_sin  # type: ignore\nfrom graficos import grafico_earm_subsistema  # type: ignore\nfrom graficos import grafico_gt_sin  # type: ignore\nfrom graficos import grafico_ghid_sin  # type: ignore\nfrom graficos import grafico_gt_subsistema  # type: ignore\nfrom graficos import grafico_ghid_subsistema  # type: ignore\nfrom graficos import exporta_dados  # type: ignore\n\nimport os\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\n\ndef main():\n    # Diretórios com as saídas do backtest no formato\n    # relato_AAAA_MM.rvX\n    dir_vigente_novo = os.getenv(\"DIR_VIGENTE_NOVO\")\n    dir_1omes_2semanas = os.getenv(\"DIR_1MES_2SEMANAS\")\n    dir_perfeito_1omes = os.getenv(\"DIR_PERFEITO_1MES\")\n    dir_perfeito = os.getenv(\"DIR_PERFEITO\")\n    dir_proposto = os.getenv(\"DIR_PROPOSTO\")\n\n    # Constroi os casos\n    vigente = Caso.constroi_caso_de_pasta(dir_vigente_novo,\n                                          \"Vigente\")\n    nome = \"SMAP 1º Mês (2 Semanas)\"\n    mes2semanas = Caso.constroi_caso_de_pasta(dir_1omes_2semanas,\n                                              nome)\n    nome = \"SMAP Perfeito 1º Mês\"\n    perfeito1mes = Caso.constroi_caso_de_pasta(dir_perfeito_1omes,\n                                               nome)\n    # nome = \"SMAP Perfeito\"\n    # perfeito = Caso.constroi_caso_de_pasta(dir_perfeito,\n    #                                        nome)\n    nome = \"SMAP Proposto\"\n    proposto = Caso.constroi_caso_de_pasta(dir_proposto,\n                                           nome)\n\n    casos = [\n             vigente,\n             mes2semanas,\n             perfeito1mes,\n            #  perfeito,\n             proposto\n             ]\n\n    saida = os.getenv(\"DIR_SAIDA_SMAP\")\n\n    # Gera os gráficos\n    # CMO por subsistema\n    grafico_cmo_subsistema(casos, saida)\n    # EARM por subsistema\n    grafico_earm_subsistema(casos, saida)\n    # GT por subsistema\n    grafico_gt_subsistema(casos, saida)\n    # Ghid por subsistema\n    grafico_ghid_subsistema(casos, saida)\n    # Déficit por subsistema\n    grafico_deficit_subsistema(casos, saida)\n    # EARM para SIN\n    grafico_earm_sin(casos, saida)\n    # GT para SIN\n    grafico_gt_sin(casos, saida)\n    # GHid para SIN\n    grafico_ghid_sin(casos, saida)\n\n    # Exporta os dados\n    for c in casos:\n        exporta_dados(c, saida)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"rjmalves/ft-newave-2021","sub_path":"backtest_smap/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2504,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"3059349980","text":"def findKthLargest(nums, k):\n    max = 0\n    for i in nums:\n        if i > max:\n            max = i\n    temp = [False for _ in range(max+1)]\n    for i in nums:\n        temp[i] = True\n    for i in range(len(temp) - 1, -1, -1):\n        if temp[i]:\n            if k == 1:\n                return i\n            else: k -= 1\n\nprint(findKthLargest([3, 5, 2, 6, 7, 8, 4, 8], 3))","repo_name":"quytrungg/DS","sub_path":"src/interview/day19.py","file_name":"day19.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"34233429034","text":"from random import getrandbits\ndef makeOneThirdCoin():     \n    n=8\n    toss=[]\n    condition = False\n    for i in range(0,n):\n        toss.append(getrandbits(1))\n    for i in range(0,n,2):\n        if toss[i] == 1:\n            sum = 0\n            for k in range(i+1,n):\n                sum = sum + toss[k]\n            if sum == 0:\n                condition = True\n    return condition\n\n# Test that probability of makeOneThirdCoin() is correct\ncount = 0\nt = 0\nnTrials = 10000000\nfor i in range(nTrials): \n    b = makeOneThirdCoin()\n    if (b):\n        count = count + 1\nprint('Count = ', count, ' probability est = ', count/nTrials)\n","repo_name":"monishnene/Algorithms","sub_path":"Assignment_2/biased_coin_test_code.py","file_name":"biased_coin_test_code.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"20044192990","text":"import logging\nimport logging.config\nimport math\nimport os\nimport pickle\nfrom dataclasses import dataclass\nfrom datetime import datetime, timedelta\nfrom hashlib import sha1\nfrom typing import BinaryIO\n\nimport pandas as pd\nfrom holidays_es import Province\n\nlogger = logging.getLogger()\n\n\ndef disk_cache(func):\n    cache_dir = \"cache\"\n    if not os.path.exists(cache_dir):\n        os.makedirs(cache_dir)\n\n    def wrapper(*args, **kwargs):\n        cache_key = sha1(\n            (\n                str(func.__module__) + str(func.__name__) +\n                str(args) + str(kwargs)\n            ).encode(\"utf-8\")\n        ).hexdigest()\n        cache_path = os.path.join(cache_dir, cache_key)\n        if os.path.exists(cache_path):\n            modification_time = datetime.fromtimestamp(\n                os.path.getmtime(cache_path))\n            if modification_time > datetime.now() - timedelta(days=1):\n                with open(cache_path, \"rb\") as f:\n                    return pickle.load(f)\n        result = func(*args, **kwargs)\n        with open(cache_path, \"wb\") as f:\n            pickle.dump(result, f)\n        return result\n\n    return wrapper\n\n\n@disk_cache\ndef get_rd_10_prices() -> pd.DataFrame:\n    rd_10_prices_url = (\n        \"https://www.mibgas.es/es/file-access/MIBGAS_Data_2023.xlsx?path=AGNO_2023/XLS\"\n    )\n    return pd.read_excel(\n        rd_10_prices_url, sheet_name=\"PGN_RD_10_2022\", names=[\"date\", \"price\"], index_col=0\n    )\n\n\ndef get_rd_10_mean_price(rd_10_prices):\n    # Get the mean of the last 30 days\n    return rd_10_prices.iloc[:30].price.mean() / 1000.0\n\n\n@dataclass(frozen=True)\nclass DataConsumption:\n    consumption_p1: float  # kWh\n    consumption_p2: float  # kWh\n    consumption_p3: float  # kWh\n    num_days: int\n\n    @property\n    def total_consumption(self) -> float:\n        return self.consumption_p1 + self.consumption_p2 + self.consumption_p3\n\n    @property\n    def p1_distribution(self) -> float:\n        return self.consumption_p1 / self.total_consumption * 100.0\n\n    @property\n    def p2_distribution(self) -> float:\n        return self.consumption_p2 / self.total_consumption * 100.0\n\n    @property\n    def p3_distribution(self) -> float:\n        return self.consumption_p3 / self.total_consumption * 100.0\n\n\n@dataclass()\nclass ElectricityCost:\n    energy_cost: float  # €/kWh\n    power_cost: float  # €/kWh\n    rd_10_cost: float  # €/kWh\n    energy_monitor_cost: float  # €/kWh\n    social_bonus_cost: float  # €/kWh\n    tax_base: float = 0.0  # €\n    electricity_cost_tax: float = 0.0  # €\n    tax: float = 0.0  # €\n    total_cost: float = 0.0  # €\n\n    def __post_init__(self):\n        ELECTRICITY_TAX = 0.5  # %\n        GENERAL_TAX = 5.0  # %\n\n        electricity_cost = self.cost_without_taxes\n\n        self.electricity_cost_tax = electricity_cost * ELECTRICITY_TAX / 100.0\n        self.tax_base = (\n            electricity_cost + self.electricity_cost_tax + self.energy_monitor_cost\n        )\n        self.tax = self.tax_base * GENERAL_TAX / 100.0\n        self.total_cost = self.tax_base + self.tax\n\n    @property\n    def cost_without_taxes(self) -> float:\n        return (\n            self.energy_cost\n            + self.power_cost\n            + self.rd_10_cost\n            + self.social_bonus_cost\n        )\n\n    def __str__(self) -> str:\n        return f\"\"\"Energy cost: {self.energy_cost:.2f} € \\nPower cost: {self.power_cost:.2f} € \\nRD10 cost: {self.rd_10_cost:.2f} € \\nEnergy cost: {self.energy_cost:.2f} € \\nTotal cost: {self.total_cost:.2f} €\"\"\"\n\n\n@dataclass(frozen=True)\nclass TariffData:\n    name: str\n    energy_cost_p1: float  # €/kWh\n    energy_cost_p2: float  # €/kWh\n    energy_cost_p3: float  # €/kWh\n    power_cost_p1: float  # €/kW/day\n    power_cost_p2: float  # €/kW/day\n    rd_10_included: bool  # The tarrif includes the RD 10 2022 into the prices\n\n    def calculate_electricity_cost(\n        self,\n        consumption: DataConsumption,\n        contracted_p1: float,\n        contracted_p2: float,\n        rd_10_mean_price: float,\n    ) -> ElectricityCost:\n\n        ENERGY_MONITOR_COST_PER_DAY = 0.02663  # €\n        SOCIAL_BONUS_COST_PER_DAY = 0.036718  # €\n\n        energy_monitor_cost = consumption.num_days * ENERGY_MONITOR_COST_PER_DAY\n        social_bonus_cost = consumption.num_days * SOCIAL_BONUS_COST_PER_DAY\n\n        power_cost = consumption.num_days * (\n            self.power_cost_p1 * contracted_p1 + self.power_cost_p2 * contracted_p2\n        )\n\n        energy_cost = (\n            consumption.consumption_p1 * self.energy_cost_p1\n            + consumption.consumption_p2 * self.energy_cost_p2\n            + consumption.consumption_p3 * self.energy_cost_p3\n        )\n\n        if self.rd_10_included:\n            rd_10_cost = 0.0\n        else:\n            rd_10_cost = consumption.total_consumption * rd_10_mean_price\n\n        return ElectricityCost(\n            energy_cost=energy_cost,\n            power_cost=power_cost,\n            rd_10_cost=rd_10_cost,\n            energy_monitor_cost=energy_monitor_cost,\n            social_bonus_cost=social_bonus_cost,\n        )\n\n\ndef get_dataframe(file: BinaryIO) -> pd.DataFrame:\n    \"\"\"\n    Calculates the energy periods consumption.\n    Returns:\n        DataConsumption instance\n    \"\"\"\n\n    # Read the file\n    df = pd.read_csv(filepath_or_buffer=file, sep=\";\", decimal=\",\")\n\n    # Parse the column \"Fecha\" as a datetime object\n    df.Fecha = pd.to_datetime(df.Fecha, format=\"%d/%m/%Y\")\n\n    # Get the National Holidays\n    df[\"year\"] = pd.DatetimeIndex(df.Fecha).year\n    years = df.year.unique()\n    holidays = {}\n    for year in years:\n        year_holidays = Province(name=\"madrid\", year=year)\n        holidays[year] = year_holidays.national_holidays()\n\n    # Create a new column indicating if the day is a weekend day or not\n    df[\"weekend\"] = df.Fecha.dt.dayofweek // 5 == 1\n    # Create a new column indicating if the day was a holiday day or not\n    df[\"holiday\"] = df.apply(lambda row: row.Fecha.date()\n                             in holidays[row.year], axis=1)\n\n    # Make the hours start from 0\n    df.Hora = df.Hora - 1\n\n    return df\n\n\ndef get_periods_consumption(df: pd.DataFrame) -> DataConsumption:\n\n    energyColumn = \"Consumo_kWh\"\n    if energyColumn not in df.columns:\n        energyColumn = \"AE_kWh\"\n\n    # P1 consumption: from 10:00 to 14:00 and from 18:00 to 22:00\n    consumption_p1 = df.loc[\n        ~df.weekend\n        & ~df.holiday\n        & ((df.Hora >= 10) & (df.Hora < 14) | (df.Hora >= 18) & (df.Hora < 22))\n    ][energyColumn].sum()\n\n    # P2 consumption: from 08:00 to 10:00, from 14:00 to 18:00 and from 22:00 to 24:00\n    consumption_p2 = df.loc[\n        ~df.weekend\n        & ~df.holiday\n        & (\n            (df.Hora >= 8) & (df.Hora < 10)\n            | (df.Hora >= 14) & (df.Hora < 18)\n            | (df.Hora >= 22) & (df.Hora < 24)\n        )\n    ][energyColumn].sum()\n\n    # P3 consumption: from 00:00 to 08:00, weekend days and holidays\n    consumption_p3 = df.loc[df.weekend | df.holiday | (df.Hora >= 0) & (df.Hora < 8)][\n        energyColumn\n    ].sum()\n\n    data = DataConsumption(\n        consumption_p1=consumption_p1,\n        consumption_p2=consumption_p2,\n        consumption_p3=consumption_p3,\n        num_days=len(df.groupby(\"Fecha\")),\n    )\n\n    # Make sure we have divided the different consumption periods correctly\n    assert math.isclose(data.total_consumption, df[energyColumn].sum())\n\n    return data\n\n\ndef get_rd_10_threshold(\n    contracted_p1: float,\n    contracted_p2: float,\n    consumption_data: pd.DataFrame,\n    tariffs_costs: list[tuple[str, float]],\n    rd_10_mean_price: float,\n    tariffs: list[TariffData]\n) -> float | None:\n\n    # Get the best tariffs with the RD10 included and not included\n    best_rd_tariff = None\n    best_non_rd_tariff = None\n    for tariff_name, tariff_cost in tariffs_costs:\n        for t in tariffs:\n            if t.name == tariff_name:\n                if t.rd_10_included and not best_rd_tariff:\n                    best_rd_tariff = t\n                elif not t.rd_10_included and not best_non_rd_tariff:\n                    best_non_rd_tariff = t\n                break\n        if best_rd_tariff and best_non_rd_tariff:\n            break\n\n    if best_rd_tariff and best_non_rd_tariff:\n\n        # Get the cost of the best tariffs\n        best_rd_10_tariff_cost = best_rd_tariff.calculate_electricity_cost(\n            consumption_data, contracted_p1, contracted_p2, rd_10_mean_price\n        )\n        best_non_rd_10_tariff_cost = best_non_rd_tariff.calculate_electricity_cost(\n            consumption_data, contracted_p1, contracted_p2, rd_10_mean_price\n        )\n\n        # Calculate the RD10 threshold\n        return (\n            best_rd_10_tariff_cost.cost_without_taxes\n            - best_non_rd_10_tariff_cost.energy_cost\n            - best_non_rd_10_tariff_cost.power_cost\n        ) / consumption_data.total_consumption\n\n\ndef get_tariffs_costs(\n    contracted_p1: float,\n    contracted_p2: float,\n    consumption_data: pd.DataFrame,\n    rd_10_mean_price: float,\n    tariffs: list[TariffData]\n) -> list[tuple[str, float]]:\n    # Get a list with the energy costs for each tariff\n    tariffs_costs = [\n        (\n            t.name,\n            t.calculate_electricity_cost(\n                consumption_data, contracted_p1, contracted_p2, rd_10_mean_price\n            ).total_cost,\n        )\n        for t in tariffs\n    ]\n    # Order the tarifs by energy cost\n    tariffs_costs.sort(key=lambda x: x[1])\n\n    return tariffs_costs\n\n\ndef get_data(\n    df: pd.DataFrame,\n    contracted_p1: float,\n    contracted_p2: float,\n    rd_10_mean_price: float,\n    tariffs: list[TariffData]\n) -> dict:\n    consumption_data = get_periods_consumption(df)\n\n    tariffs_costs = get_tariffs_costs(\n        contracted_p1, contracted_p2, consumption_data, rd_10_mean_price, tariffs\n    )\n\n    data = {\n        \"consumption_data\": consumption_data.__dict__,\n        \"tariffs\": [],\n        \"first_day\": df.iloc[0].Fecha,\n        \"last_day\": df.iloc[-1].Fecha,\n    }\n    # Print the results ordered by energy cost\n    bestTariffCost = tariffs_costs[0][1]\n    for tariff_name, tariff_cost in tariffs_costs:\n        tariff_cost_diff = tariff_cost - bestTariffCost\n        data[\"tariffs\"].append(\n            {\"name\": tariff_name,\n             \"tariff_cost\": tariff_cost,\n             \"tariff_cost_diff\": tariff_cost_diff})\n\n    th_rd_10_threshold = get_rd_10_threshold(\n        contracted_p1, contracted_p2, consumption_data, tariffs_costs, rd_10_mean_price, tariffs\n    )\n    data[\"th_rd_10_threshold\"] = th_rd_10_threshold\n\n    return data\n\n@dataclass\nclass GasDataConsumption:\n    measurement: float\n    time: datetime\n\n\ndef calculate_gas_cost(\n    consumptionA: GasDataConsumption,\n    consumptionB: GasDataConsumption,\n) -> float:\n\n    FIXED_COST_PER_DAY = 0.165370  # €\n    MONITOR_COST_PER_DAY = 0.019068  # €\n    TUR_TARIFF_COST = 0.063555  # €/kWh\n    GAS_TAX_COST = 0.002340  # €/kWh\n    TAX_COST = 5  # %\n    m3_to_kWh = 10.579\n\n    num_days = max((consumptionB.time - consumptionA.time).days, 1)\n\n    energy_consumption = (consumptionB.measurement -\n                          consumptionA.measurement) * m3_to_kWh\n\n    energy_monitor_cost = MONITOR_COST_PER_DAY * num_days\n    fixed_cost = FIXED_COST_PER_DAY * num_days\n\n    tax = GAS_TAX_COST * energy_consumption\n    energy_cost = TUR_TARIFF_COST * energy_consumption\n\n    gas_cost = energy_monitor_cost + fixed_cost + tax + energy_cost\n    return gas_cost * (1 + TAX_COST / 100)\n","repo_name":"csanz91/energy-calculator-api","sub_path":"api/source/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":11504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"544778417","text":"from typing import Any\nimport os, sys\n\nsys.path.insert(0, os.path.abspath(\"..\"))\n\nfrom celery import Celery\nfrom celery.schedules import crontab\n\nfrom scraper.archive_scraping import BASE_URL, TableType, get_table\n\napp = Celery(\"periodical_scraper\")\n\napp.config_from_object(\"scraper.celery_config\")\n\n\n@app.task(name=\"periodical_scraper.get_A_table_task\")\ndef get_A_table_task() -> None:\n    get_table(url=f\"{BASE_URL}LastA.xml\", table_type=TableType.A)\n\n\n@app.task(name=\"periodical_scraper.get_B_table_task\")\ndef get_B_table_task() -> None:\n    get_table(f\"{BASE_URL}LastB.xml\", TableType.B)\n\n\n@app.task(name=\"periodical_scraper.get_C_table_task\")\ndef get_C_table_task() -> None:\n    get_table(f\"{BASE_URL}LastC.xml\", TableType.C)\n\n\n@app.on_after_configure.connect\ndef setup_periodic_tasks(sender: Celery, **kwargs: Any) -> None:\n    sender.add_periodic_task(\n        crontab(hour=12, minute=30, day_of_week=\"1-5\"),\n        get_A_table_task.s(),\n        name=\"get table A on workdays after 12:15\",\n    )\n    sender.add_periodic_task(\n        crontab(hour=12, minute=30, day_of_week=\"wed\"),\n        get_B_table_task.s(),\n        name=\"get table B on Wednesdays after 12:15\",\n    )\n    sender.add_periodic_task(\n        crontab(hour=8, minute=30, day_of_week=\"1-5\"),\n        get_C_table_task.s(),\n        name=\"get table C on workdays after 8:15\",\n    )\n","repo_name":"Kalarebka/NBP-currency-scraper-and-api","sub_path":"scraper/periodical_scraper.py","file_name":"periodical_scraper.py","file_ext":"py","file_size_in_byte":1352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"21383549201","text":"from taipy.core.config import Config\nimport taipy as tp\nimport datetime as dt\nimport pandas as pd\nimport time\n\n# Normal function used by Taipy\ndef double(nb):\n    return nb * 2\n\ndef add(nb):\n    print(\"Wait 10 seconds in add function\")\n    time.sleep(10)\n    return nb + 10\n\n\nConfig.load('config.toml')\nConfig.configure_job_executions(mode=\"standalone\", max_nb_of_workers=2)\n\n\nif __name__==\"__main__\":\n    scenario_cfg = Config.scenarios['my_scenario']\n    tp.Core().run()\n    scenario_1 = tp.create_scenario(scenario_cfg)\n    scenario_2 = tp.create_scenario(scenario_cfg)\n    scenario_1.submit()\n    scenario_2.submit()\n\n    scenario_1 = tp.create_scenario(scenario_cfg)\n    scenario_1.submit(wait=True)\n    scenario_1.submit(wait=True, timeout=5)\n","repo_name":"Avaiga/taipy-doc","sub_path":"docs/knowledge_base/tutorials/job_execution/job_execution_toml.py","file_name":"job_execution_toml.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"85"}
+{"seq_id":"74229990997","text":"\"Test rule that uses ctx.actions.run\"\n\ndef _run_shell_rule_impl(ctx):\n    output = ctx.actions.declare_file(\"{}_out\".format(ctx.label.name))\n    (inputs, input_manifests) = ctx.resolve_tools(tools = [ctx.attr.tool])\n\n    ctx.actions.run_shell(\n        outputs = [output],\n        inputs = inputs,\n        input_manifests = input_manifests,\n        command = \"./{} {}\".format(ctx.executable.tool.path, output.path),\n    )\n\n    return [DefaultInfo(files = depset([output]))]\n\nrun_shell_rule = rule(\n    implementation = _run_shell_rule_impl,\n    attrs = {\n        \"tool\": attr.label(\n            executable = True,\n            cfg = \"exec\",\n            mandatory = True,\n        ),\n    },\n)\n","repo_name":"bazelbuild/rules_dotnet","sub_path":"dotnet/private/tests/use_as_tool/run_shell_rule/run_shell_rule.bzl","file_name":"run_shell_rule.bzl","file_ext":"bzl","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","stars":166,"dataset":"github-code","pt":"85"}
+{"seq_id":"19810894669","text":"from time import sleep\r\n\r\nfrom labquest import config\r\nfrom labquest import ngio_stop_functions as ngio_stop\r\nfrom labquest import ngio_send_cmd_get_resp as ngio_send\r\nfrom labquest import labquest_read_functions as read\r\nfrom labquest import labquest_buffer_functions as buffer\r\nbuf = buffer.lq_buffer()\r\n\r\n\r\ndef stop_measurements_clear_buffer():\r\n    \"\"\" Stop data collection and clear both the NGIO buffer and the buffer (queue)\r\n    \"\"\"\r\n\r\n    # Stop the measurements\r\n    for hDevice in config.hDevice:\r\n        parameters = [0]*14\r\n        command = 0x19   #STOP MEASUREMENTS = 19\r\n        param_bytes = 0\r\n        ngio_send.send_cmd_get_response(hDevice, command, parameters, param_bytes)       \r\n\r\n    # Clear the NGIO measurement buffer by reading any values remaining\r\n    sleep(1)\r\n    read.clear_the_lq_measurement_buffer()\r\n\r\n    # Clear the buffer\r\n    buf.buffer_clear()\r\n\r\ndef close():\r\n    \"\"\" Close any LabQuest handles, call NGIO Uninit, and reset the variables in the config file\r\n    \"\"\"\r\n\r\n    # if no devices, no device handle, or no sensors then do not try to close\r\n    if not config.device_type or not config.hDevice or not any(config.enabled_all_channels):\r\n        pass\r\n    else: \r\n        # Close the device\r\n        for hDevice in config.hDevice:\r\n            closed = ngio_stop.device_close(hDevice) \r\n\r\n    # Call NGIO_Uninit() once to 'undo' NGIO_Init()\r\n    ngio_stop.ngio_uninit()  \r\n\r\n    # clear all the variables in the config.py file   \r\n    config.logger = None \r\n    config.dll = None   \r\n    config.hLib = None   \r\n    config.hDevice = []    \r\n    config.device_type = None    \r\n    config.auto_id_list = []\r\n    config.enabled_analog_channels = []  \r\n    config.enabled_dig_channels = []    \r\n    config.enabled_all_channels = [] \r\n    config.channel_name_list = []   \r\n    config.motion = False    \r\n    config.photogate = False   \r\n    config.photogate_timing = False\r\n    config.rotary_motion = False  \r\n    config.rotary_motion_high_res = False \r\n    config.dcu = False  \r\n    config.dcu_pwm = False\r\n    config.sample_period = None\r\n    config.op_type_list = []   \r\n    config.probe_type_list = []\r\n    config.sensor_cal_list = []   \r\n    config.device_dig_channel_dictionary = []   \r\n    config.device_channel_dictionary = []\r\n\r\n\r\n","repo_name":"VernierST/labquest-py","sub_path":"labquest/labquest_stop_close_functions.py","file_name":"labquest_stop_close_functions.py","file_ext":"py","file_size_in_byte":2281,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"13217214920","text":"import logging\nimport os\nimport time\nimport uuid\n\nfrom django.conf import settings\nfrom django.contrib.auth.models import Group, User\nfrom django.core.exceptions import ValidationError\nfrom django.core.files.storage import FileSystemStorage\nfrom django.db import connections, models\nfrom django.forms.models import model_to_dict\nfrom django.utils import timezone\nfrom django.utils.translation import gettext as _\n\nfrom .model_mixins import MetadataModelMixin, ResourceModelMixin\nfrom .utils import RecursionException, check_recursion, get_cls\nfrom .validators import validate_options_json_format\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass Category(models.Model):\n    SEPARATOR = ' > '\n    name = models.CharField(_('name'), max_length=50)\n    color = models.CharField(_('color'), max_length=50, null=True, blank=True)\n    parent = models.ForeignKey(\n        'Category', verbose_name=_('parent category'), null=True, blank=True, on_delete=models.CASCADE)\n\n    class Meta:\n        verbose_name = _('Category')\n        verbose_name_plural = _('Categories')\n\n    @property\n    def title(self):\n        names = []\n        parent = self\n        while parent is not None:\n            names.insert(0, parent.name)\n            parent = parent.parent\n        return self.SEPARATOR.join(names)\n\n    def clean(self):\n        try:\n            check_recursion('parent', self)\n        except RecursionException as e:\n            raise ValidationError(str(e))\n\n    def save(self, *args, **kwargs):\n        self.full_clean()\n        return super().save(*args, **kwargs)\n\n    def __str__(self):\n        return self.title\n\n\nDB_CONNECTION_ENGINE_CHOICES = [\n    ('django.contrib.gis.db.backends.postgis', 'Postgis'),\n]\n\n\nclass DBConnection(models.Model):\n    alias = models.CharField(_('alias'), max_length=255, null=True, blank=True)\n    engine = models.CharField(_('engine'), max_length=255,\n                              choices=DB_CONNECTION_ENGINE_CHOICES)\n    name = models.CharField(_('Database name'), max_length=100, null=False, blank=False)\n    user = models.CharField(_('user'), max_length=100, null=True, blank=True)\n    password = models.CharField(_('password'), max_length=255, null=True, blank=True)\n    host = models.CharField(_('host'), max_length=100, null=True, blank=True)\n    port = models.CharField(_('port'), max_length=20, null=True, blank=True)\n\n    def db_conf(self, schema=None):\n        db = {}\n        db['ENGINE'] = self.get_engine(self.engine)\n        db['NAME'] = self.name\n        if self.user:\n            db['USER'] = self.user\n        if self.password:\n            db['PASSWORD'] = self.password\n        if self.host:\n            db['HOST'] = self.host\n        if self.port:\n            db['PORT'] = self.port\n        if schema:\n            postgres_engines = [\n                'giscube.db.backends.postgis',\n            ]\n            if self.engine in (postgres_engines):\n                db['OPTIONS'] = {\n                    'options': '-c search_path=%s,public' % schema\n                }\n\n        return db\n\n    def connection_name(self, schema=None):\n        if schema:\n            return 'giscube_connection_schema_%s_%s' % (self.pk, schema)\n        else:\n            return 'giscube_connection_%s' % self.pk\n\n    def check_connection(self):\n        db_conf = self.db_conf()\n        name = 'giscube_connection_tmp_%s' % time.time()\n        settings.DATABASES[name] = db_conf\n\n        db_conn = connections[name]\n        res = None\n        try:\n            db_conn.cursor()\n        except Exception:\n            res = False\n        else:\n            db_conn.close()\n            res = True\n        del connections[name]\n        del settings.DATABASES[name]\n        return res\n\n    def get_connection(self, schema=None):\n        self.schema = schema\n        db_conf = self.db_conf(schema)\n        name = self.connection_name(schema)\n        if not(name in settings.DATABASES):\n            settings.DATABASES[name] = db_conf\n        return connections[name]\n\n    def get_engine(self, engine):\n        if engine == 'django.contrib.gis.db.backends.postgis':\n            engine = 'giscube.db.backends.postgis'\n        return engine\n\n    def geometry_columns(self):\n        rows = []\n        conn = self.get_connection()\n        GeometryColumns = conn.ops.geometry_columns()\n        qs = GeometryColumns.objects.using(self.connection_name()).all()\n        for column in qs:\n            row = model_to_dict(column)\n            label = ''\n            if row['f_table_schema'] != '':\n                label = '\"%s\".' % row['f_table_schema']\n            label = '%s\"%s\".\"%s\" (%s, %s)' % (\n                label, row['f_table_name'], row['f_geometry_column'], row['type'], row['srid'])\n            row['label'] = label\n            rows.append(row)\n        return rows\n\n    def full_clean(self, *args, **kwargs):\n        from django.core.validators import ValidationError\n        if not self.check_connection():\n            raise ValidationError(_('Database connection error'))\n\n    def __str__(self):\n        return '%s' % self.alias or self.name\n\n    class Meta:\n        \"\"\"Meta information.\"\"\"\n        verbose_name = _('Database connection')\n        verbose_name_plural = _('Database connections')\n\n\nclass Server(models.Model):\n    name = models.CharField(_('name'), max_length=50, unique=True)\n    url = models.URLField(_('url'), null=True, blank=True)\n    token = models.CharField(_('token'), max_length=255, null=True, blank=True)\n    this_server = models.BooleanField(_('this server'), default=False)\n\n    class Meta:\n        \"\"\"Meta information.\"\"\"\n        verbose_name = _('Server connection')\n        verbose_name_plural = _('Server connections')\n\n    def __str__(self):\n        return '%s' % self.name\n\n\ndef user_asset_upload_to(instance, filename, uuid_folder=None):\n    storage_class = get_cls('USER_ASSETS_STORAGE_CLASS')\n    storage = storage_class(location='user/assets/{0}'.format(instance.user.id))\n\n    dirs = []\n    files = []\n    try:\n        dirs, files = storage.listdir('.')\n    except Exception as e:\n        if settings.DEBUG:\n            logger.warning(str(e), exc_info=True)\n    else:\n        for dir in dirs:\n            path = 'user/assets/{0}/{1}/{2}'.format(instance.user.id, dir, filename)\n            if not storage.exists(path):\n                return path\n    return 'user/assets/{0}/{1}/{2}'.format(instance.user.id, instance.uuid, filename)\n\n\nclass UserAsset(models.Model):\n    uuid = models.UUIDField(default=uuid.uuid4, editable=False, unique=True)\n    file = models.FileField(_('file'), max_length=255, upload_to=user_asset_upload_to)\n    created = models.DateTimeField(_('creation datetime'), auto_now_add=True)\n    user = models.ForeignKey(settings.AUTH_USER_MODEL, related_name='assets', on_delete=models.CASCADE)\n\n    def delete(self, *args, **kwargs):\n        super(UserAsset, self).delete(*args, **kwargs)\n        if self.file:\n            folder = os.path.dirname(self.file.name)\n            self.file.delete(save=False)\n            delete_parent = None\n            if isinstance(self.file.storage, FileSystemStorage):\n                dirs, files = self.file.storage.listdir(folder)\n                if len(files) == 0:\n                    delete_parent = os.path.join(self.file.storage.location, folder)\n            if delete_parent is not None:\n                os.rmdir(delete_parent)\n\n    class Meta:\n        verbose_name = _('User Asset')\n        verbose_name_plural = _('User Assets')\n\n\nclass GiscubeTransaction(models.Model):\n    hash = models.CharField(_('Accessed ViewSet'), max_length=32, null=False, blank=False)\n    created = models.DateTimeField(_('Access timestamp'), auto_now_add=True, editable=False, null=False, blank=False)\n    user = models.CharField(_('User'), max_length=255, null=False, blank=False)\n    url = models.CharField(_('URL'), max_length=255, null=False, blank=False)\n    request_headers = models.JSONField(_('request headers'), default=dict)\n    request_body = models.TextField(_('request body'), null=True, blank=True)\n    response_headers = models.JSONField(_('request headers'), default=dict)\n    response_status_code = models.IntegerField(_('response status code'), null=True, blank=True)\n    response_body = models.TextField(_('response body'), null=True, blank=True)\n    error = models.TextField(_('error'), null=True, blank=True)\n    traceback = models.TextField(_('traceback'), null=True, blank=True)\n\n    @staticmethod\n    def purge_old():\n        time_delete = {settings.PURGE_GISCUBETRANSACTIONS_UNIT: settings.PURGE_GISCUBETRANSACTIONS_VALUE}\n        past = timezone.datetime.today() - timezone.timedelta(**time_delete)\n        if settings.USE_TZ:\n            past = timezone.make_aware(past)\n        GiscubeTransaction.objects.filter(created__lte=past).delete()\n\n    class Meta:\n        verbose_name = _('giscube transaction')\n        verbose_name_plural = _('giscube transactions')\n\n\nclass Dataset(models.Model):\n    category = models.ForeignKey(Category, null=True, blank=True, on_delete=models.SET_NULL, related_name='datasets')\n    name = models.CharField(_('name'), max_length=50, unique=True)\n    title = models.CharField(_('title'), max_length=100, null=True, blank=True)\n    description = models.TextField(_('description'), null=True, blank=True)\n    keywords = models.CharField(_('keywords'), max_length=200, null=True, blank=True)\n    active = models.BooleanField(_('active'), default=True, help_text='Enable/disable usage')\n    visible_on_geoportal = models.BooleanField(_('visible on geoportal'), default=False)\n    options = models.TextField(\n        _('options'), null=True, blank=True, help_text='json format. Ex: {\"maxZoom\": 20}',\n        validators=[validate_options_json_format])\n    legend = models.TextField(_('legend'), null=True, blank=True)\n    anonymous_view = models.BooleanField(_('anonymous users can view'), default=False)\n    authenticated_user_view = models.BooleanField(_('authenticated users can view'), default=False)\n\n    def __str__(self):\n        return '%s' % self.title or self.name\n\n\nclass DatasetResource(ResourceModelMixin):\n    parent = models.ForeignKey(Dataset, related_name='resources', on_delete=models.CASCADE)\n\n\nclass DatasetGroupPermission(models.Model):\n    dataset = models.ForeignKey(Dataset, related_name='group_permissions', on_delete=models.CASCADE)\n    group = models.ForeignKey(Group, verbose_name=_('Group'), on_delete=models.CASCADE)\n    can_view = models.BooleanField(_('Can view'), default=True)\n\n    def __str__(self):\n        return self.group.name\n\n    class Meta:\n        verbose_name = _('Group')\n        verbose_name_plural = _('Groups')\n\n\nclass DatasetUserPermission(models.Model):\n    dataset = models.ForeignKey(Dataset, related_name='user_permissions', on_delete=models.CASCADE)\n    user = models.ForeignKey(User, verbose_name=_('User'), on_delete=models.CASCADE)\n    can_view = models.BooleanField(_('Can view'), default=True)\n\n    def __str__(self):\n        return self.user.username\n\n    class Meta:\n        verbose_name = _('User')\n        verbose_name_plural = _('Users')\n\n\nclass DatasetMetadata(MetadataModelMixin):\n    parent = models.OneToOneField(Dataset, on_delete=models.CASCADE, primary_key=True, related_name='metadata')\n\n\nclass MetadataCategory(models.Model):\n    code = models.CharField(_('code'), max_length=50, null=False, blank=False, unique=True)\n    name = models.CharField(_('name'), max_length=255, null=True, blank=True)\n\n    def __str__(self):\n        return '%s' % self.name or self.code\n\n    class Meta:\n        verbose_name = _('metadata category')\n        verbose_name_plural = _('metadata categories')\n\n\nclass BaseLayer(models.Model):\n    name = models.CharField(_('name'), max_length=255)\n    properties = models.JSONField(_('properties'), default=dict)\n\n    def __str__(self):\n        return self.name\n\n\nclass MapConfig(models.Model):\n    name = models.CharField(_('name'), max_length=50, unique=True)\n    center_lat = models.DecimalField(_('center latitude'), max_digits=8, decimal_places=6)\n    center_lng = models.DecimalField(_('center longitude'), max_digits=9, decimal_places=6)\n    initial_zoom = models.PositiveIntegerField(_('initial zoom'))\n\n    def __str__(self):\n        return self.name\n\n\nclass MapConfigBaseLayer(models.Model):\n    map_config = models.ForeignKey(MapConfig, on_delete=models.CASCADE, related_name='baselayers',\n                                   verbose_name=_('map configuration'))\n    base_layer = models.ForeignKey(BaseLayer, on_delete=models.CASCADE, verbose_name=_('base layer'))\n    order = models.PositiveIntegerField()\n\n    def __str__(self):\n        return self.map_config.name\n\n    class Meta:\n        ordering = ['order']\n","repo_name":"giscube/giscube-admin","sub_path":"giscube/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":12668,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"85"}
+{"seq_id":"74389635158","text":"import sys\n\n#debug = True\ndebug = False\ndef log(s, really = False):\n    if debug | really:\n        print(s)\n\ndef is_number(s):\n    try:\n        int(s)\n        return True\n    except ValueError:\n        return False\n\ndef doCalc(lhs, operand, rhs):\n    result = 0\n    if operand == \"*\":\n        result = lhs * rhs\n    elif operand == \"/\":\n        result = lhs / rhs\n    elif operand == \"+\":\n        result = lhs + rhs\n    elif operand == \"-\":\n        result = lhs - rhs\n    elif operand == \"%\":\n        result = lhs % rhs\n    return result\n\ndef main(tokens):\n    symbols = ['*', '/', '+', '-', '%'];\n    postFixStack = []\n    log(\"Input length : \" + str(len(tokens)))\n    log(\"Input: \" + ' '.join(str(v) for v in tokens))\n    for token in tokens:\n        log(token)\n        if is_number(token):\n            postFixStack.append(int(token))\n            log(\"Appended \" + token)\n        elif token in symbols:\n            log(token +  \" is not number\")\n            rhs = int(postFixStack.pop())\n            lhs = int(postFixStack.pop())\n            log(str(lhs) + \" \" + token + \" \" + str(rhs))\n            result = doCalc(lhs, token, rhs)\n            postFixStack.append(result)\n        else:\n            raise ValueError(\"Invalid symbol: {0}\".format(token))\n    log(\"Result: \" + str(postFixStack.pop()), True)\n    if len(postFixStack) != 0:\n        raise Exception(\"Unexpected\")\n\n\nif __name__ == \"__main__\":\n    # Test\n    # main(5, 6, 7, '*', '+', 1, '-')\n    # main(5, 6, 7, '*', '+', 1, '-','x')\n    main(sys.argv[1:])\n","repo_name":"dombrooks/python","sub_path":"PostFix.py","file_name":"PostFix.py","file_ext":"py","file_size_in_byte":1518,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"74569115796","text":"import pytest\nfrom todo_app.system.document_store import DocumentStoreConnection\nfrom todo_app.todo.models import Location, Task, Weather\nfrom django.utils import timezone\n\n\n@pytest.fixture(autouse=True)\ndef clear_document_store():\n    \"\"\"Remove all collections from the document store before each test.\"\"\"\n    db = DocumentStoreConnection().client.get_database()\n    for name in db.list_collection_names():\n        db.drop_collection(name)\n\n\n@pytest.fixture(name=\"task\")\ndef task_fixture():\n    \"\"\"Create and return a sample task.\"\"\"\n    return Task.objects.create(\n        content=\"Sample task\",\n        location=Location(lat=10, lon=20, label=\"Sample location\"),\n        weather=Weather(main=\"Snow\", temperature=0.0),\n    )\n\n\n@pytest.fixture\ndef create_active_tasks():\n    \"\"\"Create sample active tasks.\"\"\"\n    for index in range(5):\n        Task.objects.create(\n            content=f\"Sample task {index}\",\n            location=Location(lat=index, lon=index, label=f\"Location {index}\"),\n            weather=Weather(main=\"Snow\", temperature=0.0),\n        )\n\n\n@pytest.fixture\ndef create_finished_tasks():\n    \"\"\"Create sample active tasks.\"\"\"\n    for index in range(5):\n        Task.objects.create(\n            content=f\"Finished task {index}\",\n            marked_as_done_at=timezone.now(),\n            location=Location(lat=index, lon=index, label=f\"Location {index}\"),\n            weather=Weather(main=\"Snow\", temperature=0.0),\n        )\n","repo_name":"mateuszcisek/todo_app","sub_path":"conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"70988791637","text":"\"\"\"Fusion URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  url(r'^$', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.conf.urls import url, include\n    2. Add a URL to urlpatterns:  url(r'^blog/', include('blog.urls'))\n\"\"\"\n\nimport notifications.urls\nimport debug_toolbar\nfrom django.conf import settings\nfrom django.conf.urls import include, url\nfrom django.conf.urls.static import static\nfrom django.contrib import admin\nfrom django.contrib.auth import views as auth_views\n\n\nurlpatterns = [\n    url(r'^', include('applications.globals.urls')),\n    url(r'^feeds/', include('applications.feeds.urls')),\n    url(r'^admin/', admin.site.urls),\n    url(r'^academic-procedures/', include('applications.academic_procedures.urls')),\n    url(r'^aims/', include('applications.academic_information.urls')),\n    url(r'^notifications/', include('applications.notifications_extension.urls')),\n    url(r'^estate/', include('applications.estate_module.urls')),\n    url(r'^dep/', include('applications.department.urls')),\n    url(r'^programme_curriculum/',include('applications.programme_curriculum.urls')),\n    url(r'^iwdModuleV2/', include('applications.iwdModuleV2.urls')),\n    url(r'^__debug__/', include(debug_toolbar.urls)),\n    url(r'^research_procedures/', include('applications.research_procedures.urls')),\n    url(r'^accounts/', include('allauth.urls')),\n\n\n    url(r'^eis/', include('applications.eis.urls')),\n    url(r'^mess/', include('applications.central_mess.urls')),\n    url(r'^complaint/', include('applications.complaint_system.urls')),\n    url(r'^healthcenter/', include('applications.health_center.urls')),\n    url(r'^leave/', include('applications.leave.urls')),\n    url(r'^placement/', include('applications.placement_cell.urls')),\n    url(r'^filetracking/', include('applications.filetracking.urls')),\n    url(r'^spacs/', include('applications.scholarships.urls')),\n    url(r'^visitorhostel/', include('applications.visitor_hostel.urls')),\n    url(r'^office/', include('applications.office_module.urls')),\n    url(r'^finance/', include('applications.finance_accounts.urls')),\n    url(r'^purchase-and-store/', include('applications.ps1.urls')),\n    url(r'^gymkhana/', include('applications.gymkhana.urls')),\n    url(r'^library/', include('applications.library.urls')),\n    url(r'^establishment/', include('applications.establishment.urls')),\n    url(r'^ocms/', include('applications.online_cms.urls')),\n    url(r'^counselling/', include('applications.counselling_cell.urls')),\n    url(r'^hostelmanagement/', include('applications.hostel_management.urls')),\n    url(r'^income-expenditure/', include('applications.income_expenditure.urls')),\n    url(r'^hr2/', include('applications.hr2.urls')),\n    url(r'^recruitment/', include('applications.recruitment.urls')),\n] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","repo_name":"FusionIIIT/Fusion","sub_path":"FusionIIIT/Fusion/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3250,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"85"}
+{"seq_id":"29215062036","text":"from langchain.chat_models import ChatOpenAI\nfrom langchain.schema import SystemMessage, HumanMessage\n\nclass DialogueAgent:\n    def __init__(\n        self,\n        name: str,\n        system_message: SystemMessage,\n        model: ChatOpenAI,\n        is_user_controlled: bool = False,\n    ) -> None:\n        self.name = name\n        self.system_message = system_message\n        self.model = model\n        self.is_user_controlled = is_user_controlled\n        self.prefix = f\"{self.name}: \"\n        self.reset()\n\n    def reset(self):\n        self.message_history = [\"Here is the conversation so far.\"]\n\n    def send(self, message_content: str = None) -> str:\n        \"\"\"\n        Applies the chatmodel to the message history\n        and returns the message string\n        \"\"\"\n        if self.is_user_controlled:\n            if message_content is None:\n                raise ValueError(\"User controlled characters must provide a message.\")\n            return message_content\n        else:\n            message = self.model(\n                [\n                    self.system_message,\n                    HumanMessage(content=\"\\n\".join(self.message_history + [self.prefix])),\n                ]\n            )\n            return message.content\n\n    def receive(self, name: str, message: str) -> None:\n        \"\"\"\n        Concatenates {message} spoken by {name} into message history\n        \"\"\"\n        self.message_history.append(f\"{name}: {message}\")\n","repo_name":"Crescendo21/Nexus","sub_path":"Nexus/game/dialogue_agent.py","file_name":"dialogue_agent.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"28250750750","text":"import random\nfrom data.test.test_questions import brother\n\n\nclass BrotherTestUser:\n    def __init__(self, level=0, current_question=1, current_question_answer=-1, correct_answers=0):\n        self.level = level\n        self.current_question = current_question\n        self.current_question_answer = current_question_answer\n        self.correct_answers = correct_answers\n        self.time_to_answer = 20\n        self.question = {'options': [], 'answer': -1}\n        self.poll_id = -1\n\n    @staticmethod\n    def create_option():\n        first_num = random.randint(0, 9)\n        second_num = random.randint(-9, 9)\n        return f'{first_num}+{second_num}' if second_num >= 0 else f'{first_num}{second_num}'\n\n    def create_question(self):\n        options = []\n        answer = -1\n        for i in range(4):\n            option = self.create_option()\n\n            if option in brother and answer == -1:\n                answer = i\n            elif option in brother and answer > -1:\n                while option in brother:\n                    option = self.create_option()\n            elif option in options:\n                while option in options:\n                    option = self.create_option()\n            options.append(option)\n\n        if answer == -1:\n            answer = 4\n\n        options.append('Среди прмеров нет такого')\n        self.question['options'] = options\n        self.question['answer'] = answer\n","repo_name":"normal-Fag/mental_bot","sub_path":"data/test/brother_test_user/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"578626521","text":"#!/usr/local/bin/python3\n# -*- coding: utf-8 -*-\n#\n# Information about Springer references.\n#\n# Note that we don't currently get alerts from Springer.  This module supports\n# Springer articles that are identified from other sources\n\nimport quopri                             # quoted-printable encoding\nimport re\nimport alert\n\nSPRINGER_JHU_URL = \"http://link.springer.com.proxy1.library.jhu.edu/\"\nSPRINGER_URL = \"http://link.springer.com/\"\nSPRINGER_URL_LEN = len(SPRINGER_URL)\n\n\n\ndef isSpringerUrl(url):\n    \"\"\"\n    Return true if the given URL is a Springer url.\n    \"\"\"\n    return(len(url) >= SPRINGER_URL_LEN and url[0:SPRINGER_URL_LEN] == SPRINGER_URL)\n\n\ndef createHopkinsUrl(url):\n    \"\"\"\n    Given a Springer URL, convert it to a Hopkins URL\n    \"\"\"\n    # Springer URLs look like\n    # http://link.springer.com/protocol/10.1007/978-1-4939-2690-9_20\n    # Make it look like:\n    # http://link.springer.com.proxy1.library.jhu.edu/protocol/10.1007%2F978-1-4939-2690-9_20\n    urlParts = url.split(\"/\")\n    return(SPRINGER_JHU_URL + \"/\".join(urlParts[3:]))\n","repo_name":"tnabtaf/Papers","sub_path":"Springer.py","file_name":"Springer.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"43445439019","text":"# coding: utf-8\nimport logging\nimport re\nimport os\nimport shutil\nfrom cactus.utils import fileList\nfrom cactus.plugin_base import CactusPluginBase\nfrom slimit import minify\n\n\nclass MinifyJsPlugin(CactusPluginBase):\n    infile = None\n    outfile = None\n    buildpath = None\n    js_dir = None\n\n    def _prepare(self, *args, **kwargs):\n        from django.conf import settings\n        dist = kwargs.get(\"dist\", False)\n        self.buildpath = \"dist\" if dist else \"build\"\n        self.js_dir = os.path.join(self.site.paths[self.buildpath], settings.STATIC_URL_REL, 'js')\n        if not os.path.isdir(self.js_dir) or not os.listdir(self.js_dir):\n            return False\n\n        self.infile = os.path.abspath(\n            os.path.join(\n                self.js_dir,\n                self.config.get(\n                    'input_filename',\n                    'main.js'\n                )\n            )\n        )\n\n        self.outfile = os.path.abspath(\n            os.path.join(\n                self.js_dir,\n                self.config.get(\n                    'ouput_filename',\n                    'main.js'\n                )\n            )\n        )\n\n        return True\n\n    def _minify(self, infile, outfile=None):\n        if outfile is None:\n            outfile = infile\n        logging.info(\"Minifiing {0}\".format(infile))\n        f = open(infile, 'r')\n        content = f.read()\n        f.close()\n\n        f = open(outfile, 'w')\n        f.write(minify(content, mangle=False, mangle_toplevel=False))\n        f.close()\n\n    def postBuild(self, *args, **kwargs):\n        if not self._prepare(dist=False):\n            return\n\n        # Do not minify in devlopment mode, only rename file\n        try:\n            shutil.move(self.infile, self.outfile)\n        except IOError:\n            pass\n\n    def postDist(self, *args, **kwargs):\n        if not self._prepare(dist=True):\n            return\n\n        self._minify(self.infile, self.outfile)\n\n        if self.infile != self.outfile and not self.config.get('keep_unminified', True):\n            os.remove(self.infile)\n\n        if self.config.get('minify_vendor_scripts', False):\n            for filename in fileList(self.js_dir):\n                if os.path.abspath(filename) != self.outfile and not re.search(r'\\.min\\.js$', filename, re.I):\n                    self._minify(filename)\n\n        logging.info(\"done.\")\n\n    def templateContext(self, *args, **kwargs):\n        return {\n            \"main_js\": self.config.get('ouput_filename', 'main.js')\n        }\n","repo_name":"randomknowledge/Cactus_Refactored","sub_path":"cactus/plugins/minifyjs.py","file_name":"minifyjs.py","file_ext":"py","file_size_in_byte":2503,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"85"}
+{"seq_id":"32034405212","text":"from typing import Union\n\nfrom telebot.types import (\n    Message,\n    CallbackQuery\n)\n\nimport digitalocean\nfrom digitalocean import DataReadError\n\nfrom _bot import bot\nfrom utils.db import AccountsDB\nfrom .start import start\n\n\ndef add_account(d: Union[Message, CallbackQuery]):\n    t = '<b>Tambahkan Akun DO</b>\\n\\n' \\\n        'Masukan Token Digital Ocean <a href=\"https://cloud.digitalocean.com/account/api/tokens\">Ambil Disini</a> perhatikan dalam copy paste\\n\\n' \\\n        'Contoh:\\n' \\\n        '<code>token123:Komentarxxx</code>\\n' \\\n        '<code>token345</code>\\n\\n' \\\n        '/cancel Membatalkan'\n\n    msg = bot.send_message(\n        text=t,\n        chat_id=d.from_user.id,\n        parse_mode='HTML',\n        disable_web_page_preview=True\n    )\n\n    bot.register_next_step_handler(msg, add_account_next_step_handler)\n\n\ndef add_account_next_step_handler(m: Message):\n    if m.text == '/cancel':\n        start(m)\n        return\n\n    msg = bot.send_message(\n        text='Tambahkan akun...',\n        chat_id=m.from_user.id\n    )\n\n    accounts = m.text.split('\\n')\n    added_accounts = []\n    failed_accounts = []\n\n    for account in accounts:\n        if ':' in account:\n            token = account.split(':')[0]\n            remarks = account.split(':')[1]\n        else:\n            token = account\n            remarks = ''\n\n        try:\n            email = digitalocean.Account().get_object(\n                api_token=token\n            ).email\n\n            AccountsDB().save(\n                email=email,\n                token=token,\n                remarks=remarks\n            )\n\n            added_accounts.append(email)\n\n        except DataReadError:\n            failed_accounts.append(account)\n\n    t = f'<b>Umum {len(accounts)} Nomor akun</b>\\n\\n'\n\n    if added_accounts:\n        t += f'Ditambahkan dengan sukses {len(added_accounts)} Intivual：\\n'\n        for added_account in added_accounts:\n            t += f'<code>{added_account}</code>\\n'\n        t += '\\n'\n\n    if failed_accounts:\n        t += f'Tambahkan gagal {len(failed_accounts)} Intivual：\\n'\n        for failed_account in failed_accounts:\n            t += f'<code>{failed_account}</code>\\n'\n\n    bot.edit_message_text(\n        text=t,\n        chat_id=m.from_user.id,\n        message_id=msg.message_id,\n        parse_mode='HTML'\n    )\n","repo_name":"FighterTunnel/DigitalOcean-TeleBot","sub_path":"modules/add_account.py","file_name":"add_account.py","file_ext":"py","file_size_in_byte":2311,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"85"}
+{"seq_id":"6145993162","text":"import os\nimport random\n\nfrom PIL import Image\nfrom mat4py import loadmat\n\n\nclass CarDataset:\n    def __init__(self, root: str, img_folder: str, transform: None):\n        super().__init__()\n        self.root = root\n        self.transform = transform\n        self.img_folder = img_folder\n        self.img_path = os.path.join(root, img_folder)\n        anno = loadmat(os.path.join(root, f'{img_folder}_annos.mat'))\n        self.classes = anno['annotations']['class']\n        self.img_names = anno['annotations']['fname']\n        self.bbox_x1 = anno['annotations']['bbox_x1']\n        self.bbox_x2 = anno['annotations']['bbox_x2']\n        self.bbox_y1 = anno['annotations']['bbox_y1']\n        self.bbox_y2 = anno['annotations']['bbox_y2']\n        self.indexes = list(range(len(self.classes)))\n        self.label_names = loadmat(os.path.join('C:\\\\MySpace\\\\Projects\\\\PTDL\\\\lab1\\\\data\\\\LR1-1', 'cars_meta.mat'))[\n            'class_names']\n\n    def split_dataset(self, train_part: float):\n        train_number = int(train_part * self.__len__())\n        train_indexes = random.sample(self.indexes, train_number)\n        test_indexes = list(set(self.indexes) - set(train_indexes))\n\n        train_dataset = CarDataset(root=self.root, img_folder=self.img_folder, transform=self.transform)\n        train_dataset.indexes = train_indexes\n\n        test_dataset = CarDataset(root=self.root, img_folder=self.img_folder, transform=self.transform)\n        test_dataset.indexes = test_indexes\n\n        return train_dataset, test_dataset\n\n    def __getitem__(self, item):\n        index = self.indexes[item]\n        image = Image.open(os.path.join(self.img_path, self.img_names[index])).convert('RGB')\n        image.crop(box=(self.bbox_x1[index], self.bbox_y1[index], self.bbox_x2[index], self.bbox_y2[index]))\n\n        if self.transform is not None:\n            image = self.transform(image)\n\n        label = int(self.classes[index]) - 1\n        return image, label\n\n    def __len__(self):\n        return len(self.indexes)\n","repo_name":"paNoNi/PTDL","sub_path":"lab1/torch_src/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":2001,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"20129944720","text":"# fruits = \"{},{},{},{},{}\"\r\n# print(fruits.format(1,2,3,4,6))\r\n# print(fruits.format(\"1\",\"2\",\"3\",\"4\",\"5\"))\r\n# print(fruits.format(\"orange\",\"mango\",\"grapes\",\"lemon\",\"banana\"))\r\n# print(fruits.format(True,False,True , False,True))\r\n# print(fruits.format(\"orange  is super \\n\",\"BANANA IS FAVORITE FOR MONKEYS \\n\",\"mango is very sweet\\n\",\"grapes is sour \\n\",\"lemon is salt and crimy taste\"))\r\n\r\n#we can also get input using argv\r\n# from sys import argv\r\n# first,second,third = argv\r\n# # the first will be printed as the name of the file\r\n# print(f\"{first} is the winner\")\r\n# print(f\"{second} is the runner\")\r\n# print(f\"{third} is the looser\")\r\n\r\n# from sys import argv\r\n# script,file_name = argv\r\n# hello = input(\"your file will ber opened soon !\")\r\n# text = open(file_name)\r\n# red = input(\"do you want to read it \")\r\n# print(text.read())\r\n# red = input(\"do you want to read it one more \")\r\n# file_again = input(\"> enter ur file name :\")\r\n# text_new = open(file_again)\r\n# print(\"here's your file\")\r\n# print(text_new.read())\r\n\r\n# from sys import argv\r\n# script,file_name = argv\r\n# print(f\"we ar going to erase{file_name}\")\r\n# print(\"if you don't want that , hit CTRL-C.\")\r\n# print(\"if you do want that , hit RETURN\")\r\n# input(\"?\")\r\n# print(\"opening the fie......\")\r\n# target = open(file_name,\"w\")\r\n# # print(\"Truncating the file,goodbye \")\r\n# # target.truncate()\r\n# print(\"now I'm going to ask u three lines \")\r\n# line1 = input(\"line 1 :\")\r\n# line2 = input(\"line 2 :\")\r\n# line3 = input(\"line 3 :\")\r\n# print(\"I'm going to write these to the fie\")\r\n# target.write(line1)\r\n# target.write(\"\\n\")\r\n# target.write(line2)\r\n# target.write(\"\\n\")\r\n# target.write(line3)\r\n# target.write(\"\\n\")\r\n# print(\"AND finally , we close it ..\")\r\n# target.close()\r\n\r\nfrom sys import argv\r\nfrom os.path import exists\r\nscript,fromfile,tofile = argv\r\nprint(f\"we r going to copy from {fromfile} to {tofile}\")\r\nfromfile = open(fromfile)\r\nfromfiles = fromfile.read()\r\nprint(f\"check the whether {tofile} exisits ?\")\r\ntofile = open(tofile,\"w\")\r\nto_data = tofile.write(fromfiles)\r\nprint(\"the copiedd data is ::::::\")\r\n# print(tofile.read())\r\nfromfile.close()\r\ntofile.close()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# from os.path import exists\r\n# script,from_file,to_file = argv\r\n# print(f\"copying from {from_file} to {to_file}\")\r\n# in_file =open(from_file)\r\n# indata = in_file.read()\r\n# print(f\"the input file is {len(indata)} bytes long\")\r\n# print(f\"Does the output file exists? {exists(to_file)}\")\r\n# input()\r\n# out_file = open(to_file,\"w\")\r\n# out_file.write(indata)\r\n# print(\"ALright , all done , \")\r\n# out_file.close()\r\n# in_file.close()\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"jeykarlokes/complete-reference-to-python3-programs","sub_path":"python/PYTHON THE HARD WAY/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":2610,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"26712172886","text":"\"\"\"\nDate: 01/07/19\nUnderstanding matplotlib library. Using it to display an image.\n\n*) Image displayed with CV2 was not closing, it's easier with matplotlib\n*) OpenCV follows BGR order while matplotlib follows RGB order. Hence, there will be color difference if an image read\nopencv is displayed using matplotlib. So, convert from BGR2RGB first.\n\"\"\"\n\nimport cv2\nfrom matplotlib import pyplot as plt\n\n\ndef read_image(path=\"candid.png\"):\n    img = cv2.imread(path)\n    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n    # img = img[:, :, ::-1]   # Numpy convention of flipping color planes\n    return img\n\n\ndef display_image_with_mpl(img):\n    plt.imshow(img, cmap=\"gray\", interpolation=\"bicubic\")\n    plt.xticks([])  # To hide tick values on X axis\n    plt.yticks([])  # To hide tick values on Y axis\n    plt.show()\n\n\nimage = read_image()\ndisplay_image_with_mpl(image)\n","repo_name":"psukalka/morning_blues","sub_path":"open_cv_tutorials/mat_plot_lib.py","file_name":"mat_plot_lib.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"71436651159","text":"from aiohttp import web\n\nfrom api.handlers import Handler\n\ndef setup_routes(app):\n    handler = Handler()\n\n    app.router.add_routes([\n        web.get(\"/hello/{name}\", handler.hello),\n        web.get(\"/get/json\", handler.get_json),\n        web.get(\"/get/plot/{name}\", handler.get_plot),\n        web.get(\"/get/raiting/{params}\", handler.get_raiting),\n        web.get(\"/get/model/{name}\", handler.get_model),\n        web.post(\"/post/excel\", handler.post_excel),\n        web.post(\"/post/jpg\", handler.post_jpg),\n        web.post(\"/post/links\", handler.post_links)\n    ])","repo_name":"cut4cut/u-ml-api","sub_path":"api/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"12667513420","text":"\"\"\"\npygame-menu\nhttps://github.com/ppizarror/pygame-menu\n\nLEFT ARROW CLASS\nSelector with a left arrow on the item.\n\"\"\"\n\n__all__ = ['LeftArrowSelection']\n\nimport pygame\nimport pygame_menu\n\nfrom pygame_menu.widgets.selection.arrow_selection import ArrowSelection\n\nfrom pygame_menu._types import Tuple2IntType, NumberType, NumberInstance\n\n\nclass LeftArrowSelection(ArrowSelection):\n    \"\"\"\n    Widget selection left arrow class.\n    Creates an arrow to the left of the selected Menu item.\n\n    :param arrow_size: Size of arrow on x-axis and y-axis (width, height) in px\n    :param arrow_right_margin: Distance from the arrow to the widget (px)\n    :param arrow_vertical_offset: Vertical offset of the arrow (px)\n    :param blink_ms: Milliseconds between each blink; if ``0`` blinking is disabled\n    \"\"\"\n    _arrow_right_margin: int\n\n    def __init__(\n        self,\n        arrow_size: Tuple2IntType = (10, 15),\n        arrow_right_margin: int = 5,\n        arrow_vertical_offset: int = 0,\n        blink_ms: NumberType = 0\n    ) -> None:\n        assert isinstance(arrow_right_margin, NumberInstance)\n        assert arrow_right_margin >= 0, 'margin cannot be negative'\n\n        super(LeftArrowSelection, self).__init__(\n            margin_left=arrow_size[0] + arrow_right_margin,\n            margin_right=0,\n            margin_top=0,\n            margin_bottom=0,\n            arrow_vertical_offset=arrow_vertical_offset,\n            blink_ms=blink_ms\n        )\n\n        self._arrow_right_margin = arrow_right_margin\n\n    # noinspection PyMissingOrEmptyDocstring\n    def draw(self, surface: 'pygame.Surface', widget: 'pygame_menu.widgets.Widget') -> 'LeftArrowSelection':\n        # A\n        #   \\B        widget\n        # C /\n        #     <------>\n        #      margin\n        rect = widget.get_rect()\n        a = (rect.topleft[0] - self._arrow_size[0] - self._arrow_right_margin,\n             int(rect.midleft[1] - self._arrow_size[1] / 2 + self._arrow_vertical_offset))\n        b = (rect.midleft[0] - self._arrow_right_margin,\n             rect.midleft[1] + self._arrow_vertical_offset)\n        c = (rect.bottomleft[0] - self._arrow_size[0] - self._arrow_right_margin,\n             int(rect.midleft[1] + self._arrow_size[1] / 2 + self._arrow_vertical_offset))\n        super(LeftArrowSelection, self)._draw_arrow(surface, widget, a, b, c)\n        return self\n","repo_name":"ppizarror/pygame-menu","sub_path":"pygame_menu/widgets/selection/left_arrow.py","file_name":"left_arrow.py","file_ext":"py","file_size_in_byte":2356,"program_lang":"python","lang":"en","doc_type":"code","stars":487,"dataset":"github-code","pt":"85"}
+{"seq_id":"36454392560","text":"import random\n\nfor _ in range(0, 100):\n    date = '2012-01-{:0>2} {:0>2}:00:00{:0=+3}:00'.format(random.randint(\n        1, 31), random.randint(0, 23), random.randint(-11, 12))\n    with open(date.replace(' ', '-').replace(':', '') + '.md', 'w') as file:\n        print(\"\"\"\\\nTitle: {date}\nDate: {date}\n\nThis is a test for {date}\"\"\".format(date=date), file=file)\n","repo_name":"PlaidWeb/Publ","sub_path":"tests/content/date-based/make-test.py","file_name":"make-test.py","file_ext":"py","file_size_in_byte":360,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"85"}
+{"seq_id":"8375965433","text":"import numpy as np\nfrom typing import List\nfrom typing import Tuple\n\nfrom functools import reduce\n\n\ndef take_single_entity_for_each_class(predictions: List[Tuple[str, int, np.array]], base_class: int = 0) -> List[Tuple[str, int]]:\n    \"\"\"\n    Restricts the NER system output to one text span per class.\n    If the output contains more than one entity for a given class, we take the one\n    whose average confidence (probability) score is higher\n\n    An entity is defined by any contiguous span of tokens [t0, t1 ..., tn]\n    Returns a list of tokens with their associated processed classes\n    \"\"\"\n\n    def reducer(acc, x):\n        i, xs = x\n        token, pred, probs = xs\n\n        if pred not in acc:\n            acc[pred] = [[i]]\n        else:\n            last = acc[pred][-1]\n            if last[-1] == i - 1:\n                acc[pred][-1].append(i)\n            else:\n                acc[pred].append([i])\n\n        return acc\n\n    groups = reduce(reducer, enumerate(predictions), dict())\n\n    groups = ((k, v, [[predictions[i][-1] for i in xs] for xs in v]) for k, v in groups.items())\n    groups = ((k, v, [np.array(p) for p in ps]) for k, v, ps in groups)\n    groups = ((k, v, [a.mean(axis=0) for a in ax]) for k, v, ax in groups)\n    groups = ((k, v, [a[k] for a in ax]) for k, v, ax in groups)\n    groups = ((k, v, np.argmax(ax)) for k, v, ax in groups)\n\n    keep = dict(((k, set(v[a])) for k, v, a in groups))\n\n    result = []\n    for i, (token, pred, _) in enumerate(predictions):\n        if (pred == base_class) or (i in keep[pred]):\n            new_pred = pred\n        else:\n            new_pred = base_class\n        result.append((token, new_pred))\n\n    return result","repo_name":"raufer/pytorch-ner","sub_path":"src/ops/ner/entity_resolution.py","file_name":"entity_resolution.py","file_ext":"py","file_size_in_byte":1680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"73306755479","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n'''\n@File    :   BaseAlphaV2.py\n@Time    :   2022/09/02 10:05:34\n@Author  :   Liu Junli\n@Version :   1.0\n@Contact :   liujunli.xmu@foxmail.com\n'''\nimport numpy as np\nimport pandas as pd\nimport multiprocessing as mp\n\nfrom BaseAlphaBase import BaseDataSet, BaseAlphaSet, BaseFactorCheck\nfrom basesqlv3 import WriteSQLV2\nfrom config import Config\nfrom logger import get_module_logger, set_log_with_config\nfrom utils import func_timer\nimport utils\n\nidx = pd.IndexSlice\nset_log_with_config()\nBM = {\n    'sz50': '000016.SH',\n    'hs300': '000300.SH',\n    'zz1000': '000852.SH',\n    'zzlt': '000902.CSI',\n    'zz500': '000905.SH',\n    'zz800': '000906.SH',\n    'zzqz': '000985.CSI'\n}\n\ndefault_config = {\n    'date_range': {\n        'start': '2017-01-01',\n        'end': '2022-08-15'\n    },\n    'BM': BM,\n    'cne5': ['Beta', 'BooktoPrice', 'EarningsYield', 'Growth', 'Leverage', 'Liquidity', 'Momentum', 'NonLinearSize', 'ResidualVolatility', 'Size'],\n    'connect': dict(host='10.224.16.81', user='haquant', passwd='haquant', database='jydb'),\n    'stock_pools': ['all_A', 'user_defined']\n}\n\nC = Config(default_config)\n\n\nclass DataSet(BaseDataSet):\n    _provider = WriteSQLV2\n    logger = get_module_logger('file.dataset')\n\n    def __init__(self) -> None:\n        super().__init__(self._provider, conn=C.connect, read_only=True)\n\n    def _load(self):\n        cols = ['closeprice', 'firstindustrycode', 'afloats', 'adjclose']\n        self.load_data = self._provider.read('dailydata', cols, C.date_range)\n        ind_sql = \"\"\"\n        SELECT DISTINCT firstindustryname as ind_name, firstindustrycode as ind_code FROM dailydata WHERE tradedate='2020-12-31'\n        \"\"\"\n        self.inds_name_code = self._provider._read(ind_sql).dropna(how='any').astype({'ind_code': int}).set_index('ind_code')['ind_name'].to_dict()\n        self.style_factor = self._provider.read('barra_cne5_factor', C.cne5, C.date_range)\n        self.fields = pd.concat([self._load_fields(stock_pool) for stock_pool in C.stock_pools], axis=1)\n\n    @func_timer\n    def _load_fields(self, stock_pool: str):\n        if stock_pool == 'all_A':\n            fields = pd.DataFrame(1, index=self.load_data.index, columns=['all_A'])\n\n        elif stock_pool == 'user_defined':\n            fields = self._provider.read('dailydata', ['ifin_selfdefined_stockpool'], C.date_range)\n\n        elif stock_pool in C.BM.keys():\n            fields = self._provider.read('index_weight_daily', ['weight'], C.date_range, {\n                'index_code': C.BM[stock_pool]\n            }).rename(columns={\n                'component_code': 'wind_code'\n            }).reset_index().drop(columns=['index_code']).set_index(['tradedate', 'wind_code'])\n\n        fields = fields.astype(bool).sort_index()\n        fields.columns = [stock_pool]\n        return fields\n\n    @func_timer\n    def prepare_data(self):\n        with self._provider.start():\n            self._load()\n        self.logger.info('load data done!')\n        self.base_data['ind'] = self.load_data['firstindustrycode'].astype('int64', errors='ignore')\n        ind_col = [''.join(['ind', str(i)]) for i in self.inds_name_code.keys()]\n        self.ind_dummy = pd.get_dummies(self.base_data['ind'], sparse=True)\n        self.ind_dummy.columns = ind_col\n        self.base_data['cap'] = np.log(self.load_data['afloats'].mul(self.load_data['closeprice'])).to_frame()\n        self.base_data['size'] = self.base_data['cap'].groupby('tradedate').apply(lambda x: pd.qcut(x, 3, labels=['small', 'medium', 'big']))\n        self.base_data['weight'] = self.base_data['cap']\n        self.base_data['close'] = self.load_data['adjclose'][:]\n        del self.load_data\n        self.logger.info('prepare data done!')\n\n\nclass AlphaSet(BaseAlphaSet):\n    logger = get_module_logger('file.alphaset')\n\n    def load_data(self):\n        raise NotImplementedError\n\n    @func_timer\n    def pre_process(self, dataset: BaseDataSet, mode='mad', multiple=5, method='ffill', ifmp=False, w_method='cap', freq=None):\n        if freq:\n            self.alpha = utils.resample(self.alpha, freq)\n\n        if ifmp:\n            pool = mp.Pool(processes=3)\n            runs = []\n            runs.append(\n                pool.apply_async(utils.pre_process,\n                                 args=(self.alpha, dataset.base_data, dataset.style_factor, w_method, 'cne5', mode, multiple, method)))\n            runs.append(\n                pool.apply_async(utils.pre_process,\n                                 args=(self.alpha, dataset.base_data, dataset.ind_dummy.join(dataset.base_data['cap']), w_method, 'indu', mode,\n                                       multiple, method)))\n            runs.append(\n                pool.apply_async(utils.pre_process, args=(self.alpha, dataset.base_data, None, w_method, 'normal', mode, multiple, method, False)))\n            self.alpha = pd.concat([self.alpha] + [r.get() for r in runs], axis=1)\n            pool.close()\n            pool.join()\n        else:\n            result = []\n            result.append(utils.pre_process(self.alpha, dataset.base_data, dataset.style_factor, w_method, 'cne5', mode, multiple, method))\n            result.append(\n                utils.pre_process(self.alpha, dataset.base_data, dataset.ind_dummy.join(dataset.base_data['cap']), w_method, 'indu', mode, multiple,\n                                  method))\n            result.append(utils.pre_process(self.alpha, dataset.base_data, None, w_method, 'normal', mode, multiple, method, False))\n            self.alpha = pd.concat([self.alpha] + result, axis=1)\n\n\nclass FactorCheck(BaseFactorCheck):\n    def __init__(self, dataset: BaseDataSet, alphaset: BaseAlphaSet) -> None:\n        super().__init__(dataset, alphaset)\n        self.factor_suffix = 'cne5'\n\n    def get_table(self, alpha_name):\n        self.factor_check_info['因子名称'] = alpha_name\n        # self.factor_check_info['回测因子']\n        # self.factor_check_info['对照因子']\n        # self.factor_check_info['回测耗时']\n        self.table.update({'info': pd.DataFrame(self.factor_check_info, index=['值']).T})\n        cols = self.factor_data.columns[self.factor_data.columns.str.contains('_' + alpha_name + '_')]\n        self.table.update({'stats': utils.factor_return_stats(self.factor_data[cols], self.factor_return['allA'][cols])})\n        self.table.update({'IC': utils.print_IC_table(self.IC['allA'][cols]).iloc[-15:, :]})\n        cols_ = self.group_return['userdefined'].columns[self.group_return['userdefined'].columns.str.contains('_' + alpha_name + '_')]\n        self.table.update({'groups': utils.group_return_stats(self.group_return['userdefined'][cols_])})\n\n    def plot(self, alpha_name: str):\n        alpha_cols = self.factor_data.columns[self.factor_data.columns.str.contains('_' + alpha_name + '_')]\n        col_suffix = 'alpha_' + alpha_name + '_' + self.factor_suffix\n        tit = 'fac_' + self.factor_suffix\n\n        self.image.clear()\n\n        data = self.IC['allA'][alpha_cols]\n        self.image.update(dict(IC=utils.plot_line(data, ylabel='IC累计值', method='sum', suptitle='全市场股票池IC累计值', alpha_name=alpha_name)))\n\n        data = self.long_short['long_short_fields'].loc[:, idx[:, col_suffix]]\n        self.image.update(\n            dict(long_short1=utils.plot_line(data, suptitle='不同选股域的因子%s多空收益LSR' % tit, leg_txt=['自定义选股域', '全A选股域'], alpha_name=alpha_name)))\n\n        data = self.long_short['long_short_fields_ind'].loc[:, idx[:, col_suffix]]\n        self.image.update(\n            dict(long_short2=utils.plot_line(data, suptitle='不同选股域的因子%s行业分层多空收益LSR' % tit, leg_txt=['自定义选股域', '全A选股域'], alpha_name=alpha_name)))\n\n        data = self.group_return['userdefined'].loc[:, col_suffix].squeeze().unstack(level='fac_qt')\n        self.image.update(dict(groups1=utils.plot_line(data, test='mttest', suptitle='%s分组测试' % tit, alpha_name=alpha_name)))\n\n        data = self.group_return['userdefined_i_ind'].loc[:, col_suffix].squeeze().unstack(level='fac_qt')\n        self.image.update(dict(groups2=utils.plot_line(data, test='mttest', suptitle='%s行业分层分组测试' % tit, alpha_name=alpha_name)))\n\n        data = self.long_short['long_short_userdefined_o_cap'].xs(col_suffix, level=0, axis=1)\n        self.image.update(dict(groups_cap=utils.plot_line(data, suptitle='不同自由流通市值的因子多空收益LSR', alpha_name=alpha_name)))\n\n        data = self.factor_return['allA'].xs('beta', level=1).loc[:, alpha_cols]\n        self.image.update(dict(reg=utils.plot_line(data, test='fmtest', suptitle='回归系数累计值', alpha_name=alpha_name)))\n\n    @utils.func_timer\n    def factor_group_analysis(self):\n        freq = None\n        alpha_flag = self.factor_data.columns.str.startswith('alpha')\n        suffix_flag = self.factor_data.columns.str.endswith('_' + self.factor_suffix)  # cne5因子\n        alpha_cols = self.factor_data.columns[(~alpha_flag) | (suffix_flag)]\n\n        fields = self.dataset.fields.reindex(self.factor_data.index)\n        index = fields.index[fields['user_defined'].fillna(False)]\n        factor_data = self.factor_data.reindex(index)\n        if suffix_flag.sum() > 1:\n            self.group_return.update(dict(allA=utils.group_return(self.factor_data[alpha_cols], freq=freq)))\n            self.group_return.update(dict(allA_i_ind=utils.group_return(self.factor_data[alpha_cols], group_adjust=['ind'], freq=freq)))\n            self.group_return.update(dict(userdefined=utils.group_return(factor_data[alpha_cols], freq=freq)))\n            self.group_return.update(dict(userdefined_i_ind=utils.group_return(factor_data[alpha_cols], group_adjust=['ind'], freq=freq)))\n            self.group_return.update(dict(userdefined_o_cap=utils.group_return(factor_data[alpha_cols], out_group=['size'], freq=freq)))\n        else:\n            pool = mp.Pool(processes=5)\n            results = []\n            results.append(pool.apply_async(utils.group_return, args=(self.factor_data[alpha_cols], ['ind'], None, freq)))\n            results.append(pool.apply_async(utils.group_return, args=(factor_data[alpha_cols], ['ind'], None, freq)))\n            results.append(pool.apply_async(utils.group_return, args=(factor_data[alpha_cols], None, ['size'], freq)))\n            results.append(pool.apply_async(utils.group_return, args=(self.factor_data[alpha_cols], None, None, freq)))\n            results.append(pool.apply_async(utils.group_return, args=(factor_data[alpha_cols], None, None, freq)))\n            dict_keys = ['allA_i_ind', 'userdefined_i_ind', 'userdefined_o_cap', 'allA', 'userdefined']\n            for i, p in enumerate(results):\n                self.group_return.update({dict_keys[i]: p.get()})\n            pool.close()\n            pool.join()\n\n    @utils.func_timer\n    def long_short_analysis(self):\n        def long_short_return(field: str, grouer='tradedate') -> pd.DataFrame:\n            def _long_short_return(df: pd.DataFrame, field=field):\n                out = df.xs('p5', level=1, axis=0) - df.xs('p1', level=1, axis=0)\n                out.columns = pd.MultiIndex.from_product([[field], out.columns], names=['field', 'factor'])\n                return out\n\n            return self.group_return[field].groupby(grouer).apply(_long_short_return).droplevel(level=0, axis=0)\n\n        self.long_short.update(dict(long_short_fields=pd.concat([long_short_return(field) for field in ['userdefined', 'allA']], axis=1)))\n        self.long_short.update(\n            dict(long_short_fields_ind=pd.concat([long_short_return(field) for field in ['userdefined_i_ind', 'allA_i_ind']], axis=1)))\n\n        self.long_short.update(\n            dict(long_short_userdefined_o_cap=long_short_return('userdefined_o_cap', grouer=['tradedate', 'size']).droplevel(\n                level=0, axis=0).droplevel(level=0, axis=1).unstack(level='size').sort_index()))\n\n    @utils.func_timer\n    def IC_analysis(self):\n        self.IC.update(allA=utils.factor_information_coefficient(self.factor_data))\n\n    @utils.func_timer\n    def regression_analysis(self):\n        self.factor_return.update(allA=utils.factor_return(self.factor_data))\n","repo_name":"Zhongtian-Tang/BaseAlpha","sub_path":"BaseAlphaV2.py","file_name":"BaseAlphaV2.py","file_ext":"py","file_size_in_byte":12193,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"4000771965","text":"import cli_scripts.internal_spotify_apis.get as get\nimport pandas as pd\n\n\ndef test_get_error_ids_to_skip():\n    df = pd.DataFrame(\n        [(\"a\", 404), (\"b\", 404), (\"b\", 403), (\"b\", 400), (\"a\", 500), (\"c\", 401)],\n        columns=[\"track_id\", \"status_code\"],\n    )\n\n    assert get.get_error_ids_to_skip(\n        error_log_df=df, status_codes_to_skip=set([404, 403])\n    ) == set([\"a\", \"b\"])\n","repo_name":"Sejmou/spotify-charts-analysis","sub_path":"tests/cli_scripts/internal_spotify_apis/test_get.py","file_name":"test_get.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"24437968884","text":"class Solution:\n    def sortedSquares(self, nums: List[int]) -> List[int]:\n        zeroIndex = bisect.bisect(nums, 0) # use bisect's binary search to look for 0 (could also write a binary search method to search for 0)\n        neg, pos = zeroIndex-1, zeroIndex # negative pointer starts pointing before the 0, positive pointer starts pointing at it\n        n = len(nums)\n        result = []\n        while 0 <= neg and pos < n:\n            if nums[pos] <= -nums[neg]: # if the positive pointer points to a smaller or equal abs val\n                result += [nums[pos]**2] # add smaller square\n                pos += 1 # move forward\n            else:\n                result += [nums[neg]**2] # add smaller square\n                neg -= 1 # move forward\n        while 0 <= neg: # add all remaining negative numbers\n            result += [nums[neg]**2]\n            neg -= 1\n        while pos < n: # add all remaining positive numbers\n            result += [nums[pos]**2]\n            pos += 1\n        return result","repo_name":"Bloomh/LeetCode-Submissions","sub_path":"0977-squares-of-a-sorted-array/0977-squares-of-a-sorted-array.py","file_name":"0977-squares-of-a-sorted-array.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"31440278362","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Feb 10 16:23:02 2020\n\n@author: ykrempp\n\"\"\"\n\n\nimport pandas as pd\n\ndf = pd.read_excel('images_analyzed.xlsx')\n\n# import seaborn as sns\n# sns.lmplot(x='Time', y='Images_Analyzed', data=df, hue='Age')\n# sns.lmplot(x='Coffee', y='Images_Analyzed', data=df, hue='Age')\n\nfrom sklearn import linear_model\nreg = linear_model.LinearRegression()\n\nreg.fit(df[['Time', 'Coffee', 'Age']], df['Images_Analyzed']) # first 3 variables are independant, the last is the one that depends on all others\n\nprint(reg.coef_) # coefs = weights, the closest to 1 the more dependency there is. Age is not important here\n\nprint(reg.predict([[13, 2, 23]])) # predict the number of images analyzed using some variables: time = 13, coffee = 2, age = 23.","repo_name":"UniversalBuilder/Python-and-Microscopy","sub_path":"work-in-progress/multiple-linear-regression.py","file_name":"multiple-linear-regression.py","file_ext":"py","file_size_in_byte":765,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"2001329958","text":"#!/usr/bin/python\n#-*- coding: utf-8 -*-\n\nimport math\nimport time\n\ndef check_prime(n):\n    if n % 2 == 0:\n        return False\n    from_i = 3\n    to_i = int(math.sqrt(n)) + 1\n    for i in xrange(from_i, to_i, 2):\n        if i % 2 == 0:\n            return False\n    return True\n\nif __name__ == '__main__':\n    primes = []\n    number_range = xrange(1,1000000)\n    t1 = time.time()\n    for possible_prime in number_range:\n        if check_prime(possible_prime):\n            primes.append(possible_prime)\n    t2 = time.time()\n    print('Took {}'.format(t2 - t1))\n","repo_name":"ajaxhe/python-practice","sub_path":"parallel/prime/serial_prime.py","file_name":"serial_prime.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"26736135574","text":"\"\"\" PROBLEM 4\nCreate a class StringManipulation that receives a list of words wlist at the time of object\ncreation. The class must have the following methods:\nWords_of_length(length) — returns a list of all the words of length length in wlist\nWords_starts_with(char) — returns a list of all the words that start with char in wlist\nWords_ends_with(char) — returns a list of all the words that end with char in wlist\nPalindromes — returns a list of all the words that are palindromes in wlist\nTotal_words — returns the number of words in wlist\nLongest_word — that returns the longest length word in wlist . if list wlist has more than\none longest word then return the first one.\nSmallest_word that returns the smallest length word in wlist . if list wlist has more than\none smallest word then return the first one.\nCount(word) that returns the total number of occurrences of word in wlist \"\"\"\n\n#from _typeshed import SupportsReadline\n\n\nclass StringManipulation:\n    #create class constructor\n    def __init__(self,wlist) -> None:\n        self.wlist = wlist[:]\n    #word length method\n    def Words_of_length(self,length):\n        res=[]\n        for i in self.wlist:\n            if len(i) == length:\n                res.append(i)\n        return res\n    #class method words_starts_with\n    def Words_starts_with(self,char):\n        res=[]\n        for i in self.wlist:\n            if i[0]==char:\n                res.append(i)\n        return res\n    #class method words end with\n    def Words_end_with(self,char):\n        res = []\n        for i in self.wlist:\n            if i[-1] == char:\n                res.append(i)\n        return res\n    #class method for palindrome\n    def Palindromes(self):\n        res=[]\n        for i in self.wlist:\n            if i==i[::-1]:\n                res.append(i)\n        return res\n    #class method for total words\n    def Total_words(self):\n        return len(self.wlist)\n    #class method for longest word\n    def Longest_word(self):\n        maxword = self.wlist[0]\n        for i in self.wlist:\n            if len(i) > len(maxword):\n                maxword = i\n        return maxword\n    #class method for smallest word\n    def Smallest_word(self):\n        minword = self.wlist[0]\n        for i in self.wlist:\n            if len(i) < len(minword):\n                minword = i\n        return minword\n    #class method for method count\n    def Count(self,word):\n        return self.wlist.count(word)\n\n\nword = input().split(' ')\ns = StringManipulation(word)\n#calling the methods\nprint(s.Words_of_length(6))\nprint(s.Words_starts_with('s'))\nprint(s.Words_end_with('l'))\nprint(s.Palindromes())\nprint(s.Total_words())\nprint(s.Longest_word())\nprint(s.Smallest_word())\nprint(s.Count('it'))\n\n","repo_name":"BSanandu88/Coding-questions","sub_path":"Additional/week_10/ppa4.py","file_name":"ppa4.py","file_ext":"py","file_size_in_byte":2728,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"4515627349","text":"from filseparator import list_all_files as submission\nimport pandas\nfrom settings import LOWEST_NIM,HIGHEST_NIM,LIST_SOAL,PRAKTIKUM,AUTOSCORE_HEADER,AUTOSCORE_KOMENTAR\nfrom settings import USE_PRESS_ENTER_TO_CONTINUE as pakaiEnter\nfrom settings import PREVIEW_AFTER_CHECKING,RESTRICTED_CODE,ADDITIONAL,TOLERATED_MODE\nimport os\nfrom subprocess import call\nimport sheetgenerator\nfrom time import sleep\nimport openpyxl\n\n'''END OF COLOR CODE'''\n\ndef cari_dir(nfile):\n    retr = \"\"\n    for x in submission:\n        if nfile in x:\n            retr = x\n            break\n    return retr\n\ndef headercheck(flname,nim):\n    param = 0\n    with open (flname,'r') as pyprog:\n        for lines in pyprog:\n            if str(nim) in lines : param=param+1\n            if \"nim\" in lines.lower() and len(lines)>25 : param=param+1\n            if \"nama\" in lines.lower() and len(lines) > 25: param = param + 1\n            if \"tanggal\" in lines.lower() and len(lines)>15:  param=param+1\n            if \"deskripsi\" in lines.lower() and len(lines)>15: param=param+1\n\n        pyprog.close()\n    if param>=5 : return True\n    else : return False\n\ndef clrscreen():\n    if os.name==\"nt\": os.system('cls')\n    else: os.system('clear')\n\ndef printprogram(flname):\n    print(\"========\"+flname+\"========\")\n    with open (flname,'r') as pyrpog:\n        print(sheetgenerator.colors.fg.yellow)\n        for lines in pyrpog:\n            print(lines)\n        print(sheetgenerator.colors.reset)\n        pyrpog.close()\n    print(\"=================================END OF PROGRAM=================================\")\n\ndef getparentdir(childdir):\n    n = len(childdir)-1\n    while(childdir[n]!=\"\\\\\"):\n        childdir=childdir[:n:]\n        n=n-1\n    return childdir\n\ndef cekfilename(flname):\n    a = len(flname)\n    b = len(getparentdir(flname))\n    if a-b==18 : return True\n    else : return False\n\ndef checkerr(errfile):\n    err =[]\n    with open(errfile,'r') as kesalahan:\n        for k in kesalahan:\n            err.append(k)\n        kesalahan.close()\n\n    if(len(err)<=1): return False\n    else:\n        print(sheetgenerator.colors.bg.red)\n        print(\"COMPILE / RUNTIME ERROR \\n\")\n        for x in err: print(x)\n        print(sheetgenerator.colors.reset)\n        return True\n\ndef checkLossAnswer(answer,key,errfile):\n    if(checkerr(errfile)):\n        return 0\n    else:\n        ans = \"\"\n        kunjaw = \"\"\n        with open(answer,'r') as jawaban:\n            for x in jawaban:\n                ans=ans+x\n                    \n            jawaban.close()\n        with open(key,'r') as kunci:\n            for x in kunci:\n                kunjaw = kunjaw+x\n                    \n            kunci.close()\n\n        ans = ans.lower()\n        kunjaw = kunjaw.lower()\n        ans = ''.join(ans.split())\n        kunjaw = ''.join(kunjaw.split())\n        ans.replace(\".\",\"\")\n        kunjaw.replace(\".\",\"\")\n        #print(ans)\n        #print(kunjaw)\n        #sleep(1)\n        if(ans==kunjaw):\n            print(sheetgenerator.colors.fg.green+\"SELAMAT JAWABAN BENAR\"+sheetgenerator.colors.reset)\n            return 2\n        else:\n            print(sheetgenerator.colors.fg.red+\"JAWABAN SALAH !!!\"+sheetgenerator.colors.reset)\n            print(\"JAWABAN PRAKTIKAN\")\n            print(sheetgenerator.colors.fg.blue+ans+sheetgenerator.colors.reset)\n            print(\"\\nJAWABAN BENAR\")\n            print(sheetgenerator.colors.fg.green+kunjaw+sheetgenerator.colors.reset)\n            return 1\n\ndef checkAnswer(answer,key,errfile):\n    if(checkerr(errfile)):\n        return 0\n    else:\n        ans = []\n        kunjaw = []\n        with open(answer,'r') as jawaban:\n            for x in jawaban:\n                if(x==\"\"): ans.append(\"<enter>\")\n                else : ans.append(x)\n            jawaban.close()\n        with open(key,'r') as kunci:\n            for x in kunci:\n                if (x == \"\"):\n                    kunjaw.append(\"<enter>\")\n                else:\n                    kunjaw.append(x)\n            kunci.close()\n\n        if(len(ans)==len(kunjaw)):\n            w =0\n            a = True\n            while(w<len(ans) and a):\n                if(ans[w]!=kunjaw[w]) : a=False\n                else : w=w+1\n\n            if a:\n                print(sheetgenerator.colors.fg.green+\"SELAMAT JAWABAN BENAR\"+sheetgenerator.colors.reset)\n                return 2\n            else:\n                print(sheetgenerator.colors.fg.red+\"JAWABAN SALAH !!!\"+sheetgenerator.colors.reset)\n                print(\"JAWABAN PRAKTIKAN\")\n                for x in ans : print(sheetgenerator.colors.fg.lightblue+x+sheetgenerator.colors.reset)\n                print(\"\\nJAWABAN BENAR\")\n                for x in kunjaw : print(sheetgenerator.colors.fg.green+x+sheetgenerator.colors.reset)\n                return 1\n        else:\n            print(sheetgenerator.colors.fg.red+\"JAWABAN SALAH !!!\"+sheetgenerator.colors.reset)\n            print(\"JAWABAN PRAKTIKAN\")\n            for x in ans: print(sheetgenerator.colors.fg.blue+x+sheetgenerator.colors.reset)\n            print(\"\\nJAWABAN BENAR\")\n            for x in kunjaw: print(sheetgenerator.colors.fg.green+x+sheetgenerator.colors.reset)\n            return 1\n\ndef tulisatap(nim,flname,nosoal):\n    print(\"SOAL = \" + nosoal)\n    print(\"NIM = \" + str(nim))\n    print(\"fileloc : \" + flname)\n\ndef uinput(txt=\"\",lowrange=0,maxrange=0,bilbulat=True):\n    i = input(txt)\n    if bilbulat:\n        flag = True\n        if i==\"\" : flag = False \n        for a in i:\n            if (not(a>='0' and a<='9')):\n                flag =False\n                break\n\n        if flag:\n            ret = int(i)\n            if(ret>=lowrange and ret<=maxrange):\n                return ret\n            else:\n                print(\"PLEASE input number between \"+str(lowrange)+\" and \"+str(maxrange))\n                return uinput(txt, lowrange, maxrange, True)\n        else:\n            print(\"WRONG INPUT FORMAT!!!, REPEAT AGAIN\")\n            return uinput(txt,lowrange,maxrange,True)\n    else:\n        return i\n\ndef checkKomentar(flname):\n    flag ={\"kamus\":False,\"algoritma\":False}\n    with open (flname,'r') as pyprog:\n        for x in pyprog:\n            x = x.lower()\n            if \"kamus\" in x : flag[\"kamus\"] = True\n            if \"algoritma\" in x : flag[\"algoritma\"] = True\n\n            if flag[\"kamus\"] and flag[\"algoritma\"]:\n                break\n\n    return flag\n\ndef korektor(nim,flname,soal):\n    LOOPTIME = 0.1\n    tulisatap(nim,flname,soal[\"no_soal\"])\n    print(\"\\nFase 1. Pengoreksian Sourcecode \")\n    printprogram(flname)\n\n    if(soal[\"check_header\"]):\n        maxscr = soal[\"skor_max_header\"]\n        if not (headercheck(flname, nim) and cekfilename(flname) and AUTOSCORE_HEADER):\n            print(\"Header Program : \" + str(headercheck(flname, nim)))\n            print(\"Nama Program : \" + str(cekfilename(flname)))\n            nil_header = int(uinput(\"penilaian nama file dan header (0-\"+str(maxscr)+\")= \", 0, maxscr, True))\n        else:\n            print(\"Header Program : \" + str(headercheck(flname, nim)))\n            print(\"Nama Program : \" + str(cekfilename(flname)))\n            print(\"nilai maksimum untuk nama file dan header telah diberikan (\"+str(maxscr)+\"/\"+str(maxscr)+\")\")\n            nil_header = maxscr\n    else: nil_header = 0\n\n    if(soal[\"check_komentar\"]):\n        maxscr = soal[\"skor_max_komentar\"]\n        if not (checkKomentar(flname)[\"kamus\"] and checkKomentar(flname)[\"algoritma\"] and AUTOSCORE_KOMENTAR):\n            print(\"Kamus terdeteksi = \" + str(checkKomentar(flname)[\"kamus\"]))\n            print(\"Algoritma terdeteksi = \" + str(checkKomentar(flname)[\"algoritma\"]))\n            nil_komen = int(uinput(\"penilaian indentasi dan komentar (0-\"+str(maxscr)+\")= \", 0, maxscr, True))\n        else:\n            print(\"Kamus terdeteksi = \" + str(checkKomentar(flname)[\"kamus\"]))\n            print(\"Algoritma terdeteksi = \" + str(checkKomentar(flname)[\"algoritma\"]))\n            print(\"nilai maksimum untuk komentar dan indentasi telah diberikan (\"+str(maxscr)+\"/\"+str(maxscr)+\")\")\n            nil_komen = maxscr\n    else: nil_komen = 0\n\n    if(soal[\"check_penguasaan_modul\"]):\n        maxscr = soal[\"skor_max_penguasaan_modul\"]\n        nil_modul = int(uinput(\"penilaian penguasaan terhadap modul (0-\"+str(maxscr)+\")= \", 0, maxscr, True))\n    else: nil_modul = 0\n\n    clrscreen()\n\n    nil_cc = []\n    for x in soal['check_case']:\n        tulisatap(nim, flname, soal[\"no_soal\"])\n        print(\"\\nFase 2. Running sample case\")\n        try:\n            temp = soal['check_case'][x]\n            newansfile = getparentdir(flname)+\"ans_\"+x+\".txt\"\n            newerrfile = getparentdir(flname)+\"err_\"+x+\".txt\"\n            in_params = open(temp[\"stdin\"],'r')\n            correct_ans_link = temp[\"stdout\"]\n            out_params = open(newansfile,'w')\n            out_params.truncate()\n            err_params = open(newerrfile,'w')\n            err_params.truncate()\n            call([\"python\",flname],stdin=in_params,stdout=out_params,stderr=err_params,timeout=soal['timeout'])\n            if TOLERATED_MODE:\n                status_koreksi = checkLossAnswer(newansfile,correct_ans_link,newerrfile)\n            else:\n                status_koreksi = checkAnswer(newansfile,correct_ans_link,newerrfile)\n            nil_cc.append(int(status_koreksi))\n            \n        except Exception as e:\n            print(e)\n            print(\"Nilai 0 telah diberikan\")\n            nil_cc.append(0)\n\n    if(pakaiEnter):\n                print(\"tekan enter untuk lanjut\")\n                x = input()\n    else:\n                print()\n                for x in range(5):\n                    print(\". \",end=\"\")\n                    sleep(LOOPTIME)\n    clrscreen()\n    \n    if float(sum(nil_cc))/float(len(nil_cc))>=1:\n        nil_kompilasi = soal[\"skor_max_kompilasi\"]\n    else: nil_kompilasi = 0\n\n    nil_tc=[]\n    for x in soal['test_case']:\n        tulisatap(nim, flname, soal[\"no_soal\"])\n        print(\"\\nFase 3. Running test case\")\n        try:\n            temp = soal['test_case'][x]\n            newansfile = getparentdir(flname) + \"ans_\" + x + \".txt\"\n            newerrfile = getparentdir(flname) + \"err_\" + x + \".txt\"\n            in_params = open(temp[\"stdin\"], 'r')\n            correct_ans_link = temp[\"stdout\"]\n            out_params = open(newansfile, 'w')\n            out_params.truncate()\n            err_params = open(newerrfile, 'w')\n            err_params.truncate()\n            call([\"python\", flname], stdin=in_params, stdout=out_params, stderr=err_params,timeout=soal['timeout'])\n            if TOLERATED_MODE:\n                status_koreksi = checkLossAnswer(newansfile, correct_ans_link, newerrfile)\n            else:\n                status_koreksi = checkAnswer(newansfile, correct_ans_link, newerrfile)\n            \n            if(status_koreksi==0):\n                nil_tc.append(0)\n                print(\"Kompilasi error/ time limit exceeded. Nilai 0 telah diberikan untuk testcases ini\")\n                if (pakaiEnter):\n                    print(\"tekan enter untuk lanjut\")\n                    x = input()\n                else:\n                    print()\n                    for x in range(5):\n                        print(\". \", end=\"\")\n                        sleep(LOOPTIME)\n\n            elif(status_koreksi==1):\n                if soal[\"exact_mode\"]:\n                    nil_tc.append(0)\n                    print()\n                    for x in range(5):\n                        print(\". \", end=\"\")\n                        sleep(LOOPTIME)\n                else:\n                    txt = \"Penilaian anda untuk testcase ini (0-\"+str(temp[\"score\"])+\") = \"\n                    custnil = int(uinput(txt,0,temp[\"score\"],True))\n                    nil_tc.append(custnil)\n            elif(status_koreksi==2):\n                nil_tc.append(int(temp[\"score\"]))\n                if (pakaiEnter):\n                    print(\"tekan enter untuk lanjut\")\n                    x = input()\n                else:\n                    print()\n                    for x in range(5):\n                        print(\". \", end=\"\")\n                        sleep(LOOPTIME)\n        except Exception as e:\n            print(e)\n            print(\"nilai 0 telah diberikan\")\n            nil_tc.append(0)\n            if (pakaiEnter):\n                    print(\"tekan enter untuk lanjut\")\n                    x = input()\n            else:\n                print()\n                for x in range(5):\n                    print(\". \", end=\"\")\n                    sleep(LOOPTIME)\n        \n        clrscreen()\n\n    nil_program = sum(nil_tc)\n    print(\"pengoreksian soal \"+soal[\"no_soal\"]+\" untuk nim \"+str(nim)+\" telah selesai\")\n    if PREVIEW_AFTER_CHECKING:\n        print(\"=================PREVIEW PENILAIAN=================\\n\")\n        print(\"filename = \"+str(flname)+\"\\n\")\n        print(\"=================Penilaian Header==================\")\n        if soal[\"check_header\"]:\n            print(\"Penamaan File = \"+str(cekfilename(flname)))\n            print(\"Header File = \"+str(headercheck(flname, nim)))\n            print(\"Skor diberikan = \"+str(nil_header))\n            print()\n        else: print(\"SKIPPED\\n\")\n\n        print(\"=================Penilaian Komentar==================\")\n        if(soal[\"check_komentar\"]):\n            print(\"Kamus terdeteksi = \" + str(checkKomentar(flname)[\"kamus\"]))\n            print(\"Algoritma terdeteksi = \" + str(checkKomentar(flname)[\"algoritma\"]))\n            print(\"Skor diberikan = \"+str(nil_komen))\n            print()\n        else:\n            print(\"SKIPPED\\n\")\n\n        print(\"==============Penilaian Penguasaan Modul===============\")\n        if(soal[\"check_penguasaan_modul\"]):\n            print(\"Skor diberikan = \"+str(nil_modul))\n            print()\n        else:\n            print(\"SKIPPED\\n\")\n\n        print(\"=================Penilaian Kompilasi==================\")\n        print(\"penjelasan kompilasi:\")\n        print(\"kompilasi dilakukan dengan menjalankan semua check case\")\n        print(\"0 => compile error/ runtime error\")\n        print(\"1 => compile success tapi WA\")\n        print(\"2 => compile success dan jawaban benar mutlak\")\n        print(\"\\nDetail Running check case = \"+str(nil_cc))\n        print(\"Skor Diberikan = \"+str(nil_kompilasi))\n        print()\n\n        print(\"=================Penilaian Test Case==================\")\n        print(\"penilaian kebenaran program dengan mejalankan semua \\n test case\")\n        for x in range (len(nil_tc)):\n            print(\"TC\"+str(x)+\" = \"+str(nil_tc[x]))\n        print(\"\\nTotal nilai test case = \"+str(nil_program))\n\n        print(\"=================Restricted Code==================\")\n        print(\"coming soon\")\n\n        print()\n        print(\"Tekan Enter untuk lanjut, gunakan 's' untuk review source code\")\n        inp = input()\n\n        while(inp != \"\"):\n            if (inp == \"s\"):\n                printprogram(flname)\n\n            print(\"Tekan Enter untuk lanjut\")\n            inp = input()\n    else:\n        print(\"program will continue after 2,5 sec\")\n        sleep(2.5)\n    \n    clrscreen()\n    res = [nil_header,nil_komen,nil_kompilasi,nil_modul,nil_program,nil_cc,nil_tc]\n    return res\n\ndef mainprog():\n    sgen = []\n    c = 0\n\n    print(\"AWTOGRADRRRR PENGKOM\")\n    print(\"=====================\\n\\n\")\n    print(\"CEK SETTING SEBELUM MULAI PROGRAM\")\n    print(\"RANGE NIM PENGKOREKSIAN = \"+str(LOWEST_NIM)+\"-\"+str(HIGHEST_NIM))\n    print(\"PRAKTIKUM KE = \"+PRAKTIKUM)\n    print(\"JUMLAH SOAL = \"+str(len(LIST_SOAL)))\n    print(\"AUTOCHECK HEADER ON = \"+str(AUTOSCORE_HEADER))\n    print(\"AUTOCHECK KOMENTAR ON = \"+str(AUTOSCORE_KOMENTAR))\n    print(\"AUTO NEXT IF CORRECT ANSWER ON = \"+str(not pakaiEnter))\n    print(\"PREVIEW AFTER CHECKING = \",str(PREVIEW_AFTER_CHECKING))\n    print(\"RESTRICTED CODE = \"+str(RESTRICTED_CODE))\n    asdf = input(\"\\nTEKAN ENTER UNTUK LANJUT, SILAHKAN KELUAR JIKA RAGU\")\n    clrscreen()\n\n    LIST_NIM_PRAKTIKAN = []\n    for nim in range(LOWEST_NIM, HIGHEST_NIM + 1): LIST_NIM_PRAKTIKAN.append(nim)\n    for nim in ADDITIONAL : LIST_NIM_PRAKTIKAN.append(nim)\n\n    for soal in LIST_SOAL:\n        shetgen = sheetgenerator.sheetgenerator(soal)\n        sgen.append(shetgen)\n        for nim in LIST_NIM_PRAKTIKAN:\n            try:\n                filename = soal[\"type\"] + PRAKTIKUM + \"_\" + str(nim) + \"_\" + soal[\"no_soal\"] + \".py\"\n                if not cari_dir(filename)==\"\" :\n                    r = korektor(nim,cari_dir(filename),soal)\n                    sgen[c].push(nim,r[0],r[1],r[2],r[3],r[4],r[5],r[6])\n                else:\n                    sgen[c].push(nim,0,0,0,0,0,\n                                 [0 for x in range(len(soal[\"check_case\"]))],\n                                 [0 for x in range(len(soal[\"test_case\"]))])\n                #if breaksoal or breakprogram:\n                    #break\n            except Exception as e:\n                print(e)\n        c = c+1\n        #if breakprogram:\n            #break\n\n    print(\"All process was done\")\n    print(\"===============================\")\n    print(\"your file will be stored into .xlsx\")\n    outfile = input(\"namefile (without .xlsx)= \")\n    outfile.lower()\n    while(\".xlsx\" in outfile):\n        print(\"DON'T PUT .XLSX PLEASE\")\n        outfile = input(\"namefile (without .xlsx)= \")\n        outfile.lower()\n\n    try:\n        for ayy in sgen:\n            ayy.toExcel(outfile)\n\n        sgen[0].toXCL(sgen,outfile)\n        print(\"your scoring sheet successfully saved as \"+outfile+\".xlsx\")\n        print(\"plase check output folder to check .xlsx file, we also put csv as emergency score files\")\n    except Exception as e:\n        print(e)\n\n    print(\"THANKS FOR USING THIS PROGRAM\")\n    print(\"Credit by cah pesisir if'19\")\n    sleep(3)\n\nmainprog()\n\n","repo_name":"hafidabid/Awtogradrrrrr","sub_path":"awtoogradrrr.py","file_name":"awtoogradrrr.py","file_ext":"py","file_size_in_byte":17627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32601258292","text":"#!/usr/bin/env python3\n#Francisco Londono\n\nimport re\nimport pdb\nfrom collections import defaultdict \n\n# free output in MB\n#             total       used       free     shared    buffers     cached\n#Mem:          7865       7737        127          2          4       1916\n#-/+ buffers/cache:       5816       2049\n#Swap:        40959        235      40724\n\nfreeMlst = []\ncachedMlst =[]\nUsedSwaplst = []\n\ndef writeOut(ofile):\n    cnt = 1\n    for f,g,h in zip(freeMlst,cachedMlst,UsedSwaplst):\n     ofile.write(\"%s,%s,%s,%s,0,0,0\\n\" % (cnt,f,g,h))\n     cnt+=1\n\t \ndef collectSwap(line):\n    words = line.split()\n    c = words[2]   # used swap space\n    \t\n    UsedSwaplst.append(c)\n\t\ndef collect(line):\n    words = line.split()\n    #print(words)\n    a,b = words[3],words[6]\n    \n    freeMlst.append(a)\n    cachedMlst.append(b)\n\n\ndef parse(line):\n    if len(line.strip()) == 0 or line.startswith(\"-\"):\n        return\n\n    if \"Mem:\" in line:\n       collect(line)\n    elif \"Swap:\" in line:\n       collectSwap(line)\t\n    #print (\">\", line.strip())\n\nwith open (\"free.log\") as f:\n    for line in f:\n        parse(line)\n    \nwith open ('free2.dat','w') as ofile:\n     ofile.write(\"%s\\n\" % (\"#yaxis=Megabytes\"))\n     ofile.write(\"%s\\n\" % (\"#xaxis=Time-seconds\"))\n     ofile.write(\"%s\\n\" % (\"#xlabels=\"))\n     ofile.write(\"%s\\n\" % (\"#showlabels=1\"))\n     ofile.write(\"%s\\n\" % (\"#exactx=1\"))\n     ofile.write(\"%s\\n\" % (\"#format=eps,png\"))\n     ofile.write(\"%s,%s,%s,%s,%s,%s,%s\\n\" % (\"x\",\"freeM\",\"cachedM\",\"UsedSwap\",\"freeM-err\",\"cachedM-err\",\"UsedSwap-err\"))\n     \n     writeOut(ofile)\n\n\n\n\n","repo_name":"ciscofran/pidstat","sub_path":"parseFree4.py","file_name":"parseFree4.py","file_ext":"py","file_size_in_byte":1577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35620288282","text":"# Advent of code Year 2016 Day 9 solution\n# Author = ?\n# Date = December 2022\n\nwith open((__file__.rstrip(\"code.py\")+\"input.txt\"), 'r') as input_file:\n    input = input_file.read()\n\ns = input\ndef decompress(s):\n    p1 = \"\"\n\n    i = 0\n    while i < len(s):\n        if s[i] == \"(\":\n            ind = s[i:].find(\")\") + i\n            sp = s[i+1:ind]\n            sp = sp.split(\"x\")\n            num, rep = sp \n            num, rep = int(num), int(rep)\n\n            p1 += \"\".join([ s[ind+1 : ind+1+num] ] * rep)\n\n            i = ind + num + 1\n        else:\n            p1 += s[i]\n            i += 1\n    return p1\n\np1 = decompress(input)\n\nprint(\"Part One : \"+ str(len(p1)))\n\ns,i,p2 = input, 0, 0\nw = [1 for _ in range(len(s))]\n\nwhile i < len(s):\n    if s[i] == \"(\":\n        ind = s[i:].find(\")\") + i \n        sp = s[i+1:ind]\n        sp = sp.split(\"x\")\n        num, rep = int(sp[0]), int(sp[1])\n\n        for j in range(num):\n            w[ind+1+j] *= rep\n        \n        i = ind+1\n\n    else:\n        p2 += w[i]\n        i += 1\n\n\nprint(\"Part Two : \"+ str(p2))","repo_name":"Mickey253/jacob-advent-code-solutions","sub_path":"2016/9/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"33178645276","text":"import dns.resolver\n\n\ndef get_cnames(url):\n    cnames = []\n    dns_resolver = dns.resolver.Resolver()\n    try:\n        answer = dns_resolver.query(url, 'CNAME')\n    except Exception as e:\n        print(None)\n    else:\n        cname = [_.to_text() for _ in answer][0]\n        cnames.append(cname)\n        print(cnames)\n    if cnames:\n        if len(cnames) > 1 or cnames[0] != url:\n            print(True)\n        else:\n            print(False)\n\n\nif __name__ == '__main__':\n    get_cnames('www.xcqyyxx.com')\n\n\n","repo_name":"pqGC/Python_Note","sub_path":"Test/判断是否使用cdn.py","file_name":"判断是否使用cdn.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"11651468353","text":"#문제 설명\r\n#배열 array의 i번째 숫자부터 j번째 숫자까지 자르고 정렬했을 때, k번째에 있는 수를 구하려 합니다.\r\n\r\n#예를 들어 array가 [1, 5, 2, 6, 3, 7, 4], i = 2, j = 5, k = 3이라면\r\n\r\n#array의 2번째부터 5번째까지 자르면 [5, 2, 6, 3]입니다.\r\n#1에서 나온 배열을 정렬하면 [2, 3, 5, 6]입니다.\r\n#2에서 나온 배열의 3번째 숫자는 5입니다.\r\n#배열 array, [i, j, k]를 원소로 가진 2차원 배열 commands가 매개변수로 주어질 때, commands의 모든 원소에 대해 앞서 설명한 연산을 적용했을 때 나온 결과를 배열에 담아 return 하도록 solution 함수를 작성해주세요.\r\n\r\n#제한사항\r\n#array의 길이는 1 이상 100 이하입니다.\r\n#array의 각 원소는 1 이상 100 이하입니다.\r\n#commands의 길이는 1 이상 50 이하입니다.\r\n#commands의 각 원소는 길이가 3입니다.\r\n#입출력 예\r\n#array\t                commands\t                        return\r\n#[1, 5, 2, 6, 3, 7, 4]\t[[2, 5, 3], [4, 4, 1], [1, 7, 3]]\t[5, 6, 3]\r\n#입출력 예 설명\r\n#[1, 5, 2, 6, 3, 7, 4]를 2번째부터 5번째까지 자른 후 정렬합니다. [2, 3, 5, 6]의 세 번째 숫자는 5입니다.\r\n#[1, 5, 2, 6, 3, 7, 4]를 4번째부터 4번째까지 자른 후 정렬합니다. [6]의 첫 번째 숫자는 6입니다.\r\n#[1, 5, 2, 6, 3, 7, 4]를 1번째부터 7번째까지 자릅니다. [1, 2, 3, 4, 5, 6, 7]의 세 번째 숫자는 3입니다.\r\n\r\n#풀이 방법\r\n#1.커맨드 배열 안 원소를 돌아가면서 연산한다.\r\n#2.커맨드의 0번째원소부터 1번째원소까지 array를 자른다.\r\n#3.자른 array를 sort한다.\r\n#4.sort한 array에서 커맨드의 2번째원소 index에 해당하는 원소를 answer에 저장한다.\r\n#5.answer를 출력\r\n#※주의사항\r\n#k번째 수는 프로그래밍 언어로 표현하면 index인데, 1번째 원소는 index가 0이므로, 각각의 index에 -1을 해줘야 한다.\r\n\r\n\r\ndef solution(array, commands):\r\n\tanswer = []\r\n\tindex=0\r\n\tfor command in commands:\r\n\t\ttemparray = array[command[0]-1:command[1]]\r\n\t\ttemparray.sort()\r\n\t\tanswer.insert(index,temparray[command[2]-1])\r\n\t\tindex=index+1\r\n\treturn answer","repo_name":"tersver/codingstudy","sub_path":"programmers_sort_problem1.py","file_name":"programmers_sort_problem1.py","file_ext":"py","file_size_in_byte":2197,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"40787471617","text":"import torch\nfrom torch import nn\nimport math\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport os\nimport sys\nsys.path.append('..')\nfrom tools import syncer \nfrom tools import user\n\nlearning_rate = 1e-3\ndevice        = 'cuda' if torch.cuda.is_available() else 'cpu'\nn_epoch       = 200\nplot_every    = 10\n\n# Data Generation\nn_data = 10000\nn_features = 10\nx = 10 * np.random.random_sample((n_data, n_features))+20\ny = np.sum(x, axis=1)\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\n\nx_train = torch.from_numpy(x).float().to(device)\ny_train = torch.from_numpy(y).float().to(device)\n\n\n\n###  set up model\n######################\n\n# subclass the torch.nn.Module class\nclass MyModule(nn.Module, input_nfeatures=1, output_nfeatures=1):\n    # an __init__ function is needed, which has to call (with the super() function) the torch.nn.Module __init__ function\n    # here we define Modules we want to use, eg. linear layers, activation functions, or even some complicated module\n    def __init__(self):\n        super().__init__()\n        hidden1 = 50\n        hidden2 = 30\n        l1 = nn.Linear(n_features, hidden1)\n        a1 = nn.ReLU()\n        l2 = nn.Linear(hidden1, hidden2)\n        a2 = nn.ReLU()\n        self.module_list = nn.ModuleList([l1,a1,l2,a2])\n\n    # then we need to implement a forward method that specifies the network structure\n    def forward(self, x):\n        # we just connect the layers and activation functions sequentially\n        for f in self.module_list:\n            x=f(x)\n        return x\n\n\n\n\n# hidden  = 50\n# hidden2 = 30\n\n# model = torch.nn.Sequential(\n#   torch.nn.Linear(n_features, hidden),\n#   torch.nn.ReLU(),\n#   torch.nn.Linear(hidden, hidden2),\n#   torch.nn.ReLU(),\n#   torch.nn.Linear(hidden2,1),\n  \n# ).to(device)\n\nloss_fn = torch.nn.MSELoss(reduction='sum')\n\n#optimizer = torch.optim.RMSprop(model.parameters(), lr=learning_rate)\noptimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n\nlosses = []\n\n# variables for ploting results\nmodel.train()\nfor epoch in range(n_epoch):\n    # Forward pass: compute predicted y by passing x to the model.\n    y_pred = model(x_train)\n\n    # Compute and print loss.\n    loss = loss_fn(y_pred, y_train)\n    losses.append(loss.item())\n    if epoch % plot_every == 0:\n        print(epoch, loss.item())\n\n    # Before the backward pass, use the optimizer object to zero all of the\n    # gradients for the variables it will update (which are the learnable\n    # weights of the model). This is because by default, gradients are\n    # accumulated in buffers( i.e, not overwritten) whenever .backward()\n    # is called. Checkout docs of torch.autograd.backward for more details.\n    optimizer.zero_grad()\n\n    # Backward pass: compute gradient of the loss with respect to model\n    # parameters\n    loss.backward()\n\n    # Calling the step function on an Optimizer makes an update to its\n    # parameters\n    optimizer.step()\n\n        \nplt.plot(losses)\nplt.show(block=False)\nplt.savefig(os.path.join( user.plot_directory, \"loss.png\" ) )\nplt.pause(2)\n#plt.close()\n\nwith torch.no_grad():\n    model.eval()\n    y_train_pred = model(x_train)\n","repo_name":"HephyAnalysisSW/ML-pytorch","sub_path":"user/Oskar/old/fit-sum-proper.py","file_name":"fit-sum-proper.py","file_ext":"py","file_size_in_byte":3128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"5893646984","text":"\"\"\"\nMerge k sorted linked lists and return it as one sorted list.\n\nExample:\n    Input:\n        [\n          1->4->5,\n          1->3->4,\n          2->6\n        ]\n    Output:\n        1->1->2->3->4->4->5->6\n\"\"\"\nimport heapq\nfrom typing import List, Optional\n\n\n# Definition for singly-linked list.\nclass ListNode:\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\n\nclass ListMinimumNode:\n    def __init__(self, cur_node: ListNode):\n        self.cur_node = cur_node\n\n    def __lt__(self, other: 'ListMinimumNode'):\n        return self.cur_node.val < other.cur_node.val\n\n\ndef merge_lists(lists: List[Optional[ListNode]]) -> Optional[ListNode]:\n    if not lists:\n        return None\n\n    # Build a heap that always contains the next node in all lists.\n    min_nodes_heap = []\n    for list_idx in range(len(lists)):\n        list_node = lists[list_idx]\n        if list_node:\n            heapq.heappush(min_nodes_heap, ListMinimumNode(list_node))\n\n    # At this stage, start nodes has already been moved forward 1 step, and the min_nodes_heap has been populated with\n    # data from all lists.\n    first_node = None\n    result = None\n    while min_nodes_heap:\n        min_node = heapq.heappop(min_nodes_heap)\n        result_node = min_node.cur_node\n        next_node = result_node.next\n        if next_node:\n            # There is another node after this, so add it back to the heap.\n            heapq.heappush(min_nodes_heap, ListMinimumNode(next_node))\n\n        # Get lowest node from heap and add to cumulative result. Wipe the next pointer because we want to reuse the\n        # node in the result.\n        result_node.next = None\n        if not first_node:\n            first_node = result_node\n        else:\n            result.next = result_node\n        result = result_node\n\n    return first_node\n","repo_name":"r7wang/algorithm","sub_path":"src/array/merge_k_sorted_lists/default.py","file_name":"default.py","file_ext":"py","file_size_in_byte":1816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35644387930","text":"\"\"\"\n说明: 合约账户权限的测试用例\n\"\"\"\nimport json\nimport os\nimport pytest\n\n\nclass TestAccounAcl:\n    \"\"\"\n    合约账户权限的测试用例\n    \"\"\"\n\n    # 合约账号的acl是node1 node2\n    account = \"2111111111111112\"\n\n    # 被转账者\n    to_account = \"XC1111111111111211@xuper\"\n    # 合约调用\n    cname = \"multisign\"\n\n    @pytest.mark.p0\n    def test_transfer1(self, input_args):\n        \"\"\"\n        多签名转账给普通账户\n        \"\"\"\n        print(\"\\n多签名转账给普通账户\")\n        # 合约账号的acl是node1 node2\n        account = \"XC\" + self.account + \"@\" + input_args.conf.name\n\n        # 1.获取账户address, node3的address\n        to_addr = input_args.addrs[2]\n        # 2.查询被转账 账户 和 合约账户余额\n        err, befor_balan = input_args.test.xlib.get_balance(account=to_addr)\n        # 3.转账\n        keys = [input_args.keys[0], input_args.keys[1]]\n        addrs = [input_args.addrs[0], input_args.addrs[1]]\n        err, result = input_args.test.xlib.multi_transfer(\n            signkeys=keys, addrs=addrs, to=to_addr, amount=\"200\", account=account\n        )\n        assert err == 0, \"转账给合约账户 失败： \" + result\n        # 4.检查被转账余额\n        err, after_balan = input_args.test.xlib.get_balance(account=to_addr)\n        assert int(after_balan) == int(befor_balan) + int(200), \"转账给合约账户 失败： \" + result\n\n    @pytest.mark.p0\n    def test_transfer2(self, input_args):\n        \"\"\"\n        多签名转账给合约账户\n        \"\"\"\n        print(\"\\n多签名转账给合约账户\")\n\n        # 合约账号的acl是node1 node2\n        account = \"XC\" + self.account + \"@\" + input_args.conf.name\n\n        # 1.转账接收人地址是个acl账户\n        to_addr = self.to_account\n        # 2.查询被转账 账户余额\n        err, befor_balan = input_args.test.xlib.get_balance(account=to_addr)\n        # 3.转账\n        keys = [input_args.keys[0], input_args.keys[1]]\n        addrs = [input_args.addrs[0], input_args.addrs[1]]\n        err, result = input_args.test.xlib.multi_transfer(\n            signkeys=keys, addrs=addrs, to=to_addr, amount=\"100\", account=account\n        )\n        assert err == 0, \"转账给合约账户 失败： \" + result\n        # 4.检查被转账余额\n        err, after_balan = input_args.test.xlib.get_balance(account=to_addr)\n        assert int(after_balan) == int(befor_balan) + int(100), \"转账给合约账户 失败： \" + result\n\n    @pytest.mark.p0\n    def test_invoke(self, input_args):\n        \"\"\"\n        调用合约\n        \"\"\"\n        print(\"\\n调用合约\")\n        # node3调用increase\n        invoke_args = {\"key\": \"dudu\"}\n        args = json.dumps(invoke_args)\n        err, result = input_args.test.xlib.invoke_contract(\n            \"wasm\", self.cname, \"increase\", args, keys=input_args.keys[1]\n        )\n        assert err == 0, \"调用合约失败： \" + result\n\n        # 等tx上链\n        txid = input_args.test.xlib.get_txid_from_res(result)\n        err, result = input_args.test.xlib.wait_tx_on_chain(txid)\n        assert err == 0, result\n\n        # 合约账号调用get\n        # 合约账号的acl是node1 node2\n        account = \"XC\" + self.account + \"@\" + input_args.conf.name\n\n        invoke_args = {\"key\": \"dudu\"}\n        args = json.dumps(invoke_args)\n        signkeys = [input_args.keys[0], input_args.keys[1]]\n        addrs = [input_args.addrs[0], input_args.addrs[1]]\n        input_args.test.xclient.write_addrs(account, addrs)\n        err, result = input_args.test.xlib.invoke_contract(\n            \"wasm\", self.cname, \"get\", args, isMulti=\"\", account=account\n        )\n        err, result = input_args.test.xlib.multi_sign(keys=signkeys)\n        assert err == 0, \"调用合约失败： \" + result\n\n        # 等tx上链\n        txid = input_args.test.xlib.get_txid_from_res(result)\n        err, result = input_args.test.xlib.wait_tx_on_chain(txid)\n        assert err == 0, result\n\n    @pytest.mark.p0\n    def test_update_acl(self, input_args):\n        \"\"\"\n        修改账户acl\n        \"\"\"\n        print(\"\\n修改账户acl\")\n\n        # 合约账号的acl是node1 node2\n        account = \"XC\" + self.account + \"@\" + input_args.conf.name\n\n        # 设置合约账户的acl\n        acl = {\"pm\": {\"rule\": 1, \"acceptValue\": 1}, \"aksWeight\": {}}\n        # 如果需要设成其他的ak，在这里做修改\n        for addr in input_args.addrs:\n            acl[\"aksWeight\"][addr] = 0.6\n\n        # 编辑合约账户描述文件\n        # 用json.dumps直接转讲字典转换为json格式, 注意这里要用account_name，不含XC和@xuper\n        set_desc = {\n            \"module_name\": \"xkernel\",\n            \"contract_name\": \"$acl\",\n            \"method_name\": \"SetAccountAcl\",\n            \"args\": {\"account_name\": account, \"acl\": json.dumps(acl)},\n        }\n        # 创建一个临时文件来保存desc文件\n        desc = os.path.join(input_args.conf.client_path, \"set.desc\")\n        with open(desc, \"w\") as set_acl_file:\n            json.dump(set_desc, set_acl_file)\n            set_acl_file.close()\n\n        # 修改合约账户\n        err, result = input_args.test.xlib.multi_sign_tx(\n            desc=\"set.desc\",\n            acl_account=account,\n            keys=[input_args.keys[0], input_args.keys[1]],\n            addrs=[input_args.addrs[0], input_args.addrs[1]],\n        )\n        assert err == 0, \"修改合约账户失败\" + result\n\n        # 等tx上链\n        txid = input_args.test.xlib.get_txid_from_res(result)\n        err, result = input_args.test.xlib.wait_tx_on_chain(txid)\n        assert err == 0, result\n\n        err, result = input_args.test.xlib.query_acl(account=account)\n        assert err == 0, \"查询合约账户acl失败：\" + result\n        aks_weight = json.loads(result.strip(\"\\nconfirmed\"))[\"aksWeight\"]\n        # 3.返回acl\n        for key, value in aks_weight.items():\n            assert value == acl[\"aksWeight\"][key], \"合约账号的acl修改结果,不符合预期\"\n","repo_name":"limeng120/xchaintest","sub_path":"cases/acl/test_acl_1_normal.py","file_name":"test_acl_1_normal.py","file_ext":"py","file_size_in_byte":6003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"25201770387","text":"#region imports\nfrom datetime import datetime\n\nfrom AlgorithmImports import *\nfrom AlgorithmImports import Globals\n#endregion\nimport random\nfrom typing import Callable\nimport json\n\nfrom typing import List\n\nfrom common.commonUtils import CommonUtils\nfrom randomTradesAlgo.statTracker import StatTracker\n\n\nclass RandomAlgo:\n    def __init__(self):\n        self.main = None\n        self.orderedTransactions = {}\n        self.sliceDebugCount = 0\n        self.addedSecuritys = []\n        self.utils = None\n        self.statTracker = None\n\n    # Define an Initialize method.\n    # This method is the entry point of your algorithm where you define a series of settings.\n    # LEAN only calls this method one time, at the start of your algorithm.\n    def Initialize(self, main: QCAlgorithm) -> None:\n        self.main = main\n        self.orderedTransactions = {}\n        self.sliceDebugCount = 0\n        self.addedSecuritys = []\n        self.utils = CommonUtils(self.main)\n        # Set start and end dates\n        self.main.SetStartDate(2014, 1, 1)\n        #self.main.SetEndDate(2014, 3, 1)\n        self.main.SetEndDate(2015, 1, 1)\n        # Set the starting cash balance to $100,000 USD\n        self.main.SetCash(10000)\n        # Add data for the S&P500 index ETF\n\n        self.addEquitys()\n\n        self.statTracker = StatTracker(self.main)\n        self.main.Debug(\" End of init -----------\")\n\n\n    def addEquitys(self):\n        tickets= [\"LVMHF\",\"TCEHY\",\"OTGLY\"]\n        for ticket in tickets:\n                self.main.Debug(\"Adding security: \" + str(ticket))\n                sec: Security = self.main.AddSecurity(SecurityType.Equity, ticket, Resolution.Daily)\n                self.addedSecuritys.append(sec)\n\n\n    # This method receives all the data you subscribe to in discrete time slices.\n    # It's where you make trading decisions.\n    def OnData(self, slice: Slice) -> None:\n        self.utils.debugSlice(slice)\n\n        # timeStr = slice.Time.strftime(Transaction.historyDateFormat)\n        shouldTakeAction = random.randint(1, 12)\n        quantity = random.randint(20, 100)\n        buyOrSell = random.randint(1,2)\n\n        # Get all order tickets\n        orderTickets = self.main.Transactions.GetOrderTickets()\n\n        # Get open order tickets\n        openOrderTickets = self.main.Transactions.GetOpenOrderTickets()\n\n        isFilled: Callable[[OrderTicket], bool] = lambda orderTicket: (orderTicket.Status == OrderStatus.Filled)\n        filledOrderTickets = self.main.Transactions.GetOrderTickets(isFilled)\n        holdings: List[Symbol]  = [x.Key for x in self.main.Portfolio if x.Value.Invested]\n        self.makeTrades(slice)\n        self.statTracker.processSlice(slice)\n\n\n\n    def makeTrades(self, slice: Slice):\n        for symbol, trade_bar in slice.Bars.items():\n            tradeBar: TradeBar = trade_bar\n            self.main.Debug(\"Slice bar item------ \" + str(tradeBar.EndTime))\n            holding: SecurityHolding = self.main.Portfolio[symbol]\n            invested: bool = holding.Invested\n            currentQuantity: float = holding.Quantity\n            self.main.Debug(\"Currently invested \" + str(currentQuantity) + \" in \" + str(holding.Symbol))\n\n            shouldTakeAction = random.randint(1, 15)\n            quantity = random.randint(20, 40)\n            buyOrSell = random.randint(1, 2)\n            if shouldTakeAction in range(1,3):\n                if buyOrSell == 1:  # buy\n                    self.main.MarketOrder(symbol, quantity)\n                    self.main.Debug(\"buying : \" + str(symbol) + \"  \" +  str(quantity))\n                else:  # sell\n                    if currentQuantity > 0:\n                        if currentQuantity - quantity > 0:\n                            quantityToSell = quantity\n                        else:\n                            quantityToSell = currentQuantity\n                        self.main.Debug(\"selling : \" + str(symbol) + \"  \" + str(quantityToSell) )\n                        self.main.MarketOrder(symbol, (quantityToSell * (-1)))\n                    else:\n                        self.main.Debug(\"not selling \" + str(symbol) +\" because currentQuantity : \" + str(currentQuantity) )\n            else:\n                self.main.Debug(\"Not taking any action\")\n\n\n    def OnOrderEvent(self, OrderEvent):\n\n        if OrderEvent.FillQuantity == 0:\n            return\n\n        fetched = self.main.Transactions.GetOrderById(OrderEvent.OrderId)\n\n        self.main.Debug(\"{} was filled. Symbol: {}. Quantity: {}. Direction: {}\"\n                   .format(str(fetched.Type),\n                           str(OrderEvent.Symbol),\n                           str(OrderEvent.FillQuantity),\n                           str(OrderEvent.Direction)))\n\n\n    def OnEndOfAlgorithm(self) -> None:\n        self.main.Debug(\"In OnEndofAlgorithm\")\n        self.statTracker.processEndPortfolio(self.main.Portfolio)\n        currentDateAndTime = datetime.now()\n        currentTime = currentDateAndTime.strftime(\"%M_%S\")\n        self.statTracker.writeStats(Globals.DataFolder + \"/output/btstats\" + str(currentTime)+\".json\")\n","repo_name":"ari99/multi-strategy-algorithmic-trading","sub_path":"randomTradesAlgo/randomAlgo.py","file_name":"randomAlgo.py","file_ext":"py","file_size_in_byte":5062,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"37518459877","text":"sudoku = []                         # array in which the numbers of Sudoku are to be saved\nsolved = [[] for _ in range(10)]    # index: number, element: position information of row and col pair(row, col)\nempty_space = []\n\n\ndef initial_setting():  # reading file,\n    global sudoku\n\n    file = \"/Users/jennychen/Desktop/project.nosync/boilerMake/ImageReaderSrc/sudoku_solve/sudoku.txt\"\n    sudoku_file = open(file, 'r')  # making a file\n\n    while True:\n        cur_line = sudoku_file.readline()   # read file line by line\n        cur_line = cur_line.split(\" \")\n        cur_line = \"\".join(cur_line)\n        if not cur_line:                    # until it reads null\n            break\n        temp_row = []\n        for elem in cur_line[:9]:\n            elem = int(elem)                # change the type of variable for mathematical calculation\n            temp_row.append(elem)           # make a temporary list\n        sudoku.append(temp_row)\n\n    for row in range(9):\n        for col in range(9):\n            cur_number = sudoku[row][col]\n            solved[cur_number].append((row, col))   # store the information of position\n\n\ndef first_check():\n    global sudoku\n\n    for row in range(9):\n        for col in range(9):\n            cur = sudoku[row][col]\n\n            if cur == 0:  # zero can have multiple overlaps\n                continue\n\n            for idx in range(9):\n                cmp_row = sudoku[row][idx]\n                if (col != idx) and (cur == cmp_row):\n                    return False\n                cmp_col = sudoku[idx][col]\n                if (row != idx) and (cur == cmp_col):\n                    return False\n\n            # exclude numbers in the same square\n            for r in range(3):\n                for c in range(3):\n                    new_row = (row // 3) * 3 + r\n                    new_col = (col // 3) * 3 + c\n                    cmp_new_number = sudoku[new_row][new_col]\n                    if (row == new_row) and (col == new_col):\n                        continue\n                    if cur == cmp_new_number:\n                        return False\n\n    return True\n\n\ndef write_completed_sudoku():\n    global sudoku\n    answer_file = open(\"/Users/jennychen/Desktop/project.nosync/boilerMake/sudoku/public/answer.txt\", 'w')\n    for row in sudoku:\n        for col in row:\n            col = str(col)\n            answer_file.write(col + \" \")\n        answer_file.write(\"\\n\")\n\n\ndef check():\n    global sudoku, cross_hatching_flag\n    completed_number = 405     # 45 * 9 = 405\n    s = 0\n\n    for row in sudoku:\n        for col in row:\n            s = s + col\n\n    # sudoku is fully completed\n    if s == completed_number:\n        write_completed_sudoku()\n        exit()\n\n    if s < completed_number:\n        cross_hatching_flag = False\n\n\n# applying cross-hatching technique and automatically complete sudoku\ndef cross_hatching():\n    global sudoku, solved\n\n    while True:\n        plug_in_finished = True\n\n        for num in range(1, 10):    # num is the number trying to plug in\n            possible_row = [i for i in range(9)]\n            possible_col = [i for i in range(9)]\n\n            # process of removing the domain in which the number insertion is impossible\n            # since the number is unable to come out twice in the line\n            # in case a line includes the specific number (num) in one area\n            for (row, col) in solved[num]:\n                if row in possible_row:\n                    possible_row.remove(row)\n                if col in possible_col:\n                    possible_col.remove(col)\n\n            # The start of the box is the same for both row and column: 0, 3, 6th index\n            box_number = [0, 3, 6]\n\n            # exclude numbers in the same square\n            for row_box in box_number:\n                for col_box in box_number:\n                    num_of_candidate = 0    # counting the number of possible position\n                    is_in_box = False       # True = identical number exists in box / False = not exist\n\n                    for row_in_box in range(row_box, row_box + 3):\n                        for col_in_box in range(col_box, col_box + 3):\n                            cur = sudoku[row_in_box][col_in_box]\n\n                            if cur == num:  # Find a same number\n                                is_in_box = True\n\n                            # double check whether it is the domain capable of number insertion or not\n                            if (row_in_box in possible_row) and (col_in_box in possible_col):\n                                if cur == 0:    # current area is blank\n                                    num_of_candidate = num_of_candidate + 1\n                                    row_candidate = row_in_box\n                                    col_candidate = col_in_box\n\n                    # If a particular number is already in the box, there's no need to search\n                    if is_in_box: continue\n\n                    # Find a place\n                    if num_of_candidate == 1:\n                        plug_in_finished = False\n                        sudoku[row_candidate][col_candidate] = num  # insert the number\n                        possible_row.remove(row_candidate)          # eliminate current position from possible position\n                        possible_col.remove(col_candidate)          # eliminate current position from possible position\n                        solved[num].append((row_candidate, col_candidate))  # update information\n\n                    #  Error : unable to insert number\n                    if num_of_candidate == 0:\n                        answer_file = open(\"answer.txt\", 'w')\n                        answer_file.write(\"Error\")  # write an error message\n                        exit()\n\n        if plug_in_finished is True:\n            check()\n            break\n\n    if cross_hatching_flag is True:\n        return True\n    return False\n\n\ndef backtracking(blank_idx):\n    global  sudoku, empty_space\n\n    if blank_idx == len(empty_space) :\n        write_completed_sudoku()\n        blank_idx = 0\n\n    # key: number, value = bool\n    candidate = {}\n    for num in range(1, 10):\n        candidate[num] = True\n\n    cur_row, cur_col = empty_space[blank_idx]\n\n    # exclude numbers in the same column and row\n    for idx in range(9):\n        row_check = sudoku[cur_row][idx]\n        candidate[row_check] = False\n\n        col_check = sudoku[idx][cur_col]\n        candidate[col_check] = False\n\n    # exclude numbers in the same square\n    for r in range(3):\n        for c in range(3):\n            new_row = (cur_row // 3) * 3 + r\n            new_col = (cur_col // 3) * 3 + c\n            new_number = sudoku[new_row][new_col]\n            candidate[new_number] = False\n\n    # insert non-excluded numbers\n    for integer in range(1, 10):\n        if candidate[integer] is True:          # if it is possible to insert\n            sudoku[cur_row][cur_col] = integer  # insert an integer\n            backtracking(blank_idx + 1)\n\n    # remove unpromising option by using backtracking algorithm\n    sudoku[cur_row][cur_col] = 0\n\ndef fin():\n    cross_hatching_flag = True\n    initial_setting()\n\n    valid = first_check()\n\n    if not valid:\n        answer_file = open(\"/Users/jennychen/Desktop/project.nosync/boilerMake/sudoku/public/answer.txt\", 'w')\n        answer_file.write(\"Invalid Sudoku!\")\n        exit()\n\n    if not cross_hatching():\n\n        for i in range(9):\n            for j in range(9):\n                if sudoku[i][j] == 0:\n                    empty_space.append((i, j))\n\n        backtracking(0)","repo_name":"lzugaz/BoilerMake2023","sub_path":"ImageReaderSrc/sudoku_solve/sudoku_solve.py","file_name":"sudoku_solve.py","file_ext":"py","file_size_in_byte":7569,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"36742567095","text":"import string\n\n# list of unlocked steps -> remove related instructions\n# each pass 1. add newly unlocked steps to list 2. select first alphabetical unlocked step\n# step -> add related steps to unlocked\n\n# Maybe just remove instructions that are irrelevant and add element if in no more instructions\n\nfile = open(\"input\").read().split(\"\\n\")[:-1]\n\ninstructions = set((line.split(\" \")[1], line.split(\" \")[7]) for line in file)\nundone_steps = set(string.ascii_uppercase)\nunlocked_steps = undone_steps - {el[1] for el in instructions}\n\ndone_steps = []\n\nwhile undone_steps:\n    next_step = min(unlocked_steps)\n    done_steps.append(next_step)\n    undone_steps.discard(next_step)\n    unlocked_steps.discard(next_step)\n    instructions = set(el for el in instructions if el[0] != next_step)\n    unlocked_steps |= undone_steps - {el[1] for el in instructions}\n\nprint(\"Answer 1\")\n# print(\"\".join(done_steps))\n\nprint(\"Part 2 \" + 10 * \"-\")\n\n\ndef get_timing(letter):\n    return ord(letter) - 4\n\n\ninstructions = set((line.split(\" \")[1], line.split(\" \")[7]) for line in file)\nundone_steps = set(string.ascii_uppercase)\nunlocked_steps = undone_steps - {el[1] for el in instructions}\n\ndone_steps = []\ntotal_time = 0\nworkers = 5\ndoing = {}\n\nnext_step = None\n\nwhile undone_steps:\n\n    print(\"-\" * 20 + \"seconde \" + str(total_time))\n    # Assign to next step\n    while workers and unlocked_steps:\n        element = min(unlocked_steps)\n        workers -= 1\n        doing[element] = get_timing(element)\n        unlocked_steps.discard(element)\n\n    print(f\"workers {workers}\")\n    print(f\"doing {doing}\")\n\n    # Increase time\n    total_time += 1\n    doing = {task: time - 1 for task, time in doing.items() if time > 0}\n\n    # Check if done\n    done = set(el for el, time in doing.items() if time == 0)\n    print(f\"done {done}\")\n    workers += len(done)\n    done_steps.extend(list(done))  # Multiple things at the same time\n    print(f\"done_steps {done_steps}\")\n\n    # Update lists\n    doing = {key: value for key, value in doing.items() if key not in done}\n    undone_steps -= done\n    print(f\"undone_steps {undone_steps}\")\n    instructions = set(el for el in instructions if el[0] not in done)\n    print(f\"instructions {instructions}\")\n    unlocked_steps |= undone_steps - {el[1] for el in instructions}\n    unlocked_steps -= set(doing.keys())\n    print(f\"unlocked_steps {unlocked_steps}\")\n\nprint(\"\".join(done_steps))\nprint(total_time)\n","repo_name":"Ellana42/aoc_2018","sub_path":"07/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2414,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"72309325085","text":"import torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch import optim\nimport pickle\nimport random\nimport time\nimport os\nimport numpy as np\nfrom scipy import misc\nimport matplotlib.pyplot as plt\nfrom tqdm import trange\n\n\nclass VAE(nn.Module):\n    def __init__(self, zsize, layer_count=3, channels=3):\n        super(VAE, self).__init__()\n\n        d = 128\n        self.d = d\n        self.zsize = zsize\n\n        self.layer_count = layer_count\n\n        mul = 1\n        inputs = channels\n        for i in range(self.layer_count):\n            setattr(self, \"conv%d\" % (i + 1), nn.Conv2d(inputs, d * mul, 4, 2, 1))\n            setattr(self, \"conv%d_bn\" % (i + 1), nn.BatchNorm2d(d * mul))\n            inputs = d * mul\n            mul *= 2\n\n        self.d_max = inputs\n\n        self.fc1 = nn.Linear(inputs * 4 * 4, zsize)\n        self.fc2 = nn.Linear(inputs * 4 * 4, zsize)\n\n        self.d1 = nn.Linear(zsize, inputs * 4 * 4)\n\n        mul = inputs // d // 2\n\n        for i in range(1, self.layer_count):\n            setattr(self, \"deconv%d\" % (i + 1), nn.ConvTranspose2d(inputs, d * mul, 4, 2, 1))\n            setattr(self, \"deconv%d_bn\" % (i + 1), nn.BatchNorm2d(d * mul))\n            inputs = d * mul\n            mul //= 2\n\n        setattr(self, \"deconv%d\" % (self.layer_count + 1), nn.ConvTranspose2d(inputs, channels, 4, 2, 1))\n\n    def encode(self, x):\n        for i in range(self.layer_count):\n            x = F.relu(getattr(self, \"conv%d_bn\" % (i + 1))(getattr(self, \"conv%d\" % (i + 1))(x)))\n\n        x = x.view(x.shape[0], self.d_max * 4 * 4)\n        h1 = self.fc1(x)\n        h2 = self.fc2(x)\n        return h1, h2\n\n    def reparameterize(self, mu, logvar):\n        if self.training:\n            std = torch.exp(0.5 * logvar)\n            eps = torch.randn_like(std)\n            return eps.mul(std).add_(mu)\n        else:\n            return mu\n\n    def decode(self, x):\n        x = x.view(x.shape[0], self.zsize)\n        x = self.d1(x)\n        x = x.view(x.shape[0], self.d_max, 4, 4)\n        #x = self.deconv1_bn(x)\n        x = F.leaky_relu(x, 0.2)\n\n        for i in range(1, self.layer_count):\n            x = F.leaky_relu(getattr(self, \"deconv%d_bn\" % (i + 1))(getattr(self, \"deconv%d\" % (i + 1))(x)), 0.2)\n\n        x = F.tanh(getattr(self, \"deconv%d\" % (self.layer_count + 1))(x))\n        return x\n\n    def forward(self, x):\n        mu, logvar = self.encode(x)\n        mu = mu.squeeze()\n        logvar = logvar.squeeze()\n        z = self.reparameterize(mu, logvar)\n        return self.decode(z.view(-1, self.zsize, 1, 1)), mu, logvar\n\n    def weight_init(self, mean, std):\n        for m in self._modules:\n            normal_init(self._modules[m], mean, std)\n\n\ndef normal_init(m, mean, std):\n    if isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Conv2d):\n        m.weight.data.normal_(mean, std)\n        m.bias.data.zero_()\n\n\nim_size = 128\nZ_SIZE = 64\n\n\ndef loss_function(recon_x, x, mu, logvar):\n    BCE = torch.mean((recon_x - x) ** 2)\n\n    # see Appendix B from VAE paper:\n    # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014\n    # https://arxiv.org/abs/1312.6114\n    # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)\n    KLD = -0.5 * torch.mean(torch.mean(1 + logvar - mu.pow(2) - logvar.exp(), 1))\n    return BCE, KLD * 0.1\n\n\ndef process_batch(batch, device):\n    data = [x.reshape(im_size, im_size, 3).transpose((2, 0, 1)) for x in batch]\n\n    x = torch.from_numpy(np.asarray(data, dtype=np.float32)).to(device) / 127.5 - 1.\n    x = x.view(-1, 3, im_size, im_size)\n    return x\n\ndef vae_inference(input, device):\n    with torch.no_grad():\n        input = input.reshape(1, 128*128*3)\n        # input = input.reshape(1, -1)\n        batch = process_batch(input, device)\n        z_size = Z_SIZE\n        vae = VAE(zsize=z_size, layer_count=5)\n        vae.load_state_dict(torch.load('best_vae_face.pt'))\n        vae.to(device)\n        vae.eval()\n        encode_vec, _ = vae.encode(batch)\n        return encode_vec[0].cpu().numpy()\n\ndef train_vae(train_dataset, val_dataset, device):\n    batch_size = 512\n    eval_batch_size = batch_size\n    z_size = Z_SIZE\n    vae = VAE(zsize=z_size, layer_count=5)\n    vae.to(device)\n    vae.train()\n    vae.weight_init(mean=0, std=0.02)\n\n    lr = 0.001\n\n    vae_optimizer = optim.Adam(vae.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=1e-5)\n\n    train_epoch = 200\n\n    sample1 = torch.randn(128, z_size).view(-1, z_size, 1, 1)\n    train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True,\n                                           drop_last=True)\n    val_dataloader = DataLoader(val_dataset, batch_size=eval_batch_size, shuffle=True,\n                                         drop_last=False)\n    best_valid_loss = 100000\n    for epoch in range(train_epoch):\n        vae.train()\n        print(\"Train set size:\", len(train_dataset))\n        losses = []\n        epoch_start_time = time.time()\n        if epoch <= 6:\n            if (epoch + 1) % 2 == 0:\n                vae_optimizer.param_groups[0]['lr'] /= 2\n                print(\"learning rate change!\")\n        elif epoch <= 15:\n            if (epoch + 1) % 3 == 0:\n                vae_optimizer.param_groups[0]['lr'] /= 2\n                print(\"learning rate change!\")\n        elif epoch <= 31:\n            if (epoch + 1) % 4 ==0:\n                vae_optimizer.param_groups[0]['lr'] /= 2\n                print(\"learning rate change!\")\n        else:\n            if (epoch + 1) % 5 == 0:\n                vae_optimizer.param_groups[0]['lr'] /= 2\n                print(\"learning rate change!\")\n        for idx, batch in enumerate(train_dataloader):\n            vae.train()\n            vae.zero_grad()\n            batch = batch.to(device)\n            rec, mu, logvar = vae(batch)\n            loss_re, loss_kl = loss_function(rec, batch, mu, logvar)\n            loss = loss_re + loss_kl\n            loss.backward()\n            vae_optimizer.step()\n            losses.append(loss.item())\n            # TODO: realtime image-showing\n            with torch.no_grad():\n                vae.eval()\n                if (idx//batch_size) % 30 == 0:\n                    resultsample = (torch.cat([batch[0], rec[0]], dim=1) * 0.5 + 0.5)\n                    resultsample = resultsample.cpu().numpy().transpose(1, 2, 0)\n                    plt.imshow(resultsample)  \n                    plt.imsave('./generated_images/train.png'.format(idx//batch_size), resultsample)\n            # END TODO\n        epoch_end_time = time.time()\n        per_epoch_ptime = epoch_end_time - epoch_start_time\n        print(\"epoch{} training loss: {}\".format(epoch, np.mean(losses)))\n        print(\"taining time:\", per_epoch_ptime)\n        val_losses = []\n        with torch.no_grad():\n            vae.eval()\n            eval_epoch_l = []\n            for batch in val_dataloader:\n                batch = batch.to(device)\n                x_rec, mu, logvar = vae(batch)\n                loss_re, loss_kl = loss_function(x_rec, batch, mu, logvar)\n                loss = loss_re + loss_kl\n                eval_epoch_l.append(loss.item())\n            if np.mean(eval_epoch_l) < best_valid_loss:\n                print(\"save best model\")\n                torch.save(vae.state_dict(), \"./dl_saved_models/best.pt\")\n            resultsample = (torch.cat([batch[0], x_rec[0]], dim=1) * 0.5 + 0.5)\n            resultsample = resultsample.cpu().numpy().transpose(1,2,0)\n            plt.imsave('./generated_images/val_{}.png'.format(epoch), resultsample)\n            print(\"epoch{} eval loss: {}\".format(epoch, np.mean(eval_epoch_l)))\n            torch.save(vae.state_dict(), \"./dl_saved_models/checkpoint{}.pt\".format(epoch))\n    print(\"Training finish!...\")\n\n    \nif __name__ == '__main__':\n    from dataset import MyDataset\n    import torchvision.transforms as transforms\n    from torch.utils.data import DataLoader\n    train_dataset = MyDataset(data_feat='train', transform=transforms.ToTensor())\n    val_dataset = MyDataset(data_feat='val', transform=transforms.ToTensor())\n    train_vae(train_dataset, val_dataset, device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"))","repo_name":"ksc999/Wiping-VAE","sub_path":"dl_vae.py","file_name":"dl_vae.py","file_ext":"py","file_size_in_byte":8099,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34850415660","text":"class Node:\n    def __init__(self,item):\n        self.item = item\n        self.next = None\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\nll=LinkedList()\nll.head = Node(1)\nll.head.next = Node(2)\n\n# Print the linked list item\nwhile ll.head != None:\n    print(ll.head.item, end=\" \")\n    ll.head = ll.head.next\n\n    \n#print(\"hello\")\n","repo_name":"Pooja123667/DSA_Practice","sub_path":"LinkedList.py","file_name":"LinkedList.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70441309724","text":"# Alunos: Victor Matheus Alflen (vma18) e Wendel Caio Moro (wcm18)\n# Atualizado em: 24/04/2022\n# \n# Servidor de temperatura, que gera uma temperatura aleatoria dentro da faixa escolhida para a cidade\n\nimport socket\nfrom sys import argv\nfrom random import randint\nimport signal\nfrom datetime import datetime\n\n# Cria o socket\nserver = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n# Define os enderecos do servidores\nservers = {\n    \"resolute\": [ \"127.0.0.1\", 10001 ],\n    \"chad\": [ \"127.0.0.1\", 10002 ],\n    \"cairo\": [ \"127.0.0.1\", 10003 ],\n}\n\n# Define os dados para cada cidade\ncities = {\n    \"resolute\": { \n        \"name\" : \"Resolute\",\n        \"country\" : \"Canada\",\n        \"max_temp\" : -5, \n        \"min_temp\" : -40,\n    },\n    \"chad\": { \n        \"name\" : \"Chad\",\n        \"country\" : \"Chad\",\n        \"max_temp\" : 50, \n        \"min_temp\" : 20,\n    },\n    \"cairo\": { \n        \"name\" : \"Cairo\",\n        \"country\" : \"Egypt\",\n        \"max_temp\" : 50, \n        \"min_temp\" : 10,\n    }\n}\n\n# Define a cidade que cada servidor ira prover, dependendo do parametro passado no inicio da execucao\nserver_name = argv[1]\nif (server_name not in cities):\n    print(f\"\\\"{server_name}\\\" is not a known server, exiting\")\n    print(\"List of known servers: resolute, chad, cairo\")\n    exit(-1)\n\nip, port = servers[server_name]\n\n# Encerra o servidor de temperatura\ndef exit_program(signum, frame):\n    print(f\"\\n\\rExiting temperature server - {cities[server_name]['name']}, {cities[server_name]['country']}\")\n    server.close()\n    exit(0)\n\n# Obtem uma temperatura aleatoria, dependendo da cidade definida\ndef get_temperature(data):\n    if (data == \"get_temperature\"):\n        max_temp = cities[server_name][\"max_temp\"]\n        min_temp = cities[server_name][\"min_temp\"]\n        return str(randint(min_temp * 100, max_temp * 100) / 100)\n    \n    return \"error\"\n\n# Executa o cliente\ndef start_temp_server():\n\n    # Coloca o socket para escutar requisicoes\n    server.bind((ip, port))\n    server.listen(5)\n\n    print(f\"I am server {server_name}. Running on {str(ip)}:{str(port)}\")\n\n    while True:\n        # Aceita uma conecao\n        client, address = server.accept()\n        print(f\"Connection Estabilished - {address[0]}:{address[1]}\")\n\n        # Recebe os dados\n        data = client.recv(1024)\n        data = data.decode(\"utf-8\")\n        print(\"Data received:\", data)\n\n        # Gera um valor de temperatura e converte para string\n        message = get_temperature(data)\n        print(f\"Temperature got: {message} °C\")\n        \n        # Devolve a temperatura para o servidor de cache\n        client.send(bytes(message, \"utf-8\"))\n        print(f\"Temperature sent\")\n\n        # Encerra a conexão\n        client.close()\n\n# Execucao principal\nif __name__ == \"__main__\":\n    signal.signal(signal.SIGINT, exit_program)\n    now = datetime.now()\n    print(\"---------------------------------------\")\n    print(f\"Starting temperature server - {cities[server_name]['name']}, {cities[server_name]['country']}\")\n    print(\"Temperature server started at:\", now.strftime(\"%d/%m/%Y, %H:%M:%S\"))\n    print(\"---------------------------------------\")\n    start_temp_server()","repo_name":"wendelcmoro/trabalho-redes-de-computadores-2","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":3143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"20770969164","text":"def dutch_flag_sort(arr):\n  \"\"\"\n    HIGH LEVEL:\n      For the optimized solution, you want to use the two pointer technique.\n      You'll have two pointers left and right, left pointing to the first element, right pointing to the last.\n      You'll have a traveling pointer that iterates the entire array.\n      Left & right are the placeholder pointers, which we'll use to swap elements around based on what value the traveler pointer is.\n      while traveler <= right:\n        do processing based on what arr[traveler] is\n        if arr[traveler] == 0:\n          swap arr[traveler] and arr[left]\n          increment both traveler and left pointers\n        else if arr[traveler] == 1:\n          * carry on and don't swap anything.\n          increment traveler\n        else: # (arr[traveler] == 2)\n          swap arr[traveler] and arr[right]\n          decrement right, because after swapping, element @traveler may need to swapped again.\n\n      return the original array now sorted.\n\n    Time: O(n)\n    Space: O(1)\n  \"\"\"\n  traveler, left, right = 0, 0, len(arr) - 1\n  while traveler <= right:\n    if arr[traveler] == 0:\n      arr[traveler], arr[left] = arr[left], arr[traveler]\n      left += 1\n      traveler += 1\n    elif arr[traveler] == 2:\n      arr[traveler], arr[right] = arr[right], arr[traveler]\n      right -= 1\n    else:\n      traveler += 1\n  return arr\n\ndef main():\n  print(dutch_flag_sort([1, 0, 2, 1, 0]))\n  print(dutch_flag_sort([2, 2, 0, 1, 2, 0]))\n\nmain()\n","repo_name":"kevinmolina-io/GrokkingInterview","sub_path":"Two_Pointer/dutch_flag_sort.py","file_name":"dutch_flag_sort.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"13142231327","text":"import shutil\nimport os\nimport csv\nimport codecs\nimport zipfile\n\nfiles = os.listdir(os.curdir)\nstudents = {}\nfor file in files:\n    if file.endswith('.csv'):\n        with codecs.open(file, 'r',encoding='utf8' ) as File:\n            reader = csv.reader(File)\n            first = False\n            for row in reader:\n                if not first:\n                    first = True\n                    continue\n                try:\n                    name = row[1] + ' ' + row[0]\n                    group = row[2].split()[0]\n                    students[name] = group\n                except:\n                    print(\"\\nWarning! Can`t parse .csv row:\")\n                    line = \"\"\n                    for word in row:\n                        line += word + \" \"\n                    print(line)\n        break\nprint(\"Students initialized\")\n\ntemp_dir = os.curdir+\"/temp/\"\nos.mkdir(temp_dir)\nfor file in files:\n    if file.endswith('.zip'):\n        with zipfile.ZipFile(file, 'r') as zip_ref:\n            zip_ref.extractall(temp_dir)\n        break\nprint(\"Data unzipped\")\n\nresult_dir = os.curdir+\"/result/\"\nif os.path.exists(result_dir):\n    shutil.rmtree((result_dir))\nos.mkdir(result_dir)\nfor group in students.values():\n    if not os.path.exists(result_dir + group):\n        os.mkdir(result_dir + group)\nfor stud in students.keys():\n    if not os.path.exists(result_dir + students[stud] + \"/\" + stud):\n        os.mkdir(result_dir + students[stud] + \"/\" + stud)\n\ntasks_dirs = os.listdir(temp_dir)\nfor task_dir in tasks_dirs:\n    number = task_dir.split(\" - \")[0].lstrip(\"Q\")\n    task_text = temp_dir+task_dir+\"/Question text\"\n    students_dirs = os.listdir(temp_dir+task_dir)\n    for student_dir in students_dirs:\n        name = \"\"\n        try:\n            if student_dir == 'Question text':\n                continue\n            name = student_dir.split(\" - \")[1]\n        except:\n            print(\"\\nError in parsing tasks\")\n            print(\"Task dir: \"+task_dir)\n            print(\"Student dir: \"+student_dir)\n            #exit(-1)                                                               \n            continue\n        files = os.listdir(temp_dir+task_dir+\"/\"+student_dir)\n        for file in files:\n            shutil.copy(temp_dir+task_dir+\"/\"+student_dir+\"/\"+file, result_dir + students[name] + \"/\" + name +\"/\"+file)\n        shutil.copy(task_text, result_dir + students[name] + \"/\" + name + \"/Question text \"+number)\n\nshutil.make_archive('out', 'zip', result_dir)\n\nshutil.rmtree(temp_dir)\n\n\n","repo_name":"barakuda211/moodle_beautifuliser","sub_path":"moodle_beautifuliser.py","file_name":"moodle_beautifuliser.py","file_ext":"py","file_size_in_byte":2500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"4663036596","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport math\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom sklearn.naive_bayes import GaussianNB\n\n\ndef plot_consine_similarity(similarity_matrix, classes, title='Consine Similarity'):\n    \"\"\" Plot the cosine similarity matrix.\n    \"\"\"\n\n    plt.imshow(similarity_matrix, interpolation='nearest', cmap=plt.cm.Blues)\n    plt.title('Cosine Similarity')\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation=90)\n    plt.yticks(tick_marks, classes)\n\n    plt.tight_layout()\n    plt.show()\n\n\ndef plot_wifi_hotspot_signal_strengths(features, labels, label_names):\n  \"\"\" Histogram of the wifi access point signal strength.\n  \"\"\"\n\n  subplots_per_row = 3\n  rows = int(math.ceil(len(labels)/subplots_per_row))\n\n  fig, axarr = plt.subplots(rows, subplots_per_row, sharex=True, sharey=True)\n\n  for i in range(len(labels)):\n    row = int(i/subplots_per_row)\n    col = i % subplots_per_row\n    # Change the sign of wifi signal strength\n    axarr[row, col].bar(range(len(label_names)), features[i, :])\n    axarr[row, col].set_title(labels[i], fontsize=8)\n    axarr[row, col].set_xticklabels(label_names, rotation='vertical', fontsize=8)\n    axarr[row, col].set_ylabel('dBm')\n\n\n  plt.show()\n\n\ndef extract_wifi_location_features(fileName):\n  \"\"\" Extract the features (the wifi signal strength of different wifi hotspots)\n\n    Returns:\n      features: The signal strength of different wifi hotspots.\n      labels: The location names.\n      feature_names: mac addresses.\n  \"\"\"\n\n  labels = dict()\n  mac_addresses = dict()\n\n  # Construct the feature dimension and labels\n  file = open(fileName, 'r')\n  for line in file:\n    if line != '\\n':\n      # Line starting with ~^~ means location (label). Otherwise it means mac address.\n      if line[0:3] == '~^~':\n        location_name = line[4:-4] + '_' + str(len(labels))\n\n      else:\n        mac_addresss = line.split('~~')[0]\n        if mac_addresss not in mac_addresses:\n          mac_addresses[mac_addresss] = len(mac_addresses)\n\n  file.close()\n\n  # Construct the features and corresponding labels\n  labels = list()\n  features = None\n  current_features = None\n  file = open(fileName, 'r')\n  for line in file:\n    if line != '\\n':\n      if line[0:3] == '~^~':\n        labels.append(line[3:-4])\n\n        # If it's not the first label in the file,\n        # append current features (features constructed for the last label) to the global features\n        if current_features is not None:\n\n          if features is None:\n            features = current_features\n          else:\n            features = np.append(features, current_features, axis=0)\n\n        current_features = np.empty((1, len(mac_addresses)))\n        current_features[:] = np.nan\n\n      else:\n        mac_address = line.split('~~')[0]\n        strength = float(line.split('~~')[-1])\n        current_features[0, mac_addresses[mac_address]] = strength\n\n  # Add the features for the last wifi location to the entire features set\n  features = np.append(features, current_features, axis=0)\n\n  return features, labels, mac_addresses.keys()\n\n\nif __name__ == \"__main__\":\n  # Change the file name\n  fileName = \"./a3_josh_data.txt\"\n  file2 = \"./a3_josh_and_adrian_data.txt\"\n\n  # Each row of \"features\" contains scan results for each wifi scan,\n  # and each row of \"labels\" contains scan name for each wifi scan.\n\n  features, labels, label_names = extract_wifi_location_features(fileName)\n  features2, labels2, label_names2 = extract_wifi_location_features(file2)\n\n  # Plot the histogram of wifi hotspot signal strengh.\n  # You can comment it out if you don't want the plot to be shown.\n  # plot_wifi_hotspot_signal_strengths(features, labels, label_names)\n\n  # -100 dBm means no signal at all\n  features[np.isnan(features)] = -100\n  features2[np.isnan(features2)] = -100\n\n  #TODO 1: Compute the cosine similarity matrix of your own wifi signal strength\n# def cosine_similarity(a,b):\n#   numerator = np.dot(a,b)\n#   x = np.sqrt(np.sum(np.square(a)))\n#   y = np.sqrt(np.sum(np.square(b)))\n#   denominator = x*y \n#   return numerator/denominator  \n\ndef new_matrix(num):\n  matrix=np.zeros((num.shape[0], num.shape[0]))\n  for x in range(num.shape[0]):\n    for y in range(num.shape[0]):\n      matrix[x,y]=cosine_similarity(num[x,:],num[y,:])\n  return matrix\n\nsimilarity_matrix = new_matrix(features)\nplot_consine_similarity(similarity_matrix, labels)\n\nprint(new_matrix(features).shape[0])\n  #TODO 2: Compute the cosine similarity matrix of two different people's wifi scans\nsimilarity_matrix2 = new_matrix(features2)\nplot_consine_similarity(similarity_matrix2, labels2)\n\n  #TODO 3: Classify the location of the other person\nclf= GaussianNB()\nclf.fit(np.array(features2[:14]), np.array(labels2[:14]))\nfor i in range(14,35):\n    print(clf.predict(features2[i]))","repo_name":"joshua-hong-98/Visualizations","sub_path":"Wifi_Localization (4120)/wifi_localization_modified.py","file_name":"wifi_localization_modified.py","file_ext":"py","file_size_in_byte":4843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"17684990295","text":"import socket\nimport threading\n\ndef handle_client(connection, address, clients, usernames):\n    # Ask the client for a username\n    username = connection.recv(1024).decode().strip()\n    print(f\"username received: {username}\")\n    while username in usernames:\n        # Ask the client for a different username if it's not unique\n        connection.send(b\"That username is already taken. Enter a different username: \")\n        username = connection.recv(1024).decode().strip()\n    usernames.add(username)\n    connection.send(b\"username succesfull \")\n    # Add the client to the clients dictionary using their username as the key\n    clients[username] = connection\n    # Ask the client who they want to chat with\n    target_username = connection.recv(1024).decode().strip()\n    print(f\"{username}: target username received: {target_username}\")\n    if target_username in clients:\n        while True:\n            data = connection.recv(1024)\n            if not data:\n                break\n            message = data.decode()\n            clients[target_username].send(f\"{username}: {message}\".encode())\n            print(f\"for user {username} from {target_username} message: {message}\")\n    else:\n        clients[username].send(f\"Client with username {target_username} not found\".encode())\n\n# Create a socket object\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n# Bind the socket to a specific address and port\ns.bind((\"localhost\", 12345))\n\n# Listen for incoming connections\ns.listen(5)\n\nprint(\"Waiting for a connection...\")\n\nclients = {}\nusernames = set()\n\nwhile True:\n    # Accept a connection\n    connection, address = s.accept()\n    # Create a new thread for the client\n    client_thread = threading.Thread(target=handle_client, args=(connection, address, clients, usernames))\n    client_thread.start()\n\n# close the cursor\n# close the cursor and the database connection when done.\n","repo_name":"ahmadreza-76/simple-chat","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1888,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"17367363216","text":"from random import choice\nfrom colorgram import extract\nfrom turtle import Turtle, Screen\n\n\ndef extract_colors(path, number_colors):\n    \"\"\"\n    ('path/image.jpg') Use colorgram to return a color list\n    \"\"\"\n    colors = extract(\n        path,\n        number_colors\n    )\n    colors_list = []\n    for color in colors:\n        r = color.rgb.r\n        g = color.rgb.g\n        b = color.rgb.b\n        temp_tuple = (r, g, b)\n        colors_list.append(temp_tuple)\n    colors_list.remove(colors_list[0])\n    colors_list.remove(colors_list[0])\n    return colors_list\n\n\ndef draw(color_list):\n    \"\"\"\"\n    Draw an arrange of dots of 10x10 like Hirst modern art.\n    \"\"\"\n    tim = Turtle()\n    tim.speed(0)\n    screen = Screen()\n    screen.colormode(255)\n    screen.bgcolor((254, 255, 245))\n    #screen.screensize(canvwidth=700, canvheight=700)\n    tim.hideturtle()\n    tim.penup()\n    y = -225\n    for i in range(9):\n        tim.sety(y + i * 50)\n        tim.setx(-225)\n        for _ in range(9):\n            tim.dot(20, choice(color_list))\n            tim.forward(50)\n\n    screen.exitonclick()\n\n\ndef main():\n    \"\"\"\"main function\"\"\"\n    path = 'C:\\\\Users\\\\rymnd\\\\PycharmProjects\\\\day-18-start\\\\media\\\\dots.jpg'\n    number_colors = 30\n    colors = extract_colors(path, number_colors)\n    draw(colors)\n\n\nmain()\n","repo_name":"RayCarrasco/python-100-days-projects","sub_path":"day-18-start/hirst-paintin.py","file_name":"hirst-paintin.py","file_ext":"py","file_size_in_byte":1302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35901404094","text":"# run app in terminal using command bokeh serve --show covid_app.py\n\n# Data are from John Hopkins University Center for Systems Science and Engineering \n# Source: https://github.com/CSSEGISandData/COVID-19\n\n\nimport logging\nimport glob\nimport pandas as pd\nimport os\nimport geopandas as gpd\nimport json \nimport datetime\nimport itertools\nfrom bokeh.io import output_notebook, show, output_file, curdoc\nfrom bokeh.plotting import figure, ColumnDataSource\nfrom bokeh.models import GeoJSONDataSource, LinearColorMapper, ColorBar\nfrom bokeh.models import DateSlider, Select, HoverTool, DatetimeTickFormatter, NumeralTickFormatter, Toggle\nfrom bokeh.layouts import widgetbox, row, column\nfrom bokeh.models.annotations import Title\nfrom bokeh.models.widgets import Panel, Tabs\nimport colorcet \n\nlogging.basicConfig(level=logging.INFO)\n\n\n# def preprocess():\n#     \"\"\"\n#     Prepare df for plotting\n    \n#     - imports COVID-19 case and country boundary datasets\n#     - validates country names\n#     - joins datasets together\n#     - sets datatime data types\n#     - adds missing rows for countries with no cases reported on certain dates\n#     \"\"\"\n    \n#     ## import data\n\n#     confirmed = pd.read_csv('data/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')\n#     deaths = pd.read_csv('data/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')\n#     recovered = pd.read_csv('data/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv')\n\n#     # read in shapefile using Geopandas\n#     shapefile = 'data/country_boundaries/ne_110m_admin_0_countries/ne_110m_admin_0_countries.shp'\n#     gdf = gpd.read_file(shapefile)[['ADMIN', 'ADM0_A3', 'geometry']]\n\n\n#     ## prepare data\n\n#     # rename columns\n#     gdf.columns = ['country', 'country_code', 'geometry']\n\n#     confirmed_melted = prepare_metric_data(df=confirmed, metric='confirmed')\n#     deaths_melted = prepare_metric_data(df=deaths, metric='deaths')\n#     recovered_melted = prepare_metric_data(df=recovered, metric='recovered')\n\n#     df = pd.concat([confirmed_melted, deaths_melted, recovered_melted], axis=1)\n#     df.reset_index(inplace=True)\n\n\n#     # sum provinces to get totals by whole countries  \n#     df = df.groupby(['country', 'day']).agg(\n#         confirmed=pd.NamedAgg(column='confirmed', aggfunc='sum'),\n#         deaths=pd.NamedAgg(column='deaths', aggfunc='sum'),\n#         recovered=pd.NamedAgg(column='recovered', aggfunc='sum'))\n\n#     df = df.reset_index()\n\n#     # convert date to a sortable string format\n#     df['day'] = pd.to_datetime(df['day']).dt.strftime(\"%Y-%m-%d\")\n\n\n#     # keep record of original country name\n#     df['country_original'] = df['country']\n\n#     # remap country names (need to sum again after this as some countries are combined)\n\n#     country_map = {\n#         'Congo (Kinshasa)': 'Democratic Republic of the Congo',\n#         'Congo (Brazzaville)': 'Democratic Republic of the Congo',\n#         \"Cote d'Ivoire\": 'Ivory Coast',\n#         'Eswatini': 'eSwatini',\n#         'Gambia, The': 'Gambia', \n#         'The Gambia': 'Gambia', \n#         'Korea, South': 'South Korea', \n#         'North Macedonia': 'Macedonia', \n#         'Serbia': 'Republic of Serbia',\n#         'Taiwan*': 'Taiwan',\n#         'Tanzania': 'United Republic of Tanzania',\n#         'Timor-Leste': 'East Timor',\n#         'Bahamas, The': 'The Bahamas',\n#         'US': 'United States of America',\n#         'Holy See': 'Italy'    # church jurisdiction and not country (vatican)\n#     }\n\n#     df.replace({\"country\": country_map}, inplace=True)\n\n\n#     # left join so only countries we have geometries for are in the mapping dataset\n#     merged = gdf.merge(df, left_on = 'country', right_on = 'country', how='left')\n#     merged = merged.drop(['country_code'], axis=1)\n\n#     # re-convert day (including the NaN rows) to the correct string date format \n#     merged['day'] = pd.to_datetime(merged['day']).dt.strftime(\"%Y-%m-%d\")\n\n#     ## Add missing date rows for countries with no cases\n\n#     # create tmp table of all combinations of country X date\n#     country_date_cols = ['country', 'day']\n#     lists_of_uniques = [merged[col].unique() for col in country_date_cols]\n#     tmp = pd.DataFrame(list(itertools.product(*lists_of_uniques)), columns=country_date_cols)\n\n#     # add geometry from country borders\n#     tmp = tmp.merge(gdf[['country', 'geometry']], left_on = 'country', right_on = 'country', how='left')\n\n#     # outer join to add missing rows - will join on common columns\n#     merged = merged.merge(tmp, how='outer')\n\n#     # del rows with day='NaT' and NaNs\n#     merged = merged.loc[merged['day'] != 'NaT']\n#     merged.dropna(subset=['day'], inplace=True) #XXX CHECK THIS IS THE RIGHT THING TO DO!!\n\n#     # sort by date to plot time series\n#     merged.sort_values(by=['country', 'country_original', 'day'], inplace=True)\n    \n#     return merged\n\n\ndef prepare_metric_data(df, metric):\n    \"\"\"Subset df by metric based on user input\n    \n    Args:\n        df {pd.DataFrame} one row per country/date with country geometry and a column for each metric\n        metric {str} one of ['Confirmed', 'Deaths', 'Recovered']\n        \n    Returns:\n        df {pd.DataFrame}\n    \"\"\"\n    \n    # organise cols\n    df.rename({'Country/Region': 'country', 'Province/State': 'province'}, axis=1, inplace=True)\n    df.columns = [x.lower() for x in df.columns]\n    df.fillna('No data', inplace=True)\n    \n    # convert from wide to long \n    # to get one row per province/country/date\n    df = pd.melt(df, id_vars=['country', 'province', 'lat', 'long'],\n                 var_name='day', value_name=metric)\n    df.set_index(['country', 'province', 'lat', 'long', 'day'], inplace=True)\n    \n    return df.sort_values(by=['country', 'province'])\n\n\ndef dissolve_countries(gdf, iso_n3, main_country):\n    \"\"\"\n    Dissolve polygons from more than one country into a single geometry\n    For when one df has more granular country data than another, we dissolve\n    to the largest scale\n    \"\"\"\n    subset = gdf.loc[gdf['iso_n3'] == iso_n3].copy()\n    subset['country'] = main_country   \n    subset = subset.dissolve(by='iso_n3')\n    subset.reset_index(inplace=True)\n    tmp = gdf.loc[gdf['iso_n3'] != iso_n3].copy()\n    return pd.concat([tmp, subset], sort=False)\n    \ndef preprocess_geodata():\n    \"\"\"\n    Prepare country geometry and population\n    \"\"\"\n    \n    ## Prepare country borders data\n    \n    # read in shapefile using Geopandas\n    shapefile = 'data/country_boundaries/ne_110m_admin_0_countries/ne_110m_admin_0_countries.shp'\n    gdf = gpd.read_file(shapefile)[['ADMIN', 'ADM0_A3','ISO_N3','geometry']]\n\n    # rename columns\n    gdf.columns = ['country', 'country_code', 'iso_n3', 'geometry']\n    \n    gdf['iso_n3'] = gdf['iso_n3'].astype(int)\n\n    # Replace -99 with proper ISO CODES\n    gdf.loc[gdf['country'] == \"Norway\", ['iso_n3']] = 578\n    gdf.loc[gdf['country'] == \"Northern Cyprus\", ['iso_n3']] = 196    # 196 is ISOCODE of Cyprus\n    gdf.loc[gdf['country'] == \"Somaliland\", ['iso_n3']] = 706    # 706 is ISOCODE of Somalia \n    gdf.loc[gdf['country'] == \"Kosovo\", ['iso_n3']] = 688    # 688 is ISOCODE of Serbia \n    \n    gdf = dissolve_countries(gdf, iso_n3=706, main_country='Somalia')\n    gdf = dissolve_countries(gdf, iso_n3=688, main_country='Serbia')\n    gdf = dissolve_countries(gdf, iso_n3=196, main_country='Cyprus')\n    \n    \n    ## Add population (in thousands)\n    # source: https://population.un.org/wpp/Download/Standard/CSV/\n    \n    population = pd.read_csv('data/populations/WPP2019_TotalPopulationBySex.csv')\n\n    # select relevant data by time and scale\n    population = population.loc[(population['Time'] == 2020) & \n                                (population['Variant'] == 'Medium')]   \n\n    # add population to each country\n    gdf = gdf.merge(population, right_on='LocID', left_on='iso_n3', how='left')\n\n    # clean up\n    gdf.drop(['LocID', 'iso_n3', 'Location', 'Time', 'Variant', 'VarID', 'MidPeriod', 'PopMale', 'PopFemale'], \n             axis=1, inplace=True, errors='ignore')\n\n    return gdf\n\n\ndef preprocess_covid_data():\n    \"\"\"\n    Merge and clean covid case data\n    \"\"\"\n    confirmed = pd.read_csv('data/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')\n    deaths = pd.read_csv('data/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')\n    recovered = pd.read_csv('data/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv')\n    \n    confirmed_melted = prepare_metric_data(df=confirmed, metric='confirmed')\n    deaths_melted = prepare_metric_data(df=deaths, metric='deaths')\n    recovered_melted = prepare_metric_data(df=recovered, metric='recovered')\n\n    df = pd.concat([confirmed_melted, deaths_melted, recovered_melted], axis=1)\n    df.reset_index(inplace=True)\n\n    # sum provinces to get totals by whole countries  \n    df = df.groupby(['country', 'day']).agg(\n        confirmed=pd.NamedAgg(column='confirmed', aggfunc='sum'),\n        deaths=pd.NamedAgg(column='deaths', aggfunc='sum'),\n        recovered=pd.NamedAgg(column='recovered', aggfunc='sum'))\n\n    df = df.reset_index()\n\n    # convert date to a sortable string format\n    df['day'] = pd.to_datetime(df['day']).dt.strftime(\"%Y-%m-%d\")\n    \n\n    # remap country names (need to sum again after this as some countries are combined)\n    \n    country_map = {\n        'Congo (Kinshasa)': 'Democratic Republic of the Congo',\n        'Congo (Brazzaville)': 'Democratic Republic of the Congo',\n        \"Cote d'Ivoire\": 'Ivory Coast',\n        'Eswatini': 'eSwatini',\n        'Gambia, The': 'Gambia', \n        'The Gambia': 'Gambia', \n        'Korea, South': 'South Korea', \n        'North Macedonia': 'Macedonia', \n        'Serbia': 'Republic of Serbia',\n        'Taiwan*': 'Taiwan',\n        'Tanzania': 'United Republic of Tanzania',\n        'Timor-Leste': 'East Timor',\n        'Bahamas, The': 'The Bahamas',\n        'US': 'United States of America'\n        #'Holy See': 'Italy'    # church jurisdiction and not country (vatican)\n    }\n    \n    # keep record of original country name\n    df['country_original'] = df['country']\n\n    df.replace({\"country\": country_map}, inplace=True)\n    \n    return df\n\n\ndef preprocess_final_data(df, gdf):\n    \n    \"\"\"\n    Prepare final dataset used for plotting\n    \n    - joins country geometry and covid datasets together\n    - sets datatime data types\n    - adds missing rows for countries with no cases reported on certain dates\n    \n    - final df is one row per country and date, with columns for country geometry, \n    population and covid cases \n    \"\"\"\n    \n    # left join so only countries we have geometries for are in the mapping dataset\n    merged = gdf.merge(df, left_on = 'country', right_on = 'country', how='left')\n    merged = merged.drop(['country_code'], axis=1)\n\n    # re-convert day (including the NaN rows) to the correct string date format \n    merged['day'] = pd.to_datetime(merged['day']).dt.strftime(\"%Y-%m-%d\")\n\n    ## Add missing date rows for countries with no cases\n\n    # create tmp table of all combinations of country X date\n    country_date_cols = ['country', 'day']\n    lists_of_uniques = [merged[col].unique() for col in country_date_cols]\n    tmp = pd.DataFrame(list(itertools.product(*lists_of_uniques)), columns=country_date_cols)\n\n    # add geometry from country borders\n    tmp = tmp.merge(gdf[['country', 'geometry', 'PopTotal', 'PopDensity']], left_on = 'country', right_on = 'country', how='left')\n\n    # outer join to add missing rows - will join on common columns\n    merged = merged.merge(tmp, how='outer')\n\n    # del rows with day='NaT' and NaNs\n    merged = merged.loc[merged['day'] != 'NaT']\n    merged.dropna(subset=['day'], inplace=True) #XXX CHECK THIS IS THE RIGHT THING TO DO!!\n\n    # sort by date to plot time series\n    merged.sort_values(by=['country', 'country_original', 'day'], inplace=True)\n    \n    # add cases per million people in population (population is in thousands)\n    merged['confirmed_per_million'] = merged['confirmed'] / (merged['PopTotal']*1000)\n    merged['deaths_per_million'] = merged['deaths'] / (merged['PopTotal']*1000)\n    merged['recovered_per_million'] = merged['recovered'] / (merged['PopTotal']*1000)\n    \n    return merged\n\n\n# def prepare_geojsondata(df):\n#     \"\"\"Convert GeoDataFrame to GeoJSON format so it can be read by Bokeh \n#     (ColumnDataSource can't contain the multipolygons dtypes required for mapping)\n#     (for mapping, Bokeh consumes GeoJSON format which represents geographical features with JSON)\n#     \"\"\"\n#     # Read data to json.\n#     df_json = json.loads(df.to_json())\n\n#     # Convert to String like object.\n#     json_data = json.dumps(df_json)\n    \n#     # Convert JSON to GeoDataSource to be read by Bokeh\n#     geosource = GeoJSONDataSource(geojson=json_data)\n    \n#     return geosource\n\ndef source_by_date(data, selected_day):\n    \"\"\"Create geosource for date selected by user on slider. Returns pd.DataFrame.\"\"\"\n    selected_day = selected_day.strftime('%Y-%m-%d')\n    new_data = data.loc[data['day'] == selected_day] \n    return new_data\n\n\n\n## PLOT\n\ndef slider_callback(attr, old, new):\n    \"\"\"Update numbers based on selected date\"\"\"\n    day = date_slider.value\n\n    # hack to convert bokeh epoch unix time to proper date: \n    # bokeh misses the decimal point so we must divide by 1000 - Antonio logged an issue\n    day = datetime.datetime.fromtimestamp(day/1000)\n\n    new_data = source_by_date(data, day)\n    source.geojson = new_data.to_json() # overwrite the existing source's geojson\n\ndef menu_callback(attr, old, new):\n    \"\"\"Update color shading by selected metric\"\"\"\n    \n    # display raw counts\n    if toggle.active == True: \n    \n        if menu.value == 'Confirmed': \n            metric = 'confirmed'  \n            color_mapper.palette = colorcet.b_linear_blue_5_95_c73[::-1]\n            tooltips=[('Country', '@country'), ('Confirmed', '@confirmed{0,0}')]\n\n        elif menu.value == 'Deaths':\n            metric = 'deaths'\n            color_mapper.palette = colorcet.b_linear_kry_5_98_c75[::-1]\n            tooltips=[('Country', '@country'), ('Deaths', '@deaths{0,0}')]\n\n        elif menu.value == 'Recovered': \n            metric = 'recovered'\n            color_mapper.palette = colorcet.b_linear_green_5_95_c69[::-1]\n            tooltips=[('Country', '@country'), ('Recovered', '@recovered{0,0}')]\n        \n    # display cases per million of population\n    elif toggle.active == False:\n        \n        if menu.value == 'Confirmed': \n            metric = 'confirmed_per_million'  \n            color_mapper.palette = colorcet.b_linear_blue_5_95_c73[::-1]\n            tooltips=[('Country', '@country'), ('Confirmed', '@confirmed_per_million')]\n        \n        elif menu.value == 'Deaths':\n            metric = 'deaths_per_million'\n            color_mapper.palette = colorcet.b_linear_kry_5_98_c75[::-1]\n            tooltips=[('Country', '@country'), ('Deaths', '@deaths_per_million')]\n\n        elif menu.value == 'Recovered': \n            metric = 'recovered_per_million'\n            color_mapper.palette = colorcet.b_linear_green_5_95_c69[::-1]\n            tooltips=[('Country', '@country'), ('Recovered', '@recovered_per_million')]\n        \n    # update tooltips\n    p1.hover.tooltips = tooltips\n    \n    # update colours\n    vals = data[metric]    \n    color_mapper.low = vals.min()\n    color_mapper.high = vals.max()\n    country_polygons.glyph.fill_color = {'field': metric, 'transform': color_mapper}\n    color_bar.color_mapper = color_mapper\n    \n    #TODO: update title\n\n            \ngdf = preprocess_geodata()\ndf = preprocess_covid_data()\ndata = preprocess_final_data(df, gdf)\n\n# logging.info(data.head())\n# logging.info(data['day'].min())\n# logging.info(type(data['day'].min()))\n# logging.info(data['day'].max())\n# logging.info(type(data['day'].max()))\n\nstart_date = datetime.datetime.date(datetime.datetime.strptime(data['day'].min(), \"%Y-%m-%d\")) \nend_date = datetime.datetime.date(datetime.datetime.strptime(data['day'].max(), \"%Y-%m-%d\"))\n\nselected_day = end_date\nsource = source_by_date(data, selected_day)\nsource = GeoJSONDataSource(geojson=json.dumps(json.loads(source.to_json()))) # GeoJSONDataSource only works with string dates. Have to use geojsondatasource for mapping.a\n\n# set the defaults\nmetric = 'deaths'\npalette = colorcet.b_linear_kry_5_98_c75[::-1]\n\n# set the initial colour map and tooltips\nvals = data[metric]\n\ncolor_mapper = LinearColorMapper(palette=palette, low=vals.min(), high=vals.max(), \n                                 nan_color = '#d9d9d9') \n\ncolor_bar = ColorBar(color_mapper=color_mapper, label_standoff=6, \n                         location=(0,0), orientation='horizontal', formatter=NumeralTickFormatter())\n\np1 = figure(title='Global Records By United Nations Country', \n           plot_height=600 , plot_width=850, \n           toolbar_location='right', \n           tools='pan,wheel_zoom,box_zoom,reset',\n           x_axis_label='Longitude', y_axis_label='Latitude')\n\np1.xgrid.grid_line_color = None\np1.ygrid.grid_line_color = None\n\ncountry_polygons = p1.patches('xs','ys', \n          source=source,\n          fill_alpha=0.2, line_width=0.5, line_color='black',  \n          fill_color={'field': metric, 'transform': color_mapper})\n\nhover = HoverTool(tooltips=[('Country', '@country'), ('Deaths', '@deaths{0,0}')])\n\ndate_slider = DateSlider(title='Date', value=end_date, start=start_date, end=end_date, step=1)\ndate_slider.on_change('value', slider_callback)\n\np1.add_layout(color_bar, 'below')\np1.add_tools(hover)\n\nmenu = Select(options=['Confirmed', 'Deaths', 'Recovered'], value='Deaths', title='Metric')\nmenu.on_change('value', menu_callback)\n\ntoggle = Toggle(button_type='success', active=True, label='Raw counts') # button_type='primary'\ntoggle.on_change('active', menu_callback)\n\n### LAYOUT WITHOUT TABS\n#layout = row(p1, column(menu, date_slider))\n#curdoc().add_root(layout) \n\n\n### LAYOUT WITH TABS\np2 = figure(title='Individual Country Records - IN PROG', \n           plot_height=600 , plot_width=850, \n           toolbar_location='right', \n           tools='wheel_zoom, pan, reset',\n           x_axis_label='Longitude', y_axis_label='Latitude')\n\n# TAB 1: world map to show all countries, with date slider and metric selector\ntab1 = Panel(child=row(p1, column(toggle, menu, date_slider)), title='Global Records') \n\n# TAB 2: Drop down to select country, with time series bar chart for each metric, and possibly table of data by province\ntab2 = Panel(child=p2, title='Country Selector')\n\nlayout = Tabs(tabs=[tab1, tab2])\ncurdoc().add_root(layout) \n\n#curdoc().add_root(widgetbox(toggle, checkbox, radio))","repo_name":"jd12006/covid-19-bokeh","sub_path":"covid_app_workinprog.py","file_name":"covid_app_workinprog.py","file_ext":"py","file_size_in_byte":18747,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"3386682709","text":"import platform\nimport getpass\nimport os\nimport sys\nimport stat\nimport json\nimport logging\n\nfrom checkio_client.settings import conf\n\nIS_ROOT = getpass.getuser() == 'root'\n\nCONFIG_X = '''{\n  \"name\": \"com.checkio.client\",\n  \"description\": \"Chrome Native Messaging API for CheckiO Client\",\n  \"path\": \"HOST\",\n  \"type\": \"stdio\",\n  \"allowed_origins\": [\n    \"chrome-extension://mlglngjgefkbflbmelghfeijmojocnbi/\"\n  ]\n}'''\n\nEXEC_SCRIPT = '''#!EXEC\nfrom checkio_client.web_plugin import main\nmain()\n'''\n\nEXEC_SCRIPT_NAME = 'checkio_chrome_plugin.py'\n\nFILENAME = 'com.checkio.client.json'\n\nFOLDER_DARWIN_ROOT = \"/Library/Google/Chrome/NativeMessagingHosts\"\nFOLDER_DARWIN_USER = \"~/Library/Application Support/Google/Chrome/NativeMessagingHosts\"\nFOLDER_DARWIN_USER = os.path.expanduser(FOLDER_DARWIN_USER)\n\nFOLDER_LINUX_ROOT = \"/etc/opt/chrome/native-messaging-hosts\"\nFOLDER_LINUX_USER = \"~/.config/google-chrome/NativeMessagingHosts\"\nFOLDER_LINUX_USER = os.path.expanduser(FOLDER_LINUX_USER)\n\nFOLDER_WINDOW = os.path.join(conf.foldername, 'chrome')\nWIN_REG_KEY = r'Software\\Google\\Chrome\\NativeMessagingHosts\\com.checkio.client'\nWIN_BAT_FILE = 'chrome_plugin.bat'\n\nBAT_FILE_SCRIPT = '''@echo off\n\"{executable}\" {py_script}\n'''\n\nINSTALL_STEPS = []\nINSTALL_STEPS_FILE = os.path.join(conf.foldername, 'install_steps_chrome.json')\n\nINS_NEW_FILE = 'new_file'\nINS_NEW_REG = 'new_reg_cur_user'\n\nINSTALL_URL = 'http://www.checkio.org/local-editor/chrome/extension/'\n\ndef update_global_ff():\n    global CONFIG_X\n    CONFIG_X = '''{\n  \"name\": \"com.checkio.client\",\n  \"description\": \"Example host for native messaging\",\n  \"path\": \"HOST\",\n  \"type\": \"stdio\",\n  \"allowed_extensions\": [ \"{c7e3ccfd-0398-411b-8607-fa4ae25b4cd3}\" ]\n}'''\n\n    global EXEC_SCRIPT_NAME\n    EXEC_SCRIPT_NAME = 'checkio_ff_plugin.py'\n\n    global FOLDER_DARWIN_ROOT\n    FOLDER_DARWIN_ROOT = '/Library/Application Support/Mozilla/NativeMessagingHosts/'\n    global FOLDER_DARWIN_USER\n    FOLDER_DARWIN_USER = '~/Library/Application Support/Mozilla/NativeMessagingHosts/'\n    FOLDER_DARWIN_USER = os.path.expanduser(FOLDER_DARWIN_USER)\n\n    global FOLDER_LINUX_ROOT\n    FOLDER_LINUX_ROOT = '/usr/lib/mozilla/native-messaging-hosts/'\n    global FOLDER_LINUX_USER\n    FOLDER_LINUX_USER = '~/.mozilla/native-messaging-hosts/'\n    FOLDER_LINUX_USER = os.path.expanduser(FOLDER_LINUX_USER)\n\n    global WIN_REG_KEY\n    WIN_REG_KEY = r'Software\\Mozilla\\NativeMessagingHosts\\com.checkio.client'\n    global WIN_BAT_FILE\n    WIN_BAT_FILE = 'ff_plugin.bat'\n\n    global INSTALL_STEPS_FILE\n    INSTALL_STEPS_FILE = os.path.join(conf.foldername, 'install_steps_ff.json')\n\n    global FOLDER_WINDOW\n    FOLDER_WINDOW = os.path.join(conf.foldername, 'ff')\n\n    global INSTALL_URL\n    INSTALL_URL = 'http://www.checkio.org/local-editor/firefox/extension/'\n\n\ndef update_global_chromium():\n\n    global EXEC_SCRIPT_NAME\n    EXEC_SCRIPT_NAME = 'checkio_chromium_plugin.py'\n\n    global FOLDER_DARWIN_ROOT\n    global FOLDER_DARWIN_USER\n    FOLDER_DARWIN_ROOT = \"/Library/Application Support/Chromium/NativeMessagingHosts\"\n    FOLDER_DARWIN_USER = \"~/Library/Application Support/Chromium/NativeMessagingHosts\"\n    FOLDER_DARWIN_USER = os.path.expanduser(FOLDER_DARWIN_USER)\n\n    global FOLDER_LINUX_ROOT\n    global FOLDER_LINUX_USER\n    FOLDER_LINUX_ROOT = '/etc/chromium/native-messaging-hosts/'\n    FOLDER_LINUX_USER = '~/.config/chromium/NativeMessagingHosts/'\n    FOLDER_LINUX_USER = os.path.expanduser(FOLDER_LINUX_USER)\n\n    global WIN_BAT_FILE\n    WIN_BAT_FILE = 'chromium_plugin.bat'\n\n    global INSTALL_STEPS_FILE\n    INSTALL_STEPS_FILE = os.path.join(conf.foldername, 'install_steps_chromium.json')\n\n    global FOLDER_WINDOW\n    FOLDER_WINDOW = os.path.join(conf.foldername, 'chromium')\n\n\ndef add_install_step(name, value):\n    INSTALL_STEPS.append([name, value])\n\ndef save_install_steps():\n    with open(INSTALL_STEPS_FILE, 'w', encoding='utf-8') as fh:\n        json.dump(\n            INSTALL_STEPS,\n            fh\n        )\n\ndef read_install_steps():\n    with open(INSTALL_STEPS_FILE, 'r', encoding='utf-8') as fh:\n        return json.load(\n            fh\n        )\n\ndef is_installed():\n    return os.path.exists(INSTALL_STEPS_FILE)\n\n\ndef install(args=None):\n    if args.ff:\n        update_global_ff()\n\n    if args.chromium:\n        update_global_chromium()\n\n    if is_installed():\n        print('Plugin was installed before. Uninstallation...')\n        uninstall(args)\n\n    globals()['install_' + platform.system().lower()]()\n    save_install_steps()\n    configure_editor()\n    print('Installation Complete!')\n    print()\n    print('You can now install browser extension ' + INSTALL_URL)\n    print()\n\ndef install_darwin():\n    if IS_ROOT:\n        folder = FOLDER_DARWIN_ROOT\n    else:\n        folder = FOLDER_DARWIN_USER\n\n    install_x(folder)\n\ndef install_linux():\n    if IS_ROOT:\n        folder = FOLDER_LINUX_ROOT\n    else:\n        folder = FOLDER_LINUX_USER\n\n    install_x(folder)\n\ndef install_x(folder, win_bat=None):\n    conf_filename = os.path.join(folder, FILENAME)\n    script_filename = os.path.join(conf.foldername, EXEC_SCRIPT_NAME)\n\n    logging.info('Init Script File ' + script_filename)\n    os.makedirs(os.path.dirname(script_filename), exist_ok=True)\n    with open(script_filename, 'w') as fh:\n        fh.write(EXEC_SCRIPT.replace('EXEC', sys.executable))\n    add_install_step(INS_NEW_FILE, script_filename)\n\n    st = os.stat(script_filename)\n    os.chmod(script_filename, st.st_mode | stat.S_IEXEC)\n\n    logging.info('Init Config File ' + conf_filename)\n    os.makedirs(os.path.dirname(conf_filename), exist_ok=True)\n    with open(conf_filename, 'w') as fh:\n        fh.write(CONFIG_X.replace('HOST', win_bat or script_filename))\n    add_install_step(INS_NEW_FILE, conf_filename)\n\n    st = os.stat(conf_filename)\n    os.chmod(conf_filename, st.st_mode | stat.S_IRUSR)\n    return (conf_filename, script_filename)\n\ndef install_windows():\n    (conf_filename, script_filename) = install_x(FOLDER_WINDOW, WIN_BAT_FILE)\n\n    bat_file = os.path.join(FOLDER_WINDOW, WIN_BAT_FILE)\n    logging.info('Init Bat File ' + bat_file)\n\n    with open(bat_file, 'w') as fh:\n        fh.write(BAT_FILE_SCRIPT.format(\n            executable=sys.executable,\n            py_script=script_filename\n        ))\n    add_install_step(INS_NEW_FILE, bat_file)\n\n    logging.info('Init Registry Key')\n\n    import winreg\n    reg_key = winreg.CreateKey(winreg.HKEY_CURRENT_USER, WIN_REG_KEY)\n    winreg.SetValueEx(reg_key, None, 0, winreg.REG_SZ, conf_filename)\n    add_install_step(INS_NEW_REG, WIN_REG_KEY)\n\ndef uninstall(args=None):\n    if args.ff:\n        update_global_ff()\n    if args.chromium:\n        update_global_chromium()\n\n    try:\n        steps = read_install_steps()\n    except FileNotFoundError:\n        print('Plugin was not installed')\n        return\n\n    for step in steps:\n        globals()['uninstall_' + step[0]](step[1])\n\n    os.remove(INSTALL_STEPS_FILE)\n\n    print('Uninstall Complete')\n\ndef uninstall_new_file(filename):\n    try:\n        os.remove(filename)\n    except Exception as e:\n        logging.info('Unable to remove file {}: {}'.format(filename, e))\n    else:\n        logging.info('Remove file ' + filename)\n\ndef uninstall_new_reg_cur_user(reg_key):\n    import winreg\n    try:\n        winreg.DeleteKey(winreg.HKEY_CURRENT_USER, reg_key)\n    except Exception as e:\n        logging.info('Unable to remove Registry Key {}: {}'.format(reg_key, e))\n    else:\n        logging.info('Remove Registry Key ' + reg_key)\n\ndef configure_editor():\n    if not platform.system() == 'Windows':\n        return\n    default_data = conf.default_domain_data\n    editor = default_data['editor']\n    editor = input('Command that will be used for editing files [{}]:'.format(editor)) or editor\n    conf.default_domain_section['editor'] = editor.strip()\n    conf.save()\n\n","repo_name":"CheckiO/checkio-client","sub_path":"checkio_client/actions/plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":7794,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"86"}
+{"seq_id":"71752967323","text":"# -*- coding:utf-8 -*-\nfrom django import template\n\n\nregister = template.Library()\n\n\n@register.simple_tag(name='test_simpletag')\ndef test_simpletag(arg1, arg2, arg3):\n    return f'这是一个 simpletag 示例，它接收的参数分别是：{arg1}、{arg2}、{arg3}'\n\n\n@register.inclusion_tag('inclusion_tag_html.html')\ndef test_inclusiontag(name):\n    name1 = f\"{name}'s experience: \"\n    data = ['Primary programmer, Just familiar', 'Advance programmer, Skillful', 'Ultimate Programmer, Profession']\n    return {'name': name1, 'data': data}\n\n","repo_name":"xjr7670/book_practice","sub_path":"DjangoShiZhan/test_django/myproject/myapp/templatetags/test_tag.py","file_name":"test_tag.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"28292534545","text":"import requests\nimport pandas as pd\nfrom bs4 import BeautifulSoup\nfrom scraping5 import get_topics\n\nfrom multiprocessing import Pool\n\nimport time\n\n\ndef get_projects_url(topic_url,topic_name):\n    \n    response = requests.get(topic_url)\n\n    soup = BeautifulSoup(response.text, 'html.parser')\n\n    project_class = 'f3 color-fg-muted text-normal lh-condensed'\n    project_tags = soup.find_all('h3', project_class)\n\n    authors =[]\n    for project_tag in project_tags:\n        authors.append(project_tag.text.strip().split('\\n')[0])\n        \n        \n    project_url_class = 'text-bold wb-break-word'\n    project_url_tags = soup.find_all('a', project_url_class)\n    project_urls =[]\n\n\n    for project_url in project_url_tags:\n        project_urls.append(str(topic_url) + str(project_url.get('href')))\n        \n    projects_dict = {'Authors':authors,'project_urls':project_urls}\n    df = pd.DataFrame(projects_dict)\n    df.to_csv(topic_name+'.csv')\n    # print(df.head(5))\n    \n\n\nstart_time = time.time()\ntopic_urls_dict= get_topics()\n# for topic_url,topic_name in zip(topic_urls_dict['urls'], topic_urls_dict ['topics']):\n#     get_projects_url(topic_url=topic_url,topic_name = topic_name)\n\n# print(list(zip(topic_urls_dict['urls'], topic_urls_dict ['topics'])))\n# print(type(list(zip(topic_urls_dict['urls'], topic_urls_dict ['topics']))))\n\nwith Pool(4) as p:\n    p.map(get_projects_url,zip(topic_urls_dict['urls'], topic_urls_dict ['topics']))\n\nend_time= time.time()\n\nprint(\"total_time_took\"+str(end_time-start_time))","repo_name":"Ali1729/python_ali","sub_path":"Projects/scraping/scraping6.py","file_name":"scraping6.py","file_ext":"py","file_size_in_byte":1516,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"30564925549","text":"import tkinter as tk\nfrom tkinter import ttk\n\ndef key_press(event):\n    label1[\"text\"] = event.keysym\n\ndef focus_gained(event):\n    label1[\"text\"] = \"Focus gained\"\n\ndef focus_lost(event):\n    label1[\"text\"] = \"Focus lost\"\n\ndef return_key(event):\n    label1[\"text\"] = entry1.get().upper()\n\nwin = tk.Tk()\nwin.title(\"SEN4017\")\nwin.iconbitmap(\"python.ico\")\nwin.geometry(\"300x300+200+200\")\n\nentry1 = ttk.Entry(win)\nbutton1 = ttk.Button(win, text=\"Button 1\")\nlabel1 = ttk.Label(win, text=\"\", font=(\"Consoloas\", 16))\n\nentry1.pack(pady=20)\nbutton1.pack(pady=(0,20))\nlabel1.pack()\n\nentry1.bind(\"<KeyPress>\", key_press)\nentry1.bind(\"<FocusIn>\", focus_gained)\nentry1.bind(\"<FocusOut>\", focus_lost)\nentry1.bind(\"<Return>\", return_key)\n\nwin.mainloop()","repo_name":"UpbeatJupiter/GUI-Python","sub_path":"Week5/Week5e.py","file_name":"Week5e.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"32012062101","text":"# We can absouetly use the is_prime function for this probleme but we can solve this efficently with the sieve of Eratosthenes.\n\nimport time\n\"\"\"\ndef sieve_of_Eratosthenes(n):\n    primes =  [True for i in range(n+1)] #we create a list of boolean values\n    result_primes = []\n    p = 2 #initial value of p\n    while (p * p <= n):\n        if (primes[p] == True):\n            for i in range(p * 2, n+1, p):\n                primes[i] = False\n        p += 1\n\n    for p in range(2, n+1):\n        if primes[p]:\n            result_primes.append(p)\n\n    return result_primes\n\nlen(sieve_of_Eratosthenes(10000))\n\"\"\"\n\ndef main():\n    primes = [2]\n\n    i = 3\n\n    while len(primes) < 10001:\n        for j in primes:\n            if i % j == 0:\n                break\n        else:\n            primes.append(i)\n        i += 2\n\n    return primes[10001 - 1]\n\n\nif __name__ == \"__main__\":\n    start = time.time()\n    main()\n    print(time.time() - start)\n","repo_name":"furkzel/PEPSolsPy","sub_path":"1-10/pep7.py","file_name":"pep7.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35564815152","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jan 21 20:17:58 2019\n\n@author: qingyingliu\n\"\"\"\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport time\nfrom sort import *\n\nsize_list = [10*x for x in range(1,7)]\nsort_type = ['heapSort','quickSort','mergeSort','shellSort','countSort',\n             'radixSort','bucketSort','selectSort','bubbleSort','insertSort']\n\n# recode the running time\ndata = []\n\nfor size in size_list:\n    random_data = list(np.random.randint(100000,size=size))\n    \n    line = []\n    \n    #heapSort\n    start_time = time.time()\n    heapSort(random_data)\n    end_time = time.time()\n    interval = end_time - start_time\n    line.append(interval)\n    \n    #quickSort\n    start_time = time.time()\n    quickSort(random_data)\n    end_time = time.time()\n    interval = end_time - start_time\n    line.append(interval)\n    \n    #mergeSort\n    start_time = time.time()\n    mergeSort(random_data)\n    end_time = time.time()\n    interval = end_time - start_time\n    line.append(interval)\n    \n    #shellSort\n    start_time = time.time()\n    shellSort(random_data)\n    end_time = time.time()\n    interval = end_time - start_time\n    line.append(interval)\n    \n    #countSort\n    start_time = time.time()\n    countSort(random_data)\n    end_time = time.time()\n    interval = end_time - start_time\n    line.append(interval)\n    \n    #radixSort\n    start_time = time.time()\n    radixSort(random_data)\n    end_time = time.time()\n    interval = end_time - start_time\n    line.append(interval)\n    \n    #bucketSort\n    start_time = time.time()\n    bucketSort(random_data)\n    end_time = time.time()\n    interval = end_time - start_time\n    line.append(interval)\n    \n    #selectSort\n    start_time = time.time()\n    selectSort(random_data)\n    end_time = time.time()\n    interval = end_time - start_time\n    line.append(interval)\n    \n    #bubbleSort\n    start_time = time.time()\n    bubbleSort(random_data)\n    end_time = time.time()\n    interval = end_time - start_time\n    line.append(interval)\n    \n    #insertSort\n    start_time = time.time()\n    insertSort(random_data)\n    end_time = time.time()\n    interval = end_time - start_time\n    line.append(interval)\n    \n    data.append(line)\n    print('size: ' + str(size) + ' finished')\n    for j in range(10):\n        print(sort_type[j]+\": \",line[j])\n    print()\n        \n\ndf = pd.DataFrame(data,index=size_list,columns=sort_type)\ndf.index.name = 'size'\ndf.plot(legend=True)\nplt.grid(True)\nplt.xlabel('data number')\nplt.ylabel('time(second)')\nplt.show()\ndf.to_csv('runtime2.csv')\n\n","repo_name":"qingyingliu/algorithm","sub_path":"sort_algorithm/analysis1.py","file_name":"analysis1.py","file_ext":"py","file_size_in_byte":2580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3456308035","text":"# Time complexity: O(n)\n# Space complexity: O(n)\n\nclass Solution:\n    def isValid(self, s:str) -> bool:\n        stack = []\n        closeToOpen = {\")\": \"(\", \"}\": \"{\", \"]\": \"[\"}\n\n        for c in s:\n            # every key is always a closing parentheses\n            if c in closeToOpen:\n                if stack and stack[-1] == closeToOpen[c]:\n                    stack.pop()\n                else:\n                    return False\n                # if we get an open parentheses\n            else:\n                stack.append(c)\n        return True if not stack else False\n\n\n","repo_name":"Eguakun/python_dsa_and_problems","sub_path":"algorithms_and_algorithmic_techniques/stack_and_queues_problems/valid_parentheses.py","file_name":"valid_parentheses.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"19907766879","text":"\nimport mxnet as mx\nimport os\n\nclass custom_iter(mx.io.DataIter):\n    def __init__(self, data_iter):\n        super(custom_iter,self).__init__()\n        self.data_iter = data_iter\n        self.batch_size = self.data_iter.batch_size\n\n    @property\n    def provide_data(self):\n        return self.data_iter.provide_data\n\n    @property\n    def provide_label(self):\n        provide_label = self.data_iter.provide_label[0]\n\n        #return [('softmax_label', provide_label[1]), \\\n        #         ('center_loss_label', provide_label[1])]\n\n        return [('softmax_label', provide_label[1])]\n\n    def hard_reset(self):\n        self.data_iter.hard_reset()\n\n    def reset(self):\n        self.data_iter.reset()\n\n    def next(self):\n        batch = self.data_iter.next()\n        label = batch.label[0]\n\n        return mx.io.DataBatch(data=batch.data, label=[label], \\\n                pad=batch.pad, index=batch.index)\n\n\ndef get_iterator(batch_size,data_shape,i=''):\n    \"\"\"return train and val iterators for mnist\"\"\"\n    # download data\n    import numpy as np\n    eigval = np.array([55.46, 4.794, 1.148])\n    eigvec = np.array([[-0.5675, 0.7192, 0.4009],\n                       [-0.5808, -0.0045, -0.8140],\n                       [-0.5836, -0.6948, 0.4203]])\n    shape_ = data_shape\n\n    import Myaugmentation\n    aug_list_test = [\n                    mx.image.ForceResizeAug(size=(shape_+int(0.1*shape_), shape_+int(0.1*shape_))),\n                    mx.image.CenterCropAug((shape_, shape_)),\n                    mx.image.CastAug(),\n\n                    #Myaugmentation.RandNormalizeAug(1),\n    ]\n\n    aug_list_train = [\n        mx.image.ForceResizeAug(size=(shape_ + int(0.1 * shape_), shape_ + int(0.1 * shape_))),\n\n        mx.image.RandomCropAug((shape_, shape_)),\n        ##flip not suitable for charactor\n        # mx.image.HorizontalFlipAug(0.5),\n        mx.image.CastAug(),\n        # Myaugmentation.RandScale(0.5,0.5),\n        Myaugmentation.RandSub(0.5),\n        Myaugmentation.BlurAug(0.5, (5, 5)),\n\n        mx.image.ColorJitterAug(0.1, 0.1, 0.1),\n        mx.image.HueJitterAug(0.5),\n        mx.image.LightingAug(0.5, eigval, eigvec),\n        mx.image.RandomGrayAug(0.5),\n        # #### extra augmentation\n        Myaugmentation.RandomRotateAug(10, 0.5),\n\n        Myaugmentation.BlurAug(0.5, (7, 7)),\n\n        Myaugmentation.Castint8Aug(0.3),\n\n\n    ]\n\n    train_iter = mx.image.ImageIter(batch_size=batch_size,\n                                    data_shape=(3, shape_, shape_),\n                                    label_width=1,\n                                    aug_list=aug_list_train,\n                                    shuffle=True,\n                                    path_root='',\n                                    path_imglist=os.getcwd()+'/cvlst/train'+str(i)+'.lst',\n                                    )\n    val_iter = mx.image.ImageIter(batch_size=batch_size,\n                                  data_shape=(3, shape_, shape_),\n                                  label_width=1,\n                                  shuffle=False,\n                                  aug_list=aug_list_test,\n                                  path_root='',\n                                  path_imglist=os.getcwd()+'/cvlst/val'+str(i)+'.lst',\n                                 )\n\n    return (custom_iter(train_iter), custom_iter(val_iter))\n\n\n","repo_name":"610265158/tinymind_competition","sub_path":"Mydataiter.py","file_name":"Mydataiter.py","file_ext":"py","file_size_in_byte":3333,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"86"}
+{"seq_id":"2226340452","text":"import sys\nimport pandas as pd\nimport pickle\n\n# Taking two arguments model and file with data for predict\n\nif len(sys.argv) != 3:\n    print(\"USAGE: python predict.py <model> <file>\")\n\nelse:\n    model_path = sys.argv[1]\n    file_path = sys.argv[2]\n\n    # reading a model\n    print(\"Loading model...\")\n    with open(model_path, \"rb\") as f:\n        model = pickle.load(f)\n    print(\"Model loaded!\")\n\n    # Predictions for each row of argument file\n    print(\"Making Predictions...\")\n    df = pd.read_parquet(file_path)\n    df[\"total_sales\"] = model.predict(df)\n    print(df.head())\n\n    # Save predictions to a new file\n    print(\"Saving...\")\n    df.to_parquet(\"../data/predict-done-2023-08-03.parquet\", index=False)\n    print(\"Saved!\")\n\n\n","repo_name":"Amanda-Carmo/p03-batch","sub_path":"src/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"22868617597","text":"#!/usr/bin/env  python3\n# -*- coding: utf-8 -*-\n\"\"\"\nThis script is for configuration Tacacs+ service on Cisco Devices.\n\nIt seems that it has to have THIS docstring with a summary line, a blank line\nand sume more text like here. Wow.\n\"\"\"\n__author__ = \"Cesar Rodriguez\"\n__copyright__ = \"Copyright 2020\"\n__credits__ = [\"Cesar Rodriguez\"]\n__license__ = \"GPL\"\n__version__ = \"0.1.5\"\n__maintainer__ = \"Cesar Rodriguez\"\n__email__ = \"cesarrodriguezpadilla@gmail.com\"\n__status__ = \"Development\"\n\n\nfrom os import getcwd\nfrom tkinter import Tk\nfrom tkinter import filedialog\n\n\ndef window_openfile():\n    \"\"\"\n    Funcion para abrir archivo.\n\n    Esta funcion crea una ventana para abrir un archivo de texto\n    \"\"\"\n    openfile = Tk()\n    openfile.title(\"Open Device IP file.\")\n    openfile.withdraw()\n\n    directory = getcwd()\n\n    filename = filedialog.askopenfilename(\n                        initialdir=f\"{directory}/devices\",\n                        title=\"Select a File\",\n                        filetypes=(\n                            (\"txt files\", \"*.txt\"), (\"all files\", \"*.*\"))\n                        )\n\n    openfile.destroy()\n\n    device_list = []\n    for line in open(file=filename, mode=\"r\"):\n        line = line.strip(\"\\n\")\n        device_list.append(line)\n\n    return device_list\n","repo_name":"maxmaximo-github/tacacs-_config","sub_path":"functions/windowopenfile.py","file_name":"windowopenfile.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"43768537629","text":"PLACE_HOLDER = \"[name]\"\nwith open(\"./Input/Names/invited_names.txt\") as data:\n    list_names = data.readlines()\n\nwith open(\"./Input/Letters/starting_letter.txt\") as letter_files:\n    letter_content = letter_files.read()\n    for name in list_names:\n        stripped_name = name.strip()\n        new_letter = letter_content.replace(PLACE_HOLDER, stripped_name)\n\n        with open(f\"./Output/ReadyToSend/letter_for_{stripped_name}.txt\", \"w\") as write_data:\n            write_data.write(new_letter)","repo_name":"sachinanm/mail-Merging-challenge-using--file-system","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"25302828304","text":"import torch\nfrom torch.utils.data import Dataset, DataLoader\nimport torchvision\nimport torchvision.transforms as transforms\n\n\ndef load_MNIST_dataset(args):\n    mnist_transform = transforms.Compose([\n        transforms.Resize(args.image_size),\n        transforms.CenterCrop(args.image_size),\n        transforms.ToTensor()])\n    trainset = torchvision.datasets.MNIST('MNIST/processed/training.pt', download=True, transform=mnist_transform,\n                                          train=True)\n    testset = torchvision.datasets.MNIST('MNIST/processed/testing.pt', download=True, transform=mnist_transform,\n                                         train=False)\n    train_loader = DataLoader(trainset, batch_size=args.batch_size, shuffle=True)\n    test_loader = DataLoader(testset, batch_size=args.batch_size, shuffle=False)\n    return train_loader, test_loader\n","repo_name":"vuonghoangbntt/InfoGAN","sub_path":"src/data/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74834203164","text":"from PIL import Image\n\nimg = Image.open(\"MapPathfinding.png\").convert(\"RGB\")\ndata = img.getdata()\nNewData = []\nfor pix in data:\n    if pix != (255,255,255):\n        NewData.append((0,0,0))\n    else:\n        NewData.append((255,255,255))\nimg.putdata(NewData)\nimg.save(\"mapPathfindingNoirEtBlanc.png\", format=\"png\")\n","repo_name":"Intronirisme/KosmikEngine","sub_path":"2D/Pathfinding-DynamicSound/data/BlackOrWhite.py","file_name":"BlackOrWhite.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34936700096","text":"#!/usr/bin/env python\n\nG = [[1,2,3],[2,5],[4],[4,7],[6],[6],[7],[]]\nN = len(G) \nseen = [False]*N\n\ndef dfs(G,s):\n    seen[s] = True \n\n    for x in G[s]:\n        if seen[x]:\n            continue\n        dfs(G,x)\n\ns = 2\nt = 7\ndfs(G,s)\nprint(seen[t])\n\n","repo_name":"shohei/algorithm-data-structure","sub_path":"graph/st_search.py","file_name":"st_search.py","file_ext":"py","file_size_in_byte":248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"73811077405","text":"import base64\nimport boto3\nimport datetime\nfrom io import BytesIO\nfrom mimetypes import guess_extension, guess_type\nimport os\nfrom PIL import Image\nimport random\nimport re\nimport string\nimport json\n\nALLOWED_MIME_TYPES_REGEX = \"image/.+|application/pdf\"\nBUCKET_NAMES = os.environ[\"BUCKET_NAMES\"].split(\",\")\n\n\ndef success_response(data, code=200):\n    return json.dumps({\"success\": True, \"data\": data}), code\n\n\ndef failure_response(message, code=404):\n    return json.dumps({\"success\": False, \"error\": message}), code\n\n\ndef upload_image_helper(image_data, bucket_name):\n    if bucket_name not in BUCKET_NAMES:\n        return None\n    mime_type = guess_type(image_data)[0]\n    ext = guess_extension(guess_type(image_data)[0])[1:]\n    if re.fullmatch(ALLOWED_MIME_TYPES_REGEX, mime_type) is None:\n        raise Exception(f\"Extension {ext} not supported!\")\n\n    # secure way of generating random string for image name\n    salt = \"\".join(random.SystemRandom().choice(string.ascii_lowercase + string.digits) for _ in range(8))\n\n    # remove header of Base64 string\n    img_str = re.sub(\"^.*?;base64,\", \"\", image_data)\n    img_data = base64.b64decode(img_str)\n    img_filename = f\"{salt}.{ext}\"\n\n    session = boto3.session.Session()\n    client = session.client(\n        \"s3\",\n        region_name=os.environ[\"SPACES_REGION_NAME\"],\n        endpoint_url=os.environ[\"SPACES_ENDPOINT_URL\"],\n        aws_access_key_id=os.environ[\"SPACES_ACCESS_KEY_ID\"],\n        aws_secret_access_key=os.environ[\"SPACES_SECRET_ACCESS_KEY\"],\n    )\n\n    res = client.put_object(\n        Bucket=bucket_name,\n        Key=img_filename,\n        Body=BytesIO(img_data),\n        ACL=\"public-read\",\n    )\n\n    img_url = f\"{os.environ['SPACES_ENDPOINT_URL']}/{bucket_name}/{img_filename}\"\n    return img_url\n\n\ndef remove_image_helper(img_url, bucket_name):\n    session = boto3.session.Session()\n    client = session.client(\n        \"s3\",\n        region_name=os.environ[\"SPACES_REGION_NAME\"],\n        endpoint_url=os.environ[\"SPACES_ENDPOINT_URL\"],\n        aws_access_key_id=os.environ[\"SPACES_ACCESS_KEY_ID\"],\n        aws_secret_access_key=os.environ[\"SPACES_SECRET_ACCESS_KEY\"],\n    )\n    img_filename = img_url.split(\"/\")[-1]\n    # Check if `img_filename` exists\n    try:\n        client.get_object(Bucket=bucket_name, Key=img_filename)\n    except Exception as e:\n        return None\n    res = client.delete_object(\n        Bucket=bucket_name,\n        Key=img_filename,\n    )\n    return res[\"ResponseMetadata\"][\"HTTPStatusCode\"]\n","repo_name":"cuappdev/upload","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"7539593481","text":"#!/usr/bin/env python3\n\n__author__ = 'Frederic Escudie'\n__copyright__ = 'Copyright (C) 2021 IUCT-O'\n__license__ = 'GNU General Public License'\n__version__ = '1.1.0'\n__email__ = 'escudie.frederic@iuct-oncopole.fr'\n__status__ = 'prod'\n\nimport os\nimport sys\nimport unittest\n\nTEST_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nPACKAGE_DIR = os.path.dirname(TEST_DIR)\nsys.path.append(PACKAGE_DIR)\n\nfrom anacore.db.homo_sapiens.accession import AssemblyAccession, ChrAccession\n\n\n########################################################################\n#\n# FUNCTIONS\n#\n########################################################################\nclass TestAssemblyAccession(unittest.TestCase):\n    def testFromDict(self):\n        data = [\n            {\"in\": \"GRCb36\", \"expected\": \"GRCh36\"},\n            {\"in\": \"Hg18\", \"expected\": \"GRCh36\"},\n            {\"in\": \"GRCb37\", \"expected\": \"GRCh37\"},\n            {\"in\": \"GRCh37\", \"expected\": \"GRCh37\"},\n            {\"in\": \"Hg19\", \"expected\": \"GRCh37\"},\n            {\"in\": \"GRCh38\", \"expected\": \"GRCh38\"},\n            {\"in\": \"hg38\", \"expected\": \"GRCh38\"},\n            {\"in\": \"GCF_000001405.12\", \"expected\": \"GRCh36\"},\n            {\"in\": \"GCF_000001405.13\", \"expected\": \"GRCh37\"},\n            {\"in\": \"GCF_000001405.25\", \"expected\": \"GRCh37\"},\n            {\"in\": \"GCF_000001405.26\", \"expected\": \"GRCh38\"},\n            {\"in\": \"GCF_000001405.39\", \"expected\": \"GRCh38\"},\n            {\"in\": \"GCA_000001405.1\", \"expected\": \"GRCh37\"},\n            {\"in\": \"GCA_000001405.14\", \"expected\": \"GRCh37\"},\n            {\"in\": \"GCA_000001405.15\", \"expected\": \"GRCh38\"},\n            {\"in\": \"GCA_000001405.28\", \"expected\": \"GRCh38\"}\n        ]\n        for curr in data:\n            self.assertEqual(AssemblyAccession.toHumanName(curr[\"in\"]), curr[\"expected\"])\n\n\nclass TestChrAccession(unittest.TestCase):\n    def testFromDict(self):\n        data = [\n            {\"in\": \"chr1\", \"expected\": \"1\"},\n            {\"in\": \"chrM\", \"expected\": \"MT\"},\n            {\"in\": \"chrMT\", \"expected\": \"MT\"},\n            {\"in\": \"CHR1\", \"expected\": \"1\"},\n            {\"in\": \"1\", \"expected\": \"1\"},\n            {\"in\": \"M\", \"expected\": \"MT\"},\n            {\"in\": \"MT\", \"expected\": \"MT\"},\n            {\"in\": \"CM000663\", \"expected\": \"1\"},\n            {\"in\": \"CM000663.2\", \"expected\": \"1\"},\n            {\"in\": \"J01415\", \"expected\": \"MT\"},\n            {\"in\": \"NC_000001\", \"expected\": \"1\"},\n            {\"in\": \"NC_000001.12\", \"expected\": \"1\"},\n            {\"in\": \"NC_0018007\", \"expected\": \"MT\"},\n            {\"in\": \"NC_012920\", \"expected\": \"MT\"},\n        ]\n        for curr in data:\n            self.assertEqual(ChrAccession.toHumanName(curr[\"in\"]), curr[\"expected\"])\n\n\n########################################################################\n#\n# MAIN\n#\n########################################################################\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"bialimed/AnaCore","sub_path":"test/db/homo_sapiens/test_accession.py","file_name":"test_accession.py","file_ext":"py","file_size_in_byte":2888,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"13837131014","text":"from google.cloud import vision\r\nfrom google.cloud.vision import ImageAnnotatorClient\r\nfrom shutil import copyfile\r\nimport os\r\nimport io\r\n\r\n##Autenticarse con la API\r\nclient = vision.ImageAnnotatorClient()\r\n\r\n\r\n##Leer path de imagenes PYTHON3\r\nimg_path = str(input(\"Cual es la ruta(path) absoluta de las imagenes a procesar?\"+\"\\n\"))\r\n##Validar que existe el path\r\nassert os.path.exists(img_path), \"No fue posible encontrar la ruta, \"+str(img_path)\r\n\r\n##Leer el path donde se guardaran los resultados\r\nresult_path = str(input(\"Cual es la ruta(path) donde deberan guardarse los resultados?\"+\"\\n\"))\r\n##Validar que existe el path\r\nassert os.path.exists(result_path), \"No fue posible encontrar la ruta, \"+str(result_path)\r\n\r\n\r\n##Calcular numero de imagenes a procesar\r\ntotal=len([name for name in os.listdir(img_path) if os.path.isfile(os.path.join(img_path, name))])\r\n\r\n\r\n##Procesamiento de imagenes\r\n##Cargar imagenes en memoria para poder revisar en orden secuencial, se establece una variable para ir recorriendo el arreglo\r\nimages=sorted(os.listdir(img_path))\r\nimg_num=0\r\n\r\n##Se inicia un ciclo para recorrer el total de imagenes\r\nfor i in range(total):\r\n    ##Esto cuando se van eliminando del origen\r\n    images=sorted(os.listdir(img_path))\r\n\r\n    ##Se carga el nombre de la imagen\r\n    pic_name=images[img_num]\r\n    ##Se carga la imagen\r\n    pic = os.path.abspath(img_path+pic_name)\r\n    with io.open(pic, 'rb') as pic_file:\r\n        pic_content = pic_file.read()\r\n        \r\n    ##Se carga la imagen para la API\r\n    imagen = vision.Image(content=pic_content)\r\n    \r\n    ##Se hace la consulta y se guarda la respuesta en una variable\r\n    ##La appi cuenta con varias opciones, en este caso nos interesa identificar los objetos\r\n    ##OBJECT_LOCALIZATION, LANDMARK_DETECTION, FACE_DETECTION,LOGO_DETECTION,LABEL_DETECTION, DOCUMENT_TEXT_DETECTION, SAFE_SEARCH_DETECTION, IMAGE_PROPERTIES,CROP_HINTS \r\n    \r\n    response = client.object_localization(image=imagen)\r\n    \r\n    ##Se crea un directorio para almacenar las imagenes que google no pueda reconocer\r\n    if not os.path.exists(result_path+\"NPI\"):\r\n        os.mkdir(result_path+\"NPI\")\r\n    if not os.path.exists(result_path+\"Otro\"):\r\n        os.mkdir(result_path+\"Otro\")\r\n    copyfile(img_path+pic_name,result_path+'/NPI/'+pic_name)\r\n    \r\n    ##Imprimir resultados\r\n    print('Resultados para la imagen: '+pic_name)\r\n    print('=' * 30)\r\n    \r\n    ##En este caso solo se tomara el primer resultado para cada imagen\r\n    s=1\r\n    \r\n    ##Se analizan los resultados\r\n    for label in response.localized_object_annotations:\r\n        print(label.name, '(%.2f%%)' % (label.score*100.))\r\n        if s==1:\r\n            ##La imagen si se encontro por lo que se borra del directorio NPI\r\n            os.remove(result_path+'/NPI/'+pic_name)\r\n            ##Unicamente se consideran imagenes con resultados mayores al 70%\r\n            if label.score > 0.7:\r\n                ##Se revisa si existe un directorio con esa etiqueta\r\n                if not os.path.exists(result_path+label.name):\r\n                    os.mkdir(result_path+label.name)\r\n                \r\n                ##Se copia la imagen a la carpeta correspondiente\r\n                copyfile(img_path+pic_name,result_path+label.name+'/'+pic_name)\r\n                s=s+1\r\n                \r\n            else:\r\n                ##Imagenes con resultados menores al 70% se guardan en Otro\r\n                copyfile(img_path+pic_name,result_path+'/Otro/'+pic_name)\r\n                s=s+1\r\n    \r\n    ##Se suma el contador para revisar la siguiente imagen\r\n    img_num=img_num+1          \r\n            \r\n    \r\n    \r\n","repo_name":"lessbyt/lazy-biologist","sub_path":"Lazy_Biologist.py","file_name":"Lazy_Biologist.py","file_ext":"py","file_size_in_byte":3618,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34389056395","text":"input = __import__('sys').stdin.readline\n\ndef solution():\n    n = int(input())\n    points = [list(map(int, input().split())) for _ in range(n)]\n    x_list = [0 for _ in range(100001)]\n    y_list = [0 for _ in range(100001)]\n\n    for p in points:\n        [x, y] = p\n        x_list[y] += 1\n        y_list[x] += 1\n\n    ans = 0\n    for p in points:\n        [x, y] = p\n        add = (x_list[y] - 1) * (y_list[x] - 1)\n        ans += add if add > 0 else 0\n\n    print(ans)\n\nsolution()","repo_name":"jungwookim/ps","sub_path":"boj/3000/a.py","file_name":"a.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"73836440924","text":"'''\n  Author:  Jordan Leiker\n  Last Date Modified: 12/28/2020\n \n  Description:\n  This file contains the class MultiLayerPerceptrton and any additional support class/functions. \n  The MultiLayerPerceptron class implments MLP neural nets.\n'''\n\nimport numpy as np\nfrom activations import relu, sigmoid\n\nclass MultiLayerPerceptron(object):\n    def __init__(self, inputSize, numHiddenLayers, numNeuronByHiddenLayer=[], activationFunction=relu, outputSize=1, trainingIterations=1):\n        # High level parameters \n        self.inputSize = int(inputSize)\n        self.numHiddenLayers = numHiddenLayers\n        if self.numHiddenLayers > 0:\n            if (len(numNeuronByHiddenLayer) != self.numHiddenLayers):\n                print(\"ERROR - input parameter numHiddenLayers must be the length of list numNeuronsByHiddenLayer\")\n                exit\n            else:\n                self.numNeuronByHiddenLayer = numNeuronByHiddenLayer\n        self.activation = activationFunction\n        self.outputSize = int(outputSize)\n        self.trainingIterations = trainingIterations\n        \n        # Neurel Net Setup\n        ## NN has a list member for each layer, and each layer has all neurons stored in numpy array\n        ## Weights / Layer stored as:\n        #### rows are weights per neuron\n        #### columns are neurons per layer\n        #### | w1_n1   w1_n2  ...  w1_nN | \n        #### | w2_n1   w2_n2  ...  w2_nN |\n        #### | w3_n1   w3_n2  ...  w3_nN |\n        ## bias / Layer stored as:\n        #### | b_n1    b_n2   ...  b_nN  |\n        if self.numHiddenLayers > 0: \n            ### layer with input connections\n            self.weightsByLayer = [np.random.random((self.inputSize, self.numNeuronByHiddenLayer[0]))]\n            self.biasByLayer = [np.random.random((1, self.numNeuronByHiddenLayer[0]))]\n            ### all hidden layers\n            for ii in range(numHiddenLayers-1):\n                self.weightsByLayer.append(np.random.random((self.numNeuronByHiddenLayer[ii], self.numNeuronByHiddenLayer[ii+1]))) # neurons are initialized at random for now\n                self.biasByLayer.append(np.random.random((1, self.numNeuronByHiddenLayer[ii+1])))\n            ### output layer\n            self.weightsByLayer.append(np.random.random((self.numNeuronByHiddenLayer[-1], self.outputSize)))\n            self.biasByLayer.append(np.random.random((1, self.outputSize)))\n        else:\n            ### single layer\n            self.weightsByLayer = [np.random.random((self.inputSize, self.outputSize))]\n            self.biasByLayer = [np.random.random((1, self.outputSize))]\n\n\n    \"\"\"Return the networks output given an input, iData\"\"\"\n    def feedforward(self, data):\n\n        # Error check input data size\n        if data.shape[1] != self.weightsByLayer[0].shape[0]:\n            print(\"ERROR - data size must be equal to inputSize\")\n            exit()\n\n        # Iterate through each layer\n        for weight, bias in zip(self.weightsByLayer, self.biasByLayer):\n            data = self.activation(data @ weight + bias) \n\n        return data\n\n\n    def train(self, iData):\n        # Training iterations\n        for iter in range(self.trainingIterations):\n            print(\"nothing yet\")\n\n\n    def predict(self, iData):\n        pass\n\n\n","repo_name":"leikerjp/machineLearning","sub_path":"neuralNets/mlp/MultiLayerPerceptron.py","file_name":"MultiLayerPerceptron.py","file_ext":"py","file_size_in_byte":3239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"540606277","text":"from line_tracker import line_tracker\r\nfrom motor import motor\r\nimport time\r\nfrom rules import LHR\r\n\r\naddress = \"192.168.0.106\"  # Alphabot-PiZero-00\r\nmot = motor(address)\r\nlt = line_tracker(address)\r\nlt.start()\r\n\r\nth_0 = 350  # left most\r\nth_1 = 350  # left\r\nth_2 = 450  # middle\r\nth_3 = 350  # right\r\nth_4 = 350  # right most\r\n\r\nstart = True\r\nend = False\r\n\r\n# Left_hand_rule, so no turn right\r\nactions = []\r\ncheckpoints = []\r\ncount = 0\r\n\r\n\r\n# function to execute Left, Right or Straight action for turn back from the Last Checkpoint\r\ndef shortest(obj):\r\n    # when shortest() function receive char 'R', so it will turn right\r\n    if obj == 'R':\r\n        print(\"Turn right\")\r\n        mot.command(\"right\", 5, 0.5)\r\n        mot.command(\"forward\", 5, 0.5)\r\n        while lt.data[0] > th_0 and lt.data[1] > th_1:\r\n            print(lt.data, \"turn right for Right-Junction\")\r\n            mot.command(\"right\", 4, 0.2)\r\n            time.sleep(0.5)\r\n        print(lt.data, 'find route and go straight')\r\n\r\n    # when shortest() function receive char 'L', so it will turn to left\r\n    elif obj == 'L':\r\n        print(\"Turn left\")\r\n        mot.command(\"left\", 5, 0.5)\r\n        mot.command(\"forward\", 5, 0.5)\r\n        while lt.data[4] > th_4 and lt.data[3] > th_3:\r\n            print(lt.data, \"turn left for the left junction\")\r\n            mot.command(\"left\", 4, 0.2)\r\n            time.sleep(0.5)\r\n        print(lt.data, 'find route and go straight')\r\n\r\n    # when shortest() function receive char 'S', so it will go straight\r\n    else:\r\n        print(\"Straight\")\r\n        mot.command(\"forward\", 10, 0.5)\r\n\r\n\r\ntry:\r\n    while start:\r\n        # to make sure the received data are all integer\r\n        if type(lt.data) == int:\r\n            continue\r\n        # delay\r\n        time.sleep(0.5)\r\n\r\n        # when there is no T-junction\r\n        condition_2 = True\r\n\r\n        if lt.data[2] > th_2:\r\n            # it will 2 possibility, 1. adjust right and left position 2. totally lost (end route or right junction)\r\n            while lt.data[2] > th_2 and condition_2:\r\n\r\n                # slightly right till it find line detected by lt_2 or lt_1 or lt_0\r\n                if lt.data[2] > th_2 and lt.data[3] < th_3 or lt.data[4] < th_4:\r\n                    print(lt.data, 'slightly right')\r\n                    mot.command(\"right\", 4, 0.1)\r\n\r\n                # slightly left till it find line detected by lt_2 or lt_3 or lt_4\r\n                elif lt.data[2] > th_2 and lt.data[1] < th_1 or lt.data[0] < th_0:\r\n                    print(lt.data, 'slightly left')\r\n                    mot.command(\"left\", 4, 0.1)\r\n\r\n                # # when it totally lost from the line then take action to backward till find line.\r\n                # elif lt.data[0] > th_0 and lt.data[1] > th_1 and lt.data[2] > th_2 and lt.data[3] > th_3 and lt.data[4] > th_4:\r\n                #     while lt.data[0] > th_0 and lt.data[1] > th_1 and lt.data[2] > th_2 and lt.data[3] > th_3 and lt.data[4] > th_4:\r\n                #         print(lt.data, 'backward till find at least 1 line tracker detect line')\r\n                #         mot.command(\"backward\", 4, 0.2)\r\n\r\n                else:\r\n                    # this make condition_2 become false and exit from the while loop\r\n                    condition_2 = False\r\n\r\n        # It will decide to turn left, right or U-turn if the robot on the straight line.\r\n        if lt.data[2] < th_2:\r\n            # while condition lt 2 < th_2 and there is no junction at left and right, Just Go Straight\r\n            while lt.data[2] < th_2 and lt.data[0] > th_0 and lt.data[4] > th_4:\r\n                print(lt.data, 'just go straight because there is no junction on left of right')\r\n                mot.command(\"forward\", 6, 0.5)\r\n                time.sleep(0.5)\r\n\r\n            # when it detect left junction, so it will take action to turn left\r\n            if lt.data[2] < th_2 and lt.data[1] < th_1 or lt.data[0] < th_0:\r\n                print(lt.data, 'stop and turn left')\r\n                time.sleep(0.5)\r\n                # this mot.command to make sure it pass line and make at least 45 degree of left rotation\r\n                mot.command(\"left\", 8, 0.5)\r\n                mot.command(\"forward\", 5, 0.5)\r\n                # it will proceed with turn to left bit by bit till the 3rd or 4th detect the line\r\n                while lt.data[3] > th_3 and lt.data[4] > th_4:\r\n                    print(lt.data, \"turn left for Left-Junction\")\r\n                    mot.command(\"left\", 4, 0.2)\r\n                    time.sleep(0.5)\r\n                print(lt.data, 'find route and go straight')\r\n                # append L into the checkpoint function\r\n                actions.append(\"L\")\r\n                LHR(actions, checkpoints)\r\n\r\n            # After meet right junction with no left junction,it will check the straight path\r\n            elif lt.data[2] < th_2 and lt.data[3] < th_3 and lt.data[4] < th_4 and lt.data[0] > th_0:\r\n                print(lt.data, 'junction at the right and there is no left junction')\r\n                time.sleep(0.5)\r\n                # to get know whether it had a straight path or not\r\n                mot.command(\"forward\", 4, 0.2)\r\n                print(\"check whether can go straight or turn right\")\r\n                time.sleep(0.2)\r\n\r\n                # when the state from line change to all white. It assume that no straight path\r\n                if lt.data[0] > th_0 and lt.data[1] > th_1 and lt.data[2] > th_2 and lt.data[3] > th_3 and lt.data[\r\n                    4] > th_4:\r\n\r\n                    # this loop to make sure it turn back till found line at least 1 sensor will detected\r\n                    while lt.data[0] > th_0 and lt.data[1] > th_1 and lt.data[2] > th_2 and lt.data[3] > th_3 and lt.data[4] > th_4:\r\n                        print(lt.data, 'backward till find at least 1 line tracker detect line')\r\n                        mot.command(\"backward\", 4, 0.2)\r\n\r\n                    # turn right when meet right junction without left junction and straight path.\r\n                    if lt.data[2] < th_2 or lt.data[3] < th_3 and lt.data[4] < th_4 and lt.data[0] < th_0:\r\n                        time.sleep(0.5)\r\n                        print(lt.data, 'right junction without left and straight path')\r\n                        # it will continue looping turn right till 0 or 1 sensor meet the line.\r\n                        while lt.data[0] > th_0 and lt.data[1] > th_1:\r\n                            print(lt.data, \"turn right for Right-Junction\")\r\n                            mot.command(\"right\", 4, 0.2)\r\n                            time.sleep(0.5)\r\n                        print(lt.data, 'find route and go straight')\r\n                        # append R into the LHR function\r\n                        actions.append(\"R\")\r\n                        LHR(actions, checkpoints)\r\n                    else:\r\n                        # just put this if the data that we not expected\r\n                        print(lt.data, \"Maybe not turn right that we don't know DANGER \")\r\n\r\n                else:\r\n                    # it mean if have a line after go straight just now, so just go straight\r\n                    print(\"junction at right and there is straight path\")\r\n                    actions.append(\"S\")\r\n                    LHR(actions, checkpoints)\r\n\r\n            # All white, backward till meet a single line, then make U- turn while lt.data[0] > th_0 & lt.data[1] > th_1\r\n            elif lt.data[0] > th_0 and lt.data[1] > th_1 and lt.data[2] > th_2 and lt.data[3] > th_3 and lt.data[\r\n                4] > th_4:\r\n                print(lt.data, 'Dead End')\r\n                # it will turn back turn back till at least 1 of the sensor find the line.\r\n                while lt.data[0] > th_0 and lt.data[1] > th_1 and lt.data[2] > th_2 and lt.data[3] > th_3 and lt.data[\r\n                    4] > th_4:\r\n                    mot.command(\"backward\", 4, 0.1)\r\n                # this condition is to make sure there is no right and left junction\r\n                if lt.data[0] > th_0 and lt.data[4] > th_4:\r\n                    # it will turn right till 1st and 0 sensor detect a line\r\n                    while lt.data[1] > th_1 and lt.data[0] > th_0:\r\n                        print(lt.data, \"U-Turn\")\r\n                        # check if the turn right a quite hard so need to decrease the duration[turn right bit-by-bit]\r\n                        mot.command(\"right\", 4, 0.2)\r\n                        time.sleep(0.5)\r\n                    print(lt.data, 'find route and go straight')\r\n                    # append B to the LHR function\r\n                    actions.append(\"B\")\r\n                    LHR(actions, checkpoints)\r\n                else:\r\n                    # just be aware and get know maybe there is an expected input to the system\r\n                    print(lt.data, \"Maybe not U-Turn That We Don't Know DANGER\")\r\n\r\n            # 6 all black, backward to find 1 line, then make a U-turn without append \"B\" value to the LHR\r\n            elif lt.data[0] < th_0 and lt.data[1] < th_1 and lt.data[2] < th_2 and lt.data[3] < th_3 or lt.data[4] < th_4:\r\n                # go forward to check the line thick or not\r\n                mot.command(\"forward\", 4, 0.2)\r\n                # if the line thick, it will proceed with this condition\r\n                if lt.data[0] < th_0 and lt.data[1] < th_1 and lt.data[2] < th_2 and lt.data[3] < th_3 or lt.data[\r\n                    4] < th_4:\r\n                    print(lt.data, 'Reach END')\r\n                    # it will backward till it exit from the END line and meet a single line\r\n                    while lt.data[0] < th_0 and lt.data[4] < th_4:\r\n                        mot.command(\"backward\", 5, 0.5)\r\n                    # this to make sure that it exit the thick line an make U-Turn without append 'B'\r\n                    if lt.data[0] < th_0 and lt.data[4] < th_4:\r\n                        # at least 90 degree of robot will rotate to the right\r\n                        mot.command(\"right\", 10, 0.8)\r\n                        # turn right bit by bit till sensor 0 or 1 detect a single line\r\n                        while lt.data[1] > th_1 and lt.data[0] > th_0:\r\n                            print(lt.data, \"U-Turn\")\r\n                            mot.command(\"right\", 4, 0.2)\r\n                            time.sleep(0.5)\r\n                        print(lt.data, 'find route and go straight')\r\n\r\n                    time.sleep(1)\r\n                    # finish loop for start and start with end which turn back of the shortest path\r\n                    start = False\r\n                    end = True\r\n\r\n    while end:\r\n        # to make sure the received data are all integer\r\n        if type(lt.data) == int:\r\n            continue\r\n        # delay\r\n        time.sleep(0.5)\r\n\r\n        # when there is no T-junction\r\n        condition_2 = True\r\n\r\n        if lt.data[2] > th_2:\r\n            # it will 2 possibility, 1. adjust right and left position 2. totally lost (end route or right junction)\r\n            while lt.data[2] > th_2 and condition_2:\r\n\r\n                # slightly right till it find line detected by lt_2 or lt_1 or lt_0\r\n                if lt.data[2] > th_2 and lt.data[3] < th_3 or lt.data[4] < th_4:\r\n                    while lt.data[0] > th_0 and lt.data[1] > th_1 and lt.data[2] > th_2:\r\n                        print(lt.data, 'slightly right')\r\n                        mot.command(\"right\", 4, 0.1)\r\n\r\n                # slightly left till it find line detected by lt_2 or lt_3 or lt_4\r\n                elif lt.data[2] > th_2 and lt.data[1] < th_1 or lt.data[0] < th_0:\r\n                    while lt.data[4] > th_0 and lt.data[3] > th_1 and lt.data[2] > th_2:\r\n                        print(lt.data, 'slightly left')\r\n                        mot.command(\"left\", 4, 0.1)\r\n\r\n                # when it totally lost from the line then take action to backward till find line.\r\n                elif lt.data[0] > th_0 and lt.data[1] > th_1 and lt.data[2] > th_2 and lt.data[3] > th_3 and lt.data[\r\n                    4] > th_4:\r\n                    while lt.data[0] > th_0 and lt.data[1] > th_1 and lt.data[2] > th_2 and lt.data[3] > th_3 and lt.data[4] > th_4:\r\n                        print(lt.data, 'backward till find at least 1 line tracker detect line')\r\n                        mot.command(\"backward\", 4, 0.3)\r\n\r\n                else:\r\n                    condition_2 = False\r\n\r\n        if lt.data[2] < th_2:\r\n            while lt.data[2] < th_2 and lt.data[0] > th_0 and lt.data[4] > th_4:\r\n                print(lt.data, 'just go straight because there is no junction on left of right')\r\n                mot.command(\"forward\", 5, 1)\r\n\r\n            if lt.data[0] < th_0 and lt.data[1] < th_1 or lt.data[3] < th_3 and lt.data[4] < th_4:\r\n                time.sleep(0.5)\r\n                print(lt.data, \"Junction on right or left\")\r\n                if count < len(checkpoints):\r\n                    obj = checkpoints[count]\r\n                    shortest(obj)\r\n                    count += 1\r\n                else:\r\n                    print(\"Check value of count\")\r\n\r\n            else:\r\n                print(lt.data, \"Maybe there have other line tracker reading\")\r\n        else:\r\n            end = False\r\n\r\n\r\nexcept KeyboardInterrupt:\r\n    lt.stop()\r\n    mot.stop()\r\n","repo_name":"chengboonrong/robot-maze-IR","sub_path":"Robot_Challenge_4_5.py","file_name":"Robot_Challenge_4_5.py","file_ext":"py","file_size_in_byte":13297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"41222770113","text":"import aiohttp\nfrom aiogram import Router, Bot\nfrom aiogram.filters import Command\nfrom aiogram.fsm.context import FSMContext\nfrom aiogram.types import Message, CallbackQuery\n\nfrom app.config import Config\nfrom app.db.functions import Joke, User\nfrom app.keyboards.inline import yes_or_no_keyboard, pagination_keyboard, add_my_jokes_keyboard, joke_keyboard\nfrom app.states import States\n\nrouter = Router()\njoke_on_page = 2\nanswer_dict = {\n    'да': 'Пизда',\n    'мда, треш': 'Пиздец',\n    'пиздец': 'Мда, треш',\n    'нет': 'Пидора ответ',\n    'пидора ответ': 'Шлюхи аргумент',\n    'шлюхи аргумент': 'Аргумент не нужен, пидор обнаружен',\n    'аргумент не нужен, пидор обнаружен': 'Пидр засекречен твой анал не вечен',\n    'пидр засекречен твой анал не вечен': 'Мой анал не вечен твой анал помечен',\n    'мой анал не вечен твой анал помечен': 'Пошел нахуй',\n}\nheaders = {\n    'authority': 'vt.tiktok.com',\n    'pragma': 'no-cache',\n    'cache-control': 'no-cache',\n    'sec-ch-ua': '^\\\\^Google',\n    'dnt': '1',\n    'upgrade-insecure-requests': '1',\n    'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.93 '\n                  'Safari/537.36',\n    'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,'\n              'application/signed-exchange;v=b3;q=0.9',\n    'sec-fetch-site': 'none',\n    'sec-fetch-mode': 'navigate',\n    'sec-fetch-user': '^\\\\?1',\n    'sec-fetch-dest': 'document',\n    'accept-language': 'en-US,en;q=0.9',\n}\n\ncookies = {\n    'tt_webid_v2': '6935199750005992455',\n    'tt_webid': '6935199750005992455',\n    's_v_web_id': 'verify_kt9s1t0p_8eYwKjg1_4rZb_8qLr_9aXn_1j2kWm7xMxY6',\n}\n\n\n@router.message(Command(commands=[\"add_joke\"]))\nasync def add_joke_handler(message: Message, state: FSMContext):\n    await message.answer(\"Напиши текст шутки\")\n    await state.set_state(States.add_joke_text)\n\n\n@router.message(state=States.add_joke_text)\nasync def add_joke_text_handler(message: Message, state: FSMContext):\n    await message.answer(\n        f\"Вы правда хотите добавить эту шутку?\\n\\n{message.text}\",\n        reply_markup=yes_or_no_keyboard()\n    )\n    await state.set_data({\"text\": message.text})\n\n\n@router.callback_query(state=States.add_joke_text, text=\"yes\")\nasync def add_joke_yes_handler(callback_query: CallbackQuery, state: FSMContext):\n    data = await state.get_data()\n    user = await User.get(telegram_id=callback_query.from_user.id)\n    await Joke.create(text=data[\"text\"], user=user)\n    await callback_query.message.edit_text(\"Шутка добавлена\")\n    await callback_query.answer(\"Шутка добавлена\", show_alert=False)\n    await state.clear()\n\n\n@router.callback_query(state=States.add_joke_text, text=\"no\")\nasync def add_joke_no_handler(callback_query: CallbackQuery, state: FSMContext):\n    await callback_query.message.edit_text(\"Шутка не добавлена\")\n    await callback_query.answer(\"Шутка не добавлена\", show_alert=False)\n    await state.clear()\n\n\n@router.message(Command(commands=[\"joke\"]))\nasync def joke_handler(message: Message):\n    joke = await Joke.get_random()\n    await message.answer(joke.text)\n\n\n@router.message(Command(commands=[\"all_jokes\"]))\nasync def all_jokes_handler(message: Message, state: FSMContext):\n    jokes = await Joke.get_all()\n    text = \"<b>Список всех шуток:</b> \\n\\n\"\n    await state.set_state(States.all_jokes)\n    await state.set_data({\"page\": 1, \"user\": message.from_user.id})\n    for joke in jokes[0:joke_on_page]:\n        text += f\"{joke.text} \\n\\n\"\n\n    await message.answer(text, reply_markup=pagination_keyboard(1, len(jokes) // 2 + 1))\n\n\n@router.callback_query(state=States.all_jokes, text=\"page_next\")\nasync def all_jokes_next_handler(callback_query: CallbackQuery, state: FSMContext):\n    data = await state.get_data()\n    jokes = await Joke.get_all()\n    text = \"<b>Список всех шуток:</b> \\n\\n\"\n    if len(jokes) // joke_on_page + 1 <= data[\"page\"]:\n        await callback_query.answer()\n        return\n    if data[\"user\"] != callback_query.from_user.id:\n        await callback_query.answer()\n        return\n    page = data[\"page\"] + 1\n    await state.set_data({\"page\": page, \"user\": callback_query.from_user.id})\n    for joke in jokes[(page - 1) * joke_on_page:page * joke_on_page]:\n        text += f\"{joke.text} \\n\\n\"\n\n    await callback_query.message.edit_text(\n        text, reply_markup=pagination_keyboard(page, len(jokes) // joke_on_page + 1)\n    )\n    await callback_query.answer()\n\n\n@router.callback_query(state=States.all_jokes, text=\"page_prev\")\nasync def all_jokes_prev_handler(callback_query: CallbackQuery, state: FSMContext):\n    data = await state.get_data()\n    jokes = await Joke.get_all()\n    text = \"<b>Список всех шуток:</b> \\n\\n\"\n    if data[\"page\"] <= 1:\n        await callback_query.answer()\n        return\n    if data[\"user\"] != callback_query.from_user.id:\n        await callback_query.answer()\n        return\n    page = data[\"page\"] - 1\n    await state.set_data({\"page\": page, \"user\": callback_query.from_user.id})\n    for joke in jokes[(page - 1) * joke_on_page:page * joke_on_page]:\n        text += f\"{joke.text} \\n\\n\"\n\n    await callback_query.message.edit_text(\n        text, reply_markup=pagination_keyboard(page, len(jokes) // joke_on_page + 1)\n    )\n    await callback_query.answer()\n\n\n@router.message(Command(commands=[\"my_jokes\"]))\nasync def my_jokes_handler(message: Message, state: FSMContext):\n    user = await User.get(telegram_id=message.from_user.id)\n    jokes = await Joke.filter(user=user)\n    if not jokes:\n        await message.answer(\"У вас нет добавленных шуток\")\n        return\n\n    text = \"<b>Список всех твоих шуток:</b> \\n\\n\"\n\n    await state.set_state(States.my_jokes)\n    await state.set_data({\"page\": 1, \"user\": message.from_user.id})\n    await message.answer(\n        text,\n        reply_markup=add_my_jokes_keyboard(jokes=jokes[:5], page=1, max_page=len(jokes) // 5 + 1)\n    )\n\n\n@router.callback_query(text=\"my_jokes_next\")\nasync def my_jokes_next_handler(callback_query: CallbackQuery, state: FSMContext):\n    data = await state.get_data()\n    user = await User.get(telegram_id=callback_query.from_user.id)\n    jokes = await Joke.filter(user=user)\n\n    if len(jokes) // 5 + 1 <= data[\"page\"]:\n        await callback_query.answer()\n        return\n    if data[\"user\"] != callback_query.from_user.id:\n        await callback_query.answer()\n        return\n\n    page = data[\"page\"] + 1\n    await state.set_data({\"page\": page, \"user\": callback_query.from_user.id})\n\n    text = \"<b>Список всех твоих шуток:</b> \\n\\n\"\n\n    await callback_query.message.edit_text(\n        text, reply_markup=add_my_jokes_keyboard(\n            jokes=jokes[5 * (page - 1):5 * page],\n            page=page,\n            max_page=len(jokes) // 5 + 1\n        )\n    )\n    await callback_query.answer()\n\n\n@router.callback_query(text=\"my_jokes_prev\")\nasync def my_jokes_prev_handler(callback_query: CallbackQuery, state: FSMContext):\n    data = await state.get_data()\n    user = await User.get(telegram_id=callback_query.from_user.id)\n    jokes = await Joke.filter(user=user)\n\n    if data[\"page\"] <= 1:\n        await callback_query.answer()\n        return\n    if data[\"user\"] != callback_query.from_user.id:\n        await callback_query.answer()\n        return\n\n    page = data[\"page\"] - 1\n    await state.set_data({\"page\": page, \"user\": callback_query.from_user.id})\n\n    text = \"<b>Список всех твоих шуток:</b> \\n\\n\"\n\n    await callback_query.message.edit_text(\n        text, reply_markup=add_my_jokes_keyboard(\n            jokes=jokes[5 * (page - 1):5 * page],\n            page=page,\n            max_page=len(jokes) // 5 + 1\n        )\n    )\n    await callback_query.answer()\n\n\n@router.callback_query(text=\"back\")\nasync def back_handler(callback_query: CallbackQuery, state: FSMContext):\n    data = await state.get_data()\n    if data[\"user\"] != callback_query.from_user.id:\n        await callback_query.answer()\n        return\n    user = await User.get(telegram_id=data[\"user\"])\n    jokes = await Joke.filter(user=user)\n    if not jokes:\n        await callback_query.message.edit_text(\"У вас нет добавленных шуток\")\n        return\n\n    text = \"<b>Список всех твоих шуток:</b> \\n\\n\"\n\n    await state.set_state(States.my_jokes)\n    await state.set_data({\"page\": 1, \"user\": callback_query.from_user.id})\n    await callback_query.message.edit_text(\n        text\n    )\n    await callback_query.message.edit_reply_markup(\n        add_my_jokes_keyboard(jokes=jokes[:5], page=1, max_page=len(jokes) // 5 + 1)\n    )\n\n\n@router.callback_query(lambda x: x.data.startswith(\"joke_\") and x.data[5:].isdigit(), state=States.my_jokes)\nasync def joke_callback_handler(callback_query: CallbackQuery, state: FSMContext):\n    joke_id = int(callback_query.data[5:])\n    joke = await Joke.get(id=joke_id)\n    data = await state.get_data()\n    if data[\"user\"] != callback_query.from_user.id:\n        await callback_query.answer()\n        return\n\n    text = (\n        f'<b>Шутка</b> \\n\\n'\n        f'{joke.text}'\n    )\n    await callback_query.message.edit_text(text)\n    await callback_query.message.edit_reply_markup(joke_keyboard(joke_id=joke_id))\n\n    await callback_query.answer()\n\n\n@router.callback_query(lambda x: x.data.startswith(\"delete_\") and x.data[7:].isdigit(), state=States.my_jokes)\nasync def delete_joke_handler(callback_query: CallbackQuery, state: FSMContext):\n    joke_id = int(callback_query.data[7:])\n    joke = await Joke.get(id=joke_id)\n    data = await state.get_data()\n    if data[\"user\"] != callback_query.from_user.id:\n        await callback_query.answer()\n        return\n\n    await joke.delete()\n    await callback_query.message.edit_text(\"Шутка удалена\")\n\n\n@router.callback_query(lambda x: x.data.startswith(\"edit_\") and x.data[5:].isdigit(), state=States.my_jokes)\nasync def edit_joke_handler(callback_query: CallbackQuery, state: FSMContext):\n    joke_id = int(callback_query.data[5:])\n    data = await state.get_data()\n    if data[\"user\"] != callback_query.from_user.id:\n        await callback_query.answer()\n        return\n\n    await state.set_state(States.edit_joke)\n    await state.set_data({\"joke_id\": joke_id, \"user\": callback_query.from_user.id})\n    await callback_query.message.edit_text(\"Введите новый текст шутки\")\n\n    await callback_query.answer()\n\n\n@router.message(state=States.edit_joke)\nasync def edit_joke_text_handler(message: Message, state: FSMContext):\n    data = await state.get_data()\n    if data[\"user\"] != message.from_user.id:\n        return\n\n    joke_id = data[\"joke_id\"]\n    joke = await Joke.get(id=joke_id)\n    joke.text = message.text\n    await joke.save()\n    await state.clear()\n    await message.answer(\"Шутка изменена\")\n\n\n@router.message()\nasync def all_message(message: Message, bot: Bot, config: Config):\n    text = message.text\n\n    if text.startswith('https://www.tiktok.com/') or text.startswith('https://vt.tiktok.com/'):\n        try:\n            video = await return_tik_tok_video(text)\n            await message.reply_video(video)\n        except KeyError:\n            await message.reply(\"Видео не найдено\")\n            await bot.send_message(config.settings.owner_id, f\"Видео не найдено: {text}\")\n        except Exception as e:\n            print(e)\n\n    if text.lower() in answer_dict:\n        await message.reply(answer_dict[text.lower()])\n\n\nasync def return_tik_tok_video(link) -> str:\n    async with aiohttp.ClientSession() as session:\n        async with session.get(link, headers=headers, cookies=cookies) as resp:\n            video_id = resp.url.path.split('/')[-1].split('?')[0]\n\n        link = f'https://www.tiktok.com/api/item/detail/?itemId={video_id}'\n        async with session.get(link, headers=headers, cookies=cookies) as resp:\n            if resp.status == 200:\n                data = await resp.json()\n                download_url = data['itemInfo']['itemStruct']['video']['downloadAddr']\n        return download_url\n","repo_name":"yeezy-na-izi/jokes-bot","sub_path":"app/handlers/user/jokes.py","file_name":"jokes.py","file_ext":"py","file_size_in_byte":12513,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"24671226948","text":"from django.db import models\nfrom django.core.validators import MaxValueValidator, MinValueValidator\n\nfrom users.models import Degree, Domain, LearningMode, StudyProgram\n\n# Create your models here.\nclass Course(models.Model):\n  teacher = models.ForeignKey('users.Teacher', related_name='courses', on_delete=models.PROTECT, null=True)\n  students = models.ManyToManyField('users.Student', related_name='enrolled_students')\n  title = models.CharField(max_length=255)\n  link = models.URLField(max_length=200, null=True)\n  capacity = models.IntegerField(\n    default=1, \n    validators=[\n      MinValueValidator(1), \n      MaxValueValidator(100)]\n  )\n  degree = models.TextField(choices=Degree.choices, null=True)\n  semester = models.IntegerField(\n    default=1, \n    validators=[\n      MinValueValidator(1), \n      MaxValueValidator(2)]\n  )\n  class Meta:\n    verbose_name = 'course'\n    verbose_name_plural = \"courses\"\n\nclass OptionsListManager(models.Manager):\n  def get_students_sorted_by_grade(self, options_list):\n    students = list(super().get_queryset().filter(id=options_list.id)[0].students.all())\n    students.sort(key= lambda s: s.grades.get_student_current_grade(s), reverse=True)\n    return students\n\nclass OptionsList(models.Model):\n  courses = models.ManyToManyField('Course', related_name='courses')\n  students = models.ManyToManyField('users.Student', related_name='students')\n  domain = models.TextField(choices=Domain.choices, null=True)\n  learning_mode = models.TextField(choices=LearningMode.choices, null=True)\n  degree = models.TextField(choices=Degree.choices, null=True)\n  study_program = models.TextField(choices=StudyProgram.choices, null=True)\n  title = models.CharField(max_length=255)\n  year = models.IntegerField(\n    validators=[\n      MinValueValidator(1),\n      MaxValueValidator(4)\n    ],\n    default=1\n  )\n  semester = models.IntegerField(\n    validators=[\n      MinValueValidator(1),\n      MaxValueValidator(2)\n    ],\n    default=1\n  )\n\n  objects = OptionsListManager()\n  class Meta:\n    verbose_name = 'options list'\n    verbose_name_plural = \"options lists\"\n    constraints = [\n      models.UniqueConstraint(\n        fields=['domain', 'learning_mode', 'degree', 'study_program', 'year', 'semester', 'title'], name='unique_migration_options_list'\n      ),\n    ]\n\nclass StudentOptionChoiceManager(models.Manager):\n  def choices_sorted_by_order(self, student, options_list):\n    return super().get_queryset().filter(options_list=options_list, student=student).order_by('order').all()\n\nclass StudentOptionChoice(models.Model):\n  student = models.ForeignKey('users.Student', on_delete=models.CASCADE)\n  options_list = models.ForeignKey('OptionsList', on_delete=models.CASCADE)\n  course = models.ForeignKey('Course', on_delete=models.CASCADE)\n  order = models.IntegerField(\n    validators=[\n      MinValueValidator(0)\n    ]\n  )\n\n  objects = StudentOptionChoiceManager()\n  \n  class Meta:\n    verbose_name = 'student option choice'\n    verbose_name_plural = \"student option choices\"\n    constraints = [\n      models.UniqueConstraint(\n        fields=['student', 'options_list', 'course'], name='unique_migration_student_options_list_course'\n      ),\n    ]\n","repo_name":"dianapaula19/graduation-project","sub_path":"backend/courses/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"73103232604","text":"from PyQt5.QtCore import Qt, QRect\nfrom PyQt5.QtWidgets import QLabel, QWidget, QLineEdit, QListView\n\n\nclass Ui_Dialog(object):\n    def setupUi(self, Dialog):\n        Dialog.setObjectName(\"Dialog\")\n        Dialog.resize(690, 50)\n        Dialog.setWindowFlags(Qt.FramelessWindowHint)\n        Dialog.setAttribute(Qt.WA_TranslucentBackground)\n        self.lineEdit = QLineEdit(Dialog)\n        self.lineEdit.setGeometry(QRect(0, 0, 690, 50))\n        self.lineEdit.setObjectName(\"lineEdit\")\n        self.label = QLabel(Dialog)\n        self.label.setGeometry(QRect(11, 15, 20, 20))\n        self.label.setObjectName(\"label\")\n        self.widget = QWidget(Dialog, Qt.ToolTip | Qt.FramelessWindowHint)\n        self.widget.setGeometry(QRect(0, 0, 690, 0))\n        self.widget.setObjectName(\"widget\")\n        self.widget.setAttribute(Qt.WA_TranslucentBackground)\n        self.widget.setVisible(False)\n        self.listView = QListView(self.widget)\n        self.listView.setGeometry(QRect(0, 0, 690, 0))\n        self.listView.setObjectName(\"listView\")\n        self.listView.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)\n        # QMetaObject.connectSlotsByName(Dialog)\n\n    def retranslateUi(self, Dialog):\n        from Languages import Strings\n        Dialog.setWindowTitle(Strings.APP_NAME)\n","repo_name":"Xpp521/XSearch","sub_path":"SearchDialog/SearchDialog_ui.py","file_name":"SearchDialog_ui.py","file_ext":"py","file_size_in_byte":1285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35060267883","text":"\"\"\"\r\n=======================================================================\r\nScheduling with Constraint Programming\r\n=======================================================================\r\n\r\nA scheduler with configurable constraints. It takes a CSV as\r\ninput. An example CSV is included in the repository.\r\n\r\nGuidelines for the algorithm:\r\n\r\n- A given grouping of jobs should not have a value exceeding the\r\nTOTAL_VALUE_LIMIT (in hours). Note this should take into account the\r\npossibility of time overlaps, and in such situations values should be\r\ncalculated by the difference between start and end times rather than\r\nusing the value provided in the \"Value\" column from the input.\r\n\r\n- Any pairing of jobs' start and end times may overlap by at most\r\nthe value of ALLOWABLE_OVERLAP (in minutes).\r\n\r\n- The output file should be identical to the input file, with an added\r\n\"Group\" column containing a value that matches between the grouped\r\njobs.\r\n\r\nBoolean questions for pseudocode and helping plan out the algorithm steps:\r\n\r\n- Do the jobs in the grouping have a time overlap? If so, adjust the total value\r\n  accordingly.\r\n- Do the jobs in the grouping have a total value less than the TOTAL_VALUE_LIMIT?\r\n- Do the jobs in the grouping have the least duration of 'dead time' between their end\r\n  and start times, compared with all other pairings that contain one of the two jobs?\r\n\r\n\"\"\"\r\n\r\nfrom datetime import datetime, timedelta\r\nfrom typing import Any, Union\r\n\r\nimport pandas as pd\r\nfrom pandas import DataFrame, Series\r\nfrom pandas.io.parsers import TextFileReader\r\n\r\nfrom classes import Group, Job\r\nfrom config import \\\r\n    PARSE_DATES  # No longer required using strptime and formatting.\r\nfrom config import (ALLOWABLE_OVERLAP, FILE_TO_READ, OUTPUT_FILE,\r\n                    TOTAL_VALUE_LIMIT)\r\n\r\n# df: pd.DataFrame = pd.read_csv(FILE_TO_READ, parse_dates=PARSE_DATES)\r\ndf: pd.DataFrame = pd.read_csv(FILE_TO_READ)\r\noverlap = timedelta(minutes=ALLOWABLE_OVERLAP)\r\n\r\n# Create a list of Job class instances with corresponding attributes from the dataframe.\r\nlist_of_jobs = []\r\n\r\nfor x in df.index:\r\n    job_id = \"task_\" + str(df[\"Task\"].iloc[x])\r\n    job = df[\"Job\"].iloc[x]\r\n    start_time = df[\"Start\"].iloc[x]\r\n    end_time = df[\"End\"].iloc[x]\r\n    value = df[\"Value\"].iloc[x]\r\n    priority = df[\"Priority\"].iloc[x]\r\n    list_of_jobs.append(Job(job_id, job, start_time, end_time, value, priority))\r\n\r\n# --------\r\n\r\n# Testing a comparative loop in list; proof of concept.\r\n\"\"\"for x in list_of_jobs:\r\n    if x.job == \"13301\":\r\n        print(1)\r\n    else:\r\n        print(0)\"\"\"\r\n\r\n# --------\r\n\r\n# Testing a comparative loop; print matches of jobs if job start is\r\n# later than job end and sum of job values is less than 13.\r\n\"\"\"for x in list_of_jobs:\r\n    for y in list_of_jobs:\r\n        if x.start_time > (y.end_time - overlap) and x.value + y.value < 13:\r\n            i = (y.job, y.end_time, x.job, x.start_time, round(x.value + y.value, 2))\r\n            list_of_jobs.remove(y)\r\n            print(i)\"\"\"\r\n\r\n# Print leftover jobs:\r\n\"\"\"for x in list_of_jobs:\r\n    print(x.job)\"\"\"\r\n\r\n# --------\r\n\r\n# Continuation of loop; adding the matches to a group to then compare\r\n# which match is the 'best' (whichever match has the least time gap\r\n# between start and end)\r\n\r\n\"\"\"for y in list_of_jobs:\r\n    print(y.job, y.end_time)\"\"\"\r\n\r\n\"\"\"future = datetime.timedelta(hours=13)\r\ngroup = []\r\nfor x in list_of_jobs:\r\n    for y in list_of_jobs:\r\n        if x.start_time > (y.end_time - overlap) and (y.end_time - x.start_time)\r\n         < future:\r\n            z = [y, x]\r\n            #list_of_jobs.remove(y)\r\n            group.append(z)\r\n\r\n# Print the number of minutes difference between start and end times of the pairs:\r\nfor pair in group:\r\n    print(pair[0].job, pair[1].job, (pair[1].start_time - pair[0].end_time).seconds\r\n     // 60)\"\"\"\r\n\r\n# --------\r\n\r\n# New attempt at solving:\r\n\r\njobs_over_x_hours = []\r\npossible_matches = []\r\ntime_format = \"%I:%M %p\"\r\n\r\nfor job in list_of_jobs:\r\n    if job.value > 8:\r\n        list_of_jobs.remove(job)\r\n        jobs_over_x_hours.append(job)\r\n\r\nfor x in list_of_jobs:\r\n    for y in list_of_jobs:\r\n        if x.end_time < y.start_time and x.value + y.value < TOTAL_VALUE_LIMIT:\r\n            possible_matches.append([x, y])\r\n\r\ndictionary_to_compare = {}\r\nlist_to_compare = []\r\nfor match in possible_matches:\r\n    start_time = datetime.strptime(match[1].start_time, time_format)\r\n    end_time = datetime.strptime(match[0].end_time, time_format)\r\n    min_difference = start_time - (end_time - overlap)\r\n    if min_difference.days < 0:\r\n        min_difference = timedelta(\r\n            days=0,\r\n            seconds=min_difference.seconds,\r\n            microseconds=min_difference.microseconds,\r\n        )\r\n    # print(match[0].job, match[1].job, min_difference)\r\n    comparator = (match[0].job, match[1].job, min_difference)\r\n    list_to_compare.append(comparator)\r\n\r\nfor x in list_to_compare:\r\n    values = [x[1], x[2]]\r\n    dictionary_to_compare.setdefault(x[0], []).append(values)\r\n\r\ndef find_minimum(key, value_list):\r\n    return key, min(value_list, key=lambda lst: lst[1])\r\n\r\ndef find_minimum_dict(dictionary):\r\n    return dict(find_minimum(key, v) for key, v in dictionary.items())\r\n\r\nprint(find_minimum_dict(dictionary_to_compare))\r\n\r\nfor x in jobs_over_x_hours:\r\n    print(x.job + \" is leftover\")\r\n","repo_name":"zachvance/scheduling_with_constraint_programming","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"28704715175","text":"\"\"\" Contains an wrapped version of the SMAAC GAT block using Maze-Perception blocks.\"\"\"\nfrom typing import Union, List, Sequence, Dict\n\nimport torch\nfrom maze.core.annotations import override\nfrom maze.perception.blocks.shape_normalization import ShapeNormalizationBlock\nfrom torch import nn\n\nfrom maze_smaac.models.smaac_models import GATLayer\n\n\nclass SMAACGATBlock(ShapeNormalizationBlock):\n    \"\"\"A wrapped version of the SMAAC GAT block using Maze-Perception blocks.\n\n    :param in_keys: One key identifying the input tensors.\n    :param out_keys: One key identifying the output tensors.\n    :param in_shapes: List of input shapes.\n    :param nheads: The number of mutli-attention heads.\n    :param dropout: The dropout to use in the Gat layers.\n    :param number_of_gat_layers: The number of GAT layers to use.\n    \"\"\"\n\n    def __init__(self, in_keys: Union[str, List[str]], out_keys: Union[str, List[str]],\n                 in_shapes: Union[Sequence[int], List[Sequence[int]]],\n                 nheads: int, dropout: float, number_of_gat_layers: int):\n        super().__init__(in_keys=in_keys, out_keys=out_keys, in_shapes=in_shapes, in_num_dims=[3, 3],\n                         out_num_dims=3)\n        self.nheads = nheads\n        assert len(self.in_keys) == 2, 'Two keys expected, one for features one for the adj'\n        self.output_dim = self.in_shapes[0][-1]\n        self.dropout = dropout\n        self.number_of_gat_layers = number_of_gat_layers\n\n        self.layer_dict = nn.ModuleDict()\n        for i in range(number_of_gat_layers):\n            self.layer_dict[f'gat_{i}'] = GATLayer(self.output_dim, nheads, dropout)\n\n    @override(ShapeNormalizationBlock)\n    def normalized_forward(self, block_input: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:\n        \"\"\"implementation of :class:`~maze.perception.blocks.shape_normalization.ShapeNormalizationBlock` interface\n        \"\"\"\n        # prepare input tensor\n        input_tensor = block_input[self.in_keys[0]]\n        adj_tensor = block_input[self.in_keys[1]]\n\n        # forward pass\n        tmp_out_tensor = input_tensor\n        for gat_layer in self.layer_dict.values():\n            tmp_out_tensor = gat_layer(tmp_out_tensor, adj_tensor)\n\n        return {self.out_keys[0]: tmp_out_tensor}\n\n    def __repr__(self):\n        txt = f'SMAAC GAT Block x {self.number_of_gat_layers}'\n        txt += f'\\n\\t# of attn heads: {self.nheads}'\n        txt += f'\\n\\tinput/output dim: {self.output_dim}'\n        txt += f'\\n\\tdim of each head: {self.output_dim // 4}'\n        txt += f'\\n\\t[ MultiHeadAttention({self.nheads}, {self.output_dim}, {self.output_dim // 4}, ' \\\n               f'dropout={self.dropout})'\n        txt += f'\\n\\tPositionwiseFeedForward({self.output_dim}, {self.output_dim}, dropout={self.dropout}) ] x ' \\\n               f'{self.number_of_gat_layers}'\n        txt += f\"\\n\\tOut Shapes: {self.out_shapes()}\"\n        return txt\n","repo_name":"enlite-ai/maze_smaac","sub_path":"maze_smaac/models/maze_smaac_gat_block.py","file_name":"maze_smaac_gat_block.py","file_ext":"py","file_size_in_byte":2906,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"86"}
+{"seq_id":"45518331518","text":"from flask import *\ntrip = Blueprint('booking', __name__)\nimport json\nimport pymysql\nimport pymysql.cursors\nfrom dbutils.pooled_db import PooledDB\npymysql.install_as_MySQLdb()\nfrom flask_jwt_extended import *\nimport member\n\n\n# connect to the local DB\npool = PooledDB(creator=pymysql, host = \"127.0.0.1\", user = \"root\", password=\"12345678\", database='website', port= 3306)\n\n@trip.route('/api/booking', methods=['GET'])\ndef  getTrip():\n    if \"email\" in session :\n        print(session['email'])\n        email = session['email'] \n        conn = pool.connection()\n        cursor = conn.cursor()\n        \n        #cursor.execute(\"SET TRANSACTION ISOLATION LEVEL READ UNCOMMITTED;\")\n        sql = \"SELECT attraction_id, paid, date, time, price, email, stitle, address, SUBSTRING_INDEX(file, ',', 1) AS image FROM booking INNER JOIN TPtrip ON TPtrip.id = booking.attraction_id WHERE email = %s\" # ('https://www.travel.taipei/d_upload_ttn/sceneadmin/pic/11000340.jpg'\n        cursor.execute(sql, (email))\n        result = cursor.fetchone()\n        conn.commit() \n        sql = \"SELECT paid FROM booking WHERE email = %s\"\n        cursor.execute(sql, (email))\n        paid = cursor.fetchone()\n        conn.commit() \n        if not result: \n            result_JSON = json.dumps({\"data\": None,\"message\": \"尚未下訂\"})\n        elif paid == 1:\n            result_JSON = json.dumps({\"data\": False,\"message\": \"無正在下訂的訂單\"})\n        else: \n            attraction = {\n                'id':result[0],\n                'name':result[6],\n                'address':result[7],\n                'image':result[8]\n            } #{'id': 2, 'name': '大稻埕碼頭', 'address': '臺北市  大同區環河北路一段', 'image': \"('https://www.travel.taipei/d_upload_ttn/sceneadmin/pic/11000340.jpg'\"}\n            #print(result['image'][2:-1])\n            #print(\"result: \",result['time'],result['price'])\n            result_JSON = json.dumps({'data':attraction,\n                                      'date':result[2],\n                                      'time':result[3],\n                                      'price':result[4]},  indent=1, default=str)     \n        \n    else:\n        result_JSON = json.dumps({\"error\": True,\"message\": \"請登入會員\"})\n        print(\"message\", \"請登入會員\", result_JSON)\n    \n    conn.close()\n    cursor.close()\n    return Response(result_JSON, mimetype='application/json')\n\n# build a new trip\n@trip.route('/api/booking', methods=['POST'])\ndef  postTrip():\n    print(\"email: \", session['email'])\n    requestJSON = request.get_json() #{'attractionId': '2', 'date': '2022-04-07', 'time': 'morning', 'price': '2000'}\n    email = session['email']\n    attractionId = requestJSON['attractionId']\n    date = requestJSON['date']\n    price = requestJSON['price']\n    time = requestJSON['time']\n    session['price'] = price\n    print(\"attractionId, date, time, price, email: \",attractionId, date, time, price, email)\n    if date == '' or price == '' or time == '' : \n        result_JSON = json.dumps({\"error\": True ,\"message\": \"請填寫完整資料\"})\n    elif id == '':\n        result_JSON = json.dumps({\"error\": True ,\"message\": \"請登入會員\"})\n    else:\n        conn = pool.connection()\n        cursor = conn.cursor()\n        #cursor.execute(\"SET SESSION TRANSACTION ISOLATION LEVEL READ COMMITTED;\")\n        sql = \"select attraction_id FROM booking where (email = %s) ;\"\n        sql_run =  cursor.execute(sql, (email))\n        # update if there has been a booking record\n        if sql_run != 0: \n            try:\n                cursor.execute(\"SET SQL_SAFE_UPDATES=0;\")\n                cursor.execute(\"SET FOREIGN_KEY_CHECKS = 0;\")  \n                sql = \"UPDATE booking SET attraction_id=%s, date=%s, time=%s, price=%s, paid=0 WHERE email=%s;\"\n                sql_run = cursor.execute(sql,(attractionId, date, time, price, email))\n                cursor.execute(\"SET SQL_SAFE_UPDATES=1;\")\n                cursor.execute(\"SET FOREIGN_KEY_CHECKS = 1;\")\n                conn.commit() \n                result = cursor.fetchall()\n                print(\"update: \", result)\n                result_JSON = json.dumps({\"ok\": True})\n            except:\n                result_JSON = json.dumps({\"error\": True,\"message\": \"更新失敗\"})\n                \n        # insert if there is no booking record\n        else:\n            try:\n                sql = \"INSERT INTO booking (attraction_id, date, time, price, email) VALUES (%s,%s,%s,%s,%s)\"\n                sql_run = cursor.execute(sql, (attractionId, date, time, price, email))\n                cursor.execute(\"SET SQL_SAFE_UPDATES=1;\")\n                print(2)\n                conn.commit() \n                print(\"insert: \", attractionId, date, price, time)\n                result_JSON = json.dumps({\"ok\": True})\n                print(\"result: \", result_JSON) \n            except:\n                result_JSON = json.dumps({\"error\": True ,\"message\": \"下訂失敗\"})\n        conn.close()\n        cursor.close()\n    return Response(result_JSON, mimetype='application/json')\n  \n            \n# delete the trip\n@trip.route('/api/booking', methods=['DELETE'])\ndef  deleteTrip():\n    if \"email\" in session :\n        try:\n            email = session['email'] #test@gmail.com\n            conn = pool.connection()\n            cursor = conn.cursor()\n            cursor.execute(\"SET FOREIGN_KEY_CHECKS = 0;\")\n            sql = \"DELETE FROM booking WHERE email = %s;\"\n            cursor.execute(sql,(email))\n            conn.commit()\n            cursor.execute(\"SET FOREIGN_KEY_CHECKS = 1;\")\n            print(\"record(s) deleted\")\n            result_JSON = json.dumps({\"ok\": True})\n        except :\n            result_JSON = json.dumps({\"error\": True,\"message\": \"刪除失敗\"})\n            print(\"刪除失敗\")\n        finally:\n            conn.close()\n            cursor.close()\n    else :\n        result_JSON = json.dumps({\"error\": True ,\"message\": \"流程錯誤\"})\n \n    return Response(result_JSON, mimetype='application/json')","repo_name":"vicky-playground/taipei-trip","sub_path":"booking.py","file_name":"booking.py","file_ext":"py","file_size_in_byte":6001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"86"}
+{"seq_id":"5337930644","text":"from fastavro.writer import Writer\nimport hashlib\nimport logging\nimport tempfile\nimport time\n\n# Use deflate codec for c++ compatibility\nCODEC = 'deflate'\n\nSCHEMA = {\n  \"name\": \"message\",\n  \"type\": \"record\",\n  \"fields\": [\n    {\"name\": \"timestamp\", \"type\": \"long\"},\n    {\"name\": \"key\", \"type\": [\"null\", \"bytes\"]},\n    {\"name\": \"value\", \"type\": [\"null\", \"bytes\"]}\n  ]\n}\n\n\n# Allow a lot of skew with kafka. If we haven't seen a message in this amount\n# of time, then assume the topic has not been written to and flush the\n# incomplete file.\nHOUR_MS = 3600 * 1000\nKAFKA_SKEW_MS = 8 * HOUR_MS\n\n# @TODO: This could be a configuration option.\nPATH = '%(topic)s/%(partition)06d/%(offset)020d'\n\nlogger = logging.getLogger('kafka_store.buffer')\n\nclass OutputFile:\n    def __init__(self, file):\n        self.file = file\n        self.md5 = hashlib.md5()\n        self.byte_size = 0\n\n    def write(self, data):\n        self.file.write(data)\n        self.md5.update(data)\n        self.byte_size += len(data)\n\n    def flush(self):\n        return self.file.flush()\n\nclass PartitionBuffer:\n    def __init__(\n        self,\n        topic, partition, first_offset, first_timestamp_ms, max_age_ms,\n        schema=SCHEMA, codec=CODEC\n    ):\n        self._fo = tempfile.NamedTemporaryFile()\n        self._output = OutputFile(self._fo)\n        self._writer = Writer(\n            fo=self._output,\n            schema=schema,\n            codec=codec,\n        )\n\n        self.filename = self._fo.name\n        self.count = 0\n        self.closed = False\n        self.eof = False\n        self.max_age_ms = max_age_ms\n\n        self.topic = topic\n        self.partition = partition\n        self.commit_next_offset = None\n\n        self.first_offset = first_offset\n        self.final_offset = None\n        self.first_timestamp_ms = first_timestamp_ms\n\n        self.path = PATH % {\n            'topic': topic,\n            'partition': partition,\n            'offset': first_offset,\n        }\n        logger.info('Saving %s > %s', self.path, self.filename)\n\n    def mark_eof(self):\n        self.eof = True\n\n    def log(self, offset, key, value, timestamp_ms):\n        assert offset == self.first_offset + self.count\n        assert not self.closed\n        self._writer.write({\n            'key': key,\n            'value': value,\n            'timestamp': timestamp_ms,\n        })\n        self.count += 1\n        self.commit_next_offset = offset + 1\n        self.final_offset = offset\n        self.eof = False\n\n    def close(self):\n        self._writer.flush()\n        self.closed = True\n        logger.info(\n            'Closed %s > %s records=%d %.1fkB',\n            self.path, self.filename,\n            self.count,\n            self.byte_size / 1000,\n        )\n\n    @property\n    def byte_size(self):\n        return self._output.byte_size\n\n    @property\n    def md5_hex(self):\n        assert self.closed\n        return self._output.md5.hexdigest()\n\n    @property\n    def md5(self):\n        assert self.closed\n        return self._output.md5.digest()\n\n    def get_rewound_file(self):\n        assert self.closed\n        self._fo.seek(0)\n        return self._fo\n\n    def is_closed(self, timestamp_ms):\n        return (timestamp_ms - self.first_timestamp_ms) >= self.max_age_ms\n\n    def is_silent_closed(self):\n        '''\n        If a topic has been not received any new messages then close it after\n        the maximum age anyway. Add some extra wait time just incase Kafka has\n        a message that belongs in this file, but hasn't delivered it yet.\n        '''\n        if self.eof:\n            return self.is_closed(int(time.time() * 1000) - KAFKA_SKEW_MS)\n        else:\n            return False\n","repo_name":"smyte/kafka_store","sub_path":"kafka_store/buffer.py","file_name":"buffer.py","file_ext":"py","file_size_in_byte":3658,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"86"}
+{"seq_id":"9956859701","text":"import pytest\nfrom httpx import AsyncClient\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom app.tests.utils import create_artist, random_lower_string\n\n\n@pytest.mark.asyncio\nasync def test_create_artist(async_client: AsyncClient) -> None:\n    data = {\n        \"id\": random_lower_string(),\n        \"name\": random_lower_string(),\n        \"popularity\": 21,\n    }\n    response = await async_client.post(\n        \"/artists/\",\n        json=data,\n    )\n    assert response.status_code == 201\n    content = response.json()\n    assert content[\"id\"] == data[\"id\"]\n    assert content[\"name\"] == data[\"name\"]\n    assert content[\"popularity\"] == data[\"popularity\"]\n\n\n@pytest.mark.asyncio\nasync def test_read_artist(async_client: AsyncClient, db_session: AsyncSession) -> None:\n    artist_in, artist = await create_artist(db_session)\n    response = await async_client.get(f\"/artists/{artist.id}\")\n    assert response.status_code == 200\n    content = response.json()\n    assert content[\"id\"] == artist.id\n    assert content[\"name\"] == artist.name\n    assert content[\"popularity\"] == artist.popularity\n\n\n# etc etc....\n# I am not going to do all of them\n","repo_name":"pynchia/immotest","sub_path":"app/tests/api/test_artists.py","file_name":"test_artists.py","file_ext":"py","file_size_in_byte":1142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74771229405","text":"# model settings\nnorm_cfg = dict(type='SyncBN', requires_grad=True)\nmodel = dict(\n    type='EncoderDecoder',\n    backbone=dict(\n        type='BiSeNetV1EXPCFG',\n        context_path_cfg=dict(\n            type='SimpleContextPath',\n            backbone_cfg=dict(\n                type='ResNet',\n                in_channels=3,\n                depth=18,\n                num_stages=4,\n                out_indices=[2, 3],\n                dilations=(1, 1, 1, 1),\n                strides=(1, 2, 2, 2),\n                norm_cfg=norm_cfg,\n                norm_eval=False,\n                style='pytorch',\n                contract_dilation=True)),\n        spatial_path_cfg=dict(\n            type='MiTSpatialPath',\n            embed_dims=128,\n            out_channels=128,\n            num_layers=2,\n            num_heads=4,\n            patch_size=15,\n            stride=8,\n            sr_ratio=8,\n            mlp_ratio=4,\n            qkv_bias=True,\n            drop_rate=0.,\n            attn_drop_rate=0.,\n            drop_path_rate=0.,\n            act_cfg=dict(type='GELU'),\n            norm_cfg=dict(type='LN', eps=1e-6),\n            final_dowsample=True,\n            final_attn=False),\n        ffm_cfg=dict(\n            type='CPMapSPVecFFM',\n            transformer_decoder_cfg=dict(\n                type='BaseTransformerLayer',\n                attn_cfgs=dict(\n                    type='MultiheadAttention',\n                    embed_dims=128,\n                    num_heads=8,\n                    attn_drop=0.1),\n                ffn_cfgs=dict(\n                    type='FFN',\n                    embed_dims=128,\n                    feedforward_channels=512,\n                    num_fcs=2,\n                    ffn_drop=0.,\n                    act_cfg=dict(type='ReLU', inplace=True)),\n                operation_order=('self_attn', 'norm', 'cross_attn', 'norm',\n                                 'ffn', 'norm'),\n                batch_first=True),\n            in_channels=512,\n            embed_dims=128,\n            num_layers=2,\n            patch_size=1,\n            stride=None,\n            padding='corner',\n            cp_up_rate=2),\n        out_indices=(0, 2)),\n    decode_head=dict(\n        type='FCNHead',\n        in_channels=128,\n        in_index=0,\n        channels=128,\n        num_convs=1,\n        concat_input=False,\n        dropout_ratio=0.1,\n        num_classes=19,\n        norm_cfg=norm_cfg,\n        align_corners=False,\n        loss_decode=dict(\n            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),\n    auxiliary_head=dict(\n            type='FCNHead',\n            in_channels=64,\n            channels=64,\n            num_convs=1,\n            num_classes=19,\n            in_index=1,\n            norm_cfg=norm_cfg,\n            concat_input=False,\n            align_corners=False,\n            loss_decode=dict(\n                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),\n#         dict(\n#             type='FCNHead',\n#             in_channels=256,\n#             channels=64,\n#             num_convs=1,\n#             num_classes=19,\n#             in_index=1,\n#             norm_cfg=norm_cfg,\n#             concat_input=False,\n#             align_corners=False,\n#             loss_decode=dict(\n#                 type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),\n#     ],\n    # model training and testing settings\n    train_cfg=dict(),\n    test_cfg=dict(mode='whole'))\n","repo_name":"xiexinch/mseg-research","sub_path":"configs/_base_/models/bisenetv1_cfg.py","file_name":"bisenetv1_cfg.py","file_ext":"py","file_size_in_byte":3419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74328649884","text":"class Solution:\n    def maxProfit(self, prices: List[int]) -> int:\n       \n    # two poiner\n        \n        max_profit = 0\n        \n        left = 0\n        right = 1\n        while right < len(prices):\n            if prices[left] >= prices[right]:\n                left = right\n                right += 1\n            else:\n                diff = prices[right] - prices[left]\n                right += 1\n                if diff > max_profit:\n                    max_profit = diff\n        \n        return max_profit\n                \n        \n#     # kadane's\n#         curr_max = 0\n#         max_max = 0\n#         for i in range(1, len(prices)):\n#             curr_max += prices[i]-prices[i-1]\n#             curr_max = max(0, curr_max)\n#             max_max = max(curr_max, max_max)\n        \n#         return max_max","repo_name":"adityachache/algorithms-and-leetcode","sub_path":"leetcode/sliding window/easy/121-best-time-to-buy-and-sell-stocks.py","file_name":"121-best-time-to-buy-and-sell-stocks.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"41714623213","text":"from setuptools import setup\nfrom distutils.util import convert_path\n\n\n# Hack to get around version dependency problems\n# Taken from: https://stackoverflow.com/a/24517154\nmain_ns = {}\nver_path = convert_path('littlepython/version.py')\nwith open(ver_path) as ver_file:\n    exec(ver_file.read(), main_ns)\n\n\nsetup(name='littlepython',\n      version=main_ns[\"version\"],\n      description='A Super Simplified Python with a Little Syntactic Sugar',\n      url='https://github.com/DerPferd/little-python',\n      author='Jonathan Beaulieu',\n      author_email='123.jonathan@gmail.com',\n      license='MIT',\n      packages=['littlepython'],\n      zip_safe=False,\n      install_requires=['enum34;python_version<\"3.4\"'],\n      setup_requires=['pytest-runner'],\n      tests_require=['pytest', 'pytest-timeout'],\n      scripts=['bin/littlepy'],\n      classifiers=[\n            # complete classifier list: http://pypi.python.org/pypi?%3Aaction=list_classifiers\n            \"Development Status :: 3 - Alpha\",\n            \"License :: OSI Approved :: MIT License\",\n            \"Intended Audience :: Developers\",\n            \"Programming Language :: Python :: 2.7\",\n            \"Programming Language :: Python :: 3\",\n            \"Programming Language :: Python :: 3.5\",\n            \"Programming Language :: Python :: 3.6\",\n      ]\n      )\n","repo_name":"derpferd/little-python","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"41659682540","text":"from collections import deque\n\nF, S, G, U, D = map(int, input().split())\n\ndef bfs(start, visited):\n    queue = deque([(start, 0)])\n    visited[start] = 1\n\n    while queue:\n        v, cnt = queue.popleft()\n\n        if v == G:\n            return cnt\n\n        if v + U <= F and visited[v+U] == 0:\n            visited[v+U] = 1\n            queue.append((v+U, cnt+1))\n        if v - D > 0 and visited[v-D] == 0:\n            visited[v-D] = 1\n            queue.append((v-D, cnt+1))\n\n    return -1\n    \nvisited = [0] * (F+1)\nresult = bfs(S, visited)\n\nprint(result if result > -1 else \"use the stairs\")","repo_name":"rhyun9584/BOJ","sub_path":"python/5014.py","file_name":"5014.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"2018737065","text":"from django.urls import path\n\nfrom . import views\n\n\nurlpatterns = [\n    path('', views.index, name=\"index\"),\n    path('index', views.index, name=\"index\"),\n    path('book', views.book, name=\"book\"),\n    path('comment', views.comment, name=\"comment\"),\n    path('success', views.success, name='success'),\n    path('register_mail', views.register_mail, name=\"register_mail\"),\n    path('comment-handler', views.comment_handler, name=\"comment_handler\"),\n    path('product-video', views.product_video, name=\"video\"),\n    path('confirm', views.confirm, name=\"confirm\"),\n    path('product', views.product, name=\"product\")\n]\n","repo_name":"Popoola-Sinaayo/Essentials_Glam_Web_Application","sub_path":"Essential_Glams/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"30900503052","text":"import copy\nfrom functools import partial\nimport os\nfrom i6_core.corpus.convert import CorpusToStmJob\n\nimport i6_core.rasr as rasr\nfrom i6_core.recognition.scoring import ScliteJob\nfrom i6_core.returnn.config import CodeWrapper\nfrom i6_core.returnn.extract_prior import ReturnnComputePriorJob\nimport i6_core.text as text\nfrom i6_core.returnn.training import ReturnnTrainingJob\nfrom i6_core.returnn.search import (\n    ReturnnSearchJobV2,\n    SearchBPEtoWordsJob,\n    SearchWordsToCTMJob,\n)\nfrom i6_experiments.users.berger.args.returnn.config import (\n    get_network_config,\n    get_returnn_config,\n)\nfrom i6_experiments.users.berger.args.returnn.learning_rates import (\n    LearningRateSchedules,\n)\nfrom i6_experiments.users.berger.corpus.sms_wsj.data import get_bpe, get_data_inputs\nfrom i6_experiments.users.berger.network.models.fullsum_ctc_dual_output import (\n    make_blstm_fullsum_ctc_dual_output_model,\n    make_blstm_fullsum_ctc_dual_output_recog_model,\n)\nfrom i6_experiments.users.berger.recipe.summary.report import SummaryReport\nfrom i6_experiments.users.berger.systems.transducer_system import SummaryKey\nfrom sisyphus import gs, tk\nfrom ..lm.config_01_lstm_bpe import py as run_lm\n\n\n# ********** Settings **********\n\ndir_handle = os.path.dirname(__file__).split(\"config/\")[1]\nfilename_handle = os.path.splitext(os.path.basename(__file__))[0][len(\"config_\") :]\ngs.ALIAS_AND_OUTPUT_SUBDIR = f\"{dir_handle}/{filename_handle}/\"\nrasr.flow.FlowNetwork.default_flags = {\"cache_mode\": \"task_dependent\"}\n\ntrain_key = \"sms_train_si284\"\ndev_key = \"sms_cv_dev93\"\ntest_key = \"sms_test_eval92\"\n\nfrequency = 8\n\nf_name = \"gt\"\n\nnum_inputs = 40\n\nbpe_size = 100\n\n\ndef run_exp(lm_model: tk.Path, **kwargs) -> SummaryReport:\n\n    lm_cleaning = kwargs.get(\"lm_cleaning\", False)\n\n    # ********** Summary Report **********\n\n    summary_report = SummaryReport(\n        [\n            key.value\n            for key in [\n                SummaryKey.NAME,\n                SummaryKey.CORPUS,\n                SummaryKey.EPOCH,\n                SummaryKey.PRIOR,\n                SummaryKey.LM,\n                SummaryKey.WER,\n                SummaryKey.SUB,\n                SummaryKey.DEL,\n                SummaryKey.INS,\n                SummaryKey.ERR,\n            ]\n        ],\n        col_sort_key=SummaryKey.ERR.value,\n    )\n\n    # ********** Get BPEs **********\n\n    bpe_job = get_bpe(size=bpe_size, lm_cleaning=lm_cleaning)\n\n    num_classes = bpe_job.out_vocab_size  # bpe count\n    num_classes_b = num_classes + 1  # bpe count + blank\n\n    # ********** Extern data **********\n\n    train_data_inputs, dev_data_inputs, test_data_inputs, _ = get_data_inputs(\n        train_keys=[train_key],\n        dev_keys=[dev_key],\n        test_keys=[test_key],\n        freq=frequency,\n        lm_name=\"64k_3gram\",\n        recog_lex_name=\"nab-64k\",\n        delete_empty_orth=True,\n        lm_cleaning=lm_cleaning,\n    )\n\n    datasets = {\n        train_key: {\n            \"class\": \"HDFDataset\",\n            \"files\": [\n                f\"/u/berger/asr-exps/sms_wsj/20220615_dfg_multi_speaker/dependencies/hdf/8kHz/sms_train_si284_complete.gt40.bpe-100.{'updated.' if lm_cleaning else ''}hdf\"\n            ],\n            \"use_cache_manager\": False,\n            \"seq_ordering\": \"random\",\n            \"partition_epoch\": 3,\n        },\n        dev_key: {\n            \"class\": \"HDFDataset\",\n            \"files\": [\n                f\"/u/berger/asr-exps/sms_wsj/20220615_dfg_multi_speaker/dependencies/hdf/8kHz/sms_cv_dev93_complete.gt40.bpe-100.{'updated.' if lm_cleaning else ''}hdf\"\n            ],\n            \"use_cache_manager\": False,\n            \"seq_ordering\": \"sorted\",\n            \"partition_epoch\": 1,\n        },\n        test_key: {\n            \"class\": \"HDFDataset\",\n            \"files\": [\n                f\"/u/berger/asr-exps/sms_wsj/20220615_dfg_multi_speaker/dependencies/hdf/8kHz/sms_test_eval92_complete.gt40.bpe-100.{'updated.' if lm_cleaning else ''}hdf\"\n            ],\n            \"use_cache_manager\": False,\n            \"seq_ordering\": \"sorted\",\n            \"partition_epoch\": 1,\n        },\n    }\n\n    extern_data_config = {\n        \"data_time_tag\": CodeWrapper('Dim(kind=Dim.Types.Time, description=\"time\")'),\n        \"extern_data\": {\n            \"data\": {\n                \"dim\": num_inputs,\n                \"same_dim_tags_as\": {\"t\": CodeWrapper(\"data_time_tag\")},\n            },\n            \"data_separated_0\": {\n                \"dim\": num_inputs,\n                \"same_dim_tags_as\": {\"t\": CodeWrapper(\"data_time_tag\")},\n            },\n            \"data_separated_1\": {\n                \"dim\": num_inputs,\n                \"same_dim_tags_as\": {\"t\": CodeWrapper(\"data_time_tag\")},\n            },\n            \"data_clean_0\": {\n                \"dim\": num_inputs,\n                \"same_dim_tags_as\": {\"t\": CodeWrapper(\"data_time_tag\")},\n            },\n            \"data_clean_1\": {\n                \"dim\": num_inputs,\n                \"same_dim_tags_as\": {\"t\": CodeWrapper(\"data_time_tag\")},\n            },\n            \"bpe_b\": {\"dim\": num_classes_b, \"sparse\": True},\n            \"bpe_0\": {\"dim\": num_classes, \"sparse\": True},\n            \"bpe_1\": {\"dim\": num_classes, \"sparse\": True},\n        },\n        \"num_outputs\": {\n            \"bpe_0\": num_classes_b,\n            \"bpe_1\": num_classes_b,\n        },\n    }\n\n    extern_data_config_recog = copy.deepcopy(extern_data_config)\n    for key in [\n        \"data\",\n        \"data_separated_0\",\n        \"data_separated_1\",\n        \"data_clean_0\",\n        \"data_clean_1\",\n    ]:\n        extern_data_config_recog[\"extern_data\"][key][\"available_for_inference\"] = True\n\n    # ********** Training setup **********\n\n    name = \"_\".join(filter(None, [\"BLSTM_CTC_dual\", kwargs.get(\"name_suffix\", \"\")]))\n    max_pool_pre = kwargs.get(\"max_pool_pre\", [1, 1, 2])\n    max_pool_post = kwargs.get(\"max_pool_post\", [2])\n\n    train_blstm_net = {}\n\n    if kwargs.get(\"clean_data\", False):\n        from_0 = \"data:data_clean_0\"\n        from_1 = \"data:data_clean_1\"\n    else:\n        from_0 = \"data:data_separated_0\"\n        from_1 = \"data:data_separated_1\"\n\n    l2 = kwargs.get(\"l2\", 5e-06)\n    dropout = kwargs.get(\"dropout\", 0.1)\n\n    train_blstm_net, train_python_code = make_blstm_fullsum_ctc_dual_output_model(\n        num_outputs=num_classes_b,\n        from_0=from_0,\n        from_1=from_1,\n        target_key_0=\"bpe_0\",\n        target_key_1=\"bpe_1\",\n        from_mix=\"data\",\n        specaug_01_args={\n            \"max_time_num\": kwargs.get(\"max_time_num\", 2),\n            \"max_time\": kwargs.get(\"max_time\", 15),\n            \"max_feature_num\": 4,\n            \"max_feature\": 5,\n        },\n        blstm_01_args={\n            \"num_layers\": kwargs.get(\"enc_01_layers\", 4),\n            \"size\": 400,\n            \"max_pool\": max_pool_pre,\n            \"dropout\": dropout,\n            \"l2\": l2,\n        },\n        blstm_mix_args={\n            \"num_layers\": kwargs.get(\"enc_mix_layers\", 4),\n            \"size\": 400,\n            \"max_pool\": max_pool_pre,\n            \"dropout\": dropout,\n            \"l2\": l2,\n        },\n        blstm_01_mix_args={\n            \"num_layers\": kwargs.get(\"enc_01_mix_layers\", 2),\n            \"size\": 400,\n            \"max_pool\": max_pool_post,\n            \"dropout\": dropout,\n            \"l2\": l2,\n        },\n    )\n\n    num_subepochs = kwargs.get(\"num_subepochs\", 150)\n\n    train_config = get_returnn_config(\n        train_blstm_net,\n        target=None,\n        num_inputs=num_inputs,\n        num_outputs=num_classes_b,\n        num_epochs=num_subepochs,\n        extra_python=train_python_code,\n        grad_noise=kwargs.get(\"grad_noise\", 0.0),\n        grad_clip=kwargs.get(\"grad_clip\", 100.0),\n        batch_size=kwargs.get(\"batch_size\", 15000),\n        schedule=kwargs.get(\"schedule\", LearningRateSchedules.Newbob),\n        peak_lr=kwargs.get(\"peak_lr\", 2e-04),\n        learning_rate=kwargs.get(\"learning_rate\", 4e-04),\n        min_learning_rate=1e-06,\n        n_steps_per_epoch=1100,\n        use_chunking=False,\n        python_prolog=[\"from returnn.tf.util.data import Dim\"],\n        extra_config={\n            \"train\": datasets[train_key],\n            \"dev\": datasets[dev_key],\n            **extern_data_config,\n        },\n    )\n\n    train_job = ReturnnTrainingJob(\n        train_config,\n        log_verbosity=5,\n        num_epochs=num_subepochs,\n        save_interval=1,\n        keep_epochs=None,\n        time_rqmt=168,\n        mem_rqmt=8,\n    )\n\n    train_job.set_vis_name(f\"Train {name}\")\n    train_job.add_alias(f\"train_{name}\")\n\n    tk.register_output(f\"train_nn/{name}\", train_job.out_learning_rates)\n\n    # ********** Prior computation **********\n\n    prior_net = copy.deepcopy(train_blstm_net)\n    prior_net[\"output_0\"][\"class\"] = \"linear\"\n    prior_net[\"output_0\"][\"activation\"] = \"softmax\"\n    prior_net[\"output_1\"][\"class\"] = \"linear\"\n    prior_net[\"output_1\"][\"activation\"] = \"softmax\"\n    prior_net[\"output\"] = {\n        \"class\": \"combine\",\n        \"from\": [\"output_0\", \"output_1\"],\n        \"kind\": \"average\",\n    }\n    prior_net.pop(\"output_loss_0\", None)\n    prior_net.pop(\"output_loss_1\", None)\n    prior_net.pop(\"ctc_loss_0\", None)\n    prior_net.pop(\"ctc_loss_1\", None)\n\n    prior_config = copy.deepcopy(train_config)\n    prior_config.config.update(get_network_config(prior_net))\n    prior_config.config.update({\"forward_output_layer\": \"output\"})\n\n    prior_job = ReturnnComputePriorJob(\n        model_checkpoint=train_job.out_checkpoints[num_subepochs],\n        returnn_config=prior_config,\n        log_verbosity=4,\n        mem_rqmt=8,\n    )\n\n    # ********** Recognition **********\n\n    lm_scale = kwargs.get(\"lm_scale\", 1.1)\n    prior_scale = kwargs.get(\"prior_scale\", 0.3)\n\n    recog_blstm_net, recog_python_code = make_blstm_fullsum_ctc_dual_output_recog_model(\n        num_outputs=num_classes_b,\n        from_0=from_0,\n        from_1=from_1,\n        target_key_0=\"bpe_0\",\n        target_key_1=\"bpe_1\",\n        from_mix=\"data\",\n        blstm_01_args={\n            \"num_layers\": kwargs.get(\"enc_01_layers\", 4),\n            \"size\": 400,\n            \"max_pool\": max_pool_pre,\n        },\n        blstm_mix_args={\n            \"num_layers\": kwargs.get(\"enc_mix_layers\", 4),\n            \"size\": 400,\n            \"max_pool\": max_pool_pre,\n        },\n        blstm_01_mix_args={\n            \"num_layers\": kwargs.get(\"enc_01_mix_layers\", 2),\n            \"size\": 400,\n            \"max_pool\": max_pool_post,\n        },\n        lm_path=lm_model,\n        lm_scale=lm_scale,\n        lm_args={\n            \"embedding_args\": {\n                \"size\": 256,\n            },\n            \"lstm_args\": {\n                \"num_layers\": 2,\n                \"size\": 2048,\n            },\n        },\n        prior_path=prior_job.out_prior_txt_file,\n        prior_scale=prior_scale,\n    )\n\n    recog_config = get_returnn_config(\n        recog_blstm_net,\n        target=None,\n        num_inputs=num_inputs,\n        num_outputs=num_classes_b,\n        num_epochs=num_subepochs,\n        use_chunking=False,\n        extra_python=recog_python_code,\n        python_prolog=[\"from returnn.tf.util.data import Dim\"],\n        hash_full_python_code=False,\n        extra_config={\n            \"search_output_layer\": [\"ctc_decode_0\", \"ctc_decode_1\"],\n            **extern_data_config_recog,\n        },\n    )\n\n    for recog_key in [dev_key, test_key]:\n\n        search_job = ReturnnSearchJobV2(\n            search_data=datasets[recog_key],\n            model_checkpoint=train_job.out_checkpoints[num_subepochs],\n            returnn_config=recog_config,\n            output_mode=\"py\",\n            log_verbosity=5,\n            returnn_python_exe=tk.Path(gs.RETURNN_PYTHON_EXE),\n            returnn_root=tk.Path(gs.RETURNN_ROOT),\n        )\n\n        out_path = f\"nn_recog/{name}/{recog_key}_lm-{lm_scale:01.02f}_prior-{prior_scale:01.02f}_ep-{num_subepochs:03d}\"\n        search_job.add_alias(out_path)\n\n        # ********** Scoring **********\n\n        words_job = SearchBPEtoWordsJob(search_job.out_search_file)\n\n        words_job_processed = text.PipelineJob(\n            words_job.out_word_search_results,\n            [\n                'sed \"s/\\/0000_ctc_decode_0/_0\\/0000/\"',\n                'sed \"s/\\/0000_ctc_decode_1/_1\\/0000/\"',\n            ],\n            mini_task=True,\n        )\n\n        if recog_key == dev_key:\n            bliss_corpus = dev_data_inputs[recog_key].corpus_object.corpus_file\n        else:\n            bliss_corpus = test_data_inputs[recog_key].corpus_object.corpus_file\n\n        word2ctm_job = SearchWordsToCTMJob(words_job_processed.out, bliss_corpus)\n        scorer_job = ScliteJob(\n            CorpusToStmJob(bliss_corpus, non_speech_tokens=[\"<NOISE>\"]).out_stm_path,\n            word2ctm_job.out_ctm_file,\n        )\n\n        tk.register_output(f\"{out_path}.wer\", scorer_job.out_report_dir)\n\n        summary_report.add_row(\n            {\n                SummaryKey.NAME.value: name,\n                SummaryKey.CORPUS.value: recog_key,\n                SummaryKey.EPOCH.value: num_subepochs,\n                SummaryKey.PRIOR.value: prior_scale,\n                SummaryKey.LM.value: lm_scale,\n                SummaryKey.WER.value: scorer_job.out_wer,\n                SummaryKey.SUB.value: scorer_job.out_percent_substitution,\n                SummaryKey.DEL.value: scorer_job.out_percent_deletions,\n                SummaryKey.INS.value: scorer_job.out_percent_insertions,\n                SummaryKey.ERR.value: scorer_job.out_num_errors,\n            }\n        )\n\n    return summary_report\n\n\ndef py() -> SummaryReport:\n\n    cleaned_text_lm = run_lm(lm_cleaning=True)\n    lm = run_lm(lm_cleaning=False)\n\n    run_exp_clean_partial = partial(run_exp, cleaned_text_lm, lm_cleaning=True)\n    run_exp_partial = partial(run_exp, lm, lm_cleaning=False)\n\n    dir_handle = os.path.dirname(__file__).split(\"config/\")[1]\n    filename_handle = os.path.splitext(os.path.basename(__file__))[0][len(\"config_\") :]\n    gs.ALIAS_AND_OUTPUT_SUBDIR = f\"{dir_handle}/{filename_handle}/\"\n\n    summary_report = SummaryReport()\n\n    # All encoder types\n    summary_report.merge_report(\n        run_exp_clean_partial(),\n        update_structure=True,\n    )\n\n    for enc01, encmix, enc01mix in [(0, 0, 6), (6, 4, 0)]:\n        summary_report.merge_report(\n            run_exp_clean_partial(\n                name_suffix=f\"enc01-{enc01}_encmix-{encmix}_enc01mix-{enc01mix}\",\n                enc_01_layers=enc01,\n                enc_mix_layers=encmix,\n                enc_01_mix_layers=enc01mix,\n                max_pool_pre=[1, 2, 2],\n                max_pool_post=[1, 2, 2],\n            ),\n        )\n","repo_name":"rwth-i6/i6_experiments","sub_path":"users/berger/configs/sms_wsj/20220615_dfg_multi_speaker/sms_wsj_8kHz/config_05_blstm_ctc_mixed_inputs.py","file_name":"config_05_blstm_ctc_mixed_inputs.py","file_ext":"py","file_size_in_byte":14493,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"86"}
+{"seq_id":"4597674918","text":"from testharness import *\nfrom testharness.syslog import getSyslog\nfrom testharness.utility import check_status_error\nfrom testharness.tlvparser import TLVParser, tagStorage\nfrom binascii import hexlify, unhexlify\n\n# GENERATED HMAC\n#\n# VERSION 1 -----------------------------------------------------------------\n# HOSTID-6: 98A8AAED5A2BA9E228B138274FDF546D-6688D2AB8D9A36E0A50A5BF3B142AFB0\n# HOSTID-7: D1F8827DD9276F9F80F8890D3E607AC0-3CA022BA91B8024356DCDF54AD434F83\n#\n# VERSION 2 -----------------------------------------------------------------\n# HOSTID-6: C464084095AE8D1F16B5760272495565-1D45B4B6083E4A5E41C4837081F460A6\n# HOSTID-7: EDA100E8F35DCE4BD9FDA2EF7456A1E4-03E09FEB2A95FB3D97F88784B548BF4D\n#\ndef GenerateHMAC():\n    req_unsolicited = conn.connect()\n    if req_unsolicited:\n        status, buf, uns = conn.receive()\n        check_status_error(status)\n\n    #pan = b'\\x41\\x11\\x11\\x11\\x11\\x11\\x11\\x11'\n    pan = '4111111111111111'\n    c_tag.store((0xDF, 0xEC, 0x0E), pan)  # message for MAC\n\n    # expected VSS6 HMAC for TC test secrets: 98A8AAED5A2BA9E228B138274FDF546D6688D2AB8D9A36E0A50A5BF3B142AFB0\n    # 98A8AAED5A2BA9E228B138274FDF546D-6688D2AB8D9A36E0A50A5BF3B142AFB0\n    c_tag = tagStorage()\n    c_tag.store((0xDF, 0xEC, 0x23), 0x06)  # host ID\n    conn.send([0xC4, 0x22, 0x00, 0x00] , c_tag.getTemplate(0xE0))\n    log.log(\"Generate HMAC sent\")\n\n    status, buf, uns = conn.receive()\n    log.log(\"Generate HMAC response received\")\n    check_status_error(status)\n    \n    tlv = TLVParser(buf)\n    tag_output_data = (0xDF, 0xEC, 0x7B)\n    if (tlv.tagCount(tag_output_data) == 1):\n        hmac = tlv.getTag(tag_output_data)[0]\n        log.log(\"Generated HMAC HOSTID-06:\", hexlify(hmac).decode('utf-8'))\n\n        c_tag = tagStorage()\n        c_tag.store((0xDF, 0xEC, 0x0E), hmac)  # message for MAC\n        \n        # expected VSS7 HMAC for TC test secrets: D1F8827DD9276F9F80F8890D3E607AC03CA022BA91B8024356DCDF54AD434F83\n        # D1F8827DD9276F9F80F8890D3E607AC0-3CA022BA91B8024356DCDF54AD434F83\n        c_tag.store((0xDF, 0xEC, 0x23), 0x07)  # host ID\n        conn.send([0xC4, 0x22, 0x00, 0x00] , c_tag.getTemplate(0xE0))\n        log.log(\"Generate HMAC sent\")\n\n        status, buf, uns = conn.receive()\n        log.log(\"Generate HMAC response received\")\n        check_status_error(status)\n\n        tlv = TLVParser(buf)\n        tag_output_data = (0xDF, 0xEC, 0x7B)\n        if (tlv.tagCount(tag_output_data) == 1):\n            hmac = tlv.getTag(tag_output_data)[0]\n            log.log(\"Generated HMAC HOSTID-07:\", hexlify(hmac).decode('utf-8'))\n\nif __name__ == '__main__':\n    log = getSyslog()\n    conn = connection.Connection();\n    utility.register_testharness_script(GenerateHMAC)\n    utility.do_testharness()\n","repo_name":"web-projects/KEY_LOADER","sub_path":"Source/hmachasher/HMACHasher/Assets/TC_4111_generate_hmac_ASCII.py","file_name":"TC_4111_generate_hmac_ASCII.py","file_ext":"py","file_size_in_byte":2730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35045887195","text":"import os\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\nimport numpy as np\nimport torch\nfrom utils.kinematics import *\nfrom utils.mano_pth import *\nfrom render import *\nimport config\nfrom IKNetModel import IKModel\nfrom IKNet import iknet\nfrom torch.autograd import Variable\nfrom tqdm import tqdm\n\n_, _, reflen = getRef(side='left')\n\ndef get_frei_joints(index):\n    from dataUtils import FreiHandUtil\n    fre = FreiHandUtil()\n    _, joints = fre.get_parm(index)\n    side = 'left'\n    return joints, side\n\ndef get_generate_joints(index):\n    dataset_path = \"/home/liwensh2/dataset/GANeratedHands_Release/data/withObject/0001\"\n    image_num = \"%04d\" % (index)\n    joints_path = os.path.join(dataset_path, image_num + \"_joint_pos.txt\")\n    with open(joints_path, 'r') as file:\n        lines = file.readlines()\n        joints = []\n        for line in lines:\n            tmp = line.strip('\\n')\n            nums = tmp.split(',')\n            for i in range(len(nums)):\n                joints.append(float(nums[i]))\n            joints = np.array(joints).reshape(21, 3)\n\n    delta, length = xyz_to_delta(joints, MPIIHandJoints)\n    delta[1:] = delta[1:] / length[1:] * reflen[1:]\n\n    joints_scale = np.zeros_like(joints)\n    for i in range(1, 21):\n        p = MPIIHandJoints.parents[i]\n        joints_scale[i] = joints_scale[p] + delta[i]\n    _, length_scale = xyz_to_delta(joints_scale, MPIIHandJoints)\n    print(np.allclose(length_scale, reflen))\n    side = 'left'\n    return joints, joints_scale, side\n\ndef getSYNHands(index):\n    path = '/home/liwensh2/dataset/SynthHands/SynthHands_Release/female_noobject/seq01/cam01/01/'\n    img_num = \"%08d\" % (index)\n    joints_path = os.path.join(path, img_num + \"_joint_pos.txt\")\n    with open(joints_path, 'r') as file:\n        lines = file.readlines()\n        joints = []\n        for line in lines:\n            tmp = line.strip('\\n')\n            nums = tmp.split(',')\n            for i in range(len(nums)):\n                joints.append(float(nums[i]))\n        joints = np.asarray(joints).reshape((21, 3))\n    side = 'left'\n\n    joints = joints - joints[0]\n    delta, length = xyz_to_delta(joints, MPIIHandJoints)\n    delta[1:] = delta[1:] / length[1:] * reflen[1:]\n    joints_scale = np.zeros_like(joints)\n    for i in range(1,21):\n        p = MPIIHandJoints.parents[i]\n        joints_scale[i] = joints_scale[p] + delta[i]\n    _, length_scale = xyz_to_delta(joints_scale, MPIIHandJoints)\n    print(np.allclose(length_scale, reflen))\n    return joints, joints_scale, side\n\n\ndef getinput(joints, side):\n    joints = joints - joints[0]\n    delta, length = xyz_to_delta(joints, MPIIHandJoints)\n    return delta\n\n\ndef test(index):\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n    # joints,joints_scale, side = getSYNHands(index)\n    joints, joints_scale, side = get_generate_joints(index)\n    # joints, side = get_frei_joints(3)\n    concate_array = getinput(joints_scale, side)\n    input_array = torch.from_numpy(concate_array).unsqueeze(dim=0).float().to(device)\n    model = iknet()\n\n    # checkpoints = torch.load('/home/liwensh2/code/Hands3D/train/IKNet/checkpoints/res10/IKNet_left_2_500.pth')\n    # checkpoints = torch.load('/home/liwensh2/code/Hands3D/train/IKNet/checkpoints/res11/IKNet_left_480.pth')\n    # checkpoints = torch.load('/home/liwensh2/code/Hands3D/train/IKNet/checkpoints/res12/IKNet_left_500.pth')\n    # checkpoints = torch.load('/home/liwensh2/code/Hands3D/train/IKNet/checkpoints/res14/IKNet_left_500.pth')\n    checkpoints = torch.load('/home/liwensh2/code/Hands3D/train/IKNet/checkpoints/res15/IKNet_left_140.pth')\n    model.load_state_dict(checkpoints['model'])\n    # model.load_state_dict(torch.load('/home/liwensh2/code/Hands3D/train/IKNet/checkpoints/IKNet_left_3_100.pth'))\n    model = model.to(device)\n    model.eval()\n    output = model(input_array).type(torch.float32)\n    # print(output)\n\n    fig = plt.figure()\n    ax1 = plt.subplot(131, projection='3d')\n    vis_3d_joints(joints, ax=ax1)\n\n    ax2 = plt.subplot(132)\n    pose = torch.squeeze(output, dim=0).cpu().detach().float().numpy()\n    OpenDRShow(pose, np.zeros(10), side=side, ax=ax2)\n\n    pred_verts, pred_joints, _ = MANO(output,\n                                      torch.zeros(1,10).type_as(output),\n                                      side=side,\n                                      ncomps=45, use_pca=True, device=device)\n    pred_joints = pred_joints / 1000\n    pred_joints = pred_joints[0].cpu().detach().float().numpy()\n    pred_joints = pred_joints - pred_joints[0]\n\n    ax3 = plt.subplot(133, projection='3d')\n    vis_3d_joints(pred_joints, ax3)\n    plt.show()\n    error = np.sqrt((pred_joints-joints_scale)**2).mean()\n    print(error)\n    # ax3 = plt.subplot(122, projection='3d')\n    # vis_3d_joints(joints_scale, ax3)\n    # plt.show()\n\n\nif __name__ == '__main__':\n    test(4)","repo_name":"liwenssss/Hands3D","sub_path":"test/IKTest/joints_test.py","file_name":"joints_test.py","file_ext":"py","file_size_in_byte":4843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74328642524","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution:\n    def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\n        \n        currentnode = root\n        \n        while currentnode:\n            \n            # if both values are greater than root then check the right half\n            if p.val > currentnode.val and q.val > currentnode.val:\n                currentnode = currentnode.right\n                \n            # if both values are smaller than root then check the left half\n            elif p.val < currentnode.val and q.val < currentnode.val:\n                currentnode = currentnode.left\n        \n            \n            # if both of the above statements doesn't execute that means that we've encountered a split so we return root\n            else:\n                return currentnode\n\n        ","repo_name":"adityachache/algorithms-and-leetcode","sub_path":"leetcode/binary trees/easy/235-lowest-common-ancestor-of-BST.py","file_name":"235-lowest-common-ancestor-of-BST.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"72601679003","text":"from collections import deque\n\nDX_DY = [(-1,0),(0,1),(1,0),(0,-1)]\nNORTH, EAST, SOUTH, WEST = 0,1,2,3 # +1은 시계방향,-1은 반시계방향\nINVERSE_DIRECTION = {NORTH: SOUTH, EAST: WEST, SOUTH: NORTH, WEST: EAST}\n\n\nclass Dice:\n    def __init__(self):\n        # x,y 좌표(인덱스 0부터!)\n        self.x = 0\n        self.y = 0\n\n        # 해당방향에 적힌 주사위 번호\n        self.top = 1 # 위에서 보이는 윗면\n        self.bottom = 6\n        self.left = 4\n        self.right = 3\n        self.up = 2\n        self.down = 5 # !6으로 잘못썼었다.\n\n        # 방향\n        self.direction = EAST # 처음은 동쪽\n\n        # 점수\n        self.score = 0\n    \n    def move(self):\n        # 이동방향 막혀있으면 반대로 변경\n        dx,dy = DX_DY[self.direction]\n        move_x,move_y = self.x+dx, self.y+dy\n        if not is_valid(move_x,move_y):\n            self.direction = INVERSE_DIRECTION[self.direction]\n        \n        # 주사위 좌표 변경\n        dx,dy = DX_DY[self.direction]\n        self.x, self.y = self.x+dx, self.y+dy\n\n        # 주사위 칸 번호 변경\n        if self.direction == NORTH:\n            self.top, self.up, self.bottom, self.down =\\\n                self.down, self.top, self.up, self.bottom\n        elif self.direction == SOUTH:\n            self.top, self.up, self.bottom, self.down =\\\n                self.up, self.bottom, self.down, self.top\n        elif self.direction == EAST:\n            self.top, self.right, self.bottom, self.left =\\\n                self.left, self.top, self.right, self.bottom\n        elif self.direction == WEST:\n            self.top, self.right, self.bottom, self.left =\\\n                self.right, self.bottom, self.left, self.top\n\ndef is_valid(x,y):\n    \"\"\"범위안에 있는지만(인덱스 0부터!)\"\"\"\n    if 0<=x<N and 0<=y<M:\n        return True\n    return False\n\ndef get_connected_cnt(x,y):\n    \"\"\"(x,y)에서 같은수로 연결된 수 반환(BFS), O(N*M)\"\"\"\n    visited = [[False]*M for _ in range(N)]\n    cnt = 1 # (x,y)는 포함\n    visited[x][y] = True\n    queue = deque([(x,y)])\n\n    while queue:\n        x,y = queue.pop()\n        for dx, dy in DX_DY:\n            check_x = x+dx\n            check_y = y+dy\n            if is_valid(check_x,check_y) and not visited[check_x][check_y]\\\n                and graph[x][y] == graph[check_x][check_y]:\n                cnt += 1\n                visited[check_x][check_y] = True\n                queue.appendleft((check_x,check_y))\n\n    return cnt\n\n\nN,M,K = map(int, input().split())\ngraph = [list(map(int, input().split())) for _ in range(N)]\ndice = Dice()\n\n# 1. 점수판 구하기(연결된 같은칸수)\nscore = [[0]*M for _ in range(N)]\nfor i in range(N):\n    for j in range(M):\n        connected_cnt = get_connected_cnt(i,j)\n        score[i][j] += connected_cnt*graph[i][j]\n\n# 2. 주사위 이동\nfor _ in range(K):\n    # 주사위 이동(막히면 반대방향으로)\n    dice.move()\n\n    # 점수 획득\n    dice.score += score[dice.x][dice.y]\n\n    # 이동방향 결정\n    A = dice.bottom\n    B = graph[dice.x][dice.y]\n    if A > B: # 시계방향\n        dice.direction += 1\n    elif A < B:\n        dice.direction -= 1\n    dice.direction %= 4\n\n\nprint(dice.score)","repo_name":"HoYoungChun/Problem_Solving","sub_path":"삼성 SW 역량 테스트 기출/주사위 굴리기 2.py","file_name":"주사위 굴리기 2.py","file_ext":"py","file_size_in_byte":3229,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31101505234","text":"import os\nimport sys\nimport time\nimport datetime\nimport random\nimport math\nimport json\nimport yaml\nimport cv2\nimport numpy as np\nimport scipy.stats as stats\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.metrics import precision_score, recall_score, f1_score\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import confusion_matrix\nimport socket\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom collections import defaultdict, deque\nfrom operator import itemgetter\nfrom typing import Iterator, List, Optional, Union\nfrom typing import Any, BinaryIO, List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.backends.cudnn as cudnn\nimport torch.distributed as dist\nimport torchvision\nfrom torchvision import transforms\nimport torch.multiprocessing as mp\nfrom torch.utils.data.distributed import DistributedSampler\nfrom torch.utils.data.sampler import Sampler\nfrom torch.utils.data import Dataset\n\n\n\ndef unpickle(file):\n    import pickle\n    with open(file, 'rb') as fo:\n        dict = pickle.load(fo, encoding='bytes')\n    return dict\n\ndef get_open_port():\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    # Binding to port 0 will cause the OS to find an available port for us\n    sock.bind((\"\", 0))\n    port = sock.getsockname()[1]\n    sock.close()\n    # NOTE: there is still a chance the port could be taken by other processes.\n    return port\n\ndef set_sys_params(random_seed):\n    # Set random seeds for reproducibility TODO: to figure out whether it is necessary to have different random seeds\n    # on different ranks (DeiT uses different seeds)\n    seed = random_seed  #+ get_rank()\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n    np.random.seed(seed)\n    random.seed(seed)\n\n    cudnn.benchmark = True  # benchmark mode is good whenever your input sizes for your network do not vary.\n\ndef is_main_process():\n    return get_rank() == 0\n\ndef get_rank():\n    if not ddp():\n        return 0\n    return dist.get_rank()\n\ndef ddp():\n    world_size = dist.get_world_size()\n    if not dist.is_available() or not dist.is_initialized() or world_size < 2:\n        return False\n    return True\n\ndef is_dist_avail_and_initialized():\n    if not dist.is_available():\n        return False\n    if not dist.is_initialized():\n        return False\n    return True\n\ndef run_distributed_workers(rank, main_func, world_size, dist_url, args):\n    # Initialize the process group\n    dist.init_process_group(backend=\"NCCL\", init_method=dist_url, world_size=world_size, rank=rank)\n\n    # Synchronize is needed here to prevent a possible timeout after calling init_process_group\n    # See: https://github.com/facebookresearch/maskrcnn-benchmark/issues/172\n    if ddp():\n        dist.barrier()\n\n    torch.cuda.set_device(rank)\n    # print('| distributed init (rank {}): {}'.format(\n    #     rank, dist_url), flush=True)\n\n    main_func(rank, args)\n\ndef launch(main_func, args=()):\n    # Set gpu params\n    os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n    os.environ[\"CUDA_VISIBLE_DEVICES\"] = args['system_params']['gpu_ids']\n\n    world_size = args['system_params']['num_gpus']\n    port = get_open_port()\n    dist_url = f\"tcp://127.0.0.1:{port}\"\n    os.environ[\n        \"TORCH_DISTRIBUTED_DEBUG\"\n    ] = \"INFO\"\n    os.environ['CUDA_LAUNCH_BLOCKING'] = '1'\n\n    mp.spawn(\n        run_distributed_workers,\n        nprocs=world_size,\n        args=(main_func, world_size, dist_url, args),\n        daemon=False,\n    )\n\ndef has_batchnorms(model):\n    bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm)\n    for name, module in model.named_modules():\n        if isinstance(module, bn_types):\n            return True\n    return False\n\ndef synchronize():\n    if not ddp():\n        return\n    dist.barrier()\n\ndef accuracy(output, target, topk=(1,)):\n    \"\"\"Computes the accuracy over the k top predictions for the specified values of k\"\"\"\n    maxk = max(topk)\n    batch_size = target.size(0)\n    _, pred = output.topk(maxk, 1, True, True)\n    pred = pred.t()\n    correct = pred.eq(target.reshape(1, -1).expand_as(pred))\n    return [correct[:k].reshape(-1).float().sum(0) * 100. / batch_size for k in topk]\n\nclass MetricLogger(object):\n    def __init__(self, delimiter=\"\\t\"):\n        self.meters = defaultdict(SmoothedValue)\n        self.delimiter = delimiter\n\n    def update(self, **kwargs):\n        for k, v in kwargs.items():\n            if isinstance(v, torch.Tensor):\n                v = v.item()\n            assert isinstance(v, (float, int))\n            self.meters[k].update(v)\n\n    def __getattr__(self, attr):\n        if attr in self.meters:\n            return self.meters[attr]\n        if attr in self.__dict__:\n            return self.__dict__[attr]\n        raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n            type(self).__name__, attr))\n\n    def __str__(self):\n        loss_str = []\n        for name, meter in self.meters.items():\n            loss_str.append(\n                \"{}: {}\".format(name, str(meter))\n            )\n        return self.delimiter.join(loss_str)\n\n    def synchronize_between_processes(self):\n        for meter in self.meters.values():\n            meter.synchronize_between_processes()\n\n    def add_meter(self, name, meter):\n        self.meters[name] = meter\n\n    def log_every(self, iterable, print_freq, header=None):\n        i = 0\n        if not header:\n            header = ''\n        start_time = time.time()\n        end = time.time()\n        iter_time = SmoothedValue(fmt='{avg:.6f}')\n        data_time = SmoothedValue(fmt='{avg:.6f}')\n        space_fmt = ':' + str(len(str(len(iterable)))) + 'd'\n        if torch.cuda.is_available():\n            log_msg = self.delimiter.join([\n                header,\n                '[{0' + space_fmt + '}/{1}]',\n                'eta: {eta}',\n                '{meters}',\n                'time: {time}',\n                'data: {data}',\n                'max mem: {memory:.0f}'\n            ])\n        else:\n            log_msg = self.delimiter.join([\n                header,\n                '[{0' + space_fmt + '}/{1}]',\n                'eta: {eta}',\n                '{meters}',\n                'time: {time}',\n                'data: {data}'\n            ])\n        MB = 1024.0 * 1024.0\n        for obj in iterable:\n            data_time.update(time.time() - end)\n            yield obj\n            iter_time.update(time.time() - end)\n            if i % print_freq == 0 or i == len(iterable) - 1:\n                eta_seconds = iter_time.global_avg * (len(iterable) - i)\n                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n                if torch.cuda.is_available():\n                    print(log_msg.format(\n                        i, len(iterable), eta=eta_string,\n                        meters=str(self),\n                        time=str(iter_time), data=str(data_time),\n                        memory=torch.cuda.max_memory_allocated() / MB))\n                else:\n                    print(log_msg.format(\n                        i, len(iterable), eta=eta_string,\n                        meters=str(self),\n                        time=str(iter_time), data=str(data_time)))\n            i += 1\n            end = time.time()\n        total_time = time.time() - start_time\n        total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n        print('{} Total time: {} ({:.6f} s / it)'.format(\n            header, total_time_str, total_time / len(iterable)))\n\n\nclass SmoothedValue(object):\n    \"\"\"Track a series of values and provide access to smoothed values over a\n    window or the global series average.\n    \"\"\"\n\n    def __init__(self, window_size=20, fmt=None):\n        if fmt is None:\n            fmt = \"{median:.6f} ({global_avg:.6f})\"\n        self.deque = deque(maxlen=window_size)\n        self.total = 0.0\n        self.count = 0\n        self.fmt = fmt\n\n    def update(self, value, n=1):\n        self.deque.append(value)\n        self.count += n\n        self.total += value * n\n\n    def synchronize_between_processes(self):\n        \"\"\"\n        Warning: does not synchronize the deque!\n        \"\"\"\n        if not is_dist_avail_and_initialized():\n            return\n        t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')\n        dist.barrier()\n        dist.all_reduce(t)\n        t = t.tolist()\n        self.count = int(t[0])\n        self.total = t[1]\n\n    @property\n    def median(self):\n        d = torch.tensor(list(self.deque))\n        return d.median().item()\n\n    @property\n    def avg(self):\n        d = torch.tensor(list(self.deque), dtype=torch.float32)\n        return d.mean().item()\n\n    @property\n    def global_avg(self):\n        return self.total / self.count\n\n    @property\n    def max(self):\n        return max(self.deque)\n\n    @property\n    def value(self):\n        return self.deque[-1]\n\n    def __str__(self):\n        return self.fmt.format(\n            median=self.median,\n            avg=self.avg,\n            global_avg=self.global_avg,\n            max=self.max,\n            value=self.value)\n\ndef constant_scheduler(base_value, epochs, niter_per_ep, warmup_epochs=0, start_warmup_value=0, step_epoch=0):\n    warmup_schedule = np.array([])\n    warmup_iters = warmup_epochs * niter_per_ep\n    if warmup_epochs > 0:\n        warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters)\n\n    if step_epoch < epochs:\n        iters1 = step_epoch * niter_per_ep - warmup_iters\n        iters2 = epochs * niter_per_ep - step_epoch * niter_per_ep\n        schedule = [base_value]*iters1 + [base_value/10]*iters2\n    else:\n        iters1 = epochs * niter_per_ep - warmup_iters\n        schedule = [base_value]*iters1\n\n    if warmup_epochs > 0:\n        schedule = np.concatenate((warmup_schedule, schedule))\n\n    # print(schedule)\n    assert len(schedule) == epochs * niter_per_ep\n    return schedule\n\ndef restart_from_checkpoint(ckp_path, run_variables=None, **kwargs):\n    \"\"\"\n    Re-start from checkpoint\n    \"\"\"\n    if not os.path.isfile(ckp_path):\n        return\n    print(\"Found checkpoint at {}\".format(ckp_path))\n\n    # open checkpoint file\n    checkpoint = torch.load(ckp_path, map_location=\"cpu\")\n\n    # key is what to look for in the checkpoint file\n    # value is the object to load\n    # example: {'state_dict': model}\n    for key, value in kwargs.items():\n        if key in checkpoint and value is not None:\n            try:\n                msg = value.load_state_dict(checkpoint[key], strict=False)\n                if is_main_process():\n                    print(\"=> loaded '{}' from checkpoint '{}' with msg {}\".format(key, ckp_path, msg))\n            except TypeError:\n                try:\n                    msg = value.load_state_dict(checkpoint[key])\n                    if is_main_process():\n                        print(\"=> loaded '{}' from checkpoint: '{}'\".format(key, ckp_path))\n                except ValueError:\n                    if is_main_process():\n                        print(\"=> failed to load '{}' from checkpoint: '{}'\".format(key, ckp_path))\n        else:\n            if is_main_process():\n                print(\"=> key '{}' not found in checkpoint: '{}'\".format(key, ckp_path))\n\n    # re load variable important for the run\n    if run_variables is not None:\n        for var_name in run_variables:\n            if var_name in checkpoint:\n                run_variables[var_name] = checkpoint[var_name]\n\ndef layer_decay_get_params_groups(model, weight_decay, skip_list=(), get_num_layer=None, get_layer_scale=None):\n    encoder_params, encoder_names = get_params_per_model(model['model'], weight_decay, skip_list, get_num_layer, get_layer_scale, if_encoder=True)\n    if is_main_process():\n        output_path = 'yaml/params/parameter_group_printed_all.yaml'\n        with open(output_path, 'w') as outfile:\n            yaml.dump({**encoder_names,}, outfile, sort_keys=False, default_flow_style=False)\n    return list({**encoder_params, }.values())\n\ndef get_params_per_model(model, weight_decay, skip_list, get_num_layer, get_layer_scale, if_encoder=True):\n    parameter_group_names = {}\n    parameter_group_vars = {}\n    for name, param in model.named_parameters():\n        if not param.requires_grad:\n            continue\n        if len(param.shape) == 1 or name.endswith(\".bias\") or name in skip_list:\n            group_name = \"no_decay\"\n            this_weight_decay = 0.\n        else:\n            group_name = \"decay\"\n            this_weight_decay = weight_decay\n        if if_encoder:\n            if get_num_layer is not None:\n                layer_id = get_num_layer(name)\n                group_name = \"layer_%d_%s\" % (layer_id, group_name)\n            else:\n                layer_id = None\n        else:\n            group_name = 'classifier_'+group_name\n\n        if group_name not in parameter_group_names:\n            if if_encoder:\n                if get_layer_scale is not None:\n                    scale = get_layer_scale(layer_id)\n                else:\n                    scale = 1.\n            else:\n                scale = 1.\n\n            parameter_group_names[group_name] = {\n                \"weight_decay\": this_weight_decay,\n                \"params\": [],\n                \"lr_scale\": scale\n            }\n            parameter_group_vars[group_name] = {\n                \"weight_decay\": this_weight_decay,\n                \"params\": [],\n                \"lr_scale\": scale\n            }\n\n        parameter_group_vars[group_name][\"params\"].append(param)\n        parameter_group_names[group_name][\"params\"].append(name)\n    return parameter_group_vars, parameter_group_names","repo_name":"nikisamakovlis/eq2425_project3","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":13600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"11749330438","text":"\n\ndef llenarjarra3(jarra):\n    añadido=\"litro\"\n    for i in range (0,3-len(jarra)):\n        jarra.append(añadido)\n\ndef llenarjarra4(jarra):\n    añadido=\"litro\"\n    for i in range (0,4-len(jarra)):\n        jarra.append(añadido)\n\ndef traspasar(jarra1,jarra2,longitud2):\n    añadido=\"litro\"\n    for i in range(0,longitud2-len(jarra2)):\n        if(len(jarra1)!=0):\n            jarra1.pop(0)\n            jarra2.append(añadido)\n    return jarra1, jarra2\ndef vaciar(jarra):\n    jarra.clear()\ndef llenar2galones():\n    jarra3=[]\n    jarra4=[]\n    llenarjarra3(jarra3)\n    jarra3,jarra4=traspasar(jarra3,jarra4,4)\n    llenarjarra3(jarra3)\n    jarra3,jarra4=traspasar(jarra3,jarra4,4)\n    vaciar(jarra4)\n    jarra3,jarra4=traspasar(jarra3,jarra4,4)\n    return jarra3,jarra4\nif __name__ == \"__main__\":\n    jarra,jarra2=llenar2galones()\n    # llenarjarra3(jarra)\n    # jarra2=[\"litro\",\"litro\"]\n    # print(jarra)\n    # jarra,jarra2=traspasar(jarra,jarra2,4)\n    print(\"jarra3=\",jarra)\n    print(\"jarra4=\",jarra2)","repo_name":"Albertojserr/Ejercicio-Recursividad","sub_path":"Ejercicio 9.py","file_name":"Ejercicio 9.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32889736581","text":"from django.shortcuts import render\nimport requests\nfrom google_geocoding.settings import API_KEY\nfrom django.http import HttpResponse\nfrom urllib.parse import urlencode\nimport json\nfrom django.http import Http404\nfrom .forms import GeocodeForm, ReverseGeocodeForm, CalculateDistanceForm\nfrom geopy.distance import geodesic\n\ndef geocode_form(request):\n    return return_form(GeocodeForm,request)\n\ndef reverse_geocode_form(request):\n    return return_form(ReverseGeocodeForm, request)\n\ndef calculate_distance_form(request):\n    return return_form(CalculateDistanceForm, request)\n\ndef return_form(return_form,request):\n\n    if request.method == 'GET':\n        form = return_form(request.GET)\n        if form.is_valid():\n            pass  # does nothing, just trigger the validation\n    else:\n        form = return_form()\n    return render(request, 'geocode_form.html', {'form': form})\n\ndef geocode_result(request):\n\n    if 'address' in request.GET:\n        input = urlencode({'address': request.GET['address']})\n        results = get_geocode(input)\n        message = json.dumps(results)\n\n    if ['longitude', 'latitude'] == list(request.GET):\n        latitude = request.GET['latitude']\n        longitude = request.GET['longitude']\n        input = urlencode({'latlng': '{},{}'.format(latitude,longitude)})\n        results = get_geocode(input)\n        message = json.dumps(results)\n\n    if ['latitude_1', 'longitude_1', 'latitude_2', 'longitude_2'] == list(request.GET):\n        latitude_1 = request.GET['latitude_1']\n        longitude_1 = request.GET['longitude_1']\n        latitude_2 = request.GET['latitude_2']\n        longitude_2 = request.GET['longitude_2']\n        message = get_distance(latitude_1, longitude_1, latitude_2, longitude_2)\n\n    return HttpResponse(message)\n\ndef get_geocode(url_string):\n\n    response = requests.get('https://maps.googleapis.com/maps/api/geocode/json?{}&key={}'.format(url_string, API_KEY))\n    try :\n        return response.json().get('results')[0]\n    except IndexError:\n        print(\"Error with getting geocode\")\n        raise Http404\n\n\ndef get_distance(lat_1, lng_1, lat_2, lng_2):\n\n    location_1 = (lat_1,lng_1)\n    location_2 = (lat_2, lng_2)\n    return \"Location 1 and Location 2 are {} miles away from each other\".format(geodesic(location_1, location_2).miles)","repo_name":"mikeygh/google_geocoding","sub_path":"google_geocoding/geocoding/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"38349570349","text":"from django.shortcuts import render\nimport operator\n\ndef home(request):\n    return render(request, 'home.html')\n\n\ndef count(request):\n    fulltext = request.GET['fulltext']\n    wordlist = fulltext.split()\n    wordictionary = {}\n    for word in wordlist:\n        if word in wordictionary:\n            # increase\n            wordictionary[word] += 1\n        else:\n            wordictionary[word] = 1\n    sorteWord = sorted(wordictionary.items(), key=operator.itemgetter(1), reverse=True)\n    return render(request, 'count.html',\n                  {\n                      'fulltext': fulltext,\n                      'count': len(wordlist),\n                      'sorteWord': sorteWord,\n                  })\n\n\ndef about(request):\n    return render(request, 'about.html')","repo_name":"rizama/django-wordcount","sub_path":"wordcount/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"24163965640","text":"#!/usr/bin/env python\n\"\"\"\nReducer aggregates word counts by class and emits frequencies.\n\nINPUT:\n    partitionKey \\t word \\t class0_partialCount,class1_partialCount  \nOUTPUT:\n    word \\t {spamCount, hamCount, hamCountSmooth, spamCountSmooth}\n\n\"\"\"\nimport sys                                                  \nimport numpy as np  \n\n#################### YOUR CODE HERE ###################\n\nimport sys\n\n# initiatlize trackers\ncur_word = None\ncur_count = 0\nhamCount, spamCount, hamWordCount, spamWordCount, condHam, condSpam = 0,0,0,0,0,0\nhamChina, spamChina = 0,0\nUniqueWords = 0\n\nfor line in sys.stdin:\n    p_key, word, ham_cts, spam_cts = line.split()\n\n    # get number of total words in ham / spam\n    if word == '!ClassWords':\n        hamWordCount = int(ham_cts)\n        spamWordCount = int(spam_cts)\n    \n    # count first record appropriately\n    elif cur_word == None:\n        cur_count += 1\n        # tally word class counts\n        hamCount += int(ham_cts)\n        spamCount += int(spam_cts)     \n        cur_word = word\n    \n    # tally repeated word\n    elif word == cur_word:\n        cur_count += 1\n        # tally word class counts\n        hamCount += int(ham_cts)\n        spamCount += int(spam_cts)  \n    \n    else:\n        \n        if cur_word == 'ClassPriors':\n            # prepare Class Priors\n            condHam = (hamCount) / (hamCount + spamCount)\n            condSpam = (spamCount) / (hamCount + spamCount)\n            UniqueWords -= 1\n        else:\n            # add to count\n            condHam = (hamCount+1) \n            condSpam = (spamCount+1)  \n        \n        # print output           \n        print(f\"{cur_word}\\t{hamCount},{spamCount},{condHam},{condSpam}\")   \n        UniqueWords += 1\n        \n        # clear trackers\n        hamCount, spamCount, condHam, condSpam, cur_count = 0,0,0,0,0\n        checkHamWords, checkSpamWords = 0,0\n        \n        cur_count += 1\n        hamCount += int(ham_cts)\n        spamCount += int(spam_cts)  \n        cur_word = word\n\ncondHam = (hamCount+1) \ncondSpam = (spamCount+1)  \n# last record print\nprint(f\"{cur_word}\\t{hamCount},{spamCount},{condHam},{condSpam}\")\nUniqueWords += 1\n# print auxiliary data\nprint(f\"{'!UniqueWords'}\\t{UniqueWords},{0},{0},{0}\")   \nprint(f\"{'!ClassWords'}\\t{hamWordCount},{spamWordCount},{0},{0}\")   \n#################### (END) YOUR CODE ###################","repo_name":"Walekova/RecentWork","sub_path":"Main_HadoopStreaming/NaiveBayes/train_reducer_smooth.py","file_name":"train_reducer_smooth.py","file_ext":"py","file_size_in_byte":2350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"9842717677","text":"from hashlib import md5\n\nwith open(\"day_4.txt\") as f:\n    key = f.read()\n\n\ndef decrypt_md5(encoded):\n    return md5(encoded)             # decrypt hash\n\n\ndef to_hexadecimal(md5_hash):\n    return md5_hash.hexdigest()     # to hexadecimal format\n\n\ndef leading_zeros(hexadecimal, number_of_zeros):\n    if hexadecimal[:number_of_zeros] == number_of_zeros * '0':     # leading zeros required\n        return hexadecimal\n\n\ndef find_hash(key, number_of_zeros):\n    '''We are looking for a md5 hash, which in hexadecimal format is starting with specified number of leading 0.\n    Function returns a number, which must be added to given key to form above specified hash'''\n    number = 0\n    while True:\n        test_key = key + str(number)             # extending key for number\n        encoded = test_key.encode()              # encoding before hashing\n        md5_hash = decrypt_md5(encoded)          # decrypt hash\n        hexadecimal = to_hexadecimal(md5_hash)   # to hexadecimal format\n        if leading_zeros(hexadecimal, number_of_zeros):\n            return number\n        else:\n            number += 1\n\n\n# part 1: 5 leading zeros required\nprint(find_hash(key, 5))\n\n# part 2: 6 leading zeros required\nprint(find_hash(key, 6))\n","repo_name":"NelliaS/advent_of_code","sub_path":"advent_of_code_2015/day_4.py","file_name":"day_4.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35862538924","text":"import sys\nimport os\nimport random\nimport math\nimport time\nx = 0\nif os.path.exists(\"char.txt\"):\n    print('char.txt exists, please remove the file after copying the info to another file')\n    sys.exit(0)\nelse:\n    f = open('char.txt',  'x')\ntypelist = ['Strength', 'Dexterity', 'Constitution', 'Intelligence', 'Wisdom', 'Charisma']\ndef traits():\n    z = input('How many traits do you have? ')\n    z = int(z)\n    while int(z) > 0:\n        trait = input('Type in a trait of yours. ')\n        effect = input('What does this trait do? ')\n        \n        desc = input('Write a description of your trait. ')\n        f.write(f'''\n{trait}:\n{effect}\n{desc}        \n        ''')\n        z -= 1\n    \ndef weapons():\n    t = input('How many weapons do you have? ')\n    t = int(t)\n    while int(t) > 0:\n        weapon = input('Type in a weapon of yours. ')\n        damage = input('How much damage does it give (ex. 1d12, 18)? ')\n        f.write(f'''\n{weapon}:\n{damage}\n        \n        ''')\n        t -= 1\ndef skills():\n    a = input('How many skills do you have? ')\n    a = int(a)\n    while int(a) > 0:\n        skill = input('Type in a skill of yours. ')\n        f.write(f'''\n{skill}\n        \n        ''')\n        a -= 1  \n    c = input('How many feats do you have? ')\n    c = int(c)\n    while int(c) > 0:\n        feat = input('Type in a feat of yours. ')\n        bonus = input('What bonuses do you have for this feat? ')\n        f.write(f'''\n{feat}: {bonus}\n        \n        ''')\n        c -= 1 \ndef items():\n    b = input('How many items do you have? ')\n    b = int(b)\n    while int(b) > 0:\n        item = input('Type in a item of yours. ')\n        f.write(f'''\n{item} x |    |\n        \n        ''')\n        b -= 1  \ndef bkg():\n    charactername = input('Character Name? ')\n    f.write(charactername)\n    f.write('\\n')\n    characterrace = input(f\"What is {charactername}'s race? \")\n    f.write(characterrace)\n    f.write('\\n')\n    god = input('Who does your character worship? ')\n    f.write(god)\n    f.write('\\n')\n    d8 = random.randint(1,8)\n    print(d8)  \n    print('pick the backstory for your race of this number and type it in unless you aren\\'t using a new character')\n    bkg = input('Character Background(What your character was)? ')\n    f.write(bkg)\n    f.write('\\n')\n    \n    bks = input('Character Backstory ')\n    f.write(bks)\n    f.write('\\n')\ndef spells():\n    y = input('How many spells do you have? ')\n    y = int(y)\n    while int(y) > 0:\n        spell = input('Type in a spell of yours. ')\n        ranges = input('Range of the spell. ')\n        damage = input('How much damage does it give? ')\n        \n        desc = input('Write a description for your spell. ')\n        f.write(f'''\n{spell}:\ndamage: {damage}\nrange: {ranges}\n{desc}        \n        ''')\n        y -= 1\ndef mainstat():\n    print('Making stats...')\n    global x\n    while x != 6:\n        typ = typelist[x]\n        list1 = []\n        i1 = random.randint(1,6)\n        list1.append(i1)\n        i2 = random.randint(1,6)\n        list1.append(i2)\n        i3 = random.randint(1,6)\n        list1.append(i3)\n        i4 = random.randint(1,6)\n        list1.append(i4)\n        list1.sort()\n        list1.pop(0)\n        bonus = sum(list1) \n        mod = math.floor(((int(bonus) - 10)/2))\n        total = int(bonus) + int(mod)\n        if typ == 'Dexterity':\n            f.write(f'''\n{typ} + Initiatve: \\n{bonus} + feat bonus |  |\n+ {mod}\n({total})    \n    ''')\n        else:\n           f.write(f'''\n{typ}: \\n{bonus}\n+ {mod}\n({total})    \n    ''')\n\n        x += 1\n        \ndef mainstatvet():\n    global x\n    while x != 6:\n        typ = typelist[x]\n        bonus = input(f'{typ} stat ') \n        mod = input(f'{typ} mod ')\n        total = int(bonus) + int(mod)\n        if typ == 'Dexterity':\n            initb = input('Initiatve Feat Extras')\n        \n        if typ == 'Dexterity':\n           f.write(f'''\n{typ} + Initiatve: {bonus} + feat bonus |{initb}| \\n{bonus}\n+ {mod}\n({total})    \n    ''')\n        else:\n          f.write(f'''\n{typ}: \\n{bonus}\n+ {mod}\n({total})    \n    ''')\n        x += 1 \n        \ndef stats(): \n    new = input(\"Is this a brand new charcter[Y/n] \")\n    if new == 'n' or new == 'N':\n        lvl = input(f'Level of {charactername}? ')\n        mainstatvet()\n    else:\n        lvl = '1'\n        mainstat()\n        \n    maxh = input('Max Health? ')\n    f.write('\\n') \n    f.write(f'Max Health: {maxh}')\n    f.write('\\n')\n    armc = input('Armor Class? ')\n    f.write(f'Armor Class: {armc}')\n    f.write('\\n')\n    hitdie = input('What is your hit die? ')\n    f.write(f'Hit Die: {hitdie}')\n    f.write('\\n')\n\ndef init():           \n    f.write(\"Dungeons & Dragons Character Sheet\")\n    f.write('\\n')\n    f.write('This sheet was created by the dndsheetmaker by RobiTheGit a.k.a. RobiWanKenobi')\n    bkg()\n    skills()\n    traits()\n    stats()\n    spells()\n    items()\n    weapons()\n    f.write('\\n')\n    f.write('© RobiTheGit 2022')\n    print(\"Look for char.txt, rename or delete it before you run this script again, otherwise you will get a error, mainly so you don't overwrite the sheet\")\n    time.sleep(2)\n    sys.exit(0)\ninit()\n","repo_name":"RobiTheGit/dndsheetmaker","sub_path":"dndsheetmaker.py","file_name":"dndsheetmaker.py","file_ext":"py","file_size_in_byte":5112,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"31005119838","text":"from pycti import OpenCTIConnectorHelper, get_config_variable\nfrom stix2 import Bundle, Identity, Vulnerability, Infrastructure, Relationship\nfrom typing import Dict, Optional\nimport certifi\nimport json\nimport urllib\nimport ssl\nimport sys\nimport datetime\nimport time\nimport os\nimport yaml\n\n\nclass Cisa:\n    def __init__(self):\n        # Instantiate the connector helper from config\n        config_file_path = os.path.dirname(os.path.abspath(__file__)) + \"/config.yml\"\n        config = (\n            yaml.load(open(config_file_path), Loader=yaml.FullLoader)\n            if os.path.isfile(config_file_path)\n            else {}\n        )\n        self.helper = OpenCTIConnectorHelper(config)\n        # Extra config\n        self.cisa_catalog_url = get_config_variable(\n            \"CISA_CATALOG_URL\", [\"cisa\", \"catalog_url\"], config\n        )\n        self.cisa_interval = get_config_variable(\n            \"CISA_INTERVAL\", [\"cisa\", \"interval\"], config, True\n        )\n        self.update_existing_data = get_config_variable(\n            \"CONNECTOR_UPDATE_EXISTING_DATA\",\n            [\"connector\", \"update_existing_data\"],\n            config,\n        )\n        self.tlp = get_config_variable(\n            \"CONNECTOR_TLP\",\n            [\"connector\", \"tlp\"],\n            config,\n        )\n        self.confidence_level = get_config_variable(\n            \"CONNECTOR_CONFIDENCE_LEVEL\",\n            [\"connector\", \"confidence_level\"],\n            config,\n        )\n        self.created_by_stix = None\n        self.tlp_marking = None\n        self.org = \"Cybersecurity and Infrastructure Security Agency\"\n        self.opencti_url = get_config_variable(\n            \"OPENCTI_URL\", [\"opencti\", \"url\"], config\n        )\n        self.opencti_token = get_config_variable(\n            \"OPENCTI_TOKEN\", [\"opencti\", \"token\"], config\n        )\n\n    def get_interval(self):\n        return int(self.cisa_interval) * 60 * 60 * 24\n\n    def retrieve_data(self, url: str) -> Optional[str]:\n        \"\"\"\n        Retrieve data from the given url.\n\n        Parameters\n        ----------\n        url : str\n            Url to retrieve.\n\n        Returns\n        -------\n        str\n            A string with the content or None in case of failure.\n        \"\"\"\n        try:\n            return (\n                urllib.request.urlopen(\n                    url,\n                    context=ssl.create_default_context(cafile=certifi.where()),\n                )\n                .read()\n                .decode(\"utf-8\")\n            )\n        except (\n            urllib.error.URLError,\n            urllib.error.HTTPError,\n            urllib.error.ContentTooShortError,\n        ) as urllib_error:\n            self.helper.log_error(f\"Error retrieving url {url}: {urllib_error}\")\n        return None\n\n    # Get Identity info\n    def set_created_by_stix(self, org: str) -> Dict:\n        if org is None:\n            org = self.org\n        self.helper.log_info(f\"Checking CTI Service for {org}\")\n        created_by_stix = self.helper.api.identity.read(\n            filters={\"key\": \"name\", \"values\": [f\"{org}\"]}\n        )\n        if created_by_stix is None:\n            self.helper.log_info(\n                f\"{org} not found in CTI Service. Building new STIX Object\"\n            )\n            org_stix = Identity(\n                allow_custom=\"True\",\n                type=\"identity\",\n                entity_type=\"Organization\",\n                x_opencti_organization_type=\"Vendor\",\n                name=f\"{org}\",\n                description=\"The Cybersecurity and Infrastructure Security Agency is a United States federal agency, an operational component under Department of Homeland Security oversight. Its activities are a continuation of the National Protection and Programs Directorate.\",\n            )\n            self.created_by_stix = org_stix\n        else:\n            self.helper.log_info(f\"{org} found in CTI Service\")\n            type = \"identity\"\n            id = created_by_stix[\"standard_id\"]\n            name = created_by_stix[\"name\"]\n            description = created_by_stix[\"description\"]\n            org_stix = Identity(\n                allow_custom=\"True\",\n                type=f\"{type}\",\n                id=f\"{id}\",\n                name=f\"{name}\",\n                description=f\"{description}\",\n                entity_type=\"Organization\",\n                x_opencti_organization_type=\"Vendor\",\n            )\n            self.created_by_stix = org_stix\n            self.helper.log_info(f\"Created Identity Object for {org} from CTI data\")\n\n    def create_relationship_obj(self, source_ref: str, target_ref: str) -> Relationship:\n        relationship_stix = Relationship(\n            relationship_type=\"has\", source_ref=source_ref, target_ref=target_ref\n        )\n        return relationship_stix\n\n    def set_tlp_marking(self, tlp_mark):\n        if tlp_mark is None:\n            tlp_mark = self.tlp\n        self.helper.log_info(\"Retrieving TLP Data from CTI Service\")\n        marking = self.helper.api.marking_definition.read(\n            filters=[{\"key\": \"definition\", \"values\": [f\"{tlp_mark}\"]}]\n        )\n        self.tlp_marking = marking[\"standard_id\"]\n        self.helper.log_info(f\"Marking Definition: {self.tlp_marking}\")\n\n    def build_bundle(self, data):\n        self.helper.log_info(\"Building CISA Bundle\")\n        vuln = data\n        bundle = {}\n        stix_objects = []\n        vuln_cve = vuln[\"cveID\"]\n        vendor_name = vuln[\"vendorProject\"]\n        product = vuln[\"product\"]\n        description = vuln[\"shortDescription\"]\n        vuln_date = vuln[\"dateAdded\"]\n        created = f\"{vuln_date}T00:00:00.000Z\"\n        created_by_id = self.created_by_stix[\"id\"]\n        product_name = f\"{vendor_name} {product}\"\n        marking_id = self.tlp_marking\n\n        # check for existing CVE\n        self.helper.log_info(f\"Checking CTI Service for Vulnerability: {vuln_cve}\")\n        cti_vuln = self.helper.api.vulnerability.read(\n            filters={\"key\": \"name\", \"values\": [f\"{vuln_cve}\"]}\n        )\n        self.helper.log_info(f\"{vuln_cve} Found\")\n        if cti_vuln is None:\n            stix_vuln = Vulnerability(\n                type=\"vulnerability\",\n                name=f\"{vuln_cve}\",\n                description=f\"{description}\",\n                created_by_ref=self.created_by_stix[\"id\"],\n                created=f\"{created}\",\n                object_marking_refs=[f\"{marking_id}\"],\n            )\n            stix_objects.append(stix_vuln)\n        else:\n            created_by = cti_vuln[\"createdBy\"][\"standard_id\"]\n            vuln_created = cti_vuln[\"created\"]\n            vuln_id = cti_vuln[\"standard_id\"]\n            stix_vuln = Vulnerability(\n                type=\"vulnerability\",\n                id=f\"{vuln_id}\",\n                name=f\"{vuln_cve}\",\n                created_by_ref=f\"{created_by}\",\n                created=f\"{vuln_created}\",\n                object_marking_refs=[f\"{marking_id}\"],\n            )\n            stix_objects.append(stix_vuln)\n\n        # Check for existing vendor\n        self.helper.log_info(f\"Checking CTI Service for Identity: {vendor_name}\")\n        cti_vendor = self.helper.api.identity.read(\n            filters={\"key\": \"name\", \"values\": [f\"{vendor_name}\"]}\n        )\n        if cti_vendor is None:\n            stix_org = Identity(\n                allow_custom=\"True\",\n                type=\"identity\",\n                entity_type=\"Organization\",\n                name=f\"{vendor_name}\",\n                description=\"Software Vendor\",\n                created=f\"{created}\",\n                x_opencti_organization_type=\"vendor\",\n                created_by_ref=f\"{created_by_id}\",\n                object_marking_refs=[f\"{marking_id}\"],\n            )\n            stix_objects.append(stix_org)\n        else:\n            self.helper.log_info(f\"{vendor_name} was found in the CTI Service\")\n            vendor_id = cti_vendor[\"standard_id\"]\n            created = cti_vendor[\"created\"]\n            vendor_name = cti_vendor[\"name\"]\n            vendor_description = cti_vendor[\"description\"]\n            stix_org = Identity(\n                allow_custom=\"True\",\n                type=\"identity\",\n                entity_type=\"Organization\",\n                x_opencti_organization_type=\"vendor\",\n                id=f\"{vendor_id}\",\n                name=f\"{vendor_name}\",\n                description=f\"{vendor_description}\",\n                created=f\"{created}\",\n                created_by_ref=f\"{created_by_id}\",\n                object_marking_refs=[f\"{marking_id}\"],\n            )\n            stix_objects.append(stix_org)\n            org_id = stix_org[\"id\"]\n            self.helper.log_info(f\"STIX Object created for {vendor_name}\")\n        # Check for CTI Infrastructure\n        product_name = f\"{vendor_name} {product}\"\n        if vendor_name in product:\n            product_name = f\"{product}\"\n        self.helper.log_info(f\"Checking CTI Service for Infrastructure: {product_name}\")\n        cti_infra = self.helper.api.infrastructure.read(\n            filters={\"key\": \"name\", \"values\": [f\"{product_name}\"]}\n        )\n        if cti_infra is None:\n            self.helper.log_info(\n                f\"No Existing Infrastructure Object exists for: {product_name}\"\n            )\n            stix_infrastructure = Infrastructure(\n                type=\"infrastructure\",\n                name=f\"{product_name}\",\n                created=f\"{created}\",\n                created_by_ref=f\"{created_by_id}\",\n                object_marking_refs=[f\"{marking_id}\"],\n            )\n            infra_id = stix_infrastructure[\"id\"]\n            stix_objects.append(stix_infrastructure)\n        else:\n            self.helper.log_info(f\"Infrastructure: {product_name} found!\")\n            infra_id = cti_infra[\"standard_id\"]\n            self.helper.log_info(f\"Infrastructure ID: {infra_id}\")\n            stix_infrastructure = Infrastructure(\n                type=\"infrastructure\",\n                id=f\"{infra_id}\",\n                name=f\"{product_name}\",\n                created=f\"{created}\",\n                created_by_ref=f\"{created_by_id}\",\n                object_marking_refs=[f\"{marking_id}\"],\n            )\n            stix_objects.append(stix_infrastructure)\n        infra_vuln_relationship = Relationship(\n            relationship_type=\"has\",\n            source_ref=f\"{infra_id}\",\n            start_time=f\"{created}\",\n            target_ref=f\"{vuln_id}\",\n            confidence=\"100\",\n            object_marking_refs=[f\"{marking_id}\"],\n        )\n        stix_objects.append(infra_vuln_relationship)\n        infra_vendor_relationship = Relationship(\n            relationship_type=\"related-to\",\n            description=\"maintains\",\n            start_time=f\"{created}\",\n            source_ref=f\"{org_id}\",\n            target_ref=f\"{infra_id}\",\n            confidence=\"100\",\n            object_marking_refs=[f\"{marking_id}\"],\n        )\n        stix_objects.append(infra_vendor_relationship)\n        bundle = Bundle(\n            self.created_by_stix,\n            stix_vuln,\n            stix_org,\n            stix_infrastructure,\n            infra_vuln_relationship,\n            allow_custom=\"True\",\n        ).serialize()\n        self.helper.log_info(\"CISA Bundle Complete\")\n        return bundle\n\n    def process_data(self):\n        try:\n            # Get the current timestamp and check\n            timestamp = int(time.time())\n            current_state = self.helper.get_state()\n            if current_state is not None and \"last_run\" in current_state:\n                last_run = current_state[\"last_run\"]\n                self.helper.log_info(\n                    \"Connector last run: \"\n                    + datetime.datetime.utcfromtimestamp(last_run).strftime(\n                        \"%Y-%m-%d %H:%M:%S\"\n                    )\n                )\n            else:\n                last_run = None\n                self.helper.log_info(\"Connector has never run\")\n            # If the last_run is more than interval-1 day\n            if last_run is None or (\n                (timestamp - last_run) > ((int(self.cisa_interval) - 1) * 60 * 60 * 24)\n            ):\n                self.helper.log_info(\"Connector will run!\")\n\n                now = datetime.datetime.utcfromtimestamp(timestamp)\n                friendly_name = \"CISA run @ \" + now.strftime(\"%Y-%m-%d %H:%M:%S\")\n                work_id = self.helper.api.work.initiate_work(\n                    self.helper.connect_id, friendly_name\n                )\n                if self.cisa_catalog_url is not None and len(self.cisa_catalog_url) > 0:\n                    cisa_data = self.retrieve_data(self.cisa_catalog_url)\n                    self.set_created_by_stix(org=self.org)\n                    self.set_tlp_marking(tlp_mark=self.tlp)\n                    cisa_data = json.loads(cisa_data)\n                    for vuln in cisa_data[\"vulnerabilities\"]:\n                        bundle = self.build_bundle(vuln)\n                        self.send_bundle(work_id, bundle)\n\n                # Store the current timestamp as a last run\n                message = \"Connector successfully run, storing last_run as \" + str(\n                    timestamp\n                )\n                self.helper.log_info(message)\n                self.helper.set_state({\"last_run\": timestamp})\n                self.helper.api.work.to_processed(work_id, message)\n                self.helper.log_info(\n                    \"Last_run stored, next run in: \"\n                    + str(round(self.get_interval() / 60 / 60 / 24, 2))\n                    + \" days\"\n                )\n            else:\n                new_interval = self.get_interval() - (timestamp - last_run)\n                self.helper.log_info(\n                    \"Connector will not run, next run in: \"\n                    + str(round(new_interval / 60 / 60 / 24, 2))\n                    + \" days\"\n                )\n        except (KeyboardInterrupt, SystemExit):\n            self.helper.log_info(\"Connector stop\")\n            sys.exit(0)\n        except Exception as e:\n            self.helper.log_error(str(e))\n\n    def run(self):\n        self.helper.log_info(\"Fetching CISA Known Exploited Vulnerabilities...\")\n        get_run_and_terminate = getattr(self.helper, \"get_run_and_terminate\", None)\n        if callable(get_run_and_terminate) and self.helper.get_run_and_terminate():\n            self.process_data()\n            self.helper.force_ping()\n        else:\n            while True:\n                self.process_data()\n                time.sleep(60)\n\n    def send_bundle(self, work_id: str, serialized_bundle: str) -> None:\n        try:\n            self.helper.send_stix2_bundle(\n                serialized_bundle,\n                entities_types=self.helper.connect_scope,\n                update=self.update_existing_data,\n                work_id=work_id,\n            )\n        except Exception as e:\n            self.helper.log_error(f\"Error while sending bundle: {e}\")\n\n\nif __name__ == \"__main__\":\n    try:\n        connector = Cisa()\n        connector.run()\n    except Exception as e:\n        print(e)\n        time.sleep(10)\n        exit(0)\n","repo_name":"0xd6cb6d73/opencti-connectors","sub_path":"external-import/cisa-known-exploited-vulnerabilities/src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":15031,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"86"}
+{"seq_id":"74514924123","text":"import math\nimport os\nimport statistics\nimport time\nfrom functools import lru_cache, partial\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport psutil\nimport seaborn as sns\n\nimport psimulation\nimport trueskill_rate\nfrom plot_sigma import plot_sigma\n\nstart = time.time()\n\n### RUN SIMULATION\nPLAYERS = 20000\nGAMES = 10000000\nSTRATEGY = \"trueskill\"\n\"\"\"\nPLAYERS = 20000\nGAMES = 100000000\n-----------------\n\nBASE\n14273 MB    81.82s\n14275 MB    82.23s\n\nRESERVED VECTOR SIZE\n- no change\n\nCOPY\n11993 MB                with changing to pointers and carray from std::vector\n11987 MB                when simulation releasing its vector pointers\n7966 MB     89.1423s    when Players vectors directly to carray as well\n\n\"\"\"\n\n\n@lru_cache()\ndef chance_skill(diff):\n    \"\"\" Returns the chance for a player to win based on skill difference\"\"\"\n    return 1 / (1 + math.exp(-diff / 173.718))  # ELO points\n\n\n@lru_cache()\ndef chance_skill_two(a, b):\n    \"\"\" Returns the chance for a player to win based on skill difference\"\"\"\n    return chance_skill(a - b)\n\n\ndef plot_data(data, prediction_differences, match_accuracy, PLAYERS, GAMES,\n              STRATEGY):\n\n    ### PLOTTING\n    start_plotting = time.time()\n    plt.rcParams['figure.dpi'] = 150\n\n    plot_sigma(data)\n\n    ## PREDICTION DIFFERENCES\n    def plot_prediction_differences():\n        plt.figure().clear()\n        factor = 50\n        L = int(prediction_differences.size / factor)\n        means = []\n        stdevs = [[], []]\n        x = []\n        for i in range(factor):\n            mean = np.average(prediction_differences[L * i:L * (i + 1)])\n            stdev = np.std(prediction_differences[L * i:L * (i + 1)])\n            means.append(mean)\n            stdevs[0].append(mean - stdev)\n            stdevs[1].append(mean + stdev)\n            x.append(L * i)\n\n        fig, ax = plt.subplots(1, 1)\n        ax.text(0,\n                means[0] + 0.01,\n                f\"{means[0]:.3f}\",\n                color=\"#1968cf\",\n                ha=\"center\")\n        ax.text(x[-1],\n                means[-1] + 0.01,\n                f\"{means[-1]:.3f}\",\n                color=\"#1968cf\",\n                ha=\"center\")\n\n        ax.plot(x, means)\n        ax.fill_between(x, stdevs[0], stdevs[1], color=\"#1968cf\", alpha=0.2)\n        ax.set_ylabel(\"Error in matchmaking\")\n        ax.set_xlabel(\"Games\")\n        plt.grid(alpha=0.2)\n        ax.set_title(\n            f\"How matchmaking improves [{np.sum(prediction_differences)/100000:.3f}]\"\n        )\n        plt.tight_layout()\n        plt.savefig(\"img/prediction_differences.png\")\n\n    start_pred = time.time()\n    plot_prediction_differences()\n    print(\n        f\"Prediction plotting finished in {time.time()-start_pred:.3f} seconds\"\n    )\n\n    ## PLAYER HISTORY\n    def plot_mmr_history(DATAVALUES=6):\n        plt.figure().clear()\n        unique_opponents = [\n            len(np.unique(i[\"opponent_history\"])) for i in data\n        ]\n        extremes = [\n            p for p in sorted(data, key=lambda x: x[\"skill\"], reverse=True)\n        ]\n        players = data[:DATAVALUES - 2] + [extremes[0], extremes[-1]]\n        fig, ax = plt.subplots(5, 1)\n\n        for i in range(3):\n            ax[0].plot(np.linspace(0, extremes[i]['mmr_history'].size - 1,\n                                   extremes[i]['mmr_history'].size),\n                       extremes[i]['mmr_history'],\n                       label=f\"Player skill: {extremes[i]['skill']:.2f}\")\n\n            player_chances = np.vectorize(\n                partial(chance_skill_two, extremes[i]['skill']))\n\n            chances = player_chances(extremes[i]['opponent_history'])\n\n            ax[1].scatter(np.linspace(0, chances.size - 1, chances.size),\n                          chances,\n                          s=2)\n            if i == 0:\n                ax[2].plot(chances, label=\"True chances\")\n\n        ax[0].set_ylabel(\"MMR\")\n        ax[0].set_xlabel(\"Games\")\n        ax[0].legend()\n        ax[0].set_title(f\"Top 3 players\")\n        ax[0].grid(alpha=0.2)\n        ax[1].set_ylabel(\"Chances against opponents\")\n        ax[1].set_ylim(0, 1.05)\n        ax[1].grid(alpha=0.2)\n        ax[1].set_xlabel(\"Games\")\n\n        # Plot the best player\n        p = extremes[0]\n        ax[2].plot(p[\"predicted_chances\"], label=\"Predicted chances\")\n        ax[2].legend()\n        ax[2].grid(alpha=0.2)\n        ax[2].set_title(\n            f'Best player ({chance_skill(extremes[0][\"skill\"]-extremes[1][\"skill\"]):.2f} chance against second best)'\n        )\n        ax[2].set_xlabel(\"Games\")\n        ax[2].set_ylabel(\"Changes against the oppponent\")\n        ax[2].set_ylim(-0.02, 1.02)\n\n        # the worst player\n        p = extremes[-1]\n        player_chances = np.vectorize(partial(chance_skill_two, p['skill']))\n        chances = player_chances(p['opponent_history'])\n        ax[4].plot(chances, label=\"True chances\")\n        ax[4].plot(p[\"predicted_chances\"], label=\"Predicted chances\")\n        ax[4].legend()\n        ax[4].grid(alpha=0.2)\n        ax[4].set_title(\n            f'Worst player ({chance_skill(extremes[-1][\"skill\"]-extremes[-2][\"skill\"]):.2f} chance against second worst)'\n        )\n        ax[4].set_xlabel(\"Games\")\n        ax[4].set_ylabel(\"Changes against the oppponent\")\n        ax[4].set_ylim(-0.02, 1.02)\n\n        # average player\n        p = extremes[int(len(extremes) / 2)]\n        player_chances = np.vectorize(partial(chance_skill_two, p['skill']))\n        chances = player_chances(p['opponent_history'])\n        ax[3].plot(chances, label=\"True chances\")\n        ax[3].plot(p[\"predicted_chances\"], label=\"Predicted chances\")\n        ax[3].legend()\n        ax[3].grid(alpha=0.2)\n        ax[3].set_title(\"Average player\")\n        ax[3].set_xlabel(\"Games\")\n        ax[3].set_ylabel(\"Changes against the oppponent\")\n        ax[3].set_ylim(top=1)\n        ax[3].set_ylim(-0.02, 1.02)\n\n        fig.set_figheight(16)\n        fig.tight_layout(h_pad=2.1)\n        plt.savefig(\"img/Player_extremes.png\")\n\n        plt.figure().clear()\n        fig, ax = plt.subplots(2, 1)\n\n        for player in players:\n            mmr = player[\"mmr_history\"]\n            opp = player[\"opponent_history\"]\n            p = ax[0].plot(np.linspace(0, mmr.size - 1, mmr.size),\n                           mmr,\n                           linewidth=0.3)\n            ax[0].text(len(mmr) - 0.9,\n                       mmr[-1],\n                       f'{player[\"skill\"]:.3f}',\n                       ha=\"left\",\n                       va=\"center\",\n                       color=p[0].get_color())\n            p = ax[1].plot(np.linspace(0, opp.size - 1, opp.size),\n                           opp,\n                           linewidth=0.3)\n            ax[1].text(opp.size + 1,\n                       opp[-1],\n                       opp.size,\n                       ha=\"left\",\n                       color=p[0].get_color())\n\n        ax[0].set_ylabel(\"MMR\")\n        ax[0].set_xlim(0, ax[0].get_xlim()[1] * 1.1)\n        ax[1].set_ylabel(\"Opponent skill\")\n        ax[1].set_xlabel(\"Games\")\n        ax[1].set_xlim(0, ax[1].get_xlim()[1] * 1.1)\n        ax[0].set_title(\n            \"How player MMR and opponents change\\n\"\n            f\"Average unique opponents per player: {statistics.mean(unique_opponents):.2f}\"\n        )\n        ax[0].grid(alpha=0.2)\n        ax[1].grid(alpha=0.2)\n        fig.set_figheight(8)\n        plt.savefig(\"img/Player_history.png\")\n\n    start_hist = time.time()\n    plot_mmr_history()\n    print(\n        f\"MMR history plotting finished in {time.time()-start_hist:.3f} seconds\"\n    )\n\n    ### Sort data\n    data = [i for i in sorted(data, key=lambda x: x[\"skill\"])]\n    skills = np.array([i[\"skill\"] for i in data])\n    mmrs = np.array([i[\"mmr\"] for i in data])\n\n    def plot_other():\n        ## MMR - SKILL\n        plt.figure().clear()\n        fig, ax = plt.subplots()\n        ax.plot(skills, mmrs)\n\n        if STRATEGY == 'trueskill':\n            ax2 = ax.twinx()\n            ax.set_ylabel(\"Player MMR\", color=\"#1F4B73\")\n            ax2.set_ylabel(\"Player Skill\")\n        else:\n            ax2 = ax\n            ax.set_ylabel(\"Player MMR\")\n\n        ax2.plot([np.min(skills), np.max(skills)],\n                 [np.min(skills), np.max(skills)],\n                 color=\"black\",\n                 linewidth=0.5)\n\n        plt.title(f\"MMR - Skill relation ({GAMES/PLAYERS:.0f} games/player)\")\n        plt.xlabel(\"Player skill\")\n\n        plt.grid(alpha=0.2)\n        plt.tight_layout()\n        plt.savefig(\"img/MMR-Skill.png\")\n\n        ## Player skill dist\n        plt.figure().clear()\n        sns.histplot(skills, element='poly', fill=True, alpha=0.3)\n        plt.title(\n            \"Player skill distribution\\n(lines show where players have 75% chance to win against previous line)\"\n        )\n        plt.xlabel(\"Player skill\")\n        plt.ylabel(\"Player count\")\n        M = plt.ylim()[1]\n\n        # Plotting lines where a player has 75% chance to win against previous line\n        # More lines means more diverse population of skills\n        lines = 0\n        previous_skill = None\n        for skill in skills:\n            if previous_skill is None:\n                previous_skill = skill\n                continue\n\n            chance = chance_skill(skill - previous_skill)\n            if chance > 0.75:\n                previous_skill = skill\n                plt.plot([skill, skill], [0, M], \"k--\", linewidth=0.5)\n                lines += 1\n\n        plt.text(plt.xlim()[1] * 0.92, plt.ylim()[1] * 0.93, f\"#{lines}\")\n        plt.grid(alpha=0.2)\n        plt.savefig(\"img/Skill_dist.png\")\n\n        ## Player MMR dist\n        plt.figure().clear()\n        fig, ax1 = plt.subplots()\n\n        sns.histplot(mmrs, element='poly', color='red', fill=True, alpha=0.3)\n        plt.title(\n            \"Player MMR distribution\\n(lines show where players have 75% chance to win against previous line)\"\n        )\n        plt.xlabel(\"Player MMR\")\n        ax1.set_ylabel(\"Player count\", color='red')\n        M = plt.ylim()[1]\n\n        # Plot lines\n        lines = 0\n        if STRATEGY != \"trueskill\":\n            previous_mmr = None\n            for mmr in mmrs:\n                if previous_mmr is None:\n                    previous_mmr = mmr\n                    continue\n\n                chance = 1 / (1 + math.exp((previous_mmr - mmr) / 173.718))\n                if chance > 0.75:\n                    previous_mmr = mmr\n                    plt.plot([mmr, mmr], [0, M], \"k--\", linewidth=0.5)\n                    lines += 1\n\n        else:\n            minimum_mmr = min(mmrs)\n            maximum_mmr = max(mmrs)\n            current_mmr = minimum_mmr\n            while True:\n                current_mmr += 4.16666666  # BETA\n                if current_mmr > maximum_mmr:\n                    break\n                plt.plot([current_mmr, current_mmr], [0, M],\n                         \"k--\",\n                         linewidth=0.5)\n                lines += 1\n\n        plt.text(plt.xlim()[1] * 0.93, plt.ylim()[1] * 0.93, f\"#{lines}\")\n        ax2 = ax1.twinx()\n        ndata = [i for i in sorted(data, key=lambda x: x[\"mmr\"])]\n        nmmrs = [i[\"mmr\"] for i in ndata]\n        histories = [i[\"opponent_history\"].size for i in ndata]\n        ax2.scatter(nmmrs, histories, s=2)\n        ax2.set_ylabel(\n            f\"Game count per player ({min(histories)}-{max(histories)})\",\n            color='#0b47bf')\n        ax2.set_ylim(0, max(histories))\n        plt.tight_layout()\n        plt.grid(alpha=0.2)\n        plt.savefig(\"img/MMR_dist.png\")\n\n        ## Games played\n        plt.figure().clear()\n        games_played = [i[\"opponent_history\"].size for i in data]\n        sns.histplot(games_played, element='poly')\n        plt.xlabel(\"Games played\")\n        plt.ylabel(\"Player count\")\n        plt.title(\n            f\"Number of games per player\\nMedian: {statistics.median(games_played):.0f}\"\n        )\n        plt.grid(alpha=0.2)\n        plt.savefig(\"img/Games_played.png\")\n\n    plot_other()\n    print(\n        f\"Other plotting finished in {time.time()-start_plotting:.3f} seconds\")\n    print(f\"Total time: {time.time()-start:.3f} seconds\")\n\n\nif __name__ == \"__main__\":\n    psimulation.set_my_python_function(trueskill_rate.rate_1v1)\n    data, prediction_differences, match_accuracy, good_match_fraction = psimulation.run_simulation(\n        PLAYERS, GAMES, STRATEGY)\n    process = psutil.Process(os.getpid())\n    print(\n        f\"Peak memory usage during simulation: {process.memory_info().peak_wset/(1024*1024):.0f} MB\"\n    )\n\n    plot_data(data, prediction_differences, match_accuracy, PLAYERS, GAMES,\n              STRATEGY)","repo_name":"FluffyMaguro/Matchmaking_simulation","sub_path":"analyse.py","file_name":"analyse.py","file_ext":"py","file_size_in_byte":12513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"75089168917","text":"from fastapi.testclient import TestClient\nfrom main import app\n\n\nclient = TestClient(app)\n\n\ndef test_read_main():\n    response = client.get(\"/\")\n    assert response.status_code == 200\n    assert response.json() == {\"health\": \"OK\"}\n\n\ndef test_read_metrics():\n    response = client.get(\"/metrics\")\n    assert response.status_code == 200\n    assert response.json() == {'Model 1': 10,\n                               'Model 2': 100,\n                               'Model 3': 1000}\n\n\ndef test_similarity_score():\n    response = client.post(\n        \"/similarity_score\",\n        json={\"comparison_text\": \"Hello test!\", \"text\": \"Help toast.\"},\n    )\n    assert response.status_code == 200\n    assert list(response.json().keys()) == ['score']\n    assert all(isinstance(x, float) for x in response.json().values())\n\n","repo_name":"drwaterman/python-endpoint","sub_path":"test_main.py","file_name":"test_main.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"21668334193","text":"import cv2\nimport matplotlib.pyplot as plt\n\nimg = cv2.imread(\"c:/Users/AK/Pictures/ANIME/animes.jfif\",0)\ncitra_grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n_, biner = cv2.threshold(citra_grey, 50, 256, cv2.THRESH_BINARY)\n\ncv2.imshow(\"gambar\", img)\ncv2.imshow(\"gambar(greyscale)\", citra_grey)\ncv2.imshow(\"gambar(binery)\", biner)\n\nplt.figure(figsize=(8, 6))\n\nplt.subplot(2, 1, 1)\nplt.hist(citra_grey.ravel(), 150, [0, 256])\nplt.xlabel(\"Nilai Pixel\")\nplt.ylabel(\"Frekuensi\")\nplt.title(\"HISTOGRAM GAMBAR\")\n\nplt.subplot(2, 1, 2)\nplt.imshow(biner, cmap='grey')\nplt.xlabel(\"Kolom\")\nplt.ylabel(\"Baris\")\nplt.title(\"Gambar Binery\")\n\nplt.tight_layout()\nplt.show()\n\nprint(\"\\n\\nGAMBAR:\\n\", img)\nprint(\"\\n\\nBINERY\\n\", biner)\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()","repo_name":"katibinzain/gabut","sub_path":"CAMP_TASK/python06/man.py","file_name":"man.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"10896804948","text":"import os\nimport platform\nimport ruamel.yaml\n\n# internal\nfrom zax.variables import zax_yml\nfrom zax.games import Games\n\n\nclass ZaxConfig:\n    def __init__(self):\n        self.data = self.load()\n        self.games = Games(self.data[\"games\"])\n        try:\n            self.theme = self.data[\"theme\"]\n        except:\n            self.theme = \"light\"\n            self.data[\"theme\"] = \"light\"\n\n    def load(self):\n        yaml = ruamel.yaml.YAML(typ=\"rt\")\n        config = {\"games\": []}\n        if os.path.isfile(zax_yml):\n            try:\n                with open(zax_yml) as yf:\n                    config = yaml.load(yf)\n            except:\n                os.makedirs(self.config_dir, exist_ok=True)\n        return config\n\n    def save(self, game_path=None, wine_prefix=\"\", wine_debug=\"\"):\n        yaml = ruamel.yaml.YAML(typ=\"rt\")\n\n        # clean out empty wine_debug and wine_prefix stanzas from config\n        if (platform.system() != \"Windows\") and game_path:\n            new_games = []\n\n            for g in self.games.games:\n                ng = {}\n                ng[\"path\"] = g[\"path\"]\n\n                if g[\"path\"] == game_path:  # wine config for current game from window\n                    if (wine_prefix != \"\") and (wine_prefix is not None):\n                        ng[\"wine_prefix\"] = wine_prefix\n                    if (wine_debug != \"\") and (wine_debug is not None):\n                        ng[\"wine_debug\"] = wine_debug\n                else:  # wine config for other games loaded from yml\n                    if (\"wine_prefix\" in g) and (g[\"wine_prefix\"] != \"\") and (g[\"wine_prefix\"] is not None):\n                        ng[\"wine_prefix\"] = g[\"wine_prefix\"]\n                    if (\"wine_debug\" in g) and (g[\"wine_debug\"] != \"\") and (g[\"wine_debug\"] is not None):\n                        ng[\"wine_debug\"] = g[\"wine_debug\"]\n                new_games.append(ng)\n            self.games.games = new_games\n            self.data[\"games\"] = self.games.games\n\n        # sort\n        new_games = [g for g in self.games.games]\n        new_games = sorted(new_games, key=lambda k: k[\"path\"])\n        self.games.games = new_games\n        self.data[\"games\"] = self.games.games\n\n        self.data[\"theme\"] = self.theme\n        os.makedirs(os.path.dirname(zax_yml), exist_ok=True)\n        with open(zax_yml, \"w\") as yf:\n            yaml.dump(self.data, yf)\n\n    def add_game(self, path):\n        self.games.add(path)\n        self.data[\"games\"] = self.games.games\n\n    def remove_game(self, path):\n        self.games.remove(path)\n        self.data[\"games\"] = self.games.games\n\n    def scan_games(self):\n        self.games.scan()\n        self.data[\"games\"] = self.games.games\n","repo_name":"BGforgeNet/zax","sub_path":"zax/zax_config.py","file_name":"zax_config.py","file_ext":"py","file_size_in_byte":2678,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"85"}
+{"seq_id":"8444000910","text":"import os\nimport requests\nimport urllib.parse\n\nfrom flask import redirect, render_template, request, session\nfrom functools import wraps\n\n\ndef apology(message, code=400):\n    \"\"\"Render message as an apology to user.\"\"\"\n    def escape(s):\n        \"\"\"\n        Escape special characters.\n        https://github.com/jacebrowning/memegen#special-characters\n        \"\"\"\n        for old, new in [(\"-\", \"--\"), (\" \", \"-\"), (\"_\", \"__\"), (\"?\", \"~q\"),\n                         (\"%\", \"~p\"), (\"#\", \"~h\"), (\"/\", \"~s\"), (\"\\\"\", \"''\")]:\n            s = s.replace(old, new)\n        return s\n    return render_template(\"apology.html\", top=code, bottom=escape(message)), code\n\n\ndef login_required(f):\n    \"\"\"\n    Decorate routes to require login.\n    https://flask.palletsprojects.com/en/1.1.x/patterns/viewdecorators/\n    \"\"\"\n    @wraps(f)\n    def decorated_function(*args, **kwargs):\n        if session.get(\"user_id\") is None:\n            return redirect(\"/login\")\n        return f(*args, **kwargs)\n    return decorated_function\n\ndef get_food_id(food_name):\n    '''\n    Get the food id of the food\n    '''\n\n    api_key = os.environ.get(\"API_KEY\")\n\n    url = f\"https://api.nal.usda.gov/fdc/v1/search?api_key={api_key}&generalSearchInput={food_name}\"\n    #url = f'https://api.nal.usda.gov/fdc/v1/search?api_key=4xR1xirqGjxs6zA3DPlKezl1PojgFLdUwZDQw6uX'\n    response = requests.get(url)\n    data = response.json()\n\n    #if data[\"errors\"]:\n        #return None\n\n    return data[\"foods\"][0][\"fdcId\"]\n\ndef lookup(name):\n    '''\n    Get the details of the food item\n    '''\n    try:\n        api_key = os.environ.get(\"API_KEY\")\n        food_id = get_food_id(name)\n\n        url = f\"https://api.nal.usda.gov/fdc/v1/food/{food_id}?api_key={api_key}\"\n        response = requests.get(url)\n        response.raise_for_status()\n    except requests.RequestException:\n        return None\n\n    try:\n        data = response.json()\n\n        food_name = data[\"description\"]\n        calories = None\n\n        for food_nutrient in data[\"foodNutrients\"]:\n            if food_nutrient[\"nutrient\"][\"name\"] == \"Energy\":\n                calories = food_nutrient[\"amount\"]\n                break\n\n        return {\n            \"food\": food_name.capitalize(),\n            \"calories\": calories\n            }\n\n    except (KeyError, TypeError, ValueError):\n        return None\n","repo_name":"sulabhkatila/calorie-tracker","sub_path":"helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":2321,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"3614582119","text":"import requests\n\nurl = \"https://kapi.kakao.com/v1/talkchannel/create/target_user_file\"\nheaders = {\n            \"Authorization\": \"KakaoAK 34b0d17f5994bb0b93d1d15ff3718b65\",\n            \"Content-Type\": \"application/json\"\n}\nparams = {\n    \"channel_public_id\": \"_NaezG\",\n    \"file_name\": \"해외데이터팀_고객리스트\",\n    \"schema\": {\n        \"이름\": \"string\",\n        \"회사명\": \"number\",\n        \"목동발송클래스\":\"number\",\n        \"상암발송클래스\":\"number\"\n    }\n}\n\nres = requests.post(url=url, headers=headers, json=params)\n\nprint(res)","repo_name":"anwoo99/naverworks_platform","sub_path":"test/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"12086441141","text":"##\n#%%\nfrom main import *\n\ntest_input = [\n    [[0, 1500],[1000, 2000], [2000, 500], [3500, 500], [5000, 1500], [6999, 1000]],\n    [5000, 2500, -50, 0, 1000, 90, 0],\n    [4950, 2498, -51, -3, 999, 75, 1],\n    [4898, 2493, -53, -6, 997, 60, 2]\n]\t\ntest_output = [\n    [-45, 4],\n    \"-45 4\",\n    \"-45 4\"\n]\n\n\nclass EnvTest(EnvMarsLander):\n    def __init__(self,test_input):\n        super().__init__(test_input[0],test_input[1])\n        self.test_input = test_input\n\n    def reset(self):\n        self.lander.update(*self.test_input[1])\n\n\n\nevolution_number = 10\npopulation_size = 60\ngene_size = 100\ndef main():\n    env = EnvTest(test_input)\n    env.reset()\n    population = Population.generator(population_size,gene_size)\n    for _ in range(evolution_number):\n        for chromosome in population:\n            if chromosome.use(env):\n                \n                input()    \n            else:\n                \n                env.reset()\n        print(population.average_score())\n        population.selection()\n        population.mutation()\n\n\n\n# %%\n","repo_name":"Codingames-personal/MarsLander","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"71683590678","text":"#!/usr/bin/env python\n\n\"\"\"\n@author: Jordan Graesser\nDate created: 12/29/2013\n\"\"\"\n\nfrom __future__ import division\nfrom future.utils import iteritems, viewitems, itervalues\nfrom builtins import int, dict, map\n\nimport os\nimport sys\nimport time\nimport subprocess\nimport ast\nimport platform\nimport shutil\nfrom copy import copy\nimport itertools\nfrom collections import OrderedDict\nimport inspect\n\n# MpGlue\nfrom .error_matrix import error_matrix\nfrom ._moving_window import moving_window\nfrom .. import raster_tools\nfrom .. import vector_tools\nfrom ..helpers import get_path\nfrom ..errors import logger, ArrayShapeError\nfrom .ts_features import TimeSeriesFeatures\n\nMPPATH = get_path()\n\ntry:\n    from ..stats import _lin_interp\nexcept:\n\n    logger.error('Could not import _lin_interp')\n    raise ImportError\n\ntry:\n    from ..stats import _rolling_stats\nexcept:\n\n    logger.error('Could not import _rolling_stats')\n    raise ImportError\n\n# Pickle\ntry:\n    import cPickle as pickle\nexcept:\n    from six.moves import cPickle as pickle\nelse:\n    import pickle\n\n# NumPy\ntry:\n    import numpy as np\nexcept:\n\n    logger.error('NumPy must be installed')\n    raise ImportError\n\n# SciPy\ntry:\n    from scipy import stats\n    from scipy.ndimage.interpolation import zoom\n    from scipy.interpolate import interp1d\n    from scipy.spatial import distance as sci_dist\nexcept:\n\n    logger.error('SciPy must be installed')\n    raise ImportError\n\n# GDAL\ntry:\n    from osgeo import gdal\n    from osgeo.gdalconst import *\nexcept:\n\n    logger.error('GDAL must be installed')\n    raise ImportError\n\n# OpenCV\ntry:\n   import cv2\nexcept:\n\n    logger.error('OpenCV must be installed')\n    raise ImportError\n\n# Scikit-learn\ntry:\n    from sklearn import ensemble, tree, metrics, manifold, calibration\n    from sklearn.externals import joblib\n    from sklearn.feature_selection import chi2, VarianceThreshold\n    from sklearn.preprocessing import RobustScaler, StandardScaler\n    from sklearn.neighbors import KNeighborsClassifier\n    from sklearn.linear_model import LogisticRegression\n    from sklearn import svm\n    from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA\n    from sklearn.naive_bayes import GaussianNB\n    from sklearn.covariance import EllipticEnvelope\n    from sklearn.cluster import KMeans\n    from sklearn.semi_supervised import label_propagation\n    from sklearn.model_selection import GridSearchCV\n    from sklearn.decomposition import PCA as skPCA\n    from sklearn.decomposition import IncrementalPCA\n    from sklearn.gaussian_process import GaussianProcessClassifier\n    from sklearn.base import BaseEstimator, ClassifierMixin\n    from sklearn.utils.multiclass import unique_labels\nexcept:\n\n    logger.error('Scikit-learn must be installed')\n    raise ImportError\n\n# Matplotlib\ntry:\n    import matplotlib as mpl\n\n    if (os.environ.get('DISPLAY', '') == '') or (platform.system() == 'Darwin'):\n        mpl.use('Agg')\n\n    import matplotlib.pyplot as plt\n    from mpl_toolkits.mplot3d import Axes3D\n    from matplotlib.colors import ListedColormap\n    import matplotlib.ticker as ticker\nexcept:\n\n    logger.warning('Matplotlib must be installed')\n    raise ImportWarning\n\n# pd\ntry:\n    import pandas as pd\n    # import pd.rpy.common as com\nexcept:\n\n    logger.error('Pandas must be installed')\n    raise ImportError\n\n# retry\ntry:\n    from retrying import retry\nexcept:\n\n    logger.error('retrying must be installed')\n    raise ImportWarning\n\n# Pymorph\ntry:\n\n    import pymorph\n\n    PYMORPH_INSTALLED = True\n\nexcept:\n    PYMORPH_INSTALLED = False\n\n# Pystruct\ntry:\n\n    from pystruct.models import ChainCRF, GridCRF\n    import pystruct.learners as ssvm\n\n    PYSTRUCT_INSTALLED = True\n\nexcept:\n    PYSTRUCT_INSTALLED = False\n\n# LightGBM\ntry:\n\n    import lightgbm as gbm\n\n    LIGHTGBM_INSTALLED = True\n\nexcept:\n    LIGHTGBM_INSTALLED = False\n\n# XGBoost\ntry:\n\n    from xgboost import XGBClassifier\n\n    XGBOOST_INSTALLED = True\n\nexcept:\n    XGBOOST_INSTALLED = False\n\n# Catboost\ntry:\n\n    from catboost import CatBoostClassifier\n\n    CATBOOST_INSTALLED = True\n\nexcept:\n    CATBOOST_INSTALLED = False\n\n# Imbalanced-learn\ntry:\n\n    from imblearn import ensemble as imblearn\n\n    IMBLEARN_INSTALLED = True\n\nexcept:\n    IMBLEARN_INSTALLED = False\n\n# Teapot\ntry:\n\n    from tpot import TPOTClassifier\n\n    TPOT_INSTALLED = True\n\nexcept:\n    TPOT_INSTALLED = False\n\n# Scikit-garden\ntry:\n\n    import skgarden\n\n    SKGARDEN_INSTALLED = True\n\nexcept:\n    SKGARDEN_INSTALLED = False\n\n# Rtree\ntry:\n    import rtree\nexcept:\n\n    # print('Rtree must be installed to use spatial indexing')\n    pass\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\n\ndef _do_c5_cubist_predict(c5_cubist_model, classifier_name, predict_samps, rows_i=None):\n\n    \"\"\"\n    A C5/Cubist prediction function\n\n    Args:\n        c5_cubist_model (object):\n        classifier_name (str):\n        predict_samps (rpy2 array): An array of features to make predictions on.\n        rows_i (Optional[rpy2 object]): A R/rpy2 model instance of feature rows to make predictions on. If not passed,\n            predictions are made on all rows. Default is None.\n\n    Returns:\n        NumPy 1d array of class predictions\n    \"\"\"\n\n    if classifier_name == 'c5':\n\n        if not rows_i:\n            return np.array(C50.predict_C5_0(c5_cubist_model, newdata=predict_samps, type='class'), dtype='int16')\n        else:\n            return np.array(C50.predict_C5_0(c5_cubist_model, newdata=predict_samps.rx(rows_i, True),\n                                             type='class'), dtype='int64')\n\n    elif classifier_name == 'cubist':\n\n        if not rows_i:\n            return np.array(Cubist.predict_cubist(c5_cubist_model, newdata=predict_samps), dtype='float32')\n        else:\n            return np.array(Cubist.predict_cubist(c5_cubist_model, newdata=predict_samps.rx(rows_i, True)),\n                            dtype='float32')\n\n\ndef predict_c5_cubist(input_model, ip):\n\n    \"\"\"\n    A C5/Cubist prediction function for parallel predictions\n\n    Args:\n        input_model (str): The model file to load.\n        ip (list): A indice list of rows to extract from ``predict_samps``.\n    \"\"\"\n\n    with open(input_model, 'rb') as p_load:\n        ci, m, h = pickle.load(p_load)\n\n    rows_i = ro.IntVector(range(ip[0], ip[0]+ip[1]))\n\n    if ci['classifier'] == 'c5':\n        # TODO: type='prob'\n        return np.array(C50.predict_C5_0(m, newdata=predict_samps.rx(rows_i, True), type='class'), dtype='int16')\n    else:\n        return np.array(Cubist.predict_cubist(m, newdata=predict_samps.rx(rows_i, True)), dtype='float32')\n\n\ndef predict_scikit_probas_static(features,\n                                 mdl,\n                                 rw,\n                                 cw,\n                                 ipadded,\n                                 jpadded,\n                                 n_rows,\n                                 n_cols,\n                                 morphology,\n                                 do_not_morph,\n                                 plr_matrix,\n                                 plr_window_size,\n                                 plr_iterations,\n                                 d_type):\n\n    \"\"\"\n    A function to get posterior probabilities from Scikit-learn models\n\n    Args:\n        rw (int)\n        cw (int)\n        ipadded (int)\n        jpadded (int)\n        n_rows (int)\n        n_cols (int)\n        morphology (bool)\n        do_not_morph (int list)\n        plr_matrix (2d array)\n        plr_window_size (int)\n        plr_iterations (int)\n        d_type (str)\n    \"\"\"\n\n    # `probabilities` shaped as [samples x n classes]\n    probabilities = mdl.predict_proba(np.float64(features))\n\n    n_classes = probabilities.shape[1]\n\n    # Get the classes.\n    # if hasattr(mdl, 'estimators'):\n    #\n    #     if len(mdl.classes_) == n_classes:\n    #         class_list = mdl.classes_\n    #     # elif len(mdl.estimators[0][1].classes_):\n    #     #     class_list = mdl.estimators[0][1].classes_\n    #     else:\n    #\n    #         logger.exception('Could not match the class list.')\n    #         raise ValueError\n    #\n    # else:\n    class_list = mdl.classes_\n\n    # Reshape and run PLR\n    probabilities_argmax = moving_window(raster_tools.columns_to_nd(probabilities, n_classes, rw, cw),\n                                         statistic='plr',\n                                         window_size=plr_window_size,\n                                         weights=plr_matrix,\n                                         iterations=plr_iterations).argmax(axis=0)\n\n    if morphology:\n        predictions = np.zeros(probabilities_argmax.shape, dtype='uint8')\n    else:\n        predictions = np.zeros(probabilities_argmax.shape, dtype=raster_tools.STORAGE_DICT_NUMPY[d_type])\n\n    # Convert indices to classes.\n    for class_index, real_class in enumerate(class_list):\n        predictions[probabilities_argmax == class_index] = real_class\n\n    if morphology:\n\n        if isinstance(do_not_morph, list):\n\n            predictions_copy = predictions[ipadded:ipadded+n_rows,\n                                           jpadded:jpadded+n_cols].copy()\n\n            predictions = pymorph.closerec(pymorph.closerec(predictions,\n                                                            Bdil=pymorph.secross(r=3),\n                                                            Bc=pymorph.secross(r=1)),\n                                           Bdil=pymorph.secross(r=2),\n                                           Bc=pymorph.secross(r=1))[ipadded:ipadded+n_rows,\n                                                                    jpadded:jpadded+n_cols]\n\n            for do_not_morph_value in do_not_morph:\n                predictions[predictions_copy == do_not_morph_value] = do_not_morph_value\n\n            return predictions\n\n        else:\n\n            return pymorph.closerec(pymorph.closerec(predictions,\n                                                     Bdil=pymorph.secross(r=3),\n                                                     Bc=pymorph.secross(r=1)),\n                                    Bdil=pymorph.secross(r=2),\n                                    Bc=pymorph.secross(r=1))[ipadded:ipadded+n_rows,\n                                                             jpadded:jpadded+n_cols]\n\n    else:\n        return predictions[ipadded:ipadded+n_rows, jpadded:jpadded+n_cols]\n\n\ndef predict_scikit_probas(rw,\n                          cw,\n                          ipadded,\n                          jpadded,\n                          n_rows,\n                          n_cols,\n                          morphology,\n                          do_not_morph,\n                          relax_probabilities,\n                          plr_matrix,\n                          plr_window_size,\n                          plr_iterations,\n                          predict_probs,\n                          d_type,\n                          null_samples):\n\n    \"\"\"\n    A function to get posterior probabilities from Scikit-learn models\n\n    Args:\n        rw (int)\n        cw (int)\n        ipadded (int)\n        jpadded (int)\n        n_rows (int)\n        n_cols (int)\n        morphology (bool)\n        do_not_morph (int list)\n        relax_probabilities (bool)\n        plr_matrix (2d array)\n        plr_window_size (int)\n        plr_iterations (int)\n        predict_probs (bool)\n        d_type (str)\n        null_samples (tuple)\n    \"\"\"\n\n    # `probabilities` shaped as [samples x n classes]\n    probabilities = mdl.predict_proba(np.float64(features))\n\n    n_classes = probabilities.shape[1]\n\n    # Get the classes.\n    # if hasattr(mdl, 'estimators'):\n    #\n    #     if len(mdl.classes_) == n_classes:\n    #         class_list = mdl.classes_\n    #     elif len(mdl.estimators[0][1].classes_):\n    #         class_list = mdl.estimators[0][1].classes_\n    #     else:\n    #\n    #         logger.exception('Could not match the class list.')\n    #         raise ValueError\n    #\n    # else:\n    class_list = mdl.classes_\n\n    probabilities = raster_tools.columns_to_nd(probabilities, n_classes, rw, cw)\n\n    if null_samples[0].shape[0] > 0:\n\n        for pidx in range(0, n_classes):\n\n            proba_layer = probabilities[pidx]\n            proba_layer[null_samples] = 0.0\n            probabilities[pidx] = proba_layer\n\n    if relax_probabilities:\n\n        probabilities = moving_window(np.float32(probabilities),\n                                      statistic='plr',\n                                      window_size=plr_window_size,\n                                      weights=plr_matrix,\n                                      iterations=plr_iterations)\n\n    if predict_probs:\n\n        # Predict class conditional probabilities.\n        if relax_probabilities:\n            return probabilities[:, ipadded:ipadded+n_rows, jpadded:jpadded+n_cols]\n        else:\n            return probabilities\n\n    probabilities = probabilities.argmax(axis=0)\n\n    if morphology:\n        predictions = np.zeros(probabilities.shape, dtype='uint8')\n    else:\n        predictions = np.zeros(probabilities.shape, dtype=raster_tools.STORAGE_DICT_NUMPY[d_type])\n\n    # Convert indices to classes.\n    for class_index, real_class in enumerate(class_list):\n        predictions[probabilities == class_index] = real_class\n    \n    if morphology:\n\n        if isinstance(do_not_morph, list):\n\n            predictions_copy = predictions[ipadded:ipadded+n_rows, jpadded:jpadded+n_cols].copy()\n\n            predictions = pymorph.closerec(pymorph.closerec(predictions,\n                                                            Bdil=pymorph.secross(r=3),\n                                                            Bc=pymorph.secross(r=1)),\n                                           Bdil=pymorph.secross(r=2),\n                                           Bc=pymorph.secross(r=1))[ipadded:ipadded+n_rows,\n                                                                    jpadded:jpadded+n_cols]\n\n            for do_not_morph_value in do_not_morph:\n                predictions[predictions_copy == do_not_morph_value] = do_not_morph_value\n\n            return predictions\n\n        else:\n\n            return pymorph.closerec(pymorph.closerec(predictions,\n                                                     Bdil=pymorph.secross(r=3),\n                                                     Bc=pymorph.secross(r=1)),\n                                    Bdil=pymorph.secross(r=2),\n                                    Bc=pymorph.secross(r=1))[ipadded:ipadded+n_rows,\n                                                             jpadded:jpadded+n_cols]\n\n    else:\n        return predictions[ipadded:ipadded+n_rows, jpadded:jpadded+n_cols]\n\n\ndef predict_scikit(pool_iter):\n\n    \"\"\"\n    A function to predict in parallel from Scikit-learn models\n\n    Args:\n        pool_iter (int)\n    \"\"\"\n\n    ip_ = indice_pairs[pool_iter]\n\n    return mdl.predict(features[ip_[0]:ip_[0]+ip_[1]])\n\n\ndef predict_cv(ci, cs, fn, pc, cr, ig, xy, cinfo, wc):\n\n    \"\"\"\n    This is an ugly (and hopefully temporary) hack to get around the missing OpenCV model ``load`` method.\n    \"\"\"\n\n    cl = classification()\n    cl.split_samples(fn, perc_samp=pc, classes2remove=cr, ignore_feas=ig, use_xy=xy)\n    cl.construct_model(classifier_info=cinfo, class_weight=wc, be_quiet=True)\n\n    return cl.model.predict(features[ci:ci+cs])[1]\n\n\ndef get_available_models():\n\n    \"\"\"Gets a list of available models\"\"\"\n\n    return ['ab-dt', 'ab-ex-dt', 'ab-rf', 'ab-ex-rf', 'ab-dtr', 'ab-ex-dtr',\n            'ab-rfr', 'ab-ex-rfr',\n            'bag-dt', 'bag-ex-dt', 'bag-dtr', 'blag', 'blaf', 'blab', 'bayes', 'dt', 'dtr',\n            'ex-dt', 'ex-dtr', 'gb', 'gbr', 'c5', 'cubist',\n            'ex-rf', 'ex-rfr',\n            'logistic', 'nn', 'gaussian',\n            'rf', 'cvrf', 'rfr', 'cvmlp',\n            'svmc', 'svmnu', 'svmcr', 'cvsvm', 'cvsvma', 'cvsvr', 'cvsvra', 'qda',\n            'chaincrf', 'gridcrf',\n            'lightgbm', 'tpot', 'mondrian', 'catboost', 'xgboost']\n\n\nclass ParameterHandler(object):\n\n    def __init__(self, classifier):\n\n        self.equal_params = dict(trees='n_estimators',\n                                 min_samps='min_samples_split')\n\n        self.forests = ['rf',\n                        'ex-rf']\n\n        self.forests_regressed = ['rfr',\n                                  'ex-rfr']\n\n        self.bagged = ['bag-dt',\n                       'bag-ex-dt',\n                       'bag-dtr']\n\n        self.bagged_imbalanced = ['blag']\n        self.forest_imbalanced = ['blaf']\n        self.boost_imbalanced = ['blab']\n\n        self.trees = ['dt',\n                      'ex-dt']\n\n        self.trees_regressed = ['dtr',\n                                'ex-dtr']\n\n        self.boosted = ['ab-dt',\n                        'ab-ex-dt',\n                        'ab-rf',\n                        'ab-ex-rf']\n\n        self.boosted_g = ['gb']\n\n        self.boosted_g_regressed = ['gbr']\n\n        if classifier in self.forests:\n\n            self.valid_params = ['n_estimators', 'criterion', 'max_depth', 'min_samples_split',\n                                 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features',\n                                 'max_leaf_nodes', 'bootstrap', 'oob_score', 'n_jobs',\n                                 'random_state', 'verbose', 'warm_start', 'class_weight']\n\n        elif classifier in self.forests_regressed:\n\n            self.valid_params = ['n_estimators', 'criterion', 'max_depth', 'min_samples_split',\n                                 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features',\n                                 'max_leaf_nodes', 'bootstrap', 'oob_score', 'n_jobs',\n                                 'random_state', 'verbose', 'warm_start']\n\n        elif classifier in self.bagged:\n\n            self.valid_params = ['base_estimator', 'n_estimators', 'max_samples', 'max_features',\n                                 'bootstrap', 'bootstrap_features', 'oob_score', 'warm_start',\n                                 'n_jobs', 'random_state', 'verbose']\n\n        elif classifier in self.bagged_imbalanced:\n\n            self.valid_params = ['base_estimator', 'n_estimators', 'max_samples', 'max_features',\n                                 'bootstrap', 'bootstrap_features', 'oob_score', 'warm_start',\n                                 'ratio', 'replacement',\n                                 'n_jobs', 'random_state', 'verbose']\n\n        elif classifier in self.forest_imbalanced:\n\n            self.valid_params = ['n_estimators', 'criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf',\n                                 'min_weight_fraction_leaf', 'max_features', 'max_leaf_nodes',\n                                 'min_impurity_decrease', 'bootstrap', 'oob_score', 'replacement',\n                                 'n_jobs', 'verbose', 'warm_start', 'class_weight']\n\n        elif classifier in self.boost_imbalanced:\n\n            self.valid_params = ['base_estimator', 'n_estimators', 'learning_rate', 'algorithm',\n                                 'sampling_strategy', 'replacement', 'random_state']\n\n        elif classifier in self.trees:\n\n            self.valid_params = ['criterion', 'splitter', 'max_depth', 'min_samples_split',\n                                 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features',\n                                 'random_state', 'max_leaf_nodes', 'class_weight', 'presort']\n\n        elif classifier in self.trees_regressed:\n\n            self.valid_params = ['criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf',\n                                 'min_weight_fraction_leaf', 'max_features', 'random_state', 'max_leaf_nodes',\n                                 'presort']\n\n        elif classifier in self.boosted_g:\n\n            self.valid_params = ['loss', 'learning_rate', 'n_estimators', 'subsample', 'min_samples_split',\n                                 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_depth', 'init',\n                                 'random_state', 'max_features', 'verbose', 'max_leaf_nodes', 'warm_start',\n                                 'presort']\n\n        elif classifier in self.boosted_g_regressed:\n\n            self.valid_params = ['loss', 'learning_rate', 'n_estimators', 'subsample', 'min_samples_split',\n                                 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_depth', 'init',\n                                 'random_state', 'max_features', 'alpha', 'verbose', 'max_leaf_nodes',\n                                 'warm_start', 'presort']\n\n        elif classifier in self.boosted:\n            self.valid_params = ['base_estimator', 'n_estimators', 'learning_rate', 'algorithm', 'random_state']\n\n        elif classifier == 'bayes':\n\n            self.valid_params = ['priors']\n\n        elif classifier == 'nn':\n\n            self.valid_params = ['n_neighbors', 'weights', 'algorithm', 'leaf_size', 'p', 'metric',\n                                 'metric_params', 'n_jobs']\n\n        elif classifier == 'logistic':\n\n            self.valid_params = ['penalty', 'dual', 'tol', 'C', 'fit_intercept', 'intercept_scaling',\n                                 'class_weight', 'random_state', 'solver', 'max_iter', 'multi_class',\n                                 'verbose', 'warm_start', 'n_jobs']\n\n        elif classifier == 'qda':\n\n            self.valid_params = ['priors', 'reg_param', 'store_covariance', 'tol', 'store_covariances']\n\n        elif classifier == 'gaussian':\n\n            self.valid_params = ['kernel', 'optimizer', 'n_restarts_optimizer', 'max_iter_predict',\n                                 'warm_start', 'copy_X_train', 'random_state', 'multi_class', 'n_jobs']\n\n        elif classifier == 'svmc':\n\n            self.valid_params = ['C', 'kernel', 'degree', 'gamma', 'coef0', 'shrinking', 'probability',\n                                 'tol', 'cache_size', 'class_weight', 'verbose', 'max_iter',\n                                 'decision_function_shape', 'random_state']\n\n        elif classifier == 'svmnu':\n\n            self.valid_params = ['nu', 'kernel', 'degree', 'gamma', 'coef0', 'shrinking', 'probability',\n                                 'tol', 'cache_size', 'class_weight', 'verbose', 'max_iter',\n                                 'decision_function_shape', 'random_state']\n\n        elif classifier in ['chaincrf', 'gridcrf']:\n\n            self.valid_params = ['max_iter', 'C', 'n_jobs', 'show_loss_every',\n                                 'tol', 'inference_cache',\n                                 'inference_method',\n                                 'neighborhood']\n\n        elif classifier == 'lightgbm':\n\n            self.valid_params = ['boosting_type', 'num_leaves', 'max_depth', 'learning_rate', 'n_estimators',\n                                 'subsample_for_bin', 'objective', 'class_weight', 'min_split_gain',\n                                 'min_child_weight', 'min_child_samples', 'subsample', 'subsample_freq',\n                                 'colsample_bytree', 'reg_alpha', 'reg_lambda', 'random_state', 'n_jobs', 'silent',\n                                 'feature_fraction', 'bagging_freq', 'bagging_fraction', 'max_bin', 'num_boost_round']\n\n        elif classifier == 'catboost':\n\n            self.valid_params = ['iterations', 'learning_rate', 'depth', 'l2_leaf_reg', 'model_size_reg',\n                                 'rsm', 'loss_function', 'border_count', 'feature_border_type',\n                                 'fold_permutation_block_size', 'od_pval', 'od_wait', 'od_type',\n                                 'nan_mode', 'counter_calc_method', 'leaf_estimation_iterations',\n                                 'leaf_estimation_method', 'thread_count', 'random_seed',\n                                 'use_best_model', 'best_model_min_trees', 'verbose', 'silent',\n                                 'logging_level', 'metric_period', 'simple_ctr', 'combinations_ctr',\n                                 'per_feature_ctr', 'ctr_leaf_count_limit', 'store_all_simple_ctr',\n                                 'max_ctr_complexity', 'has_time', 'allow_const_label', 'classes_count',\n                                 'class_weights', 'one_hot_max_size', 'random_strength', 'name',\n                                 'ignored_features', 'train_dir', 'custom_metric', 'custom_loss', 'eval_metric',\n                                 'bagging_temperature', 'save_snapshot', 'snapshot_file', 'snapshot_interval',\n                                 'fold_len_multiplier', 'used_ram_limit', 'gpu_ram_part', 'pinned_memory_size',\n                                 'allow_writing_files', 'final_ctr_computation_mode', 'approx_on_full_history',\n                                 'boosting_type', 'task_type', 'device_config', 'devices', 'bootstrap_type',\n                                 'subsample', 'dev_score_calc_obj_block_size', 'max_depth', 'n_estimators',\n                                 'num_trees', 'num_boost_round', 'colsample_bylevel', 'random_state',\n                                 'reg_lambda', 'objective', 'eta', 'max_bin', 'scale_pos_weight', 'metadata',\n                                 'early_stopping_rounds', 'cat_features']\n\n        elif classifier == 'xgboost':\n\n            self.valid_params = ['max_depth', 'learning_rate', 'n_estimators', 'silent',\n                                 'objective', 'booster', 'n_jobs', 'nthread', 'gamma',\n                                 'min_child_weight', 'max_delta_step', 'subsample', 'colsample_bytree',\n                                 'colsample_bylevel', 'reg_alpha', 'reg_lambda', 'scale_pos_weight',\n                                 'base_score', 'random_state', 'seed', 'missing']\n\n        elif classifier == 'mondrian':\n            self.valid_params = ['n_estimators', 'max_depth', 'min_samples_split', 'random_state', 'n_jobs']\n\n        elif classifier == 'tpot':\n            self.valid_params = list()\n\n        else:\n            logger.warning('  The classifier is not supported.')\n\n    def check_parameters(self, cinfo, default_params, trials_set=False):\n\n        # Set defaults\n        for k, v in viewitems(default_params):\n\n            if (k not in cinfo) and (k in self.valid_params):\n                cinfo[k] = v\n\n        for param_key, param_value in viewitems(cinfo.copy()):\n\n            if param_key in self.equal_params:\n\n                if param_key == 'trials':\n\n                    if not trials_set:\n\n                        cinfo[self.equal_params[param_key]] = param_value\n                        del cinfo[param_key]\n\n                else:\n\n                    if self.equal_params[param_key] in cinfo:\n\n                        param_key_ = copy(param_key)\n                        param_key = self.equal_params[param_key]\n                        del cinfo[param_key_]\n\n            if (param_key not in self.valid_params) and (param_key in cinfo):\n                del cinfo[param_key]\n\n        return cinfo\n\n\nclass PickleIt(object):\n\n    \"\"\"A class for pickling objects\"\"\"\n\n    @staticmethod\n    def dump(data2dump, output_file):\n\n        with open(output_file, 'wb') as p_dump:\n\n            pickle.dump(data2dump,\n                        p_dump,\n                        protocol=pickle.HIGHEST_PROTOCOL)\n\n    @staticmethod\n    def load(input_file):\n\n        with open(input_file, 'rb') as p_load:\n            loaded_data = pickle.load(p_load)\n\n        return loaded_data\n\n\nclass Samples(object):\n\n    \"\"\"\n    A class to handle data samples\n\n    Attributes:\n        file_name (str)\n        p_vars (ndarray)\n        p_vars_test (ndarray)\n        labels (list)\n        labels_test (list)\n        use_xy (bool)\n        perc_samp (float)\n        perc_samp_each (float)\n        classes2remove (list)\n        headers (list)\n        all_samps (ndarray)\n        XY (ndarray)\n        n_samps (int)\n        n_feas (int)\n        classes (list)\n        class_counts (dict)\n        sample_weight (1d array)\n        min_observations (int)\n        class_idx (1d array)\n        clear_idx (1d array)\n        train_idx (1d array)\n        test_idx (1d array)\n        df (DataFrame)\n    \"\"\"\n\n    def __init__(self):\n        self.time_stamp = time.asctime(time.localtime(time.time()))\n\n    def split_samples(self,\n                      file_name,\n                      perc_samp=1.0,\n                      perc_samp_each=0.5,\n                      scale_data=False,\n                      class_subs=None,\n                      norm_struct=True,\n                      labs_type='int',\n                      recode_dict=None,\n                      vs_all=None,\n                      classes2remove=None,\n                      sample_weight=None,\n                      ignore_feas=None,\n                      use_xy=False,\n                      stratified=False,\n                      spacing=1000.0,\n                      x_label='X',\n                      y_label='Y',\n                      response_label='response',\n                      clear_observations=None,\n                      min_observations=10,\n                      limit_test_size=None):\n\n        \"\"\"\n        Split samples for training and testing.\n        \n        Args:\n            file_name (str or 2d array or DataFrame): Input text file, 2d array, or Pandas DataFrame\n                with samples and labels.\n            perc_samp (Optional[float]): Percent to sample from all samples. Default is .9. This parameter\n                samples from the entire set of samples, regardless of which class they are in.\n\n                *It is currently recommended to use `perc_samp_each` or `class_subs` instead of `perc_samp`.\n\n            perc_samp_each (Optional[float]): Percent to sample from each class. Default is 0. *This parameter\n                overrides ``perc_samp`` and forces a percentage of samples from each class.\n            scale_data (Optional[bool]): Whether to scale (by standardization) data. Default is False.\n            class_subs (Optional[dict]): Dictionary of class percentages or number to sample. Default is empty, or None.\n                Example:\n                    Sample by percentage = {1:.9, 2:.9, 3:.5}\n                    Sample by integer = {1:300, 2:300, 3:150}\n            norm_struct (Optional[bool]): Whether the structure of the data is normal. Default is True. \n                In the case of MpGlue, normal is (X,Y,Var1,Var2,Var3,Var4,...,VarN,Labels),\n                whereas the alternative (i.e., False) is (Labels,Var1,Var2,Var3,Var4,...,VarN)\n            labs_type (Optional[str]): Read class labels as integer ('int') or float ('float'). Default is 'int'.\n            recode_dict (Optional[dict]): A dictionary of classes to recode. Default is {}, or empty dictionary.\n            vs_all (Optional[list]): A list of classes to recode to 1, and all other classes get recoded to 0.\n                Default is None.\n            classes2remove (Optional[list]): List of classes to remove from samples. Default is [], or keep\n                all classes.\n            sample_weight (Optional[list or 1d array]): Sample weights. Default is None.\n            ignore_feas (Optional[list]): A list of feature (image layer) indexes to ignore. Default is [], or use all\n                features. *The features are sorted.\n            use_xy (Optional[bool]): Whether to use the x, y coordinates as predictive variables. Default is False.\n            stratified (Optional[bool]): Whether to stratify the samples. Default is False.\n            spacing (Optional[float]): The grid spacing (meters) to use for stratification (in ``stratified``).\n                Default is 1000.\n            x_label (str): The x coordinate label. Default is 'X'.\n            y_label (str): The y coordinate label. Default is 'Y'.\n            response_label (str): The response label. Default is 'response'.\n            clear_observations (Optional[array like]): Clear observations to filter samples by. Default is None.\n                *The array will be flattened if not 1d.\n            min_observations (Optional[int]): The minimum number of observations required in a time series.\n                *Uses `clear_observations`.\n            limit_test_size (Optional[int]): A size to limit test samples to. Default is None.\n                For example, if samples are split 30/70 for train/test and the test set is larger than needed\n                for model validation, limit the test sample pool to [`limit_test_size`, <n feas>].\n        \"\"\"\n\n        if not isinstance(class_subs, dict):\n            self.class_subs = dict()\n        else:\n            self.class_subs = class_subs\n\n        if not isinstance(recode_dict, dict):\n            recode_dict = dict()\n\n        if not isinstance(vs_all, list):\n            vs_all = list()\n\n        if not isinstance(classes2remove, list):\n            classes2remove = list()\n\n        if not isinstance(ignore_feas, list):\n            ignore_feas = list()\n\n        self.file_name = file_name\n\n        self.labels_test = None\n        self.p_vars = None\n        self.p_vars_test = None\n        self.labels = None\n        self.use_xy = use_xy\n        self.perc_samp = perc_samp\n        self.perc_samp_each = perc_samp_each\n        self.classes2remove = classes2remove\n        self.sample_weight = sample_weight\n        self.sample_weight_test = None\n        self.min_observations = min_observations\n        self.response_label = response_label\n        self.limit_test_size = limit_test_size\n\n        self.class_idx = None\n        self.clear_idx = None\n\n        self.sample_info_dict = dict()\n\n        # Open the data samples.\n        if isinstance(self.file_name, str):\n\n            self.df = pd.read_csv(self.file_name, sep=',')\n\n        elif isinstance(self.file_name, pd.DataFrame):\n\n            self.df = self.file_name\n\n        elif isinstance(self.file_name, np.ndarray):\n\n            if len(self.file_name.shape) != 2:\n\n                logger.error('  The samples array must be a 2d array.')\n                raise TypeError\n\n            headers = [x_label, y_label] + list(map(str, range(1, self.file_name.shape[1]-2))) + [self.response_label]\n\n            self.df = pd.DataFrame(self.file_name, columns=headers)\n\n            self.file_name = None\n\n        else:\n\n            logger.error('  The samples file must be a text file or a 2d array.')\n            raise TypeError\n\n        # Parse the headers.\n        self.headers = self.df.columns.values.tolist()\n\n        if norm_struct:\n\n            data_position = 2\n\n            self.headers = self.headers[self.headers.index(x_label):]\n\n            # The response index position.\n            self.label_idx = -1\n\n        else:\n\n            self.headers = self.headers[self.headers.index(self.response_label):]\n\n            # The response index position.\n            self.label_idx = 0\n\n            data_position = 0\n\n        # Parse the x variables.\n        self.all_samps = self.df.loc[:, self.headers[data_position:]].values\n\n        if isinstance(clear_observations, np.ndarray) or isinstance(clear_observations, list):\n\n            clear_observations = np.array(clear_observations, dtype='uint64').ravel()\n\n            if self.all_samps.shape[0] != len(clear_observations):\n\n                logger.error('  The clear observation and sample lengths do no match.')\n                raise AssertionError\n\n        # ---------------------------\n        # Change in array COLUMN size\n        # ---------------------------\n        # Remove specified x variables.\n        if ignore_feas:\n\n            ignore_feas = np.array(sorted([int(f-1) for f in ignore_feas]), dtype='int64')\n\n            self.all_samps = np.delete(self.all_samps, ignore_feas, axis=1)\n            self.df.drop(self.df.columns[ignore_feas+2], inplace=True, axis=1)\n\n            self.headers = self.df.columns.tolist()\n\n        if self.use_xy:\n\n            # Reorder the variables and x, y coordinates.\n            self.df = self.df[self.headers[2:-1] + self.headers[:2] + [self.headers[-1]]]\n            self.all_samps = self.df.values\n\n            self.headers = self.df.columns.tolist()\n\n        else:\n\n            # Remove the x, y coordinates.\n            self.headers = self.headers[2:]\n\n        if isinstance(self.sample_weight, list) and len(self.sample_weight) > 0:\n            self.sample_weight = np.array(self.sample_weight, dtype='float32')\n\n        # ----------------------------------\n        # Potential change in array ROW size\n        # ----------------------------------\n        # Remove unwanted classes.\n        if self.classes2remove:\n            clear_observations = self._remove_classes(self.classes2remove, clear_observations)\n\n        # ----------------------------------\n        # Potential change in array ROW size\n        # ----------------------------------\n        # Remove samples with less than\n        #   minimum time series requirement.\n        if isinstance(clear_observations, np.ndarray) and (min_observations > 0):\n            clear_observations = self._remove_min_observations(clear_observations)\n\n        # Get the number of samples and x variables.\n        #   n_samps = number of samples\n        #   n_feas = number of features minus the labels\n        self.n_samps = self.all_samps.shape[0]\n        self.n_feas = self.all_samps.shape[1] - 1\n\n        if isinstance(self.sample_weight, np.ndarray):\n            assert len(self.sample_weight) == self.n_samps\n\n        if isinstance(clear_observations, np.ndarray):\n            assert len(clear_observations) == self.n_samps\n\n        # Recode response labels.\n        if recode_dict:\n            self._recode_labels(recode_dict)\n\n        if vs_all:\n            self._recode_all(vs_all)\n\n        # Parse the x, y coordinates.\n        self.XY = self.df[[x_label, y_label]].values\n\n        # Spatial stratified sampling.\n        if stratified:\n            self._create_grid_strata(spacing)\n\n        self.train_idx = list()\n\n        # ----------------------------------\n        # Potential change in array ROW size\n        # ----------------------------------\n        # Sample a specified number per class.\n        if self.class_subs or (0 < perc_samp_each < 1):\n\n            # Create the group strata.\n            if stratified:\n                self._create_group_strata()\n\n            if not self.class_subs and (0 < perc_samp_each < 1):\n\n                for clp in self.df[self.response_label].unique():\n                    self.class_subs[int(clp)] = perc_samp_each\n\n            for class_key, cl in sorted(viewitems(self.class_subs)):\n\n                if stratified:\n                    self._stratify(class_key, cl)\n                else:\n                    self._sample_group(class_key, cl)\n\n            self.train_idx = np.array(sorted(self.train_idx), dtype='int64')\n            self.test_idx = np.array(sorted(list(set(self.df.index.tolist()).difference(self.train_idx))), dtype='int64')\n\n            if isinstance(self.limit_test_size, int):\n\n                if len(self.test_idx) > self.limit_test_size:\n\n                    self.test_idx = np.array(sorted(np.random.choice(self.test_idx,\n                                                                     size=self.limit_test_size,\n                                                                     replace=False)), dtype='int64')\n\n            test_samps = self.all_samps[self.test_idx]\n            self.all_samps = self.all_samps[self.train_idx]\n\n            if isinstance(clear_observations, np.ndarray):\n\n                # The number of clear observations at test samples\n                self.test_clear = clear_observations[self.test_idx]\n\n                # The number of clear observations at train samples\n                self.train_clear = clear_observations[self.train_idx]\n\n            if isinstance(self.sample_weight, np.ndarray):\n\n                self.sample_weight_test = self.sample_weight[self.test_idx]\n                self.sample_weight = self.sample_weight[self.train_idx]\n\n        elif ((isinstance(perc_samp, float) and (perc_samp < 1)) or (isinstance(perc_samp, int) and (perc_samp > 0))) \\\n                and (perc_samp_each == 0):\n\n            if stratified:\n\n                n_total_samps = int(perc_samp * self.n_samps)\n                n_match_samps = 0\n\n                # We need x, y coordinates, so force it.\n                if not self.use_xy:\n                    self.all_samps = np.c_[self.all_samps[:, :-1], self.XY, self.all_samps[:, -1]]\n\n                # while n_match_samps < n_total_samps:\n                #     n_match_samps = self._stratify(y_grids, x_grids, n_match_samps, n_total_samps)\n\n                test_samps = copy(self.all_samps)\n                self.all_samps = copy(self.stratified_samps)\n\n            else:\n\n                test_samps, self.all_samps, test_clear, train_clear, self.sample_weight = \\\n                    self.get_test_train(self.all_samps, perc_samp, self.sample_weight, clear_observations)\n\n                n_samples = self.all_samps.shape[0]\n\n                # Add a bit of randomness.\n                shuffle_permutations = np.random.permutation(n_samples)\n\n                self.all_samps = self.all_samps[shuffle_permutations]\n\n                if isinstance(clear_observations, np.ndarray):\n\n                    self.test_clear = np.uint64(test_clear)\n                    self.train_clear = np.uint64(train_clear[shuffle_permutations])\n\n                if isinstance(self.sample_weight, np.ndarray):\n                    self.sample_weight = self.sample_weight[shuffle_permutations]\n\n        else:\n            self.train_clear = clear_observations\n\n        self.n_samps = self.all_samps.shape[0]\n\n        # Get class labels.\n        if labs_type == 'int':\n            self.labels = np.array(self.all_samps[:, self.label_idx].ravel(), dtype='int64')\n        elif labs_type == 'float':\n            self.labels = np.array(self.all_samps[:, self.label_idx].ravel(), dtype='float32')\n        else:\n\n            logger.error('  `labs_type` should be int or float')\n            raise TypeError\n\n        if norm_struct:\n            self.p_vars = np.float32(self.all_samps[:, :self.label_idx])\n        else:\n            self.p_vars = np.float32(self.all_samps[:, 1:])\n\n        self.p_vars[np.isnan(self.p_vars) | np.isinf(self.p_vars)] = 0.\n\n        if self.class_subs or (0 < perc_samp_each < 1) or ((perc_samp < 1) and (perc_samp_each == 0)):\n\n            # Get class labels.\n            dtype_ = 'float32' if labs_type == 'float32' else 'int64'\n\n            if norm_struct:\n\n                self.labels_test = np.array(test_samps[:, self.label_idx].ravel(), dtype=dtype_)\n                self.p_vars_test = np.float32(test_samps[:, :self.label_idx])\n\n            else:\n\n                self.labels_test = np.array(test_samps[:, 1:].ravel(), dtype=dtype_)\n                self.p_vars_test = np.float32(test_samps[:, 1:])\n\n            self.p_vars_test[np.isnan(self.p_vars_test) | np.isinf(self.p_vars_test)] = 0.\n\n            self.p_vars_test_rows = self.p_vars_test.shape[0]\n            self.p_vars_test_cols = self.p_vars_test.shape[1]\n\n        # Get individual class counts.\n        self.update_class_counts()\n\n        if scale_data:\n            self._scale_p_vars()\n        else:\n\n            self.scaler = None\n            self.scaled = False\n\n        self.update_sample_info(scaler=self.scaler,\n                                scaled=self.scaled,\n                                use_xy=self.use_xy)\n\n    def update_sample_info(self, **kwargs):\n\n        self.sample_info_dict['n_classes'] = self.n_classes\n        self.sample_info_dict['classes'] = self.classes\n        self.sample_info_dict['n_feas'] = self.n_feas\n\n        for k, v in viewitems(kwargs):\n            self.sample_info_dict[k] = v\n\n    @property\n    def n_classes(self):\n        return len(self.classes)\n\n    @property\n    def classes(self):\n        return self._classes()\n\n    def _classes(self):\n\n        if hasattr(self, 'model'):\n            return self.model.classes_\n        else:\n\n            if isinstance(self.labels, np.ndarray):\n                has_labels = True if self.labels.shape[0] > 0 else False\n            else:\n                has_labels = True if self.labels else False\n\n            if has_labels:\n                return unique_labels(self.labels)\n            else:\n                return list()\n\n    @staticmethod\n    def _stack_samples(counter,\n                       test_stk,\n                       train_stk,\n                       test_samples_temp,\n                       train_samples_temp,\n                       clear_test_stk,\n                       clear_train_stk,\n                       test_clear_temp,\n                       train_clear_temp,\n                       weights_train_stk,\n                       train_weights_temp):\n\n        \"\"\"\n        Stacks sub-samples\n        \"\"\"\n\n        if counter == 1:\n\n            test_stk = test_samples_temp.copy()\n            train_stk = train_samples_temp.copy()\n\n            if isinstance(train_clear_temp, np.ndarray):\n\n                clear_test_stk = test_clear_temp.copy()\n                clear_train_stk = train_clear_temp.copy()\n\n            if isinstance(train_weights_temp, np.ndarray):\n                weights_train_stk = train_weights_temp.copy()\n\n        else:\n\n            test_stk = np.vstack((test_stk, test_samples_temp))\n            train_stk = np.vstack((train_stk, train_samples_temp))\n\n            if isinstance(train_clear_temp, np.ndarray):\n\n                clear_test_stk = np.hstack((clear_test_stk, test_clear_temp))\n                clear_train_stk = np.hstack((clear_train_stk, train_clear_temp))\n\n            if isinstance(train_weights_temp, np.ndarray):\n                weights_train_stk = np.hstack((weights_train_stk, train_weights_temp))\n\n        return test_stk, train_stk, clear_test_stk, clear_train_stk, weights_train_stk\n\n    def get_test_train(self, array2sample, sample, weights2sample, clear2sample):\n\n        \"\"\"\n        Randomly sub-samples by integer or percentage\n\n        Args:\n            array2sample (2d array): The array to sub-sample. Includes the X predictors and the y labels.\n            sample (int or float): The number or percentage to randomly sample.\n            weights2sample (1d array): The sample weights to sub-sample.\n            clear2sample (1d array): Clear observations to sub-sample.\n\n        Returns:\n            test, train, clear test, clear train\n        \"\"\"\n\n        n_samples = array2sample.shape[0]\n\n        if isinstance(sample, float):\n            random_subsample = np.random.choice(range(0, n_samples), size=int(sample*n_samples), replace=False)\n        elif isinstance(sample, int):\n\n            n_sample = sample if sample <= len(range(0, n_samples)) else len(range(0, n_samples))\n            random_subsample = np.random.choice(range(0, n_samples), size=n_sample, replace=False)\n\n        else:\n\n            logger.error('  The sample number must be an integer or float.')\n            raise TypeError\n\n        # Create the test samples.\n        test_samples = np.delete(array2sample, random_subsample, axis=0)\n\n        # Create the train samples.\n        train_samples = array2sample[random_subsample]\n\n        self.train_idx += list(random_subsample)\n\n        if isinstance(weights2sample, np.ndarray):\n            train_weight_samples = weights2sample[random_subsample]\n        else:\n            train_weight_samples = None\n\n        if isinstance(clear2sample, np.ndarray):\n\n            test_clear_samples = np.delete(clear2sample, random_subsample)\n            train_clear_samples = clear2sample[random_subsample]\n\n        else:\n\n            test_clear_samples = None\n            train_clear_samples = None\n\n        return test_samples, train_samples, test_clear_samples, train_clear_samples, train_weight_samples\n\n    def get_class_subsample(self, class_key, clear_observations):\n\n        \"\"\"\n        Sub-samples by `response` class.\n\n        Args:\n            class_key (int): The class to sub-sample.\n            clear_observations (1d array): Clear observations.\n\n        Returns:\n            Shuffled & sub-sampled ...\n                X predictors, weights, clear observations, `continue`\n        \"\"\"\n\n        # Get the indices of samples that\n        #   match the current class.\n        cl_indices = np.where(self.all_samps[:, self.label_idx] == class_key)\n\n        # Continue to the next class\n        #   if there are no matches.\n        if not np.any(cl_indices):\n            return None, None, None, True, None\n\n        # Get the samples for the current class.\n        curr_cl = self.all_samps[cl_indices]\n\n        # Add a bit of randomness.\n        shuffle_permutations = np.random.permutation(curr_cl.shape[0])\n\n        curr_cl = curr_cl[shuffle_permutations]\n\n        # Sub-sample the clear observations and\n        #   stack them with the labels.\n        if isinstance(clear_observations, np.ndarray):\n\n            current_clear = clear_observations[cl_indices]\n            current_clear = current_clear[shuffle_permutations]\n\n        else:\n            current_clear = None\n\n        # Sub-sample the sample weights and\n        #   stack them with the labels.\n        if isinstance(self.sample_weight, np.ndarray):\n\n            current_weights = self.sample_weight[cl_indices]\n            current_weights = current_weights[shuffle_permutations]\n\n        else:\n            current_weights = None\n\n        if not isinstance(clear_observations, np.ndarray) and not isinstance(self.sample_weight, np.ndarray):\n            return curr_cl, None, None, False, cl_indices\n        else:\n            return curr_cl, current_weights, current_clear, False, cl_indices\n\n    def _scale_p_vars(self):\n\n        self.scaler = RobustScaler(quantile_range=(5, 95))\n        self.scaler.fit(self.p_vars)\n\n        # Save the unscaled samples.\n        self.p_vars_original = self.p_vars.copy()\n\n        # Scale the data.\n        self.p_vars = self.scaler.transform(self.p_vars)\n\n        if isinstance(self.p_vars_test, np.ndarray):\n            self.p_vars_test = self.scaler.transform(self.p_vars_test)\n\n        self.scaled = True\n\n    def update_class_counts(self):\n\n        self.class_counts = dict()\n\n        for indv_class in self.classes:\n            self.class_counts[indv_class] = (self.labels == indv_class).sum()\n\n    def _create_group_strata(self):\n\n        groups = 'abcdefghijklmnopqrstuvwxyz'\n\n        self.df['GROUP'] = '--'\n\n        c = 0\n        gdd = 1\n\n        self.n_groups = float(len(self.y_grids) * len(self.x_grids))\n\n        # Set the groups for stratification.\n        for ygi, xgj in itertools.product(range(0, len(self.y_grids)-1), range(0, len(self.x_grids)-1)):\n\n            g = groups[c] * gdd\n\n            self.df['GROUP'] = [g if (self.x_grids[xgj] <= x_ < self.x_grids[xgj+1]) and\n                                     (self.y_grids[ygi] <= y_ < self.y_grids[ygi+1]) else\n                                gr for x_, y_, gr in zip(self.df['X'], self.df['Y'], self.df['GROUP'])]\n\n            c += 1\n            if c == len(groups):\n\n                c = 0\n                gdd += 1\n\n    def _sample_group(self, class_key, sample_size):\n\n        \"\"\"\n        Args:\n            class_key (int): The class to sample from.\n            sample_size (int): The number of samples to take.\n        \"\"\"\n\n        # DataFrame that contains the current class.\n        df_sub = self.df.query('response == {CK}'.format(CK=class_key))\n\n        # Save the original row indices.\n        df_sub['ORIG_INDEX'] = df_sub.index\n\n        # Reorder the row index.\n        df_sub.reset_index(inplace=True, drop=True)\n\n        if sample_size > df_sub.shape[0]:\n            self.train_index = df_sub.ORIG_INDEX.tolist()\n        else:\n\n            # Get `cl` samples from each response strata.\n            if isinstance(sample_size, int):\n                dfg = df_sub.sample(n=sample_size, replace=False)\n            else:\n                dfg = df_sub.sample(frac=sample_size, replace=False)\n\n            # The train indices are\n            #   the DataFrame index.\n            train_index = dfg.index.values.ravel()\n\n            # Add the original DataFrame row indices\n            #   to the full train and test indices.\n            self.train_idx += df_sub.iloc[train_index].ORIG_INDEX.tolist()\n\n    def _create_grid_strata(self, spacing):\n\n        \"\"\"Creates grid strata for sample stratification\"\"\"\n\n        min_x = self.XY[:, 0].min()\n        max_x = self.XY[:, 0].max()\n\n        min_y = self.XY[:, 1].min()\n        max_y = self.XY[:, 1].max()\n\n        self.x_grids = np.arange(min_x, max_x+spacing, spacing)\n        self.y_grids = np.arange(min_y, max_y+spacing, spacing)\n\n        self.n_grids = len(self.x_grids) * len(self.y_grids)\n\n    def _stratify(self, class_key, cl):\n\n        \"\"\"\n        Grid stratification\n\n        Args:\n            class_key (int): The class to sample from.\n            cl (int): The class sample count.\n        \"\"\"\n\n        samples_collected = 0\n\n        # DataFrame that contains the current class.\n        df_sub = self.df.query('response == {CK}'.format(CK=class_key))\n\n        # Save the original row indices.\n        df_sub['ORIG_INDEX'] = df_sub.index\n\n        train_index_sub = list()\n\n        clsamp = copy(cl)\n\n        while samples_collected < cl:\n\n            # Reorder the row index.\n            df_sub.reset_index(inplace=True, drop=True)\n\n            # Samples to take, per grid.\n            samps_per_grid = int(np.ceil(clsamp / self.n_groups))\n\n            if df_sub.shape[0] < samps_per_grid * self.n_grids:\n                break\n\n            # Get `samps_per_grid` samples from each GROUP strata.\n            dfg = df_sub.groupby('GROUP', group_keys=False).apply(lambda xr_: xr_.sample(min(len(xr_),\n                                                                                             samps_per_grid)))\n\n            # The train indices are\n            #   the DataFrame index.\n            train_index = dfg.index.values.ravel()\n\n            if (len(train_index) == 0) or (len(train_index) > df_sub.shape[0]):\n                break\n\n            # Update the train and test indices.\n            train_index_sub += df_sub.iloc[train_index].ORIG_INDEX.tolist()\n\n            # Get the total number of samples collect.\n            samples_collected = len(train_index_sub)\n\n            # Get the difference between the target\n            #   sample size and the total\n            #   collected to this point.\n            clsamp = copy(cl - samples_collected)\n\n            # Add the original DataFrame row indices\n            #   to the full train and test indices.\n            self.train_idx += df_sub.iloc[train_index].ORIG_INDEX.tolist()\n\n            # Remove the rows that were sampled.\n            df_sub.drop(np.array(sorted(list(train_index)), dtype='int64'), axis=0, inplace=True)\n\n        if len(self.train_idx) > cl:\n\n            ran = np.random.choice(range(0, len(self.train_idx)), size=cl, replace=False)\n            self.train_idx = list(np.array(self.train_idx, dtype='int64')[ran])\n\n    # def _stratify(self, y_grids, x_grids, n_match_samps, n_total_samps):\n    #\n    #     \"\"\"Grid stratification\"\"\"\n    #\n    #     for ygi, xgj in itertools.product(range(0, len(y_grids)-1), range(0, len(x_grids)-1)):\n    #\n    #         # Get all of the samples in the current grid.\n    #         gi = np.where((self.all_samps[:, -2] >= y_grids[ygi]) &\n    #                       (self.all_samps[:, -2] < y_grids[ygi+1]) &\n    #                       (self.all_samps[:, -3] >= x_grids[xgj]) &\n    #                       (self.all_samps[:, -3] < x_grids[xgj+1]))[0]\n    #\n    #         if len(gi) > 0:\n    #\n    #             # Randomly sample from the grid samples.\n    #             ran = np.random.choice(range(len(gi)), size=1, replace=False)\n    #\n    #             gi_i = gi[ran[0]]\n    #\n    #             # Remove the samples.\n    #             if n_match_samps == 0:\n    #\n    #                 # Reshape (add 1 for the labels)\n    #                 self.stratified_samps = self.all_samps[gi_i].reshape(1, self.n_feas+1)\n    #                 self.all_samps = np.delete(self.all_samps, gi_i, axis=0)\n    #\n    #             else:\n    #\n    #                 self.stratified_samps = np.r_[self.stratified_samps, self.all_samps[gi_i].reshape(1, self.n_feas+1)]\n    #                 self.all_samps = np.delete(self.all_samps, gi_i, axis=0)\n    #\n    #             n_match_samps += 1\n    #\n    #             if n_match_samps >= n_total_samps:\n    #                 return n_match_samps\n    #\n    #     return n_match_samps\n\n    def _recode_labels(self, recode_dict):\n\n        \"\"\"\n        Recodes response labels\n\n        Args:\n            recode_dict (dict): The recode dictionary.\n        \"\"\"\n\n        # new_samps = np.zeros(self.n_samps, dtype='int64')\n        temp_labels = self.all_samps[:, -1]\n        new_samps = temp_labels.copy()\n\n        for recode_key, cl in sorted(viewitems(recode_dict)):\n            new_samps[temp_labels == recode_key] = cl\n\n        self.all_samps[:, -1] = new_samps\n        self.df[self.response_label] = new_samps\n\n    def _recode_all(self, vs_all_list):\n\n        \"\"\"\n        Recodes all classes in list to 1 and all other classes to 0\n\n        Args:\n            vs_all_list (list): The list of classes to recode to 1.\n        \"\"\"\n\n        temp_labels = self.all_samps[:, -1]\n        new_samps = temp_labels.copy()\n\n        for lc_class in np.unique(temp_labels):\n\n            if lc_class in vs_all_list:\n                new_samps[temp_labels == lc_class] = 1\n            else:\n                new_samps[temp_labels == lc_class] = 0\n\n        self.all_samps[:, -1] = new_samps\n        self.df[self.response_label] = new_samps\n\n    def _remove_min_observations(self, clear_observations):\n\n        \"\"\"\n        Removes samples with less than minimum time series requirement\n\n        Args:\n            clear_observations (1d array): The clear observations.\n        \"\"\"\n\n        self.clear_idx = np.where(clear_observations >= self.min_observations)[0]\n\n        self.all_samps = self.all_samps[self.clear_idx]\n\n        self.df = self.df.iloc[self.clear_idx]\n        self.df.reset_index(inplace=True, drop=True)\n\n        if isinstance(self.sample_weight, np.ndarray):\n            self.sample_weight = self.sample_weight[self.clear_idx]\n\n        return clear_observations[self.clear_idx]\n\n    def _remove_classes(self, classes2remove, clear_observations):\n\n        \"\"\"\n        Removes specific classes from the data\n\n        Args:\n            classes2remove (list)\n            clear_observations (1d array)\n        \"\"\"\n\n        for class2remove in classes2remove:\n\n            self.class_idx = np.where(self.all_samps[:, self.label_idx] != class2remove)[0]\n\n            self.all_samps = self.all_samps[self.class_idx]\n\n            self.df = self.df.iloc[self.class_idx]\n            self.df.reset_index(inplace=True, drop=True)\n\n            if isinstance(self.p_vars, np.ndarray):\n\n                self.p_vars = np.float32(self.p_vars[self.class_idx])\n                self.labels = np.float32(self.labels[self.class_idx])\n\n            if isinstance(self.sample_weight, np.ndarray):\n                self.sample_weight = np.float32(self.sample_weight[self.class_idx])\n\n            if isinstance(clear_observations, np.ndarray):\n                clear_observations = np.uint64(clear_observations[self.class_idx])\n\n        return clear_observations\n\n    def remove_values(self, value2remove, fea_check):\n\n        \"\"\"\n        Removes values from the sample data\n\n        Args:\n            value2remove (int): The value to remove.\n            fea_check (int): The feature position to use for checking.\n\n        Attributes:\n            p_vars (ndarray)\n            labels (ndarray)\n        \"\"\"\n\n        idx = np.where(self.p_vars[:, fea_check-1] < value2remove)\n\n        self.p_vars = np.float32(np.delete(self.p_vars, idx, axis=0))\n        self.labels = np.float32(np.delete(self.labels, idx, axis=0))\n\n    def load4crf(self,\n                 predictors,\n                 labels,\n                 bands=None,\n                 scale_factor=1.0,\n                 n_jobs=1,\n                 train_x=None,\n                 train_y=None,\n                 **kwargs):\n\n        \"\"\"\n        Loads data for Conditional Random Fields on a grid\n\n        Args:\n            predictors (list): A list of images to open or a list of arrays.\n                If an `array`, a single image should be given as `rows` x `columns`. A multi-layer image should\n                be given as `layers` x `rows` x `columns.\n            bands (Optional[list]): A list of bands to open, otherwise opens all bands.\n            labels (list): A list of images to open or a list of arrays. If an `array`, a single image should\n                be given as `rows` x `columns` and must match the length of `predictors`.\n            scale_factor (Optional[float]): A scale factor for the predictors. Default is 1.0.\n            n_jobs (Optional[int]): The number of parallel jobs for `read`. Default is 1.\n            train_x (Optional[int list]): A list of left starting coordinates when `labels` is a list of 2d arrays.\n                Default is None.\n            train_y (Optional[int list]): A list of top starting coordinates when `labels` is a list of 2d arrays.\n                Default is None.\n        \"\"\"\n\n        if isinstance(predictors, list) and isinstance(labels, list):\n\n            if len(predictors) != len(labels):\n\n                logger.error('  The list lengths do not match.')\n                raise AssertionError\n\n        self.sample_info_dict = dict()\n\n        n_patches = len(predictors)\n\n        self.p_vars = None\n        self.labels = None\n        bands = None\n        data_array = None\n\n        self.im_rows = None\n        self.im_cols = None\n\n        if 'rows' in kwargs:\n\n            self.im_rows = kwargs['rows']\n            del kwargs['rows']\n\n        if 'cols' in kwargs:\n\n            self.im_cols = kwargs['cols']\n            del kwargs['cols']\n\n        # Arrange the predictors.\n        if isinstance(predictors, list):\n\n            # Get the row and column dimensions.\n            if isinstance(labels, list):\n\n                if isinstance(labels[0], str):\n\n                    with raster_tools.ropen(labels[0]) as l_info:\n\n                        self.im_rows = l_info.rows\n                        self.im_cols = l_info.cols\n\n                elif isinstance(labels[0], np.ndarray):\n                    self.im_rows, self.im_cols = labels[0].shape\n                else:\n\n                    logger.error('  The training labels must be a list of strings or ndarrays.')\n                    raise TypeError\n\n            # Get the standardization scaler.\n            if isinstance(predictors[0], str):\n\n                for pi, pim in enumerate(predictors):\n\n                    with raster_tools.ropen(pim) as i_info:\n\n                        if not isinstance(bands, list):\n                            bands = list(range(1, i_info.bands+1))\n\n                        data_array_ = i_info.read(bands=bands,\n                                                  predictions=True,\n                                                  **kwargs)\n\n                        if not isinstance(data_array, np.ndarray):\n\n                            data_array = data_array_.copy()\n\n                            bands = len(bands)\n\n                            if not isinstance(self.im_rows, int):\n\n                                self.im_rows = i_info.rows\n                                self.im_cols = i_info.cols\n\n                        else:\n                            data_array = np.vstack((data_array, data_array_))\n\n                    i_info = None\n\n                    scaler = RobustScaler(quantile_range=(2, 98)).fit(data_array / scale_factor)\n\n                    data_array = None\n\n                # Setup the predictors array.\n                self.p_vars = np.zeros((n_patches,\n                                        self.im_rows,\n                                        self.im_cols,\n                                        bands+1),           # 1 extra band as a constant\n                                       dtype='float32')\n\n                # Add a constant feature.\n                self.p_vars[:, :, :, -1] = 1\n\n                # Load each predictor.\n                for pri, predictor in enumerate(predictors):\n\n                    if ('i' in kwargs) and ('j' in kwargs):\n\n                        lab_y = 0\n                        lab_x = 0\n\n                    else:\n\n                        # Get information from the labels image.\n                        if isinstance(labels, list) and isinstance(labels[0], str):\n\n                            with raster_tools.ropen(labels[pri]) as l_info:\n\n                                lab_x = l_info.left\n                                lab_y = l_info.top\n\n                            l_info = None\n\n                        elif isinstance(train_x, list) and isinstance(train_y, list):\n\n                            lab_x = train_x[pri]\n                            lab_y = train_y[pri]\n\n                        else:\n\n                            lab_x = 0\n                            lab_y = 0\n\n                    # Scale and reshape the predictors.\n                    if n_jobs not in [0, 1]:\n\n                        self.p_vars[pri, :, :, :-1] = scaler.transform(raster_tools.read(file_name=predictor,\n                                                                                         bands=bands,\n                                                                                         y=lab_y,\n                                                                                         x=lab_x,\n                                                                                         rows=self.im_rows,\n                                                                                         cols=self.im_cols,\n                                                                                         predictions=True,\n                                                                                         n_jobs=n_jobs,\n                                                                                         **kwargs) / scale_factor).reshape(\n                            self.im_rows,\n                            self.im_cols,\n                            bands)\n\n                    else:\n\n                        with raster_tools.ropen(predictor) as i_info:\n\n                            self.p_vars[pri, :, :, :-1] = scaler.transform(i_info.read(bands=bands,\n                                                                                       y=lab_y,\n                                                                                       x=lab_x,\n                                                                                       rows=self.im_rows,\n                                                                                       cols=self.im_cols,\n                                                                                       predictions=True,\n                                                                                       **kwargs) / scale_factor).reshape(\n                                self.im_rows,\n                                self.im_cols,\n                                bands)\n\n                        i_info = None\n\n            elif isinstance(predictors[0], np.ndarray):\n\n                if len(predictors[0].shape) == 3:\n                    bands, self.im_rows, self.im_cols = predictors[0].shape\n                else:\n\n                    bands = 1\n                    self.im_rows, self.im_cols = predictors[0].shape\n\n                # Setup the predictors array.\n                self.p_vars = np.zeros((n_patches,\n                                        self.im_rows,\n                                        self.im_cols,\n                                        bands+1), dtype='float32')\n\n                # Add a constant feature.\n                self.p_vars[:, :, :, -1] = 1\n\n                # Setup a scaler for all inputs.\n                for pri, predictor in enumerate(predictors):\n\n                    if pri == 0:\n\n                        data_array = predictor.transpose(1, 2, 0).reshape(self.im_rows*self.im_cols,\n                                                                          bands).copy()\n\n                    else:\n\n                        data_array = np.vstack((data_array,\n                                                predictor.transpose(1, 2, 0).reshape(self.im_rows*self.im_cols,\n                                                                                     bands)))\n\n                scaler = RobustScaler(quantile_range=(2, 98)).fit(data_array / scale_factor)\n\n                data_array = None\n\n                for pri, predictor in enumerate(predictors):\n\n                    self.p_vars[pri, :, :, :-1] = scaler.transform(\n                        predictor.transpose(1, 2, 0).reshape(self.im_rows*self.im_cols,\n                                                             bands) / scale_factor).reshape(self.im_rows,\n                                                                                            self.im_cols,\n                                                                                            bands)\n\n        else:\n            logger.warning('  No variables were shaped for CRF.')\n\n        # Arrange the labels.\n        if isinstance(labels, list):\n\n            if isinstance(labels[0], str):\n\n                with raster_tools.ropen(labels[0]) as l_info:\n\n                    # Create the label array.\n                    self.labels = np.zeros((n_patches, l_info.rows, l_info.cols), dtype='uint8')\n\n                l_info = None\n\n                for li, label in enumerate(labels):\n\n                    with raster_tools.ropen(label) as l_info:\n                        self.labels[li] = l_info.read()\n\n                    l_info = None\n\n            elif isinstance(labels[0], np.ndarray):\n\n                rows, cols = labels[0].shape\n\n                self.labels = np.array(labels, dtype='uint8').reshape(n_patches, rows, cols)\n\n        else:\n            logger.warning('  No labels were shaped for CRF.')\n\n        if isinstance(self.p_vars, np.ndarray):\n            self.p_vars[np.isnan(self.p_vars) | np.isinf(self.p_vars)] = 0\n\n        if isinstance(self.labels, np.ndarray):\n\n            self.labels[np.isnan(self.labels) | np.isinf(self.labels)] = 0\n            self.n_samps = self.labels.size\n\n            # Check whether there are any\n            #   negative class labels.\n            if np.min(self.classes) < 0:\n\n                logger.info('  The class labels should not contain negative values, but are:')\n                logger.error(self.classes)\n                raise ValueError\n\n            # Check whether the classes begin with 0.\n            if self.classes[0] != 0:\n\n                logger.info('  The class labels must begin with 0 when using CRF models, but are:')\n                logger.error(self.classes)\n                raise ValueError\n\n            # Check whether the classes are increasing by 1.\n            if np.any(np.abs(np.diff(self.classes)) > 1):\n\n                logger.info('  The class labels should increase by 1, starting with 0, but are:')\n                logger.error(self.classes)\n                raise ValueError\n\n            self.class_counts = dict()\n\n            for indv_class in self.classes:\n                self.class_counts[indv_class] = (self.labels == indv_class).sum()\n\n        self.n_feas = self.p_vars.shape[3]\n\n        self.update_sample_info(scaler=scaler,\n                                scaled=True,\n                                use_xy=False)\n\n\nclass Visualization(object):\n\n    \"\"\"A class for data visualization\"\"\"\n\n    def __init__(self):\n        self.time_stamp = time.asctime(time.localtime(time.time()))\n\n    def vis_parallel_coordinates(self):\n\n        \"\"\"\n        Visualize time series data in parallel coordinates style\n\n        Examples:\n            >>> import mpglue as gl\n            >>>\n            >>> cl = gl.classification()\n            >>>\n            >>> cl.split_samples('/samples.txt', perc_samp_each=.5)\n            >>> cl.vis_parallel_coordinates()\n        \"\"\"\n\n        ax = plt.figure().add_subplot(111)\n\n        x = list(range(self.p_vars.shape[1]))\n\n        colors = {1: 'black', 2: 'cyan', 3: 'yellow', 4: 'red', 5: 'orange', 6: 'green',\n                  7: 'purple', 8: 'magenta', 9: '#5F4C0B', 10: '#21610B', 11: '#210B61'}\n\n        leg_items = []\n        leg_names = []\n\n        for class_label in self.classes:\n\n            idx = np.where(self.labels == class_label)\n            current_class_array = self.p_vars[idx]\n\n            for current_class in current_class_array:\n\n                p = ax.plot(x, current_class, c=colors[class_label], label=class_label)\n\n                leg_items.append(p)\n                leg_names.append(str(class_label))\n\n        plt.legend(tuple(leg_items), tuple(leg_names),\n                   scatterpoints=1,\n                   loc='upper left',\n                   ncol=3,\n                   fontsize=12)\n\n        plt.show()\n\n        plt.close()\n\n    def vis_dimensionality_reduction(self, method='pca', n_components=3, class_list=[], class_names={}, labels=None):\n\n        \"\"\"\n        Visualize dimensionality reduction\n\n        Args:\n            method (Optional[str]): Reduction method. Choices are ['pca' (default) :: Principal Components Analysis,\n                'spe' :: Spectral Embedding (also known as Laplacian Eigenmaps),\n                'tsne' :: t-distributed Stochastic Neighbor Embedding].\n            n_components (Optional[int]): The number of components to return. Default is 3.\n            class_list (Optional[list]): A list of classes to compare. The default is an empty list, or all classes.\n            class_names (Optional[dict]): A dictionary of class names. The default is an empty dictionary, so the\n                labels are the class values.\n\n        Examples:\n            >>> import mpglue as gl\n            >>>\n            >>> cl = gl.classification()\n            >>>\n            >>> cl.split_samples('/samples.txt')\n            >>> cl.vis_dimensionality_reduction(n_components=3)\n        \"\"\"\n\n        if method == 'spe':\n\n            embedder = manifold.SpectralEmbedding(n_components=n_components, random_state=0, eigen_solver='arpack')\n\n            # transform the variables\n            self.p_vars_reduced = embedder.fit_transform(self.p_vars)\n\n        elif method == 'pca':\n\n            skPCA_ = skPCA(n_components=n_components)\n            skPCA_.fit(self.p_vars)\n            self.p_vars_reduced = skPCA_.transform(self.p_vars)\n\n            # mn, eigen_values = cv2.PCACompute(self.p_vars.T, self.p_vars.T.mean(axis=0).reshape(1, -1),\n            #                                   maxComponents=n_components)\n\n            # self.p_vars_reduced = eigen_values.T\n\n        elif method == 'tsne':\n\n            tsne = manifold.TSNE(n_components=n_components, init='pca', random_state=0)\n\n            self.p_vars_reduced = tsne.fit_transform(self.p_vars)\n\n        if n_components > 2:\n            ax = plt.figure().add_subplot(111, projection='3d')\n        else:\n            ax = plt.figure().add_subplot(111)\n\n        colors = ['black', 'cyan', 'yellow', 'red', 'orange', 'green', 'purple', 'magenta',\n                  '#5F4C0B', '#21610B', '#210B61']\n\n        if class_list:\n            n_classes = len(class_list)\n        else:\n            n_classes = self.n_classes\n            class_list = self.classes\n\n        leg_items = []\n        leg_names = []\n\n        for n_class in range(0, n_classes):\n\n            if class_list:\n\n                if class_names:\n                    leg_names.append(str(class_names[class_list[n_class]]))\n                else:\n                    leg_names.append(str(class_list[n_class]))\n\n            else:\n                leg_names.append(str(class_list[n_class]))\n\n            cl_idx = np.where(self.labels == self.classes[n_class])\n\n            if n_components > 2:\n\n                curr_pl = ax.scatter(self.p_vars_reduced[:, 0][cl_idx], self.p_vars_reduced[:, 1][cl_idx],\n                                     self.p_vars_reduced[:, 2][cl_idx], c=colors[n_class],\n                                     edgecolor=colors[n_class], alpha=.5, label=leg_names[n_class])\n\n            else:\n\n                curr_pl = ax.scatter(self.p_vars_reduced[:, 0][cl_idx], self.p_vars_reduced[:, 1][cl_idx],\n                                     c=colors[n_class], edgecolor=colors[n_class], alpha=.5)\n\n            leg_items.append(curr_pl)\n\n            ax.set_xlabel('1st component')\n            ax.set_ylabel('2nd component')\n\n        ax.set_xlim3d(self.p_vars_reduced[:, 0].min(), self.p_vars_reduced[:, 0].max())\n        ax.set_ylim3d(self.p_vars_reduced[:, 1].min(), self.p_vars_reduced[:, 1].max())\n\n        if n_components > 2:\n\n            ax.set_zlim3d(self.p_vars_reduced[:, 2].min(), self.p_vars_reduced[:, 2].max())\n\n            ax.set_zlabel('3rd component')\n            ax.legend()\n\n        else:\n\n            plt.legend(tuple(leg_items), tuple(leg_names),\n                       scatterpoints=1,\n                       loc='upper left',\n                       ncol=3,\n                       fontsize=12)\n\n        if labels:\n\n            # plot x, y coordinates as labels\n            x, y = self.XY[:, 0], self.XY[:, 1]\n\n            x = x[labels]\n            y = y[labels]\n            pv = self.p_vars[labels]\n            # l = self.labels[labels]\n\n            for i in range(0, len(x)):\n\n                ax.annotate('%d, %d' % (int(x[i]), int(y[i])), xy=(pv[i, 0], pv[i, 1]), size=6, color='#1C1C1C',\n                            xytext=(-10, 10), bbox=dict(boxstyle='round,pad=0.5', fc='white', alpha=.5),\n                            arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0'),\n                            textcoords='offset points', ha='right', va='bottom')\n\n        plt.show()\n\n        plt.close()\n\n    def vis_data(self, fea_1, fea_2, fea_3=None, class_list=[], class_names={}, labels=None):\n\n        \"\"\"\n        Visualize classes in feature space\n\n        Args:\n            fea_1 (int): The first feature to plot.\n            fea_2 (int): The second feature to plot.\n            fea_3 (Optional[int]): The optional, third feature to plot. Default is None.\n            class_list (Optional[list]): A list of classes to compare. The default is an empty list, or all classes.\n            class_names (Optional[dict]): A dictionary of class names. The default is an empty dictionary, so the\n                labels are the class values.\n\n        Examples:\n            >>> import mpglue as gl\n            >>>\n            >>> cl = gl.classification()\n            >>>\n            >>> cl.split_samples('/samples.txt', classes2remove=[1, 4],\n            >>>                  class_subs={2:.1, 5:.01, 8:.1, 9:.9})\n            >>>\n            >>> cl.vis_data(1, 2)\n            >>> # or\n            >>> cl.vis_data(1, 2, fea_3=5, class_list=[3, 5, 8],\n            >>>             class_names={3: 'forest', 5: 'agriculture', 8: 'water'})\n        \"\"\"\n\n        if isinstance(fea_3, int):\n            ax = plt.figure().add_subplot(111, projection='3d')\n        else:\n            ax = plt.figure().add_subplot(111)\n\n        colors = ['black', 'cyan', 'yellow', 'red', 'orange', 'green', 'purple', 'magenta',\n                  '#5F4C0B', '#21610B', '#210B61']\n\n        if class_list:\n            n_classes = len(class_list)\n        else:\n            n_classes = self.n_classes\n            class_list = self.classes\n\n        leg_items = []\n        leg_names = []\n\n        for n_class in range(0, n_classes):\n\n            if class_list:\n\n                if class_names:\n                    leg_names.append(str(class_names[class_list[n_class]]))\n                else:\n                    leg_names.append(str(class_list[n_class]))\n\n            else:\n                leg_names.append(str(class_list[n_class]))\n\n            cl_idx = np.where(self.labels == self.classes[n_class])\n\n            if fea_3:\n\n                curr_pl = ax.scatter(self.p_vars[:, fea_1-1][cl_idx], self.p_vars[:, fea_2-1][cl_idx],\n                                     self.p_vars[:, fea_3-1][cl_idx], c=colors[n_class], edgecolor=colors[n_class],\n                                     alpha=.5, label=leg_names[n_class])\n\n            else:\n\n                curr_pl = ax.scatter(self.p_vars[:, fea_1-1][cl_idx], self.p_vars[:, fea_2-1][cl_idx],\n                                     c=colors[n_class], edgecolor=colors[n_class], alpha=.5)\n\n            leg_items.append(curr_pl)\n\n            # plt.xlabel('Feature: %d' % fea_1)\n            # plt.ylabel('Feature: %d' % fea_2)\n\n            ax.set_xlabel('Feature: %d' % fea_1)\n            ax.set_ylabel('Feature: %d' % fea_2)\n\n        limits = False\n\n        if limits:\n\n            ax.set_xlim(-1, np.max(self.p_vars[:, fea_1-1]))\n            ax.set_ylim(-1, np.max(self.p_vars[:, fea_2-1]))\n\n        if fea_3:\n\n            ax.set_zlabel('Feature: %d' % fea_3)\n            ax.legend()\n\n            # if limits:\n            #     ax.set_zlim(int(np.percentile(self.p_vars[:, fea_2-1], 1)),\n            #                 int(np.percentile(self.p_vars[:, fea_2-1], 100)))\n\n        else:\n\n            plt.legend(tuple(leg_items), tuple(leg_names),\n                       scatterpoints=1,\n                       loc='upper left',\n                       ncol=3,\n                       fontsize=12)\n\n        if labels:\n\n            # plot x, y coordinates as labels\n            x, y = self.XY[:, 0], self.XY[:, 1]\n\n            x = x[labels]\n            y = y[labels]\n            pv = self.p_vars[labels]\n            # l = self.labels[labels]\n\n            for i in range(0, len(x)):\n\n                ax.annotate('%d, %d' % (int(x[i]), int(y[i])), xy=(pv[i, fea_1-1], pv[i, fea_2-1]), size=6,\n                            color='#1C1C1C', xytext=(-10, 10), bbox=dict(boxstyle='round,pad=0.5',\n                                                                         fc='white', alpha=.5),\n                            arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0'),\n                            textcoords='offset points', ha='right', va='bottom')\n\n        plt.show()\n\n        plt.close()\n\n    def vis_decision(self, fea_1, fea_2, classifier_info={'classifier': 'rf'}, class2check=1,\n                     compare=1, locate_outliers=False):\n\n        \"\"\"\n        Visualize a model decision function\n\n        Args:\n            classifier_info (dict): Parameters for Random Forest, SVM, and Bayes.\n            fea_1 (int): The first feature to compare.\n            fea_1 (int): The second feature to compare.\n            class2check (int): The class value to visualize.\n            compare (int): Compare one classifier against itself using different parameters (1), or compare\n                several classifiers (2).\n\n        Examples:\n            >>> import mpglue as gl\n            >>>\n            >>> cl = gl.classification()\n            >>>\n            >>> # load 100% of the samples and scale the data\n            >>> cl.split_samples('/samples.txt', scale_data=True, perc_samp=1.)\n            >>>\n            >>> # semi supervised learning\n            >>> cl.semi_supervised()\n            >>>\n            >>> # or train a model\n            >>> cl.construct_model()\n            >>>\n            >>> # remove outliers in the data\n            >>> cl.remove_outliers()\n            >>>\n            >>> # plot the decision\n            >>> cl.vis_decision(1, 2)\n            >>>\n            >>> # Command line\n            >>> > ./classification.py -s /samples.txt --scale yes -p 1 --semi yes --outliers yes --decision 1,2,1,2\n        \"\"\"\n\n        self.classifier_info = classifier_info\n\n        self._default_parameters()\n\n        # take only two features\n        self.p_vars = self.p_vars[:, [fea_1-1, fea_2-1]]\n\n        # max_depth_2 = classifier_info['max_depth'] + 50\n        # C2 = classifier_info['C'] + 5\n\n        colors = ['black', 'cyan', 'yellow', 'red', 'orange', 'green', 'purple', 'magenta', '#5F4C0B', '#21610B',\n                  '#210B61']\n\n        cm = plt.cm.gist_stern # plt.cm.RdBu    # for the decision boundaries\n        cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n\n        x_min, x_max = self.p_vars[:, 0].min() - .5, self.p_vars[:, 0].max() + .5\n        y_min, y_max = self.p_vars[:, 1].min() - .5, self.p_vars[:, 1].max() + .5\n\n        xx, yy = np.meshgrid(np.arange(x_min, x_max, .05), np.arange(y_min, y_max, .05))\n\n        if compare == 1:\n\n            if 'rf' in classifier_info['classifier']:\n\n                clf1 = RandomForestClassifier(**self.classifier_info_rf)\n\n                clf2 = ExtraTreesClassifier(**self.classifier_info_rf)\n\n            elif classifier_info['classifier'] == 'svmc':\n\n                clf1 = SVC(gamma=classifier_info['gamma'], C=classifier_info['C'])\n                clf2 = SVC(gamma=classifier_info['gamma'], C=C2)\n\n            elif classifier_info['classifier'] == 'bayes':\n\n                clf1 = GaussianNB()\n                clf2 = GaussianNB()\n\n            clf1.fit(self.p_vars, self.labels)\n            clf2.fit(self.p_vars, self.labels)\n\n            ## plot the dataset first\n            ax1 = plt.subplot(121)\n            ax2 = plt.subplot(122)\n\n            ax1.set_xlim(xx.min(), xx.max())\n            ax1.set_ylim(yy.min(), yy.max())\n            ax1.set_xticks(())\n            ax1.set_yticks(())\n\n            ax2.set_xlim(xx.min(), xx.max())\n            ax2.set_ylim(yy.min(), yy.max())\n            ax2.set_xticks(())\n            ax2.set_yticks(())\n\n            # plot the decision boundary\n            if hasattr(clf1, 'decision_function'):\n                Z1 = clf1.decision_function(np.c_[xx.ravel(), yy.ravel()])[:, class2check-1]\n                Z2 = clf2.decision_function(np.c_[xx.ravel(), yy.ravel()])[:, class2check-1]\n            else:\n                Z1 = clf1.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, class2check-1]\n                Z2 = clf2.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, class2check-1]\n\n            # Put the result into a color plot\n            Z1 = Z1.reshape(xx.shape)\n            ax1.contourf(xx, yy, Z1, cmap=cm, alpha=.8)\n\n            Z2 = Z2.reshape(xx.shape)\n            ax2.contourf(xx, yy, Z2, cmap=cm, alpha=.8)\n\n        elif compare == 2:\n\n            clf1 = RandomForestClassifier(max_depth=classifier_info['max_depth'],\n                                          n_estimators=classifier_info['trees'],\n                                          max_features=classifier_info['rand_vars'],\n                                          min_samples_split=classifier_info['min_samps'],\n                                          n_jobs=-1)\n\n            clf2 = ExtraTreesClassifier(max_depth=classifier_info['max_depth'],\n                                        n_estimators=classifier_info['trees'],\n                                        max_features=classifier_info['rand_vars'],\n                                        min_samples_split=classifier_info['min_samps'],\n                                        n_jobs=-1)\n\n            clf3 = SVC(gamma=classifier_info['gamma'], C=classifier_info['C'])\n\n            clf4 = GaussianNB()\n\n            clf1.fit(self.p_vars, self.labels)\n            clf2.fit(self.p_vars, self.labels)\n\n            if locate_outliers:\n\n                weights = np.ones(len(self.labels))\n                for c, curr_c_idx in viewitems(self.class_outliers):\n\n                    class_idx = np.where(self.labels == c)\n\n                    weights[class_idx][curr_c_idx] *= 10\n\n            else:\n                weights = None\n\n            clf3.fit(self.p_vars, self.labels, sample_weight=weights)\n\n            clf4.fit(self.p_vars, self.labels)\n\n            ## plot the dataset first\n            ax1 = plt.subplot(221)\n            ax2 = plt.subplot(222)\n            ax3 = plt.subplot(223)\n            ax4 = plt.subplot(224)\n\n            ax1.set_xlim(xx.min(), xx.max())\n            ax1.set_ylim(yy.min(), yy.max())\n            ax1.set_xticks(())\n            ax1.set_yticks(())\n\n            ax2.set_xlim(xx.min(), xx.max())\n            ax2.set_ylim(yy.min(), yy.max())\n            ax2.set_xticks(())\n            ax2.set_yticks(())\n\n            ax3.set_xlim(xx.min(), xx.max())\n            ax3.set_ylim(yy.min(), yy.max())\n            ax3.set_xticks(())\n            ax3.set_yticks(())\n\n            ax4.set_xlim(xx.min(), xx.max())\n            ax4.set_ylim(yy.min(), yy.max())\n            ax4.set_xticks(())\n            ax4.set_yticks(())\n\n            ## plot the decision boundary\n            if hasattr(clf1, 'decision_function'):\n                Z1 = clf1.decision_function(np.c_[xx.ravel(), yy.ravel()])[:, class2check-1]\n            else:\n                Z1 = clf1.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, class2check-1]\n\n            if hasattr(clf2, 'decision_function'):\n                Z2 = clf2.decision_function(np.c_[xx.ravel(), yy.ravel()])[:, class2check-1]\n            else:\n                Z2 = clf2.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, class2check-1]\n\n            if hasattr(clf3, 'decision_function'):\n                Z3 = clf3.decision_function(np.c_[xx.ravel(), yy.ravel()])[:, class2check-1]\n            else:\n                Z3 = clf3.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, class2check-1]\n\n            if hasattr(clf4, 'decision_function'):\n                Z4 = clf4.decision_function(np.c_[xx.ravel(), yy.ravel()])[:, class2check-1]\n            else:\n                Z4 = clf4.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, class2check-1]\n\n            # Put the result into a color plot\n            Z1 = Z1.reshape(xx.shape)\n            ax1.contourf(xx, yy, Z1, cmap=cm, alpha=.8)\n\n            Z2 = Z2.reshape(xx.shape)\n            ax2.contourf(xx, yy, Z2, cmap=cm, alpha=.8)\n\n            Z3 = Z3.reshape(xx.shape)\n            ax3.contourf(xx, yy, Z3, cmap=cm, alpha=.8)\n\n            Z4 = Z4.reshape(xx.shape)\n            ax4.contourf(xx, yy, Z4, cmap=cm, alpha=.8)\n\n        leg_items = []\n        leg_names = []\n\n        for n_class in range(0, self.n_classes):\n\n            cl_idx = np.where(self.labels == self.classes[n_class])\n\n            # plot the training points\n            curr_pl = ax1.scatter(self.p_vars[:, 0][cl_idx], self.p_vars[:, 1][cl_idx],\n                                 c=colors[n_class], alpha=.7)#, cmap=cm_bright)\n\n            ax2.scatter(self.p_vars[:, 0][cl_idx], self.p_vars[:, 1][cl_idx],\n                                 c=colors[n_class], alpha=.7)#, cmap=cm_bright)\n\n            if compare == 2:\n\n                ax3.scatter(self.p_vars[:, 0][cl_idx], self.p_vars[:, 1][cl_idx],\n                            c=colors[n_class], alpha=.7)#, cmap=cm_bright)\n\n                ax4.scatter(self.p_vars[:, 0][cl_idx], self.p_vars[:, 1][cl_idx],\n                            c=colors[n_class], alpha=.7)#, cmap=cm_bright)\n\n            leg_items.append(curr_pl)\n            leg_names.append(str(self.classes[n_class]))\n\n        if compare == 1:\n\n            if 'rf' in classifier_info['classifier']:\n\n                ax1.set_xlabel('RF, Max. depth: %d' % classifier_info['max_depth'])\n                ax2.set_xlabel('Extreme RF, Max. depth: %d' % classifier_info['max_depth'])\n\n            elif classifier_info['classifier'] == 'SVM':\n\n                ax1.set_xlabel('C: %d' % classifier_info['C'])\n                ax2.set_xlabel('C: %d' % C2)\n\n        else:\n\n            ax1.set_xlabel('Random Forest')\n            ax2.set_xlabel('Extremely Random Forest')\n            ax3.set_xlabel('SVM')\n            ax4.set_xlabel('Naives Bayes')\n\n        plt.show()\n\n        plt.close()\n\n    def vis_series(self, class_list=[], class_names={}, smooth=True, window_size=3, xaxis_labs=[],\n                   show_intervals=True, show_raw=False):\n\n        \"\"\"\n        Visualize classes in a time series\n\n        Args:\n            class_list (Optional[list]): A list of classes to compare. Default is [], or all classes.\n            class_names (Optional[dict]): A dictionary of class names. Default is {}, so the labels\n                are the class values.\n            smooth (Optional[bool]): Whether to smooth the time series. Default is True.\n            window_size (Optional[int]): The window size to use for smoothing. Default is 3.\n            xaxis_labs (Optional[str list]): A list of labels for the x-axis. Default is [].\n            show_intervals (Optional[bool]): Whether to fill axis intervals. Default is True.\n            show_raw (Optional[bool]): Whether to plot the raw data points. Default is False.\n\n        Examples:\n            >>> import mpglue as gl\n            >>>\n            >>> cl = gl.classification()\n            >>>\n            >>> cl.split_samples('/samples.txt', classes2remove=[1, 4],\n            >>>                  class_subs={2:.1, 5:.01, 8:.1, 9:.9})\n            >>> cl.vis_series(1, class_list=[3, 5, 8],\n            >>>               class_names={3: 'forest', 5: 'agriculture', 8: 'water'})\n        \"\"\"\n\n        fig = plt.figure(facecolor='white')\n\n        ax = fig.add_subplot(111, axisbg='white')\n\n        mpl.rcParams['font.size'] = 12.\n        mpl.rcParams['font.family'] = 'Verdana'\n        mpl.rcParams['axes.labelsize'] = 8.\n        mpl.rcParams['xtick.labelsize'] = 8.\n        mpl.rcParams['ytick.labelsize'] = 8.\n\n        # new x values\n        xn_ax = np.linspace(0, self.n_feas-1, (self.n_feas-1)*10)\n\n        colors = ['black', 'cyan', 'yellow', 'red', 'orange', 'green', 'purple', 'magenta', '#5F4C0B', '#21610B',\n                  '#210B61']\n\n        if not class_list:\n            class_list = self.classes\n\n        ## setup the class names\n        ## we might not have all the classes in the current samples\n        class_names_list = [class_names[cl] for cl in class_list]\n\n        # self.df = pd.DataFrame(np.zeros((0, self.n_feas)))\n\n        leg_items = []\n        for n_class, class_name in enumerate(class_names_list):\n\n            # get the current class array indices\n            cl_idx = np.where(self.labels == self.classes[n_class])\n\n            idx2del = []\n            for ri, r in enumerate(self.p_vars[cl_idx]):\n                if r.max() == 0:\n                    idx2del.append(ri)\n\n            if idx2del:\n                vis_p_vars = np.delete(self.p_vars[cl_idx], idx2del, axis=0)\n            else:\n                vis_p_vars = np.copy(self.p_vars[cl_idx])\n\n            # df_sm = self.pd_interpolate(vis_p_vars.astype(np.float32), window_size)\n            # the_nans, x = self.nan_helper(vis_p_vars)\n            # df_sm = self.lin_interp2(x, y, the_nans)\n\n            # idx = np.arange(vis_p_vars.shape[1])\n            # df_sm = np.apply_along_axis(self.lin_interp, 1, vis_p_vars.astype(np.float32), idx)\n\n            # vis_p_vars[vis_p_vars == 0] = np.nan\n            df_sm = _lin_interp.lin_interp(vis_p_vars.astype(np.float32))\n\n            df_sm = _rolling_stats.rolling_stats(df_sm, stat='median', window_size=window_size)\n\n            df_sm_std = df_sm.std(axis=1)\n            df_sm_u = df_sm.mean(axis=1)\n            df_sm_up = df_sm_u + (1.5 * df_sm_std)\n            df_sm_um = df_sm_u - (1.5 * df_sm_std)\n\n            for idx_check in range(0, 2):\n\n                idx = np.where((df_sm[:, idx_check] > df_sm_up) | (df_sm[:, idx_check] < df_sm_um))\n\n                if len(idx[0]) > 0:\n\n                    df_sm[:, idx_check][idx] = np.median(df_sm[:, :3][idx], axis=1)\n\n            for idx_check in range(self.n_feas-1, self.n_feas-3, -1):\n\n                idx = np.where((df_sm[:, idx_check] > df_sm_up) | (df_sm[:, idx_check] < df_sm_um))\n\n                if len(idx[0]) > 0:\n\n                    df_sm[:, idx_check][idx] = np.median(df_sm[:, :3][idx], axis=1)\n\n            df_sm_u = np.nanmean(df_sm, axis=0)\n            df_sm_std = np.nanstd(df_sm, axis=0)\n\n            if smooth:\n\n                df_sm_u_int = interp1d(range(self.n_feas), df_sm_u, kind='cubic')\n                df_sm_std_int = interp1d(range(self.n_feas), df_sm_std, kind='cubic')\n\n            # add the class index\n            # df_sm.index = [class_name]*df_sm.shape[0]\n\n            # self.df = self.df.append(df_sm)\n            marker_size = .1\n            line_width = 1.5\n            alpha = .5\n\n            for r in range(0, df_sm.shape[0]):\n\n                if show_raw:\n\n                    # raw data\n                    ax.scatter(range(len(vis_p_vars[r])), vis_p_vars[r], marker='o', edgecolor='none', s=40,\n                               facecolor=colors[n_class], c=colors[n_class])\n\n                # new y values\n                if smooth:\n                    yn_cor = interp1d(range(self.n_feas), df_sm[r, :], kind='cubic')\n\n                ## Savitsky Golay filtered\n                # ax.plot(range(len(df_sm_sav[r])), df_sm_sav[r], marker='o', markeredgecolor='none', markersize=5,\n                #          markerfacecolor=colors[-1], c=colors[-1], alpha=.7, lw=2)\n\n                    ## Cubic interpolation\n                    ax.plot(xn_ax, yn_cor(xn_ax), marker='o', markeredgecolor='none', markersize=marker_size,\n                             markerfacecolor=colors[n_class], linestyle='-', c=colors[n_class], alpha=alpha,\n                             lw=line_width)\n\n                else:\n\n                    ## raw data\n                    ax.plot(range(len(df_sm[r])), df_sm[r], marker='o', markeredgecolor='none',\n                            markersize=marker_size, markerfacecolor=colors[-2], linestyle='-', c=colors[n_class],\n                            alpha=alpha, lw=line_width)\n\n            if smooth:\n\n                yn_cor = interp1d(range(self.n_feas), df_sm[-1, :], kind='cubic')\n\n                dummy = ax.scatter(xn_ax, yn_cor(xn_ax), marker='o', edgecolor='none', s=marker_size,\n                                 facecolor=colors[n_class], c=colors[n_class], alpha=alpha, lw=line_width,\n                                 label=class_name)\n\n                if show_intervals:\n\n                    ax.fill_between(xn_ax, df_sm_u_int(xn_ax)-(2*df_sm_std_int(xn_ax)),\n                                     df_sm_u_int(xn_ax)+(2*df_sm_std_int(xn_ax)), color=colors[n_class], alpha=.1)\n\n            else:\n\n                dummy = ax.scatter(range(len(df_sm[r])), df_sm[r], marker='o', edgecolor='none', s=marker_size,\n                                 facecolor=colors[n_class], c=colors[n_class], alpha=alpha, lw=line_width,\n                                 label=class_name)\n\n                if show_intervals:\n\n                    ax.fill_between(range(len(df_sm_u)), df_sm_u-(2*df_sm_std), df_sm_u+(2*df_sm_std),\n                                    color=colors[n_class], alpha=.1)\n\n            leg_items.append(dummy)\n\n            plt.ylabel('Value')\n            # plt.ylabel('Feature: %d' % fea_2)\n\n            # ax.set_xlabel('Feature: %d')\n            # ax.set_ylabel('Feature: %d' % fea)\n\n        limits = False\n\n        leg = plt.legend(tuple(leg_items), tuple(class_names_list), scatterpoints=1, loc='lower left',\n                         markerscale=marker_size*200)\n\n        leg.get_frame().set_edgecolor('#D8D8D8')\n        leg.get_frame().set_linewidth(.5)\n\n        if xaxis_labs:\n\n            ax.set_xticks(range(self.n_feas))\n            ax.set_xticklabels(xaxis_labs)\n\n        plt.xlim(0, self.n_feas)\n        plt.ylim(50, 250)\n\n        plt.setp(plt.xticks()[1], rotation=30)\n\n        plt.tight_layout()\n\n        plt.show()\n\n        plt.close(fig)\n\n    # def lin_interp(self, in_block, indices):\n    #\n    #     in_block[in_block == 0] = np.nan\n    #\n    #     not_nan = np.logical_not(np.isnan(in_block))\n    #\n    #     return np.interp(indices, indices[not_nan], in_block[not_nan]).astype(np.float32)\n\n    # def pd_interpolate(self, in_block, window_size):\n    #\n    #     in_block[in_block == 0] = np.nan\n    #\n    #     df = pd.DataFrame(in_block)\n    #\n        # linear interpolation along the x axis (layers)\n        # df = df.apply(pd.Series.interpolate, axis=1).values.astype(np.float32)\n        # df = df.apply(pd.Series.interpolate, axis=1)\n\n        # rolling mean along the x axis and converted to ndarray\n        # df = pd.rolling_median(df, window=window_size, axis=1).values\n        # df = mp.rolling_stats(df, stat='median', window_size=window_size)\n\n        # # fill the first two columns\n        # if window_size == 3:\n        #\n        #     # df[:, 0] = np.median(df[:, :window_size-1], axis=1)\n        #     # df[:, 1] = np.median(df[:, :window_size], axis=1)\n        #     # df[:, -1] = np.median(df[:, -window_size:], axis=1)\n        #     # df[:, -2] = np.median(df[:, -window_size-1:], axis=1)\n        #\n        #     df[:, 0] = np.median(df[:, :window_size-1+(window_size/2)], axis=1)\n        #     df[:, 1] = np.median(df[:, :window_size+(window_size/2)], axis=1)\n        #     df[:, -1] = np.median(df[:, -window_size-(window_size/2):], axis=1)\n        #     df[:, -2] = np.median(df[:, -window_size-1-(window_size/2):], axis=1)\n        #\n        # elif window_size == 5:\n        #\n        #     df[:, 0] = np.median(df[:, :window_size-3+(window_size/2)], axis=1)\n        #     df[:, 1] = np.median(df[:, :window_size-2+(window_size/2)], axis=1)\n        #     df[:, 2] = np.median(df[:, :window_size-1+(window_size/2)], axis=1)\n        #     df[:, 3] = np.median(df[:, :window_size+(window_size/2)], axis=1)\n        #\n        #     df[:, -1] = np.median(df[:, -window_size-(window_size/2):], axis=1)\n        #     df[:, -2] = np.median(df[:, -window_size-1-(window_size/2):], axis=1)\n        #     df[:, -3] = np.median(df[:, -window_size-2-(window_size/2):], axis=1)\n        #     df[:, -4] = np.median(df[:, -window_size-3-(window_size/2):], axis=1)\n\n        # df[np.isnan(df)] = 0\n\n        # return np.apply_along_axis(savgol_filter, 1, df, 5, 3)\n        # return df\n\n    def vis_k_means(self, image, bands2vis=[1, 2, 3], clusters=3):\n\n        \"\"\"\n        Use k-means clustering to visualize data in image\n\n        Args:\n            image (str): The image to visualize.\n            bands2vis (Optional[int list]): A list of bands to visualize. Default is [1, 2, 3].\n            clusters (Optional[int]): The number of clusters. Default is 3.\n        \"\"\"\n\n        # open the image\n        with raster_tools.ropen(image) as i_info:\n            band_arrays = [zoom(i_info.read(bands=[bd], d_type='float32'), .5) for bd in bands2vis]\n\n        rws, cls = band_arrays[0].shape[0], band_arrays[1].shape[1]\n\n        ## reshape the arrays\n        multi_d = np.empty((len(bands2vis), rws, cls)).astype(np.float32)\n\n        ctr = 0\n        for n in range(len(bands2vis)):\n\n            multi_d[ctr] = band_arrays[n]\n\n            ctr += 1\n\n        multi_d = multi_d.reshape((len(bands2vis), rws*cls)).astype(np.float32).T\n\n        # run k means clustering\n        clt = KMeans(max_iter=300, n_jobs=-1, n_clusters=clusters)\n        clt.fit(multi_d)\n\n        hst = self._centroid_histogram(clt)\n\n        bar = self._plot_colors(hst, clt.cluster_centers_)\n\n        plt.figure()\n        plt.axis('off')\n        plt.imshow(bar)\n        plt.show()\n\n        plt.close()\n\n    def _centroid_histogram(self, clt):\n\n        # grab the number of different clusters and create a histogram\n        # based on the number of pixels assigned to each cluster\n        n_labels = np.arange(0, len(np.unique(clt.labels_) + 1))\n        hist, _ = np.histogram(clt.labels_, bins=n_labels)\n\n        # normalize the histogram, such that it sums to one\n        hist = hist.astype('float')\n        hist /= hist.sum()\n\n        return hist\n\n    def _plot_colors(self, hist, centroids):\n\n        # initialize the bar chart representing the relative frequency of each of the colors\n        bar = np.zeros((50, 300, 3), dtype='uint8')\n        start_x = 0\n\n        # iterate over the percentage of each cluster and the color of each cluster\n        for (percent, color) in zip(hist, centroids):\n\n            # plot the relative percentage of each cluster\n            end_x = start_x + (percent * 300)\n\n            cv2.rectangle(bar, (int(start_x), 0), (int(end_x), 50), color.astype('uint8').tolist(), -1)\n\n            start_x = end_x\n\n        return bar\n\n\nclass Preprocessing(object):\n\n    \"\"\"A class for data preprocessing\"\"\"\n\n    def __init__(self):\n        self.time_stamp = time.asctime(time.localtime(time.time()))\n\n    def compare_features(self, f1, f2, method='mahalanobis'):\n\n        \"\"\"\n        Compares features (within samples) using distance-based methods\n\n        Args:\n            f1 (int): The first feature position to compare.\n            f2 (int): The second feature position to compare.\n            method (Optional[str]): The distance method to use. Default is 'mahalanobis'.\n        \"\"\"\n\n        dist_methods = dict(mahalanobis=sci_dist.mahalanobis,\n                            correlation=sci_dist.correlation,\n                            euclidean=sci_dist.euclidean)\n\n        if method == 'mahalanobis':\n\n            return dist_methods[method](self.p_vars[f1-1], self.p_vars[f2-1], np.linalg.cov(self.p_vars[f1-1],\n                                                                                            self.p_vars[f2-1],\n                                                                                            rowvar=0))\n\n        else:\n            return dist_methods[method](self.p_vars[f1-1], self.p_vars[f2-1])\n\n    def compare_samples(self, base_samples, compare_samples, output, id_label='Id', y_label='Y',\n                        response_label='response', dist_threshold=500, pct_threshold=.75,\n                        replaced_weight=2, semi_supervised=False, spatial_weights=False, add2base=False):\n\n        \"\"\"\n        Compares features (between samples) and removes samples\n\n        Args:\n            base_samples (str): The baseline samples.\n            compare_samples (str): The samples to compare to the baseline, ``base_samples``.\n            output (str): The output (potentially reduced) samples.\n            id_label (Optional[str]): The id label. Default is 'Id'.\n            y_label (Optional[str]): The Y label. Default is 'Y'.\n            response_label (Optional[str]): The response (or class outcome) label. Default is 'response'.\n            dist_threshold (Optional[int]): The euclidean distance threshold, where samples with distance\n                values above `dist_threshold` are removed. Default is 300.\n            pct_threshold (Optional[float]): The proportional number of image variables required\n                above 'pct_threshold`. Default is 0.75.\n            replaced_weight (Optional[int or float]): The weight value to add to new samples. Default is 2.\n            semi_supervised (Optional[bool]): Whether to apply semi-supervised learning to the\n                unselected samples. Default is False.\n            spatial_weights (Optional[bool]): Whether to apply inverse spatial weights. Default is False.\n            add2base (Optional[bool]): Whether to add the samples to the baseline set. Default is False.\n\n        Example:\n            >>> compare_samples('/2000.txt', '/2014.txt', '/2014_mod.csv')\n\n        Explained:\n            1) Get the euclidean distance between image variables.\n\n        Returns:\n            None, writes to ``output``.\n        \"\"\"\n\n        weights = None\n\n        df_base = pd.read_csv(base_samples, sep=',')\n        df_compare = pd.read_csv(compare_samples, sep=',')\n\n        # Load sample weights.\n        if os.path.isfile(base_samples.replace('.txt', '_w.txt')):\n\n            weights = PickleIt.load(base_samples.replace('.txt', '_w.txt'))\n\n            if isinstance(weights, list):\n                weights = np.array(weights, dtype='float32')\n\n        # Reset the ids in case of stacked samples.\n        df_base[id_label] = list(range(1, df_base.shape[0] + 1))\n        df_compare[id_label] = list(range(1, df_compare.shape[0] + 1))\n\n        all_headers = df_base.columns.values.tolist()\n\n        leaders = all_headers[all_headers.index(id_label):all_headers.index(y_label) + 1]\n\n        headers = all_headers[all_headers.index(y_label) + 1:all_headers.index(response_label)]\n\n        added_headers = ('_y,'.format(headers[0]).join(headers) + '_y').split(',')\n\n        df_base.rename(columns=dict(zip(headers, added_headers)), inplace=True)\n\n        if isinstance(weights, np.ndarray):\n\n            df_base['WEIGHT'] = weights\n\n            df = pd.merge(df_compare, df_base[[id_label] + added_headers + ['WEIGHT']], on=id_label, how='inner')\n\n        else:\n            # Merge column-wise, with 'compare samples' first, then 'base samples'\n            df = pd.merge(df_compare, df_base[[id_label] + added_headers], on=id_label, how='inner')\n\n        if spatial_weights:\n\n            self.index_samples(df.query('WEIGHT == 1'))\n\n            dist_weights = self.weight_samples(df.query('WEIGHT == 1'), df.query('WEIGHT != 1'))\n\n            # Calculate the inverse distance\n            df.loc[df['WEIGHT'] != 1, 'WEIGHT'] = 1. - (dist_weights['SP_DIST'] / dist_weights['SP_DIST'].max())\n\n        def e_dist(d, h1, h2):\n            return (d[h1] - d[h2]) ** 2.\n\n        df['COUNT'] = 0\n\n        # Iterate over each image variable and\n        #   calculate the euclidean distance.\n        for compare_header, base_header in zip(headers, added_headers):\n\n            df['DIST'] = e_dist(df, compare_header, base_header)\n\n            df.loc[df['DIST'] < dist_threshold, 'COUNT'] += 1\n\n        if semi_supervised:\n\n            # Add unlabeled values to samples with high distance values.\n            df.loc[df['COUNT'] < int(pct_threshold * len(headers)), 'response'] = -1\n\n            # Semi-supervised learning.\n            label_spread = label_propagation.LabelSpreading(kernel='rbf')\n            label_spread.fit(df[headers], df['response'])\n\n            # Replace the high distance samples' unlabeled responses.\n            df.loc[df['COUNT'] < int(pct_threshold * len(headers)), 'response'] = label_spread.transduction_\n\n        else:\n            df = df.query('COUNT >= {:d}'.format(int(pct_threshold * len(headers))))\n\n        # Copy the 'base samples' weights and add new weights\n        if isinstance(weights, np.ndarray):\n\n            weights_out = df['WEIGHT'].values\n            weights_out = np.where(weights_out == 1, replaced_weight, weights_out)\n\n        # Get the 'compare sample' image variables.\n        df = df[leaders + headers + ['response']]\n\n        if add2base:\n\n            # Add the original column names back.\n            df_base = df_base.rename(columns=dict(zip(added_headers, headers)))[leaders + headers + ['response']]\n\n            # Concatenate the base samples with the new samples.\n            df = pd.concat([df_base, df], axis=0)\n\n            if isinstance(weights, np.ndarray):\n\n                # Concatenate the base weights with the new weights.\n                weights_out = np.concatenate([weights, weights_out], axis=0)\n\n                assert df.shape[0] == len(weights_out)\n\n        logger.info('  Base samples: {:,d}'.format(df_base.shape[0]))\n        logger.info('  New samples: {:,d}'.format(df.shape[0]))\n\n        if os.path.isfile(output):\n            os.remove(output)\n\n        df.to_csv(output, sep=',', index=False)\n\n        if os.path.isfile(base_samples.replace('.txt', '_w.txt')):\n\n            if os.path.isfile(compare_samples.replace('.txt', '_w.txt')):\n                os.remove(compare_samples.replace('.txt', '_w.txt'))\n\n            PickleIt.dump(weights_out, compare_samples.replace('.txt', '_w.txt'))\n\n    def _index_samples(self, base_samples, x_label='X', y_label='Y'):\n\n        \"\"\"\n        Indexes samples into a RTree database\n\n        Args:\n            base_samples (DataFrame): It should only contain 'good' points.\n        \"\"\"\n\n        self.rtree_index = rtree.index.Index(interleaved=False)\n\n        # Iterate over each sample.\n        for di, df_row in base_samples.iterrows():\n\n            x = float(df_row[x_label])\n            y = float(df_row[y_label])\n\n            self.rtree_index.insert(int(df_row['UNQ']), (x, y))\n\n    def weight_samples(self,\n                       df_samples,\n                       base_query,\n                       compare_query,\n                       id_label='Id',\n                       x_label='X',\n                       y_label='Y',\n                       w_label='WEIGHT'):\n\n        \"\"\"\n        Weights samples by inverse euclidean distance\n\n        Assumptions:\n            The input dataframe (`df_samples`) should have columns for X and Y coordinates, id labels,\n            and predefined weights.\n\n        Args:\n            df_samples (Pandas DataFrame): The samples.\n            base_query (str)\n            compare_query (str)\n            id_label (Optional[str]): The id column header.\n            x_label (Optional[str]): The x coordinate column header.\n            y_label (Optional[str]): The y coordinate column header.\n            w_label (Optional[str]): The weights column header.\n\n        Examples:\n            >>> import mpglue as gl\n            >>>\n            >>> cl = gl.classification()\n            >>>\n            >>> # print(df.head())\n            >>> #  X,  Y,  Id,  WEIGHT\n            >>> # [x,  y,  l,   w]\n            >>> # where,\n            >>> #   WEIGHT is a predefined weight for each sample.\n            >>>\n            >>> # Get spatial weights.\n            >>> df = cl.weight_samples(df, 'WEIGHT == 1', 'WEIGHT != 1')\n        \"\"\"\n\n        base_samples = df_samples.query(base_query)\n        compare_samples = df_samples.query(compare_query)\n\n        base_samples['UNQ'] = list(range(0, base_samples.shape[0]))\n        compare_samples['UNQ'] = list(range(0, compare_samples.shape[0]))\n\n        # Create a RTree indexer.\n        self._index_samples(base_samples,\n                            id_label=id_label,\n                            x_label=x_label,\n                            y_label=y_label)\n\n        sp_dists = list()\n\n        # Iterate over each sample.\n        for di, df_row in compare_samples.iterrows():\n\n            # Get the x and y coordinates.\n            x1 = float(df_row[x_label])\n            y1 = float(df_row[y_label])\n\n            # Get the nearest sample.\n            n_sample_id = list(self.rtree_index.nearest((x1, y1), 1))\n\n            # Get the base sample x and y coordinates.\n            x2 = float(base_samples.loc[base_samples['UNQ'] == n_sample_id[0], x_label])\n            y2 = float(base_samples.loc[base_samples['UNQ'] == n_sample_id[0], y_label])\n\n            # Calculate the euclidean distance\n            #   between the two samples.\n            sp_dists.append(sci_dist.euclidean([x1, y1], [x2, y2]))\n\n        compare_samples['SP_DIST'] = sp_dists\n\n        df_samples.loc[df_samples[w_label] != 1, w_label] = \\\n            1. - (compare_samples['SP_DIST'] / compare_samples['SP_DIST'].max())\n\n        return df_samples\n\n    def remove_outliers(self, outliers_fraction=.25, locate_only=False):\n\n        \"\"\"\n        Removes outliers from each class by fitting an Elliptic Envelope\n\n        Args:\n            outliers_fraction (Optional[float]): The proportion of outliers. Default is .25.\n            locate_only (Optional[bool]): Whether to locate and do not remove outliers. Default is False.\n\n        Examples:\n            >>> import mpglue as gl\n            >>>\n            >>> cl = gl.classification()\n            >>>\n            >>> # Get predictive variables and class labels data.\n            >>>\n            >>> # The data should be scaled, as the the Elliptic Envelope\n            >>> #   assumes a Gaussian distribution\n            >>> cl.split_samples('/samples.txt', perc_samp=1., scale_data=True)\n            >>>\n            >>> # Search for outliers in the sample data\n            >>> #   the new p_vars are stored in the <cl> instance.\n            >>> cl.remove_outliers()\n            >>>\n            >>> # Check the outlier locations\n            >>> print cl.class_outliers\n        \"\"\"\n\n        if not self.scaled:\n\n            logger.error('  The data should be scaled prior to outlier removal.')\n            raise NameError\n\n        self.outliers_fraction = outliers_fraction\n\n        # xx, yy = np.meshgrid(np.linspace(-7, 7, self.n_samps*self.n_feas),\n        #                      np.linspace(-7, 7, self.n_samps*self.n_feas))\n\n        new_p_vars = np.empty((0, self.n_feas), dtype='float32')\n        new_labels = np.array([], dtype='int16')\n\n        self.class_outliers = {}\n\n        for check_class in self.classes:\n\n            logger.info('  Class {:d} ...'.format(check_class))\n\n            try:\n                new_p_vars, new_labels = self._remove_outliers(check_class, new_p_vars, new_labels)\n            except:\n                logger.error('  Could not fit the data for class {:d}'.format(check_class))\n                raise RuntimeError\n\n        if not locate_only:\n\n            self.p_vars = new_p_vars\n            self.labels = new_labels\n\n            self.update_class_counts()\n\n    @retry(wait_random_min=500, wait_random_max=1000, stop_max_attempt_number=5)\n    def _remove_outliers(self, check_class, new_p_vars, new_labels):\n\n        # row indices for current class\n        class_idx = np.where(self.labels == check_class)\n\n        temp_p_vars = self.p_vars[class_idx]\n        temp_labels = self.labels[class_idx]\n\n        # outlier detection\n        outlier_clf = EllipticEnvelope(contamination=.1)\n\n        try:\n            outlier_clf.fit(temp_p_vars)\n        except:\n\n            new_p_vars = np.vstack((new_p_vars, self.p_vars_original[class_idx]))\n            new_labels = np.concatenate((new_labels, temp_labels))\n\n            return new_p_vars, new_labels\n\n        y_pred = outlier_clf.decision_function(temp_p_vars).ravel()\n\n        threshold = stats.scoreatpercentile(y_pred, 100. * self.outliers_fraction)\n\n        inlier_idx = np.where(y_pred >= threshold)\n        outlier_idx = np.where(y_pred < threshold)\n\n        self.class_outliers[check_class] = outlier_idx\n\n        n_outliers = len(y_pred) - len(inlier_idx[0])\n\n        logger.info('  {:d} outliers in class {:d}'.format(n_outliers, check_class))\n\n        # temp_p_vars = temp_p_vars[inlier_idx]\n\n        temp_labels = temp_labels[inlier_idx]\n\n        # update the features\n        new_p_vars = np.vstack((new_p_vars, self.p_vars_original[class_idx][inlier_idx]))\n\n        # update the labels\n        new_labels = np.concatenate((new_labels, temp_labels))\n\n        return new_p_vars, new_labels\n\n    def semi_supervised(self,\n                        label_method='propagate',\n                        var_array=None,\n                        lab_array=None,\n                        sub_idx=None,\n                        **kwargs):\n\n        \"\"\"\n        Predict class values of unlabeled samples\n\n        Args:\n            label_method (Optional[str]): The semi-supervised label method. Default is 'propagate'. Choices\n                are ['propagate', 'spread'].\n            var_array (Optional[2d array]): An array to replace `self.p_vars`. Default is None.\n            lab_array (Optional[1d array]): An array to replace `self.labels`. Default is None.\n            sub_idx (Optional[1d array-like]): A list or array of indices to subset by. Default is None.\n            kwargs (Optional[dict]): Keyword arguments to be passed to the semi-supervised method.\n\n        Examples:\n            >>> # create the classifier object\n            >>> import mpglue as gl\n            >>>\n            >>> cl = gl.classification()\n            >>>\n            >>> # get predictive variables and class labels data, sampling 100%\n            >>> # the unknown samples should have a class value of -1\n            >>> cl.split_samples('/samples.txt', perc_samp=1.)\n            >>>\n            >>> # run semi-supervised learning to predict unknowns\n            >>> # the instances <labels>, <classes>, <n_classes>,\n            >>> # and <class_counts> are updated\n            >>> cl.semi_supervised()\n        \"\"\"\n\n        if isinstance(sub_idx, np.ndarray) or isinstance(sub_idx, list):\n\n            if isinstance(var_array, np.ndarray):\n\n                var_array = var_array[np.array(sorted(sub_idx), dtype='int64')]\n                lab_array = lab_array[np.array(sorted(sub_idx), dtype='int64')]\n\n            else:\n\n                var_array = self.p_vars[np.array(sorted(sub_idx), dtype='int64')]\n                lab_array = self.labels[np.array(sorted(sub_idx), dtype='int64')]\n\n        else:\n\n            if not isinstance(var_array, np.ndarray):\n\n                var_array = self.p_vars\n                lab_array = self.labels\n\n        unlabeled_idx = np.where(lab_array == -1)\n\n        if label_method == 'propagate':\n            ss_model = label_propagation.LabelPropagation(**kwargs)\n        else:\n            ss_model = label_propagation.LabelSpreading(**kwargs)\n\n        ss_model.fit(var_array, lab_array)\n\n        lab_array[unlabeled_idx] = ss_model.transduction_[unlabeled_idx]\n\n        return lab_array\n\n        # self.update_class_counts()\n\n        # the model parameters\n        # self._default_parameters()\n        #\n        # if classifier_info['classifier'] == 'rf':\n        #\n        #     label_spread = ensemble.RandomForestClassifier(max_depth=classifier_info['max_depth'],\n        #                                                    n_estimators=classifier_info['trees'],\n        #                                                    max_features=classifier_info['rand_vars'],\n        #                                                    min_samples_split=classifier_info['min_samps'],\n        #                                                    n_jobs=-1)\n        #\n        # elif classifier_info['classifier'] == 'ex-rf':\n        #\n        #     label_spread = ensemble.ExtraTreesClassifier(max_depth=classifier_info['max_depth'],\n        #                                                  n_estimators=classifier_info['trees'],\n        #                                                  max_features=classifier_info['rand_vars'],\n        #                                                  min_samples_split=classifier_info['min_samps'],\n        #                                                  n_jobs=-1)\n        #\n        # labeled_vars_idx = np.where(self.labels != -1)\n        # labeled_vars = self.p_vars[labeled_vars_idx]\n        # labels = self.labels[labeled_vars_idx]\n        #\n        # label_spread.fit(labeled_vars, labels)\n        #\n        # # keep the good labels\n        # unknown_labels_idx = np.where(self.labels == -1)\n        #\n        # # predict the unlabeled\n        # temp_labels = label_spread.predict(self.p_vars)\n        #\n        # # save the predictions of the unknowns\n        # self.labels[unknown_labels_idx] = temp_labels[unknown_labels_idx]\n        #\n        # # update the individual class counts\n        # self.classes = list(np.delete(self.classes, 0))\n        # self.class_counts = {}\n        # for indv_class in self.classes:\n        #     self.class_counts[indv_class] = len(np.where(self.labels == indv_class)[0])\n        #\n        # self.n_classes = len(self.classes)\n\n\nclass ModelOptions(object):\n\n    @staticmethod\n    def model_options():\n\n        return \"\"\"\\\n\n        Supported models\n\n        ===========================\n        Parameter name -- Long name\n                          *Module\n        ===========================\n\n        ab-dt       -- AdaBoost with CART (classification problems)\n                        *Scikit-learn\n        ab-ex-dt    -- AdaBoost with extremely random trees (classification problems)\n                        *Scikit-learn\n        ab-rf       -- AdaBoost with Random Forest (classification problems)\n                        *Scikit-learn\n        ab-ex-rf    -- AdaBoost with Extremely Random Forest (classification problems)\n                        *Scikit-learn\n        ab-dtr      -- AdaBoost with CART (regression problems)\n                        *Scikit-learn\n        ab-ex-dtr   -- AdaBoost with extremely random trees (regression problems)\n                        *Scikit-learn\n        bag-dt      -- Bagged Decision Trees (classification problems)\n                        *Scikit-learn              \n        bag-dtr     -- Bagged Decision Trees (regression problems)\n                        *Scikit-learn            \n        bag-ex-dt   -- Bagged Decision Trees with extremely randomized trees (classification problems)\n                        *Scikit-learn\n        blag        -- Resampled bagging (classification problems)\n                        *Imbalanced-learn\n        blaf        -- Resampled random forest (classification problems)\n                        *Imbalanced-learn\n        blab        -- Resampled boosting (classification problems)\n                        *Imbalanced-learn                        \n        bayes       -- Naives Bayes (classification problems)\n                        *Scikit-learn\n        dt          -- Decision Trees based on CART algorithm (classification problems)\n                        *Scikit-learn\n        dtr         -- Decision Trees Regression based on CART algorithm (regression problems)\n                        *Scikit-learn\n        ex-dt       -- Extra Decision Trees based on CART algorithm (classification problems)\n                        *Scikit-learn\n        ex-dtr      -- Extra Decision Trees Regression based on CART algorithm (regression problems)\n                        *Scikit-learn                        \n        catboost    -- CatBoost for Gradient Boosting (classification problems)\n                        *Catboost        \n        chaincrf    -- Linear-chain Conditional Random Fields (classification problems)\n                        *Pystruct                        \n        c5          -- C5 decision trees (classification problems)\n                        {classifier:C5,trials:10,CF:.25,min_cases:2,winnow:False,no_prune:False,fuzzy:False}\n        cubist      -- Cubist regression trees (regression problems)\n                        {classifier:Cubist,committees:5,unbiased:False,rules:100,extrapolation:10}\n        cvmlp       -- Feed-forward, artificial neural network, multi-layer perceptrons in OpenCV (classification problems)\n                        {classifier:CVMLP}                        \n        cvrf        -- Random Forests in OpenCV (classification problems)\n                        {classifier:CVRF,trees:1000,min_samps:0,rand_vars:0,max_depth:25,weight_classes:None,truncate:False}                        \n        cvsvm       -- Support Vector Machine in OpenCV (classification problems)\n                        {classifier:CVSVM,C:1,g:1.0}\n        cvsvma      -- Support Vector Machine, auto-tuned in OpenCV (classification problems)\n                        {classifier:CVSVMA}\n        cvsvmr      -- Support Vector Machine in OpenCV (regression problems)\n                        {classifier:CVSVMR,C:1,g:1.0}\n        cvsvmra     -- Support Vector Machine, auto-tuned in OpenCV (regression problems)\n                        {classifier:CVSVMRA}                        \n        ex-rf       -- Extremely Random Forests (classification problems)\n                        *Scikit-learn\n        ex-rfr      -- Extremely Random Forests (regression problems)\n                        *Scikit-learn\n        gaussian    -- Gaussian Process (classification problems)\n                        *Scikit-learn\n        gb          -- Gradient Boosted Trees (classification problems)\n                        *Scikit-learn\n        gbr         -- Gradient Boosted Trees (regression problems)\n                        *Scikit-learn\n        gridcrf     -- Pairwise Conditional Random Fields on a 2d grid (classification problems)\n                        *Pystruct    \n        lightgbm    -- Light Gradient Boosting (classification problems)\n                        *LightGBM\n        logistic    -- Logistic Regression (classification problems)\n                        *Scikit-learn                        \n        mondrian    -- Mondrian forests (classification problems)\n                        *scikit-garden                        \n        nn          -- K Nearest Neighbor (classification problems)\n                        *Scikit-learn\n        qda         -- Quadratic Discriminant Analysis (classification problems)\n                        *Scikit-learn\n        rf          -- Random Forests (classification problems)\n                        *Scikit-learn                        \n        rfr         -- Random Forests (regression problems)\n                        *Scikit-learn\n        svmc        -- C-support Support Vector Machine (classification problems)\n                        {classifier:SVMc,C:1,kernel:'rbf',g:1/n_feas}\n        svmcr       -- C-support Support Vector Machine (regression problems)\n                        {classifier:SVMcR,C:1,g:1/n_feas}\n        svmnu       -- Nu-support Support Vector Machine (classification problems)\n                        {classifier:SVMnu,C:1,kernel:'rbf',g:1/n_feas}\n        tpot        -- Tpot pipeline (classification problems)\n                        *Tpot                                                \n        xgboost     -- XGBoost for Gradient Boosting (classification problems)\n                        *XGBoost                   \n        \"\"\"\n\n\nclass VotingClassifier(BaseEstimator, ClassifierMixin):\n\n    \"\"\"\n    A voting classifier class to use prefit models instead of re-fitting\n\n    Args:\n        estimators (list of tuples): The fitted estimators.\n        weights (Optional[list, 1d array-like): The estimator weights.\n        y (1d array-like)\n        classes (1d array-like)\n    \"\"\"\n\n    def __init__(self, estimators, weights=None, y=None, classes=None):\n\n        self.estimators = estimators\n        self.weights = weights\n        self.is_prefit_model = True\n        self.y_ = y\n        self.classes_ = None\n\n        if isinstance(y, np.ndarray) or isinstance(y, list):\n            self.classes_ = unique_labels(y)\n        elif isinstance(classes, np.ndarray) or isinstance(classes, list):\n            self.classes_ = classes\n\n        if isinstance(self.weights, list):\n            self.weights = np.array(self.weights, dtype='float32')\n\n        if self.weights is None:\n            self.weights = np.ones(len(self.estimators), dtype='float32')\n\n        if len(self.weights) != len(self.estimators):\n\n            logger.error('  The length of the weights must match the length of the estimators.')\n            raise ArrayShapeError\n\n        if isinstance(self.classes_, np.ndarray) or isinstance(self.classes_, list):\n            self.n_classes_ = len(self.classes_)\n        else:\n            self.n_classes_ = 0\n\n    def predict(self, X):\n\n        \"\"\"\n        Predicts discrete classes by soft probability averaging\n\n        Args:\n            X (2d array): The predictive variables.\n        \"\"\"\n\n        # Get predictions as an index of the array position.\n        probabilities_argmax = np.argmax(self.predict_proba(X), axis=1)\n\n        predictions = np.zeros(probabilities_argmax.shape, dtype='int16')\n\n        # Convert indices to classes.\n        for class_index, real_class in enumerate(self.classes_):\n            predictions[probabilities_argmax == class_index] = real_class\n\n        return predictions\n\n    def predict_proba(self, X):\n\n        \"\"\"\n        Predicts class posterior probabilities by soft probability averaging\n\n        Args:\n            X (2d array): The predictive variables.\n        \"\"\"\n\n        clf = self.estimators[0][1]\n\n        X_probas = clf.predict_proba(X) * self.weights[0]\n\n        for clf_idx in range(1, len(self.estimators)):\n\n            clf = self.estimators[clf_idx][1]\n            X_probas += clf.predict_proba(X) * self.weights[clf_idx]\n\n        return X_probas / self.weights.sum()\n\n\nclass classification(ModelOptions, PickleIt, Preprocessing, Samples, Visualization):\n\n    \"\"\"\n    A class for image sampling and classification\n\n    Example:\n        >>> import mpglue as gl\n        >>>\n        >>> # Create the classification object.\n        >>> cl = gl.classification()\n        >>>\n        >>> # Open land cover samples and split\n        >>> #   into train and test datasets.\n        >>> cl.split_samples('/samples.txt')\n        >>>\n        >>> # Add features\n        >>> cl.add_features(['mean', 'cv'])\n        >>>\n        >>> # Train a Random Forest classification model.\n        >>> # *Note that the model is NOT saved to file in\n        >>> #   this example. However, the model IS passed\n        >>> #   to the ``cl`` instance. To use the same model\n        >>> #   after Python cleanup, save the model to file\n        >>> #   with the ``output_model`` keyword. See the\n        >>> #   ``construct_model`` function more details.\n        >>> cl.construct_model(classifier_info={'classifier': 'rf',\n        >>>                                     'trees': 1000,\n        >>>                                     'max_depth': 25})\n        >>>\n        >>> # Apply the model to predict an entire image.\n        >>> cl.predict('/image_variables.tif', '/image_labels.tif')\n    \"\"\"\n\n    def __init__(self):\n        self.time_stamp = time.asctime(time.localtime(time.time()))\n\n    def copy(self):\n        return copy(self)\n\n    def construct_model(self,\n                        input_model=None,\n                        output_model=None,\n                        classifier_info=None,\n                        class_weight=None,\n                        var_imp=True,\n                        rank_method=None,\n                        top_feas=0.5,\n                        get_probs=False,\n                        input_image=None,\n                        in_shapefile=None,\n                        out_stats=None,\n                        stats_from_image=False,\n                        calibrate_proba=False,\n                        calibrate_test=None,\n                        calibrate_labels=None,\n                        calibrate_weights=None,\n                        be_quiet=False,\n                        compress_model=False,\n                        view_calibration=None,\n                        fig_location=None,\n                        feature_list=None,\n                        append_features=False,\n                        ts_indices=None,\n                        func_applier=None):\n\n        \"\"\"\n        Loads, trains, and saves a predictive model.\n\n        Args:\n            input_model (Optional[str]): The input model name.\n            output_model (Optional[str]): The output model name.\n            classifier_info (Optional[dict]): A dictionary of classifier information. Default is {'classifier': 'rf'}.\n            class_weight (Optional[bool]): How to weight classes for priors. Default is None. Choices are\n                [None, 'percent', 'inverse'].\n                *Example when class_weight=True:\n                    IF\n                        labels = [1, 1, 1, 2, 1, 2, 3, 2, 3]\n                    THEN\n                        class_weight = {1: .22, 2: .33, 3: .44}\n            var_imp (Optional[bool]): Whether to return feature importance. Default is True.\n            rank_method (Optional[str]): The rank method to use. 'chi2' or 'rf'. Default is None.\n            top_feas (Optional[int or float]): The number or percentage of top ranked features to return.\n                Default is .5, or 50%.\n            get_probs (Optional[bool]): Whether to return class probabilities. Default is False.\n            input_image (Optional[str]): An input image for Orfeo models. Default is None.\n            in_shapefile (Optional[str]): An input shapefile for Orfeo models. Default is None.\n            out_stats (Optional[str])\n            output_stats (Optional[str]): A statistics file for Orfeo models. Default is None.\n            stats_from_image (Optional[bool]): Whether to collect statistics from the image for Orfeo models. Default\n                is False.\n            calibrate_proba (Optional[bool]): Whether to calibrate posterior probabilities with a sigmoid\n                calibration. Default is False.\n            calibrate_test (Optional[2d array-like]): An array of test samples to use for model calibration. If None,\n                self.p_vars_test and self.labels_test are used.\n            calibrate_labels (Optional[1d array-like)]: An array of test labels to use for model calibration.\n                The shape must match `calibrate_test` along the y-axis.\n            calibrate_weights (Optional[1d array-like)]: An array of sample weights to use for model calibration.\n                The shape must match `calibrate_test` along the y-axis.\n            be_quiet (Optional[bool]): Whether to be quiet and do not print to screen. Default is False.\n            compress_model (Optional[bool]): Whether to compress the model. Default is False.\n            view_calibration (Optional[int]): View the calibrated probabilities of class `view_calibration`.\n                Default is None.\n            fig_location (Optional[str]): The location to save the `view_calibration` figure. Default is None.\n            feature_list (Optional[str list]): A list of features to add to `p_vars`. Default is None.\n                Choices are ['mean', 'cv'].\n            append_features (Optional[bool]): Whether to append time series features to the existing features.\n                Default is True.\n            ts_indices (Optional[int list]): An index array for time series features. Default is None.\n            func_applier (Optional[function]): A function to apply extra features. Default is None.\n\n                E.g.,\n\n                    def func_applier(x, self):\n                        return np.concatenate((x, self.pca.transform(x)), axis=1)\n\n        Examples:\n            >>> # create the classifier object\n            >>> import mpglue as gl\n            >>>\n            >>> cl = gl.classification()\n            >>>\n            >>> # get predictive variables and class labels data\n            >>> cl.split_samples('/samples.txt')\n            >>> # or\n            >>> cl.split_samples('/samples.txt', classes2remove=[1, 4],\n            >>>                  class_subs={2:.1, 5:.01, 8:.1, 9:.9})\n            >>>\n            >>> # train a Random Forest model\n            >>> cl.construct_model(output_model='/test_model.txt',\n            >>>                    classifier_info={'classifier': 'rf',\n            >>>                                     'trees': 1000,\n            >>>                                     'max_depth': 25})\n            >>>\n            >>> # or load a previously trained RF model\n            >>> cl.construct_model(input_model='/test_model.txt')\n            >>>\n            >>> # use Orfeo to train a model\n            >>> cl.construct_model(classifier_info={'classifier': 'OR_RF', 'trees': 1000,\n            >>>                    'max_depth': 25, 'min_samps': 5, 'rand_vars': 10},\n            >>>                    input_image='/image.tif', in_shapefile='/shapefile.shp',\n            >>>                    out_stats='/stats.xml', output_model='/rf_model.xml')\n            >>>\n            >>> # or collect statistics from samples rather than the entire image\n            >>> cl.construct_model(classifier_info={'classifier': 'OR_RF', 'trees': 1000,\n            >>>                    'max_depth': 25, 'min_samps': 5, 'rand_vars': 10},\n            >>>                    input_image='/image.tif', in_shapefile='/shapefile.shp',\n            >>>                    out_stats='/stats.xml', output_model='/rf_model.xml',\n            >>>                    stats_from_image=False)\n        \"\"\"\n\n        self.input_model = input_model\n        self.output_model = output_model\n        self.var_imp = var_imp\n        self.rank_method = rank_method\n        self.top_feas = top_feas\n        self.get_probs = get_probs\n        self.compute_importances = None\n        self.in_shapefile = in_shapefile\n        self.out_stats = out_stats\n        self.stats_from_image = stats_from_image\n        self.input_image = input_image\n        self.classifier_info = classifier_info\n        self.calibrate_proba = calibrate_proba\n        self.calibrate_test = calibrate_test\n        self.calibrate_labels = calibrate_labels\n        self.calibrate_weights = calibrate_weights\n        self.class_weight = class_weight\n        self.be_quiet = be_quiet\n        self.compress_model = compress_model\n        self.view_calibration = view_calibration\n        self.fig_location = fig_location\n\n        self.feature_object = None\n        self._add_features = False\n        self.calibrated = False\n        self.feature_list = feature_list\n        self.append_features = append_features\n        self.ts_indices = ts_indices\n        self.func_applier = func_applier\n\n        if isinstance(self.view_calibration, int):\n\n            if not isinstance(self.fig_location, str):\n\n                logger.error('  The output figure location must be given with `view_calibration`.')\n                raise TypeError\n\n            if not os.path.isdir(self.fig_location):\n                os.makedirs(self.fig_location)\n\n        if isinstance(self.input_model, str):\n\n            if not os.path.isfile(self.input_model):\n\n                logger.exception('  {} does not exist.'.format(self.input_model))\n                raise OSError\n\n        if not isinstance(self.input_model, str):\n\n            # check that the model is valid\n            if 'classifier' not in self.classifier_info:\n\n                logger.exception('  The model must be declared.')\n                raise ValueError\n\n            if not isinstance(self.classifier_info['classifier'], list):\n\n                if self.classifier_info['classifier'] not in get_available_models():\n\n                    logger.exception('  {} is not a model option.'.format(self.classifier_info['classifier']))\n                    raise NameError\n\n        if isinstance(self.output_model, str):\n\n            d_name, f_name = os.path.split(self.output_model)\n            f_base, f_ext = os.path.splitext(f_name)\n\n            if not d_name and not os.path.isabs(f_name):\n                d_name = os.path.abspath('.')\n\n            self.out_acc = os.path.join(d_name, '{}_acc.txt'.format(f_base))\n\n            if os.path.isfile(self.out_acc):\n                os.remove(self.out_acc)\n\n            if 'CV' in self.classifier_info['classifier']:\n\n                if 'xml' not in f_ext.lower():\n\n                    logger.error('  The output model for OpenCV models must be XML.')\n                    raise TypeError\n\n            if not os.path.isdir(d_name):\n                os.makedirs(d_name)\n\n        if isinstance(self.rank_method, str):\n            self.compute_importances = True\n\n        if isinstance(self.class_weight, str):\n\n            class_proportions = OrderedDict()\n            class_counts_ordered = OrderedDict(self.class_counts)\n\n            # Get the proportion of samples for each class.\n            for class_value in self.classes:\n                class_proportions[class_value] = class_counts_ordered[class_value] / float(self.n_samps)\n\n                # len(np.array(self.classes)[np.where(np.array(self.classes) == class_value)]) / float(len(self.classes))\n\n            if self.class_weight == 'inverse':\n\n                # rank self.class_counts from smallest to largest\n                class_counts_ordered = OrderedDict(sorted(list(iteritems(class_counts_ordered)), key=lambda t: t[1]))\n\n                # rank class_proportions from largest to smallest\n                class_proportions = OrderedDict(sorted(list(iteritems(class_proportions)),\n                                                       key=lambda t: t[1],\n                                                       reverse=True))\n\n                # swap the proportions of the largest class counts to the smallest\n\n                self.class_weight = dict()\n\n                for (k1, v1), (k2, v2) in zip(list(iteritems(class_counts_ordered)), list(iteritems(class_proportions))):\n                    self.class_weight[k1] = v2\n\n                if 'CV' in self.classifier_info['classifier']:\n                    self.class_weight = np.array(itervalues(self.class_weight), dtype='float32')\n\n            elif self.class_weight == 'percent':\n\n                if 'CV' in self.classifier_info['classifier']:\n                    self.class_weight = np.array(itervalues(class_proportions), dtype='float32')\n                else:\n                    self.class_weight = class_proportions\n\n            else:\n                logger.error('  The weight method is not supported.')\n                raise NameError\n\n        if isinstance(self.input_model, str):\n\n            # Load the classifier parameters\n            #   and the model.\n            self._load_model()\n\n        else:\n\n            if self.feature_list:\n\n                self.feature_object = TimeSeriesFeatures()\n                self.feature_object.add_features(self.feature_list)\n\n                if not self.ts_indices:\n\n                    if self.use_xy:\n                        self.ts_indices = np.array(range(0, self.p_vars.shape[1]-2), dtype='int64')\n\n                # logger.info(self.p_vars.shape[1])\n                # logger.info(self.p_vars_test.shape[1])\n                # logger.info(self.calibrate_test.shape[1])\n                # logger.info(ts_indices.shape)\n\n                self.p_vars = self.feature_object.apply_features(X=self.p_vars,\n                                                                 ts_indices=self.ts_indices,\n                                                                 append_features=self.append_features)\n\n                if isinstance(self.p_vars_test, np.ndarray):\n\n                    self.p_vars_test = self.feature_object.apply_features(X=self.p_vars_test,\n                                                                          ts_indices=self.ts_indices,\n                                                                          append_features=self.append_features)\n\n                if isinstance(self.calibrate_test, np.ndarray):\n\n                    self.calibrate_test = self.feature_object.apply_features(X=self.calibrate_test,\n                                                                             ts_indices=self.ts_indices,\n                                                                             append_features=self.append_features)\n\n                self._add_features = True\n\n                self.sample_info_dict['n_feas'] = self.p_vars.shape[1]\n\n            self.sample_info_dict['add_features'] = self._add_features\n            self.sample_info_dict['feature_object'] = self.feature_object\n\n            # Set the model parameters.\n            self._default_parameters()\n\n            # the model instance\n            self._set_model()\n\n            if self.classifier_info['classifier'] != 'ORRF':\n\n                # get model parameters\n                if not self.get_probs:\n                    self._set_parameters()\n\n                # train the model\n                self._train_model()\n\n    def _load_model(self):\n\n        \"\"\"Loads a previously saved model\"\"\"\n\n        logger.info('  Loading {} ...'.format(self.input_model))\n\n        if '.xml' in self.input_model:\n\n            # first load the parameters\n            try:\n                self.classifier_info, __ = self.load(self.input_model)\n            except:\n                logger.error('  Could not load {}'.format(self.input_model))\n                raise OSError\n\n            # load the correct model\n            self._set_model()\n\n            # now load the model\n            try:\n                self.model.load(self.input_model)\n            except:\n\n                logger.error('  Could not load {}'.format(self.input_model))\n                raise OSError\n\n        else:\n\n            # Scikit-learn models\n            try:\n\n                # self.classifier_info, self.model = self.load(self.input_model)\n                self.classifier_info, self.model, self.sample_info_dict = joblib.load(self.input_model)\n\n                self.n_feas = self.sample_info_dict['n_feas']\n                self.scaler = self.sample_info_dict['scaler']\n                self.scaled = self.sample_info_dict['scaled']\n                self.use_xy = self.sample_info_dict['use_xy']\n                self._add_features = self.sample_info_dict['add_features']\n                self.feature_object = self.sample_info_dict['feature_object']\n\n            except:\n                logger.exception('  Could not load {}'.format(self.input_model))\n\n    def _default_parameters(self):\n        \n        \"\"\"Sets model parameters\"\"\"\n\n        if isinstance(self.classifier_info['classifier'], list):\n            return\n\n        defaults_ = dict(n_estimators=100,\n                         trials=10,\n                         max_depth=25,\n                         min_samples_split=2,\n                         min_samples_leaf=5,\n                         learning_rate=0.1,\n                         C=1.0,\n                         nu=0.5,\n                         kernel='rbf',\n                         n_jobs=-1)\n\n        # Check if model parameters are set,\n        #   otherwise, set defaults.\n\n        if 'classifier' not in self.classifier_info:\n            self.classifier_info['classifier'] = 'rf'\n\n        # Models with base estimators\n        if self.classifier_info['classifier'].startswith('ab-') or \\\n                self.classifier_info['classifier'].startswith('bag-') or \\\n                (self.classifier_info['classifier'] in ['blag', 'blab']):\n\n            class_base = copy(self.classifier_info['classifier'])\n\n            self.classifier_info['classifier'] = \\\n                self.classifier_info['classifier'][self.classifier_info['classifier'].find('-')+1:]\n\n        else:\n            class_base = 'none'\n\n        vp = ParameterHandler(self.classifier_info['classifier'])\n\n        # Check the parameters.\n        self.classifier_info_ = copy(self.classifier_info)\n        self.classifier_info_ = vp.check_parameters(self.classifier_info_, defaults_)\n\n        # Create a separate instance for\n        #   AdaBoost and Bagging base classifiers.\n        if class_base.startswith('ab-') or class_base.startswith('bag-') or (class_base in ['blag', 'blab']):\n\n            self.classifier_info_base = copy(self.classifier_info)\n            self.classifier_info_base['classifier'] = class_base\n\n            if 'trials' in self.classifier_info_base:\n\n                self.classifier_info_base['n_estimators'] = self.classifier_info_base['trials']\n                del self.classifier_info_base['trials']\n\n            else:\n                self.classifier_info_base['n_estimators'] = defaults_['trials']\n\n            vp_base = ParameterHandler(self.classifier_info_base['classifier'])\n\n            self.classifier_info_base = vp_base.check_parameters(self.classifier_info_base,\n                                                                 defaults_,\n                                                                 trials_set=True)\n\n            if 'base_estimator' in self.classifier_info_base:\n                del self.classifier_info_base['base_estimator']\n\n            self.classifier_info['classifier'] = class_base\n\n        # Random Forest in OpenCV\n        if self.classifier_info['classifier'] == 'cvrf':\n\n            if not self.input_model:\n\n                # trees\n                if 'trees' in self.classifier_info:\n                    self.classifier_info['term_crit'] = (cv2.TERM_CRITERIA_MAX_ITER,\n                                                         self.classifier_info['trees'], 0.1)\n                else:\n                    if 'term_crit' not in self.classifier_info:\n                        self.classifier_info['term_crit'] = (cv2.TERM_CRITERIA_MAX_ITER, self.DEFAULT_TREES, 0.1)\n\n                # minimum node samples\n                if 'min_samps' not in self.classifier_info:\n                    self.classifier_info['min_samps'] = int(np.ceil(0.01 * self.n_samps))\n\n                # random features\n                if 'rand_vars' not in self.classifier_info:\n                    # sqrt of feature count\n                    self.classifier_info['rand_vars'] = 0\n\n                # maximum node depth\n                if 'max_depth' not in self.classifier_info:\n                    self.classifier_info['max_depth'] = self.DEFAULT_MAX_DEPTH\n\n                if 'calc_var_importance' not in self.classifier_info:\n                    self.classifier_info['calc_var_importance'] = 0\n\n                if 'truncate' not in self.classifier_info:\n                    self.classifier_info['truncate'] = False\n\n                if 'priors' not in self.classifier_info:\n\n                    if isinstance(self.class_weight, np.ndarray):\n                        self.classifier_info['priors'] = self.class_weight\n                    else:\n                        self.classifier_info['priors'] = np.ones(self.n_classes, dtype='float32')\n\n            # MLP\n            elif self.classifier_info['classifier'] == 'cvmlp':\n\n                if not self.input_model:\n\n                    # hidden nodes\n                    try:\n                        __ = self.classifier_info['n_hidden']\n                    except:\n                        try:\n                            self.classifier_info['n_hidden'] = (self.n_feas + self.n_classes) / 2\n                        except:\n                            logger.error('  Cannot infer number of hidden nodes.')\n                            raise ValueError\n\n        elif self.classifier_info['classifier'] in ['chaincrf', 'gridcrf']:\n\n            if not PYSTRUCT_INSTALLED:\n\n                logger.warning('  Pystruct must be installed to use CRF models.\\nEnsure that pystruct and cvxopt are installed.')\n                return\n\n            if 'max_iter' not in self.classifier_info_:\n                self.classifier_info_['max_iter'] = 1000\n\n            if 'C' not in self.classifier_info_:\n                self.classifier_info_['C'] = 0.001\n\n            if 'n_jobs' not in self.classifier_info_:\n                self.classifier_info_['n_jobs'] = -1\n\n            if 'tol' not in self.classifier_info_:\n                self.classifier_info_['tol'] = 0.001\n\n            if 'inference_cache' not in self.classifier_info_:\n                self.classifier_info_['inference_cache'] = 0\n\n            if 'inference_method' not in self.classifier_info_:\n                inference_method = 'qpbo'\n            else:\n\n                inference_method = self.classifier_info_['inference_method']\n                del self.classifier_info_['inference_method']\n\n            if 'neighborhood' not in self.classifier_info_:\n                neighborhood = 4\n            else:\n\n                neighborhood = self.classifier_info_['neighborhood']\n                del self.classifier_info_['neighborhood']\n\n            self.grid_info = dict(inference_method=inference_method,\n                                  neighborhood=neighborhood)\n\n            # if 'break_on_bad' not in self.classifier_info_:\n            #     self.classifier_info_['break_on_bad'] = True\n\n            # self.classifier_info_['verbose'] = 1\n\n    def _set_model(self):\n\n        \"\"\"Sets the model object\"\"\"\n\n        # Create the model object.\n        if isinstance(self.classifier_info['classifier'], list):\n\n            self.discrete = True\n\n            classifier_info = copy(self.classifier_info)\n\n            classifier_list = list()\n\n            ci = 0\n            for classifier in classifier_info['classifier']:\n\n                self.classifier_info = copy(classifier_info)\n                self.classifier_info['classifier'] = classifier\n\n                self._default_parameters()\n\n                if classifier == 'bayes':\n                    voting_sub_model = GaussianNB(**self.classifier_info_)\n\n                elif classifier == 'nn':\n                    voting_sub_model = KNeighborsClassifier(**self.classifier_info_)\n\n                elif classifier == 'logistic':\n                    voting_sub_model = LogisticRegression(**self.classifier_info_)\n\n                elif classifier == 'rf':\n                    voting_sub_model = ensemble.RandomForestClassifier(**self.classifier_info_)\n\n                elif classifier == 'ex-rf':\n                    voting_sub_model = ensemble.ExtraTreesClassifier(**self.classifier_info_)\n\n                elif classifier == 'dt':\n                    voting_sub_model = tree.DecisionTreeClassifier(**self.classifier_info_)\n\n                elif classifier == 'ex-dt':\n                    voting_sub_model = tree.ExtraTreeClassifier(**self.classifier_info_)\n\n                elif classifier == 'ab-dt':\n\n                    voting_sub_model = ensemble.AdaBoostClassifier(base_estimator=tree.DecisionTreeClassifier(**self.classifier_info_),\n                                                                   **self.classifier_info_base)\n\n                elif classifier == 'ab-rf':\n\n                    voting_sub_model = ensemble.AdaBoostClassifier(base_estimator=ensemble.RandomForestClassifier(**self.classifier_info_),\n                                                                   **self.classifier_info_base)\n\n                elif classifier == 'ab-ex-rf':\n\n                    voting_sub_model = ensemble.AdaBoostClassifier(base_estimator=ensemble.ExtraTreesClassifier(**self.classifier_info_),\n                                                                   **self.classifier_info_base)\n\n                elif classifier == 'ab-ex-dt':\n\n                    voting_sub_model = ensemble.AdaBoostClassifier(base_estimator=tree.ExtraTreeClassifier(**self.classifier_info_),\n                                                                   **self.classifier_info_base)\n\n                elif classifier == 'bag-dt':\n\n                    voting_sub_model = ensemble.BaggingClassifier(base_estimator=tree.DecisionTreeClassifier(**self.classifier_info_),\n                                                                  **self.classifier_info_base)\n\n                elif classifier == 'bag-ex-dt':\n\n                    voting_sub_model = ensemble.BaggingClassifier(base_estimator=tree.ExtraTreeClassifier(**self.classifier_info_),\n                                                                  **self.classifier_info_base)\n\n                elif classifier == 'blag':\n\n                    if not IMBLEARN_INSTALLED:\n\n                        logger.error(\"\"\"\\\n\n                        Imbalanced learn must be installed to use the model. Install from\n                        \n                        pip install imbalanced-learn\n\n                        \"\"\")\n\n                    voting_sub_model = imblearn.BalancedBaggingClassifier(**self.classifier_info_base)\n\n                elif classifier == 'blaf':\n\n                    if not IMBLEARN_INSTALLED:\n\n                        logger.error(\"\"\"\\\n\n                        Imbalanced learn must be installed to use the model. Install from\n\n                        pip install imbalanced-learn\n\n                        \"\"\")\n\n                    voting_sub_model = imblearn.BalancedRandomForestClassifier(**self.classifier_info_base)\n\n                elif classifier == 'blab':\n\n                    if not IMBLEARN_INSTALLED:\n\n                        logger.error(\"\"\"\\\n\n                        Imbalanced learn must be installed to use the model. Install from\n\n                        pip install imbalanced-learn\n\n                        \"\"\")\n\n                    voting_sub_model = imblearn.RUSBoostClassifier(**self.classifier_info_base)\n\n                elif classifier == 'tpot':\n\n                    if not TPOT_INSTALLED:\n\n                        logger.error(\"\"\"\\\n\n                        Tpot must be installed to use the model.\n\n                        \"\"\")\n\n                    voting_sub_model = TPOTClassifier(generations=5, population_size=50, cv=5, verbosity=0)\n\n                elif classifier == 'mondrian':\n\n                    if not SKGARDEN_INSTALLED:\n\n                        logger.error(\"\"\"\\\n\n                        Scikit-garden must be installed to use the Mondrian model.\n\n                        \"\"\")\n\n                    voting_sub_model = skgarden.MondrianForestClassifier(**self.classifier_info_)\n\n                elif classifier == 'gb':\n                    voting_sub_model = ensemble.GradientBoostingClassifier(**self.classifier_info_)\n\n                elif classifier == 'qda':\n                    voting_sub_model = QDA(**self.classifier_info_)\n\n                elif classifier == 'gaussian':\n                    voting_sub_model = GaussianProcessClassifier(**self.classifier_info_)\n\n                elif classifier == 'svmc':\n                    voting_sub_model = svm.SVC(**self.classifier_info_)\n\n                elif classifier == 'svmnu':\n                    voting_sub_model = svm.NuSVC(**self.classifier_info_)\n\n                elif classifier == 'catboost':\n\n                    if not CATBOOST_INSTALLED:\n\n                        logger.error(\"\"\"\\\n\n                        Catboost must be installed to use the model.\n                                                \n                        \"\"\")\n\n                    voting_sub_model = CatBoostClassifier(**self.classifier_info_)\n\n                elif classifier == 'lightgbm':\n\n                    if not LIGHTGBM_INSTALLED:\n\n                        logger.error(\"\"\"\\\n\n                        LightGBM must be installed to use the model.\n                        \n                        # Anaconda\n                        conda install -c conda-forge lightgbm or\n                        \n                        # Python 2.x\n                        # cluster: module load cmake\n                        pip install --no-cache-dir --no-binary :all: lightgbm\n                        \n                        # Python 3.x\n                        pip install lightgbm\n\n                        \"\"\")\n\n                    voting_sub_model = gbm.LGBMClassifier(**self.classifier_info_)\n\n                elif classifier == 'xgboost':\n\n                    if not XGBOOST_INSTALLED:\n\n                        logger.error(\"\"\"\\\n\n                        XGBoost must be installed to use the model.\n\n                        \"\"\")\n\n                    voting_sub_model = XGBClassifier(**self.classifier_info_)\n\n                else:\n\n                    logger.warning('  The model, {MODEL}, is not supported'.format(MODEL=classifier))\n                    continue\n\n                # Check if the model supports sample weights.\n                try:\n                    argi = inspect.getargspec(voting_sub_model.fit)\n                except:\n                    argi = inspect.getfullargspec(voting_sub_model.fit)\n\n                supports_weights = True if 'sample_weight' in argi.args else False\n\n                logger.info('  Fitting a {MODEL} model ...'.format(MODEL=classifier))\n\n                if supports_weights:\n\n                    voting_sub_model.fit(self.p_vars,\n                                         self.labels,\n                                         sample_weight=self.sample_weight)\n\n                else:\n\n                    voting_sub_model.fit(self.p_vars,\n                                         self.labels)\n\n                if self.calibrate_proba:\n\n                    if self.n_samps >= 1000:\n\n                        cal_model = calibration.CalibratedClassifierCV(base_estimator=voting_sub_model,\n                                                                       method='isotonic',\n                                                                       cv='prefit')\n\n                    else:\n\n                        cal_model = calibration.CalibratedClassifierCV(base_estimator=voting_sub_model,\n                                                                       method='sigmoid',\n                                                                       cv='prefit')\n\n                    # # Limit the test size.\n                    # samp_thresh = 100000\n                    # if self.p_vars_test.shape[0] > samp_thresh:\n                    #\n                    #     pdf = pd.DataFrame(self.p_vars_test)\n                    #     pdf['GROUP'] = self.labels_test\n                    #\n                    #     n_groups = len(pdf.GROUP.unique())\n                    #\n                    #     group_samps = int(float(samp_thresh) / n_groups)\n                    #\n                    #     dfg = pdf.groupby('GROUP', group_keys=False).apply(lambda xr_: xr_.sample(min(len(xr_),\n                    #                                                                                   group_samps)))\n                    #\n                    #     idx = dfg.index.values.ravel()\n                    #\n                    #     p_vars_test_cal = self.p_vars_test[idx]\n                    #     labels_test_cal = self.labels_test[idx]\n                    #\n                    # else:\n                    #\n                    #     p_vars_test_cal = self.p_vars_test\n                    #     labels_test_cal = self.labels_test\n\n                    logger.info('  Calibrating a {MODEL} model ...'.format(MODEL=classifier))\n\n                    # Calibrate the model on the test data.\n                    if isinstance(self.calibrate_test, np.ndarray):\n\n                        assert self.calibrate_test.shape[0] == len(self.calibrate_labels) == len(self.calibrate_weights)\n\n                        cal_model.fit(self.calibrate_test,\n                                      self.calibrate_labels,\n                                      sample_weight=self.calibrate_weights)\n\n                    else:\n\n                        cal_model.fit(self.p_vars_test,\n                                      self.labels_test,\n                                      sample_weight=self.sample_weight_test)\n\n                    if isinstance(self.view_calibration, int):\n\n                        from sklearn.calibration import calibration_curve\n\n                        # Plot the calibrated probabilities.\n\n                        cal_prob_pos = cal_model.predict_proba(self.p_vars_test)[:, self.view_calibration-1]\n                        uncal_prob_pos = voting_sub_model.predict_proba(self.p_vars_test)[:, self.view_calibration-1]\n\n                        cal_fraction_of_positives, cal_mean_predicted_value = calibration_curve(self.labels_test,\n                                                                                                cal_prob_pos,\n                                                                                                n_bins=10)\n\n                        uncal_fraction_of_positives, uncal_mean_predicted_value = calibration_curve(self.labels_test,\n                                                                                                    uncal_prob_pos,\n                                                                                                    n_bins=10)\n\n                        ax = plt.figure().add_subplot(111)\n\n                        ax.plot(cal_mean_predicted_value,\n                                cal_fraction_of_positives,\n                                's-',\n                                c='#5F2871',\n                                label='{}, label {:d}, calibrated'.format(classifier, self.view_calibration))\n\n                        ax.plot(uncal_mean_predicted_value,\n                                uncal_fraction_of_positives,\n                                's-',\n                                c='#338A2E',\n                                label='{}, label {:d}, Uncalibrated'.format(classifier, self.view_calibration))\n\n                        ax.set_ylabel('Fraction positive')\n                        ax.set_xlabel('Mean predicted value')\n                        plt.tight_layout(pad=0.1)\n                        plt.legend()\n\n                        out_fig = os.path.join(self.fig_location,\n                                               '{}_{:d}_calibration.png'.format(classifier,\n                                                                                self.view_calibration))\n\n                        logger.info('  Calibration curves saved to {}'.format(out_fig))\n\n                        plt.savefig(out_fig, dpi=300)\n\n                        sys.exit()\n\n                    # Update the voting list.\n                    classifier_list.append((classifier,\n                                            copy(cal_model)))\n\n                else:\n\n                    # Update the voting list.\n                    classifier_list.append((classifier,\n                                            copy(voting_sub_model)))\n\n                ci += 1\n\n            vote_weights = None if 'vote_weights' not in classifier_info else classifier_info['vote_weights']\n\n            # self.model = ensemble.VotingClassifier(estimators=classifier_list,\n            #                                        voting='soft',\n            #                                        weights=vote_weights)\n\n            self.model = VotingClassifier(estimators=classifier_list,\n                                          weights=vote_weights,\n                                          y=self.labels)\n\n            # Reset the original classifier info.\n            self.classifier_info = copy(classifier_info)\n\n        else:\n\n            if self.classifier_info['classifier'] in ['ABR', 'gbr', 'ex-rfr', 'rfr', 'ex-rfr', 'SVR', 'SVRA']:\n                self.discrete = False\n            else:\n                self.discrete = True\n\n            if self.classifier_info['classifier'] == 'bayes':\n\n                self.model = GaussianNB()\n\n                # self.model = cv2.ml.NormalBayesClassifier_create()\n\n            elif self.classifier_info['classifier'] == 'CART':\n                self.model = cv2.ml.DTrees_create()\n\n            elif self.classifier_info['classifier'] in ['cvrf', 'CVRFR']:\n\n                if not self.get_probs:\n                    self.model = cv2.ml.RTrees_create()\n\n            # elif self.classifier_info['classifier'] == 'cvmlp':\n            #\n            #     if self.input_model:\n            #     self.model = cv2.ml.ANN_MLP_create()\n            #     # else:\n            #     #     self.model = cv2.ml.ANN_MLP_create(np.array([self.n_feas, self.classifier_info['n_hidden'],\n            #     #                                                  self.n_classes]))\n\n            elif self.classifier_info['classifier'] in ['cvsvm', 'cvsvma', 'CVSVMR', 'CVSVMRA']:\n                self.model = cv2.ml.SVM_create()\n\n            elif self.classifier_info['classifier'] == 'dt':\n                self.model = tree.DecisionTreeClassifier(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'dtr':\n                self.model = tree.DecisionTreeRegressor(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'ex-dt':\n                self.model = tree.ExtraTreeClassifier(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'ex-dtr':\n                self.model = tree.ExtraTreeRegressor(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'logistic':\n                self.model = LogisticRegression(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'nn':\n                self.model = KNeighborsClassifier(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'rf':\n                self.model = ensemble.RandomForestClassifier(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'ex-rf':\n                self.model = ensemble.ExtraTreesClassifier(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'rfr':\n                self.model = ensemble.RandomForestRegressor(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'ex-rfr':\n                self.model = ensemble.ExtraTreesRegressor(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'ab-dt':\n\n                self.model = ensemble.AdaBoostClassifier(base_estimator=tree.DecisionTreeClassifier(**self.classifier_info_),\n                                                         **self.classifier_info_base)\n\n            elif self.classifier_info['classifier'] == 'ab-rf':\n\n                self.model = ensemble.AdaBoostClassifier(base_estimator=ensemble.RandomForestClassifier(**self.classifier_info_),\n                                                         **self.classifier_info_base)\n\n            elif self.classifier_info['classifier'] == 'ab-ex-rf':\n\n                self.model = ensemble.AdaBoostClassifier(base_estimator=ensemble.ExtraTreesClassifier(**self.classifier_info_),\n                                                         **self.classifier_info_base)\n\n            elif self.classifier_info['classifier'] == 'ab-ex-dt':\n\n                self.model = ensemble.AdaBoostClassifier(base_estimator=tree.ExtraTreeClassifier(**self.classifier_info_),\n                                                         **self.classifier_info_base)\n\n            elif self.classifier_info['classifier'] == 'abr':\n\n                self.model = ensemble.AdaBoostRegressor(base_estimator=tree.DecisionTreeRegressor(**self.classifier_info_),\n                                                        **self.classifier_info_base)\n\n            elif self.classifier_info['classifier'] == 'abr-ex-dtr':\n\n                self.model = ensemble.AdaBoostRegressor(base_estimator=tree.ExtraTreeRegressor(**self.classifier_info_),\n                                                        **self.classifier_info_base)\n\n            elif self.classifier_info['classifier'] == 'bag-dt':\n\n                self.model = ensemble.BaggingClassifier(base_estimator=tree.DecisionTreeClassifier(**self.classifier_info_),\n                                                        **self.classifier_info_base)\n\n            elif self.classifier_info['classifier'] == 'bag-dtr':\n\n                self.model = ensemble.BaggingRegressor(base_estimator=tree.DecisionTreeRegressor(**self.classifier_info_),\n                                                       **self.classifier_info_base)\n\n            elif self.classifier_info['classifier'] == 'bag-ex-dt':\n\n                self.model = ensemble.BaggingClassifier(base_estimator=tree.ExtraTreeClassifier(**self.classifier_info_),\n                                                        **self.classifier_info_base)\n\n            elif self.classifier_info['classifier'] == 'blag':\n\n                if not IMBLEARN_INSTALLED:\n\n                    logger.error(\"\"\"\\\n\n                    Imbalanced learn must be installed to use the model. Install from\n\n                    pip install imbalanced-learn\n\n                    \"\"\")\n\n                self.model = imblearn.BalancedBaggingClassifier(**self.classifier_info_base)\n\n            elif self.classifier_info['classifier'] == 'blaf':\n\n                if not IMBLEARN_INSTALLED:\n\n                    logger.error(\"\"\"\\\n\n                    Imbalanced learn must be installed to use the model. Install from\n\n                    pip install imbalanced-learn\n\n                    \"\"\")\n\n                self.model = imblearn.BalancedRandomForestClassifier(**self.classifier_info_base)\n\n            elif self.classifier_info['classifier'] == 'blab':\n\n                if not IMBLEARN_INSTALLED:\n\n                    logger.error(\"\"\"\\\n\n                    Imbalanced learn must be installed to use the model. Install from\n\n                    pip install imbalanced-learn\n\n                    \"\"\")\n\n                self.model = imblearn.RUSBoostClassifier(**self.classifier_info_base)\n\n            elif self.classifier_info['classifier'] == 'tpot':\n\n                if not TPOT_INSTALLED:\n\n                    logger.error(\"\"\"\\\n\n                    Tpot must be installed to use the model.\n\n                    \"\"\")\n\n                self.model = TPOTClassifier(generations=5, population_size=50, cv=5, verbosity=0)\n\n            elif self.classifier_info['classifier'] == 'mondrian':\n\n                if not SKGARDEN_INSTALLED:\n\n                    logger.error(\"\"\"\\\n\n                    Scikit-garden must be installed to use the Mondrian model.\n\n                    \"\"\")\n\n                self.model = skgarden.MondrianForestClassifier(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'gb':\n                self.model = ensemble.GradientBoostingClassifier(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'gbr':\n                self.model = ensemble.GradientBoostingRegressor(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'svmc':\n                self.model = svm.SVC(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'svmnu':\n                self.model = svm.NuSVC(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'qda':\n                self.model = QDA(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'gaussian':\n                self.model = GaussianProcessClassifier(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'catboost':\n\n                if not CATBOOST_INSTALLED:\n\n                    logger.error(\"\"\"\\\n\n                    Catboost must be installed to use the model.\n\n                    \"\"\")\n\n                self.model = CatBoostClassifier(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'lightgbm':\n\n                if not LIGHTGBM_INSTALLED:\n\n                    logger.error(\"\"\"\\\n\n                    LightGBM must be installed to use the model. \n                    \n                    # Anaconda\n                    conda install -c conda-forge lightgbm or\n\n                    # Python 2.x\n                    # cluster: module load cmake\n                    pip install --no-cache-dir --no-binary :all: lightgbm\n\n                    # Python 3.x\n                    pip install lightgbm\n\n                    \"\"\")\n\n                self.model = gbm.LGBMClassifier(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'xgboost':\n\n                if not XGBOOST_INSTALLED:\n\n                    logger.error(\"\"\"\\\n\n                    XGBoost must be installed to use the model.\n\n                    \"\"\")\n\n                self.model = XGBClassifier(**self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'chaincrf':\n\n                if self.classifier_info_['n_jobs'] == 1:\n\n                    self.model = ssvm.FrankWolfeSSVM(ChainCRF(directed=True),\n                                                     **self.classifier_info_)\n\n                else:\n\n                    self.model = ssvm.OneSlackSSVM(ChainCRF(directed=True),\n                                                   **self.classifier_info_)\n\n            elif self.classifier_info['classifier'] == 'gridcrf':\n\n                try:\n\n                    # CURRENTLY EXPERIMENTAL AND DOES NOT\n                    #   SUPPORT PARALLEL PROCESSING\n                    # if self.classifier_info_['n_jobs'] == 1:\n                    #\n                    #     self.model = ssvm.FrankWolfeSSVM(GridCRF(inference_method='qpbo',\n                    #                                              neighborhood=4),\n                    #                                      **self.classifier_info_)\n                    #\n                    # else:\n\n                    self.model = ssvm.OneSlackSSVM(GridCRF(**self.grid_info),\n                                                   **self.classifier_info_)\n\n                except:\n\n                    logger.error('  The Grid CRF failed.')\n                    raise RuntimeError\n\n            elif self.classifier_info['classifier'] == 'ORRF':\n\n                # try:\n                #     import otbApplication as otb\n                # except ImportError:\n                #     raise ImportError('Orfeo tooblox needs to be installed')\n\n                v_info = vector_tools.vopen(self.in_shapefile)\n\n                if v_info.shp_geom_name.lower() == 'point':\n                    sys.exit('\\nThe input shapefile must be a polygon.\\n')\n\n                if os.path.isfile(self.out_stats):\n                    logger.info('  The statistics already exist')\n                else:\n\n                    if self.stats_from_image:\n\n                        # image statistics\n                        com = 'otbcli_ComputeImagesStatistics -il %s -out %s' % (self.input_image, self.out_stats)\n\n                        subprocess.call(com, shell=True)\n\n                    else:\n\n                        gap_1 = '    '\n                        gap_2 = '        '\n\n                        xml_string = '<?xml version=\"1.0\" ?>\\n<FeatureStatistics>\\n{}<Statistic name=\"mean\">\\n{}'.format(gap_1, gap_2)\n\n                        # gather stats from samples\n                        for fea_pos in range(0, self.n_feas):\n\n                            stat_line = '<StatisticVector value=\"%f\" />' % self.p_vars[:, fea_pos].mean()\n\n                            # add the line to the xml string\n                            if (fea_pos + 1) == self.n_feas:\n                                xml_string = '%s%s\\n%s' % (xml_string, stat_line, gap_1)\n                            else:\n                                xml_string = '%s%s\\n%s' % (xml_string, stat_line, gap_2)\n\n                        xml_string = '%s</Statistic>\\n%s<Statistic name=\"stddev\">\\n%s' % (xml_string, gap_1, gap_2)\n\n                        # gather stats from samples\n                        for fea_pos in range(0, self.n_feas):\n\n                            stat_line = '<StatisticVector value=\"%f\" />' % self.p_vars[:, fea_pos].std()\n\n                            # add the line to the xml string\n                            if (fea_pos + 1) == self.n_feas:\n                                xml_string = '%s%s\\n%s' % (xml_string, stat_line, gap_1)\n                            else:\n                                xml_string = '%s%s\\n%s' % (xml_string, stat_line, gap_2)\n\n                        xml_string = '%s</Statistic>\\n</FeatureStatistics>\\n' % xml_string\n\n                        with open(self.out_stats, 'w') as xml_wr:\n                            xml_wr.writelines(xml_string)\n\n                # app = otb.Registry.CreateApplication('ComputeImagesStatistics')\n                # app.SetParameterString('il', input_image)\n                # app.SetParameterString('out', output_stats)\n                # app.ExecuteAndWriteOutput()\n\n                if os.path.isfile(self.output_model):\n                    os.remove(self.output_model)\n\n                # train the model\n                com = 'otbcli_TrainImagesClassifier -io.il {} -io.vd {} -io.imstat {} -classifier rf \\\n                -classifier.rf.max {:d} -classifier.rf.nbtrees {:d} \\\n                -classifier.rf.min {:d} -classifier.rf.var {:d} -io.out {}'.format(self.input_image,\n                                                                                   self.in_shapefile,\n                                                                                   self.out_stats,\n                                                                                   self.classifier_info['max_depth'],\n                                                                                   self.classifier_info['trees'],\n                                                                                   self.classifier_info['min_samps'],\n                                                                                   self.classifier_info['rand_vars'],\n                                                                                   self.output_model)\n\n                subprocess.call(com, shell=True)\n\n                # app = otb.Registry.CreateApplication('TrainImagesClassifier')\n                # app.SetParameterString('io.il', input_image)\n                # app.SetParameterString('io.vd', input_shapefile)\n                # app.SetParameterString('io.imstat', output_stats)\n                # app.SetParameterString('classifier', 'rf')\n                # app.SetParameterString('classifier.rf.max', str(classifier_info['max_depth']))\n                # app.SetParameterString('classifier.rf.nbtrees', str(classifier_info['trees']))\n                # app.SetParameterString('classifier.rf.min', str(classifier_info['min_samps']))\n                # app.SetParameterString('classifier.rf.var', str(classifier_info['rand_vars']))\n                # app.SetParameterString('io.out', output_model)\n                # app.ExecuteAndWriteOutput()\n\n            else:\n\n                logger.error('  The model {} is not supported'.format(self.classifier_info['classifier']))\n                raise NameError\n\n    def _set_parameters(self):\n\n        \"\"\"Sets model parameters for OpenCV\"\"\"\n\n        #############################################\n        # Set algorithm parameters for OpenCV models.\n        #############################################\n\n        if self.classifier_info['classifier'] in ['CART', 'cvrf', 'CVEX_RF']:\n\n            self.model.setMaxDepth(self.classifier_info['max_depth'])\n            self.model.setMinSampleCount(self.classifier_info['min_samps'])\n            self.model.setCalculateVarImportance(self.classifier_info['calc_var_importance'])\n            self.model.setActiveVarCount(self.classifier_info['rand_vars'])\n            self.model.setTermCriteria(self.classifier_info['term_crit'])\n\n            if self.classifier_info['priors'].min() < 1:\n                self.model.setPriors(self.classifier_info['priors'])\n            \n            self.model.setTruncatePrunedTree(self.classifier_info['truncate'])\n\n        elif self.classifier_info['classifier'] == 'cvmlp':\n\n            n_steps = 1000\n            max_err = .0001\n            step_size = .3\n            momentum = .2\n\n            # cv2.TERM_CRITERIA_EPS\n            self.parameters = dict(term_crit=(cv2.TERM_CRITERIA_COUNT, n_steps, max_err),\n                                   train_method=cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,\n                                   bp_dw_scale=step_size,\n                                   bp_moment_scale=momentum)\n\n        elif self.classifier_info['classifier'] == 'cvsvm':\n\n            self.model.setC(self.classifier_info_svm['C'])\n            self.model.setGamma(self.classifier_info_svm['gamma'])\n            self.model.setKernel(cv2.ml.SVM_RBF)\n            self.model.setType(cv2.ml.SVM_C_SVC)\n\n            # self.parameters = dict(kernel_type=cv2.ml.SVM_RBF,\n            #                        svm_type=cv2.ml.SVM_C_SVC,\n            #                        C=self.classifier_info_svm['C'],\n            #                        gamma=self.classifier_info_svm['gamma'])\n\n        elif self.classifier_info['classifier'] == 'cvsvma':\n\n            # SVM, parameters optimized\n            self.parameters = dict(kernel_type=cv2.ml.SVM_RBF,\n                                   svm_type=cv2.ml.SVM_C_SVC)\n\n        elif self.classifier_info['classifier'] == 'CVSVMR':\n\n            # SVM regression\n            self.parameters = dict(kernel_type=cv2.ml.SVM_RBF,\n                                   svm_type=cv2.ml.SVM_NU_SVR,\n                                   C=self.classifier_info_svm['C'],\n                                   gamma=self.classifier_info_svm['gamma'],\n                                   nu=self.classifier_info_svm['nu'],\n                                   p=self.classifier_info_svm['p'])\n\n        elif self.classifier_info['classifier'] == 'CVSVMRA':\n\n            # SVM regression, parameters optimized\n            self.parameters = dict(kernel_type=cv2.ml.SVM_RBF,\n                                   svm_type=cv2.ml.SVM_NU_SVR,\n                                   nu=self.classifier_info['nu'])\n\n        else:\n\n            self.parameters = None\n\n    def _train_model(self):\n\n        \"\"\"\n        Trains a model and saves to file if prompted\n        \"\"\"\n\n        if not self.be_quiet:\n\n            if hasattr(self.model, 'is_prefit_model'):\n                logger.info('  The model has already been trained as a voting model.')\n            else:\n\n                if not hasattr(self, 'n_samps'):\n                    self.n_samps = self.p_vars.shape[0]\n\n                if not hasattr(self, 'n_feas'):\n                    self.n_feas = self.p_vars.shape[1]\n\n                if self.classifier_info['classifier'][0].lower() in 'aeiou':\n                    a_or_an = 'an'\n                else:\n                    a_or_an = 'a'\n\n                if isinstance(self.classifier_info['classifier'], list):\n\n                    logger.info('  Training a voting model with {} ...'.format(','.join(self.classifier_info['classifier'])))\n\n                else:\n\n                    logger.info('  Training {} {} model with {:,d} samples and {:,d} variables ...'.format(a_or_an,\n                                                                                                           self.classifier_info['classifier'],\n                                                                                                           self.n_samps,\n                                                                                                           self.n_feas))\n\n        # OpenCV tree-based models\n        if self.classifier_info['classifier'] in ['CART', 'cvrf', 'CVEX_RF']:\n\n            self.model.train(self.p_vars, 0, self.labels)\n            # self.model.train(self.p_vars, cv2.CV_ROW_SAMPLE, self.labels, params=self.parameters)\n\n        # OpenCV tree-based regression\n        elif self.classifier_info['classifier'] in ['CVRFR', 'CVEX_RFR']:\n\n            self.model.train(self.p_vars, cv2.CV_ROW_SAMPLE, self.labels,\n                             varType=np.zeros(self.n_feas+1, dtype='uint8'), params=self.parameters)\n\n        elif self.classifier_info['classifier'] == 'cvmlp':\n\n            # Convert input strings to binary zeros and ones, and set the output\n            # array to all -1's with ones along the diagonal.\n            targets = -1 * np.ones((self.n_samps, self.n_classes), 'float')\n\n            for i in range(0, self.n_samps):\n\n                lab_idx = sorted(self.classes).index(self.labels[i])\n\n                targets[i, lab_idx] = 1\n\n            self.model.train(self.p_vars, targets, None, params=self.parameters)\n\n        elif self.classifier_info['classifier'] in ['cvsvm', 'CVSVMR']:\n\n            if isinstance(self.rank_method, str):\n                self.rank_feas(rank_method=self.rank_method, top_feas=self.top_feas)\n                # self.model.train(self.p_vars, self.labels, varIdx=self.ranked_feas-1, params=self.parameters)\n                self.model.train(self.p_vars, self.labels, varIdx=self.ranked_feas-1)\n            else:\n                # self.model.train(self.p_vars, self.labels, params=self.parameters)\n                self.model.train(self.p_vars, 0, self.labels)\n\n        elif self.classifier_info['classifier'] in ['cvsvma', 'cvsvra']:\n\n            logger.info('  Be patient. Auto tuning can take a while.')\n\n            self.model.train_auto(self.p_vars, self.labels, None, None, params=self.parameters, k_fold=10)\n\n        elif self.classifier_info['classifier'] in ['chaincrf', 'gridcrf']:\n\n            if self.classifier_info['classifier'] == 'chaincrf':\n\n                self.p_vars, self.labels, self.p_vars_test = self._transform4crf(p_vars2reshape=self.p_vars,\n                                                                                 labels2reshape=self.labels,\n                                                                                 p_vars_test2reshape=self.p_vars_test)\n\n            self.model.fit(self.p_vars, self.labels)\n\n        # Scikit-learn models\n        else:\n\n            if not hasattr(self.model, 'is_prefit_model'):\n\n                # Check if the model supports sample weights.\n                try:\n                    argi = inspect.getargspec(self.model.fit)\n                except:\n                    argi = inspect.getfullargspec(self.model.fit)\n\n                if 'sample_weight' in argi.args:\n\n                    self.model.fit(self.p_vars,\n                                   self.labels,\n                                   sample_weight=self.sample_weight)\n\n                else:\n\n                    self.model.fit(self.p_vars,\n                                   self.labels)\n\n                if self.calibrate_proba:\n\n                    if self.n_samps >= 1000:\n\n                        self.model = calibration.CalibratedClassifierCV(base_estimator=copy(self.model),\n                                                                        method='isotonic',\n                                                                        cv='prefit')\n\n                    else:\n\n                        self.model = calibration.CalibratedClassifierCV(base_estimator=copy(self.model),\n                                                                        method='sigmoid',\n                                                                        cv='prefit')\n\n                    # # Limit the test size.\n                    # samp_thresh = 100000\n                    # if self.p_vars_test.shape[0] > samp_thresh:\n                    #\n                    #     pdf = pd.DataFrame(self.p_vars_test)\n                    #     pdf['GROUP'] = self.labels_test\n                    #\n                    #     n_groups = len(pdf.GROUP.unique())\n                    #\n                    #     group_samps = int(float(samp_thresh) / n_groups)\n                    #\n                    #     dfg = pdf.groupby('GROUP', group_keys=False).apply(lambda xr_: xr_.sample(min(len(xr_),\n                    #                                                                                   group_samps)))\n                    #\n                    #     idx = dfg.index.values.ravel()\n                    #\n                    #     p_vars_test_cal = self.p_vars_test[idx]\n                    #     labels_test_cal = self.labels_test[idx]\n                    #\n                    # else:\n                    #\n                    #     p_vars_test_cal = self.p_vars_test\n                    #     labels_test_cal = self.labels_test\n\n                    logger.info('  Calibrating a {MODEL} model ...'.format(MODEL=self.classifier_info['classifier']))\n\n                    # Calibrate the model on the test data.\n                    if isinstance(self.calibrate_test, np.ndarray):\n\n                        assert self.calibrate_test.shape[0] == len(self.calibrate_labels) == len(self.calibrate_weights)\n\n                        self.model.fit(self.calibrate_test,\n                                       self.calibrate_labels,\n                                       sample_weight=self.calibrate_weights)\n\n                    else:\n\n                        self.model.fit(self.p_vars_test,\n                                       self.labels_test,\n                                       sample_weight=self.sample_weight_test)\n\n                    feature_importances_ = None\n\n                    # Keep the feature importances.\n                    if hasattr(self.model, 'feature_importances_'):\n                        feature_importances_ = copy(self.model.feature_importances_)\n\n                    if isinstance(feature_importances_, np.ndarray):\n                        self.model.feature_importances_ = feature_importances_\n\n                    self.calibrated = True\n\n                    # if hasattr(self.model, 'estimators'):\n                    #     self.model.classes_ = self.model.estimators[0][1].classes_\n\n        if isinstance(self.output_model, str):\n\n            logger.info('  Saving model to file ...')\n\n            compress = ('zlib', 5) if self.compress_model else 0\n\n            if 'CV' in self.classifier_info['classifier']:\n\n                try:\n\n                    self.model.save(self.output_model)\n\n                    # Dump the parameters to a text file.\n                    joblib.dump([self.classifier_info,\n                                 self.model,\n                                 self.sample_info_dict],\n                                self.output_model,\n                                compress=compress,\n                                protocol=-1)\n\n                except:\n\n                    logger.error('  Could not save {} to file.'.format(self.output_model))\n                    raise IOError\n\n            else:\n\n                try:\n\n                    joblib.dump([self.classifier_info,\n                                 self.model,\n                                 self.sample_info_dict],\n                                self.output_model,\n                                compress=compress,\n                                protocol=-1)\n\n                except:\n\n                    logger.error('  Could not save {} to file.'.format(self.output_model))\n                    raise IOError\n\n            # Get test accuracy, if possible.\n            if hasattr(self, 'p_vars_test'):\n\n                if isinstance(self.p_vars_test, np.ndarray):\n\n                    # try:\n\n                    self.test_accuracy(out_acc=self.out_acc,\n                                       discrete=self.discrete)\n\n                    # except:\n                    #     logger.warning('  Could not perform model validation.')\n\n    def _transform4crf(self,\n                       p_vars2reshape=None,\n                       labels2reshape=None,\n                       p_vars_test2reshape=None):\n\n        \"\"\"\n        Transforms variables and labels for linear-chain Conditional Random Fields\n\n        Args:\n            p_vars2reshape (Optional[2d array])\n            labels2reshape (Optional[1d array like])\n            p_vars_test2reshape (Optional[2d array])\n        \"\"\"\n\n        p_vars_r = None\n        labels_r = None\n        p_vars_test_r = None\n        constant = np.array([1.0], dtype='float64').reshape(1, 1)\n\n        if isinstance(p_vars2reshape, np.ndarray):\n\n            if hasattr(self, 'n_feas'):\n\n                if isinstance(self.n_feas, int):\n                    reshape_features = self.n_feas\n                else:\n                    reshape_features = p_vars2reshape.shape[1]\n\n            else:\n                reshape_features = p_vars2reshape.shape[1]\n\n            p_vars_r = np.array([np.hstack((pv_.reshape(1, reshape_features), constant))\n                                 for pv_ in p_vars2reshape], dtype='float64')\n\n        if isinstance(labels2reshape, np.ndarray):\n            labels_r = np.array([np.array([label_], dtype='int64') for label_ in labels2reshape], dtype='int64')\n\n        if isinstance(p_vars_test2reshape, np.ndarray):\n\n            if hasattr(self, 'n_feas'):\n\n                if isinstance(self.n_feas, int):\n                    reshape_features = self.n_feas\n                else:\n                    reshape_features = p_vars_test2reshape.shape[1]\n\n            else:\n                reshape_features = p_vars_test2reshape.shape[1]\n\n            p_vars_test_r = np.array([np.hstack((pv_.reshape(1, reshape_features), constant))\n                                      for pv_ in p_vars_test2reshape], dtype='float64')\n\n        return p_vars_r, labels_r, p_vars_test_r\n\n    def predict_array(self,\n                      array2predict,\n                      rows,\n                      cols,\n                      relax_probabilities=False,\n                      plr_window_size=5,\n                      plr_matrix=None,\n                      plr_iterations=3,\n                      morphology=False,\n                      do_not_morph=None,\n                      d_type='byte'):\n\n        \"\"\"\n        Makes predictions on an array\n\n        Args:\n            array2predict (2d array): [samples x predictors]\n            rows (int): The array rows for reshaping.\n            cols (int): The array columns for reshaping.\n            relax_probabilities (Optional[bool]): Whether to relax posterior probabilities. Default is False.\n            plr_window_size (Optional[int]): The window size for probabilistic label relaxation. Default is 5.\n            plr_matrix (Optional[2d array]): The class compatibility matrix. Default is None.\n            plr_iterations (Optional[int]): The probabilistic label relaxation iterations. Default is 3.\n            morphology (Optional[bool]): Whether to apply image morphology to the predicted classes.\n                Default is False.\n            do_not_morph (Optional[int list]): A list of classes not to morph with `morphology=True`. Default is None.\n            d_type (Optional[str]): The output image data type. Default is 'byte'.\n                Choices are ['byte', 'uint16', 'uint32', 'uint64', 'int16', 'int32', 'int64'].\n                *If `morphology=True`, `d_type` is automatically set as 'byte'. For regression models, `d_type` is\n                automatically set as 'float32'.\n\n        Returns:\n            Predictions as a 2d array\n        \"\"\"\n\n        if self.classifier_info['classifier'] in ['c5', 'cubist']:\n\n            features = ro.r.matrix(array2predict,\n                                   nrow=array2predict.shape[0],\n                                   ncol=array2predict.shape[1])\n\n            features.colnames = StrVector(self.headers[:-1])\n\n            return _do_c5_cubist_predict(self.model, self.classifier_info['classifier'], features)\n\n        else:\n\n            if relax_probabilities:\n\n                return predict_scikit_probas_static(array2predict,\n                                                    self.model,\n                                                    rows,\n                                                    cols,\n                                                    0,\n                                                    0,\n                                                    rows,\n                                                    cols,\n                                                    morphology,\n                                                    do_not_morph,\n                                                    plr_matrix,\n                                                    plr_window_size,\n                                                    plr_iterations,\n                                                    self.d_type)\n\n            else:\n                return raster_tools.STORAGE_DICT_NUMPY[d_type](self.model.predict(array2predict).reshape(rows, cols))\n\n    def predict(self,\n                input_image,\n                output_image,\n                additional_layers=None,\n                band_check=-1,\n                bands=None,\n                ignore_feas=None,\n                in_stats=None,\n                in_model=None,\n                mask_background=None,\n                background_band=1,\n                background_value=0,\n                minimum_observations=0,\n                observation_band=0,\n                scale_factor=1.0,\n                row_block_size=1000,\n                col_block_size=1000,\n                n_jobs=-1,\n                n_jobs_vars=-1,\n                gdal_cache=256,\n                overwrite=False,\n                track_blocks=False,\n                predict_probs=False,\n                relax_probabilities=False,\n                plr_window_size=5,\n                plr_iterations=3,\n                plr_matrix=None,\n                write2blocks=False,\n                block_range=None,\n                morphology=False,\n                do_not_morph=None,\n                d_type='byte',\n                **kwargs):\n\n        \"\"\"\n        Applies a model to predict class labels\n\n        Args:\n            input_image (str): The input image to classify.\n            output_image (str): The output image.\n            additional_layers (Optional[list]): A list of additional images (layers) that are not part\n                of `input_image`.\n            band_check (Optional[int]): The band to check for 'no data'. Default is -1, or do not perform check. \n            bands (Optional[list]): A list of bands to open, otherwise opens all bands. Default is None.\n            ignore_feas (Optional[list]): A list of features (band layers) to ignore. Default is an empty list,\n                or use all features.\n            in_stats (Optional[str]): A XML statistics file. Default is None. *Only applicable to Orfeo models.\n            in_model (Optional[str]): A model file to load. Default is None. *Only applicable to Orfeo\n                and C5/Cubist models.\n            mask_background (Optional[str or 2d array]): An image or array to use as a background mask. Default is None.\n            background_band (int): The band from `mask_background` to use for null background value. Default is 1.\n            background_value (Optional[int]): The background value in `mask_background`. Default is 0.\n            minimum_observations (Optional[int]): A minimum number of observations in `mask_background` to be\n                recoded to 0. Default is 0, or no minimum observation filter.\n            observation_band (Optional[int]): The band position in `mask_background` of the `minimum_observations`\n                counts. Default is 0.\n            scale_factor (Optional[float]): The scale factor for CRF models. Default is 1.\n            row_block_size (Optional[int]): The row block size (pixels). Default is 1024.\n            col_block_size (Optional[int]): The column block size (pixels). Default is 1024.\n            n_jobs (Optional[int]): The number of processors to use for parallel mapping. Default is -1, or all\n                available processors.\n            n_jobs_vars (Optional[int]): The number of processors to use for parallel band loading.\n                Default is -1, or all available processors.\n            gdal_cache (Optional[int]). The GDAL cache (MB). Default is 256.\n            overwrite (Optional[bool]): Whether to overwrite an existing `output_image`. Default is False.\n            track_blocks (Optional[bool]): Whether to keep a record of processed blocks. Default is False.\n            predict_probs (Optional[bool]): Whether to write class probabilities to file in place of hard decisions.\n                Default is False.\n            relax_probabilities (Optional[bool]): Whether to relax posterior probabilities. Default is False.\n            plr_window_size (Optional[int]): The window size for probabilistic label relaxation. Default is 5.\n            plr_iterations (Optional[int]): The number of iterations for probabilistic label relaxation. Default is 3.\n            plr_matrix (Optional[2d array]): The class compatibility matrix. Default is None.\n            write2blocks (Optional[bool]): Whether to write to individual blocks, otherwise write to one image.\n                Default is False.\n                *In the event of True, each block will be given the name `base image_####base extension`.\n            block_range (Optional[list or tuple]): A start and end range for block processing. Default is None,\n                or start at the first block.\n            morphology (Optional[bool]): Whether to apply image morphology to the predicted classes.\n                Default is False.\n            do_not_morph (Optional[int list]): A list of classes not to morph with `morphology=True`. Default is None.\n            d_type (Optional[str]): The output image data type. Default is 'byte'.\n                Choices are ['byte', 'uint16', 'uint32', 'uint64', 'int16', 'int32', 'int64'].\n                *If `morphology=True`, `d_type` is automatically set as 'byte'. For regression models, `d_type` is\n                automatically set as 'float32'.\n            kwargs (Optional): Image read options passed to `mpglue.raster_tools.ropen.read`.\n            \n        Returns:\n            None, writes to `output_image`.\n\n        Examples:\n            >>> import mpglue as gl\n            >>> cl = gl.classification()\n            >>>\n            >>> # You can use x, y coordinates, but note that these must be\n            >>> #   supplied to ``predict`` also.\n            >>> cl.split_samples('/samples.txt', perc_samp_each=.7)\n            >>>\n            >>> # Random Forest with Scikit-learn\n            >>> cl.construct_model(classifier_info={'classifier': 'rf', 'trees': 100, 'max_depth': 25})\n            >>>\n            >>> # Random Forest with OpenCV\n            >>> cl.construct_model(classifier_info={'classifier': 'cvrf', 'trees': 100,\n            >>>                    'max_depth': 25, 'truncate': True})\n            >>>\n            >>> # Apply the classification model to map image class labels.\n            >>> cl.predict('/image_feas.tif', '/image_labels.tif', ignore_feas=[1, 6])\n            >>>\n            >>> # or use Orfeo to predict class labels\n            >>> cl.construct_model(classifier_info={'classifier': 'OR_RF', 'trees': 1000,\n            >>>                    'max_depth': 25, 'min_samps': 5, 'rand_vars': 10},\n            >>>                    input_image='/image.tif', in_shapefile='/shapefile.shp',\n            >>>                    out_stats='/stats.xml', output_model='/rf_model.xml')\n            >>>\n            >>> cl.predict('/image_feas.tif', '/image_labels.tif',\n            >>>            in_stats='/stats.xml', in_model='/rf_model.xml')\n        \"\"\"\n\n        self.input_image = input_image\n        self.output_image = output_image\n        self.additional_layers = additional_layers\n        self.ignore_feas = ignore_feas\n        self.bands = bands\n        self.band_check = band_check\n        self.row_block_size = row_block_size\n        self.col_block_size = col_block_size\n        self.mask_background = mask_background\n        self.background_band = background_band\n        self.background_value = background_value\n        self.minimum_observations = minimum_observations\n        self.observation_band = observation_band\n        self.scale_factor = scale_factor\n        self.in_model = in_model\n        self.gdal_cache = gdal_cache\n        self.in_stats = in_stats\n        self.n_jobs = n_jobs\n        self.n_jobs_vars = n_jobs_vars\n        self.chunk_size = (self.row_block_size * self.col_block_size) / 100\n        self.overwrite = overwrite\n        self.track_blocks = track_blocks\n        self.predict_probs = predict_probs\n        self.relax_probabilities = relax_probabilities\n        self.plr_window_size = plr_window_size\n        self.plr_iterations = plr_iterations\n        self.plr_matrix = plr_matrix\n        self.write2blocks = write2blocks\n        self.block_range = block_range\n        self.morphology = morphology\n        self.do_not_morph = do_not_morph\n        self.d_type = d_type\n        self.kwargs = kwargs\n\n        if self.n_jobs == -1:\n            self.n_jobs = joblib.cpu_count()\n\n        if not hasattr(self, 'classifier_info'):\n\n            logger.warning(\"\"\"\\\n            \n            There is no `classifier_info` object. Be sure to run `construct_model`\n            or `construct_r_model` before running `predict`.\n            \n            \"\"\")\n\n            return\n\n        if not hasattr(self, 'model'):\n\n            logger.warning(\"\"\"\\\n            \n            There is no trained `model` object. Be sure to run `construct_model`\n            or `construct_r_model` before running `predict`.\n            \n            \"\"\")\n\n            return\n\n        self.dir_name, f_name = os.path.split(self.output_image)\n        self.output_image_base, self.output_image_ext = os.path.splitext(f_name)\n\n        if not self.dir_name and not os.path.isabs(f_name):\n            self.dir_name = os.path.abspath('.')\n\n        # Delete files\n        if os.path.isfile(output_image) and self.overwrite and not self.write2blocks:\n\n            os.remove(output_image)\n\n            for ext in ['.ovr', '.aux.xml']:\n\n                if os.path.isfile(os.path.join(self.dir_name, '{}{}'.format(f_name, ext))):\n                    os.remove(os.path.join(self.dir_name, '{}{}'.format(f_name, ext)))\n\n        self.out_image_temp = os.path.join(self.dir_name, '{}_temp.tif'.format(self.output_image_base))\n        self.temp_model_file = os.path.join(self.dir_name, 'temp_model_file.txt')\n\n        if not os.path.isdir(self.dir_name):\n            os.makedirs(self.dir_name)\n\n        if self.write2blocks:\n\n            logger.info('  Predicting class labels from {} and writing to {} blocks ...'.format(self.input_image,\n                                                                                                self.output_image))\n\n        else:\n\n            logger.info('  Predicting class labels from {} and writing to {} ...'.format(self.input_image,\n                                                                                         self.output_image))\n\n        # Conditional Random Fields\n        if self.classifier_info['classifier'] == 'gridcrf':\n            self._predict4crf(**self.kwargs)\n\n        # Orfeo Toolbox application\n        elif 'OR' in self.classifier_info['classifier']:\n            self._predict4orf()\n\n        # Scikit-learn or OpenCV\n        else:\n\n            self.open_image = False\n\n            self.i_info = raster_tools.ropen(self.input_image)\n\n            # Block record keeping.\n            if self.track_blocks and not self.write2blocks:\n\n                self.record_keeping = os.path.join(self.dir_name, '{}_record.txt'.format(self.output_image_base))\n\n                if os.path.isfile(self.record_keeping):\n                    self.record_list = self.load(self.record_keeping)\n                else:\n                    self.record_list = list()\n\n            # Output image information.\n            self.o_info = self.i_info.copy()\n\n            # OUTPUT BANDS\n            if self.predict_probs:\n\n                if not hasattr(self.model, 'predict_proba'):\n\n                    logger.warning('  The model must have a `predict_proba` method to prediction class probabilities.')\n                    return\n\n                self.o_info.bands = self.n_classes\n\n            else:\n                self.o_info.bands = 1\n\n            # OUTPUT DATA TYPE\n            if self.predict_probs or (self.classifier_info['classifier'] in ['abr', 'abr-ex-dtr', 'bgr', 'bag-dtr',\n                                                                             'rfr', 'ex-rfr', 'svr', 'svra',\n                                                                             'cubist', 'dtr']):\n\n                self.o_info.update_info(storage='float32')\n                self.d_type = 'float32'\n\n            else:\n\n                if self.morphology:\n\n                    self.o_info.update_info(storage='byte')\n                    self.d_type = 'byte'\n\n                else:\n                    self.o_info.update_info(storage=self.d_type)\n\n            # Make the predictions\n            self._predict()\n\n            if self.open_image:\n                self.i_info.close()\n\n            self.o_info.close()\n\n    def _predict4crf(self, **kwargs):\n\n        self.load4crf([self.input_image],\n                      None,\n                      bands=self.bands,\n                      scale_factor=self.scale_factor,\n                      n_jobs=self.n_jobs_vars,\n                      **kwargs)\n\n        with raster_tools.ropen(self.input_image) as i_info:\n\n            o_info = i_info.copy()\n\n            # Update the output information.\n            o_info.update_info(bands=1,\n                               storage='byte')\n\n            if 'j' in kwargs:\n                o_info.update_info(left=i_info.left + (kwargs['j'] * i_info.cellY))\n\n            if 'i' in kwargs:\n                o_info.update_info(top=i_info.top - (kwargs['i'] * i_info.cellY))\n\n            if 'rows' in kwargs:\n                o_info.update_info(rows=kwargs['rows'])\n\n            if 'cols' in kwargs:\n                o_info.update_info(cols=kwargs['cols'])\n\n        del i_info\n\n        # Make class predictions and write to file.\n        raster_tools.write2raster(np.array(self.model.predict(self.p_vars),\n                                           dtype='uint8').reshape(self.im_rows,\n                                                                  self.im_cols),\n                                  self.output_image,\n                                  o_info=o_info)\n\n    def _predict4orf(self):\n\n        # make predictions\n        if isinstance(self.in_stats, str):\n\n            if isinstance(self.mask_background, str):\n\n                com = 'otbcli_ImageClassifier -in {} -imstat {} \\\n                                        -model {} -out {} -ram {:d}'.format(self.input_image,\n                                                                            self.in_stats,\n                                                                            self.in_model,\n                                                                            self.out_image_temp,\n                                                                            self.gdal_cache)\n\n            else:\n                com = 'otbcli_ImageClassifier -in {} -imstat {} \\\n                                        -model {} -out {} -ram {:d}'.format(self.input_image,\n                                                                            self.in_stats,\n                                                                            self.in_model,\n                                                                            self.output_image,\n                                                                            self.gdal_cache)\n\n        else:\n\n            if isinstance(self.mask_background, str):\n\n                com = 'otbcli_ImageClassifier -in {} -model {} -out {} -ram {:d}'.format(self.input_image,\n                                                                                         self.in_model,\n                                                                                         self.out_image_temp,\n                                                                                         self.gdal_cache)\n\n            else:\n\n                com = 'otbcli_ImageClassifier -in {} -model {} -out {} -ram {:d}'.format(self.input_image,\n                                                                                         self.in_model,\n                                                                                         self.output_image,\n                                                                                         self.gdal_cache)\n\n        try:\n            subprocess.call(com, shell=True)\n        except:\n\n            logger.warning('  Are you sure the Orfeo Toolbox is installed?')\n            return\n\n        if isinstance(self.mask_background, str):\n\n            self._mask_background()\n\n            # app = otb.Registry.CreateApplication('ImageClassifier')\n            # app.SetParameterString('in', input_image)\n            # app.SetParameterString('imstat', output_stats)\n            # app.SetParameterString('model', output_model)\n            # app.SetParameterString('out', output_map)\n            # app.ExecuteAndWriteOutput()\n\n    def _predict(self):\n\n        # Global variables for parallel processing.\n        global features, model_pp, predict_samps, indice_pairs, mdl\n\n        features = None\n        model_pp = None\n        predict_samps = None\n        indice_pairs = None\n        mdl = None\n\n        # Load the model.\n        if isinstance(self.input_model, str):\n            mdl = joblib.load(self.input_model)[1]\n        else:\n            mdl = self.model\n\n        # Set default indexing variables.\n        start_i = 0\n        start_j = 0\n        rows = self.i_info.rows\n        cols = self.i_info.cols\n        image_top = self.i_info.top\n        image_left = self.i_info.left\n        iwo = 0\n        jwo = 0\n\n        # Update variables for sub-image predictions.\n        start_i, start_j, rows, cols, iwo, jwo = self._set_indexing(start_i,\n                                                                    start_j,\n                                                                    rows,\n                                                                    cols,\n                                                                    iwo,\n                                                                    jwo)\n\n        # Determine which bands to open.\n        self._set_bands()\n\n        # Update the output raster size.\n        #\n        # *This will be overwritten in the\n        #   event `write2blocks` is set as True.\n        self.o_info.update_info(rows=rows,\n                                cols=cols)\n\n        # Setup the object to write to.\n        if not self.write2blocks:\n\n            out_raster_object = self._set_output_object()\n\n            if self.predict_probs:\n\n                for cidx in range(1, len(mdl.classes_)+1):\n\n                    out_raster_object.get_band(cidx)\n                    out_raster_object.fill(0)\n\n            else:\n\n                out_raster_object.get_band(1)\n                out_raster_object.fill(0)\n\n        block_rows, block_cols = raster_tools.block_dimensions(rows,\n                                                               cols,\n                                                               row_block_size=self.row_block_size,\n                                                               col_block_size=self.col_block_size)\n\n        # Determine the number of blocks in the image.\n        block_indices, n_blocks = self._set_n_blocks(start_i,\n                                                     start_j,\n                                                     iwo,\n                                                     jwo,\n                                                     rows,\n                                                     cols,\n                                                     block_rows,\n                                                     block_cols)\n\n        n_block = 1\n\n        for block_index in block_indices:\n\n            i = block_index[0]\n            j = block_index[1]\n            n_rows = block_index[2]\n            n_cols = block_index[3]\n            iw = block_index[4]\n            jw = block_index[5]\n            rw = block_index[6]\n            cw = block_index[7]\n            ipadded = block_index[9]\n            jpadded = block_index[10]\n\n            logger.info('  Block {:,d} of {:,d} ...'.format(n_block, n_blocks))\n\n            # Setup the object to write to.\n            if self.write2blocks:\n\n                if isinstance(self.block_range, list) or isinstance(self.block_range, tuple):\n\n                    if n_block < self.block_range[0]:\n\n                        n_block += 1\n                        continue\n\n                    if n_block > self.block_range[1]:\n                        break\n\n                self.output_image = os.path.join(self.dir_name,\n                                                 '{BASE}_{BLOCK:05d}{EXT}'.format(BASE=self.output_image_base,\n                                                                                  BLOCK=n_block,\n                                                                                  EXT=self.output_image_ext))\n\n                if os.path.isfile(self.output_image):\n\n                    if self.overwrite:\n                        os.remove(self.output_image)\n                    else:\n\n                        n_block += 1\n                        continue\n\n                # Update the output image\n                #   information for the\n                #   current block.\n                self.o_info.update_info(top=image_top - (i*self.o_info.cellY),\n                                        left=image_left + (j*self.o_info.cellY),\n                                        rows=n_rows,\n                                        cols=n_cols)\n\n                iwo = i\n                jwo = j\n\n                out_raster_object = self._set_output_object()\n\n                if not self.predict_probs:\n\n                    out_raster_object.get_band(1)\n                    out_raster_object.fill(0)\n\n            n_block += 1\n\n            if self.track_blocks and not self.write2blocks:\n\n                if n_block in self.record_list:\n\n                    logger.info('  Skipping current block ...')\n                    continue\n\n            # Check for zeros in the block.\n            if self.band_check != -1:\n\n                if self.open_image:\n\n                    self.i_info = raster_tools.ropen(self.input_image)\n                    self.open_image = False\n\n                max_check = self.i_info.read(bands=self.band_check,\n                                             i=i,\n                                             j=j,\n                                             rows=n_rows,\n                                             cols=n_cols).max()\n\n                if max_check == 0:\n\n                    # Close the block file.\n                    if self.write2blocks:\n\n                        out_raster_object.close_all()\n                        out_raster_object = None\n\n                    continue\n\n            if not self.open_image:\n\n                # Close the image information object because it\n                #   needs to be reopened for parallel ``read``.\n                self.i_info.close()\n                self.open_image = True\n\n            if 'CV' in self.classifier_info['classifier']:\n\n                if len(self.bands) != self.model.getVarCount():\n\n                    logger.error('  The number of predictive layers does not match the number of model estimators.')\n                    raise AssertionError\n\n            elif (self.classifier_info['classifier'] not in ['c5', 'cubist', 'qda', 'chaincrf']) and \\\n                    ('CV' not in self.classifier_info['classifier']):\n\n                if hasattr(self.model, 'n_features_'):\n\n                    if len(self.bands) != self.model.n_features_:\n\n                        logger.error('  The number of predictive layers does not match the number of model estimators.')\n                        raise AssertionError\n\n                if hasattr(self.model, 'base_estimator'):\n\n                    if hasattr(self.model.base_estimator, 'n_features_'):\n\n                        if len(self.bands) != self.model.base_estimator.n_features_:\n\n                            logger.error('  The number of predictive layers does not match the number of model estimators.')\n                            raise AssertionError\n\n            # Get all the bands for the tile. The shape\n            #   of the features is ([rows x columns] x features).\n            features = raster_tools.read(file_name=self.input_image,\n                                         bands=self.bands,\n                                         i=iw,\n                                         j=jw,\n                                         rows=rw,\n                                         cols=cw,\n                                         predictions=True,\n                                         d_type='float64',\n                                         n_jobs=self.n_jobs_vars)\n\n            n_samples = rw * cw\n\n            if self.use_xy:\n\n                # Create x,y coordinates for the block.\n                x_coordinates, y_coordinates = self._create_indices(iw, jw, rw, cw)\n\n                # Append the x,y coordinates to the features.\n                features = np.hstack((features,\n                                      x_coordinates,\n                                      y_coordinates))\n\n            # Reshape the features for CRF models.\n            if self.classifier_info['classifier'] == 'chaincrf':\n                features = self._transform4crf(p_vars2reshape=features)[0]\n            else:\n\n                # Scale the features.\n                if self.scaled:\n                    features = self.scaler.transform(features)\n\n            if self.func_applier:\n                features = self.func_applier(features, self)\n\n            # TODO: add to `func_applier` and remove here\n            if self.additional_layers:\n\n                additional_layers = self._get_additional_layers(iw, jw, rw, cw)\n\n                features = np.hstack((features,\n                                      additional_layers))\n\n            # Add extra predictive\n            #   time series features.\n            if self._add_features:\n\n                if not self.ts_indices:\n\n                    if self.use_xy:\n                        self.ts_indices = np.array(range(0, features.shape[1]-2), dtype='int64')\n\n                features = self.feature_object.apply_features(X=features,\n                                                              ts_indices=self.ts_indices,\n                                                              append_features=self.append_features)\n\n            features[np.isnan(features) | np.isinf(features)] = 0.0\n\n            # Get locations of empty features\n            null_samples = np.where(features.max(axis=1).reshape(rw, cw) == 0)\n\n            if 'CV' in self.classifier_info['classifier']:\n\n                if self.classifier_info['classifier'] == 'cvmlp':\n\n                    self.model.predict(features, predicted)\n\n                    predicted = np.argmax(predicted, axis=1)\n\n                else:\n\n                    # Setup the global array to write to. This avoids\n                    #   passing it to the joblib workers.\n                    # predicted = np.empty((n_samples, 1), dtype='uint8')\n\n                    predicted = joblib.Parallel(n_jobs=self.n_jobs,\n                                                max_nbytes=None)(joblib.delayed(predict_cv)(chunk,\n                                                                                            self.chunk_size,\n                                                                                            self.file_name,\n                                                                                            self.perc_samp,\n                                                                                            self.classes2remove,\n                                                                                            self.ignore_feas,\n                                                                                            self.use_xy,\n                                                                                            self.classifier_info,\n                                                                                            self.weight_classes)\n                                                                 for chunk in range(0, n_samples, self.chunk_size))\n\n                # transpose and reshape the predicted labels to (rows x columns)\n                out_raster_object.write_array(np.array(list(itertools.chain.from_iterable(predicted))).reshape(n_rows,\n                                                                                                               n_cols),\n                                              j=j-jwo,\n                                              i=i-iwo)\n\n            elif self.classifier_info['classifier'] in ['c5', 'cubist']:\n\n                # Load the predictor variables.\n                # predict_samps = pandas2ri.py2ri(pd.DataFrame(features))\n\n                predict_samps = ro.r.matrix(features, nrow=n_samples, ncol=len(self.bands))\n                predict_samps.colnames = StrVector(self.headers[:-1])\n\n                # Get chunks for parallel processing.\n                indice_pairs = list()\n                for i_ in range(1, n_samples+1, self.chunk_size):\n\n                    n_rows_ = self._num_rows_cols(i_, self.chunk_size, n_samples)\n                    indice_pairs.append([i_, n_rows_])\n\n                indice_pairs[-1][1] += 1\n\n                # Make the predictions and convert to a NumPy array.\n                if isinstance(self.input_model, str):\n\n                    predicted = joblib.Parallel(n_jobs=self.n_jobs,\n                                                max_nbytes=None)(joblib.delayed(predict_c5_cubist)(self.input_model,\n                                                                                                   ip)\n                                                                 for ip in indice_pairs)\n\n                    # Write the predictions to file.\n                    out_raster_object.write_array(np.array(list(itertools.chain.from_iterable(predicted))).reshape(n_rows,\n                                                                                                                   n_cols),\n                                                  j=j-jwo,\n                                                  i=i-iwo)\n\n                else:\n\n                    out_raster_object.write_array(_do_c5_cubist_predict(self.model,\n                                                                        self.classifier_info['classifier'],\n                                                                        predict_samps).reshape(n_rows,\n                                                                                               n_cols),\n                                                  j=j-jwo,\n                                                  i=i-iwo)\n\n            else:\n\n                # SCIKIT-LEARN MODELS\n\n                if self.predict_probs or self.relax_probabilities:\n\n                    # --------------------------------------\n                    # Posterior probability label relaxation\n                    # --------------------------------------\n\n                    if self.predict_probs:\n\n                        # Predict class conditional probabilities.\n                        predicted = predict_scikit_probas(rw,\n                                                          cw,\n                                                          ipadded,\n                                                          jpadded,\n                                                          n_rows,\n                                                          n_cols,\n                                                          self.morphology,\n                                                          self.do_not_morph,\n                                                          self.relax_probabilities,\n                                                          self.plr_matrix,\n                                                          self.plr_window_size,\n                                                          self.plr_iterations,\n                                                          self.predict_probs,\n                                                          self.d_type,\n                                                          null_samples)\n\n                        for cidx in range(0, predicted.shape[0]):\n\n                            out_raster_object.write_array(predicted[cidx],\n                                                          i=i-iwo,\n                                                          j=j-jwo,\n                                                          band=cidx+1)\n\n                    else:\n\n                        # Write the predictions to file.\n                        out_raster_object.write_array(predict_scikit_probas(rw,\n                                                                            cw,\n                                                                            ipadded,\n                                                                            jpadded,\n                                                                            n_rows,\n                                                                            n_cols,\n                                                                            self.morphology,\n                                                                            self.do_not_morph,\n                                                                            self.relax_probabilities,\n                                                                            self.plr_matrix,\n                                                                            self.plr_window_size,\n                                                                            self.plr_iterations,\n                                                                            self.predict_probs,\n                                                                            self.d_type,\n                                                                            null_samples),\n                                                      j=j-jwo,\n                                                      i=i-iwo)\n\n                else:\n\n                    # Get chunks for parallel processing.\n                    # indice_pairs = list()\n                    #\n                    # for i_ in range(0, n_samples, self.chunk_size):\n                    #\n                    #     n_rows_ = self._num_rows_cols(i_, self.chunk_size, n_samples)\n                    #     indice_pairs.append([i_, n_rows_])\n                    #\n                    # if (self.n_jobs != 0) and (self.n_jobs != 1):\n                    #\n                    #     # Make the predictions and convert to a NumPy array.\n                    #     if isinstance(self.input_model, str):\n                    #\n                    #         if platform.system() == 'Windows':\n                    #\n                    #             predicted = joblib.Parallel(n_jobs=self.n_jobs,\n                    #                                         max_nbytes=None)(joblib.delayed(predict_scikit_win)(features[ip[0]:ip[0]+ip[1]],\n                    #                                                                                             self.input_model)\n                    #                                                          for ip in indice_pairs)\n                    #\n                    #         else:\n                    #\n                    #             mdl = self.load(self.input_model)[1]\n                    #\n                    #             pool = multi.Pool(processes=self.n_jobs)\n                    #\n                    #             predicted = pool.map(predict_scikit, range(0, len(indice_pairs)))\n                    #\n                    #             pool.close()\n                    #             del pool\n                    #\n                    #     else:\n                    #\n                    #         mdl = self.model\n                    #         predicted = [predict_scikit(ip) for ip in range(0, len(indice_pairs))]\n                    #\n                    # else:\n\n                    # Make the predictions and convert to a NumPy array.\n                    # predicted = [predict_scikit(ip) for ip in range(0, len(indice_pairs))]\n\n                    # Write the predictions to file.\n                    # out_raster_object.write_array(np.array(list(itertools.chain.from_iterable(predicted))).reshape(n_rows,\n                    #                                                                                                n_cols),\n                    #                               j=j-jwo,\n                    #                               i=i-iwo)\n\n                    # Write the predictions to file.\n                    if self.morphology:\n\n                        if isinstance(self.do_not_morph, list):\n\n                            predictions = np.uint8(mdl.predict(features).reshape(rw, cw))\n\n                            predictions_copy = predictions[ipadded:ipadded+n_rows,\n                                                           jpadded:jpadded+n_cols].copy()\n\n                            predictions = pymorph.closerec(pymorph.closerec(predictions,\n                                                                            Bdil=pymorph.secross(r=3),\n                                                                            Bc=pymorph.secross(r=1)),\n                                                           Bdil=pymorph.secross(r=2),\n                                                           Bc=pymorph.secross(r=1))[ipadded:ipadded+n_rows,\n                                                                                    jpadded:jpadded+n_cols]\n\n                            for do_not_morph_value in self.do_not_morph:\n                                predictions[predictions_copy == do_not_morph_value] = do_not_morph_value\n\n                            del predictions_copy\n\n                            out_raster_object.write_array(predictions,\n                                                          j=j-jwo,\n                                                          i=i-iwo)\n\n                            del predictions\n\n                        else:\n\n                            out_raster_object.write_array(\n                                pymorph.closerec(pymorph.closerec(np.uint8(mdl.predict(features).reshape(rw, cw)),\n                                                                  Bdil=pymorph.secross(r=3),\n                                                                  Bc=pymorph.secross(r=1)),\n                                                 Bdil=pymorph.secross(r=2),\n                                                 Bc=pymorph.secross(r=1))[ipadded:ipadded+n_rows,\n                                                                          jpadded:jpadded+n_cols],\n                                j=j-jwo,\n                                i=i-iwo)\n\n                    else:\n\n                        np_dtype = raster_tools.STORAGE_DICT_NUMPY[self.d_type]\n\n                        out_raster_object.write_array(np_dtype(mdl.predict(features).reshape(n_rows,\n                                                                                             n_cols)),\n                                                      j=j-jwo,\n                                                      i=i-iwo)\n\n            features = None\n\n            # Close the block file.\n            if self.write2blocks:\n\n                out_raster_object.close_all()\n                out_raster_object = None\n\n            if self.track_blocks and not self.write2blocks:\n\n                self.record_list.append(n_block)\n\n                if os.path.isfile(self.record_keeping):\n                    os.remove(self.record_keeping)\n\n                self.dump(self.record_list,\n                          self.record_keeping)\n\n        # Close the file.\n        if not self.write2blocks:\n\n            if self.predict_probs:\n\n                for cidx in range(0, len(mdl.classes_)):\n\n                    out_raster_object.get_band(cidx+1)\n                    out_raster_object.close_band()\n\n            out_raster_object.close_all()\n            out_raster_object = None\n\n        if isinstance(self.mask_background, str) or isinstance(self.mask_background, np.ndarray):\n            self._mask_background()\n\n    def _set_indexing(self, start_i, start_j, rows, cols, iwo, jwo):\n\n        if self.kwargs:\n\n            if ('i' in self.kwargs) and ('y' not in self.kwargs):\n\n                start_i = self.kwargs['i']\n                self.o_info.update_info(top=self.o_info.top - (start_i*self.o_info.cellY))\n                iwo = start_i\n\n            elif ('i' not in self.kwargs) and ('y' in self.kwargs):\n\n                # Index the image by x, y coordinates (in map units).\n                self.kwargs['i'] = vector_tools.get_xy_offsets(self.i_info,\n                                                               x=999.,\n                                                               y=self.kwargs['y'],\n                                                               check_position=False)[3]\n\n                start_i = self.kwargs['i']\n                self.o_info.update_info(top=self.o_info.top - (start_i*self.o_info.cellY))\n                iwo = start_i\n\n            if ('j' in self.kwargs) and ('x' not in self.kwargs):\n\n                start_j = self.kwargs['j']\n                self.o_info.update_info(left=self.o_info.left + (start_j*self.o_info.cellY))\n                jwo = start_j\n\n            elif ('j' not in self.kwargs) and ('x' in self.kwargs):\n\n                # Index the image by x, y coordinates (in map units).\n                self.kwargs['j'] = vector_tools.get_xy_offsets(self.i_info,\n                                                               x=self.kwargs['x'],\n                                                               y=999.,\n                                                               check_position=False)[2]\n\n                start_j = self.kwargs['j']\n                self.o_info.update_info(left=self.o_info.left + (start_j*self.o_info.cellY))\n                jwo = start_j\n\n            if 'rows' in self.kwargs:\n\n                if self.kwargs['rows'] != -1:\n                    rows = self.kwargs['rows']\n\n            if 'cols' in self.kwargs:\n\n                if self.kwargs['cols'] != -1:\n                    cols = self.kwargs['cols']\n\n        return start_i, start_j, rows, cols, iwo, jwo\n\n    def _set_bands(self):\n\n        \"\"\"Sets the list of (feature) bands to open\"\"\"\n\n        if self.ignore_feas:\n            self.bands = sorted([bd for bd in range(1, self.i_info.bands+1) if bd not in self.ignore_feas])\n        else:\n\n            if not isinstance(self.bands, list):\n                self.bands = list(range(1, self.i_info.bands+1))\n\n    def _set_output_object(self):\n\n        \"\"\"Creates the raster object to write to\"\"\"\n\n        if self.predict_probs:\n\n            return raster_tools.create_raster(self.output_image,\n                                              self.o_info,\n                                              compress='none',\n                                              tile=False,\n                                              bigtiff='yes')\n\n        elif isinstance(self.mask_background, str) or isinstance(self.mask_background, np.ndarray):\n\n            return raster_tools.create_raster(self.out_image_temp,\n                                              self.o_info,\n                                              compress='none',\n                                              tile=False)\n\n        else:\n\n            return raster_tools.create_raster(self.output_image,\n                                              self.o_info,\n                                              tile=False)\n\n    def _set_n_blocks(self,\n                      start_i,\n                      start_j,\n                      iwo,\n                      jwo,\n                      rows,\n                      cols,\n                      block_rows,\n                      block_cols):\n\n        if self.relax_probabilities or self.morphology:\n            pad = self.plr_window_size  # int(self.plr_window_size / 2.0)\n        else:\n            pad = 0\n\n        block_indices = list()\n\n        n_blocks = 0\n\n        for i_ in range(start_i, rows+iwo, block_rows):\n\n            n_rows_ = self._num_rows_cols(i_, block_rows, rows+iwo)\n\n            # Always =i if pad=0\n            iw_ = 0 if i_ == 0 else i_ - pad\n            ipadded_ = i_ - iw_\n            rww_ = n_rows_ + pad if iw_ == 0 else n_rows_ + (pad * 2)\n            rw_ = self._num_rows_cols(iw_, rww_, rows+iwo)\n\n            for j_ in range(start_j, cols+jwo, block_cols):\n\n                n_cols_ = self._num_rows_cols(j_, block_cols, cols+jwo)\n\n                jw_ = 0 if j_ == 0 else j_ - pad\n                jpadded_ = j_ - jw_\n                cww_ = n_cols_ + pad if jw_ == 0 else n_cols_ + (pad * 2)\n                cw_ = self._num_rows_cols(jw_, cww_, cols+iwo)\n\n                block_indices.append((i_, j_, n_rows_, n_cols_, iw_, jw_, rw_, cw_, pad, ipadded_, jpadded_))\n\n                n_blocks += 1\n\n        return block_indices, n_blocks\n\n    def _mask_background(self):\n\n        \"\"\"\n        Recodes background values to zeros\n\n        Returns:\n            None, writes to ``self.output_image``.\n        \"\"\"\n\n        if isinstance(self.mask_background, str):\n            b_info = raster_tools.ropen(self.mask_background)\n\n        with raster_tools.ropen(self.out_image_temp) as m_info:\n\n            m_info.get_band(1)\n            m_info.storage = 'byte'\n\n            out_rst_object = raster_tools.create_raster(self.output_image,\n                                                        m_info,\n                                                        compress='none',\n                                                        tile=False)\n\n            out_rst_object.get_band(1)\n\n            b_rows, b_cols = m_info.rows, m_info.cols\n\n            block_rows, block_cols = raster_tools.block_dimensions(b_rows,\n                                                                   b_cols,\n                                                                   row_block_size=self.row_block_size,\n                                                                   col_block_size=self.col_block_size)\n\n            for i in range(0, b_rows, block_rows):\n\n                n_rows = self._num_rows_cols(i, block_rows, b_rows)\n\n                for j in range(0, b_cols, block_cols):\n\n                    n_cols = self._num_rows_cols(j, block_cols, b_cols)\n\n                    m_array = m_info.read(i=i,\n                                          j=j,\n                                          rows=n_rows,\n                                          cols=n_cols,\n                                          d_type='byte')\n\n                    # Get the background array.\n                    if isinstance(self.mask_background, str):\n\n                        b_array = raster_tools.read(i_info=b_info,\n                                                    bands=self.background_band,\n                                                    x=m_info.left+(j*m_info.cell.Y),\n                                                    y=m_info.top-(i*m_info.cellY),\n                                                    rows=n_rows,\n                                                    cols=n_cols,\n                                                    d_type='byte')\n\n                    else:\n                        b_array = self.mask_background[i:i+n_rows, j:j+n_cols]\n\n                    m_array[b_array == self.background_value] = 0\n\n                    if self.minimum_observations > 0:\n\n                        # Get the observation counts array.\n                        observation_array = raster_tools.read(i_info=b_info,\n                                                              bands=self.observation_band,\n                                                              i=i,\n                                                              j=j,\n                                                              rows=n_rows,\n                                                              cols=n_cols,\n                                                              d_type='byte')\n\n                        m_array[observation_array < self.minimum_observations] = 0\n\n                    out_rst_object.write_array(m_array, i, j)\n\n        m_info = None\n\n        if isinstance(self.mask_background, str):\n\n            b_info.close()\n            b_info = None\n\n        out_rst_object.close_all()\n        out_rst_object = None\n\n        os.remove(self.out_image_temp)\n\n    @staticmethod\n    def _num_rows_cols(pixel_index, block_size, rows_cols):\n        return block_size if (pixel_index + block_size) < rows_cols else rows_cols - pixel_index\n\n    def _get_feas(self, img_obj_list, i, j, n_rows, n_cols):\n\n        if self.use_xy:\n\n            x_coordinates, y_coordinates = self._create_indices(i, j, n_rows, n_cols)\n\n            feature_arrays = [x_coordinates, y_coordinates]\n\n        else:\n\n            feature_arrays = list()\n\n        # for bd in range(0, self.i_info.bands):\n        for iol in img_obj_list:\n\n            if iol[-1]:\n\n                __, __, start_j, start_i = vector_tools.get_xy_offsets(image_info=self.i_info, xy_info=iol[1])\n\n            else:\n\n                start_j, start_i = 0, 0\n\n            # print start_j, start_i\n            # sys.exit()\n\n            # if iol[3] > self.i_info.cellY:\n            #\n            #     n_cols_coarse = int((n_cols * self.i_info.cellY) / iol[3])\n            #     n_rows_coarse = int((n_rows * self.i_info.cellY) / iol[3])\n            #\n            #     coarse_array = iol[0].ReadAsArray(iol[1]+j, [2]+i, n_cols_coarse, n_rows_coarse).astype(np.float32)\n            #\n            #     row_zoom_factor = n_rows / float(n_rows_coarse)\n            #     col_zoom_factor = n_cols / float(n_cols_coarse)\n            #\n            #     feature_array = zoom(coarse_array, (row_zoom_factor, col_zoom_factor), order=2)\n            #\n            # else:\n\n            feature_arrays.append(iol[0].ReadAsArray(start_j+j, start_i+i, n_cols, n_rows).astype(np.float32))\n\n        return np.vstack(feature_arrays).reshape(self.i_info.bands, n_rows, n_cols)\n\n        # return np.vstack([img_obj_list[bd][0].ReadAsArray(img_obj_list[bd][1]+j, img_obj_list[bd][2]+i,\n        #                                                   n_cols, n_rows).astype(np.float32) for bd in\n        #                   range(0, self.i_info.bands)]).reshape(self.i_info.bands, n_rows, n_cols)\n\n    def _get_additional_layers(self, i, j, n_rows, n_cols):\n\n        \"\"\"\n        Gets additional image layers\n\n        Args:\n            i (int)\n            j (int)\n            n_rows (int)\n            n_cols (int)\n        \"\"\"\n\n        additional_stack = None\n\n        for additional_layer in self.additional_layers:\n\n            with raster_tools.ropen(additional_layer) as a_info:\n\n                if isinstance(additional_stack, np.ndarray):\n\n                    additional_stack_ = a_info.read(bands=-1,\n                                                    i=i,\n                                                    j=j,\n                                                    rows=n_rows,\n                                                    cols=n_cols,\n                                                    d_type='float32').ravel()[:, np.newaxis]\n\n                    additional_stack = np.hstack((additional_stack,\n                                                  additional_stack_))\n\n                else:\n\n                    additional_stack = a_info.read(bands=-1,\n                                                   i=i,\n                                                   j=j,\n                                                   rows=n_rows,\n                                                   cols=n_cols,\n                                                   d_type='float32').ravel()[:, np.newaxis]\n\n            a_info = None\n\n        return additional_stack\n\n    def _create_indices(self, i, j, n_rows, n_cols):\n\n        \"\"\"\n        Creates x,y coordinate indices\n\n        Args:\n            i (int)\n            j (int)\n            n_rows (int)\n            n_cols (int)\n        \"\"\"\n\n        left = self.i_info.left + (j * self.i_info.cellY)\n        top = self.i_info.top - (i * self.i_info.cellY)\n\n        # Create the longitudes\n        x_coordinates = np.arange(left,\n                                  left + (self.i_info.cellY * n_cols),\n                                  self.i_info.cellY)\n\n        x_coordinates = np.tile(x_coordinates, n_rows).reshape(n_rows, n_cols)\n\n        # Create latitudes\n        y_coordinates = np.arange(top,\n                                  top - (self.i_info.cellY * n_rows),\n                                  -self.i_info.cellY).reshape(n_rows, 1)\n\n        y_coordinates = np.tile(y_coordinates, n_cols)\n\n        return x_coordinates.ravel()[:, np.newaxis], y_coordinates.ravel()[:, np.newaxis]\n\n    @staticmethod\n    def _get_slope(elevation_array, pad=50):\n\n        elevation_array = cv2.copyMakeBorder(elevation_array, pad, pad, pad, pad, cv2.BORDER_REFLECT)\n\n        x_grad, y_grad = np.gradient(elevation_array)\n\n        return (np.pi / 2.0) - np.arctan(np.sqrt((x_grad * x_grad) + (y_grad * y_grad)))\n\n    def test_accuracy(self, out_acc=None, discrete=True, be_quiet=False):\n\n        \"\"\"\n        Tests the accuracy of a model (a model must be trained or loaded).\n\n        Args:\n            out_acc (str): The output name of the accuracy text file.\n            discrete (Optional[bool]): Whether the accuracy should assume discrete data.\n                Otherwise, assumes continuous. Default is True.\n            be_quiet (Optional[bool]): Whether to be quiet and do not print to screen. Default is False.\n\n        Returns:\n            None, writes to ``out_acc`` if given, and prints results to screen.\n\n        Examples:\n            >>> # get test accuracy\n            >>> cl.test_accuracy(out_acc='/out_accuracy.txt')\n            >>> print(cl.emat.accuracy)\n        \"\"\"\n\n        if self.classifier_info['classifier'] == 'cvmlp':\n\n            test_labs_pred = np.empty((self.p_vars_test_rows, self.n_classes), dtype='uint8')\n            self.model.predict(self.p_vars_test, test_labs_pred)\n            test_labs_pred = np.argmax(test_labs_pred, axis=1)\n\n        elif 'CV' in self.classifier_info['classifier']:\n\n            if (0 < self.perc_samp_each < 1) or ((self.perc_samp_each == 0) and (0 < self.perc_samp < 1)):\n                __, test_labs_pred = self.model.predict(self.p_vars_test)\n            else:\n                __, test_labs_pred = self.model.predict(self.p_vars)\n\n        elif self.classifier_info['classifier'] in ['c5', 'cubist']:\n\n            if (0 < self.perc_samp_each < 1) or ((self.perc_samp_each == 0) and (0 < self.perc_samp < 1)):\n\n                features = ro.r.matrix(self.p_vars_test, nrow=self.p_vars_test.shape[0],\n                                       ncol=self.p_vars_test.shape[1])\n            else:\n\n                features = ro.r.matrix(self.p_vars, nrow=self.p_vars.shape[0],\n                                       ncol=self.p_vars.shape[1])\n\n            features.colnames = StrVector(self.headers[:-1])\n\n            test_labs_pred = _do_c5_cubist_predict(self.model, self.classifier_info['classifier'],\n                                                   features)\n\n        else:\n\n            if isinstance(self.p_vars_test, np.ndarray):\n                test_labs_pred = self.model.predict(self.p_vars_test)\n            else:\n\n                # Test the train variables if no test variables exist.\n                test_labs_pred = self.model.predict(self.p_vars)\n\n        if isinstance(self.p_vars_test, np.ndarray):\n\n            if discrete:\n                self.test_array = np.int16(np.c_[test_labs_pred, self.labels_test])\n            else:\n                self.test_array = np.float32(np.c_[test_labs_pred, self.labels_test])\n\n        else:\n\n            if discrete:\n                self.test_array = np.int16(np.c_[test_labs_pred, self.labels])\n            else:\n                self.test_array = np.float32(np.c_[test_labs_pred, self.labels])\n\n        if not be_quiet:\n            logger.info('  Getting test accuracy ...')\n\n        self.emat = error_matrix()\n\n        self.emat.get_stats(po_array=self.test_array,\n                            discrete=discrete)\n\n        if out_acc:\n            self.emat.write_stats(out_acc)\n\n    def recursive_elimination(self, method='rf', perc_samp_each=.5):\n\n        \"\"\"\n        Recursively eliminates features.\n\n        Args:\n            method (Optional[str]): The method to use. Default is 'rf'. Choices are ['rf', 'chi2'].\n            perc_samp_each (Optional[float]): The percentage to sample at each iteration. Default is .5. \n\n        Returns:\n            None, plots results.\n\n        Examples:\n            >>> import mpglue as gl\n            >>>\n            >>> cl = gl.classification()\n            >>>\n            >>> cl.split_samples('/samples.txt', perc_samp=1.)\n            >>> cl.construct_model(classifier_info={'classifier': 'rf', 'trees': 500})\n            >>> cl.recursive_elimination()\n        \"\"\"\n\n        if method == 'chi2':\n\n            if not hasattr(self, 'p_vars'):\n\n                logger.error('  Be sure to run `split_samples` to create the `p_vars` variables.')\n                raise AttributeError\n\n            if not hasattr(self, 'labels'):\n\n                logger.error('  Be sure to run `split_samples` to create the `labels` variables.')\n                raise AttributeError\n\n            p_vars = self.p_vars.copy()\n\n            # Scale negative values to positive (for Chi Squared)\n            for var_col_pos in range(0, p_vars.shape[1]):\n\n                col_min = p_vars[:, var_col_pos].min()\n\n                if col_min < 0:\n                    p_vars[:, var_col_pos] = np.add(p_vars[:, var_col_pos], abs(col_min))\n\n            feas_ranked, p_val = chi2(p_vars, self.labels)\n\n        elif method == 'rf':\n\n            if not hasattr(self, 'model'):\n                logger.error('  A RF model must be trained to use RF feature importance.')\n\n            if not hasattr(self.model, 'feature_importances_'):\n                logger.error('  A RF model must be trained to use RF feature importance.')\n\n            feas_ranked = self.model.feature_importances_\n\n        else:\n            logger.error('  The feature ranking method is not supported.')\n            raise NameError\n\n        loop_len = len(feas_ranked) + 1\n\n        feas_ranked[np.isnan(feas_ranked)] = 0.\n\n        self.fea_rank = dict()\n\n        for i in range(1, loop_len):\n            self.fea_rank[i] = feas_ranked[i-1]\n\n        indices = list()\n        indice_counts = list()\n        accuracy_scores = list()\n\n        for i, s in enumerate(sorted(self.fea_rank, key=self.fea_rank.get)):\n\n            if (len(self.fea_rank) - i) <= (len(self.classes) * 2):\n\n                break\n\n            else:\n\n                indices.append(s)\n\n                self.split_samples(self.file_name,\n                                   perc_samp_each=perc_samp_each,\n                                   ignore_feas=indices,\n                                   use_xy=self.use_xy)\n\n                logger.info('  {:d} features ...'.format(self.n_feas))\n\n                self.construct_model(classifier_info=self.classifier_info)\n\n                self.test_accuracy()\n\n                # print 'Overall accuracy: {:.2f}'.format(self.emat.accuracy)\n                # print 'Kappa score: {:.2f}\\n'.format(self.emat.kappa_score)\n\n                indice_counts.append(len(indices))\n                accuracy_scores.append(self.emat.accuracy)\n\n        accuracy_scores_sm = [sum(accuracy_scores[r:r+3]) / 3. for r in range(0, len(accuracy_scores)-2)]\n        accuracy_scores_sm.insert(0, sum(accuracy_scores_sm[:2]) / 2.)\n        accuracy_scores_sm.append(sum(accuracy_scores_sm[-2:]) / 2.)\n\n        plt.plot(indice_counts, accuracy_scores_sm)\n        plt.xlabel('Number of features removed')\n        plt.ylabel('Overall accuracy')\n        plt.show()\n\n        plt.close()\n\n    def rank_feas(self, rank_text=None, rank_method='chi2', top_feas=1., be_quiet=False):\n\n        \"\"\"\n        Ranks image features by importance.\n\n        Args:\n            rank_text (Optional[str]): A text file to write ranked features to. Default is None.\n            rank_method (Optional[str]): The method to use for feature ranking. Default is 'chi2' (Chi^2). Choices are \n                ['chi2', 'rf'].\n            top_feas (Optional[float or int]): The percentage or total number of features to reduce to. \n                Default is 1., or no reduction.\n            be_quiet (Optional[bool]): Whether to be quiet and do not print to screen. Default is False.\n\n        Returns:\n            None, writes to ``rank_text`` if given and prints results to screen.\n\n        Examples:\n            >>> # rank image features\n            >>> cl.split_samples('/samples.txt', scale_data=True)\n            >>> cl.rank_feas(rank_text='/ranked_feas.txt',\n            >>>              rank_method='chi2', top_feas=.2)\n            >>> print cl.fea_rank\n            >>>\n            >>> # a RF model must be trained before feature ranking\n            >>> cl.construct_model()\n            >>> cl.rank_feas(rank_method='rf', top_feas=.5)\n        \"\"\"\n\n        if isinstance(rank_text, str):\n\n            d_name, f_name = os.path.split(rank_text)\n\n            if not os.path.isdir(d_name):\n                os.makedirs(d_name)\n\n            rank_txt_wr = open(rank_text, 'wb')\n\n        if rank_method == 'chi2':\n\n            if not hasattr(self, 'p_vars'):\n\n                logger.error('  Be sure to run `split_samples` to create the `p_vars` variables.')\n                raise AttributeError\n\n            if not hasattr(self, 'labels'):\n\n                logger.error('  Be sure to run `split_samples` to create the `labels` variables.')\n                raise AttributeError\n\n            p_vars = self.p_vars.copy()\n\n            # Scale negative values to positive (for Chi Squared)\n            for var_col_pos in range(0, p_vars.shape[1]):\n\n                col_min = p_vars[:, var_col_pos].min()\n\n                if col_min < 0:\n                    p_vars[:, var_col_pos] = np.add(p_vars[:, var_col_pos], abs(col_min))\n\n            feas_ranked, p_val = chi2(p_vars, self.labels)\n\n            loop_len = len(feas_ranked) + 1\n\n        elif rank_method == 'rf':\n\n            if not hasattr(self, 'model'):\n                logger.error('  A RF model must be trained to use RF feature importance.')\n\n            if not hasattr(self.model, 'feature_importances_'):\n                logger.error('  A RF model must be trained to use RF feature importance.')\n\n            feas_ranked = self.model.feature_importances_\n\n            loop_len = len(feas_ranked) + 1\n\n        else:\n\n            logger.error('  The feature ranking method is not supported.')\n            raise NameError\n\n        feas_ranked[np.isnan(feas_ranked)] = 0.\n\n        self.fea_rank = dict()\n\n        for i in range(1, loop_len):\n            self.fea_rank[i] = feas_ranked[i-1]\n\n        if rank_method == 'chi2':\n            title = '**********************\\n*                    *\\n* Chi^2 Feature Rank *\\n*                    *\\n**********************\\n\\nRank      Variable      Value\\n----      --------      -----'\n        elif rank_method == 'rf':\n            title = '************************************\\n*                                  *\\n* Random Forest Feature Importance *\\n*                                  *\\n************************************\\n\\nRank      Variable      Value\\n----      --------      -----'\n\n        if not be_quiet:\n            logger.info(title)\n\n        if isinstance(rank_text, str):\n            rank_txt_wr.write('%s\\n' % title)\n\n        if isinstance(top_feas, float):\n            n_best_feas = int(top_feas * len(self.fea_rank))\n        elif isinstance(top_feas, int):\n            n_best_feas = copy(top_feas)\n\n        r = 1\n        self.bad_features = list()\n\n        for s in sorted(self.fea_rank, key=self.fea_rank.get, reverse=True):\n\n            if r <= n_best_feas:\n\n                if r < 10 and s < 10:\n                    ranks = '%d         %d             %s' % (r, s, str(self.fea_rank[s]))\n                elif r >= 10 and s < 10:\n                    ranks = '%d        %d             %s' % (r, s, str(self.fea_rank[s]))\n                elif r < 10 and s >= 10:\n                    ranks = '%d         %d            %s' % (r, s, str(self.fea_rank[s]))\n                else:\n                    ranks = '%d        %d            %s' % (r, s, str(self.fea_rank[s]))\n\n                if not be_quiet:\n                    logger.info(ranks)\n\n                if isinstance(rank_text, str):\n                    rank_txt_wr.write('%s\\n' % ranks)\n\n            else:\n                # append excluded variables and remove from the \"good\" variables\n                self.bad_features.append(s)\n\n                del self.fea_rank[s]\n\n            r += 1\n\n        self.ranked_feas = np.array(sorted(self.fea_rank, key=self.fea_rank.get, reverse=True))\n\n        if not be_quiet:\n\n            logger.info('  Mean score:  %.2f' % np.average([v for k, v in viewitems(self.fea_rank)]))\n\n            logger.info('  ==================')\n            logger.info('  Excluded variables')\n            logger.info('  ==================')\n            logger.info(','.join(list(map(str, sorted(self.bad_features)))))\n\n        if isinstance(rank_text, str):\n\n            rank_txt_wr.write('\\n==================\\n')\n            rank_txt_wr.write('Excluded variables\\n')\n            rank_txt_wr.write('==================\\n')\n            rank_txt_wr.write(','.join([str(bf) for bf in sorted(self.bad_features)]))\n            rank_txt_wr.close()\n\n    def add_variable_names(self, layer_names, stat_names, additional_features=[]):\n\n        \"\"\"\n        Adds band-stat name pairs.\n\n        Args:\n            layer_names (list): A list of layer names.\n            stat_names (list): A list of statistics names.\n            additional_features (Optional[list]): Additional features. Default is [].\n\n        Examples:\n            >>> import mpglue as gl\n            >>>\n            >>> cl = gl.classification()\n            >>>\n            >>> cl.add_variable_names(['NDVI', 'EVI2', 'GNDVI', 'NDWI', 'NDBaI'],\n            >>>                       ['min', 'max', 'median', 'cv', 'jd', 'slopemx', 'slopemn'],\n            >>>                       additional_features=['x', 'y'])\n            >>>\n            >>> # get the 10th variable\n            >>> cl.variable_names[10]\n        \"\"\"\n\n        counter = 1\n        self.variable_names = dict()\n\n        for layer_name in layer_names:\n\n            for stat_name in stat_names:\n\n                self.variable_names[counter] = '{} {}'.format(layer_name, stat_name)\n\n                counter += 1\n\n        if additional_features:\n\n            for additional_feature in additional_features:\n\n                self.variable_names[counter] = additional_feature\n\n                counter += 1\n\n        for k, v in viewitems(self.variable_names):\n            logger.info(k, v)\n\n    def sub_feas(self, input_image, out_img, band_list=None):\n\n        \"\"\"\n        Subsets features. \n\n        Args:\n            input_image (str): Full path, name, and extension of a single image.\n            out_img (str): The output image.\n            band_list (Optional[list]): A list of bands to subset. Default is []. If empty, ``sub_feas`` subsets \n                the top n best features returned by ``rank_feas``.\n\n        Returns:\n            None, writes to ``out_img``.\n\n        Examples:\n            >>> import mpglue as gl\n            >>>\n            >>> cl = gl.classification()\n            >>>\n            >>> cl.split_samples('/samples.txt', scale_data=True)\n            >>> cl.rank_feas(rank_method='chi2', top_feas=.2)\n            >>>\n            >>> # apply feature rank to subset a feature image using cl.ranked_feas\n            >>> cl.sub_feas('/in_image.vrt', '/ranked_feas.vrt')\n        \"\"\"\n\n        if not hasattr(self, 'ranked_feas'):\n            sys.exit('\\nERROR!! The features need to be ranked first. See <rank_feas> method.\\n')\n\n        if not isinstance(input_image, str):\n            sys.exit('\\nERROR!! The input image needs to be specified in order set the extent.\\n')\n\n        if not os.path.isfile(input_image):\n            sys.exit('\\nERROR!! %s does not exist.\\n' % input_image)\n\n        d_name, f_name = os.path.split(out_img)\n        f_base, f_ext = os.path.splitext(f_name)\n\n        if not os.path.isdir(d_name):\n            os.makedirs(d_name)\n\n        if 'vrt' not in f_ext.lower():\n\n            out_img_orig = copy(out_img)\n            out_img = '%s/%s.vrt' % (d_name, f_base)\n\n        if not band_list:\n\n            # create the band list\n            band_list = ''\n            for fea_idx in self.ranked_feas:\n                band_list = '%s-b %d ' % (band_list, fea_idx)\n\n        logger.info('  Subsetting ranked features ...')\n\n        com = 'gdalbuildvrt %s %s %s' % (band_list, out_img, input_image)\n\n        subprocess.call(com, shell=True)\n\n        if 'tif' in f_ext.lower():\n\n            com = 'gdal_translate --config GDAL_CACHEMAX 256 -of GTiff -co TILED=YES -co COMPRESS=LZW %s %s' % \\\n                  (out_img, out_img_orig)\n\n            subprocess.call(com, shell=True)\n\n        elif 'img' in f_ext.lower():\n\n            com = 'gdal_translate --config GDAL_CACHEMAX 256 -of HFA -co COMPRESS=YES %s %s' % \\\n                  (out_img, out_img_orig)\n\n            subprocess.call(com, shell=True)\n\n    def grid_search_gridcrf(self, classifier_parameters, method='overall', output_file=None):\n\n        \"\"\"\n        Conditional Random Field SSVM parameter grid search\n\n        Args:\n            classifier_name (str): The classifier to optimize.\n            classifier_parameters (dict): The classifier parameters.\n            method (Optional[str]): The score method to use, 'overall' (default) or 'f1'. Choices are ['overall', 'f1'].\n            output_file (Optional[str]):\n\n        Examples:\n            >>> cl.load4crf(var_image_list, cdl_labels_list)\n            >>>\n            >>> cl.grid_search_gridcrf(dict(max_iter=[100, 500, 1000],\n            >>>                             C=[.001, .01, .1, 1, 10, 100],\n            >>>                             tol=[.001, .01, .1],\n            >>>                             inference_cache=[0, 100],\n            >>>                             neighborhood=[4, 8]))\n        \"\"\"\n\n        param_order = list(classifier_parameters)\n\n        # Setup the output scores table.\n        df_param_headers = '-'.join(list(classifier_parameters))\n        df = pd.DataFrame(columns=[df_param_headers])\n        df[df_param_headers] = list(itertools.product(*itervalues(classifier_parameters)))\n\n        # Setup the error object.\n        emat = error_matrix()\n\n        # Iterate over all possible parameter combinations.\n        for param_combo in list(itertools.product(*itervalues(classifier_parameters))):\n\n            # Set the current parameters.\n            current_combo = dict(zip(param_order, param_combo))\n\n            # Add the classifier name to the dictionary.\n            current_combo['classifier'] = 'gridcrf'\n            current_combo['n_jobs'] = -1\n\n            # Train the model.\n            self.construct_model(classifier_info=current_combo)\n\n            # Make predictions\n            combo_predictions = np.array(self.model.predict(self.p_vars), dtype='uint8')\n\n            # Get the model accuracy.\n            emat.get_stats(po_array=np.c_[combo_predictions.ravel(),\n                                          self.labels.ravel()])\n\n            if method == 'overall':\n                df.loc[df[df_param_headers] == param_combo, 'Accuracy'] = emat.accuracy\n\n            logger.info(param_combo)\n            logger.info(df)\n            logger.info(emat.accuracy)\n\n        best_score_index = np.argmax(df['Accuracy'].values)\n\n        logger.info('  Best score: {:f}'.format(df['Accuracy'].values[best_score_index]))\n\n        logger.info('  Best parameters:')\n        logger.info(''.join(['='] * len(df_param_headers)))\n        logger.info(df_param_headers)\n        logger.info(''.join(['='] * len(df_param_headers)))\n        logger.info(df[df_param_headers].values[best_score_index])\n\n        if isinstance(output_file, str):\n            df.to_csv(output_file, sep=',', index=False)\n\n        return df\n\n    def grid_search(self, classifier_name, classifier_parameters, file_name, k_folds=3,\n                    perc_samp=.5, ignore_feas=[], use_xy=False, classes2remove=[],\n                    method='overall', metric='accuracy', f1_class=0, stratified=False, spacing=1000.,\n                    output_file=None, calibrate_proba=False):\n\n        \"\"\"\n        Classifier parameter grid search\n\n        Args:\n            classifier_name (str): The classifier to optimize.\n            classifier_parameters (dict): The classifier parameters.\n            file_name (str): The sample file name.\n            k_folds (Optional[int]): The number of cross-validation folds. Default is 3.\n            perc_samp (Optional[float]): The percentage of samples to take at each fold. Default is .5.\n            ignore_feas (Optional[int list]): A list of features to ignore. Default is [].\n            use_xy (Optional[bool]): Whether to use x, y coordinates. Default is False.\n            classes2remove (Optional[int list]): A list of classes to remove. Default is [].\n            method (Optional[str]): The score method to use, 'overall' (default) or 'f1'. Choices are ['overall', 'f1'].\n            metric (Optional[str]): The scoring metric to use. Default is 'accuracy'.\n                Choices are ['accuracy', 'r_squared', 'rmse', 'mae', 'medae', 'mse'].\n            f1_class (Optional[int]): The class position to evaluate when ``method`` is equal to 'f1'. Default is 0,\n                or first index position.\n            stratified (Optional[bool]):\n            spacing (Optional[float]):\n            output_file (Optional[str]):\n\n        Returns:\n            DataFrame with scores.\n        \"\"\"\n\n        regressors = ['cubist', 'rfr', 'abr', 'bag-dtr', 'ex-rfr', 'ex-dtr', 'dtr']\n\n        if metric not in ['accuracy', 'r_squared', 'rmse', 'mae', 'medae', 'mse']:\n\n            logger.error('  The metric is not supported.')\n            raise NameError\n\n        if classifier_name in regressors and metric == 'accuracy':\n\n            logger.error('  Overall accuracy is not supported with regression classifiers.')\n            raise NameError\n\n        if classifier_name not in regressors and metric in ['r_squared', 'rmse', 'mae', 'medae', 'mse']:\n\n            logger.error('  Overall accuracy is the only option with discrete classifiers.')\n            raise NameError\n\n        if classifier_name in ['c5', 'cubist']:\n\n            if 'R_installed' not in globals():\n\n                logger.warning('  You must use `classification_r` to use C5 and Cubist.')\n                return\n\n            if not R_installed:\n\n                logger.warning('  R and rpy2 must be installed to use C5 or Cubist.')\n                return\n\n        if classifier_name in regressors:\n            discrete = False\n        else:\n            discrete = True\n\n        score_label = metric.upper()\n\n        param_order = list(classifier_parameters)\n\n        df_param_headers = '-'.join(param_order)\n        df_fold_headers = ('F' + '-F'.join(list(map(str, range(1, k_folds+1))))).split('-')\n\n        # Setup the output scores table.\n        df = pd.DataFrame(columns=df_fold_headers)\n        df[df_param_headers] = list(itertools.product(*itervalues(classifier_parameters)))\n\n        # Open the weights file.\n        lc_weights = file_name.replace('.txt', '_w.txt')\n\n        if os.path.isfile(lc_weights):\n            weights = self.load(lc_weights)\n        else:\n            weights = None\n\n        for k_fold in range(1, k_folds+1):\n\n            logger.info('  Fold {:d} of {:d} ...'.format(k_fold, k_folds))\n\n            self.split_samples(file_name, perc_samp_each=perc_samp, ignore_feas=ignore_feas,\n                               use_xy=use_xy, classes2remove=classes2remove, stratified=stratified,\n                               spacing=spacing, sample_weight=weights)\n\n            if classifier_name in ['c5', 'cubist']:\n\n                predict_samps = ro.r.matrix(self.p_vars, nrow=self.n_samps, ncol=self.n_feas)\n                predict_samps.colnames = StrVector(self.headers[:-1])\n\n            # Iterate over all possible combinations.\n            for param_combo in list(itertools.product(*itervalues(classifier_parameters))):\n\n                # Set the current parameters.\n                current_combo = dict(zip(param_order, param_combo))\n\n                # Add the classifier name to the dictionary.\n                current_combo['classifier'] = classifier_name\n\n                if classifier_name in ['c5', 'cubist']:\n                    self.construct_r_model(classifier_info=current_combo)\n                else:\n                    self.construct_model(classifier_info=current_combo,\n                                         calibrate_proba=calibrate_proba)\n\n                # Get the accuracy\n                self.test_accuracy(discrete=discrete)\n\n                if method == 'overall':\n                    df.loc[df[df_param_headers] == param_combo, 'F{:d}'.format(k_fold)] = getattr(self.emat, metric)\n\n                elif method == 'f1':\n                    df.loc[df[df_param_headers] == param_combo, 'F{:d}'.format(k_fold)] = self.emat.f_scores[f1_class]\n\n        df[score_label] = df[df_fold_headers].mean(axis=1)\n\n        if metric in ['accuracy', 'r_squared']:\n            best_score_index = np.argmax(df[score_label].values)\n        else:\n            best_score_index = np.argmin(df[score_label].values)\n\n        logger.info('  Best {} score: {:f}'.format(metric, df[score_label].values[best_score_index]))\n\n        logger.info('  Best parameters:')\n        logger.info(''.join(['='] * len(df_param_headers)))\n        logger.info(df_param_headers)\n        logger.info(''.join(['=']*len(df_param_headers)))\n        logger.info(df[df_param_headers].values[best_score_index])\n\n        if isinstance(output_file, str):\n            df.to_csv(output_file, sep=',', index=False)\n\n        return df\n\n    def optimize_parameters(self,\n                            file_name,\n                            classifier_info={'classifier': 'rf'},\n                            n_trees_list=[500, 1000, 1500, 2000],\n                            trials_list=[2, 5, 10],\n                            max_depth_list=[25, 30, 35, 40, 45, 50],\n                            min_samps_list=[2, 5, 10],\n                            criterion_list=['gini'],\n                            rand_vars_list=['sqrt'],\n                            cf_list=[.25, .5, .75],\n                            committees_list=[1, 2, 5, 10],\n                            rules_list=[25, 50, 100, 500],\n                            extrapolation_list=[0, 1, 5, 10],\n                            class_weight_list=[None, 'balanced', 'balanced_subsample'],\n                            learn_rate_list=[.1, .2, .4, .6, .8, 1.],\n                            bool_list=[True, False],\n                            c_list=[1., 10., 20., 100.],\n                            gamma_list=[.001, .001, .01, .1, 1., 5.],\n                            k_folds=3,\n                            perc_samp=.5,\n                            ignore_feas=[],\n                            use_xy=False,\n                            classes2remove=[],\n                            method='overall',\n                            f1_class=0,\n                            stratified=False,\n                            spacing=1000.,\n                            calibrate_proba=False,\n                            output_file=None):\n\n        \"\"\"\n        Finds the optimal parameters for a classifier by training and testing a range of classifier parameters\n        by n-folds cross-validation.\n\n        Args:\n            file_name (str): The file name of the samples.\n            classifier_info (Optional[dict]): The model parameters dictionary. Default is {'classifier': 'rf'}.\n            n_trees_list (Optional[int list]): A list of trees. Default is [500, 1000].\n            trials_list (Optional[int list]): A list of boosting trials. Default is [5, 10, 20].\n            max_depth_list (Optional[int list]): A list of maximum depths. Default is [5, 10, 20, 25, 30, 50].\n            min_samps_list (Optional[int list]): A list of minimum samples. Default is [2, 5, 10].\n            criterion_list (Optional[str list]): A list of RF criterion. Default is ['gini', 'entropy'].\n            rand_vars_list (Optional[str list]): A list of random variables. Default is ['sqrt'].\n            class_weight_list (Optional[bool]): A list of class weights.\n                Default is [None, 'balanced', 'balanced_subsample'].\n            c_list (Optional[float list]): A list of SVM C parameters. Default is [1., 10., 20., 100.].\n            gamma_list (Optional[float list]): A list of SVM gamma parameters. Default is [.001, .001, .01, .1, 1., 5.].\n            k_folds (Optional[int]): The number of N cross-validation folds. Default is 3.\n            ignore_feas (Optional[int list]): A list of features to ignore. Default is [].\n            use_xy (Optional[bool]): Whether to use x, y coordinates. Default is False.\n            classes2remove (Optional[int list]): A list of classes to remove. Default is [].\n            method (Optional[str]): The score method to use, 'overall' (default) or 'f1'.1\n            f1_class (Optional[int]): The class position to evaluate when ``method`` is equal to 'f1'. Default is 0,\n                or first index position.\n            stratified (Optional[bool]):\n            spacing (Optional[float]):\n            output_file (Optional[str]):\n\n        Returns:\n            `Pandas DataFrame` when classifier_info['classifier'] == 'c5',\n                otherwise None, prints results to screen.\n\n        Examples:\n            >>> # find the optimal parameters (max depth, min samps, trees)\n            >>> # randomly sampling 50% (with replacement) and testing on the 50% set aside\n            >>> # repeat 5 (k_folds) times and average the results\n            >>> import mpglue as gl\n            >>>\n            >>> cl = gl.classification()\n            >>>\n            >>> # Find the optimum parameters for an Extremely Randomized Forest.\n            >>> cl.optimize_parameters('/samples.txt',\n            >>>                        classifier_info={'classifier': 'ex-rf'},\n            >>>                        use_xy=True)\n            >>>\n            >>> # Find the optimum parameters for a Random Forest, but assess\n            >>> #   only one class (1st class position) of interest.\n            >>> cl.optimize_parameters('/samples.txt',\n            >>>                        classifier_info={'classifier': 'rf'},\n            >>>                        use_xy=True, method='f1', f1_class=0)\n            >>>\n            >>> # Optimizing C5 parameters\n            >>> from mpglue.classifiers import classification_r\n            >>>\n            >>> cl = classification_r()\n            >>>\n            >>> df = cl.optimize_parameters('/samples.txt', classifier_info={'classifier': 'c5'},\n            >>>                             trials_list=[2, 5, 10], cf_list=[.25, .5, .75],\n            >>>                             min_samps_list=[2, 5, 10], bool_list=[True, False],\n            >>>                             k_folds=5, stratified=True, spacing=50000.)\n            >>>\n            >>> print df\n        \"\"\"\n\n        if classifier_info['classifier'] not in ['c5', 'cubist']:\n\n            self.split_samples(file_name, perc_samp=1., ignore_feas=ignore_feas,\n                               use_xy=use_xy, classes2remove=classes2remove)\n\n            prediction_models = {'rf': ensemble.RandomForestClassifier(n_jobs=-1),\n                                 'rfr': ensemble.RandomForestRegressor(n_jobs=-1),\n                                 'ex-rf': ensemble.ExtraTreesClassifier(n_jobs=-1),\n                                 'ex-rfr': ensemble.ExtraTreesRegressor(n_jobs=-1),\n                                 'bag-dt': ensemble.BaggingClassifier(base_estimator=tree.DecisionTreeClassifier(),\n                                                                      n_jobs=-1),\n                                 'ab-dt': ensemble.AdaBoostClassifier(base_estimator=tree.DecisionTreeClassifier()),\n                                 'gb': ensemble.GradientBoostingClassifier(),\n                                 'dt': tree.DecisionTreeClassifier()}\n\n        if classifier_info['classifier'] in ['rf', 'ex-rf']:\n\n            parameters = {'criterion': criterion_list,\n                          'n_estimators': n_trees_list,\n                          'max_depth': max_depth_list,\n                          'max_features': rand_vars_list,\n                          'min_samples_split': min_samps_list,\n                          'class_weight': class_weight_list}\n\n        elif classifier_info['classifier'] in ['rfr', 'ex-rfr', 'dtr']:\n\n            parameters = {'trees': n_trees_list,\n                          'max_depth': max_depth_list,\n                          'rand_vars': rand_vars_list,\n                          'min_samps': min_samps_list}\n\n        elif classifier_info['classifier'] in ['ab-dt', 'ab-dt', 'ab-ex-dt', 'ab-rf', 'ab-ex-rf']:\n\n            parameters = {'n_estimators': n_trees_list,\n                          'trials': trials_list,\n                          'learning_rate': learn_rate_list,\n                          'max_depth': max_depth_list,\n                          'min_samps': min_samps_list,\n                          'class_weight': class_weight_list}\n\n        elif classifier_info['classifier'] in ['abr', 'abr-ex-dtr']:\n\n            parameters = {'trees': n_trees_list,\n                          'rate': learn_rate_list}\n\n        elif classifier_info['classifier'] == 'bag-dt':\n\n            parameters = {'n_estimators': n_trees_list,\n                          'warm_start': bool_list,\n                          'bootstrap': bool_list,                          \n                          'bootstrap_features': bool_list}\n\n        elif classifier_info['classifier'] == 'bag-dtr':\n\n            parameters = {'trees': n_trees_list,\n                          'warm_start': bool_list,\n                          'bootstrap': bool_list,\n                          'bootstrap_features': bool_list}\n\n        elif classifier_info['classifier'] == 'gb':\n\n            parameters = {'n_estimators': n_trees_list,\n                          'max_depth': max_depth_list,\n                          'max_features': rand_vars_list,\n                          'min_samples_split': min_samps_list,\n                          'learning_rate': learn_rate_list}\n\n        elif classifier_info['classifier'] == 'gbr':\n\n            parameters = {'trees': n_trees_list,\n                          'max_depth': max_depth_list,\n                          'rand_vars': rand_vars_list,\n                          'min_samps': min_samps_list,\n                          'learning_rate': learn_rate_list}\n\n        elif classifier_info['classifier'] == 'dt':\n\n            parameters = {'n_estimators': n_trees_list,\n                          'max_depth': max_depth_list,\n                          'max_features': rand_vars_list,\n                          'min_samples_split': min_samps_list}\n\n        elif classifier_info['classifier'] == 'svmc':\n\n            parameters = {'C': c_list,\n                          'gamma': gamma_list}\n\n        elif classifier_info['classifier'] == 'c5':\n\n            parameters = {'trials': trials_list,\n                          'min_cases': min_samps_list,\n                          'CF': cf_list,\n                          'fuzzy': bool_list}\n\n        elif classifier_info['classifier'] == 'cubist':\n\n            parameters = {'committees': committees_list,\n                          'rules': rules_list,\n                          'extrapolation': extrapolation_list,\n                          'unbiased': bool_list}\n\n        else:\n\n            logger.error('  The model cannot be optimized.')\n            return NameError\n\n        logger.info('  Finding the best paramaters for a {} model ...'.format(classifier_info['classifier']))\n\n        core_classifiers = ['c5', 'cubist', 'rf', 'rfr',\n                            'ab-rf', 'ab-ex-rf', 'ab-dt', 'ab-ex-dt',\n                            'abr', 'bag-dtr', 'ex-rf', 'ex-rfr', 'ex-dtr', 'dtr']\n\n        if classifier_info['classifier'] in core_classifiers:\n\n            return self.grid_search(classifier_info['classifier'],\n                                    parameters,\n                                    file_name,\n                                    k_folds=k_folds,\n                                    perc_samp=perc_samp,\n                                    ignore_feas=ignore_feas,\n                                    use_xy=use_xy,\n                                    classes2remove=classes2remove,\n                                    method=method,\n                                    f1_class=f1_class,\n                                    stratified=stratified,\n                                    spacing=spacing,\n                                    output_file=output_file,\n                                    calibrate_proba=calibrate_proba)\n\n        elif (method == 'overall') and (classifier_info['classifier'] not in core_classifiers):\n\n            clf = prediction_models[classifier_info['classifier']]\n\n            grid_search = GridSearchCV(clf,\n                                       param_grid=parameters,\n                                       n_jobs=classifier_info['n_jobs'],\n                                       cv=k_folds,\n                                       verbose=1)\n\n            grid_search.fit(self.p_vars, self.labels)\n\n            logger.info(grid_search.best_estimator_)\n            logger.info('  Best score: {:f}'.format(grid_search.best_score_))\n            logger.info('  Best parameters: {}'.format(grid_search.best_params_))\n\n        else:\n\n            logger.error('  The score method {} is not supported.'.format(method))\n            raise NameError\n\n    def cross_validation(self, X, y, n_splits=5, test_size=0.7, train_size=0.3, random_state=None, **kwargs):\n\n        \"\"\"\n        A cross validation function to replace scikit-learn,\n        which does not handle the built-in VotingClassifier\n\n        Example:\n            >>> cl = classification()\n            >>> cl.split_samples(<args>)\n            >>> cl.construct_model(<args>)\n            >>>\n            >>> cl.cross_validation(n_splits=5, test_size=0.7, train_size=0.3)\n            >>> print(cl.cv_scores)\n        \"\"\"\n\n        from sklearn.model_selection import StratifiedShuffleSplit\n        from sklearn.metrics import f1_score\n\n        kwargs['input_model'] = None\n        kwargs['output_model'] = None\n\n        self.cv_scores = list()\n\n        splitter = StratifiedShuffleSplit(n_splits=n_splits,\n                                          test_size=test_size,\n                                          train_size=train_size,\n                                          random_state=random_state)\n\n        for train_index, test_index in splitter.split(X, y):\n\n            # Set training data\n            self.p_vars = X[train_index]\n            self.labels = y[train_index]\n\n            # Set test data\n            p_vars_test = X[test_index]\n            labels_test = y[test_index]\n\n            self.construct_model(**kwargs)\n\n            labels_predict = self.model.predict(p_vars_test)\n\n            score = f1_score(labels_test,\n                             labels_predict,\n                             average='weighted')\n\n            self.cv_scores.append(score)\n\n    def stack_majority(self, img, output_model, out_img, classifier_info, scale_data=False, ignore_feas=[]):\n\n        \"\"\"\n        A majority vote filter.\n\n        Args:\n            img (str): The input image.\n            output_model (str): The output model.\n            out_img (str): The output map.\n            classifier_info (dict): The model parameters dictionary.\n            scale_data (Optional[bool or str]): Whether to scale the data prior to classification. \n                Default is False. *If ``scale_data`` is a string, the scaler will be loaded from the string text file.\n            ignore_features (Optional[int list]): A list of features to ignore. Default is [].\n\n        Returns:\n            None, writes results to ``out_img``.\n\n        Examples:\n            >>> import mpglue as gl\n            >>>\n            >>> cl = gl.classification()\n            >>>\n            >>> cl.split_samples('/samples.txt', scale_data=True)\n            >>>\n            >>> # setup three classifiers\n            >>> classifier_info = {'classifiers': ['rf', 'SVM', 'bayes'], 'trees': 100, 'C': 1}\n            >>> cl.stack_majority('/in_image.tif', '/out_model.xml', '/out_image.tif',\n            >>>                   classifier_info, scale_data=True)\n            >>>\n            >>> # Command line\n            >>> > ./classification.py -s /samples.txt -i /in_image.tif -mo /out_model.xml -o /out_image.tif --parameters ...\n            >>>     classifiers:RF-SVM-Bayes,trees:100,C:1 --scale yes\n        \"\"\"\n\n        d_name_mdl, f_name_mdl = os.path.split(output_model)\n        f_base_mdl, f_ext_mdl = os.path.splitext(f_name_mdl)\n\n        d_name, f_name = os.path.split(out_img)\n        f_base, f_ext = os.path.splitext(f_name)\n\n        map_list = []\n\n        for classifier in classifier_info['classifiers']:\n\n            output_model = '%s/%s_%s%s' % (d_name_mdl, f_base_mdl, classifier, f_ext_mdl)\n\n            out_image_temp = '%s/%s_%s%s' % (d_name, f_base, classifier, f_ext)\n            map_list.append(out_image_temp)\n\n            classifier_info['classifier'] = classifier\n\n            self.construct_model(output_model=output_model, classifier_info=classifier_info)\n\n            # load the model for multiproccessing\n            self.construct_model(input_model=output_model, classifier_info=classifier_info)\n\n            self.predict(img, out_image_temp, scale_data=scale_data, ignore_feas=ignore_feas)\n\n        with raster_tools.ropen(map_list[0]) as i_info:\n\n            rows, cols = i_info.rows, i_info.cols\n\n            i_info.bands = 1\n\n            with raster_tools.create_raster(out_img, i_info, bigtiff='yes') as out_rst:\n\n                out_rst.get_band(1)\n\n                rst_objs = [raster_tools.ropen(img).datasource.GetRasterBand(1) for img in map_list]\n\n                if rows >= 512:\n                    blk_size_rows = 512\n                else:\n                    blk_size_rows = copy(rows)\n\n                if cols >= 1024:\n                    block_size_cls = 1024\n                else:\n                    block_size_cls = copy(cols)\n\n                for i in range(0, rows, blk_size_rows):\n\n                    n_rows = self._num_rows_cols(i, blk_size_rows, rows)\n\n                    for j in range(0, cols, block_size_cls):\n\n                        n_cols = self._num_rows_cols(j, block_size_cls, cols)\n\n                        mode_img = np.vstack(([obj.ReadAsArray(j, i, n_cols, n_rows)\n                                               for obj in rst_objs])).reshape(len(map_list), n_rows, n_cols)\n\n                        out_mode = stats.mode(mode_img)[0]\n\n                        out_rst.write_array(out_mode, i=i, j=j)\n\n                for rst_obj in rst_objs:\n                    rst_obj.close()\n                    rst_obj = None\n\n            out_rst = None\n\n        i_info = None\n\n\ndef importr_tryhard(packname):\n\n    from rpy2.robjects.packages import importr\n    utils = importr('utils')\n\n    try:\n        __ = importr(packname)\n    except:\n        utils.install_packages(StrVector(packname))\n\n\nclass classification_r(classification):\n\n    \"\"\"\n    Class interface to R C5/Cubist\n\n    Examples:\n        >>> from mpglue.classifiers import classification_r\n        >>>\n        >>> cl = classification_r()\n        >>>\n        >>> # load the samples\n        >>> # *Note that the sample instances are stored in cl.classification, \n        >>> # structurally different than using the base\n        >>> # classification() which inherits the properties directly\n        >>> cl.split_samples('/samples.txt', classes2remove=[4, 9],\n        >>>                  class_subs={2:.9, 5:.1, 8:.9})\n        >>>\n        >>> # Train a Cubist model.\n        >>> cl.construct_r_model(output_model='/models/cubist_model', classifier_info={'classifier': 'cubist',\n        >>>                        'committees': 5, 'rules': 100, 'extrap': 10})\n        >>>\n        >>> # Predict labels with the Cubist model.\n        >>> cl.predict('/feas/image_feas.vrt', '/maps/out_labels.tif',\n        >>>            input_model='/models/cubist_model', in_samps='/samples.txt')\n        >>>\n        >>> # Train a C5 model.\n        >>> cl.construct_r_model(output_model='/models/c5_model', classifier_info={'classifier': 'c5',\n        >>>                        'trials': 10, 'c5': .25, 'min': 2})\n        >>>\n        >>> # Predict labels with the C5 model. There is no need\n        >>> #   to load a model if the prediction is applied within\n        >>> #   the same session.\n        >>> cl.predict('/feas/image_feas.vrt', '/maps/out_labels.tif')\n        >>>\n        >>> # However, you must provide the model\n        >>> #   file to predict in parallel.\n        >>> # First, load the model\n        >>> cl.construct_r_model(input_model='/models/c5_model.tree')\n        >>>\n        >>> # Then apply the predictions.\n        >>> cl.predict('/feas/image_feas.vrt', '/maps/out_labels.tif')\n    \"\"\"\n\n    global R_installed, ro, Cubist, C50, pandas2ri, StrVector\n\n    # rpy2\n    try:\n        import rpy2.robjects as ro\n        from rpy2.robjects.packages import importr\n\n        from rpy2.robjects.numpy2ri import numpy2ri\n        ro.numpy2ri.activate()\n\n        # R vector of strings\n        from rpy2.robjects.vectors import StrVector\n\n        from rpy2.robjects import pandas2ri\n        pandas2ri.activate()\n\n        # import R's utility package\n        utils = importr('utils')\n\n        R_installed = True\n\n    except:\n        R_installed = False\n\n    if R_installed:\n\n        try:\n\n            # select a mirror for R packages\n            utils.chooseCRANmirror(ind=1)  # select the first mirror in the list\n\n            # R package names\n            package_names = ('cubist', 'C50', 'raster', 'rgdal')#, 'foreach', 'doSNOW')\n\n            # Selectively install what needs to be install.\n            # We are fancy, just because we can.\n            # names_to_install = [x for x in package_names if not isinstalled(x)]\n            [importr_tryhard(px) for px in package_names]\n\n            # Install necessary libraries.\n            # if len(names_to_install) > 0:\n            #\n            #     print('Installing R packages--{} ...'.format(', '.join(names_to_install)))\n            #\n            #     utils.install_packages(StrVector(names_to_install))\n\n            # print('Importing R packages--{} ...'.format(', '.join(package_names)))\n\n            # Cubist\n            Cubist = importr('cubist', suppress_messages=True)\n\n            # C50\n            C50 = importr('C50', suppress_messages=True)\n\n            # raster\n            raster = importr('raster', suppress_messages=True)\n\n            # rgdal\n            rgdal = importr('rgdal', suppress_messages=True)\n\n            # # foreach\n            # foreach = importr('foreach', suppress_messages=True)\n            #\n            # # doSNOW\n            # doSNOW = importr('doSNOW', suppress_messages=True)\n\n        except:\n            R_installed = False\n\n    def __init__(self):\n\n        self.time_stamp = time.asctime(time.localtime(time.time()))\n\n        self.OS_SYSTEM = platform.system()\n\n    def construct_r_model(self, input_model=None, output_model=None, write_summary=False,\n                          get_probs=False, cost_array=None, case_weights=None,\n                          classifier_info={'classifier': 'cubist'}):\n\n        \"\"\"\n        Trains a Cubist model.\n\n        Args:\n            input_model (Optional[str]): The input model to load. Default is None.\n            output_model (Optional[str]): The output model to write to file. Default is None.\n                *No extension should be added. This is added automatically.\n            write_summary (Optional[bool]): Whether to write the model summary to file. Default is False.\n            get_probs (Optional[bool]): Whether to return class probabilities. Default is False.\n            cost_array (Optional[2d array]): A cost matrix, where rows are the predicted costs and columns are\n                the true costs. Default is None.\n\n                In the example below, the cost of predicting R as G is 3x more costly as the reverse, predicting\n                    R as B ix 7x more costly as the reverse, predicting G as R is 2x more costly as the reverse,\n                    and so on.\n\n                        R  G  B\n                    R [[0, 2, 4],\n                    G  [3, 0, 5],\n                    B  [7, 1, 0]]\n\n            case_weights (Optional[list or 1d array]): A list of case weights. Default is None.\n            classifier_info (dict): The model parameter dictionary: Default is {'classifier': 'cubist', \n                'committees': 5, 'rules': 100, 'extrap': 10})\n        \"\"\"\n\n        if not R_installed:\n\n            logger.warning('  R and rpy2 must be installed to use C5 or Cubist.')\n            return\n\n        self.get_probs = get_probs\n\n        # replace forward slashes for Windows\n        if self.OS_SYSTEM == 'Windows':\n            output_model = output_model.replace('\\\\', '/')\n\n        if isinstance(input_model, str):\n            self.classifier_info, self.model, self.headers = self.load(input_model)\n\n            self.input_model = input_model\n            return\n\n        else:\n            self.classifier_info = classifier_info\n\n        # Check if model parameters are set\n        #   otherwise, set defaults.\n        if self.classifier_info['classifier'] == 'cubist':\n\n            # The number of committees.\n            if 'committees' not in self.classifier_info:\n                self.classifier_info['committees'] = 5\n\n            # The number of rules.\n            if 'rules' not in self.classifier_info:\n                self.classifier_info['rules'] = 100\n\n            # Whether to use unbiased rules.\n            if 'unbiased' not in self.classifier_info:\n                self.classifier_info['unbiased'] = False\n\n            # The extrapolation percentage, between 0-100.\n            if 'extrapolation' not in self.classifier_info:\n                self.classifier_info['extrapolation'] = 10\n\n        elif self.classifier_info['classifier'] == 'c5':\n\n            # The number of boosted trials.\n            if 'trials' not in self.classifier_info:\n                self.classifier_info['trials'] = 10\n\n            # The minimum number of cases and node level.\n            if 'min_cases' not in self.classifier_info:\n                self.classifier_info['min_cases'] = 2\n\n            # Whether to apply winnowing (i.e., feature selection)\n            if 'winnow' not in self.classifier_info:\n                self.classifier_info['winnow'] = False\n\n            # Whether to turn off global pruning\n            if 'no_prune' not in self.classifier_info:\n                self.classifier_info['no_prune'] = False\n\n            # The confidence factor for pruning. Low values result\n            #   in more pruning.]\n            if 'CF' not in self.classifier_info:\n                self.classifier_info['CF'] = .25\n\n            # Whether to apply a fuzzy threshold of probabilities.\n            if 'fuzzy' not in self.classifier_info:\n                self.classifier_info['fuzzy'] = False\n\n        else:\n\n            logger.error('  The classifier must be C5 or Cubist.')\n            raise NameError\n\n        if isinstance(output_model, str):\n\n            self.model_dir, self.model_base = os.path.split(output_model)\n            self.output_model = '{}/{}.tree'.format(self.model_dir, self.model_base)\n\n            if os.path.isfile(self.output_model):\n                os.remove(self.output_model)\n\n            if not os.path.isdir(self.model_dir):\n                os.makedirs(self.model_dir)\n\n            os.chdir(self.model_dir)\n\n        ## prepare the predictive samples and labels\n        # R('samps = read.csv(file=\"%s\", head=TRUE, sep=\",\")' % file_name)\n        # samps = R['read.csv'](file_name)\n\n        # samps = com.convert_to_r_dataframe(pd.DataFrame(self.p_vars))\n        # samps = pandas2ri.py2ri(pd.DataFrame(self.p_vars))\n        # samps.colnames = self.headers[:-1]\n\n        samps = ro.r.matrix(self.p_vars, nrow=self.n_samps, ncol=self.n_feas)\n        samps.colnames = StrVector(self.headers[:-1])\n\n        if 'cubist' in self.classifier_info['classifier']:\n            labels = ro.FloatVector(self.labels)\n        elif 'c5' in self.classifier_info['classifier']:\n            labels = ro.FactorVector(pd.Categorical(self.labels))\n\n        if isinstance(case_weights, list) or isinstance(case_weights, np.ndarray):\n            case_weights = ro.FloatVector(case_weights)\n\n        if isinstance(cost_array, np.ndarray):\n\n            cost_array = ro.r.matrix(cost_array, nrow=cost_array.shape[0], ncol=cost_array.shape[1])\n            cost_array.rownames = StrVector(sorted(self.classes))\n            cost_array.colnames = StrVector(sorted(self.classes))\n\n        # samps = DataFrame.from_csvfile(self.file_name, header=True, sep=',')\n        # R('labels = samps[,c(\"%s\")]' % self.classification.hdrs[-1])\n        # labels = samps.rx(True, ro.StrVector(tuple(self.headers[-1:])))\n        # R('samps = samps[,1:%d+2]' % self.classification.n_feas)\n        # samps = samps.rx(True, ro.IntVector(tuple(range(3, self.n_feas+3))))\n\n        # Train a Cubist model.\n        if 'cubist' in self.classifier_info['classifier']:\n\n            logger.info('  Training a Cubist model with {:d} committees, {:d} rules, {:d}% extrapolation, and {:,d} samples ...'.format(self.classifier_info['committees'],\n                                                                                                                                        self.classifier_info['rules'],\n                                                                                                                                        self.classifier_info['extrapolation'],\n                                                                                                                                        self.n_samps))\n\n            # train the Cubist model\n            # R('model = Cubist::cubist(x=samps, y=labels, committees=%d, control=cubistControl(rules=%d, extrapolation=%d))' % \\\n            #   (self.classifier_info['committees'], self.classifier_info['rules'], self.classifier_info['extrap']))\n\n            self.model = Cubist.cubist(x=samps, y=labels, committees=self.classifier_info['committees'],\n                                       control=Cubist.cubistControl(rules=self.classifier_info['rules'],\n                                                                    extrapolation=self.classifier_info['extrapolation'],\n                                                                    unbiased=self.classifier_info['unbiased']))\n\n            if isinstance(output_model, str):\n\n                logger.info('  Writing the model to file ...')\n\n                # Write the Cubist model and .names to file.\n                if self.OS_SYSTEM == 'Windows':\n\n                    # R('Cubist::exportCubistFiles(model, prefix=\"%s\")' % self.model_base)\n                    Cubist.exportCubistFiles(self.model, prefix=self.model_base)\n\n                else:\n\n                    self.dump([self.classifier_info,\n                               self.model,\n                               self.headers],\n                              self.output_model)\n\n                if write_summary:\n\n                    logger.info('  Writing the model summary to file ...')\n\n                    # Write the Cubist model summary to file.\n                    with open(os.path.join(self.model_dir, '{}_summary.txt'.format(self.model_base)), 'wb') as out_tree:\n                        out_tree.write(str(Cubist.print_summary_cubist(self.model)))\n\n        elif 'c5' in self.classifier_info['classifier']:\n\n            logger.info('  Training a C5 model with {:d} trials, {:.2f} CF, {:d} minimum cases, and {:,d} samples ...'.format(self.classifier_info['trials'],\n                                                                                                                              self.classifier_info['CF'],\n                                                                                                                              self.classifier_info['min_cases'],\n                                                                                                                              self.n_samps))\n\n            # train the C5 model\n            # R('model = C50::C5.0(x=samps, y=factor(labels), trials=%d, control=C5.0Control(CF=%f, minCases=%d))' % \\\n            #   (self.classifier_info['trials'], self.classifier_info['CF'], self.classifier_info['min']))\n\n            # weights = case_weights,\n            # costs = cost_array,\n\n            self.model = C50.C5_0(x=samps, y=labels,\n                                  trials=self.classifier_info['trials'],\n                                  control=C50.C5_0Control(CF=self.classifier_info['CF'],\n                                                          minCases=self.classifier_info['min_cases'],\n                                                          winnow=self.classifier_info['winnow'],\n                                                          noGlobalPruning=self.classifier_info['no_prune'],\n                                                          fuzzyThreshold=self.classifier_info['fuzzy'],\n                                                          label='response'))\n\n            if isinstance(output_model, str):\n\n                logger.info('  Writing the model to file ...')\n\n                # Write the C5 tree to file.\n                if self.OS_SYSTEM == 'Windows':\n\n                    with open(self.output_model, 'wb') as out_tree:\n\n                        ro.globalenv['model'] = self.model\n                        out_tree.write(str(ro.r('model$tree')))\n\n                else:\n\n                    self.dump([self.classifier_info,\n                               self.model,\n                               self.headers],\n                              self.output_model)\n\n                if write_summary:\n\n                    logger.info('  Writing the model summary to file (this may take a few minutes with large trees) ...')\n\n                    # Write the C5 model summary to file.\n                    with open(os.path.join(self.model_dir, '{}_summary.txt'.format(self.model_base)), 'wb') as out_imp:\n                        out_imp.write(str(C50.print_summary_C5_0(self.model)))\n\n    def predict_c5_cubist(self, input_image, out_image, input_model=None, in_samps=None,\n                          ignore_feas=[], row_block_size=1024, col_block_size=1024,\n                          mask_background=None, background_band=0, background_value=0,\n                          minimum_observations=0, observation_band=0, n_jobs=-1, chunk_size=1024):\n\n        \"\"\"\n        Predicts class labels from C5 or Cubist model.\n\n        Args:\n            input_image (str): The image features with the same number of layers as used to train the model.\n            out_image (str): The output image.\n            input_model (Optional[str]): The full directory and base name of the model to use.\n            in_samps (Optional[str]): The image samples used to build the model.\n                *This is necessary to match the header names with Windows.\n            tree_model (str): The decision tree model to use. Default is 'cubist'. Choices are ['c5' or 'cubist'].\n        \"\"\"\n\n        global predict_samps\n\n        self.ignore_feas = ignore_feas\n        self.row_block_size = row_block_size\n        self.col_block_size = col_block_size\n        self.mask_background = mask_background\n        self.background_band = background_band\n        self.background_value = background_value\n        self.minimum_observations = minimum_observations\n        self.observation_band = observation_band\n        self.out_image = out_image\n        self.n_jobs = n_jobs\n        self.chunk_size = chunk_size\n\n        # Block record keeping.\n        d_name, f_name = os.path.split(self.out_image)\n        f_base, f_ext = os.path.splitext(f_name)\n\n        self.out_image_temp = os.path.join(d_name, '{}_temp.tif'.format(f_base))\n        self.record_keeping = os.path.join(d_name, '{}_record.txt'.format(f_base))\n\n        if os.path.isfile(self.record_keeping):\n            self.record_list = self.load(self.record_keeping)\n        else:\n            self.record_list = list()\n\n        if isinstance(input_model, str):\n            self.classifier_info, self.model, self.headers = self.load(input_model)\n\n        if self.OS_SYSTEM == 'Windows':\n\n            # input_model = input_model.replace('\\\\', '/')\n            # in_samps = in_samps.replace('\\\\', '/')\n            # input_image = input_image.replace('\\\\', '/')\n            # out_image = out_image.replace('\\\\', '/')\n\n            out_image_dir, f_name = os.path.split(out_image)\n            out_image_base, f_ext = os.path.splitext(f_name)\n\n            if not os.path.isdir(out_image_dir):\n                os.makedirs(out_image_dir)\n\n            if 'img' in f_ext.lower():\n                out_type = 'HFA'\n            elif 'tif' in f_ext.lower():\n                out_type = 'GTiff'\n            else:\n                sys.exit('\\nERROR!! The file extension is not supported.\\n')\n\n            self.model_dir, self.model_base = os.path.split(input_model)\n\n            self.input_image = input_image\n\n            # get the number of features\n            self.i_info = raster_tools.ropen(input_image)\n\n            self.n_feas = self.i_info.bands\n\n            # build the icases file\n            self._build_icases(in_samps, tree_model)\n\n            if 'c5' in tree_model:\n\n                # write the .names and .data files to text\n                self._build_C5_names(in_samps)\n\n            # self.mapC5_dir = os.path.realpath('../helpers/mapC5')\n            # python_home = 'C:/Python27/ArcGIS10.1/Lib/site-packages'\n            self.mapC5_dir = os.path.join(MPPATH, 'helpers/mapC5')\n\n            # copy the mapC5 files to the model directory\n            self._copy_mapC5(tree_model)\n\n            # change to the map_C5 model directory\n            os.chdir(self.model_dir)\n\n            # execute mapC5\n            if tree_model == 'cubist':\n\n                com = os.path.join(self.model_dir, 'mapCubist_v202.exe {} {} {}\\{}'.format(self.model_base,\n                                                                                           out_type,\n                                                                                           out_image_dir,\n                                                                                           out_image_base))\n\n            elif tree_model == 'c5':\n\n                com = os.path.join(self.model_dir, 'mapC5_v202.exe {} {} {}\\{} {}\\{}_error'.format(self.model_base,\n                                                                                                   out_type,\n                                                                                                   out_image_dir,\n                                                                                                   out_image_base,\n                                                                                                   out_image_dir,\n                                                                                                   out_image_base))\n\n            subprocess.call(com, shell=True)\n\n            self._clean_mapC5(tree_model)\n\n        else:\n\n            # Open the image.\n            self.i_info = raster_tools.ropen(input_image)\n\n            if self.ignore_feas:\n                bands = sorted([bd for bd in range(1, self.i_info.bands + 1) if bd not in self.ignore_feas])\n            else:\n                bands = list(range(1, self.i_info.bands + 1))\n\n            # Output image information.\n            self.o_info = self.i_info.copy()\n\n            # Set the number of output bands.\n            self.o_info.bands = 1\n\n            if self.classifier_info['classifier'] == 'cubist':\n                self.o_info.storage = 'float32'\n            else:\n                self.o_info.storage = 'byte'\n\n            self.o_info.close()\n\n            # Create the output image\n            if isinstance(self.mask_background, str):\n                out_raster_object = raster_tools.create_raster(self.out_image_temp, self.o_info,\n                                                               compress='none', tile=False, bigtiff='yes')\n            else:\n                out_raster_object = raster_tools.create_raster(self.out_image, self.o_info, tile=False)\n\n            out_raster_object.get_band(1)\n            out_raster_object.fill(0)\n\n            rows = self.i_info.rows\n            cols = self.i_info.cols\n\n            block_rows, block_cols = raster_tools.block_dimensions(rows, cols,\n                                                                   row_block_size=self.row_block_size,\n                                                                   col_block_size=self.col_block_size)\n\n            n_blocks = 0\n            for i in range(0, rows, block_rows):\n                for j in range(0, cols, block_cols):\n                    n_blocks += 1\n\n            n_block = 1\n\n            logger.info('  Mapping labels ...')\n\n            for i in range(0, rows, block_rows):\n\n                n_rows = self._num_rows_cols(i, block_rows, rows)\n\n                for j in range(0, cols, block_cols):\n\n                    logger.info('  Block {:,d} of {:,d} ...'.format(n_block, n_blocks))\n                    n_block += 1\n\n                    if n_block in self.record_list:\n\n                        logger.info('  Skipping current block ...')\n                        continue\n\n                    n_cols = self._num_rows_cols(j, block_cols, cols)\n\n                    features = raster_tools.read(file_name=input_image,\n                                                 bands=bands,\n                                                 i=i, j=j,\n                                                 rows=n_rows, cols=n_cols,\n                                                 predictions=True,\n                                                 d_type='float32',\n                                                 n_jobs=-1)\n\n                    # Load\n                    predict_samps = pandas2ri.py2ri(pd.DataFrame(features))\n                    predict_samps.colnames = self.headers\n\n                    # Make the predictions and convert to\n                    #   a Pandas Categorical object, followed\n                    #   by a conversion to a NumPy array.\n\n                    # Get chunks for parallel processing.\n                    samp_rows = predict_samps.shape[0]\n                    indice_pairs = []\n                    for i_ in range(0, samp_rows, self.chunk_size):\n                        n_rows_ = self._num_rows_cols(i_, self.chunk_size, samp_rows)\n                        indice_pairs.append([i_, n_rows_])\n\n                    if isinstance(self.input_model, str):\n\n                        predicted = joblib.Parallel(n_jobs=self.n_jobs,\n                                             max_nbytes=None)(joblib.delayed(self.c5_predict_parallel)(input_model, ip)\n                                                              for ip in indice_pairs)\n\n                        # Write the predictions to file.\n                        out_raster_object.write_array(np.array(list(itertools.chain.from_iterable(predicted))).reshape(n_cols, n_rows).T, i, j)\n\n                    else:\n\n                        # Write the predictions to file.\n                        out_raster_object.write_array(np.uint8(pandas2ri.ri2py(C50.predict_C5_0(self.model,\n                                                                                                newdata=predict_samps))).reshape(n_cols, n_rows).T, i, j)\n\n                    self.record_list.append(n_block)\n\n            out_raster_object.close_all()\n\n            out_raster_object = None\n\n            if isinstance(self.mask_background, str):\n\n                self._mask_background(self.out_image_temp, self.out_image, self.mask_background,\n                                      self.background_band, self.background_value, self.minimum_observations,\n                                      self.observation_band)\n\n            # ro.r('x = new(\"GDALReadOnlyDataset\", \"{}\")'.format(input_image))\n\n            # TODO: R predict functionality\n            # print(R('names(samps)'))\n\n            # R('x = new(\"GDALReadOnlyDataset\", \"%s\")' % input_image)\n            # R('feas = data.frame(getRasterTable(x))')\n            # R('names(feas) = c(\"x\", \"y\", names(samps))')\n            # R('feas = feas[1:%d+2]' % n_feas)\n            # R('feas = stack(\"%s\")' % input_image)\n            # R('predict(feas, model, filename=\"%s\", format=\"GTiff\", datetype=\"INT1U\", progress=\"window\", package=\"raster\")' % out_img)\n\n            # print(R('names(feas)'))\n\n            # R('predict(feas, fit, filename=\"%s\", format=\"GTiff\", datetype=\"INT1U\", progress=\"window\")' % out_img)\n\n    # def c5_predict_parallel(self, input_model, ip):\n    #\n    #     ci, m, h = pickle.load(file(input_model, 'rb'))\n    #\n    #     return np.uint8(pandas2ri.ri2py(C50.predict_C5_0(m, newdata=predict_samps[ip[0]:ip[0]+ip[1]])))\n\n    def _build_icases(self, in_samps, tree_model):\n\n        \"\"\"\n        Creates the icases file needed to run mapC5\n\n        Args:\n            in_samps (str): The samples used to train the model.\n            tree_model (str): 'c5' or 'cubist'\n        \"\"\"\n\n        icases_txt = os.path.join(self.model_dir, '{}.icases'.format(self.model_base))\n\n        # the output icases file\n        if os.path.isfile(icases_txt):\n            os.remove(icases_txt)\n\n        icases = open(icases_txt, 'w')\n\n        if 'cubist' in tree_model:\n            icases.write('{} ignore 1\\n'.format(self.headers[-1]))\n        elif 'c5' in tree_model:\n            icases.write('X ignore 1\\n')\n            icases.write('Y ignore 1\\n')\n\n        bd = 1\n        for hdr in self.headers[2:-1]:\n\n            icases.write('{} {} {:d}\\n'.format(hdr, self.input_image, bd))\n\n            bd += 1\n\n        icases.close()\n\n    def _build_C5_names(self, in_samps):\n\n        \"\"\"\n        Builds the C5 .names file.\n\n        Args:\n            in_samps (str): The samples used to train the model.\n        \"\"\"\n\n        names_txt = os.path.join(self.model_dir, '{}.names'.format(self.model_base))\n        data_txt = os.path.join(self.model_dir, '{}.data'.format(self.model_base))\n\n        # the output .names file\n        if os.path.isfile(names_txt):\n            os.remove(names_txt)\n\n        if os.path.isfile(data_txt):\n            os.remove(data_txt)\n\n        # create the .data file\n        shutil.copy2(in_samps, data_txt)\n\n        names = open(names_txt, 'w')\n\n        names.write('{}.\\n\\n'.format(self.headers[-1]))\n        names.write('X: ignore.\\n')\n        names.write('Y: ignore.\\n')\n\n        for hdr in self.headers[2:-1]:\n\n            names.write('{}: continuous.\\n'.format(hdr))\n\n        # write the classes\n        class_str_list = ','.join(list(map(str, sorted(self.classes))))\n        names.write('{}: {}'.format(self.headers[-1], class_str_list))\n\n        names.close()\n\n    def _copy_mapC5(self, tree_model):\n\n        \"\"\"\n        Copies files needed to run mapC5.\n\n        Args:\n            tree_model (str): The decision tree model to use ('c5' or 'cubist').\n        \"\"\"\n\n        if tree_model == 'cubist':\n\n            mapC5_list = ['gdal13.dll', 'gdal15.dll', 'install.bat', 'mapCubist_v202.exe', 'msvcp71.dll', 'msvcr71.dll']\n\n        elif tree_model == 'c5':\n\n            mapC5_list = ['gdal13.dll', 'gdal15.dll', 'install.bat', 'mapC5_v202.exe', 'msvcp71.dll', 'msvcr71.dll']\n\n        for mapC5_item in mapC5_list:\n\n            full_item = os.path.join(self.mapC5_dir, mapC5_item)\n            out_item = os.path.join(self.model_dir, mapC5_item)\n\n            if not os.path.isfile(out_item):\n                shutil.copy2(full_item, out_item)\n\n    def _clean_mapC5(self, tree_model):\n\n        \"\"\"\n        Cleans the C5 directories\n        \"\"\"\n\n        if tree_model == 'cubist':\n\n            mapC5_list = ['gdal13.dll', 'gdal15.dll', 'install.bat', 'mapCubist_v202.exe', 'msvcp71.dll', 'msvcr71.dll']\n\n        elif tree_model == 'c5':\n\n            mapC5_list = ['gdal13.dll', 'gdal15.dll', 'install.bat', 'mapC5_v202.exe', 'msvcp71.dll', 'msvcr71.dll']\n\n        for mapC5_item in mapC5_list:\n\n            full_item = os.path.join(self.model_dir, mapC5_item)\n\n            if os.path.isfile(full_item):\n                os.remove(full_item)\n\n\ndef _examples():\n\n    sys.exit(\"\"\"\\\n\n    --Find the optimum RF maximum depth--\n    classification.py -s /samples.txt -p .5 --optimize 10\n    ... would train and test (50/50) a range of depths over 10 folds cross-validation\n\n    ===============\n    Training models\n    ===============\n\n    --Train & save a Random Forest model--\n    classification.py -s /samples.txt -mo /model_rf.xml\n    ... would train and save a Random Forest model to model_rf.xml\n\n    --Train & save a Random Forest model--\n    classification.py -s /samples.txt --parameters classifier:RF,trees:2000 -mo /model_rf.xml\n    ... would train and save a Random Forest model with 2000 trees to model_rf.xml\n\n    --Train & save a Random Forest model--\n    classification.py -s /samples.txt --parameters classifier:RF,trees:2000 -ig 5,10,15 -mo /model_rf.xml\n    ... would train and save a Random Forest model with 2000 trees to model_rf.xml. The 5th, 10th, and 15th feature would\n    not be used in the model\n\n    --Train & save a Gradient Boosted Tree model--\n    classification.py -s /samples.txt -labst float --parameters classifier:Boost -mo /model_boost.xml\n    ... would train and save a Gradient Boosted Tree model with 1000 trees to model_boost.xml\n\n    --Train & save a Support Vector Machine model--\n    classification.py -s /samples.txt --parameters classifier:SVMA -mo /model_svm.xml --scale yes\n    ... would train and save an auto-tuned Support Vector Machine model to model_svm.xml\n\n    --Train & save a Cubist regression model--\n    classification.py -s /samples.txt --parameters classifier:Cubist,committees:10,extrap:20 -mo /models/Cubist\n    ... would train and save a Cubist model\n\n    --Train & save a C5 model--\n    classification.py -s /samples.txt --parameters classifier:C5,trials:10,CF:.4 -mo /models/C5\n    ... would train and save a C5 model\n\n    =======\n    Mapping\n    =======\n\n    --Load model & map image--\n    classification.py -mi /model_rf.xml -i /image_feas.tif -o /mapped_image.tif\n    ... would load a Random Forest model and map image image_feas.tif\n\n    --Load model & map image--\n    classification.py -mi /model_rf.xml -ig 5,10,15 -i /image_feas.tif -o /mapped_image.tif\n    ... would load a Random Forest model and map image image_feas.tif, ignore the 5th, 10th, and 15th image layer during\n    the classification process.\n\n    --Load model & map image--\n    classification.py -mi /model_rf.xml -i /image_feas.tif -o /mapped_image.tif --rank RF -rankt /ranked_feas.txt --accuracy /accuracy.txt\n    ... would load a Random Forest model, map image image_feas.tif, write ranked RF features to text, and write accuracy report to text\n\n    --Load model & map image--\n    classification.py -mi /model_Cubist -s /samples.txt --parameters classifier:Cubist -i /image_feas.tif -o /mapped_image.tif\n    ... would load a Cubist model model and map image image_feas.tif\n\n    ==================\n    Ranking & Accuracy\n    ==================\n\n    --Rank and subset features--\n    classification.py -s /samples.txt -i /image_feas.tif -or /image_feas_ranked.vrt --rank chi2\n    ... would rank image features with Chi^2 and write to image_feas_ranked.vrt\n\n    --Test model accuracy--\n    classification.py -s /samples.txt --accuracy /accuracy.txt -mi /model_rf.xml\n    ... would test the accuracy of model_rf.xml on 10% of data randomly sampled\n\n    --Test model accuracy--\n    classification.py -s /samples.txt --accuracy /accuracy.txt -mi /model_rf.xml -p .5\n    ... would test the accuracy of model_rf.xml on 50% of data randomly sampled\n\n    --Test model accuracy--\n    classification.py -s /samples.txt --accuracy /accuracy.txt\n    ... would test the accuracy of a new RF model on 10% of data withheld from training\n    \"\"\")\n\n\ndef _usage():\n\n    sys.exit(\"\"\"\\\n\n    classification.py ...\n\n    PRE-PROCESSING\n    ==============\n        [-s <Training samples (str) :: Default=None>]\n        [-labst <Labels type (int or float) (str) :: Default=int>]\n            *For reading samples (-s)\n        [-p <Percent to sample from all samples (float) :: Default=.9>]\n        [-pe <Percent to sample from each class (float) :: Default=None>]\n            *Overrides -p\n        [--subs <Dictionary of samples per class (dict) in place of -p :: Default={}>]\n        [--recode <Dictionary recoded class values (dict) :: Default={}>]\n        [-clrm <Classes to remove from data (int list) :: Default=[]>]\n        [-valrm <Values, based on feature, to remove from data (val,fea) :: Default=[]>]\n        [-ig <Features to ignore (int list) :: Default=[]>]\n        [-xy <Use x, y coordinates as predictive variables? :: Default=no>]\n        [--outliers <Remove outliers (str) :: Default=no>]\n        [--loc_outliers <Locate outliers and do not remove (str) :: Default=no>]\n        [--scale <Scale data (str) :: Default=no>]\n        [--semi <Semi supervised labeling (str) :: Default=no>]\n        [--visualize <Visualize data in feature space on two or three features (fea1,fea2,OPTfea3) :: Default=[]>]\n        [--decision <Visualize the decision function on two features (fea1,fea2,class,compare--1 or 2) :: Default=[]>]\n    MODEL\n    =====\n        [--parameters <Classifier & parameters (str) :: Default=classifier:RF>]\n            *Use --parameters key1:parameter,key2:parameter except with --majority, where classifiers:RF-SVM-EX_RF-Bayes, e.g.\n        [-mi <Input model (str) :: Default=None>]\n        [-mo <Output model (str), .txt for Scikit models, .xml for OpenCV models :: Default=None>]\n        [--accuracy <Accuracy of test samples withheld from training (str) :: Default=Automatic with -mo>]\n        [-probs <Get class probability layers instead of labels (str) :: Default=no>]\n        [--rank <Rank method (str) :: Default=None>]\n        [-topf <Number of top features to subset (int -total- or float -percentage-) :: Default=1.>]\n        [-or <Output ranked image (str) :: Default=None>]\n        [-rankt <Write ranked features to rankt text (str) :: Default=None>]\n        [--optimize <Optimize parameters (str) :: Default=no>]\n    MAPS\n    ====\n        [--majority <Rank the majority classification with --parameters classifiers:cl1-cl2-cl3-etc (str) :: Default=no>]\n        [-i <Input image to classify (str) :: Default=None>]\n        [-o <Output classified image (str) :: Default=None>]\n        [-addl <Additional images to use in the model (str list) :: Default=[]>]\n        [--jobs <Number of jobs for parallel mapping, with --multi (int) :: Default=-1>]\n        [-c <Chunk size for parallel mapping (int) :: Default=8000>]\n        [-bc <Band to check for zeros (int) :: Default=-1]\n    [-h <Prints this dialogue>\n    [--options <Print list of classifier options>]\n    [-e <Prints examples>\n\n    \"\"\")\n\n\ndef main():\n\n    argv = sys.argv\n\n    if argv is None:\n        sys.exit(0)\n\n    samples = None\n    img = None\n    out_img = None\n    out_img_rank = None\n    input_model = None\n    output_model = None\n    perc_samp = 0.9\n    perc_samp_each = 0\n    scale_data = False\n    labs_type = 'int'\n    class_subs = dict()\n    recode_dict = dict()\n    classes2remove = list()\n    valrm_fea = list()\n    ignore_feas = list()\n    use_xy = False\n    outrm = False\n    locate_outliers = False\n    semi = False\n    semi_kernel = 'knn'\n    feature_space = list()\n    decision_function = list()\n    header = True\n    norm_struct = True\n    classifier_info = {'classifier': 'rf'}\n    var_imp = True\n    rank_method = None\n    top_feas = 1.\n    out_acc = None\n    get_majority = False\n    optimize = False\n    rank_txt = None\n    get_probs = False\n    additional_layers = list()\n    n_jobs = -1\n    band_check = -1\n    chunk_size = 8000\n\n    i = 1\n    while i < len(argv):\n\n        arg = argv[i]\n\n        if arg == '-i':\n            i += 1\n            img = argv[i]\n\n        elif arg == '-o':\n            i += 1\n            out_img = argv[i]\n\n        elif arg == '-or':\n            i += 1\n            out_img_rank = argv[i]\n\n        elif arg == '-s':\n            i += 1\n            samples = argv[i]\n\n        elif arg == '--scale':\n            i += 1\n            scale_data = argv[i]\n\n            if scale_data == 'yes':\n                scale_data = True\n\n        elif arg == '--parameters':\n            i += 1\n\n            classifier_info = argv[i]\n            classifier_info = classifier_info.split(',')\n\n            info_dict = '{'\n            cli_ctr = 1\n            for cli in classifier_info:\n\n                cli_split = cli.split(':')\n\n                if 'classifiers' in cli:\n                    if cli_ctr == len(classifier_info):\n                        info_dict = \"%s'%s':%s\" % (info_dict, cli_split[0], cli_split[1].split('-'))\n                    else:\n                        info_dict = \"%s'%s':%s,\" % (info_dict, cli_split[0], cli_split[1].split('-'))\n                elif cli_ctr == len(classifier_info):\n                    info_dict = \"%s'%s':'%s'\" % (info_dict, cli_split[0], cli_split[1])\n                else:\n                    info_dict = \"%s'%s':'%s',\" % (info_dict, cli_split[0], cli_split[1])\n\n                cli_ctr += 1\n\n            info_dict = '%s}' % info_dict\n\n            classifier_info = ast.literal_eval(info_dict)\n\n            # convert values to integers\n            for key in classifier_info:\n                is_int = False\n                try:\n                    classifier_info[key] = int(classifier_info[key])\n                    is_int = True\n                except:\n                    pass\n\n                if not is_int:\n                    try:\n                        classifier_info[key] = float(classifier_info[key])\n                    except:\n                        pass\n\n        elif arg == '-p':\n            i += 1\n            perc_samp = float(argv[i])\n\n        elif arg == '-pe':\n            i += 1\n            perc_samp_each = float(argv[i])\n\n        elif arg == '--subs':\n            i += 1\n\n            class_subs = ''.join(argv[i])\n            class_subs = '{%s}' % class_subs\n\n            class_subs = ast.literal_eval(class_subs)\n\n        elif arg == '--recode':\n            i += 1\n\n            recode_dict = ''.join(argv[i])\n            recode_dict = '{%s}' % recode_dict\n\n            recode_dict = ast.literal_eval(recode_dict)\n\n        elif arg == '-clrm':\n            i += 1\n            classes2remove = argv[i].split(',')\n            classes2remove = list(map(int, classes2remove))\n\n        elif arg == '-valrm':\n            i += 1\n            valrm_fea = argv[i].split(',')\n            valrm_fea = list(map(int, valrm_fea))\n\n        elif arg == '-ig':\n            i += 1\n            ignore_feas = argv[i].split(',')\n            ignore_feas = list(map(int, ignore_feas))\n\n        elif arg == '-xy':\n            i += 1\n            use_xy = argv[i]\n            if use_xy == 'yes':\n                use_xy = True\n\n        elif arg == '--outliers':\n            i += 1\n            outrm = argv[i]\n            if outrm == 'yes':\n                outrm = True\n\n        elif arg == '--loc_outliers':\n            i += 1\n            locate_outliers = argv[i]\n            if locate_outliers == 'yes':\n                locate_outliers = True\n\n        elif arg == '--semi':\n            i += 1\n            semi = argv[i]\n            if semi == 'yes':\n                semi = True\n\n        elif arg == '-semik':\n            i += 1\n            semi_kernel = argv[i]\n\n        elif arg == '--visualize':\n            i += 1\n            feature_space = argv[i].split(',')\n            feature_space = list(map(int, feature_space))\n\n        elif arg == '--decision':\n            i += 1\n            decision_function = argv[i].split(',')\n            decision_function = list(map(int, decision_function))\n\n        elif arg == '--optimize':\n            i += 1\n            if argv[i] == 'yes':\n                optimize = True\n\n        elif arg == '-mi':\n            i += 1\n            input_model = argv[i]\n\n        elif arg == '-mo':\n            i += 1\n            output_model = argv[i]\n\n        elif arg == '--rank':\n            i += 1\n            rank_method = argv[i]\n\n        elif arg == '-rankt':\n            i += 1\n            rank_txt = argv[i]\n\n        elif arg == '-labst':\n            i += 1\n            labs_type = argv[i]\n\n        elif arg == '-topf':\n            i += 1\n            top_feas = argv[i]\n\n            if '.' in top_feas:\n                top_feas = float(top_feas)\n            else:\n                top_feas = int(top_feas)\n\n        elif arg == '--accuracy':\n            i += 1\n            out_acc = argv[i]\n\n        elif arg == '--majority':\n            i += 1\n            get_majority = argv[i]\n            if get_majority == 'yes':\n                get_majority = True\n\n        elif arg == '-probs':\n            i += 1\n            get_probs = argv[i]\n\n            if get_probs == 'yes':\n                get_probs = True\n\n        elif arg == '-addl':\n            i += 1\n            additional_layers = argv[i].split(',')\n\n        elif arg == '--jobs':\n            i += 1\n            n_jobs = int(argv[i])\n\n        elif arg == '-bc':\n            i += 1\n            band_check = int(argv[i])\n\n        elif arg == '-c':\n            i += 1\n            chunk_size = int(argv[i])\n\n        elif arg == '-h':\n            _usage()\n\n        elif arg == '-e':\n            _examples()\n\n        elif arg == '--options':\n            _options()\n\n        elif arg[:1] == ':':\n            logger.info('  Unrecognized command option: %s' % arg)\n            _usage()\n\n        i += 1\n\n    logger.info('\\nStart date & time --- (%s)\\n' % time.asctime(time.localtime(time.time())))\n\n    start_time = time.time()\n\n    try:\n        dummy = classifier_info['classifier']\n    except:\n        classifier_info['classifier'] = 'rf'\n\n    if 'cubist' in classifier_info['classifier'] or 'c5' in classifier_info['classifier']:\n\n        # create the C5/Cubist object\n        cl = c5_cubist()\n\n        if samples:\n\n            if rank_method:\n                scale_data = True\n\n            cl.split_samples(samples, perc_samp=perc_samp, perc_samp_each=perc_samp_each, scale_data=scale_data, \\\n                           class_subs=class_subs, header=header, norm_struct=norm_struct, labs_type=labs_type, \\\n                           recode_dict=recode_dict, classes2remove=classes2remove, ignore_feas=ignore_feas)\n\n            if valrm_fea:\n                cl.remove_values(valrm_fea[0], valrm_fea[1])\n\n            if outrm:\n                cl.remove_outliers(locate_only=locate_outliers)\n\n        # train the model\n        if output_model:\n\n            # train the C5/Cubist model\n            cl.train_c5_cubist(samples, output_model, classifier_info=classifier_info)\n\n            # cl = classification()\n\n            # cl.split_samples(samples, perc_samp=perc_samp, header=header, norm_struct=norm_struct, labs_type='float')\n\n            # out_acc = '%s/%s_acc.txt' % (c5_cubist.model_dir, c5_cubist.model_base)\n\n            # cl.test_accuracy(out_acc=out_acc, discrete=False)\n\n        # predict labels\n        if input_model and out_img:\n\n            cl.map_labels_c5_cubist(input_model, samples, img, out_img, tree_model=classifier_info['classifier'])\n\n    else:\n\n        # create the classifier object\n        cl = classification()\n\n        # get predictive variables and class labels data\n        if optimize:\n\n            cl.optimize_parameters(samples, classifier_info, perc_samp, max_depth_range=(1, 100), k_folds=optimize)\n\n            logger.info('  The optimum depth was %d' % cl.opt_depth)\n            logger.info('  The maximum accuracy was %f' % cl.max_acc)\n\n        if samples:\n\n            if rank_method:\n                scale_data = True\n\n            cl.split_samples(samples, perc_samp=perc_samp, perc_samp_each=perc_samp_each, scale_data=scale_data, \\\n                           class_subs=class_subs, header=header, norm_struct=norm_struct, labs_type=labs_type, \\\n                           recode_dict=recode_dict, classes2remove=classes2remove, ignore_feas=ignore_feas, \\\n                           use_xy=use_xy)\n\n            if feature_space:\n\n                if len(feature_space) == 3:\n                    fea_z = feature_space[2]\n                else:\n                    fea_z = None\n\n                if semi:\n                    # classified_labels = np.where(cl.labels != -1)\n                    classified_labels = None\n                else:\n                    classified_labels = None\n\n                cl.vis_data(feature_space[0], feature_space[1], fea_3=fea_z, labels=classified_labels)\n\n            if valrm_fea:\n\n                cl.remove_values(valrm_fea[0], valrm_fea[1])\n\n            if semi:\n\n                cl.semi_supervised(classifier_info, kernel=semi_kernel)\n\n                if feature_space:\n\n                    if len(feature_space) == 3:\n                        fea_z = feature_space[2]\n                    else:\n                        fea_z = None\n\n                    cl.vis_data(feature_space[0], feature_space[1], fea_3=fea_z, labels=classified_labels)\n\n            if outrm:\n\n                cl.remove_outliers(locate_only=locate_outliers)\n\n                if feature_space:\n\n                    if len(feature_space) == 3:\n                        fea_z = feature_space[2]\n                    else:\n                        fea_z = None\n\n                    cl.vis_data(feature_space[0], feature_space[1], fea_3=fea_z, labels=classified_labels)\n\n            if decision_function:\n\n                cl.vis_decision(decision_function[0], decision_function[1], classifier_info=classifier_info,\n                                class2check=decision_function[2], compare=decision_function[3],\n                                locate_outliers=locate_outliers)\n\n        if get_majority:\n\n            cl.stack_majority(img, output_model, out_img, classifier_info, scale_data, ignore_feas=ignore_feas)\n\n        if input_model or output_model or img or (rank_method == 'rf') or out_acc and not get_majority and \\\n                (rank_method != 'chi2'):\n\n            cl.construct_model(input_model=input_model, output_model=output_model, classifier_info=classifier_info,\n                               var_imp=var_imp, rank_method=rank_method, top_feas=top_feas, get_probs=get_probs)\n\n            if out_acc:\n                cl.test_accuracy(out_acc=out_acc)\n\n        if rank_method:\n\n            cl.rank_feas(rank_text=rank_txt, rank_method=rank_method, top_feas=top_feas)\n\n        if out_img and not get_majority:\n\n            # apply classification model to map image class labels\n            cl.predict(img, out_img, additional_layers=additional_layers, n_jobs=n_jobs, band_check=band_check,\n                       scale_data=scale_data, ignore_feas=ignore_feas, chunk_size=chunk_size, use_xy=use_xy)\n\n        if out_img_rank:\n\n            cl.sub_feas(img, out_img_rank)\n\n    logger.info('\\nEnd data & time -- (%s)\\nTotal processing time -- (%.2gs)\\n' %\n                (time.asctime(time.localtime(time.time())), (time.time()-start_time)))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"jgrss/mpglue","sub_path":"mpglue/classification/classification.py","file_name":"classification.py","file_ext":"py","file_size_in_byte":346719,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"85"}
+{"seq_id":"42308072049","text":"\"\"\"\nBased on zip file output of fastqc, creates a PDF summary of \nthe data file and extracts the data file for later storage in \nthe archive\n\nDependencies:\n    * R/ggplot2.\n    * Latex\n\"\"\"\n\nimport sys\nimport signal\nimport os\nimport re\nimport string\nimport tempfile\nimport logging\nimport shutil\nimport zipfile\nimport subprocess\nimport argparse\nfrom gosr.common import arghelpers\n\n#================================================================================\n# DATA\n#================================================================================\ndoc = string.Template(r\"\"\"\n\\documentclass{article}\n\\usepackage{fullpage}\n\\usepackage[usenames,dvipsnames]{color}\n\\usepackage{bookman}\n\\usepackage{rotating}\n\n\\newcommand{\\pass}{\\large{\\textbf{\\textcolor{Green}{PASS}}}}\n\\newcommand{\\warn}{\\large{\\textbf{\\textcolor{Orange}{WARN}}}}\n\\newcommand{\\fail}{\\large{\\textbf{\\textcolor{Red}{FAIL}}}}\n\n\\title{Run $safe_runid}\n\\author{gosr fastqc2pdf}\n\n\\begin{document}\n\\renewcommand*\\listfigurename{Summary}\n\\maketitle\n\\listoffigures\n%% ===========================================================================================\n<<echo=F>>=\nlibrary(ggplot2)\nlibrary(plyr)\nlibrary(reshape)\nlibrary(scales)\n\ntheme.qc                  <- theme_bw(12)\ntheme.qc$panel.border     <- theme_rect(fill = NA, size = 1, col = \"black\")\ntheme.qc$panel.grid.minor <- theme_blank()\ntheme.qc$panel.grid.major <- theme_blank()\n\nmycol <- list(yellow = rgb(1, 1, 157/255))\n@\n\\section*{Module results}\n$figures\n\n\\end{document}\n\"\"\")\n#================================================================================\n# CODE\n#================================================================================\n\ndef basic_statistics(status, datastr, tempdir):\n    \"\"\"render the Basic Statistic module for sweave\"\"\"\n    safe_datastr = datastr.replace(\"_\", \"\\_\")\n    lines = safe_datastr.split(\"\\n\")\n    fields = [c.strip().split(\"\\t\") for c in lines \\\n              if not (c.strip() == \"\" or c.startswith(\"#\"))]\n    table = \"\\n\".join(r\"%s & %s \\\\\" % (a, b) for a, b in fields)\n    section = string.Template(r\"\"\"\n\\begin{figure}[h!]\\centering\n\\begin{tabular}{rl}\n\\hline \\\\\n$table\n\\hline\n\\end{tabular}\n\\caption[\\$status~~Basic statistics]{\\$status~~Basic statistics}\n\\end{figure}\"\"\")\n    return section.substitute(locals())\n\ndef per_base_sequence_quality(status, datastr, tempdir):\n    \"\"\"render the per base sequence quality module for sweave.\n    In some cases, fastqc does not do a boxplot for each base, \n    so only start and end of the read are labeled\"\"\"\n    datafile = os.path.join(tempdir, \"perbase.tsv\")\n    plotfile = os.path.join(tempdir, \"perbase.pdf\")\n    with open(datafile, \"w\") as df:\n        lines = datastr.split(\"\\n\")\n        lines[0] = \"base\\tmean\\tmedian\\tlq\\tuq\\tp10\\tp90\"\n        for line in lines:\n            df.write(line)\n            df.write(\"\\n\")\n    section = string.Template(r\"\"\"\n\\begin{figure}[h!]\\centering\n<<perBaseQual,echo=F>>=\ndf <- read.table(\"$datafile\", sep = \"\\t\", header = T)\np<- ggplot(df) +\n    geom_boxplot(aes(x = factor(base), lower = lq, upper = uq,\n        ymin = p10, ymax = p90, middle = median), \n        stat = \"identity\", fill = \"grey80\") +\n    geom_line(aes(x = base, y = mean), col = \"blue\", size = 1) +\n    scale_x_discrete(\"Position\", breaks = c(1, max(df$base)),\n        labels = c(\"Start\", \"End\")) +\n    ylab(\"Quality\") +\n    ylim(0, 40) +\n    theme.qc\nggsave(\"$plotfile\", p, width = 6, height = 3)\n@\n\\includegraphics[width=6in, height=3in]{$plotfile}\n\\caption[\\$status~~Per base sequence quality]{\\$status~~Per base sequence\nquality.  Range of quality values; Box spans 25$^{th}$ to 75$^{th}$ percentiles;\nlines span 10$^{th}$ to 90$^{th}$ percentile; blue line is mean}\n\\end{figure}\"\"\")\n    return section.safe_substitute(locals())\n\ndef per_sequence_quality_scores(status, datastr, tempdir):\n    datafile = os.path.join(tempdir, \"perseq.tsv\")\n    plotfile = os.path.join(tempdir, \"perseq.pdf\")\n    with open(datafile, \"w\") as df:\n        lines = datastr.split(\"\\n\")\n        lines[0] = \"quality\\tn\"\n        for line in lines:\n            df.write(line)\n            df.write(\"\\n\")\n    section = string.Template(r\"\"\"\n\\begin{figure}[h!]\\centering\n<<perSeqQual,echo=F>>=\ndf <- read.table(\"$datafile\", sep = \"\\t\", header = T)\np <- ggplot(df) +\n    geom_ribbon(aes(quality, ymin = 0, ymax = n), fill = \"grey80\",\n        col = \"black\") +\n    ylab(\"Frequency\") +\n    xlab(\"Quality\") +\n    theme.qc\nggsave(\"$plotfile\", p, width = 6, height = 3)\n@\n\\includegraphics[width=6in, height=3in]{$plotfile}\n\\caption{\\$status~~Per sequence quality scores.}\n\\end{figure}\"\"\")\n    return section.safe_substitute(locals())\n\ndef per_base_sequence_content(status, datastr, tempdir):\n    datafile = os.path.join(tempdir, \"perbasecont.tsv\")\n    plotfile = os.path.join(tempdir, \"perbasecont.pdf\")\n    with open(datafile, \"w\") as df:\n        lines = datastr.split(\"\\n\")\n        lines[0] = \"pos\\tG\\tA\\tT\\tC\"\n        for line in lines:\n            df.write(line)\n            df.write(\"\\n\")\n    section = string.Template(r\"\"\"\n\\begin{figure}[h!]\\centering\n<<perBaseContent,echo=F>>=\ndf <- read.table(\"$datafile\", sep = \"\\t\", header = T)\ndf <- cbind(pos = df[, 1], sweep(df[, 2:5], 1, rowSums(df[, 2:5]), \"/\"))\ndf <- melt(df, id = \"pos\")\np <- ggplot(df) +\n    geom_line(aes(pos, value, col = variable), size = 1) +\n    xlab(\"Position\") +\n    scale_y_continuous(\"Fraction\", labels = percent,\n        limits = c(0, 0.5)) +\n    scale_color_manual(\"\", values = c(T = \"red\", C = \"blue\", \n        A = \"green\", G = \"black\")) +\n    theme.qc\nggsave(\"$plotfile\", p, width = 6, height = 3)\n@\n\\includegraphics[width=6in, height=3in]{$plotfile}\n\\caption{\\$status~~Per base sequence content}\n\\end{figure}\"\"\")\n    return section.safe_substitute(locals())\n\ndef per_base_gc_content(status, datastr, tempdir):\n    datafile = os.path.join(tempdir, \"perbasegc.tsv\")\n    plotfile = os.path.join(tempdir, \"perbasegc.pdf\")\n    with open(datafile, \"w\") as df:\n        lines = datastr.split(\"\\n\")\n        lines[0] = \"pos\\tGC\"\n        for line in lines:\n            df.write(line)\n            df.write(\"\\n\")\n    section = string.Template(r\"\"\"\n\\begin{figure}[h!]\\centering\n<<perBaseGC,echo=F>>=\ndf <- read.table(\"$datafile\", sep = \"\\t\", header = T)\np <- ggplot(df) +\n    geom_line(aes(pos, GC), size = 1) +\n    xlab(\"Position\") +\n    ylab(\"GC content [%]\") +\n    ylim(0, 100) +\n    theme.qc\nggsave(\"$plotfile\", p, width = 6, height = 3)\n@\n\\includegraphics[width=6in, height=3in]{$plotfile}\n\\caption{\\$status~~Per base GC content}\n\\end{figure}\"\"\")\n    return section.safe_substitute(locals())\n\ndef per_sequence_gc_content(status, datastr, tempdir):\n    datafile = os.path.join(tempdir, \"perseqgc.tsv\")\n    plotfile = os.path.join(tempdir, \"perseqgc.pdf\")\n    with open(datafile, \"w\") as df:\n        lines = datastr.split(\"\\n\")\n        lines[0] = \"GC\\tn\"\n        for line in lines:\n            df.write(line)\n            df.write(\"\\n\")\n    section = string.Template(r\"\"\"\n\\begin{figure}[h!]\\centering\n<<perSeqGC,echo=F>>=\ndf <- read.table(\"$datafile\", sep = \"\\t\", header = T)\ntotal <- sum(df$n)\nmeanGC <- sum(df$n * df$GC) / total\nsdGC   <- sqrt(sum((df$GC - meanGC) ^ 2 * df$n / total))\ndf$theoretical <- dnorm(df$GC, meanGC, sdGC) * total\np <- ggplot(df) +\n    geom_bar(aes(GC, n), stat = \"identity\", fill = \"grey80\", col = \"black\") +\n    geom_line(aes(GC, theoretical), col = \"blue\", size = 1) +\n    xlab(\"GC content [%]\") +\n    ylab(\"Observations\") +\n    theme.qc\nggsave(\"$plotfile\", p, width = 6, height = 3)\n@\n\\includegraphics[width=6in, height=3in]{$plotfile}\n\\caption[\\$status~~Per sequence GC content]{\\$status~~Per sequence GC content;\nObserved: bar graph; theoretical: blue line}\n\\end{figure}\"\"\")\n    return section.safe_substitute(locals())\n\ndef per_base_n_content(status, datastr, tempdir):\n    datafile = os.path.join(tempdir, \"perbasen.tsv\")\n    plotfile = os.path.join(tempdir, \"perbasen.pdf\")\n    with open(datafile, \"w\") as df:\n        lines = datastr.split(\"\\n\")\n        lines[0] = \"base\\tnn\"\n        for line in lines:\n            df.write(line)\n            df.write(\"\\n\")\n    section = string.Template(r\"\"\"\n\\begin{figure}[h!]\\centering\n<<perBaseN,echo=F>>=\ndf <- read.table(\"$datafile\", sep = \"\\t\", header = T)\np <- ggplot(df) +\n    geom_line(aes(base, nn), size = 1) +\n    scale_y_continuous(\"N [%]\", limits = c(0, 100)) +\n    xlab(\"Position\") +\n    theme.qc\nggsave(\"$plotfile\", p, width = 6, height = 3)\n@\n\\includegraphics[width=6in, height=3in]{$plotfile}\n\\caption{\\$status~~Per base N content}\n\\end{figure}\"\"\")\n    return section.safe_substitute(locals())\n\ndef sequence_length_distribution(status, datastr, tempdir):\n    datafile = os.path.join(tempdir, \"seqlen.tsv\")\n    plotfile = os.path.join(tempdir, \"seqlen.pdf\")\n    with open(datafile, \"w\") as df:\n        lines = datastr.split(\"\\n\")\n        lines[0] = \"len\\tn\"\n        for line in lines:\n            df.write(line)\n            df.write(\"\\n\")\n    section = string.Template(r\"\"\"\n\\begin{figure}[h!]\\centering\n<<seqlen,echo=F>>=\ndf <- read.table(\"$datafile\", sep = \"\\t\", header = T)\ndf <- rbind(c(min(df$len) - 1, 0), df, c(max(df$len) + 1))\np <- ggplot(df) +\n    geom_bar(aes(len, n), stat = \"identity\", fill = \"grey80\", col = \"black\") +\n    ylab(\"Observations\") +\n    xlab(\"Sequence length\") + \n    theme.qc\nggsave(\"$plotfile\", p, width = 6, height = 3)\n@\n\\includegraphics[width=6in, height=3in]{$plotfile}\n\\caption{\\$status~~Distribution of sequence lengths in run}\n\\end{figure}\"\"\")\n    return section.safe_substitute(locals())\n\ndef sequence_duplication_level(status, datastr, tempdir):\n    datafile = os.path.join(tempdir, \"dup.tsv\")\n    plotfile = os.path.join(tempdir, \"dup.pdf\")\n    with open(datafile, \"w\") as df:\n        lines = datastr.split(\"\\n\")\n        total_dup = float(lines.pop(0).split()[3])\n        total_dup = \"%.2f%%\" % total_dup\n        lines[0] = \"dup\\tn\"\n        for line in lines:\n            df.write(line)\n            df.write(\"\\n\")\n    section = string.Template(r\"\"\"\n\\begin{figure}[h!]\\centering\n<<seqDup,echo=F>>=\ndf <- read.table(\"$datafile\", sep = \"\\t\", header = T)\ndf$dupn <- 1:nrow(df)\np <- ggplot(df) +\n    geom_line(aes(dupn, n), size = 1) +\n    geom_point(aes(dupn, n), shape = 21, fill = \"white\",\n        col = \"black\", size = 2) +\n    ylab(\"Relative abundance\") +\n    scale_x_continuous(\"Duplication level\", breaks = df$dupn,\n        labels = df$dup) +\n    theme.qc +\n    opts(title=\"Sequence duplication level >= $total_dup\")\nggsave(\"$plotfile\", p, width = 6, height = 3)\n@\n\\includegraphics[width=6in, height=3in]{$plotfile}\n\\caption[\\$status~~Sequence duplication level]{\\$status~~Sequence duplication\nlevel.  This module counts the degree of duplication for every sequence in the\nset and creates a plot showing the relative number of sequences with different\ndegrees of duplication.  Only sequences that occur in the first 200,000\nsequences are considered to conserve memory. The last bin contains any sequence\nwith 10 \\emph{or more} duplicates. Reads longer than 75nts are truncated to\n50nts.}\n\\end{figure}\"\"\")\n    return section.safe_substitute(locals())\n\ndef overrepresented_sequences(status, datastr, tempdir):\n    safe_datastr = datastr.replace(\"_\", r\"\\_\").replace(\"%\", r\"\\%\")\n    lines = safe_datastr.split(\"\\n\")\n    header = \" & \".join(lines.pop(0)[1:].strip().split(\"\\t\")) + r\"\\\\ \\hline\"\n    fields = [c.strip().split(\"\\t\") for c in lines \\\n              if not c.strip() == \"\"]\n    tablelist = [header]\n    for f in fields:\n        f[2] = \"%.2f\" % float(f[2])\n        tablelist.append(\" & \".join(f) + r\"\\\\\")\n    table = \"\\n\".join(tablelist)\n    section = string.Template(r\"\"\"\n\\begin{sidewaysfigure}[h!]\\centering{\\tiny\n\\begin{tabular}{rrrp{2in}}\n\\hline \\\\\n$table\n\\hline\n\\end{tabular}}\n\\caption{\\$status~~Overrepresented sequences}\n\\end{sidewaysfigure}\"\"\")\n    return section.substitute(locals())\n\n\ndef kmer_content(status, datastr, tempdir):\n    safe_datastr = datastr.replace(\"_\", r\"\\_\").replace(\"%\", r\"\\%\")\n    lines = safe_datastr.split(\"\\n\")\n    header = \" & \".join(lines.pop(0)[1:].strip().split(\"\\t\")) + r\"\\\\ \\hline\"\n    fields = [c.strip().split(\"\\t\") for c in lines \\\n              if not c.strip() == \"\"]\n    tablelist = [header]\n    for f in fields:\n        f[2] = \"%.2f\" % float(f[2])\n        f[3] = \"%.2f\" % float(f[3])\n        tablelist.append(\" & \".join(f) + r\"\\\\\")\n    table = \"\\n\".join(tablelist)\n    section = string.Template(r\"\"\"\n\\begin{figure}[h!]\\centering{\\tiny\n\\begin{tabular}{rrrrr}\n\\hline \\\\\n$table\n\\hline\n\\end{tabular}}\n\\caption{\\$status~~Kmer content}\n\\end{figure}\"\"\")\n    return section.substitute(locals())\n\n\nmodules = {\n    \"Basic Statistics\": basic_statistics,\n    \"Per base sequence quality\": per_base_sequence_quality,\n    \"Per sequence quality scores\": per_sequence_quality_scores,\n    \"Per base sequence content\": per_base_sequence_content,\n    \"Per base GC content\": per_base_gc_content,\n    \"Per sequence GC content\": per_sequence_gc_content,\n    \"Per base N content\": per_base_n_content,\n    #\"Sequence Length Distribution\": sequence_length_distribution,\n    \"Sequence Duplication Levels\": sequence_duplication_level,\n    \"Overrepresented sequences\": overrepresented_sequences,\n    \"Kmer Content\": kmer_content\n}\n\ndef prepare_tempdir(zipfilename, datafilename):\n    \"\"\"set up the temp directory for processing data\"\"\"\n    tempdir    = tempfile.mkdtemp()\n    sys.exitfunc = lambda: shutil.rmtree(tempdir)\n    \n    zipf     = zipfile.ZipFile(zipfilename, \"r\")\n    datafile_zip_path = None\n    for zipf_member in zipf.namelist():\n        if zipf_member.endswith(datafilename):\n            datafile_zip_path = zipf_member\n            break\n    if datafile_zip_path is None:    \n        logging.error(\"zip file did not contain a '%s' file\", datafilename)\n        zipf.close()\n        sys.exit(1)\n    datafile_path = os.path.join(tempdir, datafilename)\n    with open(datafile_path, \"w\") as out:\n        out.write(zipf.read(datafile_zip_path))\n    zipf.close()\n\n    data = open(datafile_path, \"rU\").read()\n    return tempdir, datafile_path, data\n\ndef fastqc2latex(args):\n    \"\"\"parses a fastqc data file into modules and yields\n    each module as a (name, pass/fail, data) tuple\"\"\"\n    runid      = args.out_prefix\n    safe_runid = args.out_prefix.replace(\"_\", r\"\\_\")\n    datafile   = \"fastqc_data.txt\"\n    tempdir, datafile_path, data = prepare_tempdir(args.fastqc, datafile)\n    \n    # create .rnw file\n    module = re.compile(r\">>(.+?)\\s+(pass|fail|warn)\\n(.*?)>>END_MODULE\", re.DOTALL)\n    figures = []\n    for module_name, status, data in module.findall(data):\n        try:\n            figures.append(modules[module_name](status, data, tempdir))\n        except KeyError:\n            logging.warn(\"No function for processing module %s registered\",\n                    module_name)\n            continue\n    figures       = \"\\n\\n\".join(figures)\n    rnw_file_path = os.path.join(tempdir, \"%s.rnw\" % runid)\n    with open(rnw_file_path, \"w\") as out:\n        out.write(doc.safe_substitute(locals()))\n    \n    # create PDF file from .rnw\n    sweave_cmd = \"env SWEAVE_STYLEPATH_DEFAULT=TRUE R CMD Sweave {0}.rnw \" + \\\n            \"&& pdflatex {0}.tex && \" + \\\n            \"pdflatex {0}.tex && pdflatex {0}.tex\"\n    sweave = subprocess.Popen(sweave_cmd.format(runid), shell = True,\n            close_fds = True, cwd = tempdir,\n            stdout = subprocess.PIPE, stderr = subprocess.PIPE)\n    # trap SIGINT\n    def exit_nicely(signal, frame):\n        logging.warn(\"Received signal %d; terminating subprocess and exiting\",\n                signal)\n        sweave.terminate()\n        sys.exit(1)\n    signal.signal(signal.SIGINT, exit_nicely)\n    sweave_out, sweave_err = sweave.communicate()\n    if sweave.returncode != 0:\n        logging.error(\"External sweave/pdflatex failed with exit code %s\", sweave.returncode)\n        print >>sys.stderr, sweave_out\n        print >>sys.stderr, sweave_err\n        sys.exit(1)\n    else:\n        shutil.move(os.path.join(tempdir, \"%s.pdf\" % runid), \n            os.path.join(args.dir, \"%s.pdf\" % runid))\n        shutil.move(datafile_path, os.path.join(args.dir, \"%s.fastc_data.txt\" % runid))\n\n\n\n#===============================================================================\n# tool interface\n#===============================================================================\n\ndef setup(commands):\n    \"\"\"set up command line parser\"\"\"\n    cmdline = commands.add_parser(\"fastqc2pdf\",\n            help = \"\"\"Fastqc zip file to PDF converter\"\"\",\n            formatter_class = argparse.RawDescriptionHelpFormatter,\n            description     = __doc__)\n    cmdline.add_argument(\"fastqc\", type = arghelpers.infilename_check,\n            help = \"Fastqc zip file\")\n    cmdline.add_argument(\"out_prefix\", \n            help = \"prefix of output file names\")\n    cmdline.add_argument(\"--dir\", \"-d\", type = arghelpers.check_or_make_dir,\n            help = \"output directory if other than current working directory;\\\n            will be created if it does not exist [%(default)s]\",\n            default = \"./\")\n    cmdline.set_defaults(func = fastqc2latex)\n\n\n","repo_name":"wresch/gosr","sub_path":"gosr/tools/fastqc2pdf.py","file_name":"fastqc2pdf.py","file_ext":"py","file_size_in_byte":16988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"71787392278","text":"import sys\nimport time\nimport argparse\nimport torch.optim as optim\nfrom tqdm import tqdm\nfrom torch.utils.data import DataLoader\nfrom torch.autograd import Variable\nimport torch.backends.cudnn as cudnn\nfrom utils import torchPSNR, CharbonnierLoss\nfrom WeatherFormer import WeatherFormer\nfrom datasets import *\nimport torch.nn.functional as F\n\n\ndef train(args):\n\n    cudnn.benchmark = True\n    best_psnr = 0\n    best_epoch = 0\n\n    random.seed(args.seed)\n    torch.manual_seed(args.seed)\n    torch.cuda.manual_seed(args.seed)\n    torch.manual_seed(args.seed)\n\n    criterion_char = CharbonnierLoss()\n    if args.cuda:\n        criterion_char = criterion_char.cuda()\n\n    myNet = WeatherFormer()\n    if args.cuda:\n        myNet = myNet.cuda()\n\n    # optimizer\n    optimizer = optim.Adam(myNet.parameters(), lr=args.lr)\n    # schedule\n    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epoch, eta_min=1e-7)\n\n    # training dataset\n    path_train_input, path_train_target = args.train_data + 'input/', args.train_data + 'target/'\n    datasetTrain = MyTrainDataSet(path_train_input, path_train_target, patch_size=args.patch_size)\n    trainLoader = DataLoader(dataset=datasetTrain, batch_size=args.batch_size, shuffle=True,\n                             drop_last=True, num_workers=4, pin_memory=True)\n\n    # validation dataset\n    path_val_input, path_val_target = args.val_data + 'input/', args.val_data + 'target/'\n    datasetValue = MyValueDataSet(path_val_input, path_val_target, patch_size=args.patch_size)\n    valueLoader = DataLoader(dataset=datasetValue, batch_size=args.batch_size, shuffle=True,\n                             drop_last=True, num_workers=4, pin_memory=True)\n\n    # begin training\n    print('-------------------------------------------------------------------------------------------------------')\n    if os.path.exists(args.resume_state):\n        if args.cuda:  # CUDA\n            myNet.load_state_dict(torch.load(args.resume_state))\n        else:  # CPU\n            myNet.load_state_dict(torch.load(args.resume_state, map_location=torch.device('cpu')))\n\n    for epoch in range(args.epoch):\n        myNet.train()\n        iters = tqdm(trainLoader, file=sys.stdout)\n        epochLoss = 0\n        timeStart = time.time()\n        for index, (x, y) in enumerate(iters, 0):\n\n            myNet.zero_grad()\n            optimizer.zero_grad()\n\n            if args.cuda:\n                input_train, target = Variable(x).cuda(), Variable(y).cuda()\n            else:\n                input_train, target = Variable(x), Variable(y)\n\n            output_train, degradation_features = myNet(input_train)\n            l1_loss = F.l1_loss(output_train, target)\n\n            _, degradation_free_features = myNet(target)\n            char_loss = criterion_char(degradation_features, degradation_free_features)\n\n            loss = l1_loss + 0.04 * char_loss\n\n            loss.backward()\n            optimizer.step()\n            epochLoss += loss.item()\n\n            iters.set_description('Training !!!  Epoch %d / %d,  Batch Loss %.6f' % (epoch+1, args.epoch, loss.item()))\n\n        if epoch % args.val_frequency == 0:\n            myNet.eval()\n            psnr_val_rgb = []\n            for index, (x, y) in enumerate(valueLoader, 0):\n                input_, target_value = (x.cuda(), y.cuda()) if args.cuda else (x, y)\n                with torch.no_grad():\n                    output_value, _ = myNet(input_)\n                for output_value, target_value in zip(output_value, target_value):\n                    psnr_val_rgb.append(torchPSNR(output_value, target_value))\n\n            psnr_val_rgb = torch.stack(psnr_val_rgb).mean().item()\n\n            if psnr_val_rgb >= best_psnr:\n                best_psnr = psnr_val_rgb\n                best_epoch = epoch\n                torch.save(myNet.state_dict(), args.save_state)\n        scheduler.step(epoch)\n        timeEnd = time.time()\n        print(\"------------------------------------------------------------\")\n        print(\"Epoch:  {}  Finished,  Time:  {:.4f} s,  Loss:  {:.6f}, current psnr:  {:.3f}, best psnr:  {:.3f}.\".format(epoch+1, timeEnd-timeStart, epochLoss, psnr_val_rgb, best_psnr))\n        print('-------------------------------------------------------------------------------------------------------')\n    print(\"Training Process Finished ! Best Epoch : {} , Best PSNR : {:.2f}\".format(best_epoch, best_psnr))\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--seed', type=int, default=1234)\n    parser.add_argument('--epoch', type=int, default=200)\n    parser.add_argument('--batch_size', type=int, default=32)\n    parser.add_argument('--patch_size', type=int, default=128)\n    parser.add_argument('--lr', type=float, default=2e-4)\n    parser.add_argument('--train_data', type=str, default='./allweather/')\n    parser.add_argument('--val_data', type=str, default='./raindrop_a/')\n    parser.add_argument('--resume_state', type=str, default='./model_pre.pth')\n    parser.add_argument('--save_state', type=str, default='./model_best.pth')\n    parser.add_argument('--cuda', type=bool, default=True)\n    parser.add_argument('--val_frequency', type=int, default=3)\n    args = parser.parse_args()\n\n    train(args)","repo_name":"chdwyb/WeatherFormer","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"72898313239","text":"# show a GUI windows with text input and a button\nimport downloader as dl\nimport analyzer as ay\nimport player as pl\nimport helpers as h\nimport threading\n\nimport tkinter as tk\nfrom tkinter import ttk\n\n# root window\nroot = tk.Tk()\nroot.geometry('500x300')\nroot.title('Lightsaber')\nroot.grid()\nroot.grid_rowconfigure(0, weight=1)\nroot.grid_columnconfigure(0, weight=1)\ntext_box = ttk.Entry(root)\n# make the text box fill the window\n# add text\nlabel = ttk.Label(root, text=\"Enter a song name to analyze:\")\nlabel.grid(row=0, column=0)\ntext_box.insert(0, 'Enter your search query here...')\ntext_box.grid(column=0, row=1, columnspan=2, sticky='we', padx=10, pady=20)\n\npb = ttk.Progressbar(\n    root,\n    orient='horizontal',\n    mode='indeterminate',\n    length=280\n)\n# place the progressbar\npb.grid(column=0, row=2, columnspan=2, padx=10, pady=20)\n  \n  \n  \n  \n#---------------------------------------\n# --------------backend-----------------\n#---------------------------------------\n\ndef on_click(event):\n    if text_box.get() == 'Enter your search query here...':\n        text_box.delete(0, \"end\")\n        text_box.insert(0, '')\n        text_box.config(fg = 'black')\n        \ndef player_interface(file_path):\n    # load visualizer\n    path = h.visualizer(file_path)\n    \n    \n\n\ndef check(t, mode):\n    if t.is_alive():\n        root.after(100, check, t, mode)\n    else:\n        t.join()\n        pb.stop()\n        if mode == \"download\":\n            from downloader import cache_location\n            s = threading.Thread(target=ay.get_beats, args=(cache_location,))\n            s.start()\n            pb.start()\n            root.after(100, check, s, \"analyze\")\n        elif mode == \"analyze\":\n            from analyzer import beats\n            from downloader import cache_location\n            pb.stop()\n            pb.grid_remove()\n            player = threading.Thread(target=pl.main, args=(cache_location, beats))\n            player.start()\n            player_interface(cache_location)\n            \n\ndef start():\n    query = text_box.get()\n    # run in a separate thread\n    query = text_box.get()\n    # run in a separate thread\n    t = threading.Thread(target=dl.download, args=(query,))\n    # change the label text\n    label.config(text=\"Downloading \" + query)\n    t.start()\n    pb.start()\n    # check periodically if the thread is still alive\n    root.after(100, check, t, \"download\")\n        \nclicked = text_box.bind('<Button-1>', on_click)\n\n# start button\n\nstart_button = ttk.Button(root, text=\"Start\", command=start)\nstart_button.grid(column=0, row=2, padx=10, pady=10, sticky=tk.E)\n\n\nroot.mainloop()","repo_name":"valentinfrlch/lightsaber","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"73616969558","text":"import random\nfrom typing import Dict\nfrom typing import List\nfrom typing import Union\n\nfrom django.db import transaction\n\nfrom .models import Order\nfrom .models import OrderProduct\nfrom .models import Product\n\n\ndef recommend_products(product_id: str) -> Dict[str, List[Union[str, int]]]:\n    \"\"\"Return recommended products for a product with the given ID.\"\"\"\n    try:\n        recommendations = {}\n\n        # Retrieve pre-calculated similar products\n        similar_products = get_precomputed_similar_products(product_id)\n        recommendations[\"similar_products\"] = similar_products.values()\n\n        # Retrieve pre-calculated frequently bought together products\n        frequently_bought_together = get_precomputed_frequently_bought_together(\n            product_id\n        )\n        recommendations[\n            \"frequently_bought_together\"\n        ] = frequently_bought_together.values()\n\n        return recommendations\n    except Product.DoesNotExist:\n        return {}\n\n\ndef get_precomputed_similar_products(product_id: str) -> List[Product]:\n    \"\"\"Return similar products for a product with the given ID.\"\"\"\n    product = Product.objects.get(product_id=product_id)\n    similar_products = product.similar_products.all()\n\n    return similar_products\n\n\ndef get_precomputed_frequently_bought_together(product_id: str) -> List[Product]:\n    \"\"\"Return frequently bought together products for a product with the given ID.\"\"\"\n\n    product = Product.objects.get(product_id=product_id)\n    frequently_bought_together = product.frequently_bought_together.all()\n\n    return frequently_bought_together\n\n@transaction.atomic\ndef create_order(\n    *,\n    product_id: str,\n    price: float,\n    currency_code: str,\n    quantity: int,\n    address: str,\n    payment_mode: str\n) -> OrderProduct:\n    \"\"\"Create an order with the given data.\"\"\"\n    product = Product.objects.get(product_id=product_id)\n    order = Order.objects.create(\n        code=random.randint(100000, 999999),\n        address=address,\n        payment_mode=payment_mode,\n    )\n    OrderProduct.objects.create(\n        product=product,\n        order=order,\n        price=price,\n        currency_code=currency_code,\n        quantity=quantity,\n    )\n    return order\n\n\n@transaction.atomic\ndef remove_product_from_order(*, product_id: str, order_id: str) -> None:\n    \"\"\"Remove a product with the given ID from an order with the given ID.\"\"\"\n    OrderProduct.objects.filter(product_id=product_id, order_id=order_id).delete()\n\n\n@transaction.atomic\ndef add_product_to_order(\n    *, product_id: str, order_id: str, price: float, currency_code: str, quantity: int\n) -> None:\n    \"\"\"Add a product to an order with the given ID.\"\"\"\n    OrderProduct.objects.create(\n        product_id=product_id,\n        order_id=order_id,\n        price=price,\n        currency_code=currency_code,\n        quantity=quantity,\n    )\n    return Order.objects.get(order_id=order_id)\n\ndef get_order_details(order_id: str) -> Order:\n    return Order.objects.get(order_id=order_id)","repo_name":"sahilr05/product_recommender","sub_path":"recommender_app/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":3004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"72638686358","text":"# Most of this comes from https://github.com/Rapptz/discord.py/blob/master/examples/basic_voice.py\n\nimport asyncio\n\nimport discord\nimport youtube_dl\nfrom discord.ext import commands\n\nfrom utils.format import send_embed\n\n# Suppress errors\nyoutube_dl.utils.bug_reports_message = lambda: \" \"\n\nformat_options = {\n    'format': 'bestaudio/best',\n    'outtmpl': '%(extractor)s-%(id)s-%(title)s.%(ext)s',\n    'restrictfilenames': True,\n    'noplaylist': True,\n    'nocheckcertificate': True,\n    'ignoreerrors': False,\n    'logtostderr': False,\n    'quiet': True,\n    'no_warnings': True,\n    'default_search': 'auto',\n    'source_address': '0.0.0.0'\n}\n\nffmpeg_options = {\n    'options': '-vn'\n}\n\nytdl = youtube_dl.YoutubeDL(format_options)\n\n\nclass YTDLSource(discord.PCMVolumeTransformer):\n    def __init__(self, source, *, data, volume=0.5):\n        super().__init__(source, volume)\n        self.data = data\n        self.title = data.get(\"title\")\n        self.url = data.get(\"url\")\n\n    @classmethod\n    async def from_url(cls, url, *, loop=None, stream=False):\n        loop = loop or asyncio.get_event_loop()\n        data = await loop.run_in_executor(None, lambda: ytdl.extract_info(url, download=not stream))\n\n        if 'entries' in data:\n            data = data['entries'][0]\n\n        filename = data['url'] if stream else ytdl.prepare_filename(data)\n        return cls(discord.FFmpegPCMAudio(filename, **ffmpeg_options), data=data)\n\n\nasync def player_check(ctx):\n    if not ctx.voice_client:\n        return await send_embed(ctx, \"Not connected to a voice channel.\", negative=True)\n    if not ctx.author.voice:\n        return await send_embed(ctx, \"You are not connected to a voice channel.\", negative=True)\n    if ctx.author.voice.channel != ctx.voice_client.channel:\n        return await send_embed(ctx, \"You are not connected to my voice channel.\", negative=True)\n\n\nclass Music(commands.Cog, name=\"Music\"):\n    def __init__(self, bot):\n        self.bot = bot\n        global db\n        db = self.bot.db\n\n    @commands.group(aliases=[\"m\"])\n    @commands.guild_only()\n    async def music(self, ctx):\n        pass\n\n    @commands.cooldown(rate=1, per=5, type=commands.BucketType.user)\n    @music.command()\n    @commands.guild_only()\n    async def join(self, ctx):\n        \"\"\"Join a voice channel.\"\"\"\n\n        channel = ctx.author.voice.channel\n\n        if not channel:\n            await send_embed(ctx, \"You are not currently connected to a voice channel.\", negative=True)\n\n        await ctx.voice_client.move_to(channel)\n        await channel.connect()\n\n    @commands.cooldown(rate=1, per=5, type=commands.BucketType.user)\n    @music.command()\n    @commands.guild_only()\n    async def play(self, ctx, *, url):\n        \"\"\"Play from a youtube url.\"\"\"\n\n        player = await YTDLSource.from_url(url, loop=self.bot.loop)\n        ctx.voice_client.play(player, lambda e: print(f\"Error: {str(e)}\") if e else None)\n\n        await send_embed(ctx, f\"Now playing **{player.title}**.\", info=True)\n\n    @commands.cooldown(rate=1, per=5, type=commands.BucketType.user)\n    @music.command()\n    @commands.guild_only()\n    async def stream(self, ctx, *, url):\n        \"\"\"Stream from a url.\"\"\"\n\n        player = await YTDLSource.from_url(url, loop=self.bot.loop, stream=True)\n        ctx.voice_client.play(player, lambda e: print(f\"Error: {str(e)}\") if e else None)\n\n        await send_embed(ctx, f\"Now playing **{player.title}**.\", info=True)\n\n    @commands.cooldown(rate=1, per=5, type=commands.BucketType.user)\n    @music.command()\n    @commands.guild_only()\n    async def volume(self, ctx, volume: float):\n        \"\"\"Change the volume. Ranges from 0-100.\"\"\"\n\n        if player_check(ctx):\n            return\n\n        if volume < 0 or volume > 100:\n            return await send_embed(ctx, \"Invalid volume value.\", negative=True)\n\n        ctx.voice_client.source.volume = volume / 100\n        await send_embed(ctx, f\"Changed volume to {volume}\")\n\n    @commands.cooldown(rate=1, per=5, type=commands.BucketType.user)\n    @music.command()\n    @commands.guild_only()\n    async def pause(self, ctx):\n        \"\"\"Pause the music player.\"\"\"\n\n        if player_check(ctx):\n            return\n\n        if not ctx.voice_client.is_playing():\n            return await send_embed(ctx, \"Not playing anything.\", negative=True)\n\n        ctx.voice_client.pause()\n        await send_embed(ctx, \"Player paused.\")\n\n    @commands.cooldown(rate=1, per=5, type=commands.BucketType.user)\n    @music.command()\n    @commands.guild_only()\n    async def resume(self, ctx):\n        \"\"\"Resumes the music player after pausing.\"\"\"\n\n        if player_check(ctx):\n            return\n\n        if not ctx.voice_client.is_paused():\n            return await send_embed(ctx, \"Player not paused.\", negative=True)\n\n        ctx.voice_client.resume()\n        await send_embed(ctx, \"Player resumed.\")\n\n    @commands.cooldown(rate=1, per=5, type=commands.BucketType.user)\n    @music.command(aliases=[\"dc\"])\n    async def disconnect(self, ctx):\n        \"\"\"Disconnect the music player.\"\"\"\n\n        if player_check(ctx):\n            return\n\n        await ctx.voice_client.disconnect()\n        await send_embed(ctx, \"Player disconnected.\")\n\n    @play.before_invoke\n    @stream.before_invoke\n    async def voice_connected(self, ctx):\n        if not ctx.voice_client:\n            if ctx.author.voice:\n                await ctx.author.voice.channel.connect()\n            else:\n                await send_embed(ctx, \"You are not connected to a voice channel.\", negative=True)\n\n        elif ctx.voice_client.is_playing():\n            ctx.voice_client.stop()\n\n\ndef setup(bot):\n    bot.add_cog(Music(bot))\n","repo_name":"jpark9013/Discord-Bot","sub_path":"bot/cogs/music.py","file_name":"music.py","file_ext":"py","file_size_in_byte":5635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"16767011722","text":"from flask import Flask, request, jsonify\nfrom flask_cors import CORS\nfrom payloads.get_relationship_payload import ValidateRelationshipPayload\nfrom payloads.payload import Payload\nfrom resources.relationship_get_resource import RelationshipGetResource\n\napp = Flask(__name__)\nCORS(app, resources={r\"/*\": {\"origins\": \"http://localhost:8080\"}})\n\n@app.route(\"/\")\ndef get_relationship():\n    request_data = Payload.format_request_data(request)\n    ValidateRelationshipPayload(request_data).validate()\n    return RelationshipGetResource(request_data).execute()\n\n@app.errorhandler(400)\ndef generic_error(errors):\n    print(errors.description)\n    return jsonify({\"errors\": errors.description})\n\n@app.errorhandler(500)\ndef internal_error(error):\n    print(error.description)\n    return jsonify({\"errors\": [error.description]})\n\nif __name__ == '__main__':\n    app.run(debug=True)\n    ","repo_name":"PetrovStark/globalrelations-api","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"36245702528","text":"import numpy as np\nfrom scipy import integrate, interpolate\nfrom scipy import optimize\nfrom scipy.integrate import odeint\nimport matplotlib.pyplot as plt\nimport pickle\nimport pandas as pd\n\n# t_data = np.linspace(0,48,49)\n# collect data from CSV file\ncd_data = pd.read_csv('bc_covid.csv')\ndata_rows, data_cols = cd_data.shape\nt_data = np.linspace(0, data_rows-1, data_rows)\ninf_data = cd_data['Infected']\nreco_data = cd_data['Recovered']\ndead_data = cd_data['Dead']\ndef sir(y, t, param):\n    '''\n    Basic SIR model with a compartment for infected and unaware (asymptomatic)\n    and a compartment for aware (symptomatic).\n    Parameters include transmission rate, beta, social distancing factor, rho,\n    transition rate from unaware to aware, nu, and recovery rate, gamma.\n    A rate of 0.8% death rate is assumed as it matches local data.\n    A sigmoid function is used for the social distancing rate as a way\n    to \"ramp up\" social distancing in a matter of days without breaking\n    the ODE.\n    '''\n    phi = tanh(0.0, param[1], t, 46)\n    ds = -param[0]*(1-phi)*y[0]*(y[1] + y[2])\n    #S = -b(1-p)s(iu+ia)\n    diu = param[0]*(1-phi)*y[0]*(y[1] + y[2]) - 6/365. * y[1]\n    #Iu = -b(1-p)s(iu+ia) - niu\n    dia = 6/365. * y[1] - param[3]*y[2] - param[4]*y[2]\n    #Ia = nIu - gIa - mIa\n    dr = param[3]*y[2]\n    #R = gIa\n    dd = param[4]*y[2]\n    return [ds, diu, dia, dr, dd]\n\ndef tanh(y0, ym, t, tm):\n    '''\n    Sigmoid function that starts at y0 and plateaus at ym.\n    The half way point between y0 and ym will be achieved at tm.\n    '''\n    return (ym - y0)*(np.tanh(2. * ((t -tm))/tm) + 1) / 2. + y0\n\ndef sir_integrate(x, y0, p):\n    '''\n    A function to run the integration.\n    '''\n    yn = integrate.odeint(sir, y0, x, args=(p))\n    return yn\n\n\ndef ls_opt(x, fit_p):\n    '''\n    Least Squares fitting for infectious cases\n    '''\n    f = lambda y,t: sir(y, t, fit_p)\n    r = integrate.odeint(f, y0, x)\n    # print('{}'.format(r))\n    return r[:,2]\n\ndef ls_opt_p(x, fit_p):\n    '''\n    Least Squares fitting for infectious and recovered cases.\n    '''\n    f = lambda y,t: sir(y, t, fit_p)\n    r = integrate.odeint(f, y0, x)\n    # print('{}'.format(r))\n    return [r[:,2], r[:,3], r[:, 4]]\n\ndef f_resid(p):\n    inf, rec , death= ls_opt_p(t_data, p)\n    '''\n    Calculate residuals to measure the fitness of the parameters\n    '''\n    return (inf_data - inf)**2 + (reco_data - rec) + (dead_data - death)\n\nif __name__ == \"__main__\":\n    \n    # Guess parameters\n    param_g = [2.35401407e-08, 9.900000e-01,  6/365., 8.21917808e-02, 0.005]\n    y0 = [5071336, 0, 1, 0, 0]\n    res = optimize.least_squares(f_resid, param_g, bounds=([0, 0, 1/365., 5/365., 0.00001 ],\n                                [.1, .9999, 19/365., 30/365., 0.01]), ftol=10**-9,\n                                method='trf')\n    # c = optimize.least_squares(f_resid, param_g, bounds=(0, [0.05, 0.6]))\n    # (c,kvg) = optimize.curve_fit(f_resid, t_data, inf_data, p0=3.4*10**-4, bounds=([0,0,0], [1, 1, 1]))\n    # (c,kvg) = optimize.curve_fit(f_resid, t_data, inf_data, p0=2.5*10**-8)\n    print('Parameters are estimated at {}'.format(res.x))\n    params = res.x\n    # fit ODE results to interpolating spline just for fun\n    xeval=np.linspace(min(t_data), max(t_data),30) \n    gls = interpolate.UnivariateSpline(xeval, ls_opt(xeval,params), k=3, s=0)\n\n    # save variables in pickle for future use\n    with open('model_params.pkl', 'wb') as file:\n        for i in range(len(params)):\n            pickle.dump(params[i], file)\n    #pick a few more points for a very smooth curve, then plot \n    #   data and curve fit\n    xeval=np.linspace(min(t_data), max(t_data),200)\n    #Plot of the data as red dots and fit as blue line\n    plt.plot(t_data, inf_data,'.r',xeval,gls(xeval),'-b')\n    plt.xlabel('Time since Jan 28',{\"fontsize\":16})\n    plt.ylabel('Total Cases',{\"fontsize\":16})\n    plt.legend(('data','fit'),loc=0)\n    plt.show()","repo_name":"kpoore/COVID-19-BC-Model","sub_path":"calibration_sir.py","file_name":"calibration_sir.py","file_ext":"py","file_size_in_byte":3906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"7941250674","text":"import theano\nimport Mariana.useful as MUSE\n\nclass Variable(object):\n    \"\"\"docstring for Variable\"\"\"\n    def __init__(self, variableType=None, streams=[\"train\", \"test\"], **theano_kwargs):\n        super(Variable, self).__init__()\n        self.streams = streams\n        self.variables = {}\n        self.variableType = variableType\n        self.ties = set()\n\n        if variableType :\n            self.set(variableType, **theano_kwargs)\n        else :\n            self.dtype = None\n            for f in self.streams :\n                self.variables[f] = None\n        \n        self.tied = False\n\n    def isSet(self) :\n        for f in self.streams :\n            if not self.variables[f] : return False\n\n        return True\n\n    def isTied(self, stream) :\n        return stream in self.ties\n\n    def tie(self, var, stream = None) :\n        if stream is None :\n            if set(self.streams) != set(var.streams) :\n                raise ValueError( \"%s does not have the same streams. Self: %s, var: %s\" % (var, self.streams, var.streams) )\n            for f in self.streams :\n                self.variables[f] = var.variables[f]\n                self.ties.add(f)\n        else :\n            self.variables[stream] = var[stream]\n            self.ties.add(stream)\n\n    def set(self, variableType, *theano_args, **theano_kwargs) :\n        self.variableType = variableType\n        for f in self.streams :\n            self.variables[f] = variableType(*theano_args, **theano_kwargs)\n            if f in self.ties :\n                self.ties.remove(f)\n        self.dtype = self.variables[f].dtype\n    \n    def getValue(self, stream) :\n        v = self[stream]\n        if v is None :\n            raise ValueError(\"Variable has an empty value for stream: %s\" % stream)\n\n        return self[stream].get_value()\n\n    def setValue(self, stream, value) :\n        if stream not in self.streams :\n            raise KeyError(\"There is no stream by the name of: '%s'\" % stream)\n        \n        self[stream].set_value(value)\n        if stream in self.ties :\n            self.ties.remove(stream)\n\n    def __getitem__(self, stream) :\n        # print self.variables[stream]\n        \n        try :\n            return self.variables[stream]\n        except KeyError :\n            raise KeyError(\"There is no stream by the name of: '%s'\" % stream)\n    \n    def __setitem__(self, stream, newVal) :\n        try :\n            self.variables[stream] = newVal\n        except KeyError :\n            raise KeyError(\"There is no stream by the name of: '%s'\" % stream)\n        if stream in self.ties :\n            self.ties.remove(stream)\n\n    def __contains__(self, stream) :\n        \"\"\"check if the stream is supported\"\"\"\n        return stream in self.streams\n\n    def __repr__(self) :\n        return \"< Mariana %s, streams: %s>\" % (self.__class__.__name__, self.streams)\n\nclass Inputs(Variable):\n    \"\"\"docstring for Input\"\"\"\n\nclass Targets(Variable):\n    \"\"\"docstring for Input\"\"\"\n\nclass Parameter(object):\n    \"\"\"docstring for Parameter\"\"\"\n    def __init__(self, name):\n        super(Parameter, self).__init__()\n        self.name = name\n        self.theano_var = None\n        self.tiedParams = {}\n        self.master = None\n\n    def _tie(self, master) :\n        self.master = master\n\n    def isTied(self) :\n        return self.master is not None\n        # try :\n        #     print self\n        #     return self.master is not None\n        # except AttributeError :\n        #     return False\n\n    def isShared(self) :\n        return isinstance(self.theano_var, theano.compile.sharedvalue.SharedVariable)\n\n    def isSet(self) :\n        return self.theano_var is not None or self.isTied()\n\n    def tie(self, otherParam, transpose=False) :\n        if otherParam not in self.tiedParams :\n            self.tiedParams[otherParam] = transpose\n            otherParam.tie(self, transpose)\n\n    def __call__(self) :\n        return self.getVar()\n\n    def getVar(self) :\n        if self.isTied() :\n            if self.tiedParams[self.master] :\n                return self.master().T\n            else :\n                return self.master()\n        else :\n            return self.theano_var\n\n    def hasValue(self) :\n        return self.theano_var is not None\n    \n    def setValue(self, value, forceCast = True) :\n        if isinstance(value, theano.Variable) :\n            if forceCast :\n                v = MUSE.iCast_theano(value)\n            else :\n                v = value\n        else :\n            if forceCast :\n                v = theano.shared(value = MUSE.iCast_numpy(value), name = self.name)\n            else :\n                v = theano.shared(value = value, name = self.name)\n\n        self.theano_var = v\n        for p in self.tiedParams :\n            p._tie(self)\n\n    def updateValue(self, value, forceCast=False) :\n        if forceCast :\n            v = theano.shared(value = MUSE.iCast_numpy(value), name = self.name)\n        else :\n            v = value\n\n        if v.shape != self.getShape() :\n            print(\"Warning update has a different shape: %s -> %s\" %(self.shape, v.shape))\n        self.theano_var.set_value(v)\n    \n    def getValue(self) :\n        if self.theano_var is None :\n            return None\n        return self.theano_var.get_value()\n\n    def getShape(self) :\n        if self.theano_var is None :\n            return None\n        return self.getValue().shape\n\n    def __repr__(self) :\n        return \"< Mariana Parameter: %s, %s. Tied to: %s>\" % (self.name, self.getShape(), self.master)\n\nclass Losses(object):\n    \"\"\"Contains the loss for every stream\"\"\"\n    def __init__(self, layer, cost, targets, outputs):\n        super(Losses, self).__init__()\n        self.streams=targets.streams\n\n        self.layer = layer\n        self.cost = cost\n        self.targets = targets\n        self.outputs = outputs\n\n        self.store = {}\n        for k in self.streams :\n            self.store[k] = self.cost.apply(self.layer, self.targets[k], self.outputs[k], stream = k)\n\n    def __getitem__(self, k) :\n        return self.store[k]\n\n    def __setitem__(self, k, v) :\n        self.store[k] = v\n\n    def __contains__(self, stream) :\n        \"\"\"check if the stream is supported\"\"\"\n        return stream in self.streams\n\n    def __repr__(self) :\n        return \"< Mariana %s, streams: %s>\" % (self.__class__.__name__, self.streams)\n\n","repo_name":"tariqdaouda/Mariana","sub_path":"Mariana/custom_types.py","file_name":"custom_types.py","file_ext":"py","file_size_in_byte":6317,"program_lang":"python","lang":"en","doc_type":"code","stars":151,"dataset":"github-code","pt":"85"}
+{"seq_id":"71393471957","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F \nimport torchvision\nimport torchvision.transforms as transforms\nfrom torchvision.utils import save_image\nimport numpy as np \nimport matplotlib.pyplot as plt \nfrom datetime import datetime\nimport os \n\ntransforms = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize(mean= (0.5,), std= (0.5,))\n])\n\ntrain_dataset = torchvision.datasets.MNIST(root= '/home/chandanv/Drive/Courses/Pytorch:AI/Pytorch_AI/data/',\n                                            train = True,\n                                            download = True, \n                                            transform = transforms)\n\nbatch_size = 128\ntrain_dataloader = torch.utils.data.DataLoader(dataset= train_dataset, batch_size= batch_size, shuffle= True)\n\n## Discriminator\nD = nn.Sequential(\n    nn.Linear(784, 512),\n    nn.LeakyReLU(0.2),\n    nn.Linear(512, 256),\n    nn.LeakyReLU(0.2),\n    nn.Linear(256, 1)\n)\n\n## Generator\nlatent_dim = 100\nG = nn.Sequential(\n    nn.Linear(latent_dim, 256),\n    nn.LeakyReLU(0.2),\n    nn.BatchNorm1d(256, momentum= 0.7),\n    nn.Linear(256, 512),\n    nn.LeakyReLU(0.2),\n    nn.BatchNorm1d(512, momentum= 0.7),\n    nn.Linear(512, 1024),\n    nn.LeakyReLU(0.2),\n    nn.BatchNorm1d(1024, momentum= 0.7),\n    nn.Linear(1024, 784),\n    nn.Tanh()\n)\n\ndevice = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\nD = D.to(device)\nG = G.to(device)\n\ncriterion = nn.BCEWithLogitsLoss()\nd_optimizer = torch.optim.Adam(D.parameters(), lr = 0.0002, betas= (0.5, 0.999))\ng_optimizer = torch.optim.Adam(G.parameters(), lr = 0.0002, betas= (0.5, 0.999))\n\n## saving images back to 0,1\ndef scale_img(img):\n    out = (img + 1)/2\n    return out\n\nif not os.path.exists('/home/chandanv/Drive/Courses/Pytorch:AI/Pytorch_AI/data/gan_images'):\n    os.makedirs('/home/chandanv/Drive/Courses/Pytorch:AI/Pytorch_AI/data/gan_images')\n\nones_ = torch.ones(batch_size, 1).to(device)\nzeros_ = torch.zeros(batch_size, 1).to(device)\n\ng_losses = []\nd_losses = []\n\nfor epoch in range(200):\n    for inputs, _ in train_dataloader:\n\n        n = inputs.size(0)\n        inputs = inputs.reshape(n, 784).to(device)\n\n        ones = ones_[:n]\n        zeros = zeros_[:n]\n\n        ## Train discriminator\n\n        ## real images\n        real_outputs = D(inputs)\n        d_loss_real = criterion(real_outputs, ones)\n\n        ## fake images\n        noise = torch.randn(n, latent_dim).to(device)\n        fake_images = G(noise)\n        fake_outputs = D(fake_images)\n        d_loss_fake = criterion(fake_outputs, zeros)\n\n        ## gradient descent step\n        d_loss = 0.5 * (d_loss_fake + d_loss_real)\n        d_optimizer.zero_grad()\n        g_optimizer.zero_grad()\n        d_loss.backward()\n\n\n        ## Train generator\n        ## do it twice\n\n        for _ in range(2):\n            ## fake images\n            noise = torch.randn(n, latent_dim).to(device)\n            fake_images = G(noise)\n            fake_outputs = D(fake_images)\n\n            ## reverse the label\n            g_loss = criterion(fake_outputs, ones)\n\n            ## gradient descent step\n            d_optimizer.zero_grad()\n            g_optimizer.zero_grad()\n            g_loss.backward()\n            g_optimizer.step()\n\n        d_losses.append(d_loss.item())\n        g_losses.append(g_loss.item())\n    \n    print(f'Epoch: {epoch}, d_loss : {d_loss.item()}, g_loss: {g_loss.item()}')\n\n    fake_images = fake_images.reshape(-1, 1, 28, 28)\n    save_image(scale_img(fake_images), f'/home/chandanv/Drive/Courses/Pytorch:AI/Pytorch_AI/data/gan_images/{epoch+1}.png')","repo_name":"ChandanVerma/Pytorch_GANS","sub_path":"Gans_mnist.py","file_name":"Gans_mnist.py","file_ext":"py","file_size_in_byte":3577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"31076915781","text":"import numpy as np\nfrom scipy.ndimage import convolve\n\n\ndef evolve(inputs: np.array, kernel: np.array):\n    for _ in range(GENERATIONS):\n        neighbors = convolve(inputs, kernel, mode=\"constant\")\n        inputs = np.where((neighbors == 3) | (inputs & (neighbors == 2)), 1, 0)\n\n    return inputs.sum()\n\n\ndef part_one(data):\n    kernel: np.array = np.ones((3, 3, 3))\n    kernel[1, 1, 1] = 0\n\n    weights: np.array = np.zeros((13, 20, 20), dtype=int)\n    weights[GENERATIONS] = np.pad(data, GENERATIONS)\n\n    return evolve(weights, kernel)\n\n\ndef part_two(data):\n    kernel: np.array = np.ones((3, 3, 3, 3))\n    kernel[1, 1, 1, 1] = 0\n\n    weights: np.array = np.zeros((13, 13, 20, 20), dtype=int)\n    weights[GENERATIONS, GENERATIONS] = np.pad(data, GENERATIONS)\n\n    return evolve(weights, kernel)\n\n\ndef load_file(file_name: str) -> list:\n    with open(file_name, 'r') as fd:\n        return [_.strip() for _ in fd.readlines()]\n\n\ndef main():\n    raw = load_file(\"day17.txt\")\n    data = np.array([[char == \"#\" for char in line] for line in raw], dtype=int)\n\n    print(f\" Part one solution: {part_one(data):>7}\")\n    print(f\" Part two solution: {part_two(data):>7}\")\n\n\nif __name__ == '__main__':\n    GENERATIONS = 6\n    main()\n","repo_name":"raeq/adventofcode","sub_path":"src/aoc/twenty/day17.py","file_name":"day17.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"10573569167","text":"import os\nimport plistlib\nimport re\n\nfrom translate.lang import data\nfrom translate.misc.multistring import multistring\nfrom translate.storage import base\n\n\nclass StringsDictId(base.UnitId):\n    KEY_SEPARATOR = \":\"\n\n    def __str__(self):\n        s = super().__str__()\n        if s.startswith(\":\"):\n            return s[1:]\n        return s\n\n\nclass StringsDictUnit(base.DictUnit):\n    \"\"\"A single entry in a .stringsdict file.\n    One entry represents either a localized format string, or a variable used\n    within another string.\n    \"\"\"\n\n    IdClass = StringsDictId\n    format_value_type = \"\"\n\n    def __init__(self, source=None):\n        super().__init__(source=source)\n\n        loc = source or \"\"\n        if len(loc) > 0 and loc[0] == \":\":\n            loc = loc[1:]\n\n        # Check if this unit is a format string or a variable\n        split = loc.rfind(\":\")\n        if split > 0:\n            subkey = loc[(split + 1) :]\n            loc = loc[:split]\n            self.set_unitid(self.IdClass([(\"key\", loc), (\"key\", subkey)]))\n        else:\n            self.set_unitid(self.IdClass([(\"key\", loc)]))\n\n    def __eq__(self, other):\n        return (\n            super().__eq__(other) and self.format_value_type == other.format_value_type\n        )\n\n    @property\n    def outerkey(self):\n        self.get_unitid()\n\n        if len(self._unitid.parts) < 1:\n            return None\n\n        return self._unitid.parts[0][1]\n\n    @property\n    def innerkey(self):\n        self.get_unitid()\n\n        if len(self._unitid.parts) < 2:\n            return None\n\n        return self._unitid.parts[1][1]\n\n    def getid(self):\n        return self.source\n\n    def setid(self, value, unitid=None):\n        self.source = value\n        super().setid(value, unitid)\n\n\nclass StringsDictFile(base.DictStore):\n    \"\"\"Class representing a .stringsdict file.\n\n    One entry in a .stringsdict file consists of a format string, and any\n    number of variables with plural strings.\n\n    Each entry is split up into multiple translation units, containing either\n    the format string or one of the variables.\n    \"\"\"\n\n    UnitClass = StringsDictUnit\n    Name = \"iOS Stringsdict\"\n    Mimetypes = [\"application/x-plist\"]\n    Extensions = [\"stringsdict\"]\n\n    def __init__(self, inputfile=None, **kwargs):\n        super().__init__(**kwargs)\n        self.parse(inputfile)\n\n    def gettargetlanguage(self):\n        target_lang = super().gettargetlanguage()\n\n        # If targetlanguage isn't set, we try to extract it from the filename path (if any).\n        if target_lang is None and hasattr(self, \"filename\") and self.filename:\n            parent_dir = os.path.split(os.path.dirname(self.filename))[1]\n            match = re.search(r\"^(\\w*).lproj\", parent_dir)\n            if match is not None:\n                target_lang = match.group(1)\n                if target_lang.lower() == \"base\":\n                    target_lang = \"en\"\n            else:\n                target_lang = self.sourcelanguage\n\n            # Cache it\n            self.settargetlanguage(target_lang)\n\n        return target_lang\n\n    @property\n    def target_plural_tags(self):\n        \"\"\"Get all supported plural tags for the target language.\n        Note that 'zero' is always supported.\n        \"\"\"\n        target_lang = self.gettargetlanguage()\n        if target_lang is None:\n            return data.cldr_plural_categories\n\n        locale = target_lang.replace(\"_\", \"-\").split(\"-\")[0]\n        tags = data.plural_tags.get(locale, data.cldr_plural_categories).copy()\n        if \"zero\" not in tags:\n            tags.insert(0, \"zero\")\n        return tags\n\n    def parse(self, input):\n        \"\"\"Read a .stringsdict file into a dictionary, and convert it to translation units.\"\"\"\n\n        if isinstance(input, (bytes, str)):\n            plist = plistlib.loads(input)\n        elif input is not None:\n            plist = plistlib.load(input)\n        else:\n            plist = {}\n\n        for key, outer in plist.items():\n            if not isinstance(outer, dict):\n                raise ValueError(f\"{key} is not a dict\")\n            for innerkey, value in outer.items():\n                if innerkey == \"NSStringLocalizedFormatKey\":\n                    u = self.UnitClass()\n                    u.set_unitid(u.IdClass([(\"key\", key)]))\n                    u.target = str(value)\n                    self.addunit(u)\n                elif isinstance(value, dict):\n                    spec_type = value.get(\"NSStringFormatSpecTypeKey\", \"\")\n                    if spec_type and spec_type != \"NSStringPluralRuleType\":\n                        raise ValueError(\n                            f\"{innerkey} in {key} is not of NSStringPluralRuleType\"\n                        )\n\n                    plural_tags = self.target_plural_tags\n                    plural_strings = [value.get(tag, \"\") for tag in plural_tags]\n\n                    u = self.UnitClass()\n                    u.set_unitid(u.IdClass([(\"key\", key), (\"key\", innerkey)]))\n                    u.target = multistring(plural_strings)\n                    u.format_value_type = value.get(\"NSStringFormatValueTypeKey\", \"\")\n                    self.addunit(u)\n                else:\n                    raise ValueError(f\"Unexpected key {innerkey} in {key}\")\n\n    def serialize(self, out):\n        plist = {}\n\n        for u in self.units:\n            loc = u.outerkey\n            subkey = u.innerkey\n\n            if loc not in plist:\n                plist[loc] = {}\n\n            if subkey is not None:\n                plurals = {}\n                plurals[\"NSStringFormatSpecTypeKey\"] = \"NSStringPluralRuleType\"\n                plurals[\"NSStringFormatValueTypeKey\"] = u.format_value_type\n\n                plural_tags = self.target_plural_tags\n\n                if isinstance(u.target, multistring):\n                    plural_strings = u.target.strings\n                elif isinstance(u.target, list):\n                    plural_strings = u.target\n                else:\n                    plural_strings = [u.target]\n\n                # Sync plural_strings elements to plural_tags count.\n                if len(plural_strings) < len(plural_tags):\n                    plural_strings += [\"\"] * (len(plural_tags) - len(plural_strings))\n                plural_strings = plural_strings[: len(plural_tags)]\n\n                for plural_tag, plural_string in zip(plural_tags, plural_strings):\n                    if plural_string:\n                        plurals[plural_tag] = plural_string\n\n                plist[loc][subkey] = plurals\n            else:\n                plist[loc][\"NSStringLocalizedFormatKey\"] = u.target or u.source\n\n        out.write(plistlib.dumps(plist, sort_keys=False))\n","repo_name":"translate/translate","sub_path":"translate/storage/stringsdict.py","file_name":"stringsdict.py","file_ext":"py","file_size_in_byte":6662,"program_lang":"python","lang":"en","doc_type":"code","stars":796,"dataset":"github-code","pt":"85"}
+{"seq_id":"1279842438","text":"from ..helpers import requests,constants\nimport json\nfrom ...models.store import Store\nimport asyncio\nimport time\nfrom queue import PriorityQueue\nimport datetime\n\nasync def get_stores_by_zipcode(zipcode, radius = 10, curbside_only = True, next_available_timeslot = True): \n    heb_stores = PriorityQueue()\n\n    request_payload = {\"address\": zipcode, \"curbsideOnly\": curbside_only, \"radius\": radius, \"nextAvailableTimeslot\": next_available_timeslot}\n\n    response = await requests.post_async(constants.HEB_BASE_URL + \"store/locator/address\", json.dumps(request_payload))\n\n    store_creation_tasks =[]\n\n    for response_obj in response[\"stores\"]:\n        if response_obj[\"store\"] is not None and response_obj[\"distance\"] is not None: \n            store_creation_tasks.append(\n                asyncio.create_task(\n                    create_and_append_store(response_obj[\"store\"],response_obj[\"distance\"], heb_stores)\n                    )\n                )\n \n    await asyncio.gather(*store_creation_tasks)\n\n    return heb_stores\n\nasync def get_earliest_pickup_day(store_id, num_days_to_check = 30):\n    timeslot_query = \"timeslot/timeslots?store_id={store_id}&days={num_days}&fulfillment_type=pickup\"\n\n    response = await requests.get_async(constants.HEB_BASE_URL + timeslot_query.format(store_id = store_id, num_days = str(num_days_to_check)))\n\n    if response[\"items\"] is None or len(response[\"items\"]) < 1:\n        return None\n    return response[\"items\"][0][\"timeslot\"][\"startTime\"][:19]\n\n\nasync def create_and_append_store(response_dict, distance_from_zip, heb_stores):\n    new_store = Store()\n    new_store.id = int(response_dict[\"id\"])\n    \n    earliest_pickup_date = await get_earliest_pickup_day(new_store.id)\n    \n    if earliest_pickup_date is None:\n        return\n    \n    new_store.next_pickup_time = datetime.datetime.strptime(earliest_pickup_date,'%Y-%m-%dT%H:%M:%S')\n    new_store.name = response_dict[\"name\"]\n    new_store.city = response_dict[\"city\"]\n    new_store.address = response_dict[\"address1\"]\n    new_store.phone_number = response_dict[\"phoneNumber\"]\n    new_store.distance = int(distance_from_zip)\n    new_store.zipcode = response_dict[\"postalCode\"][:5]\n    new_store.location_link = constants.MAPS_URL_LAT_LONG.format(lat = response_dict[\"latitude\"], long = response_dict[\"longitude\"])\n    new_store.store_hours = response_dict[\"storeHours\"]\n    new_store.state = response_dict[\"state\"]\n    \n    heb_stores.put(new_store)\n\n\n\n\n\n\n\n\t\n","repo_name":"alexlimon/next-pickup-services","sub_path":"microservices/core/services/clients/heb_client.py","file_name":"heb_client.py","file_ext":"py","file_size_in_byte":2459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"16341655469","text":"import csv\nimport os\nimport matplotlib.pyplot as plt\n\nnew_path = os.path.join(os.path.dirname(__file__), 'all-hands.csv')\nnum_list_2d = []\ndef CSV_Creation():\n\twith open(new_path, 'r') as csv_file:\n\t\tcsv_reader = csv.reader(csv_file)\n\t\tfor row in csv_reader:\n\t\t\trow = list(map(int, row))\n\t\t\tnum_list_2d.append(row)\n\treturn num_list_2d\n# print(os.path.dirname(__file__))\n# print(new_path)\nCSV_Creation()\n# print(num_list_2d)\nx = []\ny = []\ntemp_x = []\ntemp_y = []\n# print(num_list_2d[0])\n# print(num_list_2d[1])\nfor ele in num_list_2d:\n\tfor i in range(0,ele[-1],2):\n\t\tif i < len(ele):\n\t\t\tx.append(ele[i])\n\t\t\ty.append(ele[i+1])\n# \tprint(x)\n# \tprint()\n# \tprint()\n# \tprint()\n\ttemp_x.append(x)\t\n\ttemp_y.append(y)\t\nfor ele in temp_x:\n\tprint(ele)\n\tprint()\n# plt.plot(temp_x[0], temp_y[0])\n# # plt.show()\n","repo_name":"jinarma/Python_General","sub_path":"General/rishal HW/some.py","file_name":"some.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"24013906687","text":"from typing import List, Dict, Union\nfrom utils import FileManager\nimport sys\n\n\nclass Parser:\n    \"\"\"Парсер make-файла\"\"\"\n\n    pattern = r\"((\\w+)\\s*:\\s*([\\w| |\\t]*)\\s*{([\\S\\s]*?)})\"\n    token_pattern = r\"\\w+\"\n\n\n    def __init__(self):\n        self.regexp = re.compile(self.pattern)\n        self.tokenRegexp = re.compile(self.token_pattern)\n\n\n    def _split_tokens(self, string: str) -> List[str]:\n        return self.tokenRegexp.findall(string)\n\n\n    def _split_lines(self, string: str) -> List[str]:\n        return [s.strip() for s in string.split(\"\\n\") if s]\n\n    \n    def _get_type_tokens(self, match: List[str]) -> Dict[str, Union[str, List[str]]]:\n        \"\"\"Получение словаря для исполнения кода\"\"\"\n\n        return {\n            \"target\": match[1],\n            \"deps\": self._split_tokens(match[2]),\n            \"code\": self._split_lines(match[3])\n        }\n    \n\n    def parse(self, code: str) -> List[Dict[str, Union[str, List[str]]]]:\n        \"\"\"Парсинг кода make\"\"\"\n\n        matches = self.regexp.findall(code)\n        formatted_tokens = []\n\n        for match in matches:\n            formatted_tokens.append(self._get_type_tokens(match))\n\n        return formatted_tokens\n\n\n\nif __name__ == \"__main__\":\n    print(Parser().parse(FileManager.read(sys.argv[1])))","repo_name":"supermistral/mini-make","sub_path":"parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"30605248661","text":"#!/usr/bin/env python\nimport subprocess\nimport platform\n    \n\ndef ping_ip(current_ip_address):\n        try:\n            output = subprocess.check_output(\"ping -{} 2 {}\".format('n' if platform.system().lower(\n        ) == \"windows\" else 'c', current_ip_address), shell=True, universal_newlines=True)\n            if 'unreachable' in output:\n                return False\n            else:\n                return True\n        except Exception:\n                return False\n \nif __name__ == '__main__':\n\n    current_ip_address = {'NAMESERVER' : 'DIRECCIONIP', \n                          'SERVER1' : '192.168.3.100'\n                         }  \n    \n    for key, value in current_ip_address.items():\n        if ping_ip(value):\n            print(f\"{value} {key} ONLINE \")\n            \n        else:\n            print(f\"{value} {key}   OFFLINE\")\n          \n          \n\n","repo_name":"Esteban107/testIP","sub_path":"ping.py","file_name":"ping.py","file_ext":"py","file_size_in_byte":861,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"11357190282","text":"flat = 1\nhouse = 2\ntotal = 0\n\n# for i in range(1000):\n#     total = total+flat+house\n\n# print(total)\n\n\n\nYearly_Interest = 10\nYearly_Charge = 1\ntotal = 1000\n\nfor i in range(100):\n    total = total+Yearly_Interest-Yearly_Charge\n\n#print(total)\n          ","repo_name":"WesleyLum/firsty","sub_path":"temp.py","file_name":"temp.py","file_ext":"py","file_size_in_byte":251,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"37497179131","text":"import re\nfrom ptas_core.command import CommandLine\nfrom ptas_core.status import StatusResult\nfrom ptas_core.ptasLogger import logger\nfrom ptas_core.commonApplicationUtilities import CommonApplicationUtilities\n\nclass KratosHandler :\n\t\"\"\"\n\t#-------------------------------------------------------------------------------------------------------------------\n\t# Name: KratosHandler\n\t# Description: Performs Operation on Kratos\n\t# Sends Commands by passing method names to CPTF Library Interface which calls methdods of the respective class\n\t#-------------------------------------------------------------------------------------------------------------------\n\t\"\"\"\n\n\tkratosPcIpAddress = 0\n\n\tdef __init__(self, kratosPcIpAddress) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: __init__\n\t\t# Input: Takes argument :\n\t\t# \tkratosPcIpAddress: IP Address of the Kratos PC\n\t\t# Description: Constructor that sets Kratos IP Address\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tself.kratosPcIpAddress = kratosPcIpAddress\n\n\n\tdef SetPowerSupplyOutput(self, enable) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: SetPowerSupplyOutput\n\t\t# Input: Takes argument :\n\t\t# \tenable: true/false to enable or disable Power\n\t\t# Description: Power ON/OFF Device by turning on Kratos Power ON/OFF\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Setting PowerSupply Output to \" +  enable)\n\t\tmethodString = \"-m SetPowerSupplyOutput-\" + enable\n\t\tresult = self.SendCommandAndGetResponse(methodString)\n\t\treturn result\n\n\n\tdef LoadChannelConfiguration(self, configurationFile) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: LoadChannelConfiguration\n\t\t# Input: Takes argument :\n\t\t# \tconfigurationFile: Path of the cfg file to Load\n\t\t# Description: Loads the specified configuration file on Kratos\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Loading Channel Configuration : \" +  configurationFile)\n\t\tmethodString = \"-m LoadChannelConfiguration-\" + configurationFile\n\t\treturn self.SendCommandAndGetResponse(methodString)\n\n\tdef SetDefaultOptions(self) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: SetDefaultOption\n\t\t# Input: Takes No Arguments\n\t\t# Description: Sets default of parameters :\n\t\t# checkCal : False, ignoreSelfCal : false, ignoreExtCal : True, enforceMaxVBat : False, checkConnectors : True, autoSaveData : True\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Setting Default Options - checkCal : False, ignoreSelfCal : false, ignoreExtCal : True, enforceMaxVBat : False, checkConnectors : True, autoSaveData : True\")\n\t\tmethodString = \"-m SetOption-false,false,true,false,true,true\"\n\t\treturn self.SendCommandAndGetResponse(methodString)\n\n\n\n\tdef SetUsbConnection(self, enable) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: SetUsbConnection\n\t\t# Input: Takes argument :\n\t\t# \tenable: true / false\n\t\t# Description: Enables/Disables the USB Connection on Kratos\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Setting USB Connection to : \" +  enable)\n\t\tmethodString = \"-m SetUsbConnection-\" + enable\n\t\treturn self.SendCommandAndGetResponse(methodString)\n\n\n\tdef ConfigurePowerSupply(self, voltage, currentLimit, ovp) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: ConfigurePowerSupply\n\t\t# Input: Takes argument :\n\t\t# \tvoltage: Voltage to be set on Kratos\n\t\t#\tcurrentLimit: Current Limit to be set on Kratos\n\t\t#\tovp: OVP to be set\n\t\t# Description: Sets Voltage, Current & OVP on Kratos\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info( \"Configuring Power Supply \")\n\t\tmethodString = \"-m ConfigurePowerSupply-\" + voltage + \",\" + currentLimit + \",\" + ovp\n\t\treturn self.SendCommandAndGetResponse(methodString)\n\n\n\tdef SetAcquisitionParameters(self, accuracyMode, sampleRate, acquisitionDurationInSeconds, trigger) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: SetAcquisitionParameters\n\t\t# Input: Takes argument :\n\t\t# \taccuracyMode: HIGH_ACCURACY,\n\t\t#\tsampleRate: sample rate to be set\n\t\t#\tacquisitionDurationInSeconds: duration in seconds\n\t\t#\ttrigger: Manual, Intermediate,\n\t\t# Description: Sets Acquisition Parameters\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Setting Acquisition Parameters\")\n\t\tmethodString = \"-m SetAcquisitionParameters-\" +  accuracyMode + \",\" + sampleRate + \",\" + acquisitionDurationInSeconds + \",\" + trigger;\n\t\treturn self.SendCommandAndGetResponse(methodString)\n\n\n\tdef GetSinglePlotStatistics(self, statisticList) :\n\t\tlogger.info( \"Getting Single Plot Statistics \")\n\t\tmethodString = \"-m GetSinglePlotStatistics-1,1\"\n\t\tresult = self.SendCommandAndGetResponse(methodString)\n\t\tif result.Status.HasError() :\n\t\t\tlogger.error(\"Error in Sending Command to get Single Point Statistics\")\n\t\t\treturn result\n\n\t\tcurrent = \"current : \"\n\t\tvoltage = \"voltage : \"\n\t\tmatch = re.findall ( current + '(.*?);', result.CmdResult.Output, re.DOTALL)\n\t\tif len(match) < 1 :\n\t\t\treturn StatusResult.Error(\"Could not read Current Value\")\n\n\t\t# logger.info(\"Current : \" + match[0])\n\t\tcurrent = match[0]\n\n\t\tmatch = re.findall ( voltage + '(.*?);', result.CmdResult.Output, re.DOTALL)\n\t\tif len(match) < 1 :\n\t\t\treturn StatusResult.Error(\"Could not read Voltage Value\")\n\n\t\t# logger.info(\"Voltage : \" + match[0])\n\t\tvoltage = match[0]\n\n\t\tstatisticList[0] = current\n\t\tstatisticList[1] = voltage\n\n\t\treturn StatusResult.Success()\n\n\tdef GetCurrent(self) :\n\t\tlogger.info( \"Fetching Measured Current\")\n\t\tmethodString = \"-m GetSinglePlotStatistics-1,1\"\n\t\tresult = self.SendCommandAndGetResponse(methodString)\n\t\tif result.Status.HasError() :\n\t\t\treturn None\n\n\t\tcurrent = \"current : \"\n\t\tmatch = re.findall ( current + '(.*?);', result.CmdResult.Output, re.DOTALL)\n\t\tif len(match) < 1 :\n\t\t\treturn None\n\n\t\tcurrent = match[0]\n\n\t\treturn current;\n\n\tdef GetVoltage(self) :\n\t\tlogger.info( \"Fetching Measured Voltage\")\n\t\tmethodString = \"-m GetSinglePlotStatistics-1,1\"\n\t\tresult = self.SendCommandAndGetResponse(methodString)\n\t\tif result.Status.HasError() :\n\t\t\treturn None\n\n\t\tvoltage = \"voltage : \"\n\t\tmatch = re.findall ( voltage + '(.*?);', result.CmdResult.Output, re.DOTALL)\n\t\tif len(match) < 1 :\n\t\t\treturn None\n\n\t\tvoltage = match[0]\n\n\t\treturn voltage;\n\n\tdef SetOutputDirectory(self, outputDirPath) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: SetOutputDirectory\n\t\t# Input: Takes argument :\n\t\t# \toutputDirPath: Absolute Logs Directory Path\n\t\t# Description: Sets Path on the Kratos PC to save UDAS Files\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Setting UDAS Output Directory to : \" + outputDirPath)\n\t\tmethodString = \"-m SetOutputDirectory-\" + outputDirPath\n\t\treturn self.SendCommandAndGetResponse(methodString)\n\n\n\t# Acquisition Operations\n\tdef StartAcquisition(self) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: StartAcquisition\n\t\t# Description: Starts Acquisition on Kratos\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Starting Acquisition\")\n\t\tmethodString = \"-m StartAcquisition\"\n\t\treturn self.SendCommandAndGetResponse(methodString)\n\n\n\tdef SendSwTrigger(self) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: SendSwTrigger\n\t\t# Description: Send SW Trigger command to start measurement\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Sending Software Trigger\")\n\t\tmethodString = \"-m SendSwTrigger\"\n\t\treturn self.SendCommandAndGetResponse(methodString)\n\n\n\tdef StopAcquisition(self) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: StopAcquisition\n\t\t# Description: Stops Acquisition if in progress\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Stopping Acquisition\")\n\t\tmethodString = \"-m StopAcquisition\"\n\t\treturn self.SendCommandAndGetResponse(methodString)\n\n\n\tdef GetAcquisitionStatus(self) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: GetAcquisitionStatus\n\t\t# Description: This methods checks if Acquisition is in progress\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Getting Acquisition Status\")\n\t\tmethodString = \"-m GetAcquisitionStatus-False\"\n\t\tresult = self.SendCommandAndGetResponse(methodString)\n\t\tif result.Status.HasError() :\n\t\t\treturn None\n\n\t\tacquisitionStatus = \"isAcquiring : \"\n\t\tmatch = re.findall ( acquisitionStatus + '(.*?);', result.CmdResult.Output, re.DOTALL)\n\t\tif len(match) < 1 :\n\t\t\treturn None\n\n\t\tacquisitionStatus = match[0]\n\t\treturn acquisitionStatus\n\n\n\tdef GetExtAcquisitionStatus(self) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: GetExtAcquisitionStatus\n\t\t# Description: This methods gets the Ext Acquisition\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Getting Acquisition Status\")\n\t\tmethodString = \"-m GetExtAcquisitionStatus-IDLE\"\n\t\tresult = self.SendCommandAndGetResponse(methodString)\n\t\tif result.Status.HasError() :\n\t\t\treturn None\n\n\t\textAcquisitionStatus = \"extAcquisitionStatus : \"\n\t\tmatch = re.findall ( extAcquisitionStatus + '(.*?);', result.CmdResult.Output, re.DOTALL)\n\t\tif len(match) < 1 :\n\t\t\treturn None\n\n\t\textAcquisitionStatus = match[0]\n\t\treturn extAcquisitionStatus\n\n\n\tdef GetAcquisitionError(self) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: GetAcquisitionError\n\t\t# Description: Get Acquisition Error if encountered\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Getting Acquisition Error\")\n\t\tmethodString = \"-m GetAcquisitionError\"\n\t\treturn self.SendCommandAndGetResponse(methodString)\n\n\t# end Acquisition Operations\n\n\tdef IsKratosBusy(self):\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: IsKratosBusy\n\t\t# Description: This method checks if Krtos is Busy\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Checking if Kratos is Busy\")\n\t\tmethodString = \"-m IsKratosBusy-false\"\n\t\tresult = self.SendCommandAndGetResponse(methodString)\n\t\tif result.Status.HasError() :\n\t\t\treturn None\n\n\t\tkratosStatus = \"isBusy : \"\n\t\tmatch = re.findall ( kratosStatus + '(.*?);', result.CmdResult.Output, re.DOTALL)\n\t\tif len(match) < 1 :\n\t\t\treturn None\n\n\t\tkratosStatus = match[0]\n\t\treturn kratosStatus\n\n\n\t# Calibration Operations\n\tdef SelfCalibration(self) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: SelfCalibration\n\t\t# Description: Performs Self Calibration\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Performing Self Calibration\")\n\t\tmethodString = \"-m SelfCalibration\"\n\t\treturn self.SendCommandAndGetResponse(methodString)\n\n\tdef DmmMuxCalibration(self) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: DmmMuxCalibration\n\t\t# Description: Performs Dmm Mux Calibration on Kratos\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Performing Dmm Mux Calibration\")\n\t\tmethodString = \"-m DmmMuxCalibration\"\n\t\treturn self.SendCommandAndGetResponse(methodString)\n\n\t# Calibration Operations\n\tdef CheckCalibrationStatus(self, calibrationStatusList) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: CheckCalibrationStatus\n\t\t# Description: Checks Status of Self Calibration, DMM Calibration & External Calibration\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\tlogger.info(\"Checking Calibration\")\n\t\tmethodString = \"-m CheckCalibrationStatus-false,false,false\"\n\t\tresult = self.SendCommandAndGetResponse(methodString)\n\t\tif result.Status.HasError() :\n\t\t\tlogger.error(\"Error in Sending Command to get Single Point Statistics\")\n\t\t\treturn result\n\n\t\tselfCalibrationStatus = \"selfCalOk : \"\n\t\tdmmCalibrationStatus = \"dmmCalOk : \"\n\t\texternalCalibrationStatus =\"extCalOk : \"\n\n\t\tmatch = re.findall ( selfCalibrationStatus + '(.*?);', result.CmdResult.Output, re.DOTALL)\n\t\tif len(match) < 1 :\n\t\t\treturn StatusResult.Error(\"Could not read Self Calibration Status\")\n\t\tselfCalibrationStatus = match[0]\n\n\t\tmatch = re.findall ( dmmCalibrationStatus + '(.*?);', result.CmdResult.Output, re.DOTALL)\n\t\tif len(match) < 1 :\n\t\t\treturn StatusResult.Error(\"Could not read DMM Calibration Status\")\n\t\tdmmCalibrationStatus = match[0]\n\n\t\tmatch = re.findall ( externalCalibrationStatus + '(.*?);', result.CmdResult.Output, re.DOTALL)\n\t\tif len(match) < 1 :\n\t\t\treturn StatusResult.Error(\"Could not read External Calibration Status\")\n\t\texternalCalibrationStatus = match[0]\n\n\t\tcalibrationStatusList.append(selfCalibrationStatus)\n\t\tcalibrationStatusList.append(dmmCalibrationStatus)\n\t\tcalibrationStatusList.append(externalCalibrationStatus)\n\n\t\treturn result\n\n\tdef SendCommandAndGetResponse(self, methodStrings, timeout = 120) :\n\t\t\"\"\"\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t# Name: SendCommandAndGetResponse\n\t\t# Input: Takes argument :\n\t\t# \tmethodStrings: methods name followed by their parameters.\n\t\t#\tFor example : -m methodName-param1,param2\n\t\t# Description: check If it is Qualcomm Root Build\n\t\t# Return: StatusResult() object\n\t\t#-------------------------------------------------------------------------------------------------------------------\n\t\t\"\"\"\n\t\t#cptfInterfaceExecutable = 'C:\\\\Automation\\\\PTAS_Engine\\\\CPTF_Lib_Interface\\\\CPTF_LibraryInterface.exe'\n\t\tcptfInterfaceExecutable = CommonApplicationUtilities._ResourcesProgramPath + \"CPTF_LibraryInterface\\\\Qualcomm.CPTF.LibraryInterface.exe\"\n\t\tdllPath = CommonApplicationUtilities._ResourcesProgramPath + \"CPTF_LibraryInterface\\\\Qualcomm.CPT.Automation.Plugins.HW.Kratos.dll\"\n\t\tclassName = 'Qualcomm.CPT.Automation.Plugins.HW.Kratos.KratosApi'\n\n\t\texecutionString = cptfInterfaceExecutable + \" -a \" +  dllPath + \" -c \" +  className + \" -m SetupKratos-\" + self.kratosPcIpAddress + \" \" +  methodStrings\n\t\tlogger.debug(\"Executing Command :\" +  executionString)\n\t\tresult = CommandLine.RunCommand(executionString, timeout)\n\t\tif result.Status.HasError() or 'Error' in result.CmdResult.Output :\n\t\t\tlogger.error(result.CmdResult.Output)\n\t\t\tresult.Status.AddError(result.CmdResult.Output)\n\n\t\treturn result\n","repo_name":"Master-An/cpu_power_final","sub_path":"hardware_handler/kratosApi.py","file_name":"kratosApi.py","file_ext":"py","file_size_in_byte":16527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"36801378522","text":"#You are given a positive integer N which represents the number of steps in a staircase. You can either climb 1 or 2 steps at a time.\r\n#Write a function that returns the number of unique ways to climb the stairs.\r\ndef staircase(n):\r\n    if(n < 0):\r\n        return 0 #illegal move\r\n    if(n == 1):\r\n        return 1 #just 1 step\r\n    if(n == 2):\r\n        return 2 # 1 step then 1 step, or 2 step\r\n    return staircase(n-1) + staircase(n-2)\r\n  \r\nprint(staircase(4))\r\n# 5\r\nprint(staircase(5))\r\n# 8\r\n","repo_name":"dragonsarebest/Portfolio","sub_path":"Code/13October2020.py","file_name":"13October2020.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"19173894432","text":"from flask import Flask, request, make_response, jsonify\nimport os\nimport subprocess\n\napp = Flask(__name__)\n\n@app.route('/', methods=['GET'])\ndef ping():\n    return \"pong\"\n\n@app.route('/videos/process/', methods=['POST'])\ndef process_video():\n    print('Accepted Request')\n    video_id = request.json['videoId']\n    output_path = '/storage/var/videofiles/'+video_id\n    if not os.path.exists(output_path):\n        os.makedirs(output_path)\n    return_code = subprocess.call(\"ffmpeg -i /storage/var/video/\" + video_id + \"/video.mp4 -map 0:v:0 -map 0:a\\?:0 -map 0:v:0 -map 0:a\\?:0 -map 0:v:0 -map 0:a\\?:0 -map 0:v:0 -map 0:a\\?:0 -map 0:v:0 -map 0:a\\?:0 -map 0:v:0 -map 0:a\\?:0 -b:v:0 350k -c:v:0 libx264 -filter:v:0 'scale=320:-1' -b:v:1 1000k -c:v:1 libx264 -filter:v:1 'scale=640:-1' -b:v:2 3000k -c:v:2 libx264 -filter:v:2 'scale=1280:-1' -b:v:3 245k -c:v:3 libvpx-vp9 -filter:v:3 'scale=320:-1' -b:v:4 700k -c:v:4 libvpx-vp9 -filter:v:4 'scale=640:-1' -b:v:5 2100k -c:v:5 libvpx-vp9 -filter:v:5 'scale=1280:-1' -use_timeline 1 -use_template 1 -window_size 6 -adaptation_sets 'id=0,streams=v id=1,streams=a' -f dash \" + output_path + \"/video.mpd\", shell=True)\n    if(return_code == 1):\n        return make_response(jsonify({'message': 'Error in uploading video'}), 400)\n    else:\n        return make_response(jsonify({'message': 'Video uploaded successfully'}), 200)\n\nif __name__ == \"__main__\":\n    app.run(host=\"0.0.0.0\")\n\n","repo_name":"amarchese96/ds_project","sub_path":"docker/video-processing-service/video_processing_service.py","file_name":"video_processing_service.py","file_ext":"py","file_size_in_byte":1424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"592644112","text":"# https://leetcode.com/problems/longest-substring-without-repeating-characters/\n# Given a string s, find the length of the longest substring without \n# repeating characters.\n# s = \"abcabcbb\" --> 3\n# s = \"bbbbb\" --> 1\n# s = \"pwwkew\" --> 3\n\ns = \"abcabcbb\"\ns2 = \"bbbbb\" \ns3 = \"pwwkew\" \n\ndef longest_substring(s):\n    char_set = set()\n    left = 0\n    result = 0\n    \n    for right in range(len(s)):\n        while s[right] in char_set:\n            char_set.remove(s[left])\n            left += 1\n            print(f\"Inside the while loop: s[right] is {s[right]} and char_set is {char_set}\")\n        char_set.add(s[right])\n        print(f\"Outside while loop: char_set is {char_set}\")\n        result = max(result, right - left + 1)\n        print(f\"Result is {result}\")\n    \n    return result\n\nprint(longest_substring(s))\n# print(longest_substring(s2))\n# print(longest_substring(s3))\n\n","repo_name":"patchwork109/ds-algo-python","sub_path":"arrays (lists)/longest_substring_wo_repeating_chars.py","file_name":"longest_substring_wo_repeating_chars.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"38503661628","text":"'''1.array in python store only similar types of data like only interger or only float or only string types not mixup of integer/floate/string types\n2. in python array size is expandible and srink it as per our requirement\n3. by default array not supported into python for using of array first import library of array\n4. 'i' =integer , 'u'= charecter, 'd'=floate '''\n\nimport array as arr\nvar= arr.array('i',[1,5,3,6,8,9]) # 'i' represent +ve and -ve interger values not support float or char\n\n#reverse array\nvar.reverse()\nprint(var)  #array('i', [9, 8, 6, 3, 5, 1])\n\nfor j in range(len(var)):\n    print(var[j], end=' ') # op 1 5 3 6 8 9\n\nvar.append(2)\nprint(var)  #op array('i', [1, 5, 3, 6, 8, 9, 2])\n\nvar.insert(1,10)\nprint(var)  #array('i', [1, 10, 5, 3, 6, 8, 9]) in insert 1 is index and 10 is values update on index 1\n","repo_name":"Dikasta/python_prac1","sub_path":"Array_concepts.py","file_name":"Array_concepts.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"10002471313","text":"\n# coding: utf-8\n\n# <p>Class:  USC Viterbi Data Analytics Bootcamp</p>\n# <p>Team:  Analyticus (aka Team 5)</p>\n# <p>Module:  pull_cdc_national_10_years.py<p>\n# <p>Version:  March 31, 2018\n# <p>Input:  CDC Influenza-Like-Illness CSV File containing ten years of data.</p>\n# <p>Output:  CDC json file containing ten years of CDC data normalized to HHS flu season.</p>\n\n# In[2]:\n\n\n# Import dependances.\nimport json\nimport csv\nimport pandas as pd\n\n\n# In[3]:\n\n\n# Load CDC data into a dataframe.\ndf = pd.read_csv('../data/cdc_national_10_years.csv', skiprows=[0])\n\n\n# In[4]:\n\n\n# Validate the CDC dataframe.\ndf.head()\n\n\n# In[6]:\n\n\n# Sort the data into year, week, total sequence to aid analysis.\ndf2 = df[['YEAR', 'WEEK', 'ILITOTAL']]\n\n\n# In[7]:\n\n\n# Inspect the data.\ndf2.head()\n\n\n# In[8]:\n\n\n# Inspect the data.\ndf2.tail()\n\n\n# In[9]:\n\n\n# Normalize the CDC year and week to HHS year and week.\ndf3 = pd.DataFrame()\nfor df2_index in df2.index:\n    if df2.loc[df2_index, 'WEEK'] > 39:\n        df3.loc[df2_index, 'YEAR'] = df2.loc[df2_index, 'YEAR']\n        df3.loc[df2_index, 'WEEK'] = df2.loc[df2_index, 'WEEK'] - 39\n    else:\n        df3.loc[df2_index, 'WEEK'] = df2.loc[df2_index, 'WEEK'] + 13\n        df3.loc[df2_index, 'YEAR'] = df2.loc[df2_index, 'YEAR'] - 1\n    df3.loc[df2_index, 'ILITOTAL'] = df2.loc[df2_index, 'ILITOTAL']\n    \n\n\n# In[10]:\n\n\n# Inspect the normalized data.\ndf3.head()\n\n\n# In[11]:\n\n\n# Sort the normalized data to aid analysis.\ndf4 = df3.sort_values(['YEAR', 'WEEK'])\n\n\n# In[12]:\n\n\n# Inspect the data.\ndf4.head()\n\n\n# In[13]:\n\n\n# Inspect the data.\ndf4.tail()\n\n\n# In[14]:\n\n\n# Convert year and week from type float to type int.\ndf5 = df4.loc[:,['YEAR', 'WEEK','ILITOTAL']].astype(int)\n\n\n# In[15]:\n\n\n# Inspect the year and week integers.\ndf5.head()\n\n\n# In[17]:\n\n\n# Calculate total flu cases by year.  \n# The sum will be used for calculating percentages.\ndf6 = pd.DataFrame(df5.groupby('YEAR').agg({'ILITOTAL': 'sum'}))\n\n\n# In[18]:\n\n\n# Inspect the cases sums by year.\ndf6.head()\n\n\n# In[19]:\n\n\n# Check the code needed to access the case sum.\ndf6.columns\ndf6.loc[2011, 'ILITOTAL']\n\n\n# In[20]:\n\n\n# Calculate the case percentage by dividing the week cases by the sum of cases for the year.\nfor df5_index in df5.index:\n    df6_index = df5.loc[df5_index, 'YEAR']\n    df5.loc[df5_index, 'FLU_PERCENT'] = (df5.loc[df5_index, 'ILITOTAL'] / df6.loc[df6_index, 'ILITOTAL']) * 100\n\n\n# In[21]:\n\n\n# Verify that the case percentages for a year add to 100 percent.\npd.DataFrame(df5.groupby('YEAR').agg({'FLU_PERCENT': 'sum'}))\n\n\n# In[22]:\n\n\n# Write the data to a json file.\ndf5.to_json('../data/cdc_national_10_years.json')\n\n\n# In[24]:\n\n\n# Load a dataframe with data from the just-written file for validation.\ndf7 = pd.read_json('../data/cdc_national_10_years.json')\n\n\n# In[26]:\n\n\n# Sort the data to aid validation.\ndf8 = df7.sort_values(by=['YEAR', 'WEEK'])\n\n\n# In[27]:\n\n\n# Validate CDC data.\ndf8.head()\n\n\n# In[28]:\n\n\n# Validate CDC data.\ndf8.tail()\n\n","repo_name":"zhan656/Analyticus_Project","sub_path":"python/pull_cdc_national_10_years.py","file_name":"pull_cdc_national_10_years.py","file_ext":"py","file_size_in_byte":2944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"85"}
+{"seq_id":"33949419044","text":"import unittest\n\nfrom src.graph.tarjan.template import TarjanUndirected\n\n\nclass TestGeneral(unittest.TestCase):\n    def test_undirected_graph(self):\n        # 无向无环图\n        edge = [[1, 2], [0, 3], [0, 3], [1, 2]]\n        n = 4\n        ta = TarjanUndirected()\n        cut_edge, cut_node, sub_group = ta.check_graph(edge, n)\n        assert not cut_edge\n        assert not cut_node\n        assert sub_group == [[0, 1, 2, 3]]\n\n        # 无向有环图\n        edge = [[1, 2, 3], [0, 2], [0, 1], [0]]\n        n = 4\n        cut_edge, cut_node, sub_group = ta.check_graph(edge, n)\n        assert cut_edge == [[0, 3]]\n        assert cut_node == [0]\n        assert sub_group == [[0, 1, 2], [3]]\n\n        # 无向有环图\n        edge = [[1, 2], [0, 2], [0, 1, 3], [2]]\n        n = 4\n        cut_edge, cut_node, sub_group = ta.check_graph(edge, n)\n        assert cut_edge == [[2, 3]]\n        assert cut_node == [2]\n        assert sub_group == [[0, 1, 2], [3]]\n\n        # 无向有自环图\n        edge = [[1, 2], [0, 2], [0, 1, 3], [2, 3]]\n        n = 4\n        cut_edge, cut_node, sub_group = ta.check_graph(edge, n)\n        assert cut_edge == [[2, 3]]\n        assert cut_node == [2]\n        assert sub_group == [[0, 1, 2], [3]]\n        return\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"liupengsay/PyIsTheBestLang","sub_path":"src/graph/tarjan/example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":1297,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"86"}
+{"seq_id":"23567595388","text":"from django.core import mail as dj_mail\nfrom django.conf import settings\n\nfrom mysite.templates.email.template import player_regist_template\nfrom .models import Mail\n\n\ndef email_sending():\n    for mail in Mail.objects.all():\n        data = {\n            'scenario_name': mail.scenario_name,\n            'character_name': mail.character_name,\n            'player_name': mail.player_name,\n            'player_phone_number': mail.player_phone_number\n        }\n        dj_mail.send_mail(\n            subject='[할로윈 파티] 신청 확인',\n            message=player_regist_template % data,\n            from_email=settings.EMAIL_HOST_USER,\n            recipient_list=[mail.player_email],\n            html_message=player_regist_template % data\n        )\n        mail.delete()\n","repo_name":"koyouhun/mysite","sub_path":"mysite/apps/mail/cron.py","file_name":"cron.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"16255777267","text":"'''\n#############################################################################################################\n**题目92：（链表）\n给你一个链表数组，每个链表都已经按升序排列。\n请你将所有链表合并到一个升序链��中，返回合并后的链表。\n**示例：\n输入：lists = [[1,4,5],[1,3,4],[2,6]]\n输出：[1,1,2,3,4,4,5,6]\n解释：链表数组如下：\n[\n  1->4->5,\n  1->3->4,\n  2->6\n]\n将它们合并到一个有序链表中得到。\n1->1->2->3->4->4->5->6\n#############################################################################################################\n'''\n\n'''\n暴力方法：\n两两合并，新合并好的结果再与下一个链表合并\n复杂度分析：\n时间复杂度：O(M^2*N) 其中M为链表的个数，N为链表的最长长度\n空间复杂度：O(1)\n'''\nclass Solution1(object):\n    def mergeKLists(self, lists):\n        if len(lists) == 0:\n            return None\n        res = lists[0]\n        for i in range(1, len(lists)): #n\n            res = self.merge_two_list(res, lists[i])\n        return res\n    def merge_two_list(self, lst1, lst2):\n        if lst1 == None or lst2 == None:\n            return lst1 or lst2\n        if lst1.val < lst2.val:\n            lst1.next = self.merge_two_list(lst1.next, lst2)\n            return lst1\n        else:\n            lst2.next = self.merge_two_list(lst1, lst2.next)\n            return lst2\n\n'''\n暴力方法的改进1（递归）：\n类似于归并排序，先不断去分解，直到分解为单一链表为止，然后去合并两个链表\n复杂度分析：\n时间复杂度：O(MNlogM)\n空间复杂度：O(logM)\n'''\nclass Solution3(object):\n    def mergeKLists(self, lists):\n        m = len(lists)\n        if m == 0:\n            return None\n        return self.merge(lists, 0, m - 1)\n\n    def merge(self, lists, left, right): ##分解\n        if left == right:\n            return lists[left]\n        mid = (right + left) // 2\n        lst1 = self.merge(lists, left, mid)\n        lst2 = self.merge(lists, mid + 1, right)\n        return self.merge2list(lst1, lst2)\n\n    def merge2list(self, lst1, lst2):  ##合并\n        if lst1 == None or lst2 == None:\n            return lst1 or lst2\n        if lst1.val < lst2.val:\n            lst1.next = self.merge2list(lst1.next, lst2)\n            return lst1\n        else:\n            lst2.next = self.merge2list(lst1, lst2.next)\n            return lst2\n\n\n'''\n小堆：\n根据链表的节点值构建小堆，每次拿出堆顶元素（当前最小），将堆顶元素所在链表的下一个节点加入到堆中\n对堆的操作：删除节点pop（获取堆顶元素，用堆中最后一个元素去替换堆顶元素，再将堆顶元素下沉到合适的位置）；\n增加节点push（添加到堆的末尾，在将该元素上浮到合适的位置）\n复杂度分析：\n时间复杂度：O(MNlogM)\n空间复杂度：O(M) \n'''\n# Definition for singly-linked list.\nclass ListNode(object):\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\nclass Solution4(object):\n    def __init__(self):\n        self.queue = []\n        self.size = 0\n    def mergeKLists(self, lists):\n        for lst in lists:\n            if lst:\n                self.push(lst)\n        new_node = ListNode(0)\n        new_node1 = new_node\n        while self.size > 0:\n            node = self.pop()\n            new_node1.next = node\n            if node.next:\n                self.push(node.next)\n            new_node1 = new_node1.next\n        return new_node.next\n\n    def push(self,lst):  ##构建\n        self.queue.append(lst)\n        self.size += 1\n        indx = self.size - 1\n        while indx > 0:\n            parent = (indx - 1) // 2\n            if self.queue[parent].val > self.queue[indx].val:\n                self.swap(parent, indx)\n            else:\n                break\n            indx = parent\n\n    def pop(self):  ##删除\n        top = self.queue[0]\n        self.queue[0] = self.queue[-1]\n        self.queue.pop(-1)\n        self.size -= 1\n        indx = 0\n        while self.size > indx:\n            left = indx * 2 + 1\n            right = indx * 2 + 2\n            small_indx = indx\n            if left < self.size and self.queue[left].val < self.queue[indx].val:\n                small_indx=left\n            if right < self.size and self.queue[right].val < self.queue[small_indx].val:\n                small_indx = right\n            if small_indx == indx:\n                break\n            self.swap(small_indx, indx)\n            indx = small_indx\n        return top\n\n    def swap(self,parent,indx): ##交换\n        self.queue[parent], self.queue[indx] = self.queue[indx], self.queue[parent]","repo_name":"zhangchen-fzu/my_leetcode","sub_path":"92lc合并k个链表.py","file_name":"92lc合并k个链表.py","file_ext":"py","file_size_in_byte":4681,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"29578010225","text":"import numpy as np\nimport pandas as pd\n\nmovie_df = pd.read_csv(\"/home/amirreza/Desktop/basic_machine_learning_projects/recommendation_systems/movies.csv\")\nrating_df = pd.read_csv(\"/home/amirreza/Desktop/basic_machine_learning_projects/recommendation_systems/ratings.csv\")\n\nmovie_df[\"year\"] = movie_df[\"title\"].str.extract(\"(\\(\\d\\d\\d\\d\\))\", expand=False)\nmovie_df[\"year\"] = movie_df[\"year\"].str.extract(\"(\\d\\d\\d\\d)\", expand=False)\n#movie_df[\"year\"] = pd.to_numeric(movie_df[\"year\"])\nmovie_df = movie_df.dropna()\n#movie_df[\"year\"] = movie_df[\"year\"].astype(int)\nmovie_df[\"title\"] = movie_df[\"title\"].str.replace(\"(\\(\\d\\d\\d\\d\\))\", \"\")\nmovie_df[\"title\"] = movie_df[\"title\"].apply(lambda x: x.strip())\nmovie_df[\"genres\"] = movie_df[\"genres\"].apply(lambda x: x.split(\"|\"))\nfirst_movie_df = movie_df.copy()\n\nfor index, row in movie_df.iterrows():\n    for genres in row[\"genres\"]:\n        movie_df.at[index, genres] = 1\n    #print(row[\"year\"])\n    if int(row[\"year\"]) >= 2000:\n        movie_df.at[index, \">2000\"] = 0.5\n    if int(row[\"year\"]) >= 2010:\n        movie_df.at[index, \">2000\"] = 1\nmovie_df = movie_df.fillna(0)\n\n\nrating_df[\"timestamp\"] = rating_df[\"timestamp\"] > 946080000\n\nuserInput = [\n            {'title':'Iron Man', 'rating':4.5},\n            {'title':'Interstellar', 'rating':5},\n            {'title':'Captain America: The First Avenger', 'rating':4.5},\n            {'title':'Iron Man 3', 'rating':4},\n            {'title':'Thor', 'rating':4},\n            {'title':\"Pulp Fiction\", 'rating':3.5},\n         ] \n\ninput_df = pd.DataFrame(userInput)\n\n#print(input_df)\n\nuser_input_movie = first_movie_df[movie_df[\"title\"].isin(input_df[\"title\"].tolist())]\ninput_movie = movie_df[movie_df[\"title\"].isin(input_df[\"title\"].tolist())]\ninput_movie = pd.merge(input_df, input_movie).drop(\"movieId\", axis=1).drop(\"title\", axis=1).drop(\"genres\", axis=1).drop(\"(no genres listed)\", axis=1).drop(\"year\", axis=1)\nmovie_matrix = movie_df.drop(\"title\", axis=1).drop(\"genres\", axis=1).drop(\"(no genres listed)\", axis=1).drop(\"year\", axis=1)\n\nuser_score = input_movie[\"rating\"]\nuser_genres = input_movie.drop(\"rating\", axis=1)\n\ngenres_score = user_score.transpose().dot(user_genres)\nmovie_matrix = movie_matrix.set_index(movie_matrix[\"movieId\"]).drop(\"movieId\", 1)\nrecommendation_df = (genres_score * movie_matrix).sum(axis=1) / genres_score.sum() * 100\nrecommendation_df = recommendation_df.sort_values(ascending=False)\nrecommendation_df = first_movie_df[~first_movie_df[\"movieId\"].isin(user_input_movie[\"movieId\"])].loc[first_movie_df[\"movieId\"].isin(recommendation_df.head(20).keys())]\n#recommendation_df = recommendation_df[~recommendation_df[\"movieId\"].isin(input_movie[\"movieId\"])]\n\nprint(\"\\n\\n20 best movies you should to see:\")\nprint(recommendation_df[[\"movieId\", \"title\", \"year\"]])\n","repo_name":"ARHPA/basic_machine_learning_projects","sub_path":"recommendation_systems/content_base.py","file_name":"content_base.py","file_ext":"py","file_size_in_byte":2778,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"38119298282","text":"import numpy as np\nfrom scipy.optimize import curve_fit\n\ndef get_levels( signal, sigma = 1, trace=0, t0=0, t1=-1 ):\n    \"\"\"Level detection algorithm.\n    \n    This function uses a gaussian fit around the main signal to determine the open pore current and threshold cut-off.\n        \n    Parameters\n    ----------\n    singal : numpy array\n        The signal should be fed as a array of arrays.\n        Each top-level array is threated as a trace of a signal allowing easy cross-trace analysis (for e.g. current dependent analysis).\n    sigma : int\n        The number of sigma (multiplier), that the threshold is set from the open pore.\n    trace : int\n        The trace to be analysed (n-th number array)\n    t0 : int\n        The first datapoint to be analysed in the trace.\n    t1 : int\n        The last datapoint to be analysed in the trace\n    \n    Returns\n    -------\n    tuple\n        A tuple containing (in order), centroid of the open pore current and threshold.\n        \n    \"\"\"\n    try:\n        signal = signal[ trace ][ t0:t1 ]\n        mu, std, res = gauss_deconv( abs( np.array( signal ) ) * -1 )\n        l0 = mu * np.sign( sum( signal ) ) * - 1\n        l1 = ( ( abs( std ) * sigma ) ) * np.sign( sum( signal ) ) * -1\n        return ( True, l0, l1 )\n    except:\n        return ( False, 0, 0 )\n\ndef gauss( x, *p ): \n    a, mu, sigma = p\n    return a*np.exp( -( x - mu )**2 / float( 2 * sigma**2 ) )\n\ndef gauss_deconv( signal, n_peaks = 2 ):\n    try:\n        bins = np.linspace( min( signal ), max( signal ), 2000 )    # Bins the signal\n        hist = np.histogram( np.abs( signal ) * -1, bins=bins )     # Histogram sthe signal\n        means, stds = ( [], [] )                                    # Initialise variables\n        x = np.diff(hist[1]) + hist[1][0:-1]                        # Calculate the x coordinates\n        res = hist[0].astype( 'float64' )                           # Type cast the corresponding y values\n        try:\n            for i in range( n_peaks ):\n                max_bin = np.where( max(res) == res )[0][0]         # Bin width the most counts\n                coeff, var_matrix = curve_fit(gauss,                # Fit Gaussian peak\n                                              x[max_bin-10:max_bin+10],\n                                              res[max_bin-10:max_bin+10],\n                                              p0=[ max(res), x[ max_bin ], 1 ])\n                res -= gauss(x, *coeff)                             # Subtract the Gaussian peak calculated\n                a, mu, s = coeff                                    # Unzip variables\n                means.append( mu )\n                stds.append( s )\n        except:\n            pass\n        finally:\n            if stds[ np.argmax( np.abs( means ) ) ]!=1.0:\n                return means[ np.argmax( np.abs( means ) ) ], stds[ np.argmax( np.abs( means ) ) ], res\n            else:\n                return means[ np.argmax( np.abs( means ) ) ], np.std( signal ), res\n    except:\n        pass","repo_name":"cvdelannoy/chop_n_drop_simulation","sub_path":"experimental/python_scripts/nanolyse/get_levels.py","file_name":"get_levels.py","file_ext":"py","file_size_in_byte":2995,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"29195021473","text":"import pyshorteners\r\nfrom tkinter import*\r\n\r\nwindow =Tk()\r\nwindow.geometry(\"400x720\")\r\nwindow.config(bg=\"violet\")\r\n\r\ndef short():\r\n    url = E1.get()\r\n    S = pyshorteners.Shortener().tinyurl.short(url)\r\n    E2.insert(END,S)\r\n\r\nLabel(window,text=\"Enter your URL Link\",font=\"impack 40\",bg=\"purple\",fg=\"white\").pack(fill='x')\r\nE1 = Entry(window,font=\"Ariel 20\",bd=3,width=40)\r\nE1.pack(pady=30)\r\n\r\nButton(window,text=\"CLICK\",font=\"bold 18\",bg=\"darkorchid\",fg=\"white\",command = short).pack()\r\n\r\nE2=Entry(window,font=\"impact 16 bold\",bg=\"violet\",width=24,bd=0)\r\nE2.pack(pady=30)\r\nmainloop()","repo_name":"annpurna04/CodeClause_URLShortner","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"24339474686","text":"from flask import Flask, render_template, request, session,redirect\nfrom flask_sqlalchemy import SQLAlchemy\n\n\n\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql://root:vnzBQ8qAXKmyJAlR8OXl@containers-us-west-103.railway.app:5953/railway'\ndb = SQLAlchemy(app)\n\napp.secret_key = 'fabricio'\n\n\n\nclass filmes(db.Model):\n    nome = db.Column(db.String(100), primary_key=True, nullable=False)\n    genero = db.Column(db.String(60),nullable=False)\n    diretor = db.Column(db.String(60),nullable=False)\n    ano = db.Column(db.Integer)\n\n\n\n\n@app.route('/inicio')\ndef inicio():\n    lista = filmes.query.all()\n    return render_template('index.html',lista=lista)\n\n#só pra página inicial n ficar vazia, poderia ter começado com a lista porém acho feio (não me pergunte pq)\n@app.route('/')\ndef sla():\n    return render_template('inicio.html')\n\n\n\n\n@app.route('/catalogar',methods=['POST',])\ndef catalogar():\n    nome = request.form.get('nome_cadastro')\n    genero = request.form.get('genero_cadastro')\n    diretor = request.form.get('diretor_cadastro')\n    ano = request.form.get('ano_cadastro')\n\n    #procure no banco de dados o primeiro item com o nome igual ao fornecido no form (nome=nome). Se houver não faça nada, se não houver adicione\n    if filmes.query.filter_by(nome=nome).first():\n        return redirect('/inicio')\n    else:\n        var_filme = filmes(nome=nome, genero=genero, diretor=diretor, ano=ano)\n        db.session.add(var_filme)\n        db.session.commit()\n    \n    lista = filmes.query.all()\n    return render_template('index.html',lista=lista)\n\n\n#meramente redireciona para cadastro_filme.html\n@app.route('/cadastro')\ndef cadastrar():\n        return render_template('cadastro_filme.html')\n\n\n@app.route('/editar/<string:nome>')\ndef editar(nome):\n    filminho = filmes.query.filter_by(nome=nome).first()\n    return render_template('editar.html',editFilme=filminho)\n\n\n@app.route('/atualizar',methods=['POST',])\ndef atualizar():\n    filme_att = request.form.get('filme_att')\n    film = filmes.query.filter_by(nome=filme_att).first()\n    \n    if request.form.get('nome_cadastro_edit') != '':\n        film.nome = request.form.get('nome_cadastro_edit')\n    else:\n        redirect('/atualizar')\n    film.genero = request.form.get('genero_cadastro_edit')\n    film.diretor = request.form.get('diretor_cadastro_edit')\n    film.ano = request.form.get('ano_cadastro_edit')\n\n    db.session.add(film)\n    db.session.commit()\n\n\n    return redirect('/inicio')\n\n@app.route('/deletar',methods=['POST',])\ndef deletar():\n    filme_del = request.form.get('filme_del')\n    filmes.query.filter_by(nome=filme_del).delete()\n    db.session.commit()\n\n    return redirect('/inicio')\n\n#garante que o \"app.run\" só será rodado se aberta neste arquivo\nif __name__ == \"__main__\":\n    app.run()\n\n\n\n\n\n","repo_name":"FabricioNascimentop/FilmeTeca","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2802,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"21398395470","text":"import random\r\n\r\n\r\ndef just_wasting_time():\r\n    for i in range(5):\r\n        num = random.randint(0, 10)\r\n        print(num)\r\n\r\n\r\njust_wasting_time()\r\n\r\n\r\ndef just_wasting_time_two():\r\n    total = 0\r\n    while total < 102:\r\n        print(total )\r\n        total += 1\r\n\r\n\r\njust_wasting_time_two()\r\n\r\n\r\ndef getAnswer(answerNumber):\r\n    if answerNumber == 1:\r\n        return 'It is certain'\r\n    elif answerNumber == 2:\r\n        return 'It is decidedly so'\r\n    elif answerNumber == 3:\r\n        return 'Yes'\r\n    elif answerNumber == 4:\r\n        return 'Reply hazy try again'\r\n    elif answerNumber == 5:\r\n        return 'Ask again later'\r\n    elif answerNumber == 6:\r\n        return 'Concentrate and ask again'\r\n    elif answerNumber == 7:\r\n        return 'My reply is no'\r\n    elif answerNumber == 8:\r\n        return 'Outlook not so good'\r\n    elif answerNumber == 9:\r\n        return 'Very doubtful'\r\n\r\n\r\nr = random.randint(1, 9)\r\nfortune = getAnswer(r)\r\n\r\n\r\nprint(fortune)\r\n\r\n\r\ndef spam(divideBy):\r\n    try:\r\n        return 42 / divideBy\r\n    except ZeroDivisionError:\r\n        print('can not divide by zero please enter a number greater than zero.')\r\n\r\n\r\nprint(spam(2))\r\nprint(spam(12))\r\nprint(spam(0))\r\nprint(spam(1))\r\n\r\n\r\n\r\n","repo_name":"kindyluv/My_Personal_Python_Exercises","sub_path":"Python_Exercise/py_exercise_thirty_four.py","file_name":"py_exercise_thirty_four.py","file_ext":"py","file_size_in_byte":1227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"1879607524","text":"from __future__ import absolute_import, print_function, unicode_literals, division\n\nimport math\nfrom navitiacommon import request_pb2, type_pb2, response_pb2\nfrom jormungandr.fallback_modes import FallbackModes\n\nN_DEG_TO_RAD = 0.01745329238\nEARTH_RADIUS_IN_METERS = 6372797.560856\n\n\ndef crowfly_distance_between(start_coord, end_coord):\n    lon_arc = (start_coord.lon - end_coord.lon) * N_DEG_TO_RAD\n    lon_h = math.sin(lon_arc * 0.5)\n    lon_h *= lon_h\n    lat_arc = (start_coord.lat - end_coord.lat) * N_DEG_TO_RAD\n    lat_h = math.sin(lat_arc * 0.5)\n    lat_h *= lat_h\n    tmp = math.cos(start_coord.lat * N_DEG_TO_RAD) * math.cos(end_coord.lat * N_DEG_TO_RAD)\n    return EARTH_RADIUS_IN_METERS * 2.0 * math.asin(math.sqrt(lat_h + tmp * lon_h))\n\n\ndef get_manhattan_duration(distance, speed):\n    return int((distance * math.sqrt(2)) / speed)\n\n\ndef make_speed_switcher(req):\n    from jormungandr.fallback_modes import FallbackModes\n\n    return {\n        FallbackModes.walking.name: req['walking_speed'],\n        FallbackModes.bike.name: req['bike_speed'],\n        FallbackModes.car.name: req['car_speed'],\n        FallbackModes.car_no_park.name: req['car_no_park_speed'],\n        FallbackModes.bss.name: req['bss_speed'],\n        FallbackModes.ridesharing.name: req['car_no_park_speed'],\n        FallbackModes.taxi.name: req['taxi_speed'],\n    }\n\n\nPARK_RIDE_VALUES = {'yes'}\n\n\ndef pick_up_park_ride_car_park(pt_objects):\n    from navitiacommon import type_pb2\n    from jormungandr import utils\n\n    def is_poi(pt_object):\n        return pt_object.embedded_type == type_pb2.POI\n\n    def is_park_ride(pt_object):\n        return any(\n            prop.type == 'park_ride' and prop.value.lower() in PARK_RIDE_VALUES\n            for prop in pt_object.poi.properties\n        )\n\n    park_ride_poi_filter = utils.ComposedFilter().add_filter(is_poi).add_filter(is_park_ride).compose_filters()\n\n    return list(park_ride_poi_filter(pt_objects))\n\n\ndef create_kraken_direct_path_request(\n    connector, mode, pt_object_origin, pt_object_destination, fallback_extremity, request, language\n):\n    req = request_pb2.Request()\n    req.requested_api = type_pb2.direct_path\n    req.direct_path.origin.CopyFrom(connector.make_location(pt_object_origin))\n    req.direct_path.destination.CopyFrom(connector.make_location(pt_object_destination))\n    req.direct_path.datetime = fallback_extremity.datetime\n    req.direct_path.clockwise = fallback_extremity.represents_start\n    req.direct_path.streetnetwork_params.origin_mode = connector.handle_car_no_park_modes(mode)\n    req.direct_path.streetnetwork_params.destination_mode = connector.handle_car_no_park_modes(mode)\n    req.direct_path.streetnetwork_params.walking_speed = request['walking_speed']\n    req.direct_path.streetnetwork_params.max_walking_duration_to_pt = request['max_walking_duration_to_pt']\n    req.direct_path.streetnetwork_params.bike_speed = request['bike_speed']\n    req.direct_path.streetnetwork_params.max_bike_duration_to_pt = request['max_bike_duration_to_pt']\n    req.direct_path.streetnetwork_params.bss_speed = request['bss_speed']\n    req.direct_path.streetnetwork_params.max_bss_duration_to_pt = request['max_bss_duration_to_pt']\n    req.direct_path.streetnetwork_params.car_speed = request['car_speed']\n    req.direct_path.streetnetwork_params.max_car_duration_to_pt = request['max_car_duration_to_pt']\n    req.direct_path.streetnetwork_params.language = language\n    if mode in (\n        FallbackModes.ridesharing.name,\n        FallbackModes.taxi.name,\n        FallbackModes.car_no_park.name,\n        FallbackModes.car.name,\n    ):\n        req.direct_path.streetnetwork_params.car_no_park_speed = request['{}_speed'.format(mode)]\n        req.direct_path.streetnetwork_params.max_car_no_park_duration_to_pt = request[\n            'max_{}_duration_to_pt'.format(mode)\n        ]\n\n    return req\n\n\ndef create_kraken_matrix_request(\n    connector, origins, destinations, street_network_mode, max_duration, speed_switcher, _, **kwargs\n):\n    req = request_pb2.Request()\n    req.requested_api = type_pb2.street_network_routing_matrix\n\n    req.sn_routing_matrix.origins.extend((connector.make_location(o) for o in origins))\n    req.sn_routing_matrix.destinations.extend((connector.make_location(d) for d in destinations))\n\n    req.sn_routing_matrix.mode = connector.handle_car_no_park_modes(street_network_mode)\n    req.sn_routing_matrix.speed = speed_switcher.get(street_network_mode, kwargs.get(\"walking\"))\n    req.sn_routing_matrix.max_duration = max_duration\n\n    req.sn_routing_matrix.streetnetwork_params.origin_mode = connector.handle_car_no_park_modes(\n        street_network_mode\n    )\n    req.sn_routing_matrix.streetnetwork_params.walking_speed = speed_switcher.get(\n        \"walking\", kwargs.get(\"walking\")\n    )\n    req.sn_routing_matrix.streetnetwork_params.bike_speed = speed_switcher.get(\"bike\", kwargs.get(\"bike\"))\n    req.sn_routing_matrix.streetnetwork_params.bss_speed = speed_switcher.get(\"bss\", kwargs.get(\"bss\"))\n    req.sn_routing_matrix.streetnetwork_params.car_speed = speed_switcher.get(\"car\", kwargs.get(\"car\"))\n    req.sn_routing_matrix.streetnetwork_params.car_no_park_speed = speed_switcher.get(\n        \"car_no_park\", kwargs.get(\"car_no_park\")\n    )\n\n    return req\n\n\ndef add_cycle_lane_length(response):\n    def _is_cycle_lane(path):\n        if path.HasField(str(\"cycle_path_type\")):\n            return path.cycle_path_type != response_pb2.NoCycleLane\n\n        return False\n\n    # We have multiple journeys and multiple sections in direct path\n    for journey in response.journeys:\n        for section in journey.sections:\n            # do not add cycle_lane_length for bss_rent/bss_return & walking sections\n            if section.type == response_pb2.STREET_NETWORK and section.street_network.mode == response_pb2.Bike:\n                cycle_lane_length = sum(\n                    (s.length for s in section.street_network.street_information if _is_cycle_lane(s))\n                )\n                # Since path.length are doubles and we want an int32 in the proto\n                section.cycle_lane_length = int(cycle_lane_length)\n\n    return response\n","repo_name":"hove-io/navitia","sub_path":"source/jormungandr/jormungandr/street_network/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6143,"program_lang":"python","lang":"en","doc_type":"code","stars":416,"dataset":"github-code","pt":"86"}
+{"seq_id":"34807305721","text":"#!/usr/bin/env python3\n# # -*- coding: utf-8 -*-\n\nimport logging\nimport os\n\nfrom mbtemp.devices import boards\nfrom mbtemp.template import mbt_template, mbt_template_bot, mbt_template_top\n\nlogger = logging.getLogger()\nif __name__ == \"__main__\":\n    logger.info(\"Use the script at common/generate.py instead !\")\n\n\ndef generate_board(board, defaults) -> str:\n    count = 0\n    res = \"\"\n    res += mbt_template_top.safe_substitute(\n        defaults,\n        IP_ADDR=board.ip,\n        IP_ASYN_PORT=board.ip_asyn_port,\n    )\n\n    for device in board.devices:\n        res += mbt_template.safe_substitute(\n            IP_ASYN_PORT=board.ip_asyn_port,\n            MBTEMP_ADDRESS=device.address,\n            PREFIX=device.prefix,\n            SCAN_RATE=board.scan_rate,\n            SCAN_RATE_DEVICE=\"10 second\",\n            CH1=device.channels[0],\n            CH2=device.channels[1],\n            CH3=device.channels[2],\n            CH4=device.channels[3],\n            CH5=device.channels[4],\n            CH6=device.channels[5],\n            CH7=device.channels[6],\n            CH8=device.channels[7],\n        )\n\n        count += 1\n\n    res += mbt_template_bot.safe_substitute(defaults)\n    return res\n\n\ndef generate(args, defaults):\n    logger.info(\"Generating MBTemp.\")\n    dir_name = os.path.dirname(os.path.abspath(__file__))\n\n    cmd_key = args.cmd_prefix\n\n    # MBTemp specifics\n    for board in boards:\n        res = generate_board(board, defaults)\n\n        if not os.path.exists(os.path.join(dir_name, \"ioc/cmd/\")):\n            os.makedirs(os.path.join(dir_name, \"ioc/cmd/\"))\n\n        cmd_path = os.path.join(dir_name, \"ioc/cmd/\" + cmd_key + board.file_name)\n        with open(cmd_path, \"w+\") as file:\n            file.write(res)\n\n        os.chmod(cmd_path, 0o774)\n","repo_name":"lnls-sirius/streamdevice-ioc","sub_path":"scripts/mbtemp/generate.py","file_name":"generate.py","file_ext":"py","file_size_in_byte":1760,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"43302142738","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Mar 22 20:50:13 2018\r\n\r\n@author: Michael\r\n\"\"\"\r\n\r\nfrom lxml import html\r\nimport requests\r\nfrom urllib import request\r\nimport json\r\nfrom time import sleep\r\nfrom contextlib import closing\r\n\r\n\r\ndef scrape_user_id(name):\r\n    page = requests.get('https://pubg.op.gg/user/{}?server=na'.format(name))\r\n    tree = html.fromstring(page.content)\r\n    try:\r\n        user_id = tree.xpath('/descendant::div[@data-p-user_id]')[0].attrib['data-p-user_id']\r\n        return user_id\r\n    except IndexError:\r\n        return None\r\n\r\n\r\ndef scrape_match_list(user_id):\r\n    if user_id:\r\n        offset = ''\r\n        match_ids = set()\r\n        n = 0\r\n        while n < 5:\r\n            if offset == '':\r\n                with closing(request.urlopen(\r\n                        'https://pubg.op.gg/api/users/{}/matches/recent?server=na&queue_size=4&mode=fpp'\r\n                        .format(user_id))) as url:\r\n                    data = json.loads(url.read().decode())\r\n            else:\r\n                with closing(request.urlopen(\r\n                        'https://pubg.op.gg/api/users/{}/matches/recent?server=na&queue_size=4&mode=fpp&after={}'\r\n                        .format(user_id, offset))) as url:\r\n                    data = json.loads(url.read().decode())\r\n            for j in range(0, len(data['matches']['items'])):\r\n                offset = data['matches']['items'][j]['offset']\r\n                match_id = data['matches']['items'][j]['match_id']\r\n                match_ids.add(match_id)\r\n            sleep(0)\r\n            n = n + 1\r\n        return match_ids\r\n    else:\r\n        return None\r\n\r\n\r\ndef scrape_match_data(match_id):\r\n    if match_id:\r\n        name_set = set()\r\n        with closing(request.urlopen('https://pubg.op.gg/api/matches/{}/deaths'.format(match_id))) as url:\r\n            data = json.loads(url.read().decode())\r\n        for j in range(0, len(data['deaths'])):\r\n            victim_name = data['deaths'][j]['victim']['user']['nickname']\r\n            name_set.add(victim_name)\r\n            if data['deaths'][j]['killer']:\r\n                killer_name = data['deaths'][j]['killer']['user']['nickname']\r\n                name_set.add(killer_name)\r\n        sleep(0)\r\n        return name_set, data\r\n    else:\r\n        return None\r\n","repo_name":"perfectclear/PUBG-Data","sub_path":"ScraperUtils.py","file_name":"ScraperUtils.py","file_ext":"py","file_size_in_byte":2283,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"5719257029","text":"from typing import Any, Sequence\nfrom time import process_time\nimport os\nimport pathlib\nimport pickle\nimport shutil\nfrom collections import OrderedDict\n\nfrom reprep import Report, MIME_PNG, DataNode\nfrom zuper_commons.text import remove_escapes\nfrom matplotlib import pyplot as plt\nfrom toolz import sliding_window\n\nfrom pdm4ar.exercises.ex02 import graph_search_algo\nfrom pdm4ar.exercises_def import Exercise, ExIn\nfrom pdm4ar.exercises_def.ex02.data import *\nfrom pdm4ar.exercises_def.structures import PerformanceResults\nfrom pdm4ar.exercises_def.structures import out_dir\n\n\n@dataclass(frozen=True)\nclass NodeColors:\n    default: str = \"cyan\"\n    start: str = \"orange\"\n    goal: str = \"green\"\n\n\n@dataclass(frozen=True)\nclass EdgeColors:\n    default: str = \"dimgray\"\n    path: str = \"red\"\n    gt_path: str = \"green\"\n\n\nclass TestValueEx2(ExIn, Tuple[GraphSearchProblem, str]):\n    def str_id(self) -> str:\n        return str(self[1])\n\nclass GraphImageCache:\n    \"\"\"\n    Generating images of graphs is extremely slow (over 90% of evaluation time).\n    Moreover, many of the generated images are the same, within a single run\n    and between multiple runs. This class manages images of graphs, so that identical\n    graphs are not redrawn.\n\n    The cache itself maps \"graph encodings\" to image ids. Then, there is a folder\n    which contains images of the graphs, whose file names correspond to the image ids.\n\n    We must include the node color and edge colors as attributes on the graphs.\n    This is so that if two graphs have different node colors, they will have different\n    encodings, and the GraphImageCache will know to redraw them.\n\n    It is possible that the cache enters an inconsistent state if the user interrupts the\n    program after adding/removing a file, but before the cache data has been updated.\n    We try to prevent this from happening by scheduling these writes as close together\n    as possible, and saving the cache state whenever we modify it. However, inconsistency\n    is still possible. Therefore, every time we load the cache, we check if it matches the\n    directory state, and if not, clear the cache and start over.\n    \"\"\"\n\n    CACHE_SIZE = 100\n\n    def __init__(self):\n        cache_dir = pathlib.Path(out_dir(\"02\")) / \"cache\"\n        cache_file = cache_dir / \"graph_data.pickle\"\n        create_new_cache = False\n\n        # If there is cache data present, use it to fill in our field values.\n        if os.path.exists(cache_file):\n            with open(cache_file, 'rb') as f:\n                self.__dict__ = pickle.load(f).__dict__\n\n            if not self.consistency_check(cache_dir):\n                shutil.rmtree(cache_dir)\n                create_new_cache = True\n        else:\n            create_new_cache = True\n\n        self.cache_dir = cache_dir\n        self.cache_file = cache_file\n\n        if create_new_cache:\n            os.makedirs(cache_dir, exist_ok=True)\n            self.cache = OrderedDict()\n            self.counter = 0\n\n    def consistency_check(self, cache_dir) -> bool:\n        \"\"\"It is possible that the cache enters an inconsistent state if the program is interrupted\n        between the time that someone writes/deletes and image and saves the cache data. We try to\n        make this unlikely, by grouping these actions close together. However, if it does occur,\n        we just delete the cache and start over :(\n        \"\"\"\n        image_file_names = sorted(os.listdir(cache_dir))[:-1]  # everything except the pickle file\n        if len(image_file_names) != len(self.cache):\n            return False\n        for image_id in self.cache.values():\n            if f\"{image_id}.png\" not in image_file_names:\n                return False\n        return True\n\n    def create_graph_image_node(self, graph: nx.Graph, name: str, pos, figsize) -> DataNode:\n        \"\"\"\n        Create an image node, containing the image of the graph. When creating\n        the html report, a parent node can add this image node as a child.\n        \"\"\"\n        key = GraphImageCache.graph_encoding(graph)\n        if key in self.cache:\n            self.cache.move_to_end(key, last=True)\n        else:\n            # Make room in the cache, if necessary\n            if len(self.cache) >= GraphImageCache.CACHE_SIZE:\n                self.remove_oldest_graph_from_cache()\n\n            self.add_graph_to_cache(graph, key, pos, figsize)\n\n        # Create the image node.\n        # In order to do this, we have to read in the graph image data\n        image_id = self.cache[key]\n        image_file = self.cache_dir / self.image_file(image_id)\n        with open(image_file, \"rb\") as f:\n            image_bytes = f.read()\n\n        image_node = DataNode(nid=name, data=image_bytes, mime=MIME_PNG)\n        return image_node\n\n    def save(self):\n        with open(self.cache_file, 'wb') as f:\n            pickle.dump(self, f)\n\n    def remove_oldest_graph_from_cache(self):\n        # pop the first item in the OrderedDict\n        _, image_id = self.cache.popitem(last=False)\n\n        # Delete the corresponding file\n        oldest_graph_file = self.image_file(image_id)\n        assert os.path.exists(oldest_graph_file)\n        os.remove(oldest_graph_file)\n        self.save()\n\n    def add_graph_to_cache(self, graph: nx.Graph, graph_encoding: str, pos, figsize):\n        # Draw the graph: recreate the node/edge color info based on the attributes\n        # stored on the graph components\n        node_colors = [graph.nodes[u][\"node_color\"] for u in graph.nodes]\n        edge_colors = [graph.edges[u, v][\"edge_color\"] for (u, v) in graph.edges]\n        nx.draw(graph, node_color=node_colors, edge_color=edge_colors, pos=pos, with_labels=True)\n        plt.savefig(self.image_file(self.counter), pil_kwargs={\"figsize\":figsize})\n\n        # add the graph data to our cache lookup\n        self.cache[graph_encoding] = self.counter\n        self.counter += 1\n        self.save()\n\n    def image_file(self, image_id):\n        return self.cache_dir / f\"{image_id}.png\"\n\n    @staticmethod\n    def graph_encoding(graph: nx.Graph) -> str:\n        # We need a way to convert graphs to \"keys\", that we can use as lookups\n        # for the cache. These encodings should encorporate all the data that\n        # uniquely defines a graph, and keys should only be equal if their\n        # graphs are equal. It turns out, we can use python's pickle encoding\n        # for this purpose\n        return str(pickle.dumps(graph))\n\n@dataclass(frozen=True)\nclass Ex02PerformanceResult(PerformanceResults):\n    accuracy: float\n    solve_time: float\n\n    def __post__init__(self):\n        assert 0 <= self.accuracy <= 1, self.accuracy\n        assert self.solve_time >= 0, self.solve_time\n\ndef str_from_path(path:Path) -> str:\n    return \"\".join(list(map(lambda u: f\"{u}->\", path)))[:-2]\n\n\ndef ex2_evaluation(ex_in, ex_out=None, plotGraph=True) -> Tuple[Ex02PerformanceResult, Report]:\n    # draw graph\n    graph_search_prob, algo_name = ex_in\n    test_graph = graph_search_prob.graph\n    test_queries = graph_search_prob.queries\n    graph_id = graph_search_prob.graph_id\n\n    # init rep with *unique* string id\n    r = Report(f\"Exercise2-{algo_name}-{graph_id}\")\n    cache = GraphImageCache()\n\n    G = nx.DiGraph()\n    G.add_nodes_from(test_graph.keys())\n    pic_size = max(10, int(G.number_of_nodes() / 10))\n    figsize = (pic_size, pic_size)\n    for n, successors in test_graph.items():\n        G.add_edges_from(\n                product(\n                        [\n                            n,\n                        ],\n                        successors,\n                )\n        )\n    # draw graph\n    pos = nx.get_node_attributes(G, \"pos\")\n    if not pos:\n        pos = nx.spring_layout(G, seed=1)\n    rfig = r.figure(cols=1)\n\n    default_node_colors = {n: NodeColors.default for n in G}\n    default_edge_colors = {(u, v): EdgeColors.default for (u, v) in G.edges()}\n    nx.set_node_attributes(G, values=default_node_colors, name=\"node_color\")\n    nx.set_edge_attributes(G, values=default_edge_colors, name=\"edge_color\")\n\n    graph_image = cache.create_graph_image_node(G, \"Graph\", pos, figsize)\n    rfig.add_child(graph_image)\n\n    # run algo\n    r.section(f\"{algo_name}\")\n    accuracy = []\n    solve_times = []\n    for i, query in enumerate(test_queries):\n        # Set all edge color attribute to black\n        for e in G.edges():\n            G[e[0]][e[1]][\"color\"] = EdgeColors.default\n        \n        rfig = r.figure(cols=2)\n\n        # Your algo\n        search_algo = graph_search_algo[algo_name]()\n        start_time = process_time()\n        path, opened = search_algo.search(test_graph, query[0], query[1])\n        solve_time = process_time() - start_time\n        # check path\n        if path:\n            path_str = str_from_path(path)\n            path_edges = list(sliding_window(2, path))\n        else:\n            path_str = \"No path\"\n            path_edges = []\n        # check opened\n        if opened:\n            opened_str = str_from_path(opened)\n        else:\n            opened_str = \"No opened node\"\n\n        # output message\n        msg = f\"Start: {query[0]},\\tGoal: {query[1]}\\n\"\n\n        # Ground truth\n        expected_result = ex_out[i]\n        if expected_result is not None:\n            gt_path, gt_opened = expected_result\n            correct = (path == gt_path) + (opened == gt_opened)\n            accuracy.append(correct / 2)\n            solve_times.append(solve_time)\n            gt_path_str = str_from_path(gt_path) if len(gt_path) > 0 else \"No path\"\n            gt_opened_str = str_from_path(gt_opened)\n            gt_path_edges = list(sliding_window(2, gt_path))\n            if correct == 2:\n                msg += \"Student solution : CORRECT\\n\"\n            else:\n                msg += \"Student solution : WRONG\\n\"\n        else:\n            gt_path_edges = []\n            gt_path = []\n            gt_path_str = \"Solution not given\"\n            gt_opened_str = \"Solution not given\"\n\n        msg += f\"Your algo path: {path_str}\\n\"\n        msg += f\"Your algo opened nodes: {opened_str}\\n\"\n\n        msg += f\"Ground truth path: {gt_path_str} \\n\"\n        msg += f\"Ground truth opened nodes: {gt_opened_str}\\n \\n\"\n\n        r.text(f\"{algo_name}-query{i}\", text=remove_escapes(msg))\n\n        # Plot graphs\n        if plotGraph:\n            node_colors = default_node_colors.copy()\n            node_colors[query[0]] = NodeColors.start\n            node_colors[query[1]] = NodeColors.goal\n            edge_colors = {\n                (u, v): EdgeColors.path if (u, v) in path_edges else EdgeColors.default \\\n                    for (u, v) in G.edges()\n            }\n            nx.set_node_attributes(G, values=node_colors, name=\"node_color\")\n            nx.set_edge_attributes(G, values=edge_colors, name=\"edge_color\")\n            image_node = cache.create_graph_image_node(G, f\"Path{i}\", pos, figsize)\n            rfig.add_child(image_node)\n\n            edge_colors = {\n                (u, v): EdgeColors.path if (u, v) in gt_path_edges else EdgeColors.default \\\n                    for (u, v) in G.edges()\n            }\n            nx.set_edge_attributes(G, values=edge_colors, name=\"edge_color\")\n            image_node = cache.create_graph_image_node(G, f\"GroundTruth{i}\", pos, figsize)\n            rfig.add_child(image_node)\n\n    cache.save()\n\n    # aggregate performance of each query\n    query_perf = list(map(Ex02PerformanceResult, accuracy, solve_times))\n    perf = ex2_perf_aggregator(query_perf)\n    return perf, r\n\n\ndef ex2_perf_aggregator(perf: Sequence[Ex02PerformanceResult]) -> Ex02PerformanceResult:\n    # perfomance for valid results\n    valid_acc = [p.accuracy for p in perf]\n    valid_time = [p.solve_time for p in perf]\n    try:\n        # average accuracy\n        avg_acc = sum(valid_acc) / float(len(valid_acc))\n        # average solve time\n        avg_time = sum(valid_time) / float(len(valid_time))\n    except ZeroDivisionError:\n        # None if gt wasn't provided\n        avg_acc = 0\n        avg_time = 0\n\n    return Ex02PerformanceResult(accuracy=avg_acc, solve_time=avg_time)\n\n\ndef get_exercise2() -> Exercise:\n    graph_search_problems = get_graph_search_problems(n_seed=4)\n    expected_results = ex2_get_expected_results() \n    graph_search_algos = graph_search_algo.keys()\n\n    test_values = list()\n    for ab in product(graph_search_problems, graph_search_algos):\n        test_values.append(TestValueEx2(ab))\n\n    return Exercise[TestValueEx2, Any](\n            desc='This exercise is about graph search',\n            evaluation_fun=ex2_evaluation,\n            perf_aggregator=ex2_perf_aggregator,\n            test_values=test_values,\n            expected_results=expected_results,\n    )\n","repo_name":"PDM4AR/exercises","sub_path":"src/pdm4ar/exercises_def/ex02/ex02.py","file_name":"ex02.py","file_ext":"py","file_size_in_byte":12642,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"86"}
+{"seq_id":"41697078982","text":"class Vektor3:\n    def __init__(self,x=0,y=0,z=0):\n        self.x = x\n        self.y = y\n        self.z = z\n\n    def len(self):\n        self.l = (((self.x)**2+(self.y)**2+(self.z)**2)**0.5)\n        print(f\"Der Vektor ist{self.l} lang\")\n\n\na = Vektor3(1.0, 2.0, 3.0)\na.len()","repo_name":"damianodelbiaggio/Programmierung","sub_path":"Kapitel 3/Übung 3_1_Lösung.py","file_name":"Übung 3_1_Lösung.py","file_ext":"py","file_size_in_byte":272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"42100092092","text":"import random\ndef main():\n    while True:\n        print(\"\\nWelcome to Samuel's Mini Game Suite\")\n        print(\"Don't forget to have fun!!\" )\n        print(\"1. Partner Matching Game\")\n        print(\"2. Best Brain Quiz\")\n        print(\"3. Exit\")\n        a=['1','2','3']\n        choice = input(\"Choose a game (1-3): \")\n\n        if choice == \"1\":\n            game = PartnerMatchingGame()\n            game.play_game()\n        elif choice == \"2\":\n            Best_brain()\n        elif choice == \"3\":\n            break\n        else:\n            print(\"Please enter a valid choice\")\n            main()\n\nclass PartnerMatchingGame:\n    def __init__(self):\n        self.couple_a_answers = {}\n        self.couple_b_answers = {}\n\n    def get_answers_from_couple_a(self):\n        print(\"Couple A, please provide your answers:\")\n        questions = [\n            \"What is your partner's favorite color?\",\n            \"What is your partner's dream vacation destination?\",\n            \"What is your partner's favorite movie or TV show?\",\n            \"What is your partner's go-to comfort food?\",\n            \"Who is your partner's celebrity crush?\",\n            \"What is your partner's idea of a perfect date night?\",\n            \"What is your partner's favorite hobby or pastime?\",\n            \"If your partner could have any superpower, what would it be?\",\n            \"What is your partner's most cherished childhood memory?\",\n            \"What is your partner's pet peeve or biggest annoyance?\"\n        ]\n        for i, question in enumerate(questions, 1):\n            answer = input(f\"Question {i}: {question} - Answer: \")\n            self.couple_a_answers[question] = answer\n        print(\"Answers from Couple A have been recorded.\\n\")\n\n    def get_answers_from_couple_b(self):\n        print(\"Couple B, it's your turn to guess Couple A's answers:\")\n        correct_answers = 0\n        questions = [\n            \"What is your partner's favorite color?\",\n            \"What is your partner's dream vacation destination?\",\n            \"What is your partner's favorite movie or TV show?\",\n            \"What is your partner's go-to comfort food?\",\n            \"Who is your partner's celebrity crush?\",\n            \"What is your partner's idea of a perfect date night?\",\n            \"What is your partner's favorite hobby or pastime?\",\n            \"If your partner could have any superpower, what would it be?\",\n            \"What is your partner's most cherished childhood memory?\",\n            \"What is your partner's pet peeve or biggest annoyance?\"\n        ]\n        for i, question in enumerate(questions, 1):\n            guess = input(f\"Question {i}: {question} - Guess the answer: \")\n            if guess.lower() == self.couple_a_answers[question].lower():\n                print(\"Correct!\")\n                correct_answers += 1\n            else:\n                print(f\"Opps!! Wrong answer. The correct answer is: {self.couple_a_answers[question]}\")\n        print(f\"\\nCouple B, you got {correct_answers} out of 10 answers correct.\")\n        compatibility_percentage = (correct_answers / 10) * 100\n        print(f\"You are {compatibility_percentage}% compatible\")\n        if compatibility_percentage >= 80 :\n            print(\"Purrr!! You guys are a great fit.\")\n        elif (compatibility_percentage >= 70) and (compatibility_percentage < 80):\n            print(\"You are a good pair. You can try to work things out.\")\n        else:\n            (\"Sorry you might not be a perfect fit.\")\n\n    def play_game(self):\n        self.get_answers_from_couple_a()\n        self.get_answers_from_couple_b()\n\n\ndef Best_brain():\n    score = 0\n    questions = [\n        {\"question\": \"What's the capital of France?\", \"answer\": \"paris\"},\n        {\"question\": \"Which planet is known as the Red Planet?\", \"answer\": \"mars\"},\n        {\"question\": \"What is the chemical symbol for water?\", \"answer\": \"h2o\"},\n        {\"question\": \"In which year did Christopher Columbus discover America?\", \"answer\": \"1492\"},\n        {\"question\": \"What is the square root of 16?\", \"answer\": \"4\"},\n        {\"question\": \"Who wrote 'Romeo and Juliet'?\", \"answer\": \"shakespeare\"},\n        {\"question\": \"What is the largest mammal in the world?\", \"answer\": \"blue whale\"},\n        {\"question\": \"What is the symbol for the element gold?\", \"answer\": \"au\"},\n        {\"question\": \"In which country was the game of chess invented?\", \"answer\": \"india\"},\n        {\"question\": \"Who is the first President of the United States?\", \"answer\": \"washington\"},\n        {\"question\": \"What is the formula for the area of a rectangle? (length,width)\", \"answer\": \"length x width\"},\n        {\"question\": \"What is the chemical symbol for oxygen?\", \"answer\": \"o\"},\n        {\"question\": \"Who painted the Mona Lisa?\", \"answer\": \"da vinci\"},\n        {\"question\": \"What is the largest planet in our solar system?\", \"answer\": \"jupiter\"},\n        {\"question\": \"What year did World War II end?\", \"answer\": \"1945\"},\n        {\"question\": \"What is the square of 9?\", \"answer\": \"81\"},\n        {\"question\": \"What is the capital of Japan?\", \"answer\": \"tokyo\"},\n        {\"question\": \"Who is known as the father of modern physics?\", \"answer\": \"einstein\"},\n        {\"question\": \"Which gas do plants absorb from the atmosphere?\", \"answer\": \"carbon dioxide\"},\n        {\"question\": \"Who is the author of 'To Kill a Mockingbird'?\", \"answer\": \"harper lee\"},\n        {\"question\": \"What is the boiling point of water in degrees Celsius?\", \"answer\": \"100\"},\n        {\"question\": \"What is the largest desert in the world?\", \"answer\": \"sahara\"},\n        {\"question\": \"Who wrote 'The Great Gatsby'?\", \"answer\": \"fitzgerald\"},\n        {\"question\": \"What is the chemical symbol for iron?\", \"answer\": \"fe\"},\n        {\"question\": \"In which year was the Declaration of Independence adopted?\", \"answer\": \"1776\"},\n        {\"question\": \"What is the smallest planet in our solar system?\", \"answer\": \"mercury\"},\n    ]\n    random.shuffle(questions)\n    for q in questions[:10]:\n        print(q[\"question\"])\n        user_answer = input(\"Your answer: \").lower()\n\n        if user_answer == q[\"answer\"]:\n            print(\"Good job. You had it Correct!\")\n            score += 1\n        else:\n            print(f\"Sorry, the right answer is: {q['answer']}\")\n    score = (score / 10) * 100\n\n    print(f\"You scored {score}%.\")\n    print(\"Well done. You are a genius for trying\")\n    survey=input(\"I hope you had fun? \").lower()\n    if survey == \"Yes\".lower():\n        print(\"Thank you\")\n        input(\"Please rate this quiz program over 10 (1-10): \")\n        print(\"Hope to see you play again soon\")\n    else:\n        input(\"Please rate this quiz program over 10 (1-10): \")\n        print(\"I hope you have fun next time\")\n        print(\"Bye!!\")\n\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Tomtom-debug/Samuel-s-mini-game-Suite","sub_path":"minisuite.py","file_name":"minisuite.py","file_ext":"py","file_size_in_byte":6759,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"44886687238","text":"'''\nprograma de botos de bromas\ndecir cual es el que tiene mas botos, el que tiene menos y en que posicion esta\n'''\n#programa botos\n\n#variables\ni = 0\narray = []\nmaxi = 0\nmini = 10\nposicion_max = 0\nposicion_min = 0\narray2 = []\narray3 = []\nsuma = 0\n#codigo\n\nfor i in range (6):\n    boto = int(input())\n    while (boto < 0 or boto > 10):\n        print (\"Introduce un boto válido entre 0 y 10\")\n        boto = int (input())\n\n    array.append(boto)\n    actual = array[i]\n    if (actual > maxi):\n        maxi = actual\n        posicion_max = len(array)\n    elif (actual < mini):\n        mini = actual\n        posicion_min = len(array)\n\nfor i in range (0,6):\n    if (i != posicion_min-1 and i != posicion_max-1):\n        array2.append(array[i])\n\n    if (array[i] != mini and array[i] != maxi):\n        array3.append(array[i])\n    \nfor i in range (len(array2)):\n    suma = array2[i] + suma\n\ntotal = suma / len(array2)\n\nprint (array)\nprint (maxi)\nprint (\"posicion en el array\",posicion_max)\nprint (mini)\nprint (\"posicion en el array\",posicion_min)\nprint (\"Nuevo array\", array2)\nprint (\"Con otro codigo\", array3)\nprint (\"La media de todos los valores medios(sin el min y al max) es:\",total)\n\n\n\n\n\n\n\n\n\n","repo_name":"alexcatmu/CFGS_DAM","sub_path":"PRIMERO/python/ejercicio43 arrayboto.py","file_name":"ejercicio43 arrayboto.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"23374009476","text":"from openclean_pattern.tokenize.regex import RegexTokenizer, TOKENIZER_REGEX, DefaultTokenizer, TOKENIZER_DEFAULT\n\n\nclass TokenizerFactory(object):\n    \"\"\"factory methods to create a tokenizer class object\n    \"\"\"\n\n    @staticmethod\n    def create_tokenizer(tokenizer, type_resolver=None):\n        \"\"\"Returns the tokenizer class if the input string matches the tokenizer name\n\n        Parameters\n        ----------\n        tokenizer: str\n            name string of the tokenizer\n        type_resolver: openclean_pattern.datatypes.resolver.TypeResolver (default: None)\n            type resolvers to incorporate non-basic datatypes\n        \"\"\"\n        if tokenizer == TOKENIZER_REGEX:\n            return RegexTokenizer(type_resolver=type_resolver)\n        elif tokenizer == TOKENIZER_DEFAULT:\n            return DefaultTokenizer()\n\n        raise ValueError('tokenizer: {} not found'.format(tokenizer))\n","repo_name":"VIDA-NYU/openclean-pattern","sub_path":"openclean_pattern/tokenize/factory.py","file_name":"factory.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"24615124374","text":"import torch\nimport torch.nn as nn\nfrom utils.show import show_params, show_model\nimport torch.nn.functional as F\nfrom models.conv_stft import ConvSTFT, ConviSTFT\n\nfrom models.complexnn import ComplexConv2d, ComplexConvTranspose2d, NavieComplexLSTM, complex_cat, ComplexBatchNorm\n\n\nclass DCCRN(nn.Module):\n\n    def __init__(\n            self,\n            rnn_layers=2,\n            rnn_units=128,\n            win_len=400,\n            win_inc=100,\n            fft_len=512,\n            win_type='hanning',\n            masking_mode='E',\n            use_clstm=False,\n            use_cbn=False,\n            kernel_size=5,\n            kernel_num=[16, 32, 64, 128, 256, 256]\n    ):\n        ''' \n            \n            rnn_layers: the number of lstm layers in the crn,\n            rnn_units: for clstm, rnn_units = real+imag\n\n        '''\n\n        super(DCCRN, self).__init__()\n\n        # for fft \n        self.win_len = win_len\n        self.win_inc = win_inc\n        self.fft_len = fft_len\n        self.win_type = win_type\n\n        input_dim = win_len\n        output_dim = win_len\n\n        self.rnn_units = rnn_units\n        self.input_dim = input_dim\n        self.output_dim = output_dim\n        self.hidden_layers = rnn_layers\n        self.kernel_size = kernel_size\n        # self.kernel_num = [2, 8, 16, 32, 128, 128, 128]\n        # self.kernel_num = [2, 16, 32, 64, 128, 256, 256]\n        self.kernel_num = [2] + kernel_num\n        self.masking_mode = masking_mode\n        self.use_clstm = use_clstm\n\n        # bidirectional=True\n        bidirectional = False\n        fac = 2 if bidirectional else 1\n\n        fix = True\n        self.fix = fix\n        self.stft = ConvSTFT(self.win_len, self.win_inc, fft_len, self.win_type, 'complex', fix=fix)\n        self.istft = ConviSTFT(self.win_len, self.win_inc, fft_len, self.win_type, 'complex', fix=fix)\n\n        self.encoder = nn.ModuleList()\n        self.decoder = nn.ModuleList()\n        for idx in range(len(self.kernel_num) - 1):\n            self.encoder.append(\n                nn.Sequential(\n                    # nn.ConstantPad2d([0, 0, 0, 0], 0),\n                    ComplexConv2d(\n                        self.kernel_num[idx],\n                        self.kernel_num[idx + 1],\n                        kernel_size=(self.kernel_size, 2),\n                        stride=(2, 1),\n                        padding=(2, 1)\n                    ),\n                    nn.BatchNorm2d(self.kernel_num[idx + 1]) if not use_cbn else ComplexBatchNorm(\n                        self.kernel_num[idx + 1]),\n                    nn.PReLU()\n                )\n            )\n        hidden_dim = self.fft_len // (2 ** (len(self.kernel_num)))\n\n        if self.use_clstm:\n            rnns = []\n            for idx in range(rnn_layers):\n                rnns.append(\n                    NavieComplexLSTM(\n                        input_size=hidden_dim * self.kernel_num[-1] if idx == 0 else self.rnn_units,\n                        hidden_size=self.rnn_units,\n                        bidirectional=bidirectional,\n                        batch_first=False,\n                        projection_dim=hidden_dim * self.kernel_num[-1] if idx == rnn_layers - 1 else None,\n                    )\n                )\n                self.enhance = nn.Sequential(*rnns)\n        else:\n            self.enhance = nn.LSTM(\n                input_size=hidden_dim * self.kernel_num[-1],\n                hidden_size=self.rnn_units,\n                num_layers=2,\n                dropout=0.0,\n                bidirectional=bidirectional,\n                batch_first=False\n            )\n            self.tranform = nn.Linear(self.rnn_units * fac, hidden_dim * self.kernel_num[-1])\n\n        for idx in range(len(self.kernel_num) - 1, 0, -1):\n            if idx != 1:\n                self.decoder.append(\n                    nn.Sequential(\n                        ComplexConvTranspose2d(\n                            self.kernel_num[idx] * 2,\n                            self.kernel_num[idx - 1],\n                            kernel_size=(self.kernel_size, 2),\n                            stride=(2, 1),\n                            padding=(2, 0),\n                            output_padding=(1, 0)\n                        ),\n                        nn.BatchNorm2d(self.kernel_num[idx - 1]) if not use_cbn else ComplexBatchNorm(\n                            self.kernel_num[idx - 1]),\n                        # nn.ELU()\n                        nn.PReLU()\n                    )\n                )\n            else:\n                self.decoder.append(\n                    nn.Sequential(\n                        ComplexConvTranspose2d(\n                            self.kernel_num[idx] * 2,\n                            self.kernel_num[idx - 1],\n                            kernel_size=(self.kernel_size, 2),\n                            stride=(2, 1),\n                            padding=(2, 0),\n                            output_padding=(1, 0)\n                        ),\n                    )\n                )\n\n        show_model(self)\n        show_params(self)\n        self.flatten_parameters()\n\n    def flatten_parameters(self):\n        if isinstance(self.enhance, nn.LSTM):\n            self.enhance.flatten_parameters()\n\n    def forward(self, inputs, lens=None):\n        specs = self.stft(inputs)\n        real = specs[:, :self.fft_len // 2 + 1]\n        imag = specs[:, self.fft_len // 2 + 1:]\n        spec_mags = torch.sqrt(real ** 2 + imag ** 2 + 1e-8)\n        spec_mags = spec_mags\n        spec_phase = torch.atan2(imag, real)\n        spec_phase = spec_phase\n        cspecs = torch.stack([real, imag], 1)\n        cspecs = cspecs[:, :, 1:]\n        '''\n        means = torch.mean(cspecs, [1,2,3], keepdim=True)\n        std = torch.std(cspecs, [1,2,3], keepdim=True )\n        normed_cspecs = (cspecs-means)/(std+1e-8)\n        out = normed_cspecs\n        '''\n\n        out = cspecs\n        encoder_out = []\n\n        for idx, layer in enumerate(self.encoder):\n            out = layer(out)\n            #    print('encoder', out.size())\n            encoder_out.append(out)\n\n        batch_size, channels, dims, lengths = out.size()\n        out = out.permute(3, 0, 1, 2)\n        if self.use_clstm:\n            r_rnn_in = out[:, :, :channels // 2]\n            i_rnn_in = out[:, :, channels // 2:]\n            r_rnn_in = torch.reshape(r_rnn_in, [lengths, batch_size, channels // 2 * dims])\n            i_rnn_in = torch.reshape(i_rnn_in, [lengths, batch_size, channels // 2 * dims])\n\n            r_rnn_in, i_rnn_in = self.enhance([r_rnn_in, i_rnn_in])\n\n            r_rnn_in = torch.reshape(r_rnn_in, [lengths, batch_size, channels // 2, dims])\n            i_rnn_in = torch.reshape(i_rnn_in, [lengths, batch_size, channels // 2, dims])\n            out = torch.cat([r_rnn_in, i_rnn_in], 2)\n\n        else:\n            # to [L, B, C, D]\n            out = torch.reshape(out, [lengths, batch_size, channels * dims])\n            out, _ = self.enhance(out)\n            out = self.tranform(out)\n            out = torch.reshape(out, [lengths, batch_size, channels, dims])\n\n        out = out.permute(1, 2, 3, 0)\n\n        for idx in range(len(self.decoder)):\n            out = complex_cat([out, encoder_out[-1 - idx]], 1)\n            out = self.decoder[idx](out)\n            out = out[..., 1:]\n        #    print('decoder', out.size())\n        mask_real = out[:, 0]\n        mask_imag = out[:, 1]\n        mask_real = F.pad(mask_real, [0, 0, 1, 0])\n        mask_imag = F.pad(mask_imag, [0, 0, 1, 0])\n\n        if self.masking_mode == 'E':\n            mask_mags = (mask_real ** 2 + mask_imag ** 2) ** 0.5\n            real_phase = mask_real / (mask_mags + 1e-8)\n            imag_phase = mask_imag / (mask_mags + 1e-8)\n            mask_phase = torch.atan2(\n                imag_phase,\n                real_phase\n            )\n\n            # mask_mags = torch.clamp_(mask_mags,0,100)\n            mask_mags = torch.tanh(mask_mags)\n            est_mags = mask_mags * spec_mags\n            est_phase = spec_phase + mask_phase\n            real = est_mags * torch.cos(est_phase)\n            imag = est_mags * torch.sin(est_phase)\n        elif self.masking_mode == 'C':\n            real, imag = real * mask_real - imag * mask_imag, real * mask_imag + imag * mask_real\n        elif self.masking_mode == 'R':\n            real, imag = real * mask_real, imag * mask_imag\n\n        out_spec = torch.cat([real, imag], 1)\n        out_wav = self.istft(out_spec)\n\n        out_wav = torch.squeeze(out_wav, 1)\n        # out_wav = torch.tanh(out_wav)\n        # add _ to be a in-place operation\n        out_wav = torch.clamp_(out_wav, -1, 1)\n        return out_spec, out_wav\n\n    def get_params(self, weight_decay=0.0):\n        # add L2 penalty\n        weights, biases = [], []\n        for name, param in self.named_parameters():\n            if 'bias' in name:\n                biases += [param]\n            else:\n                weights += [param]\n        params = [{\n            'params': weights,\n            'weight_decay': weight_decay,\n        }, {\n            'params': biases,\n            'weight_decay': 0.0,\n        }]\n        return params\n\n\ndef dccrn(mode='CL'):\n    if mode == 'E':\n        model = DCCRN(rnn_units=256, masking_mode='E')\n    elif mode == 'R':\n        model = DCCRN(rnn_units=256, masking_mode='R')\n    elif mode == 'C':\n        model = DCCRN(rnn_units=256, masking_mode='C')\n    elif mode == 'CL':\n        model = DCCRN(rnn_units=256, masking_mode='E',\n                      use_clstm=True, kernel_num=[32, 64, 128, 256, 256, 256])\n    else:\n        raise Exception('non-supported mode!')\n    return model\n","repo_name":"stdKonjac/DeepComplexCRN","sub_path":"models/DCCRN.py","file_name":"DCCRN.py","file_ext":"py","file_size_in_byte":9574,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"86"}
+{"seq_id":"72877408604","text":"import os\nimport numpy as np\nimport networkx as nx\n\n\nclass S2VGraph(object):\n    def __init__(self, g, label, node_tags=None, node_features=None):\n        '''\n            g: a networkx graph\n            label: an integer graph label\n            node_tags: a list of integer node tags\n            node_features: a numpy float tensor, one-hot representation of the tag that is used as input to neural nets\n            neighbors: list of neighbors (without self-loop)\n        '''\n        self.label = label\n        self.g = g\n        self.node_tags = node_tags\n        self.neighbors = []\n        self.node_features = 0\n\n        self.max_neighbor = 0\n\n\ndef load_data(dataset, degree_as_tag):\n    '''\n        dataset: name of dataset\n        test_proportion: ratio of test train split\n        seed: random seed for random splitting of dataset\n    '''\n\n    #print('loading data')\n    g_list = []\n    label_dict = {}\n    feat_dict = {}\n\n    with open('../data/dataset/%s/%s.txt' % (dataset, dataset), 'r') as f:\n        n_g = int(f.readline().strip())\n        for i in range(n_g):\n            row = f.readline().strip().split()\n            n, l = [int(w) for w in row]\n            if not l in label_dict:\n                mapped = len(label_dict)\n                label_dict[l] = mapped\n            g = nx.Graph()\n            node_tags = []\n            node_features = []\n            n_edges = 0\n            for j in range(n):\n                g.add_node(j)\n                row = f.readline().strip().split()\n                tmp = int(row[1]) + 2\n                if tmp == len(row):\n                    # no node attributes\n                    row = [int(w) for w in row]\n                    attr = None\n                else:\n                    row, attr = [int(w) for w in row[:tmp]], np.array([float(w) for w in row[tmp:]])\n                if not row[0] in feat_dict:\n                    mapped = len(feat_dict)\n                    feat_dict[row[0]] = mapped\n                node_tags.append(feat_dict[row[0]])\n\n                if tmp > len(row):\n                    node_features.append(attr)\n\n                n_edges += row[1]\n                for k in range(2, len(row)):\n                    g.add_edge(j, row[k])\n\n            if node_features != []:\n                node_features = np.stack(node_features)\n                node_feature_flag = True\n            else:\n                node_features = None\n                node_feature_flag = False\n\n            assert len(g) == n\n\n            g_list.append(S2VGraph(g, l, node_tags))\n\n            \n    for g in g_list:\n        g.neighbors = [[] for i in range(len(g.g))]\n        for i, j in g.g.edges():\n            g.neighbors[i].append(j)\n            g.neighbors[j].append(i)\n        degree_list = []\n        for i in range(len(g.g)):\n            g.neighbors[i] = g.neighbors[i]\n            degree_list.append(len(g.neighbors[i]))\n        g.max_neighbor = max(degree_list)\n\n        g.label = label_dict[g.label]\n\n        edges = [list(pair) for pair in g.g.edges()]\n        edges.extend([[i, j] for j, i in edges])\n\n        deg_list = list(dict(g.g.degree(range(len(g.g)))).values())\n\n    if degree_as_tag:\n        for g in g_list:\n            g.node_tags = list(dict(g.g.degree(range(len(g.g)))).values())\n\n    #Extracting unique tag labels   \n    tagset = set([])\n    for g in g_list:\n        tagset = tagset.union(set(g.node_tags))\n\n    tagset = list(tagset)\n    tag2index = {tagset[i]:i for i in range(len(tagset))}\n\n    for g in g_list:\n        g.node_features = np.zeros([len(g.node_tags), len(tagset)])\n        g.node_features[range(len(g.node_tags)), [tag2index[tag] for tag in g.node_tags]] = 1\n\n\n    # print('# classes: %d' % len(label_dict))\n    # print('# maximum node tag: %d' % len(tagset))\n\n    # print(\"# data: %d\" % len(g_list))\n\n    return g_list, len(label_dict)\n\n\n\ndef loadData(dataset_name):\n\t'''\n\tTBD\n\n\tParameters:\n\t\t-\n\n\tReturns:\n\t\t-\n\n\t'''\n\t# Social networks datasets\n\tif dataset_name in ['IMDBBINARY', 'IMDBMULTI', 'COLLAB']:\n\t    g_list, _ = load_data(dataset_name, True)\n\t# Bioinformatics datasets\n\telif dataset_name in ['MUTAG', 'PROTEINS', 'PTC', 'NCI1']:\n\t    g_list, _ = load_data(dataset_name, False)\n\t# Elsewise the dataset is not supported (as of yet)\n\telse:\n\t\traise ValueError(f'Dataset ({dataset_name}) is not supported!')\n\treturn g_list\n\t# ROOT = f'../data/{dataset_name}/{dataset_name}'\n\t# # Row-like graph indicator\n\t# with open(f'{ROOT}/{dataset_name}_graph_indicator.txt', 'r') as f:\n\t# \tgraph_indicator = [int(i) - 1 for i in list(f)]\n\n\t# ##########\n\t# # Nodes\n\t# num_graphs = max(graph_indicator)\n\t# node_indices = []\n\t# offset = []\n\t# c = 0\n\t# # Identify the row numbers pertaining to each graph\n\t# for i in range(num_graphs + 1):\n\t# \toffset.append(c)\n\t# \tc_i = graph_indicator.count(i)\n\t# \tnode_indices.append((c, c + c_i - 1))\n\t# \tc += c_i\n\t# # Init all the networkx graphs\n\t# graph_db = []\n\t# for i in node_indices:\n\t# \tg = nx.Graph()\n\t# \tfor j in range(i[1] - i[0] + 1):\n\t# \t\tg.add_node(j)\n\t# \tgraph_db.append(g)\n\n\t# ##########\n\t# # Edges\n\t# with open(f'{ROOT}/{dataset_name}_A.txt', 'r') as f:\n\t# \tedges = [i.split(',') for i in list(f)]\n\t# edges = [\n\t# \t(int(e[0].strip()) - 1, int(e[1].strip()) - 1) for e in edges\n\t# ]\n\t# edge_list = []\n\t# edgeb_list = []\n\t# for e in edges:\n\t# \tg_id = graph_indicator[e[0]]\n\t# \tg = graph_db[g_id]\n\t# \toff = offset[g_id]\n\t# \t#\n\t# \tif (e[0] - off, e[1] - off) not in list(g.edges()) and \\\n\t# \t   (e[1] - off, e[0] - off) not in list(g.edges()):\n\t# \t    g.add_edge(e[0] - off, e[1] - off)\n\t# \t    edge_list.append((e[0] - off, e[1] - off))\n\t# \t    edgeb_list.append(True)\n\t# \telse:\n\t# \t\tedgeb_list.append(False)\n\n\t# ##########\n\t# # Node labels\n\n\t# ##########\n\t# # Node attributes\n\n\t# ##########\n\t# # Edge labels\n\n\t# ##########\n\t# # Edge attributes\n\n\t# ##########\n\t# # Classes\n\t# if os.path.exists(f'{ROOT}/{dataset_name}_graph_labels.txt'):\n\t# \twith open(f'{ROOT}/{dataset_name}_graph_labels.txt', 'r') as f:\n\t# \t\tclasses = [i.strip() for i in list(f)]\n\t# \t# Allow multiple class graph labeling\n\t# \tclasses = [i.split(',') for i in classes]\n\t# \tcs = []\n\t# \tfor i, c in enumerate(classes):\n\t# \t\tcs.append([int(j.strip()) for j in c])\n\t# \t# Add the labels to the corresponding graphs in the main db\n\t# \tfor i, g in enumerate(graph_db):\n\t# \t\tg.graph['classes'] = cs[i]\n\n\t# ##########\n\t# # Targets\n\n\t# return graph_db\n","repo_name":"berges99/Bridging-Graph-Neural-Networks-and-Graph-Kernels","sub_path":"src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6340,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"34057016237","text":"import mesh\nimport mesh.plot.trimesh_viewer as mplot # visualize\n\n\nplane_radius = 10\nplane_width = 1\nmax_elem_area = 1 # maximum area of a triangle on the face of the plane\n\nrefinement = None # advanced\nrefine_bot = False # whether to refine both top and bot surfaces or only the top one\n\n# Create mesh\nplane = mesh.disk_create('plane', plane_radius, plane_width, max_area=max_elem_area,\n                         extra_side_node_layers='auto',\n                         refinement=refinement, refine_bot=refine_bot)\n#plane = mesh.prepare_system(plane) - sometimes needed\ntri = mesh.triangulate_system(plane) # Triangulation class\n\n# Visualize mesh\nm = mplot.triangulation_to_trimesh(tri) # class of TriMesh, suitable for plotting\nm.show() # visualize - 3D","repo_name":"icemtel/stokes","sub_path":"example/00_mesh_examples/create_disk.py","file_name":"create_disk.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"6870528547","text":"\"\"\"\"\"\nNombre: JOhan Obed Maya Morales\nFecha: 22 feb 2023\ndescripcion: par impar y nulo\n\"\"\"\"\"\nnumero = int(input(\"Introduce un número entero: \")) #qUE Se ingrese una variable llamada numero y que imprima un valor entero\n\nif numero == 0: # si numero es igual a cero \n    print(\"El número es nulo.\") #imprimira que es nulo\nelif numero % 2 == 0: # si anteriormente fue falso entonces cheqcara si es entero\n    print(\"El número es par.\") # imprimira entero\nelse:\n    print(\"El número es impar.\")# si ninguno fue verdadero entonces imprimira impar\n","repo_name":"Maya653/poo_jomm_ti21","sub_path":"reto/miercoles22.py","file_name":"miercoles22.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"24427424239","text":"\n\nimport sys\nimport pygame\nimport time\nimport copy\n\nfrom pygame.locals import *\n\nFPS = 10\nWINDOWWIDTH = 1000\nWINDOWHEIGHT = 750\nSPACESIZE = 35\nBOARDWIDTH = 19\nBOARDHEIGHT = 19\nWHITE_TILE = 'WHITE_TILE'\nBLACK_TILE = 'BLACK_TILE'\nEMPTY_SPACE = 'EMPTY_SPACE'\nANIMATIONSPEED = 25\n\nXMARGIN = int((WINDOWWIDTH - (BOARDWIDTH * SPACESIZE))/2)\nYMARGIN = int((WINDOWHEIGHT - (BOARDHEIGHT * SPACESIZE))/2)\n\nWHITE = (255, 255, 255)\nBLACK = (0, 0, 0)\nGREEN = (0, 155, 0)\nBRIGHTBLUE = (0, 50, 255)\n\nTEXTBGCOLOR1 = BRIGHTBLUE\nTEXTBGCOLOR2 = GREEN\nGRIDLINECOLOR = BLACK\nTEXTCOLOR = WHITE\n\n\ndef main():\n    global MAINCLOCK, DISPLAYSURF, FONT, BIGFONT, BGIMAGE\n\n    pygame.init()\n    MAINCLOCK = pygame.time.Clock()\n    DISPLAYSURF = pygame.display.set_mode((WINDOWWIDTH, WINDOWHEIGHT))\n    pygame.display.set_caption('Gomoku')\n    FONT = pygame.font.Font('freesansbold.ttf', 16)\n    BIGFONT = pygame.font.Font('freesansbold.ttf', 32)\n\n    boardImage = pygame.image.load('wuziqiboard.png')\n    boardImage = pygame.transform.smoothscale(boardImage, (BOARDWIDTH * SPACESIZE, BOARDHEIGHT * SPACESIZE))\n    boardImageRect = boardImage.get_rect()\n    boardImageRect.topleft = (XMARGIN, YMARGIN)\n    BGIMAGE = pygame.image.load('wuziqibackground.png')\n    BGIMAGE = pygame.transform.smoothscale(BGIMAGE, (WINDOWWIDTH, WINDOWHEIGHT))\n    BGIMAGE.blit(boardImage, boardImageRect)\n\n    # Run the main game\n    # print 'start'\n    while True:\n        if runGame() == False:\n            break\n\n\ndef runGame():\n    winner = 'tie'\n    mainBorad = getNewBoard()\n    # resetBoard(mainBorad)\n    turn = 'playerA'\n    drawBoard(mainBorad)\n    # playerATile, playerBTile = enterPlayerTile()\n    newGameSurf = FONT.render('New Game', True, TEXTCOLOR, TEXTBGCOLOR2)\n    newGameRect = newGameSurf.get_rect()\n    newGameRect.topright = (WINDOWWIDTH - 8, 10)\n\n    while True:\n        if turn == 'playerA':\n            playerTile = BLACK_TILE\n        if turn == 'playerB':\n            playerTile = WHITE_TILE\n\n        if getValidMoves(mainBorad) == []:\n            break\n        movexy = None\n        while movexy is None:\n\n            checkForQuit()\n            for event in pygame.event.get():\n                if event.type == MOUSEBUTTONUP:\n                    mousex, mousey = event.pos\n                    if newGameRect.collidepoint((mousex, mousey)):\n                        return True\n                    movexy = getSpaceClick(mousex, mousey)\n                    print(movexy)\n                    if movexy is not None and not isValidMove(mainBorad, movexy[0], movexy[1]):\n                        movexy = None\n\n            drawBoard(mainBorad)\n            # drawInfo()\n            DISPLAYSURF.blit(newGameSurf, newGameRect)\n\n            MAINCLOCK.tick(FPS)\n            pygame.display.update()\n\n        makeMove(mainBorad, playerTile, movexy[0], movexy[1])\n        drawBoard(mainBorad)\n        if hasWon(turn, mainBorad):\n            if turn == 'playerA':\n                winner = turn\n            else:\n                winner = 'playerB'\n            break\n        if getValidMoves(mainBorad) != []:\n            if turn == 'playerA':\n                turn = 'playerB'\n            else:\n                turn = 'playerA'\n\n    if winner == 'tie':\n        text = 'The game was a tie!'\n    elif winner == 'playerA':\n        text = 'BLACK win!'\n    else:\n        text = 'WHITE win!'\n\n    textSurf = FONT.render(text, True, TEXTCOLOR, TEXTBGCOLOR1)\n    textRect = textSurf.get_rect()\n    textRect.center = (int(WINDOWWIDTH / 2), int(WINDOWHEIGHT / 2))\n    DISPLAYSURF.blit(textSurf, textRect)\n\n    # Display the \"Play again?\" text with Yes and No buttons.\n    text2Surf = BIGFONT.render('Play again?', True, TEXTCOLOR, TEXTBGCOLOR1)\n    text2Rect = text2Surf.get_rect()\n    text2Rect.center = (int(WINDOWWIDTH / 2), int(WINDOWHEIGHT / 2) + 50)\n\n    # Make \"Yes\" button.\n    yesSurf = BIGFONT.render('Yes', True, TEXTCOLOR, TEXTBGCOLOR1)\n    yesRect = yesSurf.get_rect()\n    yesRect.center = (int(WINDOWWIDTH / 2) - 60, int(WINDOWHEIGHT / 2) + 90)\n\n    # Make \"No\" button.\n    noSurf = BIGFONT.render('No', True, TEXTCOLOR, TEXTBGCOLOR1)\n    noRect = noSurf.get_rect()\n    noRect.center = (int(WINDOWWIDTH / 2) + 60, int(WINDOWHEIGHT / 2) + 90)\n\n    while True:\n        # Process events until the user clicks on Yes or No.\n        checkForQuit()\n        for event in pygame.event.get(): # event handling loop\n            if event.type == MOUSEBUTTONUP:\n                mousex, mousey = event.pos\n                if yesRect.collidepoint((mousex, mousey)):\n                    return True\n                elif noRect.collidepoint((mousex, mousey)):\n                    return False\n        DISPLAYSURF.blit(textSurf, textRect)\n        DISPLAYSURF.blit(text2Surf, text2Rect)\n        DISPLAYSURF.blit(yesSurf, yesRect)\n        DISPLAYSURF.blit(noSurf, noRect)\n        pygame.display.update()\n        MAINCLOCK.tick(FPS)\n\n\n\n\n# def resetBoard(board):\n#     for x in xrange(BOARDWIDTH):\n#         for y in xrange(BOARDHEIGHT):\n#             board[x][y] = EMPTY_SPACE\n\n# def enterPlayerTile():\n\n\n# def drawInfo():\n\n\ndef getSpaceClick(mousex, mousey):\n\n    for x in range(BOARDWIDTH):\n        for y in range(BOARDHEIGHT):\n            if mousex > x * SPACESIZE + XMARGIN:\n                if mousex < (x + 1) * SPACESIZE + XMARGIN:\n                    if mousey > y * SPACESIZE + YMARGIN:\n                        if mousey < (y + 1) * SPACESIZE + YMARGIN:\n                            return x,y\n    return None\n\n\ndef hasWon(turn, b):\n    if turn == 'playerA':\n        check = BLACK_TILE\n    else:\n        check = WHITE_TILE\n\n    for x in range(BOARDWIDTH):\n\n        for y in range(BOARDHEIGHT):\n            if b[x][y] == check:\n                if x < BOARDHEIGHT - 4 and y < BOARDWIDTH - 4:\n                    if b[x][y:y+5] == [check]*5:\n                        return True\n                    if [b[x][y],b[x+1][y],b[x+2][y],b[x+3][y],b[x+4][y]] == [check]*5:\n                        return True\n                    if [b[x][y],b[x+1][y+1],b[x+2][y+2],b[x+3][y+3],b[x+4][y+4]] == [check]*5:\n                        return True\n                if x >= 4 and y < BOARDWIDTH - 4:\n                    if [b[x][y],b[x-1][y+1],b[x-2][y+2],b[x-3][y+3],b[x-4][y+4]] == [check]*5:\n                        return True\n\n\ndef makeMove(board, tile, xstart, ystart):\n    board[xstart][ystart] = tile\n\n\ndef checkForQuit():\n    for event in pygame.event.get((QUIT, KEYUP)):\n        if event.type == QUIT or (event.type == KEYUP and event.key == K_ESCAPE):\n            pygame.quit()\n            sys.exit()\n\n\ndef getValidMoves(board):\n    validMoves = []\n    for x in range(BOARDWIDTH):\n        for y in range(BOARDHEIGHT):\n            if isValidMove(board, x, y) != False:\n                validMoves.append((x, y))\n\n    return validMoves\n\n\ndef isValidMove(board, xstart, ystart):\n    if board[xstart][ystart] != EMPTY_SPACE or not isOnBoard(xstart, ystart):\n        return False\n    return True\n\n\ndef isOnBoard(x, y):\n    return x >= 0 and x < BOARDWIDTH and y >= 0 and y < BOARDHEIGHT\n\n\n\ndef drawBoard(board):\n    DISPLAYSURF.blit(BGIMAGE, BGIMAGE.get_rect())\n\n    for x in range(BOARDWIDTH + 1):\n        startx = (x * SPACESIZE) + XMARGIN\n        starty = YMARGIN\n        endx = (x * SPACESIZE) + XMARGIN\n        endy = YMARGIN + (BOARDHEIGHT * SPACESIZE)\n        pygame.draw.line(DISPLAYSURF, GRIDLINECOLOR, (startx, starty), (endx, endy))\n    for y in range(BOARDHEIGHT + 1):\n        startx = XMARGIN\n        starty = (y * SPACESIZE) + YMARGIN\n        endx = XMARGIN + (BOARDHEIGHT * SPACESIZE)\n        endy = (y * SPACESIZE) + YMARGIN\n        pygame.draw.line(DISPLAYSURF, GRIDLINECOLOR, (startx, starty), (endx, endy))\n\n    for x in range(BOARDWIDTH):\n        for y in range(BOARDHEIGHT):\n            centerx, centery = translateBoardToPixelCoord(x, y)\n            if board[x][y] == WHITE_TILE or board[x][y] == BLACK_TILE:\n                if board[x][y] == WHITE_TILE:\n                    tileColor = WHITE\n                else:\n                    tileColor = BLACK\n                pygame.draw.circle(DISPLAYSURF, tileColor, (centerx, centery), int(SPACESIZE / 2) - 4)\n\n\ndef translateBoardToPixelCoord(x, y):\n    return XMARGIN + x * SPACESIZE + int(SPACESIZE / 2), YMARGIN + y * SPACESIZE + int(SPACESIZE / 2)\n\n\ndef getNewBoard():\n    board = []\n    for i in range(BOARDWIDTH):\n        board.append([EMPTY_SPACE] * BOARDHEIGHT)\n\n    return board\n\nif __name__ == '__main__':\n    main()\n","repo_name":"qipengh/python-code","sub_path":"五子棋项目/五子棋/wuziqi.py","file_name":"wuziqi.py","file_ext":"py","file_size_in_byte":8429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"16888553981","text":"from typing import NamedTuple\n\nfrom texttable import Texttable\n\nfrom django_elastic_migrations import DEMIndexManager\nfrom django_elastic_migrations.exceptions import FirstMigrationNotRunError\nfrom django_elastic_migrations.management.commands.es import ESCommand\nfrom django_elastic_migrations.utils.django_elastic_migrations_log import get_logger\n\nlog = get_logger()\n\n\"\"\"\nData model for making a row of the output table from ./manage.py es_list\n\"\"\"\nEsListRow = NamedTuple(\n    'EsListRow', [\n        ('index_base_name', str),\n        ('index_version_name', str),\n        ('created', int),\n        ('active', int),\n        ('docs', int),\n        ('tag', str),\n    ]\n)\n\n\nclass Command(ESCommand):\n    help = \"django-elastic-migrations: list available indexes\"\n\n    def add_arguments(self, parser):\n        self.get_index_specifying_arguments(parser, include_exact=False, default_all=True)\n        parser.add_argument(\n            '--es-only', action='store_true',\n            help=\"Only list indexes in elasticsearch, without respect to the models.\"\n        )\n\n    def handle(self, *args, **options):\n        log.info(\"Available Index Definitions:\")\n\n        indexes, _, apply_all, _, _ = self.get_index_specifying_options(\n            options, require_one_include_list=['es_only'])\n\n        es_only = options.get('es_only', False)\n\n        table = Texttable(max_width=85)\n        if es_only:\n\n            table.header([\"Name\", \"Count\"])\n            [table.add_row(row) for row in DEMIndexManager.list_es_doc_counts().items()]\n\n        else:\n\n            indexes = DEMIndexManager.get_indexes()\n\n            if indexes and not apply_all:\n                new_indexes = []\n                for index in indexes:\n                    if index.get_base_name() in options['index']:\n                        new_indexes.append(index)\n                indexes = new_indexes\n\n            rows = []\n            try:\n                for dem_index in indexes:\n                    dem_index_model = dem_index.get_index_model()\n                    index_versions = dem_index_model.get_available_versions_with_prefix()\n                    row = None\n                    if index_versions:\n                        for index_version in index_versions:\n                            num_docs = DEMIndexManager.get_es_index_doc_count(index_version.name)\n                            row = EsListRow(dem_index_model.name,\n                                            index_version.name,\n                                            not (index_version.is_deleted is None),\n                                            index_version.is_active or 0,\n                                            num_docs,\n                                            index_version.tag)\n                    else:\n                        row = EsListRow(dem_index.get_base_name(), \"\", False, False, 0, \"Current (not created)\")\n                    if row:\n                        rows.append(row)\n            except AttributeError:\n                raise FirstMigrationNotRunError()\n\n            table.header([\"Index Base Name\", \"Index Version Name\", \"Created\", \"Active\", \"Docs\", \"Tag\"])\n            table.set_cols_width([20, 35, 7, 6, 5, 9])\n            # sort the rows so it's a consistent ordering; these are tuples so they sort nicely\n            [table.add_row(r) for r in sorted(rows)]\n\n        log.info(table.draw())\n        log.info(\n            \"An index version name is: \\n\"\n            \"{environment prefix}{index name}-{version primary key id}. \\n\"\n            \"Most Django Elastic Migrations management commands take the \\n\"\n            \"base name (in which case the activated version is used) or \\n\"\n            \"the specific index version name.\"\n        )\n","repo_name":"htaghizadeh/django-elastic-migrations","sub_path":"django_elastic_migrations/management/commands/es_list.py","file_name":"es_list.py","file_ext":"py","file_size_in_byte":3716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"86"}
+{"seq_id":"32212819548","text":"import unittest\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.log import Log\n\n\nclass TestMutationEvent(unittest.TestCase):\n    # Initialize the driver variable\n    driver = None\n\n    @classmethod\n    def setUpClass(cls):\n        # Initialize the Chrome WebDriver\n        cls.driver = webdriver.Chrome()\n\n        # Maximize the browser window\n        cls.driver.maximize_window()\n\n    @classmethod\n    def tearDownClass(cls):\n        # Close the driver\n        cls.driver.quit()\n\n    async def test_mutation_event(self):\n        # Using an asynchronous context manager to establish a bidi connection with the driver\n        async with self.driver.bidi_connection() as session:\n\n            # Create a log instance using the driver and session\n            log = Log(self.driver, session)\n\n        # Using an asynchronous context manager to listen for mutation events\n        async with log.mutation_events() as event:\n\n            # Navigate to a specific URL\n            self.driver.get(\"https://www.selenium.dev/selenium/web/dynamic.html\")\n\n            # Simulate a click action on an element\n            self.driver.find_element(By.ID, \"reveal\").click()\n\n        # Assert that the expected element is present in the collected mutation events\n        assert event[\"element\"] == self.driver.find_element(By.ID, \"revealed\")\n        assert event[\"attribute_name\"] == \"style\"\n        assert event[\"current_value\"] == \"\"\n        assert event[\"old_value\"] == \"display:none;\"\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n\n","repo_name":"JagatheshwaranN/Selenium_Python_Concepts","sub_path":"concepts/bidi/03_mutation_event_test.py","file_name":"03_mutation_event_test.py","file_ext":"py","file_size_in_byte":1579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70397183324","text":"import time\nimport smbus\n\nds1307_addr=0x68#DS1307 Entegresinin I2C Adresi\nds1307 = smbus.SMBus(1)\n\nseconds_addr=0x00#DS1307 second address\nminutes_addr=0x01#DS1307 minutes address\nhours_addr=0x02#DS1307 hours address\ndays_addr=0x03#DS1307 days address\ndates_addr=0x04#DS1307 dates address\nmonths_addr=0x05#DS1307 months address\nyears_addr=0x06#DS1307 years address\n\n#Kullanilmak uzere degiskenler tanimlaniyor\nseconds: int=0 \nminutes: int=0\nhours: int=0\ndays: int=0\ndates: int=0\nmonths: int=0\nyears: int=0\narray_days=['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n\n#DS1307 entegresi ile haberlesme icin 10'luk sistemden 2'lik sisteme dönüşütüren fonksiyon\ndef dec2bcd(num):\n    ones: int=0\n    tens: int=0\n    temp: int=0\n    \n    ones=int(num%10)\n    temp=int(num/10)\n    tens=int(temp<<4)\n    \n    return ones+tens\n#DS1307 entegresi ile haberlesme icin 2'lik sistemden 10'luk sisteme dönüşütüren fonksiyon\ndef bcd2dec(num):\n    s: int=0\n    s=int(num/16)\n    p: int=0\n    p=s*10\n    f: int=0\n    f=num % 16\n    o: int=0\n    o=p+f\n    return o\n\n#DS1307 entegresi Register'lara yazmak icin kullanilan fonksiyon\ndef set_time(seconds_val:  int,\n             minutes_val: int,\n             hours_val:   int,\n             days_val:    int,\n             dates_val:   int,\n             months_val:  int,\n             years_val:  int):\n    #Input degiskenleri DS1307 Register'larina yazmak icin 2'lik sisteme cevirme islemi yapiliyor         \n    seconds_val=dec2bcd(seconds_val)\n    minutes_val=dec2bcd(minutes_val)\n    hours_val=dec2bcd(hours_val)\n    days_val=dec2bcd(days_val)\n    dates_val=dec2bcd(dates_val)\n    months_val=dec2bcd(months_val)\n    years_val=dec2bcd(years_val)\n    \n    #2'lik sisteme donusturulen degiskenler DS1307 Entegresine yaziliyor\n    #(Entegre Adresi, Register Adresi, Register Degeri)\n    ds1307.write_byte_data(ds1307_addr, seconds_addr, seconds_val)\n    ds1307.write_byte_data(ds1307_addr, minutes_addr, minutes_val)\n    ds1307.write_byte_data(ds1307_addr, hours_addr, hours_val)\n    ds1307.write_byte_data(ds1307_addr, days_addr, days_val)\n    ds1307.write_byte_data(ds1307_addr, dates_addr, dates_val)\n    ds1307.write_byte_data(ds1307_addr, months_addr, months_val)\n    ds1307.write_byte_data(ds1307_addr, years_addr, years_val)\n    \n    return 0\n\ndef read_time():\n\n    #DS1307 Entegresinin Register'larindan veri okumak icin kullanilan fonksiyon\n    #Entegreden alinan veri 2'lik sistemden 10'luk sisteme donusturulerek kullanilabilir hale getiriliyor\n    seconds=ds1307.read_byte_data(ds1307_addr,seconds_addr)\n    seconds=bcd2dec(seconds)\n    \n    minutes=ds1307.read_byte_data(ds1307_addr,minutes_addr)\n    minutes=bcd2dec(minutes)\n    \n    hours=ds1307.read_byte_data(ds1307_addr,hours_addr)\n    hours=bcd2dec(hours)\n    \n    days=ds1307.read_byte_data(ds1307_addr,days_addr)\n    days=bcd2dec(days)\n    \n    dates=ds1307.read_byte_data(ds1307_addr,dates_addr)\n    dates=bcd2dec(dates)\n    \n    months=ds1307.read_byte_data(ds1307_addr,months_addr)\n    months=bcd2dec(months)\n    \n    years=ds1307.read_byte_data(ds1307_addr,years_addr)\n    years=bcd2dec(years)\n    years=years+1970+30#Years icin offset degeri mevcut. Bu deger isleme alinmazsa dogru Yil bilgisi elde edilemez \n    date_output=\"{0:04d}{1:02d}{2:02d}\" .format(years,months,dates)\n    time_output=\"{0:2d}:{1:02d}:{2:02d}\" .format(hours,minutes,seconds)\n    return date_output,time_output\n    \n#set_time(0, 28, 13, 3, 26,1,22)\n\nprint(read_time()[0]+\" \"+read_time()[1]) \n","repo_name":"Vivente-Yazilim/Entegre-Denemeleri","sub_path":"ds1307.py","file_name":"ds1307.py","file_ext":"py","file_size_in_byte":3480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"69832479965","text":"# -*- coding: utf-8 -*-\n\nfrom Queue import PriorityQueue\nfrom logging import getLogger\nfrom zlib import compress, decompress\nfrom cPickle import dumps, loads\n\nfrom base import BaseQueue\n\n\nlogger = getLogger('fetcher.queue.memory')\n\n\nclass Queue(BaseQueue):\n    '''Очередь хранящаяся в оперативной памяти'''\n\n    def __init__(self, queue_compress=False, **kwargs):\n        self._compress = queue_compress\n        self._queue = PriorityQueue()\n\n    def size(self):\n        return self._queue.qsize()\n\n    def get(self):\n        priority, data = self._queue.get()\n        if self._compress:\n            data = decompress(data)\n            data = loads(data)\n        return priority, data\n\n    def put(self, item):\n        priority, data = item[:2]\n        if self._compress:\n            data = dumps(data)\n            data = compress(data)\n        self._queue.put((priority, data))\n","repo_name":"hell10w/fetcher","sub_path":"fetcher/tasks/queues/memory.py","file_name":"memory.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"43485237646","text":"import openpyxl as xl;\r\nimport sys\r\nfrom openpyxl.styles import Font\r\n\r\n#===============Variables for the script=================#\r\n\r\ntargetExcel=\"Memory_Highlighted_Output.xlsx\"\r\nthreshold=80\r\nthresholdName=\"Avg Util(%)\"\r\nthresholdRow=2\r\n\r\n#========================================================#\r\n\r\ndef autoextract(inputfile,outputfile):\r\n\t# opening the source excel file\r\n\tfilename = inputfile\r\n\twb1 = xl.load_workbook(filename)\r\n\r\n\t# opening the destination excel file\r\n\tfilename1 = outputfile\r\n\twb2 = xl.load_workbook(filename1)\r\n\tws2 = wb2[\"Output\"]\r\n\tws3 = wb2[\"Counter\"]\r\n\r\n\tsheets = wb1.sheetnames\r\n\tprint(\"The sheets for processing include:\", sheets)\r\n\tx=len(sheets)\r\n\tfor z in range(x):\r\n\t\tws1 = wb1.worksheets[z]\r\n\r\n\t\t# calculate total number of rows and\r\n\t\t# columns in the source excel file\r\n\t\tmr = ws1.max_row\r\n\t\tmc = ws1.max_column\r\n\r\n\t\t#Initialize a variable for checking the column position of reference value.\r\n\t\treferVal=999\r\n\t\tfor a in range(1, mc + 1):\r\n\t\t\tif ws1.cell(row=thresholdRow, column=a).value == thresholdName:\r\n\t\t\t\treferVal=a\r\n\t\t\t\tbreak\r\n\r\n\t\tif referVal == 999:\r\n\t\t\t\tprint(\"ERROR: No Threshold Name cell was found.\")\r\n\t\t\t\tsys.exit(1)\r\n\r\n\t\t#Set header\r\n\t\tws2['A1'] = \"Network\"\r\n\t\tws2['B1'] = \"Device IP Address\"\r\n\t\tws2['C1'] = \"Device Name\"\r\n\t\tws2['D1'] = \"Product Series\"\r\n\t\tws2['E1'] = \"Memory Pool\"\r\n\t\tws2['F1'] = \"Min Util(%)\"\r\n\t\tws2['G1'] = \"Max Util(%)\"\r\n\t\tws2['H1'] = \"Avg Util(%)\"\r\n\r\n\t\t#Set font style\r\n\t\tws2['A1'].font = Font(bold=True)\r\n\t\tws2['B1'].font = Font(bold=True)\r\n\t\tws2['C1'].font = Font(bold=True)\r\n\t\tws2['D1'].font = Font(bold=True)\r\n\t\tws2['E1'].font = Font(bold=True)\r\n\t\tws2['F1'].font = Font(bold=True)\r\n\t\tws2['G1'].font = Font(bold=True)\r\n\t\tws2['H1'].font = Font(bold=True)\r\n\r\n\t\t#Counter to count the number of devices in a network.\r\n\t\tcount=0\r\n\r\n\t\t# copying the cell values from source\r\n\t\t# excel file to destination excel file\r\n\t\tfor i in range(1, mr + 1):\r\n\t\t\tif i>2: #Start from the 2nd row because 1st row is the header\r\n\t\t\t\tif float(ws1.cell(row=i, column=referVal).value) >= threshold:\r\n\t\t\t\t\tlastrow = len(ws2['A']) # Check the last row of Column A for appending.\r\n\t\t\t\t\tcount+=1\r\n\t\t\t\t\tws2.cell(row=lastrow+1, column=1).value = ws1.title\r\n\t\t\t\t\tfor j in range(1, mc + 1):\r\n\t\t\t\t\t\t# reading cell value from source excel file\r\n\t\t\t\t\t\tc = ws1.cell(row=i, column=j)\r\n\r\n\t\t\t\t\t\t# writing the read value to destination excel file\r\n\t\t\t\t\t\tws2.cell(row=lastrow+1, column=j+1).value = c.value\r\n\r\n\t\tws3['A1'] = \"Network\"\r\n\t\tws3['B1'] = \"Number of Devices\"\r\n\t\tlastrowCounterSheet = len(ws3['A'])  # Check the last row of Column A for appending.\r\n\t\tws3.cell(row=lastrowCounterSheet + 1, column=1).value = ws1.title\r\n\t\tws3.cell(row=lastrowCounterSheet + 1, column=2).value = count\r\n\r\n\tlastrowCounterSheet = len(ws3['A'])\r\n\tws3.cell(row=lastrowCounterSheet + 1, column=1).value = \"Total\"\r\n\r\n\t# Calculate the total and paste in a temp Cell, copy it to the original Total Cell.\r\n\tws3.cell(row=lastrowCounterSheet + 1, column=2).value = \"=SUM(B1:B\" + str(lastrowCounterSheet) + \")\"\r\n\t#print(ws3.cell(row=lastrowCounterSheet + 1, column=2).value)\r\n\r\n\t# saving the destination excel file\r\n\twb2.save(str(filename1))\r\n\r\ndef clearsheet(outputfile):\r\n\twb2 = xl.load_workbook(outputfile)\r\n\tws2 = wb2[\"Output\"]\r\n\tfor row in ws2['A1:Z999']:\r\n\t\tfor cell in row:\r\n\t\t\tcell.value = None\r\n\r\n\tws3 = wb2[\"Counter\"]\r\n\tfor row in ws3['A1:Z99']:\r\n\t\tfor cell in row:\r\n\t\t\tcell.value = None\r\n\r\n\twb2.save(str(outputfile))\r\n\r\nif __name__ == '__main__':\r\n\tif len(sys.argv) < 2:\r\n\t\tprint(\"ERROR: Please enter a valid source filename.\")\r\n\t\tsys.exit(1)\r\n\r\n\ttry:\r\n\t\tprint(\"Source filename: %s\" % (sys.argv[1]))\r\n\t\tsourceExcel = sys.argv[1]\r\n\t\tclearsheet(targetExcel)\r\n\t\tautoextract(sourceExcel,targetExcel)\r\n\t\tprint(\"The program has been completed. Please check the output file:\", targetExcel)\r\n\texcept:\r\n\t\tprint(\"Unexpected error:\", sys.exc_info()[0])\r\n\t\traise","repo_name":"tonyktliu/Excel_filtering_by_threshold","sub_path":"filteringByThreshold.py","file_name":"filteringByThreshold.py","file_ext":"py","file_size_in_byte":3873,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"37853093243","text":"import os\nimport pandas as pd\nimport numpy as np\nimport pickle\nfrom sklearn.cluster import KMeans\nimport time\nfrom scipy.spatial import distance\nimport math\n\nclassFunctions = {}\nclassFunctions['omega'] = 'omegarizeBOW'\nclassFunctions['beta'] = 'trainBeta'\nmodelsFolder = \"models\"\n\n#TODO: treat error in case its not avaliable\ndef simpleLoadClusterizer(name,version):\n\tclassName = getClusterizerName(name,version)\n\twith open(className+\".pkl\",'rb') as fid:\n\t\treturn pickle.load(fid)\n\ndef getClusterizerName(name,version):\n\treturn os.path.join(modelsFolder,name+\"-\"+version)\n\ndef getKMeansClusterizer(X, classifierName, version, re_train=False):\n\ttry:\n\t\tclassName = getClusterizerName(classifierName,version)\n\t\tfuncName = classFunctions[classifierName]\n\t\tfunc = eval(funcName)\n\t\tif(os.path.isfile(className+\".pkl\")):\n\t\t\tif(re_train == False):\n\t\t\t\ttry:\n\t\t\t\t\twith open(className+\".pkl\",'rb') as fid:\n\t\t\t\t\t\treturn pickle.load(fid)\n\t\t\t\texcept EOFError:\n\t\t\t\t\tclassifier = func(X)\n\t\t\t\t\twith open(className+\".pkl\",'wb') as fid:\n\t\t\t\t\t\tpickle.dump(classifier,fid)\n\t\t\t\t\treturn classifier\n\n\t\t# If the classifier does not exist or you have to train it again\n\t\tclassifier = func(X)\n\t\twith open(className+\".pkl\",'wb') as fid:\n\t\t    pickle.dump(classifier,fid)\n\t\treturn classifier\n\n\texcept KeyError as e:\n\t\tprint(\"Error while trying to create a function for \",classifierName)\n\ndef omegarizeBOW(bow):\n\n\tmaxScore = 10000000\n\tmaxModel = None\n\tmaxNbrCluster = None\n\n\twe = pd.DataFrame.from_dict(bow, orient='index')\n\twe.index.rename('uri', True)\n\n\tprint(\"TextML: Could assign {} word vectors\".format(we.shape[0]))\n\twe = we.dropna()\n\n\tprint(\"TextML: Initiating the Omega trainning phase\")\n\tstart_time = time.time()\n\tmat = we.as_matrix(range(300))\n\n\tscale = 0.1\n\n\t# Modificaton to make it run faster (Only for testing purposes)\n\tl_bound = 4\n\tu_bound = 16\n\n\tprint(\"Optimizing Omega\")\n\tfor num_clusters in range(l_bound,u_bound):\n\t    cluster = KMeans(n_clusters=num_clusters, max_iter=100, n_jobs=1)\n\t\t# MacOS needs to run in exactly 1 job otherwise it never stop running\n\t    # cluster = KMeans(n_clusters=num_clusters, max_iter=300, n_jobs=-1)\n\t    cluster.fit(mat)\n\t    rc = cluster.score(mat)\n\t    score = -rc / mat.shape[0]\n\t    # print(\"Score for\",num_clusters,\"is\",score)\n\t    maxModel = cluster\n\t    if(score < maxScore):\n\t        maxModel = cluster\n\t        maxScore = score\n\t        maxNbrCluster =num_clusters\n\n\tprint(\"Omega with Max Score is\",maxNbrCluster)\n\tprint(\"The maxScore is\",maxScore)\n\tlabels = maxModel.labels_\n\tprint(\"TextML: Omega train finished in %.2f seconds.\" % (time.time() - start_time))\n\treturn maxModel\n    # results = pd.DataFrame([we.index,labels]).T\n\t# return (maxModel,results)\n\t# return maxModel\n\ndef trainBeta(X):\n    l_bound = 4\n    u_bound = 16\n\n    maxScore = 10000000\n    maxModel = None\n    maxNbrCluster = None\n\n    scale = 0.1\n\n    start_time = time.time()\n\n    for num_clusters in range(l_bound,u_bound):\n        cluster = KMeans(n_clusters=num_clusters, max_iter=100, n_jobs=-1)\n        cluster.fit(X)\n        rc = cluster.score(X)\n        score = -rc / len(X)\n        # print(\"Score for\",num_clusters,\"is\",score)\n        maxModel = cluster\n        if(score < maxScore):\n            maxModel = cluster\n            maxScore = score\n            maxNbrCluster =num_clusters\n\n    print(\"Beta with Max Score is\",maxNbrCluster)\n    print(\"The maxScore is\",maxScore)\n    print(\"TextML: Beta train finished in %.2f seconds.\" % (time.time() - start_time))\n    return maxModel\n\ndef sigmoid(x):\n  return 1 / (1 + math.exp(-x))\n\ndef scoreOmega(omegaCentroid,distancesMatrix):\n\t# print(\"DISTANCE MATRIX\",distancesMatrix)\n\tscore = 0\n\ttestDiv = len(distancesMatrix)\n\tif testDiv == 0:\n\t\ttestDiv = 1\n\tfor word in distancesMatrix:\n\t\td = distance.euclidean(omegaCentroid,word)\n\t\tscore += sigmoid(d)\n\treturn score/testDiv\n\t# return score/len(distancesMatrix)\n","repo_name":"israelzinc/recommendation-system","sub_path":"ml_library.py","file_name":"ml_library.py","file_ext":"py","file_size_in_byte":3860,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"71673176603","text":"import sqlite3\nimport json\n\nfrom models import Comment, Post, User\n\n\ndef get_all_comments():\n\n    with sqlite3.connect(\"./db.sqlite3\") as conn:\n        conn.row_factory = sqlite3.Row\n        db_cursor = conn.cursor()\n\n        db_cursor.execute(\"\"\"\n        SELECT \n            c.id,\n            c.post_id,\n            c.author_id,\n            c.content\n        FROM Comments c\n        \n        \"\"\")\n\n        comments = []\n\n        dataset = db_cursor.fetchall()\n\n        for row in dataset:\n            comment = Comment(row['id'], row['post_id'],\n                              row['author_id'], row['content'])\n\n            comments.append(comment.__dict__)\n\n        return json.dumps(comments)\n\n\ndef create_comment(new_comment):\n    with sqlite3.connect(\"./db.sqlite3\") as conn:\n        db_cursor = conn.cursor()\n\n        db_cursor.execute(\"\"\"\n        INSERT INTO Comments\n            (post_id, author_id, content )\n        VALUES\n            ( ?, ?, ?);\n        \"\"\", (new_comment['post_id'], new_comment['author_id'], new_comment['content']))\n\n        id = db_cursor.lastrowid\n\n        new_comment['id'] = id\n\n    return json.dumps(new_comment)\n\n\ndef get_comments_by_post_id(post_id):\n    \"\"\"Return Comments belonging to a Post\"\"\"\n    with sqlite3.connect(\"./db.sqlite3\") as conn:\n        conn.row_factory = sqlite3.Row\n        db_cursor = conn.cursor()\n\n        # Write the SQL query to get the information you want\n        db_cursor.execute(\"\"\"\n        SELECT * FROM Comments c\n        JOIN Users u ON u.id = c.author_id\n        JOIN Posts p ON p.id = c.post_id\n        WHERE post_id = ?\n        \"\"\", (post_id, ))\n\n        # Initialize an empty list to hold all entry representations\n        comments = []\n\n        # Convert rows of data into a Python list\n        dataset = db_cursor.fetchall()\n\n        # Set representations\n        comments = create_from_query_dict_list(dataset)\n\n    # Use `json` package to properly serialize list as JSON\n    return json.dumps(comments)\n\n\ndef delete_comment(id):\n    \"\"\"\n    Removes the selected comment from the list\n\n    Args:\n        id(int): The id of the comment to be deleted\n    \"\"\"\n    with sqlite3.connect(\"./db.sqlite3\") as conn:\n        db_cursor = conn.cursor()\n\n        db_cursor.execute(\"\"\"\n            DELETE from Comments\n            WHERE id= ?\n            \"\"\", (id, ))\n\n\ndef create_from_query_dict_list(dataset):\n    \"\"\"Return list of Comment dictionary objects\"\"\"\n\n    comments = []\n    # Iterate list of data returned from database\n    for row in dataset:\n        # Set representations\n        create_dict_with_embedded_properties_comments = create_from_query_dict_comment(\n            row)\n\n        # Add the dictionary representation of the post to the list\n        comments.append(create_dict_with_embedded_properties_comments)\n\n    return comments\n\n\ndef create_from_query_dict_comment(row):\n    \"\"\"Returns Comment dictionary object\"\"\"\n\n    # Create a comment instance from the current row.\n    # Note that the database fields are specified in\n    # exact order of the parameters defined in the\n    # Entry class above.\n    comment = Comment(row['id'], row['post_id'],\n                      row['author_id'], row['content'])\n\n    # Create an User instance from the current row\n    user = User(row['author_id'],\n                row['first_name'],\n                row['last_name'],\n                row['email'],\n                row['bio'],\n                row['username'],\n                row['profile_image_url'],\n                row['created_on'],\n                row['active'])\n\n    # Add the dictionary representation of the user to the comment\n    comment.user = user.__dict__\n\n    # Create a Post instance from the current row\n    post = Post(row['post_id'], row['title'],\n                row['publication_date'], row['image_url'], row['content'], row['approved'], row['author_id'], row['category_id'])\n    # Add the dictionary representation of the post to the comment\n    comment.post = post.__dict__\n\n    return comment.__dict__\n\ndef edit_comment(id, updated_comment):\n    \n    with sqlite3.connect(\"./db.sqlite3\") as conn:\n        db_cursor = conn.cursor()\n\n        db_cursor.execute(\"\"\"\n        UPDATE Comments\n            SET\n                content = ?\n            WHERE id = ?\n        \"\"\", (updated_comment['content'], id))\n\n        rows_affected = db_cursor.rowcount\n\n    if rows_affected == 0:\n        return False\n    else:\n        return True\n","repo_name":"nss-day-cohort-56/rare-python-server-spanish-inquisition","sub_path":"views/comment_requests.py","file_name":"comment_requests.py","file_ext":"py","file_size_in_byte":4425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70364889883","text":"from PyQt5.QtCore import QPoint, QMargins\nfrom PyQt5.QtGui import QGlyphRun, QPainter, QRawFont\nfrom PyQt5.QtWidgets import QWidget\n\n\nclass QVHarfbuzzWidget(QWidget):\n    def __init__(self, vharfbuzz, size, buf):\n        self.vharfbuzz = vharfbuzz\n        self.size = size\n        self.buf = buf\n        self.setup_font()\n        self.margins = QMargins(25, 25, 25, 25)\n        super(QVHarfbuzzWidget, self).__init__()\n\n    def set_buf(self, buf):\n        self.buf = buf\n        self.update()\n\n    def setup_font(self):\n        rf = QRawFont()\n        rf.loadFromData(self.vharfbuzz.fontdata, self.size, 0)\n        self.rf = rf\n\n    def scale_point(self, x, y):\n        return QPoint(\n            x / self.vharfbuzz.upem * self.size, y / self.vharfbuzz.upem * self.size\n        )\n\n    def paintEvent(self, e):\n        if not self.buf:\n            return\n        qp = QPainter()\n        qp.begin(self)\n        g = QGlyphRun()\n        g.setRawFont(self.rf)\n        g.setGlyphIndexes([x.codepoint for x in self.buf.glyph_infos])\n        pos = (0, 0)\n        poses = []\n        for _p in self.buf.glyph_positions:\n            p = _p.position\n            # Y coordinates go down, not up.\n            poses.append(self.scale_point(pos[0] + p[0], pos[1] - p[1]))\n            pos = (pos[0] + p[2], pos[1] + p[3])\n\n        g.setPositions(poses)\n        qp.drawGlyphRun(e.rect().marginsRemoved(self.margins).bottomLeft(), g)\n        qp.end()\n","repo_name":"simoncozens/flux","sub_path":"attic/qvharfbuzz.py","file_name":"qvharfbuzz.py","file_ext":"py","file_size_in_byte":1432,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"86"}
+{"seq_id":"13279300225","text":"from os import getenv\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom dotenv import load_dotenv\nfrom flask import g\n\nload_dotenv()\n\n# Connect to the database using the env variable (from the root directory \".env\" file).\nengine  = create_engine(getenv('DB_URL'), echo=True, pool_size=20, max_overflow=0)  #manages overall DB connection\nSession = sessionmaker(bind=engine)                                                 #generates temporary connections\nBase    = declarative_base()                                                   #helps map models to the real MySQL tables\n\ndef init_db(app):\n  Base.metadata.create_all(engine)\n\n  app.teardown_appcontext(close_db)   #run \"close_db\" with the teardown function\n\ndef get_db():\n  #Avoid createing a new Session instance with each call to this function\n  if 'db' not in g:\n    # store db connection in app context\n    g.db = Session()\n\n  return g.db\n\ndef close_db(e=None):\n  db = g.pop('db', None)   #find and remove \"dg\" from \"g\"\n\n  if db is not None:\n    db.close()","repo_name":"CaptainRich/python-newsfeed","sub_path":"app/db/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1103,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34805481838","text":"#!/usr/bin/env python3.5\nimport json\nfrom json import JSONDecodeError\nimport sys\nfrom urllib.parse import urlsplit\nimport argparse\nimport re\n\nparser = argparse.ArgumentParser(description='Grep a CDRv2 JSON file up by URL host')\nparser.add_argument('host', help='Host to look for', default='.*')\nargs = parser.parse_args()\n\nlineno = 0\n\nhostre = re.compile(args.host)\n\nfor line in sys.stdin:\n    try:\n        o = json.loads(line)\n    except JSONDecodeError as err:\n        print('{0}: JSON parse error: {1}'.format(lineno, err), file=sys.stderr)\n        \n    lineno += 1\n    if 'url' in o:\n        host = \"{0.netloc}\".format(urlsplit(o['url']))\n        if re.search(hostre, host):\n            print(json.dumps(o))\n\n    else:\n        print('{0}: missing url field'.format(lineno), file=sys.stderr)\n\n","repo_name":"isoboroff/crawl-eval","sub_path":"scripts/host-grep.py","file_name":"host-grep.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"86"}
+{"seq_id":"6391259318","text":"import os, fnmatch, re, yaml\nfrom behave import *\n\n@given('the filename in the \"{env_name}\" environment variable')\ndef step_impl(context, env_name):\n  if not hasattr(context, \"filename\"):\n    context.filename = os.environ.get(env_name)\n\n@given('the {file_type} feature file has been loaded')\ndef step_impl(context, file_type):\n  if not hasattr(context, \"file_data\"):\n    process_comments = True\n    metadata_lines = []\n    story_lines = []\n    with open(context.filename) as fh:\n      for line in fh.readlines():\n        match = re.match(r'^\\s*#\\s*(.*)$', line)\n        if process_comments and match:\n          metadata_lines.append(match.group(1))\n        else:\n          match = re.match(r'^\\s*Feature:\\s*(.*)$', line)\n          if match:\n            process_comments = False\n            metadata_lines.append(\"Name: %s\" % match.group(1))\n            story_lines.append(line)\n        if not process_comments:\n          story_lines.append(line)\n\n    context.file_data = yaml.load(\"\\n\".join(metadata_lines))\n    context.file_data[\"Story\"] = \"\\n\".join(story_lines)\n\n@then('the parent directory must be \"{parent}\"')\ndef step_impl(context, parent):\n  assert os.path.split(os.path.dirname(context.filename))[1] == parent\n\n@then('the file must match the pattern \"{pattern}\"')\ndef step_impl(context, pattern):\n  basename = os.path.basename(context.filename)\n  assert re.match(pattern, basename) \n\n@then('the file length must be less than or equal to \"{length:d}\" characters')\ndef step_impl(context, length):\n  basename = os.path.basename(context.filename)\n  assert len(basename) <= length\n\n@then('the \"{field}\" field must exist')\ndef step_impl(context, field):\n  assert field in context.file_data\n  \n@then('the \"{field}\" field must be a \"{field_type}\"')\ndef step_impl(context, field, field_type):\n  type_map = {\n    \"string\": str,\n    \"str\": str,\n    \"array\": list,\n    \"list\": list,\n    \"hash\": dict,\n    \"dict\": dict\n  }\n  assert isinstance(context.file_data[field], type_map[field_type])\n\n@then('the Id must be the same as the filename Id')\ndef step_impl(context):\n  basename = os.path.basename(context.filename)\n  match = re.search(r'^(ocst|ocsc)_(\\d+)_(\\d+)_(\\d+)[a-z0-9_]*\\.feature$', basename)\n  assert match\n  ocsx_id = \"%s-%s.%s.%s\" % (match.group(1).upper(), match.group(2), match.group(3), match.group(4))\n  assert context.file_data[\"Id\"] == ocsx_id\n\n@then('the \"{field}\" field length must be {operation} \"{length:d}\" characters')\ndef step_impl(context, field, operation, length):\n  if operation == \"less than or equal to\":\n    assert len(context.file_data[field]) <= length\n  elif operation == \"greater than or equal to\":\n    assert len(context.file_data[field]) >= length\n\n@then('the \"{field}\" field must match the pattern \"{pattern}\"')\ndef step_impl(context, field, pattern):\n  assert re.match(pattern, context.file_data[field])\n\n@given('the set of allowed values')\ndef step_impl(context):\n  context.allowed_values = []\n  for row in context.table:\n    context.allowed_values.append(row[\"value\"])\n\n@then('the \"{field}\" field must be one of the allowed values')\ndef step_impl(context, field):\n  assert context.file_data[field] in context.allowed_values\n\n@then('the \"{field}\" field must be one or more of the allowed values')\ndef step_impl(context, field):\n  for value in context.file_data[field]:\n    assert value in context.allowed_values\n\n@then('each \"{field}\" value must match the pattern \"{pattern}\"')\ndef step_impl(context, field, pattern):\n  for value in context.file_data[field]:\n    assert re.match(pattern, value)\n","repo_name":"owasp-cloud-security/owasp-cloud-security","sub_path":"features/steps/steps.py","file_name":"steps.py","file_ext":"py","file_size_in_byte":3529,"program_lang":"python","lang":"en","doc_type":"code","stars":168,"dataset":"github-code","pt":"86"}
+{"seq_id":"17214041226","text":"# Write a program to find the sum of all elements of a list.\n\n# Examples\n\n# 1. Input = [1, 2, 3, 4]\n#    Output = 10\n\n# 2. Input = [0,3,-1,2]\n#    Output = 4\n\nmy_list = [1, 2, 3, 4, 5, 6]\nsum_number = 0\nfor i in my_list:\n    sum_number += i\nprint(sum_number)","repo_name":"HleyHub/HyperVergeAcademy","sub_path":"LISTS_ARRAYS/sum_of_elements_list.py","file_name":"sum_of_elements_list.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31081190351","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def binaryTreePaths(self, root: Optional[TreeNode]) -> List[str]:\n        \n        def dfs(node):\n            \n            if not node.left and not node.right:\n                return [str(node.val)]\n            if not node:\n                return\n            \n            res = []\n            if node.left:\n                for postfix in dfs(node.left):\n                    res.append(str(node.val)+\"->\"+ postfix)\n            if node.right:\n                for postfix in dfs(node.right):\n                    res.append(str(node.val)+\"->\"+postfix)\n                \n            \n            return res\n         \n        \n        return dfs(root)","repo_name":"Jyue/Leetcode","sub_path":"0257-binary-tree-paths/0257-binary-tree-paths.py","file_name":"0257-binary-tree-paths.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"7629638543","text":"from pyspark.sql.functions import *\nfrom operator import add\nfrom functools import reduce\nimport heapq\nimport plotly.express as px\n\ndef getLas(data):\n    \"\"\"Gets the local authorities from the dataset\n\n    Args:\n        data (dataframe): the dataset\n\n    Returns:\n        dataframe: the local authorities sorted in order and unique\n    \"\"\"\n    return data.select(\"la_name\").where(data.geographic_level==\"Local authority\").distinct().sort(\"la_name\")\n\ndef getLaEnrlmnts(data, la):\n    \"\"\"Gets the enrolments for the given local authority\n\n    Args:\n        data (dataframe): the dataset\n        la (string): the local authority to get enrolments for\n\n    Returns:\n        dataframe: the enrolments for that local authority broken down by year\n    \"\"\"\n    res = data.select(\"enrolments\", \"time_period\").orderBy(\"time_period\")\\\n        .filter((data.la_name.isin(la)) & (data.geographic_level==\"Local authority\") & (data.school_type == 'Total'))\n    return res.withColumn(\"enrolments\", res[\"enrolments\"].cast('int'))\n\ndef getSchls(data):\n    \"\"\"Gets the schools from the dataset\n\n    Args:\n        data (dataframe): the dataset\n\n    Returns:\n        dataframe: the schools unique and sorted\n    \"\"\"\n    return data.select(\"school_type\").where((data.school_type != 'Total') & (data.geographic_level==\"School\")).distinct().sort(\"school_type\")\n\ndef getSchlMedAbs(data, schl):\n    \"\"\"Gets school medical absences for the given school type as a sum of appointments and illness absences\n\n    Args:\n        data (dataframe): the dataset\n        schl (string): the school type\n\n    Returns:\n        dataframe: dataframe with only the total as a single col and row\n    \"\"\"\n    res = data.select(\"sess_auth_appointments\", \"sess_auth_illness\")\\\n        .filter((data.time_period == \"2017/18\") & (data.school_type.isin(schl) & (data.geographic_level==\"National\")))\\\n        .agg({\"sess_auth_appointments\" : \"sum\", \"sess_auth_illness\" : \"sum\"})\n    return res.withColumn('total', reduce(add, [col(x) for x in res.columns]))\n\ndef getYears(data):\n    \"\"\"Gets the time periods from the data set\n\n    Args:\n        data (data): the dataset\n\n    Returns:\n        dataframe_: years dataframe unique and sorted\n    \"\"\"\n    return data.select(\"time_period\").where(data.geographic_level==\"National\").distinct().sort(\"time_period\")\n\ndef getUnauthAbs(data, year, opt):\n    \"\"\"Gets unauthorised absences broken down by the option specified on the given year\n\n    Args:\n        data (data): the dataset\n        year (string): the year to match\n        opt (string): the selected option to break down the data\n\n    Returns:\n        dataframe: the unauthorised absences broken down by total number of unauthorised sessions\n    \"\"\"\n    optMap = {\"Local authority\" : \"la_name\", \"Regional\" : \"region_name\"}\n    res = data.select(optMap[opt], \"sess_unauthorised\").filter(data.time_period.isin(year) & (data.geographic_level==opt) & (data.school_type == 'Total')).sort(optMap[opt])\n    return res.withColumn(\"sess_unauthorised\", res[\"sess_unauthorised\"].cast('int'))\n\ndef getTop3Auth(data):\n    \"\"\"Gets the top 3 reasons for absences each year\n\n    Args:\n        data (data): the dataset\n\n    Returns:\n        dictionary: the top 3 reasons for absences for each year\n    \"\"\"\n    authList = [\"sess_auth_appointments\", \"sess_auth_excluded\", \"sess_auth_ext_holiday\", \"sess_auth_holiday\", \"sess_auth_illness\",\\\n         \"sess_auth_other\", \"sess_auth_religious\", \"sess_auth_study\", \"sess_auth_traveller\"]\n    authDict = dict((i, \"sum\") for i in authList)\n    res = data.select(authList + [\"time_period\"]).sort(\"time_period\").groupBy(\"time_period\").agg(authDict)\n    for col in res.schema.names:\n        if col[:3] == \"sum\":\n            res = res.withColumnRenamed(col, col[4:-1])\n\n    collect = res.collect()\n    ranking = {}\n    # heap sort and pop top 3 to get top 3\n    for row in collect:\n        ranking[row[0]] = []\n        for i in range(1, len(row)):\n            heapq.heappush(ranking[row[0]], (-row[i] if row[i] is not None else 0, res.schema.names[i][10:]))\n    res = []\n    for year in ranking:\n        res.append([year])\n        for i in range(3):\n            res[-1].append(heapq.heappop(ranking[year])[1])\n    \n    return res\n\ndef getCmpData(data, cols, sumMap, la, year, groupBy):\n    \"\"\"Gets the required data for comparison as specified\n\n    Args:\n        data (data): the dataset\n        cols (list): list of strings of columns to select\n        sumMap (dictionary): the columns to aggregate\n        la (string): local authority\n        year (string): time period\n        groupBy (string): column to group by\n\n    Returns:\n        dataframe: desired data as specified\n    \"\"\"\n    res = data.select(cols)\\\n        .where((data.la_name == la) & (data.time_period == year) & (data.geographic_level==\"Local authority\") & (data.school_type != 'Total'))\\\n        .groupBy(groupBy).agg(sumMap)\n\n    for col in res.schema.names:\n        if col[:3] == \"sum\":\n            res = res.withColumnRenamed(col, col[4:-1])\n    return res\n\ndef getPercentages(data, numer, denom):\n    \"\"\"returns a dataframe with a new column added representing a columns percentage based on the given denominator\n\n    Args:\n        data (data): the dataset\n        numer (string): numerator column\n        denom (string): denominator column\n\n    Returns:\n        _type_: _description_\n    \"\"\"\n    return data.withColumn(f\"{numer}_percent\", round(data[numer] / data[denom] * 100, 3))\n\ndef getAbsLocSchl(data, year):\n    \"\"\"Gets dataframe for relationship between absences location and schools\n\n    Args:\n        data (data): the dataset\n        year (string): time period\n\n    Returns:\n        dataframe: dataset broken down into region name overall percentage of absences and school type\n    \"\"\"\n    return data.select(\"region_name\", \"sess_overall_percent\", \"school_type\")\\\n        .filter(data.time_period.isin(year) & (data.geographic_level==\"Regional\") & (data.school_type != 'Total')).sort(\"time_period\")\n\ndef flatten(data):\n    \"\"\"flattens a dataframe\n\n    Args:\n        data (data): the dataset\n\n    Returns:\n        list: flattened dataframe\n    \"\"\"\n    return [j for i in data for j in i]\n\ndef getPercentageTable(data):\n    \"\"\"Creates a dataframe of pecentages\n\n    Args:\n        data (data): the dataset\n\n    Returns:\n        dataframe: dataframe only containing columns ending with _percent and the local authority\n    \"\"\"\n    res = data.select([c for c in data.schema.names if c.endswith(\"percent\") or c == \"la_name\"])\n    for col in res.schema.names:\n        if col[:10] == \"sess_auth_\":\n            res = res.withColumnRenamed(col, col[10:])\n        elif col[:4] == \"sess\":\n            res = res.withColumnRenamed(col, col[5:])\n    return res\n\ndef getAnalysis(data):\n    \"\"\"Gets the line chart, heat map of regions ranked by absences from best to worst over time, and the average ranking of regions over time\n\n    Args:\n        data (data): the dataset\n\n    Returns:\n        figure, figure, dictionary: the line chart, heatmap, and average rankings\n    \"\"\"\n    res = data.select(\"time_period\", \"sess_overall_percent\", \"region_name\")\\\n        .filter((data.geographic_level==\"Regional\") & (data.school_type == 'Total'))\\\n        .sort(\"time_period\")\n    nat = data.select(\"time_period\", \"sess_overall_percent\")\\\n        .filter((data.geographic_level==\"National\") & (data.school_type == 'Total'))\\\n        .sort(\"time_period\")\n    nat = nat.withColumn(\"region_name\", lit(\"Nation Wide\"))\n    nat = nat.union(res)\n    nat.show()\n    fig1 = px.line([row.asDict() for row in nat.collect()], x=\"time_period\", y=\"sess_overall_percent\", color='region_name')\n    fig1.update_layout(autotypenumbers='convert types')\n\n    labels = dict(per=\"time_period\", locs=\"region_name\", color=\"rank\")\n    pers = flatten(getYears(data).collect())\n    locs = flatten(res.select(\"region_name\").distinct().collect())\n\n    res = res.collect()\n    for i in range(len(res)):\n        res[i] = res[i].asDict()\n\n    # create an intermiedary matrix to translate the data into the correct format for the heat map\n    intMatrix = dict(zip(locs, [dict() for i in range(len(locs))]))\n    for loc in intMatrix:\n        intMatrix[loc] = dict(zip(pers, [0 for i in range(len(pers))]))\n\n    for i in range(len(res)):\n        per = res[i][\"time_period\"]\n        loc = res[i][\"region_name\"]\n        val = res[i][\"sess_overall_percent\"]\n        intMatrix[loc][per] = val\n\n    # convert the intermediary matrix into a normal 2d array for input into the heat map\n    matrix = [[0] * len(pers) for i in range(len(locs))]\n    i = 0\n    for loc in intMatrix:\n        j = 0\n        for per in intMatrix[loc]:\n            matrix[i][j] = intMatrix[loc][per]\n            j += 1\n        i += 1\n\n    # update the matrix to represent the ranks of each region each year\n    for per in range(len(matrix[0])):\n        rankings = []\n        for loc in range(len(matrix)):\n            heapq.heappush(rankings, (matrix[loc][per], loc))\n        for pos in range(1, len(matrix) + 1):\n            _, loc = heapq.heappop(rankings)\n            matrix[loc][per] = pos\n    fig2 = px.imshow(matrix, labels = labels, x = pers, y = locs)\n\n    # find the average ranking of each region using heap sort\n    rankHeap = []\n    for loc, loci in zip(locs, [i for i in range(len(matrix))]):\n        tot = 0\n        for per in range(len(matrix[0])):\n            tot += matrix[loci][per]\n        heapq.heappush(rankHeap, (tot / len(matrix[0]), loc))\n    \n    rankings = []\n    for i in range(1, len(rankHeap) + 1):\n        pos, loc = heapq.heappop(rankHeap)\n        rankings.append((i, loc))\n    return fig1, fig2, rankings","repo_name":"williamsnsung/cs5052p1","sub_path":"sparkcmds.py","file_name":"sparkcmds.py","file_ext":"py","file_size_in_byte":9596,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"22887711277","text":"import random\ncoins = 0\n\nif coins < 5:\n    print('金币不足请充值再玩！')\nwhile True:\n    money = int(input('请输入充值金额：'))\n    if money % 10 == 0:\n        coins += money //10 * 20\n        print('充值成功! 当前金币有%d个' %  coins)\n\n        print('~~~~~~~~~~开启游戏之旅~~~~~~~~~~')\n        answer = input('是否开始游戏（y/n)')\n        if answer == 'n':\n            break\n        while coins > 5 and answer == 'y':\n\n            coins -= 5\n\n            coins += 1\n\n            ran1 = random.randint(1,6)\n            ran2 = random.randint(1,6)\n\n            guess = input('洗牌完毕，请猜大小：')\n\n            if guess == '大'  and ran1 + ran2 > 6 or guess == '小' and ran1 + ran2 <= 6:\n                print('恭喜猜对了，你赢了!')\n                coins += 2\n            else:\n                print('很遗憾！本次猜错了！')\n                answer = input('是否继续游戏（y/n)')\n                if answer == 'n':\n                    break\n                \n    else:\n        print('不是十的倍数，充值失败！')\n        break","repo_name":"helenlx-xu/class-2021-python","sub_path":"Jul2021/lesson.27.0.py","file_name":"lesson.27.0.py","file_ext":"py","file_size_in_byte":1109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"11438199774","text":"from django.db import models\nfrom django.urls import reverse\nimport secrets\nfrom django.contrib.auth.models import User\nfrom .paystack import PayStack\nfrom trisolace.models import Product, Customer\n\n\n# Create your models here.\n\nclass Payment(models.Model):\n    \n\n    name = models.ForeignKey(Customer, null=True, blank=True, on_delete=models.CASCADE)\n    product = models.ForeignKey(Product, null=True, blank=True, on_delete=models.CASCADE)\n    duration = models.CharField(max_length=200, null=True)\n    amount = models.PositiveIntegerField(default='0')\n    email = models.EmailField(default=\" \")\n    ref = models.CharField(max_length=200)\n    verified = models.BooleanField(default=False)\n    date_created = models.DateTimeField(auto_now_add=True)\n\n    class Meta:\n        ordering = (\"-date_created\",)\n\n    def __str__(self) -> str:\n        return f\"{self.name} -{self.product} - GH₵ {self.amount}\"\n\n    def save(self, *args, **kwargs):\n        while not self.ref:\n            ref = secrets.token_urlsafe(50)\n            object_with_similar_ref = Payment.objects.filter(ref=ref).first()\n            if not object_with_similar_ref:\n                self.ref = ref\n        super().save(*args, **kwargs)\n\n    def amount_value(self):\n        return self.amount * 100\n\n    def verify_payment(self):\n        paystack = PayStack()\n        status, result = paystack.verify_payment(self.ref, self.amount)\n        if status:\n            self.paystack_response = result\n            if result[\"amount\"] / 100 == self.amount:\n                self.completed = True\n            self.save()\n            return True\n        return False\n\n# Create your models here.\n","repo_name":"python4GH/portal","sub_path":"core/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"39900089093","text":"from django.http import FileResponse\n\n\n# Django's FileResponse.set_headers doesn't set the Content-Length\n# header if the 'name' attribute of the file-like object it's given\n# is a relative path. Unfortunately, that seems to be the case with\n# FieldFiles (it's name is the relative path from MEDIA_ROOT).\n# See https://docs.djangoproject.com/en/2.2/_modules/django/http/response/#FileResponse\n#\n# This class overrides set_headers such that if Content-Length hasn't\n# been set and the file-like object has a 'size' attribute,\n# Content-Length is set to the value of that attribute.\nclass SizeFileResponse(FileResponse):\n    def set_headers(self, filelike):\n        super().set_headers(filelike)\n        if 'Content-Length' not in self and hasattr(filelike, 'size'):\n            self['Content-Length'] = filelike.size\n","repo_name":"eecs-autograder/autograder-server","sub_path":"autograder/rest_api/size_file_response.py","file_name":"size_file_response.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"86"}
+{"seq_id":"31451296687","text":"from src.helper import fileHelper,emailHelper\n\nswitcher = {\n        \"NEW_FILE\": fileHelper.create_new_file,\n        \"SEND_MAIL\": emailHelper.send_email,\n    }\n\n\ndef receive_event(event_type, *args, **kwargs):\n    try:\n        func = switcher[event_type]\n        func(*args, **kwargs)\n\n    except Exception as e:\n        print(\"The event is not save\" + str(e))\n        raise\n\n","repo_name":"PaulaPascualParra/ManagementOfEventsAndActions","sub_path":"src/decider/eventManagement.py","file_name":"eventManagement.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"40303645474","text":"import os\nimport smtplib\nfrom email.mime.text import MIMEText\nfrom email.header import Header\n\n\nclass Smtp(object):\n\n    def __init__(self):\n        self.enviros = os.environ\n        self.host = 'smtp.163.com'\n        self.mail_user = self.enviros['MAIL']\n        self.mail_pwd = self.enviros['PWD']\n        self.sender = self.enviros['MAIL']\n\n    def send_mail(self):\n        receivers = input(\"接受者, 以空格分割: \")\n        receivers = receivers.split(' ')\n        title = input(\"邮件标题: \")\n        content = input(\"邮件征文: \")\n        msg = MIMEText(content, \"plain\", \"utf-8\")\n        msg['From'] = self.mail_user\n        msg['To'] = ','.join(receivers)\n        msg['Subject'] = Header(title, 'utf-8')\n        try:\n            self.smtp = smtplib.SMTP()\n            self.smtp.connect(host=self.host, port=25)\n            self.smtp.login(self.mail_user, self.mail_pwd)\n            self.smtp.sendmail(self.sender, receivers, msg.as_string())\n        except smtplib.SMTPException as e:\n            print(e.args)\n\nsmtp = Smtp()\nsmtp.send_mail()","repo_name":"0xff-dev/Homeworks","sub_path":"smtp/mail_text.py","file_name":"mail_text.py","file_ext":"py","file_size_in_byte":1063,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"14146766154","text":"import sys\nfrom PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QAction, QTableWidget,QTableWidgetItem,QVBoxLayout\nfrom PyQt5.QtGui import QIcon\nfrom PyQt5.QtCore import pyqtSlot\n\nclass App(QWidget):\n\n    def __init__(self):\n        super().__init__()\n        self.title = 'PyQt5 table - pythonspot.com'\n        self.left = 0\n        self.top = 0\n        self.width = 300\n        self.height = 200\n        self.initUI()\n        \n    def initUI(self):\n        self.setWindowTitle(self.title)\n        self.setGeometry(self.left, self.top, self.width, self.height)\n        \n        self.createTable()\n\n        # Add box layout, add table to box layout and add box layout to widget\n        self.layout = QVBoxLayout()\n        self.layout.addWidget(self.tableWidget) \n        self.setLayout(self.layout) \n\n        # Show widget\n        self.show()\n\n    def createTable(self):\n       # Create table\n        self.tableWidget = QTableWidget()\n        self.tableWidget.setRowCount(4)\n        self.tableWidget.setColumnCount(3)\n        self.tableWidget.setHorizontalHeaderLabels([\"x-component\", \"y-component\",\"z-component\"])\n        self.tableWidget.setVerticalHeaderLabels([\"Description\", \"Price\"])\n        self.tableWidget.setItem(0,0, QTableWidgetItem(\"as\"))\n        self.tableWidget.setItem(0,1, QTableWidgetItem(\"Ceddll (1,2)\"))\n        self.tableWidget.setItem(0,2, QTableWidgetItem(\"Ceddll (1,2)\"))\n        # self.tableWidget.setItem(1,0, QTableWidgetItem(\"Cesll (2,1)\"))\n        # self.tableWidget.setItem(1,1, QTableWidgetItem(\"Ced2,2)\"))\n        # self.tableWidget.setItem(2,0, QTableWidgetItem(\"Ck,1)\"))\n        # self.tableWidget.setItem(2,1, QTableWidgetItem(\"Cen (3,2)\"))\n        # self.tableWidget.setItem(3,0, QTableWidgetItem(\" (4,1)\"))\n        # self.tableWidget.setItem(3,1, QTableWidgetItem(\"Celdslkjk (4,2)\"))\n        self.tableWidget.move(0,0)\n\n        # table selection change\n        # self.tableWidget.doubleClicked.connect(self.on_click)\n\n        # self.fill_table()\n\n    # @pyqtSlot()\n    # def add_element(self):\n    #     des = \"HI\"\n    #     price = 10\n\n    #     self.tableWidget.insertRow(self.items)\n    #     description_item = QTableWidgetItem(des)\n    #     price_item = QTableWidgetItem(\"{:.2f}\".format(float(price)))\n    #     price_item.setTextAlignment(Qt.AlignRight)\n\n    #     self.tableWidget.setItem(self.items, 0, description_item)\n    #     self.tableWidget.setItem(self.items, 1, price_item)\n\n    #     self.description.setText(\"\")\n    #     self.price.setText(\"\")\n\n    #     self.items += 1\n \nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n    ex = App()\n    sys.exit(app.exec_())  \n","repo_name":"nimRobotics/BTP","sub_path":"UI_dev/archive/app_dev_final/table.py","file_name":"table.py","file_ext":"py","file_size_in_byte":2645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"72880001563","text":"\"\"\"\n    A basic implimentation of a class part 1\n\"\"\"\n\nclass Person:\n    def __init__(self, name, age): #intalizer - (constructor)\n        self.name = name #property , class member variable, attruibute, field\n        self.age = age   #property , class member variable, attruibute, field\n\n\np1 = Person(\"Paul\", 48) #object  - class instance\nprint(p1.name)\nprint(p1.age)\n\n\n\"\"\"\n    In the example below the name property is private and cannot be accessed directly\n    We have made 'getters and setters' that allow access to these properties.\n\"\"\"\n\n\n\n# class Person:\n#     \"\"\"The person class is a basic human with name and age\"\"\"\n#     def __init__(self, name : str, age : int) -> None:\n#         self.__name = name\n#         self.age = age\n\n#     def get_name(self) -> str: \n#         \"\"\"Returns string conatining the persons name\"\"\"\n#         return self.__name\n    \n#     def set_name(self, name : str) -> None:\n#         \"\"\"Set the persons name, expecting a string\"\"\"\n#         if type(name) == str:\n#             self.__name = name\n#         else:\n#             print(\"[Error] : cannot set name\")\n\n# #you can't chane __name directly now.\n# p1 = Person(\"Paul\", 48)\n# p1.set_name(\"Paul Smith\")\n# print(p1.get_name())\n# print(p1.age)\n\n\n\n#These line below will crash the program as __name is not accessable\n#print(p1.__name)\n\n#try setting the name to a number\n\n#create the getters and setters for the age. With validation to ensure age is a number\n\n\n\n","repo_name":"CollegePaul/python","sub_path":"OOP/basicClass.py","file_name":"basicClass.py","file_ext":"py","file_size_in_byte":1446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"10037492454","text":"from pico2d import *\nimport random\n\n# Game object class here\nclass Grass:\n    def __init__(self):\n        self.image = load_image('grass.png')\n\n    def draw(self):\n        self.image.draw(400, 30)\n\n    def update(self):\n        pass\n\n\nclass Boy:\n    def __init__(self):\n        self.x = random.randint(100, 700)\n        self.y = 90\n        self.vx = random.randint(3, 7)\n        self.frame = random.randint(0, 7)\n        self.image = load_image('run_animation.png')\n\n    def draw(self):\n        self.image.clip_draw(self.frame*100, 0, 100, 100, self.x, self.y)\n\n    def update(self):\n        self.frame = (self.frame + 1) % 8\n        self.x += 5\n\n\nclass Ball:\n    def __init__(self):\n        self.x = random.randint(50, 750)\n        self.y = 599\n        self.vy = random.randint(30, 70) / 10\n        if random.randint(0, 1) == 0:\n            self.image = load_image('ball21x21.png')\n            self.radius = 21//2\n        else:\n            self.image = load_image('ball41x41.png')\n            self.radius = 41//2\n\n    def draw(self):\n        self.image.draw(self.x, self.y)\n\n    def update(self):\n        if self.y < 50 + self.radius:\n            self.y == 50 + self.radius\n        else:\n            self.y -= self.vy\n\n\ndef handle_events():\n    global running\n    events = get_events()\n    for event in events:\n        if event.type == SDL_QUIT:\n            running = False\n        elif event.type == SDL_KEYDOWN and event.key == SDLK_ESCAPE:\n            running = False\n\n\ndef reset_world():\n    global running\n    global grass\n    global team\n    global balls\n    global world\n\n    running = True\n    grass = Grass()\n    team = [ Boy() for _ in range(11) ]\n    balls = [ Ball() for _ in range(20) ]\n    \n    world = []\n    world.append(grass)\n    world += team\n    world += balls\n\n\ndef render_world():\n    clear_canvas()\n    for e in world:\n        e.draw()\n    update_canvas()\n\n\ndef update_world():\n    for e in world:\n        e.update()\n\n\nopen_canvas()\n\n# initialization code\nreset_world()\n\n# game main loop code\nwhile running:\n    handle_events()\n    update_world()\n    render_world()\n    delay(0.05)\n\n# finalization code\n\nclose_canvas()\n","repo_name":"Jirung-E/Drill08","sub_path":"boy_grass_object.py","file_name":"boy_grass_object.py","file_ext":"py","file_size_in_byte":2143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"6474162429","text":"from flask_restplus import fields, Namespace, Resource\n\nfrom app.models import Book\nfrom ._api import api\n\nns = Namespace('books', description='Books 增删改查 .')\n\nbook_model = ns.model('BookModel', {\n    'book_id': fields.String(readOnly=True, description='The book unique identifier'),\n    'book_name': fields.String(required=True, description='The book nickname'),\n    'price': fields.String(required=True, description='The book price'),\n})\nbook_list_model = ns.model('BookListModel', {\n    'books': fields.List(fields.Nested(book_model)),\n    'total': fields.Integer,\n})\n\n\n@ns.route(\"\")\nclass BookListApi(Resource):\n    # 初始化数据\n    books = [Book(\"三体\", '100'), Book(\"解忧杂货铺\", '25')]\n\n    @ns.doc('get_book_list')\n    @ns.marshal_with(book_list_model)\n    def get(self):\n        return {\n            \"books\": self.books,\n            \"total\": len(self.books),\n        }\n\n    @ns.doc('create_book')\n    @ns.expect(book_model)\n    @ns.marshal_with(book_model, code=201)\n    def post(self):\n        print(api.payload['book_name'], '++++')\n        book = Book(api.payload['book_name'], api.payload['price'])\n        return book","repo_name":"leeexing/python","sub_path":"flask_restful/app/apis/book_api.py","file_name":"book_api.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"1470280985","text":"import os\nimport numpy as np\nfrom scipy.io import wavfile\n\nclass APUtils:\n    __instance = None\n\n    def __init__(self):\n        \"\"\" Virtually private constructor. \"\"\"\n        if APUtils.__instance is not None:\n            raise Exception(\"This class is a singleton!\")\n        else:\n            APUtils.__instance = self\n\n    @staticmethod\n    def instance():\n        \"\"\" Static access method. \"\"\"\n        if APUtils.__instance is None:\n            APUtils()\n        return APUtils.__instance\n\n    def sinWave(self, freq, samples):\n        print(\"Generated a sin wave\")\n        pass\n\n    def read_wav_file(self, filename):\n        \"\"\"\n        Reads the wavfile for filename\n        :param filename: Name of the wav file to read\n        :return: samples: sample_rate: int, num_channels: int\n        \"\"\"\n        sample_rate, samples = wavfile.read(filename)\n        num_channels = samples.shape[1]\n        return samples, sample_rate, num_channels\n\n    def write_wav_file(self, filename, sample_rate: int, data: np.ndarray):\n        \"\"\"\n        Write data to wav file\n        :param filename: Filename of the output file to write\n        :param sample_rate: Samples/Sec of the data\n        :param data: array of data to write to file\n        :return: None\n        \"\"\"\n        if os.path.exists(filename):\n            print(f\"{filename} already exists. Skipping write ...\")\n            return\n\n        wavfile.write(filename, sample_rate, data)\n","repo_name":"SinghSiddharth01/audio-project","sub_path":"src/utils/audioProfjectUtils.py","file_name":"audioProfjectUtils.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"73921721245","text":"#Hay Points\r\nHayMoneyDictionary = {}\r\nNumWords, NumJobDescriptions = map(int, input().split(\" \"))\r\nfor i in range(NumWords):\r\n    HayWord, WordMoney = input().split(\" \")\r\n    HayMoneyDictionary[HayWord] = int(WordMoney)\r\nfor j in range(NumJobDescriptions):\r\n    a = 0\r\n    Salary = 0\r\n    while True:\r\n        Line = input()\r\n        if Line != \".\":\r\n            TempList = list(Line.split(\" \"))\r\n            for a in range(len(TempList)):\r\n                Word = TempList[a]\r\n                if Word in HayMoneyDictionary.keys():\r\n                    Salary += HayMoneyDictionary[Word]\r\n        elif Line == \".\":\r\n            print(Salary)\r\n            break","repo_name":"adityaranjan/KattisSolutions","sub_path":"haypoints.py","file_name":"haypoints.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"2672498474","text":"\"\"\"Implementation of the quicksort n log(n) algorithm\n \nDivide and conquer\n\nexample run...with random list\n\nimport random \nunsorted_list = []\nfor count in range(1,1100):\n\tunsorted_list.append(count)\n\t\nunsorted_list = random.shuffle(unsorted_list)\nsorted_list = sort(unsorted_list)\n\n#print the result\nfor number in sorted_list:\n\tprint number\n\n\n\"\"\"\n\n\ndef sort(list):\n\tif len(list) == 0 or len(list) == 1:\n\t\treturn list\n\tpivot = get_pivot(list)\n\tsmaller = []\n\tbigger = []\n\tfor number in list:\n\t\tif(number <= pivot):\n\t\t\tsmaller.append(number)\n\t\telif(number > pivot):\n\t\t\tbigger.append(number)\n\tresult = []\n\tresult.extend(sort(smaller))\n\tresult.append(pivot)\n\tresult.extend(sort(bigger))\n\treturn result\n\n\n#need to change this into a better pivot picker.\ndef get_pivot(list):\n\tfirst = list[0]\n\tlast = list[len(list)-1]\n\t#return index\n\tif first > last:\n\t\treturn list.pop(0)\n\treturn list.pop(len(list)-1)\n\t\n\n\n","repo_name":"torandersson/Algorithms","sub_path":"Sort/quicksort.py","file_name":"quicksort.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"43593553247","text":"\n\n\nclass BooleanFilterBackend(filters.BaseFilterBackend):\n    \"\"\"\n    Filter all the boolean fields with 1 or 0 instead of Boolean True or False.\n    \"\"\"\n    def filter_queryset(self, request, queryset, view):\n        boolean_filter_fields = getattr(view, 'boolean_filter_fields', [])\n        qs = queryset\n        for field in boolean_filter_fields:\n            value = request.GET.get(field)\n            if value == '1':\n                qs = qs.filter(**{field: True})\n            elif value == '0':\n                qs = qs.filter(**{field: False})\n        return qs","repo_name":"edilio/Snippets","sub_path":"django_rest.py","file_name":"django_rest.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3302033794","text":"from typing import List\n\nimport deep500 as d5\n\n# Compatiable tf version for v1\nimport tensorflow.compat.v1 as tf\ntf.disable_v2_behavior()\n\n\nclass TensorflowNetwork(d5.Network):\n    def __init__(self, device_option: d5.DeviceType, verbose=False):\n        super(TensorflowNetwork, self).__init__()\n        self.device_option = device_option\n        self.variables = {}\n        self.tensors = {}\n        self.output_names = []  # type: List[str]\n        # holds outputs of last inference\n        self.output_dict = {}\n        # map (param_name, gradient_name)\n        self.grad_names = {}\n        # session\n        self.session = None\n        # variables initialized\n        self.vars_initialized = False\n        # possibility to add log writer to monitor session\n        self.train_writer = None\n\n        self.partial_handle = None\n        self._gradients = {}\n        self.hooks = []\n\n        # saved gradient by variable\n        self.numpy_by_grad = {}\n        # gradient_names by loss\n        self.gradient_names_by_y = {}\n\n        tf_args = {}\n        if verbose:\n            tf_args['log_device_placement'] = True\n        self.session_config = tf.ConfigProto(**tf_args) if self.device_option.is_gpu() \\\n            else tf.ConfigProto(device_count={'GPU': 0}, **tf_args)\n        if self.device_option.is_gpu():\n            self.session_config.gpu_options.visible_device_list = str(self.device_option.num)\n\n        self.initializers = {}\n\n    def _teardown(self):\n        self.session.close()\n\n    def get_monitored_session(self):\n        if self.session is None:\n            self.session = tf.train.MonitoredSession(\n                session_creator=tf.train.ChiefSessionCreator(config=self.session_config), hooks=self.hooks)\n        return self.session\n\n    def get_normal_session(self):\n        if self.session is None:\n            self.session = tf.Session(config=self.session_config)\n        return self.session\n\n    def add_hooks(self, hooks):\n        self.hooks.extend(hooks)\n\n    def get_params(self):\n        return list(self.variables.keys())\n\n    def export_native(self, folder: str, inputs: List[str]):\n        inputs = {k: self.fetch_internal_tensor(k) for k in inputs}\n        outputs = {k: self.fetch_internal_tensor(k) for k in self.output_names}\n        tf.saved_model.simple_save(self.session, folder, inputs, outputs)\n\n    def _custom_parsing_context(self):\n        dev_spec = tf.DeviceSpec(device_type=(\"GPU\" if self.device_option.is_gpu() else \"CPU\"),\n                                 device_index=self.device_option.num)\n        return tf.device(dev_spec)\n\n    def gradient(self, y: str = 'loss'):\n        res = self.grad_names.get(y)\n        if res is None:\n            raise Exception('You have to call inference_and_backprop first with the correct y. Current y: {}'.format(y))\n        return res\n        \n    def add_output(self, output: str):\n        self.output_names.append(output)\n\n    def get_gradient_names(self, gradients, y):\n        names = []\n        _vars = []\n        for i, (name, var) in enumerate(list(self.variables.items())):\n            if gradients[i] is None:  # TODO HBS: Check if we really all none is none\n                continue\n            names.append((name, gradients[i]))\n            _vars.append(var)\n        self.grad_names[y] = names\n        return names, [g for g in gradients if g is not None], _vars\n\n    def fetch_tensors(self, names):\n        _names = [self.variables[name] if type(name) is str else name for name in names]\n        result = []\n        indices = []\n        for i, name in enumerate(_names):\n            if name in self.numpy_by_grad:\n                result.append(self.numpy_by_grad[name])\n            else:\n                result.append(-1)\n                indices.append(i)\n\n        if not indices:\n            return result\n        fetch_vars = [_names[i] for i in indices if i != -1]\n        output = self.get_normal_session().run(fetch_vars)\n\n        j = 0\n        out = []\n        for i, res in enumerate(result):\n            if type(res) == int:\n                out.append(output[j])\n                j += 1\n            else:\n                out.append(res)\n\n        return out\n\n    def fetch_tensor(self, name):\n        return self.fetch_tensors([name])[0]\n\n    def feed_tensor(self, name, new_value, device_option=None, is_param=False):\n        if is_param and name not in self.variables:\n            if name in self.tensors:\n                var = self.tensors[name]\n            else:\n                var = tf.get_variable(name, initializer=new_value, trainable=True)\n            self.variables[name] = var\n        else:\n            var = self.variables[name]\n            var.load(new_value, self.session)\n\n    def add_param(self, name: str, var: tf.Variable):\n        self.variables[name] = var\n\n    def fetch_variables(self, names):\n        return [self.fetch_variable(name) for name in names]\n\n    def fetch_variable(self, name):\n        return self.variables.get(name)\n\n    def feed_internal_tensor(self, name, tensor):\n        self.tensors[name] = tensor\n\n    def fetch_internal_tensor(self, name):\n        if isinstance(name, (tf.Tensor, tf.Operation)):\n            return name\n        return self.tensors.get(name) if name in self.tensors else self.variables[name]\n\n    def fetch_internal_tensors(self, names):\n        return [self.fetch_internal_tensor(name) for name in names]\n        \n    def get_input_nodes(self) -> List[str]:\n        graph = tf.get_default_graph()\n        return [n.name + ':0' for n in graph.as_graph_def().node if len(n.input) == 0 and n.op == 'Placeholder']\n\n    def get_output_nodes(self) -> List[str]:\n        return self.output_names\n","repo_name":"deep500/deep500","sub_path":"deep500/frameworks/tensorflow/tf_network.py","file_name":"tf_network.py","file_ext":"py","file_size_in_byte":5657,"program_lang":"python","lang":"en","doc_type":"code","stars":80,"dataset":"github-code","pt":"86"}
+{"seq_id":"75112226205","text":"\"\"\"\nEntry point script for document embedding and upload to index\nDepending on the value of the SSM Parameter '/student-advising/dev/retriever/RETRIEVER_NAME',\nwill upload embedded documents either to Pinecone or RDS\n\nRequires that the associated secret credentials are supplied in secrets manager:\n- Pinecone: student-advising/dev/retriever/PINECONE\n    - keys: PINECONE_KEY, PINECONE_REGION\n- RDS: student-advising/credentials/RDSCredentials\n\nNote: If using for RDS, this must be run within the same VPC\n\"\"\"\nimport subprocess\nimport torch\nimport sys\nimport requests\nsys.path.append('..')\nfrom aws_helpers.param_manager import get_param_manager\nfrom aws_helpers.rds_tools import execute_and_commit\n\nRETRIEVER_CONFIG_SSM_KEY = \"retriever\"\nRETRIEVER_NAME_SSM_KEY = \"RETRIEVER_NAME\"\n\nparam_manager = get_param_manager()\nretriever_config = param_manager.get_parameter('retriever')\nretriever_name = retriever_config[RETRIEVER_NAME_SSM_KEY]\n\nargs = [\"--compute_embeddings\", \"--clear_index\"]\nif torch.cuda.is_available() or torch.backends.mps.is_available():\n    args.append(\"--gpu_available\")\nelse:\n    args.append(\"--no-gpu_available\")\n\ntry:\n    if retriever_name == \"pinecone\":\n        subprocess.run([\"python\", \"pinecone_combined_script.py\", *args])\n    elif retriever_name == \"pgvector\":\n        subprocess.run([\"python\", \"rds_combined_script.py\", *args])\n    else:\n        raise ValueError(f\"Unsupported retriever type '{retriever_name}', supported types are 'pinecone' and 'pgvector'.\")\nexcept Exception as e:\n    raise e\n\n# Update the embedding log table\nsql = \"\"\"\n    INSERT into update_logs (datetime) \n    VALUES (current_timestamp);\n    \"\"\"\n    \nexecute_and_commit(sql)\n\n# Ping the Flask app to initialize\napp_url = param_manager.get_parameter('BEANSTALK_URL')\ninitialize_url = app_url + '/initialize'\n\ntry:\n    response = requests.get(url = initialize_url, timeout = 300)\nexcept Exception as e:\n    print(str(e))\n    raise Exception(\"Failed to initialize the Flask app after uploading embeddings\")\n\nif response.status_code != 200:\n    print(f\"Received status code {response.status_code} while initializing the Flask app at {initialize_url}\")\n    print(f\"Response text: {response.text}\")\n    raise Exception(\"Failed to initialize the Flask app after uploading embeddings\")","repo_name":"UBC-CIC/student-advising-assistant","sub_path":"embeddings/entry_point.py","file_name":"entry_point.py","file_ext":"py","file_size_in_byte":2278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"37118462264","text":"#########################################################################\n# MODEL PARAMETERS\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\n#########################################################################\n# version (0 for VGG and 1 for ResNet)\nversion = 0\n# batch size\nvideo_b_s = 2\nimage_b_s = 20\n# number of frames\nnum_frames = 5\n\n# origin shape c\nnormal_shape_c = 640\n# origin shape r\nnormal_shape_r = 360\n\n# number of cols of input images\nshape_c = 320\n\nif version == 1:\n    # number of rows of input images\n    shape_r = 256\n    # number of rows of model outputs\n    shape_r_out = 64\n    # number of cols of model outputs\n    shape_c_out = 80\nelse:\n    # number of rows of input images\n    shape_r = 256#240\n    # number of cols of model outputs\n    shape_c_out = 160\n    # number of rows of model outputs\n    shape_r_out = 128#120\n\n\n# number of rows of attention\nshape_r_attention = 64\n# number of cols of attention\nshape_c_attention = 80\n\n# number of rows of downsampled maps\nshape_r_gt = 32\n# number of cols of downsampled maps\nshape_c_gt = 40\n\n# final upsampling factor\nupsampling_factor = 16\n# number of epochs\nnb_epoch = 10\n# number of timestep\nnb_timestep = 4\n# number of learned priors\nnb_gaussian = 16\n\n# path of continuous saliency map\nmaps_path = '/maps/'\n# path of fixation maps\nfixs_path = '/fixation/maps/'\n# path of images\nframes_path = '/images/'\n\n# number of training videos\nnb_train = 100\n# number of validation videos\nnb_videos_val = 150\n# number of iterations for training using pytorch\nnb_train_pt = 100000\n# number of small test batch while training\nnb_small_test_batch = 10\n\n#########################################################################\n# TRAINING SETTINGS\t\t\t\t\t\t\t\t\t\t            \t#\n#########################################################################\n# path of training videos\nvideos_train_paths = ['./train/']\n# path of validation videos\nvideos_val_paths = ['./test/']\n# path of validation videos\nvideos_test_path = './test/'\n\nvideos_small_test_path = './train/'\noutput_path = './output/'\n\n# path of training maps\nmaps_path = '/maps/'\n# path of training fixation maps\nfixs_path = '/fixation/maps/'\n\nframes_path = '/images/'\n\n# path of training images\nimgs_path = 'D:/code/attention/staticimages/training/'","repo_name":"Nablax/ACLnet-Pytorch","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2227,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"86"}
+{"seq_id":"37802053131","text":"from scipy.integrate import quad\nfrom pylab import *\nfrom matplotlib import pyplot as plt\nimport numpy\n\ndef func(t):\n\tm = 1/(1+numpy.multiply(t,t))\n\treturn m\n\nx = numpy.linspace(0, 5.1, 101)\n\nj = arctan(x)\nprint(type(j))\n\n# Use this section for plotting integration values\n\"\"\"\nfor i in range(0, len(x)):\n\tintegration_ans, integration_error = quad(func, 0 , x[i])\n\tprint(x[i], integration_ans)\n\tplt.plot(x[i], integration_ans, 'ro')\n\nplt.plot(x[0], 0 , 'ro', label = 'integral plot')\nplt.plot(x, j, '#000000', label = 'arctan plot')\nplt.xlabel('x')\nplt.ylabel(' $\\int_{0}^{x} dx/{1+x^{2}}$')\nplt.title('Plot of $\\int_{0}^{x} dx/{1+x^{2}}$ in red and arctan in black')\nplt.legend()\nplt.show()\n\"\"\"\n\n# Use this section for plotting integration errors\n\nintegration_ans = numpy.zeros(len(x))\n\nfor k,i in zip(x,range(0, len(x))):\n\tintegration_ans[i] = quad(func, 0 , k)[0]\n\tprint(k, integration_ans)\n\nerr = abs(integration_ans-j)\n\nplt.semilogy(x, err, 'ro')\nplt.xlabel('x')\nplt.ylabel('$\\int_{0}^{x} dx/{1+x^{2}}$ error')\nplt.title('Plot of $\\int_{0}^{x} dx/{1+x^{2}}$ error')\nplt.show()\n","repo_name":"sankalpsaoji98/Python","sub_path":"Lab2/tan_inverse.py","file_name":"tan_inverse.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"25475052868","text":"from PyQt5.QtGui import *\r\nfrom PyQt5.QtWidgets import *\r\nfrom PyQt5.QtCore import *\r\nfrom PyQt5.QtPrintSupport import *\r\nfrom functions import *\r\nfrom photo_scanning import *\r\nimport sys\r\nimport re\r\nimport docx\r\nimport PyPDF2\r\n\r\nfrom predict import *\r\n\r\nimport os\r\nimport uuid\r\n\r\nFONT_SIZES = [7, 8, 9, 10, 11, 12, 13, 14, 18, 24, 36, 48, 64, 72, 96, 144, 288]\r\nIMAGE_EXTENSIONS = ['.jpg','.png','.bmp']\r\nHTML_EXTENSIONS = ['.htm', '.html']\r\n\r\ndef hexuuid():\r\n    return uuid.uuid4().hex\r\n\r\ndef splitext(p):\r\n    return os.path.splitext(p)[1].lower()\r\n\r\nclass TextEdit(QTextEdit):\r\n\r\n    def canInsertFromMimeData(self, source):\r\n\r\n        if source.hasImage():\r\n            return True\r\n        else:\r\n            return super(TextEdit, self).canInsertFromMimeData(source)\r\n\r\n    def insertFromMimeData(self, source):\r\n\r\n        cursor = self.textCursor()\r\n        document = self.document()\r\n\r\n        if source.hasUrls():\r\n\r\n            for u in source.urls():\r\n                file_ext = splitext(str(u.toLocalFile()))\r\n                if u.isLocalFile() and file_ext in IMAGE_EXTENSIONS:\r\n                    image = QImage(u.toLocalFile())\r\n                    document.addResource(QTextDocument.ImageResource, u, image)\r\n                    cursor.insertImage(u.toLocalFile())\r\n\r\n                else:\r\n                    break\r\n\r\n            else:\r\n                return\r\n\r\n\r\n        elif source.hasImage():\r\n            image = source.imageData()\r\n            uuid = hexuuid()\r\n            document.addResource(QTextDocument.ImageResource, uuid, image)\r\n            cursor.insertImage(uuid)\r\n            return\r\n\r\n        super(TextEdit, self).insertFromMimeData(source)\r\n\r\n\r\nclass MainWindow(QMainWindow):\r\n\r\n    def __init__(self, *args, **kwargs):\r\n        super(MainWindow, self).__init__(*args, **kwargs)\r\n        title = \"Система опрацювання тексту\"\r\n        \r\n        layout = QVBoxLayout()\r\n        self.editor = TextEdit()\r\n        self.editor1 = TextEdit()\r\n        self.editor.setAutoFormatting(QTextEdit.AutoAll)\r\n        self.editor.selectionChanged.connect(self.update_format)\r\n        font = QFont('Times', 12)\r\n        self.editor.setFont(font)\r\n        self.editor1.setFontPointSize(12)\r\n\r\n        self.editor1.setAutoFormatting(QTextEdit.AutoAll)\r\n        self.editor1.selectionChanged.connect(self.update_format)\r\n        self.editor1.setFont(font)\r\n        self.editor1.setFontPointSize(12)\r\n\r\n        self.path = None\r\n        label1 = QLabel(\"Оригінальний текст:\", self)\r\n        label2 = QLabel(\"Результат:\", self)\r\n        layout.addWidget(label1)\r\n        layout.addWidget(self.editor)\r\n        layout.addWidget(label2)\r\n        layout.addWidget(self.editor1)\r\n\r\n        container = QWidget()\r\n        container.setLayout(layout)\r\n        self.setCentralWidget(container)\r\n\r\n        self.status = QStatusBar()\r\n        self.setStatusBar(self.status)\r\n\r\n        file_toolbar = QToolBar(\"Файл\")\r\n        file_toolbar.setIconSize(QSize(14, 14))\r\n        self.addToolBar(file_toolbar)\r\n        file_menu = self.menuBar().addMenu(\"&Файл\")\r\n\r\n        open_file_action = QAction(QIcon(os.path.join('images', 'blue-folder-open-document.png')), \"Відкрити файл...\", self)\r\n        open_file_action.setStatusTip(\"Відкрити файл\")\r\n        open_file_action.triggered.connect(self.file_open)\r\n        file_menu.addAction(open_file_action)\r\n        file_toolbar.addAction(open_file_action)\r\n\r\n        save_file_action = QAction(QIcon(os.path.join('images', 'disk.png')), \"Зберегти\", self)\r\n        save_file_action.setStatusTip(\"Зберегти поточну сторінку\")\r\n        save_file_action.triggered.connect(self.file_save)\r\n        file_menu.addAction(save_file_action)\r\n        file_toolbar.addAction(save_file_action)\r\n\r\n        saveas_file_action = QAction(QIcon(os.path.join('images', 'disk--pencil.png')), \"Зберегти як...\", self)\r\n        saveas_file_action.setStatusTip(\"Зберегти поточну сторінку у файлі\")\r\n        saveas_file_action.triggered.connect(self.file_saveas)\r\n        file_menu.addAction(saveas_file_action)\r\n        file_toolbar.addAction(saveas_file_action)\r\n\r\n        print_action = QAction(QIcon(os.path.join('images', 'printer.png')), \"Друк...\", self)\r\n        print_action.setStatusTip(\"Друк поточної сторінки\")\r\n        print_action.triggered.connect(self.file_print)\r\n        file_menu.addAction(print_action)\r\n        file_toolbar.addAction(print_action)\r\n\r\n        edit_toolbar = QToolBar(\"Редагувати\")\r\n        edit_toolbar.setIconSize(QSize(16, 16))\r\n        self.addToolBar(edit_toolbar)\r\n        edit_menu = self.menuBar().addMenu(\"&Редагувати\")\r\n\r\n        undo_action = QAction(QIcon(os.path.join('images', 'arrow-curve-180-left.png')), \"Відмнити\", self)\r\n        undo_action.setStatusTip(\"Відмінити останню дію\")\r\n        undo_action.triggered.connect(self.editor.undo)\r\n        edit_menu.addAction(undo_action)\r\n\r\n        redo_action = QAction(QIcon(os.path.join('images', 'arrow-curve.png')), \"Відновити\", self)\r\n        redo_action.setStatusTip(\"Відновити останню дію\")\r\n        redo_action.triggered.connect(self.editor.redo)\r\n        edit_toolbar.addAction(redo_action)\r\n        edit_menu.addAction(redo_action)\r\n\r\n        edit_menu.addSeparator()\r\n\r\n        cut_action = QAction(QIcon(os.path.join('images', 'scissors.png')), \"Вирізати\", self)\r\n        cut_action.setStatusTip(\"Вирізати виділений текст\")\r\n        cut_action.setShortcut(QKeySequence.Cut)\r\n        cut_action.triggered.connect(self.editor.cut)\r\n        edit_toolbar.addAction(cut_action)\r\n        edit_menu.addAction(cut_action)\r\n\r\n        copy_action = QAction(QIcon(os.path.join('images', 'document-copy.png')), \"Скопіювати\", self)\r\n        copy_action.setStatusTip(\"Скопіювати виділений текст\")\r\n        cut_action.setShortcut(QKeySequence.Copy)\r\n        copy_action.triggered.connect(self.editor.copy)\r\n        edit_toolbar.addAction(copy_action)\r\n        edit_menu.addAction(copy_action)\r\n\r\n        paste_action = QAction(QIcon(os.path.join('images', 'clipboard-paste-document-text.png')), \"Вставити\", self)\r\n        paste_action.setStatusTip(\"Вставити\")\r\n        cut_action.setShortcut(QKeySequence.Paste)\r\n        paste_action.triggered.connect(self.editor.paste)\r\n        edit_toolbar.addAction(paste_action)\r\n        edit_menu.addAction(paste_action)\r\n\r\n        select_action = QAction(QIcon(os.path.join('images', 'selection-input.png')), \"Виділити все\", self)\r\n        select_action.setStatusTip(\"Виділити увесь текст\")\r\n        cut_action.setShortcut(QKeySequence.SelectAll)\r\n        select_action.triggered.connect(self.editor.selectAll)\r\n        edit_menu.addAction(select_action)\r\n\r\n        edit_menu.addSeparator()\r\n\r\n        format_toolbar = QToolBar(\"Форматування\")\r\n        format_toolbar.setIconSize(QSize(16, 16))\r\n        self.addToolBar(format_toolbar)\r\n        format_menu = self.menuBar().addMenu(\"&Форматування\")\r\n\r\n        text_toolbar = QToolBar(\"Аналіз тексту\")\r\n        text_toolbar.setIconSize(QSize(16, 16))\r\n        self.addToolBar(text_toolbar)\r\n        text_menu = self.menuBar().addMenu(\"&Аналіз тексту\")\r\n\r\n        self.fonts = QFontComboBox()\r\n        self.fonts.currentFontChanged.connect(self.editor.setCurrentFont)\r\n        format_toolbar.addWidget(self.fonts)\r\n\r\n        self.fontsize = QComboBox()\r\n        self.fontsize.addItems([str(s) for s in FONT_SIZES])\r\n\r\n        self.fontsize.currentIndexChanged[str].connect(lambda s: self.editor.setFontPointSize(float(s)) )\r\n        format_toolbar.addWidget(self.fontsize)\r\n\r\n        self.bold_action = QAction(QIcon(os.path.join('images', 'edit-bold.png')), \"Жирний\", self)\r\n        self.bold_action.setStatusTip(\"Жирний\")\r\n        self.bold_action.setShortcut(QKeySequence.Bold)\r\n        self.bold_action.setCheckable(True)\r\n        self.bold_action.toggled.connect(lambda x: self.editor.setFontWeight(QFont.Bold if x else QFont.Normal))\r\n        format_toolbar.addAction(self.bold_action)\r\n        format_menu.addAction(self.bold_action)\r\n\r\n        self.italic_action = QAction(QIcon(os.path.join('images', 'edit-italic.png')), \"Курсив\", self)\r\n        self.italic_action.setStatusTip(\"Курсив\")\r\n        self.italic_action.setShortcut(QKeySequence.Italic)\r\n        self.italic_action.setCheckable(True)\r\n        self.italic_action.toggled.connect(self.editor.setFontItalic)\r\n        format_toolbar.addAction(self.italic_action)\r\n        format_menu.addAction(self.italic_action)\r\n\r\n        self.underline_action = QAction(QIcon(os.path.join('images', 'edit-underline.png')), \"Підкреслення\", self)\r\n        self.underline_action.setStatusTip(\"Підкреслення\")\r\n        self.underline_action.setShortcut(QKeySequence.Underline)\r\n        self.underline_action.setCheckable(True)\r\n        self.underline_action.toggled.connect(self.editor.setFontUnderline)\r\n        format_toolbar.addAction(self.underline_action)\r\n        format_menu.addAction(self.underline_action)\r\n\r\n        format_menu.addSeparator()\r\n\r\n        self.alignl_action = QAction(QIcon(os.path.join('images', 'edit-alignment.png')), \"Вирівняти по лівій стороні\", self)\r\n        self.alignl_action.setStatusTip(\"Вирівняти по лівій стороні\")\r\n        self.alignl_action.setCheckable(True)\r\n        self.alignl_action.triggered.connect(lambda: self.editor.setAlignment(Qt.AlignLeft))\r\n        format_toolbar.addAction(self.alignl_action)\r\n        format_menu.addAction(self.alignl_action)\r\n\r\n        self.alignc_action = QAction(QIcon(os.path.join('images', 'edit-alignment-center.png')), \"Вирівняти по центру\", self)\r\n        self.alignc_action.setStatusTip(\"Вирівняти по центру\")\r\n        self.alignc_action.setCheckable(True)\r\n        self.alignc_action.triggered.connect(lambda: self.editor.setAlignment(Qt.AlignCenter))\r\n        format_toolbar.addAction(self.alignc_action)\r\n        format_menu.addAction(self.alignc_action)\r\n\r\n        self.alignr_action = QAction(QIcon(os.path.join('images', 'edit-alignment-right.png')), \"Вирівняти по правій стороні\", self)\r\n        self.alignr_action.setStatusTip(\"Вирівняти по правій стороні\")\r\n        self.alignr_action.setCheckable(True)\r\n        self.alignr_action.triggered.connect(lambda: self.editor.setAlignment(Qt.AlignRight))\r\n        format_toolbar.addAction(self.alignr_action)\r\n        format_menu.addAction(self.alignr_action)\r\n\r\n        self.key_extraction = QAction(\"Виявлення ключових слів\", self)\r\n\r\n        self.key_extraction.triggered.connect(lambda: self.editor1.setText(key_words_extraction(str(self.editor.toPlainText()))))\r\n        text_menu.addAction(self.key_extraction)\r\n\r\n        self.name_entity_recognition = QAction(\"Розпізнавання іменованих сутностей\", self)\r\n        self.name_entity_recognition.triggered.connect(lambda: self.editor1.setHtml(self.get_entities()))\r\n        text_menu.addAction(self.name_entity_recognition)\r\n        format_group = QActionGroup(self)\r\n        format_group.setExclusive(True)\r\n        format_group.addAction(self.alignl_action)\r\n        format_group.addAction(self.alignc_action)\r\n        format_group.addAction(self.alignr_action)\r\n\r\n        format_menu.addSeparator()\r\n\r\n        self._format_actions = [\r\n            self.fonts,\r\n            self.fontsize,\r\n            self.bold_action,\r\n            self.italic_action,\r\n            self.underline_action,\r\n        ]\r\n        self.update_format()\r\n        self.update_title()\r\n        self.setWindowTitle(title)\r\n        self.show()\r\n\r\n    def get_entities(self):\r\n        dicti = find_named_entities(str(self.editor.toPlainText()))\r\n        print(dicti)\r\n        tag_colors = {'B-tim': '#FFD4B2', 'I-tim': '#FFD4B2', 'B-gpe': '#FFF6BD', 'I-gpe': '#FFF6BD',\r\n        'B-geo': '#E3ACF9', 'I-geo': '#E3ACF9', 'B-per': '#86C8BC', 'I-per': '#86C8BC', 'I-org': 'pink', 'B-org': 'pink'}\r\n        dict_labels = {'Time': '#FFD4B2', 'Geopolitical term': '#FFF6BD', 'Location': '#E3ACF9', 'Person':'#86C8BC', 'Organisation': 'pink' }\r\n        result_string = \"\"\r\n        token_string = str(self.editor.toPlainText()).split()\r\n        array = []\r\n        for word in token_string:\r\n            word1 = re.sub(\"[^A-Za-z0-9 ]\",\"\",word)\r\n            if dicti.get(word1) in tag_colors:\r\n                array.append(tag_colors[dicti[word1]])\r\n            elif dicti.get(word) in tag_colors:\r\n                array.append(tag_colors[dicti[word]])\r\n            else:\r\n                array.append('O')\r\n        s = []\r\n        w = ''\r\n        res = []\r\n        for i in range(len(array)):\r\n            if len(s) != 0:\r\n                if array[i] != 'O':\r\n                    if s[-1] == array[i]:\r\n                        w += token_string[i] + \" \"\r\n                        s.append(array[i])\r\n                        res.append((w, array[i]))\r\n                        w = \"\"\r\n                        s = []\r\n                    else:\r\n                        w = token_string[i] + \" \"\r\n                        s = [array[i]]\r\n                else:\r\n                    res.append((w, s[-1]))\r\n                    w = ''\r\n                    s = []\r\n                    res.append((token_string[i], 'O'))\r\n            else:\r\n                if array[i] != 'O':\r\n                    w = token_string[i] + \" \"\r\n                    s = [array[i]] \r\n                else:\r\n                    res.append((token_string[i], 'O'))\r\n        if len(s) != 0:\r\n            res.append((w, array[len(array) - 1]))\r\n\r\n        result_string = \"\"\r\n        for key in dict_labels.keys():\r\n            result_string += f'<span style=\"border-radius: 20px; background-color:{dict_labels[key]};display: inline\">{key}</span> '\r\n        result_string += '<hr>'\r\n        for r in res:\r\n            if r[1] != 'O':\r\n                result_string += f'<span style=\"border-radius: 20px; background-color:{r[1]}; display: inline\">{r[0]}</span> '\r\n            else:\r\n                result_string += f'<span style=\"display: inline\">{r[0]}</span> '\r\n        \r\n        return result_string\r\n\r\n    def block_signals(self, objects, b):\r\n        for o in objects:\r\n            o.blockSignals(b)\r\n\r\n    def update_format(self):\r\n     \r\n        self.block_signals(self._format_actions, True)\r\n\r\n        self.fonts.setCurrentFont(self.editor.currentFont())\r\n        self.fontsize.setCurrentText(str(int(self.editor.fontPointSize())))\r\n\r\n        self.italic_action.setChecked(self.editor.fontItalic())\r\n        self.underline_action.setChecked(self.editor.fontUnderline())\r\n        self.bold_action.setChecked(self.editor.fontWeight() == QFont.Bold)\r\n\r\n        self.alignl_action.setChecked(self.editor.alignment() == Qt.AlignLeft)\r\n        self.alignc_action.setChecked(self.editor.alignment() == Qt.AlignCenter)\r\n        self.alignr_action.setChecked(self.editor.alignment() == Qt.AlignRight)\r\n\r\n        self.block_signals(self._format_actions, False)\r\n\r\n    def dialog_critical(self, s):\r\n        dlg = QMessageBox(self)\r\n        dlg.setText(s)\r\n        dlg.setIcon(QMessageBox.Critical)\r\n        dlg.show()\r\n\r\n    def file_open(self):\r\n        path, _ = QFileDialog.getOpenFileName(self, \"Відкрити файл\", \"\", \"HTML documents (*.html);Text documents (*.txt);All files (*.*)\")\r\n\r\n        try:\r\n            if '.txt' in path:\r\n                with open(path, 'rU') as f:\r\n                    text = f.read()\r\n\r\n        except Exception as e:\r\n            self.dialog_critical(str(e))\r\n\r\n        else:\r\n            self.path = path\r\n            if '.jpg' in self.path:\r\n                text = main_func(self.path)\r\n                self.editor.setText(text)\r\n                self.update_title()\r\n            elif '.docx' in self.path:\r\n                doc = docx.Document(self.path)\r\n                paragraphs = [p.text for p in doc.paragraphs]\r\n                text = '\\n'.join(paragraphs)\r\n                \r\n                self.editor.setText(text)\r\n                self.update_title()\r\n            elif '.pdf' in self.path:\r\n                with open(self.path, 'rb') as file:\r\n                    reader = PyPDF2.PdfFileReader(file)\r\n                    text = ''\r\n                    for page in range(reader.numPages):\r\n                        page_obj = reader.getPage(page)\r\n                        text += page_obj.extract_text()\r\n\r\n                self.editor.setText(text)\r\n                self.update_title()\r\n            else:\r\n                self.editor.setText(text)\r\n                self.update_title()\r\n\r\n    def file_save(self):\r\n        if self.path is None:\r\n            return self.file_saveas()\r\n\r\n        text = self.editor.toHtml() if splitext(self.path) in HTML_EXTENSIONS else self.editor.toPlainText()\r\n\r\n        try:\r\n            with open(self.path, 'w') as f:\r\n                f.write(text)\r\n\r\n        except Exception as e:\r\n            self.dialog_critical(str(e))\r\n\r\n    def file_saveas(self):\r\n        path, _ = QFileDialog.getSaveFileName(self, \"Зберегти файл\", \"\", \"HTML documents (*.html);Text documents (*.txt);All files (*.*)\")\r\n\r\n        if not path:\r\n            return\r\n\r\n        text = self.editor.toHtml() if splitext(path) in HTML_EXTENSIONS else self.editor.toPlainText()\r\n\r\n        try:\r\n            with open(path, 'w') as f:\r\n                f.write(text)\r\n\r\n        except Exception as e:\r\n            self.dialog_critical(str(e))\r\n\r\n        else:\r\n            self.path = path\r\n            self.update_title()\r\n\r\n    def file_print(self):\r\n        dlg = QPrintDialog()\r\n        if dlg.exec_():\r\n            self.editor.print_(dlg.printer())\r\n\r\n    def update_title(self):\r\n        self.setWindowTitle(\"%s\" % (os.path.basename(self.path) if self.path else \"Untitled\"))\r\n\r\n    def edit_toggle_wrap(self):\r\n        self.editor.setLineWrapMode( 1 if self.editor.lineWrapMode() == 0 else 0 )\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n    app = QApplication(sys.argv)\r\n    app.setApplicationName(\"Система опрацювання тексту\")\r\n\r\n    window = MainWindow()\r\n    app.exec_()\r\n","repo_name":"MariaPonomarenko38/Text-processing-system","sub_path":"src/main_program.py","file_name":"main_program.py","file_ext":"py","file_size_in_byte":18477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"2812564084","text":"from flask import render_template, redirect, request, url_for, flash\nfrom app import app, db\nfrom app.forms import LoginForm, RegisterForm, CreateTeam\nfrom flask_login import current_user, login_user, logout_user, login_required\nfrom app.models import User, Collection, Pieces, Dial_Attack, Dial_Damage, Dial_Defense, Dial_Movement, Team, team_and_pieces\nfrom werkzeug.urls import url_parse\n\n@app.route('/')\n@app.route('/index')\ndef index():\n    return render_template('index.html', title='Home Page')\n\n@app.route('/collection', methods=['GET', 'POST'])\ndef collection():\n    colle = Collection.query.all()\n    return render_template('collection.html', colle=colle, title='Collection')\n\n@app.route('/col/<id>')\ndef col(id):\n    #col = Pieces.query.filter_by(collection_id = int(id))\n    #ncol = Collection.query.get(int(id))\n    col = Pieces.query.filter_by(collection_id=int(id)).all()\n    #return '<h1>{}</h1>'.format(id)\n    return render_template('colpieces.html', col=col, title='Collection Pieces')\n\n@app.route('/pieces/<id>', methods=['GET', 'POST'])\ndef pieces(id):\n    p = Pieces.query.filter_by(id = int(id)).first()\n    datk = Dial_Attack.query.filter_by(pieces_id=int(id)).first()\n    ddef = Dial_Defense.query.filter_by(pieces_id=int(id)).first()\n    ddam = Dial_Damage.query.filter_by(pieces_id=int(id)).first()\n    dmov = Dial_Movement.query.filter_by(pieces_id=int(id)).first()\n    #return '<h1>{}</h1>'.format(id)\n    return render_template('pieces.html', title='Info', p=p, datk=datk, ddef=ddef, ddam=ddam, dmov=dmov)\n\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n    if current_user.is_authenticated:\n        return redirect(url_for('index'))\n\n    form = LoginForm()\n\n    if form.validate_on_submit():\n        user = User.query.filter_by(username=form.username.data).first()\n\n        if user is None or not user.check_password(form.password.data):\n            flash('Invalid username or password')\n            return redirect(url_for('login'))\n\n        login_user(user, remember=form.remember_me.data)\n        next_page = request.args.get('next')\n\n        if not next_page or url_parse(next_page).netloc != '':\n            next_page = url_for('index')\n        return redirect(next_page)\n    return render_template('login.html', title='Sign In', form = form)\n\n@app.route('/logout')\ndef logout():\n    logout_user()\n    return redirect(url_for('index'))\n\n@app.route('/register', methods=['GET', 'POST'])\ndef register():\n    if current_user.is_authenticated:\n        return redirect(url_for('index'))\n\n    form = RegisterForm()\n\n    if form.validate_on_submit():\n        user = User(username=form.username.data, email=form.email.data)\n        user.set_password(form.password.data)\n        db.session.add(user)\n        db.session.commit()\n        flash('Registration successfull!')\n        return redirect(url_for('login'))\n    return render_template('register.html', title='Register', form=form)\n\n@app.route('/user/<username>')\n@login_required\ndef user(username):\n    user = User.query.filter_by(username=username).first_or_404()\n\n    return render_template('user.html', user=user)\n\n@app.route('/newteam/<id>', methods=['GET','POST'])\n@login_required\ndef newteam(id):\n    form = CreateTeam()\n\n    if form.validate_on_submit():\n       team = Team(team_name=form.team_name.data, team_point=form.team_point.data, user_id=id)\n       db.session.add(team)\n       db.session.commit()\n       flash('team created successfully')\n       return redirect(url_for('myteam', id=current_user.id))\n    return render_template('newteam.html', form=form, title='Team')\n\n@app.route('/teampi/<id>')\n@login_required\ndef teampi(id):\n #for p in session.query(Pieces).all\n\n\n #teampi = Pieces.query.join(team_and_pieces, (team_and_pieces.c.team_id==int(id))).all()\n teampi = Pieces.query.join(team_and_pieces, (team_and_pieces.c.piece_id == Pieces.id)).filter(team_and_pieces.c.team_id == int(id)).all()\n #teampi = Pieces.query.filter_by().all()\n #return '<h1>{}</h1>'.format(pieceid)\n return render_template('teampi.html', teampi=teampi, title='Team Pieces')\n\n@app.route('/myteam')\n@login_required\ndef myteam():\n\n    myteam = Team.query.filter_by(user_id=current_user.id)\n    return render_template('myteam.html', myteam=myteam, title='Team')\n","repo_name":"P-Castro/hcbuilder","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":4247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"16522993738","text":"# print(bool(1))\n# print(bool(0))\n# print(int(True))\n# print(float(True))\n\n\n# def print_hello():\n#     name = \"gamal hisham saad\"\n#     Age = 26\n#     Exp = 5\n#     print(f\"My name is {name}  and I have {Exp} years of Experience\")\n\n# print_hello()\n\n# pi = 3.14676533899\n# print(pi.__round__(2))\n\n\n# my_list = [12, \"gamal\", 10.5, 234]\n# # print(len(my_list))\n# my_list.append(100)\n# print(my_list, len(my_list))\n# my_list.extend([200, 300])\n# print(my_list, len(my_list))\n\n# my_list.remove(12)\n# print(my_list)\n\n# number_list = [2, 3, 4, 7, 1]\n# print(max(number_list))\n# print(min(number_list))\n# print(sum(number_list))\n# print(sum(number_list[:2]))\n\n# help(print)\n\n# x = [2, 3, 67, 123, 56]\n# print(x.index(67))\n\n# y = x\n# y[0] = 1\n# print(x)\n\n# y = x[:]\n# y[0] = 1\n# print(x, y, sep=\"\\n\")\n\nmy_tuple = (2, 3, 1, 23, 43)\n# my_tuple[0] = 12 # through Erorr\n\n#----------------------------------------------------------------------------\n\n\ninputskills = input (\" please enter your skills with rank of them separte between them with comma for Ex: (python,50%,Excel,70%)  \")\n\ninskills = inputskills.split(\",\")\n\n\ndictskills = dict(zip(inskills[::2], inskills[1::2])) \n\n\ndef check_job (dictskills): \n\n    right_skills = {\n        \"python\":\"70%\",\n        \"Excel\":\"80%\",\n        \"Power_pi\":\"60%\",\n    }\n\n\n    if dictskills == right_skills :\n\n        print(\" you are qualified for this job\")\n\n    else:\n\n        print(\" you are not  qualified for this job\")\n\n    \n# check_job(dictskills)\n\n\n\n\n","repo_name":"GamalHisham/Python_course","sub_path":"Trying.py","file_name":"Trying.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"26093473106","text":"import os\nimport sys\nimport cv2\nimport numpy as np\n\n\nsys.setrecursionlimit(2 ** 20)\n# np.random.seed(2 ** 10)\n\nfrom pyagender.wide_resnet import WideResNet\n\n\nclass PyAgender():\n    \"\"\"\n    Age-Gender estimator with built-in OpenCV face detector.\n\n    Based on https://github.com/asmith26/wide_resnets_keras ResNet\n    and pre trained model from https://github.com/yu4u/age-gender-estimation\n    \"\"\"\n\n    def __init__(self,\n                 haar_cascade='pretrained_models/haarcascade_frontalface_default.xml',\n                 cv_scaleFactor=1.1,\n                 cv_minNeighbors=8,\n                 cv_minSize=(64, 64),\n                 cv_flags=cv2.CASCADE_SCALE_IMAGE,\n                 resnet_weights='pretrained_models/weights.28-3.73.hdf5',\n                 resnet_imagesize=64):\n        self.cv_face_detector = cv2.CascadeClassifier(\n            os.path.join(os.path.dirname(__file__), haar_cascade))\n        self.cv_scaleFactor = cv_scaleFactor\n        self.cv_minNeighbors = cv_minNeighbors\n        self.cv_minSize = cv_minSize\n        self.cv_flags = cv_flags\n        self.resnet_imagesize = resnet_imagesize\n        self.resnet = WideResNet(image_size=resnet_imagesize)()\n        self.resnet.load_weights(os.path.join(os.path.dirname(__file__), resnet_weights))\n\n    def detect_genders_ages(self, image):\n        \"\"\"\n        Detects all faces using OpenCV Haar cascades options provided in the constructor\n\n        :param image: image to detect\n        :return: array of dicts or empty array (no detections)\n                 {left: 34, top: 11, right: 122, bottom: 232, width:(r-l), height: (b-t),\n                  gender: 0.67, age: 23.5}\n                  \n                 gender > 0.5 == female\n        \"\"\"\n        faceregions = self.detect_faces(image, margin=0.4)\n\n        for face in faceregions:\n            face['gender'], face['age'] = self.gender_age(image,\n                                                          left=face['left'], top=face['top'],\n                                                          width=face['width'],\n                                                          height=face['height'])\n\n        return faceregions\n\n    def gender_age(self, face_image, left=0, top=0, width=None, height=None):\n        \"\"\"\n        Assuming ready to use face region on input (no detection to do)\n        \n        :param face_image: CV image object to feed into CNN age-gender estimator\n        :param left,top left upper corner of a face in the image\n        :param w,h width,height of a face (face_image == the whole face by default)\n\n        :return: [gender, age] evaluation (gender > 0.5 == female, age is float)\n        \"\"\"\n        img_h, img_w, __ = np.shape(face_image)\n\n        if width is not None:\n            img_w = width\n        if height is not None:\n            img_h = height\n\n        # Crop & resize image to 64pix box\n        test_img = PyAgender.aspect_resize(face_image[top:top + img_h, left:left + img_w],\n                                           self.resnet_imagesize, self.resnet_imagesize)\n\n        # predict ages and genders of the detected faces\n        result = self.resnet.predict(np.array([test_img]))\n        predicted_genders = result[0]\n        ages = np.arange(0, 101).reshape(101, 1)\n        predicted_ages = result[1].dot(ages).flatten()\n        # print(f'gender: {predicted_genders}')\n        # print(f'predicted_ages: {predicted_ages}')\n\n        return predicted_genders[0][0], predicted_ages[0]\n\n    def detect_faces(self, image, margin=0.2):\n        \"\"\"\n        :param image: Original image (in opencv BGR) to find faces on\n        :param padding: additional margin widht/height percentage\n        :return:\n            array of face image rectangles {left: 34, top: 11, right: 122, bottom: 232, width:(r-l), height: (b-t)}\n        \"\"\"\n        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n        img_h, img_w = np.shape(gray)\n\n        face_results = []\n\n        faces = self.cv_face_detector.detectMultiScale(gray,\n                                                       scaleFactor=self.cv_scaleFactor,\n                                                       minNeighbors=self.cv_minNeighbors,\n                                                       minSize=self.cv_minSize,\n                                                       flags=self.cv_flags)\n        for (x, y, w, h) in faces:\n            xi1 = max(int(x - margin * w), 0)\n            xi2 = min(int(x + w + margin * w), img_w - 1)\n            yi1 = max(int(y - margin * h), 0)\n            yi2 = min(int(y + h + margin * h), img_h - 1)\n            detection = {'left': xi1, 'top': yi1, 'right': xi2, 'bottom': yi2,\n                         'width': (xi2 - xi1), 'height': (yi2 - yi1)}\n            face_results.append(detection)\n\n        return face_results\n\n    @staticmethod\n    def aspect_resize(image, width, height, padding=cv2.BORDER_REPLICATE, color=[0, 0, 0]):\n        \"\"\"\n        Letterboxing image resize preserving original aspect ratio, padding if necessary\n        :param image: cv2 compatible (x,y,3) shape image data\n        :param width: desired width in pixels\n        :param height: desired height in pixels\n        :param padding: OpenCV padding strategy (see https://docs.opencv.org/3.1.0/d3/df2/tutorial_py_basic_ops.html)\n        :param color: padding cv2 color for cv2.BORDER_CONSTANT padding strategy\n\n        :return: resized copy of image in cv2 default format\n        \"\"\"\n        old_size = image.shape[:2]\n        # determine the longest side\n        ratio = min(float(height) / old_size[0], float(width) / old_size[1])\n        # resize accordingly\n        new_size = tuple([int(x * ratio) for x in old_size])\n        im = cv2.resize(image, (new_size[1], new_size[0]))\n\n        delta_w = width - new_size[1]\n        delta_h = height - new_size[0]\n\n        top, bottom = delta_h // 2, delta_h - (delta_h // 2)\n        left, right = delta_w // 2, delta_w - (delta_w // 2)\n\n        new_im = cv2.copyMakeBorder(im, top, bottom, left, right, padding, value=color)\n        return new_im\n","repo_name":"aristofun/py-agender","sub_path":"pyagender/pyagender.py","file_name":"pyagender.py","file_ext":"py","file_size_in_byte":6036,"program_lang":"python","lang":"en","doc_type":"code","stars":63,"dataset":"github-code","pt":"86"}
+{"seq_id":"23308744636","text":"from typing import Dict\nimport settings\nimport pygame\n\n\nclass Ball:\n\n    defaults = {\n        'RADIUS':   7\n    ,   'SPEED':    7\n    ,   'COLOUR':   settings.COLOURS['BLUE']\n    }\n\n    _status = {}\n\n    def __init__(self, screen, override = defaults):\n        \"\"\"\n\n        :rtype: object\n        \"\"\"\n        self._settings = override\n        self.screen = screen  # copy the reference to the screen in a local variable\n        self.reset_status()\n\n    def get_info(self) -> Dict[str, int]:\n        return {\n            'pos_x':    self._status['pos_x']\n        ,   'pos_y':    self._status['pos_y']\n        ,   'RADIUS':   self._settings['RADIUS']\n        }\n\n    def draw(self, screen) -> None:\n        ball = pygame.Surface((2*self._settings['RADIUS'],2*self._settings['RADIUS']), pygame.SRCALPHA, 32)\n        pygame.draw.circle(ball, self._settings['COLOUR'], (self._settings['RADIUS'], self._settings['RADIUS']), self._settings['RADIUS'], 0)\n        screen.blit(ball, (self._status['pos_x'] - self._settings['RADIUS'], self._status['pos_y'] - self._settings['RADIUS']))\n\n    def bounce_wall(self) -> bool:\n        if(self._status['pos_x'] <= settings.WINDOW_INNER_BORDERS['X_AXIS']['LEFT'] + self._settings['RADIUS']) or (self._status['pos_x'] >= settings.WINDOW_INNER_BORDERS['X_AXIS']['RIGHT'] - self._settings['RADIUS']):\n            self._status['direction_x'] *= -1\n            return True\n        if (self._status['pos_y'] <= settings.WINDOW_INNER_BORDERS['Y_AXIS']['TOP'] + self._settings['RADIUS']) or (self._status['pos_y'] >= settings.WINDOW_INNER_BORDERS['Y_AXIS']['BOTTOM'] - self._settings['RADIUS']):\n            self._status['direction_y'] *= -1\n            return True\n        return False\n\n    def move(self) -> None:\n\n        self._status['pos_x'] += self._status['direction_x'] * self._settings['SPEED']\n        self._status['pos_y'] += self._status['direction_y'] * self._settings['SPEED']\n\n    def reset_status(self):\n        self._status['pos_x'] = settings.WINDOW['WIDTH']//2\n        self._status['pos_y'] = settings.WINDOW['HEIGHT']//2\n        self._status['direction_x'] = 1.0\n        self._status['direction_y'] = 0.8\n","repo_name":"nennes/PyPong","sub_path":"pong/ball.py","file_name":"ball.py","file_ext":"py","file_size_in_byte":2148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"41283139145","text":"import networkx as nx\nimport three_path_sampler\nimport centered_sampler\nimport time\n\n\n#c_basic and c_advanced will be our subgraph counts, c_basic is for original algorithm, c_advanced for improved\nc_basic = [0, 0, 0, 0, 0, 0]\nc_advanced = [0, 0, 0, 0, 0, 0]\nlimit = 0\n\ng = nx.Graph()\n\n#Reading the dataset\nwith open(\"Datasets/facebook_combined.txt\") as test_graph:\n    for line in test_graph:\n        line = line.rstrip()\n        vertices = line.split(\" \")\n        if vertices[0] != vertices[1]:  #No self-loops are allowed\n            g.add_edge(vertices[0], vertices[1])\n\nstart_time = time.time()\n\n\n#Original Algorithm\n\nprint(\"Read file\")\nprint(\"Node Counts:\", len(g.nodes))\nprint(\"Edge Counts:\", len(g.edges))\n\nthree_path_sampler.init_three_path_sampler(g)\nprint(\"Initialized 3-path-sampler\")\nfirst_c_basic = three_path_sampler.three_path_sampler(g, g, 2000)  #k = 200.000 on original paper, 2000 gives us good enough results\nprint(\"3-path-sampler done\")\nc_basic[0], c_basic[1], c_basic[2], c_basic[3], c_basic[4], c_basic[5] = \\\n    first_c_basic[0], first_c_basic[1], first_c_basic[2], first_c_basic[3], first_c_basic[4], first_c_basic[5]\n    \n# Old calculation for cycle-based motifs with centered_sampler\n#c_basic[0], c_basic[1], c_basic[2] = first_c_basic[0], first_c_basic[1], first_c_basic[2]\n#centered_sampler.init_centered_sampler(g)\n#print(\"Initialized centered-sampler\")\n#last_c_basic = centered_sampler.centered_sampler(g, g, 20000)\n#print(\"Centered-sampler done\")\n#c_basic[3], c_basic[4], c_basic[5] = last_c_basic[3], last_c_basic[4], last_c_basic[5]\n\nprint(\"Original Algorithm Results: \", c_basic)\n\n\n\"\"\"\n#Improved Algorithm\n\nprint(\"Read file\")\nprint(\"Node Counts:\", len(g.nodes))\nprint(\"Edge Counts:\", len(g.edges))\n\ng_filtered = nx.Graph()\ng_filtered.add_nodes_from(g)   #Create a new graph g_filtered with all the vertices of graph g\npredictions = list(nx.adamic_adar_index(g, g.edges))\npred_p = [p for u, v, p in predictions]\nmax_prediction = max(pred_p)\nmin_prediction = min(pred_p)\navg = sum(pred_p)/len(pred_p)\nlimit = (avg - min_prediction) / (max_prediction - min_prediction)  #Calculate the limiting score with normalization\nprint(\"Min. Score:\", min_prediction)\nprint(\"Max. Score:\", max_prediction)\nprint(\"Limiting Score (Normalized):\", limit)\nfor u, v, p in predictions:  #For every edge, add only the edges with similarity score greater than average to g_filtered\n    normalized_p = (p - min_prediction) / (max_prediction - min_prediction)   #Normalize the score beween 0 and 1\n    if normalized_p >= limit:\n        g_filtered.add_edge(u, v)\n\nthree_path_sampler.init_three_path_sampler(g_filtered)\nprint(\"Initialized 3-path-sampler\")\nfirst_c_advanced = three_path_sampler.three_path_sampler(g, g_filtered, 2000)\nprint(\"3-path-sampler done\")\nc_advanced[0], c_advanced[1], c_advanced[2], c_advanced[3], c_advanced[4], c_advanced[5] = \\\n    first_c_advanced[0], first_c_advanced[1], first_c_advanced[2], first_c_advanced[3], first_c_advanced[4], first_c_advanced[5]\n\n# Old calculation for cycle-based motifs with centered_sampler\n#c_advanced[0], c_advanced[1], c_advanced[2] = first_c_advanced[0], first_c_advanced[1], first_c_advanced[2]\n#centered_sampler.init_centered_sampler(g_filtered)\n#print(\"Initialized centered-sampler\")\n#last_c_advanced = centered_sampler.centered_sampler(g, g_filtered, 2000)\n#print(\"Centered-sampler done\")\n#c_advanced[3], c_advanced[4], c_advanced[5] = last_c_advanced[3], last_c_advanced[4], last_c_advanced[5]\n\nprint(\"Improved Algorithm Results: \", c_advanced)\n\"\"\"\n\n\nprint(\"--- %s seconds ---\" % (time.time() - start_time))\n","repo_name":"SirBrundolf/DREAM-Counting-Subgraphs-by-Sampling","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"22366292444","text":"class Solution(object):\n    def singleNumber(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: int\n        \"\"\"\n        dict = {}\n        for i in nums:\n            try:\n                dict.pop(i)\n            except:\n                dict[i] = 1\n        return dict.popitem()[0]\n\n    def singleNumberMath(self, nums):\n        return 2 * sum(set(nums)) - sum(nums)\n","repo_name":"MLNC/LeetCode","sub_path":"136. Single Number.py","file_name":"136. Single Number.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"33922285017","text":"class Order:\n\n    def __init__(\n            self,\n            Time: float = None,\n            EventType: int = None,\n            OrderID: int = None,\n            Size: int = None,\n            Price: int = None,\n            Direction: int =None,\n            SenderID: str =None\n    ):\n        self.Time = Time\n        self.EventType = EventType\n        self.OrderID = OrderID\n        self.Size = Size\n        self.Price = Price\n        self.Direction = Direction\n        self.SenderID = SenderID\n\n\n    def get_order_attributes(self):\n\n        return [self.Time, self.EventType, self.OrderID, self.Size, self.Price, self.Direction, self.SenderID]\n\n\n\n","repo_name":"hibeyhan/LOBSTER_SIM","sub_path":"orders.py","file_name":"orders.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"9414762890","text":"import config as config\nfrom os import listdir\nfrom flask import Flask, request, jsonify\nfrom prediction_splitted import *\nfrom sklearn.preprocessing import Imputer\nfrom splittingPOD import *\n\n# Global Variables\ncurrentDirectoryPath = os.path.dirname(os.path.abspath(__file__))\nresultsDirectory = currentDirectoryPath + '/resultDir/'\ncurrentDirectoryPath = os.path.dirname(os.path.abspath(__file__))\npathToPOD = currentDirectoryPath + '/resultDir/'  # Contain the result of splittingPOD.py\nversion = '13-new-ctakes_new_Pat'\ntrainingResult = 'trainingResult'\ndirToWatch = currentDirectoryPath + '/tmp/toWatch'  # directory where the patients to be done are added: one empty file should be created named with the patient_id to be done\ndirForRes = currentDirectoryPath + '/tmp/forRes'\npathTocTakes = currentDirectoryPath + '/tmp/'  # need to have 2 subdirectories: 'avant' and 'apres' that must be empty. cTakes should be runing from avant to apres.\nversion = '13-new-ctakes_new_Pat'\nresultsDirectory = currentDirectoryPath + '/resultDir/'\npathToCCIs = currentDirectoryPath + \"/Anonymized_CCI-CD_without_outliers_ALL.csv\"\ncTakesUrl = \"http://localhost:9999/\"\n\nraxa_ctakes_url = \"http://\" + config.myIp + \":8080/openmrs/\"\ntime_to_wait = 2  # in sec\n\n\nif __name__ == '__main__':\n    splittingPOD()\n\n\n\ndef create_todo_dataframe(patient_id, list_col_num):\n    # Now getting the prediction for patient comming in restcall\n    pat_todo = [patient_id]\n    res_total = pd.DataFrame()\n\n    print('Formatting Data of the Patient in required Format')\n    todo = makeTodoRunServer(pat_todo)\n\n    for pat in pat_todo:\n        res_pat = []\n        for_ctakes = []\n        obs_ids_from_value_text = []\n        # Select on the patient\n        todo_pat = todo[\n            (todo['patient_id'] == pat) & (todo['concept_id'] != '33334027')]  # TODO presenting complaints\n        forPOD = todo_pat[todo_pat['value_text'].apply(\n            lambda x: 'pod' in x[:10].lower() or ('plan' not in x.lower() and 'pod' in x[10:].lower()))]\n\n        # if we have PODs\n        if not forPOD.empty:\n            # Get the POD as int\n            forPOD['POD'] = forPOD['value_text'].apply(\n                lambda x: re.search('\\d+', x).group() if re.search('\\d+', x) != None else -1).astype(int)\n            # Get the surgery date from the PODs\n            forPOD['surgeryDate'] = forPOD[['obs_datetime', 'POD']].apply(\n                lambda x: x[0] + datetime.timedelta(days=-x[1]) if (x[1] != -1) else datetime.date.min, axis=1)\n\n            # Get the surgery date from the POD0\n            list_surgery_date = forPOD['obs_datetime'][forPOD['POD'] == 0].values.tolist()\n\n            # Compare both and remove mistakes (2 surgeries with a delta in date < 3days)\n            for i in np.unique(forPOD['surgeryDate']).tolist():\n                addok = True\n                for j in list_surgery_date:\n                    if abs((i - j).days) <= 2:\n                        addok = False\n                if addok:\n                    list_surgery_date.append(i)\n\n            last = [None] * len(list_col_num)\n            last_dat = datetime.date.min\n            idC = 0\n            for dat in list(np.unique(todo_pat['obs_datetime'])):  # sorted on the fly\n                todoPatSel = todo_pat[(todo_pat['obs_datetime'] <= dat) & (todo_pat['obs_datetime'] > last_dat)]\n                last_dat = dat\n\n                # Get all the info we have up to this POD\n                now = extract_value_from_valuetext(todoPatSel, last)\n\n                # Export for cTakes :\n                for_ctakes = for_ctakes + list(todoPatSel['value_text'][(todoPatSel['value_text'] != 'nan') & (\n                        todoPatSel['value_text'] != 'None')].values)\n\n                # We are only taking obs_ids for freeText\n                obs_ids_from_value_text = obs_ids_from_value_text + list(todoPatSel['obs_id'][\n                                                                             (todoPatSel[\n                                                                                  'value_text'] != 'nan') & (\n                                                                                     todoPatSel[\n                                                                                         'value_text'] != 'None') & (\n                                                                                     todoPatSel[\n                                                                                         'concept_id'] == '160632')].values)\n\n                if obs_ids_from_value_text:\n                    print('Adding concept values from  Ctakes to dataFrame')\n                    now = update_extracted_value(todoPatSel, now, obs_ids_from_value_text)\n\n                # Get the POD of all new infos from the closest earlier surgery\n                if last != now:  # and sum(x is None for x in now)<len(now)):\n                    if not for_ctakes:\n                        for_ctakes = ['NULL']\n\n                    lres = fetch_mlob_for_obs_ids(obs_ids_from_value_text)\n\n                    for_ctakes = []\n                    obs_ids_from_value_text = []\n\n                    surgDate = getCloseSurgery(dat, list_surgery_date)\n                    pod = (dat - surgDate).days if (dat - surgDate).days < 10000 else 'max'\n                    surg_date_all = getAllSurgery(dat, list_surgery_date)\n                    min_pod = getMinPOD(dat, list_surgery_date)\n\n                    res_cTakes = [lres]\n\n                    res_pat.append([pat, surgDate, surg_date_all, min_pod, pod] + now + [str(idC)] + res_cTakes)\n                    last = now\n                    idC = idC + 1\n\n        if res_pat:\n            res_pat_df = pd.DataFrame(res_pat)\n            res_pat_df.columns = ['patient_id', 'dateSurgery', 'surgeriesPOD', 'minPOD', 'POD'] + list_col_num + [\n                'idC'] + ['rescTakes']\n            res_total = pd.concat([res_total, res_pat_df], axis=0)\n\n    return res_total\n\n\ndef convert_to_json(res):\n    resultJSON = {}\n    podArray = []\n    resultJSON['PODArray'] = podArray\n\n    for index, row in res.iterrows():\n        podJSON = {}\n        resultJSON['patientId'] = row['patient_id']\n        podJSON['POD'] = row['POD']\n        podJSON['pred'] = row['pred']\n        podArray.append(podJSON)\n\n    return jsonify(resultJSON)\n\n\ndef Num_last(x):\n    # print(\"Num_last\")\n    print(x)\n    if x is None or str(x) is 'nan':\n        return [None, None]\n    res = [None]\n    try:\n        for dat, n in x:\n            if type(n) == float or type(n) == int or n.isdigit():\n                res.append(float(n))\n    except ValueError:\n        print(x)\n    return [res[-1], None]\n\n\ndef Num_last_series(x):\n    print(\"Num_last\")\n    print(x)\n    if x is None or str(x) is 'nan':\n        return [None, None]\n    res = [None]\n    try:\n        for dat, n in x.iteritems():\n            if type(n) == float or type(n) == int or n.isdigit():\n                res.append(float(n))\n    except ValueError:\n        print(x)\n    return [res[-1], None]\n\n\ndef Num_mean(x):\n    if x is None or str(x) is 'nan':\n        return [None, None]\n    res = []\n    try:\n        for dat, n in x:\n            if type(n) == float or type(n) == int or n.isdigit():\n                res.append(float(n))\n    except ValueError:\n        print(x)\n    return [np.mean(res), np.std(res)]\n\n\ndef funOnBool_or(x):\n    print(x)\n    if x is None:\n        return None\n    res = False\n    for dat, n in x:\n        res = res or n\n    return res * 1\n\n\ndef funOnBool_last(x):\n    if x is None:\n        return None\n    res = False\n    for dat, n in x:\n        res = n\n    return res * 1\n\n\n\n\n\n\n\n\n","repo_name":"ojasgr1706/raxa_updated","sub_path":"SRIArchive/2.SRI With Comprehend Code/sri_raxa_office/source/dataPreparation.py","file_name":"dataPreparation.py","file_ext":"py","file_size_in_byte":7620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31753076973","text":"from ray.util.annotations import PublicAPI\n\nimport tensorflow as tf\n\n\n@PublicAPI(stability=\"beta\")\ndef prepare_dataset_shard(tf_dataset_shard: tf.data.Dataset):\n    \"\"\"A utility function that disables Tensorflow autosharding.\n\n    This should be used on a  TensorFlow ``Dataset`` created by calling ``to_tf()``\n    on a ``ray.data.Dataset`` returned by ``ray.train.get_dataset_shard()`` since\n    the dataset has already been sharded across the workers.\n\n    Args:\n        tf_dataset_shard (tf.data.Dataset): A TensorFlow Dataset.\n\n    Returns:\n        A TensorFlow Dataset with autosharding turned off.\n    \"\"\"\n    options = tf.data.Options()\n    options.experimental_distribute.auto_shard_policy = (\n        tf.data.experimental.AutoShardPolicy.OFF\n    )\n    return tf_dataset_shard.with_options(options)\n","repo_name":"merlinepedra/RAY-1","sub_path":"python/ray/train/tensorflow/train_loop_utils.py","file_name":"train_loop_utils.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"42475231422","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Sep 19 21:42:57 2021\nProject: Automated Soduku Solver\nWebsite: https://sudoku.com/expert/\n@author: sufian\n\"\"\"\nimport pyautogui as pg\nimport time\n\nboard = []\n\nwhile True:\n    row = list(input('Row: '))\n    ints = []\n\n    for n in row:\n        ints.append(int(n))\n    board.append(ints)\n\n    if len(board) == 9:\n        break\n    print('Row ' + str(len(board)) + ' Complete')\n\ntime.sleep(2)\n\ndef print_board(board):\n  \n  for i in range(len(board)):\n\n    #When we hit the 3 row we print a line\n    if i % 3 == 0 and i != 0:\n      print(\"- - - - - - - - - - - - - \")\n\n    #When we hit 3 column we add a '|'\n    for j in range(len(board[0])):\n        if j % 3 == 0 and j != 0 :\n          print(\" | \", end = \"\")\n\n        #Prints actual items in list to the board    \n        if j == 8:\n          print(board[i][j])\n        else:\n          print(str(board[i][j]) + \" \", end = \"\")\n\n#Function finds position of empty box in board denoted as 0\ndef find_empty(board):\n  for i in range(len(board)):\n    for j in range(len(board[0])):\n      if board[i][j] == 0:\n        return (i, j) #returns position of empty box as tuple (row, coloumn)\n  return None \n\n#Function checks if number passed to the borad is valid\ndef valid_num(board, num, pos):\n  #Check if num is in the same row \n  for i in range(len(board[0])):\n    if board[pos[0]][i] == num and pos[1] != i:\n      return False\n  \n  #Check if num is in same column \n  for i in range(len(board)):\n    if board[i][pos[1]] == num and pos[0] != i:\n      return False\n  \n  #Check if num is in the same box \n  box_x = pos[1] // 3\n  box_y = pos[0] // 3 #gives us which square we are on\n\n  for i in range(box_y * 3, box_y*3 + 3):\n    for j in range(box_x * 3, box_x*3 +3):\n      if board[i][j] == num and (i,j) != pos:\n        return False\n  \n  return True\n\ndef weboutput(board):\n    final_board = []\n    str_board = []\n    \n    for i in range(9):\n        final_board.append(board[i])\n    \n    for lists in final_board:\n        for num in lists:\n            str_board.append(str(num))\n    \n    counter = []\n    \n    for num in str_board:\n        pg.press(num)\n        pg.hotkey('right')\n        counter.append(num)\n        if len(counter) % 9 == 0:\n            pg.hotkey('down')\n            for i in range(8):\n                pg.hotkey('left')\n\ndef solve(board):\n  \n  empty_square = find_empty(board)\n  if not empty_square:\n    return True\n  else:\n    erow, ecol = empty_square\n\n  for i in range(1,10):\n    if valid_num(board, i, (erow, ecol)):\n      board[erow][ecol] = i\n\n      if solve(board):\n        return True\n      \n      board[erow][ecol] = 0\n  return False\n\nsolve(board)\nweboutput(board)\n'''\nprint(\"Soduku board: \\n\")\nprint_board(board)\nprint(\"\")\nsolve(board)\nprint(\"Your solved board: \\n\")\nprint_board(board)\n'''\n","repo_name":"BurntSouffle/SodukuSolver","sub_path":"sodukosolver.py","file_name":"sodukosolver.py","file_ext":"py","file_size_in_byte":2820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"10280152082","text":"#! /usr/bin/env python\nimport sys\nimport rospy\nimport moveit_commander\nimport geometry_msgs\n\nmoveit_commander.roscpp_initializer.roscpp_initialize(sys.argv)\nrospy.init_node('move_group_grasp', anonymous=True)\nrobot = moveit_commander.robot.RobotCommander()\n\narm_group = moveit_commander.move_group.MoveGroupCommander(\"arm\")\nhand_group = moveit_commander.move_group.MoveGroupCommander(\"gripper\")\n\narm_group.set_named_target(\"home_j\")\nplan = arm_group.go()\nhand_group.set_named_target(\"open\")\nplan = hand_group.go()\n\narm_group.set_named_target(\"home_l\")\nplan = arm_group.go(wait = False)\nrospy.sleep(1)\narm_group.stop()\n\n\n\nmoveit_commander.roscpp_initializer.roscpp_shutdown()\n","repo_name":"zzy5510/UR5-grasp-and-kinect-demo-on-gazebo","sub_path":"src/UR_with_Robotiq_grasp_gazebo/ur_tc/smart_grasping_sandbox_sample/scripts/stop_demo.py","file_name":"stop_demo.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"86"}
+{"seq_id":"73737739485","text":"import math\r\nimport numpy as np\r\n\r\nimport env, plotting, utils\r\n\r\n\r\nclass Node:\r\n    def __init__(self, n):\r\n        self.x = n[0]\r\n        self.y = n[1]\r\n        self.parent = None\r\n\r\n\r\nclass Rrt:\r\n    def __init__(self, s_start, s_goal, step_len, goal_sample_rate, iter_max):\r\n        self.s_start = Node(s_start)\r\n        self.s_goal = Node(s_goal)\r\n        self.step_len = step_len\r\n        self.goal_sample_rate = goal_sample_rate\r\n        self.iter_max = iter_max\r\n        self.vertex = [self.s_start]\r\n\r\n        self.env = env.Env()\r\n        self.plotting = plotting.Plotting(s_start, s_goal)\r\n        self.utils = utils.Utils()\r\n\r\n        self.x_range = self.env.x_range\r\n        self.y_range = self.env.y_range\r\n        self.obs_circle = self.env.obs_circle\r\n        self.obs_rectangle = self.env.obs_rectangle\r\n        self.obs_boundary = self.env.obs_boundary\r\n\r\n    def planning(self):\r\n        for i in range(self.iter_max):\r\n            node_rand = self.generate_random_node(self.goal_sample_rate)\r\n            node_near = self.nearest_neighbor(self.vertex, node_rand)\r\n            node_new = self.new_state(node_near, node_rand)\r\n\r\n            if node_new and not self.utils.is_collision(node_near, node_new):\r\n                self.vertex.append(node_new)\r\n                dist, _ = self.get_distance_and_angle(node_new, self.s_goal)\r\n\r\n                if dist <= self.step_len and not self.utils.is_collision(node_new, self.s_goal):\r\n                    self.new_state(node_new, self.s_goal)\r\n                    # path = self.extract_path(node_new)\r\n                    return self.extract_path(node_new)\r\n        return None\r\n\r\n    def generate_random_node(self, goal_sample_rate):\r\n        delta = self.utils.delta\r\n\r\n        if np.random.random() > goal_sample_rate:\r\n            return Node((np.random.uniform(self.x_range[0] + delta, self.x_range[1] - delta),\r\n                         np.random.uniform(self.y_range[0] + delta, self.y_range[1] - delta)))\r\n\r\n        return self.s_goal\r\n\r\n    @staticmethod\r\n    def nearest_neighbor(node_list, n):            #nearest with rand node\r\n        return node_list[int(np.argmin([math.hypot(nd.x - n.x, nd.y - n.y)   #return index of nearest node\r\n                                        for nd in node_list]))]             \r\n\r\n    def new_state(self, node_start, node_end):         #create new node\r\n        dist, theta = self.get_distance_and_angle(node_start, node_end)\r\n\r\n        dist = min(self.step_len, dist)\r\n        node_new = Node((node_start.x + dist * math.cos(theta),\r\n                         node_start.y + dist * math.sin(theta)))\r\n        node_new.parent = node_start\r\n\r\n        return node_new\r\n\r\n    def extract_path(self, node_end):\r\n        node = (self.s_goal.x, self.s_goal.y)\r\n        path = [Node(node)]\r\n        node_now = node_end\r\n        while node_now.parent is not None:\r\n            node_now = node_now.parent\r\n            path.append(node_now)\r\n        return path\r\n    \r\n    def coordinate_path(self,path):\r\n        coordinate_path = []\r\n        for node in path:\r\n            coordinate_path.append((node.x,node.y))\r\n        # print(len(coordinate_path))\r\n        # print(coordinate_path)\r\n        return coordinate_path\r\n\r\n    def convert_path(self,path):\r\n        path_input = []\r\n        path_input.append(path[0])\r\n        node_status = path[0]\r\n        a = len(path)\r\n        i = 1\r\n        for i in range(len(path)):\r\n            if self.utils.is_collision(node_status, path[i]): #and self.utils.is_collision(node_status,path[i+1]):\r\n                if i > 1:\r\n                    path_input.append(path[i-1])\r\n                    node_status = path[i-1]\r\n        path_input.append(path[len(path)-1])\r\n        return path_input\r\n\r\n    def coordinate_convert_path(self,path):\r\n        path_convert = []\r\n        path = self.convert_path(path)\r\n        for node in path:\r\n            path_convert.append((node.x,node.y))\r\n        return path_convert\r\n    def get_circle(self,path):\r\n        circles = []\r\n        path = self.convert_path(path)\r\n        for i in range(len(path)):\r\n            if i >0 and i < len(path)-1:\r\n                node = path[i]\r\n                r = self.utils.get_radius(node)\r\n                circles.append([node.x,node.y,r])\r\n        print(circles)\r\n        return circles\r\n\r\n    def get_point_circle(self,path):\r\n        path= self.convert_path(path)\r\n        output = []\r\n        for i in range(len(path)):\r\n            if i >0 and i < len(path)-1:\r\n                node = path[i]\r\n                r = self.utils.get_radius(node)\r\n                #print (r)\r\n                point1 = self.utils.get_intersection(path[i-1],path[i], r)\r\n                output.append(point1)\r\n                #print(point1)\r\n                point2 = self.utils.get_intersection(path[i+1],path[i], r)\r\n                output.append(point2)\r\n                #print(point2)\r\n        #print(output)\r\n        return output\r\n\r\n    @staticmethod\r\n    def get_distance_and_angle(node_start, node_end):\r\n        dx = node_end.x - node_start.x\r\n        dy = node_end.y - node_start.y\r\n        return math.hypot(dx, dy), math.atan2(dy, dx)\r\n\r\n\r\ndef main():\r\n    start = (600, 100) # Starting node\r\n    goal = (30, 200) # Goal node\r\n\r\n    rrt = Rrt(start, goal, 50, 0.01, 10000)\r\n\r\n    path = rrt.planning()\r\n    path_input = rrt.convert_path(path)\r\n    path_coordinate = rrt.coordinate_path(path)\r\n    path_convert = rrt.coordinate_convert_path(path_input)\r\n    circles = rrt.get_circle(path_input)\r\n    output = rrt.get_point_circle(path)\r\n\r\n    if path:\r\n        rrt.plotting.animation(rrt.vertex, path_coordinate, path_convert,circles,output, \"RRT\", True)\r\n    else:\r\n        print(\"No Path Found!\")\r\n   \r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","repo_name":"thuhangkhuat/RRT-path-planning-new","sub_path":"RRTalgorithm.py","file_name":"RRTalgorithm.py","file_ext":"py","file_size_in_byte":5757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70472966685","text":"import unittest\nimport json\n\n#Procedure to import file from different folder using sys, path, append\nimport sys\nsys.path.append(\".\")\nfrom Read_JSON import dataSnwProcInput,dataTransformerProc,Dx_count\n\n#Write class to run unit tests. These are created to retrieve the values from Snowflake and transformed JSONs and perform comparison \n#between them to ensure that the snowflake JSON transformation is successful when created using liquid FHIR template\n\nclass TestTransform (unittest.TestCase):    \n    \n    # if statements are written to display message in vs code for all passed tests   \n    \n    #Procedure Tests start here    \n    #Validation of CPTCODE_PRIMARY between snowflake JSON and transformed JSON\n    def test_procprimcpt(self):\n        if dataSnwProcInput['CPTCODE_PRIMARY'] == dataTransformerProc['code']['coding'][0]['code']:\n            print('CPTCODE_PRIMARY Matched')\n        #Prints message if the test fails for comparison\n        self.assertEqual(dataSnwProcInput['CPTCODE_PRIMARY'],dataTransformerProc['code']['coding'][0]['code'],\"CPTCODE_PRIMARY not Matched\")\n\n\n    #Validation of PROCEDURE_CODE_2 between snowflake JSON and transformed JSON\n    def test_proccd2(self):\n        if dataSnwProcInput['PROCEDURE_CODE_2'] == dataTransformerProc['code']['coding'][1]['code']:\n            print('PROCEDURE_CODE_2 Matched')\n        #Prints message if the test fails for comparison\n        self.assertEqual(dataSnwProcInput['PROCEDURE_CODE_2'],dataTransformerProc['code']['coding'][1]['code'],\"PROCEDURE_CODE_2 not Matched\")\n\n\n    #Validation of DATE OF SERVICE between snowflake JSON and transformed JSON\n    def test_procdos(self):\n        if dataSnwProcInput['DATE_OF_SERVICE'].split(' ')[0] == dataTransformerProc['performedPeriod']['start']:\n            print('DATE_OF_SERVICE Matched')\n        #Prints message if the test fails for comparison\n        self.assertEqual(dataSnwProcInput['DATE_OF_SERVICE'].split(' ')[0],dataTransformerProc['performedPeriod']['start'],\"DATE_OF_SERVICE not Matched\")\n\n    \n    #Validation of CENSEOID between snowflake JSON and transformed JSON\n    def test_proccensid(self):\n        if dataSnwProcInput['CENSEOID'] == dataTransformerProc['identifier'][0]['value']:\n            print('CENSEOID Matched')\n        #Prints message if the test fails for comparison\n        self.assertEqual(dataSnwProcInput['CENSEOID'],dataTransformerProc['identifier'][0]['value'],\"CENSEOID not Matched\")\n    \n\n    #Validation of MEMBER_NUMBER between snowflake JSON and transformed JSON\n    def test_procmemnm(self):\n        if dataSnwProcInput['MEMBER_NUMBER'] == dataTransformerProc['identifier'][1]['value']:\n            print('MEMBER_NUMBER Matched')\n        #Prints message if the test fails for comparison\n        self.assertEqual(dataSnwProcInput['MEMBER_NUMBER'],dataTransformerProc['identifier'][1]['value'],\"MEMBER_NUMBER not Matched\")\n\n    \n    #Validation of HICN between snowflake JSON and transformed JSON\n    def test_prochicn(self):\n        if dataSnwProcInput['HICN'] == str(dataTransformerProc['identifier'][2]['value']):\n            print('HICN Matched')\n        #Prints message if the test fails for comparison\n        self.assertEqual(dataSnwProcInput['HICN'],str(dataTransformerProc['identifier'][2]['value']),\"HICN not Matched\")\n\n    #Validation of DX Codes between snowflake JSON and transformed JSON\n    #def test_procdx1(self):\n     #   if dataSnwProcInput['DX_1'] == dataTransformerProc['reasonCode'][0]['coding'][0]['code']:\n      #      print('AdmissionDate Matched')\n        #Prints message if the test fails for comparison\n       # self.assertEqual(dataSnwProcInput['DX_1'],dataTransformerProc['reasonCode'][0]['coding'][0]['code'],\"AdmissionDate not Matched\") \n\n    #Validation of DX Codes between snowflake JSON and transformed JSON\n    def test_procdx(self):\n        #Loop runs from 0 till Dx_count\n        for y in range(Dx_count):\n            if dataSnwProcInput['DX_'+str(y+1)] == dataTransformerProc['reasonCode'][0]['coding'][y]['code']:\n                print('Diagnosis Code Matched for DX_' + str(y+1))\n            #Prints message if the test fails for comparison\n            self.assertEqual(dataSnwProcInput['DX_'+ str(y+1)],dataTransformerProc['reasonCode'][0]['coding'][y]['code'],\"Diagnosis Code not Matched for 'DX_'\" + str(y+1)) \n        \n\nif __name__ == '__main__':\n    unittest.main()","repo_name":"Command8/PY-CURES-Test-Framework","sub_path":"Test_Compare_JSONs/test_Procedure_compareJSONs.py","file_name":"test_Procedure_compareJSONs.py","file_ext":"py","file_size_in_byte":4368,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"23421291668","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 18 23:19:29 2020\n\n@author: xieyu\n\"\"\"\nimport tensorflow as tf\nimport numpy as np\n\ndef rollOneStep(dataset_x, y_predict):\n    n_step_out = y_predict.shape[0]\n    if n_step_out>0:\n        dataset_x = tf.Variable(dataset_x)\n        dataset_x[:,:-n_step_out].assign(dataset_x[:,n_step_out:])\n        dataset_x[:,-n_step_out:].assign(y_predict)\n        dataset_x = tf.convert_to_tensor(dataset_x)\n    return dataset_x\ndef rollForward(pop, \n        lstm_obj,\n        look_back=24, \n        n_step_out=1, \n        ind_sep=80, \n        n_features=1):\n    n_all = pop.shape[0]\n    n_times_predict = (n_all - ind_sep)//n_step_out\n    ind_tmp_0 = ind_sep - look_back\n    ind_tmp_end = ind_sep\n    dataset_x = pop[ind_tmp_0:ind_tmp_end]\n    dataset_x = tf.reshape(dataset_x,[1,look_back,n_features])\n    # print(dataset_x.shape)\n    y_predict = np.array([])\n    pop_predict = pop.copy()\n    for i in range(n_times_predict):\n        y_predict = lstm_obj.model.predict(dataset_x)\n        ind_tmp_predict_0 = ind_sep+i*n_step_out\n        ind_tmp_predict_end = ind_tmp_predict_0 + n_step_out\n        pop_predict[ind_tmp_predict_0:ind_tmp_predict_end] = y_predict[0]\n        # print(y_predict)\n        y_predict = tf.convert_to_tensor(y_predict, dtype=tf.float64)\n        y_predict = tf.reshape(y_predict, [1,n_step_out,n_features])\n        dataset_x = rollOneStep(dataset_x, y_predict)\n    return pop_predict\n","repo_name":"xieyu135/time_series","sub_path":"roll_forward.py","file_name":"roll_forward.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"20152858278","text":"# Import libraries\nfrom sys import argv\n\n# Establish Global Variables - Change these if your device uses a syntax\nnotes_file_name = 'My Clippings.txt'\n\n\n# Check if the usage is right 'python KindleToMD.py {TITLE OF BOOK}', else exit with error message\nif len(argv) < 2:\n    print(\"\\nERROR: No Book Title Entered. \\nUsage: 'python KindleToMD.py {TITLE OF BOOK}'\\n\")\n    exit()\n\n# Create a string from the system varibales\ntext_name = \" \".join(argv[1:])\n\n# Check if there is a notes file, if yes open it, if no print error and exit\ntry:\n    kindle_notes = open(notes_file_name, \"r\", encoding='utf-8')\nexcept FileNotFoundError:\n    print(\"\\nNotes file not accessible.\\n - Is it in the same folder as the python program?\\n - Is the file named 'My Clippings.txt'?\\n\")\n    exit()\n\n# Try to find an existing markdown notes file to read from\ntry:\n    markdown_notes = open(f\"{text_name} Notes.md\", \"r\", encoding='utf-8')\nexcept:  # If none exist, create a new file\n    markdown_notes = open(f\"{text_name} Notes.md\", \"a+\", encoding='utf-8')\n\n# Iterate through the file to find notes for the book\nline_count = 0\nnote_type = None\nnote_metadata = ''\nnotes_list = []\nfor line in kindle_notes:\n    # If we are inside of a note\n    if line_count > 0:\n        # If this is the metadata line\n        if line_count == 3:\n            #check if this is a highlight or a note\n            if line[7] == 'H':\n                note['type'] = 'highlight'\n                note['page'] = line[26]\n            else:\n                note['type'] = 'note'\n\n            # Get the page number\n            \n        \n            # Store the note metadata to print after you get the actual quote\n            note_metadata = line\n\n        # If this is the actual highlight/note\n        elif line_count == 1:\n\n            # Check if this note already exists in the file\n            for note_line in markdown_notes:\n                if str(line) in str(note_line):\n                    print(\"-----Duplicate note\\n\")\n\n            if note['type'] == 'highlight':\n                note['Highlight'] = f'> {line}>{note_metadata}\\n'\n            else:\n                note['Highlight'] = f'{line}{note_metadata}\\n'\n\n            # Add the note to the note_list\n            notes_list.append(note)\n            \n        # Subtract 1 from the line count\n        line_count -= 1\n\n    elif text_name in line: # If you find the book name in the notes file\n        line_count = 3\n        note = {}\n\n# Close the kindle notes file\nkindle_notes.close()\nprint(notes_list)\n\n\n# Write formatted ntoes to a new file\ntry:  # Create a notes markdown file to write to\n    markdown_notes = open(f\"{text_name} Notes.md\", \"x\", encoding='utf-8')\nexcept:  # If a file already exists, open it and apend to it\n    markdown_notes = open(f\"{text_name} Notes.md\", \"a\", encoding='utf-8')\n\n\n# markdown_notes.write(f'> {line}>{note_metadata}\\n')\n# markdown_notes.write(f'{line}{note_metadata}\\n')","repo_name":"Dracfo/Kindle-Notes-To-Markdown","sub_path":"KindleToMD.py","file_name":"KindleToMD.py","file_ext":"py","file_size_in_byte":2917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32177526749","text":"import os\nimport random\nfrom elasticsearch import Elasticsearch\n\n# connection parameters\n\nES_CONNECTION_URL = \"http://elastic:{ES_PASSWORD}@{ES_SERVER_ADDR}:9200/\"\n\n# document structure\n\nES_INDEX = \"demo_index\"\nES_DOCTYPE = \"events\"\nES_KEYWORD = \"Handbill not printed\"\n\n# queries\n\nES_QUERY_TEST = {\n    \"query\": {\n        \"match\": {\n            \"message\": ES_KEYWORD\n        }\n    }\n}\n\nES_QUERY_ALL = {\n    \"query\": {\n        \"match_all\": {}\n    }\n}\n\nES_QUERY_AGG = {\n    \"query\": {\n        \"bool\": {\n            \"must\": [{\n                \"match\": {\n                    \"message\": ES_KEYWORD\n                }\n            }]\n        }\n    },\n    \"aggs\": {\n        \"counter\": {\n            \"terms\": {\n                \"field\": \"message.keyword\"\n            }\n        }\n    }\n}\n\n# generate random log entries\n\ndef random_message():\n    msgs = [ES_KEYWORD, \"Handbill printed\", \"Handbill deleted\", \"Handbill created\", \"Handbill renewed\"]\n    pos = random.randint(0, len(msgs)-1)\n    return msgs[pos]\n\ndef start():\n    # server address from env\n    ES_SERVER_ADDR = os.getenv(\"ES_SERVER_ADDR\")\n    if ES_SERVER_ADDR is None:\n        print(\"ES_SERVER_ADDR not set\")\n        exit(1)\n\n    # elastic user password from env\n    ES_PASSWORD = os.getenv(\"ES_PASSWORD\")\n    if ES_PASSWORD is None:\n        print(\"ES_PASSWORD not set\")\n        exit(1)\n\n    # connect to es\n    try:\n        es = Elasticsearch(ES_CONNECTION_URL.format(ES_PASSWORD=ES_PASSWORD, ES_SERVER_ADDR=ES_SERVER_ADDR))\n        es_info = es.info()\n    except Exception:\n        print(\"Unable to connect to elasticsearch\")\n        exit(1)\n\n    print(\"Using elasticsearch at {} (ver. {})\".format(ES_SERVER_ADDR, es_info['version']['number']))\n\n    # delete our sample index\n    if es.indices.exists(ES_INDEX):\n        es.indices.delete(ES_INDEX)\n\n    # set keyword field\n    mappings = {\n        \"mappings\": {\n            ES_DOCTYPE: {\n                \"properties\": {\n                    \"message\": {\n                        \"type\": \"text\",\n                        \"fields\": {\n                            \"keyword\": {\n                                \"type\": \"keyword\"\n                            }\n                        }\n                    }\n                }\n            }\n        }\n    }\n\n    # create index\n    es.indices.create(index=ES_INDEX, body=mappings)\n\n    # counter for testing purposes\n    compare_idx = 0\n\n    # create documents\n    for i in range(1, 100):\n        body = {}\n        msg = random_message()\n        body['event'] = i\n        body['message'] = msg\n        res = es.index(index=ES_INDEX, doc_type=ES_DOCTYPE, body=body, id=i)\n        # print(\"Uploaded message #{}, result: {}\".format(str(i), res['result']))\n        if msg == ES_KEYWORD:\n            compare_idx = compare_idx + 1\n\n    # refresh\n    es.indices.refresh(index=ES_INDEX)\n\n    # search\n    result = es.search(index=ES_INDEX, doc_type=ES_DOCTYPE, body=ES_QUERY_AGG)\n    buckets = result['aggregations']['counter']['buckets']\n    if len(buckets) == 0:\n        print(\"no matches\")\n    else:\n        doc_count = 0\n        for i in buckets:\n            if i.get('key') == ES_KEYWORD:\n                doc_count = i.get('doc_count')\n                break\n\n        if doc_count > 3:\n            print(\"more than 3 (actually {})\".format(doc_count))\n        elif doc_count == 3:\n            print(\"exactly 3\")\n        else:\n            print(\"less than 3 (actually {})\".format(doc_count))\n\n        # compare findings with test\n        if doc_count == compare_idx:\n            print(\"result correct, {} == {}\".format(doc_count, compare_idx))\n        else:\n            print(\"result incorrect, {} != {}\".format(doc_count, compare_idx))\n\n    # delete index\n    es.indices.delete(ES_INDEX)\n\nif __name__ == '__main__':\n    start()\n","repo_name":"avoidik/homework_es","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"41625573456","text":"import sys\ninput = sys.stdin.readline\n\nN, M = map(int, input().split())\ndic = {}\n\nfor i in range(1, N+1):\n    a = input().strip()\n    dic[i] = a\n    dic[a] = i\n\nfor _ in range(M):\n    target = input().strip()\n\n    try:\n        print(dic.get(int(target)))\n    except:\n        print(dic.get(target))\n","repo_name":"wingunkh/BOJ","sub_path":"CLASS 3/1620 나는야 포켓몬 마스터 이다솜.py","file_name":"1620 나는야 포켓몬 마스터 이다솜.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"23717288449","text":"#pyuic5 interface.ui -o interface.py\nimport os\nimport shutil\nimport subprocess\nimport threading\nimport traceback\n\nfrom PyQt5.uic.properties import QtCore\n\nfrom functions import warnings, add_picture, update_scroll, update_model_choice, work\nfrom PyQt5 import QtWidgets\nfrom PyQt5.QtWidgets import QFileDialog, QMessageBox\n\nfrom interface import Ui_MainWindow\nfrom signals import *\nimport sys\n\n\nclass mywindow(QtWidgets.QMainWindow):\n    def __init__(self):\n        super(mywindow, self).__init__()\n\n        self.ui = Ui_MainWindow()\n        self.ui.setupUi(self)\n        self.ui.pushButton_start.clicked.connect(self.pushButton_start)\n        self.ui.pushButton_add_photo.clicked.connect(self.pushButton_add_photo)\n\n        self.ui.radioButton_upscale.clicked.connect(self.radioButton_upscale)\n        self.ui.radioButton_face.clicked.connect(self.radioButton_face)\n\n        self.ui.pushButton_add_model.clicked.connect(self.pushButton_add_model)\n\n        update_model_choice(self)\n        print(\"Upscalo is running\")\n\n    def showEvent(self, show):\n        update_scroll(self)\n\n    def keyPressEvent(self, key):\n        print(key.key())\n\n    def pushButton_add_model(self):\n        file = QFileDialog.getOpenFileName()[0]\n        models = [os.listdir(\"Real-ESRGAN\\experiments\\pretrained_models\"), os.listdir(\"GFPGAN\\experiments\\pretrained_models\")]\n        print(file.split('/')[-1])\n        print(models)\n        if str(file.split('/')[-1]) in str(models):\n            warnings('Ой', 'Эта модель уже загружена.')\n            return\n\n        if 'RealESRGAN' in file and file.endswith('.pth'):\n            shutil.copyfile(file, f\"Real-ESRGAN\\experiments\\pretrained_models\\\\{file.split('/')[-1]}\")\n\n        elif 'GFPGAN' in file and file.endswith('.pth'):\n            shutil.copyfile(file, f\"GFPGAN\\experiments\\pretrained_models\\\\{file.split('/')[-1]}\")\n        else:\n            warnings('Внимание!', 'Имя должно содержать \"RealESRGAN\" или \"GFPGAN\", а разрешение файла должно быть .pth')\n            return\n        print('Модель загружена')\n        update_model_choice(self)\n\n\n    def radioButton_upscale(self):\n        update_model_choice(self, 'upscale')\n\n\n    def radioButton_face(self):\n        update_model_choice(self, 'face')\n\n\n    def pushButton_add_photo(self):\n        file = QFileDialog.getOpenFileName()[0]\n        if file == '':\n            warnings(\"Ой\", \"Файл не выбран🤷‍\", QMessageBox.Information)\n            return\n        try:\n            shutil.copyfile(file, f\"inputs/{file.split('/')[-1]}\")\n        except Exception as er:\n            print(er)\n            print(traceback.format_exc())\n        if '.jpg' not in str(file) and '.png' not in str(file):\n            warnings(\"Внимание!\", \"Файл должен быть в формате JPG или PNG\")\n            return\n        self.ui.graphicsView_before.setScene(add_picture(file, self))\n\n        update_scroll(self)\n\n    def pushButton_start(self):\n        print(\"running...\")\n        import shlex\n\n        model = self.ui.comboBox_model.currentText()\n        print(model)\n\n        if self.ui.radioButton_upscale.isChecked():\n            arg = shlex.split(f\"python Real-ESRGAN/inference_realesrgan.py \"\n                              f\"--model_path Real-ESRGAN/experiments/pretrained_models/{model} \"\n                              f\"--input inputs \"\n                              f\"--output results \"\n                              f\"--face_enhance\")\n        else:\n            arg = shlex.split(f\"python GFPGAN/inference_gfpgan.py --upscale 2 --test_path inputs --save_root results\")\n\n        threading.Timer(0, work, [arg, self]).start()\n        print('aga')\n\n\ndef start():\n    app = QtWidgets.QApplication([])\n    application = mywindow()\n    application.show()\n    sys.exit(app.exec())\n\nif __name__ == '__main__':\n    try:\n        start()\n    except Exception as e:\n        print(\"Error: \\n\" + str(e) + \"\\n\\nTraceback: \\n\" + str(traceback.format_exc()))\n","repo_name":"cvetyshayasiren/upscalo","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"24141805358","text":"\"\"\"\r\n.. moduleauthor:: Mihael Subasic <mihael.subasic@bulme.at>\r\n\"\"\"\r\n\r\n\r\n\r\nfrom math import pi\r\n\r\nradius = float(input(\"Enter the radius: \"))\r\nheight = float(input(\"Enter the height: \"))\r\n\r\n\r\nArea = (2 * pi * radius * height)\r\nVolume = ((pi * height**2) / 3) * (3 * radius - height) \r\n\r\nprint(f'The spherical cap has a surface of {Area:.3f}')\r\nprint(f'The volume of the spherical cap is {Volume:.3f}')\r\n","repo_name":"mihalixu/Python-School","sub_path":"hw_2/spherical_cap.py","file_name":"spherical_cap.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"11158775911","text":"# coding: utf-8\nfrom wxpy import Bot, Chat\nimport re\n\nfrom snapshot_selenium import snapshot as driver\nfrom pyecharts.render import make_snapshot\nfrom pyecharts import options as opts\nfrom pyecharts.charts import Geo, Map, WordCloud\n\n\nclass Demo(Chat):\n\n    @staticmethod\n    def get_friend():\n        bot = Bot()\n        friends = bot.friends(update=True)\n\n        friend_data = []\n        for friend in friends:\n            if friend.sex == 1:\n                sex = \"男\"\n            elif friend.sex == 2:\n                sex = \"女\"\n            else:\n                sex = \"\"\n            friend_dict = {\n                \"city\": friend.city,\n                \"province\": friend.province,\n                \"sex\": sex,\n                \"signature\": friend.signature,\n\n            }\n            friend_data.append(friend_dict)\n\n        return friend_data\n\n    @staticmethod\n    def get_data(friend):\n\n        city_data = [d['city'] for d in friend if d['city']]\n        province_data = [d['province'] for d in friend if d['province']]\n\n        city_dict = {}\n        for city in city_data:\n\n            if not re.sub(\"[a-z A-Z]\", \"\", city):\n                continue\n\n            city_dict.setdefault(city, 0)\n            city_dict[city] += 1\n\n        city_list = []\n        for key, value in city_dict.items():\n            d = [key, value]\n            city_list.append(d)\n\n        province_dict = {}\n        for province in province_data:\n\n            if not re.sub(\"[a-z A-Z]\", \"\", province):\n                continue\n\n            province_dict.setdefault(province, 0)\n            province_dict[province] += 1\n\n        province_list = []\n        for key, value in province_dict.items():\n            d = [key, value]\n            province_list.append(d)\n\n        return city_list, province_list\n\n    @staticmethod\n    def geo_base(data):\n\n        city_list, province_list = data\n\n        # 好友全国省份分布图\n        geo = Geo(init_opts=opts.InitOpts(theme=\"vintage\"))\n        for city in city_list:\n            try:\n                geo.add_schema(maptype=\"china\", itemstyle_opts=opts.ItemStyleOpts(color=\"gray\"))\n                geo.add(\"微信好友分布地图\", [city], type_=\"effectScatter\", symbol_size=10)\n                geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n                geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(), title_opts=opts.TitleOpts(title=\"微信好友分布地图\"), )\n            except:\n                pass\n\n        print(\"正在制作好友全国分布图\")\n        make_snapshot(driver, geo.render(), \"geo.png\")\n\n        # 广东好友热力图\n        # geo = Geo(init_opts=opts.InitOpts(theme=\"vintage\"))\n        # for city in city_list:\n        #     try:\n        #         geo.add_schema(maptype=\"广东\", itemstyle_opts=opts.ItemStyleOpts(color=\"gray\"))\n        #         geo.add(\"广东好友热力图\", [city], type_=\"heatmap\", symbol_size=10)\n        #         geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n        #         geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(), title_opts=opts.TitleOpts(title=\"热力图\"),\n        #                             toolbox_opts=opts.ToolboxOpts())\n        #     except:\n        #         pass\n        #\n        # print(\"正在制作好友广东热力图\")\n        # make_snapshot(driver, geo.render(), \"heat.png\")\n\n        # 好友全国地理图\n        maps = Map()\n        maps.add(\"\", province_list, \"china\")\n        maps.set_global_opts(title_opts=opts.TitleOpts(title=\"微信好友分布图\"), visualmap_opts=opts.VisualMapOpts())\n\n        print(\"正在制作好友地理图\")\n        make_snapshot(driver, geo.render(), \"map.png\")\n\n        # 词云图\n        c = (\n            WordCloud()\n            .add(\"\", city_list, word_size_range=[15, 50], shape=\"diamond\", word_gap=10)\n            .set_global_opts(title_opts=opts.TitleOpts(title=\"diamond\"))\n        )\n        print(\"正在制作好友城市词云图\")\n        make_snapshot(driver, c.render(), \"world.png\")\n\n\nif __name__ == \"__main__\":\n    f = Demo.get_friend()\n    fd = Demo.get_data(f)\n    Demo.geo_base(fd)\n","repo_name":"GoJerry/wxFriend","sub_path":"echarts.py","file_name":"echarts.py","file_ext":"py","file_size_in_byte":4119,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"2534159838","text":"\"\"\"A simple key-value MySQL store for small chunks of arbitrary data.\n\nYou need a table of the following schema:\n\ncreate table cache (\n    _key                    varchar(32) primary key,\n    query                   varchar(250),\n    value                   text,\n    sticky                  boolean,\n    datetime                timestamp default current_timestamp,\n    index (sticky),\n    index (datetime)\n) charset utf8 engine MyISAM;\n\"\"\"\n\n__author__ = 'Alex Ksikes (alex.ksikes@gmail.com)'\n\nimport base64\nimport md5\nimport cPickle as pickle\nimport web\n\nDB = None\nMAX_SIZE = 10000 # maximum allowed size including the sticky values\n\nclass Cache(object):\n    def __init__(self, **db_params):\n        if not db_params:\n            db = DB\n        elif len(db_params) == 1 and db_params.get('db'):\n            db = db_params.pop('db')\n        else:\n            db = web.database(**db_params)\n        self.db = db\n        \n    def get(self, query):\n        \"\"\"\n        Get values from the cache given a query. \n        \n        The variable query is a string or an object with a unique representation.\n        An object with a unique representation must implement instance variable \"uniq\".\n        \"\"\"\n        query = get_unique_repr(query)\n        key = self._make_key(query)\n        r = self.db.select('cache', vars=dict(key=key), where='_key = $key', limit=1)\n        r = web.listget(r, 0)\n        \n        value = None\n        if r:\n            if not r.sticky:\n                self.db.query('update cache set datetime=now() where _key = $key', vars=dict(key=key))\n            value = pickle.loads(base64.b64decode(r.value))\n        return value\n        \n    def set(self, query, value, replace=False, sticky=False):\n        \"\"\"\n        Set values to the cache given a query and a value.  \n        \n        The variable query is a string or an object with a unique representation.\n        An object with a unique representation must implement instance variable \"uniq\".\n        \n        The value may be any serializable object.\n        \n        When replace is True, the value will be replaced if it already exists.\n        \n        When sticky is True, the value will not be removed from the cache unless the cache \n        is clear with also_sticky flag on.\n        \"\"\"\n        query = get_unique_repr(query)\n        key = self._make_key(query)\n        value = base64.b64encode(pickle.dumps(value))\n        \n        sql = ('insert cache (_key, query, value, sticky) values ($key, $query, $value, $sticky) '\n               'on duplicate key update datetime=now()')\n        \n        if replace:\n            sql += ', query=$query, value=$value, sticky=$sticky'\n        \n        self.db.query(sql, vars=dict(key=key, query=query, value=value, sticky=sticky))\n        self._delete_lru()\n        \n    def get_ifnot_set(self, query, value, replace=False, sticky=False):\n        value = self.get(query)\n        if not value:\n            self.set(query, value, replace, sticky)\n        return value\n    \n    def _delete_lru(self):\n        r = self.db.query('select count(*) as size from cache')\n        r = web.listget(r, 0)\n        n = r.size - MAX_SIZE \n        if n > 0:\n            self.db.query('delete from cache where sticky !=1 order by datetime limit %s' % n)\n            \n    def _make_key(self, query):\n        return md5.new(_utf8(query)).hexdigest()\n\n    def clear(self, also_sticky=False):\n        if not also_sticky:\n            where = 'sticky != 1'\n        else:\n            where = '1'\n        self.db.delete('cache', where=where)\n        \n    @classmethod\n    def make_sql_table(cls, db, drop=False):\n        if drop:\n            db.query('drop table if exists cache')\n        db.query(\n        'create table cache ('\n        '    _key            varchar(32) primary key,'\n        '    query           varchar(250),'\n        '    value           text,'\n        '    sticky          boolean,'\n        '    datetime        timestamp default current_timestamp,'\n        '    index (sticky),'\n        '    index (datetime)'\n        ') charset utf8 engine MyISAM;'\n        )\n\ndef get_unique_repr(query):\n    \"\"\"\n    Get a unique representation of the query. \n    \n    For example \"SQL_cache\" and \"sql_cache\" may be mapped to the same key.\n    \"\"\"\n    return getattr(query, 'uniq', query)\n\ndef set_DB(**db_params):\n    global DB\n    DB = web.database(**db_params)\n    DB.printing = False\n\ndef get(query):\n    return Cache(db=DB).get(query)\n    \ndef set(query, value, replace=False, sticky=False):\n    return Cache(db=DB).set(query, value, replace, sticky)\n\ndef clear(db=None, also_sticky=False):\n    db = db or DB\n    Cache(db=db).clear(also_sticky)\n\ndef make_sql_table(db=None, drop=False):\n    db = db or DB\n    Cache.make_sql_table(db, drop)\n\ndef _utf8(s):\n    if isinstance(s, unicode):\n        return s.encode(\"utf-8\")\n    assert isinstance(s, str)\n    return s","repo_name":"alexksikes/SQL-Cache","sub_path":"sql_cache.py","file_name":"sql_cache.py","file_ext":"py","file_size_in_byte":4875,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"20997214306","text":"import streamlit as st\nimport pandas as pd\nfrom database import viewQueryResult\n\n\ndef predef_queries():\n    q = [   \"Get members with Gym membership who have made payments\",\n            \"Get members with Sports membership who have made payments\",\n            \"Get members with Combination membership who have made payments\",\n            \"Get trainers who have scheduled training sessions\",\n            \"Get member with all their numbers and emails\"\n            ]\n    choice = st.selectbox(\"Choose a Query: \", q)\n    print(choice)\n    result = viewQueryResult(q.index(choice) + 1)\n    if (choice == q[0]):\n        df = pd.DataFrame(result, columns=(\"MembershipID\", \"FirstName\", \"LastName\", \"Amount\"))\n        st.dataframe(df)\n    elif (choice == q[1]):\n        df = pd.DataFrame(result, columns=(\"MembershipID\", \"FirstName\", \"LastName\", \"Amount\"))\n        st.dataframe(df)\n    elif (choice == q[2]):\n        df = pd.DataFrame(result, columns=(\"MembershipID\", \"FirstName\", \"LastName\", \"Amount\"))\n        st.dataframe(df)\n    elif (choice == q[3]):\n        df = pd.DataFrame(result, columns=(\"FirstName\", \"LastName\", \"TrainingSessionsCount\"))\n        st.dataframe(df)\n    elif (choice == q[4]):\n        df = pd.DataFrame(result, columns=(\"MembershipID\", \"FullName\", \"Emails\", \"PhoneNumbers\"))\n        st.dataframe(df)\n","repo_name":"sean-18/Sports_Management_System","sub_path":"python/queries.py","file_name":"queries.py","file_ext":"py","file_size_in_byte":1315,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"18317315137","text":"# Django imports\nfrom django.conf import settings\nfrom django.contrib import messages\nfrom django.utils import timezone\nfrom django.views.generic import *\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.mixins import LoginRequiredMixin\n\n# Local imports\nfrom .forms import*\nfrom .models import *\nfrom .utils import generate_order_id, redirect_to_payment_with_error\nfrom .decorators import have_active_order,login_required_with_message\nfrom .mixins import *\n\n# Third-party imports\nfrom django.db.models.functions import Random\nimport stripe\n\nstripe.api_key = settings.STRIPE_SECRET_KEY\n\n\n# Create your views here.\n\nclass HomeView(ListView):\n    model = Item\n    paginate_by = 10\n    template_name = 'store/home.html'\n\n    def get_queryset(self):\n        queryset = super().get_queryset()\n        random_items = queryset.order_by(Random())\n        return random_items\n\n\nclass ItemDetailView( DetailView):\n    model = Item\n    template_name = 'store/product.html'\n    context_object_name = 'item'\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        item = self.get_object()\n        is_in_cart = False\n\n        if self.request.user.is_authenticated:\n            order = self.request.user.order_set.filter(ordered=False).first()\n            if order:\n                is_in_cart = order.items.filter(item=item).exists()\n\n        context['is_in_cart'] = is_in_cart\n        return context\n\n\n@login_required_with_message(\"Please login to add items to the cart.\", \"store:checkout\")\n@have_active_order\ndef add_to_cart(request, slug):\n    item = get_object_or_404(Item, slug=slug)\n    order_item, created = OrderItem.objects.get_or_create(item=item, user=request.user, ordered=False)\n    order = Order.objects.filter(user=request.user, ordered=False).first()\n\n    if order and order.items.filter(item__slug=item.slug).exists():\n        order_item.quantity += 1\n        order_item.save()\n        messages.info(request, \"Item quantity updated.\")\n\n    else:\n        if not order:\n            order = Order.objects.create(user=request.user, ordered_date=timezone.now())\n        order.items.add(order_item)\n        messages.success(request, \"Item added to the cart.\")\n\n    return redirect(\"store:order-summary\")\n\n\n@login_required\n@have_active_order\ndef remove_from_cart(request, slug):\n    item = get_object_or_404(Item, slug=slug)\n    order = Order.objects.filter(user=request.user, ordered=False).first()\n\n    if order:\n        order_item = order.items.filter(item=item, user=request.user, ordered=False).first()\n        if order_item:\n            order_item.delete()\n            messages.error(request, \"Item removed from the cart\") \n\n    return redirect(\"store:order-summary\")\n\n\n@login_required\n@have_active_order\ndef decrease_from_cart(request, slug):\n    item = get_object_or_404(Item, slug=slug)\n    order_item = OrderItem.objects.filter(item=item, user=request.user, ordered=False).first()\n    if order_item:\n        if order_item.quantity > 1:\n            order_item.quantity -= 1\n            order_item.save()\n        else:\n            order_item.delete()  # Delete the order item from the database\n        messages.info(request, \"Item's quantity updated\")\n        return redirect(\"store:order-summary\")\n\n\nclass Cart(LoginRequiredMixin, View):\n    context = {} \n\n    def get(self, *args, **kwargs):\n        context = {}\n        try:\n            try:\n                order = Order.objects.get(user=self.request.user, ordered=False, delivered=False)\n                context = {'order': order}\n\n            except Order.DoesNotExist:\n                order = Order.objects.get(user=self.request.user, ordered=True, delivered=False)\n                return redirect('store:success')\n\n        except Order.DoesNotExist:\n            pass  # You can choose to handle this case if necessary\n\n        return render(self.request, 'store/order_summary.html', context)\n\n\nclass CheckoutView(LoginRequiredMixin,CheckoutMixin,View):\n\n    def post(self, request, *args, **kwargs):\n        form =CheckoutForm(request.POST )\n        order = Order.objects.get(user=self.request.user, ordered=False)\n\n        if form.is_valid():\n            billing_address = form.save(commit= False)\n            billing_address.user = self.request.user\n            billing_address.save()\n\n            order.billing_address = billing_address\n            order.save()\n            return redirect('store:payment')\n\n        else:\n            for field, errors in form.errors.items():\n                for error in errors:\n                    messages.error(self.request, f\"{field.capitalize()}: {error}\")\n\n            return redirect('store:checkout')\n\n\nclass PaymentView(LoginRequiredMixin,PaymentMixin,View):\n\n    def post(self, request, *args, **kwargs):\n        token = request.POST.get('stripeToken')\n        try:\n            order = Order.objects.get(user=request.user, ordered=False)\n            amount = 0\n            if order.has_redeemed:\n                amount = order.grand_total_with_redeem()\n            else:\n                amount = order.grand_total()\n            amount = int(amount*100)\n            \n            # Create a charge using Stripe API\n            charge = stripe.Charge.create(\n                    amount=amount,\n                    currency='usd',\n                    source=token,\n            )\n\n            # Create a payment record\n            payment = Payment.objects.create(\n                stripe_charge_id=charge['id'],\n                user=request.user,\n                amount=amount\n            )\n \n            # Mark order items as ordered\n            order_items = order.items.all()\n            for item in order_items:\n                item.ordered = True\n                item.save()\n\n            # Update order status and payment information\n            order.ordered = True\n            order.payment = payment\n            order.order_id = generate_order_id()\n            order.save()\n            return redirect('store:success')\n\n        except stripe.error.CardError as e:\n        # Handle card error\n            error_message = e.error.message\n            return redirect_to_payment_with_error(self,error_message)\n\n        except stripe.error.RateLimitError as e:\n            error_message = \"We're experiencing high traffic at the moment. Please try again later.\"\n            return redirect_to_payment_with_error(self,error_message)\n\n        except stripe.error.InvalidRequestError as e:\n            error_message = \"Invalid request. Please try again.\"\n            return redirect_to_payment_with_error(self,error_message)\n\n        except stripe.error.AuthenticationError as e:\n            error_message = \"Authentication with Stripe failed. Please contact support.\"\n            return redirect_to_payment_with_error(self,error_message)\n\n        except stripe.error.APIConnectionError as e:\n            error_message = \"Network error occurred. Please check your internet connection and try again.\"\n            return redirect_to_payment_with_error(self,error_message)\n\n        except stripe.error.StripeError as e:\n            error_message = 'An error occurred during payment processing. Please try again later.'\n            return redirect_to_payment_with_error(self,error_message)\n\n        except Exception as e:\n            error_message = 'A serious error occurred. Please contact support.'\n            order.ordered = False\n            order_items = order.items.all()\n            for item in order_items:\n                item.ordered = False\n                item.save()\n            order.save()\n            return redirect_to_payment_with_error(self,error_message)\n\n        except Order.DoesNotExist:\n            messages.error(request, \"Order does not exist. You don't have any active order.\")\n            return redirect('/')\n\n\ndef redeem_code(request):\n    if request.method == 'POST':\n        redeem_code = request.POST.get('redeem_code')  # Get the redeem code from the form\n        try:\n            redeem = RedeemCode.objects.get(code=redeem_code, is_valid=True,is_used= False)\n            order = Order.objects.get(user=request.user, ordered=False)\n            # Mark the redeem code as used\n            redeem.is_used = True\n            redeem.save()\n\n            # Set the has_redeemed flag in the order\n            order.has_redeemed = True\n            order.save()\n\n            messages.success(request, 'Redeem code applied successfully.')\n            return redirect('store:payment')\n                \n        except RedeemCode.DoesNotExist:\n            messages.error(request, 'Invalid redeem code. Please try again.')\n\n    return redirect('store:payment')\n\n\nclass SuccessView(LoginRequiredMixin,View):\n    def get(self, request, *args, **kwargs):\n        context = {}\n        order = None\n        try:\n            order = Order.objects.get(user=request.user, ordered=True, delivered=False)\n\n        except Order.DoesNotExist:\n            return redirect('store:home')\n\n        ordered_items = order.items.all()\n        context = {'ordered_items': ordered_items,'order':order}\n        return render(request, 'store/success.html', context)\n    \n        \n\n\n\ndef error_404(request, exception):\n    return render(request, 'store/error.html', status=404)\n","repo_name":"developer-kamran/Django-ecommerce-store","sub_path":"store/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9321,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"9323803289","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Feb 25 18:23:08 2022\n\n@author: Po-Yen Tung; Ziyuan Rao\n\"\"\"\n\n\nimport cv2\nimport os\nimport time\nimport random\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.optim import Adam, lr_scheduler\nfrom torch.utils.data import Dataset, DataLoader\nfrom scipy.spatial.distance import cdist\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import KFold\nfrom sklearn.cluster import KMeans\nfrom sklearn.mixture import GaussianMixture\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nfrom matplotlib.patches import Ellipse\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nimport seaborn as sns\nfrom Functions import *\n\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nroot = '/content/'  # 无效路径，只是用来合成新的路径,原文写的是/content/，所以所有模型都会存在D盘\n\nsns.set(color_codes=True)\n\n\nclass WAE(nn.Module):\n    def __init__(self, input_size):\n        super(WAE, self).__init__()\n        self.input_size = input_size\n\n        # encoder\n        self.encoder = nn.Sequential(\n                        nn.Linear(self.input_size, 80),\n                        nn.LayerNorm(80),\n                        nn.ReLU(),\n                        nn.Linear(80, 64),\n                        nn.LayerNorm(64),\n                        nn.ReLU(),\n                        nn.Linear(64, 48),\n                        nn.LayerNorm(48),\n                        nn.ReLU(),\n                        nn.Linear(48, 2),\n                        )   # 3层，神经元数量分别是80，64，48，每一层神经层后面都有标准化层以及一个激活函数层\n\n        # decoder\n        self.decoder = nn.Sequential(\n                        nn.Linear(2, 48),\n                        nn.LayerNorm(48),\n                        nn.ReLU(),\n                        nn.Linear(48, 64),\n                        nn.LayerNorm(64),\n                        nn.ReLU(),\n                        nn.Linear(64, 80),\n                        nn.LayerNorm(80),\n                        nn.ReLU(),\n                        nn.Linear(80, self.input_size),\n                        nn.Softmax(dim=1)   # (softmax along dimension 1)\n                        )  # 解码模型和编码模型的结构相同，但是层的作用相反\n        self.apply(weights_init)  # applying the initialization of weight and bias\n\n    \n    def forward(self, x):\n        z = self._encode(x)        # 编码数据\n        x_recon = self._decode(z)  # x_recon重新解码后的数据\n\n        return x_recon, z\n\n\n    def _encode(self, x):\n        return self.encoder(x)\n\n\n    def _decode(self, z):\n        return self.decoder(z)\n#%% Data loading, params - here you can play around with all different combinations of paramters to reduce the total loss. You can visualize your training history just to see how good your chosen set of hyperparameters performs\nsame_seeds(1)   # seed equals to 1\n\nparams = {\n    'num_epoch': 200,\n    'batch_size': 20,\n    'lr': 5e-4,\n    'weight_decay': 0.0,\n    'sigma': 8.0,\n    'MMD_lambda': 1e-4,\n    'model_name': 'WAE_v1',\n} # for WAE training\nall = pd.read_csv('../data_base.csv', header=0).iloc[:, 1:19].to_numpy()\n# header把第一行当作是名称，选取所有行，1到18列作为数据并且转为numpy.array格式\nraw_x = all[:696,:6]\nraw_y = all[:696,17].reshape(-1, 1)\ndataset = FeatureDataset(raw_x[:], raw_y[:])   # numpy to tensor\ndataloader = DataLoader(dataset, batch_size=params['batch_size'], shuffle=True)   # tensor to dataloader\n# print(raw_x[50:55])\n# %%train the WAE\nmodel = WAE(raw_x.shape[1]).to(device)   # initialize the model，前六列数据为特征数据，前六列为Fe,Ni,Co,Cr,V,Cu的成分比例\noptimizer = Adam(model.parameters(), lr=params['lr'], weight_decay=params['weight_decay'])   # adam 优化器\n\n\ndef train_WAE(model, optimizer, dataloader, params):  # WAE训练模型四要素——实例化模型，实例化优化器，数据以及参数\n    model_name = params['model_name']\n    num_epoch = params['num_epoch']\n    sigma = params['sigma']   # assuming the latent space follows Gaussian\n    MMD_lambda = params['MMD_lambda']   # WAE distance (maximum mean discrepancy)\n\n    folder_dir = os.path.join(root, model_name)   # a folder to save models\n    if not os.path.isdir(folder_dir):\n        os.mkdir(folder_dir)\n    loss_ = []\n    for epoch in range(num_epoch):\n        start_time = time.time()\n        total_loss = []   # save for plot, recon loss+MMD\n        total_recon = []   # binary cross entropy\n        total_MMD = []   # maximum mean discrepancy\n        \n        for i, data in enumerate(dataloader):  # enumerate作用于一个可迭代对象，一般用于for循环里面。\n            x = data[0].to(device)  # 一样是返回一个tensor，但是指定存储空间为cuda，详见torch.tensor.to的api\n            y = data[1].to(device)\n            model.train() # model goes to train mode\n            recon_x, z_tilde = model(x)   # latent space is Z_tilde，输入特征数据到模型中\n            # recon_x是某次编码下的特征（每次训练都会优化），z_tilde是原始数据（data_base）的前六列数据包括铁、镍、钴、铬和铜的比例。\n            z = sigma*torch.randn(z_tilde.size()).to(device)   # z is sampled from a Gaussian that has the same dimension (but no relation to z_tilde).\n\n            recon_loss = F.binary_cross_entropy(recon_x, x, reduction='mean')\n            # 算编码解码后的recon_x和原来的数据x之间的区别,reduction=‘mean’设置最后对输出进行一个平均化。\n            # recon_loss = F.mse_loss(recon_x, x, reduction='mean')\n            # recon_loss = F.l1_loss(recon_x, x, reduction='mean')\n            \n            MMD_loss = imq_kernel(z_tilde, z, h_dim=2).to(device)   # W-distance between z_tilde and z\n            MMD_loss = MMD_loss / x.size(0)   # averaging, because recon loss is mean.\n            loss = recon_loss + MMD_loss * MMD_lambda   # MM_lambda: learning-rate alike, hyperparamer\n\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n            # 优化的基本步骤，\n            # zero_grad在进行后向传播之前将梯度设置为零，避免一个batch中梯度计算受到上一个batch的影响\n            # loss.backward实施后向传播算法计算梯度\n            # 基于backward算出来的梯度以及超参数进行一次weight和bias的参数更新。每一个batch会进行一次这样的计算\n\n            total_loss.append(loss.item())  # from tensor to values\n            total_recon.append(recon_loss.item())\n            total_MMD.append(MMD_loss.item())  # 用item()从张量中取出数据并且存在列表中\n\n        avg_loss = sum(total_loss)/len(total_loss)\n        avg_recon = sum(total_recon)/len(total_recon)\n        avg_MMD = sum(total_MMD)/len(total_MMD)\n        loss_.append(avg_loss)  # 求三种类型损失函数的平均值\n\n        #scheduler.step(avg_loss)\n\n        print('[{:03}/{:03}] loss: {:.6f} Recon_loss: {:.6f}, MMD_loss:{:.6f}, time: {:.3f} sec'.format(\\\n                                        epoch+1, num_epoch, \\\n                                        avg_loss, \\\n                                        avg_recon, avg_MMD, time.time() - start_time))\n        # 优化过程的log内容\n\n        # save the model every 5 epoches 每5个epoch存一次模型到\n        if (epoch+1) % 5 == 0:\n            save_model_dir = str(model_name + \"_{}.pth\".format(epoch+1))\n            torch.save(model.state_dict(), os.path.join(folder_dir, save_model_dir))\n    return loss_ # 返回损失值列表\n\nloss_=train_WAE(model, optimizer, dataloader, params)\n\nplt.figure()  # 打开画布\n\n\"\"\"\nsns.set_style('ticks')  # 设置背景为\nplt.plot(range(len(loss_)), loss_)\nplt.show()  # 这个画出来的结果是总loss的变化图，类似原论文SI中figure S3（si中的图是reconstruction的loss）\n\"\"\"\n\n# %%Double check on the reconstructed compositions\n# one way to find out whether WAE (or any other VAE) has learned the repsentation is\n# to compare the reconstructed and original compositions.if you are not happy with the \n# reconstruction. go back to the previous step and change the params.\n# double check on the recontructed compositions\n# t = time.localtime()\n\"\"\"\nmodel_dir = os.path.join(root, '{}/{}_{}.pth'.format(params['model_name'], params['model_name'],params['num_epoch']))#load your model\nmodel = WAE(raw_x.shape[1]).to(device)\nmodel.load_state_dict(torch.load(model_dir))\nmodel.eval()  # 把模型的模式改为评估模式（前面设置为了训练模式）\nwith torch.no_grad(): #关闭梯度计算\n    test = torch.FloatTensor(raw_x).to(device)  # 转为cpu格式的张量，因为WAE是训练解码后的特征和原来的特征之间映射的联系，因此原始特征数据是目标数据。\n    recon_x, z = model(test)  # 输入测试模式的数据\n    recon_x = model.decoder(z)  # 将z解码为原来的数据，后面用于判断z和recon_x之间的\n    recon_x = recon_x.cpu().detach().numpy()\n\ncolumn_name = ['Fe', 'Ni', 'Co', 'Cr', 'V', 'Cu']  # 'VEC','AR1','AR2','PE','Density','TC','MP','FI','SI','TI','M']\n# recon_x = (recon_x * (max-min)) + min\npd.DataFrame(recon_x.round(3), columns=column_name).loc[690:695]\ncsv_data = pd.read_csv('../data_base.csv', header=0).iloc[:,1:19]\ncsv_data.iloc[690:702,:6].round(3)\n\"\"\"\n\n\n\"\"\" #暂时将这里的代码注释掉\n# %%Visualize the WAE latent space\n# Here we assign different colors to alloy with and without Copper, as we expected them to differ significantly in the latent space.\nsns.set_style('ticks')\nmodel = WAE(raw_x.shape[1]).to(device)\nmodel.load_state_dict(torch.load(model_dir)) \ndataset = FeatureDataset(raw_x[:], raw_y[:]) \nlatents = get_latents(model, dataset) \n\nlow_cu = raw_x[:,5] < 0.05\nlow_cu_latent = latents[low_cu]\nlow_cu_color = raw_y[:][low_cu]\n\nhigh_cu = raw_x[:,5] >= 0.05\nhigh_cu_latent = latents[high_cu]\nhigh_cu_color = raw_y[:][high_cu]\n\n\n# figure settings\nfig, axs = plt.subplots(figsize = (3, 3),dpi=200)\n\n# axs.set_aspect(1.)\n# axs.set_ylim(-7,7)\n# axs.set_xlim(-11,5)\n\naxs.set_yticks(np.arange(-6, 8, step=2))\naxs.set_xticks(np.arange(-10, 5, step=2))\n\naxs.set_yticklabels(np.arange(-6, 8, step=2), fontsize=7)\naxs.set_xticklabels(np.arange(-10, 5, step=2), fontsize=7)\n\n\nfor axis in ['top','bottom','left','right']:\n  axs.spines[axis].set_linewidth(1.)\n\n\naxs.tick_params(axis='both', which='major', top=False, labeltop=False, direction='out', width=1., length=4)\naxs.tick_params(axis='both', which='major', right=False, labelright=False, direction='out', width=1., length=4)\n\n# scatter1 = axs.scatter(low_cu_latent[:,0], low_cu_latent[:,1], c=low_cu_color, alpha=.75, s=10, linewidths=0, cmap='viridis')\n# scatter2 = axs.scatter(high_cu_latent[:,0], high_cu_latent[:,1], c=high_cu_color, alpha=.75, s=9, linewidths=0, cmap='Reds')\n\nscatter1 = axs.scatter(low_cu_latent[:,0], low_cu_latent[:,1], c='steelblue', alpha=.55, s=8, linewidths=0, label='Alloys w/o Cu')\nscatter2 = axs.scatter(high_cu_latent[:,0], high_cu_latent[:,1], c='firebrick', alpha=.65, s=14, linewidths=0, marker='^', label='Alloys w/ Cu')\n# scatter3 = axs.scatter(latents_exp_4[:,0], latents_exp_4[:,1], alpha=1., s=10, linewidths=.75, edgecolors='darkslategray', facecolors='w')#, label='New FeCoNiCr HEAs')\n# scatter4 = axs.scatter(latents_exp_5[:,0], latents_exp_5[:,1], alpha=1., s=16, linewidths=.75, edgecolors='darkred', facecolors='w',marker='^')#, label='New FeCoNiCrCu HEAs')\n\nhandles,labels = axs.get_legend_handles_labels()\nhandles = handles[::1]\nlabels = labels[::1]\n\nlegend_properties = {'size':7.5}\naxs.legend(handles, labels, loc='upper right', bbox_to_anchor=(1.015,1.017), handletextpad=-0.3, frameon=False, prop=legend_properties)\n# axs.legend(handles, labels, loc='upper left', bbox_to_anchor=(-0.045,1.017), handletextpad=-0.3, frameon=False, prop=legend_properties)\n\n# rect = patches.Rectangle((-19.4,15.0), 18, 4.5, linewidth=0,edgecolor=None,facecolor='k', alpha=0.03,linestyle=None,zorder=-10) #(0.2,15.4), 14, 4.1\n# axs.add_patch(rect)\n\nfig.savefig('Figure3_a.tif', bbox_inches = 'tight', pad_inches=0.01)\n\"\"\"","repo_name":"caijunfei-max/code-reading","sub_path":"Machine-learning-enabled-high-entropy-alloy-discovery/HEA-COGS/WAE.py","file_name":"WAE.py","file_ext":"py","file_size_in_byte":12274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31656239658","text":"from superdesk.tests import TestCase\nfrom .app_initialize import AppInitializeWithDataCommand\nfrom .app_scaffold_data import AppScaffoldDataCommand\nfrom superdesk import get_resource_service\n\n\nclass AppInitializeWithDataCommandTestCase(TestCase):\n\n    def setUp(self):\n        super().setUp()\n\n    def test_app_initialization(self):\n        with self.app.app_context():\n            command = AppInitializeWithDataCommand()\n            result = command.run()\n            self.assertEquals(result, 0)\n\n    def test_app_initialization_multiple_loads(self):\n        with self.app.app_context():\n            command = AppInitializeWithDataCommand()\n            result = command.run()\n            self.assertEquals(result, 0)\n            result = command.run()\n            self.assertEquals(result, 0)\n\n    def data_scaffolding_test(self):\n        with self.app.app_context():\n            command = AppInitializeWithDataCommand()\n            result = command.run()\n            self.assertEquals(result, 0)\n\n            service = get_resource_service('text_archive')\n            docs = [{\n                'type': 'text',\n                'abstract': 'test abstract {}'.format(x),\n                'headline': 'test headline {}'.format(x),\n                'body_html': 'test long story body {}'.format(x)\n            } for x in range(0, 40)]\n            service.post(docs)\n\n            stories_per_desk = 2\n            existing_desks = 1\n            command = AppScaffoldDataCommand()\n            result = command.run(stories_per_desk)\n            self.assertEquals(result, 0)\n\n            cursor = get_resource_service('desks').get_from_mongo(None, {})\n            self.assertEquals(cursor.count(), existing_desks)\n\n            cursor = get_resource_service('archive').get_from_mongo(None, {})\n            self.assertEquals(cursor.count(), existing_desks * stories_per_desk)\n","repo_name":"verifiedpixel/verifiedpixel","sub_path":"server/apps/prepopulate/app_initialization_test.py","file_name":"app_initialization_test.py","file_ext":"py","file_size_in_byte":1866,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"86"}
+{"seq_id":"13320758560","text":"import os\nfrom datetime import datetime\nfrom types import NoneType\n\nimport psycopg2\n\nfrom urllib.parse import urlparse\n\nimport inspect\n\nfrom app.utils import error, success\n\ntables = None\n\nclass SQL:\n    def __init__(self):\n        uri = os.environ['DATABASE_URL']\n        result = urlparse(uri)\n        username = result.username\n        password = result.password\n        database = result.path[1:]\n        hostname = result.hostname\n        port = result.port\n        self.conn = psycopg2.connect(\n            host=hostname,\n            database=database,\n            user=username,\n            password=password,\n            port=port)\n        self.cur = self.conn.cursor()\n        \n    def dropTable(self, name):\n        query = f\"\"\"\n            DROP TABLE IF EXISTS {name}\n        \"\"\"\n        result = self.tryExecute(query)\n        if result:\n            return result\n        self.conn.commit()\n        return success(f'Dropped table {name}')\n\n    def createTable(self, name):\n        text = []\n        columns = tables[name]\n        for n, val in columns.items():\n            text.append(f'{n} {val}')\n        text = ',\\n'.join(text)\n        query = f\"\"\"\n            CREATE TABLE IF NOT EXISTS {name} (\n                {text});\n        \"\"\"\n        result = self.tryExecute(query)\n        if result:\n            return result\n        self.conn.commit()\n        return success(f'Created table {name}')\n    \n    def insert(self, table, columns: dict):\n        # columns should be columnName: value\n        colsList = ', '.join(list(columns))\n        valsList = [f\"'{i}'\" if isinstance(i, str) else f\"{i}\" for i in columns.values()]\n        valsText = ',\\n'.join(valsList)\n        query = f\"\"\"\n            INSERT into {table} ({colsList})\n            VALUES (\n                {valsText}\n            )\n        \"\"\"\n        result = self.tryExecute(query)\n        if result:\n            return result\n        self.conn.commit()\n        return success(f'Inserted into {table}')\n    \n    def delete(self, table, where=None):\n        if where:\n            query = f\"\"\"\n                DELETE from {table} where {where}\n            \"\"\"\n        else:\n            query = f\"\"\"\n                DELETE from {table}\n            \"\"\"\n        result = self.tryExecute(query)\n        if result:\n            return result\n        self.conn.commit()\n        return success('Deleted rows')\n\n    def select(self, table, where=None):\n        if where:\n            query = f\"\"\"\n                SELECT * from {table} where {where}\n            \"\"\"\n        else:\n            query = f\"\"\"\n                SELECT * from {table}\n            \"\"\"\n        result = self.tryExecute(query)\n        if result:\n            return result\n        retList = []\n        for s in self.cur.fetchall():\n            row = {}\n            for (colname, cast), value in zip(tables[table].items(), s):\n                row.update({colname: self.typeCast(value, cast)[0]})\n            retList.append(row)\n        return success(retList)\n    \n    def typeCast(self, val, typeString):\n        # returns a value, typecast via typeString, and also the type\n        if 'TEXT' in typeString:\n            return str(val), str\n        elif 'INTEGER' in typeString or 'SERIAL' in typeString:\n            return int(val), int\n        elif 'BOOLEAN' in typeString:\n            return bool(val), bool\n        elif 'TIMESTAMP' in typeString:\n            return datetime.strptime(val, r\"%Y-%m-%d %H:%M:%S.%f\"), datetime\n        return val, NoneType\n\n    def rollback(self, data: str, e=None):\n        self.cur.execute('ROLLBACK')\n        self.conn.commit()\n        return error('Bad Request', data, e)\n\n    def tryExecute(self, query):\n        try:\n            self.cur.execute(query)\n        except Exception as e:\n            return self.rollback(f'{inspect.stack()[1].function}\\n{query}', e)\n","repo_name":"masterbbud/heroku-test","sub_path":"app/sql.py","file_name":"sql.py","file_ext":"py","file_size_in_byte":3837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"43574249745","text":"import numpy as np\nimport pandas as pd\nimport sys, os\nimport pywt\nimport wfdb\nfrom scipy import signal\n\ntarget_lv = 4\nfs_resampling = 360\nduration = 0.15 # 150ms\n\nclass DB_loading:\n    def __init__(self):\n        # path definition\n        path_base = os.path.dirname(os.path.abspath(os.path.dirname(__file__)))\n        self.path_database = path_base + '\\\\database\\\\'\n        self.report_table = pd.read_excel(path_base + '\\\\ecg_databases.xlsx')\n\n    ### filtering method\n    def dwt_idwt(self, array, wavelet='db3', level=9):\n        coeffs = pywt.wavedec(array, wavelet, level=level)\n        coeffs[0] = np.zeros_like(coeffs[0])\n        array_filtered = pywt.waverec(coeffs, wavelet)\n        return array_filtered\n\n    def lowpass_filter(self, array, fs, cutoff=40, order=5, remove_lag=True):\n        nyq = 0.5 * fs\n        normal_cutoff = cutoff / nyq\n        sos = signal.butter(order, normal_cutoff, btype='low', output='sos')\n        array_filtered = signal.sosfilt(sos, array)\n        if remove_lag == True:\n            # filter time delay -> to reduce zero lag\n            array_filtered = signal.sosfilt(sos, array_filtered[::-1])\n            return array_filtered[::-1][:len(array)]\n        else:\n            return array_filtered[:len(array)]\n\n    def filtering(self, array, fs=fs_resampling):\n        array_filtered = array.copy()\n        array_filtered = self.dwt_idwt(array_filtered)\n        array_filtered = self.lowpass_filter(array_filtered, fs)\n        return array_filtered\n\n    ### resampling\n    def resample_ecg(self, array, fs_pre, fs_post=fs_resampling):\n        t_len = len(array)\n        new_len = int(fs_post/fs_pre*t_len)\n        array_resampled = signal.resample(array, new_len)\n        array_resampled = self.filtering(array_resampled)\n        return array_resampled\n\n    def resample_label(self, label, fs_pre, fs_post=fs_resampling):\n        label_resampled = (label*fs_post/fs_pre).astype('int')\n        return label_resampled\n\n    ### transformation\n    def normalization(self, array):\n        root_squared_mean = np.mean(array**2)**0.5\n        array_norm = array/root_squared_mean/16\n        return array_norm\n\n    def sw_transform(self, array):\n        len_padding = -len(array)%2**target_lv\n        padded = np.pad(array, (0, len_padding), 'edge')\n        coeff_swt = pywt.swt(padded, 'sym4', level=target_lv, trim_approx=True)\n        # [cAn, cDn, ..., cD2, cD1]\n        coeff_swt.reverse()\n        # [cD1, cD2, ... ,cDn, cAn]\n\n        feature = coeff_swt[3]\n        feature = feature[:len(array)]\n        feature = self.normalization(feature)\n\n        diff = np.diff(array, append=array[-1])\n        diff = self.normalization(diff)\n\n        merged_feature = np.stack([feature, diff], axis=1)\n        return merged_feature\n\n    def transform(self, array, use_swt=True):\n        if use_swt == False:\n            diff = np.diff(array, append=array[-1])\n            diff = self.normalization(diff)\n            feature = diff.reshape(-1, 1)\n            return feature\n        else:\n            feature = self.sw_transform(array)\n        return feature\n\n    def make_target(self, array, label, w_size=None):\n        if w_size is None:\n            w_size = int(fs_resampling*duration)\n        target = np.zeros(array.shape[0])\n        n_label = np.array([x for x in label if x < array.shape[0]]).astype('int')\n        target[n_label] = 1\n        target = np.convolve(target, np.ones(w_size), mode='same')\n        target = np.where(target > 1, 1, target) # ?\n        return target\n\n    ### loading\n    def return_idx(self, db_name):\n        list_idx = self.report_table[(self.report_table['Database'] == db_name) & (self.report_table['Select'] == 1)].index\n        return list_idx\n\n    def nan_helper(self, array):\n        nans, x = np.isnan(array), lambda z: z.nonzero()[0]\n        array[nans] = np.interp(x(nans), x(~nans), array[~nans])\n        return array\n\n    def load_annotation(self, ann):\n        # https://archive.physionet.org/physiobank/annotations.shtml\n        beat_labels = ['N', 'L', 'R', 'B', 'A', 'a', 'J', 'S', 'V', 'r', 'F', 'e', 'j', 'n', 'E', '/', 'f', 'Q', '?']\n        in_beat_labels = np.in1d(ann.symbol, beat_labels)\n        sorted_anno = ann.sample[in_beat_labels]\n        sorted_anno = np.unique(sorted_anno)\n        sorted_anno = sorted_anno[sorted_anno >= 0]\n        return sorted_anno\n\n    def load_data(self, order, verbose=False):\n        self.order = order\n        self.table_loc = self.report_table[self.report_table.index == order].index[0]\n        database = self.report_table.loc[self.table_loc, 'Database']\n        patient = self.report_table.loc[self.table_loc, 'Patient']\n        num = self.report_table.loc[self.table_loc, 'Num']\n        path_file = self.path_database+database+'/'+str(patient)\n\n        if verbose == True:\n            print('Database : {0}, Patient : {1}'.format(database, patient))\n\n        if database != 'TELE':\n            # load data\n            record = wfdb.rdsamp(path_file)\n            ecg = record[0][:, num]\n            fs = record[1]['fs']\n\n            # load annotation\n            ann = wfdb.rdann(self.path_database+database+'/'+str(patient), 'atr')\n            label = self.load_annotation(ann)\n            mask = np.array([])\n\n            if database=='QTDB': # In QTDB database, some files had a length of 224999, not 225000.\n                if len(ecg)==224999: \n                    ecg = np.pad(ecg, (0, 1), 'edge')\n            elif database=='MIT_BIH': # In MIT_BIH database, ventricular flutter areas were removed.\n                if patient == '207': \n                    mask_1 = np.arange(14540, 22100)\n                    mask_2 = np.arange(87060, 101400)\n                    mask_3 = np.arange(554560, 589930)\n                    mask = np.concatenate([mask_1, mask_2, mask_3], axis=0)\n                    ecg[mask] = np.nan\n                    ecg = self.nan_helper(ecg)\n                    label = np.array([x for x in label if x not in mask])\n\n            elif database=='MIT_BIH_ST': # In MIT_BIH_ST database, areas without peak annotation were removed.\n                if patient == '319': \n                    mask = np.arange(142300, 186300)\n                    ecg[mask] = np.nan\n                    ecg = self.nan_helper(ecg)\n\n        else:\n            record_temp = []\n            with open(path_file+'.dat') as file:\n                dat = file.read().splitlines()\n                for d in dat:\n                    row = np.array(d.split(','), dtype='float')\n                    record_temp.append(row)\n            record_temp = np.stack(record_temp, axis=1)\n            ecg = record_temp[0,:]\n            label = record_temp[1,:]\n            label = np.where(label == 1)[0]\n            fs = 500\n            mask = record_temp[2,:] + record_temp[3,:]\n            mask = np.where(mask > 0)[0]\n\n            if patient == '244_291': # In TELE database, areas without annotation were masked.\n                mask_add = np.arange(0, 7000)\n                mask = np.concatenate([mask, mask_add], axis=0)\n            if patient == '250_300':\n                mask_add = np.arange(0, 8900)\n                mask = np.concatenate([mask, mask_add], axis=0)\n\n            ecg[mask] = np.nan\n            ecg = self.nan_helper(ecg)\n            label = np.array([x for x in label if x not in mask])\n\n        return ecg, label, fs, mask\n\n    # pipeline\n    def create_set(self, name_database, use_swt=True): \n        list_idx = self.return_idx(name_database)\n        self.metadata_patient = self.report_table.loc[list_idx,:]['Patient'].tolist()\n\n        set_dict = dict()\n        set_dict['ecg'] = []\n        set_dict['label'] = []\n        set_dict['feature'] = []\n        set_dict['target'] = []\n        set_dict['mask_array'] = []\n\n        for n, idx in enumerate(list_idx):\n            print('... Processing  {0} / {1}'.format(n+1, len(list_idx)))\n            ecg, label, fs, mask = self.load_data(idx)\n            ecg = self.resample_ecg(ecg, fs, fs_resampling)\n            label = self.resample_label(label, fs, fs_resampling)\n            mask = self.resample_label(mask, fs, fs_resampling)\n\n            feature = self.transform(ecg, use_swt=use_swt)\n            target = self.make_target(feature, label)\n            mask_array = self.make_target(feature, mask, w_size=25)\n\n            feature_diff = np.abs(feature[:,1]) # ignore flat areas (sum of absolute differences for 2 seconds < 0.1mV)\n            area_ignore = np.convolve(feature_diff, np.ones(fs_resampling*2), mode='same')\n            area_ignore = np.where(area_ignore < 0.1, 1, 0)\n            mask_array += area_ignore\n            mask_array = np.where(mask_array>0, 1, 0)\n\n            set_dict['ecg'].append(ecg)\n            set_dict['label'].append(label)\n            set_dict['feature'].append(feature)\n            set_dict['target'].append(target)\n            set_dict['mask_array'].append(mask_array)\n\n        return set_dict\n","repo_name":"dactylogram/ECG_peak_detection","sub_path":"utils/db_loader.py","file_name":"db_loader.py","file_ext":"py","file_size_in_byte":8909,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"86"}
+{"seq_id":"38737562437","text":"\"\"\"\nBuild a dataset for crossmap from wikipedia articles.\n\nWarning: This prorcedure performs very crude cleanup on wikipedia extracts.\nThe procedure tries to remove some html formatting, but the cleaning is\nad-hoc and incomplete. The attempt is to catch many of the common markups\nand to produce output that is better than without any cleanup at all.\nBut the aim is not to produce a fully clean, or even html-valid, output.\n\"\"\"\n\nimport gzip\nimport json\nimport re\nimport yaml\nfrom logging import info, error, warning\nfrom os.path import join, exists\nfrom .download import wikipedia_pageids_file, wikipedia_exintros_file\nfrom .download import read_pageids\n\n\n# ad-hoc patterns to remove some html tags\nhtml_patterns = [\"<p>|</p>|<span>|</span>\",\n                 \"<sup>|</sup>|<sub>|</sub>\",\n                 \"<b>|</b>|<i>|</i>|<a>|</a>\",\n                 \"<mi>|</mi>|<mo>|</mo>|<mn>|</mn>\",\n                 \"<msup>|</msup>|<msub>|</msub>\",\n                 \"<br>|<br/>|<semantics>|</semantics>\",\n                 \"<math>|</math>|<mstyle>|</mstyle>|<mrow>|</mrow>\",\n                 \"<annotation>|</annotation>\"]\nhtml_re = [re.compile(_) for _ in html_patterns]\n\n\ndef _rough_clean(t):\n    \"\"\"remove some html marking and whitespace\"\"\"\n\n    result = t\n    for compiled_pattern in html_re:\n        result = compiled_pattern.sub(\"\", result)\n    result = re.sub(\"\\n|\\t\", \" \", result)\n    result = re.sub(\"\\s+\", \" \", result)\n    return result.strip()\n\n\ndef _make_wikipedia_item(data, category):\n    \"\"\"create a dictionary with data, metadata\"\"\"\n\n    id = \"W:\"+str(data[\"pageid\"])\n    result = dict(title=data[\"title\"],\n                  data=_rough_clean(data[\"extract\"]),\n                  metadata=dict(category=category))\n    return id, result\n\n\ndef build_category_items(filepath, category):\n    \"\"\"read downloaded and build crossmap items\"\"\"\n\n    result = dict()\n    with gzip.open(filepath, \"rt\") as f:\n        data = json.load(f)\n    for d in data:\n        id, content = _make_wikipedia_item(d, category)\n        result[id] = content\n    return result\n\n\ndef build_wikipedia_dataset(config):\n    \"\"\"assemble data from wikipedia articles into crossmap datasets\"\"\"\n\n    category_name = \" \".join(config.wikipedia_category)\n    out_file = join(config.outdir, config.name + \".yaml.gz\")\n    if exists(out_file):\n        info(\"output file already exists: \" + out_file)\n        return\n\n    ids_file = wikipedia_pageids_file(config, category_name)\n    if not exists(ids_file):\n        error(\"file with page ids does not exist\")\n        return\n\n    # load mapping from categories to page ids\n    pages = read_pageids(ids_file)\n\n    skip = set()\n    exclude_pattern = config.wikipedia_exclude\n    with gzip.open(out_file, \"wt\") as out:\n        for category in pages.keys():\n            if re.search(exclude_pattern, category):\n                continue\n            category_file = wikipedia_exintros_file(config, category)\n            if not exists(category_file):\n                warning(\"downloaded data does not exist: \"+category)\n                continue\n            info(\"Processing: \" + category)\n            data = build_category_items(category_file, category)\n            if len(data) == 0:\n                continue\n            # avoid adding same article through several categories\n            for k in list(data.keys()):\n                if k in skip:\n                    data.pop(k)\n            skip.update(data.keys())\n            out.write(yaml.dump(data))\n\n","repo_name":"tkonopka/crossmap","sub_path":"crossprep/wikipedia/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":3456,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"4539339577","text":"from sql_alchemy import banco\n\nclass UsuarioModel(banco.Model):\n    __tablename__ = 'usuarios'\n\n    usuario_id = banco.Column(banco.Integer, primary_key=True)\n    login = banco.Column(banco.String(50))\n    senha = banco.Column(banco.String(50))\n\n    def __init__(self, login, senha):\n        self.login = login\n        self.senha = senha\n    \n    def json(self):\n        return{\n            'usuario_id': self.usuario_id,\n            'login': self.login\n        }\n    \n    @classmethod\n    def buscar_usuario(cls, usuario_id):\n        usuario = cls.query.filter_by(usuario_id=usuario_id).first()\n        if usuario:\n            return usuario\n        return None\n    \n    @classmethod\n    def buscar_por_login(cls, login):\n        usuario = cls.query.filter_by(login=login).first()\n        if usuario:\n            return usuario\n        return None\n    \n    def save_usuario(self):\n        banco.session.add(self)\n        banco.session.commit()\n    \n    def delete_usuario(self):\n        banco.session.delete(self)\n        banco.session.commit()","repo_name":"Orozimbo1/REST-APIs_Flask","sub_path":"REST_APIs/models/usuario.py","file_name":"usuario.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"8620225662","text":"import importlib\nimport os\nfrom argparse import ArgumentParser\nfrom os import path\n\nfrom pydantic import ValidationError\n\nfrom mlgame.argument.tool import get_data_from_json_file\nfrom mlgame.argument.model import UserNumConfig\nfrom mlgame.core.exceptions import GameConfigError\n\n\ndef create_game_params_parser(parser_config: dict):\n    \"\"\"\n    Generate `argparse.ArgumentParser` from `parser_config`\n\n    @param parser_config A dictionary carries parameters for creating `ArgumentParser`.\n           The key \"()\" specifies parameters for constructor of `ArgumentParser`,\n           its value is a dictionary of which the key is the name of parameter and\n           the value is the value to be passed to that parameter.\n           The remaining keys of `parser_config` specifies arguments to be added to the parser,\n           which `ArgumentParser.add_argument() is invoked`. The key is the name\n           of the argument, and the value is similar to the \"()\"\n           but for the `add_argument()`. Note that the name of the key is used as the name\n           of the argument, but if \"name_or_flags\" is specified in the dictionary of it,\n           it will be passed to the `add_argument()` instead. The value of \"name_or_flags\"\n           must be a tuple.\n           An example of `parser_config`:\n           ```\n            {\n                \"()\": {\n                    \"usage\": \"game <difficulty> <level>\"\n                },\n                \"difficulty\": {\n                    \"choices\": [\"EASY\", \"NORMAL\"],\n                    \"metavar\": \"difficulty\",\n                    \"help\": \"Specify the game style. Choices: %(choices)s\"\n                },\n                \"level\": {\n                    \"type\": int,\n                    \"help\": \"Specify the level map\"\n                },\n            }\n           ```\n    \"\"\"\n    if parser_config.get(\"()\"):\n        parser = ArgumentParser(**parser_config[\"()\"])\n    else:\n        parser = ArgumentParser()\n\n    for arg_name in parser_config.keys():\n        if arg_name != \"()\":\n            arg_config = parser_config[arg_name].copy()\n\n            name_or_flag = arg_config.pop(\"name_or_flags\", None)\n            if not name_or_flag:\n                name_or_flag = (arg_name,)\n\n            parser.add_argument(*name_or_flag, **arg_config)\n\n    return parser\n\ndef comma_separated_list(value):\n    return value.split(',')\ndef parse_game_config_data(game_config_data: dict):\n    \"\"\"\n    parse game parameter and generate data for argument parser\n    \"\"\"\n    # TODO to optimize\n    result = {}\n    params = game_config_data[\"game_params\"]\n    game_usage = \"%(prog)s \"\n    for param in params:\n        obj = {\n            \"metavar\": param[\"verbose\"],\n            \"help\": param[\"help\"]\n\n        }\n        if param[\"type\"] == \"int\":\n            obj[\"type\"] = int\n        elif param[\"type\"] == \"str\":\n            obj[\"type\"] = str\n        elif param[\"type\"] == \"list\":\n            obj[\"type\"] = comma_separated_list\n        elif param[\"type\"] == \"path\":\n            obj[\"type\"] = os.path.abspath\n\n        if \"default\" in param:\n            obj[\"nargs\"] = \"?\"\n            obj[\"default\"] = param[\"default\"]\n            game_usage += \"[\" + param[\"name\"] + \"] \"\n        else:\n            game_usage += \"<\" + param[\"name\"] + \"> \"\n\n        if \"choices\" in param:\n            choices = []\n            for choice in param[\"choices\"]:\n                if type(choice) == dict:\n                    choices.append(choice[\"value\"])\n                else:\n                    choices.append(choice)\n            obj[\"choices\"] = choices\n        if \"min\" in param and \"max\" in param:\n            obj[\"choices\"] = range(param[\"min\"], param[\"max\"] + 1)\n        \"\"\"\n        ex -t --time_to_play\n        \"\"\"\n        if \"flag\" in param:\n            obj[\"name_or_flags\"] = (f'-{param[\"flag\"]}', f'--{param[\"name\"]}')\n        else:\n            obj[\"name_or_flags\"] = (f'--{param[\"name\"]}',)\n\n        result[param[\"name\"]] = obj\n    result[\"()\"] = {\n        \"prog\": game_config_data[\"game_name\"],\n        \"game_usage\": game_usage\n    }\n    return result\n\n\nclass GameConfig:\n    \"\"\"\n    The data class storing the game defined config\n    Included game_config.json game_cls\n    \"\"\"\n\n    def __init__(self, game_folder: str):\n        \"\"\"\n        Parse the game defined config and generate a `GameConfig` instance\n        \"\"\"\n        game_config = self._load_game_config(game_folder)\n\n        config_data = get_data_from_json_file(path.join(game_folder, \"game_config.json\"))\n        try:\n            self.game_version = config_data[\"version\"]\n            self.user_num_config = UserNumConfig(**config_data[\"user_num\"])\n            self._process_game_param_dict(config_data)\n            self.game_config_parser = create_game_params_parser(self._config_to_create_parser)\n            self.game_cls = None\n            game_setup = getattr(game_config, \"GAME_SETUP\")\n            self.game_cls = game_setup[\"game\"]\n        except AttributeError:\n            raise GameConfigError(\"Missing variable 'GAME_SETUP' in the config.py\")\n        except KeyError:\n            raise GameConfigError(f\"game_config.json in {game_folder} \"\n                                  f\"should contains 'user_num', 'version', 'game_params' \")\n        except ValidationError:\n            raise GameConfigError(f\"`user_num` in game_config.json should contains 'min' and 'max' \"\n                                  f\"and user_num['min'] < user_num['max']\")\n        # self._process_game_setup_dict()\n\n    def parse_game_params(self, game_params) -> dict:\n        return self.game_config_parser.parse_args(game_params).__dict__\n\n    def _load_game_config(self, game_folder):\n        \"\"\"\n        Load the game config\n        \"\"\"\n        try:\n            # game_config = importlib.import_module(f\"{game_folder}.config\")\n\n            spec = importlib.util.spec_from_file_location(\"config\", path.join(game_folder, \"config.py\").__str__())\n            module = importlib.util.module_from_spec(spec)\n            spec.loader.exec_module(module)\n            game_config = module\n        except ModuleNotFoundError as e:\n            # print which module is not found or installed at which game_folder\n            failed_module_name = e.__str__().split(\"'\")[1]\n            msg = f\"Module '{failed_module_name}' is not found in game process\"\n            raise GameConfigError(msg)\n        else:\n            return game_config\n\n    def _process_game_param_dict(self, config_data):\n        \"\"\"\n        Convert some fields in `GAME_PARAMS`\n        \"\"\"\n        self._config_to_create_parser = parse_game_config_data(config_data)\n\n        # Append the prefix of MLGame.py usage to the `game_usage`\n        # and set it to the `usage`\n        if self._config_to_create_parser.get(\"()\") and self._config_to_create_parser[\"()\"].get(\"game_usage\"):\n            game_usage = str(self._config_to_create_parser[\"()\"].pop(\"game_usage\"))\n            self._config_to_create_parser[\"()\"][\"usage\"] = \"python MLGame.py [options] \" + game_usage\n\n        # If the game not specify \"--version\" flag,\n        # try to convert `GAME_VERSION` to a flag\n        if not self._config_to_create_parser.get(\"--version\"):\n            self._config_to_create_parser[\"--version\"] = {\n                \"action\": \"version\",\n                \"version\": self.game_version\n            }\n","repo_name":"PAIA-Playful-AI-Arena/MLGame","sub_path":"mlgame/argument/game_argument.py","file_name":"game_argument.py","file_ext":"py","file_size_in_byte":7306,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"86"}
+{"seq_id":"74389705564","text":"import re\nimport time\nimport queue\nimport threading\n\nimport cv2\nimport numpy as np\n\n\n__all__ = [ \"MediaPlayer\", \"MediaType\" ]\n\n\nclass MediaType:\n    \"\"\"Helper class for referring meida type\"\"\"\n    VIDEO = 1\n    STREAM = 2\n\n\nclass MediaPlayer:\n    \"\"\"General media player\n\n    Arguments:\n        src (str or int): opencv video source\n        queue_size (int, optional): size of frame buffering queue, default 64\n    \"\"\"\n    STATE_START = 1\n    STATE_PAUSE = 2\n    STATE_STOP = 3\n\n    def __init__(self, src, queue_size=1):\n        # Opencv capture\n        # =====================================================\n        self.capture = cv2.VideoCapture(src if not src.isdecimal() else int(src))\n        self.capture_lock = threading.Lock()\n\n        if not self.capture.isOpened():\n            raise RuntimeError(\"Cannot open camera source '{}'\".format(src))\n\n        # Player metadata\n        # =====================================================\n        src = str(src)\n        self.src = src\n        self.queue_size = queue_size\n\n        # Check source type\n        if src.startswith(\"http\") or src.startswith(\"rtsp\") or src.isdecimal():\n            self.stype = MediaType.STREAM\n            self.frame_queue = queue.Queue(maxsize=1)\n        else:\n            self.stype = MediaType.VIDEO\n            self.frame_queue = queue.Queue(maxsize=queue_size)\n\n\n        self.fps = int(self.capture.get(cv2.CAP_PROP_FPS))\n        self.total_frames = int(self.capture.get(cv2.CAP_PROP_FRAME_COUNT))\n        self.height = int(self.capture.get(cv2.CAP_PROP_FRAME_HEIGHT))\n        self.width = int(self.capture.get(cv2.CAP_PROP_FRAME_WIDTH))\n\n        # Player state\n        # =====================================================\n        self.state = MediaPlayer.STATE_PAUSE\n        self.prev_frame = np.zeros((1, 1, 3))\n        self.curr_frame = np.zeros((1, 1, 3))\n        self.fid = -1\n\n        # Buffering thread\n        # =====================================================\n        self._thread = threading.Thread(target=self._buffering, daemon=True)\n        self._thread.start()\n\n    def start(self):\n        self.state = MediaPlayer.STATE_START\n        return self\n\n    def pause(self):\n        self.state = MediaPlayer.STATE_PAUSE\n        return self\n\n    def stop(self):\n        self.state = MediaPlayer.STATE_STOP\n\n        # Critical sections\n        with self.capture_lock:\n            self.capture.release()\n        with self.frame_queue.mutex:\n            self.frame_queue.queue.clear()\n\n        return self\n\n    def jump(self, index):\n        \"\"\"Move frame pointer to specified point (index)\"\"\"\n        if self.state == MediaPlayer.STATE_PAUSE:\n            self.fid = index\n\n            # Move frame pointer & Critical section\n            with self.capture_lock:\n                self.capture.set(cv2.CAP_PROP_POS_FRAMES, index)\n                _, frame = self.capture.read()\n                if frame is not None:\n                    self.prev_frame = frame\n\n            # For previewing jumped frame\n            with self.frame_queue.mutex:\n                self.frame_queue.queue.clear()\n                self.frame_queue.queue.append(frame)\n\n    def read(self):\n        \"\"\"Return next processing frame\"\"\"\n        # Read next frame from frame queue\n        if self.state == MediaPlayer.STATE_START:\n            self.fid += 1\n            self.prev_frame = self.curr_frame\n            try:\n                self.curr_frame = self.frame_queue.get_nowait()\n            except:\n                self.curr_frame = self.prev_frame\n            ret_frame = self.curr_frame.copy()\n\n        elif self.state == MediaPlayer.STATE_PAUSE:\n            ret_frame = self.prev_frame.copy()\n\n        else:\n            raise RuntimeError(\"You cannot fetch frame from terminated player\")\n\n        return self.fid, ret_frame\n\n    def _buffering(self):\n        \"\"\"Buffering video frames into frame queue\"\"\"\n        while self.state != MediaPlayer.STATE_STOP:\n            # Fetch new frame and buffer it\n            if not self.frame_queue.full():\n                # Critical section\n                with self.capture_lock:\n                    ret, frame = self.capture.read()\n                if not ret:\n                    continue\n                self.frame_queue.put(frame)\n\n            # Slow down buffering task\n            else:\n                time.sleep(0.01)\n","repo_name":"johnnylord/mtmc-testbed","sub_path":"app/gui/media.py","file_name":"media.py","file_ext":"py","file_size_in_byte":4344,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"17596680338","text":"import csv\r\n\r\nsoma = 0\r\ntitulos = {}\r\nvalores = {}\r\ntabelas = list()\r\nnomeoticas = ('otica-visiolux','otica-central', 'otica-teffe', 'otica-prado', 'otica-clauss', 'parque-otica', 'otica-perfil',\r\n              'otica-zonatto', 'otica-ampla-visao', 'otica-miraluz', 'otica-visoluz', 'otica-anita','otica-malosti',\r\n              'otica-marine', 'otica-marin', 'visao-araucaria','mercadao-fazenda', 'estilo-visao', 'preco-popular-posto',\r\n              'preco-popular-portao','klim-otica','preco-popular-duda-loja3', 'otica-sao-braz', 'opticolor', 'outlet-dos-oculos',\r\n              'exame-visao','laboratorio-universo', 'preco-popular-cic', 'sao-braz-orleans', 'sao-braz-ponto-final',\r\n              'preco-pop-fazenda','top-vision', 'preco-pop-alm-tamandare', 'vista-alegre')\r\nfor z in range(len(nomeoticas)):\r\n    with open(\"entrada de pedidos janeiro 2023 - Respostas ao formulário 1.csv\", \"r\") as file:\r\n        reader = csv.DictReader(file)  # usando a função csv\r\n        # next(reader)  # ignorando a primeira coluna\r\n        for row in reader:\r\n            dataa = row[\"Data\"].strip()\r\n            otica = row[\"otica\"].strip()\r\n            servico = row[\"servicos\"].strip()\r\n            valor = row[\"valor\"]\r\n            if otica == nomeoticas[z]:\r\n                tabelas.append(dataa)\r\n                tabelas.append(servico)\r\n                tabelas.append(valor)\r\n\r\n    pula = 0\r\n    somaotica = 0\r\n\r\n    # escrevendo o arquivo .csv\r\n    arq = open(nomeoticas[z] + \".csv\", \"a\")\r\n    arq.write(\"Data,Servico,Valor\" + '\\n')\r\n    arq.close()\r\n\r\n    for c in tabelas:\r\n        arq = open(nomeoticas[z] + \".csv\", \"a\")\r\n        if pula == 2:\r\n            arq.write(c + '\\n')\r\n            somaotica = somaotica + float(c)\r\n            pula = 0\r\n        else:\r\n            arq.write(c + ',')\r\n            pula = pula + 1\r\n        arq.close()\r\n\r\n    arq = open(nomeoticas[z] + \".csv\", \"a\")\r\n    arq.write(str(somaotica))\r\n    arq.close()\r\n    tabelas.clear()\r\n\r\nwith open(\"entrada de pedidos janeiro 2023 - Respostas ao formulário 1.csv\", \"r\") as file:\r\n    reader = csv.DictReader(file)  # usando a função csv\r\n    # next(reader)  # ignorando a primeira coluna\r\n    for row in reader:\r\n        otica = row[\"otica\"].strip()\r\n        valor = row[\"valor\"]\r\n        soma = soma + float(valor)\r\n        if otica in valores:  # dicionario\r\n            valores[otica] = valores[otica] + float(row[\"valor\"])\r\n        if otica not in titulos:  # dicionario\r\n            titulos[otica] = 0\r\n            valores[otica] = float(row[\"valor\"])\r\n        titulos[otica] += 1\r\n\r\n# lista do fechamentos no console\r\nprint('-' * 30)\r\nprint('FEFECHAMENTO DAS ÓTICAS')\r\nprint('-' * 30)\r\nfor i in valores:\r\n    print(f'{i} = {valores[i]:.2f}')\r\nprint(soma)\r\nprint(titulos)\r\n","repo_name":"ciroberaldo/pds-em-Python","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2763,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"41246165096","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time    : 2020/9/19 7:45 下午\n# @Author  : guohua08\n# @File    : playground.py\nfrom typing import List\nimport copy\nimport collections\nimport string\n\nfrom src.linked_list.ListNode import ListNode\nfrom src.linked_list.LinkedList import LinkedList\n\n\nclass Solution:\n    def topKFrequent(self, words: List[str], k: int) -> List[str]:\n        stats = {}\n        for w in words:\n            try:\n                stats[w] += 1\n            except:\n                stats[w] = 1\n        res = []\n        for lst in sorted(stats.items(), key=lambda x: [-x[1], x[0]])[:k]:\n            # print(lst)\n            res.append(lst[0])\n        return res\n\n# [\"the\", \"day\", \"is\", \"sunny\", \"the\", \"the\", \"the\", \"sunny\", \"is\", \"is\"], k = 4\n# Output: [\"the\", \"is\", \"sunny\", \"day\"]\n\nwords = [\"the\", \"day\", \"is\", \"sunny\", \"the\", \"the\", \"the\", \"sunny\", \"is\", \"is\"]\nk = 1\nres = Solution().topKFrequent(words, k)\nprint(res)","repo_name":"xiaoye-hua/DataStructure_Algorithm_SQL","sub_path":"problems/692.py","file_name":"692.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"28753431186","text":"\"\"\"Main module.\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport pyro\nimport pyro.distributions as dist\nfrom pyro.infer import SVI, TraceMeanField_ELBO\nfrom pyro.optim import AdamW\nimport scipy\nimport torch\n\n# from .data import choose_dataloader\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader, TensorDataset\nfrom torch_geometric.data import Data\nfrom torch_geometric.utils import from_scipy_sparse_matrix\nfrom torchinfo import summary\nfrom tqdm import tqdm\n\n# from torch_geometric.loader import RandomNodeSampler\n# from .data import RandomNodeSampler\nfrom .metrics import get_metrics\nfrom .model import spatialLDAModel\nfrom .utils import (\n    check_layer,\n    get_init_bg,\n    precompute_SGC,\n)\n\n\nclass STAMP:\n    def __init__(\n        self,\n        adata,\n        n_topics=20,\n        n_layers=1,\n        hidden_size=50,\n        layer=None,\n        dropout=0.1,\n        categorical_covariate_keys=None,\n        continous_covariate_keys=None,\n        verbose=False,\n        batch_size=1024,\n        enc_distribution=\"mvn\",\n        mode=\"sign\",\n        beta=1,\n    ):\n        \"\"\"Initialize model\n\n        Args:\n            adata (_type_): AnnData  object\n            n_topics (int, optional): Number of topics to model. Defaults to 10.\n            n_layers (int, optional): Number of layers to do SGC. Defaults to 1.\n            hidden_size (int, optional):  Number of nodes in the hidden layer of the\n                encoder. Defaults to 50.\n            layer (_type_, optional): Layer where the counts data are stored. X is used\n            if None. Defaults to None.\n            dropout (float, optional): Dropout used for the encoder. Defaults to 0.2.\n            categorical_covariate_keys (_type_, optional): Categorical batch keys\n            continous_covariate_keys (_type_, optional): Continous bathc key\n            verbose (bool, optional): Print out information on the model. Defaults to\n                True.\n            batch_size (int, optional): Batch size. Defaults to 1024.\n            enc_distribution (str, optional): Encoder distribution. Choices are\n                multivariate normal. Defaults to \"mvn\".\n            mode (str, optional): sign vs sgc(simplified graph convolutions).\n            sgc leads to smoother topics. Defaults to \"sign\".\n            beta (float, optional): Beta as in Beta-VAE. Defaults to 1.\n        \"\"\"\n        pyro.clear_param_store()\n\n        self.continous_covariate_keys = continous_covariate_keys\n        self.categorical_covariate_keys = categorical_covariate_keys\n        self.hidden_size = hidden_size\n        self.n_topics = n_topics\n        self.adata = adata\n        self.n_cells = adata.shape[0]\n        self.n_genes = adata.shape[1]\n        self.hidden_size = hidden_size\n        self.dropout = dropout\n        self.n_layers = n_layers\n        self.layer = layer\n        self.batch_size = batch_size\n        self.enc_distribution = enc_distribution\n        self.beta = beta\n        self.mode = mode\n\n        bg = get_init_bg(check_layer(adata, layer))\n        self.bg_init = torch.from_numpy(bg)\n\n        self.data = self.setup(\n            adata, layer, categorical_covariate_keys, continous_covariate_keys\n        )\n\n        if mode == \"sgc\":\n            n_layers = 0\n\n        model = spatialLDAModel(\n            self.n_genes,\n            self.hidden_size,\n            self.n_topics,\n            self.dropout,\n            self.bg_init,\n            n_layers,\n            self.n_batches,\n            self.n_cells,\n            self.enc_distribution,\n            self.beta,\n        )\n        self.model = model\n        # self.model = torch.compile(model)\n\n        if verbose:\n            print(summary(self.model))\n\n    def setup(self, adata, layer, categorical_covariate_keys, continous_covariate_keys):\n        x_numpy = check_layer(adata, layer)\n        x = torch.from_numpy(x_numpy)\n\n        if self.n_layers >= 1:\n            if \"spatial_connectivities\" not in adata.obsp.keys():\n                raise KeyError(\"spatial_connectivities not found\")\n\n            # adj = SparseTensor.from_scipy(\n            #     adata.obsp[\"spatial_connectivities\"]\n            #     + scipy.sparse.identity(n=x_numpy.shape[0])\n            # )\n            # adj = adj.t()\n            edge_index = from_scipy_sparse_matrix(\n                adata.obsp[\"spatial_connectivities\"]\n                + scipy.sparse.identity(n=x_numpy.shape[0])\n            )[0]\n        else:\n            # adj = None\n            edge_index = None\n\n        self.n_batches = 0\n        self.one_hot = []\n        if categorical_covariate_keys is not None:\n            if not isinstance(categorical_covariate_keys, list):\n                raise ValueError(\"categorical_covariate_keys must be a list.\")\n\n            for categorical_covariate_key in categorical_covariate_keys:\n                self.batch_series = adata.obs[categorical_covariate_key].astype(\n                    \"category\"\n                )\n                self.n_batches += self.batch_series.nunique()\n                batch_factorize, _ = pd.factorize(self.batch_series)\n                self.batch_factorize = torch.from_numpy(batch_factorize)\n                self.one_hot.append(F.one_hot(self.batch_factorize).float())\n\n        if continous_covariate_keys is not None:\n            if not isinstance(continous_covariate_keys, list):\n                raise ValueError(\"continous_covariate_keys must be a list.\")\n\n            for continous_covariate_key in continous_covariate_keys:\n                self.batch_series = adata.obs[continous_covariate_key].astype(\"float32\")\n                self.batch_series = (\n                    self.batch_series - self.batch_series.mean()\n                ) / self.batch_series.std()\n                self.n_batches += 1\n                self.batch_factorize = torch.from_numpy(self.batch_series.values)\n                self.one_hot.append(self.batch_factorize.float().reshape(-1, 1))\n\n        if self.n_batches == 0:\n            self.n_batches += 1\n            data = Data(x=x, edge_index=edge_index)  # , adj_t=adj)\n            sgc_x = precompute_SGC(data, n_layers=self.n_layers, mode=self.mode)\n            dataset = TensorDataset(x, sgc_x)\n\n        else:\n            data = Data(\n                x=x,\n                edge_index=edge_index,\n                # adj_t=adj,\n                st_batch=torch.cat(self.one_hot, dim=1),\n            )\n            st_batch = torch.cat(self.one_hot, dim=1)\n            sgc_x = precompute_SGC(data, n_layers=self.n_layers, mode=self.mode)\n            dataset = TensorDataset(x, sgc_x, st_batch)\n        # if self.batch_size >= self.n_cells:\n\n        self.dataloader = DataLoader(\n            dataset, batch_size=self.batch_size, drop_last=False, shuffle=True\n        )\n        # else:\n        #     self.dataloader = RandomNodeSampler(\n        #         data, batch_size=self.batch_size, shuffle=True, pin_memory=False\n        #     )\n        return data\n\n    def train(\n        self,\n        max_epochs=1000,\n        learning_rate=0.01,\n        device=\"cuda:0\",\n        weight_decay=0.1,\n        early_stop=True,\n        patience=20,\n    ):\n        \"\"\"Training the data\n\n        Args:\n            max_epochs (int, optional): Maximum number of epochs to run.\n                Defaults to 2000.\n            learning_rate (float, optional): Learning rate of AdamW optimizer.\n                Defaults to 0.01.\n            device (str, optional): Which device to run model on. Use \"cpu\"\n                to run on cpu and cuda to run on gpu. Defaults to \"cuda:0\".\n            weight_decay (float, optional): Weight decay of AdamW optimizer.\n                 Defaults to 0.1.\n            early_stop (bool, optional): Whether to early stop when training plateau.\n                 Defaults to True.\n            patience (int, optional): How many epochs to stop training when\n                training plateau. Defaults to 20.\n        \"\"\"\n\n        # adam_args = {\"lr\":learning_rate, \"weight_decay\":weight_decay, \"clip_norm\": 1}\n        # optimizer = ClippedAdam(adam_args)\n        optimizer = AdamW(\n            {\"lr\": learning_rate, \"weight_decay\": weight_decay},\n            clip_args={\"clip_norm\": 1},\n        )\n\n        self.device = device\n        self.model = self.model.to(device)\n        avg_loss = np.Inf\n        # tau_prev = 0.5 * torch.ones(self.n_genes, self.n_topics).to(device)\n        # tau_prev.require_grad = False\n        if early_stop:\n            early_stopper = EarlyStopper(patience=patience)\n        # from pyro.infer.autoguide import AutoNorma\n        svi = SVI(\n            self.model.model,\n            self.model.guide,\n            optimizer,\n            loss=TraceMeanField_ELBO(),\n        )\n\n        pbar = tqdm(range(max_epochs), position=0, leave=True)\n        for epoch in pbar:\n            losses = []\n            # optimizer.zero_grad()\n            for batch_idx, batch in enumerate(self.dataloader):\n                # batch = batch.to(device)\n                if self.n_batches == 1:\n                    batch_loss = svi.step(\n                        batch[0].to(device), batch[1].to(device), None\n                    )\n                else:\n                    batch_loss = svi.step(\n                        batch[0].to(device), batch[1].to(device), batch[2].to(device)\n                    )\n\n                losses.append(float(batch_loss))\n\n            avg_loss = sum(losses) / self.n_cells\n            if np.isnan(avg_loss):\n                break\n            pbar.set_description(f\"Loss:{avg_loss:.3f}\")\n\n            if early_stop:\n                if early_stopper.early_stop(avg_loss):\n                    print(\"Early Stopping\")\n                    break\n\n    def get_metrics(self, topk=10, layer=None, TGC=True):\n        \"\"\"Get metrics\n\n        Args:\n            topk (int, optional): Number of top genes to use to score the metrics.\n                 Defaults to 10.\n            layer (_type_, optional): Which layer to use to score the metrics.\n                 If none is chosen, use X. Defaults to None.\n            TGC (bool, optional): Whether to calculate the topic gene correlation.\n                 Defaults to True.\n\n        Returns:\n            _type_: _description_\n        \"\"\"\n        adata = self.adata\n        beta = self.get_feature_by_topic()\n        topic_prop = self.get_cell_by_topic()\n        metrics = get_metrics(adata, beta, topic_prop, topk=topk, layer=layer, TGC=TGC)\n\n        return metrics\n\n    def get_prior(self, device=\"cuda:0\"):\n        self.model.to(device)\n        self.data.to(device)\n        prior_loc, prior_scale = self.model.get_prior(self.data.st_batch)\n        return prior_loc.detach().cpu().numpy(), prior_scale.detach().cpu().numpy()\n\n    def get_dispersion(self, device=\"cuda:0\"):\n        model = self.model.model_params()\n        # model.eval()\n        model.to(device)\n        self.model.set_device(device)\n        self.model.eval()\n        # self.model.to(device)\n        self.data.to(device)\n\n        pred = self.model.predictive(num_samples=10)\n        if self.n_batches > 1:\n            pred = pred(\n                self.data.x, self.data.adj_t, self.data.sgc_x, self.data.st_batch\n            )\n        else:\n            pred = pred(self.data.x, self.data.adj_t, self.data.sgc_x)\n        # rate = self.model.feature(\"rate\")\n        # rate = torch.exp(rate)\n        # rate = rate.detach().cpu().numpy()\n        rate = pred[\"disp\"].mean(axis=0).detach().cpu().numpy()\n        # ads = pred[\"ads\"].mean(axis=0).detach().cpu().numpy()\n        df = pd.DataFrame(rate, index=self.adata.var_names, columns=[\"disp\"])\n        # df[\"ads\"] = ads\n        return df\n\n    def save(self, path):\n        self.model.save(path)\n\n    def load(self, path):\n        self.model.load(path)\n        self.model.eval()\n\n    def get_cell_by_topic(self, device=\"cpu\"):\n        \"\"\"Get latent topics after training.\n\n        Args:\n            device (str, optional): What device to use. Defaults to \"cpu\".\n\n        Returns:\n            _type_: A dataframe of cell by topics where each row sum to one.\n        \"\"\"\n        model = self.model.model_params()\n        model.eval()\n        model.to(device)\n        self.data.to(device)\n\n        with torch.no_grad():\n            # if self.batch_key is None:\n            x = self.data.x\n            if (self.continous_covariate_keys is None) and (\n                self.categorical_covariate_keys is None\n            ):\n                cell_topic = self.model.guide(x, self.data.sgc_x)\n            else:\n                cell_topic = self.model.guide(x, self.data.sgc_x, self.data.st_batch)\n\n            cell_topic = cell_topic.detach().cpu().numpy()\n            cell_topic = pd.DataFrame(\n                cell_topic,\n                columns=[\"Topic\" + str(i) for i in range(1, self.n_topics + 1)],\n            )\n            cell_topic.set_index(self.adata.obs_names, inplace=True)\n\n            return cell_topic\n\n    def get_feature_by_topic(\n        self,\n        device=\"cpu\",\n        num_samples=1000,\n        pct=0.5,\n        return_softmax=False,\n    ):\n        \"\"\"Get the gene modules\n\n        Args:\n            device (str, optional): Which device to use. Defaults to \"cpu\".\n            num_samples (int, optional): Number of samples to use for calculation.\n              Defaults to 1000.\n            pct (float, optional): Depreciated . Defaults to 0.5.\n            return_softmax (bool, optional): Depreciated. Defaults to False.\n\n        Returns:\n            _type_: _description_\n        \"\"\"\n        # pseudcount of 0.5 aga aga\n        # feature_topic = feature_topic.t()\n        # Transform to torch log scale\n        self.model.to(device)\n        self.data.to(device)\n\n        # feature_topic = self.model.feature_by_topic()\n        # feature_topic = feature_topic.to(device)\n        if pct == 0.5:\n            feature_topic = self.model.feature_by_topic(\n                return_scale=False, return_softmax=return_softmax\n            )\n            feature_topic = feature_topic.to(device)\n        else:\n            feature_topic_loc, feature_topic_scale = self.model.feature_by_topic(\n                return_scale=True\n            )\n            feature_topic = dist.Normal(\n                feature_topic_loc, torch.sqrt(torch.exp(feature_topic_scale))\n            )\n            feature_topic = feature_topic.sample((num_samples,)).kthvalue(\n                int(pct * num_samples), dim=0\n            )[0]\n            feature_topic = feature_topic.to(device)\n        # feature_topic = torch.sqrt(torch.exp(feature_topic_scale))\n        # if pseudocount > 0:\n        #     if self.n_batches > 1:\n        #         bg = self.model.get_bias().min(axis=0)[0]\n        #     else:\n        #         bg = self.model.get_bias()\n        #     amt = torch.log(bg.exp() + pseudocount) - bg\n        #     feature_topic = feature_topic.t() - amt\n        #     feature_topic = feature_topic.t()\n\n        feature_topic = feature_topic.detach().cpu().numpy()\n\n        feature_topic = feature_topic[:, : self.n_topics]\n        feature_topic = pd.DataFrame(\n            feature_topic,\n            columns=[\"Topic\" + str(i) for i in range(1, self.n_topics + 1)],\n            index=self.adata.var_names,\n        )\n\n        return feature_topic\n\n    def get_background(self):\n        background = self.model.get_bias().detach().cpu().numpy()\n        background = pd.DataFrame(\n            background, index=self.adata.var_names, columns=[\"gene\"]\n        )\n        return background\n\n    # def plot_qc(self, n_obs=1000, gene=None, device=\"cuda:0\"):\n    #     self.model.eval()\n    #     self.model.to(device)\n    #     self.data.to(device)\n\n    #     if gene is None:\n    #         gene = self.adata.var_names\n    #     gene_index = np.where(self.adata.var_names == gene)[0]\n\n    #     x_plot = self.data.x.detach().cpu().numpy()\n    #     obs = random.sample(range(x_plot.shape[0]), n_obs)\n    #     x_plot = x_plot[obs, :]\n    #     x_plot = x_plot / x_plot.sum(axis=1, keepdims=True)\n\n    #     if self.batch_key is None:\n    #         w = self.get_feature_by_topic(return_softmax=True)\n    #         z = self.get_cell_by_topic()\n    #         w = w.to_numpy()\n    #         z = z.to_numpy()\n    #         z = z[obs, :]\n    #         mean = z @ w.transpose()\n    #         mean = mean\n    #     else:\n    #         w = self.get_feature_by_topic(return_softmax=True)\n    #         z = self.get_cell_by_topic()\n    #         w = w.to_numpy()\n    #         z = z.to_numpy()\n    #         z = z[obs, :]\n    #         ys = self.data.st_batch.detach().cpu().numpy()\n    #         ys = ys[obs, :]\n    #         z = np.hstack([z, ys])\n    #         mean = z @ w.transpose()\n    #         # if n_obs > x.shape[0]:\n    #     #     n_obs = x.shape[0]\n    #     x_plot = x_plot[:, gene_index].ravel()\n    #     y_plot = mean[:, gene_index].ravel()\n\n    #     plt.hist2d(\n    #         x_plot,\n    #         y_plot,\n    #         bins=100,\n    #         norm=matplotlib.colors.LogNorm(),\n    #     )\n\n\nclass EarlyStopper:\n    def __init__(self, patience=50, min_delta=0):\n        self.patience = patience\n        self.min_delta = min_delta\n        self.counter = 0\n        self.min_training_loss = np.inf\n\n    def early_stop(self, training_loss):\n        if training_loss < self.min_training_loss:\n            self.min_training_loss = training_loss\n            self.counter = 0\n        elif training_loss > (self.min_training_loss + self.min_delta):\n            self.counter += 1\n            if self.counter >= self.patience:\n                return True\n        return False\n","repo_name":"JinmiaoChenLab/scTM","sub_path":"sctm/stamp.py","file_name":"stamp.py","file_ext":"py","file_size_in_byte":17529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"72109013405","text":"#!/usr/bin/env python3\n\"\"\"\nThis is my take on a script for monitoring/dumping htlc events.\nIt prints to stdout and in CSV format to htlcstream.csv\nThere is no configurability unlike smallworlnd's stream-lnd-htlcs\n\"\"\"\n\nimport time\nimport csv\nimport traceback\nimport argparse\n\nfrom lib.nodeinterface import NodeInterface\n\nmynode = NodeInterface.fromconfig()\n\nmychannels = {}\nlastchannelfetchtime = 0\nchandatatimeout = 15\n\ndef getChanInfo(chanid):\n    \"\"\"\n    Fetches channel data from LND\n    Uses a cache that times out after `chandatatimeout` seconds\n    Also queries closed channels if `chanid` is not in open channels\n    \"\"\"\n    global lastchannelfetchtime\n    uptodate = (time.time() - lastchannelfetchtime < chandatatimeout)\n\n    if uptodate and chanid in mychannels:\n        return mychannels[chanid]\n\n    for chan in mynode.ListChannels().channels:\n        mychannels[chan.chan_id] = chan\n\n    lastchannelfetchtime = time.time()\n\n    if chanid in mychannels:\n        return mychannels[chanid]\n\n    for chan in mynode.ClosedChannels().channels:\n        mychannels[chan.chan_id] = chan\n\n    if chanid in mychannels:\n        return mychannels[chanid]\n\n    print('ERROR: Unknown chanid', chanid)\n    return None\n\ndef getAlias4ChanID(chanid):\n    chan = getChanInfo(chanid)\n    if chan is None:\n        return chanid\n    alias = mynode.getAlias(chan.remote_pubkey)\n    return alias\n\ndef getFailureAttribute(einfo, attr):\n    i = getattr(einfo, attr)\n    x = einfo.DESCRIPTOR.fields_by_name[attr]\n\n    return x.enum_type.values_by_number[i].name\n\nforward_event_cache = {}\ndef popamountsfromcache(key):\n    amount = forward_event_cache[key]['amt']\n    fee = forward_event_cache[key]['fee']\n    del forward_event_cache[key]\n    return amount, fee\n\ndef subscribeEventsPersistent():\n    failures = 0\n\n    while True:\n        events = mynode.router.SubscribeHtlcEvents()\n        try:\n            _ = mynode.GetInfo() # test connection\n            failures = 0\n            print('Connected to LND. Waiting for first event...')\n            for e in events:\n                yield e\n        except StopIteration:\n            raise\n        except Exception as e:\n            details = 'no details'\n            try:\n                details = e.details()\n            except:\n                pass\n\n            print('Error:', details)\n            unavailable = ('Connection refused' in details or 'Connection reset' in details)\n            unready = ('not yet ready' in details or 'wallet locked' in details)\n            terminated = (details == \"htlc event subscription terminated\")\n\n            if any((unavailable, unready, terminated)):\n                failures += 1\n                timeout = min(4**failures, 60*60*2)\n                print(f'Could not connect to lnd, retrying in {timeout}s')\n                time.sleep(timeout)\n                continue\n\n            print('Unhandled exception:', repr(e))\n            raise e\n\n\ndef main():\n    parser = argparse.ArgumentParser(description='Script for monitoring/dumping htlc events')\n    parser.add_argument('--persist', action=\"store_true\",\n                    help='Automatically reconnect to LND')\n    args = parser.parse_args()\n\n    if args.persist:\n        events = subscribeEventsPersistent()\n    else:\n        events = mynode.router.SubscribeHtlcEvents()\n        print('Now listening for events')\n\n    for i, event in enumerate(events):\n        try:\n            inchanid = event.incoming_channel_id\n            outchanid = event.outgoing_channel_id\n\n            outcome = event.ListFields()[-1][0].name\n            eventinfo = getattr(event, outcome)\n            eventtype = event.EventType.keys()[event.event_type]\n            timetext = time.ctime(event.timestamp_ns/1e9)\n\n            in_htlc_id = event.incoming_htlc_id\n            out_htlc_id = event.outgoing_htlc_id\n\n            inalias = outalias = 'N/A'\n            inrbal = incap = outlbal = outcap = '-'\n            if inchanid:\n                inalias = getAlias4ChanID(inchanid)\n                inchan = getChanInfo(inchanid)\n                incap = inchan.capacity\n                inrbal = inchan.remote_balance\n\n            if outchanid:\n                outalias = getAlias4ChanID(outchanid)\n                outchan = getChanInfo(outchanid)\n                # If channel is unknown (closed?) cannot guarantee these values exist\n                outcap = getattr(outchan, 'capacity', 'UNKNOWN')\n                outlbal = getattr(outchan, 'local_balance', 'UNKNOWN')\n\n            # Extract forward amount data, if available\n            amount = fee = '-'\n            if hasattr(eventinfo, 'info'):\n                if eventinfo.info.outgoing_amt_msat > 0:\n                    amt_msat = eventinfo.info.outgoing_amt_msat\n                    amount = amt_msat/1000\n                    fee = (eventinfo.info.incoming_amt_msat - amt_msat)/1000\n\n                elif eventinfo.info.incoming_amt_msat > 0:\n                    amt_msat = eventinfo.info.incoming_amt_msat\n                    amount = amt_msat/1000\n\n            # Add a note to quickly point out common scenarios\n            note = ''\n            fwdcachekey = (in_htlc_id, out_htlc_id, inchanid, outchanid)\n            if outcome == 'forward_event':\n                note = '💸 HTLC in flight.'\n                forward_event_cache[fwdcachekey] = {'amt':amount, 'fee':fee}\n\n            elif outcome == 'forward_fail_event':\n                note = '❌ Downstream fwding failure.'\n                if fwdcachekey in forward_event_cache:\n                    # This data is only found in forward_event, need to fetch it from cache\n                    amount, fee = popamountsfromcache(fwdcachekey)\n\n            elif outcome == 'link_fail_event':\n                failure_string = eventinfo.failure_string\n                failure_detail = getFailureAttribute(eventinfo, 'failure_detail')\n                wire_failure = getFailureAttribute(eventinfo, 'wire_failure')\n\n                if eventtype == 'RECEIVE' and failure_detail == 'UNKNOWN_INVOICE':\n                    note += '🛸 Probe detected. '\n\n                note += f'❌ Failure(wire: {wire_failure}, detail: {failure_detail}, string: {failure_string})'\n\n\n            elif outcome == 'settle_event' and  eventtype == 'FORWARD':\n                note = '✅ Forward successful.'\n                if fwdcachekey in forward_event_cache:\n                    # This data is only found in forward_event, need to fetch it from cache\n                    amount, fee = popamountsfromcache(fwdcachekey)\n\n            elif outcome == 'settle_event':\n                note = '✅'\n\n            print(eventtype,\n                  in_htlc_id, out_htlc_id,\n                  timetext, amount,'for', fee,\n                  inalias, f'{inrbal}/{incap}',\n                  '➜',\n                  outalias, f'{outlbal}/{outcap}',\n                  # ~ inchanid, '➜', outchanid,\n                  outcome,\n                  # ~ eventinfo,\n                  note,\n                    )\n\n            with open('htlcstream.csv', 'a', newline='') as f:\n                writer = csv.writer(f)\n\n                if i % 30 == 0:\n                    writer.writerow(['Eventtype', 'Htlc_id_in', 'Htlc_id_out',\n                                    'Timestamp', 'Amount', 'Fee',\n                                     'Alias_in','Alias_out',\n                                     'Balance_in','Capacity_in',\n                                     'Balance_out', 'Capacity_out',\n                                     'Chanid_in','Chanid_out',\n                                     'Outcome', 'Details', 'Note'])\n\n                writer.writerow([eventtype,\n                                 event.incoming_htlc_id,\n                                 event.outgoing_htlc_id,\n                                 timetext, amount, fee,\n                                 inalias, outalias,\n                                 inrbal, incap,\n                                 outlbal, outcap,\n                                 f\"{inchanid}\", f\"{outchanid}\",\n                                 outcome, eventinfo, note])\n\n        except Exception as e:\n            print('Exception while handling event.', repr(e))\n            print(event)\n            traceback.print_exc()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Gridflare/lndpytools","sub_path":"watchhtlcstream.py","file_name":"watchhtlcstream.py","file_ext":"py","file_size_in_byte":8276,"program_lang":"python","lang":"en","doc_type":"code","stars":77,"dataset":"github-code","pt":"86"}
+{"seq_id":"28879933812","text":"\nfrom utils.PicturesDB import PicturesDB\nfrom flask import session, redirect, url_for\nfrom email.message import EmailMessage\nfrom smtplib import SMTP_SSL\nfrom random import randint\nfrom os import getenv\nimport functools\n\npicturesDB = PicturesDB()\n\n\ndef auth_required(func):\n    @functools.wraps(func)\n    def wrapper(*args, **kwargs):\n        from aituNetwork.models import Users\n        if session.get('user') is None:\n            return redirect(url_for('auth.login'))\n\n        user = session['user']\n        if Users.is_user_correct(user):\n            return func(*args, **kwargs)\n\n        del session['user']\n        return redirect(url_for('auth.login'))\n\n    return wrapper\n\n\ndef send_email(to: str, token_link: str, header: str, msg: str):\n    message = EmailMessage()\n    message['Subject'] = header\n    message['From'] = getenv('SMTP_SENDER')\n    message['To'] = to\n    message.set_content(msg % token_link)\n\n    with SMTP_SSL('smtp.gmail.com', int(getenv('SMTP_PORT'))) as smtp:\n        smtp.login(getenv('SMTP_SENDER'), getenv('SMTP_PASSWORD'))\n        smtp.send_message(message)\n\n\ndef random_id():\n    from aituNetwork.models import Users\n\n    mn = 1000000\n    mx = 9999999\n\n    rand = randint(mn, mx)\n    while Users.query.filter_by(id=rand).first() is not None:\n        rand = randint(mn, mx)\n\n    return str(rand)\n","repo_name":"Mercurial5/AituNetwork","sub_path":"utils/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"12922294244","text":"from rest_framework import serializers\nfrom .models import Photo, Comment, Like\nfrom users.models import User\n\nclass FeedUserSerializer(serializers.ModelSerializer):\n    is_self = serializers.SerializerMethodField()\n    class Meta:\n        model = User\n        fields = (\n            'profile_image',\n            'username',\n            'name',\n            'post_count',\n            'followers_count',\n            'following_count',\n            'is_self',\n        )\n\n    def get_is_self(self, user):\n        if 'request' in self.context:\n            request = self.context['request']\n            if user.id == request.user.id:\n                return True\n            else:\n                return False\n        return False\n\nclass CommentSerializer(serializers.ModelSerializer):\n    owner = FeedUserSerializer(read_only=True)\n    class Meta:\n        model = Comment\n        fields = (\n            'id',\n            'message',\n            'owner'\n        )\n\nclass LikeSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Like\n        fields = '__all__'\n\n\nclass CreatePhotoSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Photo\n        fields = '__all__'\n        \nclass PhotoSerializer(serializers.ModelSerializer):\n    comments = CommentSerializer(many=True)\n    is_liked = serializers.SerializerMethodField()\n    owner = FeedUserSerializer(read_only=True)\n\n    class Meta:\n        model = Photo\n        fields = (\n                'id', \n                'image', \n                'caption', \n                'owner', \n                'comments', \n                'comment_count', \n                'is_liked',\n                'like_count',\n                )\n\n    def get_is_liked(self, obj):\n        if 'request' in self.context:\n            request = self.context['request']\n            try:\n                Like.objects.get(\n                    owner__id=request.user.id,\n                    photo__id=obj.id\n                )\n                return True\n            except Like.DoesNotExist:\n                return False\n        return False","repo_name":"develician/instagram-clone","sub_path":"gallery-backend/gallery/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2085,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"11782331416","text":"import tkinter\r\nimport time\r\nimport pyautogui\r\nimport webbrowser\r\nimport xlrd\r\n \r\nclass APP:\r\n    def __init__(self,master):\r\n        frame = tkinter.Frame(master,height=300,bd=2,width=245)\r\n        frame.pack()\r\n \r\n        self.button1 = tkinter.Button(frame,text=\"stop\",fg=\"red\",command=frame.quit,height=2,bd=2,width=8)\r\n        self.button1.place(x=25,y=50)\r\n \r\n        self.button2 = tkinter.Button(frame,text=\"start\",fg=\"green\",command=self.ScanBlog,height=2,bd=2,width=8)\r\n        self.button2.place(x=95,y=50)\r\n \r\n        self.label1 = tkinter.Label(frame,text=\"please enter refresh excel:\",bg=\"yellow\",height=2,bd=2,width=24)\r\n        self.label1.place(x=25,y=120)\r\n            \r\n        self.label2 = tkinter.Label(frame,text=\"please enter refresh times:\",bg=\"green\",height=2,bd=2,width=24)\r\n        self.label2.place(x=25,y=210)\r\n \r\n        self.label3 = tkinter.Label(frame,text=\"Refresh Web Pages\",height=3,bd=3,width=24)\r\n        self.label3.place(x=25,y=0)\r\n \r\n        self.entry1 = tkinter.Entry(frame,width=20,bd=2)\r\n        self.entry1.place(x=25,y=170)\r\n \r\n        self.entry2 = tkinter.Entry(frame,width=20,bd=2)\r\n        self.entry2.place(x=25,y=260)\r\n        \r\n    def ScanBlog(self):\r\n        file_name = self.entry1.get()\r\n        distance = int(self.entry2.get())\r\n        sleep_t = 1\r\n        sheet_name = 'url'\r\n \r\n        excel_file = xlrd.open_workbook(file_name)\r\n        sheet1 = excel_file.sheet_by_name(sheet_name)\r\n \r\n        while distance > 0:\r\n            for i in range(0,49,1):\r\n                row_value = sheet1.cell(i,1).value\r\n                webbrowser.open(row_value)\r\n                time.sleep(sleep_t)\r\n                pyautogui.hotkey('ctrl','w')\r\n            distance = distance - 1\r\n \r\nroot = tkinter.Tk()\r\napp = APP(root)\r\nroot.mainloop()\r\nroot.destroy()","repo_name":"ZhangDezhi/data-code","sub_path":"Python/ui程序示例.py","file_name":"ui程序示例.py","file_ext":"py","file_size_in_byte":1806,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"1542528942","text":"import re\n\nimport tweepy\nfrom pymongo import MongoClient\n\nconsumer_key = \"SvXCpXxVM2JzaBjJLHTFdJK61\"\nconsumer_secret = \"YBq37YK6d5pybt2eHtxnduxCSa8LkZRwBHVB2Q3N8zhxBEJunE\"\naccess_key = \"1322853776793894913-0iYSM3AcKSwNNxc6XknGJp5XZv83ok\"\naccess_secret = \"BaTRs4holXqs70GXuTA9HfQhXi175gcIr9yTnc5qmm1uv\"\n\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_key, access_secret)\napi = tweepy.API(auth, wait_on_rate_limit = True)\nresult = []\n\n\ndef clean(tweetStr):\n    tweet_regex = ' '.join(re.sub(\"([^0-9A-Za-z \\t]+)|(\\w+:\\/\\/\\S+)\", \" \", tweetStr).split())\n\n    # to remove links that start with HTTP/HTTPS in the tweet\n\n    tweet_regex = re.sub(r'https?:\\/\\/(www\\.)?[-a-zA-Z0–9@:%._\\+~#=]{2,256}\\.[a-z]{2,6}\\b([-a-zA-Z0–9@:%_\\+.~#?&//=]*)', '', tweet_regex, flags=re.MULTILINE)\n\n    # to remove other url links\n\n    tweet_regex = re.sub(r'[-a-zA-Z0–9@:%._\\+~#=]{2,256}\\.[a-z]{2,6}\\b([-a-zA-Z0–9@:%_\\+.~#?&//=]*)', '', tweet_regex, flags=re.MULTILINE)\n    return tweet_regex\n\n\n\nfor tweet in tweepy.Cursor(api.search, q = 'Storm OR Winter OR Canada OR Temperature OR Flu OR Snow OR Indoor OR Safety').items(2000):\n    data = {}\n    data['user_name'] = tweet.user.screen_name\n    data['time'] = tweet.created_at\n    data['text'] = tweet.text\n    data['location'] = tweet.user.location\n    data['retweet_count'] = tweet.retweet_count\n    result.append(data)\n    client = MongoClient(\"mongodb+srv://robindermongo:root@cluster0.hon6x.mongodb.net/test\")\n    db = client.RawDb\n    db.RawData.insert_one(data)\n    # print(one['text'])\n    if data[\"user_name\"] is not None:\n        data['user_name'] = clean(data['user_name'])\n    if data[\"text\"] is not None:\n        data['text'] = clean(data['text'])\n    if data[\"location\"] is not None:\n        data['location'] = clean(data['location'])\n    dbProcessed = client.ProcessedDb\n    dbProcessed.ProcessedData.insert_one(data)\n\n","repo_name":"robin1995dhillon/TwitterExtraction_MapReduce","sub_path":"src/main/java/Twitter.py","file_name":"Twitter.py","file_ext":"py","file_size_in_byte":1907,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3330641878","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nadd descr\n\"\"\"\n\nfrom selenium import webdriver\nimport time\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom bs4 import BeautifulSoup\n\n# from http_request_randomizer.requests.proxy.requestProxy import RequestProxy\n\n\n\nclass InstagramBot():\n    def __init__(self, email, password, path):\n        # self.proxy = RequestProxy().get_proxy_list()[0].get_address()\n        # webdriver.DesiredCapabilities.CHROME['proxy']={\n        #    \"httpProxy\":self.proxy,\n        #    \"ftpProxy\":self.proxy,\n        #    \"sslProxy\":self.proxy,\n        #    \"proxyType\":\"MANUAL\",\n        # }\n        self.browser = webdriver.Chrome(path + 'chromedriver')\n        self.email = email\n        self.password = password\n\n    def signIn(self):\n        self.browser.get('https://www.instagram.com')\n\n        # Accept cookies\n        WebDriverWait(self.browser, 10).until(\n            EC.element_to_be_clickable((By.XPATH, \"//button[text()='Accept']\"))).click()\n\n        # Insert username and pw\n        WebDriverWait(self.browser, 10).until(EC.presence_of_element_located((By.NAME, \"username\"))).send_keys(\n            self.email)\n        WebDriverWait(self.browser, 10).until(EC.presence_of_element_located((By.NAME, \"password\"))).send_keys(\n            self.password)\n        self.browser.find_element_by_name('password').send_keys(Keys.ENTER)\n\n        # Do not save credentials\n        WebDriverWait(self.browser, 10).until(\n            EC.element_to_be_clickable((By.XPATH, \"//button[text()='Not Now']\"))).click()\n\n        # Do not enable notifications\n        WebDriverWait(self.browser, 10).until(\n            EC.element_to_be_clickable((By.XPATH, \"//button[text()='Not Now']\"))).click()\n\n    def followWithUsername(self, username):\n        self.browser.get('https://www.instagram.com/' + username)\n        followButton = WebDriverWait(self.browser, 10).until(EC.element_to_be_clickable((By.CSS_SELECTOR, \"button\")))\n        if (followButton.text in ['Segui', 'Segui anche tu', 'Follow', 'Follow Back']):\n            followButton.click()\n        else:\n            print(\"You are already following this user\")\n\n    def messageWithUsername(self, username, msg):\n        self.browser.get('https://www.instagram.com/' + username)\n        msgButton = WebDriverWait(self.browser, 10).until(EC.element_to_be_clickable((By.CSS_SELECTOR, \"button\")))\n        if (msgButton.text in ['Invia un messaggio', 'Message']):\n            msgButton.click()\n            for i in range(0, 5):\n                WebDriverWait(self.browser, 10).until(\n                    EC.presence_of_element_located((By.CSS_SELECTOR, \"textarea\"))).send_keys(msg[i])\n                self.browser.find_element_by_css_selector('textarea').send_keys(Keys.ENTER)\n                time.sleep(3)\n        else:\n            print(\"You do not follow this user yet\")\n\n    def checkConvStatus(self, username):\n        self.browser.get('https://www.instagram.com/' + username)\n        msgButton = WebDriverWait(self.browser, 10).until(EC.element_to_be_clickable((By.CSS_SELECTOR, \"button\")))\n        res = dict()\n        if (msgButton.text in ['Invia un messaggio', 'Message']):\n            msgButton.click()\n            WebDriverWait(self.browser, 10).until(EC.presence_of_element_located((By.CSS_SELECTOR, \"textarea\")))\n            html = self.browser.page_source\n            pageSoup = BeautifulSoup(html, 'html.parser')\n            res['user_was_contacted'] = len(pageSoup.find_all('div', {'class': 'DMBLb'})) > 0\n            res['user_has_replied'] = len(pageSoup.find_all('a', {'class': '_2dbep qNELH kIKUG'})) > 0\n            res['user_has_viewed'] = len(pageSoup.find_all(text='Visualizzato')) > 0\n        else:\n            res['user_was_contacted'] = False\n            res['user_has_replied'] = False\n            res['user_has_viewed'] = False\n        return res\n\n    def getUserStats(self, username):\n        self.browser.get('https://www.instagram.com/' + username)\n        html = self.browser.page_source\n        pageSoup = BeautifulSoup(html, 'html.parser')\n        user_stats_dict = {\n        'user': username,\n        'numberOfPosts' : str(pageSoup.find_all('span', {'class': 'g47SY'})[0].text).replace(',',''), #fix k\n        'numberOfFollowers' : str(pageSoup.find_all('span', {'class': 'g47SY'})[1].text).replace(',',''),\n        'numberOfFollowing' : str(pageSoup.find_all('span', {'class': 'g47SY'})[2].text).replace(',','')\n        }\n        for number in ['numberOfPosts','numberOfFollowers','numberOfFollowing']:\n            if user_stats_dict[number].__contains__('k'):\n                if user_stats_dict[number].__contains__('.'):\n                    user_stats_dict[number] = user_stats_dict[number].replace('k',\"00\").replace('.','')\n                else:\n                    user_stats_dict[number] = user_stats_dict[number].replace('k',\"000\")\n\n        return user_stats_dict\n\n    def getUserFollowing(self, username, max):\n        self.browser.get('https://www.instagram.com/' + username)\n        html = self.browser.page_source\n        pageSoup = BeautifulSoup(html, 'html.parser')\n        numberOfFollowing = str(pageSoup.find_all('span', {'class': 'g47SY'})[2].text).replace('.', '').replace(',','')\n        numberOfFollowingToScrape = min(max, int(numberOfFollowing))\n        if numberOfFollowingToScrape > 0:\n            followingLink = WebDriverWait(self.browser, 10).until(\n                EC.element_to_be_clickable((By.XPATH, \"//a[@href='/\" + username + \"/following/']\")))\n            followingLink.click()\n            time.sleep(3)\n            followingList = self.browser.find_element_by_css_selector('div[role=\\'dialog\\'] ul')\n            numberOfFollowingInList = len(followingList.find_elements_by_css_selector('li'))\n            action1 = webdriver.ActionChains(self.browser).move_to_element_with_offset(followingList, 5, 200)\n            action2 = webdriver.ActionChains(self.browser)\n            while (numberOfFollowingInList < numberOfFollowingToScrape):\n                action1.click().perform()\n                action2.key_down(Keys.SPACE).key_up(Keys.SPACE).perform()\n                time.sleep(1)\n                numberOfFollowingInList = len(followingList.find_elements_by_css_selector('li'))\n                print(numberOfFollowingInList)\n                time.sleep(1)\n            following = []\n            for user in followingList.find_elements_by_css_selector('li'):\n                userLink = user.find_element_by_css_selector('a').get_attribute('href')\n                # print(userLink)\n                following.append(userLink)\n                if (len(following) == numberOfFollowingToScrape):\n                    break\n        else:\n            following = []\n        print(\"The number of followers is {}\".format(len(following)))\n        return following\n\n    def getUserInfo(self, username):\n        self.browser.get('https://www.instagram.com/' + username)\n        html = self.browser.page_source\n        pageSoup = BeautifulSoup(html, 'html.parser')\n        infoBox = pageSoup.find('div', {'class': '-vDIg'})\n        info = dict()\n        if infoBox.find('h1', {'class': 'rhpdm'}) is not None:\n            info['name'] = infoBox.find('h1', {'class': 'rhpdm'}).text\n        else:\n            info['name'] = None\n        if infoBox.find('a', {'class': 'yLUwa'}) is not None:\n            info['website'] = infoBox.find('a', {'class': 'yLUwa'}).text\n        else:\n            info['website'] = None\n\n        if infoBox.find_all('span') is not None and len(\n                [s for s in infoBox.find_all('span') if not s.has_attr('class')]) > 0:\n            info['bio'] = [s for s in infoBox.find_all('span') if not s.has_attr('class')][0].text\n        else:\n            info['bio'] = None\n        return info\n\n    def closeBrowser(self):\n        self.browser.close()\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        self.closeBrowser()\n\n\n\n","repo_name":"bonaventura-p/geoa_project","sub_path":"src/ig/insta_bot.py","file_name":"insta_bot.py","file_ext":"py","file_size_in_byte":8059,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"39251909327","text":"from os import stat\nimport random\nfrom time import time\n\nimport ahrs\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom torch.cuda import init\nfrom torch.random import initial_seed\nfrom tqdm import tqdm\n\nfrom modelmodule.snn_model_old import SNN, SNNRecurrent\n# from modelmodule.norse_model import SNN\n# from data import Dataset, get_batch, load_data_list, load_dataset_crazyflie\nfrom datamodule.data_loader import Dataset\nfrom datamodule.encoding import grf_coding, position_coding\nfrom datamodule.normalization import minmax\n\nfrom utils.quaternions import (inclination_loss, relative_angle,\n                         relative_inclination, to_euler_angles,\n                         to_euler_angles_numpy)\nfrom utils.network_utils import state_dict_to_weights_array\n\n\n###############################################################################\n# Simulation parameters\n###############################################################################\n\n# Choose between datasets\ntest_data_type = 'simulation'\n\n# Choose model\nmodel_filename = './models/snn12072021_221249.pt'\nmodel_type = \"SNN\"\nnbins = 10\nsnn_size = [6, 5]\noutput_size = 2\n\n# Choices for plotting\nplot_quaternions = False\nplot_eulers = True\n\n# Hyperparameters\nfreq = 100 # data was sampled at a certain frequency that is important for the model and the other filters\ntest_batchsize = 1\nseq_size = 2000\n\n# Data parameters\noptions = {}\noptions['encoding'] = None\noptions['normalization'] = minmax\noptions['seq_length'] = seq_size\noptions['nbins'] = int(10*6)\noptions['output'] = 'eulers'\noptions['rotate_yaw'] = False\n# skip_initial = 600 # skip the initial values to skip the liftoff in the dataset\n# normalization_type = 'minmax' # choose the normalization type (from minmax and standardize only currently)\n# gyro_max = 4.2 # absolute maximum value for normalization (these values are obtained from the used datasets)\n# acc_max = 16.8 # absolute maximum value for normalization (these values are obtained from the used datasets)\n\n###############################################################################\n# Load datasets\n###############################################################################\n\nif test_data_type == 'simulation':\n    data_folder = f'/home/sstroobants/ownCloud/PhD/Code/IMU_NN/data/training_datasets/simulation_{freq}hz__bins_minmax/Train'\n    # datasets = load_data_list(data_folder, skip_initial=skip_initial, normalization=None)\n    # val_data_not_normalized = datasets[:-1]\n    # # val_data_not_normalized = [datasets[-1]]\n\n    # datasets_normalized = load_data_list(data_folder, skip_initial=skip_initial, normalization=normalization_type, gyro_max=gyro_max, acc_max=acc_max)\n    # val_data = datasets_normalized[:-1] # these are currently unused\n    # # val_data = [datasets_normalized[-1]]\n\nelif test_data_type == 'crazyflie':\n    test_filename = '2021-02-12+09-43-34+kalman+twr+cyberzoo+optitrackstate+triangle.csv'\n    val_data_not_normalized = [load_dataset_crazyflie(test_filename, skip_initial=skip_initial, normalization=None, gyro_max=gyro_max, acc_max=acc_max)]\n    val_data = [load_dataset_crazyflie(test_filename, skip_initial=skip_initial, gyro_max=gyro_max, acc_max=acc_max)]\n\n# Set Generators to make sure data for Madgwick and Model are the same\n# g_model = torch.Generator()\n# g_madgwick = torch.Generator()\n# g_madgwick.seed()\n# g_model.set_state(g_madgwick.get_state())\n\n# Create dataloader for the PyTorch model\ndataset = Dataset(data_folder, options, inter_seq_dist=100)\n\nsmplr_model = torch.utils.data.RandomSampler(np.arange(len(dataset)))\ndata_loader = torch.utils.data.DataLoader(dataset=dataset,\n                        shuffle=False,\n                        batch_size=test_batchsize,\n                        sampler=smplr_model,\n                        drop_last=True)\n\n# Create data for the Madgwick filter\n# dataset_not_normalized = Dataset(val_data_not_normalized, seq_length=seq_size, inter_seq_dist=100)\n# smplr_madgwick = torch.utils.data.RandomSampler(np.arange(len(dataset)), generator=g_madgwick)\n# data_loader_not_normalized = torch.utils.data.DataLoader(dataset=dataset_not_normalized,\n#                         shuffle=False,\n#                         batch_size=test_batchsize,\n#                         sampler=smplr_madgwick,\n#                         drop_last=True)\n\n###############################################################################\n# Load model\n###############################################################################\n\n# Load model from file\ndevice = torch.device('cpu')\nif model_type == \"ANN\":\n    test_model = torch.load(model_filename).to(device)\nif model_type == \"SNN\":\n    test_model = SNNRecurrent(snn_size, output_size)\n    # test_model = SNN(6, 40, 2, record=True)\n    # test_model = torch.load(model_filename)\n    state_dict = torch.load(model_filename)\n    print(state_dict)\n    test_model.load_state_dict(state_dict)\n    test_model.reset()\n\n# print(state_dict)\nprint(f'model has {int(len(state_dict_to_weights_array(test_model.state_dict())))} parameters')\ncrit = torch.nn.MSELoss()\n\n###############################################################################\n# Simulate model\n###############################################################################\n\n# Initialize hidden state for the GRU\nif model_type == \"ANN\":\n    hidden = test_model.init_hidden(test_batchsize).to(device)\n\n# Calculate the model outputs and euler angles\nwith torch.no_grad():\n    test_model.eval()\n    \n    if test_data_type == 'simulation':\n        # torch.manual_seed(1349)\n        # torch.manual_seed(430213)\n        input, target = next(iter(data_loader))\n        plt.figure(figsize=[15, 7])\n        plt.subplot(2, 1, 1)\n        plt.plot(np.linspace(0, 20, 2000), input[..., :3].squeeze(0))\n        plt.title('Normalized gyroscope data')\n        plt.legend(['gyro x', 'gyro y', 'gyro z'])\n        plt.subplot(2, 1, 2)\n        plt.plot(np.linspace(0, 20, 2000), input[..., 3:].squeeze(0))\n        plt.title('Normalized accelerometer data')\n        plt.legend(['acc x', 'acc y', 'acc z'])\n        plt.xlabel('time [s]')\n        plt.show()\n        eulers = target.squeeze(0).numpy()\n        # eulers = to_euler_angles(target).squeeze(0).numpy()\n        # input_madg, target_madg = next(iter(data_loader_not_normalized))\n    elif test_data_type == 'crazyflie':\n        input, target, _ = next(iter(data_loader))\n        input_madg, target_madg, eulers = next(iter(data_loader_not_normalized))\n        eulers = eulers.squeeze(0).numpy() * np.pi / 180.0\n    \n    gyr_np = input.squeeze(0).numpy()[:, :3]\n    acc_np = input.squeeze(0).numpy()[:, 3:6]\n\n    if model_type == \"ANN\":    \n        out, hidden = test_model(input, hidden)\n    elif model_type == \"SNN\":\n        # encoded = position_coding(input, nbins)\n        out = test_model(input)\n\n    if output_size == 4:\n        euler_outs = to_euler_angles(out).squeeze(0).numpy()\n        q, q0 = inclination_loss(out, target)\n        loss = crit(q, q0)\n\n    elif output_size == 2:\n        # euler_outs = out.squeeze(1).numpy() \n        euler_outs = out.squeeze(0).numpy() \n        # loss = crit(out, to_euler_angles(target)[..., :2])\n        # loss = crit(out, target.permute(1, 0, 2))\n        loss = crit(out, target)\n\n    outs = out.squeeze(0).numpy()\n    targets = target.squeeze(0).numpy()\n    # loss = err\n    madgwick = ahrs.filters.Madgwick(gyr=gyr_np, acc=acc_np, frequency=freq)\n    madgwick_eulers = to_euler_angles_numpy(madgwick.Q)\n\nprint(loss)\nprint(np.mean(np.abs(eulers[:, 0] - euler_outs[:, 0])) * 180 / np.pi)\nprint(np.mean(np.abs(eulers[:, 1] - euler_outs[:, 1])) * 180 / np.pi)\n\npitch_err_network = (euler_outs[:, 0] - eulers[:, 0]) * 180 / np.pi\nroll_err_network = (euler_outs[:, 1] - eulers[:, 1]) * 180 / np.pi\n\npitch_err_madg = (madgwick_eulers[:, 0] - eulers[:, 0]) * 180 / np.pi\nroll_err_madg = (madgwick_eulers[:, 1] - eulers[:, 1]) * 180 / np.pi\n\n\n###############################################################################\n# Plot the outputs\n###############################################################################\n\n\nif plot_quaternions and (output_size == 4):\n    plt.figure(figsize=[10, 10])\n    plt.subplot(4, 1, 1)\n    plt.title('quaternion w')\n    plt.plot(outs[:, 0], label='network')\n    plt.plot(madgwick.Q[:, 0], label='Madgwick')\n    plt.plot(targets[:, 0], label='groundtruth')\n    plt.legend()\n\n    plt.subplot(4, 1, 2)\n    plt.title('quaternion x')\n    plt.plot(outs[:, 1], label='network')\n    plt.plot(madgwick.Q[:, 1], label='Madgwick')\n    plt.plot(targets[:, 1], label='groundtruth')\n\n    plt.legend()\n\n    plt.subplot(4, 1, 3)\n    plt.title('quaternion y')\n    plt.plot(outs[:, 2], label='network')\n    plt.plot(madgwick.Q[:, 2], label='Madgwick')\n    plt.plot(targets[:, 2], label='groundtruth')\n    plt.legend()\n\n    plt.subplot(4, 1, 4)\n    plt.title('quaternion z')\n    plt.plot(outs[:, 3], label='network')\n    plt.plot(madgwick.Q[:, 3], label='Madgwick')\n    plt.plot(targets[:, 3], label='groundtruth')\n    plt.legend()\n    plt.show()\n\n\nif plot_eulers:\n    plt.figure(figsize=[10, 10])\n\n    plt.subplot(2, 1, 1)\n    plt.title('pitch')\n    plt.plot(euler_outs[:, 0], label='network')\n    plt.plot(eulers[:, 0], label='groundtruth')\n    # plt.plot(madgwick_eulers[:, 0], label='Madgwick')\n    plt.ylabel('pitch [rad]')\n    plt.legend()\n\n    # plt.subplot(2, 2, 3)\n    # plt.title('Pitch error')\n    # plt.plot(np.zeros(len(pitch_err_madg)), 'k')\n    # plt.plot(pitch_err_network, label='network')\n    # plt.plot(pitch_err_madg, label='Madgwick')\n    # plt.xlabel('time [ms]')\n    # plt.ylabel('deg')\n    # plt.legend()\n\n    plt.subplot(2, 1, 2)\n    plt.title('roll')\n    plt.plot(euler_outs[:, 1], label='network')\n    plt.plot(eulers[:, 1], label='groundtruth')\n    # plt.plot(madgwick_eulers[:, 1], label='Madgwick')\n    plt.ylabel('roll [rad]')\n    plt.legend()\n\n    # plt.subplot(2, 2, 4)\n    # plt.title('Roll error')\n    # plt.plot(np.zeros(len(roll_err_madg)), 'k')\n    # plt.plot(roll_err_network, label='network')\n    # plt.plot(roll_err_madg, label='Madgwick')\n    # plt.xlabel('time [ms]')\n    # plt.ylabel('deg')\n    # plt.legend()\n\n    plt.show()\n\n# plt.subplot(5, 1, 4)\n# plt.title('accelerometer')\n# train_data.get_training_list()[0][0]['linear_acceleration.x'].iloc[start_i:test_size+start_i].plot()\n# train_data.get_training_list()[0][0]['linear_acceleration.y'].iloc[start_i:test_size+start_i].plot()\n# (train_data.get_training_list()[0][0]['linear_acceleration.z'].iloc[start_i:test_size+start_i]).plot()\n\n# plt.subplot(5, 1, 5)\n# plt.title('gyro')\n# train_data.get_training_list()[0][0]['angular_velocity.x'].iloc[start_i:test_size+start_i].plot()\n# train_data.get_training_list()[0][0]['angular_velocity.y'].iloc[start_i:test_size+start_i].plot()\n# train_data.get_training_list()[0][0]['angular_velocity.z'].iloc[start_i:test_size+start_i].plot()\n","repo_name":"tudelft/neuromorphic_attitude_estimation","sub_path":"evaluation/evaluate_model_real_data.py","file_name":"evaluate_model_real_data.py","file_ext":"py","file_size_in_byte":10854,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"74867260443","text":"# Search for eligable files\n\nfrom __future__ import print_function\nimport os.path\nimport re\nimport os\n\ndef find(root, reg):\n\n    if not os.path.isdir(root):\n        raise(Exception(\"Root is not a directory: %s\" % root))\n\n    # could use os.walk, but oh well we can ignore .files this way\n    for entry in os.listdir(root):\n        if not entry[:1] == \".\":\n            entry_path = os.path.join(root, entry)\n            if os.path.isdir(entry_path):\n                for loop in find(entry_path, reg):\n                    yield loop\n            elif reg.match(entry_path):\n                yield entry_path\n\n# if __name__ == '__main__':\n#     root = os.path.dirname(__file__)\n#     for file_ in find(root, re.compile(r\"^.+\\.mov$\", re.I)):\n#         print(file_)\n","repo_name":"internetimagery/handbrake-compress","sub_path":"search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":759,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"27413859488","text":"import grequests\nfrom config import token_weather, WEEK\nfrom yandex_translate import translate\nimport PIL.Image as Image\nimport datetime\n\nfrom pprint import pprint\n\nINTERVALS = ['Утро', 'День', 'Вечер', 'Ночь', ]\n\n\ndef merger_img(paths: list, path_out: str) -> str:\n    img = Image.new('RGBA', (60 * len(paths), 60))\n    img_all = []\n    for index, path in enumerate(paths):\n        img_all.append(Image.open(path).resize((60, 60)))\n        img.paste(img_all[index], (60 * index, 0))\n    img.save(path_out)\n\n    return path_out\n\n\ndef type_wind(digit: float) -> str:\n    if 0 <= digit <= 0.2:\n        digit = 'штиль'\n    elif 0.3 <= digit <= 1.5:\n        digit = 'тихий'\n    elif 1.5 <= digit <= 3.3:\n        digit = 'лёгкий'\n    elif 3.3 <= digit <= 5.4:\n        digit = 'слабый'\n    elif 5.4 <= digit <= 7.9:\n        digit = 'умеренный'\n    elif 7.9 <= digit <= 10.7:\n        digit = 'свежий'\n    elif 10.7 <= digit <= 13.8:\n        digit = 'сильный'\n    elif 13.8 <= digit <= 17.1:\n        digit = 'крепкий'\n    elif 17.1 <= digit <= 20.7:\n        digit = 'очень крепкий'\n    elif 20.7 <= digit <= 24.4:\n        digit = 'шторм'\n    elif 24.4 <= digit <= 28.4:\n        digit = 'сильный шторм'\n    elif 28.4 <= digit <= 32.6:\n        digit = 'жестокий шторм'\n    else:\n        digit = 'ураган'\n\n    return digit\n\n\ndef NSEW(deg: int) -> str:\n    if deg in (list(range(348, 361)) + list(range(0, 12))):\n        deg = 'север'\n    elif deg in range(12, 34):\n        deg = \"северо-северо-восток\"\n    elif deg in range(34, 57):\n        deg = 'северо-восток'\n    elif deg in range(57, 79):\n        deg = 'восток-северо-восток'\n    elif deg in range(79, 102):\n        deg = 'восток'\n    elif deg in range(102, 124):\n        deg = 'восток-юго-восток'\n    elif deg in range(124, 147):\n        deg = 'юго-восток'\n    elif deg in range(147, 169):\n        deg = 'юго-юго-восток'\n    elif deg in range(169, 192):\n        deg = 'юг'\n    elif deg in range(192, 214):\n        deg = 'юго-юго-запад'\n    elif deg in range(214, 237):\n        deg = 'юго-запад'\n    elif deg in range(237, 259):\n        deg = 'запад-юго-запад'\n    elif deg in range(259, 282):\n        deg = 'запад'\n    elif deg in range(282, 304):\n        deg = 'запад-северо-запад'\n    elif deg in range(304, 327):\n        deg = 'северо-запад'\n    elif deg in range(327, 348):\n        deg = 'северо-северо-запад'\n\n    return deg\n\n\ndef parse_weather_def_now(city='moscow', units='metric', lang='en') -> [str, str]:\n    city = translate(translate(city, 'en'), 'ru')\n    link = f'http://api.openweathermap.org/data/2.5/weather?q={city}&appid={token_weather}&units={units}&lang={lang}'\n    request, = grequests.map([grequests.get(link)])\n    data = request.json()\n    text = 'Город не найден'\n    if data['cod'] == 200:\n        png = f'http://openweathermap.org/img/wn/{data[\"weather\"][0][\"icon\"]}.png'\n        request_img, = grequests.map([grequests.get(png)])\n        with open(r'data/weather_img/now/photo.png', 'wb') as file:\n            file.write(request_img.content)\n\n        deg = NSEW(data[\"wind\"][\"deg\"])\n        type_ = type_wind(float(data[\"wind\"][\"speed\"]))\n        text = translate(translate(f'Погода в {data[\"name\"]} {data[\"weather\"][0][\"main\"].lower()}.\\n'\n                                   f'On the street {data[\"weather\"][0][\"description\"]}.\\n'\n                                   f'Температура колеблется от {data[\"main\"][\"temp_min\"]} '\n                                   f'до {data[\"main\"][\"temp_max\"]}°C.\\n'\n                                   f'Давление {(data[\"main\"][\"pressure\"] * 100 * 0.007501):.0f} мм рт.ст.\\n'\n                                   f'Влажность {data[\"main\"][\"humidity\"]}%\\n'\n                                   f'Ветер:\\n'\n                                   f'ᅠ* направление: {deg}, \\n'\n                                   f'ᅠ* скорость: {data[\"wind\"][\"speed\"]} м/с ({type_}).',\n                                   'en'), 'ru')\n    return text, 'data/weather_img/now/photo.png'\n\n\ndef parse_weather_def_day(city='moscow', units='metric', lang='en', cnt=8):\n    city = translate(translate(city, 'en'), 'ru')\n    link = f'http://api.openweathermap.org/data/2.5/forecast?q={city}&units={units}&lang={lang}&cnt={cnt}&appid={token_weather}'\n    request, = grequests.map([grequests.get(link)])\n    data = request.json()\n    text = 'Город не найден'\n    paths = []\n    if int(data['cod']) == 200:\n        text = ''\n        for index, name in zip(([_ for _ in range(2, cnt, 2)] + [cnt - 1, ]), INTERVALS):\n            deg = NSEW(data['list'][index][\"wind\"][\"deg\"])\n            type_ = type_wind(float(data['list'][index][\"wind\"][\"speed\"]))\n            text += name + '\\n'\n            text += (translate(translate(f\"ᅠ- {data['list'][index]['weather'][0]['description'].capitalize()}, \"\n                                         f\"температура:\"\n                                         f\" {data['list'][index]['main']['temp_min']}\"\n                                         f\" - {data['list'][index]['main']['temp_max']}°C.\\n\"\n                                         f'ᅠ- Давление'\n                                         f' {(data[\"list\"][index][\"main\"][\"pressure\"] * 100 * 0.007501):.0f} '\n                                         f'мм рт.ст.\\n'\n                                         f'ᅠ- Влажность {data[\"list\"][index][\"main\"][\"humidity\"]}%\\n'\n                                         f'ᅠ- Ветер:\\n'\n                                         f'ᅠᅠᅠ* направление: {deg}, \\n'\n                                         f'ᅠᅠᅠ* скорость: {data[\"list\"][index][\"wind\"][\"speed\"]} м/с ({type_}).\\n',\n                                         'en'), 'ru'))\n\n            png = f'http://openweathermap.org/img/wn/{data[\"list\"][index][\"weather\"][0][\"icon\"]}.png'\n            request_img, = grequests.map([grequests.get(png)])\n            with open(rf'data/weather_img/today/{name}.png', 'wb') as file:\n                paths.append(rf'data/weather_img/today/{name}.png')\n                file.write(request_img.content)\n\n    return text, merger_img(paths, r'data/weather_img/today/photo.png')\n\n\ndef parse_weather_def_tomorrow(city='moscow', units='metric', lang='en', cnt=16):\n    city = translate(translate(city, 'en'), 'ru')\n    link = f'http://api.openweathermap.org/data/2.5/forecast?q={city}&units={units}&lang={lang}&cnt={cnt}&appid={token_weather}'\n    request, = grequests.map([grequests.get(link)])\n    data = request.json()\n    text = 'Город не найден'\n    paths = []\n    if int(data['cod']) == 200:\n        text = ''\n        for index, name in zip(([_ for _ in range(10, cnt, 2)] + [cnt - 1, ]), INTERVALS):\n            deg = NSEW(data['list'][index][\"wind\"][\"deg\"])\n            type_ = type_wind(float(data['list'][index][\"wind\"][\"speed\"]))\n            text += name + '\\n'\n            text += (translate(translate(f\"ᅠ- {data['list'][index]['weather'][0]['description'].capitalize()}, \"\n                                         f\"температура:\"\n                                         f\" {data['list'][index]['main']['temp_min']}\"\n                                         f\" - {data['list'][index]['main']['temp_max']}°C.\\n\"\n                                         f'ᅠ- Давление'\n                                         f' {(data[\"list\"][index][\"main\"][\"pressure\"] * 100 * 0.007501):.0f} '\n                                         f'мм рт.ст.\\n'\n                                         f'ᅠ- Влажность {data[\"list\"][index][\"main\"][\"humidity\"]}%\\n'\n                                         f'ᅠ- Ветер:\\n'\n                                         f'ᅠᅠᅠ* направление: {deg}, \\n'\n                                         f'ᅠᅠᅠ* скорость: {data[\"list\"][index][\"wind\"][\"speed\"]} м/с ({type_}).\\n',\n                                         'en'), 'ru'))\n\n            png = f'http://openweathermap.org/img/wn/{data[\"list\"][index][\"weather\"][0][\"icon\"]}.png'\n            request_img, = grequests.map([grequests.get(png)])\n            with open(rf'data/weather_img/tomorrow/{name}.png', 'wb') as file:\n                paths.append(rf'data/weather_img/tomorrow/{name}.png')\n                file.write(request_img.content)\n\n    return text, merger_img(paths, r'data/weather_img/tomorrow/photo.png')\n\n\ndef parse_weather_def_5_days(city='moscow', units='metric', lang='en', cnt=40):\n    city = translate(translate(city, 'en'), 'ru')\n    link = f'http://api.openweathermap.org/data/2.5/forecast?q={city}&units={units}&lang={lang}&cnt={cnt}&appid={token_weather}'\n    request, = grequests.map([grequests.get(link)])\n    data = request.json()\n    text = 'Город не найден'\n    paths = []\n    if int(data['cod']) == 200:\n        text = ''\n        for index in range(0, cnt, 8):\n            index += 5\n            year, month, day = (data['list'][index]['dt_txt'].split()[0].split('-'))\n            temp = datetime.datetime(year=int(year), month=int(month), day=int(day))\n            name = WEEK[temp.weekday() + 1]\n\n            text += name + '\\n'\n            text += (translate(translate(f\"ᅠ- Днем: {data['list'][index - 1]['weather'][0]['description'].capitalize()}, \"\n                                         f\"{data['list'][index - 1]['main']['temp_max']}°C.\\n\"\n                                         f\"ᅠ- Ночью: {data['list'][index + 2]['weather'][0]['description'].capitalize()}, \"\n                                         f\"{data['list'][index + 2]['main']['temp_max']}°C.\\n\",\n                                         'en'), 'ru'))\n\n            png = f'http://openweathermap.org/img/wn/{data[\"list\"][index][\"weather\"][0][\"icon\"]}.png'\n            request_img, = grequests.map([grequests.get(png)])\n            with open(rf'data/weather_img/5_days/{name}.png', 'wb') as file:\n                paths.append(rf'data/weather_img/5_days/{name}.png')\n                file.write(request_img.content)\n\n    return text, merger_img(paths, r'data/weather_img/5_days/photo.png')\n\n\ndef parse_weather_now(*args):\n    return parse_weather_def_now(*args)\n\n\ndef parse_weather_day(*args):\n    return parse_weather_def_day(*args)\n\n\ndef parse_weather_day_tomorrow(*args):\n    return parse_weather_def_tomorrow(*args)\n\n\ndef parse_weather_day_5_days(*args):\n    return parse_weather_def_5_days(*args)\n\n\nif __name__ == '__main__':\n    # print(parse_weather_now())\n    # parse_weather_def_tomorrow()\n    print(parse_weather_def_5_days()[0])\n","repo_name":"S0IG0/Vk-bot-from-python","sub_path":"weather_links.py","file_name":"weather_links.py","file_ext":"py","file_size_in_byte":10884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"5420215165","text":"from sys import stdin, stdout\nimport string\n\n# 단지 인덱싱을 위한 알파벳 리스트\nalpha = list(string.ascii_uppercase)\n\nvalue = [['A', 0], ['B', 0], ['C', 0], ['D', 0],\n         ['E', 0], ['F', 0], ['G', 0], ['H', 0],\n         ['I', 0], ['J', 0], ['K', 0], ['L', 0],\n         ['M', 0], ['N', 0], ['O', 0], ['P', 0],\n         ['Q', 0], ['R', 0], ['S', 0], ['T', 0],\n         ['U', 0], ['V', 0], ['W', 0], ['X', 0],\n         ['Y', 0], ['Z', 0]]\n\nn = int(stdin.readline())\n\nwords = []\nnums = []\n\n# 단어 저장\nfor i in range(n):\n    word = stdin.readline().strip()\n    words.append(word)\n\n    for i in range(len(word)):\n        value[alpha.index(word[i])][1] += pow(10, len(word) - 1 - i)\n\nvalue.sort(reverse=True, key=lambda x: x[1])\n\n# 중요도 값 부여\npriority = 9\nfor x in value:\n    x[1] = priority\n\n    if priority == 0:\n        break\n\n    priority -= 1\n\n# 간편한 인덱싱을 위해 다시 알파벳 순으로 정렬\nvalue.sort()\n\n# 각 알파벳을 숫자로 치환 후 저장\nfor word in words:\n    for x in word:\n        word = word.replace(x, str(value[alpha.index(x)][1]))\n    nums.append(int(word))\n\n# 합 출력\nprint(sum(nums))\n","repo_name":"H43RO/PythonAlgorithm","sub_path":"00.Solve/BOJ/1000-2000/1339.py","file_name":"1339.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"ko","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"23749441956","text":"import numpy as np\nimport random\n\ndef replace_center_with_minus_one(d, n, m):\n    if n < 0 or m < 0 or d <= 0 or m > n:\n        raise ValueError(\"Error: Invalid values!!\")\n    \n    array = np.random.randint(10**(d - 1), 10**d, size=(n, n))\n    x = (n - m) // 2\n    y = x + m\n    \n    for i in range(x, y):\n        for j in range(x, y):\n            array[i, j] = -1\n\n    return array\n","repo_name":"canbula/NumericalAnalysis","sub_path":"Week04/hw/arrays_sedef_elmas.py","file_name":"arrays_sedef_elmas.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"86"}
+{"seq_id":"8126250145","text":"import utils.util as util\n\nimport os\nimport numpy as np\n\ndef get_dictionaries(dataset_path,train_split=0.7):\n    file_names = {}\n\n    for i,filename in enumerate(os.listdir(dataset_path)):\n        file_names[i+1] = filename\n\n    partition = {'train_ids': [], 'val_ids': []}\n\n    indices = np.arange(1,len(file_names)+1)\n    util.shuffleArray(indices,0)\n\n    n_train_images = int(train_split*len(indices))\n    partition['train_ids'] = indices[0:n_train_images]\n    partition['val_ids'] = indices[n_train_images:len(indices)+1]\n\n    return (partition,file_names)\n","repo_name":"reeshof/MTG-card-scanner","sub_path":"src/lib/card_recognition/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"5254365904","text":"\n# -------------------------- # -------------------------- #\n# Training data assembly --- # -------------------------- #\n# -------------------------- # -------------------------- #\n\nimport re\nimport numpy as np\nfrom commlit.up_scale import even_upsample\n\ndef gen_train_data(df, \n                   tgt_df=None,\n                   sent_df=None,\n                   gen_agg_feat=True,\n                   up_sample=True,\n                   rem_punct=False, \n                   rem_stop=False,\n                   min_stop_len=0.3, \n                   x_cols=None,\n                   x_split=True,\n                   drop_cols=[\"seq\", \"word\", \"alpha\"],\n                   agg_excl=[\"seq\", \"word\"],\n                   agg_excl_vec=True,\n                   quant_cols=[\"length\", \"comm_score\"],\n                   quantiles=np.arange(0.025, 1, 0.025),\n                   tgt_noise_var=\"length\",\n                   tgt_noise_mult=2,\n                   sent_norm={\"sent_length\": 50, \n                              \"noun_chunks\": 10},\n                   up_sample_param={\"n_row\": 125,\n                                    \"n_rep\": 5},\n                   seed=42):\n    \"\"\"\n    \"\"\"\n    \n    # set seed\n    np.random.seed(seed)\n    \n    # take copy\n    x = df.copy()\n    \n    # aggregate selected features\n    if gen_agg_feat:\n        if agg_excl_vec:\n            agg_excl += [f for f in x.columns if \n                         bool(re.match(\"v[0-9]+$\", f))]\n        agg_cols = [f for f in x.columns if f not in agg_excl]\n        m = x[agg_cols].groupby(\"id\").mean().reset_index()\n        \n        # add sentence features if available\n        if sent_df is not None:\n            s = sent_df.groupby(\"id\").mean().reset_index()\n            for k, v in sent_norm.items():\n                s.loc[:, k] = s[k] / v\n            m = m.merge(s, on=\"id\")\n    else:\n        m = None\n    \n    # quantile features\n    q = {}\n    if quant_cols is not None:\n        for qc in quant_cols:\n            q_df = x[x[\"alpha\"]==True].groupby(\"id\")[qc]\n            q_df = q_df.quantile(quantiles).reset_index()\n            q_df = q_df.pivot(\"id\", \"level_1\", qc)\n            q_df.columns = [\"q\" + str(np.round(i, 5)) for i in quantiles]\n            q[qc] = q_df.reset_index()  \n    \n    # drop unwanted columns\n    x = x.drop(drop_cols, axis=1)\n    \n    # remove punctuation tokens\n    if rem_punct:\n        x = x[x[\"punct\"]==False].drop([\"punct\"], axis=1)\n       \n    # remove (short) stop words    \n    if rem_stop:\n        x = x[(x[\"stop\"]==False) | \n              (x[\"length\"]>=min_stop_len)].drop([\"stop\"], axis=1)\n    \n    # drop columns with no variance, unless x_cols pre-specified\n    if x_cols is None:\n        x_std = x.std()\n        x = x.drop(x_std[x_std == 0].index.to_list(), axis=1)\n        x_cols = x.columns.to_list()\n    else:\n        x = x[x_cols]\n    \n    # upsample even number of rows per id    \n    if up_sample:\n        x = x.groupby(\"id\").apply(even_upsample,\n                                  n_row=up_sample_param[\"n_row\"],\n                                  n_rep=up_sample_param[\"n_rep\"]\n                                  ).reset_index(drop=True)\n    \n    # get the frame\n    frame = x[[\"id\", \"grp_id\"]].drop_duplicates()\n    \n    # update aggregate features\n    if gen_agg_feat:\n        m = frame.merge(m, on=\"id\")\n    if quant_cols is not None:\n        for qc in quant_cols:\n            q[qc] = frame.merge(q[qc], on=\"id\")\n    \n        \n    # create target variable\n    if tgt_df is not None:\n        y = x.groupby([\"id\", \"grp_id\"])[tgt_noise_var].mean().reset_index()\n        y.loc[:, \"diff_avg\"] = y.groupby(\"id\")[tgt_noise_var].transform(\"mean\")\n        y.loc[:, \"diff_dev\"] = 1 - y[tgt_noise_var]/y[\"diff_avg\"]\n        y = y.merge(tgt_df, on=\"id\", how=\"inner\")\n        y = y[\"target\"] + y[\"diff_dev\"]*y[\"standard_error\"]*tgt_noise_mult\n        y = y.values\n    else:\n        y = None\n        \n    # convert x to matrix input\n    x = list(x.groupby([\"id\", \"grp_id\"]))\n    x = [grp[1].drop([\"id\", \"grp_id\"], axis=1).astype(float).values \n         for grp in x]\n    x = np.stack(x)[:, :, :, np.newaxis]\n    \n    # split x into two \n    if x_split:\n        v_idx = [bool(re.match(\"v[0-9]+$\", c)) for c in x_cols if c != \"id\"]\n        a_idx = [not vi for vi in v_idx]\n        x, a = x[:, :, v_idx, :], x[:, :, a_idx, :]  \n    else:\n        a = np.array(0)    \n        \n    return x, a, y, m, q, frame, x_cols\n","repo_name":"robbie-manolache/kaggle-commlit","sub_path":"commlit/train_data.py","file_name":"train_data.py","file_ext":"py","file_size_in_byte":4397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"14361487337","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon May 31 15:17:18 2021\r\n\r\n@author: Yi\r\n\"\"\"\r\n\r\nmealCost = float(input('Enter the cost of the meal before tax and tip'))\r\ntipPercent = int(input('Enter the percentage of the cost of the meal being added as tip'))\r\ntaxPercent = int(input('Enter the percentage of the tax of the meal being added as tax'))\r\nx = round(mealCost + mealCost * tipPercent / 100 + mealCost * taxPercent / 100)\r\nprint('The total cost of a meal is ', x)\r\n","repo_name":"yiyue-zhang/30DayCodingChallenge-Hackerrank","sub_path":"Day 2 Operators solution.py","file_name":"Day 2 Operators solution.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"838149810","text":"#\n# [11] Container With Most Water\n#\n# https://leetcode.com/problems/container-with-most-water\n#\n# Medium (36.16%)\n# Total Accepted:    115284\n# Total Submissions: 318834\n# Testcase Example:  '[1,1]'\n#\n# Given n non-negative integers a1, a2, ..., an, where each represents a point\n# at coordinate (i, ai). n vertical lines are drawn such that the two endpoints\n# of line i is at (i, ai) and (i, 0). Find two lines, which together with\n# x-axis forms a container, such that the container contains the most water.\n#\n# Note: You may not slant the container and n is at least 2.\n#\n#\n\n\nclass Solution(object):\n\n    def maxArea(self, height):\n        \"\"\"\n        :type height: List[int]\n        :rtype: int\n        \"\"\"\n        lo, hi = 0, len(height) - 1\n        result = min(height[0], height[-1]) * (hi - lo)\n        while lo < hi:\n            area_curr = min(height[lo], height[hi]) * (hi - lo)\n            result = max(area_curr, result)\n            if height[lo] < height[hi]:\n                lo += 1\n            elif height[lo] > height[hi]:\n                hi -= 1\n            else:\n                lo += 1\n                hi -= 1\n        return result\n","repo_name":"imzhen/Leetcode-Exercise","sub_path":"src/container-with-most-water.py","file_name":"container-with-most-water.py","file_ext":"py","file_size_in_byte":1154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"16110057158","text":"import seaborn as sns\nimport matplotlib.pyplot as plt\ntips = sns.load_dataset('tips')\n\n# Data Set\ntips = sns.load_dataset('tips')\n# total_bill | tip | sex | smoker | day | time | size\n# 16.99 | 1.01 | Female | No | Sun | Dinner | 2\n\n# Style\n# {darkgrid, whitegrid, dark, white, ticks}\nsns.set_style('ticks')\nsns.countplot(x='sex', data=tips)\n\n# Removing spines from the plot\nsns.despine()\n\n# Setting Context\n# \"paper\", \"talk\", and \"poster\"\nsns.set_context('poster', font_scale=3)\n\n# Colormap\n# https://matplotlib.org/examples/color/colormaps_reference.html\nsns.lmplot(x='total_bill', y='tip', data=tips, hue='sex', size=2, aspect=4,\n           palette='inferno')\n\nplt.show()\n","repo_name":"leon-lei/learning-materials","sub_path":"data_science/seaborn_tutorials/seaborn_practice5_style_color.py","file_name":"seaborn_practice5_style_color.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"43441213164","text":"from __future__ import annotations\n\nfrom typing import Union\n\nimport torch\n\nfrom . import resnet, preact_resnet\n\n\ndef resnet_init(model: Union[resnet.ResNet, preact_resnet.PreActResNet], zero_init_residual=False):\n    \"\"\"Initialize a resnet following the paper [1]\n\n    (More Precisely: Delving deep into rectifiers: Surpassing human-level performance on ImageNet\n    classification - He, K. et al. (2015))\n\n    Args:\n        model (ResNet|PreActResNet): The resnet model to initialize\n        zero_init_residual (bool): Following https://arxiv.org/abs/1706.02677 and torchvision.\n            Set the weights of the final norm of each residual branch at 0 so that each block behaves\n            at first as an identity. For PreActResNet, we extend this principle and set the weights of\n            the final convolution of each residual branch to 0. (See Results in README.md)\n    \"\"\"\n    for module in model.modules():\n        if isinstance(module, torch.nn.Conv2d):\n            torch.nn.init.kaiming_normal_(module.weight, mode=\"fan_out\", nonlinearity=\"relu\")\n        elif isinstance(module, torch.nn.BatchNorm2d):\n            torch.nn.init.constant_(module.weight, 1)\n            torch.nn.init.constant_(module.bias, 0)\n\n    if zero_init_residual:\n        for module in model.modules():\n            if isinstance(module, resnet.BasicBlock):\n                torch.nn.init.constant_(module.bn2.weight, 0)\n            elif isinstance(module, resnet.Bottleneck):\n                torch.nn.init.constant_(module.bn3.weight, 0)\n            elif isinstance(module, preact_resnet.PreActBlock):\n                torch.nn.init.constant_(module.conv2.weight, 0)\n            elif isinstance(module, preact_resnet.PreActBottleneck):\n                torch.nn.init.constant_(module.conv3.weight, 0)\n","repo_name":"raphaelreme/torch-resnet","sub_path":"torch_resnet/init.py","file_name":"init.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"6863738991","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Dec  1 16:16:41 2022\n\n@author: 21205907\n\"\"\"\n\nfrom gurobipy import *\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndico_arcs ={'x_1':'AB', 'x_2':'AC', 'x_3':'AD', 'x_4':'BE', 'x_5':'BC','x_6':'BD', 'x_7':'DC', 'x_8':'DF', 'x_9':'CE', 'x_10':'CF',\n            'x_11':'EG', 'x_12':'FG'}\n\nlist_names = ['x_1', 'x_2', 'x_3', 'x_4', 'x_5', 'x_6', 'x_7', 'x_8', 'x_9', 'x_10',\n            'x_11', 'x_12', 'x_13', 'x_14', 'x_15', 'r_16', 'x_17', 'x_18', 'b_1,1',\n            'b_2,1', 'r_2', 'b_2,1', 'b_2,2']\n\n\n\ndef poids(n,alpha):\n    w = np.zeros(n)\n    for i in range(1,n+1):\n        w[i-1] = ((n-i+1)/n)**alpha - ((n-i)/n)**alpha\n    return w\n\ndef w_prime(w):\n    w_p = [w[i] - w[i+1] for i in range(len(w)-1)]\n    w_p.append(w[-1])\n    return w_p\n\n\n# on tire au hasard 20 fois les temps de trajets des deux scénarios\n# c'est donc les valeurs de z1 et z2 qui changent donc on doit modifier la matrice des contraintes\n\ndef matrice_contraintes(t1,t2):\n\n    a = [[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],  # contraintes sur arcs\n         [0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n         [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n         [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n         [1, 0, 0,-1,-1,-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n         [0, 0, 1, 0, 0, 1,-1, 0,-1,-1, 0, 0, 0, 0, 0, 0, 0, 0],\n         [0, 1, 0, 0, 1, 0, 1, 0,-1,-1, 0, 0, 0, 0, 0, 0, 0, 0],\n         [0, 0, 0, 1, 0, 0, 0, 0, 1, 0,-1, 0, 0, 0, 0, 0, 0, 0],\n         [0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,-1, 0, 0, 0, 0, 0, 0],\n         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n         [t1[0], t1[1], t1[2], t1[3], t1[4], t1[5], t1[6], t1[7], t1[8], t1[9], t1[10], t1[11], 1,-1, 0, 0, 0, 0],  # r1 z1\n         [t2[0], t2[1], t2[2], t2[3], t2[4], t2[5], t2[6], t2[7], t2[8], t2[9], t2[10], t2[11], 1, 0,-1, 0, 0, 0],  # r1 z2\n         [t1[0], t1[1], t1[2], t1[3], t1[4], t1[5], t1[6], t1[7], t1[8], t1[9], t1[10], t1[11], 0, 0, 0, 1,-1, 0],  # r2 Z1\n         [t2[0], t2[1], t2[2], t2[3], t2[4], t2[5], t2[6], t2[7], t2[8], t2[9], t2[10], t2[11], 0, 0, 0, 1, 0,-1]]  # r2 z2\n    return a\n\n\ndef solve(nbcont,nbvar,nb_scenarios,alpha,t1,t2):\n\n    n = nb_scenarios\n    nb = np.zeros(n)\n    for i in range(n):\n        nb[i] = i+1\n\n    # Intervalles de nos variables\n    lignes = range(nbcont)\n    colonnes = range(nbvar)\n\n    # Explicitation des colonnes représentants les variables rk, bik et xi\n    colonnes_rk = [12, 15]\n    colonnes_bik = [13, 14, 16, 17]\n    colonnes_x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n    # Explicitation des lignes pour les signes des contraintes\n    lignes_egal = [0, 4, 5, 6, 7, 8, 9]\n    lignes_inf = [1, 2, 3, 10, 11]\n\n    # Matrice des contraintes\n    a = matrice_contraintes(t1,t2)\n\n    # Second membre\n    b = [1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0]\n\n    # mes poids :\n    w = poids(n,alpha)\n    w_p = w_prime(w)\n    w_p1 = w_p[0]\n    w_p2 = w_p[1]\n\n    # Coefficients de la fonction objective\n    c = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, nb[0]*w_p1, -w_p1, -w_p1, nb[1]*w_p2, -w_p2, -w_p2]\n\n    # Création de modèle\n    m = Model(\"mogplex\")\n\n    # Déclaration variables de décision\n    x = []\n    for i in colonnes:\n        # les rk sont réels non bornés\n        if i in colonnes_rk:\n            x.append(m.addVar(vtype=GRB.CONTINUOUS, lb=-GRB.INFINITY, name=\"r%d\" % (i + 1)))\n\n        # les bik sont supérieurs ou égaux à 0\n        if i in colonnes_bik:\n            x.append(m.addVar(vtype=GRB.CONTINUOUS, lb=0, name=\"b%d\" % (i + 1)))\n\n        # les xi sont binaires (1 ou 0)\n        if i in colonnes_x:\n            x.append(m.addVar(vtype=GRB.BINARY, name=\"x%d\" % (i + 1)))\n    \n    # MAJ du modèle pour integrer les nouvelles variables\n    m.update()\n    obj = LinExpr();\n    obj = 0\n    for j in colonnes:\n        obj += c[j] * x[j]\n\n    # Définition de l'objectif (maximisation de la fonction objectif)\n    m.setObjective(obj, GRB.MAXIMIZE)\n\n    # Définition des contraintes\n    for i in lignes_inf:\n        m.addConstr(quicksum(a[i][j] * x[j] for j in colonnes) <= b[i], \"Contrainte%d\" % i)\n    for i in lignes_egal:\n        m.addConstr(quicksum(a[i][j] * x[j] for j in colonnes) == b[i], \"Contrainte%d\" % i)\n\n    # Résolution\n    m.optimize()\n\n    # stockage des résultats pour construire graphiques:\n    result = np.zeros((len(colonnes_x)))\n    for j in colonnes_x :\n        result[j] = x[j].x\n    # détermination du scénario : (qui fonctionnera lorsque on aura une valeur correcte de fonction objectif)\n    scenario_1 = np.zeros(12)\n    scenario_2 = np.zeros(12)\n\n    for i in range(12):\n        for j in range(10,12):\n            scenario_1[i] = a[j][i]\n        for j in range(12,14):\n            scenario_2[i] = a[j][i]\n\n    valeur_chemin = m.objVal\n    scenario = 0\n    valeur_s1 = 0\n    valeur_s2 = 0\n\n    for i in range(12):\n        valeur_s1 += scenario_1[i] * x[j].x\n        valeur_s2 += scenario_2[i] * x[j].x\n    if valeur_s1 == valeur_s2 :\n        scenario = 0\n    elif valeur_s1 == valeur_chemin :\n        scenario = 1\n    elif valeur_s2 == valeur_chemin :\n        scenario = 2\n    \n    # Affichage des résultats\n    print(\"\")\n    print('Solution optimale:')\n    for j in colonnes_x:\n        print(list_names[j], '=', x[j].x)\n        somme1 = 0\n        somme2 = 0\n        if x[j].x == 1 :\n            print('arc :',dico_arcs.get(list_names[j]))\n            print('coût arc dans scnéario 1 :',t1[j])\n            print('coût arc dans scnéario 2 :', t2[j])\n            somme1 += t1[j]\n            somme2 += t2[j]\n        print(\"coût du chemin scénario 1 :\", somme1)\n        print(\"coût du chemin scnénario 2 :\", somme2)\n    print(\"\")\n    print('Valeur de la fonction objectif :', m.objVal)\n\n    print(\"Scénario :\",scenario)\n    return result\n# fonction renvoie quels arc ont été sélectionés, soit le meilleur chemin robuste\n\n# on veut :\n# n=2\n# 20 instances de chemins\n# pour : alpha = 1, alpha = 2, alpha = 3, alpha = 4, alpha = 5 soit 5 graphiques :\n# il faudrait que ma fonction renvoie les résultats des arcs et le scénario choisit comme ça\n# je met les variables de l'autre scénario pour faire mon graph\nn = 2\nnbcont = 14\nnbvar = 18\n\n# tirage des 20 instances de temps pour mes scénarios\ntS1 = []\ntS2 = []\nfor i in range(20):\n    tS1.append(np.random.randint(1,10, size=12).tolist())\n    tS2.append(np.random.randint(1,10, size=12).tolist())\n\nfor alpha in range(1,6):\n    plt.figure()\n\n    axe_abscisse = []\n    axe_ordonne = []\n    for i in range(20):\n        result = solve(nbcont,nbvar,n,alpha, tS1[i], tS2[i]) # un array\n        for y in range(12):\n            if result[y]== 1:\n                axe_abscisse.append(tS1[i][y])\n                axe_ordonne.append(tS2[i][y])\n\n    plt.scatter(axe_abscisse,axe_ordonne)\n\n    plt.title(\"Impact de la pondération sur la robustesse, alpha %x\" %alpha)\n    plt.xlabel(\"scénario 1\")\n    plt.ylabel(\"scénario 2\")\n    nfile = \"./graph/pondération_alpha%x\" %alpha + \".png\"\n    plt.savefig(nfile)\n\nplt.show()\n","repo_name":"RoxaneC/M1-IA-ROBOTIQUE","sub_path":"S1/MOGPL-Projet/partie4/question4_3.py","file_name":"question4_3.py","file_ext":"py","file_size_in_byte":7058,"program_lang":"python","lang":"fr","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"3926552199","text":"# https://www.psycopg.org/docs/usage.html\n\nimport pandas as pd\nimport numpy as np\n\nimport yfinance as yf\nimport datetime\n\ndef finance(name_finance):\n    msft = yf.Ticker(name_finance)\n    hist = msft.history(period='max')\n    hist['finance'] = name_finance\n    hist.reset_index(inplace =True)\n    hist['Year'] = hist['Date'].apply(lambda x: x.year)\n    hist['Month'] = hist['Date'].apply(lambda x: x.month)\n    hist['WeekNum'] = hist['Date'].apply(lambda x: x.isocalendar()[1])\n    hist['Day'] = hist['Date'].apply(lambda x: datetime.datetime.strptime(str(x).split(' ')[0], '%Y-%m-%d'))\n    return hist[['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Dividends',\n                 'Stock Splits', 'finance', 'Year', 'Month', 'WeekNum','Day']]\n\nfrom os import lstat\nlst_finance = ['NKE', 'BB', 'FDX', 'CCL', 'BB.TO', 'LASE', 'URNM',\n               'VERO', 'TSLA', 'FTNT', 'CEI', 'OP', 'ADCT']\ndf = pd.DataFrame({})\nfor i in lst_finance:\n    df = df.append(finance(i))\n\n\nimport psycopg2\n\n# Connect to existing database\nconn = psycopg2.connect(\n    database=\"finance\",\n    user=\"postgres\",\n    password=\"postgres\",\n    host=\"host.docker.internal\",\n    port=\"2001\"\n)\n\n# Open cursor to perform database operation\ncur = conn.cursor()\n\nif cur is not None:\n    print('có database nè ')\nelse:\n    print('no')\n#\n# #create table\ncur.execute(\"create table finance (dtime timestamp, open_price float, \\\n            high_price float, low_price float, close_price float, \\\n            volume integer, dividends float, stock_splits float, \\\n            finance varchar(20), year integer, month integer, \\\n            week_num integer, day timestamp);\")\n\n#insert data\nfor i in df.values:\n    print(i)\n    cur.execute(\"INSERT INTO finance (dtime , open_price, high_price, low_price, close_price, volume, dividends, \\\n    stock_splits, finance, year, month, week_num, day) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\", i)\nconn.commit()\n\n# Query the database\ncur.execute(\"SELECT * FROM finance\")\nrows = cur.fetchall()\nfor row in rows:\n    print(row)\ncur.close()\nconn.close()\n","repo_name":"destroyer200/Docker-Postgresql-Superset","sub_path":"db_connect.py","file_name":"db_connect.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"24278707590","text":"\"\"\"Fixtures for zoom tests.\"\"\"\nfrom pytest import fixture\nfrom pytest_homeassistant_custom_component.async_mock import patch\n\n\n@fixture(name=\"external_url\", autouse=True)\ndef external_url_fixture(hass):\n    \"\"\"Set external URL.\"\"\"\n    hass.config.external_url = \"https://example.com\"\n\n\n@fixture(name=\"my_profile\", autouse=True)\ndef my_profile_fixture():\n    \"\"\"Set external URL.\"\"\"\n    with patch(\n        \"custom_components.zoom.ZoomAPI.async_get_my_user_profile\",\n        return_value={\"id\": \"test\", \"profile\": {}},\n    ):\n        yield\n","repo_name":"raman325/ha-zoom-automation","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"86"}
+{"seq_id":"31625509214","text":"import abc\nimport numpy as np\nimport tensorflow as tf\n\nfrom tensorflow_asr.featurizers.speech_featurizers import preemphasis, tf_preemphasis\nfrom tensorflow_asr.featurizers.speech_featurizers import depreemphasis, tf_depreemphasis\n\n\nclass SpeechFeaturizer(metaclass=abc.ABCMeta):\n    def __init__(self, speech_config: dict):\n        \"\"\"\n        speech_config = {\n            \"sample_rate\": int,\n            \"stride\": float,\n            \"preemphasis\": float,\n            \"window_size\": int,\n            \"pad_end\": bool\n        }\n        \"\"\"\n        # Samples\n        self.sample_rate = int(speech_config.get(\"sample_rate\", 16000))\n        self.window_size = int(speech_config.get(\"window_size\", 16384))\n        self.stride = int(self.window_size * speech_config.get(\"stride\", 1))\n        self.preemphasis = float(speech_config.get(\"preemphasis\", 0.95))\n        self.pad_end = bool(speech_config.get(\"pad_end\", False))\n\n    @property\n    def shape(self) -> list:\n        \"\"\" The shape of extracted features \"\"\"\n        return [self.window_size]\n\n    @abc.abstractmethod\n    def extract(self, signal):\n        \"\"\" Function to perform feature extraction \"\"\"\n        raise NotImplementedError()\n\n    @abc.abstractmethod\n    def iextract(self, slices):\n        \"\"\" Function to undo feature extraction \"\"\"\n        raise NotImplementedError()\n\n\nclass NumpySpeechFeaturizer(SpeechFeaturizer):\n\n    def extract(self, signal):\n        signal = preemphasis(signal, self.preemphasis)\n        n_samples = signal.shape[0]\n        slices = []\n        for beg_i, end_i in zip(range(0, n_samples, self.stride), range(self.window_size, n_samples + self.stride, self.stride)):\n            slice_ = signal[beg_i:end_i]\n            if slice_.shape[0] < self.window_size:\n                if self.pad_end:\n                    slice_ = np.pad(slice_, (0, self.window_size - slice_.shape[0]), 'constant', constant_values=0.0)\n                else:\n                    continue\n            if slice_.shape[0] == self.window_size:\n                slices.append(slice_)\n        return np.array(slices, dtype=np.float32)\n\n    def iextract(self, slices):\n        # slices shape = [batch, window_size]\n        signal = np.reshape(slices, [-1])\n        return depreemphasis(signal, self.preemphasis)\n\n\nclass TFSpeechFeaturizer(SpeechFeaturizer):\n\n    def extract(self, signal):\n        signal = tf_preemphasis(signal, self.preemphasis)\n        return tf.signal.frame(signal, self.window_size, self.stride, pad_end=self.pad_end, pad_value=0)\n\n    def iextract(self, slices):\n        signal = tf.reshape(slices, [-1])\n        return tf_depreemphasis(signal, self.preemphasis)\n","repo_name":"nglehuy/sasegan","sub_path":"sasegan/featurizers/speech_featurizer.py","file_name":"speech_featurizer.py","file_ext":"py","file_size_in_byte":2641,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"86"}
+{"seq_id":"72840167645","text":"import pytest\nimport requests\n\nfrom jsonschema import validate\n\n\ndef check_200(response):\n    return response.status_code == 200\n\n\nmain_url = 'https://jsonplaceholder.typicode.com/'\n\nresources = {\n    'posts': 100,\n    'comments': 500,\n    'albums': 100,\n    'photos': 5000,\n    'todos': 200,\n    'users': 10\n}\n\nschema_posts = {\n    'type': 'array',\n    'properties': {\n        'userId': {'type': 'number'},\n        'id': {'type': 'number'},\n        'title': {'type': 'string'},\n        'body': {'type': 'string'}\n    }\n}\nschema_photos = {\n    'type': 'array',\n    'properties': {\n        'albumId': {'type': 'number'},\n        'id': {'type': 'number'},\n        'title': {'type': 'string'},\n        'url': {'type': 'string'},\n        'thumbnailUrl': {'type': 'string'}\n    }\n}\nschema_comments = {\n    'type': 'array',\n    'properties': {\n        'postId': {'type': 'number'},\n        'id': {'type': 'number'},\n        'name': {'type': 'string'},\n        'email': {'type': 'string'},\n        'body': {'type': 'string'}\n    }\n}\nschema_albums = {\n    'type': 'array',\n    'properties': {\n        'userId': {'type': 'number'},\n        'id': {'type': 'number'},\n        'title': {'type': 'string'}\n    }\n}\nschema_todos = {\n    'type': 'array',\n    'properties': {\n        'userId': {'type': 'number'},\n        'id': {'type': 'number'},\n        'title': {'type': 'string'},\n        'completed': {'type': 'boolean'}\n    }\n}\nschema_users = {\n    'type': 'array',\n    'properties': {\n        'id': {'type': 'number'},\n        'name': {'type': 'string'},\n        'username': {'type': 'string'},\n        'email': {'type': 'string'},\n        'address': {\n            'type': 'array',\n            'properties': {\n                'street': {'type': 'string'},\n                'suite': {'type': 'string'},\n                'city': {'type': 'string'},\n                'zipcode': {'type': 'string'},\n            }\n        }\n    }\n}\n\n\n@pytest.mark.parametrize(\"req, num\", [(i, resources[i]) for i in resources])\ndef test_resources(req, num):\n    response = requests.get(f'{main_url}{req}')\n    assert check_200(response)\n    assert len(response.json()) == num\n\n\ndef test_post():\n    message = {'title': 'foo', 'body': 'bar', 'userId': 1}\n    post = requests.post(f'{main_url}posts', data=message)\n    assert post.status_code == 201\n    assert post.json()['id'] == 101\n\n\ndef test_put():\n    message = {'id': 1, 'title': 'totl', 'body': 'buddy', 'userId': 123}\n    put = requests.put(f'{main_url}posts/1', message)\n    assert check_200(put)\n\n\n@pytest.mark.parametrize('req, schema', [\n    ('posts', schema_posts),\n    ('comments', schema_comments),\n    ('albums', schema_albums),\n    ('photos', schema_photos),\n    ('todos', schema_todos),\n    ('users', schema_users)\n])\ndef test_validate(req, schema):\n    response = requests.get(f'{main_url}{req}')\n    assert check_200(response)\n    validate(response.json(), schema)\n\n\ndef test_delete():\n    delete = requests.delete(f'{main_url}users/1')\n    assert check_200(delete)\n\n","repo_name":"AlisaGuseva/test_automation","sub_path":"module3_homeworks/api_testing/ex9/homework_04/tests/test_jsonplaceholder.py","file_name":"test_jsonplaceholder.py","file_ext":"py","file_size_in_byte":3001,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"24594467799","text":"'''\nWe want make a package of goal kilos of chocolate. \nWe have small bars (1 kilo each) and big bars (5 kilos each). \nReturn the number of small bars to use, assuming we always use \nbig bars before small bars. Return -1 if it can't be done.\n'''\n\ndef make_chocolate(small, big, goal):\n\tsmall_amount = small * 1\n\tbig_amount = big * 5\n\t\n\tif big == 0:\n\t\tif small_amount >= goal:\n\t\t\treturn goal\n\t\telse:\n\t\t\treturn '-1'\n\t\t\t\n\tif big > 0 and big_amount < goal:\n\t\tleftover = goal - big_amount\n\t\tif small_amount >= leftover:\n\t\t\treturn leftover\n\t\telse:\n\t\t\treturn -1\n\t\t\t\n\tif big > 0 and big_amount >= goal:\n\t\tfor i in range(1, big + 1):\n\t\t\tif (i * 5) == goal:\n\t\t\t\treturn 0\n\t\t\tif (i * 5) > goal:\n\t\t\t\tif small_amount >= goal:\n\t\t\t\t\treturn goal\n\t\t\t\telse:\n\t\t\t\t\treturn -1\t\t\t\t\n\t\t\tif (i * 5) < goal and (i * 5) > (goal - 5):\n\t\t\t\tresult = i * 5\n\t\t\t\tresult_remainder = goal % result\n\t\t\t\tif small_amount >= result_remainder:\n\t\t\t\t\treturn result_remainder\n\t\t\t\telse:\n\t\t\t\t\treturn -1 ","repo_name":"MarkMikow/CodingBat-Solutions-in-Python-2.7","sub_path":"Logic-2/make_chocolate.py","file_name":"make_chocolate.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"6856198977","text":"from flask import Blueprint, render_template, redirect, url_for, request\nfrom .models import Task, db\n\nmain = Blueprint(\"main\", __name__)\n\n@main.route(\"/\")\ndef index():\n    tasks = Task.query.all()\n    return render_template(\"index.html\", tasks=tasks)\n\n@main.route(\"/add\", methods=[\"POST\"])\ndef add_task():\n    title = request.form.get(\"title\")\n    new_task = Task(title=title)\n    db.session.add(new_task)\n    db.session.commit()\n    return redirect(url_for(\"main.index\"))\n\n@main.route(\"/update/<int:task_id>\")\ndef update_task(task_id):\n    task = Task.query.get_or_404(task_id)\n    task.completed = not task.completed\n    db.session.commit()\n    return redirect(url_for(\"main.index\"))\n\n@main.route(\"/delete/<int:task_id>\")\ndef delete_task(task_id):\n    task = Task.query.get_or_404(task_id)\n    db.session.delete(task)\n    db.session.commit()\n    return redirect(url_for(\"main.index\"))\n","repo_name":"fajim1/flask-docker-ci-cd","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":888,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70029174366","text":"from day import Day\n\nsampleFile = open('../samples/day_02.txt', 'r')\nproblemFile = open('../problems/day_02.txt', 'r')\n\n\nclass Day02(Day):\n    sample = sampleFile.readlines()\n    problem = problemFile.readlines()\n    part1_sample_ans = 150\n    part2_sample_ans = 900\n\n    def part1(self, a):\n        x_pos = 0\n        depth_pos = 0\n        for x in range(len(a)):\n            val = int(a[x][a[x].index(' ') + 1:])\n            if a[x].startswith('up'):\n                depth_pos -= val\n            elif a[x].startswith('down'):\n                depth_pos += val\n            else:\n                x_pos += val\n        return x_pos * depth_pos\n\n    def part2(self, a):\n        aim = 0\n        x_pos = 0\n        depth_pos = 0\n        for x in range(len(a)):\n            val = int(a[x][a[x].index(' ') + 1:])\n            if a[x].startswith('up'):\n                aim -= val\n            elif a[x].startswith('down'):\n                aim += val\n            else:\n                x_pos += val\n                depth_pos += val * aim\n        return x_pos * depth_pos\n\n","repo_name":"dillydally414/AoC-2021","sub_path":"days/day_02.py","file_name":"day_02.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"8660852728","text":"from src import config\nfrom src.scheduler.messages import Card, MessageWeight\n\nimport time\nimport random\nimport operator\nfrom discord import Embed, Message\nfrom pytz import timezone\nfrom typing import NamedTuple, Tuple, List, Union\nfrom src.scheduler.messages import Message\nfrom datetime import datetime\nfrom apscheduler.schedulers.asyncio import AsyncIOScheduler\n\n\n# Initialize apscheduler and setup the timezone.\nscheduler = AsyncIOScheduler()\ntz = timezone(\"Europe/Brussels\")\n\n\nclass Link(NamedTuple):\n    \"\"\"\n    A \"Link\" is a bunch of text to place between\n    two sentences to \"link\" them.\n    \"\"\"\n\n    message: str\n    needPoint: bool  # True if the previous sentence need a point.\n    needUppercase: bool  # True if the following sentence need an uppercase.\n\n\n# List of linkers (text that link two sentences together)\nlinkers: Tuple = (\n    Link(message=\"\", needPoint=True, needUppercase=True),\n    Link(message=\". Et \", needPoint=False, needUppercase=False),\n)\n\n\nclass Reminder:\n    def __init__(\n        self,\n        name: str,\n        days: str,\n        hour: int,\n        minute: int,\n        mentions: bool,\n        messages: List[Message],\n    ):\n        \"\"\"\n        Core of this Bot: Create a scheduled element that will send a\n        POST request to the Discord webhook.\n\n        :param name: The name of the reminder. For clarity only.\n        :param days: Days of the week when this reminder has to trigger.\n        :param hour: Hour of the day when this reminder has to trigger.\n        :param minute: Minutes of when this reminder has to trigger.\n        :param mentions: True if this reminder has to mentions the members.\n        :param messages: List of Message object containing the text and\n                         card used to generate a Reminder.\n        \"\"\"\n        self.mentions = mentions\n        self.messages = messages\n\n        self.attendance = self.__retrieve_attendance()\n        self.text, self.embed = self.__create_text_embed()\n\n        self.__initialize(name, days, hour, minute)\n\n    def __iter_weight(self):\n\n        for message in self.messages:\n            yield message.weight.value, message\n\n    def __create_text_embed(self):\n\n        # Put the messages in a dict with their weight.\n        messages = dict(self.__iter_weight())\n\n        # EMBED\n        # The embed is taken from the smallest weight message.\n        message_embed = None\n        card: Union[Card, None] = messages[min(messages)].get_card()\n\n        if card:\n            message_embed = card.get_embed()\n\n        # MESSAGE TEXT\n        # Sort the messages by higher weight to smaller weight\n        sorted_messages = dict(\n            sorted(messages.items(), key=operator.itemgetter(0), reverse=True)\n        )\n\n        texts = []\n\n        for message in sorted_messages.values():\n            texts.append(self.__to_uppercase(message.get_message()))\n\n        message_text = \". \".join(texts) + \" !\"\n\n        return message_text, message_embed\n\n    def __retrieve_attendance(self):\n        \"\"\"Loop through every message and return the attendance details,\n        if they exists.\"\"\"\n\n        for message in self.messages:\n            if message.weight == MessageWeight.ATTENDANCE:\n\n                return message.get_attendance_details()\n\n    @staticmethod\n    def get_linker() -> Link:\n        \"\"\"Return a randomly chooser text to link to tenses.\"\"\"\n\n        return random.choice(linkers)\n\n    @staticmethod\n    def __to_uppercase(text: str) -> str:\n        \"\"\"\n        Transform the first letter of a given string to uppercase.\n        Return the result.\n        \"\"\"\n\n        return \"\".join([text[0].capitalize(), text[1:]])\n\n    @staticmethod\n    def __add_mentions(text: str) -> str:\n        \"\"\"Append the users to mention on a given text.\"\"\"\n        users = config.db.get_users_to_mention()\n\n        # Set users to empty string if the list is empty\n        if len(users) == 0:\n            users = \"\"\n\n        # Append the users to mention\n        return \"\".join([f\"{text}\\n\"] + [f\" <@{user}>\" for user in users])\n\n    def __initialize(self, name: str, days: str, hour: int, minute: int) -> None:\n        \"\"\"\n        The core of the Reminder class: This function create a scheduler to\n        post self.message and self.card on Discord via a WebRequest.\n\n        Used internally only. Parameters are the same than this class.\n        \"\"\"\n\n        @scheduler.scheduled_job(\n            \"cron\", day_of_week=days, hour=hour, minute=minute, timezone=tz\n        )\n        async def job():\n            nonlocal self\n\n            channel = config.discord.get_channel(config.DISCORD_CHANNEL_ID)\n\n            # Simulate the bot typing during 3 seconds\n            async with channel.typing():\n                time.sleep(3)\n\n                text = self.text\n\n                # Append the mentions to the message\n                if self.mentions:\n                    text = self.__add_mentions(text)\n\n                # Send the message and the card through Discord\n                # https://gist.github.com/Vexs/629488c4bb4126ad2a9909309ed6bd71\n                message: Message = await channel.send(text, embed=self.embed)\n\n                # Add a reaction to the message if the message is\n                # attendance related\n                if self.attendance:\n                    await message.add_reaction(emoji=\"\\U0001F3E0\")  # House\n                    await message.add_reaction(emoji=\"\\U0001F307\")  # City\n\n                    # Save the attendance details\n                    config.last_message = message.id\n                    config.last_attendance = self.attendance\n\n            # Job triggered\n            print(f\"[!] Job triggered: {datetime.now()} - {name}.\")\n\n        # Job registered\n        print(f\"[+] Job scheduled: {days} @ {hour}h{minute} - {name}.\")\n\n    @staticmethod\n    def start():\n        \"\"\"Start all schedulers.\"\"\"\n        scheduler.start()\n","repo_name":"jcoumont/alan-turing","sub_path":"src/scheduler/Reminder.py","file_name":"Reminder.py","file_ext":"py","file_size_in_byte":5879,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"28126884337","text":"#year   races/hany verseny    win   podiums/dobogos    poles/elso_helyrol_indul    fastests\nwith open(\"jackie.txt\",\"r\",encoding=\"utf-8\") as f:\n    fejlec = f.readline()\n    lista = [sor.strip().split(\"\\t\") for sor in f]\n    \nprint(f\"3.feladat: {len(lista)}\")\n\nlegtobb_verseny= [(sor[1], sor) for sor in lista]\nleggtob, adatok = max(legtobb_verseny)\n\nprint(f\"4.feladat: {adatok[0]}\")\n\nevek_70_es = sum([int(sor[2]) for sor in lista if 1970 <= int(sor[0]) <= 1979])\nevek_60_as = sum([int(sor[2]) for sor in lista if 1960 <= int(sor[0]) <= 1969])\n\nprint(f\"\"\"5.feladat:\n       70-es évek: {evek_70_es} megnyert verseny\n       60-es évek: {evek_60_as} megnyert verseny\"\"\")\n\n#6.feladat passz nem tom\n","repo_name":"loczylevi/okj-jackie_stewart","sub_path":"jackie.py","file_name":"jackie.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"hu","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"23697971618","text":"def noDups(nums):\r\n    \"\"\"\r\n    Assumes nums is a linked list of numbers sorted in non-decreasing order.\r\n    Modifies nums to get rid of all duplicate elements.\r\n    Returns a pointer to the first element of the modified version\r\n    of nums\r\n    \"\"\"\r\n    # so that we don't destroy nums as we nav through the elements\r\n    head = nums\r\n    # initialize change, a flag which will be used to determine if previous should be advanced\r\n    change = False\r\n    # while there are still items in the lists\r\n    while head != None:\r\n        # no change has occured, no duplicate was found\r\n        if not change:\r\n            # copy current head to previous\r\n            previous = head\r\n            # advance head to the next value\r\n            head = head['next']\r\n            # make sure you're not comparing the end of the list, which would throw an error\r\n        if head != None:\r\n            # if the values are the same, we have found a duplicate\r\n            if head['data'] == previous['data']:\r\n                # 'jump' over the duped value, by connecting the head's 'next' value to the\r\n                # previous 'next' value. Essentially removing the duplicate by dropping all references to it\r\n                # I suppose Python garbage collector will handle it in memory\r\n                # I'm sure there's a more professional way of doing this though\r\n                previous['next'] = head['next']\r\n                # advance head so we don't get an infinite loop\r\n                head = head['next']\r\n                # a change has occurred, we need to check the same previous value against the new head value\r\n                # this affects if the above if statement runs which sets previous = head, and advances head\r\n                change = True\r\n            else:\r\n                # no change has occurred, tells if that we need to move the values along\r\n                change = False\r\n    # due to the nature of linked lists nums is now in proper order\r\n    # even though we never directly called its variable name when making edits\r\n    # we simply changed the data it refers to\r\n    return nums\r\n\r\n#########################################################################################################\r\n# The following functions are included for testing purposes, so that you can easily create and print\r\n# linked lists.  You may not call these functions from any of your required functions.\r\n#########################################################################################################\r\n\r\ndef listString(linkedList):\r\n    \"\"\"\r\n    Returns a string describing the list, suitable for printing.\r\n    \"\"\"\r\n    result = '['\r\n    current = linkedList\r\n    while current != None:\r\n        result += str(current['data'])\r\n        current = current['next']\r\n        if current != None:\r\n            result += \",\"\r\n    return result + ']'\r\n\r\n\r\ndef printList(linkedList):\r\n    \"\"\"\r\n    Prints a representation of a list\r\n    \"\"\"\r\n    print(listString(linkedList))\r\n\r\n\r\ndef createList(plist):\r\n    \"\"\"\r\n    Creates and returns a linked list containing all of the elements\r\n    of the Python-style list parameter.  A useful shortcut for testing.\r\n    \"\"\"\r\n    result = None  # empty list\r\n\r\n    # loop through plist in reverse order and add each element to the\r\n    # start of the result\r\n    for index in range(len(plist) - 1, -1, -1):\r\n        result = {'data': plist[index], 'next': result}\r\n    return result\r\n\r\n\r\nlinkedList = createList([1, 2, 3, 3, 3, 4, 4, 4, 4, 5, 6])\r\n# linkedList = createList([2,2,3,5,5,5,5,7,7])\r\nprint(noDups(linkedList))\r\nprintList(noDups(linkedList))","repo_name":"TDSElliott/CISC121","sub_path":"Assignment 2/duplicates.py","file_name":"duplicates.py","file_ext":"py","file_size_in_byte":3606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"8526621673","text":"import boto3\nimport botocore\nfrom sqlalchemy.orm.attributes import flag_modified\nfrom flask_login import current_user\n\nfrom visual.core import db\nfrom visual.models import User, Estate, EstateAsset, TourVideo, BROrderAsset, BROrder\nfrom flask import render_template, redirect, url_for, request, current_app\n\nfrom .forms import EstateAssetEditForm, FilterEstatesForm, EstatesAssetsFilterForm, AddEstateForm\nfrom .. import mod, roles_required\nfrom ...util import flash_errors\n\ndef count_assets(estate):\n    order_assets = {}\n    for type in EstateAsset.TYPES:\n        assets_count = EstateAsset.query.filter_by(estate_id=estate.id, type=type).count()\n        if assets_count != 0:\n            order_assets[type] = assets_count\n    estate.cnt_assets = order_assets\n    flag_modified(estate, 'cnt_assets')\n    db.session.commit()\n\n\n@mod.route('/estates', methods=['POST', 'GET'])\n@mod.route('/estates/<int:user_id>')\n@roles_required('users')\ndef estates(user_id=None):\n    \"\"\"\n    Список объектов недвижимости\n    \"\"\"\n    def validate_form(form):\n        if form.select.data == 'id':\n            try:\n                int(form.search.data)\n            except ValueError:\n                return False\n        return True\n\n    user=None\n    q = Estate.query.options(db.joinedload(Estate.user),db.joinedload(Estate.tags))\n    if user_id:\n        user = User.query.get_or_404(user_id)\n        q = q.filter(Estate.user_id == user.id)\n\n    form = FilterEstatesForm(request.args)\n    pattern = form.search.data\n    select = form.select.data\n\n    if validate_form(form):\n        if select == 'title':\n            q = q.filter(Estate.title.ilike(f'%{pattern}%'))\n        elif select == 'id':\n            q = q.filter(Estate.id == pattern)\n        elif select == 'user_name':\n            q = q.join(User).filter(User.name.ilike(f'%{pattern}%'))\n\n    q = q.order_by(Estate.created.desc())\n    estates = q.paginate(per_page=50)\n    return render_template('admin/estates/estates.html', estates = estates, filters=form, user=user)\n\n\n@mod.route('/estates/new', methods=['POST', 'GET'])\n@mod.route('/estates/<int:estate_id>/edit', methods=['POST', 'GET'])\n@roles_required('users')\ndef estates_edit(estate_id=None):\n    \"\"\"\n    Список объектов недвижимости\n    \"\"\"\n    def populate_fields(estate, form):\n        if not estate.user_id:\n            estate.user_id = current_user.id\n        if not estate.title:\n            form.title.errors.append('Укажите название объекта')\n            return False\n        return True\n\n    if estate_id:\n        estate = Estate.query.get_or_404(estate_id)\n    else:\n        estate = Estate()\n    form = AddEstateForm(obj=estate)\n    if request.method == 'POST':\n        if form.validate_on_submit():\n            form.populate_obj(estate)\n            if  populate_fields(estate, form):\n                db.session.add(estate)\n                db.session.commit()\n                return redirect(url_for('.estates'))\n            else:\n                flash_errors(form)\n        else:\n            flash_errors(form)\n    return render_template('admin/estates/estate_edit.html', form=form, estate=estate)\n\n\n@mod.route('/estates/<int:estate_id>/delete', methods=['POST', 'GET'])\n@roles_required('users')\ndef estate_delete(estate_id):\n    estate = Estate.query.get_or_404(estate_id)\n    assets = EstateAsset.query.filter_by(estate_id=estate.id).all()\n    for asset in assets:\n        asset.delete_files()\n    db.session.delete(estate)\n    db.session.commit()\n    return redirect(url_for('.estates'))\n\n\n\n@mod.route('/estates/<int:estate_id>/assets')\n@roles_required('users')\ndef estate_assets(estate_id):\n    filters = EstatesAssetsFilterForm(request.args)\n    estate = Estate.query.get_or_404(estate_id)\n    q = db.session.query(EstateAsset).filter_by(estate_id=estate.id)\n    q = q.order_by(EstateAsset.created.desc())\n    assets = q.paginate(per_page=50)\n    return render_template('admin/estates/assets.html', assets=assets, filters=filters, estate=estate)\n\n\n@mod.route('/estates/<int:estate_id>/assets/new', methods=('GET', 'POST'))\n@mod.route('/estates/<int:estate_id>/assets/<int:asset_id>/edit', methods=('GET', 'POST'))\n@roles_required('users')\ndef estates_assets_edit(estate_id, asset_id=None):\n\n    def populate_fields(asset):\n        if asset.type == 'tour':\n            if asset.s3key:\n                if not asset.s3key.startswith('tourvideos'):\n                    asset.delete_files()\n                asset.s3key = None\n                asset.preview_s3key = None\n            asset.size = None\n            asset.height = None\n            asset.width = None\n            asset.duration = None\n            asset.tour_video = None\n        else:\n            asset.tour_id = None\n\n        if asset.type == 'tour_video':\n            if not form.tour_video_id.data:\n                form.type.errors.append('Не заполнен tour_video_id')\n                return False\n            tour_video = TourVideo.query.get(asset.tour_video_id)\n            if asset.s3key:\n                if not asset.s3key.startswith('tourvideos'):\n                    asset.delete_files()\n            asset.s3key = tour_video.video_s3_key\n            asset.preview_s3key = tour_video.preview_s3_key\n            asset.size = tour_video.size\n            asset.height = tour_video.height\n            asset.width = tour_video.width\n            asset.duration = tour_video.duration\n            asset.tour_id = None\n        else:\n            asset.tour_video_id = None\n\n        # удаляем старый добавляем новый файл\n        upload_file = request.files.get('upload_file')\n        if upload_file:\n            if asset.s3key and not asset.s3key.startswith('tourvideos'):\n                asset.delete_files()\n\n            if asset.type not in ('tour', 'tour_video'):\n                asset.save_file(upload_file)\n        return True\n\n    estate = Estate.query.get(estate_id)\n    if asset_id:\n        asset = EstateAsset.query.get_or_404(asset_id)\n    else:\n        asset = EstateAsset(estate_id=estate.id)\n\n    form = EstateAssetEditForm(obj=asset)\n    if request.method == 'POST':\n        if form.validate_on_submit():\n            form.populate_obj(asset)\n            if populate_fields(asset):\n                db.session.add(asset)\n                db.session.commit()\n                estate.cnt_assets = count_assets(estate)\n                return redirect(url_for('.estate_assets', estate_id=estate.id))\n            else:\n                flash_errors(form)\n        else:\n            flash_errors(form)\n    return render_template('admin/estates/asset_edit.html', form=form, asset=asset)\n\n\n@mod.route('/estates/<int:estate_id>/assets/<int:asset_id>/delete', methods=['POST'])\n@roles_required('users')\ndef estates_assets_delete(asset_id, estate_id):\n    asset = EstateAsset.query.get_or_404(asset_id)\n    if asset.s3key:\n        if not asset.s3key.startswith('tourvideos'):\n            asset.delete_files()\n            # delete_asset_from_s3(asset)\n    db.session.delete(asset)\n    order = Estate.query.get_or_404(estate_id)\n    count_assets(order)\n    db.session.commit()\n    return redirect(url_for('.estate_assets', estate_id=estate_id))\n\n\n@mod.route('/estates/<int:estate_id>/assets/copy', methods=('GET', 'POST'))\n@roles_required('users')\ndef estates_assets_copy(estate_id):\n    estate = Estate.query.get_or_404(estate_id)\n    id_order = request.args.get('id_order')\n    br_assets = 1\n    order = 0\n    if id_order:\n        order = BROrder.query.get_or_404(id_order)\n        br_assets = BROrderAsset.query.filter(BROrderAsset.order_id == order.id).all()\n    if request.method == 'POST':\n        form = request.form\n        for br_order_asset_id in form.getlist('for_copy'):\n            br_asset = BROrderAsset.query.get(br_order_asset_id)\n            estate.create_asset_from_brorderasset(br_asset)\n        db.session.commit()\n        return redirect(url_for('.estate_assets', estate_id=estate.id))\n    return render_template('admin/estates/assets_copy.html', br_assets=br_assets, estate=estate)\n\n","repo_name":"Eyarurr/proj_1","sub_path":"visual/admin/estates/estates.py","file_name":"estates.py","file_ext":"py","file_size_in_byte":8086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"38105425891","text":"__author__ = 'Jiri Fajtl'\n__email__ = 'ok1zjf@gmail.com'\n__version__= '1.8'\n__status__ = \"Research\"\n__date__ = \"2/1/2020\"\n__license__= \"MIT License\"\n\nimport os\nimport sys\nimport glob\nimport pickle\nimport torchvision\nfrom torchvision import transforms\nimport torch\nimport torch.nn as nn\nfrom torchsummary import summary\n\nfrom mllogger import *\nfrom datasets import CIFAR10Ex, CelebAEx, MNISTEx\nfrom models5 import *\nfrom sys_utils import *\nfrom image_utils import *\nfrom sampler import sample, interpolate_rnd, get_covb\n\n# ===================================================================================\nclass Solver():\n    def __init__(self, hps, logr):\n        self.hps = hps\n        self.logr = logr\n        self.mse = torch.nn.MSELoss()\n        self.G = None\n        self.E = None\n        self.current_best = 1e+30\n        return\n\n    def save_checkpoint(self, filename=None, epoch=0, iter=0, current_loss=None):\n\n        def add_to_state(model, name):\n            if self.hps.parallel:\n                state_dict = model.module.state_dict()\n            else:\n                state_dict = model.state_dict()\n            states.update({name:state_dict})\n\n        if filename is None:\n            filename=self.logr.model_path+'/weights-'+str(epoch)+'.cp'\n\n        path,_ = os.path.split(filename)\n        os.makedirs(path, exist_ok=True)\n\n        states = {'epoch':epoch,\n                'iter': iter,\n                'loss_eval': current_loss }\n\n        if self.G is not None:\n            add_to_state(self.G, 'gen')\n        if self.E is not None:\n            add_to_state(self.E, 'enc')\n\n        with open(filename, mode='wb+') as f:\n            torch.save(states, f)\n\n        os.system('cp '+filename +' '+ self.logr.model_path+'/last.cp')\n\n        # Save the best model\n        if current_loss is not None and current_loss < self.current_best:\n            os.system('cp '+filename +' '+ self.logr.model_path+'/best.cp')\n            self.current_best  = current_loss\n        \n        # Purge old ones\n        if self.hps.keep_last_models is not None:\n            files = [f for f in os.listdir(self.logr.model_path) if 'weights-' in f]\n            wfiles = []\n            for f in files:\n                fname,_ = os.path.splitext(f)\n                epoch = fname.split('-')[-1] \n                wfiles.append([f, int(epoch)])\n\n            wfiles.sort(key=lambda x: x[1])\n            wfiles.reverse()\n            to_remove = wfiles[self.hps.keep_last_models:]\n            for f in to_remove:\n                filename = os.path.join(self.logr.model_path, f[0])\n                os.system('rm '+filename)\n\n        return\n\n    def load_checkpoint(self, filename=None): \n        epoch=-1\n        iter = -1\n        loss_eval = -1\n\n        if filename is None:\n            filename=self.logr.model_path+'/last.cp'\n\n        if os.path.isfile(filename):\n            checkpoint = torch.load(filename, map_location=lambda storage, loc: storage)\n            epoch = checkpoint['epoch']\n            iter = checkpoint.get('iter', -1)\n            loss_eval = checkpoint.get('loss_eval', -1)\n            if 'gen' in checkpoint:\n                self.G.load_state_dict(checkpoint['gen'], strict=False)\n            if 'enc' in checkpoint:\n                self.E.load_state_dict(checkpoint['enc'], strict=False)\n\n            print(\"=> loaded checkpoint '{} (epoch {},  loss {})'\".format(filename, epoch, loss_eval))\n        else:\n            print(\"=> no checkpoint found at '{}'\".format(filename))\n\n        self.hps.epoch_start = epoch\n        self.hps.iter_start = iter\n        self.current_best = loss_eval\n        return epoch\n\n\n    def load_data(self):\n        dataroot = \"~/projects/data/\"\n\n        if self.hps.dataset=='celeba':\n            if self.hps.img_crop_size is not None:\n                transform = transforms.Compose([\n                    transforms.CenterCrop(self.hps.img_crop_size),\n                    transforms.Resize(self.hps.img_size),\n                    transforms.ToTensor(),\n                    #    transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))\n                    ])\n            else:\n                transform = transforms.Compose([\n                    transforms.Resize(self.hps.img_size),\n                    # smaller edge of the image will be matched to this number. \n                    # i.e, if height > width, then image will be rescaled to (size * height / width, size)\n                    transforms.CenterCrop(self.hps.img_size),\n                    transforms.ToTensor(),\n                    #    transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))\n                    ])\n\n            self.train_dataset= CelebAEx(dataroot+\"CelebA/\", split='train', download=True, transform=transform, \n                    corrupt_method=self.hps.corrupt_method, corrupt_args=self.hps.corrupt_args)\n\n            self.test_dataset = CelebAEx(dataroot+\"CelebA/\", split='test', download=True, transform=transform, \n                    corrupt_method=self.hps.corrupt_method, corrupt_args=self.hps.corrupt_args)\n\n\n        elif self.hps.dataset == 'mnist':\n            transform = transforms.Compose([\n                # transforms.Resize(self.hps.img_size),\n                transforms.Pad(2, fill=0),\n                transforms.ToTensor(),\n                # transforms.Normalize(mean=[0.5], std=[0.5])\n                ])\n            self.train_dataset = MNISTEx(dataroot+'MNIST', train=True, download=True, transform=transform,\n                    corrupt_method=self.hps.corrupt_method, corrupt_args=self.hps.corrupt_args)\n            self.test_dataset = MNISTEx(dataroot+'MNIST', train=False, download=True, transform=transform,\n                    corrupt_method=self.hps.corrupt_method, corrupt_args=self.hps.corrupt_args)\n\n\n        elif self.hps.dataset == 'cifar10':\n            transform = torchvision.transforms.Compose([\n                    torchvision.transforms.Resize(self.hps.img_size),\n                    torchvision.transforms.ToTensor(),\n                    # torchvision.transforms.Normalize(mean=(0.5,), std=(0.5,))\n                    ])\n            self.train_dataset = CIFAR10Ex(dataroot+'cifar10', train=True, transform=transform, download=True,\n                    corrupt_method=self.hps.corrupt_method, corrupt_args=self.hps.corrupt_args)\n            self.test_dataset = CIFAR10Ex(dataroot+'cifar10', train=False, transform=transform, download=True,\n                    corrupt_method=self.hps.corrupt_method_test, corrupt_args=self.hps.corrupt_args_test)\n\n        else:\n            print(\"Wrong dataset name:\", self.hps.dataset)\n            sys.exit(0)\n        \n\n        print('Training size:', len(self.train_dataset)) \n        self.train_dataloader = torch.utils.data.DataLoader(self.train_dataset, batch_size=self.hps.batch_size,\n                shuffle=True, num_workers=self.hps.workers, drop_last=True, pin_memory=True)\n\n        self.test_dataloader = None\n        if self.test_dataset is not None:\n            print('Test size:', len(self.test_dataset)) \n            self.test_dataloader = torch.utils.data.DataLoader(self.test_dataset, \n                    batch_size=int(self.hps.batch_size_test),\n                    shuffle=False, num_workers=8, drop_last=False, pin_memory=True)\n\n            \n        self.logr.set_samples_num('Train', len(self.train_dataloader.dataset))\n        if self.test_dataloader is not None:\n            self.logr.set_samples_num('Eval', len(self.test_dataloader.dataset))\n        return\n\n\n    def net_init(self):\n        if self.hps.use_cuda:\n            if not self.hps.parallel and self.hps.cuda_device > -1:\n                print(\"Setting CUDA device: \", self.hps.cuda_device)\n                torch.cuda.set_device(int(self.hps.cuda_device))\n\n        self.E = None\n        self.G = None\n\n        # Select model by model name\n        if self.hps.vae_model is not None:\n            gen_net_name = 'Gen'+self.hps.vae_model\n            enc_net_name = 'Enc'+self.hps.vae_model\n\n            net_class = globals()[gen_net_name]\n            self.G = net_class(self.hps)\n            weight_init(self.G)\n\n            net_class = globals()[enc_net_name]\n            self.E = net_class(self.hps)\n            weight_init(self.E)\n        \n        else:\n            print(\"No VAE model specified! Running wihout VAE\", self.hps.vae_model)\n            # sys.exit(0)\n\n        if self.G is not None and self.E is not None:\n            print(\"Encoder:\")\n            net_info(self.E)\n            print(\"Generator:\")\n            net_info(self.G)\n\n            summary(self.E.cuda(), (self.hps.channels, self.hps.img_size, self.hps.img_size))\n            summary(self.G.cuda(), (1,self.hps.zsize))\n        return\n\n    def reparam_log(self, mu, logvar):\n        std = torch.exp(0.5*logvar)\n        eps = torch.randn_like(std).cuda()\n        z = mu + eps*std\n        return z\n\n    def train(self):\n        if self.hps.parallel:\n            self.E = nn.DataParallel(self.E)\n            self.G = nn.DataParallel(self.G)\n\n        if self.G is not None and self.E is not None:\n            params = list(self.E.parameters()) + list(self.G.parameters())\n            self.optim = torch.optim.Adam(params = params , lr=self.hps.lr[0], weight_decay=self.hps.l2 )\n\n        z_static = torch.randn(self.hps.batch_size, self.hps.zsize)\n        if self.hps.use_cuda:\n            if self.G is not None and self.E is not None:\n                self.G.cuda()\n                self.E.cuda()\n            z_static =z_static.cuda()\n\n        iter = self.hps.iter_start+1\n        self.logr.iter_global = iter\n        self.ws = None\n        if self.hps.epoch_start is not None:\n            # print out config diff between the epoch we are resuming and the current config\n            past_cfg=self.logr.load_config()\n            if past_cfg is not None:\n                cfg_diff = self.hps.diff_str(past_cfg)\n                print(\"\\n** Configuration DIFF:\")\n                print(cfg_diff)\n\n            print(\"Evaluating at epoch: \", self.hps.epoch_start, flush=True)\n            self.eval_reconstruct(dataset=self.test_dataloader, at_epoch=self.hps.epoch_start, iter=iter)\n            pass\n        else:\n            self.hps.epoch_start= -1\n\n        start_from_epoch = self.hps.epoch_start+1\n        self.logr.save_config(epoch=start_from_epoch)\n        print(\"Starting training from epoch=\",start_from_epoch,\"  iter=\",iter)\n\n        mse = nn.MSELoss(reduction='sum')\n        self.zbuff = []\n        self.zbuff_classes = []\n        nsave_images=64\n        for e in range(start_from_epoch, self.hps.epochs_max):\n            self.logr.start_epoch('Train', e)\n            if self.G is not None: self.G.train() \n            if self.E is not None: self.E.train() \n\n            for i, (x, target, xc) in enumerate(self.train_dataloader):\n                # Get code directly from the dataset - bypass caching \n                batch_size = x.size(0)\n                iter += batch_size\n\n                if self.hps.use_cuda:\n                    x = x.cuda()\n                    xc = xc.cuda()\n\n                # ENCODE\n                if self.E is not None:\n                    mu, varlog, ze, _, err_quant = self.E(xc)\n                    z = self.reparam_log(mu, varlog) if self.hps.vae else mu\n                    self.zbuff.append(ze.view(z.size(0), -1).detach().cpu().numpy())\n                    target = target.view(target.size(0), -1)\n                    self.zbuff_classes.append(target)\n\n                    if self.hps.shared_weights:\n                        self.ws = self.E.layers\n\n                # DECODE\n                if self.G is not None:\n                    xr = self.G(z, self.ws)\n\n                # Calculate VAE loss\n                log_dic = {}\n                log_dic.update({'QERR': float(err_quant)})\n\n                if self.G is not None and self.E is not None:\n                    xr = xr.view(xr.size(0), -1)\n                    x = x.view(x.size(0), -1)\n                    if self.hps.binary_reco_loss:\n                        loss_reco = torch.nn.functional.binary_cross_entropy(xr, x, reduction='none').sum() /batch_size\n                    else:\n                        loss_reco = mse(xr, x) / batch_size\n                    loss = loss_reco\n\n                    if self.hps.vae:\n                        varlog = torch.clamp(varlog, -10, 10) \n                        mu = torch.clamp(mu, -10, 10) \n                        loss_kld = -0.5 * torch.sum(1 + varlog - mu.pow(2) - varlog.exp()) \n                        loss_kld = loss_kld/batch_size\n                        loss_kld = self.hps.kl_weight * loss_kld/self.hps.zsize\n                        log_dic.update({'kld_loss': float(loss_kld)})\n                        loss = loss + loss_kld\n\n                    self.optim.zero_grad() \n                    loss.backward()\n                    self.optim.step()\n\n\n                # LOGGING\n                #=====================================\n                if self.G is not None and self.E is not None:\n                    log_dic.update({'loss': float(loss), 'reco_loss':float(loss_reco )} )\n\n                self.logr.log_loss(e, iter, stage_name='Train', losses=log_dic)\n\n                if i % self.hps.print_every_batch == 0:\n                    self.logr.print_batch_stat(stage_name='Train')\n\n            self.logr.print_batch_stat('Train')\n\n\n            # Record last batch of reconstructed images\n            #======================================================================\n            x = x[:nsave_images]\n            xr = xr[:nsave_images]\n            xc = xc[:nsave_images]\n\n            size = list(x.size())\n            if len(self.hps.corrupt_args) >0:\n                size[0] = size[0]*3\n                reco_imgs = torch.stack([x, xc.view(x.size(0), -1), xr], dim=1).view(size)\n                cols = int(size[0]**0.5//3)*3\n            else:\n                size[0] = size[0]*2\n                reco_imgs = torch.stack([x, xr], dim=1).view(size)\n                cols = int(size[0]**0.5//2)*2\n\n            self.logr.log_images(reco_imgs.cpu().detach(), e, 0, 'reconstructed_train', self.hps.channels, nrow=cols) \n\n            loss_reco_avg  = None\n            if self.test_dataloader is not None:\n                loss_reco_avg  = self.eval_reconstruct(self.test_dataloader, at_epoch=e, iter=iter)\n\n            S = [np.vstack(self.zbuff), np.vstack(self.zbuff_classes)]\n            self.zbuff = []\n            self.zbuff_classes = []\n            print(\"SAVING latents...\", end='')\n            pickle.dump(S, open(self.logr.exp_path+'/latents-last.pk', 'wb'))\n            print('done')\n\n            if not os.path.isfile(self.logr.exp_path+'/latents.pk'):\n                print(\"latents.pk NOT found. Reseting best eval loss\")\n                self.current_best=9999\n\n            if loss_reco_avg is not None and loss_reco_avg < self.current_best: \n                print(\"SAVING Best latents...\", end='')\n                print(\"loss_reco_avg < self.current_best\", loss_reco_avg, self.current_best)\n                pickle.dump(S, open(self.logr.exp_path+'/latents.pk', 'wb'))\n                print('done')\n\n            self.save_checkpoint(epoch=e, iter=iter, current_loss= loss_reco_avg)\n        return\n\n    def eval(self, at_epoch=0, results_filename=None): \n        nsave_images=64\n        imgs_per_row=8\n\n        if self.G is None:\n            return\n\n        if self.hps.use_cuda:\n            if self.G is not None and self.E is not None:\n                self.G.cuda()\n                self.E.cuda()\n\n        if self.G is not None: self.G.eval()\n        if self.E is not None: self.E.eval()\n        torch.set_grad_enabled(False)\n\n\n        print(\"Generating samples(\"+self.hps.sample_method+\"). N=\",int(self.hps.gen_imgs), flush=True)\n\n        imgs_reco_dir = os.path.join(self.logr.exp_path, 'generated', 'samples_'+self.hps.sample_method)\n        os.system('rm '+imgs_reco_dir+'/*.jpg')\n\n        if self.hps.sample_method in ['cov', 'int']:\n            latents_file=self.logr.exp_path+'/latents-last.pk'\n\n            print('Loading latents from:',latents_file)\n            labels = []\n            d = pickle.load(open(latents_file, 'rb'))\n            if isinstance(d, list) and len(d) == 2:\n                d, labels = d\n\n            D = d \n            if len(d) == 2:\n                # Trim labels\n                d,D = d[0],D[0]\n\n            L,mu,H=get_covb(d)\n\n        elif self.hps.sample_method == 'random':\n            pass\n        else:\n            print(\"Wrong sample method (\",self.hps.sample_method,\") only cov,int and random are implemented\")\n            sys.exit(0)\n\n        # Generate samples\n        zr=None\n        for i in range(int(self.hps.gen_imgs)):\n\n            # Calculate covariance of the real-img latents\n            z_static = None\n            test_batch_size = int(self.hps.batch_size_test)\n            if self.hps.sample_method=='cov':\n                z = sample(L, mu, test_batch_size, neg_zero=True, zsize=self.hps.zsize, ref=zr ,attr=None)\n                z_static = torch.from_numpy(z.astype(np.float32))\n\n            if self.hps.sample_method == 'int':\n                steps = int(self.hps.interpolate_steps)\n\n                imgs_per_row = steps+2\n                z,hdists = interpolate_rnd(D, L, B=test_batch_size, steps=steps, \n                        labels=labels, set_attr = self.hps.set_attr)\n\n                nsave_images=z.shape[0]\n                z_static = torch.from_numpy(z.astype(np.float32))\n\n            if z_static is None or self.hps.sample_method == 'random':\n                if self.hps.vae:\n                    z_static = torch.randn(test_batch_size, self.hps.zsize)\n                else:\n                    zt = z_static\n                    z_static = torch.zeros([test_batch_size, self.hps.zsize]).uniform_(-1, 1)\n                    z_static = torch.clamp(z_static, min=self.hps.zclamp_min, max=self.hps.zclamp)\n\n                    z_static = roundf(z_static, self.hps.zround)\n                    if zt is not None:\n                        z_static = torch.cat([z_static, zt])\n\n            if self.hps.use_cuda:\n                z_static =z_static.cuda()\n\n            xr  = self.G(z_static, None)\n\n            tosave = int(self.hps.gen_imgs - (i)*xr.size(0))\n            save_images(xr[:tosave], self.hps.channels, self.hps.img_size, imgs_reco_dir, i*xr.size(0))\n\n            name_suffix = self.hps.sample_method\n            filename_img = self.logr.exp_path+'/generated/sample_'+str(at_epoch+i)+'_'+self.hps.sample_method\n            if self.hps.set_attr > -1:\n                filename_img += '_attr_'+str(self.hps.set_attr)\n                name_suffix += '_attr_'+str(self.hps.set_attr)\n\n            filename_img += '.pk'\n\n            xrp = xr[:nsave_images]\n            self.logr.log_images(xrp.cpu().detach(), at_epoch+i, \n                    name_suffix, 'generated', \n                    self.hps.channels, nrow=imgs_per_row) \n\n            if tosave <= xr.size(0):\n                break\n\n        torch.set_grad_enabled(True)\n\n        # Store results\n        if results_filename is not None:\n            with open(results_filename, 'wt') as f:\n                f.write('e:'+str(self.hps.epoch_start)+' loss_eval:'+str(self.current_best))\n\n        return\n\n    def eval_reconstruct(self, dataset=None, at_epoch=0, iter=0, results_filename=None): \n        nsave_images=64\n        e = at_epoch\n        mse = nn.MSELoss(reduction='sum')\n\n        if self.G is None:\n            return\n\n        if self.hps.use_cuda:\n            if self.G is not None and self.E is not None:\n                self.G.cuda()\n                self.E.cuda()\n\n        if self.G is not None: self.G.eval()\n        if self.E is not None: self.E.eval()\n        torch.set_grad_enabled(False)\n\n        dir_suffix = 'eval'\n        if self.hps.eval_train:\n            dir_suffix = 'train'\n\n        log_dic={}\n        self.eval_latents=[]\n        self.eval_classes=[]\n        rnd_batch_imgs = None\n        loss_total = 0\n        samples = 0\n        from_id = 0\n        save_img_id = np.random.randint(1, len(dataset.dataset)//self.hps.batch_size_test-1)\n\n        print(\"Evaluating samples. N=\",len(dataset.dataset),  flush=False)\n        self.logr.start_epoch('Eval', e)\n        for i, (x, target, xc) in enumerate(dataset):\n            target = target.view(target.size(0), -1)\n            batch_size = x.size(0)\n\n            if self.hps.use_cuda:\n                x = x.cuda()\n                xc = xc.cuda()\n\n            varlog=mu=None\n            loss_kld = 0\n\n            if self.E is not None:\n                mu, varlog, ze, _, err_quant = self.E(x)\n                z = self.reparam_log(mu, varlog) if self.hps.vae else mu\n                self.eval_latents.append(z.view(z.size(0), -1).detach().cpu().numpy())\n                self.eval_classes.append(target)\n\n                self.ws = None\n                if self.hps.shared_weights:\n                    self.ws = self.E.layers\n\n            if self.G is not None:\n                xr = self.G(z, self.ws)\n\n                # Calculate loss\n                xr = xr.view(xr.size(0), -1)\n                x = x.view(x.size(0), -1)\n\n                if rnd_batch_imgs is None:\n                    if save_img_id == i:\n                        rnd_batch_imgs = [x, xr]\n\n                if self.hps.binary_reco_loss:\n                    loss_reco = torch.nn.functional.binary_cross_entropy(xr, x, reduction='sum')/batch_size\n                else:\n                    loss_reco = mse(xr, x) / batch_size\n\n                if varlog is not None:\n                    # varlog = torch.clamp(varlog, -10, 10) \n                    # mu = torch.clamp(mu, -10, 10) \n                    loss_kld = -0.5 * torch.sum(1 + varlog - mu.pow(2) - varlog.exp()) \n                    loss_kld = loss_kld/batch_size\n                    loss_kld = self.hps.kl_weight * loss_kld/self.hps.zsize\n                    log_dic.update({'kld_loss': float(loss_kld)})\n\n                loss_total += float(loss_reco)\n\n                samples +=1 \n                log_dic.update({'loss': float(loss_kld+loss_reco), 'reco_loss':float(loss_reco ) })\n\n            # Record the last loss etc. This is wrong it should be average, but for now it's ok\n            # Save image and write out results\n            imgs_test_dir = self.logr.exp_path+'/../'+self.hps.dataset+'_'+dir_suffix+'/'\n            if self.hps.img_crop_size is not None:\n                imgs_test_dir = self.logr.exp_path+'/../'+self.hps.dataset+'_'+str(self.hps.img_crop_size)+'_'+dir_suffix+'/'\n\n            save_eval_imgs = True\n            if os.path.isdir(imgs_test_dir): \n                image_list = glob.glob(os.path.join(imgs_test_dir, '*.jpg'))\n\n                # Save the images only if they don't exist yet\n                if len(image_list) == len(dataset.dataset): \n                    save_eval_imgs = False\n\n            if save_eval_imgs:\n                save_images(x, self.hps.channels, self.hps.img_size, imgs_test_dir, from_id)\n\n            if self.hps.eval and not self.hps.eval_train:\n                # Save all results\n                ipath = os.path.join(self.logr.exp_path, 'reco')\n                save_images(xr, self.hps.channels, self.hps.img_size, ipath, from_id)\n\n            iter += batch_size\n            from_id += batch_size\n\n        # Record the last loss etc. This is wrong it should be average, but for now it's ok\n        loss_reco_avg = float(loss_total / samples)\n        log_dic.update({'loss': float(0), 'reco_loss':loss_reco_avg })\n        self.logr.log_loss(e, None, stage_name='Eval', losses=log_dic)\n\n        S = [np.vstack(self.eval_latents), np.vstack(self.eval_classes)]\n        pickle.dump(S, open(self.logr.exp_path+'/latents-'+dir_suffix+'.pk', 'wb'))\n\n        # Save random batch of images or the last one \n        if rnd_batch_imgs is not None:\n            x,xr = rnd_batch_imgs\n\n        # Save only the first nsave_images images\n        x = x[:nsave_images]\n        xr = xr[:nsave_images]\n            \n        size = list(x.size())\n        size[0] = size[0]*2\n        reco_imgs = torch.stack([x, xr], dim=1).view(size)\n        cols = int(size[0]**0.5//2)*2\n        self.logr.log_images(reco_imgs.cpu().detach(), e, 0, 'reconstructed_'+dir_suffix, self.hps.channels, nrow=cols) \n\n\n        self.logr.print_batch_stat('Eval')\n        torch.set_grad_enabled(True)\n        print('')\n\n        if results_filename is not None:\n            with open(results_filename, 'wt') as f:\n                f.write('e:'+str(self.hps.epoch_start)+' loss_eval:'+str(self.current_best))\n\n        return loss_reco_avg\n\n\ndef exec(hps):\n    hps.eval = False\n    hps.gen = False\n    hps.reload=False\n    hps.load_from_sys_args(sys.argv)\n\n\n    logr = MLLogger(hps)\n    experiment_name=hps.cfg+ '_res'+str(hps.img_size)+\\\n                (('_'+hps.vae_model) if hps.vae_model is not None else '')+\\\n                ('-vae' if hps.vae else '-qae')+\\\n                '-z'+str(hps.zsize)+'-'+hps.exp_suffix\n\n    # Check whether we are in the experiment directory rather than the root dir\n    exp_path = os.path.split(os.getcwd())[-1]\n    if exp_path == experiment_name:\n        experiment_name = '.'\n\n    # Open existing or create a new expriment\n    logr.open_experiment(experiment_name)\n\n    # Print system info and configuration parameters\n    print(' '.join(sys.argv))\n    # print_pkg_versions()\n    print(hps)\n\n    sv = Solver(hps, logr)\n    sv.net_init()\n    sv.load_data()\n\n    # Load model if specified\n    if hps.reload:\n        filename = logr.model_path+'/weights-'+str(int(hps.reload))+'.cp' if not isinstance(hps.reload, bool) else None\n        hps.epoch_start = sv.load_checkpoint(filename) \n    if hps.l:\n        hps.epoch_start = sv.load_checkpoint(filename=logr.model_path+'/last.cp') \n\n    if hps.eval: \n        dataloader = sv.test_dataloader\n        results_filename = logr.exp_path +'/fid-epoch.txt'\n\n        if not hps.l and not hps.reload:\n            hps.epoch_start = sv.load_checkpoint(filename=logr.model_path+'/last.cp') \n\n        if hps.gen:\n            # Novel imgs generation, interpolation, attributes modification\n            sv.eval(results_filename=results_filename)\n        else:\n            # Reconstruction evaluation\n            if hps.eval_train: \n                # On the train dataset\n                sv.eval_reconstruct(dataset=sv.train_dataloader)\n            else:\n                # On the test dataset\n                sv.eval_reconstruct(dataset=dataloader, results_filename=results_filename)\n    else:\n        sv.train()\n    return\n\n#=================================================================================\nif __name__ == \"__main__\":\n    print(\"NOT AN EXECUTABLE!\")\n\n","repo_name":"ok1zjf/LBAE","sub_path":"lbae.py","file_name":"lbae.py","file_ext":"py","file_size_in_byte":26678,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"86"}
+{"seq_id":"41247437413","text":"# @property\n# motivation: 单纯的 s.score = 1000 会使得属性被设置成不合理的值，显然我们需要进行参数检查\n# 而利用方法s.setscore()会让代码变长,且调用不方便\nclass Student(object):\n\tdef get_score(self):\n\t\treturn self._score\n\n\tdef set_score(self, value):\n\t\tif not isinstance(value, int):\n\t\t\traise ValueError('score must be an integer!')\n\t\tif value < 0 or value > 100:\n\t\t\traise ValueError('score must between 0~100！')\n\t\tself._score = Value\n\nbart = Student()\n#bart.set_score(101) \n#bart.set_score(-1)\n#bart.set_score('89') \n\n# 上述实现虽然使得参数得到了检查，但是也让代码变长，调用有一些不方便\n# property类似于装饰器,可以对调用方法进行一些修饰\nclass Student(object):\n\t\n\t@property\n\tdef score(self):\n\t\treturn self._score\n\n\t@score.setter\n\tdef score(self, value):\n\t\tif not isinstance(value, int):\n\t\t\traise ValueError('Score must be an integer!')\n\t\tif value < 0 or value > 100:\n\t\t\traise ValueError('Score must between 0~100!')\n\t\tself._score = value\n\ns = Student()\ns.score = 60  # 像调用属性一样调用方法\nprint(s.score)\n#s.score = 9999 # 出错\t\t\n\n# getter and setter： 只读 || 读写\nclass Student(object):\n\n\t@property\n\tdef birth(self):\n\t\treturn self._birth\n\n\t@birth.setter\n\tdef birth(self, value):\n\t\tself._birth = value\n\n\t@property\n\tdef age(self):\n\t\treturn 2018 - self._birth\n\n# run the method just like a attribute\nbart = Student()\nbart.birth = 2000\nprint(bart.age)\nprint(bart.birth)\n\n\n\n# Sample codes of class Screen\nclass Screen(object):\n\n\t@property\n\tdef width(self):\n\t\treturn self._width\n\n\t@width.setter\n\tdef width(self, value):\n\t\tif not (isinstance(value, int) or isinstance(value, float)):\n\t\t\traise ValueError('Width must be int or float!')\n\t\tif value <= 0:\n\t\t\traise ValueError('Width must be larger than zero!')\n\t\tself._width = value\n\n\t@property\n\tdef height(self):\n\t\treturn self._height\n\n\t@height.setter\n\tdef height(self, value):\n\t\tif not (isinstance(value, int) or isinstance(value, float)):\n\t\t\traise ValueError('Height must be int or float!')\n\t\tif value <= 0:\n\t\t\traise ValueError('Height must be larger than zero!')\n\t\tself._height = value\n\n\t@property\n\tdef resolution(self):\n\t\treturn self._width * self._height\n\ns = Screen()\ns.width = 1024\ns.height = 768\nprint('resolution =', s.resolution)\nif s.resolution == 786432:\n    print('测试通过!')\nelse:\n    print('测试失败!')\n","repo_name":"NingMeng7/Programming_Learning","sub_path":"Python/5 OOP/6_ property.py","file_name":"6_ property.py","file_ext":"py","file_size_in_byte":2388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74598619484","text":"# Basic arcade drawing\n# Draw smiley face\n\nimport arcade\nimport random\nimport math\nimport os\n\nSCREEN_WIDTH = 1950\nSCREEN_HEIGHT = 950\nSCREEN_TITLE = \"Basic Drawing\"\nRADIUS = 150\n\nHEALTHBAR_WIDTH = 50\nHEALTHBAR_HEIGHT = 6\nHEALTHBAR_OFFSET_Y = -10\n\n\nclass Health_Sprite(arcade.Sprite):\n    '''Health Sprite'''\n\n    def __init__(self, image, scale, max_health):\n        super().__init__(image, scale)\n\n        self.max_health = max_health\n        self.cur_health = max_health\n\n    def draw_health_bar(self):\n        if self.cur_health < self.max_health:\n            arcade.draw_rectangle_filled(center_x=self.center_x,\n                                         center_y=self.center_y + HEALTHBAR_OFFSET_Y,\n                                         width=HEALTHBAR_WIDTH,\n                                         height=5,\n                                         color=arcade.color.RED)\n\n        health_width = HEALTHBAR_WIDTH * (self.cur_health / self.max_health)\n\n        arcade.draw_rectangle_filled(center_x=self.center_x - 0.5 * (HEALTHBAR_WIDTH - health_width),\n                                     center_y=self.center_y - 10,\n                                     width=health_width,\n                                     height=HEALTHBAR_HEIGHT,\n                                     color=arcade.color.GREEN)\n\n\nclass Game(arcade.Window):\n    '''Main drawing window \n       and moving face. Shooter game,\n       with a smiley face being controled by you.\n       Angry faces will appear on the left side,\n       they will head to the right at different\n       speeds. Plus there is a boundary so you\n       can't get off the screen. self. tank. music.\n    '''\n\n    def __init__(self):\n        '''Initalize the drawing window\n        '''\n        # Backround\n        super().__init__(SCREEN_WIDTH, SCREEN_HEIGHT, SCREEN_TITLE)\n\n        arcade.set_background_color(arcade.color.SKY_BLUE)\n\n        self.health = 100\n        file_path = os.path.dirname(os.path.abspath(__file__))\n        os.chdir(file_path)\n\n        # Sprites\n        # Spawn the player\n        self.player = arcade.Sprite(\n            \"images\\Smiling_Face_Emoji.png\", 0.07\n        )\n\n        self.player.center_x = 0\n\n        self.player.center_y = 0\n\n        self.player.change_x = 0\n        self.player.change_y = 0\n\n        self.tank_count = 0\n        self.tank_health = 0\n        self.jet_count = 0\n        self.collided = False\n        self.collision_timer = 0.0\n        self.bullet_score = 0\n\n        # Sprite lists\n        self.fighter_jet_lists = arcade.SpriteList()\n        self.jet_missle_lists = arcade.SpriteList()\n        self.jet_missle_lists_2 = arcade.SpriteList()\n        self.jet_missle_lists_3 = arcade.SpriteList()\n        self.tank_lists = arcade.SpriteList()\n        self.tank_missle_lists = arcade.SpriteList()\n\n        self.enemy_lists = arcade.SpriteList()\n\n        self.bullet_lists = arcade.SpriteList()\n        # Spawn the player\n\n        self.player.center_x = 40\n        self.player.center_y = 300\n\n        self.player.shoot = False\n\n        # schedule always happen in setup/init\n        arcade.schedule(self.spawn_enemy, 1.0)\n\n        # schedule always happen in setup/init\n        arcade.schedule(self.spawn_tank, 1.0)\n\n        arcade.schedule(self.spawn_tank_missle, 4)\n\n        arcade.schedule(self.fighter_jet, 1.0)\n\n        arcade.schedule(self.jet_missle, 2)\n\n        arcade.schedule(self.play_music, 16)\n\n        # Music\n        self.music = arcade.load_sound(\n            \"RealPython/materials/arcade-a-primer/sounds/Apoxode_-_Electric_1.wav\")\n\n        self.collision_sound = arcade.load_sound(\n            \"RealPython/materials/arcade-a-primer/sounds/Collision.wav\")\n\n        self.game_over = arcade.load_sound(\"sounds/gameover1.wav\")\n\n        self.play_music(0)\n\n    def play_music(self, delta_time: float):\n        arcade.play_sound(\n            self.music)\n\n    def spawn_bullet(self):\n        # Spawn a bullet based on play position\n        # what is the scaling? The image may be big.\n        bullet = arcade.Sprite(\n            \"RealPython/materials/arcade-a-primer/images/missile.png\", 1.5, 0, 0, 0, 0, 0, 0, 1, 1, True)\n\n        bullet.center_x = self.player.center_x\n        bullet.center_y = self.player.center_y\n\n        bullet.velocity = (200, 0)\n\n        # add to the list so that we can update them/draw them as in the list conveniently\n        self.bullet_lists.append(bullet)\n\n    def spawn_enemy(self, delta_time: float):\n        \"\"\"Adds a new enemy to the screen\n            using schedule.\n        \"\"\"\n\n        # what is the scaling? The image may be big.\n        enemy = arcade.Sprite(\"images/angry_face.png\", 0.04)\n\n        enemy.left = random.randint(1950, 1950)\n        enemy.top = random.randint(0, 1000)\n\n        enemy.velocity = (random.randint(-200, -50), 0)\n\n        # add to the list so that we can update them/draw them as in the list conveniently\n        self.enemy_lists.append(enemy)\n\n    def spawn_tank(self, delta_time: float):\n        \"\"\" Spawns tank that \n            shoots at you.\n        \"\"\"\n        if self.tank_count < 3:\n            tank = Health_Sprite(\n                \"images/tank.png\", 0.05,\n                max_health=30)\n            tank.left = 1950\n            tank.top = random.randint(50, 900)\n\n            tank.velocity = (-150, 0)\n            self.tank_health = 20\n            self.tank_lists.append(tank)\n            self.tank_count += 1\n\n    def spawn_tank_missle(self, delta_time: float):\n        \"\"\"missles that come out from the tanks\n        \"\"\"\n\n        for tank in self.tank_lists:\n\n            tank_missle = arcade.Sprite(\n                \"RealPython/materials/arcade-a-primer/images/missile.png\")\n            total_speed = 200\n            tank_missle.center_x = tank.center_x\n            tank_missle.center_y = tank.center_y\n            x_diff = self.player.center_x - tank_missle.center_x\n            y_diff = self.player.center_y - tank_missle.center_y\n            angle = math.atan2(y_diff, x_diff)\n\n            tank_missle.angle = math.degrees(angle) + 180\n\n            x_speed = math.cos(angle) * total_speed\n            y_speed = math.sin(angle) * total_speed\n\n            tank_missle.velocity = (x_speed, y_speed)\n\n            self.tank_missle_lists.append(tank_missle)\n\n    def fighter_jet(self, delta_time: float):\n        \"\"\"Fighter_jet_boss\"\"\"\n        if self.jet_count <= 0:\n            jet = Health_Sprite(\n                \"images/fighter_jet.png\", 0.5, max_health=100\n            )\n\n            jet.left = 1200\n            jet.top = 475\n\n            jet.velocity = (-200, 0)\n\n            self.fighter_jet_lists.append(jet)\n            self.jet_count += 1\n\n    def jet_missle(self, delta_time: float):\n        \"\"\" jets missles\"\"\"\n\n        for jet in self.fighter_jet_lists:\n\n            jet_missle = arcade.Sprite(\n                \"images/blue_laser.png\", 0.05)\n            total_speed = 200\n            jet_missle.center_x = jet.center_x\n            jet_missle.center_y = jet.center_y\n            x_diff = self.player.center_x - jet_missle.center_x\n            y_diff = self.player.center_y - jet_missle.center_y\n            angle = math.atan2(y_diff, x_diff)\n\n            jet_missle.angle = math.degrees(angle) + 180\n\n            x_speed = math.cos(angle) * total_speed\n            y_speed = math.sin(angle) * total_speed\n\n            jet_missle.velocity = (x_speed, y_speed)\n\n            self.jet_missle_lists.append(jet_missle)\n\n            jet_missle_2 = arcade.Sprite(\n                \"images/blue_laser.png\", 0.05)\n            total_speed = 200\n            jet_missle_2.center_x = jet.center_x\n            jet_missle_2.center_y = jet.center_y\n            x_diff = self.player.center_x - jet_missle_2.center_x + 80\n            y_diff = self.player.center_y - jet_missle_2.center_y + 80\n            angle = math.atan2(y_diff, x_diff)\n\n            jet_missle_2.angle = math.degrees(angle) + 180\n\n            x_speed = math.cos(angle) * total_speed\n            y_speed = math.sin(angle) * total_speed\n\n            jet_missle_2.velocity = (x_speed, y_speed)\n\n            self.jet_missle_lists_2.append(jet_missle_2)\n\n            jet_missle_3 = arcade.Sprite(\n                \"images/blue_laser.png\", 0.05)\n            total_speed = 200\n            jet_missle_3.center_x = jet.center_x\n            jet_missle_3.center_y = jet.center_y\n            x_diff = self.player.center_x - jet_missle_3.center_x - 80\n            y_diff = self.player.center_y - jet_missle_3.center_y - 80\n            angle = math.atan2(y_diff, x_diff)\n\n            jet_missle_3.angle = math.degrees(angle) + 180\n\n            x_speed = math.cos(angle) * total_speed\n            y_speed = math.sin(angle) * total_speed\n\n            jet_missle_3.velocity = (x_speed, y_speed)\n\n            self.jet_missle_lists_3.append(jet_missle_3)\n\n    def on_key_press(self, symbol: int, modifiers: int):\n        if symbol == arcade.key.RIGHT:\n            # Quit immediately\n            self.player.change_x = 5\n        if symbol == arcade.key.LEFT:\n            # Quit immediately\n            self.player.change_x = -5\n        if symbol == arcade.key.UP:\n            # Quit immediately\n            self.player.change_y = 5\n        if symbol == arcade.key.DOWN:\n            # Quit immediately\n            self.player.change_y = -5\n\n    def on_key_release(self, symbol, modifiers):\n        if symbol == arcade.key.RIGHT:\n            # Quit immediately\n            self.player.change_x = 0\n        if symbol == arcade.key.LEFT:\n            # Quit immediately\n            self.player.change_x = 0\n        if symbol == arcade.key.UP:\n            # Quit immediately\n            self.player.change_y = 0\n        if symbol == arcade.key.DOWN:\n            # Quit immediately\n            self.player.change_y = 0\n\n    def on_mouse_press(self, x, y, button, modifiers):\n        if button == arcade.MOUSE_BUTTON_RIGHT:\n            # Quit immediately\n            self.spawn_bullet()\n        if button == arcade.MOUSE_BUTTON_MIDDLE:\n            # Quit immediately\n            self.spawn_bullet()\n        if button == arcade.MOUSE_BUTTON_LEFT:\n            # Quit immediately\n            self.spawn_bullet()\n\n    def on_update(self, delta_time: float):\n\n        super().on_update(delta_time)\n        self.player.center_x = self.player.center_x + self.player.change_x\n\n        super().on_update(delta_time)\n        self.player.center_y = self.player.center_y + self.player.change_y\n\n        if self.health == 0 or self.health < 0:\n            self.collision_timer += delta_time\n            if self.collision_timer > 1.0:\n                arcade.close_window()\n            return\n\n        for enemy in self.enemy_lists:\n            if self.player.collides_with_sprite(enemy):\n                self.collided = False\n                enemy.remove_from_sprite_lists()\n                self.health -= 5\n                arcade.play_sound(self.collision_sound)\n                if self.health == 0 or self.health < 0:\n                    arcade.play_sound(\n                        self.game_over)\n\n        for tank_missle in self.tank_missle_lists:\n            if self.player.collides_with_sprite(tank_missle):\n                self.collided = False\n                tank_missle.remove_from_sprite_lists()\n                self.health -= 2\n                arcade.play_sound(self.collision_sound)\n                if self.health == 0 or self.health < 0:\n                    arcade.play_sound(\n                        self.game_over)\n\n        for jet_missle in self.jet_missle_lists:\n            if self.player.collides_with_sprite(jet_missle):\n                self.collided = False\n                jet_missle.remove_from_sprite_lists()\n                self.health -= 5\n                arcade.play_sound(self.collision_sound)\n                if self.health == 0 or self.health < 0:\n                    self.collision_timer += delta_time\n                    arcade.play_sound(\n                        self.game_over)\n\n        for jet_missle_2 in self.jet_missle_lists_2:\n            if self.player.collides_with_sprite(jet_missle_2):\n                self.collided = False\n                jet_missle_2.remove_from_sprite_lists()\n                self.health -= 5\n                arcade.play_sound(self.collision_sound)\n                if self.health == 0 or self.health < 0:\n                    arcade.play_sound(\n                        self.game_over)\n\n        for jet_missle_3 in self.jet_missle_lists_3:\n            if self.player.collides_with_sprite(jet_missle_3):\n                self.collided = False\n                jet_missle_3.remove_from_sprite_lists()\n                self.health -= 5\n                arcade.play_sound(self.collision_sound)\n                if self.health == 0 or self.health < 0:\n                    arcade.play_sound(\n                        self.game_over)\n\n        for enemy in self.enemy_lists:\n            if enemy.collides_with_list(self.bullet_lists):\n                enemy.remove_from_sprite_lists()\n                self.bullet_score += 5\n\n        for bullet in self.bullet_lists:\n            hit_list = arcade.check_for_collision_with_list(\n                bullet, self.tank_lists)\n\n            if len(hit_list) > 0:\n                bullet.remove_from_sprite_lists()\n\n            for tank in hit_list:\n                if not isinstance(tank, Health_Sprite):\n                    raise TypeError(\"List contents must all be ints\")\n\n                tank.cur_health -= 2\n\n                if tank.cur_health <= 0:\n                    tank.remove_from_sprite_lists()\n\n                    self.bullet_score += 100\n                    self.tank_count -= 1\n\n        for bullet in self.bullet_lists:\n            hit_list = arcade.check_for_collision_with_list(\n                bullet, self.fighter_jet_lists)\n\n            if len(hit_list) > 0:\n                bullet.remove_from_sprite_lists()\n\n            for jet in hit_list:\n                if not isinstance(jet, Health_Sprite):\n                    raise TypeError(\"List contents must all be ints\")\n\n                jet.cur_health -= 2\n\n                if jet.cur_health <= 0:\n                    jet.remove_from_sprite_lists()\n\n                    self.bullet_score += 500\n                    self. jet_count -= 1\n\n        # IMPORTANT, without this, the sprites will NOT move at all\n        # Update the position of enemies based on speed\n        for sprite in self.enemy_lists:\n            sprite.center_x = int(\n                sprite.center_x + sprite.change_x * delta_time\n            )\n            sprite.center_y = int(\n                sprite.center_y + sprite.change_y * delta_time\n            )\n        for sprite in self.bullet_lists:\n            sprite.center_x = int(\n                sprite.center_x + sprite.change_x * delta_time\n            )\n            sprite.center_y = int(\n                sprite.center_y + sprite.change_y * delta_time\n            )\n\n        for sprite in self.tank_lists:\n            sprite.center_x = int(\n                sprite.center_x + sprite.change_x * delta_time\n            )\n            if sprite.center_x < 1500:\n                sprite.center_x = 1500\n            sprite.center_y = int(\n                sprite.center_y + sprite.change_y * delta_time\n            )\n\n        for sprite in self.tank_missle_lists:\n            sprite.center_x = sprite.center_x + sprite.change_x * delta_time\n            sprite.center_y = sprite.center_y + sprite.change_y * delta_time\n\n        for sprite in self.fighter_jet_lists:\n            sprite.center_x = sprite.center_x + sprite.change_x * delta_time\n            if sprite.center_x < 1100:\n                sprite.center_x = 1100\n            sprite.center_y = sprite.center_y + sprite.change_y * delta_time\n\n        for sprite in self.jet_missle_lists:\n            sprite.center_x = sprite.center_x + sprite.change_x * delta_time\n            sprite.center_y = sprite.center_y + sprite.change_y * delta_time\n\n        for sprite in self.jet_missle_lists_2:\n            sprite.center_x = sprite.center_x + sprite.change_x * delta_time\n            sprite.center_y = sprite.center_y + sprite.change_y * delta_time\n\n        for sprite in self.jet_missle_lists_3:\n            sprite.center_x = sprite.center_x + sprite.change_x * delta_time\n            sprite.center_y = sprite.center_y + sprite.change_y * delta_time\n\n        if self.player.top > self.height:\n            self.player.top = self.height\n        if self.player.right > self.width:\n            self.player.right = self.width\n        if self.player.bottom < 0:\n            self.player.bottom = 0\n        if self.player.left < 0:\n            self.player.left = 0\n\n        for tank in self.tank_lists:\n            start_x = tank.center_x\n            start_y = tank.center_y\n\n            dest_x = self.player.center_x\n            dest_y = self.player.center_y\n\n            x_diff = dest_x - start_x\n            y_diff = dest_y - start_y\n            angle = math.atan2(y_diff, x_diff)\n\n            tank.angle = math.degrees(angle) + 180\n\n        for jet in self.fighter_jet_lists:\n            start_x = jet.center_x\n            start_y = jet.center_y\n\n            dest_x = self.player.center_x\n            dest_y = self.player.center_y\n\n            x_diff = dest_x - start_x\n            y_diff = dest_y - start_y\n            angle = math.atan2(y_diff, x_diff)\n\n            jet.angle = math.degrees(angle) + -90\n\n    def on_draw(self):\n        '''Called when needed to draw\n        '''\n\n        arcade.start_render()\n\n        # IMPORTANT\n        # Without this, nothing will show!!!\n        self.enemy_lists.draw()\n        self.player.draw()\n        self.bullet_lists.draw()\n        self.tank_lists.draw()\n        self.tank_missle_lists.draw()\n        self.fighter_jet_lists.draw()\n        self.jet_missle_lists.draw()\n        self.jet_missle_lists_2.draw()\n        self.jet_missle_lists_3.draw()\n\n        for tank in self.tank_lists:\n            tank.draw_health_bar()\n\n        for jet in self.fighter_jet_lists:\n            jet.draw_health_bar()\n\n        time_text = f\"Score: {self.bullet_score:.0f}\"\n        arcade.draw_text(time_text, 10, 0, arcade.color.BLACK, 20)\n\n        time_text = f\"Health: {self.health:.0f}\"\n        arcade.draw_text(time_text, 1700, 0, arcade.color.LIGHT_GREEN, 20)\n\n\nif __name__ == \"__main__\":\n    app = Game()\n    arcade.run()\n\n\n# Instructions :\n# To move use the arrow keys\n# Dodge bullets and angry faces\n# Space to shoot at enemys\n","repo_name":"xwillxu/Python-Arcade","sub_path":"Shooting Games/Arcade Shooting Ship Game/14_drawing.py","file_name":"14_drawing.py","file_ext":"py","file_size_in_byte":18382,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"86"}
+{"seq_id":"44581950707","text":"import socket\r\nimport time\r\n\r\n\r\ndef is_connectable(host, port):\r\n    try:\r\n        sock = socket.create_connection((host, port), 1)\r\n    except socket.error:\r\n        return False\r\n    else:\r\n        sock.close()\r\n        return True\r\n\r\n\r\ndef wait_until_connectable(host, port, timeout=10):\r\n    count = 0\r\n    while not is_connectable(host=host, port=port):\r\n        if count >= timeout:\r\n            raise Exception(\r\n                f'The proxy server has not available by ({host}, {port}) in {timeout:d} seconds'\r\n            )\r\n        count += 1\r\n        time.sleep(1)\r\n    return True\r\n","repo_name":"romis2012/aiohttp-socks","sub_path":"tests/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":206,"dataset":"github-code","pt":"86"}
+{"seq_id":"28781705778","text":"'''\n# -*- coding: utf-8 -*-\n# @Project   : code\n# @File      : __init__.py.py\n# @Software  : PyCharm\n\n# @Author    : hetolin\n# @Email     : hetolin@163.com\n# @Date      : 2021/5/8 11:17\n\n# @Desciption: \n'''\n\nif __name__ == \"__main__\":\n    run_code = 0\n","repo_name":"hetolin/SAR-Net","sub_path":"net_respo/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"86"}
+{"seq_id":"27905045478","text":"import os\nimport xmlrpc.client\n\nimport lib.logger as logger\nimport lib.utilities as util\nimport lib.genesis as gen\n\nOS_IMAGES_DIR = gen.get_container_os_images_path() + '/'\nOS_CONFIG_DIR = OS_IMAGES_DIR + 'config/'\n\nAPACHE2_HTML_DIR = '/var/www/html/'\nKICKSTARTS_DIR = '/var/lib/cobbler/kickstarts/'\nSNIPPETS_DIR = '/var/lib/cobbler/snippets/'\nCOBBLER_USER = gen.get_cobbler_user()\nCOBBLER_PASS = gen.get_cobbler_pass()\n\n\ndef extract_iso_images(path, html_dir):\n    \"\"\"Extract ISO images into webserver directory\n\n    Args:\n        path (str): Directory path containing ISOs or path to single\n                    ISO file\n        html_dir (str): Path to root http directory\n\n    Returns:\n        list: List of tuples ('str: Extracted image directory name',\n                              'str: Relative path to kernel',\n                              'str: Relative path to initrd')\n    \"\"\"\n\n    return_list = []\n\n    if os.path.isdir(path):\n        if not path.endswith('/'):\n            path += '/'\n        file_list = os.listdir(path)\n    elif os.path.isfile(path):\n        file_list = [os.path.basename(path)]\n        path = os.path.dirname(path) + '/'\n\n    # Extract ISO into web directory for access over http\n    for _file in file_list:\n        if _file.endswith('.iso'):\n            kernel, initrd = util.extract_iso_image(path + _file, html_dir)\n            name = _file[:-4]\n            return_list.append((name,\n                                os.path.join(html_dir, kernel),\n                                os.path.join(html_dir, initrd)))\n\n    return return_list\n\n\ndef setup_image_config_files(path, html_dir):\n    \"\"\"Setup image config files\n\n    Args:\n        path (str): Directory path image config files\n        html_dir (str): Path to root http directory\n    \"\"\"\n\n    if not path.endswith('/'):\n        path += '/'\n\n    # Update preseed configurations with default user id\n\n    # Copy preseed & kickstart files to cobbler kickstart directory\n    for _file in os.listdir(path):\n        if _file.endswith('.ks') or _file.endswith('.seed'):\n            util.copy_file(path + _file, KICKSTARTS_DIR)\n\n    # Copy custom snippets to cobbler snippets directory\n    snippets_src_dir = path + 'snippets/'\n    for _file in os.listdir(snippets_src_dir):\n        util.copy_file(snippets_src_dir + _file, SNIPPETS_DIR)\n\n    # Copy apt source lists to web repo directory\n    if not os.path.isdir(html_dir + 'ubuntu_sources'):\n        os.makedirs(html_dir + 'ubuntu_sources')\n    for _file in os.listdir(path):\n        if _file.endswith('.list'):\n            util.copy_file(path + _file, html_dir + 'ubuntu_sources')\n\n\ndef cobbler_add_distro(name, kernel, initrd):\n    \"\"\"Add distro and profile to Cobbler\n\n    Args:\n        name (str): Name of distro/profile\n        kernel (str): Path to kernel\n        initrd (str): Path to initrd\n    \"\"\"\n\n    log = logger.getlogger()\n    name_list = [item.lower() for item in name.split('-')]\n    if 'ubuntu' in name_list:\n        breed = 'ubuntu'\n        for item in name_list:\n            if item == 'amd64':\n                arch = 'x86_64'\n            elif item == 'ppc64el':\n                arch = 'ppc64le'\n            elif item.startswith('14.04'):\n                os_version = 'trusty'\n            elif item.startswith('16.04'):\n                os_version = 'xenial'\n            elif item.startswith('18.04'):\n                os_version = 'bionic'\n        kernel_options = (\n            \"netcfg/dhcp_timeout=1024 \"\n            \"netcfg/choose_interface=auto \"\n            \"ipv6.disable=1\")\n        if os.path.isfile('%s%s.seed' % (KICKSTARTS_DIR, name)):\n            kickstart = '%s%s.seed' % (KICKSTARTS_DIR, name)\n        else:\n            kickstart = '%subuntu-default.seed' % KICKSTARTS_DIR\n\n    elif ('centos' in name_list) or ('rhel' in name_list):\n        breed = 'redhat'\n        for item in name_list:\n            if item == 'x86_64':\n                arch = 'x86_64'\n            elif item == 'ppc64le':\n                arch = 'ppc64le'\n            elif item.startswith('7'):\n                os_version = 'rhel7'\n        kernel_options = \"text\"\n        if os.path.isfile('%s%s.ks' % (KICKSTARTS_DIR, name)):\n            kickstart = '%s%s.ks' % (KICKSTARTS_DIR, name)\n        else:\n            kickstart = '%sRHEL-7-default.ks' % KICKSTARTS_DIR\n    else:\n        log.info(f'Cobbler distro {name} unrecognized and not added')\n        return\n\n    cobbler_server = xmlrpc.client.Server(\"http://127.0.0.1/cobbler_api\")\n    token = cobbler_server.login(COBBLER_USER, COBBLER_PASS)\n\n    new_distro_create = cobbler_server.new_distro(token)\n    cobbler_server.modify_distro(\n        new_distro_create,\n        \"name\",\n        name,\n        token)\n    cobbler_server.modify_distro(\n        new_distro_create,\n        \"arch\",\n        arch,\n        token)\n    cobbler_server.modify_distro(\n        new_distro_create,\n        \"kernel\",\n        kernel,\n        token)\n    cobbler_server.modify_distro(\n        new_distro_create,\n        \"initrd\",\n        initrd,\n        token)\n    cobbler_server.modify_distro(\n        new_distro_create,\n        \"breed\",\n        breed,\n        token)\n    cobbler_server.modify_distro(\n        new_distro_create,\n        \"os_version\",\n        os_version,\n        token)\n    cobbler_server.modify_distro(\n        new_distro_create,\n        \"kernel_options\",\n        kernel_options,\n        token)\n    cobbler_server.save_distro(new_distro_create, token)\n\n    log.info(f\"Cobbler Add Distro: name={name}\")\n    log.debug(f\"name={name} kernel={kernel} initrd{initrd}\")\n\n    new_profile_create = cobbler_server.new_profile(token)\n    cobbler_server.modify_profile(\n        new_profile_create,\n        \"name\",\n        name,\n        token)\n    cobbler_server.modify_profile(\n        new_profile_create,\n        \"distro\",\n        name,\n        token)\n    cobbler_server.modify_profile(\n        new_profile_create,\n        \"enable_menu\",\n        \"True\",\n        token)\n    cobbler_server.modify_profile(\n        new_profile_create,\n        \"kickstart\",\n        kickstart,\n        token)\n    cobbler_server.save_profile(new_profile_create, token)\n\n    log.info(\n        \"Cobbler Add Profile: name=%s, distro=%s\" %\n        (name, name))\n\n    cobbler_server.sync(token)\n    log.info(\"Running Cobbler sync\")\n\n\ndef cobbler_add_profile(distro, name):\n    log = logger.getlogger()\n    cobbler_server = xmlrpc.client.Server(\"http://127.0.0.1/cobbler_api\")\n    token = cobbler_server.login(COBBLER_USER, COBBLER_PASS)\n\n    distro_list = cobbler_server.get_distros()\n    existing_distro_list = []\n    for existing_distro in distro_list:\n        existing_distro_list.append(existing_distro['name'])\n\n    if distro not in existing_distro_list:\n        log.warning(\n            \"Cobbler Skipping Profile - Distro Unavailable: \"\n            \"name=%s, distro=%s\" %\n            (name, distro))\n        return\n\n    new_profile_create = cobbler_server.new_profile(token)\n    cobbler_server.modify_profile(\n        new_profile_create,\n        \"name\",\n        name,\n        token)\n    cobbler_server.modify_profile(\n        new_profile_create,\n        \"distro\",\n        distro,\n        token)\n    cobbler_server.modify_profile(\n        new_profile_create,\n        \"enable_menu\",\n        \"True\",\n        token)\n    cobbler_server.modify_profile(\n        new_profile_create,\n        \"kickstart\",\n        \"/var/lib/cobbler/kickstarts/%s.seed\" % name,\n        token)\n    cobbler_server.save_profile(new_profile_create, token)\n\n    log.info(\n        \"Cobbler Add Profile: name=%s, distro=%s\" %\n        (name, distro))\n\n    cobbler_server.sync(token)\n    log.info(\"Running Cobbler sync\")\n\n\nif __name__ == '__main__':\n    logger.create()\n\n    distros = extract_iso_images(OS_IMAGES_DIR, APACHE2_HTML_DIR)\n\n    setup_image_config_files(OS_CONFIG_DIR, APACHE2_HTML_DIR)\n\n    for distro in distros:\n        name = distro[0]\n        kernel = os.path.join(APACHE2_HTML_DIR, distro[1])\n        initrd = os.path.join(APACHE2_HTML_DIR, distro[2])\n        cobbler_add_distro(name, kernel, initrd)\n\n    for _file in os.listdir(OS_CONFIG_DIR):\n        if _file.endswith('.seed') or _file.endswith('.ks'):\n            profile = _file[:-5]\n            distro = _file.rsplit('.', 2)[0]\n            if profile != distro and os.path.isdir(APACHE2_HTML_DIR + distro):\n                cobbler_add_profile(distro, profile)\n","repo_name":"IBM/power-up","sub_path":"scripts/python/cobbler_add_distros.py","file_name":"cobbler_add_distros.py","file_ext":"py","file_size_in_byte":8340,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"86"}
+{"seq_id":"4006996654","text":"# Purpose: seeds the database with test data\n# Usage: SEEDING=true python3 seed_db.py\n\nimport sys\nimport os\n\nsys.path.append(\"..\")\n\nimport models\nfrom crud import create_recipe\nfrom database import SessionLocal, engine\nfrom schema_validation import NewRecipe\n\nrecipes = [\n    NewRecipe.parse_obj(\n        {\n            \"Title\": \"Bitez Burger\",\n            \"Ingredients\": \"[]\",\n            \"Instructions\": \"[]\",\n            \"TotalTime\": \"20min\",\n            \"Serves\": 6,\n            \"Tags\": \"[]\",\n            \"ImageSrc\": \"https://bitezburger.com/static/images/burger-closeup.jpg\",\n            \"Type\": \"Breakfast\",\n            \"User\": \"M&G\",\n        }\n    ),\n    NewRecipe.parse_obj(\n        {\n            \"Title\": \"Bonchon\",\n            \"Ingredients\": \"[]\",\n            \"Instructions\": \"[]\",\n            \"TotalTime\": \"20min\",\n            \"Serves\": 4,\n            \"Tags\": \"[]\",\n            \"ImageSrc\": \"https://1.bp.blogspot.com/-2xAoPwMd81M/XrCLTJbsofI/AAAAAAAApqQ/8r-zW0ozMFwD4bmM8O0WQ9wkGlNLq_LDgCLcBGAsYHQ/s1600/fried-chicken-bonchon.jpg\",\n            \"Type\": \"Lunch\",\n            \"User\": \"M&G\",\n        }\n    ),\n    NewRecipe.parse_obj(\n        {\n            \"Title\": \"Bonchon\",\n            \"Ingredients\": \"[]\",\n            \"Instructions\": \"[]\",\n            \"TotalTime\": \"20min\",\n            \"Serves\": 4,\n            \"Tags\": \"[]\",\n            \"ImageSrc\": \"https://1.bp.blogspot.com/-2xAoPwMd81M/XrCLTJbsofI/AAAAAAAApqQ/8r-zW0ozMFwD4bmM8O0WQ9wkGlNLq_LDgCLcBGAsYHQ/s1600/fried-chicken-bonchon.jpg\",\n            \"Type\": \"Lunch\",\n            \"User\": \"M&G\",\n        }\n    ),\n    NewRecipe.parse_obj(\n        {\n            \"Title\": \"Creamed Corn\",\n            \"Ingredients\": \"\"\"[\"2 cans corn\",\n                \"3 tbsp butter\",\n                \"3 tbsp flour\",\n                \"salt\",\n                \"pepper\",\n              ]\"\"\",\n            \"Instructions\": \"\"\"[\"(1) Melt butter under medium heat\",\n                \"(2) Mix flour into melted butter until paste forms\",\n                \"(3) Slowly mix in half & half, stirring in between to ensure mixture stays thick\",\n                \"(4) Continue stirring over heat until thickened & smooth\",\n                \"(5) Add corn & mix for 3 minutes\",\n                \"(6) Add salt & pepper to taste\"]\"\"\",\n            \"TotalTime\": \"20min\",\n            \"Serves\": 4,\n            \"Tags\": \"[]\",\n            \"ImageSrc\": \"https://theblondcook.com/wp-content/uploads/2018/10/easy-creamed-corn-4.jpg\",\n            \"Type\": \"Lunch\",\n            \"User\": \"M&G\",\n        }\n    ),\n]\n\nmodels.Base.metadata.create_all(bind=engine)\ndb = SessionLocal()\n\n\ndef seed_recipes():\n    for recipe in recipes:\n        create_recipe(db, recipe)\n\n\nif __name__ == \"__main__\":\n    seed_recipes()\n","repo_name":"hashtagpoop/MG-Eats","sub_path":"scripts/seed_db.py","file_name":"seed_db.py","file_ext":"py","file_size_in_byte":2707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31300613654","text":"#!/usr/bin/env python\n\nimport urllib,re\n\nurl = \"http://www.pythonchallenge.com/pc/def/linkedlist.php?nothing=\"\n\nsearch_pat = re.compile(r'next\\s*nothing\\s*is\\s*(\\d+)')\nargument = \"66831\"\n\nwhile not argument == \"\":\n\tresponse = urllib.urlopen(url+argument)\n\thtml = str(response.read())\n\tprint (html)\n\targument = ''\n\tresult = re.search(search_pat,html)\n\tif result != None:\n\t\targument = result.group(0).split()[3]\n\tif argument == '':\n\t\targument = raw_input(\":\")\n","repo_name":"ziyi19851021/challenge","sub_path":"linkedlist.py","file_name":"linkedlist.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31382443452","text":"import matplotlib.pyplot as plt\nimport matplotlib.animation as animation\n\nfig = plt.figure()\nopen(\"plot.txt\", \"w\").close()  # clear file\n\n\ndef animate(i):\n    data = open(\"plot.txt\", \"r\").read()\n    data_list = data.split(\"\\n\")\n    xs = []\n    ys = []\n    for line in data_list:\n        if len(line) > 0:\n            x, y = line.split(\" \")\n            xs.append(float(x))\n            ys.append(float(y) / 10)\n    plt.cla()\n    plt.plot(xs, ys, \"k\", lw=0.5)\n    plt.xlabel(\"time (sec)\")\n    plt.ylabel(\"temperature\")\n\n\ndef main():\n    ani = animation.FuncAnimation(fig, animate, interval=100)\n    plt.show()\n\n\nmain()\n","repo_name":"griegner/run-tcs","sub_path":"plot-live.py","file_name":"plot-live.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"1520043293","text":"from django import forms\n\nfrom catalog.models import Product, Version, Category\n\n\nclass StyleFormMixin:\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        for field_name, field in self.fields.items():\n            field.widget.attrs['class'] = 'form-control'\n\n\nclass ProductForm(StyleFormMixin, forms.ModelForm):\n    forbidden_words = ['казино', 'криптовалюта', 'крипта', 'биржа', 'дешево', 'бесплатно', 'обман', 'полиция', 'радар']\n\n    class Meta:\n        model = Product\n        fields = ('title', 'description', 'category', 'image', 'purchase_price')\n\n    def clean_title(self):\n        cleaned_data = self.cleaned_data['title']\n\n        for word in self.forbidden_words:\n            if word in cleaned_data.lower():\n                raise forms.ValidationError(f'В продукте нельзя использовать слова: {\", \".join(self.forbidden_words)}')\n\n        return cleaned_data\n\n    def clean_description(self):\n        cleaned_data = self.cleaned_data['description']\n\n        for word in self.forbidden_words:\n            if word in cleaned_data.lower():\n                raise forms.ValidationError(f'В продукте нельзя использовать слова: {\", \".join(self.forbidden_words)}.')\n\n        return cleaned_data\n\n\nclass VersionForm(StyleFormMixin, forms.ModelForm):\n    class Meta:\n        model = Version\n        fields = '__all__'\n\n\nclass CategoryForm(StyleFormMixin, forms.ModelForm):\n    class Meta:\n        model = Category\n        fields = ('title', 'description',)\n","repo_name":"MSumbaev/dj_online_store","sub_path":"catalog/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"2248301668","text":"import json\nimport re\nfrom difflib import get_close_matches\n\n# get the input of what want to search ignore the case\n# return it\n# closer word supermann fruti\n\n# open the file and store to data\nimport sys\n\ndata = json.load(open('data.json'))\n\n\ndef get_the_answer(want):\n    if want in data:\n        return data[want]\n\n    elif want.upper() in data:\n        return data[want.upper()]\n\n    elif want.title() in data:\n        return data[want.title()]\n        # the closest match can have or not so check the length\n    elif len(get_close_matches(want, data.keys())) > 0:\n        print(\"did you mean %s instead\" % get_close_matches(want, data.keys())[0])\n        print(get_close_matches(want, data.keys()))\n        decide = input(\"press y for yes or n for no \\n\")\n        if decide.lower() == \"y\":\n            # get closest match we do not consider about the upper lower\n            return data[get_close_matches(want, data.keys())[0]]\n        elif decide.lower() == \"n\":\n            print(\"you have enter the wrong key\")\n        else:\n            print(\"you have enter the wrong choice\")\n    else:\n\n        print(\"there is no \" + want + \" in the dictionary\")\n\n    return\n\n\nwhile True:\n    want = input(\"enter the word you want to find \\n\").lower()\n    while re.search('[@_!#$%^&*()<>?/}{~:|0-9]', want):\n        print(\"your word you enter contain special character which is not available\")\n        want = input(\"enter the world you want to find \\n\").lower()\n    # we make it lower first since they can screw up by input word in messy case like TaMil\n\n    out = get_the_answer(want)\n    if out is not None:\n        for var in out:\n            print(var)\n\n\n    repeat = input(\"Do you want to find any other word? press enter to continue else system will stop \\n\")\n    if repeat != \"\":\n        sys.exit(0)\n","repo_name":"kimchhengheng/learning_python","sub_path":"dic.py","file_name":"dic.py","file_ext":"py","file_size_in_byte":1800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"72418723163","text":"from sahi.predict import predict\nimport argparse\nimport os\nfrom utils import mAP, pickle_to_text, yolo_to_txt, read_file_to_tensor\nimport wandb\nfrom datetime import datetime\n\nmodel_type = \"yolov5\"\nmodel_device = 'cuda:0'\nmodel_confidence_threshold = 0.35\n\nslice_height = 1024\nslice_width = 1024\noverlap_height_ratio = 0.2\noverlap_width_ratio = 0.2\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description='Custom SAHI')\n    parser.add_argument('--model_path', default=\"../SeaDroneSee_challenge/runs/train/actual_baseline_1/weights/best.pt\", type=str)\n    parser.add_argument('--source_image_dir', default=\"../SeaDroneSee_challenge/data/images_drones/\", type=str)\n    parser.add_argument('-m', '--mode', default='sparse', type=str)\n    parser.add_argument('--fmaps_path', default='./actual_baseline_1_images_drones.npy', type=str)\n    parser.add_argument('-n', '--name', default='exp', type=str)\n    args = parser.parse_args()\n    model_confidence_threshold_arr = [0.25, 0.5, 0.75]\n    slice_arr = [512, 1024]\n    overlap_ratio_arr = [0.0, 0.2]\n    postprocess_type_arr = ['NMS', 'NMM', 'GREEDYNMM']\n    postprocess_match_metric_arr = ['IOU', 'IOS']\n    postprocess_match_threshold_arr = [0.25, 0.5, 0.75]\n    postprocess_class_agnostic_arr = [True, False]\n    cnt = 1\n    for mct in model_confidence_threshold_arr:\n        for s in slice_arr:\n            for or_ in overlap_ratio_arr:\n                for pt in postprocess_type_arr:\n                    for pmm in postprocess_match_metric_arr:\n                        for pmt in postprocess_match_threshold_arr:\n                            for pca in postprocess_class_agnostic_arr:\n                                cfg = {\n                                    'model_confidence_threshold_arr': mct,\n                                    'slice_height_arr': s,\n                                    'slice_width_arr': s,\n                                    'overlap_height_ratio_arr': or_,\n                                    'overlap_width_ratio_arr': or_,\n                                    'postprocess_type_arr': pt,\n                                    'postprocess_match_metric_arr': pmm,\n                                    'postprocess_match_threshold_arr': pmt,\n                                    'postprocess_class_agnostic_arr': pca\n                                }\n                                run = wandb.init(\n                                    project=\"sahi\",\n                                    name=datetime.now().strftime(\"%b%d_%H:%M:%S\"),\n                                    entity=\"cyr1ll\",\n                                    group='grid-search',\n                                    config=cfg\n                                )\n                                predict(\n                                    model_type=model_type,\n                                    model_path=args.model_path,\n                                    model_device=model_device,\n                                    model_confidence_threshold=mct,\n                                    source=args.source_image_dir,\n                                    slice_height=s,\n                                    slice_width=s,\n                                    overlap_height_ratio=or_,\n                                    overlap_width_ratio=or_,\n                                    export_pickle=True,\n                                    export_visual=True,\n                                    mode='standard',\n                                    image_size=768,\n                                    postprocess_type=pt,\n                                    postprocess_match_metric=pmm,\n                                    postprocess_match_threshold=pmt,\n                                    postprocess_class_agnostic=pca,\n                                    name=f\"exp{cnt}\"\n                                )\n\n                                pickle_in = f\"./runs/predict/exp{cnt}/pickles/\"\n                                pickle_to_text_dir = \"./pickles_to_text/\"\n                                print(\"Pickles at: \", pickle_in)\n\n                                gt_in = './ground_truth/gt_images_drones/'\n                                images_path = '../SeaDroneSee_challenge/data/images_drones/'\n                                gt_to_text_dir = './ground_truth/gt_images_drones_txt/'\n                                print(\"Ground truth at: \", gt_to_text_dir)\n\n                                pickle_to_text(pickle_in, pickle_to_text_dir)\n                                yolo_to_txt(gt_in, gt_to_text_dir, images_path)\n\n                                all_labels = []\n                                all_detections = []\n                                for filename in os.listdir(gt_to_text_dir):\n                                    labels = read_file_to_tensor(gt_to_text_dir, filename, n_fields=5)\n                                    detections = read_file_to_tensor(pickle_to_text_dir, filename, n_fields=6)\n\n                                    all_labels.append(labels)\n                                    all_detections.append(detections)\n\n                                res = mAP(all_detections, all_labels)\n                                wandb.log(res)\n                                print(res)\n                                cnt += 1\n                                run.finish()\n","repo_name":"LightnessOfBeing/sahi_custom","sub_path":"grid_search.py","file_name":"grid_search.py","file_ext":"py","file_size_in_byte":5352,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"6818802537","text":"import datetime\nimport PyQt5.QtCore as qtco\nimport PyQt5.QtChart as qtch\nimport PyQt5.QtWidgets as qtwi\nimport PyQt5.QtGui as qtgu\nimport sys\nimport yfinance as yf\n\n\ndef get_chart(symbol, interval, start, end):\n    ticker = yf.Ticker(symbol)\n    data_reader = ticker.history(interval=interval, start=start, end=end)\n    cstick_series = qtch.QCandlestickSeries()\n    cstick_series.setName(interval)\n    cstick_series.setIncreasingColor(qtgu.QColorConstants.Green)\n    cstick_series.setDecreasingColor(qtgu.QColorConstants.Red)\n    dates = []\n    for (ts, (opn, hgh, low, cls, vol, div, splits)) in data_reader.iterrows():\n        ts_qdt = qtco.QDateTime(ts)\n        ts_str = ts_qdt.toString(qtco.Qt.ISODate)\n        ts_flt = ts_qdt.toMSecsSinceEpoch()\n        candlestickset = qtch.QCandlestickSet(ts_flt)\n        candlestickset.setOpen(opn)\n        candlestickset.setHigh(hgh)\n        candlestickset.setLow(low)\n        candlestickset.setClose(cls)\n        cstick_series.append(candlestickset)\n        dates.append(ts_str)\n    chart = qtch.QChart()\n    chart.setTheme(qtch.QChart.ChartThemeDark)\n    chart.addSeries(cstick_series)\n    chart.setTitle(str(symbol) + \" \" + str(start) + \" \" + str(end))\n    chart.createDefaultAxes()\n    axis_x = chart.axes(qtco.Qt.Horizontal)[0]\n    axis_x.setCategories(dates)\n    axis_y = chart.axes(qtco.Qt.Vertical)[0]\n    axis_y.setMin(axis_y.min() * 0.99)\n    axis_y.setMax(axis_y.max() * 1.01)\n    chart.legend().setVisible(True)\n    chart.legend().setAlignment(qtco.Qt.AlignBottom)\n    chart_view = qtch.QChartView(chart)\n    chart_view.setRenderHint(qtgu.QPainter.Antialiasing)\n    return chart_view\n\n\ndef go():\n    a = qtwi.QApplication(sys.argv)\n    wind = qtwi.QMainWindow()\n    grid = qtwi.QGridLayout()\n    widg = qtwi.QWidget()\n    widg.setLayout(grid)\n    wind.setCentralWidget(widg)\n    symbols = ['btc-usd', 'eth-usd', 'bnb-usd',\n               'usdt-usd', 'sol-usd', 'usdc-usd',\n               'ada-usd', 'xrp-usd', 'luna1-usd']\n    positions = [(i, j) for i in range(3) for j in range(3)]\n    for position, symbol in zip(positions, symbols):\n        if symbol == '':\n            continue\n        interval = \"1h\"\n        end = datetime.datetime.now()\n        start = end - datetime.timedelta(days=2)\n        grid.addWidget(get_chart(symbol, interval, start, end), *position)\n    wind.show()\n    while True:\n        if a.hasPendingEvents():\n            a.processEvents()\n        qtco.QThread.msleep(20)\n\n\ngo()\n","repo_name":"l0/chuminator","sub_path":"chuminator.py","file_name":"chuminator.py","file_ext":"py","file_size_in_byte":2458,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"27919318556","text":"from aerosandbox.numpy import *\n\nfrom .motor import orbital_20_gearmotor\nfrom .static_parameters import *\n\nimport numpy as np\n\ndef robot_acceleration(qr, qr_dot, motor_voltages):\n    heading = qr[2]\n    heading_vel = qr_dot[2]\n\n    motor_voltages = motor_voltages[:4].reshape((4, 1))\n    qr = qr.reshape((3, 1))\n    qr_dot = qr_dot.reshape((3, 1))\n\n    R = (1 / r) * array([[1, -1, -Ll], [1, 1, Ll], [1, 1, -Ll], [1, -1, Ll]], dtype=float)\n\n    undo_rotation = transpose(rotation_matrix(heading))\n\n    wheel_speeds = R @ undo_rotation @ qr_dot\n    wheel_torques = orbital_20_gearmotor.torque(\n        motor_voltages[:4].astype(float) if isinstance(motor_voltages, np.ndarray) else motor_voltages[:4],\n        wheel_speeds)\n    forces = robot_force(wheel_torques, heading)\n\n    H = diag([m, m, Iz]) + (4 * Iw / r ** 2) * diag([1, 1, Ll ** 2])\n    K = (4 * Iw / r ** 2) * heading_vel * array([[0, 1, 0], [-1, 0, 0], [0, 0, 0]])\n    qr_ddot = linalg.inv(H) @ (forces - (K @ qr_dot))\n\n    return qr_ddot.reshape((3,1))\n\n\ndef rotation_matrix(heading):\n    return array([[cos(heading), -sin(heading), 0],\n                  [sin(heading), cos(heading), 0],\n                  [0, 0, 1]], dtype=(float if isinstance(heading, ndarray) else None))\n\n\ndef robot_force(torque, heading):\n    return (1 / r) * array([\n        sin(heading) * (torque[0] - torque[1] - torque[2] + torque[3]) + cos(heading) * (\n                torque[0] + torque[1] + torque[2] + torque[3]),\n        sin(heading) * (torque[0] + torque[1] + torque[2] + torque[3]) - cos(heading) * (\n                torque[0] - torque[1] - torque[2] + torque[3]),\n        -Ll * (torque[0] - torque[1] + torque[2] - torque[3])])\n\n\nif __name__ == '__main__':\n    state = array([0, 0., 0., 0., 0., 0.], dtype=float64).reshape((6, 1))\n    for i in range(10):\n        state = state + .1 * robot_acceleration(state, array([12., 12., 12., 12.]).reshape((4, 1)))\n\n    print(state)\n","repo_name":"KuriosityRobotics/mecanum-model-predictive-control","sub_path":"src/model-predictive-control/src/physics/dynamics.py","file_name":"dynamics.py","file_ext":"py","file_size_in_byte":1919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"17613885521","text":"from tkinter import *\r\n\r\ndef button_click(number):\r\n    current = entry.get()\r\n    entry.delete(0, END)\r\n    entry.insert(END, current + str(number))\r\n\r\ndef button_clear():\r\n    entry.delete(0, END)\r\n\r\ndef button_equal():\r\n    expression = entry.get()\r\n    \r\n    while (expression.startswith(\"0\")):\r\n        expression = expression[1:]\r\n    \r\n    result = eval(expression)\r\n    entry.delete(0, END)\r\n    entry.insert(END, result)\r\n\r\n# Create the Tkinter window\r\nwindow = Tk()\r\nwindow.title(\"Calculator\")\r\n\r\n# Create the entry field for display\r\nentry = Entry(window, width=25, borderwidth=5)\r\nentry.grid(row=0, column=0, columnspan=4, padx=10, pady=10)\r\n\r\n# Create the number buttons\r\nfor i in range(0,10):\r\n    button = Button(window, text=str(i), padx=20, pady=10, command=lambda i=i: button_click(i))\r\n    if (i==0):\r\n        button.grid(row=4, column=1, padx=5, pady=5)\r\n    else:\r\n        button.grid(row=(i+2)//3, column=(i-1)%3, padx=5, pady=5)\r\n\r\n# Create the operator buttons\r\noperators = ['+', '-', '*', '/']\r\nfor i, operator in enumerate(operators):\r\n    button = Button(window, text=operator, padx=20, pady=10, command=lambda operator=operator: button_click(operator))\r\n    button.grid(row=i+1, column=3, padx=5, pady=5)\r\n\r\n# Create the clear button\r\nbutton_clear = Button(window, text=\"C\", padx=20, pady=10, command=button_clear)\r\nbutton_clear.grid(row=4, column=0, padx=5, pady=5)\r\n\r\n# Create the equal button\r\nbutton_equal = Button(window, text=\"=\", padx=20, pady=10, command=button_equal)\r\nbutton_equal.grid(row=4, column=2, padx=5, pady=5)\r\n\r\n# Run the Tkinter event loop\r\nwindow.mainloop()\r\n","repo_name":"CADisHARD/Python_Calculator","sub_path":"calculator.py","file_name":"calculator.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74718835483","text":"#最大池化\nimport torch\nfrom torch import nn\nfrom torch.nn import MaxPool2d\nimport torchvision\nfrom torch.utils.data import DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\n\ndataset = torchvision.datasets.CIFAR100('dataset_Crfar10',train=False,transform=torchvision.transforms.ToTensor(),download=True)\nwriter = SummaryWriter('logs')\ninput = torch.tensor([[1,2,3,4,5],\n                      [4,6,7,8,9],\n                      [4,6,7,8,9],\n                      [4,6,7,8,9],\n                      [4,6,7,8,9]],dtype=torch.float32)\ninput = torch.reshape(input,(-1,1,5,5))\nprint(input.shape)\nclass Tudui(nn.Module):\n    def __init__(self):\n        super(Tudui, self).__init__()\n        self.maxpool1 = MaxPool2d(kernel_size=3, ceil_mode=True)\n\n    def forward(self, input):\n        output = self.maxpool1(input)\n        return output\n\nmaxpool =Tudui()\noutput = maxpool(input)\nprint(output)\nstep = 0\ndataloader = DataLoader(dataset,batch_size=72)\nfor data in dataloader:\n    imgs,targets = data\n    output = maxpool(imgs)\n    writer.add_images('input images',imgs,step)\n    writer.add_images('output imgs',output,step)\n    print('*********************************************')\n    print(imgs.shape)\n    print(output.shape)\n    print('*********************************************')\n    step = step + 1\nwriter.close()","repo_name":"Yuguangcheng/pytorch_test","sub_path":"11 maxpool.py","file_name":"11 maxpool.py","file_ext":"py","file_size_in_byte":1328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"21287250107","text":"import math\n\nimport pytest\nimport torch\n\nimport pyro\nimport pyro.distributions as dist\nfrom pyro.contrib.forecast import Forecaster, ForecastingModel, HMCForecaster, backtest\nfrom pyro.contrib.forecast.evaluate import DEFAULT_METRICS\nfrom pyro.util import optional\n\n\nclass Model(ForecastingModel):\n    def model(self, zero_data, covariates):\n        loc = zero_data[..., :1, :]\n        scale = pyro.sample(\"scale\", dist.LogNormal(loc, 1).to_event(1))\n\n        with self.time_plate:\n            jumps = pyro.sample(\"jumps\", dist.Normal(0, scale).to_event(1))\n        prediction = jumps.cumsum(-2)\n\n        noise_dist = dist.Normal(zero_data, 1)\n        self.predict(noise_dist, prediction)\n\n\nWINDOWS = [\n    (None, 1, None, 1, 8),\n    (None, 10, None, 10, 1),\n    (10, 1, None, 3, 5),\n    (None, 5, 10, 1, 5),\n    (7, 1, 7, 1, 7),\n    (14, 1, 7, 1, 1),\n]\n\n\n@pytest.mark.parametrize(\n    \"train_window,min_train_window,test_window,min_test_window,stride\", WINDOWS\n)\n@pytest.mark.parametrize(\"warm_start\", [False, True], ids=[\"cold\", \"warm\"])\ndef test_simple(\n    train_window, min_train_window, test_window, min_test_window, stride, warm_start\n):\n    duration = 30\n    obs_dim = 2\n    covariates = torch.zeros(duration, 0)\n    data = torch.randn(duration, obs_dim) + 4\n    forecaster_options = {\"num_steps\": 2, \"warm_start\": warm_start}\n\n    expect_error = warm_start and train_window is not None\n    with optional(pytest.raises(ValueError), expect_error):\n        windows = backtest(\n            data,\n            covariates,\n            Model,\n            train_window=train_window,\n            min_train_window=min_train_window,\n            test_window=test_window,\n            min_test_window=min_test_window,\n            stride=stride,\n            forecaster_options=forecaster_options,\n        )\n    if not expect_error:\n        assert any(window[\"t0\"] == 0 for window in windows)\n        if stride == 1:\n            assert any(window[\"t2\"] == duration for window in windows)\n        for window in windows:\n            assert window[\"train_walltime\"] >= 0\n            assert window[\"test_walltime\"] >= 0\n            for name in DEFAULT_METRICS:\n                assert name in window\n                assert 0 < window[name] < math.inf\n\n\n@pytest.mark.parametrize(\n    \"train_window,min_train_window,test_window,min_test_window,stride\", WINDOWS\n)\n@pytest.mark.parametrize(\"engine\", [\"svi\", \"hmc\"])\ndef test_poisson(\n    train_window, min_train_window, test_window, min_test_window, stride, engine\n):\n    duration = 30\n    obs_dim = 2\n    covariates = torch.zeros(duration, 0)\n    rate = torch.randn(duration, obs_dim) + 4\n    counts = dist.Poisson(rate).sample()\n\n    # Transform count data to log domain.\n    data = counts.log1p()\n\n    def transform(pred, truth):\n        pred = dist.Poisson(pred.clamp(min=1e-4).expm1()).sample()\n        truth = truth.expm1()\n        return pred, truth\n\n    if engine == \"svi\":\n        forecaster_fn = Forecaster\n        forecaster_options = {\"num_steps\": 2}\n    else:\n        forecaster_fn = HMCForecaster\n        forecaster_options = {\"num_warmup\": 1, \"num_samples\": 1}\n\n    windows = backtest(\n        data,\n        covariates,\n        Model,\n        forecaster_fn=forecaster_fn,\n        transform=transform,\n        train_window=train_window,\n        min_train_window=min_train_window,\n        test_window=test_window,\n        min_test_window=min_test_window,\n        stride=stride,\n        forecaster_options=forecaster_options,\n    )\n\n    assert any(window[\"t0\"] == 0 for window in windows)\n    if stride == 1:\n        assert any(window[\"t0\"] == 0 for window in windows)\n        assert any(window[\"t2\"] == duration for window in windows)\n    for name in DEFAULT_METRICS:\n        for window in windows:\n            assert name in window\n            assert 0 < window[name] < math.inf\n\n\ndef test_custom_warm_start():\n    duration = 30\n    obs_dim = 2\n    covariates = torch.zeros(duration, 0)\n    data = torch.randn(duration, obs_dim) + 4\n    min_train_window = 10\n\n    def forecaster_options(t0, t1, t2):\n        if t1 == min_train_window:\n            return {\"num_steps\": 2, \"warm_start\": True}\n        else:\n            return {\"num_steps\": 0, \"warm_start\": True}\n\n    backtest(\n        data,\n        covariates,\n        Model,\n        min_train_window=min_train_window,\n        test_window=10,\n        forecaster_options=forecaster_options,\n    )\n","repo_name":"pyro-ppl/pyro","sub_path":"tests/contrib/forecast/test_evaluate.py","file_name":"test_evaluate.py","file_ext":"py","file_size_in_byte":4392,"program_lang":"python","lang":"en","doc_type":"code","stars":8201,"dataset":"github-code","pt":"86"}
+{"seq_id":"24214201446","text":"# leetcode problem # 50. Pow(x, n)\n\n\"\"\"\nImplement pow(x, n), which calculates x raised to the power n (i.e., x^n).\n\nExample 1:\nInput: x = 2.00000, n = 10\nOutput: 1024.00000\n\nExample 2:\nInput: x = 2.10000, n = 3\nOutput: 9.26100\n\nExample 3:\nInput: x = 2.00000, n = -2\nOutput: 0.25000\nExplanation: 2-2 = 1/22 = 1/4 = 0.25\n\nConstraints:\n-100.0 < x < 100.0\n-2^31 <= n <= 2^31-1\nn is an integer.\nEither x is not zero or n > 0.\n-10^4 <= x^n <= 10^4\n\"\"\"\n\n\"\"\"\nMy solution: iterative building\n\nProperty of powers: X^M * X^N = X^(M+N)\nex: 2^5 * 2^7 = 2^12\n\nIdea: double the power each time a calculation is performed, then\nmultiply the answer by the largest power currently calculated without\nexceeding N\n\nKeep a stack of the previous results\n- if the current power + the last power in the stack <= N, multiply the\ncurrent result by the value in the stack, and add the last power to the current\npower\n    - add the new result and the new power to the stack\n- otherwise, pop from the stack until the requirement above is satisfied\n\nFor cases where N < 0, start with 1/X in the stack and change N to -N\n\nRuntime: O(logN) where N is the power to raise X to\nSpace: O(logN)\n\"\"\"\n\nclass Solution:\n    def myPow(self, x: float, n: int) -> float:\n        if x == 0:\n            return 0\n\n        ans = 1\n        stack = []\n\n        curPower = 0\n\n        if n < 0:\n            stack.append((1, 1 / x))\n            n = -n\n        else:\n            stack.append((1, x))\n        \n        while curPower < n:\n            if curPower + stack[-1][0] <= n:\n                ans *= stack[-1][1]\n                curPower += stack[-1][0]\n                stack.append((curPower, ans))\n            else:\n                stack.pop()\n\n        return ans\n    \n\n\"\"\"\nSolution by leetcode: Recursion with binary exponention\n\nBinary Exponention: a property of powers where X^2N == (X^2) ^ N\nex: 2^100 == 4^50 == 16^25\n\nMoreover, X^N == X * X^(N-1)\nex: 16^25 == 16 * 16^24 == 16 * 256^6\n\nThus, the power X^N can be broken down into larger bases with smaller powers\nand reconstructed instead of iteratively multiplying X by itself N times.\n\nRuntime: O(logN)\nSpace: O(logN)\n\"\"\"\n\nclass Solution:\n    def binaryExp(self, x: float, n: int) -> float:\n        # Base case, to stop recursive calls.\n        if n == 0:\n            return 1\n       \n        # Handle case where, n < 0.\n        if n < 0:\n            return 1.0 / self.binaryExp(x, -1 * n)\n       \n        # Perform Binary Exponentiation.\n        # If 'n' is odd we perform Binary Exponentiation on 'n - 1' and multiply result with 'x'.\n        if n % 2 == 1:\n            return x * self.binaryExp(x * x, (n - 1) // 2)\n        # Otherwise we calculate result by performing Binary Exponentiation on 'n'.\n        else:\n            return self.binaryExp(x * x, n // 2)\n\n    def myPow(self, x: float, n: int) -> float:\n        return self.binaryExp(x, n)","repo_name":"Stephenabcba/Algo-Practice","sub_path":"python/pow_x_n.py","file_name":"pow_x_n.py","file_ext":"py","file_size_in_byte":2862,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"11900640368","text":"import pickle\nimport random\n# from decimal import *\nimport json\nimport keras\nimport numpy as np\nimport tensorflow as tf\nfrom sklearn.utils import shuffle\nimport os\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\nclass FineTuneModel():\n    '''\n    Fine Tune Model: (Basically doing transfer learning)\n        1. Last FC layer neurons number would be the target class number + 1\n        2. Need to transform the original label to targeted class label\n    '''\n    def __init__(self, target_class_id=[0]):\n        '''\n        Set hyperparameters\n        '''\n        self.learning_rate = 0.01\n        self.epoch = 0\n        self.prune_ratio = 0.9\n\n        '''\n        For one input image :\n            1. Store all the gates infomation\n            2. Store all the gates values \n        '''\n        self.AllGateVariables = dict()\n        self.AllGateVariableValues = list()\n\n        self.target_class_id = target_class_id # assign the trim class id \n        self.target_number = len(target_class_id) + 1\n\n        self.graph = tf.Graph()\n        self.build_model(self.graph)\n        print(\"restored the pretrained model......\")\n        self.restore_model(self.graph)\n\n    '''\n    Test Accuracy\n    '''\n    def test_accuracy(self, test_images, test_labels):\n        accuracy = self.sess.run(\n            self.accuracy, feed_dict={\n            self.xs: test_images,\n            self.ys_orig: test_labels, \n            self.lr : 0.1,\n            self.is_training: False,\n            self.keep_prob: 1.0\n        }) \n\n        print(\"Test Accuracy:\" + str(accuracy))\n\n    '''\n    Fine tune training\n    '''\n    def train_model(self, input_images, input_labels):\n        if self.epoch == 5: self.learning_rate /= 10\n        if self.epoch == 50: self.learning_rate /= 10\n        if self.epoch == 100: self.learning_rate /= 10\n        self.sess.run(self.train_step, feed_dict = {\n                self.xs: input_images,\n                self.ys_orig : input_labels, \n                self.lr : self.learning_rate, \n                self.keep_prob : 1.0, \n                self.is_training : False\n            })\n        self.epoch += 1\n\n    '''\n    Restore the original network weights\n    '''\n    def restore_model(self, graph):\n        savedVariable = {}\n\n        # If GPU is needed\n        config = tf.ConfigProto()\n        config.gpu_options.allow_growth = True\n        self.sess = tf.Session(graph = graph, config = config)\n        # Else if CPU needed\n        # self.sess = tf.Session(graph = graph)\n        self.sess.run(self.init)\n\n        with graph.as_default():\n            for i in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):\n                variable = i\n                name = i.name\n                if name == 'pl:0':\n                    continue\n                if name[:4] == \"FC16\":\n                    continue\n                if name in self.AllGateVariables:\n                    continue \n                if len(name) >= 8 and name[-11:] == '/Momentum:0':\n                    name_prefix = name[:-11]\n                    name_prefix += ':0'\n                    if name_prefix in self.AllGateVariables:\n                        continue\n                name = i.name[:-2]\n                savedVariable[name] = variable\n            saver = tf.train.Saver(savedVariable)\n            # saver = tf.train.Saver(max_to_keep = None)\n            saver.restore(self.sess, \"vggNet/augmentation.ckpt-120\")\n            print(\"Restored successfully!\")\n\n    '''\n    Find mask class unit\n    '''\n    def mask_class_unit(self, classid):\n        self.test_counter = 0\n        theshold = 10\n        json_path = \"./ClassEncoding/class\" + str(classid) + \".json\"\n        with open(json_path, \"r\") as f:\n            gatesValueDict = json.load(f)\n            for idx in range(len(gatesValueDict)):\n                layer = gatesValueDict[idx]\n                name = layer[\"name\"]\n                vec = layer[\"shape\"]\n                # process name\n                name = name.split('/')[0]\n                # process vec\n                for i in range(len(vec)):\n                    if vec[i] < 10:\n                        vec[i] = 0\n                    else:\n                        vec[i] = 1\n                layer[\"name\"] = name\n                layer[\"shape\"] = vec\n\n            return gatesValueDict\n    '''\n    mask by value\n    '''\n    def mask_unit_by_value(self, classid):\n        formulizedDict = {}\n        json_path = \"./ClassEncoding/class\" + str(classid) + \".json\"\n        \n        allGatesValue = []\n\n        with open(json_path, \"r\") as f:\n            gatesValueDict = json.load(f)\n            for idx in range(len(gatesValueDict)):\n                layer = gatesValueDict[idx]\n                name = layer[\"name\"]\n                vec = layer[\"shape\"]\n                allGatesValue += vec\n                \n        allGatesValue.sort()\n        allGatesValue = allGatesValue[:int(len(allGatesValue)*0.8)]\n        \n        allGatesValue = set(allGatesValue)\n        with open(json_path, \"r\") as f:\n            gatesValueDict = json.load(f)\n            for idx in range(len(gatesValueDict)):\n                layer = gatesValueDict[idx]\n                name = layer[\"name\"]\n                vec = layer[\"shape\"]        \n                # process name\n                name = name.split('/')[0]\n                # process vec\n                for i in range(len(vec)):\n                    if vec[i] in allGatesValue or vec[i]==0:\n                        vec[i] = 0\n                    else:\n                        vec[i] = 1\n                layer[\"name\"] = name\n                layer[\"shape\"] = vec\n            with open(\"./ClassEncoding/mask1.json\", \"w\") as tmpFile:\n                json.dump(gatesValueDict, tmpFile)\n            return gatesValueDict\n\n    '''\n    Fine mask class multi, merge multi-class JSONs\n    '''\n    def mask_class_multi(self):\n        theshold = 5\n        self.test_counter = 0\n        ''' init the dict with class0.json '''\n        multiClassGates = self.mask_class_unit(self.target_class_id[0])\n        for classid in self.target_class_id:\n            if (classid == self.target_class_id[0]):\n                continue\n            ''' Merge JSONs continuously '''\n            json_path = \"./ClassEncoding/class\" + str(classid) + \".json\"\n            with open(json_path, \"r\") as f:\n                gatesValueDict = json.load(f)\n                for idx in range(len(gatesValueDict)):\n                    layer = gatesValueDict[idx]\n                    name = layer[\"name\"]\n                    vec = layer[\"shape\"]\n                    # process name\n                    name = name.split('/')[0]\n                    # process vec\n                    for i in range(len(vec)):\n                        if vec[i] < theshold:\n                            vec[i] = 0\n                        else:\n                            vec[i] = 1\n                    gatesValueDict[idx][\"name\"] = name\n                    gatesValueDict[idx][\"shape\"] = vec\n                \n                ''' Now we merge gatesValueDict and multiClassGates '''\n                for idx1 in range(len(gatesValueDict)):\n                    for idx2 in  range(len(multiClassGates)):\n                        if (gatesValueDict[idx1][\"name\"] == multiClassGates[idx2][\"name\"]):\n                            tomerge = gatesValueDict[idx1][\"shape\"]\n                            for idx3 in range(len(tomerge)):\n                                if (tomerge[idx3] == 1 and multiClassGates[idx2][\"shape\"][idx3] == 0):\n                                    multiClassGates[idx2][\"shape\"][idx3] = 1\n                                    self.test_counter += 1\n                                else:\n                                    pass\n                        else:\n                            pass\n            print(\"Furthermore, class \", str(classid), \" activate nums of neurons: \", str(self.test_counter))\n\n        return multiClassGates\n\n    def mask_class_multi_by_value(self):\n        '''\n        Calculate sum of multi-class scalars\n        '''\n        print(\"RUNNING mask_class_multi_by_value.py\")\n        # print(\"Pruning Ratio: \", self.prune_ratio)\n        multiClassGates = list()\n        for classid in self.target_class_id:\n            '''\n            Merge JSONs continuously\n            '''\n            json_path = \"./ClassEncoding/class\" + str(classid) + \".json\"\n            with open(json_path, \"r\") as f:\n                gatesValueDict = json.load(f)\n                for idx in range(len(gatesValueDict)):\n                    layer = gatesValueDict[idx]\n                    name = layer[\"name\"]\n                    vec = layer[\"shape\"]\n                    # process name\n                    name = name.split('/')[0]\n                    gatesValueDict[idx][\"name\"] = name\n                    gatesValueDict[idx][\"shape\"] = vec\n                if not multiClassGates:\n                    '''\n                    Initialize the multiClassGates\n                    '''\n                    multiClassGates = gatesValueDict\n                else:\n                    '''\n                    Now we merge gatesValueDict and multiClassGates\n                    '''\n                    for idx1 in range(len(gatesValueDict)):\n                        for idx2 in range(len(multiClassGates)):\n                            if (gatesValueDict[idx1][\"name\"] == multiClassGates[idx2][\"name\"]):\n                                tomerge = gatesValueDict[idx1][\"shape\"]\n                                for idx3 in range(len(tomerge)):\n                                    multiClassGates[idx2][\"shape\"][idx3] += tomerge[idx3]\n                            else:\n                                pass\n        '''\n        Sort & Mask for multi-class conditions\n        '''\n        allGatesValue = []\n        for idx in range(len(multiClassGates)):\n            layer = multiClassGates[idx]\n            name = layer[\"name\"]\n            vec = layer[\"shape\"]\n            allGatesValue += vec\n                \n        allGatesValue.sort()\n        allGatesValue = allGatesValue[:int(len(allGatesValue)*self.prune_ratio)]\n        allGatesValue = set(allGatesValue)\n        \n        result = multiClassGates\n\n        for idx in range(len(result)):\n            layer = result[idx]\n            name = layer[\"name\"]\n            vec = layer[\"shape\"]        \n            # process name\n            name = name.split('/')[0]\n            # process vec\n            for i in range(len(vec)):\n                if vec[i] in allGatesValue:\n                    vec[i] = 0\n                else:\n                    vec[i] = 1\n\n            layer[\"name\"] = name\n            layer[\"shape\"] = vec\n        return result\n\n    '''\n    Assign mask weights: original control gates would be 0 or 1\n    '''\n    def assign_weight(self):\n        '''\n        Encapsulate unit-class pruning and multi-class pruning print(\"PRUNE FOR CLASS\", self.target_class_id)\n        '''\n        print(\"assign weights......\")\n        maskDict = []\n        if (len(self.target_class_id) > 1):\n            maskDict = self.mask_class_multi_by_value()\n        else:\n            maskDict = self.mask_unit_by_value(self.target_class_id[0])\n\n        for tmpLayer in maskDict:\n            if (tmpLayer[\"name\"][0] == \"C\"): # if the layer is convolutional layer\n                with self.graph.as_default():\n                    layerNum = tmpLayer[\"name\"].strip(\"Conv\")\n                    name = \"Conv\" + layerNum + \"/composite_function/gate:0\"\n                    for var in tf.global_variables():\n                        if var.name == name:\n                            tmpWeights = np.array(tmpLayer[\"shape\"])\n\n                            assign = tf.assign(var, tmpWeights)\n                            self.sess.run(assign)\n    \n        print(\"assign finished!\")\n        '''\n        Save the model\n        '''\n        # with self.graph.as_default():\n        #     saver = tf.train.Saver(max_to_keep = None)\n        #     saver.save(self.sess, 'vggNet/test.ckpt')\n\n    '''\n    Build VGG Network with Control Gate Lambdas\n    '''\n    def build_model(self, graph, label_count = 100):\n        with graph.as_default():\n            '''\n            Place Holders:\n                1. input_x: data\n                2. input_y: original predicted labels\n                3. learning rate\n                4. drop keeping probability: no drop layer actually\n                5. whether in training mode: always False\n                6. penalty: regularization\n            '''\n            self.xs = tf.placeholder(\"float\", shape=[None, 32, 32, 3])\n            self.ys_orig = tf.placeholder(\"float\", shape=[None, self.target_number])\n            self.lr = tf.placeholder(\"float\", shape=[])\n            self.keep_prob = tf.placeholder(tf.float32)\n            self.is_training = tf.placeholder(\"bool\", shape=[])\n            weight_decay = 5e-4\n\n            '''\n            VGG Network Model Construction with Control Gates \n            '''\n            with tf.variable_scope(\"Conv1\", reuse = tf.AUTO_REUSE):\n                current = self.batch_activ_conv(self.xs, 3, 64, 3, self.is_training, self.keep_prob)\n            with tf.variable_scope(\"Conv2\", reuse = tf.AUTO_REUSE):\n                current = self.batch_activ_conv(current, 64, 64, 3, self.is_training, self.keep_prob)\n                current = self.maxpool2d(current, k=2)\n            with tf.variable_scope(\"Conv3\", reuse = tf.AUTO_REUSE):\n                current = self.batch_activ_conv(current, 64, 128, 3, self.is_training, self.keep_prob)\n            with tf.variable_scope(\"Conv4\", reuse = tf.AUTO_REUSE):\n                current = self.batch_activ_conv(current, 128, 128, 3, self.is_training, self.keep_prob)\n                current = self.maxpool2d(current, k=2)\n            with tf.variable_scope(\"Conv5\", reuse = tf.AUTO_REUSE):\n                current = self.batch_activ_conv(current, 128, 256, 3, self.is_training, self.keep_prob)\n            with tf.variable_scope(\"Conv6\", reuse = tf.AUTO_REUSE):\n                current = self.batch_activ_conv(current, 256, 256, 3, self.is_training, self.keep_prob)\n            with tf.variable_scope(\"Conv7\", reuse = tf.AUTO_REUSE):\n                current = self.batch_activ_conv(current, 256, 256, 1, self.is_training, self.keep_prob)\n                current = self.maxpool2d(current, k=2)\n            with tf.variable_scope(\"Conv8\", reuse = tf.AUTO_REUSE):\n                current = self.batch_activ_conv(current, 256, 512, 3, self.is_training, self.keep_prob)\n            with tf.variable_scope(\"Conv9\", reuse = tf.AUTO_REUSE):\n                current = self.batch_activ_conv(current, 512, 512, 3, self.is_training, self.keep_prob)\n            with tf.variable_scope(\"Conv10\", reuse = tf.AUTO_REUSE):\n                current = self.batch_activ_conv(current, 512, 512, 1, self.is_training, self.keep_prob)\n                current = self.maxpool2d(current, k=2)\n            with tf.variable_scope(\"Conv11\", reuse = tf.AUTO_REUSE):\n                current = self.batch_activ_conv(current, 512, 512, 3, self.is_training, self.keep_prob)\n            with tf.variable_scope(\"Conv12\", reuse = tf.AUTO_REUSE):\n                current = self.batch_activ_conv(current, 512, 512, 3, self.is_training, self.keep_prob)\n            with tf.variable_scope(\"Conv13\", reuse = tf.AUTO_REUSE):\n                current = self.batch_activ_conv(current, 512, 512, 1, self.is_training, self.keep_prob)\n                current = self.maxpool2d(current, k=2)\n                current = tf.reshape(current, [ -1, 512 ])\n            with tf.variable_scope(\"FC14\", reuse = tf.AUTO_REUSE):\n                current = self.batch_activ_fc(current, 512, 4096, self.is_training)\n            with tf.variable_scope(\"FC15\", reuse = tf.AUTO_REUSE):\n                current = self.batch_activ_fc(current, 4096, 4096, self.is_training)\n            with tf.variable_scope(\"FC16\", reuse = tf.AUTO_REUSE):\n                Wfc = self.weight_variable_xavier([ 4096, self.target_number ], name = 'W')\n                bfc = self.bias_variable([ self.target_number ])\n                self.ys_pred = tf.matmul(current, Wfc) + bfc\n\n            self.ys_pred_softmax = tf.nn.softmax(self.ys_pred)\n            '''\n            Loss Function\n            '''\n            cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n                labels = self.ys_orig, logits = self.ys_pred_softmax\n            ))\n            l2_loss = tf.add_n([tf.nn.l2_loss(var) for var in tf.trainable_variables()])\n            total_loss = l2_loss * weight_decay + cross_entropy\n            \n            '''\n            Optimizer\n            '''\n            self.train_step = tf.train.MomentumOptimizer(self.lr, 0.9, use_nesterov=True).minimize(total_loss)\n                        \n            '''\n            Check whether correct\n            '''\n            correct_prediction = tf.equal(tf.argmax(self.ys_orig, 1), tf.argmax(self.ys_pred, 1))\n            self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n\n            self.init = tf.global_variables_initializer()\n\n    '''\n    Close Session\n    '''\n    def close_sess(self):\n        self.sess.close()\n        \n\n    '''\n    Helper Builder Functions: to build model more conveniently\n    '''\n    def weight_variable_msra(self, shape, name):\n        return tf.get_variable(name = name, shape = shape, initializer = tf.contrib.layers.variance_scaling_initializer(), trainable = True)\n\n    def weight_variable_xavier(self, shape, name):\n        return tf.get_variable(name = name, shape = shape, initializer = tf.contrib.layers.xavier_initializer(), trainable = True)\n\n    def bias_variable(self, shape, name = 'bias'):\n        initial = tf.constant(0.0, shape = shape)\n        return tf.get_variable(name = name, initializer = initial, trainable = True)\n\n    def gate_variable(self, length, name = 'gate'):\n        initial = tf.constant([1.0] * length)\n        v = tf.get_variable(name = name, initializer = initial, trainable = False)\n        self.AllGateVariables[v.name] = v\n        self.AllGateVariableValues.append(v)\n        return v\n\n    def conv2d(self, input, in_features, out_features, kernel_size, with_bias=False):\n        W = self.weight_variable_msra([ kernel_size, kernel_size, in_features, out_features ], name = 'kernel')\n        conv = tf.nn.conv2d(input, W, [ 1, 1, 1, 1 ], padding='SAME')\n        gate = self.gate_variable(out_features)\n        conv = tf.multiply(conv, tf.abs(gate))\n        if with_bias:\n            return conv + self.bias_variable([ out_features ])\n        return conv\n\n    def batch_activ_conv(self, current, in_features, out_features, kernel_size, is_training, keep_prob):\n        with tf.variable_scope(\"composite_function\", reuse = tf.AUTO_REUSE):\n            current = self.conv2d(current, in_features, out_features, kernel_size)\n            current = tf.contrib.layers.batch_norm(current, scale=True, is_training=is_training, updates_collections=None, trainable=False)\n            # convValues.append(current)\n            current = tf.nn.relu(current)\n            #current = tf.nn.dropout(current, keep_prob)\n        return current\n\n    def batch_activ_fc(self, current, in_features, out_features, is_training):\n        Wfc = self.weight_variable_xavier([ in_features, out_features ], name = 'W')\n        bfc = self.bias_variable([ out_features ])\n        current = tf.matmul(current, Wfc) + bfc\n        # gate = self.gate_variable(out_features)\n        # current = tf.multiply(current, tf.abs(gate))\n        current = tf.contrib.layers.batch_norm(current, scale=True, is_training=is_training, updates_collections=None, trainable=False)\n        current = tf.nn.relu(current)\n        return current\n\n    def maxpool2d(self, x, k=2):\n        # MaxPool2D wrapper\n        return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],\n                            padding='VALID')","repo_name":"lidongyue12138/Critical-Path-Pruning","sub_path":"vggFinetuneModel.py","file_name":"vggFinetuneModel.py","file_ext":"py","file_size_in_byte":19821,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"86"}
+{"seq_id":"41315706862","text":"from typing import MutableMapping, Tuple\nimport numpy as np\n\nfrom udgs.models.forces_def.dynamics_car import dynamics_cars\n\nfrom .constants import *\nfrom .parameters import params\nfrom .indices import input_constraints, state_constraints\nfrom .objective import objective_car\nfrom .nlconstraints import nlconst_car, nlconst_carN, nlconst_car_ibr, nlconst_car_ibrN\nfrom forcespro import nlp, CodeOptions\n\n__all__ = [\"generate_forces_models\"]\n\n\ndef generate_forces_models(generate_solver: bool, to_deploy: bool, n_players: int):\n    \"\"\"\n    It defines and creates all the solver for the solution methods,\n    it returns a dictionary with key SolutionType\n    :param generate_solver:\n    :param to_deploy:\n    :param n_players:\n    :return:\n    \"\"\"\n    forces_models: MutableMapping[SolutionMethod, Tuple] = {}\n    methods2models = {(PG, LexicographicPG): ForcesPG,\n                      (IBR, LexicographicIBR): ForcesIBR}\n    for keys, forces_model in methods2models.items():\n        model, solver = _generate_forces_model(generate_solver, to_deploy, n_players, forces_model)\n        for method in keys:\n            forces_models[method] = (model, solver)\n    return forces_models\n\n\ndef _generate_forces_model(generate_solver: bool, to_deploy: bool, n_players: int, forces_model: ForcesModel):\n    \"\"\"\n    This model assumes:\n        - a state given by ...\n        - control inputs given by ...\n\n    :return:\n    \"\"\"\n    solver_name = \"solver_\" + forces_model\n    n_players_model = n_players if forces_model == ForcesPG else 1\n\n    model = nlp.SymbolicModel(params.N)\n    model.nvar = params.n_var * n_players_model\n    model.neq = params.n_states * n_players_model\n\n    if forces_model == ForcesPG:  # PG or Lexicographic PG\n        # Number of parameters\n        model.npar = params.n_opt_param + 3 * n_players_model * params.n_bspline_points\n        collision_constraints = int(n_players_model * (n_players_model - 1) / 2)\n    else:  # IBR\n        # Number of parameters\n        model.npar = params.n_opt_param + 3 * params.n_bspline_points + 2 * (n_players - 1)\n        collision_constraints = n_players - 1\n\n    obstacle_constraints = n_players_model\n    # indices of the left hand side of the dynamical constraint\n    model.E = np.concatenate([np.zeros((n_players_model * params.n_states, n_players_model * params.n_inputs)),\n                              np.eye(n_players_model * params.n_states)], axis=1)\n    model.continuous_dynamics = dynamics_cars[n_players_model]\n\n    # inequality constraints\n    model.nh = 2 * n_players_model + obstacle_constraints + collision_constraints  # Number of inequality constraints\n\n    if forces_model == ForcesPG:  # PG or Lexicographic PG\n        model.ineq = nlconst_car[n_players]\n    else:  # IBR\n        model.ineq = nlconst_car_ibr[n_players]\n\n    model.hu = []\n    model.hl = []\n    for k in range(model.nh):\n        model.hu = np.append(model.hu, np.array(0))  # upper bound for nonlinear constraints\n        model.hl = np.append(model.hl, np.array(-np.inf))  # lower bound for nonlinear constraints\n\n    # Terminal State Constraints\n    model.nhN = 3 * n_players_model + 2 + collision_constraints + obstacle_constraints\n    if forces_model == ForcesPG:  # PG or Lexicographic PG\n        model.ineqN = nlconst_carN[n_players]\n    else:  # IBR\n        model.ineqN = nlconst_car_ibrN[n_players]\n\n    model.huN = []\n    model.hlN = []\n\n    for k in range(model.nhN):\n        model.huN = np.append(model.huN, np.array(0))  # upper bound for nonlinear constraints\n        model.hlN = np.append(model.hlN, np.array(-np.inf))  # lower bound for nonlinear constraints\n\n    for k in range(params.N):\n        model.objective[k] = objective_car[n_players_model]\n\n    model.xinitidx = range(params.n_inputs * n_players_model, params.n_var * n_players_model)\n\n    # Equality constraints\n    model.ub = np.ones(params.n_var * n_players_model) * np.inf\n    model.lb = -np.ones(params.n_var * n_players_model) * np.inf\n\n    for i in range(n_players_model):\n        # delta path progress\n        upd_s_idx = i * params.n_states + (n_players_model - 1) * params.n_inputs\n        upd_i_idx = i * params.n_inputs\n\n        model.lb[params.u_idx.dS + upd_i_idx] = input_constraints.dS[0]\n        model.ub[params.u_idx.dS + upd_i_idx] = input_constraints.dS[1]\n\n        # Forward force lower bound\n        model.lb[params.u_idx.dAcc + upd_i_idx] = input_constraints.dAcc[0]\n        model.ub[params.u_idx.dAcc + upd_i_idx] = input_constraints.dAcc[1]\n\n        # slack limit\n        model.lb[params.u_idx.Slack_Lat + upd_i_idx] = 0\n        model.lb[params.u_idx.Slack_Coll + upd_i_idx] = 0\n        model.lb[params.u_idx.Slack_Obs + upd_i_idx] = 0\n\n        # Forward force lower bound\n        model.lb[params.x_idx.Acc + upd_s_idx] = state_constraints.Acc[0]\n        model.ub[params.x_idx.Acc + upd_s_idx] = state_constraints.Acc[1]\n\n        # Speed lower bound\n        model.lb[params.x_idx.Vx + upd_s_idx] = state_constraints.Vx[0]\n        model.ub[params.x_idx.Vx + upd_s_idx] = state_constraints.Vx[1]\n\n        # Steering Angle Bounds\n        model.lb[params.x_idx.Delta + upd_s_idx] = state_constraints.Delta[0]\n        model.ub[params.x_idx.Delta + upd_s_idx] = state_constraints.Delta[1]\n\n        # Path  Progress  Bounds\n        model.lb[params.x_idx.S + upd_s_idx] = state_constraints.S[0]\n        model.ub[params.x_idx.S + upd_s_idx] = state_constraints.S[1]\n\n    # CodeOptions  for FORCES solver\n    codeoptions = CodeOptions(solver_name)\n    if forces_model == ForcesPG:\n        codeoptions.maxit = 1000  # Maximum number of iterations\n    else:  # IBR\n        codeoptions.maxit = 1000\n        # Number of parameters\n    codeoptions.printlevel = 0  # Use printlevel = 2 to print progress (but not for timings)\n    # 0: no optimization, 1: optimize for size, 2: optimize for speed, 3: optimize for size & speed\n    codeoptions.optlevel = 2\n    codeoptions.printlevel = 0  # optional, on some platforms printing is not supported\n    codeoptions.cleanup = 1  # to keep necessary files for target compile\n    codeoptions.timing = 1\n    codeoptions.overwrite = 1  # 1: overwrite existing solver\n    codeoptions.BuildSimulinkBlock = 0\n    codeoptions.noVariableElimination = 1\n    codeoptions.nlp.checkFunctions = 0\n    codeoptions.nlp.integrator.type = 'ERK4'\n    codeoptions.nlp.integrator.Ts = params.dt_integrator_step\n    codeoptions.nlp.integrator.nodes = 1\n\n    if to_deploy:\n        codeoptions.useFloatingLicense = 1  # Comment out unless you got a floating license\n        codeoptions.platform = (\n            \"Docker-Gnu-x86_64\"  # Comment    out  unless  you  got  a  SW / testing  license\n        )\n\n    if generate_solver:\n        # necessary to have all the zs stack in one vector\n        output_all = (\"all_var\", list(range(0, params.N)), list(range(0, params.n_var * n_players_model)))\n        solver = model.generate_solver(codeoptions, [output_all])\n\n    else:\n        solver = nlp.Solver.from_directory(solver_name)\n    return model, solver\n","repo_name":"idsc-frazzoli/UDGs","sub_path":"src/udgs/models/forces_def/generate_model.py","file_name":"generate_model.py","file_ext":"py","file_size_in_byte":6992,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"28259431515","text":"import requests\nfrom io import BytesIO\nfrom PIL import Image, ImageDraw, ImageFont\nimport discord\nimport os\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nintent = discord.Intents.default()\nintent.members = True\nintent.message_content = True\nclient = discord.Client(intents=intent)\n\n# Replace with your own last.fm username and API key\nusername = 'insert your username'\napi_key = 'insert your last.fm API Key'\n# Set the time period to the last month\ntime_period = \"1month\"\n\n# Make a request to the last.fm API to get the user's top artists over the last month\nresponse = requests.get(f\"http://ws.audioscrobbler.com/2.0/?method=user.gettopartists&user={username}&api_key={api_key}&period={time_period}&limit=100&format=json\")\n\n# Extract the top artists from the API response\ntop_artists = response.json()['topartists']['artist']\n# Create a new image with a size of 1000x1000 pixels\nimage = Image.new(\"RGB\", (1000, 1000), (0, 0, 0))\n# Creation of the png \ndraw = ImageDraw.Draw(image)\nfont_size = 20\nfont = ImageFont.truetype('arial.ttf', font_size)\n# Loop through the top artists and paste their images onto the image\nfor i, artist in enumerate(top_artists):\n    # Make a request to the last.fm API to get the artist's image\n    response = requests.get(artist[\"image\"][-1][\"#text\"])\n    # Open the image using the Pillow library\n    artist_image = Image.open(BytesIO(response.content))\n    # Resize the image to 100x100 pixels\n    artist_image = artist_image.resize((100, 100))\n    # Calculate the x and y coordinates for the image based on the current iteration\n    x = (i % 10) * 100\n    y = (i // 10) * 100\n    # Paste the image onto the main image\n    image.paste(artist_image, (x, y))\n\n# Save the image to disk\nimage.save(\"lastfm_collage.png\")\n\n#Work in progress: implement the function in a discord bot\n@client.event\nasync def on_ready():\n    print(\"10x10 Collage bot ready for use\")\n\n","repo_name":"oton1/last.fm-10x10-collage-generator","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"13750760492","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 23 21:44:04 2022\n\n@author: raju\n\"\"\"\n\nimport numpy as np\nimport math\nimport itertools\nimport random\nimport time\nimport matplotlib.pyplot as plt\nimport sumcheck_util as util\n\n\n\nfrom timeit import default_timer as timer\n\n###For the purpose of today, we are concerned with efficient ``matrix multiplication''.\n###as opposed to our previous implementations, we will not rely so heavily on partial/full boolean sum,\n###or more generally, ``independently computing the MLE''.\n###instead, the logic must be slightly changed. The reason is that the prover will use *prior steps*\n###to deduce each stage. Fortunately, we only need *one prior stage*, which means that the verifier can pass\n###the dictionary of the step before!! Anyways, here the prover is the command.\n\n\n###NOW: we BUILD our SUMCHECK IP protocol for MatMul, for which we assume #############\n\n\n\n\n###today, I am changing the Prover code from before. In particular, L will\n###*not* be the full dictionary. It will rather be the special dictionary\n###Thaler constructs, with things like:\n### f(r_1,0,0), f(r_1,0,1),...\n    \n    \n###to implement this, we first build a general purpose function: DP_new_dict_from_old\n###input is a dictionary\n###of the form: f(r_1,..,r_{k-1},{0,1,2},b_{k+1},...), and r_k\n###and output is a dictionary of the values:\n###f(r_1,...,r_k,{0,1,2},b_{k+2},...)\n###for this, we will be assuming that f is a MLE.\n    \n###d is still 2 in these applications\n    \n###similarly, we build an iterative initialize_dict_with_params.\n###(the functionality is identical, but we are passed a vector params, and\n###we )\n    \n##this is only done for f_A. For f_B, something will have to be reversed.\n##will eventually add a flag to select for what to do! however, this will require\n##updating the test suite code, which I don't want to do right now. \n##now: flag \ndef initialize_dict_with_params(L, N, params, flag, p):\n    old_L = L\n    new_L = dict()\n    \n    for i in range(len(params)):\n        \n        digits_in_b = N-i-1\n\n#        print(\"the number of digits_in_b is\", digits_in_b)\n        for b in range(2**(digits_in_b)):\n            tuple_b = util.int_to_bin(b,digits_in_b)\n            if flag == 0: #flag = 0 means in f_A\n                elt = params[i]\n                old_params = tuple(params[:i])\n                new_L[old_params + (elt,) + tuple_b]= (\\\n                      (1-elt) * old_L[old_params + (0,) + tuple_b] + \\\n                      elt * old_L[old_params + (1,) + tuple_b] ) %p\n                      \n            elif flag ==1:\n                l = len(params)\n                elt = params[l - 1 - i]\n                if i == 0:\n                    old_params = tuple()\n                else:\n                    old_params = tuple(params[l-i : ])\n                new_L[tuple_b + (elt,) + old_params] = (\\\n                      (1-elt) * old_L[tuple_b + (0,) + old_params] + \\\n                      elt * old_L[tuple_b + (1,) + old_params]) % p\n                \n        old_L = new_L\n        new_L = dict()\n    return old_L\n\n\n\n\n#the following is the key code in the fast matrix multiplication protocol (due to\n#Thaler): it is a dynamic programming algorithm for updating the dictionary. We are given\n#an old dictionary, which corresponds to the values of the function at params, r_vec[:-1], and some bit strings\n#using DP, we build a new dictionary, which yields the values of the function at params, r_vec, and some bit_strings.\n#flag tells us the order of params_r_vec\n#if flag == 0 , then it is params, r_vec (i.e., (x,r_1))\n#if flag == 1, then it is r_vec,params (i.e., (r_3, y))\n#this is important to distinguish between the f_A and f_B cases!\n\n\n\ndef new_dict_from_old(old_L, N, params, r_vec, flag, p):\n    new_L = dict()\n    param_len = len(params)\n    #params is a tple!!\n    r_len = len(r_vec)\n    digits_in_b = N - param_len - r_len\n    \n    for b in range(2**(digits_in_b)):\n        tuple_b = util.int_to_bin(b, digits_in_b)\n        elt = r_vec[-1]\n        old_r_vec = tuple(r_vec[:-1])\n        if flag ==0: #flag = 0 means working with f_A\n\n            new_L[params + old_r_vec + (elt,) + tuple_b] = (\\\n                  (1-elt)* old_L[params + old_r_vec + (0,) + tuple_b]+\\\n                  elt * old_L[params + old_r_vec + (1,) + tuple_b]) % p\n        elif flag == 1: #flag = 1 means working with f_B\n            new_L[old_r_vec + (elt,) + tuple_b + params] = (\\\n                  (1-elt)*old_L[old_r_vec + (0,) + tuple_b + params] +\\\n                  elt * old_L[old_r_vec + (1,) + tuple_b + params])%p\n    return new_L\n                  \n\n\n#L is a list of two dictionaries, corresponding to A and B respectively\n#N is of course 2*digits, i.e., the dimension of the vector space on which\n#\\tilde{f}_A and \\tilde{f}_B are (multilinear) functions\n#params is necessary! remember, we are somehow doing a sumcheck for\n#\\tilde{f}_C(params). if params is (int_to_bin(i),int_to_bin(j))\n#this is simply the (i,j) entry of C = A*B. \n        \n    \n###running into problem: Prover_Mat_Mul must remember state!!!\n###in particular, we have now chosen the Prover (so, Prover_Mat_Mul)\n###is the command. \ndef Prover_Mat_Mul(L, N, params, p):\n    step = 0\n    #g is a list of the things I will send the verifier\n    g = []\n    #r_vec is a list of the randomness, that the verifier provides.\n    #note: for the purpose of testing this on June 18, 2022, we're going to have\n    #the prover generate the randoness.\n    r_vec = []\n    \n    #Mat_Mul corresponds to a polynomial of degree N//2.\n    \n    #namely, g(z): = f_A(params[:N//2],z)*f_B(z,params[N//2:])\n    #if params[0:N//2] and params[N//2:] are boolean, then this corresponds\n    #to a matrix entry in the product matrix. \n    \n    m = N//2\n\n    param0 = tuple(params[:m])\n    param1 = tuple(params[m:])\n    \n    #we initialize the two dictionaries we are using. \n    #at the start, current_L_A contains the values of \\tilde{f}_A(param0 + b)\n    #where b ranges over all m-bit boolean strings. \n    current_L_A = initialize_dict_with_params(L[0], N, param0, 0, p)\n    #at the start, current_L_B contains the values of \\tilde{f}_B(b + param1)\n    #where b ranges over all m-bit boolean strings. \n    current_L_B = initialize_dict_with_params(L[1], N, param1, 1, p)\n    \n    for step in range(m+1): \n        print(\"We are currently on step: \",step)\n        digits_in_b = m - step\n        #GOAL: at the end of each step, generate some ``proof'' for\n        #the verifier to check. Then we will run the verifier, who will check\n        #it, and continue.\n        \n        #what I need to send depends on the step.\n        #if step = 0, I just claim some number, which is the sum over the boolean\n        #hypercube. print this value.\n        \n        if step == 0:\n            running_sum = 0\n            for b in range(2**m):\n                running_sum = running_sum + current_L_A[param0+util.int_to_bin(b,m)]*\\\n                current_L_B[util.int_to_bin(b,m)+param1] #updating with g(b), where\n                running_sum = running_sum % p       #b ranges over {0,1}^m\n            print(\"The sum over the boolean hypercube is:\", running_sum)\n            print(\"Here, the parameters are:\",param0,\"and\",param1)\n            old_poly = [running_sum,0,0]\n            #here, old_poly is the constant poly with value running_sum. This is\n            #simply to keep syntax symmetric. We need this for check with step 1.\n            g.append(old_poly)\n            \n        #if step = 1, I send what I claim is \\sum_{b}(g(z_1,b_2,..,b_m)), which is a \n        #polynomial (quadratic) in z_1. (The sum is over b_j in {0,1})\n        #this sum concretely looks like: \n        #sum over b_j in {0,1} (dim m-1) of:\n        #f_A(param0, z_1,b_2,..b_m) * f_B(z_1,b_2,...,b_m,param1)\n        #the above is g(z_1,b_2,..,b_m)\n\n        \n        ###REWRITE ALL STEPS 1 to m (except last part) IN UNIFORM WAY.\n        else:\n#            print(\"first dictionary is:\", current_L_A)\n#            print(\"second dictionary is: \", current_L_B)\n            running_sum_0 = 0 #will represent running sum of g(r_vec, 0,b_{step},...)\n            running_sum_1 = 0 #will represent running sum of g(r_vec, 1,b_{step},...)\n            running_sum_2 = 0 ##will represent running sum of g(r_vec, 2,b_{step},...)\n            ###NOT SURE ABOUT PRECISE INDEX in the text ``b_{step}'' above, but it won't\n            ###matter, I don't need to know the exact index.\n            rtup = tuple(r_vec)\n            for b in range(2**(digits_in_b)):\n                #print(b)\n                f_A0 = current_L_A[ param0 + rtup + (0,) + util.int_to_bin(b,digits_in_b) ] #f_A(rtup, 0,b)\n                f_B0 = current_L_B[ rtup  + (0,) + util.int_to_bin(b,digits_in_b) + param1 ] #f_B(rtup, 0,b)\n                f_A1 = current_L_A[ param0 + rtup + (1,) + util.int_to_bin(b,digits_in_b) ] #f_A(rtup, 1,b)\n                f_B1 = current_L_B[ rtup + (1,) + util.int_to_bin(b,digits_in_b) + param1 ] #f_B(rtup, 1,b)\n                f_A2 = 2 * f_A1 - f_A0 #f_A(param0, 2,rtup, b)\n                f_B2 = 2 * f_B1 - f_B0 #f_B(2, rtup, b, param1)\n                                        # above is an ERROR, should be f_B(rtup, 2, b, param1)\n                                        #as of 22:04, June 18th, I've \"fixed this error\",\n\n                #formulas for f_*(2,b) only hold because both functions are multilinear.\n                # do the update phase\n            \n                running_sum_0 = (running_sum_0+ f_A0 * f_B0)%p\n                #computes sum of g(0,\\vec{b}) over all b in {0,1}^{m - 1}\n                running_sum_1 = (running_sum_1 + f_A1 * f_B1)%p\n                #computes sum of g(1,\\vec{b}) over all b in {0,1}^{m - 1}\n                running_sum_2 = (running_sum_2 + f_A2 * f_B2)%p\n            new_poly = util.quadratic_interpolate([running_sum_0, running_sum_1, running_sum_2],p)\n            g.append(new_poly)\n            #JUN 18 2022: commenting the line below out, so I can test the prover code.\n            #r = Verifier_Mat_Mul(old_poly, new_poly, L, N, step, params, p)\n            # If all goes to plan and step is in [1,m-1], the verifier will return \n            # r, a random value.\n\n            r,did_it_succeed = Verifier_Mat_Mul(L, N, step, params, r_vec, g, p)\n            if did_it_succeed == False:\n                return False\n            r_vec.append(r)\n            \n            current_L_A = new_dict_from_old(current_L_A, N, tuple(param0), r_vec, 0, p)\n            current_L_B = new_dict_from_old(current_L_B, N, tuple(param1), r_vec, 1, p)\n#            print(\"the value of randoness we choose is: \", r)\n#            print(\"the polynomial that we output is\", new_poly)\n            \n        \n        #if 1<step (verifier will act differently at the mth step)\n        #the prover will pick a random number r, and add it to r_vec.\n        ###MSK NOTE: we can have the verifier do this by just adding a flag. \n        #the prover then builds (via DP) the new dictionary, and computes\n        #the three sums that we need to via the new dictionary.\n        #the prover sends the last polynomial and the new polynomial to the verifier.\n        #(prover will remember all of the polynomials.)\n        #if verifier checks out, next step.\n        \n        #if we are at step == m, then we do the following *extra*\n        #send r_vec to verifier. Verifier will query g at r_vec, and also has a second\n        #predicted value from g_prev[-1], which was just sent.it compares these two,\n        #if they are the same, the verifier returns true, and we also return true.\n        #maybe then prover prints out the proof in a nice format.\n    #we print the proof\n    print(\"The polynomials are:\",g)\n    print(\"The randomness is:\", r_vec)\n    #finally, we return the actual value we were claiming, just so we can feed\n    #into Prover_Count_Tri protocol\n    return g[0][0]\n    \n    \ndef Verifier_Mat_Mul(L, N, step, params, r_vec, g, p):\n    if step <= 0:\n        return True\n    m = N// 2\n    if step > m:\n        print(\"too many steps!!\")\n        return False\n    g_current = g[-1]\n    g_last = g[-2]\n    \n    current_guess = (util.quadratic_evaluation(g_current, 0, p) +\\\n                     util.quadratic_evaluation(g_current,1,p)) %p\n    if step ==1:\n        old_guess = g_last[0]% p #claimed value of the sum over the boolean hypercube\n                                #because g_prev[0] is [constant, 0, 0]\n    else:\n        old_guess = util.quadratic_evaluation(g_last, r_vec[-1], p) % p\n    \n    \n    if current_guess == old_guess:\n        r = np.random.randint(0,p) #r is the randomness that the verifier chooses\n        if step < m:\n            return r, True\n        else:\n            current_poly_eval = util.quadratic_evaluation(g_current,r,p)%p\n            rtup = tuple(r_vec)+(r,)\n#            print(\"dictionary is\", L[0])\n#            print(\"params is\", params[0:m])\n#            print(\"rtup is\", rtup)\n            function_query = (util.eval_MLE(L[0], tuple(params[0:m]) + rtup, N, p) *\\\n            util.eval_MLE(L[1],rtup+tuple(params[m:]),N,p)) % p\n            if function_query == current_poly_eval:\n                print(\"The proof is correct!\")\n                return r, True\n            else:\n                print(\"The last step of the proof failed\")\n                return r, False\n    else:\n        print(\"Step\", step, \"of the proof failed\")\n        return 0,False\n\n    \n    \n#due to annoying technical issues, we will write our \"prover_count_tri\"\n#separately! Here, A will be the adjacency matrix.\n#NOT DONE YET!!!\n#here, my function will be h: F_p^N-->F_p, given by \\tilde{f}_{A^2} * \\tilde{f}_A\n#as each is multi-linear, this function is multi-quadratic. (This means total degree\n#in each variable is 2.)\ndef Prover_Count_Tri(A,n, p):\n    step = 0\n    L0, N = util.build_function_from_matrix(np.matmul(A,A),n)\n    L1, N = util.build_function_from_matrix(A,n)\n#    print(np.matmul(A,A))\n    #g is a list of the things I will send the verifier\n    g = []\n    #r_vec is a list of the randomness, that the verifier provides.\n\n    r_vec = []    \n    m = N//2\n\n    current_L_A2 = L0\n    current_L_A = L1\n    \n    for step in range(N+1): \n        print(\"We are currently on step: \",step, \"in the counting_triangles protocol\")\n        digits_in_b = N - step\n        #GOAL: at the end of each step, generate some ``proof'' for\n        #the verifier to check. Then we will run the verifier, who will check\n        #it, and continue.\n        \n        #what I need to send depends on the step.\n        #if step = 0, I just claim some number, which is the sum over the boolean\n        #hypercube. print this value.\n        \n        if step == 0:\n            running_sum = 0\n\n            for b in range(2**N):\n                running_sum = (running_sum + current_L_A2[util.int_to_bin(b,N)]*\\\n                current_L_A[util.int_to_bin(b,N)])%p \n                #compute the total sum over the boolean hypercube of the function\n                #h\n            print(\"We claim that the number of triangles * 6 is:\", running_sum)\n#            print(\"Here, the parameters are:\",param0,\"and\",param1)\n            old_poly = [running_sum,0,0]\n            #here, old_poly is the constant poly with value running_sum. This is\n            #simply to keep syntax symmetric. We need this for check with step 1.\n            g.append(old_poly)\n            \n        #if step = 1, I send what I claim is \\sum_{b}(h(z_1,b_2,..,b_m)), which is a \n        #polynomial (quadratic) in z_1. (The sum is over b_j in {0,1})\n        #this sum concretely looks like: \n        #sum over b_j in {0,1} (dim m-1) of:\n        #f_A(param0, z_1,b_2,..b_m) * f_B(z_1,b_2,...,b_m,param1)\n        #the above is h(z_1,b_2,..,b_m)\n\n        else:\n            running_sum_0 = 0 #will represent running sum of h(r_vec, 0,b_{step},...)\n            running_sum_1 = 0 #will represent running sum of h(r_vec, 1,b_{step},...)\n            running_sum_2 = 0 ##will represent running sum of h(r_vec, 2,b_{step},...)\n            ###NOT SURE ABOUT PRECISE INDEX in the text ``b_{step}'' above, but it won't\n            ###matter, I don't need to know the exact index.\n            rtup = tuple(r_vec)\n            for b in range(2**(digits_in_b)):\n                #print(b)\n                f_A2_0 = current_L_A2[ rtup + (0,) + util.int_to_bin(b,digits_in_b) ] #f_A2(rtup, 0,b)\n                f_A_0 = current_L_A[ rtup  + (0,) + util.int_to_bin(b,digits_in_b)  ] #f_A(rtup, 0,b)\n                f_A2_1 = current_L_A2[ rtup + (1,) + util.int_to_bin(b,digits_in_b) ] #f_A2(rtup, 1,b)\n                f_A_1 = current_L_A[ rtup + (1,) + util.int_to_bin(b,digits_in_b)  ] #f_A(rtup, 1,b)\n                f_A2_2 = 2 * f_A2_1 - f_A2_0 \n                f_A_2 = 2 * f_A_1 - f_A_0 \n\n                #formulas for f_*(2,b) only hold because both functions are multilinear.\n                # do the update phase\n            \n                running_sum_0 = (running_sum_0+ f_A2_0 * f_A_0)%p\n                #computes sum of g(0,\\vec{b}) over all b in {0,1}^{m - 1}\n                running_sum_1 = (running_sum_1 + f_A2_1 * f_A_1)%p\n                #computes sum of g(1,\\vec{b}) over all b in {0,1}^{m - 1}\n                running_sum_2 = (running_sum_2 + f_A2_2 * f_A_2)%p\n            new_poly = util.quadratic_interpolate([running_sum_0, running_sum_1, running_sum_2],p)\n            g.append(new_poly)\n            #JUN 18 2022: commenting the line below out, so I can test the prover code.\n            #r = Verifier_Mat_Mul(old_poly, new_poly, L, N, step, params, p)\n            # If all goes to plan and step is in [1,m-1], the verifier will return \n            # r, a random value.\n            #MODIFY VERIFIER CODE!!! ONLY PASSING L1\n            r,did_it_succeed = Verifier_Count_Tri(L1, N, step, r_vec, g, p)\n            if did_it_succeed == False:\n                return False\n            r_vec.append(r)\n            \n            current_L_A2 = new_dict_from_old(current_L_A2, N, tuple(), r_vec, 0, p)\n            current_L_A = new_dict_from_old(current_L_A, N, tuple(), r_vec, 0, p)\n            print(\"the value of randoness we choose is: \", r)\n            print(\"the polynomial that we output is\", new_poly)\n            \n        \n        #if 1<step (verifier will act differently at the mth step)\n        #the prover will pick a random number r, and add it to r_vec.\n        ###MSK NOTE: we can have the verifier do this by just adding a flag. \n        #the prover then builds (via DP) the new dictionary, and computes\n        #the three sums that we need to via the new dictionary.\n        #the prover sends the last polynomial and the new polynomial to the verifier.\n        #(prover will remember all of the polynomials.)\n        #if verifier checks out, next step.\n        \n        #if we are at step == m, then we do the following *extra*\n        #send r_vec to verifier. Verifier will query g at r_vec, and also has a second\n        #predicted value from g_prev[-1], which was just sent.it compares these two,\n        #if they are the same, the verifier returns true, and we also return true.\n        #maybe then prover prints out the proof in a nice format.\n    \n#Verifier for count triangles\ndef Verifier_Count_Tri(L1, N, step, r_vec, g, p):\n    if step <= 0:\n        return True\n    m = N// 2\n    if step > N:\n        print(\"too many steps!!\")\n        return False\n    g_current = g[-1]\n    g_last = g[-2]\n    \n    current_guess = (util.quadratic_evaluation(g_current, 0, p) +\\\n                     util.quadratic_evaluation(g_current,1,p)) %p\n    if step ==1:\n        old_guess = g_last[0]% p #claimed value of the sum over the boolean hypercube\n                                #because g_prev[0] is [constant, 0, 0]\n    else:\n        old_guess = util.quadratic_evaluation(g_last, r_vec[-1], p) % p\n    \n    \n    if current_guess == old_guess:\n        r = np.random.randint(0,p) #r is the randomness that the verifier chooses\n        if step < N:\n            return r, True\n        else:\n            current_poly_eval = util.quadratic_evaluation(g_current,r,p)%p\n            rtup = tuple(r_vec)+(r,)\n#            print(\"dictionary is\", L[0])\n#            print(\"params is\", params[0:m])\n#            print(\"rtup is\", rtup)\n            #NOW, need to do 2 queries. The first is just the normal check on\n            #\\tilde{f}_A(r_vec)\n            print(\"rtup is\", rtup)\n            A_query = util.eval_MLE(L1, rtup, N, p) %p\n            A2_query = Prover_Mat_Mul([L1,L1],N,rtup,p)%p\n#            print(\"tilde{f}_A(rtup) is:\", function_query)\n#            print(\"g_current at\", r,\"is\", current_poly_eval)\n#            print(\"g_current is\", g_current)\n            if A_query * A2_query %p == current_poly_eval % p:\n                print(\"The protocol works!!!\")\n                return r,True\n            else:\n                print(\"The last step of the count_triangles protocol failed\")\n                return r, False\n    else:\n        print(\"Step\", step, \"of the counting_triangles protocol failed\")\n        return 0,False\n","repo_name":"notnotraju/GKR","sub_path":"sumcheck_efficient.py","file_name":"sumcheck_efficient.py","file_ext":"py","file_size_in_byte":20991,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"16578281307","text":"#coding=utf-8\n\nimport sys\nimport os\nimport math\nimport pickle\nimport numpy as np\nimport tensorflow as tf\nimport scipy\nimport matplotlib\nmatplotlib.use('Agg')\nfrom matplotlib import pyplot as plt\nimport networkx as nx\n\nfrom GNN_EdgeBased.graph_class import GraphObject as EBGraphObject\nfrom GNN_EdgeBased import GNN as EBGNN\nfrom GNN_EdgeBased import standard_net as EBNet\nfrom GNN_NodeBased.graph_class import GraphObject as NBGraphObject\nfrom GNN_NodeBased import GNN as NBGNN \nfrom GNN_NodeBased import standard_net as NBNet\nfrom graph_decomposition import *\nfrom molecule_drawer import MoleculeDrawer\n\n'''\nThis variant (A1) generates node labels through a 6-way softmax. The node ordering is a breadth-first scheme with atom-type sub-ordering (H, F, O, N, C) based on the average betweenness centrality of each atom type. A graph with N nodes requires N generation steps (N-1 nodes + 1 stop). Edge trimming is performed at every step on the bonds of the newly generated node.\n'''\n\n#generator parameters\nG_INPUT_DIM = 6\t\t\t\t#input tensor width (source id + destination id + arc_label)\nG_STATE_DIM = 5\t\t\t\t#node label width\nG_OUTPUT_DIM = 6\t\t\t#target tensor width\nG_LR = 0.002\t\t\t\t#learning rate\nG_THRESHOLD = 0.001\t\t\t#state convergence threshold, in terms of relative state difference\nG_MAX_ITER = 6\t\t\t\t#maximum number of state convergence iterations\nG_TRAINING_BATCHES = 20     #number of training batches\nG_HIDDEN_UNITS_STATE = 100 \t#number of units in the hidden layer of the supernode state network\nG_HIDDEN_UNITS_OUTPUT = 60  #number of units in the hidden layer of the output network\nG_NODE_AGGREGATION = \"sum\"\t#node aggregation mode\nG_GUMBEL_SOFTMAX_TEMPERATURE = 1.0\n\n#classifier parameters\nC_INPUT_DIM = 6\t\t\t\t#input tensor width (source id + destination id + arc_label)\nC_STATE_DIM = 5\t\t\t\t#node label width\nC_OUTPUT_DIM = 3\t\t\t#target tensor width\nC_LR = 0.001\t\t\t\t#learning rate\nC_THRESHOLD = 0.001\t\t\t#state convergence threshold, in terms of relative state difference\nC_MAX_ITER = 4\t\t\t\t#maximum number of state convergence iterations\nC_TRAINING_BATCHES = 20     #number of batches in which the training set should be split\nC_HIDDEN_UNITS_STATE = 40   #number of units in the hidden layer of the state network\nC_HIDDEN_UNITS_OUTPUT = 60  #number of units in the hidden layer of the output network\nC_NODE_AGGREGATION = \"average\"#node aggregation mode\nC_GUMBEL_SOFTMAX_TEMPERATURE = 1.0\n\n#linker parameters\nL_INPUT_DIM = 6\t\t\t\t#input tensor width (source id + destination id + arc_label)\nL_STATE_DIM = 5\t\t\t\t#node label width\nL_OUTPUT_DIM = 4\t\t\t#target tensor width\nL_LR = 0.001\t\t\t\t#learning rate\nL_THRESHOLD = 0.001\t\t\t#state convergence threshold, in terms of relative state difference\nL_MAX_ITER = 6\t\t\t\t#maximum number of state convergence iterations\nL_TRAINING_BATCHES = 20     #number of batches in which the training set should be split\nL_HIDDEN_UNITS_STATE = 50   #number of units in the hidden layer of the state network\nL_HIDDEN_UNITS_OUTPUT = 50  #number of units in the hidden layer of the output network\nL_NODE_AGGREGATION = \"average\"#node aggregation mode\nL_GUMBEL_SOFTMAX_TEMPERATURE = 1.0\n\n#parameters\nRUN_SUFFIX = sys.argv[1]    #string to be appended at the end of the output file's name, in order to identify the run which produced it\nEXAMPLES = 10000\t\t\t#number of examples in the dataset\nMAX_GRAPH_SIZE = 80\t\t\t#maximum number of nodes in a graph\nstarting_node_distribution = [0.0, 0.675607, 0.096757, 0.223234, 0.004402]\npath_model_generator = \"Temp/Models/GENERATOR_/model.ckpt\"\npath_model_classifier = \"Temp/Models/CLASSIFIER_/model.ckpt\"\npath_model_linker = \"Temp/Models/LINKER_/model.ckpt\"\npath_data = \"Generated/\"\npath_results = \"Temp/Results/Construction/res_construction_\"+RUN_SUFFIX+\".txt\"\n\n\n#gpu parameters\nuse_gpu = True\ntarget_gpu = \"1\"\n\n#define parameter line\nparam_string = \"Construction_\"+RUN_SUFFIX\n\n#set target gpu as the only visible device\nif use_gpu:\n\tos.environ[\"CUDA_VISIBLE_DEVICES\"]=target_gpu\n\n#build directories\nif not os.path.exists(\"Generated/\"+RUN_SUFFIX+\"/\"):\n\tos.makedirs(\"Generated/\"+RUN_SUFFIX+\"/\")\nif not os.path.exists(\"Generated/\"+RUN_SUFFIX+\"/Graphs/\"):\n\tos.makedirs(\"Generated/\"+RUN_SUFFIX+\"/Graphs/\")\nif not os.path.exists(\"Generated/\"+RUN_SUFFIX+\"/Images/\"):\n\tos.makedirs(\"Generated/\"+RUN_SUFFIX+\"/Images/\")\n\n#initialize bond classifier\nprint(\"Initializing Bond Classifier\")\n#define a tensorflow graph for the bond classifier\nclassifier_graph = tf.Graph()\nwith classifier_graph.as_default():\n\t#define the network\n\tnet_classifier = EBNet.StandardNet(input_dim = C_INPUT_DIM, state_dim = C_STATE_DIM, output_dim = C_OUTPUT_DIM, hidden_units_state = C_HIDDEN_UNITS_STATE, hidden_units_output = C_HIDDEN_UNITS_OUTPUT, namespace=\"CLASSIFIER_\")\n\t#set the gumbel softmax temperature\n\tnet_classifier.gumbel_softmax_temperature = C_GUMBEL_SOFTMAX_TEMPERATURE\n\t#define the classifier GNN\n\tclassifier = EBGNN.GNN(net_classifier, max_it=C_MAX_ITER, input_dim=C_INPUT_DIM, output_dim = C_OUTPUT_DIM, state_dim=C_STATE_DIM, num_train_batches = C_TRAINING_BATCHES, optimizer=tf.train.AdamOptimizer, learning_rate=C_LR, threshold=C_THRESHOLD, param=param_string, namespace=\"CLASSIFIER_\")\ntf.reset_default_graph()\n\n#initialize linker\nprint(\"Initializing Linker\")\n#define a tensorflow graph for the linker\nlinker_graph = tf.Graph()\nwith linker_graph.as_default():\n\t#define the network\n\tnet_linker = EBNet.StandardNet(input_dim = L_INPUT_DIM, state_dim = L_STATE_DIM, output_dim = L_OUTPUT_DIM, hidden_units_state = L_HIDDEN_UNITS_STATE, hidden_units_output = L_HIDDEN_UNITS_OUTPUT, namespace=\"LINKER_\")\n\t#set the gumbel softmax temperature\n\tnet_linker.gumbel_softmax_temperature = L_GUMBEL_SOFTMAX_TEMPERATURE\n\t#define the linker GNN\n\tlinker = EBGNN.GNN(net_linker, max_it=L_MAX_ITER, input_dim=L_INPUT_DIM, output_dim = L_OUTPUT_DIM, state_dim=L_STATE_DIM, num_train_batches = L_TRAINING_BATCHES, optimizer=tf.train.AdamOptimizer, learning_rate=L_LR, threshold=L_THRESHOLD, param=param_string, namespace=\"LINKER_\")\ntf.reset_default_graph()\n\n#initialize generator\nprint(\"Initializing Generator\")\n#define a tensorflow graph for the generator\ngenerator_graph = tf.Graph()\nwith generator_graph.as_default():\n\t#define the network\n\tnet_generator = NBNet.StandardNet(input_dim = G_INPUT_DIM, state_dim = G_STATE_DIM, output_dim = G_OUTPUT_DIM, hidden_units_state = G_HIDDEN_UNITS_STATE, hidden_units_output = G_HIDDEN_UNITS_OUTPUT, namespace=\"GENERATOR_\")\n\t#set the gumbel softmax temperature\n\tnet_generator.gumbel_softmax_temperature = G_GUMBEL_SOFTMAX_TEMPERATURE\n\t#define the generator GNN\n\tgenerator = NBGNN.GNN(net_generator, max_it=G_MAX_ITER, input_dim=G_INPUT_DIM, output_dim = G_OUTPUT_DIM, state_dim=G_STATE_DIM, num_train_batches = G_TRAINING_BATCHES, optimizer=tf.train.AdamOptimizer, learning_rate=G_LR, threshold=G_THRESHOLD, param=param_string, namespace=\"GENERATOR_\")\ntf.reset_default_graph()\n\n#create a fake target for the generation of GraphObjects\nfake_node_target = np.zeros(G_OUTPUT_DIM)\nfake_edge_target = np.zeros(C_OUTPUT_DIM)\n\n#generate graphs\nstarting_node_floats = np.random.rand(EXAMPLES)\nfor i in range(EXAMPLES):\n\tprint(\"Generating graph \"+str(i+1)+\" of \"+str(EXAMPLES), end = '\\r')\n\t#initialize graph\n\tG = nx.DiGraph()\n\t#spawn random first node, according to the starting node probability distribution measured on the training set\n\tsnf = starting_node_floats[i]\n\tdist_sum = 0\n\tj = 0\n\twhile snf > dist_sum: \n\t\tdist_sum = dist_sum + starting_node_distribution[j]\n\t\tj += 1\n\t\tif j > len(starting_node_distribution):\n\t\t\tprint(\"DEBUG: Starting node seed: \"+str(snf))\n\t\t\tsys.exit(\"ERROR: Error encountered while sampling from starting node distribution\")\n\tstarting_type = translate_atom_inverse[j]\n\t#create first node\n\tG.add_node(0)\n\tG.nodes[0]['info'] = starting_type\n\t#call the generator to create the following nodes\n\tstop = False\n\tj = 0\n\tnode_expansion_queue = [0]\n\twhile node_expansion_queue and j < MAX_GRAPH_SIZE:\n\t\t#make a copy of the graph\n\t\tG_copy = G.copy()\n\t\t#insert an output mask value in each node of G_copy\n\t\tnode_to_expand = node_expansion_queue[0]\n\t\t#the nodes are numbered according to Breadth-First-Search, so the next node to expand is the one with the lowest index\n\t\toutput_node_indices = [node_to_expand]\n\t\t#use the output_node_indices list as the output mask\n\t\tInsertOutputMask(G_copy, output_node_indices)\n\t\t#translate the graph into a NodeBased GraphObject\n\t\tnb_graph = NetworkxDiGraphToNBGraphObject(G_copy, fake_node_target, node_aggregation = G_NODE_AGGREGATION)\n\t\t#ask the generator to create a new node\n\t\twith generator_graph.as_default():\n\t\t\tout_wrap, loss, st  = generator.Predict(nb_graph.getInputTensor(), nb_graph.getArcNode().T, nb_graph.getTargets(), nb_graph.getSetMask(), nb_graph.getOutputMask(), nb_graph.initState())\n\t\t\tnext_node_probabilities = out_wrap[0]\n\t\ttf.reset_default_graph()\n\t\tnext_node_index = np.argmax(next_node_probabilities, axis=1)\n\t\tnext_node_literal = translate_atom_inverse[next_node_index[0]+1]\n\t\t#if a 'STOP' is predicted, just jump to the next node in the expansion queue, without calling the classifier nor the linker\n\t\tif next_node_literal == 'STOP':\n\t\t\tnode_expansion_queue.pop(0)\n\t\t\tcontinue\n\t\t#otherwise add the new node to the graph\n\t\tj+=1\n\t\tG.add_node(j)\n\t\tG.nodes[j]['info'] = next_node_literal\n\t\t#add the new node to the expansion queue\n\t\tnode_expansion_queue.append(j)\n\t\t#generate the link between the new node and its \"parent\" node\n\t\tG.add_edge(node_to_expand, j)\n\t\tG.edges[node_to_expand, j]['info'] = translate_bond_direct['candidate']\n\t\t#make a copy of G\n\t\tG_copy = G.copy()\n\t\teb_graph = NetworkxDiGraphToEBGraphObject(G_copy, fake_edge_target, node_aggregation = C_NODE_AGGREGATION)\n\t\t#ask the classifier to predict the class of the link between the new node and its \"parent\" node\n\t\twith classifier_graph.as_default():\n\t\t\tout_wrap, loss, st = classifier.Predict(eb_graph.getInputTensor(), eb_graph.getArcNode().T, eb_graph.getTargets(), eb_graph.getSetMask(), eb_graph.getOutputMask(), eb_graph.initState())\n\t\t\tedge_probabilities = out_wrap[0]\n\t\ttf.reset_default_graph()\n\t\tedge_decision = np.argmax(edge_probabilities, axis=1)+1\n\t\t#update the edge label with the selected class\n\t\tG.edges[node_to_expand, j]['info'] = edge_decision\n\t\t#generate also the inverse arc\n\t\tG.add_edge(j, node_to_expand)\n\t\tG.edges[j, node_to_expand]['info'] = edge_decision\n\t\t#make a copy of G\n\t\tG_copy = G.copy()\t\t\n\t\t#link the new node to each node in the graph other than its parent and itself\n\t\tnum_arcs = len(G_copy.nodes)-2\n\t\tfor k in range(len(G_copy.nodes)):\n\t\t\tif k != j and k != node_to_expand:\n\t\t\t\tG_copy.add_edge(k,j)\n\t\t\t\tG_copy.edges[k,j]['info'] = translate_bond_direct['candidate']\n\t\t#build a fake target tensor for the linker\n\t\tfake_arc_targets = np.zeros((num_arcs,L_OUTPUT_DIM))\n\t\t#translate the graph into a GraphObject\n\t\tl_graph = NetworkxDiGraphToEBGraphObject(G_copy, fake_arc_targets, node_aggregation = L_NODE_AGGREGATION) \t\t\n\t\t#ask the linker which additional arcs should be generated and their classes\n\t\twith linker_graph.as_default():\n\t\t\tout_wrap, loss, st = linker.Predict(l_graph.getInputTensor(), l_graph.getArcNode().T, l_graph.getTargets(), l_graph.getSetMask(), l_graph.getOutputMask(), l_graph.initState())\n\t\t\tarc_probabilities = out_wrap[0]\n\t\ttf.reset_default_graph()\n\t\t#extract decisions\n\t\tarc_decisions = np.argmax(arc_probabilities, axis=1)\n\t\t#add the suggested arcs to the original graph (k iterates on nodes, l on arc decisions)\n\t\tl = 0\n\t\tfor k in range(len(G.nodes)):\n\t\t\tif k != j and k != node_to_expand:\n\t\t\t\t#check integrity\n\t\t\t\tif arc_decisions[l] not in [0,1,2,3]:\n\t\t\t\t\tsys.exit(\"ERROR: linker output is out of the expected range\")\n\t\t\t\t#retrieve decision\n\t\t\t\tif not arc_decisions[l] == 3: #3 stands for a \"DO NOT GENERATE\" decision\n\t\t\t\t\tG.add_edge(k,j)\n\t\t\t\t\tG.add_edge(j,k)\n\t\t\t\t\tG.edges[k,j]['info'] = arc_decisions[l]+1\n\t\t\t\t\tG.edges[j,k]['info'] = arc_decisions[l]+1\n\t\t\t\t#update arc decision iterator\n\t\t\t\tl += 1\n\t#translate G to an undirected graph for drawing purposes\n\tG = G.to_undirected()\n\t#save the generated graph\n\tgraph_dir = path_data+RUN_SUFFIX+\"/Graphs/G_\"+str(i)\n\tif not os.path.exists(graph_dir):\n\t\tos.makedirs(graph_dir)\n\t#pickle graph\n\tout_file = open(graph_dir+\"/graph.pkl\", \"wb\")\n\tpickle.dump(G, out_file)\n\tout_file.close()\n\t#print the generated graph to image\n\timage_path = path_data+RUN_SUFFIX+\"/Images/G_\"+str(i)+\".png\"\n\t### IMAGE DRAWING STARTS HERE ###\n\t'''\n\t#paint graph to image\n\tmd = MoleculeDrawer([0.05, 0.05, 1.0, 1.0], print_operations = False)\n\t#get array of node colors\n\tnode_list = list(G.nodes())\n\tnode_colours = MoleculeDrawer.getNodeColours(G)\n\tedge_widths = MoleculeDrawer.getEdgeWidths(G)\n\t#sketch the molecule in 2D space\n\tcoordinates_dict = md.translateGraphToStructuralFormula(G.to_undirected())\n\t#plot the image\n\tfigure = plt.figure()\n\tplot_axes = figure.add_subplot(111)\n\tplot_axes.autoscale(enable = False)\n\tnx.draw_networkx(G, coordinates_dict, ax = plot_axes, nodelist=node_list, edgelist=G.edges(), arrows=False, with_labels=True, node_size = 300, node_color=node_colours, edge_color='k', linewidths = 1.0, width=edge_widths, font_size=12, font_color='k')\n\tfigure.savefig(image_path)\n\tplt.close(figure)\n\t'''\n\t### IMAGE DRAWING ENDS HERE ###\nprint(\"\")\n\nprint(\"Execution terminated successfully\")\n","repo_name":"PietroMSB/MG2N2","sub_path":"generate_graphs.py","file_name":"generate_graphs.py","file_ext":"py","file_size_in_byte":13200,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"86"}
+{"seq_id":"73095535965","text":"\nmeasure = {\n    'm': 1,\n    \"ft\": .3048,\n    'mi' : 1609.34,\n    'km' : 1000,\n    'inch': .0254,\n    'yard': .9144\n\n}\n# Ask the user amount of distance\nuser_input= float(input('What is the distance?'))\n#Ask the user what input units\nuser_units=input('What are the units? (mi, km, ft, m, inch, yard)')\n#Here I convert the user input to a key in the directory and assign it to a varable \nvalue_dict=measure[user_units]\n#Here I do the math to find the meters\nmeters=round(value_dict*user_input, 2)\n#Ask the user what units they want outputed\noutput_units= input('What are the output units?')\n#Here I am doing the math with meters found to user desired unit output\ndesired_measured=meters/measure[output_units]\n#Here I print out the results\nprint(f'{user_input} {user_units} is {desired_measured} {output_units}')\n\n#--------Outcome----------\n#  what is the distance? 100\n# > what are the input units? ft\n# > what are the output units? mi\n# 100 ft is 0.0189394 mi","repo_name":"PdxCodeGuild/class_indri","sub_path":"anthonys/python/lab1.py","file_name":"lab1.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31011497773","text":"class Solution(object):\n    def distributeCandies(self, candies, num_people):\n        \"\"\"\n        :type candies: int\n        :type num_people: int\n        :rtype: List[int]\n        \"\"\"\n        num = [0]*num_people\n        flag = 1\n        tem = 0\n        while True:\n            for i in range(num_people):\n                if flag + sum(num) >= candies:\n                    count = i\n                    tem = 1\n                    break\n                num[i] = num[i] + flag\n                flag = flag + 1\n            if tem:\n                break\n        num[i] = candies-sum(num)+num[i]\n        return num\n\n","repo_name":"fengkai29/leetcode","sub_path":"数学/leetcode_1103_分糖果II.py","file_name":"leetcode_1103_分糖果II.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"41283147215","text":"# Algorithm 3: sample-centered\n###############################################################\n# Def: L(u, v) = Neighbors of u greater than v, e = (u, v) ∈ E.\n# Def: λe = L(u, v) * L(v, u), Λ = sum(λe for each e).\n###############################################################\n# 1 Compute λe for all edges and set pe = λe / Λ.\n# 2 Pick edge e = (u, v) with probability pe.\n# 3 Pick uniform random neighbor u' of u such that v < u'.\n# 4 Pick uniform random neighbor v' of v such that u < v'.\n# 5 Output the three edges {(u', u), (u, v), (v, v')}.\n\n\nimport numpy as np\nimport random\n\n\nbig_lambda = 0\nlambda_list = np.array([])\n\n\ndef init_sample_centered(g):\n    global big_lambda, lambda_list\n\n    big_lambda = 0\n    lambda_list = np.array([])\n    calculate_big_lambda(g)\n\n\ndef sample_centered(g, edges, k):\n    edges_picked = random.choices(edges, weights=lambda_list, k=k)  #Pick k edges with probability pe for edge e\n    samples = []\n    for e in edges_picked:\n        u, v = e[0], e[1]\n        degree_u = g.degree(u)\n        degree_v = g.degree(v)\n        neighbors_of_u_bigger_than_v = [neighbor for neighbor in g[u] if degree_v < g.degree(neighbor)\n                                        or (degree_v == g.degree(neighbor) and v < neighbor)]\n        neighbors_of_v_bigger_than_u = [neighbor for neighbor in g[v] if degree_u < g.degree(neighbor)\n                                        or (degree_u == g.degree(neighbor) and u < neighbor)]\n        if len(neighbors_of_u_bigger_than_v) == 0 or len(neighbors_of_v_bigger_than_u) == 0:\n            #If any of the neighbor lists are empty, pick another sample\n            edges_picked.append(random.choices(edges, weights=lambda_list, k=1)[0])\n            continue\n        u_ = neighbors_of_u_bigger_than_v[np.random.randint(len(neighbors_of_u_bigger_than_v))]\n        v_ = neighbors_of_v_bigger_than_u[np.random.randint(len(neighbors_of_v_bigger_than_u))]\n        centered_three_path = [(u_, u), (u, v), (v, v_)]\n        if centered_three_path is not None:\n            samples.append(centered_three_path)\n        else:  #If centered_three_path is empty, pick another sample\n            edges_picked.append(random.choices(edges, weights=lambda_list, k=1)[0])\n    return samples\n\n\ndef calculate_big_lambda(g):\n    global big_lambda\n    global lambda_list\n\n    for edge in g.edges:\n        l_uv = sum([1 for neighbor in g[edge[0]] if neighbor > edge[1]])  #L(u, v) = Neighbors of u greater than v\n        l_vu = sum([1 for neighbor in g[edge[1]] if neighbor > edge[0]])\n        lambda_e = l_uv * l_vu  #λe = L(u, v) * L(v, u)\n        lambda_list = np.append(lambda_list, lambda_e)\n        big_lambda += lambda_e  #Λ = sum(λe for each e)\n    lambda_list = lambda_list / big_lambda  #pe = λe / Λ for all edges\n","repo_name":"SirBrundolf/DREAM-Counting-Subgraphs-by-Sampling","sub_path":"sample_centered.py","file_name":"sample_centered.py","file_ext":"py","file_size_in_byte":2771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31277872441","text":"from sklearn import preprocessing\nfrom sklearn.datasets import dump_svmlight_file, load_svmlight_file\nimport numpy as np\nimport os, argparse\nimport csv, warnings\n# import matplotlib.pyplot as plt\n\nwarnings.filterwarnings(\"ignore\",category=DeprecationWarning)\n\ndef writeCSV(data, normF):\n  print('Writing %s'%normF)\n  with open(normF, \"wb\") as csv_file:\n      writer = csv.writer(csv_file, delimiter=',')\n      for line in data:\n         writer.writerow(line)\n  return\n\ndef dump_svmlight(data, label, fname):\n  print('Writing %s'%fname)\n  dump_svmlight_file(data, label, fname) \n\ndef load_svmlight(fname):\n  print('Loading %s'%fname)\n  return load_svmlight_file(fname)\n\ndef normalizeData(inpF, outF, normF):\n  min_max_scaler = preprocessing.MinMaxScaler()\n  if os.path.isfile(inpF):\n      # Pruning data\n      data, label = load_svmlight(inpF)\n      data = np.array(data.todense())\n      prune = True\n  else:\n      # Searching data\n      prune = False\n      data, label = load_svmlight(inpF + '.1')\n      data = np.array(data.todense())\n      marker = len(label)\n      cdata, clabel = load_svmlight(inpF + '.2')\n      cdata = np.array(cdata.todense())\n\n      # compile the 2 data sets\n      data = np.vstack((data, cdata))\n\n  # normalize data\n  data_minmax = min_max_scaler.fit_transform(data)\n\n  if prune:\n      dump_svmlight(data_minmax, label, outF)\n  else:\n      dump_svmlight(data_minmax[:marker], label, outF + '.1')\n      dump_svmlight(data_minmax[marker:], clabel, outF + '.2')\n\n  # Write norm params\n  min_ = min_max_scaler.data_min\n  max_ = min_max_scaler.data_max_\n  writeCSV(zip(min_, max_), normF)\n\ndef mean_variance(inpF):\n  if os.path.isfile(inpF):\n      # Pruning data\n      data, label = load_svmlight(inpF)\n      data = np.array(data.todense())\n      prune = True\n  else:\n      # Searching data\n      prune = False\n      data, label = load_svmlight(inpF + '.1')\n      data = np.array(data.todense())\n      marker = len(label)\n      cdata, clabel = load_svmlight(inpF + '.2')\n      cdata = np.array(cdata.todense())\n\n      # compile the 2 data sets\n      data = np.vstack((data, cdata))\n  \n  return np.mean(data, 0), np.var(data, 0)\n  \ndef main():\n  args = firstPassCommandLine()\n  inpF  = args.inpF\n  meanF = args.meanF\n  varF = args.varF\n  mean, variance = mean_variance(inpF)\n  np.savetxt(meanF, mean)\n  np.savetxt(varF, variance)\n  # print(variance)\n\n  # example data\n  # x = np.arange(0, len(mean))\n  # y = mean\n\n  # example variable error bar values\n  # yerr = variance\n\n  # First illustrate basic pyplot interface, using defaults where possible.\n  # fig = plt.figure()\n  # ax = fig.add_subplot(111)\n  # ax.errorbar(x, y, yerr=yerr, fmt='o', ecolor='g', markersize=5)\n  # plt.title(\"Mean and Variance of Node and Problem Features\")\n\n  # plt.show()\n\ndef firstPassCommandLine():\n\n  # Creating the parser for the input arguments\n  parser = argparse.ArgumentParser(description='Pruning network')\n\n  # Positional argument - Input XML file\n  parser.add_argument('-inpF', '--i', type=str, \\\n                       default='./sample-data/kill.train.dat',\n                       help='Input data file/Prefix', dest='inpF')\n  parser.add_argument('-meanF', '--m', type=str, \\\n                       default='./sample-data/kill.train.dat',\n                       help='Mean data file', dest='meanF')\n  parser.add_argument('-varF', '--v', type=str, \\\n                       default='./sample-data/kill.train.dat',\n                       help='Variance data file', dest='varF')\n\n  # Parse input\n  args = parser.parse_args()\n  return args\n\nif __name__ == '__main__':\n  main()\n","repo_name":"ravi-lanka-4/CoPiEr","sub_path":"scip-dagger/pyscripts/feat_var.py","file_name":"feat_var.py","file_ext":"py","file_size_in_byte":3583,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"86"}
+{"seq_id":"8157513384","text":"import rhinoscriptsyntax as rs\nimport math\n \npointList = []\n \nfor i in rs.frange(0.0, 10.0, 0.1):\n    \n    x = i\n    y = math.sin(i)\n    z = 0.0\n    \n    #play with the math to create more complex shapes!\n    #x = i*math.sin(i)\n    #y = i*math.cos(i)\n    #z = 0.0\n    \n    rs.AddPoint(x,y,z)\n    \n    pt = (x,y,z)\n    pointList.append(pt)\n \ncurve = rs.AddCurve(pointList)","repo_name":"caitlinmorris/rhinoPython","sub_path":"scripts/10_Math.py","file_name":"10_Math.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"86"}
+{"seq_id":"26455173141","text":"import json\nimport discord\nimport random\nimport datetime\nimport os\nfrom discord.ext import commands\n\nwith open('botinfo.json') as file:\n    data = json.load(file)\n\nclient = commands.Bot(command_prefix=data['prefix'])\n\nclient.remove_command('help')\n\n\ndef UTag(member):\n    return f'{member.name}#{member.discriminator}'\n\n\nasync def InBotsChannel(ctx):\n    if ctx.channel.name == 'bot-cmds':\n        return True\n    else:\n        await ctx.send(f'{ctx.author.mention} Please use the bot commands channel')\n        print('false')\n        return False\n\n\nfor filename in os.listdir('./commands'):\n    if filename.endswith('.py'):\n        client.load_extension(f'commands.{filename[:-3]}')\n        print(f'{filename} loaded!')\n\nfor filename in os.listdir('./events'):\n    if filename.endswith('.py'):\n        client.load_extension(f'events.{filename[:-3]}')\n        print(f'{filename} loaded!')\n\n\n@client.event\nasync def on_ready():\n    print('Bot is ready')\n    await client.change_presence(activity=discord.Game(name=\"!help | DriedSponge.net\"))\n\n\n# Logging\nasync def AdminLog(action, admin, member, reason, status):\n    if status == 1:\n        color = 0x44B37F\n    elif status == 2:\n        color = 0xFFA800\n    elif status == 3:\n        color = 0xFF4040\n    elif status == 4:\n        color = 0x166CD4\n\n    channel = client.get_channel(506832700502704148)\n    embed = discord.Embed(\n        color=color)\n    embed.set_author(name=action)\n    embed.add_field(name='User', value=member.mention, inline=True)\n    embed.add_field(name='Moderator', value=admin.mention, inline=True)\n    if reason is not None:\n        embed.add_field(name='Reason', value=reason, inline=True)\n    embed.timestamp = datetime.datetime.utcnow()\n    # embed.set_thumbnail(url=member.avatar_url)\n    await channel.send(embed=embed)\n\n\nclient.run(data['token'])\n","repo_name":"DriedSponge/DriedBot","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"11106783593","text":"# coding: utf-8\n__author__ = 'Ruslan N. Kosarev'\n\nimport sys\nfrom typing import Union, Tuple\nfrom pathlib import Path\nfrom datetime import datetime\nfrom omegaconf import OmegaConf, DictConfig\n\nimport torch\n\nimport random\nimport numpy as np\n\nPathType = Union[Path, str]\n\n# directory for default configs\ndefault_config_dir = Path(__file__).parents[0].joinpath('configs')\ndefault_config = default_config_dir / 'config.yaml'\n\nuser_config_dir = Path(__file__).parents[1] / 'configs'\nuser_config = user_config_dir / 'config.yaml'\n\n\ndef subdir() -> str:\n    return datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')\n\n\ndef set_seed(seed: int = None):\n    \"\"\"\n    set seed for random number generators\n\n    :param seed:\n    :return:\n    \"\"\"\n\n    if seed is not None:\n        random.seed(seed)\n        np.random.seed(seed)\n        torch.manual_seed(0)\n\n\nclass Config:\n    \"\"\"Object representing YAML settings as a dict-like object with values as fields\n    \"\"\"\n\n    def __init__(self, dct: dict = None):\n        \"\"\"Update config from dict\n        :param dct: input object\n        \"\"\"\n        if dct is None:\n            dct = dict()\n\n        for key, item in dct.items():\n            if isinstance(item, dict):\n                setattr(self, key, Config(item))\n            else:\n                setattr(self, key, item)\n\n    def __repr__(self):\n        shift = 3 * ' '\n\n        def get_str(obj, ident=''):\n            s = ''\n            for key, item in obj.items():\n                if isinstance(item, Config):\n                    s += f'{ident}{key}: \\n{get_str(item, ident=ident + shift)}'\n                else:\n                    s += f'{ident}{key}: {str(item)}\\n'\n            return s\n\n        return get_str(self)\n\n    def __getattr__(self, name):\n        return self.__dict__.get(name, Config())\n\n    def __bool__(self):\n        return bool(self.__dict__)\n\n    @property\n    def as_dict(self):\n        def as_dict(obj):\n            s = {}\n            for key, item in obj.items():\n                if isinstance(item, Config):\n                    item = as_dict(item)\n                s[key] = item\n            return s\n\n        return as_dict(self)\n\n    def items(self):\n        return self.__dict__.items()\n\n\nclass LoadConfigError(Exception):\n    pass\n\n\ndef config_paths(app_file_name, custom_config_file=None):\n    config_name = Path(app_file_name).stem + '.yaml'\n\n    paths = [\n        default_config,\n        default_config_dir.joinpath(config_name),\n        user_config,\n        user_config_dir.joinpath(config_name)\n    ]\n\n    if custom_config_file is not None:\n        paths.append(custom_config_file)\n\n    return tuple(paths)\n\n\ndef application_name():\n    return Path(sys.argv[0]).stem\n\n\ndef load_config(dct: dict = None, file: PathType = None):\n    \"\"\"Load configuration from the set of config files\n    :param dct:  Optional dictionary\n    :param file: Optional path to the custom config file\n    :return: The validated config in Config model instance\n    \"\"\"\n\n    paths = config_paths(application_name(), file)\n\n    cfg = OmegaConf.create()\n\n    for config_path in paths:\n        if not config_path.is_file():\n            continue\n\n        try:\n            new_cfg = OmegaConf.load(config_path)\n            cfg = OmegaConf.merge(cfg, new_cfg)\n        except Exception as err:\n            raise LoadConfigError(f\"Cannot load configuration from '{config_path}'\\n{err}\")\n\n    if dct is not None:\n        cfg = OmegaConf.merge(cfg, dct)\n\n    if len(cfg.keys()) == 0:\n        raise LoadConfigError(\"The configuration has not been loaded.\")\n\n    options = OmegaConf.to_container(cfg)\n    options = Config(options)\n\n    set_seed(seed=options.seed)\n\n    return options\n","repo_name":"RuslanKosarev/mlresearch","sub_path":"mlresearch/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":3669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"17484579906","text":"# LDST Address Splitter.  For misaligned address crossing cache line boundary\n\"\"\"\nLinks:\n* https://libre-riscv.org/3d_gpu/architecture/6600scoreboard/\n* http://bugs.libre-riscv.org/show_bug.cgi?id=257\n* http://bugs.libre-riscv.org/show_bug.cgi?id=216\n\"\"\"\n\n#from soc.experiment.pimem import PortInterface\n\nfrom nmigen import Elaboratable, Module, Signal, Record, Array, Const, Cat\nfrom nmutil.latch import SRLatch, latchregister\nfrom nmigen.back.pysim import Simulator, Delay\nfrom nmigen.cli import verilog, rtlil\n\nfrom soc.scoreboard.addr_match import LenExpand\n#from nmutil.queue import Queue\n\n\nclass LDData(Record):\n    def __init__(self, dwidth, name=None):\n        Record.__init__(self, (('err', 1), ('data', dwidth)), name=name)\n\n\nclass LDLatch(Elaboratable):\n\n    def __init__(self, dwidth, awidth, mlen):\n        self.addr_i = Signal(awidth, reset_less=True)\n        self.mask_i = Signal(mlen, reset_less=True)\n        self.i_valid = Signal(reset_less=True)\n        self.ld_i = LDData(dwidth, \"ld_i\")\n        self.ld_o = LDData(dwidth, \"ld_o\")\n        self.o_valid = Signal(reset_less=True)\n\n    def elaborate(self, platform):\n        m = Module()\n        comb = m.d.comb\n        m.submodules.in_l = in_l = SRLatch(sync=False, name=\"in_l\")\n\n        comb += in_l.s.eq(self.i_valid)\n        comb += self.o_valid.eq(in_l.q & self.i_valid)\n        latchregister(m, self.ld_i, self.ld_o, in_l.q & self.o_valid, \"ld_i_r\")\n\n        return m\n\n    def __iter__(self):\n        yield self.addr_i\n        yield self.mask_i\n        yield self.ld_i.err\n        yield self.ld_i.data\n        yield self.ld_o.err\n        yield self.ld_o.data\n        yield self.i_valid\n        yield self.o_valid\n\n    def ports(self):\n        return list(self)\n\ndef byteExpand(signal):\n    if(type(signal)==int):\n        ret = 0\n        shf = 0\n        while(signal>0):\n            bit = signal & 1\n            ret |= (0xFF * bit) << shf\n            signal = signal >> 1\n            shf += 8\n        return ret\n    lst = []\n    for i in range(len(signal)):\n        bit = signal[i]\n        for j in range(8): #TODO this can be optimized\n            lst += [bit]\n    return Cat(*lst)\n\nclass LDSTSplitter(Elaboratable):\n\n    def __init__(self, dwidth, awidth, dlen, pi=None):\n        self.dwidth, self.awidth, self.dlen = dwidth, awidth, dlen\n        # cline_wid = 8<<dlen # cache line width: bytes (8) times (2^^dlen)\n        cline_wid = dwidth*8  # convert bytes to bits\n\n        self.addr_i = Signal(awidth, reset_less=True)\n        # no match in PortInterface\n        self.len_i = Signal(dlen, reset_less=True)\n        self.i_valid = Signal(reset_less=True)\n        self.o_valid = Signal(reset_less=True)\n\n        self.is_ld_i = Signal(reset_less=True)\n        self.is_st_i = Signal(reset_less=True)\n\n        self.ld_data_o = LDData(dwidth*8, \"ld_data_o\") #port.ld\n        self.st_data_i = LDData(dwidth*8, \"st_data_i\") #port.st\n\n        self.exc = Signal(reset_less=True) # pi.exc TODO\n        # TODO : create/connect two outgoing port interfaces\n\n        self.sld_o_valid = Signal(2, reset_less=True)\n        self.sld_i_valid = Signal(2, reset_less=True)\n        self.sld_data_i = Array((LDData(cline_wid, \"ld_data_i1\"),\n                                 LDData(cline_wid, \"ld_data_i2\")))\n\n        self.sst_o_valid = Signal(2, reset_less=True)\n        self.sst_i_valid = Signal(2, reset_less=True)\n        self.sst_data_o = Array((LDData(cline_wid, \"st_data_i1\"),\n                                 LDData(cline_wid, \"st_data_i2\")))\n\n    def elaborate(self, platform):\n        m = Module()\n        comb = m.d.comb\n        dlen = self.dlen\n        mlen = 1 << dlen\n        mzero = Const(0, mlen)\n        m.submodules.ld1 = ld1 = LDLatch(self.dwidth*8, self.awidth-dlen, mlen)\n        m.submodules.ld2 = ld2 = LDLatch(self.dwidth*8, self.awidth-dlen, mlen)\n        m.submodules.lenexp = lenexp = LenExpand(self.dlen)\n\n        #comb += self.pi.addr_ok_o.eq(self.addr_i < 65536) #FIXME 64k limit\n        #comb += self.pi.busy_o.eq(busy)\n\n\n        # FIXME bytes not bits\n        # set up len-expander, len to mask.  ld1 gets first bit, ld2 gets rest\n        comb += lenexp.addr_i.eq(self.addr_i)\n        comb += lenexp.len_i.eq(self.len_i)\n        mask1 = Signal(mlen, reset_less=True)\n        mask2 = Signal(mlen, reset_less=True)\n        comb += mask1.eq(lenexp.lexp_o[0:mlen])  # Lo bits of expanded len-mask\n        comb += mask2.eq(lenexp.lexp_o[mlen:])   # Hi bits of expanded len-mask\n\n        # set up new address records: addr1 is \"as-is\", addr2 is +1\n        comb += ld1.addr_i.eq(self.addr_i[dlen:])\n        ld2_value = self.addr_i[dlen:] + 1\n        comb += ld2.addr_i.eq(ld2_value)\n        # exception if rolls\n        with m.If(ld2_value[self.awidth-dlen]):\n            comb += self.exc.eq(1)\n\n        # data needs recombining / splitting via shifting.\n        ashift1 = Signal(self.dlen, reset_less=True)\n        ashift2 = Signal(self.dlen, reset_less=True)\n        comb += ashift1.eq(self.addr_i[:self.dlen])\n        comb += ashift2.eq((1 << dlen)-ashift1)\n\n        #expand masks\n        mask1 = byteExpand(mask1)\n        mask2 = byteExpand(mask2)\n        mzero = byteExpand(mzero)\n\n        with m.If(self.is_ld_i):\n            # set up connections to LD-split.  note: not active if mask is zero\n            for i, (ld, mask) in enumerate(((ld1, mask1),\n                                            (ld2, mask2))):\n                ld_valid = Signal(name=\"ldi_valid%d\" % i, reset_less=True)\n                comb += ld_valid.eq(self.i_valid & self.sld_i_valid[i])\n                comb += ld.i_valid.eq(ld_valid & (mask != mzero))\n                comb += ld.ld_i.eq(self.sld_data_i[i])\n                comb += self.sld_o_valid[i].eq(ld.o_valid)\n\n            # sort out valid: mask2 zero we ignore 2nd LD\n            with m.If(mask2 == mzero):\n                comb += self.o_valid.eq(self.sld_o_valid[0])\n            with m.Else():\n                comb += self.o_valid.eq(self.sld_o_valid.all())\n            ## debug output -- output mask2 and mzero\n            ## guess second port is invalid\n\n            # all bits valid (including when data error occurs!) decode ld1/ld2\n            with m.If(self.o_valid):\n                # errors cause error condition\n                comb += self.ld_data_o.err.eq(ld1.ld_o.err | ld2.ld_o.err)\n\n                # note that data from LD1 will be in *cache-line* byte position\n                # likewise from LD2 but we *know* it is at the start of the line\n                comb += self.ld_data_o.data.eq((ld1.ld_o.data >> (ashift1*8)) |\n                                               (ld2.ld_o.data << (ashift2*8)))\n\n        with m.If(self.is_st_i):\n            # set busy flag -- required for unit test\n            for i, (ld, mask) in enumerate(((ld1, mask1),\n                                            (ld2, mask2))):\n                valid = Signal(name=\"sti_valid%d\" % i, reset_less=True)\n                comb += valid.eq(self.i_valid & self.sst_i_valid[i])\n                comb += ld.i_valid.eq(valid & (mask != mzero))\n                comb += self.sld_o_valid[i].eq(ld.o_valid)\n                comb += self.sst_data_o[i].data.eq(ld.ld_o.data)\n\n            comb += ld1.ld_i.eq((self.st_data_i << (ashift1*8)) & mask1)\n            comb += ld2.ld_i.eq((self.st_data_i >> (ashift2*8)) & mask2)\n\n            # sort out valid: mask2 zero we ignore 2nd LD\n            with m.If(mask2 == mzero):\n                comb += self.o_valid.eq(self.sst_o_valid[0])\n            with m.Else():\n                comb += self.o_valid.eq(self.sst_o_valid.all())\n\n            # all bits valid (including when data error occurs!) decode ld1/ld2\n            with m.If(self.o_valid):\n                # errors cause error condition\n                comb += self.st_data_i.err.eq(ld1.ld_o.err | ld2.ld_o.err)\n\n        return m\n\n    def __iter__(self):\n        yield self.addr_i\n        yield self.len_i\n        yield self.is_ld_i\n        yield self.ld_data_o.err\n        yield self.ld_data_o.data\n        yield self.i_valid\n        yield self.o_valid\n        yield self.sld_i_valid\n        for i in range(2):\n            yield self.sld_data_i[i].err\n            yield self.sld_data_i[i].data\n\n    def ports(self):\n        return list(self)\n\n\ndef sim(dut):\n\n    sim = Simulator(dut)\n    sim.add_clock(1e-6)\n    data = 0x0102030405060708A1A2A3A4A5A6A7A8\n    dlen = 16  # data length in bytes\n    addr = 0b1110\n    ld_len = 8\n    ldm = ((1 << ld_len)-1)\n    ldme = byteExpand(ldm)\n    dlm = ((1 << dlen)-1)\n    data = data & ldme  # truncate data to be tested, mask to within ld len\n    print(\"ldm\", ldm, hex(data & ldme))\n    print(\"dlm\", dlm, bin(addr & dlm))\n\n    dmask = ldm << (addr & dlm)\n    print(\"dmask\", bin(dmask))\n    dmask1 = dmask >> (1 << dlen)\n    print(\"dmask1\", bin(dmask1))\n    dmask = dmask & ((1 << (1 << dlen))-1)\n    print(\"dmask\", bin(dmask))\n    dmask1 = byteExpand(dmask1)\n    dmask = byteExpand(dmask)\n\n    def send_ld():\n        print(\"send_ld\")\n        yield dut.is_ld_i.eq(1)\n        yield dut.len_i.eq(ld_len)\n        yield dut.addr_i.eq(addr)\n        yield dut.i_valid.eq(1)\n        print(\"waiting\")\n        while True:\n            o_valid = yield dut.o_valid\n            if o_valid:\n                break\n            yield\n        exc = yield dut.exc\n        ld_data_o = yield dut.ld_data_o.data\n        yield dut.is_ld_i.eq(0)\n        yield\n\n        print(exc)\n        assert exc==0\n        print(hex(ld_data_o), hex(data))\n        assert ld_data_o == data\n\n    def lds():\n        print(\"lds\")\n        while True:\n            i_valid = yield dut.i_valid\n            if i_valid:\n                break\n            yield\n\n        shf = (addr & dlm)*8  #shift bytes not bits\n        print(\"shf\",shf/8.0)\n        shfdata = (data << shf)\n        data1 = shfdata & dmask\n        print(\"ld data1\", hex(data), hex(data1), shf,shf/8.0, hex(dmask))\n\n        data2 = (shfdata >> 128) & dmask1\n        print(\"ld data2\", 1 << dlen, hex(data >> (1 << dlen)), hex(data2))\n        yield dut.sld_data_i[0].data.eq(data1)\n        yield dut.sld_i_valid[0].eq(1)\n        yield\n        yield dut.sld_data_i[1].data.eq(data2)\n        yield dut.sld_i_valid[1].eq(1)\n        yield\n\n    sim.add_sync_process(lds)\n    sim.add_sync_process(send_ld)\n\n    prefix = \"ldst_splitter\"\n    with sim.write_vcd(\"%s.vcd\" % prefix, traces=dut.ports()):\n        sim.run()\n\n\nif __name__ == '__main__':\n    dut = LDSTSplitter(32, 48, 4)\n    vl = rtlil.convert(dut, ports=dut.ports())\n    with open(\"ldst_splitter.il\", \"w\") as f:\n        f.write(vl)\n\n    sim(dut)\n","repo_name":"ngi-nix/libresoc-soc","sub_path":"src/soc/scoreboard/addr_split.py","file_name":"addr_split.py","file_ext":"py","file_size_in_byte":10548,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"15891481268","text":"import matplotlib.pyplot as plt \nimport numpy as np \n\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import mean_squared_error\n\nclf = Ridge()\n\nX, y, w = make_regression(n_samples=10,n_features=10,coef=True,\n                        noise=50,random_state=42,bias=.5)\n\ncoefs = []\nerrors = []\nalphas = np.logspace(-6, 6, 200)\n\nfor a in alphas:\n    clf.set_params(alpha=a)\n    clf.fit(X,y)\n    coefs.append(clf.coef_)\n    errors.append(mean_squared_error(clf.coef_, w))\n\nplt.subplot(121)\nax = plt.gca()\nax.plot(alphas, coefs)\nax.set_xscale('log')\nplt.xlabel('alpha')\nplt.ylabel('weight')\nplt.title('Ridge coefficients as a function of the regularization')\n\nplt.subplot(122)\nax = plt.gca()\nax.plot(alphas,errors)\nax.set_xscale('log')\nplt.xlabel('alpha')\nplt.ylabel('error')\nplt.title('Coefficient error as a function of the regularization')\n\n\nplt.show()\n","repo_name":"HPHou/HPHEasyML","sub_path":"RidgeCoeffs.py","file_name":"RidgeCoeffs.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"4177432417","text":"import RPi.GPIO as GPIO\nimport math\nfrom time import sleep\t\nfrom flameSensor import get_flame\nfrom firebase import database\n\n# Funcția utilizată pentru firul de execuție care pornește alarma sau oprește alarma\ndef trigger_fire_alarm():\n\n\t# Pinul pentru buzzer\n\tBUZZER_PIN = 22\n\n\t# Inițializarea modului de numerotare al pinilor\n\tGPIO.setmode(GPIO.BOARD)\n\n\t# Configurarea pinului pentru buzzer ca ieșire\n\tGPIO.setup(BUZZER_PIN, GPIO.OUT)\n\n\tglobal PWM\n\n\t# Crearea obiectului PWM pentru pinul buzzer cu o frecvență inițială de 1 Hz\n\tPWM = GPIO.PWM(BUZZER_PIN, 1)\n\tPWM.start(0)\n\n\twhile True:\n\t\t# Obținerea stării senzorului de flăcări\n\t\tis_flame = get_flame()\n\n\t\tif not is_flame:\n\t\t\t# Pornirea PWM la 50% pentru a genera sunetul de alarmă\n\t\t\tdatabase.child(\"control\").child(\"alarma\").set(0)\n\t\t\tPWM.start(50)\n\n\t\t\t# Generarea sunetului de alarmă\n\t\t\tfor x in range(0, 361):\n\t\t\t\tsin_val = math.sin(x * (math.pi / 180))\n\n\t\t\t\t# Calcularea valorii tonului bazat pe valoarea sinusului\n\t\t\t\ttone_val = 2000 + sin_val * 500\n\n\t\t\t\t# Modificarea frecvenței PWM pentru a genera tonul corespunzător\n\t\t\t\tPWM.ChangeFrequency(tone_val)\n\t\t\t\tsleep(0.001)\n\t\telse:\n\t\t\t# Oprirea PWM în cazul în care nu se detectează flăcări\n\t\t\tdatabase.child(\"control\").child(\"alarma\").set(1)\n\t\t\tPWM.stop()\n","repo_name":"filippaliuc/smart-home-raspberry","sub_path":"buzzer.py","file_name":"buzzer.py","file_ext":"py","file_size_in_byte":1287,"program_lang":"python","lang":"ro","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"4304152945","text":"# -*- coding: utf-8 -*-\nfrom formalchemy.i18n import get_translator\nfrom formalchemy.fields import _pk\nfrom simplejson import dumps\nfrom utils import templates\nfrom random import random\n\nclass MultiFieldSetProperty(property):\n\n    def __init__(self, name):\n        self.__name__ = '_' + name\n\n    def __get__(self, instance, klass):\n        if instance is None:\n            return klass\n        return getattr(instance, self.__name__)\n\n    def __set__(self, instance, value):\n        setattr(instance, self.__name__, value)\n        for fs in instance._fs_dict.values():\n            setattr(fs, self.__name__[1:], value)\n\nclass MultiFieldSet(object):\n    \"\"\"Display more than one FieldSet:\n\n    .. sourcecode:: python\n\n        >>> from testing import *\n        >>> fs = MultiFieldSet('my_fieldsets',\n        ...             ('fs1', '', fs1))\n        >>> fs.append('fs2', 'Second fieldset', fs2)\n        >>> fs.fs1 = fs.fs1.bind(obj1)\n        >>> fs.fs2.rebind(obj2)\n        >>> print fs.render() #doctest: +ELLIPSIS +NORMALIZE_WHITESPACE\n        <div id=\"my_fieldsets_...\">\n        <fieldset id=\"fs1_...\">\n        <div>\n        ...\n        </div>\n        </fieldset>\n        <fieldset id=\"fs2_...\">\n        <legend><a href=\"#fs2_...\">Second fieldset</a></legend>\n        <div>\n        ...\n\n    \"\"\"\n    template = templates.get_template('/forms/multifieldset.mako')\n    def __init__(self, id, *fieldsets, **options):\n        if not isinstance(id, basestring):\n            raise TypeError('id must be a string. got %r' % (id,))\n        self._id = id\n        self._fs = []\n        self._fs_dict = {}\n        self.__bound_pk = None\n        self.__request = None\n        self._options = options\n        self._readonly = False\n        self._engine = None\n        self._focus = False\n        self._original_cls = None\n        for fs in fieldsets:\n            if not isinstance(fs, (tuple, list)) or len(fs) != 3:\n                raise ValueError('A form is defined by (id, title, form) got %r' % (fs,))\n            self.append(*fs)\n\n    def jsonify(self):\n        fields = []\n        for fs in self._fs:\n            for f in fs.render_fields.values():\n                fields.append((f.key, f.model_value))\n        return dict(fields)\n\n    _bound_pk = MultiFieldSetProperty('_bound_pk')\n    _request = MultiFieldSetProperty('_request')\n    focus = MultiFieldSetProperty('focus')\n    engine = MultiFieldSetProperty('engine')\n    readonly = MultiFieldSetProperty('readonly')\n\n    @property\n    def model(self):\n        if self._fs:\n            return self._fs_dict.get(self._fs[0][0]).model\n\n    @property\n    def errors(self):\n        errors = {}\n        for fs in self._fs_dict.values():\n            errors.update(fs.errors)\n        return errors\n\n    @property\n    def render_fields(self):\n        fields = {}\n        for fs in self._fs_dict.values():\n            fields.update(fs.render_fields)\n        return fields\n\n    def __getattr__(self, attr):\n        if attr in self._fs_dict:\n            return self._fs_dict.get(attr)\n        else:\n            raise AttributeError(attr)\n\n    def append(self, id, title, fs):\n        \"\"\"add a fieldset to tabs\"\"\"\n        fs.__name__ = id\n        self._fs.append((id, title))\n        self._fs_dict[id] = fs\n\n    def get(self, fs):\n        if isinstance(fs, basestring):\n            fs = self._fs_dict[fs]\n        return fs\n\n    def bind(self, model=None, **kwargs):\n        \"\"\"Bind fieldsets to model. All sub-fieldsets are bound to model.\"\"\"\n        news = []\n        for id, title in self._fs:\n            fs = self.get(id)\n            fs = fs.bind(model=model, **kwargs)\n            if model is None:\n                model = fs.model\n            news.append((id, title, fs))\n        return self.__class__(self._id, *news, **self._options.copy())\n\n    def rebind(self, model=None, **kwargs):\n        \"\"\"Bind fieldsets to model. All sub-fieldsets are bound to model.\"\"\"\n        for id, title in self._fs:\n            fs = self.get(id)\n            fs.rebind(model=model, **kwargs)\n            if model is None:\n                model = fs.model\n\n    def copy(self):\n        news = []\n        for id, title in self._fs:\n            fs = self.get(id)\n            fs = fs.bind(model=fs.model)\n            news.append((id, title, fs))\n        return self.__class__(self._id, *news, **self._options.copy())\n\n    def validate(self, *ids):\n        \"\"\"Validate fieldsets. If no ids is provided, all fieldsets are\n        validate.\"\"\"\n        fieldsets = []\n        ids = ids or self._fs_dict.keys()\n        for id in ids:\n            fieldsets.append(self.get(id))\n        validated = [fs.validate() for fs in fieldsets]\n        if False in validated:\n            return False\n        return True\n\n    def sync(self, *ids):\n        \"\"\"Sync fieldsets. If no ids is provided, all fieldsets are\n        validate.\"\"\"\n        ids = ids or self._fs_dict.keys()\n        for id in ids:\n            self.get(id).sync()\n\n    def render(self, *ids, **options):\n        fieldsets = []\n        if ids:\n            ids = [self.get(id).__name__ for id in ids]\n        else:\n            ids = self._fs_dict.keys()\n        for id, title in self._fs:\n            if id in ids:\n                fs = self._fs_dict[id]\n                fs.focus = False\n                fieldsets.append(dict(id=id, title=title, fs=fs))\n        kwargs = dict(footer='', header='')\n        kwargs.update(self._options)\n        return self.template.render_unicode(id=self._id,\n                                    rid=str(random())[2:],\n                                    fieldsets=fieldsets,\n                                    options=dumps(options),\n                                    F_=get_translator(request=self.__request),\n                                    **kwargs)\n\n\nclass Tabs(MultiFieldSet):\n    \"\"\"Display FieldSet using http://jqueryui.com/demos/tabs/:\n\n    .. sourcecode:: python\n\n        >>> from testing import *\n        >>> tabs = Tabs('my_tabs',\n        ...             ('tab1', 'My first tab', fs1),\n        ...             footer='<input type=\"submit\" name=\"%(id)s\" />')\n        >>> tabs.append('tab2', 'The second', fs2)\n        >>> tabs.tab1 = tabs.tab1.bind(obj1)\n        >>> tabs.tab2.rebind(obj2)\n        >>> print tabs.render(selected=2) #doctest: +ELLIPSIS +NORMALIZE_WHITESPACE\n        <div id=\"my_tabs_...\">\n        <ul>\n            <li><a href=\"#tab1_...\">My first tab</a></li>\n            <li><a href=\"#tab2_...\">The second</a></li>\n        </ul>\n        <div id=\"tab1_...\">...\n        </div>\n        <div id=\"tab2_...\">...\n        </div>\n        </div>\n        <script type=\"text/javascript\">\n          jQuery.fa.tabs('my_tabs_...', {\"selected\": 2});\n        </script>\n        <BLANKLINE>\n            \n    \"\"\"\n    template = templates.get_template('/forms/tabs.mako')\n\nclass Accordion(MultiFieldSet):\n    \"\"\"Work like :class:`~fa.jquery.forms.Tabs` but use\n    http://jqueryui.com/demos/accordion/\n    \"\"\"\n    template = templates.get_template('/forms/accordion.mako')\n\n","repo_name":"FormAlchemy/fa.jquery","sub_path":"fa/jquery/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":6990,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"86"}
+{"seq_id":"22852577186","text":"import sys, pathlib\nsys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))\n\nfrom mlp.utils import getXY, LoadBatch, prob_to_class\nfrom mlp.layers import Activation, Dense\nfrom mlp.models import Sequential\nfrom mpo.metaparamoptimizer import MetaParamOptimizer\nfrom util.misc import dict_to_string\n\nimport numpy as np\n\nnp.random.seed(0)\n\n# Define evaluator (function to run in MetaParamOptimizer)\ndef evaluator(x_train, y_train, x_val, y_val, experiment_path=\"\", **kwargs):\n    # Define model\n    model = Sequential(loss=\"cross_entropy\")\n    model.add(\n        Dense(nodes=10, input_dim=x_train.shape[0], weight_initialization=\"fixed\"))\n    model.add(Activation(\"softmax\"))\n\n    # Fit model\n    model.fit(X=x_train, Y=y_train, X_val=x_val, Y_val=y_val, **kwargs)\n    model.plot_training_progress(show=False, save=True, name=\"figures/\" + dict_to_string(kwargs))\n    model.save(experiment_path + \"/\" + dict_to_string(kwargs))\n\n    # Minimizing value: validation accuracy\n    val_acc = model.get_classification_metrics(x_val, y_val)[0] # Get accuracy\n    result = {\"value\": val_acc, \"model\": model}  # Save score and model\n    return result\n\nif __name__ == \"__main__\":\n    # Download & Extract CIFAR-10 Python (https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)\n    # Put it in a Data folder\n\n    # Load data\n    x_train, y_train = LoadXY(\"data_batch_1\")\n    x_val, y_val = LoadXY(\"data_batch_2\")\n    x_test, y_test = LoadXY(\"test_batch\")\n\n    # Preprocessing\n    mean_x = np.mean(x_train)\n    std_x = np.std(x_train)\n    x_train = (x_train - mean_x)/std_x\n    x_val = (x_val - mean_x)/std_x\n    x_test = (x_test - mean_x)/std_x\n\n    # Define list of parameters to try\n    dicts_list = [\n        { \"l2_reg\": 0.0, \"lr\": 0.1 },\n        { \"l2_reg\": 0.0, \"lr\": 0.001 },\n        { \"l2_reg\": 0.1, \"lr\": 0.001 },\n        { \"l2_reg\": 1.0, \"lr\": 0.001 },\n    ]\n    # Define fixed params (constant through optimization)\n    fixed_args = {\n        \"experiment_path\" : \"models/list_search/\",\n        \"x_train\" : x_train,\n        \"y_train\" : y_train,\n        \"x_val\" : x_val,\n        \"y_val\" : y_val,\n        \"batch_size\": 100,\n        \"epochs\" : 40,\n        \"momentum\" : 0.7,\n    }\n    # NOTE: The union of both dictionaries should contain all evaluator parameters\n\n    # Perform optimization\n    mpo = MetaParamOptimizer(save_path=fixed_args[\"experiment_path\"])\n    best_model = mpo.list_search(evaluator=evaluator,\n                                dicts_list=dicts_list,\n                                fixed_args=fixed_args)\n\n    # Test model\n    test_acc, test_loss = best_model[\"model\"].get_classification_metrics(x_test, y_test)\n    print(\"Test accuracy:\", test_acc)\n","repo_name":"OleguerCanal/Toy-DeepLearning-Framework","sub_path":"examples/listsearch_example.py","file_name":"listsearch_example.py","file_ext":"py","file_size_in_byte":2672,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"12752076423","text":"from evaluator import evaluate\nimport torch\nimport numpy as np\nimport json\nimport sys\n\nfrom timeit import default_timer as timer\nfrom datetime import timedelta\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\ndef run_until_observe_or_end(res):\n    cont, args, sigma = res\n    res = cont(*args)\n    while type(res) is tuple:\n        if res[2]['type'] == 'observe':\n            return res\n        cont, args, sigma = res\n        res = cont(*args)\n\n    res = (res, None, {'done' : True}) #wrap it back up in a tuple, that has \"done\" in the sigma map\n    return res\n\ndef resample_particles(particles, log_weights):\n\n    logWeights = torch.stack(log_weights)\n    Wnorm = logWeights.exp().sum()\n    Ws = logWeights.exp()\n    D = torch.distributions.Categorical(Ws/Wnorm)\n    new_particles = particles.copy()\n    for i in range(len(particles)):\n        ind = int(D.sample())\n        new_particles[i] = particles[ind]\n\n    logZ = torch.log(1/len(logWeights) * Ws.sum())\n    return logZ, new_particles\n\n\n\ndef SMC(n_particles, exp):\n\n    particles = []\n    weights = []\n    logZs = []\n    output = lambda x: x\n\n    for i in range(n_particles):\n\n        res = evaluate(exp, env=None)('addr_start', output)\n        logW = 0.\n\n\n        particles.append(res)\n        weights.append(logW)\n\n    #can't be done after the first step, under the address transform, so this should be fine:\n    done = False\n    smc_cnter = 0\n    while not done:\n    #    print('In SMC step {}, Zs: '.format(smc_cnter), logZs)\n        for i in range(n_particles): #Even though this can be parallelized, we run it serially\n            res = run_until_observe_or_end(particles[i])\n            if 'done' in res[2]: #this checks if the calculation is done\n                particles[i] = res[0]\n                if i == 0:\n                    done = True  #and enforces everything to be the same as the first particle\n                    address = ''\n                else:\n                    if not done:\n                        raise RuntimeError('Failed SMC, finished one calculation before the other')\n            else:\n                sig = res[2]\n                if i == 0: # the first particle (we can get away with this because they run sequentially)\n                    addrCurrent = sig['addr']\n                else:\n                    if not(sig['addr'] == addrCurrent):\n                        raise RuntimeError('SMC Failed, particle ', i, 'arrived to observe at address: ', sig['addr'], ' which is different to the current address: ', addrCurrent)\n\n                weights[i] = sig['logW']\n                particles[i] = res\n\n        if not done:\n            #resample and keep track of logZs\n            logZn, particles = resample_particles(particles, weights)\n            logZs.append(logZn)\n        smc_cnter += 1\n    logZ = sum(logZs)\n    return logZ, particles\n\n\nif __name__ == '__main__':\n    inc = [1,10,100,1000,10000,100000]\n    for i in range(1,5):\n        with open('programs/{}.json'.format(i),'r') as f:\n            exp = json.load(f)\n\n        fig, axes = plt.subplots(nrows = 1, ncols=len(inc),figsize=(10,10))\n\n        for j in range(len(inc)):\n            n_particles = inc[j]#None #TODO\n            start = timer() \n            logZ, particles = SMC(n_particles, exp)\n            end = timer()\n\n            print(\"Elapsed time for program \", i,\".daphne is: \",timedelta(seconds=end-start),\" seconds with \",inc[j], \" particles\")\n            print('logZ: ', logZ)\n\n            if i == 3:\n                dim = len(particles[0])\n                values = torch.stack(particles)\n                for k in range(dim):\n                    print('Mean of dim',k,':',np.mean(values[:,k].numpy()))\n                    print('Variance of dim',k,':',np.var(values[:,k].numpy()))\n\n            else:\n                particles = [torch.tensor(float(i)) for i in particles]\n                values = torch.stack(particles)\n            #TODO: some presentation of the results\n\n                print('Mean of particles: ',values.mean())\n                print('Variance of particles: ',values.var())\n\n\n            ax = axes[j]\n            ax.hist(values.numpy())\n            ax.set_title('Program ' + str(i) + ' with ' + str(inc[j]) + ' particles')\n            ax.set_xlabel('Particle Values')\n            ax.set_ylabel('PDF Estimate')\n\n        plt.show()\n","repo_name":"justinreiher/probProg_Fall2021","sub_path":"CS532-HW6/smc.py","file_name":"smc.py","file_ext":"py","file_size_in_byte":4330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"18379814656","text":"#!/Python37-32\r\n\"\"\"\r\n\r\nopen a text file called requirements.txt\r\nformat and display text to screen\r\n\r\n\"\"\"\r\nwith open(\"requirements.txt\") as f:\r\n\toutput = f.readlines()\r\nprint()\r\n#print (output)\r\n#\r\na = output[0]\r\nb = output[1]\r\nc = output[2]\r\nd = output[3]\r\ne = output[4]\r\nf = output[5]\r\ng = output[6]\r\nh = output[7]\r\n#\r\nprint (\"-\" * 20)\r\nprint (a,b,c,d,e,f,g,h)\r\nprint ()\r\n\r\n\r\n","repo_name":"dschulz6/learning-Python","sub_path":"my_open_text_file.py","file_name":"my_open_text_file.py","file_ext":"py","file_size_in_byte":378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"30142009188","text":"import random\nimport time\nfrom datetime import datetime\n\nclass Quiz:\n    def __init__(self, filename):\n        self.filename = filename\n        self.total_questions = 0\n        self.correct_answers = 0\n        self.used_numbers = set()\n        self.incorrect_responses = []\n        self.start_time = time.time()\n\n    def rand_num_gen(self):\n        question_num = random.randint(1, 900)\n        while question_num in self.used_numbers:\n            question_num = random.randint(1, 900)\n        return question_num\n\n    def ask_question(self, question_num):\n        with open(self.filename, \"r\") as file:\n            lines = file.readlines()\n\n        for i, line in enumerate(lines):\n            if f\"QUESTION {question_num}\" in line:\n                question_index = i\n                print(\"\\n\" + line)\n\n                for j in range(question_index+1, len(lines)):\n                    if \"Correct Answer\" in lines[j]:\n                        correct_answer = lines[j].split(\":\")[1].strip()\n                        break\n                    print(lines[j].strip())\n\n                user_answer = input(\"Enter your answer: \").strip()\n\n                if user_answer == correct_answer:\n                    self.correct_answers += 1\n                    print(\"Correct!\")\n                else:\n                    print(f\"Incorrect! The correct answer is {correct_answer}\")\n                    self.incorrect_responses.append(f\"QUESTION {question_num} - Incorrect answer: {user_answer}\")\n\n                self.total_questions += 1\n                break\n\n    def print_scores(self):\n        score = self.correct_answers / self.total_questions * 100\n        elapsed_time = time.time() - self.start_time\n        print(f\"Score: {self.correct_answers}/{self.total_questions} ({score:.2f}%)\")\n        print(f\"Total time taken: {elapsed_time//3600:.0f} hours and {(elapsed_time%3600)//60:.0f} minutes\")\n\n    def save_incorrect_responses(self):\n        if self.incorrect_responses:\n            # Get the current date and time\n            now = datetime.now()\n\n            # Format it as a string\n            date_time_str = now.strftime(\"%Y%m%d_%H%M%S\")\n\n            # Use it in the filename\n            filename = f\"incorrect_responses_{date_time_str}.txt\"\n\n            with open(filename, \"w\") as file:\n                for response in self.incorrect_responses:\n                    file.write(response + \"\\n\")\n\n    def run(self):\n        keep_going = True\n        while keep_going:\n            question_num = self.rand_num_gen()\n            self.used_numbers.add(question_num)\n\n            self.ask_question(question_num)\n            self.print_scores()\n\n            keep_going = input(\"\\nDo you want to continue? (y/n) \") == 'y'\n\n        self.save_incorrect_responses()\n\n\nif __name__ == '__main__':\n    quiz = Quiz(\"aws_test.txt\")\n    quiz.run()\n","repo_name":"k4u5hik/AWS-CCP-Practice-Test","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"33376911612","text":"import json\r\n\r\nfrom django.db import models\r\n\r\n# Create your models here.\r\nfrom django.contrib.auth.models import User\r\nfrom channels.layers import get_channel_layer\r\nfrom asgiref.sync import async_to_sync\r\n\r\nclass Notifications(models.Model):\r\n    user = models.ForeignKey(User, on_delete=models.CASCADE)\r\n    notification = models.TextField()\r\n    is_seen = models.BooleanField(default=False)\r\n\r\n\r\n    def save(self, *args, **kwargs):\r\n        channel_layer = get_channel_layer()\r\n        un_seen = Notifications.objects.filter(is_seen=False).count()\r\n        data = {'count':un_seen, 'current_notification':self.notification}\r\n        async_to_sync(channel_layer.group_send)(\r\n            'test_room_group_name',{\r\n                'type':'send_notification',\r\n                'value':json.dumps(data),\r\n            })\r\n\r\n        super(Notifications, self).save(*args, **kwargs)\r\n        print(\"HI\")\r\n","repo_name":"Bibin22/django-channels","sub_path":"project_1/app/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"21538885943","text":"import itertools\n# import json\n# import os.path\n\nimport numbers\nimport numpy as np\nimport matplotlib\nimport matplotlib.cm as cm\n# import matplotlib.gridspec as gridspec\nimport matplotlib.pyplot as plt\n\n# import ezmock\n# from ezmock import EZmock\n\n# import nbodykit.algorithms\n# import nbodykit.source.catalog\n\n\ndef decimal_to_filename_friendly(x, decimal_places):\n    '''\n    Converts numbers into filename-friendly strings. Decimal points are\n    replaced by 'p', and negative signs by 'n'.\n    \n    Parameters\n    ----------\n    x : real number\n        Number to convert.\n    decimal_places : int\n        Number of decimal places to represent.\n\n    Returns\n    -------\n    str\n        Filename-friendly string representation of `x`.\n    '''\n    num_str = f'{{:.{decimal_places}f}}'.format(x)\n    return num_str.replace('.', 'p').replace('-', 'n')\n\n\ndef d2ff(x, places):\n    '''\n    Alias for `decimal_to_filename_friendly`.\n    \n    See Also\n    --------\n    decimal_to_filename_friendly\n    '''\n    return decimal_to_filename_friendly(x, places)\n\n\ndef plot_ezmock_pks(ax, ezmocks, labels, colors=None, **kwargs):\n    '''\n    Plot power spectra for given EZmocks.\n    \n    ax : matplotlib.axes.Axes\n    ezmocks : list of EZmock\n        EZmocks to plot PKs for.\n    labels : list of str\n        Labels to use to identify the individual PKs.\n    colors : list of color, optional\n        Color specifiers for the lines to be used.\n    '''\n    if colors is None:\n        colors = itertools.repeat(None)\n    \n    for ezmock, label, color in zip(ezmocks, labels, colors):\n        pk_obj = ezmock.pk['real']\n        plot_pk(ax, pk_obj, label=label, color=color, linewidth=0.75, **kwargs)\n    \n\n\n# package UNIT into a nice object with the same interface as EZmock\n# TODO make them inherit the same thing\nclass EnhancedCatalog():\n    def __init__(self, name, catalog, pks, pcfs, bispecs):\n        self.catalog = catalog\n        self.pk = pks\n        self.twop_cf = pcfs\n        self.name = name\n        self.bispec = bispecs\n\n\ndef compute_pks(catalog):\n    mesh_kwargs = dict(\n        Nmesh=256,\n        compensated=True,\n        interlaced=True,\n        resampler='cic',\n    )\n    realmesh = catalog.to_mesh(\n        position='Position',\n        **mesh_kwargs,\n    )\n    realpk = nbodykit.algorithms.FFTPower(\n        realmesh,\n        mode='1d',\n        dk=0.005,\n        kmin=0,\n    )\n    \n    zmesh = catalog.to_mesh(\n        position='RSDPosition',\n        **mesh_kwargs,\n    )\n    zpk = nbodykit.algorithms.FFTPower(\n        zmesh,\n        mode='2d',\n        dk=0.005,\n        kmin=0,\n        Nmu=5,\n        los=[0,0,1],\n        poles=[0,2],\n    )\n    \n    pks = { 'real': realpk, 'zdist': zpk }\n    return pks\n\n\n# def save_realspace_catalog(catalog, fname):\n#     positions = catalog['Position'].compute()\n#     np.savetxt(fname, positions, fmt='%1.3f')\n\n\n# def save_zspace_catalog(catalog, fname):\n#     rsd_positions = catalog['RSDPosition'].compute()\n\n#     # nbodykit does not automagically apply periodic boundary conditions!\n#     np.mod(rsd_positions, catalog.attrs['BoxSize'], out=rsd_positions)\n\n#     np.savetxt(fname, rsd_positions, fmt='%1.3f')\n    \n# def import_pk_file(path):\n#     return np.genfromtxt(path, names=['k', 'power'])\n    \n# def import_cf_file(path):\n#     return np.genfromtxt(path, names=['r', 'corr'])\n\n\n# def import_catalog_with_manifest(root_dir, manifest):\n#     if not {'x', 'y', 'z'} <= set(manifest['columns']):\n#         raise ValueError('x, y, z not among columns')\n    \n#     catalog = nbodykit.source.catalog.file.CSVCatalog(\n#         os.path.join(root_dir, manifest['filename']),\n#         manifest['columns'],\n#     )\n    \n#     catalog['Position'] = nbodykit.transform.StackColumns(\n#         catalog['x'], catalog['y'], catalog['z'],\n#     )\n#     if 'vx' in manifest['columns']:\n#         catalog['Velocity'] = nbodykit.transform.StackColumns(\n#             catalog['vx'], catalog['vy'], catalog['vz'],\n#         )\n#         catalog['VelocityOffset'] = manifest['rsd_factor'] * catalog['Velocity']\n#         line_of_sight = [0, 0, 1]\n#         catalog['RSDPosition'] = \\\n#             catalog['Position'] + catalog['VelocityOffset'] * line_of_sight\n#     elif 'z_rsd' in manifest['columns']:\n#         catalog['RSDPosition'] = nbodykit.transform.StackColumns(\n#             catalog['x'], catalog['y'], catalog['z_rsd'],\n#         )\n#     else:\n#         raise NotImplementedError()\n        \n#     catalog.attrs['BoxSize'] = manifest['box_size']\n    \n#     return catalog\n\n\n        \n# def import_manifested_catalog(catalog_dir_abspath):\n#     root_dir = catalog_dir_abspath\n    \n#     manifest_path = os.path.join(root_dir, 'manifest.json')\n#     with open(manifest_path, 'r') as fp:\n#         manifest = json.load(fp)\n    \n#     catalog = None\n#     if manifest['filename'] is not None:\n#         catalog = import_catalog_with_manifest(root_dir, manifest)\n    \n#     if manifest['power_spectra']:\n#         pks_dir = os.path.join(root_dir, 'pk')\n#         pks = {\n#             'real' : import_pk_file(os.path.join(pks_dir, 'real-mono.dat')),\n#             'zdist': {\n#                 'mono': import_pk_file(os.path.join(pks_dir, 'zdist-mono.dat')),\n#                 'quad': import_pk_file(os.path.join(pks_dir, 'zdist-quad.dat')),\n#             },\n#         }\n#     elif catalog is not None:\n#         pks = compute_pks(catalog)\n#     else:\n#         raise ValueError('Cannot have no catalog and no PK!')\n    \n#     cfs = None\n#     if manifest['corr_funcs']:\n#         cfs_dir = os.path.join(root_dir, 'cf')\n#         cfs = {\n#             'real' : import_cf_file(os.path.join(cfs_dir, 'real-mono.dat')),\n#             'zdist': {\n#                 'mono': import_cf_file(os.path.join(cfs_dir, 'zdist-mono.dat')),\n#                 'quad': import_cf_file(os.path.join(cfs_dir, 'zdist-quad.dat')),\n#             },\n#         }\n\n#     bks = None\n#     if manifest['bispectra']:\n#         bks_dir = os.path.join(root_dir, 'bk')\n#         bks = {\n#             'real': np.genfromtxt(\n#                 os.path.join(bks_dir, 'real-0p1-0p2.dat'),\n#                 names=True,\n#             ),\n#             'zdist': np.genfromtxt(\n#                 os.path.join(bks_dir, 'zdist-0p1-0p2.dat'),\n#                 names=True,\n#             ),\n#         }\n    \n#     return EnhancedCatalog(manifest['short_name'], catalog, pks, cfs, bks)\n\n    \ndef plot_with_xpower(ax, x, y, exponent, yerr=None, fill=False, **kwargs):\n    '''\n    Plot y * x**exponent against x.\n    '''\n    if yerr is None:\n        return ax.plot(x, y * x**exponent, **kwargs)[0]\n    else:\n        return ax.errorbar(x, y * x**exponent, yerr * x**exponent, **kwargs)\n\n\n# def plot_with_err_and_xpower(ax, x, y, yerr, exponent, fill=False, errorbars=True, fill_kw={}, **kwargs):\n#     if not (errorbars or fill):\n#         raise ValueError('Must have either errorbars or fill to indicate error!')\n\n#     if errorbars:\n#         errorbar = ax.errorbar(x, y * x**exponent, yerr * x**exponent, **kwargs)\n#         line = errorbar[0]\n#     else:\n#         line, = ax.plot(x, y * x**exponent, **kwargs)\n    \n#     if fill:\n#         ax.fill_between(x, (y + yerr) * x**exponent, (y - yerr) * x**exponent, color=line.get_color(), linewidth=0, **fill_kw)\n        \n#     if errorbars:\n#         return errorbar\n#     else:\n#         return line\n\n\n# def plot_2pt_stats(\n#     mocks,\n#     labels,\n#     title=None,\n# ):\n#     fig, axss = plt.subplots(nrows=2, ncols=2, figsize=(8, 6))\n    \n#     for space, axs in zip(['real', 'zdist'], axss):\n#         ax1, ax2 = axs\n        \n#         for mock, label in zip(mocks, labels):            \n#             pk = mock.pk[space].power\n#             wavenums = pk['k']\n#             noiseless_power = pk['power'].real - pk.attrs['shotnoise']\n#             plot_with_xpower(ax1, wavenums, noiseless_power, 1.5, label=label, linewidth=0.75)\n\n#             cf = mock.twop_cf[space]\n#             plot_with_xpower(ax2, cf['r'], cf['corr'], 2, label=label, linewidth=0.75)\n            \n#         ax1.set_xlabel(r\"$k$ [$h \\ \\mathrm{Mpc}^{-1}$]\")\n#         ax1.set_ylabel(r\"$k^{1.5} P(k)$ [$(h^{-1}\\mathrm{Mpc})^{1.5}$]\")\n\n#         ax2.set_xlabel(r'$r$ [Mpc/$h$]')\n#         ax2.set_ylabel(r\"$r^2 \\xi_0$ [$(\\mathrm{Mpc}/h)^2$]\")\n    \n#     axss[0][0].legend()\n    \n#     if title is not None:\n#         fig.suptitle(title)\n        \n#     fig.tight_layout(rect=(0, 0, 1, 0.95))\n    \n#     return fig\n\n\n# def plot_real_2pt_stats(\n#     mocks,\n#     labels,\n#     title=None,\n# ):\n#     fig, [ax1, ax2] = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n    \n#     for mock, label in zip(mocks, labels):\n#         pk = mock.pk['real'].power\n#         wavenums = pk['k']\n#         noiseless_power = pk['power'].real - pk.attrs['shotnoise']\n#         plot_with_xpower(ax1, wavenums, noiseless_power, 1.5, label=label, linewidth=0.75)\n        \n#         cf = mock.twop_cf['real']\n#         plot_with_xpower(ax2, cf['r'], cf['corr'], 2, label=label, linewidth=0.75)\n    \n    \n#     ax1.set_xlabel(r\"$k$ [$h \\ \\mathrm{Mpc}^{-1}$]\")\n#     ax1.set_ylabel(r\"$k^{1.5} P(k)$ [$(h^{-1}\\mathrm{Mpc})^{1.5}$]\")\n    \n#     ax2.set_xlabel(r'$r$ [Mpc/$h$]')\n#     ax2.set_ylabel(r\"$r^2 \\xi_0$ [$(\\mathrm{Mpc}/h)^2$]\")\n    \n#     ax1.legend()\n    \n#     if title is not None:\n#         fig.suptitle(title)\n        \n#     fig.tight_layout(rect=(0, 0, 1, 0.95))\n    \n#     return fig\n\n\ndef get_plot_colors(val_container, cmap=cm.cividis, cmap_bounds=None):\n    if cmap_bounds is None:\n        cmap_bounds = min(val_container), max(val_container)\n    vmin, vmax = cmap_bounds\n\n    norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)\n    color_mapper = cm.ScalarMappable(norm=norm, cmap=cmap)\n    return [color_mapper.to_rgba(s) for s in val_container]\n\n\n# def compare_ezmocks_realspace(\n#     ezmock_dict,\n#     **kwargs,\n# ):\n#     '''\n#     ezmock_dict : dict\n#         dict of dicts of the form\n#         {\n#             a_val_0: {\n#                 s_val_00: mock_00,\n#                 s_val_01: mock_01,\n#                 ...\n#             },\n#             ...\n#         }\n#     '''\n#     ncols = len(ezmock_dict)\n    \n#     figsize = (4 * ncols, 3 * 2)\n#     fig, axs = plt.subplots(nrows=2, ncols=ncols, sharey='row', figsize=figsize)\n#     pk_axs, cf_axs = axs\n    \n#     is_first = True\n#     for pk_ax, cf_ax, (a_val, ezmock_subdict) in zip(pk_axs, cf_axs, ezmock_dict.items()):\n#         svals = ezmock_subdict.keys()\n#         mocks = ezmock_subdict.values()\n        \n#         plot_colors = get_plot_colors(svals)\n        \n#         plot_pks_with_fiducial(pk_ax, mocks, svals, None, colors=plot_colors, **kwargs)\n#         plot_2pcfs_with_fiducial(cf_ax, mocks, svals, None, colors=plot_colors, ylabel=is_first, legend=False, **kwargs)\n        \n#         pk_ax.set_title(f'expect_A_pdf = {a_val}')\n#         is_first = False\n    \n#     for i, ax in enumerate(pk_axs):\n#         setup_pk_ax(ax, ylabel=(i==0), xlabel=True, xlim=None)\n#         ax.legend()\n#     # pk_axs[0].legend()\n    \n#     fig.suptitle('EZmock PK/2pcf for various expect_A_pdf (panes), scatter2 (lines)')\n#     fig.tight_layout(rect=(0, 0, 1, 0.95))\n    \n#     return (fig, axs)\n\ndef compare_ezmocks_with_fiducial(\n    ezmock_dict,\n    scatter2s,\n    expect_a_pdfs,\n    fiducial=None,\n    figsize=None,\n    scatter_major=True\n):\n    major_list, minor_list = (scatter2s, expect_a_pdfs) if scatter_major else (expect_a_pdfs, scatter2s)\n    major_name = 'scatter2' if scatter_major else 'expect_A_pdf'\n    col_num = len(major_list)\n\n    plot_colors = get_plot_colors(minor_list)\n    \n    def iterate_over_minor(major_val):\n        if scatter_major:\n            return [(major_val, e) for e in minor_list]\n        else:\n            return [(e, major_val) for e in minor_list]\n\n    if figsize is None:\n        figsize = (4 * col_num, 3 * 2)\n    fig, axs = plt.subplots(\n        nrows=2, ncols=col_num, sharey='row', figsize=figsize, squeeze=False,\n        constrained_layout=True,\n    )\n    pk_axs, cf_axs = axs\n    \n    is_first = True\n    for (pk_ax, cf_ax, major_val) in zip(pk_axs, cf_axs, major_list):\n        ezmocks = [ezmock_dict[k] for k in iterate_over_minor(major_val)]\n        plot_pks_with_fiducial(pk_ax, ezmocks, minor_list, fiducial, colors=plot_colors)\n        plot_2pcfs_with_fiducial(cf_ax, ezmocks, minor_list, fiducial, colors=plot_colors, ylabel=is_first, legend=False)\n        \n        pk_ax.set_title('{} = {}'.format(major_name, major_val))\n        is_first = False\n    \n    for i, ax in enumerate(pk_axs):\n        setup_pk_ax(ax, ylabel=(i==0), xlabel=True, xlim=None)\n    pk_axs[0].legend()\n    \n    if scatter_major:\n        fig.suptitle('EZmock PK/2pcf for various scatter2 (panes), expect_A_pdf (lines)')\n    else:\n        fig.suptitle('EZmock PK/2pcf for various expect_A_pdf (panes), scatter2 (lines)')\n    # fig.tight_layout(rect=(0, 0, 1, 0.95))\n    \n    return (fig, axs)\n\n\ndef plot_with_yscale(ax, x, y, yscale, **kwargs):\n    '''\n    Plot data `(x, y)` on an Axes object `ax`, with the y-axis scaled as\n    specified by `yscale`.\n    \n    Parameters\n    ----------\n    ax : matplotlib.Axes\n        Axes to plot data on.\n    x, y : array-like or scalar\n        Same as in `matplotlib.Axes.plot`\n    yscale : number or 'log'\n        If 'log', plot on a log scale. If a real number, plot y * x**yscale.\n    \n    Returns\n    -------\n    lines\n        A list of Line2D objects representing the plotted data.\n    \n    Other parameters\n    ----------------\n    **kwargs\n        All kwargs supported by `matplotlib.Axes.plot`.\n    '''\n    if isinstance(yscale, str):\n        if yscale == 'log':\n            return ax.semilogy(x, y, **kwargs)\n        raise ValueError('Only valid string value for `yscale` is \"log\"')\n    elif not isinstance(yscale, numbers.Real):\n        raise TypeError('Did not supply `log` or real value for `yscale`!')\n\n    return plot_with_xpower(ax, x, y, yscale, **kwargs)\n\n\ndef plot_pk(ax, pk_object, yscale=1.5, start_ind=1, **kwargs):\n    Pk = pk_object.power\n    wavenums = Pk['k']\n    noiseless_power = Pk['power'].real - Pk.attrs['shotnoise']\n    \n    plot_with_yscale(ax, wavenums[start_ind:], noiseless_power[start_ind:], yscale, **kwargs)\n\n    \n# def plot_pk_pole(ax, pk_object, pole, yscale=1.5, **kwargs):\n#     if pole not in [0, 2]:\n#         raise ValueError(f'pole must be 0 or 2, was {pole}')\n#     poles = pk_object.poles\n    \n#     k = poles['k']\n#     power = poles[f'power_{pole}'].real - (pk_object.attrs['shotnoise'] if pole == 0 else 0)\n    \n#     plot_with_yscale(ax, k, power, yscale, **kwargs)\n    \n    \n# def plot_denoised_pk_real(ax, pk_arr, **kwargs):\n#     plot_with_xpower(ax, pk_arr['k'], pk_arr['power'], 1.5, **kwargs)\n\n# def plot_denoised_pk_zdist0(ax, pk_arr, **kwargs):\n#     plot_with_xpower(ax, pk_arr['k'], pk_arr['power_0'], 1.5, **kwargs)\n\n# def plot_denoised_pk_zdist2(ax, pk_arr, **kwargs):\n#     plot_with_xpower(ax, pk_arr['k'], pk_arr['power_2'], 1.5, **kwargs)\n\n    \n# def plot_cf(ax, cf, **kwargs):\n#     plot_with_xpower(ax, cf['r'], cf['corr'], 2, **kwargs)\n\n\ndef plot_pks_with_fiducial(ax, ezmocks, labels, fiducial, colors=None, **kwargs):\n    if fiducial is not None:\n        plot_pk(ax, fiducial.pk['real'], label=fiducial.name, color='r', linewidth=2)\n    plot_ezmock_pks(ax, ezmocks, labels, colors=colors, **kwargs)\n\n\ndef plot_2pcfs_with_fiducial(ax, ezmocks, labels, fiducial=None, colors=None, ylabel=True, legend=True, **kwargs):\n    if fiducial is not None:\n        fid_2pcf = fiducial.twop_cf['real']\n        plot_with_xpower(ax, fid_2pcf['r'], fid_2pcf['corr'], 2, label=fiducial.name, color='r', linewidth=2)\n    \n    if colors is None:\n        colors = itertools.repeat(None)\n    \n    for ezmock, label, color in zip(ezmocks, labels, colors):\n        cf = ezmock.twop_cf['real']\n        plot_with_xpower(ax, cf['r'], cf['corr'], 2, label=label, color=color, linewidth=0.75, **kwargs)\n    \n    ax.set_xlabel(r'$r$ [Mpc/$h$]')\n    if ylabel:\n        ax.set_ylabel(r\"$r^2 \\xi_0$ [$(\\mathrm{Mpc}/h)^2$]\")\n    if legend:\n        ax.legend()\n\n\ndef plot_bk(ax, bispec_arr, **kwargs):\n    thetas = bispec_arr['theta']\n    bs = bispec_arr['B']\n    ax.plot(thetas / np.pi, bs, **kwargs)\n\n\n# def plot_mock_bks(axs, mock, **kwargs):\n#     plot_bispec(axs[0], mock.bispec['real'], linewidth=1, **kwargs)\n#     plot_bispec(axs[1], mock.bispec['zdist'], linewidth=1, **kwargs)\n    \n\n# def setup_bk_fig(fig, axs):\n#     setup_bk_ax(axs[0], ylabel=False)\n#     setup_bk_ax(axs[1], ylabel=False)\n    \n#     fig.suptitle(r'$B(\\theta)$ ($k_2 = 2 k_1 = 0.2 h/\\mathrm{Mpc}$)')\n#     axs[0].set_ylabel('real space')\n#     axs[1].set_ylabel('$z$ space')\n\n    \ndef mocks_bispec_comparison(ezmocks, colors, labels, fiducial, fid_label, legend=True):\n    fig, [ax1, ax2] = plt.subplots(\n        nrows=2,\n        sharex=True,\n        constrained_layout=True,\n        gridspec_kw={ 'height_ratios': [2, 1] },\n    )\n    \n    catcollabs = zip(ezmocks, colors, labels)\n    catcollabs = itertools.chain(catcollabs, [[fiducial, 'red', fid_label]])\n    \n    for catalog, color, label in catcollabs:\n        plot_bk(ax1, catalog.bispec['real'], linewidth=1, label=f'{label} real', color=color)\n        plot_bk(ax1, catalog.bispec['zdist'], linewidth=1, linestyle='dashed', label=f'{label} zdist', color=color)\n    \n    cat_col_lab_zip = zip(ezmocks, colors, labels)\n    for catalog, color, label in cat_col_lab_zip:\n        thetas = catalog.bispec['real']['theta']\n        ratio_real = catalog.bispec['real']['B'] / fiducial.bispec['real']['B']\n        ax2.plot(thetas / np.pi, ratio_real, color=color, label=f'{label} real')\n    \n        ratio_zdist = catalog.bispec['zdist']['B'] / fiducial.bispec['zdist']['B']\n        ax2.plot(thetas / np.pi, ratio_zdist, linestyle='dashed', color=color, label=f'{label} zdist')\n\n    ax2.set_ylabel('Ratio (E/U)')\n    ax2.set_xlabel(r'$\\theta/\\pi$')\n    ax2.axhline(1, linestyle='dashed', color='0.5')\n    ax1.set_ylabel(r'$B(\\theta)$')\n    \n    if legend:\n        ax1.legend()\n        \n    ax1.set_title('Bispectrum for $k_2 = 2k_1 = 0.2 h/\\mathrm{Mpc}$')\n    \n    return fig, [ax1, ax2]\n\n    \ndef fog_fitting(ezmocks, fiducial=None):\n    fogs = list(ezmocks.keys())\n    \n    fog_colors = get_plot_colors(fogs)\n    \n    labels = fogs\n    colors = fog_colors\n    catalogs = list(ezmocks.values())\n    linewidths = [0.5] * len(fogs)\n    if fiducial is not None:\n        labels += [fiducial.name]\n        colors += ['red']\n        catalogs += [fiducial]\n        linewidths += [1]\n\n    return plot_mock_2pts(catalogs, labels, colors, linewidths)\n\n\n# def make_2pt_clustering_axs():\n#     fig = plt.figure(constrained_layout=True, figsize=(12,8))\n#     gs = gridspec.GridSpec(4, 3, figure=fig, height_ratios=[2,1,2,1])\n    \n#     def create_plot_row(row_ind, sharex_axs=None):\n#         if sharex_axs is None:\n#             sharex_axs = [None] * 4\n\n#         first = fig.add_subplot(gs[row_ind, 0], sharex=sharex_axs[0])\n#         axs = [first]\n#         for i in range(1, 3):\n#             axs.append(fig.add_subplot(\n#                 gs[row_ind, i], sharex=sharex_axs[i], sharey=first\n#             ))\n#         return axs\n    \n#     pk_axs = create_plot_row(0)\n#     pk_ratio_axs = create_plot_row(1, sharex_axs=pk_axs)\n#     cf_axs = create_plot_row(2)\n#     cf_ratio_axs = create_plot_row(3, sharex_axs=cf_axs)\n    \n#     data_axs = [pk_axs, cf_axs]\n#     ratio_axs = [pk_ratio_axs, cf_ratio_axs]\n    \n#     setup_pk_ax(pk_axs[0], xlabel=False, ylabel=True)\n#     setup_pk_ax(pk_axs[1], pole=0, xlabel=False, ylabel=True)\n#     setup_pk_ax(pk_axs[2], pole=2, xlabel=False, ylabel=True)\n#     setup_cf_ax(cf_axs[0], xlabel=False, ylabel=True)\n#     setup_cf_ax(cf_axs[1], pole=0, xlabel=False, ylabel=True)\n#     setup_cf_ax(cf_axs[2], pole=2, xlabel=False, ylabel=True)\n    \n#     for ax in pk_ratio_axs:\n#         setup_pk_ax(ax, xlabel=True, ylabel=False)\n#     for ax in cf_ratio_axs:\n#         setup_cf_ax(ax, xlabel=True, ylabel=False)\n        \n#     for ratio_ax_row in ratio_axs:\n#         ratio_ax_row[0].set_ylabel('ratio')\n#         for ax in ratio_ax_row:\n#             setup_ratio_ax(ax)\n    \n#     axs = np.array([pk_axs, pk_ratio_axs, cf_axs, cf_ratio_axs])\n    \n#     # turn off extra tick labels\n#     for ax in axs[:,1:].flatten():\n#         plt.setp(ax.get_yticklabels(), visible=False)\n#     for ax in pk_axs:\n#         plt.setp(ax.get_xticklabels(), visible=False)\n#     for ax in cf_axs:\n#         plt.setp(ax.get_xticklabels(), visible=False)\n    \n    \n#     return (fig, axs)\n\n\n# def plot_mock_2pts_with_fiducial(mocks, fiducial, colors, labels, fiducial_name, legend=True):\n#     fig, axs = make_2pt_clustering_axs()\n#     pk_axs, pk_ratio_axs, cf_axs, cf_ratio_axs = axs\n\n#     data_axs = [pk_axs, cf_axs]\n#     ratio_axs = [pk_ratio_axs, cf_ratio_axs]\n    \n#     for (mock, color, label) in zip(mocks, colors, labels):\n#         plot_single_2pt_stats(\n#             data_axs,\n#             mock,\n#             linewidth=0.8,\n#             color=color,\n#             label=label,\n#         )\n#         plot_2pt_fiducial_ratios(\n#             ratio_axs,\n#             mock,\n#             fiducial,\n#             color=color,\n#             label=label,\n#         )\n        \n#     plot_single_2pt_stats(\n#         data_axs,\n#         fiducial,\n#         linewidth=1.2,\n#         color='red',\n#         label=fiducial_name,\n#     )\n    \n    \n#     if legend:\n#         pk_axs[0].legend()\n    \n\ndef plot_single_2pt_stats(axs, catalog, **kwargs):\n    pkaxs, cfaxs = axs\n    \n    realpk = catalog.pk['real'].power\n    plot_with_xpower(\n        pkaxs[0],\n        realpk['k'],\n        realpk['power'].real - realpk.attrs['shotnoise'],\n        1.5,\n        **kwargs,\n    )\n    \n    zpk = catalog.pk['zdist'].poles\n    plot_with_xpower(pkaxs[1], zpk['k'], zpk['power_0'].real - zpk.attrs['shotnoise'], 1.5, **kwargs)\n    plot_with_xpower(pkaxs[2], zpk['k'], zpk['power_2'].real, 1.5, **kwargs)\n\n\n    cfs = catalog.twop_cf\n    cfs_real = cfs['real']\n    cfs_mono = cfs['zdist']['mono']\n    cfs_quad = cfs['zdist']['quad']\n\n    plot_with_xpower(\n        cfaxs[0],\n        cfs_real['r'],\n        cfs_real['corr'],\n        2,\n        **kwargs,\n    )\n    plot_with_xpower(cfaxs[1], cfs_mono['r'], cfs_mono['corr'], 2, **kwargs)\n    plot_with_xpower(cfaxs[2], cfs_quad['r'], cfs_quad['corr'], 2, **kwargs)\n\n    \n# def extract_2pt_stats(catalog):\n#     realpk = catalog.pk['real'].power\n#     k, pk = realpk['k'], realpk['power'].real - realpk.attrs['shotnoise']\n    \n#     zpk = catalog.pk['zdist'].poles\n#     zk, zpk0, zpk2 = zpk['k'], zpk['power_0'].real - zpk.attrs['shotnoise'], zpk['power_2'].real\n    \n#     fourier_stats = (k, pk, zk, zpk0, zpk2)\n    \n#     cfs = catalog.twop_cf\n#     cfs_real = cfs['real']\n#     cfs_mono = cfs['zdist']['mono']\n#     cfs_quad = cfs['zdist']['quad']\n#     real_stats = (cfs_real, cfs_mono, cfs_quad)\n    \n#     return (fourier_stats, real_stats)\n\n\n# def plot_2pt_fiducial_ratios(axs, catalog, fiducial, **kwargs):\n#     pkaxs, cfaxs = axs\n    \n#     fourier_stats, real_stats = extract_2pt_stats(catalog)\n#     k, pk, zk, zpk0, zpk2 = fourier_stats\n#     cfs_real, cfs_mono, cfs_quad = real_stats\n    \n#     fourier_stats_f, real_stats_f = extract_2pt_stats(fiducial)\n#     kf, pkf, zkf, zpk0f, zpk2f = fourier_stats_f\n#     cfs_real_f, cfs_mono_f, cfs_quad_f = real_stats_f\n    \n#     pkaxs[0].plot(k, pk/pkf, **kwargs)\n#     pkaxs[1].plot(zk, zpk0/zpk0f, **kwargs)\n#     pkaxs[2].plot(zk, zpk2/zpk2f, **kwargs)\n    \n#     cfaxs[0].plot(cfs_real['r'], cfs_real['corr']/cfs_real_f['corr'], **kwargs)\n#     cfaxs[1].plot(cfs_mono['r'], cfs_mono['corr']/cfs_mono_f['corr'], **kwargs)\n#     cfaxs[2].plot(cfs_quad['r'], cfs_quad['corr']/cfs_quad_f['corr'], **kwargs)\n    \n\ndef setup_pk_ax(ax, pole=None, xlabel=False, ylabel=False, xlim=None):\n    if xlim is None:\n        pass\n    elif xlim is True:\n        ax.set_xlim([-0.02,0.42])\n    else:\n        ax.set_xlim(xlim)\n    \n    if xlabel:\n        ax.set_xlabel(r'$k$ [$h$/Mpc]')\n    if ylabel:\n        if pole is None:\n            ax.set_ylabel(r'$k^{1.5} P(k)$ [$(\\mathrm{Mpc}/h)^{1.5}$]')\n        elif pole == 0:\n            ax.set_ylabel(r'$k^{1.5} P_0(k)$ [$(\\mathrm{Mpc}/h)^{1.5}$]')\n        elif pole == 2:\n            ax.set_ylabel(r'$k^{1.5} P_2(k)$ [$(\\mathrm{Mpc}/h)^{1.5}$]')\n\n\ndef setup_cf_ax(ax, pole=None, xlabel=False, ylabel=False):\n    if xlabel:\n        ax.set_xlabel(r'$r$ [Mpc/$h$]')\n    if ylabel:\n        if pole is None:\n            ax.set_ylabel(r'$r^2 \\xi(r)$ [$(\\mathrm{Mpc}/h)^2$]')\n        elif pole == 0:\n            ax.set_ylabel(r'$r^2 \\xi_0(r)$ [$(\\mathrm{Mpc}/h)^2$]')\n        elif pole == 2:\n            ax.set_ylabel(r'$r^2 \\xi_2(r)$ [$(\\mathrm{Mpc}/h)^2$]')\n    \n\ndef setup_bk_ax(ax, xlabel=True, ylabel=True):\n    if xlabel:\n        ax.set_xlabel(r'$\\theta/\\pi$')\n    if ylabel:\n        ax.set_ylabel(r'$B(\\theta)$ ($k_2 = 2 k_1 = 0.2 h/\\mathrm{Mpc}$)')\n\n\ndef setup_2pt_stats_axs(axs):\n    [pkaxs, cfaxs] = axs\n    \n    for ax in pkaxs:\n        setup_pk_ax(ax, xlabel=True)\n    for ax in cfaxs:\n        setup_cf_ax(ax, xlabel=True)\n    \n    pkaxs[0].set_ylabel(r'$k^{1.5} P(k)$ [$(\\mathrm{Mpc} / h)^{1.5}$]')\n    pkaxs[1].set_ylabel(r'$k^{1.5} P_0(k)$ [$(\\mathrm{Mpc} / h)^{1.5}$]')\n    pkaxs[2].set_ylabel(r'$k^{1.5} P_2(k)$ [$(\\mathrm{Mpc} / h)^{1.5}$]')\n\n    cfaxs[0].set_ylabel(r'$r^2 \\xi(r)$ [$(\\mathrm{Mpc}/h)^2$]')\n    cfaxs[1].set_ylabel(r'$r^2 \\xi_0(r)$ [$(\\mathrm{Mpc}/h)^2$]')\n    cfaxs[2].set_ylabel(r'$r^2 \\xi_2(r)$ [$(\\mathrm{Mpc}/h)^2$]')\n\n\n# def setup_ratio_ax(ax, **kwargs):\n#     # ax.set_ylim(0.7, 1.3)\n#     default_kwargs = dict(color='0.5', linestyle='dashed', linewidth=0.5)\n#     kwargs = dict(default_kwargs, **kwargs)\n#     ax.axhline(1, **kwargs)\n    \n\ndef plot_mock_2pts(catalogs, labels, colors, linewidths, title=None):\n    fig, axs = plt.subplots(\n        nrows=2, ncols=3,\n        figsize=(12, 6),\n        constrained_layout=True,\n        sharey='row',\n    )\n    [pkaxs, cfaxs] = axs\n    \n    for label, color, catalog, lw in zip(labels, colors, catalogs, linewidths):\n        kwargs = {\n            'label': str(label),\n            'color': color,\n            'linewidth': lw,\n        }\n        plot_single_2pt_stats(axs, catalog, **kwargs)\n    \n    pkaxs[0].legend()\n    \n    for ax in cfaxs:\n        ax.set_xlabel(r'$r$ [Mpc/$h$]')\n    for ax in pkaxs:\n        ax.set_xlabel(r'$k$ [$h$/Mpc]')\n\n    setup_2pt_stats_axs(axs)\n\n    if title is not None:\n        fig.suptitle(title)\n    \n    \n    return fig, axs\n","repo_name":"neutrinonerd3333/ezmock","sub_path":"ezmock/fitting_utils.py","file_name":"fitting_utils.py","file_ext":"py","file_size_in_byte":26492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"10029045829","text":"# A program to draw the olympic rings\n#\n# @author \n# @assignment CSCI 333 Assignment 1\n# @date 7/17/2022\n#\n\nfrom ezgraphics import GraphicsWindow\n\ndef drawRing(canv, color, diameter, x_pos, y_pos):\n    canv.setOutline(color)\n    canv.drawOval(x_pos, y_pos, diameter, diameter)\n\n\nwinsize_x = 640\nring_padding = 1\nline_width = 15\nwinsize_y = ((1.5 * winsize_x) / 3) + (ring_padding * 2)\n\nring_diameter = (winsize_x / 3) - (ring_padding * 2) - line_width\ntotal_space_ring_x = (ring_padding * 2) + line_width + ring_diameter\ntotal_space_ring_y = total_space_ring_x / 2\n\nwin = GraphicsWindow(winsize_x, winsize_y)\ncanvas = win.canvas()\ncanvas.setLineWidth(line_width)\n\nring_pos_x = ring_padding + (line_width / 2)\nring_pos_y = ring_pos_x\ndrawRing(canvas, \"blue\", ring_diameter, ring_pos_x, ring_pos_y)\n\nring_pos_x += total_space_ring_x\ndrawRing(canvas, \"black\", ring_diameter, ring_pos_x, ring_pos_y)\n\nring_pos_x += total_space_ring_x\ndrawRing(canvas, \"red\", ring_diameter, ring_pos_x, ring_pos_y)\n\nring_pos_x = (total_space_ring_x / 2) + (line_width / 2) + ring_padding\nring_pos_y += total_space_ring_y\ndrawRing(canvas, \"yellow\", ring_diameter, ring_pos_x, ring_pos_y)\n\nring_pos_x += total_space_ring_x\ndrawRing(canvas, \"green\", ring_diameter, ring_pos_x, ring_pos_y)\nwin.wait()\n","repo_name":"Calebm80/data-analytics","sub_path":"assn 1/Qn2.py","file_name":"Qn2.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"34367937104","text":"#!/usr/bin/env python\n\n\"\"\"Solution for the Advent of Code challenge 2016, day 20.\"\"\"\n\n__author__ = \"Serge Beaumont\"\n__date__ = \"December 2016\"\n\ndef splitLine(line):\n    start, end = line.strip().split('-')\n    return (int(start), int(end))\n\ndef sortRanges(ranges):\n    return sorted(ranges, key=lambda rng: rng[0])\n\ndef checkOverlap(rangeToCheck):\n    \"Check if there is an overlapping or connecting range in the ranges list. Assumes there is no current overlap in that list.\"\n    for rng in ranges:\n        if (rangeToCheck[0] >= (rng[0] - 1) and rangeToCheck[0] <= (rng[1] + 1)) or (rangeToCheck[1] >= (rng[0] - 1) and rangeToCheck[1] <= (rng[1] + 1)):\n            return rng\n\n# Load\nwith open(\"AoC-2016-20-data.txt\", 'r') as content_file:\n    lines = [splitLine(line) for line in content_file]\n\nlines = sortRanges(lines)\nranges = []\n\n# Parse\nfor line in lines:\n    overlap = checkOverlap(line)\n    if overlap:\n        newRange = (min(overlap[0], line[0]), max(overlap[1], line[1]))\n        ranges.remove(overlap)\n        ranges.append(newRange)\n    else:\n        ranges.append(line)\n\n# Print answer to part 1\nprint(\"Number of input ranges: {0}\".format(len(lines)))\nprint(\"Number of non-overlapping ranges: {0}\".format(len(ranges)))\nsortedRanges = sorted(ranges, key=lambda rng: rng[0])\nprint(sortedRanges)\nprint(\"Lowest free IP is: {0}\".format(sortedRanges[0][1] + 1))\n\n# Answer part 2\nMAX_IP = 4294967295\nallowedIPs = 0\ncurrentRange = sortedRanges[0]\nfor rng in sortedRanges[1:]:\n    allowedIPs += rng[0] - currentRange[1] - 1\n    currentRange = rng\nallowedIPs += MAX_IP - sortedRanges[-1][1]\nprint(\"Number of allowed IPs: {0}\".format(allowedIPs))","repo_name":"sbeaumont/AoC","sub_path":"2016/AoC-2016-20.py","file_name":"AoC-2016-20.py","file_ext":"py","file_size_in_byte":1651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"23391733807","text":"#!/usr/bin/env python3\n\"\"\"\n- cgi used for grabbing information put into html form.\n- cgitb used for error traceback, presented in the html page.\n- csv used to read data from csv files local to the server.\n- socket used to communicate between this server and the java server\n\"\"\"\nimport csv\nimport socket\nimport cgi\nimport cgitb\ncgitb.enable()\n\n\n# ---------------------------------------------------------------------------------------------------------------------\n# DESCRIPTION: REDIRECT TO TESTSCRIPT AFTER MARKING HAS COMPLETED\n# RETURNS: NOTHING\ndef redirect(question):\n    redirectURL = \"https://localhost:443/cgi-bin/testScript.py?qID=%s\" % question\n    print('Content - type: text / html\\r\\n\\r\\n')\n    print('<html>')\n    print('<head>')\n    print('<meta http-equiv=\"refresh\" content=\"0;url=' + str(redirectURL) + '\" />')\n    print('</head>')\n    print('</html>')\n\n\n# ---------------------------------------------------------------------------------------------------------------------\n# DESCRIPTION: UPDATE STUDENT CSV FILE DEPENDING ON IF THEY GOT THE ANSWER CORRECT/WRONG\n# RETURNS: NOTHING\ndef update_csv(student, question, result):\n    with open('../data/%s.csv' % student, 'r', newline='') as infile:\n        reader = csv.reader(infile)\n        arr = list(reader)\n    infile.close()\n\n    attempt = int(arr[14][int(question)])\n    bestMark = int(arr[15][int(question)])\n    if bestMark != 0 or attempt == 3:\n        return None\n    elif result == 'correct':                           # if correct then check attempt number in csv\n        attempt += 1                                    # and update mark accordingly\n        arr[14][int(question)] = attempt                # and add new mark to total mark\n        if attempt == 1:\n            mark = int(arr[3][0])\n            mark += 3\n            arr[3][0] = mark\n            arr[15][int(question)] = 3\n        if attempt == 2:\n            mark = int(arr[3][0])\n            mark += 2\n            arr[3][0] = mark\n            arr[15][int(question)] = 2\n        if attempt == 3:\n            mark = int(arr[3][0])\n            mark += 1\n            arr[3][0] = mark\n            arr[15][int(question)] = 1\n        with open('../data/%s.csv' % student, 'w', newline='') as outfile:\n            writer = csv.writer(outfile)\n            writer.writerows(arr)\n        outfile.close()\n    else:\n        attempt += 1                                   # if wrong then update attempt number\n        arr[14][int(question)] = attempt\n        with open('../data/%s.csv' % student, 'w', newline='') as outfile:\n            writer = csv.writer(outfile)\n            writer.writerows(arr)\n        outfile.close()\n\n\n# ---------------------------------------------------------------------------------------------------------------------\n# DESCRIPTION: MARK QUESTION BY CONNECTION TO JAVA SERVER, UPDATES ATTEMPTS IN APPROPRIATE STUDENT CSV FILE\n# RETURNS: TRUE IF ANSWER WAS CORRECT, FALSE IF IT WAS INCORRECT\ndef mark_question(sID, qID, setID, answer):\n    HOST = 'localhost'\n    PORT = 80\n    mark = '{\"%s\": \"%s\"}' % (setID, answer)\n\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)  # Create a socket (SOCK_STREAM means a TCP socket)\n    sock.connect((HOST, PORT))                                # Connect to server and send data\n    sock.sendall((mark + \"\\n\").encode('utf-8'))\n    result = sock.recv(2048)                                  # Receive data from the server and shut down\n    result = result.decode('utf-8')\n    sock.close()\n\n    if 'true' in result:\n        update_csv(sID, qID, 'correct')\n    else:\n        update_csv(sID, qID, 'wrong')\n\n\n# ---------------------------------------------------------------------------------------------------------------------\nform = cgi.FieldStorage()                           # STORE DATA FROM FORM\nsID = form.getvalue('sID')                          # GET USERNAME\nqID = form.getvalue('qID')                          # GET QUESTION ID ACCORDING TO LOCAL CSV\nsetID = form.getvalue('setID')                      # GET QUESTION ID ACCORDING TO QUESTION SERVER\noptionSelected = form.getvalue('MultiChoice')       # GET THE MULTI CHOICE VALUE SELECTED\nmark_question(sID, qID, setID, optionSelected)      # MARK QUESTION\nredirect(qID)                                       # REDIRECT BACK TO QUESTION VIA TESTSCRIPT\n","repo_name":"samueldelamotte/Client-to-server-commincation-in-Python-and-Java","sub_path":"testingServer_python/server/cgi-bin/markingScript.py","file_name":"markingScript.py","file_ext":"py","file_size_in_byte":4316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"35831108972","text":"import os\r\nimport torch\r\nfrom torch.utils.data import DataLoader, Dataset\r\nfrom lightning import LightningModule, Trainer\r\n\r\n\r\nclass RandomDataset(Dataset):\r\n    def __init__(self, size, num_samples):\r\n        self.len = num_samples\r\n        self.data = torch.randn(num_samples, size)\r\n\r\n    def __getitem__(self, index):\r\n        return self.data[index]\r\n\r\n    def __len__(self):\r\n        return self.len\r\n    \r\n    \r\nclass BoringModel(LightningModule):\r\n    def __init__(self):\r\n        super().__init__()\r\n        self.layer = torch.nn.Linear(32, 2)\r\n\r\n    def forward(self, x):\r\n        return self.layer(x)\r\n\r\n    def training_step(self, batch, batch_idx):\r\n        loss = self(batch).sum()\r\n        return {\"loss\": loss}\r\n\r\n    def validation_step(self, batch, batch_idx):\r\n        loss = self(batch).sum()\r\n\r\n    def configure_optimizers(self):\r\n        return torch.optim.SGD(self.layer.parameters(), lr=0.1)\r\n    \r\n    \r\ndef run():\r\n    train_data = DataLoader(RandomDataset(32, 64), batch_size=2)\r\n    val_data = DataLoader(RandomDataset(32, 64), batch_size=2)\r\n    model = BoringModel()\r\n    params = sum(p.numel() for p in model.parameters() if p.requires_grad)\r\n    print(f'TRAINABLE PARAMS: {params}')\r\n    trainer = Trainer(\r\n        default_root_dir=os.getcwd(),\r\n        limit_train_batches=1,\r\n        limit_val_batches=1,\r\n        limit_test_batches=1,\r\n        num_sanity_val_steps=0,\r\n        max_epochs=1,\r\n        enable_model_summary=True,\r\n        strategy=\"deepspeed_stage_3\",\r\n        gpus=[0]\r\n    )\r\n    trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    run()\r\n","repo_name":"pme0/DeepLightning","sub_path":"tests/_pl_issue_template.py","file_name":"_pl_issue_template.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"42811344142","text":"from keras.models import load_model\nfrom keras.models import Layer\nfrom keras import backend\nfrom grab_screen import get_scaled_grayscale, get_screen\nfrom grab_key import get_keys\nimport numpy as np\nimport pyxinput\nimport os, sys\nimport time\nimport cv2\n\nclass ScaleLayer(Layer):\n    def __init__(self, **kwargs):\n        super(ScaleLayer, self).__init__(**kwargs)\n    def call(self, x):\n        return (x-0.5)*2\n    def compute_output_shape(self, input_shape):\n        return input_shape\n    def build(self, input_shape):\n        super(ScaleLayer, self).build(input_shape)\n\ndef loss(y_true, y_pred): return backend.sum(backend.abs(y_true - y_pred))\ndef accuracy(y_true, y_pred): return 1 - backend.mean(backend.minimum(1.0,((backend.abs(y_true - y_pred))*10)))\n\nfile_number = int(sys.argv[1])\npath_models = os.path.join(os.getcwd(), '../models')\nmodel_name = 'model_' + '{0:03d}'.format(file_number)\nmodel = load_model(path_models + '\\\\' + model_name + '.h5', custom_objects={'loss': loss, 'accuracy': accuracy, 'ScaleLayer': ScaleLayer})\n\nprint(\"Starting...\\nPress O to pause\\nPress L to stop\")\n\nwait_time = 0\nfor i in range(wait_time):\n\tprint(wait_time-i)\n\ttime.sleep(1)\n\nstart = time.time()\nlast_p_time = 0\ncounter = 0\npaused = False\n\ntry:\n    joystick = pyxinput.vController()\nexcept MaxInputsReachedError:\n    print('Unable to connect controller for testing.')\n\nwhile joystick:\n\tkeys = get_keys()\n\tif 'O' in keys:\n\t\tif time.time() - last_p_time > 0.5: \n\t\t\tlast_p_time = time.time()\n\t\t\tpaused = not paused\n\t\t\tprint('{:3.2f} Paused'.format(steering), end='\\r')\n\t\t\tif paused == True: paused_time = time.time();\n\t\t\tif paused == False: start += time.time() - paused_time\n\tif 'L' in keys:\n\t\tprint(\"\\n\\nExiting...\")\n\t\tdel joystick\n\t\tbreak\n\tif not paused:\n\t\tscreen = get_screen()\n\t\tscreen = cv2.resize(screen, (100, 75))\n\t\tscreen = cv2.cvtColor(screen, cv2.COLOR_RGB2GRAY)\n\t\t# steering, throttle, brake = model.predict(screen[None, :])\n\t\t# joystick.set_value('AxisLx', steering)\n\t\t# joystick.set_value('TriggerR', throttle)\n\t\t# joystick.set_value('TriggerL', brake)\n\t\t# print('[{:3.2f}, {:3.2f}, {3.2f}]'.format(steering, throttle, brake), '@{0:4.2f}fps'.format(counter/(time.time() - start)) ,end='\\r')\n\n\t\tsteering = model.predict(screen[None, :,:, None])[0][0]\n\t\tjoystick.set_value('AxisLx', steering)\n\t\tprint('{:3.2f}'.format(steering), '@{0:4.2f}fps'.format(counter/(time.time() - start)), end='\\r')\n\n\t\tcounter += 1\n\n\n","repo_name":"saravanabalagi/self_driving_game","sub_path":"scripts/play_game.py","file_name":"play_game.py","file_ext":"py","file_size_in_byte":2419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"11484198635","text":"import os\nfrom PyQt5 import QtCore, QtGui, QtWidgets\n\nclass Ui_bruteforce_attack_dialog(object):\n    def setupUi(self, bruteforce_attack_dialog):\n        bruteforce_attack_dialog.setObjectName(\"bruteforce_attack_dialog\")\n        bruteforce_attack_dialog.resize(630, 648)\n        sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred)\n        sizePolicy.setHorizontalStretch(0)\n        sizePolicy.setVerticalStretch(0)\n        sizePolicy.setHeightForWidth(bruteforce_attack_dialog.sizePolicy().hasHeightForWidth())\n        bruteforce_attack_dialog.setSizePolicy(sizePolicy)\n        font = QtGui.QFont()\n        font.setPointSize(8)\n        bruteforce_attack_dialog.setFont(font)\n        icon = QtGui.QIcon()\n        icon.addPixmap(QtGui.QPixmap(\"Icons/bruteforce.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\n        bruteforce_attack_dialog.setWindowIcon(icon)\n        self.verticalLayout_9 = QtWidgets.QVBoxLayout(bruteforce_attack_dialog)\n        self.verticalLayout_9.setObjectName(\"verticalLayout_9\")\n        spacerItem = QtWidgets.QSpacerItem(20, 6, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)\n        self.verticalLayout_9.addItem(spacerItem)\n        self.horizontalLayout_4 = QtWidgets.QHBoxLayout()\n        self.horizontalLayout_4.setObjectName(\"horizontalLayout_4\")\n        spacerItem1 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum)\n        self.horizontalLayout_4.addItem(spacerItem1)\n        spacerItem2 = QtWidgets.QSpacerItem(20, 95, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\n        self.horizontalLayout_4.addItem(spacerItem2)\n        self.attack_server_label = QtWidgets.QLabel(bruteforce_attack_dialog)\n        self.attack_server_label.setText(\"\")\n        self.attack_server_label.setPixmap(QtGui.QPixmap(\"%s/Icons/mysql_logo.png\"%(os.getcwd())))\n        self.attack_server_label.setObjectName(\"attack_server_label\")\n        self.horizontalLayout_4.addWidget(self.attack_server_label)\n        spacerItem3 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum)\n        self.horizontalLayout_4.addItem(spacerItem3)\n        self.verticalLayout_9.addLayout(self.horizontalLayout_4)\n        spacerItem4 = QtWidgets.QSpacerItem(20, 0, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)\n        self.verticalLayout_9.addItem(spacerItem4)\n        self.groupBox = QtWidgets.QGroupBox(bruteforce_attack_dialog)\n        self.groupBox.setObjectName(\"groupBox\")\n        self.horizontalLayout_8 = QtWidgets.QHBoxLayout(self.groupBox)\n        self.horizontalLayout_8.setObjectName(\"horizontalLayout_8\")\n        self.horizontalLayout_3 = QtWidgets.QHBoxLayout()\n        self.horizontalLayout_3.setObjectName(\"horizontalLayout_3\")\n        self.mysql_radio = QtWidgets.QRadioButton(self.groupBox)\n        self.mysql_radio.setChecked(True)\n        self.mysql_radio.setObjectName(\"mysql_radio\")\n        self.horizontalLayout_3.addWidget(self.mysql_radio)\n        spacerItem5 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum)\n        self.horizontalLayout_3.addItem(spacerItem5)\n        self.oracle_radio = QtWidgets.QRadioButton(self.groupBox)\n        self.oracle_radio.setObjectName(\"oracle_radio\")\n        self.horizontalLayout_3.addWidget(self.oracle_radio)\n        spacerItem6 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum)\n        self.horizontalLayout_3.addItem(spacerItem6)\n        self.postgres_radio = QtWidgets.QRadioButton(self.groupBox)\n        self.postgres_radio.setObjectName(\"postgres_radio\")\n        self.horizontalLayout_3.addWidget(self.postgres_radio)\n        spacerItem7 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum)\n        self.horizontalLayout_3.addItem(spacerItem7)\n        self.mssql_radio = QtWidgets.QRadioButton(self.groupBox)\n        self.mssql_radio.setObjectName(\"mssql_radio\")\n        self.horizontalLayout_3.addWidget(self.mssql_radio)\n        self.horizontalLayout_8.addLayout(self.horizontalLayout_3)\n        self.verticalLayout_9.addWidget(self.groupBox)\n        spacerItem8 = QtWidgets.QSpacerItem(20, 8, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\n        self.verticalLayout_9.addItem(spacerItem8)\n        self.groupBox_3 = QtWidgets.QGroupBox(bruteforce_attack_dialog)\n        self.groupBox_3.setObjectName(\"groupBox_3\")\n        self.verticalLayout_3 = QtWidgets.QVBoxLayout(self.groupBox_3)\n        self.verticalLayout_3.setObjectName(\"verticalLayout_3\")\n        self.horizontalLayout_2 = QtWidgets.QHBoxLayout()\n        self.horizontalLayout_2.setObjectName(\"horizontalLayout_2\")\n        self.verticalLayout_2 = QtWidgets.QVBoxLayout()\n        self.verticalLayout_2.setObjectName(\"verticalLayout_2\")\n        self.label_2 = QtWidgets.QLabel(self.groupBox_3)\n        self.label_2.setObjectName(\"label_2\")\n        self.verticalLayout_2.addWidget(self.label_2)\n        self.attck_server_checkbox = QtWidgets.QCheckBox(self.groupBox_3)\n        self.attck_server_checkbox.setObjectName(\"attck_server_checkbox\")\n        self.verticalLayout_2.addWidget(self.attck_server_checkbox)\n        self.horizontalLayout_2.addLayout(self.verticalLayout_2)\n        self.verticalLayout = QtWidgets.QVBoxLayout()\n        self.verticalLayout.setObjectName(\"verticalLayout\")\n        spacerItem9 = QtWidgets.QSpacerItem(20, 40, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)\n        self.verticalLayout.addItem(spacerItem9)\n        self.attack_server_linedit = QtWidgets.QLineEdit(self.groupBox_3)\n        self.attack_server_linedit.setObjectName(\"attack_server_linedit\")\n        self.verticalLayout.addWidget(self.attack_server_linedit)\n        self.horizontalLayout = QtWidgets.QHBoxLayout()\n        self.horizontalLayout.setObjectName(\"horizontalLayout\")\n        self.attack_port_linedit = QtWidgets.QLineEdit(self.groupBox_3)\n        self.attack_port_linedit.setObjectName(\"attack_port_linedit\")\n        self.horizontalLayout.addWidget(self.attack_port_linedit)\n        self.label_3 = QtWidgets.QLabel(self.groupBox_3)\n        self.label_3.setObjectName(\"label_3\")\n        self.horizontalLayout.addWidget(self.label_3)\n        spacerItem10 = QtWidgets.QSpacerItem(46, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum)\n        self.horizontalLayout.addItem(spacerItem10)\n        self.verticalLayout.addLayout(self.horizontalLayout)\n        self.horizontalLayout_2.addLayout(self.verticalLayout)\n        self.verticalLayout_3.addLayout(self.horizontalLayout_2)\n        self.verticalLayout_9.addWidget(self.groupBox_3)\n        spacerItem11 = QtWidgets.QSpacerItem(23, 9, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\n        self.verticalLayout_9.addItem(spacerItem11)\n        self.groupBox_2 = QtWidgets.QGroupBox(bruteforce_attack_dialog)\n        self.groupBox_2.setObjectName(\"groupBox_2\")\n        self.horizontalLayout_11 = QtWidgets.QHBoxLayout(self.groupBox_2)\n        self.horizontalLayout_11.setObjectName(\"horizontalLayout_11\")\n        self.horizontalLayout_7 = QtWidgets.QHBoxLayout()\n        self.horizontalLayout_7.setObjectName(\"horizontalLayout_7\")\n        self.verticalLayout_7 = QtWidgets.QVBoxLayout()\n        self.verticalLayout_7.setObjectName(\"verticalLayout_7\")\n        self.userlist_button = QtWidgets.QPushButton(self.groupBox_2)\n        sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Preferred)\n        sizePolicy.setHorizontalStretch(11)\n        sizePolicy.setVerticalStretch(15)\n        sizePolicy.setHeightForWidth(self.userlist_button.sizePolicy().hasHeightForWidth())\n        self.userlist_button.setSizePolicy(sizePolicy)\n        self.userlist_button.setBaseSize(QtCore.QSize(9, 0))\n        self.userlist_button.setIconSize(QtCore.QSize(30, 32))\n        self.userlist_button.setObjectName(\"userlist_button\")\n        self.verticalLayout_7.addWidget(self.userlist_button)\n        self.userlist_details = QtWidgets.QLabel(self.groupBox_2)\n        self.userlist_details.setAlignment(QtCore.Qt.AlignCenter)\n        self.userlist_details.setObjectName(\"userlist_details\")\n        self.verticalLayout_7.addWidget(self.userlist_details)\n        self.horizontalLayout_7.addLayout(self.verticalLayout_7)\n        self.blank_password_checkbox = QtWidgets.QCheckBox(self.groupBox_2)\n        self.blank_password_checkbox.setObjectName(\"blank_password_checkbox\")\n        self.horizontalLayout_7.addWidget(self.blank_password_checkbox)\n        spacerItem12 = QtWidgets.QSpacerItem(0, 81, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\n        self.horizontalLayout_7.addItem(spacerItem12)\n        self.verticalLayout_6 = QtWidgets.QVBoxLayout()\n        self.verticalLayout_6.setObjectName(\"verticalLayout_6\")\n        self.wordlist_button = QtWidgets.QPushButton(self.groupBox_2)\n        sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Preferred)\n        sizePolicy.setHorizontalStretch(11)\n        sizePolicy.setVerticalStretch(15)\n        sizePolicy.setHeightForWidth(self.wordlist_button.sizePolicy().hasHeightForWidth())\n        self.wordlist_button.setSizePolicy(sizePolicy)\n        self.wordlist_button.setObjectName(\"wordlist_button\")\n        self.verticalLayout_6.addWidget(self.wordlist_button)\n        self.wordlist_details = QtWidgets.QLabel(self.groupBox_2)\n        self.wordlist_details.setAlignment(QtCore.Qt.AlignCenter)\n        self.wordlist_details.setObjectName(\"wordlist_details\")\n        self.verticalLayout_6.addWidget(self.wordlist_details)\n        self.horizontalLayout_7.addLayout(self.verticalLayout_6)\n        self.horizontalLayout_11.addLayout(self.horizontalLayout_7)\n        self.verticalLayout_9.addWidget(self.groupBox_2)\n        self.horizontalLayout_9 = QtWidgets.QHBoxLayout()\n        self.horizontalLayout_9.setObjectName(\"horizontalLayout_9\")\n        spacerItem13 = QtWidgets.QSpacerItem(25, 20, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Minimum)\n        self.horizontalLayout_9.addItem(spacerItem13)\n        self.verticalLayout_5 = QtWidgets.QVBoxLayout()\n        self.verticalLayout_5.setObjectName(\"verticalLayout_5\")\n        self.horizontalLayout_6 = QtWidgets.QHBoxLayout()\n        self.horizontalLayout_6.setObjectName(\"horizontalLayout_6\")\n        self.current_userlist = QtWidgets.QLabel(bruteforce_attack_dialog)\n        self.current_userlist.setAlignment(QtCore.Qt.AlignCenter)\n        self.current_userlist.setObjectName(\"current_userlist\")\n        self.horizontalLayout_6.addWidget(self.current_userlist)\n        spacerItem14 = QtWidgets.QSpacerItem(28, 20, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Minimum)\n        self.horizontalLayout_6.addItem(spacerItem14)\n        self.verticalLayout_5.addLayout(self.horizontalLayout_6)\n        self.progressBar_2 = QtWidgets.QProgressBar(bruteforce_attack_dialog)\n        self.progressBar_2.setObjectName(\"progressBar_2\")\n        self.verticalLayout_5.addWidget(self.progressBar_2)\n        self.horizontalLayout_9.addLayout(self.verticalLayout_5)\n        spacerItem15 = QtWidgets.QSpacerItem(58, 20, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Minimum)\n        self.horizontalLayout_9.addItem(spacerItem15)\n        self.verticalLayout_4 = QtWidgets.QVBoxLayout()\n        self.verticalLayout_4.setObjectName(\"verticalLayout_4\")\n        self.horizontalLayout_5 = QtWidgets.QHBoxLayout()\n        self.horizontalLayout_5.setObjectName(\"horizontalLayout_5\")\n        self.current_password = QtWidgets.QLabel(bruteforce_attack_dialog)\n        self.current_password.setAlignment(QtCore.Qt.AlignCenter)\n        self.current_password.setObjectName(\"current_password\")\n        self.horizontalLayout_5.addWidget(self.current_password)\n        spacerItem16 = QtWidgets.QSpacerItem(28, 20, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Minimum)\n        self.horizontalLayout_5.addItem(spacerItem16)\n        self.verticalLayout_4.addLayout(self.horizontalLayout_5)\n        self.progressBar = QtWidgets.QProgressBar(bruteforce_attack_dialog)\n        self.progressBar.setObjectName(\"progressBar\")\n        self.verticalLayout_4.addWidget(self.progressBar)\n        self.horizontalLayout_9.addLayout(self.verticalLayout_4)\n        spacerItem17 = QtWidgets.QSpacerItem(25, 20, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Minimum)\n        self.horizontalLayout_9.addItem(spacerItem17)\n        self.verticalLayout_9.addLayout(self.horizontalLayout_9)\n        spacerItem18 = QtWidgets.QSpacerItem(27, 15, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\n        self.verticalLayout_9.addItem(spacerItem18)\n        self.verticalLayout_8 = QtWidgets.QVBoxLayout()\n        self.verticalLayout_8.setObjectName(\"verticalLayout_8\")\n        self.attack_error_label = QtWidgets.QLabel(bruteforce_attack_dialog)\n        self.attack_error_label.setAlignment(QtCore.Qt.AlignCenter)\n        self.attack_error_label.setObjectName(\"attack_error_label\")\n        self.verticalLayout_8.addWidget(self.attack_error_label)\n        spacerItem19 = QtWidgets.QSpacerItem(23, 5, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\n        self.verticalLayout_8.addItem(spacerItem19)\n        self.attack_status_textBrowser = QtWidgets.QTextBrowser(bruteforce_attack_dialog)\n        sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred)\n        sizePolicy.setHorizontalStretch(0)\n        sizePolicy.setVerticalStretch(0)\n        sizePolicy.setHeightForWidth(self.attack_status_textBrowser.sizePolicy().hasHeightForWidth())\n        self.attack_status_textBrowser.setSizePolicy(sizePolicy)\n        self.attack_status_textBrowser.setObjectName(\"attack_status_textBrowser\")\n        self.verticalLayout_8.addWidget(self.attack_status_textBrowser)\n        self.verticalLayout_9.addLayout(self.verticalLayout_8)\n        self.horizontalLayout_10 = QtWidgets.QHBoxLayout()\n        self.horizontalLayout_10.setObjectName(\"horizontalLayout_10\")\n        spacerItem20 = QtWidgets.QSpacerItem(158, 37, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Minimum)\n        self.horizontalLayout_10.addItem(spacerItem20)\n        self.attack_button = QtWidgets.QPushButton(bruteforce_attack_dialog)\n        sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred)\n        sizePolicy.setHorizontalStretch(10)\n        sizePolicy.setVerticalStretch(10)\n        sizePolicy.setHeightForWidth(self.attack_button.sizePolicy().hasHeightForWidth())\n        self.attack_button.setSizePolicy(sizePolicy)\n        self.attack_button.setObjectName(\"attack_button\")\n        self.horizontalLayout_10.addWidget(self.attack_button)\n        spacerItem21 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum)\n        self.horizontalLayout_10.addItem(spacerItem21)\n        self.stop_attack_button = QtWidgets.QPushButton(bruteforce_attack_dialog)\n        sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred)\n        sizePolicy.setHorizontalStretch(10)\n        sizePolicy.setVerticalStretch(10)\n        sizePolicy.setHeightForWidth(self.stop_attack_button.sizePolicy().hasHeightForWidth())\n        self.stop_attack_button.setSizePolicy(sizePolicy)\n        self.stop_attack_button.setObjectName(\"stop_attack_button\")\n        self.horizontalLayout_10.addWidget(self.stop_attack_button)\n        spacerItem22 = QtWidgets.QSpacerItem(188, 37, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Minimum)\n        self.horizontalLayout_10.addItem(spacerItem22)\n        self.verticalLayout_9.addLayout(self.horizontalLayout_10)\n\n        self.retranslateUi(bruteforce_attack_dialog)\n        QtCore.QMetaObject.connectSlotsByName(bruteforce_attack_dialog)\n\n\n    def retranslateUi(self, bruteforce_attack_dialog):\n        bruteforce_attack_dialog.setWindowTitle(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"Database Bruteforce \", None))\n        self.groupBox.setTitle(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"Database Type\", None))\n        self.mysql_radio.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"MySQL\", None))\n        self.oracle_radio.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"Oracle\", None))\n        self.postgres_radio.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"PostgreSQL\", None))\n        self.mssql_radio.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"MS-SQL\", None))\n        self.groupBox_3.setTitle(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"Database Connection \", None))\n        self.label_2.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"MySQL Server:\", None))\n        self.attck_server_checkbox.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"MySQL Port:\", None))\n        self.label_3.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"( Default MySQL port is 3306 TCP )\", None))\n        self.groupBox_2.setTitle(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"Dictionary Attack Options\", None))\n        self.userlist_button.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"User List\", None))\n        self.userlist_details.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"\", None))\n        self.blank_password_checkbox.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"Attempt blank password\", None))\n        self.wordlist_button.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"Word List\", None))\n        self.wordlist_details.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"\", None))\n        self.stop_attack_button.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"Stop Attack\", None))\n        self.current_userlist.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"<font color=red>Not Started</font>\", None))\n        self.current_password.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"<font color=red>Not Started</font>\", None))\n        self.attack_button.setText(QtCore.QCoreApplication.translate(\"bruteforce_attack_dialog\", \"Launch Attack\", None))\n","repo_name":"Titans068/hexorbase","sub_path":"gui/bruteforce.py","file_name":"bruteforce.py","file_ext":"py","file_size_in_byte":18447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"38777022021","text":"from django.shortcuts import render\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\n\n# @api_view()\n# def hello_word(request):\n#     return Response({'msg':'HELLO Word'})\n\n# Create your views here.\n# @api_view(['GET'])\n# def hello_word(request):\n#     return Response({'msg':'HELLO Word'})\n\n@api_view(['GET','POST'])\ndef hello_word(request):\n    if request.method == \"GET\":\n        print(request.data)\n        return Response({'msg':'this is the Get Request'})\n\n    if request.method == \"POST\":\n        print(request.data)\n        return Response({'msg':'this is the post request','data':request.data})\n\n    ","repo_name":"freakraj/Django_Rest_Api-Projects","sub_path":"gs9/api9/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"6881807625","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def rob(self, root: Optional[TreeNode]) -> int:\n\n        memo = defaultdict(int)\n        def dp(node):\n            if not node:\n                return 0\n            if not node.left and not node.right:\n                return node.val\n            if node in memo:\n                return memo[node]\n            take = node.val\n            notTake = 0\n            if node.left:\n                take += dp(node.left.left) + dp(node.left.right)\n                notTake += dp(node.left)\n            if node.right:\n                take += dp(node.right.left) + dp(node.right.right)\n                notTake += dp(node.right) \n\n            memo[node] = max(take, notTake)\n            return memo[node]\n\n        return dp(root)","repo_name":"betiiy-haile/Competitve_Programming_A2SV","sub_path":"house-robber-iii.py","file_name":"house-robber-iii.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"70689223319","text":"# Minimum Spanning Tree\nfrom collections import defaultdict\nimport sys\nimport math\n\nclass FibonacciTree:\n    def __init__(self,key,weight):\n        self.key = key\n        self.weight = weight\n        self.children = []\n        self.parent = None\n        self.order = 0\n\n    def add_at_end(self,node):\n        node.parent = self\n        self.children.append(node)\n        self.order += 1\n\nclass PriorityQueue(FibonacciTree):\n    def __init__(self):\n        self.heap = []\n        self.least = None\n        self.count = 0\n    \n    def insert(self,key,weight):\n        node = FibonacciTree(key,weight)\n        self.heap.append(node)\n        self.count += 1\n        if self.least is None or node.weight < self.least.weight:\n            self.least = node\n\n    def extractMin(self):\n        node = self.least\n        self.heap.remove(node)\n        for child in node.children:\n            child.parent = None\n            self.heap.append(child)\n        self.count -= 1\n        self.consolidate()\n        return node\n    \n    def floor2log(self,count):\n        return math.frexp(count)[1] - 1\n\n    def consolidate(self):\n        l = self.floor2log(self.count) + 1\n        aux = [None]*l\n        while(self.heap != []):\n            x = self.heap.pop(0)\n            order = x.order\n            while(aux[order] is not None):\n                y = aux[order]\n                if x.weight > y.weight:\n                    x,y = y,x\n                x.add_at_end(y)\n                aux[order] = None\n                order += 1\n            aux[order] = x\n        \n        self.least = None\n        for node in aux:\n            if node is not None:\n                self.heap.append(node)\n                if self.least is None or node.weight < self.least.weight:\n                    self.least = node\n\n\nclass Graph(PriorityQueue):\n    def __init__(self,v):\n        self.count = v\n        self.edges = defaultdict(list)\n    \n    def addEdge(self,u,v,w):\n        self.edges[u].append([v,w])\n        self.edges[v].append([u,w])\n\n    def primsAlgorithm(self):\n        maxsize = sys.maxsize\n        visited = [False]*self.count\n        dist = [maxsize]*self.count\n        parent = [None]*self.count\n        pq = PriorityQueue()\n        dist[0] = 0\n        pq.insert(0,0)\n\n        while(pq.heap != []):\n            node = pq.extractMin()\n            source = node.key\n            visited[source] = True\n            sourceWeight = node.weight\n            for child in self.edges.get(source,[]):\n                target = child[0]\n                targetWeight = child[1]\n                # newWeight = sourceWeight + targetWeight\n                if not visited[target] and targetWeight < dist[target]:\n                    dist[target] = targetWeight\n                    parent[target] = source\n                    pq.insert(target,targetWeight)\n        \n        print(\"MST: Parent of each vertex {}\".format(parent))\n\n\nif __name__ == \"__main__\":\n    graph = Graph(9) \n    graph.addEdge(0, 1, 4) \n    graph.addEdge(0, 7, 8) \n    graph.addEdge(1, 2, 8) \n    graph.addEdge(1, 7, 11) \n    graph.addEdge(2, 3, 7) \n    graph.addEdge(2, 8, 2) \n    graph.addEdge(2, 5, 4) \n    graph.addEdge(3, 4, 9) \n    graph.addEdge(3, 5, 14) \n    graph.addEdge(4, 5, 10) \n    graph.addEdge(5, 6, 2) \n    graph.addEdge(6, 7, 1) \n    graph.addEdge(6, 8, 6) \n    graph.addEdge(7, 8, 7)\n    graph.primsAlgorithm()","repo_name":"Chan-Dru/g4g","sub_path":"algorithm/PrimsAlgorithm.py","file_name":"PrimsAlgorithm.py","file_ext":"py","file_size_in_byte":3356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"37540945602","text":"##############################################################################\n# OBJTrackInfo.py\n# Code Written By: Michael Feist\n#\n# This is a helper class created to store info about tracks used in\n# OBJCrowdTracking.py\n##############################################################################\n\nfrom Point import Point\nimport math\n\n# Calculates the distance between two points\ndef getDistance(a, b, c, d):\n    dx = c-a\n    dy = d-b\n\n    dx2 = dx*dx\n    dy2 = dy*dy\n\n    return math.sqrt(dx2 + dy2)\n\nclass OBJTrackInfo:\n\tdef __init__(self):\n\t\tself.started = False\n\t\tself.ended = False\n\t\tself.startFrame = 0\n\t\tself.endFrame = 0\n\n\t\t# Path\n\t\tself.points = []\n\n\t\t# How long since the object was last seen?\n\t\tself.lastFound = 0\n\n\t\t# Center of bounding box\n\t\tself.x = 0\n\t\tself.y = 0\n\n\t\t# Bounding box\n\t\tself.bx = 0\n\t\tself.by = 0\n\t\tself.bw = 0\n\t\tself.bh = 0\n\n\tdef setBoundingBox(self, x, y, w, h):\n\t\tself.bx = x\n\t\tself.by = y\n\t\tself.bw = w\n\t\tself.bh = h\n\n\tdef start(self, frame):\n\t\tif not self.started:\n\t\t\tself.startFrame = frame\n\t\tself.started = True\n\n\tdef end(self, frame):\n\t\tif not self.ended:\n\t\t\tself.endFrame = frame\n\t\tself.ended = True\n\n\tdef active(self):\n\t\treturn self.started and not self.ended\n\n\tdef getNumberOfFrames(self):\n\t\treturn len(self.points)\n\n\tdef addPoint(self, x, y):\n\t\tself.points.append(Point(x,y))\n\n\tdef applyMatrix(self, A):\n\t\tn = len(self.points)\n\t\tfor i in range(0,n):\n\t\t\tself.points[i].applyMatrix(A)\n\n\tdef split(self, frame):\n\t\tif frame < self.startFrame or frame > self.endFrame:\n\t\t\treturn None\n\n\t\ti = self.endFrame - frame\n\t\tn = len(self.points)\n\t\tnew_points = self.points[i:n]\n\t\tdel self.points[i:n]\n\n\t\ttrack = TrackInfo()\n\t\ttrack.start(frame)\n\t\ttrack.end(self.endFrame)\n\t\ttrack.points = self.points\n\n\t\tself.points = new_points\n\n\t\tself.endFrame = frame\n\n\t\treturn track\n\n\n\tdef getDistanceTraveled(self):\n\t\tnum_of_points = len(self.points)\n\n\t\tif num_of_points <= 0:\n\t\t\treturn 0.0\n\n\t\tp1 = self.points[0]\n\t\ta,b = p1.getCoords()\n\n\t\tdistance = 0.0\n\n\t\tfor i in range(1,num_of_points):\n\t\t\tp2 = self.points[i]\n\t\t\tc,d = p2.getCoords()\n\n\t\t\tdiff = getDistance(a,b,c,d)\n\n\t\t\tif diff > distance:\n\t\t\t\tdistance = diff\n\n\t\treturn distance\n\n\tdef __repr__(self):\n\t\treturn self.__str__()\n\n\tdef __str__(self):\n\t\tnum_of_points = len(self.points)\n\t\treturn \"TrackInfo(\" + \\\n\t\t\tstr(self.startFrame) + \",\" + \\\n\t\t\tstr(self.endFrame) + \",\" + \\\n\t\t\tstr(num_of_points) + \")\"\n","repo_name":"tamara-bain/CrowdMotionCapture","sub_path":"src/OBJTrackInfo.py","file_name":"OBJTrackInfo.py","file_ext":"py","file_size_in_byte":2372,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"31644263754","text":"# -*- coding: utf-8 -*-\n__author__ = 'hwj'\n\nimport matplotlib\nmatplotlib.use('Agg')\nimport argparse\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL']='3'\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" \nos.environ[\"CUDA_VISIBLE_DEVICES\"]='0'\nfrom utils_top5_simple import *\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\nimport tensorflow.compat.v1 as tf\nimport sys\n    \nclass Logger(object):\n    \"\"\"Log stdout messages.\"\"\"\n\n    def __init__(self, outfile):\n        self.terminal = sys.stdout\n        self.log = open(outfile, \"w\")\n        sys.stdout = self\n\n    def write(self, message):\n        self.terminal.write(message)\n        self.log.write(message)\n\n    def flush(self):\n        self.terminal.flush()    \n    \ndef configure_log():\n    log_file_name = os.path.join('train_log','log.log')\n    Logger(log_file_name)\n    \ndef five_crops_tf(image):\n    image = tf.squeeze(image)\n    shape = image.get_shape().as_list()\n    assert shape[:2] == [256,256]\n    crop_height = 224\n    crop_width = 224\n    cropped_shape = tf.stack([crop_height, crop_width, shape[2]])\n    offsets = [tf.to_int32(tf.stack([0, 0, 0])),\n               tf.to_int32(tf.stack([0, 32, 0])),\n               tf.to_int32(tf.stack([32, 0, 0])),\n               tf.to_int32(tf.stack([32, 32, 0])),\n               tf.to_int32(tf.stack([16, 16, 0]))]\n    image_list = []\n    for offset in offsets:\n        image_crop = tf.slice(image, offset, cropped_shape)\n        assert image_crop.get_shape().as_list()[:2] == [224,224]\n        image_list.append(tf.expand_dims(image_crop,0))\n    return tf.concat(image_list,0)\n\ndef ten_crops_tf(image):\n    image = tf.squeeze(image)\n    shape = image.get_shape().as_list()\n    assert shape[:2] == [256,256]\n    return tf.concat([five_crops_tf(image),five_crops_tf(tf.image.flip_left_right(image)),],0)\n\ndef bbox_revise(gt_box, pred_box):\n    bbox_gt = tf.constant([gt_box[0][0], gt_box[0][1], gt_box[0][2], gt_box[0][3]], dtype=tf.float32)\n    bbox_pred = tf.constant(pred_box, dtype=tf.float32)\n    cost_bbox = tf.reduce_mean(tf.square(bbox_gt - bbox_pred)) \n    bbox_grad = tf.gradients(cost_bbox, bbox_pred)\n    regress = tf.nn.l2_normalize(tf.nn.sigmoid(bbox_pred),[0]) - tf.nn.l2_normalize(bbox_grad, [0])\n    regress = 2 * regress * tf.nn.l2_normalize(bbox_grad, [0])\n    bbox_regress = bbox_pred - regress\n    bbox_regress = tf.where(bbox_regress < 0, tf.zeros_like(bbox_regress), bbox_regress)\n    bbox_regress = tf.where(bbox_regress > 223, tf.ones_like(bbox_regress)*223, bbox_regress)\n    return bbox_regress\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--eval', action='store_false', help='run offline evaluation instead of training')\n\n    parser.add_argument('--batch_size', default=1, type=int,\n                        help=\"total batch size. \"\n                        \"Note that it's best to keep per-GPU batch size in [32, 64] to obtain the best accuracy.\"\n                        \"Pretrained models listed in README were trained with batch=32x8.\")\n                        \n    parser.add_argument('--image_path', default='',\n                        help='The path for images on ILSVRC2015 validation set')\n    parser.add_argument('--annotation_path', default='',\n                        help='The path for annotations on ILSVRC2015 validation set')\n    parser.add_argument('--bounding_box_save_path', default='',\n                        help='The save path for the bounding boxes.')\n    parser.add_argument('--classify_save_path', default='',\n                        help='The save path for the classification results.')\n    parser.add_argument('--pseudo_bounding_box_save_path', default='',\n                        help='The save path for the pseudo bounding box.')\n    args = parser.parse_args()\n    batch_size = args.batch_size\n\n    eval_graph = tf.Graph()\n\n    config = tf.ConfigProto()\n    config.gpu_options.per_process_gpu_memory_fraction = 0.99\n    config.gpu_options.allow_growth = True\n    configure_log()\n\n    iou_all = []\n    error_all = []\n    iou_true = []\n    clasify = []\n    clasify_top5 = []\n    error_all_top5 = []\n    try:\n        allnum_image = len(os.listdir(args.annotation_path))\n        path_annotation = args.annotation_path\n        path_img = args.image_path\n    except:\n        print('data is wrang!!!!!')\n        exit(0)\n\n    label_name = [l.split(' ',1)[0] for l in [l.strip() for l in open('synset1.txt').readlines()]]\n\n    th_list = [0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93]\n    error1 = {}\n    error1_original = {}\n    error5 = {}\n    error5_original={}\n    kernel_list = [(0,0)]\n    for i in th_list:\n        error1[str(i)] = np.zeros((1,1)) +0.0\n        error1_original[str(i)] = np.zeros((1,1)) +0.0\n        error5[str(i)] = np.zeros((1,1)) +0.0\n        error5_original[str(i)] = np.zeros((1,1)) +0.0\n       \n    original_bbox = {'ILSVRC2012_val_'+str(i+1).zfill(8)+str('.JPEG'):{th: [] for th in th_list} for i in range(50000)}\n    for th in th_list:\n        with open(args.bounding_box_save_path+str(th)+'.txt') as f:\n            for line in f.readlines():\n                image_id, x0s, x1s, y0s, y1s = line.strip('\\n').split(',')\n                x0, x1, y0, y1 = int(x0s), int(x1s), int(y0s), int(y1s)\n                original_bbox[image_id][th].append([x0, x1, y0, y1])\n                    \n    pseudo_boxes = {'ILSVRC2012_val_'+str(i+1).zfill(8)+str('.JPEG'):[] for i in range(50000)}\n    with open(args.pseudo_bounding_box_save_path) as f:\n        for line in f.readlines():\n            image_id, x0s, x1s, y0s, y1s = line.strip('\\n').split(',')\n            x0, x1, y0, y1 = int(x0s), int(x1s), int(y0s), int(y1s)\n            pseudo_boxes[image_id].append([x0, x1, y0, y1])            \n    \n    top1 = {'ILSVRC2012_val_'+str(i+1).zfill(8)+str('.JPEG'):[] for i in range(50000)}\n    top5 = {'ILSVRC2012_val_'+str(i+1).zfill(8)+str('.JPEG'):[] for i in range(50000)}\n    with open(args.classify_save_path) as f:\n        for line in f.readlines():\n            image_id, t1, t5, pred = line.strip('\\n').split(',')\n            top1[image_id].append(t1)\n            top5[image_id].append(t5)\n    \n    for begin in range(50000):\n        tf.reset_default_graph()\n        with tf.Session() as sess:\n            operations = eval_graph.get_operations()\n            collection = eval_graph.get_all_collection_keys()\n        \n            issave = False\n            \n            batch_ture_bboxs =  [parse_xml(path_annotation+ labelname)[0] \\\n                                    for labelname in ['ILSVRC2012_val_000'+str(num_img).zfill(5)+'.xml' \\\n                                    for num_img in range(begin* batch_size + 1, (begin + 1)* batch_size + 1)]]\n            \n            batch_image_name = [imagename\\\n                                    for imagename in ['ILSVRC2012_val_000'+str(num_img).zfill(5) \\\n                                    for num_img in range(begin* batch_size + 1, (begin + 1)* batch_size + 1)]]        \n                        \n            batch_image1 = np.array([reshape_orimg(load_image2(path_img + imagename+'.JPEG',batch_ture_bboxs[(int(imagename[-5:])-1)%batch_size])[0]) \\\n                                        for imagename in batch_image_name])\n\n            for th in th_list:\n                \n                pred_box = original_bbox[str(batch_image_name[0])+str('.JPEG')][th]\n                bbox_regress = bbox_revise(pseudo_boxes[str(batch_image_name[0])+str('.JPEG')], pred_box)\n                revise_bbox = bbox_regress.eval(session=sess)\n                iou_revise = get_overlaps(batch_ture_bboxs[0], revise_bbox[0])\n                iou_original = get_overlaps(batch_ture_bboxs[0], pred_box)\n                \n                error1[str(th)][0,0] = error1[str(th)][0,0] + int(iou_revise>=0.5) * int(top1[str(batch_image_name[0])+str('.JPEG')][0])\n                error1_original[str(th)][0,0] = error1_original[str(th)][0,0] + int(iou_original>=0.5)*int(top1[str(batch_image_name[0])+str('.JPEG')][0])\n                error5[str(th)][0,0] = error5[str(th)][0,0] + int(iou_revise>=0.5)*int(top5[str(batch_image_name[0])+str('.JPEG')][0])\n                error5_original[str(th)][0,0] = error5_original[str(th)][0,0] + int(iou_original>=0.5)*int(top5[str(batch_image_name[0])+str('.JPEG')][0])\n                \n            print('######################################################################')\n            \n            for th in th_list:\n                for kernel_1,kernel_2 in kernel_list:\n                    \n                    print(time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()) ,' {} th: {}, err1:{},err1_o:{}, err5: {}, err5_o: {}'.format( \\\n                        begin+1,th,\\\n                        round(1 - (error1[str(th)][0,0]/((begin+1)*batch_size)),5),\n                        round(1 - (error1_original[str(th)][0,0]/((begin+1)*batch_size)),5),\n                        round(1 - (error5[str(th)][0,0]/((begin+1)*batch_size)),5),\n                        round(1 - (error5_original[str(th)][0,0]/((begin+1)*batch_size)),5),\n                        ))\n","repo_name":"WenjunHui1/GRCAM-Master","sub_path":"GRCAM-Master/revise_bbox.py","file_name":"revise_bbox.py","file_ext":"py","file_size_in_byte":9027,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"11597374934","text":"import numpy as np\r\nfrom math import inf, fabs\r\nfrom utils import *\r\n\r\n\r\ndef random_policy(grid_world):\r\n    \"\"\"\r\n    Creates a random policy for a grid world.\r\n\r\n    :param grid_world: the grid world.\r\n    :type grid_world: GridWorld.\r\n    :return: random policy.\r\n    :rtype: tridimensional NumPy array.\r\n    \"\"\"\r\n    dimensions = grid_world.dimensions\r\n    policy = (1.0 / NUM_ACTIONS) * \\\r\n        np.ones((dimensions[0], dimensions[1], NUM_ACTIONS))\r\n    return policy\r\n\r\n\r\ndef greedy_policy(grid_world, value, epsilon=1.0e-3):\r\n    \"\"\"\r\n    Computes a greedy policy considering a value function for a grid world. If there are more than\r\n    one optimal action for a given state, then the optimal action is chosen at random.\r\n\r\n\r\n    :param grid_world: the grid world.\r\n    :type grid_world: GridWorld.\r\n    :param value: the value function.\r\n    :type value: bidimensional NumPy array.\r\n    :param epsilon: tolerance used to consider that more than one action is optimal.\r\n    :type epsilon: float.\r\n    :return: greedy policy.\r\n    :rtype: tridimensional NumPy array.\r\n    \"\"\"\r\n    dimensions = grid_world.dimensions\r\n    policy = np.zeros((dimensions[0], dimensions[1], NUM_ACTIONS))\r\n    for i in range(dimensions[0]):\r\n        for j in range(dimensions[1]):\r\n            current_state = (i, j)\r\n            if not grid_world.is_cell_valid(current_state):\r\n                # Assuming random action if the cell is an obstacle\r\n                policy[i, j] = (1.0 / NUM_ACTIONS) * np.ones(NUM_ACTIONS)\r\n                continue\r\n            max_value = -inf\r\n            # Creating a temporary q(s, a)\r\n            action_value = np.zeros(NUM_ACTIONS)\r\n            for action in range(NUM_ACTIONS):\r\n                r = grid_world.reward(current_state, action)\r\n                action_value[action] = r\r\n                for next_state in grid_world.get_valid_sucessors((i, j), action):\r\n                    transition_prob = grid_world.transition_probability(\r\n                        current_state, action, next_state)\r\n                    action_value[action] += grid_world.gamma * \\\r\n                        transition_prob * value[next_state[0], next_state[1]]\r\n                if action_value[action] > max_value:\r\n                    max_value = action_value[action]\r\n            # This post-processing is necessary since we may have more than one optimal action\r\n            num_actions = 0\r\n            for action in range(NUM_ACTIONS):\r\n                if fabs(max_value - action_value[action]) < epsilon:\r\n                    policy[i, j, action] = 1.0\r\n                    num_actions += 1\r\n            for action in range(NUM_ACTIONS):\r\n                policy[i, j, action] /= num_actions\r\n    return policy\r\n\r\n\r\ndef policy_evaluation(grid_world, initial_value, policy, num_iterations=10000, epsilon=1.0e-5):\r\n    \"\"\"\r\n    Executes policy evaluation for a policy executed on a grid world.\r\n\r\n    :param grid_world: the grid world.\r\n    :type grid_world: GridWorld.\r\n    :param initial_value: initial value function used to bootstrap the algorithm.\r\n    :type initial_value: bidimensional NumPy array.\r\n    :param policy: policy to be evaluated.\r\n    :type policy: tridimensional NumPy array.\r\n    :param num_iterations: maximum number of iterations used in policy evaluation.\r\n    :type num_iterations: int.\r\n    :param epsilon: tolerance used in stopping criterion.\r\n    :type epsilon: float.\r\n    :return: value function of the given policy.\r\n    :rtype: bidimensional NumPy array.\r\n    \"\"\"\r\n    dimensions = grid_world.dimensions\r\n    value = np.copy(initial_value)  # value matrix from the (k+1)th iteration\r\n    old_value = None  # value matrix from the kth iteration\r\n    k = 0  # iteration\r\n    while True:\r\n        # Breaking condition\r\n        if k > num_iterations or (np.all(old_value != None) and np.all(np.abs(value - old_value) < epsilon)):\r\n            break\r\n        old_value = np.copy(value)  # get last value\r\n        new_value = np.zeros(np.shape(value))\r\n        for i in range(dimensions[0]):\r\n            for j in range(dimensions[1]):\r\n                for action in range(NUM_ACTIONS):\r\n                    current_state = i, j\r\n                    r = grid_world.reward(current_state, action)\r\n                    # calculates the sum of r(s, a) * pi(a|s) for each a e A\r\n                    new_value[i, j] = new_value[i, j] + \\\r\n                        r * policy[i, j, action]\r\n                    for state in grid_world.get_valid_sucessors(current_state, action):\r\n                        prob = policy[i, j, action] * \\\r\n                            grid_world.transition_probability(\r\n                                current_state, action, state)\r\n                        # calculates the sum of gamma * pi(a|s) * p(sl|s, a) * vk(sl) for each sl e S for each a e A\r\n                        new_value[i, j] = new_value[i, j] + \\\r\n                            grid_world.gamma * prob * old_value[state]\r\n        value = new_value\r\n        k += 1\r\n    return value\r\n\r\n\r\ndef value_iteration(grid_world, initial_value, num_iterations=10000, epsilon=1.0e-5):\r\n    \"\"\"\r\n    Executes value iteration for a grid world.\r\n\r\n    :param grid_world: the grid world.\r\n    :type grid_world: GridWorld.\r\n    :param initial_value: initial value function used to bootstrap the algorithm.\r\n    :type initial_value: bidimensional NumPy array.\r\n    :param num_iterations: maximum number of iterations used in policy evaluation.\r\n    :type num_iterations: int.\r\n    :param epsilon: tolerance used in stopping criterion.\r\n    :type epsilon: float.\r\n    :return value: optimal value function.\r\n    :rtype value: bidimensional NumPy array.\r\n    \"\"\"\r\n    dimensions = grid_world.dimensions\r\n    value = np.copy(initial_value)\r\n    old_value = None  # last value\r\n    k = 0\r\n    while True:\r\n        if k > num_iterations or (np.all(old_value != None) and np.all(abs(value - old_value) < epsilon)):\r\n            break\r\n        old_value = np.copy(value)\r\n        new_value = np.zeros(np.shape(value))\r\n        for i in range(dimensions[0]):\r\n            for j in range(dimensions[1]):\r\n                current_state = i, j\r\n                # max_value is the max(r(s, a) + sum(gamma * p(sl | s, a) * vk(sl) for each sl e S)) with a e A\r\n                max_value = -inf\r\n                for action in range(NUM_ACTIONS):\r\n                    auxiliar_value = grid_world.reward(current_state, action)\r\n                    for state in grid_world.get_valid_sucessors(current_state, action):\r\n                        transition_prob = grid_world.transition_probability(\r\n                            current_state, action, state)\r\n                        # here we update auxiliar value to compare\r\n                        auxiliar_value = auxiliar_value + grid_world.gamma * \\\r\n                            transition_prob * old_value[state]\r\n                    if max_value < auxiliar_value:\r\n                        max_value = auxiliar_value\r\n                # update\r\n                new_value[current_state] = max_value\r\n        # get vk+1\r\n        value = new_value\r\n        k += 1\r\n    return value\r\n\r\n\r\ndef policy_iteration(grid_world, initial_value, initial_policy, evaluations_per_policy=3, num_iterations=10000,\r\n                     epsilon=1.0e-5):\r\n    \"\"\"\r\n    Executes policy iteration for a grid world.\r\n\r\n    :param grid_world: the grid world.\r\n    :type grid_world: GridWorld.\r\n    :param initial_value: initial value function used to bootstrap the algorithm.\r\n    :type initial_value: bidimensional NumPy array.\r\n    :param initial_policy: initial policy used to bootstrap the algorithm.\r\n    :type initial_policy: tridimensional NumPy array.\r\n    :param evaluations_per_policy: number of policy evaluations per policy iteration.\r\n    :type evaluations_per_policy: int.\r\n    :param num_iterations: maximum number of iterations used in policy evaluation.\r\n    :type num_iterations: int.\r\n    :param epsilon: tolerance used in stopping criterion.\r\n    :type epsilon: float.\r\n    :return value: value function of the optimal policy.\r\n    :rtype value: bidimensional NumPy array.\r\n    :return policy: optimal policy.\r\n    :rtype policy: tridimensional NumPy array.\r\n    \"\"\"\r\n    value = np.copy(initial_value)\r\n    policy = np.copy(initial_policy)\r\n    old_value = None\r\n    k = 0\r\n    while True:\r\n        if k > num_iterations or (np.all(old_value != None) and np.all(abs(value - old_value) < epsilon)):\r\n            break\r\n        old_value = np.copy(value)\r\n        # policy evaluation\r\n        value = policy_evaluation(\r\n            grid_world, old_value, policy, evaluations_per_policy, epsilon)\r\n        # policy improvement\r\n        policy = greedy_policy(grid_world, value, epsilon)\r\n        k += 1\r\n    return value, policy\r\n","repo_name":"joaolrsarmento/university","sub_path":"courses/CT-213/lab10_ct213_2020/dynamic_programming.py","file_name":"dynamic_programming.py","file_ext":"py","file_size_in_byte":8770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"23454741677","text":"# Test privkey generation with valid and invalid parameters\n# Test privkey import\n# Test import to right class\n# Test pubkey generation from priv\nimport os\nimport shutil\nimport unittest\n\nfrom cryptography.hazmat.primitives.asymmetric import ec as _EC\nfrom cryptography.hazmat.primitives.asymmetric import ed25519 as _ED25519\nfrom cryptography.hazmat.primitives.asymmetric import rsa as _RSA\n\nimport src.sshkey_tools.exceptions as _EX\nfrom src.sshkey_tools.keys import (\n    EcdsaCurves,\n    EcdsaPrivateKey,\n    EcdsaPublicKey,\n    Ed25519PrivateKey,\n    Ed25519PublicKey,\n    PrivateKey,\n    PublicKey,\n    RsaPrivateKey,\n    RsaPublicKey,\n)\n\n\nclass KeypairMethods(unittest.TestCase):\n    def generateClasses(self):\n        self.rsa_key = RsaPrivateKey.generate(2048)\n        self.ecdsa_key = EcdsaPrivateKey.generate(EcdsaCurves.P256)\n        self.ed25519_key = Ed25519PrivateKey.generate()\n\n    def generateFiles(self, folder):\n        self.folder = folder\n        try:\n            os.mkdir(f\"tests/{folder}\")\n        except FileExistsError:\n            shutil.rmtree(f\"tests/{folder}\")\n            os.mkdir(f\"tests/{folder}\")\n\n        os.system(\n            f'ssh-keygen -t rsa -b 2048 -f tests/{folder}/rsa_key_sshkeygen -N \"password\" > /dev/null 2>&1'\n        )\n        os.system(\n            f'ssh-keygen -t ecdsa -b 256 -f tests/{folder}/ecdsa_key_sshkeygen -N \"\" > /dev/null 2>&1'\n        )\n        os.system(\n            f'ssh-keygen -t ed25519 -f tests/{folder}/ed25519_key_sshkeygen -N \"\" > /dev/null 2>&1'\n        )\n\n    def setUp(self):\n        self.generateClasses()\n        self.generateFiles(\"KeypairMethods\")\n\n    def tearDown(self):\n        shutil.rmtree(f\"tests/{self.folder}\")\n\n    def assertEqualPrivateKeys(\n        self, priv_class, pub_class, a, b, privkey_attr=[\"private_numbers\"]\n    ):\n        self.assertIsInstance(a, priv_class)\n        self.assertIsInstance(b, priv_class)\n\n        for att in privkey_attr:\n            try:\n                self.assertEqual(getattr(a, att), getattr(b, att))\n            except AssertionError:\n                print(\"Hold\")\n\n        self.assertEqualPublicKeys(pub_class, a.public_key, b.public_key)\n\n    def assertEqualPublicKeys(self, keyclass, a, b):\n        self.assertIsInstance(a, keyclass)\n        self.assertIsInstance(b, keyclass)\n\n        self.assertEqual(a.raw_bytes(), b.raw_bytes())\n\n    def assertEqualKeyFingerprint(self, file_a, file_b):\n        self.assertEqual(\n            0,\n            os.system(\n                f\"\"\"bash -c \"\n               diff \\\n               <( ssh-keygen -lf {file_a}) \\\n               <( ssh-keygen -lf {file_b}) \\\n            \"\n            \"\"\"\n            ),\n        )\n\n\nclass TestKeypairMethods(KeypairMethods):\n    def test_fail_assertions(self):\n        with self.assertRaises(AssertionError):\n            self.assertEqualPrivateKeys(\n                RsaPrivateKey,\n                RsaPublicKey,\n                RsaPrivateKey.from_file(\n                    f\"tests/{self.folder}/rsa_key_sshkeygen\", \"password\"\n                ),\n                EcdsaPrivateKey.from_file(f\"tests/{self.folder}/ecdsa_key_sshkeygen\"),\n            )\n\n        with self.assertRaises(AssertionError):\n            self.assertEqualPublicKeys(\n                RsaPublicKey,\n                RsaPublicKey.from_file(f\"tests/{self.folder}/rsa_key_sshkeygen.pub\"),\n                EcdsaPublicKey.from_file(f\"tests/{self.folder}/ecdsa_key_sshkeygen.pub\"),\n            )\n\n        with self.assertRaises(AssertionError):\n            self.assertEqualKeyFingerprint(\n                f\"tests/{self.folder}/rsa_key_sshkeygen\",\n                f\"tests/{self.folder}/ecdsa_key_sshkeygen\",\n            )\n\n    def test_successful_assertions(self):\n        self.assertTrue(os.path.isfile(f\"tests/{self.folder}/rsa_key_sshkeygen\"))\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/rsa_key_sshkeygen\",\n            f\"tests/{self.folder}/rsa_key_sshkeygen.pub\",\n        )\n\n        self.assertTrue(os.path.isfile(f\"tests/{self.folder}/ecdsa_key_sshkeygen\"))\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/ecdsa_key_sshkeygen\",\n            f\"tests/{self.folder}/ecdsa_key_sshkeygen.pub\",\n        )\n\n        self.assertTrue(os.path.isfile(f\"tests/{self.folder}/ed25519_key_sshkeygen\"))\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/ed25519_key_sshkeygen\",\n            f\"tests/{self.folder}/ed25519_key_sshkeygen.pub\",\n        )\n\n\nclass TestKeyGeneration(KeypairMethods):\n    def setUp(self):\n        pass\n\n    def tearDown(self):\n        pass\n\n    def test_rsa(self):\n        key_bits = [512, 1024, 2048, 4096, 8192]\n\n        for bits in key_bits:\n            key = RsaPrivateKey.generate(bits)\n\n            assert isinstance(key, RsaPrivateKey)\n            assert isinstance(key, PrivateKey)\n            assert isinstance(key.key, _RSA.RSAPrivateKey)\n            assert isinstance(key.private_numbers, _RSA.RSAPrivateNumbers)\n\n            assert isinstance(key.public_key, RsaPublicKey)\n            assert isinstance(key.public_key, PublicKey)\n            assert isinstance(key.public_key.key, _RSA.RSAPublicKey)\n            assert isinstance(key.public_key.public_numbers, _RSA.RSAPublicNumbers)\n\n    def test_rsa_incorrect_keysize(self):\n        with self.assertRaises(ValueError):\n            RsaPrivateKey.generate(256)\n\n    def test_ecdsa(self):\n        curves = [EcdsaCurves.P256, EcdsaCurves.P384, EcdsaCurves.P521]\n\n        for curve in curves:\n            key = EcdsaPrivateKey.generate(curve)\n\n            assert isinstance(key, EcdsaPrivateKey)\n            assert isinstance(key, PrivateKey)\n            assert isinstance(key.key, _EC.EllipticCurvePrivateKey)\n            assert isinstance(key.private_numbers, _EC.EllipticCurvePrivateNumbers)\n\n            assert isinstance(key.public_key, EcdsaPublicKey)\n            assert isinstance(key.public_key, PublicKey)\n            assert isinstance(key.public_key.key, _EC.EllipticCurvePublicKey)\n            assert isinstance(\n                key.public_key.public_numbers, _EC.EllipticCurvePublicNumbers\n            )\n\n    def test_ecdsa_not_a_curve(self):\n        with self.assertRaises(AttributeError):\n            EcdsaPrivateKey.generate(\"p256\")\n\n    def test_ed25519(self):\n        key = Ed25519PrivateKey.generate()\n\n        assert isinstance(key, Ed25519PrivateKey)\n        assert isinstance(key, PrivateKey)\n        assert isinstance(key.key, _ED25519.Ed25519PrivateKey)\n\n        assert isinstance(key.public_key, Ed25519PublicKey)\n        assert isinstance(key.public_key, PublicKey)\n        assert isinstance(key.public_key.key, _ED25519.Ed25519PublicKey)\n\n\nclass TestToFromFiles(KeypairMethods):\n    def setUp(self):\n        self.generateClasses()\n        self.generateFiles(\"TestToFromFiles\")\n\n    def test_encoding(self):\n        with open(\n            f\"tests/{self.folder}/rsa_key_sshkeygen\", \"r\", encoding=\"utf-8\"\n        ) as file:\n            from_string = PrivateKey.from_string(file.read(), \"password\", \"utf-8\")\n\n        with open(\n            f\"tests/{self.folder}/rsa_key_sshkeygen.pub\", \"r\", encoding=\"utf-8\"\n        ) as file:\n            from_string_pub = PublicKey.from_string(file.read(), \"utf-8\")\n\n        from_file = PrivateKey.from_file(\n            f\"tests/{self.folder}/rsa_key_sshkeygen\", \"password\"\n        )\n        from_file_pub = PublicKey.from_file(\n            f\"tests/{self.folder}/rsa_key_sshkeygen.pub\"\n        )\n\n        self.assertEqualPrivateKeys(RsaPrivateKey, RsaPublicKey, from_string, from_file)\n\n        self.assertEqualPublicKeys(RsaPublicKey, from_string_pub, from_file_pub)\n\n    def test_rsa_files(self):\n        parent = PrivateKey.from_file(\n            f\"tests/{self.folder}/rsa_key_sshkeygen\", \"password\"\n        )\n        child = RsaPrivateKey.from_file(\n            f\"tests/{self.folder}/rsa_key_sshkeygen\", \"password\"\n        )\n\n        parent_pub = PublicKey.from_file(f\"tests/{self.folder}/rsa_key_sshkeygen.pub\")\n        child_pub = RsaPublicKey.from_file(f\"tests/{self.folder}/rsa_key_sshkeygen.pub\")\n\n        parent.to_file(f\"tests/{self.folder}/rsa_key_saved_parent\", \"password\")\n        child.to_file(f\"tests/{self.folder}/rsa_key_saved_child\")\n\n        parent_pub.to_file(f\"tests/{self.folder}/rsa_key_saved_parent.pub\")\n        child_pub.to_file(f\"tests/{self.folder}/rsa_key_saved_child.pub\")\n\n        self.assertEqualPrivateKeys(RsaPrivateKey, RsaPublicKey, parent, child)\n\n        self.assertEqualPublicKeys(RsaPublicKey, parent_pub, child_pub)\n\n        self.assertEqualPublicKeys(RsaPublicKey, parent.public_key, child_pub)\n\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/rsa_key_sshkeygen\",\n            f\"tests/{self.folder}/rsa_key_saved_parent\",\n        )\n\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/rsa_key_sshkeygen.pub\",\n            f\"tests/{self.folder}/rsa_key_saved_parent.pub\",\n        )\n\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/rsa_key_saved_parent\",\n            f\"tests/{self.folder}/rsa_key_saved_child\",\n        )\n\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/rsa_key_saved_parent.pub\",\n            f\"tests/{self.folder}/rsa_key_sshkeygen.pub\",\n        )\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/rsa_key_saved_parent.pub\",\n            f\"tests/{self.folder}/rsa_key_sshkeygen.pub\",\n        )\n\n    def test_ecdsa_files(self):\n        parent = PrivateKey.from_file(f\"tests/{self.folder}/ecdsa_key_sshkeygen\")\n        child = EcdsaPrivateKey.from_file(f\"tests/{self.folder}/ecdsa_key_sshkeygen\")\n\n        parent_pub = PublicKey.from_file(f\"tests/{self.folder}/ecdsa_key_sshkeygen.pub\")\n        child_pub = EcdsaPublicKey.from_file(\n            f\"tests/{self.folder}/ecdsa_key_sshkeygen.pub\"\n        )\n\n        parent.to_file(f\"tests/{self.folder}/ecdsa_key_saved_parent\")\n        child.to_file(f\"tests/{self.folder}/ecdsa_key_saved_child\")\n\n        parent_pub.to_file(f\"tests/{self.folder}/ecdsa_key_saved_parent.pub\")\n        child_pub.to_file(f\"tests/{self.folder}/ecdsa_key_saved_child.pub\")\n\n        self.assertEqualPrivateKeys(EcdsaPrivateKey, EcdsaPublicKey, parent, child)\n\n        self.assertEqualPublicKeys(EcdsaPublicKey, parent_pub, child_pub)\n\n        self.assertEqualPublicKeys(EcdsaPublicKey, parent.public_key, child_pub)\n\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/ecdsa_key_sshkeygen\",\n            f\"tests/{self.folder}/ecdsa_key_saved_parent\",\n        )\n\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/ecdsa_key_sshkeygen.pub\",\n            f\"tests/{self.folder}/ecdsa_key_saved_parent.pub\",\n        )\n\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/ecdsa_key_saved_parent\",\n            f\"tests/{self.folder}/ecdsa_key_saved_child\",\n        )\n\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/ecdsa_key_saved_parent.pub\",\n            f\"tests/{self.folder}/ecdsa_key_sshkeygen.pub\",\n        )\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/ecdsa_key_saved_parent.pub\",\n            f\"tests/{self.folder}/ecdsa_key_sshkeygen.pub\",\n        )\n\n    def test_ed25519_files(self):\n        parent = PrivateKey.from_file(f\"tests/{self.folder}/ed25519_key_sshkeygen\")\n        child = Ed25519PrivateKey.from_file(\n            f\"tests/{self.folder}/ed25519_key_sshkeygen\"\n        )\n\n        parent_pub = PublicKey.from_file(\n            f\"tests/{self.folder}/ed25519_key_sshkeygen.pub\"\n        )\n        child_pub = Ed25519PublicKey.from_file(\n            f\"tests/{self.folder}/ed25519_key_sshkeygen.pub\"\n        )\n\n        parent.to_file(f\"tests/{self.folder}/ed25519_key_saved_parent\")\n        child.to_file(f\"tests/{self.folder}/ed25519_key_saved_child\")\n\n        parent_pub.to_file(f\"tests/{self.folder}/ed25519_key_saved_parent.pub\")\n        child_pub.to_file(f\"tests/{self.folder}/ed25519_key_saved_child.pub\")\n\n        self.assertEqualPrivateKeys(Ed25519PrivateKey, Ed25519PublicKey, parent, child)\n\n        self.assertEqualPublicKeys(Ed25519PublicKey, parent_pub, child_pub)\n\n        self.assertEqualPublicKeys(Ed25519PublicKey, parent.public_key, child_pub)\n\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/ed25519_key_sshkeygen\",\n            f\"tests/{self.folder}/ed25519_key_saved_parent\",\n        )\n\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/ed25519_key_sshkeygen.pub\",\n            f\"tests/{self.folder}/ed25519_key_saved_parent.pub\",\n        )\n\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/ed25519_key_saved_parent\",\n            f\"tests/{self.folder}/ed25519_key_saved_child\",\n        )\n\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/ed25519_key_saved_parent.pub\",\n            f\"tests/{self.folder}/ed25519_key_sshkeygen.pub\",\n        )\n        self.assertEqualKeyFingerprint(\n            f\"tests/{self.folder}/ed25519_key_saved_parent.pub\",\n            f\"tests/{self.folder}/ed25519_key_sshkeygen.pub\",\n        )\n\n\nclass TestFromClass(KeypairMethods):\n    def setUp(self):\n        self.rsa_key = _RSA.generate_private_key(public_exponent=65537, key_size=2048)\n        self.ecdsa_key = _EC.generate_private_key(curve=_EC.SECP384R1())\n        self.ed25519_key = _ED25519.Ed25519PrivateKey.generate()\n\n    def tearDown(self):\n        pass\n\n    def test_invalid_key_exception(self):\n        with self.assertRaises(_EX.InvalidKeyException):\n            PublicKey.from_class(\n                key_class=self.rsa_key, key_type=\"invalid-key-type\", comment=\"Comment\"\n            )\n\n    def test_rsa_from_class(self):\n        parent = PrivateKey.from_class(self.rsa_key)\n        child = RsaPrivateKey.from_class(self.rsa_key)\n\n        self.assertEqualPrivateKeys(RsaPrivateKey, RsaPublicKey, parent, child)\n\n    def test_ecdsa_from_class(self):\n        parent = PrivateKey.from_class(self.ecdsa_key)\n        child = EcdsaPrivateKey.from_class(self.ecdsa_key)\n\n        self.assertEqualPrivateKeys(EcdsaPrivateKey, EcdsaPublicKey, parent, child)\n\n    def test_ed25519_from_class(self):\n        parent = PrivateKey.from_class(self.ed25519_key)\n        child = Ed25519PrivateKey.from_class(self.ed25519_key)\n\n        self.assertEqualPrivateKeys(Ed25519PrivateKey, Ed25519PublicKey, parent, child)\n\n\nclass TestFromComponents(KeypairMethods):\n    def setUp(self):\n        self.generateClasses()\n\n    def tearDown(self):\n        pass\n\n    def test_rsa_from_numbers(self):\n        from_numbers = RsaPrivateKey.from_numbers(\n            n=self.rsa_key.public_key.public_numbers.n,\n            e=self.rsa_key.public_key.public_numbers.e,\n            d=self.rsa_key.private_numbers.d,\n        )\n\n        from_numbers_pub = RsaPublicKey.from_numbers(\n            n=self.rsa_key.public_key.public_numbers.n,\n            e=self.rsa_key.public_key.public_numbers.e,\n        )\n\n        self.assertEqualPublicKeys(\n            RsaPublicKey, from_numbers_pub, from_numbers.public_key\n        )\n\n        self.assertIsInstance(from_numbers, RsaPrivateKey)\n\n        self.assertEqual(\n            self.rsa_key.public_key.public_numbers.n,\n            from_numbers.public_key.public_numbers.n,\n        )\n\n        self.assertEqual(\n            self.rsa_key.public_key.public_numbers.e,\n            from_numbers.public_key.public_numbers.e,\n        )\n\n        self.assertEqual(self.rsa_key.private_numbers.d, from_numbers.private_numbers.d)\n\n  \n    def test_ecdsa_from_numbers(self):\n        from_numbers = EcdsaPrivateKey.from_numbers(\n            curve=self.ecdsa_key.public_key.key.curve,\n            x=self.ecdsa_key.public_key.public_numbers.x,\n            y=self.ecdsa_key.public_key.public_numbers.y,\n            private_value=self.ecdsa_key.private_numbers.private_value,\n        )\n\n        from_numbers_pub = EcdsaPublicKey.from_numbers(\n            curve=self.ecdsa_key.public_key.key.curve,\n            x=self.ecdsa_key.public_key.public_numbers.x,\n            y=self.ecdsa_key.public_key.public_numbers.y,\n        )\n\n        self.assertEqualPrivateKeys(\n            EcdsaPrivateKey, EcdsaPublicKey, self.ecdsa_key, from_numbers\n        )\n\n        self.assertEqualPublicKeys(\n            EcdsaPublicKey, from_numbers_pub, self.ecdsa_key.public_key\n        )\n\n        from_numbers = EcdsaPrivateKey.from_numbers(\n            curve=self.ecdsa_key.public_key.key.curve.name,\n            x=self.ecdsa_key.public_key.public_numbers.x,\n            y=self.ecdsa_key.public_key.public_numbers.y,\n            private_value=self.ecdsa_key.private_numbers.private_value,\n        )\n\n        self.assertEqualPrivateKeys(\n            EcdsaPrivateKey, EcdsaPublicKey, self.ecdsa_key, from_numbers\n        )\n\n    def test_ed25519_from_raw_bytes(self):\n        from_raw = Ed25519PrivateKey.from_raw_bytes(self.ed25519_key.raw_bytes())\n        from_raw_pub = Ed25519PublicKey.from_raw_bytes(\n            self.ed25519_key.public_key.raw_bytes()\n        )\n\n        self.assertEqualPrivateKeys(\n            Ed25519PrivateKey, Ed25519PublicKey, self.ed25519_key, from_raw, []\n        )\n\n        self.assertEqualPublicKeys(\n            Ed25519PublicKey, self.ed25519_key.public_key, from_raw_pub\n        )\n\n\nclass TestFingerprint(KeypairMethods):\n    def setUp(self):\n        self.generateFiles(\"TestFingerprint\")\n\n    def test_rsa_fingerprint(self):\n        key = RsaPrivateKey.from_file(\n            f\"tests/{self.folder}/rsa_key_sshkeygen\", \"password\"\n        )\n\n        with os.popen(f\"ssh-keygen -lf tests/{self.folder}/rsa_key_sshkeygen\") as cmd:\n            sshkey_fingerprint = cmd.read().split(\" \")[1]\n\n        self.assertEqual(key.get_fingerprint(), sshkey_fingerprint)\n\n    def test_ecdsa_fingerprint(self):\n        key = EcdsaPrivateKey.from_file(\n            f\"tests/{self.folder}/ecdsa_key_sshkeygen\",\n        )\n        with os.popen(f\"ssh-keygen -lf tests/{self.folder}/ecdsa_key_sshkeygen\") as cmd:\n            sshkey_fingerprint = cmd.read().split(\" \")[1]\n\n        self.assertEqual(key.get_fingerprint(), sshkey_fingerprint)\n\n    def test_ed25519_fingerprint(self):\n        key = Ed25519PrivateKey.from_file(\n            f\"tests/{self.folder}/ed25519_key_sshkeygen\",\n        )\n        with os.popen(\n            f\"ssh-keygen -lf tests/{self.folder}/ed25519_key_sshkeygen\"\n        ) as cmd:\n            sshkey_fingerprint = cmd.read().split(\" \")[1]\n\n        self.assertEqual(key.get_fingerprint(), sshkey_fingerprint)\n\n\nclass TestSignatures(KeypairMethods):\n    def setUp(self):\n        self.generateClasses()\n\n    def tearDown(self):\n        pass\n\n    def test_rsa_signature(self):\n        data = b\"\\x00\" + os.urandom(32) + b\"\\x00\"\n        signature = self.rsa_key.sign(data)\n\n        self.assertIsNone(self.rsa_key.public_key.verify(data, signature))\n\n        with self.assertRaises(_EX.InvalidSignatureException):\n            self.rsa_key.public_key.verify(data, signature + b\"\\x00\")\n\n    def test_ecdsa_signature(self):\n        data = b\"\\x00\" + os.urandom(32) + b\"\\x00\"\n        signature = self.ecdsa_key.sign(data)\n\n        self.assertIsNone(self.ecdsa_key.public_key.verify(data, signature))\n\n        with self.assertRaises(_EX.InvalidSignatureException):\n            self.ecdsa_key.public_key.verify(data, signature + b\"\\x00\")\n\n    def test_ed25519_signature(self):\n        data = b\"\\x00\" + os.urandom(32) + b\"\\x00\"\n        signature = self.ed25519_key.sign(data)\n\n        self.assertIsNone(self.ed25519_key.public_key.verify(data, signature))\n\n        with self.assertRaises(_EX.InvalidSignatureException):\n            self.ed25519_key.public_key.verify(data, signature + b\"\\x00\")\n\n\nclass TestExceptions(KeypairMethods):\n    def setUp(self):\n        self.generateClasses()\n\n    def tearDown(self):\n        pass\n\n    def test_invalid_private_key(self):\n        with self.assertRaises(_EX.InvalidKeyException):\n            _ = PrivateKey.from_class(KeypairMethods)\n\n    def test_invalid_ecdsa_curve(self):\n        with self.assertRaises(_EX.InvalidCurveException):\n            _ = EcdsaPublicKey.from_numbers(\n                \"abc123\",\n                x=self.ecdsa_key.public_key.public_numbers.x,\n                y=self.ecdsa_key.public_key.public_numbers.y,\n            )\n\n        with self.assertRaises(_EX.InvalidCurveException):\n            _ = EcdsaPrivateKey.from_numbers(\n                \"abc123\",\n                x=self.ecdsa_key.public_key.public_numbers.x,\n                y=self.ecdsa_key.public_key.public_numbers.y,\n                private_value=self.ecdsa_key.private_numbers.private_value,\n            )\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"scheiblingco/sshkey-tools","sub_path":"tests/test_keypairs.py","file_name":"test_keypairs.py","file_ext":"py","file_size_in_byte":20651,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"85"}
+{"seq_id":"27460560553","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Author:  Dominik Gresch <greschd@gmx.ch>\n# Date:    07.07.2016 01:05:06 CEST\n# File:    test_sparse_dense.py\n\nfrom os.path import join\n\nimport pytest\nimport tbmodels\nimport numpy as np\n\nfrom parameters import T_VALUES, KPT, SAMPLES_DIR\n\n@pytest.mark.parametrize('t1', T_VALUES)\ndef test_simple(t1, get_model):\n    model1 = get_model(*t1, sparse=True)\n    model2 = get_model(*t1, sparse=False)\n\n    for k in KPT:\n        assert np.isclose(model1.hamilton(k), model2.hamilton(k)).all()\n\n@pytest.mark.parametrize('t1', T_VALUES)\ndef test_change_to_dense(t1, get_model, models_close):\n    model1 = get_model(*t1, sparse=True)\n    model2 = get_model(*t1, sparse=False)\n    model1.set_sparse(False)\n    assert models_close(model1, model2)\n    \n@pytest.mark.parametrize('t1', T_VALUES)\ndef test_change_to_sparse(t1, get_model, models_close):\n    model1 = get_model(*t1, sparse=True)\n    model2 = get_model(*t1, sparse=False)\n    model2.set_sparse(True)\n    assert models_close(model1, model2)\n\n@pytest.mark.parametrize('hr_name', ['hr_hamilton.dat', 'wannier90_hr.dat', 'wannier90_hr_v2.dat'])\ndef test_hr(hr_name):\n    hr_file = join(SAMPLES_DIR, hr_name)\n    model1 = tbmodels.Model.from_hr_file(hr_file, occ=28, sparse=False)\n    model2 = tbmodels.Model.from_hr_file(hr_file, occ=28, sparse=True)\n    \n    for k in KPT:\n        assert np.isclose(model1.hamilton(k), model2.hamilton(k)).all()\n","repo_name":"yanguang21/TBmodels","sub_path":"tests/test_sparse_dense.py","file_name":"test_sparse_dense.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"85"}
+{"seq_id":"40273732740","text":"import json\nimport socket\n\ndef trigger_doorbell():\n    UDP_IP = socket.gethostbyname('raspberrypi.local')\n    UDP_PORT = 5005\n    MESSAGE = json.dumps({'event':'doorbell'}).encode('utf-8')\n\n    print(\"UDP target IP: %s\" % UDP_IP)\n    print(\"UDP target port: %s\" % UDP_PORT)\n    print(\"message: %s\" % MESSAGE)\n\n    sock = socket.socket(socket.AF_INET, # Internet\n                         socket.SOCK_DGRAM) # UDP\n    sock.sendto(MESSAGE, (UDP_IP, UDP_PORT))\n\nif __name__=='__main__':\n    trigger_doorbell()\n","repo_name":"molysgaard/doorbell","sub_path":"doorbell_client.py","file_name":"doorbell_client.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"14522386949","text":"\"\"\"Module for extracting GPU usage data.\"\"\"\n\nfrom datetime import datetime\nfrom typing import TypedDict\n\nimport GPUtil\nimport pandas as pd\n\n\nclass GPUData(TypedDict):\n    \"\"\"Container for GPU data.\"\"\"\n\n    name: str\n    load: float\n    memoryUtil: float\n    timestamp: datetime\n\n\ndef get_gpu_usage() -> pd.DataFrame:\n    data = []\n    for gpu in GPUtil.getGPUs():\n        gpu_data = GPUData(\n            name=gpu.name, load=gpu.load * 100, memoryUtil=gpu.memoryUtil * 100, timestamp=datetime.now()\n        )\n        data.append(gpu_data)\n\n    return pd.DataFrame(data)\n","repo_name":"42nick/gpu_usage_plotter","sub_path":"src/gpu_usage_plotter/gpu_usage_extraction.py","file_name":"gpu_usage_extraction.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"16401166174","text":"from PySide6.QtWidgets import QWidget\nfrom functions.game_functions.Hero import *\nfrom functions.game_functions.createCell import *\nfrom functions.game_functions.addMonsterInMap import *\nfrom functions.game_functions.stages.Stage import *\n\n\ndef drawGameMap(world, stage, gameWindow, heroDirection):\n\n    \n\n    if Stage.currentStage == 5:\n     \n        # tableau bidimentionnel qui contien toute les coordonner de la map (QWidget)\n        mapCell = []\n        row =[]\n        cell = []\n        xPosition = 0\n\n        \n\n        borderMap = QWidget(gameWindow)\n        borderMap.setGeometry(350, 100, 763, 565)\n        borderMap.setStyleSheet(Stage.world[world][\"stages\"][stage][\"background\"])\n\n        mapTop = QWidget(gameWindow)\n        mapTop.setGeometry(387, 37, 689, 400)\n        mapTop.setStyleSheet(Stage.world[world][\"stages\"][stage][\"top-background\"]) \n\n        Y0 = QWidget(borderMap)\n        Y0.setGeometry(0, 245, 763, 269)\n        Y0.setStyleSheet(\"border: none;\" \"background: none\")\n\n        Y1 = QWidget(borderMap)\n        Y1.setGeometry(0, 440, 763, 123)\n        Y1.setStyleSheet(\"border: none;\" \"background: none\")\n        \n        X0 = QWidget(Y0)\n        X0.setGeometry(260, 70, 195, 200)\n\n        mapCell.append([X0])\n\n        for x in range(14):\n\n            X = QWidget(Y1)\n            X.setGeometry(xPosition, 0, 125, 123)\n            X.setStyleSheet(\"border: none;\" \"background: none\")\n            xPosition = xPosition + 49\n            x = x + 1\n            cell.append(X)\n            mapCell.append(cell)\n\n        character = QWidget(mapCell[Hero.y][Hero.x])\n        character.setGeometry(0, 0, 125, 124)\n        character.setStyleSheet(\" {} \".format(heroDirection))\n\n        addMonsterInMap(mapCell, Stage.currentWorld, \"stage {}\".format(Stage.currentStage))\n\n        return borderMap, mapCell\n\n    else: \n        # permet de placer les lignes les une en dessous des autres\n        yPosition = 245\n        # compteur de boucle\n        y = 0\n        # un tableau qui contient le lignes de la map\n        row = []\n        # tableau bidimentionnel qui contien toute les coordonner de la map (QWidget)\n        mapCell = []\n\n        \n\n        borderMap = QWidget(gameWindow)\n        borderMap.setGeometry(350, 100, 763, 565)\n        borderMap.setStyleSheet(Stage.world[world][\"stages\"][stage][\"background\"])\n\n        mapTop = QWidget(gameWindow)\n        mapTop.setGeometry(387, 37, 689, 400)\n        mapTop.setStyleSheet(Stage.world[world][\"stages\"][stage][\"top-background\"])\n\n        # dans borderMap je crée 10 ligne\n        while y < 5:\n\n            Y = QWidget(borderMap)\n            Y.setGeometry(0, yPosition, 763, 123)\n            Y.setStyleSheet(\"border: none;\" \"background: none\")\n            yPosition = yPosition + 49\n            y = y + 1\n\n            # chaque ligne créer est un QWidget que j'ajoute dans la liste row\n            row.append(Y)\n\n        for i in row:\n            # a chaque itération j'appel la fonction createCellInYPosition qui\n            # retourne la liste des QWidget créer pour la ligne i que je stock dans une variable cells\n            cells = createCellInYPosition(i)\n            # je stock la liste des QWidget pour la ligne i dans la liste mapCell\n            mapCell.append(cells)\n            # voila a quoi ressemblera mapCell : mapCell [ ligne1[QWidget1, QWidget2 ], ligne2[QWidget1, QWidget2 ], ... ]\n            # mapCell contient donc toute les coordonnée X et Y de la map\n\n        character = QWidget(mapCell[Hero.y][Hero.x])\n        character.setGeometry(0, 0, 125, 124)\n        character.setStyleSheet(\" {} \".format(heroDirection))\n\n        addMonsterInMap(mapCell, Stage.currentWorld, \"stage {}\".format(Stage.currentStage))\n\n        return borderMap, mapCell\n","repo_name":"Hetic-Project/Empire-Of-Chaos","sub_path":"test/functions/game_functions/drawGameMap.py","file_name":"drawGameMap.py","file_ext":"py","file_size_in_byte":3735,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"19412699258","text":"\r\nfrom django.urls import path\r\n\r\nfrom . import views\r\n\r\nurlpatterns = [\r\n    path('', views.index, name=\"index\"),\r\n    path('unassigned/', views.UnassignedProductListView.as_view()),\r\n    path('unassignedHB/', views.UnassignedProductHBListView.as_view()),\r\n    path('losedBuyboxHB/', views.LosedBuyboxHBListView.as_view()),\r\n    path('unassignedTR/', views.UnassignedProductTRListView.as_view()),\r\n    path('losedBuyboxTR/', views.LosedBuyboxTRListView.as_view()),\r\n]\r\n","repo_name":"ibrahimdinc-software/new_stockmanage","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"38258266287","text":"import facetracker_custom as fc\nimport pygame\n\nimport os, sys, time, random\n\nPRESENT_FRAME_WRITE_PATH = \"Jiung\\jiung.png\"\n\n## 게임 요소들의 크기를 상수로 정의\nSCREEN_WIDTH_DEFAULT = 600\nSCREEN_HEIGHT_DEFAULT = 600\nINTRO_BUTTON_LARGE_WIDTH = SCREEN_WIDTH_DEFAULT*0.3\nINTRO_BUTTON_LARGE_HEIGHT = SCREEN_HEIGHT_DEFAULT*0.1\nRUN_BUTTON_MID_WIDTH = SCREEN_WIDTH_DEFAULT*0.2\nRUN_BUTTON_MID_HEIGHT = SCREEN_WIDTH_DEFAULT*0.2\nRUN_BUTTON_SMALL_WIDTH = SCREEN_WIDTH_DEFAULT*0.15\nRUN_BUTTON_SMALL_HEIGHT = SCREEN_HEIGHT_DEFAULT*0.15\nRUN_BUTTON_FIRE_WIDTH = SCREEN_WIDTH_DEFAULT*0.1\nRUN_BUTTON_FIRE_HEIGHT = SCREEN_HEIGHT_DEFAULT*0.1\n\n## Plus Value : 게임에 돌아다니는 부유물 객체의 점수를 정의\nSCORE_CHICKEN = 1000\nSCORE_BEEF = 2000\nSCORE_SUSHI = 3000\n\n## Minus Value : 게임에 돌아다니는 부유물 객체의 점수를 정의\nSCORE_CUCUMBER = 500\n\n## 게임 일시 정지에 사용되는 Flag\nSTOP = True\n\n## COLOR : 게임에 사용될 색깔 값 상수로 정의 \nBLACK = (0, 0, 0)\nRED = (255, 0, 0)\nBLUE = (0, 0, 255)\nWHITE = (255, 255, 255)\nYELLOW = (255, 255, 0)\nGOLD = (255, 215, 0)\nSILVER = (192, 192, 192)\nBRONZE = (153, 102, 0)\n\n## 유저의 생명을 관리하는 Life 클래스\nclass Life():\n    def __init__(self):\n        self.life = 3\n    def plusLife (self, value):\n        if self.life < 3:\n            self.life += value\n        else:\n            pass\n\n    def minusLife (self, value):\n        self.life -= value\n    \n\n## 유저의 점수를 관리하는 Score 클래스\nclass Score():\n    def __init__(self):\n        self.score = 0\n    # 현재 게임 모드에 따라서 점수가 반영 된다.\n    def upScore(self, value): \n        if PlusElement.DOUBLE_MODE == True: \n            if MinusElement.Minus_MODE == True: \n                self.score += int(2*0.7*value) \n            else:\n                self.score += int(2*value) \n        else:\n            if MinusElement.Minus_MODE == True: \n                self.score += int(0.7*value)\n            else: \n                self.score += value \n                \n    # 현재 게임 모드에 따라서 점수가 반영 된다.\n    def downScore(self, value): \n        if (self.score - value) > 0:\n            self.score -= value\n        else:\n            self.score = 0\n\n## 게임에 사용되는 버튼 클래스\nclass Button():\n    def __init__(self, BUTTON_IMG_PATH, x, y, w, h): # 버튼 클래스는 생성과 동시에 경로를 포함한 이미지 정보를 통해 파이게임에서 사용 가능한 이미지 객체로 생성\n        self.button = pygame.image.load(BUTTON_IMG_PATH)\n        self.button = pygame.transform.scale(self.button,(w, h))\n        self.rect = self.button.get_rect()\n        self.rect.x = x\n        self.rect.y = y\n\n    def switchImg(self, imgPath): # 눌렀을 때 이미지가 변환 되도록 해줄 메서드\n        self.button = self.button = pygame.image.load(imgPath)\n        self.button = pygame.transform.scale(self.button, (self.rect.w, self.rect.h))\n        \n    def pressed(self, mouse): # 버튼이 눌린지 아닌지 검출해줄 메서드\n        if self.rect.collidepoint(mouse) == True:\n            return True\n\n## 부유물 클래스: 점��� 클래스(plus)와, 실점(minus) 클래스로 상속한다.\nclass FloatElement: \n    # 부유물 클래스의 이동 방향을 정적변수로 선언\n    moveDir = [[0, 0], [0, 1], [1, 0], [1, 1]] \n    \n    def __init__(self,Floatter_IMG_PATH, w, h):\n        self.floatter = pygame.image.load(Floatter_IMG_PATH)\n        self.floatter = pygame.transform.scale(self.floatter,(w, h))\n        self.rect = self.floatter.get_rect()\n        self.rect.x = random.randint(0, game.SCREEN_WIDTH - w)\n        self.rect.y = random.randint(0, game.SCREEN_HEIGHT - h)\n        self.moveDir = FloatElement.moveDir[random.randrange(4)]\n        if Floatter_IMG_PATH == \"Images/chicken.png\":\n            self.type = \"chicken\"\n        elif Floatter_IMG_PATH == \"Images/beef.png\":\n            self.type = \"beef\"\n        elif Floatter_IMG_PATH == \"Images/sushi.png\":\n            self.type = \"sushi\"\n        elif Floatter_IMG_PATH == \"Images/goldApple.png\":\n            self.type = \"goldApple\"\n        elif Floatter_IMG_PATH == \"Images/medicine.png\":\n            self.type = \"medicine\"\n        elif Floatter_IMG_PATH == \"Images/cucumber.png\":\n            self.type = \"cucumber\" \n        elif Floatter_IMG_PATH == \"Images/redMushroom.png\":\n            self.type = \"redMushroom\"\n    \n    ## 난이도에 따라 다른 이동\n    def randomMove(self):\n\n        # 난이도 easy\n        if game.gameMode == \"easy\":\n            self.rect.x += random.randrange(31) - 15 # move : -15 ~ 15 \n            self.rect.y += random.randrange(11) - 5 # move : -5 ~ 5\n            # 충돌에대한 방향 보정\n            if self.rect.x > game.SCREEN_WIDTH: self.rect.x -= 30\n            elif self.rect.x < 0: self.rect.x += 30\n            if self.rect.y > game.SCREEN_HEIGHT: self.rect.y -= 30\n            elif self.rect.y < 0: self.rect.y += 30\n        \n        # 난이도 nomal\n        if game.gameMode == \"nomal\":\n            if self.moveDir == [0, 0] :\n                self.rect.x -= random.randrange(11)\n                self.rect.y -= random.randrange(11) \n            elif self.moveDir == [0, 1] :\n                self.rect.x -= random.randrange(11)\n                self.rect.y += random.randrange(11)\n            elif self.moveDir == [1, 0]:\n                self.rect.x += random.randrange(11)\n                self.rect.y -= random.randrange(11) \n            elif self.moveDir == [1, 1] :\n                self.rect.x += random.randrange(11)\n                self.rect.y += random.randrange(11)\n\n            # 충돌에대한 방향 보정\n            # x 좌표 보정\n            if self.rect.x >= game.SCREEN_WIDTH - self.rect.w : \n                self.rect.x -= 10\n                self.moveDir[0] = 0\n            elif self.rect.x < self.rect.w:\n                self.rect.x += 10\n                self.moveDir[0] = 1\n            # y좌표 보정\n            if self.rect.y >= game.SCREEN_HEIGHT-self.rect.h: \n                self.rect.y -= 10\n                self.moveDir[1] = 0\n            elif self.rect.y < self.rect.h:\n                self.rect.y += 10\n                self.moveDir[1] = 1\n\n        # 난이도 hard\n        if game.gameMode == \"hard\":\n            if self.moveDir == [0, 0] :\n                self.rect.x -= random.randrange(31)\n                self.rect.y -= random.randrange(31) \n            elif self.moveDir == [0, 1] :\n                self.rect.x -= random.randrange(31)\n                self.rect.y += random.randrange(31)\n            elif self.moveDir == [1, 0]:\n                self.rect.x += random.randrange(31)\n                self.rect.y -= random.randrange(31) \n            elif self.moveDir == [1, 1] :\n                self.rect.x += random.randrange(31)\n                self.rect.y += random.randrange(31)\n\n            # 충돌에대한 방향 보정\n            # x 좌표 보정\n            if self.rect.x >= game.SCREEN_WIDTH - self.rect.w : \n                self.rect.x -= 30\n                self.moveDir[0] = 0\n            elif self.rect.x < self.rect.w:\n                self.rect.x += 30\n                self.moveDir[0] = 1\n            # y좌표 보정\n            if self.rect.y >= game.SCREEN_HEIGHT-self.rect.h: \n                self.rect.y -= 30\n                self.moveDir[1] = 0\n            elif self.rect.y < self.rect.h:\n                self.rect.y += 30\n                self.moveDir[1] = 1\n\n# 부유물 클래스(plus, 점수 획득요소) : 부유물 객체로 부터 상속 받는다.\nclass PlusElement(FloatElement): \n    DOUBLE_MODE = False\n    def __init__(self, Floatter_IMG_PATH, w, h):\n        super().__init__(Floatter_IMG_PATH, w, h) \n\n    def eat(self):\n        if self.type == \"medicine.png\":\n            pass\n        elif self.type == \"chicken\":\n            game.score.upScore(SCORE_CHICKEN)\n        elif self.type == \"beef\":\n            game.score.upScore(SCORE_BEEF) \n        elif self.type == \"sushi\":\n            game.score.upScore(SCORE_SUSHI)\n        elif self.type == \"goldApple\":\n            PlusElement.DOUBLE_MODE = True\n            return True\n        elif self.type == \"medicine\":\n            if MinusElement.Minus_MODE == True:\n                MinusElement.Minus_MODE = False\n            game.life.plusLife(1)\n        return False\n\n# 부유물 클래스(minus, 실점 요소) : 부유물 객체로 부터 상속 받는다.\nclass MinusElement(FloatElement):\n    Minus_MODE = False\n    def __init__(self, Floatter_IMG_PATH, w, h):\n        super().__init__(Floatter_IMG_PATH, w, h)\n        \n    def eat(self):\n        if self.type == \"cucumber\":\n            game.score.downScore(SCORE_CUCUMBER) \n            game.life.minusLife(1)\n        elif self.type == \"redMushroom\":\n            MinusElement.Minus_MODE = True\n            game.life.minusLife(2)\n            \n\n\n'''\n    <게임 객체의 메서드 구성>\n    ##### 게임 세팅\n       ## 게임 객체 생성자\n       ## 게임판 생성\n                    \n    ##### 게임 관리 도우미\n       ## 문자 출력 도우미\n\n    ##### 환경 설정\n       ## 메인\n       ## 게임 난이도 설정\n       ## 유저 랭킹      \n\n    ##### 게임 요소 관리\n       ## 이미지 파일 관리\n       ## 이미지 파일 생성\n       ## 버튼 생성\n       \n    ##### 게임 인트로\n       ## 인트로 메인 화면 출력\n\n    ##### 게임 실행\n       ## 먹은 음식 검출\n       ## 거부한 음식 검출\n       ## 게임 종료\n       ## 게임 요소 정리\n       ## 부유물 객체 생성\n       ## 식사, 시간, 이벤트 체크\n       ## 게임 시작\n       ## 게임 프로그램 실행\n\n    ##### 게임 프로그램 종료\n       ## 저장 및 종료\n'''\n\n\n\nclass Game:\n    ########## 게임 세팅 ##########\n    ## 게임 세팅 : 게임 객체의 생성자 \n    def __init__(self): \n        self.userName = input(\"Insert your name in english : \") # 유저의 이름을 입력받음\n        self.userBestScore = 0\n        self.SCREEN_WIDTH = SCREEN_WIDTH_DEFAULT\n        self.SCREEN_HEIGHT = SCREEN_HEIGHT_DEFAULT\n        self.gameMode = \"nomal\"\n        self.floatElements = [[],[]]\n        self.gameTime = 60\n        self.userHistory = False\n        \n        # user 관리 파일을 유저의 열어서 최고 점수를 불러온다.\n        for user in open(\"User/userInfo.txt\", \"r\", encoding=\"UTF-8\"):\n            user = user.split()\n            if self.userName == user[0]:\n                self.userBestScore = user[1]\n                self.userHistory = True\n\n    ## 게임 세팅 : 게임판 객체 생성\n    def gameBoard(self):\n        self.img = pygame.image.load(PRESENT_FRAME_WRITE_PATH)\n        self.img = pygame.transform.scale(self.img, (self.SCREEN_WIDTH, self.SCREEN_HEIGHT))\n\n\n    ########## 게임 관리 도우미 ##########\n    ## 게임 관리 도우미: 문자 출력 도우미\n    def textHelpper(self, TEXT, SIZE, COLOR, X, Y):\n        self.gameFont = pygame.font.SysFont( 'impact', SIZE, False, False)\n        self.gameText = self.gameFont.render(f\"{TEXT}\", True, COLOR)\n        self.gameTextRect = self.gameText.get_rect() \n        self.SCREEN.blit(self.gameText, [X - self.gameTextRect.w/2, Y - self.gameTextRect.h/2])\n\n\n    ########### 환경 설정 ###########\n    ## 환경 설정 메인\n    def gameSettings(self):\n        STOP = True\n        self.SCREEN.blit(self.imgs_intro_gameScreenInfo, self.intro_gameScreenInfo[1])\n        self.SCREEN.blit(self.imgs_intro_chefInfo, self.intro_chefInfo[1])\n        while STOP:\n            self.SCREEN.blit(self.settings_modeButton.button, self.settings_modeButtonInfo[1])\n            self.SCREEN.blit(self.settings_userButton.button, self.settings_userButtonInfo[1])\n            self.SCREEN.blit(self.run_menuCancelButton.button, [self.run_menuCancelButtonInfo[1][0], self.run_menuCancelButtonInfo[1][1]])\n            pygame.display.update()\n\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    self.quitGame()\n                elif event.type == pygame.MOUSEBUTTONDOWN:\n                    \n                    if self.settings_modeButton.pressed(event.pos) == True:\n                        self.settings_modeButton.switchImg(\"Images/settings_modeButton_pressed.png\")\n                    elif self.settings_userButton.pressed(event.pos) == True:\n                        self.settings_userButton.switchImg(\"Images/settings_userButton_pressed.png\")\n                    elif self.run_menuCancelButton.pressed(event.pos) == True:\n                        self.run_menuCancelButton.switchImg(\"Images/run_menuCancelButton_pressed.png\")\n\n                elif event.type == pygame.MOUSEBUTTONUP:\n                    pauseTime = time.time()\n                    self.settings_modeButton.switchImg(\"Images/settings_modeButton.png\")\n                    self.settings_userButton.switchImg(\"Images/settings_userButton.png\")\n                    self.run_menuCancelButton.switchImg(\"Images/run_menuCancelButton.png\")\n\n                    if self.settings_modeButton.pressed(event.pos) == True:\n                        self.gameSettingsModeSelect()\n                    elif self.settings_userButton.pressed(event.pos) == True:\n                        self.gameSettingsPrintUserRanking()\n                    elif self.run_menuCancelButton.pressed(event.pos) == True:\n                        self.introScreen()\n\n    ## 환경 설정 : 게임 난이도 설정\n    def gameSettingsModeSelect(self):\n        STOP = True\n        self.SCREEN.blit(self.imgs_intro_gameScreenInfo, self.intro_gameScreenInfo[1])\n        self.SCREEN.blit(self.imgs_intro_chefInfo, self.intro_chefInfo[1])\n        while STOP:\n            self.SCREEN.blit(self.settings_modeEasyButton.button, self.settings_modeEasyButtonInfo[1])\n            self.SCREEN.blit(self.settings_modeNomalButton.button, self.settings_modeNomalButtonInfo[1])\n            self.SCREEN.blit(self.settings_modeHardButton.button, self.settings_modeHardButtonInfo[1])\n            self.SCREEN.blit(self.settings_menuCancelButton.button, [self.settings_userButtonInfo[1][0], self.settings_userButtonInfo[1][1]+ 2*self.settings_userButtonInfo[2][1]])\n\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    self.quitGame()\n                elif event.type == pygame.MOUSEBUTTONDOWN:\n                    if self.settings_modeEasyButton.pressed(event.pos) == True:\n                        self.settings_modeEasyButton.switchImg(\"Images/settings_modeEasyButton_pressed.png\")\n                    elif self.settings_modeNomalButton.pressed(event.pos) == True:\n                        self.settings_modeNomalButton.switchImg(\"Images/settings_modeNomalButton_pressed.png\")\n                    elif self.settings_modeHardButton.pressed(event.pos) == True:\n                        self.settings_modeHardButton.switchImg(\"Images/settings_modeHardButton_pressed.png\")\n                    elif self.settings_menuCancelButton.pressed(event.pos) == True:\n                        self.settings_menuCancelButton.switchImg(\"Images/run_menuCancelButton_pressed.png\")\n\n                elif event.type == pygame.MOUSEBUTTONUP:\n                    pauseTime = time.time()\n                    self.settings_modeEasyButton.switchImg(\"Images/settings_modeEasyButton.png\")\n                    self.settings_modeNomalButton.switchImg(\"Images/settings_modeNomalButton.png\")\n                    self.settings_modeHardButton.switchImg(\"Images/settings_modeHardButton.png\")\n                    self.settings_menuCancelButton.switchImg(\"Images/run_menuCancelButton.png\")\n\n                    if self.settings_modeEasyButton.pressed(event.pos) == True:\n                        self.gameMode = \"easy\"\n                        self.introScreen()\n                    elif self.settings_modeNomalButton.pressed(event.pos) == True:\n                        self.gameMode = \"nomal\"\n                        self.introScreen()\n                    elif self.settings_modeHardButton.pressed(event.pos) == True:\n                        self.gameMode = \"hard\"\n                        self.introScreen()\n                    elif self.settings_menuCancelButton.pressed(event.pos) == True:\n                        self.gameSettings()\n\n            pygame.display.update()\n\n    ## 환경 설정 : 유저 랭킹 출력\n    def gameSettingsPrintUserRanking(self):\n        STOP = True\n        self.SCREEN.blit(self.settings_userRanking, self.settings_userRankingInfo[1])\n        \n        ## 유저 정보 열람\n        userRank = []\n        for user in open(\"User/userInfo.txt\", \"r\", encoding=\"UTF-8\"):\n            user = user.split()\n            if len(userRank) == 0:\n                userRank.append(user)\n            else:\n                for rankCnt in range(len(userRank)):\n                    if int(userRank[rankCnt][1]) < int(user[1]):\n                        userRank.insert(rankCnt, user)\n                        if len(userRank) > 10: userRank.pop()\n                        break         \n        while STOP:\n            self.SCREEN.blit(self.run_menuButton.button, self.run_menuButtonInfo[1])\n\n            for rank in range(len(userRank)):\n                if rank == 0:\n                    self.textHelpper(f\"{rank+1}st {userRank[rank][0]} : {userRank[rank][1]}\", 35, GOLD, self.SCREEN_WIDTH/2, self.SCREEN_WIDTH*0.07*(rank+4))\n                elif rank == 1:\n                    self.textHelpper(f\"{rank+1}st {userRank[rank][0]} : {userRank[rank][1]}\", 33, SILVER, self.SCREEN_WIDTH/2, self.SCREEN_WIDTH*0.07*(rank+4))\n                elif rank == 2:\n                    self.textHelpper(f\"{rank+1}st {userRank[rank][0]} : {userRank[rank][1]}\", 31, BRONZE, self.SCREEN_WIDTH/2, self.SCREEN_WIDTH*0.07*(rank+4))\n                else:\n                    self.textHelpper(f\"{rank+1}st {userRank[rank][0]} : {userRank[rank][1]}\", 20, BLACK, self.SCREEN_WIDTH/2, self.SCREEN_WIDTH*0.07*(rank+4))\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    self.quitGame()\n                elif event.type == pygame.MOUSEBUTTONUP:\n                    if self.run_menuButton.pressed(event.pos) == True:\n                        self.introScreen()\n\n            pygame.display.update()\n\n\n    ########### 게임 요소 관리 ###########\n    ## 게임 요소 관리 : 이미지 파일들의 정보를 관리 ... [경로, [x,y], (width, height))]\n    def gameFactor(self): \n        self.intro_gameScreenInfo = [\"Images/intro_gameScreen.png\", [0, 0] , (self.SCREEN_WIDTH,self.SCREEN_HEIGHT)]\n        self.intro_chefInfo = [\"Images/chef.png\", [self.SCREEN_WIDTH/2 - 0.5*self.SCREEN_WIDTH/1.6, self.SCREEN_HEIGHT- self.SCREEN_HEIGHT/2], (self.SCREEN_WIDTH/1.6, self.SCREEN_HEIGHT/2)]\n        self.intro_gameStartButtonInfo = [\"Images/intro_gameStrartButton.png\", [self.SCREEN_WIDTH/2 - INTRO_BUTTON_LARGE_WIDTH/2, self.SCREEN_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT/2 - 2*INTRO_BUTTON_LARGE_HEIGHT ], (INTRO_BUTTON_LARGE_WIDTH, INTRO_BUTTON_LARGE_HEIGHT)]\n        self.intro_gameSettingsButtonInfo = [\"Images/intro_gameSettingsButton.png\", [self.SCREEN_WIDTH/2 - INTRO_BUTTON_LARGE_WIDTH/2, self.SCREEN_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT ], (INTRO_BUTTON_LARGE_WIDTH, INTRO_BUTTON_LARGE_HEIGHT)]        \n        self.intro_quitGameButtonInfo = [\"Images/intro_quitGameButton.png\", [self.SCREEN_WIDTH/2 - INTRO_BUTTON_LARGE_WIDTH/2, self.SCREEN_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT/2], (INTRO_BUTTON_LARGE_WIDTH, INTRO_BUTTON_LARGE_HEIGHT)]  \n\n        self.settings_modeButtonInfo = [\"Images/settings_modeButton.png\", [self.SCREEN_WIDTH/2 - INTRO_BUTTON_LARGE_WIDTH/2, self.SCREEN_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT/2 - 2*INTRO_BUTTON_LARGE_HEIGHT ], (INTRO_BUTTON_LARGE_WIDTH, INTRO_BUTTON_LARGE_HEIGHT)]\n        self.settings_userButtonInfo = [\"Images/settings_userButton.png\", [self.SCREEN_WIDTH/2 - INTRO_BUTTON_LARGE_WIDTH/2, self.SCREEN_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT/2- INTRO_BUTTON_LARGE_HEIGHT], (INTRO_BUTTON_LARGE_WIDTH, INTRO_BUTTON_LARGE_HEIGHT)]   \n        self.settings_modeEasyButtonInfo = [\"Images/settings_windowSettingsButton.png\", [self.SCREEN_WIDTH/2 - INTRO_BUTTON_LARGE_WIDTH/2, self.SCREEN_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT/2 - 2*INTRO_BUTTON_LARGE_HEIGHT ], (INTRO_BUTTON_LARGE_WIDTH, INTRO_BUTTON_LARGE_HEIGHT)]\n        self.settings_modeNomalButtonInfo = [\"Images/settings_modeButton.png\", [self.SCREEN_WIDTH/2 - INTRO_BUTTON_LARGE_WIDTH/2, self.SCREEN_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT ], (INTRO_BUTTON_LARGE_WIDTH, INTRO_BUTTON_LARGE_HEIGHT)]\n        self.settings_modeHardButtonInfo = [\"Images/settings_userButton.png\", [self.SCREEN_WIDTH/2 - INTRO_BUTTON_LARGE_WIDTH/2, self.SCREEN_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT/2], (INTRO_BUTTON_LARGE_WIDTH, INTRO_BUTTON_LARGE_HEIGHT)]   \n        self.settings_userRankingInfo = [\"Images/settings_userInfoScreen.png\", [0, 0], (self.SCREEN_WIDTH, self.SCREEN_HEIGHT)]\n\n        self.run_menuButtonInfo = [\"Images/run_menuButton.png\", [self.SCREEN_WIDTH-RUN_BUTTON_SMALL_WIDTH , 0], (RUN_BUTTON_SMALL_WIDTH, RUN_BUTTON_SMALL_HEIGHT)]\n        self.run_menuToIntroButtonInfo = [\"Images/run_menuToIntroButton.png\", [self.SCREEN_WIDTH/2 - INTRO_BUTTON_LARGE_WIDTH/2, self.SCREEN_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT ], (INTRO_BUTTON_LARGE_WIDTH, INTRO_BUTTON_LARGE_HEIGHT)]\n        self.run_menuCancelButtonInfo = [\"Images/run_menuCancelButton.png\", [self.SCREEN_WIDTH/2 - INTRO_BUTTON_LARGE_WIDTH/2, self.SCREEN_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT/2  ], (INTRO_BUTTON_LARGE_WIDTH, INTRO_BUTTON_LARGE_HEIGHT)]\n        self.run_lifeInfo = [[\"Images/heart1.png\", \"Images/heart2.png\", \"Images/heart3.png\"], [0, 0], (RUN_BUTTON_MID_WIDTH, RUN_BUTTON_MID_HEIGHT)]\n        self.run_restartGameButtonInfo = [\"Images/run_restartGameButton.png\", [self.SCREEN_WIDTH/2 - INTRO_BUTTON_LARGE_WIDTH/2, self.SCREEN_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT/2  ], (INTRO_BUTTON_LARGE_WIDTH, INTRO_BUTTON_LARGE_HEIGHT)] \n        self.run_yesButtonInfo = [\"Images/run_yesButton.png\", [self.SCREEN_WIDTH/2 - INTRO_BUTTON_LARGE_WIDTH/2, self.SCREEN_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT], (INTRO_BUTTON_LARGE_WIDTH, INTRO_BUTTON_LARGE_HEIGHT)]\n        self.run_noButtonInfo = [\"Images/run_noButton.png\", [self.SCREEN_WIDTH/2 - INTRO_BUTTON_LARGE_WIDTH/2, self.SCREEN_HEIGHT/2 - INTRO_BUTTON_LARGE_HEIGHT/2 ], (INTRO_BUTTON_LARGE_WIDTH, INTRO_BUTTON_LARGE_HEIGHT)]\n        self.run_fireInfo = [\"Images/fire.png\", [0, 0], (RUN_BUTTON_FIRE_WIDTH, RUN_BUTTON_FIRE_HEIGHT)]\n        self.run_bandInfo = [\"Images/band.png\", [0, 0], (RUN_BUTTON_FIRE_WIDTH, RUN_BUTTON_FIRE_HEIGHT)]\n        self.run_waitingScreenEasyInfo = [\"Images/run_waitingScreenEasy.png\", [0, 0], (self.SCREEN_WIDTH, self.SCREEN_HEIGHT)]\n        self.run_waitingScreenNomalInfo = [\"Images/run_waitingScreenNomal.png\", [0, 0], (self.SCREEN_WIDTH, self.SCREEN_HEIGHT)]\n        self.run_waitingScreenHardInfo = [\"Images/run_waitingScreenHard.png\", [0, 0], (self.SCREEN_WIDTH, self.SCREEN_HEIGHT)]\n\n\n        ## Game의 부유물 객체 관리\n        self.run_plusElementInfo = [[\"Images/chicken.png\", \"Images/beef.png\", \"Images/sushi.png\", \"Images/goldApple.png\",\"Images/medicine.png\"], [0, 0], (RUN_BUTTON_MID_WIDTH, RUN_BUTTON_MID_HEIGHT)]\n        self.run_minusElementInfo = [[\"Images/cucumber.png\",\"Images/redMushroom.png\"], [0, 0] , (RUN_BUTTON_MID_WIDTH, RUN_BUTTON_MID_HEIGHT)]\n\n    ## 게임 요소 관리 : 파이게임 이미지 객체 생성\n    def getImgs(self):\n        self.imgs_intro_gameScreenInfo = pygame.transform.scale(pygame.image.load(self.intro_gameScreenInfo[0]), self.intro_gameScreenInfo[2])\n        self.imgs_intro_chefInfo = pygame.transform.scale(pygame.image.load(self.intro_chefInfo[0]), self.intro_chefInfo[2])\n        self.imgs_run_heart1 = pygame.transform.scale(pygame.image.load(self.run_lifeInfo[0][0]), self.run_lifeInfo[2])\n        self.imgs_run_heart2 = pygame.transform.scale(pygame.image.load(self.run_lifeInfo[0][1]), self.run_lifeInfo[2])\n        self.imgs_run_heart3 = pygame.transform.scale(pygame.image.load(self.run_lifeInfo[0][2]), self.run_lifeInfo[2])\n        self.imgs_run_fire = pygame.transform.scale(pygame.image.load(self.run_fireInfo[0]), self.run_fireInfo[2])\n        self.settings_userRanking = pygame.transform.scale(pygame.image.load(self.settings_userRankingInfo[0]), self.settings_userRankingInfo[2])\n        self.imgs_run_band = pygame.transform.scale(pygame.image.load(self.run_bandInfo[0]), self.run_bandInfo[2])\n        self.imgs_run_waitingScreenEasy = pygame.transform.scale(pygame.image.load(self.run_waitingScreenEasyInfo[0]), self.run_waitingScreenEasyInfo[2])\n        self.imgs_run_waitingScreenNomal = pygame.transform.scale(pygame.image.load(self.run_waitingScreenNomalInfo[0]), self.run_waitingScreenNomalInfo[2])\n        self.imgs_run_waitingScreenHard = pygame.transform.scale(pygame.image.load(self.run_waitingScreenHardInfo[0]), self.run_waitingScreenHardInfo[2])\n\n    ## 게임 요소 관리 : 파이게임 버튼 객체 생성\n    def buttons_generate(self):\n        self.intro_gameStartButton = Button(self.intro_gameStartButtonInfo[0], self.intro_gameStartButtonInfo[1][0], self.intro_gameStartButtonInfo[1][1], self.intro_gameStartButtonInfo[2][0], self.intro_gameStartButtonInfo[2][1]) # 인트로 게임 시작 버튼 생성\n        self.intro_gameSettingsButton = Button(self.intro_gameSettingsButtonInfo[0], self.intro_gameSettingsButtonInfo[1][0], self.intro_gameSettingsButtonInfo[1][1], self.intro_gameSettingsButtonInfo[2][0], self.intro_gameSettingsButtonInfo[2][1]) #인트로 게임 설정 버튼 생성\n        self.intro_quitGameButton = Button(self.intro_quitGameButtonInfo[0], self.intro_quitGameButtonInfo[1][0], self.intro_quitGameButtonInfo[1][1], self.intro_quitGameButtonInfo[2][0], self.intro_quitGameButtonInfo[2][1] )\n\n        self.run_menuButton = Button(self.run_menuButtonInfo[0], self.run_menuButtonInfo[1][0], self.run_menuButtonInfo[1][1], self.run_menuButtonInfo[2][0], self.run_menuButtonInfo[2][1] )\n        self.run_menuToIntroButton = Button(self.run_menuToIntroButtonInfo[0], self.run_menuToIntroButtonInfo[1][0], self.run_menuToIntroButtonInfo[1][1], self.run_menuToIntroButtonInfo[2][0], self.run_menuToIntroButtonInfo[2][1] )\n        self.run_menuCancelButton = Button(self.run_menuCancelButtonInfo[0], self.run_menuCancelButtonInfo[1][0], self.run_menuCancelButtonInfo[1][1], self.run_menuCancelButtonInfo[2][0], self.run_menuCancelButtonInfo[2][1] )\n        self.run_restartGameButton = Button(self.run_restartGameButtonInfo[0], self.run_restartGameButtonInfo[1][0], self.run_restartGameButtonInfo[1][1], self.run_restartGameButtonInfo[2][0], self.run_restartGameButtonInfo[2][1] )\n        self.run_yesButton = Button(self.run_yesButtonInfo[0], self.run_yesButtonInfo[1][0], self.run_yesButtonInfo[1][1], self.run_yesButtonInfo[2][0], self.run_yesButtonInfo[2][1] )\n        self.run_noButton = Button(self.run_noButtonInfo[0], self.run_noButtonInfo[1][0], self.run_noButtonInfo[1][1], self.run_noButtonInfo[2][0], self.run_noButtonInfo[2][1] )\n\n        self.settings_modeButton = Button(self.settings_modeButtonInfo[0], self.settings_modeButtonInfo[1][0], self.settings_modeButtonInfo[1][1], self.settings_modeButtonInfo[2][0], self.settings_modeButtonInfo[2][1]) #인트로 게임 설정 버튼 생성\n        self.settings_userButton = Button(self.settings_userButtonInfo[0], self.settings_userButtonInfo[1][0], self.settings_userButtonInfo[1][1], self.settings_userButtonInfo[2][0], self.settings_userButtonInfo[2][1] )\n       \n        self.settings_modeEasyButton = Button(self.settings_modeEasyButtonInfo[0], self.settings_modeEasyButtonInfo[1][0], self.settings_modeEasyButtonInfo[1][1], self.settings_modeEasyButtonInfo[2][0], self.settings_modeEasyButtonInfo[2][1]) # 인트로 게임 시작 버튼 생성\n        self.settings_modeNomalButton = Button(self.settings_modeNomalButtonInfo[0], self.settings_modeNomalButtonInfo[1][0], self.settings_modeNomalButtonInfo[1][1], self.settings_modeNomalButtonInfo[2][0], self.settings_modeNomalButtonInfo[2][1]) #인트로 게임 설정 버튼 생성\n        self.settings_modeHardButton = Button(self.settings_modeHardButtonInfo[0], self.settings_modeHardButtonInfo[1][0], self.settings_modeHardButtonInfo[1][1], self.settings_modeHardButtonInfo[2][0], self.settings_modeHardButtonInfo[2][1] )\n        self.settings_menuCancelButton = Button(self.run_menuCancelButtonInfo[0], self.run_menuCancelButtonInfo[1][0], self.run_menuCancelButtonInfo[1][1]+self.run_menuCancelButtonInfo[2][1], self.run_menuCancelButtonInfo[2][0], self.run_menuCancelButtonInfo[2][1] )\n\n    \n    ########## 게임 인트로 ##########\n    ## 게임 인트로: 인트로 메인 화면\n    def introScreen(self):\n        intro = True\n        changeColorAndSize = [0, 1, 2, 3, 4, 5, 4, 3, 2, 1]\n        cntForTimeSlow = 0\n        while intro:\n            cntForTimeSlow += 1\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    self.quitGame()\n                elif event.type == pygame.MOUSEBUTTONDOWN:\n                    if self.intro_gameStartButton.pressed(event.pos) == True:\n                        self.intro_gameStartButton.switchImg(\"Images/intro_gameStrartButton_pressed.png\")\n                    elif self.intro_gameSettingsButton.pressed(event.pos) == True:\n                        self.intro_gameSettingsButton.switchImg(\"Images/intro_gameSettingsButton_pressed.png\")\n                    elif self.intro_quitGameButton.pressed(event.pos) == True:\n                        self.intro_quitGameButton.switchImg(\"Images/intro_quitGameButton_pressed.png\")\n\n                elif event.type == pygame.MOUSEBUTTONUP:\n                    self.intro_gameStartButton.switchImg(\"Images/intro_gameStrartButton.png\")\n                    self.intro_gameSettingsButton.switchImg(\"Images/intro_gameSettingsButton.png\")\n                    self.intro_quitGameButton.switchImg(\"Images/intro_quitGameButton.png\")\n\n                    if self.intro_gameStartButton.pressed(event.pos) == True:\n                        self.gameStart()\n                    elif self.intro_gameSettingsButton.pressed(event.pos) == True:\n                        self.gameSettings()\n                    elif self.intro_quitGameButton.pressed(event.pos) == True:\n                        self.quitGame()\n\n\n            ## 게임 스크린\n            self.SCREEN.blit(self.imgs_intro_gameScreenInfo, self.intro_gameScreenInfo[1])\n            self.SCREEN.blit(self.imgs_intro_chefInfo, self.intro_chefInfo[1])\n\n            ## 인트로 멘트\n            self.gameUserNameFont = pygame.font.SysFont( 'impact', 30+ changeColorAndSize[cntForTimeSlow%10]*2, False, False)\n            if self.userHistory:\n                self.gameUserNameText = self.gameUserNameFont.render(f\"hi, {self.userName}\", True, (changeColorAndSize[cntForTimeSlow%10]*10+60 ,0 ,changeColorAndSize[cntForTimeSlow%10]*20 ))\n            else:\n                self.gameUserNameText = self.gameUserNameFont.render(f\"Welcome {self.userName}\", True, (changeColorAndSize[cntForTimeSlow%10]*10+60 ,0 ,changeColorAndSize[cntForTimeSlow%10]*20 ))\n            self.gameUserNameRect = self.gameUserNameText.get_rect()\n            self.SCREEN.blit(self.gameUserNameText, [self.SCREEN_WIDTH/2 - self.gameUserNameRect.w/2, self.SCREEN_HEIGHT - self.gameUserNameRect.h])\n            \n            ## 유저의 최고 점수\n            self.gameUserBestScoreFont = pygame.font.SysFont( 'impact', 20+changeColorAndSize[cntForTimeSlow%10], False, False)\n            self.gameUserBestScoreText = self.gameUserBestScoreFont.render(f\"YOUR BEST SCORE : {self.userBestScore}\", True, BLACK)\n            self.gameUserBestScoreRect = self.gameUserBestScoreText.get_rect()\n            self.SCREEN.blit(self.gameUserBestScoreText, [self.SCREEN_WIDTH/2 - self.gameUserBestScoreRect.w/2, self.SCREEN_HEIGHT*0.01])\n            \n            ## 게임 버튼\n            self.SCREEN.blit(self.intro_gameStartButton.button, self.intro_gameStartButtonInfo[1])\n            self.SCREEN.blit(self.intro_gameSettingsButton.button, self.intro_gameSettingsButtonInfo[1])\n            self.SCREEN.blit(self.intro_quitGameButton.button, self.intro_quitGameButtonInfo[1])\n            \n            pygame.display.update()\n            self.clock.tick(15)\n\n    ########## 게임 실행 ##########\n    ## 게임 실행: 먹은 음식 검출\n    def isInYourMouth(self, isPlusElement, points, element): # points: x*1.1, y*1.25\n        if isPlusElement:\n            if ((points[55][1]*1.25 - points[50][1]*1.25) > 50 and points[58][0]*1.1>(element.rect.x + element.rect.w/2)>points[62][0]*1.1) and (points[50][1]*1.25<(element.rect.y+ element.rect.h/2)<points[55][1]*1.25): \n                return True\n        else:\n            if ((points[55][1]*1.25 - points[50][1]*1.25) > 50 and points[58][0]*1.1>(element.rect.x + element.rect.w/2)>points[62][0]*1.1) and (points[50][1]*1.25<(element.rect.y+ element.rect.h/2)<points[55][1]*1.25): \n                return True\n        return False\n    \n    ## 게임 실행: 거부한 음식 검출\n    def rejectOnYourMouth(self, points, element):\n        if (points[55][1]*1.25 - points[50][1]*1.25 < 35) and (points[58][0]*1.1>element.rect.x + element.rect.w/2>points[62][0]*1.1) and ( (element.rect.y)<points[50][1]*1.25<(element.rect.y + element.rect.h)):\n                return True\n        return False\n\n    ## 게임 실행: 게임 종료\n    def gameFinish(self, TYPE):\n        STOP = True\n        while STOP:\n            pygame.draw.rect(self.SCREEN, BLACK, [0, 0, self.SCREEN_WIDTH, self.SCREEN_HEIGHT])\n            if TYPE == \"GAMEOVER\":\n                self.textHelpper(\"GAME OVER\", 40, WHITE, self.SCREEN_WIDTH/2, self.SCREEN_HEIGHT*0.1)\n            elif TYPE == \"TIMEOVER\":\n                self.textHelpper(\"TIME OVER\", 40, WHITE, self.SCREEN_WIDTH/2, self.SCREEN_HEIGHT*0.1)\n             \n            self.SCREEN.blit(self.run_menuToIntroButton.button, self.run_menuToIntroButtonInfo[1])\n            self.SCREEN.blit(self.run_restartGameButton.button, self.run_restartGameButtonInfo[1])\n\n            ## 점수 객체 화면 출력\n            self.gameScoreFont = pygame.font.SysFont( 'impact', 40, False, False)\n            self.gameScoreText = self.gameScoreFont.render(f\"SCORE : {self.score.score}\", True, RED)\n            self.gameScoreTextRect = self.gameScoreText.get_rect()\n            self.SCREEN.blit(self.gameScoreText, [0, self.SCREEN_HEIGHT - self.gameScoreTextRect.h])\n                \n            if int(self.userBestScore) < self.score.score: self.userBestScore = str(self.score.score)\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    self.quitGame()\n                elif event.type == pygame.MOUSEBUTTONDOWN:\n                    if self.run_menuToIntroButton.pressed(event.pos) == True:\n                        self.run_menuToIntroButton.switchImg(\"Images/run_menuToIntroButton_pressed.png\")\n                    elif self.run_restartGameButton.pressed(event.pos) == True:\n                        self.run_restartGameButton.switchImg(\"Images/run_restartGameButton_pressed.png\")\n\n                elif event.type == pygame.MOUSEBUTTONUP:\n                    self.run_menuToIntroButton.switchImg(\"Images/run_menuToIntroButton.png\")\n                    self.run_restartGameButton.switchImg(\"Images/run_restartGameButton.png\")\n\n                    if self.run_menuToIntroButton.pressed(event.pos) == True:\n                        STOP = False\n                        self.gameFactorClear()\n                        self.introScreen()\n\n                    elif self.run_menuCancelButton.pressed(event.pos) == True:\n                        STOP = False\n                        self.gameFactorClear()\n                        self.gameStart()\n                        \n            pygame.display.update()\n\n    ## 게임 실행: 게임 요소 정리\n    def gameFactorClear(self):\n        STOP = False\n        PlusElement.DOUBLE_MODE = False\n        MinusElement.Minus_MODE = False\n        self.floatElements = [[], []]\n        self.life.life = 3\n        self.score.score = 0\n    \n    ## 게임 실행: 부유물 객체 생성\n    def floatterGenerotr(self):\n        if self.gameMode == \"easy\":\n            ## 부유물 객체의 생성 [plus : minus = 8 : 2]\n            if ((time.time() - self.startTime)%1 <0.06): # 1초에 2번 부유물 객체 생성\n                plusOrMinus = random.randrange(100)\n                if plusOrMinus < 70: # 2:8 = (minus:plus) 의 비율로 부유물 객체 생성 \n                    self.floatElements[0].append(PlusElement(self.run_plusElementInfo[0][random.randrange(0, 3)], self.run_plusElementInfo[2][0], self.run_plusElementInfo[2][1]))\n                elif 70<= plusOrMinus  <80:\n                    self.floatElements[1].append(MinusElement(self.run_minusElementInfo[0][random.randrange(0, 1)], self.run_minusElementInfo[2][0], self.run_minusElementInfo[2][1]))\n                elif 80<=plusOrMinus<85:\n                    self.floatElements[0].append(PlusElement(self.run_plusElementInfo[0][3], self.run_plusElementInfo[2][0], self.run_plusElementInfo[2][1]))\n                elif 85<=plusOrMinus<90:\n                     self.floatElements[1].append(MinusElement(self.run_minusElementInfo[0][1], self.run_minusElementInfo[2][0], self.run_minusElementInfo[2][1]))\n                elif 90<=plusOrMinus<95:\n                    self.floatElements[0].append(PlusElement(self.run_plusElementInfo[0][4], self.run_plusElementInfo[2][0], self.run_plusElementInfo[2][1]))\n        \n        elif self.gameMode == \"nomal\":\n            ## 부유물 객체의 생성 [plus : minus = 8 : 2]\n            if ((time.time() - self.startTime)%1 <0.1): # 1초에 2번 부유물 객체 생성\n                plusOrMinus = random.randrange(100)\n                if plusOrMinus < 70: # 2:8 = (minus:plus) 의 비율로 부유물 객체 생성 \n                    self.floatElements[0].append(PlusElement(self.run_plusElementInfo[0][random.randrange(0, 3)], self.run_plusElementInfo[2][0], self.run_plusElementInfo[2][1]))\n                elif 70<= plusOrMinus  <80:\n                    self.floatElements[1].append(MinusElement(self.run_minusElementInfo[0][random.randrange(0, 1)], self.run_minusElementInfo[2][0], self.run_minusElementInfo[2][1]))\n                elif 80<=plusOrMinus<85:\n                    self.floatElements[0].append(PlusElement(self.run_plusElementInfo[0][3], self.run_plusElementInfo[2][0], self.run_plusElementInfo[2][1]))\n                elif 85<=plusOrMinus<90:\n                     self.floatElements[1].append(MinusElement(self.run_minusElementInfo[0][1], self.run_minusElementInfo[2][0], self.run_minusElementInfo[2][1]))\n                elif 90<=plusOrMinus<95:\n                    self.floatElements[0].append(PlusElement(self.run_plusElementInfo[0][4], self.run_plusElementInfo[2][0], self.run_plusElementInfo[2][1]))\n        \n        elif self.gameMode == \"hard\":\n            ## 부유물 객체의 생성 [plus : minus = 8 : 2]\n            if ((time.time() - self.startTime)%1 <0.16): # 1초에 2번 부유물 객체 생성\n                plusOrMinus = random.randrange(100)\n                if plusOrMinus < 70: # 2:8 = (minus:plus) 의 비율로 부유물 객체 생성 \n                    self.floatElements[0].append(PlusElement(self.run_plusElementInfo[0][random.randrange(0, 3)], self.run_plusElementInfo[2][0], self.run_plusElementInfo[2][1]))\n                elif 70<= plusOrMinus  <80:\n                    self.floatElements[1].append(MinusElement(self.run_minusElementInfo[0][random.randrange(0, 1)], self.run_minusElementInfo[2][0], self.run_minusElementInfo[2][1]))\n                elif 80<=plusOrMinus<85:\n                    self.floatElements[0].append(PlusElement(self.run_plusElementInfo[0][3], self.run_plusElementInfo[2][0], self.run_plusElementInfo[2][1]))\n                elif 85<=plusOrMinus<90:\n                     self.floatElements[1].append(MinusElement(self.run_minusElementInfo[0][1], self.run_minusElementInfo[2][0], self.run_minusElementInfo[2][1]))\n                elif 90<=plusOrMinus<95:\n                    self.floatElements[0].append(PlusElement(self.run_plusElementInfo[0][4], self.run_plusElementInfo[2][0], self.run_plusElementInfo[2][1]))\n    \n    ## 게임 실행: 식사, 시간, 이벤트 체크\n    def eatChecker(self):\n        ## 먹은지 안 먹은지 주기적으로 검출\n        for element in self.floatElements[0]:\n            if (self.isInYourMouth(True , self.points, element)):\n                if element.eat():\n                    self.startPeverTime = time.time()\n                    \n                self.floatElements[0].remove(element)\n        \n        for element in self.floatElements[1]:\n            if (self.isInYourMouth(False , self.points, element)):\n                element.eat()\n                if self.life.life <= 0: self.gameFinish(\"GAMEOVER\") \n                self.floatElements[1].remove(element)\n            if self.rejectOnYourMouth(self.points, element):\n                self.floatElements[1].remove(element)\n\n    def timeChecker(self): \n        ## 시간 체크\n        # 타임오버\n        self.presentTime = (time.time() - self.startTime)//1 # 현재 시간 측정\n        if (self.presentTime > self.gameTime):\n            self.gameFinish(\"TIMEOVER\")\n        else:\n            self.timmerText = self.timmerFont.render(f\"{self.gameTime-int(self.presentTime)}\", True, BLACK)\n            self.timmerTextRect = self.timmerText.get_rect()\n        # 피버타임 시간 체크\n        if time.time() - self.startPeverTime > 10:\n            PlusElement.DOUBLE_MODE = False\n\n    def run_eventChecker(self):\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                self.quitGame()\n            elif event.type == pygame.MOUSEBUTTONDOWN:\n                if self.run_menuButton.pressed(event.pos) == True:\n                    STOP = True\n                    while STOP:\n                        self.SCREEN.blit(self.run_menuToIntroButton.button, self.run_menuToIntroButtonInfo[1])\n                        self.SCREEN.blit(self.run_menuCancelButton.button, self.run_menuCancelButtonInfo[1])\n\n                        for event in pygame.event.get():\n                            if event.type == pygame.QUIT:\n                                self.quitGame()\n                            elif event.type == pygame.MOUSEBUTTONDOWN:\n                                if self.run_menuToIntroButton.pressed(event.pos) == True:\n                                    self.run_menuToIntroButton.switchImg(\"Images/run_menuToIntroButton_pressed.png\")\n                                elif self.run_menuCancelButton.pressed(event.pos) == True:\n                                    self.run_menuCancelButton.switchImg(\"Images/run_menuCancelButton_pressed.png\")\n\n                            elif event.type == pygame.MOUSEBUTTONUP:\n                                pauseTime = time.time()\n                                self.run_menuToIntroButton.switchImg(\"Images/run_menuToIntroButton.png\")\n                                self.run_menuCancelButton.switchImg(\"Images/run_menuCancelButton.png\")\n\n                                if self.run_menuToIntroButton.pressed(event.pos) == True:\n                                    self.gameFactorClear()\n                                    self.introScreen()\n\n                                elif self.run_menuCancelButton.pressed(event.pos) == True:\n                                    STOP = False\n                                    self.startTime -= (time.time() - pauseTime) # 정지한 시간만큼 현재 시간 보정\n                                    \n                        pygame.display.update()\n\n    ## 게임 실행: 게임 시작 (메인화면)\n    def gameStart(self):\n        STOP = True\n        self.startPeverTime = 0\n        self.finishPeverTime = 0\n        for points in fc.run(visualize=1, max_threads=4, capture=0):\n            self.points = points\n            while STOP:\n                if self.gameMode == \"easy\": self.SCREEN.blit(self.imgs_run_waitingScreenEasy, self.run_waitingScreenEasyInfo[1])\n                elif self.gameMode == \"nomal\": self.SCREEN.blit(self.imgs_run_waitingScreenNomal, self.run_waitingScreenNomalInfo[1])\n                elif self.gameMode == \"hard\": self.SCREEN.blit(self.imgs_run_waitingScreenHard, self.run_waitingScreenHardInfo[1])\n\n                self.gamePauseFont = pygame.font.SysFont( 'impact', 40, False, False)\n                self.gamePauseText = self.gamePauseFont.render(\"Press \\\"SpaceBar\\\" to Start Game \", True, WHITE)\n                self.gamePauseTextRect = self.gamePauseText.get_rect() \n                self.SCREEN.blit(self.gamePauseText, [self.SCREEN_WIDTH/2 - self.gamePauseTextRect.w/2, self.SCREEN_HEIGHT - self.gamePauseTextRect.h])\n                \n                pygame.display.update()\n                for event in pygame.event.get():\n                    if event.type == pygame.KEYDOWN:\n                        if event.key == pygame.K_SPACE:\n                            self.startTime = time.time()\n                            STOP = False \n\n            self.run_eventChecker()\n            self.floatterGenerotr()\n            self.eatChecker()\n            self.timeChecker()\n        \n            \n            ## 부유물 30개 넘으면 Game Over\n            if (len(self.floatElements[0]) + len(self.floatElements[1]) > 30 ): self.gameFinish(\"GAMEOVER\")\n\n            ## 게임중 추가 요소는 배경 생성 후 추가해줄 것\n            self.gameBoard()\n            self.SCREEN.blit(self.img, [0, 0]) # 배경\n            self.SCREEN.blit(self.timmerText, [self.SCREEN_WIDTH/2 - self.timmerTextRect.w/2, 0])\n            self.SCREEN.blit(self.run_menuButton.button, self.run_menuButtonInfo[1])\n            ### 피버타임\n            if PlusElement.DOUBLE_MODE:\n                game.SCREEN.blit(self.imgs_run_fire, [points[67][0], points[67][1]*1.1])\n                game.SCREEN.blit(self.imgs_run_fire, [points[66][0], points[66][1]*1.1])\n            if MinusElement.Minus_MODE:\n                game.SCREEN.blit(self.imgs_run_band, [points[67][0], points[67][1]*1.1])\n                game.SCREEN.blit(self.imgs_run_band, [points[66][0], points[66][1]*1.1])\n            \n            ## 점수 객체 화면 출력\n            self.gameScoreFont = pygame.font.SysFont( 'impact', 40, False, False)\n            self.gameScoreText = self.gameScoreFont.render(f\"SCORE : {self.score.score}\", True, RED)\n            self.gameScoreTextRect = self.gameScoreText.get_rect()\n            self.SCREEN.blit(self.gameScoreText, [0, self.SCREEN_HEIGHT - self.gameScoreTextRect.h])\n\n            ## 현재 화면의 부유물 수 출력\n            self.gameFoodNumFont = pygame.font.SysFont( 'impact', 40, False, False)\n            self.gameFoodNumText = self.gameFoodNumFont.render(f\"FOOD NUMBER : {len(self.floatElements[0]) + len(self.floatElements[1])}\", True, BLUE)\n            self.gameFoodNumRect = self.gameFoodNumText.get_rect()\n            self.SCREEN.blit(self.gameFoodNumText, [self.SCREEN_WIDTH - self.gameFoodNumRect.w, self.SCREEN_HEIGHT - self.gameFoodNumRect.h])\n\n            ## 부유물 객체를 출력\n            for element in self.floatElements[0]:\n                self.SCREEN.blit(element.floatter, (element.rect.x, element.rect.y))\n            for element in self.floatElements[1]:\n                self.SCREEN.blit(element.floatter, (element.rect.x, element.rect.y))\n                \n            ## 부유물 객체의 이동\n            for elementType in self.floatElements:\n                for element in elementType:\n                    element.randomMove()\n\n\n            if self.life.life == 3:\n                self.SCREEN.blit(self.imgs_run_heart3, self.run_lifeInfo[1]) \n            elif self.life.life ==2:\n                self.SCREEN.blit(self.imgs_run_heart2, self.run_lifeInfo[1])\n            elif self.life.life ==1:\n                self.SCREEN.blit(self.imgs_run_heart1, self.run_lifeInfo[1])\n\n            pygame.display.update()\n\n    ## 게임 실행: 게임 프로그램 실행\n    def run(self):\n        pygame.init() # 파이게임 라이브러리 초기 세팅\n        self.clock = pygame.time.Clock() # timmer 사용을 위한 객체 생성 \n        self.gameFactor()  # 어떤 요소를 만들지 선언\n        self.getImgs() # gameFactor에서 선언한 요소의 이미지 파일을 불러들인다. [객체는 제외  Ex) Button ]\n        self.buttons_generate() # 모든 버튼 생성 메서드\n        self.timmerFont = pygame.font.SysFont( 'impact', 70, False, False) # 시간을 화면에 출력해줄 폰트객체 생성\n        self.life = Life() \n        self.SCREEN = pygame.display.set_mode((self.SCREEN_WIDTH, self.SCREEN_HEIGHT)) # 스크린 객체 생성\n        pygame.display.set_caption(\"Yam-Yam\") # 게임 타이틀 선언\n        self.event = pygame.event.poll() # 이벤트 객체 생성\n        self.score = Score()\n        \n        # 게임 실행 첫 화면은 인트로로 실행\n        self.introScreen()\n        \n\n    ########## 게임 프로그램 종료 ##########\n    ## 게임 프로그램 종료 : 저장 및 종료\n    def quitGame(self):\n        STOP = True\n        while STOP:\n            pygame.draw.rect(self.SCREEN, BLACK, [0, 0, self.SCREEN_WIDTH, self.SCREEN_HEIGHT])\n            self.textHelpper(\"SAVE OR NOT?\", 40, WHITE, self.SCREEN_WIDTH/2, self.SCREEN_WIDTH*0.1)\n            self.textHelpper(f\"Your Best Score : {self.userBestScore}\", 20, WHITE, self.SCREEN_WIDTH/2, self.SCREEN_WIDTH*0.2)\n            self.SCREEN.blit(self.run_noButton.button, self.run_noButtonInfo[1])\n            self.SCREEN.blit(self.run_yesButton.button, self.run_yesButtonInfo[1])\n            ## 점수 객체 화면 출력\n            \n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    self.quitGame()\n                elif event.type == pygame.MOUSEBUTTONDOWN:\n                    if self.run_yesButton.pressed(event.pos) == True:\n                        self.run_yesButton.switchImg(\"Images/run_yesButton_pressed.png\")\n                    elif self.run_noButton.pressed(event.pos) == True:\n                        self.run_noButton.switchImg(\"Images/run_noButton_pressed.png\")\n\n                elif event.type == pygame.MOUSEBUTTONUP:\n                    pauseTime = time.time()\n                    self.run_yesButton.switchImg(\"Images/run_yesButton.png\")\n                    self.run_noButton.switchImg(\"Images/run_noButton.png\")\n\n                    if self.run_yesButton.pressed(event.pos) == True:\n                        ## user 관리 파일에 유저의 최고점수를 저장한다.\n                        print(self.userHistory)\n                        if self.userHistory:\n                            file = open(\"User/userInfo.txt\", \"r\", encoding=\"UTF-8\")\n                            edit_file = []\n                            \n                            for user in file:\n                                user = user.split()\n                                if self.userName != user[0]:\n                                    edit_file.append(f\"{user[0]} {user[1]}\\n\")\n                                \n                            file = open(\"User/userInfo.txt\", \"w\", encoding=\"UTF-8\")\n                            file.write(f\"{self.userName} {self.userBestScore}\\n\")\n                            file = open(\"User/userInfo.txt\", \"a\", encoding=\"UTF-8\")\n                            for userInfo in edit_file:\n                                file.write(userInfo)\n                            STOP = False\n\n                        else :\n                            STOP = False\n                            file = open(\"User/userInfo.txt\", \"a\", encoding=\"UTF-8\")\n                            file.write(f\"{self.userName} {self.userBestScore}\\n\")\n                            STOP = False\n\n                    elif self.run_noButton.pressed(event.pos) == True:\n                        STOP = False\n            pygame.display.update()\n        \n        pygame.quit()\n        sys.exit()\n    \n  \nif __name__ == \"__main__\":\n    # generte the game\n    print(\"Welcome to our game !!\")\n    game = Game()\n    game.run()\n","repo_name":"JiungChoi/PythonProgramming2021","sub_path":"2021PythonProgramming_TeamProj_3Team/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":52487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"39725077650","text":"import sys\n\n\ndef match_character(char, pattern):\n    return True if pattern == \".\" else char == pattern\n\n\ndef match_digit(char):\n    return char.isdigit()\n\n\ndef match_alphanumeric(char):\n    return char.isalnum()\n\n\ndef match_character_groups(input_char, pattern):\n    group = pattern.split(\"]\")[0]\n    character_in_group = any(char == input_char for char in group.replace(\"^\", \"\"))\n    matched_length = [character_in_group, len(group) + 2]\n    if group.startswith(\"^\"):\n        matched_length[0] = not matched_length[0]\n    return matched_length\n\n\ndef match_zero_or_one_times(input_line, pattern):\n    return 0 if input_line[0] == pattern else -1\n\n\ndef match_one_or_more_times(input_line, pattern):\n    times = 0\n    for char in input_line:\n        if char == pattern:\n            times += 1\n        else:\n            break\n    return times - 1\n\n\ndef match_alternation(input_line, pattern):\n    alternation = pattern.split(\")\")[0]\n    alternation_length = len(alternation) + 2\n    alternatives = alternation.split(\"|\")\n    return [any(try_match(input_line, a) for a in alternatives), alternation_length]\n\n\ndef try_match(input_line, pattern):\n    pattern_ind, pattern_end = 0, len(pattern)\n    line_ind, line_end = 0, len(input_line)\n\n    while line_ind < line_end and pattern_ind < pattern_end:\n        char = input_line[line_ind]\n        pattern_left = pattern[pattern_ind:]\n\n        if pattern_left.startswith(\"\\d\") and match_digit(char):\n            pattern_ind += 2\n        elif pattern_left.startswith(\"\\w\") and match_alphanumeric(char):\n            pattern_ind += 2\n        elif pattern_left.startswith(\"[\"):\n            matched, length = match_character_groups(char, pattern_left[1:])\n            if matched:\n                pattern_ind += length\n            else:\n                return False\n        elif pattern_left.startswith(\"(\"):\n            matched, length = match_alternation(input_line[line_ind:], pattern_left[1:])\n            if matched:\n                pattern_ind += length\n            else:\n                return False\n        elif pattern_ind < pattern_end - 1 and pattern[pattern_ind + 1] == \"+\":\n            pattern_ind += 2\n            matched = match_one_or_more_times(input_line[line_ind:], pattern_left[0])\n            if matched >= 0:\n                line_ind += matched\n            else:\n                return False\n        elif pattern_ind < pattern_end - 1 and pattern[pattern_ind + 1] == \"?\":\n            pattern_ind += 2\n            line_ind += match_zero_or_one_times(input_line[line_ind:], pattern_left[0])\n        elif match_character(char, pattern_left[0]):\n            pattern_ind += 1\n        else:\n            return False\n        line_ind += 1\n\n    return pattern_ind == pattern_end\n\n\ndef match_pattern(input_line, pattern):\n    start_ind, end_ind = len(input_line) - 1, -1\n    if pattern.startswith(\"^\"):\n        start_ind = 0\n        pattern = pattern[1:]\n    if pattern.endswith(\"$\"):\n        pattern = reverse_pattern(pattern)\n        start_ind = 0\n        input_line = input_line[::-1]\n    for ind in range(start_ind, end_ind, -1):\n        if try_match(input_line[ind:], pattern):\n            return True\n    return False\n\n\ndef reverse_pattern(pattern):\n    return (\n        pattern.replace(\"\\\\w\", \"w\\\\\")\n        .replace(\"\\\\d\", \"d\\\\\")\n        .replace(\"]\", \"*\")\n        .replace(\"[\", \"]\")\n        .replace(\"*\", \"[\")[:-1][::-1]\n    )\n\n\ndef main():\n    pattern = sys.argv[2]\n    input_line = sys.stdin.read().replace(\"\\n\", \"\")\n\n    if sys.argv[1] != \"-E\":\n        print(\"Expected first argument to be '-E'\")\n        exit(1)\n\n    # Uncomment this block to pass the first stage\n    if match_pattern(input_line, pattern):\n        print(f\"matched {pattern} in {input_line}\")\n        exit(0)\n    else:\n        print(f\"no match\")\n        exit(1)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Kerman07/build-your-own-grep","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"4210021783","text":"import numpy as np\nimport pandas as pd\nimport datetime\nimport math\nfrom scipy import stats\n# from fast_arrow import StockMarketdata\n# from live_option_data import GatherData\n\ndef option_calculator(kind, spot_price, strike, expiration, IV, time_taken = None, expected_date=None, IR = 0.75):\n\n    if len(expiration.split('-')) ==3:\n        year, month, day = expiration.split('-')\n        expiration_date = datetime.datetime(int(year), int(month), int(day))\n    else:\n        year, month, day, hour, minute = expiration.split('-')\n        expiration_date = datetime.datetime(int(year), int(month), int(day), int(hour), int(minute))\n\n    # year, month, day = expiration.split('-')\n    # expiration_date = datetime.datetime(int(year), int(month), int(day))\n\n    if time_taken is not None:\n\n        today = datetime.datetime.today()\n        diff = expiration_date - today\n\n        if (today + datetime.timedelta(time_taken)).weekday() == 6: # If Sunday\n            time_taken += 1\n        if (today + datetime.timedelta(time_taken)).weekday() == 5: # If Saturday\n            time_taken += 2\n        if diff.days < 1:\n            time = ((diff.seconds/86400) - time_taken)/365\n        if diff.days >= 1:\n            time = (diff.days - time_taken)/365\n\n    if expected_date is not None:\n\n        if len(expected_date.split('-')) ==3:\n            year, month, day = expected_date.split('-')\n            expected_date = datetime.datetime(int(year), int(month), int(day))\n        else:\n            year, month, day, hour, minute = expected_date.split('-')\n            expected_date = datetime.datetime(int(year), int(month), int(day), int(hour), int(minute))\n\n        time_diff = expiration_date - expected_date\n        time = ((time_diff.seconds + time_diff.days*86400)/3600)/8760\n        # time = (expiration_date - expected_date).days/365\n        # print(expiration_date, expected_date, (time_diff.seconds + time_diff.days*86400)/3600, time)\n        # print(expiration_date, expected_date, ((expiration_date - expected_date).seconds/3600), time)\n\n    d1_numerator = np.log(spot_price/strike) + (IR/100 + ((IV)**2)/2)*time\n    d1 = d1_numerator / (IV*np.sqrt(time))\n    d2 = d1 - IV*np.sqrt(time)\n    # print(time)\n    # print(d1_numerator, d1, d2)\n\n    if kind.lower() == 'call':\n        value = spot_price * stats.norm.cdf(d1) - (strike/np.exp((IR/100)*time))*stats.norm.cdf(d2)\n\n    elif kind.lower() == 'put':\n        value = (strike/np.exp((IR/100)*time))*stats.norm.cdf(-d2) - spot_price * stats.norm.cdf(-d1)\n\n    return value\n\ndef get_option_prices(kind, spot, target, strike, expiration, IV_now, IV_expected, time_taken = None, expected_date=None, IR = 0.25):\n\n    if time_taken is not None:\n        spot_price = option_calculator(kind, spot, strike, expiration, IV, time_taken=0)\n        target_price = option_calculator(kind, target, strike, expiration, IV, time_taken=time_taken)\n\n    if expected_date is not None:\n        right_now = datetime.datetime.now()\n        right_now = f'{right_now.year}-{right_now.month}-{right_now.day}-{right_now.hour}-{right_now.minute}'\n        spot_price = option_calculator(kind, spot, strike, expiration, IV=IV_now, expected_date=right_now)\n        target_price = option_calculator(kind, target, strike, expiration, IV=IV_expected, expected_date=expected_date)\n\n    print(f'Price: ${spot_price:0.4f}')\n    print(f'Target: $ {target_price:0.4f}')\n    print(f'Reward/Risk: {abs((target_price-spot_price)/spot_price)*100:0.2f} %')\n\ndef get_spread_option_prices(kind, stop, spot, target, strike_buy, strike_sell, expiration, IV_buy, IV_sell, time_taken = None, expected_date=None, IR = 0.25):\n\n    stop_buy = option_calculator(kind, stop, strike_buy, expiration, IV=IV_buy, expected_date=expected_date)\n    stop_sell = option_calculator(kind, stop, strike_sell, expiration, IV=IV_sell, expected_date=expected_date)\n\n    spot_buy = option_calculator(kind, spot, strike_buy, expiration, IV_buy, time_taken=0)\n    spot_sell = option_calculator(kind, spot, strike_sell, expiration, IV_sell, time_taken=0)\n\n    target_buy = option_calculator(kind, target, strike_buy, expiration, IV=IV_buy, expected_date=expected_date)\n    target_sell = option_calculator(kind, target, strike_sell, expiration, IV=IV_sell, expected_date=expected_date)\n\n    stop_price = stop_buy - stop_sell\n    spot_price = spot_buy - spot_sell\n    target_price = target_buy - target_sell\n\n    print(f'Stop Price: $ {stop_price:0.4f}')\n    print(f'Buy Price : $ {spot_price:0.4f}')\n    print(f'Target Price: $ {target_price:0.4f}')\n    print(f'Reward: $ {(target_price-spot_price)*100:0.4f} -- {(target_price-spot_price)/((spot_price-stop_price))*100:0.2f}%')\n    print(f'Loss: $ {(spot_price-stop_price)*100:0.4f}')\n\n\ndef get_reward_risk(kind, stop, spot, target, strike, expiration, IV, IV_e, expected_date):\n    \n    cost = option_calculator(kind, spot, strike, expiration, IV, time_taken=0)\n    stop_loss = option_calculator(kind, stop, strike, expiration, IV_e, expected_date=expected_date)\n    target_price = option_calculator(kind, target, strike, expiration, IV_e, expected_date=expected_date)\n    loss = cost-stop_loss\n    profit = target_price-cost\n    \n    print(f'Price: $ {cost:0.4f} - Stop: $ {stop_loss:0.4f} - Target: $ {target_price:0.4f}')\n    print(f'Loss: $ {loss:0.2f} - Profit: $ {profit:0.2f}')\n    print('----------------------------------------------')\n    print(f'Reward/Risk Ratio: {abs(profit/loss)*100:0.2f} %')\n\n# def get_reward_risk2(symbol, kind, stop, spot, target, strike, expiration, IV_e, expected_date, risk, IV_jump=0, show_iv_jumps=False):\n    \n#     expiration_base = '-'.join(expiration.split('-')[0:3])\n#     GD = GatherData()\n#     option_chain = GD.main(symbol, expiration_base, kind, strike)\n\n#     today = datetime.datetime.now()\n#     data_loc_df = GD.get_day_starts(GD.date_loc_Dict)\n#     try:\n#         start_index = data_loc_df.loc[f'{today.year}-{today.month}-{today.day}'][0]\n#     except KeyError:\n#         pass\n#     else:\n#         iv_difference = option_chain['implied_volatility'][start_index] - option_chain['implied_volatility'][start_index-1]\n#         print(f'IV Jump: {iv_difference:0.3f}')\n\n#     if show_iv_jumps:\n#         iv_jumps = get_IV_openings(option_chain)\n#         stats_IV_opening_jumps(iv_jumps)\n\n#     right_now = datetime.datetime.now()\n#     if right_now < datetime.datetime(right_now.year, right_now.month, right_now.day, 6, 30, 30):\n#         print(\"Before Opening\")\n#         IV = option_chain['implied_volatility'][-1]+IV_jump\n#     else:\n#         IV = option_chain['implied_volatility'][-1]\n    \n#     IV_e = IV + (IV_e/100)\n    \n#     cost = option_calculator(kind, spot, strike, expiration, IV, time_taken=0)\n#     stop_loss = option_calculator(kind, stop, strike, expiration, IV_e, expected_date=expected_date)*100//1/100\n#     target_price = option_calculator(kind, target, strike, expiration, IV_e, expected_date=expected_date)*100//1/100\n#     loss = round(cost,2)-stop_loss\n#     profit = target_price-round(cost,2)\n    \n#     print(f'IV: {IV:0.4f} - Exected IV: {IV_e:0.4f}')\n#     print(f'Price: $ {round(cost,2):0.3f} - Stop: $ {stop_loss:0.3f} - Target: $ {target_price:0.3f}')\n#     print(f'Loss: $ {loss:0.3f} - Profit: $ {profit:0.3f}')\n#     print('----------------------------------------------')\n#     print(f'Reward/Risk Ratio: {abs(profit/loss)*100:0.2f} %')\n#     print(f\"Shares to Buy: {math.floor(risk/(loss*100))}\")\n\ndef get_stats(stock_data):\n\n    \"\"\"\n    This returns the mean and stdv or the high and low spread ohlc prices\n    \"\"\"\n    mean_oc = np.mean(np.log(stock_data['close'][-30:]/stock_data['open'][-30:]))*100\n    stdv_oc = np.std(np.log(stock_data['close'][-30:]/stock_data['open'][-30:]))*100\n    print(f'Close/Open  -  Mean: {mean_oc:0.2f}% - Stdv: {stdv_oc:0.2f}%')\n\n    mean_hl = np.mean(np.log(stock_data['high'][-30:]/stock_data['low'][-30:]))*100\n    stdv_hl = np.std(np.log(stock_data['high'][-30:]/stock_data['low'][-30:]))*100\n    print(f'High/Low  -  Mean: {mean_hl:0.2f}% - Stdv: {stdv_hl:0.2f}%')\n\n    print('\\n')\n\ndef stats_IV_opening_jumps(iv_jumps):\n    \n    differences = []\n    \n    for x, y in iv_jumps:\n        differences.append(y-x)\n    \n    print(f\"Opening IV Gaps -> Average: {np.mean(differences):0.4f} - STDV: {np.std(differences):0.4f}\")\n\n\n#################### DATA RELATED ####################\n\n# def gather_rh_prices(client, symbol, span='5year', bounds='regular'):\n#     # Gather Stock data and the closing deviation.\n#     data = StockMarketdata.historical_quote_by_symbol(client, symbol, span, bounds)\n#     data = pd.DataFrame(data['historicals'])[\n#         ['begins_at', 'open_price', 'high_price', 'low_price', 'close_price']]\n#     data['begins_at'] = data['begins_at'].astype('datetime64[ns]')\n#     data.columns = ['date', 'open', 'high', 'low', 'close']\n#     for col in data.columns[-4:]:\n#         data[col] = data[col].astype('float64')\n#     data['close pct'] = np.log(data['close']/data['close'].shift(1))\n#     data.set_index('date', inplace = True)\n\n#     return data.dropna()\n\ndef get_correlations(data1, data2):\n\n    pearson = stats.pearsonr(data1, data2)\n    spear = stats.spearmanr(data1, data2)\n    kendall = stats.kendalltau(data1, data2)\n\n    print(f'Pearson R: {pearson[0]:0.2f}')\n    print(f'Spearman R: {spear.correlation:0.2f}')\n    print(f'Kendalltau: {kendall.correlation:0.2f}')\n\ndef get_IV_openings(chain):\n    \n    started_day = False\n    iv_openings = []\n\n    for i in range(len(chain)):\n\n        if chain.index[i].hour==6 and started_day==False:\n#             print('Started Day')\n            iv_openings.append([chain['implied_volatility'][i-1],chain['implied_volatility'][i]])\n            started_day = True\n\n        if chain.index[i].hour>6 and started_day==True:\n            started_day = False\n            \n    return iv_openings","repo_name":"leoi137/ipynb-options-calculator","sub_path":"trading_formulas.py","file_name":"trading_formulas.py","file_ext":"py","file_size_in_byte":9820,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"29696763030","text":"# Author: Davis Lee\n# Description: Frontend for Proacrastinate-Smart using Dash\n\nimport functions\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output, State\n\n\napp = dash.Dash(__name__)\n\napp.layout = html.Div(children=[\n    html.Header(id='title', children=\"Procrastinate Smart!\"),\n    html.Header(id='header1', children='Please input youtube lecture video urls separated by spaces'),\n    dcc.Textarea(\n        id='inputVideos',\n        placeholder='enter urls',\n        style={'width': '100%'},\n        rows=7,\n        cols=30\n    ),\n\n    html.Button(id='submit-videos', n_clicks=0, children='Submit'),\n    html.Header(id='header2', children='', style={'display': 'none', 'width': '100%'}),\n    html.Header(id='header3', children='', style={'display': 'none'}),\n    dcc.Textarea(\n        id='inputSearch',\n        placeholder='Search Term',\n        style={'width': '30%'},\n        rows=1\n    ),\n    html.Button(id='submit-search', n_clicks=0, children='Search', style={'display': 'none'}),\n    html.Header(id='header4', children='', style={'display': 'none'}),\n\n    html.Hr(id='bar2', style={'display': 'none'}),\n    html.Div(id='output'),\n    html.Div(id='videoArray', style={'display': 'none'}),\n    html.Div(id='resultArray', children=\"\", style={'display': 'none'})\n])\n\n\n@app.callback(Output('videoArray', 'children'),\n              [Input('submit-videos', 'n_clicks')],\n              [State('inputVideos', 'value')])\ndef update_videos(n_clicks, input):\n    links = input.split()\n    links = functions.parseLinks(links)\n    return links\n\n\n@app.callback(Output('header2', 'children'),\n              [Input('submit-videos', 'n_clicks')],\n              [State('inputVideos', 'value')])\ndef update_videos(n_clicks, input):\n    return str(input.count(' ') + 1) + ' videos successfully uploaded! '\n\n\n@app.callback(Output('header3', 'children'),\n              [Input('submit-videos', 'n_clicks')],\n              [State('inputVideos', 'value')])\ndef update_header3(n_clicks, input):\n    return \" What do you want to learn?\"\n\n\n@app.callback(Output('header3', 'style'),\n              [Input('submit-videos', 'n_clicks')],\n              [State('inputVideos', 'value')])\ndef show_header3(n_clicks, input):\n    if n_clicks > 0:\n        return {'display': 'block'}\n    return {'display': 'none'}\n\n\n@app.callback(Output('header4', 'children'),\n              [Input('resultArray', 'children')],\n              [State('resultArray', 'children')])\ndef update_header4(n_clicks, input):\n    return str(int(len(input)/2)) + \" results found!\"\n\n\n@app.callback(Output('header4', 'style'),\n              [Input('resultArray', 'children')],\n              [State('inputVideos', 'value')])\ndef show_header4(n_clicks, input):\n    if len(input) > 0:\n        return {'display': 'block'}\n    return {'display': 'none'}\n\n\n@app.callback(Output('header2', 'style'),\n              [Input('submit-videos', 'n_clicks')],\n              [State('inputVideos', 'value')])\ndef show_header2(n_clicks, input):\n    if n_clicks > 0:\n        return {'display': 'block'}\n    return {'display': 'none'}\n\n\n@app.callback(Output('inputSearch', 'style'),\n              [Input('submit-videos', 'n_clicks')],\n              [State('inputVideos', 'value')])\ndef show_input_search(n_clicks, input):\n    if n_clicks > 0:\n        return {'width': '100%', 'display': 'inline-block'}\n    return {'width': '100%', 'display': 'none'}\n\n\n@app.callback(Output('submit-search', 'style'),\n              [Input('submit-videos', 'n_clicks')],\n              [State('inputVideos', 'value')])\ndef show_submit_search(n_clicks, input):\n    if n_clicks > 0:\n        return {'display': 'inline-block'}\n    return {'display': 'none'}\n\n\n@app.callback(Output('bar2', 'style'),\n              [Input('submit-videos', 'n_clicks')],\n              [State('inputVideos', 'value')])\ndef show_bar2(n_clicks, input):\n    if n_clicks > 0:\n        return {'display': 'inline-block'}\n    return {'display': 'none'}\n\n\n@app.callback(Output('resultArray', 'children'),\n              [Input('submit-search', 'n_clicks')],\n              [State('videoArray', 'children'),\n               State('inputSearch', 'value')])\ndef update_output(n_clicks, videoArray, inputSearch):\n\n    links = videoArray\n    xmlDicts = []\n    for link in links:\n        xmlDicts.append(functions.xmlToDict(link))\n    timeDicts = []\n    for xmlDict in xmlDicts:\n        timeDicts.append(functions.buildProperDict(xmlDict))\n    links2, descriptions = functions.searchAndDisplay(links, xmlDicts, timeDicts, inputSearch)\n    return links2 + descriptions\n\n\n@app.callback(Output('output', 'children'),\n              [Input('resultArray', 'children')],\n              [State('resultArray', 'children')])\ndef result_data(children1, children2):\n    dict = []\n    for i in range(0, int(len(children1)/2)):\n        # https://www.youtube.com/watch?v=SXR9CDof7qw&feature=youtu.be&t=2860\n        # https://www.youtube.com/watch?v=tBiPumGnVT4&t=16\n        # http://video.google.com/timedtext?lang=en&v=SXR9CDof7qw&t=2372.51\n        link = children1[i]\n        print(link)\n        index = link.index('v=') + 2\n        extension = link[index:]\n        link = 'https://www.youtube.com/watch?v=' + extension\n        try:\n            index2 = link.index('.', link.index('t=') + 2)\n        except ValueError:\n            index2 = -1\n\n        if index2 != -1:\n            link = link[:index2]\n        dict.append({'props': {'children': '\"' + children1[i + int((len(children1)/2))] + '\"',\n                               'href': link, 'target': '_blank'}, 'type': 'A', 'namespace': 'dash_html_components'})\n    return dict\n\n\nif __name__ == '__main__':\n    app.run_server(debug=True)\n","repo_name":"davislee7/Procrastinate-Smart","sub_path":"display.py","file_name":"display.py","file_ext":"py","file_size_in_byte":5711,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"30433075205","text":"########################################################################\n##THIS CLASS DEFINE THE CNN MODEL.                                    ##\n##THE ARCHITECTURE OF THE NETWORK IS BASED ON THAT OF LeNet-5 NETWORK.##\n########################################################################\n\n#IMPORT OF THE REQUIRED LIBRARIES\nimport torch.nn as nn\nfrom torch.nn import functional as F\n\n#INSTANTIATE THE CLASS\nclass LeNet_5(nn.Module):\n    def __init__(self,drop_1=0.5,drop_2=0.3):\n        super(LeNet_5, self).__init__()\n\n        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)\n\n        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1, padding=0)\n        self.drop1=nn.Dropout(p=drop_1)   \n\n        self.fc1 = nn.Linear(16* 5 * 5, 120)\n        self.fc2 = nn.Linear(120,84)\n        self.drop2=nn.Dropout(p=drop_2)\n        self.fc3 = nn.Linear(84, 10)\n    \n    # FORWARD PASS METHOD\n    def forward(self, x):\n        x = F.relu(F.max_pool2d(self.conv1(x),kernel_size = 2, stride=2))\n\n        x = F.relu(F.max_pool2d(self.conv2(x),kernel_size = 2, stride=2))\n        x = self.drop1(x)\n\n        x = x.view(x.size(0),-1)\n\n        x = F.relu(self.fc1(x))\n        x = self.drop2(x)\n\n        x = F.relu(self.fc2(x))\n        x = self.fc3(x)\n\n        return x","repo_name":"Lambe96/NeuralNetwork_DeepLearning","sub_path":"Homework_1-20210328/classification_task/code/Conv_net.py","file_name":"Conv_net.py","file_ext":"py","file_size_in_byte":1323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"22515600727","text":"\nimport matplotlib.pyplot as plt\nimport argparse\n\nclass puzzle:\n\n    def __init__(self, size, block):\n        # size is the size of board.\n        # block is the position (x,y) of the block\n\n        # fill the initial block as black\n        self.fill_one_square(block, 'k')\n        # draw the board\n        self.grid(size)\n        # solve it\n        self.solve(block, 1, size, 1, size)\n        # save and show\n        self.save_and_show(size, block)\n\n    # solve the puzzle with a block at board[left, right, bottom, top]\n    def solve(self, block, left, right, bottom, topr):\n        # You should recursively call solve() on four small size boards with only one block on each board\n        # You should stop the recursive call when reaching to a base case which is board 2*2\n\n        # Your code goes here:\n        quadrant = self.get_quadrant(block, left, right, bottom, topr)\n        self.fill_L(quadrant, left, right, bottom, topr)\n\n        if right - left == 1:\n            return\n\n        # Get blocks of L shape based on block's quadrant\n        cr, cc = (topr + bottom) // 2, (right + left) // 2\n        blocks = [block]\n        if quadrant == 1:\n            blocks += [(cr, cc), (cr, cc + 1), (cr + 1, cc)]\n        elif quadrant == 2:\n            blocks += [(cr, cc), (cr, cc + 1), (cr + 1, cc + 1)]\n        elif quadrant == 3:\n            blocks += [(cr, cc + 1), (cr + 1, cc), (cr + 1, cc + 1)]\n        elif quadrant == 4:\n            blocks += [(cr, cc), (cr + 1, cc), (cr + 1, cc + 1)]\n\n        # Solve for each of these blocks\n        for square in blocks:\n            square_quadrant = self.get_quadrant(square, left, right, bottom, topr)\n            if square_quadrant == 1:\n                self.solve(square, (left + right + 1)//2, right, (bottom + topr + 1)//2, topr)\n            elif square_quadrant == 2:\n                self.solve(square, left, (left + right)//2, (bottom + topr + 1)//2, topr)\n            elif square_quadrant == 3:\n                self.solve(square, left, (left + right)//2, bottom, (bottom + topr)//2)\n            elif square_quadrant == 4:\n                self.solve(square, (left + right + 1)//2, right, bottom, (bottom + topr)//2)\n\n    # return the quadrant of the block at board[left, right, bottom, top]\n    def get_quadrant(self, block, left, right, bottom, topr):\n        if block[0] - bottom < topr - block[0]:\n            quadrant = 3\n            if not (block[1] - left < right - block[1]):\n                quadrant += 1\n        else:\n            quadrant = 1\n            if block[1] - left < right - block[1]:\n                quadrant += 1\n        return quadrant\n\n    # fill a L at the center of board[left, right, bottom, top]\n    def fill_L(self, quadrant, left, right, bottom, topr):\n        # cr is row of the center\n        # cc is column of the center\n        cr, cc = (topr+bottom)//2, (right+left)//2\n        # L of type 1\n        if quadrant == 1:\n            self.fill_one_square((cr,cc), 'r')\n            self.fill_one_square((cr,cc+1), 'r')\n            self.fill_one_square((cr+1,cc), 'r')\n        # L of type 2\n        elif quadrant == 2:\n            self.fill_one_square((cr,cc), 'b')\n            self.fill_one_square((cr,cc+1), 'b')\n            self.fill_one_square((cr+1,cc+1), 'b')\n        # L of type 3\n        elif quadrant == 3:\n            self.fill_one_square((cr,cc+1), 'g')\n            self.fill_one_square((cr+1,cc), 'g')\n            self.fill_one_square((cr+1,cc+1), 'g')\n        # L of type 4\n        elif quadrant == 4:\n            self.fill_one_square((cr,cc), 'y')\n            self.fill_one_square((cr+1,cc), 'y')\n            self.fill_one_square((cr+1,cc+1), 'y')\n\n    # fill one square at postion (x,y) in color\n    def fill_one_square(self, position, color):\n        x, y = self.get_xy_coordinate(position)\n        plt.fill(x,y, color)\n\n    # position is (i,j)\n    # return [x1,x2,x3,x4], [y1,y2,y3,y4]\n    def get_xy_coordinate(self, position):\n        r1, r2 = position[0]-1, position[0]\n        c1, c2 = position[1]-1, position[1]\n        return [c1, c2, c2, c1], [r1, r1, r2, r2]\n\n    # draw the board\n    def grid(self,size):\n        for row in range(size+1):\n            x = [i for i in range(size+1)]\n            for col in range(size+1):\n                y = [col for i in range(size+1)]\n                plt.plot(x,y,'k')\n\n        for col in range(size+1):\n            y = [i for i in range(size+1)]\n            for row in range(size+1):\n                x = [col for i in range(size+1)]\n                plt.plot(x,y,'k')\n\n    # save and show the solution\n    def save_and_show(self,size,block):\n        plt.axis(\"off\")\n        plt.axis('equal')\n        plt.title(\"puzzle\")\n        plt.savefig(\"result_size_\" + str(size) + \"_block_\" + str(block[0]) + \"_\" + str(block[1]) + \".png\")\n        plt.show()\n\nif __name__ == \"__main__\":\n\n    parser = argparse.ArgumentParser(description='puzzle')\n\n    parser.add_argument('-size', dest='size', required = True, type = int, help='size of the board: 2^n')\n    parser.add_argument('-block', dest='block', required = True, nargs='+', type = int, help='position of the initial block')\n\n    args = parser.parse_args()\n\n    # size is the size of board. size must be a positive integer 2^n like 2, 4, 8, 16.\n    # block is the initial position (x,y) of the missing square. block must be two integers between 1 and size\n    # run this code in terminal like this: python puzzle.py -size 8 -block 1 1\n    game = puzzle(args.size, tuple(args.block))\n\n","repo_name":"Nathan-Hutton/Tromino-Solver","sub_path":"puzzle.py","file_name":"puzzle.py","file_ext":"py","file_size_in_byte":5471,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"13881677276","text":"import copy\nimport sys\ninput = sys.stdin.readline\n\ndef dfs(backtrack, answer):\n    pass\n\nsentence = input()\nn = int(input())\n\nletter = []\nfor i in range(n):\n    letter.append(input().rstrip())\n\ndata = []\nbacktrack = dict()\n\nfor i in letter:\n    start = 0\n    tmp = []\n    cnt = 0\n    end = len(i)\n    while start <= len(sentence):\n        part = sentence[start: end]\n        find = True\n        for j in range(len(i)):\n            if i[j] in part:\n                continue\n            else:\n                find = False\n                break\n        if find:\n            for k in range(len(i)):\n                if i[k] != part[k]:\n                    cnt += 1\n            tmp.append(start)\n            backtrack[start] = []\n            tmp.append(end)\n            tmp.append(cnt)\n            \n            start += len(i)\n            end += len(i)\n        else:\n            start += 1\n            end += 1\n    data.append(tmp)\n\nanswer = 0\n\nfor i in data:\n    if len(i) > 3:\n        for j in i[::3]:\n            backtrack[j[0]].append(j)\n    else:\n        backtrack[i[0]].append(i)\n\ndfs(backtrack, answer)\n\n\n            \n\n\n","repo_name":"rlawnsh/Algorithm_Study","sub_path":"1099번_알수없는문장.py","file_name":"1099번_알수없는문장.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"72912797397","text":"import os\nfrom copy import deepcopy\nimport numpy as np\nimport math\nimport copy\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_pdf import PdfPages\n\n# pytorch\nimport torch\nimport torch.nn as nn\n\n# nitorch\nfrom nitorch.data import show_brain\n\n\nclass Callback:\n    \"\"\"Abstract class for callbacks.\n\n    Methods\n    -------\n    reset\n        Function that resets all attributes.\n    final\n        calls `reset`. Should be executed when training is about to finish\n\n    \"\"\"\n\n    def __init__(self):\n        pass\n\n    def __call__(self):\n        pass\n\n    def reset(self):\n        pass\n\n    def final(self, **kwargs):\n        self.reset()\n\n\nclass ModelCheckpoint(Callback):\n    \"\"\"Monitors training process.\n\n    Saves model parameters after certain iterations or/and finds best parameters in all training steps.\n    Optionally, saves parameters to disk.\n\n    Parameters\n    ----------\n    path : str\n        The path where to store results.\n    retain_metric\n        The metric which will be monitored. Default: \"loss\"\n    prepend : str\n        String to prepend the filename with. Default: \"\".\n    num_iters : int\n        Number of iterations after which to store the model.\n        If set to -1, it will only store the last iteration's model. Default: -1\n    ignore_before : int\n        Ignore early iterations and do not execute callback function. Default: 0\n    store_best : bool\n        Switch whether to save the best model during training. Default: False\n    mode\n        Specifies the best metric value. \"max\" or \"min\" are allowed. Default: \"max\"\n    window : int\n        If set to integer number \"x\", retain_metric will be monitored in a window of size x.\n        Best model will be chosen according to best mean window result of all windows in retain_metric.\n        Default: None (Do not use window approach)\n    info : bool\n        Prints in combination with window mode information about current best window quantities. Default: False\n\n    Attributes\n    ----------\n    path : str\n        The path where to store results.\n    prepend : str\n        String to prepend the filename with.\n    num_iters : int\n        Number of iterations after which to store the model.\n        If set to -1, it will only store the last iteration's model.\n    ignore_before : int\n        Ignore early iterations and do not execute callback function.\n    best_model\n        Stores the best model.\n    best_res\n        Stores the best `retain_metric` result.\n    best_mean_res\n       `retain_metric` result.\n    best_window_start : int\n        Stores the starting position of the best window of size `window` over all epochs.\n    store_best : bool\n        Flag indicating whether best model will be saved to disk.\n    retain_metric\n        The retain metric. \"How to choose best models?\" Could be \"loss\" for example.\n    mode : str\n        Modus at which `retain_metric` is best. Can either be \"min\" or \"max\".\n    window : int\n        Window size.\n    info : bool\n        Prints in combination with window mode information about current best window quantities.\n\n    Methods\n    -------\n    reset\n        Resets all parameters.\n    final\n        Stores the best model to disk and calls `reset`.\n\n    \"\"\"\n\n    def __init__(\n            self,\n            path,\n            retain_metric=\"loss\",\n            prepend=\"\",\n            num_iters=-1,\n            ignore_before=0,\n            store_best=False,\n            mode=\"max\",\n            window=None,\n            info=False\n    ):\n        \"\"\"Initialization routine for class ModelCheckpoint.\"\"\"\n        super().__init__()\n        if os.path.isdir(path):\n            self.path = path\n        else:\n            os.makedirs(path)\n            self.path = path\n        # end the prepended text with an underscore if it does not\n        if not prepend.endswith(\"_\") and prepend != \"\":\n            prepend += \"_\"\n        self.prepend = prepend\n        self.num_iters = num_iters\n        self.ignore_before = ignore_before\n        self.best_model = None\n        self.best_res = -1\n        self.best_mean_res = -1\n        self.best_window_start = -1\n        self._current_window_best_res = -1\n        self._current_window_best_epoch = -1\n        self._current_window_save_idx = -1\n        self._current_window_best_model_save_idx = 0\n        if window:\n            self._state_dict_storage = [0] * window\n        else:\n            self._state_dict_storage = [0]\n        self.store_best = store_best\n        self.retain_metric = retain_metric\n        self.mode = mode\n        self.window = window\n        self.info = info\n\n    def __call__(self, trainer, epoch):\n        \"\"\"Determines what happens if class gets called.\n\n        Notes\n        -----\n        Whenever the ModelCheckpoint is called this routine gets executed. Call could happen at any point during\n        model training. Most likely ModelCheckpoint will be called after a training metric is assessed.\n\n        Parameters\n        ----------\n        trainer\n            The trainer object.\n        epoch : int\n            During training: the current epoch.\n\n        \"\"\"\n        # do not store intermediate iterations\n        if epoch >= self.ignore_before and epoch != 0:\n            if not self.num_iters == -1:\n\n                # counting epochs starts from 1; i.e. +1\n                epoch += 1\n                # store model recurrently if set\n                if epoch % self.num_iters == 0:\n                    name = self.prepend + \"training_epoch_{}.h5\".format(epoch)\n                    full_path = os.path.join(self.path, name)\n                    self.save_model(trainer, full_path)\n\n            # store current model if improvement detected\n            if self.store_best:\n                current_res = 0\n                try:\n                    # check if value can be used directly or not\n                    if isinstance(self.retain_metric, str):\n                        current_res = trainer.val_metrics[self.retain_metric][-1]\n                    else:\n                        current_res = trainer.val_metrics[self.retain_metric.__name__][-1]\n                except KeyError:\n                    print(\"Couldn't find {} in validation metrics. Using \\\n                        loss instead.\".format(self.retain_metric))\n                    current_res = trainer.val_metrics[\"loss\"][-1]\n\n                # update\n                if self.window is None:  # old update style\n                    if self._has_improved(current_res):\n                        self.best_res = current_res\n                        self.best_model = deepcopy(trainer.model.state_dict())\n                else:  # new update style\n                    # get validation metrics in certain window\n                    try:\n                        if isinstance(self.retain_metric, str):\n                            start = len(trainer.val_metrics[self.retain_metric]) - self.window\n                            start = 0 if start < 0 else start\n\n                            window_val_metrics = trainer.val_metrics[self.retain_metric][start:]\n                        else:\n                            start = len(trainer.val_metrics[self.retain_metric.__name__]) - self.window\n                            start = 0 if start < 0 else start\n                            window_val_metrics = trainer.val_metrics[self.retain_metric.__name__][start:]\n                    except KeyError:\n                        print(\n                            \"Couldn't find {} in validation metrics. Using \\\n                            loss instead.\".format(\n                                self.retain_metric\n                            )\n                        )\n                        start = len(trainer.val_metrics[self.retain_metric]) - self.window\n                        start = 0 if start < 0 else start\n                        window_val_metrics = trainer.val_metrics[\"loss\"][start:]\n\n                    # build mean\n                    mean_window_res = np.mean(window_val_metrics)\n\n                    # only safe when improvement to previous epoch detected\n                    # only a value BETTER than before can be the minimum/maximum of a\n                    # window with better mean than a previously detected window\n                    if len(window_val_metrics) == 1 \\\n                            or self._first_val_better(window_val_metrics[-1], window_val_metrics[-2]) \\\n                            or self._current_window_save_idx == -1:\n                        if self._current_window_save_idx == -1:\n                            self._current_window_save_idx = 0\n                        self._state_dict_storage[self._current_window_save_idx] = deepcopy(trainer.model.state_dict())\n                        # increase save idx and take modulo\n                        self._current_window_save_idx += 1\n                        self._current_window_save_idx = divmod(self._current_window_save_idx, self.window)[1]\n                    else:  # only increase current_window_save_idx (for modulo index calculation to work)\n                        self._current_window_save_idx += 1\n                        self._current_window_save_idx = divmod(self._current_window_save_idx, self.window)[1]\n\n                    # always update current window best result - it might be at some point overall best result\n                    current_window_best_idx = self._get_cur_win_best_idx(window_val_metrics)\n                    if current_window_best_idx == len(window_val_metrics) - 1 \\\n                            or len(window_val_metrics) == 1:  # case of improvement or initialisation\n                        # overwrite model_state saved so far\n                        self._current_window_best_model_save_idx = self._current_window_save_idx\n                        self._current_window_best_epoch = epoch\n                        self._current_window_best_res = window_val_metrics[-1]\n\n                    # check if mean has improved and copy values as best model result\n                    if self._has_window_mean_improved(mean_window_res):\n                        self.best_mean_res = mean_window_res\n                        self.best_window_start = 0 if epoch - self.window + 1 < 0 else epoch - self.window + 1\n                        # save current window best as overall best\n                        self.best_res = self._current_window_best_res\n                        self.best_model = copy.deepcopy(self._state_dict_storage[self._current_window_best_model_save_idx])\n                        if self.info:\n                            print(\"Found a window with better validation metric mean:\")\n                            print(\"\\t metric mean: {}\".format(mean_window_res))\n                            print(\"\\t epoch start: {}\".format(self.best_window_start))\n                            print(\"\\t best result: {}\".format(self.best_res))\n\n    def reset(self):\n        \"\"\"Reset module after training. Useful for cross validation.\"\"\"\n        self.best_model = None\n        self.best_res = -1\n\n    def final(self, **kwargs):\n        \"\"\"Stores best model to disk and resets results.\n\n        Parameters\n        ----------\n        kwargs\n            Variable many arguments.\n\n        \"\"\"\n        epoch = kwargs[\"epoch\"] + 1\n        if epoch >= self.ignore_before:\n            name = self.prepend + \"training_epoch_{}_FINAL.h5\".format(epoch)\n            full_path = os.path.join(self.path, name)\n            self.save_model(kwargs[\"trainer\"], full_path)\n        else:\n            print(\"Minimum iterations to store model not reached.\")\n\n        if self.best_model is not None:\n            best_model = deepcopy(self.best_model)\n            best_res = self.best_res\n            if self.window is not None:\n                print(\"Best result during training: {:.2f}.\\n In a window of size {} \"\n                      \"starting in epoch {} with best mean value of {} \\n Saving model..\".format(best_res,\n                                                                                                 self.window,\n                                                                                                 self.best_window_start,\n                                                                                                 self.best_mean_res))\n            else:\n                print(\n                    \"Best result during training: {:.2f}. Saving model..\".format(\n                        best_res\n                    )\n                )\n            name = self.prepend + \"BEST_ITERATION.h5\"\n            torch.save(best_model, os.path.join(self.path, name))\n        self.reset()\n\n    @staticmethod\n    def save_model(trainer, full_path):\n        \"\"\"Extracts a model of a trainer object and writes it to disk.\n\n        Parameters\n        ----------\n        trainer\n            The trainer object.\n        full_path\n            Path where to store the state dict of the model.\n\n        \"\"\"\n        print(\"Writing model to disk...\")\n        model = trainer.model.cpu()\n        torch.save(model.state_dict(), full_path)\n        if trainer.device is not None:\n            trainer.model.cuda(trainer.device)\n\n    def _first_val_better(self, v1, v2):\n        if self.mode == \"max\":\n            return v1 >= v2\n        elif self.mode == \"min\":\n            return v1 <= v2\n        else:\n            raise NotImplementedError(\"Only modes 'min' and 'max' available\")\n\n    def _get_cur_win_best_idx(self, val_metr):\n        if self.mode == \"max\":\n            return val_metr.index(max(val_metr))\n        elif self.mode == \"min\":\n            return val_metr.index(min(val_metr))\n        else:\n            raise NotImplementedError(\"Only modes 'min' and 'max' available\")\n\n    def _has_window_mean_improved(self, mean_window_res):\n        if self.mode == \"max\":\n            return mean_window_res >= self.best_mean_res\n        elif self.mode == \"min\":\n            # check if still standard value\n            if self.best_mean_res == -1:\n                return True\n            else:\n                return mean_window_res <= self.best_mean_res\n        else:\n            raise NotImplementedError(\"Only modes 'min' and 'max' available\")\n\n    def _has_improved(self, res):\n        if self.mode == \"max\":\n            return res >= self.best_res\n        elif self.mode == \"min\":\n            # check if still standard value\n            if self.best_res == -1:\n                return True\n            else:\n                return res <= self.best_res\n        else:\n            raise NotImplementedError(\"Only modes 'min' and 'max' available\")\n\n\nclass EarlyStopping(Callback):\n    \"\"\"Stops training when a monitored quantity has stopped improving.\n\n\n    Parameters\n    ----------\n    patience : int\n        Number of iterations without improvement after which to stop.\n    retain_metric\n        The metric which will be monitored.\n    mode : str\n        Defines if you want to maximise or minimise your metric. \"min\", \"max\" allowed.\n    ignore_before : int\n        Does not start the first window until this epoch.\n        Can be useful when training spikes a lot in early epochs. Default: 0\n    window : int\n        If set to integer number \"x\", quantity will be monitored in a window of size x.\n        Training will be stopped when mean quantity in a window has stopped improving.\n        Default: None (Do not use window approach)\n    info: bool\n        prints in combination with window mode information about current best window quantities. Default: False\n\n\n    Attributes\n    ----------\n    patience : int\n        Number of iterations without improvement after which to stop.\n    retain_metric\n        The metric which will be monitored.\n    mode : str\n        Defines if you want to maximise or minimise your metric. \"min\", \"max\" allowed.\n    ignore_before : int\n        Does not start the first window until this epoch.\n        Can be useful when training spikes a lot in early epochs.\n    window : int\n        If set to integer number \"x\", quantity will be monitored in a window of size x.\n        Training will be stopped when mean quantity in a window has stopped improving.\n    best_res\n        The best `retain_metric`.\n    best_epoch : int\n        The epoch in which `best_res` was obtained.\n    best_mean_res\n        The best mean result of window. Only if `window` is set.\n    best_window_start : int\n        Epoch where the best window (of epoch training metrics) starts.\n    info : bool\n        prints in combination with window mode information about current best window quantities.\n\n    Methods\n    -------\n    reset\n        Resets all parameters.\n    final\n        Calls `reset`.\n\n    \"\"\"\n\n    def __init__(self, patience, retain_metric, mode, ignore_before=0, window=None, info=False):\n        \"\"\"Initialization routine.\"\"\"\n        super().__init__()\n        self.patience = patience\n        self.retain_metric = retain_metric\n        self.mode = mode\n        self.ignore_before = ignore_before\n        self.best_res = -1\n        # set to first iteration which is interesting\n        self.best_epoch = self.ignore_before\n        # window mod\n        self.best_mean_res = -1\n        self.best_window_start = -1\n        self._current_window_best_res = -1\n        self._current_window_best_epoch = -1\n        self._current_window_save_idx = -1\n        self._current_window_best_model_save_idx = 0\n        if window:\n            self._state_dict_storage = [0] * window\n        else:\n            self._state_dict_storage = [0]\n        self.mode = mode\n        self.window = window\n        self.info = info\n\n    def __call__(self, trainer, epoch):\n        \"\"\"Execution of the Callback routine.\n\n        Parameters\n        ----------\n        trainer\n            The trainer object.\n        epoch : int\n            During training: The current epoch the Callback is called.\n\n        \"\"\"\n        if epoch >= self.ignore_before:\n            if epoch - self.best_epoch < self.patience:\n                if isinstance(self.retain_metric, str):\n                    current_res = trainer.val_metrics[self.retain_metric][-1]\n                else:\n                    current_res = trainer.val_metrics[self.retain_metric.__name__][-1]\n                if self.window is None:\n                    if self._has_improved(current_res):\n                        self.best_epoch = epoch\n                        self.best_res = current_res\n                        trainer.best_metric = current_res\n                        trainer.best_model = trainer.model\n                else:  # window mod\n                    # get validation metrics in certain window\n                    try:\n                        if isinstance(self.retain_metric, str):\n                            start = len(trainer.val_metrics[self.retain_metric]) - self.window\n                            start = 0 if start < 0 else start\n\n                            window_val_metrics = trainer.val_metrics[self.retain_metric][start:]\n                        else:\n                            start = len(trainer.val_metrics[self.retain_metric.__name__]) - self.window\n                            start = 0 if start < 0 else start\n                            window_val_metrics = trainer.val_metrics[self.retain_metric.__name__][start:]\n                    except KeyError:\n                        print(\n                            \"Couldn't find {} in validation metrics. Using \\\n                            loss instead.\".format(\n                                self.retain_metric\n                            )\n                        )\n                        start = len(trainer.val_metrics[self.retain_metric]) - self.window\n                        start = 0 if start < 0 else start\n                        window_val_metrics = trainer.val_metrics[\"loss\"][start:]\n\n                    # build mean\n                    mean_window_res = np.mean(window_val_metrics)\n\n                    # only safe when improvement to previous epoch detected\n                    # only a value BETTER than before can be the minimum/maximum of a\n                    # window with better mean than a previously detected window\n                    if len(window_val_metrics) == 1 \\\n                            or self._first_val_better(window_val_metrics[-1], window_val_metrics[-2]) \\\n                            or self._current_window_save_idx == -1:\n                        if self._current_window_save_idx == -1:\n                            self._current_window_save_idx = 0\n                        self._state_dict_storage[self._current_window_save_idx] = deepcopy(trainer.model.state_dict())\n                        # increase save idx and take modulo\n                        self._current_window_save_idx += 1\n                        self._current_window_save_idx = divmod(self._current_window_save_idx, self.window)[1]\n                    else:  # only increase current_window_save_idx (for modulo index calculation to work)\n                        self._current_window_save_idx += 1\n                        self._current_window_save_idx = divmod(self._current_window_save_idx, self.window)[1]\n\n                    # always update current window best result - it might be at some point overall best result\n                    current_window_best_idx = self._get_cur_win_best_idx(window_val_metrics)\n                    if current_window_best_idx == len(window_val_metrics) - 1 \\\n                            or self._current_window_best_res == -1:  # case of improvement or initialisation\n                        # overwrite model_state saved so far\n                        self._current_window_best_model_save_idx = self._current_window_save_idx\n                        self._current_window_best_epoch = epoch\n                        self._current_window_best_res = window_val_metrics[-1]\n\n                    # check if mean has improved and copy values as best model result\n                    if self._has_window_mean_improved(mean_window_res):\n                        self.best_mean_res = mean_window_res\n                        self.best_window_start = 0 if epoch - self.window + 1 < 0 else epoch - self.window + 1\n                        # save current window best as overall best\n                        self.best_res = self._current_window_best_res\n                        self.best_model = copy.deepcopy(self._state_dict_storage[self._current_window_best_model_save_idx])\n                        self.best_epoch = self._current_window_best_epoch\n                        trainer.best_metric = self._current_window_best_res\n                        trainer.best_model = trainer.model\n                        if self.info:\n                            print(\"Found a window with better validation metric mean:\")\n                            print(\"\\t metric mean: {}\".format(mean_window_res))\n                            print(\"\\t epoch start: {}\".format(self.best_window_start))\n                            print(\"\\t best result: {}\".format(self.best_res))\n\n            else:\n                # end training run\n                trainer.stop_training = True\n                if self.window is None:\n                    print(\"Early stopping at epoch {}.\\nBest model was at epoch {} with val metric score = {}\".format(\n                        epoch, self.best_epoch, self.best_res)\n                    )\n                else:\n                    print(\"Early stopping with window mode at epoch {}.\\n\"\n                          \"Best results were achieved at epoch {} with val metric score = {}.\\n\"\n                          \"Best window of size {} achieved a mean result of {} and started at epoch {}.\".format(\n                        epoch, self.best_epoch, self.best_res, self.window, self.best_mean_res, self.best_window_start)\n                    )\n\n    def reset(self):\n        \"\"\" Resets after training. Useful for cross validation.\"\"\"\n        self.best_res = -1\n        self.best_epoch = self.ignore_before\n\n    def final(self, **kwargs):\n        \"\"\"Performs a reset of the object.\"\"\"\n        self.reset()\n\n    def _has_improved(self, res):\n        if self.mode == \"max\":\n            return res > self.best_res\n        elif self.mode == \"min\":\n            # check if still standard value\n            if self.best_res == -1:\n                return True\n            else:\n                return res < self.best_res\n        else:\n            raise NotImplementedError(\"Only modes 'min' and 'max' available\")\n\n    def _first_val_better(self, v1, v2):\n        if self.mode == \"max\":\n            return v1 >= v2\n        elif self.mode == \"min\":\n            return v1 <= v2\n        else:\n            raise NotImplementedError(\"Only modes 'min' and 'max' available\")\n\n    def _get_cur_win_best_idx(self, val_metr):\n        if self.mode == \"max\":\n            return val_metr.index(max(val_metr))\n        elif self.mode == \"min\":\n            return val_metr.index(min(val_metr))\n        else:\n            raise NotImplementedError(\"Only modes 'min' and 'max' available\")\n\n    def _has_window_mean_improved(self, mean_window_res):\n        if self.mode == \"max\":\n            return mean_window_res >= self.best_mean_res\n        elif self.mode == \"min\":\n            # check if still standard value\n            if self.best_mean_res == -1:\n                return True\n            else:\n                return mean_window_res <= self.best_mean_res\n        else:\n            raise NotImplementedError(\"Only modes 'min' and 'max' available\")\n\n\ndef visualize_feature_maps(features, return_fig=False):\n    \"\"\"Visualizing 3D-features during training for custom-callbacks functions.\n\n    Can be used together with the argument 'training_time_callback' in nitorch's Trainer class.\n\n    Parameters\n    ----------\n    features\n        a tensor of features to visualize.\n    return_fig : bool\n        Flag to indicate whether to return the pyplot figure.\n        Default: False\n    Returns\n    -------\n    fig\n        The pyplot figure if `return_fig` set to True else nothing.\n\n    \"\"\"\n    if features.is_cuda:\n        features = features.cpu().detach().numpy()\n\n    num_features = len(features)\n    plt.close('all')\n    n = int(math.log2(num_features))\n    fig_size = (n * 2, n * 6)\n    fig = plt.figure(figsize=fig_size)\n\n    for i, f in enumerate(features, 1):\n        # normalize to range [0, 1] first as the values can be very small\n        if (f.max() - f.min()) != 0:\n            f = (f - f.min()) / (f.max() - f.min())\n\n            idxs = np.nonzero(f)\n            vals = np.ravel(f[idxs])\n            if len(vals):\n                # calculate the index where the mean value would lie\n                mean_idx = np.average(idxs, axis=1, weights=vals)\n                # calculate the angel ratios for each non-zero val\n                angles = (mean_idx.reshape(-1, 1) - idxs)\n                angles = angles / (np.max(abs(angles), axis=1).reshape(-1, 1))\n            else:  # if all values in f are zero, set dummy angle\n                angles = [1, 1, 1]\n\n            #             print(\"values = \",vals)\n            ax = fig.add_subplot(num_features // 3 + 1, 3, i,\n                                 projection='3d')\n            ax.set_title(\"Feature-{} in the bottleneck\".format(i))\n            ax.quiver(*idxs, angles[0] * vals, angles[1] * vals, angles[2] * vals)\n            plt.grid()\n\n        else:\n            ax = fig.add_subplot(num_features // 3 + 1, 3, i)\n            ax.text(0.5, 0.5, \"All values zero!\", transform=ax.transAxes)\n            plt.axis('off')\n\n    plt.tight_layout()\n    if return_fig:\n        return fig\n\n\nclass CAE_VisualizeTraining(Callback):\n    \"\"\"Callback that prints the model dimensions, visualizes CAE encoder outputs,\n    original image and reconstructed image during training.\n\n    Notes\n    -----\n        The forward() function of the CAE model using this callback\n        must return a (decoder_output, encoder_output) tuple.\n\n    Parameters\n    ----------\n    model\n        The pytorch model.\n    max_train_iters : int\n        The maximum number of training iterations.\n    show_epochs_list : list\n        list of epochs to visualise. Default: [] (Visualize no epochs)\n    plotFeatures : bool\n        Flag whether to plot features (True) or not (False). Default: True\n    plot_pdf_path : str\n        A path where to save figures ploted in a pdf. Default: \"\" (Do not plot into pdf)\n    cmap\n        A color map. Default: \"nipy_spectral\"\n\n    Attributes\n    ----------\n    model\n        The pytorch model.\n    max_train_iters : int\n        The maximum number of training iterations.\n    show_epochs_list : list\n        list of epochs to visualise\n    plotFeatures : bool\n        Flag whether to plot features (True) or not (False).\n    plot_pdf_path : str\n        A path where to save figures ploted in a pdf.\n    cmap\n        A color map.\n\n    \"\"\"\n\n    def __init__(self,\n                 model,\n                 max_train_iters,\n                 show_epochs_list=[],\n                 plotFeatures=True,\n                 plot_pdf_path=\"\",\n                 cmap=\"nipy_spectral\"):\n        \"\"\"Calling routine of CAE_VisualizeTraining.\n\n        Raises\n        ------\n        AttributeError\n            Thrown when a parameter is wrongly defined.\n        AssertionError\n            If `plot_pdf_path` not a path.\n            If `plotFeatures` not bool.\n            If `show_epochs_list` not a list.\n\n        \"\"\"\n        super().__init__()\n        self.model = model\n        self.max_train_iters = max_train_iters\n        if plot_pdf_path is not None:\n            assert isinstance(plot_pdf_path, str), \"plot_pdf_path is not a path!\"\n        self.plot_pdf_path = plot_pdf_path\n        assert isinstance(plotFeatures, bool), \"plotFeatures not boolean object!\"\n        self.plotFeatures = plotFeatures\n        assert isinstance(show_epochs_list, list), \"show_epochs_list is not a list!\"\n        self.show_epochs_list = show_epochs_list\n        self.cmap = cmap\n\n        # inform the model to also return the encoder output along with the decoder output\n        try:\n            if isinstance(model, nn.DataParallel):\n                model.module.set_return_encoder_out(True)\n            else:\n                model.set_return_encoder_out(True)\n        except AttributeError:\n            raise Exception(\"The CAE model must implement a setter function 'set_return_encoder_out'\\\n                for a flag 'encoder_out' which when set to true, the forward() function using this callback \\\n                must return a (decoder_output, encoder_output) tuple instead of just (encoder_output). \\\n                See the CAE class in models.py for the framework.\")\n\n    def __call__(self, inputs, labels, train_iter, epoch):\n        \"\"\"Calling the CAE_VisualizeTraining during training.\n\n        Parameters\n        ----------\n        inputs\n            Torch input tensor. Usually data of a nifti image.\n        labels\n            The label of the input data.\n        train_iter\n            The training iteration.\n        epoch\n            The current epoch.\n\n        Returns\n        -------\n        outputs\n            Output of the modeling process.\n\n        \"\"\"\n        debug = False\n        visualize_training = False\n        tmp_show_epoches_list = []\n\n        # if show_epochs_list is empty, all epoches should be plotted. Therefore, add current epoch to the list\n        if not self.show_epochs_list:\n            tmp_show_epoches_list.append(epoch)\n        else:\n            tmp_show_epoches_list = self.show_epochs_list\n\n        # check if epoch should be visualized\n        if epoch in tmp_show_epoches_list:\n            # print the model's parameter dimensions etc in the first iter\n            if train_iter == 0 and epoch == 0:\n                debug = True\n            # visualize training on the last iteration in that epoch\n            elif (train_iter == 1 and epoch == 0) or (train_iter == self.max_train_iters):\n                visualize_training = True\n\n        # for nitorch models which have a 'debug' and 'visualize_training' switch in the\n        # forward() method\n\n        # Todo: Check if  self.model.module.set_debug(debug) is still possible?\n\n        if isinstance(self.model, nn.DataParallel):\n            self.model.module.set_debug(debug)\n        else:\n            self.model.set_debug(debug)\n\n        outputs, encoder_out = self.model(inputs)\n\n        if visualize_training:\n            # check if result should be plotted in PDF\n            if self.plot_pdf_path != \"\":\n                pp = PdfPages(os.path.join(self.plot_pdf_path, \"training_epoch_\" + str(epoch) + \"_visualization.pdf\"))\n            else:\n                pp = None\n\n            # show only the first image in the batch\n            if pp is None:\n                # input image\n                show_brain(inputs[0].squeeze().cpu().detach().numpy(), draw_cross=False, cmap=self.cmap)\n                plt.suptitle(\"Input image\")\n                plt.show()\n                if not torch.all(torch.eq(inputs[0], labels[0])):\n                    show_brain(labels[0].squeeze().cpu().detach().numpy(), draw_cross=False, cmap=self.cmap)\n                    plt.suptitle(\"Expected reconstruction\")\n                    plt.show()\n                    # reconstructed image\n                show_brain(outputs[0].squeeze().cpu().detach().numpy(), draw_cross=False, cmap=self.cmap)\n                plt.suptitle(\"Reconstructed Image\")\n                plt.show()\n                # statistics\n                print(\n                    \"\\nStatistics of expected reconstruction:\\n(min, max)=({:.4f}, {:.4f})\\nmean={:.4f}\\nstd={:.4f}\".format(\n                        labels[0].min(), labels[0].max(), labels[0].mean(), labels[0].std()))\n                print(\n                    \"\\nStatistics of Reconstructed image:\\n(min, max)=({:.4f}, {:.4f})\\nmean={:.4f}\\nstd={:.4f}\".format(\n                        outputs[0].min(), outputs[0].max(), outputs[0].mean(), outputs[0].std()))\n                # feature maps\n                visualize_feature_maps(encoder_out[0])\n                plt.suptitle(\"Encoder output\")\n                plt.show()\n            else:\n                # input image\n                fig = show_brain(inputs[0].squeeze().cpu().detach().numpy(), draw_cross=False, return_fig=True,\n                                 cmap=self.cmap)\n                plt.suptitle(\"Input image\")\n                pp.savefig(fig)\n                plt.close(fig)\n                if not torch.all(torch.eq(inputs[0], labels[0])):\n                    fig = show_brain(labels[0].squeeze().cpu().detach().numpy(), draw_cross=False, cmap=self.cmap)\n                    plt.suptitle(\"Expected reconstruction\")\n                    pp.savefig(fig)\n                    plt.close(fig)\n                # reconstructed image\n                fig = show_brain(outputs[0].squeeze().cpu().detach().numpy(), draw_cross=False, return_fig=True,\n                                 cmap=self.cmap)\n                plt.suptitle(\"Reconstructed Image\")\n                pp.savefig(fig)\n                plt.close(fig)\n                # feature maps\n                if self.plotFeatures:\n                    fig = visualize_feature_maps(encoder_out[0], return_fig=True)\n                    plt.suptitle(\"Encoder output\")\n                    pp.savefig(fig)\n                    plt.close(fig)\n\n            # close the PDF\n            if pp is not None:\n                pp.close()\n\n        if isinstance(self.model, nn.DataParallel):\n            self.model.module.set_debug(False)\n        else:\n            self.model.set_debug(False)\n\n        return outputs\n","repo_name":"derEitel/patch_individual_filter_layer","sub_path":"nitorch/nitorch/callbacks.py","file_name":"callbacks.py","file_ext":"py","file_size_in_byte":35448,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"}
+{"seq_id":"73629780119","text":"import os\nimport pandas as pd\nfrom env import get_db_url\nfrom sklearn.model_selection import train_test_split\n\ndef new_zillow_data():\n    '''\n    This reads the zillow data from the Codeup db into a df\n    '''\n    sql_query = \"\"\"\n                SELECT id, propertylandusetypeid, bedroomcnt, bathroomcnt, calculatedfinishedsquarefeet, taxvaluedollarcnt, yearbuilt, taxamount, fips from properties_2017\n                WHERE propertylandusetypeid = '261';\n                \"\"\"\n    \n    # Read in DataFrame from Codeup.\n    df = pd.read_sql(sql_query, get_db_url('zillow'))\n    \n    return df\n\ndef get_zillow_data():\n    '''\n    This reads in telco data from Codeup database, writes data to\n    a csv if a local file does not exist, and returns a df.\n    '''\n    if os.path.isfile('zillow.csv'):\n        \n        # read in data from csv file if one exists\n        df = pd.read_csv('zillow.csv', index_col=0)\n        \n    else:\n        \n        # Read data from db into a DataFrame\n        df = new_zillow_data()\n        \n        # Cache to .csv\n        df.to_csv('zillow.csv')\n        \n    return df\n\n\n\ndef split_data(df, test_size=.2, validate_size=.25, col_to_stratify=None, random_state=None):\n    '''\n    This splits data into test,train and validate data\n    '''\n    # This takes in a default variable or a variable to determine target variable for stratification\n    if col_to_stratify == None:\n    # this splits the data\n        train_validate, test = train_test_split(df, test_size=test_size, random_state=random_state)\n        train, validate = train_test_split(train_validate,\n                                       test_size=validate_size,\n                                       random_state=random_state,)\n    else:                                                        \n        train_validate, test = train_test_split(df, test_size=test_size, random_state=random_state, stratify=df[col_to_stratify])\n        train, validate = train_test_split(train_validate,\n                                       test_size=validate_size,\n                                       random_state=random_state,\n                                       stratify=train_validate[col_to_stratify])\n    return train, validate, test\n\ndef wrangle_zillow():\n    '''\n    Read zillow data into a pandas DataFrame from mySQL,\n    drop property use id column, drop rows with null values,\n    return cleaned zillow DataFrame.\n    '''\n\n    # Acquire data\n\n    df = get_zillow_data()\n    \n    # drop property use id column\n    \n    df = df.drop(columns=['propertylandusetypeid'])\n\n    # Drop all rows with NaN values.\n    \n    df = df.dropna()\n\n    # converts fips to object for encoding\n\n    df['fips'] = df['fips'].astype(object)\n\n    # creates dummy variables for fips categorical\n\n    dummy_df = pd.get_dummies(df[['fips']], drop_first=True)\n    df = pd.concat([df, dummy_df], axis=1)\n\n    return df","repo_name":"DerekBixby/regression_project","sub_path":"wrangle.py","file_name":"wrangle.py","file_ext":"py","file_size_in_byte":2875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"31168319361","text":"import os\nfrom configurations import *\nfrom typing import Dict, List\nimport csv\nimport glob\n\n\ndef load_real_data() -> Dict[str, List[str]]:\n    \"\"\"\n    :return:\n    Will return a dictionary in format {'region' : [list of images in region]}\n    e.g. {'SW': [Arizona_id_302392_459, Arizona_id_306003_515, ...], 'NE': [Pennsylvania_id_129442_57, ...], ...}\n    \"\"\"\n\n    assert os.path.isfile(TRAINING_CSV_PATH), f'TRAINING_CSV_PATH: {TRAINING_CSV_PATH} is not a valid file'\n    assert os.path.isfile(VALIDATION_CSV_PATH), f'VALIDATION_CSV_PATH: {VALIDATION_CSV_PATH} is not a valid file'\n    assert os.path.isfile(IMAGES_TO_IGNORE_PATH), f'IMAGES_TO_IGNORE_PATH: {IMAGES_TO_IGNORE_PATH} is not a valid file'\n\n    images_to_ignore = []\n\n    with open(IMAGES_TO_IGNORE_PATH, 'r') as f:\n        for line in f:\n            image_basename = line.replace('\\n', '')\n            images_to_ignore.append(image_basename)\n\n    # Read training images\n    training_images = {region: [] for region in REGION_NAMES}\n    with open(TRAINING_CSV_PATH, 'r') as csvfile:\n        csvreader = csv.reader(csvfile)\n        fields = csvreader.__next__()\n        for idx, row in enumerate(csvreader):\n            id, state, region = row[0], row[3], row[4]\n            image_basename = state + '_id_' + id + '_' + str(idx)\n            if image_basename not in images_to_ignore:\n                training_images[region].append(image_basename)\n\n    # Read test images\n    validation_images = {region: [] for region in REGION_NAMES}\n    with open(VALIDATION_CSV_PATH, 'r') as csvfile:\n        csvreader = csv.reader(csvfile)\n        fields = csvreader.__next__()\n        for idx, row in enumerate(csvreader):\n            id, state, region = row[0], row[3], row[4]\n            image_basename = state + '_id_' + id + '_' + str(idx)\n            if image_basename not in images_to_ignore:\n                validation_images[region].append(image_basename)\n\n    return training_images, validation_images\n\n\ndef load_synthetic_data() -> Dict[str, List[str]]:\n    \"\"\"\n    :return:\n    Will return a dictionary in format {'region' : [list of synthetic images in region]}\n    e.g. {'SW': [Arizona_id_302392_459, Arizona_id_306003_515, ...], 'NE': [Pennsylvania_id_129442_57, ...], ...}\n    \"\"\"\n\n    synthetic_data = {region: [] for region in REGION_NAMES}\n    if not os.path.exists(SYNTHETIC_CSV_PATH):\n        print(f'Did not find csv file at {SYNTHETIC_CSV_PATH}, given by SYNTHETIC_CSV_PATH in configurations.py. '\n              f'Will return empty dictionary for synthetic images')\n        return synthetic_data\n\n    with open(SYNTHETIC_CSV_PATH, 'r') as csvfile:\n        csvreader = csv.reader(csvfile)\n        fields = csvreader.__next__()\n        for idx, row in enumerate(csvreader):\n            region = row[0]\n            filename = row[1]\n            synthetic_data[region].append(filename)\n    return synthetic_data\n\n\ndef collect_syn_data(syn_dir):\n    \"\"\"\n    :param syn_dir: path to the directory where the synthetic data is stored. Assumes that the directory is organized\n    such that there is a folder named with the region name. Following is an example of a directory structure where\n    we have all four regions.\n\n    - > syn_dir\n        - > EM\n            - > color_all_images_step608\n                - > image1.png\n                - > image2.png\n        - > NE\n        - > NW\n        - > SW\n\n    Here, color_all_images_step608 is specified in configurations.py and is the name of the directory that CityEngine\n    creates for the images that it generates\n\n    Creates a csv file with the same name as SYNTHETIC_CSV_PATH, where there is a field for region and for filename.\n    This file is then used by load_synthetic_data\n    \"\"\"\n    with open(SYNTHETIC_CSV_PATH, 'w', newline='') as file:\n        writer = csv.writer(file)\n        writer.writerow([\"region\", \"filename\"])\n        for region in REGION_NAMES:\n            region_dir_path = os.path.join(syn_dir, region, CITYENGINE_IMAGE_DIRECTORY_NAME)\n            if os.path.exists(region_dir_path):\n                print(f'Found directory for {region}')\n                paths = glob.glob(os.path.join(region_dir_path, f'*{SYN_IMAGE_EXTENSION}'))\n                for path in paths:\n                    basename = os.path.basename(path)\n                    filename = os.path.splitext(basename)[0]\n                    writer.writerow([region, filename])\n            else:\n                pass\n    return\n\n\nif __name__ == '__main__':\n    print(load_real_data())\n    print(load_synthetic_data())\n    #collect_syn_data(SYN_DIR)\n","repo_name":"Duke-BC-DL-for-Energy-Infrastructure/Experiment-Setup-Transmission-Towers","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":4549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"28206032281","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Nov 25 22:26:18 2021\r\n\r\n@author: Praveen\r\n\"\"\"\r\n\r\nprint('ABDB'.lower())\r\n\r\n\r\n# Cycle usage   \r\n\r\nfrom itertools import cycle\r\nletter = cycle('ABCD'.lower())\r\nprint(letter)\r\nn1 = ''\r\nfor value in letter:\r\n    #print(value)\r\n    n1 += value\r\n    if len(n1) > 20:\r\n        break\r\nprint(n1)\r\n    \r\n\r\nimport string\r\n\r\ndef encrypt_message(text):\r\n    alph = string.ascii_lowercase[:4]\r\n    print(alph)\r\n    new = ''\r\n    for value in text.lower():\r\n        idx = alph.index(value)\r\n        #print(idx)\r\n        encr = (idx+1) % 4\r\n        #print(encr)\r\n        new += (alph[encr])\r\n    return new\r\n\r\nprint(encrypt_message('BCAB'))","repo_name":"praveen21b/Python_labs","sub_path":"p12.py","file_name":"p12.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"73285981077","text":"import argparse\r\nimport os\r\nimport pathlib\r\nimport tensorflow \r\nfrom tensorflow.keras.preprocessing import image\r\nfrom tensorflow.keras.models import Model, load_model\r\nfrom tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\r\nfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout\r\nfrom tensorflow.keras.layers import BatchNormalization, Activation\r\nfrom tensorflow.keras.optimizers import Adam, RMSprop\r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nimport numpy as np\r\nimport pandas as pd \r\nimport matplotlib.pyplot as plt\r\nimport tensorflow as tf\r\nfrom numpy import loadtxt\r\nfrom PIL import Image, ImageOps\r\nfrom sklearn.model_selection import train_test_split\r\n\r\n\r\ndef train(images_labels_csv, IMAGE_SIZE, BATCH_SIZE, EPOCHS, model_output_path):\r\n  \r\n  \r\n  '''\r\n  Parameters\r\n  ----------\r\n  input_csv_path (file_path) : Input CSV has column for image filenames and their labels\r\n        \r\n    EPOCHS (positive_int) : Epochs of training \r\n        \r\n    BATCH_SIZE (positive_int) : Batch size of training\r\n\r\n    IMAGE_SIZE (positive_int) : The size of image after resizing when training\r\n        \r\n    model_output_path (str) : path for saving the model\r\n\r\n  Returns \r\n  -------\r\n  '''\r\n  \r\n\r\n  # Reading the csv file including the images paths and lables\r\n  train_data = pd.read_csv(images_labels_csv)\r\n  train_data['label'] = train_data['label'].astype(str)\r\n  # number of sampels\r\n  n_samples = train_data.shape[0]\r\n  Y = train_data[['label']].astype(str)\r\n  # Splitting into training and test\r\n  train_df, val_df = train_test_split(train_data, test_size=0.1)\r\n  print (\"train samples\", len(train_df))\r\n  print (\"val samples\", len(val_df))\r\n\r\n  \r\n  input_shape = (IMAGE_SIZE, IMAGE_SIZE, 3)\r\n\r\n  # train generator \r\n  ## add different pixel ranges (min, max values of pixels for normalizaiton (robust normalization)) \r\n  ## hyperparametes put them in a json file ##\r\n  train_datagen = ImageDataGenerator(rescale=1. / 255,\r\n                    height_shift_range= 0.02, \r\n                    width_shift_range=0.02, \r\n                    rotation_range=0.02, \r\n                    shear_range = 0.01,\r\n                    fill_mode='nearest',\r\n                    zoom_range=0.01)\r\n\r\n  train_generator = train_datagen.flow_from_dataframe(train_df, directory = None,\r\n                    x_col = \"filename\", y_col = \"label\",\r\n                    target_size=(IMAGE_SIZE, IMAGE_SIZE),\r\n                    batch_size=BATCH_SIZE,\r\n                    class_mode = \"categorical\", shuffle = True)\r\n\r\n  print (\"labels\", train_generator.class_indices)\r\n\r\n  # test generators \r\n  val_datagen = ImageDataGenerator(rescale=1. / 255)\r\n\r\n  val_generator = val_datagen.flow_from_dataframe(val_df, directory = None,\r\n                    x_col = \"filename\", y_col = \"label\",\r\n                    target_size=(IMAGE_SIZE, IMAGE_SIZE),\r\n                    batch_size=BATCH_SIZE,\r\n                    class_mode = \"categorical\", shuffle = True)\r\n\r\n  print (\"--------------------------------------------------------------------\")\r\n\r\n  # Model\r\n\r\n  pretrained_model = tf.keras.applications.Xception(weights='imagenet', include_top=False)\r\n  #pretrained_model.summary()\r\n  pretrained_model.trainable = False\r\n\r\n  x = pretrained_model.output\r\n  x = GlobalAveragePooling2D()(x)\r\n  x = Dense(512)(x)\r\n  x = BatchNormalization()(x)\r\n  x = Activation('relu')(x)\r\n  x = Dropout(0.5)(x)\r\n  predictions = Dense(2, activation='softmax')(x)\r\n  model = Model(inputs=pretrained_model.input, outputs=predictions)\r\n\r\n  # compiling the model\r\n  model.compile(loss='categorical_crossentropy',\r\n              optimizer='adam', \r\n              metrics=[\"accuracy\"])\r\n\r\n  filepath = \"inceptionv3_best.h5\"\r\n  checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, \r\n                             save_best_only=True, mode='min')\r\n  learning_rate_reduction = ReduceLROnPlateau(monitor='val_loss', \r\n                                            patience=10, \r\n                                            verbose=1, \r\n                                            factor=0.5, \r\n                                            min_lr=0.00001)\r\n  \r\n  callbacks_list = [checkpoint, learning_rate_reduction]\r\n\r\n  \r\n  history = model.fit_generator(\r\n      train_generator,\r\n      steps_per_epoch=train_generator.n // BATCH_SIZE,\r\n      validation_data=val_generator,\r\n      validation_steps= val_generator.n // BATCH_SIZE,\r\n      callbacks=callbacks_list,\r\n      epochs=20,\r\n      verbose=1)\r\n\r\n  plt.plot(history.history['accuracy'], label='ACC on the training set')\r\n  plt.plot(history.history['val_accuracy'], label='ACC on val set')\r\n  plt.xlabel('Epoch')\r\n  plt.ylabel('Score correct answers')\r\n  plt.legend()\r\n  plt.show()\r\n\r\n  # fine tunning of the training loop\r\n  model.load_weights(\"inceptionv3_best.h5\")\r\n  pretrained_model.trainable = False\r\n  for layer in model.layers[:290]:\r\n    layer.trainable = False\r\n  for layer in model.layers[290:]:\r\n    layer.trainable = True\r\n\r\n  model.compile(loss='categorical_crossentropy',\r\n              optimizer='adam', \r\n              metrics=[\"accuracy\"])\r\n\r\n  filepath=model_output_path\r\n  checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')\r\n  callbacks_list = [checkpoint, learning_rate_reduction]\r\n\r\n  history = model.fit_generator(\r\n      train_generator,\r\n      steps_per_epoch=train_generator.n // BATCH_SIZE,\r\n      validation_data=val_generator,\r\n      validation_steps=val_generator.n // BATCH_SIZE,\r\n      callbacks=callbacks_list,\r\n      epochs=EPOCHS,\r\n      verbose=2)\r\n\r\n\r\n  # evaluation \r\n  model = tf.keras.models.load_model(model_output_path)\r\n  loss, acc = model.evaluate_generator(generator=val_generator)\r\n  print (\"acc\", acc)\r\n\r\n  # prediction\r\n  val_generator.reset()\r\n  pred=model.predict_generator(val_generator, verbose=1)\r\n  predicted_class_indices=np.argmax(pred,axis=1)\r\n\r\n  print(\"----------------------------------------------------------------------\")\r\n\r\ndef file_path(path):\r\n    p = pathlib.Path(path)\r\n    if p.is_file():\r\n        return p\r\n    else:\r\n        raise argparse.ArgumentTypeError(\r\n            f\"Invalid argument ({path}), not a file path or file does not exist.\"\r\n        )\r\n\r\ndef nonnegative_int(i):\r\n    I = int(i)\r\n    if I >= 0:\r\n        return I\r\n    else:\r\n        raise argparse.ArgumentTypeError(\r\n            f\"Invalid argument ({i}), expected value >= 0 .\"\r\n        )\r\n\r\ndef positive_int(i):\r\n    I = int(i)\r\n    if I > 0:\r\n        return I\r\n    else:\r\n        raise argparse.ArgumentTypeError(\r\n            f\"Invalid argument ({i}), expected value > 0 .\"\r\n        )\r\n\r\ndef main():\r\n    parser = argparse.ArgumentParser(description=\"Chest X-ray classification\")\r\n    parser.add_argument('images_labels_csv', type=file_path,help='Input CSV has column for image filenames and their labels')\r\n    parser.add_argument('--IMAGE_SIZE', type=positive_int, default=299, help='The size of image after resizing when training')\r\n    parser.add_argument('--BATCH_SIZE', type=positive_int, default=16)\r\n    parser.add_argument('--EPOCHS', type=positive_int, default=40)\r\n    parser.add_argument('--model_output_path', type=str, default='./weights/inceptionv3_fine_tuned.h5', help='path for saving the model')\r\n    args = parser.parse_args()\r\n\r\n    \r\n    train(args.images_labels_csv, IMAGE_SIZE=args.IMAGE_SIZE, \r\n                  BATCH_SIZE=args.BATCH_SIZE, EPOCHS=args.EPOCHS, \r\n                  model_output_path=args.model_output_path)\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"lhncbc/NLM-PCOR-TB","sub_path":"TB_classification/code/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":7588,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"532331700","text":"\"\"\"\n\nutil\n====\n\nVarious utility routines used internally by pynbody.\n\n\"\"\"\n\nimport fractions\nimport gzip\nimport logging\nimport math\nimport os\nimport struct\nimport sys\nimport threading\nimport time\n\nimport numpy as np\n\nfrom . import units\nfrom .array import SimArray\n\nlogger = logging.getLogger('pynbody.util')\nfrom ._util import *\n\n\ndef open_(filename, *args):\n    \"\"\"Open a file, determining from the filename whether to use\n    gzip decompression\"\"\"\n\n    if (filename[-3:] == '.gz'):\n        return gzip.open(filename, *args)\n    try:\n        return open(filename, *args)\n    except OSError:\n        return gzip.open(filename + \".gz\", *args)\n\n\ndef open_with_size(filename, *args):\n    \"\"\"Open a file for reading, returning also the (decompressed)\n    file size\"\"\"\n\n    f = open_(filename, *args)\n    if isinstance(f, gzip.GzipFile):\n        fo = open(f.name, 'rb')\n        fo.seek(-4, 2)\n        r = fo.read()\n        fo.close()\n        return f, struct.unpack('<I', r)[0]\n    else:\n        f.seek(0, os.SEEK_END)\n        buflen = f.tell()\n        f.seek(4, os.SEEK_SET)\n        return f, buflen\n\n\ndef eps_as_simarray(f, eps):\n    \"\"\"Convert th given eps to a SimArray with units of f['pos'] and dtype of f['mass']\"\"\"\n    if isinstance(eps, str):\n        eps = units.Unit(eps)\n    if not isinstance(eps, units.UnitBase):\n        eps = eps * f['pos'].units\n        logger.info(f\"Considering eps = {eps}\")\n    eps_value = eps._scale\n    eps_unit = eps/eps_value\n    eps = SimArray(np.ones(len(f), dtype=f['mass'].dtype) * eps_value, eps_unit)\n    return eps\n\n\ndef get_eps(f):\n    \"\"\"The gravitational softening length is determined from (in order of\n    preference):\n    1. the array f['eps']\n    2. f.properties['eps'] (scalar or unit)\n\n    Return a SimArray with correct units and dtype (same dtype as 'mass' array)\"\"\"\n\n    try:\n        eps = f['eps']\n    except KeyError:\n        if 'eps' in f.properties:\n            eps = eps_as_simarray(f, f.properties['eps'])\n        else:\n            raise RuntimeError(\"Cannot retrieve 'eps' from SimSnap\")\n    return eps\n\n\ndef gcf(a, b):\n    while b > 0:\n        a, b = b, a % b\n    return a\n\n\ndef lcm(a, b):\n    return (a * b) // gcf(a, b)\n\n\ndef intersect_slices(s1, s2, array_length=None):\n    \"\"\"Given two python slices s1 and s2, return a new slice which\n    will extract the data of an array d which is in both d[s1] and\n    d[s2].\n\n    Note that it may not be possible to do this without information on\n    the length of the array referred to, hence all slices with\n    end-relative indexes are first converted into begin-relative\n    indexes. This means that the slice returned may be specific to\n    the length specified.\"\"\"\n\n    assert array_length is not None or \\\n        (s1.start >= 0 and s2.start >= 0 and s1.stop >= 0 and s2.start >= 0)\n\n    s1_start = s1.start\n    s2_start = s2.start\n    s1_stop = s1.stop\n    s2_stop = s2.stop\n    s1_step = s1.step\n    s2_step = s2.step\n\n    if s1_step is None:\n        s1_step = 1\n    if s2_step is None:\n        s2_step = 1\n\n    assert s1_step > 0 and s2_step > 0\n\n    if s1_start < 0:\n        s1_start = array_length + s1_start\n    if s1_start < 0:\n        return slice(0, 0)\n\n    if s2_start < 0:\n        s2_start = array_length + s2_start\n    if s2_start < 0:\n        return slice(0, 0)\n\n    if s1_stop < 0:\n        s1_stop = array_length + s1_stop\n    if s1_stop < 0:\n        return slice(0, 0)\n\n    if s2_stop < 0:\n        s2_stop = array_length + s2_stop\n    if s2_stop < 0:\n        return slice(0, 0)\n\n    step = lcm(s1_step, s2_step)\n\n    start = max(s1_start, s2_start)\n    stop = min(s1_stop, s2_stop)\n\n    if stop <= start:\n        return slice(0, 0)\n\n    s1_offset = start - s1_start\n    s2_offset = start - s2_start\n    s1_offset_x = int(s1_offset)\n    s2_offset_x = int(s2_offset)\n\n    if s1_step == s2_step and s1_offset % s1_step != s2_offset % s1_step:\n        # slices are mutually exclusive\n        return slice(0, 0)\n\n    # There is surely a more efficient way to do the following, but\n    # it eludes me for the moment\n    while s1_offset % s1_step != 0 or s2_offset % s2_step != 0:\n        start += 1\n        s1_offset += 1\n        s2_offset += 1\n        if s1_offset % s1_step == s1_offset_x % s1_step and s2_offset % s2_step == s2_offset_x % s2_step:\n            # slices are mutually exclusive\n            return slice(0, 0)\n\n    if step == 1:\n        step = None\n\n    return slice(start, stop, step)\n\n\ndef relative_slice(s_relative_to, s):\n    \"\"\"Given a slice s, return a slice s_prime with the property that\n    array[s_relative_to][s_prime] == array[s]. Clearly this will\n    not be possible for arbitrarily chosen s_relative_to and s, but\n    it should be possible for s=intersect_slices(s_relative_to, s_any)\n    which is the use case envisioned here (and used by SubSim).\n    This code currently does not work with end-relative (i.e. negative)\n    start or stop positions.\"\"\"\n\n    assert (s_relative_to.start >= 0 and s.start >= 0 and s.stop >= 0)\n\n    if s.start == s.stop:\n        return slice(0, 0, None)\n\n    s_relative_to_step = s_relative_to.step if s_relative_to.step is not None else 1\n    s_step = s.step if s.step is not None else 1\n\n    if (s.start - s_relative_to.start) % s_relative_to_step != 0:\n        raise ValueError(\"Incompatible slices\")\n    if s_step % s_relative_to_step != 0:\n        raise ValueError(\"Incompatible slices\")\n\n    start = (s.start - s_relative_to.start) // s_relative_to_step\n    step = s_step // s_relative_to_step\n    stop = start + \\\n        (s_relative_to_step - 1 + s.stop - s.start) // s_relative_to_step\n\n    if step == 1:\n        step = None\n\n    return slice(start, stop, step)\n\n\ndef chained_slice(s1, s2):\n    \"\"\"Return a slice s3 with the property that\n    ar[s1][s2] == ar[s3] \"\"\"\n\n    assert (s1.start >= 0 and s2.start >= 0 and s1.stop >= 0 and s2.stop >= 0)\n    s1_start = s1.start or 0\n    s2_start = s2.start or 0\n    s1_step = s1.step or 1\n    s2_step = s2.step or 1\n\n    start = s1_start + s2_start * s1_step\n    step = s1_step * s2_step\n    if s1.stop is None and s2.stop is None:\n        stop = None\n    elif s1.stop is None:\n        stop = start + step * (s2.stop - s2_start) // s2_step\n    elif s2.stop is None:\n        stop = s1.stop\n    else:\n        stop_s2 = start + step * (s2.stop - s2_start) // s2_step\n        stop_s1 = s1.stop\n        stop = stop_s2 if stop_s2 < stop_s1 else stop_s1\n    return slice(start, stop, step)\n\n\ndef index_before_slice(s, index):\n    \"\"\"Return an index array new_index with the property that, for a\n    slice s (start, stop and step all positive), ar[s][index] ==\n    ar[new_index].\"\"\"\n\n    start = s.start or 0\n    step = s.step or 1\n\n    assert start >= 0\n    assert step >= 0\n    assert s.stop is None or s.stop >= 0\n\n    new_index = start + index * step\n    if s.stop is not None:\n        new_index = new_index[np.where(new_index < s.stop)]\n\n    return new_index\n\n\ndef concatenate_indexing(i1, i2):\n    \"\"\"Given either a numpy array or slice for both i1 and i2,\n    return either a numpy array or slice i3 with the property that\n\n    ar[i3] == ar[i1][i2].\n\n    As a convenience, if i2 is None, i1 is returned\n    \"\"\"\n    if isinstance(i1, tuple) and len(i1) == 1:\n        i1 = i1[0]\n    if isinstance(i2, tuple) and len(i2) == 1:\n        i2 = i2[0]\n\n    if i2 is None:\n        return i1\n    if isinstance(i1, slice) and isinstance(i2, slice):\n        return chained_slice(i1, i2)\n    elif isinstance(i1, slice) and isinstance(i2, (np.ndarray, list)):\n        return index_before_slice(i1, i2)\n    elif isinstance(i1, (np.ndarray, list)) and isinstance(i2, (slice, np.ndarray)):\n        return np.asarray(i1)[i2]\n    else:\n        raise TypeError(\"Don't know how to chain these index types\")\n\n\ndef indexing_length(sl_or_ar):\n    \"\"\"Given either an array or slice, return len(ar[sl_or_ar]) for any\n    array ar which is large enough that the slice does not overrun it.\"\"\"\n\n    if isinstance(sl_or_ar, slice):\n        step = sl_or_ar.step or 1\n        diff = (sl_or_ar.stop - sl_or_ar.start)\n        return diff // step + (diff % step > 0)\n    else:\n        return len(sl_or_ar)\n\n\ndef arrays_are_same(a1, a2):\n    \"\"\"Returns True if a1 and a2 are numpy views pointing to the exact\n    same underlying data; False otherwise.\"\"\"\n    try:\n        return a1.__array_interface__['data'] == a2.__array_interface__['data'] \\\n            and a1.strides == a2.strides\n    except AttributeError:\n        return False\n\n\ndef set_array_if_not_same(a_store, a_in, index=None):\n    \"\"\"This routine checks whether a_store and a_in ultimately point to the\n    same buffer; if not, the contents of a_in are copied into a_store.\"\"\"\n    if index is None:\n        index = slice(None)\n    if not arrays_are_same(a_store[index], a_in):\n        a_store[index] = a_in\n        if not hasattr(a_in.units, \"_no_unit\"):\n            a_store.units = a_in.units\n\n\ndef index_of_first(array, find):\n    \"\"\"Returns the index to the first element in array\n    which satisfies array[index]>=find. The array must\n    be sorted in ascending order.\"\"\"\n\n    if len(array) == 0:\n        return 0\n\n    left = 0\n    right = len(array) - 1\n\n    if array[left] >= find:\n        return 0\n\n    if array[right] < find:\n        return len(array)\n\n    while right - left > 1:\n        mid = (left + right) // 2\n        if array[mid] >= find:\n            right = mid\n        else:\n            left = mid\n\n    return right\n\n\ndef equipartition(ar, nbins, vmin=None, vmax=None):\n    \"\"\"\n\n    Given an array ar, return nbins+1 monotonically increasing bin\n    edges such that the number of items in each bin is approximately\n    equal.\n\n    \"\"\"\n\n    a_s = np.sort(ar)\n\n    if vmax is not None:\n        a_s = a_s[a_s <= vmax]\n    if vmin is not None:\n        a_s = a_s[a_s > vmin]\n\n    return a_s[np.array(np.linspace(0, len(a_s) - 1, nbins + 1), dtype='int')]\n\n\ndef bisect(left, right, f, epsilon=None, eta=0, verbose=False, niter_max=200):\n    \"\"\"\n\n    Finds the value x such that f(x)=0 for a monotonically increasing\n    function f, using a binary search.\n\n    The search stops when either the bounding domain is smaller than\n    epsilon (by default 10^-7 times the original region) OR a value\n    f(x) is found such that |f(x)|<eta (by default eta=0, so this\n    criterion is never satisfied).\n\n    \"\"\"\n\n    if epsilon is None:\n        epsilon = (right - left) * 1.e-7\n\n    logger.info(\"Entering bisection search algorithm\")\n    for i in range(niter_max):\n\n        if (right - left) < epsilon:\n            return (right + left) / 2\n\n        mid = (left + right) / 2\n        z = f(mid)\n\n        logger.info(f\"{left:f} {mid:f} {right:f} {z:f}\")\n\n        if (abs(z) < eta):\n            return mid\n        elif(z < 0):\n            left = mid\n        else:\n            right = mid\n\n    raise ValueError(\"Bisection algorithm did not converge\")\n\n\ndef gauss_jordan(out):\n    \"\"\"A simple Gauss-Jordan matrix inverter. This is provided so that\n    matrices of fractions can be inverted (numpy linalg converts\n    everything to floats first.)\n\n    Don't use on large matrices -- it's slow!\n\n    Based on public domain code by Jarno Elonen.\"\"\"\n\n    h, w = out.shape\n\n    assert w > h\n\n    for y in range(0, h):\n\n        maxrow = out[y:, y].argmax() + y\n\n        (out[y], out[maxrow]) = (out[maxrow], out[y].copy())\n\n        if out[y][y] == 0:\n            # this will be a problem, see if we can do a row\n            # operation to fix it\n            for y2 in range(y+1,h):\n                if out[y2][y]!=0:\n                    out[y]+=out[y2]\n                    break\n\n            # no, out of options, must be a singular matrix\n            if out[y][y]==0:\n                raise np.linalg.linalg.LinAlgError(\"Singular matrix\")\n\n        for y2 in range(y + 1, h):    # Eliminate column y\n            c = out[y2][y] / out[y][y]\n            out[y2] -= out[y] * c\n\n    for y in range(h - 1, 0 - 1, -1):  # Backsubstitute\n        c = out[y][y]\n        for y2 in range(0, y):\n            for x in range(w - 1, y - 1, -1):\n                out[y2][x] -= out[y][x] * out[y2][y] / c\n        out[y][y] /= c\n        for x in range(h, w):       # Normalize row y\n            out[y][x] /= c\n\n    return out\n\n\ndef rational_matrix_inv(matrix):\n    \"\"\"A simple replacement for numpy linalg matrix inverse\n    which handles fractions exactly. Not suitable for large\n    matrices!\"\"\"\n\n    assert len(matrix) == len(matrix[0])\n    x = np.ndarray(\n        shape=(len(matrix), len(matrix[0]) + len(matrix)), dtype=fractions.Fraction)\n    x[:, :] = fractions.Fraction(0)\n    for i in range(len(x)):\n        x[i, len(x) + i] = fractions.Fraction(1)\n\n    for i in range(len(x)):\n        for j in range(len(x)):\n            x[i, j] = fractions.Fraction(matrix[i][j])\n\n    return gauss_jordan(x)[:, len(x):]\n\n\ndef random_rotation_matrix():\n    \"\"\"Return a random rotation matrix (Haar measure for 3x3 case), using\n    fast algorithm from Graphics Gems III\n\n    (http://tog.acm.org/resources/GraphicsGems/gemsiii/rand_rotation.c)\"\"\"\n\n    x = np.random.uniform(size=3)\n    theta = x[0]*2*math.pi\n    phi = x[1]*2*math.pi\n    z = x[2]*2\n\n    r = math.sqrt(z)\n    vx = math.sin(phi)*r\n    vy = math.cos(phi)*r\n    vz = math.sqrt(2.0-z)\n\n    st = math.sin(theta)\n    ct = math.cos(theta)\n\n    sx = vx*ct-vy*st\n    sy = vx*st+vy*ct\n\n    return np.array([[vx*sx-ct, vx*sy-st, vx*vz],\n                     [vy*sx+st, vy*sy-ct, vy*vz],\n                     [vz*sx,vz*sy,1.0-z]])\n\n\ndef cutgz(x):\n    \"\"\"Strip the .gz ending off a string\"\"\"\n    if x[-3:] == '.gz':\n        return x[:-3]\n    else:\n        return x\n\n\nclass ExecutionControl:\n\n    def __init__(self):\n        self.count = 0\n        self.on_exit = None\n\n    def __enter__(self):\n        self.count += 1\n\n    def __exit__(self, *excp):\n        self.count -= 1\n        assert self.count >= 0\n        if self.count == 0 and self.on_exit is not None:\n            self.on_exit()\n\n    def __bool__(self):\n        return self.count > 0\n\n    def __repr__(self):\n        return \"<ExecutionControl: %s>\" % ('True' if self.count > 0 else 'False')\n\n\n#################################################################\n# Code for incomplete gamma function accepting complex arguments\n#################################################################\n\ndef _gser(a, x, eps=3.e-7, itmax=700):\n    \"\"\"Series representation of the incomplete gamma\n    function, based on numerical recipes 3rd ed\"\"\"\n    if x == 0.0:\n        return 0.0\n    ap = a\n    sum = 1. / a\n    delta = sum\n    n = 1\n    while n <= itmax:\n        ap = ap + 1.\n        delta = delta * x / ap\n        sum = sum + delta\n        if (abs(delta) < abs(sum) * eps):\n            return (sum * np.exp(-x + a * np.log(x)))\n        n = n + 1\n    raise RuntimeError(\"Maximum iterations exceeded in gser\")\n\n\ndef _gcf(a, x, eps=3.e-7, itmax=200):\n    \"\"\"Continued fraction representation of the incomplete gamma\n    function, based on numerical recipes 3rd ed\"\"\"\n\n    gold = 0.\n    a0 = 1.\n    a1 = x\n    b0 = 0.\n    b1 = 1.\n    fac = 1.\n    n = 1\n    while n <= itmax:\n        an = n\n        ana = an - a\n        a0 = (a1 + a0 * ana) * fac\n        b0 = (b1 + b0 * ana) * fac\n        anf = an * fac\n        a1 = x * a0 + anf * a1\n        b1 = x * b0 + anf * b1\n        if (a1 != 0.):\n            fac = 1. / a1\n            g = b1 * fac\n            if (abs((g - gold) / g) < eps):\n                return (g * np.exp(-x + a * np.log(x)))\n            gold = g\n            n = n + 1\n    raise RuntimeError(\"Maximum iterations exceeded in gcf\")\n\n\ndef gamma_inc(a, z, eps=3.e-7):\n    \"\"\"Incomplete gamma function accepting complex z, based on algorithm\n    given in numerical recipes (3rd ed)\"\"\"\n    import scipy\n    import scipy.special\n\n    if (abs(z) < a + 1.):\n        return _gser(a, z, eps)\n    else:\n        return scipy.special.gamma(a) - _gcf(a, z, eps)\n\n\n#\n# THREAD-SAFE VERSION OF scipy.weave.inline\n#\n\ncompile_lock = threading.Lock()\n\n\ndef threadsafe_inline(*args, **kwargs):\n    \"\"\"When scipy.weave.inline is called, it may trigger a compile. We\n    only want one compilation to be going on at once, otherwise nasty\n    race conditions arise. This function wraps scipy.weave.inline to\n    be thread-safe.\"\"\"\n\n    import scipy.weave\n\n    call_frame = sys._getframe().f_back\n    if 'local_dict' not in kwargs:\n        kwargs['local_dict'] = call_frame.f_locals\n    if 'global_dict' not in kwargs:\n        kwargs['global_dict'] = call_frame.f_globals\n\n    tid = threading.currentThread().name\n    while args[0] not in scipy.weave.inline_tools.function_cache:\n        # We need a compilation, so try to acquire the compile lock\n        if compile_lock.acquire(False):\n            # acquired lock\n            try:\n                ret = scipy.weave.inline(*args, **kwargs)\n            finally:\n                compile_lock.release()\n            return ret\n        else:\n            # didn't acquire lock. Wait a while\n            time.sleep(1)\n\n    # When we reach this point, we know no compilation will be\n    # triggered, so go ahead and call\n    return scipy.weave.inline(*args, **kwargs)\n\n\n_head_type = np.dtype('i4')\n\n\ndef _thread_map(func, *args):\n\n    def r_func(*afunc):\n        try:\n            this_t = threading.current_thread()\n            this_t.ret_value = func(*afunc)\n        except Exception as e:\n            this_t.ret_excp = e\n\n    threads = []\n    for arg_this in zip(*args):\n        threads.append(threading.Thread(target=r_func, args=arg_this))\n        threads[-1].start()\n    rets = []\n    excp = None\n    for t in threads:\n        while t.is_alive():\n            # just calling t.join() with no timeout can make it harder to\n            # debug deadlocks!\n            t.join(1.0)\n        if hasattr(t, 'ret_excp'):\n            excp = t.ret_excp\n        else:\n            rets.append(t.ret_value)\n\n    if excp is None:\n        return rets\n    raise excp  # Note this is a re-raised exception from within a thread\n","repo_name":"pynbody/pynbody","sub_path":"pynbody/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":17864,"program_lang":"python","lang":"en","doc_type":"code","stars":148,"dataset":"github-code","pt":"85"}
+{"seq_id":"38986981830","text":"import sys\n\nclass MostActiveCookie:\n        \n    def filter_date(self, lines, queried_date):\n        \"\"\"\n        filter_date takes a list of lines from a csv file of format \"cookies,timestamp\" and returns a list of only the cookies that were active on the requested date and a tuple of their count on that date with the most recent timestamp for that cookie\n\n        returned list structure for clarification:\n        [(cookie, (count, most recent timestamp))]\n        str      int            str\n\n        - parameter(s) -\n        :lines: lines in the format of \"cookies,timestamp\"\n        :queried_date: the date in question for most active cookies\n\n        - returns - \n        :active_cookies_on_date (list): list of cookies with a tuple of count for the day and most recent timestamp for each\n        \"\"\"\n        log_dictionary = {}\n        for line in lines:\n\n            try:\n                cookie, datetime = line.split(\",\")\n                date, time = datetime.split(\"T\")\n            except:\n                print('\\nCSV file not in correct format. \\'cookie,timestamp\\' and timestamp needs to be in format \\'dateTtime\\'\\n') \n                exit()\n\n            if date == queried_date:\n                if cookie in log_dictionary:\n                    # max ensures that the most recent timestamp is saved\n                    log_dictionary[cookie] = (1 + log_dictionary[cookie][0], max(log_dictionary[cookie][1], time))\n                else:\n                    log_dictionary[cookie] = (1, time)\n\n        # make dictionary iterable and sort primarily by most counts to least counts then by timestamp\n        active_cookies_on_date = sorted(list(log_dictionary.items()), key = lambda x : (x[1][0], x[1][1]), reverse=True)\n\n        return active_cookies_on_date\n\n\n\n    def filter_most_counted(self, active_cookies_on_date):\n        \"\"\"\n        filter_most_counted puts the cookies that were counted the most and their timestamp into a list and sorts them from most recent to furthest\n\n        - parameter(s) -\n        :active_cookies_on_date (list): list of cookies with a tuple of count for the day and most recent timestamp for each\n\n        - returns - \n        :output (list = [str]): list of cookies that were the most active on the date\n\n        \"\"\"\n        output = []\n        if active_cookies_on_date:\n            max_count = active_cookies_on_date[0][1][0]\n            for cookie, (count, _) in active_cookies_on_date:\n                if count != max_count:\n                    break\n                output.append(cookie)\n        return output\n\n\n\nif __name__ == \"__main__\":\n    \n    # Turn commandline arguments into the goat\n    try:\n        filename, request_date = sys.argv[1], sys.argv[3]\n    except:\n        print(\"\\nIncorrect number of arguments\\n\")\n        exit()\n    \n    # Open file and put each line as an element to a list/array\n    try:\n        with open(filename) as open_file:\n            lines = open_file.readlines()[1:]\n    except:\n        print(\"\\nFile not valid\\n\")\n        exit()\n\n    mac = MostActiveCookie()\n\n    active_cookies_on_date = mac.filter_date(lines, request_date)\n\n    output = mac.filter_most_counted(active_cookies_on_date)\n    \n    for cookie in output:\n        print(cookie)","repo_name":"jionkim00/most_active_cookies","sub_path":"most_active_cookie.py","file_name":"most_active_cookie.py","file_ext":"py","file_size_in_byte":3230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"74124565717","text":"import re, io, json, random\r\n\r\nnormalize_chars={'Š':'S', 'š':'s', 'Ð':'Dj','Ž':'Z', 'ž':'z', 'À':'A', 'Á':'A', 'Â':'A', 'Ã':'A', 'Ä':'A',\r\n    'Å':'A', 'Æ':'A', 'Ç':'C', 'È':'E', 'É':'E', 'Ê':'E', 'Ë':'E', 'Ì':'I', 'Í':'I', 'Î':'I',\r\n    'Ï':'I', 'Ñ':'N', 'Ń':'N', 'Ò':'O', 'Ó':'O', 'Ô':'O', 'Õ':'O', 'Ö':'O', 'Ø':'O', 'Ù':'U', 'Ú':'U',\r\n    'Û':'U', 'Ü':'U', 'Ý':'Y', 'Þ':'B', 'ß':'Ss','à':'a', 'á':'a', 'â':'a', 'ã':'a', 'ä':'a',\r\n    'å':'a', 'æ':'a', 'ç':'c', 'è':'e', 'é':'e', 'ê':'e', 'ë':'e', 'ì':'i', 'í':'i', 'î':'i',\r\n    'ï':'i', 'ð':'o', 'ñ':'n', 'ń':'n', 'ò':'o', 'ó':'o', 'ô':'o', 'õ':'o', 'ö':'o', 'ø':'o', 'ù':'u',\r\n    'ú':'u', 'û':'u', 'ü':'u', 'ý':'y', 'ý':'y', 'þ':'b', 'ÿ':'y', 'ƒ':'f',\r\n    'ă':'a', 'î':'i', 'â':'a', 'ș':'s', 'ț':'t', 'Ă':'A', 'Î':'I', 'Â':'A', 'Ș':'S', 'Ț':'T',}\r\nalphabets=io.open(\"src/alphabets.txt\", mode=\"r\", encoding=\"utf-8\").read().strip().split(\"\\n\")\r\nemojis=json.load(io.open(\"src/emojis.json\", mode=\"r\", encoding=\"utf-8\"))\r\nfor alphabet in alphabets[1:]:\r\n    for ind, char in enumerate(alphabet):\r\n        try:normalize_chars[char]=alphabets[0][ind]\r\n        except: print(alphabet, len(alphabet), len(alphabets[0]));break\r\nnormalize_chars.update({i:i for i in 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'})\r\n\r\nnormal_map=str.maketrans(normalize_chars)\r\ndel normalize_chars\r\n\r\nbot_prefixes=tuple(io.open(\"src/prefixes.txt\", mode=\"r\", encoding=\"utf-8\").read().strip().split(\"\\n\"))\r\nnames=io.open(\"src/names.txt\", mode=\"r\", encoding=\"utf-8\").read().strip().split(\"\\n\")\r\nreplace_names={}\r\n\r\ndef gen_name(username):\r\n    try:\r\n        int(username)\r\n        try: out_name=replace_names[username]\r\n        except: \r\n            out_name=random.choice(names)\r\n            replace_names[username]=out_name\r\n        return out_name\r\n    except: return \" @\"+random.choice(names)\r\n\r\ndef convemojis(i):\r\n    if i in emojis: return emojis[i]\r\n    return i\r\n\r\n#precompile regex\r\nr1=re.compile(r'@Deleted User')\r\nr2=re.compile(r'https?:\\/\\/(?:www\\.)?[-a-zA-Z0-9@:%._\\+~#=\\n]{1,256}\\.[a-zA-Z0-9()]{1,6}\\b(?:[-a-zA-Z0-9()@:%_\\+.~#?&\\/=]*)|:[^\\n\\s]+?:|[\\w\\-\\.]+@(?:[\\w-]+\\.)+[\\w-]{2,4}|(?:\\+\\d{1,2}\\s)?\\(?\\d{3}\\)?[\\s.-]\\d{3}[\\s.-]\\d{4}|```.+?```\\n?|(?:\\\\n)+|\\b(?:a*ha+h[ha]*|o?l+o+l+[ol]*)\\b|[^a-z0-9.,:;\\'\\”@!?\\s\\<\\>\\/\\-\\+\\=\\(\\)\\[\\]*_'+''.join(emojis)+r']+|(?<=[a-z.,\\':;!?\\/]) +(?=[.,\\'!?\\/])|([,\\':;\\s\\/\\(\\)\\[\\]\\+\\-\\<\\>\\=])\\1+|([_])\\2{2,}|([a-z.!?*])\\3{3,}|(: )(?:> (?:.*?)(?:\\n+|\\\\n+|$))+', flags=re.DOTALL | re.IGNORECASE)\r\nr3=re.compile(r'[\\U00003000\\U0000205F\\U0000202F\\U0000200A\\U00002000-\\U00002009\\U00001680\\U000000A0\\t]+| {2,}')\r\nr4=re.compile(r\"(.{3,})\\1\", re.IGNORECASE | re.DOTALL)\r\n\r\ndef clean(text, author=False):\r\n    if text.lower() == \"welc\" or (\"welcome\" in text.lower()): return None #welcome is the bane of exisitence and needs to be culled\r\n    if \"@everyone\" in text.lower() or \"@here\" in text.lower(): return None #no need for these kinds of pings, and messages in them are even more useless.\r\n    if text[text.find(': ')+2:].strip().lower().startswith(bot_prefixes): return None #handle bot commands\r\n    if author and text.startswith(\"Deleted User\"): text=gen_name(author)+text[len(\"Deleted User\"):]\r\n    \r\n    text=text.translate(normal_map)#handle special chars from other langs\r\n    text= re.sub(r1, gen_name, text.strip()) #replace \"deleted users\" with names\r\n    text= re.sub(r2, r\"\\1\\2\\2\\3\\3\\3\\4\", text.strip()) #remove urls, emails, code blocks, custom emojis, non-emoji, punctuation, letters, and phone numbers\r\n    text= re.sub(r3, \" \", text.strip()) #handle... interesting spaces\r\n    text= \"\".join(list(map(convemojis,text.strip()))) #translate emojis to their `:text:` shorthand form\r\n    text= \"\\\\n\".join([ln.strip().strip(\"\\t\") for ln in text.split(\"\\n\")]) #handle newlines\r\n    if text.startswith(\": \"): text=gen_name(author)+text\r\n\r\n    if not (text[text.find(':')+1:].strip() in [\"\", \"\\\\n\", \"\\n\", \" \", \"\\t\"] or text[text.find(':')+1:].strip().lower().startswith(bot_prefixes)): \r\n        return text.lstrip((\"!.,^#\")).strip().replace(\"\\t\", \" \")\r\n    else: return None","repo_name":"JEF1056/clean-discord","sub_path":"src/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":4137,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"85"}
+{"seq_id":"2618243870","text":"#!/usr/bin/env python\n'''\nThis script extracts MOGREPS-G from the Met Office MASS data archive for particular period. It extracts data for particular variables and stores them in the specified location. Aggregation of data and other processing is done separately.\n\nFuture work is to make this more general, and specify paramters through a JSON file so this can easily run for many different time periods, possibly as the backend to a data catalog of some sort.\n'''\nimport pathlib\nimport datetime\nimport subprocess\nimport sys\nimport logging\nroot_mass =  'moose:/opfc/atm/mogreps-g/lev1/'\n\nnum_periods = 10\nstart_ref_time = datetime.datetime(2020,2,14,12)\nforecast_ref_time_range = [start_ref_time + datetime.timedelta(hours=6)*i1 for i1 in range(num_periods)]\nleadtime_hours = 15\n\nvariables_to_extract = [\n    \"cloud_amount_of_total_cloud\",\n    \"cloud_amount_on_height_levels\",\n    \"pressure_on_height_levels\",\n    \"temperature_on_height_levels\",\n    \"relative_humidity_on_height_levels\",\n    \"wind_direction_on_height_levels\",\n    \"wind_speed_on_height_levels\",\n    \"rainfall_accumulation-PT03H\",\n    \"snowfall_accumulation-PT03H\",\n    \"rainfall_rate\",\n    \"snowfall_rate\",\n    \"height_of_orography\",\n    \"pressure_at_mean_sea_level\",\n]\ncurrent_time = datetime.datetime.now()\nlogs_directory = pathlib.Path('/data/users/shaddad/precip_rediagnosis/logs')\ncurrent_timestamp = '{ct.year:04d}{ct.month:02d}{ct.day:02d}{ct.hour:02d}{ct.minute:02d}{ct.second:02d}'.format(ct=current_time)\nlogger = logging.getLogger('extract_mass')\nlogger.setLevel(logging.INFO)\n\nformatter = logging.Formatter('%(asctime)s | %(levelname)s | %(message)s',\n                              '%m-%d-%Y %H:%M:%S')\n\nhandler1 = logging.FileHandler(logs_directory / f'extract_mass_{current_timestamp}.log')\nhandler1.setLevel(logging.INFO)\nhandler1.setFormatter(formatter)\nlogger.addHandler(handler1)\n\nhandler1 = logging.StreamHandler(sys.stdout)\nhandler1.setLevel(logging.INFO)\nhandler1.setFormatter(formatter)\nlogger.addHandler(handler1)\n\nlogger.debug('Extracting files from mass')\nlogger.info(f'forecast reference times {forecast_ref_time_range}')\nlogger.info(f'variables to extract {variables_to_extract}')\n\nmass_root = pathlib.Path('moose:/opfc/atm/')\ndataset = 'mogreps-g'\nsubset = 'lev1'\nforecast_ref_template = '{frt.year:04d}{frt.month:02d}{frt.day:02d}T{frt.hour:02d}00Z.nc.file'\nfname_template = '{vt.year:04d}{vt.month:02d}{vt.day:02d}T{vt.hour:02d}00Z-PT{lead_time:04d}H00M-{var_name}.nc'\ndest_root = pathlib.Path('/scratch/shaddad/precip_rediagnosis')\nmass_cmd_template = 'moo get {args} {files} {dest_dir}'\nfor var1 in variables_to_extract:\n    extract_path_list = []\n    for fcst_ref_time in forecast_ref_time_range:\n        validity_time = fcst_ref_time + datetime.timedelta(hours=leadtime_hours)\n        mass_path = (mass_root /\n                     dataset /\n                     subset /\n                     forecast_ref_template.format(frt=fcst_ref_time)\n                     / fname_template.format(vt=validity_time,\n                                             lead_time=leadtime_hours,\n                                             var_name=var1)\n                     )\n        extract_path_list += [str(mass_path)]\n    output_dir =  dest_root / dataset\n    mass_get_cmd = mass_cmd_template.format(files=' '.join(extract_path_list),\n                                            dest_dir=str(output_dir),\n                                            args='-f')\n    logger.info(f'running command:\\n{mass_get_cmd}')\n\n    try:\n        cmd_output = subprocess.check_output(mass_get_cmd, shell=True)\n    except subprocess.CalledProcessError as err1:\n        logger.error(f'return code = {err1.returncode}\\noutput = {err1.output}')\n\n    logger.info(f'get command output:\\n{cmd_output}')\n\nlogger.info(f'files output to {dest_root}')\n\n\n","repo_name":"informatics-lab/precip_rediagnosis","sub_path":"data_prep/.ipynb_checkpoints/extract_mass-checkpoint.py","file_name":"extract_mass-checkpoint.py","file_ext":"py","file_size_in_byte":3813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"35807724799","text":"import os\nfrom configparser import SafeConfigParser, NoSectionError, NoOptionError\n\nimport gtk\nfrom gtk import gdk\nimport gobject\n\nfrom .canvas import Canvas\nfrom .chartable import CharTable\n\nfrom tegaki.recognizer import Recognizer\n\nclass RecognizerWidgetBase(gtk.HBox):\n\n    DEFAULT_CANVAS_WIDTH = 250\n\n    __gsignals__ = {\n\n        \"commit-string\" :         (gobject.SIGNAL_RUN_LAST, \n                                   gobject.TYPE_NONE,\n                                   [gobject.TYPE_STRING])\n    }\n\n    def __init__(self):\n        gtk.HBox.__init__(self)\n\n        self._recognizer = None\n        self._search_on_stroke = True\n\n        self._create_ui()\n        self.clear_canvas()\n        self.clear_characters()\n\n        self._load_preferences()\n\n    def _load_preferences(self):\n        pm = PreferenceManager()\n        pm.load()\n        self.set_drawing_stopped_time(pm[\"GENERAL\"][\"DRAWING_STOPPED_TIME\"])\n        self.set_search_on_stroke(pm[\"GENERAL\"][\"SEARCH_ON_STROKE\"])\n        self.set_selected_model(pm[\"GENERAL\"][\"SELECTED_MODEL\"])\n        self.set_draw_annotations(pm[\"GENERAL\"][\"DRAW_ANNOTATIONS\"])\n\n    def _save_preferences(self):\n        pm = PreferenceManager()\n        pm[\"GENERAL\"][\"DRAWING_STOPPED_TIME\"] = self.get_drawing_stopped_time()\n        pm[\"GENERAL\"][\"SEARCH_ON_STROKE\"] = self.get_search_on_stroke()\n        pm[\"GENERAL\"][\"SELECTED_MODEL\"] = self.get_selected_model()\n        pm[\"GENERAL\"][\"DRAW_ANNOTATIONS\"] = self.get_draw_annotations()\n        pm.save()\n\n    def _create_toolbar_separator(self):\n        self._toolbar.pack_start(gtk.HSeparator(), expand=False)\n\n    def _create_clear_button(self):\n        self._clear_button = gtk.Button()\n        image = gtk.image_new_from_stock(gtk.STOCK_CLEAR, gtk.ICON_SIZE_BUTTON)\n        self._clear_button.set_image(image)\n        self._clear_button.connect(\"clicked\", self._on_clear)\n        self._toolbar.pack_start(self._clear_button, expand=False)\n\n    def _create_find_button(self):\n        self._find_button = gtk.Button()\n        image = gtk.image_new_from_stock(gtk.STOCK_FIND, gtk.ICON_SIZE_BUTTON)\n        self._find_button.set_image(image)\n        self._find_button.connect(\"clicked\", self._on_find)\n        self._toolbar.pack_start(self._find_button, expand=False)\n\n    def _create_undo_button(self):\n        self._undo_button = gtk.Button()\n        image = gtk.image_new_from_stock(gtk.STOCK_UNDO, gtk.ICON_SIZE_BUTTON)\n        self._undo_button.set_image(image)\n        self._undo_button.connect(\"clicked\", self._on_undo)\n        self._toolbar.pack_start(self._undo_button, expand=False)\n\n    def _create_prefs_button(self):\n        self._prefs_button = gtk.Button()\n        image = gtk.image_new_from_stock(gtk.STOCK_PREFERENCES,\n                                         gtk.ICON_SIZE_BUTTON)\n        self._prefs_button.set_image(image)\n        self._prefs_button.connect(\"clicked\", self._on_prefs)\n        self._toolbar.pack_start(self._prefs_button, expand=False)\n\n    def _create_models_button(self):\n        self._models_button = gtk.Button(\"Models\")\n        self._models_button.connect(\"button-press-event\", self._on_models)\n        self._toolbar.pack_start(self._models_button, expand=False)\n\n    def _create_model_menu(self):\n        menu = gtk.Menu()\n\n        all_models = Recognizer.get_all_available_models()\n\n        if len(all_models) == 0:\n            return None\n\n        i = 0\n        for r_name, model_name, meta in all_models:\n            item = gtk.MenuItem(\"%d. %s (%s)\" % (i+1, model_name, r_name))\n            item.connect(\"activate\", self._on_activate_model, i)\n            menu.append(item)\n            i += 1\n\n        return menu\n\n    def _create_canvas(self, canvas_name):\n        canvas = Canvas()\n        canvas.set_size_request(self.DEFAULT_CANVAS_WIDTH,\n                                self.DEFAULT_CANVAS_WIDTH)\n\n        canvas.connect(\"button-press-event\",\n                       self._on_canvas_button_press,\n                       canvas_name)\n\n        canvas.connect(\"drawing-stopped\",\n                       self._on_canvas_drawing_stopped,\n                       canvas_name)\n\n        canvas.connect(\"stroke-added\",\n                       self._on_canvas_stroke_added,\n                       canvas_name)\n\n        setattr(self, canvas_name, canvas)\n\n        frame = gtk.Frame()\n        frame.add(canvas)\n\n        setattr(self, canvas_name + \"_frame\", frame)\n\n    def _create_chartable(self):    \n        self._chartable_frame = gtk.Frame()\n        self._chartable = CharTable()\n        self._chartable_frame.add(self._chartable)\n\n        self._chartable.connect(\"character-selected\", \n                                self._on_character_selected)\n\n    def _on_models(self, button, event):\n        menu = self._create_model_menu()\n        if menu:\n            menu.show_all()\n            menu.popup(None, None, None, event.button, event.time)\n        else:\n            parent = self.get_toplevel()\n            msg = \"No recognizers and/or no models installed!\"\n            dialog = ErrorDialog(parent, msg).run()\n\n    def _on_activate_model(self, item, i):\n        self.set_selected_model(i)\n        self._save_preferences()\n\n    def _on_find(self, button):\n        self.find()\n\n    def _on_undo(self, button):\n        self.revert_stroke()\n\n    def _on_prefs(self, button):\n        parent = self.get_toplevel()\n        if not parent.flags() & gtk.TOPLEVEL:\n            parent = None\n        pref_dialog = PreferenceDialog(parent)\n\n        pref_dialog.connect(\"response\", self._on_pref_validate)\n\n        pref_dialog.set_search_on_stroke(self.get_search_on_stroke())\n        pref_dialog.set_drawing_stopped_time(self.get_drawing_stopped_time())\n        pref_dialog.set_draw_annotations(self.get_draw_annotations())\n\n        pref_dialog.run()\n\n    def _on_pref_validate(self, dialog, response):\n        if response == gtk.RESPONSE_OK:\n            if dialog.get_search_on_stroke():\n                self.set_search_on_stroke(True)\n            else:\n                self.set_drawing_stopped_time(dialog.get_drawing_stopped_time())\n            self.set_draw_annotations(dialog.get_draw_annotations())\n            self._save_preferences()\n\n        dialog.destroy()\n\n    def _on_clear(self, button):\n        self.clear_canvas()\n\n    def clear_all(self):\n        self.clear_characters()\n        self.clear_canvas()\n\n    def get_search_on_stroke(self):\n        return self._search_on_stroke\n\n    def set_search_on_stroke(self, enabled):\n        self._search_on_stroke = enabled\n\n    def get_characters(self):\n        return self._chartable.get_characters()\n\n    def get_selected_model(self):\n       return self._models_button.selected_model\n\n    def set_selected_model(self, i):\n        try:\n            r_name, model_name, meta = Recognizer.get_all_available_models()[i]\n\n            klass = Recognizer.get_available_recognizers()[r_name]\n            self._recognizer = klass()\n            self._recognizer.set_model(meta[\"name\"])\n            self._models_button.set_label(meta[\"shortname\"])\n            # a hack to retain the model id the button\n            self._models_button.selected_model = i\n\n            self._ready = True\n        except IndexError:\n            self._ready = False\n\n    def get_toolbar_vbox(self):\n        return self._toolbar\n\nclass SimpleRecognizerWidget(RecognizerWidgetBase):\n\n    def __init__(self):\n        RecognizerWidgetBase.__init__(self)\n\n    def _create_toolbar(self):\n        self._toolbar = gtk.VBox(spacing=2)\n        self._create_find_button()\n        self._create_toolbar_separator()\n        self._create_undo_button()\n        self._create_clear_button()\n        self._create_toolbar_separator()\n        self._create_models_button()\n        self._create_prefs_button()\n\n    def _create_ui(self):\n        self._create_canvasbox()\n        self._create_chartable()\n\n        vbox = gtk.VBox(spacing=2)\n        vbox.pack_start(self._canvasbox, expand=True)\n        vbox.pack_start(self._chartable_frame, expand=False)\n\n        self._create_toolbar()\n        self.set_spacing(2)\n        self.pack_start(vbox, expand=True)\n        self.pack_start(self._toolbar, expand=False)\n\n    def _create_canvasbox(self):\n        self._create_canvas(\"_canvas\")\n        self._canvasbox = self._canvas_frame  \n\n    def _on_canvas_button_press(self, widget, event, curr_canv):\n        pass\n  \n    def _on_canvas_drawing_stopped(self, widget, curr_canv):\n        if not self._search_on_stroke:\n            self.find()\n\n    def _on_canvas_stroke_added(self, widget, curr_canv):\n        if self._search_on_stroke:\n            self.find()\n\n    def _on_character_selected(self, chartable, event):\n        chars = self._chartable.get_characters()\n        selected = self._chartable.get_selected()\n        self.emit(\"commit-string\", chars[selected])\n\n    def clear_canvas(self):\n        self._canvas.clear()\n        self.clear_characters()\n\n    def clear_characters(self):\n        self._chartable.clear() \n\n    def get_drawing_stopped_time(self):\n        return self._canvas.get_drawing_stopped_time()\n\n    def set_drawing_stopped_time(self, time_msec):\n        self._search_on_stroke = False\n        self._canvas.set_drawing_stopped_time(time_msec)\n\n    def get_draw_annotations(self):\n        return self._canvas.get_draw_annotations()\n\n    def set_draw_annotations(self, active):\n        self._canvas.set_draw_annotations(active)\n\n    def revert_stroke(self):\n        self._canvas.revert_stroke()\n        if self._search_on_stroke:\n            self.find()\n\n    def find(self):\n        if not self._ready:\n            return\n\n        writing = self._canvas.get_writing().copy()\n\n        if writing.get_n_strokes() > 0:\n            candidates = self._recognizer.recognize(writing, n=9)\n            candidates = [char for char, prob in candidates]\n            self._chartable.set_characters(candidates)\n\n    def get_writing(self):\n        self._canvas.get_writing()\n\n    def set_writing(self, writing):\n        self._canvas.set_writing(writing)\n\nclass SmartRecognizerWidget(RecognizerWidgetBase):\n\n    OTHER_CANVAS_COLOR = (0xFFFF, 0xFFFF, 0xFFFF) \n    CURR_CANVAS_COLOR =  [x * 256 for x in (255, 235, 235)]\n\n    def __init__(self):\n        RecognizerWidgetBase.__init__(self)\n\n    def _create_toolbar(self):\n        self._toolbar = gtk.VBox(spacing=2)\n        self._create_commit_button()\n        self._create_del_button()\n        self._create_toolbar_separator()\n        self._create_find_button()\n        self._create_toolbar_separator()\n        self._create_undo_button()\n        self._create_clear_button()\n        self._create_toolbar_separator()\n        self._create_models_button()\n        self._create_prefs_button()\n\n    def _create_commit_button(self):\n        self._commit_button = gtk.Button()\n        image = gtk.image_new_from_stock(gtk.STOCK_OK, gtk.ICON_SIZE_BUTTON)\n        self._commit_button.set_image(image)\n        self._commit_button.connect(\"clicked\", self._on_commit)\n        self._toolbar.pack_start(self._commit_button, expand=False)\n\n    def _create_del_button(self):\n        self._del_button = gtk.Button(\"Del\")\n        self._del_button.connect(\"clicked\", self._on_delete)\n        self._toolbar.pack_start(self._del_button, expand=False)\n\n    def _create_ui(self):\n        self._create_canvasbox()\n        self._create_chartable()\n\n        vbox = gtk.VBox(spacing=2)\n        vbox.pack_start(self._chartable_frame, expand=False)\n        vbox.pack_start(self._canvasbox, expand=True)\n\n        self._create_toolbar()\n        self.set_spacing(2)\n        self.pack_start(vbox, expand=True)\n        self.pack_start(self._toolbar, expand=False)\n\n    def _create_canvasbox(self):\n        self._canvasbox = gtk.HBox(spacing=2)\n        self._create_canvas(\"_canvas1\")\n        self._create_canvas(\"_canvas2\")\n        self._canvasbox.pack_start(self._canvas1_frame)\n        self._canvasbox.pack_start(self._canvas2_frame)\n\n    def _find(self, canvas):\n        if not self._ready:\n            return\n\n        writing = getattr(self, canvas).get_writing()\n\n        if writing.get_n_strokes() == 0:\n            return\n\n        writing = writing.copy()\n        candidates = self._recognizer.recognize(writing)\n        candidates = [char for char, prob in candidates]     \n        \n        if candidates:\n            candidate_list = CandidateList(candidates)\n\n            if canvas == self._last_completed_canvas:\n                # update the current character if the same canvas was used\n                last = len(self.get_characters()) - 1\n                self.replace_character(last, candidate_list)\n                self._writings[last] = writing\n            else:\n                # append character otherwise\n                self.add_character(candidate_list)\n                self._writings.append(writing)\n\n        self._last_completed_canvas = canvas\n\n    def _other_canvas(self, canvas):\n        if canvas == \"_canvas1\":\n            othr_canv = \"_canvas2\"\n        else:\n            othr_canv = \"_canvas1\"\n        return othr_canv\n    \n    def _set_canvas_focus(self, curr_canv):\n        othr_canv = self._other_canvas(curr_canv)\n        self._focused_canvas = curr_canv\n\n        # set background color\n        for canvas, color in ((curr_canv, self.CURR_CANVAS_COLOR),\n                            (othr_canv, self.OTHER_CANVAS_COLOR)):\n\n            getattr(self, canvas).set_background_color(*color)\n\n    def _on_canvas_button_press(self, widget, event, curr_canv):\n        othr_canv = self._other_canvas(curr_canv)\n\n        if self._focused_canvas == othr_canv:\n            getattr(self, curr_canv).clear()\n\n            if getattr(self, othr_canv).get_writing().get_n_strokes() > 0 and \\\n               self._last_completed_canvas != othr_canv and \\\n               not self._search_on_stroke:\n\n                self._find(othr_canv)\n\n        self._set_canvas_focus(curr_canv)\n  \n    def _on_canvas_drawing_stopped(self, widget, curr_canv):\n        if self._focused_canvas == curr_canv and not self._search_on_stroke:\n            self._find(curr_canv)\n\n    def _on_canvas_stroke_added(self, widget, curr_canv):\n        if self._search_on_stroke:\n            self._find(curr_canv)\n\n    def _on_commit(self, button):\n        chars = self.get_selected_characters()\n        if len(chars) > 0:\n            self.clear_all()\n            self.emit(\"commit-string\", \"\".join(chars))\n\n    def _on_delete(self, button):\n        self.delete_character()\n\n    def _on_character_selected(self, chartable, event):\n        selected = self._chartable.get_selected()\n\n        candidates = self._characters[selected]\n        popup = CandidatePopup(candidates)\n        popup.move(int(event.x_root), int(event.y_root) + \\\n                    int(self._chartable.allocation.height/3))\n\n\n        popup.connect(\"character-selected\", self._on_candidate_selected)\n        popup.connect(\"hide\", self._on_popup_close)\n        popup.connect(\"edit-character\", self._on_edit_character)\n        popup.connect(\"delete-character\", self._on_delete_character)\n\n        popup.popup()\n\n    def _on_candidate_selected(self, popup, event):\n        char_selected = self._chartable.get_selected()\n        cand_selected = popup.get_selected()\n        self._characters[char_selected].selected = cand_selected\n        self._chartable.set_characters(self.get_selected_characters())\n        self._chartable.unselect()\n       \n    def _on_edit_character(self, popup):\n        char_selected = self._chartable.get_selected()\n        edit_window = gtk.Window()\n        edit_window.set_title(\"Edit character\")\n        rw = SimpleRecognizerWidget()\n        rw.set_writing(self._writings[char_selected])\n        edit_window.add(rw)\n\n        parent = self.get_toplevel()\n        if parent.flags() & gtk.TOPLEVEL:\n            edit_window.set_transient_for(parent)\n            edit_window.set_position(gtk.WIN_POS_CENTER_ON_PARENT)\n            edit_window.set_type_hint(gdk.WINDOW_TYPE_HINT_DIALOG)\n        edit_window.set_modal(True)\n       \n        rw.connect(\"commit-string\", self._on_commit_edited_char, char_selected)\n        \n        edit_window.show_all()\n\n    def _on_commit_edited_char(self, rw, char, char_selected):\n        candidate_list = CandidateList(rw.get_characters())\n        candidate_list.set_selected(char)\n        self.replace_character(char_selected, candidate_list)\n        rw.get_parent().destroy()\n\n    def _on_delete_character(self, popup):\n        char_selected = self._chartable.get_selected()\n        self.remove_character(char_selected)\n\n    def _on_popup_close(self, popup):\n        self._chartable.unselect()\n\n    def clear_canvas(self):\n        self._canvas1.clear()\n            \n        if self._canvas2:\n            self._canvas2.clear()\n        \n        self._set_canvas_focus(\"_canvas1\")\n        self._last_completed_canvas = None\n\n    def delete_character(self):\n        try:\n            self._characters.pop()\n            self._writings.pop()\n            self._chartable.set_characters(self.get_selected_characters())\n            self._chartable.unselect()\n        except IndexError:\n            pass\n\n    def clear_characters(self):\n        self._characters = []\n        self._writings = []\n        self._chartable.clear() \n\n    def add_character(self, candidate_list):\n        if len(candidate_list) > 0:\n            self._characters.append(candidate_list)\n            self._chartable.set_characters(self.get_selected_characters())\n\n    def replace_character(self, index, candidate_list):\n        if len(candidate_list) > 0:\n            try:\n                self._characters[index] = candidate_list\n                self._chartable.set_characters(self.get_selected_characters())\n            except IndexError:\n                pass\n\n    def remove_character(self, index):\n        length = len(self._chartable.get_characters())\n        if length > 0 and index <= length - 1:\n            del self._characters[index]\n            del self._writings[index]\n            self._chartable.set_characters(self.get_selected_characters())    \n       \n    def get_selected_characters(self):\n        return [char[char.selected] for char in self._characters]\n\n    def get_drawing_stopped_time(self):\n        return self._canvas1.get_drawing_stopped_time()\n\n    def set_drawing_stopped_time(self, time_msec):\n        self._search_on_stroke = False\n        for canvas in (self._canvas1, self._canvas2):\n            canvas.set_drawing_stopped_time(time_msec)\n\n    def get_draw_annotations(self):\n        return self._canvas1.get_draw_annotations()\n\n    def set_draw_annotations(self, active):\n        for canvas in (self._canvas1, self._canvas2):\n            canvas.set_draw_annotations(active)\n\n    def revert_stroke(self):\n        if self._focused_canvas:\n            getattr(self, self._focused_canvas).revert_stroke()\n\n    def find(self):\n        if self._focused_canvas:\n            self._find(self._focused_canvas)\n\nclass CandidatePopup(gtk.Window):\n\n    __gsignals__ = {\n\n        \"character_selected\" : (gobject.SIGNAL_RUN_LAST,\n                                gobject.TYPE_NONE,\n                                [gobject.TYPE_PYOBJECT]),\n\n        \"edit-character\"     : (gobject.SIGNAL_RUN_LAST, \n                                gobject.TYPE_NONE,\n                                []),\n\n        \"delete-character\"   : (gobject.SIGNAL_RUN_LAST, \n                                gobject.TYPE_NONE,\n                                [])\n    }\n\n    def __init__(self, candidates):\n        gtk.Window.__init__(self, gtk.WINDOW_POPUP)\n        self._candidates = candidates\n        self._create_ui()\n\n    def get_selected(self):\n        return self._chartable.get_selected()\n\n    def _create_ui(self):\n        self.add_events(gdk.BUTTON_PRESS_MASK)\n\n        self.set_title(\"Candidates\")\n\n        frame = gtk.Frame()\n        self._chartable = CharTable()\n        self._chartable.add_events(gdk.BUTTON_PRESS_MASK)\n        self._chartable.set_characters(self._candidates)\n        self._chartable.set_layout(CharTable.LAYOUT_HORIZONTAL)\n        max_width, max_height = self._chartable.get_max_char_size()\n        self._chartable.set_size_request(int(max_width*3.5),\n                                         int(max_height*3.5))\n        frame.add(self._chartable)\n\n        self.connect(\"button-press-event\", self._on_button_press)\n        self._chartable.connect(\"character-selected\",\n                                self._on_character_selected)\n\n        vbox = gtk.VBox(spacing=2)\n        vbox.pack_start(frame)\n\n        self._edit_button = gtk.Button()\n        image = gtk.image_new_from_stock(gtk.STOCK_EDIT,\n                                         gtk.ICON_SIZE_BUTTON)\n        self._edit_button.set_image(image)\n        self._edit_button.set_relief(gtk.RELIEF_NONE)\n        self._edit_button.connect(\"clicked\", self._on_edit)\n\n        self._delete_button = gtk.Button()\n        image = gtk.image_new_from_stock(gtk.STOCK_DELETE,\n                                         gtk.ICON_SIZE_BUTTON)\n        self._delete_button.set_image(image)\n        self._delete_button.set_relief(gtk.RELIEF_NONE)\n        self._delete_button.connect(\"clicked\", self._on_delete)\n\n        self._close_button = gtk.Button()\n        image = gtk.image_new_from_stock(gtk.STOCK_CLOSE,\n                                         gtk.ICON_SIZE_BUTTON)\n        self._close_button.set_image(image)\n        self._close_button.set_relief(gtk.RELIEF_NONE)\n        self._close_button.connect(\"clicked\", self._on_close)\n\n        frame = gtk.Frame()\n        buttonbox = gtk.HBox()\n        buttonbox.pack_start(self._edit_button, expand=False)\n        buttonbox.pack_start(self._delete_button, expand=False)\n        buttonbox.pack_start(self._close_button, expand=False)\n        frame.add(buttonbox)\n        vbox.pack_start(frame)\n\n        self.add(vbox)\n\n    def _on_close(self, button):\n        self.popdown()\n\n    def _on_edit(self, button):\n        self.emit(\"edit-character\")\n        self.popdown()\n\n    def _on_delete(self, button):\n        self.emit(\"delete-character\")\n        self.popdown()\n\n    def _on_character_selected(self, chartable, event):\n        self.emit(\"character-selected\", event)\n\n    def _on_button_press(self, window, event):\n        # If we're clicking outside of the window or in the chartable\n        # close the popup\n        if (event.window != self.window or\n            (tuple(self.allocation.intersect(\n                   gdk.Rectangle(x=int(event.x), y=int(event.y),\n                                 width=1, height=1)))) == (0, 0, 0, 0)):\n            self.popdown()\n\n    def popup(self):\n        self.show_all()\n\n        # grab pointer\n        self.grab_add()\n        gdk.pointer_grab(self.window,\n                         True,\n                         gdk.BUTTON_PRESS_MASK|\n                         gdk.BUTTON_RELEASE_MASK|\n                         gdk.POINTER_MOTION_MASK,\n                         None, None, \n                         gtk.get_current_event_time())\n\n    def popdown(self):\n        gdk.pointer_ungrab(gtk.get_current_event_time())\n        self.grab_remove()\n        self.destroy()\n\nclass CandidateList(list):\n    def __init__(self, initial_candidates=[]):\n        self.extend(initial_candidates)\n        self.selected = 0\n\n    def get_selected(self):\n        try:\n            return self[self.selected]\n        except IndexError:\n            return None\n\n    def set_selected(self, name):\n        try:\n            i = self.index(name)\n            self.selected = i\n        except ValueError:\n            pass\n\nclass ErrorDialog(gtk.MessageDialog):\n\n    def __init__(self, parent, msg):\n        gtk.MessageDialog.__init__(self, parent, gtk.DIALOG_MODAL,\n                                   gtk.MESSAGE_ERROR, gtk.BUTTONS_OK, msg)\n\n        self.connect(\"response\", lambda w,r: self.destroy())\n\nclass PreferenceManager(dict):\n\n    def __init__(self):\n        dict.__init__(self)\n        self._init_paths()\n        self._init_dirs()\n        self._init_defaults()\n\n    def _init_defaults(self):\n        self[\"GENERAL\"] = {}\n\n    def _init_paths(self):\n        try:\n            self._home_dir = os.environ['HOME']\n            self._tegaki_dir = os.path.join(self._home_dir, \".tegaki\")\n        except KeyError:\n            self._home_dir = os.environ['USERPROFILE']\n            self._tegaki_dir = os.path.join(self._home_dir, \"tegaki\")\n\n        self._conf_file = os.path.join(self._tegaki_dir, \"recognizer.ini\")\n\n    def _init_dirs(self):\n        if not os.path.exists(self._tegaki_dir):\n            os.makedirs(self._tegaki_dir)\n\n    def load(self):\n        config = SafeConfigParser()\n        config.read(self._conf_file)\n\n        for opt, dflt, meth  in [(\"SEARCH_ON_STROKE\", True, config.getboolean),\n                                 (\"DRAWING_STOPPED_TIME\", 0, config.getint),\n                                 (\"SELECTED_MODEL\", 0, config.getint),\n                                 (\"DRAW_ANNOTATIONS\", 1, config.getboolean)]:\n\n            try:\n                self[\"GENERAL\"][opt] = meth(\"GENERAL\", opt)\n            except (NoSectionError, NoOptionError, ValueError) as e:\n                self[\"GENERAL\"][opt] = dflt\n\n    def save(self):\n        config = SafeConfigParser()\n        \n        for section in list(self.keys()):\n            if not config.has_section(section):\n                config.add_section(section)\n\n            for opt, value in list(self[section].items()):\n                config.set(section, opt, str(value))\n\n        f = open(self._conf_file, \"w\")\n        config.write(f)\n        f.close()\n\nclass PreferenceDialog(gtk.Dialog):\n\n    def __init__(self, parent):\n        gtk.Dialog.__init__(self)\n        self._init_dialog(parent)\n        self._create_ui()\n\n    def _init_dialog(self, parent):\n        self.add_button(gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL)\n        self.add_button(gtk.STOCK_OK, gtk.RESPONSE_OK)\n        self.set_default_response(gtk.RESPONSE_OK)\n        self.set_has_separator(True)\n        self.set_transient_for(parent)\n        self.set_border_width(6)\n        self.set_modal(True)\n        self.set_title(\"Preferences\")\n\n    def _create_ui(self):\n        self._search_on_stroke = gtk.RadioButton(group=None, \n                                                 label=\"Search on stroke\")\n        self._search_on_stroke.connect(\"toggled\", self._on_search_on_stroke)\n\n        \n        self._search_after = gtk.RadioButton(group=self._search_on_stroke,\n                                             label=\"Search after:\")\n        self._search_after.connect(\"toggled\", self._on_search_after)\n        adjustment = gtk.Adjustment(value=0, lower=0, upper=3000, step_incr=100,\n                                    page_incr=0, page_size=0)\n        self._spinbutton = gtk.SpinButton(adjustment)\n        self._spinbutton.set_sensitive(False)\n        self._search_after_hbox = gtk.HBox(spacing=2)\n        self._search_after_hbox.pack_start(self._search_after, expand=False)\n        self._search_after_hbox.pack_start(self._spinbutton, expand=False)\n        self._search_after_hbox.pack_start(gtk.Label(\"[msecs]\"), expand=False)\n\n        self._draw_annotations = gtk.CheckButton(label=\"Draw annotations\")\n\n        main_vbox = self.get_child()\n        main_vbox.set_spacing(10)\n        main_vbox.pack_start(self._search_on_stroke)\n        main_vbox.pack_start(self._search_after_hbox)\n        main_vbox.pack_start(self._draw_annotations)\n        self.show_all()\n\n    def _on_search_on_stroke(self, radiobutton):\n        self._spinbutton.set_sensitive(False)\n\n    def _on_search_after(self, radiobutton):\n        self._spinbutton.set_sensitive(True)\n\n    def get_search_on_stroke(self):\n        return self._search_on_stroke.get_active()\n\n    def set_search_on_stroke(self, active):\n        self._search_on_stroke.set_active(active)\n        self._search_after.set_active(not(active))\n\n    def get_draw_annotations(self):\n        return self._draw_annotations.get_active()\n\n    def set_draw_annotations(self, active):\n        self._draw_annotations.set_active(active)\n\n    def get_search_after(self):\n        return self._search_after.get_active()\n\n    def set_search_after(self, active):\n        self._search_after.set_active(active)\n        self._search_on_stroke.set_active(not(active))\n\n    def get_drawing_stopped_time(self):\n        return int(self._spinbutton.get_value())\n\n    def set_drawing_stopped_time(self, time):\n        self._spinbutton.set_value(int(time))\n\nif __name__ == \"__main__\":\n    import sys\n\n    try:\n        simple = int(sys.argv[1])\n    except IndexError:\n        simple = False\n\n    window = gtk.Window()\n\n    if simple:\n        recognizer_widget = SimpleRecognizerWidget()\n    else:\n        recognizer_widget = SmartRecognizerWidget()\n\n    def on_commit_string(rw, string):\n        print(string)\n\n    recognizer_widget.connect(\"commit-string\", on_commit_string)\n\n    window.add(recognizer_widget)\n    window.show_all()\n\n    gtk.main()","repo_name":"tegaki/tegaki","sub_path":"tegaki-pygtk/tegakigtk/recognizer.py","file_name":"recognizer.py","file_ext":"py","file_size_in_byte":28812,"program_lang":"python","lang":"en","doc_type":"code","stars":235,"dataset":"github-code","pt":"85"}
+{"seq_id":"39101670058","text":"def factNum(n):\n    f=1\n    for i in range(1,n+1):\n        f = f*i\n    return (f)\n\nprint(factNum(10))\n\n\n############## FACTORIAL ####################\n\ndef factorialNum(n):\n    if n==0:\n        return 1\n    return n*factorialNum(n-1)\n\nprint(factorialNum(10))\n\ndef fib(n):\n    fibo = [0,1]\n    for i in range(2,n):\n        c = fibo[i-2 ]+fibo[i-1]\n        fibo.append(c)\n    return fibo\nprint(fib(10))","repo_name":"Mainul-Hasan07/python-basic","sub_path":"Day-4/factorialNum.py","file_name":"factorialNum.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"39659387679","text":"from icecream import ic\ndef search(find: int,lt:[int]):\n  size=len(lt)\n  mid=size//2\n  \n  if lt[mid]==find:\n    return(mid)\n  else:\n    if lt[mid]>find:\n      lt=lt[:mid]\n    else:\n      lt=lt[mid:]\n    return search(find,lt)\n    \n  return \"not found\"\ndef search2(find) -> int:\n  lt=[1,2,3,4,5]\n  start=0\n  end=len(lt)-1\n  while start<end:\n    mid=(start+end)//2\n    \n    if find > lt[mid]:\n      start=mid+1\n    else:\n      #if find==lt[mid]:\n      #  return mid\n      end=mid\n    ic(start,mid,end)\n  if find==lt[start]:\n    return start\n  else: return -1\nimport requests\nrs=requests.get('')\nprint(rs.content)\n\n","repo_name":"lekhit/binary-search","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"10241970132","text":"import pandas as pd\nimport pathlib\n\n# get relative data folder\nPATH = pathlib.Path(__file__).parent\nDATA_PATH = PATH.joinpath(\"data\").resolve()\n\ntsne_dict = {\n    \"tsne_sentiment\": pd.read_csv(DATA_PATH.joinpath(\"tsne_bert_base_cased_sentiment.csv\")),\n    \"tsne_offensive\": pd.read_csv(DATA_PATH.joinpath(\"tsne_bert_base_cased_offensive.csv\")),\n    \"tsne_hate\": pd.read_csv(DATA_PATH.joinpath(\"tsne_bert_base_cased_hate.csv\")),\n}\n\n# for every entry in tsne_dict calculate the average and standard deviation of the columns x and y in the dataframe\ndef get_tsne_stats(df):\n    return {\n            'avg_x': df['x'].mean(),\n            'avg_y': df['y'].mean(),\n            'std_x': df['x'].std(),\n            'std_y': df['y'].std(),\n        }\n\n# remove all points that are above the mean + 2*std\ndef remove_outliers(df, stats):\n    df = df.drop(df[(abs(df['x']) > 1.5*(abs(stats['avg_x']) + stats['std_x'])) | (abs(df['y']) > 1.5*(abs(stats['avg_y']) + stats['std_y']))].index)\n    # multiply the y axis by the difference std[x]/std[y]\n    df['y'] = df['y'] * (stats['std_x'] / stats['std_y'])\n    return df\n\n# ensure equal distribution of the data by the label column\ndef equal_distribution(df, label):\n    # count the number of occurences of each label in the label column\n    labels_occurences = df[label].value_counts()\n    # split the dataframe into dataframes by the label column\n    print(labels_occurences)\n    df_by_label = {}\n    for l in labels_occurences.index:\n        print(l)\n        df_by_label[l] = df[df[label] == l]\n    # truncate each dataframe to the minimum labels_occurences\n    for l in df_by_label:\n        min = labels_occurences.min()\n        df_by_label[l] = df_by_label[l].sample(n=min//2)\n    # concatenate the dataframes\n    df = pd.concat(df_by_label.values())\n    return df\n\n# call remove_outliers function on all dataframes in the tsne_dict\nfor key, value in tsne_dict.items():\n    # split every tsne_dict by epoch, then by layer\n    epoch_dict = {}\n    for epoch in value['epoch'].unique():\n        epoch_dict[epoch] = value[value['epoch'] == epoch]\n        # split the dataframe by layer\n        layer_dict = {}\n        for layer in epoch_dict[epoch]['layer'].unique():\n            layer_dict[layer] = epoch_dict[epoch][epoch_dict[epoch]['layer'] == layer]\n            # remove outliers\n            # print layer_dict type\n            print(type(layer_dict[layer]))\n            # print layer_dict first 5 rows\n            print(layer_dict[layer].head())\n            layer_dict[layer] = remove_outliers(layer_dict[layer], get_tsne_stats(layer_dict[layer]))\n            # equalize the distribution of the data by the label column\n            layer_dict[layer] = equal_distribution(layer_dict[layer], 'label')\n        \n        # concatenate the dataframes\n        epoch_dict[epoch] = pd.concat(layer_dict.values())\n    \n    # concatenate the dataframes\n    tsne_dict[key] = pd.concat(epoch_dict.values())\n    \n# save the new dataframes to csv files\ndef save_to_csv(dict, folder):\n    # if the folder isnt created yet, create it\n    if not DATA_PATH.joinpath(folder).exists():\n        DATA_PATH.joinpath(folder).mkdir()\n        \n    for key, value in dict.items():\n        # save dataframes without index column\n        value.to_csv(DATA_PATH.joinpath(folder + \"/\" + key + \".csv\"), index=False)\n\n# call save_to_csv on all tsne_dict dataframes\nsave_to_csv(tsne_dict, \"preprocess_tsne_data\")","repo_name":"amrohendawi/unraveling-bert-article","sub_path":"data_extraction/preprocess_tsne_results.py","file_name":"preprocess_tsne_results.py","file_ext":"py","file_size_in_byte":3418,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"80"}
+{"seq_id":"33820215374","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport sys\nimport argparse\nimport logging\nfrom add_french_noise import create_logger, read_rep\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('lex', help='Path of Lexique383.tsv')\n    parser.add_argument('-art', action='store_true', help=\"Articles\")\n    parser.add_argument('-pre', action='store_true', help=\"Prepositions\")\n    parser.add_argument('-pro', action='store_true', help=\"Pronouns\")\n    parser.add_argument('-adv', action='store_true', help=\"Adverbs\")\n    parser.add_argument('-pun', action='store_true', help=\"Punctuation\")\n    parser.add_argument('-log', default='info', help=\"Logging level [debug, info, warning, critical, error] (info)\")\n    args = parser.parse_args()\n    create_logger('stderr',args.log)\n    punctuation = [',', '.', ':', ';', '\\'', '\"', '!', '?', '<', '>', '(', ')', '-']\n    rep = read_rep(args.lex)\n    pos2mot = rep['pos2mot']\n    for pos in pos2mot:\n        if pos != 'ART' and pos != 'PRO' and pos != 'PRE' and pos != 'ADV':\n            continiue\n        if pos == 'ART' and not args.art:\n            continue\n        elif pos == 'PRE' and not args.pre:\n            continue\n        elif pos == 'PRO' and not args.pro:\n            continue\n        elif pos == 'ADV' and not args.adv:\n            continue\n        for mot in pos2mot[pos]:\n            print(mot)\n    if args.pun:\n        print('\\n'.join(punctuation))\n\n        \n        \n","repo_name":"jmcrego/gramerco","sub_path":"scripts/noiser/txt2app.py","file_name":"txt2app.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"19738237533","text":"import os\n\n\n\n\n\n\ndef get_files():\n    sample_f = os.path.join(os.getcwd(), 'summary')\n    for filepath,dirnames,filenames in os.walk(sample_f):\n        for filename in filenames:\n            print(os.path.join(filepath.replace('summary', 'result', 1), filename))\n\n\nget_files()","repo_name":"scxbx/docx_project","sub_path":"household_residence_census/play_ground.py","file_name":"play_ground.py","file_ext":"py","file_size_in_byte":275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"73089600897","text":"import argparse # تستخدم لتوضيح استخدامات الكود\r\nimport socket\r\nimport time # تستخدم لأنشاء الاتصال\r\nfrom colorama import init, Fore # تستخدم لعرض النصوص بالألوان في موجه الاوامر\r\nfrom threading import Thread, Lock # تستخدم للمسارات\r\nfrom queue import Queue # تستخدم لإدارة المسارات\r\n\r\ninit()\r\nGREEN = Fore.GREEN # ضبط اللون الاخضر\r\nRESET = Fore.RESET # اعادة ضبط السطر في تيرمينال \r\nRED = Fore.RED # ضبط اللون الاحمر\r\n\r\n\r\nTHREADS_NUMBER = 200 # يمكنك تغيير او مضاعفىة ذلك العدد\r\n\r\nQ = Queue()\r\nprint_lock = Lock()\r\n\r\ndef fast_scan(port):\r\n    try:\r\n        scan = socket.socket() # انشاء كائن من المكتبة سوكيت\r\n        scan.connect((host, port)) # الاتصال بالهدف باستخدام الهوست والبورت\r\n    except:\r\n        with print_lock:\r\n            print(f\"{RED}{host}:{port} is closed  {RESET}\", end='\\r')\r\n    else:\r\n        with print_lock:\r\n            print(f\"{GREEN}{host}:{port} is open          {RESET}\")\r\n    finally:\r\n        scan.close() # اغلاق الاتصال في النهاية مهما كانت حالة الاتصال\r\n\r\ndef scan_thread():\r\n    global Q\r\n    while True:\r\n        bringer = Q.get() # الحصول علي رقم البورت من الكيو queue\r\n        fast_scan(bringer) # فحص البورت الذي تم جلبه\r\n        Q.task_done() # اخبار الكيو ان فحص المنفذ قد تم\r\n        \r\ndef main(host, ports):\r\n    global Q\r\n    start = time.time()\r\n    for t in range(THREADS_NUMBER): # البدء في تنفيذ كل مسار (100)\r\n        t = Thread(target=scan_thread) # وضع الدالة السابقة في مسار لفحص المنقذ وهكذا\r\n        t.daemon = True # نقوم بتفعيل ذلك الخيار للانتهاء من المسارات عن انتهاء الدالة الخاصة بنا main\r\n        t.start() # البدء في تنفيذ المسار تلو الاخر\r\n\r\n    for bringer in ports: # نقوم باستخراج كل بورت من قائمة البورتات التي سنمررها للدالة\r\n        Q.put(bringer) # نضع كل بورت في الكيو حتي تقوم الدالة السابقة في استخراجه وبدء فحصه\r\n    Q.join() # نقوم بانتظار المسارات حتي تنتهي\r\n    end = time.time()\r\n    print(f'Time taken {round(end-start, 2)} seconds')\r\n\r\nif __name__ == \"__main__\": # نقطه استدعاء وتشغيل البرنامج\r\n\r\n    parser = argparse.ArgumentParser(description=\"Simple port scanner\") # اظهار معلومات حول استخدامات الاسكريبت\r\n    parser.add_argument(\"--ports\", \"-p\", dest=\"port_range\", default=\"1-65535\", help=\"Port range to scan, default is 1-65535 (all ports)\")\r\n    parser.add_argument(\"host\", help=\"Host to scan.\") # معلومات عن الهدف الذي يجب تضمينه\r\n    args = parser.parse_args() # استخراج الهدف ومدي المنافذ من موجة الاوامر\r\n    host, port_range = args.host, args.port_range # وضع الهوست او الهدف في متفير والبورت في متغير\r\n\r\n    start_port, end_port = port_range.split(\"-\") # استخراج بداية البورت ونهايته\r\n    start_port, end_port = int(start_port), int(end_port) # تحويل الثيم الي integer\r\n\r\n    ports = [ p for p in range(start_port, end_port)] # توليد قائمه البورتات\r\n\r\n    main(host, ports) # تمرير الهوست وقائمة المنافذ الي الدالة main","repo_name":"Oscar404M/python_pentesting","sub_path":"infromation_gathering/port_scanning/Port Scanner using socket/fast_scanner.py","file_name":"fast_scanner.py","file_ext":"py","file_size_in_byte":3694,"program_lang":"python","lang":"ar","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"18350649545","text":"import math\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef first_digital(x):\n    while x >= 10:\n        x //= 10\n    return x\n\n\nif __name__ == \"__main__\":\n    n = 1\n    frequency = [0] * 9\n    list_m = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n    for i in range(1, 1000):\n        n *= i\n        m = first_digital(n)\n        frequency[m - 1] += 1\n    print(frequency)\n    plt.plot(list_m, frequency, 'r-', linewidth=2)\n    plt.plot(list_m, frequency, 'go', markersize=8)\n    plt.grid(True)\n    plt.show()\n","repo_name":"juzhong180236/NewPython","sub_path":"APP_utils/Algorithm/Machine_Learning/Test/九点分布.py","file_name":"九点分布.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"6669623051","text":"'''\nParsing GTDB-Tk output tables into one folder\n6/8/21\n'''\n\n\ndef gtdbtk_parse(in_path, suffix, out_path):\n    '''\n    imports individual GTDB-Tk output summary tsv\n    inputs:\n        file_path - path to folder where GTDB outputs are saved - string\n        suffix - gtdbtk file suffix, like \"*.summary.tsv\" - string\n            you can also search subdirectories like this \"/*/*.summary.tsv\"\n            make sure that you have slashes in the right locations\n        out_path - path and name of output file\n    '''\n    import glob\n    import pandas as pd\n\n    full_path = in_path + suffix\n\n    files = glob.glob(full_path)\n\n    data_list = [] # all data will be imported from csv to df, then appended to this list\n    column_names = [] # where column names will be stored\n    \n    first = True # used to save the column names from the first tsv read\n    \n    for i in files:\n        if first: # if first loop, save the column names as a list and append to list\n            first = False\n            df = pd.read_csv(i, sep = \"\\t\", header = 0)\n            df.insert(0, \"file\", i) # add a new column to 0 index with the file name\n            column_names = df.columns.tolist() # saving column names as a list\n            data_list.extend(df.values.tolist()) # appending data to master list\n        else: # otherwise just append the data\n            df = pd.read_csv(i, sep = \"\\t\", header = 0)\n            df.insert(0, \"file\", i) # add a new column to 0 index with the file name\n            data_list.extend(df.values.tolist())\n\n    data_df = pd.DataFrame(data_list, columns=column_names)\n    data_df.to_csv(out_path, index=False)\n\ngtdbtk_parse(\".\",\"/*/*summary.tsv\",\"./test.csv\")\n","repo_name":"sahutt/NOBPhage","sub_path":"mag_pipeline/gtdbtk_parse.py","file_name":"gtdbtk_parse.py","file_ext":"py","file_size_in_byte":1680,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"80"}
+{"seq_id":"31421897635","text":"from django.shortcuts import render,redirect\nimport json\nfrom django.http import JsonResponse\nfrom .models import Mapping\nfrom django.views.decorators.csrf import csrf_protect\nfrom Program.models import Program\nfrom Class.models import Class\nfrom Class.models import new_Class_model\nfrom timetable.models import Timetable\nfrom course.models import Course\nfrom course.models import new_Course_model\nfrom Staff.models import Staff\nfrom Room.models import Room\nfrom accounts.models import User\nfrom DCapp.models import input_values\nimport random\n# Create your views here.\n\n\ntt_id=Timetable.objects.latest('user_id_id','tt_id').tt_id\nno_course=new_Course_model.objects.latest('user_id_id','tt_id').no_courses\nno_classes=new_Class_model.objects.latest('user_id_id','tt_id').no_classes\nno_periods=input_values.objects.latest('user_id_id','tt_id').no_periods\nno_days=input_values.objects.latest('user_id_id','tt_id').no_days\ndata=Mapping.objects.values('class_data').distinct()\n\n\ndef generate_timetable(request):\n    global user_id\n    user_id=request.user.id\n    \n    print(data)\n    return render(request,'generate_timetable.html',{'sec_data':sec_data})\n\n\ndef mapping1(request):\n    tt_id1=Timetable.objects.latest()\n    tt_id=tt_id1.tt_id\n    # no_course=new_Course_model.objects.latest('user_id_id').no_courses\n    # no_classes=new_Class_model.objects.latest('user_id_id').no_classes\n    # print(no_course,no_classes)\n\n    user_id=request.user.id\n    tt_id=Timetable.objects.latest('user_id_id','tt_id').tt_id\n    data=Mapping.objects.filter(tt_id=tt_id).values()\n    global courses\n    courses=Course.objects.filter(user_id_id=user_id,tt_id=tt_id).values()\n    print(data)\n    global classes\n    classes=Class.objects.filter(user_id_id=user_id,tt_id=tt_id).values()\n    global rooms\n    rooms=Room.objects.filter(user_id_id=user_id,tt_id=tt_id).values()\n    global staff\n    staff=Staff.objects.filter(user_id_id=user_id,tt_id=tt_id).values()\n    if(request.method==\"POST\"):\n        class_data=request.POST['class_data']\n        course_data=request.POST['course_data']\n        room_data=request.POST['room_data']\n        staff_data=request.POST['staff_data']\n        time_data=request.POST['time_data']\n        en=Mapping(class_data=class_data,course_data=course_data,room_data=room_data,staff_data=staff_data,time_data=time_data,user_id_id=user_id,tt_id=tt_id)\n        en.save()\n\n       \n    return render(request,'mappings.html',{'courses':courses,'classes':classes, 'rooms':rooms, 'staff':staff,'data':data})\n\n\n\n#Timetable generation \narr_days=[]\nperiods_plus_days=[]\nsections=[[[0, 0,0,0,0], [0, 0,0,0,0],[0, 0,0,0,0],[0, 0,0,0,0],[0, 0,0,0,0]], [[0, 0,0,0,0], [0, 0,0,0,0],[0, 0,0,0,0],[0, 0,0,0,0],[0, 0,0,0,0]]]\nfor i in range(no_days):\n    arr_days.append(0)\nfor i in range(no_periods):\n    periods_plus_days.append(arr_days)\n\nfor i in range(2):\n    sections.append(periods_plus_days)\neach_section=[]\nall_classes=[]\n\nfor i in range(2):\n    each_section.append(data[i]['class_data'])\n\n\nfor j in each_section:\n    all_classes.append(Mapping.objects.filter(class_data=j).values())\n\nprint(sections)\nprint(all_classes[0][2])\ncomparisons=0\nhashmaps={0:0,1:0,2:0,3:0,4:0}\n\ndef random_generation():\n    \n    index=random.randint(0,4)\n    hashmaps[index]=hashmaps[index]+1\n    while(hashmaps[index]>2):\n        hashmaps[index]=hashmaps[index]-1\n        index=random.randint(0,4)\n        hashmaps[index]+=1\n    print(index,\" \",hashmaps)\n    return index\n\n\n\nfor classes in range(0,2):\n    for row in range(0,no_days):\n        for column in range(0,no_periods):\n            if((classes==0)):\n                index=random_generation()\n                sections[classes][row][column]=all_classes[0][index]\n            else:\n                i=0\n                index=random_generation()\n                sections[classes][row][column]=all_classes[classes][index]\n                while ((i<comparisons) and sections[classes][row][column]['staff_data']==sections[classes-1][row][column]['staff_data']):\n                    index=random_generation()\n                    sections[classes][row][column]=all_classes[classes][index]\n                    i=i+1\n        hashmaps={0:0,1:0,2:0,3:0,4:0}\n\n    hashmaps={0:0,1:0,2:0,3:0,4:0}\n    comparisons+=1\n\nsec_data=[]          \nfor i in range (len(sections)-2):\n    print(sections[i])\n    sec_data.append(sections[i])\n    print(\"\\n\")\n\nprint(len(sec_data))\n\n\n\n\n# for i in range(0,5):\n#     random_generation()\n\n","repo_name":"attreyee-muk/mini_project_final","sub_path":"project1/mapping/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"28272227746","text":"import torch\nimport time\nimport numpy as np\nimport sys\nimport math\nsys.path.append('.')\nimport wrapper\nimport wrapper.surrogate\n\n\ndef forward_backward(fun, x, alpha):\n    fun(x, alpha).sum().backward()\n\ndef cmp(fun1, fun2, device, x_shape=[1024], cal_times=1024):\n    x = torch.rand(x_shape, device=device)\n    alpha = torch.ones([1], device=device)\n    with torch.no_grad():\n        t1 = wrapper.cal_fun_t(cal_times, fun1, x, alpha)\n        t2 = wrapper.cal_fun_t(cal_times, fun2, x, alpha)\n    print('forward', t1, t2, 'speed =', round(t1 / t2, 2))\n    wrapper.assert_equal(fun1(x, alpha), fun2(x, alpha), 1e-5)\n    x.requires_grad_(True)\n    t1 = wrapper.cal_fun_t(cal_times, forward_backward, fun1, x, alpha)\n    x.grad.zero_()\n    t2 = wrapper.cal_fun_t(cal_times, forward_backward, fun2, x, alpha)\n    x.grad.zero_()\n    print('forward and backward', t1, t2, 'speed =', round(t1 / t2, 2))\n    forward_backward(fun1, x, alpha)\n    x1_grad = x.grad.clone()\n    x.grad.zero_()\n    forward_backward(fun2, x, alpha)\n    x2_grad = x.grad.clone()\n    x.grad.zero_()\n    wrapper.assert_equal(x1_grad, x2_grad, 1e-5)\n\n\n\ndef cmp_sigmoid(device, x_shape=[1024], cal_times=1024):\n    class sigmoid(torch.autograd.Function):\n        @staticmethod\n        def forward(ctx, x, alpha):\n            if x.requires_grad:\n                ctx.save_for_backward(x, alpha)\n            return x.ge(0).to(x)\n\n        @staticmethod\n        def backward(ctx, grad_output):\n            grad_x = None\n            if ctx.needs_input_grad[0]:\n                sgax = (ctx.saved_tensors[0] * ctx.saved_tensors[1]).sigmoid_()\n                grad_x = grad_output * (1 - sgax) * sgax * ctx.saved_tensors[1]\n\n            return grad_x, None\n    cmp(sigmoid.apply, wrapper.surrogate.sigmoid.apply, device, x_shape, cal_times)\n    \n\ndef cmp_atan(device, x_shape=[1024], cal_times=1024):\n    class atan(torch.autograd.Function):\n        @staticmethod\n        def forward(ctx, x, alpha):\n            if x.requires_grad:\n                ctx.save_for_backward(x, alpha)\n            return x.ge(0).to(x)\n\n        @staticmethod\n        def backward(ctx, grad_output):\n            grad_x = None\n            if ctx.needs_input_grad[0]:\n                grad_x = ctx.saved_tensors[1] / 2 / (1 + (math.pi / 2 * ctx.saved_tensors[1] * ctx.saved_tensors[0]).pow_(2)) * grad_output\n\n            return grad_x, None\n    cmp(atan.apply, wrapper.surrogate.atan.apply, device, x_shape, cal_times)\n\ndevice = 'cuda:7'\ncmp_sigmoid(device)\ncmp_atan(device)\n\n\n    \n\n","repo_name":"Yanqi-Chen/spikingjellyCppExt","sub_path":"surrogate/benchmark.py","file_name":"benchmark.py","file_ext":"py","file_size_in_byte":2511,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"80"}
+{"seq_id":"30185960995","text":"import dash_html_components as html\nimport dash_bootstrap_components as dbc\n\n# needed only if running this as a single page app\n#external_stylesheets = [dbc.themes.LUX]\n\n#app = dash.Dash(__name__, external_stylesheets=external_stylesheets)\n\n# --------------------------------------------------------------------------------------------------------------------------------------\n#Functions to Create Bootstrap Cards\ndef Card(header, title, text, button_text, href):\n    card = dbc.Card(\n        [   \n            dbc.CardHeader(header),\n            dbc.CardBody([\n                #html.H4('{}'.format(header), style={'text-align':'left'}, className=\"card-header\"),\n                html.H2('{}'.format(title), style={'text-align':'left'}, className=\"card-title\"),\n                html.P('{}'.format(text), style={'text-align':'left'}, className=\"card-text\"),\n                dbc.Button(button_text, href=href, color=\"primary\", className=\"mr-1\"),\n            ]),\n            \n        ],\n        #body=True,\n        color=\"light\",\n        inverse=False,\n    )\n    return card\n\n# --------------------------------------------------------------------------------------------------------------------------------------\n# Cards\n\ncard1 = Card('Permits In Progress...', \"See applications in progress today and make a forecast\", \n             'Data includes permits that havent been issued yet. Here, you can also compare types of permits,' +\n             ' and see relevant information about processing times, resources and productivity.' + \n             ' Plus, make a forcast based on current queues in the system and available resources.', \n             'Go to Permits In Progess', \"/appInProgress\")\n\ncard2 = Card('BP: Status of Applications', \"See the historical behavior of queues in the system\", \n             'Data includes all permits received until today that have been issued, as well as applications in progress.' + \n             ' The chart displays the status of all aplications week by week.' + \n             ' It was developed based on the last process completed in the application and its outcome at the beggining of each week', \n             'Go to BP: Status of Applications', \"/statuses\")\n\ncard3 = Card('BP: Complete vs. Incomplete Applications', \"Compare complete vs. incomplete applications historically\", \n             'Data only includes permits that have been already issued.' + \n             ' Here, you can compare processing times, and the percentage of applications that requested more information at Intage and Plans Examination Review stages.',\n             'Go to BP: Complete vs. Incomplete Applications', \"/MIRstatus\")\n\ncard4 = Card('BP: Model Development', \"Develop and test the predictive model based on relevant features\", \n             'Data only includes permits that have been already issued.' + \n             ' Here, you can compare the correlation of data features against processing time and their influence in the predictive model.',\n             'Go to BP: Model Development', \"/appRegressionBP\")\n\nlist_group = dbc.ListGroup(\n    [\n        dbc.ListGroupItem(\"Intake Review: initial screening review of the application.\"),\n        dbc.ListGroupItem(\"Plans Examination Review: Thorough review of the applications.\"),\n        dbc.ListGroupItem(\"Resources: People in charge of reviewing applications.\"),\n        dbc.ListGroupItem(\"Rate: Number of applications reviewed by one resource in certain interval of time.\"),\n        dbc.ListGroupItem(\"Productivity: Number of unique applications reviewed per week by all resources\"),\n        dbc.ListGroupItem(\"Target Segment: Indicates the average processing time between two specified processes.\"),\n    ]\n)\ncard5 = dbc.Card(\n              [   \n            dbc.CardHeader('Glossary'),\n            dbc.CardBody([\n                html.H2('{}'.format(\"Get familiar with COE terms and their definitions\"), style={'text-align':'left'}, className=\"card-title\"),\n                list_group,\n                html.Br(),\n                dbc.Button('Back to top', href=\"/\", color=\"primary\", className=\"mr-1\"),\n            ]), \n        ],\n        color=\"light\",\n        inverse=False,\n    )\n\n# --------------------------------------------------------------------------------------------------------------------------------------\n# HOME APP LAYOUT\nlayout = html.Div([\n                html.Div([\n                    dbc.Container([\n                        #Title\n                        html.H1(\"City of Edmonton Dashboard for Predictive Analytics\", className=\"text-left\"),\n                        html.Br(),\n                        dbc.CardDeck([card1, card2]),\n                        html.Br(),\n                        dbc.CardDeck([card3, card4]),\n                        html.Br(),\n                        dbc.CardDeck([card5]),\n                        #dbc.CardGroup([card1, card2]),\n                        #3rd Row - 2 Cards\n                        #dbc.CardGroup([card4, card5]),\n                    ])\n                ])\n            ],\n)","repo_name":"josedanielcl18/ceo-permitting-processes-timelines","sub_path":"apps/home.py","file_name":"home.py","file_ext":"py","file_size_in_byte":5003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"34785516499","text":"# -*- coding: utf-8 -*-\n\ndef main():\n    a = input(\"Введите A: \")\n    b = input(\"Введите B: \")\n    c = input(\"Введите C: \")\n    d = input(\"Введите D: \")\n\n    print(\"Уравнение: {}x + {}y + {}z = {}\".format(a, b, c, d))\n\n    count = 0\n    answers = []\n    for i in range(1, d):\n        for j in range(1, d):\n            for k in range(1, d):\n                if i * a + j * b + k * c == d:\n                    count += 1\n                    answers.append(\"{}-ый набор: {}, {}, {}\".format(count, i, j, k))\n\n    print(\"Всего решений: {}\".format(count))\n    for i in answers:\n        print(i)\n\n\nmain()","repo_name":"IrinaEvsei/Python","sub_path":"lab1/lab1task4.py","file_name":"lab1task4.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"24868091842","text":"import sys\nfrom enum import Enum\nfrom pathlib import Path\nfrom typing import List\n\nconversion_backends = [\n    (\"NGFF_ZARR\", \"ngff_zarr\"),\n    (\"ZARR_ARRAY\", \"zarr\"),\n    (\"ITK\", \"itk\"),\n    (\"TIFFFILE\", \"tifffile\"),\n    (\"IMAGEIO\", \"imageio\"),\n]\nconversion_backends_values = [b[1] for b in conversion_backends]\nConversionBackend = Enum(\"ConversionBackend\", conversion_backends)\n\n\ndef detect_cli_io_backend(input: List[str]) -> ConversionBackend:\n    if (Path(input[0]) / \".zarray\").exists():\n        return ConversionBackend.ZARR_ARRAY\n\n    extension = \"\".join(Path(input[0]).suffixes).lower()\n\n    ngff_zarr_supported_extensions = (\".zarr\",)\n    if extension in ngff_zarr_supported_extensions:\n        return ConversionBackend.NGFF_ZARR\n\n    itk_supported_extensions = (\n        \".bmp\",\n        \".dcm\",\n        \".gipl\",\n        \".hdf5\",\n        \".jpg\",\n        \".jpeg\",\n        \".iwi\",\n        \".iwi.cbor\",\n        \".lsm\",\n        \".mnc\",\n        \".mnc.gz\",\n        \".mnc2\",\n        \".mgh\",\n        \".mhz\",\n        \".mha\",\n        \".mhd\",\n        \".mrc\",\n        \".nia\",\n        \".nii\",\n        \".nii.gz\",\n        \".hdr\",\n        \".nrrd\",\n        \".nhdr\",\n        \".png\",\n        \".pic\",\n        \".vtk\",\n        \".isq\",  # Requires pip install itk-ioscanco,\n        \".fdf\",  # Requires pip install itk-iofdf\n    )\n\n    if extension in itk_supported_extensions:\n        return ConversionBackend.ITK\n\n    try:\n        import tifffile\n\n        tifffile_supported_extensions = [\n            f\".{ext}\" for ext in tifffile.TIFF.FILE_EXTENSIONS\n        ]\n        if extension in tifffile_supported_extensions:\n            return ConversionBackend.TIFFFILE\n    except ImportError:\n        from rich import print\n\n        print(\"[red]Please install the [i]tifffile[/i] package\")\n        sys.exit(1)\n\n    return ConversionBackend.IMAGEIO\n","repo_name":"thewtex/ngff-zarr","sub_path":"ngff_zarr/detect_cli_io_backend.py","file_name":"detect_cli_io_backend.py","file_ext":"py","file_size_in_byte":1830,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"80"}
+{"seq_id":"22415290749","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Date    : 2018-02-23 07:01:55\n# @Author  : Zhi Liu (zhiliu.mind@gmail.com)\n# @Link    : http://iridescent.ink\n# @Version : $1.0$\n\nimport numpy as np\nimport pyaibox as pb\n\n\ndatafolder = pb.data_path('optical')\nxr = pb.imread(datafolder + 'Einstein256.png')\nxi = pb.imread(datafolder + 'LenaGRAY256.png')\n\nx = xr + 1j * xi\n\ny = pb.ct2rt(x, axis=0)\nz = pb.rt2ct(y, axis=0)\n\nprint(x.shape, y.shape, z.shape)\nprint(x.dtype, y.dtype, z.dtype)\n\nprint(np.min(np.abs(x)), np.max(np.abs(x)))\nprint(np.min(np.abs(y)), np.max(np.abs(y)))\nprint(np.min(np.abs(z)), np.max(np.abs(z)))\n\n\nplt = pb.imshow([x.real, x.imag, y.real, y.imag, z.real, z.imag], nrows=3, ncols=2,\n                titles=['original(real)', 'original(imag)', 'converted(real)', \n                'converted(imag)', 'reconstructed(real)', 'reconstructed(imag)'])\nplt.show()\n","repo_name":"antsfamily/pyaibox","sub_path":"examples/misc/demo_complex2real_fft.py","file_name":"demo_complex2real_fft.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"21713491091","text":"import torch\nimport os\nimport sys\nimport json\nfrom tqdm import tqdm, trange\nimport imageio\nfrom dataloader import DataLoader\nfrom render import *\nfrom modules import *\nfrom utils import *\n\ntorch.set_default_tensor_type('torch.cuda.FloatTensor')\n\n\"\"\"=============== GLOBAL ARGUMENTS ===============\"\"\"\nconfig_filepath = sys.argv[1]\ndemo_type = int(sys.argv[2])\nwith open(config_filepath, 'r') as config_file:\n    config = json.load(config_file)\n\noutput_path = config['output_path']\nexperiment_name = config['experiment_name']\ndata_path = config['data_path']\n\nuse_dir = config['use_dir'] if 'use_dir' in config else True\nz_dim = config['z_dim'] if 'z_dim' in config else 1024\n\nrender_near = config['render_near'] if 'render_near' in config else 0.5\nrender_far = config['render_far'] if 'render_far' in config else 1.5\n\nresolution = 128\nrender_coarse_sample_num = 32\nrender_coarse_sample_num = 64\n\n\"\"\"=============== START ===============\"\"\"\n# Model\ngenerator = Generator(z_dim, resolution, render_near, render_far, 12, render_coarse_sample_num, render_fine_sample_num, 0.3, 0.15, use_dir)\n\n# Load log directory\nlog_path = os.path.join(output_path, experiment_name)\ncheck_points = [os.path.join(log_path, f) for f in sorted(os.listdir(log_path)) if 'tar' in f]\nprint('Found check_points', check_points)\nif len(check_points) > 0:\n    check_point_path = check_points[-1]\n    print('Reloading from', check_point_path)\n    check_point = torch.load(check_point_path)\n    generator.load_state_dict(check_point['generator'])\n\nif demo_type == 0:\n    save_demo(generator, './demo.png', 8, 8)\nelif demo_type == 1:\n    n_pose = 8\n    poses = [[0.2 * np.cos(2 * np.pi * i / n_pose), 0.2 * np.sin(2 * np.pi * i / n_pose)] for i in range(n_pose)]\n    demo_multiview(generator, './demo_multiview.png', poses, 8)\nelif demo_type == 2:\n    n_pose = 9\n    poses = [[0.15 * (i - (n_pose - 1) / 2), 0] for i in range(n_pose)]\n    demo_multiview(generator, './demo_extrapolate.png', poses, 8)\nelif demo_type == 3:\n    n_pose = 5\n    poses = [[0, 0, 6 + 6 * i] for i in range(n_pose)]\n    demo_multiview(generator, './demo_fov.png', poses, 8)\nelif demo_type == 4:\n    # Render\n    poses = [[angle, 0] for angle in np.linspace(-1, 1, 40 + 1)[:-1]]\n    demo_video(generator, './demo.gif', poses)\nelif demo_type == 5:\n    demo_interpolate(generator, './demo_interpolate.png', 9)\nelif demo_type == 6:\n    demo_style_mix(generator, './demo_style_mix.png', 8)\n\n","repo_name":"JeffreyXiang/MSRA-practice-project","sub_path":"pi_GAN/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":2429,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"8701065499","text":"# -*- coding:utf-8 -*-\r\n# 此程序用于验证，所用数据集为验证集\r\nimport tensorflow as tf\r\nimport cv2 as cv\r\nimport readTFRescord\r\nimport netForward\r\nimport ceshi_3\r\nimport resNetForward\r\nimport netBackward\r\nimport time\r\nimport numpy as np\r\n\r\n\r\n# 测试集文件存放路径\r\ntestFile = \"D:\\\\Desktop\\\\python3\\\\paper_2\\\\data\\\\picture\\\\0hp\\\\test\\\\test.tfrecords\"\r\nBATCH_SIZE = 1000\r\n\r\n\r\ndef test():\r\n    with tf.Graph().as_default() as g:\r\n        x = tf.placeholder(tf.float32, [None, 64, 64, 1])\r\n        y_ = tf.placeholder(tf.float32, [None, 10])\r\n        x_data = tf.reshape(x, [-1, 64, 64, 1])\r\n        y = ceshi_3.forward(x_data)\r\n\r\n        saver = tf.train.Saver()\r\n\r\n        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\r\n        ans = tf.argmax(y, 1)\r\n        lab = tf.argmax(y_, 1)\r\n        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\r\n\r\n        data_batch, label_batch = readTFRescord.read_and_decode(testFile, BATCH_SIZE)\r\n\r\n        with tf.Session() as sess:\r\n\r\n            ckpt = tf.train.get_checkpoint_state(netBackward.MODEL_SAVE_PATH)\r\n            if ckpt and ckpt.model_checkpoint_path:\r\n                saver.restore(sess, ckpt.model_checkpoint_path)\r\n\r\n                coord = tf.train.Coordinator()\r\n                threads = tf.train.start_queue_runners(sess=sess, coord=coord)\r\n\r\n                d, ys = sess.run([data_batch, label_batch])\r\n\r\n                noi = abs(np.random.normal(0, 64, [BATCH_SIZE, 64, 64, 1]))  # 噪声的形状要与数据匹配\r\n                noise = np.array(noi, dtype='uint8')\r\n                datas = cv.add(d, noise)\r\n\r\n                labels = readTFRescord.onehot(ys)\r\n\r\n                start_time = time.time()\r\n                accuracy_score = sess.run(accuracy, feed_dict={x: datas, y_: labels})\r\n                ans = sess.run(ans,feed_dict={x: datas, y_: labels})\r\n                lab = sess.run(lab,feed_dict={x: datas, y_: labels})\r\n                for i in range(BATCH_SIZE):\r\n                    if ans[i] != lab[i]:\r\n                        print(\"output = %d ; label = %d\" % (ans[i], lab[i]))\r\n                end_time = time.time()\r\n                print('time:', (end_time - start_time))\r\n                print('The total accuracy is:%f' % accuracy_score)\r\n\r\n                coord.request_stop()\r\n                coord.join(threads)\r\n\r\n            else:\r\n                print('No checkpoint file found')\r\n                return\r\n\r\n\r\ndef main():\r\n    test()\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Shane1911/-","sub_path":"netTest.py","file_name":"netTest.py","file_ext":"py","file_size_in_byte":2549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"41664788090","text":"from django.shortcuts import render\n\n# Create your views here.\nfrom .forms import Predict_Form\nfrom client.models import UserProfileInfo\nfrom django.shortcuts import get_object_or_404, render\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom .knapsack_functions import *\n\n\n@login_required(login_url='/')\ndef PredictRisk(request, pk):\n\n    predicted = False\n    predictions = {}\n    list_of_sums = []\n\n    # predictions = {\n    #     'SVC': '1',\n    #     'LogisticRegression': '1',\n    #     'NaiveBayes': '1',\n    #     'DecisionTree': '0',\n    # }\n\n    appliances = {\n        'Coffee maker': 1200,\n        'Clothes washer': 500,\n        'Clothes dryer': 5000,\n        'Dishwasher': 2400,\n        'Hair dryer': 1875,\n        'Heater': 1500,\n        'Clothes iron': 1800,\n        'Water pump': 1100,\n        'Laptop': 50,\n        'Radio': 400,\n        'Refrigerator': 725,\n        'Television': 110,\n        'Vacuum cleaner': 1440,\n        'Microwave oven': 1100,\n        'Toaster': 1400,\n        'Ceiling Fans': 110\n    }\n\n\n\n    if request.session.has_key('user_id'):\n        u_id = request.session['user_id']\n\n    if request.method == 'POST':\n        form = Predict_Form(data=request.POST)\n        profile = get_object_or_404(UserProfileInfo, pk=pk)\n\n        if form.is_valid():\n            features = [[form.cleaned_data['appliance1'], form.cleaned_data['appliance2'],\n                         form.cleaned_data['appliance3'], form.cleaned_data['appliance4'],\n                         form.cleaned_data['appliance5'], form.cleaned_data['appliance6'],\n                         form.cleaned_data['appliance7'], form.cleaned_data['appliance8'],\n                         form.cleaned_data['appliance9'], form.cleaned_data['appliance10'],\n                         form.cleaned_data['priority1'], form.cleaned_data['priority2'],\n                         form.cleaned_data['priority3'], form.cleaned_data['priority4'],\n                         form.cleaned_data['priority5'], form.cleaned_data['priority6'],\n                         form.cleaned_data['priority7'], form.cleaned_data['priority8'],\n                         form.cleaned_data['priority9'], form.cleaned_data['priority10'],\n                         form.cleaned_data['time1'],  form.cleaned_data['time2'],\n                         form.cleaned_data['time3'],  form.cleaned_data['time4'],\n                         form.cleaned_data['time5'],  form.cleaned_data['time6'],\n                         form.cleaned_data['time7'],  form.cleaned_data['time8'],\n                         form.cleaned_data['time9'],  form.cleaned_data['time10'],\n                         form.cleaned_data['price_of_electricity'], form.cleaned_data['price_of_high_tariffs'],\n                         form.cleaned_data['limit_of_time_hours']]]\n\n            # return HttpResponse(features)\n            main_list = features[0]                 # list of all elements retrieved from the user\n            list_names = main_list[0:10]            # list of names of items chosen from user\n            list_priorities = main_list[10:20]      # list of the priorities chosen from user\n            list_time = main_list[20:30]            # list of time for each appliance chosen from use\n            length = len(main_list)                 # len of list\n\n            price_in_kwh = main_list[30]            # price of electricity dollar per kilowatt per hours\n            increase_in_percent = main_list[31]     # percent increase of price for high tariffs\n            limit_of_time_in_hours = main_list[32]  # if the client want to limit time of all appliances' operations\n\n\n            print(list_names)\n            print(list_priorities)\n            print(list_time)\n            print(price_in_kwh)\n            print(increase_in_percent)\n            print(limit_of_time_in_hours)\n            print(length)\n\n            # return HttpResponse(main_list)\n\n            dictionary_of_items = {}\n            list_watts = []                        # watts for each appliance\n            list_watts_multiply_hours = []         # multiply watts per hour with number of hours of appliance usage\n            list_price_for_all_watts_spend = []    # multiply amount of watts used with price (kilo watts per hours)\n            list_after_increased_prices = []       # multiply amount of watts with price of high tariffs\n            iterator = 0\n\n            for x in list_names:\n                if x in appliances.keys():\n                    lista = []\n                    list_watts.append(appliances[x])                          # list of watts per hour by appliance\n\n                    hours_watts = int(appliances[x]) * int(list_time[iterator])  # multiply watts by time spend\n                    list_watts_multiply_hours.append(hours_watts)                # get the results in a list\n\n                    price_of_used_watts = int(hours_watts * int(price_in_kwh) /1000)  # multiply all watts and the price\n                    list_price_for_all_watts_spend.append(price_of_used_watts)        # get the results in a list\n\n                    high_tariffs = (int(price_in_kwh) * int(increase_in_percent))/100\n                    price_of_used_watts_high_tariffs = int(hours_watts * (int(price_in_kwh) + high_tariffs)/1000)\n                    list_after_increased_prices.append(price_of_used_watts_high_tariffs)\n\n                    lista.append(price_of_used_watts_high_tariffs)\n                    lista.append(list_priorities[iterator])\n                    lista.append(appliances[x])\n                    lista.append(list_time[iterator])\n\n\n                    dictionary_of_items[x] = lista\n\n                    iterator = iterator + 1\n\n            price_limit = sum(list_price_for_all_watts_spend)\n            price_limit1 = sum(list_after_increased_prices)\n\n            print(list_watts)\n            print(list_watts_multiply_hours)\n            print(list_price_for_all_watts_spend)\n            print(list_after_increased_prices)\n            print(\"first price\", price_limit)\n            print(\"second price\", price_limit1)\n            print(\"my dictionary\", dictionary_of_items)\n\n            # return HttpResponse(features)\n\n            \"\"\"                             \n                        DYNAMIC PROGRAMING => using Knapsack to give the optimal solution of appliances                                               \n            \"\"\"\n\n            table = dynamic_programming_solution(price_limit, list_after_increased_prices, list_priorities)\n            optimal_solution = get_selected_items_list(price_limit, table, list_after_increased_prices,\n                                                       list_priorities, dictionary_of_items)\n\n            predictions = dict(optimal_solution[2])\n\n            print('this is the solution isheAllah', optimal_solution)\n            print('this is the new dictionary insheAllah', predictions)\n\n            sum1 = 0\n            sum2 = 0\n            sum3 = 0\n            sum4 = 0\n\n\n\n            for key, values in predictions.items():\n                sum1 = sum1 + values[0]\n                sum2 = sum2 + values[1]\n                sum3 = sum3 + values[2]\n                sum4 = sum4 + values[3]\n\n            list_of_sums.append(sum1)\n            list_of_sums.append(sum2)\n            list_of_sums.append(sum3)\n            list_of_sums.append(sum4)\n\n\n            pred = form.save(commit=False)\n\n            # l = [predictions['SVC'],\n            #      predictions['LogisticRegression'],\n            #      predictions['NaiveBayes'],\n            #      predictions['DecisionTree']]\n            #\n            # count = l.count('1')\n            #\n            # result = False\n            #\n            # if count >= 2:\n            #     result = True\n            #     pred.num = 1\n            # else:\n            #     pred.num = 0\n\n            pred.profile = profile\n\n            pred.save()\n            predicted = True\n\n            # colors = {}\n            #\n            # if predictions['SVC'] == '0':\n            #     colors['SVC'] = \"table-success\"\n            # elif predictions['SVC'] == '1':\n            #     colors['SVC'] = \"table-danger\"\n            #\n            # if predictions['LogisticRegression'] == '0':\n            #     colors['LR'] = \"table-success\"\n            # else:\n            #     colors['LR'] = \"table-danger\"\n            #\n            # if predictions['NaiveBayes'] == '0':\n            #     colors['NB'] = \"table-success\"\n            # else:\n            #     colors['NB'] = \"table-danger\"\n            #\n            # if predictions['DecisionTree'] == '0':\n            #     colors['DT'] = \"table-success\"\n            # else:\n            #     colors['DT'] = \"table-danger\"\n\n    if predicted:\n        return render(request, 'solution.html',\n                      {'form': form, 'predicted': predicted, 'user_id': u_id, 'list_of_sums': list_of_sums,\n                          'predictions': predictions})\n\n    else:\n        form = Predict_Form()\n\n        return render(request, 'solution.html',\n                      {'form': form, 'predicted': predicted, 'user_id': u_id, 'list_of_sums': list_of_sums,\n                       'predictions': predictions})\n","repo_name":"ermeba/knapsack_problem","sub_path":"knapsack_solution/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"23168802700","text":"# -*- coding: utf-8 -*-\n\"\"\"\nUtility for deleting IAM users and associated resources.\n\nUnlike the AWS Management Console, when you delete a user programmatically,\nyou must delete the items attached to the user manually, or the deletion fails.\nFor more information, see Deleting an IAM User:\n\nhttps://docs.aws.amazon.com/IAM/latest/UserGuide/id_users_manage.html#id_users_deleting_cli\n\nBefore attempting to delete a user, remove the following items:\n\n  * Password (DeleteLoginProfile)\n    - delete-login-profile --user-name <user-name>\n    - delete_login_profile(UserName=<user_name>)\n\n  * Access Keys (DeleteAccessKey)\n    - delete-access-key --access-key-id <access-key-id>\n    - delete_access_key(AccessKeyId=<access_key_id>)\n\n  * Signing Certificate (DeleteSigningCertificate)\n    - delete-signing-certificate --certificate-id <certificate-id>\n    - delete_signing_certificate(CertificateId=<certificate_id>)\n\n  * SSH Public Key (DeleteSSHPublicKey)\n    - delete-ssh-public-key --user-name <user-name> --ssh-public-key-id <ssh-public-key-id>\n    - delete_ssh_public_key(UserName=<user_name>, SSHPublicKeyId=<ssh_public_key_id>)\n\n  * Git Credentials (DeleteServiceSpecificCredential)\n    - delete-service-specific-credential --service-specific-credential-id <service-specific-credential-id>\n    - delete_service_specific_credentials(ServiceSpecificCredentialId=<service_specific_credential_id>)\n\n  * Multi-factor Authentication (MFA) Device (DeactivateMFADevice, DeleteVirtualMFADevice)\n    - deactivate-mfa-device --user-name <user-name> --serial-number <serial-number>\n    - deactivate_mfa_device(UserName=<user_name>, SerialNumber=<serial_number>)\n    - delete-virtual-mfa-device --serial_number <serial-number>\n    - delete_virtual_mfa_device(SerialNumber=<serial_number>)\n\n  * Inline Policies (DeleteUserPolicy)\n    - delete-user-policy --user-name <user-name> --policy-name <policy-name>\n    - delete_user_policy(UserName=<user_name>, PolicyName=<policy_name>)\n\n  * Attached Managed Policies (DetachUserPolicy)\n    - detach-user-policy --user-name <user-name> --policy-arn <policy-arn>\n    - detach_user_policy(UserName=<user_name>, PolicyArn=<policy_arn>)\n\n  * Group Memberships (RemoveUserFromGroup)\n    - remove-user-from-group --user-name <user-name> --group-name <group-name>\n    - remove_user_from_group(UserName=<user_name>, GroupName=<group_name>)\n\nFinally, delete the user:\n\n  * Delete User (DeleteUser)\n   - delete-user --user-name <user-name>\n   - delete_user(UserName=<user_name>)\n\"\"\"\n\nimport boto3\nimport click\n\nimport logging\n\n# configure Logger instance\nlogger = logging.getLogger()\nf = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\nfh = logging.FileHandler('logs.txt')\nfh.setLevel(logging.WARNING)\nfh.setFormatter(f)\nlogger.addHandler(fh)\n\n\n@click.command()\n@click.option('--user-name', default=None, help='Name of the user.')\n@click.option('--dry-run',\n              is_flag=True,\n              help='Echo AWS CLI commands without executing them.')\ndef main(user_name: str, dry_run: bool) -> None:\n    client = boto3.client('iam')\n\n    access_key_ids = \\\n        [x['AccessKeyId']\n         for x in client.list_access_keys(\n             UserName=user_name)['AccessKeyMetadata']]\n    certificate_ids = \\\n        [x['CertificateId']\n         for x in client.list_signing_certificates(\n             UserName=user_name)['Certificates']]\n    ssh_public_key_ids = \\\n        [x['SSHPublicKeyId']\n         for x in client.list_ssh_public_keys(\n             UserName=user_name)['SSHPublicKeys']]\n    service_specific_credential_ids = \\\n        [x['ServiceSpecificCredentialId']\n         for x in client.list_service_specific_credentials(\n             UserName=user_name)['ServiceSpecificCredentials']]\n    serial_numbers = \\\n        [x['SerialNumber']\n         for x in client.list_mfa_devices(\n             UserName=user_name)['MFADevices']]\n    policy_names = \\\n        [x\n         for x in client.list_user_policies(\n             UserName=user_name)['PolicyNames']]\n    policy_arns = \\\n        [x['PolicyArn']\n         for x in client.list_attached_user_policies(\n             UserName=user_name)['AttachedPolicies']]\n    group_names = \\\n        [x['GroupName']\n         for x in client.list_groups_for_user(\n             UserName=user_name)['Groups']]\n\n    if dry_run:\n        print('aws iam delete-login-profile --user-name {}'.format(user_name))\n        print('aws iam list-access-keys --user-name {}'.format(user_name))\n        for access_key_id in access_key_ids:\n            print('aws iam delete-access-key --access-key-id {}'.format(\n                access_key_id))\n        print('aws iam list-signing-certificates --user-name {}'.format(\n            user_name))\n        for certificate_id in certificate_ids:\n            print('aws iam delete-signing-certificate --certificate-id {}'.\n                  format(certificate_id))\n        print('aws iam list-ssh-public-keys --user-name {}'.format(user_name))\n        for ssh_public_key_id in ssh_public_key_ids:\n            print(\n                'aws iam delete-ssh-public-key --user-name --ssh-public-key-id {}'\n                .format(user_name, ssh_public_key_id))\n        print(\n            'aws iam list-service-specific-credentials --user-name {}'.format(\n                user_name))\n        for service_specific_credential_id in service_specific_credential_ids:\n            print(\n                'aws iam delete-service-specific-credential --service-specific-credential-id {}'\n                .format(service_specific_credential_id))\n        print('aws iam list-mfa-devices --user-name {}'.format(user_name))\n        for serial_number in serial_numbers:\n            print(\n                'aws iam deactivate-mfa-device --user-name {} --serial-number {}'\n                .format(user_name, serial_number))\n            print(\n                'aws iam delete-virtual-mfa-device --serial-number {}'.format(\n                    serial_number))\n        print('aws iam list-user-policy --user-name {}'.format(user_name))\n        for policy_name in policy_names:\n            print('aws iam delete-user-policy --user-name {} --policy-name {}'.\n                  format(user_name, policy_name))\n        print('aws iam list-attached-user-policies --user-name {}'.format(\n            user_name))\n        for policy_arn in policy_arns:\n            print('aws iam detach-user-policy --user-name {} --policy-arn {}'.\n                  format(user_name, policy_arn))\n        print('aws iam list-groups-for-user --user-name {}'.format(user_name))\n        for group_name in group_names:\n            print(\n                'aws iam remove-user-from-group --user-name {} --group-name {}'\n                .format(user_name, group_name))\n        print('aws iam delete-user --user-name {}'.format(user_name))\n\n    else:\n        client.delete_login_profile(UserName=user_name)\n        for access_key_id in access_key_ids:\n            client.delete_access_key(AccessKeyId=access_key_id)\n        for certificate_id in certificate_ids:\n            client.delete_signing_certificate(CertificateId=certificate_id)\n        for ssh_public_key_id in ssh_public_key_ids:\n            client.delete_ssh_public_key(SSHPublicKeyId=ssh_public_key_id)\n        for service_specific_credential_id in service_specific_credential_ids:\n            client.delete_service_specific_credential(\n                ServiceSpecificCredentialId=service_specific_credential_id)\n        for serial_number in serial_numbers:\n            client.deactivate_mfa_device(UserName=user_name,\n                                         SerialNumber=serial_number)\n            client.delete_virtual_mfa_device(SerialNumber=serial_number)\n        for policy_name in policy_names:\n            client.delete_user_policy(UserName=user_name,\n                                      PolicyName=policy_name)\n        for policy_arn in policy_arns:\n            client.detach_user_policy(UserName=user_name, PolicyArn=policy_arn)\n        for group_name in group_names:\n            client.remove_user_from_group(UserName=user_name,\n                                          GroupName=group_name)\n        client.delete_user(UserName=user_name)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"nickolashkraus/aws-scripts","sub_path":"sdk/iam-delete-user/iam_delete_user.py","file_name":"iam_delete_user.py","file_ext":"py","file_size_in_byte":8204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"7830075688","text":"from re import findall, sub\nfrom math import log\nfrom stopword import stopword\n\n# class tfidf\nclass TfIdf:\n    def __init__(self, kalimat):\n        # untuk menyimpan kalimat awal\n        self.kalimat = kalimat\n        # array menyimpan hasil casefold\n        self.casefold = []\n        # array menyimpan hasil pemisahan berdasarkan .\n        self.perkalimat = []\n        # array menyimpan hasil filter\n        self.filter = []\n        # array menyimpan hasil token (memecah menjadi array per-kata)\n        self.token = []\n        # array menyimpan hasil stopword (menghilangkan stopword)\n        self.stopword_val = []\n        # array menyimpan kata-kata yang unik saja\n        self.unique = []\n        # arary menyimpan jumlah kemunculan kata-kata unik dalam dokumen ini saja\n        self.frekuensi_term = {}\n        # arary menyimpan jumlah kemunculan kata-kata unik dalam dokumen ini saja, namun dengan lengkap di kalimat mana\n        self.frekuensi_term_perkalimat = {}\n        # arary menyimpan jumlah kemunculan kata-kata unik dalam semua dokumen  **tidak terpakai sejauh ini\n        self.dokumen_term = {}\n        # arary menyimpan jumlah kemunculan kata-kata unik dalam semua dokumen tapi dispesifikan perdokumen**tidak terpakai sejauh ini\n        self.dokumen_term_perdoc = {}\n        # nilai idf, dan tfidf\n        self.idf_val = {}\n        self.tf_val = {}\n        self.tf_idf_val = {}\n\n    # function process, function yang dipanggil di document.py\n    def process(self):\n        self.case_folding()\n        self.pemecah_kalimat()\n        self.filtering()\n        self.stopword_removal()\n        self.tokenizing()\n        self.set_unique()\n        self.set_frekuensi_term()\n\n    # huruf kecil\n    def case_folding(self):\n        self.case_fold = self.kalimat.casefold()\n\n    # misahin berdasarkan titik\n    def pemecah_kalimat(self):\n        # function sub untuk mengganti parameter1, keparameter2 pada parameter3\n        # dalam kasus ini mengganti enter menjadi spasi pada var self.case_fold, \n        # keluaraanya masuk one_liner\n        one_liner = sub('(\\r\\n)|[\\r\\n]', ' ', self.case_fold)\n        # misahan berdsarkan .\n        kalimat_pecahan = one_liner.split('.')\n        for index, line in enumerate(kalimat_pecahan):\n            self.perkalimat.append(line)\n\n    # filter symbol, ngilangin spasi lebih\n    def filtering(self):\n        for index, line in enumerate(self.perkalimat):\n            filter_kalimat = sub('[\\d+\\!\\(\\<\\]\\@\\)\\:\\|\\#\\.\\;\\\\\\$\\,\\/\\+\\%\\„\\?\\=\\^\\\"\\{\\_\\&\\}\\,\\*\\>\\[\\t\\r\\n(\\(.*\\))]|(\\d+\\/\\d+\\/\\d+)', '', line)\n            # mengubah - atau spasi yang leibh dari 1 menjadi 1 spasi\n            filter_kalimat = sub('[\\-|\\s{2,}]', ' ', filter_kalimat)\n            # menghilangkan spasi di awal dan di akhir\n            filter_kalimat = sub('(^\\s+|\\s+$)(.*)', r'\\2', filter_kalimat)\n            if filter_kalimat:\n                self.filter.append(filter_kalimat)     \n\n    # menghilangkan kata-kata yang tidak penting\n    def stopword_removal(self):\n        stopwords = stopword.get_stopwod_stringed()\n        for index, line in enumerate(self.filter):\n            remove_stopword = sub(stopwords, '', line)\n            self.stopword_val.append(remove_stopword)\n\n    # memecah kata-kata menjadi array\n    def tokenizing(self):\n        for index, line in enumerate(self.stopword_val):\n            tokenize = line.split()\n            self.token.append(tokenize)\n\n    # mengambil kata-kata yang unik saja\n    def set_unique(self):\n        self.unique = self.token.copy()\n        unique = self.unique[0].copy()\n        for i in range(1, len(self.unique)):\n            for index, word in enumerate(self.unique[i]):\n                if word not in unique:\n                    unique.append(word)\n        self.unique = unique\n\n    # menghitung jumlah kemunculan kata-kata pada kalimat-kalimat\n    def set_frekuensi_term(self):\n        # unique = ['rencana', 'jakarta']\n        for i, iv in enumerate(self.unique):\n            nums = [] # S0: 1, S2: 2, S3: 0, ..., S7: 0\n            total = 0\n            for l, lv in enumerate(self.stopword_val):\n                num = 0\n                regex = '\\\\b%s\\\\b' % iv\n                appear = findall(regex, lv) # return ['rencana', 'rencana']\n                num = len(appear) # 2\n                total += num\n                nums.append(num)\n            kamus = {}\n            # kamus = {'asd': kv}\n            for k, kv in enumerate(nums):\n                kamus['S%d' % k] = kv\n            self.frekuensi_term_perkalimat[iv] = kamus\n            self.frekuensi_term[iv] = total\n\n    def set_dokumen_term(self, dokumen_term):\n        self.dokumen_term = dokumen_term.copy()\n \n    def set_dokumen_term_perdoc(self, dokumen_term, doc_name):\n        self.dokumen_term_perdoc[doc_name] = dokumen_term\n\n    # Melakukan pengecekan array kata unique pada it_idf ini\n    def check_doc_unique(self, unique):\n        dokumen_term = {}\n        for i, iv in enumerate(unique):\n            nums = 0\n            for l, lv in enumerate(self.stopword_val):\n                regex = '\\\\b%s\\\\b' % iv\n                appear = findall(regex, lv)\n                num = len(appear)\n                nums += num\n            dokumen_term[iv] = nums;\n        return dokumen_term\n\n    def set_idf_value(self):\n        # if len(self.dokumen_term_perdoc) == 0:\n        n_doc = len(self.dokumen_term_perdoc) + 1\n        for i in self.frekuensi_term:\n            nDf = ( 1 if self.frekuensi_term[i] > 0 else 0 ) + self.dokumen_term[i]\n            self.idf_val[i] = log(n_doc/nDf, 10)\n\n    def set_tf_value(self):\n        for i in self.frekuensi_term:\n            if self.frekuensi_term[i] == 0:\n                self.tf_val[i] = 0\n            else:\n                self.tf_val[i] = 1+log(self.frekuensi_term[i], 10)\n\n    # menghitung tf-idf\n    def set_tf_idf_value(self):\n        for i in self.tf_val:\n            self.tf_idf_val[i] = self.tf_val[i] * self.idf_val[i]\n        \n        \n","repo_name":"Nagaku/tfidf","sub_path":"tf_idf.py","file_name":"tf_idf.py","file_ext":"py","file_size_in_byte":5928,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"38604891481","text":"from collections import deque\r\nimport sys\r\nimport heapq\r\n\r\nn,b = map(int,sys.stdin.readline().strip().split())\r\n\r\nmatrix = []\r\nfor i in range(n):\r\n    temp = list(map(int,sys.stdin.readline().split()))\r\n    matrix.append(temp)\r\n\r\ndef matrix_solve(matrixA, matrixB):\r\n    global n\r\n    result = [[0 for _ in range(n)]for _ in range(n)]\r\n    for i in range(n):\r\n        for j in range(n):\r\n            for k in range(n):\r\n                result[i][j] += matrixA[i][k]*matrixB[k][j]\r\n            result[i][j] = result[i][j]%1000\r\n\r\n    return result\r\n\r\ndef problem_solve(b):\r\n    global n\r\n    if b == 1:\r\n        for i in range(n):\r\n            for j in range(n):\r\n                matrix[i][j] = matrix[i][j]%1000\r\n        return matrix\r\n    elif b%2 == 0:\r\n        half = problem_solve(b//2)\r\n        return matrix_solve(half,half)\r\n    else:\r\n        half = problem_solve(b//2)\r\n        temp = matrix_solve(half,half)\r\n        return matrix_solve(temp,matrix)\r\n\r\n\r\n\r\nlist = problem_solve(b)\r\nfor i in range(n):\r\n    print(' '.join(map(str,list[i])))","repo_name":"songarden/baekjun-solving","sub_path":"백준/Gold/10830. 행�� 제곱/행렬 제곱.py","file_name":"행렬 제곱.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"24358003280","text":"import collections\nimport json\nimport os\nimport shutil\nimport sys\nimport identifiers_hash\n\n\ndef show_cmdline_usage():\n    script_name = os.path.split(__file__)[1]\n    print(f'Usage: \\n'\n        f'1) For extracting the vba_imphash and displaying the identifiers for a single file: '\n        f'{script_name} file_path\\n'\n        f'****Example****: {script_name} \"details 07.20.doc.old\"\\n\\n\\n'\n        f'2) For clustering files based on the computed vba_imphash:\\n'\n        f'  a) Without creating the clusters on disk: '\n        f'{script_name} unclustered_files_path\\n'\n        f'  ****Example****: {script_name} \"/home/test/Unclustered files/\"\\n\\n'\n        f'  b) Creating the clusters on disk: '\n        f'{script_name} unclustered_files_path clusters_destination_path\\n'\n        f'  ****Example****: {script_name} \"/home/test/Unclustered files/\" '\n        f'\"/home/test/Clusters/\"\\n\\n\\n'\n        f'7z needs to be installed and available as a command.\\n'\n        f'In case the clustering files version of the command line is used, the script creates '\n        f'the following .json files containing relevant information in the current working '\n        f'directory:  \"vba_imphash_clusters.json\", \"imphash_identifiers.json\", '\n        f'\"non_imphash_identifiers.json\".')\n\n\ndef extract_vba_imphash_from_single_file(file_path):\n    vba_imphash, list_imphash_identifiers, list_non_imphash_identifiers = \\\n        identifiers_hash.compute_imphash(file_path)\n    print(f'Import identifiers: {list_imphash_identifiers}.\\n'\n        f'NON-Import identifiers: {list_non_imphash_identifiers}.\\n'\n        f'VBA import hash = {vba_imphash}.')\n\n\ndef cluster_office_files_directory(dir_path):\n    dict_clusters = {}\n    dict_imphash_identifiers = {}\n    dict_non_imphash_identifiers = {}\n\n    for file_path in _get_file_names_in_path(dir_path):\n        print('')\n        vba_imphash, list_imphash_identifiers, list_non_imphash_identifiers = \\\n            identifiers_hash.compute_imphash(file_path)\n        _display_info_about_vba_imphash(dict_clusters, vba_imphash, list_imphash_identifiers, \n            file_path)\n        _update_dict_clusters(dict_clusters, vba_imphash, file_path)\n        _update_dict_identifiers(dict_imphash_identifiers, list_imphash_identifiers)\n        _update_dict_identifiers(dict_non_imphash_identifiers, list_non_imphash_identifiers)\n    \n    _display_dict_clusters(dict_clusters)\n    _save_dicts_to_disk(dict_clusters, dict_imphash_identifiers, dict_non_imphash_identifiers)\n\n\ndef _get_file_names_in_path(dir_path):\n    for file_name in os.listdir(dir_path):\n        file_path = os.path.join(dir_path, file_name)\n        if not os.path.isfile(file_path):\n            continue\n        yield file_path\n\n\ndef _display_info_about_vba_imphash(dict_clusters, vba_imphash, list_imphash_identifiers, \n        file_path):\n    if vba_imphash in dict_clusters:\n        return\n    print(f'File {file_path} has the vba imphash {vba_imphash} from the identifiers '\n        f'{list_imphash_identifiers}.')\n\n\ndef _update_dict_clusters(dict_clusters, vba_imphash, file_path):\n    if vba_imphash not in dict_clusters:\n        dict_clusters[vba_imphash] = [file_path]\n    else:\n        dict_clusters[vba_imphash].append(file_path)\n\n\ndef _update_dict_identifiers(dict_identifiers, list_identifiers_found):\n    for identifier in list_identifiers_found:\n        if identifier not in dict_identifiers:\n            dict_identifiers[identifier] = 1\n        else:\n            dict_identifiers[identifier] += 1\n\n\ndef _display_dict_clusters(dict_clusters):\n    print('\\n' * 3 + '*' * 100)\n    dict_clusters = dict(sorted(dict_clusters.items(), key=lambda item: len(item[1])))\n    i = 0\n    for cluster_name, list_files_in_cluster_paths in dict_clusters.items():\n        i += 1\n        list_file_names = [os.path.split(x)[1] for x in list_files_in_cluster_paths]\n        print(f'{i}) Cluster {cluster_name}. Len = {len(list_file_names)}.\\n'\n            f'Files: {list_file_names}')\n\n\ndef _save_dicts_to_disk(dict_clusters, dict_imphash_identifiers, dict_non_imphash_identifiers):\n    _save_dict_clusters_to_disk(dict_clusters)\n    _save_dict_identifiers_to_disk(dict_imphash_identifiers, 'imphash_identifiers.json')\n    _save_dict_identifiers_to_disk(dict_non_imphash_identifiers, 'non_imphash_identifiers.json')\n\n\ndef _save_dict_clusters_to_disk(dict_clusters):\n    dict_clusters = dict(sorted(dict_clusters.items(), key=lambda item: len(item[1])))\n    dict_clusters_ordered = collections.OrderedDict()\n    for cluster_name, list_files in dict_clusters.items():\n        dict_clusters_ordered[cluster_name] = list_files\n    _save_object_to_json_file(dict_clusters, 'vba_imphash_clusters.json')\n\n\ndef _save_object_to_json_file(object_to_save, json_file_path):\n    with open(json_file_path, 'w') as fh:\n        json.dump(object_to_save, fh, indent=4)\n\n\ndef _save_dict_identifiers_to_disk(dict_identifiers, dict_file_name):\n    dict_identifiers = dict(sorted(dict_identifiers.items(), key=lambda item: item[1]))\n    dict_identifiers_ordered = collections.OrderedDict()\n    for identifier, nr in dict_identifiers.items():\n        dict_identifiers_ordered[identifier] = nr\n    _save_object_to_json_file(dict_identifiers_ordered, dict_file_name)\n\n\ndef create_clusters_on_disk(clusters_dest_path):\n    dict_clusters = _load_json_from_disk('vba_imphash_clusters.json')\n    for cluster_name, list_files in dict_clusters.items():\n        _create_single_cluster(clusters_dest_path, cluster_name, list_files)\n\n\ndef _load_json_from_disk(json_file_path):\n    with open(json_file_path, 'r') as fh:\n        return json.load(fh)\n\n\ndef _create_single_cluster(clusters_dest_path, cluster_name, list_files_paths_in_cluster):\n    nr_files_in_cluster = len(list_files_paths_in_cluster)\n    cluster_name = f'{str(nr_files_in_cluster).zfill(5)}_{cluster_name}'\n    cluster_path = os.path.join(clusters_dest_path, cluster_name)\n    os.mkdir(cluster_path)\n    for file_path in list_files_paths_in_cluster:\n        shutil.copy(file_path, cluster_path)\n\n\ndef main():\n    nr_args = len(sys.argv)\n    if (nr_args == 1) or (nr_args > 3):\n        show_cmdline_usage()\n        return\n    \n    if nr_args == 2:\n        if os.path.isfile(sys.argv[1]):\n            extract_vba_imphash_from_single_file(file_path=sys.argv[1])\n        else:\n            cluster_office_files_directory(dir_path=sys.argv[1])\n    elif nr_args == 3:\n        cluster_office_files_directory(dir_path=sys.argv[1])\n        create_clusters_on_disk(clusters_dest_path=sys.argv[2])\n    \n\nif __name__ == '__main__':\n    main()\n","repo_name":"0x1Avram/vba_imphash","sub_path":"vba_imphash.py","file_name":"vba_imphash.py","file_ext":"py","file_size_in_byte":6548,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"1100327836","text":"# coding: utf-8\n\n# This file is part of the Printrun suite.\n#\n# Printrun is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# Printrun is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with Printrun.  If not, see <http://www.gnu.org/licenses/>.\n\nimport sys\nimport struct\nimport math\nimport logging\n\nimport numpy\nimport numpy.linalg\n\ndef normalize(v):\n    return v / numpy.linalg.norm(v)\n\ndef genfacet(v):\n    veca = v[1] - v[0]\n    vecb = v[2] - v[1]\n    vecx = numpy.cross(veca, vecb)\n    vlen = numpy.linalg.norm(vecx)\n    if vlen == 0:\n        vlen = 1\n    normal = vecx / vlen\n    return (normal, v)\n\nI = numpy.identity(4)\n\ndef homogeneous(v, w = 1):\n    return numpy.append(v, w)\n\ndef applymatrix(facet, matrix = I):\n    return genfacet([matrix.dot(homogeneous(x))[:3] for x in facet[1]])\n\ndef ray_triangle_intersection(ray_near, ray_dir, v123):\n    \"\"\"\n    Möller–Trumbore intersection algorithm in pure python\n    Based on http://en.wikipedia.org/wiki/M%C3%B6ller%E2%80%93Trumbore_intersection_algorithm\n    \"\"\"\n    v1, v2, v3 = v123\n    eps = 0.000001\n    edge1 = v2 - v1\n    edge2 = v3 - v1\n    pvec = numpy.cross(ray_dir, edge2)\n    det = edge1.dot(pvec)\n    if abs(det) < eps:\n        return False, None\n    inv_det = 1. / det\n    tvec = ray_near - v1\n    u = tvec.dot(pvec) * inv_det\n    if u < 0. or u > 1.:\n        return False, None\n    qvec = numpy.cross(tvec, edge1)\n    v = ray_dir.dot(qvec) * inv_det\n    if v < 0. or u + v > 1.:\n        return False, None\n\n    t = edge2.dot(qvec) * inv_det\n    if t < eps:\n        return False, None\n\n    return True, t\n\ndef ray_rectangle_intersection(ray_near, ray_dir, p0, p1, p2, p3):\n    match1, _ = ray_triangle_intersection(ray_near, ray_dir, (p0, p1, p2))\n    match2, _ = ray_triangle_intersection(ray_near, ray_dir, (p0, p2, p3))\n    return match1 or match2\n\ndef ray_box_intersection(ray_near, ray_dir, p0, p1):\n    x0, y0, z0 = p0[:]\n    x1, y1, z1 = p1[:]\n    rectangles = [((x0, y0, z0), (x1, y0, z0), (x1, y1, z0), (x0, y1, z0)),\n                  ((x0, y0, z1), (x1, y0, z1), (x1, y1, z1), (x0, y1, z1)),\n                  ((x0, y0, z0), (x1, y0, z0), (x1, y0, z1), (x0, y0, z1)),\n                  ((x0, y1, z0), (x1, y1, z0), (x1, y1, z1), (x0, y1, z1)),\n                  ((x0, y0, z0), (x0, y1, z0), (x0, y1, z1), (x0, y0, z1)),\n                  ((x1, y0, z0), (x1, y1, z0), (x1, y1, z1), (x1, y0, z1)),\n                  ]\n    rectangles = [(numpy.array(p) for p in rect)\n                  for rect in rectangles]\n    for rect in rectangles:\n        if ray_rectangle_intersection(ray_near, ray_dir, *rect):\n            return True\n    return False\n\ndef emitstl(filename, facets = [], objname = \"stltool_export\", binary = True):\n    if filename is None:\n        return\n    if binary:\n        with open(filename, \"wb\") as f:\n            buf = b\"\".join([b\"\\0\"] * 80)\n            buf += struct.pack(\"<I\", len(facets))\n            facetformat = struct.Struct(\"<ffffffffffffH\")\n            for facet in facets:\n                l = list(facet[0][:])\n                for vertex in facet[1]:\n                    l += list(vertex[:])\n                l.append(0)\n                buf += facetformat.pack(*l)\n            f.write(buf)\n    else:\n        with open(filename, \"w\") as f:\n            f.write(\"solid \" + objname + \"\\n\")\n            for i in facets:\n                f.write(\"  facet normal \" + \" \".join(map(str, i[0])) + \"\\n   outer loop\\n\")\n                for j in i[1]:\n                    f.write(\"    vertex \" + \" \".join(map(str, j)) + \"\\n\")\n                f.write(\"   endloop\" + \"\\n\")\n                f.write(\"  endfacet\" + \"\\n\")\n            f.write(\"endsolid \" + objname + \"\\n\")\n\nclass stl:\n\n    _dims = None\n\n    def _get_dims(self):\n        if self._dims is None:\n            minx = float(\"inf\")\n            miny = float(\"inf\")\n            minz = float(\"inf\")\n            maxx = float(\"-inf\")\n            maxy = float(\"-inf\")\n            maxz = float(\"-inf\")\n            for normal, facet in self.facets:\n                for vert in facet:\n                    if vert[0] < minx:\n                        minx = vert[0]\n                    if vert[1] < miny:\n                        miny = vert[1]\n                    if vert[2] < minz:\n                        minz = vert[2]\n                    if vert[0] > maxx:\n                        maxx = vert[0]\n                    if vert[1] > maxy:\n                        maxy = vert[1]\n                    if vert[2] > maxz:\n                        maxz = vert[2]\n            self._dims = [minx, maxx, miny, maxy, minz, maxz]\n        return self._dims\n    dims = property(_get_dims)\n\n    def __init__(self, filename = None):\n        self.facet = (numpy.zeros(3), (numpy.zeros(3), numpy.zeros(3), numpy.zeros(3)))\n        self.facets = []\n        self.facetsminz = []\n        self.facetsmaxz = []\n\n        self.name = \"\"\n        self.insolid = 0\n        self.infacet = 0\n        self.inloop = 0\n        self.facetloc = 0\n        if filename is None:\n            return\n        with open(filename,encoding=\"ascii\",errors=\"ignore\") as f:\n            data = f.read()\n        if \"facet normal\" in data[1:300] and \"outer loop\" in data[1:300]:\n            lines = data.split(\"\\n\")\n            for line in lines:\n                if not self.parseline(line):\n                    return\n        else:\n            logging.warning(\"Not an ascii stl solid - attempting to parse as binary\")\n            f = open(filename, \"rb\")\n            buf = f.read(84)\n            while len(buf) < 84:\n                newdata = f.read(84 - len(buf))\n                if not len(newdata):\n                    break\n                buf += newdata\n            facetcount = struct.unpack_from(\"<I\", buf, 80)\n            facetformat = struct.Struct(\"<ffffffffffffH\")\n            for i in range(facetcount[0]):\n                buf = f.read(50)\n                while len(buf) < 50:\n                    newdata = f.read(50 - len(buf))\n                    if not len(newdata):\n                        break\n                    buf += newdata\n                fd = list(facetformat.unpack(buf))\n                self.name = \"binary soloid\"\n                facet = [fd[:3], [fd[3:6], fd[6:9], fd[9:12]]]\n                self.facets.append(facet)\n                self.facetsminz.append((min(x[2] for x in facet[1]), facet))\n                self.facetsmaxz.append((max(x[2] for x in facet[1]), facet))\n            f.close()\n            return\n\n    def intersect_box(self, ray_near, ray_far):\n        ray_near = numpy.array(ray_near)\n        ray_far = numpy.array(ray_far)\n        ray_dir = normalize(ray_far - ray_near)\n        x0, x1, y0, y1, z0, z1 = self.dims\n        p0 = numpy.array([x0, y0, z0])\n        p1 = numpy.array([x1, y1, z1])\n        return ray_box_intersection(ray_near, ray_dir, p0, p1)\n\n    def intersect(self, ray_near, ray_far):\n        ray_near = numpy.array(ray_near)\n        ray_far = numpy.array(ray_far)\n        ray_dir = normalize(ray_far - ray_near)\n        best_facet = None\n        best_dist = float(\"inf\")\n        for facet_i, (normal, facet) in enumerate(self.facets):\n            match, dist = ray_triangle_intersection(ray_near, ray_dir, facet)\n            if match and dist < best_dist:\n                best_facet = facet_i\n                best_dist = dist\n        return best_facet, best_dist\n\n    def rebase(self, facet_i):\n        normal, facet = self.facets[facet_i]\n        u1 = facet[1] - facet[0]\n        v2 = facet[2] - facet[0]\n        n1 = u1.dot(u1)\n        e1 = u1 / math.sqrt(n1)\n        u2 = v2 - u1 * v2.dot(u1) / n1\n        e2 = u2 / numpy.linalg.norm(u2)\n        e3 = numpy.cross(e1, e2)\n        # Ensure Z direction if opposed to the normal\n        if normal.dot(e3) > 0:\n            e2 = - e2\n            e3 = - e3\n        matrix = [[e1[0], e2[0], e3[0], 0],\n                  [e1[1], e2[1], e3[1], 0],\n                  [e1[2], e2[2], e3[2], 0],\n                  [0, 0, 0, 1]]\n        matrix = numpy.array(matrix)\n        # Inverse change of basis matrix\n        matrix = numpy.linalg.inv(matrix)\n        # Set first vertex of facet as origin\n        neworig = matrix.dot(homogeneous(facet[0]))\n        matrix[:3, 3] = -neworig[:3]\n        newmodel = self.transform(matrix)\n        return newmodel\n\n    def cut(self, axis, direction, dist):\n        s = stl()\n        s.facets = []\n        f = min if direction == 1 else max\n        for _, facet in self.facets:\n            minval = f([vertex[axis] for vertex in facet])\n            if direction * minval > direction * dist:\n                continue\n            vertices = []\n            for vertex in facet:\n                vertex = numpy.copy(vertex)\n                if direction * (vertex[axis] - dist) > 0:\n                    vertex[axis] = dist\n                vertices.append(vertex)\n            s.facets.append(genfacet(vertices))\n        s.insolid = 0\n        s.infacet = 0\n        s.inloop = 0\n        s.facetloc = 0\n        s.name = self.name\n        for facet in s.facets:\n            s.facetsminz += [(min(x[2] for x in facet[1]), facet)]\n            s.facetsmaxz += [(max(x[2] for x in facet[1]), facet)]\n        return s\n\n    def translation_matrix(self, v):\n        matrix = [[1, 0, 0, v[0]],\n                  [0, 1, 0, v[1]],\n                  [0, 0, 1, v[2]],\n                  [0, 0, 0, 1]\n                  ]\n        return numpy.array(matrix)\n\n    def translate(self, v = [0, 0, 0]):\n        return self.transform(self.translation_matrix(v))\n\n    def rotation_matrix(self, v):\n        z = v[2]\n        matrix1 = [[math.cos(math.radians(z)), -math.sin(math.radians(z)), 0, 0],\n                   [math.sin(math.radians(z)), math.cos(math.radians(z)), 0, 0],\n                   [0, 0, 1, 0],\n                   [0, 0, 0, 1]\n                   ]\n        matrix1 = numpy.array(matrix1)\n        y = v[0]\n        matrix2 = [[1, 0, 0, 0],\n                   [0, math.cos(math.radians(y)), -math.sin(math.radians(y)), 0],\n                   [0, math.sin(math.radians(y)), math.cos(math.radians(y)), 0],\n                   [0, 0, 0, 1]\n                   ]\n        matrix2 = numpy.array(matrix2)\n        x = v[1]\n        matrix3 = [[math.cos(math.radians(x)), 0, -math.sin(math.radians(x)), 0],\n                   [0, 1, 0, 0],\n                   [math.sin(math.radians(x)), 0, math.cos(math.radians(x)), 0],\n                   [0, 0, 0, 1]\n                   ]\n        matrix3 = numpy.array(matrix3)\n        return matrix3.dot(matrix2.dot(matrix1))\n\n    def rotate(self, v = [0, 0, 0]):\n        return self.transform(self.rotation_matrix(v))\n\n    def scale_matrix(self, v):\n        matrix = [[v[0], 0, 0, 0],\n                  [0, v[1], 0, 0],\n                  [0, 0, v[2], 0],\n                  [0, 0, 0, 1]\n                  ]\n        return numpy.array(matrix)\n\n    def scale(self, v = [0, 0, 0]):\n        return self.transform(self.scale_matrix(v))\n\n    def transform(self, m = I):\n        s = stl()\n        s.facets = [applymatrix(i, m) for i in self.facets]\n        s.insolid = 0\n        s.infacet = 0\n        s.inloop = 0\n        s.facetloc = 0\n        s.name = self.name\n        for facet in s.facets:\n            s.facetsminz += [(min(x[2] for x in facet[1]), facet)]\n            s.facetsmaxz += [(max(x[2] for x in facet[1]), facet)]\n        return s\n\n    def export(self, f = sys.stdout):\n        f.write(\"solid \" + self.name + \"\\n\")\n        for i in self.facets:\n            f.write(\"  facet normal \" + \" \".join(map(str, i[0])) + \"\\n\")\n            f.write(\"   outer loop\" + \"\\n\")\n            for j in i[1]:\n                f.write(\"    vertex \" + \" \".join(map(str, j)) + \"\\n\")\n            f.write(\"   endloop\" + \"\\n\")\n            f.write(\"  endfacet\" + \"\\n\")\n        f.write(\"endsolid \" + self.name + \"\\n\")\n        f.flush()\n\n    def parseline(self, l):\n        l = l.strip()\n        if l.startswith(\"solid\"):\n            self.insolid = 1\n            self.name = l[6:]\n        elif l.startswith(\"endsolid\"):\n            self.insolid = 0\n            return 0\n        elif l.startswith(\"facet normal\"):\n            l = l.replace(\", \", \".\")\n            self.infacet = 1\n            self.facetloc = 0\n            normal = numpy.array([float(f) for f in l.split()[2:]])\n            self.facet = (normal, (numpy.zeros(3), numpy.zeros(3), numpy.zeros(3)))\n        elif l.startswith(\"endfacet\"):\n            self.infacet = 0\n            self.facets.append(self.facet)\n            facet = self.facet\n            self.facetsminz += [(min(x[2] for x in facet[1]), facet)]\n            self.facetsmaxz += [(max(x[2] for x in facet[1]), facet)]\n        elif l.startswith(\"vertex\"):\n            l = l.replace(\", \", \".\")\n            self.facet[1][self.facetloc][:] = numpy.array([float(f) for f in l.split()[1:]])\n            self.facetloc += 1\n        return 1\n\nif __name__ == \"__main__\":\n    s = stl(\"../../Downloads/frame-vertex-neo-foot-x4.stl\")\n    for i in range(11, 11):\n        working = s.facets[:]\n        for j in reversed(sorted(s.facetsminz)):\n            if j[0] > i:\n                working.remove(j[1])\n            else:\n                break\n        for j in (sorted(s.facetsmaxz)):\n            if j[0] < i:\n                working.remove(j[1])\n            else:\n                break\n\n        print(i, len(working))\n    emitstl(\"../../Downloads/frame-vertex-neo-foot-x4-a.stl\", s.facets, \"emitted_object\")\n# stl(\"../prusamendel/stl/mendelplate.stl\")\n","repo_name":"kliment/Printrun","sub_path":"printrun/stltool.py","file_name":"stltool.py","file_ext":"py","file_size_in_byte":13806,"program_lang":"python","lang":"en","doc_type":"code","stars":2224,"dataset":"github-code","pt":"80"}
+{"seq_id":"34595941618","text":"import copy\nimport os\nfrom functools import partial\nfrom pathlib import Path\nfrom typing import List, Tuple\n\nimport hydra\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport pytorch_lightning as pl\nimport scipy\nimport torch\nfrom hydra.utils import get_original_cwd\nfrom omegaconf import DictConfig, OmegaConf\nfrom sklearn.neighbors import KDTree\n\nfrom src.dataset.datamodule import GsdcDatamodule, interpolate_vel\nfrom src.dataset.utils import get_groundtruth\nfrom src.modeling.pl_model import LitModel\nfrom src.postprocess.metric import print_metric\nfrom src.postprocess.postporcess import (apply_kf_smoothing, filter_outlier,\n                                         mean_with_other_phones)\nfrom src.postprocess.visualize import add_distance_diff\nfrom src.utils.util import set_random_seed\n\npd.set_option(\"display.max_rows\", 100)\nSEED = 42\n\n\ndef check_test_df(path_a, path_b):\n    df_a = pd.read_csv(path_a)\n    df_b = pd.read_csv(path_b)\n    df_a = df_a.rename(columns={\"latDeg\": \"latDeg_gt\", \"lngDeg\": \"lngDeg_gt\"})\n    df = pd.merge(df_a, df_b, on=[\"phone\", \"millisSinceGpsEpoch\"])\n    met_df = print_metric(df=df)\n    return met_df\n\n\ndef load_dataset(is_test: bool = True) -> Tuple[pd.DataFrame, pd.DataFrame]:\n    data_dir = Path(\n        get_original_cwd(), \"../input/google-smartphone-decimeter-challenge\"\n    )\n    fname = \"test\" if is_test else \"train\"\n    df = pd.read_csv(data_dir / f\"baseline_locations_{fname}.csv\")\n\n    if not is_test:\n        # merge graoundtruth\n        df = df.merge(\n            get_groundtruth(data_dir),\n            on=[\"collectionName\", \"phoneName\", \"millisSinceGpsEpoch\"],\n        )\n\n    # area_df from\n    # https://www.kaggle.com/columbia2131/area-knn-prediction-train-hand-label\n    area_df = pd.read_csv(\n        Path(get_original_cwd()) / f\"./src/meta_data/{fname}_area.csv\"\n    )\n\n    df = apply_kf_smoothing(df=df)\n    df = add_distance_diff(df=df, is_test=is_test)\n\n    if is_test:\n        area_df = area_df.rename(columns={\"area_pred\": \"area_target\"})\n\n    df = pd.merge(df, area_df[[\"collectionName\", \"area_target\"]], on=[\"collectionName\"])\n    return df, area_df\n\n\ndef interpolate_vel_df(\n    vel_pred_df: pd.DataFrame,\n    posi_pred_df: pd.DataFrame,\n    abs_targets: List[str] = [\"latDeg\", \"lngDeg\"],\n    pred_targets: List[str] = [\"latDeg_diff_prev\", \"lngDeg_diff_prev\"],\n):\n    T_ref = posi_pred_df.loc[:, \"millisSinceGpsEpoch\"].values\n    rel_positions = vel_pred_df.loc[:, pred_targets].fillna(0.0).values\n    T_rel = vel_pred_df.loc[:, \"millisSinceGpsEpoch\"].values\n\n    # padding and interpolate, exclude the fisrt nan\n    delta_xy_hat = interpolate_vel(\n        velocity=rel_positions, base_time=T_rel, ref_time=T_ref, drop_first_vel=True\n    )\n    return T_ref, delta_xy_hat\n\n\ndef plot_velocity(\n    vel_pred_df: pd.DataFrame,\n    posi_pred_df: pd.DataFrame,\n    phone: str,\n    pred_targets: List[str] = [\"latDeg_diff_prev\", \"lngDeg_diff_prev\"],\n    is_test: bool = False,\n) -> None:\n    posi_pred_df = posi_pred_df[posi_pred_df[\"phone\"] == phone].fillna(0.0)\n    posi_pred_df = add_distance_diff(df=posi_pred_df, is_test=is_test)\n\n    vel_pred_df = vel_pred_df[vel_pred_df[\"phone\"] == phone].fillna(0.0)\n\n    T_ref, delta_xy_hat = interpolate_vel_df(\n        vel_pred_df=vel_pred_df, posi_pred_df=posi_pred_df, pred_targets=pred_targets,\n    )\n    for_df = {\n        \"phone\": np.repeat(phone, delta_xy_hat.shape[0]),\n        \"millisSinceGpsEpoch\": T_ref,\n    }\n    for_df.update(\n        {stft_target: delta_xy_hat[:, i] for i, stft_target in enumerate(pred_targets)}\n    )\n    vel_pred_df = pd.DataFrame(for_df).fillna(0.0)\n    fig, axes = plt.subplots(figsize=(15, 8), nrows=3, sharex=True)\n\n    axes[0].plot(\n        posi_pred_df[\"millisSinceGpsEpoch\"],\n        posi_pred_df[\"latDeg_diff_prev\"],\n        # \"--\",\n        label=\"baseline\",\n    )\n    axes[0].plot(\n        vel_pred_df[\"millisSinceGpsEpoch\"],\n        vel_pred_df[\"latDeg_diff_prev\"],\n        label=\"pred\",\n    )\n\n    axes[1].plot(\n        posi_pred_df[\"millisSinceGpsEpoch\"],\n        posi_pred_df[\"lngDeg_diff_prev\"],\n        label=\"baseline\",\n    )\n    axes[1].plot(\n        vel_pred_df[\"millisSinceGpsEpoch\"],\n        vel_pred_df[\"lngDeg_diff_prev\"],\n        label=\"pred\",\n    )\n    if not is_test:\n        axes[0].plot(\n            posi_pred_df[\"millisSinceGpsEpoch\"],\n            posi_pred_df[\"latDeg_gt_diff_prev\"],\n            \"--\",\n            label=\"gt\",\n        )\n        axes[1].plot(\n            posi_pred_df[\"millisSinceGpsEpoch\"],\n            posi_pred_df[\"lngDeg_gt_diff_prev\"],\n            \"--\",\n            label=\"gt\",\n        )\n\n        axes[2].plot(\n            posi_pred_df[\"millisSinceGpsEpoch\"],\n            np.abs(\n                posi_pred_df[\"latDeg_diff_prev\"].values\n                - posi_pred_df[\"latDeg_gt_diff_prev\"].values\n            ),\n            \"--\",\n            label=\"baseline\",\n        )\n        axes[2].plot(\n            posi_pred_df[\"millisSinceGpsEpoch\"],\n            np.abs(\n                vel_pred_df[\"latDeg_diff_prev\"].values\n                - posi_pred_df[\"latDeg_gt_diff_prev\"].values\n            ),\n            label=\"pred\",\n        )\n    yscale = \"linear\"\n\n    axes[0].set_yscale(yscale)\n    axes[0].set_ylabel(\"deg velocity\")\n\n    axes[1].set_yscale(yscale)\n    axes[1].set_ylabel(\"deg velocity\")\n    axes[2].set_yscale(\"log\")\n    axes[2].set_ylim(1.0e-8, 1.0e-4)\n\n    axes[1].set_xlabel(\"time step\")\n    axes[0].grid()\n    axes[1].grid()\n    axes[2].grid()\n\n    title = phone\n    fig.suptitle(title)\n    axes[0].legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n    axes[1].legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n    axes[2].legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n    save_path = os.path.join(os.getcwd(), f\"{phone}_vel.png\")\n    print(save_path)\n    plt.savefig(save_path, dpi=300, bbox_inches=\"tight\")\n\n\ndef rel_pred(\n    phone: str,\n    vel_pred_df: pd.DataFrame,\n    posi_pred_df: pd.DataFrame,\n    stft_targets: List[str],\n    is_test: bool = False,\n) -> pd.DataFrame:\n    posi_pred_df = posi_pred_df[posi_pred_df[\"phone\"] == phone].fillna(0.0)\n    posi_pred_df = add_distance_diff(df=posi_pred_df, is_test=is_test)\n    vel_pred_df = vel_pred_df[vel_pred_df[\"phone\"] == phone].fillna(0.0)\n\n    targets = [\"latDeg\", \"lngDeg\"]\n\n    T_ref, delta_xy_hat = interpolate_vel_df(\n        vel_pred_df=vel_pred_df, posi_pred_df=posi_pred_df, pred_targets=stft_targets\n    )\n    delta_xy_hat[:1] = 0.0\n    pred_posi = np.cumsum(delta_xy_hat, axis=0)\n    rel_posi_mean = pred_posi.mean(axis=0)\n\n    abs_posi_mean = posi_pred_df.loc[:, targets].values.mean(axis=0)\n\n    pred_posi = pred_posi - rel_posi_mean + abs_posi_mean\n    print(pred_posi.mean(axis=0) - abs_posi_mean)\n    posi_pred_df.loc[:, targets] = pred_posi\n\n    return posi_pred_df\n\n\ndef calc_avg_vel(df: pd.DataFrame, is_database: bool = False, add_future: bool = False):\n    if is_database:\n        global_targets = [\"latDeg_gt\", \"lngDeg_gt\"]\n    else:\n        global_targets = [\"latDeg\", \"lngDeg\"]\n\n    local_targets = [key + \"_diff_prev\" for key in global_targets]\n    window_sizes = [5, 15, 45]\n    new_targets = []\n    dfs = []\n    for phone, df_ in df.groupby(\"phone\"):\n        for window_size in window_sizes:\n            tri_center = (\n                df_[local_targets]\n                .rolling(window=window_size, min_periods=1, center=False)\n                .mean()\n            ).fillna(0.0)\n            for target in local_targets:\n                new_targets.append(target + \"_\" + str(window_size))\n                df_[new_targets[-1]] = tri_center[target]\n                if add_future:\n                    next_target = new_targets[-1].replace(\"prev\", \"next\")\n                    df_[next_target] = (\n                        df_[new_targets[-1]].shift(-window_size).fillna(0.0)\n                    )\n                    new_targets.append(next_target)\n        dfs.append(df_)\n\n    new_targets = sorted(list(set(new_targets)))\n    df = pd.concat(dfs, axis=0)\n    local_targets.extend(new_targets)\n    local_targets = local_targets + [\"latDeg_diff_next\", \"lngDeg_diff_next\"]\n    df.reset_index(drop=False, inplace=True)\n    return df, global_targets, local_targets\n\n\ndef knn_search(\n    phone: str,\n    vel_pred_df: pd.DataFrame,\n    posi_pred_df: pd.DataFrame,\n    data_df: pd.DataFrame,\n    global_targets_gt=List[str],\n    local_targets_gt=List[str],\n    is_test: bool = False,\n):\n\n    # choose phone data\n    posi_pred_df = posi_pred_df[posi_pred_df[\"phone\"] == phone]\n    posi_pred_df = add_distance_diff(df=posi_pred_df, is_test=is_test)\n    posi_pred_df = posi_pred_df.fillna(0.0)\n    vel_pred_df = vel_pred_df[vel_pred_df[\"phone\"] == phone].fillna(0.0)\n    area = posi_pred_df.area_target.to_numpy()[0]\n    if posi_pred_df.area_target.to_numpy()[0] != 2:\n        return posi_pred_df\n\n    un_used = posi_pred_df[posi_pred_df[\"phone\"] == phone].collectionName.unique()[0]\n    data_df = data_df.loc[data_df.collectionName != un_used]\n    leaf_size = 40\n    k = 10 if area == 2 else 5\n    k_local = 3\n    local_leaf_size = 10\n    global_tree = KDTree(data_df[global_targets_gt], leaf_size=leaf_size)\n\n    posi_pred_df, global_targets, local_targets = calc_avg_vel(df=posi_pred_df)\n    dists, inds = global_tree.query(posi_pred_df[global_targets], k=k)\n    # print(\"mean, std\", np.mean(dists), np.std(dists))\n    for i, (dist, ind) in enumerate(zip(dists, inds)):\n        if area == 2:\n            pass\n        elif area == 1:\n            deg_vel = vel_pred_df.iloc[i][[\"latDeg_diff_prev\", \"lngDeg_diff_prev\"]]\n            if np.any(deg_vel > 5.0e-6):\n                continue\n        else:\n            continue\n        local_data_df = data_df.iloc[ind]\n        query_state = posi_pred_df.iloc[i]\n        local_tree = KDTree(local_data_df[local_targets_gt], leaf_size=local_leaf_size)\n        local_dist, local_ind = local_tree.query(\n            query_state[local_targets].to_numpy().reshape(1, -1), k=k_local\n        )\n\n        posi_pred_df.loc[\n            posi_pred_df.millisSinceGpsEpoch == query_state.millisSinceGpsEpoch,\n            global_targets,\n        ] = (\n            local_data_df.iloc[local_ind[0]][global_targets_gt].to_numpy().mean(axis=0)\n        )\n    return posi_pred_df\n\n\ndef mask_with_velocity(\n    phone: str,\n    vel_pred_df: pd.DataFrame,\n    posi_pred_df: pd.DataFrame,\n    stft_targets: List[str],\n    is_test: bool = False,\n) -> pd.DataFrame:\n    targets = [\"latDeg\", \"lngDeg\"]\n\n    posi_pred_df = posi_pred_df[posi_pred_df[\"phone\"] == phone].fillna(0.0)\n    posi_pred_df = add_distance_diff(df=posi_pred_df, is_test=is_test)\n    vel_pred_df = vel_pred_df[vel_pred_df[\"phone\"] == phone].fillna(0.0)\n\n    deg_preds = posi_pred_df.loc[:, targets].values\n\n    T_ref, delta_xy_hat = interpolate_vel_df(\n        vel_pred_df=vel_pred_df, posi_pred_df=posi_pred_df, pred_targets=stft_targets\n    )\n\n    kf_vel = posi_pred_df.loc[:, stft_targets].values\n\n    delta_xy_hat[:1] = 0.0\n    kf_vel[:1] = 0.0\n\n    low_pred_vel = np.all(np.abs(delta_xy_hat) < 5e-7, axis=1)\n\n    prev_part = 0\n\n    low_vel_area = list(np.where(np.diff(T_ref[low_pred_vel], axis=0) > 5000)[0])\n    low_vel_area.append(low_pred_vel.sum())\n    local_means = []\n    for part_ind in low_vel_area:\n        part_ind = part_ind + 1\n        slice_target = np.where(low_pred_vel)[0][prev_part:part_ind]\n        local_degs = np.take(deg_preds, slice_target, axis=0)\n        local_span = np.take(T_ref, slice_target, axis=0)\n        if local_span.max() - local_span.min() > 5000:\n            local_mean = np.median(local_degs, axis=0, keepdims=True)\n            local_means.append(np.repeat(local_mean, local_degs.shape[0], axis=0))\n        else:\n            local_means.append(local_degs)\n\n        prev_part = part_ind\n\n    deg_preds[low_pred_vel] = np.concatenate(local_means, axis=0)\n\n    kf_vel = np.diff(deg_preds, axis=0)\n    kf_vel = np.pad(kf_vel, [[1, 0], [0, 0]], mode=\"constant\", constant_values=0.0)\n    disagreement_mask = np.all(np.abs(delta_xy_hat - kf_vel) > 0.09e-4, axis=1)\n    disagreement_mask[:-1] += disagreement_mask[1:]\n\n    disagreement_mask[0] = False\n    disagreement_mask[-1] = False\n\n    deg_preds_filtered = copy.deepcopy(deg_preds[~disagreement_mask])\n    T_ref_filtered = copy.deepcopy(T_ref[~disagreement_mask])\n    deg_preds = scipy.interpolate.interp1d(T_ref_filtered, deg_preds_filtered, axis=0)(\n        T_ref\n    )\n\n    posi_pred_df.loc[:, targets] = deg_preds\n    return posi_pred_df\n\n\n@hydra.main(config_path=\"./src/config\", config_name=\"test_config\")\ndef main(conf: DictConfig) -> None:\n    set_random_seed(SEED)\n\n    is_test = not conf.test_with_val\n    print(\"start conv position prediction\")\n    for model_path in conf.test_weights.model_paths:\n        model_path = Path(\n            get_original_cwd(), conf.test_weights.weights_dir, model_path[1]\n        )\n        conf_path = model_path / conf.test_weights.conf_name\n        model_conf = OmegaConf.load(conf_path)\n        ckpt_path = list(model_path.glob(conf.test_weights.ckpt_regex))\n\n        assert len(ckpt_path) == 1\n        model_conf.ckpt_path = str(ckpt_path[0])\n        print(\"\\t\\t ==== TEST MODE ====\")\n        print(\"load from: \", model_conf.ckpt_path)\n\n        # add missing keys\n        model_conf.tta_with_kf = conf.tta_with_kf\n        model_conf.use_flip_tta = conf.use_flip_tta\n        model_conf.test_with_val = conf.test_with_val\n        model_conf.test_sampling_delta = conf.test_sampling_delta\n        datamodule = GsdcDatamodule(\n            conf=model_conf,\n            val_fold=model_conf.val_fold,\n            batch_size=model_conf.batch_size,\n            aug_mode=model_conf.aug_mode,\n            num_workers=model_conf.num_workers,\n            is_debug=model_conf.is_debug,\n        )\n        datamodule.prepare_data()\n        datamodule.setup(stage=\"test\")\n        model = LitModel.load_from_checkpoint(\n            model_conf.ckpt_path, conf=model_conf, dataset_len=-1\n        )\n        pl.trainer.seed_everything(seed=SEED)\n        trainer = pl.Trainer(gpus=1)\n        trainer.test(model, datamodule=datamodule)\n        torch.cuda.empty_cache()\n\n    csv_name = f\"pred_test_flip_{conf.use_flip_tta}_d{conf.test_sampling_delta}.csv\"\n    vel_pred_paths = [\n        [\n            model_path[0],\n            Path(get_original_cwd(), conf.test_weights.weights_dir, model_path[1])\n            / csv_name,\n        ]\n        for model_path in conf.test_weights.model_paths\n    ]\n    vel_preds = []\n\n    for path in vel_pred_paths:\n        df = pd.read_csv(path[1])\n        vel_preds.append(path[0] * df.loc[:, conf.stft_targets].values)\n\n    vel_pred_df = pd.read_csv(vel_pred_paths[0][1])\n    vel_pred_df.loc[:, conf.stft_targets] = np.sum(np.stack(vel_preds), axis=0)\n\n    print(\"loading baseline positions\")\n    posi_pred_df, area_df = load_dataset(is_test=is_test)\n\n    phone_list = vel_pred_df[\"phone\"].unique()\n    # baseline\n    df = posi_pred_df.loc[posi_pred_df.phone.isin(phone_list)]\n    if not is_test:\n        print(\"baseline\")\n        met_df = print_metric(df=df)\n        print(met_df)\n\n    print(\"Ensemble, conv pred & ligtgbm\")\n    gbm_df = pd.read_csv(Path(get_original_cwd(), conf.gbm_pred_path))\n    conv_df = pd.read_csv(Path(get_original_cwd(), conf.conv_pred_path))\n\n    targets = [\"latDeg\", \"lngDeg\"]\n    for phone in phone_list:\n        df_ = gbm_df.loc[gbm_df.phone == phone]\n        cname = phone.split(\"_\")[0]\n        area = area_df.loc[area_df.collectionName == cname][\"area_target\"].to_numpy()[0]\n        if len(df_) > 0:\n            if area == 0:\n                posi_pred_df.loc[posi_pred_df.phone == phone, targets] = (\n                    df_.loc[:, targets].to_numpy() * 0.6\n                    + conv_df.loc[conv_df.phone == phone, targets].to_numpy() * 0.4\n                )\n            else:\n                posi_pred_df.loc[posi_pred_df.phone == phone, targets] = (\n                    df_.loc[:, targets].to_numpy() * 0.3\n                    + conv_df.loc[conv_df.phone == phone, targets].to_numpy() * 0.7\n                )\n        else:\n            if area == 0:\n                posi_pred_df.loc[posi_pred_df.phone == phone, targets] = (\n                    posi_pred_df.loc[posi_pred_df.phone == phone, targets].to_numpy()\n                    * 0.6\n                    + conv_df.loc[conv_df.phone == phone, targets].to_numpy() * 0.4\n                )\n            elif area == 1:\n                posi_pred_df.loc[posi_pred_df.phone == phone, targets] = (\n                    posi_pred_df.loc[posi_pred_df.phone == phone, targets].to_numpy()\n                    * 0.2\n                    + conv_df.loc[conv_df.phone == phone, targets].to_numpy() * 0.8\n                )\n            elif area == 2:\n                posi_pred_df.loc[posi_pred_df.phone == phone, targets] = (\n                    posi_pred_df.loc[posi_pred_df.phone == phone, targets].to_numpy()\n                    * 0.10\n                    + conv_df.loc[conv_df.phone == phone, targets].to_numpy() * 0.9\n                )\n\n    if not is_test:\n        met_df = print_metric(df=posi_pred_df)\n        print(met_df)\n\n    print(\"conv speed & disagreement mask\")\n    dfs = []\n    for phone in phone_list:\n        dfs.append(\n            mask_with_velocity(\n                phone=phone,\n                vel_pred_df=vel_pred_df,\n                posi_pred_df=posi_pred_df,\n                stft_targets=conf.stft_targets,\n                is_test=is_test,\n            )\n        )\n    df = pd.concat(dfs, axis=0)\n    if not is_test:\n        met_df = print_metric(df=df)\n        print(met_df)\n\n    print(\"outlier\")\n    df = filter_outlier(df=df, one_direction=True)\n    if not is_test:\n        print_metric(df=df)\n\n    print(\"mean pred\")\n    df = mean_with_other_phones(df=df)\n    if not is_test:\n        met_df = print_metric(df=df)\n        print(met_df)\n\n    print(\"knn at downtown\")\n    dfs = []\n    data_df, _ = load_dataset(is_test=False)\n    data_df = data_df.fillna(0.0)\n\n    data_df, global_targets_gt, local_targets_gt = calc_avg_vel(\n        df=data_df, is_database=True\n    )\n    for phone in phone_list:\n        dfs.append(\n            knn_search(\n                phone=phone,\n                vel_pred_df=vel_pred_df,\n                posi_pred_df=df,\n                data_df=data_df,\n                global_targets_gt=global_targets_gt,\n                local_targets_gt=local_targets_gt,\n                is_test=is_test,\n            )\n        )\n    df = pd.concat(dfs, axis=0)\n    if not is_test:\n        met_df = print_metric(df=df)\n        print(met_df)\n\n    print(\"kalmann filtering \")\n    df_down = df.loc[df.area_target == 2]\n    df_down = apply_kf_smoothing(df=df_down)\n    df = df.loc[df.area_target != 2]\n    df = pd.concat([df, df_down], axis=0)\n    df = df.sort_values([\"phone\", \"millisSinceGpsEpoch\"]).reset_index(drop=True)\n    if not is_test:\n        met_df = print_metric(df=df)\n        print(met_df)\n\n    if is_test:\n        targets = [\"phone\", \"millisSinceGpsEpoch\", \"latDeg\", \"lngDeg\"]\n        df = df.loc[:, targets]\n        sample_sub = os.path.join(\n            get_original_cwd(),\n            \"../input/google-smartphone-decimeter-challenge/sample_submission.csv\",\n        )\n        sample_sub = pd.read_csv(sample_sub)\n        orig_len = (len(sample_sub), len(df))\n        sample_sub = sample_sub.loc[:, targets[:2]]\n        df = pd.merge(left=sample_sub, right=df, on=targets[:2])\n        after_len = (len(sample_sub), len(df))\n        print(orig_len, after_len)\n        save_path = Path.cwd() / \"./submission.csv\"\n        df.loc[:, targets].to_csv(save_path, index=False)\n        print(f\"Save submission file on {str(save_path)}\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Fkaneko/kaggle_Google_Smartphone_Decimeter_Challenge","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":19606,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"80"}
+{"seq_id":"19565684801","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Sep 20 21:19:47 2017\n\n@author: aayushgadia\n\"\"\"\n\ndays_list=[]\nfilename=input('Enter a file name: ')\nf_in=open(filename)\nfor line in f_in:\n    if line.startswith('From '):\n        word_list=line.split()\n        day=word_list[2]\n        days_list.append(day)\n\n   \nd=dict()       \nfor day in days_list:\n    if day not in d:\n        d[day]=1\n        \n    else:\n        d[day]+=1\n       \nprint(d)        \n\n\n#it will be line.startswith('From ')....it is From then space........\n#earlier i just wrote....'From' & did not put any space...after From.....","repo_name":"gadia-aayush/Python_for_Informatics-Solutions","sub_path":"9.3[nice qn].py","file_name":"9.3[nice qn].py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"30730043089","text":"import warnings\nfrom PIL import ImageFile\nfrom skimage.io import imread\nimport os\nfrom download import download_image\n\n# warnings.filterwarnings(\"error\")\n# ImageFile.LOAD_TRUNCATED_IMAGES = True\n\n\ndef _download(image_id, image_root):\n\n    print(\"image {} is downloading...\".format(image_id))\n    image_root = os.path.join(image_root, \"append\")\n    download_image(image_id=image_id, image_root=image_root)\n    image_filepath = os.path.join(image_root, image_id)\n    image = imread(fname=image_filepath + \"_.jpg\")\n    image = resize_image(\n        image=image, large_side=self._image_large_side\n    )\n    imsave(fname=image_filepath + \".jpg\", arr=image)\n    # image_filepath = os.path.join(self._image_root, image_id + \".jpg\")\n    # imsave(fname=image_filepath, arr=image)\n\nif __name__ == \"__main__\":\n    image_id = \"310261\"\n    # image_id = \"532402\"\n    # image_id = \"53\"\n    image_root = \"e:/src/jupyter/datasets/AVA/images\"\n    image_filepath = os.path.join(image_root, image_id + \".jpg\")\n    print(imread(fname=image_filepath).shape)\n    # print(\"image id: {}\".format(image_id))\n\n    # if os.path.isfile(image_filepath):\n    #     try:\n    #         warnings.warn(imread(fname=image_filepath))\n    #     except:\n    #         _download(image_id=image_id, image_root=image_root)\n    # else:\n    #     _download(image_id=image_id, image_root=image_root)\n","repo_name":"zhaoyin214/nima_pytorch","sub_path":"utils/warning_cap.py","file_name":"warning_cap.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"6715570933","text":"import torch\nfrom models.EEGSimpleConv import EEGSimpleConv\nimport numpy as np\nimport random\n\nimport wandb ### uncomment this line to use wandb\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\n#batch_size = 288\n\n\n\n        \n\ndef load_data(dict_config):\n    \"\"\"Returns X and Y according to the configuration dict\n\n    Parameters:\n    dict_config (dict): configuration dict\n\n    Returns:\n    X (list of torch tensors): EEG time series\n    Y (list of torch tensors): Associated labels\n\n   \"\"\"\n    dict_config = correct_dict_0(dict_config)\n    dict_classes = {'BNCI':4,'Cho':2,'Physionet':4,'Zhou':3,'Weibo':4,'Large':2}\n    dict_config['n_classes'] = dict_classes[dict_config['dataset']]\n    path=dict_config['path']\n    dataset=dict_config['dataset']\n    if dict_config['dataset'] not in ['BNCI','Zhou']:\n        dict_config['EOG']== False  \n    EOG = dict_config['EOG']\n    ea = '_EA' if dict_config['EA'] else ''\n    s = '_s' if dict_config['session'] else ''\n    x_path = path + '/' +dataset+'/X'+ea+s+'.pt'\n    if 'online' not in dict_config.keys():\n        dict_config['online']=False\n    if dict_config['online']==True:\n        if dict_config['within']: \n            X = torch.load(path + '/' +dataset+'/X_EA_online_ft.pt')\n            Y = torch.load(path + '/' +dataset+'/Y_s.pt')\n        else : \n            X = torch.load(path + '/' +dataset+'/X_s.pt')\n            Y = torch.load(path + '/' +dataset+'/Y_s.pt')\n            assert dict_config['BN'] == False and dict_config['EA'] == False\n    else : \n        X = torch.load(x_path)\n        Y = torch.load(path + '/' +dataset+'/Y'+s+'.pt')\n    if EOG:\n        print('warning EOG not supported in this code anymore')\n        assert EOG ==False\n    if dict_config['model']=='EEGSimpleConv':\n        n_chan = X[0][0].shape[1] if dict_config['session'] else X[0].shape[1]\n        dict_config['params'].append(n_chan)\n        dict_config['params'].append(dict_config['n_classes'])  \n        dict_config['params'].append(dict_config['sfreq'])\n        if dict_config['reg_subject']:\n            n_subjects = len(X)\n            dict_config['params'].append(n_subjects)   \n    # Check \n    if dict_config['reg_subject']:\n        assert len(dict_config['params'])==9\n    else  :\n        assert len(dict_config['params'])==7   \n    \n    return X,Y    \n\n\ndef loaders(idx,X,Y,lmso,nsplit,session,reg_subject,within=False,mdl=False):\n    \"\"\"Returns data loaders to train and test for a given split\n\n    Parameters:\n    idx (int): id of the split\n    X (list of torch tensors): EEG time series\n    Y (list of torch tensors): Associated labels\n    lmso (bool): True if LMSO, False if LOSO\n    nsplit (int): Number of splits\n    session (bool): True if session else False\n    reg_subject (bool): True if subject-wise regularization else False\n    within (bool): True if Within-Subject evaluation else False\n    mdl (bool): True if MDL evluation else False\n    \n    Returns:\n    train_loader (torch dataloader)\n    test_loader (list of zipped test set)\n   \"\"\"\n    n_chan = X[0][0].shape[1]\n    if reg_subject:\n        Y_subject = [[torch.tensor([i]*XXX.shape[0]) for XXX in XX] for i,XX in enumerate(X)] if session else  [torch.tensor([i]*XX.shape[0]) for i,XX in enumerate(X)] \n    if lmso :\n        if session ==False : \n            n_subjects = len(X) \n            inf = (idx * n_subjects) // nsplit\n            sup = (n_subjects + idx * n_subjects) // nsplit \n            print(inf,sup)\n            train_X = torch.cat(X[:inf] + X[sup:])\n            train_Y = torch.cat(Y[:inf] + Y[sup:])\n            test_X = X[inf:sup]\n            test_Y = Y[inf:sup]\n            if reg_subject:#temp\n                train_Y_subject = torch.cat(Y_subject[:inf] + Y_subject[sup:]) \n                test_Y_subject = Y_subject[inf:sup]\n        if session:\n            n_subjects = len(X) \n            inf = (idx * n_subjects) // nsplit\n            sup = (n_subjects + idx * n_subjects) // nsplit \n            print(inf,sup)\n            X_ = X[:inf] + X[sup:]\n            Y_ = Y[:inf] + Y[sup:]\n            train_X = torch.cat([item for sublist in X_ for item in sublist])\n            train_Y = torch.cat([item for sublist in Y_ for item in sublist])\n            test_X = [item for sublist in X[inf:sup] for item in sublist]\n            test_Y = [item for sublist in Y[inf:sup] for item in sublist]\n            if reg_subject:#temp\n                train_Y_subject =torch.cat([item for sublist in Y_subject[:inf] + Y_subject[sup:] for item in sublist])\n                test_Y_subject = [item for sublist in Y_subject[inf:sup] for item in sublist]\n    else :\n        if mdl:\n            X_ = X[:idx] + X[idx+1:] + [[X[idx][0]]]\n            Y_ = Y[:idx] + Y[idx+1:] + [[Y[idx][0]]]\n            train_X = torch.cat([item for sublist in X_ for item in sublist])\n            train_Y = torch.cat([item for sublist in Y_ for item in sublist])\n            test_X = [X[idx][1]]\n            test_Y = [Y[idx][1]]\n            if reg_subject:\n                Y_subject_ = Y_subject[:idx] + Y_subject[idx+1:]  + [[Y_subject[idx][0]]]\n                train_Y_subject = torch.unbind(torch.cat([item for sublist in Y_subject_ for item in sublist]))\n                test_Y_subject = [Y_subject[idx][1]]\n            \n        elif within :\n            train_X = X[idx][0]\n            test_X = [X[idx][1]]\n            train_Y = Y[idx][0]\n            test_Y = [Y[idx][1]]\n            \n        elif within==False and session:\n            X_ = X[:idx] + X[idx+1:]\n            Y_ = Y[:idx] + Y[idx+1:]\n            train_X = torch.cat([item for sublist in X_ for item in sublist])\n            train_Y = torch.cat([item for sublist in Y_ for item in sublist])\n            test_X = X[idx]\n            test_Y = Y[idx]\n            if reg_subject:\n                Y_subject_ = Y_subject[:idx] + Y_subject[idx+1:]\n                train_Y_subject = torch.unbind(torch.cat([item for sublist in Y_subject_ for item in sublist]))\n                test_Y_subject = Y_subject[idx]\n        else:\n            train_X = torch.cat(X[:idx] + X[idx+1:])\n            train_Y = torch.cat(Y[:idx] + Y[idx+1:])\n            test_X = [X[idx]]\n            test_Y = [Y[idx]]\n            if reg_subject:\n                train_Y_subject = torch.unbind(torch.cat(Y_subject[:idx] + Y_subject[idx+1:])) #temp\n                test_Y_subject = [Y_subject[idx]]\n    \n    if within:\n        batch_size = 16\n    else :\n        batch_size = 288\n    mean = train_X.transpose(1,2).reshape(-1, n_chan).mean(dim = 0)\n    std = train_X.transpose(1,2).reshape(-1, n_chan).std(dim = 0)\n    train_X = (train_X - mean.unsqueeze(0).unsqueeze(2)) / std.unsqueeze(0).unsqueeze(2)\n\n    train_X = torch.unbind(train_X)\n    train_Y = torch.unbind(train_Y)\n    train_data = list(zip(train_X, train_Y,train_Y_subject)) if reg_subject else list(zip(train_X, train_Y))\n    train_loader = torch.utils.data.DataLoader(train_data, batch_size = batch_size, shuffle = True, drop_last = True, num_workers = 8)\n    test_X = [(XX - mean.unsqueeze(0).unsqueeze(2)) / std.unsqueeze(0).unsqueeze(2) for XX in test_X]\n    test_loader = list(zip(test_X, test_Y,test_Y_subject)) if reg_subject else list(zip(test_X, test_Y))\n    return train_loader, test_loader\n\n    \ndef train(epoch, model, criterion, optimizer, train_loader, mixup = False,T=0.1,preload_reg=False):\n    \"\"\"Train the model for one epoch\n\n    Parameters:\n    epoch (int): epoch number\n    model (torch model)\n    criterion (torch loss)\n    optimizer (torch optimizer)\n    train_loader (torch dataloader)\n    mixup (bool): True if use mixup else False\n    T (float): Temperature to ponderate the subject-wise regularization term in the loss\n    preload_reg (bool): True if the pretrained model was trained using subject-wise reguralization\n    \n    Returns:\n    Train accuracy\n   \"\"\"\n    losses, scores = [], []\n    cont = True\n    model.train()\n    for batch_idx, batch_data in enumerate(train_loader):\n        reg_subject =True if len(batch_data)==3 else False\n        if reg_subject:\n            data,target,target_subject = batch_data\n            data,target,target_subject = data.to(device), target.to(device),target_subject.to(device)\n        else : \n            data,target = batch_data\n            data,target = data.to(device), target.to(device)\n        optimizer.zero_grad()\n        if mixup:\n            mm = random.random()\n            perm = torch.randperm(data.shape[0])\n            if reg_subject or preload_reg:\n                output,output_subject = model(mm * data + (1 - mm) * data[perm])\n            else :\n                output = model(mm * data + (1 - mm) * data[perm]) \n        else:\n            if reg_subject or preload_reg:\n                output,output_subject = model(data)\n            else : \n                output = model(data)\n        decisions = torch.argmax(output, dim = 1)\n        scores.append((decisions == target).float().mean().item())\n        if mixup:\n            loss = mm * criterion(output, target) + (1 - mm) * criterion(output, target[perm])\n        else:\n            loss = criterion(output, target)\n        if reg_subject:\n            #loss2 = criterion(output_subject,target_subject) #\n            loss2 = mm * criterion(output_subject, target_subject) + (1 - mm) * criterion(output_subject, target_subject[perm])  \n            loss = (loss + T*loss2)/2\n        loss.backward()\n        optimizer.step()\n        losses.append(loss.item())\n    print(\"\\r{:3d} {:3.3f} {:3.3f} \".format(epoch + 1, np.mean(losses), np.mean(scores)), end='')\n    return np.mean(scores)\n\ndef test(epoch, model, test_loader,bn, confusions = False,preload_reg=False):\n    \"\"\"Test the model for one epoch\n\n    Parameters:\n    epoch (int): epoch number\n    model (torch model)\n    test_loader (list of zipped test set)\n    bn (bool): True if use BN trick else False\n    confusions (bool): True if print confusion matrix else False\n    preload_reg (bool): True if the pretrained model was trained using subject-wise reguralization\n    \n    Returns:\n    Test accuracy\n   \"\"\"\n    if confusions:\n        confs = torch.zeros((4,4))\n    score, count = 0, 0\n    if bn :\n        model.train()\n    else : \n        model.eval()\n    with torch.no_grad():\n        for batch_idx, batch_data in enumerate(test_loader):\n            reg_subject =True if len(batch_data)==3 else False\n            if reg_subject:\n                (data, target,_) = batch_data\n            else : \n                (data, target) = batch_data\n            data, target = data.to(device), target.to(device)\n            if reg_subject or preload_reg:\n                output,_ = model(data)\n            else : \n                output = model(data)\n            decisions = torch.argmax(output, dim = 1)\n            if confusions:\n                for j in range(4):\n                    for k in range(4):\n                        confs[j][k] += (decisions[torch.where(target == j)[0]] == k).int().sum().item()\n            score += (decisions == target).int().sum().item()\n            count += target.shape[0]\n    print(\"\\r{:3d} test: {:.3f} \".format(epoch, score / count), end = '')\n    if confusions:\n        print(confs)\n    return (score / count)\n    \n\ndef train_test(params, dict_config,X,Y):\n    \"\"\"Train and test the model according to the configuration dict\n\n    Parameters:\n    params (list): model paramaters\n    dict_config (dict): configuration dict\n    X (list of torch tensors): EEG time series\n    Y (list of torch tensors): Associated labels\n    \n    Returns:\n    Test accuracy\n   \"\"\"\n    dict_config['params'] = params\n    dict_config,params_model = correct_dict_1(dict_config,params)\n    if dict_config['preload_reg']==True:\n        params_model = params + [len(Y)]\n    if dict_config['use_wandb']:\n        wandb.init(project=\"simpleconv_c\", entity='brain-imt',config = dict_config,settings=wandb.Settings(start_method='fork'))\n    \n    \n    model = instanciate_model(dict_config['model'],params_model)\n    number_params =  np.sum([m.numel() for m in model.parameters()])\n    print(params,number_params, \"params\")\n    runs = dict_config['runs']\n    n_split = 9 if dict_config['lmso'] else len(X)\n    scores = []\n    for idx_ in range(n_split):\n        print(\"Split:\", idx_)\n        train_loader, test_loader = loaders(idx_,X,Y,dict_config['lmso'],n_split,dict_config['session'],dict_config['reg_subject'],within=dict_config['within'],mdl=dict_config['mdl'])\n        criterion = torch.nn.CrossEntropyLoss()\n        for n_run in range(runs):\n            if dict_config['load_model']:\n                model = instanciate_model(dict_config['model'],params_model).to(device)\n                checkpoint = torch.load(dict_config['load_model_path']+'/model_'+str(idx_)+'_'+str(n_run)+'.pt')\n                model.load_state_dict(checkpoint['model_state_dict'])\n                optimizer = torch.optim.Adam(model.parameters())\n                optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n            else:\n                model = instanciate_model(dict_config['model'],params_model).to(device)\n                optimizer = torch.optim.Adam(model.parameters())\n            scheduler = torch.optim.lr_scheduler.StepLR(optimizer = optimizer, step_size = (4*dict_config['n_epochs'])//5, gamma = 0.1)\n            for epoch in range(dict_config['n_epochs']):\n                train_acc = train(epoch, model, criterion, optimizer, train_loader, mixup = dict_config['mixup'],T=dict_config['T'],preload_reg=dict_config['preload_reg'])\n                if epoch ==dict_config['n_epochs'] -1 : #%2==0\n                    if dict_config['save_model']:\n                        torch.save({'model_state_dict':model.state_dict(),'optimizer_state_dict': optimizer.state_dict()},dict_config['save_model_path']+'/model_w_'+str(idx_)+'_'+str(n_run)+'.pt')\n                    score = test(epoch, model, test_loader,dict_config['BN'],preload_reg=dict_config['preload_reg'])\n                scheduler.step()\n            scores.append(score)\n            \n            if n_run == runs - 1:\n                print(\" average: {:.3f}\".format(np.mean(scores[-runs:])))\n            else:\n                print()\n\n    std_sub = np.array(scores).reshape(n_split,runs).mean(1).std()\n    std = np.array(scores).reshape(n_split,runs).T.mean(1).std()\n    print(\"{:.3f}\".format(np.mean(scores)))\n    if dict_config['use_wandb']:\n        wandb.log({'test_acc':np.mean(scores),'scores':scores,'number_params':number_params,'std':std,'std_sub':std_sub})\n        wandb.finish()\n\n\n    return np.mean(scores)\n\n\ndef instanciate_model(model_name, params_model):\n    \"\"\"Initialize the model\n\n    Parameters:\n    model_name (string): Name of the model (only valid for EEGSimpleConv)\n    params_model (list): model paramaters\n    \n    Returns:\n    torch model\n   \"\"\"\n    if model_name == 'EEGSimpleConv':\n        return EEGSimpleConv(*params_model)\n\n\n    \n### Two functions to ensure there are no inconsistencies in the use of the dictionnary\n    \ndef correct_dict_0(dict_config):\n    if dict_config['dataset'] not in ['BNCI','Zhou','Large']:\n        dict_config['lmso']   =True\n        dict_config['EOG']    =False\n        dict_config['session']=False\n        print('no EOG or session in Physionet or Cho')\n    else :\n        dict_config['lmso']   =False\n        dict_config['sfreq'] = 250\n    if dict_config['dataset']=='Cho':\n        dict_config['sfreq'] = 512\n    if dict_config['dataset']=='Physionet':\n        dict_config['sfreq'] = 160\n    print('Sampling Freq: '+str(dict_config['sfreq']))\n    \n    if dict_config['within']==True or dict_config['mdl']=='True':\n        assert dict_config['dataset']=='BNCI'\n    if dict_config['evaluation']=='cross':\n        assert dict_config['within']==False\n        assert dict_config['mdl']==False\n    if dict_config['within']==True:\n        assert dict_config['reg_subject']==False\n    return dict_config\n\n\ndef correct_dict_1(dict_config,params):\n    if dict_config['reg_subject']:\n        dict_config['T'] = params[0]\n        params_model = params[1:]\n        assert len(params_model)==8 and len(params)==9\n    else : \n        dict_config['T'] = None\n    if len(params)==9:\n        assert dict_config['reg_subject']==True\n    else : \n        params_model = params\n        assert len(params_model)==7\n    return dict_config,params_model","repo_name":"elouayas/EEGSimpleConv","sub_path":"scripts/scripts.py","file_name":"scripts.py","file_ext":"py","file_size_in_byte":16234,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"80"}
+{"seq_id":"73431011457","text":"import pygame\nimport sys\nimport random\n\n\nclass Block(pygame.sprite.Sprite):\n    \"\"\"The base class for all entities\n    Returns: An entity object\n    Functions:\n    Attributes:\"\"\"\n\n    def __init__(self, imagelist, screenwidth, screenheight, *groups):\n        pygame.sprite.Sprite.__init__(self, *groups)\n        self.imagelist = {}\n        for key, imagefile in imagelist.iteritems():\n            self.imagelist[key] = pygame.image.load(imagefile)\n            if self.imagelist[key].get_alpha() is None:\n                self.imagelist[key] = self.imagelist[key].convert()\n            else:\n                self.imagelist[key] = self.imagelist[key].convert_alpha()\n        self.image = self.imagelist.itervalues().next()\n        self.rect = self.image.get_rect()\n        self.screen = pygame.display.get_surface()\n        self.area = self.screen.get_rect()\n        x = random.randint(0, (screenwidth/self.rect.width) - 1)\n        y = random.randint(0, (screenheight/self.rect.height) - 1)\n        self.location = {'x': x, 'y': y}\n        self.rect = self.rect.move(x*32, y*32)\n        self.radius = 8\n        self.hp = 5\n","repo_name":"BenFielding/intelligent-chalice-game","sub_path":"block.py","file_name":"block.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"40753752336","text":"#!/usr/bin/env python3\nfrom lox.parser import Parser\nfrom lox.scanner import Scanner\n\ncode = \"\"\"\nclass Breakfast {\n  cook() {\n    print \"Eggs a-fryin'!\";\n  }\n\n  serve(who) {\n    print \"Enjoy your breakfast, \" + who + \".\";\n  }\n}\nvar breakfast = Breakfast();\nvar person = \"me\";\nbreakfast.cook().serve(person);\n\"\"\"\n\ntokens = Scanner(code).scan_tokens()\nparser = Parser(tokens)\nstatements = parser.parse()\nbreakpoint()\nprint(\"end\")\n","repo_name":"zormit/plox","sub_path":"playground.py","file_name":"playground.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"40044461209","text":"def triangle(nums):\n    for i in range(2,len(nums)):\n        if nums[i-2]+nums[i-1]>nums[i]: return True\n    return False\nn=int(input())\nnums=[int(x) for x in input().split()]\nnums.sort()\nif triangle(nums): print(\"YES\")\nelse: print(\"NO\")\n\n        ","repo_name":"AdamZhouSE/pythonHomework","sub_path":"Code/CodeRecords/2801/60846/275642.py","file_name":"275642.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"80"}
+{"seq_id":"30916575577","text":"class Solution:\n    def twoSum(self, nums: List[int], target: int) -> List[int]:\n\n        # double for loop approach\n        # Time: O(N^2); Space: O(1)\n        # for i in range(len(nums)):\n        #     for j in range(i+1, len(nums)):\n        #         if nums[i] + nums[j] == target:\n        #             return i, j\n\n        \n        # optimize with dict as cache\n        #  Time: O(N); Space: O(N)\n        cache = {}\n\n        for idx, num in enumerate(nums):\n            comp = target - num\n            if comp in cache:\n                return cache[comp], idx\n            cache[num] = idx\n\n        return -1, -1        ","repo_name":"mowafess/leetcode-solution","sub_path":"0001-two-sum/0001-two-sum.py","file_name":"0001-two-sum.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"22214315467","text":"#!/usr/bin/python3\nimport os\nimport time\nfrom bs4 import BeautifulSoup\nimport zipper\nimport threading\nimport time\n\nclass myThread (threading.Thread):\n   def __init__(self, threadID, name, counter):\n      threading.Thread.__init__(self)\n      self.threadID = threadID\n      self.name = name\n      self.counter = counter\n   def run(self):\n      print(\"%s: %s\" % (self.name, time.ctime(time.time())))\n\n# @see https://www.tutorialspoint.com/python/python_multithreading.htm\ndef run_threads():\n   threads = []\n   # create new threads\n   thread1 = myThread(1, \"Thread-1\", 1)\n   thread2 = myThread(2, \"Thread-2\", 2)\n   # start new Threads\n   thread1.start()\n   thread2.start()\n   # add threads to thread list\n   threads.append(thread1)\n   threads.append(thread2)\n   # wait for all threads to complete\n   for t in threads:\n      t.join()\n   print(\"Exiting Main Thread\")\n\n# FUNCTION Scrape el resultado de busqueda en dos farma\ndef scrapeMain(html:BeautifulSoup, dir:str, issue_idx:int):\n   #headings = html.findAll('div', attrs={'class':'heading'})\n   #title = headings[0].find('h3').text.replace(':', '_').replace(',', '_').replace(' ', '_')\n   cover = html.find('div', attrs={'class':'col cover'}).find('img', src=True)\n   img_url:str = cover['src']\n   if not(img_url.startswith('http')):\n      img_url = \"https://readcomiconline.to\" + img_url\n   print(\"title: \" + dir + \"cover: \" + img_url)\n\n\n   cover_file = dir + \"/cover.jpg\"\n   zipper.get_image(img_url, cover_file)\n\n   issues = html.find('ul', attrs={'class':'list'}).findAll('li')\n   for issue in issues:\n      co = issue.find('a', href=True)\n      co_title = co.find('span').text\n      if -1 != co_title.find('#'):\n         co_title = \"issue-\" + str(co_title[co_title.find('#')+1:]).zfill(3)\n      if -1 != co_title.find('Part'):\n         co_title = \"issue-\" + str(co_title[co_title.find('Part')+4:-1]).zfill(3)\n      else:\n         co_title = \"issue-\" + str(issue_idx).zfill(3)\n      co_dir = dir + \"/\" + co_title\n      co_url = \"https://readcomiconline.to\" + co['href']\n      scrapeIssue(co_title, co_url, co_dir)\n      # next issue\n      issue_idx = issue_idx+1\n      time.sleep(5)\n\ndef scrapeIssue(co_title:str, co_url:str, co_dir:str):\n   print(\" \" + co_title + \": \" + co_url)\n   if not os.path.exists(co_dir):\n         os.makedirs(co_dir)\n   co_html = zipper.get_html(co_url).prettify()\n   img_idx = 0\n   html_idx: int = co_html.find('lstImages.push')\n   while -1 != html_idx:\n      co_html = co_html[html_idx:]\n      img_url = co_html[:co_html.find('\");')]\n      img_url = img_url.replace('lstImages.push(\"', '').replace('\")', '')\n      img_title = \"img-\" + str(img_idx).zfill(3) + \".jpg\"\n      print(img_title + \": \" + img_url)\n      zipper.get_image(img_url, co_dir + \"/\" + img_title)\n      # next img\n      img_idx = img_idx+1\n      co_html = co_html[co_html.find(';'):]\n      html_idx = co_html.find('lstImages.push')\n      time.sleep(5)\n\n# execute ./bin/python3 src/comic-thread.py\ndef main():\n   try:\n      URL = 'https://readcomiconline.to/Comic/'\n      title = 'The-Savage-Sword-Of-Conan'\n      html = zipper.get_html(URL + title)\n      # dir destiny\n      dir = title.replace(':', '_').replace(',', '_').replace(' ', '_').replace('(', '').replace(')', '')\n      if not os.path.exists(dir):\n         os.makedirs(dir)\n      \n      issue_idx = 96\n      scrapeMain(html, dir, issue_idx)\n\n      zipper.zip(dir, dir + '.cbz')\n      #scrapeIssue('TPB', 'https://readcomiconline.to/Comic/Marvel-Zombies-2006/TPB?id=158840', 'Marvel_Zombies_(2006)/issue-TPB')\n   except Exception as e:\n      print(e)\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"smanero/python","sub_path":"web-s-py/src/comic-thread.py","file_name":"comic-thread.py","file_ext":"py","file_size_in_byte":3598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"72850068099","text":"# Import Libraries\r\nimport scipy.constants as sp\r\nimport math\r\n\r\n# Variables\r\n\r\nn1_turns = 4600\r\nn2_turns = 2600\r\nradius_c1 = 0.04\r\nradius_c2 = 0.02\r\ndistance = 0.4\r\ncurrent = 0.05\r\nfreq = 2100\r\n\r\n# Calculations\r\n\r\nmu0_4pi = sp.mu_0/(4*sp.pi)\r\narea_c1 = math.pow(radius_c1,2) * sp.pi\r\narea_c2 = math.pow(radius_c2,2) * sp.pi\r\nang_freq = freq * 2 * sp.pi\r\nl_third = 1/math.pow(distance,3)\r\nemf_c2 = n1_turns * n2_turns * mu0_4pi * 2 * area_c1 * area_c2 * current * ang_freq * l_third\r\n\r\n# Print\r\nprint(\"The maximum voltage of the second coil voltage =\", emf_c2,\"V\")\r\n","repo_name":"phys2331/idyll-material","sub_path":"computational_activities/Trinkets/22.23_HW9.py","file_name":"22.23_HW9.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"32106499857","text":"# 给定一个仅包含数字 2-9 的字符串，返回所有它能表示的字母组合。答案可以按 任意顺序 返回。 \n# \n#  给出数字到字母的映射如下（与电话按键相同）。注意 1 不对应任何字母。 \n# \n#  \n# \n#  \n# \n#  示例 1： \n# \n#  \n# 输入：digits = \"23\"\n# 输出：[\"ad\",\"ae\",\"af\",\"bd\",\"be\",\"bf\",\"cd\",\"ce\",\"cf\"]\n#  \n# \n#  示例 2： \n# \n#  \n# 输入：digits = \"\"\n# 输出：[]\n#  \n# \n#  示例 3： \n# \n#  \n# 输入：digits = \"2\"\n# 输出：[\"a\",\"b\",\"c\"]\n#  \n# \n#  \n# \n#  提示： \n# \n#  \n#  0 <= digits.length <= 4 \n#  digits[i] 是范围 ['2', '9'] 的一个数字。 \n#  \n#  Related Topics 哈希表 字符串 回溯 👍 1680 👎 0\n\n\n# leetcode submit region begin(Prohibit modification and deletion)\nfrom typing import List\n\n\nclass Solution:\n    def letterCombinations(self, digits: str) -> List[str]:\n        if len(digits) == 0:\n            return []\n        az = [chr(i) for i in range(ord('a'), ord('z') + 1)]\n        d = dict()\n        start = 0\n        for i, num in enumerate([3, 3, 3, 3, 3, 4, 3, 4]):\n            d[i+2] = az[start: start+num]\n            start += num\n        ans = []\n\n        def backtrack(s: List[str]):\n            if len(digits) == len(s):\n                ans.append(''.join(s))\n                return\n            digit = int(digits[len(s)])\n            for c in d[digit]:\n                s.append(c)\n                backtrack(s)\n                s.pop(-1)\n\n        backtrack([])\n        return ans\n\n\nif __name__ == '__main__':\n    print(Solution().letterCombinations(''))\n# leetcode submit region end(Prohibit modification and deletion)\n","repo_name":"AISEALs/AISEALs","sub_path":"src/leetcode/leetcode/editor/cn/[17]电话号码的字母组合.py","file_name":"[17]电话号码的字母组合.py","file_ext":"py","file_size_in_byte":1626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"16606555680","text":"'''\n    Test for chat modules\n'''\n\nimport sqlite3\nfrom chat import chat_api as c\n\ndef test_add_reading():\n    response = c.put({\n        'request': 'POST',\n        'sender_ID': 1,\n        'receiver_ID': 2,\n        'conversation_ID': 1,\n        'message_type': 1,\n        'message': \"Nice to meet you!\",\n        'time': \"2022-02-15 00:00:00\",\n    })\n    assert response['success'] == True\n\ndef test_get_reading():\n    response = c.get({\n        'request': 'GET',\n        'conversation_ID': 1\n    })\n    # Deleting test dummy entry\n    connection = sqlite3.connect('database/health_care_DB.db')\n    cursor = connection.cursor()\n    cursor.execute('''DELETE FROM messages WHERE conversation_ID==1 AND message=='Nice to meet you!';''')\n    connection.commit()\n    connection.close()\n    assert response['success'] == True\n","repo_name":"saadullah01/health-data-managing-API","sub_path":"chat_module/test_chat.py","file_name":"test_chat.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"40547846292","text":"import os\nfrom moviepy.editor import VideoFileClip\n\ndef extract_audio_from_video(video_path, audio_file):\n    try:\n        videoClip = VideoFileClip(video_path)\n        audioClip = videoClip.audio\n\n        converted_file = audioClip.write_audiofile(audio_file)\n        print(converted_file)\n\n    finally:\n        if audioClip:\n            audioClip.close()\n        if videoClip:\n            videoClip.close()\n\n# 使用例\nvideo_path = \"/path/to/movieData\"\naudio_file = \"./path/to/audioDataName\"\n\nextract_audio_from_video(video_path, audio_file)","repo_name":"sho55/movie-transcribe","sub_path":"src/movie_to_audio.py","file_name":"movie_to_audio.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"20901208109","text":"import time\nfrom rpi_ws281x import PixelStrip, Color\n\n# LED strip configuration\nLED_COUNT = 60         # Number of LED pixels.\nLED_PIN = 18           # GPIO pin connected to the pixels (18 uses PWM!).\nLED_FREQ_HZ = 800000   # LED signal frequency in hertz (usually 800khz)\nLED_DMA = 10           # DMA channel to use for generating signal (try 10)\nLED_BRIGHTNESS = 255   # Set to 0 for darkest and 255 for brightest\nLED_INVERT = False     # True to invert the signal (when using NPN transistor level shift)\nLED_CHANNEL = 0        # set to '1' for GPIOs 13, 19, 41, 45 or 53\n\n# Define the colors of the rainbow\nRED = Color(255, 0, 0)\nORANGE = Color(255, 127, 0)\nYELLOW = Color(255, 255, 0)\nGREEN = Color(0, 255, 0)\nBLUE = Color(0, 0, 255)\nINDIGO = Color(75, 0, 130)\nVIOLET = Color(148, 0, 211)\n\n# Define a function to create a rainbow effect\ndef rainbow(strip, wait_ms=20):\n    for j in range(256):\n        for i in range(strip.numPixels()):\n            pixel_index = (i * 256 // strip.numPixels()) + j\n            strip.setPixelColor(i, wheel(pixel_index & 255))\n        strip.show()\n        time.sleep(wait_ms / 1000.0)\n\n# Define a function to convert a color value to a wheel value\ndef wheel(pos):\n    if pos < 85:\n        return Color(pos * 3, 255 - pos * 3, 0)\n    elif pos < 170:\n        pos -= 85\n        return Color(255 - pos * 3, 0, pos * 3)\n    else:\n        pos -= 170\n        return Color(0, pos * 3, 255 - pos * 3)\n\n# Create an instance of the PixelStrip class\nstrip = PixelStrip(LED_COUNT, LED_PIN, LED_FREQ_HZ, LED_DMA, LED_INVERT, LED_BRIGHTNESS, LED_CHANNEL)\nstrip.begin()\n\n# Run the rainbow effect\nwhile True:\n    rainbow(strip)\n","repo_name":"ConnectLive/UnderArmourLED","sub_path":"New_Pixel_Tape_Test.py","file_name":"New_Pixel_Tape_Test.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"44602485978","text":"# The function definition goes here\ndef is_num_in_range(number):\n    if number > 1 and number < 555:\n        answer = str(number) + \" is in range.\"\n    else:\n        answer = str(number) + \" is outside the range!\"\n    \n    return answer\n\n\n\nnum = int(input(\"Enter a number: \"))\n\nresult = is_num_in_range(num)\n\nprint (result)\n\n# You call the function here","repo_name":"vesteinnbjarna/Forritun_Vesteinn","sub_path":"Tímaverkefni/Assignment 7/A7.3.py","file_name":"A7.3.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"34775985915","text":"#!/usr/bin/env python3\n\"\"\"File colours.py\"\"\"\nfrom typing import Match, Final, Pattern\nimport re\n\n# Initial control character:\n_control_character: Final[str] = '\\033'\n# Regex to validate basic colour / control strings.\n_is_four_bit_regex: Pattern = re.compile(r'^\\033\\[(?P<value>\\d+)m$')\n_is_eight_bit_regex: Pattern = re.compile(r'^\\033\\[(?P<fgBg>[34]8);5;(?P<value>\\d{1,3})m$')\n_is_sixteen_bit_regex: Pattern = re.compile(r'^\\033\\[(?P<fgBg>[34]8);2;(?P<red>\\d{1,3});(?P<green>\\d{1,3});(?P<blue>\\d{1,3})m$')\n# List of valid 4 bit control values.\n_control_values: Final[list[str]] = [\n    '0', '01', '02', '03', '04', '05', '06', '07', '08', '09'\n]\n# List of valid 4 bit foreground colour values.\n_four_bit_fg_values: Final[list[str]] = [\n    '30', '31', '32', '33', '34', '35', '36', '37', '90', '91', '92', '93', '94', '95', '96', '97'\n]\n# List of valid 4 bit background colour values.\n_four_bit_bg_values: Final[list[str]] = [\n    '40', '41', '42', '43', '44', '45', '46', '47', '100', '101', '102', '103', '104', '105', '106', '107'\n]\n# Valid 8 bit foreground / background values:\n_eight_bit_fg_value: Final[str] = '38'\n_eight_bit_bg_value: Final[str] = '48'\n# Valid 16 bit foreground / background values:\n_sixteen_bit_fg_value: Final[str] = '38'\n_sixten_bit_bg_value: Final[str] = '48'\n\n\ndef __value_is_valid__(value: str) -> bool:\n    str_value = value\n    int_value = int(value)\n    if 0 <= int_value <= 9:\n        if len(str_value) == 1:\n            return True\n    elif 10 <= int_value <= 99:\n        if len(str_value) == 2:\n            return True\n    elif 100 <= int_value <= 255:\n        if len(str_value) == 3:\n            return True\n    return False\n\n\nclass ColourError(Exception):\n    \"\"\"Class for all colour errors.\"\"\"\n    _errorMessages = {\n        0: 'No Error.',\n        1: 'TypeError: value must be an int between 0 and 255, inclusive.',\n        2: 'TypeError: red must be an int between 0 and 255, inclusive.',\n        3: 'TypeError: green must be an int between 0 and 255, inclusive.',\n        4: 'TypeError: blue must be an int between 0 and 255, inclusive.',\n        5: 'TypeError: value must be a str.',\n        6: 'ValueError: value must be an int between 0 and 255, inclusive.',\n        7: 'ValueError: red value must be an int between 0 and 255, inclusive.',\n        8: 'ValueError: green value must be an int between 0 and 255, inclusive.',\n        9: 'ValueError: blue value must be an int between 0 and 255, inclusive.',\n        10: 'Value is not valid control string.',\n        11: 'Value is not valid colour string.',\n        12: 'Value is not valid four bit colour string.',\n        13: 'Value is not valid eight bit colour string.',\n        14: 'Value is not valid sixteen bit colour string.',\n        15: 'Value is not valid foreground colour string.',\n        16: 'Value is not valid background colour string.',\n    }\n\n    def __init__(self,\n                 error_number: int,\n                 *args: object\n                 ) -> None:\n        \"\"\"\n        Initialize the error.\n        :param error_number: int, The error message number.\n        :param args: object, Additional arguments to pass to Exception.\n        \"\"\"\n        super().__init__(*args)\n        self.error_number: int = error_number\n        self.error_message: str = self._errorMessages[error_number]\n        return\n\n\nclass Colours(object):\n    \"\"\"\n    Classes and methods to store and generate, and validate colour / control strings.\n        Class: Colours(object), Stores control strings by name, and validation methods.\n            Class: Colours.ColourError(Exception)\n            Method: Colours.is_control(value, raiseOnError), Return True if value is valid 4-Bit control string.\n            Method: Colours.is_colour(value, raiseOnError), Return True if value is valid colour string, regardless of\n                                                                bit length.\n            Method: Colours.is_four_bit(value, raiseOnError), Return True if value is valid 4-Bit colour string.\n            Method: Colours.is_eight_bit(value, raiseOnError), Return True if value is valid 8-Bit colour string.\n            Method: Colours.is_sixteen_bit(value, raiseOnError), Return True if value is valid 16-Bit colour string.\n            Method: Colours.is_foreground(value, raiseOnError), Return True if value is valid foreground colour string.\n            Method: Colours.is_background(value, raiseOnError), Return True if value is valid background colour string.\n            Class: Colours.fg(object), Stores 4-Bit foreground colour values by name.\n                Method: Colours.fg.colour(value), Generate an 8-Bit foreground colour string, given colour value.\n                Method: Colours.fg.rgb(red, green, blue), Generate a 16-Bit foreground colour string, given red, green,\n                                                            and blue values.\n            Class: Colours.bg(object), Stores 4-Bit background colour values by name.\n                Method: Colours.bg.colour(value), Generate an 8-Bit background colour string, given colour value.\n                Method: Colours.bg.rgb(red, green, blue), Generate a 16-Bit background colour string, given red, green,\n                                                            and blue values.\n    \"\"\"\n\n    # Control strings:\n    reset: Final[str] = '\\033[0m'\n    bold: Final[str] = '\\033[01m'\n    disable: Final[str] = '\\033[02m'\n    underline: Final[str] = '\\033[04m'\n    blink: Final[str] = '\\033[05m'  # Note also '\\033[06m'\n    reverse: Final[str] = '\\033[07m'\n    invisible: Final[str] = '\\033[08m'\n    strikeThrough: Final[str] = '\\033[09m'\n\n    @staticmethod\n    def is_control(value: object, raise_on_false: bool = False) -> bool:\n        \"\"\"\n        Return True if value is a valid 4-bit control string.\n        :param value: str, The value to check.\n        :param raise_on_false: Raise ColourError instead of returning False\n        :raises ColourError: If raise_on_false is True, and would return False.\n        :return: bool: True if value is a valid 4-bit colour value.\n        \"\"\"\n        if not isinstance(value, str):\n            if raise_on_false:\n                raise ColourError(10)\n            else:\n                return False\n        four_bit_match: Match = _is_four_bit_regex.match(value)\n        if four_bit_match is not None:\n            if four_bit_match['value'] in _control_values:\n                return True\n        if raise_on_false:\n            raise ColourError(10)\n        else:\n            return False\n\n    @staticmethod\n    def is_four_bit(value: object, raise_on_false: bool = False) -> bool:\n        \"\"\"\n        Return True if value is a valid 4-bit colour string.\n        :param value: str, The value to check.\n        :param raise_on_false: bool, If true raises ColourError instead of returning False.\n        :raises ColourError:If raise_on_false is true, and would return False.\n        :return: bool, True if value is valid 4-bit colour string.\n        \"\"\"\n        if not isinstance(value, str):\n            if raise_on_false:\n                raise ColourError(12)\n            else:\n                return False\n        four_bit_match: Match = _is_four_bit_regex.match(value)\n        four_bit_values: list[str] = _four_bit_fg_values + _four_bit_bg_values\n        if four_bit_match is not None:\n            if four_bit_match['value'] in four_bit_values:\n                return True\n        if raise_on_false:\n            raise ColourError(12)\n        else:\n            return False\n\n    @staticmethod\n    def is_eight_bit(value: object, raise_on_false: bool = False) -> bool:\n        \"\"\"\n        Returns True if value is valid 8-Bit colour string.\n        :param value: str, The value to check.\n        :param raise_on_false: If true raises ColourError instead of returning False.\n        :raises ColourError: If raise_on_false is True, and would return False.\n        :return: bool, True if valid 8-bit colour string.\n        \"\"\"\n        if not isinstance(value, str):\n            if raise_on_false:\n                raise ColourError(13)\n            else:\n                return False\n        eight_bit_match: Match = _is_eight_bit_regex.match(value)\n        if eight_bit_match is not None:\n            if __value_is_valid__(eight_bit_match['value']):\n                return True\n        if raise_on_false:\n            raise ColourError(13)\n        else:\n            return False\n\n    @staticmethod\n    def is_sixteen_bit(value: object, raise_on_false=False) -> bool:\n        \"\"\"\n        Return True if value is valid 16-bit colour string.\n        :param value: str, The value to check.\n        :param raise_on_false: If True, raises ColourError instead of returning False.\n        :raises ColourError: If raise_on_false is True, and would return False.\n        :return: bool, True if value is a valid 16-bit colour string.\n        \"\"\"\n        if not isinstance(value, str):\n            if raise_on_false:\n                raise ColourError(14)\n            else:\n                return False\n        sixteen_bit_match: Match = _is_sixteen_bit_regex.match(value)\n        if sixteen_bit_match is not None:\n            red_good = __value_is_valid__(sixteen_bit_match['red'])\n            green_good = __value_is_valid__(sixteen_bit_match['green'])\n            blue_good = __value_is_valid__(sixteen_bit_match['blue'])\n            if red_good is True and green_good is True and blue_good is True:\n                return True\n        if raise_on_false:\n            raise ColourError(14)\n        else:\n            return False\n\n    @classmethod\n    def is_colour(cls, value: object, raise_on_false: bool = False) -> bool:\n        \"\"\"\n        Return True if value is a valid colour string of any bit length.\n        :param value: The value to check.\n        :param raise_on_false: If True, raises ColourError instead of returning False.\n        :raises ColourError: If raise_on_false is True and would return False.\n        :return: bool, True if valid colour string.\n        \"\"\"\n        if not isinstance(value, str):\n            if raise_on_false:\n                raise ColourError(11)\n            else:\n                return False\n        if cls.is_four_bit(value) is True or cls.is_eight_bit(value) is True or cls.is_sixteen_bit(value) is True:\n            return True\n        if raise_on_false:\n            raise ColourError(11)\n        else:\n            return False\n\n    @classmethod\n    def is_foreground(cls, value: object, raise_on_false: bool = False) -> bool:\n        \"\"\"\n        Return True if value is valid foreground colour of any bit length.\n        :param value: The value to check.\n        :param raise_on_false: If True, raises ColourError instead of returning False.\n        :raises ColourError: If raise_on_false is True and would return False.\n        :return: bool, True if value is valid foreground colour string.\n        \"\"\"\n        if not isinstance(value, str):\n            if raise_on_false:\n                raise ColourError(15)\n            else:\n                return False\n        if not cls.is_colour(value):\n            if raise_on_false:\n                raise ColourError(15)\n            else:\n                return False\n        four_bit_match: Match = _is_four_bit_regex.match(value)\n        eight_bit_match: Match = _is_eight_bit_regex.match(value)\n        sixteen_bit_match: Match = _is_sixteen_bit_regex.match(value)\n        if four_bit_match is not None:\n            if four_bit_match['value'] in _four_bit_fg_values:\n                return True\n        elif eight_bit_match is not None:\n            if eight_bit_match['fgBg'] == _eight_bit_fg_value:\n                return True\n        elif sixteen_bit_match is not None:\n            if sixteen_bit_match['fgBg'] == _sixteen_bit_fg_value:\n                return True\n        if raise_on_false:\n            raise ColourError(15)\n        else:\n            return False\n\n    @classmethod\n    def is_background(cls, value: object, raise_on_false: bool = False) -> bool:\n        \"\"\"\n        Return True if value is valid background colour of any bit length.\n        :param value: The value to check.\n        :param raise_on_false: If True, raises ColourError instead of returning False.\n        :raises ColourError: If raise_on_false is True and would return False.\n        :return: bool, True f valid background colour.\n        \"\"\"\n        if not isinstance(value, str):\n            if raise_on_false:\n                raise ColourError(16)\n            else:\n                return False\n        if not cls.is_colour(value):\n            if raise_on_false:\n                raise ColourError(16)\n            else:\n                return False\n        four_bit_match: Match = _is_four_bit_regex.match(value)\n        eight_bit_match: Match = _is_eight_bit_regex.match(value)\n        sixteen_bit_match: Match = _is_sixteen_bit_regex.match(value)\n        if four_bit_match is not None:\n            if four_bit_match['value'] in _four_bit_bg_values:\n                return True\n        elif eight_bit_match is not None:\n            if eight_bit_match['fgBg'] == _eight_bit_bg_value:\n                return True\n        elif sixteen_bit_match is not None:\n            if sixteen_bit_match['fgBg'] == _sixten_bit_bg_value:\n                return True\n        if raise_on_false:\n            raise ColourError(16)\n        else:\n            return False\n\n    # Foreground\n    class fg(object):\n        \"\"\"Foreground Colours.\"\"\"\n        # 4 bit colour (16 Colours)\n        black: Final[str] = '\\033[30m'\n        red: Final[str] = '\\033[31m'\n        green: Final[str] = '\\033[32m'\n        orange: Final[str] = '\\033[33m'\n        blue: Final[str] = '\\033[34m'\n        purple: Final[str] = '\\033[35m'\n        cyan: Final[str] = '\\033[36m'\n        light_grey: Final[str] = '\\033[37m'\n        dark_grey: Final[str] = '\\033[90m'\n        light_red: Final[str] = '\\033[91m'\n        light_green: Final[str] = '\\033[92m'\n        yellow: Final[str] = '\\033[93m'\n        lightblue: Final[str] = '\\033[94m'\n        pink: Final[str] = '\\033[95m'\n        light_cyan: Final[str] = '\\033[96m'\n        white: Final[str] = '\\033[97m'\n\n        # 8 bit foreground colour (256 Colours):\n        @staticmethod\n        def colour(value: int) -> str:\n            \"\"\"\n            Get an 8-Bit foreground colour:\n            :param value: int, The colour value (0-255).\n            :raises ColourError: On type error, or value error.\n            :return: str, The 8-bit colour string.\n            \"\"\"\n            # Type check:\n            if not isinstance(value, int):\n                raise ColourError(1)\n            # Value check:\n            if value < 0 or value > 255:\n                raise ColourError(6)\n            return '\\033[38;5;%im' % value\n\n        # 16 bit foreground colour (65,536 Colours)\n        @staticmethod\n        def rgb(red: int, green: int, blue: int) -> str:\n            \"\"\"\n            Get a 16-Bit foreground colour.\n            :param red: int, The red value (0-255).\n            :param green: int, The green value (0-255).\n            :param blue: int, The blue value (0-255).\n            :return: str, The 16-bit colour string.\n            \"\"\"\n            # Type check:\n            if not isinstance(red, int):\n                raise ColourError(2)\n            if not isinstance(green, int):\n                raise ColourError(3)\n            if not isinstance(blue, int):\n                raise ColourError(4)\n            # Value check:\n            if red < 0 or red > 255:\n                raise ColourError(7)\n            if green < 0 or green > 255:\n                raise ColourError(8)\n            if blue < 0 or blue > 255:\n                raise ColourError(9)\n            return '\\033[38;2;%i;%i;%im' % (red, green, blue)\n\n    # Background:\n    class bg(object):\n        \"\"\"Background Colours.\"\"\"\n        # 4 bit colour (16 Colours)\n        black: Final[str] = '\\033[40m'\n        red: Final[str] = '\\033[41m'\n        green: Final[str] = '\\033[42m'\n        orange: Final[str] = '\\033[43m'\n        blue: Final[str] = '\\033[44m'\n        purple: Final[str] = '\\033[45m'\n        cyan: Final[str] = '\\033[46m'\n        light_grey: Final[str] = '\\033[47m'\n        dark_grey: Final[str] = '\\033[100m'\n        light_red: Final[str] = '\\033[101m'\n        light_green: Final[str] = '\\033[102m'\n        yellow: Final[str] = '\\033[103m'\n        lightblue: Final[str] = '\\033[104m'\n        pink: Final[str] = '\\033[105m'\n        light_cyan: Final[str] = '\\033[106m'\n        white: Final[str] = '\\033[107m'\n\n        # 8-bit background colour (255 Colours):\n        @staticmethod\n        def colour(value: int) -> str:\n            \"\"\"\n            Get an 8-bit background colour.\n            :param value : int, The colour number. 0-255.\n            :raises ColourError : On type error or value error\n            :returns: str, The colour string.\n            \"\"\"\n            if not isinstance(value, int):\n                raise ColourError(1)\n            if value < 0 or value > 255:\n                raise ColourError(6)\n            return '\\033[48;5;%im' % value\n\n        # 16-bit background colour (65,536 Colours)\n        @staticmethod\n        def rgb(red: int, green: int, blue: int):\n            \"\"\"\n            Get a 16-bit background colour.\n            :param red: int, The red value (0-255).\n            :param green: int, The green value (0-255).\n            :param blue: int, The blue value (0-255).\n            :raises ColourError: On type error or value error.\n            :return: str, The 16-bit colour string.\n            \"\"\"\n            # Type check:\n            if not isinstance(red, int):\n                raise ColourError(2)\n            if not isinstance(green, int):\n                raise ColourError(3)\n            if not isinstance(blue, int):\n                raise ColourError(4)\n            # Value check:\n            if red < 0 or red > 255:\n                raise ColourError(7)\n            if green < 0 or green > 255:\n                raise ColourError(8)\n            if blue < 0 or blue > 255:\n                raise ColourError(9)\n            return '\\033[48;2;%i;%i;%im' % (red, green, blue)\n\n\nif __name__ == '__main__':\n    # 4 bit colour:\n    print(\"4 bit colour test:\")\n    print(Colours.fg.red, \"This\", \"is\", Colours.bg.dark_grey, \"a\", Colours.strikeThrough, \"test\", Colours.reset, \"\\n\")\n    # 8 bit colour:\n    print(\"8 bit colour test:\")\n    for i in range(256):\n        colourString = Colours.fg.colour(i) + '%3i' % i\n        if i == 0:\n            colourString += Colours.bg.white\n        else:\n            colourString += Colours.bg.black\n        print(colourString, end='')\n        print(Colours.reset + ' ', end='')\n        if not i % 20:\n            print()\n    print(Colours.reset)\n    # 16 bit colour:\n    print(\"16 bit colour test:\")\n    for i in range(0, 256):\n        print(Colours.fg.rgb(i, 0, 0) + '\\u2588', end='')\n    print()\n    for i in range(0, 256):\n        print(Colours.fg.rgb(0, i, 0) + '\\u2588', end='')\n    print()\n    for i in range(0, 256):\n        print(Colours.fg.rgb(0, 0, i) + '\\u2588', end='')\n    print(Colours.reset)\n\n    # Test colour validations:\n    fourBitControlTest = Colours.reverse\n    fourBitFgTest = Colours.fg.black\n    fourBitBgTest = Colours.bg.black\n    eightBitFgTest = Colours.fg.colour(0)\n    eightBitBgTest = Colours.bg.colour(0)\n    sixteenBitFgTest = Colours.fg.rgb(0, 0, 0)\n    sixteenBitBgTest = Colours.bg.rgb(255, 255, 255)\n    badColour = \"bad_colour\"\n    print(\"Four bit validations:\")\n    print(\"CONTROL is_control:\")\n    print(Colours.is_control(fourBitControlTest))\n    print(\"CONTROL is_four_bit:\")\n    print(Colours.is_four_bit(fourBitControlTest))\n    print()\n    print(\"FG is_colour:\")\n    print(Colours.is_colour(fourBitFgTest))\n    print(\"FG is_foreground:\")\n    print(Colours.is_foreground(fourBitFgTest))\n    print(\"FG is_background:\")\n    print(Colours.is_background(fourBitFgTest))\n    print(\"FG is_control:\")\n    print(Colours.is_control(fourBitFgTest))\n    print(\"FG is 8 bit:\")\n    print(Colours.is_eight_bit(fourBitFgTest))\n    print()\n    print(\"BG is_colour:\")\n    print(Colours.is_colour(fourBitBgTest))\n    print(\"BG is_foreground:\")\n    print(Colours.is_foreground(fourBitBgTest))\n    print(\"BG is_background:\")\n    print(Colours.is_background(fourBitBgTest))\n    print(\"BG is 16 bit:\")\n    print(Colours.is_sixteen_bit(fourBitBgTest))\n    print(\"BG is 4 bit:\")\n    print(Colours.is_four_bit(fourBitBgTest))\n    print()\n\n    print(\"Eight bit Tests:\")\n    print(\"FG is_colour:\")\n    print(Colours.is_colour(eightBitFgTest))\n    print(\"FG is_foreground:\")\n    print(Colours.is_foreground(eightBitFgTest))\n    print(\"FG is background:\")\n    print(Colours.is_background(eightBitFgTest))\n    print(\"FG is 4 bit:\")\n    print(Colours.is_four_bit(eightBitFgTest))\n    print()\n    print(\"BG is_colour:\")\n    print(Colours.is_colour(eightBitBgTest))\n    print(\"BG is_foreground:\")\n    print(Colours.is_foreground(eightBitBgTest))\n    print(\"BG is_background:\")\n    print(Colours.is_background(eightBitBgTest))\n    print(\"BG is 16 bit:\")\n    print(Colours.is_sixteen_bit(eightBitBgTest))\n    print(\"BG is 8 bit:\")\n    print(Colours.is_eight_bit(eightBitBgTest))\n    print()\n\n    print(\"Sixteen bit tests:\")\n    print(\"FG is Colour:\")\n    print(Colours.is_colour(sixteenBitFgTest))\n    print(\"FG is_foreground:\")\n    print(Colours.is_foreground(sixteenBitFgTest))\n    print(\"FG is_background:\")\n    print(Colours.is_background(sixteenBitFgTest))\n    print(\"FG is 4 Bit:\")\n    print(Colours.is_four_bit(sixteenBitFgTest))\n    print()\n    print(\"BG is_colour:\")\n    print(Colours.is_colour(sixteenBitBgTest))\n    print(\"BG is_foreground:\")\n    print(Colours.is_foreground(sixteenBitBgTest))\n    print(\"BG is_background:\")\n    print(Colours.is_background(sixteenBitBgTest))\n    print(\"BG is 8 bit:\")\n    print(Colours.is_eight_bit(sixteenBitBgTest))\n    print(\"BG is 16 bit:\")\n    print(Colours.is_sixteen_bit(sixteenBitBgTest))\n\n    exit(0)\n","repo_name":"pnearing/PapertrailLogDownloader","sub_path":"colours.py","file_name":"colours.py","file_ext":"py","file_size_in_byte":21920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"31961836950","text":"import module\n# unique to module\nimport dns.resolver\n\nclass Module(module.Module):\n\n    def __init__(self, params):\n        module.Module.__init__(self, params, query='SELECT DISTINCT host FROM hosts WHERE host IS NOT NULL AND ip_address IS NULL')\n        self.info = {\n            'Name': 'Hostname Resolver',\n            'Author': 'Tim Tomes (@LaNMaSteR53)',\n            'Description': 'Resolves the IP address for a host. Updates the \\'hosts\\' table with the results.',\n            'Comments': [\n                'Note: Nameserver must be in IP form.'\n            ]\n        }\n\n    def module_run(self, hosts):\n        q = self.get_resolver()\n        for host in hosts:\n            try:\n                answers = q.query(host)\n            except dns.resolver.NXDOMAIN:\n                self.verbose('%s => Unknown' % (host))\n            except dns.resolver.NoAnswer:\n                self.verbose('%s => No answer' % (host))\n            except (dns.resolver.NoNameservers, dns.resolver.Timeout):\n                self.verbose('%s => DNS Error' % (host))\n            else:\n                for i in range(0, len(answers)):\n                    if i == 0:\n                        self.query('UPDATE hosts SET ip_address=? WHERE host=?', (answers[i].address, host))\n                    else:\n                        data = {\n                            'host': self.to_unicode(host),\n                            'ip_address': self.to_unicode(answers[i].address)\n                        }\n                        self.insert('hosts', data, data.keys())\n                    self.output('%s => %s' % (host, answers[i].address))\n","repo_name":"badfish5150/recon-ng","sub_path":"modules/recon/hosts-hosts/resolve.py","file_name":"resolve.py","file_ext":"py","file_size_in_byte":1618,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"30527763160","text":"import random\nfrom enum import Enum\nfrom typing import Tuple\n\nfrom pyarcade.cards import Suits\nfrom pyarcade.abstract_game import AbstractGame\n\n\nclass Ranks(Enum):\n    \"\"\"\n    Enumerated Type for card ranks of War.\n    \"\"\"\n    TWO = 2\n    THREE = 3\n    FOUR = 4\n    FIVE = 5\n    SIX = 6\n    SEVEN = 7\n    EIGHT = 8\n    NINE = 9\n    TEN = 10\n    JACK = 11\n    QUEEN = 12\n    KING = 13\n    ACE = 14\n\n\nclass War(AbstractGame):\n\n    def __init__(self):\n        \"\"\"\n        Sets up player one and two's initial hands.\n        \"\"\"\n        AbstractGame.__init__(self)\n        self.player_one_hand = []\n        self.player_two_hand = []\n        self.last_turn_winner = 0  # Since there is no last turn winner.\n        self.new_game()\n\n    def play_turn(self, pile: [(Ranks, Suits)]) -> bool:\n        \"\"\"\n        Args:\n            pile: Cards to be given to winner of turn.\n\n        Returns:\n            True if the game is to be continued, False if there is a winner.\n        \"\"\"\n        # Check if there is a winner. Need check here also in case where after\n        # 3 cards flipped for war results in a player having 0 cards left.\n        if len(self.player_one_hand) == 0:\n            self.last_turn_winner = 2\n            self.update_current_history()\n            return False\n        elif len(self.player_two_hand) == 0:\n            self.last_turn_winner = 1\n            self.update_current_history()\n            return False\n\n        player_one_card = War.flip(self.player_one_hand)\n        player_two_card = War.flip(self.player_two_hand)\n        pile.extend([player_one_card, player_two_card])\n        evaluation = War.compare_to(player_one_card, player_two_card)\n\n        # Turn evaluation.\n        if evaluation != 0:\n            if evaluation > 0:\n                self.last_turn_winner = 1\n                self.player_one_hand.extend(pile)\n                self.update_current_history()\n            elif evaluation < 0:\n                self.last_turn_winner = 2\n                self.player_two_hand.extend(pile)\n                self.update_current_history()\n\n            # Check if there is a winner.\n            if len(self.player_one_hand) == 0:\n                return False\n            elif len(self.player_two_hand) == 0:\n                return False\n            else:\n                return True\n        else:\n            return self.war(pile)\n\n    def war(self, pile: [(Ranks, Suits)]) -> bool:\n        \"\"\"\n        Case where turn results in a war. Each player adds 3 additional cards\n        to the pile before playing a new turn.\n\n        Args:\n            pile: Cards to be given to winner of turn.\n\n        Returns:\n            Recursive call to play_turn with an updated pile.\n        \"\"\"\n        if len(self.player_one_hand) < 3:\n            self.last_turn_winner = 2\n            self.player_one_hand = []\n            return False\n        if len(self.player_two_hand) < 3:\n            self.last_turn_winner = 1\n            self.player_two_hand = []\n            return False\n\n        for count in range(0, 3):\n            # Check if there is a winner.\n            if len(self.player_one_hand) == 0:\n                self.last_turn_winner = 2\n                return False\n            elif len(self.player_two_hand) == 0:\n                self.last_turn_winner = 1\n                return False\n            else:\n                player_one_card = War.flip(self.player_one_hand)\n                player_two_card = War.flip(self.player_two_hand)\n                pile.extend([player_one_card, player_two_card])\n\n        return self.play_turn(pile)\n\n    def enter_user_turn(self, cmd: str) -> bool:\n        \"\"\"\n        Args:\n            cmd: \"Flip Card\"\n        Returns:\n            Outcome of turn. (False if there is a winner)\n        \"\"\"\n        turn_outcome = self.play_turn([])\n\n        if turn_outcome:\n            return True\n        else:\n            self.update_entire_history()\n            return False\n\n    def get_last_turn(self) -> Tuple[str, str, int, int, bool, int]:\n        \"\"\"\n        Returns:\n            A Tuple containing:\n            str: Player One's card.\n            str: Player Two's card.\n            int: Number of cards Player One has.\n            int: Number of cards Player Two has.\n            bool: Returns True if there is a winner, False otherwise.\n            int: 1 or 2 depending on who won last turn.\n        \"\"\"\n\n        if len(self.player_one_hand) == 0 or len(self.player_two_hand) == 0:\n            return \"\", \"\", len(self.player_one_hand), \\\n                   len(self.player_two_hand), True, self.last_turn_winner\n\n        player_one_card = War.to_str([self.player_one_hand[0]])\n        player_two_card = War.to_str([self.player_two_hand[0]])\n\n        return player_one_card, player_two_card, len(self.player_one_hand),\\\n            len(self.player_two_hand), False, self.last_turn_winner\n\n    def reset_game(self):\n        self.new_game()\n        self.current_history = []\n\n    def clear_game(self):\n        self.reset_game()\n        self.entire_history = []\n\n    def new_game(self):\n        \"\"\"\n        Deals new hands to players.\n        \"\"\"\n        deck = War.generate_new_deck()\n        shuffled = War.shuffle_deck(deck)\n        self.player_one_hand, self.player_two_hand = War.deal_hands(shuffled)\n        self.last_turn_winner = 0  # Since there is no last turn winner.\n\n    @staticmethod\n    def generate_new_deck() -> [(Ranks, Suits)]:\n        \"\"\"\n        Returns:\n            New deck of cards.\n        \"\"\"\n        deck = []\n        for rank in Ranks:\n            for suit in Suits:\n                deck.append((rank, suit))\n        return deck\n\n    @staticmethod\n    def shuffle_deck(deck: [(Ranks, Suits)]) -> [(Ranks, Suits)]:\n        \"\"\"\n        Args:\n            deck: List of cards to be shuffled.\n\n        Returns:\n            Shuffled deck.\n        \"\"\"\n        for idx in range((len(deck) - 1), -1, -1):\n            card = deck[idx]\n            random_position = random.randint(0, idx)\n            deck[idx] = deck[random_position]\n            deck[random_position] = card\n\n        return deck\n\n    @staticmethod\n    def deal_hands(deck: [(Ranks, Suits)]) \\\n            -> ([(Ranks, Suits)], [(Ranks, Suits)]):\n        \"\"\"\n        Args:\n            deck: Deck which cards will be dealt.\n\n        Returns:\n            Two lists of 26 cards.\n        \"\"\"\n        return deck[:26], deck[26:]\n\n    @staticmethod\n    def to_str(deck: [(Ranks, Suits)]) -> str:\n        \"\"\"\n        Args:\n            deck: List of cards.\n\n        Returns:\n            String containing all ranks in hand.\n        \"\"\"\n        hand = \"\"\n        for card in deck:\n            hand += card[0].name + \", \"\n        return hand[:-2]\n\n    @staticmethod\n    def flip(deck: [(Ranks, Suits)]):\n        \"\"\"\n        Args:\n            deck: Deck of cards.\n\n        Returns:\n            Top card of the deck.\n        \"\"\"\n        return deck.pop(0)\n\n    @staticmethod\n    def compare_to(card_one: (Ranks, Suits), card_two: (Ranks, Suits)) -> int:\n        \"\"\"\n        Args:\n            card_one: Player one card.\n            card_two: Player two card.\n\n        Returns:\n            Positive number if player one wins turn, negative if player two\n            wins turn, zero if there is a war.\n        \"\"\"\n        if card_one[0].value > card_two[0].value:\n            return 1\n        elif card_one[0].value < card_two[0].value:\n            return -1\n        else:\n            return 0\n\n    @staticmethod\n    def get_regex_pattern() -> str:\n        \"\"\"\n        Gets regex pattern for War.\n        \n        Returns:\n            Pattern match for game.\n        \"\"\"\n        return r\"^\\s*(flip card)\\s*$\"\n\n    def update_current_history(self):\n        \"\"\"\n        Adds each player's hand and previous turn winner to current history.\n        \"\"\"\n        player_one = War.to_str(self.player_one_hand)\n        player_two = War.to_str(self.player_two_hand)\n        last_turn_winner = self.last_turn_winner\n        self.current_history.append((player_one, player_two, last_turn_winner))\n\n    def update_entire_history(self):\n        \"\"\"\n        Adds winner of game, and current history, to entire history.\n        \"\"\"\n        winner = self.last_turn_winner\n        self.entire_history.append((winner, self.current_history))\n","repo_name":"brandonkwintner/pyarcade","sub_path":"frontend/pyarcade/war.py","file_name":"war.py","file_ext":"py","file_size_in_byte":8240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"47565335365","text":"from tkinter import *\r\nfrom src.GUI import windows\r\nfrom src.config import Config\r\n\r\n# объект Config должен быть создан только 1 раз !\r\ncfg = Config()\r\n\r\napp = Tk()\r\nwindows.KeyCheckerWindow(app, cfg.key_checker)\r\napp.mainloop()\r\n\r\napp = Tk()\r\nwindows.MainWindow(app, cfg.main)\r\napp.mainloop()\r\n","repo_name":"Groooof/apps_auto_sender","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"39891752599","text":"t = int(input())\nfor i in range(t):\n    num = int(input())\n    record = []\n    for i in range(num):\n        record.append(i+1)\n    pos = 0\n    for j in range(num-1):\n        del record[(pos+1)%len(record)]\n        if pos<len(record):\n            pos=(pos+1)%len(record)\n        else:\n            pos=0\n    print(record[0])","repo_name":"AdamZhouSE/pythonHomework","sub_path":"Code/CodeRecords/2197/60661/235398.py","file_name":"235398.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"80"}
+{"seq_id":"13744753536","text":"import os\nimport shlex\n\nfrom .util.cli import *\nfrom .constants import *\n\nfrom .file.rec.data_record import DataRecord\n\nfrom .file.binary_file import SearchError\nfrom .file.serial_file import SerialFile\nfrom .file.sequential_file import SequentialFile\nfrom .file.index_file import IndexFile\n\n\nclass App:\n    def __init__(self, init_data_path: str) -> None:\n        self.rec = DataRecord()\n\n        self.ser = SerialFile(ser_path, self.rec, F)\n        self.seq = SequentialFile(seq_path, self.rec, F)\n        self.active = IndexFile(active_path, self.rec, F)\n\n        self.__init_data(init_data_path)\n\n\n    def __init_data(self, init_data_path: str) -> None:\n        with open(init_data_path, \"r\") as f:\n            lines = f.readlines()\n            for line in lines:\n                cols = shlex.split(line)\n                self.seq.insert_record(\n                    self.rec.get_rec(int(cols[0]), cols[1],  \n                                    cols[2],  int(cols[3]),  float(cols[4]), 0))\n        self.generate_active()\n\n\n    def make_new_db(self) -> None:\n        def valid_path(path):\n            try: open(path, \"wb\").close()\n            except: return False\n            return True\n\n        self.__new_db_path(\n            valid=valid_path,\n            err=\"Putanja nije validna.\")\n\n\n    def change_db(self) -> None:\n        self.__new_db_path(\n            valid=lambda path : os.path.isfile(path),\n            err=\"Datoteka ne postoji ili putanja nije validna.\")\n        \n\n    def __new_db_path(self, valid, err: str) -> None:\n        new_path = input(\"Putanja datoteke: \").strip()\n        new_path = os.path.abspath(new_path)\n\n        if not valid(new_path):\n            Cli.print_warning(err)\n            return\n\n        self.active = IndexFile(new_path, self.rec, F)\n\n\n# --------------------------------------\n    def print_active(self) -> None:\n        print()\n        self.active.print_file()\n        input(\"\\nUnesite karakter da nastavite \")\n    \n    def print_serial(self):\n        print()\n        self.ser.print_file()\n        input(\"\\nUnesite karakter da nastavite \")\n\n    def print_sequential(self):\n        print()\n        self.seq.print_file()\n        input(\"\\nUnesite karakter da nastavite \")\n# --------------------------------------\n\n\n    def add_ser(self) -> None:\n        print()\n        try:\n            rec = self.__get_record()\n            self.ser.insert_record(rec)\n\n            input(\"\\nUnesite karakter da nastavite \")\n        except InputError as e1:\n            Cli.print_warning(e1)\n        except SearchError:\n            Cli.print_warning(\"Podaci sa unetim evidencionim brojem već postoje.\")\n\n\n\n    def generate_sequential(self) -> None:\n        self.seq.reformat_file()\n        records = []\n        with open(self.ser.path, \"rb\") as f:\n            while True:\n                block = self.ser.read_block(f)\n                if not block:\n                    break\n\n                for rec in block:\n                    records.append(rec)\n\n        records = sorted(records, key=lambda d:d[\"id\"])\n        for rec in records:\n            self.seq.insert_record(rec)\n        self.ser.reformat_file()\n\n    \n    def generate_active(self) -> None:\n        self.active.reformat_file()\n        self.active.init_file(self.seq)\n\n\n    def find_active(self) -> None:\n        print()\n        try:\n            id = Cli.input_digit(\"Evidencioni broj za pretragu: \")\n            found = self.active.find_by_id(id)\n            if found:\n                i, j, rec = found\n                if j == 0:\n                    print(\"\\nzona prekoračenja\", rec)\n                else:\n                    print(f\"\\nblok {i}, slog {j}\", rec)\n\n                input(\"\\nUnesite karakter da nastavite \")\n            else:\n                Cli.print_warning(\"Podaci sa unetim evidencionim brojem ne postoje.\")\n        except InputError as e:\n            Cli.print_warning(e)\n\n\n    def add_active(self) -> None:\n        print()\n        try:\n            rec = self.__get_record()\n            self.active.insert_record(rec)\n\n            input(\"\\nUnesite karakter da nastavite \")\n        except InputError as e1:\n            Cli.print_warning(e1)\n        except SearchError:\n            Cli.print_warning(\"Podaci sa unetim evidencionim brojem već postoje.\")\n\n\n    def update_active(self) -> None:\n        print()\n        try:\n            id = Cli.input_digit(\"Evidencioni broj: \", max_len=9)\n            date = Cli.input_datetime(\"Datum i vreme odstrela (dd/MM/YYYY hh:mm): \")\n            found = self.active.find_by_id(id)\n            if found:\n                _, _, rec = found\n                rec[\"date\"] = date\n                self.active.update_record(rec)\n\n                input(\"\\nUnesite karakter da nastavite \")\n            else:\n                Cli.print_warning(\"Podaci sa unetim evidencionim brojem ne postoje.\")\n        except InputError as e:\n            Cli.print_warning(e)\n\n\n    def remove_active(self) -> None:\n        print()\n        try:\n            id = Cli.input_digit(\"Evidencioni broj za brisanje: \")\n            self.active.delete_by_id(id)\n\n            input(\"\\nUnesite karakter da nastavite \")\n        except InputError as e:\n            Cli.print_warning(e)\n        except SearchError as e:\n            Cli.print_warning(\"Podaci sa unetim evidencionim brojem ne postoje.\")\n\n\n    # dobavljanje sloga iz konzole\n    def __get_record(self) -> dict:\n        new_id = Cli.input_digit(\"Evidencioni broj: \", max_len=9)\n        type = Cli.input_str(\"Vrsta divljači: \", 40)\n        date = Cli.input_datetime(\"Datum i vreme odstrela (dd/MM/YYYY hh:mm): \")\n        ammunition = Cli.input_digit(\"Oznaka municije: \", max_len=7)\n        weight = Cli.input_float(\"Težina (kg): \", pos=True)\n\n        return self.rec.get_rec(new_id, type, date, ammunition, weight, 0)\n","repo_name":"DimitrijeG/fax","sub_path":"S3/index-sequential-file-{data-organization}/app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"34947893054","text":"import cv2 as cv\nimport numpy as np\n\n# (1) OpenCV 버전 확인\n# print(\"Hello OpenCV\", cv.__version__)\n\n# (2) OpenCV 이미지 불러오기\n# img = cv.imread('soodal.jpeg')\n#\n# if img is None:\n#     print('Image load failed!')\n#     exit()\n# cv.imshow('image', img)\n# cv.waitKey()\n\n# (3) 이미지 타입 확인하기\n\n# def func1():\n#     img = cv.imread('soodal.jpeg',cv.IMREAD_GRAYSCALE)\n#\n#     if img is None:\n#         print('Image load failed')\n#         return\n#\n#     print('type(img):', type(img))\n#     print('img.shape:',img.shape)\n#\n#     if len(img.shape) == 2:\n#         print('img is a grayscale image')\n#     elif len(img.shape) == 3:\n#         print('img is a truecolor image')\n#\n#     cv.imshow('img',img)\n#     cv.waitKey()\n#     cv.destroyAllWindows()\n#\n# func1()\n\n# (4) 행렬의 복사 - copy() 연산 사용\n\n# def fun2():\n#     img = cv.imread('soodal.jpeg')\n#\n#     img1 = img\n#     img2 = img.copy()\n#\n#     img[:, :] = (0, 255, 255) # yellow color\n#\n#     cv.imshow('img', img)\n#     cv.imshow('img1', img1)\n#     cv.imshow('img2', img2)\n#     cv.waitKey()\n#     cv.destroyAllWindows()\n#\n# fun2()\n\n# (5) 부분 행렬 추출\n\n# def fun3():\n#     img = cv.imread('soodal.jpeg',cv.IMREAD_GRAYSCALE)\n#\n#     img1 = img[200:400, 200:400]\n#     img2 = img[200:400, 200:400].copy()\n#\n#     img1 += 20\n#\n#     cv.imshow('img',img)\n#     cv.imshow('img1', img1)\n#     cv.imshow('img2', img2)\n#     cv.waitKey()\n#     cv.destroyAllWindows()\n#\n# fun3()\n\n# (6) 부분행렬 추출 후 반전\ndef fun4():\n    img = cv.imread('../chapter04_02/soodal.jpeg')\n\n    img1 = img[100:400, 100:400]\n    img2 = img[100:400, 100:400].copy()\n\n    out = img1.copy()\n    out = 255 - out\n\n    img1 += 20\n\n    cv.imshow('img',img)\n    # cv.imshow('img1', img1)\n    cv.imshow('img2', img2)\n    cv.imshow('img1',out)\n    cv.waitKey()\n    cv.destroyAllWindows()\n\nfun4()\n\n","repo_name":"ohjungmin317/2022_Computer_Vision","sub_path":"chapter03/chapter_03.py","file_name":"chapter_03.py","file_ext":"py","file_size_in_byte":1869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"1107364786","text":"import os\nimport shutil\nimport time\nimport unittest as ut\n\n# Needed for log config.\nimport configuration as cfg\nimport fixtures\n\n\nclass MinerFTSFileOperationsTest(fixtures.TrackerMinerFTSTest):\n    \"\"\"\n    Move, update, delete the files and check the text indexes are updated accordingly.\n    \"\"\"\n\n    def setUp(self):\n        fixtures.TrackerMinerFTSTest.setUp(self)\n\n        no_monitored_dir = self.path(\"test-no-monitored\")\n        os.makedirs(no_monitored_dir, exist_ok=True)\n\n    def test_01_removal_of_file(self):\n        \"\"\"\n        When removing the file, its text contents disappear from the index\n        \"\"\"\n        TEXT = \"automobile is red and big and whatnot\"\n        self.basic_test(TEXT, \"automobile\")\n\n        id = self._query_id(self.uri(self.testfile))\n        with self.tracker.await_delete(\n            fixtures.DOCUMENTS_GRAPH, id, timeout=cfg.AWAIT_TIMEOUT\n        ):\n            os.remove(self.path(self.testfile))\n\n        results = self.search_word(\"automobile\")\n        self.assertEqual(len(results), 0)\n\n    def test_02_empty_the_file(self):\n        \"\"\"\n        Emptying the file, the indexed words are also removed\n        \"\"\"\n        TEXT = \"automobile is red and big and whatnot\"\n        self.basic_test(TEXT, \"automobile\")\n\n        self.set_text(\"\")\n        results = self.search_word(\"automobile\")\n        self.assertEqual(len(results), 0)\n\n    def test_03_update_the_file(self):\n        \"\"\"\n        Changing the contents of the file, updates the index\n        \"\"\"\n        TEXT = \"automobile is red and big and whatnot\"\n        self.basic_test(TEXT, \"automobile\")\n\n        self.set_text(\"airplane is blue and small and wonderful\")\n\n        results = self.search_word(\"automobile\")\n        self.assertEqual(len(results), 0)\n\n        results = self.search_word(\"airplane\")\n        self.assertEqual(len(results), 1)\n\n    # Skip the test_text_13... feel, feet, fee in three diff files and search feet\n\n    def __recreate_file(self, filename, content):\n        if os.path.exists(filename):\n            os.remove(filename)\n\n        f = open(filename, \"w\")\n        f.write(content)\n        f.close()\n\n    def test_04_on_unmonitored_file(self):\n        \"\"\"\n        Set text in an unmonitored file. There should be no results.\n        \"\"\"\n        TEXT = \"automobile is red\"\n\n        TEST_15_FILE = \"test-no-monitored/fts-indexing-test-15.txt\"\n        self.__recreate_file(self.path(TEST_15_FILE), TEXT)\n\n        results = self.search_word(\"automobile\")\n        self.assertEqual(len(results), 0)\n\n        os.remove(self.path(TEST_15_FILE))\n\n    def test_05_move_file_unmonitored_monitored(self):\n        \"\"\"\n        Move file from unmonitored location to monitored location and index should be updated\n        \"\"\"\n\n        TEXT = \"airplane is beautiful\"\n        TEST_16_SOURCE = \"test-no-monitored/fts-indexing-text-16.txt\"\n        TEST_16_DEST = \"test-monitored/fts-indexing-text-16.txt\"\n\n        self.__recreate_file(self.path(TEST_16_SOURCE), TEXT)\n        # the file is supposed to be ignored by tracker, so there is no notification..\n        time.sleep(2)\n\n        results = self.search_word(\"airplane\")\n        self.assertEqual(len(results), 0)\n\n        with self.await_document_inserted(TEST_16_DEST, content=TEXT):\n            shutil.copyfile(self.path(TEST_16_SOURCE), self.path(TEST_16_DEST))\n\n        results = self.search_word(\"airplane\")\n        self.assertEqual(len(results), 1)\n\n        os.remove(self.path(TEST_16_SOURCE))\n        os.remove(self.path(TEST_16_DEST))\n\n    # skip test for a file in a hidden directory\n\n\nif __name__ == \"__main__\":\n    fixtures.tracker_test_main()\n","repo_name":"GNOME/tracker-miners","sub_path":"tests/functional-tests/test_fts_file_operations.py","file_name":"test_fts_file_operations.py","file_ext":"py","file_size_in_byte":3617,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"80"}
+{"seq_id":"17122137215","text":"import re\nimport os\n\ndir_path = os.path.dirname(os.path.realpath(__file__))\ndir_path = dir_path.replace('\\\\', '/')\n\ntxtfilepath = str(dir_path) + '/testspeed.txt'\ncsvfilepath = str(dir_path) + '/testspeed.csv'\n\npingregex = re.compile(\".*: (\\d*\\.\\d*) ms.*\")\ndownregex = re.compile(\".*Download: (\\d*\\.\\d*) Mbit/s.*\")\nupregex = re.compile(\".*Upload: (\\d*\\.\\d*) Mbit/s\")\ncsvfile = open(csvfilepath, \"w\")\ncsvfile.write(\"Date Time,Ping (ms),Down (Mbps),Up (Mbps),,\\n\")\n\nwith open(txtfilepath) as f:\n    lines = f.readlines()\n\ndate, time, ping, down, up = \"\", \"\", \"\", \"\", \"\"\nfor line in lines:\n    if re.search(\".*\\d{2}/\\d{2}/\\d{4}.*\", line):\n        date = line[4:14]\n    if re.search(\"(\\d{1,2}:){2}\\d{2}\\.\\d{1,9}\", line):\n        time = line[:8]\n    if re.search(\"Hosted by\", line):\n        ping = pingregex.match(line).group(1)\n    if re.search(\"Download:\", line):\n        down = downregex.match(line).group(1)\n    if re.search(\"Upload:\", line):\n        up = upregex.match(line).group(1)\n        csvfile.write(\"%s %s,%s,%s,%s,,\\n\" % (date, time, ping, down, up))\n\ncsvfile.close()\n\n\n","repo_name":"ForgeDH/speedTracker","sub_path":"SpeedTracker/conversion.py","file_name":"conversion.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"17636067191","text":"from dataclasses import dataclass\nfrom collections import namedtuple\nPoint = namedtuple(\"Point\", [\"x\", \"y\"])\n\n@dataclass\nclass Map:\n    min: Point\n    max: Point\n    elevations: dict[Point, int]\n\ndef main():\n    with open(\"input.txt\", \"r\") as f:\n        lines = [line.rstrip() for line in f.readlines()]\n    \n    S: Point = None\n    E: Point = None\n\n    elevations = {}\n    for y, line in enumerate(lines):\n        for x, char in enumerate(line):\n            if char == \"S\":\n                S = Point(x, y)\n                char = \"a\"\n            elif char == \"E\":\n                E = Point(x, y)\n                char = \"z\"\n            elevations[Point(x, y)] = ord(char) - ord(\"a\")\n    \n    map = Map(min(elevations.keys()), max(elevations.keys()), elevations)\n    \n    print(dijkstra(map, S, E))\n\ndef dijkstra(map: Map, S: Point, E: Point):\n    unvisited = set(map.elevations.keys())\n    tentative_distances = dict()\n\n    for node in map.elevations.keys():\n        tentative_distances[node] = float(\"inf\")\n    tentative_distances[S] = 0\n\n    current = S\n    while unvisited:\n        for neighbor in possible_moves(map, current):\n            if neighbor in unvisited:\n                new_distance = tentative_distances[current] + 1\n                tentative_distances[neighbor] = min(new_distance, tentative_distances[neighbor])\n        unvisited.remove(current)\n        if E not in unvisited:\n            return tentative_distances[E]\n        \n        current = find_next(unvisited, tentative_distances)\n    \n    raise Exception(\"?\")\n\ndef find_next(unvisited, tentative_distances):\n    smallest_distance = float(\"inf\")\n    next = None\n    for node in unvisited:\n        if tentative_distances[node] < smallest_distance:\n            next = node\n            smallest_distance = tentative_distances[node]\n    return next\n\ndef possible_moves(map: Map, cur: Point) -> list[Point]:\n    up = Point(cur.x, cur.y - 1)\n    down = Point(cur.x, cur.y + 1)\n    left = Point(cur.x - 1, cur.y)\n    right = Point(cur.x + 1, cur.y)\n    return [target for target in [up, down, left, right] if possible_move(map, cur, target)]\n\ndef possible_move(map: Map, cur: Point, target: Point):\n    if target.x > map.max.x or target.x < map.min.x \\\n        or target.y > map.max.y or target.y < map.min.y:\n        return False\n    \n    return map.elevations[target] <= map.elevations[cur] + 1\n\nif __name__ == \"__main__\":\n    import cProfile\n    cProfile.run(\"main()\")\n    #main()\n","repo_name":"spkl/aoc2022","sub_path":"Day12/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":2451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"37826911198","text":"import json\nimport pickle\nimport random\n\nimport nltk\nimport numpy\nfrom nltk.stem import LancasterStemmer\nfrom tensorflow_core.python.keras.layers import Dense\nfrom tensorflow_core.python.keras.models import Sequential\nfrom tensorflow_core.python.keras.models import model_from_yaml\n\nfrom EventMangerAPI.models import UserQuestions\n\nnltk.download('punkt')\n\nstemmer = LancasterStemmer()\n\nwith open(\"EventMangerAPI/intents.json\") as file:\n    data = json.load(file)\n\ntry:\n    with open(\"EventMangerAPI/chatbodmodel.pickle\", \"rb\") as f:\n        words, labels, training, output = pickle.load(f)\nexcept:\n    words = []\n    labels = []\n    docs_x = []\n    docs_y = []\n\n    for intent in data[\"intents\"]:\n        for pattern in intent[\"patterns\"]:\n            wrds = nltk.word_tokenize(pattern)\n            words.extend(wrds)\n            docs_x.append(wrds)\n            docs_y.append(intent[\"tag\"])\n\n        if intent[\"tag\"] not in labels:\n            labels.append(intent[\"tag\"])\n\n    words = [stemmer.stem(w.lower()) for w in words if w != \"?\"]\n    words = sorted(list(set(words)))\n\n    labels = sorted(labels)\n\n    training = []\n    output = []\n\n    out_empty = [0 for _ in range(len(labels))]\n\n    for x, doc in enumerate(docs_x):\n        bag = []\n\n        wrds = [stemmer.stem(w.lower()) for w in doc]\n\n        for w in words:\n            if w in wrds:\n                bag.append(1)\n            else:\n                bag.append(0)\n\n        output_row = out_empty[:]\n        output_row[labels.index(docs_y[x])] = 1\n\n        training.append(bag)\n        output.append(output_row)\n\n    training = numpy.array(training)\n    output = numpy.array(output)\n\n    with open(\"EventMangerAPI/chatbodmodel.pickle\", \"wb\") as f:\n        pickle.dump((words, labels, training, output), f)\n\ntry:\n    # load YAML and create model\n    yaml_file = open('EventMangerAPI/chatbodmodel.yaml', 'r')\n    loaded_model_yaml = yaml_file.read()\n    yaml_file.close()\n    myChatModel = model_from_yaml(loaded_model_yaml)\n    # # load weights into new model\n    myChatModel.load_weights(\"EventMangerAPI/chatbodmodel.h5\")\n    print(\"Loaded model from disk\")\n\n\nexcept:\n    # Make the neural network\n    myChatModel = Sequential()\n    myChatModel.add(Dense(8, input_shape=[len(words)], activation='relu'))\n    myChatModel.add(Dense(len(labels), activation='softmax'))\n\n    # optimize the model\n    myChatModel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n    # train the model\n    myChatModel.fit(training, output, epochs=1000, batch_size=8)\n\n    # serialize model to YAML\n    model_yaml = myChatModel.to_yaml()\n    with open(\"EventMangerAPI/chatbodmodel.yaml\", \"w\") as yaml_file:\n        yaml_file.write(model_yaml)\n    # serialize weights to HDF5\n    myChatModel.save_weights(\"EventMangerAPI/chatbodmodel.h5\")\n    print(\"Saved model to disk\")\n\n\ndef bag_of_words(s, words):\n    bag = [0 for _ in range(len(words))]\n\n    s_words = nltk.word_tokenize(s)\n    s_words = [stemmer.stem(word.lower()) for word in s_words]\n\n    for se in s_words:\n        for i, w in enumerate(words):\n            if w == se:\n                bag[i] = 1\n\n    return numpy.array(bag)\n\n\ndef chat(inp):\n    responses = \"I didn't get that, try again!\"\n\n    currentText = bag_of_words(inp, words)\n    currentTextArray = [currentText]\n    numpyCurrentText = numpy.array(currentTextArray)\n    results = myChatModel.predict(numpyCurrentText[0:1])\n    results_index = numpy.argmax(results)\n    tag = labels[results_index]\n\n    if numpy.all((numpyCurrentText == 0)):\n        uq = UserQuestions(text=inp)\n        uq.save()\n        return \"I didn't get that, try again\"\n\n    if results[0][results_index] > 0.7:\n        for tg in data[\"intents\"]:\n            if tg['tag'] == tag:\n                responses = tg['responses']\n\n        return random.choice(responses)\n    else:\n        uq = UserQuestions(text=inp)\n        uq.save()\n        return \"I didn't get that, try again!\"\n","repo_name":"SandeepaFernando/EventManagerWebAPI","sub_path":"EventMangerAPI/ChatBotAPI.py","file_name":"ChatBotAPI.py","file_ext":"py","file_size_in_byte":3937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"39592931230","text":"# 작년에 소수나라에 다녀온 하나는, 올해는 문자열나라로 관광을 가려고 한다. 문자열나라에서는 특이하게 알파벳 대문자로 구성된 문자열을 화폐로 사용한다.\n# 문자열나라에서 'A'는 1의 가치, 'B'는 2의 가치, ..., 'Z'는 26의 가치를 가지고 있으며, 이 알파벳들을 붙여 화폐로 쓰일 문자열을 만든다. 예를 들어, \"HOnGIK\"의 가치는 8 + 15 + 14 + 7 + 9 + 11 = 64가 된다.\n# 소수나라에서 특이한 화폐 때문에 큰 스트레스를 받았던 하나는, 이번에는 정확한 소비 계획을 세워 미리 문자열 화폐로 돈을 환전해가려고 한다. 하나가 가져갈 문자열은 딱 하나이며, 길이는 n이고, 가치는 x여야 한다. 그리고 물론 알파벳 대문자로만 이루어져 있어야 한다.\n# 그런데 환전소에서는 사전 순으로 앞서는 문자열을 우선적으로 환전해준다고 한다! 여행 준비에 정신이 없는 하나를 위해, 조건을 만족하면서 사전 순으로 가장 앞서는 문자열 구해주자.\n\nn, x = map(int, input().split())\n\nif n * 26 < x or n > x:\n    print('!')\nelse:\n    string_list = ['A'] * n\n    x -= n\n    i = n - 1\n\n    while x > 0:\n        if x >= 25:\n            string_list[i] = 'Z'\n            i -= 1\n            x -= 25\n        else:\n            string_list[i] = chr(x + 65)\n            break\n    ans = ''\n    for i in string_list:\n        ans+=i\n    # print(''.join(string_list))\n    print(ans)","repo_name":"ksunbum97/algorithm_test","sub_path":"백준/17828.py","file_name":"17828.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"72594396758","text":"import json\nfrom socket import socket, AF_INET, SOCK_STREAM\nfrom typing import Tuple, Dict, List, Union, Any\n\n\nclass ClientController:\n    def __init__(self, host: str, port: int, name: str) -> None:\n        \"\"\"\n        Initialize client controller\n        :param host: Host to connect\n        :param port: port number\n        :param name: name of client\n        \"\"\"\n        self.server: Union[socket, None] = None\n        self.host: str = host\n        self.port: int = port\n        self.name: str = name\n\n        self.is_terminated: bool = False\n\n    def connect(self) -> str:\n        \"\"\"\n        Connect to server\n        :return: True if connected, False otherwise\n        \"\"\"\n        try:\n            self.server = socket(AF_INET, SOCK_STREAM)\n            self.server.connect((self.host, self.port))\n\n            # send name to server\n            self.server.send(self.name.encode())\n\n            # receive message from server\n            message = self.server.recv(1024).decode()\n\n            # if message is connected then return\n            return message\n\n        except Exception as e:\n            if type(e) == ConnectionRefusedError:  # if connection refused\n                return 'Connection refused'\n            elif type(e) == TimeoutError:          # if connection timeout\n                return 'Connection timeout'\n            else:\n                return 'Unknown error'\n\n    def close(self) -> None:\n        \"\"\"\n        Close server\n        :return: None\n        \"\"\"\n        self.server.close()\n\n    def receive_message(self) -> str:\n        \"\"\"\n        Receive message from server\n        :return: message\n        \"\"\"\n        message = ''\n        try:\n            while message == '':\n                message = self.server.recv(1024).decode()\n        except:\n            message = 'Connection closed'\n        return message\n\n    def send_message(self, message: str) -> None:\n        \"\"\"\n        Send message to server\n        :param message: message\n        :return: None\n        \"\"\"\n        self.server.send(message.encode())\n\n    @property\n    def is_connected(self) -> bool:\n        \"\"\"\n        Check connection\n        :return: True if connected, False otherwise\n        \"\"\"\n        try:\n            self.send_message('')\n            return True\n        except Exception:\n            print('Disconnected')\n            return False\n","repo_name":"furkankyildirim/CS408-Computer-Networks","sub_path":"client/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":2356,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"4485760062","text":"# import standard libraries\nimport abc\nimport logging\nfrom typing import Any, Dict, List, Set\n# import local files\nfrom schemas.ExtractionMode import ExtractionMode\nfrom schemas.Schema import Schema\nfrom utils.Logger import Logger\n\nclass ExtractorSchema(Schema):\n    def __init__(self, name:str, all_elements:Dict[str, Any]):\n        self._enabled     : Set[ExtractionMode]\n        self._type_name   : str\n        self._description : str\n\n        if not isinstance(all_elements, dict):\n            all_elements = {}\n            Logger.Log(f\"For {name} Extractor config, all_elements was not a dict, defaulting to empty dict\", logging.WARN)\n\n        if \"type\" in all_elements.keys():\n            self._type_name = ExtractorSchema._parseType(all_elements['type'])\n        else:\n            self._type_name = name\n        if \"enabled\" in all_elements.keys():\n            self._enabled = ExtractorSchema._parseEnabled(all_elements['enabled'])\n        else:\n            self._enabled = {ExtractionMode.DETECTOR, ExtractionMode.SESSION, ExtractionMode.PLAYER, ExtractionMode.POPULATION}\n            Logger.Log(f\"{name} config does not have an 'enabled' element; defaulting to enabled=True\", logging.WARN)\n        if \"description\" in all_elements.keys():\n            self._description = ExtractorSchema._parseDescription(all_elements['description'])\n        else:\n            self._description = \"No Description\"\n            Logger.Log(f\"{name} config does not have an 'description' element; defaulting to description='{self._description}'\", logging.WARN)\n\n        _leftovers = { key : val for key,val in all_elements.items() if key not in {\"type\", \"enabled\", \"description\"} }\n        super().__init__(name=name, other_elements=_leftovers)\n\n    @property\n    def TypeName(self) -> str:\n        return self._type_name\n\n    @property\n    def Enabled(self) -> Set[ExtractionMode]:\n        return self._enabled\n\n    @property\n    def Description(self) -> str:\n        return self._description\n    \n    @staticmethod\n    def _parseType(extractor_type):\n        ret_val : str\n        if isinstance(extractor_type, str):\n            ret_val = extractor_type\n        else:\n            ret_val = str(extractor_type)\n            Logger.Log(f\"Extractor type was not a string, defaulting to str(type) == {ret_val}\", logging.WARN)\n        return ret_val\n\n    @staticmethod\n    def _parseEnabled(enabled):\n        ret_val : Set[ExtractionMode] = set()\n        if isinstance(enabled, bool):\n            if enabled:\n                ret_val = {ExtractionMode.DETECTOR, ExtractionMode.SESSION, ExtractionMode.PLAYER, ExtractionMode.POPULATION}\n            else:\n                ret_val = set()\n        elif isinstance(enabled, list):\n            for mode in enabled:\n                mode = str(mode)\n                if mode.upper() == \"DETECTOR\":\n                    ret_val.add(ExtractionMode.DETECTOR)\n                elif mode.upper() == \"SESSION\":\n                    ret_val.add(ExtractionMode.SESSION)\n                elif mode.upper() == \"PLAYER\":\n                    ret_val.add(ExtractionMode.PLAYER)\n                elif mode.upper() == \"POPULATION\":\n                    ret_val.add(ExtractionMode.POPULATION)\n                else:\n                    Logger.Log(f\"Found unrecognized element of 'enabled': {mode}\", logging.WARN)\n        else:\n            ret_val = {ExtractionMode.DETECTOR, ExtractionMode.SESSION, ExtractionMode.PLAYER, ExtractionMode.POPULATION}\n            Logger.Log(f\"'enabled' element has unrecognized type {type(enabled)}; defaulting to enable all modes\", logging.WARN)\n        return ret_val\n    \n    @staticmethod\n    def _parseDescription(description):\n        ret_val : str\n        if isinstance(description, str):\n            ret_val = description\n        else:\n            ret_val = str(description)\n            Logger.Log(f\"Extractor description was not a string, defaulting to str(description) == {ret_val}\", logging.WARN)\n        return ret_val\n","repo_name":"opengamedata/opengamedata-core","sub_path":"schemas/games/ExtractorSchema.py","file_name":"ExtractorSchema.py","file_ext":"py","file_size_in_byte":3963,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"85"}
+{"seq_id":"6113683500","text":"# -*- coding: utf-8 -*-\n\n'''\nCODE 설명\nPytorch 에서 custom 으로 생성한 데이터와 모델을 이용해서 실험을 돌림,\nCNN 모델을 생성해서 돌리는 것\nopencv-python == cv2\n'''\n\n#custom_mnist_model\n\nfrom torch.utils.data.dataset import Dataset\nimport torch\nfrom M_model import LeNet\nfrom tensorboardX import SummaryWriter\nimport torchvision.datasets as mdatset\nimport torchvision.transforms as transforms\ndef save_checkpoint(state, filename = \"checkpoint.pth.bar\"):\n\n    torch.save(state, filename)\n\n\ndef accuracy(output, target, topk=(1,)):\n    \"\"\"Computes the precision@k for the specified values of k\"\"\"\n    maxk = max(topk)\n    batch_size = target.size(0)\n\n    _, pred = output.topk(maxk, 1, True, True)\n\n    pred = pred.t()\n    correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n    res = []\n    for k in topk:\n        correct_k = correct[:k].view(-1).float().sum(0)\n        res.append(correct_k.mul_(100.0 / batch_size))\n\n    return res\nclass AverageMeter(object):\n\n    def __init__(self):\n        self.reset()\n\n    def reset(self):\n        self.val = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n\n    def update(self, val, n = 1):\n        self.val = val\n        self.sum += val * n # sum = sum + val * n\n        self.count += n\n        self.avg = self.sum / self.count\n\ndef train(my_dataset_loader,model,criterion,optimizer,epoch,writer):\n\n    model.train()\n\n    losses = AverageMeter()\n    top1 = AverageMeter()\n\n    for i, data in enumerate(my_dataset_loader, 0):\n        # Forward pass: Compute predicted y by passing x to the model\n\n        # fc 구조 이기 때문에 일렬로 쫙피는 작업이 필요하다.\n        images, label = data\n\n        images = torch.autograd.Variable(images)\n        label = torch.autograd.Variable(label)\n\n\n        # 그냥 images를 하면 에러가 난다. 데이터 shape이 일치하지 않아서\n        y_pred = model(images)\n\n        # Compute and print loss\n        loss = criterion(y_pred, label)\n\n        #print(epoch, loss.item())\n\n        # Zero gradients, perform a backward pass, and update the weights.\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n        output = y_pred.float()\n        loss = loss.float()\n\n       # print('output', output, type(output))\n\n        prec1 = accuracy(output.data, label)[0]\n\n        prec_2 = accuracy(output.data, label)\n\n        #print(\"prec1\", (prec1))\n        #print(\"item prec1\", prec1.item())\n\n        #print('loss.item', loss)\n        #print('real loss item ', loss.item())\n\n        losses.update(loss.item(), images.size(0))\n        top1.update(prec1.item(), images.size(0))\n\n    writer.add_scalar('Train/loss', losses.avg, epoch)\n    writer.add_scalar('Train/accuaracy', top1.avg, epoch)\n\n\n\ndef test(my_dataset_loader, model, criterion, epoch,test_writer):\n    losses = AverageMeter()\n    top1 = AverageMeter()\n    model.eval()\n    for i, data in enumerate(my_dataset_loader, 0):\n        # Forward pass: Compute predicted y by passing x to the model\n\n        # fc 구조 이기 때문에 일렬로 쫙피는 작업이 필요하다.\n        images, label = data\n\n        # 그냥 images를 하면 에러가 난다. 데이터 shape이 일치하지 않아서c\n        y_pred = model(images)\n\n        # Compute and print loss\n        loss = criterion(y_pred, label)\n\n        output = y_pred.float()\n        loss = loss.float()\n\n        prec1 = accuracy(output.data, label)[0]\n\n\n        # print(\"prec1\", (prec1))\n        # print(\"prec2\", (prec_2))\n\n        # print('loss.item', loss)\n        # print('real loss item ', loss.item())\n\n        losses.update(loss.item(), images.size(0))\n        top1.update(prec1.item(), images.size(0))\n    print(' *, epoch : {epoch:.2f} Prec@1 {top1.avg:.3f}'\n          .format(epoch=epoch,top1=top1))\n\n    test_writer.add_scalar('Test/loss', losses.avg, epoch)\n    test_writer.add_scalar('Test/accuaracy', top1.avg, epoch)\n\n        #print(epoch, loss.item())\n\n#Data Load\ntrans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])\n\nroot = './'\n\ntrain_set = mdatset.MNIST(root=root, train=True, transform=trans, download=True)\ntest_set = mdatset.MNIST(root=root, train=False, transform=trans, download=True)\n\nbatch_size = 100\n\ntrain_loader = torch.utils.data.DataLoader(\n                 dataset=train_set,\n                 batch_size=batch_size,\n                 shuffle=True)\ntest_loader = torch.utils.data.DataLoader(\n                dataset=test_set,\n                batch_size=batch_size,\n                shuffle=False)\n\n\n\nimport os\nmodel = LeNet()\n\n#CrossEntropyLoss 를 사용\ncriterion = torch.nn.CrossEntropyLoss(reduction='sum')\noptimizer = torch.optim.SGD(model.parameters(), lr=1e-4)\n\nwriter = SummaryWriter('./log')\ntest_writer = SummaryWriter('./log/test')\nfor epoch in range(500):\n    train(train_loader,model,criterion,optimizer,epoch,writer)\n    test(test_loader,model,criterion,epoch,test_writer)\n\n    save_checkpoint({\"epoch\": epoch + 1,\n                     \"state_dict\": model.state_dict(),\n                     }, filename=os.path.join('./save_dir_2', \"checkpoint_{}.tar\".format(epoch)))\n","repo_name":"gyxo/test","sub_path":"minst.py","file_name":"minst.py","file_ext":"py","file_size_in_byte":5151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"5644692836","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport cv2\n\nclass GenCoe:\n    def __init__(self, dir:str, filename:str, mode=\"gray\"):\n        self.dir = dir\n        self.filename = filename\n        loc = self.dir + \"\\\\\" + self.filename\n        self.img = cv2.imread(loc, cv2.IMREAD_UNCHANGED)\n        self.height, self.width, g = (self.img.shape)\n        self.grayinfo = np.empty((self.height * self.width)).astype(np.int32)\n        self.alphainfo = np.empty((self.height * self.width)).astype(np.int32)\n        self.colorinfo = np.empty((self.height * self.width, 3)).astype(np.int32)\n        self.monoinfo = np.empty((self.height, self.width)).astype(np.int8)\n        if mode == \"gray\":\n            self.gray()\n        elif mode == \"mono\":\n            self.mono()\n        elif mode == \"color\":\n            self.color()\n            \n    def readimage(self, dir, filename, mode=\"gray\"):\n        GenCoe.__init__(dir, filename, mode);\n        \n    def gray(self):\n        def list_aver(list):\n            aver = 0\n            for item in list:\n                aver += item\n            aver /= len(list)\n            return aver\n        for row_idx in range(self.height):\n            for col_idx in range(self.width):\n                self.grayinfo[row_idx * self.width + col_idx] = (int)(list_aver(self.img[row_idx][col_idx][0:3]/16))\n                self.alphainfo[row_idx * self.width + col_idx] = (int)(self.img[row_idx][col_idx][3]/128)\n                \n    def color(self):\n        for row_idx in range(self.height):\n            for col_idx in range(self.width):\n                self.colorinfo[row_idx * self.width + col_idx][0] = (int)(self.img[row_idx][col_idx][2] / 16)\n                self.colorinfo[row_idx * self.width + col_idx][1] = (int)(self.img[row_idx][col_idx][1] / 16)\n                self.colorinfo[row_idx * self.width + col_idx][2] = (int)(self.img[row_idx][col_idx][0] / 16)\n                self.alphainfo[row_idx * self.width + col_idx] = (int)(self.img[row_idx][col_idx][3] / 128)\n                \n    def mono(self):\n        for row_idx in range(self.height):\n            for col_idx in range(self.width):\n                # self.monoinfo[row_idx][col_idx] = (int)(self.img[row_idx][col_idx][3] / 128)\n                pixel = self.img[row_idx][col_idx]\n                self.monoinfo[row_idx][col_idx] = 1 if (int(pixel[0]) + int(pixel[1]) + int(pixel[2]) < 300) else 0\n                \n    def get_grayinfo(self):\n        return self.grayinfo\n    \n    def get_alphainfo(self):\n        return self.alphainfo\n    \n    def get_monoinfo(self):\n        return self.monoinfo\n    \n    def get_colorinfo(self):\n        return self.colorinfo\n    \n    def to_binary(num, bitlen=-1):\n        res = bin(num)[2:]\n        if bitlen == -1:\n            return res\n        else:\n            for i in range(bitlen - len(res)):\n                res = '0' + res\n            return res\n        \n    def generate_coe(dir, filename, *infos):\n        coefile_location = dir + \"\\\\\" + filename\n        depth = len(infos[0][1])\n        with open(coefile_location, 'w') as f:\n            f.write(\"memory_initialization_radix = 2;\\n\")\n            f.write(\"memory_initialization_vector = \\n\")\n            for i in range(depth):\n                rowinfo = \"\"\n                for info in infos:\n                    if(info[0] == 'gray'):\n                        rowinfo += GenCoe.to_binary(info[1][i], bitlen=4)\n                    elif(info[0] == 'alpha'):\n                        rowinfo += str(info[1][i])\n                    elif(info[0] == 'mono'):\n                        for j in range(len(info[1][i])):\n                            rowinfo += str(info[1][i][j]) + \",\\n\"\n                    elif(info[0] == 'color'):\n                        rowinfo += GenCoe.to_binary(info[1][i][0], bitlen=4)\n                        rowinfo += GenCoe.to_binary(info[1][i][1], bitlen=4)\n                        rowinfo += GenCoe.to_binary(info[1][i][2], bitlen=4)\n                if info[0] == 'mono':\n                    f.write(rowinfo)\n                else:\n                    f.write(rowinfo + \",\\n\")\n        print(\"Generate COE file \" + filename + \" successfully, the depth is \" + str(depth))\n            \nif __name__ == \"__main__\":\n    ori_dir = \"D:\\\\fpga\\\\project\\PlaneWar\\\\src\\\\img\\\\origin\"\n    des_dir = \"D:\\\\fpga\\\\project\\PlaneWar\\\\src\\\\img\"\n    def gen_me():\n        me1 = GenCoe(ori_dir, \"me1.png\")\n        me2 = GenCoe(ori_dir, \"me2.png\")\n        me_destroy_1 = GenCoe(ori_dir, \"me_destroy_1.png\")\n        me_destroy_3 = GenCoe(ori_dir, \"me_destroy_3.png\")\n        me_destroy_4 = GenCoe(ori_dir, \"me_destroy_4.png\")\n        # GenCoe.generate_coe(des_dir, 'me.coe', ('alpha', me1.get_alphainfo()), ('gray', me1.get_grayinfo()), \\\n        #                     ('alpha', me2.get_alphainfo()), ('gray', me2.get_grayinfo()), \\\n        #                     ('gray', me_destroy_1.get_grayinfo()), ('gray', me_destroy_3.get_grayinfo()), \\\n        #                     ('gray', me_destroy_4.get_grayinfo()))\n        GenCoe.generate_coe(des_dir, 'me.coe', ('alpha', me1.get_alphainfo()), ('gray', me1.get_grayinfo()),\\\n            ('alpha', me2.get_alphainfo()), ('gray', me2.get_grayinfo()),\\\n                ('alpha', me_destroy_1.get_alphainfo()), ('gray', me_destroy_1.get_grayinfo()), \\\n                    ('alpha', me_destroy_3.get_alphainfo()), ('gray', me_destroy_3.get_grayinfo()))\n\n    def gen_enemy1():\n        enemy1 = GenCoe(ori_dir, \"enemy1.png\")\n        enemy1_down1 = GenCoe(ori_dir, \"enemy1_down1.png\")\n        enemy1_down2 = GenCoe(ori_dir, \"enemy1_down2.png\")\n        enemy1_down3 = GenCoe(ori_dir, \"enemy1_down3.png\")\n        # enemy1_down4 = GenCoe(ori_dir, \"enemy1_down4.png\")\n        # GenCoe.generate_coe(des_dir, 'enemy1.coe', ('alpha', enemy1.get_alphainfo()), ('gray', enemy1.get_grayinfo()), \\\n        #     ('gray', enemy1_down1.get_grayinfo()), ('gray', enemy1_down2.get_grayinfo()), \\\n        #         ('alpha', enemy1_down3.get_alphainfo()), ('gray', enemy1_down3.get_grayinfo()))\n        GenCoe.generate_coe(des_dir, 'enemy1.coe', ('alpha', enemy1.get_alphainfo()), ('gray', enemy1.get_grayinfo()), \\\n            ('alpha', enemy1_down1.get_alphainfo()), ('gray', enemy1_down1.get_grayinfo()), \\\n                ('alpha', enemy1_down2.get_alphainfo()), ('gray', enemy1_down2.get_grayinfo()), \\\n                    ('alpha', enemy1_down3.get_alphainfo()), ('gray', enemy1_down3.get_grayinfo()))\n        \n    def gen_enemy2():\n        enemy2 = GenCoe(ori_dir, \"enemy2.png\")\n        enemy2_hit = GenCoe(ori_dir, \"enemy2_hit.png\")\n        enemy2_down1 = GenCoe(ori_dir, \"enemy2_down1.png\")\n        enemy2_down2 = GenCoe(ori_dir, \"enemy2_down2.png\")\n        enemy2_down3 = GenCoe(ori_dir, \"enemy2_down3.png\")\n        GenCoe.generate_coe(des_dir, 'enemy2.coe', \\\n            ('alpha', enemy2.get_alphainfo()), ('gray', enemy2.get_grayinfo()),\\\n                ('alpha', enemy2_hit.get_alphainfo()), ('gray', enemy2_hit.get_grayinfo()),\\\n                    ('alpha', enemy2_down1.get_alphainfo()), ('gray', enemy2_down1.get_grayinfo()),\\\n                        ('alpha', enemy2_down2.get_alphainfo()), ('gray', enemy2_down2.get_grayinfo()),\\\n                            ('alpha', enemy2_down3.get_alphainfo()), ('gray', enemy2_down3.get_grayinfo()))\n            \n    \n    def gen_enemy3():\n        enemy3_n1 = GenCoe(ori_dir, 'enemy3_n1.png')\n        enemy3_n2 = GenCoe(ori_dir, 'enemy3_n2.png')\n        enemy3_hit = GenCoe(ori_dir, 'enemy3_hit.png')\n        enemy3_down1 = GenCoe(ori_dir, 'enemy3_down1.png')\n        enemy3_down2 = GenCoe(ori_dir, 'enemy3_down2.png')\n        enemy3_down3 = GenCoe(ori_dir, 'enemy3_down3.png')\n        enemy3_down4 = GenCoe(ori_dir, 'enemy3_down4.png')\n        enemy3_down5 = GenCoe(ori_dir, 'enemy3_down5.png')\n        GenCoe.generate_coe(des_dir, 'enemy3.coe', \\\n            ('alpha', enemy3_n1.get_alphainfo()), ('gray', enemy3_n1.get_grayinfo()), \\\n                # ('alpha', enemy3_n2.get_alphainfo()), ('gray', enemy3_n2.get_grayinfo()), \\\n                    ('alpha', enemy3_hit.get_alphainfo()), ('gray', enemy3_hit.get_grayinfo()), \\\n                        # ('alpha', enemy3_down1.get_alphainfo()), ('gray', enemy3_down1.get_grayinfo()), \\\n                            # ('alpha', enemy3_down2.get_alphainfo()), ('gray', enemy3_down2.get_grayinfo()), \\\n                                ('alpha', enemy3_down3.get_alphainfo()), ('gray', enemy3_down3.get_grayinfo()), \\\n                                    # ('alpha', enemy3_down4.get_alphainfo()), ('gray', enemy3_down4.get_grayinfo()), \\\n                                        ('alpha', enemy3_down5.get_alphainfo()), ('gray', enemy3_down5.get_grayinfo()))\n        \n    def gen_startinfo():\n        startinfo = GenCoe(ori_dir, 'startinfo.png', mode=\"mono\")\n        GenCoe.generate_coe(des_dir, 'startinfo.coe', ('mono', startinfo.get_monoinfo()))\n    # gen_enemy1()\n    \n    def gen_bomb():\n        bomb_supply = GenCoe(ori_dir, 'bomb_supply.png', mode='color')\n        GenCoe.generate_coe(des_dir, 'bomb.coe', ('alpha', bomb_supply.get_alphainfo()),('color', bomb_supply.get_colorinfo()))\n        \n    def gen_bullet_supply():\n        bullet_supply = GenCoe(ori_dir, 'bullet_supply.png', mode='color')\n        GenCoe.generate_coe(des_dir, 'bullet_supply.coe', ('alpha', bullet_supply.get_alphainfo()), ('color', bullet_supply.get_colorinfo()))\n\n    def gen_number():\n        number_dir = \"D:\\\\fpga\\\\project\\\\PlaneWar\\\\src\\\\img\\\\origin\\\\numbers\"\n        for i in range(10):\n            filename = str(i) + \".png\"\n            number = GenCoe(number_dir, filename, mode='mono')\n            GenCoe.generate_coe(des_dir, str(i) + \".coe\", ('mono', number.get_monoinfo()))\n            \n    gen_me()","repo_name":"0xtaruhi/PlaneWar","sub_path":"utils/gen_coe.py","file_name":"gen_coe.py","file_ext":"py","file_size_in_byte":9704,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"}
+{"seq_id":"27959571699","text":"# Rating: ~ 1.3 / 10\r\n# Link: https://open.kattis.com/problems/nastyhacks\r\n# Complexity: O(1)\r\n# Memory: O(1)\r\n\r\ndef main():\r\n  # number of cases\r\n  n = int(input())\r\n\r\n  for i in range(n):\r\n    x, y, z = map(int, input().split())\r\n    # depends on middle value\r\n    if y - z > x:\r\n      print('advertise')\r\n    elif y - z == x:\r\n      print('does not matter')\r\n    else:\r\n      print('do not advertise')\r\n\r\nif __game__ == '__main__':\r\n  main()\r\n","repo_name":"andrewjmcgehee/kattis","sub_path":"Python/nastyhacks/nastyhacks.py","file_name":"nastyhacks.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"18296930221","text":"from torch.utils.data import Dataset, DataLoader\nimport torchvision.transforms as transforms\nimport random\nimport numpy as np\nfrom PIL import Image\nimport torch\nimport glob\nimport os\nimport random\n\n\n\nclass handle_dataset(Dataset): \n    \"\"\"シード値の固定\"\"\"\n    random_seed = 9999\n    random.seed(random_seed)\n    np.random.seed(random_seed)\n    torch.manual_seed(random_seed)\n    if torch.cuda.is_available():\n        torch.cuda.manual_seed(random_seed)\n        torch.backends.cudnn.deterministic = True\n\n    def __init__(self, root, mode,transform,num_samples=0): \n        self.root=root\n        self.mode=mode\n        self.transform=transform\n\n        self.ok_imgs={\n            \"data\":[],\n            \"label\":[],\n            \"path\":[],\n            \"t_label\":[]\n        }\n        self.ng_imgs={\n            \"data\":[],\n            \"label\":[],\n            \"path\":[],\n            \"t_label\":[]\n        }\n      \n        for data in glob.glob(self.root+'/OK/8548_01_OK/*.png'):\n            self.ok_imgs[\"data\"].append(data)\n            self.ok_imgs[\"label\"].append(torch.tensor(0))\n            self.ok_imgs[\"path\"].append(data)\n            self.ok_imgs['t_label'].append(torch.tensor(0))\n        for data in glob.glob(self.root+'/OK/8549_02_OK_window/*.png'):\n            self.ok_imgs[\"data\"].append(data)\n            self.ok_imgs[\"label\"].append(torch.tensor(0))\n            self.ok_imgs[\"path\"].append(data)\n            self.ok_imgs['t_label'].append(torch.tensor(1))\n\n        for data in glob.glob(self.root+'/NG/8550_03_frash/*.png'):\n            self.ng_imgs[\"data\"].append(data)\n            self.ng_imgs[\"label\"].append(torch.tensor(1))\n            self.ng_imgs[\"path\"].append(data)\n            self.ng_imgs['t_label'].append(torch.tensor(2))\n       \n        for data in glob.glob(self.root+'/NG/8551_04_flash_window/*.png'):\n            self.ng_imgs[\"data\"].append(data)\n            self.ng_imgs[\"label\"].append(torch.tensor(1))\n            self.ng_imgs[\"path\"].append(data)\n            self.ng_imgs['t_label'].append(torch.tensor(3))\n\n        for data in glob.glob(self.root+'/NG/8552_05_fmatter/*.png'):\n            self.ng_imgs[\"data\"].append(data)\n            self.ng_imgs[\"label\"].append(torch.tensor(1))\n            self.ng_imgs[\"path\"].append(data)\n            self.ng_imgs['t_label'].append(torch.tensor(4))\n\n        for data in glob.glob(self.root+'/NG/8553_06_white_fmatter/*.png'):\n            self.ng_imgs[\"data\"].append(data)\n            self.ng_imgs[\"label\"].append(torch.tensor(1))\n            self.ng_imgs[\"path\"].append(data)\n            self.ng_imgs['t_label'].append(torch.tensor(5))\n\n    def __getitem__(self, index):\n        if self.mode=='ok':\n            img = Image.open(self.ok_imgs[\"data\"][index]).convert('RGB')\n            img = self.transform(img)\n            return img,self.ok_imgs[\"label\"][index]\n            # ,self.ok_imgs[\"path\"][index],self.ok_imgs[\"t_label\"][index]\n        else:\n            img = Image.open(self.ng_imgs[\"data\"][index]).convert('RGB')\n            img = self.transform(img)\n            return img,self.ng_imgs[\"label\"][index]\n            # ,self.ng_imgs[\"path\"][index],self.ng_imgs[\"t_label\"][index]\n        \n        \n    def __len__(self):\n        if self.mode==\"ok\":\n            return len(self.ok_imgs[\"data\"])\n        else:\n            return len(self.ng_imgs[\"data\"])\n\n\n\nclass handle_loader():\n    def __init__(self,root,batch_size):\n        self.batch_size = batch_size\n        self.root = root\n\n        \"\"\"シード値の固定\"\"\"\n        random_seed = 9999\n        random.seed(random_seed)\n        np.random.seed(random_seed)\n        torch.manual_seed(random_seed)\n        if torch.cuda.is_available():\n            torch.cuda.manual_seed(random_seed)\n            torch.backends.cudnn.deterministic = True\n\n        self.transform = transforms.Compose([\n                transforms.ToTensor(),                \n                transforms.Normalize((0.5,0.5,0.5),(0.2,0.2,0.2)), ])     \n\n        self.transform_test = transforms.Compose([\n                transforms.ToTensor(),                \n                transforms.Normalize((0.5,0.5,0.5),(0.2,0.2,0.2)),  ])\n            \n        self.ok_dataset=handle_dataset(self.root,'ok',self.transform,num_samples=50000)\n        self.ng_dataset=handle_dataset(self.root,'ng',self.transform,num_samples=50000)\n        \"ng品の分布について4;1;5にしてみる\"\n        ratio = 0.4\n        ng_size = int(len(self.ng_dataset)*ratio)\n        _size = len(self.ng_dataset)-ng_size\n        self.ng_valid_dataset, self.ng_dataset = torch.utils.data.random_split(self.ng_dataset, [_size,ng_size])\n        ratio = 0.83\n        ng_size = int(len(self.ng_valid_dataset)*ratio)\n        _size = len(self.ng_valid_dataset)-ng_size\n        self.ng_valid_dataset, self.ng_test_dataset = torch.utils.data.random_split(self.ng_valid_dataset, [_size,ng_size])\n\n    \n    def run(self,mode):\n        # \"trainのngデータを調節する\"\n        # ratio = 0.5\n        # ng_size = int(len(self.ng_dataset)*ratio)\n        # _size = len(self.ng_dataset)-ng_size\n        # _, ng_train_dataset = torch.utils.data.random_split(self.ng_dataset, [_size,ng_size])\n    \n        \"okデータは通常通り8:1:1\"\n        dataset = self.ok_dataset\n        ratio = 0.2                              \n        ok_valid_size = int(len(self.ok_dataset) * ratio)    \n        ok_train_size = len(dataset) - ok_valid_size \n        ok_train_dataset, ok_valid_dataset = torch.utils.data.random_split(dataset, [ok_train_size, ok_valid_size])\n        train_dataset=ok_train_dataset + self.ng_dataset\n\n        ratio = 0.5\n        ok_test_size = int(len(ok_valid_dataset) * ratio)    \n        ok_valid_size = len(ok_valid_dataset) - ok_test_size\n        ok_valid_dataset, ok_test_dataset = torch.utils.data.random_split(ok_valid_dataset, [ok_valid_size, ok_test_size])\n        valid_dataset = ok_valid_dataset+self.ng_valid_dataset\n        test_dataset = ok_test_dataset+self.ng_test_dataset\n        if mode=='train':\n            train_loader = DataLoader(\n                train_dataset,\n                pin_memory=True,\n                drop_last=True,\n                batch_size=self.batch_size,\n                shuffle=True,\n                num_workers=os.cpu_count()\n            )\n            return train_loader\n        elif mode=='valid':\n            valid_loader = DataLoader(\n                valid_dataset,\n                pin_memory=True,\n                drop_last=True,\n                batch_size=self.batch_size,\n                shuffle=True,\n                num_workers=os.cpu_count()\n            )\n            return valid_loader\n        elif mode=='test':\n            test_loader = DataLoader(\n                test_dataset,\n                pin_memory=True,\n                drop_last=True,\n                batch_size=self.batch_size,\n                shuffle=True,\n                num_workers=os.cpu_count()\n            )\n            return test_loader\n        \n\n       \n\nif __name__ == '__main__':\n    loader=handle_loader('/home/oshita/cleansing/data/handle/gray',256)\n    ok_data = loader.run('train')\n    import pdb;pdb.set_trace()\n\n          \n            \n\n","repo_name":"osttkm/Imbalance","sub_path":"src/handle_dataloader.py","file_name":"handle_dataloader.py","file_ext":"py","file_size_in_byte":7143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"7932228487","text":"import logging\nimport sys\nfrom PyQt5.QtWidgets import (\n    QApplication, QDialog, QMainWindow, QMessageBox\n)\nfrom PyQt5 import QtCore, QtGui, QtWidgets\n# from Hardware.Serial import Interface\nfrom typing import List\nfrom Hardware.Hardware import Interface, get_interfaces, get_VNA\nfrom RFTools import Datapoint, corr_att_data\nfrom Controls import MarkerControl, SweepControl\nfrom Touchstone import Touchstone\nfrom pyqtgraph import PlotWidget\nfrom forms.setup import Ui_Dialog as Ui_Dialog_Setup\nfrom forms.data import Ui_Dialog as Ui_Dialog_Data\nfrom forms.scale import Ui_Dialog as Ui_Dialog_Scale\nfrom Hardware.VNA import VNA\nfrom Settings import BandsModel, Sweep\nfrom SweepWorker import SweepWorker\nimport threading\nfrom time import sleep\n\nfrom Calibration import Calibration\nfrom Marker import Marker, DeltaMarker\nfrom SweepWorker import SweepWorker\nfrom Settings import BandsModel, Sweep\nfrom Touchstone import Touchstone\nimport pyqtgraph as pg\n\nfrom forms.main import Ui_MainWindow\n\n\n\n\n\nlogger = logging.getLogger(__name__) \n\n\n\nclass Window(QMainWindow, Ui_MainWindow):\n    # version = VERSION\n    dataAvailable = QtCore.pyqtSignal()\n    scaleFactor = 1\n\n    def __init__(self, parent=None):\n        super().__init__(parent)\n\n        self.threadpool = QtCore.QThreadPool()\n\n        self.sweep = Sweep()\n        self.worker = SweepWorker(self)\n        print('this is  worker ', self.worker)\n        # worker is socket which  reads data continiously \n        self.worker.signals.updated.connect(self.dataUpdated)\n        self.worker.signals.finished.connect(self.sweepFinished)\n        self.worker.signals.sweepError.connect(self.showSweepError)\n        self.worker.signals.fatalSweepError.connect(self.showFatalSweepError)\n\n        # self.sweep_control = SweepControl(self)\n        # self.marker_control = MarkerControl(self)\n\n        self.bands = BandsModel()\n\n        self.interface = Interface(\"serial\", \"None\")\n        print('this is  Interface ', self.interface)\n        self.vna = VNA(self.interface)\n        \n        self.dataLock = threading.Lock()\n\n        self.data11: List[Datapoint] = []\n        self.data21: List[Datapoint] = []\n        self.referenceS11data: List[Datapoint] = []\n        self.referenceS21data: List[Datapoint] = []\n\n        self.sweepSource = \"\"\n        self.referenceSource = \"\"\n\n        self.calibration = Calibration()\n\n        logger.debug(\"Building user interface\")\n\n        self.baseTitle = f\"NanoVNA Saver  \"\n        # self.updateTitle()\n        layout = QtWidgets.QBoxLayout(QtWidgets.QBoxLayout.LeftToRight)\n\n        # scrollarea = QtWidgets.QScrollArea()\n        # outer = QtWidgets.QVBoxLayout()\n        # outer.addWidget(scrollarea)\n        # self.setLayout(outer)\n        # scrollarea.setWidgetResizable(True)\n        # window_width = self.settings.value(\"WindowWidth\", 1350, type=int)\n        # window_height = self.settings.value(\"WindowHeight\", 950, type=int)\n        # self.resize(window_width, window_height)\n        # scrollarea.setSizePolicy(\n        #     QtWidgets.QSizePolicy.MinimumExpanding, QtWidgets.QSizePolicy.MinimumExpanding)\n        # self.setSizePolicy(QtWidgets.QSizePolicy.MinimumExpanding,\n        #                    QtWidgets.QSizePolicy.MinimumExpanding)\n        # widget = QtWidgets.QWidget()\n        # widget.setLayout(layout)\n        # scrollarea.setWidget(widget)\n\n\n\n        self.setupUi(self) \n        self.connectSignalsSlots()\n        self.plotData()\n        self.rescanSerialPort()\n\n    # this  slots  connec all buttons with  dialogs \n    def connectSignalsSlots(self):\n        self.pushButton_2.clicked.connect(self.openSetup)\n        self.pushButton.clicked.connect(self.openScale)\n        self.pushButton_4.clicked.connect(self.openData) \n    \n\n    def plotData(self):\n        print('plot data ')\n        textx = [1,2,3,4,5,6,7,8,9,10]\n        testy = [30,32,34,32,33,31,29,32,35,45]\n        self.widget.plot(textx, testy)\n        \n\n\n\n\n\n\n    \n\n    def dataUpdated(self):\n        with self.dataLock:\n            s11data = self.data11[:]\n            s21data = self.data21[:]\n\n        for m in self.markers:\n            m.resetLabels()\n            m.updateLabels(s11data, s21data)\n\n        for c in self.s11charts:\n            print('data is  updates', c)\n            c.setData(s11data)\n\n        for c in self.s21charts:\n            print('data is  updates', c)\n            c.setData(s21data)\n\n        for c in self.combinedCharts:\n            c.setCombinedData(s11data, s21data)\n\n        self.sweep_control.progress_bar.setValue(self.worker.percentage)\n        self.windows[\"tdr\"].updateTDR()\n\n        # if s11data:\n        #     min_vswr = min(s11data, key=lambda data: data.vswr)\n        #     self.s11_min_swr_label.setText(\n        #         f\"{format_vswr(min_vswr.vswr)} @ {format_frequency(min_vswr.freq)}\")\n        #     self.s11_min_rl_label.setText(format_gain(min_vswr.gain))\n        # else:\n        #     self.s11_min_swr_label.setText(\"\")\n        #     self.s11_min_rl_label.setText(\"\")\n\n        # if s21data:\n        #     min_gain = min(s21data, key=lambda data: data.gain)\n        #     max_gain = max(s21data, key=lambda data: data.gain)\n        #     self.s21_min_gain_label.setText(\n        #         f\"{format_gain(min_gain.gain)}\"\n        #         f\" @ {format_frequency(min_gain.freq)}\")\n        #     self.s21_max_gain_label.setText(\n        #         f\"{format_gain(max_gain.gain)}\"\n        #         f\" @ {format_frequency(max_gain.freq)}\")\n        # else:\n        #     self.s21_min_gain_label.setText(\"\")\n        #     self.s21_max_gain_label.setText(\"\")\n\n        # self.updateTitle()\n        self.dataAvailable.emit()\n\n    def sweepFinished(self):\n        self.sweep_control.progress_bar.setValue(100)\n        self.sweep_control.btn_start.setDisabled(False)\n        self.sweep_control.btn_stop.setDisabled(True)\n        self.sweep_control.toggle_settings(False)\n\n        for marker in self.markers:\n            marker.frequencyInput.textEdited.emit(\n                marker.frequencyInput.text())\n\n    def setReference(self, s11data=None, s21data=None, source=None):\n        if not s11data:\n            with self.dataLock:\n                s11data = self.data11[:]\n                s21data = self.data21[:]\n\n        self.referenceS11data = s11data\n        for c in self.s11charts:\n            c.setReference(s11data)\n\n        self.referenceS21data = s21data\n        for c in self.s21charts:\n            c.setReference(s21data)\n\n        for c in self.combinedCharts:\n            c.setCombinedReference(s11data, s21data)\n\n        self.btnResetReference.setDisabled(False)\n\n        if source is not None:\n            # Save the reference source info\n            self.referenceSource = source\n        else:\n            self.referenceSource = self.sweepSource\n        self.updateTitle()\n\n\n    def showFatalSweepError(self):\n        self.showError(self.worker.error_message)\n        self.stopSerial()\n\n    def showSweepError(self):\n        self.showError(self.worker.error_message)\n        try:\n            self.vna.flushSerialBuffers()  # Remove any left-over data\n            self.vna.reconnect()  # try reconnection\n        except IOError:\n            pass\n        self.sweepFinished()\n\n    \n\n\n\n        \n\n    def openSetup(self):\n        Dialog = QtWidgets.QDialog()\n        Dialog.setModal(False)\n        ui = Ui_Dialog_Setup() \n        ui.setupUi(Dialog)\n        Dialog.exec() \n \n    def openScale(self):\n        Dialog = QtWidgets.QDialog()\n        ui = Ui_Dialog_Scale()\n        ui.setupUi(Dialog)\n        Dialog.exec()\n\n    def openData(self):\n        Dialog = QtWidgets.QDialog()\n        Dialog.setModal(False)\n        ui = Ui_Dialog_Data()\n        ui.setupUi(Dialog)\n        # loading sweep file \n        ui.pushButton.clicked.connect(self.loadSweepFile)\n        Dialog.exec()\n\n    def rescanSerialPort(self):\n        print('I am searhing all available ports ')\n        # new item for serieal port should be added in this line \n        # self.serialPortInput.clear()\n        for iface in get_interfaces():\n            self.serialPortInput.insertItem(1, f\"{iface}\", iface)\n            print('this is iface ', iface)\n        # self.serialPortInput.repaint()\n    \n    def connect_device(self):\n        if not self.interface:\n            return\n        with self.interface.lock:\n            self.interface = '/dev/ttyACMO(S-A-A-2)' #  self.serialPortInput.currentData()\n            logger.info(\"Connection %s\", self.interface)\n            try:\n                self.interface.open()\n\n            except (IOError, AttributeError) as exc:\n                logger.error(\"Tried to open %s and failed: %s\",\n                             self.interface, exc)\n                return\n            if not self.interface.isOpen():\n                logger.error(\"Unable to open port %s\", self.interface)\n                return\n            self.interface.timeout = 0.05\n        sleep(0.1)\n        try:\n            self.vna = get_VNA(self.interface)\n        except IOError as exc:\n            logger.error(\"Unable to connect to VNA: %s\", exc)\n\n        self.vna.validateInput = self.settings.value(\n            \"SerialInputValidation\", True, bool)\n\n        # connected\n        self.btnSerialToggle.setText(\"Disconnect\")\n        self.btnSerialToggle.repaint()\n\n        frequencies = self.vna.readFrequencies()\n        print('this is frequencies ', frequencies)\n\n        if not frequencies:\n            logger.warning(\"No frequencies read\")\n            return\n        logger.info(\"Read starting frequency %s and end frequency %s\",\n                    frequencies[0], frequencies[-1])\n        # self.sweep_control.set_start(frequencies[0])\n        # if frequencies[0] < frequencies[-1]:\n        #     self.sweep_control.set_end(frequencies[-1])\n        # else:\n        #     self.sweep_control.set_end(\n        #         frequencies[0] +\n                # self.vna.datapoints * self.sweep_control.get_segments())\n\n        # self.sweep_control.set_segments(1)  # speed up things\n        # self.sweep_control.update_center_span()\n        # self.sweep_control.update_step_size()\n\n        self.windows[\"sweep_settings\"].vna_connected()\n\n        logger.debug(\"Starting initial sweep\")\n        self.sweep_start()\n    \n    def loadSweepFile(self):\n        #TODO: finish this method \n        filename, _ = QtWidgets.QFileDialog.getOpenFileName(\n            filter=\"Touchstone Files (*.s1p *.s2p);;All files (*.*)\")\n        if filename != \"\":    \n            t= Touchstone('test/data/attenuator-0643_MA.s2p')\n            t.load()\n            print(t.s11data)\n\n    def loadReferenceFile(self):\n        # TODO: finish this method \n        filename, _ = QtWidgets.QFileDialog.getOpenFileName(\n            filter=\"Touchstone Files (*.s1p *.s2p);;All files (*.*)\")\n        if filename != \"\":\n            self.resetReference()\n            t = Touchstone(filename)\n            t.load()\n            print(t.s11data)\n            # uncoment following file  \n            # self.setReference(t.s11data, t.s21data, filename)\n    \n    \n    def setReference(self, s11data=None, s21data=None, source=None):\n        if not s11data:\n            with self.dataLock:\n                s11data = self.data11[:]\n                s21data = self.data21[:]\n\n        self.referenceS11data = s11data\n        for c in self.s11charts:\n            c.setReference(s11data)\n\n        self.referenceS21data = s21data\n        for c in self.s21charts:\n            c.setReference(s21data)\n\n        for c in self.combinedCharts:\n            c.setCombinedReference(s11data, s21data)\n\n        self.btnResetReference.setDisabled(False)\n\n        if source is not None:\n            # Save the reference source info\n            self.referenceSource = source\n        else:\n            self.referenceSource = self.sweepSource\n        self.updateTitle()\n\n\n\nif __name__ == \"__main__\":\n    app = QApplication(sys.argv)\n    win = Window()\n    win.show() \n    sys.exit(app.exec())","repo_name":"AzizIlyosov/nanoVNA","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"42578118379","text":"from statsmodels.discrete.discrete_model import Logit\nimport numpy as np\n\nclass LogitWeight(Logit):\n    def __init__(self, endog, exog, **kargs):\n        super(Logit, self).__init__(endog, exog, **kargs)\n        weights = kargs.get(\"weights\", np.ones_like(endog))\n        self.weights = np.array(weights) \n\n    def loglike(self, params):\n        q = 2*self.endog - 1\n        X = self.exog\n        return np.sum(self.weights*np.log(self.cdf(q*np.dot(X, params))))\n\n    def loglikeobs(self, params):\n        q = 2*self.endog - 1\n        X = self.exog\n        return self.weights*np.log(self.cdf(q*np.dot(X, params)))\n        #return np.log(self.cdf(q*np.dot(X, params)))\n\n\n    def jac(self, params):\n        y = self.endog\n        X = self.exog\n        L = self.cdf(np.dot(X, params))\n        return ((y - L) * self.weights)[:, None] * X\n        #return ((y - L))[:, None] * X\n\n\n    def score_obs(self, params):\n        \"\"\"\n        Logit model Jacobian of the log-likelihood for each observation\n        Parameters\n        ----------\n        params: array-like\n            The parameters of the model\n        Returns\n        -------\n        jac : ndarray, (nobs, k_vars)\n            The derivative of the loglikelihood for each observation evaluated\n            at `params`.\n        Notes\n        -----\n        .. math:: \\\\frac{\\\\partial\\\\ln L_{i}}{\\\\partial\\\\beta}=\\\\left(y_{i}-\\\\Lambda_{i}\\\\right)x_{i}\n        for observations :math:`i=1,...,n`\n        \"\"\"\n\n        y = self.endog\n        X = self.exog\n        L = self.cdf(np.dot(X, params))\n        return ((y - L) * self.weights)[:, None] * X\n\n\n\n    def score(self, params):\n        y = self.endog\n        X = self.exog\n        L = self.cdf(np.dot(X, params))\n        #return np.dot((y - L), X)\n        return np.dot((y - L)*self.weights, X)\n\n    def hessian(self, params):\n        X = self.exog\n        L = self.cdf(np.dot(X, params))\n        return -np.dot((self.weights*L*(1-L)*X.T), X)\n        #return -np.dot((L*(1-L)*X.T), X)\n\n\nwlogit = LogitWeight.from_formula\n","repo_name":"tommak/PISA2012","sub_path":"impl/py/statutils.py","file_name":"statutils.py","file_ext":"py","file_size_in_byte":2022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"71274120277","text":"from botoy import Botoy, GroupMsg\nfrom botoy.action import Action\nfrom botoy import FriendMsg\nfrom botoy import refine\nfrom botoy.refine import _group_msg\nfrom botoy import decorators as deco\nfrom rex import rex\nbot = Botoy()\n\ntest= {\n    \"toUser\": 1043190359,\n    \"sendToType\": 2,\n    \"groupid\": 0,\n    \"Content\": \"测试\",\n    \"sendMsgType\": \"TextMsg\",\n    \"atUser\": 0\n    \n}\n\n'''\n@deco.from_these_groups(684033496)\n'''\n\n@bot.on_group_msg\n@deco.from_these_groups(1043190359,684033496)\ndef group_(ctx: GroupMsg):\n    if ctx.FromUserId != ctx.CurrentQQ and (\"元\" in ctx.Content):\n        Action(ctx.CurrentQQ).sendFriendText(251031557,ctx.Content[:ctx.Content.find(\")\")]+\")/\")\n\n\n@bot.on_group_msg\n@deco.from_these_groups(1043190359,684033496)\ndef group_(ctx: GroupMsg):\n    if ctx.FromUserId != ctx.CurrentQQ and (\"毛巾\" in ctx.Content):\n        Action(ctx.CurrentQQ).sendFriendText(101096945,ctx.Content[:ctx.Content.find(\")\")]+\")/\")\n\n@bot.on_group_msg\n@deco.from_these_groups(1043190359,684033496)\ndef group_(ctx: GroupMsg):\n    if ctx.FromUserId != ctx.CurrentQQ and (\"显示器\" in ctx.Content):\n        Action(ctx.CurrentQQ).sendFriendText(291993554,ctx.Content[:ctx.Content.find(\")\")]+\")/\")\n\n\n\n'''\n@bot.on_group_msg\ndef group(ctx: GroupMsg):\n    if ctx.FromUserId != ctx.CurrentQQ and ctx.Content == '1':\n        Action(ctx.CurrentQQ).sendGroupText(2655226230,\"1\")\n'''\n\n@bot.on_friend_msg\ndef friend(ctx:FriendMsg):\n    if ctx.FromUin != ctx.CurrentQQ and ctx.Content == '你好':\n        Action(ctx.CurrentQQ).sendFriendText(251031557,ctx.Content)    \n\n\n\nif __name__ == '__main__':\n    bot.run()","repo_name":"CrazyGriferman/PiaoQQBot","sub_path":"miniproject.py","file_name":"miniproject.py","file_ext":"py","file_size_in_byte":1605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"2366147657","text":"\"\"\"\n\n@author Jinal Shah\n\n\"\"\"\nimport wordcloud\n\ntext = ''\nheight = int(input('Height of wordcloud: '))\nwidth = int(input(\"Width of wordcloud: \"))\n\ncloud = wordcloud.WordCloud(height=height,width=width)\nres = input(\"Do you have a file with all your text?(Yes/No): \")\n\nif res == 'No':\n    words = []\n\n    response = 'Yes'\n    word = input(\"First Word: \")\n\n    while response == 'Yes':\n        words.append(word)\n        word = input(\"Next Word: \")\n        response = input(\"Would you like to add another word?(Yes/No) \")\n\n    for x in words:\n        text += x + \" \"\n\n    cloud.generate_from_text(text=text)\n\n    cloud.to_file('wordcloud.png')\n\nelse:\n    fileName = input(\"Enter the filename: \")\n    file = open(file=fileName,mode='r')\n\n    for x in file.readlines():\n        text += x + \" \"\n\n    cloud.generate_from_text(text=text)\n\n    cloud.to_file('wordcloud.png')\n","repo_name":"JinalShah2002/Word_Cloud","sub_path":"WordCloud.py","file_name":"WordCloud.py","file_ext":"py","file_size_in_byte":865,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"7664315242","text":"# import sys\nfrom utils.base import save_fig\nfrom global_variable import STATISTIC_IMAGE_PATH\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nimport statsmodels.api as sm\n# sys.path.append(\"..\")\n\n\ndef basic_statistic(data: pd.DataFrame) -> None:\n    print(\"Some basic statistic information of the designated data set:\")\n    print(data.describe())\n\n\ndef is_null_value(data: pd.DataFrame) -> None:\n    print(\"Check which column has null values:\")\n    print(\"--\" * 10)\n    print(data.isnull().any())\n    print(\"--\" * 10)\n\n\ndef is_imputed(data: pd.DataFrame) -> bool:\n    \"\"\"Check whether the data set has null value or not\n\n    :param data: pd.DataFrame, the data set\n    :return: bool, True if it has null value\n    \"\"\"\n    flag = data.isnull().any().any()\n    if flag:\n        print(\"Tip: you'd better use imputation techniques to deal with the missing values.\")\n    else:\n        print(\"Tip: you don't need to deal with the missing values, we'll just pass this part!\")\n    return flag\n\n\ndef ratio_null_vs_filled(data: pd.DataFrame) -> None:\n    print('The ratio of the null values in each column:')\n    print(\"--\" * 10)\n    print(data.isnull().mean().sort_values(ascending=False))\n    print(\"--\" * 10)\n\n\ndef correlation_plot(col: pd.Index, df: pd.DataFrame) -> None:\n    \"\"\"A heatmap describing the correlation between the required columns\n\n    :param col: pd.Index, a list of columns that need to plot\n    :param df: pd.DataFrame, the dataframe\n    \"\"\"\n    plot_df = df[col]\n    plot_df_cor = plot_df.corr()\n    plt.figure(figsize=(20, 20))\n    sns.heatmap(plot_df_cor, cmap='coolwarm', annot=True, linewidths=.5)\n    print(\"Successfully calculate the pair-wise correlation coefficient among the selected columns.\")\n    save_fig(\"correlation_plot\", STATISTIC_IMAGE_PATH)\n\n\ndef distribution_plot(col: pd.Index, df: pd.DataFrame) -> None:\n    \"\"\"The histogram containing the respective distribution subplots of the required columns\n\n    :param col: pd.Index, a list of columns that need to plot\n    :param df: pd.DataFrame, the dataframe\n    \"\"\"\n    n = int(np.sqrt(len(col))) + 1\n    plt.figure(figsize=(n*2, n*2))\n    df.hist()\n    print(\"Successfully plot the distribution plot of the selected columns.\")\n    save_fig(\"distribution_histogram\", STATISTIC_IMAGE_PATH)\n\n\ndef probability_plot(col: pd.Index, df_origin: pd.DataFrame, df_impute: pd.DataFrame) -> None:\n    \"\"\"A large graph containing the respective probability plots (origin vs. impute) of the required columns\n\n    :param col: pd.Index, a list of columns that need to plot\n    :param df_origin: pd.DataFrame, the original dataframe\n    :param df_impute: pd.DataFrame, the dataframe after missing value imputation\n    \"\"\"\n    r, c = len(col) // 4 + 1, 4\n    fig = plt.figure(figsize=(c*8, r*8))\n    for i in range(len(col)):\n        feature = col[i]\n        pp_origin = sm.ProbPlot(df_origin[feature].dropna(), fit=True)\n        pp_impute = sm.ProbPlot(df_impute[feature], fit=True)\n        ax = fig.add_subplot(r, c, i+1)\n        pp_origin.ppplot(line=\"45\", other=pp_impute, ax=ax)\n        plt.title(f\"{feature}, origin data vs. imputed data\")\n    print(\"Successfully graph the respective probability plot (origin vs. impute) of the selected columns\")\n    save_fig(\"probability_plot\", STATISTIC_IMAGE_PATH)\n","repo_name":"RuiRaeZhu/geochemistrypy","sub_path":"geochemistrypy/plot/statistic_plot.py","file_name":"statistic_plot.py","file_ext":"py","file_size_in_byte":3316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"85"}
+{"seq_id":"41772364019","text":"# -*- coding:utf-8 -*-\n\nimport re\n\na = \"not 404 found 张三 99 深圳\"\nalist = a.split(\" \")\nres = re.findall('\\d+|[a-zA-Z]+', a)\nprint(list(res))\n\na = (1,)\nprint(type(a))\n","repo_name":"lillianfly/python","sub_path":"tmp/testfile.py","file_name":"testfile.py","file_ext":"py","file_size_in_byte":172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"32161149946","text":"import sys\nfrom urllib.request import urlopen\n\n\n# http://sixty-north.com/c/t.txt\n\ndef split_words(url):\n    with urlopen(url) as story:\n        story_words = []\n        for line in story:\n            line_words = line.decode('utf-8').split()\n            for word in line_words:\n                story_words.append(word)\n    return story_words\n    \n    \ndef print_words(words):\n    \"\"\"Prints the list of words passed to the function \n        Args:\n            words: A list\n\n        Returs:\n            None\n    \"\"\"\n    print(words)\n    return\n\ndef main(url): \n    new_words = split_words(url)\n    print_words(new_words)\n\n\n\nif __name__ == \"__main__\":\n    main(sys.argv[1])\n    print(\"called\")\nelse:\n    print(\"not main\")","repo_name":"ramanujamk/python-sample","sub_path":"url.py","file_name":"url.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"13965392765","text":"\n# coding: utf-8\n\n# In[35]:\n\na = open(\"C:/Users/saiad/Desktop/IE Project/reviews_Electronics_split_9/reviews_Electronics_3.txt\").read()\n\n\n# In[2]:\n\nimport re\nimport nltk\n\n\n# In[36]:\n\nsents = nltk.sent_tokenize(a)\n\n\n# In[10]:\n\nmystring = (\"feature\", \"delivered late\", \"delayed\", \"Shipment delayed\",\"arrived early\",\"late delivered\",\n             \"\")\ntext = str()\nfor line in sents:\n    if any(x in line for x in mystring):\n    #if mystring in line:\n          text = text + line\n\n\n# In[38]:\n\nwith open('C:/Users/saiad/Desktop/IE Project/Output/reviews_Electronics_3.txt', 'w') as f:\n    f.writelines(text)\n\n\n# In[36]:\n\nb = open(\"C:/Users/saiad/Desktop/IE Project/Output/output3.txt\").read()\n\n\n# In[18]:\n\nimport re\nc = re.findall(r\"asin..\\s['\\w]+\",b)\nd = re.findall(r\"title':\\s['\\w\\s&-]+\",b)\n\n\n# In[23]:\n\nwith open('C:/Users/saiad/Desktop/IE Project/Output/output1.txt', 'w') as f:\n    f.writelines(c)\n\n\n# In[37]:\n\nd = b.replace(\"title\",\"\\n\")\n\n\n# In[38]:\n\nwith open('C:/Users/saiad/Desktop/IE Project/Output/output3.txt', 'w') as f:\n    f.writelines(d)\n\n\n# Code to extract the dates\n\ndatesextr = open(\"C:/Users/saiad/Desktop/IE Project/Output/combined_shipment_electronics_neg.txt\").read()\n\ndatesextrs = re.findall(r'(reviewTime\":\\s\"[\\d\\s,]+)',datesextr)\n\nwith open('C:/Users/saiad/Desktop/IE Project/Output/combined_shipment_electronics_neg_dates.txt', 'w') as f:\n    f.writelines(datesextrs)\n\n\n\n\n","repo_name":"saicumbulam/Amazon-Shipment-Delay","sub_path":"Shipment Delay.py","file_name":"Shipment Delay.py","file_ext":"py","file_size_in_byte":1394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"10436158422","text":"import datetime\nfrom urllib.parse import unquote_plus\n\nimport pytest\n\nfrom src.api.disk_api.model import ResourcesResponse\n\n\n@pytest.mark.usefixtures(\"auth\")\nclass TestDiskApi:\n    \"\"\"\n    Тестирование API Яндекс.Диска\n    \"\"\"\n\n    @pytest.mark.parametrize(\"folder, file1, file2\",\n                             [('disk:/TEST-' + str(datetime.datetime.now()),\n                               'файл для копирования.txt',\n                               'переименованный файл.txt')])\n    def test_by_tz(self, disk_api, folder, file1, file2):\n        \"\"\"\n        Сценарий по ТЗ.\n        Шаги :\n        1. Выполнить запрос на создание новой папки с названием из folder.\n        2. Скопировать файл file1 в созданную папку.\n        3. Переименовать файл file1 в file2.\n        OP : Код ответа соответствует требованиям (201, 202), тело ответа соответствуют требованиям.\n        \"\"\"\n        # 1\n        response = disk_api.create_dir(path=folder,\n                                       type_response=ResourcesResponse,\n                                       header=self.auth)\n        assert response.status_code == 201\n        # 2\n        response = disk_api.copy_file_or_dir(from_path=file1,\n                                             to_path=f\"{folder}/{file1}\",\n                                             type_response=ResourcesResponse,\n                                             header=self.auth)\n        assert response.status_code in [201, 202]\n        # 3\n        response = disk_api.move_file_or_dir(from_path=f\"{folder}/{file1}\",\n                                             to_path=f\"{folder}/{file2}\",\n                                             type_response=ResourcesResponse,\n                                             header=self.auth)\n        # OP\n        path_in_response = unquote_plus(response.data.href).split('=')[-1]\n        assert response.status_code in [201, 202]\n        assert path_in_response == f\"{folder}/{file2}\"\n        assert response.data.method == \"GET\"\n\n    # ------------------------------------------------------------------------------------------------------------------\n    def test_disk_info(self, disk_api):\n        \"\"\"\n        Получение общей информации по диску пользователя.\n        \"\"\"\n        response = disk_api.get_user_disk_info(type_response=None, header=self.auth)\n        assert response.status_code == 200\n\n    def test_mkdir(self, disk_api):\n        \"\"\"\n        Создание и удаление папки.\n        \"\"\"\n        folder = 'disk:/TEST' + str(datetime.datetime.now())\n        # 1\n        response = disk_api.create_dir(path=folder,\n                                       type_response=ResourcesResponse,\n                                       header=self.auth)\n        assert response.status_code == 201\n        # 2\n        response = disk_api.delete_file_or_dir(path=folder,\n                                               type_response=None,\n                                               header=self.auth)\n        assert response.status_code in [204, 202]\n\n    def test_copy_file(self, disk_api):\n        \"\"\"\n        Копирование файла.\n        \"\"\"\n        response = disk_api.copy_file_or_dir(from_path='файл для копирования.txt',\n                                             to_path='disk:/TEST/файл для копирования.txt',\n                                             overwrite='true',\n                                             type_response=ResourcesResponse,\n                                             header=self.auth)\n        assert response.status_code in [201, 202]\n","repo_name":"BortnikovaOlga/ydisk_api","sub_path":"test/test_disk_api.py","file_name":"test_disk_api.py","file_ext":"py","file_size_in_byte":3884,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"21195036863","text":"#Sam Krimmel\n#5/7/18\n#quiz5.py\n\ndef penultimate(L):\n    return L[len(L)-2]\n\ndef plusEquals(numbers,num):\n    for i in range(0,len(numbers)):\n        numbers[i] = numbers[i]+num\n    return numbers\n    \ndef smallest(numbers):\n    smallest = sum(numbers)\n    for item in numbers:\n        if item < smallest:\n            smallest = item\n    return smallest\n    \nprint(penultimate([3,4,5,6,7]))\nprint(plusEquals([1,2,3,4],10))\nprint(smallest([1,2,3,4]))\n\n","repo_name":"samkrimmel/unit5","sub_path":"quiz5.py","file_name":"quiz5.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"19823962522","text":"from __future__ import print_function\r\nfrom __future__ import division\r\nfrom scipy.optimize import minimize\r\nfrom sklearn.gaussian_process import GaussianProcessRegressor\r\nfrom sklearn.gaussian_process.kernels import Matern\r\nfrom sklearn.neighbors import KernelDensity\r\nimport numpy as np\r\nfrom scipy.stats import norm\r\n\r\n\r\n\r\ndef _hashable(x):\r\n    return tuple(map(float,x))\r\n\r\n\r\ndef unique_rows(a):\r\n    if a.size == 0:\r\n        return np.empty((0,))\r\n\r\n    order = np.lexsort(a.T)\r\n    reorder = np.argsort(order)\r\n\r\n    a = a[order]\r\n    diff = np.diff(a, axis=0)\r\n    ui = np.ones(len(a), 'bool')\r\n    ui[1:] = (diff != 0).any(axis=1)\r\n\r\n    return ui[reorder]\r\n\r\ndef ensure_rng(random_state=None):\r\n\r\n    if random_state is None:\r\n        random_state = np.random.RandomState()\r\n    elif isinstance(random_state, int):\r\n        random_state = np.random.RandomState(random_state)\r\n    else:\r\n        assert isinstance(random_state, np.random.RandomState)\r\n    return random_state\r\n\r\n\r\nclass UtilityFunction(object):\r\n\r\n    def __init__(self,kind,kappa,xi):\r\n\r\n        self.kappa=kappa\r\n\r\n        self.xi=xi\r\n\r\n        if kind not in ['ucb','ei','poi','tpe']:\r\n            err=\"Please choose one of ucb,ei,poi\"\r\n            raise NotImplementedError(err)\r\n        else:\r\n            self.kind=kind\r\n\r\n\r\n    def utility(self,x,gp,y_max,xt,yt):\r\n        if self.kind=='ucb':\r\n            return self._ucb(x,gp,self.kappa)\r\n        if self.kind=='ei':\r\n            return self._ei(x,gp,y_max,self.xi)\r\n        if self.kind=='poi':\r\n            return self._poi(x,gp,y_max,self.xi)\r\n        if self.kind=='tpe':\r\n            return self.tpe(x,xt,yt)\r\n\r\n    @staticmethod\r\n    def _ucb(x,gp,kappa):\r\n        mean,std=gp.predict(x,return_std=True)\r\n        return mean+kappa*std\r\n\r\n\r\n    @staticmethod\r\n    def _ei(x,gp,y_max,xi):\r\n        mean,std=gp.predict(x,return_std=True)\r\n        z=(mean-y_max-xi)/std\r\n        return (mean-y_max-xi)*norm.cdf(z)+std*norm.pdf(z)\r\n\r\n    @staticmethod\r\n    def _poi(x,gp,y_max,xi):\r\n        mean,std=gp.predict(x,return_std=True)\r\n        z=(mean-y_max-xi)/std\r\n        return norm.cdf(z)\r\n\r\n    @staticmethod\r\n    def tpe(x_trail,x,y):\r\n\r\n        best_ratio = 0.2\r\n        n_best = int(len(y) * best_ratio)\r\n        y_sorted_index = np.argsort(y)\r\n\r\n        k=0\r\n        x_sorted=np.zeros([len(x),len(x[0])])\r\n        for i in y_sorted_index:\r\n            for j in range(len(x[0])):\r\n                x_sorted[k][j]=x[i][j]\r\n            k+=1\r\n\r\n        x_best = x_sorted[len(y) - n_best:]\r\n        x_worst = x_sorted[:len(y) - n_best]\r\n\r\n        kd_b=KernelDensity(bandwidth=0.1)\r\n        kd_w=KernelDensity(bandwidth=0.1)\r\n        kd_best=kd_b.fit(x_best)\r\n        kd_worst=kd_w.fit(x_worst)\r\n\r\n        best_proba=kd_best.score_samples(x_trail)\r\n        worst_proba=kd_worst.score_samples(x_trail)\r\n\r\n        ei=np.abs(worst_proba)/np.abs(best_proba)\r\n\r\n        return ei\r\n\r\nclass TargetSpace:\r\n\r\n    def __init__(self,f,bounds,random_state=None):\r\n\r\n\r\n        self.random_state =ensure_rng(random_state)\r\n\r\n        self.f=f\r\n        self.keys=list(bounds.keys())\r\n        self.bounds=np.array(list(bounds.values()),dtype=np.float)\r\n        self.dim=len(self.keys)\r\n\r\n        self._length = 0 # of observations\r\n\r\n\r\n        self._Xarr=None\r\n        self._Yarr=None\r\n\r\n        # views of preallocated data\r\n        self._Xview=None\r\n        self._Yview = None\r\n        self._cache={}\r\n\r\n    @property\r\n    def getX(self):\r\n        return self._Xview\r\n\r\n    @property\r\n    def getY(self):\r\n        return self._Yview\r\n\r\n    def __contains__(self, x):\r\n        return _hashable(x) in self._cache\r\n\r\n    def __len__(self):\r\n        return self._length\r\n\r\n    def _dict_to_points(self,points_dict):\r\n\r\n        param_tup_lens=[]\r\n\r\n        for key in self.keys:\r\n            param_tup_lens.append(len(list(points_dict[key])))\r\n\r\n\r\n        if all([e==param_tup_lens[0] for e in param_tup_lens]):\r\n            pass\r\n        else:\r\n            raise ValueError('The same number of initialization points must be entered for every parameter')\r\n\r\n        all_points=[]\r\n        for key in self.keys:\r\n            all_points.append(points_dict[key])\r\n\r\n\r\n        points=list(map(list,zip(*all_points)))\r\n        return points\r\n\r\n\r\n    def observe_point(self,x):\r\n        '''finding y=f(x)'''\r\n\r\n        x=np.asarray(x).ravel()\r\n        assert x.size==self.dim,\"x must have the same dimension\"\r\n\r\n        if x in self:\r\n            '''lookup for the cache'''\r\n            y=self._cache[_hashable(x)]\r\n        else:\r\n            '''find the target function'''\r\n            params=dict(zip(self.keys,x))\r\n            y=self.f(**params)\r\n            self.add_observation(x,y)\r\n        return y\r\n\r\n\r\n\r\n\r\n    def add_observation(self,x,y):\r\n\r\n        if self._length>=self._n_alloc_rows:\r\n            self.allocate((self._length+1)*2)\r\n\r\n        x=np.asarray(x).ravel()\r\n\r\n        self._cache[_hashable(x)]=y\r\n\r\n        self._Xarr[self._length] = x\r\n        self._Yarr[self._length] = y\r\n\r\n        self._length+=1\r\n\r\n        self._Xview=self._Xarr[:self._length]\r\n        self._Yview=self._Yarr[:self._length]\r\n\r\n\r\n\r\n    def allocate(self,num,fast=True):\r\n\r\n        if num <=self._n_alloc_rows:\r\n            raise ValueError('num must be larger than current array length')\r\n\r\n        self._assert_internal_invariants()\r\n\r\n        _Xnew=np.empty((num,self.bounds.shape[0]))\r\n        _Ynew=np.empty(num)\r\n\r\n\r\n        if self._Xarr is not None:\r\n            _Xnew[:self._length]=self._Xarr[:self._length]\r\n            _Ynew[:self._length]=self._Yarr[:self._length]\r\n\r\n        self._Xarr=_Xnew\r\n        self._Yarr=_Ynew\r\n\r\n        self._Xview=self._Xarr[:self._length]\r\n        self._Yview=self._Yarr[:self._length]\r\n\r\n    @property\r\n    def _n_alloc_rows(self):\r\n        return 0 if self._Xarr is None else self._Xarr.shape[0]\r\n\r\n    def random_points(self,num):\r\n        '''Creates random points within the bounds of a space variable'''\r\n\r\n        data=np.empty((num,self.dim))\r\n\r\n        for col,(lower,upper) in enumerate(self.bounds):\r\n             data.T[col]=self.random_state.uniform(lower,upper,size=num)\r\n        return data\r\n\r\n    def max_point(self):\r\n        '''Return current max points that best maximizes the target function'''\r\n\r\n        return {'max_val':self.getY.max(),\r\n                'max_params': dict(zip(self.keys,\r\n                                       self.getX[self.getY.argmax()]))}\r\n\r\n    def _assert_internal_invariants(self, fast=True):\r\n        \"\"\"\r\n        Run internal consistency checks to ensure that data structure\r\n        assumptions have not been violated.\r\n        \"\"\"\r\n        if self._Xarr is None:\r\n            assert self._Yarr is None\r\n            assert self._Xview is None\r\n            assert self._Yview is None\r\n        else:\r\n            assert self._Yarr is not None\r\n            assert self._Xview is not None\r\n            assert self._Yview is not None\r\n            assert len(self._Xview) == self._length\r\n            assert len(self._Yview) == self._length\r\n            assert len(self._Xarr) == len(self._Yarr)\r\n\r\n            if not fast:\r\n                # run slower checks\r\n                assert np.all(unique_rows(self.X))\r\n\r\n\r\ndef acq_max(ac,gp,y_max,xt,yt,bounds,random_state,n_warmup=100000,n_iter=250):\r\n    '''function to find the maximum of the acquisition function'''\r\n    x_tries=random_state.uniform(bounds[:,0],bounds[:,1],\r\n                                 size=(n_warmup,bounds.shape[0]))\r\n    ys=ac(x_tries,gp=gp,y_max=y_max,xt=xt,yt=yt)\r\n    x_max=x_tries[ys.argmax()]\r\n    max_acq=ys.max()\r\n\r\n    x_seeds=random_state.uniform(bounds[:,0],bounds[:,1],\r\n                                 size=(n_iter,bounds.shape[0]))\r\n\r\n    for x_try in x_seeds:\r\n        res=minimize(lambda x:-ac(x.reshape(1,-1),gp=gp,y_max=y_max,xt=xt,yt=yt),\r\n                     x_try.reshape(1,-1),\r\n                     bounds=bounds,\r\n                     method=\"L-BFGS-B\")\r\n\r\n\r\n        #see if success\r\n        if not res.success:\r\n            continue\r\n\r\n        #store if better than the previous minimum/maximum\r\n        if max_acq is None or -res.fun[0]>=max_acq:\r\n            x_max=res.x\r\n            max_acq=-res.fun[0]\r\n\r\n    return np.clip(x_max,bounds[:,0],bounds[:,1])\r\n\r\n\r\n\r\nclass Optimizer():\r\n    def __init__(self,f,bounds,random_state=None,verbose=1):\r\n        self.bounds=bounds\r\n        self.random_state=ensure_rng(random_state)\r\n        self.gp=GaussianProcessRegressor(kernel=Matern(nu=2.5),\r\n                                         n_restarts_optimizer=25,\r\n                                         random_state=self.random_state)\r\n        self.space=TargetSpace(f,bounds,self.random_state)\r\n        self.initialized = False\r\n        self.i = 0# used for iterations\r\n        self.util = None #utility function\r\n\r\n        self._acqkw = {'n_warmup': 100000, 'n_iter': 250}\r\n        self.init_points = []\r\n        self.x_init = []\r\n        self.y_init = []\r\n\r\n        self.res={} #output dictionary\r\n        self.res['max']={'max_val':None,\r\n                         'max_params': None}\r\n        self.res['all']={'values':[],'params':[]}\r\n        self.verbose = verbose\r\n\r\n    def init(self,init_points):\r\n        #init_points is number of randomly sampled points to probe\r\n        rand_points=self.space.random_points(init_points)\r\n        self.init_points.extend(rand_points)\r\n\r\n\r\n        for x in self.init_points:\r\n            y=self.space.observe_point(x)\r\n\r\n        if self.x_init:\r\n            x_init=np.vstack(self.x_init)\r\n            y_init=np.hstack(self.y_init)\r\n            for x,y in zip(x_init,y_init):\r\n                self.space.add_observation(x,y)\r\n\r\n\r\n        self.initialized=True\r\n\r\n\r\n    def explore(self,points_dict,eager=False):\r\n\r\n        if eager:\r\n            points=self.space._dict_to_points(points_dict)\r\n            for x in points:\r\n                self.space.observe_point(x)\r\n        else:\r\n            points=self.space._dict_to_points(points_dict)\r\n            self.init_points=points\r\n\r\n\r\n    def maximize(self,init_points=5,n_iter=25,acq='ei',kappa=2.576,xi=0.0,**gp_params):\r\n        '''main optimization method'''\r\n        self.util=UtilityFunction(kind=acq,kappa=kappa,xi=xi)\r\n\r\n        if not self.initialized:\r\n            self.init(init_points)\r\n\r\n        y_max=self.space.getY.max()\r\n\r\n        self.gp.set_params(**gp_params)\r\n\r\n        self.gp.fit(self.space.getX,self.space.getY)\r\n\r\n        x_max=acq_max(ac=self.util.utility,\r\n                      gp=self.gp,\r\n                      y_max=y_max,\r\n                      yt=self.space.getY,\r\n                      xt=self.space.getX,\r\n                      bounds=self.space.bounds,\r\n                      random_state=self.random_state,\r\n                      **self._acqkw)\r\n\r\n        for i in range(n_iter):\r\n            while x_max in self.space:\r\n                x_max=self.space.random_points(1)[0]\r\n\r\n            y=self.space.observe_point(x_max)\r\n\r\n            #update the gp\r\n            self.gp.fit(self.space.getX,self.space.getY)\r\n\r\n            #update the best params seen so far\r\n            self.res['max']=self.space.max_point()\r\n            self.res['all']['values'].append(y)\r\n            self.res['all']['params'].append(dict(zip(self.space.keys,x_max)))\r\n\r\n            if self.space.getY [ -1] > y_max:\r\n                y_max=self.space.getY [-1]\r\n\r\n\r\n            #maximize the acquisition function to find the next probing point\r\n            x_max=acq_max(ac=self.util.utility,\r\n                          gp=self.gp,\r\n                          y_max=y_max,\r\n                          yt=self.space.getY,\r\n                          xt=self.space.getX,\r\n                          bounds=self.space.bounds,\r\n                          random_state=self.random_state,\r\n                          **self._acqkw)\r\n\r\n            self.i +=1","repo_name":"mmangliyeva/HyperparameterOptimization","sub_path":"BayesianTPE_V3.py","file_name":"BayesianTPE_V3.py","file_ext":"py","file_size_in_byte":11839,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"39107865218","text":"import matplotlib.pyplot as pyplot\nimport numpy\n\n# Calculate angle and position\nSAMPLE_RATE = 100\nANGULAR_VELOCITY = 45\nRADIUS = 1\n\nstarting_stationary = numpy.zeros(2 * SAMPLE_RATE)  # 2 seconds\naccelerating = numpy.linspace(0, ANGULAR_VELOCITY, 5 * SAMPLE_RATE)  # 5 seconds\nconstant_velocity = numpy.full(3 * SAMPLE_RATE, ANGULAR_VELOCITY)  # 3 seconds\ndecelerating = numpy.linspace(ANGULAR_VELOCITY, 0, 5 * SAMPLE_RATE)  # 5 seconds\nending_stationary = numpy.zeros(2 * SAMPLE_RATE)  # 2 seconds\n\nomega = numpy.concatenate((starting_stationary, accelerating, constant_velocity, decelerating, ending_stationary))\n\ntime = numpy.linspace(0, (len(omega) - 1) / SAMPLE_RATE, len(omega))\n\ntheta = (numpy.cumsum(omega) / SAMPLE_RATE) % 360\n\nx = RADIUS * numpy.cos(numpy.radians(theta))\ny = RADIUS * numpy.sin(numpy.radians(theta))\n\n# Write to file\nwith open(\"circle.csv\", \"w\") as file:\n    file.write(\"Time (s),X (m),Y (m),Z (m),X (deg),Y (deg),Z (deg)\\n\")\n\n    for index, _ in enumerate(time):\n        file.write(str(time[index]) + \",\" + str(x[index]) + \",\" + str(y[index]) + \",0,0,0,\" + str(theta[index]) + \"\\n\")\n\n# Plot\n_, axes = pyplot.subplots(nrows=3, sharex=True)\n\naxes[0].plot(time, omega, label=\"omega\")\naxes[0].set_ylabel(\"Angular velocity (°/s)\")\naxes[0].grid()\naxes[0].legend()\n\naxes[1].plot(time, theta, label=\"theta\")\naxes[1].set_ylabel(\"Angle (°)\")\naxes[1].grid()\naxes[1].legend()\n\naxes[2].plot(time, x, label=\"x\")\naxes[2].plot(time, y, label=\"y\")\naxes[2].set_ylabel(\"Position (m)\")\naxes[2].set_xlabel(\"Time (s)\")\naxes[2].grid()\naxes[2].legend()\n\npyplot.show()\n","repo_name":"xioTechnologies/IMU-Simulator","sub_path":"Examples/circle/circle.py","file_name":"circle.py","file_ext":"py","file_size_in_byte":1574,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"34953033624","text":"# Faça um programa que lê as duas notas parciais obtidas por um aluno numa disciplina ao longo de um semestre,\n# e calcule a sua média. A atribuição de conceitos obedece à tabela abaixo:\n#   Média de Aproveitamento  Conceito\n#   Entre 9.0 e 10.0        A\n#   Entre 7.5 e 9.0         B\n#   Entre 6.0 e 7.5         C\n#   Entre 4.0 e 6.0         D\n#   Entre 4.0 e zero        E\n\nnota1 = float(input('Digite a nota 1: '))\nnota2 = float(input('Digite a nota 2: '))\nmedia = (nota1 + nota2) / 2\nconceito = ''\nsituação = ''\n\nif 0 <= media < 4:\n    conceito = 'E'\nelif 4 <= media < 6:\n    conceito = 'D'\nelif 6 <= media < 7.5:\n    conceito = 'C'\nelif 7.5 <= media < 9:\n    conceito = 'B'\nelif 9 <= media <= 10:\n    conceito = 'A'\n\nif conceito in 'ABC':\n    situação = 'APROVADO!!'\nelse:\n    situação = 'REPROVADO'\n\n\nprint(f'Aluno tirou nas provas {nota1, nota2} e ficou com média {media}, CONCEITO: {conceito}')\nprint(situação)\n","repo_name":"eduspj/exerciciospython","sub_path":"EstruturaDeDecisao/exerc14.py","file_name":"exerc14.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"6629784287","text":"from collections import deque \n\nN = int(input())\ngraph = list()\nfor _ in range(N):\n    graph.append(list(map(int,input().split())))\n\n\ndx = [-1,1,0,0]\ndy = [0,0,-1,1]\nanswer = 0 \nfor h  in range(1,101):\n    v = [[0]*N for _ in range(N)] \n    def BFS(y,x):\n        v[y][x] = 1 \n        q = deque()\n        q.append((y,x))\n        while q: \n            y,x = q.popleft()\n            for i in range(4):\n                nx = x+dx[i]\n                ny = y + dy[i]\n                if 0<=nx<N and 0<=ny<N and graph[ny][nx] < h and v[ny][nx] ==0:\n                    v[ny][nx] = 1 \n                    q.append((ny,nx))\n\n\n    def BFS_safe(y,x):\n        v[y][x] = 1 \n        q = deque()\n        q.append((y,x))\n        while q: \n                y,x = q.popleft()\n                for i in range(4):\n                    nx = x+dx[i]\n                    ny = y + dy[i]\n                    if 0<=nx<N and 0<=ny<N and v[ny][nx] ==0:\n                        v[ny][nx] = 1 \n                        q.append((ny,nx))\n\n        \n\n    # 높이는 N \n    # 잠기는 지역 파악 \n    for i in range(N):\n        for j in range(N):\n            if graph[i][j] < h and v[i][j] == 0:\n                BFS(i,j)\n    temp_answer = 0 \n    for i in range(N):\n        for j in range(N):\n            if v[i][j] == 0: \n                temp_answer += 1\n                BFS_safe(i,j)\n    answer = max(answer,temp_answer)\n\nprint(answer)","repo_name":"JunYupKim/go_algorithm","sub_path":"baekjoon/2468/2468.py","file_name":"2468.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"573249377","text":"import json\nimport logging\n\nfrom appAlarm.utils import return_result\nfrom appAlarm.models import NavigationModel, NavigationLevelTwoModel\nfrom django.core import serializers\n\nlogger = logging.getLogger('django')\n\n\nclass NavigationManage:\n\n    def get_all_navigation(self):\n        try:\n            all_data = json.loads(serializers.serialize(\"json\", NavigationModel.objects.all().order_by(\"id\")))\n\n            data = []\n            for i in all_data:\n                tmp_dict = {}\n                level_two_data = json.loads(serializers.serialize(\"json\",\n                                                                  NavigationLevelTwoModel.objects.filter(\n                                                                      level_one_name=i[\"fields\"][\n                                                                          \"level_one_name\"]).order_by(\"id\")))\n                tmp_dict[\"pk\"] = i[\"pk\"]\n                tmp_dict[\"name\"] = i[\"fields\"][\"level_one_name\"]\n                tmp_dict[\"childs\"] = level_two_data\n                data.append(tmp_dict)\n        except Exception as e:\n            logger.error(\"查询菜单栏结果失败，原因：{message}\".format(message=e))\n            return return_result.result(False, message=\"查询结果失败\")\n        return return_result.result(True, data=data)\n","repo_name":"wangyue369/alartPlatformPython","sub_path":"appAlarm/manages/NavigationManage.py","file_name":"NavigationManage.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"1684087401","text":"import matplotlib\nmatplotlib.use('Agg')\n\nimport os\nimport io\nimport base64\nimport numpy as np\nimport numpy.matlib\nimport scipy.signal\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg\nfrom scipy.io import loadmat\nfrom utils import prepare_gt\n\n\ndef visualize_mil(\n    videoname, \n    frame_cnt, \n    path_to_MIL_test_output_dir,\n    gt_file_pth):\n    '''\n    Parameters:\n    ---------\n        videoname: \n            Videoname of a particular choosen test set\n        frame_cnt: \n            Frame length for corresponding test set\n        path_to_MIL_test_output_dir:\n            Path to directory containing all test set's prediction (MIL)\n        gt_file_pth:\n            Path to file containing ground-truth at temporal-level\n\n    Returns:\n    ---------\n        x: \n            Interval for frames count on the x-axis\n        y:\n            Scores at the y-axis for corresponding frame intervals.\n        gt_bar: \n            Numpy array containing the values for plotting ground-truth bar chart.\n\n    '''\n    fps = 30\n    # Obtain temporal annotations\n    ground_truth = os.listdir(gt_file_pth)[0]\n    ground_truth = os.path.join(gt_file_pth, ground_truth)\n    gtdf = prepare_gt(ground_truth)\n    gtdf = gtdf[gtdf['videoname'] == videoname]\n    gtdf = gtdf[['start', 'end']].values * fps\n\n    gt_bar = np.zeros((1, frame_cnt))\n    for gt_rows in range(len(gtdf)):\n        start_frame = int(gtdf[gt_rows, 0])\n        end_frame = int(gtdf[gt_rows, 1])\n        gt_bar[:, start_frame: end_frame] = 1\n\n    assert(gt_bar.shape[1] == frame_cnt)\n\n    \n    videoname_MIL = videoname + '_C'\n    video_MIL_output = os.path.join(\n        path_to_MIL_test_output_dir,\n        videoname_MIL\n    )\n\n    predictions_mil = loadmat(video_MIL_output)\n    predictions_mil = predictions_mil['predictions']\n\n    #---- Prediction of scores for each 32 segments in MIL ------\n    num_seg = 32\n    total_segments = np.linspace(1, frame_cnt, num=num_seg+1)\n    total_segments = total_segments.round()\n\n    Frames_Score = []\n    count = -1\n    for iv in range(0, num_seg):\n        F_Score = np.matlib.repmat(\n            predictions_mil[iv],\n            1,\n            (int(total_segments[iv+1])-int(total_segments[iv]))\n        )\n        count = count + 1\n        if count == 0:\n            Frames_Score = F_Score\n        if count > 0:\n            Frames_Score = np.hstack((Frames_Score, F_Score))\n\n    x = np.linspace(1, frame_cnt, frame_cnt)\n    scores = Frames_Score\n    scores1 = scores.reshape((scores.shape[1],))\n    y = scipy.signal.savgol_filter(scores1, 101, 3)\n    x = x.tolist()\n    y = y.tolist()\n\n    return x, y, gt_bar\n\n\n\ndef plot_mil_charts(x, y, gt_bar):\n    '''\n    Parameters:\n    ---------\n        x: \n            Frames of selected video along the x-axis in chart.\n        y: \n            Scores of every frame along the y-axis in chart.\n        gt_bar:\n            Values of ground-truth (temporal-level) for plotting bar chart.\n\n\n    Returns:\n    ---------\n        milImageB64String:\n            Base-64 string representation of charts plot for MIL\n    '''\n\n    x, y = x[1:], y\n    height = gt_bar.flatten().tolist()[1:]\n    assert(len(x) == len(height))\n    assert(len(x) == len(y))\n\n    fig = plt.figure()\n    frame_cnt = len(x)\n    xmin = 0\n    xmax = frame_cnt\n    ymin = 0\n    ymax = 1\n    plt.axis([xmin, xmax, ymin, ymax])\n    plt.bar(x, height=height, width=1.0, color='#893101')\n    plt.plot(x, y, color='green', linewidth=3)\n    plt.yticks([])\n    fig.tight_layout()\n    \n    # Convert fig to PNG image\n    fig_image = io.BytesIO()\n    FigureCanvasAgg(fig).print_png(fig_image)\n\n    # Encode PNG image to base64 string\n    milImageB64String = \"data:image/png;base64,\"\n    milImageB64String += base64.b64encode(fig_image.getvalue()).decode('utf8')\n    return milImageB64String","repo_name":"VivaaindreanNg/fyp_demo","sub_path":"weakly_supervised/mil.py","file_name":"mil.py","file_ext":"py","file_size_in_byte":3828,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"31223846440","text":"import discord\nfrom discord.ext import commands\nfrom functions import *\nimport json\nimport asyncio\nfrom discord import Button, ButtonStyle\n\ndocs = {\n\n    \"aliases\":['adminpings'],\n\n    \"usage\":\"!adminping\",\n\n    \"description\":\"If a user pings/mentions an admin role, this feature will send a message to the designated channel that links to the ping/mention. This allows for easier ping tracking instead of searching through an entire channel to find the ping.\\n\\nOnce a channel is set, this feature will automatically activate. Undesignating a channel will also disable this feature.\",\n\n    \"category\":\"admin-administrative\"\n    \n    }\n\ndef setup(client):\n    @client.command(aliases=['adminpings'])\n    async def adminping(ctx):\n        if not hasAdminRole(ctx) and checkOwner(ctx):\n            await ctx.reply(\"You do not have permission to use this command!\", delete_after=20)\n            return\n\n        sqlCursor.execute('SELECT adminPingChannel FROM serverDB WHERE serverId = %s', (ctx.guild.id,))\n        channelData = sqlCursor.fetchone()[0]\n\n        view = discord.ui.View()\n        button1 = discord.ui.Button(label=\"Set\", style=ButtonStyle.green, custom_id='add')\n        button2 = discord.ui.Button(label=\"Remove\", style=ButtonStyle.red, custom_id='remove')\n        button3 = discord.ui.Button(label=\"Done\", style=ButtonStyle.gray, custom_id='done')\n        view.add_item(button1)\n        if channelData == None:\n            content = 'No Admin Ping channel has been set'\n            content2 = 'Please set a channel to enable the Admin Ping feature.'\n        else:\n            view.add_item(button2)\n            content = f'`#{ctx.guild.get_channel(channelData)}` is currently set as the Admin Ping channel.'\n            content2 = 'Use `SET` to overwrite this channel, or `REMOVE` to remove the channel and disable this feature.'\n        view.add_item(button3)\n\n        msg1 = await ctx.send(embed=discord.Embed(title=content, description=content2), view=view)\n\n        def checkButton(m):\n            return m.message == msg1 and m.user == ctx.author\n        try:\n            interacted = await client.wait_for('interaction', timeout=300, check=checkButton)\n        except asyncio.TimeoutError:\n            await msg1.edit(content='Timed out!', view=None)\n        else:\n            await interacted.response.defer()\n            await msg1.edit(view=None)\n            \n            if interacted.data['custom_id'] == 'done':\n                return\n            elif interacted.data['custom_id'] == 'add':\n                def makeOptions(myList):\n                    return discord.SelectOption(label=f'#{myList[1][0]}', value=f'{myList[1][1]}')\n\n                options = list(map(makeOptions, list(enumerate(list(map(lambda data: (data.name, data.id) ,ctx.guild.text_channels)), start=1))))\n                options.append(discord.SelectOption(label=f'Cancel', value=f'0'))\n                view = discord.ui.View()\n                myMenu = discord.ui.Select(placeholder=\"Choose a channel to set\", options=options)\n                view.add_item(myMenu)\n                msg2 = await ctx.send(view=view)\n\n                def checkButton(m):\n                    return m.message == msg2 and m.user == ctx.author\n                try:\n                    interacted = await client.wait_for('interaction', timeout=300, check=checkButton)\n                except asyncio.TimeoutError:\n                    await msg2.edit(content='Timed out!', view=None)\n                else:\n                    await interacted.response.defer()\n                    optionSelected = int(interacted.data[\"values\"][0])\n                    await msg2.delete()\n\n                    if optionSelected == 0:\n                        await ctx.send(embed=discord.Embed(title=\"Set Channel cancelled\"))\n                    else:\n                        sql = 'UPDATE serverDB SET adminPingChannel = %s WHERE serverId = %s'\n                        val = (optionSelected, ctx.guild.id)\n                        sqlCursor.execute(sql, val)\n                        sqlDb.commit()\n\n                        await ctx.send(embed=discord.Embed(title=f'#{client.get_channel(optionSelected).name} set as Admin Ping channel.'))\n\n            elif interacted.data['custom_id'] == 'remove':\n                sql = 'UPDATE serverDB SET adminPingChannel = %s WHERE serverId = %s'\n                val = (None, ctx.guild.id)\n                sqlCursor.execute(sql, val)\n                sqlDb.commit()\n\n                embed=discord.Embed(title=f'`#{ctx.guild.get_channel(channelData)}` removed as the Admin Ping channel')\n                await ctx.send(embed=embed)","repo_name":"llkyz/Server-Lizard","sub_path":"commandList/adminping.py","file_name":"adminping.py","file_ext":"py","file_size_in_byte":4621,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"}
+{"seq_id":"8309081437","text":"from Tools.usefulDecorators import printAllParameters\nimport pickle\nfrom settings.settings import CSimulationData\n\n\nclass CSimulation:\n    \"\"\"\n    In this class the parameters of the simulation are saved. The methods \"run\" and \"performTimeStep\" are the frame of\n    the simulation.\n    \"\"\"\n    def __init__(self, settings=None):\n        if settings is None:\n            self.settings = CSimulationData()\n        else:\n            self.settings = settings\n        # set the environment in which the population is placed\n        self.env = self.settings.settings_dict['classType_of_environment'](\n                    **self.settings.settings_dict['classType_settings']['classType_of_environment'])\n\n        # create a population of males on random positions in that environment\n        # ToDo: Consider better solutions instead of adding smth to the settings\n        self.settings.settings_dict['classType_settings']['classType_of_population']['set_initial_position_in_the_environment'] = \\\n            self.env.place_item_in_environment\n\n        self.population = self.settings.settings_dict['classType_of_population'](\n            **self.settings.settings_dict['classType_settings']['classType_of_population'])\n\n        # information about the current simulation\n        self.selected_individual = None\n        self.running = True\n        self.pause = False\n\n        # display information\n        # self.graphicsSimulation.init_display()\n    def perform_time_step(self):\n        \"\"\"\n        Performes one iteration of the simulation\n        :return:\n        \"\"\"\n        try:\n            self.population.update_states()\n        except:\n            print(\"The population could not be updated anymore.\")\n            print(self.population)\n            self.running = False\n            return\n        self.env.update(self.population.males, self.population.females, self.collision_occured)\n        self.settings.step_counter += 1\n\n    def run(self):\n        \"\"\"\n        Runs until the user terminates the simulation\n        :return:\n        \"\"\"\n        while self.running:\n            if not self.pause:\n                self.perform_time_step()\n            self.hook_for_user_control()\n        print(\"The simulation is over.\")\n\n    def run_n_timesteps(self, n):\n        \"\"\"\n        run n timesteps\n        :param n: number of timesteps\n        :return:\n        \"\"\"\n        for _ in range(n):\n            if self.running:\n                if not self.pause:\n                    self.perform_time_step()\n                self.hook_for_user_control()\n\n    def hook_for_user_control(self):\n        \"\"\"\n        This is a hook for decorators, that want to interact with users\n        \"\"\"\n        pass\n\n    def collision_occured(self, male, female):\n        \"\"\"\n        The method is not called in this class. Instead it is given as a function pointer to the environment class\n        in the method \"performTimeStep\". Every time a female individual crashes into a male this method is called.\n        The method then checks for possible mating in the population.\n        :param male: male individual (class CIndividual)\n        :param female: female individual (class CIndividual)\n        :return:\n        \"\"\"\n        self.settings.collision_counter += 1\n        self.settings.average_number_of_collisions_per_timestep = \\\n            self.settings.collision_counter/self.settings.step_counter\n        self.population.check_for_mating(male, female)\n\n    def quit_simulation(self):\n        self.running = False\n\n    def pause_simulation(self):\n        self.pause = False if self.pause else True\n\n    def __str__(self):\n        z = str(self.settings)\n        return z\n\n    def save(self):\n        print(\"Save simulation\")\n        pickle.dump(self.population, open(\"saved/population\"+str(self.settings.step_counter)+\".p\", \"wb\"))\n        pickle.dump(self.env, open(\"saved/environment\"+str(self.settings.step_counter)+\".p\", \"wb\"))\n        pickle.dump(self.settings, open(\"saved/settings\"+str(self.settings.step_counter)+\".p\", \"wb\"))\n","repo_name":"courtiol/choosiness","sub_path":"CSimulation.py","file_name":"CSimulation.py","file_ext":"py","file_size_in_byte":4012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"22034817103","text":"import uvicorn\nfrom fastapi import FastAPI, HTTPException\nfrom playhouse.shortcuts import model_to_dict, IntegrityError\n\nfrom calculations import *\nfrom database import Station, Observation, clear_db\nfrom ml import make_prediction_agg, serialize_model, train_model\nfrom schemas import *\nfrom utils import log\n\napp = FastAPI()\n\nAGG_OBSERVATION_SAMPLE_SIZE = 5\nAGG_OBSERVATION_SAMPLE_P = 0.5\n\n\n@app.get(\"/\")\nasync def root():\n    return {\"message\": \"Hello World\"}\n\n\n@app.post(\"/create_station\")\nasync def create_station(station: StationCreationSchema):\n    try:\n        station = Station.create(**station.dict())\n        return model_to_dict(station)\n    except IntegrityError:\n        raise HTTPException(409, 'station name already exists')\n\n\n@app.post(\"/enable_training\")\nasync def station_enable_training(station_name: str):\n    return station_set_training(station_name, True)\n\n\n@app.post(\"/disable_training\")\nasync def station_disable_training(station_name: str):\n    return station_set_training(station_name, False)\n\n\ndef station_set_training(station_name: str, enabled: bool):\n    station = Station.get_or_none(Station.name == station_name)\n    if station is None:\n        raise HTTPException(404, 'station not found')\n    else:\n        log.info(f'Setting training enabled: {enabled} for {station}')\n        station.is_training = enabled\n        station.save()\n        if not enabled:\n            model = train_model(station)\n            station.trained_model = serialize_model(model)\n            station.save()\n        return {'status': 'ok'}\n\n\n@app.post(\"/make_observation\")\nasync def make_observation(observation_data: ObservationCreationSchema):\n    station = Station.get_or_none(Station.name == observation_data.station_name)\n    if station is None:\n        raise HTTPException(404, 'station not found')\n    new_observation = Observation.create(station=station.name,\n                                         is_training=station.is_training,\n                                         time=observation_data.time or datetime.now(),\n                                         sample_frequency=observation_data.sample_frequency,\n                                         sample_count=len(observation_data.sample_data),\n                                         sample_data=observation_data.sample_data,\n                                         rms=calc_rms(observation_data.sample_data),\n                                         crest=calc_crest(observation_data.sample_data),\n                                         peak_to_peak=calc_peak_to_peak(observation_data.sample_data),\n                                         kurtosis=calc_kurtosis(observation_data.sample_data))\n    if not station.is_training:\n        # We get a few previous datapoints in order to make N predictions instead of just 1, which\n        # may be subject to noise\n        observations_select = Observation.select(\n            Observation.station,\n            Observation.is_training,\n            Observation.time,\n            Observation.rms,\n            Observation.crest,\n            Observation.peak_to_peak,\n            Observation.kurtosis,\n        ).where(Observation.station == station). \\\n            order_by(Observation.time.desc()).limit(AGG_OBSERVATION_SAMPLE_SIZE)\n        observations = list(observations_select)\n        anomaly_detected = make_prediction_agg(observations, AGG_OBSERVATION_SAMPLE_P)\n        new_observation.is_anomaly = anomaly_detected\n        new_observation.save()\n        return {'anomaly': anomaly_detected}\n    else:\n        log.info(f'Station {station} in training mode; no prediction made')\n        return {'ok': True}\n\n\n@app.post('/clear_db')\nasync def do_clear_db():\n    log.info('Clearing DB...')\n    clear_db()\n    return {'status': 'ok'}\n\n\n@app.get('/station')\nasync def get_station(station_name: str, max_observations: int = 10):\n    station = Station.get_or_none(Station.name == station_name)\n    if station is None:\n        raise HTTPException(404, 'station not found')\n    observations_select = Observation.select(\n        Observation.time,\n        Observation.sample_frequency,\n        Observation.sample_count,\n        Observation.rms,\n        Observation.crest,\n        Observation.peak_to_peak,\n        Observation.kurtosis,\n    ).where(Observation.station == station).limit(max_observations)\n    observations = [model_to_dict(obs, fields_from_query=observations_select) for obs in observations_select]\n    return {'station': model_to_dict(station), 'observations': observations}\n\n\nif __name__ == \"__main__\":\n    uvicorn.run(\"main:app\", host=\"0.0.0.0\", port=8000, reload=False)\n","repo_name":"pedrovhb/tcc","sub_path":"service/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"74670303636","text":"import math\nfrom collections import OrderedDict\nimport numpy as np\nimport pandas as pd\n\ndef kurasawe(x):\n    return kurasawe_f1(x), kurasawe_f2(x)\n\ndef kurasawe_f1(x):\n    n = len(x)\n\n    f1 = 0\n    for i in range(n-1):\n        f1 += -10*math.exp(-0.2*(x[i]**2+x[i+1]**2)**0.5)\n    return f1\n\ndef kurasawe_f2(x):\n    n = len(x)\n\n    f2 = 0\n    for i in range(n):\n        f2 += abs(x[i])**0.8+5*math.sin(x[0]**3)\n\n    return f2\n\ndef costfunction(x,weights,obj_functions):\n    assert len(weights) == len(obj_functions)\n\n    m = len(weights)\n\n    C = 0\n    for i in range(m):\n        C += weights[i] * obj_functions[i]\n\n    return C\n\nif __name__ == \"__main__\":\n    from scipy.stats import uniform\n\n    n_design_variables = 3\n    n_obj_functions = 2\n    n_simulations = 100000\n\n    x_sampler = [uniform(-5,10) for i in range(n_design_variables)]\n\n    column_names = ['id'] \\\n            + ['x{}'.format(i+1) for i in range(n_design_variables)] \\\n            + ['f{}'.format(i+1) for i in range(n_obj_functions)]\n    data = []\n    for i in range(n_simulations):\n        x = [u.rvs() for u in x_sampler]\n        f1,f2 = kurasawe(x)\n        row = [i] + x + [f1,f2]\n        data.append([i] + x + [f1,f2])\n\n    df = pd.DataFrame(data,\n                 columns=column_names)\n    print(df)\n\n    import matplotlib.pyplot as plt\n\n    fig, ax = plt.subplots(1,)\n    ax.scatter(df['f1'],df['f2'],c='black',marker=\".\",s=1)\n    #ax.set_xlim(-20,-14)\n    #ax.set_ylim(-12,2)\n    plt.show()\n    fig.savefig('pareto_mc_sampling.eps',dps=1200)\n","repo_name":"eragasa/ufl_dissertation","sub_path":"chapter3/make_monte_carlo_sampling_plot.py","file_name":"make_monte_carlo_sampling_plot.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"16584726390","text":"isimSayisi =(int)(input())\nisimListe=[]\nilkHarf=[]\nharfKontrol=[]\n\nsayac=0\nsayac2=0\n\nfor i in range(isimSayisi):\n    isim = input()\n    isimListe += isim.split()\n\nisimListe.sort()\n\nfor i in range(isimSayisi):\n    ilkHarf += isimListe[i][0]\n    sayac = (int)(ilkHarf.count(isimListe[i][0]))\n    if sayac >= 5:\n        harfKontrol+=isimListe[i][0]\n        sayac2=1\na=sorted(harfKontrol)\n\nb=set(harfKontrol)\nc=list(b)\nc.sort()\nprint(\"\".join(c),end=\"\")\n\nif sayac2!=1:\n    print(\"inzva\")\n","repo_name":"dATA-Teknoloji-Toplulugu/Sorular-ve-Cozumler","sub_path":"inzva/online-contest/algo-team-mk.py","file_name":"algo-team-mk.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"22986001110","text":"from collections import defaultdict\n\nsynonyms = defaultdict(list)\n\nn = int(input())\n\nfor i in range(n):\n    word,syn = input() , input()\n    synonyms[word].append(syn)\n\nfor word,syn in synonyms.items():\n    print(f\"{word} - {', '.join(syn)}\")\n\n","repo_name":"angelovang/Soft_Uni_problems_solutions","sub_path":"Fundamental_05_2022/Fundamental_lab/Dictionary/Word Synonyms.py","file_name":"Word Synonyms.py","file_ext":"py","file_size_in_byte":244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"7992971514","text":"\ndef solve():\n    params = input().split()\n\n    numParts = int(params[0])\n    days = int(params[1])\n    uniqueParts = {}\n\n    for i in range(days):\n        part = input()\n        uniqueParts[part] = \"replaced\"\n        if len(uniqueParts) == numParts:\n            print(i+1)\n            return\n    print(\"paradox avoided\")\nsolve()\n\n","repo_name":"cyberLaVoy/kattis","sub_path":"python3/boatParts.py","file_name":"boatParts.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70606665885","text":"# !/usr/bin/python\n# coding: utf-8\n\n\nclass JulyExerciseList(object):\n    def __init__(self):\n        pass\n\n    @staticmethod\n    def frequency_sort(src: str) -> str:\n        \"\"\"\n        给定一个字符串，请将字符串里的字符按照出现的频率降序排列\n\n        :param src: \"tree\"\n        :return: \"eert\"\n        \"\"\"\n        record_dic = dict()\n        for s in src:\n            if not record_dic.get(s):\n                record_dic[s] = 1\n                continue\n            record_dic[s] += 1\n        res_sort = sorted(record_dic.items(), key=lambda x: x[1], reverse=True)\n        result = ''\n        for s, num in res_sort:\n            result += s*num\n        return result\n\n    @staticmethod\n    def seq_sort(src: str) -> str:\n        \"\"\"\n        给定一个字符串，请将字符串里的字符按照出现的频率降序排列\n\n        :param src: 'tree'\n        :return: 'eetr'\n        \"\"\"\n        str_record = list()\n        num_list = list()\n        for s in src:\n            if s not in str_record:\n                str_record.append(s)\n                num_list.append(1)\n                continue\n            i = str_record.index(s)\n            num_list[i] = num_list[i] + 1\n        for i in range(1, len(num_list)):\n            for j in range(0, i):\n                if num_list[i] > num_list[j]:\n                    num_list[i], num_list[j] = num_list[j], num_list[i]\n                    str_record[i], str_record[j] = str_record[j], str_record[i]\n        res = ''\n        for i, s in enumerate(str_record):\n            res = res + num_list[i] * s\n        return res\n\n    @staticmethod\n    def find_error_num(src):\n        \"\"\"\n        集合 src 包含从 1 到n的整数。不幸的是，因为数据错误，导致集合里面某一个数字复制了成了集合里面的另外一个数字的值，\n        导致集合 丢失了一个数字 并且 有一个数字重复 。\n        给定一个数组 nums 代表了集合 src 发生错误后的结果。请你找出重复出现的整数，再找到丢失的整数，将它们以数组的形式返回。\n\n        :param src:\n        :return:\n        \"\"\"\n        seq_list = sorted(src)\n        n = len(seq_list)\n        rep = None\n        for i in range(1, n):\n            if seq_list[i] == seq_list[i-1]:\n                rep = seq_list[i]\n                break\n        right = set(range(1, n+1))\n        lost = right.difference(set(src)).pop()\n        return [rep, lost]\n\n    @staticmethod\n    def count_atom(formula: str) -> str:\n        \"\"\"\n        考察压栈出栈\n        formula = \"K4(ON(SO3)2)2\"\n\n        :param formula:\n        :return:\n        \"\"\"\n        n = len(formula)\n        i = 0\n        flag = 0\n        stack = []\n        while i < n:\n            # 查找单个原子表达式和数量，以及栈的初始值\n            atom = ['', 1, flag]\n            if formula[i].isupper():\n                atom[0] = atom[0] + formula[i]\n                i += 1\n                while i < n and formula[i].islower():\n                    atom[0] += formula[i]\n                    i += 1\n                if i < n and formula[i].isdigit():\n                    atom[1] = int(formula[i])\n                    i += 1\n                stack.append(atom)\n\n            # 遇到左括号，加一层栈\n            elif formula[i] == '(':\n                flag += 1\n                i += 1\n            # 遇到右扩号，查找将对应一层栈原子需要乘的数字，并在存的栈列表里，查找对应一层栈的原子，乘以倍数后，栈降低一层\n            elif formula[i] == ')':\n                i += 1\n                multi = 1\n                if formula[i].isdigit():\n                    multi = int(formula[i])\n                    i += 1\n                end = len(stack)\n                while stack and stack[end-1][2] == flag:\n                    stack[end-1][1] = stack[end-1][1] * multi\n                    stack[end-1][2] = stack[end-1][2] - 1\n                    end -= 1\n                flag -= 1\n\n        # 处理合并栈内相同的元素\n        stack_dic = dict()\n        for tmp_atom in stack:\n            stack_dic[tmp_atom[0]] = stack_dic.get(tmp_atom[0], 0) + tmp_atom[1]\n\n        # 按字典序输出\n        res = sorted(stack_dic.items(), key=lambda x: x[0])\n        result = ''\n        for atom, num in res:\n            multi = '' if num == 1 else str(num)\n            result += atom + multi\n\n        return result\n\n    def count_pairs(self, deliciousness: list) -> int:\n        \"\"\"\n        计算两种餐品和为2的幂\n\n        :param deliciousness:\n        :return:\n        \"\"\"\n        num = 0\n        n = len(deliciousness)\n        exist = set()\n        for i in range(n-1):\n            first_num = deliciousness[i]\n            for j in range(i+1, n):\n                second_num = deliciousness[j]\n                sum_res = first_num + second_num\n                if sum_res == 2 or sum_res == 1:\n                    num += 1\n                    exist.add(sum_res)\n                    continue\n                elif sum_res in exist:\n                    num += 1\n                    continue\n                else:\n                    boolean_value, exist_set = self.back_method(sum_res)\n                    if boolean_value:\n                        num += 1\n                        exist.update(exist_set)\n        return num\n\n    def back_method(self, src, exist=None):\n        if not exist:\n            exist = set()\n        exist.add(src)\n        if src == 1:\n            return True, exist\n        elif src % 2:\n            return False, set()\n        tmp = src // 2\n        exist.add(tmp)\n        return self.back_method(tmp, exist)\n\n    @staticmethod\n    def num_sub_array_with_sum(nums: list, goal: int) -> int:\n        \"\"\"\n        给定一个二元数组nums， 和一个数goal\n        计算有多少子数组sub，其元素和等于goal\n        \n        :param nums: \n        :param goal: \n        :return: \n        \"\"\"\n        # nums[i]不是0就是1\n        # 1 <= nums.length <= 3 * 10**4\n        # 0 <= goal <= nums.length\n\n        # 从 length == goal开始取子集\n        # 当遍历同一长度的子集后，都大于goal时，停止\n        result = 0\n        n = 1\n        start_len = 1\n        while n <= len(nums):\n            flag = 0\n            for i in range(0, len(nums) + 1 - start_len):\n                sub = nums[i: i+start_len]\n                if sum(sub) == goal:\n                    result += 1\n                else:\n                    flag += 1\n            if flag == len(nums) + 1 - start_len and result > 0:\n                break\n            start_len += 1\n            n += 1\n\n        return result\n\n    # @staticmethod\n    # def numSubarraysWithSum(nums: list[int], goal: int) -> int:\n    #     from collections import Counter\n    #     res = 0\n    #     sum1 = 0\n    #     c = Counter()\n    #     for num in nums:\n    #         c[sum1] += 1\n    #         sum1 += num\n    #         res += c[sum1-goal]\n    #     return res\n\n\nclass TimeMap:\n\n    def __init__(self):\n        \"\"\"\n        Initialize your data structure here.\n        \"\"\"\n        # 总计调用120000.提前开辟空间？\n        self.dic = dict()\n        self.key = [''] * 120001\n        self.value = [''] * 120001\n        self.timestamp = [0] * 120001\n\n    def set(self, key: str, value: str, timestamp: int) -> None:\n        \"\"\"\n        1、每次存储时，如果key/value存在，则更新时间戳\n\n        :param key:\n        :param value:\n        :param timestamp:\n        :return:\n        \"\"\"\n        # key = 'food' value1 = 'hi1' stamp = 32\n        # 总计调用120000次，时间搓递增。时间搓作为索引下标，并将时间搓填入对应的位置\n        self.timestamp[timestamp] = timestamp\n        self.key[timestamp] = key\n        self.value[timestamp] = value\n\n\n\n        # if not self.dic.get(key):\n        #     self.dic[key] = {'value': [value], 'timestamp': [timestamp]}\n        # else:\n        #     self.dic[key].get('value').append(value)\n        #     self.dic[key].get('timestamp').append(timestamp)\n\n    def get(self, key: str, timestamp: int) -> str:\n        \"\"\"\n        1、如果get到key value，对比时间戳，当存储的时间戳大于输入，返回''\n        2、1 <= timestamp <= 10**7\n\n        :param key:\n        :param timestamp:\n        :return:\n        \"\"\"\n        if self.timestamp[timestamp] == 0:\n            for i in range(timestamp-1, 0, -1):\n                if self.timestamp[i] > 0 and self.key[i] == key:\n                    return self.value[i]\n        else:\n            if self.key[timestamp] == key:\n                return self.value[timestamp]\n        return ''\n\n\n\n\n\n        # res = self.dic.get(key)\n        # if not res:\n        #     return ''\n        # time_lst = res.get('timestamp')\n        # if timestamp in time_lst:\n        #     tmp_index = time_lst.index(timestamp)\n        #     return res.get('value')[tmp_index]\n        # elif timestamp < time_lst[0]:\n        #     return ''\n        # else:\n        #     for i in range(len(time_lst)-1, -1, -1):\n        #         if time_lst[i] < timestamp:\n        #             return res.get('value')[i]\n\n    @staticmethod\n    def mains(put1, put2):\n        lst = []\n        for i, style in enumerate(put1):\n            if style == 'set':\n                key, value, timestamp = put2[i]\n                obj.set(key, value, timestamp)\n                lst.append(None)\n            else:\n                key, timestamp = put2[i]\n                param_2 = obj.get(key, timestamp)\n                lst.append(param_2)\n        return lst\n\n\nclass MajorityElement(object):\n    def __init__(self):\n        pass\n\n    @staticmethod\n    def majority_element(nums: list) -> int:\n        \"\"\"\n\n        :param nums:\n        :return:\n        \"\"\"\n        n = len(nums)\n        record_dict = dict()\n        for s in nums:\n            if not record_dict.get(s):\n                record_dict[s] = 1\n                continue\n            record_dict[s] += 1\n        res = sorted(record_dict.items(), key=lambda x: x[1])\n        if res[-1][1] * 2 > n:\n            return res[-1][0]\n        return -1\n\n    @staticmethod\n    def majority_element_improve(nums: list) -> int:\n        \"\"\"\n\n        :param nums:\n        :return:\n        \"\"\"\n        from collections import Counter\n        count_list = sorted(Counter(nums).items(), key=lambda x: x[1])\n        if count_list[-1][1] * 2 > len(nums):\n            return count_list[-1][0]\n        return -1\n\n    @staticmethod\n    def majority_element_improve_prove(nums: list) -> int:\n        \"\"\"\n        投票算法。消消乐\n\n        :param nums:\n        :return:\n        \"\"\"\n        n = len(nums)\n        cur = -1\n        record_people = 0\n        for num in nums:\n            if not record_people:\n                cur = num\n            if cur == num:\n                record_people += 1\n            else:\n                record_people -= 1\n\n        return cur if record_people and nums.count(cur) * 2 > n else -1\n\n\nclass HIndex(object):\n\n    @staticmethod\n    def high_index(citations: list) -> int:\n        \"\"\"\n\n        :param citations:\n        :return:\n        \"\"\"\n        from collections import Counter\n        citation_statistic = sorted(Counter(citations).items(), key=lambda x: x[0])\n        h_list = list()\n        for i, item in enumerate(citation_statistic):\n            # 被引用次数 > 对应的论文数\n            be_cited_times, paper_nums = item\n            # [1, 11]\n            if be_cited_times <= paper_nums:\n                h_list.append(be_cited_times)\n            # [11, 2], [12, 3], [13, 14]\n            else:\n                sums = 0\n                for _, num in citation_statistic[i:]:\n                    sums += num\n                    if sums >= be_cited_times:\n                        break\n                if sums >= be_cited_times:\n                    h_list.append(be_cited_times)\n                else:\n                    h_list.append(sums)\n        return sorted(h_list)[-1]\n\n    @staticmethod\n    def improve(citations: list) -> int:\n        from collections import defaultdict\n        citations = sorted(citations)\n        counter = defaultdict(int)\n        n = len(citations)\n        print(citations)\n        for i in range(n):\n            # 排序后，从[i,n]的文章引用次数都是大于citations[i]的，所以直接计数候选h指数为n-1\n            counter[citations[i]] = n - i\n            print(counter)\n            # 即题意中“h 指数是指他（她）的 （N 篇论文中）总共有 h 篇论文分别被引用了至少 h 次”\n            if n - i <= citations[i]:\n                return n - i\n\n        return 0\n\n\nclass MinAbsoluteSumDiff(object):\n\n    @staticmethod\n    def min_absolute_sum_diff(nums1: list, nums2: list) -> int:\n        \"\"\"\n\n        :param nums1:\n        :param nums2:\n        :return:\n        \"\"\"\n        import bisect\n        # 1 求两个列表的差值绝对值和，若果和为0，直接返回\n        n = len(nums1)\n        diff = sum([abs(nums1[i] - nums2[i]) for i in range(n)])\n        if not diff:\n            return 0\n        ans = float('Inf')\n        # 2 对nums1进行升序排序\n        nums_seq = sorted(nums1)\n        # 3 使用bisect模块，遍历循环nums2的元素，通过插值法查找nums2每一个元素nums2[j]，对应排序后nums1中与nums2[j]最接近的元素索引。\n        # 4 比较替换索引附近两个元素，取差值最小的作为替换元素\n        for j, num_j in enumerate(nums2):\n            # bisect_left返回nums1中与num_j最接近的x索引，若果有多个x，返回最左边的一个x对应的索引\n            idx = bisect.bisect_left(nums_seq, num_j)\n            if idx:\n                # [1 -> num_seq[idx-1], 3 -> num_seq[idx]]\n                ans = min(ans, diff - abs(nums1[j] - nums2[j]) + abs(nums_seq[idx-1] - num_j))\n            if idx < n:\n                ans = min(ans, diff - abs(nums1[j] - nums2[j]) + abs(nums_seq[idx] - num_j))\n        return ans % (10 ** 9 + 7)\n\n\nclass MaximumElementAfterDecrementingAndRearranging(object):\n    @staticmethod\n    def maximum_element_after_decrementing_and_rearranging(arr: list) -> int:\n        \"\"\"\n        贪心算法\n        1、升序排列\n        2、第一个数定义为1\n        3、当后一个数减前一个数大于1，则后一个数等于前一个数+1，因为最多+1（提目限制只能加不能减）\n\n        :param arr:\n        :return:\n        \"\"\"\n        n = len(arr)\n        arr_seq = sorted(arr)\n        arr_seq[0] = 1\n        for i in range(1, n):\n            if arr_seq[i] - arr_seq[i-1] > 1:\n                arr_seq[i] = arr_seq[i-1] + 1\n        print(arr_seq)\n        return arr_seq[-1]\n\n\nclass MaxArray(object):\n    @staticmethod\n    def max_array(nums):\n        \"\"\"\n        暴力解\n        求序列的最大子序列和\n        :param nums: [-2,1,-3,4,-1,2,1,-5,4],\n        :return:\n        \"\"\"\n        if not nums:\n            return\n\n        n = len(nums)\n        if not n:\n            return\n        ans = float('-inf')\n        for i in range(1, n+1):\n            for j in range(0, n-i+1):\n                # i=2 j=0\n                sub_array = sum(nums[j: j + i])\n                ans = max(ans, sub_array)\n        return ans\n\n    @staticmethod\n    def max_array_improve(nums: list) -> int:\n        \"\"\"\n        动态规划\n        求序列的最大子序列和\n\n        1、包含nums[i]在内的前i项最大子序和为D[i]\n        a、当第i项≤0时，D[i] = D[i-1]\n        b、当第i项＞0时，D[i] = max(D[i-1]+arr[i], D[i-1])\n        :param nums:\n        :return:\n        \"\"\"\n        # 前i项元素的包含nums[i]在内打最大子序和为dp[i]\n        n = len(nums)\n        dp = [0] * n\n        dp[0] = nums[0]\n        ans = dp[0]\n        for i in range(1, n):\n            # 前i项元素，包含nums[i]在内的最大子序和\n            dp[i] = max(dp[i-1] + nums[i], nums[i])\n            print(dp[i], nums[i])\n            # ans记录了前i项的最大自序和\n            ans = max(dp[i], ans)\n        return ans\n\n    @staticmethod\n    def maxSubArray(nums: list) -> int:\n        l = len(nums)\n        # 特殊情况判断\n        if l<1:\n            return 0\n        elif l==1:\n            return nums[0]\n\n        # 初始化赋值\n        D = [0] * l\n        D[-1] = nums[-1]\n        max = D[-1]\n\n        i = l-2  # 注意，i是从l-2开始，也就是倒数第二位开始，因为倒数第一位已经初始化赋值nums[-1]了\n        while i>=0:\n            if D[i+1]>0:\n                D[i] = nums[i] + D[i+1]\n            else:\n                D[i] = nums[i]\n            if D[i]>max:\n                max = D[i]\n            i = i-1\n            print(max)\n        return max\n\n\nclass GroupGrams:\n\n    @staticmethod\n    def group_grams(strings: list) -> list:\n        import collections\n        default_dict = collections.defaultdict(list)\n        for strs in strings:\n            seq_str_lst = ''.join(sorted(strs))\n            default_dict[seq_str_lst].append(strs)\n        return list(default_dict.values())\n\n\nif __name__ == '__main__':\n    obj = MinAbsoluteSumDiff()\n    inputs1 = [1, 7, 5]\n    inputs2 = [2, 3, 5]\n    result = obj.min_absolute_sum_diff(inputs1, inputs2)\n    print(result)\n\n","repo_name":"luof-508/arithmetic_repo","sub_path":"plugins/leetcode/exercise_dir/july/july.py","file_name":"july.py","file_ext":"py","file_size_in_byte":17235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"27001423309","text":"#!bin/python\nfrom IPython import embed\nimport numpy\nimport matplotlib.pyplot as plt\nimport scipy.stats as stats\nimport ss_plotting.make_plots as ss;\ndata = numpy.loadtxt(open(\"./dot_data.txt\", \"rb\"), delimiter=' ');\n\ntheta = numpy.linspace(0, 3.14 * 2)\nxes = numpy.sin(theta) * 2\nyes = numpy.cos(theta) * 2\n\nss.plot(series=[(xes, yes), (data[:, 0], data[:, 1]), (data[:, 2], data[:, 3])], \n                series_colors=['red', 'blue', 'green'], \n                series_labels=['Dot Perimeter', 'Touches -- Dense Tracking', 'Touches -- Open Loop'],\n                 plot_xlabel='X error (cm)', \n                 plot_ylabel=\"Y error (cm)\", \n                 savefile=\"./dots.pdf\",\n                 line_styles=['-', '', ''],\n                 plot_markers=['', '.', 'x'],\n                 plot_xlim=(-8, 8),\n                 plot_ylim=(-8, 8),\n                  savefile_size = (3, 3));\n\nplt.show();\n","repo_name":"mklingen/Thesis","sub_path":"img/plot_dots.py","file_name":"plot_dots.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"72754389724","text":"\"\"\"Program to add even numbers from and including 1 - 100\"\"\"\n\nsum_even = 0\nsum_odd = 0\n\nfor i in range(101):\n    if i % 2 == 0:\n        sum_even += i\nprint(f\"The sum of even numbers between 1 and 100 inclusive is: {sum_even}\")\n\nfor i in range(101):\n    if i % 2 != 0:\n        sum_odd += i\nprint(f\"The sum of odd numbers between 1 and 100 inclusive is: {sum_odd}\")\n\n\ndef sum_numbers(start_number, end_number, odd_or_even):\n    sum_num = 0\n    if odd_or_even == \"odd\":\n        for i in range(start_number, end_number):\n            if i % 2 != 0:\n                sum_num += i\n    elif odd_or_even == \"even\":\n        for i in range(start_number, end_number + 1):\n            if i % 2 == 0:\n                sum_num += i\n    return sum_num\n\n\nstart_number = int(input(\"Enter the first number: \"))\nend_number = int(input(\"Enter the last number: \"))\nodd_or_even = input(\"Odd or even numbers? \")\nprint(sum_numbers(start_number, end_number, odd_or_even))\n\n","repo_name":"HarryCashel/100days","sub_path":"Day05 - Loops/Exercise 3 - Adding Even Numbers.py","file_name":"Exercise 3 - Adding Even Numbers.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"18505122989","text":"import os\nimport requests\nfrom celery import shared_task\n\nfrom control.functions import check_client_response\nfrom webhook.utils.get_objects import get_message, get_ticket\nfrom webhook.utils.logger import Logger\nfrom webhook.utils.tools import (\n    get_event_status,\n    message_is_saved,\n    get_current_period,\n    get_contact_number,\n    message_exists_in_digisac,\n    update_ticket_last_message,\n)\n\nlogger = Logger(__name__)\n\n\ndef load_env(var):\n    return os.environ.get(var, os.getenv(var))\n\n\nIS_LOCALHOST = load_env(\"IS_LOCALHOST\")\nWEBHOOK_API = load_env(\"WEBHOOK_API_LOCAL\") if IS_LOCALHOST else load_env(\"WEBHOOK_API\")\n\n\n##-- Handler to events\n@shared_task(name=\"handler_task\")\ndef manage(data):\n    event_handlers = {\n        \"message.created\": (handle_message_created, [\"id\", \"isFromMe\"]),\n        \"message.updated\": (handle_message_updated, [\"id\"]),\n        \"ticket.created\": (handle_ticket_created, [\"id\", \"contactId\", \"lastMessageId\"]),\n        \"ticket.updated\": (handle_ticket_updated, [\"id\"]),\n    }\n\n    event = data.get(\"event\")\n    data = data.get(\"data\")\n    event_handler_func, params = event_handlers.get(event, (None, []))\n\n    try:\n        if (event == \"message.created\") and data.get(\"type\", None) == \"ticket\":\n            return f\"Event: {event} has ticket type, avoiding to prevent message.created error\"\n    except AttributeError:\n        return f\"Event: {event} has some bug\"\n\n    if event_handler_func:\n        args = [data.get(param) for param in params]\n\n        event_handler_func.apply_async(args=args, kwargs={\"data\": data})\n\n    return f\"Event: {event} handled to the refers function\"\n\n\n##-- Tasks to handle events\n@shared_task(name=\"create_message\")\ndef handle_message_created(message_id, isFromMe, data=...):\n    message_exists = message_exists_in_digisac(message_id=message_id)\n    message_saved = message_is_saved(message_id=message_id)\n\n    if message_exists and message_saved:\n        handle_message_updated.apply_async(args=[message_id], kwargs={\"data\": data})\n        return \"Mensagem já existe mandada pra atualização\"\n\n    url = f\"{WEBHOOK_API}/messages/create\"\n\n    contact_id = data.get(\"contactId\")\n    date = get_current_period()\n    number = get_contact_number(contact_id=contact_id)\n    parameters = {\"phone\": number, \"period\": date}\n    message_type = data.get(\"type\")\n    message_body = {\n        \"message_id\": message_id,\n        \"contact_id\": contact_id,\n        \"timestamp\": data.get(\"timestamp\"),\n        \"status\": data[\"data\"][\"ack\"],\n        \"ticket\": data.get(\"ticketId\"),\n        \"message_type\": data.get(\"type\"),\n        \"is_from_me\": isFromMe,\n        \"text\": data.get(\"text\", message_type),\n    }\n\n    if number is None:\n        raise ValueError(\n            f\"Consulta do telefone:{number} na {url} com id {contact_id} falhou\"\n        )\n    # if not isFromMe:\n    response = requests.post(url, json=message_body, params=parameters)\n\n    update_ticket_last_message(ticket_id=data.get(\"ticketId\"))\n\n    if not isFromMe and not response.status_code in range(400, 501):\n        check_client_response.apply_async(args=[contact_id])\n\n    if response.status_code == 409:\n        return \"Esta mensagem já existe por algum motivo chegou até aqui novamente. Verifique os logs\"\n\n    if response.status_code != 201:\n        text = (\n            f\"Failed to create message_id: {message_id}\\n{response}-\\n{response.text}\"\n        )\n        raise ValueError(text)\n\n    return response\n\n\n@shared_task(name=\"update_message\")\ndef handle_message_updated(message_id, data=...):\n    if not message_id:\n        return \"Message vazio. diabo é isso?\"\n\n    message_exists = message_exists_in_digisac(message_id=message_id)\n    message_saved = message_is_saved(message_id)\n\n    if message_exists and not message_saved:\n        handle_message_created.apply_async(\n            args=[message_id, data.get(\"isFromMe\")], kwargs={\"data\": data}\n        )\n        return \"Mensagem existe e não foi salva antes\"\n    try:\n        data = data.get(\"data\")\n        message = get_message(message_id=message_id)\n        actual_status = get_event_status(\"message\", message_id=message_id)\n        status = data[\"ack\"][0] if isinstance(data[\"ack\"], tuple) else data.get(\"ack\")\n        if actual_status < status:\n            if message:\n                message.status = status\n                message.save()\n        else:\n            return f\"Status:{status} menor que o atual da mensagem com id: {message_id}\"\n\n    except Exception as e:\n        logger.debug(e)\n        raise Exception(\n            f\"erro: {str(e)} - actual_staus:{type(actual_status)} e status = {type(status)}\"\n        )\n\n    return \"Mensagem atualizada com sucesso\"\n\n\n@shared_task(name=\"create_ticket\")\ndef handle_ticket_created(ticket_id, contact_id, last_message_id, data=...):\n    url = f\"{WEBHOOK_API}/messages/create/ticket\"\n    params = {\n        \"id\": ticket_id,\n        \"period\": get_current_period(),\n        \"contact\": contact_id,\n        \"last_message\": last_message_id,\n    }\n\n    response = requests.post(url, params=params)\n    if response.status_code != 201:\n        text = (\n            f\"Failed to create ticket with id: {ticket_id}\\n{response}-{response.text}\"\n        )\n        logger.debug(text)\n        return text\n\n    return response\n\n\n@shared_task(name=\"update_ticket\")\ndef handle_ticket_updated(ticket_id, data=...):\n    actual_status = get_event_status(\"ticket\", ticket_id=ticket_id)\n    last_message_id = data.get(\"lastMessageId\")\n    is_open = data.get(\"isOpen\")\n\n    if actual_status and not is_open:\n        ticket = get_ticket(ticket_id=ticket_id)\n\n        try:\n            if ticket:\n                ticket.is_open = is_open\n                ticket.last_message_id = last_message_id\n                ticket.save()\n\n        except Exception as e:\n            raise ValueError(str(e))\n\n        return \"Ticket atualizado com sucesso\"\n    else:\n        return f\"Ticket com id: {ticket_id} já está fechado ou ainda não foi criado\"\n","repo_name":"psousaj/webhook","sub_path":"messages_api/event.py","file_name":"event.py","file_ext":"py","file_size_in_byte":5959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"17003258270","text":"import csv\r\nfrom selenium import webdriver\r\nfrom datetime import datetime\r\nfrom shutil import copyfile\r\n#CSV\r\ndef csv_url_reader(url_obj):\r\n    reader=csv.DictReader(url_obj,delimiter =',')\r\n    for line in reader:\r\n        url=line[\"URL\"]\r\n        user_agent = \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.83 Safari/537.36\"\r\n        options = webdriver.ChromeOptions()\r\n        chrome_prefs = {}\r\n        options.experimental_options[\"prefs\"] = chrome_prefs\r\n        chrome_prefs[\"profile.default_content_settings\"] = {\"images\": 2}\r\n        chrome_prefs[\"profile.managed_default_content_settings\"] = {\"images\": 2}\r\n        options.headless = False\r\n        options.add_argument(f'user-agent={user_agent}')\r\n        options.add_argument(\"--window-size=1920,1080\")\r\n        options.add_argument('--ignore-certificate-errors')\r\n        options.add_argument('--allow-running-insecure-content')\r\n        options.add_argument(\"--disable-extensions\")\r\n        options.add_argument(\"--proxy-server='direct://'\")\r\n        options.add_argument(\"--proxy-bypass-list=*\")\r\n        options.add_argument(\"--start-maximized\")\r\n        options.add_argument('--disable-gpu')\r\n        options.add_argument('--disable-dev-shm-usage')\r\n        options.add_argument('--no-sandbox')\r\n        driver = webdriver.Chrome(executable_path=\"chromedriver.exe\", options=options)\r\n        driver.get(url)\r\n        driver.implicitly_wait(10) #Now loop it to execute only one website at a time and printing the FP and Name (Doubt)\r\n        FP=driver.find_element_by_xpath('//*[@id=\"content\"]/div[2]/div/div[1]/div/div/div[2]/div[2]/div[1]/div/div[1]/div[2]/div[2]/span').text\r\n        FP1=FP.replace('◎','')\r\n        Name=driver.find_element_by_xpath('//*[@id=\"content\"]/div[2]/div/div[1]/div/div/div[2]/div[1]/div/div/div/div[1]').text\r\n        driver.close()\r\n        print(Name)\r\n        print(FP1)\r\n        Name_list=[Name]\r\n        FP_list=[FP1]\r\n        print(Name_list)\r\n        print(FP_list)\r\n        row=Name_list+FP_list\r\n        with open('Floorprice.csv','a',encoding='utf-8',newline='') as f:\r\n            thewriter = csv.writer(f)\r\n            thewriter.writerow(row)\r\n\r\n      \r\n\r\nif __name__==\"__main__\":\r\n    with open('Floorprice.csv','w',encoding='utf-8',newline='') as f:\r\n        data=csv.writer(f)\r\n        data.writerow(['Name','FP'])\r\n    with open(\"goodprojects.csv\",'r') as url_obj:\r\n        csv_url_reader(url_obj)\r\n\r\ndatetime_backup=datetime.now()\r\nprint(datetime_backup)\r\n\r\nstr_datetime_backup=str(datetime_backup).replace('-','.')\r\na=str_datetime_backup[0:13]\r\na=str(a).replace('-',\"\") \r\nprint(a)\r\n\r\npath_input=r'D:\\Python Programs\\Floor Price NFT\\Floorprice.csv'\r\npath_output=r'D:\\Python Programs\\Floor Price NFT\\Backups' + '\\\\' + a +\" Floor price.csv\"\r\n\r\ncopyfile(path_input,path_output) ","repo_name":"Antonykings/NFT-Floor-Price-Tracker","sub_path":"Floor Price NFT/Floor_Price.py","file_name":"Floor_Price.py","file_ext":"py","file_size_in_byte":2843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74718835163","text":"import torch\nimport torchvision\nfrom torch import nn\nfrom torch.nn import Conv2d\nfrom torch.utils.data import DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\n\ndataset = torchvision.datasets.CIFAR100('dataset_Crfar10',train = False,transform=torchvision.transforms.ToTensor()\n                                        ,download=True)\ndataloader = DataLoader(dataset,batch_size=64)\nwriter = SummaryWriter('logs')\n\nclass Process(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv2d = Conv2d(in_channels=3,out_channels=72,kernel_size=3,stride=1)#定义卷积层\n    def forward(self,x):\n        x = self.conv2d(x)\n        return x\n\nimg_process = Process()\nstep = 0\nfor data in dataloader:\n    imgs,targets = data\n    output = img_process(imgs)\n\n    output = torch.reshape(output, (-1, 3, 30,30))\n    writer.add_images('input imgs',imgs,step)\n    writer.add_images('output imgs', output, step)\n    step = step+1\nwriter.close()","repo_name":"Yuguangcheng/pytorch_test","sub_path":"10 nn.Conv2d.py","file_name":"10 nn.Conv2d.py","file_ext":"py","file_size_in_byte":962,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"41532456304","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Apr 19 23:23:56 2015\n\n@author: evanyao, robertchen, rak, anie\n\"\"\"\nimport math\n\nclass DijkstraDataStructure:\n    ''' \n    Underlying Data structure for Dijkstra's Algorithm \n    ''' \n    name = \"\"\n    data_structure = []\n    indices = []\n    \n    def __init__(self, graph): \n        self.name = \"Default List Dijkstra's\" \n        # tracks index of key in data_structure\n        self.indices = [None] * graph.num_stations\n        \n    def deleteMin(self): \n        pass  \n        \n    def insert(self, key, value): \n        pass\n    \n    def decreaseKey(self, key, new_value):\n        position = self.indices[key]\n        # if node has been bumped out of queue then must redo search process to\n        # also adjust adjacent node distances\n        if position == None:\n            self.insert(key, new_value)\n            return\n        if new_value < self.data_structure[position][1]:\n            self.data_structure[position] = (key, new_value)\n            self.push_up(position)\nclass Priority(DijkstraDataStructure): \n    ''' \n    Priority Queue in List form for Dijkstra's \n    '''\n    def deleteMin(self):\n        node = self.data_structure.pop(0)\n        # update index trackers\n        length = len(self.indices)\n        for i in range(0,length):\n            if self.indices[i] != None:\n                self.indices[i] -= 1\n        self.indices[node[0]] = None\n        return node\n    # performs insertion sort with new node    \n    def insert(self, key, value):\n        node = (key, value)\n        length = len(self.data_structure)\n        if length == 0:\n            self.data_structure.append(node)\n            self.indices[key] = 0\n        else:\n            self.data_structure.append(node)\n            self.indices[key] = length\n            self.push_up(length)\n    # rebalancing method\n    def push_up(self, position):\n        key, value = self.data_structure[position]\n        new_position = 0\n        # find smallest node greater than our pushed node\n        for new_position in range(0, position):\n            if value <= self.data_structure[new_position][1]:\n                break\n        # update indices of nodes between our new and old position\n        for i in range(new_position, position):\n            self.indices[self.data_structure[i][0]] += 1\n        self.data_structure.pop(position)\n        self.data_structure.insert(new_position, (key,value))\n        self.indices[key] = new_position\n    \nclass DaryHeap(DijkstraDataStructure): \n    '''\n    d-ary Heap for Dijkstra's Algorithm\n    '''\n    def __init__(self, graph, d = 2):\n        DijkstraDataStructure.__init__(self,graph)\n        self.name = \"d-ary Heap Dijkstra's\"\n        self.d = d\n    # swap two elements of the priority queue\n    def swap(self, ind1, ind2):\n        temp = self.data_structure[ind1]\n        self.data_structure[ind1] = self.data_structure[ind2]\n        self.data_structure[ind2] = temp\n        \n    # compares child to parent in case child needs to swap with parent    \n    def push_up(self, position):\n        if position == 0:\n            return\n        key, value = self.data_structure[position]\n        parent = self.data_structure[(position-1)//self.d]\n        while value < parent[1] and position != 0:\n            self.swap(position, (position-1)//self.d)\n            self.indices[parent[0]] = position\n            position = (position-1)//self.d\n            self.indices[key] = position\n            parent = self.data_structure[(position-1)//self.d]\n            \n    def insert(self, key, value):\n        node = (key, value)\n        position = len(self.data_structure)        \n        self.data_structure.append(node)        \n        self.indices[key] = position\n        self.push_up(position)\n    def deleteMin(self):\n        length = len(self.data_structure)\n        if length == 0:\n            return None\n        self.swap(0,-1)        \n        min_node = self.data_structure.pop()\n        length -= 1\n        self.indices[min_node[0]] = None\n        position = 0\n        if length == 0:\n            return min_node\n        # log condition checks if node has reached bottom level of heap\n        while position == 0 or int(math.log(length, self.d)) - int(math.log(position + 1, self.d)) > 0:\n            # determine child with smallest value\n            best_child = None\n            best_child_value = float(\"inf\")\n            search = (length - 1)%self.d\n            if search == 0 and length > 0:\n                search = self.d\n            for i in range(1, search + 1):\n                if self.d * position + i >= length:\n                    break\n                test_value = self.data_structure[self.d * position + i][1]\n                if test_value < min(self.data_structure[position][1], \n                                    best_child_value):\n                    best_child = self.d * position + i\n                    best_child_value = test_value\n            # if needed, swap with smallest child\n            if best_child != None:\n                self.indices[self.data_structure[best_child][0]] = position\n                self.swap(position, best_child)\n                position = best_child\n                self.indices[self.data_structure[position][0]] = position\n            else:\n                self.indices[self.data_structure[position][0]] = position\n                return min_node\n        self.indices[self.data_structure[position][0]] = position\n        return min_node","repo_name":"alexnie01/cs51final","sub_path":"_CS_51_Project/code, data/data_structures.py","file_name":"data_structures.py","file_ext":"py","file_size_in_byte":5450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"18964093479","text":"import numpy as np\n\nimport shutil, os\n\nYears = 10\n\nPAP = 0.1\nT = 1. - PAP\n\nPhi = 1e-5 #Initial Conditions\n\ndx =1e-4#1e-3#1 #1e-4#1\ndt =1e-4#1 #1e-4#1\n\nN = 100#50 #Number of terms in Fourier Series\n\n\nlogMinEta = -3\nlogMaxEta = 1\nEtaNum = 100\netaList = np.logspace(logMinEta,logMaxEta,EtaNum)#np.arange(1e-1,1e2,1e-1)#1e-4,1e-0,1e-4)#np.arange(1e-4,5e-2,1e-4)\n\nMaxrho = 1\nMinrho = 0.05\ndrho = 0.05\nrhoList = np.arange(Minrho,Maxrho,drho)#np.arange(0,1.0,0.1)#np.arange(0,1.0,0.01)\n\n#SaveFile\nSaveDirName = (\"Saved_Plots/\"+\n                \"Maxrho_\"+'{:.1E}'.format(Maxrho) +\n                \"_Minrho_\"+'{:.1E}'.format(Minrho) +\n                \"_drho_\"+'{:.1E}'.format(drho) +\n                \"_dx_\"+'{:.1E}'.format(dx) +\n                \"_Years_%d_\"%(Years) +\n                \"_logMaxEta_\"+'{:.1E}'.format(logMaxEta)+\n                \"_logMinEta_\"+'{:.1E}'.format(logMinEta)+\n                \"_EtaNum_%d\"%(EtaNum) + \"_PAP_\" + '{:.1E}'.format(PAP) +\n                \"_Phi_\"+'{:.1E}'.format(Phi) + \n                \"_N_%d\"%(N))\n\n","repo_name":"tt386/Simplified_Stratonovitch","sub_path":"Heat_Equation/Analytical_Dedimensionalised_Periodic/Many_Years/Params.py","file_name":"Params.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"33027012613","text":"class Solution(object):\n    def findMin(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: int\n        \"\"\"\n        start = 0\n        end = len(nums) - 1\n        while start < end and nums[start] >= nums[end]:\n            mid = (start + end) / 2\n            if nums[mid] < nums[end]:\n                end = mid\n            elif nums[start] < nums[mid]:\n                start = mid + 1\n            else:\n                return min(self.findMin(nums[:mid+1]), self.findMin(nums[mid+1:]))\n        return nums[start]\n","repo_name":"moonlightshadow123/leetcode_solutions","sub_path":"array/154-Find-Minimum-in-Rotated-Sorted-Array-II/do_not_let_it_out_and_recursive.py","file_name":"do_not_let_it_out_and_recursive.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"71306889565","text":"from tkinter import *\nfrom PIL import ImageTk, Image\n\n\nroot = Tk()\nroot.title(\"Windows\")\n\n\ndef open():\n    global new_image1\n    top = Toplevel()\n    top.title(\"Second Window\")\n    my_image1 = Image.open(\"./Pictures/Emerger.png\")\n    resized1 = my_image1.resize((500, 450), Image.ANTIALIAS)\n    new_image1 = ImageTk.PhotoImage(resized1)\n    my_label = Label(top, image=new_image1).pack()\n    button2 = Button(top, text=\"Close Window\", command=top.destroy).pack()\n    \n    \n    \nbutton = Button(root, text=\"Open Second Window\", command=open).pack()\n\n\nmainloop()","repo_name":"xxhernandoxx/graphicinterface","sub_path":"windows.py","file_name":"windows.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32558247887","text":"houston_pollution = pollution[pollution.city  ==  'Houston']\n\n# Make array orangred for day 330 of year 2014, otherwise lightgray\nhouston_colors = ['orangered' if (day  ==  330) & (year  ==  2014) else 'lightgray' \n                  for day,year in zip(houston_pollution.day, houston_pollution.year)]\n\nsns.regplot(x = 'NO2',\n            y = 'SO2',\n            data = houston_pollution,\n            fit_reg = False, \n            # Send scatterplot argument to color points \n            scatter_kws = {'facecolors': houston_colors, 'alpha': 0.7})\nplt.show()","repo_name":"skupriienko/Datacamp-Python-Exercises","sub_path":"Data Visualization with Python/Improving Your Data Visualizations in Python/Highlighting your data/1_Hardcoding a highlight.py","file_name":"1_Hardcoding a highlight.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"86"}
+{"seq_id":"74448186203","text":"import numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns\n\n\n\nimport os\n\nfor dirname, _, filenames in os.walk('/kaggle/input/pickle-ieee'):\n\n    for filename in filenames:\n\n        print(os.path.join(dirname, filename))\ntrain = pd.read_pickle('/kaggle/input/pickle-ieee/Train.pkl')\n\ntest = pd.read_pickle('/kaggle/input/pickle-ieee/Test.pkl')\ny = train['isFraud']\n\ndel train['isFraud']\nimport lightgbm as lgbm\nfrom sklearn.model_selection import RandomizedSearchCV\nparams = {\n\n    'num_leaves': [500,600,700,800],\n\n    'feature_fraction': list(np.arange(0.1,0.5,0.1)),\n\n    'bagging_fraction': list(np.arange(0.1,0.5,0.1)),\n\n    'min_data_in_leaf': [100,120,140,160],\n\n    'learning_rate': [0.05],\n\n    'reg_alpha': list(np.arange(0.1,0.5,0.1)),\n\n    'reg_lambda': list(np.arange(0.1,0.5,0.1)),\n\n}\nmodel = lgbm.LGBMClassifier(random_state=42,metric='auc',verbosity=-1,objective='binary',max_depth=-1)\ngrid = RandomizedSearchCV(model,param_distributions=params,n_iter=15,cv=3,scoring='roc_auc')\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(\n\n    train, y, test_size=0.15, random_state=42)\ngrid.fit(X_train,y_train)\ngrid.best_params_\ngrid.best_score_\nfrom sklearn.metrics import classification_report, roc_auc_score\nprint(classification_report(y_test,grid.predict(X_test)))\nprint(roc_auc_score(y_test,grid.predict_proba(X_test)[:,1]))","repo_name":"aorursy/new-nb-6","sub_path":"rohan9889_best-params-lightgbm-ieee-fraud.py","file_name":"rohan9889_best-params-lightgbm-ieee-fraud.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"13256709604","text":"\"\"\"\nusage: run_itmo.py [-h] [--itmo {linear,fhdr}] [--tmo {reinhard,drago}] ldr_path hdr_path\n\nRun an ITMO\n\npositional arguments:\n  ldr_path              Path of LDR image\n  hdr_path              Path of output HDR image\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --itmo {linear,fhdr}  What itmo to use (default: fhdr)\n  --tmo {reinhard,drago}\n                        What tmo to use (default: reinhard)\n\"\"\"\nimport include_parent_path\nimport argparse\nfrom util import load_ldr_image, save_hdr_image, save_ldr_image\nfrom itmo import fhdr, linear\nfrom tmo import drago, reinhard\n\np = argparse.ArgumentParser(description=\"Run an ITMO\", formatter_class=argparse.ArgumentDefaultsHelpFormatter)\np.add_argument(\"--itmo\", help=\"What itmo to use\", choices=[\"linear\", \"fhdr\"], default=\"fhdr\")\np.add_argument(\"--tmo\", help=\"What tmo to use\", choices=[\"reinhard\", \"drago\"], default=\"reinhard\")\np.add_argument(\"ldr_path\", help=\"Path of LDR image\")\np.add_argument(\"hdr_path\", help=\"Path of output HDR image\")\n\nargs = p.parse_args()\n\n\n# selecting the itmo\nif args.itmo == \"linear\":\n  itmo_func = linear\nelse:\n  itmo_func = fhdr\n\n\n# selecting tmo\nif args.tmo == \"reinhard\":\n  tmo_func = reinhard\nelse:\n  tmo_func = drago\n\nldr = load_ldr_image(args.ldr_path)\nhdr = itmo_func(ldr)\nhdr_tmo = tmo_func(hdr)\nsave_hdr_image(hdr, args.hdr_path)\nsave_ldr_image(hdr_tmo, args.hdr_path.replace(\".hdr\", \".jpg\"))","repo_name":"wingerse/itmo","sub_path":"src/scripts/run_itmo.py","file_name":"run_itmo.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"14437148965","text":"from flask import Blueprint, request, redirect, render_template, url_for, jsonify\nfrom models import Cliente\nfrom forms import ClienteForm\nfrom app import db\n\nappCliente = Blueprint('appCliente', __name__, template_folder='templates')\n\n@appCliente.route('/cliente/index')\ndef Inicio():\n    listClientes = Cliente.query.all()\n    return render_template('clienteIndex.html', clientes = listClientes)\n\n@appCliente.route('/cliente/agregar', methods = ['GET', 'POST'])\ndef Agregar():\n    cliente = Cliente()\n    newCliente = ClienteForm(obj = cliente)\n    if request.method == 'POST':\n        if newCliente.validate_on_submit():\n            newCliente.populate_obj(cliente)\n            db.session.add(cliente)\n            db.session.commit()\n            return RedirectIndex()\n    return render_template('clienteAgregar.html', newCliente = newCliente)\n\n@appCliente.route('/cliente/editar/<int:id>',methods=[\"GET\", \"POST\"])\ndef Editar(id):\n    cliente = Cliente.query.get_or_404(id)\n    editCliente = ClienteForm(obj = cliente)\n    if request.method == 'POST':\n        if editCliente.validate_on_submit():\n            editCliente.populate_obj(cliente)\n            db.session.commit()\n            return RedirectIndex()\n    return render_template('clienteEditar.html', editCliente = editCliente)\n\n@appCliente.route('/cliente/eliminar/<int:id>')\ndef Eliminar(id):\n    cliente = Cliente.query.get_or_404(id)\n    db.session.delete(cliente)\n    db.session.commit()\n    return RedirectIndex()\n\n#Rutas para peticiones HTTP (JSONs)\n@appCliente.route('/cliente/json/index')\ndef InicioJson():\n    try:\n        clientes = Cliente.query.all()\n        listCliente = []\n        for c in clientes:\n            cliente = {}                               #Diccionario vacío\n            cliente[\"id_cliente\"] = c.id_cliente       \n            cliente[\"nombre\"] = c.nombre               #Llena los campos para parsearlo a JSON\n            cliente[\"apellido\"] = c.apellido\n            cliente[\"telefono\"] = c.telefono\n            cliente[\"email\"] = c.email\n            listCliente.append(cliente)\n        return jsonify({'clientes':listCliente})\n    except Exception as ex:\n        return jsonify({\"status\" : 400, \"mensaje\" : ex})\n    \n@appCliente.route('/cliente/json/agregar', methods = [\"POST\"])\ndef AgregarJson():\n    try:\n        if request.method == \"POST\":\n            cliente = Cliente()\n            json = request.get_json()\n            cliente.nombre = json['nombre']\n            cliente.apellido = json['apellido']\n            cliente.telefono = json['telefono']\n            cliente.email = json['email']\n            db.session.add(cliente)\n            db.session.commit()\n            return jsonify({'status':200, 'mensaje':'Cliente agregado por petición HTTP'})\n    except Exception as ex:\n        return jsonify({'status':400, 'mensaje':ex})\n    \n@appCliente.route('/cliente/json/editar', methods = [\"POST\"])\ndef EditarJson():\n    try:\n        if request.method == \"POST\":\n            json = request.get_json()\n            cliente = Cliente.query.get_or_404(json['id_cliente'])\n            cliente.nombre = json['nombre']\n            cliente.apellido = json['apellido']\n            cliente.telefono = json['telefono']\n            cliente.email = json['email']\n            db.session.commit()\n            return jsonify({'status':200, 'mensaje':'Cliente editado por petición HTTP'})\n    except Exception as ex:\n        return jsonify({'status':400, 'mensaje':ex})\n    \n@appCliente.route('/cliente/json/eliminar', methods = [\"POST\"])\ndef EliminarJson():\n    try:\n        json = request.get_json()\n        cliente = Cliente.query.get_or_404(json['id_cliente'])\n        db.session.delete(cliente)\n        db.session.commit()\n        return jsonify({\"status\":200, \"mensaje\":f\"Cliente {json['id_cliente']} eliminado por peticion HTTP\"})\n    except Exception as ex:\n        return jsonify({'status':400, 'mensaje':ex})\n    \ndef RedirectIndex():\n    return redirect(url_for('appCliente.Inicio'))","repo_name":"JonathanAL003/MultiparadigmaRepo","sub_path":"Unidad 2 - Practicas/Practica 3/flask/routes/cliente/clientes.py","file_name":"clientes.py","file_ext":"py","file_size_in_byte":3978,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"38964732998","text":"# ---------------------------------------------------------------------------------\n# - IMPORT -\nfrom flask import render_template, request, redirect, flash, session\nfrom flask_app import app\nfrom flask_app.models.user import User\nfrom flask_bcrypt import Bcrypt\n\nbcrypt = Bcrypt(app)\n# ---------------------------------------------------------------------------------\n\n\n\n# ---------------------------------------------------------------------------------\n# - LOGIN & REGISTRATION PAGE -\n@app.route('/')\ndef registration_and_login():\n    return render_template('reg_and_log.html')\n\n# - VALIDATE & PROCESS REGISTRATION -\n@app.route('/register', methods=[\"POST\"])\ndef register():\n    if not User.validate_register(request.form):\n        return redirect('/')\n    pw_hash = bcrypt.generate_password_hash(request.form['password'])\n    print(pw_hash)\n    data = {\n        \"username\" : request.form['username'],\n        \"email\" : request.form['email'],\n        \"password\" : pw_hash\n    }\n    user_id = User.save(data)\n    session['user_id'] = user_id\n    return redirect(f'/edit/{user_id}')\n\n# - VALIDATE & PROCESS LOGIN -\n@app.route('/login', methods=[\"POST\"])\ndef login():\n    data = {\"username\" : request.form['username']}\n    user_in_db = User.get_by_username(data)\n    if not user_in_db:\n        flash(\"No account with that username!\")\n        return redirect('/')\n    if not bcrypt.check_password_hash(user_in_db.password, request.form['password']):\n        flash(\"Incorrect password!\")\n        return redirect('/')\n    session['user_id'] = user_in_db.id\n    user_id = session['user_id']\n    return redirect(f'/profile/{user_id}')\n# ---------------------------------------------------------------------------------\n\n\n\n# ---------------------------------------------------------------------------------\n# - BROWSE USERS -\n@app.route('/users')\ndef users():\n    users = User.get_all()\n    return render_template('users.html', all_users = users)\n\n# - VIEW USER -\n@app.route('/profile/<int:user_id>')\ndef user(user_id):\n    data = {\n        \"user_id\" : user_id\n    }\n    show_user = User.get_by_id(data)\n    return render_template('user_prof.html', show_user = show_user)\n# ---------------------------------------------------------------------------------\n\n\n\n# ---------------------------------------------------------------------------------\n# - EDIT USER -\n@app.route('/edit/<int:user_id>')\ndef edit_user(user_id):\n    if \"user_id\" not in session:\n        flash(\"Please register or login before continuing!\")\n        return redirect('/')\n    if  not session['user_id'] == user_id:\n        flash(\"You don't have access to that page!\")\n        return redirect(f'/profile/{user_id}')\n    data = {\n        \"user_id\" : user_id\n    }\n    show_user = User.get_by_id(data)\n    return render_template('edit_user.html', show_user = show_user)\n\n# - VALIDATE AND PROCESS USER EDIT -\n@app.route('/update_user/<int:user_id>', methods=[\"POST\"])\ndef update_user(user_id):\n    if not User.validate_profile(request.form):\n        return redirect(f'/edit/{user_id}')\n    data = {\n        \"name\" : request.form['name'],\n        \"pronouns\" : request.form['pronouns'],\n        \"birthday\" : request.form['birthday'],\n        \"twitter\" : request.form['twitter'],\n        \"about_me\" : request.form['about_me'],\n        \"user_id\" : user_id\n    }\n    User.edit_user(data)\n    return redirect(f'/profile/{user_id}')\n# ---------------------------------------------------------------------------------\n\n\n\n# ---------------------------------------------------------------------------------\n# - SHOW USER'S CHARACTERS -\n@app.route('/characters/<int:user_id>')\ndef character_list(user_id):\n    data = {\n        \"user_id\" : user_id\n    }\n    user_charas= User.get_all_characters(data)\n    return render_template('character_list.html', user_charas = user_charas)\n\n# - FAVORITE CHARACTER -\n@app.route('/save_favorite/<int:character_id>', methods=[\"POST\"])\ndef favorite(character_id):\n    if not User.validate_fave(request.form):\n        return redirect(f'/character/{character_id}')\n    data = {\n        \"user_id\" : request.form['user_id'],\n        \"character_id\" : request.form['character_id']\n    }\n    User.favorite_character(data)\n    flash(\"Successfully favorited!\")\n    return redirect(f'/character/{character_id}')\n\n# - SHOW USER'S FAVORITES -\n@app.route('/favorites/<int:user_id>')\ndef favorites_list(user_id):\n    data = {\n        \"user_id\" : user_id\n    }\n    user_faves = User.get_all_favorites(data)\n    return render_template('fave_list.html', user_faves = user_faves)\n# ---------------------------------------------------------------------------------\n\n\n\n# ---------------------------------------------------------------------------------\n# - LOGOUT -\n@app.route('/logout')\ndef logout():\n    session.clear()\n    return redirect('/')\n# ---------------------------------------------------------------------------------","repo_name":"Flightless-System/Character-Directory","sub_path":"flask_app/controllers/users.py","file_name":"users.py","file_ext":"py","file_size_in_byte":4890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"73748888925","text":"from time import sleep\nimport rospy\nfrom elite_msgs.srv import JointTrajectoryMove, JointTrajectoryMoveResponse, JointTrajectoryMoveRequest\n# from elite._moveml import EC\nfrom elite import EC\n\n\n\nclass JointTrajectoryMoveLService():  # pylint: disable=R0903\n    \"\"\"关节轨迹运动服务\"\"\"\n\n    def __init__(self) -> None:\n        rospy.loginfo(\"JointTrajectoryMoveLService is started...\")\n        self.joint_trajectory_move_server = rospy.Service(\n            'joint_trajectory_movel', JointTrajectoryMove, self.handle_trajectory_movel_)\n\n    def handle_trajectory_movel_(self, request: JointTrajectoryMoveRequest):\n        \"\"\"处理运动请求\"\"\"\n        response = JointTrajectoryMoveResponse()\n        length = request.length\n        time_stamp = list(request.time_stamp)\n        joint = list(request.joint)\n        self.elite_robot: EC\n        self.elite_robot.ml_init(length=length, point_type=0, ref_joint=self.elite_robot.current_joint, ref_frame=[\n                                 0, 0, 0, 0, 0, 0], ret_flag=0)\n        for i in range(len(time_stamp)):\n            temp_joint = joint[i*8:i*8+6]\n            temp_time = time_stamp[i]\n            self.elite_robot.ml_push(  # pylint: disable=E1101\n                temp_time, temp_joint)\n            sleep(0.005)\n        print(\"end_push\", self.elite_robot.ml_end_push())  # pylint: disable=E1101\n        if self.elite_robot.ml_check_push_result() == EC.MlPushResult.CORRECT:\n            self.elite_robot.ml_run(  # pylint: disable=E1101\n                speed_percent=100.0)  # 全速运行，速度交由Moveit控制\n        else:\n            print(\"push result\", self.elite_robot.ml_check_push_result())\n        if request.is_blocking:\n            self.elite_robot.wait_stop()  # pylint: disable=E1101\n\n        response.result = True\n        return response\n","repo_name":"Elite-Robots/ROS","sub_path":"src/elite_driver/src/elite_driver/joint_trajectory_movel.py","file_name":"joint_trajectory_movel.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"86"}
+{"seq_id":"18382345888","text":"import math\n\nclass IndoorScorer():\n    PRECIPITATION_WEIGHT = 0.7\n    TEMPERATURE_WEIGHT = 0.1\n    CLOUDS_WEIGHT = 0.04\n    WIND_SPEED_WEIGHT = 0.03\n    DAY_TIME_WEIGHT = 0.06\n    HUMIDITY_WEIGHT = 0.02\n    OPTIMAL_TEMPERATURE_LOW = 20\n    OPTIMAL_TEMPERATURE_HIGH = 25\n    HUMIDITY_LOW = 0.4\n    HUMIDITY_HIGH = 0.55\n    AIR_QUALITY_WEIGHT = 0.05\n\n    @staticmethod\n    def temperature_score(temperature):\n        if IndoorScorer.OPTIMAL_TEMPERATURE_LOW <= temperature <= IndoorScorer.OPTIMAL_TEMPERATURE_HIGH:\n            return 0\n        elif temperature < IndoorScorer.OPTIMAL_TEMPERATURE_LOW:\n            return 1 - math.exp(-0.04 * (IndoorScorer.OPTIMAL_TEMPERATURE_LOW - temperature))\n        else:\n            return 1 - math.exp(0.2 * (IndoorScorer.OPTIMAL_TEMPERATURE_HIGH - temperature))\n        \n    @staticmethod\n    def day_time_score(current_time, sunrise_time, sunset_time):\n        if sunrise_time <= current_time <= sunset_time:\n            return IndoorScorer.DAY_TIME_WEIGHT\n        return 0\n\n    @staticmethod\n    def humidity_score(humidity):\n        if humidity < IndoorScorer.HUMIDITY_LOW or humidity > IndoorScorer.HUMIDITY_HIGH:\n            return IndoorScorer.HUMIDITY_WEIGHT\n        return 0\n\n    def score(self, temperature, wind_speed, humidity, precipitation, clouds, air_quality, sunrise_time, sunset_time, current_time):\n        score = (IndoorScorer.PRECIPITATION_WEIGHT * (1 - math.exp(-10 * precipitation)) +\n                 IndoorScorer.TEMPERATURE_WEIGHT * self.temperature_score(temperature) +\n                 self.day_time_score(current_time, sunrise_time, sunset_time) +\n                 IndoorScorer.CLOUDS_WEIGHT * (1 - math.exp(-3 * clouds)) +\n                 IndoorScorer.WIND_SPEED_WEIGHT * (1 - math.exp(-0.3 * wind_speed)) +\n                 self.humidity_score(humidity) +\n                 IndoorScorer.AIR_QUALITY_WEIGHT * math.exp(0.5 * (1 - air_quality)))\n        return round(score, 2)\n","repo_name":"HelsinkiUniCollab/WeatherBasedRecommender","sub_path":"recommender-back/src/services/scoring/indoor_scorer.py","file_name":"indoor_scorer.py","file_ext":"py","file_size_in_byte":1942,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"86"}
+{"seq_id":"29488815255","text":"import os\nimport time\nfrom subprocess import call\nfrom config import Folder, Commands\n\n\ndef develop_preparations() -> None:\n    for f in [\n        Commands.install_additional, \n        Commands.install_main\n    ]:\n        os.system(f)\n\ndef check_folder(path:str) -> None:\n    return os.path.exists(path) or os.mkdir(path)\n\ndef develop_file_merged(file_name:str) -> str:\n    return f\"merged_{file_name}.pdf\"\n\ndef develop_folders() -> None:\n    for f in [\n        Folder.storage,\n        Folder.input,\n        Folder.output,\n    ]:\n        check_folder(f)\n\ndef develop_file_transform():\n    for files in os.listdir(Folder.input):\n        path = os.path.join(Folder.input, files)\n        if os.path.splitext(files)[1].lower()=='.djvu':\n            time_start = time.time()\n            path_output = os.path.join(Folder.output, f\"{os.path.splitext(files)[0]}.pdf\")\n            call(f'ddjvu -format=pdf \"{path}\" \"{path_output}\"', shell=True)\n            print('--------------------------------------------------------------------------------')\n            print('Finished:', files, '\\nIt took time:', round(time.time()-time_start, 2), 'seconds')\n            print('ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ')\n        os.remove(path)\n\n\nif __name__ == \"__main__\":\n    develop_preparations()\n    develop_folders()\n    develop_file_transform()","repo_name":"kungfu-kenny/DjvuTransform","sub_path":"djvu_transform.py","file_name":"djvu_transform.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"71351064285","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\n\n\"\"\"\nhttps://leetcode.com/problems/balance-a-binary-search-tree/description/\n\nTime complexity: O(n), n = len(root)\nSpace complexity: O(n)\n\"\"\"\nclass Solution:\n    def balanceBST(self, root: TreeNode) -> TreeNode:\n        vals = []\n\n        def dfs(node: TreeNode):\n            if node is None: return\n            vals.append(node.val)\n            dfs(node.left)\n            dfs(node.right)\n\n        dfs(root)\n        vals.sort()\n\n        def to_balance_BST(l: int, r: int) -> TreeNode:\n            if l > r: return None\n            if l == r: return TreeNode(vals[l])\n            mid = (r + l) // 2\n            cur = TreeNode(vals[mid])\n            cur.left = to_balance_BST(l, mid - 1)\n            cur.right = to_balance_BST(mid + 1, r)\n            return cur\n\n        return to_balance_BST(0, len(vals) - 1)\n","repo_name":"dextermallo/Dev-Notes","sub_path":"leetcode/1382.Balance-a-Binary-Search-Tree.py","file_name":"1382.Balance-a-Binary-Search-Tree.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"822128112","text":"from django.urls import path, include\nfrom rest_framework.routers import DefaultRouter\nfrom . import views\n\nrouter = DefaultRouter()\nrouter.register(r'stores', views.StoreViewSet)\nrouter.register(r'foods', views.FoodViewSet)\nrouter.register(r'food-reviews', views.FoodReviewViewSet)\n\nurlpatterns = [\n    path('', include(router.urls))\n]","repo_name":"MrMeta/fooddiary-backend","sub_path":"app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"18321059622","text":"import cimodel.data.simple.ios_definitions as ios_definitions\n\n\nclass IOSNightlyJob:\n    def __init__(self,\n                 variant,\n                 is_upload=False):\n\n        self.variant = variant\n        self.is_upload = is_upload\n\n    def get_phase_name(self):\n        return \"upload\" if self.is_upload else \"build\"\n\n    def get_common_name_pieces(self, with_version_dots):\n\n        extra_name_suffix = [self.get_phase_name()] if self.is_upload else []\n\n        common_name_pieces = [\n            \"ios\",\n        ] + ios_definitions.IOS_VERSION.render_dots_or_parts(with_version_dots) + [\n            \"nightly\",\n            self.variant,\n            \"build\",\n        ] + extra_name_suffix\n\n        return common_name_pieces\n\n    def gen_job_name(self):\n        return \"_\".join([\"pytorch\"] + self.get_common_name_pieces(False))\n\n    def gen_tree(self):\n        extra_requires = [x.gen_job_name() for x in BUILD_CONFIGS] if self.is_upload else []\n\n        props_dict = {\n            \"build_environment\": \"-\".join([\"libtorch\"] + self.get_common_name_pieces(True)),\n            \"requires\": extra_requires,\n            \"context\": \"org-member\",\n            \"filters\": {\"branches\": {\"only\": \"nightly\"}},\n        }\n\n        if not self.is_upload:\n            props_dict[\"ios_arch\"] = self.variant\n            props_dict[\"ios_platform\"] = ios_definitions.get_platform(self.variant)\n            props_dict[\"name\"] = self.gen_job_name()\n\n        template_name = \"_\".join([\n            \"binary\",\n            \"ios\",\n            self.get_phase_name(),\n        ])\n\n        return [{template_name: props_dict}]\n\n\nBUILD_CONFIGS = [\n    IOSNightlyJob(\"x86_64\"),\n    IOSNightlyJob(\"arm64\"),\n]\n\n\nWORKFLOW_DATA = BUILD_CONFIGS + [\n    IOSNightlyJob(\"binary\", is_upload=True),\n]\n\n\ndef get_workflow_jobs():\n    return [item.gen_tree() for item in WORKFLOW_DATA]\n","repo_name":"snuspl/nimble","sub_path":".circleci/cimodel/data/simple/nightly_ios.py","file_name":"nightly_ios.py","file_ext":"py","file_size_in_byte":1844,"program_lang":"python","lang":"en","doc_type":"code","stars":248,"dataset":"github-code","pt":"86"}
+{"seq_id":"1545175897","text":"import sys\nimport os\nimport pandas as pd\n\npd.set_option('display.max_columns', None)\nimport pickle\nimport numpy as np\nimport math\nimport tensorflow as tf\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import train_test_split\nimport random\nfrom LSTM import LSTM\nfrom sklearn import metrics\n\n\ndef load_pkl(path):\n    with open(path, 'rb') as f:\n        obj = pickle.load(f)\n        return obj\n\n\ndef load_data(plane, strain, dir):\n    print('Plane ' + plane + ', Strain ' + strain)\n    data = pd.read_csv(dir + plane + '_data.csv', encoding='utf-8')\n    # M = pd.read_csv(dir+plane+'_mean.csv', encoding='utf-8')\n    # S = pd.read_csv(dir+plane+'_sd.csv', encoding='utf-8')\n\n    print('*** data is loaded')\n\n    return data\n\n\ndef data_extract(data, strain, test_number):\n    # length of a series\n    sequence_length = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n    # length of a test of a plane\n    lengths = data.groupby('number')['0'].count().tolist()\n    # batch size\n    batch_size = 64\n    # 30 feature name\n    feature_name = data.columns.values.tolist()[1:31]\n    # label name\n    label_name = strain\n\n    batch_data = []\n    batch_label = []\n    # random series length\n    index_sequence_length = sequence_length[random.randint(0, len(sequence_length) - 1)]\n    # which test\n    if test_number == None:\n        number = random.randint(0, len(lengths) - 1)\n    else:\n        number = test_number\n\n    for i in range(batch_size):\n        index_row = random.randint(0, lengths[number] - index_sequence_length - 1)\n        d = data[data['number'] == number].reset_index(drop=True).loc[index_row:index_row + index_sequence_length - 1,\n            feature_name].values.tolist()\n        label = data[data['number'] == number].reset_index(drop=True).loc[\n            index_row + index_sequence_length - 1, label_name]\n        batch_data.append(d)\n        batch_label.append([label])\n\n    return batch_data, batch_label\n\n\ndef training(plane, strain, test_number, dir, fold, training_epochs, train_dropout_prob, hidden_dim, fc_dim, key,\n             model_path,\n             learning_rate=[1e-5, 2e-2], lr_decay=2000, earlystopping=10):\n    # load data\n    data = load_data(plane=plane, strain=strain, dir=dir)\n    if test_number == None:\n        number_train_batches = 20000\n    else:\n        number_train_batches = 2000\n    input_dim = 30\n    output_dim = 1\n\n    # model built\n    lstm = LSTM(input_dim, output_dim, hidden_dim, fc_dim, key)\n    loss, y_pre, y_label = lstm.get_cost_acc()\n    lr = learning_rate[0] + tf.train.exponential_decay(learning_rate[1],\n                                                       lstm.step,\n                                                       lr_decay,\n                                                       1 / np.e)\n\n    optimizer = tf.train.AdamOptimizer(learning_rate=lr).minimize(loss)\n    best_valid_loss = 1e10\n\n    init = tf.global_variables_initializer()\n    saver = tf.train.Saver()\n\n    EP = 0\n    # train\n    with tf.Session() as sess:\n        sess.run(init)\n\n        for epoch in range(training_epochs):\n            # Loop over all batches\n            for i in range(number_train_batches):\n                # batch_xs is [number of patients x sequence length x input dimensionality]\n                batch_xs, batch_ys = data_extract(data, strain, test_number)\n                step = epoch * number_train_batches + i\n                sess.run(optimizer,\n                         feed_dict={lstm.input: batch_xs, lstm.labels: batch_ys, lstm.keep_prob: train_dropout_prob,\n                                    lstm.step: step})\n                print('Training epoch ' + str(epoch) + ' batch ' + str(i) + ' done')\n\n            # valid\n\n            batch_xs, batch_ys = data_extract(data, strain, test_number)\n            loss, y_pre, y_true = sess.run(lstm.get_cost_acc(), feed_dict={lstm.input: batch_xs,\n                                                                           lstm.labels: batch_ys,\n                                                                           lstm.keep_prob: train_dropout_prob})\n\n            print(\"validation MSE = {:.3f}\".format(loss))\n            MAE = metrics.mean_absolute_error(y_pre, y_true)\n            print(\"validation MAE = {:.3f}\".format(MAE))\n            print('epoch ' + str(epoch) + ' done........................')\n            if (loss <= best_valid_loss):\n                EP = 0\n                best_valid_loss = loss\n                print(\"[*] Best loss so far! \")\n                saver.save(sess, model_path + 'model' + str(fold) + '/')\n                print(\"[*] Model saved at\", model_path + 'model' + str(fold) + '/', flush=True)\n            else:\n                EP = EP + 1\n                if EP > earlystopping:\n                    print(\"Early stopping! Fold \" + str(fold) + \" training is over!\")\n                    saver.save(sess, model_path + 'model' + str(fold) + '/')\n                    print(\"[******] Model saved at\", model_path + 'model' + str(fold) + '/', flush=True)\n\n        print(\"Fold \" + str(fold) + \" training is over!\")\n        saver.save(sess, model_path + 'model' + str(fold) + '/')\n        print(\"[******] Model saved at\", model_path + 'model' + str(fold) + '/', flush=True)\n\n\ndef testing(plane, strain, test_number, dir, hidden_dim, fc_dim, key, model_path, fold):\n    data = load_data(plane=plane, strain=strain, dir=dir)\n\n    input_dim = 30\n    output_dim = 1\n\n    test_dropout_prob = 1.0\n    lstm_load = LSTM(input_dim, output_dim, hidden_dim, fc_dim, key)\n\n    saver = tf.train.Saver()\n    with tf.Session() as sess:\n        saver.restore(sess, model_path + 'model' + str(fold) + '/')\n\n        batch_xs, batch_ys = data_extract(data, strain, test_number)\n        loss, y_pre, y_true = sess.run(lstm_load.get_cost_acc(), feed_dict={lstm_load.input: batch_xs,\n                                                                            lstm_load.labels: batch_ys, \\\n                                                                            lstm_load.keep_prob: test_dropout_prob})\n\n        MAE = metrics.mean_absolute_error(y_pre, y_true)\n        print(\"Test Loss = {:.3f}\".format(loss))\n        print(\"validation MAE = {:.3f}\".format(MAE))\n\n\ndef main(training_mode, plane, strain, test_number, data_path, fold, learning_rate, lr_decay, training_epochs,\n         dropout_prob, hidden_dim, fc_dim,\n         model_path, earlystopping):\n    \"\"\"\n    :param training_mode:  1 train，0 test，\n    :param plane:  P123-P127，\n    :param strain:  30-35，\n    :param test_number:  which test，\n    :param data_path:\n    :param fold: 5-fold\n    :param learning_rate:\n    :param training_epochs:\n    :param dropout_prob: dropout，1 keep all\n    :param hidden_dim:\n    :param fc_dim:\n    :param model_path: save/load model path\n    \"\"\"\n    training_mode = int(training_mode)\n    path = str(data_path)\n\n    # train\n    if training_mode == 1:\n        learning_rate = learning_rate\n        lr_decay = lr_decay\n        training_epochs = int(training_epochs)\n        dropout_prob = float(dropout_prob)\n        hidden_dim = int(hidden_dim)\n        fc_dim = int(fc_dim)\n        model_path = str(model_path)\n        training(plane, strain, test_number, path, fold, training_epochs, dropout_prob, hidden_dim, fc_dim,\n                 training_mode, model_path,\n                 learning_rate, lr_decay, earlystopping)\n\n    # test\n    elif training_mode == 0:\n        hidden_dim = int(hidden_dim)\n        fc_dim = int(fc_dim)\n        model_path = str(model_path)\n        testing(plane, strain, test_number, path, hidden_dim, fc_dim, training_mode, model_path, fold)\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) > 1:\n        training_mode = int(sys.argv[1])\n        plane = sys.argv[2]\n        strain = sys.argv[3]\n        if sys.argv[4] == 'None':\n            test_number = None\n        else:\n            test_number = int(sys.argv[4])\n        main(training_mode=training_mode, plane=plane, strain=strain, test_number=test_number,\n             data_path='../../data_per_plane/', fold=1,\n             learning_rate=[1e-5, 2e-2], lr_decay=2000,\n             training_epochs=15, dropout_prob=0.25, hidden_dim=64, fc_dim=32, model_path='save_LSTM/', earlystopping=10)\n    else:\n        main(training_mode=1, plane='P123', strain='30', test_number=0, data_path='../../data_per_plane/', fold=1,\n             learning_rate=[1e-5, 2e-2], lr_decay=2000,\n             training_epochs=15, dropout_prob=0.25, hidden_dim=64, fc_dim=32, model_path='save_LSTM/', earlystopping=10)\n","repo_name":"SCXsunchenxi/PLANE611","sub_path":"code/model/temporal_models/LSTMmain.py","file_name":"LSTMmain.py","file_ext":"py","file_size_in_byte":8551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3223859755","text":"from numpy import pad\nimport torch\nimport torch.nn as nn\n\n\nclass TSautoencoder(nn.Module):\n\tdef __init__(self, input_dim, ts_len, noise=False):\n\t\tsuper(TSautoencoder, self).__init__()\n\t\tmodules = []\n\t\tself.hidden_dims = [1000, 100]\n\t\tinput_dim_tmp = ts_len\n\t\tfor h_dim in self.hidden_dims:\n\t\t\tmodules.append(\n                nn.Sequential(\n                    nn.Linear(input_dim_tmp, h_dim),\n                    nn.BatchNorm1d(h_dim),\n                    nn.ReLU())\n            )\n\t\t\tinput_dim_tmp = h_dim\n\t\tself.encoder = nn.Sequential(*modules)\n\t\tself.fc_mu = nn.Linear(self.hidden_dims[-1], self.hidden_dims[-1])\n\t\tself.fc_var = nn.Linear(self.hidden_dims[-1], self.hidden_dims[-1])\n\t\t\t\n\t\tself.hidden_dims.reverse()\n\t\tmodules = []\n\t\t# modules.append()\n\t\tfor i in range(len(self.hidden_dims)-1):\n\t\t\tmodules.append(\n\t\t\t\tnn.Sequential(\n                    nn.Linear(self.hidden_dims[i], self.hidden_dims[i+1]),\n                    nn.BatchNorm1d(self.hidden_dims[i+1]),\n                    nn.ReLU())\n            )\n\t\t\n\t\tself.decoder = nn.Sequential(*modules)\n\t\tself.decoder_input = nn.Linear(self.hidden_dims[-1], ts_len)\n\t\tself.noise = noise\n\t\n\tdef reparameterize(self, mu, logvar):\n\t\tstd = torch.exp(0.5 * logvar)\n\t\teps = torch.randn_like(std)\n\t\treturn eps * std + mu\n\n\tdef forward(self, x):\n\t\tx = x.T\n\t\tE = self.encoder(x)\n\t\tmu = self.fc_mu(E)\n\t\tlog_var = self.fc_var(E)\n\t\tE_2 = self.reparameterize(mu, log_var)\n\t\trx = self.decoder(E_2)\n\t\trx = self.decoder_input(rx)\n\t\tif self.noise:\n\t\t\tE_1 = self.reparameterize(mu, log_var)\n\t\t\trx1 = self.decoder(E_1)\n\t\t\trx1 = self.decoder_input(rx1)\n\t\t\treturn rx.T, rx1.T\n\t\treturn E, mu, log_var, rx.T\n\n\nclass ProjectionHead_target(nn.Module):\n\tdef __init__(self, input_dim):\n\t\tsuper(ProjectionHead_target, self).__init__()\n\t\tself.fc1 = nn.Linear(input_dim, input_dim)\n\t\tself.fc2 = nn.Linear(input_dim, input_dim)\n\n\tdef forward(self, x):\n\t\tx = self.fc1(x)\n\t\tx = torch.relu(x)\n\t\tx = self.fc2(x)\n\t\tx = x.squeeze()\n\t\treturn x\n    \n\nclass SimpleConvGLU_double(nn.Module):\n    def __init__(self, input_dim, num_nodes):\n        super(SimpleConvGLU_double, self).__init__()\n        self.input_dim = input_dim\n        self.num_nodes = num_nodes\n        self.conv1_left = nn.Conv1d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2)\n        self.conv1_right = nn.Conv1d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2)\n        self.glu1_conv = nn.GLU()\n        self.conv2_left = nn.Conv1d(in_channels=16, out_channels=1, kernel_size=9, stride=1, padding=4)\n        self.conv2_right = nn.Conv1d(in_channels=16, out_channels=1, kernel_size=9, stride=1, padding=4)\n        self.glu2_conv = nn.GLU()\n        self.conv_fc = nn.Conv1d(in_channels=input_dim, out_channels=100, kernel_size=1, stride=1)\n        self.bn_conv = nn.BatchNorm1d(input_dim)\n        self.bn_conv2 = nn.BatchNorm1d(input_dim)\n        self.fc1_left = nn.Linear(input_dim, 1000)\n        self.fc1_right = nn.Linear(input_dim, 1000)\n        self.bn = nn.BatchNorm1d(1000)\n        self.glu1 = nn.GLU()\n        self.fc2_left = nn.Linear(1000, 100)\n        self.fc2_right = nn.Linear(1000, 100)\n        self.glu2 = nn.GLU()\n    def forward(self, x):\n        x = x.unsqueeze(1)\n        x_left = self.conv1_left(x) # N * 1 * T\n        x_right = self.conv1_right(x) # N * 1 * T\n        x = self.glu1_conv(torch.cat((x_left, x_right),dim=-1))\n        x_tmp = x.reshape(x.shape[0], self.input_dim, -1)\n        x_tmp = self.bn_conv(x_tmp)\n        x = x_tmp.reshape(x.shape[0], -1, self.input_dim)\n        x = torch.relu(x)\n        x_left = self.conv2_left(x) # N * 1 * T\n        x_right = self.conv2_right(x) # N * 1 * T\n        x = self.glu2_conv(torch.cat((x_left, x_right),dim=-1))\n        x_tmp = x.reshape(x.shape[0], self.input_dim, -1)\n        x_tmp = self.bn_conv2(x_tmp)\n        x = x_tmp.reshape(x.shape[0], -1, self.input_dim)\n        x = torch.relu(x)\n        x = x.squeeze()\n        i1_left = self.fc1_left(x)\n        i1 = self.bn(i1_left)\n        i1 = torch.relu(i1)\n        i2_left = self.fc2_left(i1)\n        return i2_left\n\n    \nclass LR(nn.Module):\n    def __init__(self, input_dim, output_dim):\n        super(LR, self).__init__()\n        self.fc = nn.Linear(input_dim, output_dim)\n    def forward(self, x):\n        out = self.fc(x)\n        return out\n\n\nclass EMA():\n    def __init__(self, beta):\n        super().__init__()\n        self.beta = beta\n\n    def update_average(self, old, new):\n        if old is None:\n            return new\n        return old * self.beta + (1 - self.beta) * new","repo_name":"sunnydmx/CLGR","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":4530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"5874787446","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 31 15:24:03 2015\n\n@author: Taikor\n\"\"\"\ncategories = ['alt.atheism', 'soc.religion.christian',\n              'comp.graphics', 'sci.med']\n    \n          \nfrom sklearn.datasets import fetch_20newsgroups\n\ntwenty_train = fetch_20newsgroups(subset='train',\ncategories=categories, shuffle=True, random_state=42)\n\n\nfrom sklearn.feature_extraction.text import CountVectorizer\n\ncount_vect = CountVectorizer()\nX_train_counts = count_vect.fit_transform(twenty_train.data)\nX_train_counts.shape\n\n\nfrom sklearn.feature_extraction.text import TfidfTransformer\ntf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts)\nX_train_tf = tf_transformer.transform(X_train_counts)\nX_train_tf.shape\n\n\ntfidf_transformer = TfidfTransformer()\nX_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\nX_train_tfidf.shape\n\n\nfrom sklearn.naive_bayes import MultinomialNB\nclf = MultinomialNB().fit(X_train_tfidf, twenty_train.target)\n\ndocs_new = ['take a pill, in god', 'i got a headache']\nX_new_counts = count_vect.transform(docs_new)\nX_new_tfidf = tfidf_transformer.transform(X_new_counts)\n\npredicted = clf.predict(X_new_tfidf)\n\nfor doc, category in zip(docs_new, predicted):\n    print('%r => %s' % (doc, twenty_train.target_names[category]))\n","repo_name":"TaikorRoy/miscellaneous","sub_path":"python/test_skitlearn_nlp.py","file_name":"test_skitlearn_nlp.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70889978525","text":"#!/usr/bin/python3\n\"\"\" Make Change Module \"\"\"\n\n\ndef makeChange(coins, total):\n    \"\"\"\n    Calculates the fewest number of coins needed to meet a given total.\n\n    Args:\n        coins (list): A list of coin values.\n        total (int): The target total.\n\n    Returns:\n        int: number of coins needed.\n             else Returns -1.\n\n    Raises:\n        None\n\n    \"\"\"\n\n    if total <= 0:\n        return 0\n\n    remaining = total\n    coin_count = 0\n    coin_index = 0\n    sorted_coins = sorted(coins, reverse=True)\n    num_coins = len(coins)\n\n    while remaining > 0:\n        if coin_index >= num_coins:\n            return -1\n\n        if remaining - sorted_coins[coin_index] >= 0:\n            remaining -= sorted_coins[coin_index]\n            coin_count += 1\n        else:\n            coin_index += 1\n\n    return coin_count\n","repo_name":"calvean/alx-interview","sub_path":"0x08-making_change/0-making_change.py","file_name":"0-making_change.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"25456779641","text":"import os\nimport settings\n\n# ########### BEGIN -- Edit your settings ###\n# The following constants are joined to build STATIC_ROOT and MEDIA_ROOT\nMEDIA_CONTAINER_PATH = '/path/to/prj_media/'\nSTATIC_CONTAINER_PATH = '/path/to/prj_static/'\nPRJ_NAME = 'django_project_name'\n# ########### END -- No need to edit anything more below this section ###\n\n# Fallback to default storage and media serve from local filesystem\nif getattr(settings, 'LOCAL_STORAGE', False):\n    STATICFILES_STORAGE = 'django.contrib.staticfiles.storage.StaticFilesStorage'\n    DEFAULT_FILE_STORAGE = 'django.core.files.storage.FileSystemStorage'\n    STATIC_ROOT = os.path.join(STATIC_CONTAINER_PATH, PRJ_NAME)\n    MEDIA_ROOT = os.path.join(MEDIA_CONTAINER_PATH, PRJ_NAME)\n    STATIC_URL = '/static/'\n    MEDIA_URL = '/media/'\n\n# Django debug toolbar\nif getattr(settings, 'ENABLE_DEBUG_TOOLBAR', False):\n    try:\n        from debug_toolbar import VERSION\n    except ImportError:\n        pass\n    else:\n        DEBUG_TOOLBAR_VERSION = VERSION  # Dummy assign to avoid flake8 complain\n        from settings import INSTALLED_APPS\n        INSTALLED_APPS = INSTALLED_APPS + ('debug_toolbar',)\n","repo_name":"Nomadblue/nomad-web","sub_path":"conf/localsettings_sample.py","file_name":"localsettings_sample.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"30916032365","text":"\"\"\"\n--- Day 18: Boiling Boulders ---\nYou and the elephants finally reach fresh air. You've emerged near the base of a large volcano that seems\nto be actively erupting! Fortunately, the lava seems to be flowing away from you and toward the ocean.\n\nBits of lava are still being ejected toward you, so you're sheltering in the cavern exit a little longer.\nOutside the cave, you can see the lava landing in a pond and hear it loudly hissing as it solidifies.\n\nDepending on the specific compounds in the lava and speed at which it cools, it might be forming obsidian!\nThe cooling rate should be based on the surface area of the lava droplets,\nso you take a quick scan of a droplet as it flies past you (your puzzle input).\n\nBecause of how quickly the lava is moving, the scan isn't very good; its resolution is quite low and, as a result,\nit approximates the shape of the lava droplet with 1x1x1 cubes on a 3D grid, each given as its x,y,z position.\n\nTo approximate the surface area, count the number of sides of each cube that are not immediately connected\nto another cube. So, if your scan were only two adjacent cubes like 1,1,1 and 2,1,1,\neach cube would have a single side covered and five sides exposed, a total surface area of 10 sides.\n\nHere's a larger example:\n\n2,2,2\n1,2,2\n3,2,2\n2,1,2\n2,3,2\n2,2,1\n2,2,3\n2,2,4\n2,2,6\n1,2,5\n3,2,5\n2,1,5\n2,3,5\nIn the above example, after counting up all the sides that aren't connected to another cube, the total surface area is 64.\n\nWhat is the surface area of your scanned lava droplet?\n\nYour puzzle answer was 3346.\n\n--- Part Two ---\nSomething seems off about your calculation. The cooling rate depends on exterior surface area, but your calculation also included the surface area of air pockets trapped in the lava droplet.\n\nInstead, consider only cube sides that could be reached by the water and steam as the lava droplet tumbles into the pond. The steam will expand to reach as much as possible, completely displacing any air on the outside of the lava droplet but never expanding diagonally.\n\nIn the larger example above, exactly one cube of air is trapped within the lava droplet (at 2,2,5), so the exterior surface area of the lava droplet is 58.\n\nWhat is the exterior surface area of your scanned lava droplet?\n\nYour puzzle answer was 1980.\n\nBoth parts of this puzzle are complete! They provide two gold stars: **\n\"\"\"\n\nimport sys\nimport math\nfrom copy import deepcopy\nfrom collections import defaultdict, deque\ninfile = sys.argv[1] if len(sys.argv)>1 else '18.in'\ndata = open(infile).read().strip()\nlines = [x for x in data.split('\\n')]\n\nP = set()\nfor line in lines:\n    x,y,z = line.split(',')\n    x,y,z = int(x),int(y),int(z)\n    P.add((x,y,z))\n\nOUT = set()\nIN = set()\ndef reaches_outside(x,y,z,part):\n    if (x,y,z) in OUT:\n        return True\n    if (x,y,z) in IN:\n        return False\n    SEEN = set()\n    Q = deque([(x,y,z)])\n    while Q:\n        x,y,z = Q.popleft()\n        if (x,y,z) in P:\n            continue\n        if (x,y,z) in SEEN:\n            continue\n        SEEN.add((x,y,z))\n        if len(SEEN) > (5000 if part==2 else 0):\n            for p in SEEN:\n                OUT.add(p)\n            return True\n        Q.append((x+1,y,z))\n        Q.append((x-1,y,z))\n        Q.append((x,y+1,z))\n        Q.append((x,y-1,z))\n        Q.append((x,y,z+1))\n        Q.append((x,y,z-1))\n    for p in SEEN:\n        IN.add(p)\n    return False\n\ndef solve(part):\n    OUT.clear()\n    IN.clear()\n    ans = 0\n    for (x,y,z) in P:\n        if reaches_outside(x+1,y,z,part):\n            ans += 1\n        if reaches_outside(x-1,y,z,part):\n            ans += 1\n        if reaches_outside(x,y+1,z,part):\n            ans += 1\n        if reaches_outside(x,y-1,z,part):\n            ans += 1\n        if reaches_outside(x,y,z+1,part):\n            ans += 1\n        if reaches_outside(x,y,z-1,part):\n            ans += 1\n    return ans\nprint(solve(1))\nprint(solve(2))","repo_name":"kachnazkoci/adventOfCode22","sub_path":"20221218.py","file_name":"20221218.py","file_ext":"py","file_size_in_byte":3895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"28984795395","text":"import math\nimport argparse\nimport json\nimport sys\nimport os\nfrom pprint import pprint\nfrom getpass import getpass\n\nfrom web3 import Web3\nfrom web3.logs import DISCARD\nimport requests\nimport rlp\n\nfrom state_proof import request_block_header, request_account_proof\n\n\nORACLE_CONTRACT_ADDRESS = '0x602C71e4DAC47a042Ee7f46E0aee17F94A3bA0B6'\n\n\ndef main():\n    parser = argparse.ArgumentParser(\n        description=\"Patricia Merkle Trie Proof Generating Tool\",\n        formatter_class=argparse.RawTextHelpFormatter)\n\n    parser.add_argument(\"-b\", \"--block-number\",\n        help=\"Block number, defaults to `latest - 15`\")\n\n    parser.add_argument(\"-r\", \"--rpc\",\n        default=\"http://localhost:8545\",\n        help=\"URL of a full node RPC endpoint, e.g. http://localhost:8545\")\n\n    parser.add_argument(\"-k\", \"--keyfile\",\n        help=\"Send transaction and sign it using the keyfile at the provided path\")\n\n    parser.add_argument(\"-g\", \"--gas-price\",\n        help=\"Use the specified gas price\")\n\n    parser.add_argument(\"--contract\",\n        default=ORACLE_CONTRACT_ADDRESS,\n        help=\"Oracle contract address\")\n\n    args = parser.parse_args()\n    w3 = Web3(Web3.HTTPProvider(args.rpc))\n\n    block_number = args.block_number if args.block_number is not None else w3.eth.block_number - 15\n    oracle_contract = get_oracle_contract(args.contract, w3)\n    params = oracle_contract.functions.getProofParams().call()\n\n    (block_number, block_header, pool_acct_proof, steth_acct_proof,\n        pool_storage_proofs, steth_storage_proofs) = generate_proof_data(\n            rpc_endpoint=args.rpc,\n            block_number=block_number,\n            pool_address=params[0],\n            steth_address=params[1],\n            pool_slots=params[2:4],\n            steth_slots=params[4:10],\n        )\n\n    header_blob = rlp.encode(block_header)\n\n    proofs_blob = rlp.encode(\n        [pool_acct_proof, steth_acct_proof] +\n        pool_storage_proofs +\n        steth_storage_proofs\n    )\n\n    print(f\"\\nBlock number: {block_number}\\n\")\n    print(\"Header RLP bytes:\\n\")\n    print(f\"0x{header_blob.hex()}\\n\")\n    print(\"Proofs list RLP bytes:\\n\")\n    print(f\"0x{proofs_blob.hex()}\\n\")\n\n    if args.keyfile is None:\n        return\n\n    print(f\"Will send transaction calling `submitState` on {oracle_contract.address}\")\n\n    private_key = load_private_key(args.keyfile, w3)\n    account = w3.eth.account.privateKeyToAccount(private_key)\n    nonce = w3.eth.get_transaction_count(account.address)\n    gas_price = int(args.gas_price) if args.gas_price is not None else w3.eth.gas_price\n\n    tx = oracle_contract.functions.submitState(header_blob, proofs_blob).buildTransaction({\n        'gasPrice': gas_price,\n        'gas': 3000000,\n        'nonce': nonce,\n    })\n\n    signed = w3.eth.account.sign_transaction(tx, private_key)\n\n    print(f\"Sending transaction from {account.address}, gas price {gas_price}...\")\n\n    tx_hash = w3.eth.send_raw_transaction(signed.rawTransaction)\n\n    print(f\"Transaction sent: {tx_hash.hex()}\\nWaiting for inclusion...\\n\")\n\n    receipt = w3.eth.waitForTransactionReceipt(tx_hash)\n    pprint(dict(receipt))\n\n    if int(receipt['status']) != 1:\n        print(\"\\nTransaction failed\")\n    else:\n        print_event(\"SlotValuesUpdated\", receipt, oracle_contract)\n        print_event(\"PriceUpdated\", receipt, oracle_contract)\n\n\ndef get_oracle_contract(address, w3):\n    dir = os.path.dirname(__file__)\n    interface_path = os.path.join(dir, '../interfaces/StableSwapStateOracle.json')\n    with open(interface_path) as abi_file:\n        abi = json.load(abi_file)\n        return w3.eth.contract(address=address, abi=abi)\n\n\ndef load_private_key(path, w3):\n    with open(path) as keyfile:\n        encrypted_key = keyfile.read()\n        password = getpass()\n        return w3.eth.account.decrypt(encrypted_key, password)\n\n\ndef print_event(name, receipt, contract):\n    # https://github.com/ethereum/web3.py/issues/1738\n    logs = contract.events[name]().processReceipt(receipt, DISCARD)\n    if len(logs) != 0:\n        print(f\"\\n{name} event:\")\n        for key, value in logs[0]['args'].items():\n            print(f\"  {key}: {value}\")\n    else:\n        print(f\"\\nNo {name} event generated\")\n\n\ndef generate_proof_data(\n    rpc_endpoint,\n    block_number,\n    pool_address,\n    steth_address,\n    pool_slots,\n    steth_slots,\n):\n    block_number = \\\n        block_number if block_number == \"latest\" or block_number == \"earliest\" \\\n        else hex(int(block_number))\n\n    (block_number, block_header) = request_block_header(\n        rpc_endpoint=rpc_endpoint,\n        block_number=block_number,\n    )\n\n    (pool_acct_proof, pool_storage_proofs) = request_account_proof(\n        rpc_endpoint=rpc_endpoint,\n        block_number=block_number,\n        address=pool_address,\n        slots=pool_slots,\n    )\n\n    (steth_acct_proof, steth_storage_proofs) = request_account_proof(\n        rpc_endpoint=rpc_endpoint,\n        block_number=block_number,\n        address=steth_address,\n        slots=steth_slots,\n    )\n\n    return (\n        block_number,\n        block_header,\n        pool_acct_proof,\n        steth_acct_proof,\n        pool_storage_proofs,\n        steth_storage_proofs,\n    )\n\n\nif __name__ == \"__main__\":\n    main()\n    exit(0)\n","repo_name":"succinctlabs/eth-proof-of-consensus","sub_path":"contracts/lib/curve-merkle-oracle/offchain/generate_steth_price_proof.py","file_name":"generate_steth_price_proof.py","file_ext":"py","file_size_in_byte":5225,"program_lang":"python","lang":"en","doc_type":"code","stars":102,"dataset":"github-code","pt":"86"}
+{"seq_id":"37535097472","text":"import asyncio\r\nimport random\r\nimport time\r\n\r\n\r\nasync def part1(n):\r\n    i = random.randint(0, 10)\r\n    print(f\"part1({n}) sleeping for {i} seconds\")\r\n    await asyncio.sleep(i)\r\n    result = f\"result{n}-1\"\r\n    print(f\"Returning part1({n}) == {result}\")\r\n    return result\r\n\r\n\r\nasync def part2(n, arg):\r\n    i = random.randint(0, 10)\r\n    print(f\"part2({n, arg}) sleeping for {i} seconds\")\r\n    await asyncio.sleep(i)\r\n    result = f\"result{n}-2 derived from {arg}\"\r\n    print(f\"Returning part2({n, arg}) == {result}\")\r\n    return result\r\n\r\n\r\nasync def chain(n):\r\n    start = time.perf_counter()\r\n    p1 = await part1(n)\r\n    p2 = await part2(n, p1)\r\n    end = time.perf_counter() - start\r\n    print(f\"Chain({n}) => {p2} took {end:0.2f} seconds.\")\r\n\r\n\r\nasync def main(*args):\r\n    await asyncio.gather(*(chain(arg) for arg in args))\r\n\r\n\r\nif __name__ == '__main__':\r\n    t = time.perf_counter()\r\n    random.seed(444)\r\n    inp = [1, 2, 3]\r\n    asyncio.run(main(*inp))\r\n    elapsed = time.perf_counter() - t\r\n    print(f\"Took {elapsed:0.2f} seconds.\")\r\n","repo_name":"prabhupad26/simple_apscheduler_example","sub_path":"chained.py","file_name":"chained.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"29288469491","text":"import json\n\nfrom flask import request, Flask\nfrom flask_sqlalchemy import SQLAlchemy\n\nimport raw_data\n\napp = Flask(__name__)\n\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = \"sqlite:///homework16_again.db\"  #:memory:\napp.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False  # отключить\n\ndb = SQLAlchemy(app)  # связь базы данных с приложением\n\n\nclass User(db.Model):\n    __tablename__ = \"user\"\n    id = db.Column(db.Integer, primary_key=True)\n    first_name = db.Column(db.String)\n    last_name = db.Column(db.String)\n    age = db.Column(db.Integer)\n    email = db.Column(db.String)\n    role = db.Column(db.String)  # должность\n    phone = db.Column(db.String)\n\n    def do_dict(self):  # питонячий формат\n        return {\n            \"id\": self.id,\n            \"first_name\": self.first_name,\n            \"last_name\": self.last_name,\n            \"age\": self.age,\n            \"email\": self.email,\n            \"role\": self.role,\n            \"phone\": self.phone\n        }\n\n\nclass Order(db.Model):  # заказ\n    __tablename__ = 'order'\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String)\n    description = db.Column(db.String)  # описание\n    start_date = db.Column(db.String)\n    end_date = db.Column(db.String)\n    address = db.Column(db.String)\n    price = db.Column(db.Integer)\n    customer_id = db.Column(db.Integer, db.ForeignKey(\"user.id\"))  # покупатель\n    executor_id = db.Column(db.Integer, db.ForeignKey(\"user.id\"))  # исполнитель\n\n    def do_dict(self):\n        return {\n            \"id\": self.id,\n            \"name\": self.name,\n            \"description\": self.description,\n            \"start_date\": self.start_date,\n            \"end_date\": self.end_date,\n            \"address\": self.address,\n            \"price\": self.price,\n            \"customer_id\": self.customer_id,\n            \"executor_id\": self.executor_id\n        }\n\n\nclass Offer(db.Model):  # Предложение/ скидка\n    __tablename__ = \"offer\"\n    id = db.Column(db.Integer, primary_key=True)\n    executor_id = db.Column(db.Integer, db.ForeignKey(\"user.id\"))  # исполнитель\n    order_id = db.Column(db.Integer, db.ForeignKey(\"order.id\"))  # номер заказа\n\n    def do_dict(self):\n        return {\n            \"id\": self.id,\n            \"executor_id\": self.executor_id,\n            \"order_id\": self.order_id\n        }\n\n\ndef init_database():\n    db.drop_all()  # очистить\n    db.create_all()  # залить\n\n    for user_data in raw_data.users:  # если не переделывать данные в JSON формат\n        new_user = User(\n            id=user_data[\"id\"],\n            first_name=user_data[\"first_name\"],\n            last_name=user_data[\"last_name\"],\n            age=user_data[\"age\"],\n            email=user_data[\"email\"],\n            role=user_data[\"role\"],\n            phone=user_data[\"phone\"]\n        )\n\n        db.session.add(new_user)  # добавляем в базу данных\n        db.session.commit()  # делаем коммит\n\n    for order_data in raw_data.orders:\n        new_order = Order(\n            id=order_data[\"id\"],\n            name=order_data[\"name\"],\n            description=order_data[\"description\"],\n            start_date=order_data[\"start_date\"],\n            end_date=order_data[\"end_date\"],\n            address=order_data[\"address\"],\n            price=order_data[\"price\"],\n            customer_id=order_data[\"customer_id\"],\n            executor_id=order_data[\"executor_id\"]\n        )\n\n        db.session.add(new_order)\n        db.session.commit()\n\n    for offer_data in raw_data.offers:\n        new_offer = Offer(\n            id=offer_data['id'],\n            executor_id=offer_data['executor_id'],\n            order_id=offer_data['order_id']\n        )\n\n        db.session.add(new_offer)\n        db.session.commit()\n\n\n# --------------------Users-----------------\n# ---------соединяя 2-3 метода в одной вьюшке------------\n@app.route(\"/users\", methods=[\"GET\", \"POST\"])\ndef users():\n    if request.method == \"GET\":\n        result = []\n        for u in User.query.all():\n            result.append(u.do_dict())\n\n        return json.dumps(result), 200\n\n    if request.method == \"POST\":\n        user_data = json.loads(request.data)\n        new_user = User(\n            id=user_data[\"id\"],\n            first_name=user_data[\"first_name\"],\n            last_name=user_data[\"last_name\"],\n            age=user_data[\"age\"],\n            email=user_data[\"email\"],\n            role=user_data[\"role\"],\n            phone=user_data[\"phone\"]\n        )\n\n        db.session.add(new_user)\n        db.session.commit()\n\n        return \"Пользователь добавлен\", 201  # на создание\n\n\n@app.route(\"/users/<int:uid>\", methods=[\"GET\", \"PUT\", \"DELETE\"])\ndef user(uid: int):\n    if request.method == \"GET\":  # берем юзера по id\n        return json.dumps(User.query.get(uid).do_dict()), 200\n\n    if request.method == \"PUT\":\n        user_data = json.loads(request.data)\n        u = User.query.get(uid)\n        # id PUTом изменить не получиться\n        u.first_name = user_data[\"first_name\"]\n        u.last_name = user_data[\"last_name\"]\n        u.age = user_data[\"age\"]\n        u.email = user_data[\"email\"]\n        u.role = user_data[\"role\"]\n        u.phone = user_data[\"phone\"]\n\n        db.session.add(u)\n        db.session.commit()\n        return \"Пользователь изменен\", 204  # обновление\n\n    if request.method == \"DELETE\":\n        u = User.query.get(uid)\n\n        db.session.delete(u)\n        db.session.commit()\n\n        return \"Пользователь удален\", 204  # обновление\n\n\n# --------------------Orders-----------------\n\n@app.route(\"/orders\", methods=[\"GET\", \"POST\"])\ndef orders():\n    if request.method == \"GET\":\n        result = []\n        for u in Order.query.all():\n            result.append(u.do_dict())\n\n        return json.dumps(result), 200\n\n    if request.method == \"POST\":\n        order_data = json.loads(request.data)\n        new_order = Order(\n            id=order_data[\"id\"],\n            name=order_data[\"name\"],\n            description=order_data[\"description\"],\n            start_date=order_data[\"start_date\"],\n            end_date=order_data[\"end_date\"],\n            address=order_data[\"address\"],\n            price=order_data[\"price\"],\n            customer_id=order_data[\"customer_id\"],\n            executor_id=order_data[\"executor_id\"]\n        )\n\n        db.session.add(new_order)\n        db.session.commit()\n\n        return \"Заказ добавлен\", 201  # на создание\n\n\n@app.route(\"/orders/<int:uid>\", methods=[\"GET\", \"PUT\", \"DELETE\"])\ndef order(uid: int):\n    if request.method == \"GET\":  # берем юзера по id\n        return json.dumps(Order.query.get(uid).do_dict()), 200\n\n    if request.method == \"PUT\":\n        order_data = json.loads(request.data)\n        u = Order.query.get(uid)\n        # id PUTом изменить не получиться\n        u.name = order_data[\"name\"]\n        u.description = order_data[\"description\"]\n        u.start_date = order_data[\"start_date\"]\n        u.end_date = order_data[\"end_date\"]\n        u.address = order_data[\"address\"]\n        u.price = order_data[\"price\"]\n        u.customer_id = order_data[\"customer_id\"]\n        u.executor_id = order_data[\"executor_id\"]\n\n        db.session.add(u)\n        db.session.commit()\n        return \"Заказ изменен\", 204  # обновление\n\n    if request.method == \"DELETE\":\n        u = Order.query.get(uid)\n\n        db.session.delete(u)\n        db.session.commit()\n\n        return \"Заказ удален\", 204  # обновление\n\n\n# --------------------Offers-----------------\n\n@app.route(\"/offers\", methods=[\"GET\", \"POST\"])\ndef offers():\n    if request.method == \"GET\":\n        result = []\n        for u in Offer.query.all():\n            result.append(u.do_dict())\n\n        return json.dumps(result), 200\n\n    if request.method == \"POST\":\n        offer_data = json.loads(request.data)\n        new_offer = Offer(\n            id=offer_data[\"id\"],\n            order_id=offer_data[\"order_id\"],\n            executor_id=offer_data[\"executor_id\"]\n        )\n\n        db.session.add(new_offer)\n        db.session.commit()\n\n        return \"Выполнение заказа добавлено\", 201  # на создание\n\n\n@app.route(\"/offers/<int:uid>\", methods=[\"GET\", \"PUT\", \"DELETE\"])\ndef offer(uid: int):\n    if request.method == \"GET\":  # берем юзера по id\n        return json.dumps(Offer.query.get(uid).do_dict()), 200\n\n    if request.method == \"PUT\":\n        offer_data = json.loads(request.data)\n        u = Offer.query.get(uid)\n        # id PUTом изменить не получиться\n        u.order_id = offer_data[\"order_id\"]\n        u.executor_id = offer_data[\"executor_id\"]\n\n        db.session.add(u)\n        db.session.commit()\n\n        return \"Выполнение заказа изменено\", 204  # обновление\n\n    if request.method == \"DELETE\":\n        u = Offer.query.get(uid)\n\n        db.session.delete(u)\n        db.session.commit()\n\n        return \"Выполнение заказа удалено\", 204  # обновление\n\n\nif __name__ == '__main__':\n    init_database()\n    app.run(host='127.0.0.1', port=6000, debug=True)\n","repo_name":"Klever-Jenya/home_work_16_again_2","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":9417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"36000422995","text":"N,K=map(int,input().split())\r\nlst=[]\r\nfor _ in range(N):\r\n    lst.append(list(map(int,input().split())))\r\nA=[[0 for _ in range(K+1)] for _ in range(N+1)]\r\nfor i in range(1,N+1):\r\n    for j in range(K+1):\r\n        if j>=lst[i-1][0]:\r\n            A[i][j]=max(A[i-1][j],A[i-1][j-lst[i-1][0]]+lst[i-1][1])\r\n        else:\r\n            A[i][j]=A[i-1][j]\r\nprint(A[N][K])","repo_name":"jbkarvens/baekjoon","sub_path":"백준/Gold/12865. 평범한 배낭/평범한 배낭.py","file_name":"평범한 배낭.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"28203786173","text":"import mimetypes\nimport re\n\nimport sys\nfrom django.shortcuts import render, get_object_or_404\nfrom django.views.generic import View\nfrom django.http import JsonResponse, Http404, StreamingHttpResponse\nfrom wsgiref.util import FileWrapper\n\nfrom .Transformations import *\nfrom .Tints import Tint\nfrom .Enhancements import *\nfrom .KernelFilters import KernelFilterApplier\nfrom .ColorFilters import FilterManager\nfrom .models import ColorTint, ColorFilter, KernelFilter, ImageFunction\n\nfrom io import BytesIO\nfrom PIL import Image\nfrom base64 import b64encode, b64decode\n\n\nclass Editor(View):\n    template_name = 'ImageEditor/editor.html'\n\n    def get(self, request):\n        color_tints = ColorTint.objects.all()\n        color_filters = ColorFilter.objects.all()\n        kernel_filters = KernelFilter.objects.all()\n        adjustments = ImageFunction.objects.filter(function_type='Adjustment')\n        transforms = ImageFunction.objects.filter(function_type='Transform')\n        context = {'color_tints': color_tints, 'adjustments': adjustments,\n                   'color_filters': color_filters, 'transforms': transforms,\n                   'kernel_filters': kernel_filters}\n        return render(request, self.template_name, context)\n\n\nclass ImageOperation(View):\n    def post(self, request, operation_type):\n        class_dict = self.get_class_dict()  # Get dict of all available operations\n\n        if operation_type not in class_dict:  # Raise 404 if invalid operation\n            raise Http404\n\n        # If the last operation is not the same as the last one, get an the update image from the canvas\n        # and store it in the user's current session\n\n        # If the last operation was the same as the current one, edit the previously saved image\n        # This solves the scenario where the user is testing out different values of sharpness, for examaple\n        # as updating the image for each harpness change would cause the sharpness enhancement to be applied to\n        # a previously sharpened image.\n        if request.session.get('LAST_OPERATION') != operation_type:\n            request.session['LAST_OPERATION'] = operation_type\n            image_base64 = request.session.get('current_image_base64')\n            request.session['pre_operation_image_base64'] = image_base64\n\n        else:\n            image_base64 = request.session.get('pre_operation_image_base64')\n\n        image_bytes = self.decode_base64_image(image_base64)\n        image = Image.open(image_bytes)\n\n        # Instantiate the approrpriate image operation\n        class_to_instantiate = get_object_or_404(ImageFunction, display_name=operation_type).class_name\n        operation = class_dict[class_to_instantiate](request.POST.get('params'))\n\n        # process the current image\n        output_image = operation.process(image)\n        output_image_base64 = self.encode_base64_image(output_image)\n        request.session['current_image_base64'] = output_image_base64\n\n        image.close()\n\n        return JsonResponse({'processed_image': output_image_base64})\n\n    @staticmethod\n    def decode_base64_image(base64_string):\n        # Remove the \"data:image/jpg;base64,\" tag at the beginning of the string\n        # Would lead to a corrupted image otherwise\n        # reg = re.compile(\"data:image/(.*?);\")\n        # self.format = reg.match(base64_string[:25]).group(1)\n        img_data = re.sub('^data:image/.+;base64,', '', base64_string)\n        return BytesIO(b64decode(img_data))\n\n    @staticmethod\n    def encode_base64_image(image):\n        buffered_image = BytesIO()\n        image.save(buffered_image, format('JPEG'))\n        return 'data:image/jpeg;base64,' + b64encode(buffered_image.getvalue()).decode()\n\n    @staticmethod\n    def get_class_dict():\n        \"\"\"Return a dict of all the available classes in the global namespace\"\"\"\n        class_dict = globals()\n        return class_dict\n\n\nclass ImageUploadHandler(View):\n    def post(self, request):\n        request.session['LAST_OPERATION'] = \"\"\n        request.session['original_image_base64'] = request.POST['imgBase64']\n        request.session['pre_operation_image_base64'] = request.POST['imgBase64']\n        request.session['current_image_base64'] = request.POST['imgBase64']\n        request.session.set_expiry(0)\n\n        return JsonResponse({\"OK\": \"IT WORKS\"})\n\n\ndef reset_image(request):\n    if request.method == 'POST':\n        try:\n            original_image = request.session.get('original_image_base64')\n        except KeyError:\n            raise Http404(\"Image could not be reset\")\n\n        request.session['pre_operation_image_base64'] = original_image\n        request.session['current_image_base64'] = original_image\n\n        return JsonResponse({'processed_image': original_image})\n\n\ndef download_image(request):\n    if request.method == 'GET':\n        try:\n            decoded_image = ImageOperation.decode_base64_image(request.session['current_image_base64'])\n        except KeyError:\n            raise Http404(\"No image to download\")\n        chunk_size = 8192\n        response = StreamingHttpResponse(FileWrapper(decoded_image, chunk_size),\n                                         content_type='image/jpeg')\n        response['Content-Disposition'] = \"attachment; filename=image-download.jpeg\"\n        return response\n","repo_name":"ShadyF/theia-web","sub_path":"ImageEditor/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5279,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"70112013404","text":"import numpy as np\nfrom PIL import Image\n\n\ndef ft_load(path: str) -> np.array:\n    \"\"\"\n    throw error when unable to open img\n    otherwise return img data array\n    \"\"\"\n\n    try:\n        img = Image.open(path)\n        arr = np.array(img)\n        return arr\n    except FileNotFoundError:\n        print('FileNotFoundError: Unable to open ' + path)\n\n\n# if __name__ == \"__main__\":\n#     print(ft_load('.jpg'))\n","repo_name":"NEIL-smtg/42_python_data_science","sub_path":"python_01/ex03/load_image.py","file_name":"load_image.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"24955802109","text":"# mypy: allow-untyped-defs\n\nimport time\nimport uuid\n\nfrom threading import Timer\n\nfrom ..data.device import Device\nfrom ..testing.event_dispatcher import DEVICES, DEVICE_ADDED_EVENT, DEVICE_REMOVED_EVENT\nfrom ..utils.user_agent_parser import parse_user_agent\nfrom ..utils.serializer import serialize_device\nfrom ..data.exceptions.not_found_exception import NotFoundException\n\n\nDEVICE_TIMEOUT = 60000  # 60sec\nRECONNECT_TIME = 5000   # 5sec\n\nclass DevicesManager:\n    def initialize(self, event_dispatcher):\n        self.devices = {}\n        self._event_dispatcher = event_dispatcher\n        self._timer = None\n\n    def create_device(self, user_agent):\n        browser = parse_user_agent(user_agent)\n        name = \"{} {}\".format(browser[\"name\"], browser[\"version\"])\n        token = str(uuid.uuid1())\n        last_active = int(time.time() * 1000)\n\n        device = Device(token, user_agent, name, last_active)\n\n        self._event_dispatcher.dispatch_event(\n            DEVICES,\n            DEVICE_ADDED_EVENT,\n            serialize_device(device))\n        self.add_to_cache(device)\n\n        self._set_timer(DEVICE_TIMEOUT)\n\n        return device\n\n    def read_device(self, token):\n        if token not in self.devices:\n            raise NotFoundException(f\"Could not find device '{token}'\")\n        return self.devices[token]\n\n    def read_devices(self):\n        devices = []\n        for key in self.devices:\n            devices.append(self.devices[key])\n\n        return devices\n\n    def update_device(self, device):\n        if device.token not in self.devices:\n            return\n        self.devices[device.token] = device\n\n    def delete_device(self, token):\n        if token not in self.devices:\n            return\n        device = self.devices[token]\n        del self.devices[token]\n        self._event_dispatcher.dispatch_event(\n            DEVICES,\n            DEVICE_REMOVED_EVENT,\n            serialize_device(device))\n\n    def refresh_device(self, token):\n        if token not in self.devices:\n            return\n        device = self.devices[token]\n        device.last_active = int(time.time() * 1000)\n        self.update_device(device)\n\n    def post_event(self, handle, event_type, data):\n        if event_type is None:\n            return\n        self._event_dispatcher.dispatch_event(handle, event_type, data)\n\n    def post_global_event(self, event_type, data):\n        self.post_event(DEVICES, event_type, data)\n\n    def _set_timer(self, timeout):\n        if self._timer is not None:\n            return\n\n        def handle_timeout(self):\n            self._timer = None\n            now = int(time.time() * 1000)\n            timed_out_devices = []\n            for token in self.devices:\n                device = self.devices[token]\n                if now - device.last_active < DEVICE_TIMEOUT:\n                    continue\n                timed_out_devices.append(token)\n\n            for token in timed_out_devices:\n                self.delete_device(token)\n\n            oldest_active_time = None\n            for token in self.devices:\n                device = self.devices[token]\n                if oldest_active_time is None:\n                    oldest_active_time = device.last_active\n                else:\n                    if oldest_active_time > device.last_active:\n                        oldest_active_time = device.last_active\n            if oldest_active_time is not None:\n                self._set_timer(now - oldest_active_time)\n\n        self._timer = Timer(timeout / 1000.0, handle_timeout, [self])\n        self._timer.start()\n\n    def add_to_cache(self, device):\n        if device.token in self.devices:\n            return\n\n        self.devices[device.token] = device\n","repo_name":"servo/servo","sub_path":"tests/wpt/tests/tools/wave/testing/devices_manager.py","file_name":"devices_manager.py","file_ext":"py","file_size_in_byte":3696,"program_lang":"python","lang":"en","doc_type":"code","stars":24247,"dataset":"github-code","pt":"86"}
+{"seq_id":"72528517724","text":"\"\"\"\nGiven the root of a binary tree, calculate the vertical order traversal of the binary tree.\n\nFor each node at position (row, col), its left and right children will be at positions (row + 1, col - 1) and (row + 1, col + 1) respectively. The root of the tree is at (0, 0).\n\nThe vertical order traversal of a binary tree is a list of top-to-bottom orderings for each column index starting from the leftmost column and ending on the rightmost column. There may be multiple nodes in the same row and same column. In such a case, sort these nodes by their values.\n\nReturn the vertical order traversal of the binary tree.\n\n \n\nExample 1:\n\n\nInput: root = [3,9,20,null,null,15,7]\nOutput: [[9],[3,15],[20],[7]]\nExplanation:\nColumn -1: Only node 9 is in this column.\nColumn 0: Nodes 3 and 15 are in this column in that order from top to bottom.\nColumn 1: Only node 20 is in this column.\nColumn 2: Only node 7 is in this column.\nExample 2:\n\n\nInput: root = [1,2,3,4,5,6,7]\nOutput: [[4],[2],[1,5,6],[3],[7]]\nExplanation:\nColumn -2: Only node 4 is in this column.\nColumn -1: Only node 2 is in this column.\nColumn 0: Nodes 1, 5, and 6 are in this column.\n          1 is at the top, so it comes first.\n          5 and 6 are at the same position (2, 0), so we order them by their value, 5 before 6.\nColumn 1: Only node 3 is in this column.\nColumn 2: Only node 7 is in this column.\nExample 3:\n\n\nInput: root = [1,2,3,4,6,5,7]\nOutput: [[4],[2],[1,5,6],[3],[7]]\nExplanation:\nThis case is the exact same as example 2, but with nodes 5 and 6 swapped.\nNote that the solution remains the same since 5 and 6 are in the same location and should be ordered by their values.\n \n\nConstraints:\n\nThe number of nodes in the tree is in the range [1, 1000].\n0 <= Node.val <= 1000\n\n\"\"\"\n# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\n\"\"\"\n不能直接按照314的做法，这里要行数相同时才会按照大小来排序\n\"\"\"\nclass Solution:\n    def verticalTraversal(self, root: Optional[TreeNode]) -> List[List[int]]:\n        if not root:\n            return []\n        \n        self.position = []\n        self.dfs(root, 0, 0)\n        self.position.sort(key = lambda x:(x[1], x[0], x[2]))\n        res = []\n        i = 0\n        while i < len(self.position):\n            pos = self.position[i][1]\n            temp = []\n            while i < len(self.position) and self.position[i][1] == pos:\n                temp.append(self.position[i][2])\n                i += 1\n            res.append(temp)\n        return res\n    \n    def dfs(self, root, row, col):\n        if not root:\n            return \n        self.position.append((row, col, root.val))\n        self.dfs(root.left, row + 1, col - 1)\n        self.dfs(root.right, row + 1, col + 1)\n\n","repo_name":"ling67/Algorithm-LeetCode","sub_path":"Solutions/0987.Vertical_Order_Traversal_of_a_Binary_Tree.py","file_name":"0987.Vertical_Order_Traversal_of_a_Binary_Tree.py","file_ext":"py","file_size_in_byte":2848,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"13173099023","text":"# 🚨 Don't change the code below 👇\nage = input(\"What is your current age? \")\n# 🚨 Don't change the code above 👆\n\n#Write your code below this line 👇\n\n#365 days in a year\n#52 weeks in a year\n#12 months in a year\n\n#You have x days, y weeks, and z months left.\n\nninetyDays= (90 * 365)\nninetyWeeks= (90 * 52)\nninetyMonths= (90 * 12)\n\ndays= (ninetyDays) - (int(age) * 365) \nweeks= (ninetyWeeks) - (int(age) * 52) \nmonths= (ninetyMonths)- (int(age) * 12)\n\nprint(f\"You have {days} days, {weeks} weeks, and {months} months left based on a life expectancy of 90 years old.\")","repo_name":"Kmclaurin99/dataEngineer101","sub_path":"mathFunctions/lifeinWeeks.py","file_name":"lifeinWeeks.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"73497245405","text":"import openai\nfrom gtts import gTTS\nfrom aiogram import Bot\nfrom aiogram.types import Message, FSInputFile\nfrom ..kayboards.replay import reply_keyboard\nfrom pycbrf.toolbox import ExchangeRates\nimport datetime\n\n\ndef currency(val):\n    rates = ExchangeRates(datetime.date.today())\n    result = rates[val].value\n    return str(result)\n\n\nasync def get_voice(message: Message, bot: Bot):\n    if message.text == '/start':\n        await message.answer('hello my friendo',\n                             reply_markup=reply_keyboard)\n    elif message.text == 'show the course a dollar':\n        await message.answer(currency('USD') + ' dollar')\n    elif message.text == 'show the course a euro':\n        await message.answer(currency('EUR') + ' euro')\n    elif message.text == 'show the course a yen':\n        await message.answer(currency('JPY') + ' yen')\n    else:\n        response = openai.Completion.create(\n            model=\"text-davinci-003\",\n            prompt=str(message.text),\n            max_tokens=700,\n            temperature=0\n        )\n        audio = gTTS(text=response.choices[0].text, lang=\"ru\", slow=False)\n        audio.save('txt.mp3')\n        send_audio = FSInputFile(path='txt.mp3')\n        await bot.send_voice(message.chat.id, voice=send_audio)\n\n","repo_name":"kiipariss/git_aiogram","sub_path":"src/handler/basic.py","file_name":"basic.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"6503881609","text":"import time\n\n\ndef fx(a, b):\n    time.sleep(1)\n    aim_list = []\n    a_length = len(a)\n    b_length = len(b)\n    a_cursor = 0\n    b_cursor = 0\n\n    for i in range(a_length + b_length):\n        if a_cursor == a_length:\n            aim_list += b[b_cursor:]\n            break\n        elif b_cursor == b_length:\n            aim_list += a[a_cursor:]\n            break\n        else:\n            if a[a_cursor] < b[b_cursor]:\n                aim_list.append(a[a_cursor])\n                a_cursor += 1\n            else:\n                aim_list.append(b[b_cursor])\n                b_cursor += 1\n\n    return aim_list\n\n\ndef fx2(a, b):\n    time.sleep(1)\n    c = a + b\n    # c = sorted(c)\n    c.sort()\n    return c\n\n\ndef fx3(a, b):\n    time.sleep(1)\n    res = []\n    while a or b:\n        if not a:\n            res.extend(b)\n            return res\n        elif not b:\n            res.extend(a)\n            return res\n        else:\n            res.append(a.pop(0) if a[0] <= b[0] else b.pop(0))\n\n\ndef merge_sort(nums1, nums2):\n    time.sleep(1)\n    m = []\n    i, j = 0, 0\n    l_1, l_2 = len(nums1) - 1, len(nums2) - 1\n    # 当i，j的索引位置小于等于索引最大值的时候\n    while i <= l_1 and j <= l_2:\n        if nums1[i] <= nums2[j]:\n            m.append(nums1[i])\n            i += 1\n        else:\n            m.append(nums2[j])\n            j += 1\n    m = m + nums1[i:] + nums2[j:]\n    return m\n\n\nif __name__ == '__main__':\n    a = [1] * 50 + [2] * 100 + [3] * 1000 + [4] * 20 + [5] * 10 + [6] * 20\n    b = [2] * 30 + [4] * 100 + [6] * 100 + [8] * 100 + [9] * 100\n\n    ts2 = time.time()\n    res2 = fx2(a, b)\n    td2 = time.time()\n    print('方式2耗时', td2 - ts2)\n    # print(res2)\n\n    ts4 = time.time()\n    res4 = merge_sort(a, b)\n    td4 = time.time()\n    print('方式4耗时', td4 - ts4)\n    # print(res3)\n\n    ts = time.time()\n    res = fx(a, b)\n    t1 = time.time()\n    print('方式1耗时', t1 - ts)\n    # print(res)\n\n    ts3 = time.time()\n    res3 = fx3(a, b)\n    td3 = time.time()\n    print('方式3耗时', td3 - ts3)\n    # print(res3)\n\n    aim = [1, 4, 2, 1, 3]\n    aim.sort()\n\n    blist = [1, 2, 6, 4, 2, 3, 1]\n    clist = sorted(blist)\n    print(aim)\n    print(clist)\n    if (td3 - ts3) > (t1 - ts):\n        print('算法时间长')\n","repo_name":"kongklw/prc_project","sub_path":"sunfa/combine_arry.py","file_name":"combine_arry.py","file_ext":"py","file_size_in_byte":2254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"26594523262","text":"import json\r\nfrom sys import stdout\r\ndata = json.load(open(\"items.json\"))\r\n\r\nfor row in data:\r\n    year = row['year']\r\n    for par in row['participants']:\r\n        if par['place'] == 1:\r\n            winner = par['performer']\r\n            winning = par['song']\r\n            descr = par['name']\r\n    \r\n    stdout.write((u\"%s\\t%s\\t%s\\t\\t%s\\r\\n\" % (year, winner, winning, descr)).encode('utf-8'))\r\n","repo_name":"kolen/eurovision-parser","sub_path":"extract1.py","file_name":"extract1.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"30164758515","text":"import torch\nimport torch.nn.functional as F\nimport numpy as np\nimport os\nimport argparse\nfrom scipy import misc\nimport cv2\nfrom lib.Network_Res2Net_GRA_NCD import Network\nfrom utils.data_val import test_dataset\nimport yaml\nfrom utils.eval import *\nimport torch.backends.cudnn as cudnn\nimport imageio\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--testsize', type=int, default=352, help='testing size')\nparser.add_argument('--pth_path', type=str,\n                    default='/home/liuxiangyu/SINet-V2-multi-class/snapshot/SINet_V2/Net_epoch_best.pth')\nparser.add_argument('--gpu_id', type=str, default='1', help='train use gpu')\nwith open('/home/liuxiangyu/SINet-V2-multi-class/config.yaml', 'r', encoding='utf-8') as f:\n    cfg = yaml.safe_load(f)\n    parser.set_defaults(**cfg)\nopt = parser.parse_args()\n\n\nfor _data_name in ['COD10K']:\n    data_path = './Dataset/TestDataset/{}/'.format(_data_name)\n    save_path = './res/{}/{}/'.format(opt.pth_path.split('/')[-2], _data_name)\n    # set the device for training\n    if opt.gpu_id == '0':\n        os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n        print('USE GPU 0')\n    elif opt.gpu_id == '1':\n        os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n        print('USE GPU 1')\n    cudnn.benchmark = True\n    model = Network(num_classes=len(opt.CLASSES), imagenet_pretrained=False)\n    model.load_state_dict(torch.load(opt.pth_path))\n    model.cuda()\n    model.eval()\n\n    os.makedirs(save_path, exist_ok=True)\n    image_root = '{}/Imgs/'.format(data_path)\n    gt_root = '{}/GT/'.format(data_path)\n    test_loader = test_dataset(image_root, gt_root, opt.testsize, opt.CLASSES)\n    confusion_matrix = torch.zeros(len(opt.CLASSES), len(opt.CLASSES)).cuda()\n\n    for i in range(test_loader.size):\n        image, gt, name, _ = test_loader.load_data()\n        image = image.cuda()\n\n        res5, res4, res3, res2 = model(image)\n        # res2使用argmax获得类别\n        res = res2.squeeze()\n        res = res.argmax(axis=0).type(torch.uint8)\n        res = res.unsqueeze(0)\n        res = res.unsqueeze(0)\n        # resize到原大小，采用临近插值法，不会出现新的数值\n        res = F.interpolate(res, size=np.array(gt).shape, mode='nearest')\n        res = res.squeeze()\n        # 计算混淆矩阵\n        gt = torch.from_numpy(np.array(gt)).cuda()\n        confusion_matrix += get_confusion_matrix(res, gt, len(opt.CLASSES))\n        # colorize 对输出图着色\n        colormap = get_colormap(len(opt.CLASSES))\n        print('> {} - {} '.format(_data_name, name), end='')\n        rgb = decode_segmap(np.array(res.cpu()),\n                            colormap=colormap, classes=opt.CLASSES)\n        # 使用imageio保存彩色输出图\n        imageio.imsave(save_path+name, rgb)\n    # 计算iou\n    iou = cal_iou(confusion_matrix, len(opt.CLASSES))\n    # 打印每个类别的iou\n    for i in range(len(opt.CLASSES)):\n        # 输出字符串对齐\n        print('{:15}{}'.format(opt.CLASSES[i], iou[i]))\n    # 计算目标类miou\n    print('{:15}{}'.format('miou', torch.mean(iou[1:])))\n","repo_name":"25252www/SINet-V2-multi-class","sub_path":"MyTesting.py","file_name":"MyTesting.py","file_ext":"py","file_size_in_byte":3063,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32905933021","text":"import comet_ml\nimport sys\n\nimport time\nimport signal\nimport gin\nimport argparse\nfrom deepgene.utils import train_model, test_model\n\n\ndef handler(signum, frame):\n    print('Received signal to end running', signum)\n    raise KeyboardInterrupt\n\n\nsignal.signal(signal.SIGUSR1, handler)\nsignal.signal(signal.SIGTERM, handler)\nsignal.signal(signal.SIGINT, handler)\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(prog='Genomics')\n    parser.add_argument(\"--config\", type=str, default=\"\")\n    action_parsers = parser.add_subparsers(title='actions', dest='action')\n    train_parser = action_parsers.add_parser('train')\n\n    predict_parser = action_parsers.add_parser('test')\n    predict_parser.add_argument('--path', type=str)\n    args = parser.parse_args()\n\n    print(args.config)\n    gin.parse_config_file(args.config)\n\n    print(gin.config._CONFIG)\n    if args.action == \"train\":\n        train_model(configs=gin.config._CONFIG)\n    elif args.action == \"test\":\n        test_model()\n    else:\n        ValueError(\"Choose train or predict\")","repo_name":"Genomics-HSE/deepgene","sub_path":"old_main.py","file_name":"old_main.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"11223548532","text":"import argparse\nimport torch\nimport json\nimport optmizor\nfrom torch.optim.lr_scheduler import StepLR\nfrom torchvision import datasets, transforms\nfrom model import NonConvex\nfrom tqdm import tqdm\nfrom torch import nn\nimport os\nfrom utils.ploter import AverageMeter, RecordWriter\n\nMODEL_DICT = {\n    'Func1': NonConvex.Function1\n}\n\n\ndef main(args):\n    use_cuda = torch.cuda.is_available()\n    torch.manual_seed(args.seed)\n    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n\n    print('device {}'.format(device))\n\n    model = NonConvex.Function1().to(device)\n\n    print(model)\n\n\n    if args.optimizer == 'SGD':\n        writer = RecordWriter('NonConvex_{}_{}'.format(args.optimizer, args.lr))\n        optimizer = optmizor.SGD(model.parameters(), lr=args.lr)\n    elif args.optimizer == 'SGD-M':\n        optimizer = optmizor.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\n    elif args.optimizer == 'FGD-K':\n        writer = RecordWriter('NonConvex_{}_{}'.format(args.optimizer, args.lr))\n        optimizer = optmizor.KGD(model.parameters(), lr=args.lr, device=device)\n    elif args.optimizer == 'ARMAGD':\n        print(args.memory)\n        writer = RecordWriter('NonConvex_{}_{}_{}'.format(args.optimizer, args.lr, args.memory))\n        optimizer = optmizor.ARMAGD(model.parameters(), lr=args.lr, memory=args.memory, device=device)\n    elif args.optimizer == 'MASGD':\n        print(args.optimizer, args.memory)\n        writer = RecordWriter('NonConvex_{}_{}_{}'.format(args.optimizer, args.lr, args.memory))\n        optimizer = optmizor.MASGD(model.parameters(), lr=args.lr, memory=args.memory, device=device)\n    elif args.optimizer == 'FGD-W':\n        print(args.optimizer)\n        writer = RecordWriter('NonConvex_{}_{}'.format(args.optimizer, args.lr))\n        optimizer = optmizor.WTGD(model.parameters(), lr=args.lr, device=device)\n    elif args.optimizer == 'Adam':\n        print(args.optimizer)\n        writer = RecordWriter('NonConvex_{}_{}'.format(args.optimizer, args.lr))\n        optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n    else:\n        print('Optimizer not defined')\n        exit()\n\n\n    writer.update('x', [model.x.data.cpu().numpy().tolist()])\n    writer.update('y', [model.y.data.cpu().numpy().tolist()])\n    for epoch in range(1, args.epochs + 1):\n        output = model()\n        optimizer.zero_grad()\n        print('[{}/{}] Output:{}'.format(epoch, args.epochs + 1, output))\n        output.backward()\n        optimizer.step()\n        writer.update('output', [output.data.cpu().numpy().tolist()])\n        writer.update('x', [model.x.data.cpu().numpy().tolist()])\n        writer.update('x_grad', [model.x.grad.cpu().numpy().tolist()])\n        writer.update('y', [model.y.data.cpu().numpy().tolist()])\n        writer.update('y_grad', [model.y.grad.cpu().numpy().tolist()])\n\n\n    writer.write()\n\n\nif __name__ == '__main__':\n    # Training settings\n    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n    parser.add_argument('--optimizer', type=str, default='Adam')\n    parser.add_argument('--memory', nargs=\"+\", type=float, default=[0.1, 0.8])\n    parser.add_argument('--epochs', type=int, default=500, metavar='N',\n                        help='number of epochs to train (default: 14)')\n    parser.add_argument('--lr', type=float, default=0.03, metavar='LR',\n                        help='learning rate (default: 1.0)')\n    parser.add_argument('--momentum', type=float, default=0.9, metavar='MOM',\n                        help='momentum (default: 0.9)')\n    parser.add_argument('--use-cuda', action='store_true', default=True,\n                        help='disables CUDA training')\n\n    parser.add_argument('--seed', type=int, default=1, metavar='S',\n                        help='random seed (default: 1)')\n    parser.add_argument('--log-interval', type=int, default=50, metavar='N',\n                        help='how many batches to wait before logging training status')\n    parser.add_argument('--save-model', default=True,\n                        help='For Saving the current Model')\n    parser.add_argument('--cuda', type=str, default='0')\n\n    args = parser.parse_args()\n    main(args)\n","repo_name":"Adamdad/Filter-Gradient-Decent","sub_path":"NUMBERICAL_exp.py","file_name":"NUMBERICAL_exp.py","file_ext":"py","file_size_in_byte":4168,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"86"}
+{"seq_id":"31332051679","text":"# _*_coding: utf-8 _*_\n\nfrom flask import Flask,request,jsonify,Response, make_response\nfrom flask_restful import Resource,Api,reqparse\nimport numpy as np\nimport tensorflow.compat.v1 as tf\nimport model.ChakeList as md\nimport json\nimport pymysql\nfrom flask_cors import CORS\n\ntf.disable_v2_behavior()\n\napp = Flask(__name__)\napi = Api(app)\n\nCORS(app)\n\ndb = pymysql.connect(host='127.0.0.1', port=3306, user='root', passwd='root', database='testdb', charset='utf8')\n\ncursor = db.cursor()\n\n#cursor.execute(\"CREATE TABLE opinion(name VARCHAR(255), colonoscopy int(10), gastroscopy int(10), tcd int(10), thorax int(10), thyroid int(10))\")\n\nclass main(Resource):\n    def get(self):\n        try:\n\n            return \"Hello World\"\n        except Exception as e:\n            app.logger.error(e)\n            return {'error':str(e)}\n\nclass Index(Resource):\n    def get(self):\n        try:\n            ls = [{'id': 1, 'name': 'kim'}, {'id': 2, 'name': 'lee'}]\n            lsj = json.dumps(ls)\n            resp = make_response(lsj)\n            resp.mimetype = 'application/x-www-form-urlencoded'\n            return jsonify(ls)\n        except Exception as e:\n            app.logger.error(e)\n            return {'error':str(e)}\n    def post(self):\n        try:\n            data = request.get_json()\n\n            print(data)\n\n            result1 = md.Colonoscopy_load(data)\n            result2 = md.Gastroscopy_load(data)\n            result3 = md.Tcd_load(data)\n            result4 = md.Thorax_load(data)\n            result5 = md.Thyroid_load(data)\n\n            res = {\"Colonoscopy\": result1, \"Gastroscopy\": result2, \"Tcd\":result3,\"Thorax\":result4,\"Thyroid\":result5}\n\n            sql ='INSERT INTO opinion (name, colonoscopy, gastroscopy, tcd, thorax, thyroid) VALUES(%s,%s,%s,%s,%s,%s)'\n\n            cursor.execute(sql,(data['name'],result1,result2,result3,result4,result5))\n\n            db.commit()\n\n            print(res)\n\n            return jsonify(res)\n\n        except Exception as e:\n            app.logger.error(e)\n            return {'error':str(e)}\n\napi.add_resource(main,'/')\napi.add_resource(Index,'/TEST2')\n\n\nif __name__ == '__main__':\n    app.run(host=\"0.0.0.0\",port=\"5000\")","repo_name":"dlwns258/pjtAIP","sub_path":"ServerProject.py","file_name":"ServerProject.py","file_ext":"py","file_size_in_byte":2169,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70236105245","text":"import torch\n\n# 임의의 상태 텐서 생성 (예: 4개의 상태, 각 상태는 2개의 특성을 가짐)\nstates = torch.tensor([[1.0, 2.0], [2.5, 3.0], [3.0, 1.5], [4.5, 2.0]])\n\n\n\n# 각 상태에 대해 최대 Q-값 찾기\nmax_Q_values = states.max(1)[0]\n\nprint(\"Maximum Q-value for each state:\\n\", max_Q_values)\n","repo_name":"rnscks/CableAutoRouting","sub_path":"DQNProject/TensorMaxExample.py","file_name":"TensorMaxExample.py","file_ext":"py","file_size_in_byte":317,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"11590776464","text":"import nltk\nfrom nltk.stem import WordNetLemmatizer\nlemmatizer = WordNetLemmatizer()\nimport pickle\nimport numpy as np\n\nfrom keras.models import load_model\nmodel = load_model('chatbot_model.h5')\nimport json\nimport random\nintents = json.loads(open('job_intents.json', encoding='utf-8').read())\nwords = pickle.load(open('words.pkl','rb'))\nclasses = pickle.load(open('classes.pkl','rb'))\n\n\ndef clean_up_sentence(sentence):\n    sentence_words = nltk.word_tokenize(sentence)\n    sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]\n    return sentence_words\n\n# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence\n\ndef bow(sentence, words, show_details=True):\n    # tokenize the pattern\n    sentence_words = clean_up_sentence(sentence)\n    # bag of words - matrix of N words, vocabulary matrix\n    bag = [0]*len(words)\n    for s in sentence_words:\n        for i,w in enumerate(words):\n            if w == s:\n                # assign 1 if current word is in the vocabulary position\n                bag[i] = 1\n                if show_details:\n                    print (\"found in bag: %s\" % w)\n    return(np.array(bag))\n\ndef predict_class(sentence, model):\n    # filter out predictions below a threshold\n    p = bow(sentence, words, show_details=False)\n    res = model.predict(np.array([p]))[0]\n    ERROR_THRESHOLD = 0.25\n    results = [[i,r] for i,r in enumerate(res) if r>ERROR_THRESHOLD]\n    # sort by strength of probability\n    results.sort(key=lambda x: x[1], reverse=True)\n    return_list = []\n    for r in results:\n        return_list.append({\"intent\": classes[r[0]], \"probability\": str(r[1])})\n    return return_list\n\ndef getResponse(ints, intents_json):\n    tag = ints[0]['intent']\n    list_of_intents = intents_json['intents']\n    for i in list_of_intents:\n        if(i['tag']== tag):\n            result = random.choice(i['responses'])\n            break\n        else:\n            result = \"You must ask the right questions\"\n    return result\n\ndef chatbot_response(msg):\n    ints = predict_class(msg, model)\n    res = getResponse(ints, intents)\n    return res\n","repo_name":"tatiblockchain/python-deep-learning-chatbot","sub_path":"python-deep-learning-chatbot/processor.py","file_name":"processor.py","file_ext":"py","file_size_in_byte":2124,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"86"}
+{"seq_id":"4680502825","text":"def e_primo(n):\n    i = 2\n    primo = True  \n\n    while (i < n) and primo:\n        if (n % i) == 0:\n            primo = False\n        i = i + 1\n\n    return primo\n    \n# n = int(input(\"Digite um número inteiro: \"))\n# f = e_primo(n)\n# print(f)\n\ndef maior_primo(n):\n    primo = e_primo(n)\n    \n    if primo == True:\n        return n\n\n    else:\n        while primo == False:\n            n = n - 1\n            primo = e_primo(n)\n            \n            if (primo == True):\n                return n\n\n# f2 = maior_primo(n)\n# print(f2)\n\n","repo_name":"luisavitoria/introducao-curso-basico-python","sub_path":"code/maiorprimo.py","file_name":"maiorprimo.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"1921073707","text":"# -*- coding=utf-8 -*-\nfrom pandas import Series, DataFrame\nimport pandas as pd\nimport redis\n\n# 造数据\ndata = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],\n        'year': [2000, 2001, 2002, 2001, 2002],\n        'pop': [1.5, 1.7, 3.6, 2.4, 2.9]}\nframe = DataFrame(data)\n\n# 获取redis连接\npool = redis.ConnectionPool(host='127.0.0.1', port=6379)\nr = redis.Redis(connection_pool=pool)\n\n# r.set(\"frame\", frame)\n# a = r.get(\"frame\")\n\n# 存储\nr.set(\"frame\", frame.to_msgpack(compress='zlib'))\n# 读取\na = pd.read_msgpack(r.get(\"frame\"))\nprint(a)\n","repo_name":"yanshixiao/swyjjtj","sub_path":"minor_problem/数据框存储至redis.py","file_name":"数据框存储至redis.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"14554118023","text":"import helpers\nimport sqlite3\nimport os\n\n\ndef ranger(input):\n    # create a db, pass it to needed files \n    \n\n    with sqlite3.connect(\":memory:\") as conn:\n        helpers.create_db(conn)\n    if type(input) == str and os.path.isfile(input):\n        int_dict, slot_dict, int_no = helpers.make_int_dictionary(input)\n        helpers.write_to_db(helpers.create_lists_of_tupples(int_dict, slot_dict, int_no), conn)\n        rangable_ints = helpers.range_out_of_set(helpers.uniquely_configured_int_groups(int_dict))\n    elif type(input) == dict:\n        int_dict, db_info = helpers.load_ints(input)\n        helpers.write_to_db(db_info, conn)\n        rangable_ints = helpers.range_out_of_set(helpers.uniquely_configured_int_groups(int_dict, structured=True))\n        # pprint(rangable_ints)\n\n    \n    list_of_indexes = []\n    for chuncks in rangable_ints:\n        int_index_list = []\n        for interfaces in chuncks:\n            with conn:\n                conn.row_factory = sqlite3.Row\n                query = \"select * from interfaces where int_name = '{}'\".format(interfaces)\n                result = conn.execute(query)\n                int_index_list.append(result.fetchone()[0])\n        list_of_indexes.append(sorted(int_index_list))\n\n\n    main_sep_list = []\n    ardicil_groups = []\n    separated_list = []\n    for chunk in sorted(list_of_indexes):\n        for int_index in chunk:\n            if int_index == chunk[0]:\n                ardicil_groups = []\n                ardicil_groups.append(int_index)\n                prev = int_index\n                continue\n            if (int_index == prev + 1 and \n                helpers.db_query(int_index, 5, conn) == helpers.db_query(prev, 5, conn) + 1 and\n                helpers.db_query(int_index, 4, conn) == helpers.db_query(prev, 4, conn) and\n                helpers.db_query(int_index, 2, conn) == helpers.db_query(prev, 2, conn) and\n                helpers.db_query(int_index, 3, conn) == helpers.db_query(prev, 3, conn)):\n                ardicil_groups.append(int_index)\n                prev = int_index\n                continue\n            else:\n                separated_list.append(ardicil_groups)\n                ardicil_groups = []\n                ardicil_groups.append(int_index)\n                prev = int_index\n        else:\n            separated_list.append(ardicil_groups)\n            ardicil_groups = []\n        separated_list.sort()\n        main_sep_list.append(separated_list)\n        separated_list = []\n\n    # print(main_sep_list)\n\n    \n    whole_rangable_int = sum(sum(main_sep_list, []), [])\n\n    # ToDo extract repeating pattern for both input types to function \n    # or do it using decorator\n    if type(input) == str and os.path.isfile(input):\n        for key in int_no:\n            if key not in whole_rangable_int:\n                print('interface ' + helpers.db_query(key, 1, conn))\n                print(helpers.db_query(key, 6, conn))\n                print(\"!\")\n        for chunk in main_sep_list:\n            # in this chunk interface configs are the same\n            for range_div in helpers.cisco_range_packer(chunk):\n                range_pack = []\n                for sub_chunk in range_div:\n                    if len(sub_chunk) > 1:\n                        max = len(sub_chunk) - 1\n                        range_pack.append(\n                            (helpers.db_query(sub_chunk[0], 1, conn) + '-' + str(helpers.db_query(sub_chunk[max], 5, conn))))\n                    else:\n                        range_pack.append((helpers.db_query(sub_chunk[0], 1, conn)))\n                if len(range_pack) == 1 and \"-\" not in range_pack[0]:\n                    print('interface ', end='')\n                    print(\", \".join(range_pack))\n                    print(helpers.db_query(chunk[0][0], 6, conn))\n                    print(\"!\")\n                else:\n                    print('interface range ', end='')\n                    print(\", \".join(range_pack))\n                    print(helpers.db_query(chunk[0][0], 6, conn))\n                    print(\"!\")\n    elif type(input) == dict:\n        result = {}\n        for key in int_dict:\n            if helpers.db_query_id(key, conn) not in whole_rangable_int:\n                result[key] = int_dict[key]\n        for chunk in main_sep_list:\n            # in this chunk interface configs are the same\n            for range_div in helpers.cisco_range_packer(chunk):\n                range_pack = []\n                for sub_chunk in range_div:\n                    # print(sub_chunk)\n                    if len(sub_chunk) > 1:\n                        max = len(sub_chunk) - 1\n                        range_pack.append((helpers.db_query(sub_chunk[0], 1, conn) + '-' + str(helpers.db_query(sub_chunk[max], 5, conn))))\n                    else:\n                        range_pack.append((helpers.db_query(sub_chunk[0], 1, conn)))\n                if len(range_pack) == 1 and \"-\" not in range_pack[0]:\n                    result[\", \".join(range_pack)] = int_dict[helpers.db_query(chunk[0][0], 1, conn)]\n                else:\n                    result[\"range \" + \", \".join(range_pack)] = int_dict[helpers.db_query(chunk[0][0], 1, conn)]\n        return result\n\nif __name__ == \"__main__\":\n    #Todo: make a package, make range_it as main, others as helpers\n    #Todo: test with large quantity of data, empty str, emtpry, structure, wrong int name etc...\n    #Todo: check if sqlmemory will be working fine in case of flask multiaccess\n    #Todo: convert to OOP\n\n    sample_ints = {\n        \"GigabitEthernet0/1\": {},\n        \"GigabitEthernet0/2\": {},\n        \"GigabitEthernet0/4\": {},\n        \"GigabitEthernet0/3\": {},\n        \"GigabitEthernet1/0/1\": {},\n        \"GigabitEthernet1/0/2\": {},\n        \"GigabitEthernet1/0/4\": {\"mode\": \"access\"},\n        \"GigabitEthernet3/4/2\": {\"mode\": \"access\"},\n        \"GigabitEthernet3/4/3\": {\"mode\": \"access\"},\n        \"GigabitEthernet3/5/3\": {\"mode\": \"trunk\"},\n        \"GigabitEthernet3/6/3\": {\"mode\": \"trunk\"},\n        \"GigabitEthernet4/0/3\": {},\n        \"GigabitEthernet4/1/3\": {\"mode\": \"trunk\"},\n        \"GigabitEthernet4/2/3\": {},\n        \"GigabitEthernet2/0/4\": {},\n        \"GigabitEthernet3/1/4\": {},\n        \"GigabitEthernet3/1/5\": {}\n    }\n\n    config_file = os.path.join(os.path.dirname(__file__), 'test_config.txt')\n    ranger(config_file)\n    # print(ranger(sample_ints))","repo_name":"nicataliyev/rangify","sub_path":"rangify/rangify.py","file_name":"rangify.py","file_ext":"py","file_size_in_byte":6330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"13845948785","text":"tipo = input(\" Indique el tipo del producto: \").upper()\nprecio = float(input(\" Indique el precio del producto: \"))\ndescuento = 0\n\nif (precio > 0 and (tipo == \"A\" or tipo == \"B\" or tipo == \"C\")):\n    if tipo == \"A\":\n        descuento = 7\n    elif tipo == \"C\" or precio < 500:\n        descuento = 12  \n    else:\n        descuento = 9\n        \nprint(\" El precio tras la renaja es\", precio * (1 - (descuento / 100)))        \n\ninput(\"\\n Pulse enter para salir\")\n","repo_name":"Stinger-dev/Programacion","sub_path":"Python/Tema_1/Boletin_3/Ejercicio09.py","file_name":"Ejercicio09.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"18848151567","text":"import turtle as t\nfrom random import randint as r\n\ndef shape(side):\n    for _ in range(side):\n        t.right(int(360/side))\n        t.fd(100)\n\nt.colormode(255)\nt.speed(0)\nt.hideturtle()\nt.bgcolor(0, 0, 0)\n\nfor i in range(int(360/10)):\n    t.color(r(0, 255), r(0, 255), r(0, 255))\n    t.pensize(2)\n    t.circle(100)\n    # t.circle(150)\n    # shape(5)\n    shape(8)\n    t.setheading(t.heading() + 10)\n\nt.exitonclick()","repo_name":"Dhiraj-birajdar/Dhiraj_VSCode","sub_path":"Python/100_Days_code/spirograph.py","file_name":"spirograph.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"72001866837","text":"from PIL import Image\n\nimage = Image.open('monro.jpg')\n\nred_pixels, green_pixels, blue_pixels = image.split()\n\ncrop_pixels1 = 100\ncrop_pixels2 = 50\n\nfirst_red_picture = red_pixels.crop((crop_pixels1,\n                                     0,\n                                     red_pixels.width,\n                                     red_pixels.height))\n\nsecond_red_picture = red_pixels.crop((crop_pixels2,\n                                      0,\n                                      red_pixels.width - crop_pixels2,\n                                      red_pixels.height))\n\nfirst_blue_picture = blue_pixels.crop((0,\n                                       0,\n                                       blue_pixels.width - crop_pixels1,\n                                       blue_pixels.height))\n\nsecond_blue_picture = blue_pixels.crop((crop_pixels2,\n                                        0,\n                                        blue_pixels.width - crop_pixels2,\n                                        blue_pixels.height))\n\ngreen_picture = green_pixels.crop((crop_pixels2,\n                                   0,\n                                   green_pixels.width - crop_pixels2,\n                                   red_pixels.height))\n\nred_pixels = Image.blend(first_red_picture, second_red_picture, 0.5)\nblue_pixels = Image.blend(first_blue_picture, second_blue_picture, 0.5)\ngreen_pixels = Image.blend(green_picture, green_picture, 0.5)\n\nimage = Image.merge('RGB', (red_pixels, green_pixels, blue_pixels))\n\ncoefficient = 0\nwidth = 0\nheight = 0\n\nif image.width > image.height:\n    coefficient = image.width / 80\n    width = int(image.width / coefficient)\n    height = int(image.height / coefficient)\nelse:\n    coefficient = image.height / 80\n    width = int(image.width / coefficient)\n    height = int(image.height / coefficient)\n\nimage.thumbnail((width, height))\nimage.save('small.jpg')\n","repo_name":"aseventura/dvmn_basics","sub_path":"lesson_4/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"28985266003","text":"#If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3, 5, 6 and 9. The sum of these multiples is 23.\r\n#Find the sum of all the multiples of 3 or 5 below 1000.\r\n\r\nmaxNumber  = int(input('Enter a maximum number: '))\r\nmultipleOne = int(input('Enter a multiple: '))\r\nmultipleTwo = int(input('Enter another multiple: '))\r\n\r\nsum = 0\r\nfor num in range(maxNumber):\r\n    if ( num % multipleOne == 0 ) or ( num % multipleTwo == 0 ):\r\n        sum += num\r\nprint(sum)","repo_name":"Beeinmay/Project-Euler","sub_path":"Problem 1.py","file_name":"Problem 1.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"32457665878","text":"# Nengo Cerebellum Test Code; Andreas Stöckel, Terry Stewart; 2020\n\nimport nengo\nimport nengo_bio as bio\nimport numpy as np\nimport pytry\nimport matplotlib\n\nimport sys, os\nsys.path.append(os.path.dirname(__file__))\nfrom granule_golgi_circuit import GranuleGolgiCircuit\n\nimport scipy.stats\n\n# Run using\n# pytry model/blink_trial.py --mode two_populations_dales_principle --use_spatial_constraints True --n_pcn_golgi_convergence 100 --n_pcn_granule_convergence 5 --n_granule_golgi_convergence 100 --n_golgi_granule_convergence 5 --n_golgi_golgi_convergence 100 --n_granule 10000 --n_golgi 100 --data_dir=exp --data_filename two_populations_dales_principle_detailed --data_format npz\n\ndef 𐌈(**kwargs):\n    return kwargs\n\nclass EyeblinkReflex(nengo.Process):\n    \"\"\"\n    This class implements the trajectory generation for the agonist and\n    antagonist muscles generating an eyeblink.\n    \"\"\"\n\n    P_AGONIST    = [ 0.29104462, -0.5677302,  0.0125624,  81.2206584]\n    T_AGONIST    = 250e-3\n    P_ANTAGONIST = [ 0.33981309, 10.7624779,  0.3168123,   3.4498503]\n    T_ANTAGONIST = 275e-3\n\n    def __init__(self):\n        super().__init__(default_size_in=1, default_size_out=2)\n\n    @staticmethod\n    def skewnorm(x, mu, a, std, s):\n        return (s * std) * scipy.stats.skewnorm.pdf(x, a, mu, std)\n\n    def make_step(self, shape_in, shape_out, dt, rng, state=None):\n        # Compute the convolutions\n        def mkconv(P, T0, T1=1.0):\n            ts = np.arange(T0, T1, dt)\n            return EyeblinkReflex.skewnorm(ts, *P)\n\n        conv_ag = mkconv(\n            EyeblinkReflex.P_AGONIST, EyeblinkReflex.T_AGONIST)\n        conv_an = mkconv(\n            EyeblinkReflex.P_ANTAGONIST, EyeblinkReflex.T_ANTAGONIST)\n\n        state=np.zeros(1 + len(conv_ag) + len(conv_an))\n        I_lx = slice(0, 1)                                 # last x\n        I_ag = slice(I_lx.stop, I_lx.stop + len(conv_ag))  # agonist\n        I_an = slice(I_ag.stop, I_ag.stop + len(conv_an))  # antagonist\n\n        def step(t, x, state=state):\n            # Compute the differential, compute a positive and negative branch\n            # of the differential\n            lx, ag, an = state[I_lx], state[I_ag], state[I_an]\n            dx = (x - lx[0]) / dt\n            dxp, dxn = np.maximum(0, dx), np.maximum(0, -dx)\n\n            # Store the last x value\n            lx[0] = x\n\n            # Shift the history by one element and insert the new\n            # agonist/antagonist\n            ag[1:] = ag[:-1]\n            an[1:] = an[:-1]\n            ag[0] = dxp\n            an[0] = dxn\n\n            # Return the current state as output\n            return np.array((\n                np.sum(conv_ag * ag) * dt,\n                np.sum(conv_an * an) * dt))\n\n        return step\n\n\n\nclass Eyelid(nengo.Process):\n    \"\"\"\n    The Eyelid class models the eylid. It turns an agonist and antagonist input\n    into an eyelid \"closedness\" between zero (full closed) and one (full open).\n    \"\"\"\n\n    def __init__(self):\n        super().__init__(default_size_in=2, default_size_out=1)\n        self.step = self.make_step(2, 1, 1e-3, None, None)\n\n\n    def make_step(self, shape_in, shape_out, dt, rng, state=None):\n        state=np.zeros(1)\n        state[0] = 0.0\n\n        def step(t, x, state=state):\n            # Update the eyelid location\n            state[0] = np.clip(state[0] + (np.clip(x[0], 0, None) -\n                                          (np.clip(x[1], 0, None))) * dt, 0, 1)\n\n            # Return the state as output\n            return np.array((state[0],))\n\n        setattr(step, 'state', state);\n\n        return step\n\n    def __call__(self, *args, **kwargs):\n        return self.step(*args, **kwargs)\n\n    @property\n    def _nengo_html_(self):\n        return \"\"\"\n<svg width=\"200\" height=\"100\" viewBox=\"-1.9 -1.5 3.5 2.75\" version=\"1.1\">\n\t<defs>\n\t\t<clipPath clipPathUnits=\"userSpaceOnUse\" id=\"clipPath973\">\n\t\t\t<path style=\"fill:#ffffff\" d=\"M -2.3,0 C -2.3,0 -0.9,{o} 0,{o} 1,{o} 2.3,0 2.3,0 2.3,0 1,-{o} 0,-{o} -1,-{o} -2.3,0 -2.3,0 Z\" />\n\t\t</clipPath>\n\t</defs>\n\t<path\n\t\tstyle=\"fill:#ffffff;stroke:#000000;stroke-width:0.04px\"\n\t\td=\"M -2.3,0 C -2.3,0 -0.9,{o} 0,{o} 1,{o} 2.3,0 2.3,0 2.3,0 1,-{o} 0,-{o} -1,-{o} -2.3,0 -2.3,0 Z\" />\n\t<path\n\t\tstyle=\"fill:#000000\"  clip-path=\"url(#clipPath973)\"\n\t\td=\"m 0.00568441,-0.83354084 a 0.80501685,0.80501685 0 0 0 -0.80511881,0.8051188 0.80501685,0.80501685 0 0 0 0.80511881,0.80511881 0.80501685,0.80501685 0 0 0 0.8051188,-0.80511881 0.80501685,0.80501685 0 0 0 -0.8051188,-0.8051188 z m 0,0.57877603 a 0.22641097,0.22641097 0 0 1 0.22634277,0.22634277 0.22641097,0.22641097 0 0 1 -0.22634277,0.22634277 0.22641097,0.22641097 0 0 1 -0.22634277,-0.22634277 0.22641097,0.22641097 0 0 1 0.22634277,-0.22634277 z\" />\n</svg>\"\"\".format(o=1.0 - self.step.state[0])\n\n\n\ndef make_lmu(q=6, theta=1.0):\n    # Do Aaron's math to generate the matrices\n    #  https://github.com/arvoelke/nengolib/blob/master/nengolib/synapses/analog.py#L536\n    Q = np.arange(q, dtype=np.float64)\n    R = (2*Q + 1)[:, None] / theta\n    j, i = np.meshgrid(Q, Q)\n\n    A = np.where(i < j, -1, (-1.)**(i-j+1)) * R\n    B = (-1.)**Q[:, None] * R\n    return A, B\n\ndef add_labels(model, locals):\n    for k, v in locals.items():\n        if isinstance(v, (nengo.Node, nengo.Ensemble)) and v.label is None:\n            v.label = k\n\n\nclass BlinkTrial(pytry.PlotTrial):\n    def params(self):\n        # Build a temporary Golgi-Granule circuit instance to get the default parameters\n        with nengo.Network() as model:\n            pre = bio.Ensemble(n_neurons=100, dimensions=1)\n            net = GranuleGolgiCircuit(pre)\n\n        # Experiment setup\n        self.param('time between trials', period=0.8)\n        self.param('time between tone and puff', t_delay=0.15)\n        self.param('tone length', t_tone=0.1)\n        self.param('puff length', t_puff=0.1)\n        self.param('number of trials', n_trials=4)\n        self.param('only run minimal model', do_minimal=True)\n\n        # Recording\n        self.param('save data from plots', save_plot_data=True)\n        self.param('probe resolution', sample_every=0.001)\n\n        # Motor model\n        self.param('eyelid opening constant', eye_bias=4)\n\n        # Non-granule-golgi time constants\n        self.param('learning rate', learning_rate=1e-4)\n        self.param('tau for learning rule', tau_pre=0.2)\n        self.param('tau for error feedback', tau_error=0.2)\n        self.param('tau for purkinje output', tau_purkinje=0.01)\n\n        # Granule Golgi Circuit\n        self.param('q', q=net.q)\n        self.param('theta', theta=net.theta)\n        self.param('tau for granule', tau=net.tau)\n        self.param('use cosine intercept distribution', use_cosine=True)\n        self.param('granule golgi mode', mode=net.mode)\n        self.param('use_spatial_constraints', use_spatial_constraints=net.use_spatial_constraints)\n        self.param('n_pcn_golgi_convergence', n_pcn_golgi_convergence=-1)\n        self.param('n_pcn_granule_convergence', n_pcn_granule_convergence=-1)\n        self.param('n_granule_golgi_convergence', n_granule_golgi_convergence=-1)\n        self.param('n_golgi_granule_convergence', n_golgi_granule_convergence=-1)\n        self.param('n_golgi_golgi_convergence', n_golgi_golgi_convergence=-1)\n        self.param('n_granule', n_granule=net.n_granule)\n        self.param('n_golgi', n_golgi=net.n_golgi)\n        self.param('n_pcn', n_pcn=100)\n        self.param('pcn_max_rates_lower', pcn_max_rates_lower=25)\n        self.param('pcn_max_rates_upper', pcn_max_rates_upper=75)\n        self.param('bias_mode', bias_mode=\"jbias_realistic_pcn_intercepts\")\n\n\n    def evaluate(self, p, plt):\n        # Make sure the convergence numbers are either non-negative or None\n        if p.n_pcn_golgi_convergence < 0:\n            p.n_pcn_golgi_convergence = None\n        if p.n_pcn_granule_convergence < 0:\n            p.n_pcn_granule_convergence = None\n        if p.n_granule_golgi_convergence < 0:\n            p.n_granule_golgi_convergence = None\n        if p.n_golgi_granule_convergence < 0:\n            p.n_golgi_granule_convergence = None\n        if p.n_golgi_golgi_convergence < 0:\n            p.n_golgi_golgi_convergence = None\n\n        t_tone_start = 0.0\n        t_tone_end = t_tone_start + p.t_tone\n        t_puff_start = t_tone_end + p.t_delay\n        t_puff_end = t_puff_start + p.t_puff\n\n        def puff_func(t):\n            if t_puff_start < t % p.period < t_puff_end:\n                return 1\n            else:\n                return 0\n\n        def tone_func(t):\n            if t_tone_start < t % p.period < t_tone_end:\n                return 1\n            else:\n                return 0\n\n        model = nengo.Network()\n        with model:\n\n            ###########################################################################\n            # Setup the conditioned stimulus (i.e., a tone) and the unconditioned     #\n            # stimulus (i.e., a puff)                                                 #\n            ###########################################################################\n            nd_tone = nengo.Node(tone_func)\n            nd_puff = nengo.Node(puff_func)\n\n\n            ###########################################################################\n            # Setup the reflex generator and the eye-motor system                     #\n            ###########################################################################\n\n            # The reflex pathway is across the Trigeminal nucleus in the brainstem;\n            # we don't model this in this particular model\n\n            # Scaling factor that has to be applied to the reflex trajectory to scale\n            # it to a range from 0 to 1\n            reflex_scale = 1.0 / 25.0\n\n            # The reflex system takes an input and produces the reflex trajectory on\n            # the rising edge (convolves the differential of the input with the\n            # trajectory)\n            nd_reflex = nengo.Node(EyeblinkReflex()) # Unscaled output\n            nd_reflex_out = nengo.Node(size_in=1)    # Normalised output\n            nengo.Connection(nd_reflex[0], nd_reflex_out, transform=reflex_scale,\n                             synapse=None)\n\n            if not p.do_minimal:\n                # The eyelid component represents the state of the eye in the world.\n                # It receives two inputs, an agonist input (closing the eye, dim 0) and an\n                # antagonist input (opening the eye, dim 1).\n                nd_eyelid = nengo.Node(Eyelid())         # Unscaled input\n                eyelid_in = nengo.Ensemble(n_neurons=100, dimensions=2)\n\n                nengo.Connection(eyelid_in, nd_eyelid[0], transform=1.0 / reflex_scale,\n                                 function=lambda x: max(x[0], x[1]),\n                                 synapse=0.005)\n\n                # Constantly open the eye a little bit\n                nd_eye_bias = nengo.Node(p.eye_bias)\n                nengo.Connection(nd_eye_bias, nd_eyelid[1])\n\n                # We can't detect the puff if the eye is closed, multiply the output from\n                # nd_puff with the amount the eye is opened. This is our unconditioned\n                # stimulus\n                # NOTE: Currently disabled by commenting out the line below\n                c0, c1 = nengo.Node(size_in=1), nengo.Node(size_in=1) # Only for GUI\n                nengo.Connection(nd_eyelid, c0, synapse=None)\n                nengo.Connection(c0, c1, synapse=None)\n            #    nengo.Connection(c1, nd_us[1], synapse=None)\n\n            # Connect the unconditioned stimulus to the reflex generator\n            nd_us = nengo.Node(lambda t, x: x[0] * (1 - x[1]), size_in=2, size_out=1)\n            nengo.Connection(nd_puff, nd_us[0], synapse=None)\n            nengo.Connection(nd_us, nd_reflex)\n            if not p.do_minimal:\n                nengo.Connection(nd_reflex_out, eyelid_in[0])\n\n            ###########################################################################\n            # Generate a neural representation of the conditioned stimulus            #\n            ###########################################################################\n\n            # Make sure that \"bias_mode\" is valid\n            assert p.bias_mode in {\n                None,\n                \"uniform_pcn_intercepts\", \"realistic_pcn_intercepts\", \"very_realistic_pcn_intercepts\",\n                \"jbias_uniform_pcn_intercepts\", \"jbias_realistic_pcn_intercepts\", \"jbias_very_realistic_pcn_intercepts\",\n                \"exc_jbias_uniform_pcn_intercepts\", \"exc_jbias_realistic_pcn_intercepts\", \"exc_jbias_very_realistic_pcn_intercepts\",\n                \"inh_jbias_uniform_pcn_intercepts\", \"inh_jbias_realistic_pcn_intercepts\", \"inh_jbias_very_realistic_pcn_intercepts\",\n            }\n\n            rng = np.random\n            pcn_encs = rng.choice([-1, 1], p.n_pcn)\n            if not p.bias_mode is None:\n                if \"very_realistic_pcn_intercepts\" in p.bias_mode:\n                    pcn_xis = rng.uniform(-0.35, 0.95, p.n_pcn)\n                elif \"realistic_pcn_intercepts\" in p.bias_mode:\n                    pcn_xis = rng.uniform(-0.15, 0.95, p.n_pcn)\n                else:\n                    pcn_xis = rng.uniform(-0.95, 0.95, p.n_pcn)\n\n            pcn_max_rates = nengo.dists.Uniform(p.pcn_max_rates_lower, p.pcn_max_rates_upper)\n\n            nd_cs = nengo.Node(size_in=1)\n            ens_pcn = bio.Ensemble(n_neurons=p.n_pcn,\n                                   dimensions=1,\n                                   p_exc=1.0,\n                                   label=\"ens_pcn\",\n                                   max_rates=pcn_max_rates,\n                                   encoders=pcn_encs.reshape(-1, 1),\n                                   intercepts=pcn_xis,)\n            nengo.Connection(nd_tone, nd_cs, synapse=None)\n            nengo.Connection(nd_cs, ens_pcn)\n\n            ###########################################################################\n            # Generate a LMU representation of the conditioned stimulus               #\n            ###########################################################################\n\n            bio_bias_mode = None\n            if not p.bias_mode is None:\n                if \"exc_jbias_\" in p.bias_mode:\n                    bio_bias_mode = bio.ExcJBias\n                elif \"inh_jbias_\" in p.bias_mode:\n                    bio_bias_mode = bio.InhJBias\n                elif \"jbias_\" in p.bias_mode:\n                    bio_bias_mode = bio.JBias\n                else:\n                    bio_bias_mode = bio.Decode\n\n            kwargs = 𐌈(\n                mode=p.mode,\n                use_spatial_constraints=p.use_spatial_constraints,\n                n_pcn_golgi_convergence=p.n_pcn_golgi_convergence,\n                n_pcn_granule_convergence=p.n_pcn_granule_convergence,\n                n_granule_golgi_convergence=p.n_granule_golgi_convergence,\n                n_golgi_granule_convergence=p.n_golgi_granule_convergence,\n                n_golgi_golgi_convergence=p.n_golgi_golgi_convergence,\n                n_granule=p.n_granule,\n                n_golgi=p.n_golgi,\n                qp_solver_extra_args={'max_iter': 200},\n                tau=p.tau,\n                q=p.q,\n                theta=p.theta,\n                golgi_intercepts=nengo.dists.CosineSimilarity(p.q+2) if p.use_cosine else nengo.dists.Uniform(-1,1),\n                granule_intercepts=nengo.dists.CosineSimilarity(p.q+2) if p.use_cosine else nengo.dists.Uniform(-1,1),\n                bias_mode_granule=bio_bias_mode,\n            )\n            net_granule_golgi = GranuleGolgiCircuit(\n                ens_pcn,\n                **kwargs\n            )\n\n            ###########################################################################\n            # Learn the connection from the Granule cells to the Purkinje cells via   #\n            # input from the Inferior Olive                                           #\n            ###########################################################################\n\n            # This is the US pathway; the data is relayed from the Trigeminal nucleus\n            # to the Interior Olive.\n\n            ens_cn = nengo.Ensemble(n_neurons=100, dimensions=1)\n            ens_prn = nengo.Ensemble(n_neurons=100, dimensions=1)\n            ens_io = nengo.Ensemble(n_neurons=100, dimensions=1)\n            ens_purkinje = nengo.Ensemble(n_neurons=100, dimensions=1)\n\n            # Represent the error signal in ens_io\n            nengo.Connection(nd_reflex_out[0], ens_io, transform=-1)\n            nengo.Connection(ens_cn, ens_prn, transform=1, synapse=p.tau_error)\n            nengo.Connection(ens_prn, ens_io, transform=1, synapse=p.tau_error)\n\n            # Project from the granule onto the Purkinje cells\n            c_learn = nengo.Connection(\n                net_granule_golgi.ens_granule.neurons, ens_purkinje,\n                transform=np.zeros((ens_purkinje.dimensions, net_granule_golgi.ens_granule.n_neurons)),\n                learning_rule_type=nengo.learning_rules.PES(learning_rate=p.learning_rate, pre_synapse=p.tau_pre))\n            nengo.Connection(ens_io, c_learn.learning_rule)\n\n            ###########################################################################\n            # Project from CN onto the motor system\n            ###########################################################################\n\n            nengo.Connection(ens_purkinje, ens_cn)\n            if not p.do_minimal:\n                nengo.Connection(ens_cn, eyelid_in[1])\n\n            p_nd_reflex_out = nengo.Probe(nd_reflex_out, sample_every=p.sample_every)\n            if not p.do_minimal:\n                p_eyelid = nengo.Probe(nd_eyelid, sample_every=p.sample_every)\n            p_purkinje = nengo.Probe(ens_purkinje, synapse=p.tau_purkinje, sample_every=p.sample_every)\n            p_granule = nengo.Probe(net_granule_golgi.ens_granule, synapse=0.03, sample_every=p.sample_every)\n\n        add_labels(model, locals=locals())\n\n        sim = nengo.Simulator(model)\n        with sim:\n            sim.run(p.period*p.n_trials)\n\n        dt = p.sample_every\n        steps = int(p.period/dt)\n\n        purk = sim.data[p_purkinje].reshape(-1, steps).T\n        v = np.clip(purk[:,:],0,np.inf)*dt/reflex_scale\n        pos = np.cumsum(v, axis=0)\n\n        if plt:\n            t = np.arange(steps)*dt\n\n            ax1 = plt.subplot(4, 1, 1)\n            ax1.set_ylabel('granule')\n            ax2 = plt.subplot(4, 1, 2)\n            ax2.set_ylabel('purkinje')\n            ax3 = plt.subplot(4, 1, 3)\n            ax3.set_ylabel('eye position\\n(due to reflex)')\n            ax4 = plt.subplot(4, 1, 4)\n            ax4.set_ylabel('eye position\\n at puff start')\n            ax4.set_xlabel('trial')\n\n            n_steps = len(sim.data[p_purkinje])\n            cmap = matplotlib.cm.get_cmap(\"viridis\")\n            for i in range(0, n_steps, steps):\n                color = cmap(i / n_steps)\n                #if not p.do_minimal:\n                #    ax1.plot(t, sim.data[p_eyelid][i:i+steps], label='eyelid %d'%(i//steps), ls='--')\n                ax2.plot(t, sim.data[p_purkinje][i:i+steps], color=color)\n                ax3.plot(t, np.cumsum(np.abs(sim.data[p_purkinje][i:i+steps]))*dt/reflex_scale, color=color)\n            ax1.plot(t, sim.data[p_granule][:steps])\n            ax2b = ax2.twinx()\n            ax2b.plot(t, sim.data[p_nd_reflex_out][:steps], c='k', ls='--')\n            ax4.plot(pos[int(t_puff_start/dt)])\n\n        r = dict(final_pos=pos[int(t_puff_start/dt),-1],\n                 pos_at_puff_start=pos[int(t_puff_start/dt)],\n                 )\n        if p.save_plot_data:\n            r['purkinje']=sim.data[p_purkinje]\n            r['granule']=sim.data[p_granule][:steps]\n            r['reflex']=sim.data[p_nd_reflex_out][:steps]\n        return r\n","repo_name":"astoeckel/phd_thesis","sub_path":"code/chapters/05_cerebellum/model/blink_trial.py","file_name":"blink_trial.py","file_ext":"py","file_size_in_byte":19749,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"}
+{"seq_id":"15686696963","text":"\"\"\"View tests for the ``booking`` app.\"\"\"\nfrom django.test import TestCase\n\nfrom django_libs.tests.mixins import ViewRequestFactoryTestMixin\nfrom mixer.backend.django import mixer\n\nfrom .. import views\nfrom ..models import Booking\n\n\nclass BookingCreateViewTestCase(ViewRequestFactoryTestMixin, TestCase):\n    view_class = views.BookingCreateView\n\n    def setUp(self):\n        self.user = mixer.blend('auth.User')\n\n    def test_view(self):\n        self.is_callable(add_session=True)\n\n        data = {\n            'gender': 'mr',\n            'forename': 'Foo',\n            'surname': 'Bar',\n            'nationality': 'DE',\n            'street1': 'Foostreet 12',\n            'city': 'Foocity',\n            'zip_code': 'ABC123',\n            'country': 'DE',\n        }\n        self.is_postable(data=data, add_session=True,\n                         to_url_name='booking_detail')\n        self.assertEqual(Booking.objects.count(), 1, msg=(\n            'One booking should have been created.'))\n        self.assertTrue(Booking.objects.all()[0].session.session_key, msg=(\n            'Booking should have a session key.'))\n        self.is_postable(data=data, add_session=True,\n                         to_url_name='booking_detail')\n        self.assertEqual(Booking.objects.count(), 2, msg=(\n            'Another booking should have been created.'))\n\n        self.is_postable(data=data, user=self.user, add_session=True,\n                         to_url_name='booking_detail')\n        self.assertEqual(self.user.bookings.count(), 1, msg=(\n            'User should have a new booking.'))\n        self.assertTrue(Booking.objects.all()[0].user.username, msg=(\n            'Booking should have a user.'))\n\n\nclass BookingDetailViewTestCase(ViewRequestFactoryTestMixin, TestCase):\n    view_class = views.BookingDetailView\n\n    def setUp(self):\n        self.user = mixer.blend('auth.User')\n        self.booking = mixer.blend('booking.Booking')\n\n    def get_view_kwargs(self):\n        return {'pk': self.booking.pk}\n\n    def test_view(self):\n        self.is_not_callable()\n        self.is_not_callable(user=self.user)\n        self.booking = mixer.blend('booking.Booking', user=self.user)\n        self.is_callable(user=self.user)\n\n\nclass BookingListViewTestCase(ViewRequestFactoryTestMixin, TestCase):\n    view_class = views.BookingListView\n\n    def setUp(self):\n        self.user = mixer.blend('auth.User')\n        mixer.blend('booking.Booking', user=self.user)\n\n    def test_view(self):\n        self.is_callable(user=self.user)\n","repo_name":"bitlabstudio/django-booking","sub_path":"booking/tests/views_tests.py","file_name":"views_tests.py","file_ext":"py","file_size_in_byte":2510,"program_lang":"python","lang":"en","doc_type":"code","stars":207,"dataset":"github-code","pt":"85"}
+{"seq_id":"13585564861","text":"from dash import Dash, dcc, html, Input, Output, State\nimport json\nimport numpy as np\nimport time\nimport plotly.express as px\nimport pandas as pd\ndef create_table(df):\n    header = html.Thead(\n        html.Tr([html.Th(col) for col in df.columns])\n    )\n    body = html.Tbody([\n        html.Tr([\n            html.Td(df.iloc[i][col]) for col in df.columns\n        ]) for i in range(len(df))\n    ])\n    return header, body\n\napp = Dash(\n    __name__,\n    external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\n)\n\napp.layout = html.Div(\n    [\n        dcc.Dropdown(id='dropdown', options=[\"Normal\", \"Uniform\"]),\n        dcc.Graph(id='graph'),\n        html.Table(id='table'),\n\n        # dcc.Store для хранения промежуточных значений\n        dcc.Store(id='intermediate-value')\n    ]\n)\n\n@app.callback(\n    Output('intermediate-value', 'data'),\n    Input('dropdown', 'value')\n)\ndef preprocessing(value):\n    print(\"Sleeping...\")\n    time.sleep(3)\n    if value == \"Normal\":\n        data = np.random.random(size=(100, 2)).tolist()\n    else:\n        data = np.random.uniform(10, 20, size=(100, 2)).tolist()\n    return json.dumps(data)\n\n@app.callback(\n    Output('graph', 'figure'),\n    Input('intermediate-value', 'data')\n)\ndef update_graph(data):\n    data_df = pd.DataFrame(json.loads(data), columns=['x', 'y'])\n    return px.scatter(data_df, x=\"x\", y=\"y\")\n\n@app.callback(\n    Output('table', 'children'),\n    Input('intermediate-value', 'data')\n)\ndef update_table(data):\n    data_df = pd.DataFrame(json.loads(data), columns=['x', 'y'])\n    return create_table(data_df)\n\nif __name__ == '__main__':\n    app.run_server(debug=True)    \n","repo_name":"blokhinnv/py-packages","sub_path":"dash/02_callbacks/store.py","file_name":"store.py","file_ext":"py","file_size_in_byte":1668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"36040148","text":"#!/usr/bin/env python\n\nimport unittest\nfrom seqdiff import *\n\nclass TestSeq(unittest.TestCase):\n    def test_ref_parsing(self):\n        l = \"Ref XYZ 3\"\n        ref, tlen = grab_ref_and_tag_len(l)\n        self.assertEqual(ref, \"XYZ\")\n        self.assertEqual(tlen, 4)\n\n        l = \"Ref  XYZ 3\"\n        ref, tlen = grab_ref_and_tag_len(l)\n        self.assertEqual(ref, \"XYZ\")\n        self.assertEqual(tlen, 5)\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"justicz/seqdiff","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"32708145305","text":"'''Module to define one implementation of the general interface to the chatbot'''\nimport interpreter\nimport message_processor\nimport bot_abstract\n\n\nclass BotRivescript(bot_abstract.BotInterface):\n    '''Concrete class to define a general interface for the chatbot'''\n    def __init__(self, preprocessor=None,\n                       interpreter=None,\n                       postprocessor=None):\n        '''The chatbot interface includes an optional message preprocessing and\n        reply postprocessing layers'''\n        self._preprocessor = preprocessor\n        self._interpreter = interpreter\n        self._postprocessor = postprocessor\n\n    def createUserSession(self, userInfo):\n        self._interpreter.createUserSession(userInfo)\n\n    def reply(self, message):\n        userid = message.getUserid()\n        messagecontent = self._preprocess(message.getContent())\n        reply = self._interpreter.reply(userid, messagecontent)\n        reply = self._postprocess(reply)\n\n        return reply\n\n    def _preprocess(self, message):\n        '''To tell the preprocessor to preprocess the message (if the\n        preprocessor has been initialized)'''\n        if self._preprocessor is not None:\n            return self._preprocessor.process(message)\n        else:\n            return  message\n\n    def _postprocess(self, reply):\n        '''To tell the postprocessor to postprocess the message (if the\n        postprocessor has been initialized)'''\n        if self._postprocessor is not None:\n            return self._postprocessor.process(reply)\n        else:\n            return reply\n","repo_name":"andreallorerung/peach-chatbot-alpha","sub_path":"FlaskWebProject/chatbot/botinterface/bot_rivescript.py","file_name":"bot_rivescript.py","file_ext":"py","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"2603112524","text":"# Set up possible directions correpsonding to each symbol\nDIRECTIONS = {'>': (0, 1), 'v': (1, 0), '<': (0, -1), '^': (-1, 0)}\nSYMBOLS = {v: k for k, v in DIRECTIONS.items()}\n\ndef get_input(input_file: str='Inputs/Day24_Inputs.txt') -> tuple:\n    \"\"\"\n    Parse an input file giving the layout of a valley, bounded by walls (#) and containing empty\n    space (.) surrounded by a series of moving blizzards (>, v, <, ^) represented by arrows\n    indicating their direction of movement. Extracts the initial blizzard layout, the entrance and\n    exit from the valley and the dimensions of the valley.\n\n    Parameters\n    ----------\n    input_file : str, optional\n        Input file giving the valley layout.\n        The default is 'Inputs/Day24_Inputs.txt'.\n\n    Returns\n    -------\n    all_blizzards : dict(tuple: list(tuple))\n        Dictionary of lists of the initial coordinates of every blizzard going in a given direction,\n        hashed by the unit vector of that direction, in the form (x, y).\n\n    journey_bounds : tuple(tuple(int))\n        The coordinates (x, y) of the entrance and exit from the valley in the form (entrance,\n        exit).\n\n    valley_bounds : tuple(int)\n        The dimensions of the valley in the form (length, width).\n\n    \"\"\"\n    # Parse input file\n    with open(input_file) as f:\n        lines = [line.strip() for line in f.readlines()]\n    # Calculate valley dimensions, discounting the walls\n    valley_length = len(lines) - 2\n    valley_width = len(lines[0]) - 2\n    # Find the coordinates of the start (first row) and end (last row)\n    begin = (-1, lines[0].index('.') - 1)\n    end = (valley_length, lines[-1].index('.') - 1)\n    # Initliase empty blizzard position dictionary\n    all_blizzards = {direction: [] for direction in DIRECTIONS.values()}\n    # For each row of the valley within the walls\n    for i, line in enumerate(lines[1:-1]):\n        # Check each character\n        for j, c in enumerate(line.strip('#')):\n            if c == '.':\n                continue\n            # If a blizzard, add to the list corresponding to its direction\n            all_blizzards[DIRECTIONS[c]].append((i, j))\n\n    return all_blizzards, (begin, end), (valley_length, valley_width)\n\ndef draw_valley(all_blizzards: dict, journey_bounds: tuple, valley_bounds: tuple,\n                player_pos: tuple) -> None:\n    \"\"\"\n    Draw the current valley layout, with walls marked as '#', blizzards marked by arrows of their\n    direction, or the number of blizzards in that spot if there are multiple, empty spots as '.'\n    and the player position marked as 'E'.\n\n    Parameters\n    ----------\n    all_blizzards : dict(tuple: list(tuple))\n        Dictionary of lists of the initial coordinates of every blizzard going in a given direction,\n        hashed by the unit vector of that direction, in the form (x, y).\n    journey_bounds : tuple(tuple(int))\n        The coordinates (x, y) of the entrance and exit from the valley in the form (entrance,\n        exit).\n    valley_bounds : tuple(int)\n        The dimensions of the valley in the form (length, width).\n    player_pos : tuple(int)\n        Current player position in the form (x, y).\n\n    Returns\n    -------\n    None.\n\n    \"\"\"\n    # Print the upper wall with entrance\n    print('#' + ''.join('E' if (-1, j) == player_pos else '.' if j == journey_bounds[0][1] else '#' \\\n                        for j in range(valley_bounds[1])) + '#')\n    # For each row\n    for i in range(valley_bounds[0]):\n        # Add left wall\n        row = '#'\n        for j in range(valley_bounds[1]):\n            # Add player if found\n            if (i, j) == player_pos:\n                row += 'E'\n                continue\n            # Count how many blizzards on the same spot and add correpsonding character\n            matched_symbols = []\n            for direction, blizzards in all_blizzards.items():\n                if (i, j) in blizzards:\n                    matched_symbols.append(SYMBOLS[direction])\n            if len(matched_symbols) == 0:\n                row += '.'\n            elif len(matched_symbols) == 1:\n                row += matched_symbols[0]\n            else:\n                row += str(len(matched_symbols))\n        # Add right wall and print\n        print(row + '#')\n\n    # Print the lower wall with exit\n    print('#' + ''.join('E' if (valley_bounds[0] + 1, j) == player_pos else '.' if j == journey_bounds[1][1] else '#' \\\n                        for j in range(valley_bounds[1])) + '#')\n\ndef add_coords(curr: tuple, dir_: tuple, bounds: tuple=None, moves: int=1) -> tuple:\n    \"\"\"\n    Finds the new coordinates after moving a given number of spaces in a given direction from\n    initial coordinates. If bounds are specified, the coordinates will wrap around when these\n    boundaries are exceeded.\n\n    Parameters\n    ----------\n    curr : tuple(int)\n        The initial coordinates.\n    dir_ : tuple(int)\n        Unit vector of the direction being moved in.\n    bounds : tuple(int) or None, optional\n        If not None, then the upper boundaries of the grid in the form (x_max, y_max).\n        The default is None.\n    moves : int, optional\n        The number of spaces to move.\n        The default is 1.\n\n    Returns\n    -------\n    new_pos : tuple(int)\n        Coordinates of the new position after the movement.\n\n    \"\"\"\n    # If boundaries, perform wrapping with % b\n    if bounds:\n        return tuple(map(lambda x, y, b : (x + moves*y) % b, curr, dir_, bounds))\n    # Else just add the unit vector multplied by the number of moves\n    else:\n        return tuple(map(lambda x, y : x + moves*y, curr, dir_))\n\ndef coord_is_in_bounds(coord: tuple, bounds: tuple, journey_bounds: tuple) -> tuple:\n    \"\"\"\n    Checks if a set of coordinates are within a set of boundaries, or match an element from a\n    separate, special set of coordinates.\n\n    Parameters\n    ----------\n    coord : tuple(int)\n        Coordinate to check in the form (x, y).\n    bounds : tuple(int)\n        Upper boundaries in the form (x_max, y_max).\n    journey_bounds : tuple(tuple(int))\n        Tuple of coordinates outside the bounds which should also pass this check.\n\n    Returns\n    -------\n    is_in_bounds : bool\n        Whether the coordinate is within the given bounds or special set of coordinates.\n\n    \"\"\"\n    # Check if coords are in special set, or if coords are between 0 and upper bounds\n    return coord in journey_bounds or all(map(lambda c, b : 0 <= c < b, coord, bounds))\n\ndef move_blizzards(all_blizzards: dict, valley_bounds: tuple, moves: int=1) -> dict:\n    \"\"\"\n    Move a set of blizzards in a valley a set number of positions from given starting points, in\n    the directions in which they are moving. If a blizzard moves outside given bounds of the valley\n    then the coordinates should wrap around to 0.\n\n    Parameters\n    ----------\n    all_blizzards : dict(tuple: list(tuple))\n        Dictionary of lists of the initial coordinates of every blizzard going in a given direction,\n        hashed by the unit vector of that direction, in the form (x, y).\n    valley_bounds : tuple(int)\n        The dimensions of the valley in the form (length, width).\n    moves : int, optional\n        The number of spaces for each blizzard to move.\n        The default is 1.\n\n    Returns\n    -------\n    next_blizzards : dict(tuple: list(tuple))\n        Dictionary of lists of the final coordinates of every blizzard after the movements.\n\n    \"\"\"\n    # Create copy of blizzards to edit\n    next_blizzards = all_blizzards.copy()\n    # For the blizzards in each direction\n    for direction, blizzards in all_blizzards.items():\n        # Set up lambda for movement in current direction\n        move_in_dir = lambda t : add_coords(t, direction, valley_bounds, moves)\n        # Map onto blizzards moving in that direction\n        next_blizzards[direction] = list(map(move_in_dir, blizzards))\n\n    return next_blizzards\n\ndef find_possible_moves(curr_pos: tuple, next_blizzards: dict, valley_bounds: tuple,\n                        journey_bounds: tuple) -> set:\n    \"\"\"\n    Finds the possible next moves for a player in a given position in a valley containing a set\n    of moving blizzards. Possible moves are up, down, left, right or no move, but a player cannot\n    move/remain in a position which will be occupied by a blizzard. Additionally, the player cannot\n    move outside given valley bounds, except into given entrance and exit points.\n\n    Parameters\n    ----------\n    curr_pos : tuple(int)\n        Current player position in the form (x, y).\n    next_blizzards : dict(tuple: list(tuple))\n        Dictionary of lists of the initial coordinates of every blizzard going in a given direction,\n        hashed by the unit vector of that direction, in the form (x, y).\n    valley_bounds : tuple(int)\n        The dimensions of the valley in the form (length, width).\n    journey_bounds : tuple(tuple(int))\n        The coordinates (x, y) of the entrance and exit from the valley in the form (entrance,\n        exit).\n\n    Returns\n    -------\n    next_positions : set(tuple(int))\n        List of the potential coordinates which the player can move to.\n\n    \"\"\"\n    # If the current position will not contain a blizzard next turn, not moving is an option\n    if curr_pos not in next_blizzards:\n        next_positions = {curr_pos}\n    # Else remaining here is not an option\n    else:\n        next_positions = set()\n    # For each possible movement direction\n    for direction in DIRECTIONS.values():\n        # Find new coordinates after this move\n        next_pos = add_coords(curr_pos, direction)\n        # If this position is within the boundaries and will not contain a blizzard next turn,\n        # it is an option\n        if coord_is_in_bounds(next_pos, valley_bounds, journey_bounds) and \\\n            next_pos not in next_blizzards:\n            next_positions.add(next_pos)\n\n    return next_positions\n\n### All this is just to find the lowest common multiple of two numbers ###\n                                                                         #\ndef next_prime(n: int) -> int:                                           #\n    \"\"\"                                                                  #\n    Finds the next highest prime number after a given integer.           #\n                                                                         #\n    Parameters                                                           #\n    ----------                                                           #\n    n : int                                                              #                                                                         #                                                                         #\n        The current number.                                              #\n                                                                         #                                                                         #                                                                         #\n    Returns                                                              #\n    -------                                                              #\n    n : int                                                              #\n        The next highest prime number after the given integer.           #\n                                                                         #\n    \"\"\"                                                                  #\n    n += 1                                                               #\n    # While the number has any factors between 2 and sqrt(n), increment  #\n    while not all(n%i for i in range(2, int(n**0.5))):                   #\n        n += 1                                                           #\n                                                                         #\n    return n                                                             #\n                                                                         #\ndef prime_factors(n: int) -> set:                                        #\n    \"\"\"                                                                  #\n    Finds the prime factors of a given integer.                          #\n                                                                         #\n    Parameters                                                           #\n    ----------                                                           #\n    n : int                                                              #\n        Number to find the prime factors for.                            #\n                                                                         #\n    Returns                                                              #\n    -------                                                              #\n    factors : set(tuple(int))                                            #\n        Set of the prime factors of the given number, in the form        #\n        (factor, occurance).                                             #\n                                                                         #\n    \"\"\"                                                                  #\n                                                                         #\n    factors = set()                                                      #\n    # Start at 2                                                         #\n    f = 2                                                                #\n    # While there are remaining factors                                  #\n    while n > 1:                                                         #\n        # Count occurances of current factor                             #\n        num = 1                                                          #\n        while n%f == 0:                                                  #\n            n /= f                                                       #\n            # Add new factor with occurance count                        #\n            factors.add((f, num))                                        #\n            num += 1                                                     #\n        # Check next highest prime number                                #\n        f = next_prime(f)                                                #\n                                                                         #\n    return factors                                                       #\n                                                                         #\nimport operator                                                          #\nfrom functools import reduce                                             #\n                                                                         #\ndef gcd(a: int, b: int) -> int:                                          #\n    \"\"\"                                                                  #\n    Find the greatest common divisor of two integers.                    #\n                                                                         #\n    Parameters                                                           #\n    ----------                                                           #\n    a : int                                                              #\n        First integer.                                                   #                                                                         #\n    b : int                                                              #\n        Second integer.                                                  #\n                                                                         #\n    Returns                                                              #\n    -------                                                              #\n    gcd : int                                                            #\n        Greatest common divisor of the two integers.                     #\n                                                                         #\n    \"\"\"                                                                  #\n    # Find prime factors of the numbers                                  #\n    a_f = prime_factors(a)                                               #\n    b_f = prime_factors(b)                                               #\n    # Find all common prime factors                                      #\n    c_f = [f[0] for f in a_f.intersection(b_f)]                          #\n    # GCD is the product of these common factors                         #\n    return reduce(operator.mul, c_f, 1)                                  #\n                                                                         #\ndef lcm(a: int, b: int) -> int:                                          #\n    \"\"\"                                                                  #\n    Find the lowest common multiple of two integers.                     #\n                                                                         #\n    Parameters                                                           #\n    ----------                                                           #\n    a : int                                                              #\n        First integer.                                                   #\n    b : int                                                              #\n        Second integer.                                                  #\n                                                                         #\n    Returns                                                              #\n    -------                                                              #\n    lcm : int                                                            #\n        Lowest common multiple of the two integers.                      #\n                                                                         #\n    \"\"\"                                                                  #\n    # Find GCD of the numbers, then multiply by the uncommon factors     #\n    # from both numbers                                                  #\n    return int((a / gcd(a, b)) * b)                                      #\n                                                                         #\n##########################################################################\n\ndef find_fastest_route_bfs(journey_bounds: tuple, all_blizzard_states: list, valley_bounds: tuple,\n                           start_moves: int=0) -> list:\n    \"\"\"\n    Performs a breadth-first search to find the fewest number of moves required to move between\n    given journey bounds, in a valley of given dimensions containing a set of blizzards which\n    move in set directions every move and which the player cannot share the same position with at\n    any time.\n\n    Parameters\n    ----------\n    journey_bounds : tuple(tuple(int))\n        The coordinates (x, y) of the entrance and exit from the valley in the form (entrance,\n        exit).\n    all_blizzard_states : list(set(tuple))\n        Dictionary of lists of the initial coordinates of every blizzard going in a given direction,\n        hashed by the unit vector of that direction, in the form (x, y).\n    valley_bounds : tuple(int)\n        The dimensions of the valley in the form (length, width).\n    start_moves : int, optional\n        The number of moves to start at.\n        The default is 0.\n\n    Returns\n    -------\n    fewest_moves : int\n        The fewest number of moves required to move between given journey bounds, after the given\n        number of starting moves.\n\n    \"\"\"\n    # Initalise queue of states for BFS with (position, moves_to_reach) describing each state\n    queue = [(journey_bounds[0], start_moves)]\n    # Initalise set of checked states\n    checked_states = set()\n\n    # While there are unchecked states\n    while queue:\n        # Get next highest priority state\n        curr_pos, curr_moves = queue.pop(0)\n        # Find possible next positions from current position\n        next_positions = find_possible_moves(curr_pos,\n                                             all_blizzard_states[(curr_moves + 1) % len(all_blizzard_states)],\n                                             valley_bounds, journey_bounds)\n        # If we have reached the target end point, return the current move count\n        if journey_bounds[1] in next_positions:\n            return curr_moves + 1\n        # Else for each possible next position (if any)\n        for pos in next_positions:\n            # Create correpsonding state\n            next_state = (pos, curr_moves + 1)\n            # Check if this has already been checked\n            if next_state not in checked_states:\n                # If not, add to the queue and set of checked states\n                queue.append(next_state)\n                checked_states.add(next_state)\n\ndef Day24_Part1(input_file: str='Inputs/Day24_Inputs.txt') -> int:\n    \"\"\"\n    Finds the fewest number of moves required to reach the other side of valley containing a\n    set of blizzards which move in set directions every move and which the player cannot share the\n    same position with at any time. The layout of the valley is given in an input file, with the\n    valley walls marked as '#', empty space as '.' and blizzards represented by arrows indicating\n    their direction of their movement ('>', 'v', '<', '^'). When blizzards reach a valley wall,\n    they wrap around to the opposite side and continue moving in the same direction.\n\n    Parameters\n    ----------\n    input_file : str, optional\n        Input file containing the valley layout.\n        The default is 'Inputs/Day24_Inputs.txt'.\n\n    Returns\n    -------\n    start_to_end : int\n        The fewest number of moves required to reach the other side of valley.\n\n    \"\"\"\n    # Extract initial blizzard positions and journey and valley boundaries from input file\n    all_blizzards, journey_bounds, valley_bounds = get_input(input_file)\n\n    # Find fewest number of moves required for the blizzards to recover their initial positions\n    num_blizzard_states = lcm(*valley_bounds)\n    all_blizzard_states = []\n    # Find every possible unique blizzard layout\n    for moves in range(num_blizzard_states + 1):\n        all_blizzard_states.append(move_blizzards(all_blizzards, valley_bounds, moves))\n\n    # Assert that the blizzards recovered their initial positions at the end\n    assert all_blizzard_states[0] == all_blizzard_states[-1]\n\n    # Join together all blizzard positions into a single set\n    all_blizzard_states = [set(bliz for direc in blizzard_state.values() for bliz in direc) \\\n                           for blizzard_state in all_blizzard_states[:-1]]\n\n    # Perform a breadth-first search through the valley to find the lowest number of moves required\n    start_to_end = find_fastest_route_bfs(journey_bounds, all_blizzard_states, valley_bounds)\n\n    return start_to_end\n\ndef Day24_Part1and2(input_file: str='Inputs/Day24_Inputs.txt') -> tuple:\n    \"\"\"\n    Finds the fewest number of moves required to reach the other side of valley containing a\n    set of blizzards which move in set directions every move and which the player cannot share the\n    same position with at any time. Then finds the fewest number of moves to do this, then go back\n    to the start and then go back to the end all in a row, with the blizzards continuing to move\n    the whole time. The layout of the valley is given in an input file, with the valley walls\n    marked as '#', empty space as '.' and blizzards represented by arrows indicating their\n    direction of their movement ('>', 'v', '<', '^'). When blizzards reach a valley wall, they wrap\n    around to the opposite side and continue moving in the same direction. \n\n    Parameters\n    ----------\n    input_file : str, optional\n        Input file containing the valley layout.\n        The default is 'Inputs/Day24_Inputs.txt'.\n\n    Returns\n    -------\n    start_to_end : int\n        The fewest number of moves required to reach the other side of valley.\n    start_to_end_and_back_and_back : int\n        The fewest number of moves required to reach the other side of valley, then go back to the\n        start, and then go back to the end again.\n\n    \"\"\"\n    # Extract initial blizzard positions and journey and valley boundaries from input file\n    all_blizzards, journey_bounds, valley_bounds = get_input(input_file)\n    \n    # Find fewest number of moves required for the blizzards to recover their initial positions\n    num_blizzard_states = lcm(*valley_bounds)\n    all_blizzard_states = []\n    # Find every possible unique blizzard layout\n    for moves in range(num_blizzard_states + 1):\n        all_blizzard_states.append(move_blizzards(all_blizzards, valley_bounds, moves))\n\n    # Assert that the blizzards recovered their initial positions at the end\n    assert all_blizzard_states[0] == all_blizzard_states[-1]\n\n    # Join together all blizzard positions into a single set\n    all_blizzard_states = [set(bliz for direc in blizzard_state.values() for bliz in direc) \\\n                           for blizzard_state in all_blizzard_states[:-1]]\n\n    # Perform a breadth-first search through the valley to find the lowest number of moves required\n    # to go from the start to the end the first time\n    start_to_end = find_fastest_route_bfs(journey_bounds, all_blizzard_states, valley_bounds)\n    \n    # Perform a breadth-first search through the valley to find the lowest number of moves required\n    # to go from the end back to the start\n    start_to_end_and_back = find_fastest_route_bfs(journey_bounds[::-1], all_blizzard_states,\n                                                   valley_bounds, start_to_end)\n    \n    # Perform a breadth-first search through the valley to find the lowest number of moves required\n    # to go from the start to the end the second time\n    start_to_end_and_back_and_back = find_fastest_route_bfs(journey_bounds, all_blizzard_states,\n                                                            valley_bounds, start_to_end_and_back)\n\n    return start_to_end, start_to_end_and_back_and_back\n","repo_name":"joshlomas99/Advent-of-Code-2022","sub_path":"Day24.py","file_name":"Day24.py","file_ext":"py","file_size_in_byte":26089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"1952412379","text":"def HomePage(request):\r\n    print(request.__dict__)\r\n    for item in request.GET.values():\r\n        print(item)\r\n    if request.user.is_authenticated:\r\n        cur_user = UserProfile.objects.get(username=request.user.username)\r\n        if not cur_user.auth_token:\r\n            logout(request)\r\n    if request.method == 'POST':\r\n        form = Searchform(request.POST)\r\n        if form.is_valid():\r\n            search = form.cleaned_data['search']\r\n            print(search)\r\n            buys = buy.objects.filter(Q(Q(title__contains=search)| Q(content__contains=search)) & Q(ordered=False))\r\n            return render(request, 'glpage/indexb.html', {'buys': buys, 'title': 'Главная', 'form': form})\r\n    else:\r\n        form = Searchform()\r\n        buys = buy.objects.filter(remain__gt = 0)\r\n        return render(request, 'glpage/indexb.html', {'buys': buys, 'title': 'Главная', 'form': form})\r\n\r\n\r\ndef about(request):\r\n    print(request.user)\r\n    return render(request, 'glpage/about.html')\r\n\r\n\r\ndef create(request):\r\n    if request.method == 'POST':\r\n        form = buyform(request.headers)\r\n        if form.is_valid():\r\n            print(form.cleaned_data)\r\n            created = buy.objects.create(**form.cleaned_data)\r\n            return redirect('home')\r\n    else:\r\n        form = buyform()\r\n    context = {'form': form, }\r\n    return render(request, 'glpage/create.html', context)\r\n\r\n\r\ndef order(request, tovar_id):\r\n    tovar = buy.objects.get(pk=tovar_id)\r\n    current_user = request.user\r\n    if tovar.ordered_by.filter(pk=request.user.id):\r\n        tovar.ordered_by.remove(current_user)\r\n        tovar.remain+=1\r\n    else:\r\n        tovar.ordered_by.add(current_user)\r\n        if not tovar.ordered:\r\n            tovar.ordered = True\r\n        tovar.remain-=1\r\n    if not tovar.ordered_by.all():\r\n        tovar.ordered = False\r\n    tovar.save()\r\n    return redirect(tovar)\r\n\r\n\r\nclass TovarByCat(ListView):\r\n    model = buy\r\n    template_name = 'glpage/category.html'\r\n    context_object_name = 'buys'\r\n    allow_empty = False\r\n\r\n    def get_context_data(self, *, object_list=None, **kwargs):\r\n        context = super().get_context_data(**kwargs)\r\n        title = category.get_cat_id_by_slug(self.kwargs['category_id'])\r\n        current_cat = category.objects.get(title=title)\r\n        context['title']=category.objects.get(pk=current_cat.pk)\r\n        return context\r\n\r\n    def get_queryset(self):\r\n        title = category.get_cat_id_by_slug(self.kwargs['category_id'])\r\n        current_cat = category.objects.get(title=title)\r\n        return buy.objects.filter(category=current_cat.pk, ordered=False)\r\n\r\n\r\n\r\n\r\ndef tovar_page(request,tovar_id):\r\n    tovar = get_object_or_404(buy, pk=tovar_id)\r\n    current_user = request.user\r\n    if tovar.ordered_by.filter(pk=current_user.id):\r\n        ordered_or_not = 'Удалить из корзины'\r\n    else:\r\n        ordered_or_not = 'Добавить в корзину'\r\n    context = {'tovar': tovar, 'ordered_or_not': ordered_or_not}\r\n    return render(request, 'glpage/tovar.html', context)\r\n\r\n\r\nclass Packet(ListView):\r\n    model = buy\r\n    context_object_name = 'buys'\r\n    template_name = 'glpage/order.html'\r\n\r\n    def get_queryset(self):\r\n        return buy.objects.filter(Q(ordered=True) & Q())\r\n\r\n    def get_context_data(self, *, object_list=None, **kwargs):\r\n        context = super().get_context_data(**kwargs)\r\n        context['title'] = 'Корзина'\r\n        price_order = 0\r\n        for item in buy.objects.filter(ordered=True):\r\n            price_order+=item.price\r\n        context['price_order'] = price_order\r\n        return context\r\n\r\n\r\ndef user_Packet(request):\r\n    current_user = request.user\r\n    ordered_things = current_user.buy_set.all()\r\n    price_packet = 0\r\n    for item in ordered_things:\r\n        price_packet += item.price\r\n    context = {'buys':ordered_things, 'user':current_user, 'price_order': price_packet} # price_order\r\n    return render(request, 'glpage/order.html', context)\r\n\r\ndef register(request):\r\n    if request.method == 'POST':\r\n        form = UserRegistration(request.POST)\r\n        if form.is_valid():\r\n            print(form.cleaned_data)\r\n            form.save()\r\n            messages.success(request, 'Registered')\r\n            username = form.data['username']\r\n            password = form.data['password1']\r\n            user_login_form = {\r\n                \"username\" : username,\r\n                \"password\" : password,\r\n            }\r\n            url = \"http://127.0.0.1:8000/auth/token/login\"\r\n            resp = requests.post(url, data=user_login_form)\r\n            if 'auth_token' in resp.json():\r\n                user = authenticate(request, username=username, password=password)\r\n                login(request, user)\r\n                print(resp.json()['auth_token'])\r\n                if UserProfile.objects.filter(username=username):\r\n                    print(UserProfile.objects.filter(username=username))\r\n                    curuserprof = UserProfile.objects.get(username=username)\r\n                    curuserprof.auth_token = resp.json()['auth_token']\r\n                    curuserprof.save()\r\n                else:\r\n                    UserProfile.objects.create(username=username, user=request.user, auth_token=resp.json()['auth_token'])\r\n                return redirect('home')\r\n        else:\r\n            messages.error(request, 'Not registered')\r\n    else:\r\n        form = UserRegistration()\r\n    context = {'form': form, }\r\n    return render(request, 'glpage/register.html', context)\r\n\r\n\r\ndef orderingprocess(request):\r\n    current_user = request.user\r\n    ordered_things = current_user.buy_set.all()\r\n    if request.method == 'POST':\r\n        form = OrderModelform(request.POST)\r\n        if form.is_valid():\r\n            order = OrderModel(ordered_by_user=current_user.username,ordered_by_name=form.cleaned_data['ordered_by_name'], ordered_by_phone = form.cleaned_data['ordered_by_phone'], transport = form.cleaned_data['transport'], address = form.cleaned_data['address'])\r\n            order.save()\r\n            for item in current_user.buy_set.all():\r\n                order.ordered_things.add(item)\r\n            order.save()\r\n            return redirect('home')\r\n    else:\r\n        form = OrderModelform()\r\n    context = {\r\n        'form':form,\r\n        'title':'Ordering',\r\n        'buys': current_user.buy_set.all()\r\n    }\r\n    return render(request, 'glpage/ordering.html', context)\r\n\r\n\r\nclass catAPI(APIView):\r\n    permission_classes = (IsAdminUser, )\r\n    def get(self, request):\r\n        return Response({'category':CategorySerializer(category.objects.all(), many=True).data})\r\n\r\n\r\ndef loginbyapi(request):\r\n    message = 'Вход'\r\n    if request.method == 'POST':\r\n        form = LoginByAPIForm(request.POST)\r\n        if form.is_valid():\r\n            username = form.data['username']\r\n            password = form.data['password']\r\n            print(form.cleaned_data['choice'])\r\n            user_login_form = {\r\n                \"username\" : username,\r\n                \"password\" : password,\r\n            }\r\n            url = \"http://127.0.0.1:8000/auth/token/login\"\r\n            resp = requests.post(url, data=user_login_form)\r\n            if 'auth_token' in resp.json():\r\n                user = authenticate(request, username=username, password=password)\r\n                login(request, user)\r\n                print(resp.json()['auth_token'])\r\n                if UserProfile.objects.filter(username=username):\r\n                    print(UserProfile.objects.filter(username=username))\r\n                    curuserprof = UserProfile.objects.get(username=username)\r\n                    curuserprof.auth_token = resp.json()['auth_token']\r\n                    curuserprof.save()\r\n                else:\r\n                    UserProfile.objects.create(username=username, user=request.user, auth_token=resp.json()['auth_token'])\r\n                return redirect('home')\r\n            else:\r\n                message = 'You have failed you loging'\r\n    else:\r\n        form = LoginByAPIForm()\r\n    context = {\r\n        'messaga':message,\r\n        'form':form\r\n    }\r\n    return render(request, 'glpage/loginbyapi.html', context)\r\n\r\n\r\ndef logoutbyapi(request):\r\n    if not request.user.is_authenticated:\r\n        return redirect('home')\r\n    url = \"http://127.0.0.1:8000/auth/token/logout\"\r\n    currpofile = UserProfile.objects.get(username=request.user.username)\r\n    header = 'Token '+currpofile.auth_token\r\n    currpofile.auth_token = ''\r\n    currpofile.save()\r\n    headers = {\"Authorization\": header}\r\n    resp = requests.post(url, headers=headers)\r\n    logout(request)\r\n    return redirect('home')\r\n\r\n\r\ndef study(request, number):\r\n    tov = buy.objects.get(pk=22)\r\n    context = {\r\n        'number': number,\r\n        'object': tov,\r\n        'title': 'Name'\r\n    }\r\n    return render(request, 'glpage/study1.html', context)\r\n","repo_name":"ArtsiomSt/Django-OnlineShop","sub_path":"glpage_with_api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"71352057239","text":"from typing import List\nclass Solution:\n    def maxProduct(self, words: List[str]) -> int:\n        n=len(words)\n\n        wrdset=[set(i) for i in words]\n\n        product=[len(words[i])*len(words[j]) for i in range(n) for j in range(i,n) if wrdset[i].isdisjoint(wrdset[j])]\n        if len(product)==0:\n            return 0\n\n        return max(product)\n\ns=Solution()\nassert 16==s.maxProduct([\"abcw\",\"baz\",\"foo\",\"bar\",\"xtfn\",\"abcdef\"])\nassert 4==s.maxProduct([\"a\",\"ab\",\"abc\",\"d\",\"cd\",\"bcd\",\"abcd\"])","repo_name":"awanesh92/ProgramPractice","sub_path":"MxProdofWrdLength.py","file_name":"MxProdofWrdLength.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"20505788829","text":"\nclass Node:\n  def __init__(self, data):\n    self.left = None\n    self.right = None\n    self.data = data\n\n\ndef isBTSymmetric(root):  \n  return False\n\n\n\n\n\n\n\n\n\n\n\ndef assertEqual(a, b, desc):\n  if (a == b):\n    print('{} ... PASS'.format(desc))\n  else:\n    print('{desc} ... FAIL: {a} != {b}'.format(desc=desc, a=a, b=b))\n\n\n\ndesc = 'single node - should be symmetric'\nroot = Node(4)\nassertEqual(isBTSymmetric(root), True, desc)\n\ndesc = 'shallow BT - should be symmetric'\nroot = Node(4)\nroot.left = Node(3)\nroot.right = Node(3)\nroot.left.left = Node(9)\nroot.left.right = Node(5)\nroot.right.left = Node(5)\nroot.right.right = Node(9)\nassertEqual(isBTSymmetric(root), True, desc)\n\ndesc = 'shallow BT - not symmetric'\nroot = Node(4)\nroot.left = Node(3)\nroot.right = Node(3)\nroot.left.left = Node(9)\nroot.left.right = Node(5)\nroot.right.left = Node(2)\nroot.right.right = Node(9)\nassertEqual(isBTSymmetric(root), False, desc)","repo_name":"dinhtq/coding_practice","sub_path":"trees/bt_symmetric.py","file_name":"bt_symmetric.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"35395371844","text":"'''\nUSAGE - The script is a backup utility for Splunk that provides the user \n        with a choice of backing up either configuration files, indexed \n        data, or both. It defines default source and destination directories \n        for each type of backup. The script uses shutil library to copy \n        the source directories to the destination directories. The user \n        is prompted to input the source and destination directories if \n        the default directories are not used. Finally, the script runs \n        an infinite loop until the user enters a valid input for the backup type.\n        \nAUTHOR - https://github.com/Ahendrix9624\n'''\nimport os\nimport shutil\n\n# Set constants for default directories\nDEFAULT_CONFIG_SRC_DIR = '/opt/splunk/etc'\nDEFAULT_CONFIG_DEST_DIR = '/opt/splunk_backup/configs'\nDEFAULT_INDEX_SRC_DIR = '/opt/splunk/var/lib/splunk/defaultdb/db'\nDEFAULT_INDEX_DEST_DIR = '/opt/splunk_backup/indexed_data'\n\ndef backup_configs(src_dir=DEFAULT_CONFIG_SRC_DIR, dest_dir=DEFAULT_CONFIG_DEST_DIR):\n    # Use default directories if input is empty\n    config_src_dir = input(f'Enter configuration files source directory [{DEFAULT_CONFIG_SRC_DIR}]: ')\n    config_dest_dir = input(f'Enter configuration files destination directory [{DEFAULT_CONFIG_DEST_DIR}]: ')\n    zip_backup = input(\"Do you want to compress the backup files? (y/n): \")\n    if zip_backup.lower() == \"y\":\n        compress = True\n    else:\n        compress = False\n    src_dir = src_dir or DEFAULT_CONFIG_SRC_DIR\n    dest_dir = dest_dir or DEFAULT_CONFIG_DEST_DIR\n\n    shutil.rmtree(dest_dir, ignore_errors=True)\n    shutil.copytree(src_dir, dest_dir)\n    print(f'\\nConfiguration files backed up successfully to {dest_dir}.\\n')\n    if compress:\n        compress_files(dest_dir, '.zip')\n\ndef backup_indexed_data(src_dir=DEFAULT_INDEX_SRC_DIR, dest_dir=DEFAULT_INDEX_DEST_DIR):\n    # Use default directories if input is empty\n    index_src_dir = input(f'Enter indexed data source directory [{DEFAULT_INDEX_SRC_DIR}]: ')\n    index_dest_dir = input(f'Enter indexed data destination directory [{DEFAULT_INDEX_DEST_DIR}]: ')\n    zip_backup = input(\"Do you want to compress the backup files? (y/n): \")\n    if zip_backup.lower() == \"y\":\n        compress = True\n    else:\n        compress = False\n    src_dir = src_dir or DEFAULT_INDEX_SRC_DIR\n    dest_dir = dest_dir or DEFAULT_INDEX_DEST_DIR\n\n    shutil.rmtree(dest_dir, ignore_errors=True)\n    shutil.copytree(src_dir, dest_dir)\n    print(f'\\nIndexed data backed up successfully to {dest_dir}.\\n')\n    if compress:\n        compress_files(dest_dir, '.zip')\n\ndef compress_files(src_dir, extension):\n    # Create a compressed ZIP archive of the source directory in the same directory\n    zip_filename = src_dir + extension\n    shutil.make_archive(base_name=src_dir, format='zip', root_dir=os.path.dirname(src_dir), base_dir=os.path.basename(src_dir))\n    print(f'\\n{zip_filename} compressed successfully.\\n')\n\nwhile True:\n    print('Choose backup type:')\n    print('1. Configuration files')\n    print('2. Indexed data')\n    print('3. Both')\n    choice = input('Enter choice (1/2/3): ')\n    if choice in ['1', '2', '3']:\n        break\n    print('Invalid input, please try again.')\n\nif choice == '1':\n    backup_configs()\nelif choice == '2':\n    backup_indexed_data()\nelif choice == '3':\n    backup_configs()\n    backup_indexed_data()\n","repo_name":"Ahendrix9624/splunk_scripts","sub_path":"splunk-backup/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3369,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"18665697105","text":"import pymongo\nimport os,sys\nimport re\nimport random\n\nclass ClassRoom:\n    # 链接本地客户端\n    __myclient = pymongo.MongoClient(\"mongodb://localhost:27017\")\n    # 创建数据库\n    __mydb = __myclient[\"MMKeyDB\"]\n    # 创建新的集合\n    __mycol = __mydb[\"ClassRoom_test\"]\n\n    # 判断是否输入id或是输入name，如果有输入则转译\n    def Name2Id(room_id,name):\n        bool_n  = bool(re.match(\"教\\d{1}-\\d{3}\",name))\n        bool_id = bool(re.match(\"B\\d{1}R\\d{3}\",room_id))\n        if not (bool_id or bool_n):\n            return False\n        elif bool_n:\n            room_id = \"B\" + name[1] + \"R\" + name[3:6]\n        else:\n            name = \"教\" + room_id[1] + \"-\" + room_id[3:6]\n\n        return room_id,name\n\n    # 将id转换为name\n    def Id2Name(self,room_id):\n        if room_id :\n            name = \"教\" + room_id[1] + \"-\" + room_id[3:6]\n            return room_id,name\n        else:\n            return '',''\n\n    def __init__(self,\n                 room_id  = \"\",\n                 seats    = \"\",\n                 status   = 0,\n                 event    = []):\n\n        DuringList = ['8:00~9:35',\n                      '9:50~11:25',\n                      '13:45~15:20',\n                      '15:35~17:10',\n                      '18:30~21:00',\n                      '11:30~12:15',\n                      '21:00~21:45']\n        if not(self.Id2Name(room_id)):\n            self.WrongFlag = 1\n        else:\n            self.id,self.name = self.Id2Name(room_id)\n            self.seats    = seats\n            self.during   = DuringList[int(self.id[-1])] if self.id else ''\n            self.status    = status\n            self.event    = event\n            # ClassRoom.PullClassroom(self)\n\n\n    def PullClassroom(self):\n        result = self.__mycol.find_one({ \"_id\": self.id })\n        if result:\n            self.name   = self.name      or result['name']\n            self.seats  = self.seats     or result['seats']\n            self.during = self.during    or result['during']\n            self.status = self.status    or result['status']\n            self.event  = self.event     or result['event']\n            return self\n        else:\n            return False\n\n\n    def TurnDict(self):\n        mydict = {\n            \"_id\"      : self.id ,\n            \"name\"     : self.name,\n            \"seats\"    : self.seats,\n            \"during\"   : self.during,\n            \"status\"   : self.status,\n            \"event\"    : self.event}\n        return mydict\n    \n\n    # 有则更新，无则创建\n    def PushClassroom(self):\n        mydict = self.TurnDict()\n        if self.__mycol.find_one({ \"_id\": self.id }):\n            myquery = {\"_id\" : self.id}\n            self.__mycol.update_one(myquery,{'$set':mydict})\n            return \"Acc_Updated\"\n        else:\n            self.__mycol.insert_one(mydict) # 上传新的document\n            return \"Acc_Created\"\n\n\n    def ShowAll(self):\n        allCol = self.__mycol.find()\n        return allCol\n\n\n    # 删除教室记录\n    def Delete(self):\n        self.__mycol.delete_one({\"_id\": self.id})\n        return \"Deleted\"\n\n\nif __name__ == '__main__':\n    # 先pull 再update\n    for i in range(2,5):\n        for j in range(1,4):\n            for k in range(1,10):\n                # 座位按房间号用2取余\n                if k % 2 == 0:\n                    seats = 80\n                else:\n                    seats = 180\n                for n in range(0,5):\n                    id = 'B'+str(i)+'R'+str(j)+str(k).zfill(2)+str(n)\n                    ClassRoom(id,seats=seats).PushClassroom()","repo_name":"Ap01lo/mmk","sub_path":"ClassRoomCopy.py","file_name":"ClassRoomCopy.py","file_ext":"py","file_size_in_byte":3563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"28342581887","text":"import unittest\n\nfrom pyTerra import api, image\nimport datetime\n\n\nclass Object:\n    pass\n    \n    \nimagePresence='true'\nMaxItems='10'\nplaceName='Ames'\ntheme = 'DOQ'\nscale = \"Scale4m\"\nptype = 'CityTown'\n\n\n# \n# #Extended around Baker field\n# if large:\n#     ul_x = 433714.25\n#     ul_y = 4661043.80\n#     lr_x = 438603.35\n#     lr_y = 4656591.96\n# else:\n# #Baker field\n#     ul_x = 436521.25\n#     ul_y = 4659253.80\n#     lr_x = 437142.35\n#     lr_y = 4658582.96\n\n\nlg_ul = Object()\nlg_ul.X = 433714.25\nlg_ul.Y = 4661043.80\nlg_ul.Zone = 15\n\nlg_lr = Object()\nlg_lr.X = 438603.35\nlg_lr.Y = 4656591.96\nlg_lr.Zone = 15\n\n\nul = Object()\nul.X = 436521.25\nul.Y = 4659253.80\nul.Zone = 15\n\nlr = Object()\nlr.X = 436521.25\nlr.Y = 4659253.80\nlr.Zone = 15\n\nscale = 'Scale2m'\ntheme = 'Ortho'\nfilename = 'test.jpg'\n\nclass ImageTest(unittest.TestCase):\n    def testFetchSmallImage(self):\n        \"\"\"Fetching small image works\"\"\"\n        img = image.TerraImage(ul, lr, scale, theme, lr.Zone, \"/tmp\")\n        t = img.download()\n        self.assertEqual(t.size[0], 200)\n        self.assertEqual(t.size[1], 200)\n        self.assertEqual(t.mode, 'RGB')\n        dates = [datetime.datetime(1994, 4, 16, 0, 0)]\n        self.assertEqual(img.dates, dates)\n\n    def testFetchLargeImage(self):\n        \"\"\"Fetching large image works\"\"\"\n        img = image.TerraImage(lg_ul, lg_lr, scale, theme, lr.Zone, \"/tmp\")\n        t = img.download()\n        self.assertEqual(t.size[0], 2600)\n        self.assertEqual(t.size[1], 2400)\n        self.assertEqual(t.mode, 'RGB')\n        self.assertEqual(img.number_of_tiles, 156)\n\n","repo_name":"hobu/pyTerra","sub_path":"tests/image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"85"}
+{"seq_id":"74099882199","text":"def collatz(number):\n    if (number % 2) == 0:\n        print(number // 2)\n        return number // 2\n\n    elif (number % 2) == 1:\n        result = 3 * number + 1\n        print(result)\n        return result  \n\ntry:\n    x = input(\"Enter a number: \")\n    while x != 1:\n        x = collatz(int(x))\n\n\nexcept ValueError:\n    print(\"User must enter an integer.\")  ","repo_name":"BasiliskOps/collatz","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"74605302039","text":"import numpy as np\nimport unittest as ut\nimport itertools as it\nimport cell\nfrom cell import R, G, B, rot, dot, cell_to_rgb, rgb_to_cell, iterate_cell\nimport bbworld\nfrom bbworld import board_from_pic, pic_from_board, _get_neighbours, iterate_board, save_pic, load_pic\nfrom PIL import Image\n\nclass CellsTest(ut.TestCase):\n    def setUp(self):\n        self.colors      = (0, R, G, B)\n        self.blanks      = ((0,0), (0,R), (0,G), (0,B))\n        self.red_in      = (R,G)\n        self.blue_in     = (B,R)\n        self.green_in    = (G,B)\n        self.incoming    = (self.red_in, self.blue_in, self.green_in)\n    \n    def tearDown(self):\n        pass\n\n    def test_cell_to_rgb(self):\n        self.assertEqual(cell_to_rgb(R), (255, 0 ,0))\n        self.assertEqual(cell_to_rgb(G), (0, 255, 0))\n        self.assertEqual(cell_to_rgb(B), (0, 0, 255))\n        self.assertEqual(cell_to_rgb(0), (255, 255, 255))\n\n    def test_rgb_to_cell(self):\n        self.assertEqual(rgb_to_cell((255, 0, 0)), R)\n        self.assertEqual(rgb_to_cell((0, 255, 0)), G)\n        self.assertEqual(rgb_to_cell((0, 0, 255)), B)\n        self.assertEqual(rgb_to_cell((255, 255, 255)), 0)\n\n    def test_rot(self):\n        self.assertEqual(rot(R), G)\n        self.assertEqual(rot(G), B)\n        self.assertEqual(rot(B), R)\n        self.assertEqual(rot(0), 0)\n\n    def test_dot(self):\n        for S in self.colors:\n            #S.S=0\n            self.assertEqual(dot(S, S), 0)\n            #S.0 = 0\n            self.assertEqual(dot(S, 0), S)\n            self.assertEqual(dot(0, S), S)\n            #S.S+ = S-\n            self.assertEqual(dot(S, rot(S)), rot(rot(S)))\n            self.assertEqual(dot(rot(S), S), rot(rot(S)))\n            #S.S- = S+\n            self.assertEqual(dot(S, rot(rot(S))), rot(S))\n            self.assertEqual(dot(rot(rot(S)), S), rot(S))\n\n    def test_iterate_cell_on_blanks(self):\n        for S in self.colors:\n            for neighbours in it.combinations_with_replacement(self.blanks, 4):\n                self.assertEqual(0, iterate_cell(cell=S, neighbours=neighbours))\n\n\n    def test_iterate_incoming(self):\n        for t in self.incoming:\n            for S in self.colors:\n                for blank_neighbours in it.combinations_with_replacement(self.blanks, 3):\n                    neighbours = (t, ) + blank_neighbours\n                    self.assertEqual(\n                        dot(S,t[0]),\n                        iterate_cell(cell=S, neighbours = neighbours)\n                    )\n\n    def test_iterate_outgoing(self):\n        for S in {R, G, B}:\n            for blank_neighbours in it.combinations_with_replacement(self.blanks, 3):\n                neighbours = ((rot(S),0), ) + blank_neighbours\n                #print(S, neighbours)\n                self.assertEqual(\n                    rot(S),\n                    iterate_cell(cell=S, neighbours=neighbours)\n                    )\n    # More complicated tests may be in order sometime?\n\n\nclass WorldTest(ut.TestCase):\n    def setUp(self):\n        fname0_0    = \"test_images/00_step00.png\"\n        fname0_1    = \"test_images/00_step01.png\"\n        self.rgb0_0_open = np.asarray(Image.open(fname0_0))\n        self.rgb0_1_open = np.asarray(Image.open(fname0_1))\n        self.board0_0 =np.array(\n                    [   [R,G,0,0],\n                        [0,0,0,0],\n                        [0,0,0,0],\n                        [0,0,0,0],\n                        [0,B,0,0]])\n        self.board0_1 =np.array(\n                    [   [G,B,0,R],\n                        [0,G,0,0],\n                        [0,0,0,0],\n                        [0,0,0,0],\n                        [0,0,0,0]])\n\n    def tearDown(self):\n        pass\n    \n    def test_board_from_pic(self):\n        self.assertTrue(\n            np.array_equiv(\n                board_from_pic(self.rgb0_0_open),\n                self.board0_0\n            )\n        )\n        self.assertTrue(\n            np.array_equiv(\n                board_from_pic(self.rgb0_1_open),\n                self.board0_1\n            )\n        )\n\n    def test_pic_from_board(self):\n        self.assertTrue(\n            np.array_equiv(\n                self.rgb0_0_open,\n                pic_from_board(self.board0_0)\n            )\n        )\n        self.assertTrue(\n            np.array_equiv(\n                self.rgb0_1_open,\n                pic_from_board(self.board0_1)\n            )\n        )\n        \n    def test_pic_io(self):\n        save_pic(self.board0_0, \"test_images/savetest.png\")\n        b0 = load_pic(\"test_images/savetest.png\")\n        self.assertTrue(\n            np.array_equal(b0, self.board0_0)\n        )\n\n    def test_get_neighbours(self):\n        neighbours = _get_neighbours(self.board0_0, x=1, y=2, w=4, h=5)\n        self.assertEqual(neighbours.count((0,0)), 2)\n        self.assertEqual(neighbours.count((0,G)), 1)\n        self.assertEqual(neighbours.count((0,B)), 1)\n        \n        neighbours = _get_neighbours(self.board0_0, x=2, y=0, w=4, h=5)\n        self.assertEqual(neighbours.count((G,R)), 1)\n        self.assertEqual(neighbours.count((0,R)), 1)\n        self.assertEqual(neighbours.count((0,0)), 2)\n\n    def test_bbworld_iterate(self):\n        self.assertTrue(\n            np.array_equal( iterate_board(self.board0_0), self.board0_1 )\n        )\n        # Make sure that it didn't modify board0_0:\n        self.assertFalse(\n            np.array_equal(self.board0_0, self.board0_1)\n            )\n\n\ntests = [   CellsTest,\n            WorldTest\n            ]\n\n\nif __name__ == '__main__':\n    for test in tests:\n        ut.TextTestRunner(verbosity=2).run(\n            ut.TestLoader().loadTestsFromTestCase(test  )\n        )\n","repo_name":"lynnpepin/brainbow","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":5625,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"}
+{"seq_id":"23513260032","text":"import torch\nimport torch.nn as nn\nimport numpy as np\n\n\n##This implementation follows the Keras implementation available here: https://github.com/FIBLAB/DeepSTN\nclass DeepSTN(nn.Module):\n    '''\n    Implementation of the model DeepSTN+. Paper link: https://dl.acm.org/doi/10.1145/3477577\n\n    Parameters\n    ..........\n    H (Int, Optional) - Grid Height, Default: 21\n    W (Int, Optional) - Grid Width, Default: 12\n    channel (Int, Optional) - No of Channels/features, Default: 2\n    c (Int, Optional) - Closeness temporal length, Default: 3\n    p (Int, Optional) - Period temporal length, Default: 4\n    t (Int, Optional) - Trend temporal length, Default: 4\n    pre_F (Int, Optional) - Default: 64\n    conv_F (Int, Optional) - Default: 64\n    R_N (Int, Optional) - Default: 2\n    is_plus (Int, Optional) - Default: True\n    plus (Int, Optional) - Default: 8\n    rate (Int, Optional) - Default: 2\n    is_pt (Int, Optional) - Default: True\n    P_N (Int, Optional) - Default: 6\n    T_F (Int, Optional) - Default: 28\n    PT_F (Int, Optional) - Default: 6\n    T (Int, Optional) - Default: 24\n    dropVal (Int, Optional) - Default: 0\n    kernel1 (Int, Optional) - Default: 1\n    isPT_F (Int, Optional) - Decides whether PT_model uses one more Conv after _Multiplying PoI and Time, 1 recommended. Default: 1\n    '''\n\n    def __init__(self, H=21, W=12, channel=2,\\\n        c=3, p=4, t=4,\\\n        pre_F=64, conv_F=64, R_N=2,\\\n        is_plus=True,\\\n        plus=8, rate=1,\\\n        is_pt=True,\\\n        P_N=9, T_F=7*8, PT_F=9, T=24,\\\n        dropVal=0,\\\n        kernel1=1,\\\n        isPT_F=1):\n        super(DeepSTN, self).__init__()\n\n        self._device = None\n\n        self.H = H\n        self.W = W\n        self.T = T\n        self.P_N = P_N\n        self.is_pt = is_pt\n        self.kernel1 = kernel1\n        self.isPT_F = isPT_F\n        self.is_plus = is_plus\n        self.R_N = R_N\n\n        self.channel_c = channel*c\n        self.channel_p = channel*p\n        self.channel_t = channel*t\n\n        self.conv1 = nn.Conv2d(self.channel_c, pre_F, kernel_size=1, padding = \"same\")\n        self.conv2 = nn.Conv2d(self.channel_p, pre_F, kernel_size=1, padding = \"same\")\n        self.conv3 = nn.Conv2d(self.channel_t, pre_F, kernel_size=1, padding = \"same\")\n\n        self.ptTrans = _PT_trans(P_N, PT_F, T, T_F, H, W, isPT_F)\n        self.cpt1_0 = _Conv_unit1(pre_F*3+PT_F*isPT_F+P_N*(not isPT_F), conv_F, dropVal, H, W)\n        self.cpt0_0 = _Conv_unit0(pre_F*3+PT_F*isPT_F+P_N*(not isPT_F), conv_F, dropVal, H, W)\n\n        self.cpt1_1 = _Conv_unit1(pre_F*3, conv_F, dropVal, H, W)\n        self.cpt0_1 = _Conv_unit0(pre_F*3, conv_F, dropVal, H, W)\n\n        self.resPlus = _Res_plus(conv_F, plus, rate, dropVal, H, W)\n        self.resNormal = _Res_normal(conv_F, dropVal, H, W)\n\n        self.relu = torch.relu\n        self.batchNorm2d = nn.BatchNorm2d(num_features=conv_F, eps=0.001, momentum=0.99, affine=False)\n        self.dropout = nn.Dropout(dropVal)\n        self.conv4 = nn.Conv2d(conv_F, channel, kernel_size=1, padding = \"same\")\n        self.tanh = torch.tanh\n        \n\n    def forward(self, input_c, input_p, input_t, input_time = None, input_poi = None):\n        '''\n        Parameters\n        ..........\n        input_c (Tensor) - Closeness sequence part of the input sample\n        input_p (Tensor) - Period sequence part of the input sample\n        input_t (Tensor) - Trend sequence part of the input sample\n        input_time (Tensor, Optional) - Temporal part of input sample when is_pt is True. Default: None\n        input_poi (Tensor, Optional) - POI part of input sample when is_pt is True. Default: None\n        '''\n\n        if self._device == None:\n            if input_c.is_cuda:\n                self._device = torch.device(\"cuda\")\n            elif input_c.is_mps:\n                self._device = torch.device(\"mps\")\n            else:\n                self._device = torch.device(\"cpu\")\n            self.ptTrans._set_device(self._device)\n\n        input_c = input_c.view(-1, self.channel_c, self.H, self.W)\n        input_p = input_p.view(-1, self.channel_p, self.H, self.W)\n        input_t = input_t.view(-1, self.channel_t, self.H, self.W)\n\n        c_out1 = self.conv1(input_c)\n        p_out1 = self.conv2(input_p)\n        t_out1 = self.conv3(input_t)\n\n        if self.is_pt:\n            input_time = input_time.view(-1, self.T+7, self.H, self.W)\n\n            if input_poi != None:\n                input_poi = input_poi.view(-1, self.P_N, self.H, self.W)\n            \n            poi_time = self.ptTrans(input_time, input_poi)\n\n            cpt_con1 = torch.cat((c_out1, p_out1, t_out1, poi_time), axis=1)\n\n            if self.kernel1:\n                cpt = self.cpt1_0(cpt_con1)\n            else:\n                cpt = self.cpt0_0(cpt_con1)\n        else:\n            cpt_con1 = torch.cat((c_out1,p_out1,t_out1), axis=1)\n            if self.kernel1:\n                cpt = self.cpt1_1(cpt_con1)\n            else:\n                cpt = self.cpt0_1(cpt_con1)\n\n        if self.is_plus:\n            for i in range(self.R_N):\n                cpt = self.resPlus(cpt)\n        else:\n            for i in range(self.R_N):\n                cpt = self.resNormal(cpt)\n\n        \n        cpt_out = self.relu(cpt)\n        cpt_out = self.batchNorm2d(cpt_out)\n        cpt_out = self.dropout(cpt_out)\n        cpt_out = self.conv4(cpt_out)\n        cpt_out = self.tanh(cpt_out)\n\n        return cpt_out\n\n\n\nclass _Conv_unit0(nn.Module):\n    def __init__(self, Fin, Fout, dropVal, H, W):\n        super(_Conv_unit0, self).__init__()\n        self.Fin = Fin\n        self.H = H\n        self.W = W\n        self.relu = torch.relu\n        self.batchNorm2d = nn.BatchNorm2d(num_features=Fin, eps=0.001, momentum=0.99, affine=False)\n        self.dropout = nn.Dropout(dropVal)\n        self.conv = nn.Conv2d(Fin, Fout, kernel_size=3, padding = \"same\")\n\n    def forward(self, x):\n        x = x.view(-1, self.Fin, self.H, self.W)\n        x = self.relu(x)\n        x = self.batchNorm2d(x)\n        x = self.dropout(x)\n        x = self.conv(x)\n        return x\n\nclass _Conv_unit1(nn.Module):\n    def __init__(self, Fin, Fout, dropVal, H, W):\n        super(_Conv_unit1, self).__init__()\n        self.Fin = Fin\n        self.H = H\n        self.W = W\n        self.relu = torch.relu\n        self.batchNorm2d = nn.BatchNorm2d(num_features=Fin, eps=0.001, momentum=0.99, affine=False)\n        self.dropout = nn.Dropout(dropVal)\n        self.conv = nn.Conv2d(Fin, Fout, kernel_size=1, padding = \"same\")\n\n    def forward(self, x):\n        x = x.view(-1, self.Fin, self.H, self.W)\n        x = self.relu(x)\n        x = self.batchNorm2d(x)\n        x = self.dropout(x)\n        x = self.conv(x)\n        return x\n\n\n## The following implementation of _Multiply class was proposed here: https://discuss.pytorch.org/t/how-to-create-a-_Multiply-layer-which-supports-backprop/56220\nclass _Multiply(nn.Module):\n    def __init__(self):\n        super(_Multiply, self).__init__()\n\n    def forward(self, x):\n        result = torch.ones(x[0].size()).to(self._device)\n\n        for t in x:\n            result *= t\n\n        return t\n\n    def _set_device(self, _device):\n        self._device = _device\n\n\n\nclass _Res_plus(nn.Module):\n    def __init__(self, F, Fplus, rate, dropVal, H, W):\n        super(_Res_plus, self).__init__()\n        self.F = F\n        self.Fplus = Fplus\n        self.rate = rate\n        self.H = H\n        self.W = W\n\n        self.cl_conv1A = _Conv_unit0(F, F-Fplus, dropVal, H, W)\n        self.cl_conv1B = nn.AvgPool2d(kernel_size=rate, stride=rate) ## there was padding = valid in keras version\n        self.relu = torch.relu\n        self.batchNorm2d = nn.BatchNorm2d(num_features=F, eps=0.001, momentum=0.99, affine=False)\n        self.plus_conv = nn.Conv2d(F, Fplus*H*W, kernel_size=(int(np.floor(H/rate)),int(np.floor(W/rate))))\n        self.cl_conv1 = _Conv_unit0(F, F, dropVal, H, W)\n\n    def forward(self, x):\n        x_org = x.view(-1, self.F, self.H, self.W)\n        x2 = self.cl_conv1A(x_org)\n        if self.rate !=1:\n            x = self.cl_conv1B(x_org)\n        else:\n            x = x_org\n        x = self.relu(x)\n        x = self.batchNorm2d(x)\n        x = self.plus_conv(x)\n        x = x.view(-1, self.Fplus, self.H, self.W)\n        x = torch.cat((x2, x), axis=1)\n        x = self.cl_conv1(x)\n        x = x_org + x\n\n        return x\n\n\nclass _Res_normal(nn.Module):\n    def __init__(self, F, dropVal, H, W):\n        super(_Res_normal, self).__init__()\n        self.F = F\n        self.H = H\n        self.W = W\n\n        self.cl_conv1 = _Conv_unit0(F, F, dropVal, H, W)\n        self.cl_conv2 = _Conv_unit0(F, F, dropVal, H, W)\n\n    def forward(self, x):\n        x_org = x.view(-1, self.F, self.H, self.W)\n        x = self.cl_conv1 (x_org)\n        x = self.cl_conv2 (x)\n        x = x_org + x\n\n        return x\n\n\n\nclass _T_trans(nn.Module):\n    def __init__(self, T, T_F, H, W):\n        super(_T_trans, self).__init__()\n        self.T = T\n        self.H = H\n        self.W = W\n\n        self.T_mid = nn.Conv2d(T+7, T_F, kernel_size=1, padding = \"same\")\n        self.T_act = torch.relu\n        self.T_fin = nn.Conv2d(T_F, 1, kernel_size=1, padding = \"same\")\n\n\n    def forward(self, x):\n        x = x.view(-1, self.T + 7, self.H, self.W)\n        x = self.T_mid(x)\n        x = self.T_act (x)\n        x = self.T_fin (x)\n        x = self.T_act (x)\n\n        return x\n\n\nclass _PT_trans(nn.Module):\n    def __init__(self, P_N, PT_F, T, T_F, H, W, isPT_F):\n        super(_PT_trans, self).__init__()\n        self.P_N = P_N\n        self.T = T\n        self.H = H\n        self.W = W\n        self.isPT_F = isPT_F\n\n        self.t_trans = _T_trans(T, T_F, H, W)\n        self.multiply = _Multiply()\n        if P_N > 0:\n            self.conv = nn.Conv2d(P_N, PT_F, kernel_size=1, padding = \"same\")\n        else:\n            self.conv = nn.Conv2d(1, PT_F, kernel_size=1, padding = \"same\")\n\n    def forward(self, input_time, input_poi = None):\n        input_time = input_time.view(-1, self.T+7, self.H, self.W)\n\n        t_x = self.t_trans(input_time)\n\n        if input_poi != None:\n            input_poi = input_poi.view(-1, self.P_N, self.H, self.W)\n\n            if self.P_N >= 2:\n                t_x = torch.cat(tuple([t_x]*self.P_N), axis=1)\n\n            poi_time = self.multiply(torch.stack([input_poi, t_x]))\n        else:\n            poi_time = self.multiply(torch.stack([t_x]))\n\n        if self.isPT_F:\n            poi_time = self.conv(poi_time)\n\n        return poi_time\n\n    def _set_device(self, _device):\n        self.multiply._set_device(_device)\n\n\n\n","repo_name":"wherobots/GeoTorchAI","sub_path":"geotorchai/models/grid/deep_stn_net.py","file_name":"deep_stn_net.py","file_ext":"py","file_size_in_byte":10507,"program_lang":"python","lang":"en","doc_type":"code","stars":429,"dataset":"github-code","pt":"85"}
+{"seq_id":"38632399650","text":"import cv2\nimport numpy as np\nimport socket\nimport sys\nimport pickle\nimport struct\n\ncap=cv2.VideoCapture(0)\nclientsocket=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\nclientsocket.connect(('localhost',8089))\n\nwhile True:\n    ret,frame=cap.read()\n    cv2.imshow('frame', frame)\n    key = cv2.waitKey(1)\n    if key == ord('q'):\n        # Serialize frame\n        data = pickle.dumps(frame)\n\n        # Send message length first\n        message_size = struct.pack(\"L\", len(data)) ### CHANGED\n        # Send size 0 if want to terminate\n\n        # Then data\n        clientsocket.sendall(message_size + data)\n\n        # Receive price\n        total = clientsocket.recv(4096)\n        print(total)\n\n        # Release cv2\n        cap.release()\n        cv2.destroyAllWindows()\n        exit(1)\n\n        \n","repo_name":"MiyaTests/store-counter","sub_path":"client2.py","file_name":"client2.py","file_ext":"py","file_size_in_byte":793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"28134384130","text":"from .base import *  # noqa\nfrom .base import env\n\n# GENERAL\n# ------------------------------------------------------------------------------\n# https://docs.djangoproject.com/en/dev/ref/settings/#debug\nDEBUG = True\nALLOW_ADMIN_URL = DEBUG or env.bool(\"ALLOW_ADMIN_URL\", False)\n# https://docs.djangoproject.com/en/dev/ref/settings/#secret-key\nSECRET_KEY = env('DJANGO_SECRET_KEY', default='q6fvzuvhe9DOS9EpWlhGahCOdnEzH8mfUGLt0EC7LWwYSyDPHI59VRBfrI31qtcT')\n# https://docs.djangoproject.com/en/dev/ref/settings/#allowed-hosts\nALLOWED_HOSTS = [\n    \"localhost\",\n    \"0.0.0.0\",\n    \"127.0.0.1\",\n]\n\n# CACHES\n# ------------------------------------------------------------------------------\n# https://docs.djangoproject.com/en/dev/ref/settings/#caches\nCACHES = {\n    'default': {\n        'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',\n        'LOCATION': ''\n    }\n}\n\n# TEMPLATES\n# ------------------------------------------------------------------------------\n# https://docs.djangoproject.com/en/dev/ref/settings/#templates\nTEMPLATES[0]['OPTIONS']['debug'] = DEBUG  # noqa F405\n\n# EMAIL\n# ------------------------------------------------------------------------------\n# https://docs.djangoproject.com/en/dev/ref/settings/#email-backend\nEMAIL_BACKEND = env('DJANGO_EMAIL_BACKEND', default='django.core.mail.backends.console.EmailBackend')\n# https://docs.djangoproject.com/en/dev/ref/settings/#email-host\nEMAIL_HOST = 'localhost'\n# https://docs.djangoproject.com/en/dev/ref/settings/#email-port\nEMAIL_PORT = 1025\n\n# django-debug-toolbar\n# ------------------------------------------------------------------------------\n# https://django-debug-toolbar.readthedocs.io/en/latest/installation.html#prerequisites\nINSTALLED_APPS += ['debug_toolbar']  # noqa F405\n# https://django-debug-toolbar.readthedocs.io/en/latest/installation.html#middleware\nMIDDLEWARE += ['debug_toolbar.middleware.DebugToolbarMiddleware']  # noqa F405\n# https://django-debug-toolbar.readthedocs.io/en/latest/configuration.html#debug-toolbar-config\nDEBUG_TOOLBAR_CONFIG = {\n    'DISABLE_PANELS': [\n        'debug_toolbar.panels.redirects.RedirectsPanel',\n    ],\n    'SHOW_TEMPLATE_CONTEXT': True,\n}\n# https://django-debug-toolbar.readthedocs.io/en/latest/installation.html#internal-ips\nINTERNAL_IPS = ['127.0.0.1', '10.0.2.2']\n\n\n# django-extensions\n# ------------------------------------------------------------------------------\n# https://django-extensions.readthedocs.io/en/latest/installation_instructions.html#configuration\nINSTALLED_APPS += ['django_extensions']  # noqa F405\n\n# Your stuff...\n# ------------------------------------------------------------------------------\n# Disable caching when working locally.\nCACHES.update({\n    k: {\n        'BACKEND': 'django.core.cache.backends.dummy.DummyCache',\n        'TIMEOUT': 0,\n    } for k in (\n        'default', 'post_preview'\n    )\n})\n\n# Optionally enable cache for post_preview\nif os.environ.get('ENABLE_POST_PREVIEW_CACHE'):\n    CACHES['post_preview'] = {\n        'BACKEND': 'django.core.cache.backends.db.DatabaseCache',\n        'LOCATION': 'post_preview_cache',\n        'TIMEOUT': None,\n    }\n","repo_name":"contactr2m/cosme","sub_path":"config/settings/local.py","file_name":"local.py","file_ext":"py","file_size_in_byte":3124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"13333049175","text":"import urllib2\nimport os\nimport tarfile\nimport inspect\nimport time\nimport sys\n\nMAIN_PATH =  os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) # script directory\nsys.path.append(MAIN_PATH+ \"/src\")\nimport std_tensorflow\n\n\n\n# (for parallelization, how many cores are used;\n# where does 'the mitigated data transfer overhead' come from)? \n# How much time on loading;training;classifying\n# Is the Python ML code used by both systems the same? \n# Are there any drawbacks of storing models as BLOBs (e.g., serialization cost)? \n# The authors should try to clarify such things and provide more insights into the advantages of the proposed integration.\n\nprint(\"Cleaning Database\")\nos.system('mclient '+ MAIN_PATH+'/src/dropschema.sql')\n\nprint(\"Downloading Cifar 100\")\nresponse = urllib2.urlopen('https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz')\nzipcifar= response.read()\nwith open(MAIN_PATH+\"/cifar100.tar.gz\", 'w') as f:\n    f.write(zipcifar)\n\ntar = tarfile.open(MAIN_PATH+\"/cifar100.tar.gz\", \"r:gz\")\ntar.extractall()\ntar.close()\n\nprint(\"Creating Database\")\nos.system('mclient '+ MAIN_PATH+'/src/schema.sql')\nos.system('mclient '+ MAIN_PATH+'/src/loadimages.sql')\nsql = \"COPY LOADER INTO image_class FROM loadClass(\\'\"+MAIN_PATH+\"/cifar-100-python\\');\"\nos.system('mclient -s ' +\"\\\"\" + sql +\"\\\"\")\nsql = \"COPY LOADER INTO image_superclass FROM loadSuperclass(\\'\"+MAIN_PATH+\"/cifar-100-python\\');\"\nos.system('mclient -s ' +\"\\\"\" + sql +\"\\\"\")\nsql = \"COPY LOADER INTO cifar100 FROM loadImages(\\'\"+MAIN_PATH+\"/cifar-100-python\\');\"\nos.system('mclient -s ' +\"\\\"\" + sql +\"\\\"\")\n\nprint(\"Training Models MonetDB/Tensorflow\")\nstart_time_monet = time.time()\nmodeldir = os.path.join(MAIN_PATH, \"databasemodels\")\nos.system('mkdir -p '+ modeldir)\nos.system('mclient '+ MAIN_PATH+'/src/trainmodel.sql')\nsql = \"SELECT trainmodel(id, '%s') FROM image_superclass GROUP BY id;\" % (modeldir,)\nos.system('mclient -s ' +\"\\\"\" + sql.replace(\"\\n\", \" \") +\"\\\"\")\nend_time_monet = time.time()\nprint(\"--- %s MonetDB (Training + Loading) seconds ---\" % (end_time_monet - start_time_monet))\n\n\nprint(\"Classifying Models MonetDB/Tensorflow\")\nstart_time_monet = time.time()\nos.system('mclient '+ MAIN_PATH+'/src/classification.sql')\nsql = \"SELECT classification (model_path,name) FROM classificationmodel;\"\nos.system('mclient -s ' +\"\\\"\" + sql +\"\\\"\")\nend_time_monet = time.time()\nprint(\"--- %s MonetDB (Classifying) seconds ---\" % (end_time_monet - start_time_monet))\n\n\nprint(\"Training/Classifying Models Standard Tensorflow\")\nos.system('mkdir '+ MAIN_PATH+'/tensorflowmodels')\nstart_time_tensor = time.time()\nstd_tensorflow.run(MAIN_PATH)\nend_time_tensor = time.time()\n\n\nprint(\"--- %s MonetDB seconds ---\" % (end_time_monet - start_time_monet))\nprint(\"--- %s TensorFlow seconds ---\" % (end_time_tensor - start_time_tensor))","repo_name":"pdet/EnsembleLearningMonetDBTensorflow","sub_path":"experiment.py","file_name":"experiment.py","file_ext":"py","file_size_in_byte":2811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"19871726239","text":"# Standard Library\nfrom unittest.mock import patch, MagicMock\n\n# IRango\nfrom src.repositories.restaurants.repository import RestaurantsRepository\nfrom src.services.restaurants.service import RestaurantsService\n\n\n@patch.object(RestaurantsRepository, \"find_one\")\ndef test_delete_restaurant_restaurant_not_found(find_one_patch: MagicMock):\n    payload = {\n        \"restaurant_id\": 123\n    }\n\n    find_one_patch.return_value = None\n\n    expected = {\"message\": f\"Restaurant not found\", \"status_code\": 204}\n\n    result = RestaurantsService.delete_restaurant(payload)\n\n    assert result == expected\n\n\n@patch.object(RestaurantsRepository, \"find_one\")\n@patch.object(RestaurantsRepository, \"delete_one\")\ndef test_delete_restaurant(delete_one_patch: MagicMock, find_one_patch: MagicMock):\n    payload = {\n        \"restaurant_id\": 1,\n\n    }\n\n    restaurant_info = {\n        \"name\": \"large burger\"\n    }\n\n    find_one_patch.return_value = restaurant_info\n    delete_one_patch.return_value = True\n\n    expected = {\"message\": f\"Restaurant deleted\", \"status_code\": 204}\n    result = RestaurantsService.delete_restaurant(payload)\n\n    assert result == expected\n\n","repo_name":"SMarkus27/irango-api-python","sub_path":"tests/services/restaurants/test_delete_restaurant.py","file_name":"test_delete_restaurant.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"71639825237","text":"#!/usr/bin/env python\n\nimport timeit\n\n# https://www.hackerrank.com/challenges/kangaroo/problem\n\n# = Runtime Error\n# = RecursionError: maximum recursion depth exceeded in comparison\n# def kangaroo(x1, v1, x2, v2):\n#     if x1 == x2 or x1+v1 == x2+v2: return \"YES\"\n#     if x1  > x2: return \"NO\"\n#     if v1  < v2: return \"NO\"\n#     return kangaroo(x1+v1, v1, x2+v2, v2)\n\ndef kangaroo(x1, v1, x2, v2):\n    return \"NO\"\n\n\nif __name__ == '__main__':\n    result = kangaroo(2, 1, 1, 2)\n    print(result == \"YES\", result)\n    result = kangaroo(0, 2, 5, 3)\n    print(result == \"NO\", result)\n    result = kangaroo(0, 3, 4, 2)\n    print(result == \"YES\", result)\n    result = kangaroo(43, 2, 70, 2)\n    print(result == \"NO\", result)\n","repo_name":"azimut/challenges","sub_path":"hackerrank/easy/number-line-jumps.py","file_name":"number-line-jumps.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"20194184534","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nimport argparse\n\nfrom googlecloudsdk.api_lib.container.fleet import util as fleet_util\nfrom googlecloudsdk.api_lib.container.fleet.policycontroller import protos\nfrom googlecloudsdk.calliope import base as calliope_base\nfrom googlecloudsdk.command_lib.container.fleet.features import base\nfrom googlecloudsdk.command_lib.container.fleet.policycontroller import command\nfrom googlecloudsdk.command_lib.container.fleet.policycontroller import deployment_configs as deployment\nfrom googlecloudsdk.command_lib.container.fleet.policycontroller import flags\nfrom googlecloudsdk.core import exceptions\n\n\n@calliope_base.Hidden\n@calliope_base.ReleaseTracks(\n    calliope_base.ReleaseTrack.ALPHA, calliope_base.ReleaseTrack.BETA\n)\nclass Remove(base.UpdateCommand, command.PocoCommand):\n  \"\"\"Removes configuration properties from Policy Controller components.\n\n  Remove customizations of on-cluster components in Policy Controller. These\n  components are managed as individual kubernetes deployments (e.g. 'admission')\n  in the gatekeeper-system namespace.\n\n  When removing a 'toleration' property, it must match exactly, including the\n  key, value and effect flag (if originally specified).\n\n  ## EXAMPLES\n\n  To remove the replica count for a component:\n\n    $ {command} admission replica-count\n\n  To remove the replica count for a component across all fleet memberships:\n\n    $ {command} admission replica-count --all-memberships\n\n  To remove a toleration with key 'my-key' on a component:\n\n    $ {command} admission toleration my-key\n\n  To remove a toleration with key 'my-key' and 'my-value' on a component:\n\n    $ {command} admission toleration my-key=my-value\n\n  To remove a toleration with key 'my-key' and 'my-value' on a component, along\n  with the effect 'NoSchedule':\n\n    $ {command} admission toleration my-key=my-value --effect=NoSchedule\n\n  To remove a memory limit:\n\n    $ {command} audit memory-limit\n\n  To remove a memory request:\n\n    $ {command} mutation memory-request\n\n  To remove a cpu limit:\n\n    $ {command} admission cpu-limit\n\n  To remove a cpu request:\n\n    $ {command} audit cpu-request\n  \"\"\"\n\n  feature_name = 'policycontroller'\n\n  @classmethod\n  def Args(cls, parser):\n    cmd_flags = flags.PocoFlags(parser, 'remove deployment configuration')\n    cmd_flags.add_memberships()\n\n    parser.add_argument(\n        'deployment',\n        choices=deployment.G8R_COMPONENTS,\n        help=(\n            'The PolicyController deployment component (i.e, \"admission\", '\n            ' \"audit\" or \"mutation\" from which to remove configuration.'\n        ),\n    )\n    parser.add_argument(\n        'property',\n        choices=deployment.SUPPORTED_PROPERTIES,\n        help='Property to be removed.',\n    )\n    parser.add_argument(\n        'value',\n        nargs=argparse.OPTIONAL,\n        default=None,\n        help=(\n            'This is only required to remove a toleration. It should not be'\n            ' included for any other property.'\n        ),\n    )\n    parser.add_argument(\n        '--effect',\n        choices=deployment.K8S_SCHEDULING_OPTIONS,\n        help=(\n            'Applies only to \"toleration\" property. To be removed, tolerations'\n            ' must match exactly, including the effect setting.'\n        ),\n        type=str,\n    )\n\n  def Run(self, args):\n    # All the membership specs for this feature.\n    specs = self._membership_specs(args)\n\n    for _, spec in specs.items():\n      cfgs = protos.additional_properties_to_dict(\n          spec.policycontroller.policyControllerHubConfig.deploymentConfigs\n      )\n      deployment_cfg = cfgs.get(\n          args.deployment,\n          self.messages.PolicyControllerPolicyControllerDeploymentConfig(),\n      )\n\n      cfgs[args.deployment] = self.set_deployment_config(\n          deployment_cfg,\n          args.property,\n          args.value,\n          args.effect,\n      )\n\n      # Convert back to a list of additionalProperties.\n      # TODO(b/290215626) If empty, ensure it's removed from proto.\n      dcv = protos.set_additional_properties(\n          self.messages.PolicyControllerHubConfig.DeploymentConfigsValue(), cfgs\n      )\n      spec.policycontroller.policyControllerHubConfig.deploymentConfigs = dcv\n\n    return self.merge_specs(specs)\n\n  def _membership_specs(self, args):\n    memberships = [\n        fleet_util.MembershipPartialName(p)\n        for p in base.ParseMembershipsPlural(\n            args, search=True, prompt=True, prompt_cancel=False, autoselect=True\n        )\n    ]\n    specs = self.hubclient.ToPyDict(self.GetFeature().membershipSpecs)\n    return {\n        path: spec\n        for path, spec in specs.items()\n        if fleet_util.MembershipPartialName(path) in memberships\n    }\n\n  def set_deployment_config(self, deployment_cfg, prop, value, effect):\n    if prop == 'toleration':\n      return deployment.remove_toleration(deployment_cfg, value, effect)\n    if value is not None:  # Only valid for toleration.\n      raise exceptions.Error(\n          '\"value\" argument only accepted when removing a toleration.'\n      )\n    if effect is not None:\n      raise exceptions.Error(\n          '\"effect\" flag only accepted when removing a toleration.'\n      )\n    if prop == 'cpu-limit':\n      return deployment.update_cpu_limit(self.messages, deployment_cfg, None)\n    if prop == 'cpu-request':\n      return deployment.update_cpu_request(self.messages, deployment_cfg, None)\n    if prop == 'memory-limit':\n      return deployment.update_mem_limit(self.messages, deployment_cfg, None)\n    if prop == 'memory-request':\n      return deployment.update_mem_request(self.messages, deployment_cfg, None)\n    if prop == 'replica-count':\n      return deployment.update_replica_count(deployment_cfg, None)\n\n  def merge_specs(self, specs):\n    orig = self.hubclient.ToPyDict(self.GetFeature().membershipSpecs)\n    merged = {path: specs.get(path, spec) for path, spec in orig.items()}\n    self.Update(\n        ['membership_specs'],\n        self.messages.Feature(\n            membershipSpecs=self.hubclient.ToMembershipSpecs(merged)\n        ),\n    )\n","repo_name":"google-cloud-sdk-unofficial/google-cloud-sdk","sub_path":"lib/surface/container/fleet/policycontroller/deployment/remove.py","file_name":"remove.py","file_ext":"py","file_size_in_byte":6140,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"85"}
+{"seq_id":"3854774902","text":"#From https://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/\n#LSTM Development\nimport numpy    #importing main lib\nfrom keras.models import Sequential\nfrom keras.layers import LSTMV2, Dense\nfrom keras.layers import Dropout\nfrom keras.layers import LSTM\nfrom keras.callbacks import CallbackList, ModelCheckpoint\nfrom keras.utils import np_utils\nimport os \nimport glob\n\n#Number of epochs (set here)\nepoch = 20\n\n#Loading and lowercasing text\nfilename = \"wordlist.10000\"     #Configure wordlist here\nraw_text = open(filename, 'r', encoding='utf-8').read()\nraw_text = raw_text.lower()\n\n#Preperation to convert text to integers\nchars = sorted(list(set(raw_text)))\nchars_to_int = dict((c, i) for i, c in enumerate(chars))\n\n#Summarise Dataset\nn_chars = len(raw_text)\nn_vocab = len(chars)\nprint (\"Total Characters: \", n_chars)\nprint (\"Total Vocabs: \", n_vocab)\n\n#Prepare dataset of I/O pairs encoded as integers\nseq_length = 100    #Set Sequence length\ndataX = []\ndataY = []\nfor i in range(0, n_chars - seq_length):\n    seq_in = raw_text[i:i + seq_length]\n    seq_out = raw_text[i + seq_length]\n    dataX.append([chars_to_int[char]for char in seq_in])\n    dataY.append(chars_to_int[seq_out])\nn_patterns = len(dataX)\nprint(\"Total Patterns: \", n_patterns)\n\n#Input Sequences to form\nX = numpy.reshape(dataX, (n_patterns, seq_length, 1)) #X reshape to be [samples, time steps, features]\nX = X / float(n_vocab) # normalise\ny = np_utils.to_categorical(dataY) # one hot encode the output variable\n\n#LSTM Define\nmodel = Sequential()\nmodel.add(LSTMV2(256, input_shape=(X.shape[1], X.shape[2])))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(y.shape[1], activation='softmax'))\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])\n\n#Define Checkpoint\nfilepath=\"weights-improvement-{epoch}-{loss:.4f}.hdf5\"\ncheckpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')\ncallbacks_list = [checkpoint]\n\n#Fitting of model to data\nmodel.fit(X, y, epochs=epoch, batch_size=128, callbacks=callbacks_list) # Change batch_size according to ram (experimental)\n\n#Find last epoch HDF5\nfor file in glob.glob(\"./weights-improvement-{}-*.hdf5\".format(epoch)):\n    os.rename(file,'model.hdf5') #Rename last hdf5 to model.hdf5\n\n#Delete other model files\nfor weights in glob.glob(\"weights*.hdf5\"):\n    os.remove(weights) #Removes other weights-improvement files to save space","repo_name":"leezhiwei/WordGenerator-Tensorflow","sub_path":"textprocessingandtraining.py","file_name":"textprocessingandtraining.py","file_ext":"py","file_size_in_byte":2437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"15217014716","text":"import telebot\nimport pyowm\n\nowm = pyowm.OWM('1195a34f05fbee64f2bf544da26f0982')\nbot = telebot.TeleBot('857328939:AAErdqGLGe93RRQ40s7ck-KnHO84AVHjoy4')\n\n\n@bot.message_handler(content_types=['text'])\ndef send_echo(message):\n    observation = owm.weather_at_place(message.text)\n    w = observation.get_weather()\n    temp = w.get_temperature('celsius')['temp']\n\n    answer = 'В городе' + message.text + 'сейчас' + w.get_detailed_status() + '\\n'\n    answer += 'Температура сейчас в районе ' + str(temp) + '\\n\\n'\n    # bot.reply_to(message, message.text)\n\n    bot.send_message(message.chat.id, answer)\n\n\nbot.polling(none_stop=True)\n","repo_name":"slavaroot/weatherbot.github.io","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"7167978058","text":"from datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\n\nptn_start_date = datetime(2023, 1, 1)\n\nfor i in range(12):\n    ptn_year = ptn_start_date.strftime(\"%y\")\n    ptn_month = ptn_start_date.strftime(\"%m\")\n    ptn_start = ptn_start_date.strftime(\"%Y-%m-%d\")\n    ptn_end_date = ptn_start_date + relativedelta(months=1)\n    ptn_end = ptn_end_date.strftime(\"%Y-%m-%d\")\n\n    partition_str = f\"CREATE TABLE IF NOT EXISTS history_{ptn_year}_{ptn_month} PARTITION OF histories FOR VALUES FROM ('{ptn_start}') TO ('{ptn_end}')\"\n\n    print(partition_str)\n\n    ptn_start_date = ptn_end_date\n","repo_name":"T1rax/movies_auth_service","sub_path":"flask_app/old_files/commands_examples/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"8226291308","text":"from fastapi import APIRouter\n\nrouter = APIRouter(\n    prefix=\"/items\",\n    tags=[\"Items\"],\n)\n\n\n@router.get(\"\")\ndef get_items():\n    return {\n        \"data\": [\n            {\n                \"id\": 1,\n                \"value\": \"cookies\",\n            },\n            {\n                \"id\": 2,\n                \"value\": \"candies\",\n            },\n            {\n                \"id\": 3,\n                \"value\": \"chocolates\",\n            },\n        ]\n    }\n\n\n@router.get(\"/{item_id}\")\ndef get_item(item_id: int):\n    return {\n        \"item\": {\"id\": item_id},\n    }\n\n\n@router.post(\"\")\ndef create_item(data: dict):\n    return {\n        \"item\": data,\n    }\n","repo_name":"MakarovaIV/otus","sub_path":"homework_03/api/items.py","file_name":"items.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"11373720335","text":"from third import third_time\r\ndef second_time(a,b,c,d):\r\n    print(\"                             Alice In Boaderland -> Beauty Contest\")\r\n    print()\r\n    marks_list = [0, 0, 0, 0]\r\n    Round = 1\r\n    while True:\r\n        # Taking players inputs\r\n        try:\r\n            player1 = int(input(str(a)+\" Enter a number between 0 - 100: \"))\r\n            if player1 < 0 or player1 > 100:\r\n                print(\"Enter a number between 0 and 100\")\r\n                continue\r\n            player2 = int(input(str(b)+\" Enter a number between 0 - 100: \"))\r\n            if player2 < 0 or player2 > 100:\r\n                print(\"Enter a number between 0 and 100\")\r\n                continue\r\n            player3 = int(input(str(c)+\" Enter a number between 0 - 100: \"))\r\n            if player3 < 0 or player3 > 100:\r\n                print(\"Enter a number between 0 and 100\")\r\n                continue\r\n            player4 = int(input(str(d)+\" Enter a number between 0 - 100: \"))\r\n            if player4 < 0 or player4 > 100:\r\n                print(\"Enter a number between 0 and 100\")\r\n                continue\r\n        except ValueError as e:\r\n            print(\"Please Enter a number between 0 - 100\")\r\n            break\r\n\r\n        print()\r\n        print(\"------------------------------Round:- \" + str(Round) + \"------------------------------------\")\r\n        print(str(a)+\"         \"+str(b)+\"        \"+str(c)+\"        \"+str(d))\r\n        print(\"---------------------------------------------------------------------------\")\r\n        print(\"  \" + str(player1) + \"              \" + str(player2) + \"             \" + str(player3) + \"            \" + str(player4))\r\n        Round = int(Round)\r\n\r\n        # taking average\r\n        avg = (player1 + player2 + player3 + player4) / 4\r\n        death_num = avg * 0.8\r\n\r\n        # Gap between player 1 and death number\r\n        player1_gap = player1 - death_num\r\n        player1_gap = abs(player1_gap)\r\n\r\n        # Gap between player 2 and death number\r\n        player2_gap = player2 - death_num\r\n        player2_gap = abs(player2_gap)\r\n\r\n        # Gap between player 3 and death number\r\n        player3_gap = player3 - death_num\r\n        player3_gap = abs(player3_gap)\r\n\r\n        # Gap between player 4 and death number\r\n        player4_gap = player4 - death_num\r\n        player4_gap = abs(player4_gap)\r\n\r\n        min_player_gap = min(player1_gap, player2_gap, player3_gap, player4_gap)\r\n\r\n        if player1_gap == min_player_gap: #player1 = a [0,0,0,0]\r\n            # Marking system\r\n            for i in range(len(marks_list)):\r\n                if i == 0:\r\n                    continue\r\n                else:\r\n                    marks_list[i] -= 1\r\n            print(\"Score:-\")\r\n            print(\" \" + str(marks_list[0]) + \"               \" + str(marks_list[1]) + \"             \" + str(marks_list[2]) + \"            \" + str(marks_list[3]))\r\n            print()\r\n            print(\"                         Death number is: \" + str(round(death_num, 2)))\r\n            print(\"                     Congratulations \"+str(a)+\" won\")\r\n            print(\"---------------------------------------------------------------------------\")\r\n\r\n        elif player2_gap == min_player_gap:\r\n            # Marking system\r\n            for i in range(len(marks_list)):\r\n                if i == 1:\r\n                    continue\r\n                else:\r\n                    marks_list[i] -= 1\r\n            print(\"Score:-\")\r\n            print(\" \" + str(marks_list[0]) + \"               \" + str(marks_list[1]) + \"             \" + str(marks_list[2]) + \"            \" + str(marks_list[3]))\r\n            print()\r\n            print(\"                         Death number is: \" + str(round(death_num, 2)))\r\n            print(\"                     Congratulations \"+str(b)+\" won\")\r\n            print(\"---------------------------------------------------------------------------\")\r\n\r\n        elif player3_gap == min_player_gap:\r\n            # Marking system\r\n            for i in range(len(marks_list)):\r\n                if i == 2:\r\n                    continue\r\n                else:\r\n                    marks_list[i] -= 1\r\n            print(\"Score:-\")\r\n            print(\" \" + str(marks_list[0]) + \"               \" + str(marks_list[1]) + \"             \" + str(marks_list[2]) + \"            \" + str(marks_list[3]))\r\n            print()\r\n            print(\"                         Death number is: \" + str(round(death_num, 2)))\r\n            print(\"                     Congratulations \"+str(c)+\" won\")\r\n            print(\"---------------------------------------------------------------------------\")\r\n\r\n        elif player4_gap == min_player_gap:\r\n            # Marking system\r\n            for i in range(len(marks_list)):\r\n                if i == 3:\r\n                    continue\r\n                else:\r\n                    marks_list[i] -= 1\r\n            print(\"Score:-\")\r\n            print(\" \" + str(marks_list[0]) + \"               \" + str(marks_list[1]) + \"             \" + str(marks_list[2]) + \"            \" + str(marks_list[3]))\r\n            print()\r\n            print(\"                         Death number is: \" + str(round(death_num, 2)))\r\n            print(\"                     Congratulations \"+str(d)+\" won\")\r\n            print(\"---------------------------------------------------------------------------\")\r\n\r\n        else:\r\n            print(\"No one win\")\r\n        Round = Round + 1\r\n\r\n        if marks_list[0] == -10:\r\n            print(str(a)+\" Eliminated\")   #a = player1 in second round\r\n            e, f, g = b, c, d\r\n            break\r\n        elif marks_list[1] == -10:\r\n            print(str(b)+\" Eliminated\")\r\n            e, f, g = a, c, d\r\n            break\r\n        elif marks_list[2] == -10:\r\n            print(str(c)+\" Eliminated\")\r\n            e, f, g = a, b, d\r\n            break\r\n        elif marks_list[3] == -10:\r\n            print(str(d)+\" Eliminated\")\r\n            e, f, g = a, b, c\r\n            break\r\n\r\n    third_time(e,f,g)","repo_name":"ImKavinduSandaruwan/Beauty-Contest","sub_path":"second.py","file_name":"second.py","file_ext":"py","file_size_in_byte":5960,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"11997907865","text":"import unittest\nfrom kreis import Kreis\n\nclass Kreis_test(unittest.TestCase):\n\n    def test_umfang(self):\n        k = Kreis(3)\n        self.assertEqual(k.umfang(), 12.5663704)\n\n    def test_flaeche(self):\n        k = Kreis\n        self.assertEqual(k.flaeche(), 12.5663704)\n\nif __name__=='__main__':\n    Kreis_test.main()","repo_name":"Anthony9375/python-2020","sub_path":"src/Stunde 16/test_kreis.py","file_name":"test_kreis.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"4841211280","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n\n\"\"\"\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import Select\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import TimeoutException\nfrom time import sleep\nimport random\n\n\n\n\n\ndef fillscreen(browser, web_app, data=None, subj_type=\"SPO\", prod_type=\"FC\"):\n    nova_zadost_url = \"Processing/processStart.aspx?definitionID={AB260856-E6EA-42D4-998C-175099591E9A}\"\n    browser.get(web_app + nova_zadost_url)\n\n    # subj_type = \"PO\"  # SPO, FOP, PO\n    # prod_type = \"FC\"   # FC - úvěr , FO - operativní leasing\n\n    if data is None:\n        data = get_data(subj_type, prod_type)\n\n    xp_fc = '//span[contains(text(), \"' + \"úvěr\" + '\")]'\n    xp_fo = '//span[contains(text(), \"' + \"operativní leasing\" + '\")]'\n    try:\n        label_fc = WebDriverWait(browser, 4).until(\n            EC.presence_of_element_located((By.XPATH, xp_fc))\n        )\n        # print(\"element  loaded\")\n    except TimeoutException:\n        print(\"Time exceeded!\")\n    label_fo = browser.find_element_by_xpath(xp_fo)\n\n    if prod_type == \"FC\":\n        label_fc.click()\n    elif prod_type == \"FO\":\n        label_fo.click()\n\n    label_nove = browser.find_element_by_xpath(\n        '//span[contains(text(), \"' + \"nové\" + '\")]'\n    )\n    label_ojete = browser.find_element_by_xpath(\n        '//span[contains(text(), \"' + \"ojeté\" + '\")]'\n    )\n\n    if data[\"vehicle\"][\"VehicleStatus\"] == \"1\":\n        label_nove.click()\n    elif data[\"vehicle\"][\"VehicleStatus\"] == \"0\":\n        label_ojete.click()\n\n    label_osobni = browser.find_element_by_xpath(\n        '//span[contains(text(), \"' + \"osobní\" + '\")]'\n    )\n    label_uzitkove = browser.find_element_by_xpath(\n        '//span[contains(text(), \"' + \"užitkové\" + '\")]'\n    )\n\n    if data[\"vehicle\"][\"VehicleMode\"] == \"1\":\n        label_osobni.click()\n    elif data[\"vehicle\"][\"VehicleMode\"] == \"3\":\n        label_uzitkove.click()\n\n    # vyrobce = browser.find_element_by_id(\"__VehicleMaker\")\n    # Select(vyrobce).select_by_value(\"L\") #Lexus\n\n    sleep(0.2)\n    model_type = browser.find_element_by_id(\"__VehicleModelType\")\n    sleep(0.2)\n    Select(model_type).select_by_value(data[\"vehicle\"][\"VehicleModelType\"])\n\n    sleep(0.2)\n    model = browser.find_element_by_id(\"__VehicleModel\")\n    sleep(0.2)\n    Select(model).select_by_value(data[\"vehicle\"][\"VehicleModel\"])\n\n    sleep(0.2)\n    equipment = browser.find_element_by_id(\"__EquipmentLevel\")\n    sleep(0.2)\n    Select(equipment).select_by_value(data[\"vehicle\"][\"EquipmentLevel\"])\n\n    if data[\"vehicle\"][\"VehicleStatus\"] == \"0\":\n        cylinder_volume = browser.find_element_by_id(\"__CylinderVolume\")\n        if cylinder_volume.is_enabled():\n            cylinder_volume.send_keys(data[\"vehicle\"][\"CylinderVolume\"])\n\n        usage_start_date = browser.find_element_by_id(\"__UsageStartDate\")\n        usage_start_date.send_keys(data[\"vehicle\"][\"UsageStartDate\"])\n\n        covered_km = browser.find_element_by_id(\"__CoveredKilometres\")\n        covered_km.send_keys(data[\"vehicle\"][\"CoveredKilometres\"])\n\n    #    sleep(0.2)\n    #    subj_type_dd = browser.find_element_by_id(\"__SubjType\")\n    #    vehicle_operation = browser.find_element_by_id(\"__VehicleOperation\")\n    #    vat_r_buttons = browser.find_elements_by_name(\"__VATPayer\")\n    #    if subj_type == \"SPO\":\n    #        Select(subj_type_dd).select_by_value(\"1\")\n    #    elif subj_type == \"FOP\":\n    #        Select(subj_type_dd).select_by_value(\"2\")\n    #        sleep(0.2)\n    #        Select(vehicle_operation).select_by_value(\"1\")\n    #        vat_r_buttons[0].click() #platce DPH ano\n    #\n    #    elif subj_type == \"PO\":\n    #        Select(subj_type_dd).select_by_value(\"3\")\n    #        sleep(0.2)\n    #        Select(vehicle_operation).select_by_value(\"1\")\n    #        vat_r_buttons[0].click() #platce DPH ano\n\n    #    #cena doplňků\n    #    accessories_price = browser.find_element_by_id(\"__AccessoriesPrice\")\n    #    accessories_price.send_keys(data[\"vehicle\"][\"AccessoriesPriceVAT\"])\n    #\n    #    # sleva s dph\n    #    sleep(0.1)\n    #    discount_price = browser.find_element_by_id(\"__DiscountPrice\")\n    #    discount_price.send_keys(data[\"vehicle\"][\"DiscountPrice\"])\n    #\n\n    vehicle_usage_elem = browser.find_element_by_id(\"__VehicleUsage\")\n\n    if subj_type == \"SPO\":\n        Select(vehicle_usage_elem).select_by_value(\"1\")\n    elif subj_type in [\"FOP\", \"PO\"]:\n        Select(vehicle_usage_elem).select_by_value(\"0\")\n\n    # cena s dph\n    sleep(0.1)\n    calc_price = browser.find_element_by_id(\"__CalculationPrice\")\n    calc_price.send_keys(data[\"vehicle\"][\"CalculationPrice\"])\n\n    sleep(0.2)\n    dealer = browser.find_element_by_id(\"__TFSCDealer\")\n    sleep(0.2)\n    Select(dealer).select_by_value(data[\"contract\"][\"TFSCDealer\"])\n\n    sleep(0.4)\n    campaign_elem = browser.find_element_by_id(\"__CampaignCode\")\n\n    campaign_code = data[\"contract\"][\"CampaignCode\"]\n    css_sel = f\"option[value={campaign_code}]\"\n    try:\n        WebDriverWait(campaign_elem, 4).until(\n            EC.presence_of_element_located((By.CSS_SELECTOR, css_sel))\n        )\n        # print(\"Option loaded\")\n    except TimeoutException:\n        print(\"CampaignCode Time exceeded!\")\n    Select(campaign_elem).select_by_value(data[\"contract\"][\"CampaignCode\"])\n\n    sleep(0.4)\n    pocet_mesicu_elem = browser.find_element_by_id(\"__NoOfInstalmentsMax\")\n    WebDriverWait(pocet_mesicu_elem, 3).until(\n        EC.presence_of_element_located(\n            (By.CSS_SELECTOR, \"option:nth-child(2)\")\n        )  # css indexuje od 1\n    )\n    Select(pocet_mesicu_elem).select_by_index(1)\n\n    if prod_type == \"FO\":\n        sleep(0.4)\n        rocni_najezd = browser.find_element_by_id(\"__StepMileAgePerYear\")\n        Select(rocni_najezd).select_by_index(1)\n\n    sleep(0.2)\n\n    pojisteni = [\n        \"__InsuranceCoName\",\n        \"__MTPLInsuranceLimits\",\n        \"__InsuranceCoNameMotor\",\n        \"__MotorInsuranceParticipation\",\n    ]\n    for p in pojisteni:\n        elem = browser.find_element_by_id(p)\n        WebDriverWait(elem, 3).until(\n            EC.presence_of_element_located(\n                (By.CSS_SELECTOR, \"option:nth-child(2)\")\n            )  # css indexuje od 1\n        )\n        Select(elem).select_by_index(1)\n        sleep(0.1)\n\n    doplnkove_pojisteni = [\n        (\"__MotorSIWindscreenInsurance\", \"__MotorSIWindscreenLimit\"),\n        (\"__MotorSILuggageInsurance\", \"__MotorSILuggageLimit\"),\n        (\"__MotorSIVehicleRentInsurance\", \"__MotorSIVehicleRentDays\"),\n        (\"__MotorAPISeatInsurance\", \"__MotorAPISeatAmount\"),\n        (\"__MotorSIPlusInsurance\", \"__MotorSIPlusType\"),\n    ]\n\n    for p in doplnkove_pojisteni:\n        cb = browser.find_element_by_id(p[0])\n        cb.click()\n        sleep(0.2)\n        Select(browser.find_element_by_id(p[1])).select_by_index(1)\n    Select(browser.find_element_by_id(\"__MotorAPIMultiple\")).select_by_index(1)\n\n    _body = browser.find_element_by_css_selector(\"body\")\n    _body.send_keys(Keys.PAGE_DOWN)\n\n\ndef get_data(subj_type, prod_type):\n\n    data = {\n        \"applicant\": {},\n        \"contract\": {},\n        \"vehicle\": {\n            \"VehicleStatus\": \"0\",  # nové 1 , ojeté 0\n            \"VehicleMode\": \"1\",  # osobní 1, užitkové 3\n        },\n    }\n\n    if prod_type == \"FC\":\n        data[\"contract\"][\"OpType\"] = \"FC\"\n        data[\"contract\"][\"TFSCDealer\"] = \"1373\"\n        data[\"contract\"][\"CampaignCode\"] = \"KAMPAN_PRO_FC_GENIO_V_15_018\"\n        data[\"contract\"][\"NoOfInstalmentsMax\"] = \"72\"\n\n        data[\"vehicle\"][\"VehicleModelType\"] = \"AUHTS\"\n        data[\"vehicle\"][\"VehicleModel\"] = \"000636\"\n        data[\"vehicle\"][\"EquipmentLevel\"] = \"CT__AUHTSMC15_T0006363L__\"\n        data[\"vehicle\"][\"VehicleOperation\"] = \"1\"\n        data[\"vehicle\"][\"AccessoriesPriceVAT\"] = \"7500\"\n        data[\"vehicle\"][\"CalculationPrice\"] = \"456789\"\n        data[\"vehicle\"][\"DiscountPrice\"] = \"1586\"\n\n    elif prod_type == \"FO\":\n        data[\"contract\"][\"OpType\"] = \"FO\"\n        data[\"contract\"][\"TFSCDealer\"] = \"1373\"\n        data[\"contract\"][\"CampaignCode\"] = \"KAMPAN_PRO_FO_RENT_V_15_064\"\n        data[\"contract\"][\"NoOfInstalmentsMax\"] = \"72\"\n\n        data[\"vehicle\"][\"VehicleModelType\"] = \"AUR\"\n        data[\"vehicle\"][\"VehicleModel\"] = \"000621\"\n        data[\"vehicle\"][\"EquipmentLevel\"] = \"CT__AUR__MC15_T000621JH__\"\n        data[\"vehicle\"][\"VehicleOperation\"] = \"1\"\n        data[\"vehicle\"][\"AccessoriesPriceVAT\"] = \"7500\"\n        data[\"vehicle\"][\"DiscountPrice\"] = \"1586\"\n\n    if subj_type == \"SPO\":\n        data[\"applicant\"][\"SubjType\"] = \"1\"\n        data[\"applicant\"][\"VATPayer\"] = \"0\"\n    elif subj_type == \"FOP\":\n        data[\"applicant\"][\"SubjType\"] = \"2\"\n        data[\"applicant\"][\"VATPayer\"] = \"1\"\n    elif subj_type == \"PO\":\n        data[\"applicant\"][\"SubjType\"] = \"3\"\n        data[\"applicant\"][\"VATPayer\"] = \"1\"\n\n    if data[\"vehicle\"][\"VehicleStatus\"] == \"0\":\n        data[\"vehicle\"][\"UsageStartDate\"] = \"21.12.2018\"\n        data[\"vehicle\"][\"CylinderVolume\"] = \"1794\"\n        data[\"vehicle\"][\"CoveredKilometres\"] = \"5999\"\n\n        data[\"contract\"][\"CampaignCode\"] = \"KAMPAN_PRO_FC_KREDIT_V_15_031\"\n\n        # __UsageStartDate, __CylinderVolume, __CoveredKilometres\n\n    return data\n\n\n# browser, web_app = open_browser()\n# login()\n# fillscreen(browser, web_app)\n\n\"\"\"\nbrowser, web_app = open_browser()\nlogin(browser)\n\nfillscreen(browser, web_app)\n\n\nfillscreen(browser, web_app, subj_type = \"SPO\", prod_type = \"FO\")\nfillscreen(browser, web_app, subj_type = \"FOP\", prod_type = \"FO\")\nfillscreen(browser, web_app, subj_type = \"PO\", prod_type = \"FO\")\n\n\n\n\n\n\nfor subj_type in [\"SPO\", \"FOP\", \"PO\"]:\n    for prod_type in [\"FC\", \"FO\"]:\n        fillscreen(browser, web_app, \n                   subj_type = subj_type, prod_type = prod_type)\n        sleep(2)\n        browser.back()\n        sleep(1)\n        \n\"\"\"\n\n#    \"VehicleMaker\":\"T\",\n#    \"VehicleModelType\":\"AUHTS\",\n#    \"VehicleModel\":\"000636\",\n#    \"EquipmentLevel\": \"CT__AUHTSMC15_T0006363L__\",\n#    \"VehicleOperation\":\"1\",\n#    \"AccessoriesPriceVAT\":\"7500\",\n#    \"DiscountPrice\":\"1586\",\n#    \"TFSCDealer\":\"1373\",\n#    \"NoOfInstalmentsMax\":\"72\",\n#\n#\n#    \"InsuranceCoName\": \"\",\n#    \"MTPLInsuranceLimit\":\"\",\n#    \"InsuranceCoNameMotor\":\"\",\n#\n#    'MotorSIWindscreenInsurance': '1',\n#    'MotorSILuggageInsurance': '1',\n#    'MotorSIVehicleRentInsurance': '1',\n#    'MotorAPISeatInsurance': '1',\n#    'MotorSIPlusInsurance': '1',\n#    \"MotorAPIMultiple\":\"1\",\n#\n#    'MotorSIWindscreenLimit': '1',\n#    'MotorSILuggageLimit': '1',\n#    'MotorSIVehicleRentDays': '1',\n#    'MotorAPISeatAmount': '1',\n#    'MotorSIPlusType': '1',\n\n","repo_name":"mh70cz/py_old","sub_path":"toyota/toyota_old/selenium/zz_task_1.py","file_name":"zz_task_1.py","file_ext":"py","file_size_in_byte":10738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"39545399096","text":"from typing import List\n\nimport pytest\n\n\nclass Solution:\n    def trap(self, height: List[int]) -> int:\n        i = 0\n        n = len(height)\n        result = 0\n        while i < n - 1:\n            # Find the left height\n            if height[i] <= height[i + 1]:\n                i += 1\n            else:\n                l_height = height[i]\n                j = i + 1\n                r_height = 0\n                r_idx = j\n                # Find the right height thats:\n                # 1. Taller than left height, or\n                # 2. The rightmost tallest from [left+1, end]\n                while j < n and r_height < l_height:\n                    # Start counting right height only after we passed a valley\n                    if height[j] <= height[j - 1]:\n                        j += 1\n                        continue\n                    # Ignore some plateaus\n                    if height[j] >= r_height and height[j] != height[j - 1]:\n                        r_idx = j\n                        r_height = height[j]\n                    j += 1\n                # Can only accumulate water up to the lower height\n                h = min(l_height, r_height)\n                for idx in range(i + 1, r_idx):\n                    result += max(h - height[idx], 0)\n                i = r_idx\n\n        return result\n\n    def trap_lr_array(self, height: List[int]) -> int:\n        n = len(height)\n        # tallest height from left\n        l_heights = [height[0]]\n        # tallest height from right\n        r_heights = [height[-1]]\n        for i in range(1, n):\n            l_heights.append(max(l_heights[-1], height[i]))\n            r_heights.append(max(r_heights[-1], height[n - 1 - i]))\n        result = 0\n        for i, h in enumerate(height):\n            # area of each column, is the\n            # (lower of either heights) - (curr column height)\n            # ignore negative results\n            result += max(0, min(l_heights[i], r_heights[n - 1 - i]) - h)\n        return result\n\n    def trap_two_pointer(self, height: List[int]) -> int:\n        n = len(height)\n        l = 0\n        r = n - 1\n        max_l = 0\n        max_r = 0\n        result = 0\n        while l <= r:\n            # Process the lower side first\n            if height[l] <= height[r]:\n                if height[l] >= max_l:\n                    max_l = height[l]\n                else:\n                    result += max_l - height[l]\n                l += 1\n            else:\n                if height[r] >= max_r:\n                    max_r = height[r]\n                else:\n                    result += max_r - height[r]\n                r -= 1\n        return result\n\n    def trap_stack(self, height: List[int]) -> int:\n        stack = []\n        result = 0\n        i = 0\n        while i < len(height):\n            # Append to stack until we pass a valley\n            if not stack or height[i] <= height[stack[-1]]:\n                stack.append(i)\n                i += 1\n            else:\n                curr = stack.pop()\n                # only process if there's a valley, not just an uphill\n                if stack:\n                    result += (min(height[i], height[stack[-1]]) - height[curr]) * (\n                        i - stack[-1] - 1\n                    )\n        return result\n\n    def trap_stack_2(self, height: List[int]) -> int:\n        n = len(height)\n        stack = []\n\n        result = 0\n        for i in range(n):\n            if not stack or height[i] < height[stack[-1]]:\n                stack.append(i)\n            else:\n                while stack and height[i] >= height[stack[-1]]:\n                    center_height = height[stack.pop()]\n                    if not stack:\n                        continue\n                    width = i - stack[-1] - 1\n                    min_wall_height = min(height[i], height[stack[-1]])\n                    result += (min_wall_height - center_height) * width\n                stack.append(i)\n        return result\n\n\n@pytest.mark.parametrize(\n    \"case,expected\",\n    [\n        ([0, 1, 0, 2, 1, 0, 1, 3, 2, 1, 2, 1], 6),\n        ([4, 2, 0, 3, 2, 5], 9),\n    ],\n)\ndef test_solution(case, expected):\n    solution = Solution()\n    assert solution.trap(case) == expected\n    assert solution.trap_lr_array(case) == expected\n    assert solution.trap_two_pointer(case) == expected\n    assert solution.trap_stack(case) == expected\n","repo_name":"mammothb/lc-solutions","sub_path":"array_strings/42_trapping_rain_water/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":4359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"8690969815","text":"import numpy as np\nfrom scipy.sparse.linalg import cg\nimport time\nfrom itertools import islice\nfrom fractions import Fraction\nimport scipy.sparse.linalg as spl\nfrom scipy.linalg import solve\nimport matplotlib.pyplot as plt\n\ndef conjugate_grad(func, b, tol):\n    itr=0 \n    n = len(b)\n    x = np.zeros(n)\n    r = b - multiply(func,x)\n    d = np.copy(r)\n    rk_norm = np.linalg.norm(r)\n    curve_x = [x]\n    curve_r = [rk_norm]\n    curve_itr = [itr]\n    for i in range (n):\n        Ad = multiply(func,d)\n        rr = np.dot(r,r)\n        alpha = rr / d.dot(Ad)\n        x += alpha * d\n        r -= alpha * Ad\n        rk_norm = np.linalg.norm(r)\n        if rk_norm < tol:\n            break\n        else:\n            beta = np.dot(r,r)/ rr \n            d = r + (beta*d)\n            itr += 1\n            curve_x.append(x)\n            curve_r.append(rk_norm)\n            curve_itr.append(itr)   \n    return x, curve_itr,curve_r\n    \ndef multiply(f,x):\n    n = len(x)\n    r = np.zeros(n)\n    for i in range (n):\n        r[i] = sum([f(i, j) * x[j] for j in range(n)])\n    return r\n\ndef inverse(A,tol):\n    n= A.shape[0]\n    X = []\n    B = np.identity(n)\n    for i in range(n):\n        x,i,r = conjugate_grad(A,B[:,i],tol)\n        X.append(x)\n    return np.array(X).T\n\ndef delta(i,j):\n    return 1 if i==j else 0    \n\ndef func(i,j):\n    return 0.5*(delta(i+1,j)+delta(i-1,j)+(2*delta(i,j))) + (0.2**2)*delta(i,j)\n\nA = np.array([[func(i, j) for j in range(20)] for i in range(20)])\n\nprint(np.linalg.inv(A))\n#print(\"Conjugate gradient time\",time.process_time()-tic1)\n#print(\"The solution for Ax = b is {0} and error is {0}\".format(x,error))\nb = np.zeros(20)\nb[2] =1\nx,i,r = conjugate_grad(func,b,1e-6)\nerror = multiply(func,x)-b\nprint(x,\"\\n\",error)\nprint(A)\n\n## Inverse of the matrix\n#tic2 = time.process_time()\n#A_inv = inverse(A,tol)\n#print(\"The inverse of the matrix by conjugate gradient method is \\n {0}\".format(A_inv))\n#print(\"Conjugate gradient inverse time\",time.process_time()-tic2)\n\n#plt.plot(i,r)\n#plt.xlabel(\"Iteration\")\n#plt.ylabel(\"Residue\")\n#plt.show()\n","repo_name":"vaishakhi123/P452-Codes","sub_path":"vaishakhi/cg_fly.py","file_name":"cg_fly.py","file_ext":"py","file_size_in_byte":2055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"2878867037","text":"from django.shortcuts import render\nfrom django.views.generic import TemplateView\nfrom django.http import HttpResponse\n\nfrom watson_developer_cloud import LanguageTranslatorV3\nfrom watson_developer_cloud import WatsonApiException\n\nimport json\n\nDEFAULT_LANG = 'ja'\n\n\nclass IndexView(TemplateView):\n    template_name = 'translate/index.html'\n\n    def get(self, request, *args, **kwargs):\n        context = {'source_lang': DEFAULT_LANG}\n        return self.render_to_response(context) \n\n    def post(self, request, *args, **kwargs):\n        context = ''\n        if request.POST['source']:\n            try:\n                language_translator = LanguageTranslatorV3(\n                    version='2018-05-01',\n                    iam_apikey='',\n                    url='https://gateway-tok.watsonplatform.net/language-translator/api'\n                )\n\n                translation = language_translator.translate(\n                text=request.POST['source'],\n                source=request.POST['source_lang'],\n                target=request.POST['target_lang'],\n                ).get_result()\n\n                if translation:\n                    context = translation['translations'][0]['translation']\n\n            except WatsonApiException as ex:\n                print(\"Method failed with status code \" + str(ex.code) + \": \" + ex.message)\n                context = ''\n\n        return HttpResponse(context)\n        # return self.render_to_response(context)\n","repo_name":"Takumetal/language-translate","sub_path":"translate/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"2432176570","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\n# 创建噪声\nNUM = 300\nnp.random.seed(4)\nNoise = np.random.randn(NUM)  # 高斯分布随机噪声\nNoise_std = np.random.randn(NUM)  # 测量噪声\n\nX = [0] * NUM\nY = [0] * NUM\n\n# 创建数据集     Y=sin(0.2X)\nfor i in range(1, NUM):\n    X[i] = np.sin(0.2 * i)\n\nNoise_std_ = np.square(np.var(Noise_std))  # 求方差\nNoise_ = np.square(np.var(Noise))\n\nP = [0] * NUM  # 每次的最优偏差\nK = [0] * NUM  # 卡尔曼增益\n\nS = X + Noise_std  # 测量值\n\nfor i in range(1, NUM):\n    P[i] = np.square(P[i - 1]) + 0.1 * Noise_\n\n    K[i] = 0.1 * np.sqrt(P[i] / (Noise_std_ + P[i]))\n\n    Y[i] = Y[i - 1] + K[i] * (S[i] - Y[i - 1])\n\n    P[i] = np.sqrt((1 - K[i]) * P[i])\n    print(P[i])\n\nplt.plot(X, color='r', label='True')        # 数据集\nplt.plot(S, color='g', label='Scaling')     # 数据测量获取值（带噪声）\nplt.plot(Y, color='b', label='Est')         # 过滤后的数据\nplt.legend()\n\nplt.show()\n","repo_name":"fucilazo/Dragunov","sub_path":"卡尔曼滤波/陀螺仪.py","file_name":"陀螺仪.py","file_ext":"py","file_size_in_byte":969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"42417893988","text":"import hashlib\nimport mnemonic_code\nimport hmac\nfrom utils import pbkdf2\nimport binascii\nimport optparse\nimport sys\nfrom utility_adapters import bitcoin_secp256k1\nfrom utils import base58\nimport pubkey_address \nimport tkinter\nfrom functools import partial \nimport pyqrcode\nfrom ecdsa import SigningKey, SECP256k1\n\nmessage1 = []\nentries = []\n\nword_list = []\n\ndef on_button(y, entry, toplevel):\n        #print(\"%d: %s\" % (y, entry.get()))\n        entries[y] = entry.get()\n        if entries[y] in word_list:\n            message1[y].set('%d:correct' % (y+1))\n        else:\n            message1[y].set('%d:wrong' % (y+1))\n        toplevel.destroy()\n\ndef callback2(test_message):\n        #print('entries = %s' % entries)\n        joined_word_key_list = ' '.join(entries)\n        is_valid = mnemonic_code.verifyMnemonicWordCodeString(joined_word_key_list)\n        print('is valid = %r' % is_valid)\n        if is_valid:\n            test_message.set(\"Correct\")\n        else:\n            test_message.set(\"Wrong\")\n\ndef callback(y: int):\n    toplevel = tkinter.Toplevel()\n    message = tkinter.StringVar()\n    entry = tkinter.Entry(toplevel, textvariable=message, width=10)\n    button = tkinter.Button(toplevel, text=\"Get\", command=lambda y=y, entry=entry, toplevel=toplevel: on_button(y, entry, toplevel))\n    entry.pack()\n    button.pack()\n\nkey_selector = ''\ndef on_button_selector(entry, root):\n    global key_selector\n    key_selector = str(entry.get())\n    #print('key_selector = %s' % key_selector)\n    root.destroy()\n\n\n# implementation of BIP32\n# mainnet: 0x0488B21E public, 0x0488ADE4 private; testnet: 0x043587CF public, 0x04358394 private\n\ndef hash160(secret: bytes):\n        secrethash = hashlib.sha256(secret).digest()\n        h = hashlib.new('ripemd160')\n        h.update(secrethash)\n        secret_hash160 = h.digest()\n        return secret_hash160\n\ndef generateSeedFromStr(code: str, salt: str):\n        seed = pbkdf2.pbkdf2(hashlib.sha512, code, salt, 2048, 64)\n        #print('seed = %s' % bytes.decode(binascii.hexlify(seed)))\n        return seed\n\ndef generateMasterKeys(seed: bytes):\n        h = hmac.new(bytes(\"Bitcoin seed\", 'utf-8'),seed, hashlib.sha512).digest()\n        private_key = int(binascii.hexlify(h[0:32]), 16)\n        chaincode = h[32:64]\n        return private_key, chaincode\n\ndef encodedSerializationKeys(key: int, chaincode: bytes, depth: int, is_private: bool, is_mainnet: bool, child_index=0, parent_key=0):\n        if is_private == True:\n                if is_mainnet == True:\n                        version = b'\\x04\\x88\\xAD\\xE4'\n                else:\n                        version = b'\\x04\\x35\\x83\\x94'\n        else:\n                if is_mainnet == True:\n                        version = b'\\x04\\x88\\xB2\\x1E'\n                else:\n                        version = b'\\x04\\x35\\x87\\xCF'\n        if depth == 0:\n                # for root key\n                parent_fingerprint = b'\\x00\\x00\\x00\\x00'\n        else:\n                parent_fingerprint = hash160(binascii.unhexlify('%064x' % parent_key))[0:4]\n\n        key_b = b'\\x00' + binascii.unhexlify('%064x' % key)\n        child_number = binascii.unhexlify('%08x' % child_index)                \n        serialized_key = version + bytes([depth]) + parent_fingerprint + child_number + chaincode + key_b\n        #print('serialized key = %s' % bytes.decode(binascii.hexlify(serialized_key)))\n        h = hashlib.sha256(hashlib.sha256(serialized_key).digest()).digest()\n        #print('hash = %s' % bytes.decode(binascii.hexlify(h)))\n        serialized_key_with_checksum = int(binascii.hexlify(serialized_key + h[0:4]), 16)\n        #print('with checksum: %x' % serialized_key_with_checksum)\n        encoded_serialized_key = base58.base58_encode(serialized_key_with_checksum)\n\n        return encoded_serialized_key\n\ndef generateChildAtIndex(privkey: int, chaincode: bytes, index: int):\n        if index >= (1<<31):\n                # hardened\n                #print('hardened')\n                h = hmac.new(chaincode, b'\\x00' + binascii.unhexlify('%064x' % privkey) + binascii.unhexlify('%08x' % index), hashlib.sha512).digest()\n                #print('child seed = %s' % bytes.decode(binascii.hexlify(b'\\x00' + binascii.unhexlify('%064x' % privkey) + binascii.unhexlify('%08x' % index))))\n                #print('h = %s' % bytes.decode(binascii.hexlify(h)))\n        else:\n                # normal\n                privkey_s = '%064x' % privkey\n                privkey_b = binascii.unhexlify(privkey_s)\n                sk = SigningKey.from_string(privkey_b, curve=SECP256k1)\n                vk = sk.get_verifying_key()\n\n                full_pubkey_b = b'\\x04' + vk.to_string()\n                pubkey = pubkey_address.compressPubkey(full_pubkey_b)\n                h = hmac.new(chaincode, pubkey + binascii.unhexlify('%08x' % index), hashlib.sha512).digest()\n        childprivkey = (int(binascii.hexlify(h[0:32]), 16) + privkey) % bitcoin_secp256k1.N\n        #print('h[0:32] = %x' % int(binascii.hexlify(h[0:32]), 16))\n        #print('privkey = %x' % privkey)\n        child_chaincode = h[32:64]\n        return childprivkey, child_chaincode\n\ndef generatePrivkeyPubkeyPair(keypath: str, seed: bytes, compressed: bool):\n        keypath_list = keypath.replace(' ', '').split('/')\n        print(keypath_list)\n        if keypath_list[0] != 'm':\n                return None\n        for key in keypath_list:\n                if key == 'm':\n                        privkey, chaincode = generateMasterKeys(seed)\n                else:\n                        if \"'\" in key:\n                                index = int(key[:-1]) + (1<<31)\n                        else:\n                                index = int(key)\n                        privkey, chaincode = generateChildAtIndex(privkey, chaincode, index)\n                #print('key = %s' % key)\n                #print('private key = %x, chaincode = %s' % (privkey, bytes.decode(binascii.hexlify(chaincode))))\n        privkey_s = '%064x' % privkey\n        privkey_b = binascii.unhexlify(privkey_s)\n        sk = SigningKey.from_string(privkey_b, curve=SECP256k1)\n        vk = sk.get_verifying_key()\n\n        full_pubkey_b = b'\\x04' + vk.to_string()\n        pubkey = pubkey_address.compressPubkey(full_pubkey_b)\n#        print('privkey = %x, pubkey = %s' % (privkey, bytes.decode(binascii.hexlify(pubkey))))\n        return privkey, pubkey\n\nif __name__ == '__main__':\n        parser = optparse.OptionParser(usage=\"python3 hd_wallet.py -s <Salt>\")\n        parser.add_option('-s', '--salt', action='store', dest='salt', help='Add salt to secret')\n        (args, _) = parser.parse_args()\n        if args.salt == None:\n                print (\"Missing required argument\")\n                sys.exit(1)\n\n        word_list = mnemonic_code.getMnemonicWordList()\n\n        #top = tkinter.Tk()\n        top = tkinter.Toplevel()\n        top.title(\"RUN ON START TEST\")\n        #frame = tkinter.Frame(top)\n        testvar = tkinter.StringVar()\n        testvar.set(\"Test\")\n        get = tkinter.Button(top, textvariable=testvar, command=lambda:callback2(testvar))\n        #get = tkinter.Button(top, text='Get', command=lambda:callback2(testvar))\n\n        for y in range(0,12):\n                entries.append('')\n                message1.append(tkinter.StringVar())\n                message1[y].set('%d:unset' % (y+1))\n                #b = tkinter.Button(frame, textvariable=message1[y],   command=lambda y=y: callback(y))\n                b = tkinter.Button(textvariable=message1[y],   command=lambda y=y: callback(y))\n                b.grid(row=0,column=y)\n        #frame.pack()\n        get.pack()\n\n        top.mainloop()\n\n        #mnemonic_code_list = mnemonic_code.getMnemonicWordCodeString(12)\n        #mnemonic_code = \" \".join(mnemonic_code_list)\n        mnemonic_code_str = \" \".join(entries)\n        print('is valid = %r' % mnemonic_code.verifyMnemonicWordCodeString(mnemonic_code_str))\n        #print('mnemonic code: %s' % mnemonic_code)\n        seed_b = generateSeedFromStr(mnemonic_code_str, \"mnemonic\" + args.salt)\n\n        master_privkey, master_chaincode = generateMasterKeys(seed_b)\n\n        if master_privkey == 0 or master_privkey >= 0xFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFEFFFFFC2F:\n                print('invalid master key')\n\n        #print('master private key = %x, master chaincode = %s' % (master_privkey, bytes.decode(binascii.hexlify(master_chaincode))))\n        encoded_serialized_key = encodedSerializationKeys(master_privkey, master_chaincode, 0, True, True)\n        #print('Encoded Serialized Key = %s' % encoded_serialized_key)\n\n        # for hardened\n        child_privkey, child_chaincode = generateChildAtIndex(master_privkey, master_chaincode, 1<<31)\n        #print('child private key = %x, child chaincode = %s' % (child_privkey, bytes.decode(binascii.hexlify(child_chaincode))))\n\n        # for normal\n        child_privkey, child_chaincode = generateChildAtIndex(master_privkey, master_chaincode, 0)\n        #print('child private key = %x, child chaincode = %s' % (child_privkey, bytes.decode(binascii.hexlify(child_chaincode))))\n\n        #print('seed = %s' % bytes.decode(binascii.hexlify(seed_b)))\n\n        #privkey_i, chaincode = generatePrivkeyPubkeyPair('m / 5\\'/ 6', seed_b, True)\n        #key_selector = 'm/10/2'\n        root = tkinter.Tk()\n        selector = tkinter.StringVar()\n        entry = tkinter.Entry(root, textvariable=selector, width=20)\n        button = tkinter.Button(root, text=\"Get\", command=lambda entry=entry, root=root: on_button_selector(entry, root))\n        entry.pack()\n        button.pack()\n        root.mainloop()\n\n        #print('inside main: key_selector = %s' % key_selector)\n        privkey_i, chaincode = generatePrivkeyPubkeyPair(key_selector, seed_b, True)\n        privkey_wif = pubkey_address.privkeyHex2Wif(privkey_i)\n        address_s = pubkey_address.pubkey2address(chaincode)\n        #print('keys at m/5\\'/6: private key = %s, public key = %s, addess = %s' % (privkey_wif, bytes.decode(binascii.hexlify(chaincode)), address_s))\n        print('keys at %s: privkey_i = %x, private key = %s, public key = %s, addess = %s' % (key_selector, privkey_i, privkey_wif, bytes.decode(binascii.hexlify(chaincode)), address_s))\n\n\n#        root = tkinter.Tk()\n#        root.attributes(\"-fullscreen\", True)\n#        T = tkinter.Text(root, height=10, width=100, font=(\"Helvetica\", 32))\n#        T.pack()\n#        T.insert(tkinter.END, address_s)\n#        root.mainloop()\n\n        code = pyqrcode.create(address_s)\n        code_xbm = code.xbm(scale=5)\n        top = tkinter.Tk()\n        top.attributes(\"-fullscreen\", True)\n        code_bmp = tkinter.BitmapImage(data=code_xbm)\n        code_bmp.config(foreground=\"black\")\n        code_bmp.config(background=\"white\")\n        label = tkinter.Label(image=code_bmp)\n        label.pack()\n        top.mainloop()\n\n        if input('address = ') == address_s:\n            print('address is valid')\n        if input('public key = ') == bytes.decode(binascii.hexlify(chaincode)):\n            print('public key is valid')\n        if input('private key = ') == privkey_wif:\n            print('private key is valid')\n\n\n#if __name__ == '__main__':\n#        mnemonic_code = input\n","repo_name":"vizeet/SimpleBitcoinWallet","sub_path":"hd_wallet.py","file_name":"hd_wallet.py","file_ext":"py","file_size_in_byte":11183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"26380912917","text":"from sendgrid import sendgrid\nfrom sendgrid import message\n\nimport webapp2\nimport jinja2\nimport os\nimport logging\n\nimport ConfigParser\n\nfrom google.appengine.ext import ndb\nimport tusers\n\nfrom models import PreRegRecord\n\nJINJA_ENVIRONMENT = jinja2.Environment(\n    loader=jinja2.FileSystemLoader(os.path.dirname(__file__)),\n    extensions=['jinja2.ext.autoescape'],\n    autoescape=True)\n\n\nclass RecipientsHandler(webapp2.RequestHandler):\n  def get(self):\n    user = tusers.get_current_user()\n\n    if user:\n      #Get the requested tournament\n      tid = self.request.get('t')\n      key = ndb.Key('Tournament', int(tid))\n      t = key.get()\n\n      if (t and user.key in t.owner):\n        reg = t.preRegRecord().get()\n        if (reg == None):\n          reg = PreRegRecord(parent=key)\n          reg.open = False\n          reg.put()\n\n        iJudges = reg.indyJudges()\n        teams = reg.teams()\n        institutions = reg.institutions()\n\n\n        template_values = {\n          'user' : user,\n          't' : t,\n          'logout' : tusers.create_logout_url('/'),\n          'r' : reg,\n          'ijudges' : iJudges,\n          'jcount' : reg.totalJudgeCount(),\n          'teams' : teams,\n          'institutions' : institutions,\n          'tcount' : reg.totalTeamCount(),\n          'icount' : institutions.count(limit=500),\n        }\n        template = JINJA_ENVIRONMENT.get_template('view/recipients.html')\n        self.response.write(template.render(template_values))\n\n      else:\n        self.redirect('/tournaments')\n    else:\n      self.redirect(tusers.create_login_url(self.request.uri))\n\nclass SendHandler(webapp2.RequestHandler):\n  def post(self):\n    user = tusers.get_current_user()\n\n    if user:\n      #Get the requested tournament\n      tid = self.request.get('t')\n      key = ndb.Key('Tournament', int(tid))\n      t = key.get()\n\n      if (t and user.key in t.owner):\n        reg = t.preRegRecord().get()\n        if (reg == None):\n          reg = PreRegRecord(parent=key)\n          reg.open = False\n          reg.put()\n\n        iJudges = reg.indyJudges()\n        teams = reg.teams()\n        institutions = reg.institutions()\n\n        mail_from = \"Tournatrack\"\n        subject = t.name\n        body = self.request.get('mbody')\n        body += \"\\n******\\nYou are receiving this email because you registered for the %s at http://tournatrack.com\"%t.name\n\n        logging.info(body)\n\n        #Get the username and password from the config file\n        Config = ConfigParser.ConfigParser()\n        Config.read('settings.ini')\n\n        uname = Config.get('mail', 'uname')\n        pw = Config.get('mail', 'password')\n\n        #Open connection to SendGrid\n        sendGrid = sendgrid.SendGridClient(uname, pw, secure=True)\n\n        #Create the message\n        comm = message.Mail(from_email=mail_from, from_name=\"Tournatrack\", subject=subject, text=body)\n\n        #Add teams if necessary\n        if self.request.get('teams'):\n          for t in teams:\n            owner = t.user.get()\n            comm.add_to(owner.preferredEmail())\n\n            if owner.full_name:\n              comm.add_to_name(owner.full_name)\n            else:\n              comm.add_to_name(\"Debater\")\n\n        #Add judges if necessary\n        if self.request.get('judges'):\n          for j in iJudges:\n            owner = j.user.get()\n            comm.add_to(owner.preferredEmail())\n\n            if owner.full_name:\n              comm.add_to_name(owner.full_name)\n            else:\n              comm.add_to_name(\"Debater\")\n\n        #Add institutions if necessary\n          logging.info(\"Going to institutions\")\n          for i in institutions:\n            owner = i.user.get()\n            comm.add_to(owner.preferredEmail())\n\n            if owner.full_name:\n              comm.add_to_name(owner.full_name)\n            else:\n              comm.add_to_name(\"Debater\")\n\n\n        #Add the owner\n        comm.add_to(user.preferredEmail())\n        comm.add_to_name(user.full_name)\n\n        logging.info(sendGrid.send(comm))\n\n\n        template_values = {\n          'user' : user,\n          't' : t,\n          'logout' : tusers.create_logout_url('/'),\n        }\n        template = JINJA_ENVIRONMENT.get_template('view/message_sent.html')\n        self.response.write(template.render(template_values))\n\n      else:\n        self.redirect('/tournaments')\n    else:\n      self.redirect(tusers.create_login_url(self.request.uri))\n\n\napp = webapp2.WSGIApplication([\n  ('/comms', RecipientsHandler),\n  ('/comms/send', SendHandler)\n], debug=True)\n","repo_name":"sarrionandia/tournatrack","sub_path":"comms.py","file_name":"comms.py","file_ext":"py","file_size_in_byte":4503,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"}
+{"seq_id":"1607048491","text":"import logging\nimport os\n\nfrom scraper.io import filter_persons, get_data, get_files\n\nlogger = logging.getLogger(__name__)\n\n\ndef run(directory, names_source):\n    files_with_meta = get_files(directory)\n    persons = filter_persons(get_data(names_source))\n    filenames = rename(files_with_meta, persons)\n    rewrite(filenames)\n    return directory\n\n\ndef rename(filenames, persons):  # noqa: WPS231\n\n    for file in filenames:\n        for person, info in persons.items():\n            if file['last_name'] in person:\n                person_id = info.get('person_id')\n                section_id = info.get('section_id')\n\n                if person_id and person_id != 'NULL':\n                    new_name = person_id\n                else:\n                    new_name = 'section_{0}'.format(section_id)\n\n                new_path = _change_path(file['path'], new_name)\n                file['new_path'] = new_path\n\n    return filenames\n\n\ndef _change_path(old_path, new_name):\n    parent_dir = os.path.abspath(os.path.join(old_path, os.pardir))\n    return os.path.join(parent_dir, new_name)\n\n\ndef rewrite(files):\n    for file in files:\n        old_path = file.get('path')\n        new_path = file.get('new_path')\n        if new_path:\n            os.rename(old_path, '{0}.jpg'.format(new_path))\n            logger.info('File {0} was renamed to {1}'.format(old_path, new_path))  # noqa: E501\n","repo_name":"sgmdlt/wsf_test","sub_path":"scraper/rename.py","file_name":"rename.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"24180151285","text":"import datetime\nimport pytz\nfrom datetime import timedelta\nfrom django.utils import timezone as util_timezone\n\nfrom django.db.models import Sum, Avg, Count, F, ExpressionWrapper, FloatField\nfrom django.db.models.functions import TruncDay, TruncYear, TruncQuarter, TruncMonth, TruncWeek\n\n\nfrom api.models import *\nfrom pytz import timezone\n\ndef summary_5_view(user, startdate, enddate, viewformat):\n    machine = Machine.objects.filter(user=user)\n    machine_list = []\n    for i in machine:\n        machine_list.append(i.toDic()[\"machine_id\"])\n    #----Souren---24/04/21-------#    \n    machine_details = Machine.objects.get(machine_id=machine_list[0])\n    starttime = machine_details.shift1_start_time\n    machine_total_shift = machine_details.machine_total_shift\n    if machine_total_shift == 2:\n        endtime=machine_details.shift2_end_time\n    else:\n        endtime = machine_details.shift3_end_time\n    shift1_time_duration = machine_details.shift1_time_duration\n    shift2_time_duration = machine_details.shift2_time_duration\n    if machine_total_shift==3:\n        shift3_time_duration = machine_details.shift3_time_duration\n    else:\n        shift3_time_duration=0\n    if (shift1_time_duration+shift2_time_duration+shift3_time_duration==24.00):\n        enddate = (datetime.datetime.strptime(enddate, '%Y-%m-%d')+timedelta(days=1)).strftime('%Y-%m-%d')\n\n    local_zone = MachineTimeZone.objects.get(machine_id=machine_list[0]).machine_timezone    \n    \n    starttimestamp = int(timezone(local_zone).localize(datetime.datetime.strptime(\n        str(startdate) + ' ' + str(starttime), \"%Y-%m-%d %H:%M:%S\")).timestamp())\n    endtimestamp = int(timezone(local_zone).localize(datetime.datetime.strptime(\n        str(enddate) + ' ' + str(endtime), \"%Y-%m-%d %H:%M:%S\")).timestamp())\n\n    if viewformat == 'Day' or viewformat == 'Week' or viewformat == 'Month':\n        weightgainloss = PipeDataProcessed.objects.annotate(group_day=TruncDay('site_local_time')).values('group_day').annotate(weightloss__sum__kg=ExpressionWrapper(Sum(F('weightloss') * 1.0 / 1000), output_field=FloatField()), weightgain__sum__kg=ExpressionWrapper(Sum(F('weightgain') * 1.0 / 1000), output_field=FloatField(\n        )), weight__sum__kg=ExpressionWrapper(Sum(F('weight') * 1.0 / 1000), output_field=FloatField()), weight_net__sum__kg=ExpressionWrapper(Sum(F('weightgain') + F('weightloss')) * 1.0 / 1000, output_field=FloatField())).filter(timestamp__gte=starttimestamp, timestamp__lte=endtimestamp, machine_id__in=machine_list)\n        weightgainloss.query.clear_ordering(force_empty=True)\n    elif viewformat == 'Quarter' or viewformat == 'Year':\n        weightgainloss = PipeDataProcessed.objects.annotate(group_day=TruncMonth('site_local_time')).values('group_day').annotate(weightloss__sum__kg=ExpressionWrapper(Sum(F('weightloss') * 1.0 / 1000), output_field=FloatField()), weightgain__sum__kg=ExpressionWrapper(Sum(F('weightgain') * 1.0 / 1000), output_field=FloatField(\n        )), weight__sum__kg=ExpressionWrapper(Sum(F('weight') * 1.0 / 1000), output_field=FloatField()), weight_net__sum__kg=ExpressionWrapper(Sum(F('weightgain') + F('weightloss')) * 1.0 / 1000, output_field=FloatField())).filter(timestamp__gte=starttimestamp, timestamp__lte=endtimestamp, machine_id__in=machine_list)\n        weightgainloss.query.clear_ordering(force_empty=True)\n    else:\n        raise Exception(\"Please select proper format\")\n\n    print(starttimestamp, endtimestamp, viewformat)\n\n    weightgainlossdic = {\n        \"weightdate\": [],\n        \"weight\": [],\n        \"weightgain\": [],\n        \"weightloss\": [],\n        \"netgain\": []\n    }\n\n    weight = weightgain = weightloss = netgain = 0\n    index = 0\n    for i in weightgainloss:\n        if viewformat == 'Day' or viewformat == 'Week' or viewformat == 'Month':\n            weightgainlossdic[\"weightdate\"].append(\n                i['group_day'].strftime(\"%d/%m/%Y %A\"))\n        else:\n            weightgainlossdic[\"weightdate\"].append(\n                i['group_day'].strftime(\"%B, %Y\"))\n        weightgainlossdic[\"weight\"].append(\n            round(i['weight__sum__kg'], 2) if i['weight__sum__kg'] != None else 0)\n        weight += i['weight__sum__kg']\n        weightgainlossdic[\"weightloss\"].append(\n            round(i['weightloss__sum__kg'], 2) if i['weightloss__sum__kg'] != None else 0)\n        weightloss += i['weightloss__sum__kg']\n        weightgainlossdic[\"weightgain\"].append(\n            round(i['weightgain__sum__kg'], 2) if i['weightgain__sum__kg'] != None else 0)\n        weightgain += i['weightgain__sum__kg']\n        weightgainlossdic[\"netgain\"].append(\n            round(i['weight_net__sum__kg'], 2) if i['weight_net__sum__kg'] != None else 0)\n        netgain += i['weight_net__sum__kg']\n        index += 1\n        if (viewformat == 'Month' or viewformat == 'Week') and index == 7:\n            weightgainlossdic[\"weightdate\"].append(\"Total\")\n            weightgainlossdic[\"weight\"].append(\n                0 if weight == None else round(weight, 2))\n            weightgainlossdic[\"weightgain\"].append(\n                0 if weightgain == None else round(weightgain, 2))\n            weightgainlossdic[\"weightloss\"].append(\n                0 if weightloss == None else round(weightloss, 2))\n            weightgainlossdic[\"netgain\"].append(\n                0 if netgain == None else round(netgain, 2))\n            \n            weightgainlossdic[\"weightdate\"].append(\"%\")\n            weightgainlossdic[\"weight\"].append('')\n            weightgainlossdic[\"weightgain\"].append(\n                0 if weightgain == None or weight == 0 else round((weightgain / weight) * 100, 2))\n            weightgainlossdic[\"weightloss\"].append(\n                0 if weightloss == None or weight == 0 else round((weightloss / weight) * 100, 2))\n            weightgainlossdic[\"netgain\"].append(\n                0 if netgain == None or netgain == 0 else round((netgain / weight) * 100, 2))\n\n            weightgainlossdic[\"weightdate\"].append(\"\")\n            weightgainlossdic[\"weight\"].append('')\n            weightgainlossdic[\"weightgain\"].append('')\n            weightgainlossdic[\"weightloss\"].append('')\n            weightgainlossdic[\"netgain\"].append('')\n\n            weightgainlossdic[\"weightdate\"].append(\"\")\n            weightgainlossdic[\"weight\"].append('')\n            weightgainlossdic[\"weightgain\"].append('')\n            weightgainlossdic[\"weightloss\"].append('')\n            weightgainlossdic[\"netgain\"].append('')\n\n            weight = weightgain = weightloss = netgain = 0\n            index = 0\n\n    if ((viewformat == 'Month' or viewformat == 'Week') and index != 7) or ((viewformat == 'Day' or viewformat == 'Year' or viewformat == 'Quarter')):\n        weightgainlossdic[\"weightdate\"].append(\"Total\")\n        weightgainlossdic[\"weight\"].append(0 if weight == None else round(weight, 2))\n        weightgainlossdic[\"weightgain\"].append(\n            0 if weightgain == None else round(weightgain, 2))\n        weightgainlossdic[\"weightloss\"].append(\n            0 if weightloss == None else round(weightloss, 2))\n        weightgainlossdic[\"netgain\"].append(\n            0 if netgain == None else round(netgain, 2))\n\n        weightgainlossdic[\"weightdate\"].append(\"%\")\n        weightgainlossdic[\"weight\"].append('')\n        weightgainlossdic[\"weightgain\"].append(\n            0 if weightgain == None or weightgain == 0 else round((weightgain / weight) * 100, 2))\n        weightgainlossdic[\"weightloss\"].append(\n            0 if weightloss == None or weightloss == 0 else round((weightloss / weight) * 100, 2))\n        weightgainlossdic[\"netgain\"].append(\n            0 if netgain == None or netgain == 0 else round((netgain / weight) * 100, 2))\n\n    return 'summary_5.html', {\n        \"machine\": machine,\n        \"startdate\": startdate,\n        \"enddate\": enddate,\n        \"viewformat\": viewformat,\n        \"summary_name\": \"MIS 5: TOTAL OUTPUT VS WEIGHTGAIN / LOSS\",\n        \"pipecountsum\": zip(\n            weightgainlossdic[\"weightdate\"],\n            weightgainlossdic[\"weight\"],\n            weightgainlossdic[\"weightgain\"],\n            weightgainlossdic[\"weightloss\"],\n            weightgainlossdic[\"netgain\"]\n        )\n    }\n","repo_name":"AmitMozitronics/IPPT_WebApp","sub_path":"frontend/summaryview_v2/total_output_vs_weight_gain_or_loss_v2.py","file_name":"total_output_vs_weight_gain_or_loss_v2.py","file_ext":"py","file_size_in_byte":8157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"40606980534","text":"import torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom models.layers import get_all_indices, Embedding, MLP\n\nclass GraphAttentionLayer(nn.Module):\n\n    def __init__(self, ninfeat, noutfeat, nhead, dropout, alpha):\n        super().__init__()\n        self.nhead = nhead\n\n        self.W = nn.ParameterList()\n        self.a = nn.ModuleList()\n        for _ in range(nhead):\n            self.W.append(nn.Parameter(torch.zeros(size=(ninfeat, noutfeat))))\n            nn.init.xavier_uniform_(self.W[-1].data, gain=1.414)\n            self.a.append(nn.Linear(2*noutfeat, 1, bias=False))\n\n        self.dropout = nn.Dropout(p=dropout)\n        self.leakyrelu = nn.LeakyReLU(alpha)\n\n    def forward(self, x, adj):\n        '''\n        :param x:       FloatTensor B*F*E1\n        :param adj:     FloatTensor F*F\n        :return:        FloatTensor B*F*(headxE2)\n        '''\n        nfield = x.size(1)\n        zero_vec = -9e15 * torch.ones_like(adj)\n        mask = torch.where(adj > 0, adj, zero_vec)                                  # F*F\n\n        h_list = []\n        vi_indices, vj_indices = get_all_indices(nfield)\n        for head in range(self.nhead):\n            h = torch.einsum('bfi,io->bfo', x, self.W[head])                        # B*F*E2\n            vi, vj = h[:, vi_indices], h[:, vj_indices]\n            hh = torch.cat([vi, vj], dim=2)                                         # B*(FxF)*(2xE2)\n\n            e = self.leakyrelu(self.a[head](hh))                                    # B*(FxF)*1\n\n            attn = torch.einsum('bxy,xy->bxy', e.view(-1, nfield, nfield), mask)    # B*F*F\n            attn = self.dropout(F.softmax(attn, dim=-1))                            # B*F*F\n\n            h_prime = torch.einsum('bxy,bye->bxe', attn, h)                         # B*F*E2\n            h_list.append(h_prime)\n\n        return torch.cat(h_list, dim=2)                                             # B*F*(headxE2)\n\nclass GATModel(nn.Module):\n    \"\"\"\n        Model:  Graph Attention Networks\n        Ref:    P Veličković, et al. Graph Attention Networks, 2018.\n        \"\"\"\n    def __init__(self, nfield, nfeat, nemb, gat_layers, gat_hid, mlp_layers, mlp_hid, dropout, alpha=0.2, nhead=8):\n        super().__init__()\n        self.embedding = Embedding(nfeat, nemb)\n\n        self.gat_layers = gat_layers\n        self.gats = torch.nn.ModuleList()\n        ninfeat = nemb\n        for _ in range(gat_layers):\n            self.gats.append(GraphAttentionLayer(ninfeat, gat_hid, nhead, dropout, alpha))\n            ninfeat = nhead*gat_hid\n\n        self.dropout = nn.Dropout(p=dropout)\n        self.affine = MLP(nfield*ninfeat, mlp_layers, mlp_hid, dropout)\n\n    def forward(self, x, adj=None):\n        \"\"\"\n        :param x:   {'id': LongTensor B*F, 'value': FloatTensor B*F}\n        :param adj:     FloatTensor F*F, default fully connected\n        :return:    y of size B, Regression and Classification (+sigmoid)\n        \"\"\"\n        h = self.embedding(x)                                                   # B*F*E\n        if adj is None:\n            adj = torch.ones((h.size(1), h.size(1)), dtype=h.dtype, device=h.device)\n        for l in range(self.gat_layers):\n            h = self.gats[l](h, adj)                                            # B*F*(nheadxgat_hid)\n            h = F.elu(self.dropout(h))\n\n        y = self.affine(h.view(h.size(0), -1))                                  # B*1\n        return y.squeeze(1)                                                     # B","repo_name":"nusdbsystem/ARM-Net","sub_path":"models/gat.py","file_name":"gat.py","file_ext":"py","file_size_in_byte":3470,"program_lang":"python","lang":"en","doc_type":"code","stars":64,"dataset":"github-code","pt":"85"}
+{"seq_id":"555557152","text":"# Created by Ben Kung\nimport pygame\n\nimport item\n\n\n# Sort functions that take a list of items and sort by specified method\n\n\n# helper function that removes the nulls from the list\n# and returns the count of number of nulls removed\ndef remove_null(list):\n    num = 0\n    null_pos = []\n    # loop through and find the position of all nulls\n    for index in range(0, len(list)):\n        if list[index] is None:\n            num += 1\n            null_pos.append(index)\n    # flip order to not create problems with pop()\n    null_pos.reverse()\n    # remove all the nulls\n    for index in range(0, len(null_pos)):\n        list.pop(null_pos[index])\n    return num\n\n\n# sort min to max id, creates new list\n# uses insertion sort algorithm\n# takes a list of item and returns a new list of items sorted\ndef sort_by_id(list):\n    new_list = []\n    # remove nulls to sort\n    null_num = remove_null(list)\n    # insertion sort\n    while len(list) >= 1:\n        index = 0\n        for place in range(0, len(list)):\n            if list[place].get_id() < list[index].get_id():\n                index = place\n        new_list.append(list[index])\n        list.pop(index)\n    # add nulls back\n    for index in range(0, null_num):\n        new_list.append(None)\n\n    return new_list\n\n\n# sort by name A-Z, creates new list\n# uses insertion sort\ndef sort_by_name(list):\n    new_list = []\n    # remove nulls to sort\n    null_num = remove_null(list)\n    # insertion sort\n    while len(list) >= 1:\n        index = 0\n        for place in range(0, len(list)):\n            if list[place].get_name() < list[index].get_name():\n                index = place\n        new_list.append(list[index])\n        list.pop(index)\n    # add nulls back\n    for index in range(0, null_num):\n        new_list.append(None)\n    return new_list\n\n\n# sort by item type A-Z, creates new list\n# uses insertion sort\ndef sort_by_type(list):\n    new_list = []\n    null_num = remove_null(list)\n    while len(list) >= 1:\n        index = 0\n        for place in range(0, len(list)):\n            if list[place].get_type() < list[index].get_type():\n                index = place\n        new_list.append(list[index])\n        list.pop(index)\n    # add nulls back\n    for index in range(0, null_num):\n        new_list.append(None)\n    return new_list\n\n\n# sort by item quantity lowest to highest\n# uses insertion sort\ndef sort_by_number(list):\n    new_list = []\n    null_num = remove_null(list)\n\n    dictionary = {}\n    # count the total number of each item and stores as a dictionary\n    for item in range(0, len(list)):\n        if list[item].get_name() in dictionary:\n            dictionary[list[item].get_name()] += list[item].get_num()\n        else:\n            dictionary[list[item].get_name()] = list[item].get_num()\n    for x, y in dictionary.items():\n        print(x, y)\n\n    # perform insertion sort\n    while len(dictionary) >= 1:\n        # get the minimum item name\n        key_min = min(dictionary.keys(), key=(lambda k: dictionary[k]))\n        print(key_min, \"min key\", dictionary[key_min], \"value\")\n        pop_list = [] # store all places to pop\n        # add all instances of min item to front of the list\n        for item in range(0, len(list)):\n            if list[item].get_name() == key_min:\n                new_list.append(list[item])\n                pop_list.append(item)\n        # remove item from list\n        pop_list.sort(reverse = True)\n        for index in range(0, len(pop_list)):\n            list.pop(pop_list[index])\n        # remove key\n        dictionary.pop(key_min)\n\n    # add nulls back\n    for index in range(0, null_num):\n        new_list.append(None)\n    return new_list\n\n\n# currently using placeholder boolean to determine\n# whether is highlighted, will use later method to\n# determine later\n# sort using bucket sort algorithm\ndef sort_by_highlight(list):\n    highlight_list = []\n    non_highlight_list = []\n    null_num = remove_null(list)\n    # do a first pass to separate highlighted items from non highlighted\n    for i in range(0, len(list)):\n        if list[i].highlight_sort:\n            highlight_list.append(list[i])\n        else:\n            non_highlight_list.append(list[i])\n    # add non highlighted to the back\n    highlight_list.extend(non_highlight_list)\n    # add nulls back\n    for index in range(0, null_num):\n        highlight_list.append(None)\n    return highlight_list\n\n\ndef tester():\n    item1 = item.Item(0, \"Weapon\", \"Sword\", \"An ancient sword passed down\", 1, (0, 0), \"./Assets/sword.png\")\n    item2 = item.Item(1, \"Food\", \"Apple\", \"An apple picked fresh from a tree\", 32, (0, 0), \"./Assets/apple.png\")\n    item3 = item.Item(2, \"Mineral\", \"Gem\", \"A precious gemstone\", 3, (0, 0), \"./Assets/gem.png\")\n    item4 = None\n    item5 = None\n\n    item_list = [item1, item4, item3, item2, item5]\n\n    print(\"starting list\")\n    for i in range(0, len(item_list)):\n        if item_list[i] is not None:\n            print(item_list[i].get_name())\n\n    # sort by id\n    item_list = sort_by_id(item_list)\n    print(\"Sorting by id\")\n    for i in range(0, len(item_list)):\n        if item_list[i] is not None:\n            print(item_list[i].get_name())\n        else:\n            print(\"Null\")\n\n    # sort by item name\n    item_list = sort_by_name(item_list)\n    print(\"Sorting by name\")\n    for i in range(0, len(item_list)):\n        print(item_list[i].get_name())\n\n    # sort by item type\n    item_list = sort_by_type(item_list)\n    print(\"Sorting by type\")\n    for i in range(0, len(item_list)):\n        print(item_list[i].get_name())\n\n    # sort by item number\n    item_list = sort_by_number(item_list)\n    print(\"Sorting by number\")\n    for i in range(0, len(item_list)):\n        print(item_list[i].get_name())\n\n    # sort by highlight\n    item1.set_highlight(False)\n    item2.set_highlight(True)\n    item3.set_highlight(True)\n\n    item_list = sort_by_highlight(item_list)\n    print(\"Sorting by highlight\")\n    for i in range(0, len(item_list)):\n        print(item_list[i].get_name())\n\n\n# tester()\n","repo_name":"HactuallyBenji/PyGameInventory","sub_path":"sortingStuff.py","file_name":"sortingStuff.py","file_ext":"py","file_size_in_byte":5980,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"28212178839","text":"# October 7, 2022\n# first attempt at making flappy bird.\n# not using any OOP at all except those imported.\n# THIS FILE IS NOT THE FINAL FILE; SEE \"flap2.py\"\n\nimport pygame, random, time, os, json\n\nos.chdir(os.path.dirname(__file__))\npygame.init()\n\n# window size\nwindow_width = 1300\nwindow_height = 800\n\n# setting up the window\nwindow = pygame.display.set_mode((window_width, window_height))\nwindowCaptions = [\"Flabby Bird\", \"Flapster\"] # for the lols\nrandomCaptionIndex = random.randint(0, (len(windowCaptions)-1))\npygame.display.set_caption(windowCaptions[randomCaptionIndex])\n\n# define pygame-specific variables\nfont = pygame.font.SysFont(None, 72)\nbg = pygame.image.load(r\"SamplePics\\starwarsbg.jpg\")\nbg = pygame.transform.scale(bg, (window_width,window_height))\npipe = pygame.image.load(r\"SamplePics\\pipe.png\")\npipeRect = pipe.get_rect()\ntopPipe = pygame.transform.flip(pipe, False, True)\ntopPipeRect = topPipe.get_rect()\nplayer = pygame.image.load(r\"SamplePics\\flappyBird.png\")\nplayer = pygame.transform.scale(player, (100, 100))\nplayerRect = player.get_rect()\nplayRect = pygame.Rect(window_width//2-80, window_height//2, 160, 80)\nplayText = \"Play\"\n\n# define variables\ngameOver = True\nclicked = False\npipeExist = False\nscore = 0\npipeVel = 5\nplayerVel = 7\naccl = .5\nplayerRect.x = 215\nplayerRect.y = window_height//2\npipeRect.x = window_width-500\n\n# define color rgb tuples\nred = (255, 0, 0)\ngreen = (0, 255, 0)\nblue = (0, 0, 255)\n\n\n# getting save data\n# read the file and parse the data into \"playerData\" variable\nwith open('flap.json', 'r') as file:\n    encodedPlayerData = file.read()\n    playerData = json.loads(encodedPlayerData)\n    file.close()\n    \n# parse the player data\nhighScore = playerData['highScore']\n\n# functions\n# save function\ndef save():\n    global highScore\n    playerData['highScore'] = highScore\n    with open('flap.json', 'w') as f:\n        json.dump(playerData, f, indent=4)\n        f.close()\n\n# to draw home (game over) screen\ndef drawHome():\n    pygame.draw.rect(window, green, playRect)\n    renderedText = font.render(playText, True, blue)\n    window.blit(renderedText, (window_width//2-50, window_height//2+10))\n\n# to draw screen\ndef drawScreen():\n    # draw bg and player\n    window.blit(bg, (0,0))\n    window.blit(player, playerRect)\n\n    global highScore\n    # change high score if needed\n    if score > highScore:\n        highScore = score\n\n    # draw the score on the screen\n    scoreRect = pygame.Rect(window_width-400,50,350,50)\n    scoreString = \"Score: \" + str(score)\n    renderedScore = font.render(scoreString, True, blue)\n\n    # draw the high score on the screen\n    highScoreRect = pygame.Rect(window_width-400,100,350,50)\n    highScoreString = \"High Score: \" + str(highScore)\n    renderedHighScore = font.render(highScoreString, True, blue)\n\n    # drawing score and high score, and their respective rectangles\n    pygame.draw.rect(window, green, scoreRect)\n    pygame.draw.rect(window, green, highScoreRect)\n    window.blit(renderedScore, (window_width-400, 50))\n    window.blit(renderedHighScore, (window_width-400, 100))\n\n# checking win conditions\ndef checkWin():\n    global gameOver\n    # check if gameOver\n    if playerRect.y >= window_height or playerRect.y <= 0:\n        gameOver = True\n    # check player-pipe collision\n    if playerRect.colliderect(pipeRect) or playerRect.colliderect(topPipeRect):\n        gameOver = True\n\n\n# game\nrun = True\nwhile run:\n\n    # checking the score\n    if playerRect.x == pipeRect.x + 50:\n        score += 1\n\n    # if game over\n    if gameOver:\n        drawScreen()\n        drawHome()\n        pipeVel = 0\n        accl = 0\n        playerVel = 0\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                run = False\n            if event.type == pygame.MOUSEBUTTONDOWN and clicked == False:\n                clicked = True\n            if event.type == pygame.MOUSEBUTTONUP and clicked == True:\n                clicked = False\n                pos = pygame.mouse.get_pos()\n                if playRect.collidepoint(pos):\n                    clicked = False\n                    pipeExist = False\n                    score = 0\n                    pipeVel = 5\n                    playerVel = 7\n                    accl = .5\n                    playerRect.x = 215\n                    playerRect.y = window_height//2\n                    pipeRect.x = window_width-500\n                    gameOver = False\n    # events for in-game\n    else:\n        drawScreen()\n        for event in pygame.event.get():\n            pos = pygame.mouse.get_pos()\n            if event.type == pygame.QUIT:\n                run = False\n            if event.type == pygame.MOUSEBUTTONDOWN: # and clicked == False:\n            #     clicked = True\n            # if event.type == pygame.MOUSEBUTTONUP and clicked == True:\n            #     clicked = False\n                # make character jump (set velocity to point upwards)\n                playerVel = 12.5\n            if event.type == pygame.K_SPACE:\n                playerVel = 12.5\n\n    # move pipes and record score\n    if pipeExist == False:\n        pipeRect.y = window_height//random.uniform(1.1,3)\n        topPipeRect.y = pipeRect.y-1170\n        # print(pipeRect.y)\n        pipeExist = True\n\n    if pipeRect.x >= -370 and pipeExist:\n        window.blit(pipe, pipeRect)\n        topPipeRect.x = pipeRect.x\n        window.blit(topPipe, topPipeRect)\n    else:\n        pipeRect.x = window_width\n        topPipeRect.x = window_width\n        pipeExist = False\n    # move the pipe\n    pipeRect.x -= pipeVel\n\n    # make the player fall\n    playerVel -= accl\n    playerRect.y -= playerVel\n\n    checkWin()\n\n    # 60 fps means ~16ms per frame\n    # made 10ms to account for python execution time\n    time.sleep(.01)\n    pygame.display.update()\n\nsave()\n\npygame.quit()\n","repo_name":"omgdory/FlapPy-Bird","sub_path":"flap.py","file_name":"flap.py","file_ext":"py","file_size_in_byte":5802,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"7061080150","text":"from imports import *\r\n\r\n\r\n\r\nclass WorkerSignals(QObject):\r\n    result = pyqtSignal(bool, tuple)\r\n\r\n\r\nclass AWorker(QRunnable):\r\n    def __init__(self, fn, *args, **kwargs):\r\n        super(AWorker, self).__init__()\r\n        self.fn = fn\r\n        self.args = args\r\n        self.kwargs = kwargs\r\n        self.signals = WorkerSignals()\r\n\r\n    @pyqtSlot()\r\n    def run(self):\r\n        try:\r\n            data = self.fn(*self.args, **self.kwargs)\r\n            self.signals.result.emit(True, tuple(data))\r\n        except:\r\n            self.signals.result.emit(False, tuple(data))\r\n\r\n\r\n\r\nclass AExecutor(QRunnable):\r\n    def __init__(self, fn, *args, **kwargs):\r\n        super(AExecutor, self).__init__()\r\n        self.fn = fn\r\n        self.args = args\r\n        self.kwargs = kwargs\r\n\r\n    @pyqtSlot()\r\n    def run(self):\r\n        self.fn(*self.args, **self.kwargs)\r\n\r\n\r\n\r\nclass functions_Chart():\r\n    def __init__(self):\r\n        self.address = Web3.toChecksumAddress(\"0x34faa80fec0233e045ed4737cc152a71e490e2e3\") #TIGS\r\n\r\n    def reinit(self, address):\r\n        self.address = Web3.toChecksumAddress(address)\r\n\r\n    def currentUnixTime(self):\r\n        date = datetime.utcnow()\r\n        utc_time = calendar.timegm(date.utctimetuple())\r\n        return utc_time\r\n\r\n    def doRequest(self, candleTime, candleHistory):\r\n        currentUnixTime = int(self.currentUnixTime())\r\n        unix = candleHistory * 60 * 60\r\n        realCandelTime = int(currentUnixTime - unix)\r\n        reqJ = requests.get(f'https://api.trading-tigers.com/PancakeSwap/Chart/{self.address}/{candleTime}&from={currentUnixTime}&to={realCandelTime}').text\r\n        return json.loads(reqJ)['data']\r\n\r\n\r\nclass MainWindow(QDialog):\r\n    def __init__(self):\r\n        QDialog.__init__(self)\r\n        fplt.foreground = '#dca101'\r\n        fplt.background = '#272c36'\r\n        self.ui = Ui_Dialog()\r\n        self.chart_functions = functions_Chart()\r\n        self.GS = Global_Settings()\r\n        self.US = User_Settings()\r\n        self.OS = LimitOrders_Settings()\r\n        self.limitorders = LimitOrders_Algo()\r\n        self.web3 = Web3_UI_Functions()\r\n        self.math = Math_UI_Functions()\r\n        self.uif = UI_Functions()\r\n        self.ui.setupUi(self)\r\n\r\n        self.threadpool = QThreadPool.globalInstance()\r\n        self.setWindowTitle('Trading Tigers Toolkit')\r\n        self.setWindowFlags(QtCore.Qt.FramelessWindowHint)\r\n        self.setAttribute(QtCore.Qt.WA_TranslucentBackground)\r\n        \r\n        self.ui.btn_minimize.clicked.connect(lambda: self.showMinimized())\r\n        self.ui.btn_close.clicked.connect(lambda: self.close())\r\n        #BUTTONS & CHANGED QTEXTEDIT\r\n        self.ui.token_address.textChanged.connect(lambda: self.addressChange())\r\n        self.ui.ButtonchartHelper_MA20.clicked.connect(lambda: self.changeIndicatorSettings(\"MA20\"))\r\n        self.ui.ButtonChartHelper_Volume.clicked.connect(lambda: self.changeIndicatorSettings(\"VOLUME\"))\r\n        self.ui.pushButton_error_OK.clicked.connect(lambda: self.ui.stackedWidget_16.setCurrentWidget(self.ui.Chart_Loaded))\r\n        self.ui.pushButton_Status_OK.clicked.connect(lambda: self.ui.stackedWidget_16.setCurrentWidget(self.ui.Chart_Loaded))\r\n        self.ui.pushButton_Status_OK.clicked.connect(lambda: self.ui.stackedWidget_16.setCurrentWidget(self.ui.Chart_Loaded))\r\n        self.ui.pushButton_Cancel_Confirm.clicked.connect(lambda: self.ui.stackedWidget_16.setCurrentWidget(self.ui.Chart_Loaded))\r\n        self.ui.pushButton_Cancel_Order.clicked.connect(lambda: self.ui.stackedWidget_16.setCurrentWidget(self.ui.Chart_Loaded))\r\n        self.ui.pushButton_Reject_Approve.clicked.connect(lambda: self.ui.stackedWidget_16.setCurrentWidget(self.ui.Chart_Loaded))\r\n\r\n\r\n\r\n        self.linkStartStopButtons()\r\n        self.ui.pushButton_STOP_LimitOrders.clicked.connect(lambda: self.ButtonStopOrderALGO())\r\n        self.ui.pushButton_START_LimitOrders.clicked.connect(lambda: self.ButtonStartOrderALGO())\r\n\r\n\r\n        self.initButtonValidators()\r\n        self.initButtonsChart()\r\n        self.initButtonsBToken()\r\n        self.initButtonsOrderType()\r\n        self.initButtonsMenu()\r\n        self.initChart()\r\n        self.initTradeInputs()\r\n\r\n\r\n        self.ui.frame_Settings_error.hide()\r\n\r\n        self.ui.Input_Settings_PrivateKey.setEchoMode(QLineEdit.Password)\r\n\r\n        update_Price = QtCore.QTimer(self, interval=10000, timeout=self.update_Price)\r\n        update_Price.start()\r\n\r\n        update_wallet_balance = QtCore.QTimer(self, interval=10000, timeout=self.update_wallet_balance)\r\n        update_wallet_balance.start()\r\n\r\n        AllowanceChecker = QtCore.QTimer(self, interval=5000, timeout=self.checkTokenAllowances)\r\n        AllowanceChecker.start()\r\n\r\n        update_OpenOrders = QtCore.QTimer(self, interval=10000, timeout=self.initOpenOrders)\r\n        update_OpenOrders.start()\r\n\r\n        self.LimitOrderAlgo = QtCore.QTimer(self, interval=int(self.US.OQI * 1000), timeout=self.RunLimitOrderAlgorithumus)\r\n\r\n        if self.US.AUTOSTART_ORDERS:\r\n            if self.GS.O_ONLINE:\r\n                self.LimitOrderAlgo.start()\r\n\r\n\r\n        # DROP SHADOW\r\n        self.shadow = QGraphicsDropShadowEffect(self)\r\n        self.shadow.setBlurRadius(15)\r\n        self.shadow.setXOffset(0)\r\n        self.shadow.setYOffset(0)\r\n        self.shadow.setColor(QColor(0, 0, 0, 150))\r\n        self.ui.main_frame.setGraphicsEffect(self.shadow)\r\n\r\n        ##############\r\n        self.selectBaseToken()\r\n        self.selectIndicatorsButtons()\r\n        self.selectCandleTime()\r\n        self.selectCandleHistoryTime()\r\n        self.selectOrderType()\r\n        self.selectMenuButton()\r\n\r\n        self.update_wallet_balance()\r\n        self.ui.token_address.setText(str(Web3.toChecksumAddress(\"0x34faa80fec0233e045ed4737cc152a71e490e2e3\")))\r\n\r\n\r\n\r\n        def moveWindow(event):\r\n            if event.buttons() == Qt.LeftButton:\r\n                self.move(self.pos() + event.globalPos() - self.dragPos)\r\n                self.dragPos = event.globalPos()\r\n                event.accept()\r\n        self.ui.frame_top_btns.mouseMoveEvent = moveWindow\r\n\r\n        self.window().axs = [self.ax]\r\n        self.show()\r\n\r\n    def resizeEvent(self, event):\r\n        self.resizeFunction()\r\n        return super(MainWindow, self).resizeEvent(event)\r\n\r\n    def resizeFunction(self):\r\n        print('Height: ' + str(self.height()) + ' | Width: ' + str(self.width()))\r\n\r\n\r\n    def mousePressEvent(self, event):\r\n        self.dragPos = event.globalPos()\r\n   \r\n    def addressChange(self):\r\n        token_address = self.ui.token_address.text()\r\n        if Web3.isAddress(token_address) == True:\r\n            if self.web3.checkIsTokenContract(Web3.toChecksumAddress(token_address)):\r\n                self.token_address = Web3.toChecksumAddress(token_address)\r\n                self.ui.token_address.setText(self.token_address)\r\n                self.loadTokenAddress()\r\n\r\n    def RunLimitOrderAlgorithumus(self):\r\n        self.limitorders.start()\r\n            \r\n    def linkStartStopButtons(self):\r\n        self.ui.pushButton_STOP_LimitOrders.clicked.connect(lambda: self.ButtonStopOrderALGO())\r\n        self.ui.pushButton_START_LimitOrders.clicked.connect(lambda: self.ButtonStartOrderALGO())\r\n        self.initOrderAlgoButtons(True)\r\n\r\n\r\n    def initOrderAlgoButtons(self, firstStart):\r\n        if firstStart:\r\n            if self.US.AUTOSTART_ORDERS:\r\n                if self.GS.O_ONLINE:\r\n                    self.ui.label_Status_LIMITORDERS.setText(\"ONLINE\")\r\n                    self.ui.label_Status_LIMITORDERS.setStyleSheet(Ui_Styles().BUY_label)\r\n                else:\r\n                    self.ui.label_Status_LIMITORDERS.setText(\"OFFLINE\")\r\n                    self.ui.label_Status_LIMITORDERS.setStyleSheet(Ui_Styles().SELL_label)\r\n            else:\r\n                self.ui.label_Status_LIMITORDERS.setText(\"OFFLINE\")\r\n                self.ui.label_Status_LIMITORDERS.setStyleSheet(Ui_Styles().SELL_label)\r\n        else:\r\n            if self.GS.O_ONLINE:\r\n                self.ui.label_Status_LIMITORDERS.setText(\"ONLINE\")\r\n                self.ui.label_Status_LIMITORDERS.setStyleSheet(Ui_Styles().BUY_label)\r\n            else:\r\n                self.ui.label_Status_LIMITORDERS.setText(\"OFFLINE\")\r\n                self.ui.label_Status_LIMITORDERS.setStyleSheet(Ui_Styles().SELL_label)\r\n\r\n\r\n    def ButtonStopOrderALGO(self):\r\n        self.GS.changeGlobalSetting(\"ORDERS_ONLINE\", False)\r\n        self.initOrderAlgoButtons(False)\r\n        self.LimitOrderAlgo.stop()\r\n\r\n    def ButtonStartOrderALGO(self):\r\n        self.GS.changeGlobalSetting(\"ORDERS_ONLINE\", True)\r\n        self.initOrderAlgoButtons(False)\r\n        self.LimitOrderAlgo.start()\r\n\r\n\r\n    def loadTokenAddress(self):\r\n        self.chart_functions.reinit(self.token_address)\r\n        self.loadAddButton()\r\n        self.update_TokenInfos()\r\n        self.update_Price()\r\n        self.update_chart_dex()\r\n        self.update_TokenTax()\r\n        self.update_TokenAllowance()\r\n        self.resetInputs()\r\n\r\n    def update_TokenAllowance(self):\r\n        self.threadpool.start(AExecutor(self.checkTokenAllowances))\r\n\r\n    def checkTokenAllowances(self):\r\n        try:\r\n            if self.GS.B_TOKEN == \"BUSD\":\r\n                ALLOWANCE = int(self.web3.checkAlloanceSwapper(self.web3.BUSD, self.US.ADDRESS)) / 10**18\r\n                if float(ALLOWANCE) <= float(self.ui.Input_LB.text()) or float(ALLOWANCE) < float(self.ui.Input_MB.text()):\r\n                    self.changeApproveButtonsBUY(True)\r\n                else:\r\n                    self.changeApproveButtonsBUY(False)\r\n            else:\r\n                self.changeApproveButtonsBUY(False)\r\n            if self.token_address != self.web3.WBNB:\r\n                ALLOWANCE = int(self.web3.checkAlloanceSwapper(self.token_address, self.US.ADDRESS)) / 10**self.jsonTokenData['tokenDecimals']\r\n                if float(ALLOWANCE) <= float(self.ui.Input_LS.text()) or float(ALLOWANCE) < float(self.ui.Input_MS.text()):\r\n                    self.changeApproveButtonsSELL(True)\r\n                else:\r\n                    self.changeApproveButtonsSELL(False)\r\n            else:\r\n                self.changeApproveButtonsSELL(False)\r\n\r\n        except:\r\n            pass\r\n\r\n    \r\n    def changeApproveButtonsBUY(self, bool):\r\n        try:\r\n            self.ui.ButtonExecute_MB.clicked.disconnect()\r\n        except:\r\n            pass\r\n        try:\r\n            self.ui.ButtonPlace_LB.clicked.disconnect()\r\n        except:\r\n            pass\r\n\r\n        if bool:\r\n            self.ui.ButtonExecute_MB.clicked.connect(lambda: self.ApproveToken(self.web3.BUSD))\r\n            self.ui.ButtonExecute_MB.setText(\"APPROVE\")\r\n            self.ui.ButtonPlace_LB.clicked.connect(lambda: self.ApproveToken(self.web3.BUSD))\r\n            self.ui.ButtonPlace_LB.setText(\"APPROVE\")\r\n        else:\r\n            self.ui.ButtonExecute_MB.clicked.connect(lambda: self.ConfirmPageMarket(\"MARKET\",\"BUY\"))\r\n            self.ui.ButtonExecute_MB.setText(\"EXECUTE BUY\")\r\n            self.ui.ButtonPlace_LB.clicked.connect(lambda: self.ConfirmPageMarket(\"LIMIT\",\"BUY\"))\r\n            self.ui.ButtonPlace_LB.setText(\"PLACE ORDER\")\r\n\r\n\r\n    def changeApproveButtonsSELL(self, bool):\r\n        try:\r\n            self.ui.ButtonExecute_MS.clicked.disconnect()\r\n        except:\r\n            pass\r\n        try:\r\n            self.ui.ButtonPlace_LS.clicked.disconnect()\r\n        except:\r\n            pass\r\n\r\n        if bool:\r\n            self.ui.ButtonExecute_MS.clicked.connect(lambda: self.ApproveToken(self.token_address))\r\n            self.ui.ButtonExecute_MS.setText(\"APPROVE\")\r\n            self.ui.ButtonPlace_LS.clicked.connect(lambda: self.ApproveToken(self.token_address))\r\n            self.ui.ButtonPlace_LS.setText(\"APPROVE\")\r\n        else:\r\n            self.ui.ButtonExecute_MS.clicked.connect(lambda: self.ConfirmPageMarket(\"MARKET\", \"SELL\"))\r\n            self.ui.ButtonExecute_MS.setText(\"EXECUTE SELL\")\r\n            self.ui.ButtonPlace_LS.clicked.connect(lambda: self.ConfirmPageMarket(\"LIMIT\",\"SELL\"))\r\n            self.ui.ButtonPlace_LS.setText(\"PLACE ORDER\")\r\n\r\n\r\n    def ConfirmPageMarket(self, type, side):\r\n        status = True\r\n        if str(self.jsonTokenData[\"tokenSymbol\"]) == str(self.GS.B_TOKEN):\r\n            status = False\r\n            self.ui.label_ErrorMessage.setText(\"FROM and TO TOKEN IS SAME, Wrapped TOKENS not Supported!\")\r\n            self.ui.stackedWidget_16.setCurrentWidget(self.ui.Error_Page)\r\n\r\n        if status:\r\n            if type == \"MARKET\":\r\n                if float(self.ui.Input_MB.text()) != 0 or float(self.ui.Input_MS.text()) !=0:\r\n                    if side == \"SELL\":\r\n\r\n                        self.ui.Confirm_OrderType.setText(type + \" \" + side)\r\n                        self.ui.Confirm_OrderType.setStyleSheet(Ui_Styles().SELL_label)\r\n                        self.ui.Confirm_Symbol_In.setText(self.jsonTokenData[\"tokenSymbol\"])\r\n                        self.ui.Confirm_Symbol_Out.setText(self.GS.B_TOKEN)\r\n                        self.ui.Confirm_Amount_In.setText(str(self.ui.Input_MS.text()))\r\n                        self.ui.Confirm_Amount_Out.setText(str(self.ui.Output_MS.text()))\r\n                        self.ui.stackedWidget_16.setCurrentWidget(self.ui.Submit_Page)\r\n\r\n                        if self.GS.B_TOKEN == \"BNB\":\r\n                            Status, Estimate_Fee, Signed_TX = self.web3.createSignedTransactionTokentoETH(\r\n                                self.US.ADDRESS, self.token_address, float(self.ui.Input_MS.text()),self.jsonTokenData[\"tokenDecimals\"], self.US.SLIPPAGE,  self.US.GAS,  self.US.PRIVKEY)\r\n                        elif self.GS.B_TOKEN == \"BUSD\":\r\n                            if self.token_address == self.web3.WBNB:\r\n                                Status, Estimate_Fee, Signed_TX = self.web3.createSignedTransactionETHtoToken(\r\n                                    self.US.ADDRESS, self.web3.BUSD, float(self.ui.Input_MS.text()), self.US.SLIPPAGE,  self.US.GAS,  self.US.PRIVKEY)\r\n                        elif self.GS.B_TOKEN == \"BUSD\":\r\n                            Status, Estimate_Fee, Signed_TX = self.web3.createSignedTransactionTokentoToken(\r\n                                self.US.ADDRESS, self.token_address, self.web3.BUSD, float(self.ui.Input_MS.text()), self.jsonTokenData[\"tokenDecimals\"], self.US.SLIPPAGE,  self.US.GAS,  self.US.PRIVKEY)\r\n                        if Status == True:\r\n                            self.ui.Confirm_TX_FEE_ETH.setText(str(self.math.FloatToCleanSting(Estimate_Fee)))\r\n                            try:\r\n                                self.ui.pushButton_Submit_Confirm.clicked.diconnect()\r\n                            except:\r\n                                pass\r\n                            self.ui.pushButton_Submit_Confirm.clicked.connect(lambda: self.SubmitTransaction())\r\n                            self.ui.stackedWidget_16.setCurrentWidget(self.ui.Submit_Page)\r\n                            self.Signed_TX = Signed_TX\r\n                        else:\r\n                            self.ui.label_ErrorMessage.setText(str(Status))\r\n                            status = False\r\n                    elif side == \"BUY\":\r\n\r\n                        self.ui.Confirm_OrderType.setText(type + \" \" + side)\r\n                        self.ui.Confirm_OrderType.setStyleSheet(Ui_Styles().BUY_label)\r\n                        self.ui.Confirm_Symbol_In.setText(self.GS.B_TOKEN)\r\n                        self.ui.Confirm_Symbol_Out.setText(self.jsonTokenData[\"tokenSymbol\"])\r\n                        self.ui.Confirm_Amount_In.setText(str(self.ui.Input_MB.text()))\r\n                        self.ui.Confirm_Amount_Out.setText(str(self.ui.Output_MB.text()))  \r\n\r\n                        if self.GS.B_TOKEN == \"BNB\":\r\n                            Status, Estimate_Fee, Signed_TX= self.web3.createSignedTransactionETHtoToken(\r\n                                self.US.ADDRESS, self.token_address, float(self.ui.Input_MB.text()), self.US.SLIPPAGE,  self.US.GAS,  self.US.PRIVKEY)\r\n                        elif self.GS.B_TOKEN == \"BUSD\":\r\n                            Status, Estimate_Fee, Signed_TX= self.web3.createSignedTransactionTokentoToken(\r\n                                self.US.ADDRESS, self.web3.BUSD, self.token_address, float(self.ui.Input_MB.text()), 18, self.US.SLIPPAGE,  self.US.GAS,  self.US.PRIVKEY)\r\n                        if Status == True:\r\n                            self.ui.Confirm_TX_FEE_ETH.setText(str(self.math.FloatToCleanSting(Estimate_Fee)))\r\n                            try:\r\n                                self.ui.pushButton_Submit_Confirm.clicked.diconnect()\r\n                            except:\r\n                                pass\r\n                            self.ui.pushButton_Submit_Confirm.clicked.connect(lambda: self.SubmitTransaction())\r\n                            self.ui.stackedWidget_16.setCurrentWidget(self.ui.Submit_Page)\r\n                            self.Signed_TX = Signed_TX\r\n                    \r\n                        else:\r\n                            self.ui.label_ErrorMessage.setText(str(Status))\r\n                            status = False\r\n                else:\r\n                    self.ui.label_ErrorMessage.setText(\"Check your Input or Output Amount!\")\r\n                    status = False\r\n\r\n\r\n            if type == \"LIMIT\":\r\n                if float(self.ui.Input_LB.text()) != 0 or float(self.ui.Input_LS.text()) !=0:\r\n                    if float(self.ui.Price_LB.text()) != 0 or float(self.ui.Price_LS.text()) !=0:\r\n                        try:\r\n                            self.ui.pushButton_Submit_Order.clicked.disconnect()\r\n                        except:\r\n                            pass\r\n                        if side == \"BUY\":\r\n                            self.LO = self.createOrderDict(\"BUY\", self.GS.B_TOKEN,\r\n                             float(self.ui.Input_LB.text()), self.jsonTokenData[\"tokenSymbol\"],\r\n                              float(self.ui.Price_LB.text()), self.token_address)\r\n                              \r\n                            status = True\r\n                        elif side == \"SELL\":\r\n                            self.LO = self.createOrderDict(\"SELL\", self.jsonTokenData[\"tokenSymbol\"],\r\n                             float(self.ui.Input_LS.text()), self.GS.B_TOKEN,\r\n                             float(self.ui.Price_LS.text()), self.token_address)\r\n                            status = True\r\n\r\n                        if status:\r\n                            self.ui.Order_OrderType.setText(str(side))\r\n                            if side == \"BUY\":\r\n                                self.ui.Order_OrderType.setStyleSheet(Ui_Styles().BUY_label)\r\n                            if side == \"SELL\":\r\n                                self.ui.Order_OrderType.setStyleSheet(Ui_Styles().SELL_label)\r\n                            self.ui.Order_Amount.setText(str(self.LO[\"INPUT_AMOUNT\"]))\r\n                            self.ui.Order_Symbol_In.setText(self.LO[\"INPUT_SYMBOL\"])\r\n                            self.ui.Order_Price.setText(str(self.LO[\"TARGET_PRICE\"]) + \" $\")\r\n                            self.ui.Confirm_Orderway.setText(str(self.LO[\"INPUT_SYMBOL\"] +\" -> \" + self.LO[\"OUTPUT_SYMBOL\"]))\r\n                            self.ui.stackedWidget_16.setCurrentWidget(self.ui.page_LimitOrder)\r\n                            self.ui.pushButton_Submit_Order.clicked.connect(lambda: self.saveOrder())\r\n\r\n\r\n                    else:\r\n                        self.ui.label_ErrorMessage.setText(\"Check your Target Price!\")\r\n                        status = False\r\n                else:\r\n                    self.ui.label_ErrorMessage.setText(\"Check your Input Amount!\")\r\n                    status = False\r\n\r\n        if not status:\r\n            self.ui.stackedWidget_16.setCurrentWidget(self.ui.Error_Page)\r\n\r\n            \r\n    def createOrderDict(self, side, INPUT_SYMBOL, INPUT_AMOUNT, OUTPUT_SYMBOL, TARGET_PRICE, TOKEN_ADDRESS):\r\n        return {\r\n        \"ID\": str(''.join(random.choice(string.ascii_letters) for x in range(5))).upper(),\r\n        \"SIDE\": side,\r\n        \"INPUT_SYMBOL\": INPUT_SYMBOL,\r\n        \"INPUT_AMOUNT\": INPUT_AMOUNT,\r\n        \"OUTPUT_SYMBOL\": OUTPUT_SYMBOL,\r\n        \"TARGET_PRICE\": TARGET_PRICE,\r\n        \"TOKEN_ADDRESS\": TOKEN_ADDRESS\r\n        }\r\n\r\n    def saveOrder(self):\r\n        self.OS.addNewOrder(self.LO)\r\n        self.ui.stackedWidget_16.setCurrentWidget(self.ui.Chart_Loaded)\r\n        self.initOpenOrders()\r\n        self.resetInputs()\r\n        del self.LO\r\n\r\n\r\n    def ApproveToken(self, Token_Address):\r\n        try:\r\n            self.ui.pushButton_Submit_Approve.clicked.disconnect()\r\n        except:\r\n            pass\r\n        Status = True\r\n        try:\r\n\r\n            self.ui.Approve_Symbol.setText(self.jsonTokenData[\"tokenSymbol\"])\r\n            self.ui.Approve_Symbol.setText(self.jsonTokenData[\"tokenSymbol\"])\r\n            status, Estimate_Fee, Signed_TX = self.web3.createSignedTransactionApproveMAX(self.US.ADDRESS, Token_Address, self.US.GAS, self.US.PRIVKEY)\r\n            self.ui.Approve_TX_FEE_ETH.setText(self.math.FloatToCleanSting(Estimate_Fee))\r\n            if status == True:\r\n                self.ui.stackedWidget_16.setCurrentWidget(self.ui.Approve_Page)\r\n                self.ui.pushButton_Submit_Approve.clicked.connect(lambda: self.SubmitTransaction())\r\n                self.Signed_TX = Signed_TX\r\n            else:\r\n                self.ui.label_ErrorMessage.setText(str(status))\r\n                Status = False\r\n        except Exception as e:\r\n            Status = False\r\n            self.ui.label_ErrorMessage.setText(str(e))\r\n            print(e)\r\n\r\n        if not Status:\r\n            self.ui.stackedWidget_16.setCurrentWidget(self.ui.Error_Page)\r\n\r\n    \r\n    def waitTransaction(self):\r\n        try:\r\n            self.ui.Transaction_Succesfully.hide()\r\n        except:\r\n            pass\r\n        try:\r\n            self.ui.Transaction_Fail.hide()\r\n        except:\r\n            pass\r\n        result = self.web3.awaitTransaction(self.tx)\r\n        if result: # 1 == True :)\r\n            self.ui.Transaction_Succesfully.show()\r\n            self.ui.Input_TX_HEX.setText(str(self.tx.hex()))\r\n            self.ui.stackedWidget_16.setCurrentWidget(self.ui.page_TX_Status)\r\n        else:\r\n            self.ui.Transaction_Fail.show()\r\n            self.ui.Input_TX_HEX.setText(str(self.tx.hex()))\r\n            self.ui.stackedWidget_16.setCurrentWidget(self.ui.page_TX_Status)\r\n        del self.tx\r\n        \r\n\r\n    def SubmitTransaction(self):\r\n        try:\r\n            self.ui.stackedWidget_16.setCurrentWidget(self.ui.chart_isLoading) \r\n            if self.US.WAIT_TX_STATUS:\r\n                self.tx = self.web3.SubmitTransaction(self.Signed_TX)\r\n                self.waitTransaction()\r\n            else:\r\n                self.web3.SubmitTransaction(self.Signed_TX)\r\n                self.ui.stackedWidget_16.setCurrentWidget(self.ui.Chart_Loaded)\r\n            self.resetInputs()\r\n            del self.Signed_TX\r\n        except:\r\n            self.ui.stackedWidget_16.setCurrentWidget(self.ui.Chart_Loaded)\r\n            pass\r\n        \r\n        \r\n\r\n\r\n\r\n    def loadAddButton(self):\r\n        for token in self.US.TOKEN_LIST:\r\n            AllreadyinList =  Web3.toChecksumAddress(self.token_address) == Web3.toChecksumAddress(token)\r\n            if AllreadyinList:\r\n                break\r\n        try:\r\n            self.ui.pushButton_AddtoList.clicked.disconnect()\r\n        except:\r\n            pass\r\n        if AllreadyinList:\r\n            self.ui.pushButton_AddtoList.hide()\r\n        else:\r\n            self.ui.pushButton_AddtoList.show()\r\n            self.ui.pushButton_AddtoList.clicked.connect(self.addTokenAddressToList)\r\n\r\n    def addTokenAddressToList(self):\r\n        self.US.TOKEN_LIST.append(self.token_address)\r\n        self.US.changeUserSetting(\"TOKEN_LIST\",self.US.TOKEN_LIST)\r\n        self.loadAddButton()\r\n    \r\n    def update_TokenTax(self):\r\n        self.threadpool.start(AExecutor(self.refresh_tokenTax))\r\n\r\n    def update_TokenInfos(self):\r\n        self.threadpool.start(AExecutor(self.refresh_token_infos))\r\n\r\n    def refresh_tokenTax(self):\r\n        Successfull, buy_tax, sell_tax  = self.web3.checkTokenTax(self.token_address)\r\n        if Successfull == True:\r\n            self.ui.label_token_BuyTax_2.setText(str(buy_tax)+ \" %\")\r\n            self.ui.label_token_BuyTax.setText(str(buy_tax)+ \" %\")\r\n            self.ui.label_token_SellTax_2.setText(str(sell_tax)+ \" %\")\r\n            self.ui.label_token_SellTax.setText(str(sell_tax)+ \" %\")\r\n        else:\r\n            self.ui.label_token_BuyTax_2.setText(\"?? %\")\r\n            self.ui.label_token_BuyTax.setText(\"?? %\")\r\n            self.ui.label_token_SellTax_2.setText(\"?? %\")\r\n            self.ui.label_token_SellTax.setText(\"?? %\")\r\n\r\n\r\n    def refresh_token_infos(self):\r\n        try:\r\n            data = self.web3.fetchWalletTokenInformations(self.US.ADDRESS, [self.token_address])\r\n\r\n            if str(data[1][0]) == \"WBNB\":\r\n                self.jsonTokenData = {'tokenName': \"Binance Coin\", 'tokenSymbol': \"BNB\", 'tokenDecimals': 18 }\r\n            else:\r\n                self.jsonTokenData = {'tokenName': data[0][0], 'tokenSymbol': data[1][0], 'tokenBalance': float(data[2][0] / 10**int(data[4][0])), 'tokenPrice': float(data[3][0] / 10**18), 'tokenBalance_USD': float(data[2][0] / 10**int(data[4][0]) * data[3][0] / 10**18), 'tokenDecimals': data[4][0] }\r\n\r\n            namestr = str(self.jsonTokenData['tokenName']) + \" ( \" + str(self.jsonTokenData['tokenSymbol']) + \" )\" \r\n            self.ui.TokenName.setText(namestr)\r\n            self.ui.TokenName_LB.setText(namestr)\r\n            self.ui.TokenName_LS.setText(namestr)\r\n            self.ui.TokenName_MS.setText(namestr)\r\n            self.ui.TokenName_MB.setText(namestr)\r\n            self.initTradeSymbols()\r\n        except Exception as e:\r\n            print(e)\r\n            name = self.web3.fetchName(self.token_address)\r\n            self.ui.TokenName.setText(name)\r\n            self.ui.TokenName_LB.setText(name)\r\n            self.ui.TokenName_LS.setText(name)\r\n            self.ui.TokenName_MS.setText(name)\r\n            self.ui.TokenName_MB.setText(name)\r\n\r\n\r\n\r\n\r\n    def update_Price(self):\r\n        try:\r\n            self.current_price = self.math.FloatToCleanSting(self.web3.fetchCurrentPrice(self.token_address))\r\n            self.ui.TokenPrice.setText(str(self.current_price)+ \" $  \")\r\n        except Exception as e:\r\n            print(e)\r\n            pass\r\n\r\n\r\n\r\n    def refresh_datas(self, a, b):\r\n        if a == True:\r\n            first_Price = float(b[0][3][0])\r\n            percent = round(self.math.get_change(float(self.current_price), float(first_Price)), 2)\r\n            p = str(percent) + \" %\"\r\n            if str(p[-0]) == \"-\":\r\n                self.ui.TokenPriceChange.setStyleSheet(u\"color:rgb(196, 49, 69);\\nfont-weight: bold;\\nbackground:transparent;\\nfont-size: 10pt;\")\r\n                prefix= \"\"\r\n            else:\r\n                self.ui.TokenPriceChange.setStyleSheet(u\"color:#006626;\\nfont-weight: bold;\\nbackground:transparent;\\nfont-size: 10pt;\")\r\n                prefix= \"+\"\r\n            self.ui.TokenPriceChange.setText( str(prefix + p))\r\n            fplt.candlestick_ochl(b[0], ax = self.ax)\r\n            if self.GS.MA20 == True:\r\n                fplt.plot(b[1], legend = \"MA-20\", ax = self.ax, size=3)\r\n            if self.GS.VOLUME == True:\r\n                fplt.volume_ocv(b[2], ax=self.axo)\r\n            fplt.refresh()\r\n            self.ui.stackedWidget_16.setCurrentWidget(self.ui.Chart_Loaded)\r\n            print(\"updated Chart\")\r\n        else:\r\n            print(\"Request Fail!\")\r\n\r\n\r\n\r\n\r\n    def fetchTokenData(self):\r\n        try:\r\n            req = self.chart_functions.doRequest(self.GS.candleTimeMin, self.GS.candleHistory)\r\n            df = pd.DataFrame(req)\r\n            df = df.rename(columns={'t':'Time', 'o':'Open', 'c':'Close', 'h':'High', 'l':'Low', 'v':'Volume'})\r\n            o = df['Open']#.apply(float(self.math.FloatToCleanSting))\r\n            c = df['Close']#.apply(float(self.math.FloatToCleanSting))\r\n            h = df['High']#.apply(float(self.math.FloatToCleanSting))\r\n            l = df['Low']#.apply(float(self.math.FloatToCleanSting))\r\n            v = df['Volume']#.apply(float(self.math.FloatToCleanSting))\r\n            candles = df['Time'], o, c, h, l\r\n            ma20 = df.Close.rolling(20).mean()\r\n            volume = df[\"Time\"], o, c, v\r\n            data = candles, ma20, volume\r\n            return data\r\n        except Exception as e:\r\n            return e\r\n\r\n\r\n    def fetchWalletBalances(self):\r\n        t = []\r\n        for i in range(0, len(self.US.TOKEN_LIST), 40):\r\n            t.append(self.US.TOKEN_LIST[i:i+40])\r\n        data = [[],[],[],[],[]]\r\n        for token in t:\r\n            d = self.web3.fetchWalletTokenInformations(self.US.ADDRESS, token)\r\n            data[0].extend(d[0])\r\n            data[1].extend(d[1])\r\n            data[2].extend(d[2])\r\n            data[3].extend(d[3])\r\n            data[4].extend(d[4])\r\n        i = 0\r\n        rou = []\r\n        ETHdata = self.web3.fetchEthWalletInfos(self.US.ADDRESS)\r\n        rou.append({\r\n            'tokenAddress': self.web3.WBNB,\r\n            'tokenName': \"Binance Coin\",\r\n            'tokenSymbol': \"BNB\",\r\n            'tokenBalance': float(ETHdata[0] / 10**18),\r\n            'tokenPrice': float(ETHdata[1]),\r\n            'tokenBalance_USD': float(ETHdata[0] * ETHdata[1] / 10**18),\r\n            'tokenDecimals': 18\r\n            })\r\n\r\n        for tokenAddress in self.US.TOKEN_LIST:\r\n            rou.append({\r\n            'tokenAddress': tokenAddress,\r\n            'tokenName': data[0][i],\r\n            'tokenSymbol': data[1][i],\r\n            'tokenBalance': float(data[2][i] / 10**int(data[4][i])),\r\n            'tokenPrice': float(data[3][i] / 10**18),\r\n            'tokenBalance_USD': float(data[2][i] / 10**int(data[4][i]) * data[3][i] / 10**18),\r\n            'tokenDecimals': data[4][i]\r\n            })\r\n            i += 1\r\n        rou.sort(key=lambda x: x.get('tokenBalance_USD'), reverse=True)\r\n        return rou\r\n\r\n\r\n    def update_chart_dex(self):\r\n        self.ui.stackedWidget_16.setCurrentWidget(self.ui.chart_isLoading)\r\n        self.ax.reset()\r\n        self.axo.reset()\r\n        worker = AWorker(self.fetchTokenData)\r\n        worker.signals.result.connect(self.refresh_datas)\r\n        self.threadpool.start(worker)\r\n\r\n\r\n    def update_wallet_balance(self):\r\n        worker = AWorker(self.fetchWalletBalances)\r\n        worker.signals.result.connect(self.editScrollAreaWidget)\r\n        self.threadpool.start(worker)\r\n\r\n    def calcMBuyOutput(self):\r\n        InputAmount = self.ui.Input_MB.text()\r\n        try:\r\n            float(InputAmount)\r\n            if float(InputAmount) != float(0):\r\n                status = True\r\n            else:\r\n                status = False\r\n        except:\r\n            status = False  \r\n        if status == True:\r\n            Slippage = (float(InputAmount) / 100 * self.US.SLIPPAGE)\r\n            Amount = int( (float(InputAmount) - float(Slippage)) * 10**18 )\r\n            if self.GS.B_TOKEN == \"BNB\":\r\n                BaseAddress = self.web3.WBNB\r\n            elif self.GS.B_TOKEN == \"BUSD\":\r\n                BaseAddress = self.web3.BUSD\r\n            OutputAmount = self.math.FloatToCleanSting(\r\n                self.web3.fetchOutputAmount(\r\n                    Amount, BaseAddress,\r\n                    self.token_address)[0] / 10**self.jsonTokenData[\"tokenDecimals\"]\r\n                    )\r\n            self.ui.Output_MB.textChanged.disconnect()\r\n            self.ui.Output_MB.setText(OutputAmount)\r\n            self.ui.Output_MB.textChanged.connect(self.calcMBuyInput)\r\n\r\n\r\n    def calcMSellOutput(self):\r\n        InputAmount = self.ui.Input_MS.text()\r\n        try:\r\n            float(InputAmount)\r\n            if float(InputAmount) != float(0):\r\n                status = True\r\n            else:\r\n                status = False\r\n        except:\r\n            status = False  \r\n        if status == True:\r\n            Slippage = (float(InputAmount) / 100 * self.US.SLIPPAGE)\r\n            Amount = int( (float(InputAmount) - float(Slippage)) * 10**self.jsonTokenData['tokenDecimals'] )\r\n            print(Amount)\r\n            if self.GS.B_TOKEN == \"BNB\":\r\n                BaseAddress = self.web3.WBNB\r\n            elif self.GS.B_TOKEN == \"BUSD\":\r\n                BaseAddress = self.web3.BUSD\r\n            OutputAmount = self.math.FloatToCleanSting(\r\n                self.web3.fetchOutputAmount(\r\n                    Amount, \r\n                    self.token_address,\r\n                    BaseAddress\r\n                    )[0] / 10**18)\r\n            self.ui.Output_MS.textChanged.disconnect()\r\n            self.ui.Output_MS.setText(OutputAmount)\r\n            self.ui.Output_MS.textChanged.connect(self.calcMSellInput)\r\n\r\n\r\n    def calcMSellInput(self):\r\n        OutputAmount = self.ui.Output_MS.text()\r\n        try:\r\n            float(OutputAmount)\r\n            if float(OutputAmount) != float(0):\r\n                status = True\r\n            else:\r\n                status = False\r\n        except:\r\n            status = False  \r\n        if status == True:\r\n            Slippage = (float(OutputAmount) / 100 * self.US.SLIPPAGE)\r\n            Amount = int( (float(OutputAmount) + float(Slippage)) * 10**self.jsonTokenData['tokenDecimals'])\r\n            print(Amount)\r\n            if self.GS.B_TOKEN == \"BNB\":\r\n                BaseAddress = self.web3.WBNB\r\n            elif self.GS.B_TOKEN == \"BUSD\":\r\n                BaseAddress = self.web3.BUSD\r\n            InputAmount = self.math.FloatToCleanSting(\r\n                self.web3.fetchOutputAmount(\r\n                    Amount, \r\n                    BaseAddress,\r\n                    self.token_address\r\n                    )[0] / 10**18)\r\n            self.ui.Input_MS.textChanged.disconnect()\r\n            self.ui.Input_MS.setText(InputAmount )\r\n            self.ui.Input_MS.textChanged.connect(self.calcMSellOutput)\r\n\r\n\r\n    def calcMBuyInput(self):\r\n        OutputAmount = self.ui.Output_MB.text()\r\n        try:\r\n            float(OutputAmount)\r\n            if float(OutputAmount) != float(0):\r\n                status = True\r\n            else:\r\n                status = False\r\n        except:\r\n            status = False  \r\n        if status == True:\r\n            Slippage = (float(OutputAmount) / 100 * self.US.SLIPPAGE)\r\n            Amount = int( (float(OutputAmount) + float(Slippage)) * 10**self.jsonTokenData['tokenDecimals'] )\r\n            print(Amount)\r\n            if self.GS.B_TOKEN == \"BNB\":\r\n                BaseAddress = self.web3.WBNB\r\n            elif self.GS.B_TOKEN == \"BUSD\":\r\n                BaseAddress = self.web3.BUSD\r\n            InputAmount = self.math.FloatToCleanSting(\r\n                self.web3.fetchOutputAmount(\r\n                    Amount, \r\n                    self.token_address,\r\n                    BaseAddress\r\n                    )[0] / 10**18)\r\n            print(InputAmount)\r\n            self.ui.Input_MB.textChanged.disconnect()\r\n            self.ui.Input_MB.setText(InputAmount)\r\n            self.ui.Input_MB.textChanged.connect(self.calcMBuyOutput)\r\n\r\n\r\n    def resetInputs(self):\r\n        self.ui.Output_MS.setText(str(0))\r\n        self.ui.Input_MS.setText(str(0))\r\n        self.ui.Output_MB.setText(str(0))\r\n        self.ui.Input_MB.setText(str(0))\r\n        self.ui.Input_LS.setText(str(0))\r\n        self.ui.Price_LS.setText(str(0))\r\n        self.ui.Input_LB.setText(str(0))\r\n        self.ui.Price_LB.setText(str(0))\r\n\r\n\r\n    def checkSaveSettings(self):\r\n        try:\r\n            self.ui.frame_Settings_error.hide()\r\n        except:pass\r\n        try:\r\n            self.ui.label_Settings_error.show()\r\n        except:pass       \r\n        try:\r\n            self.ui.frame_Settings_success.hide()\r\n        except:pass      \r\n        e = True\r\n        if len(self.ui.Input_Settings_Address.text()) != 0:\r\n            if Web3.isAddress(self.ui.Input_Settings_Address.text()):\r\n                self.US.changeUserSetting(\"ADDRESS\",Web3.toChecksumAddress(self.ui.Input_Settings_Address.text()))\r\n        if len(self.ui.Input_Settings_PrivateKey.text()) != 0:\r\n            try:\r\n                self.US.changeUserSetting(\"SECRETKEY\", self.ui.Input_Settings_PrivateKey.text())\r\n            except Exception as e:\r\n                self.showSettingsError(e)\r\n                print(e)\r\n                e = False\r\n        if len(self.ui.Input_Settings_provider.text()) != 0:\r\n            try:\r\n                providerTest = web3ProviderTester().runTest(self.ui.Input_Settings_provider.text())\r\n                if providerTest:\r\n                    self.US.changeUserSetting(\"PROVIDER\", self.ui.Input_Settings_provider.text())\r\n            except Exception as e:\r\n                self.showSettingsError(e)\r\n                print(e)\r\n                e = False\r\n        if len(self.ui.Input_Settings_Slippage.text()) != 0:\r\n            try:\r\n                float(self.ui.Input_Settings_Slippage.text())\r\n                self.US.changeUserSetting(\"SLIPPAGE\", float(self.ui.Input_Settings_Slippage.text()))\r\n            except Exception as e:\r\n                self.showSettingsError(e)\r\n                print(e)\r\n                e = False\r\n        if len(self.ui.Input_Settings_GAS.text()) != 0:\r\n            try:\r\n                float(self.ui.Input_Settings_GAS.text())\r\n                self.US.changeUserSetting(\"GAS_GWEI\", float(self.ui.Input_Settings_GAS.text()))\r\n            except Exception as e:\r\n                self.showSettingsError(e)\r\n                print(e)\r\n                e = False\r\n        if len(self.ui.Input_Settings_OQI.text()) != 0:\r\n            try:\r\n                float(self.ui.Input_Settings_OQI.text())\r\n                self.US.changeUserSetting(\"ORDER_QUERY_INTERVAL\", float(self.ui.Input_Settings_OQI.text()))\r\n            except Exception as e:\r\n                self.showSettingsError(e)\r\n                print(e)\r\n                e = False\r\n        if len(self.ui.Input_Settings_SQI.text()) != 0:\r\n            try:\r\n                float(self.ui.Input_Settings_SQI.text())\r\n                self.US.changeUserSetting(\"SNIPER_QUERY_INTERVAL\", float(self.ui.Input_Settings_SQI.text()))\r\n            except Exception as e:\r\n                self.showSettingsError(e)\r\n                print(e)\r\n                e = False\r\n        self.US.changeUserSetting(\"WAIT_TX_STATUS\", self.ui.checkBox_WaitTXStatus.isChecked())\r\n        self.US.changeUserSetting(\"AUTOSTART_ORDERS\", self.ui.checkBox_AutoStart_Orders.isChecked())\r\n        self.US.changeUserSetting(\"AUTOSTART_SNIPER\", self.ui.checkBox_AutoStart_Sniper.isChecked())\r\n        if e:\r\n            self.ui.frame_Settings_error.show()\r\n            self.ui.label_Settings_error.hide()\r\n\r\n        \r\n    def showSettingsError(self, e):\r\n        try:\r\n            self.ui.label_Settings_error.setText(str(e))\r\n            self.ui.label_Settings_error.show()\r\n        except:\r\n            pass\r\n        \r\n        \r\n    def changeIndicatorSettings(self, SETTING):\r\n        if SETTING == \"MA20\":\r\n            if self.GS.MA20 == True:\r\n                self.GS.changeGlobalSetting(\"MA20\", False)\r\n            else:\r\n                self.GS.changeGlobalSetting(\"MA20\", True)\r\n        if SETTING == \"VOLUME\":\r\n            if self.GS.VOLUME == True:\r\n                self.GS.changeGlobalSetting(\"VOLUME\", False)\r\n            else:\r\n                self.GS.changeGlobalSetting(\"VOLUME\", True)\r\n        self.selectIndicatorsButtons()\r\n        self.update_chart_dex()\r\n\r\n\r\n    def changeCandleTime(self, TIME):\r\n        self.GS.changeGlobalSetting(\"CandleTimeMin\", TIME)\r\n        self.selectCandleTime()\r\n        self.update_chart_dex()\r\n\r\n    def changeCandleHistoryTime(self, TIME):\r\n        self.GS.changeGlobalSetting(\"CandleHistory\", TIME)\r\n        self.selectCandleHistoryTime()\r\n        self.update_chart_dex()\r\n\r\n    def changeBToken(self, B_TOKEN):\r\n        self.GS.changeGlobalSetting(\"BASE_TOKEN\", B_TOKEN)\r\n        self.selectBaseToken()\r\n        self.resetInputs()\r\n        self.update_TokenAllowance()\r\n\r\n    def changeOrderType(self, ORDER_TYPE):\r\n        self.GS.changeGlobalSetting(\"ORDER_TYPE\", ORDER_TYPE)\r\n        self.selectOrderType()\r\n\r\n    def changeMenuButton(self, MENU_BUTTON):\r\n        self.GS.changeGlobalSetting(\"MENU_BUTTON\", MENU_BUTTON)\r\n        self.selectMenuButton()\r\n\r\n    def initSettingsInputs(self):\r\n        self.ui.Input_Settings_Address.setText(self.US.ADDRESS)\r\n        self.ui.Input_Settings_PrivateKey.setText(self.US.PRIVKEY)\r\n        self.ui.Input_Settings_Slippage.setText(str(self.US.SLIPPAGE))\r\n        self.ui.Input_Settings_provider.setText(self.US.PROVIDER)\r\n        self.ui.Input_Settings_OQI.setText(str(self.US.OQI))\r\n        self.ui.Input_Settings_SQI.setText(str(self.US.SQI))\r\n        self.ui.Input_Settings_GAS.setText(str(self.US.GAS))\r\n        self.ui.checkBox_WaitTXStatus.setChecked(self.US.WAIT_TX_STATUS)\r\n        self.ui.checkBox_AutoStart_Orders.setChecked(self.US.AUTOSTART_ORDERS)\r\n        self.ui.checkBox_AutoStart_Sniper.setChecked(self.US.AUTOSTART_SNIPER)\r\n        try:\r\n            self.ui.pushButton_Save_Settings.clicked.disconnect()\r\n        except:\r\n            pass\r\n        try:\r\n            self.ui.pushButton_Discard_Settings.clicked.disconnect()\r\n        except:\r\n            pass\r\n        self.ui.pushButton_Save_Settings.clicked.connect(self.checkSaveSettings)\r\n        self.ui.pushButton_Discard_Settings.clicked.connect(self.initSettingsInputs)\r\n        \r\n\r\n    def initTradeInputs(self):\r\n        self.ui.Input_MB.textChanged.connect(self.calcMBuyOutput)\r\n        self.ui.Input_MS.textChanged.connect(self.calcMSellOutput)\r\n        self.ui.Output_MB.textChanged.connect(self.calcMBuyInput)\r\n        self.ui.Output_MS.textChanged.connect(self.calcMSellInput)\r\n    \r\n\r\n    def initTradeSymbols(self):\r\n        self.ui.Input_MB_tokenSymbol.setText(self.GS.B_TOKEN)\r\n        self.ui.Output_MB_tokenSymbol.setText(str(self.jsonTokenData[\"tokenSymbol\"]))\r\n        self.ui.Input_MS_tokenSymbol.setText(str(self.jsonTokenData[\"tokenSymbol\"]))\r\n        self.ui.Output_MS_tokenSymbol.setText(self.GS.B_TOKEN)\r\n        self.ui.Input_LB_tokenSymbol.setText(self.GS.B_TOKEN)\r\n\r\n        self.ui.Output_LB_tokenSymbol.setText(\"$\")\r\n        self.ui.Input_LS_tokenSymbol.setText(str(self.jsonTokenData[\"tokenSymbol\"]))\r\n        self.ui.Output_LS_tokenSymbol.setText(\"$\")\r\n\r\n\r\n    def initChart(self):\r\n        self.movie = QMovie(r'ui/icons/Spinner.gif')\r\n        self.ui.chart_loading.setMovie(self.movie)\r\n        self.ui.chart_loading.setAlignment(Qt.AlignCenter)\r\n        self.movie.setScaledSize(QSize(275, 275))\r\n        self.movie.start()\r\n        self.sizegrip = QSizeGrip(self.ui.frame_size_grip)\r\n        self.sizegrip.setStyleSheet(\"width: 20px; height: 20px; margin 0px; padding: 0px;\")\r\n        area = DockArea()\r\n        dock_0 = Dock(\"Chart\", hideTitle=True, closable = False)\r\n        self.ax = fplt.create_plot_widget(master=area,rows=1, init_zoom_periods=10**10)\r\n        self.axo = self.ax.overlay()\r\n        area.axs = [self.ax]\r\n        dock_0.addWidget(self.ax.ax_widget, 1, 0, 1, 2)\r\n        area.addDock(dock_0)\r\n        self.layChart = QVBoxLayout()\r\n        self.layChart.setContentsMargins(0, 0, 0, 0)\r\n        self.layChart.addWidget(area)\r\n        self.ui.chart_container.setLayout(self.layChart)\r\n\r\n\r\n    def initOpenOrders(self):\r\n        self.OS.LoadOrders()\r\n        layout = self.ui.scrollArea_ordersWidgetContents.layout()\r\n        for i in reversed(range(layout.count())): \r\n            layout.itemAt(i).widget().deleteLater()\r\n        for order in self.OS.ORDERS:\r\n            o = self.uif.createOrderContainer(self, order[\"ID\"], order[\"INPUT_SYMBOL\"], order[\"OUTPUT_SYMBOL\"], order[\"SIDE\"], order[\"TARGET_PRICE\"], order[\"INPUT_AMOUNT\"])\r\n            layout.addWidget(o)\r\n\r\n\r\n    def initButtonValidators(self):\r\n        NumberValidator = QDoubleValidator(0.0, 5.0, 8)\r\n        self.ui.Input_LB.setValidator(NumberValidator)\r\n        self.ui.Input_LS.setValidator(NumberValidator)\r\n        self.ui.Price_LS.setValidator(NumberValidator)\r\n        self.ui.Price_LB.setValidator(NumberValidator)\r\n        self.ui.Input_MB.setValidator(NumberValidator)\r\n        self.ui.Input_MS.setValidator(NumberValidator)\r\n        self.ui.Output_MB.setValidator(NumberValidator)\r\n        self.ui.Output_MS.setValidator(NumberValidator)\r\n\r\n    def initButtonsChart(self):\r\n        self.ui.pushButton_Chart_1m.clicked.connect(lambda: self.changeCandleTime(1))\r\n        self.ui.pushButton_Chart_5m.clicked.connect(lambda: self.changeCandleTime(5))\r\n        self.ui.pushButton_Chart_10m.clicked.connect(lambda: self.changeCandleTime(10))\r\n        self.ui.pushButton_Chart_30m.clicked.connect(lambda: self.changeCandleTime(30))\r\n        self.ui.pushButton_Chart_1h.clicked.connect(lambda: self.changeCandleTime(60))\r\n        self.ui.pushButton_Chart_1d.clicked.connect(lambda: self.changeCandleTime(1440))\r\n        self.ui.pushButton_ChartTime_1h.clicked.connect(lambda: self.changeCandleHistoryTime(1))\r\n        self.ui.pushButton_ChartTime_1d.clicked.connect(lambda: self.changeCandleHistoryTime(24))\r\n        self.ui.pushButton_ChartTime_1w.clicked.connect(lambda: self.changeCandleHistoryTime(168))\r\n        self.ui.pushButton_ChartTime_1m.clicked.connect(lambda: self.changeCandleHistoryTime(672))\r\n        self.ui.pushButton_ChartTime_1y.clicked.connect(lambda: self.changeCandleHistoryTime(8064))\r\n\r\n    def initButtonsBToken(self):\r\n        self.ui.Button_Base_BNB_MB.clicked.connect(lambda: self.changeBToken(\"BNB\"))\r\n        self.ui.Button_Base_BNB_MS.clicked.connect(lambda: self.changeBToken(\"BNB\"))\r\n        self.ui.Button_Base_BUSD_MB.clicked.connect(lambda: self.changeBToken(\"BUSD\"))\r\n        self.ui.Button_Base_BUSD_MS.clicked.connect(lambda: self.changeBToken(\"BUSD\"))\r\n        self.ui.Button_Base_BNB_LB.clicked.connect(lambda: self.changeBToken(\"BNB\"))\r\n        self.ui.Button_Base_BNB_LS.clicked.connect(lambda: self.changeBToken(\"BNB\"))\r\n        self.ui.Button_Base_BUSD_LS.clicked.connect(lambda: self.changeBToken(\"BUSD\"))\r\n        self.ui.Button_Base_BUSD_LB.clicked.connect(lambda: self.changeBToken(\"BUSD\"))\r\n\r\n    def initButtonsOrderType(self):\r\n        self.ui.Button_Market.clicked.connect(lambda: self.changeOrderType(\"MARKET\"))\r\n        self.ui.Button_Limit.clicked.connect(lambda: self.changeOrderType(\"LIMIT\"))\r\n\r\n    def initButtonsMenu(self):\r\n        self.ui.pushButton_Wallet.clicked.connect(lambda: self.changeMenuButton(\"WALLET\"))\r\n        self.ui.pushButton_Orders.clicked.connect(lambda: self.initOpenOrders())\r\n        self.ui.pushButton_Orders.clicked.connect(lambda: self.changeMenuButton(\"ORDERS\"))\r\n        self.ui.pushButton_Settings.clicked.connect(lambda: self.initSettingsInputs())\r\n        self.ui.pushButton_Settings.clicked.connect(lambda: self.changeMenuButton(\"SETTINGS\"))\r\n\r\n\r\n    def selectCandleTime(self):\r\n        if self.GS.candleTimeMin == 1:\r\n            self.ui.pushButton_Chart_1m.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n        else:\r\n            self.ui.pushButton_Chart_1m.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n        if self.GS.candleTimeMin == 5:\r\n            self.ui.pushButton_Chart_5m.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n        else:\r\n            self.ui.pushButton_Chart_5m.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n        if self.GS.candleTimeMin == 10:\r\n            self.ui.pushButton_Chart_10m.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n        else:\r\n            self.ui.pushButton_Chart_10m.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n        if self.GS.candleTimeMin == 30:\r\n            self.ui.pushButton_Chart_30m.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n        else:\r\n            self.ui.pushButton_Chart_30m.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n        if self.GS.candleTimeMin == 60:\r\n            self.ui.pushButton_Chart_1h.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n        else:\r\n            self.ui.pushButton_Chart_1h.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n        if self.GS.candleTimeMin == 1440:\r\n            self.ui.pushButton_Chart_1d.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n        else:\r\n            self.ui.pushButton_Chart_1d.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n\r\n\r\n    def selectCandleHistoryTime(self):\r\n        if self.GS.candleHistory == 1:\r\n            self.ui.pushButton_ChartTime_1h.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n        else:\r\n            self.ui.pushButton_ChartTime_1h.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n        if self.GS.candleHistory == 24:\r\n            self.ui.pushButton_ChartTime_1d.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n        else:\r\n            self.ui.pushButton_ChartTime_1d.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n        if self.GS.candleHistory == 168:\r\n            self.ui.pushButton_ChartTime_1w.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n        else:\r\n            self.ui.pushButton_ChartTime_1w.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n        if self.GS.candleHistory == 672:\r\n            self.ui.pushButton_ChartTime_1m.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n        else:\r\n            self.ui.pushButton_ChartTime_1m.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n        if self.GS.candleHistory == 8064:\r\n            self.ui.pushButton_ChartTime_1y.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n        else:\r\n            self.ui.pushButton_ChartTime_1y.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n\r\n    def selectIndicatorsButtons(self):\r\n        if self.GS.MA20 == True:\r\n            self.ui.ButtonchartHelper_MA20.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n        else:\r\n            self.ui.ButtonchartHelper_MA20 .setStyleSheet(Ui_Styles.btn_CandelTime)\r\n        if self.GS.VOLUME == True:\r\n            self.ui.ButtonChartHelper_Volume.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n        else:\r\n            self.ui.ButtonChartHelper_Volume.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n\r\n\r\n    def selectBaseToken(self):\r\n        if self.GS.B_TOKEN == \"BNB\":\r\n            self.ui.Button_Base_BNB_MB.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n            self.ui.Button_Base_BNB_MS.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n            self.ui.Button_Base_BNB_LB.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n            self.ui.Button_Base_BNB_LS.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n            self.ui.Button_Base_BUSD_MB.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n            self.ui.Button_Base_BUSD_MS.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n            self.ui.Button_Base_BUSD_LB.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n            self.ui.Button_Base_BUSD_LS.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n\r\n        elif self.GS.B_TOKEN == \"BUSD\":\r\n            self.ui.Button_Base_BUSD_MB.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n            self.ui.Button_Base_BUSD_MS.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n            self.ui.Button_Base_BUSD_LB.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n            self.ui.Button_Base_BUSD_LS.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n            self.ui.Button_Base_BNB_MB.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n            self.ui.Button_Base_BNB_MS.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n            self.ui.Button_Base_BNB_LB.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n            self.ui.Button_Base_BNB_LS.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n        try:\r\n            self.initTradeSymbols()\r\n        except:\r\n            pass\r\n    \r\n    def selectOrderType(self):\r\n        if self.GS.OT == \"MARKET\":\r\n            self.ui.Button_Market.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n            self.ui.Button_Limit.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n            self.ui.stackedWidget_17.setCurrentWidget(self.ui.Trade_Market)\r\n        elif self.GS.OT == \"LIMIT\":\r\n            self.ui.Button_Limit.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n            self.ui.Button_Market.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n            self.ui.stackedWidget_17.setCurrentWidget(self.ui.Trade_Limit)\r\n\r\n\r\n    def selectMenuButton(self):\r\n        if self.GS.BM == \"WALLET\":\r\n            self.ui.pushButton_Wallet.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n            self.ui.pushButton_Orders.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n            self.ui.pushButton_Settings.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n            self.ui.stackedWidget_2.setCurrentWidget(self.ui.WalletOverview)\r\n        elif self.GS.BM == \"ORDERS\":\r\n            self.ui.pushButton_Orders.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n            self.ui.pushButton_Settings.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n            self.ui.pushButton_Wallet.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n            self.ui.stackedWidget_2.setCurrentWidget(self.ui.OrdersOverview)\r\n        elif self.GS.BM == \"SETTINGS\":\r\n            self.ui.pushButton_Orders.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n            self.ui.pushButton_Settings.setStyleSheet(Ui_Styles.btn_CandelTime_Select)\r\n            self.ui.pushButton_Wallet.setStyleSheet(Ui_Styles.btn_CandelTime)\r\n            self.ui.stackedWidget_2.setCurrentWidget(self.ui.SettingsOverview)\r\n\r\n\r\n\r\n    def editScrollAreaWidget(self, status, rou):\r\n        if status == True:\r\n            Balance_all = float()\r\n            layout = self.ui.scrollAreaWidgetContents.layout()\r\n            for i in reversed(range(layout.count())): \r\n                layout.itemAt(i).widget().deleteLater()\r\n            for info in rou:\r\n                if float(info['tokenBalance']) > 0.0000001:\r\n                    Balance_all += info['tokenBalance_USD']\r\n                    c = self.uif.createWalletTokenContainer(self.ui, info['tokenAddress'], info['tokenSymbol'], info['tokenName'], info['tokenPrice'], info['tokenBalance'], info['tokenBalance_USD'])\r\n                    layout.addWidget(c)\r\n            self.ui.Balance_all.setText(\"$ \" + self.math.FloatToCleanSting(Balance_all))\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    app = QApplication(sys.argv)\r\n    window = MainWindow()\r\n    app.setWindowIcon(QIcon('ui/icons/favicon.ico'))\r\n    window.setWindowIcon(QIcon('ui/icons/favicon.ico'))\r\n    sys.exit(app.exec_())\r\n","repo_name":"Trading-Tiger/PancakeSwap_BSC_LimitOrders_BOT_GUI","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":54180,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"85"}
+{"seq_id":"1664743783","text":"import os\nimport shutil\nimport docx\n\nfrom datetime import datetime\nfrom openpyxl import load_workbook\nfrom parse import TBC\n\nRESOURCES_FOLDER = os.path.abspath('resources')\n\n\ndef get_new_filename(record, document_name, file_extension):\n    \"\"\"Generates the filename of an output document\n\n    Args:\n        record(Record): A Record object\n        document_name(str): The name of the document that is being exported\n        file_extension(str): The file extension of the documents to copy\n\n    Returns:\n        str: The new filename\n\n    \"\"\"\n    now = datetime.now()\n    curr_year = now.year\n    curr_month = now.strftime('%b')\n\n    new_filename = (\n        f'{record.client.last_name}, '\n        f'{record.client.first_name} - '\n        f'{document_name} - '\n        f'{curr_year} '\n        f'{curr_month}'\n        f'.{file_extension}'\n    )\n\n    return new_filename\n\n\ndef copy_resources_to_export(record, export_folder, file_extension):\n    \"\"\"Copies all files with the given extension from the resources folder to the export folder\n\n    Args:\n        record(Record): A Record object\n        export_folder(str): The absolute path of the folder to export to\n        file_extension(str): The file extension of the documents to copy\n\n    Returns:\n        list(str): The list of destination file absolute paths\n\n    \"\"\"\n    paths = []\n    for item in os.listdir(RESOURCES_FOLDER):\n        if not item.endswith(f'.{file_extension}'):\n            continue\n\n        document_name = item[:item.index(f'.{file_extension}')]\n        src = os.path.join(RESOURCES_FOLDER, item)\n        dst = os.path.join(export_folder, get_new_filename(record, document_name, file_extension))\n        shutil.copyfile(src, dst)\n\n        paths.append(dst)\n\n    return paths\n\n\ndef word_export(record, export_folder):\n    \"\"\"Exports the data in a Record object into all of the output word documents\n\n    Args:\n        record(Record): A Record object\n        export_folder(str): The absolute path of the folder to export to\n\n    Returns:\n        None\n\n    \"\"\"\n    placeholder_to_val = {\n        '[title]': record.client.title,\n        '[full_name]': record.client.full_name,\n        '[dob]': record.client.dob,\n        '[gender]': record.client.gender,\n        '[address]': str(record.client.address),\n        '[house_number]': record.client.address.house_number,\n        '[street]': record.client.address.street,\n        '[suburb]': record.client.address.suburb,\n        '[state]': record.client.address.state,\n        '[home_phone_number]': record.client.home_phone_number,\n        '[mobile_phone_number]': record.client.mobile_phone_number,\n        '[email_address]': record.client.email_address,\n        '[ndis_number]': record.client.ndis_number,\n        '[plan_start_date]': record.plan.start_date,\n        '[plan_end_date]': record.plan.end_date,\n        '[core_supports_categories]': record.supports['Core'].categories,\n        '[capacity_building_supports_categories]': record.supports['Capacity Building'].categories,\n        '[capital_supports_categories]': record.supports['Capital'].categories,\n        '[core_supports_total]': record.supports['Core'].total,\n        '[capacity_building_supports_total]': record.supports['Capacity Building'].total,\n        '[capital_supports_total]': record.supports['Capital'].total,\n        '[funded_supports_total]': record.funded_supports_total,\n        '[support_coordination_hours]': record.support_coordination_hours\n    }\n\n    goals = []\n    for value in record.supports.values():\n        if value.goals != TBC:\n            for goal in value.goals:\n                goals.append(goal)\n\n    goals.extend(['' for _ in range(12 - len(goals))])\n\n    def search_and_replace(paragraph):\n        for placeholder in placeholder_to_val.keys():\n            if placeholder in paragraph.text:\n                value = placeholder_to_val[placeholder]\n                if 'categories' in placeholder:\n                    if value == TBC:\n                        string = TBC\n                    else:\n                        string = ''\n                        for category in value:\n                            string += f'{category[0]}: {category[1]}\\n'\n                else:\n                    string = value\n\n                paragraph.text = paragraph.text.replace(\n                    placeholder,\n                    string\n                )\n            elif '[goal]' in paragraph.text:\n                paragraph.text = paragraph.text.replace('[goal]', goals[0])\n                goals.pop(0)\n            elif '[sc1]' in paragraph.text:\n                if record.support_coordination_management_type.lower() == 'ndia-managed':\n                    paragraph.text = '   X'\n                else:\n                    paragraph.text = ''\n            elif '[sc2]' in paragraph.text:\n                if record.support_coordination_management_type.lower() == 'self-managed':\n                    paragraph.text = '   X'\n                else:\n                    paragraph.text = ''\n\n    paths = copy_resources_to_export(record, export_folder, 'docx')\n    for path in paths:\n        doc = docx.Document(path)\n        for paragraph in doc.paragraphs:\n            search_and_replace(paragraph)\n\n        for table in doc.tables:\n            for row in table.rows:\n                for cell in row.cells:\n                    for paragraph in cell.paragraphs:\n                        search_and_replace(paragraph)\n\n        doc.save(path)\n\n\ndef excel_export(record, export_folder='', optional_xml_path=''):\n    \"\"\"Exports the data in a Record object into all of the output excel documents\n\n    Args:\n        record(Record): A Record object\n        export_folder(str): The absolute path of the folder to export to (optional)\n        optional_xml_path(str): The path of an xml document to append data to if a new one\n            should not be created (optional)\n\n    Returns:\n        None\n\n    \"\"\"\n    data = (\n        record.client.title,\n        'CLIENT',\n        record.client.first_name,\n        record.client.last_name,\n        record.client.home_phone_number,\n        record.client.mobile_phone_number,\n        record.client.gender,\n        record.client.dob,\n        record.client.email_address,\n        record.additional_email_address,\n        record.service_region_id,\n        f'{record.client.address.house_number} {record.client.address.street}',\n        '',\n        record.client.address.suburb,\n        record.client.address.state,\n        record.client.address.postcode,\n        record.client.ndis_number,\n        record.plan.start_date,\n        record.plan.end_date,\n        'Support Coordination Client',\n        'Inactive',\n        TBC,\n        TBC,\n        TBC,\n        TBC,\n        'off',\n        TBC,\n        TBC,\n        TBC,\n        TBC,\n        'Yes',\n        TBC,\n        TBC,\n        'No',\n        'Yes',\n        'No',\n        TBC,\n        TBC,\n        'None',\n        TBC,\n        TBC,\n        'Megan King',\n        'Megan King'\n    )\n\n    if export_folder:\n        path = copy_resources_to_export(record, export_folder, 'xlsx')[0]\n    else:\n        path = optional_xml_path\n\n    wb = load_workbook(filename=path)\n    ws = wb.active\n    ws.append(data)\n    wb.save(path)\n\n\ndef record_export(record, export_folder):\n    \"\"\"Exports the data in a Record object into a blank text file\n\n    Args:\n        record(Record): A Record object\n        export_folder(str): The absolute path of the folder to export to (optional)\n\n    Returns:\n        None\n\n    \"\"\"\n    export_path = os.path.join(export_folder, get_new_filename(record, 'Data', 'txt'))\n    with open(export_path, 'w') as file:\n        file.write(str(record))\n","repo_name":"Pwnion/NDIS-Doc-Parser","sub_path":"export.py","file_name":"export.py","file_ext":"py","file_size_in_byte":7631,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"1969025821","text":"from fastapi import APIRouter, Body, Depends, Path, Query\nfrom starlette.status import *\nfrom starlette.exceptions import HTTPException\nfrom typing import List\nfrom motor.motor_asyncio import AsyncIOMotorClient\nfrom uuid import UUID, uuid4\n\nfrom server.config import *\nfrom server.database import get_database\nfrom server.models.assets import *\nfrom server.models.users import *\nfrom server.routes.users import fastapi_users\n\nrouter = APIRouter()\n\n@router.post(\"\", response_model=Asset, status_code=HTTP_201_CREATED)\nasync def create_asset(\n    asset: AssetCreate,\n    db: AsyncIOMotorClient = Depends(get_database),\n    user: User = Depends(fastapi_users.get_current_user),\n):\n    asset = asset.dict()\n    asset[\"id\"] = str(uuid4())\n    asset[\"user_id\"] = str(user.id)\n    await db[\"assets\"].insert_one(asset)\n\n    return Asset(**asset)\n\n\n@router.get(\"\", response_model=List[Asset], status_code=HTTP_200_OK)\nasync def get_all_assets(\n    db: AsyncIOMotorClient = Depends(get_database),\n    user: User = Depends(fastapi_users.get_current_user),\n):\n    assets = db[\"assets\"].find({\"user_id\": str(user.id)})\n    return [Asset(**asset) async for asset in assets]\n\n\n@router.get(\"/{id}\", response_model=Asset, status_code=HTTP_200_OK)\nasync def get_asset(\n    id,\n    db: AsyncIOMotorClient = Depends(get_database),\n    user: User = Depends(fastapi_users.get_current_user),\n):\n    asset = await db[\"assets\"].find_one({\"id\": id, \"user_id\": str(user.id)})\n    if not asset:\n        raise HTTPException(status_code=404, detail=\"Asset not found\")\n    return Asset(**asset)\n\n\n@router.delete(\"/{id}\", status_code=HTTP_200_OK)\nasync def delete_asset(\n    id,\n    db: AsyncIOMotorClient = Depends(get_database),\n    user: User = Depends(fastapi_users.get_current_user),\n):\n\n    asset = await db[\"assets\"].find_one({\"id\": id, \"user_id\": str(user.id)})\n    if not asset:\n        raise HTTPException(status_code=404, detail=\"Asset not found\")\n\n    await db[\"assets\"].delete_one({\"id\": id, \"user_id\": str(user.id)})\n\n    return Asset(**asset)\n","repo_name":"Aeturnum-Network/aeturnum","sub_path":"backend/server/routes/assets.py","file_name":"assets.py","file_ext":"py","file_size_in_byte":2026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"21828214347","text":"from __future__ import annotations\n\nimport logging\nimport math\nimport os\n\nimport libtbx.utils\nfrom dials.array_family import flex\nfrom dxtbx.serialize import load\n\nfrom xia2.Driver.DriverFactory import DriverFactory\nfrom xia2.Handlers.Phil import PhilIndex\n\nlogger = logging.getLogger(\"xia2.Wrappers.Dials.Index\")\n\n\ndef Index(DriverType=None):\n    \"\"\"A factory for IndexWrapper classes.\"\"\"\n\n    DriverInstance = DriverFactory.Driver(DriverType)\n\n    class IndexWrapper(DriverInstance.__class__):\n        def __init__(self):\n            DriverInstance.__class__.__init__(self)\n            self.set_executable(\"dials.index\")\n\n            self._sweep_filenames = []\n            self._spot_filenames = []\n            self._unit_cell = None\n            self._space_group = None\n            self._maximum_spot_error = None\n            self._detector_fix = None\n            self._beam_fix = None\n            self._indexing_method = \"fft3d\"\n            self._p1_cell = None\n            self._indxr_input_cell = None\n            self._indxr_input_lattice = None\n            self._reflections_per_degree = None\n            self._fft3d_n_points = None\n            self._histogram_binning = None\n            self._nearest_neighbor_percentile = None\n\n            self._experiment_filename = None\n            self._indexed_filename = None\n\n            self._nref = None\n            self._rmsd_x = None\n            self._rmsd_y = None\n            self._rmsd_z = None\n\n            self._max_cell = None\n            self._max_cell_max_height_fraction = None\n            self._min_cell = None\n\n            self._d_min_start = None\n\n            self._phil_file = None\n            self._outlier_algorithm = None\n            self._close_to_spindle_cutoff = None\n\n        def add_sweep_filename(self, sweep_filename):\n            self._sweep_filenames.append(sweep_filename)\n\n        def add_spot_filename(self, spot_filename):\n            self._spot_filenames.append(spot_filename)\n\n        def set_indexer_input_lattice(self, lattice):\n            self._indxr_input_lattice = lattice\n\n        def set_indexer_user_input_lattice(self, user):\n            self._indxr_user_input_lattice = user\n\n        def set_indexer_input_cell(self, cell):\n            if not isinstance(cell, type(())) or len(cell) != 6:\n                raise RuntimeError(\"cell must be a 6-tuple de floats\")\n\n            self._indxr_input_cell = tuple(map(float, cell))\n\n        def set_detector_fix(self, detector_fix):\n            self._detector_fix = detector_fix\n\n        def set_beam_fix(self, beam_fix):\n            self._beam_fix = beam_fix\n\n        def set_reflections_per_degree(self, reflections_per_degree):\n            self._reflections_per_degree = int(reflections_per_degree)\n\n        def set_fft3d_n_points(self, n_points):\n            self._fft3d_n_points = n_points\n\n        def set_histogram_binning(self, histogram_binning):\n            self._histogram_binning = histogram_binning\n\n        def set_nearest_neighbor_percentile(self, nearest_neighbor_percentile):\n            self._nearest_neighbor_percentile = nearest_neighbor_percentile\n\n        def get_experiments_filename(self):\n            return self._experiment_filename\n\n        def get_indexed_filename(self):\n            return self._indexed_filename\n\n        def set_phil_file(self, phil_file):\n            self._phil_file = phil_file\n\n        def set_outlier_algorithm(self, outlier_algorithm):\n            self._outlier_algorithm = outlier_algorithm\n\n        def get_nref_rmsds(self):\n            return self._nref, (self._rmsd_x, self._rmsd_y, self._rmsd_z)\n\n        def set_max_cell(self, max_cell=None, max_height_fraction=None):\n            if max_cell is not None:\n                self._max_cell = max_cell\n            if max_height_fraction is not None:\n                self._max_cell_max_height_fraction = max_height_fraction\n\n        def set_min_cell(self, min_cell):\n            self._min_cell = min_cell\n\n        def set_close_to_spindle_cutoff(self, close_to_spindle_cutoff):\n            self._close_to_spindle_cutoff = close_to_spindle_cutoff\n\n        def run(self, method):\n            logger.debug(\"Running dials.index\")\n\n            self.clear_command_line()\n            for f in self._sweep_filenames:\n                self.add_command_line(f)\n            for f in self._spot_filenames:\n                self.add_command_line(f)\n            if len(self._sweep_filenames) > 1:\n                self.add_command_line(\"auto_reduction.action=fix\")\n            self.add_command_line(\"indexing.method=%s\" % method)\n            nproc = PhilIndex.params.xia2.settings.multiprocessing.nproc\n            self.set_cpu_threads(nproc)\n            self.add_command_line(\"indexing.nproc=%i\" % nproc)\n            if PhilIndex.params.xia2.settings.small_molecule:\n                self.add_command_line(\"filter_ice=false\")\n            if self._reflections_per_degree is not None:\n                self.add_command_line(\n                    \"reflections_per_degree=%i\" % self._reflections_per_degree\n                )\n            if self._fft3d_n_points is not None:\n                self.add_command_line(\n                    \"fft3d.reciprocal_space_grid.n_points=%i\" % self._fft3d_n_points\n                )\n            if self._close_to_spindle_cutoff is not None:\n                self.add_command_line(\n                    \"close_to_spindle_cutoff=%f\" % self._close_to_spindle_cutoff\n                )\n            if self._outlier_algorithm:\n                self.add_command_line(\"outlier.algorithm=%s\" % self._outlier_algorithm)\n            if self._max_cell:\n                self.add_command_line(\"max_cell=%g\" % self._max_cell)\n            if self._max_cell_max_height_fraction is not None:\n                self.add_command_line(\n                    \"max_height_fraction=%g\" % self._max_cell_max_height_fraction\n                )\n            if self._min_cell:\n                self.add_command_line(\"min_cell=%d\" % self._min_cell)\n            if self._histogram_binning is not None:\n                self.add_command_line(\n                    \"max_cell_estimation.histogram_binning=%s\" % self._histogram_binning\n                )\n            if self._nearest_neighbor_percentile is not None:\n                self.add_command_line(\n                    \"max_cell_estimation.nearest_neighbor_percentile=%s\"\n                    % self._nearest_neighbor_percentile\n                )\n            if self._d_min_start:\n                self.add_command_line(\"d_min_start=%f\" % self._d_min_start)\n            if self._indxr_input_lattice is not None:\n                from xia2.Experts.SymmetryExpert import lattice_to_spacegroup_number\n\n                self._symm = lattice_to_spacegroup_number(self._indxr_input_lattice)\n                self.add_command_line(\"known_symmetry.space_group=%s\" % self._symm)\n            if self._indxr_input_cell is not None:\n                self.add_command_line(\n                    'known_symmetry.unit_cell=\"%s,%s,%s,%s,%s,%s\"'\n                    % self._indxr_input_cell\n                )\n            if self._maximum_spot_error:\n                self.add_command_line(\n                    \"maximum_spot_error=%.f\" % self._maximum_spot_error\n                )\n            if self._detector_fix:\n                self.add_command_line(\"detector.fix=%s\" % self._detector_fix)\n            if self._beam_fix:\n                self.add_command_line(\"beam.fix=%s\" % self._beam_fix)\n            if self._phil_file is not None:\n                self.add_command_line(self._phil_file)\n\n            self._experiment_filename = os.path.join(\n                self.get_working_directory(), \"%d_indexed.expt\" % self.get_xpid()\n            )\n            self._indexed_filename = os.path.join(\n                self.get_working_directory(), \"%d_indexed.refl\" % self.get_xpid()\n            )\n            self.add_command_line(\"output.experiments=%s\" % self._experiment_filename)\n            self.add_command_line(\"output.reflections=%s\" % self._indexed_filename)\n\n            self.start()\n            self.close_wait()\n\n            if not os.path.isfile(self._experiment_filename) or not os.path.isfile(\n                self._indexed_filename\n            ):\n                # Indexing failed\n                with open(self.get_log_file()) as fh:\n                    if \"No suitable lattice could be found\" in fh.read():\n                        raise libtbx.utils.Sorry(\n                            \"No suitable indexing solution could be found.\\n\\n\"\n                            \"You can view the reciprocal space with:\\n\"\n                            \"dials.reciprocal_lattice_viewer %s\"\n                            % \" \".join(\n                                os.path.normpath(\n                                    os.path.join(self.get_working_directory(), p)\n                                )\n                                for p in self._sweep_filenames + self._spot_filenames\n                            )\n                        )\n                    else:\n                        raise RuntimeError(\n                            \"dials.index failed, see log file for more details: %s\"\n                            % self.get_log_file()\n                        )\n\n            self.check_for_errors()\n\n            for record in self.get_all_output():\n                if \"Too few reflections to parameterise\" in record:\n                    logger.debug(record.strip())\n\n            self._experiment_list = load.experiment_list(self._experiment_filename)\n            self._reflections = flex.reflection_table.from_file(self._indexed_filename)\n\n            crystal = self._experiment_list.crystals()[0]\n            self._p1_cell = crystal.get_unit_cell().parameters()\n\n            refined_sel = self._reflections.get_flags(\n                self._reflections.flags.used_in_refinement\n            )\n            refl = self._reflections.select(refined_sel)\n            xc, yc, zc = refl[\"xyzcal.px\"].parts()\n            xo, yo, zo = refl[\"xyzobs.px.value\"].parts()\n\n            self._nref = refl.size()\n            self._rmsd_x = math.sqrt(flex.mean(flex.pow2(xc - xo)))\n            self._rmsd_y = math.sqrt(flex.mean(flex.pow2(yc - yo)))\n            self._rmsd_z = math.sqrt(flex.mean(flex.pow2(zc - zo)))\n\n    return IndexWrapper()\n","repo_name":"xia2/xia2","sub_path":"src/xia2/Wrappers/Dials/Index.py","file_name":"Index.py","file_ext":"py","file_size_in_byte":10263,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"85"}
+{"seq_id":"20508338950","text":"class Solution(object):\n    def threeSum(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: List[List[int]]\n        \"\"\"\n        answer_list = []\n        nums.sort()\n        print(nums)\n        for index, test_num in enumerate(nums[:-2]):\n            if index != 0 and test_num == nums[index-1]:\n                continue\n            left_index = index + 1\n            right_index = len(nums) - 1\n            while left_index < right_index:\n                left = nums[left_index]\n                right = nums[right_index]\n                sum = test_num + left + right\n                if sum > 0:\n                    right_index -= 1\n                elif sum < 0:\n                    left_index += 1\n                elif sum == 0:\n                    answer_list.append([test_num, nums[left_index], nums[right_index]])\n                    while left == nums[left_index] and left_index < right_index:\n                        left_index += 1\n\n        return answer_list\n\n\nsol = Solution()\nprint(sol.threeSum([-1,0,1,2,-1,-4]))","repo_name":"haddeeann/tidbits","sub_path":"leetcode/python/15/3sum.py","file_name":"3sum.py","file_ext":"py","file_size_in_byte":1042,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"}
+{"seq_id":"12104448073","text":"import os\nimport sys\nimport enum\nimport glob\nimport stat\nimport shutil\nimport fnmatch\nimport filecmp\nfrom itertools import filterfalse\nimport argparse\nimport time\n\n\nclass Response(enum.Enum):\n    Ok = 0\n    SourceNotExist = 1\n    UnknownType = 2\n    UnknownMethod = 3\n    Skip = 4\n\n\nclass Method:\n    Copy = \"copy\"\n    Move = \"move\"\n    Link = \"link\"\n    Symlink = \"symlink\"\n\n\nclass Statistics:\n    def __init__(self):\n        self.correct_lines = 0\n        self.skipped_lines = 0\n        self.incorrect_lines = 0\n        self.total_lines = 0\n        self.succeeded_transfers = 0\n        self.skipped_transfers = 0\n        self.failed_transfers = 0\n\n\nclass ArgumentParserError(Exception): pass\n\n\nclass ThrowingArgumentParser(argparse.ArgumentParser):\n    def error(self, message):\n        raise ArgumentParserError(message)\n\n\n# compare two files\ndef compareFiles(file1, file2, shallow=True):\n    return filecmp.cmp(file1, file2, shallow=shallow)\n\n\n# compare two directories (reworked dircmp)\ndef compareDirs(dir1, dir2, shallow=True):\n    dir1_list = os.listdir(dir1)\n    dir2_list = os.listdir(dir2)\n    dir1_list.sort()\n    dir2_list.sort()\n\n    a = dict(zip(map(os.path.normcase, dir1_list), dir1_list))\n    b = dict(zip(map(os.path.normcase, dir2_list), dir2_list))\n    common = list(map(a.__getitem__, filter(b.__contains__, a)))\n    dir1_only = list(map(a.__getitem__, filterfalse(b.__contains__, a)))\n    dir2_only = list(map(b.__getitem__, filterfalse(a.__contains__, b)))\n    # if we have objects in only one directory then they are different\n    if dir1_only or dir2_only:\n        return False\n\n    common_dirs = []\n    common_files = []\n    common_funny = []\n    for x in common:\n        a_path = os.path.join(dir1, x)\n        b_path = os.path.join(dir2, x)\n\n        ok = 1\n        try:\n            a_stat = os.stat(a_path)\n        except OSError:\n            ok = 0\n        try:\n            b_stat = os.stat(b_path)\n        except OSError:\n            ok = 0\n\n        if ok:\n            a_type = stat.S_IFMT(a_stat.st_mode)\n            b_type = stat.S_IFMT(b_stat.st_mode)\n            if a_type != b_type:\n                common_funny.append(x)\n            elif stat.S_ISDIR(a_type):\n                common_dirs.append(x)\n            elif stat.S_ISREG(a_type):\n                common_files.append(x)\n            else:\n                common_funny.append(x)\n        else:\n            common_funny.append(x)\n    # if we have invalid objects then report directories are different\n    if common_funny:\n        return False\n\n    same_files, diff_files, funny_files = filecmp.cmpfiles(dir1, dir2, common_files, shallow=shallow)\n    # if we have different files or invalid objects then report directories are different\n    if diff_files or funny_files:\n        return False\n\n    # compare subdirs\n    for x in common_dirs:\n        a_x = os.path.join(dir1, x)\n        b_x = os.path.join(dir2, x)\n        # report if subdirs have differencies\n        if not compareDirs(a_x, b_x, shallow=shallow):\n            return False\n\n    return True\n\n\n# return list with filenames in path directory that match patterns\ndef ignoredNames(path: str, patterns):\n    names = os.listdir(path)\n    ignored_names = []\n    for pattern in patterns:\n        ignored_names.extend(fnmatch.filter(names, pattern))\n    return set(ignored_names)\n\n\n# transfer a file from src to dst\ndef transferFile(src, dst, method=Method.Copy, force=False):\n    # check if dst object exists\n    if os.path.exists(dst):\n        if not force:\n            # skip file if src and dst are equal\n            if compareFiles(src, dst):\n                return Response.Skip\n        os.remove(dst)\n    # if not, make sure we have dst dir\n    else:\n        dst_dirname, dst_basename = os.path.split(dst)\n        if not os.path.exists(dst_dirname):\n            os.makedirs(dst_dirname)\n    # transfer file by selected method\n    if method == Method.Link:\n        os.link(src, dst)\n    elif method == Method.Symlink:\n        os.symlink(src, dst)\n    elif method == Method.Copy:\n        shutil.copy2(src, dst)\n    elif method == Method.Move:\n        shutil.move(src, dst)\n    else:\n        return Response.UnknownMethod\n    return Response.Ok\n\n\n# transfer a directory from src to dst\ndef transferDir(src, dst, method=Method.Copy, force=False, ignorepatterns=[], onlyfiles=False, deletedst=False,\n                keeppatterns=[]):\n    # check if dst object exists\n    if os.path.exists(dst):\n        # if they are the same then skip them if force is false\n        if not force:\n            if compareDirs(src, dst):\n                return Response.Skip\n        # delete dst dir content\n        if deletedst:\n            keep_names = ignoredNames(dst, keeppatterns)\n            filenames = [f for f in os.listdir(dst) if f not in keep_names]\n            for filename in filenames:\n                filepath = os.path.join(dst, filename)\n                if os.path.isfile(filepath) or os.path.islink(filepath):\n                    os.remove(filepath)\n                elif os.path.isdir(filepath):\n                    shutil.rmtree(filepath)\n    # transfer only files from src directory\n    if onlyfiles:\n        ignored_names = ignoredNames(src, ignorepatterns)\n        filenames = [f for f in os.listdir(src)\n                     if os.path.isfile(os.path.join(src, f)) and f not in ignored_names]\n        for filename in filenames:\n            resp = transferFile(os.path.join(src, filename), os.path.join(dst, filename), method=method)\n            if resp is not Response.Ok:\n                return resp\n    # transfer whole src directory\n    else:\n        # transfer dir by selected method\n        if method == Method.Link:\n            shutil.copytree(src, dst, copy_function=os.link, dirs_exist_ok=True,\n                            ignore=shutil.ignore_patterns(*ignorepatterns))\n        elif method == Method.Symlink:\n            shutil.copytree(src, dst, copy_function=os.symlink, dirs_exist_ok=True,\n                            ignore=shutil.ignore_patterns(*ignorepatterns))\n        elif method == Method.Copy:\n            shutil.copytree(src, dst, dirs_exist_ok=True,\n                            ignore=shutil.ignore_patterns(*ignorepatterns))\n        elif method == Method.Move:\n            shutil.copytree(src, dst, copy_function=shutil.move, dirs_exist_ok=True,\n                            ignore=shutil.ignore_patterns(*ignorepatterns))\n            shutil.rmtree(src)\n        else:\n            return Response.UnknownMethod\n    return Response.Ok\n\n\n# make transfer for file or directory\ndef makeTransfer(src, dst, method=Method.Copy, force=False, ignorepatterns=[], onlyfiles=False, deletedst=False,\n                 keeppatterns=[]):\n    # check source object existence\n    if os.path.exists(src):\n        # source objects is a file or a link\n        if os.path.isfile(src) or os.path.islink(src):\n            return transferFile(src, dst, method=method, force=force)\n        # source object is a directory\n        elif os.path.isdir(src):\n            return transferDir(src, dst, method=method, force=force, ignorepatterns=ignorepatterns, onlyfiles=onlyfiles,\n                               deletedst=deletedst, keeppatterns=keeppatterns)\n        # unknown type of source object\n        else:\n            return Response.UnknownType\n    # source object do not exist\n    else:\n        return Response.SourceNotExist\n\n\n# parse line\ndef parseLine(line: str, lpars: ThrowingArgumentParser, lstat: Statistics):\n    # check line len is correct\n    if len(line) == 0:\n        return\n    # check comment\n    if line[0] == '#':\n        print(\"  Skip line: \" + line[1:] + \"\")\n        lstat.skipped_lines += 1\n        return\n    try:\n        line_args = lpars.parse_args(line.split())\n        input_path = line_args.input.strip().strip('\"')\n        output_path = line_args.output.strip().strip('\"')\n        if input_path == \"\" or output_path == \"\":\n            raise Exception(\"Input or output is empty\")\n\n        method = line_args.method\n        force = line_args.force\n        ignorepatterns = [ip.strip().strip('\"') for ip in line_args.ignorepatterns]\n        onlyfiles = line_args.onlyfiles\n        deletedst = line_args.deletedst\n        keeppatterns = [kp.strip().strip('\"') for kp in line_args.keeppatterns]\n\n        print(\"  Handle line: \" + line[1:] + \"\")\n        print(\"    \" + method.capitalize() + \" \\\"\" + input_path + \"\\\" --> \\\"\" + output_path + \"\\\" ...\")\n        lstat.correct_lines += 1\n        res = makeTransfer(input_path, output_path, method=method, force=force,\n                           ignorepatterns=ignorepatterns, onlyfiles=onlyfiles, deletedst=deletedst,\n                           keeppatterns=keeppatterns)\n        if res == Response.Ok:\n            lstat.succeeded_transfers += 1\n            print(\"    Ok\")\n        elif res == Response.SourceNotExist:\n            print(\"    Fail: source object not exist\")\n            lstat.failed_transfers += 1\n        elif res == Response.UnknownType:\n            print(\"    Fail: unknown type of source object \")\n            lstat.failed_transfers += 1\n        elif res == Response.UnknownMethod:\n            print(\"    Fail: unknown transfer method\")\n            lstat.failed_transfers += 1\n        elif res == Response.Skip:\n            lstat.skipped_transfers += 1\n            print(\"    Skip\")\n    except Exception as e:\n        print(\"  Cannot handle line: \" + line + \", because \" + str(e))\n        lstat.incorrect_lines += 1\n\n\nif __name__ == '__main__':\n    try:\n        print(\"File-copy-helper script starts\")\n\n        parser = ThrowingArgumentParser(description='Arg parser')\n        parser.add_argument('-l', '--lines', metavar='lines', nargs=\"+\", default=[],\n                            help='lines to parse')\n        parser.add_argument('-f', '--files', metavar='files', nargs=\"+\", default=[],\n                            help='files with lines to parse')\n        parser.add_argument('-d', '--dir', metavar='dir', type=str, default=\"\",\n                            help='directory with files, default: directory with script')\n        parser.add_argument('-fp', '--filepattern', metavar='filepattern', type=str, default='*.txt',\n                            help='pattern of files to parse lines, default: \\'*.txt\\'')\n        parser.add_argument('-es', '--endsleep', metavar='endsleep', type=int, default='0',\n                            help='sleep seconds at the end of script, default: 0')\n        args = parser.parse_args()\n\n        app_lines = args.lines\n        print(\"App lines: \" + str(app_lines))\n        app_files = args.files\n        print(\"App files: \" + str(app_files))\n        app_dirname, app_basename = os.path.split(sys.argv[0])\n        app_dir = os.path.abspath(os.path.join(app_dirname, args.dir))\n        print(\"App directory: \" + app_dir)\n        app_filepattern = args.filepattern\n        print(\"App file pattern: \" + app_filepattern)\n        app_endsleep = int(args.endsleep)\n\n        line_parser = ThrowingArgumentParser(description=\"Line parser\")\n        line_parser.add_argument('-i', '--input', metavar='input', type=str, default=\"\",\n                                 help=\"input path to file/directory\")\n        line_parser.add_argument('-o', '--output', metavar='output', type=str, default=\"\",\n                                 help=\"output path to file/directory\")\n        line_parser.add_argument('-m', '--method', metavar='method', type=str, default=Method.Copy,\n                                 help=\"method for file transfer, available methods: \\'\" +\n                                      Method.Link + \"\\' to make hardlink, \\'\" +\n                                      Method.Symlink + \"\\' to make symbolic link, \\'\" +\n                                      Method.Copy + \"\\' to copy, \\'\" + Method.Move + \"\\' to cut, \"\n                                      \"default: \\'\" + Method.Copy + \"\\'\")\n        line_parser.add_argument('-f', '--force', action='store_true',\n                                 help=\"force call transfer function if objects are the same\")\n        line_parser.add_argument('-ip', '--ignorepatterns', metavar='ignorepatterns', nargs=\"+\", default=[],\n                                 help=\"ignore patterns for skipping objects\")\n        line_parser.add_argument('-of', '--onlyfiles', action='store_true',\n                                 help=\"transfer only files in directory\")\n        line_parser.add_argument('-dd', '--deletedst', action='store_true',\n                                 help=\"delete destination content\")\n        line_parser.add_argument('-kp', '--keeppatterns', metavar='keeppatterns', nargs=\"+\", default=[],\n                                 help=\"keep patterns for objects in destination directory if -dd is active\")\n\n        stat = Statistics()\n        # parse lines\n        if len(app_lines):\n            linelist = list(filter(None, (line.strip() for line in app_lines)))\n            stat.total_lines += len(linelist)\n            print(\"Parse \" + str(len(linelist)) + \" line(s) ...\")\n            for line in linelist:\n                parseLine(line, line_parser, stat)\n        # handle files\n        else:\n            linelist_filenames = []\n            # handle separate files\n            if len(app_files):\n                print(\"Scan separate files ...\")\n                for af in app_files:\n                    filename = af.strip().strip('\"')\n                    if not os.path.isabs(filename):\n                        filename = os.path.join(app_dir, filename)\n                    if os.path.exists(filename):\n                        linelist_filenames.append(filename)\n            # scan directory for files with selected filepattern\n            else:\n                print(\"Scan app directory ...\")\n                if not os.path.exists(app_dir):\n                    raise Exception(\"Incorrect directory: \" + app_dir)\n                linelist_filenames = glob.glob(os.path.join(app_dir, app_filepattern))\n            if len(linelist_filenames) == 0:\n                print(\"No files to parse found\")\n            else:\n                print(\"Found \" + str(len(linelist_filenames)) + \" file(s) to parse\")\n                # iterate over all line files\n                for linelist_filename in linelist_filenames:\n                    # open each line file\n                    with open(linelist_filename, \"r\") as file:\n                        linelist = list(filter(None, (line.strip() for line in file)))\n                        stat.total_lines += len(linelist)\n                        print(\"Handle file: \\\"\" + linelist_filename + \"\\\", lines to parse: \" + str(len(linelist)))\n                        # iterate over every line in file\n                        for line in linelist:\n                            parseLine(line, line_parser, stat)\n\n        print(\"Correct/skipped/incorrect/total lines: \" + str(stat.correct_lines) + \"/\" +\n              str(stat.skipped_lines) + \"/\" + str(stat.incorrect_lines) + \"/\" + str(stat.total_lines) + \", \\n\"\n              \"Succeeded/skipped/incorrect/total transfers: \" + str(stat.succeeded_transfers) + \"/\" +\n              str(stat.skipped_transfers) + \"/\" + str(stat.incorrect_lines) + \"/\" + str(stat.correct_lines))\n        time.sleep(app_endsleep)\n    except Exception as e:\n        print(str(e))\n","repo_name":"nialister/file-copy-helper","sub_path":"file-copy-helper.py","file_name":"file-copy-helper.py","file_ext":"py","file_size_in_byte":15242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"74901023636","text":"from __future__ import unicode_literals\napp_name = \"misscix_theme\"\napp_title = \"Miss Cix Website Şablonu\"\napp_publisher = \"Harpiya Yazılım Teknolojileri\"\napp_description = \"Miss Cix Websitesi Şablonu\"\napp_icon = \"fa fa-globe\"\napp_color = \"orange\"\napp_email = \"info@harpiya.com\"\napp_url = \"https://harpiya.com\"\napp_version = \"0.0.1\"\nhide_in_installer = True\n# home_page = \"home\"\n\nwebsite_context = {\n\t\"disable_website_theme\": True\n}\n\n# Includes in <head>\n# ------------------\n\n# include js, css files in header of desk.html\n# app_include_css = \"/assets/misscix_theme/css/misscix_theme.css\"\n# app_include_js = \"/assets/misscix_theme/js/misscix_theme.js\"\n\n# include js, css files in header of web template\n# web_include_css = \"/assets/misscix_theme/css/misscix-web.css\"\n# web_include_js = \"/assets/misscix_theme/js/misscix.min.js\"\n\n# Installation\n# ------------\n\n# before_install = \"misscix_theme.install.before_install\"\n# after_install = \"misscix_theme.install.after_install\"\n\n# Desk Notifications\n# ------------------\n# See frappe.core.notifications.get_notification_config\n\n# notification_config = \"misscix_theme.notifications.get_notification_config\"\n\n# Permissions\n# -----------\n# Permissions evaluated in scripted ways\n\n# permission_query_conditions = {\n# \t\"Event\": \"frappe.core.doctype.event.event.get_permission_query_conditions\",\n# }\n#\n# has_permission = {\n# \t\"Event\": \"frappe.core.doctype.event.event.has_permission\",\n# }\n\n# Document Events\n# ---------------\n# Hook on document methods and events\n\n# doc_events = {\n# \t\"*\": {\n# \t\t\"on_update\": \"method\",\n# \t\t\"on_cancel\": \"method\",\n# \t\t\"on_trash\": \"method\"\n#\t}\n# }\n\n# Scheduled Tasks\n# ---------------\n\n# scheduler_events = {\n# \t\"all\": [\n# \t\t\"misscix_theme.tasks.all\"\n# \t],\n# \t\"daily\": [\n# \t\t\"misscix_theme.tasks.daily\"\n# \t],\n# \t\"hourly\": [\n# \t\t\"misscix_theme.tasks.hourly\"\n# \t],\n# \t\"weekly\": [\n# \t\t\"misscix_theme.tasks.weekly\"\n# \t]\n# \t\"monthly\": [\n# \t\t\"misscix_theme.tasks.monthly\"\n# \t]\n# }\n\n# Testing\n# -------\n\n# before_tests = \"misscix_theme.install.before_tests\"\n","repo_name":"harpiya/misscix_theme","sub_path":"misscix_theme/hooks.py","file_name":"hooks.py","file_ext":"py","file_size_in_byte":2026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"27990520070","text":"import time\nfrom collections import defaultdict, deque\nfrom threading import Thread\n\nfrom library.event import EventArgs, EventId\n\n\nclass Stopwatch:\n    \"\"\"Замеряет время выполнения блока кода.\"\"\"\n    def __init__(self):\n        self.result_ms = None\n        self.start_time = None\n\n    def __enter__(self):\n        self.result_ms = None\n        self.start_time = time.time()\n        return self\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        self.result_ms = (time.time() - self.start_time) * 1000\n\n\nclass Timer:\n\n    def __init__(self, services, interval_ms):\n        self._services = services\n        self._stopwatch = Stopwatch()\n        self.interval_ms = interval_ms\n        self.tick_count = 0\n        self.running = False\n        self._working_thread = Thread(target=self._working_cycle, daemon=True)\n\n    def _working_cycle(self):\n        while self.running:\n            with self._stopwatch:\n                self._services.event_dispatcher.fire(\n                    EventId.TICK, EventArgs(self, time=self.tick_count)\n                )\n            self.tick_count += 1\n            interval = self.interval_ms - int(self._stopwatch.result_ms)\n            if not self.running:\n                break\n            if interval > 0:\n                time.sleep(interval / 1000)\n\n    def start(self):\n        self.running = True\n        self._working_thread.start()\n\n    def stop(self):\n        self.running = False\n\n\nclass Scheduler:\n\n    def __init__(self, services):\n        self._services = services\n        self.current_time = 0\n        self._planned_events = defaultdict(deque)\n        self._services.event_dispatcher.register_handler(\n            EventId.TICK, self._on_tick, 0\n        )\n\n    def schedule(self, delay, event_id):\n        self._planned_events[self.current_time + delay].append(event_id)\n\n    def _on_tick(self, event_args):\n        self.current_time = event_args.time\n        while self._planned_events[self.current_time]:\n            event_id = self._planned_events[self.current_time].popleft()\n            self._services.event_dispatcher.fire(event_id, EventArgs(self))\n\n    def reset(self):\n        self._planned_events = defaultdict(deque)\n\n    def store(self):\n        result = defaultdict(list)\n        for time_ in self._planned_events.keys():\n            for planned_event in self._planned_events[time_]:\n                result[time_ - self.current_time].append(planned_event.name)\n        return result\n\n    def load(self, data):\n        for time_ in data:\n            for planned_event in data[time_][1:-1].split(', '):\n                time_ = int(time_) + self.current_time\n                self._planned_events[time_].append(\n                    EventId[planned_event[1:-1]]\n                )\n","repo_name":"nexusasx10/Pacman","sub_path":"library/time.py","file_name":"time.py","file_ext":"py","file_size_in_byte":2778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"19873151939","text":"# coding: utf-8\n\nfrom __future__ import absolute_import\nfrom datetime import date, datetime  # noqa: F401\n\nfrom typing import List, Dict  # noqa: F401\n\nfrom swagger_server.models.base_model_ import Model\nfrom swagger_server import util\n\n\nclass StationShort(Model):\n    \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n    Do not edit the class manually.\n    \"\"\"\n    def __init__(self, station_id: int=None, name: str=None):  # noqa: E501\n        \"\"\"StationShort - a model defined in Swagger\n\n        :param station_id: The station_id of this StationShort.  # noqa: E501\n        :type station_id: int\n        :param name: The name of this StationShort.  # noqa: E501\n        :type name: str\n        \"\"\"\n        self.swagger_types = {\n            'station_id': int,\n            'name': str\n        }\n\n        self.attribute_map = {\n            'station_id': 'stationId',\n            'name': 'name'\n        }\n        self._station_id = station_id\n        self._name = name\n\n    @classmethod\n    def from_dict(cls, dikt) -> 'StationShort':\n        \"\"\"Returns the dict as a model\n\n        :param dikt: A dict.\n        :type: dict\n        :return: The StationShort of this StationShort.  # noqa: E501\n        :rtype: StationShort\n        \"\"\"\n        return util.deserialize_model(dikt, cls)\n\n    @property\n    def station_id(self) -> int:\n        \"\"\"Gets the station_id of this StationShort.\n\n\n        :return: The station_id of this StationShort.\n        :rtype: int\n        \"\"\"\n        return self._station_id\n\n    @station_id.setter\n    def station_id(self, station_id: int):\n        \"\"\"Sets the station_id of this StationShort.\n\n\n        :param station_id: The station_id of this StationShort.\n        :type station_id: int\n        \"\"\"\n\n        self._station_id = station_id\n\n    @property\n    def name(self) -> str:\n        \"\"\"Gets the name of this StationShort.\n\n\n        :return: The name of this StationShort.\n        :rtype: str\n        \"\"\"\n        return self._name\n\n    @name.setter\n    def name(self, name: str):\n        \"\"\"Sets the name of this StationShort.\n\n\n        :param name: The name of this StationShort.\n        :type name: str\n        \"\"\"\n\n        self._name = name\n","repo_name":"SoSorryTT/Data-Acquisition-and-Integration","sub_path":"rain-api-python-DAQ/stub/swagger_server/models/station_short.py","file_name":"station_short.py","file_ext":"py","file_size_in_byte":2212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"34421336231","text":"import discord\nfrom discord.ext import commands\nfrom mac_vendor_lookup import AsyncMacLookup\nimport pyshorteners\nfrom requests import get\n\n\n\nclass extras(commands.Cog):\n    def init(self, client):\n        self.client = client\n    \n    \n#url shortner\n    @commands.command()\n    async def urls(self,ctx,arg):\n      s = pyshorteners.Shortener()\n      embed=discord.Embed(title=\"URL Shortener\", color=0xff0505)\n      embed.add_field(name=\"Your original URL:\", value=arg, inline=False)\n      embed.add_field(name=\"Your shortened value:\", value=s.tinyurl.short(arg), inline=False)\n      embed.set_footer(text=\"Hope this achives your goals\")\n      await ctx.author.send(embed=embed)\n\n    #mac address lookup\n    @commands.command()\n    async def mac(self,ctx,arg):\n      mac = AsyncMacLookup()\n      embed=discord.Embed(title=\"MAC address Lookup\", color=0xff0505)\n      embed.add_field(name=\"Your MAC address:\", value=arg, inline=False)\n      embed.add_field(name=\"Your identifd manufacturer:\", value=await mac.lookup(arg), inline=False)\n      embed.set_footer(text=\"Hope this achives your goals\")\n      await ctx.author.send(embed=embed)\n    \n    @commands.command() #has a small limit-future api key invest?\n    async def geo(self,ctx, arg): \n     loc = get('https://ipapi.co/' + arg +'/json')\n     await ctx.author.send(loc.json())\n    \n\n \n\ndef setup(client):\n    client.add_cog(extras(client))","repo_name":"mharisss/BeckettBot","sub_path":"cogs/extras.py","file_name":"extras.py","file_ext":"py","file_size_in_byte":1391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"38708491535","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.decomposition import PCA\n\nprices_link = \"C:\\\\Учеба\\\\Машобчик\\\\4 Неделя\\\\close_prices.csv\"\ndow_link = \"C:\\\\Учеба\\\\Машобчик\\\\4 Неделя\\\\djia_index.csv\"\n# Remove date-column\nprices_data = pd.read_csv(prices_link, delimiter=',').iloc[:, 1:]\ndow_data = pd.read_csv(dow_link, delimiter=',').iloc[:, 1:]\n# Обучаем анализатор метода главных компонент\npca = PCA(n_components=10)\npca.fit(prices_data)\n# Смотрим дисперсию по каждой компоненте\nprint(\"Дисперсия покомпонентно:\")\nprint(pca.explained_variance_ratio_)\nprint(\"Дисперсия от четырех компонент:\")\nprint(np.sum(pca.explained_variance_ratio_[:4]))\n# Преобразовываем данные\nprices_data_trans = pca.transform(prices_data)\n# Смотрим корелляцию между первой компонентой и индексом Доу-Джонс\ncorrelation = np.corrcoef(prices_data_trans[:, 0], dow_data.to_numpy()[:,0])\nprint(\"Коэффициент корелляции между первой компонентой и индексом Доу-Джонс: \")\nprint(round(correlation[0,1], 2))\n# Какая компания имеет наибольший вес в первой компоненте\n#print(pca.components_)\nfirst_component = pca.components_[0, :]\nmax_index = np.argmax(first_component)\nprint(\"Максимальный вес в первой компоненте у компании\")\nprint(prices_data.columns[max_index], first_component[max_index])\n","repo_name":"SadBattlecruiser/Coursera-Machine-Larning","sub_path":"4 Неделя/principal_component_analysis.py","file_name":"principal_component_analysis.py","file_ext":"py","file_size_in_byte":1644,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"71802524420","text":"# -*- coding: utf-8 -*-\r\n\r\n\"\"\"\r\nRoutes and views for the flask application.\r\n\"\"\"\r\n\r\nfrom siteinterface import app\r\nfrom math import floor, ceil\r\nimport os, sys\r\nfrom datetime import datetime,timedelta\r\nimport time\r\nimport logging\r\nimport zipfile\r\nfrom logic.PointNameAnalysis.PointChiller import PointChiller\r\nfrom logic.PointNameAnalysis.PointPump import PointPump\r\nfrom logic.PointNameAnalysis.PointChMotor import PointChMotor\r\nfrom logic.PointNameAnalysis.PointBase import PointBase\r\nfrom logic.PointNameAnalysis.PointCTFan import PointCTFan\r\nfrom logic.PointNameAnalysis.PointCT import PointCT\r\nfrom logic.PointNameAnalysis.PointColdMeter import PointColdMeter\r\nfrom logic.PointNameAnalysis.PointGSHP import PointGSHP\r\nfrom logic.PointNameAnalysis.PointGSHPMotor import PointGSHPMotor\r\nfrom logic.PointNameAnalysis.PointMainpipe import PointMainpipe\r\nfrom pypinyin import pinyin, lazy_pinyin\r\n\r\nclass PointNameAI(PointBase):\r\n    __instance = None\r\n\r\n\r\n    def __init__(self):\r\n        self._data = dict()\r\n        self._pointList = dict()\r\n\r\n    @classmethod\r\n    def getInstance(self):\r\n        if(self.__instance == None):\r\n            self.__instance = PointNameAI()\r\n        return self.__instance\r\n\r\n    def analysis_description(self, strDescription, roomDefine, nPointIdx):\r\n        rv = dict(result='', rate=1.0)\r\n        strResultName = ''\r\n\r\n        strRoomName = self.find_room_name(strDescription, roomDefine)\r\n\r\n        if strDescription == \"1#主机-冷冻水供水温度\":\r\n            print(\"found\")\r\n\r\n        nEquipNo = self.is_GSHP_motor(strDescription)\r\n        if nEquipNo > 0:\r\n            nGSHPNo = self.get_GSHP_no_of_motor(strDescription)\r\n            strResultName = PointGSHPMotor(\"GSHPMotor\", nEquipNo, nGSHPNo, strRoomName).analysis_description(strDescription)\r\n            if strResultName:\r\n                return strResultName\r\n\r\n        nEquipNo = self.is_ch_motor(strDescription)\r\n        if nEquipNo > 0:\r\n            nChillerNo = self.get_chiller_no_of_motor(strDescription)\r\n            strResultName = PointChMotor('ChMotor', nEquipNo, nChillerNo, strRoomName).analysis_description(strDescription)\r\n            if strResultName:\r\n                return strResultName\r\n\r\n        nEquipNo = self.is_ch_coldmeter(strDescription)\r\n        if nEquipNo > 0:\r\n            nChillerNo = self.get_chiller_no_of_coldmeter(strDescription)\r\n            if nEquipNo==1:\r\n                strResultName = PointColdMeter('ChEvapColdMeter', nChillerNo, strRoomName).analysis_description(strDescription)\r\n                if strResultName:\r\n                    return strResultName\r\n            if nEquipNo==2:\r\n                strResultName = PointColdMeter('ChCondColdMeter', nChillerNo, strRoomName).analysis_description(strDescription)\r\n                if strResultName:\r\n                    return strResultName\r\n\r\n        nEquipNo = self.is_chw_coldmeter(strDescription)\r\n        if nEquipNo > 0:\r\n            if nEquipNo == 1:\r\n                strResultName = PointColdMeter('PriChWColdMeter', nEquipNo, strRoomName).analysis_description(strDescription)\r\n                if strResultName:\r\n                    return strResultName\r\n\r\n        nEquipNo = self.is_cw_coldmeter(strDescription)\r\n        if nEquipNo > 0:\r\n            if nEquipNo == 1:\r\n                strResultName = PointColdMeter('CWColdMeter', nEquipNo, strRoomName).analysis_description(strDescription)\r\n                if strResultName:\r\n                    return strResultName\r\n\r\n        # chiller\r\n        nEquipNo = self.is_chiller(strDescription)\r\n        if nEquipNo > 0:\r\n            strResultName =  PointChiller(nEquipNo, strRoomName).analysis_description(strDescription)\r\n            if strResultName:\r\n                return strResultName\r\n\r\n        nEquipNo = self.is_ct_fan(strDescription)\r\n        if nEquipNo>0:\r\n            nCTNo = self.get_ct_no_of_fan(strDescription)\r\n            strResultName = PointCTFan('CTFan', nEquipNo, nCTNo, strRoomName).analysis_description(strDescription)\r\n            if strResultName:\r\n                return strResultName\r\n\r\n        nEquipNo = self.is_pri_chwp(strDescription)\r\n        if nEquipNo > 0:\r\n            strResultName = PointPump('PriChWP', nEquipNo, strRoomName).analysis_description(strDescription)\r\n            if strResultName:\r\n                return strResultName\r\n\r\n        nEquipNo = self.is_sec_chwp(strDescription)\r\n        if nEquipNo > 0:\r\n            strResultName = PointPump('SecChWP', nEquipNo, strRoomName).analysis_description(strDescription)\r\n            if strResultName:\r\n                return strResultName\r\n\r\n        nEquipNo = self.is_cwp(strDescription)\r\n        if nEquipNo > 0:\r\n            strResultName = PointPump('CWP', nEquipNo, strRoomName).analysis_description(strDescription)\r\n            if strResultName:\r\n                return strResultName\r\n\r\n        nEquipNo = self.is_hwp(strDescription)\r\n        if nEquipNo > 0:\r\n            strResultName = PointPump('HWP', nEquipNo, strRoomName).analysis_description(strDescription)\r\n            if strResultName:\r\n                return strResultName\r\n\r\n        nEquipNo = self.is_higharea_sec_chwp(strDescription)\r\n        if nEquipNo > 0:\r\n            strResultName = PointPump('HighAreaSecChWP', nEquipNo, strRoomName).analysis_description(strDescription)\r\n            if strResultName:\r\n                return strResultName\r\n\r\n        nEquipNo = self.is_ct(strDescription)\r\n        if nEquipNo > 0:\r\n            strResultName = PointCT('CT', nEquipNo, strRoomName).analysis_description(strDescription)\r\n            if strResultName:\r\n                return strResultName\r\n\r\n        # GSHP\r\n        nEquipNo = self.is_ground_source_heat_pump(strDescription)\r\n        if nEquipNo > 0:\r\n            strResultName = PointGSHP(\"GSHP\", nEquipNo, strRoomName).analysis_description(strDescription)\r\n            if strResultName:\r\n                return strResultName\r\n\r\n        nEquipNo = self.is_mainpipe(strDescription)\r\n        if nEquipNo > 0:\r\n            strResultName = PointMainpipe(\"Mainpipe\", strRoomName).analysis_description(strDescription)\r\n            if strResultName:\r\n                return strResultName\r\n\r\n        if self.is_undefined_point(strDescription):\r\n            return \"undefined_point_%02d\" % nPointIdx\r\n\r\n        if self.have(strDescription, ['室外']) and self.have(strDescription, ['干球温度', '温度']):\r\n            strResultName = 'OutdoorTdbin'\r\n            return strResultName\r\n        elif self.have(strDescription, ['室外']) and self.have(strDescription, ['相对湿度', '湿度']):\r\n            strResultName = 'OutdoorRH'\r\n            return strResultName\r\n        elif self.have(strDescription, ['机房']) and self.have(strDescription, ['相对湿度', '湿度']) and self.have_not_one(strDescription, ['室外']):\r\n            strResultName = '%sChillerPlantRoomRH' % strRoomName\r\n            return strResultName\r\n        elif self.have(strDescription, ['机房']) and self.have(strDescription, ['干球温度', '温度']) and self.have_not_one(strDescription, ['室外']):\r\n            strResultName = '%sChillerPlantRoomTdbin' % strRoomName\r\n            return strResultName\r\n\r\n        if self.is_after_filter(strDescription, \"分集水器\", \"分水器\") >= 0:\r\n            if strDescription.find(\"温度\") >= 0:\r\n                return '%sPriChWTempSupply' % strRoomName\r\n            elif self.have_all(strDescription, [\"压力\", \"虚拟\"]):\r\n                return '%sVirtualPriChWPressureSupply' % strRoomName\r\n            elif strDescription.find(\"压力\") >= 0:\r\n                return '%sPriChWPressureSupply' % strRoomName\r\n        if self.is_after_filter(strDescription, \"分集水器\", \"集水器\") >= 0:\r\n            if strDescription.find(\"温度\") >= 0:\r\n                return '%sPriChWTempReturn' % strRoomName\r\n            elif self.have_all(strDescription, [\"压力\", \"虚拟\"]):\r\n                return '%sVirtualPriChWPressureReturn' % strRoomName\r\n            elif strDescription.find(\"压力\") >= 0:\r\n                return '%sPriChWPressureReturn' % strRoomName\r\n\r\n        if self.have_all(strDescription, ['冷冻','旁通', '开度', '设定']):\r\n            strResultName = '%sPriChWBypassValveOpenRatioSetting01' % strRoomName\r\n            return strResultName\r\n        elif self.have_all(strDescription, ['冷却','旁通', '开度', '设定']):\r\n            strResultName = '%sCWBypassValveOpenRatioSetting01' % strRoomName\r\n            return strResultName\r\n        elif self.have_all(strDescription, ['冷冻','旁通', '开度', '反馈']):\r\n            strResultName = '%sPriChWBypassValveOpenRatio01' % strRoomName\r\n            return strResultName\r\n        elif self.have_all(strDescription, ['冷却','旁通', '开度', '反馈']):\r\n            strResultName = '%sCWBypassValveOpenRatio01' % strRoomName\r\n            return strResultName\r\n        elif self.have_all(strDescription, [\"冷却水\", \"供水\", \"温度\"]):\r\n            strResultName = '%sCWTempSupply' % strRoomName\r\n            return strResultName\r\n        elif self.have_all(strDescription, [\"冷却水\", \"回水\", \"温度\"]):\r\n            strResultName = '%sCWTempReturn' % strRoomName\r\n            return strResultName\r\n        elif self.have_all(strDescription, [\"冷冻水\", \"供水温度\", \"设定\"]) or self.have_all(strDescription, [\"冷冻水\", \"送水温度\", \"设定\"]):\r\n            strResultName = '%sPriChWTempSupplySetting' % strRoomName\r\n            return strResultName\r\n        elif self.have_all(strDescription, [\"冷冻水\", \"供水\", \"温度\"]):\r\n            strResultName = '%sPriChWTempSupply' % strRoomName\r\n            return strResultName\r\n        elif self.have_all(strDescription, [\"冷冻水\", \"回水\", \"温度\"]):\r\n            strResultName = '%sPriChWTempReturn' % strRoomName\r\n            return strResultName\r\n        elif self.have_all(strDescription, [\"冷冻水\", \"供水\", \"压力\"]):\r\n            strResultName = '%sPriChWPressureSupply' % strRoomName\r\n            return strResultName\r\n        elif self.have_all(strDescription, [\"冷冻水\", \"回水\", \"压力\"]):\r\n            strResultName = '%sPriChWPressureReturn' % strRoomName\r\n            return strResultName\r\n\r\n\r\n        strTrans = lazy_pinyin(strDescription)\r\n\r\n        strAllFinal =  'U_'+ '_'.join(strTrans)\r\n\r\n        strAllFinal = strAllFinal.replace('#', '_')\r\n        strAllFinal = strAllFinal.replace('.', '_')\r\n        strAllFinal = strAllFinal.replace(' ', '_')\r\n        strAllFinal = strAllFinal.replace('/', '_')\r\n        strAllFinal = strAllFinal.replace('\\\\', '_')\r\n        strAllFinal = strAllFinal.replace('*', '_')\r\n        strAllFinal = strAllFinal.replace('-', '_')\r\n\r\n        return strAllFinal","repo_name":"sadelover/dompysite","sub_path":"logic/PointNameAnalysis/PointNameAI.py","file_name":"PointNameAI.py","file_ext":"py","file_size_in_byte":10704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"72913715139","text":"\n#\n# A class to represent calendar dates       \n#\n\nclass Date:\n    \"\"\" A class that stores and manipulates dates that are\n        represented by a day, month, and year.\n    \"\"\"\n\n    # The constructor for the Date class.\n    def __init__(self, init_month, init_day, init_year):\n        \"\"\" constructor that initializes the three attributes  \n            in every Date object (month, day, and year)\n        \"\"\"\n        \n        self.month=init_month\n        self.day=init_day\n        self.year=init_year\n\n\n    # The function for the Date class that returns a Date\n    # object in a string representation.\n    def __repr__(self):\n        \"\"\" This method returns a string representation for the\n            object of type Date that it is called on (named self).\n        \"\"\"\n        s = '%02d/%02d/%04d' % (self.month, self.day, self.year)\n        return s\n\n    def is_leap_year(self):\n        \"\"\" Returns True if the called object is\n            in a leap year. Otherwise, returns False.\n        \"\"\"\n        if self.year % 400 == 0:\n            return True\n        elif self.year % 100 == 0:\n            return False\n        elif self.year % 4 == 0:\n            return True\n        return False\n\n    def copy(self):\n        \"\"\" Returns a new object with the same month, day, year\n            as the called object (self).\n        \"\"\"\n        new_date = Date(self.month, self.day, self.year)\n        return new_date\n\n\n    #part 3\n    def advance_one(self):\n        \"\"\"advance_one(self) that changes the called object so that \n        it represents one calendar day after the date that it originally represented.\n        \"\"\"\n        days_in_month = [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n        # to check if we should go to the next year\n        if self.day + 1 > days_in_month[self.month] and self.month == 12:\n            self.month = 1\n            self.day = 1\n            self.year += 1\n        # to check if it is a leap year\n        elif self.day + 1 > days_in_month[self.month] and self.is_leap_year() == True and self.month == 2 and self.day < 29:\n            self.day += 1\n        # to check if the month should be incremented\n        elif self.day + 1 > days_in_month[self.month]:\n            self.month += 1 \n            self.day = 1\n        # to just increment the day by one\n        else:\n            self.day += 1\n        \n    # Part 4\n    def advance_n(self, n):\n        \"\"\"advance_n(self, n) that changes the calling object so that \n        it represents n calendar days after the date it originally \n        represented. Additionally, the method should print all of the \n        dates from the starting date to the finishing date, inclusive of \n        both endpoints.\n        \"\"\"\n        print(self)\n        for day in range(n):\n            self.advance_one()\n            print(self)\n    \n    # Part 5\n    def __eq__(self, other):\n        \"\"\"__eq__(self, other) that returns True if the called object (self) \n        and the argument (other) represent the same calendar date \n        (i.e., if the have the same values for their day, month, and \n         year attributes). Otherwise, this method should return False.\n\n        \"\"\"\n        if self.day==other.day and self.month==other.month and self.year==other.year:\n            return True\n        else:\n            return False\n        \n    #Part 6\n    def is_before(self, other):\n        \"\"\"is_before(self, other) that returns True if the called object \n        represents a calendar date that occurs before the calendar \n        date that is represented by other. If self and other represent \n        the same day, or if self occurs after other, the method should \n        return False.\n        \"\"\"\n        # first check year\n        if self.year > other.year:\n            return False\n        elif self.year < other.year:\n            return True\n        # months in the same year\n        if self.month > other.month:\n            return False\n        elif self.month < other.month:\n            return True\n        # days in the same month\n        if self.day > other.day:\n            return False\n        elif self.day < other.day:\n            return True\n        # same day\n        elif self.day == other.day and self.month == other.month:\n            \n            return False\n        \n\n        \n    # Part 7\n    def is_after(self, other):\n        \"\"\"is_after(self, other) that returns True if the calling object \n        represents a calendar date that occurs after the calendar date \n        that is represented by other. If self and other represent the \n        same day, or if self occurs before other, the method should return False.\n        \"\"\"\n        if self.__eq__(other) == True:\n            return False\n        elif self.is_before(other) == True:\n            return False\n        else:\n            return True\n    \n    # Part 8\n    def days_between(self,other):\n        \"\"\"days_between(self, other) that returns an integer that represents \n        the number of days between self and other.\n        \"\"\"\n        self_date = self.copy()\n        other_date = other.copy()\n        \n        \n        num_days_btw = 0\n        if self.__eq__(other) == True:\n            num_days_btw = 0\n            \n        elif self.is_before(other) == True:\n            \n            while self_date.is_before(other_date) == True:\n                self_date.advance_one()\n                num_days_btw -= 1\n            \n        elif self.is_before(other) == False:\n            \n            while self_date.is_after(other_date) == True:\n                other_date.advance_one()\n                num_days_btw += 1\n        \n        return num_days_btw \n\n      \n        \n    \n    # Part 9\n    def day_name(self):\n        \"\"\"day_name(self) that returns a string that indicates the name of \n        the day of the week of the Date object that calls it. In other words, \n        the method should return one of the following strings: 'Monday', 'Tuesday', \n        'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'.\n        \"\"\"\n        week_days = ['Monday', 'Tuesday', 'Wednesday', \n             'Thursday', 'Friday', 'Saturday', 'Sunday']\n        known_date=Date(11, 11, 2019)\n        days_btw=self.days_between(known_date)\n        day_name_index=days_btw%7\n        day_name=week_days[day_name_index]\n        return day_name\n    \n    \n        \n        \n        \n    \n        \n        \n        \n            \n    \n            \n        \n","repo_name":"KrishnanShwetha/calendar_search","sub_path":"calendar_search.py","file_name":"calendar_search.py","file_ext":"py","file_size_in_byte":6402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"27684933496","text":"import telebot\nfrom random import *\nfrom telebot import types  # Кнопки тг импорт\nimport os #Картинки из папок)\nimport emoji\nimport requests\nfrom googletrans import Translator, constants\nfrom pprint import pprint\nimport database\nfrom pycoingecko import CoinGeckoAPI\ncg = CoinGeckoAPI()\nfrom database import SQLite\nbot = telebot.TeleBot(\"5618060312:AAFKPwJBIHbbK4unvjLJem_22cm5Ck5vago\")\napp_id = '6752774e07260308a55a41713b6715ad'\n\ndb = SQLite(\"user_telebot.db\")\nsqlite_create_table_query = '''CREATE TABLE IF NOT EXISTS users (\n                                        id integer,\n                                        name TEXT ,\n                                        surname text ,\n                                        age integer );'''\ndb.execute(sqlite_create_table_query)\nsqlite_create_table_admin = '''CREATE TABLE IF NOT EXISTS admin (\n                                        id integer,\n                                        name TEXT);'''\ndb.execute(sqlite_create_table_admin)\n# db.add_admin_execut(345092919, \"Марго\")\n\nmain_keyboard = types.ReplyKeyboardMarkup(True)  # Кнопки\nmain_keyboard.row(\"Магический шар\", 'Помощь', \"Погода\")\nmain_keyboard.row(\"Меню\", \"Регистрация\", \"Переводчик\")\nmain_keyboard.row(\"Игра угадай число\", \"Получить фото\")\nmain_keyboard.row(\"Стикер\", \"Калькулятор\", \"Криптовалюта\")\n\nno_keyboard = types.ReplyKeyboardMarkup(True)\nno_keyboard.row(\"Нет\")\n\nyes_no_keyboatd = types.ReplyKeyboardMarkup(True)\nyes_no_keyboatd.row(\"Да\", \"Нет\")\n\ncryptocurrency_keyboard = types.ReplyKeyboardMarkup(True)\ncryptocurrency_keyboard.row('Bitcoin', 'Ethereum')\ncryptocurrency_keyboard.row('EUR', 'RUB', 'USD')\n\ncrypto_keyboard = types.ReplyKeyboardMarkup(True)\ncrypto_keyboard.row('Bitcoin', 'Ethereum')\n\ncurrency_keyboard = types.ReplyKeyboardMarkup(True)\ncurrency_keyboard.row('EUR', 'RUB', 'USD')\n\nadmin_keyboard = types.ReplyKeyboardMarkup(True)\nadmin_keyboard.row('users bd', 'del user', 'add admin')\nadmin_keyboard.row(\"del admin\", \"check admin\")\n\ncalculator_keyboard = types.ReplyKeyboardMarkup(True)\ncalculator_keyboard.row(\"Простые вычисления\", \"Дроби\", \"Степени\")\n\nid_admins = [345092919]\n\n@bot.message_handler(commands=[\"start\"])\ndef send_welcome(message):\n    bot.send_message(message.from_user.id, \"Привет, я бот помощник. Буду помогать тебе!\",\n                     reply_markup=main_keyboard)  # Кнопка клавиатура\n\n\n@bot.message_handler(commands=[\"помощь\"])\ndef send_welcome(message):\n    bot.send_message(message.from_user.id, \"Пока не могу помочь, меня разрабатывают\",\n                     reply_markup=main_keyboard)  # Кнопка клавиатура\n\n\n@bot.message_handler(commands=[\"magic_ball\"])\ndef send_welcome(message):\n    bot.send_message(message.from_user.id, \"Привет, я магический шар! Напиши свой вопрос!\")\n    bot.register_next_step_handler(message, game_play)\n\n\n@bot.message_handler(commands=[\"help\"])\ndef send_welcome(message):\n    bot.send_message(message.from_user.id, \"Пока не могу помочь, меня разрабатывают\")\n\n@bot.message_handler(commands=[\"guess_the_number\"])\ndef send_welcome(message):\n    bot.send_message(message.from_user.id, \"Добро пожаловать в игру 'Угадай число'!\")\n    start_game(message)\n\n@bot.message_handler(content_types=[\"text\"])\ndef send_message(message):\n    text = message.text.lower()\n    if text == \"помощь\":\n        bot.send_message(message.from_user.id, \"Пока не могу помочь, меня разрабатывают\")\n    elif text == \"магический шар\" or text == \"game\":\n        bot.send_message(message.from_user.id, \"Привет, я магический шар! Напиши свой вопрос!\")\n        bot.register_next_step_handler(message, game_play)\n    elif text == \"меню\" or text == \"/menu\":\n        bot.send_message(message.from_user.id, \"Ты можешь выбрать нужное тебе действие: \\n \" +\n                         \"/help - Помощь \\n\" + \"/magic_ball - Игра магический шар \\n\" + \"/guess_the_number - Игра угадай число \\n\")\n    elif text == \"регистрация\":\n        bot.send_message(message.from_user.id, \"Напишите Ваше имя\")\n        bot.register_next_step_handler(message, get_name)\n    elif text == \"игра угадай число\":\n        bot.send_message(message.from_user.id, \"Добро пожаловать в игру 'Угадай число'!\")\n        start_game(message)\n    elif text == \"получить фото\":\n        bot.send_photo(message.from_user.id, open(\"тт.jpg\", \"rb\"))\n        bot.send_photo(message.from_user.id, \"https://i.imgur.com/77YeKfh.jpeg\")\n        bot.send_video(message.from_user.id, 'https://i.imgur.com/URY3Wd4.mp4')\n    elif text == \"получить мем\":\n        get_files_names_memes(message)\n    elif text == \"стикер\":\n        stikers = [\"CAACAgIAAxkBAAEF_OxjOryYP3pX0KWxfRwqC3s-11YWOwACLgADN7ORFJ6m-GsXdZUWKgQ\",\n                   \"CAACAgIAAxkBAAEF_OpjOryW4Lx5aDVRTWOBR4jB_cgc6QACLwADN7ORFF5aVpEyrfDtKgQ\",\n                   \"CAACAgIAAxkBAAEF_OhjOryUB34CR-BIscbForTnEgvDpwACog8AAjx1GEi-eZ90LVK_IioE\",\n                   \"CAACAgIAAxkBAAEF_OZjOryS1m2GbgQJEIQuUKzmjaz-RgACEA4AAniAGEigRfnK2vZrjyoE\",\n                   \"CAACAgIAAxkBAAEF_ORjOryPXhTl3TXlX9mo7wrJgptcsAACbRIAAsvG0UhbxGS4C108yyoE\",\n                   \"CAACAgIAAxkBAAEF_OJjOryNFaP4moyVKvqwPf8hdRLp-wACLxYAAnujyEirRjCqmOc1uyoE\",\n                   \"CAACAgIAAxkBAAEF_OBjOryLtYJs8pj55xlyhH2UhQzQowACgBkAAr0ByUjmqj-AiCmidyoE\",\n                   \"CAACAgIAAxkBAAEF_N5jOryIoEu0tG4bp343JT6I5B6g_AACqBIAAlCHyEgmu776I8ZkVSoE\",\n                   \"CAACAgIAAxkBAAEF_NRjOrvwhRsE7SmIQ1PUdGJh0sI9qAACCxEAAnv90Ug6UurudObDbyoE\"]\n        stiker = choice(stikers)\n        bot.send_sticker(message.from_user.id, stiker)\n    elif text == \"смайлик\":\n        bot.send_message(message.from_user.id, emoji.emojize(\":thumbs_up:\"))\n    elif text == 'погода':\n        bot.send_message(message.from_user.id, \"Напишите нужный Вам город!)\")\n        bot.register_next_step_handler(message, weather)\n    elif text == \"переводчик\":\n        bot.send_message(message.from_user.id, \"Вы хотите получить список всех языков? Напишите 'Да' или  'Нет' ;)\", reply_markup= yes_no_keyboatd)\n        bot.register_next_step_handler(message, translate_yes_no)\n    elif text == \"криптовалюта\":\n        bot.send_message(message.from_user.id, \"Нажмите на нужную Вам валюту:3\", reply_markup=cryptocurrency_keyboard)\n        bot.register_next_step_handler(message, cryptocurrency_get)\n    elif text == \"админ панель\":\n        if db.check_admin_execut(message.from_user.id):\n            bot.send_message(message.from_user.id, \"Привет, Админ!\", reply_markup=admin_keyboard)\n            bot.register_next_step_handler(message, admin_bd)\n        else:\n            bot.send_message(message.from_user.id, \"ТЫ НЕ ПРОЙДЕШЬ!\", reply_markup=main_keyboard)\n\n\n\n# сделать перевод только из крипты в долабы, и т.д и наоборот\n\n\n\n\ndef admin_del_admin(message):\n    id_admin = message.text\n    if int(id_admin) in id_admins:\n        bot.send_message(message.from_user.id, \"Его нельзя удалить\", reply_markup=main_keyboard)\n    else:\n        if db.del_admin_execut(id_admin):\n            bot.send_message(message.from_user.id, \"Вы успешно удалили Админа\", reply_markup=main_keyboard)\n        else:\n            bot.send_message(message.from_user.id, \"Произошла ошибка:(\", reply_markup=main_keyboard)\n\ndef admin_get_user_del(message):\n    user_id = message.text\n    if db.del_execut(user_id):\n        bot.send_message(message.from_user.id, \"Вы успешно удалили пользователя\", reply_markup=main_keyboard)\n    else:\n        bot.send_message(message.from_user.id, \"Произошла ошибка:(\", reply_markup=main_keyboard)\n\ndef admin_add_admin(message):\n    id_admin, name_admin = message.text.split()\n    if db.add_admin_execut(id_admin, name_admin):\n        bot.send_message(message.from_user.id, \"Вы успешно добавили Админа\", reply_markup=main_keyboard)\n    else:\n        bot.send_message(message.from_user.id, \"Что-то пошло не так..\", reply_markup=main_keyboard)\n\ndef admin_bd(message):\n    try:\n        command = message.text.lower()\n        if command == 'users bd':\n            users = \"\"\n            data_base_get_users = db.select_execute()\n            for i in data_base_get_users:\n                users += \" \" + str(i)\n                users += \"\\n\"\n            bot.send_message(message.from_user.id, users, reply_markup=main_keyboard)\n        elif command == 'del user':\n            bot.send_message(message.from_user.id, \"Напишите ID\")\n            bot.register_next_step_handler(message, admin_get_user_del)\n        elif command == 'add admin':\n            bot.send_message(message.from_user.id, 'Напишите id и Имя пользователя используя только пробел')\n            bot.register_next_step_handler(message, admin_add_admin)\n        elif command == \"del admin\":\n            bot.send_message(message.from_user.id, \"Напишите ID\")\n            bot.register_next_step_handler(message, admin_del_admin)\n        elif command == \"check admin\":\n            admins = \"\"\n            data_base_get_admin = db.list_admin_execut()\n            for i in data_base_get_admin:\n                admins += \" \" + str(i)\n                admins += \"\\n\"\n            bot.send_message(message.from_user.id, admins, reply_markup=main_keyboard)\n\n\n\n    except:\n        print('Бд пустая')\n        bot.send_message(message.from_user.id, \"Произошла ошибка\", reply_markup=main_keyboard)\n\ndef cryptocurrency_output(message, currency, quantity):\n    try:\n        global cg\n        output = message.text.lower()\n        print(output, currency, quantity)\n        crypto = ['bitcoin', 'ethereum']\n        currency_money = ['eur', 'rub', 'usd']\n        if currency in crypto:\n            money = cg.get_price(ids=currency, vs_currencies=output)\n            money_total = money[currency][output] * quantity\n            bot.send_message(message.from_user.id, output + \" = \" + str(money_total), reply_markup=main_keyboard)\n        elif currency in currency_money:\n            money = cg.get_price(ids=output, vs_currencies=currency)\n            money_total = quantity / money[output][currency]\n            bot.send_message(message.from_user.id, output + \" = \" + str(money_total), reply_markup=main_keyboard)\n    except Exception as error:\n        print(Exception)\n        bot.send_message(message.from_user.id, \"Переводить можно только из крипты в валюту и наоборот. Попробуйте еще раз:)\",\n                         reply_markup=main_keyboard)\ndef cryptocurrency_quantity(message, currency):\n    try:\n        quantity = message.text\n        if \",\" in quantity:\n            quantity = quantity.replace(\",\", \".\")\n        quantity = float(quantity)\n        crypto = ['bitcoin', 'ethereum']\n        currency_money = ['eur', 'rub', 'usd']\n        if currency in crypto:\n            bot.send_message(message.from_user.id, \"Нажмите на валюту в которую перевести:3\", reply_markup=currency_keyboard)\n            bot.register_next_step_handler(message, cryptocurrency_output, currency, quantity)\n        elif currency in currency_money:\n            bot.send_message(message.from_user.id, \"Нажмите на валюту в которую перевести:3\",\n                             reply_markup=crypto_keyboard)\n            bot.register_next_step_handler(message, cryptocurrency_output, currency, quantity)\n\n\n\n    except Exception as error:\n        bot.send_message(message.from_user.id, \"Вам нужно написать число, попробуйте еще раз:((\",\n                         reply_markup=main_keyboard)\ndef cryptocurrency_get(message):\n\n    currency = message.text.lower()\n    bot.send_message(message.from_user.id, \"Напишите количество:3\")\n    bot.register_next_step_handler(message, cryptocurrency_quantity, currency)\n\ndef translate_language(message, words):\n    try:\n        language = message.text\n        print(words)\n        print(language)\n        translator = Translator()\n        translation = translator.translate(words, dest=language)\n        bot.send_message(message.from_user.id, translation.origin + ' ' + translation.src + \"\\n\" + translation.text + ' ' + translation.dest, reply_markup=main_keyboard)\n    except Exception as error:\n        bot.send_message(message.from_user.id, \"Вы неправильно ввели язык, посмотрите список языков\", reply_markup=main_keyboard)\ndef translate(message):\n    words = message.text\n\n    bot.send_message(message.from_user.id, \"Напишите нужный Вам язык в формате 'RU', 'EN'\")\n    bot.register_next_step_handler(message, translate_language, words)\n\ndef translate_language_full(message):\n    language_full = [\"ru - Русский\", \"en - Английский\", \"de - Немецкий\", \"af - Африканский\", \"am - Амхарский\",\n                     \"ar - Арабский\", \"az - Азербайджанский\", \"be - Белорусский\", \"bg - Болгарский\",\n                     \"bn - Бенгальский\",\n                     \"bs - Боснийский\", \"ca - Каталанский\", \"ceb - Сербский\", \"co - Корсиканский\",\n                     \"cs - Чешский\", \"cy - Валлийский\",\n                     \"da - Датский\", \"el - Греческий\", \"eo - Эсперанто\", \"es - Испанский\", \"et - Эстонский\",\n                     \"eu - Баскский\", \"fa - Персидский\",\n                     \"fi - Финский\", \"fr - Французский\", \"fy - Фризский\", \"ga - Ирландский\",\n                     \"gd - Шотландскийгэльский\", \"gl - Галисийский\",\n                     \"gu - Гуджарати\", \"ha - Хауса\", \"haw - Гавайский\", \"he - Иврит\", \"hi - Хинди\",\n                     \"hmn - Хмонг\", \"hr - Хорватский\",\n                     \"ht - Гаитянскийкреольский\", \"hu - Венгерский\", \"hy - Армянский\", \"id - Индонезийский\",\n                     \"ig - Игбо\", \"is - Исландский\",\n                     \"it - Итальянский\", \"iw - Иврит\", \"ja - Японский\", \"jw - Яванский\", \"ka - Грузинский\",\n                     \"kk - Казахский\", \"km - Кхмерский\",\n                     \"kn - Каннада\", \"ko - Корейский\", \"ku - Курдский(курманджи)\", \"ky - Киргизский\",\n                     \"la - Латинский\", \"lb - Л��ксембургский\"]\n\n\n    bot.send_message(message.from_user.id, '\\n'.join(language_full))\n    bot.send_message(message.from_user.id, \"Напишите Ваш текст :3\")\n    bot.register_next_step_handler(message, translate)\n\ndef translate_yes_no(message):\n    answer = message.text.lower()\n    print(answer)\n    if answer == \"yes\" or answer == \"да\":\n        translate_language_full(message)\n    else:\n        bot.send_message(message.from_user.id, \"Напишите Ваш текст :3\")\n        bot.register_next_step_handler(message, translate)\n\n\n\n\ndef check_weather_big_one(message, number, info):\n    global app_id\n    info_city = info[\"list\"][number]\n    id_city = info_city[\"id\"]\n    answer = requests.get(\"http://api.openweathermap.org/data/2.5/weather\",\n                          params={'id': id_city, 'units': 'metric', 'lang': 'ru', 'APPID': app_id})\n    info = answer.json()\n    temp = int(info[\"main\"][\"temp\"])\n    temp_min_and_max = [int(info[\"main\"][\"temp_min\"]), int(info[\"main\"][\"temp_max\"])]\n    weather = info[\"weather\"][0][\"description\"]\n\n\n    bot.send_message(message.from_user.id, \"Температура: \" + str(temp) + \"\\n\" + \"Минимальная температура \" + str(temp_min_and_max[0]) + '\\n' + 'Наибольшая темпепратура ' + str(temp_min_and_max[1]) + \"\\n\" + \"Погода: \" + weather, reply_markup=main_keyboard)\n\n\ndef number_user_weather(message, info):\n    number = message.text\n    if number.isdigit() and 0 < int(number) <= (len(info)):\n        number = int(number) - 1\n        check_weather_big_one(message, number, info)\n    else:\n        bot.send_message(message.from_user.id,\"Вы написани не правильный номер, повторите попытку!\")\n        bot.register_next_step_handler(message, number_user_weather, info)\n\ndef citi_big_one(message, info):\n    global info_city\n    for i in range(len(info[\"list\"])):\n        bot.send_message(message.from_user.id, str(i + 1) + \": \" + info[\"list\"][i][\"name\"]+ \" \" + info[\"list\"][i][\"sys\"][\"country\"])\n    bot.send_message(message.from_user.id, \"Напишите номер: \")\n    bot.register_next_step_handler(message, number_user_weather, info)\n\ndef weather(message):\n    city = message.text\n    global app_id\n    try:\n        answer = requests.get(\"http://api.openweathermap.org/data/2.5/find\",\n                              params={'q': city, 'units': 'metric', 'lang': 'ru', 'APPID': app_id})\n        info = answer.json()\n        number = 0\n        if len(info[\"list\"]) > 1:\n            citi_big_one(message, info)\n        else:\n            check_weather_big_one(message, number, info)\n    except Exception as error:\n        bot.send_message(message.from_user.id, \"Этот город не найден :(\", reply_markup=main_keyboard)\n    number = 0\n\n\ndef get_files_names_memes(message):\n    files = os.listdir(path=\"./meme\")\n    mem = choice(files)\n    bot.send_photo(message.from_user.id, open(\"./meme/\" + mem, \"rb\"))\n    files.remove(mem)\ndef Yes_no_check_quit(message):\n    text = message.text.lower()\n    total = 0\n    if text == \"да\" or text == \"lf\" or text == \"yes\":\n        start_game(message)\n    else:\n        bot.send_message(message.from_user.id, \"Приходи еще!\",\n                         reply_markup=main_keyboard)\n\n\n\ndef process_game(message, random_num, total):\n    numbers = message.text\n    total += 1\n    if numbers.isdigit() and 1 <= int(numbers) <= 100:\n        numbers = int(numbers)\n        if random_num == numbers:\n            bot.send_message(message.from_user.id, \"Вы угадали, поздравляем! Это была \" + str(total) + \" попытка!\")\n            bot.send_message(message.from_user.id, \"Хотите сыграть еще? Напишите 'Да' или 'Нет' : \",\n                             reply_markup=yes_no_keyboatd)\n            bot.register_next_step_handler(message, Yes_no_check_quit)\n\n\n        elif random_num < numbers:\n            bot.send_message(message.from_user.id, \"Попытка № \" + str(total) + \": \" + \"Слишком много, попробуйте еще раз\")\n            bot.register_next_step_handler(message, process_game, random_num, total)\n\n        else:\n            bot.send_message(message.from_user.id, \"Попытка № \" + str(total) + \" : \" + ' Слишком мало, попробуйте еще раз')\n            bot.register_next_step_handler(message, process_game, random_num, total)\n\n    else:\n        bot.send_message(message.from_user.id, \"Введите корректное число от 1 до 100\")\n        bot.register_next_step_handler(message, process_game, random_num, total)\n\n\ndef start_game(message):\n    bot.send_message(message.from_user.id, \"Введи число от 1 до 100: \")\n    random_num = randint(1, 100)\n    total = 0\n    bot.register_next_step_handler(message, process_game, random_num, total)\n\n\n\n\ndef get_name(message):\n    name = message.text\n\n    bot.send_message(message.from_user.id, \"Напишите Вашу фамилию\")\n    bot.register_next_step_handler(message, get_surname, name)\n\n\ndef get_surname(message, name):\n    surname = message.text\n\n    bot.send_message(message.from_user.id, \"Напишите Ваш возраст(Используйте только цифры)\")\n    bot.register_next_step_handler(message, get_number, name, surname)\n\n\ndef get_number(message, name, surname):\n    age = message.text\n    res = \"Ваше имя: \" + name + \"\\n\"\n    res += \"Ваша фамилия: \" + surname + \"\\n\"\n    res += \"Ваш возраст: \" + age + \"\\n\"\n    bot.send_message(message.from_user.id, \"Ваши данные: \" + \"\\n\" + res)\n    id_user = message.from_user.id\n    sqlite_select_query = 'INSERT INTO users (id, name, surname, age) VALUES ( ' + str(\n        id_user) + ', \\'' + name + '\\',' + ' \\'' + surname + '\\', ' + age + ');'\n    if db.execute(sqlite_select_query):\n        bot.send_message(message.from_user.id, \"Вы успешно зарегистрировались :3\", reply_markup=main_keyboard)\n    else:\n        bot.send_message(message.from_user.id, \"Возникли какие-то неполадки, попробуйте еще раз:((\", reply_markup=main_keyboard)\n\n\n\ndef game_play(message):\n    text = message.text.lower()\n    if text == \"нет\" or text == \"ytn\" or text == \"no\":\n        bot.send_message(message.from_user.id,\n                         \"Жду тебя снова!\", reply_markup=main_keyboard)\n\n        return\n    else:\n        answer = [\"Бесспорно\", \"Предрешено\", \"Никаких сомнений\", \"Определённо да\", \"Можешь быть уверен в этом\",\n                  \"Мне кажется - да\",\n                  \"Вероятнее всего\", \"Хорошие перспективы\", \"Знаки говорят - да\", \"Да\", \"Пока неясно, попробуй снова\",\n                  \"Спроси позже\",\n                  \"Лучше не рассказывать\", \"Сейчас нельзя предсказать\", \"Сконцентрируйся и спроси опять\",\n                  \"Даже не думай\",\n                  \"Мой ответ - нет\",\n                  \"По моим данным - нет\", \"Перспективы не очень хорошие\", \"Весьма сомнительно\"]\n\n    bot.send_message(message.from_user.id, choice(answer))\n    bot.send_message(message.from_user.id,\n                     \"Ты хочешь еще что-то спросить? \\n Напиши вопрос или 'Нет' если хочешь закончить \",\n                     reply_markup=no_keyboard)\n    bot.register_next_step_handler(message, game_play)\n\n\n\nbot.polling(none_stop=True, timeout=123)","repo_name":"Margolapo4ka/Telobot_Test_Bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":23260,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"26729286604","text":"\"\"\"Main file of GUI application. All functions, related to\nrendering are in this module. This file is basically\ndescribes frontend of adserver\n\"\"\"\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport numpy as np\nfrom dash.dependencies import Input, Output, State\n\nimport util\nimport server\n\nexternal_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\napp = dash.Dash(\n    __name__,\n    external_stylesheets=external_stylesheets,\n)\napp.config['suppress_callback_exceptions'] = True\n\n# store grid functions in this dictionary\n# this solution is limited to one user apps\n# and cannot be used for multiple users\ngrid_store = {}\n\n\napp.layout = html.Div([\n    html.H1('Ad server'),\n\n    html.Div('''Implementation of a system that simulates the behavior of an\n    advertising server, described as a system of differential\n    equations.'''),\n\n    html.Hr(),\n    html.H4('Functions input'),\n\n    dcc.Tabs(id=\"function-input-tabs\", value='params', children=[\n        dcc.Tab(label='Load as file', value='file'),\n        dcc.Tab(label='Input parameters', value='params'),\n    ]),\n\n    html.Div(id='function-input-content'),\n\n    html.Hr(),\n    html.H4('Parameters'),\n\n    util.get_numeric_input('cauchy-x0', 'x₀'),\n    util.get_numeric_input('cauchy-y0', 'y₀'),\n    util.get_numeric_input('cauchy-beta', 'β'),\n    util.get_numeric_input('cauchy-tau', 'τ'),\n    util.get_numeric_input('auto-beta0', 'auto β₀'),\n    util.get_numeric_input('auto-beta1', 'auto β₁'),\n\n    html.Br(),\n    html.Button(id='build-plot', n_clicks_timestamp='0', children='plot', className='mgn-l mgn-t'),\n    html.Button(id='auto', n_clicks_timestamp='0', children='auto', className='mgn-l'),\n    html.Br(),\n    html.P(\n        '''When using auto mode, you don\\'t need to specify x₀, y₀ and β. You only\n        need to specify the region for β\n        ''',\n        className='mgn',\n    ),\n\n    html.Hr(),\n    html.H4('Plots'),\n    html.Div(id='plots-container'),\n])\n\n\n@app.callback(\n    Output('function-input-content', 'children'),\n    [Input('function-input-tabs', 'value')]\n)\ndef render_function_input(value):\n    \"\"\"Rendering input components for functions.\n\n    Based on chosen input type construct the requested input components\n    for ρ(x), S(t), z(t) (probability density, shows plan and real traffic)\n    \"\"\"\n    global grid_store\n    grid_store = {}\n    if value == 'file':\n        return html.Div([\n            html.Div([\n                html.P('Load csv file in following format: x,f(x)'),\n                html.Div([\n                    html.Div(\n                        util.get_file_upload('rho-file', 'Probability density ρ(x) '),\n                        className='four columns',\n                    ),\n                    html.Div(\n                        util.get_file_upload('plan-file', 'Shows plan S(t) '),\n                        className='four columns',\n                    ),\n                    html.Div(\n                        util.get_file_upload('traffic-file', 'Traffic z(t) '),\n                        className='four columns'\n                    ),\n                ], className='row'),\n            ], className='row mgn'),\n            html.Div(id='file-validation', className='row mgn'),\n            html.Div(id='placeholder_file', style={'display': 'none'}),\n        ])\n    if value == 'params':\n        return html.Div([\n            html.P('Parameters for ρ(x) = ax(b - x)'),\n            util.get_numeric_input('pdf-a', 'a'),\n            util.get_numeric_input('pdf-b', 'b'),\n\n            html.P('Parameters for S(t) = mt + n·sin(kt)'),\n            util.get_numeric_input('plan-m', 'm'),\n            util.get_numeric_input('plan-n', 'n'),\n            util.get_numeric_input('plan-k', 'k'),\n\n            html.P('Parameters for z(t) = pt + q·cos(rt)'),\n            util.get_numeric_input('traffic-p', 'p'),\n            util.get_numeric_input('traffic-q', 'q'),\n            util.get_numeric_input('traffic-r', 'r'),\n\n            html.Br(),\n            html.Div(id='placeholder_param', style={'display': 'none'}),\n        ], className='mgn')\n\n\n@app.callback(\n    Output('placeholder_file', 'children'),\n    [Input('rho-file', 'contents'),\n     Input('plan-file', 'contents'),\n     Input('traffic-file', 'contents')]\n)\ndef save_functions_from_files(pdf_file, plan_file, traffic_file):\n    if pdf_file and plan_file and traffic_file:\n        grid_store['pdf'] = util.parse_contents(pdf_file)\n        grid_store['plan'] = util.parse_contents(plan_file)\n        grid_store['traffic'] = util.parse_contents(traffic_file)\n    return ''\n\n\n@app.callback(\n    Output('placeholder_param', 'children'),\n    [Input('pdf-a', 'value'),\n     Input('pdf-b', 'value'),\n     Input('plan-m', 'value'),\n     Input('plan-n', 'value'),\n     Input('plan-k', 'value'),\n     Input('traffic-p', 'value'),\n     Input('traffic-q', 'value'),\n     Input('traffic-r', 'value'),\n     Input('cauchy-tau', 'value')]\n)\ndef save_functions_from_params(\n        pdf_a, pdf_b,\n        plan_m, plan_n, plan_k,\n        traffic_p, traffic_q, traffic_r,\n        tau,\n):\n    params = [pdf_a, pdf_b, plan_m, plan_n, plan_k, traffic_p, traffic_q, traffic_r, tau]\n    if all(p is not None for p in params):\n        grid_store['pdf'] = util.tabulate_probability_density(pdf_a, pdf_b)\n        grid_store['plan'] = util.tabulate_plan(plan_m, plan_n, plan_k, tau)\n        grid_store['traffic'] = util.tabulate_traffic(traffic_p, traffic_q, traffic_r, tau)\n    return ''\n\n\n@app.callback(\n    Output('plots-container', 'children'),\n    [Input('build-plot', 'n_clicks_timestamp'),\n     Input('auto', 'n_clicks_timestamp')],\n    [State('cauchy-x0', 'value'),\n     State('cauchy-y0', 'value'),\n     State('cauchy-beta', 'value'),\n     State('cauchy-tau', 'value'),\n     State('auto-beta0', 'value'),\n     State('auto-beta1', 'value')]\n)\ndef render_plots(simple, auto, x0, y0, beta, tau, beta0, beta1):\n    if int(simple) > int(auto):\n        return render_plots_simple(x0, y0, beta, tau)\n    else:\n        return render_plots_auto(beta0, beta1, tau)\n\n\ndef render_plots_simple(x0, y0, beta, tau):\n    if (\n            all(k in grid_store for k in ['pdf', 'plan', 'traffic']) and\n            all(p is not None for p in [x0, y0, beta, tau])\n    ):\n        # x(t), y(t)\n        real_shows, threshold = server.solve(\n            grid_store['pdf'],\n            grid_store['plan'],\n            grid_store['traffic'],\n            x0, y0, beta, tau,\n        )\n\n        # C_1(beta), C_2(beta)\n        crit1 = server.crit1(real_shows, threshold, grid_store['pdf'], x0, tau)\n        crit2 = server.crit2(real_shows, grid_store['plan'])\n\n        # |S(t) - x(t)|\n        diff = np.zeros_like(real_shows)\n        diff[:, 0] = real_shows[:, 0]\n        diff[:, 1] = np.abs(grid_store['plan'][:, 1] - real_shows[:, 1])\n\n        return [\n            html.Div([\n                dcc.Graph(\n                    figure=util.plot_lines(\n                        [real_shows, grid_store['plan'], grid_store['traffic']],\n                        ['Shows', 'Plan', 'Traffic'],\n                        'Shows',\n                        yaxis='value',\n                        xaxis='time',\n                    )\n                ),\n            ], className='row'),\n            html.Div([\n                dcc.Graph(\n                    figure=util.plot_lines(\n                        [threshold],\n                        ['threshold y(t)'],\n                        'Threshold',\n                        yaxis='value',\n                        xaxis='time',\n                    ),\n                    className='six columns',\n                ),\n                html.Div(\n                    children=f'C₁ = {crit1:.2f}  C₂ = {crit2:.2f}',\n                    className='six columns criterions',\n                )\n            ], className='row'),\n            html.Div([\n                dcc.Graph(\n                    figure=util.plot_lines(\n                        [grid_store['pdf']],\n                        ['pdf'],\n                        'Probability density',\n                        yaxis='value',\n                        xaxis='x',\n                    ),\n                    className='six columns',\n                ),\n                dcc.Graph(\n                    figure=util.plot_lines(\n                        [diff],\n                        ['diff'],\n                        '|S(t) - x(t)|',\n                        xaxis='time',\n                        yaxis='value',\n                    ),\n                    className='six columns',\n                )\n            ], className='row')\n        ]\n    return 'Input all parameters and hit plot button to see relevant plots'\n\n\ndef render_plots_auto(beta0, beta1, tau):\n    if all(p is not None for p in [beta0, beta1, tau]):\n        # initializations\n        beta = np.linspace(beta0, beta1, 20)\n        x0 = 0\n        y0 = 0.5\n        loss = []\n\n        # find optimal beta\n        for b in beta:\n            real_shows, threshold = server.solve(\n                grid_store['pdf'],\n                grid_store['plan'],\n                grid_store['traffic'],\n                x0, y0, b, tau,\n            )\n\n            # C_1(beta), C_2(beta)\n            crit1 = server.crit1(real_shows, threshold, grid_store['pdf'], x0, tau)\n            crit2 = server.crit2(real_shows, grid_store['plan'])\n            loss.append(crit1 + 10 * crit2)\n\n        loss = np.array(loss)\n        best = beta[np.argmin(loss)]\n\n        print('Best beta=', best)\n\n        # now find some optimal points\n        nx, ny = (5, 5)\n        criterions = np.zeros((nx, ny))\n\n        xs = np.linspace(0, grid_store['traffic'][0, 1], nx)\n        ys = np.linspace(0, 1, ny)\n\n        for i in range(nx):\n            for j in range(ny):\n                x = xs[i]\n                y = ys[j]\n                real_shows, threshold = server.solve(\n                    grid_store['pdf'],\n                    grid_store['plan'],\n                    grid_store['traffic'],\n                    x, y, best, tau,\n                )\n\n                # C_1(beta), C_2(beta)\n                crit1 = server.crit1(real_shows, threshold, grid_store['pdf'], x, tau)\n                crit2 = server.crit2(real_shows, grid_store['plan'])\n                criterions[i, j] = crit1 + 10 * crit2\n\n        i, j = np.unravel_index(np.argmin(criterions, axis=None), criterions.shape)\n        best_x = xs[i]\n        best_y = ys[j]\n\n        # get 3 best x, y and plot solutions for them\n        idxs = np.dstack(\n            np.unravel_index(np.argsort(criterions.ravel())[:3], criterions.shape)\n        )[0]\n\n        solutions = []\n        for x, y in idxs:\n            real_shows, threshold = server.solve(\n                grid_store['pdf'],\n                grid_store['plan'],\n                grid_store['traffic'],\n                xs[x], ys[y], best, tau,\n            )\n            solutions.append(real_shows)\n\n        # compute with best params\n        real_shows, threshold = server.solve(\n            grid_store['pdf'],\n            grid_store['plan'],\n            grid_store['traffic'],\n            best_x, best_y, best, tau,\n        )\n\n        diff = np.zeros_like(real_shows)\n        diff[:, 0] = real_shows[:, 0]\n        diff[:, 1] = np.abs(grid_store['plan'][:, 1] - real_shows[:, 1])\n\n        return [\n            # Best shows function\n            html.Div([\n                dcc.Graph(\n                    figure=util.plot_lines(\n                        [real_shows, grid_store['plan'], grid_store['traffic']],\n                        ['Shows', 'Plan', 'Traffic'],\n                        f'Shows for best x₀={best_x:.2f} y₀={best_y:.2f} β={best:.2f}',\n                        yaxis='value',\n                        xaxis='time',\n                    )\n                ),\n            ], className='row'),\n            html.Div([\n                dcc.Graph(\n                    figure=util.plot_lines(\n                        [threshold],\n                        ['threshold y(t)'],\n                        f'Threshold for best x₀={best_x:.2f} y₀={best_y:.2f} β={best:.2f}',\n                        yaxis='value',\n                        xaxis='time',\n                    ),\n                    className='six columns',\n                ),\n                dcc.Graph(\n                    figure={\n                        'data': [\n                            {'x': beta, 'y': loss, 'type': 'line', 'name': 'loss'}\n                        ],\n                        'layout': {\n                            'title': 'Loss',\n                            'xaxis': {'title': 'beta'},\n                            'yaxis': {'title': 'C₁ + 10C₂'},\n                        }\n                    },\n                    className='six columns',\n                )\n            ], className='row'),\n            html.Div([\n                dcc.Graph(\n                    figure=util.plot_lines(\n                        [grid_store['pdf']],\n                        ['pdf'],\n                        'Probability density',\n                        yaxis='value',\n                        xaxis='x',\n                    ),\n                    className='six columns',\n                ),\n                dcc.Graph(\n                    figure=util.plot_lines(\n                        [diff],\n                        ['diff'],\n                        '|S(t) - x(t)|',\n                        xaxis='time',\n                        yaxis='value',\n                    ),\n                    className='six columns',\n                )\n            ], className='row'),\n\n            html.Div([\n                dcc.Graph(\n                    figure={\n                        'data': [\n                            {\n                                'x': np.repeat(xs, nx),\n                                'y': np.tile(ys, ny),\n                                'z': criterions.flatten(),\n                                'mode': 'markers',\n                                'type': 'scatter3d',\n                                'marker': {\n                                    'size': 12,\n                                    'opacity': 0.8,\n                                },\n                            }\n                        ],\n                        'layout': {\n                            'title': 'Loss',\n                            'xaxis': {'title': 'x'},\n                            'yaxis': {'title': 'y'},\n                            'zaxis': {'title': 'loss'}\n                        }\n                    },\n                    className='six columns'\n                ),\n                dcc.Graph(\n                    figure={\n                        'data': [\n                            {'x': grid_store['plan'][:, 1], 'y': s[:, 1], 'type': 'line', 'name': f'x₀={x}, y₀={y}'}\n                            for s, (x, y) in zip(solutions, idxs)\n                        ],\n                        'layout': {\n                            'title': '(S, x) plots for 3 best points',\n                            'xaxis': {'title': 'S(t)'},\n                            'yaxis': {'title': 'x(t)'},\n                        }\n                    },\n                    className='six columns'\n                )\n            ])\n        ]\n\n\nif __name__ == '__main__':\n    app.run_server(host='localhost', debug=True)\n","repo_name":"puhsu/numerical_methods","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":15271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"1908888043","text":"from abc import ABCMeta, abstractmethod\nimport re\nimport sys\n\nfrom inginious.common.base import id_checker\n\n\nclass BasicBox(object, metaclass=ABCMeta):\n    \"\"\" A basic abstract problem box. A box is a small input for a problem. A problem can contain multiple boxes \"\"\"\n\n    @abstractmethod\n    def get_type(self):\n        \"\"\" Return the type of this box \"\"\"\n        return None\n\n    def get_problem(self):\n        \"\"\" Return the problem to which this box is linked \"\"\"\n        return self._problem\n\n    def get_id(self):\n        \"\"\" Return the _id of this box \"\"\"\n        return self._id\n\n    def input_is_consistent(self, task_input, default_allowed_extension, default_max_size):  # pylint: disable=unused-argument\n        \"\"\" Check if an input for this box is consistent. Return true if this is case, false else \"\"\"\n        try:\n            return self.get_complete_id() in task_input\n        except:\n            return False\n\n    def get_complete_id(self):\n        \"\"\" Returns the complete _id of this box. This _id is unique among all problems and boxes in an exercice \"\"\"\n        pid = str(self.get_problem().get_id())\n        bid = str(self.get_id())\n        if bid != \"\":\n            return pid + \"/\" + bid\n        else:\n            return pid\n\n    def __init__(self, problem, boxid, boxdata_):\n        \"\"\" Constructor. problem is a BasicProblem (or derivated) instance, boxid a an alphanumeric _id and boxdata is the data for this box. \"\"\"\n        if not id_checker(boxid) and not boxid == \"\":\n            raise Exception(\"Invalid box _id: \" + boxid)\n        self._id = boxid\n        self._problem = problem\n\n\nclass TextBox(BasicBox):\n    \"\"\"Text box. Simply shows text.\"\"\"\n\n    def get_type(self):\n        return \"text\"\n\n    def input_is_consistent(self, task_input, default_allowed_extension, default_max_size):\n        # do not call input_is_consistent from BasicBox.\n        return True\n\n    def __init__(self, problem, boxid, boxData):\n        super(TextBox, self).__init__(problem, boxid, boxData)\n        if \"content\" not in boxData:\n            raise Exception(\"Box _id \" + boxid + \" with type=text do not have content.\")\n        self._content = boxData['content']\n\n\nclass FileBox(BasicBox):\n    \"\"\"\n        File box. Allow to send a file to the inginious.backend.\n        The input for this box must be a dictionnary, containing two keys:\n        ::\n\n            {\n                \"filename\": \"thefilename.txt\",\n                \"value\": \"the content of the file\"\n            }\n\n    \"\"\"\n\n    def get_type(self):\n        return \"file\"\n\n    def input_is_consistent(self, taskInput, default_allowed_extension, default_max_size):\n        if not BasicBox.input_is_consistent(self, taskInput, default_allowed_extension, default_max_size):\n            return False\n\n        try:\n            if not taskInput[self.get_complete_id()][\"filename\"].endswith(tuple(self._allowed_exts or default_allowed_extension)):\n                return False\n\n            if sys.getsizeof(taskInput[self.get_complete_id()][\"value\"]) > (self._max_size or default_max_size):\n                return False\n        except:\n            return False\n        return True\n\n    def __init__(self, problem, boxid, boxData):\n        super(FileBox, self).__init__(problem, boxid, boxData)\n        self._allowed_exts = boxData.get(\"allowed_exts\", None)\n        self._max_size = boxData.get(\"max_size\", None)\n\n\nclass InputBox(BasicBox):\n    \"\"\" Input box. Displays an input object \"\"\"\n\n    def get_type(self):\n        return \"input\"\n\n    def input_is_consistent(self, taskInput, default_allowed_extension, default_max_size):\n        if not BasicBox.input_is_consistent(self, taskInput, default_allowed_extension, default_max_size):\n            return False\n\n        if self._max_chars != 0 and len(taskInput[self.get_complete_id()]) > self._max_chars:\n            return False\n\n        # do not allow empty answers\n        try:\n            if len(taskInput[self.get_complete_id()]) == 0:\n                if self._optional:\n                    taskInput[self.get_complete_id()] = self._default_value\n                else:\n                    return False\n        except:\n            return False\n\n        if self._input_type == \"integer\":\n            try:\n                int(taskInput[self.get_complete_id()])\n            except:\n                return False\n\n        if self._input_type == \"decimal\":\n            try:\n                float(taskInput[self.get_complete_id()])\n            except:\n                return False\n        return True\n\n    def __init__(self, problem, boxid, boxData):\n        super(InputBox, self).__init__(problem, boxid, boxData)\n        if boxData[\"type\"] == \"input-text\":\n            self._input_type = \"text\"\n            self._default_value = \"\"\n        elif boxData[\"type\"] == \"input-integer\":\n            self._input_type = \"integer\"\n            self._default_value = \"0\"\n        elif boxData[\"type\"] == \"input-decimal\":\n            self._input_type = \"decimal\"\n            self._default_value = \"0.0\"\n        else:\n            raise Exception(\"No such box type \" + boxData[\"type\"] + \" in box \" + boxid)\n\n        self._optional = boxData.get(\"optional\", False)\n\n        if \"maxChars\" in boxData and isinstance(boxData['maxChars'], int) and boxData['maxChars'] > 0:\n            self._max_chars = boxData['maxChars']\n        elif \"maxChars\" in boxData:\n            raise Exception(\"Invalid maxChars value in box \" + boxid)\n        else:\n            self._max_chars = 0\n\n\nclass MultilineBox(BasicBox):\n    \"\"\" Multiline Box \"\"\"\n\n    def get_type(self):\n        return \"multiline\"\n\n    def input_is_consistent(self, taskInput, default_allowed_extension, default_max_size):\n        if not BasicBox.input_is_consistent(self, taskInput, default_allowed_extension, default_max_size):\n            return False\n        if self._max_chars != 0 and len(taskInput[self.get_complete_id()]) > self._max_chars:\n            return False\n        # do not allow empty answers\n        if len(taskInput[self.get_complete_id()]) == 0:\n            if self._optional:\n                taskInput[self.get_complete_id()] = \"\"\n            else:\n                return False\n        return True\n\n    def __init__(self, problem, boxid, boxData):\n        super(MultilineBox, self).__init__(problem, boxid, boxData)\n        if \"maxChars\" in boxData and isinstance(boxData['maxChars'], int) and boxData['maxChars'] > 0:\n            self._max_chars = boxData['maxChars']\n        elif \"maxChars\" in boxData:\n            raise Exception(\"Invalid maxChars value in box \" + boxid)\n        else:\n            self._max_chars = 0\n\n        self._optional = boxData.get(\"optional\", False)\n\n        if \"lines\" in boxData and isinstance(boxData['lines'], int) and boxData['lines'] > 0:\n            self._lines = boxData['lines']\n        elif \"lines\" in boxData:\n            raise Exception(\"Invalid lines value in box \" + boxid)\n        else:\n            self._lines = 8\n\n        if re.match(r'[a-z0-9\\-_\\.]+$', boxData.get(\"language\", \"\"), re.IGNORECASE):\n            self._language = boxData.get(\"language\", \"\")\n        elif boxData.get(\"language\", \"\"):\n            raise Exception(\"Invalid language \" + boxData[\"language\"])\n        else:\n            self._language = \"plain\"\n","repo_name":"JuezUN/INGInious-old","sub_path":"inginious/common/tasks_code_boxes.py","file_name":"tasks_code_boxes.py","file_ext":"py","file_size_in_byte":7241,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"29359167892","text":"import sys\nimport concurrent.futures\nfrom Harasser import ApiHarasser, RequestType\nfrom models import RiseoAccountRegistration\nimport random\nimport string\nfrom database.models import Sponsor\nimport json\nfrom functions import read_data_from_file\nfrom database.operations import get_all_sponsors, get_session, create_registered_user\nfrom config import FILES_DIR\nimport logging\n\nlogging.basicConfig(filename=\"assets/logger.log\",\n                             format='%(levelname)s:%(message)s', level=logging.INFO)\n\n\nSPONSORS = read_data_from_file(FILES_DIR / \"valid_sponsors.txt\")\n\nSESSION = get_session()\nHEADERS = {\n\n    \"Host\": \"api.riseoo.com\",\n    \"User-Agent\": \"Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/111.0\",\n    \"Accept\": \"application/json, text/plain, */*\",\n    \"Accept-Language\": \"en-US,en;q=0.5\",\n    \"Accept-Encoding\": \"gzip, deflate, br\",\n    \"Content-Type\": \"application/json\",\n    \"Authorization\": \"Bearer undefined\",\n    \"Content-Length\": \"336\",\n    \"Origin\": \"https://portal.riseoo.com\",\n    \"Connection\": \"keep-alive\",\n    \"Referer\": \"https://portal.riseoo.com/\",\n    \"Sec-Fetch-Dest\": \"empty\",\n    \"Sec-Fetch-Mode\": \"cors\",\n    \"Sec-Fetch-Site\": \"same-site\",\n    \"TE\": \"trHEilers\"\n}\n\n\ndef generate_record(sponsor: str) -> RiseoAccountRegistration:\n    random_number = random.randint(100, 999)\n    random_letters = ''.join(random.choices(string.ascii_letters, k=4))\n\n    username = f\"{sponsor}{random_letters}\"\n    password = f\"#{username.capitalize()}{random_number}#\"\n    email = f\"{username}@gmail.com\"\n\n    return RiseoAccountRegistration(**{\"sponsorUsername\": sponsor, \"side\": 1, \"username\": username, \"password\": password, \"fullName\": username, \"emailAddress\": email,\n                                       \"contactNumber\": \"1-31212343212\", \"countryID\": 219, \"ipAddress\": \"123.123.123.123\", \"title\": \"Mr.\", \"panID\": \"\", \"bankAccountHolderName\": \"\", \"bankAccountNumber\": \"\", \"bankAccountIFSC\": \"\", \"bankAccountType\": 0})\n\n\ndef do_harass(sponsor: Sponsor):\n    global SESSION\n\n    harasser = ApiHarasser(\n        \"https://api.riseoo.com/Registration\", RequestType.POST, HEADERS)\n\n    user_data = generate_record(sponsor.sponsor_username)\n\n    response = harasser.do_request(\n        data=user_data.json(), is_json=True)\n\n    if response.ok:\n        try:\n            parsed_response = response.json()\n            if \"isSuccess\" in list(parsed_response.keys()) and parsed_response['isSuccess']:\n                create_registered_user(SESSION, user_data.username, user_data.password,\n                                       user_data.emailAddress, sponsor.sponsor_username)\n\n        except json.JSONDecodeError:\n            logging.error(\"Response is not JSON Serializable\")\n\n\ndef main():\n    global SESSION\n\n    sponsors: list[Sponsor] = get_all_sponsors(SESSION)\n\n    workers = 50\n    if len(sys.argv) > 1:\n        if sys.argv[1].isnumeric():\n            workers = int(sys.argv[1])\n\n    print(f\"== Initialized with {workers} workers ==\\n\")\n    with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as executor:\n        futures = [executor.submit(do_harass, sponsor)\n                   for sponsor in sponsors]\n        for future in concurrent.futures.as_completed(futures):\n            try:\n                future.result()\n            except Exception as exc:\n                logging.error(f\"Job failed: {exc}\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"rickturner2001/Riseoo-API-Harasser","sub_path":"registration_clogger.py","file_name":"registration_clogger.py","file_ext":"py","file_size_in_byte":3412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"71314189057","text":"def count_wins(dice1, dice2):\r\n  assert len(dice1) == 6 and len(dice2) == 6\r\n  dice1_wins, dice2_wins = 0, 0\r\n\r\n  for i in dice1:\r\n    for j in dice2:\r\n      if i > j:\r\n        dice1_wins += 1\r\n      elif i < j:\r\n        dice2_wins += 1\r\n  return (dice1_wins, dice2_wins)\r\n\r\n\r\ndef find_the_best_dice(dices):\r\n\r\n  assert all(len(dice) == 6 for dice in dices)\r\n\r\n  for i in dices:\r\n    counter = 0\r\n\r\n\r\n    for j in dices:\r\n      if i != j:\r\n        dice1_wins, dice2_wins = count_wins(i, j)\r\n        if dice1_wins > dice2_wins:\r\n          counter += 1\r\n          if counter == len(dices) - 1:\r\n            return dices.index(i)\r\n  return -1\r\n\r\n\r\ndef compute_strategy(dices):\r\n  assert all(len(dice) == 6 for dice in dices)\r\n  strategy = dict()\r\n\r\n  index_best_die = find_the_best_dice(dices)\r\n  if index_best_die != -1:\r\n    update_d = {'choose_first': True, 'first_dice': index_best_die}\r\n    strategy.update(update_d)\r\n  else:\r\n    strategy.update({'choose_first': False})\r\n    for i in dices:\r\n      for j in dices:\r\n        if i != j:\r\n          dice1_wins, dice2_wins = count_wins(i, j)\r\n          if dice1_wins > dice2_wins:\r\n            update = {dices.index(j): dices.index(i)}\r\n            strategy.update(update)\r\n\r\n  return strategy\r\n\r\n\r\ndices = [[1, 1, 4, 6, 7, 8], [2, 2, 2, 6, 7, 7], [3, 3, 3, 5, 5, 8]]\r\nx = compute_strategy(dices)\r\nprint(x)","repo_name":"engkangkoh/Dice-Game","sub_path":"dice game.py","file_name":"dice game.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"15759980488","text":"from picamera import PiCamera, Color\nfrom time import sleep\nimport time\nfrom datetime import datetime, timedelta\n#import dateutil.tz\n#from pytz import timezone\nimport os\n\nimport RPi.GPIO as GPIO\n\nGPIO.setmode(GPIO.BOARD)\n\ndateString = '%H:%M:%S %d/%m/%Y %Z'\ncamera = PiCamera()\n#camera.resolution = (2592, 1944)\n\n#camera.framerate = 15\n\n#camera.resolution = (64,64)\n#camera.resolution = (128,128)\ncamera.resolution = (640, 480)\ncamera.annotate_text_size = 25\n\n#camera.start_preview(alpha=200)\nGPIO.setup(8, GPIO.OUT)\n\n\nGPIO.output(8,GPIO.LOW)#turn led on and wait a few seconds\nfor i in range(2):\n#    sleep(1800)\n    GPIO.output(8, GPIO.HIGH)\n    camera.start_preview()\n    sleep(3)\n    camera.capture('/home/pi/Plantography/camera/frame%s.jpg' %i)\n    camera.annotate_text = (datetime.now()+ timedelta(hours = 1)).strftime(dateString);\n    camera.capture('/home/pi/Plantography/camera/latest.jpg')\n    camera.stop_preview()\n    print (\"capture completed\")\n    os.system(\"sudo cp /home/pi/Plantography/camera/latest.jpg /var/www/html/images\")\n    os.system(\"sudo cp /home/pi/Plantography/camera/frame%s.jpg /var/www/html/images\"%i)\n    print (\"copy completed\")\n    GPIO.output(8,GPIO.LOW)\n","repo_name":"rantistic/Plantography","sub_path":"camera/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"19507771966","text":"\ndef partOne():\n\n    valid = 0\n    file = open(\"Day2/input.txt\", \"r\")\n    for line in file:\n        lineList = line.split(\" \")\n        range = lineList[0].split(\"-\")\n        target = lineList[1][0]\n        targetCount = 0\n\n        for char in lineList[2]:\n            if char == target:\n                targetCount+=1\n\n        if targetCount >= int(range[0]) and targetCount <= int(range[1]):\n            valid += 1\n\n    file.close()\n    print(valid)\n\n\ndef partTwo():\n\n    valid = 0\n    file = open(\"Day2/input.txt\", \"r\")\n    for line in file:\n        lineList = line.split(\" \")\n        indices = lineList[0].split(\"-\")\n        target = lineList[1][0]\n\n        if (lineList[2][int(indices[0])-1] == target and lineList[2][int(indices[1])-1] != target) or (lineList[2][int(indices[1])-1] == target and lineList[2][int(indices[0])-1] != target):\n            valid += 1\n\n    file.close()\n    print(valid)\n\n\npartOne()\npartTwo()\n\n","repo_name":"austins1999/AdventOfCode2020","sub_path":"Day2/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"13448853447","text":"from urllib.parse import urlparse\n\n\ndef parse_kafka_url(val):\n    \"\"\"Extracts the group ID, broker addresses, and topic names from a Kafka URL.\n\n    The URL should be in this form:\n        ``kafka://[groupid@]broker[,broker2[,...]]/topic[,topic2[,...]]``\n\n    The returned group ID and topic may be None if they aren't in the URL.\n\n    \"\"\"\n    parsed = urlparse(val)\n    if parsed.scheme != \"kafka\":\n        raise ValueError(\"invalid kafka URL: must start with 'kafka://'\")\n\n    split_netloc = parsed.netloc.split(\"@\", maxsplit=1)\n    if len(split_netloc) == 2:\n        group_id = split_netloc[0]\n        broker_addresses = split_netloc[1].split(\",\")\n    else:\n        group_id = None\n        broker_addresses = split_netloc[0].split(\",\")\n\n    topics = parsed.path.lstrip(\"/\")\n    if len(topics) == 0:\n        split_topics = None\n    else:\n        split_topics = topics.split(\",\")\n    return group_id, broker_addresses, split_topics\n","repo_name":"astronomy-commons/adc-streaming","sub_path":"adc/kafka.py","file_name":"kafka.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"80"}
+{"seq_id":"40818613621","text":"from quirks.mainApp import _addressCoordsStuff\r\nimport sqlite3\r\n\r\n# fetching coords for order location\r\nconn1 = sqlite3.connect('washaDB/orders.db')\r\ncur1 = conn1.cursor()\r\ncur1.execute(\"SELECT * FROM orders WHERE orderNum = :orderNum\",\r\n                    {'orderNum':2021112000000001})\r\nfetchedList = cur1.fetchall()\r\nfetchedTup = fetchedList[0]\r\nfetchedAddress = fetchedTup[6]\r\n# coords = \r\n_addressCoordsStuff.addressCoord(fetchedAddress)\r\n# print(coords)","repo_name":"rajmanish23/MREC-vishesh21","sub_path":"washa/backup/trash/addressCoordsTest.py","file_name":"addressCoordsTest.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"80"}
+{"seq_id":"13617196517","text":"# Se le pide al usuario que ingrese los dos números\nnumero1 = float(input(\"Ingrese el primer número: \"))\nnumero2 = float(input(\"Ingrese el segundo número: \"))\n\n# Se verifica si los dos números son iguales\nif numero1 == numero2:\n    print(\"Los dos números son iguales\")\n\n# Se verifica si los dos números son diferentes\nelif numero1 != numero2:\n# Se verifica si el primer número es mayor que el segundo\n    if numero1 > numero2:\n        print(\"El primer número es mayor que el segundo\")\n# Se verifica si el segundo número es mayor o igual que el primero\n    if numero2 >= numero1:\n        print(\"El segundo número es mayor o igual que el primero\")\nelse: \n    print(\"Digite valores numéricos\")","repo_name":"BryanQC1021/python0223","sub_path":"listasDeEjercicios(semana1)/ejercicio7.py","file_name":"ejercicio7.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"es","doc_type":"code","dataset":"github-code","pt":"80"}
+{"seq_id":"45297576602","text":"from bs4 import BeautifulSoup\nfrom selenium import webdriver as wd\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.support.wait import WebDriverWait as wdw\nfrom selenium.webdriver.support import expected_conditions as ec\nfrom selenium.common.exceptions import TimeoutException\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium.common.exceptions import ElementClickInterceptedException\nfrom selenium.webdriver.common.by import By as by\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By as by\nfrom selenium.common.exceptions import NoSuchElementException\nfrom openpyxl import Workbook\nfrom time import sleep\n\noption = wd.ChromeOptions()\nprefs = {\"profile.default_content_setting_values\":{\"notifications\":2}}\noption.add_experimental_option(\"prefs\", prefs)\n\n \ndriver = wd.Chrome(\"C:\\\\Users\\\\user\\\\Desktop\\\\chromedriver.exe\", options = option)\ndriver.get(\"https://www.linetv.tw/\")\n#連續劇\n\ndef change_page():\n    driver.execute_script(\"window.scrollTo(0,document.body.scrollHeight)\")\n    try :\n        wdw(driver, 3).until(ec.element_to_be_clickable((by.CSS_SELECTOR, 'a[class=\"Pagination-nextLink\"][aria-disabled=\"false\"]'))).click()\n    except (ElementClickInterceptedException, TimeoutException, NoSuchElementException):\n        return False\n    return True\n\nlang_t = 8\nlkyu = []\nurl = []\nwhile lang_t < 10:\n    kind_t = 2\n    #語言\n    wdw(driver, 5).until(ec.element_to_be_clickable((by.XPATH, '//*[@id=\"root\"]/div/div/div/div/nav/div/div[1]/div[2]/a['+str(lang_t)+']'))).click()\n    sleep(1)\n    #類別\n    while True:\n        year_t = 2\n        try :\n            wdw(driver, 5).until(ec.element_to_be_clickable((by.XPATH, '//*[@id=\"root\"]/div/div/div/div/nav/div/div[2]/div[2]/a['+str(kind_t)+']'))).click()\n        except (TimeoutException, NoSuchElementException):\n            break\n        sleep(1)\n        #年份\n        while True:\n            url_temp = 1\n            url_total = 1\n            try :\n                wdw(driver, 5).until(ec.element_to_be_clickable((by.XPATH, '//*[@id=\"root\"]/div/div/div/div/nav/div/div[3]/div[2]/a['+str(year_t)+']'))).click()\n            except (TimeoutException, NoSuchElementException):\n                print(\"going to next kind\")\n                break\n            sleep(3)\n            #抓網頁\n            while True:\n                try :\n                    url.append(driver.find_element(by.XPATH, '//*[@id=\"root\"]/div/div/div/div/section/div[2]/a['+ str(url_temp) +']').get_attribute(\"href\"))\n                except NoSuchElementException:\n                    if change_page():\n                        print(\"going to next page.\")\n                        url_temp = 0\n                        url_total -= 1\n                        sleep(3)\n                    else :\n                        print(url_total)\n                        driver.execute_script(\"window.scrollTo(0,0)\")\n                        print(\"going to next year.\")\n                        lkyu.append([lang_t - 1, kind_t - 1, year_t - 1, url_total - 1])\n                        break\n                sleep(0.5)\n                url_temp += 1\n                url_total += 1\n            year_t += 1\n        kind_t += 1\n    print(\"going to next language\")\n    lang_t += 1\n    import requests as req\n\ndef driver_get(dri, u, u_t):\n    try:\n        dri.get(u[u_t])\n    except TimeoutException:\n        print(\"get error\")\n        driver_get(dri, u, u_t)\n        \ndef ban_18(dri):\n    sleep(2)\n    if len(dri.find_elements(by.CSS_SELECTOR, 'div[class=\"ReactModalPortal\"]')) == 4:\n        wdw(dri, 3).until(ec.element_to_be_clickable((by.XPATH, '/html/body/div[4]/div/div/div/div/button[2]'))).click()\n\ndef roll_down(dri):\n    try :\n        dri.execute_script(\"window.scrollTo(0,1080)\")\n    except :\n        print(\"roll error\")\n        driver.refresh()\n        ban_18(dri)\n        roll_down(dri)\n    sleep(1)\n\ndef in_c(s,sc):\n    try :\n        temp_c = round(float(s.find('script').text[(sc.text).find('\"rating_avg\":') + 13:(sc.text).find('\"rating_avg\":') + 17]), 1)\n    except ValueError:\n        temp_c = 0\n    return temp_c\n\ndef in_d(dri):\n    try :\n        temp_d = wdw(driver, 3).until(ec.element_to_be_clickable((by.XPATH, '//*[@id=\"react-tabs-0\"]'))).text\n    except (TimeoutException, NoSuchElementException):\n        driver.refresh()\n        ban_18(dri)\n        roll_down(dri)\n        return in_d(dri)\n    temp_d = int(temp_d[temp_d.find(\"(\") + 1:temp_d.find(\")\")])\n    return temp_d\n\n\ndef in_e(sc):\n    if \"普遍級\" in sc.text:\n        temp_e = \"普遍級\"\n    elif \"保護級\" in sc.text:\n        temp_e = \"保護級\"\n    elif \"輔導十二歲級\" in sc.text:\n        temp_e = \"輔導十二歲級\"\n    elif \"限制級\" in sc.text:\n        temp_e = \"限制級\"\n    else :\n        temp_e = \"None\"\n    return temp_e\n\ndef in_f(s):\n    try :\n        temp_f = s.find(\"div\", class_=\"flex-none text-brand-black bg-brand-yellow-400 text-12 font-500 px-2\").text\n    except AttributeError:\n        temp_f = False\n    if not temp_f:\n        temp_f = 0\n    else :\n        temp_f = 1\n    return temp_f\n\nwb = Workbook()\nws = wb.active\nws.append([\"語言\", \"種類\", \"年份\", \"類型與集數\", \"標題\", \"評分\", \"留言數\", \"分級\", \"VIP\"])\nprint(len(url), len(lkyu))\nurl_intimes = 0\n\nfor i in lkyu:\n    print(i)\n    temp = []\n    if i[0] == 1:\n        temp.append(\"日本\")\n    elif i[0] == 2:\n        temp.append(\"其他地區\")\n#     elif i[0] == 3:\n#         temp.append(\"大陸\")\n#     elif i[0] == 4:\n#         temp.append(\"日本\")\n#     elif i[0] == 5:\n#         temp.append(\"新加坡\")\n#     elif i[0] == 6:\n#         temp.append(\"泰國\")\n#     elif i[0] == 7:\n#         temp.append(\"香港\")\n#     elif i[0] == 8:\n#         temp.append(\"其他地區\")\n\n    if i[1] == 1:\n        temp.append(\"Ani-One專區\")\n    elif i[1] == 2:\n        temp.append(\"熱血\")\n    elif i[1] == 3:\n        temp.append(\"王道\")\n    elif i[1] == 4:\n        temp.append(\"懸疑\")\n    elif i[1] == 5:\n        temp.append(\"勵志\")\n    elif i[1] == 6:\n        temp.append(\"科幻\")\n    elif i[1] == 7:\n        temp.append(\"青春\")\n    elif i[1] == 8:\n        temp.append(\"幽默\")\n    elif i[1] == 9:\n        temp.append(\"校園\")\n    elif i[1] == 10:\n        temp.append(\"料理\")\n    elif i[1] == 11:\n        temp.append(\"格鬥\")\n    elif i[1] == 12:\n        temp.append(\"家庭\")\n    elif i[1] == 13:\n        temp.append(\"友情\")\n    elif i[1] == 14:\n        temp.append(\"愛情\")\n    elif i[1] == 15:\n        temp.append(\"運動\")\n    elif i[1] == 16:\n        temp.append(\"妖怪\")\n    elif i[1] == 17:\n        temp.append(\"恐怖\")\n    elif i[1] == 18:\n        temp.append(\"職人\")\n    elif i[1] == 19:\n        temp.append(\"耽美\")\n\n    if i[2] == 1:\n        temp.append(\"2023\")\n    elif i[2] == 2:\n        temp.append(\"2022\")\n    elif i[2] == 3:\n        temp.append(\"2021\")\n    elif i[2] == 4:\n        temp.append(\"2020\")\n    elif i[2] == 5:\n        temp.append(\"2019\")\n    elif i[2] == 6:\n        temp.append(\"2018\")\n    elif i[2] == 7:\n        temp.append(\"2017\")\n    elif i[2] == 8:\n        temp.append(\"2016\")\n    elif i[2] == 9:\n        temp.append(\"2011-2015\")\n    elif i[2] == 10:\n        temp.append(\"2000-2010\")\n    elif i[2] == 11:\n        temp.append(\"2000年以前\")\n\n    if i[3] == 0:\n        continue\n    else :\n        for t in range(i[3]):\n            res = req.get(url[url_intimes])\n            soup = BeautifulSoup(res.text)\n            driver_get(driver, url, url_intimes)\n            print(\"connect success\", t+1)\n            ban_18(driver)\n            roll_down(driver)\n            url_intimes += 1\n            scripts = soup.find('script')\n            a = soup.find(\"div\", class_ = \"flex items-end mt-2\").text\n            b = soup.find(\"h1\", class_ = \"sr-only\").text\n            c = in_c(soup, scripts)\n            d = in_d(driver)\n            e = in_e(scripts)\n            f = in_f(soup)\n            temp.append(a) #類型與集數\n            temp.append(b) #標題\n            temp.append(c) #評分\n            temp.append(d) #留言數\n            temp.append(e) #分級(普遍...)\n            temp.append(f) #VIP\n            ws.append(temp)\n            for pop in range(6):\n                temp.pop()\n    wb.save(\"anime_3.xlsx\")","repo_name":"pchou887/Line-TV-Recommend-ChatBot","sub_path":"LineTvSeleniumCrawler.py","file_name":"LineTvSeleniumCrawler.py","file_ext":"py","file_size_in_byte":8271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"13724212606","text":"import numpy as np\nimport torch\n\n\nclass LinUCB:\n\n    def __init__(self, d, theta, delta_mat, noise_level):\n        self.d = d\n        self.noise_level = noise_level\n        # theta - simple case: have one dimension equal to 1\n        self.theta = theta\n        # initial matrix A\n        self.delta = delta_mat\n        self.A = self.delta * torch.eye(d)\n        self.A_t = self.delta * torch.eye(d)\n        # theta hat\n        # theta_hat = torch.zeros(d, requires_grad=True)\n        self.theta_hat = torch.ones(d)  # torch.matmul(torch.inverse(torch.eye(d)), )\n        # beta of confidence bonus\n        self.lambda_ = 1\n        self.delta_beta = 0.01\n        self.beta = np.sqrt(self.lambda_) + np.sqrt(2 * np.log(1 / self.delta_beta) + d * np.log(1 + 1 / (d * self.lambda_)))\n        # V_tau matrix\n        self.V_tau = torch.eye(d) * 1\n        # U_tau matrix\n        self.U_tau = torch.zeros(d)\n\n    # OBJECTIVE FUNCTION FOR GRADIENT DESCENT WITH GOAL OF FINDING BEST BLOCK USING UCBs\n    def UCB(self, action):\n        ucb = 0.0\n        y = torch.dot(action, self.theta_hat)\n        z = torch.sqrt(torch.matmul(torch.t(action), torch.matmul(torch.inverse(self.V_tau), action)))\n        ucb = (y + self.beta * z)\n\n        return ucb\n\n    def oracle(self, action, lr):  # , obj_plot):\n        n_iter = 100\n        for epoch in range(n_iter):\n            obj = self.UCB(action)\n            #print(obj)\n            obj.backward() # check if argument is causing errors retain_graph=True\n            with torch.no_grad():\n                action += lr * action.grad  # difficult to tune this rate\n                # we project - sure about norm in 1D?\n                norm = torch.norm(action).item()\n                action /= norm\n                action.grad.zero_()\n        return action\n\n    def gen_A(self, past_actions, alpha):\n        mat = torch.eye(self.d) + torch.matmul(past_actions.T, past_actions)\n        #mat = torch.linalg.matrix_power(torch.inverse(mat), alpha)\n        mat_tmp = mat.detach().numpy()\n        eigenv_tmp, U_tmp = np.linalg.eigh(mat_tmp)\n        U = torch.tensor(U_tmp)\n        eigenv = torch.tensor(eigenv_tmp)\n        res = torch.matmul(torch.matmul(U, torch.diag(torch.pow(eigenv, alpha))), U.T)\n        return mat\n\n\n    def gen_A_Greedysubopt(self, past_actions):\n        m, d = past_actions.shape\n        init_mat = torch.zeros((d, d))\n        init_mat[0, 0] = 1\n        e_1 = torch.zeros((d))\n        e_1[0] = 1\n        e_2 = torch.zeros((d))\n        e_2[1] = 1\n        mat_1 = torch.zeros((d, d))\n        mat_2 = torch.zeros((d, d))\n        for i in range(m):\n            mat_1 += torch.matmul(past_actions[i, :], e_1) * torch.outer(e_1, e_1)\n            mat_2 += torch.matmul(past_actions[i, :], e_2) * torch.outer(e_2, e_2)\n        res = init_mat + mat_1 + mat_2\n        return res\n\n    def compute_rwd(self, action, A_t):\n        a_tilde_t = torch.matmul(A_t, action)\n        y = torch.dot(a_tilde_t, self.theta)\n        rwd = np.random.normal(loc=y, scale=self.noise_level, size=1) #+ self.noise_level * np.random.randn(1)\n        return rwd\n\n    def update_coeff(self, rwd, action):\n        rwd = torch.tensor(rwd)\n        # UPDATE U MATRIX USED TO COMPUTE THETA_HAT\n        self.U_tau += (rwd * action)\n        # UPDATE V_tau\n        self.V_tau += torch.matmul(torch.t(action), action)\n\n    def update_beta(self, s):\n        # UPDATE BOUND ON CONFIDENCE SET\n        self.beta = np.sqrt(self.lambda_) + np.sqrt(\n            2 * np.log(1 / self.delta_beta) + self.d * np.log(1 + (s + 1) / (self.d * self.lambda_)))\n\n    def est_theta_hat(self):\n        # UPDATE THE ESTIMATE OF THETA HAT\n        self.theta_hat = torch.matmul((torch.inverse(self.V_tau)), self.U_tau)\n\n","repo_name":"GiuliaClerici/Linear-Bandits-with-Memory","sub_path":"LinUCB.py","file_name":"LinUCB.py","file_ext":"py","file_size_in_byte":3707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"5035254551","text":"\"\"\" Utility to print a task-operator in a standard formatting\"\"\"\nfrom string import digits\nfrom . import object_to_str\n\ndef task_to_str(task_id):\n    \"\"\" Given an sml Identifier root of a task-operator, \n        returns a string representation of the task \n        e.g. 'move1(gray fork, on1(table))' \"\"\"\n    task_handle = task_id.GetChildString(\"task-handle\")\n\n    # Get the root identifier of each argument\n    arg_list = ( (arg_name, task_id.GetChildId(arg_name)) for arg_name in (\"arg1\", \"arg2\", \"arg3\", \"start-clause\", \"end-clause\", \"when-clause\") )\n    # Convert each non-null argument to a string\n    parsed_args = ( task_arg_to_str(arg[0], arg[1]) for arg in arg_list if arg[1] is not None)\n\n    return task_handle + \"(\" + \", \".join(parsed_args) + \")\"\n\ndef task_arg_to_str(arg_name, arg_id):\n    \"\"\" Given an sml Identifier root of a task argument, \n        returns a string representation of the argument \"\"\"\n    arg_type = arg_id.GetChildString(\"arg-type\")\n    if arg_type == \"object\":\n        return object_to_str(arg_id.GetChildId(\"id\"))\n    elif arg_type == \"partial-predicate\":\n        handle_str = arg_id.GetChildString(\"handle\")\n        obj2_str = object_to_str(arg_id.GetChildId(\"2\"))\n        return \"%s(%s)\" % ( handle_str, obj2_str )\n    elif arg_type == \"waypoint\":\n        wp_id = arg_id.GetChildId(\"id\")\n        return wp_id.GetChildString(\"handle\")\n    elif arg_type == \"concept\":\n        return arg_id.GetChildString(\"handle\")\n    elif arg_type == \"measure\":\n        return arg_id.GetChildString(\"number\") + \" \" + arg_id.GetChildString(\"unit\")\n    elif arg_type == \"coordinate\":\n        coord_id = arg_id.GetChildId(\"coord\")\n        return \"{%.2f, %.2f}\" % ( coord_id.GetChildFloat(\"x\"), coord_id.GetChildFloat(\"y\") )\n    elif arg_type == \"temporal-clause\":\n        return arg_name.split('-')[0] + pred_set_to_str(arg_id)\n    return \"?\"\n\ndef pred_set_to_str(arg_id):\n    \"\"\" Given an sml Identifier root of a predicate set, \n        returns a string representation of the predicate set\n        e.g. '{ on1(red block, table), right1(red block, blue block) }' \"\"\"\n    num_preds = arg_id.GetChildInt(\"pred-count\")\n    parsed_preds = []\n    for i in range(num_preds):\n        pred = arg_id.GetChildId(str(i+1))\n        parsed_preds.append(predicate_to_str(pred))\n    return \"{ \" + \", \".join(parsed_preds) + \" }\"\n\ndef predicate_to_str(pred_id):\n    \"\"\" Given an sml Identifier root of a predicate, \n        returns a string representation of the predicate\n        e.g. 'on1(red block, table)' \"\"\"\n    pred_type = pred_id.GetChildString(\"type\")\n    pred_handle = pred_id.GetChildString(\"handle\")\n    if pred_type == \"unary\":\n        obj1_str = object_to_str(pred_id.GetChildId(\"1\"))\n        return \"%s(%s)\" % (pred_handle, obj1_str)\n    elif pred_type == \"relation\":\n        obj1_str = object_to_str(pred_id.GetChildId(\"1\"))\n        obj2_str = object_to_str(pred_id.GetChildId(\"2\"))\n        return \"%s(%s, %s)\" % (pred_handle, obj1_str, obj2_str)\n    elif pred_type == \"clocktime\":\n        hour = pred_id.GetChildInt(\"hour\")\n        minute = pred_id.GetChildInt(\"minute\")\n        return \"%d:%d\" % (hour, minute)\n    elif pred_type == \"duration\":\n        number = pred_id.GetChildInt(\"number\")\n        unit = pred_id.GetChildString(\"unit\")\n        return \"%d %s\" % (number, unit)\n    else:\n        return \"?\"\n\n","repo_name":"SoarGroup/rosie","sub_path":"python/rosie/tools/task_to_str.py","file_name":"task_to_str.py","file_ext":"py","file_size_in_byte":3327,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"80"}
+{"seq_id":"9326003516","text":"import pytest\nfrom bigchaindb_smart_assets.policy import PolicyParser\n\n\ndef print_lexer(_lexer):\n    while True:\n        tok = _lexer.token()\n        if not tok:\n            break\n        print(tok, tok.type)\n\n\ndef test_policy_lexer_string():\n    test_inputs = [\n        '3 * 4 + 5 * 6 AND 8'\n        'AND/6*4',\n        'BANDANA',\n        'x == djsjh + AND + d > d >= 4',\n        'transaction.metadata[\"data\"] == \"test\" OR 4 + 3',\n        \"transaction.inputs[0].public_keys['value'] == 'somekey'\",\n        \"comma, comma\",\n        \"LEN(2, 2) == 2\",\n        \"LEN([2, 2, 3]) == 3\"\n    ]\n\n    for test_input in test_inputs:\n        parser = PolicyParser()\n        parser.input(test_input)\n        print_lexer(parser)\n\n\n@pytest.mark.bdb\n@pytest.mark.usefixtures('inputs')\ndef test_policy_lexer_transaction(b, user_pk):\n    test_inputs = [\n        \"transaction.inputs[0].owners_before == 'somekey'\"\n    ]\n\n    transaction = b.get_transaction(b.get_owned_ids(user_pk)[0].txid)\n\n    for test_input in test_inputs:\n        lexer = PolicyParser(transaction=transaction)\n        lexer.input(test_input)\n        print_lexer(lexer)\n\n\ndef test_policy_grammar_string():\n    test_inputs = [\n        (' \"3\" * (4 + 5 * 6) == 102', False),\n        (' 3 * (4 + 5 * 6) > 100', True),\n        (' 3 * (4 + 5 * 6) < 103', True),\n        (\"3 * (4 + 5 * 6) > 100 AND ('TEST' == 'TEST' OR 'DUMMY' == 'TEST')\", True),\n        ('\"TEST\" == \"TEST\"', True),\n        ('1 == 1 AND 3 == 3', True),\n        ('1 == 1 AND 3 == \"DUMMY\"', False),\n        ('1 == 1 OR 3 == \"DUMMY\"', True),\n        ('LEN(2, 2) == 2', True),\n        ('LEN([1, 2, 3]) == 3', True),\n        ('SUM([1, 2, 3]) == 6', True)\n    ]\n\n    for test_input in test_inputs:\n        parser = PolicyParser()\n        result = parser.parse(test_input[0], lexer=parser.lexer)\n        assert result == test_input[1]\n\n\n@pytest.mark.bdb\n@pytest.mark.usefixtures('inputs')\ndef test_policy_grammar_transaction(b, user_pk):\n    test_inputs = [\n        ('transaction.operation == \"CREATE\"', True),\n        ('transaction.outputs[0].public_keys[0] == \"{}\"'.format(user_pk), True),\n        ('LEN(transaction.outputs[0].public_keys[0]) == 1', True),\n        ('AMOUNT(transaction.outputs)*3 == 3', True),\n        (\"transaction.outputs[0].public_keys[0] == '{}'\".format(user_pk), True),\n    ]\n\n    transaction = b.get_transaction(b.get_owned_ids(user_pk)[0].txid)\n\n    for test_input in test_inputs:\n        parser = PolicyParser(transaction=transaction)\n        result = parser.parse(test_input[0], lexer=parser.lexer)\n        assert result == test_input[1]\n","repo_name":"bigchaindb/bigchaindb-smart-assets","sub_path":"tests/test_policy.py","file_name":"test_policy.py","file_ext":"py","file_size_in_byte":2569,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"80"}
+{"seq_id":"71577117379","text":"import re\nimport numpy as np\n\nNUM_MOTORS = 12\nDOFS_PER_LEG = 3\n\nOBS_SIZE = 34\nACTION_SIZE = 12\nHOST = '127.0.0.5'\nPORT = 12345\nACTION_SIZE = 12\nOBS_SIZE = 34","repo_name":"FrankTianTT/laikago_robot","sub_path":"robot_reality/laikago_constant.py","file_name":"laikago_constant.py","file_ext":"py","file_size_in_byte":157,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"80"}
+{"seq_id":"17192217193","text":"from selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service as ChromeService\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.common.by import By\nimport time\n\n# options:\noptions = webdriver.ChromeOptions()\n\n# driver\ndriver = webdriver.Chrome(\n    service=ChromeService(ChromeDriverManager().install()),\n    options=options,\n)\n\ntry:\n    driver.get('https://vk.com/')\n    time.sleep(2)\n\n    email_input = driver.find_element(By.ID, 'index_email')\n    email_input.clear()  # очистить поле(если там уже что-то есть)\n    email_input.send_keys('+79505479134')\n    time.sleep(10)\n\n    login_btn = driver.find_element(\n        By.CLASS_NAME,\n        'VkIdForm__signInButton'\n    ).click()\n    time.sleep(60)\n\n    news_link = driver.find_element(By.ID, 'l_nwsf').click()\n    time.sleep(10)\n\n\nexcept Exception as ex:\n    print(ex)\nfinally:\n    driver.close()\n    driver.quit()\n","repo_name":"ordenmeny/Web_scraping","sub_path":"learning selenium/chromedriver/main2.py","file_name":"main2.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"15101616442","text":"import os\nimport os.path\nimport yaml\nimport re\nimport logging\nimport sqlite3\nimport lxml.html\nfrom parsing.parser import Parser\nfrom util import (parse_for_url, escape, unescape, get_modules_objects, \n                  first, chain_call, download_page, download_page_decoded)\nfrom wstbot_locals import WEB_ENCODING, URL_REGEX_PREFIX\n\nlogger = logging.getLogger(\"wstbot\")\n\n# paths\nMEDIA_DB_PATH = os.path.join(\"data\", \"media.db\")\nSITEINFO_FILE_PATH = os.path.join(\"data\", \"siteinfo.yaml\")\nSOURCES_PATH = os.path.join(\"parsing\", \"information_retrieval_sources\")\n\n# what kinds of links should be stored?\nSTORE_IMAGES = True\nSTORE_LINKS = True # meaning all other links\nITEMS_PER_PAGE = 15\n\n# siteinfo stuff\nFIRST_COLOR = \"purple\"\nFIRST_STYLE = \"bold\"\nREST_COLOR = \"green\"\nREST_STYLE = \"default\"\n\nclass InformationRetrieval(Parser):\n\n    def __init__(self, *args):\n        super().__init__(*args)\n\n        self.media = self.init_media()\n        self.siteinfo = Siteinfo(self.msg_formats)\n\n        self.sources = get_modules_objects(SOURCES_PATH, f=lambda x: x(self.bot))\n\n    def init_media(self):\n        if not os.path.exists(MEDIA_DB_PATH):\n            logger.warning(\"Path does not exist: {0}. Did you forget to run the setup?\"\n                    .format(os.path.abspath(MEDIA_DB_PATH)))\n            return None\n        return Media(self.msg_formats)\n\n    def parse(self, msg, nick):\n        url = parse_for_url(msg)\n        if url is None:\n            return\n\n        source = None\n        info = None\n        title = None\n\n        def info_from_sources():\n            nonlocal source\n            for s in self.sources:\n                info = s.find_info(url)\n                if info is not None:\n                    source = s\n                    return info\n            return None\n\n        # try to find info by using the source modules\n        info_from_modules = lambda: info_from_sources()\n        # find infos using siteinfo object\n        info_from_siteinfo = lambda: self.siteinfo.find_info(url)\n        # try them in order; if the first one succeeds, the second one is not called\n        r = info_from_modules() or info_from_siteinfo() \n        if r is not None:\n            info, title = r\n            logger.debug(\"title: {}\".format(title))\n\n        if msg.strip()[-1] != \"#\":\n            # process all urls/links\n            matches = re.findall(\"(\" + URL_REGEX_PREFIX + \"\\S+)\\s*\", msg)\n            for url in matches:\n                # store media\n                if source is not None:\n                    # use the media handler of the object that was used for information retrieval\n                    type_, url = source.find_media_info(url)\n                    self.media.store_media(url, title=title, type_=type_)\n                else:\n                    # use the builtin media handlers\n                    self.media.store_media(url, title=title)\n\n        return info\n\nclass Siteinfo:\n\n    def __init__(self, msg_formats):\n        self.sitedata = None\n        self.msg_formats = msg_formats\n        self.mtime = None\n        self.load_yaml_siteinfo()\n\n    def load_yaml_siteinfo(self):\n        mtime = os.path.getmtime(SITEINFO_FILE_PATH)\n        if self.mtime is None or mtime > self.mtime:\n            # load siteinfo data\n            with open(SITEINFO_FILE_PATH, \"r\") as siteinfo_file:\n                self.sitedata = yaml.safe_load(siteinfo_file)\n            self.mtime = os.path.getmtime(SITEINFO_FILE_PATH)\n\n    def patterns_for_url(self, url):\n        \"\"\"Get the information dict from the yaml file for the url contained in msg.\n        Returns a tuple (url, resource_dict) where info is the yaml dict\"\"\"\n\n        # reload siteinfo data if it has changed\n        self.load_yaml_siteinfo()\n                \n        for resource_dict in self.sitedata[\"sources\"]:\n            try:\n                match = re.search(resource_dict[\"url pattern\"], url)\n            except:\n                logger.warning(\"Bad url pattern: \" + resource_dict[\"url pattern\"])\n                continue\n            if not match:\n                continue\n\n            url = match.group(1)\n            logger.info(\"Found siteinfo url for \" + resource_dict[\"name\"] + \"!\")\n            logger.info(\"url: \" + url)\n\n            return (url, resource_dict)\n\n    def search_site(self, url, resource_dict):\n        \"\"\"Downloads the URL's content, searches for the paths and patterns\n        and builds a message out of the matched data.\n\n        Arguments: resource_dict contains the paths, patterns and additional data for\n        the url.\n        \"\"\"\n\n        if self.sitedata is None:\n            return\n\n        # retrieve content\n        try:\n            content = download_page(url).decode(WEB_ENCODING, \"replace\")\n        except:\n            return\n        if content is None:\n            return\n\n        message = None\n        title = None\n\n        def info_xpath():\n            # try to find info using xpath\n            root = lxml.html.fromstring(content)\n            items = root.xpath(info[\"xpath\"])\n            logger.debug(\"using xpath: \" + info[\"xpath\"])\n            if items is not None and len(items) >= 1:\n                return items[0]\n            else:\n                return None\n\n        def info_regex():\n            # try to find info using a regex pattern\n            logger.debug(\"using regex: \" + info[\"pattern\"])\n            match = re.search(info[\"pattern\"], content)\n            if match is None:\n                logger.warning(\"Could not find info! (match == None) with pattern: \" + info[\"pattern\"])\n                return None\n            if match.groups() is None:\n                logger.warning(\"match.groups() was None\")\n                return None\n            if len(match.groups()) <= 0:\n                logger.warning(\"Found match but no groups\")\n                return None\n\n            return match.group(1)\n\n        for info in resource_dict[\"patterns\"]:\n            if not \"pattern\" in info and not \"xpath\" in info:\n                logger.error(\"siteinfo entry does not contain a path or pattern!\")\n                break\n\n            infodata = None\n            # try regex first because it seems to be faster\n            if \"pattern\" in info:\n                infodata = info_regex()\n            # try xpath if there was no pattern or regex was unsuccessful\n            if infodata is None and \"xpath\" in info:\n                infodata = info_xpath()\n\n            if infodata is None:\n                logger.warning(\"infodata was None!\")\n                break\n\n            logger.debug(\"\\ninfodata:\\n\")\n            logger.debug(infodata)\n\n            if infodata is None or infodata == \"\":\n                continue\n\n            logger.info(\"found info data: \" + infodata)\n            infodata = unescape(infodata)\n            infodata = escape(infodata)\n\n            infodata = infodata.strip()\n            if title is None:\n                title = infodata\n                \n            color = REST_COLOR\n            style = REST_STYLE\n            if message is None:\n                message = \"\"\n                color = FIRST_COLOR\n                style = FIRST_STYLE\n            message += self.msg_formats.get(style, self.msg_formats.get(color, infodata))\n            if info != resource_dict[\"patterns\"][-1]:\n                message += \" \" + self.sitedata[\"separator\"] + \" \"\n\n        # cut last separator if there is one\n        sep = self.sitedata[\"separator\"]\n        if message is not None and message.strip()[-len(sep):] == sep:\n            message = message.strip()[:-len(sep)].strip()\n            \n        return message, title\n\n    def find_info(self, url):\n        \"\"\"Find information in the page at the specified url.\"\"\"\n        r = self.patterns_for_url(url)\n        if r is None:\n            return\n        url, resource_dict = r\n        return self.search_site(url, resource_dict)\n\nclass Media:\n    \"\"\"Parse for URLs that could be of interest and store them\"\"\"\n\n    def __init__(self, msg_formats):\n        self.msg_formats = msg_formats\n        \n    def store_media(self, url, title=None, type_=None):\n        if type_ is None:\n            # try the builtin types\n            media_info = chain_call(url, [self.parse_image, self.parse_link])\n\n            if media_info is None:\n                logger.warning(\"media was not stored\")\n                return \n            type_, url = media_info\n\n        if title is None:\n            title = \"\"\n\n        # write\n        with sqlite3.connect(MEDIA_DB_PATH) as conn:\n            cur = conn.cursor()\n            cur.execute(\"insert into media (type, title, url) values (?, ?, ?)\",\n                    (type_, title, url))\n            conn.commit()\n\n    def parse_link(self, url):\n        if not STORE_LINKS:\n            return None\n\n        return (\"link\", url)\n\n    def parse_image(self, url):\n        if not STORE_IMAGES:\n            return None\n\n        # prefix for URLs in general\n        match = re.search(\"(\" + URL_REGEX_PREFIX + \".*(\\.jpeg|\\.jpg|\\.png|\\.gif))\", url, re.IGNORECASE)\n        if match is None:\n            return None\n\n        logger.info(\"Found image url: \" + url)\n        return (\"image\", url)\n\nCLASS_ = InformationRetrieval\n","repo_name":"tlang0/wstbot","sub_path":"parsing/information_retrieval.py","file_name":"information_retrieval.py","file_ext":"py","file_size_in_byte":9151,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"72935271299","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# template from CruzHacks 2019\n\nfrom flask import Flask, request, jsonify \nfrom flask_sqlalchemy import SQLAlchemy \nfrom flask_marshmallow import Marshmallow \nimport os\n        \napp = Flask(__name__)\nbasedir = os.path.abspath(os.path.dirname(__file__))\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///' + os.path.join(basedir, 'crud.sqlite')\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n\ndb = SQLAlchemy(app)\nma = Marshmallow(app)\n\n# hacker model\nclass Hacker(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(64), unique=False)\n    age = db.Column(db.Integer, unique=False)\n    school = db.Column(db.String(64), unique=False)\n    major = db.Column(db.String(64), unique=False)\n    email = db.Column(db.String(64), unique=False)\n    phone = db.Column(db.String(32), unique=True)\n    physicalAccommodations = db.Column(db.Boolean, unique=False)\n    dietaryAccommodations = db.Column(db.Boolean, unique=False)\n    isCheckedIn = db.Column(db.Boolean, unique=False)\n\n    def __init__(self, name, age, school, major, email, phone, \\\n            physicalAccommodations, dietaryAccommodations, isCheckedIn):\n        self.name = name\n        self.age = age\n        self.school = school\n        self.major = major\n        self.email = email\n        self.phone = phone\n        self.physicalAccommodations = physicalAccommodations\n        self.dietaryAccommodations = dietaryAccommodations\n        self.isCheckedIn = isCheckedIn\n      \n# hacker schema\nclass HackerSchema(ma.Schema):\n    class Meta:\n        fields = ('name', 'age', 'school', 'major', 'email', 'phone', \\\n                'physicalAccommodations', 'dietaryAccommodations', 'isCheckedIn')\n\nhacker_schema = HackerSchema()\nhackers_schema = HackerSchema(many=True)\n\n# main page with no info on it\n@app.route(\"/\")\ndef main_page():\n    return \"\"\n\n# endpoint \n@app.route(\"/hackers\", methods=[\"POST\"])\n\ndef add_hacker():\n    name = request.json['name']\n    age = request.json['age']\n    school = request.json['school']\n    major = request.json['major']\n    email = request.json['email']\n    phone = request.json['phone']\n    physicalAccommodations = request.json['physicalAccommodations']\n    dietaryAccommodations = request.json['dietaryAccommodations']\n    isCheckedIn = request.json['isCheckedIn']\n    \n    new_hacker = Hacker(name, age, school, major, email, phone, \\\n            physicalAccommodations, dietaryAccommodations, isCheckedIn)\n        \n    db.session.add(new_hacker)\n    db.session.commit()\n    return str(new_hacker)\n\n# endpoint to show all hackers\n@app.route(\"/hackers\", methods=[\"GET\"])\ndef get_hacker():\n    all_hackers = Hacker.query.all()\n    result = hackers_schema.dump(all_hackers)\n    return jsonify(result.data)\n\n# endpoint to get hacker detail by id\n@app.route(\"/hackers/<id>\", methods=[\"GET\"])\ndef hacker_detail(id):\n    hacker = Hacker.query.get(id)\n    return hacker_schema.jsonify(hacker)\n\n# endpoint to update hacker\n@app.route(\"/hackers/<id>\", methods=[\"PUT\"])\ndef hacker_update(id):\n    hacker = Hacker.query.get(id)\n    hacker.name = request.json['name']\n    hacker.age = request.json['age']\n    hacker.school = request.json['school']\n    hacker.major = request.json['major']\n    hacker.email = request.json['email']\n    hacker.phone = request.json['phone']\n    hacker.physicalAccommodations = request.json['physicalAccommodations']\n    hacker.physicalAccommodations = request.json['dietaryAccommodations']\n    hacker.isCheckedIn = request.json['isCheckedIn']\n\n    db.session.commit()\n    return hacker_schema.jsonify(hacker)\n\n# endpoint to delete hacker\n@app.route(\"/hackers/<id>\", methods=[\"DELETE\"])\ndef hacker_delete(id):\n    hacker = Hacker.query.get(id)\n    db.session.delete(hacker)\n    db.session.commit()\n\n    return hacker_schema.jsonify(hacker)\n\nif __name__ == '__main__':\n    app.run(debug=True) \n","repo_name":"hlnwu/CruzHacksChallengeBE","sub_path":"flask/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"21309450398","text":"import utils\r\nimport leer_archivo\r\nimport tablas\r\n\r\ndef run():\r\n    data = leer_archivo.leer_archivo('./poblacion\\world_population.csv')\r\n    data = list(filter(lambda item: item['Continent']== 'South America', data))\r\n\r\n    paises = list(map(lambda x: x['Country/Territory'], data))\r\n    porcentajes = list(map(lambda x: float(x['World Population Percentage']), data))\r\n    tablas.generar_grafica_barras(paises, porcentajes)\r\n    tablas.generar_grafica_pie(paises, porcentajes)\r\n\r\nrun()","repo_name":"mariana-ruge/Python","sub_path":"Proyectos/Graficas Poblacion/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"74337799939","text":"network = input(\"Введите ip-сеть в формате a.a.a.a/b\"\"\\n\")\r\n\r\nip, mask = network.split(\"/\")\r\nip_list = ip.split(\".\")\r\nmask = int(mask)\r\n\r\noct1, oct2, oct3, oct4 = [\r\n    int(ip_list[0]),\r\n    int(ip_list[1]),\r\n    int(ip_list[2]),\r\n    int(ip_list[3]),\r\n]\r\n\r\nbin_ip= '{:08b}{:08b}{:08b}{:08b}'.format(oct1, oct2, oct3, oct4)\r\nbin_network = bin_ip[0:mask] + \"0\" * (32 - mask)\r\n\r\nnetwork_oct1, network_oct2, network_oct3, network_oct4 = [\r\n    int(bin_network[0:8], 2),\r\n    int(bin_network[8:16], 2),\r\n    int(bin_network[16:24], 2),\r\n    int(bin_network[24:32], 2),\r\n]\r\n\r\nprint(oct1, oct2, oct3, oct4)\r\nprint(bin_ip)\r\nprint(bin_network)\r\n\r\nbin_mask = \"1\" * mask + \"0\" * (32 - mask)\r\nm1, m2, m3, m4 = [\r\n    int(bin_mask[0:8], 2),\r\n    int(bin_mask[8:16], 2),\r\n    int(bin_mask[16:24], 2),\r\n    int(bin_mask[24:32], 2),\r\n]\r\nprint (bin_mask)\r\nprint(m1, m2, m3, m4)\r\n\r\n\r\n\r\n\r\nip_output = \"\"\"\r\nNetwork:\r\n{0:<8}  {1:<8}  {2:<8}  {3:<8}\r\n{0:08b}  {1:08b}  {2:08b}  {3:08b}\"\"\"\r\n\r\nmask_output = \"\"\"\r\nMask:\r\n/{0}\r\n{1:<8}  {2:<8}  {3:<8}  {4:<8}\r\n{1:08b}  {2:08b}  {3:08b}  {4:08b}\r\n\"\"\"\r\n\r\nprint(ip_output.format(network_oct1, network_oct2, network_oct3, network_oct4))\r\nprint(mask_output.format(mask, m1, m2, m3, m4))","repo_name":"KedOFF/Python_practice","sub_path":"7-8/задание 7 день 7.py","file_name":"задание 7 день 7.py","file_ext":"py","file_size_in_byte":1226,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"10234892553","text":"import cv2, utils\nimport os\nimport random\n\nvalue = [random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)]\nidx = 0\n\nfor file in os.listdir('./raw_train_data'):\n    img = cv2.imread('./raw_train_data/' + file, cv2.IMREAD_COLOR)\n    img_raw_width, img_raw_height = img.shape[:2]\n    img = cv2.copyMakeBorder(img, (int)(img_raw_height * 0.5), (int)(img_raw_height * 0.5), (int)(img_raw_width * 0.5), (int)(img_raw_width * 0.5),\n                             cv2.BORDER_CONSTANT, None, value)\n    img_padded_width, img_padded_height = img.shape[:2]\n\n    for i in range(7):\n        idx += 1\n        angle = random.randint(-20, 20)\n        rotated_img = utils.rotate_img(img, angle)\n        bottom_border = (int)(img_padded_height / 2 - img_raw_height / 2)\n        left_border = (int)(img_padded_width / 2 - img_raw_width / 2)\n        cropped_img = rotated_img[left_border:(left_border + img_raw_width), bottom_border:(bottom_border + img_raw_height)]\n        cv2.imwrite('./train_data/' + str(angle) + '/' + str(idx) + '.jpg', cropped_img)","repo_name":"ans5925/photo_horizon_correction","sub_path":"make_train_data.py","file_name":"make_train_data.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"14693148095","text":"\"\"\"\n窗口菜单\n1. 要实现窗口菜单，自定义的类需要继承 QMainWindow\n2. 分为两种，一种是菜单（menu），一种是动作（action）\n- 动作是点击后，具体执行的逻辑，\n- 菜单可以添加动作\n\n3. 还有一种浮动菜单\n\"\"\"\nimport sys\n\nfrom PyQt5.QtGui import QIcon, QPixmap\nfrom PyQt5.QtWidgets import QMainWindow, QApplication, QAction\n\n\nclass AppWidget(QMainWindow):\n\n    def __init__(self):\n        super().__init__()\n        self.resize(700, 400)\n        self.setWindowTitle(\"QMenuBar\")\n        self.init_ui()\n\n    def init_ui(self):\n        bar = self.menuBar()\n        # 添加一个 File 一级菜单\n        file = bar.addMenu(\"File\")\n        file.addAction(\"Open\")\n        # 添加一个 New 一级菜单\n        new = bar.addMenu(\"New\")\n        ## 给 new 添加动作\n        new.addAction('创建新文件')\n\n\n        # 通过 Action 添加第三个菜单\n        setting = QAction(\"Settings\", self)\n        setting.setShortcut(\"Ctrl+S\")\n        bar.addAction(setting)\n\n        # 给 file 菜单添加子菜单\n        edit = file.addMenu(\"Edit\")\n        copy = edit.addAction(\"Copy\")\n        copy.setShortcut(\"Ctrl+C\")\n        edit.addAction(\"Paste\")\n\n        # 给 file 菜单绑定事件\n        file.triggered[QAction].connect(self.process_triggered)\n\n        \"\"\"图标菜单\"\"\"\n        log_action = QAction(QIcon(QPixmap(\"data/logo.jpg\")), \"图标\", self)\n        bar.addAction(log_action)\n\n    def process_triggered(self, q):\n        print(q.text() + \" is triggered\")\ndef main():\n    app = QApplication(sys.argv)\n    window = AppWidget()\n    window.show()\n    sys.exit(app.exec_())\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"qwli7/PyQt5_Practice","sub_path":"05-基本控件/09-QMenubar.py","file_name":"09-QMenubar.py","file_ext":"py","file_size_in_byte":1683,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"80"}
+{"seq_id":"71955020098","text":"import json\nimport urllib\nimport requests\nimport Config\nimport logging\n\nlogging.basicConfig(level=logging.DEBUG)\n\n\n# Class to access the new Twitch 'HELIX' API\n# The class receives it's authentication from a dictionary (HEADERS) in the Config.py file.\n# The \"Bearer\" token must have the required scopes to perform successful API calls.\nclass TwitchApi:\n    def __init__(self, channel_name=Config.CHANNEL_NAME):\n        self.channel_id = self.get_user_id(channel_name)\n\n    # Function to make a JSON request to the Twitch API\n    def json_data(self, url, user=None):\n        try:\n            req = urllib.request.Request(url, headers=Config.HEADERS)\n            resp = urllib.request.urlopen(req)\n            twitch_data = json.loads(json.dumps(json.loads(resp.read())))\n            return twitch_data\n        except Exception as e:\n            logging.error(\"Error parsing JSON data.\", e)\n\n    # Get the ID of a given username\n    def get_user_id(self, username=Config.CHANNEL_NAME):\n        try:\n            url = 'https://api.twitch.tv/helix/users?login=' + username\n            user_data = self.json_data(url, username)\n            user_id = user_data['data'][0]['id']\n            return user_id\n        except Exception as e:\n            logging.error(\"Unable to retrieve the user ID for \" + username, e)\n\n    # Gather all available data for a specified user\n    def get_user_data(self, username=Config.CHANNEL_NAME):\n        try:\n            url = 'https://api.twitch.tv/helix/users?login=' + username\n            user_data = self.json_data(url, username)\n            user = user_data['data'][0]\n            return user\n        except Exception as e:\n            logging.error(\"Unable to retrieve the data for \" + username, e)\n\n    def get_channel_id(self, channel_name=Config.CHANNEL_NAME):\n        try:\n            return self.channel_id\n        except Exception as e:\n            logging.error(\"Could not retrieve channel ID for \" + channel_name, e)\n\n    # Get first 100 moderators of a channel. \"Pagination\" must be used for more than 100 results.\n    # Request requires a valid Bearer (Helix Oauth) token.\n    def get_moderators(self):\n        try:\n            url = 'https://api.twitch.tv/helix/moderation/moderators?broadcaster_id=' + self.channel_id\n            moderator_data = self.json_data(url)\n            moderator_names = []\n            for index, item in enumerate(moderator_data['data']):\n                moderator_names.append(item['user_name'])\n            return moderator_names\n        except Exception as e:\n            logging.error(\"Could not retrieve Moderator list for the channel\", e)\n\n    def get_followers(self):\n        try:\n            url = 'https://api.twitch.tv/helix/users/follows?to_id=' + self.channel_id\n            follower_data = self.json_data(url)\n            follower_names = []\n            for index, item in enumerate(follower_data['data']):\n                follower_names.append(item['from_name'])\n            return follower_names\n        except Exception as e:\n            logging.error(\"Could not retrieve Follower list for the channel\", e)\n\n    # Check if a specified user is a moderator in the channel\n    def is_moderator(self, viewer):\n        try:\n            viewer_id = self.get_user_id(viewer)\n            url = 'https://api.twitch.tv/helix/moderation/moderators?broadcaster_id=' + self.channel_id + '&user_id=' + viewer_id\n            moderator_data = self.json_data(url)\n            if moderator_data['data'] is None:\n                return False\n            else:\n                return True\n        except Exception as e:\n            logging.error(\"Unable to determine if \" + viewer + \" is a moderator.\", e)\n\n    # Check if a specified user is following the channel\n    def is_follower(self, viewer):\n        try:\n            viewer_id = self.get_user_id(viewer)\n            url = 'https://api.twitch.tv/helix/users/follows?to_id=' + self.channel_id + '&from_id=' + viewer_id\n            follow_data = self.json_data(url)\n            if follow_data['total'] == 0:\n                return False\n            else:\n                return True\n        except Exception as e:\n            logging.error(\"Unable to determine if \" + viewer + \" is following the channel.\", e)\n\n    # Check if a viewer is subscribed to teh channel\n    def is_subscriber(self, viewer):\n        try:\n            viewer_id = self.get_user_id(viewer)\n            url = 'https://api.twitch.tv/helix/subscriptions?broadcaster_id=' + self.channel_id + '&user_id=' + viewer_id + '&tier'\n            sub_data = self.json_data(url)\n            if not sub_data['data']:\n                return False\n            else:\n                return True\n        except Exception as e:\n            logging.error(\"Unable to determin if \" + viewer + \" is subscribed to the channel.\", e)\n\n    # Creates a clip from the live stream. Test when live as\n    # when not live it shows a previously created clip.\n    def create_clip(self):\n        try:\n            url = 'https://api.twitch.tv/helix/clips?broadcaster_id=' + self.channel_id\n            clip_data = self.json_data(url)\n            return clip_data\n        except Exception as e:\n            logging.error(\"Couldn't create clip.\", e)\n\n    # Check if a user is banned from the channel\n    def is_banned(self, viewer):\n        try:\n            viewer_id = self.get_user_id(viewer)\n            url = 'https://api.twitch.tv/helix/moderation/banned?broadcaster_id=' + self.channel_id + '&user_id=' + viewer_id\n            banned_data = self.json_data(url)\n            if banned_data['data'] is None:\n                return False\n            else:\n                return True\n        except Exception as e:\n            logging.error(\"Unable to check if \" + viewer + \" is banned from the channel.\", e)\n\n    # Get followed channel since date\n    def follower_since(self, viewer):\n        try:\n            viewer_id = self.get_user_id(viewer)\n            url = 'https://api.twitch.tv/helix/users/follows?to_id=' + self.channel_id + '&from_id=' + viewer_id\n            follow_data = self.json_data(url)\n            if follow_data['total'] == 0:\n                return None\n            else:\n                return follow_data['data'][0]['followed_at']\n        except Exception as e:\n            logging.error(\"Unable to determine if \" + viewer + \" is following the channel.\", e)\n\n    # The following functions use the Twitch V5 API and require a separate token (OAuth)\n    def update_channel(self, title, game):\n        try:\n            url = 'https://api.twitch.tv/kraken/channels/' + self.get_channel_id() + '?api_version=5'\n            headers = Config.V5HEADERS\n            data = {'channel[status]': title, 'channel[game]': game, 'channel[channel_feed_enabled]': 'true'}\n            response = requests.put(url=url, headers=headers, params=data)\n            return response\n        except Exception as e:\n            logging.error('Unable to perform V5 API call', e)\n            return None\n\n    # The below will retrieve current \"Chatters\" in a channel.\n    # THESE ARE NOT A TWITCH API FUNCTIONS - UNDOCUMENTED\n    # This has a delayed refresh time (currently unknown).\n    # Note: Due to some viewers/bots being connected anon to the channel\n    # this will only show chatters and not all viewers.\n    def get_chatter_data(self, channel):\n        try:\n            url = 'https://tmi.twitch.tv/group/user/' + channel + '/chatters'\n            chatter_data = self.json_data(url)\n            return chatter_data\n        except Exception as e:\n            logging.error('Unable to retrieve chatter data. ', e)\n            return None\n\n    def all_chatter_names(self, channel):\n        try:\n            chatter_data = self.get_chatter_data(channel)['chatters']\n            chatters = [item for sublist in chatter_data.values() for item in sublist]\n            return chatters\n        except Exception as e:\n            logging.error('Unable to retrieve chatter names. ', e)\n            return None\n","repo_name":"borgej/BrexBot","sub_path":"TwitchApi.py","file_name":"TwitchApi.py","file_ext":"py","file_size_in_byte":7963,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"29938558825","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport picklefield.fields\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('rant', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Emoji',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('word', models.CharField(help_text=b'Emojis for tweets', max_length=255, null=True, blank=True)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Quotation',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('text', models.CharField(help_text=b'Quotations by Gary', max_length=255, null=True, blank=True)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.AddField(\n            model_name='clip',\n            name='tags',\n            field=picklefield.fields.PickledObjectField(help_text=b'A list of words that describe this clip', null=True, editable=False, blank=True),\n            preserve_default=True,\n        ),\n    ]\n","repo_name":"mdee/buseybot","sub_path":"rant/migrations/0002_auto_20150109_2033.py","file_name":"0002_auto_20150109_2033.py","file_ext":"py","file_size_in_byte":1361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"15123034310","text":"#import pypozyx\nimport sys, time\nfrom pypozyx import PozyxSerial, get_first_pozyx_serial_port, POZYX_SUCCESS, SingleRegister, EulerAngles, Acceleration, UWBSettings\n#pozyx = PozyxLib()  # PozyxSerial has PozyxLib's functions, just for generality\nCURSOR_UP_ONE = '\\x1b[1A'\nERASE_LINE = '\\x1b[2K'\nis_cursor_up = False\n#print(pypozyx.get_first_pozyx_serial_port())\npozyx = PozyxSerial(get_first_pozyx_serial_port())\nwho_am_i = SingleRegister()\n# get the data, passing along the container\nstatus = pozyx.getWhoAmI(who_am_i)\nacceleration = Acceleration()\neuler_angles = EulerAngles()\nuwb_settings = UWBSettings()\n\n# check the status to see if the read was successful. Handling failure is covered later.\nif status == POZYX_SUCCESS:\n    # print the container. Note how a SingleRegister will print as a hex string by default.\n    print('Who Am I: {}'.format(who_am_i)) # will print '0x43'\n\nwhile True:\n    # initalize the Pozyx as above\n\n    # initialize the data container\n\n    # and repeat\n    # initialize the data container\n    # get the data, passing along the container\n    if is_cursor_up:\n        sys.stdout.write(CURSOR_UP_ONE)\n        sys.stdout.write(CURSOR_UP_ONE)\n        sys.stdout.write(ERASE_LINE)\n    pozyx.getAcceleration_mg(acceleration)\n    print('Accleration: {}'.format(acceleration))\n\n    # initialize the data container\n    # get the data, passing along the container\n    sys.stdout.write(ERASE_LINE)\n    pozyx.getEulerAngles_deg(euler_angles)\n    print('Euler Angle: {}'.format(euler_angles))\n    sys.stdout.write(ERASE_LINE)\n    pozyx.getUWBSettings(uwb_settings)\n    print('UWB Settings: {}'.format(uwb_settings), end='\\r')\n    time.sleep(1)\n    is_cursor_up =True\n","repo_name":"srikanthsrnvs/Pozyx","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1684,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"22241414529","text":"import csv;\nimport pandas as pd;\nimport datetime ;\nimport time;\nimport os;\n\ncwd = str.format(os.getcwd())\n\n\ndf=pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv')\ndf['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')\nnw_dt=df.sort_values(['date','location'],ascending=False).groupby('location',as_index=False).first()\ndt=nw_dt[nw_dt['date']!=pd.to_datetime('today').date().strftime(format='%Y-%m-%d')].head(10)\ndt.to_csv(cwd+'/Top10.csv',sep='\\t')\n\nlc = pd.read_csv('./countries.txt')\n#new cases, total cases, new vaccinations and total vaccinations\nnw_dt=df.sort_values(['date','location'],ascending=False)\nfor country in lc:\n    \n    f_dt=nw_dt[nw_dt['location']==country].groupby('location',as_index=True).agg(\n    {\n         'total_cases':sum, \n         'new_cases': sum,  \n         'new_vaccinations': sum,\n         'total_vaccinations': sum\n    })\n    f_dt.to_csv(cwd+'/listedCountries'+time.strftime(\"%Y%m%d-%H%M%S\")+'.csv',sep='\\t',mode='a', header=False)\n","repo_name":"Venu4thFeb/Covid19Cases","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"20878509889","text":"import networkx as nx\nimport matplotlib.pyplot as plt\n\n# create sgraph bt merging ctree            \ndef s_graph(IG, CTs):\n    Sgraph = nx.Graph()\n    for i in range(len(CTs)):\n        CTREES = CTs[i]\n        for ctree in CTREES:\n            Sgraph = nx.compose(ctree,Sgraph)\n        \n    return Sgraph","repo_name":"madhudamor24/Query-based-text-summarization","sub_path":"modules/sgraph.py","file_name":"sgraph.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"21565630041","text":"import unittest\nimport sys\nimport os\n\nimport numpy as np\nfrom skimage.draw import line\nimport matplotlib.pyplot as plt\n\nsys.path.append(os.path.abspath(\"../\"))\nfrom DisneyDisp import edge_confidence, sample_radiance, score_computation, estimate_disp, propagation\n\n\n\nclass TestPopagation(unittest.TestCase):\n\n    def setUp(self):\n        # We create an artificial lf\n        imgs = np.zeros((50,100,150), dtype=np.uint8)\n        for i in range(50):\n            for j in range(2):\n                rr, cc = line(0, 10+j+i, 99, 10+j+i)\n                imgs[i, rr, cc] = 75\n            for j in range(5):\n                rr, cc = line(0, 12+j+i, 99, 12+j+i)\n                imgs[i, rr, cc] = 125\n            for j in range(2):\n                rr, cc = line(0, 17+j+i, 99, 17+j+i)\n                imgs[i, rr, cc] = 75\n            for j in range(5):\n                rr, cc = line(0, 20+j+2*i, 99, 20+j+2*i)\n                imgs[i, rr, cc] = 175\n            for j in range(10):\n                rr, cc = line(0, 35+j+2*i, 99, 35+j+2*i)\n                imgs[i, rr, cc] = 250\n            #ski.io.imsave(\"img_{i}.png\".format(i=i), imgs[i])\n\n        # We create epis out of it\n        self.epis = np.zeros((100,50,150), dtype=np.uint8)\n        for i in range(100):\n            self.epis[i] = np.reshape(imgs[:,i], (50,150))\n            #ski.io.imsave(\"epi_{i}.png\".format(i=i), epis[i])\n        self.epi = self.epis[50]\n\n        self.disp = np.zeros((100,50,150), dtype=np.float32)\n        self.disp[self.epis == 75] = -1\n        self.disp[self.epis == 125] = -1\n        self.disp[self.epis == 175] = -2\n        self.disp[self.epis == 250] = -2\n        self.disp[self.disp == 0] = np.nan\n\n\n    def test_propagation(self):\n        disp = np.full((100,50,150), fill_value=np.nan, dtype=np.float32)\n        disp[:,25] = self.disp[:,25]\n        disp[disp == 0] = np.nan\n        Ds, plot = propagation(disp, self.epis, self.epis[:,25], 25, threshold=0.1, DEBUG=True)\n        #plt.imsave(\"propagation.png\", Ds[50], )\n        np.testing.assert_array_equal(Ds[50],self.disp[50])\n\n\n\n    def doCleanups(self):\n        pass\n\n\nif __name__ == '__main__':\n    unittest.main()","repo_name":"manuSrep/DisneyDispPy","sub_path":"tests/TestPropagation.py","file_name":"TestPropagation.py","file_ext":"py","file_size_in_byte":2148,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"80"}
+{"seq_id":"19884775289","text":"import logging\n\nfrom .BookCleaner import BookCleaner\nfrom .DatabaseManager import DatabaseManager\nfrom .Dictionary import YellowBridgeDictionary\nfrom .DeckManager import DeckManager\n\nlogger = logging.getLogger('autoanki')\nlogger.setLevel(logging.INFO)\nlogging.basicConfig(\n    # filename='HISTORYlistener.log',\n    level=logging.DEBUG,\n    format='%(asctime)s.%(msecs)03d %(levelname)s %(module)s - %(funcName)s: %(message)s',\n    datefmt='%Y-%m-%d %H:%M:%S',\n)\n\nclass AutoAnki:\n\n    def __init__(self, database_filepath='autoanki.db'):\n        \"\"\"\n        Creates an instance of autoanki.\n        This creates a book cleaner, database connection, and deck maker\n        :param database_filepath: The filepath for the database\n        \"\"\"\n        logger.info(\"autoanki: Connecting to database...\")\n\n        self.database_filepath = database_filepath\n\n        self.book_cleaner = BookCleaner()\n        if not DatabaseManager.is_database(database_filepath):\n            logger.info(\"Creating database...\")\n            DatabaseManager.create_autoanki_db(database_filepath)\n            logger.info(\"Done creating database.\")\n        self.database_manager = DatabaseManager(database_filepath)\n        self.dictionary = YellowBridgeDictionary()\n        self.deck_manager = DeckManager()\n\n        logger.info(\"autoanki: Connected!\")\n\n    def add_book(self, book_path: str, book_name: str = 'New Book'):\n        \"\"\"\n        Add a directory ful of files to the database\n        :param book_path: The filepath to the directory that contains the files to add. e.g. lost_prince.txt\n        :param book_name: The name of the book being added e.g. \"Lost Prince\"\n        :return:\n        \"\"\"\n\n        logger.debug(f\"autoanki: Adding book from [{book_path}]\")\n\n        # Clean the book\n        if not self.book_cleaner.clean(book_path):\n            logger.warning(\"autoanki: Unable to clean book [\" + book_name + \"].\")\n            return\n\n        # Add the book to the database\n        if not self.database_manager.add_book(book_path, book_name):\n            logger.warning(\"Unable to add [\" + book_name + \"] to database.\")\n            return\n\n        logger.info(\"autoanki: Added [\" + book_path + \"].\")\n\n    def complete_unfinished_definitions(self):\n        \"\"\"\n        autoanki contains an internal definitions' table that is scraped from the internet. As words are added to\n        autoanki, their definitions must be found. This function passively finds definitions and adds them to the table\n        :return: None\n        \"\"\"\n\n        # TODO Make progress par for unfinished records\n        logger.info(\"Checking for records...\")\n        self.database_manager.cursor.execute(\"SELECT word FROM dictionary WHERE definition IS NULL\")\n        response_rows = self.database_manager.cursor.fetchall()\n        while len(response_rows) > 0:\n            self.database_manager.cursor.execute(\"SELECT word FROM dictionary WHERE definition IS NULL\")\n            response_rows = self.database_manager.cursor.fetchall()\n            if len(response_rows) > 0:\n                logger.info(\"Adding \" + str(len(response_rows)) + \" rows to dictionary table\")\n                for row in response_rows:\n                    word = row[0]\n\n                    # TODO This is a bad way of doing it, but find word is returning all of the parameters to\n                    #     add to the database\n                    # TODO create a dictionary that gets words from a file, not the internet\n                    params = self.dictionary.find_word(word)\n                    self.database_manager.complete_definition(params)\n\n            else:\n                logger.info(\"No new rows to complete in dictionary table\")\n            # time.sleep(2)\n\n    @staticmethod\n    def is_database(db_path):\n        return DatabaseManager.is_database(db_path)\n\n    @staticmethod\n    def create_autoanki_db(db_path: str):\n        DatabaseManager.create_autoanki_db(db_path)\n\n    def create_deck(self, deck_name: str, filepath: str):\n        \"\"\"\n        Creates a deck file in the directory of the main file.\n        :return:\n        \"\"\"\n        # FEATURE Add more options for how the deck looks\n        # FEATURE get files from only one book, not the whole database\n\n        logger.info(\"Generating deck file [\" + deck_name + \".apk ]\")\n        words = self.database_manager.get_all_completed_definitions()\n\n        deck_path = self.deck_manager.generate_deck_file(words, deck_name, filepath)\n        if deck_path is None:\n            logger.warning(\"Was not able to create deck file for [\", deck_name, \"]\")\n        else:\n            logger.info(\"Generated deck file [\" + deck_path + \"]\")\n\n    @property\n    def book_list(self):\n        \"\"\"\n        Get a list of the books in the database\n        :return: List of book names\n        \"\"\"\n        return self.database_manager.book_list\n\n    @book_list.setter\n    def book_list(self, value):\n        pass\n\n    @property\n    def unfinished_entries(self):\n        return self.database_manager.unfinished_definitions()\n\n    @unfinished_entries.setter\n    def unfinished_entries(self, value):\n        pass\n\n\nif __name__ == '__main__':\n\n    aa = AutoAnki()\n    print(aa.book_list)\n","repo_name":"timmy6figures/autoanki","sub_path":"src/autoanki/AutoAnki.py","file_name":"AutoAnki.py","file_ext":"py","file_size_in_byte":5171,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"80"}
+{"seq_id":"1598222541","text":"# -*- coding:UTF-8 -*- \r\nfrom django.contrib import admin\r\nfrom dfat.models import LogInput\r\nfrom dfat.models import task\r\nfrom dfat.models import unittest\r\nfrom dfat.models import log, smtasklist, smnodevtasklist\r\n\r\nclass configLog(admin.ModelAdmin):\r\n\tlist_display = ('id', 'date', 'user', 'subject', 'content')\r\n\t\r\nclass configTask(admin.ModelAdmin):\r\n\tlist_display = ('id', \"tid\", \"uid\", \"subject\", \"status\", \"last_sync\", \"last_modify\")\r\n\t\r\nclass configsmtasklist(admin.ModelAdmin):\r\n\tlist_display = ('id', \"tid\", \"uid\", \"subject\", \"status\", \"last_sync\", \"last_modify\")\r\n\r\nadmin.site.register(LogInput, configLog)\r\nadmin.site.register(task, configTask)\r\nadmin.site.register(smtasklist, configsmtasklist)\r\nadmin.site.register(smnodevtasklist)\r\nadmin.site.register(unittest)\r\nadmin.site.register(log)","repo_name":"laughgege/backup-some-code","sub_path":"python/djangoProject/dfat/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":802,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"72623823297","text":"\n'''\n官方教程\nhttps://plot.ly/python/\n全部的参数解析 https://plot.ly/python/reference/#scatter\n'''\n\n\n# 1、一个综合的例子-bar\nif __name__ == '__main__1':\n    import plotly.offline as py\n    import plotly.graph_objs as go\n\n    figure = go.Figure(\n        # 两个数据放到同一个chart中\n        data=[\n            # 不能像matplotlib那样两个y放在一起\n            go.Bar(\n                x=[1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n                   2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012],\n                y=[219, 146, 112, 127, 124, 180, 236, 207, 236, 263,\n                   350, 430, 474, 526, 488, 537, 500, 439],\n                name='Rest of world',\n                marker=go.bar.Marker(\n                    color='rgb(55, 83, 109)'\n                )\n            ),\n            go.Bar(\n                x=[1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n                   2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012],\n                y=[16, 13, 10, 11, 28, 37, 43, 55, 56, 88, 105, 156, 270,\n                   299, 340, 403, 549, 499],\n                name='China',\n                marker=go.bar.Marker(\n                    color='rgb(26, 118, 255)'\n                )\n            )\n        ],\n        # 这个chart的样子\n        layout=go.Layout(\n            title='US Export of Plastic Scrap',\n            showlegend=True,\n            legend=go.layout.Legend(\n                x=0,\n                y=1.0\n            ),\n            margin=go.layout.Margin(l=100, r=10, t=100, b=20)  # chart本身到边框的距离，单位px\n        )\n    )\n\n    py.plot(figure, filename='tmp/temp-plot.html', auto_open=True, )\n\n    '''\n    一、长代码的经验：\n        1、多括号的处理，\n        2、多参数的处理，不同参数处于同一缩进，参数单开一行\n        3、不同块空一行\n    '''\n\n\n# 2、plotly的基础\nif __name__ == '__main__2':\n    import plotly.offline as py\n    import plotly.graph_objs as go\n    import numpy as np\n\n    N = 100\n    x = np.random.rand(N)\n    y = np.random.rand(N)\n    colors = np.random.rand(N)\n    sz = np.random.rand(N) * 30\n\n    fig = go.Figure(data=None, layout=None, frames=None, skip_invalid=False)  # 有四个参数，没有size的参数\n    # 添加chart，就是data参数的内容\n    fig.add_scatter(x=x,\n                    y=y,\n                    mode='markers',  # 'markers'是显示点\n                    marker={'size': sz,\n                            'color': colors,\n                            'opacity': 0.6,\n                            'colorscale': 'Viridis'\n                            })\n\n    # 1、图像发布成html\n    url = py.plot(fig, filename='tmp/temp-plot.html', auto_open=True, )\n\n    # 2、输出到静态图像orca，其中orca是一个服务器，能够发布图像\n    import plotly.io as pio\n    pio.write_image(fig, r'D:\\py36 projects\\dash_tutorial\\tmp\\fig1.png')\n\n    pio.orca.config  # 输出的一些config\n    pio.orca.status\n\n    pio.orca.shutdown_server()\n\n    # 3、privacy\n    # 这个很多是在存到plotly的服务器的那里\n\n    # 4、Sending Data to Charts in Python\n    if __name__ == '__main__':\n        # import plotly.plotly as py  # 这个是放到plotly的服务器上的，\n        import plotly.offline as py  # 这个是发布到离线的本地文件的\n        import plotly.graph_objs as go\n\n        data = [go.Scatter(x=[1, 2], y=[3, 4])]\n\n        plot_url = py.plot(data, filename='tmp/my_plot.html',auto_open=True,)\n\n        # 更新数据？\n        new_data = [go.Scatter(x=[3, 4], y=[3, 2])]\n\n        plot_url = py.plot(data, filename='tmp/my_plot.html', fileopt='append')  # 本地没办法这样更新\n\n\n# 3、Scatter\nif __name__ == '__main__':\n    import plotly.offline as py\n    import plotly.graph_objs as go\n\n    import numpy as np\n    import pandas as pd\n\n    # mode参数：marker, lines+markers, lines\n    # 附带visible,showlegend,legendgroup\n    # opacity, name\n    if __name__ == '__main__':\n\n        N = 100\n        random_x = np.linspace(0, 1, N)\n        random_y0 = np.random.randn(N) + 5\n        random_y1 = np.random.randn(N)\n        random_y2 = np.random.randn(N) - 5\n\n        # Create traces\n        trace0 = go.Scatter(\n            x=random_x,\n            y=random_y0,\n            mode='markers',\n            name='markers',\n            # visible = False,\n            # showlegend=False,\n            # legendgroup='test legendgroup',  # 不知道是干嘛的\n        )\n        trace1 = go.Scatter(\n            x=random_x,\n            y=random_y1,\n            mode='lines+markers',\n            name='lines+markers',\n            # legendgroup='test legendgroup',  # 不知道是干嘛的\n        )\n        trace2 = go.Scatter(\n            x=random_x,\n            y=random_y2,\n            mode='lines',\n            name='lines'\n        )\n\n        data = [trace0, trace1, trace2]\n        url = py.plot(data, filename='tmp/scatter_tutorial.html', auto_open=True, )\n\n\n    # markers参数，\n    if __name__ == '__main__':\n        N = 500\n\n        trace0 = go.Scatter(\n            x=np.random.randn(N),\n            y=np.random.randn(N) + 2,\n            name='Above',\n            mode='markers',\n            marker=dict(\n                size=10,\n                color='rgba(152, 0, 0, .8)',\n                line=dict(\n                    width=2,\n                    color='rgb(0, 0, 0)'\n                )\n            ),\n        )\n\n        trace1 = go.Scatter(\n            x=np.random.randn(N),\n            y=np.random.randn(N) - 2,\n            name='Below',\n            mode='markers',\n            marker=dict(\n                size=10,\n                color='rgba(255, 182, 193, .9)',\n                line=dict(\n                    width=2,\n                )\n            )\n        )\n\n        data = [trace0, trace1]\n\n        # layout是整个chart，title也是大标题\n        layout = dict(title='Styled Scatter',\n                      yaxis=dict(zeroline=False),  # 不显示x轴\n                      xaxis=dict(zeroline=False)\n                      )\n\n        fig = dict(data=data, layout=layout)\n        url = py.plot(fig, filename='tmp/scatter_tutorial.html', auto_open=True, )\n\n    # Data Labels on Hover\n    # 默认的hover是以x为主的，同时读取对应的y，x的标签在轴下，y标签在点的边上，如果一个x对应多个y则有多个标签\n    # hovermode = 'closet'就只放到一个标签中，并且根据点来去，不是根据x来取\n    # text的y的标签下显示对应的x标签，做一个和x轴的数字的对应\n    if __name__ == '__main__':\n        l = []\n        y = []\n        data = pd.read_csv(\"https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv\")\n        # Setting colors for plot.\n        N = 53\n        c = ['hsl(' + str(h) + ',50%' + ',50%)' for h in np.linspace(0, 360, N)]\n\n        for i in range(int(N)):\n            y.append((2000 + i))\n            trace0 = go.Scatter(\n                x=data['Rank'],\n                y=data['Population'] + (i * 1000000),\n                mode='markers',\n                marker=dict(size=14,\n                            line=dict(width=1),\n                            color=c[i],\n                            opacity=0.3\n                            ), name=y[i],\n                text=data['State'] # The hover text goes here...\n            )\n            l.append(trace0)\n\n        layout = go.Layout(\n            title='Stats of USA States',\n            hovermode='closest',\n            xaxis=dict(\n                title='Rank',\n                ticklen=5,\n                zeroline=False,\n                gridwidth=2,\n            ),\n            yaxis=dict(\n                title='Population',\n                ticklen=5,\n                gridwidth=2,\n            ),\n            showlegend=False\n        )\n        fig = go.Figure(data=l, layout=layout)\n        url = py.plot(fig, filename='tmp/scatter_tutorial.html', auto_open=True, )\n\n    # 颜色深浅作为第三维\n    if __name__ == '__main__':\n        trace1 = go.Scatter(\n            y=np.random.randn(500),\n            mode='markers',\n            marker=dict(\n                size=16,\n                color=np.random.randn(500),  # set color equal to a variable\n                colorscale='Viridis',\n                showscale=True\n            )\n        )\n        data = [trace1]\n        url = py.plot(data, filename='tmp/scatter_tutorial.html', auto_open=True, )\n\n\n    # 大数据模式，Scattergl\n    if __name__ == '__main__':\n        N = 100000\n        trace = go.Scattergl(\n            x=np.random.randn(N),\n            y=np.random.randn(N),\n            mode='markers',\n            marker=dict(\n                color='#FFBAD2',\n                line=dict(width=1)\n            )\n        )\n        data = [trace]\n        url = py.plot(data, filename='tmp/scatter_tutorial.html', auto_open=True, )\n\n\n    # hover相关，line相关，fill\n    if __name__ == '__main__':\n        N = 100\n        random_x = np.linspace(0, 1, N)\n        random_y0 = np.random.randn(N) + 5\n        random_y1 = np.random.randn(N)\n        random_y2 = np.random.randn(N) - 5\n\n        # Create traces\n        trace0 = go.Scatter(\n            x=random_x,\n            y=random_y0,\n            mode='markers',\n            name='markers',\n            # hoverinfo='skip'  # Any combination of \"x\", \"y\", \"z\", \"text\", \"name\" joined with a \"+\"\n            # hoverlabel=dict(\n            #     bgcolor='yellow',  # 默认是点的颜色\n            #     bordercolor='black',\n            #     font=dict(\n            #         family='',  # 字体\n            #         size='',  #\n            #         color='',  # 颜色\n            #     ),\n            #     namelength = -1, # -1：trace的全称，0-3：显示的字符数\n            # ),\n            # hoveron='points',\n            # hovertemplate='Price: %{y:$.2f}'  # 自定义hoverbox的显示模板，会覆盖hoverinfo\n        )\n        trace1 = go.Scatter(\n            x=random_x,\n            y=random_y1,\n            mode='lines+markers',\n            name='lines+markers',\n            fill='tonextx',  # tozeroy从x轴往上下，toself是线性拟合线为基准，tonextx不知道@todo\n            fillcolor='green',\n\n        )\n        trace2 = go.Scatter(\n            x=random_x,\n            y=random_y2,\n            mode='lines',\n            name='lines',\n            # line=dict(\n            #     color='red',\n            #     width=5,\n            #     shape='spline',  # 平滑方式vh是方波图，spline是平滑\n            #     smoothing=1.3,  # 平滑度0-1.3\n            #     dash='15px', # 样式，实行，空心，点状，或者是5px\n            #     simplify=True  # 是否去掉共线性的点\n            # )\n        )\n\n        data = [trace0, trace1, trace2]\n        url = py.plot(data, filename='tmp/scatter_tutorial.html', auto_open=True, )\n\n\n    # marker\n    if __name__ == '__main__':\n        N = 100\n        random_x = np.linspace(0, 1, N)\n        random_y0 = np.random.randn(N) + 5\n\n\n        # Create traces\n        trace0 = go.Scatter(\n            x=random_x,\n            y=random_y0,\n            mode='markers',\n            name='markers',\n            marker=dict(\n                # symbol='star',\n                opacity=0.5,\n                size=12,\n                # maxdisplayed=10, # 最大显示数\n                line=dict(  # marker的边线\n                    color='red',\n                    width=3,\n                    cauto=True, # 是否自动设置\n                gradient='radial', # 渐进模式\n                ),\n            )\n        )\n\n        data = [trace0]\n        url = py.plot(data, filename='tmp/scatter_tutorial.html', auto_open=True, )\n\n\n    # stack\n\n\n    # error_x和error_y\n\n\n    # xcalendar和ycalendar\n\n\n    # textfont和textposition\n\n\n    # selected和unselected，拖动选择框来select\n    if __name__ == '__main__':\n        N = 100\n        random_x = np.linspace(0, 1, N)\n        random_y0 = np.random.randn(N) + 5\n\n\n        # Create traces\n        trace0 = go.Scatter(\n            x=random_x,\n            y=random_y0,\n            mode='markers',\n            name='markers',\n            selected=dict(\n                marker=dict(\n                    color='red'\n                )\n            )\n        )\n\n        data = [trace0]\n        url = py.plot(data, filename='tmp/scatter_tutorial.html', auto_open=True, )\n\n    # color,cauto,cmin,cmax,cmid,colorscale, autocolorscale, reversescale,showscale\n\n\n# 4、line，还是用scatter的\nif __name__ == '__main__':\n    import plotly.offline as py\n    import plotly.graph_objs as go\n\n    import numpy as np\n\n    # mode参数，其实就跟scatter一样，这里就不写了\n\n    # line style,line的dash参数，同scatter\n    if __name__ == '__main__':\n\n        # Add data\n        month = ['January', 'February', 'March', 'April', 'May', 'June', 'July',\n                 'August', 'September', 'October', 'November', 'December']\n        high_2000 = [32.5, 37.6, 49.9, 53.0, 69.1, 75.4, 76.5, 76.6, 70.7, 60.6, 45.1, 29.3]\n        low_2000 = [13.8, 22.3, 32.5, 37.2, 49.9, 56.1, 57.7, 58.3, 51.2, 42.8, 31.6, 15.9]\n        high_2007 = [36.5, 26.6, 43.6, 52.3, 71.5, 81.4, 80.5, 82.2, 76.0, 67.3, 46.1, 35.0]\n        low_2007 = [23.6, 14.0, 27.0, 36.8, 47.6, 57.7, 58.9, 61.2, 53.3, 48.5, 31.0, 23.6]\n        high_2014 = [28.8, 28.5, 37.0, 56.8, 69.7, 79.7, 78.5, 77.8, 74.1, 62.6, 45.3, 39.9]\n        low_2014 = [12.7, 14.3, 18.6, 35.5, 49.9, 58.0, 60.0, 58.6, 51.7, 45.2, 32.2, 29.1]\n\n        # Create and style traces\n        trace0 = go.Scatter(\n            x=month,\n            y=high_2014,\n            name='High 2014',\n            line=dict(\n                color=('rgb(205, 12, 24)'),\n                width=4)\n        )\n        trace1 = go.Scatter(\n            x=month,\n            y=low_2014,\n            name='Low 2014',\n            line=dict(\n                color=('rgb(22, 96, 167)'),\n                width=4, )\n        )\n        trace2 = go.Scatter(\n            x=month,\n            y=high_2007,\n            name='High 2007',\n            line=dict(\n                color=('rgb(205, 12, 24)'),\n                width=4,\n                dash='dash')  # dash options include 'dash', 'dot', and 'dashdot'\n        )\n        trace3 = go.Scatter(\n            x=month,\n            y=low_2007,\n            name='Low 2007',\n            line=dict(\n                color=('rgb(22, 96, 167)'),\n                width=4,\n                dash='dash')\n        )\n        trace4 = go.Scatter(\n            x=month,\n            y=high_2000,\n            name='High 2000',\n            line=dict(\n                color=('rgb(205, 12, 24)'),\n                width=4,\n                dash='dot')\n        )\n        trace5 = go.Scatter(\n            x=month,\n            y=low_2000,\n            name='Low 2000',\n            line=dict(\n                color=('rgb(22, 96, 167)'),\n                width=4,\n                dash='dot')\n        )\n        data = [trace0, trace1, trace2, trace3, trace4, trace5]\n\n        # Edit the layout\n        layout = dict(title='Average High and Low Temperatures in New York',\n                      xaxis=dict(title='Month'),\n                      yaxis=dict(title='Temperature (degrees F)'),\n                      )\n\n        fig = dict(data=data, layout=layout)\n\n        py.plot(fig, filename='tmp/line_tutorial.html', auto_play=True)\n\n\n    # Connect Data Gaps，x有空值connectgaps参数\n    if __name__ == '__main__':\n        trace1 = go.Scatter(\n            x=[1, 2, 3, 4, 5,\n               6, 7, 8, 9, 10,\n               11, 12, 13, 14, 15],\n            y=[10, 20, None, 15, 10,\n               5, 15, None, 20, 10,\n               10, 15, 25, 20, 10],\n            name='<b>No</b> Gaps',  # Style name/legend entry with html tags\n            connectgaps=True\n        )\n        trace2 = go.Scatter(\n            x=[1, 2, 3, 4, 5,\n               6, 7, 8, 9, 10,\n               11, 12, 13, 14, 15],\n            y=[5, 15, None, 10, 5,\n               0, 10, None, 15, 5,\n               5, 10, 20, 15, 5],\n            name='Gaps',\n        )\n\n        data = [trace1, trace2]\n\n        fig = dict(data=data)\n        py.plot(fig, filename='tmp/line_tutorial.html', auto_play=True)\n\n\n    # interpolation hv,vh的关系\n    if __name__ == '__main__':\n        trace1 = go.Scatter(\n            x=[1, 2, 3, 4, 5],\n            y=[1, 3, 2, 3, 1],\n            mode='lines+markers',\n            name=\"'linear'\",\n            hoverinfo='name',\n            line=dict(\n                shape='linear'\n            )\n        )\n        trace2 = go.Scatter(\n            x=[1, 2, 3, 4, 5],\n            y=[6, 8, 7, 8, 6],\n            mode='lines+markers',\n            name=\"'spline'\",\n            text=[\"tweak line smoothness<br>with 'smoothing' in line object\"],\n            hoverinfo='text+name',\n            line=dict(\n                shape='spline'\n            )\n        )\n        trace3 = go.Scatter(\n            x=[1, 2, 3, 4, 5],\n            y=[11, 13, 12, 13, 11],\n            mode='lines+markers',\n            name=\"'vhv'\",\n            hoverinfo='name',\n            line=dict(\n                shape='vhv'\n            )\n        )\n        trace4 = go.Scatter(\n            x=[1, 2, 3, 4, 5],\n            y=[16, 18, 17, 18, 16],\n            mode='lines+markers',\n            name=\"'hvh'\",\n            hoverinfo='name',\n            line=dict(\n                shape='hvh'\n            )\n        )\n        trace5 = go.Scatter(\n            x=[1, 2, 3, 4, 5],\n            y=[21, 23, 22, 23, 21],\n            mode='lines+markers',\n            name=\"'vh'\",\n            hoverinfo='name',\n            line=dict(\n                shape='vh'\n            )\n        )\n        trace6 = go.Scatter(\n            x=[1, 2, 3, 4, 5],\n            y=[26, 28, 27, 28, 26],\n            mode='lines+markers',\n            name=\"'hv'\",\n            hoverinfo='name',\n            line=dict(\n                shape='hv'\n            )\n        )\n        data = [trace1, trace2, trace3, trace4, trace5, trace6]\n        layout = dict(\n            legend=dict(\n                y=0.5,\n                traceorder='reversed',\n                font=dict(\n                    size=16\n                )\n            )\n        )\n        fig = dict(data=data, layout=layout)\n        py.plot(fig, filename='tmp/line_tutorial.html', auto_play=True)\n\n\n    # Label Lines with Annotations:annotations是在layout里面的\n    if __name__ == '__main__':\n        title = 'Main Source for News'\n\n        labels = ['Television', 'Newspaper', 'Internet', 'Radio']\n\n        colors = ['rgb(67,67,67)', 'rgb(115,115,115)', 'rgb(49,130,189)', 'rgb(189,189,189)']\n\n        mode_size = [8, 8, 12, 8]\n\n        line_size = [2, 2, 4, 2]\n\n        x_data = [\n            [2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2013],\n            [2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2013],\n            [2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2013],\n            [2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2013],\n        ]\n\n        y_data = [\n            [74, 82, 80, 74, 73, 72, 74, 70, 70, 66, 66, 69],\n            [45, 42, 50, 46, 36, 36, 34, 35, 32, 31, 31, 28],\n            [13, 14, 20, 24, 20, 24, 24, 40, 35, 41, 43, 50],\n            [18, 21, 18, 21, 16, 14, 13, 18, 17, 16, 19, 23],\n        ]\n\n        traces = []\n\n        for i in range(0, 4):\n            traces.append(go.Scatter(\n                x=x_data[i],\n                y=y_data[i],\n                mode='lines',\n                line=dict(color=colors[i], width=line_size[i]),\n                connectgaps=True,\n            ))\n\n            traces.append(go.Scatter(\n                x=[x_data[i][0], x_data[i][11]],\n                y=[y_data[i][0], y_data[i][11]],\n                mode='markers',\n                marker=dict(color=colors[i], size=mode_size[i])\n            ))\n\n        layout = go.Layout(\n            xaxis=dict(\n                showline=True,\n                showgrid=False,\n                showticklabels=True,\n                linecolor='rgb(204, 204, 204)',\n                linewidth=2,\n                ticks='outside',\n                tickcolor='rgb(204, 204, 204)',\n                tickwidth=2,\n                ticklen=5,\n                tickfont=dict(\n                    family='Arial',\n                    size=12,\n                    color='rgb(82, 82, 82)',\n                ),\n            ),\n            yaxis=dict(\n                showgrid=False,\n                zeroline=False,\n                showline=False,\n                showticklabels=False,\n            ),\n            autosize=False,\n            margin=dict(\n                autoexpand=False,\n                l=100,\n                r=20,\n                t=110,\n            ),\n            showlegend=False\n        )\n\n        annotations = []\n\n        # Adding labels\n        for y_trace, label, color in zip(y_data, labels, colors):\n            # labeling the left_side of the plot\n            annotations.append(dict(xref='paper', x=0.05, y=y_trace[0],  # paper是相对于画图区域的比例，y就是在chart中坐标的位置了\n                                    xanchor='right', yanchor='middle',\n                                    text=label + ' {}%'.format(y_trace[0]),\n                                    font=dict(family='Arial',\n                                              size=16),\n                                    showarrow=False))\n            # labeling the right_side of the plot\n            annotations.append(dict(xref='paper', x=0.95, y=y_trace[11],\n                                    xanchor='left', yanchor='middle',\n                                    text='{}%'.format(y_trace[11]),\n                                    font=dict(family='Arial',\n                                              size=16),\n                                    showarrow=False))\n        # Title\n        annotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05,\n                                xanchor='left', yanchor='bottom',\n                                text='Main Source for News',\n                                font=dict(family='Arial',\n                                          size=30,\n                                          color='rgb(37,37,37)'),\n                                showarrow=False))\n        # Source\n        annotations.append(dict(xref='paper', yref='paper', x=0.5, y=-0.1,\n                                xanchor='center', yanchor='top',\n                                text='Source: PewResearch Center & ' +\n                                     'Storytelling with data',\n                                font=dict(family='Arial',\n                                          size=12,\n                                          color='rgb(150,150,150)'),\n                                showarrow=False))\n\n        layout['annotations'] = annotations\n\n        fig = go.Figure(data=traces, layout=layout)\n        py.plot(fig, filename='tmp/line_tutorial.html', auto_play=True)\n\n\n    # Filled Lines\n    if __name__ == '__main__':\n        x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n        x_rev = x[::-1]\n\n        # Line 1\n        y1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n        y1_upper = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n        y1_lower = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n        y1_lower = y1_lower[::-1]\n\n        # Line 2\n        y2 = [5, 2.5, 5, 7.5, 5, 2.5, 7.5, 4.5, 5.5, 5]\n        y2_upper = [5.5, 3, 5.5, 8, 6, 3, 8, 5, 6, 5.5]\n        y2_lower = [4.5, 2, 4.4, 7, 4, 2, 7, 4, 5, 4.75]\n        y2_lower = y2_lower[::-1]\n\n        # Line 3\n        y3 = [10, 8, 6, 4, 2, 0, 2, 4, 2, 0]\n        y3_upper = [11, 9, 7, 5, 3, 1, 3, 5, 3, 1]\n        y3_lower = [9, 7, 5, 3, 1, -.5, 1, 3, 1, -1]\n        y3_lower = y3_lower[::-1]\n\n        trace1 = go.Scatter(\n            x=x + x_rev,\n            y=y1_upper + y1_lower,\n            fill='tozerox',\n            fillcolor='rgba(0,100,80,0.2)',\n            line=dict(color='rgba(255,255,255,0)'),\n            showlegend=False,\n            name='Fair',\n        )\n        trace2 = go.Scatter(\n            x=x + x_rev,\n            y=y2_upper + y2_lower,\n            fill='tozerox',\n            fillcolor='rgba(0,176,246,0.2)',\n            line=dict(color='rgba(255,255,255,0)'),\n            name='Premium',\n            showlegend=False,\n        )\n        trace3 = go.Scatter(\n            x=x + x_rev,\n            y=y3_upper + y3_lower,\n            fill='tozerox',\n            fillcolor='rgba(231,107,243,0.2)',\n            line=dict(color='rgba(255,255,255,0)'),\n            showlegend=False,\n            name='Fair',\n        )\n        trace4 = go.Scatter(\n            x=x,\n            y=y1,\n            line=dict(color='rgb(0,100,80)'),\n            mode='lines',\n            name='Fair',\n        )\n        trace5 = go.Scatter(\n            x=x,\n            y=y2,\n            line=dict(color='rgb(0,176,246)'),\n            mode='lines',\n            name='Premium',\n        )\n        trace6 = go.Scatter(\n            x=x,\n            y=y3,\n            line=dict(color='rgb(231,107,243)'),\n            mode='lines',\n            name='Ideal',\n        )\n\n        data = [trace1, trace2, trace3, trace4, trace5, trace6]\n\n        layout = go.Layout(\n            paper_bgcolor='rgb(255,255,255)',\n            plot_bgcolor='rgb(229,229,229)',\n            xaxis=dict(\n                gridcolor='rgb(255,255,255)',\n                range=[1, 10],  # 显示范围了\n                showgrid=True,\n                showline=False,  # 边线\n                showticklabels=True,\n                tickcolor='rgb(127,127,127)',\n                ticks='outside',\n                zeroline=False\n            ),\n            yaxis=dict(\n                gridcolor='rgb(255,255,255)',\n                showgrid=True,\n                showline=False,\n                showticklabels=True,\n                tickcolor='rgb(127,127,127)',\n                ticks='outside',\n                zeroline=False\n            ),\n        )\n        fig = go.Figure(data=data, layout=layout)\n        py.plot(fig, filename='tmp/line_tutorial.html', auto_play=True)\n\n\n# 5、bar\nif __name__ == '__main__':\n    import plotly.offline as py\n    import plotly.graph_objs as go\n\n    import numpy as np\n\n    # Grouped Bar Chart，layout的barmode='group'\n    if __name__ == '__main__':\n        trace1 = go.Bar(\n            x=['giraffes', 'orangutans', 'monkeys'],\n            y=[20, 14, 23],\n            name='SF Zoo'\n        )\n        trace2 = go.Bar(\n            x=['giraffes', 'orangutans', 'monkeys'],\n            y=[12, 18, 29],\n            name='LA Zoo'\n        )\n\n        data = [trace1, trace2]\n        layout = go.Layout(\n            barmode='group'\n        )\n\n        fig = go.Figure(data=data, layout=layout)\n        py.plot(fig, filename='tmp/line_tutorial.html', auto_play=True)\n\n\n    # Stacked Bar Chart，layout的barmode='stack'\n    if __name__ == '__main__':\n        trace1 = go.Bar(\n            x=['giraffes', 'orangutans', 'monkeys'],\n            y=[20, 14, 23],\n            name='SF Zoo'\n        )\n        trace2 = go.Bar(\n            x=['giraffes', 'orangutans', 'monkeys'],\n            y=[12, 18, 29],\n            name='LA Zoo'\n        )\n\n        data = [trace1, trace2]\n        layout = go.Layout(\n            barmode='stack'\n        )\n\n        fig = go.Figure(data=data, layout=layout)\n        py.plot(fig, filename='tmp/line_tutorial.html', auto_play=True)\n\n\n    # Bar Chart with Hover Text\n    if __name__ == '__main__':\n        trace0 = go.Bar(\n            x=['Product A', 'Product B', 'Product C'],\n            y=[20, 14, 23],\n            text=['27% market share', '24% market share', '19% market share'],\n            marker=dict(\n                color='rgb(158,202,225)',\n                line=dict(\n                    color='rgb(8,48,107)',\n                    width=1.5,\n                )\n            ),\n            opacity=0.6\n        )\n\n        data = [trace0]\n        layout = go.Layout(\n            title='January 2013 Sales Report',\n        )\n\n        fig = go.Figure(data=data, layout=layout)\n        py.plot(fig, filename='tmp/line_tutorial.html', auto_play=True)\n\n\n    # Bar Chart with Direct Labels，textposition='auto'\n    if __name__ == '__main__':\n        x = ['Product A', 'Product B', 'Product C']\n        y = [20, 14, 23]\n\n        data = [go.Bar(\n            x=x,\n            y=y,\n            text=y,\n            textposition='auto',\n            marker=dict(\n                color='rgb(158,202,225)',\n                line=dict(\n                    color='rgb(8,48,107)',\n                    width=1.5),\n            ),\n            opacity=0.6\n        )]\n        py.plot(data, filename='tmp/line_tutorial.html', auto_play=True)\n\n    # Grouped Bar Chart with Direct Labels\n    if __name__ == '__main__':\n        x = ['Product A', 'Product B', 'Product C']\n        y = [20, 14, 23]\n        y2 = [16, 12, 27]\n\n        trace1 = go.Bar(\n            x=x,\n            y=y,\n            text=y,\n            textposition='auto',\n            marker=dict(\n                color='rgb(158,202,225)',\n                line=dict(\n                    color='rgb(8,48,107)',\n                    width=1.5),\n            ),\n            opacity=0.6\n        )\n\n        trace2 = go.Bar(\n            x=x,\n            y=y2,\n            text=y2,\n            textposition='auto',\n            marker=dict(\n                color='rgb(58,200,225)',\n                line=dict(\n                    color='rgb(8,48,107)',\n                    width=1.5),\n            ),\n            opacity=0.6\n        )\n\n        data = [trace1, trace2]\n        py.plot(data, filename='tmp/line_tutorial.html', auto_play=True)\n\n\n    # Rotated Bar Chart Labels，layout的xaxis=dict(tickangle=-45)\n    if __name__ == '__main__':\n        trace0 = go.Bar(\n            x=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\n               'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'],\n            y=[20, 14, 25, 16, 18, 22, 19, 15, 12, 16, 14, 17],\n            name='Primary Product',\n            marker=dict(\n                color='rgb(49,130,189)'\n            )\n        )\n        trace1 = go.Bar(\n            x=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\n               'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'],\n            y=[19, 14, 22, 14, 16, 19, 15, 14, 10, 12, 12, 16],\n            name='Secondary Product',\n            marker=dict(\n                color='rgb(204,204,204)',\n            )\n        )\n\n        data = [trace0, trace1]\n        layout = go.Layout(\n            xaxis=dict(tickangle=-45),\n            barmode='group',\n        )\n\n        fig = go.Figure(data=data, layout=layout)\n        py.plot(fig, filename='tmp/line_tutorial.html', auto_play=True)\n\n\n    # Customizing Individual Bar Colors\n    if __name__ == '__main__':\n        trace0 = go.Bar(\n            x=['Feature A', 'Feature B', 'Feature C',\n               'Feature D', 'Feature E'],\n            y=[20, 14, 23, 25, 22],\n            marker=dict(\n                color=['rgba(204,204,204,1)', 'rgba(222,45,38,0.8)',\n                       'rgba(204,204,204,1)', 'rgba(204,204,204,1)',\n                       'rgba(204,204,204,1)']),\n        )\n\n        data = [trace0]\n        layout = go.Layout(\n            title='Least Used Feature',\n        )\n\n        fig = go.Figure(data=data, layout=layout)\n        py.plot(fig, filename='tmp/line_tutorial.html', auto_play=True)\n\n    # Customizing Individual Bar Widths\n    if __name__ == '__main__':\n        trace0 = go.Bar(\n            x=[1, 2, 3, 5.5, 10],\n            y=[10, 8, 6, 4, 2],\n            width=[0.8, 0.8, 0.8, 3.5, 4]\n        )\n\n        data = [trace0]\n\n        fig = go.Figure(data=data)\n        py.plot(fig, filename='tmp/line_tutorial.html', auto_play=True)\n\n    # Customizing Individual Bar Base，base=[-500, -600, -700]\n    if __name__ == '__main__':\n        data = [\n            go.Bar(\n                x=['2016', '2017', '2018'],\n                y=[500, 600, 700],\n                base=[-500, -600, -700],\n                marker=dict(\n                    color='red'\n                ),\n                name='expenses'\n            ),\n            go.Bar(\n                x=['2016', '2017', '2018'],\n                y=[300, 400, 700],\n                base=0,\n                marker=dict(\n                    color='blue'\n                ),\n                name='revenue'\n            )\n        ]\n\n        fig = go.Figure(data=data)\n        py.plot(fig, filename='tmp/line_tutorial.html', auto_play=True)\n\n\n    # Colored and Styled Bar Chart\n    if __name__ == '__main__':\n        trace1 = go.Bar(\n            x=[1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n               2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012],\n            y=[219, 146, 112, 127, 124, 180, 236, 207, 236, 263,\n               350, 430, 474, 526, 488, 537, 500, 439],\n            name='Rest of world',\n            marker=dict(\n                color='rgb(55, 83, 109)'\n            )\n        )\n        trace2 = go.Bar(\n            x=[1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n               2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012],\n            y=[16, 13, 10, 11, 28, 37, 43, 55, 56, 88, 105, 156, 270,\n               299, 340, 403, 549, 499],\n            name='China',\n            marker=dict(\n                color='rgb(26, 118, 255)'\n            )\n        )\n        data = [trace1, trace2]\n        layout = go.Layout(\n            title='US Export of Plastic Scrap',\n            xaxis=dict(\n                tickfont=dict(\n                    size=14,\n                    color='rgb(107, 107, 107)'\n                )\n            ),\n            yaxis=dict(\n                title='USD (millions)',\n                titlefont=dict(\n                    size=16,\n                    color='rgb(107, 107, 107)'\n                ),\n                tickfont=dict(\n                    size=14,\n                    color='rgb(107, 107, 107)'\n                )\n            ),\n            legend=dict(\n                x=0,\n                y=1.0,\n                bgcolor='rgba(255, 255, 255, 0)',\n                bordercolor='rgba(255, 255, 255, 0)'\n            ),\n            barmode='group',\n            bargap=0.15,\n            bargroupgap=0\n        )\n\n        fig = go.Figure(data=data, layout=layout)\n        py.plot(fig, filename='tmp/line_tutorial.html', auto_play=True)\n\n\n    # Waterfall Bar Chart\n    if __name__ == '__main__':\n        x_data = ['Product<br>Revenue', 'Services<br>Revenue',\n                  'Total<br>Revenue', 'Fixed<br>Costs',\n                  'Variable<br>Costs', 'Total<br>Costs', 'Total']\n        y_data = [400, 660, 660, 590, 400, 400, 340]\n        text = ['$430K', '$260K', '$690K', '$-120K', '$-200K', '$-320K', '$370K']\n\n        # Base\n        trace0 = go.Bar(\n            x=x_data,\n            y=[0, 430, 0, 570, 370, 370, 0],\n            marker=dict(\n                color='rgba(1,1,1, 0.0)',\n            )\n        )\n        # Revenue\n        trace1 = go.Bar(\n            x=x_data,\n            y=[430, 260, 690, 0, 0, 0, 0],\n            marker=dict(\n                color='rgba(55, 128, 191, 0.7)',\n                line=dict(\n                    color='rgba(55, 128, 191, 1.0)',\n                    width=2,\n                )\n            )\n        )\n        # Costs\n        trace2 = go.Bar(\n            x=x_data,\n            y=[0, 0, 0, 120, 200, 320, 0],\n            marker=dict(\n                color='rgba(219, 64, 82, 0.7)',\n                line=dict(\n                    color='rgba(219, 64, 82, 1.0)',\n                    width=2,\n                )\n            )\n        )\n        # Profit\n        trace3 = go.Bar(\n            x=x_data,\n            y=[0, 0, 0, 0, 0, 0, 370],\n            marker=dict(\n                color='rgba(50, 171, 96, 0.7)',\n                line=dict(\n                    color='rgba(50, 171, 96, 1.0)',\n                    width=2,\n                )\n            )\n        )\n        data = [trace0, trace1, trace2, trace3]\n        layout = go.Layout(\n            title='Annual Profit- 2015',\n            barmode='stack',\n            paper_bgcolor='rgba(245, 246, 249, 1)',\n            plot_bgcolor='rgba(245, 246, 249, 1)',\n            showlegend=False\n        )\n\n        annotations = []\n\n        for i in range(0, 7):\n            annotations.append(dict(x=x_data[i], y=y_data[i], text=text[i],\n                                    font=dict(family='Arial', size=14,\n                                              color='rgba(245, 246, 249, 1)'),\n                                    showarrow=False, ))\n            layout['annotations'] = annotations\n\n        fig = go.Figure(data=data, layout=layout)\n        py.plot(fig, filename='tmp/line_tutorial.html', auto_play=True)\n\n\n    # Bar Chart with Relative Barmode\n    if __name__ == '__main__':\n        x = [1, 2, 3, 4]\n\n        trace1 = {\n            'x': x,\n            'y': [1, 4, 9, 16],\n            'name': 'Trace1',\n            'type': 'bar'\n        }\n        trace2 = {\n            'x': x,\n            'y': [6, -8, -4.5, 8],\n            'name': 'Trace2',\n            'type': 'bar'\n        }\n        trace3 = {\n            'x': x,\n            'y': [-15, -3, 4.5, -8],\n            'name': 'Trace3',\n            'type': 'bar'\n        }\n\n        trace4 = {\n            'x': x,\n            'y': [-1, 3, -3, -4],\n            'name': 'Trace4',\n            'type': 'bar'\n        }\n\n        data = [trace1, trace2, trace3, trace4]\n        layout = {\n            'xaxis': {'title': 'X axis'},\n            'yaxis': {'title': 'Y axis'},\n            'barmode': 'relative',\n            'title': 'Relative Barmode'\n        }\n        py.plot({'data': data, 'layout': layout}, filename='tmp/line_tutorial.html', auto_play=True)\n\n\n# 6 pie\nif __name__ == '__main__':\n    import plotly.offline as py\n    import plotly.graph_objs as go\n\n    # Styled Pie Chart\n    if __name__ == '__main__':\n        labels = ['Oxygen', 'Hydrogen', 'Carbon_Dioxide', 'Nitrogen']\n        values = [4500, 2500, 1053, 500]\n        colors = ['#FEBFB3', '#E1396C', '#96D38C', '#D0F9B1']\n\n        trace = go.Pie(labels=labels, values=values,\n                       hoverinfo='label+percent', textinfo='value',\n                       textfont=dict(size=20),\n                       marker=dict(colors=colors,\n                                   line=dict(color='#000000', width=2)),\n\n                )\n\n        py.plot([trace], filename='tmp/piechart_tutorial.html', auto_play=True)\n\n    # Donut Chart\n    if __name__ == '__main__':\n        fig = {\n            \"data\": [\n                {\n                    \"values\": [16, 15, 12, 6, 5, 4, 42],\n                    \"labels\": [\n                        \"US\",\n                        \"China\",\n                        \"European Union\",\n                        \"Russian Federation\",\n                        \"Brazil\",\n                        \"India\",\n                        \"Rest of World\"\n                    ],\n                    \"domain\": {\"x\": [0, .48]},\n                    \"name\": \"GHG Emissions\",\n                    \"hoverinfo\": \"label+percent+name\",\n                    \"hole\": .4,\n                    \"type\": \"pie\"\n                },\n                {\n                    \"values\": [27, 11, 25, 8, 1, 3, 25],\n                    \"labels\": [\n                        \"US\",\n                        \"China\",\n                        \"European Union\",\n                        \"Russian Federation\",\n                        \"Brazil\",\n                        \"India\",\n                        \"Rest of World\"\n                    ],\n                    \"text\": [\"CO2\"],\n                    \"textposition\": \"inside\",\n                    \"domain\": {\"x\": [.52, 1]},\n                    \"name\": \"CO2 Emissions\",\n                    \"hoverinfo\": \"label+percent+name\",\n                    \"hole\": .4,\n                    \"type\": \"pie\"\n                }],\n            \"layout\": {\n                \"title\": \"Global Emissions 1990-2011\",\n                \"annotations\": [\n                    {\n                        \"font\": {\n                            \"size\": 20\n                        },\n                        \"showarrow\": False,\n                        \"text\": \"GHG\",\n                        \"x\": 0.20,\n                        \"y\": 0.5\n                    },\n                    {\n                        \"font\": {\n                            \"size\": 20\n                        },\n                        \"showarrow\": False,\n                        \"text\": \"CO2\",\n                        \"x\": 0.8,\n                        \"y\": 0.5\n                    }\n                ]\n            }\n        }\n        py.plot(fig, filename='tmp/piechart_tutorial.html', auto_play=True)\n\n    # Pie Chart Subplots\n    if __name__ == '__main__':\n        fig = {\n            'data': [\n                {\n                    'labels': ['1st', '2nd', '3rd', '4th', '5th'],\n                    'values': [38, 27, 18, 10, 7],\n                    'type': 'pie',\n                    'name': 'Starry Night',\n                    'marker': {'colors': ['rgb(56, 75, 126)',\n                                          'rgb(18, 36, 37)',\n                                          'rgb(34, 53, 101)',\n                                          'rgb(36, 55, 57)',\n                                          'rgb(6, 4, 4)']},\n                    'domain': {'x': [0, .48],\n                               'y': [0, .49]},\n                    'hoverinfo': 'label+percent+name',\n                    'textinfo': 'none'\n                },\n                {\n                    'labels': ['1st', '2nd', '3rd', '4th', '5th'],\n                    'values': [28, 26, 21, 15, 10],\n                    'marker': {'colors': ['rgb(177, 127, 38)',\n                                          'rgb(205, 152, 36)',\n                                          'rgb(99, 79, 37)',\n                                          'rgb(129, 180, 179)',\n                                          'rgb(124, 103, 37)']},\n                    'type': 'pie',\n                    'name': 'Sunflowers',\n                    'domain': {'x': [.52, 1],\n                               'y': [0, .49]},\n                    'hoverinfo': 'label+percent+name',\n                    'textinfo': 'none'\n\n                },\n                {\n                    'labels': ['1st', '2nd', '3rd', '4th', '5th'],\n                    'values': [38, 19, 16, 14, 13],\n                    'marker': {'colors': ['rgb(33, 75, 99)',\n                                          'rgb(79, 129, 102)',\n                                          'rgb(151, 179, 100)',\n                                          'rgb(175, 49, 35)',\n                                          'rgb(36, 73, 147)']},\n                    'type': 'pie',\n                    'name': 'Irises',\n                    'domain': {'x': [0, .48],\n                               'y': [.51, 1]},\n                    'hoverinfo': 'label+percent+name',\n                    'textinfo': 'none'\n                },\n                {\n                    'labels': ['1st', '2nd', '3rd', '4th', '5th'],\n                    'values': [31, 24, 19, 18, 8],\n                    'marker': {'colors': ['rgb(146, 123, 21)',\n                                          'rgb(177, 180, 34)',\n                                          'rgb(206, 206, 40)',\n                                          'rgb(175, 51, 21)',\n                                          'rgb(35, 36, 21)']},\n                    'type': 'pie',\n                    'name': 'The Night Café',\n                    'domain': {'x': [.52, 1],\n                               'y': [.51, 1]},\n                    'hoverinfo': 'label+percent+name',\n                    'textinfo': 'none'\n                }\n            ],\n            'layout': {'title': 'Van Gogh: 5 Most Prominent Colors Shown Proportionally',\n                       'showlegend': False}\n        }\n\n        py.plot(fig, filename='tmp/piechart_tutorial.html', auto_play=True)\n\n\n# 7、Filled Area Plots in Python\nif __name__ == '__main__':\n    import plotly.offline as py\n    import plotly.graph_objs as go\n\n    # Basic Overlaid Area Chart\n    if __name__ == '__main__':\n        trace1 = go.Scatter(\n            x=[1, 2, 3, 4],\n            y=[0, 2, 3, 5],\n            fill='tozeroy'\n        )\n        trace2 = go.Scatter(\n            x=[1, 2, 3, 4],\n            y=[3, 5, 1, 7],\n            fill='tonexty'\n        )\n\n        data = [trace1, trace2]\n        py.plot(data, filename='tmp/fill_tutorial.html', auto_play=True)\n\n    # Overlaid Area Chart Without Boundary Lines，tonexty就是邻近的y\n    if __name__ == '__main__':\n        trace1 = go.Scatter(\n            x=[1, 2, 3, 4],\n            y=[0, 2, 3, 5],\n            fill='tozeroy',\n            mode='none'\n        )\n        trace2 = go.Scatter(\n            x=[1, 2, 3, 4],\n            y=[3, 5, 1, 7],\n            fill='tonexty',\n            mode='none'\n        )\n\n        data = [trace1, trace2]\n        py.plot(data, filename='tmp/fill_tutorial.html', auto_play=True)\n\n    # Interior Filling for Area Chart\n    if __name__ == '__main__':\n        trace0 = go.Scatter(\n            x=[1, 2, 3, 4],\n            y=[3, 4, 8, 3],\n            fill=None,\n            mode='lines',\n            line=dict(\n                color='rgb(143, 19, 131)',\n            )\n        )\n        trace1 = go.Scatter(\n            x=[1, 2, 3, 4],\n            y=[1, 6, 2, 6],\n            fill='tonexty',\n            mode='lines',\n            line=dict(\n                color='rgb(143, 19, 131)',\n            )\n        )\n\n        data = [trace0, trace1]\n        py.plot(data, filename='tmp/fill_tutorial.html', auto_play=True)\n\n    # Stacked Area Chart\n    if __name__ == '__main__':\n        # Add original data\n        x = ['Winter', 'Spring', 'Summer', 'Fall']\n\n        trace0 = dict(\n            x=x,\n            y=[40, 60, 40, 10],\n            hoverinfo='x+y',\n            mode='lines',\n            line=dict(width=0.5,\n                      color='rgb(131, 90, 241)'),\n            stackgroup='one'\n        )\n        trace1 = dict(\n            x=x,\n            y=[20, 10, 10, 60],\n            hoverinfo='x+y',\n            mode='lines',\n            line=dict(width=0.5,\n                      color='rgb(111, 231, 219)'),\n            stackgroup='one'\n        )\n        trace2 = dict(\n            x=x,\n            y=[40, 30, 50, 30],\n            hoverinfo='x+y',\n            mode='lines',\n            line=dict(width=0.5,\n                      color='rgb(184, 247, 212)'),\n            stackgroup='one'\n        )\n        data = [trace0, trace1, trace2]\n\n        fig = dict(data=data)\n        py.plot(fig, filename='tmp/fill_tutorial.html', auto_play=True)\n\n\n    # Stacked Area Chart with Normalized Values，stackgroup he groupnorm\n    if __name__ == '__main__':\n        trace0 = dict(\n            x=['Winter', 'Spring', 'Summer', 'Fall'],\n            y=['40', '20', '30', '40'],\n            mode='lines',\n            line=dict(width=0.5,\n                      color='rgb(184, 247, 212)'),\n            stackgroup='one',\n            groupnorm='percent'\n        )\n        trace1 = dict(\n            x=['Winter', 'Spring', 'Summer', 'Fall'],\n            y=['50', '70', '40', '60'],\n            mode='lines',\n            line=dict(width=0.5,\n                      color='rgb(111, 231, 219)'),\n            stackgroup='one'\n        )\n        trace2 = dict(\n            x=['Winter', 'Spring', 'Summer', 'Fall'],\n            y=['70', '80', '60', '70'],\n            mode='lines',\n            line=dict(width=0.5,\n                      color='rgb(127, 166, 238)'),\n            stackgroup='one'\n        )\n        trace3 = dict(\n            x=['Winter', 'Spring', 'Summer', 'Fall'],\n            y=['100', '100', '100', '100'],\n            mode='lines',\n            line=dict(width=0.5,\n                      color='rgb(131, 90, 241)'),\n            stackgroup='one'\n        )\n        data = [trace0, trace1, trace2, trace3]\n        layout = go.Layout(\n            showlegend=True,\n            xaxis=dict(\n                type='category',\n            ),\n            yaxis=dict(\n                type='linear',\n                range=[1, 100],\n                dtick=20,\n                ticksuffix='%'\n            )\n        )\n        fig = dict(data=data, layout=layout)\n        py.plot(fig, filename='tmp/fill_tutorial.html', auto_play=True)\n\n    # Select Hover Points\n    if __name__ == '__main__':\n        trace0 = go.Scatter(\n            x=[0, 0.5, 1, 1.5, 2],\n            y=[0, 1, 2, 1, 0],\n            fill='toself',\n            fillcolor='#ab63fa',\n            hoveron='points+fills',\n            line=dict(\n                color='#ab63fa'\n            ),\n            text=\"Points + Fills\",\n            hoverinfo='text'\n        )\n\n        trace1 = go.Scatter(\n            x=[3, 3.5, 4, 4.5, 5],\n            y=[0, 1, 2, 1, 0],\n            fill='toself',\n            fillcolor='#e763fa',\n            hoveron='points',\n            line=dict(\n                color='#e763fa'\n            ),\n            text=\"Points only\",\n            hoverinfo='text'\n        )\n\n        data = [trace0, trace1]\n\n        layout = go.Layout(\n            title=\"hover on <i>points</i> or <i>fill</i>\",\n            xaxis=dict(\n                range=[0, 5.2]\n            ),\n            yaxis=dict(\n                range=[0, 3]\n            )\n        )\n\n        fig = go.Figure(data=data, layout=layout)\n        py.plot(fig, filename='tmp/fill_tutorial.html', auto_play=True)\n\n\n# 8、Dot Plots in Python\nif __name__ == '__main__':\n    import plotly.offline as py\n    import plotly.graph_objs as go\n\n    # basic\n    if __name__ == '__main__':\n        trace1 = {\"x\": [72, 67, 73, 80, 76, 79, 84, 78, 86, 93, 94, 90, 92, 96, 94, 112],\n                  \"y\": [\"Brown\", \"NYU\", \"Notre Dame\", \"Cornell\", \"Tufts\", \"Yale\",\n                        \"Dartmouth\", \"Chicago\", \"Columbia\", \"Duke\", \"Georgetown\",\n                        \"Princeton\", \"U.Penn\", \"Stanford\", \"MIT\", \"Harvard\"],\n                  \"marker\": {\"color\": \"pink\", \"size\": 12},\n                  \"mode\": \"markers\",\n                  \"name\": \"Women\",\n                  \"type\": \"scatter\"\n                  }\n\n        trace2 = {\"x\": [92, 94, 100, 107, 112, 114, 114, 118, 119, 124, 131, 137, 141, 151, 152, 165],\n                  \"y\": [\"Brown\", \"NYU\", \"Notre Dame\", \"Cornell\", \"Tufts\", \"Yale\",\n                        \"Dartmouth\", \"Chicago\", \"Columbia\", \"Duke\", \"Georgetown\",\n                        \"Princeton\", \"U.Penn\", \"Stanford\", \"MIT\", \"Harvard\"],\n                  \"marker\": {\"color\": \"blue\", \"size\": 12},\n                  \"mode\": \"markers\",\n                  \"name\": \"Men\",\n                  \"type\": \"scatter\",\n                  }\n\n        data = [trace1, trace2]\n        layout = {\"title\": \"Gender Earnings Disparity\",\n                  \"xaxis\": {\"title\": \"Annual Salary (in thousands)\", },\n                  \"yaxis\": {\"title\": \"School\"}}\n\n        fig = go.Figure(data=data, layout=layout)\n        py.plot(fig, filename='tmp/dot_tutorial.html', auto_play=True)\n\n\n    # Styled Categorical Dot Plot\n    if __name__ == '__main__':\n        country = ['Switzerland (2011)', 'Chile (2013)', 'Japan (2014)',\n                   'United States (2012)', 'Slovenia (2014)', 'Canada (2011)',\n                   'Poland (2010)', 'Estonia (2015)', 'Luxembourg (2013)', 'Portugal (2011)']\n        voting_pop = [40, 45.7, 52, 53.6, 54.1, 54.2, 54.5, 54.7, 55.1, 56.6]\n        reg_voters = [49.1, 42, 52.7, 84.3, 51.7, 61.1, 55.3, 64.2, 91.1, 58.9]\n\n        trace0 = go.Scatter(\n            x=voting_pop,\n            y=country,\n            mode='markers',\n            name='Percent of estimated voting age population',\n            marker=dict(\n                color='rgba(156, 165, 196, 0.95)',\n                line=dict(\n                    color='rgba(156, 165, 196, 1.0)',\n                    width=1,\n                ),\n                symbol='circle',\n                size=16,\n            )\n        )\n        trace1 = go.Scatter(\n            x=reg_voters,\n            y=country,\n            mode='markers',\n            name='Percent of estimated registered voters',\n            marker=dict(\n                color='rgba(204, 204, 204, 0.95)',\n                line=dict(\n                    color='rgba(217, 217, 217, 1.0)',\n                    width=1,\n                ),\n                symbol='circle',\n                size=16,\n            )\n        )\n\n        data = [trace0, trace1]\n        layout = go.Layout(\n            title=\"Votes cast for ten lowest voting age population in OECD countries\",\n            xaxis=dict(\n                showgrid=False,\n                showline=True,\n                linecolor='rgb(102, 102, 102)',\n                titlefont=dict(\n                    color='rgb(204, 204, 204)'\n                ),\n                tickfont=dict(\n                    color='rgb(102, 102, 102)',\n                ),\n                showticklabels=True,\n                dtick=10,\n                ticks='outside',\n                tickcolor='rgb(102, 102, 102)',\n            ),\n            margin=dict(\n                l=140,\n                r=40,\n                b=50,\n                t=80\n            ),\n            legend=dict(\n                font=dict(\n                    size=10,\n                ),\n                yanchor='middle',\n                xanchor='right',\n            ),\n            width=800,\n            height=600,\n            paper_bgcolor='rgb(254, 247, 234)',\n            plot_bgcolor='rgb(254, 247, 234)',\n            hovermode='closest',\n        )\n        fig = go.Figure(data=data, layout=layout)\n        py.plot(fig, filename='tmp/dot_tutorial.html', auto_play=True)\n\n\n# 9、Table\nif __name__ == '__main__':\n    import plotly.offline as py\n    import plotly.graph_objs as go\n\n    # basic，最基础的也能移动列\n    if __name__ == '__main__':\n        trace = go.Table(\n            header=dict(values=['A Scores', 'B Scores']),\n            cells=dict(values=[[100, 90, 80, 90],\n                               [95, 85, 75, 95]]))\n\n        data = [trace]\n        py.plot(data, filename='tmp/table_tutorial.html', auto_play=True)\n\n\n    # Styled Table，layout = dict(width=500, height=300)定义高度和宽度\n    if __name__ == '__main__':\n        trace = go.Table(\n            header=dict(values=['A Scores', 'B Scores'],\n                        line=dict(color='#7D7F80'),\n                        fill=dict(color='#a1c3d1'),\n                        align=['left'] * 5),\n            cells=dict(values=[[100, 90, 80, 90],\n                               [95, 85, 75, 95]],\n                       line=dict(color='#7D7F80'),\n                       fill=dict(color='#EDFAFF'),\n                       align=['left'] * 5))\n\n        layout = dict(width=500, height=300)\n        data = [trace]\n        fig = dict(data=data, layout=layout)\n        py.plot(fig, filename='tmp/table_tutorial.html', auto_play=True)\n\n\n    # Use a Panda's Dataframe\n    if __name__ == '__main__':\n        import pandas as pd\n\n        df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv')\n\n        trace = go.Table(\n            header=dict(values=list(df.columns),\n                        fill=dict(color='#C2D4FF'),\n                        align=['left'] * 5),\n            cells=dict(values=[df.Rank, df.State, df.Postal, df.Population],\n                       fill=dict(color='#F5F8FF'),\n                       align=['left'] * 5))\n\n        data = [trace]\n        py.plot(data, filename='tmp/table_tutorial.html', auto_play=True)\n\n\n    # Changing Row and Column Size，这里定义宽高 columnorder=[1, 2], columnwidth=[80, 400],\n    if __name__ == '__main__':\n        values = [['Salaries', 'Office', 'Merchandise', 'Legal', '<b>TOTAL<br>EXPENSES</b>'],\n                  [\n                       \"Lorem ipsum dolor sit amet, tollit discere inermis pri ut. Eos ea iusto timeam, an prima laboramus vim. Id usu aeterno adversarium, summo mollis timeam vel ad\",\n                       \"Lorem ipsum dolor sit amet, tollit discere inermis pri ut. Eos ea iusto timeam, an prima laboramus vim. Id usu aeterno adversarium, summo mollis timeam vel ad\",\n                       \"Lorem ipsum dolor sit amet, tollit discere inermis pri ut. Eos ea iusto timeam, an prima laboramus vim. Id usu aeterno adversarium, summo mollis timeam vel ad\",\n                       \"Lorem ipsum dolor sit amet, tollit discere inermis pri ut. Eos ea iusto timeam, an prima laboramus vim. Id usu aeterno adversarium, summo mollis timeam vel ad\",\n                       \"Lorem ipsum dolor sit amet, tollit discere inermis pri ut. Eos ea iusto timeam, an prima laboramus vim. Id usu aeterno adversarium, summo mollis timeam vel ad\"\n                  ]\n                 ]\n\n        trace0 = go.Table(\n            columnorder=[1, 2],\n            columnwidth=[80, 400],\n            header=dict(\n                values=[['<b>EXPENSES</b><br>as of July 2017'],\n                        ['<b>DESCRIPTION</b>']],\n                line=dict(color='#506784'),\n                fill=dict(color='#119DFF'),\n                align=['left', 'center'],\n                font=dict(color='white', size=12),\n                height=40\n            ),\n            cells=dict(\n                values=values,\n                line=dict(color='#506784'),\n                fill=dict(color=['#25FEFD', 'white']),\n                align=['left', 'center'],\n                font=dict(color='#506784', size=12),\n                height=30\n            ))\n\n        data = [trace0]\n        py.plot(data, filename='tmp/table_tutorial.html', auto_play=True)\n\n\n    # Alternating Row Colors\n    if __name__ == '__main__':\n        headerColor = 'grey'\n        rowEvenColor = 'lightgrey'\n        rowOddColor = 'white'\n\n        trace0 = go.Table(\n            header=dict(\n                values=[['<b>EXPENSES</b>'],\n                        ['<b>Q1</b>'],\n                        ['<b>Q2</b>'],\n                        ['<b>Q3</b>'],\n                        ['<b>Q4</b>']],\n                line=dict(color='#506784'),\n                fill=dict(color=headerColor),\n                align=['left', 'center'],\n                font=dict(color='white', size=12)\n            ),\n            cells=dict(\n                values=[\n                    ['Salaries', 'Office', 'Merchandise', 'Legal', '<b>TOTAL</b>'],\n                    [1200000, 20000, 80000, 2000, 12120000],\n                    [1300000, 20000, 70000, 2000, 130902000],\n                    [1300000, 20000, 120000, 2000, 131222000],\n                    [1400000, 20000, 90000, 2000, 14102000]\n                ],\n                line=dict(color='#506784'),\n                fill=dict(color=[rowOddColor, rowEvenColor, rowOddColor, rowEvenColor, rowOddColor]),\n                align=['left', 'center'],\n                font=dict(color='#506784', size=11)\n            ))\n\n        data = [trace0]\n\n        py.plot(data, filename='tmp/table_tutorial.html', auto_play=True)\n\n    # Row Color Based on Variable\n    if __name__ == '__main__':\n        import pandas as pd\n        import colorlover as cl\n\n        colors = cl.scales['5']['seq']['Blues']\n        data = {'Year': [2010, 2011, 2012, 2013, 2014],\n                'Color': colors}\n        df = pd.DataFrame(data)\n\n        trace0 = go.Table(\n            header=dict(\n                values=[\"Color\", \"<b>YEAR</b>\"],\n                line=dict(color='white'),\n                fill=dict(color='white'),\n                align=['center'],\n                font=dict(color='black', size=12)\n            ),\n            cells=dict(\n                values=[df.Color, df.Year],\n                line=dict(color=[df.Color]),\n                fill=dict(color=[df.Color]),\n                align='center',\n                font=dict(color='black', size=11)\n            ))\n\n        data = [trace0]\n        py.plot(data, filename='tmp/table_tutorial.html', auto_play=True)\n\n\n    # Cell Color Based on Variable\n    if __name__ == '__main__':\n        import numpy as np\n        import colorlover as cl\n\n        colors = cl.scales['9']['seq']['Reds']\n        a = np.random.randint(low=0, high=9, size=10)\n        b = np.random.randint(low=0, high=9, size=10)\n        c = np.random.randint(low=0, high=9, size=10)\n\n        trace0 = go.Table(\n            header=dict(\n                values=['<b>Column A</b>', '<b>Column B</b>', '<b>Column C</b>'],\n                line=dict(color='white'),\n                fill=dict(color='white'),\n                align='center',\n                font=dict(color='black', size=12)\n            ),\n            cells=dict(\n                values=[a, b, c],\n                line=dict(color=[np.array(colors)[a], np.array(colors)[b],\n                                 np.array(colors)[c]]),\n                fill=dict(color=[np.array(colors)[a], np.array(colors)[b],\n                                 np.array(colors)[c]]),\n                align='center',\n                font=dict(color='white', size=11)\n            ))\n\n        data = [trace0]\n\n        py.plot(data, filename='tmp/table_tutorial.html', auto_play=True)\n\n\n# 10、Multiple Chart Types in Python\nif __name__ == '__main__':\n    import plotly.offline as py\n    import plotly.graph_objs as go\n\n    # Line Chart and a Bar Chart\n    if __name__ == '__main__':\n        trace1 = go.Scatter(\n            x=[0, 1, 2, 3, 4, 5],\n            y=[1.5, 1, 1.3, 0.7, 0.8, 0.9]\n        )\n        trace2 = go.Bar(\n            x=[0, 1, 2, 3, 4, 5],\n            y=[1, 0.5, 0.7, -1.2, 0.3, 0.4]\n        )\n\n        data = [trace1, trace2]\n        py.plot(data, filename='tmp/multi_tutorial.html', auto_play=True)\n\n\n    # A Contour and Scatter Plot of the Method of Steepest Descent\n    if __name__ == '__main__':\n        import json\n        import six.moves.urllib\n\n        response = six.moves.urllib.request.urlopen(\n            'https://raw.githubusercontent.com/plotly/datasets/master/steepest.json')\n        data = json.load(response)\n\n        trace1 = go.Contour(\n            z=data['contour_z'][0],\n            y=data['contour_y'][0],\n            x=data['contour_x'][0],\n            ncontours=30,\n            showscale=False\n        )\n        trace2 = go.Scatter(\n            x=data['trace_x'],\n            y=data['trace_y'],\n            mode='markers+lines',\n            name='steepest',\n            line=dict(\n                color='black'\n            )\n        )\n\n        data = [trace1, trace2]\n        py.plot(data, filename='tmp/multi_tutorial.html', auto_play=True)\n","repo_name":"ein0920/dash_tutorial","sub_path":"plotly_tutorial/plotly_1_basic_charts.py","file_name":"plotly_1_basic_charts.py","file_ext":"py","file_size_in_byte":62378,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"41835581573","text":"def parseBallot(line):\n  p, ballot = line.split()\n  return int(p), dict(zip(map(lambda x: ord(x) - ord('A'), ballot), list(range(len(ballot)))))\n\nn, m = map(int, input().split())\nballots = list(map(parseBallot, [input() for _ in range(m)]))\n\ngraph = dict(zip(list(range(n)), [set() for _ in range(n)]))\nfor v in range(n):\n  for w in range(v + 1, n):\n    i_p, j_p = 0, 0\n    for p, ballot in ballots:\n      if ballot[v] < ballot[w]:\n        i_p += p\n      else:\n        j_p += p\n    graph[v].add(w) if i_p > j_p else graph[w].add(v)\n\nreversed_graph = dict(zip(list(range(n)), [set() for _ in range(n)]))\nfor v in graph.keys():\n  for w in graph.keys():\n    if v != w and v in graph[w]:\n      reversed_graph[v].add(w)\n\ndef dfs(graph, v, visited):\n  visited.add(v)\n  for w in graph[v]:\n    if w not in visited:\n      dfs(graph, w, visited)\n\nfor i in range(n):\n  beats = set()\n  dfs(graph, i, beats)\n  beaten_by = reversed_graph[i]\n  print(\"{}: can{} win\".format(chr(ord('A') + i), '' if beaten_by.issubset(beats) else \"'t\"))\n","repo_name":"omarchehab98/open.kattis.com-problems","sub_path":"pearwise/pearwise.py","file_name":"pearwise.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"80"}
+{"seq_id":"25373085538","text":"import argparse\nimport logging\nimport os\nimport re\nfrom abc import ABC, abstractmethod\nfrom contextlib import ExitStack\nfrom itertools import cycle\nfrom pathlib import Path\nfrom time import time, sleep\nfrom typing import ContextManager, Dict, List, Optional, Sequence\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = \"3\"\n\n# pylint: disable=wrong-import-position\n# flake8: noqa: E402\n\ntry:\n    import tensorflow as tf\nexcept ImportError:\n    tf = None\nimport numpy as np\n\nfrom tf_bodypix.utils.timer import LoggingTimer\nfrom tf_bodypix.utils.image import (\n    ImageSize,\n    resize_image_to,\n    get_image_size,\n    box_blur_image\n)\nfrom tf_bodypix.utils.s3 import iter_s3_file_urls\nfrom tf_bodypix.download import (\n    ALL_TENSORFLOW_LITE_BODYPIX_MODEL_PATHS,\n    BodyPixModelPaths,\n    TensorFlowLiteBodyPixModelPaths,\n    download_model\n)\nfrom tf_bodypix.tflite import get_tflite_converter_for_model_path\nfrom tf_bodypix.model import (\n    load_model,\n    VALID_MODEL_ARCHITECTURE_NAMES,\n    PART_CHANNELS,\n    BodyPixModelWrapper,\n    BodyPixResultWrapper\n)\nfrom tf_bodypix.source import get_image_source, get_threaded_image_source, T_ImageSource\nfrom tf_bodypix.sink import (\n    T_OutputSink,\n    get_image_output_sink_for_path,\n    get_show_image_output_sink\n)\ntry:\n    from tf_bodypix.draw import draw_poses\nexcept ImportError as exc:\n    _draw_import_exc = exc\n    def draw_poses(*_, **__):  # type: ignore\n        raise _draw_import_exc\n\n\nLOGGER = logging.getLogger(__name__)\n\n\nDEFAULT_MODEL_TF_PATH = BodyPixModelPaths.MOBILENET_FLOAT_50_STRIDE_16\n\n\nDEFAULT_MODEL_TFLITE_PATH = TensorFlowLiteBodyPixModelPaths.MOBILENET_FLOAT_75_STRIDE_16_FLOAT16\n\n\nDEFAULT_MODEL_PATH = (\n    DEFAULT_MODEL_TF_PATH if tf is not None\n    else DEFAULT_MODEL_TFLITE_PATH\n)\n\n\nclass SubCommand(ABC):\n    def __init__(self, name, description):\n        self.name = name\n        self.description = description\n\n    @abstractmethod\n    def add_arguments(self, parser: argparse.ArgumentParser):\n        pass\n\n    @abstractmethod\n    def run(self, args: argparse.Namespace):\n        pass\n\n\ndef add_common_arguments(parser: argparse.ArgumentParser):\n    parser.add_argument(\n        \"--debug\",\n        action=\"store_true\",\n        help=\"Enable debug logging\"\n    )\n\n\ndef add_model_arguments(parser: argparse.ArgumentParser):\n    parser.add_argument(\n        \"--model-path\",\n        default=DEFAULT_MODEL_PATH,\n        help=\"The path or URL to the bodypix model.\"\n    )\n    parser.add_argument(\n        \"--model-architecture\",\n        choices=VALID_MODEL_ARCHITECTURE_NAMES,\n        help=(\n            \"The model architecture.\"\n            \" It will be guessed from the model path if not specified.\"\n        )\n    )\n    parser.add_argument(\n        \"--output-stride\",\n        type=int,\n        help=(\n            \"The output stride to use.\"\n            \" It will be guessed from the model path if not specified.\"\n        )\n    )\n    parser.add_argument(\n        \"--internal-resolution\",\n        type=float,\n        default=0.5,\n        help=(\n            \"The internal resolution factor to resize the input image to\"\n            \" before passing it the model.\"\n        )\n    )\n    parser.add_argument(\n        \"--threshold\",\n        type=float,\n        default=0.75,\n        help=\"The mask threshold.\"\n    )\n    parser.add_argument(\n        \"--mask-blur\",\n        type=int,\n        default=0,\n        help=\"The blur radius for the mask.\"\n    )\n    parser.add_argument(\n        \"--mask-mean-count\",\n        type=int,\n        default=0,\n        help=\"The number of masks to average to smooth the results.\"\n    )\n    parser.add_argument(\n        \"--mask-cache-time\",\n        type=float,\n        default=0,\n        help=(\n            \"For how long, in seconds, the mask model result should be cached.\"\n            \" e.g. if the model is very slow, you could let it calculate every second only.\"\n            \" of course that would be visible when moving quickly\"\n        )\n    )\n\n\ndef _fourcc_type(text: str) -> str:\n    if not text:\n        return text\n    if len(text) != 4:\n        raise TypeError(\n            'fourcc code must have exactly four characters, e.g. MJPG; but was: %r' % text\n        )\n    return text\n\n\ndef add_source_arguments(parser: argparse.ArgumentParser):\n    source_group = parser.add_argument_group('source')\n    source_group.add_argument(\n        \"--source\",\n        required=True,\n        help=\"The path or URL to the source image or webcam source.\"\n    )\n    image_size_help = (\n        \"If width and height are specified, the source will be resized.\"\n        \"In the case of the webcam, it will be asked to produce that resolution if possible\"\n    )\n    source_group.add_argument(\n        \"--source-width\",\n        type=int,\n        help=image_size_help\n    )\n    source_group.add_argument(\n        \"--source-height\",\n        type=int,\n        help=image_size_help\n    )\n    source_group.add_argument(\n        \"--source-fourcc\",\n        type=_fourcc_type,\n        default=\"MJPG\",\n        help=\"The fourcc code to select the source to, e.g. MJPG\"\n    )\n    source_group.add_argument(\n        \"--source-fps\",\n        type=int,\n        default=None,\n        help=(\n            \"Limit the source frame rate to desired FPS.\"\n            \" If provided, it will attempt to set the frame rate on the source device if supported.\"\n            \" Otherwise it will slow down the frame rate.\"\n            \" Use '0' for a fast as possible fps.\"\n        )\n    )\n    source_group.add_argument(\n        \"--source-threaded\",\n        action='store_true',\n        help=\"if set, will read from the source in a thread (experimental).\"\n    )\n\n\ndef add_output_arguments(parser: argparse.ArgumentParser):\n    output_group = parser.add_mutually_exclusive_group(required=True)\n    output_group.add_argument(\n        \"--show-output\",\n        action=\"store_true\",\n        help=\"Shows the output in a window.\"\n    )\n    output_group.add_argument(\n        \"--output\",\n        help=\"The path to the output file.\"\n    )\n\n\ndef get_image_source_for_args(args: argparse.Namespace) -> T_ImageSource:\n    image_size = None\n    if args.source_width and args.source_height:\n        image_size = ImageSize(height=args.source_height, width=args.source_width)\n    image_source = get_image_source(\n        args.source,\n        image_size=image_size,\n        fourcc=args.source_fourcc,\n        fps=args.source_fps\n    )\n    if args.source_threaded:\n        return get_threaded_image_source(image_source)\n    return image_source\n\n\ndef get_output_sink(args: argparse.Namespace) -> ContextManager[T_OutputSink]:\n    if args.show_output:\n        return get_show_image_output_sink()\n    if args.output:\n        return get_image_output_sink_for_path(args.output)\n    raise RuntimeError('no output sink')\n\n\ndef load_bodypix_model(args: argparse.Namespace) -> BodyPixModelWrapper:\n    local_model_path = download_model(args.model_path)\n    if args.model_path != local_model_path:\n        LOGGER.info('loading model: %r (downloaded from %r)', local_model_path, args.model_path)\n    else:\n        LOGGER.info('loading model: %r', local_model_path)\n    return load_model(\n        local_model_path,\n        internal_resolution=args.internal_resolution,\n        output_stride=args.output_stride,\n        architecture_name=args.model_architecture\n    )\n\n\ndef get_mask(\n    bodypix_result: BodyPixResultWrapper,\n    masks: List[np.ndarray],\n    timer: LoggingTimer,\n    args: argparse.Namespace,\n    resize_method: Optional[str] = None\n) -> np.ndarray:\n    mask = bodypix_result.get_mask(args.threshold, dtype=np.float32, resize_method=resize_method)\n    if args.mask_blur:\n        timer.on_step_start('mblur')\n        mask = box_blur_image(mask, args.mask_blur)\n    if args.mask_mean_count >= 2:\n        timer.on_step_start('mmean')\n        masks.append(mask)\n        if len(masks) > args.mask_mean_count:\n            masks.pop(0)\n        if len(masks) >= 2:\n            mask = np.mean(masks, axis=0)\n    LOGGER.debug('mask.shape: %s (%s)', mask.shape, mask.dtype)\n    return mask\n\n\nclass ListModelsSubCommand(SubCommand):\n    def __init__(self):\n        super().__init__(\"list-models\", \"Lists available bodypix models (original models)\")\n\n    def add_arguments(self, parser: argparse.ArgumentParser):\n        add_common_arguments(parser)\n        parser.add_argument(\n            \"--storage-url\",\n            default=\"https://storage.googleapis.com/tfjs-models\",\n            help=\"The base URL for the storage containing the models\"\n        )\n\n    def get_model_paths(self, storage_url: str) -> Sequence[str]:\n        return [\n            file_url\n            for file_url in iter_s3_file_urls(storage_url)\n            if re.match(r'.*/bodypix/.*/model.*\\.json', file_url)\n        ]\n\n    def run(self, args: argparse.Namespace):  # pylint: disable=unused-argument\n        print('\\n'.join(self.get_model_paths(storage_url=args.storage_url)))\n\n\nclass ListTensorFlowLiteModelsSubCommand(SubCommand):\n    def __init__(self):\n        super().__init__(\"list-tflite-models\", \"Lists available tflite bodypix models\")\n\n    def add_arguments(self, parser: argparse.ArgumentParser):\n        add_common_arguments(parser)\n\n    def get_model_paths(self) -> Sequence[str]:\n        return ALL_TENSORFLOW_LITE_BODYPIX_MODEL_PATHS\n\n    def run(self, args: argparse.Namespace):  # pylint: disable=unused-argument\n        print('\\n'.join(self.get_model_paths()))\n\n\nclass ConvertToTFLiteSubCommand(SubCommand):\n    def __init__(self):\n        super().__init__(\"convert-to-tflite\", \"Converts the model to a tflite model\")\n\n    def add_arguments(self, parser: argparse.ArgumentParser):\n        add_common_arguments(parser)\n        parser.add_argument(\n            \"--model-path\",\n            default=DEFAULT_MODEL_TF_PATH,\n            help=\"The path or URL to the bodypix model.\"\n        )\n        parser.add_argument(\n            \"--output-model-file\",\n            required=True,\n            help=\"The path to the output file (tflite model).\"\n        )\n        parser.add_argument(\n            \"--optimize\",\n            action='store_true',\n            help=\"Enable optimization (quantization).\"\n        )\n        parser.add_argument(\n            \"--quantization-type\",\n            choices=['float16', 'float32', 'int8'],\n            help=\"The quantization type to use.\"\n        )\n\n    def run(self, args: argparse.Namespace):  # pylint: disable=unused-argument\n        LOGGER.info('converting model: %s', args.model_path)\n        converter = get_tflite_converter_for_model_path(download_model(\n            args.model_path\n        ))\n        tflite_model = converter.convert()\n        if args.optimize:\n            LOGGER.info('enabled optimization')\n            converter.optimizations = [tf.lite.Optimize.DEFAULT]\n        if args.quantization_type:\n            LOGGER.info('quanization type: %s', args.quantization_type)\n            quantization_type = getattr(tf, args.quantization_type)\n            converter.target_spec.supported_types = [quantization_type]\n            converter.inference_input_type = quantization_type\n            converter.inference_output_type = quantization_type\n        LOGGER.info('saving tflite model to: %s', args.output_model_file)\n        Path(args.output_model_file).write_bytes(tflite_model)\n\n\nclass AbstractWebcamFilterApp(ABC):\n    def __init__(self, args: argparse.Namespace):\n        self.args = args\n        self.bodypix_model = None\n        self.output_sink = None\n        self.image_source = None\n        self.image_iterator = None\n        self.timer = LoggingTimer()\n        self.masks: List[np.ndarray] = []\n        self.exit_stack = ExitStack()\n        self.bodypix_result_cache_time = None\n        self.bodypix_result_cache = None\n\n    @abstractmethod\n    def get_output_image(self, image_array: np.ndarray) -> np.ndarray:\n        pass\n\n    def get_mask(self, *args, **kwargs):\n        return get_mask(\n            *args, masks=self.masks, timer=self.timer, args=self.args, **kwargs\n        )\n\n    def get_bodypix_result(self, image_array: np.ndarray) -> BodyPixResultWrapper:\n        assert self.bodypix_model is not None\n        current_time = time()\n        if (\n            self.bodypix_result_cache is not None\n            and current_time < self.bodypix_result_cache_time + self.args.mask_cache_time\n        ):\n            return self.bodypix_result_cache\n        self.bodypix_result_cache = self.bodypix_model.predict_single(image_array)\n        self.bodypix_result_cache_time = current_time\n        return self.bodypix_result_cache\n\n    def __enter__(self):\n        self.exit_stack.__enter__()\n        self.bodypix_model = load_bodypix_model(self.args)\n        self.output_sink = self.exit_stack.enter_context(get_output_sink(self.args))\n        self.image_source = self.exit_stack.enter_context(get_image_source_for_args(self.args))\n        self.image_iterator = iter(self.image_source)\n        return self\n\n    def __exit__(self, *args, **kwargs):\n        self.exit_stack.__exit__(*args, **kwargs)\n\n    def next_frame(self):\n        self.timer.on_frame_start(initial_step_name='in')\n        try:\n            image_array = next(self.image_iterator)\n        except StopIteration:\n            return False\n        LOGGER.debug('image_array: %r (%r)', image_array.shape, image_array.dtype)\n        self.timer.on_step_start('model')\n        output_image = self.get_output_image(image_array)\n        self.timer.on_step_start('out')\n        self.output_sink(output_image)\n        self.timer.on_frame_end()\n        return True\n\n    def run(self):\n        try:\n            self.timer.start()\n            while self.next_frame():\n                pass\n            if self.args.show_output:\n                LOGGER.info('waiting for window to be closed')\n                while not self.output_sink.is_closed:\n                    sleep(0.5)\n        except KeyboardInterrupt:\n            LOGGER.info('exiting')\n\n\nclass AbstractWebcamFilterSubCommand(SubCommand):\n    def add_arguments(self, parser: argparse.ArgumentParser):\n        add_common_arguments(parser)\n        add_model_arguments(parser)\n        add_source_arguments(parser)\n        add_output_arguments(parser)\n\n    @abstractmethod\n    def get_app(self, args: argparse.Namespace) -> AbstractWebcamFilterApp:\n        pass\n\n    def run(self, args: argparse.Namespace):\n        with self.get_app(args) as app:\n            app.run()\n\n\nclass DrawMaskApp(AbstractWebcamFilterApp):\n    def get_output_image(self, image_array: np.ndarray) -> np.ndarray:\n        resize_method = None\n        result = self.get_bodypix_result(image_array)\n        self.timer.on_step_start('get_mask')\n        mask = self.get_mask(result, resize_method=resize_method)\n        if self.args.colored:\n            self.timer.on_step_start('get_cpart_mask')\n            mask_image = result.get_colored_part_mask(\n                mask, part_names=self.args.parts, resize_method=resize_method\n            )\n        elif self.args.parts:\n            self.timer.on_step_start('get_part_mask')\n            mask_image = result.get_part_mask(\n                mask, part_names=self.args.parts, resize_method=resize_method\n            ) * 255\n        else:\n            if LOGGER.isEnabledFor(logging.DEBUG):\n                LOGGER.debug(\n                    'mask: %r (%r, %r) (%s)',\n                    mask.shape, np.min(mask), np.max(mask), mask.dtype\n                )\n            mask_image = mask * 255.0\n        if self.args.mask_alpha is not None:\n            self.timer.on_step_start('overlay')\n            LOGGER.debug('mask.shape: %s (%s)', mask.shape, mask.dtype)\n            alpha = self.args.mask_alpha\n            try:\n                if tf is not None:\n                    if mask_image.dtype == tf.int32:\n                        mask_image = tf.cast(mask_image, tf.float32)\n                else:\n                    image_array = np.asarray(image_array).astype(np.float32)\n                if LOGGER.isEnabledFor(logging.DEBUG):\n                    LOGGER.debug(\n                        'mask_image: %r (%r, %r) (%s)',\n                        mask_image.shape, np.min(mask_image), np.max(mask_image), mask_image.dtype\n                    )\n            except TypeError:\n                pass\n            output = np.clip(\n                image_array * (1 - alpha) + mask_image * alpha,\n                0.0, 255.0\n            )\n            return output\n        return mask_image\n\n\nclass DrawMaskSubCommand(AbstractWebcamFilterSubCommand):\n    def __init__(self):\n        super().__init__(\"draw-mask\", \"Draws the mask for the input\")\n\n    def add_arguments(self, parser: argparse.ArgumentParser):\n        super().add_arguments(parser)\n        parser.add_argument(\n            \"--mask-alpha\",\n            type=float,\n            help=\"The opacity of mask overlay to add.\"\n        )\n        parser.add_argument(\n            \"--add-overlay-alpha\",\n            dest='mask_alpha',\n            type=float,\n            help=\"Deprecated, please use --mask-alpha instead.\"\n        )\n        parser.add_argument(\n            \"--colored\",\n            action=\"store_true\",\n            help=\"Enable generating the colored part mask\"\n        )\n        parser.add_argument(\n            \"--parts\",\n            nargs=\"*\",\n            choices=PART_CHANNELS,\n            help=\"Select the parts to output\"\n        )\n\n    def get_app(self, args: argparse.Namespace) -> AbstractWebcamFilterApp:\n        return DrawMaskApp(args)\n\n\nclass DrawPoseApp(AbstractWebcamFilterApp):\n    def get_output_image(self, image_array: np.ndarray) -> np.ndarray:\n        result = self.get_bodypix_result(image_array)\n        self.timer.on_step_start('get_pose')\n        poses = result.get_poses()\n        LOGGER.debug('number of poses: %d', len(poses))\n        output_image = draw_poses(\n            image_array.copy(), poses,\n            keypoints_color=(255, 100, 100),\n            skeleton_color=(100, 100, 255)\n        )\n        return output_image\n\n\nclass DrawPoseSubCommand(AbstractWebcamFilterSubCommand):\n    def __init__(self):\n        super().__init__(\"draw-pose\", \"Draws the pose estimation\")\n\n    def get_app(self, args: argparse.Namespace) -> AbstractWebcamFilterApp:\n        return DrawPoseApp(args)\n\n\nclass BlurBackgroundApp(AbstractWebcamFilterApp):\n    def get_output_image(self, image_array: np.ndarray) -> np.ndarray:\n        result = self.get_bodypix_result(image_array)\n        self.timer.on_step_start('get_mask')\n        mask = self.get_mask(result)\n        self.timer.on_step_start('bblur')\n        background_image_array = box_blur_image(image_array, self.args.background_blur)\n        self.timer.on_step_start('compose')\n        output = np.clip(\n            background_image_array * (1 - mask)\n            + image_array * mask,\n            0.0, 255.0\n        )\n        return output\n\n\nclass BlurBackgroundSubCommand(AbstractWebcamFilterSubCommand):\n    def __init__(self):\n        super().__init__(\"blur-background\", \"Blurs the background of the webcam image\")\n\n    def add_arguments(self, parser: argparse.ArgumentParser):\n        super().add_arguments(parser)\n        parser.add_argument(\n            \"--background-blur\",\n            type=int,\n            default=15,\n            help=\"The blur radius for the background.\"\n        )\n\n    def get_app(self, args: argparse.Namespace) -> AbstractWebcamFilterApp:\n        return BlurBackgroundApp(args)\n\n\nclass ReplaceBackgroundApp(AbstractWebcamFilterApp):\n    def __init__(self, *args, **kwargs):\n        self.background_image_iterator = None\n        super().__init__(*args, **kwargs)\n\n    def get_next_background_image(self, image_array: np.ndarray) -> np.ndarray:\n        if self.background_image_iterator is None:\n            background_image_source = self.exit_stack.enter_context(get_image_source(\n                self.args.background,\n                image_size=get_image_size(image_array)\n            ))\n            self.background_image_iterator = iter(cycle(background_image_source))\n        return next(self.background_image_iterator)\n\n    def get_output_image(self, image_array: np.ndarray) -> np.ndarray:\n        background_image_array = self.get_next_background_image(image_array)\n        result = self.get_bodypix_result(image_array)\n        self.timer.on_step_start('get_mask')\n        mask = self.get_mask(result)\n        self.timer.on_step_start('compose')\n        background_image_array = resize_image_to(\n            background_image_array, get_image_size(image_array)\n        )\n        output = np.clip(\n            background_image_array * (1 - mask)\n            + image_array * mask,\n            0.0, 255.0\n        )\n        return output\n\n\nclass ReplaceBackgroundSubCommand(AbstractWebcamFilterSubCommand):\n    def __init__(self):\n        super().__init__(\"replace-background\", \"Replaces the background of a person\")\n\n    def add_arguments(self, parser: argparse.ArgumentParser):\n        add_common_arguments(parser)\n        add_model_arguments(parser)\n        add_source_arguments(parser)\n\n        parser.add_argument(\n            \"--background\",\n            required=True,\n            help=\"The path or URL to the background image.\"\n        )\n\n        add_output_arguments(parser)\n\n    def get_app(self, args: argparse.Namespace) -> AbstractWebcamFilterApp:\n        return ReplaceBackgroundApp(args)\n\n\nSUB_COMMANDS: List[SubCommand] = [\n    ListModelsSubCommand(),\n    ListTensorFlowLiteModelsSubCommand(),\n    ConvertToTFLiteSubCommand(),\n    DrawMaskSubCommand(),\n    DrawPoseSubCommand(),\n    BlurBackgroundSubCommand(),\n    ReplaceBackgroundSubCommand()\n]\n\nSUB_COMMAND_BY_NAME: Dict[str, SubCommand] = {\n    sub_command.name: sub_command for sub_command in SUB_COMMANDS\n}\n\n\ndef parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace:\n    parser = argparse.ArgumentParser(\n        'TensorFlow BodyPix (TF BodyPix)',\n        formatter_class=argparse.ArgumentDefaultsHelpFormatter\n    )\n    subparsers = parser.add_subparsers(dest=\"command\")\n    subparsers.required = True\n    for sub_command in SUB_COMMANDS:\n        sub_parser = subparsers.add_parser(\n            sub_command.name, help=sub_command.description,\n            formatter_class=argparse.ArgumentDefaultsHelpFormatter\n        )\n        sub_command.add_arguments(sub_parser)\n\n    args = parser.parse_args(argv)\n    return args\n\n\ndef run(args: argparse.Namespace):\n    sub_command = SUB_COMMAND_BY_NAME[args.command]\n    sub_command.run(args)\n\n\ndef main(argv: Optional[List[str]] = None):\n    args = parse_args(argv)\n    if args.debug:\n        logging.getLogger().setLevel(logging.DEBUG)\n    LOGGER.debug(\"args: %s\", args)\n    run(args)\n\n\nif __name__ == '__main__':\n    logging.basicConfig(level='INFO')\n    main()\n","repo_name":"de-code/python-tf-bodypix","sub_path":"tf_bodypix/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":22640,"program_lang":"python","lang":"en","doc_type":"code","stars":112,"dataset":"github-code","pt":"80"}
+{"seq_id":"50754335745","text":"from __future__ import unicode_literals, division, absolute_import, print_function\n\n__license__   = 'GPL v3'\n__copyright__ = '2011, Grant Drake'\n\nimport six\nimport os, time, traceback, re\n\nfrom calibre import CurrentDir, guess_type\nfrom calibre.ebooks.chardet import strip_encoding_declarations\nfrom calibre.ebooks.conversion.plumber import OptionValues\nfrom calibre.ebooks.metadata.opf2 import OPF\nfrom calibre.ebooks.metadata.meta import set_metadata\nfrom calibre.ebooks.oeb.base import XPath\nfrom calibre.customize.ui import apply_null_metadata\nfrom calibre.libunzip import extract as zipextract\nfrom calibre.ptempfile import TemporaryDirectory\n\nfrom calibre_plugins.modify_epub.container import ExtendedContainer, OPF_NS\nfrom calibre_plugins.modify_epub.covers import CoverUpdater\nfrom calibre_plugins.modify_epub.css import CSSUpdater\nfrom calibre_plugins.modify_epub.jacket import (remove_legacy_jackets, remove_all_jackets,\n                                                add_replace_jacket)\nfrom calibre_plugins.modify_epub.margins import MarginsUpdater\n\nITUNES_FILES = ['iTunesMetadata.plist', 'iTunesArtwork']\nBOOKMARKS_FILES = ['META-INF/calibre_bookmarks.txt']\nOS_FILES = ['.DS_Store', 'thumbs.db']\nALL_ARTIFACTS = ITUNES_FILES + BOOKMARKS_FILES + OS_FILES\n\nclass TAG:\n    content = ''    #actual content\n    pair = 0        #tag pair\n    e_type = 0      #1=OPEN 2=CLOSE 3=CONTAINED 4=TEXT OR CR/LF 9=REMOVE-EMPTY-SPAN\n\ndef modify_epub(log, title, epub_path, calibre_opf_path, cover_path, options):\n    start_time = time.time()\n    modifier = BookModifier(log)\n    new_book_path = modifier.process_book(title, epub_path, calibre_opf_path,\n                                          cover_path, options)\n    if new_book_path:\n        log('ePub updated in %.2f seconds'%(time.time() - start_time))\n    else:\n        log('ePub not changed after %.2f seconds'%(time.time() - start_time))\n    return new_book_path\n\n\nclass BookModifier(object):\n\n    def __init__(self, log):\n        self.log = log\n\n    def process_book(self, title, epub_path, calibre_opf_path, cover_path, options):\n        self.log('  Modifying: ', epub_path)\n        try:\n            self._restore_metadata_from_opf(calibre_opf_path, cover_path)\n            self._setup_user_options()\n\n            # If the user is updating metadata, we need to do this as a separate\n            # step at the start, because it takes a stream object as input so is\n            # run before we have written any container changes to disk below.\n            is_metadata_updated = False\n            if options['update_metadata']:\n                is_metadata_updated = self._update_metadata_and_cover(epub_path)\n\n            # Extract the epub into a temp directory\n            with TemporaryDirectory('_modify-epub') as tdir:\n                with CurrentDir(tdir):\n                    zipextract(epub_path, tdir)\n\n                    # Use our own simplified wrapper around an ePub that will\n                    # preserve the file structure and css\n                    container = ExtendedContainer(tdir, self.log)\n                    is_modified = self._process_book(container, options)\n                    if is_modified:\n                        container.write(epub_path)\n\n            # Only return path to the ePub if we have changed it\n            if is_metadata_updated or is_modified:\n                return epub_path\n        except:\n            self.log.exception('%s - ERROR: %s' %(title, traceback.format_exc()))\n        finally:\n            if calibre_opf_path and os.path.exists(calibre_opf_path):\n                os.remove(calibre_opf_path)\n            if cover_path and os.path.exists(cover_path):\n                os.remove(cover_path)\n\n    def _restore_metadata_from_opf(self, calibre_opf_path, cover_path):\n        '''\n        Create an mi object from our copy of the latest Calibre metadata\n        stored in an OPF, so that we can perform functions that update\n        the book metadata, such as generating a new jacket.\n        '''\n        if calibre_opf_path and os.path.exists(calibre_opf_path):\n            with open(calibre_opf_path, 'r') as f:\n                calibre_opf = OPF(f, os.path.dirname(calibre_opf_path))\n            self.mi = calibre_opf.to_book_metadata()\n\n        # Store our link to a copy of the book cover, so that we can perform\n        # functions such as replacing the cover image.\n        self.cover_path = cover_path\n\n    def _update_metadata_and_cover(self, epub_path):\n        self.log('\\tUpdating metadata and cover')\n        # Populate our mi object with the cover data\n        if self.cover_path:\n            if os.access(self.cover_path, os.R_OK):\n                fmt = self.cover_path.rpartition('.')[-1]\n                data = open(self.cover_path, 'rb').read()\n                self.mi.cover_data = (fmt, data)\n        with open(epub_path, 'r+b') as f:\n            with apply_null_metadata:\n                set_metadata(f, self.mi, stream_type='epub')\n        return True # Going to \"assume\" it did something\n\n    def _process_book(self, container, options):\n        is_changed = False\n\n        # MANIFEST OPTIONS\n        if options['remove_missing_files']:\n            is_changed |= self._remove_missing_files(container)\n        if options['add_unmanifested_files']:\n            is_changed |= self._process_unmanifested_files(container, add=True)\n        elif options['remove_unmanifested_files']:\n            is_changed |= self._process_unmanifested_files(container, add=False)\n        if options['flatten_toc']:\n            is_changed |= self._flatten_toc(container)\n        if options['remove_broken_ncx_links']:\n            is_changed |= self._remove_broken_ncx_links(container)\n\n        # ADOBE OPTIONS\n        if options['zero_xpgt_margins'] and not options['remove_xpgt_files']:\n            is_changed |= self._zero_xpgt_margins(container)\n        if options['remove_xpgt_files']:\n            is_changed |= self._remove_xpgt_files(container)\n        if options['remove_page_map']:\n            is_changed |= self._remove_pagemaps(container)\n        if options['remove_gp_page_map']:\n            is_changed |= self._remove_gp_pagemaps(container)\n        if options['remove_drm_meta_tags']:\n            is_changed |= self._remove_drm_meta_tags(container)\n\n        # JACKET OPTIONS\n        if options['remove_legacy_jackets'] and not options['remove_all_jackets']:\n            is_changed |= remove_legacy_jackets(container, self.log)\n        if options['remove_all_jackets']:\n            is_changed |= remove_all_jackets(container, self.log)\n        if options['add_replace_jacket']:\n            if options['jacket_end_book']:\n                jacket_end_book = True\n            else:\n                jacket_end_book = False\n            is_changed |= add_replace_jacket(container, self.log, self.mi, self.opts.output_profile, jacket_end_book)\n\n        # METADATA/COVER OPTIONS\n        if options['remove_broken_covers']:\n            is_changed |= self._remove_broken_covers(container)\n        if options['remove_cover'] and not options['insert_replace_cover']:\n            is_changed |= self._remove_cover(container)\n        if options['remove_non_dc_elements']:\n            is_changed |= self._remove_non_dc_elements(container)\n\n        # HTML/STYLE OPTIONS\n        if options['encode_html_utf8']:\n            is_changed |= self._encode_html_utf8(container)\n        if options['remove_embedded_fonts']:\n            is_changed |= self._remove_embedded_fonts(container)\n        if options['rewrite_css_margins']:\n            is_changed |= self._rewrite_css_margins(container)\n        if options['append_extra_css']:\n            is_changed |= self._append_extra_css(container)\n        if options['remove_javascript']:\n            is_changed |= self._remove_javascript(container)\n        if options['smarten_punctuation']:\n            is_changed |= self._smarten_punctuation(container)\n\n        # FILE OPTIONS\n        if options['strip_kobo']:\n            is_changed |= self._strip_kobo(container)\n        if options['remove_itunes_files']:\n            is_changed |= self._remove_files_if_exist(container, ITUNES_FILES)\n        if options['remove_calibre_bookmarks']:\n            is_changed |= self._remove_files_if_exist(container, BOOKMARKS_FILES)\n        if options['remove_os_artifacts']:\n            is_changed |= self._remove_files_if_exist(container, OS_FILES)\n        if options['remove_unused_images']:\n            is_changed |= self._remove_unused_images(container)\n        if options['strip_spans']:\n            is_changed |= self._strip_spans(container)\n        if options['unpretty']:\n            is_changed |= self._unpretty(container)\n\n        # WARNING: This must be the very last option run, because afterwards\n        # the container object may not be perfectly synchronised with changes\n        # made by inserting or updating covers.\n        # Rather than re-initialising all the internal dictionaries etc. for\n        # now will get away with it by running no modifications after it.\n        if options['insert_replace_cover']:\n            is_changed |= self._insert_replace_cover(container)\n\n        return is_changed\n\n    def _remove_files_if_exist(self, container, files):\n        '''\n        Helper function to remove items from manifest whose filename is\n        in the set of 'files'\n        '''\n        dirtied = False\n        self.log('\\tLooking for files to remove:', files)\n        files = [f.lower() for f in files]\n        for name in list(container.name_path_map.keys()):\n            found = False\n            if name.lower() in files:\n                found = True\n            if not found:\n                for f in files:\n                    if name.lower().endswith('/'+f):\n                        found = True\n                        break\n            if found:\n                self.log('\\t  Found file to remove:', name)\n                container.delete_from_manifest(name)\n                dirtied = True\n        return dirtied\n\n    def _remove_unused_images(self, container):\n        self.log('\\tLooking for unused images')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot remove unused images from DRM encrypted book')\n            return False\n\n        dirtied = container.remove_unused_images(container.get_image_names())\n        return dirtied\n\n    def _remove_missing_files(self, container):\n        self.log('\\tLooking for redundant entries in manifest')\n        missing_files = set(container.mime_map.keys()) - set(container.name_path_map.keys())\n        dirtied = False\n        for name in missing_files:\n            self.log('\\t  Found entry to remove:', name)\n            container.delete_from_manifest(name)\n            dirtied = True\n        if dirtied:\n            container.set(container.opf_name, container.opf)\n        return dirtied\n\n    def _process_unmanifested_files(self, container, add=False):\n        self.log('\\tLooking for unmanifested files')\n        all_artifacts = [f.lower() for f in ALL_ARTIFACTS]\n        dirtied = False\n        for name in list(container.manifest_worthy_names()):\n            # Special exclusion for bookmarks, plist files and other OS artifacts\n            known_artifact = False\n            if name.lower() in all_artifacts:\n                known_artifact = True\n            if not known_artifact:\n                for a in all_artifacts:\n                    if name.lower().endswith('/'+a):\n                        known_artifact = True\n                        break\n            if known_artifact:\n                continue\n\n            item = container.get_manifest_item_for_name(name)\n            if item is None:\n                if add:\n                    self.log('\\t  Found file to to add:', name)\n                    ext = os.path.splitext(name)[1]\n                    mt = None   # Let the mime-type be guessed from the extension\n                    if ext.lower().startswith('.htm'):\n                        # If this is really an xhtml file, need to explicitly declare it\n                        raw = container.get_raw(name)\n                        if raw.find('xmlns=\"http://www.w3.org/1999/xhtml\"') != -1:\n                            mt = guess_type('a.xhtml')[0]\n                            self.log('\\t Switching mimetype to:', mt)\n                    container.add_name_to_manifest(name, mt)\n                else:\n                    self.log('\\t  Found file to to remove:', name)\n                    container.delete_name(name)\n                dirtied = True\n        if dirtied:\n            container.set(container.opf_name, container.opf)\n        return dirtied\n\n    def _remove_non_dc_elements(self, container):\n        self.log('\\tLooking for non dc: elements in manifest')\n        if not container.opf_name:\n            self.log('\\t  No opf manifest found')\n            return False\n        to_remove = []\n        metadata = container.opf.xpath('//opf:metadata', namespaces={'opf':OPF_NS})[0]\n        for child in metadata:\n            try:\n                if not child.tag.startswith('{http://purl.org/dc/'):\n                    to_remove.append(child)\n                    self.log('\\t  Removing child:', child.tag)\n            except:\n                # Dunno how to elegantly handle in lxml parsing\n                # text like <!-- stuff --> which blows up when\n                # calling the .tag function.\n                to_remove.append(child)\n                self.log('\\t  Removing child of commented out text:', child.text)\n        if to_remove:\n            for node in to_remove:\n                metadata.remove(node)\n            container.set(container.opf_name, container.opf)\n        return bool(to_remove)\n\n    def _flatten_toc(self, container):\n        self.log('\\tLooking for NCX to flatten')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot flatten TOC NCX in DRM encrypted book')\n            return False\n        return container.flatten_toc()\n\n    def _remove_broken_ncx_links(self, container):\n        self.log('\\tLooking for broken links in the NCX')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot remove broken NCX links in DRM encrypted book')\n            return False\n        html_names_map = dict((k.lower(), True) for k in container.get_html_names())\n        return container.delete_broken_toc_links(html_names_map)\n\n    def _zero_xpgt_margins(self, container):\n        dirtied = False\n        self.log('\\tLooking for Adobe xpgt page template margins')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot zero xpgt margins in DRM encrypted book')\n            return False\n        for name in container.get_xpgt_names():\n            data = container.get_parsed_etree(name)\n            if hasattr(data, 'xpath'):\n                for elem in data.xpath(\n                        '//*[@margin-bottom or @margin-top '\n                        'or @margin-left or @margin-right]'):\n                    for margin in ('left', 'right', 'top', 'bottom'):\n                        attr = 'margin-'+margin\n                        elem.attrib.pop(attr, None)\n                        dirtied = True\n            if dirtied:\n                self.log('\\t  Removed page margins from:', name)\n                container.set(name, data)\n                break\n        return dirtied\n\n    def _remove_xpgt_files(self, container):\n        dirtied = False\n        self.log('\\tLooking for Adobe xpgt files and links to remove')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot remove xpgt files from DRM encrypted book')\n            return False\n\n        for name in list(container.get_xpgt_names()):\n            self.log('\\t  Found xpgt file to to remove:', name)\n            container.delete_from_manifest(name)\n            dirtied = True\n\n        for name in container.get_html_names():\n            html = container.get_parsed_etree(name)\n            try:\n                xpgt_links = XPath('//h:link[(@rel=\"stylesheet\" or @rel=\"xpgt\") and @href]')(html)\n            except:\n                xpgt_links = []\n            for xpgt_link in xpgt_links:\n                href = xpgt_link.get('href').lower()\n                if href.endswith('.xpgt'):\n                    xpgt_link.getparent().remove(xpgt_link)\n                    self.log('\\t  Removed xpgt link from:', name)\n                    container.set(name, html)\n                    dirtied = True\n\n        # Look for import statments for xpgt files. Will support any of:\n        # @import url(path); @import url(\"path\"); @import url('path'); @import \"path\"\n        # Plus the variations of semi-colon delimited or inlined style\n        RE_CSS_IMPORT1 = re.compile(r'@import url\\([\\'\\\"]*(.*?)[\\'\"]*\\)[^;<\\-]*;?', re.UNICODE | re.DOTALL)\n        RE_CSS_IMPORT2 = re.compile(r'@import\\s+\"(.*?)\"[^;<\\-]*;?', re.UNICODE | re.DOTALL)\n\n        def compare_import_match(match, name, data):\n            if match.group(1).lower().endswith('.xpgt'):\n                data = data.replace(match.group(0), '')\n                self.log('\\t  Removed xpgt @import from:', name)\n                container.set(name, data)\n                return True\n\n        for name in list(container.get_css_names()) + list(container.get_html_names()):\n            data = container.get_raw(name)\n            for match in RE_CSS_IMPORT1.finditer(data):\n                if compare_import_match(match, name, data):\n                    dirtied = True\n            for match in RE_CSS_IMPORT2.finditer(data):\n                if compare_import_match(match, name, data):\n                    dirtied = True\n        return dirtied\n\n    def _remove_drm_meta_tags(self, container):\n        RE_DRM_META = re.compile(r'(\\n*\\s*)?<meta [^>]*?name=\"adept\\.[expctd\\.]*?resource\"[^>]*?>', re.UNICODE | re.IGNORECASE)\n        dirtied = False\n        self.log('\\tLooking for Adobe DRM meta tags to remove')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot remove Adobe meta tags from DRM encrypted book')\n            return False\n        for name in container.get_html_names():\n            html = container.get_raw(name)\n            new_html = RE_DRM_META.sub('', html)\n            if html != new_html:\n                dirtied = True\n                container.set(name, new_html)\n                self.log('\\t  Removed meta tag from:', name)\n        return dirtied\n\n    def _rewrite_css_margins(self, container):\n        self.log('\\tLooking for CSS margins')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot modify css margins in DRM encrypted book')\n            return False\n        mu = MarginsUpdater(self.log, container)\n        dirtied = mu.rewrite_css_margins()\n        return dirtied\n\n    def _append_extra_css(self, container):\n        self.log('\\tLooking for extra CSS to append')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot append extra css in DRM encrypted book')\n            return False\n        mu = CSSUpdater(self.log, container)\n        dirtied = mu.rewrite_css()\n        return dirtied\n\n    def _remove_embedded_fonts(self, container):\n        RE_FONT_FACE = re.compile(r'@font\\-face[^}]+?}\\s*', re.UNICODE | re.IGNORECASE)\n        self.log('\\tLooking for embedded fonts')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot remove embedded fonts from DRM encrypted book')\n            return False\n        dirtied = False\n        for name in list(container.name_path_map.keys()):\n            if name.lower().endswith('.ttf') or name.lower().endswith('.otf'):\n                self.log('\\t  Found font to remove:', name)\n                container.delete_from_manifest(name)\n                dirtied = True\n\n        self.log('\\tLooking for css @font-face style declarations')\n        for name in container.get_css_names():\n            css = container.get_raw(name)\n            new_css = RE_FONT_FACE.sub('', css)\n            if css != new_css:\n                dirtied = True\n                container.set(name, new_css)\n                self.log('\\t  Removed @font-face from:', name)\n\n        self.log('\\tLooking for inline @font-face style declarations')\n        for name in container.get_html_names():\n            html = container.get_raw(name)\n            new_html = RE_FONT_FACE.sub('', html)\n            if html != new_html:\n                dirtied = True\n                container.set(name, new_html)\n                self.log('\\t  Removed @font-face from:', name)\n        return dirtied\n\n    def _encode_html_utf8(self, container):\n        self.log('\\tLooking for html files to remove charset meta tags/encode to utf-8')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot switch a DRM encrypted book to UTF-8 encoding')\n            return False\n\n        dirtied = False\n        for name in container.get_html_names():\n            html = container.get_raw(name)\n            try:\n                new_html = strip_encoding_declarations(html)\n                #new_html = new_html.encode('utf-8')\n                if not new_html.strip().startswith('<?xml'):\n                    new_html = '<?xml version=\"1.0\" encoding=\"utf-8\"?>'+new_html\n                    new_html = re.sub(r'<\\?xml([^\\?]*?)\\?><', r'<?xml\\1?>\\n<', new_html)\n                if new_html != html:\n                    dirtied = True\n                    container.set(name, new_html)\n                    self.log('\\t  Switched to UTF-8 encoding for:', name)\n            except:\n                pass\n        return dirtied\n\n    def _smarten_punctuation(self, container):\n        from calibre.utils.smartypants import smartyPants\n        from calibre.ebooks.chardet import substitute_entites\n        from calibre.ebooks.conversion.utils import HeuristicProcessor\n        from uuid import uuid4\n        dirtied = False\n        self.log('\\tApplying smarten punctuation')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot smarten punctuation in DRM encrypted book')\n            return False\n\n        def smarten_punctuation_for_page(html):\n            preprocessor = HeuristicProcessor(None, self.log)\n            start = 'calibre-smartypants-'+str(uuid4())\n            stop = 'calibre-smartypants-'+str(uuid4())\n            html = html.replace('<!--', start)\n            html = html.replace('-->', stop)\n            html = preprocessor.fix_nbsp_indents(html)\n            html = smartyPants(html)\n            html = html.replace(start, '<!--')\n            html = html.replace(stop, '-->')\n            # convert ellipsis to entities to prevent wrapping\n            html = re.sub(r'(?u)(?<=\\w)\\s?(\\.\\s?){2}\\.', '&hellip;', html)\n            # convert double dashes to em-dash\n            html = re.sub(r'\\s--\\s', u'\\u2014', html)\n            return substitute_entites(html)\n\n        for name in container.get_html_names():\n            html = container.get_raw(name)\n            new_html = smarten_punctuation_for_page(html)\n            if html != new_html:\n                dirtied = True\n                container.set(name, new_html)\n                self.log('\\t  Smartened punctuation in:', name)\n        return dirtied\n\n    def _unpretty(self, container):\n        dirtied = False\n        self.log('\\tUnprettying files')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot de-indent a DRM encrypted book')\n            return False\n\n        def unpretty_for_page(html_text):\n            if re.search(r'<pre\\s*([^>]*?)>', html_text, re.I):\n                self.log('\\t  Skipped:', name, ' - not safe to unpretty files which contain PRE elements.');\n            else:\n                html_text = re.sub(r'\\r\\n?', r'\\n', html_text)\n                html_text = re.sub(r'<!--([\\s\\S]*?)-->', r'', html_text)\n                html_text = re.sub(r'</(b|h)r>', r'', html_text)\n                html_text = re.sub(r'!DOCTYPE([^>]*?)\\n([^>]*?)>', r'!DOCTYPE\\1 \\2>', html_text)\n                html_text = re.sub(r'!DOCTYPE([^>]*?)>\\s*', r'!DOCTYPE\\1>\\n', html_text)\n                html_text = re.sub(r'>\\n\\s+<', r'>\\n<', html_text)\n                html_text = re.sub(r'\\s+</([^>]+)>', r'</\\1> ', html_text)\n                html_text = re.sub(r'[^\\S\\n]+\\n', r'\\n', html_text)\n                html_text = re.sub(r'<(\\S+)([^/>]*?) style=\"display: ?none;?\"([^/>]*?)></\\1>', r'', html_text)\n                html_text = re.sub(r'>\\s*<(html|head|title|meta|link|style|body|h\\d|ul|ol|li|p|div|section|nav|tr|td)([^>]*?)(/?)>', r'>\\n<\\1\\2\\3>', html_text)\n                html_text = re.sub(r'<(h\\d|li|p|div|section|nav|td)([^/>]*?)>\\s*<(span|b|i|a|small)', r'<\\1\\2><\\3', html_text)\n                html_text = re.sub(r'<(span|b|i|a|u|em|strong|small)([^>]*?)> <(span|b|i|a|u|em|strong|small)', r' <\\1\\2><\\3', html_text)\n                html_text = re.sub(r'>\\s+<(span|b|i|a|u|em|strong|big|small)', r'> <\\1', html_text)\n                html_text = re.sub(r'\\s*<(section|nav|div)([^>]*?)>', r'\\n<\\1\\2>', html_text)\n                html_text = re.sub(r'<(section|nav|div)([^>]*?)>\\s*', r'<\\1\\2>\\n', html_text)\n                html_text = re.sub(r'\\s*</(title|body|html)>\\s*', r'</\\1>\\n', html_text)\n                html_text = re.sub(r'\\s*</(h\\d|ul|ol|p|table|tr)>\\s*', r'</\\1>\\n\\n', html_text)\n                html_text = re.sub(r'\\s*<(b|h)r([^>]*?)/?>\\s*', r'<\\1r\\2/>\\n', html_text)\n                html_text = re.sub(r'<(meta|link)([^>]*?)/?>\\s*', r'<\\1\\2/>\\n', html_text)\n                html_text = re.sub(r'>\\n*<(body|h\\d|ul|ol|p|hr|table)( ?)', r'>\\n\\n<\\1\\2', html_text)\n                html_text = re.sub(r'<(body|table|tr)([^>]*?)>\\n*', r'<\\1\\2>\\n', html_text)\n                html_text = re.sub(r'<td([^>]*?)>\\n+', r'<td\\1>\\n', html_text)\n                html_text = re.sub(r'\\n+</td>', r'\\n</td>', html_text)\n                html_text = re.sub(r'\\s*</(div|section|nav|table|tr|ul|ol|body)>', r'\\n</\\1>', html_text)\n                html_text = re.sub(r'\\s*</head>\\s*', r'\\n</head>\\n\\n', html_text)\n                html_text = re.sub(r'\\s*</(body|style)>', r'\\n</\\1>', html_text)\n                html_text = re.sub(r'/html>\\s+', r'/html>', html_text)\n                html_text = re.sub(r' +', r' ', html_text)\n            return html_text\n\n        for name in container.get_html_names():\n            orig_html = container.get_raw(name)\n            html = orig_html\n            new_html = unpretty_for_page(html)\n            while html != new_html:\n                dirtied = True\n                html = new_html;\n                new_html = unpretty_for_page(html)\n            if orig_html != new_html:\n                container.set(name, new_html)\n                self.log('\\t  De-indented:', name)\n        return dirtied\n\n    def _remove_pagemaps(self, container):\n        self.log('\\tLooking for pagemaps')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot remove pagemaps from DRM encrypted book')\n            return False\n        dirtied = False\n        dirtied |= self._remove_gp_pagemaps(container)\n\n        for name in list(container.get_pagemap_names()):\n            mapcode = container.get_raw(name)\n            self.log('\\t  Removing pagemap file:', name)\n            container.delete_from_manifest(name)\n            dirtied = True\n            html = container.get_raw(container.opf_name)\n            new_html = re.sub(r'<spine page-map=\"([^\"]+?)\"', r'<spine', html)\n            if html != new_html:\n                container.set(container.opf_name, new_html)\n                dirtied = True\n        return dirtied\n\n    def _remove_gp_pagemaps(self, container):\n        self.log('\\tLooking for Google Play pagemaps')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot remove pagemaps from DRM encrypted book')\n            return False\n        dirtied = False\n        RE_GBS_ANCHOR1 = re.compile(r'<div( style=\"display:none;\")?>\\s*<a id=\"GBS\\.\\d+\\.\\d+\"/>\\s*</div>', re.UNICODE | re.IGNORECASE)\n        RE_GBS_ANCHOR2 = re.compile(r'<a id=\"GBS\\.\\d+\\.\\d+\"/>', re.UNICODE | re.IGNORECASE)\n\n        for name in list(container.get_pagemap_names()):\n            mapcode = container.get_raw(name)\n            gbscheck = re.compile(r'#GBS\\.\\d+\\.\\d+')\n            if gbscheck.search(mapcode) is not None:\n                for hname in container.get_html_names():\n                    html = container.get_raw(hname)\n                    new_html = RE_GBS_ANCHOR1.sub('', html)\n                    new_html = RE_GBS_ANCHOR2.sub('', new_html)\n                    if html != new_html:\n                        dirtied = True\n                        container.set(hname, new_html)\n                        html = new_html\n                        self.log('\\t  Removed Google Play anchors from:', hname)\n                self.log('\\t  Removing Google Play pagemap file:', name)\n                container.delete_from_manifest(name)\n                dirtied = True\n                html = container.get_raw(container.opf_name)\n                new_html = re.sub(r'<spine page-map=\"([^\"]+?)\"', r'<spine', html)\n                if html != new_html:\n                    container.set(container.opf_name, new_html)\n                    dirtied = True\n        return dirtied\n\n    def _strip_spans(self, container):\n        dirtied = False\n        self.log('\\tStripping spans')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot strip spans in DRM encrypted book')\n            return False\n\n        def strip_span_for_page(html_text):\n            HTML_ENTITY = []\n\n            html_text = re.sub(r'<(\\S+)([^/>]*?) style=\"display: ?none;\"([^/>]*?)></\\1>', r'', html_text)\n            html_text = re.sub(r'<(\\S+)([^/>]*?)></\\1>', r'<\\1\\2/>', html_text)\n            html_text = re.sub(r'<([^>]*?)(\\s+?)/>', r'<\\1/>', html_text)\n            html_text = re.sub(r'</(b|h)r>', r'', html_text)\n            html_text = re.sub(r'<(b|h)r([^/>]*?)/?>', r'<\\1r\\2/>', html_text)\n            html_text = re.sub(r'<(b|i|u|a|em|strong|span|big|small)/>', r'', html_text)\n            html_text = re.sub(r'<\\?dp([^>]*?)\\?>\\n?', r'', html_text)\n\n            entities = re.split(r'(<.+?>)', html_text)\n\n            total = 0\n            for entity in entities:\n                if entity:\n                    entity = container.decode(entity)\n                    total += 1\n                    this_entity = TAG()\n                    this_entity.content = entity\n                    if entity == u'<span>':\n                        this_entity.e_type = 9\n                    elif entity[-2:] == u'/>':\n                        this_entity.e_type = 3\n                    elif entity[0] != u'<':\n                        this_entity.e_type = 4\n                    elif entity[:2] == u'</':\n                        this_entity.e_type = 2\n                    else:\n                        this_entity.e_type = 1\n                    HTML_ENTITY.append(this_entity)\n\n            pos = -1\n            PAIR = 0\n            while pos < total-1:\n                pos+=1\n                if HTML_ENTITY[pos].e_type == 2:\n                    PAIR += 1\n                    HTML_ENTITY[pos].pair = PAIR\n                    pair_pos = pos\n                    while True:\n                        pair_pos += -1\n                        if pair_pos<0 : break\n                        e_type = HTML_ENTITY[pair_pos].e_type\n                        if e_type == 1 or e_type==9:\n                            if HTML_ENTITY[pair_pos].pair == 0:\n                                HTML_ENTITY[pair_pos].pair = PAIR\n                                if e_type == 9: HTML_ENTITY[pos].e_type = 9\n                                break\n\n            output = []\n            for entry in HTML_ENTITY:\n                if entry.e_type < 9:\n                    output.append(entry.content)\n\n            out_text = ''.join(output)\n            return out_text\n\n        for name in container.get_html_names():\n            orig_html = container.get_raw(name)\n            html = orig_html\n            new_html = strip_span_for_page(html)\n            while html != new_html:\n                dirtied = True\n                html = new_html;\n                new_html = strip_span_for_page(html)\n            if orig_html != new_html:\n                container.set(name, new_html)\n                self.log('\\t  Stripped spans in:', name)\n        return dirtied\n\n    def _strip_kobo(self, container):\n        dirtied = False\n        self.log('\\tStripping Kobo remnants')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot strip Kobo remnants in DRM encrypted book')\n            return False\n\n        RE_KOBO_META1 = re.compile(r'\\s*<!-- kobo-style -->', re.UNICODE | re.IGNORECASE)\n        RE_KOBO_META2 = re.compile(r'\\s*<script[^>]*? src=\"[^\"]*?js/kobo(|-android)\\.js\"(/|></script)>', re.UNICODE | re.IGNORECASE)\n        RE_KOBO_META3 = re.compile(r'\\s*<style[^>]*? id=\"kobo[\\s\\S]*?</style>', re.UNICODE | re.IGNORECASE)\n        RE_KOBO_META4 = re.compile(r'\\s*<link[^>]*? href=\"[^\"]*?css/kobo(|-android)\\.css\"[\\s\\S]*?(/|></link)>', re.UNICODE | re.IGNORECASE)\n        dirtied = False\n        for name in container.get_html_names():\n            html = container.get_raw(name)\n            new_html = RE_KOBO_META1.sub('', html)\n            new_html = RE_KOBO_META2.sub('', new_html)\n            new_html = RE_KOBO_META3.sub('', new_html)\n            new_html = RE_KOBO_META4.sub('', new_html)\n            if html != new_html:\n                dirtied = True\n                container.set(name, new_html)\n                self.log('\\t  Removed Kobo HEAD elements from:', name)\n\n        for name in list(container.name_path_map.keys()):\n            if name.lower().endswith('js/kobo.js'):\n                self.log('\\t  Removed kobo.js file:', name)\n                container.delete_from_manifest(name)\n                dirtied = True\n            elif name.lower().endswith('css/kobo.css'):\n                self.log('\\t  Removed kobo.css file:', name)\n                container.delete_from_manifest(name)\n                dirtied = True\n            elif name.lower() == 'rights.xml':\n                self.log('\\t  Removed rights.xml file:', name)\n                container.delete_from_manifest(name)\n                dirtied = True\n\n        def strip_kobo_for_page(html_text):\n            HTML_ENTITY = []\n\n            html_text = re.sub(r'<(\\S+)([^/>]*?)></\\1>', r'<\\1\\2/>', html_text)\n            html_text = re.sub(r'<([^>]*?)(\\s+?)/>', r'<\\1/>', html_text)\n            html_text = re.sub(r'<span([^>]+?) id=\"kobo([^\"]+?)\"', r'<span id=\"kobo\\2\"\\1', html_text)\n            html_text = re.sub(r'</(b|h)r>', r'', html_text)\n            html_text = re.sub(r'<(b|h)r([^/>]*?)/?>', r'<\\1r\\2/>', html_text)\n            html_text = re.sub(r'<(b|i|u|a|em|strong|span|big|small)/>', r'', html_text)\n\n            entities = re.split(r'(<.+?>)', html_text)\n\n            total = 0\n            for entity in entities:\n                if entity:\n                    entity = container.decode(entity)\n                    total += 1\n                    this_entity = TAG()\n                    this_entity.content = entity\n                    if entity[:15] == u'<span id=\"kobo.':\n                        this_entity.e_type = 9\n                    elif entity[-2:] == u'/>':\n                        this_entity.e_type = 3\n                    elif entity[0] != u'<':\n                        this_entity.e_type = 4\n                    elif entity[:2] == u'</':\n                        this_entity.e_type = 2\n                    else:\n                        this_entity.e_type = 1\n                    HTML_ENTITY.append(this_entity)\n\n            pos = -1\n            PAIR = 0\n            while pos < total-1:\n                pos+=1\n                if HTML_ENTITY[pos].e_type == 2:\n                    PAIR += 1\n                    HTML_ENTITY[pos].pair = PAIR\n                    pair_pos = pos\n                    while True:\n                        pair_pos += -1\n                        if pair_pos<0 : break\n                        e_type = HTML_ENTITY[pair_pos].e_type\n                        if e_type == 1 or e_type==9:\n                            if HTML_ENTITY[pair_pos].pair == 0:\n                                HTML_ENTITY[pair_pos].pair = PAIR\n                                if e_type == 9: HTML_ENTITY[pos].e_type = 9\n                                break\n\n            output = []\n            for entry in HTML_ENTITY:\n                if entry.e_type < 9:\n                    output.append(entry.content)\n\n            out_text = ''.join(output)\n            return out_text\n\n        for name in container.get_html_names():\n            html = container.get_raw(name)\n            new_html = strip_kobo_for_page(html)\n            if html != new_html:\n                dirtied = True\n                container.set(name, new_html)\n                self.log('\\t  Stripped Kobo spans in:', name)\n        return dirtied\n\n    def _remove_javascript(self, container):\n        dirtied = False\n        self.log('\\tLooking for inline javascript blocks to remove')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot remove javascript from DRM encrypted book')\n            return False\n        for name in container.get_html_names():\n            html = container.get_parsed_etree(name)\n            try:\n                scripts = XPath('//h:script[@type=\"text/javascript\"]')(html)\n            except:\n                scripts = []\n            if scripts:\n                for script in scripts:\n                    script.getparent().remove(script)\n                    self.log('\\t  Removed script block from:', name)\n                dirtied = True\n                container.set(name, html)\n\n        self.log('\\tLooking for .js files to remove')\n        for name in list(container.name_path_map.keys()):\n            if name.lower().endswith('.js'):\n                self.log('\\t  Found .js file to remove:', name)\n                container.delete_from_manifest(name)\n                dirtied = True\n        return dirtied\n\n    def _remove_broken_covers(self, container):\n        dirtied = False\n        self.log('\\tLooking for html pages containing only broken image links')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot remove broken covers from DRM encrypted book')\n            return False\n\n        avail_image_names = {x.lower() : True for x in container.get_image_names()}\n        if not avail_image_names:\n            return False\n\n        names_to_delete = []\n\n        for html_name in container.get_html_names():\n            delete_candidate = False\n            for image_name, orig_href, _node in container.get_page_image_names(html_name):\n                if image_name.lower() in avail_image_names:\n                    # This page has at least one valid link\n                    delete_candidate = False\n                    break\n                else:\n                    # This page has a broken link\n                    self.log('\\t    Broken image link: \"%s\" in: %s'%(orig_href, html_name))\n                    delete_candidate = True\n\n            if delete_candidate:\n                names_to_delete.append(html_name)\n\n        for html_name in names_to_delete:\n            # Verify there is no other text within the body of this document.\n            if container.get_body_text(html_name):\n                self.log('\\t  Body contains other text so will not be removed:', html_name)\n                continue\n            else:\n                dirtied = True\n                self.log('\\t  Removing html containing only broken image link:', html_name)\n                container.delete_from_manifest(html_name)\n        return dirtied\n\n    def _remove_cover(self, container):\n        self.log('\\tRemove cover')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot remove cover in DRM encrypted book')\n            return False\n        cu = CoverUpdater(self.log, container, None, None)\n        cu.remove_existing_cover()\n        return True\n\n    def _insert_replace_cover(self, container):\n        self.log('\\tInsert or replace cover')\n        if container.is_drm_encrypted():\n            self.log('ERROR - cannot insert/replace cover in DRM encrypted book')\n            return False\n        if not self.cover_path:\n            self.log('\\t  ERROR - no cover image assigned to this book in the library')\n            return False\n\n        cu = CoverUpdater(self.log, container, self.cover_path, self.opts)\n        cu.insert_or_replace_cover()\n        return True\n\n    def _setup_user_options(self):\n        '''\n        Initialise the self.opts which is required for passing to some of the\n        tasks within this plugin that are utilising calibre pipeline code or\n        are wanting to lookup the user's default values\n        '''\n        def get_user_margins():\n            default_margins = {\n                'margin_right' : 5.0,\n                  'margin_top' : 5.0,\n                 'margin_left' : 5.0,\n               'margin_bottom' : 5.0,\n                        }\n            prefs_margins = {}\n\n            from calibre.ebooks.conversion.config import load_defaults\n            ps = load_defaults('page_setup')\n            if 'margin_top' in ps:\n                prefs_margins = ps\n            else:\n                prefs_margins = default_margins\n\n            for s, v in six.iteritems(prefs_margins):\n                setattr(self.opts, s, v)\n\n        def get_epub_output_options():\n            default_values = {\n                'preserve_cover_aspect_ratio' : False,\n                'no_svg_cover' : False\n                        }\n            prefs_options = {}\n\n            from calibre.ebooks.conversion.config import load_defaults\n            ps = load_defaults('epub_output')\n            if 'preserve_cover_aspect_ratio' in ps:\n                prefs_options = ps\n            else:\n                prefs_options = default_values\n\n            for s, v in six.iteritems(prefs_options):\n                setattr(self.opts, s, v)\n\n        self.opts = OptionValues()\n        get_user_margins()\n        get_epub_output_options()\n        self.opts.output_profile = self._get_output_profile()\n        self.opts.dest = self.opts.output_profile\n\n    def _get_output_profile(self):\n        from calibre.ebooks.conversion.config import load_defaults\n        from calibre.customize.ui import output_profiles\n        ps = load_defaults('page_setup')\n        output_profile_name = 'default'\n        if 'output_profile' in ps:\n            output_profile_name = ps['output_profile']\n        for x in output_profiles():\n            if x.short_name == output_profile_name:\n                return x\n        self.log.warn('Output Profile %s is no longer available, using default'%output_profile_name)\n        for x in output_profiles():\n            if x.short_name == 'default':\n                return x\n","repo_name":"kiwidude68/calibre_plugins","sub_path":"modify_epub/modify.py","file_name":"modify.py","file_ext":"py","file_size_in_byte":42779,"program_lang":"python","lang":"en","doc_type":"code","stars":114,"dataset":"github-code","pt":"80"}
+{"seq_id":"571839585","text":"import socket\nfrom select import select\n\nfor_read = []\nfor_write = []\nready_to_read = []\nready_to_write = []\n\nserver_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserver_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\nserver_socket.bind((\"localhost\", 5001))\nserver_socket.listen()\n\nfor_read.append(server_socket)\n\nready_to_read, ready_to_write, _ = select(for_read, for_write, [])\n\nclient_socket: socket.socket\n\nif server_socket in ready_to_read:\n    client_socket, address = server_socket.accept()\n    for_read.append(client_socket)\n    print(\"receive connection from:\", address)\n\nprint(\"before 1 select\")\nready_to_read, ready_to_write, _ = select(for_read, for_write, [])\n\nif client_socket in ready_to_read:\n    buf = client_socket.recv(4096)\n    print(buf.decode())\n\nprint(\"before 2 select\")\nready_to_read, ready_to_write, _ = select(for_read, for_write, [])\n\nif client_socket in ready_to_read:\n    buf = client_socket.recv(4096)\n    print(buf.decode())\n\n\n","repo_name":"viktor471/training","sub_path":"python/net/tcpip/server/test/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"33090181653","text":"import numpy as np\n\ndef tsdiff(ts):\n    \"\"\"\n    apply backward difference to time series ts\n    \"\"\"\n    ts1=np.zeros(np.shape(ts))\n    nr=len(ts1)\n    ts1[0]=ts[0]\n    j=1\n    while j<nr:\n        ts1[j]=ts[j]-ts[j-1]\n        j=j+1\n    return ts1\n\nclass histd(object):\n\n    \"\"\"\n    define class of daily output\n    \"\"\"\n    def __init__(self,vars):\n        \"\"\"\n        \"\"\"\n        self._vars=vars\n        self.nvars=len(vars)\n        self._data=np.zeros((self.nvars,366))\n        self.recs=0\n    def add_record(self,datav):\n        \"\"\"\n        add a new data record\n        \"\"\"\n#        print(datav)\n        self._data[:,self.recs]=datav\n#        print(self._data[:,self.recs])\n        self.recs=self.recs+1\n\n    def get_tsvarj(self,j):\n        \"\"\"\n        get time series for the jth variable\n        \"\"\"\n        if j>self.nvars:\n            raise RuntimeError(\"The queried variable is outside the list\")\n        return np.reshape(self._data[j-1,0:self.recs],(self.recs))\n\n    def get_tsvars(self,var):\n        \"\"\"\n        get time series of variable var\n        \"\"\"\n        j=0\n        for v in self._vars:\n            if v==var:\n#                print('j=%d,%s'%(j,self._vars[j]))\n#                print(self._data[j-1,0:self.recs])\n                return np.reshape(self._data[j,0:self.recs],(self.recs))\n            else:\n                j=j+1\n        raise RuntimeError(\"The queried variable is not in the list\")\n\ndef ischar(c):\n    \"\"\"\n    determine if c is a legitimate\n    \"\"\"\n    if c=='_' or c=='[' or c==']' or c=='.' or c=='/' or c=='+':\n        return True\n    if ord(c)>=ord('0') and ord(c)<=ord('9'):\n        return True\n    if ord(c)>=ord('a') and ord(c)<=ord('z'):\n        return True\n    if ord(c)>=ord('A') and ord(c)<=ord('Z'):\n        return True\n    return False\n\ndef getvarls(tline):\n    \"\"\"\n    get list of variables\n    \"\"\"\n    docp=False\n    v=''\n    vl=[]\n    nvar=0\n    for c in tline:\n        if ischar(c):\n            if not docp:\n                docp=True\n            v=v+c\n        else:\n            if docp:\n                vl.append(v)\n                docp=False\n                v=''\n                nvar=nvar+1\n    if v:\n        vl.append(v)\n        nvar=nvar+1\n    return vl,nvar\n\ndef dcread(fnm):\n    \"\"\"\n    read daily carbon output file\n    \"\"\"\n    with open(fnm,\"r\") as infile:\n        line=infile.readline()\n        tline=line.strip()\n        vars,nvars=getvarls(tline)\n        print(\"totally %d variables read in, including\"%nvars)\n        j=0\n        for var in vars:\n            j=j+1\n            print('%d: %s'%(j,var))\n        dchist=histd(vars)\n\n        line=infile.readline()\n        while line:\n            tline=line.strip()\n#            print(tline)\n            sarr=tline.split()\n            datav=np.zeros(nvars)\n            for n in range(nvars):\n#                print('%d,%s'%(n,sarr[n]))\n                datav[n]=float(sarr[n])\n            dchist.add_record(datav)\n            line=infile.readline()\n    return dchist\n\n\n#dchist1=dcread('/Users/jinyuntang/work/ecosys_sims/run_2017_and_new/outputs/010101998dc')\n#dchist2=dcread('/Users/jinyuntang/work/ecosys_sims/run_2017_and_new/outputs_2017_code/010101998dc')\n\ndchist2=dcread('/Users/jinyuntang/work/ecosys_sims/point1pt_outputs/010102008dc')\n\n#rh1=dchist1.get_tsvars('ECO_RH')\n#rh1=tsdiff(rh1)\n\nrh2=dchist2.get_tsvars('ECO_RH')\nrh2=tsdiff(rh2)\n\ndoy=dchist2.get_tsvarj(1)\n\nimport matplotlib.pyplot as plt\n\n#plt.plot(doy,rh2,doy,rh1)\nplt.plot(doy,rh2)\nplt.show()\n","repo_name":"jinyun1tang/EcoSIM","sub_path":"python_tools/dcreader.py","file_name":"dcreader.py","file_ext":"py","file_size_in_byte":3469,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"80"}
+{"seq_id":"14598492194","text":"import pynecone as pc\n\nfrom pcweb.base_state import State\nfrom pcweb.templates.docpage import (\n    docdemo,\n    doctext,\n    demo_box_style,\n    doccode,\n    doclink,\n    subheader,\n)\n\ninput_state = \"\"\"class InputState(State):\n    text: str = \"Type something...\"\n\"\"\"\nbasic_input_example = \"\"\"pc.vstack(\n    pc.text(InputState.text, color_scheme=\"green\"),\n    pc.input(on_change=InputState.set_text)\n)\n\"\"\"\nexec(input_state)\n\ninput_blur_state = \"\"\"class InputBlurState(State):\n    text: str = \"Type something...\"\n\n    def set_text(self, text):\n        self.text = text.upper()\n\"\"\"\nblur_input_example = \"\"\"pc.vstack(\n    pc.text(InputBlurState.text),\n    pc.input(placeholder=\"Type something...\", on_blur=InputBlurState.set_text)\n)\n\"\"\"\nexec(input_blur_state)\nclear_input_state = \"\"\"\nclass ClearInputState(State):\n    text: str\n\n    def clear_text(self):\n        self.text = \"\"\n\"\"\"\nclear_input_example = \"\"\"pc.vstack(\n    pc.input(\n        value=ClearInputState.text,\n        on_change=ClearInputState.set_text,\n    ),\n    pc.button(\"Clear\", on_click=ClearInputState.clear_text),\n)\n\"\"\"\n\nexec(clear_input_state)\nkey_press_state = \"\"\"\nclass KeyPressInputState(State):\n    text: str\n\n    def clear_text(self):\n        self.text = \"\"\n\n    def on_key_down(self, key):\n        if key == \"Enter\":\n            self.text = self.text.upper()\n\"\"\"\nexec(key_press_state)\nkey_press_example = \"\"\"pc.input(\n    placeholder=\"Type and press enter...\",\n    value=KeyPressInputState.text,\n    on_change=KeyPressInputState.set_text,\n    on_key_down=KeyPressInputState.on_key_down,\n)\n\"\"\"\ninput_type_example = \"\"\"pc.vstack(\n    pc.input(type_=\"password\"),\n    pc.input(type_=\"date\"),\n)\"\"\"\npassword_example = \"\"\"pc.password()\"\"\"\n\n\ndef render_input():\n    return pc.vstack(\n        doctext(\"The input component is used to receive text input from the user.\"),\n        docdemo(\n            basic_input_example,\n            state=input_state,\n            comp=eval(basic_input_example),\n            context=True,\n        ),\n        doctext(\n            \"The input component can also use the \",\n            pc.code(\"on_blur\"),\n            \" event handler to only change the state when the user clicks away from the input. This is useful for performance reasons, as the state will only be updated when the user is done typing.\",\n        ),\n        docdemo(\n            blur_input_example,\n            state=input_blur_state,\n            comp=eval(blur_input_example),\n            context=True,\n        ),\n        doctext(\n            \"The input component can also be hooked up to a state using the \",\n            pc.code(\"value\"),\n            \" prop. \",\n            \"This lets you control the value of the input from the state.\",\n        ),\n        docdemo(\n            clear_input_example,\n            state=clear_input_state,\n            comp=eval(clear_input_example),\n            context=True,\n        ),\n        doctext(\n            \"You can also use the \",\n            pc.code(\"on_key_down\"),\n            \" and \",\n            pc.code(\"on_key_up\"),\n            \" event handlers to listen for key presses.\",\n        ),\n        docdemo(\n            key_press_example,\n            state=key_press_state,\n            comp=eval(key_press_example),\n            context=True,\n        ),\n        doctext(\n            \"You can change the type of input by using the \",\n            pc.code(\"type_\"),\n            \" prop. For example you can create a password input or a date picker. \",\n        ),\n        docdemo(input_type_example),\n        doctext(\n            \"We also provide a \",\n            pc.code(\"pc.password\"),\n            \" component as a shorthand for the password input.\",\n        ),\n        docdemo(password_example),\n        align_items=\"start\",\n    )\n","repo_name":"cturner-confluent/pcweb","sub_path":"pcweb/pages/docs/component_lib/forms/input.py","file_name":"input.py","file_ext":"py","file_size_in_byte":3727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"80"}
+{"seq_id":"7083382889","text":"class Solution:\n    def mergeTriplets(self, triplets: List[List[int]], target: List[int]) -> bool:\n        res = set()\n        for trip in triplets:\n            # if any of the current triplets values are greater than the current targets,\n            # we don't want to use it to max out, since it won't work\n            if trip[0] > target[0] or trip[1] > target[1] or trip[2] > target[2]:\n                continue # skip iteration\n            for i, val in enumerate(trip):\n                if val == target[i]:\n                    res.add(i) # has to be a set so that you can add indices without dupes\n        \n        return len(res) == 3","repo_name":"andrewkvu/leetcode_grind","sub_path":"1899-merge-triplets-to-form-target-triplet/1899-merge-triplets-to-form-target-triplet.py","file_name":"1899-merge-triplets-to-form-target-triplet.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"74715883779","text":"import os\nimport itertools\nimport subprocess\nimport shutil\nimport json\n\nfrom myhdl import Cosimulation, Signal, intbv\nimport pytest\n\nfrom common_util import ROOT_DIR, IMPL_DIR, TB_DIR, spikehard_named_params\n\n\n@pytest.fixture(autouse=True)\ndef expose_markers_option_fixture(request, pytestconfig):\n  request.cls.markers_option = pytestconfig.getoption('-m')\n  request.cls.fast_marker = \"fast\" in request.cls.markers_option\n  request.cls.ci_marker = \"ci\" in request.cls.markers_option\n\n\nclass basic_test_util():\n  @staticmethod\n  def run_subtests(testcase, run_test=None, keys=None, all_args=None, **kwargs):\n    if run_test is None:\n      run_test = testcase.run_test\n\n    if (keys is None) != (all_args is None):\n      raise Exception(\"keys is specified if and only if all_args is specified\")\n\n    if keys is None:\n      all_args = itertools.product(*kwargs.values())\n      keys = list(kwargs.keys())\n      test_count = 1\n      for v in kwargs.values():\n        test_count *= len(v)\n    else:\n      test_count = len(all_args)\n\n    for test_idx, args in enumerate(all_args):\n      subtest_kwargs = dict(zip(keys, args))\n      with testcase.subTest(**subtest_kwargs):\n        testcase.logger.debug(\"==>> running subtest ({}/{}) - args: ({}) <<==\".format(test_idx + 1,\n                              test_count, \", \".join([\"{} = {}\".format(k, v) for k, v in subtest_kwargs.items()])))\n        run_test(**subtest_kwargs)\n\n  @staticmethod\n  def correct_filepath(test_params):\n    return os.path.join(test_params.memory_filepath, 'tb_correct.txt')\n\n  @staticmethod\n  def input_filepath(test_params):\n    return os.path.join(test_params.memory_filepath, 'tb_input.txt')\n\n  @staticmethod\n  def num_inputs_filepath(test_params):\n    return os.path.join(test_params.memory_filepath, 'tb_num_inputs.txt')\n\n  @staticmethod\n  def num_outputs_filepath(test_params):\n    return os.path.join(test_params.memory_filepath, 'tb_num_outputs.txt')\n\n  @staticmethod\n  def spikehard_test_params_filepath(memory_filepath):\n    return os.path.join(memory_filepath, 'tb_spikehard_params.json')\n\n  @staticmethod\n  def gen_test_params(model_name, altered=False, num_axons=256, num_neurons=256, num_ticks_to_check=None, dma_bus_width=32, dma_frame_header_word_width=32, router_buffer_depth=4, clock_cycles_per_tick=None):\n    if num_axons == num_neurons:\n      test_name = str(num_axons)\n    else:\n      test_name = '{}_{}'.format(num_axons, num_neurons)\n\n    memory_filepath = '{}/memory_files/{}{}/{}'.format(TB_DIR, \"altered/\" if altered else \"\", model_name, test_name)\n    if not os.path.exists(memory_filepath):\n      print('unrecognised axon/neuron combination')\n      quit()\n\n    with open(basic_test_util.spikehard_test_params_filepath(memory_filepath), 'r') as f:\n      config_json_data = json.loads(f.read())\n\n    if num_ticks_to_check is not None:\n      config_json_data[\"num_ticks_to_check\"] = num_ticks_to_check\n    if clock_cycles_per_tick is not None:\n      config_json_data[\"clock_cycles_per_tick\"] = clock_cycles_per_tick\n\n    test_params = spikehard_named_params(**config_json_data,\n                                         dma_bus_width=dma_bus_width,\n                                         dma_frame_header_word_width=dma_frame_header_word_width,\n                                         router_buffer_depth=router_buffer_depth,\n                                         memory_filepath=memory_filepath)\n\n    return test_params\n\n\nclass myhdl_util():\n  @staticmethod\n  def gen_cosimulation(dut, work_dir, params, *ports, src_dirs=None):\n    if src_dirs is None:\n      src_dirs = [IMPL_DIR]\n\n    cmd = \"iverilog -o {}.o\".format(dut)\n\n    for d in src_dirs:\n      cmd += \" -I {}\".format(d)\n\n    for field in params._fields:\n      value = getattr(params, field)\n      try:\n        v = int(value)\n      except:\n        if isinstance(value, str):\n          cmd += \" -D{}=\\\\\\\"{}\\\\\\\"\".format(field.upper(), value)\n        continue\n      cmd += \" -D{}={}\".format(field.upper(), value)\n\n    cmd += \" -s {}\".format(dut)\n\n    for d in src_dirs:\n      cmd += \" {}/*.v\".format(d)\n    cmd += \" {}/{}.v\".format(work_dir, dut)\n\n    cmd += \" -g2005-sv\"\n\n    subprocess.run(cmd, check=True, shell=True)\n\n    ports_dict = {}\n    for ps in ports:\n      ports_dict.update(ps._asdict())\n\n    return Cosimulation(\"vvp -m {}/iverilog/myhdl.vpi {}.o\".format(TB_DIR, dut), **ports_dict)\n\n  @staticmethod\n  def gen_signal(num_bits, num_signals=1):\n    if num_signals == 1:\n      if num_bits == 1:\n        return Signal(bool(0))\n      else:\n        return Signal(intbv(0, min=0, max=(2**num_bits) - 1)[num_bits:0])\n    else:\n      return [myhdl_util.gen_signal(num_bits) for _ in range(num_signals)]\n\n  @staticmethod\n  def to_int(signal) -> int:\n    if hasattr(signal, 'val'):\n      return int(bin(signal.val), 2)\n    else:\n      return int(bin(signal), 2)\n\n  @staticmethod\n  def to_intbv(value, num_bits):\n    v = intbv(value, min=0, max=(2**num_bits) - 1)\n    assert len(v) == num_bits\n    return v\n\n  @staticmethod\n  def wait_for(clk, signal, value=1, timeout=100000):\n    if timeout is None:\n      while myhdl_util.to_int(signal) != value:\n        yield clk.posedge\n      return\n    else:\n      for _ in range(timeout):\n        if myhdl_util.to_int(signal) == value:\n          return\n        yield clk.posedge\n      raise TimeoutError(\"timed out whilst waiting for signal\")\n\n  @staticmethod\n  def initialise_accelerator(testcase, input_ports, output_ports, tx_size=2 ** 31, rx_size=2 ** 31):\n    # Initialise inputs\n    input_ports.conf_done.next = 0\n    input_ports.dma_read_ctrl_ready.next = 0\n    input_ports.dma_read_chnl_valid.next = 0\n    input_ports.dma_write_ctrl_ready.next = 0\n    input_ports.dma_write_chnl_ready.next = 0\n\n    # Waiting for nothing\n    yield input_ports.clk.posedge\n    yield input_ports.clk.posedge\n\n    # Initialise accelerator\n    yield input_ports.clk.posedge\n    input_ports.rst.next = 0\n    for _ in range(15):\n      yield input_ports.clk.posedge\n    yield input_ports.clk.posedge\n    input_ports.rst.next = 1\n    for _ in range(5):\n      yield input_ports.clk.posedge\n    yield input_ports.clk.posedge\n    if hasattr(input_ports, \"conf_info_tx_size\"):\n      input_ports.conf_info_tx_size.next = tx_size\n    if hasattr(input_ports, \"conf_info_rx_size\"):\n      input_ports.conf_info_rx_size.next = rx_size\n    input_ports.conf_done.next = 1\n    yield input_ports.clk.posedge\n    input_ports.conf_done.next = 0\n    yield input_ports.clk.posedge\n\n  @staticmethod\n  def __dma_word_size(word_width) -> int:\n    return {8: 0, 16: 1, 32: 2, 64: 3}[word_width]\n\n  @staticmethod\n  def service_read_request(testcase,\n                           input_ports,\n                           output_ports,\n                           read_offset,\n                           read_length,\n                           dma_bus_width,\n                           word_width,\n                           make_read_word,\n                           noc_delay=None,\n                           timeout=100000):\n    if noc_delay is None:\n      def zero() -> int:\n        return 0\n\n      noc_delay = zero\n\n    dma_bus_mask = (2 ** dma_bus_width) - 1\n    wait_for = lambda *args: myhdl_util.wait_for(input_ports.clk, *args, timeout=timeout)\n\n    def make_read_data(beat_idx):\n      if dma_bus_width >= word_width:\n        v = 0\n        words_per_beat = dma_bus_width // word_width\n        for i in range(words_per_beat):\n          v |= make_read_word(beat_idx * words_per_beat + i) << (i * word_width)\n      else:\n        beats_per_word = word_width // dma_bus_width\n        v = (make_read_word(beat_idx // beats_per_word) >> ((beat_idx % beats_per_word) * dma_bus_width)) & dma_bus_mask\n\n      return myhdl_util.to_intbv(v, dma_bus_width)\n\n    for _ in range(noc_delay()):\n      yield input_ports.clk.posedge\n\n    # Wait for (dma_read_ctrl_valid && have dma_read_ctrl_ready).\n    input_ports.dma_read_ctrl_ready.next = 1\n    yield input_ports.clk.posedge\n    yield from wait_for(output_ports.dma_read_ctrl_valid)\n    input_ports.dma_read_ctrl_ready.next = 0\n\n    # Check that length, word size, etc are as expected.\n    testcase.assertEqual(myhdl_util.to_int(output_ports.dma_read_ctrl_data_index), read_offset)\n    testcase.assertEqual(myhdl_util.to_int(output_ports.dma_read_ctrl_data_length), read_length)\n    testcase.assertEqual(myhdl_util.to_int(output_ports.dma_read_ctrl_data_size),\n                         myhdl_util.__dma_word_size(word_width))\n\n    # Start sending data immediately afterwards with random delay between packets.\n    for beat_idx in range(read_length):\n      # Wait a predetermined or random number of clock ticks before sending data.\n      for _ in range(noc_delay()):\n        yield input_ports.clk.posedge\n\n      # Send next beat of data\n      input_ports.dma_read_chnl_data.next = make_read_data(beat_idx)\n      input_ports.dma_read_chnl_valid.next = 1\n      yield input_ports.clk.posedge\n\n      # Wait for (dma_read_chnl_valid && have dma_read_chnl_ready).\n      yield from wait_for(output_ports.dma_read_chnl_ready)\n      input_ports.dma_read_chnl_valid.next = 0\n\n  @staticmethod\n  def service_write_request(testcase,\n                            input_ports,\n                            output_ports,\n                            write_offset,\n                            write_length,\n                            dma_bus_width,\n                            word_width,\n                            check_write_word,\n                            noc_delay=None,\n                            timeout=100000):\n    if noc_delay is None:\n      def zero() -> int:\n        return 0\n\n      noc_delay = zero\n\n    word_mask = (2 ** word_width) - 1\n    wait_for = lambda *args: myhdl_util.wait_for(input_ports.clk, *args, timeout=timeout)\n\n    write_word = 0\n\n    def check_write_data(beat_idx, value):\n      nonlocal write_word\n\n      value = myhdl_util.to_int(value)\n      if dma_bus_width >= word_width:\n        words_per_beat = dma_bus_width // word_width\n        for i in range(words_per_beat):\n          word_idx = beat_idx * words_per_beat + i\n          word_value = (value >> (i * word_width)) & word_mask\n          check_write_word(word_idx, word_value)\n      else:\n        beats_per_word = word_width // dma_bus_width\n        if (beat_idx % beats_per_word) == 0:\n          write_word = 0\n        write_word |= (value << (dma_bus_width * (beat_idx % beats_per_word)))\n        if ((beat_idx + 1) % beats_per_word) == 0:\n          check_write_word(beat_idx // beats_per_word, write_word)\n\n    for _ in range(noc_delay()):\n      yield input_ports.clk.posedge\n\n    # Wait for (dma_write_ctrl_valid && have dma_write_ctrl_ready).\n    input_ports.dma_write_ctrl_ready.next = 1\n    yield input_ports.clk.posedge\n    yield from wait_for(output_ports.dma_write_ctrl_valid)\n    input_ports.dma_write_ctrl_ready.next = 0\n\n    # Check that length, word size, etc are as expected.\n    testcase.assertEqual(myhdl_util.to_int(output_ports.dma_write_ctrl_data_index), write_offset)\n    testcase.assertEqual(myhdl_util.to_int(output_ports.dma_write_ctrl_data_length), write_length)\n    testcase.assertEqual(myhdl_util.to_int(output_ports.dma_write_ctrl_data_size),\n                         myhdl_util.__dma_word_size(word_width))\n\n    # Start reading data immediately afterwards with random delay between when ready.\n    for beat_idx in range(write_length):\n      # Wait a predetermined or random number of clock ticks before ready to receive data.\n      for _ in range(noc_delay()):\n        yield input_ports.clk.posedge\n      input_ports.dma_write_chnl_ready.next = 1\n      yield input_ports.clk.posedge\n\n      # Wait for (dma_write_chnl_ready && have dma_write_chnl_valid).\n      yield from wait_for(output_ports.dma_write_chnl_valid)\n      input_ports.dma_write_chnl_ready.next = 0\n\n      # Validate.\n      check_write_data(beat_idx, output_ports.dma_write_chnl_data)\n","repo_name":"sld-columbia/spikehard","sub_path":"hardware/util/test_util.py","file_name":"test_util.py","file_ext":"py","file_size_in_byte":11848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"39894168219","text":"n=int(input())\nfor x in range(0,n):\n    numbers=list(map(int,input().split(\" \")))\n    a=numbers[0]\n    b=numbers[1]\n    sum=0\n    for i in range(1,b+1):\n        sum=sum+i\n    if sum>a:\n        print(0)\n    else:\n        print(1)","repo_name":"AdamZhouSE/pythonHomework","sub_path":"Code/CodeRecords/2207/60638/252078.py","file_name":"252078.py","file_ext":"py","file_size_in_byte":228,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"80"}
+{"seq_id":"21580632171","text":"import unittest\nfrom unittest.mock import patch, MagicMock\nfrom asset_manager.connectors.KrakenConnector import KrakenConnector\nfrom asset_manager.assets.Crypto import Crypto\nfrom asset_manager.mappers.PriceMapper import PriceMapper\nfrom asset_manager.mappers.MapperConnection import MapperConnection\nimport pandas as pd\n\nclass CryptoTest(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        MapperConnection(\"testengine\")\n\n    def setUp(self):\n        self.prices = pd.read_pickle(\"tests/test_data/etheur.pkl\")\n        self.crypto1 = Crypto(\"T\")\n        self.crypto2 = Crypto(\"T2\")\n\n        self.last_date = self.prices.index[-1] \n\n    def test_update_prices(self):\n        self.crypto1.kraken_connector.get_prices = MagicMock(return_value=\"UP-TO-DATE\")\n        with patch.object(PriceMapper, \"get_last_saved_date\", return_value=self.last_date):\n            self.crypto1.update_prices(1)\n        self.crypto1.kraken_connector.get_prices.assert_called_with(self.last_date, \"XXTEUR\", 1)\n\n        self.crypto2.kraken_connector.get_prices = MagicMock(return_value=\"UP-TO-DATE\")\n        with patch.object(PriceMapper, \"get_last_saved_date\", return_value=self.last_date):\n            self.crypto2.update_prices(5)\n        self.crypto2.kraken_connector.get_prices.assert_called_with(self.last_date, \"XT2EUR\", 5)\n\n        self.crypto2.kraken_connector.get_prices = MagicMock(return_value=self.prices[:5])\n        self.crypto2.price_mapper.save_prices = MagicMock(return_value=True)\n        with patch.object(PriceMapper, \"get_last_saved_date\", return_value=self.prices.index[0]):\n            self.crypto2.update_prices(1)\n        self.assertEqual(\"T2\", self.crypto2.price_mapper.save_prices.call_args[0][0])\n        self.assertEqual(1 , self.crypto2.price_mapper.save_prices.call_args[0][2])\n        self.assertTrue(self.prices[:5].equals(self.crypto2.price_mapper.save_prices.call_args[0][1]))\n\n        self.crypto2.kraken_connector.get_prices = MagicMock(return_value=self.prices[5:])\n        self.crypto2.price_mapper.save_prices = MagicMock(return_value=True)\n        with patch.object(PriceMapper, \"get_last_saved_date\", return_value=self.prices.index[4]):\n            self.crypto2.update_prices(1)\n        self.assertTrue(self.prices.equals(self.crypto2.prices), \"Prices df was not appended correctly to the existing prices table.\")\n    \n    def test_clean_prices(self):\n        one_gap = pd.read_pickle(\"tests/test_data/gaps.pkl\")\n        two_gaps = one_gap.drop(one_gap.index[2])\n        one_gap_filled = pd.read_pickle(\"tests/test_data/gaps_filled.pkl\")\n        two_gaps_filled = pd.read_pickle(\"tests/test_data/two_gaps_filled.pkl\")\n\n        self.crypto1.prices=one_gap\n        self.crypto1.clean_prices()\n        self.assertTrue(self.crypto1.prices.equals(one_gap_filled), \"The gaps in the prices table were not filled correctly.\")\n        self.assertTrue(self.crypto1.prices.loc[self.crypto1.prices.origin==\"O\"].equals(one_gap), \"The original data was altered during the cleaning process.\")\n\n        self.crypto1.prices=two_gaps\n        self.crypto1.clean_prices()\n        self.assertTrue(self.crypto1.prices.equals(two_gaps_filled), \"The gaps in the prices table were not filled correctly.\")\n        self.assertTrue(self.crypto1.prices.loc[self.crypto1.prices.origin==\"O\"].equals(two_gaps), \"The original data was altered during the cleaning process.\")","repo_name":"miquel-vv/asset_manager","sub_path":"tests/asset_tests/test_crypto.py","file_name":"test_crypto.py","file_ext":"py","file_size_in_byte":3376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"43015020140","text":"import scrapy\n\nclass PostsSpider(scrapy.Spider):\n    name = 'posts'\n    allowed_domains = ['djpen.kemendag.go.id']\n    start_urls = ['http://djpen.kemendag.go.id/app_frontend/imp_profiles/view/1']\n\n\n    def parse(self, response):\n        page_number = 1\n        while page_number<15002:\n            self.link = f'http://djpen.kemendag.go.id/app_frontend/imp_profiles/view/{page_number}'\n            page_number += 1\n            yield scrapy.Request(url = self.link, callback = self.parse_item)\n\n\n    def parse_item(self, response):\n        title = response.css(\"#body > div > div.left.grid_9 > h1 ::text\").get()\n        address = ''.join(response.css(\"#body > div > div.left.grid_9 > div.article > div > p:nth-child(1) ::text\").getall())\n        address = address.replace(\" \",\"\")\n        address = address.replace(\"\\t\",\"\")\n        address = address.replace(\"\\n\",\"\")\n        detail = response.css(\".detail\")\n        full_desc = ''.join(response.css(\".detail ::text\").getall())\n        full_desc = full_desc.replace(\" \",\"\")\n        full_desc = full_desc.replace(\"\\t\",\"\")\n        full_desc = full_desc.replace(\"\\n\",\"\")\n        data = {}\n        if title:\n            data['page'] = self.link[-1]\n            data['title'] = title\n            data['address'] = address\n            data['phone'] = detail.xpath(\"p[2]/text()\").get()\n            data['fax'] = detail.xpath(\"p[3]/text()\").get()\n            data['email'] = detail.xpath(\"p[4]/text()\").get()\n            data['website'] = detail.xpath(\"p[5]/text()\").get()\n            data['contact'] = response.css(\"#body > div > div.left.grid_9 > div.article > div > ul:nth-child(7) > li ::text\").getall()\n            data['products'] = response.css(\"#body > div > div.left.grid_9 > div.article > div > ul:nth-child(9) > li ::text\").getall()\n            data['full_desc'] = full_desc\n        else:\n            data.remove()\n        yield data","repo_name":"arrlanyhars/scraping-djpen-scrapy","sub_path":"djpen/spiders/posts.py","file_name":"posts.py","file_ext":"py","file_size_in_byte":1884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"31715468005","text":"#*******\n#* Read input from STDIN\n#* Use: echo or print to output your result to STDOUT, use the /n constant at the end of each result line.\n#* Use: sys.stderr.write() to display debugging information to STDERR\n#* ***/\nimport sys\n\nlines = []\nfor line in sys.stdin:\n\tlines.append(line.rstrip('\\n'))\n\np1_lat, p1_lng, p2_lat, p2_lng = map (float, lines[0].split())\n\nnb_ppl = int (lines[1])\n\nnb_ppl_in = 0\nfor ppl in lines [2:nb_ppl+2]:\n    ppl_lat, ppl_lng = map (float, ppl.split (\" \")) \n    if p1_lat <= ppl_lat <= p2_lat and p1_lng <= ppl_lng <= p2_lng:\n        nb_ppl_in += 1\n\nprint (str(nb_ppl_in))","repo_name":"mcandries/b-dev-test","sub_path":"M2016_ex2/M2016_ex2.py","file_name":"M2016_ex2.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"22078755427","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nfrom tensorflow.python.training import training_util\nimport ray\nfrom ray.tune.result import TrainingResult\nfrom ray.tune.trainable import Trainable\nfrom ray.tune.hpo_scheduler import HyperOptScheduler\nimport tensorflow as tf\nimport hyperopt.hp as hp\n\nimport iris_data\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--batch_size', default=100, type=int, help='batch size')\nparser.add_argument('--train_steps', default=1000, type=int,\n                    help='number of training steps')\n\ndef my_model(features, labels, mode, params):\n    \"\"\"DNN with three hidden layers, and dropout of 0.1 probability.\"\"\"\n    # Create three fully connected layers each layer having a dropout\n    # probability of 0.1.\n    net = tf.feature_column.input_layer(features, params['feature_columns'])\n    for units in params['hidden_units']:\n        net = tf.layers.dense(net, units=units, activation=tf.nn.relu)\n\n    # Compute logits (1 per class).\n    logits = tf.layers.dense(net, params['n_classes'], activation=None)\n\n    # Compute predictions.\n    predicted_classes = tf.argmax(logits, 1)\n    if mode == tf.estimator.ModeKeys.PREDICT:\n        predictions = {\n            'class_ids': predicted_classes[:, tf.newaxis],\n            'probabilities': tf.nn.softmax(logits),\n            'logits': logits,\n        }\n        return tf.estimator.EstimatorSpec(mode, predictions=predictions)\n\n    # Compute loss.\n    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)\n\n    # Compute evaluation metrics.\n    accuracy = tf.metrics.accuracy(labels=labels,\n                                   predictions=predicted_classes,\n                                   name='acc_op')\n    metrics = {'accuracy': accuracy}\n    tf.summary.scalar('accuracy', accuracy[1])\n\n    if mode == tf.estimator.ModeKeys.EVAL:\n        return tf.estimator.EstimatorSpec(\n            mode, loss=loss, eval_metric_ops=metrics)\n\n    # Create training op.\n    assert mode == tf.estimator.ModeKeys.TRAIN\n\n    optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)\n    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())\n    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)\n\n\nclass TestTrainable(Trainable):\n    def _setup(self):\n        self.steps = 0\n        self.session = tf.Session()\n        (train_x, train_y), (test_x, test_y) = iris_data.load_data()\n        self.train_x = train_x\n        self.train_y = train_y\n\n        self.test_x = test_x\n        self.test_y = test_y\n\n        # Feature columns describe how to use the input.\n        my_feature_columns = []\n        for key in train_x.keys():\n            my_feature_columns.append(tf.feature_column.numeric_column(key=key))\n\n        layer_size = int(self.config['layer_size'])\n\n        # Build 2 hidden layer DNN with 10, 10 units respectively.\n        self.classifier = tf.estimator.Estimator(\n            model_fn=my_model,\n            params={\n                'feature_columns': my_feature_columns,\n                # Two hidden layers of 10 nodes each.\n                'hidden_units': [layer_size, layer_size],\n                # The model must choose between 3 classes.\n                'n_classes': 3,\n            })\n\n        self.saver = None\n        self.global_step_tensor = training_util._get_or_create_global_step_read()  # pylint: disable=protected-access\n\n    def _train(self):\n        self.classifier.train(\n            input_fn=lambda: iris_data.train_input_fn(self.train_x, self.train_y, 10),\n            steps=100)\n\n        self.steps = self.steps + 100\n\n        eval_result = self.classifier.evaluate(\n            input_fn=lambda: iris_data.eval_input_fn(self.test_x, self.test_y, 10))\n\n        return TrainingResult(timesteps_this_iter=100, timesteps_total=self.steps, mean_validation_accuracy=eval_result['accuracy'])\n\n    def _save(self, checkpoint_dir):\n        #saver must be set here, otherwise there will be no variables to have\n        if self.saver is None:\n            self.saver = tf.train.Saver()\n        return self.saver.save(\n            self.session, checkpoint_dir + \"/save\",\n            global_step=self.steps)\n\n    def _restore(self, checkpoint_path):\n        return self.saver.restore(self.session, checkpoint_path)\n\n\nif __name__ == '__main__':\n    ray.init()\n    config = {'iris_test': {\n        'run': 'iris_test',\n        'stop': {'mean_validation_accuracy': 0.999999999999},\n        \"trial_resources\": {\"cpu\": 1, \"gpu\": 0},\n        'repeat': 1,\n        'config': {\n            'space': {\n                'layer_size': hp.uniform('layer_size', 10, 100),\n            },\n        }\n    }}\n\n    hpo_sched = HyperOptScheduler(max_concurrent=4, reward_attr=\"mean_validation_accuracy\")\n    ray.tune.register_trainable(\"iris_test\", TestTrainable)\n    ray.tune.run_experiments(config, verbose=True, scheduler=hpo_sched)\n\n    #tf.logging.set_verbosity(tf.logging.INFO)\n    #tf.app.run(main)","repo_name":"sseveran/ray-tensorflow-trainable","sub_path":"estimator.py","file_name":"estimator.py","file_ext":"py","file_size_in_byte":5024,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"80"}
+{"seq_id":"71433200924","text":"class Player:\n    def __init__(self, from_user):\n        self.id = from_user.id\n        self.username = from_user.username\n        self.name = from_user.full_name\n        self.cards = []\n        self.balance = 0\n        self.starting_balance = 0   # Amount of chips player started game with\n        self.current_stage_bet = 0\n        self.all_in = 0     # If player is all-in stores amount of chips he had at a start of hand\n\n    def send_cards(self, bot):\n        \"\"\"Send player private message with his cards\"\"\"\n        bot.send_message(self.id, f\"Your cards:\\n{self.cards[0]}{self.cards[1]}\")\n\n    def __eq__(self, other):\n        if self.id == other.id and self.username == other.username:\n            return True\n        else:\n            return False\n\n","repo_name":"Delminho/poker-telegram-bot","sub_path":"player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74812270685","text":"import q5\n\nif __name__ == \"__main__\":\n    s1 = \"paraparaparadise\"\n    s2 = \"paragraph\"\n\n    s1_bi_gram_char = q5.n_gram_char(s1, 2)\n    s2_bi_gram_char = q5.n_gram_char(s2, 2)\n\n    s1_bi_set = set(s1_bi_gram_char)\n    s2_bi_set = set(s2_bi_gram_char)\n\n    s1_or_s2 = s1_bi_set | s2_bi_set\n    print(s1_or_s2)\n\n    s1_and_s2 = s1_bi_set & s2_bi_set\n    print(s1_and_s2)\n\n    s1_minus_s2 = s1_bi_set - s2_bi_set\n    print(s1_minus_s2)\n\n    s1_contain_se = \"se\" in s1_bi_set\n    s2_contain_se = \"se\" in s2_bi_set\n    print(s1_contain_se)\n    print(s2_contain_se)","repo_name":"bond-kaneko/nlp100","sub_path":"part1/q6.py","file_name":"q6.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"39651968820","text":"#!/usr/bin/env python\nimport time\nimport struct\nfrom kobot.srv import I2CService, I2CServiceResponse\nimport smbus\nimport rospy\nfrom kobot.msg import floor_sensor\n\n\nclass ADS1115:\n    i2c = None\n\n    # IC Identifiers\n    __IC_ADS1115 = 0x01\n\n    # Config Register\n    __ADS1115_REG_CONFIG_DR_8SPS = 0x0000  # 8 samples per second\n    __ADS1115_REG_CONFIG_DR_16SPS = 0x0020  # 16 samples per second\n    __ADS1115_REG_CONFIG_DR_32SPS = 0x0040  # 32 samples per second\n    __ADS1115_REG_CONFIG_DR_64SPS = 0x0060  # 64 samples per second\n    __ADS1115_REG_CONFIG_DR_128SPS = 0x0080  # 128 samples per second\n    __ADS1115_REG_CONFIG_DR_250SPS = 0x00A0  # 250 samples per second (default)\n    __ADS1115_REG_CONFIG_DR_475SPS = 0x00C0  # 475 samples per second\n    __ADS1115_REG_CONFIG_DR_860SPS = 0x00E0  # 860 samples per second\n\n    __ADS1015_REG_CONFIG_CQUE_MASK = 0x0003\n    # Assert ALERT/RDY after one conversions\n    __ADS1015_REG_CONFIG_CQUE_1CONV = 0x0000\n    # Assert ALERT/RDY after two conversions\n    __ADS1015_REG_CONFIG_CQUE_2CONV = 0x0001\n    # Assert ALERT/RDY after four conversions\n    __ADS1015_REG_CONFIG_CQUE_4CONV = 0x0002\n    # Disable the comparator and put ALERT/RDY in high state (default)\n    __ADS1015_REG_CONFIG_CQUE_NONE = 0x0003\n\n    __ADS1015_REG_CONFIG_CMODE_MASK = 0x0010\n    # Traditional comparator with hysteresis (default)\n    __ADS1015_REG_CONFIG_CMODE_TRAD = 0x0000\n    __ADS1015_REG_CONFIG_CMODE_WINDOW = 0x0010  # Window comparator\n\n    __ADS1015_REG_CONFIG_CPOL_MASK = 0x0008\n    # ALERT/RDY pin is low when active (default)\n    __ADS1015_REG_CONFIG_CPOL_ACTVLOW = 0x0000\n    # ALERT/RDY pin is high when active\n    __ADS1015_REG_CONFIG_CPOL_ACTVHI = 0x0008\n    # Determines if ALERT/RDY pin latches once asserted\n    __ADS1015_REG_CONFIG_CLAT_MASK = 0x0004\n    # Non-latching comparator (default)\n    __ADS1015_REG_CONFIG_CLAT_NONLAT = 0x0000\n    __ADS1015_REG_CONFIG_CLAT_LATCH = 0x0004  # Latching comparator\n\n    __ADS1015_REG_CONFIG_MODE_MASK = 0x0100\n    __ADS1015_REG_CONFIG_MODE_CONTIN = 0x0000  # Continuous conversion mode\n    # Power-down single-shot mode (default)\n    __ADS1015_REG_CONFIG_MODE_SINGLE = 0x0100\n\n    __ADS1015_REG_CONFIG_PGA_MASK = 0x0E00\n    __ADS1015_REG_CONFIG_PGA_6_144V = 0x0000  # +/-6.144V range\n    __ADS1015_REG_CONFIG_PGA_4_096V = 0x0200  # +/-4.096V range\n    __ADS1015_REG_CONFIG_PGA_2_048V = 0x0400  # +/-2.048V range (default)\n    __ADS1015_REG_CONFIG_PGA_1_024V = 0x0600  # +/-1.024V range\n    __ADS1015_REG_CONFIG_PGA_0_512V = 0x0800  # +/-0.512V range\n    __ADS1015_REG_CONFIG_PGA_0_256V = 0x0A00  # +/-0.256V range\n\n    __ADS1015_REG_CONFIG_MUX_MASK = 0x7000\n    # Differential P = AIN0, N = AIN1 (default)\n    __ADS1015_REG_CONFIG_MUX_DIFF_0_1 = 0x0000\n    # Differential P = AIN0, N = AIN3\n    __ADS1015_REG_CONFIG_MUX_DIFF_0_3 = 0x1000\n    # Differential P = AIN1, N = AIN3\n    __ADS1015_REG_CONFIG_MUX_DIFF_1_3 = 0x2000\n    # Differential P = AIN2, N = AIN3\n    __ADS1015_REG_CONFIG_MUX_DIFF_2_3 = 0x3000\n    __ADS1015_REG_CONFIG_MUX_SINGLE_0 = 0x4000  # Single-ended AIN0\n    __ADS1015_REG_CONFIG_MUX_SINGLE_1 = 0x5000  # Single-ended AIN1\n    __ADS1015_REG_CONFIG_MUX_SINGLE_2 = 0x6000  # Single-ended AIN2\n    __ADS1015_REG_CONFIG_MUX_SINGLE_3 = 0x7000  # Single-ended AIN3\n\n    # Config Register\n    __ADS1015_REG_CONFIG_OS_MASK = 0x8000\n    # Write: Set to start a single-conversion\n    __ADS1015_REG_CONFIG_OS_SINGLE = 0x8000\n    # Read: Bit = 0 when conversion is in progress\n    __ADS1015_REG_CONFIG_OS_BUSY = 0x0000\n    # Read: Bit = 1 when device is not performing a conversion\n    __ADS1015_REG_CONFIG_OS_NOTBUSY = 0x8000\n\n    # Pointer Register\n    __ADS1015_REG_POINTER_MASK = 0x03\n    __ADS1015_REG_POINTER_CONVERT = 0x00\n    __ADS1015_REG_POINTER_CONFIG = 0x01\n    __ADS1015_REG_POINTER_LOWTHRESH = 0x02\n    __ADS1015_REG_POINTER_HITHRESH = 0x03\n\n    # Dictionaries with the sampling speed values\n    # These simplify and clean the code\n    # (avoid the abuse of if/elif/else clauses)\n    spsADS1115 = {\n        8: __ADS1115_REG_CONFIG_DR_8SPS,\n        16: __ADS1115_REG_CONFIG_DR_16SPS,\n        32: __ADS1115_REG_CONFIG_DR_32SPS,\n        64: __ADS1115_REG_CONFIG_DR_64SPS,\n        128: __ADS1115_REG_CONFIG_DR_128SPS,\n        250: __ADS1115_REG_CONFIG_DR_250SPS,\n        475: __ADS1115_REG_CONFIG_DR_475SPS,\n        860: __ADS1115_REG_CONFIG_DR_860SPS\n    }\n\n    # Dictionariy with the programable gains\n    pgaADS1x15 = {\n        6144: __ADS1015_REG_CONFIG_PGA_6_144V,\n        4096: __ADS1015_REG_CONFIG_PGA_4_096V,\n        2048: __ADS1015_REG_CONFIG_PGA_2_048V,\n        1024: __ADS1015_REG_CONFIG_PGA_1_024V,\n        512: __ADS1015_REG_CONFIG_PGA_0_512V,\n        256: __ADS1015_REG_CONFIG_PGA_0_256V\n    }\n\n    # Constructor\n    def __init__(self, address=0x48, ic=__IC_ADS1115, debug=False):\n        self.address = address\n        self.debug = debug\n        # Set pga value, so that getLastConversionResult() can use it,\n        # any function that accepts a pga value must update this.\n        self.pga = 6144\n        self.I_min = 200\n        self.I_max = 2400\n\n        try:\n            self.i2c = smbus.SMBus(1)\n            try:\n                val = self.readADCSingleEnded(0, 4096, 64)\n            except IOError:\n                rospy.loginfo(\"Tryin 0x49\")\n                try:\n                    self.address = 0x49\n                    val = self.readADCSingleEnded(0, 4096, 64)\n                    rospy.loginfo(\"Switched to 0x49\")\n                except IOError:\n                    rospy.loginfo(\"Could not find 0x49 device\")\n        except IOError:\n            rospy.loginfo(\"Could not find i2c device\")\n\n\n    def get_params(self):\n        \"\"\"\n        LBA params. are checked constantly and is\n        updated if necessary\n        \"\"\"\n        if rospy.has_param('qrd_indx'):\n            # fetch a group (dictionary) of parameters\n            self.qrd_indx = rospy.get_param('qrd_indx')\n        else:  # feed default vals\n            self.qrd_indx = [0,3]\n\n    def readADCSingleEnded(self, channel=0, pga=6144, sps=64):\n        '''\n        Gets a single-ended ADC reading from the specified\n        channel in mV. The sample rate for this mode\n        (single-shot) can be used to lower the noise\n        (low sps) or to lower the power consumption (high sps)\n        by duty cycling, see datasheet page 14 for more info.\n        The pga must be given in mV, see page 13 for the\n        supported values.\n        '''\n\n        # With invalid channel return -1\n        if (channel > 3):\n            return -1\n\n        # Disable comparator, Non-latching, Alert/Rdy active low\n        # traditional comparator, single-shot mode\n        config = self.__ADS1015_REG_CONFIG_CQUE_NONE | \\\n            self.__ADS1015_REG_CONFIG_CLAT_NONLAT | \\\n            self.__ADS1015_REG_CONFIG_CPOL_ACTVLOW | \\\n            self.__ADS1015_REG_CONFIG_CMODE_TRAD | \\\n            self.__ADS1015_REG_CONFIG_MODE_SINGLE\n\n        # Set sample per seconds, defaults to 250sps\n        config |= self.spsADS1115.setdefault(\n            sps, self.__ADS1115_REG_CONFIG_DR_250SPS)\n\n        # Set PGA/voltage range, defaults to +-6.144V\n        config |= self.pgaADS1x15.setdefault(\n            pga, self.__ADS1015_REG_CONFIG_PGA_6_144V)\n        self.pga = pga\n\n        # Set the channel to be converted\n        if channel == 3:\n            config |= self.__ADS1015_REG_CONFIG_MUX_SINGLE_3\n        elif channel == 2:\n            config |= self.__ADS1015_REG_CONFIG_MUX_SINGLE_2\n        elif channel == 1:\n            config |= self.__ADS1015_REG_CONFIG_MUX_SINGLE_1\n        else:\n            config |= self.__ADS1015_REG_CONFIG_MUX_SINGLE_0\n\n        # Set 'start single-conversion' bit\n        config |= self.__ADS1015_REG_CONFIG_OS_SINGLE\n\n        # Write config register to the ADC\n        byte_val_list = [(config >> 8) & 0xFF, config & 0xFF]\n        try:\n            self.i2c.write_i2c_block_data(\n                self.address, self.__ADS1015_REG_POINTER_CONFIG, byte_val_list)\n        except IOError:\n            rospy.logerr(\"Floor sensor I2C Write Error in channel: {}\".format(channel))\n\n        # self.frame_data(self.address, self.__ADS1015_REG_POINTER_CONFIG,\n        #   byte_val_list)\n\n        # Wait for the ADC conversion to complete\n        # The minimum delay depends on the sps: delay >= 1/sps\n        # use 0.05 sec since we dont need more\n        delay = 1/float(sps) + 0.001\n        rospy.sleep(delay)\n        try:\n            # Read the conversion results\n            result = self.i2c.read_i2c_block_data(\n                self.address, self.__ADS1015_REG_POINTER_CONVERT, 2)\n            # Return a mV value for the ADS1115\n            # (Take signed values into account as well)\n            val = (int(result[0]) << 8) | (int(result[1]))\n            if val > 0x7FFF:\n                return (val - 0xFFFF)*self.pga/32768.0\n            else:\n                return ((result[0] << 8) | (result[1]))*self.pga/32768.0\n        except IOError:\n            # assume black when we have I2C error\n            rospy.logerr(\"Floor sensor I2C Read Error in channel: {}\".format(channel))\n            raise IOError\n            return 4095\n\n    def intensity_filter(self, intensity_list):\n        \"\"\"\n        Filter only the valid intensity sensor vals.\n        defined by qrd_indx list, disregard remaining sensor\n        vals.\n        \"\"\"\n        valid_intensity_list = []\n        for indx in range(len(self.qrd_indx)):\n            I = intensity_list[indx]\n            if I < self.I_min:\n                I = self.I_min\n            elif I > self.I_max:\n                I = self.I_max\n            I -= self.I_min\n            # map values to 8-bit\n            I = int(I/(self.I_max - self.I_min) * 255.0)\n            # 255 -> highest intensity\n            # 0 -> lowest intensity\n            # reverse intensity definition\n            I = 255 - I\n            valid_intensity_list.append(I)\n        return valid_intensity_list\n      \n\ndef start():\n    \"\"\"\n    this node is responsible for all the I2C communication\n    \"\"\"\n    # node name is also used in .launch file\n    rospy.init_node(\"floor_sensors\")\n    floor_sensors_pub = rospy.Publisher(\n        \"/sensors/floor_sensor\", floor_sensor, queue_size=1)\n    ads = ADS1115()\n    floor_sensors_msg = floor_sensor()\n    floor_sensors_freq = 10\n    if rospy.has_param('floor_sensor_freq'):\n        floor_sensors_freq = rospy.get_param('floor_sensor_freq')\n    rate = rospy.Rate(floor_sensors_freq)  # Hz\n    while not rospy.is_shutdown():\n        ads.get_params()\n        sensor_vals = []\n        for i in ads.qrd_indx:\n            # order cw from left front\n            try:\n                val = ads.readADCSingleEnded(i, 4096, 64)\n            except IOError:\n                rospy.loginfo(\"IOErr \")\n            # rospy.sleep(0.005)\n            sensor_vals.append(val)\n        filtered_sensor_vals = ads.intensity_filter(sensor_vals)\n        floor_sensors_msg.intensity = filtered_sensor_vals\n        floor_sensors_pub.publish(floor_sensors_msg)\n        rate.sleep()\n\n\n# start from command-line\nif __name__ == '__main__':\n    start()\n","repo_name":"cemoke/kobot","sub_path":"src/scripts/sub_system/floor_sensor.py","file_name":"floor_sensor.py","file_ext":"py","file_size_in_byte":11070,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"15929730316","text":"# 나의 풀이\nimport copy\n\n\ndef check(n, arr):\n    for r in range(n):\n        for c in range(n):\n            if arr[r + n][c + n] != 1:\n                return False\n    return True\n\n\ndef rotate_a_matrix_by_90_degree(key):\n    m = len(key)\n    result = [[0] * m for _ in range(m)]\n    for r in range(m):\n        for c in range(m):\n            result[c][m - 1 - r] = key[r][c]\n    return result\n\n\ndef solution(key, lock):\n    m, n = len(key), len(lock)\n    array = [[0] * 3 * n for _ in range(3 * n)]\n    for r in range(n):\n        for c in range(n):\n            array[r + n][c + n] = lock[r][c]\n    ori = copy.deepcopy(array)\n\n    for i in range(4):\n        for x in range(2 * n):\n            for y in range(2 * n):\n                for r in range(m):\n                    for c in range(m):\n                        array[r + x][c + y] += key[r][c]\n                if check(n, array):\n                    return True\n                else:\n                    array = copy.deepcopy(ori)\n        key = rotate_a_matrix_by_90_degree(key)\n    return False\n\n# 책 풀이\ndef rotate_a_matrix_by_90_degree(a):\n  n = len(a)\n  m = len(a)\n  result = [[0] * n for _ in range(m)]\n  for i in range(n):\n    for j in range(m):\n      result[j][n - i - 1] = a[i][j]\n  return result\n\ndef check(new_lock):\n  lock_length = len(new_lock) // 3\n  for i in range(lock_length, lock_length * 2):\n    for j in range(lock_length, lock_length * 2):\n      if new_lock[i][j] != 1:\n        return False\n  return True\n\ndef solution(key, lock):\n  n = len(lock)\n  m = len(key)\n  new_lock = [[0] * (n * 3) for _ in range(n * 3)]\n  for i in range(n):\n    for j in range(n):\n      new_lock[i + n][j + n] = lock[i][j]\n\n  for rotation in range(4):\n    key = rotate_a_matrix_by_90_degree(key)\n    for x in range(n * 2):\n      for y in range(n * 2):\n        for i in range(m):\n          for j in range(m):\n            new_lock[x + i][y + j] += key[i][j]\n        if check(new_lock) == True:\n          return True\n        for i in range(m):\n          for j in range(m):\n            new_lock[x + i][y + j] -= key[i][j]\n  return False\n\nprint(solution([[0, 0, 0], [1, 0, 0], [0, 1, 1]], [[1, 1, 1], [1, 1, 0], [1, 0, 1]]))\n","repo_name":"developerTae/python-for-coding-test","sub_path":"1회/구현 문제/q10_자물쇠와 열쇠.py","file_name":"q10_자물쇠와 열쇠.py","file_ext":"py","file_size_in_byte":2174,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"18908491057","text":"from dataclasses import dataclass\nfrom typing import List\n\n\n@dataclass\nclass SectionItem:\n    path: str = None\n    text: str = None\n    is_color: bool = False\n\n    @classmethod\n    def from_dict(cls, section_item_dict):\n        return cls(\n            path=section_item_dict.get(\"path\", None),\n            text=section_item_dict.get(\"text\", None),\n            is_color=section_item_dict.get(\"isColor\", False),\n        )\n\n\n@dataclass\nclass Section:\n    title: str\n    items: List[SectionItem]\n\n    @classmethod\n    def from_dict(cls, section_dict):\n        return cls(\n            title=section_dict[\"title\"],\n            items=[\n                SectionItem.from_dict(item_dict)\n                for item_dict in section_dict[\"items\"]\n            ]\n        )\n","repo_name":"pokey/visa_application","sub_path":"visa_application/models/section.py","file_name":"section.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"10260961568","text":"import sys,os\nsys.path.append(os.pardir)\nimport numpy as np\nfrom dataset.mnist import load_mnist\nimport pickle\ndef get_data():\n    (x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, flatten=True, one_hot_label=True)\n    return x_train,t_train\n\ndef softmax(a):\n    a = a.T   # 각 행의 최대값을 가져온다.\n    c = np.max(a, axis=0)\n    exp_a = np.exp(a-c)\n    sum_exp_a = np.sum(exp_a, axis=0)\n    y = exp_a / sum_exp_a\n    return  y.T\n\ndef init_network():\n    with open(\"C:\\data\\sample_weight.pkl\", 'rb') as f:\n        network = pickle.load(f)\n    return network\n\ndef sigmoid(x):\n    return 1 / (1 + np.exp(-x))\n\ndef predict(network, x):\n    w1, w2, w3 = network['W1'], network['W2'], network['W3']\n    b1, b2, b3 = network['b1'], network['b2'], network['b3']\n    a1 = np.dot(x, w1) + b1\n    z1 = sigmoid(a1)\n    a2 = np.dot(z1, w2) + b2\n    z2 = sigmoid(a2)\n    a3 = np.dot(z2, w3) + b3\n    y = softmax(a3)\n    return y\n\ndef crossEntropyerror(y,t):\n    delta = 1e-7\n    print('y', y)   # y에 확률 들어오고\n    print(len(y))   # 0~~9 10줄\n    print('t', t)   # t에 레이블 들어온다.(원-핫 코딩)\n    print(len(t))   # 0~~9 10줄\n    return -np.sum(t*np.log(y+delta)) / len(y)   # 각 행에 대한 오차율 평균\n\ntrain = get_data()\n# print('train1111', train)\n# print(len(train))\n\nx_train = train[0]\nt_train = train[1]\n\nprint(x_train.shape)\nprint(t_train.shape)\n\ntrain_size = 60000\nbatch_size = 10\nbatch_mask = np.random.choice(train_size,batch_size)\nprint(batch_mask)\nx_batch = x_train[batch_mask]\nt_batch = t_train[batch_mask]\n\ny = predict(init_network(), x_batch)\np = np.argmax(y, axis=1)\nprint(p)\n\nprint('CEE:', crossEntropyerror(y, t_batch))   # 오차율","repo_name":"gh506015/source_code","sub_path":"mnist.py","file_name":"mnist.py","file_ext":"py","file_size_in_byte":1705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31011387483","text":"class Solution(object):\n    def maxProduct(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: int\n        \"\"\"\n        dpmax = [0] * len(nums)\n        dpmin = [0] * len(nums)\n        dpmax[0] = dpmin[0] = nums[0]\n        for i in range(1,len(nums)):\n            a1 = dpmax[i-1]*nums[i]\n            a2 = dpmin[i-1]*nums[i]\n            a3 = nums[i]\n            dpmax[i] = max(a1,a2,a3)\n            dpmin[i] = min(a1,a2,a3)\n        return max(dpmax)\n","repo_name":"fengkai29/leetcode","sub_path":"动态规划/leetcode_152_乘积最大子序列.py","file_name":"leetcode_152_乘积最大子序列.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"2416908057","text":"import pycurl\nfrom io import BytesIO\nimport re\nimport random\nimport threading\nimport queue\n\n\nclass CheckProccess(threading.Thread):\n    def __init__(\n        self,\n        timeout=10,\n        queue=None,\n        result_queue=None,\n        check_country=True,\n        check_address=True,\n    ):\n        threading.Thread.__init__(self)\n        self.queue = queue\n        self.result_queue = result_queue\n        self.timeout = timeout\n        self.get_ip_url = \"http://ifconfig.me/ip\"\n        self.ip = self.get_ip()\n        self.check_country = check_country\n        self.check_address = check_address\n\n    def run(self):\n        \"\"\"Запуск потока\"\"\"\n        while True:\n            proxy = self.queue.get()\n\n            # if isset login:password\n            proxy_parts = proxy.split(\":\")\n            if len(proxy_parts) == 4:\n                user = proxy_parts[2]\n                password = proxy_parts[3]\n                proxy = \"{}:{}\".format(proxy_parts[0], proxy_parts[1])\n            else:\n                user = False\n                password = False\n\n            self.check_proxy(proxy, user=user, password=password)\n\n            self.queue.task_done()\n\n    def get_ip(self):\n        r = self.send_query(url=self.get_ip_url)\n\n        if not r:\n            return \"\"\n\n        return r[\"response\"]\n\n    def send_query(self, proxy=False, url=None, user=None, password=None):\n        response = BytesIO()\n        c = pycurl.Curl()\n\n        c.setopt(c.URL, url or self.get_ip_url)\n        c.setopt(c.WRITEDATA, response)\n        c.setopt(c.TIMEOUT, self.timeout)\n\n        if user is not None and password is not None:\n            c.setopt(c.PROXYUSERPWD, f\"{user}:{password}\")\n\n        c.setopt(c.SSL_VERIFYHOST, 0)\n        c.setopt(c.SSL_VERIFYPEER, 0)\n\n        if proxy:\n            c.setopt(c.PROXY, proxy)\n\n        # Perform request\n        try:\n            c.perform()\n        except Exception as e:\n            # print(e)\n            return False\n\n        # Return False if the status is not 200\n        if c.getinfo(c.HTTP_CODE) != 200:\n            return False\n\n        # Calculate the request timeout in milliseconds\n        timeout = round(c.getinfo(c.CONNECT_TIME) * 1000)\n\n        # Decode the response content\n        response = response.getvalue().decode(\"utf-8\")\n\n        return {\"timeout\": timeout, \"response\": response}\n\n    def parse_anonymity(self, r):\n        if self.ip in r:\n            return \"Transparent\"\n\n        privacy_headers = [\n            \"VIA\",\n            \"X-FORWARDED-FOR\",\n            \"X-FORWARDED\",\n            \"FORWARDED-FOR\",\n            \"FORWARDED-FOR-IP\",\n            \"FORWARDED\",\n            \"CLIENT-IP\",\n            \"PROXY-CONNECTION\",\n            \"X-REAL-IP\",\n            \"X-PROXY-ID\",\n            \"HTTP-FORWARDED\",\n            \"FORWARDED_FOR\",\n            \"X_FORWARDED FORWARDED\",\n            \"CLIENT_IP\",\n            \"PROXY-CONNECTION\",\n            \"XROXY-CONNECTION\",\n            \"X-IMForwards\",\n        ]\n\n        if any([header in r for header in privacy_headers]):\n            return \"Anonymous\"\n\n        return \"Elite\"\n\n    def get_country(self, ip):\n        r = self.send_query(url=\"https://ip2c.org/\" + ip)\n\n        if r and r[\"response\"][0] == \"1\":\n            r = r[\"response\"].split(\";\")\n            return [r[3], r[1]]\n\n        return [\"-\", \"-\"]\n\n    def check_proxy(self, proxy, user=None, password=None):\n        protocols = {}\n        timeout = 0\n\n        # Test the proxy for each protocol\n        for protocol in [\"http\", \"socks4\", \"socks5\"]:\n            r = self.send_query(\n                proxy=protocol + \"://\" + proxy, user=user, password=password\n            )\n            # Check if the request failed\n            if not r:\n                continue\n\n            protocols[protocol] = r\n            timeout += r[\"timeout\"]\n\n        # Check if the proxy failed all tests\n        if len(protocols) == 0:\n            return\n        r = protocols[random.choice(list(protocols.keys()))][\"response\"]\n\n        # Get country\n        if self.check_country:\n            country = self.get_country(proxy.split(\":\")[0])\n\n        # Check anonymity\n        anonymity = self.parse_anonymity(r)\n\n        # Check timeout\n        timeout = timeout // len(protocols)\n\n        # Check remote address\n        if self.check_address:\n            remote_regex = r\"REMOTE_ADDR = (\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})\"\n            remote_addr = re.search(remote_regex, r)\n            if remote_addr:\n                remote_addr = remote_addr.group(1)\n\n        results = {\n            \"protocols\": list(protocols.keys()),\n            \"anonymity\": anonymity,\n            \"timeout\": timeout,\n            \"address\": proxy,\n        }\n\n        if self.check_country:\n            results[\"country\"] = country[0]\n            results[\"country_code\"] = country[1]\n\n        if self.check_address:\n            results[\"remote_address\"] = remote_addr\n\n        self.result_queue.put(results)\n\n\nclass RPrint(threading.Thread):\n    def __init__(self, result_queue, output, is_json=False):\n        threading.Thread.__init__(self)\n        self.result_queue = result_queue\n        self.shutdown = False\n        self.output = output\n        self.is_json = is_json\n\n    def run(self):\n        while not self.shutdown:\n            result = self.result_queue.get()\n            print(result)\n            \"\"\" write data in output file \"\"\"\n            with open(self.output, \"a\") as output_file:\n                if self.is_json:\n                    output_file.write(\"{}\\n\".format(result))\n                else:\n                    output_file.write(\"{}\\n\".format(result[\"address\"]))\n            self.result_queue.task_done()\n\n    def terminate(self):\n        self.shutdown = True\n\n\nclass ProxyDuck:\n    def __init__(\n        self,\n        input_filepath=None,\n        output_filepath=None,\n        threads=200,\n        timeout=15,\n        check_address=True,\n        check_country=True,\n        json_file_output=False,\n    ):\n        self.proxy_list = queue.Queue()\n        self.result_queue = queue.Queue()\n        self.input_filepath = input_filepath\n        self.output_filepath = output_filepath\n        self.threads = threads\n        self.check_address = check_address\n        self.check_country = check_country\n        self.json_file_output = json_file_output\n        self.timout = timeout\n\n    def start(self):\n        with open(self.input_filepath) as proxy_file:\n            proxy_arr = proxy_file.readlines()\n\n        for proxy in proxy_arr:\n            self.proxy_list.put(proxy.strip())\n\n        if len(proxy_arr) < self.threads:\n            self.threads = len(proxy_arr)\n\n        for _ in range(self.threads):\n            proccess_worker = CheckProccess(\n                self.timout,\n                self.proxy_list,\n                self.result_queue,\n                self.check_address,\n                self.check_country,\n            )\n            proccess_worker.daemon = True\n            proccess_worker.start()\n\n        \"\"\" Set thread for printing results \"\"\"\n        rprint = RPrint(self.result_queue, self.output_filepath, self.json_file_output)\n        rprint.daemon = True\n        rprint.start()\n        self.proxy_list.join()\n        rprint.terminate()\n","repo_name":"foozzi/ProxyDuck","sub_path":"proxyduck/proxyduck.py","file_name":"proxyduck.py","file_ext":"py","file_size_in_byte":7206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"13244870968","text":"import pytest\nfrom contextlib import contextmanager\nfrom io import StringIO\nimport sys\nfrom assertpy import assert_that\nfrom swarmci import parse_args\nfrom swarmci.errors import SwarmCIError\nfrom swarmci import build_tasks_hierarchy\nfrom swarmci.task import Task, TaskType, TaskFactory\n\n\ndef describe_build_tasks_hierarchy():\n    def given_no_stages():\n        def expect_error_raised():\n            config = {\n                \"foo\": \"bar\"\n            }\n\n            with pytest.raises(SwarmCIError) as excinfo:\n                build_tasks_hierarchy(config, TaskFactory())\n\n            assert_that(str(excinfo.value)).is_equal_to('Did not find \"stages\" key in the .swarmci file.')\n\n    def given_stages_not_a_list():\n        def expect_error_raised():\n            config = {\n                \"stages\": \"bar\"\n            }\n\n            with pytest.raises(SwarmCIError) as excinfo:\n                build_tasks_hierarchy(config, TaskFactory())\n\n            assert_that(str(excinfo.value)).is_equal_to(\n                'The value of the \"stages\" key should be a list in the .swarmci file.')\n\n    def expect_build_task_returned():\n        config = {\n            'stages': [\n                {\n                    'name': 'foo_stage',\n                    'jobs': [\n                        {\n                            'name': 'foo_job',\n                            'commands': [\n                                'test command'\n                            ]\n                        }\n                    ]\n                }\n            ]\n        }\n        task = build_tasks_hierarchy(config, TaskFactory())\n\n        assert_that(task).is_instance_of(Task)\n        assert_that(task.task_type).is_equal_to(TaskType.BUILD)\n\n\n@contextmanager\ndef capture_sys_output():\n    capture_out, capture_err = StringIO(), StringIO()\n    current_out, current_err = sys.stdout, sys.stderr\n    try:\n        sys.stdout, sys.stderr = capture_out, capture_err\n        yield capture_out, capture_err\n    finally:\n        sys.stdout, sys.stderr = current_out, current_err\n\n\ndef describe_parse_args():\n    def given_version_option():\n        def expect_package_and_version_returned():\n            with pytest.raises(SystemExit):\n                with capture_sys_output() as (stdout, stderr):\n                    parse_args(['--version'])\n\n            assert_that(stdout.getvalue()).matches(r'SwarmCI \\d+\\.\\d+\\.\\d+')\n\n    def given_file_option():\n        def expect_file_set_in_output():\n            expected_filename = 'foo.bar'\n            actual_args = parse_args(['--file', expected_filename])\n            assert_that(actual_args.file).is_equal_to(expected_filename)\n","repo_name":"ghostsquad/swarmci","sub_path":"tests/isolation/test_init.py","file_name":"test_init.py","file_ext":"py","file_size_in_byte":2637,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"86"}
+{"seq_id":"19571928453","text":"import pika\n\nconnection = pika.BlockingConnection(parameters=pika.ConnectionParameters(\"localhost\"))\nchannel = connection.channel()\n\nqueue_name = \"tut1\"\nchannel.queue_declare(queue=queue_name)\n\nmessage = \"Hello World\"\n\nchannel.basic_publish(\n    exchange=\"\",\n    routing_key=queue_name,\n    body=message\n)\n\nprint(f\" [x] '{message}' sent 👉\")\n\nconnection.close()","repo_name":"vishwakarmad1999/rabbitmq-pocs","sub_path":"tutorial-1/producer.py","file_name":"producer.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"24642282547","text":"import math\nfrom io import StringIO\nfrom colorama import init, Fore, Back, Style\n\ninit(convert=True)\n\ndef show_tree(tree, total_width=36, fill=' ', **kw):\n    \"\"\"Pretty-print a tree.\"\"\"\n    print(tree)    \n    output = StringIO()\n    last_row = -1\n    for i, n in enumerate(tree):\n        if i:\n            row = int(math.floor(math.log(i + 1, 2)))\n        else:\n            row = 0\n        if row != last_row:\n            output.write('\\n')\n        columns = 2 ** row\n        col_half_width = int(math.floor(total_width / columns / 2))\n        output_char = str(n)\n        if i == kw['fromIndex']:\n            output_char = Fore.YELLOW + output_char + Style.RESET_ALL\n        elif i == kw['toIndex']:\n            output_char = Fore.BLUE + output_char + Style.RESET_ALL\n        output.write(col_half_width * fill + output_char + col_half_width * fill)\n        last_row = row\n    print(output.getvalue())\n    print('-' * total_width)\n    print()","repo_name":"Dengyy/Blog","sub_path":"python/utils/treeVisual.py","file_name":"treeVisual.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"4713274980","text":"import os\nfrom pathlib import Path\n\nfrom Aufgabe_2.utils import log_timing, log\nfrom Aufgabe_2.Aufgabe_2_2.rc4.rc4 import fixed_rc4\n\n\n@log_timing()\ndef iv_and_stream_cipher_generator(n=256, rounds=2, iv_length=3, key_length=5, tuple_amount=1000, cache=False):\n    \"\"\"\n    Method for generation of (iv, main key and stream key) pairs as required by Exercise 2.2\n    Modes:\n        64-bit WEP(WEP-40): 40 bit key, 24-bit iv\n        128-bit WEP(WEP-104): 104 bit key, 26-bit iv\n    :param n: Base of the integer group\n    :param rounds: Amount of rounds the pseudo random generator should be called\n    :param iv_length: Length of the initializing vector in bytes\n    :param key_length: Length of the key in bytes. Typically ranges from 5 to 64 with maximum of 256\n    :param tuple_amount: Amount of iv and stream keys to be generated\n    :return: A set of iv and stream key tuples and the main key\n    \"\"\"\n    key_file_name = \"main_key\"\n    data_file_name = \"stream_cipher\"\n    if cache:\n        if all(Path(file).exists() for file in [key_file_name, data_file_name]):\n            return load_cache(key_file_name, data_file_name)\n\n    log(\"Proceeding with: length={}, amount={}, rounds={}, n={}\".format(key_length, tuple_amount, rounds, n))\n\n    # Generate random key\n    main_key = bytearray(os.urandom(key_length))\n    log(\"Using main key: {}\".format(main_key), level=0)\n\n    iv_stream_set = []\n    for i in range(tuple_amount):\n        # Generate random iv\n        iv = bytearray(os.urandom(iv_length))\n        stream_cipher = fixed_rc4(iv, main_key, cipher_length=rounds * n, n=n)\n        iv_stream_set.append((iv, stream_cipher))\n\n    if cache:\n        export_cache(iv_stream_set, main_key, key_file_name, data_file_name)\n    return iv_stream_set, main_key\n\n\ndef export_cache(iv_stream_set, main_key, key_file_name, data_file_name):\n    with open(key_file_name, 'wb') as output:\n        output.write(main_key)\n    with open(data_file_name, 'w') as output:\n        output.write(str(iv_stream_set))\n\n\ndef load_cache(key_file_name, data_file_name):\n    with open(key_file_name, 'rb') as file:\n        main_key = file.read()\n    with open(data_file_name, 'r') as file:\n        iv_and_stream_set = eval(file.read())\n    return iv_and_stream_set, main_key\n","repo_name":"doctordebug/Hackerpraktikum","sub_path":"Aufgabe_2/Aufgabe_2_2/wep/iv_and_cipher_generator.py","file_name":"iv_and_cipher_generator.py","file_ext":"py","file_size_in_byte":2253,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"16390726496","text":"import re\r\ndef CodelandUsernameValidation(string):\r\n  count = 0\r\n  if((len(string) > 4) and (len(string) < 25)):\r\n    count += 1  \r\n  \r\n  l = list(string)\r\n  if(l[0].isalpha()):\r\n    count += 1\r\n  \r\n  if((len(string)-1) != '_'):\r\n    count += 1\r\n  \r\n  res = bool(re.match(\"^[A-Za-z0-9_-]*$\",string))\r\n  if(res == True):\r\n    count += 1\r\n  \r\n  if(count == 4):\r\n    result = True\r\n  else:\r\n    result = False\r\n# code goes here\r\n  return result\r\n\r\n# keep this function call here \r\nprint(CodelandUsernameValidation(input()))","repo_name":"akshetty2808/Coderbyte-Solutions","sub_path":"Codeland_Username_Validation.py","file_name":"Codeland_Username_Validation.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"72970584285","text":"from datetime import datetime\nfrom syscalls import *\n\nbig_file = open('./audit.txt', 'r')\nsmall_file3 = open('./parsed_audit.txt', 'w')\ncount = 0\n\ntry: \n   for line in big_file:\n      lins = line.split()\n      for word in range(len(lins)):\n         if lins[word][:8]=='syscall=':\n            try:\n               call=calls[int(lins[word][8:])]\n               # if call in enable_syscalls:\n               output = call+ \" \"\n               small_file3.write(output)\n               count+=1\n               # else:\n               #    break\n            except:\n               pass\n         elif lins[word][:3] == 'a0=':\n            ans = lins[word][3:]+' '+lins[word+1][3:]+' '+lins[word+2][3:]+' '+lins[word+3][3:]+'\\n'\n            small_file3.write(ans)\n            break\n\nexcept Exception as e:\n   nfile = open('./error', 'w')\n   nfile.write(str(e))\nbig_file.close()\nsmall_file3.close()\n\n","repo_name":"Altruy/Audit-Logs","sub_path":"scripts/parsing_audit.py","file_name":"parsing_audit.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"17026037187","text":"import gi\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import GLib, Gio, Gtk, Gdk\n\nfrom datalijst import DataLijst, ColumnDef\nfrom barcodeprint import BarcodePrinter\nfrom nieuwe_barcodes_dialog import NewBarcodesDialog\n\nclass BarcodesWidget(Gtk.Grid):\n    def __init__(self, parent, database):\n        super().__init__(orientation=Gtk.Orientation.VERTICAL)\n        self.parent = parent\n        self.db = database\n        self.invalidated = False\n\n        self.printer = BarcodePrinter()\n\n        columns = (\n                ColumnDef(\"Code\", \"isbn\", datatype=str),\n                ColumnDef(\"Geprint\", \"printtime\"),\n                ColumnDef(\"Opmerking\", \"notes\"),\n            )\n        self.lijst = DataLijst(columns, show_primary_key=True, expand=True, multiselect=True)\n        self.selection = self.lijst.view.get_selection()\n        self.selection.connect(\"changed\", self.on_select_row)\n        self.lijst.view.connect(\"row-activated\", self.on_row_activated)\n    \n        self.nieuw_button = Gtk.Button(\"Genereer nieuwe streepjescodes\")\n        self.nieuw_button.connect(\"clicked\", self.on_nieuw)\n        self.verwijder_button = Gtk.Button(\"Verwijder\")\n        self.verwijder_button.connect(\"clicked\", self.on_verwijder)\n        self.print_button = Gtk.Button(\"Print\")\n        self.print_button.connect(\"clicked\", self.on_print)\n\n        self.bestaand_check = Gtk.CheckButton(label=\"Ook bestaande boeken\")\n        self.bestaand_check.connect(\"clicked\", self.refresh)\n        self.geprint_check = Gtk.CheckButton(label=\"Ook geprinte streepjescodes\")\n        self.geprint_check.connect(\"clicked\", self.refresh)\n\n        self.bulk_select_button = Gtk.Button(label=\"Selecteer pagina vol\")\n        self.bulk_select_button.connect(\"clicked\", self.on_bulk_select)\n        self.bulk_label = Gtk.Label(\"(dubbel-klikken op een regel kan ook)\")\n\n        # layout\n        #\n        knoppen_grid = Gtk.Grid(\n                margin=12,\n                column_spacing=8,\n                row_spacing=8,\n            )\n        knoppen_grid.attach(self.nieuw_button, 1, 0, 1, 1)\n        knoppen_grid.attach(self.verwijder_button, 1, 1, 1, 1)\n        knoppen_grid.attach(self.print_button, 1, 2, 1, 1)\n\n        filter_grid = Gtk.Grid(\n                margin=12,\n                column_spacing=8,\n                row_spacing=8,\n            )\n        filter_grid.attach(self.bestaand_check, 1, 0, 1, 1)\n        filter_grid.attach(self.geprint_check, 1, 1, 1, 1)\n\n        selecteren_grid = Gtk.Grid(\n                margin=12,\n                column_spacing=8,\n                row_spacing=8,\n            )\n        selecteren_grid.attach(self.bulk_select_button, 1, 0, 1, 1)\n        selecteren_grid.attach(self.bulk_label, 1, 1, 1, 1)\n\n        selecteren_paneel = Gtk.Frame(label=\"Selecteren\")\n        selectie_paneel = Gtk.Frame(label=\"Filteren\")\n        actie_paneel = Gtk.Frame(label=\"Acties\")\n\n        selecteren_paneel.add(selecteren_grid)\n        selectie_paneel.add(filter_grid)\n        actie_paneel.add(knoppen_grid)\n\n        paneel = Gtk.Grid(\n                margin=16,\n                column_spacing=16,\n                row_spacing=16,\n                orientation=Gtk.Orientation.HORIZONTAL,\n                hexpand=True,\n            )\n        paneel.attach(selectie_paneel, 0, 0, 1, 1)\n        paneel.attach(selecteren_paneel, 1, 0, 1, 1)\n        paneel.attach(Gtk.Label(hexpand=True), 2, 0, 1, 1)\n        paneel.attach(actie_paneel, 3, 0, 1, 1)\n\n        self.add(paneel)\n        self.add(self.lijst)\n        self.refresh()\n\n    def refresh(self, dummy=None):\n        self.invalidated = True\n        bestaand = None if self.bestaand_check.get_active() else False\n        printed = None if self.geprint_check.get_active() else False\n        self.barcodes = self.db.get_barcodes(\n                with_book=bestaand,\n                printed=printed,\n            )\n        self.lijst.load(self.barcodes)\n        self.disable_print_button_if_needed()\n        self.disable_delete_button_if_needed()\n        self.bulk_select_button.set_label(\"Selecteer hele pagina erbij\\n({} codes)\".format(self.get_labels_per_page()))\n\n    def get_labels_per_page(self):\n        self.printer.loadconfig()\n        return self.printer.get_codes_per_page()\n\n    def get_selected_rows(self):\n        model, path = self.selection.get_selected_rows()\n        if not path:\n            return []\n        result = list()\n        for row in path:\n            rec = [col for col in model[row]]\n            rec.insert(0, row.get_indices()[0])\n            result.append(tuple(rec))\n        return result\n\n    def disable_delete_button_if_needed(self):\n        enabled = False\n        for row in self.get_selected_rows():\n            enabled = True\n            if not row[3]:\n                enabled = False\n                break\n        self.verwijder_button.set_sensitive(enabled)\n\n    def disable_print_button_if_needed(self):\n        self.print_button.set_sensitive(self.get_selected_rows())\n\n    def on_verwijder(self, button):\n        for row in self.get_selected_rows():\n            self.db.delete_barcode_record(row[1])\n        self.refresh()\n\n    def on_nieuw(self, button):\n        aantal = None\n        dialog = NewBarcodesDialog(self.parent, int(self.get_labels_per_page()))\n        response = dialog.run()\n        if response == Gtk.ResponseType.OK:\n            str_aantal = dialog.aantal.get_text()\n            try:\n                aantal = int(str_aantal)\n            except ValueError:\n                aantal = None\n                errdialog = Gtk.MessageDialog(self.parent, 0, Gtk.MessageType.ERROR,\n                        Gtk.ButtonsType.CANCEL, 'Error: \"{}\" is geen geldig aantal'.format(str_aantal))\n                errdialog.run()\n                errdialog.destroy()\n            if aantal:\n                self.db.create_new_barcodes(num=aantal)\n                self.refresh()\n        dialog.destroy()\n\n    def on_print(self, button):\n        codes = [row[1] for row in self.get_selected_rows()]\n        self.printer.print(codes)\n        for code in codes:\n            self.db.mark_barcode_printed(code)\n        self.refresh()\n\n    def on_select_row(self, view):\n        self.disable_delete_button_if_needed()\n        self.disable_print_button_if_needed()\n\n    def on_row_activated(self, view, path, column):\n        self.auto_select_codes(path.get_indices()[0])\n\n    def on_bulk_select(self, button):\n        rows = self.get_selected_rows()\n        last_index = -1\n        if rows:\n            last_index = rows[-1][0]\n        if len(rows) == 1:\n            last_index -= 1   # als er 1 regel geselecteerd was niet 7 selecteren. dat 'voelt' niet goed.\n        self.auto_select_codes(last_index + 1)\n\n    def auto_select_codes(self, start=0, num=None):\n        if num == None:\n            num = self.get_labels_per_page()\n\n        startpath = Gtk.TreePath(start)\n\n        max_idx = max(len(self.barcodes) - 1, 0)\n        end_idx = min(start + num - 1, max_idx)\n        endpath = Gtk.TreePath(end_idx)\n\n        self.selection.select_range(startpath, endpath)\n        self.disable_delete_button_if_needed()\n","repo_name":"hkoof/boekleen","sub_path":"barcodeswidget.py","file_name":"barcodeswidget.py","file_ext":"py","file_size_in_byte":7091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"40570601125","text":"import math\n\nfrom pyasn1.type import namedtype, tag, univ\n\nfrom tlsspy.log import log\nfrom tlsspy.util import (\n    bytes_to_long,\n    num_bytes,\n    long_to_bytes,\n    pow_mod,\n)\n\n\nPKCS1_PREFIX = dict(\n    # RFC 4337 section 9\n    md2       = '3020300c06082a864886f70d020205000410'.decode('hex'),\n    md5       = '3020300c06082a864886f70d020505000410'.decode('hex'),\n    sha1      = '3021300906052b0e03021a05000414'.decode('hex'),\n    sha224    = '302d300d06096086480165030402040500041c'.decode('hex'),\n    sha256    = '3031300d060960864801650304020105000420'.decode('hex'),\n    sha384    = '3041300d060960864801650304020205000430'.decode('hex'),\n    sha512    = '3051300d060960864801650304020305000440'.decode('hex'),\n    ripemd160 = '3021300906052B2403020105000414'.decode('hex'),\n)\n\n\nclass Modulus(univ.OctetString):\n    tagSet = univ.OctetString.tagSet.tagImplicitly(\n        tag.Tag(tag.tagClassUniversal, tag.tagFormatSimple, 0x02)\n    )\n\n\nclass RSAPublicKey(univ.Sequence):\n    componentType = namedtype.NamedTypes(\n        namedtype.NamedType('modulus', Modulus()),\n        namedtype.NamedType('exponent', univ.Integer()),\n    )\n\n    def get_bits(self):\n        # The modulus is always padded with a NULL byte, so here we calculate\n        # the number of data bytes and convert them to bits\n        #return 8 * (len(self.getComponentByName('modulus')._value) - 1)\n        modulus = self.get_modulus()[1:]\n        return 8 * len(modulus)\n\n    def get_exponent(self):\n        return self.getComponentByName('exponent')._value\n\n    def get_modulus(self):\n        return self.getComponentByName('modulus')._value\n\n    def get_modulus_long(self):\n        return bytes_to_long(bytearray(self.get_modulus()))\n\n    def verify(self, signature, data, signature_algorithm):\n        exponent = self.get_exponent()\n        modulus = self.get_modulus_long()\n        modulus_size = num_bytes(modulus)\n        signature_size = len(signature)\n        if modulus_size != signature_size:\n            log.debug(\n                'Signature length {0} does not match our key size {1}'.format(\n                    signature_size,\n                    modulus_size,\n                )\n            )\n            return False\n\n        prefix = self._add_pkcs1_prefix(data, signature_algorithm)\n        padded = self._add_pkcs1_padding(prefix, 1)\n        c = bytes_to_long(bytearray(signature))\n        if c >= modulus:\n            log.debug('Signature data exceeds modulus')\n            return False\n\n        m = pow_mod(c, exponent, modulus)\n        check = long_to_bytes(m, modulus_size)\n        return check == padded\n\n    def _add_pkcs1_prefix(self, data, signature_algorithm):\n        signature_algorithm = signature_algorithm.lower()\n        signature_algorithm = signature_algorithm.replace('withrsaencryption', '')\n        return bytearray(PKCS1_PREFIX.get(signature_algorithm, 0)) + data\n\n    def _add_pkcs1_padding(self, value, block_type):\n        modulus = self.get_modulus_long()\n        modulus_size = num_bytes(modulus)\n        pad_size = (modulus_size - (len(value) + 3))\n\n        if block_type == 1:  # signature padding\n            pad = [0xff] * pad_size\n\n        elif block_type == 2:  # encryption padding\n            pad = bytearray(0)\n            while len(pad) < pad_size:\n                pad_bytes = get_random_bytes(pad_size * 2)\n                pad = filter(None, pad_bytes)\n                pad = pad[:pad_size]\n\n        else:\n            raise TypeError('Invalid block type')\n\n        padding = bytearray([0, block_type] + pad + [0])\n        padded_value = padding + value\n        return padded_value\n","repo_name":"tehmaze/tlsspy","sub_path":"tlsspy/asn1_models/rsa.py","file_name":"rsa.py","file_ext":"py","file_size_in_byte":3611,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"30407998957","text":"from flask.json import jsonify\r\nfrom pymysql.cursors import Cursor\r\nfrom init import mysql\r\nfrom contextlib import closing\r\nimport  datetime\r\n\r\n \r\n \r\ndef get_formaPagoCliente(idComprador):\r\n    try:\r\n        conect = mysql.connect()\r\n        with  closing(conect.cursor()) as cursor:\r\n            cursor.execute('SELECT fp.pago_id, fp.pago_numero_tarjeta FROM tbl_formas_pago fp WHERE fp.pago_comprador=%s',(idComprador))\r\n            result=cursor.fetchall()\r\n            return (result)\r\n    except Exception as ex:\r\n        return ('error', repr(ex))\r\n\r\ndef get_DirrecionEnvio(idComprador):\r\n    try:\r\n        conect = mysql.connect()\r\n        with  closing(conect.cursor()) as cursor:\r\n            cursor.execute('SELECT en.envio_id, d.direcciion_pais,d.direccion_provincia,d.direccion_canton,en.envio_casillero FROM tbl_direccion_envio en inner JOIN tbl_direcciones d on d.direccion_id=en.direccion_id WHERE en.comprador_id=%s',(idComprador))\r\n            result=cursor.fetchall()\r\n            return (result)\r\n    except Exception as ex:\r\n        return ('error', repr(ex))\r\n\r\ndef getCantidadProducto(idComprador):\r\n    try:\r\n        conect = mysql.connect()\r\n        with  closing(conect.cursor()) as cursor:\r\n            cursor.execute('SELECT ca.carrito_cant,p.producto_cantidad,p.producto_precio,p.producto_oferta,p.producto_cost_env,p.producto_id,p.producto_nombre from tbl_carritos ca INNER JOIN tbl_compradores c on c.comprador_id=ca.comprador_id inner JOIN tbl_productos p on p.producto_id=ca.producto_id WHERE c.comprador_id=%s',(idComprador))\r\n            result=cursor.fetchall()\r\n            return (result)\r\n    except Exception as ex:\r\n        return ('error', repr(ex))\r\n\r\ndef getFormaPagoSeleccionada(idFormaPago):\r\n    try:\r\n        conect = mysql.connect()\r\n        with  closing(conect.cursor()) as cursor:\r\n            cursor.execute('SELECT fg.pago_saldo,fg.pago_cvv FROM tbl_formas_pago fg WHERE fg.pago_id=%s',(idFormaPago))\r\n            return ('ok', cursor.fetchone())   \r\n    except Exception as ex:\r\n        return ('error', repr(ex))\r\n\r\ndef insertarFactura(idComprador,idFormaPago,subtotal,factura_total,idDirrecion):\r\n    try:\r\n        fecha=datetime.datetime.now()\r\n        conect = mysql.connect()\r\n        query = \"INSERT INTO tbl_facturas (factura_comprador,factura_metodo_pago,facura_fecha_hora,factura_subtotal,factura_total,envio_id) VALUES (%s,%s,%s,%s,%s,%s)\"\r\n\r\n        data = (idComprador,idFormaPago,fecha,subtotal,factura_total,idDirrecion)\r\n        with closing(conect.cursor()) as cursor:\r\n            cursor.execute(query, data)\r\n            conect.commit()\r\n            id_insert =  cursor.lastrowid \r\n            resp=('ok', id_insert)\r\n        return resp\r\n    except  Exception as ex:\r\n        return ('error', repr(ex))\r\n\r\ndef agregarDetalle(idProducto,idFactura,cantidad,precioFinal):\r\n    try:\r\n        conect = mysql.connect()\r\n        query = \"INSERT INTO  tbl_detalle(detalle_producto,detalle_factura,detalle_cantidad,detalle_precio_final) VALUES (%s,%s,%s,%s)\"\r\n\r\n        data = (idProducto,idFactura,cantidad,precioFinal)\r\n        with closing(conect.cursor()) as cursor:\r\n            cursor.execute(query, data)\r\n            conect.commit()\r\n            id_insert =  cursor.lastrowid \r\n            resp=('ok', id_insert)\r\n        return resp\r\n    except  Exception as ex:\r\n        return ('error', repr(ex))","repo_name":"RoberthFallas/API_REST_proyecto_p4","sub_path":"services/srv_compra.py","file_name":"srv_compra.py","file_ext":"py","file_size_in_byte":3371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"1514744048","text":"from replicator.MultinomialReplicator import MultinomialReplicator, MultinomialReplica\n\n\nclass BinomialReplicator(MultinomialReplicator):\n    def __init__(self, regime):\n        super(BinomialReplicator, self).__init__(regime)\n\n    @staticmethod\n    def _addLossDictFunc(cModel):\n        def addLossDict(lossDict: dict, lossDictsList: list, widthRatio: float, trainedPathIdx: list):\n            trainedPathWidth = cModel.currWidth()\n            trainPathWidthRatio = cModel.currWidthRatio()\n\n            diffList = []\n            for layerIdx, layer in enumerate(cModel.layersList()):\n                diff = trainedPathWidth[layerIdx] - layer.alphaWidthMean().item()\n                diffList.append(diff)\n\n            lossDictsList.append((lossDict, diffList, trainPathWidthRatio))\n\n        return addLossDict\n\n    @staticmethod\n    def iterateOverSamples(replica: MultinomialReplica, lossFunc, data, pathsHistoryDict, lossDictsList, gpu: int):\n        cModel = replica.getModel()\n\n        addLossDictFunc = BinomialReplicator._addLossDictFunc(cModel)\n        generateTrainParams = MultinomialReplicator.generateTrainParams\n\n        BinomialReplicator.evaluateSample(replica, lossFunc, data, pathsHistoryDict, lossDictsList, generateTrainParams, addLossDictFunc)\n\n\nclass BlockBinomialReplicator(MultinomialReplicator):\n    def __init__(self, regime):\n        super(BlockBinomialReplicator, self).__init__(regime)\n\n    @staticmethod\n    def _addLossDictFunc(cModel):\n        def addLossDict(lossDict: dict, lossDictsList: list, widthRatio: float, trainedPathIdx: list):\n            trainedPathWidth = cModel.currWidth()\n            trainPathWidthRatio = cModel.currWidthRatio()\n\n            alphasDict = cModel.alphasDict()\n            widthDiffDict = {}\n            for width, alphaWidth in alphasDict.items():\n                # take one of alpha layers index, in order to get actual alpha width in current partition\n                layerIdx = alphaWidth.layersIdxList()[0]\n                widthDiffDict[width] = trainedPathWidth[layerIdx] - alphaWidth.mean(width).item()\n\n            lossDictsList.append((lossDict, widthDiffDict, trainPathWidthRatio))\n\n        return addLossDict\n\n    @staticmethod\n    def iterateOverSamples(replica: MultinomialReplica, lossFunc, data, pathsHistoryDict, lossDictsList, gpu: int):\n        cModel = replica.getModel()\n\n        addLossDictFunc = BlockBinomialReplicator._addLossDictFunc(cModel)\n        generateTrainParams = MultinomialReplicator.generateTrainParams\n\n        BlockBinomialReplicator.evaluateSample(replica, lossFunc, data, pathsHistoryDict, lossDictsList, generateTrainParams, addLossDictFunc)\n","repo_name":"yochaiz/Slimmable","sub_path":"replicator/BinomialReplicator.py","file_name":"BinomialReplicator.py","file_ext":"py","file_size_in_byte":2645,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"86"}
+{"seq_id":"12734976145","text":"from voluptuous import Any, Schema\nfrom . import ModelApiResource\nfrom ._validation import datetime, datetime_to_str\nfrom ._variable import Variable\n\nSTATUS_INITIALIZING = 'INITIALIZING'\nSTATUS_INCOMPLETE = 'INCOMPLETE'\nSTATUS_COMPLETE = 'COMPLETE'\nSTATUS_EXPIRED = 'EXPIRED'\n\n_forecast_deserialize = Schema({\n    'id': Any(None, str, unicode),\n    'model': Any(str, unicode),\n    'createTime': datetime,\n    'initTime': datetime,\n    'variables': Any(str, unicode),\n    'status': Any(str, unicode),\n    'model_id': Any(str, unicode),\n    'expirationTime': datetime\n})\n\n_forecast_serialize = Schema({\n    'id': Any(None, str, unicode),\n    'model_id': Any(str, unicode),\n    'initTime': datetime_to_str,\n    'status': Any(str, unicode),\n    'expirationTime': datetime_to_str\n})\n\n\nclass _ForecastMixin(object):\n\n    def get_variables(self, **kwargs):\n        return Variable.find(forecast_id=self.id, **kwargs)\n\n    def add_variable(self, platform_product_id, platform_forecast_id):\n        variable = Variable()\n        variable.forecast_id = self.id\n        variable.platform_forecast_product_id = platform_product_id\n        variable.platform_forecast_id = platform_forecast_id\n        variable.save()\n        return variable\n\n\nclass Forecast(ModelApiResource, _ForecastMixin):\n\n    _path = '/models/{model_id}/forecasts'\n\n    _args = Schema({\n        'start': datetime_to_str,\n        'end': datetime_to_str,\n        'initTime': datetime_to_str,\n        'limit': int,\n        'sort': Any('asc', 'desc'),\n        'status': Any(str, unicode)\n    })\n\n    _deserialize = _forecast_deserialize\n\n    _serialize = _forecast_serialize\n\n    @classmethod\n    def find(cls, id_=None, **kwargs):\n        if id_ is not None:\n            return _ForecastById.find(id_)\n        return super(Forecast, cls).find(**kwargs)\n\n    def save(self, **kwargs):\n        if self.id:\n            forecast = _ForecastById()\n            forecast._data = self._data\n            forecast.save(**kwargs)\n            return forecast.id\n        return super(Forecast, self).save(**kwargs)\n\n\nclass _ForecastById(ModelApiResource, _ForecastMixin):\n\n    _path = '/forecasts'\n\n    _deserialize = _forecast_deserialize\n\n    _serialize = _forecast_serialize\n\n\nclass LatestForecast(ModelApiResource, _ForecastMixin):\n\n    _path = '/models/{model_id}/latest-forecast'\n\n    _deserialize = _forecast_deserialize\n\n    _serialize = _forecast_serialize\n","repo_name":"wdtinc/skywise-model-py","sub_path":"skywisemodel/forecast.py","file_name":"forecast.py","file_ext":"py","file_size_in_byte":2409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70161508124","text":"from Stack.Stack import Stack\n\n\n# 利用栈来匹配括号 ([{\ndef par_check(symbolString):\n    s = Stack()\n    is_mark = True\n    index = 0\n    while index < len(symbolString) and is_mark:\n        symbol = symbolString[index]\n        if symbol in '([{':\n            s.push(symbol)\n        else:\n            if s.isEmpty():\n                is_mark = False\n            else:\n                top = s.pop()\n                if not matches(top, symbol):\n                    is_mark = False\n        index = index + 1\n    if is_mark and s.isEmpty():\n        return True\n    else:\n        return False\n\n\ndef matches(top, symbol):\n    opens = '([{'\n    closer = ')]}'\n    return opens.index(top) == closer.index(symbol)\n\n\nprint(par_check('()[]{}'))\nprint(par_check('{[]()()}'))\nprint(par_check('({[][][][]})()'))\n\n","repo_name":"yang529593122/python_study","sub_path":"study_04/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"72210372125","text":"import torch \nfrom torch.autograd import Variable \nimport torchvision.transforms as transforms\nfrom PIL import Image , ImageOps\n\nimgsize = 224, 224 \n\n#PIL 이미지를 tensor 로 변경 해준다. \ndef image_to_tensor(pil_image):\n\t# 이미지 사이지 재설정 \n\tresized = ImageOps.fit(pil_image , imgsize , Image.ANTIALIAS)\n\t# torch tensor 로 변환해준다. \n\tloader = transforms.Compose([\n\t\ttransforms.ToTensor()])\n\treturn loader(resized).unsqueeze(0) \n\n# load model \ndef load_model():\n\treturn torch.load(\"finetunned_model\")\n\n# path 에 있는 이미지의 라벨을 가져 와준다. \ndef is_coat(path):\n\tone_image = load_image(path)\n\timage_tensor = image_to_tensor(one_image)\n\timage_as_variable = Variable(image_tensor)\n\tmodel = load_model()\n\tmodel.eval()\n\tprobabilities = model.forward(image_as_variable)\n\tprint(probabilities)\n\tcoat_prob = get_coat_probability(probabilities)\n\tprint(\"coat probability :{}%\".format(coat_prob*100))\n\tget_probability(probabilities)\n\treturn coat_prob\n\ndef get_probability(all_probabilities):\n\tprobs = all_probabilities.data.numpy()[0]\n\tprint(probs[0])\n\tprint(probs[1])\n\tprint(probs[2])\n\tprint(probs[3])\n\tprint(probs[4])\n\treturn \"stop\"\n\ndef get_coat_probability(all_probabilities):\n\tprobs = all_probabilities.data.numpy()[0]\n\tindex = 0\n\tmaxValue = probs[0]\n\tif probs[0] > probs[1] and probs[0] > probs[2] and probs[0] > probs[3] and probs[0] > probs[4]:\n\t\treturn \"coat\"\n\tif probs[2] > probs[0] and probs[2] > probs[1] and probs[2] > probs[3] and probs[2] > probs[4]:\n\t\treturn \"padding\" \n\tif probs[4] > probs[0] and probs[4] > probs[1] and probs[4] > probs[2] and probs[4] > probs[3]:\n\t\treturn \"short\"\n\tif probs[3] > probs[0] and probs[3] > probs[1] and probs[3] > probs[2] and probs[3] > probs[4]:\n\t\treturn \"shirt\"\n\tif probs[1] > probs[0] and probs[1] > probs[2] and probs[1] > probs[3] and probs[1] > probs[4]:\n\t\treturn \"hood\"\n\n\t\ndef load_image(path):\n\treturn Image.open(path)","repo_name":"kanghowoo/OSSP","sub_path":"WeatherAndCloth_model.py","file_name":"WeatherAndCloth_model.py","file_ext":"py","file_size_in_byte":1915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"2024332266","text":"N = 9\n\ndef output_file(a):\n    with open('output.txt', 'r+') as file:\n        for i in range(N):\n            for j in range(N):\n                print(a[i][j], end=\" \")\n                grid[i][j] = str(grid[i][j])\n                file.write(a[i][j])\n            file.write('\\n')\n            print()\n\n\ndef rowcol(grid, row, col, num):\n    for x in range(9):\n        if grid[row][x] == num:\n            return False\n\n    for x in range(9):\n        if grid[x][col] == num:\n            return False\n\n    start_Row = row - row % 3\n    start_Col = col - col % 3\n    for i in range(3):\n        for j in range(3):\n            if grid[i + start_Row][j + start_Col] == num:\n                return False\n    return True\n\n\ndef solution(grid, row, col):\n    if row == N - 1 and col == N:\n        return True\n    if col == N:\n        row += 1\n        col = 0\n    if grid[row][col] > 0:\n        return solution(grid, row, col + 1)\n    for num in range(1, N + 1, 1):\n        if rowcol(grid, row, col, num):\n            grid[row][col] = num\n            if solution(grid, row, col + 1):\n                return True\n        grid[row][col] = 0\n    return False\n\n\nwith open('input.txt') as file:\n    field = enumerate(file.read().splitlines())\n    grid = list()\n    for index, element in field:\n        grid.append(list(element))\n    for i in range(len(grid)):\n        for j in range(len(grid[0])):\n            grid[i][j] = int(grid[i][j])\n\nif solution(grid, 0, 0):\n    output_file(grid)\nelse:\n    print(\"Impossible\")\n","repo_name":"MaxZmoZg/SUDOKU","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"11024006969","text":"\"\"\"moodle_xml és una extensió d'sphinx que implementa el\n:term:`domain` `moodle` i el :term:`builder` `moodle` que genera el\nformat Moodle XML\n\n\"\"\"\n\nimport functools\n\n\nfrom docutils import nodes\nfrom docutils.parsers.rst import directives, states\n\n\nfrom sphinx.builders.html import SingleFileHTMLBuilder\nfrom sphinx.builders import Builder\nfrom sphinx.domains import Domain, ObjType, Index\nfrom sphinx.locale import l_, _\nfrom sphinx.roles import XRefRole\nfrom sphinx.util.compat import Directive\n\n\nformatMake = \"html\"\n#\n# Nodes del doctree\n#\n\nclass Question(nodes.General, nodes.Element):\n    \"\"\"Question doctree node\"\"\"\n    pass\n\nclass Name(nodes.General, nodes.Element):\n    \"\"\"Name doctree node\"\"\"\n    pass\n\nclass QAText(nodes.General, nodes.Element):\n    \"\"\"QAText doctree node\"\"\"\n    pass\n\nclass QuestionText(nodes.General, nodes.Element):\n    \"\"\"QuestionText doctree node\"\"\"\n    pass\n\nclass Answer(nodes.General, nodes.Element):\n    \"\"\"Answer doctree node\"\"\"\n    pass\n\nclass Feedback(nodes.General, nodes.Element):\n    \"\"\"Feedback doctree node\"\"\"\n    pass\n\n\n#\n# Visitor methods\n#\n\ndef findSections(node):\n    totalSection = 0\n    while node.tagname != 'document':\n        node = node.parent\n        if node.tagname == 'section':\n            totalSection += 1\n    return totalSection+1\n\ndef html_visitors(name, formatMake):\n    def visit(self, node):\n        if formatMake == 'html':\n            if node.parent.tagname == 'Name':\n                self.body.append('<h' + str(findSections(node)) +'>')\n            if node.tagname == 'Feedback' and node.parent.tagname == 'Question':\n                if node.parent.get('realimentacio'):\n                    self.body.append('<table><tr><th>Retroacció Global:</th><td>')\n                else:\n                    self.body.append('<!--')\n        elif formatMake == 'moodle':\n            print(name)\n            d = { k: node.attributes[k] for k in node.attributes if node.attributes[k] != [] }\n            d.pop('single', None)\n            self.body.append(self.starttag(node, '{}'.format(name), **d))\n\n    def depart(self, node):\n        if formatMake == 'html':\n            if node.tagname == 'Feedback' and node.parent.tagname == 'Question':\n                if node.parent.get('realimentacio'):\n                    self.body.append('</td></tr></table><br><ol>')\n                else:\n                    self.body.append('--><ol>')\n            # if name == 'questiontext':\n                # self.body.append('')\n            if node.parent.tagname == 'Name':\n                self.body.append('</h' + str(findSections(node)) +'>')\n            if node.tagname == 'Answer':\n                if node.get('realimentacio'):\n                    self.body.append('</td></tr></table></li><br>')\n                else:\n                    self.body.append('-->')\n        elif formatMake == 'moodle':\n            self.body.append('</{}>\\n'.format(name))\n\n    return visit, depart\n\ndef text_visitors(name, formatMake):\n    v, d = html_visitors(name, formatMake)\n\n    def visit(self, node):\n        v(self, node)\n        if formatMake == 'html':\n            if node.parent.tagname == 'Answer':\n                self.body.append('<li>')\n        elif formatMake == 'moodle':\n            print(name)\n            d = { k: node.attributes[k] for k in node.attributes if node.attributes[k] != [] }\n            d.pop('single', None)\n            self.body.append(self.starttag(node, '{}'.format(name), **d))\n\n    def depart(self, node):\n        # print(node.parent.tagname)\n        if formatMake == 'html':\n            if node.parent.tagname == 'Answer':\n                if node.parent.get('realimentacio'):\n                    self.body.append('<table><tr><th>Puntuació:</th>' +\n                            '<td>' + str(node.parent.get('fraction')) + '%</td></tr>' +\n                            '<tr><th>Retroacció:</th><td>')\n                else:\n                    self.body.append('<!--')\n        elif formatMake == 'moodle':\n            self.body.append('</{}>\\n'.format(name))\n        d(self, node)\n\n    return visit, depart\n\ndef question_visitors(name, formatMake):\n    visit, d = html_visitors(name, formatMake)\n\n    def depart(self, node):\n        if formatMake == 'html':\n            # if node.tagname == 'Feedback' and node.parent.tagname == 'Question':\n            # print(node.child.tagname)\n            # print(node.tagname)\n            self.body.append('</ol>')\n        d(self, node)\n\n    return visit, depart\n\n# def moodle_visitors(name):\n    # def visit(self, node):\n        # d = { k: node.attributes[k] for k in node.attributes if node.attributes[k] != [] }\n        # d.pop('single', None)\n        # self.body.append(self.starttag(node, '{}'.format(name), **d))\n    # def depart(self, node):\n        # self.body.append('</{}>\\n'.format(name))\n    # return visit, depart\n\n\n#\n# Directives\n#\n\ndef opt_type(argument):\n    \"\"\"Conversion function for the \"type\" option.\"\"\"\n    return directives.choice(argument, ('multichoice', 'truefalse'))\n\nclass MoodleQuestion(Directive):\n    \"\"\":rst:dir:`Question directive <moodle:question>`.\n\n    \"\"\"\n\n    required_arguments = 1\n    optional_arguments = 0\n    final_argument_whitespace = True\n    option_spec = {\n        'type': opt_type,\n        'feedback': directives.unchanged,\n        'single': directives.flag,\n    }\n    has_content = True\n\n    def run(self):\n        env = self.state.document.settings.env  # sphinx.environment.BuildEnvironment\n        config = env.config                     # sphinx.config.Config\n        if 'realimentacio' in config:\n            realimentacio = config['realimentacio']\n        else:\n            realimentacio = False\n\n\n        # Raise an error if the directive does not have contents.\n        self.assert_has_content()\n        # Creem un node question, que acabarà de farcir la crida a\n        # `nested_parse`.\n        question_node = Question()\n        question_node['type'] = self.options['type']\n        question_node['single'] = 'single' in self.options\n        question_node['realimentacio'] = realimentacio\n        # Extraiem el títol i l'afegim a un node name\n        title_text = self.arguments[0].strip()\n        text_nodes, messages = self.state.inline_text(title_text,\n                                                      self.lineno)\n        name_node = Name()\n        text_node = QAText()\n        text_node += text_nodes + messages\n        name_node += text_node\n        # Afegim el node name al node question\n        question_node += name_node\n        # Parse the directive contents.\n        question_text_node = QuestionText()\n        # question_text_node['format'] = 'html'\n        textqt_node = QAText()\n        self.state.nested_parse(self.content, self.content_offset,\n                                textqt_node)\n        question_text_node += textqt_node\n        question_node += question_text_node\n        if 'feedback' in self.options:\n            feedback_node = Feedback()\n            # feedback_node['format'] = 'html'\n            feedback_node['fraction'] = self.arguments[0].strip()\n            textf_node = QAText()\n            text_nodes, messages = self.state.inline_text(self.options['feedback'], self.lineno)\n            textf_node += text_nodes + messages\n            feedback_node += textf_node\n            question_node += feedback_node\n        return [question_node]\n\n\nclass MoodleAnswer(Directive):\n    \"\"\"rst:dir:`Answer directive <moodle:question>`\"\"\"\n\n    required_arguments = 1\n    optional_arguments = 0\n    final_argument_whitespace = False\n    option_spec = {\n        'feedback': directives.unchanged,\n    }\n    has_content = True\n\n    def run(self):\n        env = self.state.document.settings.env  # sphinx.environment.BuildEnvironment\n        config = env.config                     # sphinx.config.Config\n        if 'realimentacio' in config:\n            realimentacio = config['realimentacio']\n        else:\n            realimentacio = False\n\n        # Raise an error if the directive does not have contents.\n        self.assert_has_content()\n        text = '\\n'.join(self.content)\n        # Creem un node answer\n        answer_node = Answer()\n        answer_node['fraction'] = self.arguments[0].strip()\n        # answer_node['format'] = 'html'\n        answer_node['realimentacio'] = realimentacio\n        # Creem un node feedback\n        # Parse the directive contents.\n        texta_node = QAText()\n        self.state.nested_parse(self.content, self.content_offset,\n                                texta_node)\n        answer_node += texta_node\n        if 'feedback' in self.options:\n            feedback_node = Feedback()\n            # feedback_node['format'] = 'html'\n            feedback_node['fraction'] = self.arguments[0].strip()\n            textf_node = QAText()\n            text_nodes, messages = self.state.inline_text(self.options['feedback'], self.lineno)\n            textf_node += text_nodes + messages\n            feedback_node += textf_node\n            answer_node += feedback_node\n        return [answer_node]\n\n\n#\n# Dominis\n#\n\nclass MoodleDomain(Domain):\n    \"\"\"Moodle XML language domain.\"\"\"\n    name = 'moodle'\n    label = 'Moodle XML'\n    object_types = {\n        'question':  ObjType(l_('question'),  'quest'),\n    }\n\n    directives = {\n        'question': MoodleQuestion,\n        'answer':   MoodleAnswer,\n    }\n    roles = {\n        'quest':   XRefRole(),\n    }\n    initial_data = {\n        'questions': {},     # questname -> docname, questtype\n    }\n    indices = [\n        # MoodleQuestionIndex,\n    ]\n\n    def clear_doc(self, docname):\n        for questname, (fn, _) in self.data['questions'].items():\n            if fn == docname:\n                del self.data['questions'][questname]\n\n    def get_objects(self):\n        for questname, (docname, questtype) in self.data['questions'].items():\n            yield (questname, questname, questtype, docname, questname, 1)\n\n\n#\n# Builders\n#\n\nclass MoodleBuilder(SingleFileHTMLBuilder):\n    \"\"\"This builder produces Moodle XML format\"\"\"\n\n    name = 'moodle'\n    format = 'moodle'\n    out_suffix = '.xml'\n\n\n#\n# doctree-read handler\n#\n\ndef doctree_read_handler(app, doctree):\n    for node in doctree.traverse(Answer):\n        node.parent.remove(node)\n        node.parent.parent.parent += node\n\n#\n# Inicialització\n#\n\ndef setup(app):\n    app.add_config_value('realimentacio', False, True)\n\n    app.add_builder(MoodleBuilder)\n    app.add_domain(MoodleDomain)\n\n    # global formatMake\n    # formatMake= app.buildername\n\n    app.add_node(Question, html=question_visitors('question', app.buildername))\n    app.add_node(Name, html=html_visitors('name', app.buildername))\n    app.add_node(QAText, html=text_visitors('text', app.buildername))\n    app.add_node(QuestionText, html=html_visitors('questiontext', app.buildername))\n    app.add_node(Answer, html=html_visitors('answer', app.buildername))\n    app.add_node(Feedback, html=html_visitors('feedback', app.buildername))\n\n    # app.add_node(Question, moodle=moodle_visitors('question'))\n    # app.add_node(Name, moodle=moodle_visitors('name'))\n    # # app.add_node(QAText, moodle=moodle_visitors('text'))\n    # app.add_node(QuestionText, moodle=moodle_visitors('questiontext'))\n    # app.add_node(Answer, moodle=moodle_visitors('answer'))\n    # app.add_node(Feedback, moodle=moodle_visitors('feedback'))\n\n    app.connect('doctree-read', doctree_read_handler)\n","repo_name":"ipsums19/moodle_xml","sub_path":"moodle_xml/moodle_xml.py","file_name":"moodle_xml.py","file_ext":"py","file_size_in_byte":11357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74817894043","text":"def hey(phrase):\n    phrase = phrase.strip()\n    question = bool(phrase.endswith('?'))\n    yell = bool(phrase.isupper())\n    if not phrase:\n        return 'Fine. Be that way!'\n    elif question and yell:\n        return 'Calm down, I know what I\\'m doing!'\n    elif question:\n        return 'Sure.'\n    elif yell:\n        return 'Whoa, chill out!'\n    else:\n        return 'Whatever.'\n","repo_name":"confar/exercism","sub_path":"bob/bob.py","file_name":"bob.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"4549505205","text":"from PyQt5.QtCore import Qt\r\nfrom PyQt5.QtWidgets import QApplication, QWidget, QPushButton, QLabel, QVBoxLayout, QHBoxLayout, QRadioButton, QButtonGroup\r\n\r\napp = QApplication([])\r\nwindow = QWidget()\r\nwindow.setWindowTitle('Мое первое приложение')\r\nwindow.resize(600, 400)\r\n\r\nlayout_main = QVBoxLayout()\r\n\r\nlayoutH1 = QHBoxLayout()\r\nlayoutH2 = QHBoxLayout()\r\nlayoutH3 = QHBoxLayout()\r\nlayoutH4 = QHBoxLayout()\r\nlayoutH5 = QHBoxLayout()\r\n\r\nbtn_answer1 = QRadioButton('1')\r\nbtn_answer2 = QRadioButton('2')\r\nbtn_answer3 = QRadioButton('3')\r\nbutton1 = QPushButton('Проверить')\r\ntext = QLabel('')\r\n\r\nbtn_answer1.setChecked(True)\r\n\r\nbutton_group = QButtonGroup()\r\n\r\nbutton_group.addButton(btn_answer1, id = 1)\r\nbutton_group.addButton(btn_answer2, id = 2)\r\nbutton_group.addButton(btn_answer3, id = 3)\r\n\r\nlayoutH1.addWidget(btn_answer1, alignment = Qt.AlignLeft)\r\nlayoutH2.addWidget(btn_answer2, alignment = Qt.AlignLeft)\r\nlayoutH3.addWidget(btn_answer3, alignment = Qt.AlignLeft)\r\nlayoutH4.addWidget(button1, alignment = Qt.AlignCenter)\r\nlayoutH5.addWidget(text, alignment = Qt.AlignCenter)\r\n\r\nbutton_group.checkedId()\r\n\r\ntext.setText(\"Выбрана кнопка под номером \" + str(button_group.checkedId()))\r\n\r\nlayout_main.addLayout(layoutH1)\r\nlayout_main.addLayout(layoutH2)\r\nlayout_main.addLayout(layoutH3)\r\nlayout_main.addLayout(layoutH4)\r\nlayout_main.addLayout(layoutH5)\r\nwindow.setLayout(layout_main)\r\nwindow.show()\r\napp.exec_() ","repo_name":"0mar228/PyQt2","sub_path":"amir5.py","file_name":"amir5.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"1428214700","text":"import os\n\nDEBUG = True\nTESTING = True ##Testing environment for pytest\n##Secret key to handle cookie encryption\nSECRET_KEY = '9e08f244b77f44ca910461519b2e0ed6'\nif os.environ.get('DATABASE_URL') is None:\n    SQLALCHEMY_DATABASE_URI = 'postgresql:///business_finder'\nelse:\n    SQLALCHEMY_DATABASE_URI = os.environ['DATABASE_URL']","repo_name":"dennohtu/businessFinder","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"33073086934","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nfrom configparser import ConfigParser\n\n\nclass ModConfig(object):\n\n    def __init__(self, path):\n        self.cfg = ConfigParser()\n        self.cfg_path = path\n\n        self.read()\n\n    def read(self):\n        if os.path.exists(self.cfg_path):\n            self.cfg.read(self.cfg_path)\n\n        else:\n            self.cfg[\"MODULE\"] = {}\n            self.cfg[\"MODULE\"][\"total\"] = \"lib.cores.mods\"\n            self.cfg[\"MODULE\"][\"enable\"] = \"lib.cores.mods\"\n\n            with open(self.cfg_path, \"w\") as configfile:\n                self.cfg.write(configfile)\n\n    def get_value_by_col_key(self, col, key):\n        result = self.cfg.get(col, key).strip().split(\"\\n\")\n        result = [r for r in result if r]\n\n        return result\n","repo_name":"swoiow/blog","sub_path":"lib/iniReader.py","file_name":"iniReader.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"73141312605","text":"'''\nCS 115 A, Spring 2017 - Test 2, Questions 7 and 8\n\nAuthor: Bhavin Soni\nPledge: I pledge my honor that I have abided by the Stevens Honor System\n'''\n\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\n' RULES: You can use Canvas to download this file and upload your solution.\n' You can use Eclipse to edit and run your program. You should NOT look at\n' other programs in Eclipse, you should NOT use any other programs, and you\n' should NOT use any notes or books.\n' According to the Honor Code, you should report any student who appears\n' to be violating these rules.\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\n\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\n' Question 7 (10 points)\n' Implement this function using recursion.\n' Tribonacci numbers follow a pattern similar to Fibonacci numbers, except\n' that the next number in the sequence is the sum of the previos three\n' numbers.\n' The sequence is as follows: 0, 0, 1, 1, 2, 4, 7, 13, 24, 44, ...\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\ndef tribonacci(n):\n    '''Returns the nth Tribonacci number. The 0th number is 0, the 1st number\n    is 0, the 2nd number is 1, and any number beyond that is the sum of the\n    previous three numbers.\n    Examples:\n    tribonacci(0) -> 0\n    tribonacci(2) -> 1\n    tribonacci(5) -> 4'''\n    if n <= 1:\n        return 0\n    elif n == 2:\n        return 1\n    else:\n        return tribonacci(n-1) + tribonacci(n-2) + tribonacci(n-3)\n\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\n' Question 8 (20 points)\n' Implement this function using recursion AND memoization.\n' Tribonacci numbers follow a pattern similar to Fibonacci numbers, except\n' that the next number in the sequence is the sum of the previos three\n' numbers.\n' The sequence is as follows: 0, 0, 1, 1, 2, 4, 7, 13, 24, 44, ...\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\ndef trib_memo(n):\n    '''Returns the nth Tribonacci number. The 0th number is 0, the 1st number\n    is 0, the 2nd number is 1, and any number beyond that is the sum of the\n    previous three numbers. Uses memoization to improve performance.'''\n    def tribfast(n, memo):\n        if n in memo:\n            return memo[n]\n        elif n <= 1:\n            result = 0\n        elif n == 2:\n            result = 1\n        else:\n            result = tribfast(n-1, memo) + tribfast(n-2, memo) + tribfast(n-3, memo)\n        \n        memo[n] = result\n        return result \n    \n    return tribfast (n, {})\n\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\n' Code to test your work. DO NOT TOUCH.\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\ndef test(test_num, function, arg, expected_output):\n    received = function(arg)\n    try:\n        assert(received == expected_output)\n        print('Test %d passed.' % test_num)\n    except:\n        if isinstance(arg, str):\n            print('Test %d failed: %s(\\'%s\\') should be %s.' %\n                  (test_num, function.__name__, arg, expected_output))\n            print('   -- Received %s.' % received)\n        else:\n            print('Test %d failed: %s(%s) should be %s.' %\n                  (test_num, function.__name__, arg, expected_output))\n            print('   -- Received %s.' % received)\n\ntest(1, tribonacci, 0, 0)\ntest(2, tribonacci, 1, 0)\ntest(3, tribonacci, 2, 1)\ntest(4, tribonacci, 3, 1)\ntest(5, tribonacci, 4, 2)\ntest(6, tribonacci, 7, 13)\ntest(7, tribonacci, 9, 44)\ntest(8, tribonacci, 10, 81)\ntest(9, tribonacci, 11, 149)\ntest(10, tribonacci, 12, 274)\n\ntest(11, trib_memo, 0, 0)\ntest(12, trib_memo, 1, 0)\ntest(13, trib_memo, 2, 1)\ntest(14, trib_memo, 3, 1)\ntest(15, trib_memo, 4, 2)\ntest(16, trib_memo, 7, 13)\n# If you memoize correctly, these function calls will return nearly\n# instantaneously.\ntest(17, trib_memo, 30, 15902591)\ntest(18, trib_memo, 49, 1697490356184)\ntest(19, trib_memo, 50, 3122171529233)\ntest(20, trib_memo, 60, 1383410902447554)\n","repo_name":"bhavsoni/CS115b---Intro-To-Computer-Science","sub_path":"test2_q78.py","file_name":"test2_q78.py","file_ext":"py","file_size_in_byte":4089,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"1143663922","text":"from fastapi import FastAPI\nimport json\nfrom elasticsearch import Elasticsearch, helpers\n\napp = FastAPI()\n\nes_usr = Elasticsearch(\"http://localhost:9200\", timeout=600)\nes_index = \"fortaleza_infected\"\n\n\n@app.get(\"/api\")\ndef read_root():\n    return {\"name\": \"api-server\", \"status\": \"active\"}\n\n\n@app.get(\"/api/users/{device_id}/selfreport\")\ndef get_selfreport(device_id: str):\n    query = {\"query\": {\"term\": {\"mobileId\": {\"value\": device_id}}}}\n    try:\n        return es_usr.search(index=es_index, body=query)\n    except Exception as e:\n        return {\"error\": str(e)}\n","repo_name":"shaojunluo/mobile_covid19_tracker","sub_path":"production/api-server/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"9039389530","text":"import sys, time \t\nimport requests, json\nimport system_handler as sysHand\nfrom tor import getSafeSession\nfrom alternate_agent import getRamdomUserAgent\n\n\ndef getSingleWord(word, proxy, agent):\n\tDATA_STATUS_OK = 200\n\n\turl = \"https://googledictionaryapi.eu-gb.mybluemix.net/?define=\" + word + \"&lang-en\"\n\t#response = requests.get(url)\n\t#response = requests.get(url, proxies={\"http\": proxy, \"https\": proxy}, headers = {'User-Agent': agent})\n\t\n\tresponse = session.get(url, headers=headers)\n\n\tif (response.status_code == DATA_STATUS_OK):\n\t\tif (response.content):\n\t\t\ttry:\n\t\t\t\tdata = json.loads(response.content)\n\t\t\t\tstatusMessage = \"Successfully get the word: \" + word\n\t\t\t\t\n\t\t\t\t#print(data)\n\t\t\t\treturn (data, statusMessage) \n\t\t\texcept:\t\t\t\t\n\t\t\t\tstatusMessage = \"An exception occurred while getting \" + word\n\t\t\t\treturn (None, statusMessage)\n\t\t\t\t\n\t\t\n\telse:\t\t\n\t\tstatusMessage = \"Fail to remotely get the word \" + word\n\t\treturn (None, statusMessage)\n\n\n\ndef RunCode(START_NUMBER, session, headers):\n\tPATH_IN = \"E:/FULLTEXT/SPECIALTY/NLTK_Words_List.txt\"\n\tPATH_DATA_OUT = \"E:/FULLTEXT/GOOGLE/RAW\"\n\tPATH_LOG_OUT = \"E:/FULLTEXT/GOOGLE/LOG\"\n\tSTOP_NUMBER = START_NUMBER + 100\n\n\tpathDataOut, pathStatusOut = sysHand.getIncrementPath(START_NUMBER, PATH_DATA_OUT, PATH_LOG_OUT)\n\n\twordList = sysHand.getWordFromTextFile(PATH_IN)\n\n\tresults = []\n\tstatus = []\n\n\tfor i in range(START_NUMBER, STOP_NUMBER):\n\t    word = wordList[i]\n\t    (data, message) = getSingleWord(word, session, headers)\n\t    print(i, ':',  message)\n\t    status.append(str(i) + ' ' + message)\n\t    if (data):\n\t    \tresults.append(data)\n\t    time.sleep(3)\n\n\tsysHand.writeDataToJSON(results, pathDataOut)\n\tsysHand.writeListToFile(status, pathStatusOut)\n\n\nif __name__ == '__main__':\n\tSTART_NUMBER = 39900\n\tSTOP_NUMBER\t = START_NUMBER + 20000\n\tSTEP_NUMBER = 100\n\tfor i in range(START_NUMBER, STOP_NUMBER, STEP_NUMBER):\n\t\tprint('starting at:', i)\n\t\tsession = getSafeSession()\n\t\tprint('using proxies:', session.proxies)\n\t\tuser_agent = getRamdomUserAgent()\n\t\tprint('using agent:', user_agent)\n\t\theaders = {'User-Agent': user_agent}\n\t\tRunCode(i, session, headers)\n\t\ttime.sleep(10)\n\t\t#initNumber \n\t\t\n","repo_name":"andytrandaoanh/test-dict","sub_path":"google_dic.py","file_name":"google_dic.py","file_ext":"py","file_size_in_byte":2136,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"18698915964","text":"import random\nimport sys\n\naddressRange = 62500000 # 250 GB\nreadRatio = 0.9 # % of read operations, 0 is write operation and 1 is read operation\nmaxRecords = 10000000\ncount = 0\n\nwhile True:\n    op = 0 if random.random() > readRatio else 1\n    print(\"{} {}\".format(random.randint(0,sys.maxsize % addressRange), op))\n    count += 1\n    if count == maxRecords:\n        break\n","repo_name":"MingHongYang/HybridCache","sub_path":"tool/genRandomWorkload.py","file_name":"genRandomWorkload.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"21812230387","text":"#!/usr/bin/python3\n\nimport sys\nfrom xml.dom import minidom\n\nxmlDoc = minidom.getDOMImplementation().createDocument(None, 'disc', None)\nrootNode = xmlDoc.documentElement\n\nfor tagName in ['artist', 'album', 'genre', 'date', 'discnumber']:\n    print('Enter', tagName + ': ', file=sys.stderr)\n    tagValue = sys.stdin.readline().strip()\n    if tagValue != \"\":\n        newNode = rootNode.appendChild(xmlDoc.createElement(tagName))\n        newNode.appendChild(xmlDoc.createTextNode(tagValue))\n\nprint('Enter each track name and press return (Ctrl-D when done)',\n        file=sys.stderr)\ntracks = sys.stdin.readlines()\nfor i in range(len(tracks)):\n    trackTag = rootNode.appendChild(xmlDoc.createElement('track'))\n    titleTag = trackTag.appendChild(xmlDoc.createElement('title'))\n    trackNumTag = trackTag.appendChild(xmlDoc.createElement('tracknumber'))\n    trackNumTag.appendChild(xmlDoc.createTextNode(str(i + 1)))\n    titleTag.appendChild(xmlDoc.createTextNode(tracks[i].strip()))\n\nxmlDoc.writexml(sys.stdout, addindent='    ', newl='\\n', encoding='utf-8')\n","repo_name":"Dockheas23/music-utilities","sub_path":"scripts/quickInfo.py","file_name":"quickInfo.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3559248900","text":"class Solution:\n    def beautySum(self, s: str) -> int:\n        n = len(s)\n        maxm = [0] * 26\n        minm = [float('inf')] * 26\n        left = right = 0\n        ans = 0\n        while left < n:\n            index = ord(s[right]) - ord('a')\n            maxm[index] += 1\n            if minm[index] == float('inf'):\n                minm[index] = 1\n            else:\n                minm[index] += 1\n            maxcount = max(maxm)\n            mincount = min(minm)\n            ans += maxcount - mincount\n            right += 1\n            if right == n:\n                leftindex = ord(s[left]) - ord('a')\n                maxm[leftindex] -= 1\n                minm[leftindex] -= 1\n                if minm[leftindex] == 0:\n                    minm[leftindex] = float('inf')\n                left += 1\n                right = left\n                maxm = [0] * 26\n                minm = [float('inf')] * 26\n        return ans\n\n\ns = \"aabcb\"\nprint(Solution().beautySum(s))\n","repo_name":"chenfu2017/Algorithm","sub_path":"leetcode/5682. 所有子字符串美丽值之和.py","file_name":"5682. 所有子字符串美丽值之和.py","file_ext":"py","file_size_in_byte":967,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"21870710990","text":"import numpy as np\nimport math\nfrom matplotlib import pyplot as plt\nfrom prettytable import PrettyTable\n\n# log(1-x)\nx = 0.5\ntrue_derivative = -1/(1-x)\nh_vector = np.geomspace(0.4, 0.4*math.pow(0.2, 19), num=20)\nderivative_vector = (np.log(1-x-h_vector) - np.log(1-x+h_vector))/(2*h_vector)\n\nerror_vector = np.abs(true_derivative - derivative_vector)\ntable = PrettyTable()\ntable.add_column('h', h_vector)\ntable.add_column('derivative', derivative_vector)\ntable.add_column('Absolute error', error_vector)\nprint(table)\n\nprint(f'The smallest error for h = {h_vector[np.argmin(error_vector)]}')\nfig, ax = plt.subplots()\n\nax.plot(h_vector, error_vector)\nplt.title(\"(log(1-x)'\")\nplt.xlabel('h')\nplt.ylabel('absolute error')\nplt.yscale('log')\nplt.xscale('log')\nplt.show()\n","repo_name":"SzymonK1306/Numerical-methods","sub_path":"MN2/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31086024277","text":"#จงเขียนฟังก์ชั่นสำหรับการ insert แบบ Binary Search Tree (BST) โดยที่ input ตัวแรกจะเป็น root เสมอและจงเขียนฟังก์ชั่นสำหรับการหาค่าที่ใกล้เคียง input ที่รับเข้ามาที่สุดที่อยู่ใน BST ที่ทำการ insert ครบแล้ว\n# รูปแบบการรับ input จะแบ่งโดย '/'\n# 1.ชุดของ BST ที่จะทำการ insert โดยตัวแรกจะเป็น root เสมอ\n# 2.ค่าที่จะนำมาเปรียบเทียบกับค่าใน BST ที่ทำการ insert แล้ว\n\n# รูปแบบ output \n# จะ printTree ทุกครั้งที่มีการ insert ค่าเข้าและเมื่อทำการ insert จบจะเรียกใช้ฟังก์ชั่น closestValue(root,value) และแสดงค่าที่ใกล้เคียงที่สุดจาก BST \n# *** ถ้าหากค่าที่รับเข้ามาเทียบมีอยู่ใน BST ให้ return ค่านั้นออกมาได้เลย และหากมีค่าที่อยู่ใกล้มากกว่า 1 จำนวนให้แสดงจำนวนที่มากที่สุดที่อยู่ใกล้ค่านั้น ***\n\n\nclass Node:\n    def __init__(self, data):\n        self.data = data\n        self.left = None\n        self.right = None\n    \n    def __str__(self):\n        return str(self.data)\n\nclass BST:\n    def __init__(self):\n        self.root = None\n\n    def insert(self, data):\n        if self.root == None:\n            self.root = Node(data)\n            return self.root\n        node = self.root\n        while True:\n            if node.data <= data:\n                if node.right == None:\n                    node.right = Node(data)\n                    return self.root\n                node = node.right\n            else:\n                if node.left == None:\n                    node.left = Node(data)\n                    return self.root\n                node = node.left\n                \n    def printTree(self, node, level = 0):\n        if node != None:\n            self.printTree(node.right, level + 1)\n            print('     ' * level, node)\n            self.printTree(node.left, level + 1)\n\ndef closestValue(node,n):\n    a = node.data\n    son = node.left if n < a else node.right\n    if son == None:\n        return a\n    b = closestValue(son,n)\n    return min((a,b), key=lambda x: abs(n-x))\n            \nif __name__ == \"__main__\":\n    Data = input('Enter Input : ').split('/')\n    cV = int(Data[1])\n    t = BST()\n    inp = [int(i) for i in Data[0].split()]\n    for data in inp:\n        root = t.insert(data)\n        t.printTree(root)\n        print('--------------------------------------------------')\n    print('Closest value of {} : {}'.format(cV,closestValue(t.root,cV)))","repo_name":"j4m3ee/DataStruct","sub_path":"lab08 Tree2/02 Closest Value.py","file_name":"02 Closest Value.py","file_ext":"py","file_size_in_byte":3187,"program_lang":"python","lang":"th","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"6137229226","text":"\nimport os\nimport random\nimport csv\nimport glob\nfrom shutil import copy2\n\n\ndef data_set_split(src_data_folder, target_data_folder, train_scale=0.8, val_scale=0.2, test_scale=0.0, to_csv=True):\n    \"\"\"\n    读取源数据文件夹，生成划分好的文件夹，分为train、validation、test三个文件夹进行\n    :param to_csv: 建立标签文件\n    :param src_data_folder: 源文件夹\n    :param target_data_folder: 目标文件夹\n    :param train_scale: 训练集比例\n    :param val_scale: 验证集比例\n    :param test_scale: 测试集比例\n    :return:\n    \"\"\"\n    print(\"开始数据集划分\")\n    class_names = os.listdir(src_data_folder)\n    # 在目标目录下创建文件夹\n    split_names = ['train', 'validation', 'test']\n    for split_name in split_names:\n        split_path = os.path.join(target_data_folder, split_name)\n        if os.path.isdir(split_path):\n            pass\n        else:\n            os.mkdir(split_path)\n        # 然后在split_path的目录下创建类别文件夹\n        for class_name in class_names:\n            class_split_path = os.path.join(split_path, class_name)\n            if os.path.isdir(class_split_path):\n                pass\n            else:\n                os.mkdir(class_split_path)\n\n    # 按照比例划分数据集，并进行数据图片的复制\n    # 首先进行分类遍历\n    for class_name in class_names:\n        current_class_data_path = os.path.join(src_data_folder, class_name)\n        current_all_data = os.listdir(current_class_data_path)\n        current_data_length = len(current_all_data)\n        current_data_index_list = list(range(current_data_length))\n        random.shuffle(current_data_index_list)\n\n        train_folder = os.path.join(os.path.join(target_data_folder, 'train'), class_name)\n        val_folder = os.path.join(os.path.join(target_data_folder, 'validation'), class_name)\n        test_folder = os.path.join(os.path.join(target_data_folder, 'test'), class_name)\n        train_stop_flag = int(current_data_length * train_scale)\n        val_stop_flag = int(current_data_length * (train_scale + val_scale))\n        current_idx = 0\n        train_num = 0\n        val_num = 0\n        test_num = 0\n        for i in current_data_index_list:\n            src_img_path = os.path.join(current_class_data_path, current_all_data[i])\n            if current_idx < train_stop_flag:\n                copy2(src_img_path, train_folder)\n                # print(\"{}复制到了{}\".format(src_img_path, train_folder))\n                train_num = train_num + 1\n            elif (current_idx >= train_stop_flag) and (current_idx < val_stop_flag):\n                copy2(src_img_path, val_folder)\n                # print(\"{}复制到了{}\".format(src_img_path, val_folder))\n                val_num = val_num + 1\n            else:\n                copy2(src_img_path, test_folder)\n                # print(\"{}复制到了{}\".format(src_img_path, test_folder))\n                test_num = test_num + 1\n\n            current_idx = current_idx + 1\n\n        print('*'*20, \"{}\".format(class_name), '*'*20)\n        print(\n            \"{}类按照{}：{}：{}的比例划分完成，一共{}张图片\".format(class_name, train_scale, val_scale, test_scale, current_data_length))\n        print(\"训练集{}：{}张\".format(train_folder, train_num))\n        print(\"验证集{}：{}张\".format(val_folder, val_num))\n        print(\"测试集{}：{}张\".format(test_folder, test_num))\n    if to_csv:\n        for split_name in split_names:\n            split_path = os.path.join(target_data_folder, split_name)\n            est_csv(split_path)\n\n\ndef est_csv(root: str):\n    name2label = {}\n    for name in sorted(os.listdir(os.path.join(root))):\n        if not os.path.isdir(os.path.join(root, name)):\n            continue\n\n        name2label[name] = len(name2label.keys())\n    images = []\n    for name in name2label.keys():\n\n        images += glob.glob(os.path.join(root, name, '*.png'))\n        images += glob.glob(os.path.join(root, name, '*.jpg'))\n        images += glob.glob(os.path.join(root, name, '*.jpeg'))\n        images += glob.glob(os.path.join(root, name, '*.bmp'))\n\n    print(len(images), images)\n\n    random.shuffle(images)\n    with open(os.path.join(root, 'label.csv'), mode='w', newline='') as f:\n        writer = csv.writer(f)\n        for img in images:\n            name = img.split(os.sep)[-2]\n            label = name2label[name]\n            writer.writerow([img, label])\n        print('writen into csv file:', 'label.csv')\n\n\nif __name__ == '__main__':\n    src_data = \"D:/两个网络/N个分类-偏心0.2\"   # todo 修改你的原始数据集路径\n    target_data = \"E:/服务器连接用/N个分类-偏心0.2\"  # todo 修改为你要存放的路径\n    if not os.path.isdir(target_data):\n        os.makedirs(target_data)\n    data_set_split(src_data, target_data, to_csv=False)\n    # est_csv(\"D:/两个网络/单张拟合-再训练\")\n","repo_name":"Yready/Paper_regression_classification","sub_path":"data_split.py","file_name":"data_split.py","file_ext":"py","file_size_in_byte":4914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"7130791433","text":"from typing import Dict\nimport pandas as pd\n\n\ndef get_item_dict(dataframe: pd.DataFrame) -> Dict[int, float]:\n    \"\"\"Make a dict with all unique items from the dataframe and starting value 0.\n\n    Args:\n        dataframe: the dataframe from which the items are taken.\n\n    Returns:\n        a dictionary with unique items as keys and 0 as values.\n    \"\"\"\n    result = {}\n    for item in dataframe['item'].unique():\n        result[item] = 0\n    return result\n\n\ndef calculate_apc(dataframe: pd.DataFrame) -> Dict[int, float]:\n    \"\"\"Sum up the total artist play count (apc).\n\n    Used in the Kullback-Leibler formula for converting ratings.\n\n    Args:\n        dataframe with an item and rating header.\n\n    Returns:\n        a dictionary with key:item, value:apc.\n    \"\"\"\n    apc = get_item_dict(dataframe)\n\n    for _, row in dataframe.iterrows():\n        apc[row['item']] += row['rating']\n\n    return apc\n\n\ndef calculate_alc(dataframe: pd.DataFrame) -> Dict[int, float]:\n    \"\"\"Sum up the total artist listener count (alc).\n\n    Used in the Kullback-Leibler formula for converting ratings.\n\n    Args:\n        dataframe with an item and rating header.\n\n    Returns:\n        a dictionary with key:item, value:alc.\n    \"\"\"\n    alc = get_item_dict(dataframe)\n\n    for _, row in dataframe.iterrows():\n        alc[row['item']] += 1\n\n    return alc\n","repo_name":"TheMinefreak23/fairreckitlib","sub_path":"src/fairreckitlib/data/ratings/count.py","file_name":"count.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"4485133392","text":"\"\"\"\nVillager class\n\"\"\"\n\nclass Villager(Unit):\n    \"\"\"docstring for Villager class\"\"\"\n\n    def __init__(self):\n        super().__init__()\n        self.wood = 0.388 # wood/sec\n        self.berries = 0.310\n        self.gold = 0.379\n        self.stone = 0.359\n        self.fish = 0.426\n        self.sheep = 0.330\n        self.hunt = 0.408\n        self.farm = 0.319\n\n        self.carry = 10\n        self.speed = 0.8\n\n        self.hp = 40\n        self.attack = 3\n        self.melee_armor = 1\n        self.pierce_armor = 1\n\n        return self\n","repo_name":"james38/aoe2","sub_path":"Villager.py","file_name":"Villager.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"4778558705","text":"from model import Shape_Effi_B7, Shape_Effi_B0, Shape_ResNet152, Shape_ResNet18, MyModel\nfrom utils import train_model, load_data, draw_graph\nimport torch\n\n\ndef main(learning_rate=0.1, batch_size=64, epoch=100):\n    dataloader, class_num = load_data(batch_size=batch_size)\n\n    model = MyModel(class_num=class_num)\n\n    criterion = torch.nn.CrossEntropyLoss()\n    optimizer_adam = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-4)\n\n    lmbda = lambda epoch: 0.98739\n\n    exp_lr_scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer_adam, lr_lambda=lmbda)\n\n    model, best_idx, best_acc, train_loss, train_acc, valid_loss, valid_acc = train_model(model, criterion,\n                                                                                          optimizer_adam,\n                                                                                          exp_lr_scheduler,\n                                                                                          num_epochs=epoch,\n                                                                                          dataloader=dataloader)\n\n    draw_graph(best_idx, train_acc, train_loss, valid_acc, valid_loss)\n\n\nif __name__ == '__main__':\n    learning_rate = 0.0001\n    batch_size = 32\n    epoch = 50\n    main(learning_rate=learning_rate, batch_size=batch_size, epoch=epoch)\n","repo_name":"mindw96/Pill_Shape_Classifier","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"69796318044","text":"from typing import Dict\n\ngraph = {\n    \"a\": [\"c\"],\n    \"b\": [\"c\"],\n    \"c\": [\"f\", \"g\", \"h\"],\n    \"f\": [\"y\"],\n    \"g\": [\"x\"],\n    \"h\": [],\n    \"y\": [\"z\"],\n    \"z\": [],\n    \"x\": [],\n\n}\n\ntopolSorted = []\n\n\ndef topSort(graph, visited, ans):\n    for node in graph:\n        if node not in visited:\n            dfs(graph, visited, node, ans)\n        ans.append(node)\n\n\ndef dfs(graph: Dict, visited: Dict, node: str, ans):\n    visited[node] = True\n    children = graph[node]\n    for child in children:\n        if child not in visited:\n            visited[child] = True\n            dfs(graph, visited, child, ans)\n\n\ntopSort(graph, {}, topolSorted)\n\nprint((topolSorted))\n","repo_name":"haxul/algorithm_tasks_solving","sub_path":"python/tasks/Graphs.py","file_name":"Graphs.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"44529756578","text":"### A python program to perform various operations on a stack using Stack class\n\nfrom stack import Stack\n\ns = Stack()\n\nchoice=0\nwhile choice<5:\n    print(\"Stack operations\")\n    print(\"1. Push element\")\n    print(\"2. Pop element\")\n    print(\"3. Peep element\")\n    print(\"4. Search for element\")\n    print(\"5. Exit\")\n\n    choice = int(input(\"Your choice : \"))\n\n    if choice == 1:\n        element = int(input(\"Enter the element\"))\n        s.push(element)\n\n    elif choice == 2:\n        element = s.pop()\n        if element == -1:\n            print(\"The stack is empty\")\n\n        else:\n            print(\"THe popped element is : \", element)\n\n    elif choice == 3:\n        element = s.peep()\n        print(\"Topmost element : \", element)\n\n    elif choice == 4:\n        element = int(input(\"Enter the element to be searched : \"))\n        pos = s.search(element)\n        if pos == -1:\n            print(\"The stack is empty\")\n        elif pos == -2:\n            print(\"The element not found\")\n        else:\n            print(\"The element is found at : \", pos)\n\n    else:\n        break\n\n    print(\"Stack = \", s.display())","repo_name":"Divyen-Patil/Data-Structures","sub_path":"Stack datastructure.py","file_name":"Stack datastructure.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"40083643792","text":"from numba import njit\n\n@njit\ndef solve(N, A, B, C):\n    ans = 10**10\n    for a in range(0, 10000):\n        for b in range(0, 10000-a):\n            d = N - a*A - b*B\n            if d < 0:\n                break\n            if d % C == 0:\n                c = d//C\n                ans = min(ans, a+b+c)\n    return ans  \n\ndef main():\n    N = int(input())\n    A, B, C = map(int, input().split())\n    print(solve(N, A, B, C))\n\nmain()\n","repo_name":"aruma256/kyopro","sub_path":"src/AtCoder/typical90/016.py","file_name":"016.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"5564455606","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Jan 17 22:20:20 2021\r\n\r\n@author: Mani\r\n\"\"\"\r\n\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.metrics import  accuracy_score , classification_report , confusion_matrix , plot_roc_curve\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\ndef model_test(X_oversample,y_oversample,xtest,ytest,choice = 1):\r\n    if choice == 1:\r\n        from sklearn.linear_model import LogisticRegression\r\n        #from sklearn.model_selection import KFold,cross_val_score\r\n        log=LogisticRegression()\r\n        log.fit(X_oversample,y_oversample)\r\n        ypred=log.predict(xtest)\r\n        #CLASSIFICATION REPORT\r\n        print(classification_report(ytest,ypred))\r\n        #ROC CURVE\r\n        plot_roc_curve(log , xtest , ytest)\r\n        plt.show()\r\n        \r\n        #BIAS AND VARIANCE\r\n        #kf = KFold(shuffle=True , n_splits=5 , random_state=7)\r\n        #score = cross_val_score(log , X , y , cv=kf , scoring='roc_auc')\r\n        #bias1 = np.mean(1-score)\r\n        #variance1 = np.std(score , ddof=1)\r\n        #print(bias1 , variance1)\r\n        \r\n    elif choice == 2:\r\n        from sklearn.naive_bayes import GaussianNB\r\n        from sklearn.model_selection import KFold,cross_val_score\r\n        NB = GaussianNB()\r\n        NB.fit(X_oversample,y_oversample)\r\n        ypred=NB.predict(xtest)\r\n        #CLASSIFICATION REPORT\r\n        print(classification_report(ytest,ypred))\r\n        #ROC CURVE\r\n        plot_roc_curve(NB , xtest , ytest)\r\n        plt.show()\r\n        \r\n    elif choice == 3:\r\n        from sklearn.neighbors import KNeighborsClassifier\r\n        KNN = KNeighborsClassifier()\r\n        KNN.fit(X_oversample,y_oversample)\r\n        ypred=KNN.predict(xtest)\r\n        print(classification_report(ytest,ypred))\r\n        plot_roc_curve(KNN , xtest , ytest)\r\n        plt.show()\r\n        \r\n    elif choice == 4:\r\n        from sklearn.ensemble import GradientBoostingClassifier\r\n        GBoost=GradientBoostingClassifier(n_estimators=100)\r\n        GBoost.fit(X_oversample,y_oversample)\r\n        ypred=GBoost.predict(xtest)\r\n        print(classification_report(ytest,ypred))\r\n        plot_roc_curve(GBoost , xtest , ytest)\r\n        plt.show()\r\n\r\n    elif choice ==5:\r\n        from xgboost import XGBClassifier\r\n        XGB = XGBClassifier()\r\n        XGB\r\n        XGB.fit(X_oversample,y_oversample,eval_metric='auc')\r\n        XGB\r\n        ypred = XGB.predict(xtest)\r\n        print(classification_report(ytest,ypred))\r\n        plot_roc_curve(XGB , xtest , ytest)\r\n        plt.show()","repo_name":"Rajeev01Prasad/DBDA-End-Project","sub_path":"py_scripts/ModelBuilding.py","file_name":"ModelBuilding.py","file_ext":"py","file_size_in_byte":2521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"1694059233","text":"# Banding Machine\nprint('[음료 자판기]')\nprint('1 : 콜라 (1200원) ')\nprint('2 : 사이다 (1000원) ')\nprint('3 : 물 (500원) ')\nprint('4 : 커피 (1500원) ')\noption = input('음료를 골라주세요 (1-4) : ')\npayment = int(input('돈을 넣어주세요 : '))\npayback = payment\nchange = 0\n### Select Menu and Pay\nif payment >= 500:\n    if option == '1':\n        menu = '콜라'\n        change =  payment - 1200\n    elif option == '2':\n        menu = '사이다'\n        change = payment - 1000\n    elif option == '3':\n        menu = '물'\n        change = payment - 500\n    elif option == '4':\n        menu = '커피'\n        change = payment - 1500\n    else:\n        print('매뉴를 다시 선택하여 주세요 (1-4)')\nelse:print('금액이 부족합니다')\n### If Change < 0, Withdraw the Money and Display instruction\nif change < 0: \n    print('금액이 부족합니다. 다른 메뉴를 선택하세요')\nelse:\n    print()\n    print('[', menu, ']가 나왔습니다.')\n    print('잔돈은 ',change, ' 원 입니다.')\n\n\n\n","repo_name":"coolmich0904/Practice_Python","sub_path":"dailyexamples/bandingMachine.py","file_name":"bandingMachine.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"70738866838","text":"from argparse import ArgumentParser\nimport os\nimport random\nimport sys\n\nimport wikipedia\nfrom wikipedia import WikipediaException\n\nbase = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\nsys.path.extend([base])\n\nfrom common.session import get_mongo\nfrom logic import CacheSecretLogic\n\n\ndef main():\n    redis = None  # no need for redis as this is \"dry\"\n    mongo = get_mongo()\n    wikipedia.set_lang('zh')\n    secret = ''\n    while True:\n        try:\n            page = wikipedia.page(wikipedia.random(1)).content\n        except WikipediaException:\n            continue\n        words = list(set(page.split()))\n        secret = random.choice(words)\n        if input(f'I chose {secret[::-1]}. Ok? [Ny] > ') in ('y', 'Y'):\n            break\n\n    csl = CacheSecretLogic(mongo, redis, secret, None)\n    csl.set_secret(dry=True)\n\n    cache = csl.cache[::-1]\n    for rng in (range(1, 11), range(100, 1000, 100)):\n        for i in rng:\n            score, w = cache[i]\n            print(f\"{i}: {score}: {w[::-1]}\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"amashk/semantle-zh","sub_path":"scripts/pick_random_word.py","file_name":"pick_random_word.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"23073727310","text":"import numpy as np \r\nimport pandas as pd\r\n\r\nimport random\r\nimport tensorflow as tf\r\nseed = 1 #(angka dapat berupa 0-9)\r\nrandom.seed(seed)\r\nnp.random.seed(seed)\r\ntf.random.set_seed(seed)\r\n\r\nnp.set_printoptions(suppress=True)\r\ndf = pd.read_excel('dataset/train.xlsx', sheet_name='Sheet1')\r\nprint(df)\r\n\r\nfrom sklearn.preprocessing import LabelEncoder\r\nPredictors = ['IR740nm', 'IR770nm', 'IR800nm', 'IR830nm', 'IR880nm']\r\n\r\ndf = pd.DataFrame(df, columns=['IR740nm', 'IR770nm', 'IR800nm', 'IR830nm', 'IR880nm', 'class'])\r\nx = df.iloc[:, 0:5].values\r\ny = df.iloc[:, -1].values\r\n\r\n# print(x, y)\r\n\r\n\r\ndf.info()\r\n\r\nfrom sklearn.model_selection import train_test_split\r\nx_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)\r\n\r\nprint('X_train  ', x_train.shape)\r\nprint('y_train  ', y_train.shape)\r\nprint('X_test   ', x_test.shape)\r\nprint('y_test   ', y_test.shape)\r\n\r\nfrom sklearn.preprocessing import StandardScaler\r\nsc = StandardScaler()\r\n# scPredict = StandardScaler()\r\n# scTarget = StandardScaler()\r\n\r\n# PredSF = sc.fit(x)\r\n# TarVarSF = sc.fit(y)\r\n\r\n# x = PredSF.transform(x)\r\n# y = TarVarSF.transform(y)\r\n# scPredictFit = scPredict.fit(x)\r\n# scTargetFit = scTarget.fit(y)\r\n\r\n# xx = scPredictFit.transform(x)\r\n# yy = scTargetFit.transform(y)\r\n\r\nx_train = sc.fit_transform(x_train)\r\nx_test = sc.fit_transform(x_test)\r\n# y_train = sc.fit_transform(y_train)\r\n# y_test = sc.fit_transform(y_test)\r\n\r\n# y_test = sc.fit_transform(y_test)\r\n# y_train = sc.fit_transform(y)\r\n# y_test = sc.fit_transform(y)\r\nprint(x_train)\r\nprint(x_test)\r\nprint(y_train)\r\nprint(y_test)\r\nimport tensorflow as tf \r\n\r\nmodel = tf.keras.models.Sequential()\r\nmodel.add(tf.keras.layers.Dense(units=5, activation='relu'))\r\nmodel.add(tf.keras.layers.Dense(units=10, activation='relu'))\r\nmodel.add(tf.keras.layers.Dense(units=10, activation='relu'))\r\n# model.add(tf.keras.layers.Dense(units=20, activation='relu'))\r\nmodel.add(tf.keras.layers.Dense(units=3, activation='softmax'))\r\n\r\nmodel.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\r\n\r\nhistory = model.fit(x_train, y_train, epochs=800, batch_size=128)\r\n\r\n\r\n\r\n\r\n\r\nPredictions= model.predict(x_test)\r\nprint('Predictions\\n',Predictions)\r\n\r\n# y_test = sc.fit_transform(x)\r\n\r\nfor i in range(len(Predictions)):\r\n\tprint(np.argmax(Predictions[i]))\r\n# scaling the predicted kadar/alb data back to original scale\r\n# Predictions= x_train.inverse_transform(Predictions)\r\n# print('Predictions\\n',Predictions)\r\n\r\n# y_test_orig=y_test.inverse_transform(y_test)\r\n# print('y_test_orig',y_test_orig)\r\n\r\n# # # scaling the y_test kadar/alb data to back original scale\r\n# # x_test= x_test.inverse_transform(x_test)\r\n# # print('x_test_orig\\n',x_test_orig)\r\n\r\n# # # scaling the test data back to original scale\r\n# Test_Data= x_test.inverse_transform(x_test)\r\n# print('Test_Data\\n',Test_Data)\r\n\r\n# # y_pred = model.predict(x_test)\r\n# # print('Predictions\\n', y_pred) \r\n\r\n# TestingData= pd.DataFrame(data=Test_Data, columns=Predictors)\r\n# print('TestingData\\n',TestingData)\r\n# TestingData['kelas']= y_test_orig\r\n# TestingData['Predicted kelas']= Predictions\r\n# TestingData.head()\r\n\r\n\r\n\r\n\r\ntesting = model.evaluate(x_test, y_test, batch_size=10)\r\n\r\nhistory.history.keys()\r\n# testing.keys()\r\n\r\nimport matplotlib.pyplot as plt\r\n\r\nplt.plot(history.history['loss'])\r\nplt.title('model loss')\r\nplt.ylabel('loss')\r\nplt.xlabel('epoch')\r\nplt.legend(['train'], loc='upper left')\r\nplt.show()\r\n\r\n\r\nplt.plot(history.history['accuracy'])\r\nplt.title('model accuracy')\r\nplt.ylabel('accuracy')\r\nplt.xlabel('epoch')\r\nplt.legend(['train'], loc='upper left')\r\nplt.show()\r\n\r\n\r\n\r\n\r\n######################################################\r\n# ANN for prediction Kadar, ALB #panjang gelombang NIR\r\n\r\n# import pandas as pd \r\n# import numpy as np \r\n\r\n# # untuk menghindari perubahan nilai hasil setiap running\r\n# import random\r\n# import tensorflow as tf\r\n\r\n# seed = 1 #(angka dapat berupa 0-9)\r\n# random.seed(seed)\r\n# np.random.seed(seed)\r\n# tf.random.set_seed(seed)\r\n\r\n\r\n# # to remove the scientific notation from numpy arrays\r\n# np.set_printoptions(suppress=True)\r\n\r\n# # read the data\r\n# Data = pd.read_excel('dataset/train.xlsx', sheet_name='Sheet1')\r\n# Data.head()\r\n\r\n# # separate the target and predictor variables\r\n# # TargetVariable    = ['Kadar']\r\n# TargetVariable = ['class']\r\n\r\n# # Predictors = ['IR770nm', 'IR800nm', 'IR830nm', 'IR880nm']\r\n# Predictors = ['IR740nm', 'IR800nm', 'IR880nm']\r\n\r\n# X = Data[Predictors].values\r\n# y = Data[TargetVariable].values\r\n\r\n# Data.info()\r\n\r\n# ## standarization of data ##\r\n# from sklearn.preprocessing import StandardScaler\r\n# PredictorsScaler  = StandardScaler()\r\n# TargetVariScaler = StandardScaler()\r\n\r\n# # storing the fit object for later reference\r\n# PredictorsScalerFit\t= PredictorsScaler.fit(X)\r\n# TargetVariScalerFit = TargetVariScaler.fit(y) \r\n\r\n# # generating the standarized values of X and y\r\n# X = PredictorsScalerFit.transform(X)\r\n# y = TargetVariScalerFit.transform(y.reshape(-1))[:,0]\r\n\r\n\r\n# # split the data into training and testing set\r\n# from sklearn.model_selection import train_test_split\r\n# # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state= 42) # karena depan belakang, 0.2 = 42 data\r\n# # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, random_state= 42)\r\n# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state= 0)\r\n\r\n# print(X_train)\r\n# print(X_test)\r\n# print(y_train)\r\n# print(y_test)\r\n# # quick sanity check with the shapes of training and testing datasets\r\n# print('X_train  ', X_train.shape)\r\n# print('y_train  ', y_train.shape)\r\n# print('X_test   ', X_test.shape)\r\n# print('y_test   ', y_test.shape)\r\n\r\n\r\n\r\n# # importing the libraries\r\n# from keras.models import Sequential\r\n# from keras.layers import Dense\r\n\r\n# # Create ANN model\r\n# model = Sequential()\r\n# print(model)\r\n\r\n# # defining the input layer and first hidden layer, both are same!\r\n# model.add(Dense(units=3, activation='relu'))\r\n\r\n# # defining the second layer of the model\r\n# # after the fist layer we don't have to specify input_dim as keras configure it automatically\r\n# model.add(Dense(units=10, activation='relu'))\r\n# model.add(Dense(units=10, activation='relu'))\r\n\r\n# # model.add(Dense(units=5, kernel_initializer='normal', activation='tanh'))\r\n\r\n# # the output neuron is a single fully connected node\r\n# # since we will be predicting a single number\r\n# model.add(Dense(units=3, activation='softmax'))\r\n\r\n# # compiling the model\r\n# model.compile(optimizer='rmsprop', loss='kullback_leibler_divergence', metrics=['accuracy'])\r\n\r\n# # fitting the ANN to the training set\r\n# # model.fit(X_train, y_train, batch_size=5, epochs=50, verbose=1)\r\n\r\n# # a = model.fit(X_train, y_train, batch_size=2, epochs=50, verbose=1)\r\n# # print(a)\r\n\r\n# ####################################################################################################\r\n# ###=================== defining a function to find the best parameters for ANN===================###\r\n# # def FunctionFindBestParams(X_train, y_train, X_test, y_test):\r\n\r\n# # \t# defining the list of hyper parameters to try\r\n# # \tbatch_size_list = [5,10,15,20, 25, 30, 50]\r\n# # \tepochs_list\t\t= [5,10,50,100, 200, 400, 1000]\r\n\r\n# # \timport pandas as pd \r\n# # \tSearchResultsData=pd.DataFrame(columns=['TrialNumber', 'Parameters', 'Accuracy'])\r\n\r\n# # \t# initializing the trials\r\n# # \tTrialNumber=0\r\n# # \tfor batch_size_trial in batch_size_list:\r\n# # \t\tfor epochs_trial in epochs_list:\r\n# # \t\t\tTrialNumber+=1\r\n\r\n# # \t\t\t# create ANN model\r\n# # \t\t\tmodel = Sequential()\r\n\r\n# # \t\t\t# defining the first layer of the model\r\n# # \t\t\tmodel.add(Dense(units=5, input_dim=X_train.shape[1], kernel_initializer='normal', activation='relu'))\r\n\r\n# # \t\t\t# defining the second layer of the model\r\n# # \t\t\tmodel.add(Dense(units=5, kernel_initializer='normal', activation='relu'))\r\n\r\n# # \t\t\t# the output neuron is a single fully connected node\r\n# # \t\t\t# since we will be predciting a single number\r\n# # \t\t\tmodel.add(Dense(units=1, kernel_initializer='normal'))\r\n\r\n# # \t\t\t# compiling the model\r\n# # \t\t\tmodel.compile(loss='mean_squared_error', optimizer='adam')\r\n\r\n# # \t\t\t# fitting the ANN to the training set\r\n# # \t\t\tmodel.fit(X_train, y_train, batch_size = batch_size_trial, verbose=0)\r\n\r\n# # \t\t\tMAPE = np.mean(100*(np.abs(y_test - model.predict(X_test)) / y_test))\r\n\r\n# # \t\t\t# printing the results of the current iteration\r\n# # \t\t\tprint(TrialNumber, 'Parameters:', 'batch_size:', batch_size_trial, '-', 'epochs:', epochs_trial, '-', 'Accuracy:', 100-MAPE)\r\n\r\n# # \t\t\tSearchResultsData=SearchResultsData.append(pd.DataFrame(data=[[TrialNumber, str(batch_size_trial)+'-'+str(epochs_trial), 100-MAPE]],\r\n# # \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcolumns=['TrialNumber', 'Parameters', 'Accuracy']))\r\n\r\n# # \treturn(SearchResultsData)\r\n\r\n\r\n\r\n# # ############################################\r\n# # # calling the function\r\n# # ResultsData = FunctionFindBestParams(X_train, y_train, X_test, y_test)\r\n\r\n\r\n\r\n# # # %matplotlib inline\r\n# # import matplotlib.pyplot as plt \r\n# # ResultsData.plot(x='Parameters', y='Accuracy', figsize=(15,4), kind='line')\r\n\r\n# # plt.show()\r\n\r\n\r\n\r\n# #################################################\r\n# # didapatkan parameter terbaik batch_size = 10, epochd=1000\r\n# # Using the best set of parameters found, training the model again and predicting the kadar/alb on testing data.\r\n\r\n# # fitting the ANN to the training data set\r\n# history = model.fit(X_train, y_train, batch_size=128, epochs=100)\r\n# print('history\\n',history)\r\n\r\n# # generating predictions on testing data\r\n# Predictions= model.predict(X_test)\r\n# print('Predictions\\n',Predictions)\r\n\r\n# # scaling the predicted kadar/alb data back to original scale\r\n# Predictions= TargetVariScalerFit.inverse_transform(Predictions)\r\n# print('Predictions\\n',Predictions)\r\n\r\n# # scaling the y_test kadar/alb data to back original scale\r\n# y_test_orig=TargetVariScalerFit.inverse_transform(y_test)\r\n# print('y_test_orig\\n',y_test_orig)\r\n\r\n# # scaling the test data back to original scale\r\n# Test_Data= PredictorsScalerFit.inverse_transform(X_test)\r\n# print('Test_Data\\n',Test_Data)\r\n\r\n# TestingData= pd.DataFrame(data=Test_Data, columns=Predictors)\r\n# print('TestingData\\n',TestingData)\r\n# TestingData['kelas']= y_test_orig\r\n# TestingData['Predicted kelas']= Predictions\r\n# TestingData.head()\r\n\r\n\r\n# # TestingData= pd.DataFrame(data=Test_Data, columns=Predictors)\r\n# # TestingData['ALB']= y_test_orig\r\n# # TestingData['Predicted ALB']= Predictions\r\n# # TestingData.head()\r\n\r\n\r\n# print(TestingData)\r\n\r\n\r\n# ######\r\n# \"\"\" Using the final trained model, now we are generating the prediction error \r\n# for each row in testing data as the Absolute Percentage Error.Taking the average \r\n# for all the rows is known as Mean Absolute Percentage Error(MAPE) \"\"\"\r\n\r\n\r\n# # computing the absolute percent error\r\n# APE= 100*(abs(TestingData['kelas'] - TestingData['Predicted kelas']) / TestingData['kelas'])\r\n# TestingData['APE']= APE \r\n\r\n# print('The Accuracy of ANN model is:', 100 - np.mean(APE))\r\n# TestingData.head()\r\n\r\n# # \"\"\" \r\n# # \tSaat pencarian parameter terbaik, hasil yang didapatkan akan selalu berubah, begitu juga\r\n# # \tdengan hasil prediksi akan selalu berubah jika dilakukan running ulang program py nya, ini\r\n# # \tterjadi karena :\r\n\r\n# # \t\t\tWhy the accuracy comes different every time I train ANN?\r\n\r\n# # \tEven when you use the same hyperparameters, the result will be slightly \r\n# # \tdifferent for each run of ANN. This happens because the initial step for \r\n# # \tANN is the random initialization of weights. So every time you run the code, \r\n# # \tthere are different values that get assigned to each neuron as weights and bias,\r\n# # \t hence the final outcome also differs slightly.\r\n\r\n# # untuk mengatasi hal tsb kita harus menambahkan : (diawal program)\r\n\t\t\t\t\t\r\n# # seed = 1 (angka dapat berupa 0-9)\r\n# # random.seed(seed)\r\n# # np.random.seed(seed)\r\n# # tf.random.set_seed(seed)\r\n\r\n# # \"\"\"\r\n\r\n# history.history.keys()\r\n\r\n# import matplotlib.pyplot as plt\r\n\r\n# plt.plot(history.history['loss'])\r\n# plt.title('model loss')\r\n# plt.ylabel('loss')\r\n# plt.xlabel('epoch')\r\n# plt.legend(['train'], loc='upper left')\r\n# plt.show()\r\n\r\n\r\n# plt.plot(history.history['accuracy'])\r\n# plt.title('model accuracy')\r\n# plt.ylabel('accuracy')\r\n# plt.xlabel('epoch')\r\n# plt.legend(['train'], loc='upper left')\r\n# plt.show()","repo_name":"dilham1108/ANNBackProp","sub_path":"modellBackProp.py","file_name":"modellBackProp.py","file_ext":"py","file_size_in_byte":12342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"18443053784","text":"def listes_proprietes(numero_joueur, monopoly, joueurs):\r\n    L=[]\r\n    for l in monopoly['possédé']:\r\n        if monopoly['possédé'][l]==numero_joueur :\r\n            L.append(l)\r\n    return (L)\r\n\r\ndef revendre(dette, numero_joueur, indice_proprietaire, monopoly, joueurs): #La dette est égale à la somme que le joueur doit payer moins l'argent qu'il possédait.\r\n    print(\"Vous devez vendre des propriétés pour régler votre dette.\")\r\n    somme=0\r\n    print(\"Voici les propriété que vous possédez et leurs prix :\")\r\n    L=listes_proprietes(numero_joueur, monopoly, joueurs)\r\n    for l in L:\r\n        print(l + \" peut se vendre à \" + str(int(monopoly['prix_achat'][l]*0.9)) + \".\")\r\n        somme+=monopoly['prix_achat'][l]\r\n    if somme >= dette : #La somme des valeurs de toutes ses propriétés est supérieur à ça dette, il peut donc la rembourser \r\n        print(\"Vous pouvez rembourser votre dette.\")\r\n        restant=dette\r\n        while restant > 0 : # Il faut vendre jusqu'à ce qu'on puisse rembourser.\r\n            print(\"Il reste \" + str(restant) + \" à rembourser.\")\r\n            choix = input(\"Entrez le nom du bien que vous souhaitez vendre. \")\r\n            if choix in L :\r\n                restant-=int(monopoly['prix_achat'][choix]*0.9)\r\n                monopoly['possédé'][choix]=0\r\n                L.remove(choix)\r\n        joueurs['argent'][str(numero_joueur)]+=(-1)*restant\r\n        joueurs['argent'][str(indice_proprietaire)]+=dette\r\n        print(\"Votre dette est remboursée, l'argent restant de la vente est ajouté à votre compte.\")\r\n    else :\r\n        print(\"Il vous est impossible de rembourser votre dette.\")\r\n        for l in L :\r\n            monopoly['possédé'][l]=0\r\n        joueurs['argent'][str(indice_proprietaire)]+=somme\r\n        joueurs['argent'][str(numero_joueur)]=somme-dette # une valeur négative d'argent implique une défaite\r\n\r\n","repo_name":"MandragoreVR/Monopoly","sub_path":"Sprint 2 Finalisé/revente.py","file_name":"revente.py","file_ext":"py","file_size_in_byte":1893,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"16524118166","text":"import cv2\r\n\r\ndef on_trackbar(x):\r\n    pass\r\n\r\ncv2.namedWindow('Canny')\r\n\r\ncv2.createTrackbar('low threshold', 'Canny', 0, 1000, on_trackbar)\r\ncv2.createTrackbar('high threshold', 'Canny', 0, 1000, on_trackbar)\r\n\r\ncv2.setTrackbarPos('low threshold', 'Canny', 50)\r\ncv2.setTrackbarPos('high threshold', 'Canny', 150)\r\n\r\nimg_gray = cv2.imread('yeon000.jpg', cv2.IMREAD_GRAYSCALE)\r\n\r\nwhile(1):\r\n    low = cv2.getTrackbarPos('low threshold', 'Canny')\r\n    high = cv2.getTrackbarPos('high threshold', 'Canny')\r\n\r\n    img_canny = cv2.Canny(img_gray, low, high)\r\n\r\n    cv2.imshow('Canny', img_canny)\r\n\r\n    if cv2.waitKey(1) & 0xFF == 13:\r\n        cv2.imwrite(\"result.jpg\", img_canny)\r\n        break\r\n\r\n    if cv2.waitKey(1) & 0xFF == 27:\r\n        break\r\n\r\ncv2.destroyAllWindows()\r\n\r\nimg_result = cv2.imread('result.jpg')\r\nkernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))\r\nimg_result = cv2.dilate(img_result, kernel, iterations = 1)\r\n\r\nimg_r = 255 - img_canny\r\ncv2.imwrite(\"r.jpg\", img_r)\r\ncv2.imshow('result', img_result)\r\ncv2.waitKey()\r\n","repo_name":"songdogeon/Pixel-Art","sub_path":"drawing.py","file_name":"drawing.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"70052604119","text":"import gettext\nimport re\n\nfrom otopi import plugin\nfrom otopi import util\n\nfrom ovirt_engine_setup import constants as osetupcons\n\n\ndef _(m):\n    return gettext.dgettext(message=m, domain='ovirt-engine-setup')\n\n\n@util.export\nclass Plugin(plugin.PluginBase):\n    \"\"\"\n    SELinux configuration plugin.\n    \"\"\"\n\n    def __init__(self, context):\n        super(Plugin, self).__init__(context=context)\n        self._enabled = True\n\n    @plugin.event(\n        stage=plugin.Stages.STAGE_INIT,\n    )\n    def _init(self):\n        self.environment[osetupcons.SystemEnv.SELINUX_CONTEXTS] = []\n        self.environment[osetupcons.SystemEnv.SELINUX_RESTORE_PATHS] = []\n        self.environment[osetupcons.SystemEnv.SELINUX_BOOLEANS] = []\n        self.environment[osetupcons.SystemEnv.SELINUX_PORTS] = []\n\n    @plugin.event(\n        stage=plugin.Stages.STAGE_SETUP,\n        condition=lambda self: self._enabled,\n    )\n    def _setup(self):\n        self.command.detect('selinuxenabled')\n        self.command.detect('semanage')\n        self.command.detect('semodule')\n        self.command.detect('restorecon')\n\n    @plugin.event(\n        stage=plugin.Stages.STAGE_VALIDATION,\n        condition=lambda self: self._enabled,\n    )\n    def _validation(self):\n        if (\n            self.environment[osetupcons.CoreEnv.DEVELOPER_MODE] or\n            self.command.get('selinuxenabled', optional=True) is None\n        ):\n            self._enabled = False\n        else:\n            rc, stdout, stderr = self.execute(\n                (\n                    self.command.get('selinuxenabled'),\n                ),\n                raiseOnError=False,\n            )\n            self._enabled = rc == 0\n\n    @plugin.event(\n        stage=plugin.Stages.STAGE_MISC,\n        condition=lambda self: self._enabled,\n        name=osetupcons.Stages.SETUP_SELINUX,\n        priority=plugin.Stages.PRIORITY_LOW,\n    )\n    def _misc(self):\n        for entry in self.environment[osetupcons.SystemEnv.SELINUX_PORTS]:\n            rc, stdout, stderr = self.execute(\n                (self.command.get('semanage'), 'port', '-l')\n            )\n            if not any(\n                re.match(\n                    '{t}.*{p}'.format(t=entry['type'], p=entry['port']),\n                    line\n                ) for line in stdout\n            ):\n                rc, stdout, stderr = self.execute(\n                    (\n                        self.command.get('semanage'),\n                        'port',\n                        '-a',\n                        '-t', entry['type'],\n                        '-p', entry['protocol'],\n                        entry['port']\n                    ),\n                )\n        for entry in self.environment[osetupcons.SystemEnv.SELINUX_CONTEXTS]:\n            rc, stdout, stderr = self.execute(\n                (self.command.get('semanage'), 'fcontext', '-C', '-l')\n            )\n            if not any(\n                re.match(\n                    '{p}.*{t}'.format(p=entry['pattern'], t=entry['type']),\n                    line\n                ) for line in stdout\n            ):\n                rc, stdout, stderr = self.execute(\n                    (\n                        self.command.get('semanage'),\n                        'fcontext',\n                        '-a',\n                        '-t', entry['type'],\n                        entry['pattern']\n                    )\n                )\n        for path in self.environment[\n            osetupcons.SystemEnv.SELINUX_RESTORE_PATHS\n        ]:\n            rc, stdout, stderr = self.execute(\n                (\n                    self.command.get('restorecon'),\n                    '-r',\n                    path\n                )\n            )\n            if rc != 0:\n                self.logger.error(\n                    _('Failed to refresh SELINUX context for {path}').format(\n                        path=path\n                    )\n                )\n        for entry in self.environment[osetupcons.SystemEnv.SELINUX_BOOLEANS]:\n            rc, stdout, stderr = self.execute(\n                (\n                    self.command.get('semanage'),\n                    'boolean',\n                    '--modify',\n                    '--{state}'.format(state=entry['state']),\n                    entry['boolean']\n                )\n            )\n            if rc != 0:\n                self.logger.error(\n                    _(\n                        'Failed to modify selinux boolean {boolean}, please '\n                        'make sure it is set to {state}.'\n                    ).format(\n                        boolean=entry['boolean'],\n                        state=entry['state'],\n                    )\n                )\n\n\n# vim: expandtab tabstop=4 shiftwidth=4\n","repo_name":"oVirt/ovirt-engine","sub_path":"packaging/setup/plugins/ovirt-engine-common/base/system/selinux.py","file_name":"selinux.py","file_ext":"py","file_size_in_byte":4716,"program_lang":"python","lang":"en","doc_type":"code","stars":452,"dataset":"github-code","pt":"85"}
+{"seq_id":"74786013718","text":"\"\"\"\nFile: anagram.py\nName: Eden\n----------------------------------\nThis program recursively finds all the anagram(s)\nfor the word input by user and terminates when the\ninput string matches the EXIT constant defined\nat line 19\n\nIf you correctly implement this program, you should see the\nnumber of anagrams for each word listed below:\n    * arm -> 3 anagrams\n    * contains -> 5 anagrams\n    * stop -> 6 anagrams\n    * tesla -> 10 anagrams\n    * spear -> 12 anagrams\n\"\"\"\n\n# Constants\nFILE = 'dictionary.txt'     # This is the filename of an English dictionary\nEXIT = '-1'                 # Controls when to stop the loop\n\ncount = 0\nword_list = []              # The input words' index put in this list\nchosen_list = []            # The letter program choose will be put in this list\npython_list = []            # Take the words in dictionary to this list\nanswer_list = []            # The anagrams this program find\n\n\ndef main():\n    global word_list, chosen_list, python_list, answer_list\n\n    read_dictionary()\n    print('Welcome to stanCode \"Anagrams Generator\" (or ' + str(EXIT) + ' to quit) ')\n\n    while True:\n        word = input('Find anagrams for: ')\n        if word == EXIT:\n            break\n\n        print('Searching...')\n        find_anagrams(word)\n\n        print(f\"{len(answer_list)} anagrams: {answer_list}\")\n\n        word_list = []\n        answer_list = []\n\n\ndef read_dictionary():\n    global python_list\n    with open(FILE, 'r') as f:\n        for line in f:\n            line = line.split()\n            python_list += line\n\n\ndef find_anagrams(s):\n    \"\"\"\n    This function use recursion to find all the possible permutations of s (the user input).\n    :param s: string, the user input word\n    :return: nothing / this function only adds words into the global variable'searching_list'\n    \"\"\"\n\n    # Take the index of input word to word_list\n    for i in range(len(s)):\n        word_list.append(i)\n\n    find_anagrams_helper(s)\n\n\ndef find_anagrams_helper(s):\n    global count, answer_list, word_list, chosen_list\n\n    if len(chosen_list) == len(word_list):\n\n        # Use index to convert into words\n        ans = ''\n        for ch in chosen_list:\n            ans += s[ch]\n\n        if ans in python_list and ans not in answer_list:\n            answer_list.append(ans)\n            print(ans)\n            print('Searching...')\n\n    else:\n        answer = ''\n        if has_prefix(answer) is True:\n            for j in range(len(word_list)):\n                element = word_list[j]\n\n                if element not in chosen_list:\n\n                    chosen_list.append(element)\n                    answer += s[element]\n                    find_anagrams_helper(s)\n\n                    chosen_list.pop()\n\n\ndef has_prefix(sub_s):\n\n    global word_list, chosen_list, python_list\n\n    \"\"\"\n    This function can make sure that the current string(recorded in index) is in the dictionary.\n    (or it will return False and stop finding.\n    :param sub_s: a sub_string we want to search\n    :return: (bool) If there is any words with prefix stored in current list(recorded in index)\n    \"\"\"\n\n    for word in python_list:\n        if word.startswith(sub_s) is True:\n            return True\n    return False\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"EdenHuang2056/sc_project","sub_path":"stancode_project/boggle_game_solver/anagram.py","file_name":"anagram.py","file_ext":"py","file_size_in_byte":3245,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"28138553458","text":"'''\r\nThe Coin Flip Game\r\nv2.0\r\n-----------------\r\nProject: [bini-tek]\\\\\r\nCode by: Ash Dawod\r\n\r\n2018\r\n'''\r\nimport math\r\nimport random\r\n\r\n'''\r\nThis is a simple game where the computer randomly \"flips a coin\" and tells you how many times it get \"Heads\" or \"Tails\"\r\nThe user gets to tell the computer how many times to flip the coin\r\n'''\r\n\r\n'''\r\n//////////////////////\r\nDefining the Functions\r\n//////////////////////\r\n'''\r\n\r\n#-- The coin_flip() Function\r\n#---------------------------\r\ndef coin_flip():\r\n    #Ask user how many flips should it do\r\n    howMany = int(input(\"\\nHow many times do you want to flip the coin? \"))\r\n\r\n    # Set up Heads & Tails as 0\r\n    h = 0  # <-- H for Heads\r\n    t = 0  # <-- T for Tails\r\n\r\n\r\n    #Setting up the FLIP WHILE loop\r\n    #This is to go through the number of flips\r\n    i = 0 # <-- the counter for the While loop\r\n    while (i < howMany):\r\n        # The random Flip (f)\r\n        # The range is between 1 and 2\r\n        f = random.randint(1,2)\r\n\r\n        # If the Flip (f) is 1, add one to Heads (h)\r\n        if f == 1:\r\n            h = h + 1\r\n\r\n        # If the Flip (f) is NOT 1, add one to Tails (t)\r\n        else:\r\n            t = t + 1\r\n\r\n        # Add '1' to the While loop counter after each Flip\r\n        i = i +1\r\n\r\n    #Display the result for Heads & Tails\r\n    print(\"\\nHeads: \", h) # <-- Heads\r\n    print(\"Tails: \", t) # <-- Tails\r\n\r\n\r\n\r\n'''\r\n//////////////////////\r\nEND of Defining the Functions\r\n//////////////////////\r\n'''\r\n\r\n\r\n#Dispaly the Name of the Game\r\nprint(\"\\nThe Coin Flip Game [v2.0]\"\r\n      \"\\n   [bini-tek]\\\\\\\\\"\r\n      \"\\n--------------------------\\n\")\r\n\r\n\r\n#Setting up the WHILE loop to do 2 main things:\r\n#1: To call the coin_flip() function\r\n#2: To ask if the user wants do it again\r\nwhile_again = 'y' # <-- the answer to the loop\r\nwhile (while_again.lower() == 'y'):\r\n    coin_flip()\r\n    #Ask if the user wants to do it again\r\n    while_again = input(\"\\nWould you like to do it again? Y/N: \")\r\n\r\n#If the answer is not 'y'\r\n#Thank the user & Exit\r\nprint(\"\\nThanks for Playing\"\r\n      \"\\n[--Game Over--]\")\r\nexit()\r\n","repo_name":"ashNOLOGY/Python_Projects","sub_path":"The_CoinFlip_Game_v2.py","file_name":"The_CoinFlip_Game_v2.py","file_ext":"py","file_size_in_byte":2081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"74872637397","text":"from django.urls import path\nfrom . import views\n\napp_name = 'project'\nurlpatterns = [\n\t# ex: /project/\n    path('', views.dashboard, name='dashboard'),\n    path('proses', views.proses, name='proses'),\n    path('desc', views.desc, name='desc'),\n    path('table', views.table, name='table'),\n    path('visualisasi', views.visualisasi, name='visualisasi'),\n    path('data', views.data, name='data'),\n]","repo_name":"DaffaFauzan/KNN","sub_path":"damin/project/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"10431927218","text":"from selenium import webdriver\nfrom time import sleep\n\n'''xpath定位'''\ntry:\n    driver = webdriver.Chrome()\n    driver.get('https://www.baidu.com')\n    # 绝对路径\n    driver.find\n    driver.find_element_by_xpath('/html/body/div[1]/div[1]/div[5]/div/div/form/span[1]/input').send_keys('美国大选')\n    driver.find_element_by_xpath('//*[@id=\"su\"]').click()\nexcept Exception as e:\n    print(e)\nfinally:\n    sleep(3)\n    driver.quit()\n","repo_name":"gaohuan1479962371/gaohuan","sub_path":"day01/xpath定位.py","file_name":"xpath定位.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"33882550980","text":"#!/bin/python3\n\nimport sys\n\n# Too Slow: timeout\n#def solve(a):\n#     # Complete this function\n#     for i in range(len(a)):\n#         if sum(a[:i]) == sum(a[i+1:]):\n#             return \"YES\"\n#     return \"NO\" \n\ndef solve(a):\n    # Complete this function\n    sum = 0\n    i = 0\n    j = len(a)-1\n    while(i != j):\n        if(sum >=  0):\n            sum -= a[j]\n            j -= 1\n        else:\n            sum += a[i]\n            i += 1\n    if sum == 0:\n        return \"YES\"\n    else:\n        return \"NO\"\n    \nT = int(input().strip())\nfor a0 in range(T):\n    n = int(input().strip())\n    a = list(map(int, input().strip().split(' ')))\n    result = solve(a)\n    print(result)\n\n","repo_name":"YuliangZHENG6/hackerrank","sub_path":"Algorithms/Search/SherlockArray.py","file_name":"SherlockArray.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"6931276724","text":"import sys\n\nclass Node:\n  def __init__(self, left=None, right=None):\n    self.left = left;\n    self.right = right;\n\n\ndef creteFrequencyMap(string):\n  charFrequency = {}\n\n  for ch in string:\n    if ch in charFrequency:\n      charFrequency[ch] += 1;\n    else:\n      charFrequency[ch] = 1;\n\n  return charFrequency\n\ndef convertFrequencyMapToTupple(charFrequencyMap):\n  charTupple = list();\n  \n  for key in charFrequencyMap:\n    charTupple.append([charFrequencyMap[key], key]);\n\n  charTupple.sort(key = sortBasedOnFirstValue, reverse = True);\n\n  return charTupple;\n\ndef sortBasedOnFirstValue(val): \n    return val[0]\n\ndef createHuffmanTree(charTupple):\n\n  \n  while(len(charTupple) > 1):\n    # print(charTupple)\n    l = charTupple.pop();\n    r = charTupple.pop();\n\n    sum = l[0] + r[0];\n    node = Node(l, r);\n\n    charTupple.append([sum, node]);\n    \n    charTupple.sort(key = sortBasedOnFirstValue, reverse = True);\n\n  return charTupple[0];\n\ndef createCharToBinaryCodeMap(node, prefix =\"\", code=None):\n    \n    if(code is None):\n      code = {};\n\n    if(type(node[1].left[1]) is str):\n      code[node[1].left[1]] = prefix + '0';\n    else:\n      createCharToBinaryCodeMap(node[1].left, prefix + '0', code)\n\n    if(type(node[1].right[1]) is  str):\n      code[node[1].right[1]] = prefix + '1';\n    else:\n      createCharToBinaryCodeMap(node[1].right, prefix + '1', code)\n\n    return code;\n    \ndef createEncodedString(chToBinaryCodeMap, String):\n  encoded_string = '';\n\n  for ch in String:\n    encoded_string =  encoded_string + chToBinaryCodeMap[ch] + '-'\n  \n  return encoded_string[:-1]\n\ndef createBinaryToCharMap(chToBinaryCodeMap):\n  code = {};\n\n  for key in chToBinaryCodeMap:\n    code[chToBinaryCodeMap[key]] = key;\n  \n  return code\n\ndef getCodeForSingleFrequencyMap(charFrequencyMap):\n  code = {}\n  for key in charFrequencyMap:\n    code = {\n      key:'0'\n    }\n  return code;\n\n    \n  \ndef huffman_encoding(string):\n\n  #if string is empty return same string back\n  if len(string) is 0:\n    return string, {}\n  \n  charFrequencyMap = creteFrequencyMap(string);\n  \n  #if there is only one character encode it with 0\n  if len(charFrequencyMap) is 1:\n      charToBinaryMap = getCodeForSingleFrequencyMap(charFrequencyMap)\n      encodedString = createEncodedString(charToBinaryMap, string);\n      binaryToChar = createBinaryToCharMap(charToBinaryMap);\n      return encodedString, binaryToChar;\n  \n  charTupple = convertFrequencyMapToTupple(charFrequencyMap);\n  root = createHuffmanTree(charTupple);\n  charToBinaryMap = createCharToBinaryCodeMap(root, '')\n  encodedString = createEncodedString(charToBinaryMap, string);\n  binaryToCharMap = createBinaryToCharMap(charToBinaryMap);\n\n  return encodedString, binaryToCharMap\n\ndef huffman_decoding(encodedString, codeMap):\n  \n  if len(encodedString) is 0:\n    return ''\n  \n  encodedList = encodedString.split(\"-\")\n  decodedString = '';\n\n  for item in encodedList:\n    decodedString += codeMap[item]\n  \n  return decodedString;\n\n\n#Test-1\na_great_sentence = \"The bird is the word\"\nencoded_str, code = huffman_encoding(a_great_sentence)\ndecoded_str = huffman_decoding(encoded_str, code)\nprint(encoded_str)\n# 11101-001-000-10-11100-011-010-1101-10-011-11111-10-11110-001-000-10-11001-11000-010-1101\nprint(decoded_str)\n# The bird is the word\n\n#Test-2\nempty_string = \"\"\nencoded_str, code = huffman_encoding(empty_string)\ndecoded_str = huffman_decoding(encoded_str, code)\nprint(encoded_str)\n# \nprint(decoded_str)\n#\n\n#Test-3\nrare_string = \"aaaaaaaaaaa\"\nencoded_str, code = huffman_encoding(rare_string)\ndecoded_str = huffman_decoding(encoded_str, code)\nprint(encoded_str)\n# 0-0-0-0-0-0-0-0-0-0-0\nprint(decoded_str)\n# aaaaaaaaaaa\n\n\n","repo_name":"metanitesh/Data-Structure-Algorithm-Python","sub_path":"projects/DA-project-2/nitesh-project-2/problem-3.py","file_name":"problem-3.py","file_ext":"py","file_size_in_byte":3660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"23066063573","text":"\n\nclass AmountOfTime(object):\n    time_type_multiple = {'year': 12 * 30 * 24 * 60, 'month': 30 * 24 * 60, 'day': 24 * 60, 'hour': 60, 'second': 1}\n\n    def __init__(self, amount, time_type):\n        self.type = time_type\n        self.amount = amount\n        self.value = self.time_type_multiple.get(time_type) * amount\n\n    def get_time(self, time_type):\n        return self.value/self.time_type_multiple.get(time_type)\n\n\n# 不考虑复利的情况\ndef get_each_month_interest(interest_rates, time, money):\n    interest_list = []\n    interest_rates = interest_rates/12\n    for i in range(int(time.get_time('month'))):\n        interest_list.append(interest_rates * money * (i + 1))\n    return interest_list\n\n\ndef get_compound_interest(compound_interest_cycle, interest_list):\n    interest = 0\n    num = int(len(interest_list)/compound_interest_cycle) * compound_interest_cycle\n    for i in range(num):\n        interest += interest_list[i]\n    return interest\n\n\n# 考虑复利的情况\ndef get_each_month_interest_(interest_rates, time, money, cycle):\n    interest_list = []\n    interest_rates = interest_rates/12\n    for i in range(int(time.get_time('month'))):\n        interest = get_compound_interest(cycle, interest_list)\n        interest_list.append(interest_rates * (money * (i + 1) + interest))\n    return interest_list\n\n\ndef get_interest(interest_rates, time, money):\n    return sum(get_each_month_interest(interest_rates, time, money))\n\n\ndef get_interest_(interest_rates, time, money, cycle):\n    return sum(get_each_month_interest_(interest_rates, time, money, cycle))\n\n\ndef get_principal_and_interest(interest_rates, time, money):\n    principal = time.get_time('month') * money\n    return get_interest(interest_rates, time, money) + principal\n\n\ndef get_principal_and_interest_(interest_rates, time, money, cycle):\n    principal = time.get_time('month') * money\n    return get_interest_(interest_rates, time, money, cycle) + principal\n\n\nif __name__ == '__main__':\n    annual_interest_rates = 4.5 / 100\n    month_ = 12\n    cycles = 6\n    monthly_deposit_money = 4100\n\n    time_ = AmountOfTime(month_, 'month')\n\n    # 不考虑复利的情况\n    moneys = get_interest(annual_interest_rates, time_, monthly_deposit_money)\n    print('收益 =', moneys)\n    print('本利和 =', month_ * monthly_deposit_money + moneys)\n\n    # 考虑复利的情况\n    moneys = get_interest_(annual_interest_rates, time_, monthly_deposit_money, cycles)\n    print('收益 =', moneys)\n    print('本利和 =', month_ * monthly_deposit_money + moneys)\n\n    # 哎！ 穷啊！\n","repo_name":"Zhongwb95/LittleTool","sub_path":"tool/economy/fixed_deposit.py","file_name":"fixed_deposit.py","file_ext":"py","file_size_in_byte":2561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"1766831878","text":"import pandas as pd\nimport sqlalchemy\nfrom apscheduler.schedulers.background import BackgroundScheduler\nfrom decouple import config\n\n\"\"\"\nThese scripts make new SQL tables and update them. The values that are added to the new tables\nare the difference between two records. This is used because in the main tables\nthat we are writing our data is with accumulation and it is hard to wrap\nyour head around. Having the difference between the days is easier for analysis\nand also for making reports.\n\nThe index of the tables is the \"pk\" and also the values are sorted by the \"pk\".\n\nWhen updating the tables these scrips delete the old ones and replace them with a new one.\nIf the tables exist. If they do not exist they just create a new one.\n\nThere is a separate script for every table - water, gas, electricity, compressors.\n\nThe scripts are made with a scheduler so we can update the time how often we update\nthese tables. Currently they are set to 1800 seconds or 30 minutes.\n\"\"\"\n\n\ndef update_water_database():\n    database = (\n        f\"postgresql://{config('DB_USER')}:{config('DB_PASSWORD')}\"\n        f\"@localhost:{config('DB_PORT')}/{config('DB_NAME')}\"\n    )\n    engine = sqlalchemy.create_engine(database)\n    sql_data = pd.read_sql_table(\"water\", engine)\n    df = pd.DataFrame(sql_data)\n    df = df.sort_values(by=[\"pk\"])\n    df[\n        [\n            \"total_m3\",\n            \"casting\",\n            \"high_pressure_casting\",\n            \"glazing\",\n            \"sorting\",\n            \"administration\",\n        ]\n    ] = df[\n        [\n            \"total_m3\",\n            \"casting\",\n            \"high_pressure_casting\",\n            \"glazing\",\n            \"sorting\",\n            \"administration\",\n        ]\n    ].diff()\n    df.set_index(\"pk\", inplace=True)\n    df.to_sql(\"Daily Water consumption\", engine, if_exists=\"replace\")\n    print(\"Water database updated !\")\n\n\ndef update_gas_database():\n    database = (\n        f\"postgresql://{config('DB_USER')}:{config('DB_PASSWORD')}\"\n        f\"@localhost:{config('DB_PORT')}/{config('DB_NAME')}\"\n    )\n    engine = sqlalchemy.create_engine(database)\n    sql_data = pd.read_sql_table(\"natural_gas\", engine)\n    df = pd.DataFrame(sql_data)\n    df = df.sort_values(by=[\"pk\"])\n    df[\n        [\n            \"total_Nm3\",\n            \"casting\",\n            \"high_pressure_casting\",\n            \"glazing\",\n            \"sorting\",\n            \"administration\",\n            \"kilns\",\n            \"shuttle_kilns\",\n        ]\n    ] = df[\n        [\n            \"total_Nm3\",\n            \"casting\",\n            \"high_pressure_casting\",\n            \"glazing\",\n            \"sorting\",\n            \"administration\",\n            \"kilns\",\n            \"shuttle_kilns\",\n        ]\n    ].diff()\n    df.set_index(\"pk\", inplace=True)\n    df.to_sql(\"Daily natural gas consumption\", engine, if_exists=\"replace\")\n    print(\"Gas database updated !\")\n\n\ndef update_electricity_database():\n    database = (\n        f\"postgresql://{config('DB_USER')}:{config('DB_PASSWORD')}\"\n        f\"@localhost:{config('DB_PORT')}/{config('DB_NAME')}\"\n    )\n    engine = sqlalchemy.create_engine(database)\n    sql_data = pd.read_sql_table(\"electricity\", engine)\n    df = pd.DataFrame(sql_data)\n    df = df.sort_values(by=[\"pk\"])\n    df[\n        [\n            \"total_kwh\",\n            \"casting\",\n            \"high_pressure_casting\",\n            \"glazing\",\n            \"sorting\",\n            \"administration\",\n            \"kilns\",\n            \"shuttle_kilns\",\n        ]\n    ] = df[\n        [\n            \"total_kwh\",\n            \"casting\",\n            \"high_pressure_casting\",\n            \"glazing\",\n            \"sorting\",\n            \"administration\",\n            \"kilns\",\n            \"shuttle_kilns\",\n        ]\n    ].diff()\n    df.set_index(\"pk\", inplace=True)\n    df.to_sql(\"Daily electricity consumption\", engine, if_exists=\"replace\")\n    print(\"Electricity database updated !\")\n\n\ndef update_compressors_database():\n    database = (\n        f\"postgresql://{config('DB_USER')}:{config('DB_PASSWORD')}\"\n        f\"@localhost:{config('DB_PORT')}/{config('DB_NAME')}\"\n    )\n    engine = sqlalchemy.create_engine(database)\n    sql_data = pd.read_sql_table(\"compressors\", engine)\n    df = pd.DataFrame(sql_data)\n    df = df.sort_values(by=[\"pk\"])\n    df[\n        [\n            \"total_kwh\",\n            \"compressor_one\",\n            \"compressor_two\",\n            \"compressor_three\",\n            \"compressor_four\",\n            \"compressor_five\",\n        ]\n    ] = df[\n        [\n            \"total_kwh\",\n            \"compressor_one\",\n            \"compressor_two\",\n            \"compressor_three\",\n            \"compressor_four\",\n            \"compressor_five\",\n        ]\n    ].diff()\n    df.set_index(\"pk\", inplace=True)\n    df.to_sql(\"Daily compressors consumption\", engine, if_exists=\"replace\")\n    print(\"Compressors database updated !\")\n\n\nscheduler = BackgroundScheduler(timezone=\"Europe/Sofia\")\nscheduler.add_job(func=update_water_database, trigger=\"interval\", seconds=1800)\nscheduler.add_job(func=update_gas_database, trigger=\"interval\", seconds=1800)\nscheduler.add_job(func=update_electricity_database, trigger=\"interval\", seconds=1800)\nscheduler.add_job(func=update_compressors_database, trigger=\"interval\", seconds=1800)\n","repo_name":"ZhivkoB3/FlaskProject","sub_path":"scripts/update_database.py","file_name":"update_database.py","file_ext":"py","file_size_in_byte":5213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"13644217452","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('', views.onboarding, name='onboarding'),\n    path('emotions/', views.emotions_view, name='emotions_view'),\n    path('login/', views.login_view, name='login_view'),\n    path('login_submit/', views.login_submit, name='login_submit'),\n    path('signup/', views.signup_view, name='signup_view'),\n    path('signup_submit/', views.signup_submit, name='signup_submit'),\n    path('logout/', views.logout_view, name='logout_view'),\n    path('sleep/', views.sleep_view, name='sleep_view'),\n    path('profile/', views.profile_view, name='profile_view'),\n    path('mood/', views.mood_view, name='mood_view'),\n    path('mood-slider/', views.mood_slider_view, name='mood_slider_view'),\n    path('home/', views.home_view, name='home_view'),\n    path('suggestions/', views.suggestions_view, name='suggestion_view'),\n    path('choices/', views.choices_view, name='choices_view'),\n\n]\n","repo_name":"PennSpark/sp22-blue-AnxieTea","sub_path":"anxietea/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":942,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"27917196051","text":"\"\"\"\nA basic implementation of von Mises Fisher distribution in Python. Supported functionalities include\ndensity evaluation (both log and normal scale) and randomly simulate sample from a vMF. \n\n.. math::\n    p(x) = c_d(\\kappa) exp(\\kappa \\mu^\\top x)\n\n@Author: Lei Fang lf28@sta\n@Date: 18/02/2022\n\nExample: \n# initialisation a vMF instance with `\\mu= [1.0, 0, 0]` and `\\kappa=1.0`\nvmf = VonMisesFisher([1,0,0], 1)\n# generate 10 samples from a vMF\ndata = vmf.sample(10)\n# evaluate log probability density of data\n# the data is assumed to be a n by d matrix, i.e. each row is one observation\nvmf.logpdf(data)\n\nLimitation:\n    \n\nAttributes:\n    μ: the center of the vMF; it should be unit one directional vector\n    κ>0: concentration parameter\n\"\"\"\n\n\nimport numpy as np\nimport numpy.linalg as linalg\nfrom scipy.special import ive\n\nclass VonMisesFisher:\n    \"\"\"\n    This is a class for mathematical operations on complex numbers.\n      \n    Attributes:\n        μ (np.array): the center of the vMF; it should be unit one directional vector\n        κ>0 (float): concentration parameter\n    \"\"\"\n    \n    def __init__(self, μ, κ=1.0, rng=None):\n        \"\"\"\n        The constructor for von Mises Fisher\n  \n        Parameters:\n           μ (np.array): the center of the vMF; it should be unit one directional vector\n           κ>0 (float): concentration parameter\n           rng (np.random.default_rng): random number generator\n        \"\"\"\n        self.μ = np.array(μ) / linalg.norm(μ)\n        self.κ = κ\n        self.dim = len(μ)\n        if rng is None:\n            self.rng = np.random.default_rng()\n        else:\n            self.rng = rng\n\n    def logpdf(self, xs):\n        \"\"\"\n        The function to return the log probability density evaluated at xs\n  \n        Parameters:\n            xs (np.array): a n by d array; the locations that the density evaluates at \n          \n        Returns:\n            logLik (np.array): a n by 1 array of the log densities\n        \"\"\"\n        logLik = self.__logCdk() + self.κ * (xs @ self.μ)\n        return logLik \n\n    def pdf(self, xs):\n        \"\"\"\n        The function to return the probability density evaluated at xs\n  \n        Parameters:\n            xs (np.array): a n by d array; the locations that the density evaluates at \n          \n        Returns:\n            lik (np.array): a n by 1 array of the densities\n        \"\"\"\n        return np.exp(self.logpdf(xs))\n\n    def __logCdk(self):\n        \"\"\"\n        The function to return the log transformed normalising constant of the vMF\n        .. math::\n            \\ln c_d(\\kappa) = \\ln \\left (\\frac{\\kappa^{d/2-1}}{(2\\pi)^{d/2} I_{d/2-1}(\\kappa)} \\right )\n        \n        where `I_{\\nu}(x)` is the modified Bessel function of the first kind\n        \n        Returns:\n            ComplexNumber: A complex number which contains the sum.\n        \"\"\"\n        d = self.dim\n        return (d/2 -1) * np.log(self.κ) - d/2 * np.log(2*np.pi) - self.__logBesseli(d/2-1, self.κ)\n\n    def sample(self, n=1):\n        \"\"\"\n        The function to randomly sample from the vMF\n  \n        Parameters:\n            n (int): the number of samples to generate\n          \n        Returns:\n            samples (np.array): a n by d matrix of the generated samples\n        \"\"\"\n        # Wood method to random sample from a vMF; \n        # this implementation is based on the algorithm VM* of A.T.A. Wood\n        # A. T. A. Wood. Simulation of the von-Mises Fisher\n        # Distribution. Comm Stat. Comput. Simul. (1994) v. 23 157-164\n        # shorthand notations\n        κ = self.κ\n        μ = self.μ\n        d = self.dim\n        rng = self.rng\n        t1 = np.sqrt(4*κ*κ + (d-1)*(d-1))\n        b = (-2*κ + t1 )/(d-1)\n        x0 = (1-b)/(1+b)\n        # place holder for samples\n        samples = np.zeros((n,d)) \n        m = (d-1)/2\n        c = κ*x0 +(d-1) * np.log(1-x0**2) \n        for i in range(n):\n            t = -1000\n            u =1\n            while t < np.log(u):\n                # a beta random sample\n                z = rng.beta(m, m)\n                u = rng.random()\n                w = (1-(1+b)*z)/(1-(1-b)*z)\n                t = κ*w + (d-1) * np.log(1-x0*w) - c\n            v = self.__unitrand(d-1, rng)\n            samples[i,0:-1] = np.sqrt(1- w*w) * v \n            samples[i,-1] = w\n\n        v,b = self.__house(μ)\n        Q = np.eye(d) - b * np.outer(v,v)\n\n        for i in range(n):\n            tmpv = Q @ samples[i,:]\n            samples[i,:] = tmpv\n        return samples\n  \n    @staticmethod\n    def __logBesseli(ν, x):\n        \"\"\"\n        Private static helper method to calculate log transformed the modified Bessel function of the first kind\n  \n        Parameters:\n            ν (float): order of the bessel function\n            x (float): input of the function\n          \n        Returns:\n            logb(float): the value of the function\n        \"\"\"\n        # calculate log transformed the modified besseli function of the first kind : log(Iν(x)), use exponential scale to deal over overflow\n        # use approximation to deal with numerical issues (when ν is too large)\n        if ν < 5:\n            logb = x + np.log(ive(ν, x))\n        else:\n            logb = VonMisesFisher.__logBesselli_approx(ν, x)\n        return logb\n\n    @staticmethod\n    def __logBesselli_approx(ν, x):\n        \"\"\"\n        Private static helper method to approximate log transformed the modified Bessel function of the first kind for large order\n  \n        Parameters:\n            ν (float): order of the bessel function\n            x (float): input of the function\n          \n        Returns:\n            logb(float): the value of the function\n        \"\"\"\n        # when nu is too large, need to deal with underflow\n        # approximation from Eqn 9.7.7 of Abramowitz and Stegun\n        # http://www.math.sfu.ca/~cbm/aands/page_378.htm\n        frac = x/ν\n        square = 1+ frac**2\n        root = np.sqrt(square)\n        eta = root + np.log(frac) - np.log(1+root)\n        logb = - np.log(np.sqrt(2*np.pi*ν)) + ν * eta - 0.25 * np.log(square)\n        return logb\n\n\n\n    @staticmethod\n    def __unitrand(d, rng):\n        \"\"\"\n        The function to return a random unit length vectors of length d on a hyperphere\n  \n        Parameters:\n            d (int): the length of the random vector\n            rng (np.random.default_rng): random number generator\n          \n        Returns:\n            v: a random vector uniformly distributed on a hypersphere\n        \"\"\"\n        v = rng.standard_normal(d)\n        v = v / linalg.norm(v)\n        return v\n    \n    @staticmethod\n    def __house(x):\n        \"\"\"\n        Householder vector reflection transformation to reduce x to b*e_n (e_n is the n-th standard basis vector)\n        H = np.eye(n)-b*np.outer(v,v) is the householder matrix that will transform\n        \n        Parameters:\n            x (np.array): input vector\n          \n        Returns:\n            v, b: transformation's vector and scalar\n        \"\"\"\n        # householder transformation\n        n = len(x)\n        s = x[0:-1] @ x[0:-1]\n        v = np.append(x[0:-1], 1)\n        if s == 0:\n            b =0\n        else:\n            m = np.sqrt(x[-1]*x[-1] + s)\n            if x[-1] <= 0:\n                v[-1] = x[-1] - m\n            else:\n                v[-1] = -s/(x[-1] +m)\n      \n            b = 2* v[-1] * v[-1]/(s+v[-1]*v[-1])\n            v = v/ v[-1]\n        return v, b","repo_name":"Jynxer/Unsupervised-Learning","sub_path":"vmf.py","file_name":"vmf.py","file_ext":"py","file_size_in_byte":7404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"4488143572","text":"from solver import Solver\nfrom model import Model\n\n#absolute path:\n#pythonic mode:\n#import sys\n#sys.path.insert(0, '/home/clayton/Dt/codes/intpy_dev/')\n#4Lnx mode: export PYTHONPATH='/home/clayton/Dt/codes/intpy_dev/'\n#4Win mode: SET PYTHONPATH='path/to/directory'\n#echo $PYTHONPATH\n\nfrom intpy.intpy import initialize_intpy\nimport time\n\n@initialize_intpy(__file__)\ndef main():\n    dimensionality = (2,2)\n    nx = 0.15\n    ny = 0.15\n    delta_t = 0.1\n    start_time = time.perf_counter()\n    model = Model(nx, ny, dimensionality)\n    solver = Solver(model, delta_t)\n    solver.solve()\n    end_time = time.perf_counter()\n    print(end_time - start_time)\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"claytonchagas/intpy_dev","sub_path":"heat_distribution_lu/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"71075975958","text":"from discord.ext import commands\r\nimport pickle\r\n\r\n\r\ndef get_money():\r\n    try:\r\n        with open('money.pkl', 'rb') as f:\r\n            return pickle.load(f)\r\n    except:\r\n        return {}\r\n\r\n\r\nmoney = get_money()\r\n\r\ndef dump_money():\r\n    try:\r\n        with open('money.pkl', 'wb') as f:\r\n            pickle.dump(money, f)\r\n    except Exception as e:\r\n        print(\"PICKLING ERROR: \" + str(e))\r\n        return\r\n\r\n    with open('backupmoney.pkl', 'wb') as f:\r\n        pickle.dump(money, f)\r\n\r\n\r\ndef add_amount(id, amount):\r\n    try:\r\n        money[id] += amount\r\n    except:\r\n        money[id] = 100 + amount\r\n\r\n    dump_money()\r\n\r\ndef get_amount(id):\r\n    try:\r\n        return money[id]\r\n    except:\r\n        money[id] = 100\r\n        dump_money()\r\n        return 100\r\n\r\n\r\ndef set_amount(id, amount):\r\n    money[id] = amount\r\n\r\n\r\nclass Money(commands.Cog):\r\n    def __init__(self, bot):\r\n        self.bot = bot\r\n        self.money = get_money()\r\n        dump_money()\r\n\r\n\r\n    @commands.command(brief=\"Check your money amount.\",aliases=[\"money\"])\r\n    async def getmoney(self,ctx):\r\n        await ctx.send(\"**You have ∰\" + str(get_amount(ctx.author.id)) + \"**\")\r\n\r\n\r\n    @commands.command(brief=\"Add or remove money.\")\r\n    async def addmoney(self,ctx,amount : int,id : int):\r\n        if ctx.author.id != 137001076284063744:\r\n            return await ctx.send(\"You can't do that!\")\r\n\r\n        add_amount(id,amount)\r\n        await ctx.send(\"**Given ∰\" + str(amount) + \" to <@\" + str(id) + \">!**\")\r\n\r\n\r\n    @commands.command(brief=\"Get some money if you're out.\")\r\n    async def bankrupt(self,ctx):\r\n        amount = get_amount(ctx.author.id)\r\n        \r\n        if amount != 0:\r\n            return await ctx.send(\"Hey, you have money! What are you trying to pull here?\")\r\n        else:\r\n            set_amount(ctx.author.id,100)\r\n            return await ctx.send(\"A mysterious benefactor has granted you ∰100\")\r\n\r\n    @commands.command(brief=\"See the richest of the rich!\")\r\n    async def richlist(self,ctx,page : int = 1):\r\n        get_amount(ctx.author.id) #ensure you're on the list\r\n\r\n        richlist = money.items()\r\n        richlist = sorted(richlist,key=lambda x: x[1])[::-1]\r\n\r\n        if page < 1:\r\n            await ctx.send(\"Uh oh! You friccin moron! That's an invalid page number!\")\r\n            return\r\n\r\n        personcount = len(richlist)\r\n\r\n        if 10 * (page - 1) > personcount:\r\n            await ctx.send(\"Uh oh! You friccin moron! There aren't that many people with money!\")\r\n            return\r\n\r\n        text = \"```\\n\"\r\n\r\n        for i in range(10):\r\n            try:\r\n                j = 10 * (page - 1) + i\r\n                person = richlist[j]\r\n\r\n                text += str(j + 1) + \") \" + self.bot.get_user(person[0]).name + \" - ∰\" + str(person[1]) + \"\\n\"\r\n\r\n\r\n            except:\r\n                continue\r\n\r\n        text += \"```\\n\"\r\n        amt = get_amount(ctx.author.id)\r\n        text += \"You are #\" + str(richlist.index((ctx.author.id,amt)) + 1) + \"/\" + str(len(richlist)) + \" (∰\" + str(amt) + \")\"\r\n        await ctx.send(text)\r\n\r\n\r\n    @commands.command(brief=\"Reset everyone.\")\r\n    async def reset(self,ctx, *, verif):\r\n        if ctx.author.id != 137001076284063744:\r\n            return await ctx.send(\"You can't do that!\")\r\n\r\n        if verif == \"yes i am absolutely sure\":\r\n            for person in money:\r\n                money[person] = 100\r\n\r\n        dump_money()\r\n\r\n\r\ndef setup(bot):\r\n    bot.add_cog(Money(bot))\r\n","repo_name":"Bazinga9000/Bazbot","sub_path":"money.py","file_name":"money.py","file_ext":"py","file_size_in_byte":3472,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"}
+{"seq_id":"14418590511","text":"from uuid import UUID\n\nfrom fastapi import FastAPI, WebSocket\nfrom fastapi.middleware.cors import CORSMiddleware\n# from fastapi.middleware.httpsredirect import HTTPSRedirectMiddleware\n\nfrom app.models import Answer, Message, Question, Room, Topic\nfrom app.services import (\n    create_answer,\n    create_message,\n    create_question,\n    create_room,\n    create_topic,\n    get_answers,\n    get_message_by_uuid,\n    get_message_similarity,\n    get_question_by_id,\n    get_questions,\n    get_random_question_by_topic_id,\n    get_room_by_uuid,\n    get_topics,\n    update_question_by_id,\n    get_questions_by_topic_id,\n)\nfrom app.utils import load_audio\n\nfrom .schemas import (\n    CreateAnswerRequest,\n    CreateQuestionRequest,\n    CreateRoomRequest,\n    CreateTopicRequest,\n    RoomMessage,\n    UpdateQuestionRequest,\n)\n\nimport time\nimport json\nfrom app.grpc.unicron import get_unicron_stub\nfrom app.grpc.unicron import unicron_pb2 as unicron_grpc_messages\n\n# app = FastAPI()\napp = FastAPI(ssl_keyfile=\"/etc/letsencrypt/live/mockmentor.ru/privkey.pem\", ssl_certfile=\"/etc/letsencrypt/live/mockmentor.ru/fullchain.pem\")\n\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=[\"*\"],\n    # allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\n# app.add_middleware(HTTPSRedirectMiddleware)\n\n@app.get('/topics')\nasync def fetch_topics() -> list[Topic]:\n    return get_topics()\n\n\n@app.post('/topics')\nasync def insert_topics(topic_request: CreateTopicRequest) -> Topic:\n    return create_topic(**topic_request.dict())\n\n\n@app.get('/questions/')\nasync def fetch_questions() -> list[Question]:\n    return get_questions()\n\n\n@app.post('/questions/')\nasync def insert_question(question_request: CreateQuestionRequest) -> Question:\n    return create_question(**question_request.dict())\n\n\n@app.put('/questions/{question_id}')\nasync def edit_question(\n    question_id: int, question_request: UpdateQuestionRequest\n) -> list[Topic]:\n    if not get_question_by_id(question_id):\n        raise Exception\n    return update_question_by_id(id=question_id, **question_request.dict())\n\n\n@app.get('/answers/')\nasync def fetch_answers(question_id: int | None) -> list[Answer]:\n    return get_answers(question_id=question_id)\n\n\n@app.post('/answers/')\nasync def insert_answer(answer_request: CreateAnswerRequest) -> Answer:\n    return create_answer(**answer_request.dict())\n\n\n@app.get('/messages/{message_uuid}')\nasync def fetch_message(message_uuid: UUID) -> Message:\n    return get_message_by_uuid(message_uuid)\n\n\n@app.post('/rooms')\nasync def insert_room(room_request: CreateRoomRequest) -> Room:\n    return create_room(room_request.topic_id)\n\n\n@app.websocket('/rooms/{room_uuid}')\nasync def ws_room(room_uuid: UUID, websocket: WebSocket):\n    await websocket.accept()\n\n    room = get_room_by_uuid(room_uuid)\n    questions = get_questions_by_topic_id(room.topic_id)\n\n    while(len(questions) != 0):\n        time.sleep(1)\n        \n        # question = get_random_question_by_topic_id(room.topic_id)\n        question = questions.pop()\n\n        answers = get_answers(question_id=question.id)\n\n        message = create_message(room_uuid=room_uuid, text=question.text)\n        room_message = RoomMessage(\n            uuid=message.uuid,\n            text=message.text,\n            type=\"websocket.send\"\n            # audio=load_audio(message.audio_file_name)\n        )\n\n        msg = {}\n        msg['mtype'] = \"question\"\n        msg['text'] = message.text\n        json_data = json.dumps(msg)\n        message = await websocket.send_text(json_data)\n        # await websocket.send(room_message.dict())\n\n        ws_message = await websocket.receive()\n        \n        if(\"text\" in ws_message):\n            try:\n                text = ws_message['text']\n                room_message = RoomMessage(text=text, type=\"websocket.send\")\n                message = create_message(\n                    uuid=room_message.uuid,\n                    text=room_message.text,\n                    audio=room_message.audio,\n                    room_uuid=room_uuid,\n                )\n                similarity = get_message_similarity(text, answers)\n\n                msg = {}\n                msg['mtype'] = \"correctness\"\n                msg['text'] = f'Your answer\\'s correctness: {round(similarity, 3)}'\n                json_data = json.dumps(msg)\n                message = await websocket.send_text(json_data)\n                \n                # message = await websocket.send_text(f'Your answer\\'s correctness {round(similarity, 3)}')\n\n            except ValueError as err:\n                await websocket.send_json({'error': str(err)})\n        \n        elif(\"bytes\" in ws_message):\n            try:\n                b_bytes = ws_message['bytes'] # <class 'bytes'>\n                request = unicron_grpc_messages.TextifyRequest(audio=b_bytes)\n                text_obj = get_unicron_stub().textify(request)\n\n                text = text_obj.text\n                # text = str(text_obj)\n                msg = {}\n                msg['mtype'] = \"textify\"\n                msg['text'] = text\n                json_data = json.dumps(msg)\n                message = await websocket.send_text(json_data)\n                \n                room_message = RoomMessage(text=text, type=\"websocket.send\")\n                message = create_message(\n                    uuid=room_message.uuid,\n                    text=room_message.text,\n                    audio=room_message.audio,\n                    room_uuid=room_uuid,\n                )\n\n\n                similarity = get_message_similarity(text, answers)\n                msg = {}\n                msg['mtype'] = \"correctness\"\n                msg['text'] = f'Your answer\\'s correctness: {round(similarity, 3)}'\n                json_data = json.dumps(msg)\n                message = await websocket.send_text(json_data)\n                # message = await websocket.send_text(f'Your answer\\'s correctness {round(similarity, 3)}')\n\n            except ValueError as err:\n                await websocket.send_json({'error': str(err)})\n\n    final_text = \"You answered all questions! Well done!\"\n    message = create_message(room_uuid=room_uuid, text=final_text)\n    room_message = RoomMessage(\n        uuid=message.uuid,\n        text=message.text,\n        type=\"websocket.send\"\n        # audio=load_audio(message.audio_file_name)\n    )\n\n    await websocket.send(room_message.dict())\n    websocket.close()\n\n","repo_name":"InnoBrainstrormOG/OptimusPrime","sub_path":"app/api/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":6404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"36752939687","text":"#!/bin/python3\r\n\r\nimport math\r\nimport os\r\nimport random\r\nimport re\r\nimport collections\r\nimport sys\r\n\r\n# Complete the repeatedString function below.\r\ndef repeatedString(s, n):\r\n    a=0\r\n    if 'a' not in s:\r\n        return 0\r\n    x=collections.Counter(s)['a']\r\n    if n%len(s)!=0:\r\n        a=s[:n%len(s)].count('a')\r\n        return (x) * (n//len(s))+(a)\r\n    return math.floor((n*x)/len(s))\r\n\r\nif __name__ == '__main__':\r\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\r\n\r\n    s = input()\r\n\r\n    n = int(input())\r\n\r\n    result = repeatedString(s, n)\r\n\r\n    fptr.write(str(result) + '\\n')\r\n\r\n    fptr.close()\r\n","repo_name":"sashank007/Hackerrank-Solutions","sub_path":"Problem Solving/RepeatedString.py","file_name":"RepeatedString.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"35751014217","text":"from collections.abc import Mapping\nimport itertools\nimport logging\nimport os\nimport re\nimport yaml\n\nfrom odemis import model\nfrom odemis.util import mock\n\n\n# Detect duplicate keys on mappings (e.g., two components with the same name)\n# Currently Pyyaml fail to detect that error: http://pyyaml.org/ticket/128 (contains patch)\n# We extend the class, to make it behave as we need.\nfrom yaml.constructor import ConstructorError\nfrom yaml.nodes import MappingNode\n\n\nclass SafeLoader(yaml.SafeLoader):\n    def __init__(self, stream):\n        self._root = os.path.dirname(stream.name)  # Directory containing the YAML file\n        super(SafeLoader, self).__init__(stream)\n\n    def construct_mapping(self, node, deep=False):\n        # From BaseConstructor\n        if not isinstance(node, MappingNode):\n            raise ConstructorError(None, None,\n                    \"expected a mapping node, but found %s\" % node.id,\n                    node.start_mark)\n        # From SafeContrusctor\n        self.flatten_mapping(node)\n        # From BaseConstructor\n        mapping = {}\n        for key_node, value_node in node.value:\n            key = self.construct_object(key_node, deep=deep)\n            try:\n                hash(key)\n            except TypeError as exc:\n                raise ConstructorError(\"while constructing a mapping\", node.start_mark,\n                        \"found unacceptable key (%s)\" % exc, key_node.start_mark)\n            value = self.construct_object(value_node, deep=deep)\n            if key in mapping:\n                # TODO: Raise a ConstructorError (as defined in YAML), once all\n                # offending files are fixed. Or do a deep merge instead?\n                logging.warning(\"Mapping already has key (%s) defined, will override it. \"\n                                \"Semantic will soon change, do not rely on this behaviour. \"\n                                \"Only define the key once.\",\n                                key)\n#                 raise ConstructorError(\"while constructing a mapping\", node.start_mark,\n#                                        \"key (%s) already defined\" % (key,),\n#                                        key_node.start_mark)\n            mapping[key] = value\n        return mapping\n\n    def include(self, node):\n        \"\"\"\n        Method for including values defined in a yaml file into another yaml file. This can be used to add values to\n        a specified key in a dict or outside a dict (e.g. to define the content of a component definition).\n\n        :param node (yaml.nodes.ScalarNode): Contains both the key which indicated to including another file, and the\n        path to the file.\n        :return: Data which from external file which needs to be included.\n        \"\"\"\n        try:\n            filename = os.path.join(self._root, self.construct_scalar(node))  # For supporting relative paths\n            with open(filename, 'r') as f:\n                logging.info(\"Loading file '%s' via the !include key\", filename)\n                return yaml.load(f, SafeLoader)\n\n        except FileNotFoundError as error:\n            # Informing the user in case the user forgot a comma and maybe received an unclear error as result\n            if any(c in filename for c in (\"!\", \"\\n\", \"\\\\\", \"/\", \" \", \":\", \"<\")):\n                logging.error(\"File not found, probably because an invalid character is used or a comma is forgotten \"\n                              \"when using include in a dict.\")\n            raise FileNotFoundError(error)\n\n    def construct_yaml_map(self, node):\n        \"\"\"\n        Method overwriting the original \"construct_yaml_map\" which adds the functionality to extend/update a dict in a\n        yaml file with an external yaml file via the use of the key \"!extend\"\n        For double defined keys the values contained in the last \"!extend\" key will be used.\n        :param node (Mapping node):\n        \"\"\"\n        data = {}\n        yield data\n\n        extended_data = []  # Copy of node.value, with the !extend'ed entries replaced by their content\n        # Check all nodes for the \"!extend\" key\n        for key_node, value_node in node.value:\n            if key_node.tag == \"!extend\":\n                filename = os.path.join(self._root, key_node.value)  # For supporting relative paths\n                logging.info(\"Loading file '%s' via the !extend key\", filename)\n                try:\n                    with open(filename, 'r') as f:\n                        try:\n                            node_extension = SafeLoader(f).get_single_node()  # Get the data from the external file.\n                        except yaml.parser.ParserError as error:\n                            raise ParseError(\"Parsing of file '%s' using the '!extend' key failed with the error:\\n%s\"\n                                             % (key_node.value, error))\n                except FileNotFoundError as error:\n                    if any(c in filename for c in (\"!\", \"\\n\", \"\\\\\", \"/\", \" \", \":\", \"<\")):\n                        logging.error(\"File not found, probably because an invalid character is used \"\n                                      \"or a comma is forgotten when using the '!extend' key in a dict.\")\n\n                    raise FileNotFoundError(error)\n\n                # Add the ScalarNodes (key+value) from the \"extend\" file to the main MappingNode\n                extended_data.extend(node_extension.value)\n            else:\n                extended_data.append((key_node, value_node))\n\n        node.value = extended_data\n\n        try:\n            value = self.construct_mapping(node)\n        except yaml.parser.ParserError as error:\n            try:  # A try/catch part because accessing the node structure during an parsing error might fail.\n                for (key_node, value_node) in node.value:\n                    if key_node.tag == \"!include\" or value_node.tag == \"!include\":\n                        filename = value_node.value if value_node.tag == \"!include\" else key_node.value\n                        error = ParseError(\"Parsing of file '%s' using the '!include' key failed with the error:\\n%s\"\n                                           % (filename, error))\n                        break\n                    if key_node.tag == \"!extend\" or value_node.tag == \"!extend\":\n                        filename = value_node.value if value_node.tag == \"!extend\" else key_node.value\n                        error = ParseError(\"Parsing of file '%s' using the '!extend' key failed with the error:\\n%s\"\n                                           % (filename, error))\n                        break\n            except:\n                pass\n            logging.error(\"Parsing of the input file failed with the following traceback: \\n%s\", error)\n            raise error\n\n        data.update(value)\n\n# Add yaml merger features to the SafeLoader class to combine multiple yaml files into one\n# Add the key '!include' by adding a constructor which allows to include external yaml files\nSafeLoader.add_constructor('!include', SafeLoader.include)\n# Overwrite construct_yaml_map with custom mapper which adds support for the \"!extend\" key to extend a dictionary\nSafeLoader.yaml_constructors['tag:yaml.org,2002:map'] = SafeLoader.construct_yaml_map\n\nclass ParseError(Exception):\n    pass\n\n\nclass SemanticError(Exception):\n    pass\n\n# the classes that we provide in addition to the device drivers\n# Nice name => actual class name\ninternal_classes = {\"Microscope\": \"odemis.model.Microscope\",\n                    # The following is deprecated, as it can now be directly used\n                    # as \"actuator.MultiplexActuator\"\n                    \"CombinedActuator\": \"odemis.driver.actuator.MultiplexActuator\",\n                    }\ndef get_class(name):\n    \"\"\"\n    name (str): class name given as \"package.module.class\" or \"module.class\" or\n      one of the internal classes name\n    returns the class object\n    Raises:\n         SemanticError in case an error is detected.\n         ParseError in case names are not possible module names\n    \"\"\"\n    if name in internal_classes:\n        module_name, class_name = internal_classes[name].rsplit(\".\", 1)\n    else:\n        # It comes from the user, so check carefully there is no strange characters\n        if not re.match(r\"(\\w|\\.)+\\.(\\w)+\\Z\", name):\n            raise ParseError(\"Syntax error in microscope file: \"\n                             \"class name '%s' is malformed.\" % name)\n\n        names = name.rsplit(\".\", 1)\n        if len(names) < 2:\n            raise ParseError(\"Syntax error in microscope file: \"\n                \"class name '%s' is not in the form 'module.method'.\" % name)\n        module_name = \"odemis.driver.\" + names[0] # always look in drivers directory\n        class_name = names[1]\n\n    try:\n        mod = __import__(module_name, fromlist=[class_name])\n    except ImportError as ex:\n        raise SemanticError(\"Error in microscope file: \"\n            \"fail loading module '%s' (class '%s'): %s.\" % (module_name, class_name, ex))\n#        return None # DEBUG\n\n    try:\n        the_class = getattr(mod, class_name)\n    except AttributeError:\n        raise SemanticError(\"Error in microscope file: \"\n                \"module '%s' has no class '%s'.\" % (module_name, class_name))\n\n    return the_class\n\n# A component can reference other components as 'children' or as 'dependencies'.\n#  * A dependency is a component which is needed by another one (a functional dependency/requirement)\n#  * A child is a component which is created by another one (provided/creation)\n# It used to be the case that 'children' and 'dependencies' were provided in the same attribute\n# called 'children'. Therefore we need to provide compatibility with both ways of defining references.\n# The microscope class is special. It only has a .children attribute and no .dependencies\n# attribute because we want to be able to list all the components.\n\n\nclass Instantiator(object):\n    \"\"\"\n    manages the instantiation of a whole model\n    \"\"\"\n\n    def __init__(self, inst_file, settings_file=None, container=None, create_sub_containers=False,\n                 dry_run=False):\n        \"\"\"\n        inst_file (file): opened file that contains the YAML\n        settings_file (file or None): opened settings file in YAML format.\n          If None, persistent settings will be not be stored.\n        container (Container): container in which to instantiate the components\n        create_sub_containers (bool): whether the leave components (components which\n           have no children created separately) are running in isolated containers\n        dry_run (bool): if True, it will check the semantic and try to instantiate the\n          model without actually any driver contacting the hardware. It will also\n          be stricter, and some issues which are normally just warnings will be\n          considered errors.\n        \"\"\"\n        self.ast = self._parse_instantiation_model(inst_file)  # AST of the model to instantiate\n        self._can_persist = settings_file is not None\n        self._persistent_props, self._persistent_mds = self._parse_settings(settings_file)\n        self.root_container = container # the container for non-leaf components\n\n        self.microscope = None # the root of the model (Microscope component)\n        self._microscope_name = None  # the name of the microscope\n        self._microscope_ast = None # the definition of the Microscope\n        self.components = set() # all the components created\n        self.sub_containers = {}  # container's name -> container: all the sub-containers created for the components\n        self._comp_container = {}  # comp name -> container: the container that runs the given component\n        self.create_sub_containers = create_sub_containers # flag for creating sub-containers\n        self.dry_run = dry_run # flag for instantiating mock version of the components\n\n        self._check_structure()\n        self._preparate_microscope()\n\n        # update/fill up the model with implicit information\n        self._fill_creator()\n\n        # Sanity checks\n        self._check_affects(strict=dry_run)\n        self._check_duplicate_roles()\n\n        # TODO: if the microscope has a known role, check it has the minimum\n        # required sub-components (with the right roles) and otherwise raise\n        # SemanticError\n\n        # TODO: check here that each class is loadable.\n\n        # TODO: check there is no cyclic dependencies on the parents/children\n\n    def _check_structure(self):\n        \"\"\"\n        The AST should be a dict[str, dict[str, ...]]: name of component -> definition\n        Each component definition must have at least the \"role\" key\n        \"\"\"\n        if not isinstance(self.ast, dict):\n            raise ParseError(f\"Microscope file must be a dictionary of component names -> definition, \"\n                             f\"but got a {type(self.ast).__name__}\")\n\n        for n, a in self.ast.items():\n            if not isinstance(n, str):\n                raise ParseError(f\"Component name should be string but got: {n}\")\n            if not isinstance(a, dict):\n                raise ParseError(f\"Definition of component \\\"{n}\\\" should be a dict, \"\n                                 f\"but got a {type(a).__name__}\")\n\n            # Check inside the component definition\n            for arg in a.keys():\n                if not isinstance(arg, str):\n                    raise ParseError(f\"Definition of component \\\"{n}\\\" should only have keys of type str, \"\n                                     f\"but got a {type(arg).__name__}\")\n            if \"role\" not in a:\n                raise SemanticError(f\"Definition of component \\\"{n}\\\" is missing the mandatory key 'role'.\")\n\n    def _preparate_microscope(self):\n        \"\"\"\n        Find the microscope definition and do some updates on the definition if\n        needed. In particular, Microscope used to be special with 3 types of\n        child. In case the definition has not been updated, we do it here.\n        \"\"\"\n        # look for the microscope def\n        microscopes = [(n, a) for n, a in self.ast.items() if a.get(\"class\") == \"Microscope\"]\n        if len(microscopes) == 1:\n            cname, microscope = microscopes[0]\n        elif len(microscopes) > 1:\n            raise SemanticError(\"Error in microscope file: \"\n                                \"there are several Microscopes (%s).\" %\n                                \", \".join(n for n, a in microscopes))\n        else:\n            raise SemanticError(\"Error in microscope file: \"\n                                \"no Microscope component found.\")\n\n        self._microscope_name = cname\n        self._microscope_ast = microscope\n\n        # We used to request that all the \"useful\" components (the ones with a role)\n        # be listed as \"children\" of the microscope. However, there are \"dependencies\",\n        # not \"children\" (ie, created by that code). In addition, for already a long\n        # time all the code relies on .ghost and .alive to find all the components.\n        # Even longer time ago, we had \"actuators\", \"detectors\", and \"emitters\".\n        # They used to be converted to \"children\".\n        # => it's now useless, and we just force it empty, to make sure to really not rely on it.\n        for a in (\"actuators\", \"detectors\", \"emitters\", \"children\"):\n            if a in microscope:\n                logging.warning(\"Removing deprecated parameter %s from the Microscope\", a)\n                del microscope[a]\n\n    def _fill_creator(self):\n        \"\"\"\n        Add the \"parents\" field (= reverse of children) and update the creator\n         field for the components that don't have it explicitly set.\n        \"\"\"\n        # update the children by adding a \"parents\" attribute\n        for name, comp in self.ast.items():\n            references = list(comp.get(\"children\", {}).values()) + list(comp.get(\"dependencies\", {}).values())\n            for ref in references:\n                # detect direct loop\n                if ref == name:\n                    raise SemanticError(\"Error in microscope file: \"\n                                        \"component %s is child/dependency of itself.\" % ref)\n                if ref not in self.ast:\n                    raise SemanticError(\"Error in microscope file: \"\n                                        \"component %s references unknown child/dependency %s.\" %\n                                        (name, ref))\n\n                if \"parents\" not in self.ast[ref].keys():\n                    self.ast[ref][\"parents\"] = []\n                self.ast[ref][\"parents\"].append(name)\n\n        # For each component which is created by delegation (= no class):\n        # * if no creator specified, use its parent (and error if multiple parents)\n        # * if creator specified, check it's one of the parents (deprecated, since the use of dependencies)\n        for name, comp in self.ast.items():\n            if \"class\" in comp:\n                continue\n            parents = comp.get(\"parents\", [])  # If empty, will raise an error just later\n            if \"creator\" in comp:\n                creator_name = comp[\"creator\"]\n                if creator_name not in parents:\n                    raise SemanticError(\"Error in microscope file: component %s \"\n                            \"is creator of component %s but doesn't have it as a child.\"\n                            % (creator_name, name))\n            else:\n                # If one parent is Microscope, it's dropped for the \"creator\"\n                # guess because Microscope is known to create no component.\n                parents = [p for p in parents if self.ast[p].get(\"class\") != \"Microscope\"]\n\n                if len(parents) == 0:\n                    raise SemanticError(\"Error in microscope file: component \\\"%s\\\" \"\n                            \"has no class specified and is not child of any component.\" % name)\n                else:\n                    creator = None\n                    for p in parents:\n                        if name in self.ast[p].get(\"children\", {}).values() and creator:\n                            raise SemanticError(\"Error in microscope file: component \\\"%s\\\" \"\n                                    \"has to be created by one of its parents %s, but no \"\n                                    \"creator is designated.\" % (name, tuple(parents)))\n                        elif name in self.ast[p].get(\"children\", {}).values() and not creator:\n                            creator = p\n                    if creator:\n                        comp[\"creator\"] = creator\n                        logging.debug(\"Identified %s as creator of %s\",\n                                      creator, name)\n\n    def _check_cyclic(self):\n\n        # TODO\n        # in theory, it's done, but if there is a dependency loop some components\n        # will never be instantiable\n        instantiated = set(c.name for c in self._instantiator.components)\n        left = set(self._instantiator.ast.keys()) - instantiated\n        if left:\n            raise SemanticError(\"Some components could not be instantiated due \"\n                                \"to cyclic dependency: %s\" %\n                                (\", \".join(left)))\n\n    def _check_affects(self, strict=False):\n        \"\"\"\n        Check that the affects of the components are correct.\n        In particular, it checks that all affects points to existing components\n        strict (bool): if strict, will raise an error, instead of just printing\n          warnings\n        \"\"\"\n        for cname, attrs in self.ast.items():\n            affects = attrs.get(\"affects\", [])\n            for affcname in affects:\n                if affcname not in self.ast:\n                    if strict:\n                        raise SemanticError(\"Component '%s' affects non-existing component '%s'.\" % (cname, affcname))\n                    else:\n                        logging.warning(\"Component '%s' affects non-existing component '%s'.\", cname, affcname)\n\n    def _check_duplicate_roles(self):\n        \"\"\"\n        Check that any component with a role is unique\n        In particular, it checks that all affects points to existing components\n        \"\"\"\n        roles = {}  # Dict role -> comp name\n        for cname, attrs in self.ast.items():\n            role = attrs.get(\"role\")\n            if role is not None:\n                if role == \"None\" or role == \"none\":\n                    logging.warning(\"Component '%s' has role '%s', which mostly likely should be set to 'null'\",\n                                    cname, role)\n                    continue\n                if role in roles:\n                    ocname = roles[role]\n                    raise SemanticError(\"Components '%s' and '%s' both have role '%s', but roles should be unique.\" %\n                                        (ocname, cname, role))\n                else:\n                    roles[role] = cname\n\n    def _parse_instantiation_model(self, inst_file):\n        \"\"\"\n        Converts the instantiation model odm.yaml file into a python representation\n\n        inst_file (file): opened file that contains the yaml\n        Note that in practice is does almost no checks but try to decode the yaml file.\n        So there is only syntax check, but tries to makes errors clear to understand\n        and fix.\n        returns (dict str -> dict):  python representation of a yaml file\n\n        Raises:\n            ParseError in case there is a syntax error\n        \"\"\"\n        try:\n            # The standard PyYAML loader is dangerous as it can create any python object\n            data = yaml.load(inst_file, SafeLoader)\n        except yaml.YAMLError as exc:\n            logging.error(\"Syntax error in microscope file: %s\", exc)\n            if hasattr(exc, 'problem_mark'):\n                mark = exc.problem_mark\n                # display the line\n                inst_file.seek(0)\n                line = list(itertools.islice(inst_file, mark.line, mark.line + 1))[0]\n                logging.error(\"%s\", line.rstrip(\"\\n\"))\n                # display the column\n                logging.error(\" \" * mark.column + \"^\")\n            raise ParseError(\"Syntax error in microscope file.\")\n\n        logging.debug(\"YAML file read like this: \" + str(data))\n\n        return data\n\n    def _make_args(self, name):\n        \"\"\"\n        Create init arguments for a component instance and its children\n        name (str): name that will be given to the component instance\n        returns (dict (str -> value)): init argument for the component instance\n        \"\"\"\n        attr = self.ast[name]\n        # it's not an error if there is not init attribute, just not specific arguments\n        init = attr.get(\"init\", {}).copy()\n\n        # it's an error to specify \"name\" and \"role\" in the init\n        if \"name\" in init:\n            raise SemanticError(\"Error in microscope file: \"\n                \"component '%s' should not have a 'name' entry in the init.\" % name)\n        init[\"name\"] = name\n        if \"role\" in init:\n            raise SemanticError(\"Error in microscope file: \"\n                \"component '%s' should not have a 'role' entry in the init.\" % name)\n        if \"role\" not in attr:\n            raise SemanticError(\"Error in microscope file: \"\n                                \"component '%s' has no role specified.\" % name)\n        init[\"role\"] = attr[\"role\"]\n\n        class_name = attr.get(\"class\", None)\n        if self.dry_run and not class_name == \"Microscope\":\n            # mock class needs some hints to create the fake VAs\n            init[\"_vas\"] = list(attr.get(\"properties\", {}).keys())\n\n        # microscope take a special \"model\" argument which is AST itself\n        if class_name == \"Microscope\":\n            init[\"model\"] = self.ast\n\n        # create recursively the children\n        if \"children\" in init:\n            raise SemanticError(\"Error in microscope file: \"\n                \"component '%s' should not have a 'children' entry in the init.\" % name)\n        if \"dependencies\" in init:\n            raise SemanticError(\"Error in microscope file: \"\n                \"component '%s' should not have a 'dependencies' entry in the init.\" % name)\n        if \"children\" in attr and not class_name == \"Microscope\":\n            init[\"children\"] = {}\n            init[\"dependencies\"] = {}\n            children_names = attr[\"children\"]\n\n            for internal_role, child_name in children_names.items():\n                child_attr = self.ast[child_name]\n                # Two types of children creation:\n                if \"creator\" in child_attr and child_attr[\"creator\"] == name:\n                    # the child creation is delegated... to this component\n                    # => it'll be created by the component, so\n                    # we just pass the init arguments\n                    init[\"children\"][internal_role] = self._make_args(child_name)\n                else:\n                    # the child has a class or is created by another component\n                    # => we explicitly reuse it\n                    init[\"dependencies\"][internal_role] = self._get_component_by_name(child_name)\n        if \"dependencies\" in attr and class_name == \"Microscope\":\n            raise SemanticError(\"Error in microscope file: \"\n                                \"microscope class '%s' should not have 'dependencies' argument.\" % name)\n        if \"dependencies\" in attr:\n            if init.get(\"dependencies\"):\n                # In case some children are dependencies (ie, old-style), but there\n                # are also explicit \"dependencies\" used, warn, as it should be\n                # completely converted.\n                logging.warning(\"Mix legacy dependent-component (%s) + dependencies\", init[\"dependencies\"].keys())\n            else:\n                init[\"dependencies\"] = {}\n            dep_names = attr[\"dependencies\"]\n            for internal_role, dep_name in dep_names.items():\n                init[\"dependencies\"][internal_role] = self._get_component_by_name(dep_name)\n\n        # take care of power supplier argument\n        if \"power_supplier\" in init:\n            raise SemanticError(\"Error in microscope file: \"\n                \"component '%s' should not have a 'power_supplier' entry in the init.\" % name)\n        if \"power_supplier\" in attr:\n            psu_name = attr[\"power_supplier\"]\n            init[\"power_supplier\"] = self._get_component_by_name(psu_name)\n\n        return init\n\n    def is_leaf(self, name):\n        \"\"\"\n        says whether a component is a leaf or not. A \"leaf\" is a component which\n          has no dependencies.\n        name (str): name of the component instance\n        \"\"\"\n        attr = self.ast[name]\n\n        if attr.get(\"dependencies\", {}):\n            return False\n\n        # For backwards compatibility, also check the \"dependent children\"\n        children_names = attr.get(\"children\", {}).values()\n        for child_name in children_names:\n            child_attr = self.ast[child_name]\n            if \"class\" in child_attr:\n                # the child has a class => it will be instantiated separately\n                return False\n\n        return True\n\n    def _get_container(self, name):\n        \"\"\"\n        Find the best container to instantiate a component\n        name (str): name of the component to instantiate\n        return (None or container): None means a new container must be created\n        \"\"\"\n        attr = self.ast[name]\n        if attr.get(\"class\") == \"Microscope\":\n            # The Microscope (root) is special\n            return self.root_container\n\n        # If it's a leaf, use its own container\n        if self.create_sub_containers and self.is_leaf(name):\n            return None\n\n        # If it's not a leaf, it's probably a wrapper (eg, MultiplexActuator),\n        # which is simple Python code and so doesn't need to run in a\n        # separate container. If clearly it wraps just one other component,\n        # use the same container, otherwise, use the root container\n\n        # Get the dependencies\n        dependency_names = attr.get(\"dependencies\", {})\n        deps_cont = set()\n        for child_name in dependency_names.values():\n            try:\n                cont = self._comp_container[child_name]\n            except KeyError:\n                logging.warning(\"Component %s was not created yet, but %s depends on it\", child_name, name)\n                continue\n            deps_cont.add(cont)\n        # Ensure backwards compatibility with old-style children (children = dependencies + delegated comps)\n        children_names = attr.get(\"children\", {})\n        for child_name in children_names.values():\n            if \"class\" in self.ast[child_name]:\n                try:\n                    cont = self._comp_container[child_name]\n                except KeyError:\n                    logging.warning(\"Component %s was not created yet, but %s depends on it\", child_name, name)\n                    continue\n                deps_cont.add(cont)\n\n        if len(deps_cont) == 1:\n            return deps_cont.pop()\n\n        # Multiple dependencies -> just use the root container then\n        return self.root_container\n\n    def _instantiate_comp(self, name):\n        \"\"\"\n        Instantiate a component\n        name (str): name that will be given to the component instance\n        returns (HwComponent): an instance of the component\n        Raises:\n            SemanticError in case an error in the model is detected.\n        \"\"\"\n        attr = self.ast[name]\n        class_name = attr[\"class\"]\n        class_comp = get_class(class_name)\n\n        # create the arguments:\n        # name (str)\n        # role (str)\n        # children:\n        #  * explicit creation: (dict str -> HwComponent) internal name -> comp\n        #  * delegation: (dict str -> dict) internal name -> init arguments\n        # anything else is passed as is\n        args = self._make_args(name)\n\n        logging.debug(\"Going to instantiate %s (%s) with args %s\",\n                      name, class_name, args)\n\n        # if the component is connected to a PowerSupplier we first need to turn\n        # it on before we instantiate it\n        if \"power_supplier\" in args:\n            f = args[\"power_supplier\"].supply({name: True})\n            f.result()\n\n        if self.dry_run and not class_name == \"Microscope\":\n            # mock class for everything but Microscope (because it is safe)\n            args[\"_realcls\"] = class_comp\n            class_comp = mock.MockComponent\n\n        try:\n            cont = self._get_container(name)\n            if cont is None:\n                # new container has the same name as the component\n                cont, comp = model.createInNewContainer(name, class_comp, args)\n                self.sub_containers[name] = cont\n            else:\n                logging.debug(\"Creating %s in container %s\", name, cont)\n                comp = model.createInContainer(cont, class_comp, args)\n            self._comp_container[name] = cont\n        except Exception:\n            logging.error(\"Error while instantiating component %s.\", name)\n            raise\n\n        self.components.add(comp)\n        # Add all the children, which were created by delegation, to our list of components.\n        self.components |= comp.children.value\n        for child in comp.children.value:\n            self._comp_container[child.name] = cont\n\n        return comp\n\n    def _get_component_by_name(self, name):\n        \"\"\"\n        Find a component by its name in the set of instantiated components\n        name (str): name of the component\n        return HwComponent\n        Raises:\n             LookupError: if no component is found\n        \"\"\"\n        for comp in self.components:\n            if comp.name == name:\n                return comp\n        raise LookupError(\"No component named '%s' found\" % name)\n\n    def get_required_components(self, name):\n        \"\"\"\n        Return all the components required (but not created) by the component for\n          instantiation\n        name (str): name of the component\n        return (set of str): the name of the components that will be required when\n          instantiating the given component, (not including the component itself)\n        \"\"\"\n        ret = set()\n\n        attrs = self.ast[name]\n        dependencies = attrs.get(\"dependencies\", {}).values()\n        for n in dependencies:\n            ret.add(n)\n            ret |= self.get_required_components(n)\n\n        try:\n            ret.add(attrs[\"power_supplier\"])\n        except KeyError:\n            pass  # no power supplier\n\n        # Support legacy code\n        children = attrs.get(\"children\", {}).values()\n        for n in children:\n            cattrs = self.ast[n]\n            if cattrs.get(\"creator\") != name and n not in ret:\n                ret.add(n)\n                ret |= self.get_required_components(n)\n\n        return ret\n\n    def get_children_names(self, name):\n        \"\"\"\n        Return all the components created by delegation when creating the given\n         component (including the given component)\n        name (str): name of the component\n        return (set of str): the name of the components that will be created when\n          instantiating the given component, including the component itself\n        \"\"\"\n        ret = {name}\n        for n, attrs in self.ast.items():\n            if attrs.get(\"creator\") == name:\n                # Note: by passing ret, and checking it's not already added,\n                # we could handle cyclic creation... but it's better to fail here\n                # than trying to instantiate such beast.\n                ret |= self.get_children_names(n)\n\n        return ret\n\n    @classmethod\n    def get_children(cls, root):\n        \"\"\"\n        Return the set of components which are referenced from the given component\n         (via children)\n        root (HwComponent): the component to start from\n        returns (set of HwComponents): all the children, including the component\n          itself.\n        \"\"\"\n        ret = {root}\n        for child in root.children.value:\n            ret |= cls.get_children(child)\n\n        return ret\n\n    def instantiate_microscope(self):\n        \"\"\"\n        Generates the just the microscope component\n\n        Raises:\n            SemanticError: an error in the model is detected. Note that\n            (obviously) not every error can be detected.\n            LookupError\n            ParseError\n            Exception (dependent on the driver): in case initialisation of a driver fails\n        \"\"\"\n        self.microscope = self._instantiate_comp(self._microscope_name)\n        return self.microscope\n\n    def _update_properties(self, comp_name):\n        \"\"\"\n        Set the VA values as defined in the \"properties\" section of the component. In case\n        of persistent properties (specified in \"persistent\" section of component) try\n        to restore the latest value from the settings file. If this fails,\n        use the VA definition in the \"properties\" section instead.\n\n        comp_name (str): name of the component for which to set the VAs\n        \"\"\"\n        attrs = self.ast[comp_name]\n        comp = self._get_component_by_name(comp_name)\n\n        # Initialize persistent properties from settings file\n        props, _ = self.get_persistent(comp_name)\n        persistent_props_values = self._persistent_props.get(comp_name, {})\n        for prop_name in props:\n            # If persistent property wasn't saved in settings file, still check for errors\n            try:\n                va = getattr(comp, prop_name)\n                if not isinstance(va, model.VigilantAttributeBase):\n                    raise AttributeError\n            except AttributeError:\n                raise SemanticError(\"Error in microscope file: \"\n                                    \"Component '%s' has no property '%s' (defined as persistent).\" % (comp_name, prop_name))\n\n            # Load value from settings file if available\n            if prop_name in persistent_props_values:\n                value = persistent_props_values[prop_name]\n                try:\n                    va.value = value\n                except Exception as exp:\n                    logging.warning(\"Error in settings file: \"\n                                     \"%s.%s = '%s' failed due to '%s'\" %\n                                     (comp_name, prop_name, value, exp))\n                    del persistent_props_values[prop_name]\n\n        if \"properties\" in attrs:\n            for prop_name, value in attrs[\"properties\"].items():\n                if prop_name in persistent_props_values:\n                    continue  # Already known, from persistent settings\n                try:\n                    va = getattr(comp, prop_name)\n                except AttributeError:\n                    raise SemanticError(\"Error in microscope file: \"\n                            \"Component '%s' has no property '%s'.\" % (comp_name, prop_name))\n                if not isinstance(va, model.VigilantAttributeBase):\n                    raise SemanticError(\"Error in microscope file: \"\n                            \"Component '%s' has no property (VA) '%s'.\" % (comp_name, prop_name))\n                try:\n                    va.value = value\n                except Exception as exp:\n                    raise ValueError(\"Error in microscope file: \"\n                                     \"%s.%s = '%s' failed due to '%s'\" %\n                                     (comp_name, prop_name, value, exp))\n\n    def _update_metadata(self, comp_name):\n        \"\"\"\n        Update the metadata as defined in the \"metadata\" section of the component. In case\n        of persistent metadata (specified in \"persistent\" section of component) try\n        to restore the latest value from the settings file.\n\n        comp_name (str): name of the component for which to set the metadata\n        \"\"\"\n        attrs = self.ast[comp_name]\n        comp = self._get_component_by_name(comp_name)\n        compmd = {}\n\n        # Initialize persistent metadata from settings file\n        _, mds = self.get_persistent(comp_name)\n        persistent_mds_values = self._persistent_mds.get(comp_name, {})\n        for md_name in mds:\n            # Also check persistent metadata that are specified in the model file, but were not saved\n            # in the settings file. If something goes wrong, it should fail now rather than at the\n            # end when we're trying to write the metadata to the settings file.\n            try:\n                # To indicate the metadata name:\n                # UPPER_CASE_NAME -> use model.MD_UPPER_CASE_NAME\n                # We could also accept the actual MD string, but it'd get more\n                # complicated and not really help anyone.\n                fullname = \"MD_\" + md_name\n                md = getattr(model, fullname)\n            except AttributeError:\n                raise SemanticError(\"Error in microscope file: \"\n                      \"Component '%s' has unknown metadata '%s'.\" % (comp_name, md_name))\n\n            if md_name in persistent_mds_values:\n                compmd[md] = persistent_mds_values[md_name]\n\n        # Initialize other metadata from model file\n        if \"metadata\" in attrs:\n            for md_name, value in attrs[\"metadata\"].items():\n                try:\n                    fullname = \"MD_\" + md_name\n                    md = getattr(model, fullname)\n                except AttributeError:\n                    raise SemanticError(\"Error in microscope file: \"\n                        \"Component '%s' has unknown metadata '%s'.\" % (comp_name, md_name))\n\n                if md in compmd:\n                    continue  # Already known, from persistent settings\n                compmd[md] = value\n\n        # Only update if metadata not empty. Updating a component with empty metadata should not\n        # have any effect, but it's safer not to update it when it's not necessary.\n        if compmd:\n            comp.updateMetadata(compmd)\n\n    def _update_affects(self, name):\n        \"\"\"\n        Update .affects of the given component, and of all the components which\n         affects the given component\n\n        name (str): the (new) component\n        \"\"\"\n        comp = self._get_component_by_name(name)\n        attrs = self.ast[name]\n        # Just set all the components (by name), even if they are not yet active\n        comp.affects.value = attrs.get(\"affects\", [])\n        logging.debug(\"Updating affect %s -> %s\", name,\n                      \", \".join(comp.affects.value))\n\n    def instantiate_component(self, name):\n        \"\"\"\n        Generate the component (and its children)\n        All the dependencies that are created by separate instantiation must already\n        have been created.\n        It will take care of updating the .children VA of the microscope if\n         needed.\n\n        return (Component): the new component created. Note that more component\n         might have been created (by delegation). You can find them by looking\n         at the .children VA of the new component.\n        Raises:\n            LookupError: if the component doesn't exists in the AST or\n                  if the children should have been created and are not.\n            ValueError: if the component has already been instantiated\n            KeyError: if component should be created by delegation\n        \"\"\"\n        for c in self.components:\n            if c.name == name:\n                raise ValueError(\"Trying to instantiate again component %s\" % name)\n\n        comp = self._instantiate_comp(name)\n\n        # Post-instantiation changes\n        newcmps = self.get_children(comp) # that includes comp itself\n        for c in newcmps:\n            self._update_properties(c.name)\n            self._update_metadata(c.name)\n            self._update_affects(c.name)\n\n        return comp\n\n    def get_instantiables(self, instantiated=None):\n        \"\"\"\n        Find the components that are currently not yet instantiated, but\n        could be directly (ie, all their children or power_supplier created explicitly are\n        already instantiated)\n        instantiated (None or set of str): the names of the components already\n          instantiated. If it's None, it will use the list of all the components\n          ever instantiated.\n        return (set of str): names of all the components that are instantiable\n        \"\"\"\n        comps = set()\n        if instantiated is None:\n            instantiated = set(c.name for c in self.components)\n        for n, attrs in self.ast.items():\n            if n in instantiated: # should not be already instantiated\n                continue\n            if \"class\" not in attrs: # created by delegation\n                continue\n            if \"power_supplier\" in attrs:\n                psuname = attrs[\"power_supplier\"]\n                logging.debug(\"%s has a power_supplier %s\", n, psuname)\n                if psuname not in instantiated:\n                    logging.debug(\"Component %s is not instantiable yet\", n)\n                    continue\n            deps = list(attrs.get(\"dependencies\", {}).values())\n            # support legacy code\n            deps += [c for c in attrs.get(\"children\", {}).values() if self.ast[c].get(\"creator\") != n]\n            for name in deps:\n                if name not in instantiated:\n                    logging.debug(\"Component %s is not instantiable yet\", n)\n                    break\n            else:\n                comps.add(n)\n\n        return comps\n\n    def read_yaml(self, f):\n        \"\"\"\n        Read content of YAML file. This is a wrapper for yaml.safe_load that returns an\n        empty dictionary in case a YAMLError is raised or the file is empty.\n        f (File or None): opened YAML file\n        return (dict): dictionary with file contents or empty dictionary. The format is:\n          comp -> ('metadata'|'properties' -> (key -> value)) \n        \"\"\"\n        if f is None:\n            return {}\n\n        # Make sure we start from the beginning, useful if the function\n        # is called several times in a row.\n        f.seek(0)\n        try:\n            data = yaml.safe_load(f)\n            if not isinstance(data, dict):\n                logging.warning(\"Settings file reset.\")\n                data = {}\n        except yaml.YAMLError as ex:\n            logging.warning(\"Settings file reset after loading failed with Exception %s.\" % ex)\n            data = {}\n        return data\n\n    def _parse_settings(self, settings_file):\n        \"\"\"\n        Parse settings file and return persistent properties and metadata in useful format.\n        settings_file (File or None): opened settings file. If the file is empty or corrupted, two empty\n          dict will be returned.\n        return (list of 2 nested dicts): persistent properties and persistent metadata for\n          each component in the settings file.\n          props[comp][prop_name] --> value, mds[comp][md_name] --> value\n        \"\"\"\n        data = self.read_yaml(settings_file)\n        # Rearrange data from data[comp][type][prop_name] --> type[comp][prop_name],\n        # type is 'properties' or 'metadata'\n        mds = {}\n        props = {}\n        for comp in data:\n            mds[comp] = data[comp].get('metadata', {})\n            props[comp] = data[comp].get('properties', {})\n            if not isinstance(mds[comp], Mapping):\n                raise ValueError(\"Persistent metadata for component %s is not a mapping.\" % comp)\n            if not isinstance(props[comp], Mapping):\n                raise ValueError(\"Persistent properties for component %s is not a mapping.\" % comp)\n        return props, mds\n\n    def get_persistent(self, comp_name):\n        \"\"\"\n        List all persistent properties and metadata as specified in the model file.\n        comp_name (str): name of the component\n        return (2 lists of str): VA names, metadata keys\n        \"\"\"\n        attrs = self.ast[comp_name]\n        persistent = attrs.get(\"persistent\", {})\n        prop_names = persistent.get(\"properties\", [])\n        md_names = persistent.get(\"metadata\", [])\n\n        if not self._can_persist and (prop_names or md_names):\n            logging.warning(\"Component %s has persistent settings, but no persistent file available\",\n                            comp_name)\n\n        return prop_names, md_names\n\n","repo_name":"delmic/odemis","sub_path":"src/odemis/odemisd/modelgen.py","file_name":"modelgen.py","file_ext":"py","file_size_in_byte":46142,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"85"}
+{"seq_id":"4395817297","text":"#!/Users/spherik/anaconda3/envs/mayavi_env/bin/python\n\n# Mostrar dipSignal a part (mes amunt)\n# Checkbox normalitzar senyals x = x-min/max-min\n# menu toogle visualize per ocultar dipol - done\n# mostrar correl_slider i time_slider\n# Veure el num de dipols mostrats (despres de filtrar per correlacio)\n\n\n# First, and before importing any Enthought packages, set the ETS_TOOLKIT\n# environment variable to qt4, to tell Traits that we will use Qt.\nimport os\nos.environ['ETS_TOOLKIT'] = 'qt4'\n# By default, the PySide binding will be used. If you want the PyQt bindings\n# to be used, you need to set the QT_API environment variable to 'pyqt'\n#os.environ['QT_API'] = 'pyqt'\n\n# To be able to use PySide or PyQt4 and not run in conflicts with traits,\n# we need to import QtGui and QtCore from pyface.qt\nfrom pyface.qt import QtGui, QtCore\n# Alternatively, you can bypass this line, but you need to make sure that\n# the following lines are executed before the import of PyQT:\n#   import sip\n#   sip.setapi('QString', 2)\n\nimport scipy.io as sio\nimport numpy as np\n\nfrom MPLQWidget import MPLQWidget\nfrom MayaviQWidget import MayaviQWidget\nfrom ChacoQWidget import ChacoQWidget\n################################################################################\n# The QMainWindow\nclass MainWindow(QtGui.QMainWindow):\n    def __init__(self):\n        QtGui.QMainWindow.__init__(self)\n        self._BuildLayout()\n        self._BuildMenu()\n\n    def _OpenMatFile(self):\n        title = \"Select mat file\"\n        filename = QtGui.QFileDialog.getOpenFileName(self,\n\t\t\t\t\t\ttitle,\n\t\t\t\t\t\tos.path.expanduser(\"~\"),\n\t\t\t\t\t\t\"Mat files (*.mat)\")\n\n        if filename:\n            mat_data = sio.loadmat(filename)\n            # Anatomy meshes\n            self.cortex_vertices = mat_data['cortex_vertices']\n            self.cortex_triangles = mat_data['cortex_triangles']-1\n            self.head_vertices = mat_data['head_vertices']\n            self.head_triangles = mat_data['head_triangles']-1\n\n            # Simulated signal\n            self.virtual_sensor_positions = mat_data['posVS']\n            self.time = mat_data['time'][0,:]\n            self.virtual_sensors_signals = mat_data['signalproj']\n            self.correl = np.abs(mat_data['correl'])\n\n            # Dipole\n            self.dipole_position = mat_data['origDipolePos'][0]\n            self.dipole_momentum = mat_data['origDipoleMom'][0]/np.linalg.norm(mat_data['origDipoleMom'][0])\n            self.dipole_signal = mat_data['dipSignal'][0]\n\n            print(self.dipole_position)\n            print(self.dipole_momentum)\n            print(self.virtual_sensors_signals.shape)\n\n            self.time_slider.setRange(0, self.time.shape[0])\n            self.time_slider.setSingleStep(1)\n            self.time_slider.setTickInterval(10)\n            self.time_slider.setTickPosition(QtGui.QSlider.TicksBelow)\n\n            self.correl_slider.setRange(0, self.correl.shape[1])\n            self.correl_slider.setSingleStep(1)\n            self.correl_slider.setTickInterval(10)\n            self.correl_slider.setTickPosition(QtGui.QSlider.TicksBelow)\n\n            self.time_slider.valueChanged.connect(self._TimeChanged)\n            self.correl_slider.valueChanged.connect(self._CorrelChanged)\n\n            self._BuildScene()\n            self._BuildPlot()\n\n    def _BuildLayout(self):\n        container = QtGui.QWidget()\n        container.setWindowTitle(\"Brain signal visualizer\")\n        # define a \"complex\" layout to test the behaviour\n        layout = QtGui.QVBoxLayout(container) #QGridLayout(container)\n\n        # Plot and scene\n        #scroll = QtGui.QScrollArea(self)\n        self.plot_widget = QtGui.QWidget()\n        self.mpl_widget = ChacoQWidget(self)\n\n        vbox = QtGui.QVBoxLayout()\n        vbox.addWidget(self.mpl_widget)  # the chaco canvas\n        self.plot_widget.setLayout(vbox)\n\n        self.mayavi_widget = MayaviQWidget(self)\n\n        visualization_layout = QtGui.QSplitter(container)\n        visualization_layout.addWidget(self.plot_widget)\n        visualization_layout.addWidget(self.mayavi_widget)\n        layout.addWidget(visualization_layout)\n\n        # Controls\n        self.time_slider = QtGui.QSlider(self)\n        self.time_slider.setOrientation(QtCore.Qt.Horizontal)\n        self.time_slider.setRange(0,1000)\n        self.time_slider.setValue(0)\n        self.time_slider.setEnabled(False)\n\n        self.time_text = QtGui.QLineEdit()\n        self.time_text.setText('0')\n        self.time_text.editingFinished.connect(self._TimeTextChanged)\n        self.time_text.setEnabled(False)\n\n        self.correl_slider = QtGui.QSlider(self)\n        self.correl_slider.setOrientation(QtCore.Qt.Horizontal)\n        self.correl_slider.setRange(0,1000)\n        self.correl_slider.setValue(0)\n\n        self.correl_text = QtGui.QLineEdit()\n        self.correl_text.setText('0')\n        self.correl_text.editingFinished.connect(self._CorrelTextChanged)\n        self.correl_text.setEnabled(False)\n\n        controls_layout = QtGui.QGridLayout(container)\n        controls_layout.addWidget(self.time_slider,0,0)\n        controls_layout.addWidget(self.time_text,0,1)\n        controls_layout.addWidget(self.correl_slider,1,0)\n        controls_layout.addWidget(self.correl_text,1,1)\n        layout.addLayout(controls_layout)\n        controls_layout.activate()\n        self.correl_text.resize(self.correl_text.sizeHint())\n        self.time_text.resize(self.time_text.sizeHint())\n        #layout.addWidget(mayavi_widget, 1, 1)\n\n        container.show()\n        self.setCentralWidget(container)\n\n    def _BuildMenu(self):\n        bar = self.menuBar()\n\n        # File menu\n        file_menu = bar.addMenu('File')\n\n        open_action = QtGui.QAction('Open',self,triggered = self._OpenMatFile)\n        open_action.setShortcut('Ctrl+O')\n        file_menu.addAction(open_action)\n\n        # Visualization menuBar\n        visualization_menu = bar.addMenu('Visualization')\n\n        self.head_visual_action = QtGui.QAction('Head', self, triggered = self._ToggleHeadVisibility)\n        self.head_visual_action.setCheckable(True)\n        self.head_visual_action.setChecked(True)\n        self.head_visual_action.setEnabled(False)\n        visualization_menu.addAction(self.head_visual_action)\n\n        self.cortex_visual_action = QtGui.QAction('Cortex', self, triggered = self._ToggleCortexVisibility)\n        self.cortex_visual_action.setCheckable(True)\n        self.cortex_visual_action.setChecked(True)\n        self.cortex_visual_action.setEnabled(False)\n        visualization_menu.addAction(self.cortex_visual_action)\n\n        self.dipole_visual_action = QtGui.QAction('Dipole',self, triggered = self._ToggleDipoleVisibility)\n        self.dipole_visual_action.setCheckable(True)\n        self.dipole_visual_action.setChecked(True)\n        self.dipole_visual_action.setEnabled(False)\n        visualization_menu.addAction(self.dipole_visual_action)\n\n    def _BuildScene(self):\n        selected_correl_value = self.correl.min()\n        self.selected_ids = np.ravel_multi_index(np.where(self.correl>=selected_correl_value), self.correl.shape)\n        self.mayavi_widget.SetPoints(self.virtual_sensor_positions[:,0],\n                                     self.virtual_sensor_positions[:,1],\n                                     self.virtual_sensor_positions[:,2],\n                                     self.virtual_sensors_signals[self.time_slider.value()-1,self.selected_ids])\n\n        self.mayavi_widget.SetScalarsRange(self.virtual_sensors_signals.min(),self.virtual_sensors_signals.max())\n        self.mayavi_widget.ToggleScalarBarVisibility(True)\n\n        self.mayavi_widget.AddCortex(self.cortex_vertices, self.cortex_triangles)\n        self.mayavi_widget.AddHead(self.head_vertices, self.head_triangles)\n\n        self.mayavi_widget.AddDipole(self.dipole_position, self.dipole_momentum)\n\n    def _BuildPlot(self):\n        self.mpl_widget.SetDipoleSignal(self.time,self.dipole_signal)\n        self.mpl_widget.SetSignals(self.time, self.virtual_sensors_signals[self.selected_ids,:], self.time_slider.value()-1)\n\n    def _TimeChanged(self, value):\n        self.mayavi_widget.SetScalars(self.virtual_sensors_signals[self.selected_ids,value])\n        self.mpl_widget.SetTimeIndex(value)\n        self.time_text.setText(str(value))\n\n    def _CorrelChanged(self, value):\n        selected_correl_value = self.correl.min()+((self.correl.max()-self.correl.min())*float(value)/(self.correl.shape[1]-1))\n        self.selected_ids = np.ravel_multi_index(np.where(self.correl>=selected_correl_value), self.correl.shape)\n\n        # Update 3D scene\n        self.mayavi_widget.SetPoints(self.virtual_sensor_positions[self.selected_ids,0],\n                                     self.virtual_sensor_positions[self.selected_ids,1],\n                                     self.virtual_sensor_positions[self.selected_ids,2],\n                                     self.virtual_sensors_signals[self.selected_ids,self.time_slider.value()-1])\n        # Update signal Plot\n        self.mpl_widget.SetSignals(self.time,self.virtual_sensors_signals[self.selected_ids,:],time_index = self.time_slider.value()-1)\n\n        self.correl_text.setText(str(value))\n\n    def _TimeTextChanged(self):\n        value = int(self.time_text.text())\n        self.time_slider.setValue(value)\n        self.mayavi_widget.SetScalars(self.virtual_sensors_signals[self.selected_ids,value])\n        self.mpl_widget.SetTimeIndex(value)\n\n    def _CorrelTextChanged(self):\n        value = int(self.correl_text.text())\n        self.correl_slider.setValue(value)\n\n        selected_correl_value = self.correl.min()+((self.correl.max()-self.correl.min())*float(value)/(self.correl.shape[1]-1))\n        self.selected_ids = np.ravel_multi_index(np.where(self.correl>=selected_correl_value), self.correl.shape)\n\n        # Update 3D scene\n        self.mayavi_widget.SetPoints(self.virtual_sensor_positions[self.selected_ids,0],\n                                     self.virtual_sensor_positions[self.selected_ids,1],\n                                     self.virtual_sensor_positions[self.selected_ids,2],\n                                     self.virtual_sensors_signals[self.selected_ids,self.time_slider.value()-1])\n        # Update signal Plot\n        self.mpl_widget.SetSignals(self.virtual_sensors_signals[self.selected_ids,:], time_index = self.time_slider.value()-1)\n\n    def _ToggleHeadVisibility(self, is_checked):\n        self.mayavi_widget.ToggleHeadVisibility(is_checked)\n\n    def _ToggleCortexVisibility(self, is_checked):\n        self.mayavi_widget.ToggleCortexVisibility(is_checked)\n\n    def _ToggleDipoleVisibility(self, is_checked):\n        self.mayavi_widget.ToggleDipoleVisibility(is_checked)\n\nif __name__ == \"__main__\":\n    # Don't create a new QApplication, it would unhook the Events\n    # set by Traits on the existing QApplication. Simply use the\n    # '.instance()' method to retrieve the existing one.\n    app = QtGui.QApplication.instance()\n\n    window = MainWindow()\n\n    window.show()\n\n    # Start the main event loop.\n    app.exec_()\n","repo_name":"spherik/meg","sub_path":"QtMEG/qt_meg.py","file_name":"qt_meg.py","file_ext":"py","file_size_in_byte":11018,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"16995578165","text":"import torch\nimport torch.nn as nn\nfrom .mlp import MLP\n\nclass Net(nn.Module):\n    \"\"\"\n    EWC [Kirkpatrick et al., 2017], where the loss is regularized to avoid catastrophic forgetting\n    \"\"\"\n    def __init__(\n        self,\n        n_inputs: int,\n        n_outputs: int,\n        n_tasks: int,\n        args\n    ):\n        \"\"\"\n        Parameters\n        ----------\n        n_inputs : int\n            Number of inputs\n\n        n_outputs : int\n            Number of outputs\n\n        n_tasks : int\n            Number of tasks\n\n        args\n            Command line arguments\n        \"\"\"\n        super(Net, self).__init__()\n        num_layers = args.num_layers\n        hidden_size = args.hidden_size\n        self.reg = args.memory_strength\n\n        self.net = MLP([n_inputs] + [hidden_size] * num_layers + [n_outputs])\n\n        # Set up optimizer\n        self.opt = torch.optim.SGD(self.parameters(), lr=args.lr)\n\n        # Set up losses\n        self.loss = nn.CrossEntropyLoss()\n        self.num_classes_per_task = n_outputs\n        self.n_outputs = n_outputs\n        self.n_memories = args.n_memories\n\n        # Set up memories\n        self.current_task = 0\n        self.fisher = {}\n        self.optpar = {}\n        self.memx = None\n        self.memy = None\n\n\n    def forward(self, x, t):\n        output = self.net(x)\n        return output\n    \n    def observe(self, x, t, y):\n        self.train()\n\n        if t != self.current_task:\n            self.opt.zero_grad()\n\n            # Compute Fisher\n            self.loss(self(self.memx, self.current_task), self.memy).backward()\n            self.fisher[self.current_task] = []\n            self.optpar[self.current_task] = []\n\n            for p in self.net.parameters():\n                pd = p.data.clone()\n                pg = p.grad.data.clone().pow(2)\n                self.fisher[self.current_task].append(pg)\n                self.optpar[self.current_task].append(pd)\n\n            self.current_task = t\n            self.memx = None\n            self.memy = None\n\n        if self.memx is None:\n            self.memx = x.data.clone()\n            self.memy = y.data.clone()\n        else:\n            if self.memx.size()[0] < self.n_memories:\n                self.memx = torch.cat((self.memx, x.data.clone()), 0)\n                self.memy = torch.cat((self.memy, y.data.clone()), 0)\n                if self.memx.size()[0] > self.n_memories:\n                    self.memx = self.memx[:self.n_memories]\n                    self.memy = self.memy[:self.n_memories]\n\n        self.opt.zero_grad()\n        loss = self.loss(self(x, t), y)\n        for tt in range(t):\n            for i, p in enumerate(self.net.parameters()):\n                l = self.reg * self.fisher[tt][i]\n                l = l * (p - self.optpar[tt][i]).pow(2)\n                loss += l.sum()\n\n        loss.backward()\n        self.opt.step()","repo_name":"Minhchuyentoancbn/Continual-Learning","sub_path":"GEM/model/ewc.py","file_name":"ewc.py","file_ext":"py","file_size_in_byte":2846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"19821100358","text":"# pylint: skip-file\n\"\"\"add includes_excludes_key to datapoint table\n\nRevision ID: d59ee5722264\nRevises: 36a3a319fd8f\nCreate Date: 2022-10-14 11:54:15.344773\n\n\"\"\"\nimport sqlalchemy as sa\nfrom alembic import op\n\n# revision identifiers, used by Alembic.\nrevision = \"d59ee5722264\"\ndown_revision = \"36a3a319fd8f\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column(\n        \"datapoint\", sa.Column(\"includes_excludes_key\", sa.String(), nullable=True)\n    )\n    op.drop_constraint(\"unique_datapoint\", \"datapoint\", type_=\"unique\")\n    op.create_unique_constraint(\n        \"unique_datapoint\",\n        \"datapoint\",\n        [\n            \"report_id\",\n            \"start_date\",\n            \"end_date\",\n            \"dimension_identifier_to_member\",\n            \"context_key\",\n            \"includes_excludes_key\",\n            \"metric_definition_key\",\n            \"source_id\",\n        ],\n    )\n    # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_constraint(\"unique_datapoint\", \"datapoint\", type_=\"unique\")\n    op.create_unique_constraint(\n        \"unique_datapoint\",\n        \"datapoint\",\n        [\n            \"report_id\",\n            \"start_date\",\n            \"end_date\",\n            \"dimension_identifier_to_member\",\n            \"context_key\",\n            \"metric_definition_key\",\n            \"source_id\",\n        ],\n    )\n    op.drop_column(\"datapoint\", \"includes_excludes_key\")\n    # ### end Alembic commands ###\n","repo_name":"Recidiviz/pulse-data","sub_path":"recidiviz/persistence/database/migrations/justice_counts/versions/2022_10_14_1154_d59ee5722264_add_includes_excludes_key_to_datapoint_.py","file_name":"2022_10_14_1154_d59ee5722264_add_includes_excludes_key_to_datapoint_.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"85"}
+{"seq_id":"2038667065","text":"from pexpect import popen_spawn\nimport os\nimport json\nfrom gamestate import GameState\n\nclass Game():\n    round_info = None\n    child = None\n    ATTACKS = [\"Rock\", \"Paper\",\"Scissors\",\"Shoot the moom\",\"ROF Rock\",\"ROF Paper\",\"ROF Scissors\",\"ROF Shoot the moon\", \"ROF ROF\"]\n    def __init__(self):\n        self.child = child\n        self.gamestate = GameState(1,1)\n        self.game_finished = False\n        \n    def __del__(self):\n        self.child.proc.terminate()\n        \n    def sendAttack(self, attackArr):\n        cooldowns = list(self.gamestate.me_cooldowns.values())\n        for i, cooldown in enumerate(cooldowns):\n            if cooldown != 0:\n                if i == 4:\n                    for j in range(len(attackArr)):\n                        if j >= 4: attackArr[j] = 0\n                else:\n                    attackArr[i] = 0\n        attackIndex = attackArr.index(max(attackArr)) + 1\n        \n\n        if(attackIndex > 4):\n                self.child.sendline(\"{}\\r\\n\".format(5).encode('UTF-8'))\n                self.child.sendline(\"{}\\r\\n\".format(attackIndex - 4).encode('UTF-8'))\n        else:\n                self.child.sendline(\"{}\\r\\n\".format(attackIndex).encode('UTF-8'))\n\n        if self.gamestate.me_health <= 0 or self.gamestate.op_health <= 0:\n            self.game_finished = True\n\n    def get_fitness(self):\n        if self.gamestate.op_health < 0:\n            self.gamestate.op_health = 0\n        return self.gamestate.me_health - self.gamestate.op_health\n\n    def get_net_data(self):\n        return self.gamestate.get_net_data()\n\n    @staticmethod\n    def remember(gamestate):\n        json_string = Game.child.readline()\n        Game.round_info = json.loads(str(json_string)[2:-5])\n        gamestate.remember_turn(Game.round_info)\n        print(json_string)","repo_name":"uwp-mlc/BattlePetsGeneticBot","sub_path":"ClassProject/ProjectWorkspace/FurbiesFighters/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":1786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"22896892256","text":"from pyrogram import idle\n\nfrom config import *\nfrom PunyaAlby import BOTLOG_CHATID, LOGGER, LOOP, bots\nfrom PunyaAlby.helpers.misc import git, heroku\n\nMSG_ON = \"\"\"\n**ALBY-PYROBOT DIAKTIFKAN**🔥\n    (\\︵/) \n　⫺( •ᆺ•)⫹ \n┏━∪ ━━━━━━━━\n➠ **Userbot Version -** `{}`\n➠ **Ketik** `{}alby` **untuk Mengecheck Bot**\n┗━━━━━━━━━━\n\"\"\"\n\n\nasync def main():\n    for bot in bots:\n        try:\n            await bot.start()\n            bot.me = await bot.get_me()\n            await bot.join_chat(\"ruangdiskusikami\")\n            await bot.join_chat(\"ruangprojects\")\n            await bot.join_chat(\"ruang_gabutku\")\n            await bot.send_message(BOTLOG_CHATID, MSG_ON.format(BOT_VER, CMD_HANDLER))\n        except Exception as a:\n            LOGGER(\"main\").warning(a)\n    await idle()\n\n\nif __name__ == \"__main__\":\n    LOGGER(\"PunyaAlby\").info(\"Starting ALBY-PYROBOT\")\n    git()\n    heroku()\n    LOGGER(\"PunyaAlby\").info(f\"ALBY-PYROBOT v{BOT_VER} [🔥 BERHASIL DIAKTIFKAN! 🔥]\")\n    LOOP.run_until_complete(main())\n","repo_name":"bitchlah/cadangan","sub_path":"PunyaAlby/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"3948005017","text":"import random\n\nNUM_STANDARD_DICE = 5\n\ndef __mostFrequent(List):\n    maxElement = max(set(List), key = List.count)\n    numElement = List.count(maxElement)\n    return numElement, maxElement\n\n# Roll a dice once\ndef __individualRoll():\n    return random.randrange(1,7)\n    \n# Roll 5 dice once each\ndef standardRoll(numDice: int = NUM_STANDARD_DICE):\n    values = []\n\n    for i in range(numDice):\n        values.append(__individualRoll())\n\n    return values\n\n# This is weighted since we will save the best\n# dice from the previous roll and reroll the rest\ndef weightedRoll(previousRoll: list):\n    countMostFrequentOccurence, mostFrequentDiceElement = __mostFrequent(previousRoll)\n    savedDice = [mostFrequentDiceElement] * countMostFrequentOccurence\n\n    # Roll remaining dice that are not the majority\n    roll = standardRoll(NUM_STANDARD_DICE-countMostFrequentOccurence)\n    combination = savedDice + roll\n    \n    return combination \n\nif __name__ == \"__main__\":\n    print(\"Cannot run this file as a stand alone script.\")","repo_name":"DennisPlaydon/yahtzee-calculator","sub_path":"engine/yahtzeeRoller.py","file_name":"yahtzeeRoller.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"35727210411","text":"# 조합 dp | bj 5569 출근 경로\nimport sys\n\n# import itertools\n# from math import factorial\n# from collections import deque\n\n# sys.setrecursionlimit(int(1e9))\ninp = sys.stdin.readline\n\nw, h = map(int, inp().split())\n# dp의 첫인자가 0 => 1칸이동 1 => 2칸이동 즉, 1이면 방향전환이 가능하다는것\n# dp의 두번째인자가 0 => 동으로 이동 1 => 북으로 이동\ndp = [[[[0, 0] for _ in range(2)] for _ in range(101)] for _ in range(101)]\n\nMOD = 100000\n\nfor row in range(2, h + 1):\n    dp[row][1][0][0] = 1\nfor col in range(2, w + 1):\n    dp[1][col][0][1] = 1\n\nfor row in range(2, h + 1):\n    for col in range(2, w + 1):\n        # 방향 전환이 불가능한 경우 = 첫번째 인자가 0인 경우\n        # 이전칸에서 방향 전환을 한 경우이다\n        dp[row][col][1][0] = dp[row - 1][col][0][1] % MOD\n        dp[row][col][1][1] = dp[row][col - 1][0][0] % MOD\n        # 방향전환이 가능한 경우는 = 첫번째 인자가 1인 경우\n        # 이전칸에서 방향전환이 가능할때 + 불가능할때 이다\n        dp[row][col][0][0] = (dp[row - 1][col][0][0] + dp[row - 1][col][1][0]) % MOD\n        dp[row][col][0][1] = (dp[row][col - 1][0][1] + dp[row][col - 1][1][1]) % MOD\n\nanswer = 0\nfor i in range(2):\n    for j in range(2):\n        answer += dp[h][w][i][j]\n\nprint(answer%MOD)\n","repo_name":"chj3748/TIL","sub_path":"Algorithm/baekjoon/bj_5569#.py","file_name":"bj_5569#.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"}
+{"seq_id":"39253716926","text":"# -----------\n# Automating Video\n# Week 1\n\n# Switch between two videos frames \n# -----------\n\nfrom random import randint, uniform\nimport moviepy.editor as mp\n\n# load the videos\nvideo = mp.VideoFileClip(\"videos/president.mp4\")\nvideo2 = mp.VideoFileClip(\"videos/day.mp4\")\n\nclips = []\n\n# create random short videos with the input videos\nfor i in range(0, 10):\n  start = uniform(0, video.duration - 1)\n  end = start + 1\n  clip1 = video.subclip(start, end)\n\n  start = uniform(0, video2.duration - 1)\n  end = start + 1\n  clip2 = video2.subclip(start, end)\n\n  clips.append(clip1)\n  clips.append(clip2)\n\n# make the videos\ncomposition = mp.concatenate(clips)\ncomposition.write_videofile(\"videos/juxtapose.mp4\", fps=25)\n","repo_name":"cvalenzuela/automating_video","sub_path":"inclass/week1/switch_bt_videos.py","file_name":"switch_bt_videos.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"}
+{"seq_id":"31897271802","text":"import os\nimport random\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader\nfrom torchvision import transforms\nfrom torchvision.datasets import ImageFolder\nfrom models.moriaty import Moriaty\nfrom models.watson import Watson\nfrom torchsummary import summary\n\n\ndef model_summary_custom(model):\n    \"\"\"Print out pretty model summary including parameter counts\"\"\"\n\n    print(\"\\nLayer_name\" + \"\\t\" * 7 + \"Number of Parameters\")\n    print(\"=\" * 100)\n\n    model_parameters = [layer for layer in model.parameters()]\n    layer_name = [child for child in model.children()]\n    j, total_params = 0, 0\n    print(\"\\t\" * 10)\n    for i in layer_name:\n        print()\n        param = 0\n        try:\n            bias = (i.bias is not None)\n        except:\n            bias = False  \n        if not bias:\n            param = model_parameters[j].numel() + model_parameters[j+1].numel()\n            j = j+2\n        else:\n            param = model_parameters[j].numel()\n            j = j+1\n        print(str(i) + \"\\t\" * 3 + \"Parameters in Layer: \" + str(param))\n        total_params += param\n    print(\"=\" * 100)\n    print(f\"Total Params: {total_params}\\n\")\n\n\ndef model_summary(model, model_name, printModel=False):\n    \"\"\"Print out pretty model summary including parameter counts\"\"\"\n\n    if printModel:\n        print(\"Model:\")\n        print(model)\n        print()\n\n    print(\"Model summary:\")\n    if model_name in ['Moriaty', 'Moriaty_untrained']:\n        model_summary_custom(model)\n    else:\n        summary(model, (3, 224, 224))\n\n\ndef set_seed(seed):\n    \"\"\"Set ALL random seeds\"\"\"\n\n    random.seed(seed)\n    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    if torch.cuda.is_available():\n        torch.cuda.manual_seed_all(seed)\n        torch.backends.cudnn.deterministic = True\n\n\nclass EarlyStopping:\n    \"\"\"Early stops the training if validation loss doesn't improve after a given patience.\"\"\"\n\n    def __init__(self, patience=7, verbose=False, delta=0, path='checkpoint.pt', trace_func=print, saveEveryEpoch=False):\n        \"\"\"\n        Args:\n            patience (int): How long to wait after last time validation loss improved.\n                            Default: 7\n            verbose (bool): If True, prints a message for each validation loss improvement. \n                            Default: False\n            delta (float): Minimum change in the monitored quantity to qualify as an improvement.\n                            Default: 0\n            path (str): Path for the checkpoint to be saved to.\n                            Default: 'checkpoint.pt'\n            trace_func (function): trace print function.\n                            Default: print            \n        \"\"\"\n\n        self.patience = patience\n        self.verbose = verbose\n        self.counter = 0\n        self.best_score = None\n        self.early_stop = False\n        self.val_loss_min = np.Inf\n        self.delta = delta\n        self.path = path\n        self.trace_func = trace_func\n        self.saveEveryEpoch = saveEveryEpoch\n\n    def __call__(self, val_loss, model, epoch):\n        score = -val_loss\n\n        if self.saveEveryEpoch:\n            path = self.path[:-3] + \"_epoch_\" + str(epoch) + \".pt\"\n            self.save_checkpoint(val_loss, model, path)\n\n        if self.best_score is None:\n            self.best_score = score\n            self.save_checkpoint(val_loss, model, self.path)\n        elif score < self.best_score + self.delta:\n            self.counter += 1\n            self.trace_func(f'EarlyStopping counter: {self.counter} out of {self.patience}')\n            if self.counter >= self.patience:\n                self.early_stop = True\n        else:\n            self.best_score = score\n            self.save_checkpoint(val_loss, model, self.path)\n            self.counter = 0\n\n    def save_checkpoint(self, val_loss, model, path):\n        \"\"\"Saves model when validation loss decreases.\"\"\"\n\n        if self.verbose:\n            self.trace_func(f'Val loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model...\\n')\n        torch.save(model.state_dict(), path)\n        self.val_loss_min = val_loss\n\n\ndef load_model(model_name, config, device, finetune=False):\n    \"\"\"\"Load specified model from checkpoint onto device.\"\"\"\n\n    if model_name == \"Moriaty_untrained\":\n        path_model = None\n        model = Moriaty().to(device)\n    elif model_name == \"Moriaty\":\n        path_model = config['path_model_moriaty']\n        model = Moriaty().to(device)\n        model.load_state_dict(torch.load(path_model, map_location=device))\n    elif model_name == 'Moriaty_adv':\n        path_model = config['path_model_moriaty_adv']\n        model = Moriaty().to(device)\n        model.load_state_dict(torch.load(path_model, map_location=device))\n    elif model_name == \"Polimi\":\n        from polimi.gan_vs_real_detector import Detector as PolimiNet\n        path_model = None\n        model = PolimiNet(device) # note: object is not a neural net\n    elif model_name == \"Lestrade\":\n        path_model = None\n        model = Watson(finetune=finetune).to(device)\n    elif model_name == \"Watson\":\n        path_model = config['path_model_watson']\n        model = Watson(finetune=finetune).to(device)\n        model.load_state_dict(torch.load(path_model, map_location=device))\n    elif model_name == 'Sherlock':\n        path_model = config['path_model_sherlock']\n        model = Watson(finetune=finetune).to(device)\n        model.load_state_dict(torch.load(path_model, map_location=device))\n    else:\n        raise ValueError(\"Need to specify 'Lestrade', 'Sherlock', 'Watson' or 'Polimi'\")\n\n    print(f\"Loaded model: {model_name} onto device: {device} from: {path_model}\")\n\n    return model, model_name, path_model, device\n\n\ndef load_data(data_path, batch_size, model_name, seed, num_workers, adverserial_training=False):\n    \"\"\"\"\n    Load data from specified path and return dataloader with batch size.\n\n    Automatic class assignments by ImageFolder function are done in order \n    of folders in specified directory, so to obtain implicit class assignments \n    (0: real, 1: fake) need to rename real FFHQ image folders as \"ffhq\" and \n    fake StyleGAN image folders as \"stylegan\" (f < s for alphabetical ordering).\n    This naming is also strictly necessary to pass the assert statement.\n\n    Data folder structure/naming:\n        - train\n            - ffhq\n            - stylegan2\n        - val\n            - ffhq\n            - stylegan2\n        - test\n            - ffhq\n            - stylegan3\n    \"\"\"\n    print('modelname:', model_name)\n\n    # Set seed\n    def seed_worker(worker_id):\n        worker_seed = torch.initial_seed() % 2**32\n        np.random.seed(worker_seed)\n        random.seed(worker_seed)\n    g = torch.Generator()\n    g.manual_seed(seed)\n\n    if model_name in ['Lestrade', 'Watson', 'Sherlock']:\n        if adverserial_training == True:\n            print(\"Use transformation for ResNet18 but without normalization\")\n            transform = transforms.Compose([\n                transforms.Resize(224),\n                transforms.ToTensor(),\n            ])\n        else:\n            print(\"Use transformation for ResNet18 with normalization\")\n            transform = transforms.Compose([\n                transforms.Resize(224),\n                transforms.ToTensor(),\n                transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n        ])\n    else:\n        print(\"Use no transformation\")\n        transform = transforms.Compose([\n            transforms.ToTensor()\n        ])\n\n    data = ImageFolder(root=data_path, transform=transform)\n    dataloader = DataLoader(\n        data, \n        batch_size=batch_size, \n        shuffle=True,\n        num_workers=num_workers,\n        worker_init_fn=seed_worker,\n        generator=g\n    )\n\n    assert data.class_to_idx == {'ffhq': 0, 'stylegan2': 1} or data.class_to_idx == {'ffhq': 0, 'stylegan3': 1}\n    assert len(np.unique(data.targets)) == 2, \"More than two classes.\"\n    print(\"Dataset size:\", len(dataloader.dataset))\n    print(\"Class mapping:\", data.class_to_idx) # 0: Real, 1: Fake\n    print(f\"Batch size: {batch_size}\")\n\n    return dataloader\n","repo_name":"municola/robust-deepfake-detector","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":8169,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"85"}
+{"seq_id":"11641345667","text":"from __future__ import unicode_literals\nimport os\nimport frappe\nimport libzfs\nimport subprocess\nfrom frappe.utils import cint\nfrom frappe.model.document import Document\nfrom zfs_admin.utils import run_command, sync_properties\n\nclass ZFSPool(Document):\n\t@property\n\tdef zpool(self):\n\t\tif not hasattr(self, \"_zpool\"):\n\t\t\tself._zpool = libzfs.ZFS().get(self.name)\n\t\treturn self._zpool\n\n\tdef sync(self):\n\t\tself.sync_properties()\n\t\tself.sync_vdev()\n\t\tself.save()\n\t\tself.sync_datasets()\n\n\tdef on_update(self):\n\t\tself.update_disks()\n\n\tdef sync_vdev(self):\n\t\t\"\"\"Sync virtual devices\"\"\"\n\t\tself.load_vdevs()\n\t\tself.fix_vdev_ordering()\n\n\tdef sync_datasets(self):\n\t\t\"\"\"Sync dataset info\"\"\"\n\t\tself.added = []\n\n\t\t# sync root dataset\n\t\tself.sync_one_dataset(self.zpool.root_dataset)\n\t\tself.added.append(self.zpool.root_dataset.name)\n\n\t\t# sync all children\n\t\tfor c in self.zpool.root_dataset.children_recursive:\n\t\t\tself.sync_one_dataset(c)\n\n\t\t# sync all snapshots\n\t\tfor c in self.zpool.root_dataset.snapshots_recursive:\n\t\t\tself.sync_one_dataset(c)\n\n\t\t# delete unsued\n\t\tfor d in frappe.db.sql_list(\"\"\"select name from `tabZFS Dataset`\n\t\t\twhere zfs_pool = %s and name not in ({0})\"\"\".format(\", \".join([\"%s\"] * len(self.added))),\n\t\t\t\t[self.name] + self.added):\n\t\t\tfrappe.delete_doc(\"ZFS Dataset\", d)\n\n\tdef sync_one_dataset(self, d):\n\t\tif frappe.db.exists(\"ZFS Dataset\", d.name):\n\t\t\tzdataset = frappe.get_doc(\"ZFS Dataset\", d.name)\n\t\telse:\n\t\t\tzdataset = frappe.new_doc(\"ZFS Dataset\")\n\t\t\tzdataset.name = d.name\n\n\t\tzdataset.sync_properties(d)\n\t\tzdataset.zfs_pool = self.name\n\t\tzdataset.save()\n\n\t\tself.added.append(zdataset.name)\n\n\tdef update_disks(self):\n\t\t\"\"\"Update Disk with pool and health status\"\"\"\n\t\tfor vdev in self.virtual_devices:\n\t\t\tif vdev.type == \"disk\":\n\t\t\t\tdisk_name = vdev.device_name\n\n\t\t\t\t# TODO: diskname wit partion suffix like ada0p1\n\t\t\t\t# this needs to be synced better\n\t\t\t\tif disk_name[-2] == \"p\":\n\t\t\t\t\tdisk_name = disk_name[:-2]\n\n\t\t\t\tdisk = frappe.get_doc(\"Disk\", disk_name)\n\t\t\t\tdisk.zfs_pool = self.name\n\t\t\t\tdisk.health = vdev.status\n\t\t\t\tdisk.save()\n\n\tdef load_vdevs(self):\n\t\t\"\"\"Load videvs from libzfs\"\"\"\n\t\tfor group in (\"data\", \"cache\", \"log\", \"spare\"):\n\t\t\tgroup_vdevs = self.zpool.groups.get(group)\n\t\t\tadded = {}\n\n\t\t\tfor i, vdev in enumerate(group_vdevs):\n\t\t\t\tparent_row = self.add_vdev(vdev)\n\n\t\t\t\tif parent_row.type == \"disk\":\n\t\t\t\t\tparent_row.device_name = self.get_disk_name(vdev.path)\n\t\t\t\telse:\n\t\t\t\t\tvdev_len = len([v for v in group_vdevs if v.type==vdev.type])\n\t\t\t\t\tif vdev_len > 1:\n\t\t\t\t\t\t# name as mirror-1, mirror-2 etc.\n\t\t\t\t\t\tvdev_id = added.setdefault(vdev.type, 1)\n\t\t\t\t\t\tparent_row.device_name = \"{0}-{1}\".format(vdev.type, vdev_id)\n\t\t\t\t\t\tadded[vdev.type] += 1\n\t\t\t\t\telse:\n\t\t\t\t\t\tparent_row.device_name = vdev.type\n\n\t\t\t\t\tfor disk in vdev.children:\n\t\t\t\t\t\trow = self.add_vdev(disk, True)\n\t\t\t\t\t\trow.group_type = parent_row.type\n\t\t\t\t\t\trow.device_name = self.get_disk_name(disk.path)\n\t\t\t\t\t\trow.parent_device_name = parent_row.device_name\n\n\tdef fix_vdev_ordering(self):\n\t\t\"\"\"Remove unused vdev records and order them so that the groups and disks\n\t\tappear below each other\"\"\"\n\t\tnew_list = []\n\t\tfor d in getattr(self, \"virtual_devices\", []):\n\t\t\tif getattr(d, \"mapped\", False):\n\t\t\t\tnew_list.append(d)\n\n\t\t# reorder in groups\n\t\tnew_order = []\n\t\tfor d in new_list:\n\t\t\tif d.parent_device_name: continue\n\t\t\tnew_order.append(d)\n\t\t\td.idx = len(new_order)\n\t\t\tif d.type != \"disk\":\n\t\t\t\tfor child in new_list:\n\t\t\t\t\tif child.parent_device_name == d.device_name:\n\t\t\t\t\t\tnew_order.append(child)\n\t\t\t\t\t\tchild.idx = len(new_order)\n\n\t\tself.virtual_devices = new_order\n\n\tdef get_disk_name(self, disk_path):\n\t\treturn os.path.split(disk_path)[-1]\n\n\tdef add_vdev(self, vdev, is_child=False):\n\t\t\"\"\"Add a new virtual device row\"\"\"\n\t\trow = self.get_vdev_row(vdev.guid)\n\t\tif not row:\n\t\t\trow = self.append(\"virtual_devices\", {\"guid\": vdev.guid})\n\n\t\trow.status = vdev.status\n\t\trow.type = vdev.type\n\t\trow.guid = vdev.guid\n\t\tif not is_child:\n\t\t\trow.size = vdev.size\n\n\t\trow.mapped = True\n\n\t\treturn row\n\n\tdef sync_properties(self):\n\t\t\"\"\"Sync ZFS Pool properties\"\"\"\n\t\tsync_properties(self, self.zpool.properties)\n\n\tdef get_vdev_row(self, guid):\n\t\tfor d in getattr(self, \"virtual_devices\", []):\n\t\t\tif str(d.guid) == str(guid):\n\t\t\t\treturn d\n\n\tdef zpool_add(self, type, disk1, disk2):\n\t\t\"\"\"Runs zpool add\"\"\"\n\t\tself.has_permission(\"write\")\n\n\t\tif type.lower()==\"disk\":\n\t\t\targs = [\"sudo\", \"zpool\", \"add\", self.name, disk1]\n\t\telse:\n\t\t\targs = [\"sudo\", \"zpool\", \"add\", self.name, type.lower(), disk1, disk2]\n\n\t\tout = run_command(args)\n\t\tif out==\"okay\":\n\t\t\tself.sync()\n\t\t\treturn out\n\n\tdef zpool_detach(self, disk):\n\t\t\"\"\"Runs zpool detach\"\"\"\n\t\tself.has_permission(\"write\")\n\t\tout = run_command([\"sudo\", \"zpool\", \"detach\", self.name, disk])\n\t\tif out==\"okay\":\n\t\t\tself.sync()\n\t\t\treturn out\n\n\tdef zpool_destroy(self):\n\t\t\"\"\"Runs zpool destroy\"\"\"\n\t\tself.has_permission(\"delete\")\n\n\t\tout = run_command([\"sudo\", \"zpool\", \"destroy\", self.name])\n\t\tif out==\"okay\":\n\t\t\t# remove references from disk\n\t\t\tfrappe.db.sql(\"update tabDisk set zfs_pool='' where zfs_pool=%s\", self.name)\n\n\t\t\t# delete zfs dataset records\n\t\t\tfor d in frappe.db.get_all(\"ZFS Dataset\", filters={\"zfs_pool\": self.name}):\n\t\t\t\tfrappe.delete_doc(\"ZFS Dataset\", d.name)\n\n\t\t\t# delete record\n\t\t\tself.delete()\n\t\t\treturn \"okay\"\n\ndef zpool_create(name, type, disk1, disk2):\n\t\"\"\"zpool create\"\"\"\n\tif type==\"Disk\":\n\t\targs = [\"sudo\", \"zpool\", \"create\", name, disk1]\n\telse:\n\t\targs = [\"sudo\", \"zpool\", \"create\", name, type.lower(), disk1, disk2]\n\n\tif run_command(args)==\"okay\":\n\t\tzfs_pool = frappe.new_doc(\"ZFS Pool\")\n\t\tzfs_pool.name = name\n\t\tzfs_pool.sync()\n\t\treturn \"okay\"\n","repo_name":"rmehta/zfs_admin","sub_path":"zfs_admin/zfs_admin/doctype/zfs_pool/zfs_pool.py","file_name":"zfs_pool.py","file_ext":"py","file_size_in_byte":5571,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"85"}
+{"seq_id":"17502532948","text":"from typing import List, Optional, Dict\nimport datetime\nfrom django.db import models, transaction\nfrom django.utils import timezone\nfrom django.utils.html import format_html\nfrom django.core.exceptions import ValidationError\nfrom django.contrib.postgres.fields import JSONField\nfrom django.conf import settings\n\nfrom project.common_data import Choices\nfrom project import common_data\nfrom project.util.site_util import absolute_reverse, get_site_name\nfrom project.util.instance_change_tracker import InstanceChangeTracker\nfrom users.models import JustfixUser\nfrom .landlord_lookup import lookup_landlord\n\n\nLOB_STRICTNESS_HELP_URL = \\\n    \"https://lob.com/resources/guides/general/verification-of-mailing-addresses\"\n\nLOC_MAILING_CHOICES = Choices.from_file('loc-mailing-choices.json')\n\nLOC_REJECTION_CHOICES = Choices.from_file('loc-rejection-choices.json')\n\nUSPS_TRACKING_URL_PREFIX = common_data.load_json(\"loc.json\")[\"USPS_TRACKING_URL_PREFIX\"]\n\n# The amount of time a user has to change their letter of request\n# content after originally submitting it.\nLOC_CHANGE_LEEWAY = datetime.timedelta(hours=1)\n\n# Maximum length of a landlord address.\nADDR_LENGTH = 1000\n\n\nclass AccessDateManager(models.Manager):\n    @transaction.atomic\n    def set_for_user(self, user: JustfixUser, dates: List[datetime.date]):\n        self.filter(user=user).delete()\n        self.bulk_create([\n            AccessDate(user=user, date=date)\n            for date in dates\n        ])\n\n    def get_for_user(self, user: JustfixUser) -> List[datetime.date]:\n        return [ad.date for ad in user.access_dates.all()]\n\n\nclass AccessDate(models.Model):\n    class Meta:\n        unique_together = ('user', 'date')\n\n    user = models.ForeignKey(\n        JustfixUser, on_delete=models.CASCADE, related_name='access_dates',\n        help_text=\"The user whose dwelling this access date this is for.\")\n\n    date = models.DateField(\n        help_text=\"The date on which the user's dwelling will be accessible.\")\n\n    objects = AccessDateManager()\n\n\nclass LandlordDetails(models.Model):\n    '''\n    This represents the landlord details for a user's address, either\n    manually entered by them or automatically looked up by us (or a\n    combination of the two, if the user decided to change what we\n    looked up).\n    '''\n\n    user = models.OneToOneField(\n        JustfixUser, on_delete=models.CASCADE, related_name='landlord_details',\n        help_text=\"The user whose landlord details this is for.\")\n\n    name = models.CharField(\n        max_length=100, help_text=\"The landlord's name.\")\n\n    address = models.CharField(\n        max_length=ADDR_LENGTH,\n        help_text=\"The full mailing address for the landlord.\")\n\n    lookup_date = models.DateField(\n        null=True,\n        blank=True,\n        help_text=\"When we last tried to look up the landlord's details.\"\n    )\n\n    is_looked_up = models.BooleanField(\n        default=False,\n        help_text=(\n            \"Whether the name and address was looked up automatically, \"\n            \"or manually entered by the user.\"\n        )\n    )\n\n    @property\n    def address_lines_for_mailing(self) -> List[str]:\n        '''Return the full mailing address as a list of lines.'''\n\n        if not self.address:\n            return []\n        return self.address.split('\\n')\n\n    @classmethod\n    def create_lookup_for_user(cls, user: JustfixUser) -> Optional['LandlordDetails']:\n        '''\n        Create an instance of this class by attempting to look up details on the\n        given user's address.\n\n        Assumes that the user does not yet have an instance of this class associated\n        with them.\n\n        If the lookup fails, this method will still create an instance of this class,\n        but it will set the lookup date, so that another lookup can be attempted\n        later.\n\n        However, if the user doesn't have any address information, this will return\n        None, as it has no address to lookup the landlord for.\n        '''\n\n        if hasattr(user, 'onboarding_info'):\n            oi = user.onboarding_info\n            info = lookup_landlord(\n                oi.full_address,\n                oi.pad_bbl,\n                oi.pad_bin\n            )\n            details = LandlordDetails(\n                user=user,\n                lookup_date=timezone.now()\n            )\n            if info:\n                details.name = info.name\n                details.address = info.address\n                details.is_looked_up = True\n            details.save()\n            return details\n        return None\n\n\nclass AddressDetails(models.Model):\n    '''\n    A model that maps address \"blobs\" of text to individual fields that\n    represent the address.\n    '''\n\n    class Meta:\n        verbose_name_plural = \"Address details\"\n\n    created_at = models.DateTimeField(auto_now_add=True)\n\n    updated_at = models.DateTimeField(auto_now=True)\n\n    address = models.CharField(\n        max_length=ADDR_LENGTH,\n        unique=True,\n        help_text=(\n            'This is the address represented as a single string. Sometimes '\n            'we need to manually break it up into its constituent parts so '\n            'it is easier for machines to understand, which is what all '\n            'the other fields in this model are for.'\n        )\n    )\n\n    primary_line = models.CharField(\n        max_length=255,\n        blank=True,\n        help_text='Usually the first line of the address, e.g. \"150 Court Street\"'\n    )\n\n    secondary_line = models.CharField(\n        max_length=255,\n        blank=True,\n        help_text='Optional. Usually the second line of the address, e.g. \"Suite 2\"'\n    )\n\n    urbanization = models.CharField(\n        max_length=80,\n        blank=True,\n        help_text='Optional. Only used for addresses in Puerto Rico.'\n    )\n\n    city = models.CharField(\n        max_length=80,\n        blank=True,\n        help_text='The city of the address, e.g. \"Brooklyn\".'\n    )\n\n    state = models.CharField(\n        max_length=2,\n        blank=True,\n        help_text='The two-letter state for the address, e.g. \"NY\".'\n    )\n\n    zip_code = models.CharField(\n        max_length=10,\n        blank=True,\n        help_text='The zip code of the address, e.g. \"11201\" or \"94107-2282\".'\n    )\n\n    is_definitely_deliverable = models.BooleanField(\n        verbose_name=(\n            \"This address is definitely deliverable \"\n            \"(manually override Lob's address verification)\"\n        ),\n        default=False,\n        help_text=(\n            \"If you know for certain that this address is deliverable, even if Lob thinks \"\n            \"otherwise, check this box. This will manually override Lob's verification \"\n            \"and force any letters sent to this address to be mailed, assuming you have \"\n            \"configured your \"\n            f\"<a href=\\\"{LOB_STRICTNESS_HELP_URL}\\\" \"\n            \"target=\\\"_blank\\\" rel=\\\"noopener nofollow\\\">Lob strictness settings</a> to 'Relaxed'.\"\n        )\n    )\n\n    notes = models.TextField(\n        blank=True,\n        help_text=(\n            \"Notes about this address. In particular, if you had to override the deliverability \"\n            \"of this address or change it in non-trivial ways, please add your rationale here.\"\n        )\n    )\n\n    # Attributes that map to keys used by Lob's verifications API:\n    LOB_ATTRS = ['primary_line', 'secondary_line', 'urbanization', 'city', 'state', 'zip_code']\n\n    def is_populated(self) -> bool:\n        '''\n        Return whether the model contains enough filled-out fields to be a useful\n        substitute to the address string.\n        '''\n\n        return bool(self.primary_line and self.city and self.state and self.zip_code)\n\n    def as_lob_params(self) -> Dict[str, str]:\n        '''\n        Returns a dictionary representing the address that can be passed directly\n        to Lob's verifications API: https://lob.com/docs#us_verifications_create\n        '''\n\n        if not self.is_populated():\n            return {'address': self.address}\n        result: Dict[str, str] = {}\n        for attr in self.LOB_ATTRS:\n            value = getattr(self, attr)\n            if value:\n                result[attr] = value\n        return result\n\n    def __str__(self):\n        return self.address.replace(\"\\n\", \" / \")\n\n\nclass LetterRequest(models.Model):\n    '''\n    A completed letter of complaint request submitted by a user.\n    '''\n\n    created_at = models.DateTimeField(auto_now_add=True)\n\n    updated_at = models.DateTimeField(auto_now=True)\n\n    user = models.OneToOneField(\n        JustfixUser, on_delete=models.CASCADE, related_name='letter_request')\n\n    mail_choice = models.TextField(\n        max_length=30,\n        choices=LOC_MAILING_CHOICES.choices,\n        help_text=\"How the letter of complaint will be mailed.\")\n\n    html_content = models.TextField(\n        blank=True,\n        help_text=\"The HTML content of the letter at the time it was requested.\"\n    )\n\n    lob_letter_object = JSONField(\n        blank=True,\n        null=True,\n        help_text=(\n            \"If the letter was sent via Lob, this is the JSON response of the API call that \"\n            \"was made to send the letter, documented at https://lob.com/docs/python#letters.\"\n        )\n    )\n\n    tracking_number = models.CharField(\n        max_length=100,\n        blank=True,\n        help_text=(\n            \"The tracking number for the letter. Note that when this is changed, \"\n            \"the user will be notified via SMS and added to a LOC follow-up campaign, \"\n            \"if one has been configured.\"\n        ),\n    )\n\n    letter_sent_at = models.DateTimeField(\n        null=True,\n        blank=True,\n        help_text=\"When the letter was mailed through the postal service.\"\n    )\n\n    rejection_reason = models.CharField(\n        max_length=100,\n        blank=True,\n        choices=LOC_REJECTION_CHOICES.choices,\n        help_text=\"The reason we didn't mail the letter, if applicable.\"\n    )\n\n    def __init__(self, *args, **kwargs) -> None:\n        super().__init__(*args, **kwargs)\n        self.__tracker = InstanceChangeTracker(self, ['mail_choice', 'html_content'])\n        self.__tracking_number_tracker = InstanceChangeTracker(self, ['tracking_number'])\n\n    @property\n    def will_we_mail(self) -> bool:\n        '''\n        Whether or not the user wants us to mail the letter for them.\n        '''\n\n        return self.mail_choice == LOC_MAILING_CHOICES.WE_WILL_MAIL\n\n    @property\n    def admin_pdf_url(self) -> str:\n        '''\n        A link where an administrative/staff user can view the\n        letter of complaint as a PDF.\n\n        If we don't have enough information to generate such a link,\n        this will be an empty string.\n        '''\n\n        if self.pk is None:\n            return ''\n        return absolute_reverse('loc_for_user', kwargs={'user_id': self.user.pk})\n\n    def __str__(self):\n        if not (self.created_at and self.user and self.user.full_name):\n            return super().__str__()\n        return (\n            f\"{self.user.full_name}'s letter of complaint request from \"\n            f\"{self.created_at.strftime('%A, %B %d %Y')}\"\n        )\n\n    @property\n    def lob_letter_html_description(self) -> str:\n        '''\n        Return an HTML string that describes the mailed Lob letter. If\n        the letter has not been sent through Lob, return an empty string.\n        '''\n\n        lob_url = self.lob_url\n        return lob_url and format_html(\n            'The letter was <a href=\"{}\" rel=\"noreferrer noopener\" target=\"_blank\">'\n            'sent via Lob</a> with the tracking number {} and '\n            \"has an expected delivery date of {}.\",\n            lob_url,\n            self.lob_letter_object['tracking_number'],\n            self.lob_letter_object['expected_delivery_date']\n        )\n\n    @property\n    def lob_url(self) -> str:\n        '''\n        Return the URL on Lob where more information about the mailed Lob\n        version of this letter can be found.\n\n        If the letter has not been sent through Lob, return an empty string.\n        '''\n\n        if not self.lob_letter_object:\n            return ''\n        ltr_id = self.lob_letter_object['id']\n\n        # This URL structure isn't formally documented anywhere, it was\n        # just inferred, so it could technically break at any time, but\n        # it's better than nothing!\n        return f\"https://dashboard.lob.com/#/letters/{ltr_id}\"\n\n    @property\n    def usps_tracking_url(self) -> str:\n        '''\n        Return the URL on the USPS website where more information about\n        the mailed letter can be found.\n\n        If the letter has not been sent, return an empty string.\n        '''\n\n        if not self.tracking_number:\n            return ''\n\n        return f\"{USPS_TRACKING_URL_PREFIX}{self.tracking_number}\"\n\n    def can_change_content(self) -> bool:\n        if self.__tracker.original_values['mail_choice'] == LOC_MAILING_CHOICES.USER_WILL_MAIL:\n            return True\n        if self.lob_letter_object is not None:\n            return False\n        if self.tracking_number:\n            return False\n        if self.created_at is None:\n            return True\n        return timezone.now() - self.created_at < LOC_CHANGE_LEEWAY\n\n    def clean(self):\n        super().clean()\n        user = self.user\n\n        if self.rejection_reason and self.tracking_number:\n            raise ValidationError('Letter cannot be both rejected and mailed!')\n\n        if user and self.mail_choice == LOC_MAILING_CHOICES.WE_WILL_MAIL:\n            if user.issues.count() == 0 and user.custom_issues.count() == 0:\n                raise ValidationError(\n                    'Please select at least one issue from the issue checklist.')\n            if user.access_dates.count() == 0:\n                raise ValidationError(\n                    'Please provide at least one access date.')\n            if not hasattr(user, 'landlord_details'):\n                raise ValidationError(\n                    'Please provide contact information for your landlord.')\n\n        if self.__tracker.has_changed() and not self.can_change_content():\n            raise ValidationError('Your letter is already being mailed!')\n\n    def regenerate_html_content(self, author: str):\n        from .views import render_letter_body\n\n        header = (\n            f'<!-- This HTML was generated by {author} on '\n            f'{datetime.datetime.now()} using git revision '\n            f'{settings.GIT_INFO.get_version_str()}. -->\\n'\n        )\n        self.html_content = header + render_letter_body(self.user)\n\n    def _on_tracking_number_changed(self):\n        if not self.tracking_number:\n            return\n        self.user.send_sms_async(\n            f\"{get_site_name()} here - \"\n            f\"We've mailed the letter of complaint to your landlord. \"\n            f\"You can track its progress here: {self.usps_tracking_url} \"\n            f\"(link may take a day to update)\"\n        )\n        self.user.send_sms_async(\n            f\"We'll follow up in about a week to see how things are going.\"\n        )\n        self.user.trigger_followup_campaign_async(\"LOC\")\n\n    def save(self, *args, **kwargs):\n        super().save(*args, **kwargs)\n        self.__tracker.set_to_unchanged()\n\n        if self.__tracking_number_tracker.has_changed():\n            self._on_tracking_number_changed()\n\n        self.__tracking_number_tracker.set_to_unchanged()\n","repo_name":"ma8642/tenants2","sub_path":"loc/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":15339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"85"}
+{"seq_id":"37854752838","text":"from rest_framework import serializers\n\nfrom . import models\n\n\nclass GenreSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.Genre\n        fields = '__all__'\n\n\nclass PersonSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.Person\n        fields = '__all__'\n\n\nclass RoleSerializer(serializers.ModelSerializer):\n    # name = serializers.StringRelatedField(source='person')\n    # Return list of names ['..'] instead a list of objects {name: '...'}\n    def to_representation(self, obj):\n        return obj.person.credited_name\n\n    def to_internal_value(self, data):\n        role = {\n            \"person_id\": data\n        }\n        return role\n\n    class Meta:\n        model = models.Role\n\n        fields = ['person']\n\n\nclass Country:\n    pass\n\n\nclass CountrySerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Country\n        fields = '__all__'\n\nclass MovieSerializer(serializers.ModelSerializer):\n\n    class Meta:\n        model = models.Movie\n        fields = '__all__'\n\n    def create(self, validated_data):\n        genre_data = validated_data.pop('genres')\n        movie = models.Movie.objects.create(**validated_data)\n        for genre in genre_data:\n            movie.genres.add(genre.pk)\n        return movie\n\n    def update(self, instance, validated_data):\n        print(validated_data)\n        genre_data = validated_data.pop('genres')\n        instance.movie_title = validated_data.get('movie_title', instance.movie_title)\n        instance.runtime = validated_data.get('runtime', instance.runtime)\n        instance.released = validated_data.get('released', instance.released)\n        instance.country = validated_data.get('country', instance.country)\n        instance.save()\n\n        instance.genres.clear()\n\n        for genre in genre_data:\n            print(genre)\n            instance.genres.add(genre.pk)\n\n        return instance\n","repo_name":"raphi98/GreenGuru","sub_path":"backend/wapdev2/yamod/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"71470869077","text":"def main():\n    \"\"\"Searches for a record from the file 'records.txt'.\"\"\"\n    found = False\n    search = input('Enter a description to search for: ')\n    records_file = open('records.txt', 'r')\n    descr = records_file.readline()\n\n    while descr != '':\n        qty = float(records_file.readline())\n        descr = descr.rstrip('\\n')\n        if descr == search:\n            print('Description:', descr)\n            print('Quantity:', qty)\n            print()\n            found = True\n\n        descr = records_file.readline()\n\n    records_file.close()\n\n    if not found:\n        print('That item was not found in the file.')\n\n\nmain()\n","repo_name":"squidmin/python-labs","sub_path":"14_files_and_exceptions/13_search_records.py","file_name":"13_search_records.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"14901808594","text":"import drawlibrary as dl\nimport naivesim as ns\n\n# todo - refactor functions to accomodate knockoutstage procedure rather than separate functions\n# todo - automate this more, rather than in test?\n# todo - sort prints by records\n\nclass Tournamentmaster:\n    def __init__(self, players):\n        # assume number of players is correct, and always 32 -> so max 5 rounds\n        self.active_players = players\n        self.eliminated_players = []\n        self.games_played = 0\n        self.matchups = None\n        self.latest_winners = None\n        self.knockoutstage = False\n        self.knockoutbracket = None\n\n    def get_active_players(self):\n        return self.active_players\n\n    def get_all_players(self):\n        return self.active_players + self.eliminated_players\n\n    def get_eliminated_players(self):\n        return self.eliminated_players\n\n    def get_player(self, name):\n        for player in self.active_players:\n            if player.name == name:\n                return player\n        for player in self.eliminated_players:\n            if player.name == name:\n                return player\n        return None\n\n    def get_matchups(self):\n        return self.matchups\n\n    def get_knockout_brackets(self):\n        return self.knockoutbracket\n\n    def simulate_round(self):\n        self.latest_winners = ns.generate_results(self.matchups)\n        self.__update_records()\n        self.print_results()\n\n    def __update_records(self):\n        self.games_played += 1\n        for i in range(0,len(self.matchups)):\n            (p1,p2) = self.matchups[i]\n            winner = self.latest_winners[i]\n            if winner is p1:\n                p1.update_record(True)\n                p2.update_record(False)\n                (p2w, p2l) = p2.get_record()\n                if p2l > 2:\n                    self.active_players.remove(p2)\n                    self.eliminated_players.append(p2)\n            else:\n                p2.update_record(True)\n                p1.update_record(False)\n                (p1w, p1l) = p1.get_record()\n                if p1l > 2:\n                    self.active_players.remove(p1)\n                    self.eliminated_players.append(p1)\n        if self.games_played is 5:\n            self.knockoutstage = True\n            # todo - handle knockoutstage scenario\n\n    def draw_matchups(self):\n        players_by_losses = self.__sort_by_records()\n        newmatchups = []\n        for i in range(0, self.games_played+1):\n            newmatchups += dl.draw_matchups(players_by_losses[i])\n        self.matchups = newmatchups\n\n    # sort list of players by records for drawing purposes\n    def __sort_by_records(self):\n        players_by_losses = {}\n        for i in range(0,self.games_played+1):\n            players_by_losses[i] = []\n        for player in self.active_players:\n            (pwin, ploss) = player.get_record()\n            players_by_losses[ploss] += [player]\n        return players_by_losses\n\n    def draw_knockout_brackets(self):\n        players_by_losses = self.__sort_by_records()\n        self.knockoutbracket = dl.draw_knockout_brackets(players_by_losses[0],players_by_losses[1],players_by_losses[2])\n\n    def simulate_ko_round(self):\n        bracketsleft = len(self.knockoutbracket)\n        # each bracket has 2 matches, except if 1 bracket left\n        if bracketsleft is 1:\n            if len(self.knockoutbracket[0]) is 2:\n                winners = ns.generate_results(self.knockoutbracket[0])\n                self.knockoutbracket = [[(winners[0],winners[1])]]\n            else:\n                winner = ns.generate_results(self.knockoutbracket[0])[0]\n                print(\"YOUR WINNER IS \" + str(winner.get_name()))\n        else:\n            allwinners = []\n            for bracket in self.knockoutbracket:\n                winners = ns.generate_results(bracket)\n                allwinners += winners\n            newbrackets = []\n            newnumbrackets = int(bracketsleft/2)\n            for i in range(0,newnumbrackets):\n                newbrackets += [[(allwinners[4*i+0],allwinners[4*i+1]),(allwinners[4*i+2],allwinners[4*i+3])]]\n            self.knockoutbracket = newbrackets\n\n    # assume that winners list and matchups match up\n    def print_results(self):\n        for i in range(0,len(self.matchups)):\n            (p1,p2) = self.matchups[i]\n            winner = self.latest_winners[i]\n            if winner is p1:\n                print(str(p1.get_name()) + \" BEATS \" + str(p2.get_name()))\n            else:\n                print(str(p2.get_name()) + \" BEATS \" + str(p1.get_name()))\n\n    def print_matchups(self):\n        for (p1, p2) in self.matchups:\n            print(str(p1.get_name()) + \" VS \" + str(p2.get_name()))\n\n    def print_all_records(self):\n        for player in self.active_players:\n            name, team, record = player.get_profile()\n            (wins, losses) = record\n            print(name + \" : (\" +  str(wins) +\"-\"+str(losses)+\")\")\n        for player in self.eliminated_players:\n            name, team, record = player.get_profile()\n            (wins, losses) = record\n            print(name + \" : (\" +  str(wins) +\"-\"+str(losses)+\")\")\n\n    def print_active_records(self):\n        for player in self.active_players:\n            name, team, record = player.get_profile()\n            (wins, losses) = record\n            print(name + \" : (\" +  str(wins) +\"-\"+str(losses)+\")\")\n\n    def print_ko_draw(self):\n        index = 1\n        numbrackets = len(self.knockoutbracket)\n        for bracket in self.knockoutbracket:\n            if numbrackets is 1:\n                if len(self.knockoutbracket[0]) is 1:\n                    print(\"YOUR FINAL:\")\n                else:\n                    print(\"SEMIS:\")\n            elif numbrackets is 2:\n                if index is 1:\n                    print(\"TOP HALF:\")\n                else:\n                    print(\"BOTTOM HALF:\")\n            else:\n                print(\"Section \"+str(index)+\":\")\n            for (p1,p2) in bracket:\n                print(str(p1.get_name()) + \" VS \" + str(p2.get_name()))\n            index +=1\n\n    def print_KO_results(self):\n        pass\n","repo_name":"zfegd/Swiss-Format-Tracker","sub_path":"tournamentmaster.py","file_name":"tournamentmaster.py","file_ext":"py","file_size_in_byte":6091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"70913035797","text":"import argparse\nimport os\nimport gc\nimport sys\nsys.path.append('./Grounded-Segment-Anything')\n\nimport numpy as np\nimport torch\nfrom PIL import Image\n\nfrom segment_anything import build_sam, SamPredictor, build_sam_vit_l, build_sam_vit_b, sam_model_registry\nfrom maskrcnn_benchmark.config import cfg\nfrom maskrcnn_benchmark.engine.predictor_glip import GLIPDemo\n\n\nclass SemanticPredGLIPSAM():\n\n    def __init__(self, args, categories):\n        self.caption = ' . '.join(categories) + '.'\n        self.num_sem_categories = args.num_sem_categories\n\n        # update the config options with the config file\n        # manual override some options\n        cfg.local_rank = 0\n        cfg.num_gpus = 1\n        cfg.merge_from_file(args.det_config_file)\n        cfg.merge_from_list([\"MODEL.WEIGHT\", args.det_weight])\n        cfg.merge_from_list([\"MODEL.DEVICE\", \"cuda\"])\n\n        self.glip_demo = GLIPDemo(\n            cfg,\n            # min_image_size=800,\n            confidence_threshold=args.det_thresh,\n            show_mask_heatmaps=False\n        )\n        self.thresh = args.det_thresh\n        self.device = \"cuda:0\"\n\n        # initialize SAM\n        self.predictor = SamPredictor(sam_model_registry[args.sam_type](checkpoint=args.sam_checkpoint).to(self.device))\n        torch.cuda.set_device(args.sem_gpu_id)\n\n    def get_prediction(self, img):\n        H, W, _ = img.shape\n        # convert to RGB\n        rgb = img[:, :, [2, 1, 0]]\n\n        top_predictions = self.glip_demo.inference(img, self.caption)\n        print(top_predictions)\n\n        labels = top_predictions.get_field('labels').numpy()\n        print('seg labels: ', labels)\n\n        if self.glip_demo.cfg.MODEL.RPN_ARCHITECTURE == \"VLDYHEAD\":\n            plus = 1\n        else:\n            plus = 0\n\n        semantic_input = np.zeros((H, W, self.num_sem_categories))\n\n        if labels.shape[0] > 0:\n            boxes_filt = top_predictions.bbox\n\n            self.predictor.set_image(rgb)\n            transformed_boxes = self.predictor.transform.apply_boxes_torch(boxes_filt, (H, W)).to(self.device)\n\n            masks, _, _ = self.predictor.predict_torch(\n                point_coords=None,\n                point_labels=None,\n                boxes=transformed_boxes.to(self.device),\n                multimask_output=False,\n            )\n\n            masks = masks.cpu().numpy() * 1.0\n\n            for j in range(len(labels)):\n                class_idx = labels[j]\n                if class_idx <= self.num_sem_categories:\n                    obj_mask = masks[j]\n                    semantic_input[:, :, class_idx - plus] += np.squeeze(obj_mask)\n\n        return semantic_input, img\n","repo_name":"liangcici/MO-VLN","sub_path":"agents/utils/glip_sam_prediction.py","file_name":"glip_sam_prediction.py","file_ext":"py","file_size_in_byte":2641,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"85"}
+{"seq_id":"13153072042","text":"from pacfish import PAData\nfrom pacfish.qualitycontrol import CompletenessChecker, ConsistencyChecker\n\n\ndef quality_check_pa_data(pa_data: PAData, verbose: bool = False, log_file_path: str = None) -> bool:\n    \"\"\"\n    This is a convenience method that instantiates both a completeness and a consistency checker\n    and evaluates the given PAData instance.\n\n    Parameters\n    ----------\n    pa_data: PAData\n        The PAData instance to check\n    verbose: bool\n        Specifies if the log report should be printed to the console\n    log_file_path: str\n        A path to a log file to write to\n\n    Return\n    ------\n    bool\n        True if and only if all completeness and quality checks are passed.\n    \"\"\"\n    completeness = CompletenessChecker(verbose=verbose, log_file_path=log_file_path)\n    consistency = ConsistencyChecker(verbose=verbose, log_file_path=log_file_path)\n\n    b1 = completeness.check_acquisition_meta_data(pa_data.meta_data_acquisition)\n    b2 = consistency.check_acquisition_meta_data(pa_data.meta_data_acquisition)\n\n    b3 = completeness.check_device_meta_data(pa_data.meta_data_device)\n    b4 = consistency.check_device_meta_data(pa_data.meta_data_device)\n\n    b5 = consistency.check_binary_data(pa_data.binary_time_series_data)\n\n    return b1 and b2 and b3 and b4 and b5","repo_name":"IPASC/PACFISH","sub_path":"pacfish/qualitycontrol/PADataIntegrityCheck.py","file_name":"PADataIntegrityCheck.py","file_ext":"py","file_size_in_byte":1298,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"85"}
+{"seq_id":"22798112674","text":"import tornado.websocket\nfrom models import users\n\nnotifiers = {}\n\nclass NotificationsWebSocket(tornado.websocket.WebSocketHandler):\n\n    def open(self):\n        self.user = int(self.get_secure_cookie(\"user\"))\n        notifiers[self.user] = self\n        print('Notifications connected.')\n\n    def on_message(self, target_id):\n        print('--------MATCH----------')\n        print(self.user, target_id)\n        # check all connections and notify the other matched user\n        target_id = int(target_id)\n        if int(target_id) in notifiers:\n            user = users.get_user(self.user)\n            notifiers[int(target_id)].write_message(user['name'])\n\n    def on_close(self):\n        if self in notifiers:\n            notifiers.remove(self)\n\n","repo_name":"omarmjhd/codr","sub_path":"server/api/notifications.py","file_name":"notifications.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"85"}
+{"seq_id":"34806116745","text":"#!/usr/bin/env python\n# coding: utf-8\n# author email: erfan.molaei@gmail.com\nimport os\nimport pickle\nimport joblib\n\nimport numpy as np\nfrom scipy.stats import norm\n\nimport tensorflow as tf\nfrom tensorflow.keras import layers\nimport datetime\n\nfrom utils import load_datasets, encode_sequences\nfrom matplotlib.colors import LinearSegmentedColormap\nimport matplotlib.pyplot as plt \n\n# from Loss import plotLoss\n# from Latent import plotLatent1d, plotLatent2d\n\n# class metricsByEpoch(keras.callbacks.Callback):\n\n#     def on_epoch_end(self, epoch, target_epochs):\n#         if epoch in target_epoch:\n\n       \n\ndef __create_callbacks__(metric, ld=6, epochs=1, optimizer='adam', batch_size=1):\n    # cpk_path = f'./drive/My Drive/ShiLab/training_checkpoints/HLA/mixed_pop/mixed_best_model_{kfold}.h5'\n\n    # checkpoint = tf.keras.callbacks.ModelCheckpoint(\n    #     filepath=cpk_path,\n    #     monitor= metric,\n    #     mode='min',\n    #     save_best_only=True,\n    #     verbose=1,\n    # )\n\n    log_dir = \"logs/fit/\" + f\"latent_dim_{ld}_bs_{batch_size}_epochs_{epochs}_optimizer_{optimizer}_\" + \\\n              datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n    tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n\n    reducelr = tf.keras.callbacks.ReduceLROnPlateau(\n        monitor=metric,\n        mode='min',\n        factor=0.2,\n        patience=10,\n        verbose=0\n    )\n\n    earlystop = tf.keras.callbacks.EarlyStopping(\n        monitor=metric,\n        mode='min',\n        patience=25,\n        verbose=1,\n        restore_best_weights=True\n    )\n\n    callbacks = [\n        #  checkpoint,\n        #         reducelr,\n        #         earlystop,\n        tensorboard_callback]\n\n    return callbacks\n\n\n@tf.function()\ndef _data_mapper(X_sample, q):\n    # return tf.one_hot(X_sample, q)\n    return tf.reshape(tf.one_hot(X_sample, q), [X_sample.shape[0], q, 1])\n    # return tf.reshape(tf.one_hot(X_sample, q), [-1])\n\n\ndef _get_dataset(X, bs, q, training=True):\n    AUTO = tf.data.experimental.AUTOTUNE\n    dataset = tf.data.Dataset.from_tensor_slices((X))\n    if training:\n        dataset = dataset.shuffle(X.shape[0], reshuffle_each_iteration=True)\n        dataset = dataset.repeat()\n    # Add Attention Mask\n    dataset = dataset.map(lambda x: _data_mapper(x, q), num_parallel_calls=AUTO, deterministic=False)\n    # Prefetech to not map the whole dataset\n    dataset = dataset.prefetch(AUTO)\n    dataset = dataset.batch(bs, drop_remainder=False)\n    return dataset\n\nclass Sampling(layers.Layer):\n    \"\"\"Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.\"\"\"\n\n    def call(self, inputs, training=None):\n        z_mean, z_log_var = inputs\n        batch = tf.shape(z_mean)[0]\n        dim = tf.shape(z_mean)[1]\n        epsilon = tf.keras.backend.random_normal(shape=(batch, dim))\n        return z_mean + tf.exp(0.5 * z_log_var) * epsilon\n\n\nclass BaseVAE(tf.keras.Model):\n    def __init__(self, encoder, decoder, **kwargs):\n        super(BaseVAE, self).__init__(**kwargs)\n        self.encoder = encoder\n        self.decoder = decoder\n        self._set_inputs(inputs=self.encoder.inputs, outputs=decoder.outputs)\n        self.total_loss_tracker = tf.keras.metrics.Mean(name=\"total_loss\")\n        self.reconstruction_loss_tracker = tf.keras.metrics.Mean(\n            name=\"reconstruction_loss\"\n        )\n        self.kl_loss_tracker = tf.keras.metrics.Mean(name=\"kl_loss\")\n        self.hamming_loss_tracker = tf.keras.metrics.Mean(name=\"hamming_loss\")\n\n    @property\n    def metrics(self):\n        return [\n            self.total_loss_tracker,\n            self.reconstruction_loss_tracker,\n            self.kl_loss_tracker,\n            self.hamming_loss_tracker,\n        ]\n\n    #def call(self, inputs, training=None):\n    #    return self.decoder(self.encoder(inputs, training=training)[-1], training=training)\n\n    def train_step(self, data):\n        with tf.GradientTape() as tape:\n            z_mean, z_log_var, z = self.encoder(data)\n            reconstruction = self.decoder(z)\n            reconstruction_loss = tf.reduce_mean(\n                tf.reduce_sum(\n                    tf.keras.losses.binary_crossentropy(data, reconstruction), axis=(1, 2)\n                )\n            )\n            kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n            kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))\n            # hamming_loss = tfa.metrics.hamming_loss_fn(data, reconstruction, threshold=0.6, mode='multiclass')\n            total_loss = reconstruction_loss + kl_loss\n\n        grads = tape.gradient(total_loss, self.trainable_weights)\n        self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n        self.total_loss_tracker.update_state(total_loss)\n        self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n        self.kl_loss_tracker.update_state(kl_loss)\n        # self.hamming_loss_tracker.update_state(hamming_loss)\n        return {\n            \"loss\": self.total_loss_tracker.result(),\n            \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n            \"kl_loss\": self.kl_loss_tracker.result(),\n            # \"hamming_loss\": self.hamming_loss_tracker.result(),\n        }\n\n    def test_step(self, data):\n        # Compute predictions\n        z_mean, z_log_var, z = self.encoder(data)\n        reconstruction = self.decoder(z)\n        # Updates the metrics tracking the loss\n        # hamming_loss = tfa.metrics.hamming_loss_fn(data, reconstruction, threshold=0.6, mode='multiclass')\n        reconstruction_loss = tf.reduce_mean(\n            tf.reduce_sum(\n                tf.keras.losses.binary_crossentropy(data, reconstruction), axis=(1, 2)\n            )\n        )\n        kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n        kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))\n        total_loss = reconstruction_loss + kl_loss\n        self.total_loss_tracker.update_state(total_loss)\n        self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n        self.kl_loss_tracker.update_state(kl_loss)\n        # self.hamming_loss_tracker.update_state(hamming_loss)\n        return {\n            \"loss\": self.total_loss_tracker.result(),\n            \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n            \"kl_loss\": self.kl_loss_tracker.result(),\n            # \"hamming_loss\": self.hamming_loss_tracker.result(),\n        }\n\n\nclass VAE:\n    \"\"\"\n        Abstract Base class for all VAEs.  Does not specify layers, you must\n        subclass it and provide an _enc_dec_layers method. The inputs are\n        shaped as (L, q, 1) and the outputs of decoder layers should be of size L*q in any shape.\n    \"\"\"\n\n    def __init__(self, save_path='saved models', **kwargs):\n        self.optimizer = None\n        self.METRIC = None\n        self.N = None\n        self.save_root_dir = save_path + os.path.sep\n        self.optimizers = {'adam': tf.keras.optimizers.Adam(),\n                           'rmsprop': tf.keras.optimizers.RMSprop(),\n                           'sgd': tf.keras.optimizers.SGD()}\n        self.L, self.q = None, None\n        self.batch_size, self.latent_dim = None, None\n        self.z_mean = None\n        self.z_log_var = None\n        self._sampling = None\n        self.vae = None\n        self.hist = None\n\n    def instantiate_model(self, L, q, latent_dim, batch_size, activation, **kwargs):\n        self.L, self.q = L, q\n        self.batch_size, self.latent_dim = batch_size, int(latent_dim)\n        enc_layers, dec_layers = self._enc_dec_layers(self.L, self.q,\n                                                      self.latent_dim, self.batch_size,\n                                                      activation=activation, **kwargs)\n\n        # Build the encoder\n        encoder_inputs = x = tf.keras.layers.Input(shape=(self.L, self.q, 1))\n        for layer in enc_layers:\n            x = layer(x)\n\n        self.z_mean = layers.Dense(self.latent_dim, name=\"z_mean\")(x)\n        self.z_log_var = layers.Dense(latent_dim, name=\"z_log_var\")(x)\n        self._sampling = z = Sampling()([self.z_mean, self.z_log_var])\n        encoder = tf.keras.Model(encoder_inputs, [self.z_mean, self.z_log_var, z], name=\"encoder\")\n\n        # Build the decoder\n        latent_inputs = x = tf.keras.layers.Input(shape=(self.latent_dim,))\n        for layer in dec_layers:\n            x = layer(x)\n        decoder_outputs = layers.Reshape((self.L, self.q, 1))(x)\n        decoder = tf.keras.Model(latent_inputs, decoder_outputs, name=\"decoder\")\n        self.vae = BaseVAE(encoder, decoder, **kwargs)\n\n    def _enc_dec_layers(self, L, q, latent_dim, batch_size, activation, **kwargs):\n        raise NotImplementedError()\n\n    def train_vae(self, train_seqs, epochs, optimizer,\n                  val_seqs=None, save_history=True, verbose=2,\n                  use_callbacks=True):\n        \"\"\"\n            train_seqs: ndarray of shape (n_samples, L)\n            val_seqs: ndarray of shape (n_samples, L)\n            optimizer: string\n        \"\"\"\n        if val_seqs is not None:\n            self.METRIC = \"val_loss\"\n        else:\n            self.METRIC = \"loss\"\n\n        assert (self.L == train_seqs.shape[1])\n        self.N = train_seqs.shape[0]\n        self.optimizer = self.optimizers[optimizer]\n        steps_per_epoch = np.ceil(train_seqs.shape[0] / self.batch_size)\n        validation_steps = np.ceil(val_seqs.shape[0] / self.batch_size)\n        assert (train_seqs.shape[0] <= val_seqs.shape[0])\n\n        x_train = _get_dataset(train_seqs, self.batch_size, self.q, training=True)\n        x_valid = _get_dataset(val_seqs, self.batch_size, self.q, training=False)\n\n        callbacks = __create_callbacks__(metric=self.METRIC,\n                                         ld=self.latent_dim,\n                                         epochs=epochs,\n                                         optimizer=self.optimizer._name,\n                                         batch_size=self.batch_size)\n        # super(BaseVAE, self).build(input_shape)\n        self.vae.compile(optimizer=self.optimizer)\n        self.hist = self.vae.fit(x_train,\n                            epochs=epochs,\n                            steps_per_epoch=steps_per_epoch,\n                            validation_steps=validation_steps,\n                            validation_data=x_valid,\n                            callbacks=callbacks if use_callbacks else None,\n                            verbose=verbose,\n                            )\n        # if save_history:\n        #     with open(\n        #             f\"logs/logs_Latent_{self.latent_dim}_batch_size_\"\n        #             f\"{self.batch_size}_epochs_{epochs}_optimizer_{self.optimizer._name}\" + \".pkl\",\n        #             'wb') as f:\n        #         pickle.dump(hist.history, f)\n\n    def summarize(self):\n        self.vae.encoder.summary()\n        self.vae.decoder.summary()\n\n    def save_model(self, name, path):\n        print(\"Saving Model...\")\n        path = path +\"/model/\"\n        tf.keras.models.save_model(self.vae.encoder, path + name + \"_enc\", save_format=\"tf\", include_optimizer=True)\n        \n        tf.keras.models.save_model(self.vae.decoder, path + name + \"_dec\", save_format=\"tf\")\n\n        with open(path + '{}_param.pkl'.format(name), 'wb') as f:\n            d = (self.batch_size, self.L, self.q, self.latent_dim,\n                 self.vae.optimizer.get_config(), self.__class__.__name__)\n            joblib.dump(d, f)\n        \n        print(\"Model Saved Successfully!\")\n\n    def _extract_layers(self):\n        # self.encoder = self.vae.get_layer('encoder')\n        # self.decoder = self.vae.get_layer('decoder')\n        self.z_mean = self.vae.encoder.get_layer('z_mean').output\n        self.z_log_var = self.vae.encoder.get_layer('z_log_var').output\n        self._sampling = Sampling()([self.z_mean, self.z_log_var])\n\n    def load_model(self, name, path):\n        print(\"Loading Model...\")\n        path = path + \"/model/\"\n        with open(path + '{}_param.pkl'.format(name), 'rb') as f:\n            d = joblib.load(f)\n            self.batch_size, self.L, self.q, self.latent_dim, opt_config, cls = d\n\n        encoder = tf.keras.models.load_model(path + name + \"_enc\", compile=False)\n        decoder = tf.keras.models.load_model(path + name + \"_dec\", compile=False)\n        self.vae = BaseVAE(encoder, decoder)\n\n        self._extract_layers()\n        # self.vae.optimizer.from_config(opt_config)\n        print(\"Model Loaded Successfully!\")\n        pass\n\n    def encode(self, data):\n        z_mean, z_log_var, _ = self.vae.encoder.predict(tf.keras.utils.to_categorical(data, self.q))\n        return z_mean, z_log_var\n\n    def decode_bernoulli(self, z):\n        brnll = self.vae.decoder.predict(z)\n        brnll = brnll.reshape((z.shape[0], self.L, self.q))\n        # clip like in Keras categorical_crossentropy used in vae_loss\n        brnll = np.clip(brnll, 1e-7, 1 - 1e-7)\n        brnll = brnll / np.sum(brnll, axis=-1, keepdims=True)\n        return brnll\n\n    def single_sample(self, data):\n        return self.vae.predict(tf.keras.utils.to_categorical(data, self.q))\n\n    def lELBO(self, seqs, n_samples=1000):\n        N, L = seqs.shape\n        rN, rL = np.arange(N)[:,None], np.arange(L)\n\n        zm, zlv = self.vae.encode(seqs)\n        zstd = np.exp(zlv/2)\n\n        kl_loss = 0.5*np.sum(1 + zlv - np.square(zm) - np.exp(zlv), axis=-1)\n\n        xent_loss = np.zeros(N, dtype=float)\n        for n in range(n_samples):\n            z = norm.rvs(zm, zstd)\n            brnll = self.decode_bernoulli(z)\n            xent_loss += np.sum(-np.log(brnll[rN, rL, seqs]), axis=-1)\n        xent_loss /= n_samples\n\n        return xent_loss - kl_loss\n\n    def logp(self, seqs, n_samples=1000):\n        N, L = seqs.shape\n        rN, rL = np.arange(N)[:,None], np.arange(L)\n\n        zm, zlv = self.vae.encode(seqs)\n        zstd = np.exp(zlv/2)\n\n        logp = None\n        for n in range(n_samples):\n            z = norm.rvs(zm, zstd)\n            brnll = self.decode_bernoulli(z)\n\n            lqz_x = np.sum(norm.logpdf(z, zm, zstd), axis=-1)\n            lpx_z = np.sum(np.log(brnll[rN, rL, seqs]), axis=-1)\n            lpz = np.sum(norm.logpdf(z, 0, 1), axis=-1)\n            lpxz = lpz + lpx_z\n\n            if logp is None:\n                logp = lpxz - lqz_x\n            else:\n                np.logaddexp(logp, lpxz - lqz_x, out=logp)\n\n        return logp - np.log(n_samples)\n\n    def generate(self, N):\n        # returns a generator yielding sequences in batches\n        assert(N % self.batch_size == 0)\n\n        print(\"\")\n        for n in range(N // self.batch_size):\n            print(\"\\rGen {}/{}\".format(n*self.batch_size, N), end='')\n\n            z = norm.rvs(0., 1., size=(self.batch_size, self.latent_dim))\n            brnll = self.decode_bernoulli(z)\n\n            c = np.cumsum(brnll, axis=2)\n            c = c/c[:,:,-1,None] # correct for fp error\n            r = np.random.rand(self.batch_size, self.L)\n\n            seqs = np.sum(r[:,:,None] > c, axis=2, dtype='u1')\n            yield seqs\n        print(\"\\rGen {}/{}   \".format(N, N))\n\n    def getLoss(self):\n        loss = {}\n\n        loss['kl'] = self.hist.history['kl_loss']\n        loss['val_kl'] = self.hist.history['val_kl_loss']\n        loss['rec'] = self.hist.history['reconstruction_loss']\n        loss['val_rec'] = self.hist.history['val_reconstruction_loss']\n        loss['total'] = self.hist.history['loss']\n        loss['val_total'] = self.hist.history['val_loss']\n\n        return loss\n\n    # def get_config(self):\n    #     return{\"optimizer\": self.optimizer,\n    #            \"METRIC\": self.METRIC,\n    #            \"N\": self.N,               \n    #            \"save_root_dir\": self.save_root_dir,\n    #            \"optimizers\": self.optimizers,\n    #            \"L\": self.L,\n    #            \"q\": self.q,\n    #            \"batch_size\": self.batch_size,\n    #            \"latent_dim\": self.latent_dim,\n    #            \"z_mean\": self.z_mean,\n    #            \"z_log_var\": self.z_log_var,\n    #            \"_sampling\": self._sampling,\n    #            \"vae\": self.vae,\n    #            \"hist\": self.hist\n    #            }\n    \n    # @classmethod\n    # def from_config(cls, config):\n    #     return cls(**config)\n\nclass SVAE(VAE):\n    def __init__(self, **kwargs):\n        super(SVAE, self).__init__(**kwargs)\n        self._is_graph_network=False\n\n    def instantiate_model(self, L, q, latent_dim, batch_size, activation, inner_dim):\n        self.inner_dim = inner_dim\n        super(SVAE, self).instantiate_model(L, q, latent_dim, batch_size, activation)\n\n    def _enc_dec_layers(self, L, q, latent_dim, batch_size, activation):\n        enc_layers = [layers.Flatten(),\n                      layers.Dense(self.inner_dim, activation=activation),\n                      layers.Dropout(0.3),\n                      layers.Dense(self.inner_dim, activation=activation),\n                      layers.BatchNormalization(),\n                      layers.Dense(self.inner_dim, activation=activation)]\n\n        dec_layers = [layers.Dense(self.inner_dim, activation=activation),\n                      layers.Dense(self.inner_dim, activation=activation),\n                      layers.Dropout(0.3),\n                      layers.Dense(self.inner_dim, activation=activation),\n                      layers.Dense(L * q, activation='sigmoid'),\n                      ]\n\n        return enc_layers, dec_layers\n\n    def get_config(self):\n        return{\"optimizer\": self.optimizer,\n                \"METRIC\": self.METRIC,\n                \"N\": self.N,               \n                \"save_root_dir\": self.save_root_dir,\n                \"optimizers\": self.optimizers,\n                \"L\": self.L,\n                \"q\": self.q,\n                \"batch_size\": self.batch_size,\n                \"latent_dim\": self.latent_dim,\n                \"z_mean\": self.z_mean,\n                \"z_log_var\": self.z_log_var,\n                \"_sampling\": self._sampling,\n                \"vae\": self.vae,\n                \"hist\": self.hist\n                }\n    \n    @classmethod\n    def from_config(cls, config):\n        return cls(**config)\n","repo_name":"jclamanna/neoTesting","sub_path":"newVaes.py","file_name":"newVaes.py","file_ext":"py","file_size_in_byte":18106,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"30819474711","text":"# this is the run file\nfrom datetime import datetime\nfrom simulation_0_0 import simulate\n\n\ndef run(param):\n    start = datetime.now()\n    now = start.strftime(\"%d/%m/%Y %H:%M:%S\")\n    print(' =============================================== \\n',\n          'DECISION SUPPORT FOR SUSTAINABLE PROCESS DESIGN \\n',\n          now, '\\n',\n          '===============================================',\n          )\n\n    months = 240 + 48\n\n    sim = simulate(months, table=False, plot=True, Excel_p=False,\n                   # manufacturer settings\n                   capacity_root_coeff=2.0, speed_of_build=0.3, time_to_build=6.0,\n                   # regulator settings\n                   notice_period=30, fraction=0.1, start_levy=0.5, ratio_jump=0.2, wait_time=48,\n                   compliance_threshold=0.5,\n                   decade_jump=0.8)\n\n    end = datetime.now()\n    now = end.strftime(\"%d/%m/%Y %H:%M:%S\")\n    print('\\n ==================== \\n',\n          'SIMULATION COMPLETE \\n',\n          now, '\\n',\n          '==================== \\n',\n          )\n    return\n\n\nif __name__ == '__main__':\n    for x in [1.0]:\n        run(x)\n","repo_name":"EugeCarr/decision-support-2","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"75165313558","text":"\"\"\"Main OOP implementation of our application.\"\"\"\n# Internal Imports\nimport maze\nimport tkColourPicker\nfrom database import Database\nfrom help import HelpMenu\n\n# External imports\n# Tkinter, ttk and messagebox libraries used for GUI aspects of the program.\nimport tkinter as tk\nimport tkinter.ttk as ttk\nimport tkinter.messagebox as mb\nfrom tkinter.filedialog import asksaveasfilename, askopenfilename\n# os used for handling directories.\nimport os\n# sys used for getting resource paths for packed program.\nfrom utils import getResourcePath, loadFont\n\n\"\"\"\nBUGS:\n    MAJOR - When maze is solved, it can then be continued from solving\n    menus, causing issues\n    MINOR - Slight visual blank area at bottom of maze on occasion.\n\"\"\"\n\n\"\"\"\nTODO:\n    Help Button, HOME PAGE, Top left\n    WHEN MAZE LOADED, CHANGE APP Title TO REFLECT FILE\n\"\"\"\n\n\nclass Application(tk.Tk):\n    \"\"\"Class used to house the GUI aspects of the application.\"\"\"\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"Initialise Application class.\"\"\"\n        super().__init__()\n\n        # Set the Title and icon of our application.\n        self.title(\"PathFinding\")\n        self.Title = \"PathFinding\"\n\n        # Set default background and foreground colours for all widgets.\n        self.tk_setPalette(background=\"#D9D9D9\", foreground=\"#000000\")\n\n        # Set our programs icon for the top left.\n        self.iconbitmap(getResourcePath(\"assets/maze.ico\"))\n\n        # Load in Database\n        dirPath = os.path.join(os.environ['APPDATA'], 'PathFinding')\n        if not os.path.exists(dirPath):\n            os.makedirs(dirPath)\n        filePath = os.path.join(dirPath, \"userData.db\")\n        self.db = Database(filePath, maze.tileColours)\n\n        # Add current user\n        self.userID, colours = self.db.loginUser(os.getlogin())\n\n        # Stop the user from resizing the screen.\n        self.screenSize = 750\n        # Take away 5 to account for border of screen so maze fits properly\n        self.minsize(width=self.screenSize - 5, height=self.screenSize - 5)\n\n        self.resizable(False, False)\n\n        # Create all styles to be used for our GUI.\n        self.loadStyles()\n\n        # Ensure all placed items are centered.\n        self.grid_columnconfigure(0, weight=1)\n\n        # Create a dictionary for the screen tabs and populate it.\n        self.frames = dict()\n\n        # Load in all tabs and put them in our dictionary.\n        self.frames[HomeScreen] = HomeScreen(self)\n        self.frames[MazeScreen] = MazeScreen(self)\n\n        # Load the homescreen\n        self.changeFrame(HomeScreen)\n\n        # Create a dictionary for the side menus and populate it.\n        self.menus = dict()\n\n        # Load in all menus and put them in our dictionary.\n        self.menus[MenuList] = MenuList(self)\n        self.menus[ColourSettings] = ColourSettings(self)\n        self.menus[SolverSettings] = SolverSettings(self)\n        self.menus[GenerationSettings] = GenerationSettings(self)\n        self.menus[SolverMenu] = SolverMenu(self)\n        self.menus[EditMenu] = EditMenu(self)\n\n        # Load Maze\n        self.loadMaze()\n\n        # Load in Top Menu\n        self.loadTopMenu()\n\n        # Bind for Easter Egg\n        self.EESequence = [38, 38, 40, 40, 37, 39, 37, 39, 66, 65]\n        self.EEPos = 0\n        self.bind(\"<Key>\", self.keyPress)\n\n    def keyPress(self, event):\n        \"\"\"Handle key presses on the main menu.\"\"\"\n        if event.keycode == self.EESequence[self.EEPos]:\n            self.EEPos += 1\n        else:\n            self.EEPos = 0\n\n        if self.EEPos == len(self.EESequence):\n            mb.showinfo(self.Title,\n                        u\"Found it!\\n31.9505\\u00B0 S, 115.8605\\u00B0 E\")\n            self.EEPos = 0\n\n    def loadStyles(self):\n        \"\"\"\n        Load the different styles needed for labels, buttons as well as\n        loading in a custom font.\n        \"\"\"\n        # Load in a custom font\n        loadFont(\"assets/fonts/AlegreyaSansSC-Regular.ttf\")\n        # Load a style object\n        self.style = ttk.Style()\n\n        # Set the default background for all widgets to a specific colour\n        self.style.configure(\".\", background=\"#d9d9d9\")\n\n        # Load in styles for any situation we need.\n        self.style.configure(\"Header.TLabel\",\n                             font=(\"Alegreya Sans SC Regular\",\n                                   15, \"bold italic\"))\n\n        self.style.configure(\"Subheading.TLabel\",\n                             font=(\"Alegreya Sans SC Regular\", 13))\n\n        self.style.configure(\"Footer.TLabel\",\n                             font=(\"Alegreya Sans SC Regular\", 12, \"roman\"))\n\n        self.style.configure(\"Settings.TButton\",\n                             height=2, width=20,\n                             font=(\"Alegreya Sans SC Regular\", 15))\n\n    def loadTopMenu(self):\n        \"\"\"\n        Method for loading in the top menu of the screen.\n        \"\"\"\n        self.menubar = tk.Menu(self)\n\n        self.menubar.add_command(label=\"Home\",\n                                 command=lambda: self.changeFrame(HomeScreen))\n\n        fileMenu = tk.Menu(self.menubar, tearoff=False)\n        fileMenu.add_command(label=\"Save Maze\", command=self.saveMazeFile)\n        fileMenu.add_command(label=\"Load Maze\", command=self.loadMazeFile)\n        fileMenu.add_separator()\n        fileMenu.add_command(label=\"Save Solve\",\n                             command=lambda: print(\"Save Solve\"))\n        fileMenu.add_command(label=\"Load Solve\",\n                             command=lambda: print(\"Load Solve\"))\n\n        self.menubar.add_cascade(label=\"File\", menu=fileMenu)\n\n        self.menubar.add_command(label=\"Generate Maze\",\n                                 command=self.generateMaze)\n\n        # If we are currently solving a maze, this brings up the Solver Menu,\n        # otherwise it solves the Maze.\n        self.menubar.add_command(label=\"Solve Current Maze\",\n                                 command=lambda: self.changeMenu(SolverMenu)\n                                 if self.maze.solving else self.solveMaze())\n\n        self.menubar.add_command(label=\"Enter edit mode\",\n                                 command=self.editMode)\n\n        self.menubar.add_command(label=\"Settings\",\n                                 command=lambda: self.changeMenu(MenuList))\n\n    def changeFrame(self, newFrame):\n        \"\"\"\n        Internal method for changing the current frame shown on screen.\n        Arguments:\n            newFrame -- The frame to change to\n        \"\"\"\n        # Loop through all frames that are currently on the grid and remove\n        # them from the grid\n        for frame in self.grid_slaves():\n            if frame.grid_info()[\"column\"] == 0:\n                frame.grid_forget()\n        # If we're going to the home screen, remove the menubar from the Top\n        # Otherwise, place our menubar there.\n        if newFrame == HomeScreen:\n            self.changeMenu(None)\n            emptyMenu = tk.Menu(self)\n            self.config(menu=emptyMenu)\n        else:\n            self.config(menu=self.menubar)\n        # Load the new frame\n        frame = self.frames[newFrame]\n        # Place our new frame onto the grid\n        frame.grid(row=0, column=0)\n        frame.grid(row=0, column=0, sticky=\"N\")\n\n    def changeMenu(self, newMenu):\n        \"\"\"\n        Internal method for changing the current frame shown on screen.\n        Arguments:\n            newFrame -- The frame to change to\n        \"\"\"\n        # Loop through all frames that are currently on the grid and remove\n        # them from the grid\n        for frame in self.grid_slaves():\n            if frame.grid_info()[\"column\"] == 1:\n                frame.grid_forget()\n\n        # Disable edit mode\n        self.frames[MazeScreen].editMode = False\n\n        if newMenu is not None:\n            # Load the new frame\n            frame = self.menus[newMenu]\n            # Place our new frame onto the grid\n            frame.grid(row=0, column=1, sticky=\"NE\")\n\n    def saveMazeFile(self):\n        \"\"\"\n        Method used to translate a maze object into a binary file.\n        Displays a dialog box for user to select file path and\n        translates object.\n        Arguments:\n            NONE\n        \"\"\"\n        # Open a file save dialog for the user.\n        filePath = asksaveasfilename(initialdir=\"./saves/\",\n                                     filetypes=[('MAZ Files', '.maz')],\n                                     title=\"Where to save file?\")\n\n        # If the file path fits the form of '*.maz' then save it.\n        # Otherwise don't.\n        if filePath.endswith(\".maz\"):\n            self.maze.toFile(filePath)\n        elif filePath.strip(\" \") == \"\":\n            return\n        else:\n            self.maze.toFile(filePath+\".maz\")\n\n    def loadMazeFile(self):\n        \"\"\"\n        Load a maze object from a binary file.\n\n        Displays a dialog box for user to select file path and\n        translates object.\n        Arguments:\n            NONE\n        \"\"\"\n        if self.maze.solving:\n            mb.showerror(\"ERROR\", \"Maze already being solved\")\n            return\n        # Display a dialog box to get file path.\n        filePath = askopenfilename(initialdir=\"./saves/\",\n                                   filetypes=[('MAZ Files', '.maz')],\n                                   title=\"Please select a .MAZ file\")\n        # Ensure the filepath has a .maz suffix and if not, show an error.\n        if filePath.endswith(\".maz\"):\n            self.maze.fromFile(filePath)\n            self.changeFrame(MazeScreen)\n            self.title(\"PathFinding | {}\".format(filePath))\n        elif filePath != \"\":\n            mb.showerror(self.Title, \"That is not a valid filename.\\n \" +\n                                     \"Please ensure the filePath fits \" +\n                                     \"the form of '*.maz'\")\n\n    def loadMaze(self, size=51):\n        \"\"\"\n        Load in a blank Maze object.\n        Arguments:\n            size -- The width and height of the new Maze\n        \"\"\"\n        # Load the frame we are going to attach it to.\n        frame = self.frames[MazeScreen]\n        self.maze = None\n        self.maze = maze.Maze(frame, canvasSize=self.screenSize, size=size)\n        self.maze.canvas.grid(row=0, column=0)\n\n    def generateMaze(self):\n        \"\"\"\n        Method used to generate a maze based on current settings\n        from the settings menu.\n        Arguments:\n            NONE\n        \"\"\"\n        if self.maze.solving:\n            mb.showerror(\"ERROR\", \"Maze already being solved\")\n            return\n        # Copy the GenerationSettings for easier referencing.\n        settings = self.menus[GenerationSettings]\n\n        # Get the algorithm choice and load that algorithms Generator.\n        algorithm = settings.algorithmChoice.get()\n\n        if algorithm == \"Flood Fill\":\n            from generators.floodfill import Generator\n        elif algorithm == \"Kruskals Algorithm\":\n            from generators.kruskals import Generator\n        elif algorithm == \"Subchamber Division\":\n            from generators.subchamberdivision import Generator\n        elif algorithm == \"Blank Maze\":\n            from generators.blankmaze import Generator\n        else:\n            mb.showerror(\"ERROR\", \"Error loading generator, \" +\n                                  \"please choose another generator\")\n            return\n\n        # Load in the size of the maze from settings\n        size = int(settings.mazeSize.get())\n\n        # Reset the current maze and generate a new one from the\n        # loaded generator.\n        self.loadMaze(size)\n        Generator(self.maze)\n\n        # Reset the programs title\n        self.title(\"PathFinding\")\n\n    def solveMaze(self):\n        \"\"\"\n        Method used to solve a maze based on current settings from\n        the settings menu.\n        Arguments:\n            NONE\n        \"\"\"\n        if self.maze.solving:\n            mb.showerror(\"ERROR\", \"Maze already being solved\")\n            return\n\n        # Copy the GenerationSettings for easier referencing.\n        settings = self.menus[SolverSettings]\n\n        # Get the algorithm choice and load that algorithms Generator.\n        algorithm = settings.solverChoice.get()\n\n        if algorithm == \"Recursive Backtracker\":\n            from solvers.recursivebacktracker import Solver\n        elif algorithm == \"Dijkstra's Algorithm\":\n            from solvers.dijkstras import Solver\n        elif algorithm == \"A*\":\n            from solvers.AStar import Solver\n        else:\n            mb.showerror(\"ERROR\", \"Error loading solver, \" +\n                                  \"please choose another solver\")\n            return\n\n        self.changeMenu(SolverMenu)\n        # Use the solver to solve our maze.\n        self.maze.unvisitTiles()\n        self.maze.solving = True\n        self.solver = Solver(self, self.maze, settings, self.menus[SolverMenu])\n\n    def editMode(self):\n        if not self.maze.solving:\n            self.changeMenu(EditMenu)\n\n            self.frames[MazeScreen].editMode = True\n        else:\n            mb.showerror(self.Title, \"Cannot enter edit mode whilst \" +\n                                     \"maze is being solved.\")\n\n\nclass HomeScreen(tk.Frame):\n    \"\"\"\n    Class for the applications Home Screen\n    \"\"\"\n    def __init__(self, parent):\n        super().__init__()\n        \"\"\"\n        Arguments\n            parent -- The parent tkinter object for this screen.\n        \"\"\"\n        self.parent = parent\n\n        self.loadGUI()\n\n    def loadGUI(self):\n        \"\"\"\n        Method used to load the GUI aspects of the homescreen.\n        Arguments:\n            NONE\n        \"\"\"\n        self.titleImage = tk.PhotoImage(\n            file=getResourcePath(\"assets/home/title.png\"))\n        self.title = ttk.Label(self, image=self.titleImage,\n                               text=\"Path Finding Thing\",\n                               style=\"Title.TLabel\")\n        self.title.grid(row=0, column=0, pady=50)\n\n        self.settingsImage = tk.PhotoImage(\n            file=getResourcePath(\"assets/home/settings.png\"))\n        self.settingsButton = tk.Button(\n            self, image=self.settingsImage,\n            command=lambda: self.parent.changeMenu(MenuList), borderwidth=0)\n        self.settingsButton.grid(row=0, column=0, sticky=\"NE\", pady=5, padx=5)\n\n        self.helpImage = tk.PhotoImage(\n            file=getResourcePath(\"assets/home/help.png\"))\n        self.helpButton = tk.Button(self, image=self.helpImage,\n                                    command=self.showHelp, borderwidth=0)\n        self.helpButton.grid(row=0, column=0, sticky=\"NW\", pady=5, padx=5)\n\n        self.generateImage = tk.PhotoImage(\n            file=getResourcePath(\"assets/home/generate.png\"))\n        self.generateButton = tk.Button(self, image=self.generateImage,\n                                        command=self.generateMaze,\n                                        borderwidth=0)\n        self.generateButton.grid(row=1, column=0, pady=30)\n\n        self.loadImage = tk.PhotoImage(\n            file=getResourcePath(\"assets/home/load.png\"))\n        self.loadButton = tk.Button(self, image=self.loadImage,\n                                    command=self.parent.loadMazeFile,\n                                    borderwidth=0)\n        self.loadButton.grid(row=2, column=0, pady=30)\n\n        self.grid_rowconfigure(10, weight=100)\n\n        self.footer = ttk.Label(self, text=\"Created By Felix J. Randle\",\n                                style=\"Footer.TLabel\")\n        self.footer.grid(row=10, column=0, sticky=\"S\", pady=(80, 0))\n\n    def generateMaze(self):\n        \"\"\"\n        Method used to generate a maze and change the screen.\n        Arguments:\n            NONE\n        \"\"\"\n        self.parent.generateMaze()\n        self.parent.changeFrame(MazeScreen)\n\n    def showHelp(self):\n        \"\"\"\n        Method for showing the help menu.\n        \"\"\"\n        self.helpMenu = HelpMenu()\n\n\nclass MazeScreen(tk.Frame):\n    \"\"\"\n    Class for the applications Maze Screen\n    \"\"\"\n    def __init__(self, parent):\n        \"\"\"\n        Arguments:\n            parent -- The parent tkinter object for this screen.\n        \"\"\"\n        super().__init__()\n        self.parent = parent\n\n        self.editMode = False\n\n\nclass MenuList(tk.Frame):\n    \"\"\"\n    Class for the applications Menu list\n    \"\"\"\n    def __init__(self, parent):\n        \"\"\"\n        Arguments:\n            parent -- The parent tkinter object for this screen.\n        \"\"\"\n        super().__init__()\n        self.parent = parent\n\n        self.loadWidgets()\n\n    def loadWidgets(self):\n        \"\"\"\n        Method for creating all the pages widgets\n        \"\"\"\n        self.exitImage = tk.PhotoImage(\n            file=getResourcePath(\"assets/settings/exit.png\"))\n        self.exitButton = tk.Button(\n            self, image=self.exitImage,\n            command=lambda: self.parent.changeMenu(None),\n            borderwidth=0)\n        self.exitButton.grid(row=0, column=0, sticky=\"NE\", pady=5, padx=5)\n\n        self.ColourSettingsIcon = tk.PhotoImage(\n            file=getResourcePath(\"assets/settings/colourTitle.png\"))\n        tk.Button(self, image=self.ColourSettingsIcon, borderwidth=0,\n                  command=lambda: self.parent.changeMenu(ColourSettings)\n                  ).grid(row=0, column=0, pady=70, padx=40)\n\n        self.SolverSettingsIcon = tk.PhotoImage(\n            file=getResourcePath(\"assets/settings/solverTitle.png\"))\n        tk.Button(self, image=self.SolverSettingsIcon, borderwidth=0,\n                  command=lambda: self.parent.changeMenu(SolverSettings)\n                  ).grid(row=5, column=0, pady=70, padx=40)\n\n        self.GenerationSettingsIcon = tk.PhotoImage(\n            file=getResourcePath(\"assets/settings/generationTitle.png\"))\n        tk.Button(self, image=self.GenerationSettingsIcon, borderwidth=0,\n                  command=lambda: self.parent.changeMenu(GenerationSettings)\n                  ).grid(row=10, column=0, pady=70, padx=40)\n\n\nclass SettingsMenu(tk.Frame):\n    def __init__(self, parent, back=None):\n        \"\"\"\n        Arguments:\n            parent -- The parent tkinter object for this screen\n            back -- None if there is no menu to go back to. Otherwise\n            the menu that the back button should lead to.\n        \"\"\"\n        super().__init__()\n        self.parent = parent\n\n        # If we need a back button, load the icon and create the button.\n        if back is not None:\n            self.backImage = tk.PhotoImage(\n                file=getResourcePath(\"assets/settings/back.png\"))\n            self.backButton = tk.Button(\n                self, image=self.backImage,\n                command=lambda: parent.changeMenu(back),\n                borderwidth=0)\n            self.backButton.grid(row=0, column=0, sticky=\"NW\", pady=5, padx=5)\n\n        # Create the exit button after loading the icon.\n        self.exitImage = tk.PhotoImage(\n            file=getResourcePath(\"assets/settings/exit.png\"))\n        self.exitButton = tk.Button(self, image=self.exitImage,\n                                    command=self.exitMenu, borderwidth=0)\n        self.exitButton.grid(row=0, column=0, sticky=\"NE\", pady=5, padx=5)\n\n    def exitMenu(self):\n        \"\"\"\n        Method for exiting the current menu.\n        \"\"\"\n        self.parent.changeMenu(None)\n\n    def loadTitle(self, source):\n        \"\"\"\n        Load a Title image from the given source.\n        \"\"\"\n        self.titleImage = tk.PhotoImage(file=source)\n\n        tk.Label(self, image=self.titleImage).grid(row=0, column=0,\n                                                   pady=50, padx=40)\n\n\nclass ColourSettings(SettingsMenu):\n    \"\"\"\n    Class for the applications Colour Menu\n    \"\"\"\n    def __init__(self, parent):\n        \"\"\"\n        Arguments:\n            parent -- The parent tkinter object for this screen.\n        \"\"\"\n        super().__init__(parent, MenuList)\n        self.parent = parent\n\n        self.loadTitle(getResourcePath(\"assets/settings/colourTitle.png\"))\n\n        self.loadWidgets()\n\n    def loadWidgets(self):\n        \"\"\"\n        Method used to create menu's widgets\n        \"\"\"\n        # Loop through all the keys and items in our current tileColours\n        for key, item in maze.tileColours.items():\n            # Remove all underscores from the keys string version\n            tileName = key.name.replace(\"_\", \" \").title()\n\n            # Create a container to place our items in\n            container = tk.Frame(self, width=200)\n            container.grid(row=key.value + 1, column=0, pady=0)\n\n            title = ttk.Label(container, style=\"Header.TLabel\", text=tileName)\n            title.grid(row=0, column=0, columnspan=19)\n\n            # Create widgets for picking both the foreground and background of\n            # each of the tiles.\n            fgTitle = ttk.Label(container, style=\"Subheading.TLabel\",\n                                text=\"FG :\")\n            fgTitle.grid(row=1, column=0)\n\n            fgColour = tkColourPicker.ColourPicker(container, 2, 1, key=key,\n                                                   command=self.setColour,\n                                                   index=1)\n            fgColour.grid(row=1, column=1)\n            fgColour.set(maze.tileColours[key][1])\n\n            divider = ttk.Label(container, style=\"Subheading.TLabel\",\n                                text=\"          \")\n            divider.grid(row=1, column=3)\n\n            bgTitle = ttk.Label(container, style=\"Subheading.TLabel\",\n                                text=\"BG :\")\n            bgTitle.grid(row=1, column=4)\n\n            bgColour = tkColourPicker.ColourPicker(container, 2, 1, key=key,\n                                                   command=self.setColour,\n                                                   index=0)\n            bgColour.grid(row=1, column=5)\n            bgColour.set(maze.tileColours[key][0])\n\n        # Add a reload button to use the users changes without reloading\n        # the entire maze.\n        self.reloadButton = ttk.Button(self, style=\"Settings.TButton\",\n                                       text=\"Reload Colours\",\n                                       command=self.updateColours)\n        self.reloadButton.grid(row=100, column=0, pady=3)\n\n    def setColour(self, key, newColour, index):\n        \"\"\"\n        Method to change the colour stored in both the database and the\n        tileColours dictionary\n        \"\"\"\n        if newColour is not None:\n            maze.tileColours[key][index] = newColour\n\n            self.parent.db.updateUserColours(self.parent.userID,\n                                             key.name.upper(), index,\n                                             newColour)\n\n    def updateColours(self):\n        \"\"\"\n        Method to update the colours across the entire maze\n        \"\"\"\n        for row in self.parent.maze.tiles:\n            for tile in row:\n                tile.updateColour()\n\n\nclass GenerationSettings(SettingsMenu):\n    def __init__(self, parent):\n        \"\"\"\n        Arguments:\n            parent -- The parent tkinter object for this screen.\n        \"\"\"\n        super().__init__(parent, MenuList)\n        self.parent = parent\n\n        # Load a Title button with the given file\n        self.loadTitle(getResourcePath(\"assets/settings/generationTitle.png\"))\n\n        self.container = tk.Frame(self)\n        self.container.grid(row=10, column=0, sticky=\"S\")\n\n        ttk.Label(self.container, text=\"Generation Algorithm\",\n                  style=\"Header.TLabel\").grid(row=1, column=0, pady=20)\n\n        generators = (\n            \"Flood Fill\",\n            \"Subchamber Division\",\n            \"Kruskals Algorithm\",\n            \"Blank Maze\"\n        )\n\n        self.algorithmChoice = ttk.Combobox(self.container, values=generators,\n                                            state=\"readonly\", width=20,\n                                            font=(\"arial\", 14))\n        self.algorithmChoice.current(2)\n        self.algorithmChoice.grid(row=2, column=0, pady=20)\n\n        ttk.Label(self.container, text=\"Maze Size\",\n                  style=\"Header.TLabel\").grid(row=3, column=0, pady=20)\n\n        self.mazeSize = ttk.Scale(self.container, from_=21, to=75,\n                                  orient=tk.HORIZONTAL, value=37,\n                                  command=self.oddOnly, length=200)\n        self.mazeSize.grid(row=4, column=0, pady=20)\n\n        self.mazeSizeLabel = ttk.Label(self.container, text=51,\n                                       style=\"Header.TLabel\")\n        self.mazeSizeLabel.grid(row=5, column=0, pady=20)\n\n    def oddOnly(self, event):\n        value = self.mazeSize.get()\n        if (int(value) != value):\n            if int(value) % 2 == 0:\n                value += 1\n            self.mazeSize.set(int(value))\n\n        self.mazeSizeLabel.config(text=int(value))\n\n\nclass SolverSettings(SettingsMenu):\n    def __init__(self, parent):\n        \"\"\"\n        Arguments:\n            parent -- The parent tkinter object for this screen.\n        \"\"\"\n        super().__init__(parent, MenuList)\n\n        self.loadTitle(getResourcePath(\"assets/settings/solverTitle.png\"))\n\n        solvers = (\n            \"Recursive Backtracker\",\n            \"Dijkstra's Algorithm\",\n            \"A*\"\n        )\n\n        self.solverChoice = ttk.Combobox(self, values=solvers,\n                                         state=\"readonly\", width=20,\n                                         font=(\"arial\", 14))\n        self.solverChoice.set(solvers[2])\n        self.solverChoice.grid(row=2, column=0, pady=20)\n\n        self.autoStepEnabled = True\n        self.autoStepButton = ttk.Button(self, text=\"Disable AutoStep\",\n                                         style=\"Settings.TButton\",\n                                         command=self.toggleAutoStep)\n        self.autoStepButton.grid(row=3, column=0, pady=20)\n\n        self.speedsFrame = tk.Frame(self)\n        self.speedsFrame.grid(row=4, column=0, pady=20)\n\n        self.speedDisplay = ttk.Label(self.speedsFrame,\n                                      text=\"Current Speed: X1\",\n                                      style=\"Subheading.TLabel\")\n        self.speedDisplay.grid(row=0, column=0, columnspan=1000)\n\n        self.speeds = [1, 2, 5, 10, 50, 100]\n        self.speedItems = {}\n\n        for speed in self.speeds:\n            image = tk.PhotoImage(\n                file=getResourcePath(\"assets/speeds/X{}.png\".format(speed)))\n            button = tk.Button(self.speedsFrame, image=image,\n                               command=lambda x=speed: self.setSpeed(x),\n                               borderwidth=0)\n            button.grid(row=1, column=speed)\n\n            self.speedItems.update({speed: {\"image\": image, \"button\": button}})\n\n        self.speed = tk.DoubleVar()\n        self.speed.set(1)\n        self.speed.trace(\"w\", self.updateLabel)\n\n    def toggleAutoStep(self):\n        if self.autoStepEnabled:\n            self.autoStepButton[\"text\"] = \"Enable AutoStep\"\n            self.autoStepEnabled = False\n        else:\n            self.autoStepButton[\"text\"] = \"Disable AutoStep\"\n            self.autoStepEnabled = True\n\n    def setSpeed(self, newSpeed):\n        self.speed.set(newSpeed)\n\n    def updateLabel(self, *args):\n        speed = int(self.speed.get())\n        self.speedDisplay.config(text=f\"Current Speed: X{speed}\")\n        self.parent.menus[SolverMenu].speedDisplay.config(\n            text=f\"Current Speed: X{speed}\")\n\n    def solveMaze(self):\n        self.parent.solveMaze()\n\n\nclass SolverMenu(SettingsMenu):\n    def __init__(self, parent):\n        super().__init__(parent)\n\n        self.loadTitle(getResourcePath(\"assets/settings/solverTitle.png\"))\n\n        self.autoStepControls = tk.Frame(self)\n        self.autoStepControls.grid(row=1, column=0)\n\n        self.play = tk.PhotoImage(\n            file=getResourcePath(\"assets/solving/playButton.png\"))\n        self.playButton = tk.Button(self.autoStepControls,\n                                    image=self.play,\n                                    command=self.startAutoStep, borderwidth=0)\n        self.playButton.grid(row=0, column=0)\n\n        self.pause = tk.PhotoImage(\n            file=getResourcePath(\"assets/solving/pauseButton.png\"))\n        self.pauseButton = tk.Button(self.autoStepControls, image=self.pause,\n                                     command=self.stopAutoStep, borderwidth=0)\n        self.pauseButton.grid(row=0, column=1)\n\n        self.stop = tk.PhotoImage(\n            file=getResourcePath(\"assets/solving/stopButton.png\"))\n        self.stopButton = tk.Button(self.autoStepControls, image=self.stop,\n                                    command=self.stopSolve, borderwidth=0)\n        self.stopButton.grid(row=0, column=2)\n\n        self.speedsFrame = tk.Frame(self)\n        self.speedsFrame.grid(row=4, column=0, pady=20)\n\n        self.speedDisplay = ttk.Label(self.speedsFrame,\n                                      text=\"Current Speed: X1\",\n                                      style=\"Subheading.TLabel\")\n        self.speedDisplay.grid(row=0, column=0, columnspan=1000)\n\n        self.speeds = [1, 2, 5, 10, 50, 100]\n        self.speedItems = {}\n\n        for speed in self.speeds:\n            image = tk.PhotoImage(\n                file=getResourcePath(\"assets/speeds/X{}.png\".format(speed)))\n            button = tk.Button(\n                self.speedsFrame, image=image,\n                command=lambda x=speed: self.parent.solver.setSpeed(x),\n                borderwidth=0)\n            button.grid(row=1, column=speed)\n\n            self.speedItems.update({speed: {\"image\": image, \"button\": button}})\n\n        self.stepButton = tk.Button(self, width=10, height=2, text=\"Step\",\n                                    command=self.step)\n        self.stepButton.grid(row=10, column=0, pady=5)\n\n        self.advancedInfo = tk.IntVar()\n        self.advancedInfoButton = ttk.Checkbutton(\n            self, text=\"Show Advanced Information?\",\n            variable=self.advancedInfo)\n        self.advancedInfoButton.grid(row=11, column=0, pady=5)\n        self.advancedInformationFrame = tk.Frame(self)\n        self.advancedInformationFrame.grid(row=15, column=0, pady=10)\n\n        self.advancedInformation = ttk.Label(\n            self.advancedInformationFrame,\n            text=\"Advanced Solver Information\",\n            style=\"Subheading.TLabel\")\n        self.advancedInformation.grid(row=0, column=0, pady=10)\n\n        self.stepCount = tk.Label(self.advancedInformationFrame,\n                                  text=\"Steps: 0\",\n                                  font=(\"Helvetica\", 12, \"bold italic\"))\n        self.stepCount.grid(row=1, column=0)\n\n    def startAutoStep(self):\n        if not self.parent.solver.solved:\n            self.parent.solver.autorun = True\n            self.parent.solver.step()\n\n    def stopAutoStep(self):\n        self.parent.solver.autorun = False\n\n    def stopSolve(self):\n        if mb.askyesno(\"End Solve?\",\n                       \"Are you sure you want to stop the current solve?\"):\n            self.stopAutoStep()\n            self.parent.maze.solving = False\n            self.parent.solver = None\n            self.parent.changeMenu(None)\n\n            self.parent.after(1, self.parent.maze.unvisitTiles)\n\n    def step(self):\n        self.parent.solver.step() if not self.parent.solver.autorun else \\\n            mb.showerror(\"ERROR\", \"Cannot force step whilst autorunning\")\n        self.parent.solver.step(force=True) if not self.parent.solver.autorun \\\n            else mb.showerror(\"ERROR\", \"Cannot force step whilst autorunning\")\n\n    def updateLabel(self, newValue):\n        self.autoStepDelayLabel.config(text=\"{:.3f}\".format(float(newValue)))\n        self.parent.solver.delay = float(newValue)\n\n\nclass EditMenu(SettingsMenu):\n    def __init__(self, parent):\n        super().__init__(parent)\n\n        self.loadTitle(getResourcePath(\"assets/settings/solverTitle.png\"))\n\n        ttk.Label(self, text=\"Current Tile:\",\n                  style=\"Header.TLabel\").grid(row=1, column=0)\n\n        self.currentTileLabel = ttk.Label(self, text=\"None\",\n                                          style=\"Subheading.TLabel\")\n        self.currentTileLabel.grid(row=2, column=0, pady=5)\n\n        self.currentTile = tk.Button(self, width=2, height=1, bg=\"grey\")\n        self.currentTile.grid(row=3, column=0, pady=5)\n\n        ttk.Label(self, text=\"Please click on a tile to select it\",\n                  style=\"Subheading.TLabel\").grid(row=5, column=0)\n\n        availableTiles = [\"WALL\", \"PATH\", \"START\", \"END\"]\n\n        currentRow = 10\n\n        firstItem = True\n\n        for key, item in maze.tileColours.items():\n            if key.name in availableTiles:\n                if firstItem:\n                    self.changeTile(key, key.name.title(), item)\n\n                    firstItem = False\n                ttk.Label(self, text=key.name.title(),\n                          style=\"Subheading.TLabel\").grid(row=currentRow,\n                                                          column=0, pady=5)\n                tk.Button(self, width=2, height=1, bg=item[1],\n                          highlightbackground=item[0],\n                          command=lambda key=key, name=key.name.title(),\n                          colours=item:\n                          self.changeTile(key, name, colours)\n                          ).grid(row=currentRow + 1, column=0, pady=5)\n\n                currentRow += 5\n\n    def changeTile(self, key, tileName, colours):\n        self.currentTileLabel.config(text=tileName)\n        self.currentTile.config(bg=colours[1])\n\n        try:\n            self.parent.maze.currentEdit = key\n        except AttributeError:\n            pass\n\n    def exitMenu(self):\n        self.parent.frames[MazeScreen].editMode = False\n        self.parent.changeMenu(None)\n","repo_name":"FelixRandle/PathFinding","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":33702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"28147543441","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jan 13 21:25:06 2019\n\n@author: Aidan\n\"\"\"\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\ntd = np.array([1, 4, 8, 16])\navg_time = np.array([307.31, 77.45, 39.46, 27.19])\n\n\none = np.array([308.097, 304.901, 309.126, 305.047, 309.379])\nfour = np.array([76.9488, 78.2809, 76.8299, 78.2478, 76.9655])\neight = np.array([39.4189, 39.8951, 39.2331, 39.9419, 38.8113 ])\nsixteen = np.array([24.2101, 25.7484, 24.177, 31.6769, 30.1301 ])\nto_plot = [one, four, eight, sixteen]\nfig=plt.figure(1,figsize=(9,6))\nax=fig.add_subplot(111)\nplt.xlabel(\"Number of threads\")\nplt.ylabel(\"Runtime (seconds)\")\nplt.title(\"Shared Memory Performance\")\nbp=ax.boxplot(to_plot)\nfig.savefig('boxplot.png',bbox_inches='tight')\nplt.show()\n\nplt.xlabel(\"Number of shared memory threads\")\nplt.ylabel(\"Runtime (seconds)\")\nplt.title(\"Shared Memory Performance\")\nplt.scatter(td, avg_time)\nplt.show()\n\nefficiency = [0.992, 0.973, 0.706]\n\nplt.xlabel(\"Number of shared memory threads\")\nplt.ylabel(\"Efficiency\")\nplt.title(\"Parallel efficiency\")\nplt.scatter(td[1:], efficiency)\nplt.show()\n\n\nobjects = (\"Static\", \"Dynamic\")\ny_pos = np.arange(len(objects))\ntime = [32.28, 21.06]\n \nplt.bar(y_pos, time, align='center', alpha=0.5)\nplt.xticks(y_pos, objects)\nplt.ylabel('Runtime (seconds)')\nplt.title('Parallel Scheduling')\nplt.show()\n","repo_name":"PuppyQ08/CS420HW","sub_path":"molecular-dynamics-master/Plots/runtime_analysis.py","file_name":"runtime_analysis.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"35938133312","text":"\n# Anniversary_Promo_2018.py\n# Author: Jason Greve\n# Client: ObjectRocket\n# Created: 2018-SEP-17-MON\n# Updated:\n# Description: Data for Farmers_Market.py (Register Class)\n\n\nfrom collections import OrderedDict\nfrom Anniversary_Specials_2018 import AnniversarySpecials2018 as AnSpec\n\n# OrderedDict ensures that APOM will be applied before APPL.\n# This ensures that the customer will get the largest discount\n# when buying 3 or more bags of apples and one or more bags of \n# oatmeal. (The rules interact and are not commutative)\n\n# The following example shows different ways to calculate the \n# price for a bag of apples when both APOM and APPL apply:\n# (6 * 0.5) * 0.75 = 2.25   APOM >> APPL \"ratio\" discount\n# (6 * 0.75) * 0.5 = 2.25   APPL >> APOM \"ratio\" discount\n# (6 * 0.5) - 1.25 = 1.75   APOM >> APPL \"fixed\" discount <<--- Best Price for Customer\n# (6 - 1.25) * 0.5 = 2.375  APPL >> APOM \"fixed\" discount\n\nanniversary_promo_2018 = OrderedDict()\nanniversary_promo_2018['BOGO'] = AnSpec.bogo\nanniversary_promo_2018['CHMK'] = AnSpec.chmk\nanniversary_promo_2018['APOM'] = AnSpec.apom\nanniversary_promo_2018['APPL'] = AnSpec.appl\n\n\n\n","repo_name":"jbgreve/ObjectRocket","sub_path":"Anniversary_Promo_2018.py","file_name":"Anniversary_Promo_2018.py","file_ext":"py","file_size_in_byte":1134,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"}
+{"seq_id":"71597805405","text":"import argparse\nimport logging\nfrom datetime import datetime\nfrom math import ceil\nimport os\nimport sys\nfrom multiprocessing import Pool\n\nfrom numpy import float64, poly\n\nfrom core.boundary import Boundary\nfrom core.config import Config\nfrom core.filler import PointsFiller\nfrom core.frame import create_frames\n\nfrom core.plotter import Plotter\nfrom gds_parser import GDSParser\n\ndef get_boundary(polygons):\n    x_max = polygons[0].points[0].x\n    x_min = polygons[0].points[0].x\n    y_max = polygons[0].points[0].y\n    y_min = polygons[0].points[0].y\n\n    for polygon in polygons:\n        for point in polygon.points:\n            if point.x > x_max:\n                x_max = point.x\n            if point.x < x_min:\n                x_min = point.x\n            if point.y > y_max:\n                y_max = point.y\n            if point.y < y_min:\n                y_min = point.y\n\n    return x_max, x_min, y_max, y_min\n\ndef map_frame_polygons(frames, polygons):\n    \"\"\"map polygons to frames, one polygon may be placed in several frames\"\"\"\n    map_fp = {}\n\n    for frame in frames:\n        for polygon in polygons:\n            if frame.intersect_polygon(polygon):\n                if frame not in map_fp:\n                    map_fp[frame] = []\n                map_fp[frame].append(polygon)\n\n    return map_fp\n\ndef check_drc(polygons):\n    for i, p1 in enumerate(polygons[:-1]):\n        print(f'check polygon {i+1} of {len(polygons)}')\n        for p2 in polygons[i+1:]:\n            if p1.intersect_polygon(p2):\n                return (p1, p2)\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument('gds_file', help=\"input GDSII file name with path\")\n    parser.add_argument('direction', default=\"left-right\", help=\"beam movement direction, possuble valuses are: left-rignt or down-top\")\n    parser.add_argument('frame_size', help=\"size of FIB frame in um. For example 51.2\", type=float64)\n    parser.add_argument('time_in_point', default=4000, help=\"time for FIB staying in one point, integer\", type=int)\n    parser.add_argument('-v', '--verbose', default=False, help=\"enable debug messages\", type=bool)\n\n\n    args = parser.parse_args()\n\n    #  load parsed arguments to config\n    config = Config()\n\n    config.gds_file = args.gds_file\n    config.direction = args.direction\n    config.frame_size = args.frame_size\n    config.time_in_point = args.time_in_point\n    config.verbose = args.verbose\n    config.frame_points = 4096\n    config.output_path = f'output/processed_{datetime.today().strftime(\"%A_%d_%B_%Y__%I_%M_%S\")}'\n\n    # calculate step\n    config.fib_step = config.frame_size / config.frame_points\n    # calculate multiplier\n    config.multiply = config.frame_points / config.frame_size\n\n    os.makedirs(config.output_path, exist_ok=True)\n\n    logging.basicConfig(filename='main.log', level=logging.DEBUG)\n    log = logging.getLogger(__name__)\n    log.info('Started with parameters:')\n    for k, v in config.__dict__.items():\n        log.info(f'\\t{k}\\t{v}')\n\n    # parse GDSII and extract polygons\n    print(f'parsing {config.gds_file}')\n    gds_parser = GDSParser(config.gds_file)\n    gds_parser.parse()\n    polygons = gds_parser.objects\n    print(f'polygons extracted: {len(polygons)}')\n    print('done')\n    print()\n\n    print('extracting boundary')\n    x_max, x_min, y_max, y_min = get_boundary(polygons)\n    boundary = Boundary()\n    boundary.set_boundary(x_min, x_max, y_min, y_max)\n    print(boundary)\n    print('done')\n    print()\n\n    print('checking drc...')\n    res = check_drc(polygons)\n    if res:\n        p1, p2 = res\n        print(f'Error')\n        print('----------------------------')\n        print(f'polygon\\n{p1}\\n\\nintersects polygon\\n{p2}')\n        print('----------------------------')\n        print('Fix GDS and rerun program')\n        sys.exit(0)\n\n    # generate frames of defied size\n    print('creating frames...')\n    frames = create_frames(\n        boundary.xmax,\n        boundary.xmin,\n        boundary.ymax,\n        boundary.ymin,\n        config.frame_size\n    )\n    # plot frames\n    config.multiply = 1\n    plotter = Plotter(ceil(x_max - x_min), ceil(y_max - y_min))\n    plotter.plot(\n        polygons=polygons,\n        frames=frames,\n        d_x=x_min,\n        d_y=y_min,\n        output_path=os.path.join(config.output_path, 'figure.png')\n    )\n\n    print(f'frames created: {len(frames)}')\n    print('done')\n    print()\n\n    print('mapping polygons to frames...')\n    map_fp = map_frame_polygons(frames, polygons)\n    print(f'frames with polygons: {len(map_fp)}')\n    print('done')\n    print()\n\n    print('processing')\n\n\n    i = 1\n    for f, polys in map_fp.items():\n        print('-------------------------------')\n        print(f'processing {i} of {len(map_fp)} frame\\n{f}')\n        filler = PointsFiller(f, polys)\n        filler.fill()\n        print('printing...')\n        filler.print_points()\n        print('plotting...')\n        filler.plot()\n        print('done')\n\n        i += 1\n\n    print('==============================================')\n    print('GDS file processing done')\n","repo_name":"ghost211221/fib_points","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5056,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"17158560097","text":"import numpy as np\n\nprint(\"Método de Jacobi\")\n\nn=3\na=np.array([[4., -1., 0.], [-1., 4., -1.], [0., -1, 4]])\nb=np.ones(n)\neps = 1.0e-06\nitmax = 2\n\nprint(\"Matriz A:\")\nprint(np.matrix(a))\nprint('Matriz B:')\nprint(np.matrix(b))\n\nalfa=np.zeros(n)\nx=np.zeros(n)\nxold=np.zeros(n)\n\n\nprint(\"Verificando critério das linhas ou diagonal dominância\")\nfor i in range(0,n):\n    for j in range(0,i):\n        alfa[i] = alfa[i]+np.abs(a[i,j])\n    for j in range(i+1,n):\n        alfa[i] = alfa[i]+np.abs(a[i,j])\n    alfa[i] = alfa[i] / np.abs(a[i,i])\n    print(\"alfa\",i+1,\"=\",alfa[i])\nalfamax = np.max(alfa)\nif (alfamax >= 1.):\n    print(\" critério das linhas não satisfeito, taxa definida arbitrariamente\")\n    taxa = 10000\nelse:\n    taxa=alfamax/(1.-alfamax)\nprint(\"alfamax = \",alfamax)\n#\nprint(\"x (\",0,\")  = \", np.matrix(x))\nprint('')\nfor k in range(0,itmax):\n    for i in range(0,n):\n        x[i]=b[i]\n        for j in range(0,i):\n            x[i] = x[i]-a[i,j]*xold[j]\n        for j in range(i+1,n):\n            x[i] = x[i]-a[i,j]*xold[j]\n        x[i] = x[i] / a[i,i]\n    error = taxa*np.max(np.abs(x-xold))\n    print(\"após iteração \",k+1, \"erro estimado \",error)\n    print(\"x (\",k+1,\")  = \", np.matrix(x))\n    print('')\n    if (error < eps):\n        break\n    xold = np.copy(x)\n#\n#    Verificando se temos a solução bem aproximada\n#\nax = np.matmul(a,x)\nprint(\" Ax = \",ax)\nprint(\"máximo do resíduo\",np.max(np.abs(b-ax)))\n","repo_name":"politecmedio/numerico","sub_path":"jacobi.py","file_name":"jacobi.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"42798321878","text":"import flask\nimport datetime\n\nimport talisker\nfrom canonicalwebteam.flask_base.app import FlaskBase\nfrom canonicalwebteam.yaml_responses.flask_helpers import (\n    prepare_deleted,\n    prepare_redirects,\n)\nfrom canonicalwebteam import image_template\n\nfrom webapp.handlers import add_headers\nfrom webapp.jaasai.views import jaasai\nfrom webapp.template_utils import current_url_with_query, static_url\n\nsession = talisker.requests.get_session()\n\n\ndef create_app(testing=False):\n    app = FlaskBase(\n        __name__,\n        \"jaas.ai\",\n        template_folder=\"../templates\",\n        static_folder=\"../static\",\n    )\n\n    app.testing = testing\n    app.after_request(add_headers)\n    app.before_request(prepare_redirects(\"redirects.yaml\"))\n    app.before_request(\n        prepare_redirects(\"permanent-redirects.yaml\", permanent=True)\n    )\n    app.before_request(prepare_deleted())\n\n    # Handlers\n    # ===\n    @app.errorhandler(404)\n    def page_not_found(error):\n        \"\"\"\n        For 404 pages, display the 404.html template,\n        passing through the error description.\n        \"\"\"\n\n        return flask.render_template(\"404.html\", error=error.description), 404\n\n    @app.errorhandler(500)\n    def internal_server_error(error):\n        \"\"\"\n        For 500 pages, display the 500.html template,\n        passing through the error.\n        \"\"\"\n\n        return flask.render_template(\"500.html\", error=error), 500\n\n    @app.errorhandler(410)\n    def gone(error):\n        \"\"\"\n        For 410 pages, display the 410.html template,\n        passing through the error.\n        \"\"\"\n\n        return flask.render_template(\"410.html\", error=error), 410\n\n    # Blueprints\n    # ===\n    app.register_blueprint(jaasai)\n\n    # Dashboard and redirect views\n    # ===\n    @app.route(\"/models\")\n    @app.route(\"/models/<path:path>\")\n    @app.route(\"/controllers\")\n    @app.route(\"/controllers/<path:path>\")\n    def dashboard_index(path=None):\n        \"\"\"\n        Send /models and /controllers to the index page\n        \"\"\"\n\n        return flask.render_template(\"dashboard/index.html\")\n\n    @app.route(\"/config.js\")\n    @app.route(\"/manifest.json\")\n    @app.route(\"/version.json\")\n    @app.route(\"/ghost-bundle.svg\")\n    def dashboard_files():\n        \"\"\"\n        Load dashboard files directly\n        \"\"\"\n        return flask.send_from_directory(\n            \"../templates/dashboard\",\n            flask.request.path.strip(\"/\"),\n        )\n\n    @app.route(\"/q/\")\n    @app.route(\"/q/<path:path>\")\n    def search_redirect(path=None):\n        \"\"\"\n        Handle redirects from jujucharms.com search URLS to the jaas.ai format.\n        e.g. /q/k8s/demo?sort=-name&series=xenial will redirect to\n        /search?q=k8s+demo&sort=-name&series=xenial\n        \"\"\"\n        query_string = []\n        if path:\n            query_string.append(\"q={}\".format(path.replace(\"/\", \"+\")))\n        if flask.request.query_string:\n            query_string.append(str(flask.request.query_string, \"utf-8\"))\n        return flask.redirect(\n            \"/search?{}\".format(\"&\".join(query_string)), code=302\n        )\n\n    @app.route(\"/<charm_or_bundle_name>\")\n    @app.route(\"/<charm_or_bundle_name>/<series_or_version>\")\n    @app.route(\"/<charm_or_bundle_name>/<series_or_version>/<version>\")\n    def details_redirect(\n        charm_or_bundle_name,\n        series_or_version=None,\n        version=None,\n    ):\n        charmhub_url = \"https://charmhub.io/\" + charm_or_bundle_name\n        return flask.redirect(charmhub_url, code=301)\n\n    # Template filters\n    # ===\n    @app.template_filter(\"pluralize\")\n    def pluralize_filter(count):\n        if int(count) > 1:\n            return \"s\"\n        else:\n            return \"\"\n\n    @app.context_processor\n    def inject_utilities():\n        return {\n            \"current_url_with_query\": current_url_with_query,\n            \"static_url\": static_url,\n        }\n\n    @app.context_processor\n    def inject_today_date():\n        return {\"current_year\": datetime.date.today().year}\n\n    app.jinja_env.add_extension(\"jinja2.ext.do\")\n\n    @app.context_processor\n    def utility_processor():\n        return {\"image\": image_template}\n\n    return app\n","repo_name":"canonical/jaas.ai","sub_path":"webapp/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4143,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"86"}
+{"seq_id":"32051502876","text":"#!/usr/bin/env python3\n\nimport molsturm\nimport common\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\n\nBASIS_SETS = [\"STO-3G\", \"cc-pVDZ\", \"pc-1\", \"pc-3\", \"cc-pV6Z\"]\n\n\ndef plot_overview():\n    plt.close()\n    plt.figure(figsize=(5.5, 3.5))\n\n    for baset in BASIS_SETS:\n        bas = molsturm.construct_basis(\"gaussian\", \"H\", basis_set_name=baset)\n        coeff = common.do_hydrogen_scf(bas)\n\n        rab = np.linspace(0, 1, 5000), np.linspace(1, 10, 5000)\n        r = np.concatenate(rab)\n        r, locen = common.hydrogen_local_energy(bas, coeff, r, dr=1e-6)\n        plt.plot(r, locen, label=baset)\n\n    plt.xlabel(common.XLABEL)\n    plt.ylabel(common.ELLABEL)\n\n    plt.ylim(-5, 5)\n    if -0.5 not in plt.yticks()[0]:\n        plt.yticks(list(plt.yticks()[0]) + [-0.5])\n    plt.legend(loc='upper right')\n\n\ndef plot_closeup():\n    plt.close()\n    plt.figure(figsize=(5.5, 3.5))\n\n    for baset in BASIS_SETS:\n        bas = molsturm.construct_basis(\"gaussian\", \"H\", basis_set_name=baset)\n        coeff = common.do_hydrogen_scf(bas)\n\n        rab = np.linspace(0, 0.05, 5000), np.linspace(0.05, 0.5, 5000)\n        r = np.concatenate(rab)\n        r, locen = common.hydrogen_local_energy(bas, coeff, r, dr=1e-6)\n        plt.plot(r, locen, label=baset)\n\n    plt.xlabel(common.XLABEL)\n    plt.ylabel(common.ELLABEL)\n\n    plt.ylim(-5, 5)\n    if -0.5 not in plt.yticks()[0]:\n        plt.yticks(list(plt.yticks()[0]) + [-0.5])\n\n\ndef plot_relative_error(log=False):\n    plt.close()\n    plt.figure(figsize=(5.5, 3.5))\n\n    for baset in BASIS_SETS:\n        bas = molsturm.construct_basis(\"gaussian\", \"H\", basis_set_name=baset)\n        coeff = common.do_hydrogen_scf(bas)\n\n        rab = np.linspace(0, 1, 10000), np.linspace(1, 10, 10000)\n        r = np.concatenate(rab)\n        r, err = common.hydrogen_relative_error(bas, coeff, r)\n\n        if log:\n            plt.semilogy(r, np.abs(err), label=baset)\n            extra = \"absolute of \"\n        else:\n            plt.plot(r, err, label=baset)\n            extra = \"\"\n\n    plt.xlabel(common.XLABEL)\n    plt.ylabel(extra + common.RELABEL)\n\n    plt.legend(loc='lower right')\n    plt.ylim(-0.2, 0.2)\n\n\ndef main():\n    common.setup()\n\n    plot_overview()\n    plt.savefig(\"local_energy_cgto.pdf\", bbox_inches=\"tight\")\n\n    plot_closeup()\n    plt.savefig(\"local_energy_cgto_zoom.pdf\", bbox_inches=\"tight\")\n\n    plot_relative_error(log=False)\n    plt.savefig(\"relative_error_cgto.pdf\", bbox_inches=\"tight\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"mfherbst/dissertation","sub_path":"4_solving_hf/hydrogen_comparison/cgtos.py","file_name":"cgtos.py","file_ext":"py","file_size_in_byte":2484,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"2529350057","text":"from django.http import HttpResponseRedirect\nfrom django.shortcuts import get_object_or_404, render\nfrom django.contrib.auth import logout as auth_logout\n\nfrom models import Problem, ProblemSolution, User\nfrom forms import UserForm\n\n\ndef index(request):\n\treturn render(request, 'index.html')\n\ndef logout(request):\n    \"\"\"Logs out user\"\"\"\n    auth_logout(request)\n    return HttpResponseRedirect('/')\n\ndef detail(request, problem_id):\n    problem = get_object_or_404(Problem, pk=problem_id)\n\n    if request.user.is_authenticated():\n        solved = ProblemSolution.objects.get_or_none(problem=problem, user=request.user)\n        if solved is not None:\n            solution = solved.solution\n            return render(request, 'problem/detail.html', { 'problem': problem, 'solved': solved.checked, 'load': solved is not None, 'solution': solution })\n\n    return render(request, 'problem/detail.html', {'problem': problem, 'solved': False, 'load': False })\n\ndef problems(request):\n    prob_solved = []\n    if request.user.is_authenticated():\n        prob_solved = ProblemSolution.objects.filter(user=request.user, checked=True)\n        prob_solved = map(lambda prob: prob.problem.id, prob_solved)\n    \n\n    categories = ['Starter', 'Easy', 'Medium', 'Hard']\n    probs = list(Problem.objects.all())\n    probs.sort(key=lambda t: categories.index(t.category))\n\n    return render(request, 'problem/list.html', {'problems': probs, 'solved': prob_solved})\n\ndef search(request):\n    if 'search' not in request.POST:\n        return problems(request)\n\n    name = request.POST['search']\n    prob_solved = []\n\n    categories = ['Starter', 'Easy', 'Medium', 'Hard']\n    probs = list(Problem.objects.filter(title__icontains=name))\n    probs.sort(key=lambda t: categories.index(t.category))\n\n    if request.user.is_authenticated():\n        prob_solved = ProblemSolution.objects.filter(user=request.user)\n        prob_solved = map(lambda prob: prob.problem.id, prob_solved)\n    \n    return render(request, 'problem/list.html', {'problems': probs, 'solved': prob_solved})\n\ndef profile(request, user):\n    user = get_object_or_404(User, username=user)\n\n    prob_solved = map(lambda prob: prob.problem.id, ProblemSolution.objects.filter(user=user, checked=True))\n    problems_cat = map(lambda prob: prob.category, Problem.objects.filter(id__in=prob_solved))\n    points = 0\n\n    for p in problems_cat:\n        if p == \"Starter\":\n            points += 10\n        elif p == \"Easy\":\n            points += 50\n        elif p == \"Medium\":\n            points += 250\n        elif p == \"Hard\":\n            points += 2000\n\n    return render(request, 'profile/profile.html', {'u': user, 'problems': len(prob_solved), 'points': points})\n\ndef profile_edit(request):\n    if not request.user.is_authenticated():\n        return HttpResponseRedirect('/')\n    return render(request, 'profile/edit.html')\n\ndef profile_save(request):\n    if not request.user.is_authenticated():\n        return HttpResponseRedirect('/')\n    if request.method == 'POST':\n        form = UserForm(request.POST)\n\n        username = request.POST['username']\n        user_error = None\n        user = None\n\n        try:\n           user =  User.objects.get(username=username)\n        except User.DoesNotExist:\n           pass\n        \n        if user is not None and user.username.lower() != request.user.username.lower():\n            user_error = 'Username is already taken!'\n        if form.is_valid() and user_error is None:\n            request.user.username = username\n            request.user.first_name = form.cleaned_data['first_name']\n            request.user.last_name = form.cleaned_data['last_name']\n            request.user.bio = form.cleaned_data['bio']\n            request.user.website = form.cleaned_data['website']\n            request.user.facebook = form.cleaned_data['facebook']\n            request.user.twitter = form.cleaned_data['twitter']\n            request.user.linkedin = form.cleaned_data['linkedin']\n            request.user.github = form.cleaned_data['github']\n\n            request.user.save()\n\n            return render(request, 'profile/edit.html', {'ok': 'Changes saved!'})\n        else:\n            return render(request, 'profile/edit.html', {'ok': None, 'errors': form.errors, 'user_error': user_error})\n\n    return HttpResponseRedirect('/profile/edit/')","repo_name":"joseotoro/GoCodeGo","sub_path":"gocodego/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4320,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"25788830548","text":"# -*- coding: utf-8 -*-\nimport re\n\n\nclass RegexBuilder:\n    r\"\"\"Builds regex using arguments passed into a pattern template.\n\n    Builds a regex object for which the pattern is made from an argument\n    passed into a template. If more than one argument is passed (iterable),\n    each pattern is joined by \"|\" (regex alternation 'or') to create a\n    single pattern.\n\n    Args:\n        pattern_args (iteratable): String element(s) to be each passed to\n            ``pattern_func`` to create a regex pattern. Each element is\n            ``re.escape``'d before being passed.\n        pattern_func (callable): A 'template' function that should take a\n            string and return a string. It should take an element of\n            ``pattern_args`` and return a valid regex pattern group string.\n        flags: ``re`` flag(s) to compile with the regex.\n\n    Example:\n        To create a simple regex that matches on the characters \"a\", \"b\",\n        or \"c\", followed by a period::\n\n            >>> rb = RegexBuilder('abc', lambda x: \"{}\\.\".format(x))\n\n        Looking at ``rb.regex`` we get the following compiled regex::\n\n            >>> print(rb.regex)\n            'a\\.|b\\.|c\\.'\n\n        The above is fairly simple, but this class can help in writing more\n        complex repetitive regex, making them more readable and easier to\n        create by using existing data structures.\n\n    Example:\n        To match the character following the words \"lorem\", \"ipsum\", \"meili\"\n        or \"koda\"::\n\n            >>> words = ['lorem', 'ipsum', 'meili', 'koda']\n            >>> rb = RegexBuilder(words, lambda x: \"(?<={}).\".format(x))\n\n        Looking at ``rb.regex`` we get the following compiled regex::\n\n            >>> print(rb.regex)\n            '(?<=lorem).|(?<=ipsum).|(?<=meili).|(?<=koda).'\n\n    \"\"\"\n\n    def __init__(self, pattern_args, pattern_func, flags=0):\n        self.pattern_args = pattern_args\n        self.pattern_func = pattern_func\n        self.flags = flags\n\n        # Compile\n        self.regex = self._compile()\n\n    def _compile(self):\n        alts = []\n        for arg in self.pattern_args:\n            arg = re.escape(arg)\n            alt = self.pattern_func(arg)\n            alts.append(alt)\n\n        pattern = \"|\".join(alts)\n        return re.compile(pattern, self.flags)\n\n    def __repr__(self):  # pragma: no cover\n        return str(self.regex)\n\n\nclass PreProcessorRegex:\n    r\"\"\"Regex-based substitution text pre-processor.\n\n    Runs a series of regex substitutions (``re.sub``) from each ``regex`` of a\n    :class:`gtts.tokenizer.core.RegexBuilder` with an extra ``repl``\n    replacement parameter.\n\n    Args:\n        search_args (iteratable): String element(s) to be each passed to\n            ``search_func`` to create a regex pattern. Each element is\n            ``re.escape``'d before being passed.\n        search_func (callable): A 'template' function that should take a\n            string and return a string. It should take an element of\n            ``search_args`` and return a valid regex search pattern string.\n        repl (string): The common replacement passed to the ``sub`` method for\n            each ``regex``. Can be a raw string (the case of a regex\n            backreference, for example)\n        flags: ``re`` flag(s) to compile with each `regex`.\n\n    Example:\n        Add \"!\" after the words \"lorem\" or \"ipsum\", while ignoring case::\n\n            >>> import re\n            >>> words = ['lorem', 'ipsum']\n            >>> pp = PreProcessorRegex(words,\n            ...                        lambda x: \"({})\".format(x), r'\\\\1!',\n            ...                        re.IGNORECASE)\n\n        In this case, the regex is a group and the replacement uses its\n        backreference ``\\\\1`` (as a raw string). Looking at ``pp`` we get the\n        following list of search/replacement pairs::\n\n            >>> print(pp)\n            (re.compile('(lorem)', re.IGNORECASE), repl='\\1!'),\n            (re.compile('(ipsum)', re.IGNORECASE), repl='\\1!')\n\n        It can then be run on any string of text::\n\n            >>> pp.run(\"LOREM ipSuM\")\n            \"LOREM! ipSuM!\"\n\n    See :mod:`gtts.tokenizer.pre_processors` for more examples.\n\n    \"\"\"\n\n    def __init__(self, search_args, search_func, repl, flags=0):\n        self.repl = repl\n\n        # Create regex list\n        self.regexes = []\n        for arg in search_args:\n            rb = RegexBuilder([arg], search_func, flags)\n            self.regexes.append(rb.regex)\n\n    def run(self, text):\n        \"\"\"Run each regex substitution on ``text``.\n\n        Args:\n            text (string): the input text.\n\n        Returns:\n            string: text after all substitutions have been sequentially\n            applied.\n\n        \"\"\"\n        for regex in self.regexes:\n            text = regex.sub(self.repl, text)\n        return text\n\n    def __repr__(self):  # pragma: no cover\n        subs_strs = []\n        for r in self.regexes:\n            subs_strs.append(\"({}, repl='{}')\".format(r, self.repl))\n        return \", \".join(subs_strs)\n\n\nclass PreProcessorSub:\n    r\"\"\"Simple substitution text preprocessor.\n\n    Performs string-for-string substitution from list a find/replace pairs.\n    It abstracts :class:`gtts.tokenizer.core.PreProcessorRegex` with a default\n    simple substitution regex.\n\n    Args:\n        sub_pairs (list): A list of tuples of the style\n            ``(<search str>, <replace str>)``\n        ignore_case (bool): Ignore case during search. Defaults to ``True``.\n\n    Example:\n        Replace all occurences of \"Mac\" to \"PC\" and \"Firefox\" to \"Chrome\"::\n\n            >>> sub_pairs = [('Mac', 'PC'), ('Firefox', 'Chrome')]\n            >>> pp = PreProcessorSub(sub_pairs)\n\n        Looking at the ``pp``, we get the following list of\n        search (regex)/replacement pairs::\n\n            >>> print(pp)\n            (re.compile('Mac', re.IGNORECASE), repl='PC'),\n            (re.compile('Firefox', re.IGNORECASE), repl='Chrome')\n\n        It can then be run on any string of text::\n\n            >>> pp.run(\"I use firefox on my mac\")\n            \"I use Chrome on my PC\"\n\n    See :mod:`gtts.tokenizer.pre_processors` for more examples.\n\n    \"\"\"\n\n    def __init__(self, sub_pairs, ignore_case=True):\n        def search_func(x):\n            return u\"{}\".format(x)\n\n        flags = re.I if ignore_case else 0\n\n        # Create pre-processor list\n        self.pre_processors = []\n        for sub_pair in sub_pairs:\n            pattern, repl = sub_pair\n            pp = PreProcessorRegex([pattern], search_func, repl, flags)\n            self.pre_processors.append(pp)\n\n    def run(self, text):\n        \"\"\"Run each substitution on ``text``.\n\n        Args:\n            text (string): the input text.\n\n        Returns:\n            string: text after all substitutions have been sequentially\n            applied.\n\n        \"\"\"\n        for pp in self.pre_processors:\n            text = pp.run(text)\n        return text\n\n    def __repr__(self):  # pragma: no cover\n        return \", \".join([str(pp) for pp in self.pre_processors])\n\n\nclass Tokenizer:\n    r\"\"\"An extensible but simple generic rule-based tokenizer.\n\n    A generic and simple string tokenizer that takes a list of functions\n    (called `tokenizer cases`) returning ``regex`` objects and joins them by\n    \"|\" (regex alternation 'or') to create a single regex to use with the\n    standard ``regex.split()`` function.\n\n    ``regex_funcs`` is a list of any function that can return a ``regex``\n    (from ``re.compile()``) object, such as a\n    :class:`gtts.tokenizer.core.RegexBuilder` instance (and its ``regex``\n    attribute).\n\n    See the :mod:`gtts.tokenizer.tokenizer_cases` module for examples.\n\n    Args:\n        regex_funcs (list): List of compiled ``regex`` objects. Each\n            functions's pattern will be joined into a single pattern and\n            compiled.\n        flags: ``re`` flag(s) to compile with the final regex. Defaults to\n            ``re.IGNORECASE``\n\n    Note:\n        When the ``regex`` objects obtained from ``regex_funcs`` are joined,\n        their individual ``re`` flags are ignored in favour of ``flags``.\n\n    Raises:\n        TypeError: When an element of ``regex_funcs`` is not a function, or\n            a function that does not return a compiled ``regex`` object.\n\n    Warning:\n        Joined ``regex`` patterns can easily interfere with one another in\n        unexpected ways. It is recommanded that each tokenizer case operate\n        on distinct or non-overlapping chracters/sets of characters\n        (For example, a tokenizer case for the period (\".\") should also\n        handle not matching/cutting on decimals, instead of making that\n        a seperate tokenizer case).\n\n    Example:\n        A tokenizer with a two simple case (*Note: these are bad cases to\n        tokenize on, this is simply a usage example*)::\n\n            >>> import re, RegexBuilder\n            >>>\n            >>> def case1():\n            ...     return re.compile(\"\\,\")\n            >>>\n            >>> def case2():\n            ...     return RegexBuilder('abc', lambda x: \"{}\\.\".format(x)).regex\n            >>>\n            >>> t = Tokenizer([case1, case2])\n\n        Looking at ``case1().pattern``, we get::\n\n            >>> print(case1().pattern)\n            '\\\\,'\n\n        Looking at ``case2().pattern``, we get::\n\n            >>> print(case2().pattern)\n            'a\\\\.|b\\\\.|c\\\\.'\n\n        Finally, looking at ``t``, we get them combined::\n\n            >>> print(t)\n            're.compile('\\\\,|a\\\\.|b\\\\.|c\\\\.', re.IGNORECASE)\n             from: [<function case1 at 0x10bbcdd08>, <function case2 at 0x10b5c5e18>]'\n\n        It can then be run on any string of text::\n\n            >>> t.run(\"Hello, my name is Linda a. Call me Lin, b. I'm your friend\")\n            ['Hello', ' my name is Linda ', ' Call me Lin', ' ', \" I'm your friend\"]\n\n    \"\"\"\n\n    def __init__(self, regex_funcs, flags=re.IGNORECASE):\n        self.regex_funcs = regex_funcs\n        self.flags = flags\n\n        try:\n            # Combine\n            self.total_regex = self._combine_regex()\n        except (TypeError, AttributeError) as e:  # pragma: no cover\n            raise TypeError(\n                \"Tokenizer() expects a list of functions returning \"\n                \"regular expression objects (i.e. re.compile). \" + str(e)\n            )\n\n    def _combine_regex(self):\n        alts = []\n        for func in self.regex_funcs:\n            alts.append(func())\n\n        pattern = \"|\".join(alt.pattern for alt in alts)\n        return re.compile(pattern, self.flags)\n\n    def run(self, text):\n        \"\"\"Tokenize `text`.\n\n        Args:\n            text (string): the input text to tokenize.\n\n        Returns:\n            list: A list of strings (token) split according to the tokenizer cases.\n\n        \"\"\"\n        return self.total_regex.split(text)\n\n    def __repr__(self):  # pragma: no cover\n        return str(self.total_regex) + \" from: \" + str(self.regex_funcs)\n","repo_name":"pndurette/gTTS","sub_path":"gtts/tokenizer/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":10908,"program_lang":"python","lang":"en","doc_type":"code","stars":2027,"dataset":"github-code","pt":"86"}
+{"seq_id":"27273976801","text":"class Solution:\n    def titleToNumber( columnTitle: str) -> int:\n        l = {chr(64+i):i for i in range(1,27)}\n        ind = 0\n        out = 0\n        for i in columnTitle[::-1]:\n            out +=l[i]*26**ind\n            ind+=1\n        return out\n    print(titleToNumber(\"XYZ\"))","repo_name":"yesetoda/leetcodeSolutions","sub_path":"excelSheetColumnNumber.py","file_name":"excelSheetColumnNumber.py","file_ext":"py","file_size_in_byte":280,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"7343333825","text":"# reduce no le aplica la función al primer número de la lista para funciones dobles. \n\nfrom functools import reduce\n\ndef SumatorioClasico(l):\n    resultado = 0\n    for valor in l:\n        resultado += valor\n        \n    return resultado\n\ndef SumatorioDobleClasico(l):\n    resultado = 0\n    for valor in l:\n        resultado += valor*2\n        \n    return resultado\n\ndef ProductoTotal(l):\n    resultado = 0\n    for valor in l:\n        resultado *= valor\n    return resultado\n\n\nlista = [1, 3, -1, 15, 9]\n\nsumatorio = reduce(lambda x, y: x + y, lista)\n\n# Creo una copia de la lista.\n\nl = lista[:]\n\n# Añado el neutro para la suma en la posición cero: lista = [0, 1, 3, -1, 15, 9]\n# Como reduce no afecta al primero, si metemos el cero, entonces ahora si que afecta al 1. \n\nl.insert(0,0)\nsumatorioDobles = reduce(lambda x, y: x + y*2, l)\n\n\n\nprint(sumatorio)\nprint(sumatorioDobles)\n\n\n        \n    ","repo_name":"yalaska04/m02_boot_0","sub_path":"ReduceProblems.py","file_name":"ReduceProblems.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"20357545759","text":"###################################################################\n#   Music Analyzer\n#   Victor Peralta\n#   This Program prompts the user for a playlist data file from apple\n#   music or itunes to analyze. The program then dislpays a report of\n#   contents of the file.\n#\n####################################################################\n\nimport csv\n#This function asks the user for the name of a file path and returns that name as a string\ndef get_user_info():  \n    info = \" \"\n    while(True):\n        try:\n            info = input(\"Please provide the name of your playlist data file: (.csv/.txt) \")\n            if(\".txt\" not in info and \".csv\" not in info):      #Ensures user at least submits a .txt/,csv file name\n                print(\"Please enter a .txt or .csv file: \")\n                continue\n        except:\n            print('Something went wrong. Please try again')\n            continue\n        else:\n            break\n    return info\n\n#This function takes in the name of a file, opens it, reads the file as a tab delimited file, and puts that reading into a list\n#This list is then returned from the function\ndef load_music(file_path):\n    music_list = []\n    try:\n        with open(file_path, 'r', encoding='utf-16', newline='') as file:\n            csv_reader = csv.reader(file, delimiter = '\\t')\n            for row_of_music_as_string in csv_reader:\n                music_list.append(row_of_music_as_string)\n    except:\n        print(f\"An error occured when trying to open {file_path}\")\n\n    return music_list\n\n#This function calculates and prints the number of songs for each year within the playlist\ndef num_songs_in_year(mlist):          \n    years = []              #This will hold the different years\n    info2 = []              #This will hold all occurences of each year\n    for row in mlist:       #traverse throught provided list, in order to populate our lists\n        try:\n            if(row[16] not in years):       #check for duplicates\n                years.append(row[16])     \n            info2.append(row[16])           #duplicates included\n        except:\n            continue\n    \n    years.remove(\"Year\")\n    years.sort()\n    years.pop(0)            #Assumes that there was a blank year value in the file. Which is why we remove it from consideration\n    info2.remove(\"Year\")    #Both removes of year are just so we don't consider this string in calculations\n    \n    for year in years:      #Prints the number of songs for each year\n        print(f\"\\nNumber of songs in the year {year}: {info2.count(year)}\")\n\n#This function finds the longest song with the greatest Time value in the given list.\n#This song(or songs')'s info is then displayed to the user\ndef find_longest_song(mlist):\n    info = []           #This will hold all instances of largest Time values\n    largest = 0\n    lindex = 0          #This is the index of the last value put into info (-1)\n    for row in mlist:\n        try:\n            if(int(row[11]) >= largest):\n               largest = int(row[11])\n                  \n               info.append(row)\n               lindex+=1  \n        except:\n            continue\n\n    llist = []          # This holds the rows of information that share the greatest values in Time\n    llist.append(info[lindex-1])\n    for row in info:\n        if(row[11] == info[lindex-1]):      #We know that the last index in info is the greatest, but we check if any other song in info shares this Time value\n            llist.append(row)   \n\n    iterable = 0\n    print(f\"Longest song(s) (based on time):\")\n    for row in llist:\n         print(f\"Name: {llist[iterable][0]} Artist: {llist[iterable][1]} Time: {largest}\")       \n         iterable+=1\n    print(\"\\n\")\n#This function finds the shortest song with the least Time value in the given list\n#The song's(or songs') info is then displayed to the user\ndef find_shortest_song(mlist):\n    info = []       #This will hold every instance of the smallest value\n    smallest = 999999999999999999999\n    lindex = 0\n    for row in mlist:\n        try:\n            if(int(row[11]) <= smallest):\n               smallest = int(row[11])   \n               info.append(row)\n               lindex+=1  \n        except:\n            continue\n\n    slist = []          #This will hold mutiple of songs that hold the same lowest Time value\n    slist.append(info[lindex-1])\n    for row in info:\n        if(row[11] == info[lindex-1]):      #We know the last index of info is the shortest song, but we have to check entire list to see if any other songs have the same value \n            slist.append(row)     \n    iterable2 = 0\n\n    print(f\"Shortest song(s) (based on time):\")\n    for row in slist:\n         print(f\"Name: {slist[iterable2][0]} Artist: {slist[iterable2][1]} Time: {smallest}\")       \n         iterable2 += 1\n    print(\"\\n\")\n\n#This function provides: the number of songs, the longest song by Name and\n#Artist (list multiple if more than one), the shortest song by Name and Artist (\n#list multiple if more than one), of each genre in the given list.\ndef collect_genre_info(mlist):\n    index = 0\n    genres = []         #this holds every genre in the list\n    info2 = []          #this holds every instance of a genre in the list\n    for row in mlist:\n        try:\n            if(row[9] not in genres):\n                genres.append(row[9])\n                \n            info2.append(row[9])\n        except:\n            continue\n        index+=1\n    genres.remove(\"Genre\")\n    genres.sort()\n    genres.pop(0)               #Assumes that ther is at least one blank genre spot, which is why it is removed\n    \n    info2.remove(\"Genre\")\n    \n    for genre in genres:\n        print(f\"\\nNumber of {genre} songs: {info2.count(genre)}\")\n        songs = []\n        for row in mlist:\n            if(row[9] == genre):\n                songs.append(row)\n\n        find_longest_song(songs)        \n        find_shortest_song(songs)       #Calls to functions made earlier\n\n#his function calculates the number of songs that have been played in a given list  \n#This value is then returned\ndef get_num_songs_played(mlist):\n    info = []\n    for row in mlist:\n        info.append(row[25])\n    info.sort()\n    \n    info.pop(len(info)-1)   #Removes the \"Plays' column in the first row\n    count = 0\n    for value in info:\n        if(value.isnumeric()):\n            count+=1\n    return count\n\n#This funciton calculates the number of songs that have not been played in a given list\n#This value is then returned\ndef get_num_songs_not_played(mlist):\n    info = []\n    for row in mlist:\n        info.append(row[25])\n    info.sort()\n    \n    info.pop(len(info)-1)           #Removes the \"Plays' column in the first row\n    count = 0\n    for value in info:\n        if(not value.isnumeric()):\n            count+=1\n    return count\ndef main():\n    while(True):\n        file_path = get_user_info()\n        mlist = load_music(file_path)\n        if(mlist):\n            print(f\"Numer of songs in {file_path}: {len(mlist)-1}\\n\")\n            num_songs_in_year(mlist)\n            print(\"\\n\")\n            find_longest_song(mlist)\n            find_shortest_song(mlist)\n            collect_genre_info(mlist)\n            count = get_num_songs_not_played(mlist)\n            print(f\"\\nThe amount of songs not played is {count}\")\n            count2 = get_num_songs_played(mlist)\n            print(f\"The amount of songs played is {count2}\\n\")\n        another_analyze = input(\"\\nWould you like to analyze another music file?: (y/n) \")\n        if(another_analyze != 'y'):\n            break\n\nmain()\n","repo_name":"ElM3roM3ro/CS","sub_path":"PythonApps/music_analyzer/music_analyzer.py","file_name":"music_analyzer.py","file_ext":"py","file_size_in_byte":7528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"42523016519","text":"import os, os.path\nimport json\nfrom ase.io import read\nimport ase.db\nfrom gpaw import GPAW, PW, FermiDirac\nfrom gpaw.response.df import DielectricFunction\nfrom ase.parallel import parprint\n\ndef excited(base_dir=\"./\"):\n    gs_gpw = os.path.join(base_dir, \"gs.gpw\")\n    es_gpw = os.path.join(base_dir, \"es.gpw\")\n    if os.path.exists(es_gpw):\n        parprint(\"Excited states calculated, will use gpw file directly!\")\n        return\n    \n    if not os.path.exists(gs_gpw):\n        raise FileNotFoundError(\"Ground state not calculated!\")\n\n    curr_dir = os.path.dirname(os.path.abspath(__file__))\n    param_file = os.path.join(curr_dir, \"../parameters.json\")\n    if os.path.exists(param_file):\n        params = json.load(open(param_file, \"r\"))\n    else:\n        raise FileNotFoundError(\"no parameter file!\")\n\n    calc = GPAW(gs_gpw)\n    calc.set(**params[\"es\"])\n    calc.get_potential_energy()\n    calc.diagonalize_full_hamiltonian(nbands=60)\n    calc.write(es_gpw, mode=\"all\")  # full matrix\n\ndef permittivity(base_dir=\"./\", mode=\"df\"):\n    curr_dir = os.path.dirname(os.path.abspath(__file__))\n    es_gpw = os.path.join(base_dir, \"es.gpw\")\n    param_file = os.path.join(curr_dir, \"../parameters.json\")\n\n    if os.path.exists(param_file):\n        params = json.load(open(param_file, \"r\"))\n    else:\n        raise FileNotFoundError(\"no parameter file!\")\n\n    # No reason to use 2D truncation for dielectric\n    params[\"df\"].pop(\"truncation\", None)\n    params[\"tetra\"].pop(\"truncation\", None)\n    \n    if not os.path.exists(es_gpw):\n        raise FileNotFoundError(\"Excited state not calculated!\")\n    \n    if mode not in (\"df\", \"tetra\"):\n        raise ValueError(\"Mode should be df or tetra\")\n    \n    data_file = os.path.join(base_dir,\n                             \"eps_{}.npz\".format(mode))\n\n    if os.path.exists(data_file):\n        parprint(\"Polarizability file exists!\")\n        return 0\n    \n    df = DielectricFunction(calc=es_gpw,\n                            **params[mode])\n    eps0x, epsx = df.get_dielectric_function(q_c=[0, 0, 0],\n                                            direction=\"x\",\n                                            filename=None)\n    eps0y, epsy = df.get_dielectric_function(q_c=[0, 0, 0],\n                                            direction=\"y\",\n                                            filename=None)\n    eps0z, epsz = df.get_dielectric_function(q_c=[0, 0, 0],\n                                            direction=\"z\",\n                                            filename=None)\n    \n    freq = df.get_frequencies()\n    data = dict(frequencies=freq,\n                eps_x=epsx,\n                eps_y=epsy,\n                eps_z=epsz,\n                eps_x0=eps0x,\n                eps_y0=eps0y,\n                eps_z0=eps0z,)\n    from ase.parallel import world\n    import numpy\n    if world.rank == 0:\n        numpy.savez_compressed(data_file, **data)\n","repo_name":"alchem0x2A/2D_dielectric","sub_path":"src/dielectric.py","file_name":"dielectric.py","file_ext":"py","file_size_in_byte":2886,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"42086927166","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[13]:\n\n\nget_ipython().run_line_magic('config', 'IPCompleter.greedy=True')\n\n\n# In[2]:\n\n\nimport pandas as pd\nimport os\nimport csv\n\n\n# In[9]:\n\n\nmiPath = \"C:/Users/User/Documents/Udemy-Cursos/DataScience-Python/python-ml-course/datasets\"\nmiArchivo =\"/titanic/titanic3.xls\"\nmiArchivo2 =\"/titanic/titanic3.xlsx\"\n\n\n# In[10]:\n\n\ntitanic3 = pd.read_excel(miPath + \"/\" + miArchivo, \"titanic3\")\ntitanic2 = pd.read_excel(miPath + \"/\" + miArchivo2, \"titanic3\")\n\n\n# In[11]:\n\n\ntitanic2.head()\n\n\n# In[ ]:\n\n\n\n\n\n# In[12]:\n\n\ntitanic3.to_csv(miPath + \"/titanic/archivo-Titanic-CSV.csv\")\n\n","repo_name":"pablopetito/DataSciense","sub_path":"tema-Leer-DataExcel.py","file_name":"tema-Leer-DataExcel.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"15027735777","text":"import matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.signal import hilbert\nfrom obspy import Stream, Trace\n\n\ndef stack_all(stream, pws=False):\n    \"\"\"\n    Stack all traces in ``Stream`` objects.\n\n    Args:\n        stream (:class:`~obspy.core.Stream`):\n            Contains traces to stack\n        pws (bool):\n            Enables Phase-Weighted Stacking\n\n    Returns:\n        :class:`~obspy.core.Trace`:\n            Stacked trace\n\n    \"\"\"\n\n    # Copy stats from stream\n    str_stats = stream[0].stats\n\n    # Initialize arrays\n    tmp = np.zeros(len(stream[0].data))\n    weight = np.zeros(len(stream[0].data), dtype=complex)\n\n    # Stack all traces\n    for tr in stream:\n        tmp += tr.data\n        hilb = hilbert(tr.data)\n        phase = np.arctan2(hilb.imag, hilb.real)\n        weight += np.exp(1j * phase)\n\n    # Normalize\n    tmp = tmp / float(len(stream))\n\n    # Phase-weighting\n    if pws:\n        weight = weight / float(len(stream))\n        weight = np.real(abs(weight))\n    else:\n        weight = np.ones(len(stream[0].data))\n\n    # Put back into traces\n    stack = Trace(data=weight * tmp, header=str_stats)\n\n    return stack\n\n\ndef rf_wiggles(\n    rflist,\n    btyp=\"baz\",\n    wvtype=\"P\",\n    pws=False,\n    tmin=-5.0,\n    tmax=20,\n    scale=None,\n    pcolor=\"red\",\n    ncolor=\"blue\",\n    save=False,\n    axs=None,\n    ftitle=\"Figure_rf_wiggle\",\n    fmt=\"png\",\n    plot_kwargs={\"linewidth\": 0.1, \"color\": \"black\"},\n    figure_kwargs={},\n):\n    \"\"\"\n    Plot receiver function seismograms sorted by back-azimuth or slowness.\n\n    Args:\n        rflist (list or prs.Result):\n            list of `obspy.core.Stream <https://tinyurl.com/3u8dv8s8>`_ objects\n            containing receiver functions\n        btyp (str):\n            Type of sorting for panel\n        wvtype (str):\n            Wavet type ('P', 'SV', or 'SH')\n        pws (bool):\n            Enables Phase-Weighted Stacking\n        tmin (float):\n            Lower bound of time axis (s)\n        tmax (float):\n            Upper bound of time axis (s)\n        scale (float):\n            Amplitude scaling factor\n        save (bool):\n            Whether or not to save the figure\n        axs (4*tuple):\n            matplotlib axes to place the subplots.\n\n                * axs[0]: radial stack\n                * axs[1]: radial section\n                * axs[2]: transverse stack\n                * axs[3]: transverse section\n\n        pcolor (str):\n            Color to fill positive wiggles\n        ncolor (str):\n            Color to fill negative wiggles\n        ftitle (str):\n            Filename of figure to be saved (without format suffix, see fmt)\n        fmt (str):\n            Output format ('png', 'jpg', or 'eps', etc.)\n        plot_kwargs (dict):\n            Keyword arguments passed to\n            `matplotlib.pyplot.plot() <https://tinyurl.com/2p9xy7hs>`_\n        figure (dict):\n            Keyword arguments passed to\n            `matplotlib.pyplot.figure() <https://tinyurl.com/yc3w956b>`_\n\n    Returns:\n        :class:`matplotlib.pyplot.figure` :\n            Figure handle\n    \"\"\"\n\n    if not (btyp == \"baz\" or btyp == \"slow\" or btyp == \"dist\"):\n        raise ValueError('type has to be \"baz\" or \"slow\" or \"dist\"')\n\n    if not fmt in [\"png\", \"PNG\", \"jpg\", \"JPG\", \"eps\", \"EPS\", \"pdf\", \"PDF\"]:\n        raise ValueError(\"'fmt' has to be one of 'png', 'jpg', 'eps', 'pdf'\")\n\n    # Re-order streams in list\n    str1 = Stream(traces=[st[0] for st in rflist.rfs])\n    str2 = Stream(traces=[st[1] for st in rflist.rfs])\n\n    # Get stacked traces\n    tr1 = stack_all(str1, pws=pws)\n    tr2 = stack_all(str2, pws=pws)\n\n    # Time axis\n    time = str1[0].stats.taxis\n\n    # Initialize figure\n    if not axs:\n        fig = plt.figure(**figure_kwargs)\n        plt.clf()\n\n        # Get more control on subplots\n        ax1 = fig.add_axes([0.1, 0.825, 0.3, 0.05])\n        ax2 = fig.add_axes([0.1, 0.1, 0.3, 0.7])\n        ax3 = fig.add_axes([0.45, 0.825, 0.3, 0.05])\n        ax4 = fig.add_axes([0.45, 0.1, 0.3, 0.7])\n    else:\n        ax1, ax2, ax3, ax4 = axs\n        fig = ax1.get_figure()\n\n    # Plot stack of all traces from str1 on top left\n    ax1.fill_between(\n        time,\n        0.0,\n        tr1.data,\n        where=tr1.data + 1e-6 <= 0.0,\n        facecolor=ncolor,\n        linewidth=0,\n        interpolate=True,\n    )\n    ax1.fill_between(\n        time,\n        0.0,\n        tr1.data,\n        where=tr1.data + 1e-6 >= 0.0,\n        facecolor=pcolor,\n        linewidth=0,\n        interpolate=True,\n    )\n    ax1.plot(time, tr1.data, **plot_kwargs)\n    ax1.set_ylim(-np.max(np.abs(tr1.data)), np.max(np.abs(tr1.data)))\n    ax1.set_yticks(())\n    ax1.set_xticks(())\n    ax1.set_title(\"Radial\")\n    ax1.set_xlim(tmin, tmax)\n\n    # Plot stack of all SH traces on top right\n    ax3.fill_between(\n        time,\n        0.0,\n        tr2.data,\n        where=tr2.data + 1e-6 <= 0.0,\n        facecolor=ncolor,\n        linewidth=0,\n        interpolate=True,\n    )\n    ax3.fill_between(\n        time,\n        0.0,\n        tr2.data,\n        where=tr2.data + 1e-6 >= 0.0,\n        facecolor=pcolor,\n        linewidth=0,\n        interpolate=True,\n    )\n    ax3.plot(time, tr2.data, **plot_kwargs)\n    ax3.set_xlim(tmin, tmax)\n    ax3.set_ylim(-np.max(np.abs(tr1.data)), np.max(np.abs(tr1.data)))\n    ax3.set_yticks(())\n    ax3.set_xticks(())\n    ax3.set_title(\"Transverse\")\n\n    axes = [ax2, ax4]\n    streams = [str1, str2]\n\n    for ax, st in zip(axes, streams):\n\n        # Plot sorted traces from str1 on bottom left panel\n        for tr in st:\n\n            if scale:\n                maxval = scale\n                # Define y axis\n                if btyp == \"baz\":\n                    y = tr.stats.baz\n                elif btyp == \"slow\":\n                    y = tr.stats.slow\n                elif btyp == \"dist\":\n                    y = tr.stats.slow\n            else:\n                # Define y axis\n                if btyp == \"baz\":\n                    y = tr.stats.baz\n                    maxval = 180\n                elif btyp == \"slow\":\n                    y = tr.stats.slow\n                    maxval = 0.02\n                elif btyp == \"dist\":\n                    y = tr.stats.slow\n                    maxval = 20\n\n            # Fill positive in red, negative in blue\n            ax.fill_between(\n                time,\n                y,\n                y + tr.data * maxval,\n                where=tr.data + 1e-6 <= 0.0,\n                facecolor=ncolor,\n                linewidth=0,\n                interpolate=True,\n            )\n            ax.fill_between(\n                time,\n                y,\n                y + tr.data * maxval,\n                where=tr.data + 1e-6 >= 0.0,\n                facecolor=pcolor,\n                linewidth=0,\n                interpolate=True,\n            )\n            ax.plot(time, y + tr.data * maxval, **plot_kwargs)\n\n        ax.set_xlim(tmin, tmax)\n\n        if btyp == \"baz\":\n            ax.set_ylim(-5, 370)\n            ax.set_ylabel(\"Back-azimuth (degree)\")\n\n        elif btyp == \"slow\":\n            if wvtype == \"P\":\n                ax.set_ylim(0.038, 0.082)\n            elif wvtype == \"S\":\n                ax.set_ylim(0.07, 0.125)\n            elif wvtype == \"SKS\":\n                ax.set_ylim(0.03, 0.06)\n            ax.set_ylabel(\"Slowness (s/km)\")\n        elif btyp == \"dist\":\n            if wvtype == \"P\":\n                ax.set_ylim(28.0, 92.0)\n            elif wvtype == \"S\":\n                ax.set_ylim(53.0, 107.0)\n            elif wvtype == \"SKS\":\n                ax.set_ylim(83.0, 117.0)\n            ax.set_ylabel(\"Distance (degree)\")\n\n        ax.set_xlabel(\"Time (seconds)\")\n        ax.grid(ls=\":\")\n\n    # Remove labels on axis 4\n    ax4.set_ylabel(\"\")\n    ax4.set_yticklabels(())\n\n    if save:\n        plt.savefig(ftitle + \".\" + fmt, bbox_inches=\"tight\", format=fmt)\n    else:\n        plt.show()\n\n    return fig\n\n\ndef stream_wiggles(\n    streamlist,\n    btyp=\"baz\",\n    wvtype=\"P\",\n    tmin=-5.0,\n    tmax=20.0,\n    scale=None,\n    pcolor=\"red\",\n    ncolor=\"blue\",\n    save=False,\n    axs=None,\n    ftitle=\"Figure_pw_wiggles\",\n    fmt=\"png\",\n    plot_kwargs={\"linewidth\": 0.1, \"color\": \"black\"},\n    figure_kwargs={},\n):\n    \"\"\"\n    Plot displacement seismograms sorted by back-azimuth or slowness.\n\n    Args:\n        streamlist (list or prs.Result):\n            list of `obspy.core.Stream <https://tinyurl.com/3u8dv8s8>`_ objects\n            containing displacement seismograms\n        btyp (str):\n            Type of sorting for panel\n        wvtype (str):\n            Wave type ('P', 'SV', or 'SH')\n        tmin (float):\n            Lower bound of time axis (s)\n        tmax (float):\n            Upper bound of time axis (s)\n        scale (float):\n            Scaling factor\n        pcolor (str):\n            Color to fill positive wiggles\n        ncolor (str):\n            Color to fill negative wiggles\n        save (bool):\n            Whether or not to save the figure\n        axs (3*tuple):\n            matplotlib axes to place the subplots.\n\n                * axs[0]: P or R section\n                * axs[1]: V or T section\n                * axs[2]: H or Z section\n\n        ftitle (str):\n            Filename of figure to be saved (without format suffix, see fmt)\n        fmt (str):\n            Output format ('png', 'jpg', or 'eps', etc.)\n        plot_kwargs (dict):\n            Keyword arguments passed to\n            `matplotlib.pyplot.plot() <https://tinyurl.com/2p9xy7hs>`_\n        figure (dict):\n            Keyword arguments passed to\n            `matplotlib.pyplot.figure() <https://tinyurl.com/yc3w956b>`_\n\n    Returns:\n        :class:`matplotlib.pyplot.figure` :\n            Figure handle\n    \"\"\"\n\n    if not (btyp == \"baz\" or btyp == \"slow\" or btyp == \"dist\"):\n        msg = 'type has to be \"baz\" or \"slow\" or \"dist\"'\n        raise ValueError(msg)\n\n    # Re-order streams in list\n    str1 = Stream(traces=[st[0] for st in streamlist.streams])\n    str2 = Stream(traces=[st[1] for st in streamlist.streams])\n    str3 = Stream(traces=[st[2] for st in streamlist.streams])\n\n    # Time axis\n    time = str1[0].stats.taxis\n\n    # Initialize figure\n    if not axs:\n        fig = plt.figure(**figure_kwargs)\n        plt.clf()\n\n        # Get more control on subplots\n        ax1 = fig.add_axes([0.1, 0.1, 0.25, 0.83])\n        ax2 = fig.add_axes([0.4, 0.1, 0.25, 0.83])\n        ax3 = fig.add_axes([0.7, 0.1, 0.25, 0.83])\n        axes = [ax1, ax2, ax3]\n    else:\n        axes = axs\n        fig = ax1.get_figure()\n\n    streams = [str1, str2, str3]\n\n    for ax, st in zip(axes, streams):\n\n        for tr in st:\n            if scale:\n                maxval = scale\n                # Define y axis\n                if btyp == \"baz\":\n                    y = tr.stats.baz\n                elif btyp == \"slow\":\n                    y = tr.stats.slow\n                elif btyp == \"dist\":\n                    y = tr.stats.slow\n            else:\n                # Define y axis\n                if btyp == \"baz\":\n                    y = tr.stats.baz\n                    maxval = 100\n                elif btyp == \"slow\":\n                    y = tr.stats.slow\n                    maxval = 0.02\n                elif btyp == \"dist\":\n                    y = tr.stats.slow\n                    maxval = 20\n\n            # Fill positive in red, negative in blue\n            ax.fill_between(\n                time,\n                y,\n                y + tr.data * maxval,\n                where=tr.data + 1e-6 <= 0.0,\n                facecolor=ncolor,\n                linewidth=0,\n                interpolate=True,\n            )\n            ax.fill_between(\n                time,\n                y,\n                y + tr.data * maxval,\n                where=tr.data + 1e-6 >= 0.0,\n                facecolor=pcolor,\n                linewidth=0,\n                interpolate=True,\n            )\n            ax.plot(time, y + tr.data * maxval, **plot_kwargs)\n\n        ax.set_xlim(tmin, tmax)\n\n        if btyp == \"baz\":\n            ax.set_ylim(-5, 370)\n            ax.set_ylabel(\"Back-azimuth (degree)\")\n\n        elif btyp == \"slow\":\n            if wvtype == \"P\":\n                ax.set_ylim(0.038, 0.082)\n            elif wvtype == \"S\":\n                ax.set_ylim(0.07, 0.125)\n            elif wvtype == \"SKS\":\n                ax.set_ylim(0.03, 0.06)\n            ax.set_ylabel(\"Slowness (s/km)\")\n        elif btyp == \"dist\":\n            if wvtype == \"P\":\n                ax.set_ylim(28.0, 92.0)\n            elif wvtype == \"S\":\n                ax.set_ylim(53.0, 107.0)\n            elif wvtype == \"SKS\":\n                ax.set_ylim(83.0, 117.0)\n            ax.set_ylabel(\"Distance (degree)\")\n\n        ax.set_xlabel(\"Time (seconds)\")\n        ax.set_title(st[0].stats.channel)\n        ax.grid(ls=\":\")\n\n    # Remove labels on axes 2 and 3\n    axes[1].set_ylabel(\"\")\n    axes[2].set_ylabel(\"\")\n    axes[1].set_yticklabels(())\n    axes[2].set_yticklabels(())\n\n    if save:\n        plt.savefig(ftitle + \".\" + fmt, bbox_inches=\"tight\", format=fmt)\n    else:\n        plt.show()\n\n    return fig\n\n\ndef seis_wiggles(\n    stream,\n    tmin=-5.0,\n    tmax=20.0,\n    save=False,\n    ftitle=\"Figure_pw_wiggles_3c\",\n    fmt=\"png\",\n    figure_kwargs={},\n):\n    \"\"\"\n    Plots 3-component wiggles.\n\n    Args:\n        stream (:class:`~obspy.core.Stream`):\n            `obspy.core.Stream <https://tinyurl.com/3u8dv8s8>`_ containing 3 traces\n        tmin (float):\n            Lower bound of time axis (s)\n        tmax (float, optional):\n            Upper bound of time axis (s)\n        save (bool):\n            Whether or not to save the figure\n        ftitle (str):\n            Filename of figure to be saved (without format suffix, see fmt)\n        fmt (str):\n            Output format ('png', 'jpg', or 'eps', etc.)\n\n    Returns:\n        :class:`matplotlib.pyplot.figure` :\n            Figure handle\n    \"\"\"\n\n    nt = stream[0].stats.npts\n    dt = stream[0].stats.delta\n\n    # Clear figure\n    fig = plt.figure(**figure_kwargs)\n\n    # Time axis\n    time = stream[0].stats.taxis\n\n    max1 = np.max(np.abs(stream[0].data))\n    max2 = np.max(np.abs(stream[1].data))\n    max3 = np.max(np.abs(stream[2].data))\n\n    ax = fig.add_subplot(313)\n    maxv = np.max(np.array([max1, max2, max3]))\n    ax.plot(time, stream[2].data / maxv, \"k\", label=\"Vertical component\", lw=0.75)\n    ax.set_xlim(tmin, tmax)\n    ax.set_ylim(-1.1, 1.1)\n    ax.set_xlabel(\"Time following $P$-wave arrival (seconds)\")\n\n    plt.legend()\n\n    ax = fig.add_subplot(312)\n    ax.plot(time, stream[0].data / maxv, \"k\", label=\"North component\", lw=0.75)\n    ax.set_xlim(tmin, tmax)\n    ax.set_ylim(-1.1, 1.1)\n    ax.set_xticklabels(())\n\n    plt.legend()\n\n    ax = fig.add_subplot(311)\n    ax.plot(time, stream[1].data / maxv, \"k\", label=\"East component\", lw=0.75)\n    ax.set_xlim(tmin, tmax)\n    ax.set_ylim(-1.1, 1.1)\n    ax.set_xticklabels(())\n\n    plt.legend()\n\n    plt.legend()\n    plt.tight_layout()\n\n    if save:\n        plt.savefig(ftitle + \".\" + fmt, bbox_inches=\"tight\", format=fmt)\n    else:\n        plt.show()\n\n    return fig\n","repo_name":"paudetseis/PyRaysum","sub_path":"pyraysum/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":14986,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"86"}
+{"seq_id":"25107047363","text":"import os\nimport pytest\nfrom unittest.mock import MagicMock\nfrom genie.testbed import load\nfrom lab_api.services import ISE, Appliance, DNAC, DeviceLab\n\n\n# DeviceLab Setup\n\n\n@pytest.fixture()\ndef testbed():\n    # Setup testbed for testing\n    yield load(os.getenv(\"CIVLAB_NETDEVICE_DATA\"))\n\n\n@pytest.fixture()\ndef devicelab(testbed):\n    # Setup LabDevice object\n    obj = DeviceLab(testbed)\n    yield obj\n\n\n@pytest.fixture()\ndef device():\n    # Mock device\n    obj = MagicMock()\n    obj.connect = MagicMock()\n    obj.api.get_platform_default_dir = MagicMock(return_value=\"test\")\n    obj.api.verify_file_exists = MagicMock(return_value=True)\n    obj.api.get_running_config_dict = MagicMock(return_value=\"test\")\n    obj.api.get_config_from_file = MagicMock(return_value=\"test\")\n    obj.api.compare_config_dicts = MagicMock(return_value=\"\")\n    yield obj\n\n\n# DeviceLab tests\n\n\ndef test_devicelab_testbed(testbed, devicelab):\n    assert testbed == devicelab.testbed\n\n\ndef test_devicelab_default_cfg_file(testbed, devicelab):\n    assert testbed.custom.default_cfg_file == devicelab.default_cfg_file\n\n\ndef test_devicelab_get_default_cfg_exists(devicelab, device):\n    devicelab.get_default_cfg_exists(device)\n    device.api.get_platform_default_dir.assert_called_once()\n    device.api.verify_file_exists.assert_called_once_with(device.default_cfg_path)\n    assert device.default_config_exists\n\n\ndef test_devicelab_get_default_cfg_exists_all(devicelab, device):\n    devicelab.get_default_cfg_exists = MagicMock()\n    devicelab.get_default_cfg_exists_all()\n    devicelab.get_default_cfg_exists.assert_called()\n\n\ndef test_devicelab_get_running_default_cfg_diff(devicelab, device):\n    device.connect()\n    devicelab.get_running_default_cfg_diff(device)\n    device.api.get_running_config_dict.assert_called_once()\n    device.api.get_config_from_file.assert_called_with(\n        device.default_dir, devicelab.default_cfg_file\n    )\n    device.api.compare_config_dicts.assert_called_with(\n        device.running_cfg, device.default_cfg\n    )\n\n\ndef test_devicelab_get_running_default_cfg_diff_all(devicelab, device):\n    devicelab.get_running_default_cfg_diff = MagicMock()\n    devicelab.get_running_default_cfg_diff_all()\n    devicelab.get_running_default_cfg_diff.assert_called()\n\n\ndef test_devicelab_reset_device_to_default(devicelab, device):\n    # Mock setup\n    devicelab.get_running_default_cfg_diff = MagicMock(return_value=\"test\")\n    device.default_cfg_path = \"test\"\n    device.api.execute_copy_to_startup_config = MagicMock()\n    device.api.execute_reload = MagicMock()\n    # Test\n    devicelab.reset_device_to_default(device, sleep=1, timeout=2)\n    # Asserts\n    device.api.execute_copy_to_startup_config.assert_called_once_with(\n        device.default_cfg_path\n    )\n    device.api.execute_reload.assert_called_once_with(\n        prompt_recovery=False,\n        reload_creds=\"default\",\n        sleep_after_reload=1,\n        timeout=2,\n    )\n\n\n# Appliance Setup\n\n\n@pytest.fixture()\ndef appliance(mocker):\n    mocker.patch(\"paramiko.SSHClient\")\n    appl = Appliance(\"10.1.1.1\", \"test\", \"test\", 22)\n    yield appl\n\n\n@pytest.fixture()\ndef dnac(mocker):\n    mocker.patch(\"paramiko.SSHClient\")\n    dnac = DNAC(\"10.1.1.1\", \"test\", \"test\", 22)\n    yield dnac\n\n\n@pytest.fixture()\ndef ise(mocker):\n    mocker.patch(\"paramiko.SSHClient\")\n    ise = ISE(\"10.1.1.1\", \"test\", \"test\", 22)\n    yield ise\n\n\n# Test Appliance\n\n\ndef test_appliance_connect(mocker, appliance):\n    mocker.patch.object(Appliance, \"_connect_ssh\")\n    r = appliance.connect()\n    Appliance._connect_ssh.assert_called_once()\n    assert r\n    assert appliance.connected\n\n\ndef test_appliance_disconnect(mocker, appliance):\n    appliance.connect()\n    mocker.patch.object(appliance, \"_disconnect\")\n    appliance._disconnect.return_value = False\n    r = appliance.disconnect()\n    appliance._disconnect.assert_called_once()\n    assert not r\n\n\ndef test_appliance__disconnect(mocker, appliance):\n    appliance.connect()\n    mocker.patch.object(appliance, \"_ssh\")\n    mocker.patch.object(appliance, \"_do_before_disconnect\")\n    mocker.patch.object(appliance, \"_do_after_disconnect\")\n    r = appliance._disconnect()\n    appliance._ssh.close.assert_called_once()\n    appliance._do_before_disconnect.assert_called_once()\n    appliance._do_after_disconnect.assert_called_once()\n    assert not r\n\n\ndef test_appliance__connect_ssh(mocker, appliance):\n    mocker.patch.object(appliance, \"_ssh\")\n    r = appliance._connect_ssh()\n    appliance._ssh.connect.assert_called_once_with(\"10.1.1.1\", 22, \"test\", \"test\")\n    appliance._ssh.invoke_shell.assert_called_once()\n    assert r\n\n\ndef test_appliance__send_command(mocker, appliance):\n    cmd = \"test\"\n    cmd_nl = f\"{cmd}\\n\"\n    mocker.patch.object(appliance, \"_shell\")\n    mocker.patch.object(appliance, \"_read_shell\")\n    appliance._shell.send.return_value = len(cmd_nl.encode(\"utf-8\"))\n    appliance._read_shell.return_value = cmd\n    r = appliance._send_command(cmd)\n    appliance._shell.send.assert_called_once_with(cmd_nl)\n    appliance._read_shell.assert_called_once()\n    assert r == cmd\n\n\ndef test_appliance__read_shell_recv_ready(mocker, appliance):\n    data = \"test\"\n    mocker.patch.object(appliance, \"_shell\")\n    appliance._shell.recv_ready.return_value = True\n    appliance._shell.recv.return_value = data.encode(\"utf-8\")\n    r = appliance._read_shell(wait=1)\n    appliance._shell.recv_ready.assert_called_once()\n    assert r == data\n\n\n# Test DNAC\n\n\ndef test_dnac__handle_kong_prompt(mocker, dnac):\n    dummy_usr_prompt = \" [administration] username \"\n    dummy_pwd_prompt = \" [administration] password \"\n    mocker.patch.object(dnac, \"_send_command\")\n    dnac._send_command.return_value = dummy_pwd_prompt\n    r = dnac._handle_kong_prompt(dummy_usr_prompt)\n    dnac._send_command.assert_called_with(\"test\")\n    dnac._send_command.assert_called_with(dnac.password)\n    assert r == dummy_pwd_prompt\n\n\ndef test_dnac__parse_maglev_restore_history(dnac):\n    with open(\"mock_data/dnac_maglev_restore_history\") as stream:\n        data = stream.read()\n    r = dnac._parse_maglev_restore_history(data, dnac._default_result)\n    assert \"ee84abae-a11b-45c6-94c3-d578cfe888f6\" == r[\"restore_id\"]\n\n\ndef test_dnac_get_status(mocker, dnac):\n    dummy_resp = 'test'\n    mocker.patch.object(dnac, \"_send_command\")\n    mocker.patch.object(dnac, \"_handle_kong_prompt\")\n    mocker.patch.object(dnac, \"_parse_maglev_restore_history\")\n    dnac._send_command.return_value = dummy_resp\n    dnac._handle_kong_prompt.return_value = dummy_resp\n    dnac._parse_maglev_restore_history.return_value = dummy_resp\n    dnac.get_status()\n    dnac._send_command.assert_called_once_with(dnac._status_cmd)\n    dnac._handle_kong_prompt.assert_called_once_with(dummy_resp)\n    dnac._parse_maglev_restore_history.assert_called_once_with(dummy_resp, dnac._default_result)\n\n\n# Test ISE\n\n\ndef test_ise__parse_show_restore_history(ise):\n    with open(\"mock_data/ise_show_restore_history\") as stream:\n        data = stream.read()\n    r = ise._parse_show_restore_history(data)\n    assert \"ise-with-users-ready4DNACb-CFG10-220325-1518.tar.gpg\" == r[\"restore_file\"]\n\n\ndef test_ise___do_after_connect(mocker, ise):\n    mocker.patch.object(ise, \"_handle_session_prompt\")\n    mocker.patch.object(ise, \"_send_command\")\n    ise._do_after_connect()\n    ise._handle_session_prompt.assert_called_once()\n    ise._send_command.assert_called_once_with(ise._term_len_cmd)\n\n\ndef test_ise__handle_session_prompt(mocker, ise):\n    mocker.patch.object(ise, \"_read_shell\")\n    mocker.patch.object(ise, \"_send_command\")\n    ise._read_shell.return_value = \" press <Enter> to start a new one: \"\n    ise._handle_session_prompt()\n    ise._read_shell.assert_called_once()\n    ise._send_command.assert_called_once_with(\"\")\n\n\ndef test_ise_get_status(mocker, ise):\n    dummy_resp = \"test\"\n    mocker.patch.object(ise, \"_send_command\")\n    mocker.patch.object(ise, \"_parse_show_restore_history\")\n    ise._send_command.return_value = dummy_resp\n    ise._parse_show_restore_history.return_value = dummy_resp\n    r = ise.get_status()\n    assert r == dummy_resp\n    ise._send_command.assert_called_once_with(ise._status_cmd)\n    ise._parse_show_restore_history.assert_called_once_with(dummy_resp)\n","repo_name":"sambyers/fedciv-lab-api","sub_path":"tests/test_services.py","file_name":"test_services.py","file_ext":"py","file_size_in_byte":8227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"4655270629","text":"import pandas as pd\r\nimport statistics\r\n\r\ndf = pd.read_csv('test_score.csv')\r\n\r\nMath_score = df['math score'].tolist()\r\n\r\nMean = statistics.mean(Math_score)\r\nMedian = statistics.median(Math_score)\r\nMode = statistics.mode(Math_score)\r\nsd = statistics.stdev(Math_score)\r\n\r\nsd1start, sd1end = Mean - sd, Mean + sd\r\nsd2start, sd2end = Mean - (2*sd), Mean + (2*sd)\r\nsd3start, sd3end = Mean - (3*sd), Mean + (3*sd)\r\n\r\ndata_within_sd1 = [ data for data in Math_score if data > sd1start and data < sd1end]\r\ndata_within_sd2 = [ data for data in Math_score if data > sd2start and data < sd2end]\r\ndata_within_sd3 = [ data for data in Math_score if data > sd3start and data < sd3end]\r\n\r\nprint('{}% of data lies within 1sd'.format(len(data_within_sd1)*100/len(Math_score)))\r\nprint('{}% of data lies within 2sd'.format(len(data_within_sd2)*100/len(Math_score)))\r\nprint('{}% of data lies within 3sd'.format(len(data_within_sd3)*100/len(Math_score)))","repo_name":"Aanadanbiswas/PRO-109","sub_path":"PRO-109/score.py","file_name":"score.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"20825877393","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Dec 28 10:01:30 2016\n\nThe Fibonacci sequence is defined by the recurrence relation:\n\nFn = Fn−1 + Fn−2, where F1 = 1 and F2 = 1.\nHence the first 12 terms will be:\n\nF1 = 1\nF2 = 1\nF3 = 2\nF4 = 3\nF5 = 5\nF6 = 8\nF7 = 13\nF8 = 21\nF9 = 34\nF10 = 55\nF11 = 89\nF12 = 144\nThe 12th term, F12, is the first term to contain three digits.\n\nWhat is the index of the first term in the Fibonacci sequence to contain 1000 digits?\n\"\"\"\n\ndef number_25(x):\n    fibo = [1, 1]\n    length = len(str(fibo[len(fibo)-1]))\n    while length < x:\n        fibo.append(fibo[len(fibo)-1]+fibo[len(fibo)-2])\n        length = len(str(fibo[len(fibo)-1]))\n    return len(fibo)-1","repo_name":"zlatankr/Coding-Challenges","sub_path":"ProjectEuler/Project Euler 25.py","file_name":"Project Euler 25.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"7927045","text":"import js\nfrom RobotRaconteur.Client import *\nimport importlib_resources\nimport traceback\nimport numpy as np\nimport base64\nfrom RobotRaconteurCompanion.Util.GeometryUtil import GeometryUtil\nfrom pyri.webui_browser import util\nfrom pyri.webui_browser.util import to_js2\n\nclass NewRobotOriginCalibrationDialog:\n    def __init__(self, new_name, core, device_manager):\n        self.vue = None\n        self.core = core\n        self.device_manager = device_manager\n        self.new_name = new_name\n\n    def init_vue(self,vue):\n        self.vue = vue\n\n    def handle_create(self,*args):\n        try:\n            robot_local_device_name = getattr(self.vue,\"$data\").robot_selected\n            intrinsic_calib = getattr(self.vue,\"$data\").camera_intrinsic_selected\n            extrinsic_calib = getattr(self.vue,\"$data\").camera_extrinsic_selected\n            image_sequence_global_name = getattr(self.vue,\"$data\").image_sequence_selected\n            aruco_dict = getattr(self.vue,\"$data\").aruco_dict_selected\n            aruco_tag_id = int(getattr(self.vue,\"$data\").aruco_tag_id)\n            aruco_tag_size = float(getattr(self.vue,\"$data\").aruco_tag_size)\n            xyz = np.zeros((3,),dtype=np.float64)\n            rpy = np.zeros((3,),dtype=np.float64)\n            xyz[0] = float(getattr(self.vue,\"$data\").marker_pose_x)\n            xyz[1] = float(getattr(self.vue,\"$data\").marker_pose_y)\n            xyz[2] = float(getattr(self.vue,\"$data\").marker_pose_z)\n            rpy[0] = float(getattr(self.vue,\"$data\").marker_pose_r_r)\n            rpy[1] = float(getattr(self.vue,\"$data\").marker_pose_r_p)\n            rpy[2] = float(getattr(self.vue,\"$data\").marker_pose_r_y)\n\n            rpy = np.deg2rad(rpy)\n\n            robot_calib = self.core.device_manager.get_device_subscription(\"vision_robot_calibration\").GetDefaultClient()\n            geom_util = GeometryUtil(client_obj = robot_calib)\n            marker_pose = geom_util.xyz_rpy_to_pose(xyz, rpy)\n            \n            self.core.create_task(do_calibration(robot_local_device_name,intrinsic_calib,extrinsic_calib,\\\n                image_sequence_global_name,aruco_dict,aruco_tag_id, aruco_tag_size, marker_pose, self.new_name,self.core))\n        except:\n            traceback.print_exc()\n\n    def handle_hidden(self,*args):\n        try:\n            l = getattr(self.vue,\"$el\")\n            l.parentElement.removeChild(l)\n        except:\n            traceback.print_exc()\n        \n        \nasync def do_show_new_robot_origin_calibration_dialog(new_name: str, variable_type: str, variable_tags: str, core: \"PyriWebUIBrowser\"):\n    try:\n        \n        core.device_manager.connect_device(\"vision_robot_calibration\")\n\n        dialog_html = importlib_resources.read_text(__package__,\"new_calibrate_robot_origin_dialog.html\")\n\n        dialog_obj = NewRobotOriginCalibrationDialog(new_name, core, core.device_manager)\n\n        el = js.document.createElement('div')\n        el.id = \"new_calibrate_robot_origin_dialog_wrapper\"\n        js.document.getElementById(\"wrapper\").appendChild(el)\n\n        dialog = js.Vue.new(to_js2({\n            \"el\": \"#new_calibrate_robot_origin_dialog_wrapper\",\n            \"template\": dialog_html,\n            \"data\":\n            {\n                \"robot_selected\": \"\",\n                \"robot_select_options\": [],\n                \"camera_intrinsic_selected\": \"\",\n                \"camera_intrinsic_select_options\": [],\n                \"camera_extrinsic_selected\": \"\",\n                \"camera_extrinsic_select_options\": [],\n                \"image_sequence_selected\": \"\",\n                \"image_sequence_select_options\": [],\n                \"aruco_dict_selected\": \"\",\n                \"aruco_dict_select_options\": [],\n                \"aruco_tag_id\": \"120\",\n                \"aruco_tag_size\": \"0.06\",\n                \"marker_pose_x\": \"0\",\n                \"marker_pose_y\": \"0\",\n                \"marker_pose_z\": \"0\",\n                \"marker_pose_r_r\": \"0\",\n                \"marker_pose_r_p\": \"0\",\n                \"marker_pose_r_y\": \"0\",\n                \n            },\n            \"methods\":\n            {\n                \"handle_create\": dialog_obj.handle_create,\n                \"handle_hidden\": dialog_obj.handle_hidden\n            }\n        }))\n\n        dialog_obj.init_vue(dialog)\n\n        robots = []\n        robot_names = util.get_devices_with_type(core, \"com.robotraconteur.robotics.robot.Robot\")\n        robots = util.device_names_to_dropdown_options(robot_names)\n        \n        getattr(dialog,\"$data\").robot_select_options = to_js2(robots)\n        if len(robots) > 0:\n            getattr(dialog,\"$data\").robot_selected = robots[0][\"value\"]\n\n        db = core.device_manager.get_device_subscription(\"variable_storage\").GetDefaultClient()\n\n        intrins_var_names = await db.async_filter_variables(\"globals\",\"\",[\"camera_calibration_intrinsic\"],None)\n        intrins_vars = []\n        for v in intrins_var_names:\n            intrins_vars.append({\"value\": v, \"text\": v})\n        getattr(dialog,\"$data\").camera_intrinsic_select_options = to_js2(intrins_vars)\n        if len(intrins_vars) > 0:\n            getattr(dialog,\"$data\").camera_intrinsic_selected = intrins_vars[0][\"value\"]\n\n        extrins_var_names = await db.async_filter_variables(\"globals\",\"\",[\"camera_calibration_extrinsic\"],None)\n        extrins_vars = []\n        for v in extrins_var_names:\n            extrins_vars.append({\"value\": v, \"text\": v})\n        getattr(dialog,\"$data\").camera_extrinsic_select_options = to_js2(extrins_vars)\n        if len(extrins_vars) > 0:\n            getattr(dialog,\"$data\").camera_extrinsic_selected = extrins_vars[0][\"value\"]\n\n        \n        seq_var_names = await db.async_filter_variables(\"globals\",\"\",[\"image_sequence\"],None)\n\n        seq_vars = []\n        for v in seq_var_names:\n            seq_vars.append({\"value\": v, \"text\": v})\n        getattr(dialog,\"$data\").image_sequence_select_options = to_js2(seq_vars)\n        if len(seq_vars) > 0:\n            getattr(dialog,\"$data\").image_sequence_selected = seq_vars[0][\"value\"]\n\n        aruco_dicts = ['DICT_4X4_100', 'DICT_4X4_1000', 'DICT_4X4_250', \\\n            'DICT_4X4_50', 'DICT_5X5_100', 'DICT_5X5_1000', 'DICT_5X5_250', \\\n            'DICT_5X5_50', 'DICT_6X6_100', 'DICT_6X6_1000', 'DICT_6X6_250', \\\n            'DICT_6X6_50', 'DICT_7X7_100', 'DICT_7X7_1000', 'DICT_7X7_250', \\\n            'DICT_7X7_50', 'DICT_APRILTAG_16H5', 'DICT_APRILTAG_16h5', 'DICT_APRILTAG_25H9', \\\n            'DICT_APRILTAG_25h9', 'DICT_APRILTAG_36H10', 'DICT_APRILTAG_36H11', 'DICT_APRILTAG_36h10', \\\n            'DICT_APRILTAG_36h11', 'DICT_ARUCO_ORIGINAL']\n\n        aruco_opts = [{\"value\": v,\"text\": v} for v in aruco_dicts]\n\n        getattr(dialog,\"$data\").aruco_dict_select_options = to_js2(aruco_opts)\n        getattr(dialog,\"$data\").aruco_dict_selected = 'DICT_6X6_250'\n        \n        getattr(dialog,\"$bvModal\").show(\"new_vision_camera_calibrate_robot_origin\")\n    except:\n        js.alert(f\"Calibration failed:\\n\\n{traceback.format_exc()}\")\n\ndef show_new_robot_origin_calibration_dialog(new_name: str, variable_type: str, variable_tags: str, core: \"PyriWebUIBrowser\"):\n    core.create_task(do_show_new_robot_origin_calibration_dialog(new_name, variable_type, variable_tags, core))\n\nasync def do_calibration(robot_local_device_name, intrinsic_calib, extrinsic_calib, \\\n    image_sequence_global_name, aruco_dict, aruco_tag_id, aruco_tag_size, marker_pose, new_name, core):\n\n    try:\n        robot_calib = core.device_manager.get_device_subscription(\"vision_robot_calibration\").GetDefaultClient()\n\n        calib_res = await robot_calib.async_calibrate_robot_origin(robot_local_device_name, intrinsic_calib, \\\n            extrinsic_calib, image_sequence_global_name, aruco_dict, aruco_tag_id, aruco_tag_size, \\\n            marker_pose, new_name, None)\n\n    except:\n        js.alert(f\"Calibration failed:\\n\\n{traceback.format_exc()}\")\n        return\n\n    try:\n        do_show_new_robot_origin_calibration_dialog2(new_name, calib_res.robot_pose, calib_res.display_images, core)\n    except:\n        traceback.print_exc()\n\n\ndef do_show_new_robot_origin_calibration_dialog2(new_name: str, robot_pose, display_images, core: \"PyriWebUIBrowser\"):\n    try:\n        dialog2_html = importlib_resources.read_text(__package__,\"new_calibrate_robot_origin_dialog2.html\")\n\n        robot_calib = core.device_manager.get_device_subscription(\"vision_robot_calibration\").GetDefaultClient()\n        geom_util = GeometryUtil(client_obj = robot_calib)\n        marker_xyz, marker_rpy, _, _ = geom_util.named_pose_to_xyz_rpy(robot_pose.pose)\n\n        el = js.document.createElement('div')\n        el.id = \"new_calibrate_robot_origin_dialog2_wrapper\"\n        js.document.getElementById(\"wrapper\").appendChild(el)\n\n        def handle_hidden(*args):\n            try:\n                el.parentElement.removeChild(el)\n            except:\n                traceback.print_exc()\n\n        x = f\"{marker_xyz[0]:4e}\"\n        y = f\"{marker_xyz[1]:4e}\"\n        z = f\"{marker_xyz[2]:4e}\"\n        r_r = f\"{marker_rpy[0]:4e}\"\n        r_p = f\"{marker_rpy[1]:4e}\"\n        r_y = f\"{marker_rpy[2]:4e}\"\n        \n\n        imgs = []\n        i=0\n        for d in display_images:\n            d_encoded = str(base64.b64encode(d.data))[2:-1]\n            d2 = {\n                \"id\": i,\n                \"caption\": f\"Calibration result {i+1}\",\n                \"img\": \"data:image/jpeg;base64,\" + d_encoded\n            }\n            del d_encoded\n            imgs.append(d2)\n            i+=1\n            #TODO: check for png?\n\n        dialog = js.Vue.new(to_js2({\n            \"el\": \"#new_calibrate_robot_origin_dialog2_wrapper\",\n            \"template\": dialog2_html,\n            \"data\":\n            {\n                \"x\": x,\n                \"y\": y,\n                \"z\": z,\n                \"r_r\": r_r,\n                \"r_p\": r_p,\n                \"r_y\": r_y,\n                \"display_images\": imgs\n            },\n            \"methods\":\n            {\n                \"handle_hidden\": handle_hidden\n            }\n\n        }))\n\n        getattr(dialog,\"$bvModal\").show(\"new_vision_camera_calibrate_robot_origin2\")\n\n    except:\n        traceback.print_exc()","repo_name":"pyri-project/pyri-vision-browser","sub_path":"src/pyri/vision_browser/dialogs/new_calibrate_robot_origin_dialog.py","file_name":"new_calibrate_robot_origin_dialog.py","file_ext":"py","file_size_in_byte":10108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"24409756321","text":"from neural_net import NeuralNetwork, NetworkFramework\nfrom neural_net import Node, Target, Input\nimport random\n\n\n# <--- Problem 3, Question 1 --->\n\ndef FeedForward(network, input):\n  \"\"\"\n  Arguments:\n  ---------\n  network : a NeuralNetwork instance\n  input   : an Input instance\n\n  Returns:\n  --------\n  Nothing\n\n  Description:\n  -----------\n  This function propagates the inputs through the network. That is,\n  it modifies the *raw_value* and *transformed_value* attributes of the\n  nodes in the network, starting from the input nodes.\n\n  Notes:\n  -----\n  The *input* arguments is an instance of Input, and contains just one\n  attribute, *values*, which is a list of pixel values. The list is the\n  same length as the number of input nodes in the network.\n\n  i.e: len(input.values) == len(network.inputs)\n\n  This is a distributed input encoding (see lecture notes 7 for more\n  informations on encoding)\n\n  In particular, you should initialize the input nodes using these input\n  values:\n\n  network.inputs[i].raw_value = input[i]\n  \"\"\"\n  network.CheckComplete()\n  numInputs = len(input.values)\n  #print input.values\n  # 1) Assign input values to input nodes\n  for i in range(numInputs):\n    network.inputs[i].raw_value = input.values[i]\n    network.inputs[i].transformed_value = input.values[i]\n  # 2) Propagates to hidden layer\n  for i in range(len(network.hidden_nodes)):\n    raw = NeuralNetwork.ComputeRawValue(network.hidden_nodes[i])\n    network.hidden_nodes[i].raw_value = raw\n    network.hidden_nodes[i].transformed_value = NeuralNetwork.Sigmoid(raw)\n  # 3) Propagates to the output layer\n  for i in range(len(network.outputs)):\n    raw = NeuralNetwork.ComputeRawValue(network.outputs[i])\n    network.outputs[i].raw_value = raw\n    network.outputs[i].transformed_value = NeuralNetwork.Sigmoid(raw)\n\n  #  network.outputs[3].raw_value\n\n#< --- Problem 3, Question 2\n\ndef Backprop(network, input, target, learning_rate):\n  \"\"\"\n  Arguments:\n  ---------\n  network       : a NeuralNetwork instance\n  input         : an Input instance\n  target        : a target instance\n  learning_rate : the learning rate (a float)\n\n  Returns:\n  -------\n  Nothing\n\n  Description:\n  -----------\n  The function first propagates the inputs through the network\n  using the Feedforward function, then backtracks and update the\n  weights.\n\n  Notes:\n  ------\n  The remarks made for *FeedForward* hold here too.\n\n  The *target* argument is an instance of the class *Target* and\n  has one attribute, *values*, which has the same length as the\n  number of output nodes in the network.\n\n  i.e: len(target.values) == len(network.outputs)\n\n  In the distributed output encoding scenario, the target.values\n  list has 10 elements.\n\n  When computing the error of the output node, you should consider\n  that for each output node, the target (that is, the true output)\n  is target[i], and the predicted output is network.outputs[i].transformed_value.\n  In particular, the error should be a function of:\n\n  target[i] - network.outputs[i].transformed_value\n  \n  \"\"\"\n  network.CheckComplete()\n\n  # 1) We first propagate the input through the network\n  FeedForward(network,input)\n\n  delta_out = []\n\n  # 2) Then we compute the errors and update the weigths starting with the last layer\n  for j in range(len(network.outputs)):\n    myoutput = network.outputs[j]\n    y = target[j]\n    s = myoutput.transformed_value\n    e = y - s\n    delta = (e * s * (1 - s))\n    delta_out.append(delta)\n    for m in range(len(myoutput.inputs)):\n      myoutput.weights[m].value = myoutput.weights[m].value + (learning_rate*myoutput.inputs[m].transformed_value*delta)\n  \n  # 3) We now propagate the errors to the hidden layer, and update the weights there too\n  for j in range(len(network.hidden_nodes)):\n    mynode = network.hidden_nodes[j]\n    e = sum(map(lambda n: mynode.forward_weights[n].value*delta_out[n], range(len(mynode.forward_weights))))\n    s = mynode.transformed_value\n    delta = e * s * (1 - s)\n    for m in range(len(mynode.inputs)):\n      mynode.weights[m].value = mynode.weights[m].value + (learning_rate*mynode.inputs[m].transformed_value*delta)\n  \n\n# <--- Problem 3, Question 3 --->\n\ndef Train(network, inputs, targets, learning_rate, epochs):\n  \"\"\"\n  Arguments:\n  ---------\n  network       : a NeuralNetwork instance\n  inputs        : a list of Input instances\n  targets       : a list of Target instances\n  learning_rate : a learning_rate (a float)\n  epochs        : a number of epochs (an integer)\n\n  Returns:\n  -------\n  Nothing\n\n  Description:\n  -----------\n  This function should train the network for a given number of epochs. That is,\n  run the *Backprop* over the training set *epochs*-times\n  \"\"\"\n  network.CheckComplete()\n\n  for i in range(epochs):\n    for j in range(len(inputs)):\n      Backprop(network, inputs[j], targets[j], learning_rate)\n      if j == 0:\n        first = [n.value for n in network.weights]\n      if j == 1:\n        second = [n.value for n in network.weights]\n\n  \n\n\n# <--- Problem 3, Question 4 --->\n\nclass EncodedNetworkFramework(NetworkFramework):\n  def __init__(self):\n    \"\"\"\n    Initializatio.\n    YOU DO NOT NEED TO MODIFY THIS __init__ method\n    \"\"\"\n    super(EncodedNetworkFramework, self).__init__() # < Don't remove this line >\n    \n  # <--- Fill in the methods below --->\n\n  def EncodeLabel(self, label):\n    \"\"\"\n    Arguments:\n    ---------\n    label: a number between 0 and 9 (ARE THESE INTS??!!)\n\n    Returns:\n    ---------\n    a list of length 10 representing the distributed\n    encoding of the output.\n\n    Description:\n    -----------\n    Computes the distributed encoding of a given label.\n\n    Example:\n    -------\n    0 => [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n    3 => [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n\n    Notes:\n    ----\n    Make sure that the elements of the encoding are floats.\n    \n    \"\"\"\n    # Replace line below by content of function\n    assert(isinstance(label,int)), \"label must be an integer\"\n    assert(label > -1 and label < 10), \"label must be between 0 and 9\"\n    array = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n    array[label] = float(1)\n    return array\n\n  def GetNetworkLabel(self):\n    \"\"\"\n    Arguments:\n    ---------\n    Nothing\n\n    Returns:\n    -------\n    the 'best matching' label corresponding to the current output encoding\n\n    Description:\n    -----------\n    The function looks for the transformed_value of each output, then decides \n    which label to attribute to this list of outputs. The idea is to 'line up'\n    the outputs, and consider that the label is the index of the output with the\n    highest *transformed_value* attribute\n\n    Example:\n    -------\n\n    # Imagine that we have:\n    map(lambda node: node.transformed_value, self.network.outputs) => [0.2, 0.1, 0.01, 0.7, 0.23, 0.31, 0, 0, 0, 0.1, 0]\n\n    # Then the returned value (i.e, the label) should be the index of the item 0.7,\n    # which is 3\n    \n    \"\"\"\n    # Replace line below by content of function\n    lst = map(lambda node: node.transformed_value, self.network.outputs)\n    label = lst.index(max(lst))\n    return label\n\n  def Convert(self, image):\n    \"\"\"\n    Arguments:\n    ---------\n    image: an Image instance\n\n    Returns:\n    -------\n    an instance of Input\n\n    Description:\n    -----------\n    The *image* arguments has 2 attributes: *label* which indicates\n    the digit represented by the image, and *pixels* a matrix 14 x 14\n    represented by a list (first list is the first row, second list the\n    second row, ... ), containing numbers whose values are comprised\n    between 0 and 256.0. The function transforms this into a unique list\n    of 14 x 14 items, with normalized values (that is, the maximum possible\n    value should be 1).\n    \n    \"\"\"\n    # Replace line below by content of function\n    # function goes through image pixels first through rows then through columns\n    dim = 14\n    assert (dim == len(image.pixels)), \"dimension mismatch\"\n\n    inputs = Input()\n\n    for i in range(dim):\n      for j in range(dim):\n        newpixel = float(image.pixels[i][j]/256.0)\n        inputs.values.append(newpixel)\n\n    return inputs\n\n  def InitializeWeights(self):\n    \"\"\"\n    Arguments:\n    ---------\n    Nothing\n\n    Returns:\n    -------\n    Nothing\n\n    Description:\n    -----------\n    Initializes the weights with random values between [-0.01, 0.01].\n\n    Hint:\n    -----\n    Consider the *random* module. You may use the the *weights* attribute\n    of self.network.\n    \n    \"\"\"\n    # replace line below by content of function\n    for weight in self.network.weights:\n      weight.value = random.uniform(-0.01, 0.01)\n\n# Problem 3, Q5: Since our output values are between 0 and 1, normalizing the input values\n# prevents us from having to normalize in later functions\n\n#<--- Problem 3, Question 6 --->\n\nclass SimpleNetwork(EncodedNetworkFramework):\n  def __init__(self):\n    \"\"\"\n    Arguments:\n    ---------\n    Nothing\n\n    Returns:\n    -------\n    Nothing\n\n    Description:\n    -----------\n    Initializes a simple network, with 196 input nodes,\n    10 output nodes, and NO hidden nodes. Each input node\n    should be connected to every output node.\n    \"\"\"\n    super(SimpleNetwork, self).__init__() # < Don't remove this line >\n    \n    # 1) Adds an input node for each pixel. \n    # defines the dimensions of the image\n    DIM = 14\n    DIGITS = 10\n    newinputs = []\n    for i in range(DIM*DIM):\n      newin = Node()\n      newinputs.append(newin)\n      self.network.AddNode(newin,self.network.INPUT)\n\n    # 2) Add an output node for each possible digit label.\n    for j in range(DIGITS):\n      newout = Node()\n      for k in range(DIM*DIM):\n        newout.AddInput(newinputs[k],None,self.network)\n      self.network.AddNode(newout,self.network.OUTPUT)\n\n\n\n\n#<---- Problem 3, Question 7 --->\n\nclass HiddenNetwork(EncodedNetworkFramework):\n  def __init__(self, number_of_hidden_nodes=15):\n    \"\"\"\n    Arguments:\n    ---------\n    number_of_hidden_nodes : the number of hidden nodes to create (an integer)\n\n    Returns:\n    -------\n    Nothing\n\n    Description:\n    -----------\n    Initializes a network with a hidden layer. The network\n    should have 196 input nodes, the specified number of\n    hidden nodes, and 10 output nodes. The network should be,\n    again, fully connected. That is, each input node is connected\n    to every hidden node, and each hidden_node is connected to\n    every output node.\n    \"\"\"\n    super(HiddenNetwork, self).__init__() # < Don't remove this line >\n\n    # 1) Adds an input node for each pixel\n    # from above\n    DIM = 14\n    DIGITS = 10\n    newinputs = []\n    newhidden = []\n    for i in range(DIM*DIM):\n      newin = Node()\n      newinputs.append(newin)\n      self.network.AddNode(newin,self.network.INPUT)\n    # 2) Adds the hidden layer\n    for i in range(number_of_hidden_nodes):\n      newhid = Node()\n      for k in range(DIM*DIM):\n        newhid.AddInput(newinputs[k],None,self.network)\n      newhidden.append(newhid)\n      self.network.AddNode(newhid,self.network.HIDDEN)\n    # 3) Adds an output node for each possible digit label.\n    for j in range(DIGITS):\n      newout = Node()\n      for k in range(number_of_hidden_nodes):\n        newout.AddInput(newhidden[k],None,self.network)\n      self.network.AddNode(newout,self.network.OUTPUT)\n    \n\n#<--- Problem 3, Question 8 ---> \n\nclass CustomNetwork(EncodedNetworkFramework):\n  def __init__(self, number_of_hidden_nodes=10):\n    \"\"\"\n    Arguments:\n    ---------\n    number_of_hidden_nodes : the number of hidden nodes to create (an integer) for each quadrant\n\n    Returns:\n    --------\n    Nothing\n\n    Description:\n    -----------\n    Has a hidden layer that is not fully connected. The image is split into 4 quadrants, which are \n    trained on separately before combined\n    0   1   2   3   4   5   6  | 7   8   9   10  11  12  13\n    14  15  16  17  18  19  20 | 21  22  23  24  25  26  27\n    28  29  30  31  32  33  34 | 35  36  37  38  39  40  41\n    42  43  44  45  46  47  48 | 49  50  51  52  53  54  55\n    56  57  58  59  60  61  62 | 63  64  65  66  67  68  69\n    70  71  72  73  74  75  76 | 77  78  79  80  81  82  83 \n    84  85  86  87  88  89  90 | 91  92  93  94  95  96  97\n    ---------------------------|---------------------------  \n    98  99  100 ...            |\n    ...                        |\n    182 183 184 185 186 187 188| 189 190 191 192 193 194 195\n\n    quadrant 1: i < 98, i%14 < 7\n    quadrant 2: i < 98, i%14 > 6\n    quadrant 3: i > 97, i%14 < 7\n    quadrant 4: i > 97, i%14 > 6\n\n\n    \"\"\"\n    super(CustomNetwork, self).__init__() # <Don't remove this line>\n    \n    # 1) Adds an input node for each pixel\n    # from above\n    DIM = 14\n    DIGITS = 10\n    q1inputs = []\n    q2inputs = []\n    q3inputs = []\n    q4inputs = []\n    q1hidden = []\n    q2hidden = []\n    q3hidden = []\n    q4hidden = []\n    for i in range(DIM*DIM):\n      newin = Node()\n      if i < 98 and i%14 < 7:\n        q1inputs.append(newin)\n      elif i < 98 and i%14 > 6:\n        q2inputs.append(newin)\n      elif i > 97 and i%14 < 7:\n        q3inputs.append(newin)\n      else:\n        q4inputs.append(newin)\n      self.network.AddNode(newin,self.network.INPUT)\n\n    # 2) Adds the hidden layer\n    for i in range(number_of_hidden_nodes):\n      newq1 = Node()\n      newq2 = Node()\n      newq3 = Node()\n      newq4 = Node()\n      for k in range(DIM*DIM/4):\n        newq1.AddInput(q1inputs[k],None,self.network)\n        newq2.AddInput(q2inputs[k],None,self.network)\n        newq3.AddInput(q3inputs[k],None,self.network)\n        newq4.AddInput(q4inputs[k],None,self.network)\n      q1hidden.append(newq1)\n      q2hidden.append(newq2)\n      q3hidden.append(newq3)\n      q4hidden.append(newq4)\n      self.network.AddNode(newq1,self.network.HIDDEN)\n      self.network.AddNode(newq2,self.network.HIDDEN)\n      self.network.AddNode(newq3,self.network.HIDDEN)\n      self.network.AddNode(newq4,self.network.HIDDEN)\n    # 3) Adds an output node for each possible digit label.\n    for j in range(DIGITS):\n      newout = Node()\n      for k in range(number_of_hidden_nodes):\n        newout.AddInput(q1hidden[k],None,self.network)\n        newout.AddInput(q2hidden[k],None,self.network)\n        newout.AddInput(q3hidden[k],None,self.network)\n        newout.AddInput(q4hidden[k],None,self.network)\n      self.network.AddNode(newout,self.network.OUTPUT)\n","repo_name":"kslin/CS181","sub_path":"hw2/neural_net_impl.py","file_name":"neural_net_impl.py","file_ext":"py","file_size_in_byte":14328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35355226496","text":"from telethon import events\n\nimport asyncio\n\nfrom userbot.utils import admin_cmd\n\n@borg.on(admin_cmd(\"impress\"))\nasync def _(event):\n    if event.fwd_from:\n        return\n    animation_interval = 2\n    animation_ttl = range(0,36)\n    #input_str = event.pattern_match.group(1)\n   # if input_str == \"impress\":\n    await event.edit(\"impress\")\n    animation_chars = [\n            \"Suno na👀\",\n            \"❤️ I LOVE U ❤️\",\n            \"🥺 PLZZ MERI GF BN JAO 🥺\",\n            \"🙏 HAMESHA KHUSH RAKHUGA 🙏\",\n            \"🔥 APNE SE JYADA TUMSE LOVE KRTA HU 😘\",\n            \"💝 APAN SATH RAHEGE POORI LIFE 💝\",\n            \"💘 MERI JAAN HE TU 💓\",\n            \"😊 POORI LIFE SAATH RAHUGA 🥺❤️\",\n            \"😘 MERI LIFE HE TU 😘\",\n            \"😍 TERE SARE NAKHRE SEH LUGA 😍\",\n            \"🙂 HAR BAAT MANUGA ☺️\",\n            \"💥ME LOVE U MORE THEN MYSELF💥\",\n        ]\n\n    for i in animation_ttl:\n         \n        await asyncio.sleep(animation_interval)\n        await event.edit(animation_chars[i % 18])\n\n","repo_name":"dixitpal79/Sabkabaap","sub_path":"userbot/plugins/Impress.py","file_name":"Impress.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"28971765035","text":"__author__ = 'alefur'\n\nfrom spsGUIActor.common import ComboBox\nfrom spsGUIActor.control import ControlPanel, CommandsGB\nfrom spsGUIActor.widgets import ValueGB, DoubleSpinBoxGB, CmdButton, CustomedCmd\n\n\nclass MoveCmd(CustomedCmd):\n    limits = dict(penta=(-450, 450),\n                  detector=(0, 12))\n\n    def __init__(self, controlPanel, stage):\n        CustomedCmd.__init__(self, controlPanel, buttonLabel='MOVE')\n\n        self.stage = stage\n        l_bound, u_bound = MoveCmd.limits[stage]\n\n        self.combo = ComboBox()\n        self.combo.addItems(['abs', 'rel'])\n\n        self.distSpinbox = DoubleSpinBoxGB('Dist', l_bound, u_bound, 3)\n\n        self.addWidget(self.combo, 0, 1)\n        self.addWidget(self.distSpinbox, 0, 2)\n\n    def buildCmd(self):\n        reference = '' if self.combo.currentText() == 'rel' else self.combo.currentText()\n        cmdStr = 'sac move %s=%.2f %s' % (self.stage, self.distSpinbox.getValue(), reference)\n        return cmdStr\n\n\nclass StagePanel(ControlPanel):\n    def __init__(self, controlDialog, stage):\n        self.stage = stage\n        ControlPanel.__init__(self, controlDialog)\n        self.addCommandSet(StageCommands(self, stage))\n\n    def createWidgets(self):\n        label = self.stage.capitalize()\n        self.state = ValueGB(self.moduleRow, 'ls%s' % label, '', 0, '{:s}')\n        self.substate = ValueGB(self.moduleRow, 'ls%s' % label, '', 1, '{:s}')\n        self.position = ValueGB(self.moduleRow, 'ls%s' % label, 'Position', 2, '{:.3f}')\n\n    def setInLayout(self):\n        self.grid.addWidget(self.state, 0, 0)\n        self.grid.addWidget(self.substate, 0, 1)\n        self.grid.addWidget(self.position, 0, 2)\n\n\nclass StageCommands(CommandsGB):\n    def __init__(self, controlPanel, stage):\n        CommandsGB.__init__(self, controlPanel)\n        self.statusButton = CmdButton(controlPanel=controlPanel, label='STATUS', cmdStr='sac stages %s status' % stage)\n        self.initButton = CmdButton(controlPanel=controlPanel, label='INIT', cmdStr='sac stages %s init' % stage)\n\n        self.moveCmd = MoveCmd(controlPanel=controlPanel, stage=stage)\n\n        self.grid.addWidget(self.statusButton, 0, 0)\n        self.grid.addWidget(self.initButton, 0, 1)\n        self.grid.addLayout(self.moveCmd, 1, 0, 1, 3)\n","repo_name":"Subaru-PFS/ics_spsGUIActor","sub_path":"python/spsGUIActor/sac/stage.py","file_name":"stage.py","file_ext":"py","file_size_in_byte":2258,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"23375117703","text":"class Solution:\n    def balancedStringSplit(self, s: str) -> int:\n        balance = 0\n        out = 0\n\n        for letter in s:\n            if letter == 'R':\n                balance += 1\n            else:\n                balance -= 1\n\n            if balance == 0:\n                out += 1\n            print(out, letter)\n\n        return out\n\n\ns = \"RLRRLLRLRL\"\ns = \"RLLLLRRRLR\"\ns = \"RLRRRLLRLL\"\nprint(Solution().balancedStringSplit(s))\n","repo_name":"zzz136454872/leetcode","sub_path":"balancedStringSplit.py","file_name":"balancedStringSplit.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"21025970354","text":"from flask import Blueprint, session, render_template, redirect\nimport logging\nfrom netaddr import *\n\nfrom frontend.lib import web_handler, ip_lookup\n\nip_details_pages_blueprint = Blueprint('ip_detail_pages_blueprint', __name__)\n\n#PER INSTANCE SYSTEM USERS PAGE\n@ip_details_pages_blueprint.route(\"/ip_details/<ip>\", methods=[\"GET\", \"POST\"])\ndef ip_details(ip):\n    if web_handler.basic_page_verify(session[\"id\"]) == True:\n        if IPAddress(ip).is_private() is True:\n            return render_template(\"index.html\", heading=\"Private IP Lookup\", messages=\"You have tried to perform a lookup on a private IP address. These IP Addresses are you exclusively for private networks such as internal business or home networks\") \n        elif IPAddress(ip).is_loopback() is True:\n            return render_template(\"index.html\", heading=\"Loopback IP Lookup\", messages=\"You have tried to perform a lookup on a loopback IP address.\") \n        else:\n            try:\n                ip_details = ip_lookup.get_ip_details(ip)\n                final_tup = [\n                    [\"IP\", ip_details[\"ip\"]], \n                    [\"Country\", ip_details[\"country\"]], \n                    [\"Region\", ip_details[\"region\"]], \n                    [\"City\", ip_details[\"city\"]], \n                    [\"ISP\", ip_details[\"connection\"][\"isp\"]], \n                    [\"Organisation\", ip_details[\"connection\"][\"org\"]]\n                    ]\n                fol_map = ip_lookup.show_ip_map(ip_details)\n                fol_map.get_root().width = \"800px\"\n                fol_map.get_root().height = \"600px\"\n                fol_iframe = fol_map.get_root()._repr_html_()\n                return render_template(\"iframe.html\", heading=\"IP Details\", messages=final_tup, iframe=fol_iframe)\n            except:\n                return render_template(\"index.html\", heading=\"Error When Looking Up IP Details\", messages=\"Error\")\n    else:\n        web_handler.user_auth_error_page()","repo_name":"Trippik/PfSense_Dashboard-Frontend","sub_path":"frontend/blueprints/ip_detail_pages.py","file_name":"ip_detail_pages.py","file_ext":"py","file_size_in_byte":1937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"41913615909","text":"from typing import Iterable\n\nfrom .text_model import GroupedText, ModeText\nfrom ..model import Mode, InvalidCharacterError\n\n\ndef char2mode(char: str) -> Mode:\n    \"\"\"\n    文字に対応している最も低位のモードを取得する\n\n    :param char: 文字\n    :return: 対応するモード\n    \"\"\"\n    modes = [\n        Mode.Numeric,\n        Mode.AlphaNumeric,\n        Mode.Kanji,\n        Mode.EightBitByte,\n    ]\n    for mode in modes:\n        if mode.is_valid(char):\n            return mode\n    raise InvalidCharacterError(f'Invalid character', char)\n\n\ndef grouping(text: str) -> GroupedText:\n    \"\"\"\n    同じモードが連続する箇所でグループ化する\n\n    :param text: グループ化するテキスト\n    :return: グループ化したテキスト\n    \"\"\"\n\n    modes = [char2mode(c) for c in text]\n    if len(modes) == 0:\n        return GroupedText()\n\n    # 下のループのために番兵を仕込む\n    modes += [...]\n    text += '$'\n\n    # モードが連続する箇所をまとめる\n    result = GroupedText()\n    prev = ModeText(modes[0], text[0])\n    for mode, char in zip(modes[1:], text[1:]):\n        if mode == prev.mode:\n            prev.text += char\n        else:\n            result.append(prev)\n            prev = ModeText(mode, char)\n    return result\n\n\ndef list2str(lst: Iterable):\n    \"\"\"リストを文字列に変換する\"\"\"\n    return '[' + ', '.join((str(x) for x in lst)) + ']'\n","repo_name":"tkamiya22/mkmqr","sub_path":"mkmqr/optimization/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1431,"program_lang":"python","lang":"ja","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"24767940639","text":"import chromadb\r\nfrom chromadb.config import Settings\r\n#settings is used to configure the database \r\n\r\n#chromadb client is created by calling the Client constructor \r\n#client constructor is passed a Settings object as an argument\r\n#'chroma_db_impl' is used to specify the type of database to be used\r\n#'persist_directory' specifies the directory where the database will be stored\r\n#client = chromadb.Client(Settings(chroma_db_impl='duckdb+parquet', persist_directory='/db'))\r\nclient = chromadb.PersistentClient(path=\"/db\")\r\n\r\n#Reseting the database so that all the consecutive commands can run smoothly\r\nclient.delete_collection(name='Students')\r\nclient.delete_collection(name='Students2')\r\n\r\n#A collection object is crated using the client, it is similar to creating a table in a traditional database\r\ncollection = client.create_collection(name='Students')\r\n\r\n\r\n#initializing text about student, club, and university\r\nstudent_info = \"\"\"\r\nAlexandra Thompson, a 19-year-old computer science sophomore with a 3.7 GPA,\r\nis a member of the programming and chess clubs who enjoys pizza, swimming, and hiking\r\nin her free time in hopes of working at a tech company after graduating from the University of Washington.\r\n\"\"\"\r\n\r\nclub_info = \"\"\"\r\nThe university chess club provides an outlet for students to come together and enjoy playing\r\nthe classic strategy game of chess. Members of all skill levels are welcome, from beginners learning\r\nthe rules to experienced tournament players. The club typically meets a few times per week to play casual games,\r\nparticipate in tournaments, analyze famous chess matches, and improve members' skills.\r\n\"\"\"\r\n\r\nuniversity_info = \"\"\"\r\nThe University of Washington, founded in 1861 in Seattle, is a public research university\r\nwith over 45,000 students across three campuses in Seattle, Tacoma, and Bothell.\r\nAs the flagship institution of the six public universities in Washington state,\r\nUW encompasses over 500 buildings and 20 million square feet of space,\r\nincluding one of the largest library systems in the world.\r\n\"\"\"\r\n\r\n\r\n#The data is added with metadata and unique IDs\r\n#chromadb will automatically convert the text into embeddings and store it in the Studnets collection\r\n#It uses the  'all-MiniLM-L6-v2' model to convert text into embeddings\r\ncollection.add(\r\n    documents = [student_info, club_info, university_info],\r\n    metadatas= [{'source': 'student_info'}, {'source': 'club_info'}, {'source': 'university_info'}],\r\n    ids= ['id1', 'id2', 'id3']\r\n)\r\n\r\n\r\n#the query function is used to used to ask questions in natural language for a similarity search\r\n#It will convert the query into embedding and use similarity search to come up with similar results\r\nresults = collection.query(\r\n    query_texts=['What is the student name?'],\r\n    n_results=2\r\n)\r\n\r\n\r\n# results variable consists of the 2 files that I closest to our expected output\r\nprint(results)\r\n\r\n\r\n#we can choose the embedding function or even create our oen embedding function\r\n#text documents are then added to create embeddings\r\nfrom chromadb.utils import embedding_functions\r\nimport openai\r\nopenai.api_key = ('add_openai_api_key')\r\nopenai_ef = embedding_functions.OpenAIEmbeddingFunction(\r\n                model_name=\"text-embedding-ada-002\"\r\n            )\r\nstudents_embeddings = openai_ef([student_info, club_info, university_info])\r\nprint(students_embeddings)\r\n\r\n#Now we will use the above mentioned embedding model in the new database\r\n#We will now use the 'get_or_create_collection', as the name suggests it will either create a collection or get it if it already exists\r\n#we used the embedding function to create the embeddings and then feed it to the collection\r\ncollection2 = client.get_or_create_collection(name='Students2')\r\n\r\ncollection2.add(\r\n    embeddings= students_embeddings,\r\n    documents= [student_info, club_info, university_info],\r\n    metadatas= [{'source': 'student info'}, {'source': 'club info'}, {'source': 'university info'}],\r\n    ids= ['id1', 'id2', 'id3']\r\n)\r\n\r\n#We can use an easier way wherein instead of feeding the embeddings we will specify the embedding function while creating of the collection\r\ncollection2 = client.get_or_create_collection(name='Students2', embedding_function=openai_ef)\r\n\r\ncollection2.add(\r\n    documents = [student_info, club_info, university_info],\r\n    metadatas= [{'source': 'student info'}, {'source': 'club info'}, {'source': 'university info'}],\r\n    ids = ['id1', 'id2', 'id3']\r\n)\r\n\r\nresults = collection2.query(\r\n    query_texts=['What is the student name?'],\r\n    n_results=2\r\n)\r\n\r\nprint(results)\r\n\r\n#Updating data\r\n#To alter the text or the meta data we provide the id\r\ncollection2.update(\r\n    ids=[\"id1\"],\r\n    documents=[\"Kristiane Carina, a 19-year-old computer science sophomore with a 3.7 GPA\"],\r\n    metadatas=[{\"source\": \"student info\"}],\r\n)\r\n\r\nresults = collection2.query(\r\n    query_texts=[\"What is the student name?\"],\r\n    n_results=2\r\n)\r\n\r\nprint(results)\r\n\r\n#Removing data\r\ncollection2.delete(ids = ['id1'])\r\n\r\nresults = collection2.query(\r\n    query_texts=['What is the student name?'],\r\n    n_results=2\r\n)\r\n\r\nprint(results)\r\n\r\n#Collection Management\r\nvector_collections = client.create_collection(\"vectordb\")\r\n\r\n\r\nvector_collections.add(\r\n    documents=[\"This is Chroma DB CheatSheet\",\r\n               \"This is Chroma DB Documentation\",\r\n               \"This document Chroma JS API Docs\"],\r\n    metadatas=[{\"source\": \"Chroma Cheatsheet\"},\r\n    {\"source\": \"Chroma Doc\"},\r\n    {'source':'JS API Doc'}],\r\n    ids=[\"id1\", \"id2\", \"id3\"]\r\n)\r\n\r\n#Count the records in the collection\r\nvector_collections.count()\r\n\r\n#View all the records in the collection\r\nvector_collections.get()\r\n\r\n#change the name of the collection\r\nvector_collections.modify(name='chroma_info')\r\n\r\n#list all the collections in the client\r\nclient.list_collections()\r\n\r\n#Access a new collection\r\nvector_collection_new = client.get_collection(name='chroma_info')\r\n\r\n#Delete a collection\r\nclient.delete_collection(name='chroma_info')\r\nclient.list_collections()\r\n\r\n#Delete the entire database/client\r\nclient.reset()\r\nclient.list_collections()\r\n\r\n\r\n\r\n","repo_name":"Noor-Kaur/ChromaDB-Tutorial","sub_path":"chromadb_tutorial.py","file_name":"chromadb_tutorial.py","file_ext":"py","file_size_in_byte":6099,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"23853848930","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 15 00:33:38 2020\n\n@author: canok\n\"\"\"\n\n\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jun 14 03:27:45 2020\n\n@author: canok\n\"\"\"\n\n\nimport numpy as np\nimport keras\nfrom PIL import Image\nimport os \nimport glob\nimport cv2\nfrom keras.preprocessing import image\nimport numpy as np\nfrom natsort import natsorted, ns\nimport pandas as pd\n\n\n\n######################### DataGenerator\n\n\ncurrent_dir = os.path.dirname(__file__)\n\n\npath_org=os.path.join(current_dir,'signatures/full_org')\npath_frog= os.path.join(current_dir,'signatures/full_forg')\n\ndef get_dataframe():\n  forg_images=[]\n  for indis in range(1,56):#55 different classes   #56\n    if(indis==41):\n      continue\n    else:\n        for j in range(1,25): #25\n            #path=os.path.join(path_frog,'/forgeries_'+str(indis)+'_'+str(j)+'.png')\n            path=(path_frog+'/forgeries_'+str(indis)+'_'+str(j)+'.png')\n              #img=cv2.imread(path)\n            # p.append(path)\n            forg_images.append(path)\n      \n    \n  org_images=[]\n  for indis in range(1,56):#55 different classes   #56\n    if(indis==41):\n      continue\n    else:\n      for j in range(1,25): #24 farklı gerçek-gerçek veya gerçek-sahte imza çifti\n          #path=os.path.join(path_org,'/original_'+str(indis)+'_'+str(j)+'.png')\n          path=(path_org+'/original_'+str(indis)+'_'+str(j)+'.png')\n          #img=cv2.imread(path)\n          #p.append(path)\n\n          org_images.append(path)  \n\n  no_of_ppl = len(org_images)//24 #her kisi icin 24 sample var\n    #find the number of class  \n  \n  raw_data = {\"sing_1\":[], \"sign_2\":[], \"label\":[]}\n  \n  for i in range(no_of_ppl):\n    i1_batch_1 = []\n    i1_batch_2 = []\n    i2_batch = []\n\n    start = i*24\n    end = (i+1)*24\n    \n    for j in range(start,end): \n      i1_batch_1.append(org_images[j])\n      i1_batch_2.append(org_images[j])\n      raw_data[\"label\"].append(1)#0\n\n    temp_rot = (i1_batch_1[-12:]+i1_batch_1[:-12])\n    i1_batch_1.extend(i1_batch_2)\n\n    for elem in temp_rot:\n      i2_batch.append(elem)\n\n    for j in range(start,end): \n      i2_batch.append(forg_images[j])\n      raw_data[\"label\"].append(0)#1\n\n    raw_data[\"sing_1\"].extend(i1_batch_1)\n    raw_data[\"sign_2\"].extend(i2_batch)\n  df = pd.DataFrame(raw_data, columns = [\"sing_1\",\"sign_2\",\"label\"])\n  df=df.reindex(df.index)\n  return df\n\n\n\n\n\n\nfrom sklearn.model_selection import train_test_split\n\n\ndef get_dataset():\n  df = get_dataframe()\n  #print(df.shape)\n  \n  train_set, val_set = train_test_split(df,test_size=0.3,random_state=0)\n  \n  return train_set, val_set\n\nds_train,ds_val = get_dataset()\nprint(len(ds_val))\n\n\nimport numpy as np\nimport keras\nfrom PIL import Image\nimport cv2\n\n\n\n\n\n\n\n\nclass SignatureSequence(keras.utils.Sequence):\n    \n    def __init__(self, df, batch_size, dim):\n        self.dim = dim\n        self.batch_size = batch_size\n        self.df = df\n        self.labels = df[\"label\"]\n        \n        self.on_epoch_end()\n\n    def __len__(self):\n        s_df=self.df.shape[0]\n        n=np.floor(s_df/self.batch_size)\n        return int(n)\n\n    def __getitem__(self, indis):\n         #indexes\n        batches = self.indises[indis*self.batch_size:(indis+1)*self.batch_size]\n        items = [self.df.iloc[k] for k in batches]\n        part1,part2 = self.generator(items)\n        return part1,part2\n\n    def on_epoch_end(self):\n        self.indises = np.arange(self.df.shape[0])\n        \n\n    def generator(self, items):\n        part_1 = np.empty((self.batch_size, *self.dim,1))#working with gray images\n        part_2 = np.empty((self.batch_size, *self.dim,1))#working with gray images\n        label = np.empty((self.batch_size), dtype=int)\n        \n        for i in range(len(items)):\n            #image 1\n            signature_1 = cv2.imread(items[i][\"sing_1\"])\n           \n            resized_signature = cv2.resize(signature_1,(220,155))\n            gray_signature=cv2.cvtColor(resized_signature, cv2.COLOR_BGR2GRAY)\n            ret,thr_img = cv2.threshold(gray_signature, 0, 255, cv2.THRESH_OTSU)\n            normalized_signature=thr_img/255\n            signature_expanded = normalized_signature[:, :, np.newaxis]\n            signature_1=np.array(signature_expanded)\n\n            #image 2\n            signature_2 = cv2.imread(items[i][\"sign_2\"])\n            \n            resized_signature = cv2.resize(signature_2,(220,155))\n            gray_signature=cv2.cvtColor(resized_signature, cv2.COLOR_BGR2GRAY)\n            ret,thr_img = cv2.threshold(gray_signature, 0, 255, cv2.THRESH_OTSU)\n            normalized_signature=thr_img/255\n            signature_expanded = normalized_signature[:, :, np.newaxis]\n            signature_2=np.array(signature_expanded)\n            \n\n            \n          \n            label[i] = items[i][\"label\"]\n            part_1[i,] = signature_1\n            part_2[i,] = signature_2 \n\n        return [part_1 ,part_2], label\n\n###DataGenerator\n\n\n\n\nimport numpy as np\nimport keras\nfrom PIL import Image\nimport cv2\n\n\n\n\n\nimg_width= 155\nimg_height = 220\n\n\ndim=(img_width,img_height)\nbatch_size=64\ntrain_datagen = SignatureSequence(ds_train,batch_size,dim)\nvalidation_datagen = SignatureSequence(ds_val,batch_size,dim)\n\n###DataGenerator\n\n\n\n\n\n\nfrom keras.models import load_model\nfrom keras import backend as K\n\n\ndef contrastive_loss(l, y_pred):\n    #l=label(y_true)\n    margin = 1\n    #http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n    margin_square = (K.maximum(margin - y_pred, 0))**2\n\n    # α,β= ½ \n    return K.mean((l * (y_pred)**2)*l + (1 - l)* margin_square)\n\n\n\nmod = load_model('siyam_model.h5',custom_objects={'contrastive_loss':contrastive_loss})\n\n\n\ny_pred = mod.predict_generator(validation_datagen)#, steps=25\n\nprint(\"count  pred:\"+str(len(y_pred)))\nds_labels=validation_datagen.labels[0:768]\nprint(\"count of labels: \"+str(len(ds_labels)))\n\n\n\n\n\n\n\nfrom keras.models import load_model\nfrom keras import backend as K\n\n\ndef contrastive_loss(l, y_pred):\n    #l=label(y_true)\n    margin = 1\n    #http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n    margin_square = (K.maximum(margin - y_pred, 0))**2\n\n    # α,β= ½ \n    return K.mean((l * (y_pred)**2)*l + (1 - l)* margin_square)\n\n\n\nmod = load_model('siyam_RMSProp_model.h5',custom_objects={'contrastive_loss':contrastive_loss})\n\n\n\ny_pred = mod.predict_generator(validation_datagen)#, steps=25\nds_labels=validation_datagen.labels[0:768]\n\n\n\n\n\ndef accuracy_hesapla(y_pred, y_true):\n    '''Compute ROC accuracy with a range of thresholds on distances.\n    '''\n    df_max = np.max(y_pred)\n    df_min = np.min(y_pred)\n    number_of_sim = np.sum(y_true == 1)\n    number_of_diff = np.sum(y_true == 0)\n   \n    step = 0.01\n    max_acc = 0\n    best_thresh=0\n    \n    true_pos_rate_list=[]\n    for distance in np.arange(df_min, df_max+step, step):\n        idx1 = y_pred.ravel() <= distance\n        idx2 = y_pred.ravel() > distance\n       \n        true_pos_rate = float(np.sum(y_true[idx1] == 1)) / number_of_sim\n        true_neg_rate  = float(np.sum(y_true[idx2] == 0)) / number_of_diff\n        acc = 0.5 * (true_pos_rate + true_neg_rate )       \n#       \n        \n        true_pos_rate_list.append(true_pos_rate)\n        if (acc > max_acc):\n            max_acc, best_thresh = acc, distance\n           \n    return max_acc, best_thresh,true_pos_rate_list\n\n\nmax_acc,threshold,tpr_list=accuracy_hesapla(y_pred,ds_labels)\n\nprint(\"Max Accuracy:\"+str(max_acc))\nprint(\"Best thresh:\"+str(threshold))\n\n\nfrom sklearn import metrics as m\n#threshold=0.5203162277571391\nconfusionmatrix = m.confusion_matrix(ds_labels, y_pred <threshold)\n\nprint('false acceptance', confusionmatrix[0, 1] / confusionmatrix[0, :].sum())\nprint('false rejection', confusionmatrix[1, 0] / confusionmatrix[1, :].sum())\nprint('accuracy', m.accuracy_score(ds_labels, y_pred < threshold))\n\n\n\n\n\nfrom sklearn.metrics import classification_report\n\nprint(classification_report(ds_labels, y_pred <threshold))\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"CantOkan/ENG402_Graduation_Project","sub_path":"performance_model.py","file_name":"performance_model.py","file_ext":"py","file_size_in_byte":7897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"26227007392","text":"from app.models import db, Comment\n\n\n# Adds a demo user, you can add other users here if you want\ndef seed_comments():\n    comment_1 = Comment(\n        comment=\"I made a little bit more of the sauce than the recipe called for the second time to coat the tofu. The first time I made it, I followed the recipe, and while the broccoli was very good, the tofu was super bland. Marinating the tofu beforehand can also help!\", user_id=1, recipe_id=1)\n    comment_2 = Comment(\n        comment=\"added mushrooms and doubled the sauce! It was delicious :) I also marinated the tofu for an hour\", user_id=2, recipe_id=1)\n    comment_3 = Comment(\n        comment=\"This was a BIG hit. I followed the advice to double the sauce (basically everything but the tofu and broccoli) and it was perfect.\", user_id=3, recipe_id=1)\n    comment_4 = Comment(\n        comment=\"This was delicious. Needed a lot more soy sauce but I’m now officially a tofu person\", user_id=1, recipe_id=1)\n    comment_5 = Comment(\n        comment=\"This was great! I marinated the tofu in soy sauce so it would have some flavor. I also added in onion, bell peppers, mushrooms, and carrots to make it a stir fry.\", user_id=1, recipe_id=2)\n    comment_6 = Comment(\n        comment=\"I made it with tricolor quinoa! It was delicious!\", user_id=2, recipe_id=2)\n    comment_7 = Comment(\n        comment=\"add some water chestnuts and some sriracha. thank me later\", user_id=3, recipe_id=2)\n    comment_8 = Comment(\n        comment=\"It’s my first time making n drinking a smoothie,and I don’t eat any fruits or vegetables at all. I made this and it was easy and good!! I’ll make it again 4 sure! If u want it more sweet - add tbsp of honey ( for me it was sweet enough)\", user_id=1, recipe_id=4)\n    comment_9 = Comment(\n        comment=\"DELICIOUS!!! I make it so much I honestly love it !\", user_id=3, recipe_id=4)\n    comment_10 = Comment(\n        comment=\"I adde chia seeds, flax seeds & oats for added thickness & fiber to keep for for longer. It helps if you want the smoothie to replace a meal.\", user_id=2, recipe_id=4)\n    comment_11 = Comment(\n        comment=\"I used dried oregano instead of dried basil. Still very tasty! 😋\", user_id=1, recipe_id=3)\n    comment_12 = Comment(\n        comment=\"These where great! Me and my sister made them for Christmas “dinner” (lunch😂)\", user_id=3, recipe_id=3)\n    comment_13 = Comment(\n        comment=\"I added mushrooms and onions too it! Amazing ❤️\", user_id=1, recipe_id=5)\n    comment_14 = Comment(\n        comment=\"Garlic Kale was so so good!!!!! This is a must do again dish!!!\", user_id=3, recipe_id=5)\n    comment_15 = Comment(\n        comment=\"This is a pretty accurate jambalaya! As someone from New Orleans, I was skeptical of the recipe at first but it’s great. Honestly I was surprised.\", user_id=1, recipe_id=6)\n    comment_16 = Comment(\n        comment=\"Wow!!! This came out sooooo delicious. Definitely will continue to make this over and over and over again. Yummmmmmmy!!!\", user_id=2, recipe_id=6)\n    \n    db.session.add(comment_1)\n    db.session.add(comment_2)\n    db.session.add(comment_3)\n    db.session.add(comment_4)\n    db.session.add(comment_5)\n    db.session.add(comment_6)\n    db.session.add(comment_7)\n    db.session.add(comment_8)\n    db.session.add(comment_9)\n    db.session.add(comment_10)\n    db.session.add(comment_11)\n    db.session.add(comment_12)\n    db.session.add(comment_13)\n    db.session.add(comment_14)\n    db.session.add(comment_15)\n    db.session.add(comment_16)\n\n    db.session.commit()\n\n\n# Uses a raw SQL query to TRUNCATE the users table.\n# SQLAlchemy doesn't have a built in function to do this\n# TRUNCATE Removes all the data from the table, and RESET IDENTITY\n# resets the auto incrementing primary key, CASCADE deletes any\n# dependent entities\ndef undo_comments():\n    db.session.execute('TRUNCATE comments RESTART IDENTITY CASCADE;')\n    db.session.commit()\n","repo_name":"nicopierson/hotpotato","sub_path":"app/seeds/comments.py","file_name":"comments.py","file_ext":"py","file_size_in_byte":3930,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"86"}
+{"seq_id":"21162487791","text":"import os\nimport csv\n\ndef buscar_precio(fruta):\n    file_name = 'precios.csv'\n    with open(file_name, 'rt') as file:\n        while(True): \n            try:\n                linea = next(file)\n                linea = linea.split(',')\n                if linea[0] == fruta:\n                    print(linea[1])\n                    break\n            except StopIteration:\n                print('No se encontró')\n                break\n\n\n\n\nbuscar_precio('Lechuga')\n","repo_name":"milenalebe/Programacion_en_Python_UNSAM","sub_path":"Clase02/buscar_precio.py","file_name":"buscar_precio.py","file_ext":"py","file_size_in_byte":459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74342901725","text":"# setup.py\n\nimport pathlib\n\nfrom setuptools import setup, find_packages\n\n# The directory containing this file\nHERE = pathlib.Path(__file__).parent\n\n# The text of the README file\nREADME = (HERE / \"README.md\").read_text()\n\n# The text of the VERSION file\nVERSION = (HERE / 'geoipsets/VERSION').read_text()\n\n# This call to setup() does all the work\nsetup(\n    name=\"geoipsets\",\n    version=VERSION,\n    description=\"Utility to build country-specific IP sets for ipset/iptables and nftables.\",\n    long_description=README,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/chr0mag/geoipsets\",\n    license=\"GPLv3\",\n    classifiers=[\n        \"Operating System :: POSIX :: Linux\",\n        \"License :: OSI Approved :: GNU General Public License v3 (GPLv3)\",\n        \"Programming Language :: Python :: 3\",\n        \"Programming Language :: Python :: 3.8\",\n        \"Programming Language :: Python :: 3.9\",\n        \"Programming Language :: Python :: 3.10\",\n        \"Programming Language :: Python :: 3.11\",\n    ],\n    packages=find_packages(exclude=(\"tests\",)),\n    include_package_data=True,\n    install_requires=[\"requests\", \"beautifulsoup4\"],\n    entry_points={\n        \"console_scripts\": [\n            \"geoipsets=geoipsets.__main__:main\",\n        ]\n    },\n)\n","repo_name":"chr0mag/geoipsets","sub_path":"python/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1278,"program_lang":"python","lang":"en","doc_type":"code","stars":72,"dataset":"github-code","pt":"86"}
+{"seq_id":"43206962581","text":"# Задайте список из вещественных чисел. \n# Напишите программу, которая найдёт разницу между \n# максимальным и минимальным значением дробной части элементов.\n# Пример:\n# - [1.1, 1.2, 3.1, 5, 10.01] => 0.19\nimport random  \nN = int(input('Введите размер списка: '))\na = [round(random.randint(1, 1000) * random.random(), 2) for i in range(N)]\nprint('Исходный массив: ', *a)\nmaxDrob = 0\nminDrob = 1\nfor i in range(N):\n    drob = a[i]\n    if a[i] > 1:\n        drob = round(a[i] % int(a[i]), 2) \n    if drob > maxDrob:\n        maxDrob = drob\n    if drob < minDrob:\n        minDrob = drob\nprint(f'Разница между максимальным и минимальным значением дробной части элементов: {round(maxDrob - minDrob, 2)}')","repo_name":"VitaminPC/PythonPractise","sub_path":"3/task3.py","file_name":"task3.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"9796186059","text":"from django import forms\nfrom datetime import datetime\nfrom codemirror.widgets import CodeMirrorTextarea\nfrom .models import PriceNavigator\nfrom django.utils.html import mark_safe\nfrom django.conf import settings\nfrom .utils import SHOP_PRICE_DIR\n\n\nclass PriceNavigatorForm(forms.ModelForm):\n\n    class Meta:\n        model = PriceNavigator\n        widgets = {\n            'template': CodeMirrorTextarea(mode='xml', config={\n                'fixedGutter': True,\n                'lineWrapping': True,\n            }),\n        }\n        exclude = []\n\n    def __init__(self, *args, **kwargs):\n        super(PriceNavigatorForm, self).__init__(*args, **kwargs)\n        if self.instance:\n            prices_url = settings.MEDIA_URL + SHOP_PRICE_DIR.split(\n                '/media/')[-1]\n            files_links = []\n            for lang in settings.LANGUAGES:\n                files_links.append(\n                    '<a href=\"{lang_url}\">{lang_name}</a>'.format(**{\n                        'lang_url': '%s%s_%s' % (\n                            prices_url, lang[0], self.instance.file_name),\n                        'lang_name': lang[1]\n                    })\n                )\n            file_name_help_text = mark_safe(' / '.join(files_links))\n            self.fields['file_name'].help_text = file_name_help_text\n\n\n    def clean_update_times(self):\n        update_times = self.cleaned_data.get('update_times')\n        if update_times:\n            try:\n                now = datetime.now()\n                [datetime.combine(now, datetime.strptime(update_time.strip(\n                    ' '), '%H:%M').time()) for update_time in update_times.split(',')]\n            except:\n                raise forms.ValidationError(_(u\"Invalid data\"))\n        return update_times","repo_name":"phonxis/konkord","sub_path":"konkord/apps/price_navigator/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"36834812572","text":"import sys\n\nstring = list(sys.stdin.readline().rstrip())\nanswer = []\n\n# 대문자: 65~90\n# 소문자: 97~122\n\nfor i in string:\n    alpha = ord(i)\n    if (65<=alpha<=90):\n        new = alpha+13\n        if (new) > 90:\n            new -= 26\n        answer.append(chr(new))\n        new = 0    \n    elif (97<=alpha<=122):\n        new = ord(i)+13\n        if (new) > 122:\n            new-= 26\n        answer.append(chr(new))\n        new = 0\n    else:\n        answer.append(i)\n    \nfor i in answer:\n    print(i, end='')\n    ","repo_name":"drizzle0171/StudyAlgorithm","sub_path":"BOJ/11655: ROC13.py","file_name":"11655: ROC13.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"45234321317","text":"class Solution:\n    def minimumTotal(self, triangle) -> int:\n        #         self.ans = float('inf')\n\n        #         def bk(row, col, num):\n        #             if row > len(triangle) - 1:\n        #                 self.ans = min(self.ans, num)\n        #                 return\n        #             bk(row + 1, col, num + triangle[row][col])\n        #             bk(row + 1, col + 1, num + triangle[row][col])\n        #         bk(0, 0, 0)\n\n        #         return self.ans\n        if len(triangle) == 0: return 0\n        if len(triangle) == 1: return triangle[0][0]\n\n        for i in range(1, len(triangle)):\n            for j in range(len(triangle[i])):\n                if j == 0:\n                    triangle[i][j] += triangle[i - 1][j]\n                elif j == len(triangle[i]) - 1:\n                    triangle[i][j] += triangle[i - 1][j - 1]\n                else:\n                    triangle[i][j] += min(triangle[i - 1][j], triangle[i - 1][j - 1])\n        return min(triangle[-1])\n    \ndef main():\n    triangle = [[]]\n    a = Solution()\n    print(a.minimumTotal(triangle))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"fukode/leetcode","sub_path":"101-120/minimumTotal.py","file_name":"minimumTotal.py","file_ext":"py","file_size_in_byte":1131,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"8517047963","text":"# pandas读取文件：csv & json\n# 读取csv：\nimport pandas as pd\ndata = pd.read_csv(\"path\",chunksize= 1000) # 分块读入,每一块1000行\ndata = pd.read_csv(\"path\",)\n# 写入csv\ndata.to_csv(\"path\")\n\n# 读取json\n# python：\n#   读取 ==> python_object = json.loads(json) ;\n#   写入 ==> json =  json.dumps(python_object);\ndata = pd.read_json(\"path_or_pro\")","repo_name":"P79N6A/pyprojects","sub_path":"newprojects/pyProject-learning/pandas_learning/pandas_files.py","file_name":"pandas_files.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"72314010524","text":"import unified_planning\nfrom unified_planning.shortcuts import *\nfrom unified_planning.domains import Domain\n\n\nclass Stuck_Car(Domain):\n    def __init__(self, kind, deadline, object_amount, garbage_amount=None):\n        Domain.__init__(self, 'stuck_car', kind)\n        self.object_amount = object_amount\n        self.user_types()\n        self.objects()\n        self.fluents()\n        self.actions()\n        self.add_goal(deadline)\n\n    def user_types(self):\n        Robot = UserType('Robot')\n        Car = UserType('Car')\n        GasPedal = UserType('GasPedal')\n        Rock = UserType('Rock')\n        BodyPart = UserType('BodyPart')\n\n        self.userTypes = dict(Robot=Robot, Car=Car, GasPedal=GasPedal, Rock=Rock, BodyPart=BodyPart)\n\n    def objects(self):\n        \"\"\" Init things that can be pushed \"\"\"\n\n        \"\"\" Init robot \"\"\"\n        robot_names = ['r' + str(i) for i in range(self.object_amount)]\n        robots = [unified_planning.model.Object(r, self.userTypes['Robot']) for r in robot_names]\n        self.problem.add_objects(robots)\n\n        \"\"\" Init car \"\"\"\n        car_names = ['c' + str(i) for i in range(self.object_amount)]\n        cars = [unified_planning.model.Object(c, self.userTypes['Car']) for c in car_names]\n        self.problem.add_objects(cars)\n\n        gasPedal = unified_planning.model.Object('gasPedal', self.userTypes['GasPedal'])\n        self.problem.add_object(gasPedal)\n\n        \"\"\" Init rocks \"\"\"\n        rocks_names = ['bad', 'good']\n        rocks = [unified_planning.model.Object(r, self.userTypes['Rock']) for r in rocks_names]\n        self.problem.add_objects(rocks)\n\n        \"\"\" Init body parts -\n            when performing an action at least one of the body parts will be occupied\n        \"\"\"\n        bodyParts_names = ['hands', 'legs']\n        bodyParts = [unified_planning.model.Object(b, self.userTypes['BodyPart']) for b in bodyParts_names]\n        self.problem.add_objects(bodyParts)\n\n    def fluents(self):\n        car_out = unified_planning.model.Fluent('car_out', BoolType(), c=self.userTypes['Car'])\n        self.problem.add_fluent(car_out, default_initial_value=False)\n\n        tired = unified_planning.model.Fluent('tired', BoolType(), ro=self.userTypes['Robot'])\n        self.problem.add_fluent(tired, default_initial_value=False)\n\n        got_rock = unified_planning.model.Fluent('got_rock', BoolType(), ro=self.userTypes['Robot'],\n                                                 r=self.userTypes['Rock'])\n        self.problem.add_fluent(got_rock, default_initial_value=False)\n\n        free = unified_planning.model.Fluent('free', BoolType(), ro=self.userTypes['Robot'],\n                                             b=self.userTypes['BodyPart'])\n        if self.kind == 'combination':\n            self.problem.add_fluent(free, default_initial_value=False)\n        if self.kind == 'regular':\n            self.problem.add_fluent(free, default_initial_value=True)\n\n        rock_under_car = unified_planning.model.Fluent('rock_under_car', BoolType(), c=self.userTypes['Car'],\n                                                       r=self.userTypes['Rock'])\n        self.problem.add_fluent(rock_under_car, default_initial_value=False)\n\n        gas_pressed = unified_planning.model.Fluent('gas_pressed', BoolType(), c=self.userTypes['Car'])\n        self.problem.add_fluent(gas_pressed, default_initial_value=False)\n\n        if self.kind == 'combination':\n            ready = unified_planning.model.Fluent('ready', BoolType(), ro=self.userTypes['Robot'],\n                                                  b=self.userTypes['BodyPart'])\n            self.problem.add_fluent(ready, default_initial_value=True)\n\n    def add_goal(self, deadline):\n        car_out = self.problem.fluent_by_name('car_out')\n        cars_list = self.get_objects(['c' + str(i) for i in range(self.object_amount)])\n\n        for c in cars_list:\n            self.problem.add_goal(car_out(c))\n\n        deadline_timing = Timing(delay=deadline, timepoint=Timepoint(TimepointKind.START))\n        self.problem.set_deadline(deadline_timing)\n\n    def use_bodyPart(self, action, robot, bodyPart):\n        ready, free = self.get_fluents(['ready', 'free'])\n\n        if self.kind == 'combination':\n            action.add_precondition(OverallPreconditionTiming(), ready(robot, bodyPart), True)\n            action.add_effect(ready(robot, bodyPart), False)\n            action.add_effect(free(robot, bodyPart), True)\n\n        if self.kind == 'regular':\n            self.use(action, free(robot, bodyPart))\n\n    def actions(self):\n        self.rest_action()\n        self.place_rock_action()\n        self.search_rock_action()\n        self.push_car_action()\n        self.push_gas_action()\n        self.push_car_gas_action()\n        if self.kind == 'combination':\n            self.turn_on()\n\n    def tired_prob(self, robot):\n        tired = self.problem.fluent_by_name('tired')\n\n        def tired_probability(state, actual_params):\n            p = 0.4\n            robot_param = actual_params.get(robot)\n            return {p: {tired(robot_param): True}, 1 - p: {tired(robot_param): False}}\n\n        return tired_probability\n\n    def push_prob(self, car, probs):\n        car_out, rock_under_car = self.get_fluents(['car_out', 'rock_under_car'])\n        bad, good = self.get_objects(['bad', 'good'])\n\n        def push_probability(state, actual_params):\n            # The probability of getting the car out when pushing\n            p = 1\n            car_param = actual_params.get(car)\n            predicates = state.predicates\n\n            if car_out(car_param) not in predicates:\n                # The bad rock is under the car\n                if rock_under_car(car_param, bad) in predicates:\n                    p = probs['bad']\n\n                # The good rock is under the car\n                elif rock_under_car(car_param, good) in predicates:\n                    p = probs['good']\n\n                # There isn't a rock under the car\n                else:\n                    p = probs['none']\n\n            return {p: {car_out(car_param): True}, 1 - p: {}}\n\n        return push_probability\n\n    def turn_on(self):\n        ready, free = self.get_fluents(['ready', 'free'])\n\n        turn_on = unified_planning.model.InstantaneousAction('turn_on', robot=self.userTypes['Robot'],\n                                                             bodyPart=self.userTypes['BodyPart'])\n        robot = turn_on.parameter('robot')\n        bodyPart = turn_on.parameter('bodyPart')\n\n        turn_on.add_precondition(free(robot, bodyPart), True)\n        turn_on.add_effect(free(robot, bodyPart), False)\n        turn_on.add_effect(ready(robot, bodyPart), True)\n\n        self.problem.add_action(turn_on)\n\n    def rest_action(self):\n        \"\"\" Rest Action \"\"\"\n        tired, free = self.get_fluents(['tired', 'free'])\n        hands, legs = self.get_objects(['hands', 'legs'])\n\n        rest = unified_planning.model.DurativeAction('rest', robot=self.userTypes['Robot'])\n        robot = rest.parameter('robot')\n\n        if self.kind == 'regular':\n            rest.add_precondition(OverallPreconditionTiming(), free(robot, hands), True)\n            rest.add_precondition(OverallPreconditionTiming(), free(robot, legs), True)\n\n        if self.kind == 'combination':\n            self.use_bodyPart(rest, robot, hands)\n            self.use_bodyPart(rest, robot, legs)\n\n        rest.set_fixed_duration(1)\n        rest.add_effect(tired(robot), False)\n\n        self.problem.add_action(rest)\n\n    def place_rock_action(self):\n        \"\"\" Place a rock under the car Action \"\"\"\n        tired, got_rock, rock_under_car, free = self.get_fluents(['tired', 'got_rock', 'rock_under_car', 'free'])\n        hands, legs = self.get_objects(['hands', 'legs'])\n\n        place_rock = unified_planning.model.DurativeAction('place_rock', robot=self.userTypes['Robot'],\n                                                           car=self.userTypes['Car'], rock=self.userTypes['Rock'])\n        robot = place_rock.parameter('robot')\n        car = place_rock.parameter('car')\n        rock = place_rock.parameter('rock')\n\n        place_rock.set_fixed_duration(2)\n        place_rock.add_precondition(OverallPreconditionTiming(), got_rock(robot, rock), True)\n        place_rock.add_precondition(StartPreconditionTiming(), tired(robot), False)\n\n        self.use_bodyPart(place_rock, robot, hands)\n        self.use_bodyPart(place_rock, robot, legs)\n\n        place_rock.add_effect(rock_under_car(car, rock), True)\n        place_rock.add_effect(got_rock(robot, rock), False)\n        place_rock.add_probabilistic_effect([tired(robot)], self.tired_prob(robot))\n\n        self.problem.add_action(place_rock)\n\n    def search_rock_action(self):\n        \"\"\" Search a rock Action\n            the robot can find a one of the rocks\"\"\"\n\n        tired, free, got_rock = self.get_fluents(['tired', 'free', 'got_rock'])\n        bad, good, hands = self.get_objects(['bad', 'good', 'hands'])\n\n        search = unified_planning.model.action.DurativeAction('search', robot=self.userTypes['Robot'])\n        robot = search.parameter('robot')\n        search.set_fixed_duration(2)\n\n        search.add_precondition(StartPreconditionTiming(), tired(robot), False)\n\n        self.use_bodyPart(search, robot, hands)\n\n        def rock_probability(state, actual_params):\n            # The probability of finding a good rock when searching\n            p = 0.1\n            robot_param = actual_params.get(robot)\n            return {p: {got_rock(robot_param, bad): True},\n                    1 - p: {got_rock(robot_param, good): True}}\n\n        search.add_probabilistic_effect([got_rock(robot, bad), got_rock(robot, good)], rock_probability)\n        self.problem.add_action(search)\n\n    def push_gas_action(self):\n        \"\"\" Push Gas Pedal Action\n        The probability of getting the car out is lower than push car but the robot won't get tired\"\"\"\n\n        tired, car_out, free, = self.get_fluents(['tired', 'car_out', 'free'])\n        legs = self.problem.object_by_name('legs')\n\n        push_gas = unified_planning.model.action.DurativeAction('push_gas', robot=self.userTypes['Robot'],\n                                                                car=self.userTypes['Car'])\n        robot = push_gas.parameter('robot')\n        car = push_gas.parameter('car')\n        push_gas.set_fixed_duration(2)\n\n        push_gas.add_precondition(StartPreconditionTiming(), tired(robot), False)\n\n        self.use_bodyPart(push_gas, robot, legs)\n\n        push_gas.add_probabilistic_effect([car_out(car)], self.push_prob(car, probs=dict(bad=0.2, good=0.4, none=0.1)))\n        self.problem.add_action(push_gas)\n\n    def push_car_action(self):\n        \"\"\" Push Car Action\n            The probability of getting the car out is higher than push gas but the robot can get tired\"\"\"\n\n        tired, car_out, free = self.get_fluents(['tired', 'car_out', 'free'])\n        hands, legs = self.get_objects(['hands', 'legs'])\n\n        push_car = unified_planning.model.action.DurativeAction('push_car', robot=self.userTypes['Robot'],\n                                                                car=self.userTypes['Car'])\n        robot = push_car.parameter('robot')\n        car = push_car.parameter('car')\n        push_car.set_fixed_duration(2)\n\n        push_car.add_precondition(StartPreconditionTiming(), tired(robot), False)\n\n        self.use_bodyPart(push_car, robot, hands)\n\n        push_car.add_probabilistic_effect([car_out(car)], self.push_prob(car, probs=dict(bad=0.3, good=0.48, none=0.1)))\n        push_car.add_probabilistic_effect([tired(robot)], self.tired_prob(robot))\n        self.problem.add_action(push_car)\n\n    def push_car_gas_action(self):\n        tired, car_out, free = self.get_fluents(['tired', 'car_out', 'free'])\n        hands, legs = self.get_objects(['hands', 'legs'])\n\n        push_car_gas = unified_planning.model.action.DurativeAction('push_car_gas', robot=self.userTypes['Robot'],\n                                                                    car=self.userTypes['Car'])\n        robot = push_car_gas.parameter('robot')\n        car = push_car_gas.parameter('car')\n        push_car_gas.set_fixed_duration(4)\n\n        push_car_gas.add_precondition(StartPreconditionTiming(), tired(robot), False)\n\n        self.use_bodyPart(push_car_gas, robot, hands)\n        self.use_bodyPart(push_car_gas, robot, legs)\n\n        push_car_gas.add_probabilistic_effect([car_out(car)],\n                                              self.push_prob(car, probs=dict(bad=0.4, good=0.9, none=0.2)))\n        push_car_gas.add_probabilistic_effect([tired(robot)], self.tired_prob(robot))\n\n        self.problem.add_action(push_car_gas)\n\n# run_regular(kind='regular', deadline=10, search_time=1, search_depth=20, selection_type='avg',exploration_constant=10)\n","repo_name":"taliBerman5/CSTP","sub_path":"unified_planning/domains/stuck_car.py","file_name":"stuck_car.py","file_ext":"py","file_size_in_byte":12756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"44477115908","text":"# 2022_03_02_1\n# 약수 구하기\nn, k = map(int, input().split())\ncount = 0\nfor i in range(1, n+1):\n    if n % i == 0:\n        count +=1\n    if count == k:\n        break\n    if i >= n and count < k:\n        i = 0\n        break\nprint(i) # 조건 잘못 설정시 포문이 끝까지 돌아 i가 최대 범위로 반환\n# 배열(리스트)에 모든 약수를 저장하고 k가 리스트 길이보다 크면 0반환하는 방법도 있다. ","repo_name":"motoloj/CodingStudy2022","sub_path":"baek_joon/2501.py","file_name":"2501.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"16239257864","text":"# Import from cohortextractor\nfrom cohortextractor import StudyDefinition, patients\n\n# Import variables\nfrom data_processing import loop_over_codes\nfrom common_variables import common_variables\n\n\n# Import codelist\nfrom codelist import proactive_codes\n\nfrom data_processing import loop_over_codes\n\n\n# Study definition\nstudy = StudyDefinition(\n    # set index_date\n    index_date=\"2019-04-01\",\n    # Define default expectations\n    default_expectations={\n        \"date\": {\"earliest\": \"2019-04-01\", \"latest\": \"2022-02-01\"},\n        \"incidence\": \"1\",\n        \"rate\": \"uniform\",\n    },\n    # Define population inclusion criteria\n    population=patients.satisfying(\n        \"\"\"\n            (has_proactive_code) AND\n            (age > 0 AND age <= 120) AND\n            (region != \"\") AND\n            (imd_quintile != 0)\n        \"\"\",\n        has_proactive_code=patients.with_these_clinical_events(\n            proactive_codes, between=[\"index_date\", \"index_date + 6 days\"]\n        ),\n    ),\n    # proactive care date\n    # Code to loop over proactive_codes to find the first match in the period\n    **loop_over_codes(\n        proactive_codes, \"index_date\", returning=\"number_of_matches_in_period\"\n    ),\n    # Loop over the common variables\n    **common_variables\n)\n","repo_name":"opensafely/Uptake-of-NHS-home-interventions-during-COVID-19","sub_path":"analysis/study_definition_proactive.py","file_name":"study_definition_proactive.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3213847517","text":"import json\n\n# 打开包含 JSON 数据的文件\n\nvalue = {\"srcmsg\": \"\", \"dstmsg\": \"\", \"edge_type\": \"\", \"time\": \"\"}\nopen_set = [\"ProcessStart\"]\nread_set = [\"RegistryOpen\", \"ProcessDCStart\", \"FileIORead\", \"RegistryQuery\", \"RegistryEnumerateKey\",\n            \"RegistryEnumerateValueKey\", \"RegistryQueryValue\", \"RegistryQueryMultipleValue\", \"FileIOQueryInfo\"]\ncreate_set = [\"RegistryCreate\", \"FileIOCreate\", \"RegistryKCBCreate\", \"FileIOFileCreate\"]\nmessage_set = [\"TcpIpSendIPV4\", \"TcpIpRecvIPV4\", \"TcpIpDisconnectIPV4\", \"TcpIpRetransmitIPV4\", \"TcpIpReconnectIPV4\",\n               \"TcpIpTCPCopyIPV4\", \"TcpIpAcceptIPV4\", \"TcpIpFailed\"]\nmodify_set = [\"RegistrySetValue\", \"RegistrySetInformation\"]\nstart_set = [\"TcpIpConnectIPV4\"]\nrename_set = [\"FileIORename\"]\ndelete_set = [\"RegistryDelete\", \"RegistryDeleteValue\", \"RegistryKCBDelete\", \"FileIODelete\", \"FileIOFileDelete\"]\nterminal_set = [\"ProcessEnd\"]\nwrite_set = [\"FileIOWrite\"]\nunknown_set = [\"ThreadCSwitch\", \"DiskIOWrite\", \"DiskIOWriteInit\", \"ProcessDCEnd\", \"ProcessDefunct\", \"ThreadEnd\",\n               \"ThreadDCEnd\", \"RegistryKCBRundownEnd\", \"FileIOOperationEnd\", \"FileIOCleanup\", \"FileIOClose\",\n               \"RegistryKCBRundownBegin\", \"FileIOSetInfo\", \"DiskIORead\", \"DiskIOReadInit\", \"FileIODirEnum\",\n               \"ProcessPerfCtr\", \"ProcessPerfCtrRundown\", \"RegistryVirtualize\", \"RegistryClose\", \"RegistryFlush\",\n               \"FileIOFlush\", \"DiskIOFlushInit\", \"DiskIOFlushBuffers\", \"DiskIODrvComplReq\", \"DiskIODrvComplReqRet\",\n               \"DiskIODrvComplRout\", \"DiskIODrvMjFnCall\", \"DiskIODrvMjFnRet\", \"PerfInfoThreadDPC\", \"PerfInfoDPC\",\n               \"PerfInfoTimerDPC\", \"PerfInfoISR\", \"PerfInfoSysClEnter\", \"PerfInfoSysClEnter\", \"ImageLoad\",\n               \"ImageUnload\", \"ImageDCStart\", \"ImageDCEnd\", \"FileIODirNotify\", \"FileIOFSControl\", \"FileIOName\",\n               \"FileIOFileRundown\", \"ALPC-Receive-Message\", \"ALPC-Send-Message\", \"ALPC-Unwait\",\n               \"ALPC-Wait-For-New-Message\", \"ALPC-Wait-For-Reply\", \"ThreadStart\", \"ThreadDCStart\", \"TcpIpSendIPV6\",\n               \"TcpIpRecvIPV6\", \"TcpIpDisconnectIPV6\", \"TcpIpRetransmitIPV6\", \"TcpIpReconnectIPV6\", \"TcpIpTCPCopyIPV6\",\n               \"TcpIpConnectIPV6\", \"TcpIpAcceptIPV6\"]\n\nregistry_set = [\"RegistryCreate\", \"RegistryOpen\", \"RegistryDelete\", \"RegistryQuery\", \"RegistrySetValue\",\n                \"RegistryDeleteValue\", \"RegistryQueryValue\", \"RegistryEnumerateKey\", \"RegistryEnumerateValueKey\",\n                \"RegistryQueryMultipleValue\", \"RegistrySetInformation\", \"RegistryFlush\", \"RegistryKCBCreate\",\n                \"RegistryKCBDelete\", \"RegistryKCBRundownBegin\", \"RegistryKCBRundownEnd\", \"RegistryVirtualize\",\n                \"RegistryClose\"]\nprocess_set = [\"ProcessStart\", \"ProcessEnd\", \"ProcessDCStart\", \"ProcessDCEnd\", \"ProcessDefunct\", \"ProcessPerfCtr\",\n               \"ProcessPerfCtrRundown\"]\nthread_set = [\"ThreadStart\", \"ThreadEnd\", \"ThreadDCStart\", \"ThreadDCEnd\", \"ThreadCSwitch\"]\nnetwork_set = [\"TcpIpSendIPV4\", \"TcpIpSendIPV6\", \"TcpIpRecvIPV4\", \"TcpIpDisconnectIPV4\", \"TcpIpRetransmitIPV4\",\n               \"TcpIpReconnectIPV4\", \"TcpIpTCPCopyIPV4\", \"TcpIpRecvIPV6\", \"TcpIpDisconnectIPV6\", \"TcpIpRetransmitIPV6\",\n               \"TcpIpReconnectIPV6\", \"TcpIpTCPCopyIPV6\", \"TcpIpConnectIPV4\", \"TcpIpAcceptIPV4\", \"TcpIpConnectIPV6\",\n               \"TcpIpAcceptIPV6\", \"TcpIpFailed\"]\nfileio_set = [\"FileIOCreate\", \"FileIODirEnum\", \"FileIODirNotify\", \"FileIOSetInfo\", \"FileIODelete\", \"FileIORename\",\n              \"FileIOQueryInfo\", \"FileIOFSControl\", \"FileIOName\", \"FileIOFileCreate\", \"FileIOFileDelete\",\n              \"FileIOFileRundown\", \"FileIOOperationEnd\", \"FileIORead\", \"FileIOWrite\", \"FileIOCleanup\", \"FileIOClose\",\n              \"FileIOFlush\"]\ndiskio_set = [\"DiskIOWrite\", \"DiskIORead\", \"DiskIOReadInit\", \"DiskIOWriteInit\", \"DiskIOFlushInit\", \"DiskIOFlushBuffers\",\n              \"DiskIODrvComplReq\", \"DiskIODrvComplReqRet\", \"DiskIODrvComplRout\", \"DiskIODrvMjFnCall\",\n              \"DiskIODrvMjFnRet\"]\nalpc_set = [\"ALPC-Receive-Message\", \"ALPC-Send-Message\", \"ALPC-Unwait\", \"ALPC-Wait-For-New-Message\",\n            \"ALPC-Wait-For-Reply\"]\nother_set = [\"PerfInfoThreadDPC\", \"PerfInfoDPC\", \"PerfInfoTimerDPC\", \"PerfInfoISR\", \"PerfInfoSysClEnter\",\n             \"PerfInfoSysClEnter\", \"ImageLoad\", \"ImageUnload\", \"ImageDCStart\", \"ImageDCEnd\"]\n\n\ndef getEdge_type(eventname):\n    if eventname in open_set: return \"OPEN\"\n    if eventname in read_set: return \"READ\"\n    if eventname in create_set: return \"CREATE\"\n    if eventname in message_set: return \"MESSAGE\"\n    if eventname in modify_set: return \"MODIFY\"\n    if eventname in start_set: return \"START\"\n    if eventname in rename_set: return \"RENAME\"\n    if eventname in delete_set: return \"DELETE\"\n    if eventname in terminal_set: return \"TERMINAL\"\n    if eventname in write_set: return \"WRITE\"\n    if eventname in unknown_set: return \"FALSE\"\n\n\ndef getSrcmsg(eventname, event):\n    ret = {}\n    if eventname in message_set:\n        ret[\"FLOW\"] = event[\"args\"][\"saddr\"] + \":\" + str(event[\"args\"][\"sport\"])\n    else:\n        ret[\"PROCESS\"] = event[\"PName\"]\n    return ret\n\n\ndef getDstmsg(eventname, event):\n    ret = {}\n    if eventname in message_set:\n        ret[\"FLOW\"] = event[\"args\"][\"daddr\"] + \":\" + str(event[\"args\"][\"dport\"])\n    elif eventname in registry_set:\n        ret[\"FILE\"] = event[\"args\"][\"KeyName\"]\n    elif eventname in start_set:\n        ret[\"PROCESS\"] = event[\"args\"][\"ImageFileName\"]\n    elif eventname in fileio_set:\n        try:\n            ret[\"FILE\"] = event[\"args\"][\"OpenPath\"]\n        except:\n            ret[\"FILE\"] = event[\"args\"][\"FileName\"]\n    return ret\n\n\ni = 1\nwith open(\"test.json\", \"r\") as source_file, open(\"output.json\", \"w\") as target_file, open(\"prase_error.txt\",\n                                                                                          \"w\") as error_file:\n    # 逐行读取源文件\n    for line in source_file:\n        try:\n            data = json.loads(line)\n            eventName = data[\"Event\"]\n            edge_type = getEdge_type(eventName)\n            value[\"time\"] = data[\"TimeStamp\"]\n            value[\"edge_type\"] = getEdge_type(eventName)\n            value[\"srcmsg\"] = getSrcmsg(eventName, data)\n            value[\"dstmsg\"] = getDstmsg(eventName, data)\n            if value[\"dstmsg\"] != \"{}\":\n                print(i)\n                i = i + 1\n                target_file.write(json.dumps(value) + \"\\n\")\n            else:\n                continue\n        except:\n            error_file.write(line + \"\\n\")\n","repo_name":"Wind-Enchanter/node_detection","sub_path":"parse_log/parse.py","file_name":"parse.py","file_ext":"py","file_size_in_byte":6465,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"12792948334","text":"import os\n\n#file_arr = ['crop\\\\1', 'crop\\\\2_start', 'crop\\\\2_end', 'crop\\\\3_start', 'crop\\\\3_end', 'crop\\\\4_start', 'crop\\\\4_end', 'crop\\\\5_start', 'crop\\\\5_end', 'crop\\\\6', 'crop\\\\7', 'crop\\\\8', 'crop\\\\9', 'crop\\\\10', 'crop\\\\11']   # 15 classes\n\n#file_arr = ['crop\\\\1', 'crop\\\\2_start', 'crop\\\\2_end', 'crop\\\\3_start', 'crop\\\\3_end', 'crop\\\\4_start', 'crop\\\\4_end', 'crop\\\\5_start', 'crop\\\\5_end', 'crop\\\\6', 'crop\\\\7', 'crop\\\\9', 'crop\\\\10']   # 13 classes\n\nfile_arr = ['crop\\\\1', 'crop\\\\2_start', 'crop\\\\2_end', 'crop\\\\3', 'crop\\\\4_start', 'crop\\\\4_end', 'crop\\\\5', 'crop\\\\6', 'crop\\\\7', 'crop\\\\9', 'crop\\\\10']   # 11 classes\n\nperson_index_1 = [[1,136],[137,150],[151,160],[161,162],[163,173],[174,179],[180,188],[178,196],[197,204],[205,217],[218,245],[246,264],[265,276]]\n\nperson_index_2 = [[1,83],[84,97],[98,112],[113,125],[126,144],[145,154],[155,169],[170,184],[185,199],[200,214],[215,235],[236,263],[264,281]]\n\nperson_index_3 = [[1,76],[77,90],[91,105],[106,118],[119,137],[138,147],[148,162],[163,177],[178,192],[193,207],[208,228],[229,256],[257,274]]\n\nperson_index_4 = [[],[1,15],[16,29],[30,37],[38,62],[63,80],[81,95],[96,110],[111,125],[126,139],[140,160],[161,180],[]]\n\nperson_index_5 = [[],[1,15],[16,31],[32,46],[47,69],[64,78],[79,93],[94,108],[109,122],[123,137],[138,157],[158,177],[]]\n\nperson_index_6 = [[],[1,15],[16,30],[31,42],[43,56],[57,71],[72,86],[87,101],[102,115],[116,130],[131,150],[151,170],[]]\n\nperson_index_7 = [[],[1,14],[15,27],[28,39],[40,54],[55,71],[72,85],[86,100],[101,115],[116,131],[132,151],[152,172],[173,191]]\n\ndef MOD_X(MOD_NUM=3):\n\n    print('MOD_' + str(MOD_NUM))\n\n    f_train = open('my_train.txt', 'w')\n    f_test = open('my_test.txt', 'w')\n\n    #size_1 = 34\n    #size_2 = 38\n\n    label_arr = [' 1\\n', ' 2\\n', ' 3\\n', ' 4\\n', ' 5\\n', ' 6\\n', ' 7\\n', ' 8\\n', ' 9\\n', ' 10\\n', ' 11\\n', ' 12\\n', ' 13\\n', ' 14\\n', ' 15\\n']\n\n    for arr in range(len(file_arr)):\n        cou = 0\n        for dirPath, dirNames, fileNames in os.walk(file_arr[arr]):\n            #print(dirPath, dirNames)\n            s = 'D:\\\\Code\\\\Hololens_Project\\\\Dataset\\\\my_dataset_holo\\\\' + dirPath + ' ' + str(len(fileNames)) + label_arr[arr]\n            if len(fileNames) != 0:\n                if cou % MOD_NUM == 0:\n                    f_test.write(s)\n                    cou += 1\n                else:\n                    f_train.write(s)\n                    cou += 1\n\n\n    f_train.close()\n    f_test.close()\n\ndef for_7_class(MOD_NUM=3):\n    f_train = open('my_train.txt', 'w')\n    f_test = open('my_test.txt', 'w')\n\n    file_arr = ['crop\\\\1', 'crop\\\\2_start', 'crop\\\\2_end', 'crop\\\\3', 'crop\\\\4', 'crop\\\\5', 'crop\\\\6']\n    label_arr = [' 1\\n', ' 2\\n', ' 3\\n', ' 4\\n', ' 5\\n', ' 6\\n', ' 7\\n']\n\n    for arr in range(len(file_arr)):\n        cou = 0\n        for dirPath, dirNames, fileNames in os.walk(file_arr[arr]):\n            #print(dirPath, dirNames)\n            s = 'D:\\\\Code\\\\Hololens_Project\\\\Dataset\\\\my_dataset_holo\\\\' + dirPath + ' ' + str(len(fileNames)) + label_arr[arr]\n            if len(fileNames) != 0:\n                if cou % MOD_NUM == 0:\n                    f_test.write(s)\n                    cou += 1\n                else:\n                    f_train.write(s)\n                    cou += 1\n\n\n    f_train.close()\n    f_test.close()\n\ndef for_cross_val_7_class():\n    f_train = open('my_train.txt', 'w')\n    f_test = open('my_test.txt', 'w')\n\n    file_arr = ['crop\\\\1', 'crop\\\\2_start', 'crop\\\\2_end', 'crop\\\\3', 'crop\\\\4', 'crop\\\\5', 'crop\\\\6']\n    label_arr = [' 1\\n', ' 2\\n', ' 3\\n', ' 4\\n', ' 5\\n', ' 6\\n', ' 7\\n']\n\n    #test_person = [1,4,7]   # 13個人當中選3個來當testing data\n    test_person = [11]   # 13個人當中選1個來當testing data\n\n    print('test_person = ', end='')\n    print(test_person)\n\n    for arr in range(len(file_arr)):\n        for dirPath, dirNames, fileNames in os.walk(file_arr[arr]):\n            #print(dirPath, dirNames)\n            s = 'D:\\\\Code\\\\Hololens_Project\\\\Dataset\\\\my_dataset_holo\\\\' + dirPath + ' ' + str(len(fileNames)) + label_arr[arr]\n            if len(dirPath) == 13 or len(dirPath) == 17 or len(dirPath) == 15:\n                if len(dirPath) == 13:\n                    my_index = int(dirPath.split('_')[-1])\n                elif len(dirPath) == 17 or len(dirPath) == 15:\n                    my_index = int(dirPath.split('\\\\')[-1])\n                for_loop_cou = 0\n                for i in range(len(test_person)):\n                    if dirPath.split('\\\\')[1] == '1' and person_index_1[test_person[i]][0] <= my_index and my_index <= person_index_1[test_person[i]][1]:\n                        f_test.write(s)\n                    elif dirPath.split('\\\\')[1] == '2_start' and person_index_2[test_person[i]][0] <= my_index and my_index <= person_index_2[test_person[i]][1]:\n                        f_test.write(s)\n                    elif dirPath.split('\\\\')[1] == '2_end' and person_index_3[test_person[i]][0] <= my_index and my_index <= person_index_3[test_person[i]][1]:\n                        f_test.write(s)\n                    elif dirPath.split('\\\\')[1] == '3' and person_index_4[test_person[i]][0] <= my_index and my_index <= person_index_4[test_person[i]][1]:\n                        f_test.write(s)\n                    elif dirPath.split('\\\\')[1] == '4' and person_index_5[test_person[i]][0] <= my_index and my_index <= person_index_5[test_person[i]][1]:\n                        f_test.write(s)\n                    elif dirPath.split('\\\\')[1] == '5' and person_index_6[test_person[i]][0] <= my_index and my_index <= person_index_6[test_person[i]][1]:\n                        f_test.write(s)\n                    elif dirPath.split('\\\\')[1] == '6' and person_index_7[test_person[i]][0] <= my_index and my_index <= person_index_7[test_person[i]][1]:\n                        f_test.write(s)\n                    else:\n                        for_loop_cou += 1\n                if for_loop_cou == len(test_person):\n                    f_train.write(s)\n\n    f_train.close()\n    f_test.close()\n\n\nif __name__ == \"__main__\":\n    MOD_X(MOD_NUM=4)\n    #for_7_class(MOD_NUM=3)\n    #for_cross_val_7_class()\n    ","repo_name":"chang-chih-yao/Hololens_Project","sub_path":"Dataset/my_dataset_holo/gen_train_test_file.py","file_name":"gen_train_test_file.py","file_ext":"py","file_size_in_byte":6101,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"86"}
+{"seq_id":"42746106294","text":"#Binary Search\n\ndef binary_search(nums,target):\n  low=mid=0\n  high=len(nums)-1\n  while low<=high:\n    mid=(low+high)//2\n    if(nums[mid]<target):\n      low=mid+1\n    elif(nums[mid]>target):\n      high=mid-1\n    else:\n      return mid\n  return -1\n\n# Test array\nnums = [ 2, 3, 4, 10, 40 ]\ntarget = 10\n \n# Function call\nresult = binary_search(nums, target)\n \nif result != -1:\n    print(\"Element is present at index\", str(result))\nelse:\n    print(\"Element is not present in array\")\n","repo_name":"krithika117/program-solutions","sub_path":"binary-search.py","file_name":"binary-search.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"23179872180","text":"# Importa Symbol - possibilidade de operacoes com simbolos\n# Importa solve - permite solucao de equacoes\n# Importa funcoes seno(sin), cosseno(cos), tangente(tan)\n\nfrom sympy import Symbol, solve, sin, cos, tan\n\n# Define uma nova funcao\n\n\ndef calcula_f(x):\n    return (sin(2 * x))**2 - cos(2 * x) - tan(2*x) - 1\n\n\nprint(\"\\n\" * 100)\n\n# Define x como uma variavel\nx = Symbol('x')\n\n# Resolve a equacao calcula_f = 0\ny = calcula_f(x)\n\nresultado = solve(y)\nprint(\"\\n\")\nprint(\"Resultado da equacao (sin(2*x))**2 - cos(2 * x) - tan(2*x) - 1 = 0\")\nprint(\"x = \", resultado)\n","repo_name":"IgorTerriaga/MathwithPython","sub_path":"EquacaoInequacao/SolucaoEquacaoTrigonometrica.py","file_name":"SolucaoEquacaoTrigonometrica.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3581624379","text":"from django.core.paginator import Paginator\nfrom django.shortcuts import render, redirect\nfrom django.views import View\nfrom Tour.models.Package import Package\n\nclass Package_page(View):\n    def get(self, request):\n        # Get the search query from the request\n        search_query = request.GET.get('search', '')\n\n        # Get all packages\n        all_packages = Package.get_all_packages(search_query)\n\n        # Set the number of packages to display per page\n        packages_per_page = 12\n\n        # Get the current page number from the request's GET parameters\n        page_number = request.GET.get('page', 1)\n\n        # Create a Paginator instance and get the current page\n        paginator = Paginator(all_packages, packages_per_page)\n        current_page = paginator.get_page(page_number)\n\n        return render(request, 'Package_page.html', {'Packages': current_page, 'search_query': search_query})\n\n    def post(self, request):\n        # Check if the form is submitted for adding to the wishlist\n        if 'package' in request.POST:\n            # Get the package ID from the submitted form\n            package = request.POST.get('package')\n\n            # Retrieve the user's wishlist from the session or create an empty dictionary\n            wishlist = request.session.get('wishlist', {})\n\n            # Check if the package is already in the wishlist\n            if package not in wishlist:\n                # Package is not in the wishlist, add it to the dictionary\n                wishlist[package] = 1  # You can use any value; here, I'm using 1\n\n                # Update the session with the modified wishlist\n                request.session['wishlist'] = wishlist\n\n                # Print the updated wishlist to the console for debugging\n                print(request.session['wishlist'])\n\n        # Redirect the user back to the 'Package_page'\n        return redirect('Package_page')\n","repo_name":"Sagun9391/Final_Project","sub_path":"Tour/Views/package.py","file_name":"package.py","file_ext":"py","file_size_in_byte":1905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"17037165665","text":"from tkinter import *\n# import tkinter.ttk import *\n\nfrom plotdata import regression_plot\nfrom stats import stats_columns\n\n\nclass Application(Frame):\n    def __init__(self, master=None):\n        super().__init__(master)\n        self.master = master\n        self.b = Button(master, text=\"Blammo!\", command=lambda: self.onClick())\n        self.b.pack()\n\n    def onClick(self):\n        anotherWindow=Application(self.master)\n        anotherWindow.pack()\n\n\nroot = Tk()\napp = Application(master=root)\napp.mainloop()\n","repo_name":"jessica-dyer/wiggles_work","sub_path":"dataview.py","file_name":"dataview.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"38747738823","text":"import time\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.chrome.options import Options\nfrom concurrent.futures import ProcessPoolExecutor\nimport os\nimport threading\nimport pandas as pd\nfrom naver_reviews import naver_reviews_list\nfrom search_restaurant_url import restaurant\nfrom image_crawling import image_crawling\nfrom inform_restaurant import inform_restaurant\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n\nchrome_options = Options()\nchrome_options.add_argument(\"--no-sandbox\")\nchrome_options.add_argument(\"--headless\")\n\n\ndef fetch_review(url):\n    print(f\"{os.getpid()} process | {threading.get_ident()} thread, {url}\")\n    try:\n        driver = webdriver.Chrome(\n            \"/Users/seop/Documents/GitHub/Trend_Analysis_and_Recommendation_System_Project/Place_crawling/chromedriver\",\n            chrome_options=chrome_options,\n        )\n        driver.get(url[:-4] + \"review/visitor\")\n    except:\n        print(url, \"| HTTP Error 500: Internal Server Error\")\n    return naver_reviews_list(driver, url, 5)\n\n\ndef fetch_image_food(url):\n    # print(f\"{os.getpid()} process | {threading.get_ident()} thread, {url}\")\n    try:\n        driver = webdriver.Chrome(\n            \"/Users/seop/Documents/GitHub/Trend_Analysis_and_Recommendation_System_Project/Place_crawling/chromedriver\",\n            chrome_options=chrome_options,\n        )\n        driver.get(url[:-4] + \"photo?filterType=음식\")\n    except:\n        print(url, \"| HTTP Error 500: Internal Server Error\")\n    return image_crawling(driver, url, 30)\n\n\ndef fetch_image_inner(url):\n    # print(f\"{os.getpid()} process | {threading.get_ident()} thread, {url}\")\n    try:\n        driver = webdriver.Chrome(\n            \"/Users/seop/Documents/GitHub/Trend_Analysis_and_Recommendation_System_Project/Place_crawling/chromedriver\",\n            chrome_options=chrome_options,\n        )\n        driver.get(url[:-4] + \"photo?filterType=내부\")\n    except:\n        print(url, \"| HTTP Error 500: Internal Server Error\")\n    return image_crawling(driver, url, 30)\n\n\ndef main():\n    df = pd.DataFrame(\n        columns=[\n            \"name\",\n            \"address\",\n            \"sort\",\n            \"menu\",\n            \"mean_price\",\n            \"score\",\n            \"people_give_score\",\n            \"review_count\",\n        ]\n    )\n\n    region_df = pd.read_csv(\"/Users/seop/Downloads/Report.csv\")\n    region_df = region_df.drop(index=[0, 1, 2], axis=0)\n\n    for region in region_df[\"법정동\"][:1]:\n\n        print(\"현재 지역 :\", region)\n        urls = restaurant(region, 3)\n\n        executor = ProcessPoolExecutor(max_workers=10)\n\n        result_rivew = list(executor.map(fetch_review, urls))\n        result_food = list(executor.map(fetch_image_food, urls))\n        result_inner = list(executor.map(fetch_image_inner, urls))\n\n        for idx, url in enumerate(urls):\n            result_df = inform_restaurant(url)\n            df = pd.concat(\n                [df, pd.DataFrame(result_df, index=[idx])], ignore_index=False\n            )\n\n        result = {}\n        result[\"review_list\"] = []\n        result[\"img_food\"] = []\n        result[\"img_inner\"] = []\n\n        for i, j, k in zip(result_rivew, result_food, result_inner):\n\n            result[\"review_list\"].append(i)\n            result[\"img_food\"].append(j)\n            result[\"img_inner\"].append(k)\n\n        result_selenium = pd.DataFrame(result)\n        df = pd.concat([df, result_selenium], axis=1)\n        df.to_csv(f\"{region}.csv\", encoding=\"utf-8-sig\")\n\n    return df, result_selenium\n\n\nif __name__ == \"__main__\":\n\n    start = time.time()\n    df, result_selenium = main()\n    end = time.time()\n    print(end - start, \" second\")\n\n# 60초\n","repo_name":"assayw119/Trend_Analysis_and_Recommendation_System_Project","sub_path":"Place_crawling/naver_test.py","file_name":"naver_test.py","file_ext":"py","file_size_in_byte":3702,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"4304179845","text":"# -*- coding: utf-8 -*-\nfrom formalchemy.ext.pylons.controller import _ModelsController as Base\nfrom fa.jquery.utils import TemplateEngine\nfrom fa.jquery.utils import Flash\nfrom fa.jquery.fanstatic_resources import fa_admin, fa_jqgrid\nfrom js import jqueryui\nfrom js import jqgrid\nfrom webhelpers.html import literal\nfrom formalchemy.ext.pylons.controller import model_url\nfrom formalchemy.ext.pylons.controller import request\nfrom formalchemy import fields\nfrom formalchemy import fatypes\nfrom routes.util import GenerationException\nget_lang = __import__(\"pylons\").i18n.translation.get_lang\nfrom simplejson import dumps\nimport renderers\nimport logging\n\nlog = logging.getLogger(__name__)\n\nclass _ModelsController(Base):\n    engine = TemplateEngine()\n    template = 'restfieldset.mako'\n\n    def update_resources(self):\n        jqueryui.redmond.need()\n        fa_admin.need()\n        needed_resource = getattr(jqgrid, 'jqgrid_i18n_%s' % get_lang(),\n                                  jqgrid.jqgrid_i18n_en)\n        needed_resource.need()\n        fa_jqgrid.need()\n\n    def index(self, *args, **kwargs):\n        kwargs['pager'] = ''\n        return Base.index(self, *args, **kwargs)\n\n    def get_page(self, **kwargs):\n        if 'collection' not in kwargs:\n            model = self.get_model()\n            params = request.params\n            session = self.Session()\n            fields = model._sa_class_manager\n            collection = session.query(model)\n            # FIXME: use id by default but should use pk field\n            sidx = params.get('sidx', 'id').decode()\n            if sidx and fields.has_key(sidx):\n                sidx = fields[sidx]\n                sord = params.get('sord', 'asc').decode().lower()\n                if sord in ['asc', 'desc']:\n                    collection = collection.order_by(getattr(sidx, sord)())\n            if 'searchField' in params:\n                field = fields.get(params['searchField'], None)\n                if field:\n                    op = params['searchOper']\n                    value = params['searchString']\n                    if op == 'cn':\n                        value = '%%%s%%' % value\n                        filter = field.ilike(value)\n                    else:\n                        filter = field==value\n                    collection = collection.filter(filter)\n            kwargs.update(collection=collection)\n        if 'items_per_page' not in kwargs:\n            kwargs.update(items_per_page=int(request.GET.get('rows', 20)))\n        return Base.get_page(self, **kwargs)\n\n    def render_xhr_format(self, fs=None, **kwargs):\n        html = Base.render_xhr_format(self, fs=fs, **kwargs)\n        if fs and request.POST and 'field' not in request.GET:\n            flash = Flash()\n            if fs.errors:\n                errors = [f.label() for f in fs.render_fields.values() if f.errors]\n                flash.error('Field(s) %s have errors' % ','.join(errors))\n            else:\n                flash.info('Record saved')\n            html = literal(html) + flash.render()\n        return html\n\n    def update_grid(self, grid, *args, **kwargs):\n        for field in grid.render_fields.values():\n            metadata = dict(search=0)\n            searchoptions = dict(sopt=['eq', 'cn'])\n            if field.is_relation:\n                metadata.update(width=100)\n            elif isinstance(field.type, fatypes.Text):\n                field.set(renderer=renderers.ellipsys(field.renderer))\n                metadata.update(search=1)\n            elif isinstance(field.type, (fatypes.String, fatypes.Unicode)):\n                metadata.update(search=1)\n            elif isinstance(field.type, (fatypes.Date, fatypes.Integer)):\n                metadata.update(width=70, align='\"center\"')\n            elif isinstance(field.type, fatypes.DateTime):\n                metadata.update(width=120, align='\"center\"')\n            elif isinstance(field.type, fatypes.Boolean):\n                metadata.update(width=30, align='\"center\"')\n            if metadata['search']:\n                metadata['searchoptions'] = dumps(searchoptions)\n            metadata.update(field.metadata)\n            field.set(metadata=metadata)\n\ndef ModelsController(cls, prefix_name, member_name, collection_name):\n    \"\"\"wrap a controller with :class:~formalchemy.ext.pylons.controller._ModelsController\"\"\"\n    return type(cls.__name__, (cls, _ModelsController),\n                dict(prefix_name=prefix_name, member_name=member_name, collection_name=collection_name))\n\ndef RelationRenderer(renderer=fields.SelectFieldRenderer, **jq_options):\n    class Renderer(renderer):\n        def render(self, *args, **kwargs):\n            html = super(Renderer, self).render(*args, **kwargs)\n            fk_class = self.field.relation_type()\n            model_name = fk_class.__name__\n            try:\n                field_url = '%s.xhr?field=%s' % (model_url('model', id=fields._pk(self.field.model)), self.field.key)\n            except GenerationException:\n                field_url = '%s.xhr?field=%s' % (model_url('new_model'), self.field.key)\n            new_url = '%s.xhr' % model_url('new_model', model_name=model_name)\n            html += literal('<button class=\"new_relation_item\" alt=\"%s\" href=\"%s\">New %s</button>' % (\n                                                field_url, new_url, model_name))\n            return html\n    return renderers.jQueryFieldRenderer('relation', show_input=True, renderer=Renderer, **jq_options)\n\n@renderers.alias(RelationRenderer, renderer=renderers.checkboxset())\ndef relations(): pass\n\n@renderers.alias(RelationRenderer, renderer=renderers.radioset())\ndef relation(): pass\n\ntry:\n    import pyramid_formalchemy\nexcept ImportError:\n    # this only works with pylons 1.0\n    renderers.default_renderers['dropdown'] = RelationRenderer()\n\n","repo_name":"FormAlchemy/fa.jquery","sub_path":"fa/jquery/pylons.py","file_name":"pylons.py","file_ext":"py","file_size_in_byte":5798,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"86"}
+{"seq_id":"44374417154","text":"import pandas as pd\nimport sqlite3\nimport surprise\nimport os\n# Python이 실행될 때 DJANGO_SETTINGS_MODULE이라는 환경 변수에 현재 프로젝트의 settings.py파일 경로를 등록합니다.\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"recoduct.settings\")\n# 이제 장고를 가져와 장고 프로젝트를 사용할 수 있도록 환경을 만듭니다.\nimport django\ndjango.setup()\n\nfrom catalog.models import Profile\nfrom catalog.models import Item\nfrom catalog.models import Rate\nfrom django.contrib.auth.models import User\n\n\ndef load_item():\n    con = sqlite3.connect(\"C:\\dev\\glowpickDB.db\")\n    item_df = pd.read_sql(\"SELECT product_id, name, product_image FROM merged_data\", con)\n    return item_df\n\n\ndef load_user():\n    con = sqlite3.connect(\"C:\\dev\\glowpickDB.db\")\n    user_df = pd.read_sql(\"SELECT user_id, age, gender, skin_type, nickname, profile_image FROM merged_data\", con)\n    return user_df\n\n\ndef load_rate():\n    con = sqlite3.connect(\"C:\\dev\\glowpickDB.db\")\n    rate_df = pd.read_sql(\"SELECT user_id, product_id, rating, contents, created_at FROM merged_data\", con)\n    return rate_df\n\n\nif __name__=='__main__':\n#    item_df = load_item()\n#    for idx in range(len(item_df)):\n#        Item(item_id=item_df.iloc[idx, 0], name=item_df.iloc[idx, 1], image=item_df.iloc[idx, 2], brand='이니스프리').save()\n#    print(\"완료\")\n    user_df = load_user()\n    for idx in range(len(user_df)):\n        user_obj = User(id=user_df.iloc[idx, 0], password=user_df.iloc[idx, 0], username=user_df.iloc[idx, 0])\n        user_obj.save()\n        Profile(user=user_obj, profile_id=user_df.iloc[idx, 0], age=user_df.iloc[idx, 1], gender=user_df.iloc[idx, 2], skin_type=user_df.iloc[idx, 3], nickname=user_df.iloc[idx, 4], image=user_df.iloc[idx, 5]).save()\n    print(\"완료\")\n    rate_df = load_rate()\n    for idx in range(len(rate_df)):\n        Rate(user_id=rate_df.iloc[idx, 0], item_id=rate_df.iloc[idx, 1], rate=rate_df.iloc[idx, 2], review=rate_df.iloc[idx, 3], created_at=rate_df.iloc[idx, 4]).save()\n    print(\"완료\")\n\n#prediction_df = load_prediction()\n#for idx in range(len(prediction_df)):\n#Prediction(user_id=prediction_df.iloc[idx, 0], item_id=prediction_df.iloc[idx, 1], prediction=prediction_df.iloc[idx, 2]).save()\n\n#userId=pd.to_numeric(df['user_id'])\n#productId=pd.to_numeric(df['product_id'])\n#rating=pd.to_numeric(df['rating'])\n\n#temp={\"user\":userId,'item':productId,'rating':rating}\n\n#df2=pd.DataFrame(temp,columns = ['user', 'item','rating'])","repo_name":"dlawjdqls10/finalProject","sub_path":"data_load.py","file_name":"data_load.py","file_ext":"py","file_size_in_byte":2489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"6080381462","text":"import os\nimport json\nimport subprocess\nimport shutil\nfrom pathlib import Path\nfrom contextlib import contextmanager\nfrom .utils import temp_dir, print_header, print_section, format_filesize, \\\n    print_info, print_error, print_warn, listify\nfrom .utils.env import ensure_root\nfrom .utils.command import safe_run\nfrom .utils.zstd_tar import compress_directory, decompress_tarball, ensure_tar_zstd\nfrom .nspawn import nspawn_run_helper_persist, nspawn_run_persist\nfrom .aptcache import APTCache\n\n\nclass OSBase:\n    '''\n    Describes an OS base registered with debspawn\n    '''\n\n    def __init__(self, gconf, suite, arch, variant=None, base_suite=None, cachekey=None):\n        self._gconf = gconf\n        self._suite = suite\n        self._base_suite = base_suite\n        self._arch = arch\n        self._variant = variant\n        self._name = self._make_name()\n        self._results_dir = self._gconf.results_dir\n        self._cachekey = cachekey\n        if self._cachekey:\n            self._cachekey = self._cachekey.replace(' ', '')\n\n        self._aptcache = APTCache(self)\n\n        # ensure we can (de)compress zstd tarballs\n        ensure_tar_zstd()\n\n    def _make_name(self):\n        if not self._arch:\n            out, _, ret = safe_run(['dpkg-architecture', '-qDEB_HOST_ARCH'])\n            if ret != 0:\n                raise Exception('Running dpkg-architecture failed: {}'.format(out))\n\n            self._arch = out.strip()\n        if self._variant:\n            return '{}-{}-{}'.format(self._suite, self._arch, self._variant)\n        else:\n            return '{}-{}'.format(self._suite, self._arch)\n\n    @property\n    def name(self) -> str:\n        return self._name\n\n    @property\n    def suite(self) -> str:\n        return self._suite\n\n    @property\n    def base_suite(self) -> str:\n        return self._base_suite\n\n    @property\n    def arch(self) -> str:\n        return self._arch\n\n    @property\n    def variant(self) -> str:\n        return self._variant\n\n    @property\n    def global_config(self):\n        return self._gconf\n\n    @property\n    def aptcache(self):\n        return self._aptcache\n\n    @property\n    def has_base_suite(self) -> bool:\n        return True if self.base_suite and self.base_suite != self.suite else False\n\n    @property\n    def results_dir(self):\n        Path(self._results_dir).mkdir(parents=True, exist_ok=True)\n        return self._results_dir\n\n    @results_dir.setter\n    def results_dir(self, path):\n        self._results_dir = path\n        Path(self._results_dir).mkdir(exist_ok=True)\n\n    def _copy_helper_script(self, osroot_path):\n        script_location = os.path.join(osroot_path, 'usr', 'lib', 'debspawn')\n        Path(script_location).mkdir(parents=True, exist_ok=True)\n        script_fname = os.path.join(script_location, 'dsrun')\n\n        if os.path.isfile(script_fname):\n            os.remove(script_fname)\n        shutil.copy2(self._gconf.dsrun_path, script_fname)\n\n        os.chmod(script_fname, 0o0755)\n\n    def get_image_location(self):\n        return os.path.join(self._gconf.osroots_dir, '{}.tar.zst'.format(self.name))\n\n    def get_image_cache_dir(self):\n        cache_img_dir = os.path.join(self._gconf.osroots_dir, 'dcache', self.name)\n        Path(cache_img_dir).mkdir(parents=True, exist_ok=True)\n        return cache_img_dir\n\n    def get_cache_image_location(self):\n        if not self._cachekey:\n            return None\n        return os.path.join(self.get_image_cache_dir(), '{}.tar.zst'.format(self._cachekey))\n\n    def get_config_location(self):\n        return os.path.join(self._gconf.osroots_dir, '{}.json'.format(self.name))\n\n    def exists(self):\n        return os.path.isfile(self.get_image_location())\n\n    def cacheimg_exists(self):\n        location = self.get_cache_image_location()\n        if not location:\n            return False\n        return os.path.isfile(location)\n\n    def ensure_exists(self):\n        '''\n        Ensure the container image exists, and terminate the\n        program with an error code in case it does not.\n        '''\n        import sys\n        if not self.exists():\n            print_error('The container image for \"{}\" does not exist. Please create it first.'.format(self.name))\n            sys.exit(3)\n\n    def new_nspawn_machine_name(self):\n        import platform\n        from random import choice\n        from string import ascii_lowercase, digits\n\n        nid = ''.join(choice(ascii_lowercase + digits) for _ in range(4))\n\n        # on Linux, the maximum hostname length is 64, so we simple set this as general default for\n        # debspawn here.\n        # shorten the hostname part or replace the suffix, depending on what is longer.\n        # This should only ever matter if the hostname of the system already is incredibly long\n        uniq_suffix = '{}-{}'.format(self.name, nid)\n        if len(uniq_suffix) > 48:\n            uniq_suffix = ''.join(choice(ascii_lowercase + digits) for _ in range(12))\n        node_name_prefix = platform.node()[:63 - len(uniq_suffix)]\n\n        return '{}-{}'.format(node_name_prefix, uniq_suffix)\n\n    def _write_config_json(self, mirror, components, extra_suites, extra_source_lines):\n        '''\n        Create configuration file for this container base image\n        '''\n\n        print_info('Saving configuration settings.')\n        data = {'Suite': self.suite,\n                'Architecture': self.arch}\n        if self.variant:\n            data['Variant'] = self.variant\n        if mirror:\n            data['Mirror'] = mirror\n        if components:\n            data['Components'] = components\n        if extra_suites:\n            data['ExtraSuites'] = extra_suites\n        if extra_source_lines:\n            data['ExtraSourceLines'] = extra_source_lines\n\n        with open(self.get_config_location(), 'wt') as f:\n            f.write(json.dumps(data, sort_keys=True, indent=4))\n\n    def _clear_image_tree(self, image_dir):\n        ''' Clear files from a directory tree that we don't want in the tarball. '''\n\n        if os.path.ismount(image_dir):\n            print_warn('Preparing OS tree for compression, but /dev is still mounted.')\n            return\n\n        for sdir, _, files in os.walk(os.path.join(image_dir, 'dev')):\n            for f in files:\n                fname = os.path.join(sdir, f)\n                if os.path.lexists(fname) and not os.path.isdir(fname) and not os.path.ismount(fname):\n                    os.remove(fname)\n\n    def _create_internal(self, mirror=None, components=None, extra_suites=[], extra_source_lines=None, show_header=True):\n        ''' Create new container base image (internal method) '''\n\n        if self.exists():\n            print_error('An image already exists for this configuration. Can not create a new one.')\n            return False\n\n        # ensure image location exists\n        Path(self._gconf.osroots_dir).mkdir(parents=True, exist_ok=True)\n\n        if show_header:\n            print_header('Creating new base: {} [{}]'.format(self.suite, self.arch))\n        else:\n            print_section('Creating new base: {} [{}]'.format(self.suite, self.arch))\n\n        print('Using mirror: {}'.format(mirror if mirror else 'default'))\n        if self.variant:\n            print('variant: {}'.format(self.variant))\n        cmd = ['debootstrap',\n               '--arch={}'.format(self.arch),\n               '--include=python3-minimal,eatmydata']\n        if components:\n            cmd.append('--components={}'.format(','.join(components)))\n        if self.variant:\n            cmd.append('--variant={}'.format(self.variant))\n\n        with temp_dir() as tdir:\n            bootstrap_suite = self.suite\n            if self.has_base_suite:\n                bootstrap_suite = self.base_suite\n            cmd.extend([bootstrap_suite, tdir])\n            print('Bootstrap suite: {}'.format(bootstrap_suite))\n            if extra_suites:\n                print('Additional suites: {}'.format(', '.join(extra_suites)))\n            if extra_source_lines:\n                print('Custom sources.list lines will be added:')\n                for line in extra_source_lines.split('\\\\n'):\n                    print('    {}'.format(line))\n            if mirror:\n                cmd.append(mirror)\n\n            print_section('Bootstrap')\n            proc = subprocess.run(cmd)\n            if proc.returncode != 0:\n                return False\n\n            # create helper script runner\n            self._copy_helper_script(tdir)\n\n            # if we bootstrapped the base suite, add the primary suite to\n            # sources.list. We also add any explicit extra suites and source lines\n            if self.has_base_suite or extra_suites or extra_source_lines:\n                import re\n\n                sourceslist_fname = os.path.join(tdir, 'etc', 'apt', 'sources.list')\n                if not mirror:\n                    with open(sourceslist_fname, 'r') as f:\n                        contents = f.read()\n                        matches = re.findall('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', contents)\n                        if not matches:\n                            print_error('Unable to detect default APT repository URL (no regex matches).')\n                            return False\n                        mirror = matches[0]\n                    if not mirror:\n                        print_error('Unable to detect default APT repository URL.')\n                        return False\n\n                if not components:\n                    components = ['main']  # FIXME: We should really be more clever here, e.g. depend on python-apt and parse sources.list properly\n                with open(sourceslist_fname, 'a') as f:\n                    if self.has_base_suite:\n                        f.write('deb {mirror} {suite} {components}\\n'.format(mirror=mirror, suite=self.suite, components=' '.join(components)))\n\n                    if extra_suites:\n                        f.write('\\n')\n                        for esuite in extra_suites:\n                            if esuite == self.suite or esuite == bootstrap_suite:\n                                # don't add existing suites multiple times\n                                continue\n                            f.write('deb {mirror} {esuite} {components}\\n'.format(mirror=mirror, esuite=esuite, components=' '.join(components)))\n\n                    if extra_source_lines:\n                        f.write('\\n')\n                        for line in extra_source_lines.split('\\\\n'):\n                            f.write('{}\\n'.format(line.strip()))\n\n            print_section('Configure')\n            if nspawn_run_helper_persist(self, tdir, self.new_nspawn_machine_name(), '--update') != 0:\n                return False\n\n            print_section('Creating Tarball')\n            self._clear_image_tree(tdir)\n            compress_directory(tdir, self.get_image_location())\n\n        # store configuration settings, so we can later recreate this tarball\n        # or just display information about it\n        self._write_config_json(mirror, components, extra_suites, extra_source_lines)\n\n        return True\n\n    def create(self, mirror=None, components=None, extra_suites=[], extra_source_lines=None):\n        ''' Create new container base image (internal method) '''\n        ensure_root()\n\n        if self.exists():\n            print_error('This configuration has already been created. You can only delete or update it.')\n            return False\n\n        ret = self._create_internal(mirror=mirror,\n                                    components=components,\n                                    extra_suites=extra_suites,\n                                    extra_source_lines=extra_source_lines,\n                                    show_header=True)\n        if ret:\n            print_info('Done.')\n\n        return ret\n\n    def delete(self):\n        ''' Remove container base image '''\n        ensure_root()\n\n        if not self.exists():\n            print_error('Can not delete \"{}\": The configuration does not exist.'.format(self.name))\n            return False\n\n        print_header('Removing base image {}'.format(self.name))\n\n        print_section('Deleting cache')\n        # remove packages cache\n        cache_size = self._aptcache.clear()\n        print_info('Removed {} cached packages.'.format(cache_size))\n        self._aptcache.delete()\n        # remove cached images\n        shutil.rmtree(self.get_image_cache_dir())\n        print_info('Cache directory removed.')\n\n        print_section('Deleting base tarball')\n        os.remove(self.get_image_location())\n\n        config_fname = self.get_config_location()\n        if os.path.isfile(config_fname):\n            print_section('Deleting configuration manifest')\n            os.remove(config_fname)\n\n        print_info('Done.')\n        return True\n\n    @contextmanager\n    def new_instance(self, basename=None):\n        with temp_dir() as tdir:\n            if self.cacheimg_exists():\n                image_fname = self.get_cache_image_location()\n            else:\n                image_fname = self.get_image_location()\n            decompress_tarball(image_fname, tdir)\n            yield tdir, self.new_nspawn_machine_name()\n\n    def make_instance_permanent(self, instance_dir):\n        ''' Add changes done in the current instance to the main tarball of this OS tree, replacing it. '''\n\n        # remove unwanted files from the tarball\n        self._clear_image_tree(instance_dir)\n\n        if self._cachekey:\n            tarball_name = self.get_cache_image_location()\n        else:\n            tarball_name = self.get_image_location()\n        tarball_name_old = '{}.old'.format(tarball_name)\n\n        if os.path.isfile(tarball_name):\n            os.replace(tarball_name, tarball_name_old)\n        compress_directory(instance_dir, tarball_name)\n        if os.path.isfile(tarball_name_old):\n            os.remove(tarball_name_old)\n\n        tar_size = os.path.getsize(tarball_name)\n        if self._cachekey:\n            print_info('New compressed tarball size (for {}) is {}'.format(self._cachekey, format_filesize(tar_size)))\n        else:\n            print_info('New compressed tarball size is {}'.format(format_filesize(tar_size)))\n\n    def update(self):\n        ''' Update container base image '''\n        ensure_root()\n\n        if not self.exists():\n            print_error('Can not update \"{}\": The configuration does not exist.'.format(self.name))\n            return False\n\n        print_header('Updating container image')\n\n        with self.new_instance() as (instance_dir, machine_name):\n            # ensure helper script runner exists and is up to date\n            self._copy_helper_script(instance_dir)\n\n            print_section('Update')\n            if nspawn_run_helper_persist(self, instance_dir, self.new_nspawn_machine_name(), '--update') != 0:\n                return False\n\n            print_section('Recreating tarball')\n            self.make_instance_permanent(instance_dir)\n\n        print_section('Cleaning up cache')\n        cache_size = self._aptcache.clear()\n        print_info('Removed {} cached packages.'.format(cache_size))\n        # remove now-outdated cached images\n        shutil.rmtree(self.get_image_cache_dir())\n\n        print_info('Done.')\n        return True\n\n    def recreate(self):\n        ''' Recreate a container base image '''\n        ensure_root()\n\n        if not self.exists():\n            print_error('Can not recreate \"{}\": The image does not exist.'.format(self.name))\n            return False\n\n        config_fname = self.get_config_location()\n        if not os.path.isfile(config_fname):\n            print_error('Can not recreate \"{}\": Unable to find configuration data for this image.'.format(self.name))\n            return False\n\n        print_header('Recreating container image')\n\n        # read configuration data\n        with open(config_fname, 'rt') as f:\n            cdata = json.loads(f.read())\n            self._suite = cdata.get('Suite', self.suite)\n            self._arch = cdata.get('Architecture', self.arch)\n            self._variant = cdata.get('Variant', self.variant)\n            mirror = cdata.get('Mirror')\n            components = cdata.get('Components')\n            extra_suites = cdata.get('ExtraSuites', [])\n            extra_source_lines = cdata.get('ExtraSourceLines')\n\n        print_section('Deleting cache')\n        cache_size = self._aptcache.clear()\n        print_info('Removed {} cached packages.'.format(cache_size))\n        self._aptcache.delete()\n        print_info('Cache directory removed.')\n\n        # move old image tarball out of the way\n        image_name = self.get_image_location()\n        image_name_old = self.get_image_location() + '.old'\n        if os.path.isfile(image_name_old):\n            print_info('Removing cruft image')\n            os.remove(image_name_old)\n        os.rename(image_name, image_name_old)\n        print_info('Old tarball moved.')\n\n        # ty to create the tarball again\n        try:\n            ret = self._create_internal(mirror=mirror,\n                                        components=components,\n                                        extra_suites=extra_suites,\n                                        extra_source_lines=extra_source_lines,\n                                        show_header=False)\n        except Exception as e:\n            print_error('Error while trying to create image: {}'.format(str(e)))\n            ret = False\n\n        if ret:\n            if os.path.isfile(image_name_old):\n                print_info('Removing old image')\n                os.remove(image_name_old)\n\n            print_info('Removing outdated cached images')\n            shutil.rmtree(self.get_image_cache_dir())\n\n            print_info('Done.')\n            return True\n        else:\n            print_info('Restoring old tarball')\n            if os.path.isfile(image_name):\n                print_info('Removing failed new image')\n                os.remove(image_name)\n            os.rename(image_name_old, image_name)\n            print_info('Recreation failed.')\n            return False\n\n    def login(self, persistent=False, allowed=[]):\n        ''' Interactive shell login into the container '''\n        ensure_root()\n\n        if not self.exists():\n            print_info('Can not enter \"{}\": The configuration does not exist.'.format(self.name))\n            return False\n\n        print_header('Login (persistent changes) for {}'.format(self.name) if persistent else 'Login for {}'.format(self.name))\n        with self.new_instance() as (instance_dir, machine_name):\n            # ensure helper script runner exists and is up to date\n            self._copy_helper_script(instance_dir)\n\n            # run an interactive shell in the new container\n            nspawn_run_persist(self,\n                               instance_dir,\n                               self.new_nspawn_machine_name(),\n                               '/srv',\n                               verbose=True,\n                               allowed=allowed)\n\n            if persistent:\n                print_section('Recreating tarball')\n                self.make_instance_permanent(instance_dir)\n            else:\n                print_info('Changes discarded.')\n\n        print_info('Done.')\n        return True\n\n    def _copy_command_script_to_instance_dir(self, instance_dir: str, command_script: str) -> str:\n        '''\n        Copy a script from the host to the current instance directory and make it\n        executable.\n        Contains the path to the executable script as seen from inside the container.\n        '''\n        host_script = os.path.abspath(command_script)\n        if not os.path.isfile(host_script):\n            return None\n\n        script_location = os.path.join(instance_dir, 'srv', 'tmp')\n        Path(script_location).mkdir(parents=True, exist_ok=True)\n        script_fname = os.path.join(script_location, os.path.basename(host_script))\n\n        if os.path.isfile(script_fname):\n            os.remove(script_fname)\n        shutil.copy2(host_script, script_fname)\n        os.chmod(script_fname, 0o0755)\n\n        return os.path.join('/srv', 'tmp', os.path.basename(host_script))\n\n    def run(self, command, build_dir, artifacts_dir, init_command=None, copy_command=False, header_msg=None, allowed=[]):\n        ''' Run an arbitrary command or script in the container '''\n        ensure_root()\n\n        if not self.exists():\n            print_error('Can not run command in \"{}\": The base image does not exist.'.format(self.name))\n            return False\n\n        if len(command) <= 0:\n            print_error('No command was given. Can not continue.')\n            return False\n\n        if isinstance(init_command, str):\n            if init_command:\n                import shlex\n                init_command = shlex.split(init_command)\n        init_command = listify(init_command)\n\n        # ensure we have absolute paths\n        if build_dir:\n            build_dir = os.path.abspath(build_dir)\n        if artifacts_dir:\n            artifacts_dir = os.path.abspath(artifacts_dir)\n\n        if self._cachekey and init_command and not self.cacheimg_exists():\n            print_header('Preparing template for `{}`'.format(self._cachekey))\n\n            # we do not have a cached image prepared, let's do that now!\n            with self.new_instance() as (instance_dir, machine_name):\n                # ensure helper script runner exists and is up to date\n                self._copy_helper_script(instance_dir)\n\n                if copy_command:\n                    # copy initialization script from host to container\n                    host_script = init_command[0]\n                    init_command[0] = self._copy_command_script_to_instance_dir(instance_dir, host_script)\n                    if not init_command[0]:\n                        print_error('Unable to find initialization script \"{}\", can not copy it to the container. Exiting.'.format(host_script))\n                        return False\n\n                r = nspawn_run_helper_persist(self,\n                                              instance_dir,\n                                              machine_name,\n                                              '--prepare-run',\n                                              '/srv')\n                if r != 0:\n                    print_error('Container setup failed.')\n                    return False\n                # we do not want some permissions to be in effect here,\n                # as they may have unwanted effects on the final cached image\n                banned_permissions = ['full-dev', 'full-proc', 'read-kmods']\n                filtered_allowed = []\n                for perm in allowed:\n                    if perm not in banned_permissions:\n                        filtered_allowed.append(perm)\n                r = nspawn_run_persist(self,\n                                       instance_dir,\n                                       machine_name,\n                                       '/srv',\n                                       init_command,\n                                       allowed=filtered_allowed)\n                if r != 0:\n                    return False\n\n                print_info('Storing prepared image in cache')\n                self.make_instance_permanent(instance_dir)\n\n        if header_msg:\n            print_header(header_msg)\n        if self._cachekey and init_command and self.cacheimg_exists():\n            print_info('Using cached container image `{}`'.format(self._cachekey))\n\n        with self.new_instance() as (instance_dir, machine_name):\n            # ensure helper script runner exists and is up to date\n            self._copy_helper_script(instance_dir)\n\n            if copy_command:\n                # copy the script from the host into our container and execute it there\n                host_script = command[0]\n                command[0] = self._copy_command_script_to_instance_dir(instance_dir, host_script)\n                if not command[0]:\n                    print_error('Unable to find script \"{}\", can not copy it to the container. Exiting.'.format(host_script))\n                    return False\n\n            r = nspawn_run_helper_persist(self,\n                                          instance_dir,\n                                          machine_name,\n                                          '--prepare-run',\n                                          '/srv')\n            if r != 0:\n                print_error('Container setup failed.')\n                return False\n\n            print_section('Running Task')\n\n            nspawn_flags = []\n            chdir = '/srv'\n            if artifacts_dir:\n                nspawn_flags.extend(['--bind={}:/srv/artifacts/'.format(os.path.normpath(artifacts_dir))])\n            if build_dir:\n                nspawn_flags.extend(['--bind={}:/srv/build/'.format(os.path.normpath(build_dir))])\n                chdir = '/srv/build'\n\n            r = nspawn_run_persist(self,\n                                   instance_dir,\n                                   machine_name,\n                                   chdir,\n                                   command,\n                                   nspawn_flags,\n                                   allowed=allowed)\n            if r != 0:\n                return False\n\n        print_info('Done.')\n        return True\n\n\ndef print_container_base_image_info(gconf):\n    '''\n    Search for all available container base images and list information\n    about them.\n    '''\n    from glob import glob\n\n    osroots_dir = gconf.osroots_dir\n    tar_files = []\n    if os.path.isdir(osroots_dir):\n        tar_files = list(glob(os.path.join(osroots_dir, '*.tar.zst')))\n    if not tar_files:\n        print_info('No container base images have been found!')\n        return False\n    tar_files_len = len(tar_files)\n\n    for i, tar_fname in enumerate(tar_files):\n        img_basepath = os.path.splitext(os.path.splitext(tar_fname)[0])[0]\n        config_fname = img_basepath + '.json'\n        imgid = os.path.basename(img_basepath)\n        print('[{}]'.format(imgid))\n\n        cache_files = list(glob(os.path.join(osroots_dir, 'dcache', imgid, '*.tar.zst')))\n        cached_names = []\n        for cfile in cache_files:\n            cname = os.path.basename(os.path.splitext(os.path.splitext(cfile)[0])[0])\n            cached_names.append(cname)\n\n        # read configuration data if it exists\n        if os.path.isfile(config_fname):\n            with open(config_fname, 'rt') as f:\n                cdata = json.loads(f.read())\n            for key, value in cdata.items():\n                if type(value) is list:\n                    value = '; '.join(value)\n                print('{} = {}'.format(key, value))\n\n        tar_size = os.path.getsize(tar_fname)\n        print('Size = {}'.format(format_filesize(tar_size)))\n\n        if cached_names:\n            print('CachedImages = {}'.format('; '.join(cached_names)))\n        if i != tar_files_len - 1:\n            print()\n","repo_name":"angdraug/debspawn","sub_path":"debspawn/osbase.py","file_name":"osbase.py","file_ext":"py","file_size_in_byte":26941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"86"}
+{"seq_id":"35650583005","text":"import numpy as np\nimport sys\nimport os\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nimport os\nfrom shape import Shape\n\nfrom pathlib import Path\nfrom file_reader import FileReader\n\ndef test_barycenter(shape):\n    assert abs(shape.processed_vertices.mean(axis=0).max())<1e-5, \"Not translated to barycenter!\"\n\ndef test_alignment(shape):\n    vertices = shape.processed_vertices.flatten()\n    vertices = vertices.reshape((3,-1),order=\"F\")\n    cov = np.cov(vertices)\n    eigenvalues, eigenvectors = np.linalg.eig(cov)\n    assert (abs(eigenvectors.sum(axis=0) -1) <1e-5).sum()==3, \"Eigenvectors are not aligned with standard basis!\"\n\ndef test_bounding(shape):\n    assert ((shape.bounding_rect_vertices.reshape((-1,3)).max(axis=0) - shape.processed_vertices.max(axis=0))==0).sum()==3, \"Pos values above bounding box!\"\n    assert ((abs(shape.bounding_rect_vertices.reshape((-1,3)).min(axis=0)) - abs(shape.processed_vertices.min(axis=0)))==0).sum()==3, \"Neg values below bounding box!\"\n   \n\n\n\n\n\nif __name__ == '__main__':\n\n    ant_path = Path(r\"data/benchmark/db/0/m0/m0.off\")\n    path = Path(r\"data/test.ply\")\n    max_path = Path('data/benchmark/db/17/m1755/m1755.off')\n    problem_path = \"data/benchmark/db/2/m201/m201.off\"\n    path = ant_path\n\n\n\n    reader = FileReader()\n    vertices, element_dict, info = reader.read(path)\n    shape = Shape(vertices,element_dict,info)\n    shape.process_shape()\n    \n    \n    test_barycenter(shape)\n    test_alignment(shape)\n    test_bounding(shape)\n    \n","repo_name":"SamGalanakis/MIR_Pipeline_Project","sub_path":"src/tests/normalization_test.py","file_name":"normalization_test.py","file_ext":"py","file_size_in_byte":1517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70796206044","text":"import logging\nfrom typing import Dict, List, Set, Union\n\nimport sympy\nfrom pysmt.formula import FNode\nfrom pysmt.shortcuts import (\n    GE,\n    LE,\n    REAL,\n    TRUE,\n    And,\n    Equals,\n    Minus,\n    Or,\n    Plus,\n    Real,\n    Symbol,\n    Times,\n)\n\nfrom funman.model.model import Model\nfrom funman.translate.simplifier import FUNMANSimplifier\nfrom funman.utils.sympy_utils import (\n    rate_expr_to_pysmt,\n    series_approx,\n    sympy_subs,\n    to_sympy,\n)\n\nfrom .translate import Encoder, Encoding\n\nl = logging.getLogger(__file__)\n\n\nclass PetrinetEncoder(Encoder):\n    _transition_rate_cache: Dict[str, List[sympy.Expr]] = {}\n\n    def encode_model(self, model: \"Model\") -> Encoding:\n        \"\"\"\n        Encode a model into an SMTLib formula.\n\n        Parameters\n        ----------\n        model : Model\n            model to encode\n\n        Returns\n        -------\n        Encoding\n            formula and symbols for the encoding\n        \"\"\"\n        return Encoding(formula=TRUE(), symbols={})\n\n    def _encode_next_step(\n        self,\n        scenario: \"AnalysisScenario\",\n        step: int,\n        next_step: int,\n        substitutions={},\n    ) -> FNode:\n        l.debug(f\"Encoding step: {step} to {next_step}\")\n        state_vars = scenario.model._state_vars()\n        transitions = scenario.model._transitions()\n\n        step_size = next_step - step\n        current_state = {\n            scenario.model._state_var_id(s): self._encode_state_var(\n                scenario.model._state_var_name(s), time=step\n            )\n            for s in state_vars\n        }\n        next_state = {\n            scenario.model._state_var_id(s): self._encode_state_var(\n                scenario.model._state_var_name(s), time=next_step\n            )\n            for s in state_vars\n        }\n\n        time_var = scenario.model._time_var()\n        if time_var:\n            time_var_name = scenario.model._time_var_id(time_var)\n            time_symbol = self._encode_state_var(\n                time_var_name\n            )  # Needed so that there is a pysmt symbol for 't'\n            current_time_var = self._encode_state_var(time_var_name, time=step)\n            next_time_var = self._encode_state_var(\n                time_var_name, time=next_step\n            )\n            current_state[time_var_name] = current_time_var\n            next_state[time_var_name] = next_time_var\n\n        # Each transition corresponds to a term that is the product of current state vars and a parameter\n        transition_terms = {\n            scenario.model._transition_id(t): self._encode_transition_term(\n                t,\n                current_state,\n                next_state,\n                scenario,\n                substitutions=substitutions,\n            )\n            for t in transitions\n        }\n\n        if self.config.substitute_subformulas:\n            if all(\n                isinstance(t, sympy.Expr)\n                for k, v in transition_terms.items()\n                for t in v\n            ):\n                # substitutions are FNodes\n                # transition terms are sympy.Expr\n                # convert relevant substitutions to sympy.Expr\n                # sympy subs transition term with converted subs\n                # simplify/approximate substituted formula\n                # convert to pysmt formula\n                # TODO store substitutions as both FNode and pysmt.Expr to avoid extra conversion\n                transition_terms = {\n                    k: [\n                        FUNMANSimplifier.sympy_simplify(\n                            t,\n                            parameters=scenario.model_parameters(),\n                            substitutions=substitutions,\n                            threshold=self.config.series_approximation_threshold,\n                            taylor_series_order=self.config.taylor_series_order,\n                        )\n                        for t in v\n                    ]\n                    for k, v in transition_terms.items()\n                }\n            else:\n                transition_terms = {\n                    k: v.substitute(substitutions)\n                    for k, v in transition_terms.items()\n                }\n        else:\n            # Need to convert transition terms to pysmt without substituting\n            transition_terms = {\n                k: [rate_expr_to_pysmt(t, current_state) for t in v]\n                for k, v in transition_terms.items()\n            }\n\n        # for each var, next state is the net flow for the var: sum(inflow) - sum(outflow)\n        net_flows = []\n        for var in state_vars:\n            state_var_flows = []\n            for transition in transitions:\n                state_var_id = scenario.model._state_var_id(var)\n\n                transition_id = scenario.model._transition_id(transition)\n                outflow = scenario.model._num_flow_from_state_to_transition(\n                    state_var_id, transition_id\n                )\n                inflow = scenario.model._flow_into_state_via_transition(\n                    state_var_id, transition_id\n                )\n                net_flow = inflow - outflow\n\n                if net_flow != 0:\n                    state_var_flows.append(\n                        [\n                            Times(Real(net_flow) * t)\n                            for t in transition_terms[transition_id]\n                        ]\n                    )\n            if len(state_var_flows) > 0:\n                # FIXME: the below should involve computing update as the cross product of all transition_rate equations\n                flows = Plus(\n                    Times(\n                        Real(step_size),\n                        Plus(\n                            [s[0] for s in state_var_flows]\n                        ),  # FIXME see above\n                    ),  # .simplify()\n                    current_state[state_var_id],\n                )  # .simplify()\n                if self.config.substitute_subformulas:\n                    # flows = flows.substitute(substitutions)\n                    flows = FUNMANSimplifier.sympy_simplify(\n                        # flows.substitute(substitutions),\n                        to_sympy(\n                            flows.substitute(substitutions).simplify(),\n                            scenario.model._symbols(),\n                        ),\n                        parameters=scenario.model_parameters(),\n                        threshold=self.config.series_approximation_threshold,\n                        taylor_series_order=self.config.taylor_series_order,\n                    )\n            else:\n                flows = current_state[state_var_id]\n                # .substitute(substitutions)\n\n            net_flows.append(Equals(next_state[state_var_id], flows))\n            if self.config.substitute_subformulas:\n                substitutions[next_state[state_var_id]] = flows\n\n        # If any variables depend upon time, then time updates need to be encoded.\n        if time_var is not None:\n            time_increment = (\n                Plus(current_time_var, Real(step_size))\n                .substitute(substitutions)\n                .simplify()\n            )\n            time_update = Equals(next_time_var, time_increment)\n            if self.config.substitute_subformulas:\n                substitutions[next_time_var] = time_increment\n        else:\n            time_update = TRUE()\n\n        return (\n            And(net_flows + [time_update]),\n            substitutions,\n        )\n\n    def _define_init(\n        self, scenario: \"AnalysisScenario\", init_time: int = 0\n    ) -> FNode:\n        initial_state, substitutions = super()._define_init(\n            scenario, init_time=init_time\n        )\n        # state_var_names = scenario.model._state_var_names()\n        # initial_substitution = {}\n\n        if self.config.use_compartmental_constraints:\n            compartmental_bounds = self._encode_compartmental_bounds(\n                scenario, 0\n            )\n            if self.config.substitute_subformulas and substitutions:\n                compartmental_bounds = compartmental_bounds.substitute(\n                    substitutions\n                ).simplify()\n        else:\n            compartmental_bounds = TRUE()\n        initial_state = And(initial_state, compartmental_bounds).simplify()\n\n        return initial_state, substitutions\n\n    def _encode_compartmental_bounds(\n        self,\n        scenario: \"AnalysisScenario\",\n        step,\n        substitutions: Dict[FNode, FNode] = {},\n    ):\n        bounds = []\n\n        if self.config.normalize:\n            population = Real(1.0)\n        else:\n            population = (\n                Plus(\n                    [\n                        self._encode_state_var(\n                            scenario.model._state_var_name(var1), time=step\n                        )\n                        for var1 in scenario.model._state_vars()\n                    ]\n                )\n                .substitute(substitutions)\n                .simplify()\n            )\n\n        for var in scenario.model._state_vars():\n            lb = GE(\n                self._encode_state_var(\n                    scenario.model._state_var_name(var), time=step\n                ),\n                Real(0.0),\n            )\n            ub = LE(\n                self._encode_state_var(\n                    scenario.model._state_var_name(var), time=step\n                ),\n                population,\n            )\n\n            bounds += [lb, ub]\n        # noise_var = Symbol(\"noise\", REAL)\n        noise_const = Real(1e-3)\n        sum_vars = Plus(\n            [\n                self._encode_state_var(\n                    scenario.model._state_var_name(var), time=step\n                )\n                for var in scenario.model._state_vars()\n            ]\n        )\n        total = And(\n            LE(sum_vars, Plus(population, noise_const)),\n            LE(Minus(population, noise_const), sum_vars),\n        )\n\n        return And(bounds + [total])\n\n    def _encode_transition_term(\n        self, transition, current_state, next_state, scenario, substitutions={}\n    ) -> Union[sympy.Expr, FNode]:\n        transition_id = scenario.model._transition_id(transition)\n        input_edges = scenario.model._input_edges()\n        output_edges = scenario.model._output_edges()\n        state_subs = {s: str(f) for s, f in current_state.items()}\n\n        ins = [\n            current_state[scenario.model._edge_source(edge)]\n            for edge in input_edges\n            if scenario.model._edge_target(edge) == transition_id\n        ]\n        # The model expresses each rate with untimed variable symbols.\n        # If not yet approximated, approximate and cache term.\n        # Substitute current time variable symbols\n        if (\n            scenario.model._transition_id(transition)\n            not in self._transition_rate_cache\n        ):\n            model_transition_rates = scenario.model._transition_rate(\n                transition\n            )\n            if all(\n                isinstance(r, str) or isinstance(r, float)\n                for r in model_transition_rates\n            ):\n                self._transition_rate_cache[\n                    scenario.model._transition_id(transition)\n                ] = model_transition_rates\n            elif all(\n                isinstance(r, sympy.Expr) for r in model_transition_rates\n            ):\n                self._transition_rate_cache[\n                    scenario.model._transition_id(transition)\n                ] = [\n                    series_approx(\n                        (\n                            r\n                            if isinstance(r, sympy.Expr)\n                            else to_sympy(r, scenario.model._symbols())\n                        ),\n                        vars=[\n                            mp.name\n                            for mp in scenario.model_parameters()\n                            if mp in scenario.synthesized_parameters()\n                        ],\n                    )\n                    for r in scenario.model._transition_rate(transition)\n                ]\n            else:\n                raise Exception(\n                    f\"Cannot encode model transition rate: {model_transition_rates}\"\n                )\n\n        transition_rates = []\n        for r in self._transition_rate_cache[\n            scenario.model._transition_id(transition)\n        ]:\n            if isinstance(r, sympy.Expr):\n                # is a custom rate expression\n                transition_rates.append(sympy_subs(r, state_subs))\n            elif isinstance(r, str):\n                # Is a single parameter\n                transition_rates.append(substitutions[Symbol(r, REAL)])\n            elif isinstance(r, float):\n                # Is a constant\n                transition_rates.append(Real(r))\n\n        if all(isinstance(t, sympy.Expr) for t in transition_rates):\n            return transition_rates  # Need to build Or(transition_rates) later after converting to FNodes\n        else:\n            return (\n                Or([(Times([tr] + ins)) for tr in transition_rates])\n                # .substitute(substitutions)\n                # .simplify()\n            )\n\n    def _get_timed_symbols(self, model: Model) -> Set[str]:\n        \"\"\"\n        Get the names of the state (i.e., timed) variables of the model.\n\n        Parameters\n        ----------\n        model : Model\n            The petrinet model\n\n        Returns\n        -------\n        List[str]\n            state variable names\n        \"\"\"\n        state_vars = set(model._state_var_names())\n        time_var = model._time_var()\n        if time_var:\n            state_vars.add(f\"timer_{time_var.id}\")\n        return state_vars\n","repo_name":"siftech/funman","sub_path":"src/funman/translate/petrinet.py","file_name":"petrinet.py","file_ext":"py","file_size_in_byte":13745,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"10459873676","text":"# films/forms.py\n\nfrom django import forms\nfrom films.models import Film, Director\nfrom .models import Review\n\n\nclass FilmForm(forms.ModelForm):\n    class Meta:\n        model = Film\n        fields = ('title', 'created_in_country',\n                  'available_in_countries', 'category', 'director')\n\n\nclass DirectorForm(forms.ModelForm):\n    class Meta:\n        model = Director\n        fields = ('first_name', 'last_name')\n\n\nclass ReviewForm(forms.ModelForm):\n    class Meta:\n        model = Review\n        fields = ['content']\n        widgets = {\n            'content': forms.Textarea(attrs={'rows': 4}),\n        }\n\n    def save(self, commit=True, user=None):\n        review = super().save(commit=False)\n        if user:\n            review.review_author = user\n        if commit:\n            review.save()\n        return review\n","repo_name":"davidholo2/DI_Bootcamp_April2023","sub_path":"Week6/Day2/dc/FilmProject/films/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"44170216612","text":"class Solution:\n\n    def merge(self,head1,len1,head2,len2):\n        vHead=ListNode(0)\n        cur=vHead\n        p=q=0\n        while p<len1 or q<len2:\n            if p<len1 and ( q>=len2 or head1.val<head2.val ):\n                p+=1\n                cur.next=head1\n                head1=head1.next\n            else:\n                q+=1\n                cur.next=head2\n                head2=head2.next\n            cur=cur.next\n        cur.next=None\n        return vHead.next\n\n    def sort0(self, length):\n        if length==1:\n            head=self.cur\n            self.cur=self.cur.next\n            return head\n        lHead=self.sort0(length//2)\n        rHead=self.sort0(length-length//2)\n        return self.merge(lHead,length//2,rHead,length-length//2)\n\n    # @param head, a ListNode\n    # @return a ListNode\n    def sortList(self, head):\n        if head==None:\n            return None\n        length=0\n        cur=head\n        while cur!=None:\n            length+=1\n            cur=cur.next\n        self.cur=head\n        return self.sort0(length)","repo_name":"coderfengyun/TCSE2012","sub_path":"WeeklyCode/week6/Sort List/chenhao.py","file_name":"chenhao.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"37580620344","text":"import copy\n\n# local imports\nimport utilities\n\nMETRIC_PREFIX = 'custom.cloudmonitoring.googleapis.com/kubernetes.io/'\nMETRIC_NAMES = [\n    METRIC_PREFIX + 'cpu/usage',\n    METRIC_PREFIX + 'memory/page_faults',\n    METRIC_PREFIX + 'memory/usage',\n    METRIC_PREFIX + 'memory/working_set',\n    METRIC_PREFIX + 'network/rx',\n    METRIC_PREFIX + 'network/rx_errors',\n    METRIC_PREFIX + 'network/tx',\n    METRIC_PREFIX + 'network/tx_errors',\n    METRIC_PREFIX + 'uptime'\n]\n\n\ndef _get_container_labels(container, parent_pod):\n  \"\"\"Returns key/value pairs identifying all metrics of this container.\n\n  Args:\n    container: the container object to annotate.\n    parent_pod: the parent pod of 'container'.\n\n  Returns:\n  A dictionary of key/value pairs.\n  If any error was detected, returns None.\n  \"\"\"\n  if not utilities.is_wrapped_object(container, 'Container'):\n    return None\n  if not utilities.is_wrapped_object(parent_pod, 'Pod'):\n    return None\n\n  pod_id = utilities.get_attribute(\n      parent_pod, ['properties', 'metadata', 'uid'])\n  if not utilities.valid_string(pod_id):\n    return None\n\n  hostname = utilities.get_attribute(\n      parent_pod, ['properties', 'spec', 'nodeName'])\n  if not utilities.valid_string(hostname):\n    return None\n\n  short_container_name = utilities.get_attribute(\n      container, ['properties', 'metadata', 'name'])\n  if not utilities.valid_string(short_container_name):\n    return None\n\n  return {\n      'pod_id': pod_id,\n      'hostname': hostname,\n      'container_name': short_container_name\n  }\n\n\ndef _get_node_labels(node):\n  \"\"\"Returns key/value pairs identifying all metrics of this node.\n\n  Args:\n    node: the node object to annotate.\n\n  Returns:\n  A dictionary of key/value pairs.\n  If any error was detected, returns None.\n  \"\"\"\n  if not utilities.is_wrapped_object(node, 'Node'):\n    return None\n\n  hostname = utilities.get_attribute(node, ['properties', 'metadata', 'name'])\n  if not utilities.valid_string(hostname):\n    return None\n\n  return {\n      'pod_id': '',\n      'hostname': hostname,\n      'container_name': '/'\n  }\n\n\ndef _make_gcm_metrics(labels_dict):\n  \"\"\"Generate a descriptor of GCM metrics from 'labels_dict'.\n\n  Args:\n    labels_dict: the key/value pairs that identify all metrics of the\n    current resource.\n\n  Returns:\n  A dictionary containing the descriptor of the GCM metrics.\n  See below for details.\n  If 'labels_dict' is None, returns None.\n\n  Typical output is:\n  {\n    'gcm': {\n      'names': ['.../cpu/usage', '.../memory/page_faults', ...],\n      'project': PROJECT,\n      'labels_prefix': PREFIX,\n      'labels': {\n         'pod_id': POD_ID, 'hostname': HOSTNAME,\n         'container_name': CONTAINER_NAME }\n    }\n  }\n  \"\"\"\n  if not labels_dict:\n    return None\n\n  assert isinstance(labels_dict, dict)\n\n  return {'gcm': {\n      'names': copy.deepcopy(METRIC_NAMES),\n      'project': '_unknown_',\n      'labels': copy.deepcopy(labels_dict),\n      'labels_prefix': METRIC_PREFIX + 'label/'\n  }}\n\n\ndef annotate_container(container, parent_pod):\n  \"\"\"Annotate the given container with Heapster GCM metric information.\n\n  Args:\n    container: the container object to annotate.\n    parent_pod: the parent pod of 'container'.\n\n  Raises:\n    AssertionError: if the input arguments are invalid\n  \"\"\"\n  assert utilities.is_wrapped_object(container, 'Container')\n  assert utilities.is_wrapped_object(parent_pod, 'Pod')\n\n  m = _make_gcm_metrics(_get_container_labels(container, parent_pod))\n  if m is not None:\n    if 'annotations' not in container:\n      container['annotations'] = {}\n    container['annotations']['metrics'] = m\n\n\ndef annotate_node(node):\n  \"\"\"Annotate the given node with Heapster GCM metric information.\n\n  Args:\n    node: the node object to annotate.\n\n  Raises:\n    AssertionError: if the input argument is invalid.\n  \"\"\"\n  assert utilities.is_wrapped_object(node, 'Node')\n\n  m = _make_gcm_metrics(_get_node_labels(node))\n  if m is not None:\n    if 'annotations' not in node:\n      node['annotations'] = {}\n    node['annotations']['metrics'] = m\n","repo_name":"google/cluster-insight","sub_path":"collector/metrics.py","file_name":"metrics.py","file_ext":"py","file_size_in_byte":4032,"program_lang":"python","lang":"en","doc_type":"code","stars":99,"dataset":"github-code","pt":"86"}
+{"seq_id":"43627533321","text":"#! /usr/bin/python\n\nimport sys, os\nimport datetime\nimport argparse\n\nimport gts.readers\nimport gts.timeseries as ts\nimport gts.tsploters\n\nEXIT_SUCCESS = 0\nEXIT_FAILURE = 1\n\nparser = argparse.ArgumentParser(\n    description = 'This tool is used to read in a DSO formated time-series file  '\n    'and clear and sort its contents.'\n)\n\nparser.add_argument('-s', '--station',\n    action = 'store',\n    required = True,\n    help = 'The name of the station; the time-series file will be assumed to be: '\n    '\\\"<station-name>.cts\\\"',\n    metavar = 'STATION_NAME',\n    dest = 'tsf'\n)\n\nparser.add_argument('-c', '--comment',\n    action = 'store',\n    required = False,\n    help = 'If specified, only records containing this string as description will '\n    'be read and exported',\n    metavar = 'COMMENT',\n    dest = 'reccom',\n    default = None\n)\n\nparser.add_argument('-t', '--topocentric',\n    action = 'store_true',\n    required = False,\n    help = 'Transform the time-series from (geocentric) cartesian to a topocentric '\n    'reference frame',\n    #metavar = 'TRANSFORM_TOPOCENTRIC',\n    dest = 'totopo'\n)\n\n\n##  Parse command line arguments\nargs = parser.parse_args()\n\n##  The time-series file\nts_file = args.tsf + '.cts'\n\n##  read in the time-series file\nots = gts.readers.read_cts(ts_file, args.reccom)\n\n##  trasnform to topocentric if needed\nif args.totopo:\n    ots = ots.transform(ts.CoordinateType.Topocentric)\n\n##  print the time-series\nots.print_as_cts()\n\nsys.exit(EXIT_SUCCESS)\n","repo_name":"xanthospap/gnss-ts","sub_path":"python/tsclear.py","file_name":"tsclear.py","file_ext":"py","file_size_in_byte":1481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31464894585","text":"import pandas as pd\nimport logging\nimport re\nimport datetime\nimport json\nfrom typing import List, Union, Optional, Any\n\nfrom googleapiclient.errors import HttpError as GoogleApiHttpError\nfrom google.api_core.exceptions import ResourceExhausted, InvalidArgument, PermissionDenied\nimport google.analytics.data_v1beta.types as ga_data_types\n\nfrom . import googlepandas as gpd\nfrom .utils import general_utils\nfrom .utils.ga4_parser import parse_ga4_response, parse_ga3_response, join_ga4_responses\nfrom . import pga_logger\n\n\nclass MissingID(AttributeError):\n    \"\"\"Error raised when Property ID is not specified\"\"\"\n    def __init__(self, message=\"Missing ID\"):\n        self.message = message\n        super().__init__(self.message)\n\n\nclass GoogalyticsWrapper:\n    \"\"\"\n    The GoogalyticsWrapper requires the following arguments to access data:\n    - for GSC data: sc_domain. This is the url-like string you see in the Google Search Console web application\n    when selecting the site. It is either a full url (e.g. `https://www.example.com/`) or something like `sc_domain:example.com`\n    - for GA3 data: the \"view_id\" you see in \"settings\" on the GA web application. This is usually an 8- or 9-digit number, passed as a string\n    - for GA4 data: the ga4 property id.\n    \"\"\"\n    def __init__(self,\n                 gsc_resource,\n                 ga3_resource,\n                 ga4_resource,\n                 sc_domain: str = None,\n                 view_id: str = None,\n                 ga4_property_id: str = None):\n\n        self.sc_domain: str = sc_domain\n        self.view_id: str = view_id\n        self.ga4_property_id: str = ga4_property_id\n\n        self._api_test_gsc: dict = dict()\n        self._api_test_ga3: dict = dict()\n        self._api_test_ga4: dict = dict()\n\n        self.gsc_resource = gsc_resource\n        self.ga3_resource = ga3_resource\n        self.ga4_resource = ga4_resource\n\n        pga_logger.debug(f\"initialising GoogalyticsWrapper object\")\n\n    # *****************************************************************\n    # *** GAPI_WRAPPER STATS ******************************************\n\n    def __dict__(self) -> dict:\n        _dates_test = self.available_dates\n        gsc_date_range_str = general_utils.date_range_string(dates=_dates_test.get(\"GSC\"),\n                                                     alternate_text=\"No dates available from GSC\")\n        ga3_date_range_str = general_utils.date_range_string(dates=_dates_test.get(\"GA3\"),\n                                                     alternate_text=\"No dates available from GA3\")\n\n        return {\n            \"API config\": {\n                \"GSC sc-domain\": self.sc_domain,\n                \"GA3 View ID\": self.view_id,\n                \"GA4 Property ID\": self.ga4_property_id\n            },\n            \"API status\": {\n                \"GSC status\": self.api_test_gsc.get('status'),\n                \"GA3 status\": self.api_test_ga3.get('status'),\n                \"GSC error\": self.api_test_gsc.get('error'),\n                \"GA3 error\": self.api_test_ga3.get('error')\n            },\n            \"Available datas\": {\n                \"GSC\": gsc_date_range_str,\n                \"GA3\": ga3_date_range_str\n            }\n        }\n\n    def __repr__(self):\n        _s = \"GoogalyticsWrapper object:\\n\"\n        _s += json.dumps(self.__dict__(), indent=2)\n        return _s\n\n    @property\n    def api_summary(self) -> dict:\n        _dates_test = self.available_dates\n        _sc_domain = \"\"\n        if re.match(\"sc-domain:.+\", self.sc_domain):\n            _sc_domain = self.sc_domain\n        return {\"GA3 view id\": self.view_id,\n                \"sc-domain\": _sc_domain,\n                \"GA3 API\": self.api_test_ga3.get('status'),\n                \"GSC API\": self.api_test_gsc.get('status'),\n                \"GA3 dates\": len(_dates_test.get(\"GA3\")),\n                \"GSC dates\": len(_dates_test.get(\"GSC\")),\n                }\n\n    def _perform_api_test_gsc(self):\n        \"\"\"test GSC API\"\"\"\n        pga_logger.debug(f\"{self.__class__.__name__}.api_test() :: testing GSC api\")\n\n        _api_error = None\n\n        try:\n            _ = self.get_gsc_response(start_date=datetime.date.today() + datetime.timedelta(days=-7),\n                                      raise_http_error=True)\n            _api_status = \"Success\"\n            pga_logger.debug(f\"{self.__class__.__name__}.api_test() :: GSC api successful\")\n        except GoogleApiHttpError as http_e:\n            _api_status = \"HttpError\"\n            _api_error = http_e.reason.split('See also')[0]\n            pga_logger.debug(f\"{self.__class__.__name__}.api_test() :: GSC api failed\")\n        except Exception as http_e:\n            _api_status = \"Other Error\"\n            _api_error = repr(http_e)\n            pga_logger.debug(f\"{self.__class__.__name__}.api_test() :: GSC api failed\")\n            # The HttpError for GSC contains this unhelpful \"See also this answer to a question...\"\n            # which is just a link to an FAQ with a 404 error\n\n        self._api_test_gsc = {'status': _api_status, 'error': _api_error, 'timestamp': datetime.datetime.utcnow()}\n\n    @property\n    def api_test_gsc(self) -> dict:\n        if self._api_test_gsc.get('status') is None:\n            self._perform_api_test_gsc()\n        return self._api_test_gsc\n\n    @property\n    def api_test_ga3(self) -> dict:\n        if self._api_test_ga3.get('status') is None or not general_utils.test_time(self._api_test_ga3.get('timestamp'), 3600):\n            self._perform_api_test_ga3()\n        return self._api_test_ga3\n\n    def _perform_api_test_ga3(self):\n        \"\"\"test GA API\"\"\"\n        pga_logger.debug(f\"{self.__class__.__name__}.api_test() :: testing GA api\")\n        _api_error = None\n        if not self.view_id:\n            _api_status = \"No view id\"\n        try:\n            _ = self.get_ga3_response(start_date=datetime.date.today() + datetime.timedelta(days=-7),\n                                      raise_http_error=True, log_error=False)\n            _api_status = \"Success\"\n            pga_logger.debug(f\"{self.__class__.__name__}.api_test() :: GA api successful\")\n        except GoogleApiHttpError as http_e:\n            _api_status = \"HttpError\"\n            _api_error = repr(http_e)\n            pga_logger.debug(f\"{self.__class__.__name__}.api_test() :: GA api failed\")\n        except Exception as http_e:\n            _api_status = \"Other Error\"\n            _api_error = repr(http_e)\n            pga_logger.debug(f\"{self.__class__.__name__}.api_test() :: GA api failed\")\n        self._api_test_ga3 = {'status': _api_status, 'error': _api_error, 'timestamp': datetime.datetime.utcnow()}\n\n    @property\n    def api_test_ga4(self) -> dict:\n        return self._api_test_ga4\n\n    # *****************************************************************************************\n\n    @property\n    def available_dates(self) -> dict:\n        gsc_date_list: List[datetime.date] = self.get_dates(result=\"GSC\")\n        ga3_date_list: List[datetime.date] = self.get_dates(result=\"GA3\")\n        ga4_date_list: List[datetime.date] = self.get_dates(result=\"GA4\")\n        return {\"GA3\": ga3_date_list, \"GA4\": ga4_date_list, \"GSC\": gsc_date_list}\n\n    # *** Calls to Google API *****************************************************************\n\n    def get_inspection_response(self,\n                                inspection_url: str):\n        gsc_request = {\n            'siteUrl': self.sc_domain,\n            'inspectionUrl': inspection_url,\n        }\n        gsc_response = self.gsc_resource.urlInspection().index().inspect(body=gsc_request).execute()\n        return gsc_response\n\n    def get_gsc_response(self,\n                         start_date: Union[str, datetime.date],\n                         end_date: Optional[Union[str, datetime.date]] = None,\n                         gsc_dimensions: Optional[Union[List[str], str]] = None,\n                         row_limit: int = 25000,\n                         start_row: int = 0,\n                         raise_http_error: bool = False,\n                         _print_log: bool = False):\n\n        # dimension \"searchAppearance\" cannot be used alongside any other dimension\n\n        if end_date is None:\n            end_date = start_date\n\n        if isinstance(start_date, str):\n            start_date = datetime.datetime.strptime(start_date, \"%Y-%m-%d\").date()\n        if isinstance(end_date, str):\n            end_date = datetime.datetime.strptime(end_date, \"%Y-%m-%d\").date()\n\n        start_date_string = start_date.strftime(\"%Y-%m-%d\")\n        end_date_string = end_date.strftime(\"%Y-%m-%d\")\n\n        if gsc_dimensions is None:\n            gsc_dimensions = ['country', 'device', 'page', 'query']\n        elif isinstance(gsc_dimensions, str):\n            gsc_dimensions = [gsc_dimensions]\n\n        gsc_request = {\n            'startDate': start_date_string,\n            'endDate': end_date_string,\n            'dimensions': gsc_dimensions,\n            # 'dimensionFilterGroups': [{\n            #     'filters': [\n            #         {'dimension': 'country', 'expression': 'GBR'}\n            #     ]\n            # }],\n            # 'aggregationType': aggregation_type,\n            'rowLimit': row_limit,\n            'startRow': start_row\n        }\n\n        try:\n            gsc_response = self.gsc_resource.searchanalytics().query(siteUrl=self.sc_domain,\n                                                                     body=gsc_request).execute()\n        except GoogleApiHttpError as http_error:\n            if re.match(\".*user does not have sufficient permissions\", repr(http_error).lower()):\n                pga_logger.error(\n                    f\"{self.__class__.__name__}.get_gsc_response() :: user does not have sufficient permissions\")\n            if raise_http_error:\n                raise http_error\n            else:\n                if _print_log:\n                    print(f\"{self.__class__.__name__}.get_gsc_response() :: GoogleApiHttpError\")\n                    print(http_error)\n                return None\n\n        try:\n            _rows = gsc_response.get(\"rows\", None)\n        except AttributeError:\n            _rows = None\n\n        if _rows is None:\n            pga_logger.debug(f\"{self.__class__.__name__}.get_gsc_response() :: empty gsc response\")\n            if _print_log:\n                print(f\"{self.__class__.__name__}.get_gsc_response() :: empty gsc response\")\n            # raise EmptyResponseError(\"GSC\", start_date=start_date, end_date=end_date)\n            return None\n\n        return gsc_response\n\n    def get_ga3_response(self,\n                         start_date: datetime.date,\n                         end_date: datetime.date,\n                         dimensions: list[str] | str | None = None,\n                         metrics: list[str] | str | None = None,\n                         ga_filters: dict | None = None,\n                         raise_http_error: bool = False,\n                         log_error: bool = True,\n                         filter_google_organic: bool = False,\n                         _print_log: bool = False) -> Optional[dict]:\n\n        ga_dimensions = ['ga:' + _s if not re.match(r'ga:', _s) else _s for _s in dimensions]\n        ga_metrics = ['ga:'+_s if not re.match(r'ga:', _s) else _s for _s in metrics]\n\n        r = self._ga3_response_raw(\n            start_date=start_date,\n            end_date=end_date,\n            ga_dimensions=ga_dimensions,\n            ga_metrics=ga_metrics,\n            ga_filters=ga_filters,\n            filter_google_organic=filter_google_organic,\n            page_token=None\n        )\n\n        data = r.get('reports', [{}])[0].get('data', {}).get('rows', [])\n        column_header = r.get('reports', [{}])[0].get('columnHeader')\n        next_page_token = r.get('reports', [{}])[0].get('nextPageToken')\n        error = r.get('error', None)\n        error_type = r.get('error_type', None)\n\n        while next_page_token and not error:\n            r = self._ga3_response_raw(\n                start_date=start_date,\n                end_date=end_date,\n                ga_dimensions=ga_dimensions,\n                ga_metrics=ga_metrics,\n                ga_filters=ga_filters,\n                filter_google_organic=filter_google_organic,\n                page_token=next_page_token\n            )\n            error = r.get('error', None)\n            error_type = r.get('error_type', None)\n            _d = r.get('reports', [dict()])[0].get('data', dict()).get('rows', [])\n            data.extend(_d)\n            next_page_token = r.get('reports', [{}])[0].get('nextPageToken')\n\n        # print(f\"\\ndimensions: {ga_dimensions} \\n\"\n        #       f\"metrics: {ga_metrics} \\n \"\n        #       f\"data: len={len(data)}, \\n\"\n        #       f\"error_type: {error_type} \\n\"\n        #       f\"column_header: {column_header} \\n\")\n\n        if column_header is not None:\n            response = parse_ga3_response(column_header=column_header, response_rows=data)\n        else:\n            response = {\n                'dimension_headers': dimensions,\n                'metric_headers': metrics,\n                'row_count': 0,\n                'rows': []\n            }\n\n        response['response_type'] = 'GA3'\n        response['start_date'] = start_date\n        response['end_date'] = end_date\n\n        response['error'] = error\n        response['error_type'] = error_type\n\n        return response\n\n    def _ga3_response_raw(self,\n                          start_date: datetime.date,\n                          end_date: datetime.date,\n                          ga_dimensions: list[str],\n                          ga_metrics: List[str],\n                          ga_filters: dict | None = None,\n                          filter_google_organic: bool = False,\n                          return_raw_response: bool = False,\n                          page_token: str = None,\n                          _print_log: bool = False):\n\n        if not self.view_id:\n            _r = {\n                'error': PermissionError(\"view_id is not set\"),\n                'error_type': 'missing_view_id'\n            }\n            return _r\n\n        start_date_string = start_date.strftime(\"%Y-%m-%d\")\n        end_date_string = end_date.strftime(\"%Y-%m-%d\")\n\n        _dfc = []  # dimension filter clauses\n        _mfc = []  # metric filter clauses\n        _orderby = []\n\n        if ga_filters:\n            for filter_dict in ga_filters:\n                if not isinstance(filter_dict.get('filters'), list):\n                    continue\n                if len(filter_dict.get('filters')) != 0:\n                    if filter_dict.get('filters')[0].get('dimensionName'):\n                        _dfc.append(filter_dict)\n                    elif filter_dict.get('filters')[0].get('metricName'):\n                        _mfc.append(filter_dict)\n\n        if filter_google_organic is True:\n            _dfc.append({\"operator\": 'OR',\n                         \"filters\": [{\"dimensionName\": 'ga:sourceMedium',\n                                      \"not\": 'false',\n                                      \"operator\": 'EXACT',\n                                      \"expressions\": ['google / organic'],\n                                      \"caseSensitive\": 'false'\n                                      }]\n                         })\n\n        if 'ga:itemRevenue' in ga_metrics:\n            _orderby.append({\n                \"fieldName\": 'ga:itemRevenue',\n                \"orderType\": 'VALUE',\n                \"sortOrder\": 'DESCENDING'\n            })\n\n        _request_dict = {\n            'viewId': self.view_id,\n            'dateRanges': [{'startDate': start_date_string, 'endDate': end_date_string}],\n            # 'dimensions': [{'name': 'ga:productName'}],\n            # 'metrics': [{'expression': 'ga:itemRevenue'}]\n            'dimensions': [{'name': _d} for _d in ga_dimensions],\n            \"dimensionFilterClauses\": _dfc,\n            \"metricFilterClauses\": _mfc,\n            \"orderBys\": _orderby,\n            'metrics': [{'expression': _m} for _m in ga_metrics],\n            'pageSize': 100_000\n        }\n\n        if page_token:\n            _request_dict.update({'pageToken': page_token})\n\n        ga3_request = {'reportRequests': [_request_dict]}\n\n        _error = None\n        _error_type = None\n        try:\n            ga3_response = self.ga3_resource.reports().batchGet(body=ga3_request).execute()\n            if return_raw_response:\n                pga_logger.info(f\"{self.__class__.__name__}.get_ga3_response() :: returning raw response\")\n                return ga3_response\n        except GoogleApiHttpError as http_error:\n            _error = http_error\n            _msg = ''\n            if re.match(\".*user does not have sufficient permissions\", repr(http_error).lower()):\n                _error_type = 'insufficient_permissions'\n                _msg = f\"{self.__class__.__name__}.get_ga3_response() :: user does not have sufficient permissions\"\n            if re.match(\".*viewid must be set\", repr(http_error).lower()):\n                _error_type = 'missing_view_id'\n                _msg = f\"{self.__class__.__name__}.get_ga3_response() :: view id is not set\"\n            ga3_response = None\n        except Exception as _e:\n            _error = _e\n            _error_type = 'other'\n            ga3_response = None\n\n        if ga3_response is None:\n            ga3_response = dict()\n        else:\n            try:\n                _rows = ga3_response.get('reports', [])[0].get('data').get('rows', None)\n            except (AttributeError, KeyError) as _e:\n                _rows = None\n\n            if _rows is None:\n                _error = AttributeError('ga3_response in incorrect format.')\n                _error_type = 'empty_response'\n                pga_logger.debug(f\"{self.__class__.__name__}.get_ga3_response() :: empty ga response\")\n                # raise EmptyResponseError(\"GA3\", start_date=start_date, end_date=end_date)\n\n        ga3_response['error'] = _error\n        ga3_response['error_type'] = _error_type\n\n        return ga3_response\n\n    def _ga4_response_raw(self,\n                         start_date: datetime.date,\n                         end_date: datetime.date,\n                         ga4_dimensions: list[ga_data_types.Dimension],\n                         ga4_metrics: list[ga_data_types.Metric],\n                         limit: int,\n                         offset: int):\n\n        if not self.ga4_property_id:\n            raise MissingID(\"ga4_property_id is not set\")\n\n        request = ga_data_types.RunReportRequest(\n            property=f\"properties/{self.ga4_property_id}\",\n            dimensions=ga4_dimensions,\n            metrics=ga4_metrics,\n            date_ranges=[\n                ga_data_types.DateRange(\n                    start_date=start_date.strftime(\"%Y-%m-%d\"),\n                    end_date=end_date.strftime(\"%Y-%m-%d\")\n                )\n            ],\n            limit=limit,\n            offset=offset,\n            return_property_quota=True\n        )\n        ga4_response = self.ga4_resource.run_report(request)\n\n        return ga4_response\n\n    def get_ga4_response(self,\n                         start_date: datetime.date,\n                         end_date: datetime.date,\n                         dimensions: list[str],\n                         metrics: list[str],\n                         limit: int | None = None) -> (list, dict, Any):\n\n        ga_dimensions = [ga_data_types.Dimension(name=_) for _ in dimensions]\n        ga_metrics = [ga_data_types.Metric(name=_) for _ in metrics]\n\n        request_limit = 100_000\n        if limit is None:\n            limit = 1_000_000_000\n        elif limit < 100_000:\n            request_limit = limit\n\n        complete: bool = False\n        error_type: str | None = None\n        responses: list[dict] = []\n        error = None\n        offset: int = 0\n\n        while not complete:\n            num_tries = 0\n            success = False\n            response = dict()\n            while num_tries < 3 and not success:\n                error = None\n                try:\n                    ga4_response = self._ga4_response_raw(\n                        start_date=start_date,\n                        end_date=end_date,\n                        ga4_dimensions=ga_dimensions,\n                        ga4_metrics=ga_metrics,\n                        limit=request_limit,\n                        offset=offset\n                    )\n                    response = parse_ga4_response(ga4_response)\n                    success = True\n                except PermissionDenied as _permission_denied_error:\n                    complete = True\n                    error_type = 'permission_denied'\n                    error = _permission_denied_error\n                    num_tries = 1_000\n                except ResourceExhausted as _resource_exhausted_error:\n                    error_type = 'quota_reached'\n                    complete = True\n                    error = _resource_exhausted_error\n                    num_tries = 1_000\n                except MissingID as _id_error:\n                    complete = True\n                    error_type = 'missing_id'\n                    error = _id_error\n                    num_tries = 1_000\n                except InvalidArgument as _invalid_argument_error:\n                    if re.search(r\"metrics are incompatible\", _invalid_argument_error.message):\n                        error_type = 'invalid_arguments'\n                    complete = True\n                    error = _invalid_argument_error\n                    num_tries = 1_000\n                except Exception as _e:\n                    error = _e\n                    num_tries += 1\n\n            if success:\n                offset += response.get('row_count', 0)\n                if offset >= limit:\n                    complete = True\n                if response.get('row_count', 0) == 0 or offset >= response.get('meta_row_count', 1_000_000_000):\n                    complete = True\n\n                responses.append(response)\n\n        if len(responses) > 1:\n            response = join_ga4_responses(responses)\n            if response.get('row_count', 0) == 0:\n                error = AttributeError(\"Empty response\")\n                error_type = \"empty_response\"\n        elif len(responses) == 1:\n            response = responses[0]\n            if response.get('row_count', 0) == 0:\n                error = AttributeError(\"Empty response\")\n                error_type = \"empty_response\"\n        else:\n            response = {\n                'response_type': 'GA4',\n                'dimension_headers': dimensions,\n                'metric_headers': metrics,\n                'rows': []\n            }\n\n        response['start_date'] = start_date\n        response['end_date'] = end_date\n\n        response['error'] = error\n        response['error_type'] = error_type\n\n        return response\n\n    def get_dates(self,\n                  result: str,\n                  start_date: Optional[Union[datetime.date, str, int]] = None,\n                  end_date: Optional[Union[datetime.date, str]] = None,\n                  reverse: bool = False) -> List[datetime.date]:\n\n        # set the end_date to yesterday by default.\n        # GA data is \"available\" for today but it is not the whole day.\n        if end_date is None:\n            end_date = datetime.date.today() + datetime.timedelta(days=-1)\n\n        if isinstance(start_date, str):\n            start_date = datetime.datetime.strptime(start_date, '%Y-%m-%d').date()\n        if isinstance(end_date, str):\n            end_date = datetime.datetime.strptime(end_date, '%Y-%m-%d').date()\n\n        if isinstance(start_date, int):\n            start_date = end_date + datetime.timedelta(days=-1 * start_date)\n\n        if re.match(r\"GA3\", result):\n            if start_date is None:\n                start_date = datetime.date.today() + datetime.timedelta(days=-1500)\n            dimensions = ['ga:date']\n            metrics = ['ga:sessions']\n        elif re.match(r\"GA4\", result):\n            if start_date is None:\n                start_date = datetime.date.today() + datetime.timedelta(days=-1500)\n            dimensions = ['date']\n            metrics = ['sessions']\n        elif re.match(r\"GSC\", result):\n            if start_date is None:\n                start_date = datetime.date.today() + datetime.timedelta(days=-500)\n            dimensions = ['date']\n            metrics = None\n        else:\n            raise KeyError(f\"invalid result {result}\")\n\n        _df = self.get_df(result=result,\n                          start_date=start_date,\n                          end_date=end_date,\n                          dimensions=dimensions,\n                          metrics=metrics,\n                          add_boolean_metrics=False)\n\n        if \"record_date\" not in _df.columns or len(_df) == 0:\n            return []\n\n        if \"record_date\" not in _df.columns or len(_df) == 0:\n            return []\n        else:\n            return sorted(list(_df[\"record_date\"]), reverse=reverse)\n\n    # *****************************************************************************************\n    # *** Return dataframe *****************************************************************\n\n    def get_df(self,\n               result: str,\n               start_date: Union[str, datetime.date] = None,\n               end_date: Optional[Union[str, datetime.date]] = None,\n               dimensions: Optional[Union[str, List[str]]] = None,\n               metrics: Optional[Union[str, List[str]]] = None,\n               row_limit: Optional[int] = None,\n               url_list: Optional[Union[str, List[str]]] = None,\n               filter_google_organic: bool = False,\n               filters: List[dict] = None,\n               add_boolean_metrics: bool = False,\n               _return_response: bool = False,\n               raise_errors: bool = True\n               ) -> Union[gpd.GADataFrame, gpd.GSCDataFrame, pd.DataFrame]:\n        \"\"\"\n        The `get_df` method accepts the following values for the `result` argument:\n        - \"GSC\": for Google Search Console data\n        - \"GA3\": for Google Analytics 3 (UA) data\n        - \"URL\": for Google Search Console URL inspection data\n        - \"GA4\": for Google Analytics 4 data (note, this is not yet available in production)\n        \"\"\"\n\n        if start_date is None:\n            if result == \"GSC\":\n                start_date = datetime.date.today() + datetime.timedelta(days=-3)\n            else:\n                start_date = datetime.date.today()\n\n        if end_date is None:\n            end_date = start_date\n\n        if isinstance(start_date, str):\n            start_date = datetime.datetime.strptime(start_date, '%Y-%m-%d').date()\n        if isinstance(end_date, str):\n            end_date = datetime.datetime.strptime(end_date, '%Y-%m-%d').date()\n\n        if isinstance(dimensions, str):\n            dimensions = [dimensions]\n        if isinstance(metrics, str):\n            metrics = [metrics]\n        if isinstance(url_list, str):\n            url_list = [url_list]\n\n        if re.match(r\"GA4\", result):\n            return self._get_analytics_df(\n                response_type='GA4',\n                start_date=start_date,\n                end_date=end_date,\n                dimensions=dimensions,\n                metrics=metrics,\n                add_boolean_metrics=add_boolean_metrics,\n                limit=row_limit,\n                filters=filters,\n                return_response=_return_response,\n            )\n        elif re.match(r\"GA3\", result):\n            return self._get_analytics_df(\n                response_type='GA3',\n                start_date=start_date,\n                end_date=end_date,\n                dimensions=dimensions,\n                metrics=metrics,\n                filters=filters,\n                add_boolean_metrics=add_boolean_metrics,\n                return_response=_return_response\n            )\n        elif re.match(r\"GSC\", result) and result != \"GSCQ\":\n            if row_limit is None:\n                row_limit = 100000\n            return self._get_gsc_df(start_date=start_date,\n                                    end_date=end_date,\n                                    gsc_dimensions=dimensions,\n                                    row_limit=row_limit,\n                                    add_boolean_metrics=add_boolean_metrics)\n        elif result == 'GSCQ':\n            if row_limit is None:\n                row_limit = 100000\n            return self._get_gsc_df(start_date=start_date,\n                                    end_date=end_date,\n                                    gsc_dimensions=['query'],\n                                    row_limit=row_limit,\n                                    add_boolean_metrics=add_boolean_metrics)\n        elif result == 'URL':\n            return self._get_urlinspection_df(url_list=url_list)\n        else:\n            raise KeyError(f\"invalid result {result}\")\n\n    def _get_gsc_df_raw(self,\n                        start_date: datetime.date,\n                        end_date: datetime.date,\n                        row_limit: int,\n                        gsc_dimensions: List[str]) -> Optional[gpd.GSCDataFrame]:\n\n        gsc_response = self.get_gsc_response(start_date=start_date, end_date=end_date,\n                                             gsc_dimensions=gsc_dimensions,\n                                             row_limit=min(row_limit, 25000))\n\n        if gsc_response is None:\n            # Make an empty GSCDataFrame\n            gsc_df = gpd.GSCDataFrame(df_input=None,\n                                      gsc_dimensions=gsc_dimensions)\n            _response_aggregation = None\n        else:\n            # Make a dataframe of the gsc response\n            gsc_df = gpd.from_response(response=gsc_response,\n                                       response_type=\"GSC\",\n                                       gsc_dimensions=gsc_dimensions)\n            _response_aggregation = gsc_df.response_aggregation\n\n        if (row_limit > 25000) and (len(gsc_df) == 25000):\n            temp_row_limit = row_limit - 25000\n            temp_start_row = 25000  # not 25001: \"Zero-based index of the first row in the response\"\n            frames = [gsc_df]\n            while temp_row_limit > 0:\n\n                gsc_response2 = self.get_gsc_response(start_date=start_date, end_date=end_date,\n                                                      gsc_dimensions=gsc_dimensions,\n                                                      row_limit=min(temp_row_limit, 25000),\n                                                      start_row=temp_start_row)\n                if gsc_response2 is None:\n                    break  # exit the while loop if we get an empty response\n\n                # Make a dataframe of the second gsc response\n                new_gsc_df = gpd.from_response(response=gsc_response2,\n                                               response_type=\"GSC\",\n                                               gsc_dimensions=gsc_dimensions)\n                frames.append(new_gsc_df)\n\n                temp_row_limit -= 25000\n                temp_start_row += 25000\n\n            if len(frames) > 1:\n                gsc_df = gpd.GSCDataFrame(pd.concat(frames, ignore_index=True),\n                                          gsc_dimensions=gsc_dimensions)\n\n        gsc_df.response_aggregation = _response_aggregation\n\n        return gsc_df\n\n    def _get_gsc_df(self,\n                    start_date: datetime.date,\n                    end_date: datetime.date,\n                    row_limit: int = 200000,\n                    gsc_dimensions: Optional[List[str]] = None,\n                    add_boolean_metrics: bool = True) -> Optional[gpd.GSCDataFrame]:\n\n        if gsc_dimensions is None:\n            gsc_dimensions = ['date', 'country', 'device', 'page', 'query']\n\n        gsc_df = self._get_gsc_df_raw(start_date=start_date,\n                                      end_date=end_date,\n                                      row_limit=row_limit,\n                                      gsc_dimensions=gsc_dimensions)\n\n        if gsc_df is None:\n            return None\n\n        if add_boolean_metrics:\n            gsc_df.add_question_column()\n            gsc_df.add_transactional_column()\n            gsc_df.add_investigation_column()\n\n        return gsc_df\n\n    def _get_analytics_df(self,\n                    response_type: str,\n                    start_date: datetime.date | str,\n                    end_date: datetime.date | str | None = None,\n                    dimensions: Optional[List[str]] = None,\n                    metrics: list[str] | list[list[str]] = None,\n                    add_boolean_metrics: bool = True,\n                    filters: Optional[dict] = None,\n                    limit: int | None = 100_000_000,\n                    return_response: bool = False,\n                    raise_errors: bool = False) -> gpd.GADataFrame:\n\n        if dimensions is None:\n            dimensions = ['dateHour']\n        elif isinstance(dimensions, str):\n            dimensions = dimensions.split(';')\n        dimensions = [re.sub(r\"^ga:\", \"\", _) for _ in dimensions]\n\n        if metrics is None:\n            metrics = ['itemRevenue']\n\n        if not end_date:\n            end_date = start_date\n        if isinstance(start_date, str):\n            start_date = general_utils.parse_date(start_date)\n        if isinstance(end_date, str):\n            end_date = general_utils.parse_date(end_date)\n        if end_date < start_date:\n            raise ValueError(\"date range incompatible: end_date < start_date\")\n        if start_date > datetime.date.today():\n            raise ValueError(\"date range incompatible: start_date in the future\")\n\n        if isinstance(metrics, str):\n            metrics = metrics.split(';')\n        if all(isinstance(_, str) for _ in metrics):\n            metrics = [metrics]\n\n        metrics_list: list[list[str]] = []\n        for _list in metrics:\n            metrics_list.extend([_list[10 * i:10 * i + 10] for i in range((len(_list) - 1) // 10 + 1)])\n        metrics_list = [[re.sub(r\"^ga:\", \"\", _m) for _m in _sub_list] for _sub_list in metrics_list]\n\n        responses: list = []\n        breaking_error: bool = False\n        breaking_error_type: str | None = None\n        for _metrics in metrics_list:\n            if response_type == 'GA3':\n                _r = self.get_ga3_response(\n                    start_date=start_date,\n                    end_date=end_date,\n                    dimensions=dimensions,\n                    metrics=_metrics,\n                    ga_filters=filters,\n                    raise_http_error=False\n                )\n            elif response_type == 'GA4':\n                _r = self.get_ga4_response(\n                    start_date=start_date,\n                    end_date=end_date,\n                    dimensions=dimensions,\n                    metrics=_metrics,\n                    limit=limit\n                )\n            else:\n                raise KeyError(\"response_type not recognised\")\n\n            responses.append(_r)\n            if _r.get('error_type') is not None:\n                if _r.get('error_type') != 'empty_response':\n                    breaking_error = True\n                    breaking_error_type = _r.get('error_type')\n                    break\n\n        if return_response:\n            return responses\n\n        if raise_errors:\n            _errors = [_r.get('error') for _r in responses if _r.get('error') is not None]\n            if len(_errors) > 0:\n                raise _errors[0]\n\n        if breaking_error:\n            frames = []\n        else:\n            frames = [gpd.from_response(response=_r) for _r in responses]\n\n        # if len(frames)>0:\n        #     return frames\n\n        if len(frames) == 0:\n            dataframe = gpd.GADataFrame(df_input=None,\n                                     dimensions=dimensions,\n                                     metrics=general_utils.expand_list(metrics_list),\n                                     start_date=start_date,\n                                     end_date=end_date,\n                                     error = breaking_error_type)\n\n        elif all(len(_frame) == 0 for _frame in frames):\n            dataframe = gpd.GADataFrame(df_input=None,\n                                     dimensions=dimensions,\n                                     metrics=general_utils.expand_list(metrics_list),\n                                     start_date=start_date,\n                                     end_date=end_date,\n                                     error = breaking_error_type)\n        elif len(frames) == 1:\n            dataframe = frames[0]\n        else:\n            dataframe = frames[0]\n            for i in range(1, len(frames)):\n                dataframe = dataframe.join_on_dimensions(frames[i], how=\"outer\")\n            if len(dataframe) == 0 and not dataframe.error:\n                dataframe.error = 'empty_response'\n\n        if add_boolean_metrics:\n            dataframe.add_google_organic_column()\n            dataframe.add_has_item_column()\n            dataframe.add_new_user_column()\n            dataframe.add_shopping_stage_all_column()\n            dataframe.add_has_site_search_column()\n\n        dataframe.fill_nan_with_zeros()\n\n        return dataframe\n\n\n    def urlinspection_dict(self, url: str, inspection_index: int = None) -> dict:\n        _now = datetime.datetime.utcnow()\n        _d = {\"record_date\": _now.date(),\n              \"record_time\": _now.time(),\n              \"url\": url}\n\n        try:\n            response = self.get_inspection_response(url)\n        except Exception as _e:\n            _d.update({\"response\": repr(_e)})\n            return _d\n\n        _inspectionResult = response.get(\"inspectionResult\")\n        if _inspectionResult is None:\n            _d.update({\"response\": \"empty\"})\n            return _d\n\n        _d.update({\"response\": \"success\"})\n\n        _indexStatusResult = _inspectionResult.get(\"indexStatusResult\")\n        if _indexStatusResult is not None:\n            _last_crawl_time = _indexStatusResult.get(\"lastCrawlTime\")\n            if _last_crawl_time is not None:\n                _last_crawl_time = datetime.datetime.strptime(_last_crawl_time, \"%Y-%m-%dT%H:%M:%SZ\")\n            _d.update({\"index_status_result_verdict\": _indexStatusResult.get(\"verdict\"),\n                       \"coverage_state\": _indexStatusResult.get(\"coverageState\"),\n                       \"robotstxt_state\": _indexStatusResult.get(\"robotsTxtState\"),\n                       \"indexing_state\": _indexStatusResult.get(\"indexingState\"),\n                       \"last_crawl_time\": _last_crawl_time,\n                       \"page_fetch_state\": _indexStatusResult.get(\"pageFetchState\"),\n                       \"google_canonical\": _indexStatusResult.get(\"googleCanonical\"),\n                       \"user_canonical\": _indexStatusResult.get(\"userCanonical\"),\n                       \"sitemap\": _indexStatusResult.get(\"sitemap\"),\n                       \"referring_urls\": _indexStatusResult.get(\"referringUrls\"),\n                       \"crawled_as\": _indexStatusResult.get(\"crawledAs\")})\n\n        _mobileUsabilityResult = _inspectionResult.get(\"mobileUsabilityResult\")\n        if _mobileUsabilityResult is not None:\n            _d.update({\"mobile_usability_result_verdict\": _mobileUsabilityResult.get(\"verdict\")\n                       })\n\n        _mobileUsabilityIssue = _inspectionResult.get(\"mobileUsabilityIssue\")\n        if _mobileUsabilityIssue is not None:\n            _d.update({\"mobile_usability_result_verdict\": _mobileUsabilityResult.get(\"verdict\")\n                       })\n        return _d\n\n    def _get_urlinspection_df(self,\n                              url_list: List[str]) -> pd.DataFrame:\n        if isinstance(url_list, str):\n            url_list = [url_list]\n        pga_logger.info(f\"{self.__class__.__name__}.get_urlinspection_df() :: \"\n                        f\"requesting url inspection for {len(url_list)} urls\")\n        _frames = []\n        for _i, url in enumerate(url_list):\n            _frames.append(self.urlinspection_dict(url, inspection_index=_i))\n\n        if len(_frames) == 0:\n            return pd.DataFrame()\n\n        df = pd.DataFrame(_frames)\n\n        df.rename(columns={'url': 'url_full'}, inplace=True)\n\n        df['url_parameter'] = df['url_full'].apply(general_utils.url_extract_parameter)\n        df['url'] = df['url_full'].apply(general_utils.strip_url)\n        df['url_nodomain'] = df['url_full'].apply(general_utils.url_strip_domain)\n\n        return df\n","repo_name":"Blink-SEO/pygoogalytics","sub_path":"pygoogalytics/googalytics_wrapper.py","file_name":"googalytics_wrapper.py","file_ext":"py","file_size_in_byte":40439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"5420688605","text":"from sys import stdin\nfrom collections import deque\n\ndx = [-1, 1, 0, 0]\ndy = [0, 0, -1, 1]\n\nn, m, k = map(int, stdin.readline().split())\nvisited = [[-1] * m for _ in range(n)]\ngraph = []\nfor _ in range(n):\n    graph.append(list(stdin.readline().strip()))\nx1, x2, y1, y2 = map(int, stdin.readline().split())\nx1, x2, y1, y2 = x1 - 1, x2 - 1, y1 - 1, y2 - 1\n\nqueue = deque([(x1, x2)])\nvisited[x1][x2] = 0\n\nwhile queue:\n    x, y = queue.popleft()\n    if x == y1 and y == y2:\n        break\n    for i in range(4):\n        # 한 방향으로 제한 거리까지 이동해보기\n        for j in range(k):\n            nx = x + dx[i] * (j + 1)\n            ny = y + dy[i] * (j + 1)\n            if nx < 0 or nx >= n or ny < 0 or ny >= m or graph[nx][ny] == '#':\n                break\n            if visited[nx][ny] == visited[x][y] + 1:\n                continue\n            elif visited[nx][ny] == -1:\n                visited[nx][ny] = visited[x][y] + 1\n                queue.append((nx, ny))\n            else:\n                break\n\nprint(visited[y1][y2])\n","repo_name":"H43RO/PythonAlgorithm","sub_path":"00.Solve/BOJ/16000-17000/16930.py","file_name":"16930.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"24231492711","text":"inp = 1634\r\nnum = inp\r\ndup = inp\r\nsum = 0\r\nno_of_digits = 0\r\n\r\nwhile dup > 0:\r\n  dup = int(dup / 10)\r\n  no_of_digits = no_of_digits + 1\r\n\r\nwhile num > 0:\r\n  part = num % 10\r\n  print(part)\r\n  val = (part**no_of_digits)\r\n  sum = sum + val\r\n  print('SUM = {} and VAL = {}\\n'.format(sum,val))\r\n  num = int(num / 10)\r\n\r\nif sum == inp:\r\n  print(\"Armstrong Number:\",sum)\r\nelse:\r\n  print(\"Not Armstrong Number:\",sum)","repo_name":"GitSyedUmar/GitPrograms","sub_path":"Own Sample Programs/ArmstrongNumber.py","file_name":"ArmstrongNumber.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34280557989","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Nov 10 08:20:33 2017\n\n@author: Stebbins\n\"\"\"\n\nimport random\nimport time\nfrom itertools import combinations\n\nnumber = [\"1\",\"2\",\"3\"] # number of shapes\nshape = [\"S\",\"D\",\"C\"]  # Square, Diamond, Circle\ncolors = [\"R\",\"G\",\"P\"] # Red, Green, Purple\nfill = [\"S\",\"E\",\"T\"]   # Solid, Empty, Transparent\n\n# Create 81 cards, 1 of each combination\noutput_list = []\nfor i in number:\n    for j in shape:\n        for k in colors:\n            for m in fill:\n                temp = i+j+k+m\n                output_list.append(temp)\n                \n#print (output_list)\n#print(\"--------------------------------------------------------\")\n\n            \n\n#print (temp_list)\n#print(\"--------------------------------------------------------\")\n#print(output_list)\n#print(\"--------------------------------------------------------\")\n\ndef allUnique(x):\n    seen = set()\n    return not any(i in seen or seen.add(i) for i in x)\n\ndef allSame(x):\n   return x[1:] == x[:-1]\n\ndef dealHand():\n    # create a copy of the main list and shuffle it\n    temp_list = output_list[:]\n    random.shuffle(temp_list)    \n    \n    #Grab the first 12 cards out of the shuffled list for this hand\n    this_hand = temp_list[0:6]\n#    print (this_hand)\n#    print(\"--------------------------------------------------------\")\n\n    sets_in_hand = 0\n    #Look for a match\n    for combo in combinations(this_hand,3):\n#        print (combo)\n        \n        temp1 = combo[0][0]+combo[1][0]+combo[2][0]\n        if allSame(temp1) == True or allUnique(temp1) == True:\n            \n            temp2 = combo[0][1]+combo[1][1]+combo[2][1]\n            if allSame(temp2) == True or allUnique(temp2) == True:\n                \n                temp3 = combo[0][2]+combo[1][2]+combo[2][2]\n                if allSame(temp3) == True or allUnique(temp3) == True:\n                    \n                    temp4 = combo[0][3]+combo[1][3]+combo[2][3]\n                    if allSame(temp4) == True or allUnique(temp4) == True:\n#                        print(\"SET!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\")\n                        sets_in_hand = sets_in_hand + 1\n#    print(\"Total sets in this hand = \",sets_in_hand)\n    \n    handToString(this_hand,sets_in_hand)\n    \n    if sets_in_hand > 0:\n        return True\n    else:\n        return False\n    \ndef handToString(input_hand,sets_in_hand):\n    f = open('OUTPUT.txt','a')  \n    \n    output_string = \"\"\n    i = 1\n    for each in input_hand:\n        output_string = output_string + str(i) + \",\" + each[0] + \",\" + each[1] + \",\" + each[2] + \",\" + each[3] + \"/\"\n        i = i + 1\n    output_string  = output_string + str(sets_in_hand)\n#    print (output_string)\n    f.write('\\n'+output_string)\n    f.close\n    \nt = time.time()\nhands = 100000\nhands_with_a_set = 0\nfor i in range (0,hands):\n    temp = dealHand()\n    \n    if temp == True:\n        hands_with_a_set = hands_with_a_set + 1\n              \nelapsed_time = time.time() - t\nprint(\"\")\nprint(\"Number of hands out of\",hands,\"that contain a set is =\",hands_with_a_set)\nprint(\"time to complete =\",elapsed_time,\"seconds\")    ","repo_name":"mikestebbins/SetCardGameGenerator","sub_path":"SetCardGameHandBuilder.py","file_name":"SetCardGameHandBuilder.py","file_ext":"py","file_size_in_byte":3077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"13589422251","text":"from datetime import timedelta, datetime\nfrom random import randint\n\nfrom airflow import DAG\nfrom airflow.contrib.operators.dataproc_operator import DataProcHiveOperator\n\nUSERNAME = 'asamoilov'\n\ndefault_args = {\n    'owner': USERNAME,\n    'start_date': datetime(2012, 1, 1, 0, 0, 0)\n}\n\ndag = DAG(\n    USERNAME + '_data_lake_etl_issue',\n    default_args=default_args,\n    description='Data Lake ETL tasks',\n    schedule_interval=\"0 0 1 1 *\",\n)\n\nods_issue = DataProcHiveOperator(\n    task_id='ods_issue',\n    dag=dag,\n    query=\"\"\"\n        INSERT OVERWRITE TABLE asamoilov.ods_issue PARTITION (year = {{ execution_date.year }})\n        SELECT CAST(user_id AS BIGINT) as user_id,\n            CAST(start_time AS TIMESTAMP) AS start_time,\n            CAST(end_time AS TIMESTAMP) AS end_time,\n            title,\n            description,\n            service\n        FROM asamoilov.stg_issue WHERE year(start_time) = {{ execution_date.year }};\n    \"\"\",\n    cluster_name='cluster-dataproc',\n    job_name=USERNAME + '_ods_issue_{{ execution_date.year }}_{{ params.job_suffix }}',\n    params={\"job_suffix\": randint(0, 100000)},\n    region='europe-west3',\n)\n","repo_name":"alexandersamoylov/de-training-airflow","sub_path":"data_lake_etl_issue.py","file_name":"data_lake_etl_issue.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"20356426596","text":"from pymongo import MongoClient\nfrom bson import ObjectId\nimport cv2\n\n\n\n\n\nclass profile:\n    def __init__(self,server=''):\n        if server =='':\n            self.client = MongoClient('mongodb://10.34.33.28:33333')\n        else:\n            self.client =MongoClient(server)\n        self.db = self.client.database\n        self.profilelist =[]\n\n\n\n\n    def create (self,imagepath, source, web_url, image_url, datetime, keyword, height, width):\n        prev_id = self.db.crawl.count()\n\n        documentformatter={\n            \"source\":source,\n            \"web url\": web_url,\n            \"image url\":image_url,\n            \"keyword\":keyword,\n            \"height\":height,\n            \"width\":width,\n            \"datetime\": datetime,\n            \"_id\" : ObjectId(repr(prev_id+1))\n        }\n        self.db.crawl.insert([documentformatter])\n\n\n    def find_bykeyword (self,tags):\n        count = self.db.crawl.find({\"keyword\":{\"$all\":tags}}).count()\n        if count  > 0:\n            for users in count:\n                self.profilelist[users] = self.db.crawl.find_one({\"keyword\":{\"$all\":tags}}).skip(users)\n            return self.db.crawl.find({\"keyword\":{\"$all\":tags}}).count()\n        else:\n            return -1\n\n    def update(self, tag, change):\n        numupdates = self.find_bykeyword(tag)\n\n        if numupdates > 0:\n            self.db.collection.update({\"keyword\":tag},{\"keyword\":change} )\n#update keywords\n\n    \n    def delete(self, tag):\n        numdeletes = self.find_bykeyword(tag)\n        if numdeletes > 0:\n            self.db.collection.remove({\"keyword\":tag})\n\n\ntest = profile()\ntest.imagepath = print(\"\\home\\katiechang\\Documents\\\\\")\n\n\n\n\n        \n","repo_name":"kkaatttiechang/Database","sub_path":"document.py","file_name":"document.py","file_ext":"py","file_size_in_byte":1660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32947684508","text":"import numpy as np\nimport random\nimport copy\n\nclass Solver:\n    def __init__(self, vehicle_count, track_count, vehicle_lengths, vehicle_series,\n                 vehicle_restrictions, track_lengths, departure_times,\n                 schedule_type, blocking_tracks):\n\n        #########################\n        # Load instance data #\n        #########################\n        self.vehicle_count = vehicle_count\n        self.track_count = track_count\n        self.vehicle_lengths = vehicle_lengths\n        self.vehicle_series = vehicle_series\n        self.vehicle_restrictions = np.array(vehicle_restrictions)\n        self.track_lengths = track_lengths\n        self.departure_times = departure_times\n        self.schedule_type = schedule_type\n        self.blocking_tracks = blocking_tracks\n\n        self.track_length_sum = sum(self.track_lengths)\n        self.vehicle_length_sum = sum(self.vehicle_lengths)\n\n        # tracks that are not blocking and are not blocked\n        blocked = [item - 1 for sublist in self.blocking_tracks.values() for item in sublist]\n        blocking = [item - 1 for item in self.blocking_tracks.keys()]\n        self.nonblocking_tracks = [t for t in list(range(self.track_count))\n                                   if t not in blocked + blocking]\n\n        # initial solution\n        self.initial_solution = self.generate_initial_solution()\n\n    def global_goal_first(self, solution):\n        # first subfunction\n\n        f_1 = 0\n        temp_first = None\n        for first, second in zip(solution.series_on_track, solution.series_on_track[1:]):\n            if first is not None and second is not None and first != second:\n                f_1 += 1\n            elif first is not None and second is None:\n                temp_first = first\n            elif first is None and second is not None and temp_first:\n                if temp_first != second:\n                    f_1 += 1\n                temp_first = None\n        p_1 = 1.0 / (solution.used_tracks_count - 1)\n\n        # second subfunction\n        f_2 = solution.used_tracks_count\n        p_2 = 1.0 / self.track_count\n\n        # third subfunction\n        f_3 = 0\n        for used, leftover in zip(solution.series_on_track, solution.unused_track_capacity):\n            if used is not None:\n                f_3 += leftover\n        p_3 = 1.0 / (self.track_length_sum - self.vehicle_length_sum)\n        \n        return (p_1 * f_1) + (p_2 * f_2) + (p_3 * f_3)\n\n    def global_goal_second(self, solution):\n        # first subfunction\n        g_1 = 0\n        for track_schedule in solution.schedule:\n            if len(track_schedule) > 1:\n                for first, second in zip(track_schedule, track_schedule[1:]):\n                    if self.schedule_type[first] == self.schedule_type[second]:\n                        g_1 += 1\n        r_1 = 1.0 / (self.vehicle_count - solution.used_tracks_count)\n\n        # second subfunction\n        g_2 = 0\n        temp_first = None\n        for first, second in zip(solution.schedule, solution.schedule[1:]):\n            if (len(first) != 0 and len(second) != 0 and\n                    self.schedule_type[first[-1]] == self.schedule_type[second[0]]):\n                g_2 += 1\n            elif len(first) != 0 and len(second) == 0:\n                temp_first = first[-1]\n            elif len(first) == 0 and len(second) != 0 and temp_first:\n                if self.schedule_type[temp_first] == self.schedule_type[second[0]]:\n                    g_2 += 1\n                temp_first = None\n        r_2 = 1.0 / (solution.used_tracks_count - 1)\n\n        # third subfunction\n        g_3 = 0\n        pair_counter = 0\n        for track_schedule in solution.schedule:\n            if len(track_schedule) > 1:\n                for first, second in zip(track_schedule, track_schedule[1:]):\n                    g_3 += self.__get_vehicle_departure_gap_factor(first, second)\n                    pair_counter += 1\n        r_3 = 1.0 / (15 * pair_counter)\n\n        return (r_1 * g_1) + (r_2 * g_2) + (r_3 * g_3)\n\n    def __get_vehicle_departure_gap_factor(self, vehicle_1, vehicle_2):\n        deprature_diff = self.departure_times[vehicle_2] - self.departure_times[vehicle_1]\n        if deprature_diff >= 10 and deprature_diff <= 20:\n            return 15\n        elif deprature_diff > 20:\n            return 10\n        else:\n            return -4 * (10 - deprature_diff)\n\n    def fitness_func(self, solution):\n        return self.global_goal_second(solution) / self.global_goal_first(solution)\n\n    def generate_initial_solution(self):\n        s = Solution(self.track_count, self.track_lengths)\n\n        # generate vehicle list and sort it by departure time (this is priority!)\n        vehicles = list(range(self.vehicle_count))\n        vehicles_sorted = self.__sort_by_departure_time(vehicles)\n\n        tracks = list(range(self.track_count))\n\n        for vehicle in vehicles_sorted:\n            track_availability = self.vehicle_restrictions[vehicle]\n            # Get all tracks that have current vehicle series assigned to them\n            # and find the best one that vehicle fits in if it exists\n            assigned_tracks = [t for t in tracks\n                               if s.series_on_track[t] == self.vehicle_series[vehicle]]\n            best_capacity = None\n            best_track = None\n            for t in assigned_tracks:\n                if not track_availability[t]:\n                    continue\n                # check for blocking tracks constraint validation\n                blocked_tracks = self.blocking_tracks.get(t + 1)\n                if blocked_tracks:\n                    invalid_flag = False\n                    for bt in blocked_tracks:\n                        if len(s.schedule[bt - 1]) > 0:\n                            if (self.departure_times[s.schedule[bt - 1][0]] <\n                                    self.departure_times[vehicle]):\n                                invalid_flag = True\n                    if invalid_flag:\n                        continue\n\n                new_capacity = s.unused_track_capacity[t] - self.vehicle_lengths[vehicle] - 0.5\n                if new_capacity < 0:\n                    continue\n                elif best_capacity is None or best_capacity > new_capacity:\n                    best_capacity = new_capacity\n                    best_track = t\n\n            if best_track is not None:\n                s.unused_track_capacity[best_track] = best_capacity\n                s.schedule[best_track].append(vehicle)\n\n            # no track in assigned track was found, need to assign new track to this vehicle series\n            else:\n                # generate list of tracks that current vehicle can park on\n                available_tracks = [t for t in tracks\n                                    if track_availability[t] and s.series_on_track[t] is None]\n                # get blocking and non blocking tracks first, if there is no such track left,\n                # only then take from blocked tracks\n                blocking_tracks = [t - 1 for t in self.blocking_tracks.keys()]\n                usable_tracks = [t for t in available_tracks\n                                 if t in self.nonblocking_tracks + blocking_tracks]\n                if len(usable_tracks) == 0:\n                    usable_tracks = available_tracks\n                # find track that can store smallest number of vehicle series and use that one\n                # this prioritizes less flexible tracks for vehicles that can go into them\n                best_can_hold_types = self.vehicle_count + 1\n                best_track = None\n                for t in usable_tracks:\n                    can_hold_types = list(self.vehicle_restrictions[:, t]).count(True)\n                    if (can_hold_types < best_can_hold_types and\n                            self.vehicle_lengths[vehicle] <= self.track_lengths[t]):\n                        best_can_hold_types = can_hold_types\n                        best_track = t\n\n                if best_track is not None:\n                    s.unused_track_capacity[best_track] -= self.vehicle_lengths[vehicle]\n                    s.series_on_track[best_track] = self.vehicle_series[vehicle]\n                    s.used_tracks_count += 1\n                    s.schedule[best_track].append(vehicle)\n                else:\n                    s.unscheduled_vehicles.add(vehicle)\n\n        return s\n\n    def __sort_by_departure_time(self, target_list):\n        zipped_pairs = zip(self.departure_times, target_list)\n        z = [x for _, x in sorted(zipped_pairs)]\n        return z\n\n    def is_valid(self, solution):\n        \"\"\"This function checks if solution respects all of constraints.\"\"\"\n        tracks = list(range(self.track_count))\n        for track, track_index in zip(solution.schedule, tracks):\n            if len(track) > 1:\n                for first, second in zip(track, track[1:]):\n                        if self.departure_times[first] > self.departure_times[second]:\n                            return (False,\n                                    'Vehicle {} departs later than vehicle {}!'.format(first + 1,\n                                                                                       second + 1))\n                        if self.vehicle_series[first] != self.vehicle_series[second]:\n                            return (False,\n                                    'Vehicle {} is not same series as vehicle {}!'.format(first + 1,\n                                                                                          second + 1))\n            for vehicle in track:\n                if not self.vehicle_restrictions[vehicle][track_index]:\n                    return (False,\n                            'Vehicle {} is restricted to park on track {}!'.format(vehicle + 1,\n                                                                                   track_index + 1))\n            if solution.unused_track_capacity[track_index] < 0:\n                return (False,\n                        'Track {} is over its capacity!'.format(track_index + 1))\n        for blocking_track in self.blocking_tracks.keys():\n            for blocked_track in self.blocking_tracks[blocking_track]:\n                blocking_schedule = solution.schedule[blocking_track - 1]\n                blocked_schedule = solution.schedule[blocked_track - 1]\n                if len(blocked_schedule) > 0 and len(blocking_schedule) > 0:\n                    if (self.departure_times[blocking_schedule[-1]] >\n                            self.departure_times[blocked_schedule[0]]):\n                        #print(self.departure_times[blocking_schedule[-1]])\n                        #print(self.departure_times[blocked_schedule[0]])\n                        return (False,\n                                'First vehicle in blocked track {} departs sooner than last vehicle in blocking track {}'.format(blocked_track,\n                                                                                                                                 blocking_track))\n        return (True, '')\n\n    def generate_neighbourhood(self, initial_solution, neighbourhood_length):\n        neighbourhood = set()\n\n        # try to add unscheduled vehicles to produce neighbourhood\n        # check if unused capacity is bigger then some of the unscheduled vehicles\n        \n        unscheduled_neighbourhood = self.generate_unscheduled_neughbourhood(initial_solution)\n        for s in unscheduled_neighbourhood:\n            if self.is_valid(s)[0] != False and s.schedule != self.initial_solution.schedule:\n                neighbourhood.add(s)\n        \n        while len(neighbourhood) < neighbourhood_length:\n            s = copy.deepcopy(initial_solution)\n            if len(self.initial_solution.unscheduled_vehicles) > 0:\n                # add unscheduled vehicles to scheduled\n                s.schedule.append(list(s.unscheduled_vehicles))\n\n            tracks_count = len(s.schedule)\n\n            # find random track\n            selected_track1_index = random.randrange(tracks_count)\n            # randomly find track and cannot choose two empty tracks\n            selected_track2_index = random.randrange(tracks_count)\n            while len(s.schedule[selected_track1_index]) == 0 and len(s.schedule[selected_track2_index]) == 0:\n                selected_track2_index = random.randrange(tracks_count)            \n\n            selected_track2_count = len(s.schedule[selected_track2_index]) \n            selected_track1_count = len(s.schedule[selected_track1_index])\n            if selected_track1_count > 0 and selected_track2_count > 0:\n                \n                selected_vehicle1_index = random.randrange(selected_track1_count)\n                selected_vehicle2_index = random.randrange(selected_track2_count)\n\n                # swap vehicles\n                tmp = s.schedule[selected_track1_index][selected_vehicle1_index]\n                s.schedule[selected_track1_index][selected_vehicle1_index] = s.schedule[selected_track2_index][selected_vehicle2_index] \n                s.schedule[selected_track2_index][selected_vehicle2_index] = tmp\n            elif selected_track1_count == 0:\n                # first track is empty\n                selected_vehicle2_index = random.randrange(selected_track2_count)\n                selected_vehicle2 = s.schedule[selected_track2_index].pop(selected_vehicle2_index)\n                s.schedule[selected_track1_index].append(selected_vehicle2) \n            elif selected_track2_count == 0:\n                # second track is empty\n                selected_vehicle1_index = random.randrange(selected_track1_count)\n                selected_vehicle1 = s.schedule[selected_track1_index].pop(selected_vehicle1_index)\n                s.schedule[selected_track2_index].append(selected_vehicle1)\n            # update solution\n            # remove unscheduled vehicles\n            if len(s.unscheduled_vehicles) > 0:\n                unscheduled_vehicles =  set(s.schedule.pop())\n                s.unscheduled_vehicles = unscheduled_vehicles\n\n            s.used_tracks_count = self.count_used_tracks(s)\n            s.series_on_track = self.initialize_series_on_track(s)\n            s.unused_track_capacity = self.update_unused_track_capacity(s)\n            if self.is_valid(s)[0] != False and s.schedule != self.initial_solution.schedule:\n                neighbourhood.add(s)\n\n        return neighbourhood\n\n    def generate_unscheduled_neughbourhood(self, solution):\n        unused_track_capacity = solution.unused_track_capacity\n        unscheduled_neighbourhood = []\n\n        for vehicle in solution.unscheduled_vehicles:\n            for track_number in range(0, len(solution.schedule)):\n                if unused_track_capacity[track_number] >= self.vehicle_lengths[vehicle] + 1:\n                    # vehicles can park between all other vehicles in track\n                    # add vehicle to schedule between all elements\n                    for vehicle_position in range(0, len(solution.schedule[track_number]) + 1):\n                        s = copy.deepcopy(solution)\n                        s.schedule[track_number].insert(vehicle_position, vehicle)\n                        s.unscheduled_vehicles.remove(vehicle)\n                        s = self.update_solution(s)\n                        unscheduled_neighbourhood.append(s)\n                elif unused_track_capacity[track_number] >= self.vehicle_lengths[vehicle] + 0.5:\n                    # vehicle can park as first or last in track\n                    s = copy.deepcopy(solution)\n                    s.schedule[track_number].insert(0, vehicle)\n                    s.unscheduled_vehicles.remove(vehicle)\n                    s = self.update_solution(s)\n                    unscheduled_neighbourhood.append(s)\n\n                    s = copy.deepcopy(solution)\n                    s.schedule[track_number].append()\n                    s.unscheduled_vehicles.remove(vehicle)\n                    s = self.update_solution(s)\n                    unscheduled_neighbourhood.append(s)\n\n        return unscheduled_neighbourhood\n\n    def update_solution(self, solution):\n        s = copy.deepcopy(solution)\n        s.used_tracks_count = self.count_used_tracks(s)\n        s.series_on_track = self.initialize_series_on_track(s)\n        s.unused_track_capacity = self.update_unused_track_capacity(s)\n        return s\n\n    def count_used_tracks(self, solution):\n        count = 0\n        for track in solution.schedule:\n            if len(track) != 0:\n                count += 1\n        return count\n\n    def initialize_series_on_track(self, solution):\n        series_on_track = [None] * self.track_count\n        for i in range(0, self.track_count):\n            if (len(solution.schedule[i]) > 0):\n                series_on_track[i] = self.vehicle_series[solution.schedule[i][0]]\n        return series_on_track\n\n    def update_unused_track_capacity(self, solution):\n        track_lengths = self.track_lengths.copy()\n        unused_tracks_capacity = []\n        for track, unused_track in zip(solution.schedule, track_lengths):\n            for vehicle in track:\n                unused_track -= (self.vehicle_lengths[vehicle] + 0.5)\n            unused_track += 0.5\n            unused_tracks_capacity.append(unused_track)\n        return unused_tracks_capacity\n\n    def taboo_search(self, taboo_duration, iterations, neighbourhood_length, reset_iteration):\n        taboo_list = []\n        best_solution = self.initial_solution\n        current_solution = best_solution\n        current_iteration = 0\n\n        while current_iteration < iterations:\n            \n            neighbourhood = self.generate_neighbourhood(best_solution, neighbourhood_length)\n\n            for neighbour in neighbourhood:\n                if not (neighbour in taboo_list) and self.fitness_func(best_solution) < self.fitness_func(neighbour):\n                    best_solution = neighbour\n                    taboo_list.insert(0, neighbour)\n                    if taboo_duration == len(taboo_list):\n                        taboo_list.pop()\n\n            current_iteration += 1\n            if current_iteration % reset_iteration == 0 or current_iteration == iterations - 1:\n                if self.fitness_func(current_solution) < self.fitness_func(best_solution):\n                    # print('current:', current_solution)\n                    current_solution = best_solution\n                best_solution = self.initial_solution\n            print(current_iteration)\n        return current_solution\n\n\nclass Solution:\n    def __init__(self, track_count, track_lengths):\n\n        ##################################\n        # Initialize solution attributes #\n        ##################################\n\n        # List notes which series of vehicle is parked on which track\n        self.series_on_track = [None] * track_count\n        self.used_tracks_count = 0\n\n        # List of empty space left in each track\n        self.unused_track_capacity = track_lengths.copy()\n\n        # Result data structure - list of tracks where every element is list of vehicles\n        self.schedule = [[] for _ in range(track_count)]\n\n        # Set of vehicles that couldn't fit anywhere\n        self.unscheduled_vehicles = set()\n\n    def __str__(self):\n        string_schedule = []\n        for track in self.schedule:\n            string_schedule.append(' '.join([str(v + 1) for v in track]) if len(track) > 0 else '')\n        return '\\n'.join(string_schedule)\n        \n    def __eq__(self, other):\n        if isinstance(other, Solution):\n            return ((self.schedule == other.schedule) and (self.unscheduled_vehicles == other.unscheduled_vehicles) and (self.series_on_track == other.series_on_track)\n            and self.used_tracks_count == other.used_tracks_count and self.unused_track_capacity == other.unused_track_capacity)\n        else:\n            return False\n    def __hash__(self):\n        return hash((tuple(self.series_on_track), self.used_tracks_count,(tuple(val) for val in self.schedule), tuple(self.unused_track_capacity), tuple(self.unscheduled_vehicles)))\n    pass\n","repo_name":"creationspirit/public-transport-garage-optimization","sub_path":"heuristic.py","file_name":"heuristic.py","file_ext":"py","file_size_in_byte":20038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35290168848","text":"# python3 -m pytest test_parameterization.py -v -s\n\nimport requests\nimport json\nimport jsonpath\nimport pytest\nclass TestCases:\n    file = open('/Users/user/Desktop/APIAutomation/GetRequest/postData.json', 'r')\n    json_input = file.read()\n    requests_json = json.loads(json_input)\n    @pytest.mark.parametrize(\"nameJob\", requests_json)\n    def test_create_new_user(self,nameJob):\n        urlPOST = \"https://reqres.in/api/users\"\n        response = requests.post(urlPOST, nameJob)\n        print(response.content)\n        assert response.status_code == 201\n        # print(response.headers.get('Content-Length'))\n        response_json1 = json.loads(response.text)\n        # print(requests_json)\n        id = jsonpath.jsonpath(response_json1, 'id')\n        print(\"New located id is:\"+id[0])\n\n    @pytest.mark.parametrize(\"userIDs\", [\"2\",\"7\"])\n    def test_fetch_user_data(self,userIDs):\n        urlGET = \"https://reqres.in/api/users/\"+(userIDs)\n        response = requests.get(urlGET)\n        json_response = json.loads(response.text)\n        #print(json_response)\n        data = jsonpath.jsonpath(json_response, 'data')\n        print(data)\n        assert response.status_code == 200\n\n    @pytest.mark.parametrize(\"updateUserID\", [\"2\"])\n    def test_update_user_data(self,updateUserID):\n        urlPut = \"https://reqres.in/api/users/\"+(updateUserID)\n        response = requests.put(urlPut)\n        assert response.status_code == 200\n        response_json1 = json.loads(response.text)\n        updated = jsonpath.jsonpath(response_json1, 'updatedAt')\n        print(\"Updated data is at: \"+updated[0])\n\n    @pytest.mark.parametrize(\"deleteUserID\", [\"2\"])\n    def test_delete_user_data(self,deleteUserID):\n        urlDELETE = \"https://reqres.in/api/users/\"+(deleteUserID)\n        response = requests.delete(urlDELETE)\n        assert response.status_code == 204","repo_name":"pitz-qa/API_Automation","sub_path":"Python_pytest/GetRequest/testCases/test_parameterization.py","file_name":"test_parameterization.py","file_ext":"py","file_size_in_byte":1852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"27866142640","text":"import streamlit as st\nimport markdown\nimport matplotlib.image as mpimg\n\ndef main():\n    st.title(\"Ipotesi di Business plan\")\n\n    show_footer()\n\ndef show_footer():\n   \n    st.markdown(\"\"\" \n    ## Business model canvas\n   \n     \"\"\")\n    img2=mpimg.imread('p5_bmc.png')\n    st.image(img2, caption='',use_column_width=True)\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"hananeRaziq/project_work_iot_FAV","sub_path":"pag5_business_plan.py","file_name":"pag5_business_plan.py","file_ext":"py","file_size_in_byte":361,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"11807033746","text":"from typing import Dict, NoReturn, Any\r\n\r\nfrom tp2_utils.message_pipeline.operations.group_aggregates.count import Count\r\nfrom tp2_utils.message_pipeline.operations.group_aggregates.group_aggregate import GroupAggregate\r\nfrom tp2_utils.message_pipeline.operations.group_aggregates.sum import Sum\r\n\r\n\r\nclass Mean(GroupAggregate):\r\n    def __init__(self, mean_value: str, mean_suffix: str = '_mean'):\r\n        \"\"\"\r\n\r\n        :param mean_value: the value to calculate the mean\r\n        :param mean_suffix: the suffix to add to the output key\r\n        \"\"\"\r\n        self.mean_value = mean_value\r\n        self.mean_suffix = mean_suffix\r\n        self.count = Count()\r\n        self.sum = Sum(mean_value)\r\n\r\n    def add(self, key: str, values: Dict) -> NoReturn:\r\n        \"\"\"\r\n        Adds an element to the group statistic\r\n\r\n        :param key: the key of the element\r\n        :param values: the values associated\r\n        \"\"\"\r\n        self.count.add(key, values)\r\n        self.sum.add(key, values)\r\n\r\n    def dump(self) -> Dict[Any, Dict]:\r\n        \"\"\"\r\n        Dumps all the statistics to a dict\r\n        :return: the dict with the group value as key and a dict of statistics\r\n        \"\"\"\r\n        counts = self.count.dump()\r\n        sums = self.sum.dump()\r\n        return {k: {self.mean_value + self.mean_suffix: v[self.mean_value + '_sum'] / counts[k]['count']}\r\n                for k, v in sums.items()}\r\n","repo_name":"jian01/tp2-distro","sub_path":"tp2_utils_package/tp2_utils/message_pipeline/operations/group_aggregates/mean.py","file_name":"mean.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"41592190502","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\"\"\"\nApply susceptibility distortion correction (SDC)\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\n.. topic :: Abbreviations\n\n    fmap\n        fieldmap\n    VSM\n        voxel-shift map -- a 3D nifti where displacements are in pixels (not mm)\n    DFM\n        displacements field map -- a nifti warp file compatible with ANTs (mm)\n\n\"\"\"\nfrom __future__ import print_function, division, absolute_import, unicode_literals\n\nimport pkg_resources as pkgr\n\nfrom niworkflows.nipype.pipeline import engine as pe\nfrom niworkflows.nipype.interfaces import afni, ants, fsl, utility as niu\nfrom niworkflows.interfaces import CopyHeader\nfrom niworkflows.interfaces.registration import ANTSApplyTransformsRPT, ANTSRegistrationRPT\n\nfrom fmriprep.interfaces import itk\nfrom fmriprep.interfaces import ReadSidecarJSON\nfrom fmriprep.interfaces.bids import DerivativesDataSink\n\nfrom fmriprep.interfaces import StructuralReference\nfrom fmriprep.workflows.util import init_enhance_and_skullstrip_epi_wf\n\n\ndef init_sdc_unwarp_wf(reportlets_dir, omp_nthreads, fmap_bspline,\n                       fmap_demean, debug, name='sdc_unwarp_wf'):\n    \"\"\"\n    This workflow takes in a displacements fieldmap and calculates the corresponding\n    displacements field (in other words, an ANTs-compatible warp file).\n\n    It also calculates a new mask for the input dataset that takes into account the distortions.\n    The mask is restricted to the field of view of the fieldmap since outside of it corrections\n    could not be performed.\n\n    .. workflow ::\n        :graph2use: orig\n        :simple_form: yes\n\n        from fmriprep.workflows.fieldmap.unwarp import init_sdc_unwarp_wf\n        wf = init_sdc_unwarp_wf(reportlets_dir='.', omp_nthreads=8,\n                                fmap_bspline=False, fmap_demean=True,\n                                debug=False)\n\n\n    Inputs\n\n        in_reference\n            the reference image\n        in_mask\n            a brain mask corresponding to ``in_reference``\n        name_source\n            path to the original _bold file being unwarped\n        fmap\n            the fieldmap in Hz\n        fmap_ref\n            the reference (anatomical) image corresponding to ``fmap``\n        fmap_mask\n            a brain mask corresponding to ``fmap``\n\n\n    Outputs\n\n        out_reference\n            the ``in_reference`` after unwarping\n        out_reference_brain\n            the ``in_reference`` after unwarping and skullstripping\n        out_warp\n            the corresponding :abbr:`DFM (displacements field map)` compatible with\n            ANTs\n        out_jacobian\n            the jacobian of the field (for drop-out alleviation)\n        out_mask\n            mask of the unwarped input file\n        out_mask_report\n            reportled for the skullstripping\n\n    \"\"\"\n\n    workflow = pe.Workflow(name=name)\n    inputnode = pe.Node(niu.IdentityInterface(\n        fields=['in_reference', 'in_reference_brain', 'in_mask', 'name_source',\n                'fmap_ref', 'fmap_mask', 'fmap']), name='inputnode')\n    outputnode = pe.Node(niu.IdentityInterface(\n        fields=['out_reference', 'out_reference_brain', 'out_warp', 'out_mask',\n                'out_jacobian', 'out_mask_report']), name='outputnode')\n\n    meta = pe.Node(ReadSidecarJSON(), name='meta')\n\n    # Register the reference of the fieldmap to the reference\n    # of the target image (the one that shall be corrected)\n    ants_settings = pkgr.resource_filename('fmriprep', 'data/fmap-any_registration.json')\n    if debug:\n        ants_settings = pkgr.resource_filename(\n            'fmriprep', 'data/fmap-any_registration_testing.json')\n    fmap2ref_reg = pe.Node(\n        ANTSRegistrationRPT(\n            generate_report=True, from_file=ants_settings, output_inverse_warped_image=True,\n            output_warped_image=True, num_threads=omp_nthreads),\n        name='fmap2ref_reg')\n    fmap2ref_reg.interface.num_threads = omp_nthreads\n\n    ds_reg = pe.Node(\n        DerivativesDataSink(base_directory=reportlets_dir,\n                            suffix='fmap_reg'), name='ds_reg')\n\n    # Map the VSM into the EPI space\n    fmap2ref_apply = pe.Node(ANTSApplyTransformsRPT(\n        generate_report=True, dimension=3, interpolation='BSpline', float=True),\n                             name='fmap2ref_apply')\n\n    fmap_mask2ref_apply = pe.Node(ANTSApplyTransformsRPT(\n        generate_report=False, dimension=3, interpolation='NearestNeighbor',\n        float=True),\n        name='fmap_mask2ref_apply')\n\n    ds_reg_vsm = pe.Node(\n        DerivativesDataSink(base_directory=reportlets_dir,\n                            suffix='fmap_reg_vsm'), name='ds_reg_vsm')\n\n    # Fieldmap to rads and then to voxels (VSM - voxel shift map)\n    torads = pe.Node(niu.Function(function=_hz2rads), name='torads')\n\n    gen_vsm = pe.Node(fsl.FUGUE(save_unmasked_shift=True), name='gen_vsm')\n    # Convert the VSM into a DFM (displacements field map)\n    # or: FUGUE shift to ANTS warping.\n    vsm2dfm = pe.Node(itk.FUGUEvsm2ANTSwarp(), name='vsm2dfm')\n    jac_dfm = pe.Node(ants.CreateJacobianDeterminantImage(\n        imageDimension=3, outputImage='jacobian.nii.gz'), name='jac_dfm')\n\n    unwarp_reference = pe.Node(ANTSApplyTransformsRPT(dimension=3,\n                                                      generate_report=False,\n                                                      float=True,\n                                                      interpolation='LanczosWindowedSinc'),\n                               name='unwarp_reference')\n\n    fieldmap_fov_mask = pe.Node(niu.Function(function=_fill_with_ones), name='fieldmap_fov_mask')\n\n    fmap_fov2ref_apply = pe.Node(ANTSApplyTransformsRPT(\n        generate_report=False, dimension=3, interpolation='NearestNeighbor',\n        float=True),\n        name='fmap_fov2ref_apply')\n\n    apply_fov_mask = pe.Node(fsl.ApplyMask(), name=\"apply_fov_mask\")\n\n    enhance_and_skullstrip_epi_wf = init_enhance_and_skullstrip_epi_wf()\n\n    workflow.connect([\n        (inputnode, meta, [('name_source', 'in_file')]),\n        (inputnode, fmap2ref_reg, [('fmap_ref', 'moving_image')]),\n        (inputnode, fmap2ref_apply, [('in_reference', 'reference_image')]),\n        (fmap2ref_reg, fmap2ref_apply, [\n            ('composite_transform', 'transforms')]),\n        (inputnode, fmap_mask2ref_apply, [('in_reference', 'reference_image')]),\n        (fmap2ref_reg, fmap_mask2ref_apply, [\n            ('composite_transform', 'transforms')]),\n        (inputnode, ds_reg_vsm, [('name_source', 'source_file')]),\n        (fmap2ref_apply, ds_reg_vsm, [('out_report', 'in_file')]),\n        (inputnode, fmap2ref_reg, [('in_reference_brain', 'fixed_image')]),\n        (inputnode, ds_reg, [('name_source', 'source_file')]),\n        (fmap2ref_reg, ds_reg, [('out_report', 'in_file')]),\n        (inputnode, fmap2ref_apply, [('fmap', 'input_image')]),\n        (inputnode, fmap_mask2ref_apply, [('fmap_mask', 'input_image')]),\n        (fmap2ref_apply, torads, [('output_image', 'in_file')]),\n        (meta, gen_vsm, [(('out_dict', _get_ec), 'dwell_time'),\n                         (('out_dict', _get_pedir_fugue), 'unwarp_direction')]),\n        (meta, vsm2dfm, [(('out_dict', _get_pedir_bids), 'pe_dir')]),\n        (torads, gen_vsm, [('out', 'fmap_in_file')]),\n        (vsm2dfm, unwarp_reference, [('out_file', 'transforms')]),\n        (inputnode, unwarp_reference, [('in_reference', 'reference_image')]),\n        (inputnode, unwarp_reference, [('in_reference', 'input_image')]),\n        (vsm2dfm, outputnode, [('out_file', 'out_warp')]),\n        (vsm2dfm, jac_dfm, [('out_file', 'deformationField')]),\n        (inputnode, fieldmap_fov_mask, [('fmap_ref', 'in_file')]),\n        (fieldmap_fov_mask, fmap_fov2ref_apply, [('out', 'input_image')]),\n        (inputnode, fmap_fov2ref_apply, [('in_reference', 'reference_image')]),\n        (fmap2ref_reg, fmap_fov2ref_apply, [('composite_transform', 'transforms')]),\n        (fmap_fov2ref_apply, apply_fov_mask, [('output_image', 'mask_file')]),\n        (unwarp_reference, apply_fov_mask, [('output_image', 'in_file')]),\n        (apply_fov_mask, enhance_and_skullstrip_epi_wf, [('out_file', 'inputnode.in_file')]),\n        (apply_fov_mask, outputnode, [('out_file', 'out_reference')]),\n        (enhance_and_skullstrip_epi_wf, outputnode, [\n            ('outputnode.mask_file', 'out_mask'),\n            ('outputnode.out_report', 'out_mask_report'),\n            ('outputnode.skull_stripped_file', 'out_reference_brain')]),\n        (jac_dfm, outputnode, [('jacobian_image', 'out_jacobian')]),\n    ])\n\n    if not fmap_bspline:\n        workflow.connect([\n            (fmap_mask2ref_apply, gen_vsm, [('output_image', 'mask_file')])\n        ])\n\n    if fmap_demean:\n        # Demean within mask\n        demean = pe.Node(niu.Function(function=_demean), name='demean')\n\n        workflow.connect([\n            (gen_vsm, demean, [('shift_out_file', 'in_file')]),\n            (fmap_mask2ref_apply, demean, [('output_image', 'in_mask')]),\n            (demean, vsm2dfm, [('out', 'in_file')]),\n        ])\n\n    else:\n        workflow.connect([\n            (gen_vsm, vsm2dfm, [('shift_out_file', 'in_file')]),\n        ])\n\n    return workflow\n\n\ndef init_pepolar_unwarp_wf(fmaps, bold_file, omp_nthreads, layout=None,\n                           fmaps_pes=None, bold_file_pe=None,\n                           name=\"pepolar_unwarp_wf\"):\n    \"\"\"\n    This workflow takes in a set of EPI files with opposite phase encoding\n    direction than the target file and calculates a displacements field\n    (in other words, an ANTs-compatible warp file).\n\n    This procedure works if there is only one '_epi' file is present\n    (as long as it has the opposite phase encoding direction to the target\n    file). The target file will be used to estimate the field distortion.\n    However, if there is another '_epi' file present with a matching\n    phase encoding direction to the target it will be used instead.\n\n    Currently, different phase encoding dimension in the target file and the\n    '_epi' file(s) (for example 'i' and 'j') is not supported.\n\n    The warp field correcting for the distortions is estimated using AFNI's\n    3dQwarp, with displacement estimation limited to the target file phase\n    encoding direction.\n\n    It also calculates a new mask for the input dataset that takes into\n    account the distortions.\n\n    .. workflow ::\n        :graph2use: orig\n        :simple_form: yes\n\n        from fmriprep.workflows.fieldmap.unwarp import init_pepolar_unwarp_wf\n        wf = init_pepolar_unwarp_wf(fmaps=['/dataset/sub-01/fmap/sub-01_epi.nii.gz'],\n                                    fmaps_pes=['j-'],\n                                    bold_file='/dataset/sub-01/func/sub-01_task-rest_bold.nii.gz',\n                                    bold_file_pe='j',\n                                    omp_nthreads=8)\n\n\n    Inputs\n\n        in_reference\n            the reference image\n        in_reference_brain\n            the reference image skullstripped\n        in_mask\n            a brain mask corresponding to ``in_reference``\n        name_source\n            not used, kept for signature compatibility with ``init_sdc_unwarp_wf``\n\n    Outputs\n\n        out_reference\n            the ``in_reference`` after unwarping\n        out_reference_brain\n            the ``in_reference`` after unwarping and skullstripping\n        out_warp\n            the corresponding :abbr:`DFM (displacements field map)` compatible with\n            ANTs\n        out_mask\n            mask of the unwarped input file\n        out_mask_report\n            reportlet for the skullstripping\n\n    \"\"\"\n    if not bold_file_pe:\n        bold_file_pe = layout.get_metadata(bold_file)[\"PhaseEncodingDirection\"]\n\n    usable_fieldmaps_matching_pe = []\n    usable_fieldmaps_opposite_pe = []\n    args = '-noXdis -noYdis -noZdis'\n    rm_arg = {'i': '-noXdis',\n              'j': '-noYdis',\n              'k': '-noZdis'}[bold_file_pe[0]]\n    args = args.replace(rm_arg, '')\n\n    for i, fmap in enumerate(fmaps):\n        if fmaps_pes:\n            fmap_pe = fmaps_pes[i]\n        else:\n            fmap_pe = layout.get_metadata(fmap)[\"PhaseEncodingDirection\"]\n        if fmap_pe[0] == bold_file_pe[0]:\n            if len(fmap_pe) != len(bold_file_pe):\n                add_list = usable_fieldmaps_opposite_pe\n            else:\n                add_list = usable_fieldmaps_matching_pe\n            add_list.append(fmap)\n\n    if len(usable_fieldmaps_opposite_pe) == 0:\n        raise Exception(\"None of the discovered fieldmaps has the right \"\n                        \"phase encoding direction. Possibly a problem with \"\n                        \"metadata. If not, rerun with '--ignore fieldmaps' to \"\n                        \"skip distortion correction step.\")\n\n    workflow = pe.Workflow(name=name)\n    inputnode = pe.Node(niu.IdentityInterface(\n        fields=['in_reference', 'in_reference_brain', 'in_mask', 'name_source']), name='inputnode')\n\n    outputnode = pe.Node(niu.IdentityInterface(\n        fields=['out_reference', 'out_reference_brain', 'out_warp', 'out_mask',\n                'out_mask_report']),\n        name='outputnode')\n\n    prepare_epi_opposite_wf = init_prepare_epi_wf(ants_nthreads=omp_nthreads,\n                                                  name=\"prepare_epi_opposite_wf\")\n    prepare_epi_opposite_wf.inputs.inputnode.fmaps = usable_fieldmaps_opposite_pe\n\n    qwarp = pe.Node(afni.QwarpPlusMinus(pblur=[0.05, 0.05],\n                                        blur=[-1, -1],\n                                        noweight=True,\n                                        minpatch=9,\n                                        nopadWARP=True,\n                                        environ={'OMP_NUM_THREADS': str(omp_nthreads)},\n                                        args=args),\n                    name='qwarp')\n    qwarp.interface.num_threads = omp_nthreads\n\n    workflow.connect([\n        (inputnode, prepare_epi_opposite_wf, [('in_reference_brain', 'inputnode.ref_brain')]),\n        (prepare_epi_opposite_wf, qwarp, [('outputnode.out_file', 'base_file')]),\n    ])\n\n    if usable_fieldmaps_matching_pe:\n        prepare_epi_matching_wf = init_prepare_epi_wf(ants_nthreads=omp_nthreads,\n                                                      name=\"prepare_epi_matching_wf\")\n        prepare_epi_matching_wf.inputs.inputnode.fmaps = usable_fieldmaps_matching_pe\n\n        workflow.connect([\n            (inputnode, prepare_epi_matching_wf, [('in_reference_brain', 'inputnode.ref_brain')]),\n            (prepare_epi_matching_wf, qwarp, [('outputnode.out_file', 'source_file')]),\n        ])\n    else:\n        workflow.connect([(inputnode, qwarp, [('in_reference_brain', 'source_file')])])\n\n    to_ants = pe.Node(niu.Function(function=_fix_hdr), name='to_ants')\n\n    cphdr_warp = pe.Node(CopyHeader(), name='cphdr_warp')\n\n    unwarp_reference = pe.Node(ANTSApplyTransformsRPT(dimension=3,\n                                                      generate_report=False,\n                                                      float=True,\n                                                      interpolation='LanczosWindowedSinc'),\n                               name='unwarp_reference')\n\n    enhance_and_skullstrip_epi_wf = init_enhance_and_skullstrip_epi_wf()\n\n    workflow.connect([\n        (inputnode, cphdr_warp, [('in_reference', 'hdr_file')]),\n        (qwarp, cphdr_warp, [('source_warp', 'in_file')]),\n        (cphdr_warp, to_ants, [('out_file', 'in_file')]),\n        (to_ants, unwarp_reference, [('out', 'transforms')]),\n        (inputnode, unwarp_reference, [('in_reference', 'reference_image'),\n                                       ('in_reference', 'input_image')]),\n        (unwarp_reference, enhance_and_skullstrip_epi_wf, [('output_image', 'inputnode.in_file')]),\n        (unwarp_reference, outputnode, [('output_image', 'out_reference')]),\n        (enhance_and_skullstrip_epi_wf, outputnode, [\n            ('outputnode.mask_file', 'out_mask'),\n            ('outputnode.out_report', 'out_report'),\n            ('outputnode.skull_stripped_file', 'out_reference_brain')]),\n        (to_ants, outputnode, [('out', 'out_warp')]),\n    ])\n\n    return workflow\n\n\ndef init_prepare_epi_wf(ants_nthreads, name=\"prepare_epi_wf\"):\n    \"\"\"\n    This workflow takes in a set of EPI files with with the same phase\n    encoding direction and returns a single 3D volume ready to be used in\n    field distortion estimation.\n\n    The procedure involves: estimating a robust template using FreeSurfer's\n    'mri_robust_template', bias field correction using ANTs N4BiasFieldCorrection\n    and AFNI 3dUnifize, skullstripping using FSL BET and AFNI 3dAutomask,\n    and rigid coregistration to the reference using ANTs.\n\n    .. workflow ::\n        :graph2use: orig\n        :simple_form: yes\n\n        from fmriprep.workflows.fieldmap.unwarp import init_prepare_epi_wf\n        wf = init_prepare_epi_wf(ants_nthreads=8)\n\n\n    Inputs\n\n        fmaps\n            list of 3D or 4D NIfTI images\n        ref_brain\n            coregistration reference (skullstripped and bias field corrected)\n\n    Outputs\n\n        out_file\n            single 3D NIfTI file\n\n    \"\"\"\n    inputnode = pe.Node(niu.IdentityInterface(fields=['fmaps', 'ref_brain']),\n                        name='inputnode')\n\n    outputnode = pe.Node(niu.IdentityInterface(fields=['out_file']),\n                         name='outputnode')\n\n    split = pe.MapNode(fsl.Split(dimension='t'), iterfield='in_file',\n                       name='split')\n\n    merge = pe.Node(\n        StructuralReference(auto_detect_sensitivity=True,\n                            initial_timepoint=1,\n                            fixed_timepoint=True,  # Align to first image\n                            intensity_scaling=True,\n                            # 7-DOF (rigid + intensity)\n                            no_iteration=True,\n                            subsample_threshold=200,\n                            out_file='template.nii.gz'),\n        name='merge')\n\n    enhance_and_skullstrip_epi_wf = init_enhance_and_skullstrip_epi_wf()\n\n    ants_settings = pkgr.resource_filename('fmriprep',\n                                           'data/translation_rigid.json')\n    fmap2ref_reg = pe.Node(ants.Registration(from_file=ants_settings,\n                                             output_warped_image=True,\n                                             num_threads=ants_nthreads),\n                           name='fmap2ref_reg')\n    fmap2ref_reg.interface.num_threads = ants_nthreads\n\n    workflow = pe.Workflow(name=name)\n\n    def _flatten(l):\n        return [item for sublist in l for item in sublist]\n\n    workflow.connect([\n        (inputnode, split, [('fmaps', 'in_file')]),\n        (split, merge, [(('out_files', _flatten), 'in_files')]),\n        (merge, enhance_and_skullstrip_epi_wf, [('out_file', 'inputnode.in_file')]),\n        (enhance_and_skullstrip_epi_wf, fmap2ref_reg, [\n            ('outputnode.skull_stripped_file', 'moving_image')]),\n        (inputnode, fmap2ref_reg, [('ref_brain', 'fixed_image')]),\n        (fmap2ref_reg, outputnode, [('warped_image', 'out_file')]),\n    ])\n\n    return workflow\n\n\n# Helper functions\n# ------------------------------------------------------------\n\n\ndef _fix_hdr(in_file):\n    import nibabel as nb\n    import os\n\n    nii = nb.load(in_file)\n    hdr = nii.header.copy()\n    hdr.set_data_dtype('<f4')\n    hdr.set_intent('vector', (), '')\n\n    out_file = os.path.abspath(\"warpfield.nii.gz\")\n\n    nb.Nifti1Image(nii.get_data().astype('<f4'), nii.affine, hdr).to_filename(out_file)\n\n    return out_file\n\n\ndef _get_ec(in_dict):\n    return float(in_dict['EffectiveEchoSpacing'])\n\n\ndef _get_pedir_bids(in_dict):\n    return in_dict['PhaseEncodingDirection']\n\n\ndef _get_pedir_fugue(in_dict):\n    return in_dict['PhaseEncodingDirection'].replace('i', 'x').replace('j', 'y').replace('k', 'z')\n\n\ndef _hz2rads(in_file, out_file=None):\n    \"\"\"Transform a fieldmap in Hz into rad/s\"\"\"\n    from math import pi\n    import nibabel as nb\n    from fmriprep.utils.misc import genfname\n    if out_file is None:\n        out_file = genfname(in_file, 'rads')\n    nii = nb.load(in_file)\n    data = nii.get_data() * 2.0 * pi\n    nb.Nifti1Image(data, nii.get_affine(),\n                   nii.get_header()).to_filename(out_file)\n    return out_file\n\n\ndef _demean(in_file, in_mask, out_file=None):\n    import numpy as np\n    import nibabel as nb\n    from fmriprep.utils.misc import genfname\n\n    if out_file is None:\n        out_file = genfname(in_file, 'demeaned')\n    nii = nb.load(in_file)\n    msk = nb.load(in_mask).get_data()\n    data = nii.get_data()\n    data -= np.median(data[msk > 0])\n    nb.Nifti1Image(data, nii.affine, nii.header).to_filename(\n        out_file)\n    return out_file\n\n\ndef _fill_with_ones(in_file):\n    import nibabel as nb\n    import numpy as np\n    import os\n\n    nii = nb.load(in_file)\n    data = np.ones(nii.shape)\n\n    out_name = os.path.abspath(\"out.nii.gz\")\n    nb.Nifti1Image(data, nii.affine, nii.header).to_filename(out_name)\n\n    return out_name\n","repo_name":"vsoch/fmriprep-debug","sub_path":"fmriprep/workflows/fieldmap/unwarp.py","file_name":"unwarp.py","file_ext":"py","file_size_in_byte":21126,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"42696236444","text":"import pandas as pd\nimport numpy as np\nimport yaml\ndef np_encoder(object):\n    if isinstance(object, np.generic):\n        return object.item()\n\n\n# def save_data(data):\n#     if data['ten']=='iot':\n#         thoi_gian=data['thoi_gian']\n#         # t=np.array(thoi_gian.replace(\"[\",\"\").replace(\"]\",\"\").split(\", \")).astype(int)\n#         t=thoi_gian\n#         from datetime import datetime\n#         d = datetime(t[0],t[1],t[2],t[3],t[4],t[5],)+ pd.Timedelta(hours=7)\n#         data['thoi_gian']=d.strftime(\"%a %b %d %H:%M:%S %Y\")\n    \n#     df = pd.read_csv('weather.csv')\n#     df = df.dropna()\n#     if len(df.columns)==len(pd.DataFrame(pd.Series(data)).T.columns):\n#         df = df.append(pd.DataFrame(pd.Series(data)).T, ignore_index=True)\n\n#         df = df.dropna()\n#         df.to_csv('weather.csv', index=None)\n#         return True\n#     else:\n#         return False\n\n\n\ndef get_config(config_path: str):\n    if config_path.endswith(\"json\"):\n        with open(config_path, 'r') as f:\n            config = json.load(f)\n    elif config_path.endswith(\"yml\"):\n        with open(config_path, 'r') as f:\n            config = yaml.load(f, Loader=yaml.FullLoader)\n    return config\n","repo_name":"nvhuy898/weather_api","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31047024655","text":"from odoo import models\nfrom odoo.exceptions import UserError\nfrom odoo.tests.common import Form\n\n\nCOUNTRY_EAS = {\n    'HU': 9910,\n\n    'AD': 9922,\n    'AL': 9923,\n    'BA': 9924,\n    'BE': 9925,\n    'BG': 9926,\n    'CH': 9927,\n    'CY': 9928,\n    'CZ': 9929,\n    'DE': 9930,\n    'EE': 9931,\n    'UK': 9932,\n    'GR': 9933,\n    'HR': 9934,\n    'IE': 9935,\n    'LI': 9936,\n    'LT': 9937,\n    'LU': 9938,\n    'LV': 9939,\n    'MC': 9940,\n    'ME': 9941,\n    'MK': 9942,\n    'MT': 9943,\n    'NL': 9944,\n    'PL': 9945,\n    'PT': 9946,\n    'RO': 9947,\n    'RS': 9948,\n    'SI': 9949,\n    'SK': 9950,\n    'SM': 9951,\n    'TR': 9952,\n    'VA': 9953,\n\n    'SE': 9955,\n\n    'FR': 9957\n}\n\n\nclass AccountEdiFormat(models.Model):\n    ''' This edi_format is \"abstract\" meaning that it provides an additional layer for similar edi_format (formats\n    deriving from EN16931) that share some functionalities but needs to be extended to be used.\n    '''\n    _inherit = 'account.edi.format'\n\n    ####################################################\n    # Export\n    ####################################################\n\n    def _get_bis3_values(self, invoice):\n        values = super()._get_ubl_values(invoice)\n        values.update({\n            'customization_id': 'urn:cen.eu:en16931:2017#compliant#urn:fdc:peppol.eu:2017:poacc:billing:3.0',\n            'profile_id': 'urn:fdc:peppol.eu:2017:poacc:billing:01:1.0',\n        })\n\n        all_tax_detail_per_tax = {}\n        for line_vals in values['invoice_line_vals_list']:\n            line_vals['price_subtotal_with_no_tax_closing'] = line_vals['line'].price_subtotal\n            tax_detail_per_tax = {}\n            for tax_detail_vals in line_vals['tax_detail_vals_list']:\n                tax = tax_detail_vals['tax']\n\n                tax_percent = tax.amount\n                tax_category = 'S' if tax_percent else 'Z'\n                key = (tax_category, tax_percent)\n                tax_detail_per_tax.setdefault(key, {\n                    'base': tax_detail_vals['tax_base_amount'],\n                    'base_currency': tax_detail_vals['tax_base_amount_currency'],\n                    'amount': 0.0,\n                    'amount_currency': 0.0,\n                    'tax_percent': tax_percent,\n                    'tax_category': tax_category,\n                })\n                vals = tax_detail_per_tax[key]\n\n                vals['amount'] += tax_detail_vals['tax_amount_closing']\n                vals['amount_currency'] += tax_detail_vals['tax_amount_currency_closing']\n                delta_tax = tax_detail_vals['tax_amount_currency'] - tax_detail_vals['tax_amount_currency_closing']\n                line_vals['price_subtotal_with_no_tax_closing'] += delta_tax\n\n            if len(tax_detail_per_tax) > 1:\n                raise UserError(\"Multiple vat percentage not supported on the same invoice line\")\n\n            line_vals['tax_detail_vals'] = list(tax_detail_per_tax.values())[0]\n\n            for key, tax_vals in tax_detail_per_tax.items():\n                all_tax_detail_per_tax.setdefault(key, {\n                    **tax_vals,\n                    'base': 0.0,\n                    'base_currency': 0.0,\n                    'amount': 0.0,\n                    'amount_currency': 0.0,\n                })\n                vals = all_tax_detail_per_tax[key]\n                vals['base'] += tax_vals['base']\n                vals['base_currency'] += tax_vals['base_currency']\n                vals['amount'] += tax_vals['amount']\n                vals['amount_currency'] += tax_vals['amount_currency']\n\n        values['tax_detail_vals_list'] = list(all_tax_detail_per_tax.values())\n        values['total_untaxed_amount'] = sum(x['price_subtotal_with_no_tax_closing'] for x in values['invoice_line_vals_list'])\n        values['total_tax_amount'] = sum(x['amount'] for x in values['tax_detail_vals_list'])\n        values['total_tax_amount_currency'] = sum(x['amount_currency'] for x in values['tax_detail_vals_list'])\n\n        for partner_vals in (values['customer_vals'], values['supplier_vals']):\n            partner = partner_vals['partner']\n            if partner.country_id.code in COUNTRY_EAS:\n                partner_vals['bis3_endpoint'] = partner.vat\n                partner_vals['bis3_endpoint_scheme'] = COUNTRY_EAS[partner.country_id.code]\n\n        return values\n\n    ####################################################\n    # Import\n    ####################################################\n\n    def _bis3_get_extra_partner_domains(self, tree):\n        \"\"\" Returns an additional domain to find the partner of the invoice based on specific implementation of BIS3.\n        TO OVERRIDE\n\n        :returns: a list of domains\n        \"\"\"\n        return []\n\n    def _decode_bis3(self, tree, invoice):\n        \"\"\" Decodes an EN16931 invoice into an invoice.\n        :param tree:    the UBL (EN16931) tree to decode.\n        :param invoice: the invoice to update or an empty recordset.\n        :returns:       the invoice where the UBL (EN16931) data was imported.\n        \"\"\"\n        def _find_value(path, root=tree):\n            element = root.find(path)\n            return element.text if element is not None else None\n\n        element = tree.find('./{*}InvoiceTypeCode')\n        if element is not None:\n            type_code = element.text\n            move_type = 'in_refund' if type_code == '381' else 'in_invoice'\n        else:\n            move_type = 'in_invoice'\n\n        default_journal = invoice.with_context(default_move_type=move_type)._get_default_journal()\n\n        with Form(invoice.with_context(default_move_type=move_type, default_journal_id=default_journal.id)) as invoice_form:\n            # Reference\n            element = tree.find('./{*}ID')\n            if element is not None:\n                invoice_form.ref = element.text\n\n            # Dates\n            element = tree.find('./{*}IssueDate')\n            if element is not None:\n                invoice_form.invoice_date = element.text\n            element = tree.find('./{*}DueDate')\n            if element is not None:\n                invoice_form.invoice_date_due = element.text\n\n            # Currency\n            currency = self._retrieve_currency(_find_value('./{*}DocumentCurrencyCode'))\n            if currency:\n                invoice_form.currency_id = currency\n\n            # Partner\n            specific_domain = self._bis3_get_extra_partner_domains(tree)\n            invoice_form.partner_id = self._retrieve_partner(\n                name=_find_value('./{*}AccountingSupplierParty/{*}Party/*/{*}Name'),\n                phone=_find_value('./{*}AccountingSupplierParty/{*}Party/*/{*}Telephone'),\n                mail=_find_value('./{*}AccountingSupplierParty/{*}Party/*/{*}ElectronicMail'),\n                vat=_find_value('./{*}AccountingSupplierParty/{*}Party/{*}PartyTaxScheme/{*}CompanyID'),\n                domain=specific_domain,\n            )\n\n            # Lines\n            for eline in tree.findall('.//{*}InvoiceLine'):\n                with invoice_form.invoice_line_ids.new() as invoice_line_form:\n                    # Product\n                    invoice_line_form.product_id = self._retrieve_product(\n                        default_code=_find_value('./{*}Item/{*}SellersItemIdentification/{*}ID', eline),\n                        name=_find_value('./{*}Item/{*}Name', eline),\n                        barcode=_find_value('./{*}Item/{*}StandardItemIdentification/{*}ID[@schemeID=\\'0160\\']', eline)\n                    )\n\n                    # Quantity\n                    element = eline.find('./{*}InvoicedQuantity')\n                    quantity = element is not None and float(element.text) or 1.0\n                    invoice_line_form.quantity = quantity\n\n                    # Price Unit\n                    element = eline.find('./{*}Price/{*}PriceAmount')\n                    price_unit = element is not None and float(element.text) or 0.0\n                    line_extension_amount = element is not None and float(element.text) or 0.0\n                    invoice_line_form.price_unit = price_unit or line_extension_amount / invoice_line_form.quantity or 0.0\n\n                    # Name\n                    element = eline.find('./{*}Item/{*}Description')\n                    invoice_line_form.name = element is not None and element.text or ''\n\n                    # Taxes\n                    tax_elements = eline.findall('./{*}Item/{*}ClassifiedTaxCategory')\n                    invoice_line_form.tax_ids.clear()\n                    for tax_element in tax_elements:\n                        invoice_line_form.tax_ids.add(self._retrieve_tax(\n                            amount=_find_value('./{*}Percent', tax_element),\n                            type_tax_use=invoice_form.journal_id.type\n                        ))\n\n        return invoice_form.save()\n","repo_name":"Kinal-dev/crm","sub_path":"addons/account_edi_ubl_bis3/models/account_edi_format.py","file_name":"account_edi_format.py","file_ext":"py","file_size_in_byte":8794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"13360366059","text":"# Tests for sprinkler.py\nimport unittest\nimport time\nimport pickle\nfrom mock import *\n\n# We need to mock RPi (that is used internally by \n# the sprinkler to active the pins) in order to make\n# the test run.\nmock = Mock()\nwith patch.dict('sys.modules', {'RPIO': mock, 'RPi': mock}):\n\timport sprinkler\n\n\tclass SprinklerTest(unittest.TestCase):\n\n\t\tdef setUp(self):\n\t\t\tself.sprinkler = sprinkler.Sprinkler('configuration.yaml', 5, [ 1, 2 ])\n\n\t\tdef test_when_operate_called_setup_pid(self):\n\t\t\t# when\n\t\t\tself.sprinkler.start()\n\t\t\ttime.sleep(0.5) # Just make sure sprinkler has read the file\n\t\t\t# then - we know the pid location\n\t\t\t_f = open('/tmp/sprinkler.pid')\n\t\t\t_isRunning = pickle.load(_f)\n\t\t\t_f.close()\n\t\t\t# assert\n\t\t\tassert _isRunning is 1\n\n\t\tdef test_when_finished_operate_setup_pid_to_0(self):\n\t\t\t# when\n\t\t\t#self.sprinkler.start()\n\t\t\ttime.sleep(0.5) # Just make sure sprinkler has read the file\n\t\t\t# then\n\t\t\ttime.sleep(6)\n\t\t\t_f = open('/tmp/sprinkler.pid')\n\t\t\t_isRunning = pickle.load(_f)\n\t\t\t_f.close()\n\t\t\t# assert\n\t\t\tassert _isRunning is 0\n","repo_name":"djalexd/py-sprinkler","sub_path":"test_sprinkler.py","file_name":"test_sprinkler.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"1445662105","text":"\"\"\"\nMLSTRUCTFP - SETUP\n\nSetup distribution.\n\"\"\"\n\n# Library imports\nfrom setuptools import setup, find_packages\nimport MLStructFP\n\n# Load readme\nwith open('README.rst') as f:\n    long_description = f.read()\n\n# Load requirements\nrequirements = [\n    'IPython == 8.12.2',\n    'matplotlib == 3.5.3',\n    'numpy == 1.18.5',\n    'opencv-python == 4.5.1.48',\n    'Pillow == 9.5.0',\n    'plotly == 5.11.0',\n    'requests == 2.31.0',\n    'six == 1.16.0',\n    'tabulate == 0.9.0'\n]\n\n# Setup library\nsetup(\n    author=MLStructFP.__author__,\n    author_email=MLStructFP.__email__,\n    classifiers=[\n        'Natural Language :: English',\n        'Operating System :: OS Independent',\n        'Programming Language :: Python :: 3.8',\n        'Programming Language :: Python',\n        'Topic :: Scientific/Engineering',\n        'Topic :: Scientific/Engineering :: Artificial Intelligence',\n        'Topic :: Scientific/Engineering :: Image Recognition',\n        'Topic :: Scientific/Engineering :: Visualization'\n    ],\n    description=MLStructFP.__description__,\n    long_description=long_description,\n    include_package_data=True,\n    install_requires=requirements,\n    extras_require={\n        'test': ['nose2[coverage_plugin]']\n    },\n    keywords=MLStructFP.__keywords__,\n    name='MLStructFP',\n    packages=find_packages(exclude=[\n        '.idea',\n        '.ipynb_checkpoints',\n        'test'\n    ]),\n    platforms=['any'],\n    project_urls={\n        'Bug Tracker': MLStructFP.__url_bug_tracker__,\n        'Documentation': MLStructFP.__url_documentation__,\n        'Source Code': MLStructFP.__url_source_code__\n    },\n    python_requires='>=3.8',\n    url=MLStructFP.__url__,\n    version=MLStructFP.__version__\n)\n","repo_name":"MLSTRUCT/MLSTRUCT-FP","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1705,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"72402305884","text":"import pyspark\nfrom delta import *\n\nfrom spark.session_manager import SparkSessionManager\n\nif __name__ == '__main__':\n    builder = pyspark.sql.SparkSession.builder.appName(\"main\") \\\n        .config(\"hive.metastore.uris\", \"thrift://localhost:9083\")\n\n    spark = configure_spark_with_delta_pip(builder) \\\n        .enableHiveSupport() \\\n        .getOrCreate()\n\n    r = spark.conf.get(\"spark.sql.catalogImplementation\")\n    print(r)\n","repo_name":"jinho-yoo-jack/spark_with_deltalake","sub_path":"lakehouse/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"11981186977","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jul  9 20:08:41 2020\n\n@author: jiching\n\n\"\"\"\nimport numpy as np\nimport pandas as pd\nfrom pandas.core.frame import DataFrame\n\ndef save_1array(title,path,array1,name1):\n    aaa=DataFrame(array1,columns=[str(name1)])\n    aaa.to_csv(path+'/'+str(title)+'.csv',index=False)\n\ndef save_2array(title,path,array1,array2,name1,name2):\n    aaa=DataFrame(array1,columns=[str(name1)])\n    bbb=DataFrame(array2,columns=[str(name2)])\n    n2 = pd.concat([aaa,bbb],axis=1)\n    n2.to_csv(path+'/'+str(title)+'.csv',index=False)\n   \ndef save_3array(title,path,array1,array2,array3,name1,name2,name3):\n    aaa=DataFrame(array1,columns=[str(name1)])\n    bbb=DataFrame(array2,columns=[str(name2)])\n    ccc=DataFrame(array3,columns=[str(name3)])\n    n3 = pd.concat([aaa,bbb,ccc],axis=1)\n    n3.to_csv(path+'/'+str(title)+'.csv',index=False)\n    \ndef save_4array(title,path,array1,array2,array3,array4,name1,name2,name3,name4):\n    aaa=DataFrame(array1,columns=[str(name1)])\n    bbb=DataFrame(array2,columns=[str(name2)])\n    ccc=DataFrame(array3,columns=[str(name3)])\n    ddd=DataFrame(array4,columns=[str(name4)])\n    n4 = pd.concat([aaa,bbb,ccc,ddd],axis=1)\n    n4.to_csv(path+'/'+str(title)+'.csv',index=False) \n\ndef save_5array(title,path,array1,array2,array3,array4,array5,name1,name2,name3,name4,name5):\n    aaa=DataFrame(array1,columns=[str(name1)])\n    bbb=DataFrame(array2,columns=[str(name2)])\n    ccc=DataFrame(array3,columns=[str(name3)])\n    ddd=DataFrame(array4,columns=[str(name4)])\n    eee=DataFrame(array5,columns=[str(name5)])\n    n5 = pd.concat([aaa,bbb,ccc,ddd,eee],axis=1)\n    n5.to_csv(path+'/'+str(title)+'.csv',index=False)\n    \ndef save_6array(title,path,array1,array2,array3,array4,array5,array6,name1,name2,name3,name4,name5,name6):\n    aaa=DataFrame(array1,columns=[str(name1)])\n    bbb=DataFrame(array2,columns=[str(name2)])\n    ccc=DataFrame(array3,columns=[str(name3)])\n    ddd=DataFrame(array4,columns=[str(name4)])\n    eee=DataFrame(array5,columns=[str(name5)])\n    fff=DataFrame(array6,columns=[str(name6)])\n    n6 = pd.concat([aaa,bbb,ccc,ddd,eee,fff],axis=1)\n    n6.to_csv(path+'/'+str(title)+'.csv',index=False)\n\ndef read_data(title,path):\n    file=pd.read_csv(path+'/'+title+'.csv')\n    file=np.array(file)\n    return file\ndef read_data_column(title,path,column_index):\n    file=pd.read_csv(path+'/'+title+'.csv')\n    temp2=file[(column_index-1)]\n    temp2=temp2.tolist()\n    return temp2\ndef save_1txt(title,path,array1):\n    f = open(path +\"/\"+ title + \".txt\",'w')\n    for kk in range(len(array1)):\n        f.write('%f\\n'%array1[kk])\n    f.close()\ndef save_2txt(title,path,array1,array2):\n    f = open(path +\"/\"+ title + \".txt\",'w')\n    for kk in range(len(array1)):\n        f.write('%f '%array1[kk])\n        f.write('%f\\n'%array2[kk])\n    f.close()\ndef save_3txt(title,path,array1,array2,array3):\n    f = open(path +\"/\"+ title + \".txt\",'w')\n    for kk in range(len(array1)):\n        f.write('%f '%array1[kk])\n        f.write('%f '%array2[kk])\n        f.write('%f\\n'%array3[kk])\n    f.close()\ndef save_4txt(title,path,array1,array2,array3,array4):\n    f = open(path +\"/\"+ title + \".txt\",'w')\n    for kk in range(len(array1)):\n        f.write('%f '%array1[kk])\n        f.write('%f '%array2[kk])\n        f.write('%f '%array3[kk])\n        f.write('%f\\n'%array4[kk])\n    f.close()\ndef save_5txt(title,path,array1,array2,array3,array4,array5):\n    f = open(path +\"/\"+ title + \".txt\",'w')\n    for kk in range(len(array1)):\n        f.write('%f '%array1[kk])\n        f.write('%f '%array2[kk])\n        f.write('%f '%array3[kk])\n        f.write('%f '%array4[kk])\n        f.write('%f\\n'%array5[kk])\n    f.close()\ndef save_6txt(title,path,array1,array2,array3,array4,array5,array6):\n    f = open(path +\"/\"+ title + \".txt\",'w')\n    for kk in range(len(array1)):\n        f.write('%f '%array1[kk])\n        f.write('%f '%array2[kk])\n        f.write('%f '%array3[kk])\n        f.write('%f '%array4[kk])\n        f.write('%f '%array5[kk])\n        f.write('%f\\n'%array6[kk])\n    f.close()\n","repo_name":"chingchenn/pythonFLAC","sub_path":"function_savedata.py","file_name":"function_savedata.py","file_ext":"py","file_size_in_byte":4027,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"24905883837","text":"import argparse\nimport json\nimport os\nimport random\nimport tarfile\n\nparser = argparse.ArgumentParser(\n    description=\"Convert an existing ASR dataset to tarballs compatible with TarredAudioToTextDataLayer.\"\n)\nparser.add_argument(\n    \"--manifest_path\", default=None, type=str, required=True, help=\"Path to the existing dataset's manifest.\"\n)\n\n# Optional arguments\nparser.add_argument(\n    \"--target_dir\",\n    default='./tarred',\n    type=str,\n    help=\"Target directory for resulting tarballs and manifest. Defaults to `./tarred`. Creates the path if ncessary.\",\n)\nparser.add_argument(\n    \"--num_shards\",\n    default=1,\n    type=int,\n    help=\"Number of shards (tarballs) to create. Used for partitioning data among workers.\",\n)\nparser.add_argument(\n    '--max_duration',\n    default=None,\n    type=float,\n    help='Maximum duration of audio clip in the dataset. By default, it is None and will not filter files.',\n)\nparser.add_argument(\n    '--min_duration',\n    default=None,\n    type=float,\n    help='Minimum duration of audio clip in the dataset. By default, it is None and will not filter files.',\n)\nparser.add_argument(\n    \"--shuffle\",\n    action='store_true',\n    help=\"Whether or not to randomly shuffle the samples in the manifest before tarring/sharding.\",\n)\nparser.add_argument(\"--shuffle_seed\", type=int, help=\"Random seed for use if shuffling is enabled.\")\nargs = parser.parse_args()\n\n\ndef create_shard(entries, target_dir, new_entries, shard_id):\n    \"\"\"Creates a tarball containing the audio files from `entries`.\n    \"\"\"\n    tar = tarfile.open(os.path.join(target_dir, f'audio_{shard_id}.tar'), mode='w')\n\n    for entry in entries:\n        # We squash the filename since we do not preserve directory structure of audio files in the tarball.\n        base, ext = os.path.splitext(entry['audio_filepath'])\n        base = base.replace('/', '_')\n        # Need the following replacement as long as WebDataset splits on first period\n        base = base.replace('.', '_')\n        squashed_filename = f'{base}{ext}'\n        tar.add(entry['audio_filepath'], arcname=squashed_filename)\n\n        new_entry = {\n            'audio_filepath': squashed_filename,\n            'duration': entry['duration'],\n            'text': entry['text'],\n            'shard_id': shard_id,  # Keep shard ID for recordkeeping\n        }\n        new_entries.append(new_entry)\n\n    tar.close()\n\n\ndef main():\n    manifest_path = args.manifest_path\n    target_dir = args.target_dir\n    num_shards = args.num_shards\n    max_duration = args.max_duration\n    min_duration = args.min_duration\n    shuffle = args.shuffle\n    seed = args.shuffle_seed\n\n    if not os.path.exists(target_dir):\n        os.makedirs(target_dir)\n\n    # Read the existing manifest\n    entries = []\n    filtered_entries = 0\n    with open(manifest_path, 'r') as m:\n        for line in m:\n            entry = json.loads(line)\n            if (max_duration is None or entry['duration'] < max_duration) and (\n                min_duration is None or entry['duration'] > min_duration\n            ):\n                entries.append(entry)\n            else:\n                filtered_entries += 1\n\n    if filtered_entries > 0:\n        print(f\"Filtered {filtered_entries} files.\")\n\n    if shuffle:\n        random.seed(seed)\n        print(\"Shuffling...\")\n        random.shuffle(entries)\n\n    # Create shards and updated manifest entries\n    new_entries = []\n    print(f\"Remainder: {len(entries) % num_shards}\")\n    for i in range(num_shards):\n        start_idx = (len(entries) // num_shards) * i\n        end_idx = start_idx + (len(entries) // num_shards)\n        print(f\"Shard {i} has entries {start_idx} ~ {end_idx}\")\n        if i == num_shards - 1:\n            # We discard in order to have the same number of entries per shard.\n            print(f\"Have {len(entries) - end_idx} entries left over that will be discarded.\")\n\n        create_shard(entries[start_idx:end_idx], target_dir, new_entries, i)\n\n    # Write manifest\n    new_manifest_path = os.path.join(target_dir, 'tarred_audio_manifest.json')\n    with open(new_manifest_path, 'w') as m2:\n        for entry in new_entries:\n            json.dump(entry, m2)\n            m2.write('\\n')\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"kssteven418/Q-ASR","sub_path":"scripts/convert_to_tarred_audio_dataset.py","file_name":"convert_to_tarred_audio_dataset.py","file_ext":"py","file_size_in_byte":4222,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"86"}
+{"seq_id":"71531919963","text":"import zmq\nimport sys\nimport json\nfrom os import remove, mkdir\nfrom os.path import exists\nfrom hashlib import sha256\n\ncontext = zmq.Context()\nsockets = {}\nproxySocket = context.socket(zmq.REQ)\nproxySocket.connect(\"tcp://localhost:7556\")\nPS = 1024*1024*5\n\ndef full_hash(name):\n    f = open(name, \"rb\")\n    sha = sha256()\n    i = 0\n    while True:\n        f.seek(PS*i)\n        data = f.read(PS)\n        if data:\n            i+=1\n            sha.update(data)\n        else:\n            f.close()\n            return sha.hexdigest()\n\ndef hash_parts(name):\n    h = []                  #Lista de hashes de cada una de  las partes\n    sha = sha256()          # Variable para el hash del archihvo completo\n    with open(name, \"rb\") as f:\n        while True:\n            data = f.read(PS)\n            if data:\n                h.append(sha256(data).hexdigest())       #Se agrega el sha de una parte a la lista\n                sha.update(data)                                #Se actualiza el sha del archivo completo agregando una parte\n            else:\n                break\n    return sha.hexdigest(), h\n\ndef download(name):\n    # try:\n    print(name)\n    proxySocket.send_multipart((b\"#client-download\", name.encode()))\n    parts = proxySocket.recv_json()\n    if not \"Error\" in parts:\n        d = {}\n        for server in parts:\n            socket = context.socket(zmq.REQ)\n            socket.connect(server)\n            for p in parts[server]:\n                path = \"downloaded/\"+parts[server][p]\n                socket.send_multipart((b\"#d\", parts[server][p].encode()))\n                msg = socket.recv_multipart()\n                if msg[0] == b\"ok\":\n                    sha = sha256(msg[2]).hexdigest()\n                    if sha == parts[server][p]:\n                        msg[1] = msg[1].decode()\n                        f = open(path, \"wb\")\n                        f.write(msg[2])\n                        f.close()\n                        d[int(p)] = msg[1]\n                else:\n                    print(\"Error del servidor\")\n        s = dict(sorted(d.items()))\n        file = open(\"downloaded/\"+name,\"ab\")\n        for p in s:\n            part = open(\"downloaded/\"+s[p], \"rb\")\n            data = part.read()\n            file.seek(PS*p)\n            file.write(data)\n            part.close()\n            remove(\"downloaded/\"+s[p])\n        file.close()\n    else:\n        print(\"Error del proxy\")\n\ndef upload(name):\n    try:\n        sha_file, parts = hash_parts(name)\n        msg = [b\"#client-upload\", sha_file.encode(), name.encode()]\n        for p in parts:\n            msg.append(p.encode())\n        proxySocket.send_multipart(tuple(msg))\n        d = proxySocket.recv_json()\n        i = 0\n        for server in d:\n            socket = context.socket(zmq.REQ)\n            socket.connect(server)\n            f = open(name, \"rb\")\n            print(server)\n            for p in d[server]:\n                print(p)\n                socket.send(b\"#u\")\n                socket.recv()\n                f.seek(PS*int(p))\n                data = f.read(PS)\n                sha = sha256(data).hexdigest()\n                if not sha == d[server][p]:\n                    raise Exception\n                i+=1\n                socket.send_multipart((sha.encode(),data,sha_file.encode(), name.encode()))\n                sha_server = socket.recv().decode()\n                if sha == sha_server:\n                    print(\"Parte\",i,\"subida\")\n                else:\n                     raise Exception\n            f.close()\n            socket.close()\n        dictt = json.load(open(\"files.json\",\"r\"))\n        if not name in dictt[\"files\"]:\n            dictt[\"files\"][name] = sha_file\n        if not sha_file in dictt[\"parts\"]:\n            dictt[\"parts\"][sha_file] = d\n        with open(\"files.json\", \"w+\") as file:\n            file.write(json.dumps(dictt,indent=4))\n            file.close()\n    except:\n         print(\"Error\")\n         socket.send(b\"end\")\n\ndef listar():\n    proxySocket.send(b\"#list\")\n    cad = proxySocket.recv().decode()\n    files = cad.split(\",\")\n    for f in files:\n        print(f)\n\ndef main():\n    if not exists(\"downloaded\"):\n        mkdir(\"downloaded\")\n    if len(sys.argv) >= 2:\n        if sys.argv[1] == \"upload\":\n            print(\"Subiendo...\")\n            upload(sys.argv[2])\n            print(\"Terminado\")\n        elif sys.argv[1] == \"download\":\n            download(sys.argv[2])\n        elif sys.argv[1] == \"listar\":\n            listar()\n    else:\n        print(\"Uso incorrecto\")\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"maicandrew/ClienteServidor","sub_path":"Proxy/client/cliente.py","file_name":"cliente.py","file_ext":"py","file_size_in_byte":4528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"5893641604","text":"\"\"\"\nFind the maximum sublist sum of an array containing positive and negative integers.\n\"\"\"\nfrom typing import List, Optional\n\nNO_SOLUTION = -999\n\n\ndef max_sublist_sum(data: List[int]) -> Optional[int]:\n    # return _solve_brute_force(data)\n    # return _solve_iterative(data)\n    return _solve_linear(data)\n\n\ndef _solve_linear(data: List[int]) -> Optional[int]:\n    # For the whole array from 0 -> (n-1):\n    #   - If index 0 is negative, we don't want that element.\n    #   - If index 0 is positive, and sum(index 0 -> k) is negative, then item k must be sufficiently negative to wipe\n    #     out positivity from 0 -> k-1.\n    #\n    # Index: 0                    k\n    #        P------------------>PN\n    #        ----------------------\n    #\n    # Notes:\n    #   - There is no way for sum(index 1 -> k-1) to be more positive than what k would wipe out, because that would\n    #     imply index 0 to be negative, and if index 0 is negative, there's no reason to start accumulating from that\n    #     index.\n    #   - During the time when we're traversing the array, we should record our best solution.\n    #\n    # If we want to track the indices of the best solution:\n    #   - Track the start index of the current solution.\n    #   - Every time we update the best solution, the best solution goes from start index of the current solution to\n    #     current index.\n    best_solution = NO_SOLUTION\n    cur_solution = 0\n    for val in data:\n        cumulative_sum = cur_solution + val\n\n        # Update the best solution.\n        best_solution = max(best_solution, cumulative_sum)\n\n        # Decide whether or not to reset the current solution.\n        if cumulative_sum < 0:\n            cur_solution = 0\n        else:\n            cur_solution = cumulative_sum\n\n    return best_solution if best_solution != NO_SOLUTION else None\n\n\ndef _solve_iterative(data: List[int]) -> int:\n    max_sum = 0\n    for i in range(0, len(data)):\n        cumulative_sum = 0\n        for j in range(i, len(data)):\n            cumulative_sum += data[j]\n            if cumulative_sum > max_sum:\n                max_sum = cumulative_sum\n    return max_sum\n\n\ndef _solve_brute_force(data: List[int]) -> int:\n    # O(n^3) because for each of n^2 sublists, we have to pull up to n items from the list.\n    max_sum = 0\n    for i in range(0, len(data)):\n        for j in range(i + 1, len(data) + 1):\n            cur_sum = sum(data[i:j])\n            print('{}-{}: {}'.format(i, j, cur_sum))\n            if cur_sum > max_sum:\n                max_sum = cur_sum\n    return max_sum\n","repo_name":"r7wang/algorithm","sub_path":"src/array/max_sublist_sum/default.py","file_name":"default.py","file_ext":"py","file_size_in_byte":2551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"12611749825","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan  7 10:59:23 2016\n\n@author: Philip Djakovic\n\"\"\"\n\nimport markup as mup\nimport codecs\n\n\n\"\"\"\nDefinition Section-Klasse -> für jede Section wird Objekt\ndieser Klasse erstellt und landet in einer Liste\n\"\"\"\nclass section:\n    \n    def __init__(self, name, parameters):\n        self.name = name\n        self.parameters = parameters\n\n     \n\"\"\"\nDefinition Parameter-Klasse\n-> für jeden Parameter einer Section wird Objekt dieser Klasse erstellt,\nalle Parameter einer Section landen in einer Liste und diese Liste wird\nmit dem jew. Section-Objekt über das Element parameters verknüpft\n\"\"\"      \nclass parameter:\n    \n    def __init__(self, paraname, erkl, default, bed):\n        self.paraname = paraname\n        self.erkl = erkl\n        self.default = default\n        self.bed = bed\n\n\n\"\"\"\nFunktion zum Herausschneiden des Section-Namen aus einer Zeile\n\"\"\"\ndef sectInLine(line):\n    start = line.index('[') + 1\n    end = line.index(']')\n    return line[start:end]\n\n\n\"\"\"\nFunktion zum Herausschneiden aller Parameter-relevanten Infos\naus einer Zeile -> Name, Erklärung, Default-Wert, Bedingung\nNeues Parameter-Objekt mit diesen Infos wird erstellt\n\"\"\"\ndef parInLine(line):\n    name_start = 0\n    name_end = line.index('=')\n    erkl_start = line.index('\"')\n    erkl_end = line.index('\"', erkl_start+1)\n    def_start = line.index('(')\n    def_end = line.index(',')\n    bed_start = line.index('\\'', def_end)\n    bed_end = line.index('\\'', bed_start+1)\n    \n    name = line[name_start:name_end].strip()\n    erkl = line[erkl_start+1:erkl_end]\n    default = (line[def_start+1:def_end].strip()).replace('\\'', '')\n    bed = line[bed_start+1:bed_end]\n    \n    newpar = parameter(name, erkl, default, bed)\n    return newpar\n\n\n\"\"\"\nFunktion zum Erstellen des HTML-Eintrags eines Parameters + Infos\nmittels markup.py\n\"\"\"  \ndef createtable(page, par):\n    page.h3(par.paraname)\n    page.p(par.erkl)\n    page.table(cellpadding=5)\n    page.tr()\n    page.td()\n    page.b('Default')\n    page.td.close()\n    page.td(par.default)\n    page.tr.close()\n    page.tr()\n    page.td()\n    page.b('Condition')\n    page.td.close()\n    page.td(par.bed)\n    page.tr.close()\n    page.table.close()\n    page.br()\n    return page\n\n\nsect_list = []\n#UTF-8-Einlesen sonst werden Umlaute falsch angezeigt\nf1 = codecs.open('parameters.db', 'r', 'UTF-8')\n\n\"\"\"\nSchleife über alle Zeilen der parameters.db\n-> Section-Zeile: Name herausschneiden, neues Section-Objekt erstellen\n                -> zu Section-Liste hinzufügen\n-> Parameter-Zeile: alle relevanten Parameter-Infos filtern -> Objekt erstellen\n                -> zu Parameter-Liste des jew. Section-Objekts (letzter\n                Section-Listeneintrag) hinzufügen\n\"\"\"\nfor line in f1:\n    if '=' not in line and '[' in line:\n        sect_name = sectInLine(line)        \n        newsect = section(sect_name,[])   \n        sect_list.append(newsect)       \n        \n    if '=' in line:\n        newpar = parInLine(line)\n        sect_list[len(sect_list)-1].parameters.append(newpar)\n    \nf1.close()\n\n#alphabetische Sortierung der Section-Liste nach dem Section-Namen\nsect_list.sort(key = lambda x: x.name)\n\n#Initialisierung von index.html mittels markup.py\nmainpage = mup.page()\nmainpage.init(title = 'index')\nmainpage.h1('Liste aller Sections')\n\n\n\"\"\"\nSchleife über alle Section-Listeneinträge\n-> Erstellen der Section-Liste in index.html mittels markup.py\n-> Sortierung der Parameter-Einträge jeder Section\n-> Erstellen der Section-HTML-Seiten mittels markup.py\n-> Befüllen der Seiten mit entspr. Parametern\n-> Erstellen der Section-HTML-Dateien mit markup-Inhalt\n\"\"\"\nfor sect in sect_list:\n    name = sect.name\n    \n    sect_path = name + '.html'\n    mainpage.a(name, href=sect_path)\n    mainpage.br()\n    \n    pars = sect.parameters\n    \n    pars.sort(key = lambda x: x.paraname)\n    sect_page = mup.page()\n    sect_header = 'Liste aller Parameter von ' + name\n    sect_page.init(title = name)\n    sect_page.h1(sect_header)\n        \n    for par in pars: \n        sect_page = createtable(sect_page, par)        \n        \n    f3 = open(sect_path, 'w')\n    f3.write(str(sect_page))\n    f3.close()\n\n\n\"\"\"\nErstellen der Index-Datei -> mit markup-Inhalt mainpage befüllen \n\"\"\"\nindex_path = 'index.html'\n\nf2 = open(index_path, 'w')\nf2.write(str(mainpage))\nf2.close()","repo_name":"hobler/miniTopSim15","sub_path":"manual.py","file_name":"manual.py","file_ext":"py","file_size_in_byte":4321,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"7144978554","text":"\"\"\"Add Label to each component port.\"\"\"\n\nfrom __future__ import annotations\n\nfrom collections.abc import Callable\nfrom functools import partial\n\nimport gdsfactory as gf\nfrom gdsfactory.component import Component, ComponentReference\nfrom gdsfactory.component_layout import _parse_layer\nfrom gdsfactory.port import Port\nfrom gdsfactory.typings import ComponentOrReference, Label, LayerSpec\n\n\ndef get_input_label_text_dash(\n    port: Port,\n    gc: ComponentReference | Component,\n    gc_index: int | None = None,\n    component_name: str | None = None,\n    prefix: str = \"\",\n    suffix: str = \"\",\n) -> str:\n    \"\"\"Returns text with `GratingName-ComponentName-PortName`.\"\"\"\n    gc_name = gc.name if isinstance(gc, Component) else gc.parent.name\n    return f\"{prefix}{gc_name}-{component_name or port.parent.name}-{port.name}{suffix}\"\n\n\ndef get_input_label_text_dash_loopback(\n    port: Port,\n    gc: ComponentReference | Component,\n    gc_index: int | None = None,\n    component_name: str | None = None,\n    prefix: str = \"\",\n) -> str:\n    \"\"\"Returns text with `GratingName-ComponentName-Loopback`.\"\"\"\n    gc_name = gc.name if isinstance(gc, Component) else gc.parent.name\n    return f\"{prefix}{gc_name}-{component_name or port.parent.name}-loopback_{gc_index}\"\n\n\ndef get_input_label_text(\n    port: Port,\n    gc: ComponentReference | Component,\n    gc_index: int | None = None,\n    component_name: str | None = None,\n    component_prefix: str = \"\",\n    prefix: str = \"opt\",\n    suffix: str = \"\",\n) -> str:\n    \"\"\"Returns text string for an optical port based on grating coupler.\n\n    {label_prefix}_{polarization}_{wavelength_nm}_({prefix}{component_name})\n\n    Args:\n        port: to label.\n        gc: grating coupler component or reference.\n        gc_index: grating_coupler index, which grating_coupler we are labelling.\n        component_name: optional name.\n        component_prefix: component prefix.\n        prefix: prefix to add to the label.\n    \"\"\"\n    polarization = gc.info.get(\"polarization\") or gc.metadata_child.get(\"polarization\")\n    wavelength = gc.info.get(\"wavelength\") or gc.metadata_child.get(\"wavelength\")\n\n    if polarization not in [\"te\", \"tm\"]:\n        raise ValueError(f\"polarization {polarization!r} needs to be [te, tm]\")\n    if not isinstance(wavelength, int | float) or not 0.5 < wavelength < 5.0:\n        raise ValueError(\n            f\"{wavelength} needs to be > 0.5um and < 5um. Make sure it's in um\"\n        )\n\n    component_name = component_name or port.parent.metadata_child.get(\"name\")\n\n    text = f\"{prefix}_{polarization}_{int(wavelength*1e3)}_({component_prefix}{component_name}){suffix}\"\n    if isinstance(gc_index, int):\n        text += f\"_{gc_index}_{port.name}\"\n    else:\n        text = f\"_{port.name}\"\n\n    return text\n\n\nget_input_label_text_loopback = partial(get_input_label_text, prefix=\"loopback_\")\n\n\ndef get_input_label(\n    port: Port,\n    gc: ComponentReference,\n    gc_index: int | None = None,\n    gc_port_name: str = \"o1\",\n    layer_label: LayerSpec = \"TEXT\",\n    component_name: str | None = None,\n    get_input_label_text_function=get_input_label_text,\n) -> Label:\n    \"\"\"Returns a label with component info for a given grating coupler.\n\n    Test equipment to extract grating coupler coordinates and match it to the component.\n\n    Args:\n        port: port to label.\n        gc: grating coupler reference.\n        gc_index: grating coupler index.\n        gc_port_name: name of grating coupler port.\n        layer_label: layer of the label.\n        component_name: for the label.\n        get_input_label_text_function: function to get input label.\n    \"\"\"\n    text = get_input_label_text_function(\n        port=port, gc=gc, gc_index=gc_index, component_name=component_name\n    )\n\n    if gc_port_name is None:\n        gc_port_name = list(gc.ports.values())[0].name\n\n    layer, texttype = gf.get_layer(layer_label)\n    return Label(\n        text=text,\n        origin=gc.ports[gc_port_name].center,\n        anchor=\"o\",\n        layer=layer,\n        texttype=texttype,\n    )\n\n\nget_input_label_dash = partial(\n    get_input_label, get_input_label_text_function=get_input_label_text_dash\n)\n\n\ndef get_input_label_electrical(\n    port: Port,\n    gc_index: int = 0,\n    component_name: str | None = None,\n    layer_label: LayerSpec = \"TEXT\",\n    gc: ComponentReference | None = None,\n) -> Label:\n    \"\"\"Returns a label to test component info for a given electrical port.\n\n    This is the label used by T&M to extract grating coupler coordinates\n    and match it to the component.\n\n    Args:\n        port: to label.\n        gc_index: index of the label.\n        component_name: Optional component_name.\n        layer_label: for label.\n        gc: ignored.\n    \"\"\"\n    if component_name:\n        name = component_name\n    elif isinstance(port.parent, gf.Component):\n        name = port.parent.name\n    else:\n        name = port.parent.ref_cell.name\n\n    text = f\"elec_{gc_index}_({name})_{port.name}\"\n    layer_label = gf.get_layer(layer_label)\n    layer, texttype = _parse_layer(layer_label)\n    return Label(\n        text=text,\n        origin=port.center,\n        anchor=\"o\",\n        layer=layer,\n        texttype=texttype,\n    )\n\n\ndef add_labels(\n    component: Component,\n    get_label_function: Callable = get_input_label_electrical,\n    layer_label: LayerSpec = \"TEXT\",\n    gc: Component | None = None,\n    **kwargs,\n) -> Component:\n    \"\"\"Returns component with labels on ports.\n\n    Args:\n        component: to add labels to.\n        get_label_function: function to get label.\n        layer_label: layer_label.\n        gc: Optional grating coupler.\n\n    keyword Args:\n        layer: port GDS layer.\n        prefix: with in port name.\n        suffix: select ports with port name suffix.\n        orientation: in degrees.\n        width: for ports to add label.\n        layers_excluded: List of layers to exclude.\n        port_type: optical, electrical, ...\n        clockwise: if True, sort ports clockwise, False: counter-clockwise.\n\n    Returns:\n        original component with labels.\n    \"\"\"\n    ports = component.get_ports_list(**kwargs)\n\n    for i, port in enumerate(ports):\n        label = get_label_function(\n            port=port,\n            gc=gc,\n            gc_index=i,\n            component_name=component.name,\n            layer_label=layer_label,\n        )\n        component.add(label)\n\n    return component\n\n\ndef add_siepic_labels(\n    component: Component,\n    model: str = \"auto\",\n    library: str = \"auto\",\n    label_layer: LayerSpec = \"DEVREC\",\n    spice_params: dict | list | str | None = None,\n    label_spacing: float = 0.2,\n) -> Component:\n    \"\"\"Adds labels and returns the same component.\n\n    Args:\n        component: component.\n        model: Lumerical Interconnect model.\n            'auto' attempts to extract this from the cross_section.\n        library: Lumerical Interconnect library.\n            'auto' attempts to extract this from the cross_section.\n        label_layer: layer for writing SiEPIC labels.\n        spice_params: spice parameters (in microns).\n            Either pass in a dict with parameter, value pairs, or pass\n            a list of values to extract from component info.\n        label_spacing: separation distance between labels in um.\n    \"\"\"\n    c = component\n\n    labels = []\n    if model:\n        if model == \"auto\" and \"model\" in c.info:\n            model = c.info[\"model\"]\n        labels.append(f\"Component={model}\")\n    if library:\n        if library == \"auto\" and \"library\" in c.info:\n            library = c.info[\"library\"]\n        labels.append(f\"Lumerical_INTERCONNECT_library={library}\")\n    if spice_params and c.info[\"layout_model_property_pairs\"]:\n        if spice_params == \"auto\":\n            pairs = c.info[\"layout_model_property_pairs\"]\n            spice_params = {pair[1]: c.info[pair[0]] for pair in pairs}\n        param_str = \"\"\n        for param in spice_params:\n            val = spice_params[param]\n            param_str += f\"{param}={val:.3f}u \"\n        labels.append(f\"Spice_param:{param_str}\")\n\n    for i, text in enumerate(labels):\n        c.add_label(\n            text=text, position=(0, i * label_spacing), layer=label_layer, anchor=\"w\"\n        )\n    return c\n\n\ndef add_labels_to_ports(\n    component: Component,\n    label_layer: LayerSpec = \"TEXT\",\n    port_type: str | None = None,\n    **kwargs,\n) -> Component:\n    \"\"\"Add labels to component ports.\n\n    Args:\n        component: to add labels.\n        label_layer: layer spec for the label.\n        port_type: to select ports.\n\n    keyword Args:\n        layer: select ports with GDS layer.\n        prefix: select ports with prefix in port name.\n        suffix: select ports with port name suffix.\n        orientation: select ports with orientation in degrees.\n        width: select ports with port width.\n        layers_excluded: List of layers to exclude.\n        port_type: select ports with port_type (optical, electrical, vertical_te).\n        clockwise: if True, sort ports clockwise, False: counter-clockwise.\n    \"\"\"\n    ports = component.get_ports_list(port_type=port_type, **kwargs)\n    for port in ports:\n        component.add_label(text=port.name, position=port.center, layer=label_layer)\n\n    return component\n\n\ndef add_labels_to_ports_x_y(\n    component: Component,\n    label_layer: LayerSpec = \"TEXT\",\n    port_type: str | None = None,\n    **kwargs,\n) -> Component:\n    \"\"\"Add labels to component ports. Prepends -x-y coordinates\n\n    Args:\n        component: to add labels.\n        label_layer: layer spec for the label.\n        port_type: to select ports.\n\n    keyword Args:\n        layer: select ports with GDS layer.\n        prefix: select ports with prefix in port name.\n        suffix: select ports with port name suffix.\n        orientation: select ports with orientation in degrees.\n        width: select ports with port width.\n        layers_excluded: List of layers to exclude.\n        port_type: select ports with port_type (optical, electrical, vertical_te).\n        clockwise: if True, sort ports clockwise, False: counter-clockwise.\n    \"\"\"\n    ports = component.get_ports_list(port_type=port_type, **kwargs)\n    for port in ports:\n        x, y = port.center\n        component.add_label(\n            text=f\"{port.name}/{int(x)}/{int(y)}\",\n            position=port.center,\n            layer=label_layer,\n        )\n\n    return component\n\n\nadd_labels_to_ports_electrical = partial(\n    add_labels_to_ports,\n    port_type=\"electrical\",\n)\nadd_labels_to_ports_optical = partial(\n    add_labels_to_ports,\n    port_type=\"optical\",\n)\nadd_labels_to_ports_vertical_dc = partial(\n    add_labels_to_ports,\n    port_type=\"vertical_dc\",\n)\nadd_labels_to_ports_opt = partial(add_labels_to_ports, prefix=\"opt\", port_type=None)\n\n\ndef get_labels(\n    component: ComponentOrReference,\n    get_label_function: Callable = get_input_label_electrical,\n    layer_label: LayerSpec = \"TEXT\",\n    gc: Component | None = None,\n    component_name: str | None = None,\n    **kwargs,\n) -> list[Label]:\n    \"\"\"Returns component labels on ports.\n\n    Args:\n        component: to add labels to.\n        get_label_function: function to get label.\n        layer_label: layer_label.\n        gc: Optional grating coupler.\n        component_name: optional component name.\n\n    keyword Args:\n        layer: port GDS layer.\n        prefix: look for prefix in port name.\n        suffix: select ports with port name suffix.\n        orientation: in degrees.\n        width: for ports to add label.\n        layers_excluded: List of layers to exclude.\n        port_type: optical, electrical, ...\n        clockwise: if True, sort ports clockwise, False: counter-clockwise.\n\n    Returns:\n        list of component labels.\n    \"\"\"\n    labels = []\n    ports = component.get_ports_list(**kwargs)\n    component_name = component_name or component.name\n\n    for i, port in enumerate(ports):\n        label = get_label_function(\n            port=port,\n            gc=gc,\n            gc_index=i,\n            component_name=component_name,\n            layer_label=layer_label,\n        )\n        labels.append(label)\n\n    return labels\n\n\nif __name__ == \"__main__\":\n    # c = gf.components.mzi_phase_shifter()\n    # add_labels_ports(c, c.get_ports_list(port_type=\"electrical\"), prefix=\"pad_\")\n    # from gdsfactory.tests.test_labels import test_add_labels_electrical\n    # c = test_add_labels_optical()\n    # c = test_add_labels_electrical()\n    # c = gf.routing.add_fiber_single(c)\n\n    c = gf.components.pad(decorator=add_labels_to_ports_vertical_dc)\n    c.show(show_ports=True)\n","repo_name":"gdsfactory/gdsfactory","sub_path":"gdsfactory/add_labels.py","file_name":"add_labels.py","file_ext":"py","file_size_in_byte":12531,"program_lang":"python","lang":"en","doc_type":"code","stars":318,"dataset":"github-code","pt":"86"}
+{"seq_id":"733979556","text":"# -*- coding:utf-8 -*-\n# 使用CNN实现验证码识别\n#   Author  :   fx\n#   E-mail  :   pythonist@126.com\nimport numpy as np\nimport tensorflow as tf\nfrom utils import read_data\n\ntrain_dir = \"data\"\ntest_dir = \"test_data\"\n# train标志着是训练还是测试\ntrain = False\n# 模型最后的保存路径\nmodel_path = \"model/image_model\"\n\n# 这是样本的标签种类\nchar_to_digit = [\"零\",\"壹\",\"贰\",\"叁\",\"肆\",\"伍\",\"陆\",\"柒\",\"捌\",\"玖\",\"拾\",\"一\",\"二\",\"三\",\"四\",\"五\",\"六\",\"七\",\"八\",\"九\",\"加\",\"减\",\"乘\",\"除\"]\n\nfpaths, datas, labels = read_data(train_dir)\ntest_fpath, test_datas, test_labels = read_data(test_dir)\ndata_len = datas.shape[0]\n\n# n_classes 表示有多少类图片\nn_classes = len(set(labels))\n\n\n# 定义占位符，存放图片和对应的标签 图片数据大小为30*26*1，存放的数据为像素值归一化后的值\nX = tf.placeholder(tf.float32, [None, 30, 26, 1])\nY = tf.placeholder(tf.int32, [None])\n\n# drop为dropout参数，为一个百分比，表示反向传播时，选取一部分参数不进行更新，减少过拟合，训练时为0.25，测试时为0\ndrop = tf.placeholder(tf.float32)\n\n# 定义第一层卷积，20个卷积核，核大小为1*1，即全卷积，relu激活\nconv1 = tf.layers.conv2d(X, 20, 1, activation=tf.nn.relu)\n\n# 定义第二层卷积, 20个卷积核, 核大小为1*1，Relu激活\nconv2 = tf.layers.conv2d(conv1, 20, 1, activation=tf.nn.relu)\n\n\n# 将三维向量拉伸为一维\nflat = tf.layers.flatten(conv2)\n\n# 全连接，将输入转换成一个1000维向量，还是采用relu激活\nfc = tf.layers.dense(flat, 1000, activation=tf.nn.relu)\n\n# 计算dropout\ndrop_func = tf.layers.dropout(fc, drop)\n\n# 这里再次全连阶，压缩到与分类维度对应的向量\nlogits = tf.layers.dense(drop_func, n_classes)\n\n# tf.argmax返回的是指定维度上最大值的索引值index\n# 这里pred_labels可以用来标志最后的预测结果\npred_labels = tf.argmax(logits, 1)\n\n\n# 损失函数采用交叉熵，\nloss = tf.nn.softmax_cross_entropy_with_logits(\n    labels=tf.one_hot(Y, n_classes),\n    logits=logits\n)\n# softmax_cross_entropy_with_logits返回的不是一个具体的值，而是一个向量，这里需要求平均值\nm_loss = tf.reduce_mean(loss)\n\n# 定义优化器，学习率为0.001\noptimizer = tf.train.AdamOptimizer(learning_rate=1e-3).minimize(m_loss)\n\n\n# saver用来保存训练的模型\nsaver = tf.train.Saver()\nif __name__ == '__main__':\n    with tf.Session() as sess:\n\n        if train:\n            print(\"train\")\n            # init\n            sess.run(tf.global_variables_initializer())\n            # 迭代50次\n            for i in range(100):\n                _, loss_v = sess.run([optimizer, m_loss], feed_dict={\n                                                                    X: datas,\n                                                                    Y: labels,\n                                                                    drop: 0,\n                                                                    }\n                                     )\n                if i % 10 == 0:\n                    print(\"step:{}-->loss:{}\".format(i, loss_v))\n            saver.save(sess, model_path)\n            print(\"Done!，save as :{}\".format(model_path))\n        else:\n            # 测试\n            print(\"test\")\n            saver.restore(sess, model_path)\n            print(\"recover from:{}\".format(model_path))\n            # label_map是模型输出值与实际分类标签的分类\n            label_map = {k: v for k, v in enumerate(char_to_digit)}\n            pred_val = sess.run(pred_labels, feed_dict={\n                X: test_datas,\n                Y: test_labels,\n                drop: 0\n            })\n            err_count = 0\n            for fpath, real_label, predicted_label in zip(test_fpath, test_labels, pred_val):\n                # 将预测的标签索引值转换为对应分类\n                real_label_name = label_map[real_label]\n                pred_name = label_map[predicted_label]\n                if real_label_name != pred_name:\n                    err_count += 1\n            print(1 - err_count/len(test_datas))\n\n","repo_name":"Super-Storm/tensorflow-captcha","sub_path":"CNN_captcha.py","file_name":"CNN_captcha.py","file_ext":"py","file_size_in_byte":4152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"41653043675","text":"# Flow control\ndisastertype = 'tornado'\n\nif disastertype == 'earthquake':\n    print('woah!')\nelif disastertype == 'hurricane':\n    print('nooo!')\nelse:\n    print('oh no!')\n\n# Ternary if (in-line)\nmy_values = { 'red': ['7'], 'blue': [0], 'green': [3] }\nred = my_values.get('red', [''])\nred = int(red[0]) if red[0] else 0\nprint(red)\n\n# Looping\ncount = 1\n\n#while count > 0:\n#    stuff = input('String to capitalize [q to quit]: ')\n#\n#    if stuff == 'q':\n#        break; # continue also supported\n#\n#    print(stuff.upper())\n#    count += 1\n\nrabbits = ['tom', 'bugs', 'roger', 'jerry']\n\nfor name in rabbits:\n    print(name)\n\nword = 'mississippi'\n\nfor c in word:\n    print(c)\n\nauthors = {\n        'king': ['It', 'Christine', 'The Shining'],\n        'card': ['Ender''s Game', 'Speaker for the Dead'],\n        'heinlein': ['Stranger in a Strange Land', 'Starship Troopers']\n    }\n\nfor author in authors:\n    print(author)\n\nfor books in authors.values():\n    print(books)\n\nfor author, books in authors.items(): # tuple assignment\n    print(author, books)\n\nindexes = [1, 2, 3]\nvalues = ['one', 'two', 'three']\n\nfor index, value in zip(indexes, values):\n    print(index, value)\n\nprint(list(zip(indexes, values))) # list of tuples\nprint(dict(zip(indexes, values))) # dictionary\n\nfor n in range(0, 10, 2): # slice\n    print(n)\n\n# Use enumerate over range when scanning a list:\nflavor_list = ['one', 'two', 'three']\nfor i, flavor in enumerate(flavor_list):\n    print('%d: %s' % (i + 1, flavor))\n\n# Comprehensions\nnumbers = [n for n in range(0, 11, 2)] # kind of like math set notation \nodds = [n + 1 for n in range(0, 11, 2)]\nodds2 = [n for n in range(0, 51) if n % 2 == 1]\nxy = [(x, y) for x in range(1, 5) if x % 2 == 0 for y in range(1, 5) if y % 2 == 1] # cartesian product (l to r)\n\nmatrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\nflat = [x for row in matrix for x in row] # l to r, row then element\nprint(flat)\n\nsquared = [[x**2 for x in row] for row in matrix] # square a matrix\nprint(squared)\n\nprint(numbers)\nprint(odds)\nprint(odds2)\nprint(xy)\n\nword = 'hello world!!!'\nletter_counts = {letter: word.count(letter) for letter in set(word)}\nletter_index = {letter: word.index(letter) for letter in set(word)}\n\nprint(letter_counts)\nprint(letter_index)\n\nset1 = {n for n in range(1, 10, 3)}\n\nprint(set1)\n\n# Generator comprehensions (like an enumerator)\n# More memory efficient than comprehensions (like a stream)\nnums = (n for n in range(0, 10))\n\nprint(nums)\n\nfor n in nums: # enumerate\n    print(n)\n\nprint(list(nums)) # empty (implicitly iterates)\n\n# Functions!\n\ndef myFirstFun(anything = 'default value'):\n    return 'Hello World!' + anything\n\nprint(myFirstFun(anything = '!!!'))\n\ndef printArgs(*args):\n    print(args)\n\ndef log(message, *values):  # any number of args after the first\n    if not values:\n        print(message)\n    else:\n        values_str = ', '.join(str(x) for x in values)\n        print(f'{message}: {values_str}')\n\ndef printArgsKV(**args): # create dictionary\n    print(args)\n\n# * indicates the end of positional arguments and the beginning of keyword-only arguments\ndef safe_division_c(number, divisor, *,\n                    ignore_overflow=False,\n                    ignore_zero_division=False):\n    pass\n\nprintArgs('1', '2', '3')\n\nl = ['1', '2', '3', '4']\nprintArgs(*l) # scatters list into arguments\n\nprintArgsKV(key1='val1', key2='val2', key3='val3')\n\ndef times3(*args):\n    return sum(args)\n\ndef invoke(func, *args): # function delegate\n    print(func(*args))\n\ninvoke(times3, 1, 2, 3, 4, 5)\n\ndef outer(a, b):\n    def inner(c, d):\n        return c - d\n    return a + b + inner(a, b)\n\nprint(outer(1, 2))\n\ndef returnClosure(str1):\n    'Returns a closure: function'\n    def inner():\n        print(\"The arg is: %s\" % str1)\n\n    return inner\n\nfunc1 = returnClosure(\"hellow\")\nfunc1()\n\ndef iterate1(words, func1):\n    for w in words:\n        print(func1(w))\n\niterate1(['a', 'nice', 'day'], lambda word: word.upper() + '!') # lambda syntax\n\n# Generator functions / iterators\n# More memory efficient than building a list and returning it.\ndef my_range(first, last, step):\n    num = first\n\n    while first < last:\n        yield first # creates state machine\n        first += step\n\nfor n in my_range(0, 10, 2):\n    print(n)\n\n# Decorator: takes a func as an arg and returns another function\ndef docFunc(func):\n    def decorateFunc(*args, **kwargs):\n        print('Running function:', func.__name__)\n        print('Positional arguments:', args)\n        print('Keyword arguments:', kwargs)\n        result = func(*args, **kwargs)\n        print('Result:', result)\n        return result\n    return decorateFunc\n\ndef myAdd(p, q, **kwargs):\n    return p + q\n\nnewFunc = docFunc(myAdd) # manual decorator assignment\nnewFunc(2, 4, key1='val1', key2='val2')\n\n# Or\n# Same as: myAdd2 = docFunc(myAdd2)\n@docFunc\ndef myAdd2(p, q, **kwargs):\n    return p + q\n\nmyAdd2(4, 6, key1='val1', key2='val2')\n\ndef squareIt(func):\n    def newFunction(*args, **kwargs):\n        result = func(*args, **kwargs)\n        return result * result\n    \n    return newFunction\n\n# Chained decorators\n@docFunc\n@squareIt\ndef myAdd3(p, q, **kwargs):\n    return p + q\n\nmyAdd3(1, 2)\n\n# Global variable scope\nanimal = \"kitten\"\n\ndef changeGlobal():\n    global animal # without this variable is scoped to function\n    animal = animal.upper() + '!'\n\nprint(animal)\nchangeGlobal()\nprint(animal)\n\ndef changeLocal():\n    animal = 'tiger'\n    print('locals: ', locals())\n\nchangeLocal()\nprint('globals: ', globals())\n\n# Exception / Try Catch\ntry:\n    mylist = [1, 2, 3]\n    print(mylist[5])\nexcept IndexError as e:\n    print(\"Out of bounds!: \", e)\nexcept Exception as e:\n    print(\"Unknown: \", e)\nfinally:\n    print('Always run this!')\n\nmylist2 = {1, 2, 3, 4}\n\nfor n in mylist2:\n    if n % 2 == 0:\n        raise Exception('Even number!')\n\n# Enums\nfrom enum import Enum\n\nclass State(Enum):\n    COMPLETE = 1\n    PARTIAL = 2\n    BLANK = 3\n\n","repo_name":"shermanflan/py-methods","sub_path":"py-techniques/codestructures.py","file_name":"codestructures.py","file_ext":"py","file_size_in_byte":5885,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"34754802296","text":"\nclass Solution:\n    def longestIncreasingPath(self, matrix: List[List[int]]) -> int:\n        if not matrix:\n            return 0\n        \n        m, n = len(matrix), len(matrix[0])\n        memo = [[0] * n for _ in range(m)]  # 初始化记忆化数组\n        \n        def dfs(i, j):\n            if memo[i][j] > 0:  # 如果已经计算过路径长度，直接返回之前的结果\n                return memo[i][j]\n            \n            directions = [(0, 1), (0, -1), (1, 0), (-1, 0)]\n            res = 1  # 如果四个方向都搜索不到递增路径，则默认路径长度为 1\n            for di, dj in directions:  # 对于每一个方向\n                ni, nj = i + di, j + dj\n                if 0 <= ni < m and 0 <= nj < n and matrix[ni][nj] > matrix[i][j]:\n                    # 如果当前方向对应位置的数值严格大于当前位置的数值\n                    # 沿着当前方向继续递归，得到从当前位置出发的最长递增路径长度\n                    res = max(res, dfs(ni, nj) + 1)\n            \n            memo[i][j] = res  # 记录当前位置的最长递增路径长度\n            return res\n        \n        ans = 0\n        for i in range(m):\n            for j in range(n):\n                ans = max(ans, dfs(i, j))  # 枚举每个位置，计算所有位置中最长递增路径的长度\n        return ans\n\n# https://leetcode.cn/problems/longest-increasing-path-in-a-matrix/submissions/\n","repo_name":"leejamesss/Leetcode_daily","sub_path":"8.8/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"39967960486","text":"class TreeNode:\n    def __init__(self, data):\n        self.data = data\n        self.left = None\n        self.right = None\n\n    def __str__(self) -> str: #Retornando o dado do nó em string\n        return str(self.data)\n\nclass BinaryTree:\n    def __init__(self, data = None):\n        if data:\n            node = TreeNode(data)\n            self.root = node\n        else:\n            self.root = None\n\n    def simetric_traversal(self, node = None): #Encaminhamento em ordem\n        if node is None:\n            node = self.root #Partindo por padrão a partir da raiz\n        if node.left:\n            print('(', end='')\n            self.simetric_traversal(node.left)\n        print(node, end = '')\n        if node.right:\n            self.simetric_traversal(node.right)\n            print(')', end='')\n\n    def postorder_traversal(self, node = None):\n        if node is None:\n            node = self.root #Partindo por padrão a partir da raiz\n        if node.left:\n            self.postorder_traversal(node.left)\n        if node.right:\n            self.postorder_traversal(node.right)\n        print(node, end='')\n\n    def height(self, node = None):\n        if node is None:\n            node = self.root #Partindo por padrão a partir da raiz\n        hleft = 0\n        hright = 0\n        if node.left:\n            hleft = self.height(node.left)\n        if node.right:\n            hright = self.height(node.right)\n        if hright > hleft:\n            return hright + 1 \n        return hleft + 1\n\n        \n\ndef postorder_example_tree():\n    tree = BinaryTree()\n    n1 = TreeNode('I')\n    n2 = TreeNode('N')\n    n3 = TreeNode('S')\n    n4 = TreeNode('C')\n    n5 = TreeNode('R')\n    n6 = TreeNode('E')\n    n7 = TreeNode('V')\n    n8 = TreeNode('A')\n    n10 = TreeNode('-')\n    n9 = TreeNode('S')\n    n0 = TreeNode('E')\n\n    n0.left = n6\n    n0.right = n9\n    n6.left = n1\n    n6.right = n5\n    n5.left = n2\n    n5.right = n4\n    n4.right = n3\n    n9.left = n10\n    n10.right = n8\n    n8.right = n7\n\n    tree.root = n0\n    return tree\n\ndef inorder_traversal_example():\n    tree = BinaryTree()\n    n1 = TreeNode('a')\n    n2 = TreeNode('+')\n    n3 = TreeNode('*')\n    n4 = TreeNode('b')\n    n5 = TreeNode('-')\n    n6 = TreeNode('/')\n    n7 = TreeNode('c')\n    n8 = TreeNode('d')\n    n9 = TreeNode('e')\n\n    n6.left = n7\n    n6.right = n8\n    n5.left = n6\n    n5.right = n9\n    n3.left = n4\n    n3.right = n5\n    n2.left = n1\n    n2.right = n3\n\n    tree.root = n2\n    tree.simetric_traversal()\n    print()\n\n    '''\n            '+'\n        /    \\\n        'a'     '*'\n                /   \\ \n            'b'   '-'\n                    /   \\\n                '/'  'e'\n                /  \\ \n            'c'  'd'\n    '''\n\nif __name__ == \"__main__\":\n\n    tree = postorder_example_tree()\n    print('Percurso em pós ordem:')\n    tree.postorder_traversal()\n    print(f'\\nAltura: {tree.height()}')","repo_name":"tuliooarauj/algoritmo-estrutura_dados","sub_path":"Arvores Binaris/tree.py","file_name":"tree.py","file_ext":"py","file_size_in_byte":2871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32105612293","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"Console script for cmip6_object_store.\"\"\"\n\nimport argparse\nimport os\nimport shutil\nimport sys\n\nfrom cmip6_object_store import CONFIG, logging\nfrom cmip6_object_store.cmip6_zarr.batch import BatchManager\nfrom cmip6_object_store.cmip6_zarr.caringo_store import CaringoStore\nfrom cmip6_object_store.cmip6_zarr.compare import compare_zarrs_with_ncs\nfrom cmip6_object_store.cmip6_zarr.results_store import get_results_store, get_verification_store\nfrom cmip6_object_store.cmip6_zarr.task import TaskManager\nfrom cmip6_object_store.cmip6_zarr.intake_cat import create_intake_catalogue\nfrom cmip6_object_store.cmip6_zarr.utils import (\n    get_credentials,\n    get_zarr_url,\n)\n\nLOGGER = logging.getLogger(__file__)\n\n\ndef _add_arg_parser_run(parser):\n\n    _add_arg_parser_project(parser, description=\"convert to Zarr in Object Store\")\n\n    group = parser.add_mutually_exclusive_group()\n\n    group.add_argument(\n        \"--slurm-array-member\",\n        action=\"store_true\",\n        required=False,\n        help=(\"Not for interactive use. \\n\"\n              \"Batch number will be taken from SLURM_ARRAY_TASK_ID environment variable.\"),\n    )\n\n    group.add_argument(\n        \"-b\",\n        \"--batches\",\n        type=str,\n        default=\"all\",\n        required=False,\n        help=\"Batches to run, default is 'all'. Also accepts comma separated \"\n        \"list of batch numbers and/or ranges specified with a hyphen. E.g: \"\n        \"'1,2,3' or '1-5'.\",\n    )\n\n    parser.add_argument(\n        \"-d\",\n        \"--datasets\",\n        type=str,\n        default=None,\n        required=False,\n        help=\"Datasets to run. Also accepts comma separated \"\n        \"list of datasets. E.g.: -d cmip6...gn.v20190910,cmip6...v20200202\",\n    )\n\n    parser.add_argument(\n        \"-r\",\n        \"--run-mode\",\n        type=str,\n        default=\"lotus\",\n        required=False,\n        help=\"Mode to run in, either 'lotus' (default) or 'local'.\",\n    )\n\n\ndef _range_to_list(range_string, sep):\n    start, end = [int(val) for val in range_string.split(sep)]\n    return list(range(start, end + 1))\n\n\ndef parse_args_run(args):\n    # Parse batches into a single value\n    slurm_array_member = args.slurm_array_member\n    batches = args.batches\n    datasets = args.datasets\n\n    if slurm_array_member:\n        batches = [int(os.environ[\"SLURM_ARRAY_TASK_ID\"])]\n    \n    elif batches == \"all\":\n        batches = None\n    else:\n        items = batches.split(\",\")\n        batches = []\n\n        for item in items:\n            if \"-\" in item:\n                batches.extend(_range_to_list(item, \"-\"))\n            else:\n                batches.append(int(item))\n\n        batches = sorted(list(set(batches)))\n\n    if datasets:\n        datasets = datasets.split(\",\")\n\n    return args.project, batches, datasets, args.run_mode\n\n\ndef run_main(args):\n    project, batches, datasets, run_mode = parse_args_run(args)\n\n    tm = TaskManager(project, batches=batches, datasets=datasets, run_mode=run_mode)\n    tm.run_tasks()\n\n\ndef _add_arg_parser_project(parser, description=\"\"):\n\n    parser.add_argument(\n        \"-p\",\n        \"--project\",\n        type=str,\n        default=CONFIG[\"workflow\"][\"default_project\"],\n        required=False,\n        help=f\"Project {description}\",\n    )\n\n\ndef parse_args_project(args):\n    return args.project\n\n\ndef _add_arg_parser_create(parser):\n    _add_arg_parser_project(parser)\n    parser.add_argument(\"--all\", action=\"store_true\",\n                        help=\"include all datasets in batches (default is to check if already done)\")\n    \ndef create_main(args):\n    project = parse_args_project(args)\n    bm = BatchManager(project, exclude_done=not args.all)\n    bm.create_batches()\n\n\ndef _add_arg_parser_intake(parser):\n    _add_arg_parser_project(parser, description=\"to create intake catalog for\")\n    parser.add_argument(\"--limit\", type=int,\n                        help=\"maximum number of datasets to include\")\n\ndef intake_main(args):\n    project = parse_args_project(args)\n    create_intake_catalogue(project, limit=args.limit)\n    \n    \ndef _add_arg_parser_clean(parser):\n\n    _add_arg_parser_project(parser, description=\"to clean out directories for\")\n\n    parser.add_argument(\n        \"-D\",\n        \"--delete-objects\",\n        action=\"store_true\",\n        help=\"Delete all the objects in the Object Store - DANGER!!!\",\n    )\n\n    parser.add_argument(\n        \"-b\",\n        \"--buckets\",\n        default=[],\n        nargs=\"*\",\n        help=\"Identifiers of buckets TO DELETE!\",\n    )\n\n\ndef parse_args_clean(args):\n    return args.project, args.delete_objects, args.buckets\n\n\ndef clean_main(args):\n    project, delete_objects, buckets_to_delete = parse_args_clean(args)\n\n    if delete_objects:\n        resp = input(\"DO YOU REALLY WANT TO DELETE THE BUCKETS? [Y/N] \")\n        if resp != \"Y\":\n            print(\"Exiting.\")\n            sys.exit()\n\n    batch_dir = BatchManager(project)._version_dir\n    log_dir = os.path.join(CONFIG[\"log\"][\"log_base_dir\"], project)\n    to_delete = [log_dir, batch_dir]\n\n    for dr in to_delete:\n        if os.path.isdir(dr):\n            LOGGER.warning(f\"Deleting: {dr}\")\n            shutil.rmtree(dr)\n\n    if buckets_to_delete:\n        LOGGER.warning(\"Starting to delete buckets from Object Store!\")\n        caringo_store = CaringoStore(creds=get_credentials())\n\n        for bucket in buckets_to_delete:\n            LOGGER.warning(f\"DELETING BUCKET: {bucket}\")\n            caringo_store.delete(bucket)\n\n\ndef _add_arg_parser_list(parser):\n\n    _add_arg_parser_project(parser, description=\"to list directories for\")\n\n    parser.add_argument(\n        \"-c\",\n        \"--count-only\",\n        action=\"store_true\",\n        help=\"Only show the total count of records processed.\",\n    )\n\n\ndef parse_args_list(args):\n    return args.project, args.count_only\n\n\ndef list_main(args):\n    project, count_only = parse_args_list(args)\n    results_store = get_results_store(project)\n    \n    records = results_store.get_successful_runs()\n\n    if not count_only:\n        for _, dataset_id in records:\n            zarr_url = get_zarr_url(dataset_id, project)\n            print(f\"Record: {zarr_url}\")\n\n    print(f\"\\nTotal records: {len(records)}\")\n\n\ndef verify_main(args):\n    project = parse_args_project(args)\n    results_store = get_results_store(project)\n    verified_store = get_verification_store(project)\n    \n    successes, out_of = compare_zarrs_with_ncs(project, dataset_id=args.dataset)\n    print(f\"\\nVerified {successes} out of {out_of} datasets.\")\n\n    print(\"\\n\\nResults of all verifications so far:\")\n\n    n_verified_successes = verified_store.count_successes()\n    n_total_successes = results_store.count_successes()\n    n_total = results_store.count_results()\n\n    print(f\"\"\"{n_verified_successes} successfully verified\n{n_total_successes} total claimed successes\n{n_total} total results including failures\"\"\")\n\n\ndef show_errors_main(args):\n    project = parse_args_project(args)\n    results_store = get_results_store(project)    \n\n    errors = results_store.get_failed_runs()\n    \n    for dataset_id, error in errors.items():\n        print(\"\\n===================================================\")\n        print(f\"{dataset_id}:\")\n        print(\"===================================================\\n\")\n        print(\"\\t\" + error)\n\n    print(f\"\\nFound {len(errors)} errors.\")\n\n\ndef main():\n    \"\"\"Console script for cmip6_object_store.\"\"\"\n    main_parser = argparse.ArgumentParser()\n    main_parser.set_defaults(func=lambda args: main_parser.print_help())\n    subparsers = main_parser.add_subparsers()\n\n    run_parser = subparsers.add_parser(\"run\")\n    _add_arg_parser_run(run_parser)\n    run_parser.set_defaults(func=run_main)\n\n    create_parser = subparsers.add_parser(\"create-batches\")\n    _add_arg_parser_create(create_parser)\n    create_parser.set_defaults(func=create_main)\n\n    clean_parser = subparsers.add_parser(\"clean\")\n    _add_arg_parser_clean(clean_parser)\n    clean_parser.set_defaults(func=clean_main)\n\n    list_parser = subparsers.add_parser(\"list\")\n    _add_arg_parser_list(list_parser)\n    list_parser.set_defaults(func=list_main)\n\n    verify_parser = subparsers.add_parser(\"verify\")\n    _add_arg_parser_project(verify_parser)\n\n    verify_parser.add_argument(\n        \"-d\",\n        \"--dataset\",\n        type=str,\n        default=None,\n        required=False,\n        help=\"Single dataset ID to verify (defaults to choosing a sample)\"\n    )\n\n    verify_parser.set_defaults(func=verify_main)\n\n    intake_parser = subparsers.add_parser(\"create-intake\")\n    _add_arg_parser_intake(intake_parser)\n    intake_parser.set_defaults(func=intake_main)\n\n    show_errors_parser = subparsers.add_parser(\"show-errors\")\n    _add_arg_parser_project(show_errors_parser)\n    show_errors_parser.set_defaults(func=show_errors_main)\n\n    args = main_parser.parse_args()\n    args.func(args)\n\n\nif __name__ == \"__main__\":\n\n    sys.exit(main())  # pragma: no cover\n","repo_name":"cedadev/cmip6-object-store","sub_path":"cmip6_object_store/cmip6_zarr/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":8919,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"8578113712","text":"from fastapi import APIRouter, Request\nfrom app.core.tools.database import find_one, update_one \n\nfrom app.core.tools.jwt import FastJWT\n\ntelegram_router = APIRouter(prefix=\"/telegram\")\n\n@telegram_router.post(\"/set\")\nasync def get_profile_items_event(request: Request, telegram_id: str):\n    auth_token = request.headers[\"Authorisation\"]\n    auth_token = await FastJWT().decode(auth_token)\n    user = await find_one(\"users_db\", {\"email\": auth_token[\"email\"]})\n\n    user[\"telegram\"] = telegram_id\n    await update_one(\"users_db\", {\"email\": auth_token[\"email\"]}, user)\n\n    return {\"message\": \"Updated.\"}","repo_name":"denver-code/stuff_accounting_backend","sub_path":"v1/private/profile/telegram.py","file_name":"telegram.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32906885282","text":"#Napisać funkcję sum_seq(sequence) obliczającą sumę liczb zawartych w sekwencji, która może zawierać zagnieżdżone\n# podsekwencje. Wskazówka: rozważyć wersję rekurencyjną, a sprawdzanie, czy element jest sekwencją,\n# wykonać przez isinstance(item, (list, tuple)).\n\ndef sum_seq(sequence):\n    result = 0\n    if isinstance(sequence, (list,tuple)) == True:\n        for item in sequence:\n           result += sum_seq(item)\n    else:\n        return sequence\n    return result\n\nprint(sum_seq([(1,[1,2]),3,[2,3],(1,1)])) # result = 14","repo_name":"zarrock256/python","sub_path":"4.6.py","file_name":"4.6.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"28898021379","text":"#!/usr/bin/env python\r\n## Win32 EXE Builder using PyInstaller 2.0\r\n## Luis Rodil-Fernandez <luis@greenhost.nl>\r\n##\r\n## As of pyinstaller 2 there's no need for the 2-step process\r\n## executables can now be build in a single call:\r\n##\r\n## $ python pyinstaller.py [opts] yourprogram.py\r\n##\r\n## see documentation for further details:\r\n## http://www.pyinstaller.org/export/v2.0/project/doc/Manual.html?format=raw\r\nimport os, sys\r\nimport time, string\r\nimport subprocess\r\nimport shutil\r\nimport urlparse\r\n\r\nPYINST_DEFAULT_PATH = 'C:\\Python27\\Lib\\site-packages\\PyInstaller'\r\n\r\n# Apps to build, specifying the entry point script and the icon to\r\n# embed in the executable file as a resource\r\nAPPS = [\r\n\t\t{'script': 'viperclient.py', 'icon': 'resources/icons/online.ico'}\r\n\t   ]\r\n\r\n# PyInstaller options\r\nOPTS = ['--onefile', '--noconsole']\r\n\r\n# Additional application resources\r\nRES  = ['README.md', 'resources', 'third-party/tap-windows', 'third-party/openvpn'] #, 'dist/viperclient.exe']\r\n\r\n# Build byproducts to delete after build\r\nCLEAN = ['dist/viperclient.exe']\r\n\r\n# third-party binaries\r\nOVPN_DOWNLOAD_URL = \"http://swupdate.openvpn.org/community/releases/\"\r\nOVPN_DOWNLOAD = \"openvpn-install-{0}-{1}.exe\"\r\nOVPN_VERSION = \"2.3.6-I601\"\r\nTUNTAP_DOWNLOAD = \"tap-windows-{0}.exe\"\r\nTUNTAP_VERSION = \"9.21.1\"\r\n\r\n# relative to the CWD\r\ndef get_build_path():\r\n\tp = os.path.join(os.getcwd(), \"dist\")\r\n\tif not os.path.exists(p):\r\n\t\tos.makedirs(p)\r\n\r\n\treturn p\r\n\r\ndef get_ovpn_platform_suffix():\r\n\t\"\"\" get the platform from the python API and translate it to the suffix used in OpenVPN releases \"\"\"\r\n\tPLATFORM = {'32bit' : \"i686\", '64bit' : \"x86_64\"}\r\n\timport platform\r\n\tb, l = platform.architecture()\r\n\treturn PLATFORM[b]\r\n\r\ndef download_binaries():\r\n\timport urllib\r\n\tdownloader = urllib.URLopener()\r\n\r\n\tovpnfn = OVPN_DOWNLOAD.format(OVPN_VERSION, get_ovpn_platform_suffix())\r\n\tovpnurl = urlparse.urljoin(OVPN_DOWNLOAD_URL, ovpnfn)\r\n\tprint(\"Downloading\", ovpnurl)\r\n\tdownloader.retrieve(ovpnurl, ovpnfn)\r\n\r\n\ttuntapfn = TUNTAP_DOWNLOAD.format(TUNTAP_VERSION)\r\n\ttuntapurl = urlparse.urljoin(OVPN_DOWNLOAD_URL, tuntapfn)\r\n\tprint(\"Downloading\", tuntapurl)\r\n\tdownloader.retrieve(tuntapurl, tuntapfn)\r\n\r\n# absolute path to PyInstaller - expects an environment variable pointing to it\r\ndef get_pyinstaller_path():\r\n\tp = os.getenv(\"PYINSTALLER_HOME\")\r\n\tif p:\r\n\t\treturn p\r\n\telse:\r\n\t\tprint(\"(!!!) The environment variable PYINSTALLER_HOME is not defined, resorting to default...\")\r\n\t\treturn PYINST_DEFAULT_PATH\r\n\r\n# build actual command call to PyInstaller\r\ndef create_executables():\r\n\tprint(\"Creating executables...\")\r\n\tpath = get_pyinstaller_path()\r\n\tcmd = os.path.join(path, \"main.py\")\r\n\topt = string.join(OPTS)\r\n\tfor app in APPS:\r\n\t\t# build actual command to execute for this application\r\n\t\tsc = cmd + \" \" + opt + \" -i \" + app['icon'] + \" \" + app['script']\r\n\t\tprint(sc)\r\n\t\tsubprocess.call(sc, shell=True)\r\n\r\n\r\n# Copy additional resources\r\ndef copy_resources():\r\n\tprint(\"Including resources...\")\r\n\tfor r in RES:\r\n\t\tprint(\"Copying resource %s\" % (r,))\r\n\t\tif not os.path.exists(r):\r\n\t\t\tprint(\"WARNING: The requested resource %s could not be copied because source doesn't exist.\" % (r,))\r\n\t\t\tcontinue\r\n\t\tif os.path.isdir(r):\r\n\t\t\tprint(\"\\t-> as directory tree\")\r\n\t\t\tshutil.copytree(r, os.path.join(get_build_path(), os.path.basename(r)) )\r\n\t\telse:\r\n\t\t\tprint(\"\\t-> as file\")\r\n\t\t\tshutil.copy(r, os.path.join(get_build_path(), os.path.basename(r)) )\r\n\r\n# Build windows services (PyInstaller doesn't seem to support services)\r\ndef py2exe_build_services():\r\n\tprint(\"Building the windows service...\")\r\n\tsubprocess.call(\"python setup.py py2exe\", shell=True)\r\n\tprint(\"Sometimes when executing the py2exe command from a python script certain DLLs are not copied.\\n\\nRun this command to make sure they are copied: python setup.py py2exe\")\r\n\r\ndef cleanup():\r\n\tfor r in CLEAN:\r\n\t\tprint(\"Cleaning byproduct %s\" % (r,))\r\n\t\tif os.path.isfile(r):\r\n\t\t\tos.remove(r)\r\n\t\telse:\r\n\t\t\tprint(\"File doesn't exist, skipping.\")\r\n\r\n##\r\n## Main loop\r\n##\r\nif __name__ == '__main__':\r\n\t#create_executables()\r\n\tcopy_resources()\r\n\tcleanup()\r\n\t#py2exe_build_services()\r\n\r\n","repo_name":"greenhost/viper","sub_path":"buildexe.py","file_name":"buildexe.py","file_ext":"py","file_size_in_byte":4111,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"86"}
+{"seq_id":"13700018914","text":"\nwith open('kek.43319559636b94db1c945834340b65d68f90b6ecbb70925f7b24f6efc5c2524e.txt', 'r') as readin:\n    for line in readin:\n        output = line.split()\n\nprint(output)\nmorse = ''\nflag = 1\nfor each in output:\n    if each[0] == 'K' and each[1] == 'E' and each[2] == 'K':\n        if flag == 0:\n            morse = morse + ('.' * (len(each) - 3))\n        else:\n            morse = morse + ('-' * (len(each) - 3))\n    if each[0] == 'T' and each[1] == 'O' and each[2] == 'P':\n        morse = morse + ' '\n        flag = (flag + 1)%2\nfp = open('morsecode.txt', 'w')\nfp.write(morse)\nfp.close()\n","repo_name":"rd-li/CTF","sub_path":"htv/others/topkek/topkek.py","file_name":"topkek.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"12482460462","text":"import json\nimport re\nimport traceback\nfrom helpers import aeshelper\nfrom objects import glob\nfrom common.ripple import userUtils\n\nfrom . import flavours\nfrom . import ice_coffee\nfrom . import police\n\n#Cornflakes is nice when 90% is sugar\nsugar = {\n\t\"hash\": [],\n\t\"path\": [],\n\t\"file\": [],\n\t\"title\": []\n}\n\ninitialized_eggs = False\n\n#Eggs\ndef init_eggs():\n\teggs = glob.db.fetchAll(\"SELECT * FROM eggs\", [])\n\tif eggs is not None:\n\t\tfor egg in eggs:\n\t\t\tif egg[\"type\"] not in [\"hash\", \"path\", \"file\", \"title\"]:\n\t\t\t\tcontinue\n\t\t\tsugar[egg[\"type\"]].append(egg)\n\n\tcompile_regex()\n\n\tinitialized_eggs = True\n\ndef compile_regex():\n\t#Cache regex searches\n\tfor carbohydrates in sugar:\n\t\tfor speed in sugar[carbohydrates]:\n\t\t\tif speed[\"is_regex\"]:\n\t\t\t\tspeed[\"regex\"] = re.compile(speed[\"value\"])\n\n#Since this is still being worked on everything is in a try catch\ndef bake(submit, score):\n\t\"\"\"\n\tWe have deprecated the process list scanning.\n\tThere is no config to re-enable this.\n\tIf you know what you are doing however you know how to re-enable this feature.\n\t\"\"\"\n\treturn\n\ttry:\n\t\tif not initialized_eggs:\n\t\t\tinit_eggs()\n\t\t\n\t\tif not score.passed:\n\t\t\treturn\n\t\t\n\t\tdetected = []\n\t\tflags = 0\n\n\t\tif \"osuver\" in submit.request.arguments:\n\t\t\taeskey = \"osu!-scoreburgr---------{}\".format(submit.get_argument(\"osuver\"))\n\t\telse:\n\t\t\taeskey = \"h89f2-890h2h89b34g-h80g134n90133\"\n\t\tiv = submit.get_argument(\"iv\")\n\n\t\tscore_data = aeshelper.decryptRinjdael(aeskey, iv, submit.get_argument(\"score\"), True).split(\":\")\n\t\tusername = score_data[1].strip()\n\n\t\tuser_id = userUtils.getID(username)\n\t\trestricted = userUtils.isRestricted(user_id)\n\n\t\tif restricted == True or user_id == 0: #We dont care about this since this person is already taken care off\n\t\t\treturn\n\n\t\tflags = score_data[17].count(' ')\n\n\t\ttry:\n\t\t\tpl = aeshelper.decryptRinjdael(aeskey, iv, submit.get_argument(\"pl\"), True).split(\"\\r\\n\")\n\t\texcept:\n\t\t\tpolice.call(\"Unable to decrypt process list from USERNAME()\", user_id=user_id)\n\t\t\tdetected.append({\n\t\t\t\t\"tag\":\"Unable to decrypt process list (Hacked)\",\n\t\t\t\t\"ban\": False\n\t\t\t\t})\n\t\t\teat(score, {}, detected, flags)\n\t\t\treturn\n\n\t\tpl = sell(pl)\n\n\t\t#I dont really like chocolate that much >.<\n\t\tfor p in pl:\n\t\t\tfor t in sugar.keys():\n\t\t\t\tif p[t] is None:\n\t\t\t\t\tcontinue\n\n\t\t\t\tfor speed in sugar[t]:\n\t\t\t\t\tif speed in detected:\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t\tif speed[\"is_regex\"]:\n\t\t\t\t\t\tif \"regex\" not in speed: #Some weird bug where it unsets itself\n\t\t\t\t\t\t\tspeed[\"regex\"] = re.compile(speed[\"value\"])\n\n\t\t\t\t\t\tif speed[\"regex\"].search(p[t]) is not None:\n\t\t\t\t\t\t\t\tdetected.append(speed)\n\t\t\t\t\telse:\n\t\t\t\t\t\tif speed[\"value\"] == p[t]:\n\t\t\t\t\t\t\tdetected.append(speed)\n\n\t\teat(score, pl, detected, flags)\n\texcept:\n\t\tpolice.call(traceback.format_exc(), discord_m=True)\n\t\tpolice.call(\"Oh no! The cake is on fire! Abort!\")\n\ndef sell(processes):\n\tformatted_pl = []\n\tfor p in processes: #Formats the process list\n\t\ttry:\n\t\t\tt = p.split(\" | \", 1)\n\t\t\ttry:\n\t\t\t\td = t[0].split(\" \", 1)\n\t\t\t\tfile_hash = d[0]\n\t\t\t\tfile_path = d[1]\n\t\t\texcept:\n\t\t\t\tfile_hash = None\n\t\t\t\tfile_path = None\n\n\t\t\th = t[1].split(\" (\", 1)\n\t\t\tfile_name = h[0]\n\n\t\t\tfile_title = None\n\t\t\tif len(h[1]) > 1:\n\t\t\t\tfile_title = h[1][:-1]\n\n\t\t\tformatted_pl.append({\"hash\":file_hash, \"path\":file_path,\n\t\t\t\t\t\t\t\t \"file\":file_name, \"title\":file_title})\n\t\texcept:\n\t\t\tcontinue\n\n\treturn formatted_pl\n\ndef eat(score, processes, detected, flags):\n\tif flavours.config is None:\n\t\tpolice.cache_config()\n\n\tdo_restrict = False\n\tfor toppings in detected:\n\t\tif toppings[\"ban\"]:\n\t\t\tdo_restrict = True\n\n\ttag_list = [x[\"tag\"] for x in detected]\n\n\thax_flags = flags & ~ice_coffee.IGNORE_HAX_FLAGS\n\tbeatmap_id = get_beatmap_id(score.fileMd5)[\"beatmap_id\"]\n\t\n\tusername = userUtils.getUsername(score.playerUserID)\n\n\tfields = [\n\t\t{\n\t\t\t\"name\": \"BeatmapID: {}\".format(beatmap_id),\n\t\t\t\"value\": \"[Download Beatmap](http://{}/b/{})\".format(flavours.config[\"urls\"][\"main_domain\"], beatmap_id),\n\t\t\t\"inline\": True\n\t\t},\n\t\t{\n\t\t\t\"name\": \"ScoreID: {}\".format(score.scoreID),\n\t\t\t\"value\": \"[Download Replay](http://{}/web/replays/{})\".format(flavours.config[\"urls\"][\"main_domain\"], score.scoreID),\n\t\t\t\"inline\": True\n\t\t}\n\t]\n\n\tif len(detected) > 0:\n\t\treason = \" & \".join(tag_list)\n\t\tif len(reason) > 86:\n\t\t\treason = \"reasons...\"\n\n\t\textra_data = \"\"\n\t\tif hax_flags != 0:\n\t\t\textra_data = \"\\nHad bad flags: ({}) -> ({})\".format(flags, make_flags_string(flags))\n\n\t\tif do_restrict:\n\t\t\tuserUtils.restrict(score.playerUserID)\n\t\t\tuserUtils.appendNotes(score.playerUserID, \"Restricted due to {}\".format(reason))\n\t\t\tpolice.call(\"{} was restricted for: {} {}\".format(username, reason, extra_data), \n\t\t\t\tdiscord_m=True,\n\t\t\t\tembed_args={\n\t\t\t\t\t\t\"color\": 0xd9534f,\n\t\t\t\t\t\t\"title\": \"Bad cake detected\",\n\t\t\t\t\t\t\"title_url\": \"http://old.{}/index.php?p=129&sid={}\".format(flavours.config[\"urls\"][\"main_domain\"], score.scoreID),\n\t\t\t\t\t\t\"desc\": \"Restricted for: {} {}\".format(reason, extra_data),\n\t\t\t\t\t\t\"author\": username,\n\t\t\t\t\t\t\"author_icon\": \"http://a.{}/{}\".format(flavours.config[\"urls\"][\"main_domain\"], score.playerUserID),\n\t\t\t\t\t\t\"author_url\": \"http://{}/u/{}\".format(flavours.config[\"urls\"][\"main_domain\"], score.playerUserID),\n\t\t\t\t\t\t\"thumbnail\": flavours.config[\"images\"][\"bad_cake_ban\"],\n\t\t\t\t\t\t\"fields\": fields\n\t\t\t\t\t}\n\t\t\t\t)\n\t\telse:\n\t\t\tuserUtils.appendNotes(score.playerUserID, reason)\n\t\t\tpolice.call(\"{} submitted bad cake: {} {}\".format(username, reason, extra_data), \n\t\t\t\tdiscord_m=True,\n\t\t\t\tembed_args={\n\t\t\t\t\t\t\"color\": 0xf0ad4e,\n\t\t\t\t\t\t\"title\": \"Bad cake detected\",\n\t\t\t\t\t\t\"title_url\": \"http://old.{}/index.php?p=129&sid={}\".format(flavours.config[\"urls\"][\"main_domain\"], score.scoreID),\n\t\t\t\t\t\t\"desc\": \"Had bad cake: {} {}\".format(reason, extra_data),\n\t\t\t\t\t\t\"author\": username,\n\t\t\t\t\t\t\"author_icon\": \"http://a.{}/{}\".format(flavours.config[\"urls\"][\"main_domain\"], score.playerUserID),\n\t\t\t\t\t\t\"author_url\": \"http://{}/u/{}\".format(flavours.config[\"urls\"][\"main_domain\"], score.playerUserID),\n\t\t\t\t\t\t\"thumbnail\": flavours.config[\"images\"][\"bad_cake\"],\n\t\t\t\t\t\t\"fields\": fields\n\t\t\t\t\t}\n\t\t\t\t)\n\telif hax_flags != 0:\n\t\tpolice.call(\"{} submitted bad flags: ({}) -> ({})\".format(username, flags, make_flags_string(flags)),\n\t\t\tdiscord_m=True, \n\t\t\tembed_args={\n\t\t\t\t\t\"color\": 0xf0ad4e,\n\t\t\t\t\t\"title\": \"Bad flags detected\",\n\t\t\t\t\t\"title_url\": \"http://old.{}/index.php?p=129&sid={}\".format(flavours.config[\"urls\"][\"main_domain\"], score.scoreID),\n\t\t\t\t\t\"desc\": \"({}) -> ({})\".format(flags, make_flags_string(flags)),\n\t\t\t\t\t\"author\": username,\n\t\t\t\t\t\"author_icon\": \"http://a.{}/{}\".format(flavours.config[\"urls\"][\"main_domain\"], score.playerUserID),\n\t\t\t\t\t\"author_url\": \"http://{}/u/{}\".format(flavours.config[\"urls\"][\"main_domain\"], score.playerUserID),\n\t\t\t\t\t\"thumbnail\": flavours.config[\"images\"][\"bad_flag\"],\n\t\t\t\t\t\"fields\": fields\n\t\t\t\t}\n\t\t\t)\n\n\tglob.db.execute(\"INSERT INTO cakes(id, userid, score_id, processes, detected, flags) VALUES (NULL,%s,%s,%s,%s,%s)\", [score.playerUserID, score.scoreID, json.dumps(processes), json.dumps(tag_list), flags])\n\ndef make_flags_string(i):\n\ts = []\n\tflags = [e for e in ice_coffee.Flags]\n\n\tfor flag in flags:\n\t\tif i & flag.value and i & ~ice_coffee.IGNORE_HAX_FLAGS:\n\t\t\ts.append(flag.name)\n\t\n\treturn \", \".join(s)\n\ndef get_beatmap_id(hash):\n\tquery = \"SELECT beatmap_id,beatmapset_id FROM beatmaps WHERE beatmap_md5 = %s\"\n\treturn glob.db.fetch(query, [hash])","repo_name":"light-ripple/Light-Ripple-Windows","sub_path":"lets/secret/butterCake.py","file_name":"butterCake.py","file_ext":"py","file_size_in_byte":7202,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"86"}
+{"seq_id":"8256330475","text":"\"\"\"\nA unit test for the pyxsim analysis module.\n\"\"\"\n\nimport os\nimport shutil\nimport tempfile\n\nimport numpy as np\nfrom numpy.testing import assert_allclose\nfrom soxs import ApecGenerator\nfrom yt.utilities.cosmology import Cosmology\nfrom yt.utilities.physical_constants import clight\n\nfrom pyxsim import CIESourceModel, make_photons, project_photons\nfrom pyxsim.tests.utils import (\n    BetaModelSource,\n    ParticleBetaModelSource,\n    events_ks_testing,\n)\n\ncosmo = Cosmology()\n\nckms = clight.in_units(\"km/s\").v\n\n\ndef test_beta_model(check_dir):\n    bms = BetaModelSource()\n    do_beta_model(bms, check_dir)\n\n\ndef test_beta_model_nomove(check_dir):\n    bms = BetaModelSource()\n    do_beta_model(bms, check_dir, axis=\"x\", prng=89)\n\n\ndef test_beta_model_offaxis(check_dir):\n    bms = BetaModelSource()\n    do_beta_model(bms, check_dir, axis=[1.0, -2.0, 5.0], prng=78)\n\n\ndef test_particle_beta_model(check_dir):\n    bms = ParticleBetaModelSource()\n    do_beta_model(bms, check_dir, prng=29)\n\n\ndef test_particle_beta_model_nomove(check_dir):\n    bms = ParticleBetaModelSource()\n    do_beta_model(bms, check_dir, axis=\"x\", prng=72)\n\n\ndef test_particle_beta_model_offaxis(check_dir):\n    bms = ParticleBetaModelSource()\n    do_beta_model(bms, check_dir, prng=67, axis=[1.0, -2.0, 5.0])\n\n\ndef do_beta_model(source, check_dir, axis=\"z\", prng=None):\n    tmpdir = tempfile.mkdtemp()\n    curdir = os.getcwd()\n    os.chdir(tmpdir)\n\n    if prng is None:\n        prng = source.prng\n\n    ds = source.ds\n\n    A = 30000.0\n    exp_time = 1.0e4\n    redshift = 0.05\n    nH_sim = 0.02\n\n    sphere = ds.sphere(\"c\", (0.5, \"Mpc\"))\n\n    kT_sim = source.kT\n    Z_sim = source.Z\n\n    thermal_model = CIESourceModel(\"apec\", 0.1, 11.5, 20000, Z_sim, prng=prng)\n    make_photons(\"my_photons\", sphere, redshift, A, exp_time, thermal_model)\n\n    D_A = cosmo.angular_diameter_distance(0.0, redshift).to_value(\"cm\")\n\n    norm_sim = sphere.quantities.total_quantity((\"gas\", \"emission_measure\"))\n    norm_sim *= 1.0e-14 / (4 * np.pi * D_A * D_A * (1.0 + redshift) * (1.0 + redshift))\n    norm_sim = float(norm_sim.in_cgs())\n\n    v1, v2 = sphere.quantities.weighted_standard_deviation(\n        (\"gas\", \"velocity_z\"), (\"gas\", \"emission_measure\")\n    )\n\n    if isinstance(axis, str):\n        if axis == \"z\":\n            fac = 1.0\n        else:\n            fac = 0.0\n    else:\n        axis /= np.sqrt(np.dot(axis, axis))\n        fac = np.dot(axis, [0.0, 0.0, 1.0])\n\n    sigma_sim = fac * float(v1.in_units(\"km/s\"))\n    mu_sim = -fac * float(v2.in_units(\"km/s\"))\n\n    project_photons(\n        \"my_photons\",\n        \"my_events\",\n        axis,\n        [30.0, 45.0],\n        absorb_model=\"tbabs\",\n        nH=nH_sim,\n        prng=prng,\n    )\n\n    redshift_sim = (1.0 + mu_sim / ckms) * (1.0 + redshift) - 1.0\n\n    agen = ApecGenerator(0.3, 8.0, 10000)\n    spec = agen.get_spectrum(kT_sim, Z_sim, redshift_sim, norm_sim, velocity=sigma_sim)\n    spec.apply_foreground_absorption(nH_sim, model=\"tbabs\")\n\n    pvalue = events_ks_testing(\"my_events.h5\", spec, exp_time, A, check_dir)\n\n    assert pvalue > 0.05\n\n    os.chdir(curdir)\n    shutil.rmtree(tmpdir)\n\n\ndef test_vapec_beta_model(check_dir):\n    bms = BetaModelSource()\n\n    tmpdir = tempfile.mkdtemp()\n    curdir = os.getcwd()\n    os.chdir(tmpdir)\n\n    prng = 50\n\n    ds = bms.ds\n\n    A = 30000.0\n    exp_time = 1.0e5\n    redshift = 0.05\n    nH_sim = 0.02\n\n    sphere = ds.sphere(\"c\", (0.5, \"Mpc\"))\n\n    kT_sim = bms.kT\n    Z_sim = bms.Z\n    O_sim = bms.O\n    Ca_sim = bms.Ca\n\n    var_elem = {\"O\": (\"stream\", \"oxygen\"), \"Ca\": (\"stream\", \"calcium\")}\n\n    thermal_model = CIESourceModel(\n        \"apec\", 0.1, 11.5, 20000, (\"gas\", \"metallicity\"), var_elem=var_elem, prng=prng\n    )\n\n    make_photons(\"my_photons\", sphere, redshift, A, exp_time, thermal_model)\n\n    D_A = cosmo.angular_diameter_distance(0.0, redshift).to(\"cm\")\n\n    norm_sim = sphere.quantities.total_quantity(\"emission_measure\")\n    norm_sim *= 1.0e-14 / (4 * np.pi * D_A * D_A * (1.0 + redshift) * (1.0 + redshift))\n    norm_sim = float(norm_sim.in_cgs())\n\n    project_photons(\n        \"my_photons\",\n        \"my_events\",\n        \"z\",\n        [30.0, 45.0],\n        absorb_model=\"tbabs\",\n        nH=nH_sim,\n        prng=prng,\n        no_shifting=True,\n    )\n\n    agen = ApecGenerator(0.2, 10.0, 10000, var_elem=[\"O\", \"Ca\"])\n    spec = agen.get_spectrum(\n        kT_sim, Z_sim, redshift, norm_sim, elem_abund={\"O\": O_sim, \"Ca\": Ca_sim}\n    )\n    spec.apply_foreground_absorption(nH_sim, model=\"tbabs\")\n\n    pvalue = events_ks_testing(\"my_events.h5\", spec, exp_time, A, check_dir)\n\n    print(pvalue)\n    assert pvalue > 0.05\n\n    os.chdir(curdir)\n    shutil.rmtree(tmpdir)\n\n\ndef test_beta_model_fields():\n    bms = BetaModelSource()\n    ds = bms.ds\n\n    redshift = 0.2\n\n    sphere = ds.sphere(\"c\", (0.5, \"Mpc\"))\n\n    kT_sim = bms.kT\n    Z_sim = bms.Z\n\n    D_A = cosmo.angular_diameter_distance(0.0, redshift).to_value(\"cm\")\n    D_L = cosmo.luminosity_distance(0.0, redshift).to_value(\"cm\")\n\n    norm = (\n        1.0e-14\n        * sphere.sum((\"gas\", \"emission_measure\")).v\n        / (4.0 * np.pi * D_A * D_A * (1 + redshift) ** 2)\n    )\n\n    agen = ApecGenerator(0.1, 11.5, 2000)\n\n    spec = agen.get_spectrum(kT_sim, Z_sim, redshift, norm)\n    pflux, eflux = spec.get_flux_in_band(0.5 / (1.0 + redshift), 7.0 / (1.0 + redshift))\n    lum = 4.0 * np.pi * D_L**2 * eflux.value\n    plum = 4.0 * np.pi * D_L**2 * pflux.value / (1.0 + redshift)\n\n    thermal_model = CIESourceModel(\"apec\", 0.1, 11.5, 2000, Z_sim)\n\n    xray_fields = thermal_model.make_source_fields(ds, 0.5, 7.0)\n    lum1 = sphere.sum(xray_fields[1]).value\n    plum1 = (sphere[xray_fields[-2]] * sphere[\"cell_volume\"]).sum().value\n    plum2 = sphere[xray_fields[-1]].sum().value\n\n    int_fields = thermal_model.make_intensity_fields(\n        ds, 0.5 / (1.0 + redshift), 7.0 / (1.0 + redshift), redshift=redshift\n    )\n    angular_scale = 1.0 / cosmo.angular_scale(0.0, redshift).to(\"cm/arcsec\")\n\n    eflux2 = (sphere[int_fields[0]] * sphere[\"cell_volume\"]).sum() * angular_scale**2\n    pflux2 = (sphere[int_fields[1]] * sphere[\"cell_volume\"]).sum() * angular_scale**2\n\n    assert np.abs(lum1 - lum) / lum < 0.001\n    assert np.abs(plum1 - plum) / plum < 0.01\n    assert np.abs(plum2 - plum) / plum < 0.01\n\n    assert np.abs(eflux2.value - eflux.value) / eflux.value < 0.001\n    assert np.abs(pflux2.value - pflux.value) / pflux.value < 0.01\n\n\ndef test_beta_model_spectrum():\n    bms = BetaModelSource()\n    ds = bms.ds\n\n    redshift = 0.2\n\n    sphere = ds.sphere(\"c\", (0.5, \"Mpc\"))\n\n    kT_sim = bms.kT\n    Z_sim = bms.Z\n\n    D_A = cosmo.angular_diameter_distance(0.0, redshift).to_value(\"cm\")\n\n    norm1 = 1.0e-14 * sphere.sum((\"gas\", \"emission_measure\")).v\n    norm2 = norm1 / (4.0 * np.pi * D_A * D_A * (1 + redshift) ** 2)\n\n    agen = ApecGenerator(0.2, 7.0, 2000)\n\n    spec1 = agen.get_spectrum(kT_sim, Z_sim, redshift, norm2)\n\n    thermal_model = CIESourceModel(\"apec\", 0.2, 7.0, 2000, Z_sim)\n    spec2 = thermal_model.make_spectrum(\n        sphere, 0.2, 7.0, 2000, redshift=redshift, cosmology=cosmo\n    )\n    assert_allclose(spec1.flux.value, spec2.flux.value)\n\n    spec3 = agen.get_spectrum(kT_sim, Z_sim, 0.0, norm1)\n\n    spec4 = thermal_model.make_spectrum(sphere, 0.2, 7.0, 2000)\n\n    assert_allclose(spec3.flux.value, spec4.flux.value)\n","repo_name":"jzuhone/pyxsim","sub_path":"pyxsim/tests/test_beta_model.py","file_name":"test_beta_model.py","file_ext":"py","file_size_in_byte":7275,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"86"}
+{"seq_id":"30734990036","text":"import configparser\nimport datetime\nfrom enum import Enum\nimport os\n\nimport openpyxl\nimport yaml\n\nSEPARATOR = \"_\"\nTEMPLATE_SUFFIX = SEPARATOR + \"template\"\n# TODO: someday: move this into the config\nUNITS_SUFFIX = SEPARATOR + \"units\"\nPHI_SUFFIX = SEPARATOR + \"phi\"\n# All field names should be lowercase and contain only alphanumeric and underscores.\n# No field name can start with a number\nFIELD_NAME_REGEX = \"^[a-z][a-z0-9_]*$\"\nNON_WIZARD_XLSX_ERROR_PREFIX = \"Spreadsheet does not appear to have been produced by QIIMP: \"\n# TODO: someday: this duplicates a definition in xlsx_builder, which would be a circular reference here; refactor!\nWORKBOOK_PASSWORD = \"kpcofGs\"  # Kingdom phylum class order family Genus species\nSAMPLE_NAME_HEADER = \"sample_name\"\nNAME_KEY = \"name\"\nDISPLAY_NAME_KEY = \"display_name\"\nDOWNLOAD_URL_FOLDER = \"/download\"\nPACKAGE_URL_FOLDER = \"/package\"\nUPLOAD_URL_FOLDER = \"/upload\"\n# Per Austin, make default column width \"at least wide enough to handle\n# host_scientific_name <snip> our largest mandatory metadata title\";\n# Sizing approach (magic # 1.25 ~= width of 1 character) from\n# https://stackoverflow.com/questions/29463274/simulate-autofit-column-in-\n# xslxwriter#comment90864296_37218180\nMIN_COL_WIDTH = len(\"host_scientific_name\") * 1.25\n\n\ndef get_single_key_and_subdict(a_dict):\n    if len(a_dict.keys()) != 1:\n        raise ValueError(\n            \"Dictionary '{0}' is mis-structured; must have only one top-level key.\".format(a_dict))\n\n    single_key = list(a_dict.keys())[0]\n    return single_key, a_dict[single_key]\n\n\ndef load_yaml_from_wizard_xlsx(filepath, yaml_sheetname):\n    assumed_cell = \"A1\"\n    wb = openpyxl.load_workbook(filename=filepath)\n    check_is_metadata_wizard_file(wb, yaml_sheetname, filepath)\n\n    yaml_sheet = wb[yaml_sheetname]\n    yaml_string = yaml_sheet[assumed_cell].value\n    yaml_dict = yaml.load(yaml_string)\n    return yaml_dict\n\n\n# TODO: someday: grrr ... this doesn't really belong here, I feel, but can't move it xlsx_basics because that\n# would create a circular reference, so some refactoring is called for ...\ndef check_is_metadata_wizard_file(openpyxl_workbook, yaml_sheetname, filepath):\n    sheet_names = openpyxl_workbook.sheetnames\n    if yaml_sheetname not in sheet_names:\n        error_msg = \"{0}'{1}' .\".format(NON_WIZARD_XLSX_ERROR_PREFIX, filepath)\n        raise ValueError(error_msg)\n\n\ndef _load_yaml_from_fp(filepath):\n    with open(filepath, 'r') as stream:\n        result = yaml.load(stream)\n    return result\n\n\nclass ValidationKeys(Enum):\n    type = \"type\"\n    required = \"required\"\n    allowed = \"allowed\"\n    default = \"default\"\n    empty = \"empty\"\n    anyof = \"anyof\"\n    min_inclusive = \"min\"\n    min_exclusive = \"min_exclusive\"\n    max_inclusive = \"max\"\n    max_exclusive = \"max_exclusive\"\n    forbidden = \"forbidden\"\n    regex = \"regex\"\n    unique = \"unique\"\n\n\nclass CerberusDataTypes(Enum):\n    Text = \"string\"\n    Integer = \"integer\"\n    Decimal = \"number\"\n    DateTime = \"datetime\"\n    # Note: time is NOT a built-in Cerberus data type;\n    # if we ever decide to actually use Cerberus validation of the\n    # QIIMP schemas, we will need to create this as a custom Cerberus type\n    # (see http://docs.python-cerberus.org/en/stable/customize.html#new-types )\n    Time = \"time\"\n\n\nclass EbiMissingValues(Enum):\n    # values from https://www.ebi.ac.uk/ena/about/missing-values-reporting\n    ebi_not_applicable = \"not applicable\"\n    ebi_not_collected = \"not collected\"\n    ebi_not_provided = \"not provided\"\n    ebi_restricted = \"restricted access\"\n\n\nclass InputNames(Enum):\n    study_name = \"study_name\"\n    field_name = \"field_name\"\n    field_type = \"field_type\"\n    field_desc = \"field_desc\"\n    allowed_missing_vals = \"allowed_missing_vals[]\"\n    default_value = \"default_value\"\n    allowed_missing_default_select = \"allowed_missing_default_select\"\n    categorical_default_select = \"categorical_default_select\"\n    continuous_default = \"continuous_default\"\n    boolean_default_select = \"boolean_default_select\"\n    text_default = \"text_default\"\n    datetime_default = \"datetime_default\"\n    true_value = \"true_value\"\n    false_value = \"false_value\"\n    data_type = \"data_type\"\n    categorical_values = \"categorical_values\"\n    minimum_comparison = \"minimum_comparison\"\n    minimum_value = \"minimum_value\"\n    maximum_comparison = \"maximum_comparison\"\n    maximum_value = \"maximum_value\"\n    units = \"units\"\n    is_unitless = \"is_unitless\"\n    is_phi = \"is_phi\"\n    environment = \"env\"\n    sample_type = \"sample_type\"\n\n\nclass FieldTypes(Enum):\n    Boolean = \"boolean\"\n    Categorical = \"categorical\"\n    Continuous = \"continuous\"\n    Text = CerberusDataTypes.Text.value\n\n\ndef _get_field_type_to_tooltip_dict():\n    return {\n        FieldTypes.Text.value: \"Free Text\",\n        FieldTypes.Boolean.value: \"Boolean (True/False)\",\n        FieldTypes.Categorical.value: \"Categorical (Group A, B, C, etc.)\",\n        FieldTypes.Continuous.value: \"Continous (Numbers, dates, etc.)\"\n    }\n\n\nclass DefaultTypes(Enum):\n    no_default = \"no_default\"\n    boolean_default = \"boolean_default\"\n    allowed_missing_default = \"allowed_missing_default\"\n    categorical_default = \"categorical_default\"\n    continuous_default = \"continuous_default\"\n    text_default = \"text_default\"\n\n\nclass MetadataWizardState(object):\n    def __init__(self):\n        # I think this should NOT be in the config; new versions SHOULD involve changing code.\n        self.VERSION = \"v0.3\"\n\n        # TODO: someday: these file path definitions should move into the config\n        self.RESERVED_WORDS_YAML_PATH = \"reserved_words.yaml\"\n        self.REGEX_YAML_PATH = 'regex_definitions.yaml'\n        self.README_TEXT_PATH = \"readme_template.txt\"\n        self.DEFAULT_LOCALES_YAML_PATH = \"default_locales.yaml\"\n        self.ENVIRONMENTS_YAML_PATH = \"environments.yaml\"\n        self.SAMPLETYPES_YAML_PATH = \"sampletypes.yaml\"\n        self.FIELD_TYPE_TOOLTIPS = _get_field_type_to_tooltip_dict()\n        self.TUTORIAL_LINK = \"http://metadata-wizard-tutorial.readthedocs.io/en/latest/\"\n        self.TUTORIAL_BLURB = \"Need help? Visit the <a target='_blank' href='\" + self.TUTORIAL_LINK + \\\n                              \"'>tutorial</a>! (Opens in new tab.)\"\n\n        self.install_dir = None\n        self.static_path = None\n        self.url_subfolder = None\n        self.static_url_folder = None\n        self.static_url_prefix = None\n        self.packages_dir_path = None\n        self.settings_dir_path = None\n        self.templates_dir_path = None\n        self.client_scripts_dir_path = None\n\n        self.main_url = None\n        self.partial_package_url = None\n        self.partial_download_url = None\n        self.partial_upload_url = None\n        self.full_upload_url = None\n        #self.full_merge_url = None\n        self.listen_port = None\n        self.use_ssl = True\n        self.protocol = None\n\n        self.regex_handler = None\n\n        self.default_locales_list = None\n        self.reserved_words_list = None\n        self.displayname_by_sampletypes_list = None\n        self.environment_definitions = None\n\n        self.combinations_display_dicts_list = None\n        self.envs_display_dicts_list = None\n        self.sampletype_display_dicts_list = None\n        self.parent_stack_by_env_name = None\n        self.env_schemas = None\n\n        # self.merge_info_by_merge_id = {}\n\n    def set_up(self, is_deployed):\n        self.install_dir = os.path.dirname(__file__)\n        self.settings_dir_path = os.path.join(self.install_dir, \"settings\")\n        self.packages_dir_path = os.path.join(self.settings_dir_path, \"packages\")\n        self.templates_dir_path = os.path.join(self.install_dir, \"templates\")\n        self.client_scripts_dir_path = os.path.join(self.install_dir, \"client_scripts\")\n\n        self._get_config_values(is_deployed)\n\n        self.use_ssl = bool(self.certificate_file and self.key_file)\n        self.protocol = \"https\" if self.use_ssl else \"http\"\n        if self.static_path == \"\": self.static_path = self.install_dir\n\n        self.static_url_prefix = self._get_url(self.static_url_folder)\n        self.partial_package_url = self._get_url(PACKAGE_URL_FOLDER)\n        self.partial_download_url = self._get_url(DOWNLOAD_URL_FOLDER)\n        self.partial_upload_url = self._get_url(UPLOAD_URL_FOLDER)\n        self.full_upload_url = self._get_url(UPLOAD_URL_FOLDER, True)\n        # self.full_merge_url = \"{0}://{1}/merge\".format(self.protocol, self.main_url)\n\n        self.regex_handler = RegexHandler(self._get_settings_item_path(self.REGEX_YAML_PATH))\n        self.reserved_words_list = _load_yaml_from_fp(self._get_settings_item_path(self.RESERVED_WORDS_YAML_PATH))\n        self.default_locales_list = _load_yaml_from_fp(self._get_settings_item_path(self.DEFAULT_LOCALES_YAML_PATH))\n        self.displayname_by_sampletypes_list = _load_yaml_from_fp(self._get_settings_item_path(self.SAMPLETYPES_YAML_PATH))\n        self.environment_definitions = _load_yaml_from_fp(self._get_settings_item_path(self.ENVIRONMENTS_YAML_PATH))\n\n    def set_env_and_sampletype_infos(self, env_and_sampletype_infos_tuple):\n        # NB: These values come from metadata_package_schema_builder.load_environment_and_sampletype_info; a more\n        # explicit output rather than an arbitrarily ordered tuple wouldn't be a bad idea :)\n        self.combinations_display_dicts_list = env_and_sampletype_infos_tuple[0]\n        self.envs_display_dicts_list = env_and_sampletype_infos_tuple[1]\n        self.sampletype_display_dicts_list = env_and_sampletype_infos_tuple[2]\n        self.parent_stack_by_env_name = env_and_sampletype_infos_tuple[3]\n        self.env_schemas = env_and_sampletype_infos_tuple[4]\n\n    def get_output_path(self, file_name):\n        return os.path.join(self.install_dir, self.get_partial_output_path(file_name))\n\n    def get_partial_output_path(self, file_name):\n        return os.path.join(\"output\", file_name)\n\n    def make_readme_text(self):\n        with open(self._get_settings_item_path(self.README_TEXT_PATH), 'r') as f:\n            readme_text = f.read()\n\n        now = datetime.datetime.now()\n        readme_text = readme_text.replace(\"VERSION\", self.VERSION)\n        readme_text = readme_text.replace(\"GENERATION_TIMESTAMP\", now.strftime(\"%Y-%m-%d %H:%M:%S\"))\n        return readme_text\n\n    def _get_config_values(self, is_deployed):\n        section_name = \"DEPLOYED\" if is_deployed else \"LOCAL\"\n        config_parser = configparser.ConfigParser(interpolation=configparser.ExtendedInterpolation())\n        config_parser.read_file(open(os.path.join(self.settings_dir_path, 'config.txt')))\n\n        self.static_path = os.path.expanduser(config_parser.get(section_name, \"static_path\"))\n        self.url_subfolder = config_parser.get(section_name, \"url_subfolder\")\n        self.static_url_folder = config_parser.get(section_name, \"static_url_folder\")\n        self.listen_port = os.path.expanduser(config_parser.get(section_name, \"listen_port\"))\n        self.main_url = config_parser.get(section_name, \"main_url\")\n        self.certificate_file = self._apply_default_path(os.path.expanduser(config_parser.get(section_name, 'CERTIFICATE_FILE')))\n        self.key_file = self._apply_default_path(os.path.expanduser(config_parser.get(section_name, 'KEY_FILE')))\n\n    def _apply_default_path(self, file_name):\n        # assume that, if the file name doesn't already include a path,\n        # the file is located in the settings directory\n        result = file_name\n        if result:\n            if not os.path.dirname(file_name):\n                result = os.path.join(self.settings_dir_path, file_name)\n        return result\n\n    def _get_settings_item_path(self, item_file_name):\n        return self._get_item_path(self.settings_dir_path, item_file_name)\n\n    def _get_url(self, desired_subfolder=\"\", make_full_url=False):\n        result = self.url_subfolder + desired_subfolder + \"/\"\n        if make_full_url:\n            result = \"{0}://{1}{2}\".format(self.protocol, self.main_url, result)\n        return result\n\n    @staticmethod\n    def _get_item_path(parent_dir, file_name):\n        return os.path.join(parent_dir, file_name)\n\n\nclass RegexHandler(object):\n    FORMULA_KEY = \"formula\"\n    REGEX_KEY = \"regex\"\n    MESSAGE_KEY = \"message\"\n\n    def __init__(self, regex_definitions_yaml_fp):\n        with open(regex_definitions_yaml_fp) as f:\n            self._dict_of_regex_dicts = yaml.load(f)\n\n    def get_regex_val_by_name(self, regex_name):\n        return self._get_relevant_item_dict_if_any(regex_name, self.REGEX_KEY)\n\n    def get_formula_or_message_for_regex(self, regex_value, get_formula=True):\n        result = None\n        for _, details_dict in self._dict_of_regex_dicts.items():\n            curr_regex_value = details_dict[self.REGEX_KEY]\n            if curr_regex_value == regex_value:\n                result = details_dict[self.FORMULA_KEY] if get_formula else \\\n                    details_dict[self.MESSAGE_KEY]\n                break\n\n        if result is None:\n            raise ValueError(\"unrecognized regex {0}\".format(regex_value))\n        return result\n\n    def _get_relevant_item_dict_if_any(self, section_key, item_key):\n        result = None\n        if section_key in self._dict_of_regex_dicts:\n            section_dict = self._dict_of_regex_dicts[section_key]\n            if item_key in section_dict:\n                result = section_dict[item_key]\n\n        return result\n","repo_name":"ucsd-ccbb/qiimp","sub_path":"qiimp/metadata_wizard_settings.py","file_name":"metadata_wizard_settings.py","file_ext":"py","file_size_in_byte":13368,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"29992485534","text":"import numpy as np\nfrom sklearn import datasets\nfrom sklearn.neighbors import KNeighborsClassifier\n\niris = datasets.load_iris()\niris_X = iris.data   # use iris_X.shape --> SHAPE shows the dimensions. it's a property of numpy arrays\niris_Y = iris.target\n\nnp.random.seed(0)\nindices = np.random.permutation(len(iris_X))\nprint(indices)\niris_X_train = iris_X[indices[:-10]] # use index -10 as [:-10] to avoid having to specify the length of the array and just select all data from 0 except the last 10\niris_Y_train = iris_Y[indices[:-10]] # WHAT IS indices? --> is the array went through a permutation with all the indices (all indices are the size of iris_X\niris_X_test = iris_X[indices[-10:]]\niris_Y_test = iris_Y[indices[-10:]]\n\nknn = KNeighborsClassifier() # typing knn in the console shows you hte details of the classifier or regressor\nknn.fit(iris_X_train,iris_Y_train)\nprint(knn.predict(iris_X_test))\nprint(iris_Y_test) #changing the seed used for the permutation, determines different train and test and therefore changes the fit\n\n#PRESS SHIFT 2 TIMES TO SEARCH EVERYWHERE","repo_name":"fedegott/Intro_Machine_Learning","sub_path":"Intro_ML.py","file_name":"Intro_ML.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34184874627","text":"from know_me import models\nfrom know_me.serializers import subscription_serializers\n\n\ndef test_serialize_inactive():\n    \"\"\"\n    If an inactive subscription is serialized, ``is_active`` should be\n    ``False`` and all other fields should be ``None``.\n    \"\"\"\n    subscription = models.Subscription(is_active=False)\n    serializer = subscription_serializers.SubscriptionSerializer(subscription)\n\n    assert serializer.data == {\n        \"apple_receipt\": None,\n        \"is_active\": False,\n        \"is_legacy_subscription\": False,\n    }\n\n\ndef test_serialize_apple_receipt(apple_receipt_factory):\n    \"\"\"\n    If a subscription backed by an Apple receipt is serialized, it\n    should return information about the Apple receipt.\n    \"\"\"\n    receipt = apple_receipt_factory(subscription__is_active=True)\n    serializer = subscription_serializers.SubscriptionSerializer(\n        receipt.subscription\n    )\n\n    # Child serializers\n    receipt_serializer = subscription_serializers.AppleReceiptInfoSerializer(\n        receipt\n    )\n\n    assert serializer.data == {\n        \"apple_receipt\": receipt_serializer.data,\n        \"is_active\": True,\n        \"is_legacy_subscription\": False,\n    }\n\n\ndef test_serialize_legacy(subscription_factory):\n    \"\"\"\n    If a subscription is marked as a legacy subscription, it should\n    include a flag indicating that.\n    \"\"\"\n    subscription = subscription_factory(is_legacy_subscription=True)\n    serializer = subscription_serializers.SubscriptionSerializer(subscription)\n\n    assert serializer.data == {\n        \"apple_receipt\": None,\n        \"is_active\": subscription.is_active,\n        \"is_legacy_subscription\": True,\n    }\n","repo_name":"knowmetools/km-api","sub_path":"km_api/know_me/journal/tests/serializers/test_subscription_serializer.py","file_name":"test_subscription_serializer.py","file_ext":"py","file_size_in_byte":1653,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"14545601579","text":"from django.test import TestCase\nfrom django.db import IntegrityError\nfrom ..models import *\n\nclass StockTestCases(TestCase):\n    # Create a Stock item\n    def setUp(self):\n        self.org_code = generate_random_org_code()\n        self.org = Organisation.objects.create(code = self.org_code, name = \"Org\", logo=\"img\")\n        self.p_name = \"Eggs\"\n        self.p = Produce.objects.create(organisation=self.org, name=self.p_name)\n        self.pv_name = \"Variety\"\n        self.pv = ProduceVariety.objects.create(variety=self.pv_name, produce_id=self.p)\n        self.pqs_suffix = \"kg\"\n        self.pqs_base = 1000\n        self.pqs = ProduceQuantitySuffix.objects.create(suffix=self.pqs_suffix, base_equivalent = self.pqs_base, produce_id = self.p)\n        self.s_name = \"Supplier\"\n        self.s_pn = \"012456789\"\n        self.s = Supplier.objects.create(organisation=self.org, name = self.s_name, phone_number = self.s_pn)\n        self.ac_name = \"Area Code\"\n        self.ac_desc = \"It's an area code\"\n        self.ac = AreaCode.objects.create(organisation=self.org, area_code = self.ac_name, description = self.ac_desc)\n        self.qty = 123\n        self.aqty = 123\n        self.date_seeded = \"2022-10-01\"\n        self.date_planted = \"2022-10-02\"\n        self.date_picked = \"2022-10-03\"\n        self.ehd = \"2022-12-01\"\n        self.stock = Stock.objects.create(organisation=self.org, quantity=self.qty, quantity_available=self.aqty, date_seeded=self.date_seeded, date_planted = self.date_planted, date_picked = self.date_picked, ehd = self.ehd, date_completed = None, produce_id = self.p, variety_id = self.pv, quantity_suffix_id = self.pqs, supplier_id = self.s, area_code_id = self.ac)\n\n    # Test whether the fields of the created stock are correct or not\n    def test_stock_fields(self):\n        self.assertEquals(self.stock.quantity, self.qty)\n        self.assertEquals(self.stock.quantity_available, self.aqty)\n        self.assertEquals(self.stock.date_seeded, self.date_seeded)\n        self.assertEquals(self.stock.date_planted, self.date_planted)\n        self.assertEquals(self.stock.date_picked, self.date_picked)\n        self.assertEquals(self.stock.ehd, self.ehd)\n        self.assertEquals(self.stock.date_completed, None)\n        self.assertEquals(self.stock.produce_id, self.p)\n        self.assertEquals(self.stock.variety_id, self.pv)\n        self.assertEquals(self.stock.quantity_suffix_id, self.pqs)\n        self.assertEquals(self.stock.supplier_id, self.s)\n        self.assertEquals(self.stock.area_code_id, self.ac)\n\n    # Ensure that null org raises integrity error\n    def test_stock_creation_exception(self):\n        with self.assertRaises(IntegrityError):\n            Stock.objects.create(organisation=None, quantity_available=self.aqty, date_seeded=self.date_seeded, date_planted = self.date_planted, date_picked = self.date_picked, ehd = self.ehd, date_completed = None, produce_id = self.p, variety_id = self.pv, quantity_suffix_id = self.pqs, supplier_id = self.s, area_code_id = self.ac)","repo_name":"MatthewKarko/Farmware","sub_path":"farmware/core/api/tests/test_stock_model.py","file_name":"test_stock_model.py","file_ext":"py","file_size_in_byte":3012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"39302115491","text":"# 완전검색\ndef dfs(arr, path, visited, res):\n    global min_num\n    if sum(visited) == n:\n        res += arr[path[-1][0]]\n        if min_num > res:\n            min_num = res\n    else:\n        prior = path[-1]\n        for i in range(n):\n            if visited[i]:\n                continue\n            else:\n                visited[i] = 1\n                res += arr[path[-1][0]]\n                path.append(i)\n\nimport sys\nsys.stdin = open('input.txt', 'r')\nfor tc in range(1, int(input())+1):\n    n = int(input())\n    arr = [list(map(int, input().split())) for _ in range(n)]\n    visited = [0] * n\n    visited[0] = 1\n    min_num = 99999\n    res = 0\n    dfs(arr, [0], visited, res)\n\n\n","repo_name":"otterji/algorithms","sub_path":"1909/190918/카트/카트.py","file_name":"카트.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"1180968732","text":"#Python libraries for math and graphics\nimport numpy as np\nimport mpmath as mp\nimport matplotlib.pyplot as plt\nfrom numpy import linalg as LA\n\nimport sys                                          #for path to external scripts\n#sys.path.insert(0,'/storage/emulated/0/github/cbse-papers/CoordGeo')         #path to my scripts\nsys.path.insert(0,'/sdcard/fwc/circle/CoordGeo')\n\n\n#local imports\nfrom line.funcs import *\nfrom triangle.funcs import *\nfrom conics.funcs import circ_gen\n\n#if using termux\nimport subprocess\nimport shlex\n#end if\ndef circ_gen(O,r):\n\tlen = 50\n\ttheta = np.linspace(0,2*np.pi,len)\n\tx_circ = np.zeros((2,len))\n\tx_circ[0,:] = r*np.cos(theta)\n\tx_circ[1,:] = r*np.sin(theta)\n\tx_circ = (x_circ.T + O).T\n\treturn x_circ\n\ndef line_gen(A,B):\n  len =10\n  dim = A.shape[0]\n  x_AB = np.zeros((dim,len))\n  lam_1 = np.linspace(0,1,len)\n  for i in range(len):\n    temp1 = A + lam_1[i]*(B-A)\n    x_AB[:,i]= temp1.T\n  return x_AB\n\ndef line_dir_pt(m,G,k1,k2):\n   len = 10\n   dim = G.shape[0]\n   x_LC = np.zeros((dim,len))\n   lam_1 = np.linspace(k1,k2,len)\n   for i in range(len):\n     temp1 = G + lam_1[i]*m\n     x_LC[:,i]= temp1.T\n   return x_LC\n\n#for intersection tangent\nx1 = np.array(([5,0],[0,5]))\ne,p = np.linalg.eig(x1)\nw = np.array(([np.sqrt(e[0]),np.sqrt(e[1])]))\nq = np.array(([np.sqrt(e[0]),-np.sqrt(e[1])]))\nK = np.array(([w,q]))\nn3 = p@w\nn4 = p@q\nC = np.array(([9.8,2.8]))\nN = np.linalg.inv(K)@C\nprint(N)\nprint(\"norm :\",n3)\nprint(\"norm :\",n4)\nb = np.array(([2.23,-2.23]))\nc = np.array(([2.23,2.23]))\n\n#Standard basis vectors\ne1 = np.array((1,0)).reshape(2,1)\ne2 = np.array((0,1)).reshape(2,1)\n\n#Input parameters\nr1  = 1\nr2 = 4\ntheta=np.pi/3\nh=np.array((2/3,0)).reshape(2,1)\nV1 = np.eye(2)\nu1 = np.array((-2,-1)).reshape(2,1)\nf1 =4\nV2=np.eye(2)\nu2=np.array((-6,-4)).reshape(2,1)\nf2 = 36\nS1 = (V1@h+u1)@(V1@h+u1).T-(h.T@V1@h+2*u1.T@h+f1)*V1\nS2 = (V2@h+u2)@(V2@h+u2).T-(h.T@V2@h+2*u2.T@h+f2)*V2\nO1 = -u1.T\nO2 = -u2.T\nprint(\"S matrix :\",S1,S2)\nm = np.array(([6,-8]))\nn = np.array(([8,6]))\np = np.array(([2,1]))\nC = 32\nf0 = u1.T@V1@u1-f1\ni = (f0)/(n.T@V1@n)\nki = np.sqrt(i)\nX = V1*ki*n-u1\nP = np.array(([m@p,C]))\nM = np.vstack(([m,n]))\nX = np.linalg.inv(M)@P\nA = np.array(([2/3,0]))\nprint(\"intersection :\",X)\n\nk1 = 0.5\nk2 = -0.5\nx_X = line_dir_pt(m,X,k1,k2)\n\nk1 = 1\nk2 = -1\nx_A = line_dir_pt(b,N,k1,k2)\n\nk1 = 3\nk2 = -1.5\nx_c = line_dir_pt(c,N,k1,k2)\n\n#Intermediate parameters\nf01 = np.abs(-f1+u1.T@LA.inv(V1)@u1)\nf02 = np.abs(-f2+u2.T@LA.inv(V2)@u2)\n\n#Eigenvalues and eigenvectors\nD_vec1,P1 = LA.eig(S1)\nlam1 = D_vec1[0]\nlam2 = D_vec1[1]\np1 = P1[:,1].reshape(2,1)\np2 = P1[:,0].reshape(2,1)\nD = np.diag(D_vec1)\nt1= np.sqrt(np.abs(D_vec1))\nnegmat = np.block([e1,-e2])\nt2 = negmat@t1\n\n\n#Normal vectors to the conic\nn1 = P1@t1\nn2 = P1@t2\nprint(\":\",n1,n2)\nx=n1@n2\ny = np.linalg.norm(n1)*np.linalg.norm(n2)\ntheta = np.arccos(x/y)\ntheta1 = theta*180/np.pi\nprint(\"theta: \",theta1)\n\n#kappa\nden1 = n1.T@LA.inv(V1)@n1\nden2 = n2.T@LA.inv(V1)@n2\nk1 = np.sqrt(f01/(den1))\nk2 = np.sqrt(f01/(den2))\n\n#q11 = LA.inv(V1)@((k1*n1-u1.T).T)\nq12 = LA.inv(V1)@((-k1*n1-u1.T).T)\n#q21 = LA.inv(V1)@((k2*n2-u1.T).T)\nq22 = LA.inv(V1)@((-k2*n2-u1.T).T)\nprint(\"point of contact :\",q12,q22)\n\n\n#Eigenvalues and eigenvectors\nD_vec2,P2 = LA.eig(S2)\nlam11 = D_vec2[0]\nlam21 = D_vec2[1]\np11 = P2[:,1].reshape(2,1)\np21 = P2[:,0].reshape(2,1)\nD1 = np.diag(D_vec2)\nt11= np.sqrt(np.abs(D_vec2))\nnegmat = np.block([e1,-e2])\nt21 = negmat@t11\n\n#Normal vectors to the conic\nn11 = P2@t11\nn21 = P2@t21\nprint(\"normal :\",n1,n11,n2,n21)\n#kappa\nden11 = n11.T@LA.inv(V2)@n11\nden21 = n21.T@LA.inv(V2)@n21\n\nk11 = np.sqrt(f02/(den11))\nk21 = np.sqrt(f02/(den21))\n\n#q11_1 = LA.inv(V2)@((k11*n11-u2.T).T)\nq12_1 = LA.inv(V2)@((-k11*n11-u2.T).T)\n#q21_1 = LA.inv(V2)@((k21*n21-u2.T).T)\nq22_1 = LA.inv(V2)@((-k21*n21-u2.T).T)\nprint(\"point of contact :\",q12_1,q22_1)\n\n#Generating the lines\nx_hq22 = line_gen(h,q22)\nx_hq12 = line_gen(h,q12)\nx_q22q22_1 = line_gen(q22,q22_1)\nx_q12q12_1 = line_gen(q12,q12_1)\n\n\n##Generating the circle\nx_circ= circ_gen(O1,r1)\nx_circ1=circ_gen(O2,r2)\n\n##Plotting all lines\nplt.plot(x_hq22[0,:],x_hq22[1,:],color='green')\nplt.plot(x_hq12[0,:],x_hq12[1,:],color='red')\nplt.plot(x_q22q22_1[0,:],x_q22q22_1[1,:],label='$tangent1$',color='green')\nplt.plot(x_q12q12_1[0,:],x_q12q12_1[1,:],label='$tangent2$',color='red')\nplt.plot(x_X[0,:],x_X[1,:],color='black',label='$tangent3$')\nplt.plot(x_A[0,:],x_A[1,:],color='blue')\nplt.plot(x_c[0,:],x_c[1,:],color='violet')\n\n#Plotting the circle\nplt.plot(x_circ[0,:],x_circ[1,:],label='$Circle$')\nplt.plot(x_circ1[0,:],x_circ1[1,:],label='$circle1$')\n\n#Labeling the coordinates\ntri_coords = np.vstack((h.T,q12.T,q22.T,O1,O2,q12_1.T,q22_1.T,X.T)).T\nplt.scatter(tri_coords[0,:], tri_coords[1,:])\nvert_labels = ['h','q12','q22','O1','O2','q12_1','q22_1','X']\nfor i, txt in enumerate(vert_labels):\n    plt.annotate(txt, # this is the text\n                 (tri_coords[0,i], tri_coords[1,i]), # this is the point to label\n                 textcoords=\"offset points\", # how to position the text\n                 xytext=(0,10), # distance from text to points (x,y)\n                 ha='center') # horizontal alignment can be left, right or center\n\nplt.xlabel('$x$')\nplt.ylabel('$y$')\nplt.legend(loc='best')\nplt.grid() # minor\nplt.axis('equal')\n#\n#if using termux\nplt.savefig('/sdcard/fwc/circle/fig.pdf')\n#subprocess.run(shlex.split(\"termux-open /sdcard/fwc/circle/circle.pdf\"))\nplt.show()\n","repo_name":"Sairaghavendra36/Fwc-2022","sub_path":"matrices/circle/code/circle.py","file_name":"circle.py","file_ext":"py","file_size_in_byte":5394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"24290387510","text":"import os\nimport argparse\nimport requests\nfrom dotenv import load_dotenv\nfrom work_with_files import save_images\nfrom work_with_files import define_filetype\nfrom pathlib import Path\n\n\ndef get_response_images(params):\n    url_day_photo = 'https://api.nasa.gov/planetary/apod'\n    response = requests.get(\n        url_day_photo,\n        params=params\n    )\n    response.raise_for_status()\n    response_images = response.json()\n    return response_images\n\n\ndef get_APOD(params, day=None):\n    response_images = get_response_images(params)\n    os.makedirs('Photos_of_the_day', exist_ok=True)\n    if day:\n        response_images = [response_images]\n    for response_image in response_images:\n        date = response_image['date']\n        if response_image['media_type'] != \"image\":\n            print(f'За дату {date} нет изображения')\n\n        else:\n            try:\n                url = response_image['hdurl']\n            except KeyError:\n                url = response_image['url']\n            filetype = define_filetype(url)\n            save_images(\n                url,\n                Path.cwd() / 'Photos_of_the_day' / f'image_for_{date}{filetype}'\n            )\n\n\ndef main():\n    load_dotenv()\n    nasa_api = os.environ['NASA_API']\n    parser = argparse.ArgumentParser(\n        description='Программа скачивает фотографии дня в файл '\n                    '\"Photos_of_the_day\" в рабочей директории,'\n                    ' (если нет создает)'\n    )\n    parser.add_argument(\n        \"--count\",\n        type=int,\n        help=\"Укажите количество случайных фотографий\"\n    )\n    parser.add_argument(\n        \"--day\",\n        help=\"Укажите день, за который нужно\"\n             \" получить фото, в формате YYYY-MM-DD\"\n    )\n    parser.add_argument(\n        \"--days\",\n        help=\"Укажите промежуток дней, за которые хотите\"\n             \" получить фотографии,\"\n             \" в формате YYYY-MM-DD-YYYY-MM-DD\"\n    )\n    args = parser.parse_args()\n    if args.count:\n        params = {\n            'count': args.count,\n            'api_key': nasa_api\n        }\n        get_APOD(params)\n    if args.day:\n        params = {\n            'date': args.day,\n            'api_key': nasa_api\n        }\n        get_APOD(params, args.day)\n    if args.days:\n        params = {\n            'start_date': args.days[:10],\n            'end_date': args.days[11:],\n            'api_key': nasa_api\n        }\n        get_APOD(params)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"BAIBASH1/Download_photos_from_NASA","sub_path":"get_APOD.py","file_name":"get_APOD.py","file_ext":"py","file_size_in_byte":2681,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34073341847","text":"#!/usr/bin/env python3\n#\n# Cross Platform and Multi Architecture Advanced Binary Emulation Framework\n#\n\nimport sys\nsys.path.append(\"../..\")\n\nfrom qiling.core import Qiling\nfrom qiling.const import QL_VERBOSE\nfrom qiling.extensions.mcu.gd32vf1 import gd32vf103\nfrom qiling.const import QL_ARCH, QL_OS\n\n\nql = Qiling(['../rootfs/mcu/gd32vf103/blink.hex'], archtype=QL_ARCH.RISCV64, ostype=QL_OS.MCU,\n            env=gd32vf103, verbose=QL_VERBOSE.DEBUG)\n\nql.hw.create('rcu')\nql.hw.create('gpioa').watch()\nql.hw.create('gpioc').watch()\n\ndelay_cycles_begin = 0x800015c\ndelay_cycles_end = 0x800018c\n\n\ndef skip_delay(ql):\n    ql.arch.regs.pc = delay_cycles_end\n\n\nql.hook_address(skip_delay, delay_cycles_begin)\nql.hw.gpioc.hook_set(13, lambda : print('Set PC13'))\n\nql.run(count=20000)\n","repo_name":"qilingframework/qiling","sub_path":"examples/mcu/gd32vf103_blink.py","file_name":"gd32vf103_blink.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":4307,"dataset":"github-code","pt":"86"}
+{"seq_id":"23762152269","text":"#load CSV \nimport csv\nimport numpy as np\nfrom pandas import read_csv\nfrom pandas import set_option\n\n# load using csv\n# filename = 'pima-indians-diabetes.data.csv'\n# raw_data = open(filename, 'r')\n# reader = csv.reader(raw_data, delimiter = ',', quoting = csv.QUOTE_NONE)\n# x = list(reader)\n# data = np.array(x).astype('float')\n# print(data.shape)\n\n# load using pandas and some operations\nfilename = 'pima-indians-diabetes.data.csv'\nnames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\ndata = read_csv(filename, names = names)\nset_option('display.width', 100)\nset_option('precision', 3)\ndescription = data.describe()\npeek = data.head(20)\ntypes = data.dtypes\nclass_counts = data.groupby('class').size()\ncorrelations = data.corr(method = 'pearson')\nskew = data.skew()\nprint(skew)\n","repo_name":"KrisCheng/Hitchhiker-Guide-to-Machine-Learning","sub_path":"archive/Model/others/load_csv.py","file_name":"load_csv.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"86"}
+{"seq_id":"42315033847","text":"# -*- coding:utf-8 -*-\n\n__author__ = 'huanghf'\n\n\"\"\"\n在给定的网格中，每个单元格可以有以下三个值之一：\n值 0 代表空单元格；\n值 1 代表新鲜橘子；\n值 2 代表腐烂的橘子。\n每分钟，任何与腐烂的橘子（在 4 个正方向上）相邻的新鲜橘子都会腐烂。\n返回直到单元格中没有新鲜橘子为止所必须经过的最小分钟数。如果不可能，返回 -1。\n\n示例 1：\n输入：[[2,1,1],[1,1,0],[0,1,1]]\n输出：4\n\n示例 2：\n输入：[[2,1,1],[0,1,1],[1,0,1]]\n输出：-1\n解释：左下角的橘子（第 2 行， 第 0 列）永远不会腐烂，因为腐烂只会发生在 4 个正向上。\n\n示例 3：\n输入：[[0,2]]\n输出：0\n解释：因为 0 分钟时已经没有新鲜橘子了，所以答案就是 0 。\n\n提示：\n1 <= grid.length <= 10\n1 <= grid[0].length <= 10\ngrid[i][j] 仅为 0、1 或 2\n\n链接: https://leetcode-cn.com/problems/rotting-oranges/\n\"\"\"\n\nfrom typing import List\n\n\nclass Solution:\n    def orangesRotting(self, grid: List[List[int]]) -> int:\n        \"\"\"\n        带状态的 bfs\n        :param grid:\n        :return:\n        \"\"\"\n        m, n = len(grid), len(grid[0])\n        ds = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n\n        # 初始队列\n        queue = [[i, j, 0] for i in range(m) for j in range(n) if grid[i][j] == 2]\n        res = 0\n\n        while queue:\n            i, j, res = queue.pop(0)  # i, j 存储已经传染的橘子的坐标, res 表示当前已经经过的时间\n            for dx, dy in ds:\n                x = i + dx\n                y = j + dy\n                if (0 <= x < m and 0 <= y < n and grid[x][y] == 1):\n                    grid[x][y] = 2\n                    queue.append([x, y, res + 1])\n\n        # 还有橘子没有腐烂\n        if any(grid[i][j] == 1 for i in range(m) for j in range(n)):\n            return -1\n\n        return res\n\n\ngrid = [[2, 1, 1],\n        [1, 1, 0],\n        [0, 1, 1]]\n\ns = Solution()\nprint(s.orangesRotting(grid))\n","repo_name":"lovehhf/LeetCode","sub_path":"994_腐烂的橘子.py","file_name":"994_腐烂的橘子.py","file_ext":"py","file_size_in_byte":1976,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"1590400181","text":"from unittest import mock\nfrom unittest.mock import PropertyMock\n\nimport pytest\nfrom airflow import models\nfrom impala.hiveserver2 import HiveServer2Connection, HiveServer2Cursor\n\nfrom astronomer.providers.apache.hive.hooks.hive import HiveCliHookAsync\n\nTEST_TABLE = \"test_table\"\nTEST_SCHEMA = \"test_schema\"\nTEST_POLLING_INTERVAL = 5\nTEST_PARTITION = \"state='FL'\"\nTEST_METASTORE_CONN_ID = \"metastore_default\"\nTEST_CONN_TYPE = \"metastore\"\nTEST_PORT = 10000\nTEST_HOST = \"localhost\"\n\n\nclass TestHiveCliHookAsync:\n    @mock.patch(\"astronomer.providers.apache.hive.hooks.hive.HiveCliHookAsync.get_connection\")\n    @mock.patch(\"airflow.configuration.AirflowConfigParser.get\")\n    @mock.patch(\"impala.hiveserver2.connect\")\n    def test_get_hive_client_with_conf(self, mock_get_connect, mock_get_conf, mock_get_connection):\n        \"\"\"Checks the connection to hive client\"\"\"\n        mock_get_connect.return_value = mock.AsyncMock(HiveServer2Connection)\n        mock_get_conf.return_value = \"kerberos\"\n        mock_get_connection.return_value = models.Connection(\n            conn_id=TEST_METASTORE_CONN_ID,\n            conn_type=TEST_CONN_TYPE,\n            port=TEST_PORT,\n            host=TEST_HOST,\n        )\n        hook = HiveCliHookAsync(TEST_METASTORE_CONN_ID)\n        result = hook.get_hive_client()\n        assert isinstance(result, HiveServer2Connection)\n\n    @mock.patch(\"astronomer.providers.apache.hive.hooks.hive.HiveCliHookAsync.get_connection\")\n    @mock.patch(\"impala.hiveserver2.connect\")\n    def test_get_hive_client(self, mock_get_connect, mock_get_connection):\n        \"\"\"Checks the connection to hive client\"\"\"\n        mock_get_connect.return_value = mock.AsyncMock(HiveServer2Connection)\n        mock_get_connection.return_value = models.Connection(\n            conn_id=TEST_METASTORE_CONN_ID,\n            conn_type=TEST_CONN_TYPE,\n            port=TEST_PORT,\n            host=TEST_HOST,\n        )\n        hook = HiveCliHookAsync(TEST_METASTORE_CONN_ID)\n        result = hook.get_hive_client()\n        assert isinstance(result, HiveServer2Connection)\n\n    @pytest.mark.asyncio\n    @pytest.mark.parametrize(\n        \"result,response\",\n        [\n            ([\"123\"], \"success\"),\n            ([], \"failure\"),\n        ],\n    )\n    @mock.patch(\"astronomer.providers.apache.hive.hooks.hive.HiveCliHookAsync.get_connection\")\n    @mock.patch(\"astronomer.providers.apache.hive.hooks.hive.HiveCliHookAsync.get_hive_client\")\n    async def test_partition_exists(self, mock_get_client, mock_get_connection, result, response):\n        \"\"\"\n        Tests to check if a partition in given table in hive\n        is found or not\n        \"\"\"\n        hook = HiveCliHookAsync(metastore_conn_id=TEST_METASTORE_CONN_ID)\n        hiveserver_connection = mock.AsyncMock(HiveServer2Connection)\n        mock_get_client.return_value = hiveserver_connection\n        cursor = mock.AsyncMock(HiveServer2Cursor)\n        hiveserver_connection.cursor.return_value = cursor\n        cursor.is_executing = PropertyMock(side_effect=[True, False])\n        cursor.fetchall.return_value = result\n        res = await hook.partition_exists(\"test_table\", TEST_SCHEMA, TEST_PARTITION, TEST_POLLING_INTERVAL)\n        assert res == response\n\n    @pytest.mark.parametrize(\n        \"partition,expected\",\n        [\n            (\"user_profile/city=delhi\", (\"default\", \"user_profile\", \"city=delhi\")),\n            (\"user.user_profile/city=delhi\", (\"user\", \"user_profile\", \"city=delhi\")),\n        ],\n    )\n    def test_parse_partition_name_success(self, partition, expected):\n        \"\"\"Assert that `parse_partition_name` correctly parse partition string\"\"\"\n        actual = HiveCliHookAsync.parse_partition_name(partition)\n        assert actual == expected\n\n    def test_parse_partition_name_exception(self):\n        \"\"\"Assert that `parse_partition_name` throw exception if partition string not correct\"\"\"\n        with pytest.raises(ValueError):\n            HiveCliHookAsync.parse_partition_name(\"user_profile.city=delhi\")\n\n    @pytest.mark.parametrize(\n        \"result,expected\",\n        [\n            ([\"123\"], True),\n            ([], False),\n        ],\n    )\n    @mock.patch(\"astronomer.providers.apache.hive.hooks.hive.HiveCliHookAsync.get_connection\")\n    @mock.patch(\"astronomer.providers.apache.hive.hooks.hive.HiveCliHookAsync.get_hive_client\")\n    def test_check_partition_exists(self, mock_get_client, mock_get_connection, result, expected):\n        \"\"\"Assert that `check_partition_exists` return True if partition found else return False.\"\"\"\n        hook = HiveCliHookAsync(metastore_conn_id=TEST_METASTORE_CONN_ID)\n        hiveserver_connection = mock.AsyncMock(HiveServer2Connection)\n        mock_get_client.return_value = hiveserver_connection\n        cursor = mock.AsyncMock(HiveServer2Cursor)\n        hiveserver_connection.cursor.return_value = cursor\n        cursor.is_executing.return_value = False\n        cursor.fetchall.return_value = result\n        actual = hook.check_partition_exists(TEST_SCHEMA, \"test_table\", TEST_PARTITION)\n        assert actual == expected\n","repo_name":"astronomer/astronomer-providers","sub_path":"tests/apache/hive/hooks/test_hive.py","file_name":"test_hive.py","file_ext":"py","file_size_in_byte":5045,"program_lang":"python","lang":"en","doc_type":"code","stars":122,"dataset":"github-code","pt":"86"}
+{"seq_id":"33931086697","text":"#!/usr/bin/env python3\nimport machine_data_structures as ds\nimport collections\n\n# Holds all machines\nall_machines=[]\n\n# Define default machines, used if actual machine can't be determined\ndefault_win = ds.machine('unknown_win', 'win', ('ssh', 'sh', 'bash', 'vim', 'git', 'tmux', 'opt'), {})\ndefault_mac = ds.machine('unknown_mac', 'mac', ('ssh', 'sh', 'bash', 'vim', 'git', 'tmux', 'opt'), {})\ndefault_nix = ds.machine('unknown_nix', 'nix', ('ssh', 'sh', 'bash', 'vim', 'X', 'git', 'gnome2', 'selected_editor', 'tmux', 'opt'), {})\n\n# Define known machines\nall_machines.append(\n  ds.machine('deb7',\n             'nix',\n             ('ssh', 'sh', 'bash', 'kde', 'vim', 'X', 'git', 'gnome2', 'selected_editor', 'tmux', 'opt'),\n             {}))\nall_machines.append(\n  ds.machine('wintermute',\n             'nix',\n             ('ssh', 'sh', 'bash', 'kde', 'vim', 'X', 'git', 'gnome2', 'selected_editor', 'tmux', 'opt'),\n             {}))\nall_machines.append(\n  ds.machine('aurora',\n             'nix',\n             ('ssh', 'sh', 'bash', 'kde', 'vim', 'X', 'git', 'gnome2', 'selected_editor', 'tmux', 'opt'),\n             {}))\nall_machines.append(\n  ds.machine('base',\n             'nix',\n             ('ssh', 'sh', 'bash', 'git', 'opt', 'X'),\n             {'prereq.sh': ('root',)}))\nall_machines.append(\n  ds.machine('developer',\n             'nix',\n             ('ssh', 'sh', 'bash', 'git', 'opt'),\n             {}))\nall_machines.append(\n  ds.machine('perigee.local',\n             'mac',\n             ('ssh', 'sh', 'bash', 'vim', 'git', 'tmux', 'opt'),\n             {}))\nall_machines.append(\n  ds.machine('AE-3NJ28V1',\n             'win',\n             ('ssh', 'sh', 'bash', 'vim', 'git', 'tmux', 'opt'),\n             {}))\n\nemacs_setup_scripts = collections.OrderedDict()\nemacs_setup_scripts['python2.sh'] = ()\nemacs_setup_scripts['java10.sh'] = ()\nemacs_setup_scripts['clojure.sh'] = ()\nemacs_setup_scripts['heroku.sh'] = ()\nemacs_setup_scripts['emacs.sh'] = ('25','root')\nemacs_setup_scripts['name.sh'] = ('emacs',)\nall_machines.append(\n  ds.machine('emacs',\n             'nix',\n             ('ssh', 'sh', 'bash', 'git', 'opt'),\n             emacs_setup_scripts))\n","repo_name":"hibes/dotfiles","sub_path":"machine_configuration.py","file_name":"machine_configuration.py","file_ext":"py","file_size_in_byte":2162,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"38695719548","text":"from bot import Bot\nfrom server import SERVER_THREAD\n\n\ndef parse_token(filename: str = \"token.key\") -> str | None:\n    token = None\n    try:\n        with open(filename) as token_file:\n            content = token_file.read()\n            token = content.strip()\n            return token\n    except Exception:\n        return None\n\n\ntoken = parse_token()\nif token is None:\n    print(\"Failed to parse a token from 'token.key' file.\")\n    exit(-1)\n\nbot = Bot(token)\nbot.run()\n\ntry:\n    SERVER_THREAD.join(0.1)\nexcept Exception:\n    pass\n","repo_name":"ivatolm/itmo-schedule-export","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31521470665","text":"symbols = []\nf = open('symbols.txt', 'r')\ns = f.readline()\nwhile(s):\n    symbols.append(s.replace('\\n',''))\n    s = f.readline()\nf.close()\n\nfile = '/home/suneel/Downloads/CASH_Orders_20012021.DAT'\noutputfiles = [s.replace('b','').lower() for s in symbols]\n\nfor i,symbol in enumerate(symbols):\n    f = open(file, 'r')\n    ell = f.readline()\n    data = []\n    while(ell):\n        if(ell[38:48] == symbol):\n            session = ell[0:2]\n            orderid = ell[6:22]\n            time = ell[22:36]\n            side = ell[36:37]\n            action = ell[37:38]\n            dquant = ell[50:58]\n            fquant = ell[58:66]\n            price = ell[66:74]\n            triggerprice = ell[74:82]\n            market = ell[82:83]\n            stoploss = ell[83:84]\n            ioc = ell[84:85]\n            row = '|'.join([session, orderid, side, action, time, price, fquant, dquant, market, ioc, stoploss, triggerprice])\n            data.append(row)\n        ell = f.readline()\n\n    f.close()\n    f = open('/home/suneel/Teaching/Finance_Analytics/data/highfreq/' + outputfiles[i]+'.order', 'w')\n    data = '\\n'.join(data)\n    f.write(data)\n    f.close()\n\n\n","repo_name":"prakhar-chaurasiya/final_project_nism","sub_path":"extract_stock_orders.py","file_name":"extract_stock_orders.py","file_ext":"py","file_size_in_byte":1148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"73429961564","text":"#!/usr/bin/python3\n\nimport sys\nfrom collections import defaultdict\n\nTHRESHOLD=2\n\ndebugging = True\ndef dprint(msg):\n    if debugging:\n        print(msg)\n\nif len(sys.argv) != 1:\n    sys.stderr.write(\"Usage: %s < parse_trees_file > struct_freq\\n\" % sys.argv[0])\n    sys.exit(1)\n\nword2id = {}\nid2word = []\ndef getWord(num):\n    if 0 <= num and num < len(id2word):\n        return id2word[num]\n    return \"-UNK-\"\ndef getID(word):\n    if word not in word2id:\n        word2id[word] = len(id2word)\n        id2word.append(word)\n    return word2id[word]\ndef getPhrase(idvec):\n    return str.join(' ', map(getWord, idvec))\ndef getIDVec(phrase):\n    if type(phrase) == str:\n        return tuple( map(getID, phrase.split()) )\n    return tuple( map(getID, phrase) )\n\ndef parse(expr, i = 0):\n    cont = ''\n    while i < len(expr):\n        #dprint('Expr[%s]: %s' % (i, expr[i]))\n        if expr[i] == '(':\n            #dprint('Push')\n            cont = []\n            while i < len(expr):\n                if expr[i] == ')':\n                    #dprint(\"Closing: %s\" % cont)\n                    return cont, i + 1\n                item, i = parse(expr, i + 1)\n                if item:\n                    #print(\"Appending: %s\" % item)\n                    cont.append(item)\n            return cont, i\n        elif expr[i] == ')':\n            #dprint(\"Closing: \" + cont)\n            return cont, i\n        elif expr[i] == ' ':\n            #dprint('Elem: ' + cont)\n            return cont, i\n        else:\n            cont += expr[i]\n            i += 1\n    return cont, i\n\ndef extractPhrase(tree):\n    words = ()\n    if type(tree) == list:\n        for elem in tree[1:None]:\n            words += extractPhrase(elem)\n    else:\n        words += (getID(tree),)\n    return words\n\ndef countPhrases(tree, counter = defaultdict(int)):\n    if type(tree) == list:\n        if len(tree) >= 3:\n            words = extractPhrase(tree)\n            #print(words)\n            counter[words] += 1\n        for elem in tree[1:None]:\n            countPhrases(elem, counter)\n    else:\n        #print(getIDVec(tree))\n        counter[getIDVec(tree)] += 1\n\ncounter = defaultdict(int)\nfor line in sys.stdin:\n    line = line.strip()\n    tree, _ = parse(line)\n    #print(tree)\n    if tree:\n        countPhrases(tree, counter)\n\nfor key in counter.keys():\n    count = counter[key]\n    if count >= THRESHOLD:\n        print(\"%s\\t%s\" % (getPhrase(key),count))\n\n","repo_name":"akivajp/naacl2016","sub_path":"script/count-parsed-phrases.py","file_name":"count-parsed-phrases.py","file_ext":"py","file_size_in_byte":2407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32896262471","text":"from genomics_utils import LightningModuleExtended\n\n\nclass FullModelTraining(LightningModuleExtended):\n    def training_step(self, batch, batch_ix, hiddens):\n        X_batch, y_batch = batch\n        logits, hiddens = self.forward(X_batch, hiddens)\n        y_one_hot = one_hot_encoding(y_batch, self.n_output, device=self.device)\n        loss = self.loss(logits, y_one_hot)\n        self.log(\"train_loss\", loss, on_step=True, on_epoch=True)\n        return {'loss': loss,\n                'hiddens': hiddens\n                }\n\n    def test_step(self, batch, batch_idx) -> Any:\n        X_batch, y_batch = batch\n        logits = self.forward(X_batch)\n        preds = torch.exp(logits)\n        preds = torch.flatten(preds, start_dim=0, end_dim=1)\n    \n        # y_batch = torch.argmax(y_batch, dim=-1)\n        y = y_batch.flatten()\n    \n        preds = preds.cpu().detach()\n        self.logger.log_coalescent_heatmap(self.name, [preds.T, y.T], batch_idx)\n    \n    \n    \n    def tbptt_split_batch(self, batch: torch.Tensor, split_size: int) -> list:\n        X_batch = batch[0]\n        Y_batch = batch[1]\n        batch_len = len(X_batch)\n        \n        distances_dims = [len(X) for X in batch[0]]\n        max_time_dim = np.max(distances_dims)\n        \n        splits = []\n        x_split_size = split_size\n        y_t_indexes = [0 for _ in range(batch_len)]\n        \n        for x_t in range(0, max_time_dim, x_split_size):\n            batch_split = []\n            \n            split_x = [[] for _ in range(batch_len)]\n            split_y = [[] for _ in range(batch_len)]\n            \n            for batch_idx in range(batch_len):\n                if x_t > len(X_batch[batch_idx]):\n                    continue\n                elif x_t + x_split_size > len(X_batch[batch_idx]):\n                    current_x_sample = X_batch[batch_idx][x_t: len(X_batch[batch_idx])]\n                else:\n                    current_x_sample = X_batch[batch_idx][x_t:x_t + x_split_size]\n                \n                y_t = y_t_indexes[batch_idx]\n                y_split_size = int(np.sum(current_x_sample))\n                split_x[batch_idx] = current_x_sample\n                split_y[batch_idx] = Y_batch[batch_idx][y_t:y_t + y_split_size]\n                \n                y_t_indexes[batch_idx] = y_t + y_split_size\n            \n            batch_split.append(split_x)\n            batch_split.append(split_y)\n            \n            splits.append(batch_split)\n        \n        return splits\n\ndef one_hot_encoding(y_data, num_class, device):\n    batch_size, seq_len = y_data.shape\n    y_one_hot = torch.FloatTensor(batch_size, seq_len, num_class).to(device)\n    \n    y_one_hot.zero_()\n    y_one_hot.scatter_(2, y_data.unsqueeze(2), 1)\n    return y_one_hot","repo_name":"Genomics-HSE/DeepModels2","sub_path":"src/full_models/training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":2736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"29061602852","text":"# -*- coding: utf-8 -*-\n# create by yihui 11:32 18/12/19\nimport logging\nimport os\nfrom logging.handlers import TimedRotatingFileHandler\n\nfrom src.env.EnvWrapper import env_wrapper\n\n\nclass LoggerWrapper:\n    def __init__(self):\n        self._logger = {}\n        self._console_init = False\n\n    @staticmethod\n    def _get_path(action, path=\"\"):\n        \"\"\"\n        根据日志名，创建对应的日志路径\n        :param path:\n        :return:\n        \"\"\"\n        if action != 'logs':\n            action = \"logs/\" + action + \"/\"\n\n        path = env_wrapper.get_module_path() + \"/\" + action + path\n        if not os.path.exists(path):\n            # 当目录不存在时，主动创建\n            os.makedirs(path)\n\n        return path\n\n    def _gen_logger(self, path='logs', log_name='Crawler'):\n        base_logger = logging.getLogger(log_name)\n        base_logger.setLevel(logging.INFO)\n\n        log_file = self._get_path(path, log_name) + \"/\" + log_name + \".log\"\n        ch = TimedRotatingFileHandler(log_file, when='D', encoding=\"utf-8\")\n        ch.setLevel(logging.INFO)\n        formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\n        ch.setFormatter(formatter)\n        base_logger.addHandler(ch)\n        base_logger.propagate = 0\n\n        if env_wrapper.console_log_enable():  # and not self._console_init:\n            console = logging.StreamHandler()\n            console.setLevel(logging.DEBUG)\n            console.setFormatter(formatter)\n            base_logger.addHandler(console)\n            self._console_init = True\n\n        return base_logger\n\n    def get_logger(self, name=None):\n        if name is None:\n            key = env_wrapper.get_current_task_name()\n        else:\n            key = name\n\n        if key not in self._logger:\n            log_name, path = key, env_wrapper.get_current_task_name()\n            self._logger[key] = self._gen_logger(path, log_name)\n\n        return self._logger[key]\n\n    def error(self, msg, name=None):\n        log = self.get_logger(name)\n        log.error(msg)\n\n    def warn(self, msg, name=None):\n        log = self.get_logger(name)\n        log.warning(msg)\n\n    def info(self, msg, name=None):\n        log = self.get_logger(name)\n        log.info(msg)\n\n    def debug(self, msg, name=None):\n        log = self.get_logger(name)\n        log.debug(msg)\n\n    def exception(self, msg, name=None):\n        \"\"\"\n        打印堆栈信息\n        :param msg:\n        :param name:\n        :return:\n        \"\"\"\n        log = self.get_logger(name)\n        log.exception(msg)\n\n\nSpiderLogger = LoggerWrapper()\nlogger = SpiderLogger.get_logger\ndebug = SpiderLogger.debug\ninfo = SpiderLogger.info\nerror = SpiderLogger.error\nwarning = SpiderLogger.warn\nexception = SpiderLogger.exception\n","repo_name":"liuyueyi/python-task-engine","sub_path":"src/plugins/logger/LoggerWrapper.py","file_name":"LoggerWrapper.py","file_ext":"py","file_size_in_byte":2757,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"73548545245","text":"from threading import Thread\nfrom time import sleep\nfrom typing import Iterable, Union\n\nfrom microbit_protocol.commands import MicrobitCommand\nfrom microbit_protocol.commands.microbit.display import (\n    DISPLAY_MAX_X,\n    DISPLAY_MAX_Y,\n    DISPLAY_MIN_X,\n    DISPLAY_MIN_Y,\n    LED_MAX_VALUE,\n    LED_MIN_VALUE,\n    MicrobitDisplayClearCommand,\n    MicrobitDisplayOffCommand,\n    MicrobitDisplayOnCommand,\n    MicrobitDisplayReadLightLevelCommand,\n    MicrobitDisplaySetPixelCommand,\n    MicrobitDisplayShowCommand,\n)\nfrom microbit_protocol.peer import MicrobitPeer\n\nfrom microbit_client.image import Image\n\n\nclass Display:\n    \"\"\"Represents a micro:bit's display client.\"\"\"\n\n    def __init__(self, peer: MicrobitPeer) -> None:\n        \"\"\"Initialises `self` to a new Display instance.\n\n        Args:\n            peer (MicrobitPeer): The peer to communicate with.\n        \"\"\"\n        self.__peer = peer\n        self.__light_level = 0\n        self.on()\n        self.clear()\n\n        def listener(command: MicrobitCommand) -> None:\n            if isinstance(command, MicrobitDisplayReadLightLevelCommand):\n                self.__light_level = command.light_level\n\n        peer.add_listener(listener)\n\n    def get_pixel(self, x: int, y: int) -> int:\n        \"\"\"Gets the brightness of the LED at the given position.\n\n        Args:\n            x (int): The x position of the LED.\n            y (int): The y position of the LED.\n\n        Returns:\n            int: The brightness of the LED.\n        \"\"\"\n        assert isinstance(x, int), f\"x must be an int, not {type(x).__name__}\"\n        assert isinstance(y, int), f\"y must be an int, not {type(y).__name__}\"\n\n        if (\n            x < DISPLAY_MIN_X\n            or DISPLAY_MAX_X < x\n            or y < DISPLAY_MIN_Y\n            or DISPLAY_MAX_Y < y\n        ):\n            raise ValueError(f\"invalid position {x}, {y}\")\n\n        return self.__pixels[y][x]\n\n    def set_pixel(self, x: int, y: int, value: int) -> None:\n        \"\"\"Sets the brightness of the LED at the given position.\n\n        Args:\n            x (int): The x position of the LED.\n            y (int): The y position of the LED.\n            value (int): The brightness of the LED.\n\n        Raises:\n            ValueError: If `value` is not between 0 and 9 inclusive.\n            ValueError: If `x` or `y` are not between 0 and 4 inclusive.\n        \"\"\"\n        assert isinstance(x, int), f\"x must be an int, not {type(x).__name__}\"\n        assert isinstance(y, int), f\"y must be an int, not {type(y).__name__}\"\n        assert isinstance(\n            value, int\n        ), f\"value must be an int, not {type(value).__name__}\"\n\n        if value < LED_MIN_VALUE or LED_MAX_VALUE < value:\n            raise ValueError(\"brightness out of bounds\")\n\n        if (\n            x < DISPLAY_MIN_X\n            or DISPLAY_MAX_X < x\n            or y < DISPLAY_MIN_Y\n            or DISPLAY_MAX_Y < y\n        ):\n            raise ValueError(f\"invalid position {x}, {y}\")\n\n        self.__pixels[y][x] = value\n\n        self.__peer.send_command(MicrobitDisplaySetPixelCommand(x=x, y=y, value=value))\n\n    def clear(self) -> None:\n        \"\"\"Set the brightness of all LEDs to 0 (off).\"\"\"\n        self.__pixels = [[0 for _ in range(5)] for _ in range(5)]\n        self.__peer.send_command(MicrobitDisplayClearCommand())\n\n    def show(  # noqa: PLR0913\n        self,\n        image: Union[Image, str, float, int, Iterable[Image]],\n        delay: int = 400,\n        *,\n        wait: bool = True,\n        loop: bool = False,\n        clear: bool = False,\n    ) -> None:\n        \"\"\"Displays an image on the micro:bit's display.\n\n        Args:\n            image (Union[Image, str, float, int, Iterable[Image]]): The image\n                to display.\n            delay (int, optional): The delay between each frame in milliseconds.\n                Defaults to 400.\n            wait (bool, optional): Whether to wait for the animation to finish.\n                Defaults to True.\n            loop (bool, optional): Whether to loop the animation.\n                Defaults to False.\n            clear (bool, optional): Whether to clear the display after the animation.\n                Defaults to False.\n\n        Raises:\n            ValueError: If `delay` is negative.\n        \"\"\"\n        if isinstance(image, Image):\n            self.__send_image(image)\n            return\n\n        assert isinstance(image, (str, float, int, Iterable)), (\n            \"image must be a str, float, int or Iterable[Image],\"\n            f\"got {type(image).__name__}\"\n        )\n        assert isinstance(\n            delay, int\n        ), f\"delay must be an int, not {type(delay).__name__}\"\n        assert isinstance(wait, bool), f\"wait must be a bool, not {type(wait).__name__}\"\n        assert isinstance(loop, bool), f\"loop must be a bool, not {type(loop).__name__}\"\n        assert isinstance(\n            clear, bool\n        ), f\"clear must be a bool, not {type(clear).__name__}\"\n\n        if delay < 0:\n            raise ValueError(\"delay must be positive\")\n\n        if isinstance(image, (int, float)):\n            image = str(image)\n\n        images: Iterable[Image]\n        if isinstance(image, str):\n            images = [Image(letter) for letter in image]\n        else:\n            images = image\n\n        def target() -> None:\n            self.__send_images(images, delay)\n            while loop:\n                self.__send_images(images, delay)\n\n            if clear:\n                self.clear()\n\n        if wait:\n            target()\n        else:\n            Thread(target=target, daemon=True).start()\n\n    def scroll(  # noqa: PLR0913\n        self,\n        text: Union[str, int, float],\n        delay: int = 150,\n        *,\n        wait: bool = True,\n        loop: bool = False,\n        monospace: bool = False,\n    ) -> None:\n        \"\"\"Scrolls text across the micro:bit's display.\n\n        Args:\n            text (Union[str, int, float]): The text to scroll.\n            delay (int, optional): The delay between each frame in milliseconds.\n                Defaults to 150.\n            wait (bool, optional): Whether to wait for the animation to finish.\n                Defaults to True.\n            loop (bool, optional): Whether to loop the animation.\n                Defaults to False.\n            monospace (bool, optional): Whether to use monospace font.\n                Defaults to False.\n\n        Raises:\n            ValueError: If `delay` is negative.\n        \"\"\"\n        assert isinstance(\n            text, (str, int, float)\n        ), f\"text must be a str, int or float, not {type(text).__name__}\"\n        assert isinstance(\n            delay, int\n        ), f\"delay must be an int, not {type(delay).__name__}\"\n        assert isinstance(wait, bool), f\"wait must be a bool, not {type(wait).__name__}\"\n        assert isinstance(loop, bool), f\"loop must be a bool, not {type(loop).__name__}\"\n        assert isinstance(\n            monospace, bool\n        ), f\"monospace must be a bool, not {type(monospace).__name__}\"\n\n        if delay < 0:\n            raise ValueError(\"delay must be positive\")\n\n        if isinstance(text, (int, float)):\n            text = str(text)\n\n        if monospace:\n            image = self.__get_scroll_image_monospace(text)\n        else:\n            image = self.__get_scroll_image(text)\n\n        def target() -> None:\n            self.__scroll_image(image, delay)\n            while loop:\n                self.__scroll_image(image, delay)\n\n        if wait:\n            target()\n        else:\n            Thread(target=target, daemon=True).start()\n\n    def on(self) -> None:\n        \"\"\"Turns the display on.\"\"\"\n        self.__is_on = True\n        self.__peer.send_command(MicrobitDisplayOnCommand())\n\n    def off(self) -> None:\n        \"\"\"Turns the display off.\"\"\"\n        self.__is_on = False\n        self.__peer.send_command(MicrobitDisplayOffCommand())\n\n    def is_on(self) -> bool:\n        \"\"\"Returns whether the display is on.\n\n        Returns:\n            bool: Whether the display is on.\n        \"\"\"\n        return self.__is_on\n\n    def read_light_level(self) -> int:\n        \"\"\"Reads the light level from the display.\n\n        Returns:\n            int: The light level.\n        \"\"\"\n        return self.__light_level\n\n    def __scroll_image(self, image: Image, delay: int) -> None:\n        \"\"\"Scrolls an image across the display.\n\n        Args:\n            image (Image): The image to scroll.\n            delay (int): The delay between each frame in milliseconds.\n        \"\"\"\n        for i in range(image.width() + 1):\n            self.__send_image(image.crop(i, 0, 5, 5))\n            sleep(delay / 1000)\n\n    def __send_images(self, images: Iterable[Image], delay: int) -> None:\n        \"\"\"Sends images to the display.\n\n        Args:\n            images (Iterable[Image]): The images to send.\n            delay (int): The delay between each frame in milliseconds.\n        \"\"\"\n        for image in images:\n            self.__send_image(image)\n            sleep(delay / 1000)\n\n    def __send_image(self, image: Image) -> None:\n        \"\"\"Sends an image to the display.\n\n        Args:\n            image (Image): The image to send.\n        \"\"\"\n        self.__peer.send_command(\n            MicrobitDisplayShowCommand(\n                image=[\n                    [image.get_pixel(x, y) for x in range(image.width())]\n                    for y in range(image.height())\n                ]\n            )\n        )\n\n    @classmethod\n    def __get_scroll_image_monospace(cls, text: str) -> Image:\n        \"\"\"Gets an image of the text in monospace to scroll.\n\n        Args:\n            text (str): The text to scroll.\n\n        Returns:\n            Image: The image of the text.\n        \"\"\"\n        scroll_image = Image(4 + 5 * len(text), 5)\n\n        for i, char in enumerate(text):\n            scroll_image.blit(Image(char), 0, 0, 5, 5, 4 + 5 * i, 0)\n\n        return scroll_image\n\n    @classmethod\n    def __get_scroll_image(cls, text: str) -> Image:\n        \"\"\"Gets an image of the text to scroll.\n\n        Args:\n            text (str): The text to scroll.\n\n        Returns:\n            Image: The image of the text.\n        \"\"\"\n        images: list[Image] = []\n        images_width = 0\n\n        for char in text:\n            if char == \" \":\n                image = Image(3, 5)\n            else:\n                image = cls.__remove_image_void(Image(char))\n\n            images.append(image)\n            images_width += image.width()\n\n        scroll_image = Image(4 + images_width + len(images) - 1, 5)\n\n        current_width = 4\n        for image in images:\n            scroll_image.blit(image, 0, 0, image.width(), 5, current_width, 0)\n            current_width += image.width() + 1\n\n        return scroll_image\n\n    @classmethod\n    def __remove_image_void(cls, image: Image) -> Image:\n        \"\"\"Removes the void pixels from an image.\n\n        Args:\n            image (Image): The image to remove the void pixels from.\n\n        Returns:\n            Image: The image without the void pixels.\n        \"\"\"\n        start = 0\n        while start < image.width() and cls.__is_column_void(image, start):\n            start += 1\n\n        end = image.width() - 1\n        while start < end and cls.__is_column_void(image, end):\n            end -= 1\n\n        width = end - start + 1\n\n        return image.crop(start, 0, width, image.height())\n\n    @classmethod\n    def __is_column_void(cls, image: Image, column: int) -> bool:\n        \"\"\"Returns whether a column is void.\n\n        Args:\n            image (Image): The image to check.\n            column (int): The column to check.\n\n        Returns:\n            bool: Whether the column is void.\n        \"\"\"\n        for y in range(image.height()):\n            if image.get_pixel(column, y) != 0:\n                return False\n        return True\n","repo_name":"BergLucas/microbit-python-simulator","sub_path":"src/microbit_client/display.py","file_name":"display.py","file_ext":"py","file_size_in_byte":11790,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"32109456303","text":"import argparse\nimport shlex\nimport os\nimport sys\nimport toml\nimport time\n\n\ndef main(args, subject=None, message=None, to_email=None, bcc=None,\n         sender=None, reply_to=None):\n    \"\"\"\n    Sends a test email message to check configurations.\n    \"\"\"\n\n    # Get the current time and date\n    localtime = time.asctime(time.localtime(time.time()))\n     # Use configurations for required fields if nothing is provided\n    if not subject:\n        subject = \"Test message\"\n    if not message:\n        message = (\"<p>This is a test email. If you are reading this, the \"\n        \"configured  values probably work!</p><p>{}</p><p>Current \"\n        \"configuration: {}</p>\").format(localtime, cfg.email)\n    if not to_email:\n        to_email = cfg.email.admin_emails\n    if not sender:\n        sender = cfg.email.from_email\n    if not reply_to:\n        reply_to = cfg.email.reply_to\n\n    # Attempt to send emails\n    actions.send_email(\n        subject,\n        message,\n        to_email,\n        bcc,\n        sender,\n        reply_to=reply_to\n    )\n\ndef bootstrap(args):\n    \"\"\"\n    Configures the program so that it can function correctly. This is done by\n    changing into the arbiter directory and then importing arbiter functions.\n    \"\"\"\n    # Make the path to files absolute. This makes behavior consistent when\n    # changing directories. Otherwise, configuration files would be relative to\n    # the arbiter/ directory\n    args.configs = [os.path.abspath(path) for path in args.configs]\n    os.chdir(args.arbdir)\n    insert(args.arbdir)\n    insert(args.etc)\n\n    import cfgparser\n    try:\n        if not cfgparser.load_config(*args.configs, check=False):\n            print(\"There was an issue with the specified configuration (see \"\n                  \"above). You can investigate this with the cfgparser.py \"\n                  \"tool.\")\n            sys.exit(2)\n    except (TypeError, toml.decoder.TomlDecodeError) as err:\n        print(\"Configuration error:\", str(err), file=sys.stderr)\n        sys.exit(2)\n\n\ndef insert(context):\n    \"\"\"\n    Appends a path to into the python path.\n    \"\"\"\n    context_path = os.path.dirname(__file__)\n    sys.path.insert(0, os.path.abspath(os.path.join(context_path, context)))\n\n\ndef arbiter_environ():\n    \"\"\"\n    Returns a dictionary with the ARB environment variables. If a variable is\n    not found, it is not in the dictionary.\n    \"\"\"\n    env = {}\n    env_vars = {\n        \"ARBETC\": (\"-e\", \"--etc\"),\n        \"ARBDIR\": (\"-a\", \"--arbdir\"),\n        \"ARBCONFIG\": (\"-g\", \"--config\")\n    }\n    for env_name, ignored_prefixes in env_vars.items():\n        env_value = os.environ.get(env_name)\n        if not env_value:\n            continue\n        warn = lambda i, s: print(\"{} in {} {}\".format(i, env_name, s))\n        expanded_path = lambda p: os.path.expandvars(os.path.expanduser(p))\n\n        for prefix in ignored_prefixes:\n            if env_value.startswith(prefix):\n                env_value = env_value.lstrip(prefix).lstrip()\n                break\n\n        if env_name == \"ARBCONFIG\":\n            config_paths = shlex.split(env_value, comments=False, posix=True)\n            valid_paths = []\n            for path in config_paths:\n                if not os.path.isfile(expanded_path(path)):\n                    warn(path, \"does not exist\")\n                    continue\n                valid_paths.append(path)\n\n            if valid_paths:\n                env[env_name] = valid_paths\n            continue\n\n        expanded_value = expanded_path(env_value)\n        if not os.path.exists(expanded_value):\n            warn(env_value, \"does not exist\")\n            continue\n        if not os.path.isdir(expanded_value):\n            warn(env_value, \"is not a directory\")\n            continue\n        if env_name == \"ARBDIR\" and not os.path.exists(expanded_value + \"/arbiter.py\"):\n            warn(env_value, \"does not contain arbiter modules! (not arbiter/ ?)\")\n            continue\n        if env_name == \"ARBETC\" and not os.path.exists(expanded_value + \"/integrations.py\"):\n            warn(env_value, \"does not contain etc modules! (no integrations.py)\")\n            continue\n        env[env_name] = expanded_value\n    return env\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Arbiter email tester\")\n    arb_environ = arbiter_environ()\n    parser.add_argument(\"-a\", \"--arbdir\",\n                        type=str,\n                        help=\"Sets the directory in which arbiter modules \"\n                             \"are loaded from. Defaults to $ARBDIR if \"\n                             \"present or ../arbiter otherwise.\",\n                        default=arb_environ.get(\"ARBDIR\", \"../arbiter\"),\n                        dest=\"arbdir\")\n    parser.add_argument(\"-g\", \"--config\",\n                        type=str,\n                        nargs=\"+\",\n                        help=\"The configuration files to use. Configs will be \"\n                             \"cascaded together starting at the leftmost (the \"\n                             \"primary config) going right (the overwriting \"\n                             \"configs). Defaults to $ARBCONFIG if present or \"\n                             \"../etc/config.toml otherwise.\",\n                        default=arb_environ.get(\"ARBCONFIG\", [\"../etc/config.toml\"]),\n                        dest=\"configs\")\n    parser.add_argument(\"-e\", \"--etc\",\n                        type=str,\n                        help=\"Set the directory in which configurable modules \"\n                             \"are loaded from (e.g. integrations.py). If a \"\n                             \"required module does not exist in the given \"\n                             \"directory, the default module will be loaded \"\n                             \"from $ARBETC if present or ../etc otherwise.\",\n                        default=arb_environ.get(\"ARBETC\", \"../etc\"),\n                        dest=\"etc\")\n    parser.add_argument(\"--to\",\n                        type=str,\n                        nargs=\"+\",\n                        default=[],\n                        help=\"The users who will receive the test message\",\n                        dest=\"to\")\n    parser.add_argument(\"--bcc\",\n                        type=str,\n                        nargs=\"+\",\n                        default=[],\n                        help=\"Users to be added to BCC headers of messages\",\n                        dest=\"bcc\")\n    parser.add_argument(\"--from\",\n                        type=str,\n                        default=\"\",\n                        help=\"The sending email address\",\n                        dest=\"sender\")\n    parser.add_argument(\"--replyto\",\n                        type=str,\n                        default=\"\",\n                        help=\"The reply-to address of emails\",\n                        dest=\"replyto\")\n    parser.add_argument(\"--message\",\n                        type=str,\n                        default=\"\",\n                        help=\"The message to send, if a custom message is \"\n                             \"desired\",\n                        dest=\"message\")\n    parser.add_argument(\"--subject\",\n                        type=str,\n                        default=\"\",\n                        help=\"The subject to send, if a custom subject is \"\n                             \"desired\",\n                        dest=\"subject\")\n    args = parser.parse_args()\n    bootstrap(args)\n    from cfgparser import cfg, shared\n    import actions\n    main(args, subject=args.subject, message=args.message, to_email=args.to,\n         bcc=args.bcc, sender=args.sender, reply_to=args.replyto)\n","repo_name":"CHPC-UofU/arbiter2","sub_path":"tools/test_email.py","file_name":"test_email.py","file_ext":"py","file_size_in_byte":7575,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"86"}
+{"seq_id":"42314595377","text":"# -*- coding:utf-8 -*-\n\n__author__ = 'huanghf'\n\n\"\"\"\n给定两个以升序排列的整形数组 nums1 和 nums2, 以及一个整数 k。\n\n定义一对值 (u,v)，其中第一个元素来自 nums1，第二个元素来自 nums2。\n\n找到和最小的 k 对数字 (u1,v1), (u2,v2) ... (uk,vk)。\n\n示例 1:\n\n输入: nums1 = [1,7,11], nums2 = [2,4,6], k = 3\n输出: [1,2],[1,4],[1,6]\n解释: 返回序列中的前 3 对数：\n     [1,2],[1,4],[1,6],[7,2],[7,4],[11,2],[7,6],[11,4],[11,6]\n示例 2:\n\n输入: nums1 = [1,1,2], nums2 = [1,2,3], k = 2\n输出: [1,1],[1,1]\n解释: 返回序列中的前 2 对数：\n     [1,1],[1,1],[1,2],[2,1],[1,2],[2,2],[1,3],[1,3],[2,3]\n示例 3:\n\n输入: nums1 = [1,2], nums2 = [3], k = 3 \n输出: [1,3],[2,3]\n解释: 也可能序列中所有的数对都被返回:[1,3],[2,3]\n\"\"\"\n\nimport heapq\n\n\nclass Solution(object):\n    def kSmallestPairs(self, nums1, nums2, k):\n        \"\"\"\n        优化堆\n        :param nums1:\n        :param nums2:\n        :param k:\n        :return:\n        \"\"\"\n        if not nums1 or not nums2:\n            return []\n        m, n = len(nums1), len(nums2)\n        heap = []\n        res = []\n        vis = [[0]*n for _ in range(m)]\n        heapq.heappush(heap, (nums1[0] + nums2[0], 0, 0))\n        while k and heap:\n            x, i, j = heapq.heappop(heap)\n            res.append([nums1[i], nums2[j]])\n            if i < m - 1 and not vis[i+1][j]:\n                heapq.heappush(heap, (nums1[i + 1] + nums2[j], i + 1, j))\n                vis[i+1][j] = 1\n            if j < n - 1 and not vis[i][j+1]:\n                heapq.heappush(heap, (nums1[i] + nums2[j + 1], i, j + 1))\n                vis[i][j+1] = 1\n            k -= 1\n        return res\n\n    def kSmallestPairs2(self, nums1, nums2, k):\n        \"\"\"\n        暴力做法\n        :type nums1: List[int]\n        :type nums2: List[int]\n        :type k: int\n        :rtype: List[List[int]]\n        \"\"\"\n        nums = [[x, y] for x in nums1 for y in nums2]\n        return heapq.nsmallest(k, nums, key=lambda x: x[0] + x[1])\n\n\ns = Solution()\nnums1 = [1, 2, 4]\nnums2 = [-1, 1, 2]\nk = 100\nprint(s.kSmallestPairs(nums1, nums2, k))\nprint(s.kSmallestPairs2(nums1, nums2, k))\n","repo_name":"lovehhf/LeetCode","sub_path":"373_查找和最小的K对数字.py","file_name":"373_查找和最小的K对数字.py","file_ext":"py","file_size_in_byte":2173,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"25322602441","text":"# coding=utf-8\n\n\"\"\"\nrequirements:\n1. 用当前项目的coroutine, 替换本文件的coroutine\n2. 用当前项目的Future, 替换本文件的Future\n\n\"\"\"\n\nimport time\nimport hashlib\n\nfrom tornado.gen import coroutine, Future\n\n\ndef _timestamp():\n    return int(time.time())\n\n\ndef _params_sign(*args, **kwargs):\n\n    result = []\n\n    if args:\n        result.extend(str(val) for val in args)\n\n    if kwargs:\n\n        kwargs_part = r'&'.join(r'{0}={1}'.format(key, val) for key, val in sorted(kwargs.items(), key=lambda x: x[0]))\n\n        result.append(kwargs_part)\n\n    raw_str = r'&'.join(result)\n\n    raw_b = bytes(raw_str, r'utf-8')\n\n    return hashlib.md5(raw_b).hexdigest()\n\n\nclass TimedCache(object):\n\n    def __init__(self, ttl=0):\n\n        self._ttl = ttl\n        self._data = dict()\n\n    def get(self, key):\n\n        result = None\n\n        if key in self._data:\n\n            item = self._data[key]\n\n            if item[0] > _timestamp():\n                result = item[1]\n            else:\n                del self._data[key]\n\n        return result\n\n    def set(self, key, val, expire=0):\n\n        if expire <= 0:\n            expire = self._ttl\n\n        now_time = _timestamp()\n\n        self._data[key] = (now_time + expire, val)\n\n        del_keys = []\n\n        for key, val in self._data.items():\n            if val[0] < now_time:\n                del_keys.append(key)\n\n        for key in del_keys:\n            del self._data[key]\n\n    def delete(self, key):\n\n        if key in self._data:\n            del self._data[key]\n\n    def exists(self, key):\n\n        result = False\n\n        if key in self._data:\n\n            item = self._data[key]\n\n            if item[0] > _timestamp():\n                result = True\n            else:\n                del self._data[key]\n\n        return result\n\n    def size(self):\n\n        length = 0\n\n        now_time = _timestamp()\n\n        del_keys = []\n\n        for key, val in self._data.items():\n            if val[0] > now_time:\n                length += 1\n            else:\n                del_keys.append(key)\n\n        for key in del_keys:\n            del self._data[key]\n\n        return length\n\n\ndef func_cache(expire, func):\n\n    __cache = TimedCache()\n\n    def __wrapper(*args, **kwargs):\n\n        func_sign = _params_sign(func, *args, **kwargs)\n\n        result = __cache.get(func_sign)\n\n        if result is None:\n\n            result = func(*args, **kwargs)\n\n            if isinstance(result, Future):\n                result = yield result\n\n            __cache.set(func_sign, result, expire)\n\n        return result\n\n    return coroutine(__wrapper)\n","repo_name":"XTAYJGDUFVF/prize_server","sub_path":"prize_server/util/mem_cache.py","file_name":"mem_cache.py","file_ext":"py","file_size_in_byte":2593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"4394881261","text":"from rembg import remove, new_session\nimport sys, os, shutil, cv2, re\nimport numpy as np\nimport multiprocessing\nimport tqdm\nimport base64\n\nn=0\n\n#Create mask as <filename>_mask.png at pathToFolder\ndef createMask(dirpath, mask_abs_path):\n    extensions = ['.jpg','.JPG','.png','.PNG']\n    paths = [str for str in os.listdir(dirpath) if any(sub in str for sub in extensions)]\n    no_of_image = len(paths)\n    paths = [(dirpath, str, mask_abs_path, no_of_image) for str in sorted(paths) if 'mask' not in str]\n    pool = multiprocessing.Pool()\n    pool.starmap(run_mask, paths)\n    pool.close()\n    pool.join\n    #for path in paths:\n    #    run_mask(*path)\n\ndef run_mask(dirpath, file, mask_abs_path, no_of_image):\n    input_path = os.path.join(dirpath,file)\n    #ext = file.split('.')[-1]\n    ext = 'png'\n    output_path = os.path.join(dirpath, file.split('.')[0]+f'_mask.{ext}')\n    finalmask_path = os.path.join(mask_abs_path, file.split('.')[0]+f'_mask.{ext}')\n    maskprocess=False\n    if not os.path.exists(finalmask_path):\n        maskprocess=True\n        with open(input_path, 'rb') as i:\n            input = i.read()\n        session=new_session('u2netp')\n        output_byte = remove(data=input, session=session, only_mask=True)\n        convertMask(output_path,output_byte)  \n    \n    global n\n    if maskprocess:\n        print(file,'masked')\n        #print(file,'masked','('+str(n+1)+'/'+str(no_of_image)+')')\n    else:\n        print(os.path.basename(finalmask_path),'already exists')\n        #print(finalmask_path,'already exists','('+str(n+1)+'/'+str(no_of_image)+')')\n    n+=1\n    \n    return 0\n    \n#convert and replaces mask to single channel, black n white\ndef convertMask(image_path, image_byte):\n    #image_path = path\n    im = np.frombuffer(image_byte, np.uint8)\n    im = cv2.imdecode(im, cv2.IMREAD_COLOR)\n    im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\n    im = np.greater_equal(im, 40) #10\n    im = np.where(im, 255, 0)\n    cv2.imwrite(image_path, im)\n\n#move masks to pathToMask directory\ndef moveMask(path0, path1):\n    if not os.path.exists(path1):\n        os.mkdir(path1)\n    for file in os.listdir(path0):\n        if '_mask' in file:\n            input_path = os.path.join(path0, file)\n            output_path = os.path.join(path1,file)\n            if not os.path.exists(output_path):\n                shutil.move(input_path,output_path)\n\ndef main():\n    pathToFolder = sys.argv[1]\n    if not os.path.exists(pathToFolder):\n        print('rembg: folder does not exists: ',pathToFolder)\n        sys.exit(1)\n    if len(sys.argv) > 2:\n        pathToMask = sys.argv[2]\n    else:\n        parent_Folder = os.path.join(pathToFolder, os.pardir)\n        pathToMask = os.path.join(parent_Folder,'masks')\n    print('rembg: image directory:', pathToFolder)\n    print('rembg: mask directory:',pathToMask)    \n    createMask(pathToFolder,pathToMask)\n    moveMask(pathToFolder, pathToMask)\n    print('rembg: completed')\n\nmain()","repo_name":"rexliuser/scripts_odm","sub_path":"removeBG_parallel.py","file_name":"removeBG_parallel.py","file_ext":"py","file_size_in_byte":2929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"12784595770","text":"import logging\nfrom asyncio import sleep\nfrom datetime import datetime, timedelta\nfrom typing import Optional\n\nimport requests\nfrom aiogram import Bot\nfrom aiogram.exceptions import TelegramForbiddenError\nfrom aiogram.types import ChatPermissions, Chat, User, Message\n\nSTATE_PASS = 'pass'\nSTATE_NOT_PERFORMED = 'not performed'\n\n\n# STATE_FAIL = 'fail'\n\n\nclass UserState:\n    def __init__(self, chat_id: int, group_tim_msg: Message, state: str = STATE_NOT_PERFORMED,\n                 private_tip_msg: Optional[Message] = None):\n        self.chat_id = chat_id\n        self.group_tip_msg = group_tim_msg\n        self.state = state\n        self.private_tip_msg = private_tip_msg  # 私聊中的提示信息，完成后删除\n\n\nuser_states: dict[int, UserState] = {}\n\n\nasync def verification(chat: Chat,\n                       group_tip_message: Message,\n                       user: User,\n                       bot: Bot,\n                       shutup_before_verification: bool,\n                       test_time: int,\n                       ban: bool,\n                       ban_time: int | None):\n    \"\"\"\n     缓存数据，发送提示信息，等待私聊验证，超市则踢出用户\n     :param chat: 群组\n     :param group_tip_message: 提示信息，验证后删除\n     :param user: 被验证用户\n     :param bot: Bot\n     :param shutup_before_verification: 在通过验证前是否禁言\n     :param test_time: 超过这个时间将被封禁\n     :param ban: 验证失败后是否封禁\n     :param ban_time: 封禁时间，空为永久\n     \"\"\"\n    user_states[user.id] = UserState(chat.id, group_tip_message)  # 缓存正在用户的信息\n\n    if shutup_before_verification:\n        await bot.restrict_chat_member(\n            chat_id=chat.id,\n            user_id=user.id,\n            permissions=ChatPermissions(),\n        )\n\n    await sleep(test_time)\n\n    if user_states[user.id].state == STATE_PASS:\n        \"\"\"\n        通过验证后消息的删除，与通过的提醒在 handlers.web_callback_handler 中处理了\n        \"\"\"\n        user_states.pop(user.id)\n        return\n\n    msg = await bot.send_message(chat.id, f\"{user.full_name[0] + '███' + user.full_name[-1]} 验证超时，已被踢出\")\n    await bot.ban_chat_member(\n        chat_id=chat.id,\n        user_id=user.id,\n        until_date=datetime.now() + timedelta(seconds=ban_time) if ban_time is not None else None)\n\n    if not ban:\n        await bot.unban_chat_member(chat.id, user.id)\n    elif ban_time is not None:\n        try:\n            await bot.send_message(user.id, f\"未通过验证，已被踢出，请在 {test_time} 秒后重试\")\n        except TelegramForbiddenError:\n            logging.info(f\"{user.full_name} 未私聊，无法发送消息\")\n    else:\n        try:\n            await bot.send_message(user.id, f\"未通过验证，已被永久封禁，请联系管理员\")\n        except TelegramForbiddenError:\n            logging.info(f\"{user.full_name} 未私聊，无法发送消息\")\n    user_states.pop(user.id)\n    await sleep(10)\n\n    await bot.delete_message(chat.id, msg.message_id)\n    await bot.delete_message(chat.id, group_tip_message.message_id)\n\n\n# 验证客户端传回来的 recaptcha response\ndef verify_recaptcha(user_data: str, token: str, proxy: str = None):\n    data = {\n        \"secret\": token,\n        \"response\": user_data\n    }\n    result = requests.post(\n        url=\"https://www.google.com/recaptcha/api/siteverify\",\n        data=data,\n        proxies={\"https\": proxy} if proxy else None\n    ).json()\n    if \"score\" in result:  # for v3\n        return result['success'] and result['score'] > 0.5\n    return result['success']  # for v2\n\n\ndef verify_turnstile(user_data: str, token: str, proxy: str = None):\n    data = {\n        \"secret\": token,\n        \"response\": user_data\n    }\n    result = requests.post(\n        url=\"https://challenges.cloudflare.com/turnstile/v0/siteverify\",\n        data=data,\n        proxies={\"https\": proxy} if proxy else None\n    ).json()\n    return result['success']\n","repo_name":"tobyprime/VerificationBot","sub_path":"verification.py","file_name":"verification.py","file_ext":"py","file_size_in_byte":4028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32702967078","text":"\n#### Usage: python3 this.py\n####    <reference_dir> <indexed_sequences.csv> <parms.txt> <marginals.txt> <best_scenarios_counts.csv> [Pgen_counts.csv] [should_sort_idx_output]\n#### Options: [should_sort_idx_output] (default: True)\n#### Input: <best_scenarios_counts.csv>  15 columns\n####  Note: <reference_dir> is actually not used so far\n#### Output: STDOUT  .csv (';'-separated, idx-sorted)\n####            add 3+4+8+8+6(+1)=29(30) columns\n####              3(16-18th) V_name,D_name,J_name\n####              4(19-22th) P(V),P(J|V),P(D|V,J),P(V,D,J)\n####              2*4=8(23-30th) V3_delLen, P(V3_delLen|V), D5_delLen, P(.|D), D3_delLen, P(,|D), J5_delLen, P(.|J)\n####              4*2=8(31-38th) VD_insLen, P(VD_insLen), VD_insNt, P(VD_insNt), DJ_...\n####              1(39th) P_recomb = P(V,D,J)*P(V3_delLen|V)*...*P(VD_insLen)*P(VD_InsNt)*...\n####              1(40th) P_mismatch = error_rate ^ num_mismatch\n####              1(41th) P_scenario = P_recomb * P_mismatch\n####              1(42th) P_posterior = P_scenario / P_read\n####              1(43th) P_read = sum P_scenario\n####              1(44th) P_gen  = sum P_recomb\n####              1(45th) Pgen (if [Pgen_counts.csv] is provided)\n####           each best_scenario followed by 6 lines for sequence alignment: read, match, VDJjoin, V, D, J\n\nimport sys\nimport numpy as np\n\nref_dirPath, idx_seq_filename, parms_filename, marginals_filename, bestScenarios_filename = sys.argv[1:6]\nPgen_filename = None\nif len(sys.argv) > 6:\n\tPgen_filename = sys.argv[6]\n\ndef str2bool(x):\n\ttry:\n\t\tx = int(x)\n\t\treturn x > 0\n\texcept ValueError:\n\t\treturn x.lower() in ('true', 't', 'yes', 'y')\n\nshould_sort_idx_output = True\nif len(sys.argv) > 7:\n\tshould_sort_idx_output = str2bool(sys.argv[7])\n\t\n\n# for debug\nprint('ref_dirPath = {}'.format(ref_dirPath), file=sys.stderr)\nprint('idx_seq_filename = {}'.format(idx_seq_filename), file=sys.stderr)\nprint('parms_filename = {}'.format(parms_filename), file=sys.stderr)\nprint('marginals_filename = {}'.format(marginals_filename), file=sys.stderr)\nprint('bestScenarios_filename = {}'.format(bestScenarios_filename), file=sys.stderr)\nprint('Pgen_filename = {}'.format(Pgen_filename), file=sys.stderr)\n\n\n\ndef str_left(str, Len, fill=''):\n\tif Len > len(str):\n\t\treturn str + fill * (Len - len(str))\n\telse:\n\t\treturn str[0:Len]\n\t\ndef str_right(str, Len, fill=''):\n\tif Len > len(str):\n\t\treturn fill * (Len - len(str)) + str\n\telse:\n\t\treturn str[(len(str)-Len):]\n\ndef str_trim_left(str, Len):\n\treturn str[Len:]\n\ndef str_trim_right(str, Len):\n\tif Len > len(str):\n\t\treturn ''\n\telse:\n\t\treturn str[0:(len(str)-Len)]\n\ndef str_left_match(str, target):\n\treturn str_left(str, len(target)) == target\n\ndef str_right_match(str, target):\n\treturn str_right(str, len(target)) == target\n\n\ndef read_parms_Event_list(parms_filename):\n\tparms_event = {}\n\t# parms_event[nick] = {key_idx:name, ..., name:int(idx)}\n\tVDJ_alleles = {'V':{}, 'D':{}, 'J':{}}\n\t# VDJ_alleles['V'/'D'/'J'] = {key_idx:seq, name:seq}\n\terror_rate = -1\n\t\n\twith open(parms_filename, 'r') as fin:\n\t\tpart = ''\n\t\tsubpart = ''\n\t\ttype, gene, side, prio, nick = [''] * 5\n\t\tfor line in fin:\n\t\t\tline = line.rstrip()\n\t\t\tif line[0] == '@':\n\t\t\t\tpart = line[1:]\n\t\t\t\tif part != 'Event_list':\n\t\t\t\t\tpass   # do nothing\n#\t\t\t\t\tbreak   # end of reading\n\t\t\telif line[0] == '#':\n\t\t\t\tif part == 'Event_list':\n\t\t\t\t\tsubpart = line[1:]\n\t\t\t\t\ttype, gene, side, prio, nick = subpart.split(';')\n\t\t\t\t\tif nick not in parms_event:\n\t\t\t\t\t\tparms_event[nick] = {}\n\t\t\t\telif part == 'ErrorRate':\n\t\t\t\t\tsubpart = line[1:]\n\t\t\telif line[0] == '%':\n\t\t\t\tif part == 'Event_list':\n\t\t\t\t\tF = line[1:].split(';')\n\t\t\t\t\tname = F[0]\n\t\t\t\t\tidx = F[len(F)-1]\n\t\t\t\t\tparms_event[nick]['key_' + idx] = name\n\t\t\t\t\tparms_event[nick][name] = int(idx)\n\t\t\t\tif nick == 'v_choice' or nick == 'j_choice' or nick == 'd_gene':\n\t\t\t\t\tVDJ = nick[0].upper()\n\t\t\t\t\tseq = F[1]\n\t\t\t\t\tVDJ_alleles[VDJ]['key_' + idx] = seq\n\t\t\t\t\tVDJ_alleles[VDJ][name] = seq\n\t\t\telse:\n\t\t\t\tif part == 'ErrorRate' and subpart == 'SingleErrorRate':\n\t\t\t\t\terror_rate = float(line)\n\t\t\t\t\tsubpart = 'SingleErrorRate_alreadyRead'\n\t\t\t\t\t\n\treturn parms_event, VDJ_alleles, error_rate\n\t\t\t\t\ndef read_marginals(marginals_filename):\n\tmarginals = {}\n\t# marginals[nick] = np.array()\n\n\twith open(marginals_filename, 'r') as fin:\n\t\tfor line in fin:\n\t\t\tline = line.rstrip()\n\t\t\tif line[0] == '@':\n\t\t\t\tnick = line[1:]\n\t\t\telif line[0] == '$':\n\t\t\t\tdim = line[5:(len(line)-1)]\n\t\t\t\tdim = list(map(int, dim.split(',')))\n\t\t\t\tmarginals[nick] = np.zeros(dim)\n\t\t\telif line[0] == '#':\n\t\t\t\tnamedDim = line[2:(len(line)-1)]\n\t\t\t\tif namedDim == '':\n\t\t\t\t\theadIdxes = []\n\t\t\t\telse:\n\t\t\t\t\tnamedDim = namedDim.split('],[')\n\t\t\t\t\theadIdxes = [int(x.split(',')[1]) for x in namedDim]\n\t\t\telif line[0] == '%':\n\t\t\t\tvec = list(map(float, line[1:].split(',')))\n\t\t\t\tif len(headIdxes) == 0:\n\t\t\t\t\tmarginals[nick] = np.array(vec)\n\t\t\t\t\tif str_right_match(nick, '_dinucl'):\n\t\t\t\t\t\tmarginals[nick] = np.reshape(vec, (4,4))\n\t\t\t\telif len(headIdxes) == 1:\n\t\t\t\t\tmarginals[nick][headIdxes[0]] = vec\n\t\t\t\telif len(headIdxes) == 2:\n\t\t\t\t\tmarginals[nick][headIdxes[0]][headIdxes[1]] = vec\n\t\t\t\telse:\n\t\t\t\t\tprint('Warning: dim > 3 ?!', file=sys.stderr)\n\t\n\treturn marginals\n\n\nVDJ2nick = {\n\t'V' : 'v_choice',\n\t'J' : 'j_choice',\n\t'D' : 'd_gene'\n}\nVDJdel2fullPrefix = {\n\t'V3' : 'Deletion_V_gene_Three_prime',\n\t'D5' : 'Deletion_D_gene_Five_prime',\n\t'D3' : 'Deletion_D_gene_Three_prime',\n\t'J5' : 'Deletion_J_gene_Five_prime'\n}\nntCode2Nt = {\n\t'0' : 'A',\n\t'1' : 'C',\n\t'2' : 'G',\n\t'3' : 'T'\n}\nnt2intCode = {\n\t'A' : 0,\n\t'C' : 1,\n\t'G' : 2,\n\t'T' : 3\n}\ndef getNtFromNtCode(ntCode):\n\tif ntCode in ntCode2Nt:\n\t\treturn ntCode2Nt[ntCode]\n\telse:\n\t\tprint('Warning: unknown ntCode={}, so I return ?'.format(ntCode), file=sys.stderr)\n\t\treturn '?'\n\ndef get_VDJidx(VDJname, VDJ):\n\tVDJidx = -1\n\tif VDJname in parms_event[VDJ2nick[VDJ]]:\n\t\tVDJidx = parms_event[VDJ2nick[VDJ]][VDJname]\n\telse:\n\t\tprint('Warning: cannot find {} {}'.format(VDJ, VDJname), file=sys.stderr)\n\treturn VDJidx\n\ndef get_P_VDJchoice(Vidx, Didx, Jidx):\n\tans = [-1] * 4\n\t# ans = [P(V), P(J|V), P(D|V,J), P(V,D,J)]\n\tif Vidx < marginals['v_choice'].shape[0]:\n\t\tans[0] = marginals['v_choice'][Vidx]\n\t\tif Jidx < marginals['j_choice'][Vidx].shape[0]:\n\t\t\tans[1] = marginals['j_choice'][Vidx][Jidx]\n\t\t\tif Didx < marginals['d_gene'][Vidx][Jidx].shape[0]:\n\t\t\t\tans[2] = marginals['d_gene'][Vidx][Jidx][Didx]\n\t\t\t\tans[3] = ans[0] * ans[1] * ans[2]\n\treturn ans\n\n\ndef fields_to_field2idx(fields):\n\tans = {}\n\tfor i, x in enumerate(fields):\n\t\tans[x] = i\n\treturn ans\n\ndef fields_prefix_match_idx(query, fields):\n\tquery_len = len(query)\n\tans = []\n\tfor i in range(len(fields)):\n\t\tif fields[i][0:query_len] == query:\n\t\t\tans.append(i)\n\treturn ans\n\t\ndef read_Pgen(Pgen_filename):\n\tans = {}\n\t# ans[idx] = Pgen_estimate\n\t\n\tis_headline = True\n\tfields = []\n\tfield2idx = {}\n\twith open(Pgen_filename, 'r') as fin:\n\t\tfor line in fin:\n\t\t\tline = line.rstrip()\n\t\t\tF = line.split(';')\n\t\t\tif is_headline:\n\t\t\t\tis_headline = False\n\t\t\t\tfields = F\n\t\t\t\tfield2idx = fields_to_field2idx(F)\n\t\t\telse:\n\t\t\t\tif 'seq_index' not in field2idx:\n\t\t\t\t\tprint('Warning: I cannot find seq_idx column in Pgen file {}'.format(Pgen_filename), file=sys.stderr)\n\t\t\t\tif 'Pgen_estimate' not in field2idx:\n\t\t\t\t\tprint('Warning: I cannot find Pgen_estimate column in Pgen file {}'.format(Pgen_filename), file=sys.stderr)\n#\t\t\t\tidx, Pgen = F[0:2]\n\t\t\t\tidx = F[field2idx['seq_index']]\n\t\t\t\tPgen = F[field2idx['Pgen_estimate']]\n\t\t\t\tans[idx] = Pgen\n\n\treturn ans\n\ndef read_idx_seq(idx_seq_filename):\n\tinput_idx2seq = {}\n\tis_headline = True\n\tfields = []\n\tfield2idx = {}\n\twith open(idx_seq_filename, 'r') as fin:\n\t\tfor line in fin:\n\t\t\tline = line.rstrip()\n\t\t\tF = line.split(';')\n\t\t\tif is_headline:\n\t\t\t\tis_headline = False\n\t\t\t\tfields = F\n\t\t\t\tfield2idx = fields_to_field2idx(F)\n\t\t\telse:\n\t\t\t\tif 'seq_index' not in field2idx:\n\t\t\t\t\tprint('Warning: I cannot find seq_idx column in idx_seq file {}'.format(idx_seq_filename), file=sys.stderr)\n\t\t\t\tif 'sequence' not in field2idx:\n\t\t\t\t\tprint('Warning: I cannot find sequence column in idx_seq file {}'.format(idx_seq_filename), file=sys.stderr)\n\t\t\t\t\t\n\t\t\t\tidx = F[field2idx['seq_index']]\n\t\t\t\tseq = F[field2idx['sequence']]\n\t\t\t\tinput_idx2seq['key_' + idx] = seq\n\treturn input_idx2seq\n\t\n\t\ndef get_P_delLen(VDJidx, VDJdelLenIdx):\n\tVDJdelLen = {}\n\tP_VDJdel = {}\n\tfor VDJdel in ('V3', 'D5', 'D3', 'J5'):\n\t\tnick = VDJdel[0].lower() + '_' + VDJdel[1] + '_del'\n\t\tnowLenIdx = VDJdelLenIdx[VDJdel]\n\t\tP_VDJdel[VDJdel] = marginals[nick][VDJidx[VDJdel[0]]][nowLenIdx]\n\t\tif VDJdel == 'D3':\n\t\t\t# P(D3|D,D5)\n\t\t\tP_VDJdel[VDJdel] = marginals[nick][VDJidx[VDJdel[0]]][VDJdelLenIdx['D5']][nowLenIdx]\n\t\t\t# I have checked and confirmed this extraction is correct\n\t\tVDJdelLen[VDJdel] = int(parms_event[nick]['key_' + str(nowLenIdx)])\n#\t\t## for simple convenience\n#\t\tif VDJdelLen[VDJdel] < 0:\n#\t\t\tVDJdelLen[VDJdel] = 0\n\treturn P_VDJdel, VDJdelLen\n\ndef get_P_insLen(VDJinsLenIdx):\n\tVDJinsLen = {}\n\tP_VDJins = {}\n\tfor VDJins in ('VD', 'DJ'):\n\t\tnick = VDJins.lower() + '_ins'\n\t\tnowLenIdx = VDJinsLenIdx[VDJins]\n\t\tP_VDJins[VDJins] = marginals[nick][nowLenIdx]\n\t\tVDJinsLen[VDJins] = int(parms_event[nick]['key_' + str(nowLenIdx)])\n\treturn P_VDJins, VDJinsLen\n\ndef get_P_insDinucl(VDJinsNt, initNt={}):\n\tP_VDJinsNt = {}\n\tfor VDJins in ('VD', 'DJ'):\n\t\tnick = VDJins.lower() + '_dinucl'\n\t\tntVec = VDJinsNt[VDJins]\n\t\tans = 1\n\t\tfor i in range(len(ntVec)):\n\t\t\tprevNt = None\n\t\t\tif i > 0:\n\t\t\t\tprevNt = ntVec[i-1]\n\t\t\telif VDJins in initNt:\n\t\t\t\tprevNt = initNt[VDJins]\n\t\t\tnextNt = ntVec[i]\n#\t\t\tprint('{} {}'.format(prevNt, nextNt))   # for debug\n\t\t\tif prevNt is None or prevNt == '':\n\t\t\t\tprint('Warning: no initNt, so I skip the first base transition (when calling get_P_insDinucl)', file=sys.stderr)\n\t\t\t\tcontinue   # skip the first nt if initNt == None (prevNt==None)\n\t\t\tif prevNt == '?' or nextNt == '?':\n\t\t\t\tprint('Warning: prevNt == \"?\" or nextNt == \"?\", so I skip this base transition (when calling get_P_insDinucl)', file=sys.stderr)\n\t\t\t\tcontinue   # skip the first nt if prevNt == \"?\" or nextNt == \"?\"\n\t\t\tprevNtCode = nt2intCode[prevNt]\n\t\t\tnextNtCode = nt2intCode[nextNt]\n\t\t\tans *= marginals[nick][prevNtCode, nextNtCode]\n\t\tP_VDJinsNt[VDJins] = ans\n\treturn P_VDJinsNt\n\ndef get_match_mismatch_str(query, subjt, caseSensitive=False):\n\tif not caseSensitive:\n\t\tquery = query.upper()\n\t\tsubjt = subjt.upper()\n\t\t\n\tfrom itertools import zip_longest\n\tans = []\n\tfor a, b in zip_longest(query, subjt):\n\t\tif a is None:\n\t\t\tbreak\n\t\tif b is None:\n\t\t\tans.append(a)\n\t\telif a==b:\n\t\t\tans.append('.')\n\t\telse:\n\t\t\tans.append(a)\n\treturn ''.join(ans)\n\t\ndef find_max_match_shift(query, subjt):\n\tmax_match = -1\n\tmax_match_shift = 0\n\tfor shift in range(0, -len(query), -1):\n\t\tnum_match = get_match_mismatch_str(query, ' '*(-shift) + subjt).count('.')\n#\t\tprint('{} {}'.format(shift, num_match))   # for debug\n\t\tif num_match > max_match:\n\t\t\tmax_match_shift = shift\n\t\t\tmax_match = num_match\n\tfor shift in range(1, len(subjt)):\n\t\tnum_match = get_match_mismatch_str(' '*(shift) + query, subjt).count('.')\n#\t\tprint('{} {}'.format(shift, num_match))   # for debug\n\t\tif num_match > max_match:\n\t\t\tmax_match_shift = shift\n\t\t\tmax_match = num_match\n\treturn max_match_shift\n\ndef continuous_LCS_DP(query, subjt, caseSensitive=False):\n\tif not caseSensitive:\n\t\tquery = query.upper()\n\t\tsubjt = subjt.upper()\n\t\t\n\tlen1 = len(query)\n\tlen2 = len(subjt)\n\tmax_LCS_len = 0\n\tmax_len_i, max_len_j = -1, -1\n\tDPmat = np.zeros((len1+1, len2+1), dtype='int')\n\tfor i in range(1, len1+1):\n\t\tfor j in range(1, len2+1):\n\t\t\tif query[i-1] == subjt[j-1]:\n\t\t\t\tDPmat[i,j] = DPmat[i-1,j-1] + 1\n\t\t\t\tif DPmat[i,j] > max_LCS_len:\n\t\t\t\t\tmax_LCS_len = DPmat[i,j]\n\t\t\t\t\tmax_len_i, max_len_j = i, j\n\t\n\treturn max_LCS_len, max_len_i, max_len_j\n\t# max_match_shift = j - i\n\ndef generate_scenario_alignment_str(input_read_seq, VDJidx, VDJdelLen, VDJinsNt, VDJinsLen={}, output_prefix=''):\n\tVDJseq = {}\n\tfor VDJ in ('V', 'D', 'J'):\n\t\tVDJseq[VDJ] = VDJ_alleles[VDJ]['key_' + str(VDJidx[VDJ])]\n\tmyVDJinsLen = {}\n\tfor VDJins in ('VD', 'DJ'):\n\t\tif VDJins in VDJinsLen:\n\t\t\tif VDJinsLen[VDJins] != len(VDJinsNt[VDJins]):\n\t\t\t\tprint('Warning: len(VDJinsNt[\"{}\"]={} != VDJinsLen={}'.format(VDJins, len(VDJinsNt[VDJins]), VDJinsLen[VDJins]), file=sys.stderr)\n\t\tmyVDJinsLen[VDJins] = len(VDJinsNt[VDJins])\n\t\n\t# del\n#\tVseq = str_trim_right(VDJseq['V'], VDJdelLen['V3'])\n#\tDseq = str_trim_right(str_trim_left(VDJseq['D'], VDJdelLen['D5']), VDJdelLen['D3'])\n#\tJseq = str_trim_left(VDJseq['J'], VDJdelLen['J5'])\n\tif VDJdelLen['V3'] >= 0:\n\t\tVseq = str_trim_right(VDJseq['V'], VDJdelLen['V3'])\n\telse:\n\t\tVseq = VDJseq['V'] + '?' * - VDJdelLen['V3']\n\tif VDJdelLen['D5'] >= 0:\n\t\tDseq = str_trim_left(VDJseq['D'], VDJdelLen['D5'])\n\telse:\n\t\tDseq = '?' * - VDJdelLen['D5'] + VDJseq['D']\n\tif VDJdelLen['D3'] >= 0:\n\t\tDseq = str_trim_right(Dseq, VDJdelLen['D3'])\n\telse:\n\t\tDseq = Dseq + '?' * - VDJdelLen['D3'] \n\tif VDJdelLen['J5'] >= 0:\n\t\tJseq = str_trim_left(VDJseq['J'], VDJdelLen['J5'])\n\telse:\n\t\tJseq = '?' * - VDJdelLen['J5'] + VDJseq['J']\n\t## 2018-12-04 Note: I found that IGoR DJins dinuc is probably from J to D (3'->5' direction, + strand)\n\tVDJjoinSeq = Vseq.upper() + VDJinsNt['VD'].lower() + Dseq.upper() + ''.join(reversed(VDJinsNt['DJ'])).lower() + Jseq.upper()\n\tVDJseqStart = {'V' : 0}\n\tVDJseqStart['D'] = VDJseqStart['V'] + len(Vseq) + myVDJinsLen['VD'] - VDJdelLen['D5']\n\tVDJseqStart['J'] = VDJseqStart['V'] + len(Vseq) + myVDJinsLen['VD'] + len(Dseq) + myVDJinsLen['DJ'] - VDJdelLen['J5']\n\t\n#\tmyShift = find_max_match_shift(input_read_seq, VDJjoinSeq)\n\ttmp = continuous_LCS_DP(input_read_seq, VDJjoinSeq)\n\tmyShift = tmp[2] - tmp[1]\n\t\n\tans = output_prefix\n\tif myShift >= 0:\n\t\tans += ' ' * myShift + input_read_seq\n\t\tans += '\\n' + output_prefix + get_match_mismatch_str(' ' * myShift + input_read_seq, VDJjoinSeq)\n\telse:\n\t\tans += input_read_seq\n\t\tans += '\\n' + output_prefix + get_match_mismatch_str(input_read_seq, ' ' * (-myShift) + VDJjoinSeq)\n\t\toutput_prefix += ' ' * (-myShift)\n\tans += '\\n' + output_prefix + VDJjoinSeq\n\tans += '\\n' + output_prefix + ' '*VDJseqStart['V'] + Vseq.upper() + str_right(VDJseq['V'], VDJdelLen['V3']).lower()\n\tans += '\\n' + output_prefix + ' '*VDJseqStart['D'] + str_left(VDJseq['D'], VDJdelLen['D5']).lower() + Dseq.upper() + str_right(VDJseq['D'], VDJdelLen['D3']).lower()\n\tans += '\\n' + output_prefix + ' '*VDJseqStart['J'] + str_left(VDJseq['J'], VDJdelLen['J5']).lower() + Jseq.upper()\n\treturn ans, VDJjoinSeq, Vseq, Dseq, Jseq\n\t\n\t\ndef read_bestScenarios(bestScenarios_filename):\n\tcontents = {}\n\tinput_idx_order = []\n\t# contents[idx][rank] = F(list of the line)\n\n\tis_headline = True\n\tfields = []\n\tfield2idx = {}\n\tVDJchoice_colidx = {'V':-1, 'D':-1, 'J':-1}\n\tVDJdel_colidx = {'V':-1, 'D':-1, 'J':-1}\n\tVDJins_colidx = {'V':-1, 'D':-1, 'J':-1}\n\tVDJinsNt_colidx = {'V':-1, 'D':-1, 'J':-1}\n\tseq_idx_colidx, scenario_rank_colidx, mismatch_colidx = -1, -1, -1\n\twith open(bestScenarios_filename, 'r') as fin:\n\t\tfor line in fin:\n\t\t\tline = line.rstrip()\n\t\t\tF = line.split(';')\n\t\t\tif is_headline:\n\t\t\t\tis_headline = False\n\t\t\t\tfields = F\n\t\t\t\tfield2idx = fields_to_field2idx(F)\n\t\t\t\t\n\t\t\t\tfor VDJ in ('V','D','J'):\n\t\t\t\t\tVDJchoice_colidx[VDJ] = fields_prefix_match_idx('GeneChoice_'+VDJ+'_gene_', fields)\n\t\t\t\t\tif len(VDJchoice_colidx[VDJ]) <= 0:\n\t\t\t\t\t\tprint('Error: cannot find GeneChoice_{}_gene_ column in bestScenarios input file {}'.format(VDJ, bestScenarios_filename), file=sys.stderr)\n\t\t\t\t\t\treturn None\n\t\t\t\t\tif len(VDJchoice_colidx[VDJ]) > 1:\n\t\t\t\t\t\tprint('Warning: multiple GeneChoice_{}_gene_ columns in bestScenarios input file {}. I just use the left-most column'.format(VDJ, bestScenarios_filename), file=sys.stderr)\n\t\t\t\t\tVDJchoice_colidx[VDJ] = VDJchoice_colidx[VDJ][0]\n\t\t\t\tfor VDJdel in ('V3', 'D5', 'D3', 'J5'):\n\t\t\t\t\tVDJdel_colidx[VDJdel] = fields_prefix_match_idx(VDJdel2fullPrefix[VDJdel], fields)\n\t\t\t\t\tif len(VDJdel_colidx[VDJdel]) <= 0:\n\t\t\t\t\t\tprint('Error: cannot find ' + VDJdel2fullPrefix[VDJdel] + ' column in bestScenarios input file {}'.format(VDJ, bestScenarios_filename), file=sys.stderr)\n\t\t\t\t\t\treturn None\n\t\t\t\t\tif len(VDJdel_colidx[VDJdel]) > 1:\n\t\t\t\t\t\tprint('Warning: multiple ' + VDJdel2fullPrefix[VDJdel] + ' columns in bestScenarios input file {}. I just use the left-most column'.format(VDJ, bestScenarios_filename), file=sys.stderr)\n\t\t\t\t\tVDJdel_colidx[VDJdel] = VDJdel_colidx[VDJdel][0]\n\t\t\t\tfor VDJins in ('VD', 'DJ'):\n\t\t\t\t\tVDJins_colidx[VDJins] = fields_prefix_match_idx('Insertion_'+VDJins+'_gene', fields)\n\t\t\t\t\tif len(VDJins_colidx[VDJins]) <= 0:\n\t\t\t\t\t\tprint('Error: cannot find Insertion_{}_gene column in bestScenarios input file {}'.format(VDJins, bestScenarios_filename), file=sys.stderr)\n\t\t\t\t\t\treturn None\n\t\t\t\t\tif len(VDJins_colidx[VDJins]) > 1:\n\t\t\t\t\t\tprint('Warning: multiple Insertion_{}_gene columns in bestScenarios input file {}. I just use the left-most column'.format(VDJins, bestScenarios_filename), file=sys.stderr)\n\t\t\t\t\tVDJins_colidx[VDJins] = VDJins_colidx[VDJins][0]\n\t\t\t\t\n\t\t\t\t\tVDJinsNt_colidx[VDJins] = fields_prefix_match_idx('DinucMarkov_'+VDJins+'_gene', fields)\n\t\t\t\t\tif len(VDJinsNt_colidx[VDJins]) <= 0:\n\t\t\t\t\t\tprint('Error: cannot find DinucMarkov_{}_gene column in bestScenarios input file {}'.format(VDJins, bestScenarios_filename), file=sys.stderr)\n\t\t\t\t\t\treturn None\n\t\t\t\t\tif len(VDJinsNt_colidx[VDJins]) > 1:\n\t\t\t\t\t\tprint('Warning: multiple DinucMarkov_{}_gene columns in bestScenarios input file {}. I just use the left-most column'.format(VDJins, bestScenarios_filename), file=sys.stderr)\n\t\t\t\t\tVDJinsNt_colidx[VDJins] = VDJinsNt_colidx[VDJins][0]\n\t\t\t\t\n\t\t\t\tif 'seq_index' in field2idx:\n\t\t\t\t\tseq_idx_colidx = field2idx['seq_index']\n\t\t\t\telse:\n\t\t\t\t\tprint('Error: cannot find Mismatches column in bestScenarios input file {}'.format(bestScenarios_filename), file=sys.stderr)\n\t\t\t\t\treturn None\n\t\t\t\tif 'scenario_rank' in field2idx:\n\t\t\t\t\tscenario_rank_colidx = field2idx['scenario_rank']\n\t\t\t\telse:\n\t\t\t\t\tprint('Error: cannot find Mismatches column in bestScenarios input file {}'.format(bestScenarios_filename), file=sys.stderr)\n\t\t\t\t\treturn None\n\t\t\t\tif 'Mismatches' in field2idx:\n\t\t\t\t\tmismatch_colidx = field2idx['Mismatches']\n\t\t\t\telse:\n\t\t\t\t\tprint('Warning: cannot find Mismatches column in bestScenarios input file {}'.format(bestScenarios_filename), file=sys.stderr)\n\t\t\telse:\n#\t\t\t\tidx, rank = F[0:2]\n\t\t\t\tidx = F[seq_idx_colidx]\n\t\t\t\trank = F[scenario_rank_colidx]\n\t\t\t\tinput_read_seq = ''\n\t\t\t\tif 'key_' + idx in input_idx2seq:\n\t\t\t\t\tinput_read_seq = input_idx2seq['key_' + idx]\n\t\t\t\tVDJidx = {'V':-1, 'D':-1, 'J':-1}\n\t\t\t\tVDJname = {'V':'?','D':'?','J':'?'}\n\t\t\t\tVDJdelLenIdx = {'V3':-1, 'D5':-1, 'D3':-1, 'J5':-1}\n\t\t\t\tVDJinsLenIdx = {'VD':-1, 'DJ':-1}\n\t\t\t\tVDJinsNt = {'VD':'?', 'DJ':'?'}\n\t\t\t\tfor VDJ in ('V','D','J'):\n\t\t\t\t\tVDJidx[VDJ] = F[VDJchoice_colidx[VDJ]]\n\t\t\t\t\tVDJidx[VDJ] = VDJidx[VDJ][1:(len(VDJidx[VDJ])-1)]   # remove outer ()\n\t\t\t\t\tif 'key_' + VDJidx[VDJ] in parms_event[VDJ2nick[VDJ]]:\n\t\t\t\t\t\tVDJname[VDJ] = parms_event[VDJ2nick[VDJ]]['key_' + VDJidx[VDJ]]\n\t\t\t\t\tVDJidx[VDJ] = int(VDJidx[VDJ])\n\t\t\t\tfor VDJdel in ('V3', 'D5', 'D3', 'J5'):\n\t\t\t\t\tVDJdelLenIdx[VDJdel] = F[VDJdel_colidx[VDJdel]]\n\t\t\t\t\tVDJdelLenIdx[VDJdel] = int(VDJdelLenIdx[VDJdel][1:(len(VDJdelLenIdx[VDJdel])-1)])   # remove outer ()\n\t\t\t\tfor VDJins in ('VD', 'DJ'):\n\t\t\t\t\tVDJinsLenIdx[VDJins] = F[VDJins_colidx[VDJins]]\n\t\t\t\t\tVDJinsLenIdx[VDJins] = int(VDJinsLenIdx[VDJins][1:(len(VDJinsLenIdx[VDJins])-1)])   # remove outer ()\n\t\t\t\t\tVDJinsNt[VDJins] = F[VDJinsNt_colidx[VDJins]]\n\t\t\t\t\tVDJinsNt[VDJins] = VDJinsNt[VDJins][1:(len(VDJinsNt[VDJins])-1)]   # remove outer ()\n#\t\t\t\t\tprint(VDJins)   # for debug\n#\t\t\t\t\tprint('\"' + VDJinsNt[VDJins] + '\"')\n#\t\t\t\t\tprint(F)   # for debug\n\t\t\t\t\tif len(VDJinsNt[VDJins]) > 0:\n\t\t\t\t\t\tVDJinsNt[VDJins] = ''.join([ getNtFromNtCode(x) for x in VDJinsNt[VDJins].split(',') ])\n\t\t\t\t\telse:\n\t\t\t\t\t\tVDJinsNt[VDJins] = ''\n\t\t\t\tmismatches = []\n\t\t\t\tif mismatch_colidx >= 0:\n\t\t\t\t\tmismatches = F[mismatch_colidx]\n\t\t\t\t\tmismatches = mismatches[1:(len(mismatches)-1)]   # remove outer ()\n\t\t\t\t\tmismatches = mismatches.split(',')\n\t\t\t\tif len(mismatches) == 1 and mismatches[0] == '':\n\t\t\t\t\tmismatches = []\n\t\t\t\t\t\n####            add 3+4+1(+1)=8(9) columns\n####              3 V_name,D_name,J_name\n\t\t\t\tF.append(VDJname['V'])\n\t\t\t\tF.append(VDJname['D'])\n\t\t\t\tF.append(VDJname['J'])\n####              4 P(V),P(J|V),P(D|V,J),P(V,D,J)\n#\t\t\t\tprint(VDJidx)   # for debug\n#\t\t\t\tprint(get_P_VDJchoice(VDJidx['V'], VDJidx['D'], VDJidx['J']))\n\t\t\t\tP_VDJchoice = get_P_VDJchoice(VDJidx['V'], VDJidx['D'], VDJidx['J'])\n\t\t\t\tF.extend(list(map(str, P_VDJchoice)))\n\t\t\t\tP_scenario = P_VDJchoice[3]\n####              2*4=8 V3_delLen, P(V3_delLen|V), D5_delLen, P(.|D), D3_delLen, P(,|D, D5_delLen), J5_delLen, P(.|J)\n\t\t\t\tP_VDJdel, VDJdelLen = get_P_delLen(VDJidx, VDJdelLenIdx)\n#\t\t\t\tprint(VDJidx)   # for debug\n#\t\t\t\tprint(VDJdelLenIdx)   # for debug\n#\t\t\t\tprint(VDJdelLen)   # for debug\n#\t\t\t\tprint(P_VDJdel)   # for debug\n\t\t\t\t\n\t\t\t\tfor VDJdel in ('V3', 'D5', 'D3', 'J5'):\n\t\t\t\t\tF.extend(list(map(str, (VDJdelLen[VDJdel], P_VDJdel[VDJdel]))))\n\t\t\t\t\tP_scenario *= P_VDJdel[VDJdel]\n####              4*2=8 VD_insLen, P(VD_insLen), VD_insNt, P(VD_insNt), DJ_...\n\t\t\t\tP_VDJins, VDJinsLen = get_P_insLen(VDJinsLenIdx)\n\t\t\t\talignStr, VDJjoinSeq, Vseq, Dseq, Jseq = generate_scenario_alignment_str(input_read_seq, VDJidx, VDJdelLen, VDJinsNt, VDJinsLen)\n\t\t\t\tinitNt = {'VD':str_right(Vseq,1), 'DJ':str_right(Dseq,1)}\n\t\t\t\tif initNt['DJ'] == '':\n\t\t\t\t\tinitNt['DJ'] = str_right(VDJinsNt['VD'], 1)\n\t\t\t\tP_VDJinsNt = get_P_insDinucl(VDJinsNt, initNt)\n\t\t\t\tfor VDJins in ('VD', 'DJ'):\n\t\t\t\t\tF.extend(list(map(str, (VDJinsLen[VDJins], P_VDJins[VDJins], VDJinsNt[VDJins], P_VDJinsNt[VDJins]))))\n\t\t\t\t\tP_scenario *= P_VDJins[VDJins] * P_VDJins[VDJins] * P_VDJinsNt[VDJins]\n####              1 P_mismatch = error_rate ^ num_mismatch\n\t\t\t\tP_mismatch = (error_rate/3) ** len(mismatches) * (1-error_rate) ** len(input_read_seq)\n#\t\t\t\tprint('{} {} {}'.format(mismatches, len(mismatches), P_mismatch))   # for debug\n\t\t\t\tP_recomb = P_scenario\n\t\t\t\tP_scenario *= P_mismatch\n\t\t\t\tF.append(str(P_recomb))\n\t\t\t\tF.append(str(P_mismatch))\n####              1 P_scenario = P(V,D,J)*P(V3_delLen|V)*...*P(VD_insLen)*P(VD_InsNt)*...\n\t\t\t\tF.append(str(P_scenario))\n#\t\t\t\tfor i, x in enumerate(F):   # for debug\n#\t\t\t\t\tprint('{} {}'.format(i, x))\n#\t\t\t\tbreak\n\t\t\t\t\n\t\t\t\tF.append(alignStr)   # push, wait for pop\n\t\t\t\t\n####              1 P(V,D,J)_weighted_mean (weighted by scenario_proba_cond_seq)\n####              1 P_scenario_weighted_mean (weighted by scenario_proba_cond_seq)\n####              1 Pgen (if [Pgen_counts.tsv] is provided)\n\n\t\t\t\tif idx not in contents:\n\t\t\t\t\tcontents[idx] = {}\n\t\t\t\t\tinput_idx_order.append(idx)\n\t\t\t\tcontents[idx][rank] = F\n\n\toutput_fields = fields\n\toutput_fields.extend(['V_name', 'D_name', 'J_name', 'P_V', 'P_J_given_V', 'P_D_given_V_J', 'P_VDJ'])\n\tfor VDJdel in ('V3', 'D5', 'D3', 'J5'):\n\t\toutput_fields.extend([VDJdel + '_delLen', 'P_' + VDJdel + '_delLen_given_' + VDJdel[0]])\n\t\tif VDJdel == 'D3':\n\t\t\toutput_fields[len(output_fields)-1] = 'P_D3_delLen_given_D_and_D5_delLen'\n\tfor VDJins in ('VD', 'DJ'):\n\t\toutput_fields.extend([VDJins + '_insLen', 'P_' + VDJins + '_insLen', VDJins + 'insNt', 'P_' + VDJins + 'insNt'])\n\toutput_fields.append('P_recomb')\n\toutput_fields.append('P_mismatch')\n\toutput_fields.append('P_scenario')\n#\tfor i, x in enumerate(output_fields):   # for debug\n#\t\tprint('{} {}'.format(i, x))\n\treturn {'fields':output_fields, 'contents':contents, 'input_idx_order':input_idx_order}\n\n\n\n\nprint('Now read two model files ...', file=sys.stderr)\nparms_event, VDJ_alleles, error_rate = read_parms_Event_list(parms_filename)\nmarginals = read_marginals(marginals_filename)\ninput_idx2seq = read_idx_seq(idx_seq_filename)\n\nif Pgen_filename is not None:\n\tprint('Now read Pgen file ...', file=sys.stderr)\n\tPgen_hash = read_Pgen(Pgen_filename)\n\nprint('Now read best_scenariors file ...', file=sys.stderr)\nbestScenario_hash = read_bestScenarios(bestScenarios_filename)\n\n####              1(39th) P_recomb = P(V,D,J)*P(V3_delLen|V)*...*P(VD_insLen)*P(VD_InsNt)*...\n####              1(40th) P_mismatch = error_rate ^ num_mismatch\n####              1(41th) P_scenario = P_recomb * P_mismatch\n####              1(42th) P_posterior = P_scenario / P_read\n####              1(43th) P_read = sum P_scenario\n####              1(44th) P_gen  = sum P_recomb\n####              1(45th) Pgen (if [Pgen_counts.csv] is provided)\noutput_fields = bestScenario_hash['fields']\nfield2idx = fields_to_field2idx(output_fields)\noutput_fields.append('P_posterior')\noutput_fields.append('P_read')\noutput_fields.append('P_gen')\nif Pgen_filename is not None:\n\toutput_fields.append('Pgen_estimate')\nprint(';'.join(output_fields))\n## sort, 2-pass for weighted mean, and output\nif should_sort_idx_output:\n\tidxes = sorted(bestScenario_hash['contents'].keys(), key=int)\nelse:\n\tidxes = bestScenario_hash['input_idx_order']\nfor idx in idxes:\n\tthis_seqIdx_Fs = bestScenario_hash['contents'][idx].values()\n#\tprint(this_seqIdx_Fs)   # for debug\n\tnum_sce = len(this_seqIdx_Fs)\n#\tsce_probs = [ F[field2idx['scenario_proba_cond_seq']]  for F in this_seqIdx_Fs ]\n#\tsce_probs = list(map(float, sce_probs))\n#\tsum_sce_prob = sum(sce_probs)\n\t\n\tP_scenarios = [ F[field2idx['P_scenario']]  for F in this_seqIdx_Fs ]\n\tP_scenarios = list(map(float, P_scenarios))\n\tP_read = sum(P_scenarios)\n\tP_recombs = [ F[field2idx['P_recomb']]  for F in this_seqIdx_Fs ]\n\tP_recombs = list(map(float, P_recombs))\n#\tP_posteriors = [x/P_read for x in P_scenarios]\n\tP_gen = sum(P_recombs)\n\t\n\tfor rank in sorted(bestScenario_hash['contents'][idx].keys(), key=int):\n\t\tF = bestScenario_hash['contents'][idx][rank]\n\t\talignStr = F[len(F)-1]\n\t\tF = F[0:(len(F)-1)]   # pop\n\t\t\n\t\tF.append(str(float(F[field2idx['P_scenario']]) / P_read))   # P_posterior\n\t\tF.append(str(P_read))\n\t\tF.append(str(P_gen))\n\t\tif Pgen_filename is not None:\n\t\t\tif idx in Pgen_hash:\n\t\t\t\tF.append(Pgen_hash[idx])\n\t\t\telse:\n\t\t\t\tF.append('-1')\n#\t\tprint(F)   # for debug\n\t\tprint(';'.join(F))\n\t\tprint(alignStr)\n","repo_name":"Yyx2626/HTGTSrep","sub_path":"IGoR/scripts/yyx_annotate_bestScenarios.20181206.py","file_name":"yyx_annotate_bestScenarios.20181206.py","file_ext":"py","file_size_in_byte":25745,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"31751441453","text":"import pytest\n\nimport ray\nfrom ray.tests.conftest import *  # noqa\n\nNUM_REPEATS = 10\nNUM_TASKS = 10\n\n\n# This test can be flaky if there is resource deadlock between the pipeline\n# stages. Run it a lot to ensure no regressions.\ndef test_basic_actors(shutdown_only):\n    ray.init(num_cpus=2)\n    for _ in range(NUM_REPEATS):\n        ds = ray.data.range(NUM_TASKS)\n        ds = ds.window(blocks_per_window=1)\n        assert sorted(ds.map(lambda x: x + 1, compute=\"actors\").take()) == list(\n            range(1, NUM_TASKS + 1)\n        )\n\n\nif __name__ == \"__main__\":\n    import sys\n\n    sys.exit(pytest.main([\"-v\", __file__]))\n","repo_name":"merlinepedra/RAY-1","sub_path":"python/ray/data/tests/test_pipeline_nohang.py","file_name":"test_pipeline_nohang.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"34831147188","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport glob\nimport re\nimport subprocess\nimport os\nimport argparse\nimport csc\n\ndef DefineArguments():\n    \"\"\"Argument parser.\"\"\"\n    parser = argparse.ArgumentParser(\n        description='Determine which CSC to generate State Machine tests.')\n    parser.add_argument(\n        '-c',\n        '--csc',\n        metavar='CSC',\n        dest='csc',\n        type = str.lower,\n        required=True,\n        choices=str(csc.csc_array)[1:-1],\n        help='''For which CSC do you want to generate tests? ''')\n    args = parser.parse_args()\n    return args\n\ndef CreateLocalVars(csc):\n\t# Create/Open GlobalVars file.\n\tprint(\"Creating \" + csc + \"_LocalVars.robot\")\n\ttry:\n\t\tos.chdir(\"robotframework_\" + csc)\n\texcept FileNotFoundError:\n\t\tos.makedirs(\"robotframework_\" + csc, exist_ok=False)\n\t\tos.chdir(\"robotframework_\" + csc)\n\tfinally:\n\t\tf = open(csc + \"_LocalVars.txt\",\"w\")\n\n\t# Create the header.\n\tf.write(\"# vi:syntax=cmake\\n\")\n\tf.write(\"#  Arguments file for testing the \" + csc + \"\\n\")\n\tf.write(\"\\n\")\n\n\t# Define location test reports\n\tf.write(\"#  Output directory\\n\")\n\tf.write(\"-d \" + os.environ['HOME'] + \"/Reports/\" + csc + \"_RegressionTests\\n\")\n\tf.write(\"\\n\")\n\n\t# Define tests to skip\n\tf.write(\"#  Specify tags to NOT run\\n\")\n\tf.write(\"-e skipped\\n\")\n\tf.write(\"#-e TSS*\\n\")\n\tf.write(\"\\n\")\n\n\t# Define non-critical tags\n\tf.write(\"# Specify non-critical failures\\n\")\n\tf.write(\"--noncritical TSS*\\n\")\n\tf.write(\"\\n\")\n\n\t# Set dry run mode\n\tf.write(\"# Dry run mode\\n\")\n\tf.write(\"#--dryrun\\n\")\n\tf.write(\"\\n\")\n\n\t# User informatin\n\tf.write(\"#  Redefine default variables\\n\")\n\tf.write(\"--variable UserName:FILLMEIN\\n\")\n\tf.write(\"--variable PassWord:FILLMEIN\\n\")\n\tf.write(\"--variable OpenspliceVersion:6.7.170523OSS\\n\")\n\tf.write(\"--variable OpenspliceDate:2017-07-31\\n\")\n\tf.write(\"--variable SALVersion:3.7.0\\n\")\n\tf.write(\"\\n\")\n\n\t# Return to calling directory.\n\tos.chdir(\"../\")\n\nif __name__ == '__main__':\n\t# Get the arguments.\n\targs = DefineArguments()\n\n\tCreateLocalVars(args.csc)\n","repo_name":"lsst-ts/robotframework_template","sub_path":"Vars.py","file_name":"Vars.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"13192520937","text":"import BaseHTTPServer\nimport handlerutils\nimport rssfeed\nimport datetime\nimport iso8601\nimport textwrap\nfrom utctz import UTCTZ\nimport urlparse\nimport htmlutils\n\n_rss_mime_type = 'text/xml'\n_html_mime_type = 'text/html'\n_plain_mime_type = 'text/plain'\n_allowed_fetch_domains = set(('facebook.com','www.facebook.com'))\n_allowed_fetch_schemes = set(('http','https'))\n_allowed_fetch_ports = set((80,443,None))\n\nclass GateHandler(BaseHTTPServer.BaseHTTPRequestHandler,\n    handlerutils.HandlerUtilsMixIn):\n    def __init__(self, request, client_address, server, env):\n        self.env = env\n        BaseHTTPServer.BaseHTTPRequestHandler.__init__(self,\n            request, client_address, server)\n\n    def r_hello(self):\n        \"\"\" Test route \"\"\"\n        self.finalize('Hello World!\\n',\n            headers = (('Content-Type', _plain_mime_type),))\n\n    def r_feed(self):\n        if self.command == 'HEAD':\n            self.finalize(headers = (('Content-Type', _rss_mime_type),))\n            return\n\n        args = self.get_args()\n        if 'id' not in args:\n            self.send_error(400, 'Missing required query parameter: id')\n            return\n\n        try:\n            ID = int(args['id'])\n        except:\n            self.send_error(400, 'Unable to parse integer parameter: id')\n            return\n\n        try:\n            feedobj = self.env.graphapi.get_feed(ID, limit=100)\n        except:\n            self.send_error(500, 'Unable to fetch feed')\n            return\n            \n        try:\n            groupobj = self.env.graphapi.get_group(ID)\n            groupname = groupobj['name']\n        except:\n            groupname = 'Facebook feed %d' % (ID,)\n            \n        feed = rssfeed.RSSFeed(\n            'https://facebook.com/%s' % (ID,),\n            groupname,\n            groupname,\n            datetime.datetime.now(UTCTZ())\n        )\n        \n        for post in feedobj['data']:\n            posturl = 'https://facebook.com/' + post['id']\n            feed.append_item(posturl,\n                textwrap.wrap(post['message'])[0] + '...' if 'message' in post else post.get('story') or post['id'],\n                post['message'] if 'message' in post else post.get('story', '') + ':' + posturl,\n                iso8601.parse_date(post['updated_time']),\n                post['id']\n            )\n\n        self.send_response(200)\n        self.send_header('Content-Type', _rss_mime_type)\n        self.send_header('Connection', 'close')\n        self.end_headers()\n        feed.marshal(self.wfile)\n\n    def r_icon(self):\n        self.serve_static('favicon.ico')\n\n    def r_resolver(self):\n        if self.command == 'HEAD':\n            self.finalize(headers = (('Content-Type', _plain_mime_type),))\n            return\n\n        args = self.get_args()\n        if 'url' not in args:\n            self.send_error(400, 'Missing required query parameter: url')\n            return\n\n        url = args['url']\n        uc = urlparse.urlparse(url, 'http')\n\n        if uc.username is not None or uc.password is not None:\n            self.send_error(400, 'HTTP Basic auth not allowed')\n            return\n\n        if uc.port not in _allowed_fetch_ports:\n            self.send_error(400, 'Port not allowed')\n            return\n\n        if uc.scheme not in _allowed_fetch_schemes:\n            self.send_error(400, 'URL scheme not allowed')\n            return\n\n        if uc.hostname not in _allowed_fetch_domains:\n            self.send_error(400, 'URL domain not allowed')\n            return\n\n        try:\n            text = self.env.fbuser.fetch_url(url)\n        except:\n            self.send_error(500, 'Unable to fetch URL')\n            return\n\n        try:\n            te = htmlutils.TagExtractor(['html','head','meta'], [('property', 'al:android:url')])\n            te.feed(text)\n        except:\n            self.send_error(500, 'Unable to parse document body')\n            return\n\n        if te.found is None:\n            self.send_error(500, 'Unable to parse document body')\n            return\n\n        ta = dict(te.found)\n        if 'content' not in ta:\n            self.send_error(500, 'Unable to parse document body')\n            return\n\n        ID = ''.join([c for c in ta['content'][::-1] if c.isdigit()][::-1])\n        self.finalize(ID)\n\n    def r_index(self):\n        self.serve_static('index.html')\n\n    routes = {\n        '/': r_index,\n        '/index.html': r_index,\n        '/index.htm': r_index,\n        '/rss/v1.0/hello': r_hello,\n        '/rss/v1.0/feed': r_feed,\n        '/resolve/v1.0/page_id': r_resolver,\n        '/favicon.ico': r_icon\n    }\n\n    def do_GET(self):\n        self.route(self.routes)\n","repo_name":"Snawoot/fbfeed2rss","sub_path":"gatehandler.py","file_name":"gatehandler.py","file_ext":"py","file_size_in_byte":4616,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"11068122972","text":"# https://leetcode.com/problems/number-of-provinces/\n\n# CodeHelp\n\nclass Solution:\n    def dfs(self, vis, src, isConnected):\n        vis[src] = True\n        # row -> src\n        # col -> we will write a loop\n        size = len(isConnected[src])\n        for col in range(size):\n            if src != col and isConnected[src][col] == 1:\n                # col is a nbr\n                if col not in vis or not vis[col]:  # map\n                # if not vis[col]:                    #list but In cpp same used in map\n                    self.dfs(vis, col, isConnected)\n\n    def findCircleNum(self, isConnected: List[List[int]]) -> int:\n\n        # <int, bool> map\n        visited = {}\n\n        # n = len(isConnected)\n        # visited = [0] * n\n\n        count = 0\n        n = len(isConnected)\n        # i represents nodes here\n        for i in range(n):\n            if i not in visited:   # map\n            # if not visited[i]:       # list but In cpp same used in map\n                self.dfs(visited, i, isConnected)\n                count += 1\n        return count\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n        # # Devsnest\n        # n = len(isConnected)\n        # # Create a visited list to keep track of which cities have been visited\n        # visited = [0] * n\n        # # Initialize the number of provinces to 0\n        # num_provinces = 0\n\n        # for i in range(n):\n        #     # If the city has not been visited yet\n        #     if visited[i] == 0:\n        #         # Use BFS to traverse all the cities that are directly or indirectly connected to it\n        #         queue = deque([i])\n        #         visited[i] = 1\n        #         while queue:\n        #             city = queue.popleft()\n        #             for j in range(n):\n        #                 if isConnected[city][j] == 1 and visited[j] == 0:\n        #                     queue.append(j)\n        #                     visited[j] = 1\n        #         # Increase the number of provinces by 1\n        #         num_provinces += 1\n        # return num_provinces","repo_name":"anshawasthi01/Supreme-DSA","sub_path":"20. Graph - II/1. Graph/1. Number of Provinces[L - 547].py","file_name":"1. Number of Provinces[L - 547].py","file_ext":"py","file_size_in_byte":2028,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"15124992606","text":"from flask import Flask, render_template, request,jsonify\nimport numpy as np\nfrom keras.models import load_model\nfrom keras.preprocessing.image import load_img, img_to_array\nfrom keras.applications.vgg16 import preprocess_input\nfrom flask_cors import CORS\n\napp = Flask(__name__)\nCORS(app)\nmodel_path = \"E:\\pymodel\\model\\modelMLtest.h5\"\nmodel = load_model(model_path)\n\n@app.route('/', methods=['GET'])\ndef index():\n    return render_template('testml.html')\n\n@app.route('/predict', methods=['POST'])\ndef predict():\n    imagefile = request.files['imagefile']\n    image_path = \"./images/\" + imagefile.filename\n    imagefile.save(image_path)\n\n    # โหลดรูปภาพและปรับขนาด\n    image = load_img(image_path, target_size=(256, 256))\n    image = img_to_array(image)\n    image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))\n    image = preprocess_input(image)\n\n    # ทำนายผลลัพธ์ด้วยโมเดล\n    yhat = model.predict(image)\n    class_index = np.argmax(yhat)\n    \n    # รายชื่อคลาส\n    label_names = ['Astrophytum', 'Mammillaria', 'Melocactus', 'cactus']\n    classification = label_names[class_index]\n\n    return render_template('testml.html', prediction=classification)\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', port=5000, debug=True)","repo_name":"KheperX/ml_cactus","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"36319212149","text":"#! usr/bin/env Python3\ndef synchronize(l1, l2):\n    mapping = dict(zip(sorted(l1), sorted(l2)))\n    return [mapping[x] for x in l1]\n\nwhile True:\n    n = int(input())\n    if n != 0:\n        listOne, listTwo = [], []\n        for i in range(n): listOne.append(int(input()))\n        for i in range(n): listTwo.append(int(input()))\n        ans = (synchronize(listOne, listTwo))\n        for i in range(len(ans)):\n            print(ans[i])\n    else: break\n","repo_name":"FarOutWest/Kattis","sub_path":"synclists.py","file_name":"synclists.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"37223458339","text":"from core import FreqOracleClient\n\nimport numpy as np\nimport math\nimport random\n\nclass MultiPCMSMeanClient(FreqOracleClient):\n    def __init__(self, epsilon, hash_funcs, m, is_hadamard=False, index_mapper=None):\n        super().__init__(epsilon, None, index_mapper)\n        self.sketch_based = True\n        self.is_hadamard = is_hadamard\n        self.update_params(hash_funcs, m, epsilon)\n\n    def update_params(self, hash_funcs=None, m=None, epsilon=None, index_mapper=None):\n        if hash_funcs is not None:\n            self.hash_funcs = hash_funcs\n            self.k = len(self.hash_funcs)\n\n        self.epsilon = epsilon if epsilon is not None else self.epsilon\n        self.m = m if m is not None else self.m\n\n        if epsilon is not None:\n            if self.is_hadamard:\n                self.prob = 1 / (1 + math.pow(math.e, self.epsilon))\n            else:\n                self.prob = 1 / (1 + math.pow(math.e, self.epsilon / self.m))\n\n    def _one_hot(self, data):\n        j = random.randint(0, self.k-1)\n        h_j = self.hash_funcs[j]\n        v = [0] * self.m if self.is_hadamard else np.full(self.m, -1)\n        for i in range(len(data)):\n            v[h_j(str(data[i]))] = 1\n        return v, j\n\n    def _perturb(self, data):\n        v, j = self._one_hot(data)  # modify the encode, and self.prob\n        np.random.seed()\n        r = np.random.rand(*v.shape)\n        v[r < self.prob] *= -1 # \"flip\" bits with prob\n        return v, j\n\n    def privatise(self, data):\n        return self._perturb(data)","repo_name":"Lyinger/PrivSketch","sub_path":"frequency_oracles/pcms/multi_pcms_mean_client.py","file_name":"multi_pcms_mean_client.py","file_ext":"py","file_size_in_byte":1518,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"27293307258","text":"import datetime\nimport os\nimport sys\nimport time\nimport warnings\n\nsys.path.append('../utils')\n# import presets\nimport torch\nimport torch.utils.data\nimport torchvision\nimport torchvision.transforms\nimport transforms\nimport utils\nfrom torch import nn\nfrom torch.utils.data.dataloader import default_collate\nfrom torchvision.transforms.functional import InterpolationMode\n\n@torch.no_grad()\ndef get_all_preds(model, loader):\n    all_preds = torch.tensor([])\n    for batch in loader:\n        images, labels = batch\n\n        preds = model(images)\n        all_preds = torch.cat((all_preds, preds) ,dim=0)\n\n    return all_preds    \n\n@torch.no_grad()\ndef get_acc(test_loader, model, device):    \n    correct = 0\n    total = 0\n    model.eval()\n    \n    # with torch.no_grad():\n    for data in test_loader:\n        images, targets = data\n        images, targets = images.to(device), targets.to(device)\n        outputs = model(images)\n        _, predicted = torch.max(outputs.data, 1)\n        total += targets.size(0)\n        correct += (predicted == targets).sum().item()\n\n    return 100 * correct / total\n\ndef train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args, model_ema=None, scaler=None):\n    model.train()\n\n    header = f\"Epoch: [{epoch}]\"\n    for i, (image, target) in enumerate((data_loader)):\n        image, target = image.to(device), target.to(device)\n        with torch.cuda.amp.autocast(enabled=scaler is not None):\n            output = model(image)\n            loss = criterion(output, target)\n\n        optimizer.zero_grad()\n        if scaler is not None:\n            scaler.scale(loss).backward()\n            if args.clip_grad_norm is not None:\n                # we should unscale the gradients of optimizer's assigned params if do gradient clipping\n                scaler.unscale_(optimizer)\n                nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad_norm)\n            scaler.step(optimizer)\n            scaler.update()\n        else:\n            loss.backward()\n            if args.clip_grad_norm is not None:\n                nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad_norm)\n            optimizer.step()\n\n        if model_ema and i % args.model_ema_steps == 0:\n            model_ema.update_parameters(model)\n            if epoch < args.lr_warmup_epochs:\n                # Reset ema buffer to keep copying weights during warmup period\n                model_ema.n_averaged.fill_(0)\n                \n    # acc1 = get_acc(test_loader, model, device)\n    \n    \ndef get_args_parser(add_help=True):\n    import argparse\n\n    parser = argparse.ArgumentParser(description=\"PyTorch Classification Training\", add_help=add_help)\n\n    parser.add_argument(\"--data-path\", default=\"/datasets01/imagenet_full_size/061417/\", type=str, help=\"dataset path\")\n    parser.add_argument(\"--model\", default=\"resnet18\", type=str, help=\"model name\")\n    parser.add_argument(\"--device\", default=\"cuda\", type=str, help=\"device (Use cuda or cpu Default: cuda)\")\n    parser.add_argument(\n        \"-b\", \"--batch-size\", default=32, type=int, help=\"images per gpu, the total batch size is $NGPU x batch_size\"\n    )\n    parser.add_argument(\"--epochs\", default=90, type=int, metavar=\"N\", help=\"number of total epochs to run\")\n    parser.add_argument(\n        \"-j\", \"--workers\", default=16, type=int, metavar=\"N\", help=\"number of data loading workers (default: 16)\"\n    )\n    parser.add_argument(\"--opt\", default=\"sgd\", type=str, help=\"optimizer\")\n    parser.add_argument(\"--lr\", default=0.1, type=float, help=\"initial learning rate\")\n    parser.add_argument(\"--momentum\", default=0.9, type=float, metavar=\"M\", help=\"momentum\")\n    parser.add_argument(\n        \"--wd\",\n        \"--weight-decay\",\n        default=1e-4,\n        type=float,\n        metavar=\"W\",\n        help=\"weight decay (default: 1e-4)\",\n        dest=\"weight_decay\",\n    )\n    parser.add_argument(\n        \"--norm-weight-decay\",\n        default=None,\n        type=float,\n        help=\"weight decay for Normalization layers (default: None, same value as --wd)\",\n    )\n    parser.add_argument(\n        \"--bias-weight-decay\",\n        default=None,\n        type=float,\n        help=\"weight decay for bias parameters of all layers (default: None, same value as --wd)\",\n    )\n    parser.add_argument(\n        \"--transformer-embedding-decay\",\n        default=None,\n        type=float,\n        help=\"weight decay for embedding parameters for vision transformer models (default: None, same value as --wd)\",\n    )\n    parser.add_argument(\n        \"--label-smoothing\", default=0.0, type=float, help=\"label smoothing (default: 0.0)\", dest=\"label_smoothing\"\n    )\n    parser.add_argument(\"--mixup-alpha\", default=0.0, type=float, help=\"mixup alpha (default: 0.0)\")\n    parser.add_argument(\"--cutmix-alpha\", default=0.0, type=float, help=\"cutmix alpha (default: 0.0)\")\n    parser.add_argument(\"--lr-scheduler\", default=\"steplr\", type=str, help=\"the lr scheduler (default: steplr)\")\n    parser.add_argument(\"--lr-warmup-epochs\", default=0, type=int, help=\"the number of epochs to warmup (default: 0)\")\n    parser.add_argument(\n        \"--lr-warmup-method\", default=\"constant\", type=str, help=\"the warmup method (default: constant)\"\n    )\n    parser.add_argument(\"--lr-warmup-decay\", default=0.01, type=float, help=\"the decay for lr\")\n    parser.add_argument(\"--lr-step-size\", default=30, type=int, help=\"decrease lr every step-size epochs\")\n    parser.add_argument(\"--lr-gamma\", default=0.1, type=float, help=\"decrease lr by a factor of lr-gamma\")\n    parser.add_argument(\"--lr-min\", default=0.0, type=float, help=\"minimum lr of lr schedule (default: 0.0)\")\n    parser.add_argument(\"--print-freq\", default=10, type=int, help=\"print frequency\")\n    parser.add_argument(\"--output-dir\", default=\".\", type=str, help=\"path to save outputs\")\n    parser.add_argument(\"--resume\", default=\"\", type=str, help=\"path of checkpoint\")\n    parser.add_argument(\"--start-epoch\", default=0, type=int, metavar=\"N\", help=\"start epoch\")\n    parser.add_argument(\n        \"--cache-dataset\",\n        dest=\"cache_dataset\",\n        help=\"Cache the datasets for quicker initialization. It also serializes the transforms\",\n        action=\"store_true\",\n    )\n    parser.add_argument(\n        \"--sync-bn\",\n        dest=\"sync_bn\",\n        help=\"Use sync batch norm\",\n        action=\"store_true\",\n    )\n    parser.add_argument(\n        \"--test-only\",\n        dest=\"test_only\",\n        help=\"Only test the model\",\n        action=\"store_true\",\n    )\n    parser.add_argument(\"--auto-augment\", default=None, type=str, help=\"auto augment policy (default: None)\")\n    parser.add_argument(\"--ra-magnitude\", default=9, type=int, help=\"magnitude of auto augment policy\")\n    parser.add_argument(\"--augmix-severity\", default=3, type=int, help=\"severity of augmix policy\")\n    parser.add_argument(\"--random-erase\", default=0.0, type=float, help=\"random erasing probability (default: 0.0)\")\n\n    # Mixed precision training parameters\n    parser.add_argument(\"--amp\", action=\"store_true\", help=\"Use torch.cuda.amp for mixed precision training\")\n\n    # distributed training parameters\n    parser.add_argument(\"--world-size\", default=1, type=int, help=\"number of distributed processes\")\n    parser.add_argument(\"--dist-url\", default=\"env://\", type=str, help=\"url used to set up distributed training\")\n    parser.add_argument(\n        \"--model-ema\", action=\"store_true\", help=\"enable tracking Exponential Moving Average of model parameters\"\n    )\n    parser.add_argument(\n        \"--model-ema-steps\",\n        type=int,\n        default=32,\n        help=\"the number of iterations that controls how often to update the EMA model (default: 32)\",\n    )\n    parser.add_argument(\n        \"--model-ema-decay\",\n        type=float,\n        default=0.99998,\n        help=\"decay factor for Exponential Moving Average of model parameters (default: 0.99998)\",\n    )\n    parser.add_argument(\n        \"--use-deterministic-algorithms\", action=\"store_true\", help=\"Forces the use of deterministic algorithms only.\"\n    )\n    parser.add_argument(\n        \"--interpolation\", default=\"bilinear\", type=str, help=\"the interpolation method (default: bilinear)\"\n    )\n    parser.add_argument(\n        \"--val-resize-size\", default=256, type=int, help=\"the resize size used for validation (default: 256)\"\n    )\n    parser.add_argument(\n        \"--val-crop-size\", default=224, type=int, help=\"the central crop size used for validation (default: 224)\"\n    )\n    parser.add_argument(\n        \"--train-crop-size\", default=224, type=int, help=\"the random crop size used for training (default: 224)\"\n    )\n    parser.add_argument(\"--clip-grad-norm\", default=None, type=float, help=\"the maximum gradient norm (default None)\")\n    parser.add_argument(\"--ra-sampler\", action=\"store_true\", help=\"whether to use Repeated Augmentation in training\")\n    parser.add_argument(\n        \"--ra-reps\", default=3, type=int, help=\"number of repetitions for Repeated Augmentation (default: 3)\"\n    )\n    parser.add_argument(\"--weights\", default=None, type=str, help=\"the weights enum name to load\")\n    parser.add_argument(\"--backend\", default=\"PIL\", type=str.lower, help=\"PIL or tensor - case insensitive\")\n    parser.add_argument(\"--use-v2\", action=\"store_true\", help=\"Use V2 transforms\")\n    return parser    ","repo_name":"YuBeomGon/vit_cifar10","sub_path":"utils/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":9289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3898767302","text":"# -*- coding: utf-8 -*-\n# project: PycharmProjects\n# file: loggerhandler\n# author: gichen\n# create time: 2018/6/7 4:23 PM\n# product name: PyCharm\nimport logging\nimport os\nimport re\nimport sys\nimport time\nfrom stat import ST_MTIME\n\ntry:\n    import uwsgi\n\n    uwsgi_mode = True\nexcept:\n    uwsgi_mode = False\n\n# prepare env\nfrom logging.handlers import TimedRotatingFileHandler\n\n\ndef check_version():\n    if sys.version_info < (3, 0):\n        reload(sys)\n        sys.setdefaultencoding('utf-8')\n        sys.getfilesystemencoding = lambda: 'UTF-8'\n        reload(sys)\n    # else:\n    #     sys.stdout.write('Please use python 2.x to run this script!\\n')\n    #     sys.exit(255)\n\n\ncheck_version()\n\n# basicConfig 打印library日志\nlogging.basicConfig(level=logging.INFO)\nfor hd in logging.getLogger().handlers:\n    if isinstance(hd, logging.StreamHandler):\n        hd.setLevel(20)\n\n\n# make sure dir exist\ndef __makesuredirexist__(path):\n    if not os.path.exists(path):\n        sys.stdout.write('path does not exist: {}\\n'.format(path))\n        sys.stdout.write('auto create {}\\n'.format(path))\n        os.makedirs(path, 0o775)\n\n\nclass mylogger():\n    def __init__(self, classname, log_path, when='midnight', interval=1, backupCount=0, level=10):\n        \"\"\"\n        ���定保存日志的文件路径，日志级别，以及调用文件\n        将日志存入到指定的文件中\n        :param classname:\n        :param log_path:\n        :param when:\n        :param interval:\n        :param backupCount:\n        :param level: default DEBUG=10\n        \"\"\"\n        # self attributes setting\n        if uwsgi_mode:\n            wk_id = uwsgi.worker_id()\n            self.log_path = log_path + '.{}'.format(wk_id)\n        else:\n            self.log_path = log_path\n\n        # 创建log文件父目录\n        __makesuredirexist__(os.path.dirname(log_path))\n\n        # 创建一个logger\n        self.logger = logging.getLogger(classname)\n        self.logger.setLevel(level)\n        self.logger.propagate = 0\n\n        # 创建一个handler，用于写入日志文件\n        fh = TimedRotatingFileHandler(log_path, when=when, interval=interval, backupCount=backupCount, encoding='utf-8')\n        fh.setLevel(logging.DEBUG)\n\n        # 再创建一个handler，用于输出到控制台\n        ch = logging.StreamHandler()\n        ch.setLevel(logging.INFO)\n\n        # 定义handler的输出格式\n        formatter = logging.Formatter('[%(asctime)s - %(name)s - %(levelname)s - %(process)d] %(message)s')\n        fh.setFormatter(formatter)\n        ch.setFormatter(formatter)\n\n        # 给logger添加handler\n        for hdlr in self.logger.handlers:\n            self.logger.removeHandler(hdlr)\n        self.logger.addHandler(fh)\n        self.logger.addHandler(ch)\n\n        #  添加下面一句，在记录日志之后移除句柄\n        # self.logger.removeHandler(ch)\n        # self.logger.removeHandler(fh)\n        # 关闭打开的文件\n        fh.close()\n        ch.close()\n\n    def _check_basefilename(self):\n        \"\"\"\n        Only if uwsgi_mode is True, then check basefilename works.\n        Aim to locate correct log file in case of multi processes.\n        :return:\n        \"\"\"\n        if uwsgi_mode:\n            wk_id = uwsgi.worker_id()\n            for h in self.logger.handlers:\n                if isinstance(h, TimedRotatingFileHandler):\n                    if h.baseFilename.endswith('.log'):\n                        base_filename = h.baseFilename + '.{}'.format(wk_id)\n                        h.baseFilename = base_filename\n                        self.__reset_log_file(h)\n                        continue\n                    if re.match(r\".*\\.[0-9]\", h.baseFilename) and not re.match(r\".*\\.{}\".format(wk_id), h.baseFilename):\n                        base_filename = '.'.join(h.baseFilename.split('.')[:-1]) + '.{}'.format(wk_id)\n                        h.baseFilename = base_filename\n                        self.__reset_log_file(h)\n                        continue\n\n    def __reset_log_file(self, handler):\n        \"\"\"\n        change log file stream;\n        change rolloverat\n        re-open stream\n        :param handler:\n        :return:\n        \"\"\"\n        if handler.stream:\n            handler.stream.close()\n            handler.stream = None\n        if os.path.exists(handler.baseFilename):\n            t = os.stat(handler.baseFilename)[ST_MTIME]\n        else:\n            t = int(time.time())\n        handler.stream = handler._open()\n        newRolloverAt = handler.computeRollover(t)\n        while newRolloverAt <= t:\n            newRolloverAt = newRolloverAt + handler.interval\n        handler.rolloverAt = newRolloverAt\n\n    def getlog(self):\n        return self.logger\n\n    def debug(self, msg, *args, **kwargs):\n        self._check_basefilename()\n        self.logger.debug(msg, *args, **kwargs)\n\n    def info(self, msg, *args, **kwargs):\n        self._check_basefilename()\n        self.logger.info(msg, *args, **kwargs)\n\n    def error(self, msg, *args, **kwargs):\n        self._check_basefilename()\n        self.logger.error(msg, *args, **kwargs)\n\n    def warning(self, msg, *args, **kwargs):\n        self._check_basefilename()\n        self.logger.warning(msg, *args, **kwargs)\n\n    def critical(self, msg, *args, **kwargs):\n        self._check_basefilename()\n        self.logger.critical(msg, *args, **kwargs)\n","repo_name":"xunfengqiyang/baseProjects","sub_path":"python3/bin/Utils/loggerhandler.py","file_name":"loggerhandler.py","file_ext":"py","file_size_in_byte":5339,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"8114847287","text":"import sys\n\nfrom match import Match\nfrom team import Team\nfrom player import Batsman\n\n\ndef get_players_probability():\n    \"\"\" players probability \"\"\"\n    return {\n        'Kirat Boli': { '0':5, '1':30, '2':25, '3':10, '4':15, '5':1 ,'6':9,'out':5 },\n        'N.S Nodhi': { '0':10, '1':40, '2':20, '3':5, '4':10, '5':1, '6':4, 'out':10 },\n        'R Rumrah': { '0':20, '1':30, '2':15, '3':5, '4':5, '5':1, '6':4, 'out':20 },\n        'Shashi Henra': { '0':30, '1':25, '2':5, '3':0, '4':5, '5':1, '6':4, 'out':30 }\n    }\n\ndef get_inputs():\n    \"\"\" take inputs from the user \"\"\" \n    team_1 = str(input(\"Enter Batting Team: \"))\n    team_2 = str(input(\"Enter Bowling Team: \"))\n    no_of_balls = input(\"Enter No of Balls: \")\n    no_of_wickets = input(\"Enter no of wickets: \")\n    runs_to_win = input(\"Enter runs to win: \")\n\n    return team_1, team_2, no_of_balls, no_of_wickets, runs_to_win\n\ndef create_team(team, is_batting=True):\n    return Team(team, is_batting)\n\ndef create_batsman(player_name, team, probability):\n    return Batsman(player_name, team, probability)\n\n\ndef validation(team_1, team_2, no_of_balls, no_of_wickets, runs_to_win):\n\n    \"\"\" validation of team name, balls, wickets and runs to win \"\"\"\n    if team_1 == team_2:\n        return \"Both teams can't be of same name\"\n    try:\n        no_of_balls = int(no_of_balls)\n    except Exception:\n        return \"no of balls should be an integer\"\n\n    try:\n        no_of_wickets = int(no_of_wickets)\n    except Exception as e:\n        return \"no of wickets should be an integer\"\n\n    try:\n        runs_to_win = int(runs_to_win)\n    except Exception as e:\n        return \"no of runs should be an integer\"\n\ndef main(team_1, team_2, no_of_balls, no_of_wickets, runs_to_win):\n    \n    \"\"\" main func \"\"\"\n    val = validation(team_1, team_2, no_of_balls, no_of_wickets, runs_to_win)\n    if val:\n        return val\n\n    no_of_balls = int(no_of_balls)\n    no_of_wickets = int(no_of_wickets)\n    runs_to_win = int(runs_to_win)\n\n    team_1 = create_team(team_1, True)\n    team_2 = create_team(team_2, False)\n    \n    players_probability = get_players_probability()\n\n    player_1 = create_batsman(\"Kirat Boli\", team_1, players_probability[\"Kirat Boli\"])\n    player_1.came_to_bat = True\n\n    player_2 = create_batsman(\"N.S Nodhi\", team_1, players_probability[\"N.S Nodhi\"])\n    player_2.came_to_bat = True\n\n    player_3 = create_batsman(\"R Rumrah\", team_1, players_probability[\"R Rumrah\"])\n    player_4 = create_batsman(\"Shashi Henra\", team_1, players_probability[\"Shashi Henra\"])\n\n    striker = player_1\n    non_striker = player_2\n    rest_players = [player_3, player_4]\n\n    match = Match(no_of_balls, no_of_wickets, runs_to_win, team_1, team_2, striker, non_striker, rest_players)\n\n    output = match.main()\n    return output\n\n    \n\nif __name__ == \"__main__\":\n\n    \"\"\" calling the main func \"\"\"\n    team_1, team_2, no_of_balls, no_of_wickets, runs_to_win = get_inputs()\n    results = main(team_1, team_2, no_of_balls, no_of_wickets, runs_to_win)\n    for result in results:\n        print (result)\n","repo_name":"poojasalecha/probability_a_team_can_win","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"43618085930","text":"'''\n歡迎訂閱我們的Youtube頻道Coding Kevin BKH，影片中會使用動畫解釋演算法，同時也有Leetcode實戰解題。\nCheck out our Youtube channel, we explain the solutions step by step. Please subscribe!\nThe audio is in Mandarin\n\nCoding Kevin BKH\n\n'''\n\n### solution 1 ###\nclass Solution:\n    def shortestToChar(self, S: str, C: str) -> List[int]:\n        pos = [i for i in range(len(S)) if S[i] == C]\n        pos_len = len(pos)\n        dis = []\n        curr = 0\n        for i in range(len(S)):\n            if curr >= pos_len:\n                dis.append(abs(pos[curr-1]-i))\n            elif curr-1 < 0:\n                dis.append(abs(pos[curr]-i))\n            else:\n                dis.append(min(abs(pos[curr]-i), abs(pos[curr-1]-i)))\n            if curr < pos_len and i == pos[curr]:\n                curr += 1\n        return dis\n\n### solution 2 ###\nclass Solution:\n    def shortestToChar(self, S: str, C: str) -> List[int]:\n        import math\n\n        dis = []\n        prev = None\n        for i in range(len(S)):\n            if S[i] == C:\n                prev = i\n            if prev != None:\n                dis.append(i-prev)\n            else:\n                dis.append(math.inf)\n        prev = None\n        for i in range(len(dis)-1, -1, -1):\n            if S[i] == C:\n                prev = i\n            if prev != None:\n                dis[i] = min(prev-i, dis[i])\n        return dis\n","repo_name":"codingkevinbkh/leetcode","sub_path":"python/problems-0801-0900/0821-shortest-distance-to-a-character.py","file_name":"0821-shortest-distance-to-a-character.py","file_ext":"py","file_size_in_byte":1412,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"86"}
+{"seq_id":"70352064604","text":"import redis\nimport app.actions.ec2 as action\nimport app.config.default as cfg\nfrom app.notifications.client import post_message_init\n# State\nstate_active = \"1\"\nstate_inactive = \"0\"\n\n\n# Redis\nr = redis.Redis(host=cfg.redis_host, port=cfg.redis_port, db=0)\npub_sub = r.pubsub()\npub_sub.psubscribe('*:battleNetworkStatus')\npost_message_init()\n\n# Dicts\nnetworks_state = {}\ninstance_state = {}\n\n\ndef start_network(network_name):\n    print('starting network {}'.format(network_name))\n    h_get_all = r.hgetall(name='mappingInstanceNetwork')\n    for key, value in h_get_all.items():\n        instance_state[key.decode(\"utf-8\")] = value.decode(\"utf-8\")\n    action.instance_action_start(instance_id=[tuple(instance_state[network_name].split())])\n\n\ndef stop_network(network_name):\n    print('stopping network {}'.format(network_name))\n    h_get_all = r.hgetall(name='mappingInstanceNetwork')\n    for key, value in h_get_all.items():\n        instance_state[key.decode(\"utf-8\")] = value.decode(\"utf-8\")\n    action.instance_action_stop(instance_id=[tuple(instance_state[network_name].split())])\n\n\ndef convert_hash_set(networks_state):\n    result = new_d = {network.decode('utf-8'): state.decode('utf-8') for network, state in networks_state.items()}\n    return result\n\n\ndef add_networks(old_state, new_state):\n    for network, state in new_state.items():\n        if network in old_state:\n            continue\n        old_state[network] = state\n        if state == state_active:\n            start_network(network)\n        else:\n            stop_network(network)\n\n\ndef update_networks(old_state, new_state):\n    for network, state in new_state.items():\n        if (not network in old_state) or (state == old_state[network]):\n            continue\n        old_state[network] = state\n        if state == state_active:\n            start_network(network)\n        else:\n            stop_network(network)\n\n\ndef remove_deleted_networks(old_state, new_state):\n    for network in list(old_state.keys()):\n        if network in new_state:\n            continue\n        state = old_state.pop(network, None)\n        if state == state_active:\n            stop_network(network)\n\n\nwhile True:\n    for m in pub_sub.listen():\n        if cfg.debug:\n            print(m)\n        updated_networks_state = r.hgetall('battleNetworkStatus')\n        updated_networks_state = convert_hash_set(updated_networks_state)\n        if cfg.debug:\n            print('old_state:')\n            print(networks_state)\n            print('new_state:')\n            print(updated_networks_state)\n        add_networks(networks_state, updated_networks_state)\n        update_networks(networks_state, updated_networks_state)\n        remove_deleted_networks(networks_state, updated_networks_state)\n        if cfg.debug:\n            print('old_state:')\n            print(networks_state)\n            print('new_state:')\n            print(updated_networks_state)","repo_name":"nikolay-semenov/scripts","sub_path":"instance-start-stop/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2894,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"29376292556","text":"# https://programmers.co.kr/learn/courses/30/lessons/12977\n# 프로그래머스 <소수 만들기>\nnums = [1,2,7,6,4]\n\"\"\"def prime_check(number):\n    flag = 0\n    for i in range(2, number):\n        if number % i == 0:\n            flag = 1\n            break\n    if flag == 1:\n        return False\n    else:\n        return True\"\"\"\n# 에라토스테네스의 체 활용하기\ndef prime_list(n):\n    sieve = [True] * n\n    m = int(n ** 0.5)\n    for i in range(2, m+1):\n        if sieve[i]:\n            for j in range(i+i, n, i):\n                sieve[j] = False\n    return [i for  i in range(2,n) if sieve[i]]\ndef solution(nums):\n    answer = 0\n    tmp = 0\n    prime = prime_list(3000)\n    for i in range(len(nums)):\n        for j in range(i+1, len(nums)):\n            for k in range(j+1, len(nums)):\n                if (nums[i] + nums[j] + nums[k]) in prime:\n                    answer +=1 \n    return answer\nprint(solution(nums))\n\n\n\"\"\"for i in range(len(nums)-2):\n        tmp += nums[i]\n        for j in range(i+1, len(nums)-1):\n            tmp += nums[j]\n            for k in range(j+1, len(nums)):\n                tmp += nums[k]\n                if prime_check(tmp):\n                    tmp -= nums[k]\n                    answer += 1\n                else:\n                    tmp -= nums[k]\n            tmp -= nums[j]\n        tmp -= nums[i]\"\"\"","repo_name":"201810756/Programmers_practice","sub_path":"소수 만들기.py","file_name":"소수 만들기.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"725097121","text":"import rhgamestationFiles\nfrom generators.Generator import Generator\nimport os.path\nimport glob\nimport sys\nimport shutil\nimport amiberryController\nimport sys\nimport amiberryConfig\nimport binascii\nfrom settings.unixSettings import UnixSettings\n\nmountPointWHDL = rhgamestationFiles.amiberryMountPoint + \"/WHDL\"\nwhdFilespath = rhgamestationFiles.BIOS + \"/amiga/whdl\"\n\n\ndef generateWHDL(fullName, romFolder, gameName, amigaHardware, controller):\n    print(\"execute WHDLoad : <%s>\" % os.path.join(romFolder, gameName))\n\n    # ----- check Bios -----\n    if not amiberryConfig.hasKickstarts(amigaHardware, whdl=True):\n        raise IOError(\"No %s kickstarts found\" % amigaHardware)\n\n    amiberryConfig.initMountpoint(rhgamestationFiles.amiberryMountPoint)\n    os.makedirs(mountPointWHDL)\n\n    # ------------ copy WHDL structure Files ------------\n    print(\"Copy WHDL bootstrap files from %s to %s\" % (whdFilespath, mountPointWHDL))\n    # TODO REDO IN PYTHON (not easily done)\n    os.popen('cp -R ' + whdFilespath + '/* ' + mountPointWHDL)\n\n    # ---- copy BIOS as equivalent into WHDL structure ----\n    whdlKickstarts = os.path.join(mountPointWHDL, \"Devs\", \"Kickstarts\")\n    shutil.copy2(os.path.join(rhgamestationFiles.BIOS, \"kick13.rom\"), os.path.join(whdlKickstarts, \"kick33180.A500\"))\n    shutil.copy2(os.path.join(rhgamestationFiles.BIOS, \"kick13.rom\"), os.path.join(whdlKickstarts, \"kick33192.A500\"))\n    shutil.copy2(os.path.join(rhgamestationFiles.BIOS, \"kick13.rom\"), os.path.join(whdlKickstarts, \"kick34005.A500\"))\n    # shutil.copy2(os.path.join(rhgamestationFiles.BIOS,\"kick20.rom\"),os.path.join(whdlKickstart,))\n    shutil.copy2(os.path.join(rhgamestationFiles.BIOS, \"kick31.rom\"), os.path.join(whdlKickstarts, \"kick40068.A1200\"))\n\n    # ------------ copy game folder & uae ------------\n    whdlDir = os.path.join(romFolder, gameName)\n    whdlZip = whdlDir + \".zip\"\n    if os.path.exists(whdlZip):\n        print(\"Unzip game folder %s\" % whdlZip)\n        os.popen('unzip \"' + whdlZip + '\" -d ' + mountPointWHDL)\n    else:\n        print(\"Copy game folder and uae %s\" % whdlDir)\n        # TODO REDO IN PYTHON (not easily done)\n        os.popen('cp -R \"' + whdlDir + '/\"* ' + mountPointWHDL)\n    shutil.copy2(whdlDir + \".uae\", os.path.join(rhgamestationFiles.amiberryMountPoint, \"amiberry\", \"conf\", \"uaeconfig.uae\"))\n\n    # ------------ Build reference ------------\n    referenceFiles = {}\n    buildReferenceFile(mountPointWHDL, referenceFiles)\n    # for key, val in referenceFiles.items():\n    #     print key, \"=>\", val\n\n    # ------------ copy game saved files from previous sessions ------------\n    handleBackupToGame(gameName, amigaHardware)\n\n    # ------------ Complete UAE ----------------\n    uaeConfig = os.path.join(rhgamestationFiles.amiberryMountPoint, \"amiberry\", \"conf\", \"uaeconfig.uae\")\n\n    fUaeConfig = UnixSettings(uaeConfig, separator='', defaultComment=';')\n    uaeConfigIsEmpty = os.path.getsize(uaeConfig) == 0\n\n    # Needed or too speedy\n    amiberryConfig.generateConfType(fUaeConfig)\n    # Allow custom controllers conf in file\n    if uaeConfigIsEmpty or not ';controls' in open(uaeConfig).read():\n        amiberryController.generateControllerConf(fUaeConfig)\n\n    amiberryController.generateSpecialKeys(fUaeConfig, controller)\n    amiberryConfig.generateGUIConf(fUaeConfig, 'false')\n    amiberryConfig.generateKickstartPathWHDL(fUaeConfig, amigaHardware)\n    # Allow custom hardware conf in file\n    if uaeConfigIsEmpty or not ';hardware' in open(uaeConfig).read():\n        amiberryConfig.generateHardwareConf(fUaeConfig, amigaHardware)\n    # Add Z3 Mem to load whole game in memory\n    amiberryConfig.generateZ3Mem(fUaeConfig)\n    if uaeConfigIsEmpty or not ';graphics' in open(uaeConfig).read():\n        amiberryConfig.generateGraphicConf(fUaeConfig)\n    amiberryConfig.generateSoundConf(fUaeConfig)\n    generateHardDriveConf(fUaeConfig)\n\n    # ------------ Create StartupSequence with right slave files ------------\n    customLaunch = os.path.join(romFolder, gameName + \".whdl\")\n    gotAddedParams = os.path.exists(customLaunch) and not os.path.getsize(customLaunch) == 0\n\n    fStartupSeq = open(os.path.join(mountPointWHDL, \"S\", \"Startup-Sequence\"), \"a+\")\n    try:\n        slaveFiles = [filename for filename in os.listdir(mountPointWHDL) if\n                      filename.endswith(\".Slave\") or filename.endswith(\".slave\")]\n        print(slaveFiles)\n        if len(slaveFiles) == 0:\n            raise IOError(\"This is not a valid WHD game\")\n\n        for slaveFile in slaveFiles:\n            if gotAddedParams:\n                addedParams = open(customLaunch, \"r\").readlines()[0].rstrip('\\n\\r ')\n                print(\"Using slave file %s with custom params %s\" % (slaveFile, addedParams))\n                fStartupSeq.write(\"WHDload \" + slaveFile + \" Preload \" + addedParams + \"\\n\")\n            else:\n                print(\"Using slave file %s\" % slaveFile)\n                fStartupSeq.write(\"WHDload \" + slaveFile + \" Preload\\n\")\n\n        # comment for now\n        # fStartupSeq.write(\"exitemu\\n\")\n    finally:\n        fStartupSeq.close()\n\n    return referenceFiles\n\n\ndef generateHardDriveConf(fUaeConfig):\n    fUaeConfig.save(\"rtg_nocustom\", \"true\")\n    fUaeConfig.save(\"filesystem2\", \"rw,DH0:DH0:\" + mountPointWHDL + \"/,0\")\n    fUaeConfig.save(\"uaehf0\", \"dir,rw,DH0:DH0:\" + mountPointWHDL + \"/,0\")\n\n\ndef handleBackupToGame(gameName, amigaHardware):\n    saveDir = os.path.join(rhgamestationFiles.amiberrySaves, amigaHardware, gameName)\n    if os.path.exists(saveDir):\n        print(\"Copy saved files to game folder. From %s to %s\" % (saveDir, mountPointWHDL))\n        restoreModifiedFiles(saveDir, mountPointWHDL)\n    else:\n        print(\"No saved data.\")\n\n\ndef handleBackupFromGame(fullName, romFolder, gameName, amigaHardware, reference):\n    # ------------ clean WHDL structure Files before backup of backups ------------\n    shutil.rmtree(os.path.join(mountPointWHDL, 'S'))\n    shutil.rmtree(os.path.join(mountPointWHDL, 'C'))\n    shutil.rmtree(os.path.join(mountPointWHDL, 'Devs'))\n\n    # ------------ detect changes in remaining games files for backuping saves ------------\n    saveDir = os.path.join(rhgamestationFiles.amiberrySaves, amigaHardware, gameName)\n    romDir = os.path.join(romFolder, gameName)\n    print(\"Backup changed files from %s to %s\" % (mountPointWHDL, saveDir))\n    backupModifiedFiles(mountPointWHDL, saveDir, reference)\n\n\ndef makedirectories(path):\n    try:\n        os.makedirs(path)\n    except OSError as exc:  # Python >2.5\n        if exc.errno == errno.EEXIST and os.path.isdir(path):\n            pass\n        else:\n            raise\n\n\ndef backupModifiedFiles(source, target, reference):\n    print(\"backup dir <%s> to <%s>\" %(source, target))\n    for f in os.listdir(source):\n        sourcePath = os.path.join(source, f)\n        destinationPath = os.path.join(target, f)\n        print (\"backup file : %s/%s\" %(source, f))\n        if os.path.isdir(sourcePath):\n            backupModifiedFiles(sourcePath, destinationPath, reference)\n        else:\n            if not sourcePath in reference:\n                # if this is a new file, just copy\n                print(\"new file : %s backup %s -> %s\" % (f, source, target))\n                # create target path if necessary\n                if not os.path.isdir(target):\n                    makedirectories(target)\n                shutil.copy2(sourcePath, target)\n            else:\n                # file exists, compate datetime to detect any change\n                datetime = int(os.path.getmtime(sourcePath))\n                if datetime != reference[sourcePath]:\n                    # create target path if necessary\n                    if not os.path.isdir(target):\n                        makedirectories(target)\n                    print(\"changed file : %s backup %s -> %s\" % (f, source, target))\n                    shutil.copy2(sourcePath, target)\n\n\ndef restoreModifiedFiles(source, target):\n    print(\"restore dir <%s> to <%s>\" %(source, target))\n    for f in os.listdir(source):\n        sourcePath = os.path.join(source, f)\n        destinationPath = os.path.join(target, f)\n        print (\"restore file : %s/%s\" %(source, f))\n        if os.path.isdir(sourcePath):\n            restoreModifiedFiles(sourcePath, destinationPath)\n        else:\n            if not os.path.isdir(target):\n                makedirectories(target)\n            shutil.copy2(sourcePath, target)\n\n\ndef buildReferenceFile(source, referenceFiles):\n    for f in os.listdir(source):\n        sourcePath = os.path.join(source, f)\n        if os.path.isdir(sourcePath):\n            buildReferenceFile(sourcePath, referenceFiles)\n        else:\n            referenceFiles[sourcePath] = int(os.path.getmtime(sourcePath))\n\n\ndef CRC32_from_file(filename):\n    buf = open(filename, 'rb').read()\n    buf = (binascii.crc32(buf) & 0xFFFFFFFF)\n    return \"%08X\" % buf\n","repo_name":"pmoran13800/rhgamestation-configgen","sub_path":"configgen/generators/amiberry/whdlGenerator.py","file_name":"whdlGenerator.py","file_ext":"py","file_size_in_byte":8842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"13452084416","text":"# -*- coding: utf-8 -*-\n__all__ = ('TemplateTransformationSchema', 'TemplateTransformationSchemaConfig')\n\nimport typing\nfrom typing import Any, ClassVar, Iterator, Mapping, NoReturn, Optional\n\nimport attr\nfrom comby import Comby\n\nfrom .base import Transformation, TransformationSchema\nfrom .config import TransformationSchemaConfig\nfrom .. import exceptions as exc\n\nif typing.TYPE_CHECKING:\n    from ..problem import Problem\n    from ..snippet import SnippetDatabase\n\n\n@attr.s(auto_attribs=True)\nclass TemplateTransformationSchema(TransformationSchema):\n    _problem: 'Problem' = attr.ib(repr=False, eq=False, hash=False)\n    _comby: 'Comby' = attr.ib(repr=False, eq=False, hash=False)\n    match: str\n    rewrite: str\n\n    def find_all_in_file(self, filename: str) -> Iterator[Transformation]:\n        m = (\"template-based transformations should be supported in \"\n             \"Darjeeling within the next few days (Dec. 6, 2020).\")\n        raise NotImplementedError(m)\n\n\n@attr.s(frozen=True, auto_attribs=True)\nclass TemplateTransformationSchemaConfig(TransformationSchemaConfig):\n    NAME: ClassVar[str] = 'template'\n\n    match: str\n    rewrite: str\n\n    @classmethod\n    def from_dict(cls,\n                  dict_: Mapping[str, Any],\n                  dir_: Optional[str] = None\n                  ) -> TransformationSchemaConfig:\n        def err(message: str) -> NoReturn:\n            raise exc.BadConfigurationException(message)\n\n        def read_string_property(name: str) -> str:\n            if name not in dict_:\n                err(f'missing \"{name}\" property in template transformation config')\n\n            value = dict_[name]\n\n            if not isinstance(value, str):\n                err(f'expected \"{name}\" property in template transformation config'\n                    f' to be a str but was a {value.__class__.__name__}')\n\n            return value\n\n        match = read_string_property('match')\n        rewrite = read_string_property('rewrite')\n\n        return TemplateTransformationSchemaConfig(\n            match=match,\n            rewrite=rewrite,\n        )\n\n    def build(self,\n              problem: 'Problem',\n              snippets: 'SnippetDatabase'\n              ) -> 'TransformationSchema':\n        return TemplateTransformationSchema(\n            problem=problem,\n            comby=problem.environment.comby,\n            match=self.match,\n            rewrite=self.rewrite,\n        )\n","repo_name":"squaresLab/Darjeeling","sub_path":"src/darjeeling/transformation/template.py","file_name":"template.py","file_ext":"py","file_size_in_byte":2410,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"86"}
+{"seq_id":"16110162068","text":"import unittest\nfrom unittest.mock import patch\nfrom employee import Employee\n\n\nclass TestEmployee(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        print('setupClass')\n\n    @classmethod\n    def tearDownClass(cls):\n        print('teardownClass')\n\n    def setUp(self):\n        print('setUp')\n        self.emp_1 = Employee('Leon', 'Lei', 50000)\n        self.emp_2 = Employee('John', 'Smith', 60000)\n\n    def tearDown(self):\n        print('tearDown\\n')\n\n    def test_email(self):\n        print('test_email')\n        self.assertEqual(self.emp_1.email, 'Leon.Lei@gmail.com')\n        self.assertEqual(self.emp_2.email, 'John.Smith@gmail.com')\n\n        self.emp_1.first = 'Leonidas'\n        self.emp_2.first = 'Jane'\n\n        self.assertEqual(self.emp_1.email, 'Leonidas.Lei@gmail.com')\n        self.assertEqual(self.emp_2.email, 'Jane.Smith@gmail.com')\n\n    def test_fullname(self):\n        print('test_fullname')\n        self.assertEqual(self.emp_1.fullname, 'Leon Lei')\n        self.assertEqual(self.emp_2.fullname, 'John Smith')\n\n        self.emp_1.first = 'Alpha'\n        self.emp_2.first = 'Gamma'\n        self.emp_1.last = 'Beta'\n        self.emp_2.last = 'Delta'\n\n        self.assertEqual(self.emp_1.fullname, 'Alpha Beta')\n        self.assertEqual(self.emp_2.fullname, 'Gamma Delta')\n\n    def test_apply_raise(self):\n        print('test_apply_raise')\n        self.emp_1.apply_raise()\n        self.emp_2.apply_raise()\n\n        self.assertEqual(self.emp_1.pay, 52500)\n        self.assertEqual(self.emp_2.pay, 63000)\n\n    def test_monthly_schedule(self):\n        print('test_monthly_schedule')\n        with patch('employee.requests.get') as mocked_get:\n            mocked_get.return_value.ok = True\n            mocked_get.return_value.text = 'Success'\n\n            schedule = self.emp_1.monthly_schedule('May')\n            mocked_get.assert_called_with('http://company.com/Lei/May')\n            self.assertEqual(schedule, 'Success')\n\n            # Mock situation where web site is down\n            mocked_get.return_value.ok = False\n\n            schedule = self.emp_2.monthly_schedule('June')\n            mocked_get.assert_called_with('http://company.com/Smith/June')\n            self.assertEqual(schedule, 'Bad Response!')\n\n\nif __name__ == '__main__':\n    unittest.main()","repo_name":"leon-lei/learning-materials","sub_path":"unit_testing_tutorials/test_employee.py","file_name":"test_employee.py","file_ext":"py","file_size_in_byte":2292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35804381059","text":"# This class simplifies the input from a robot's sensors into two values,\n# representing the nearest neighbour (of a particular object type) that is detected:\n# (1) distance (in the range [0, 1], where 0 = no object detected, and 1 = object detected as close as possible)\n# (2) angle (in the range (-1, 1], where negative represents left and positive represents right)\n\nimport util.globals as globals\nimport util.categorise as categorise\nimport numpy as np\n\nclass RadarSensor:\n\n  def __init__(self, agent, type, range, fov=(-180, 180)):\n    self.agent = agent\n    self.type = type # wall, dog or sheep\n    self.range = range # sensory radius in pixels\n    self.fov = fov # tuple with max left angle and max right angle in degrees\n\n  def detect(self, normalised=True):\n    # get all distance detections and sensor angles (in either absolute values (pixels and degrees) or normalised values)\n    distances = self.agent.get_all_distances() * globals.config.get(\"gSensorRange\", \"int\")\n    distances[distances > self.range] = -1 # undetected (becomes 0 when normalised)\n    if normalised:\n      distances[distances != -1] = 1 - (distances[distances != -1] / self.range)\n      angles = self.agent.get_all_sensor_angles() / np.pi\n      min_angle = self.fov[0] / 180\n      max_angle = self.fov[1] / 180\n      undetected_distance = 0\n      closest_distance = 0\n    else:\n      angles = (self.agent.get_all_sensor_angles() / np.pi) * 180\n      min_angle = self.fov[0]\n      max_angle = self.fov[1]\n      undetected_distance = -1\n      closest_distance = 9999999999999\n    # filter distance detections based on object type for radar\n    if self.type == \"wall\":\n      is_walls = self.agent.get_all_walls()\n      distances = np.where(is_walls, distances, undetected_distance)\n    elif self.type == \"dog\":\n      robot_ids = self.agent.get_all_robot_ids()\n      distances = list(map(lambda i: distances[i] if categorise.is_dog(robot_ids[i]) else undetected_distance, range(len(robot_ids))))\n    elif self.type == \"sheep\":\n      robot_ids = self.agent.get_all_robot_ids()\n      distances = list(map(lambda i: distances[i] if categorise.is_sheep(robot_ids[i]) else undetected_distance, range(len(robot_ids))))\n    distances[distances == -1] = undetected_distance\n    # exclude distance detections that are outside FOV range\n    distances = [distances[i] for i in range(len(angles)) if min_angle <= angles[i] and angles[i] <= max_angle]\n    angles = [angle for angle in angles if min_angle <= angle and angle <= max_angle]\n    # get nearest distance detection to return\n    closest_index = -1\n    for i in range(len(distances)):\n      if (normalised and distances[i] > closest_distance) or (not normalised and distances[i] != -1 and distances[i] < closest_distance):\n        closest_index = i\n        closest_distance = distances[i]\n    if closest_index == -1:\n      return (undetected_distance, 0)\n    else:\n      return (distances[closest_index], angles[closest_index])\n","repo_name":"scotthallauer/sheepdogai","sub_path":"controller/radar.py","file_name":"radar.py","file_ext":"py","file_size_in_byte":2954,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"38366267116","text":"#!/usr/bin/python\n\n# (c) 2019, NetApp, Inc\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n\nANSIBLE_METADATA = {'metadata_version': '1.1',\n                    'status': ['preview'],\n                    'supported_by': 'community'}\n\nDOCUMENTATION = '''\nauthor: NetApp Ansible Team (@carchi8py) <ng-ansibleteam@netapp.com>\ndescription:\n  - Create/Delete/Modify Name Service Switch\nextends_documentation_fragment:\n  - netapp.ontap.netapp.na_ontap\nmodule: na_ontap_name_service_switch\noptions:\n  state:\n    choices: ['present', 'absent']\n    description:\n      - Whether the specified ns-switch should exist or not.\n    default: present\n    type: str\n  vserver:\n    description:\n      - Name of the vserver to use.\n    required: true\n    type: str\n  database_type:\n    description:\n      - Name services switch database.\n    choices: ['hosts','group', 'passwd', 'netgroup', 'namemap']\n    required: true\n    type: str\n  sources:\n    description:\n      - Type of sources.\n      - Possible values include files,dns,ldap,nis.\n    type: list\n    elements: str\n\nshort_description: \"NetApp ONTAP Manage name service switch\"\n'''\n\nEXAMPLES = \"\"\"\n    - name: create name service database\n      na_ontap_name_service_switch:\n        state: present\n        database_type: namemap\n        sources: files,ldap\n        vserver: \"{{ Vserver name }}\"\n        username: \"{{ netapp_username }}\"\n        password: \"{{ netapp_password }}\"\n        hostname: \"{{ netapp_hostname }}\"\n\n    - name: modify name service database sources\n      na_ontap_name_service_switch:\n        state: present\n        database_type: namemap\n        sources: files\n        vserver: \"{{ Vserver name }}\"\n        username: \"{{ netapp_username }}\"\n        password: \"{{ netapp_password }}\"\n        hostname: \"{{ netapp_hostname }}\"\n\"\"\"\n\nRETURN = \"\"\"\n\"\"\"\n\nimport traceback\nfrom ansible.module_utils.basic import AnsibleModule\nfrom ansible.module_utils._text import to_native\nimport ansible_collections.netapp.ontap.plugins.module_utils.netapp as netapp_utils\nfrom ansible_collections.netapp.ontap.plugins.module_utils.netapp_module import NetAppModule\n\nHAS_NETAPP_LIB = netapp_utils.has_netapp_lib()\n\n\nclass NetAppONTAPNsswitch(object):\n    \"\"\"\n    Class with NVMe service methods\n    \"\"\"\n\n    def __init__(self):\n\n        self.argument_spec = netapp_utils.na_ontap_host_argument_spec()\n        self.argument_spec.update(dict(\n            state=dict(required=False, type='str', choices=['present', 'absent'], default='present'),\n            vserver=dict(required=True, type='str'),\n            database_type=dict(required=True, type='str', choices=['hosts', 'group', 'passwd', 'netgroup', 'namemap']),\n            sources=dict(required=False, type='list', elements='str')\n        ))\n\n        self.module = AnsibleModule(\n            argument_spec=self.argument_spec,\n            supports_check_mode=True\n        )\n\n        self.na_helper = NetAppModule()\n        self.parameters = self.na_helper.set_parameters(self.module.params)\n\n        if HAS_NETAPP_LIB is False:\n            self.module.fail_json(msg=\"the python NetApp-Lib module is required\")\n        else:\n            self.server = netapp_utils.setup_na_ontap_zapi(module=self.module, vserver=self.parameters['vserver'])\n\n    def get_name_service_switch(self):\n        \"\"\"\n        get current name service switch config\n        :return: dict of current name service switch\n        \"\"\"\n        nss_iter = netapp_utils.zapi.NaElement('nameservice-nsswitch-get-iter')\n        nss_info = netapp_utils.zapi.NaElement('namservice-nsswitch-config-info')\n        db_type = netapp_utils.zapi.NaElement('nameservice-database')\n        db_type.set_content(self.parameters['database_type'])\n        query = netapp_utils.zapi.NaElement('query')\n        nss_info.add_child_elem(db_type)\n        query.add_child_elem(nss_info)\n        nss_iter.add_child_elem(query)\n        result = self.server.invoke_successfully(nss_iter, True)\n        return_value = None\n        if result.get_child_by_name('num-records') and int(result.get_child_content('num-records')) == 1:\n            nss_sources = result.get_child_by_name('attributes-list').get_child_by_name(\n                'namservice-nsswitch-config-info').get_child_by_name('nameservice-sources')\n            sources = [sources.get_content() for sources in nss_sources.get_children()]\n            return_value = {\n                'sources': sources\n            }\n        return return_value\n\n    def create_name_service_switch(self):\n        \"\"\"\n        create name service switch config\n        :return: None\n        \"\"\"\n        nss_create = netapp_utils.zapi.NaElement('nameservice-nsswitch-create')\n        nss_create.add_new_child('nameservice-database', self.parameters['database_type'])\n        nss_sources = netapp_utils.zapi.NaElement('nameservice-sources')\n        nss_create.add_child_elem(nss_sources)\n        for source in self.parameters['sources']:\n            nss_sources.add_new_child('nss-source-type', source.strip())\n        try:\n            self.server.invoke_successfully(nss_create,\n                                            enable_tunneling=True)\n        except netapp_utils.zapi.NaApiError as error:\n            self.module.fail_json(msg='Error on creating name service switch config on vserver %s: %s'\n                                      % (self.parameters['vserver'], to_native(error)),\n                                  exception=traceback.format_exc())\n\n    def delete_name_service_switch(self):\n        \"\"\"\n        delete name service switch\n        :return: None\n        \"\"\"\n        nss_delete = netapp_utils.zapi.NaElement.create_node_with_children(\n            'nameservice-nsswitch-destroy', **{'nameservice-database': self.parameters['database_type']})\n        try:\n            self.server.invoke_successfully(nss_delete,\n                                            enable_tunneling=True)\n        except netapp_utils.zapi.NaApiError as error:\n            self.module.fail_json(msg='Error on deleting name service switch config on vserver %s: %s'\n                                      % (self.parameters['vserver'], to_native(error)),\n                                  exception=traceback.format_exc())\n\n    def modify_name_service_switch(self, modify):\n        \"\"\"\n        modify name service switch\n        :param modify: dict of modify attributes\n        :return: None\n        \"\"\"\n        nss_modify = netapp_utils.zapi.NaElement('nameservice-nsswitch-modify')\n        nss_modify.add_new_child('nameservice-database', self.parameters['database_type'])\n        nss_sources = netapp_utils.zapi.NaElement('nameservice-sources')\n        nss_modify.add_child_elem(nss_sources)\n        if 'sources' in modify:\n            for source in self.parameters['sources']:\n                nss_sources.add_new_child('nss-source-type', source.strip())\n        try:\n            self.server.invoke_successfully(nss_modify, enable_tunneling=True)\n        except netapp_utils.zapi.NaApiError as error:\n            self.module.fail_json(msg='Error on modifying name service switch config on vserver %s: %s'\n                                  % (self.parameters['vserver'], to_native(error)),\n                                  exception=traceback.format_exc())\n\n    def apply(self):\n        netapp_utils.ems_log_event(\"na_ontap_name_service_switch\", self.server)\n        current = self.get_name_service_switch()\n        cd_action, modify = None, None\n        cd_action = self.na_helper.get_cd_action(current, self.parameters)\n        modify = self.na_helper.get_modified_attributes(current, self.parameters)\n\n        if self.na_helper.changed:\n            if self.module.check_mode:\n                pass\n            else:\n                if cd_action == 'create':\n                    self.create_name_service_switch()\n                elif cd_action == 'delete':\n                    self.delete_name_service_switch()\n                elif modify:\n                    self.modify_name_service_switch(modify)\n        self.module.exit_json(changed=self.na_helper.changed)\n\n\ndef main():\n    '''Applyoperations from playbook'''\n    nss = NetAppONTAPNsswitch()\n    nss.apply()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"ansible-collections/netapp","sub_path":"ansible_collections/netapp/ontap/plugins/modules/na_ontap_name_service_switch.py","file_name":"na_ontap_name_service_switch.py","file_ext":"py","file_size_in_byte":8301,"program_lang":"python","lang":"en","doc_type":"code","stars":46,"dataset":"github-code","pt":"86"}
+{"seq_id":"11301200711","text":"import os, sys, argparse, logging\nimport json\nimport zlib\nimport base64\nimport struct\nimport urllib.request\nimport urllib.error\n\n\ntry: import NexonAPI\nexcept ImportError:\n\tNexonAPI = None\nelse:\n\timport getpass\n#endtry\n\n\nclass PatchServerError(Exception): pass\n\nclass PatchServer:\n\t# Ideally one would log in and retrieve this from the Nexon API, but I'm not going to publish that!\n\tGAME_ID = \"10200\"\n\tBASE_URL = \"https://download2.nexon.net/Game/nxl/games/\" + GAME_ID + \"/\"\n\n\tHASH_URL = \"{gameID}.{version}R.manifest.hash\"\n\tMANIFEST_URL = \"{hash}\"\n\tPART_URL = \"{gameID}/{part:.2}/{part}\"\n\n\tdef __init__(self):\n\t\t# But if you have a library for it already...\n\t\tif NexonAPI:\n\t\t\tself.BASE_URL = NexonAPI.getBaseURL()\n\t\t#endif\n\n\t\tself.local_version = None\n\t\tself.target_version = None\n\n\t\tself.manifest = None\n\t\tself.manifestVersion = None\n\t#enddef\n\n\tdef _getURL(self, url, fileName=None, serverName=None):\n\t\ttry:\n\t\t\treturn urllib.request.urlopen(url)\n\t\texcept urllib.error.HTTPError as err:\n\t\t\tif fileName is None: fileName = url.split(\"/\")[-1]\n\t\t\traise PatchServerError(\"Error retrieving {}: {}\".format(fileName, str(err)))\n\t\texcept urllib.error.URLError as err:\n\t\t\tif serverName is None: serverName = url.split(\"/\", maxsplit=3)[2]\n\t\t\traise PatchServerError(\"Could not connect {}: {}\".format(serverName, str(err)))\n\t\t#endtry\n\t#enddfe\n\n\tdef getWebLaunchStatus(self):\n\t\t\"\"\" Returns true if the web launcher thinks the game is up. \"\"\"\n\t\tconn = self._getURL(\"http://www.nexon.net/json/game_status.js\", \"status file\")\n\n\t\t# Have to de-JSONp this.\n\t\tresponse = json.loads(conn.read()[len(\"nexon.games.playGame(\") : -2].decode(\"utf8\"))\n\n\t\t# This is Mabi's ID here\n\t\tstatus = response[\"SVG012\"]\n\t\tlogging.info(\"Web launch status is: {}.\".format(\"UP\" if status else \"DOWN\"))\n\t\treturn status\n\t#enddef\n\n\tdef legacyGetLatestVersion(self):\n\t\t\"\"\" Get the latest version as reported by the legacy launcher info. \"\"\"\n\t\tconn = self._getURL(\"http://mabipatchinfo.nexon.net/patch/patch.txt\", \"patch info file\")\n\n\t\t# Format is a list of var=val, one per line.\n\t\ttxt = conn.read().decode(\"utf8\").split(\"\\n\")\n\t\tfor line in txt:\n\t\t\tvar, val = line.split(\"=\", maxsplit=1)\n\t\t\tif var.strip() == \"main_version\": return int(val.strip())\n\t\t#endfor\n\n\t\traise PatchServerError(\"Version not found in patch info.\")\n\t#enddef\n\n\tdef getLatestVersion(self):\n\t\t\"\"\" Get the latest version as reported by some server. \"\"\"\n\t\tif NexonAPI is None:\n\t\t\tver = self.legacyGetLatestVersion()\n\t\telse:\n\t\t\tver = NexonAPI.getLatestVersion()\n\t\t#endif\n\n\t\tlogging.info(\"Read latest version as {}.\".format(ver))\n\t\tself.target_version = ver\n\t\treturn ver\n\t#enddef\n\n\tdef getLocalVersion(self, path):\n\t\t\"\"\" Get the verion of the Mabinogi installed at the given path. \"\"\"\n\t\ttry:\n\t\t\twith open(os.path.join(path, \"version.dat\"), \"rb\") as f:\n\t\t\t\tver = struct.unpack(\"<I\", f.read())[0]\n\n\t\t\t\tself.local_version = ver\n\t\t\t\tlogging.info(\"Read local version as {}.\".format(ver))\n\t\t\t\treturn ver\n\t\t\t#endwith\n\t\texcept FileNotFoundError:\n\t\t\traise PatchServerError(\"Unable to find version.dat at \" + path)\n\t\t#endif\n\t#enddef\n\n\tdef getManifest(self, version=None):\n\t\t\"\"\" Get the manifest file from the server and decode it. \"\"\"\n\t\tversion = version or self.target_version\n\n\t\tif version == self.manifestVersion:\n\t\t\tlogging.debug(\"Reusing manifest from cache.\")\n\t\t\treturn self.manifest\n\t\t#endif\n\n\t\tproperties = {\n\t\t\t\"gameID\": self.GAME_ID,\n\t\t\t\"version\": version\n\t\t}\n\n\t\t# First download the hash\n\t\thashURL = self.BASE_URL + self.HASH_URL.format(**properties)\n\n\t\tconn = self._getURL(hashURL, \"hash file (\" + hashURL + \")\", \"patch server\")\n\n\t\tproperties[\"hash\"] = conn.read().strip().decode(\"utf8\")\n\n\t\tlogging.debug(\"Hash downloaded.\")\n\n\t\t# Now download the manifest\n\t\tmanifestURL = self.BASE_URL + self.MANIFEST_URL.format(**properties)\n\n\t\tconn = self._getURL(manifestURL, \"manifest file (\" + manifestURL + \")\", \"patch server\")\n\t\t\n\t\tmanifest = conn.read()\n\t\tmanifest = zlib.decompress(manifest)\n\t\t# TODO: handle zlib errors\n\n\t\tlogging.debug(\"Manifest decompressed.\")\n\n\t\tmanifest = json.loads(manifest.decode(\"utf8\"))\n\n\t\t# Decode filenames.\n\t\tfiles = manifest[\"files\"]\n\t\tkeys = list(files.keys())\n\t\tencoding = manifest[\"filepath_encoding\"]\n\t\tfor key in keys:\n\t\t\t# Decode the filename.\n\t\t\tfilename = os.path.join(*base64.b64decode(key).decode(encoding).split(\"\\\\\"))\n\t\t\tfiles[filename] = files[key]\n\t\t\tdel files[key]\n\t\t#endfor\n\n\t\tself.manifest = manifest\n\t\tself.manifestVersion = version\n\n\t\treturn manifest\n\t#enddef\n\n\tdef dumpManifest(self, filename, manifest=None):\n\t\t\"\"\" Dump the given or last retrieved manifest to a file. \"\"\"\n\t\tmanifest = manifest or self.manifest\n\n\t\twith open(filename, \"w\") as f:\n\t\t\tjson.dump(manifest, f, indent=4, sort_keys=True)\n\t\t#endwith\n\t#enddef\n\n\tdef diffManifests(self, m1, m2):\n\t\t\"\"\" Diff two manifests' files and return whether to create, update, or delete each changed file. \"\"\"\n\t\tf1, f2 = m1[\"files\"], m2[\"files\"]\n\t\tchanges, statuses = {}, {}\n\t\tupdated, created, deleted = 0, 0, 0\n\n\t\tfor fn, d2 in f2.items():\n\t\t\tif fn in f1:\n\t\t\t\t# Both versions have this file, compare mtime.\n\t\t\t\td1 = f1[fn]\n\t\t\t\tif d1[\"mtime\"] != d2[\"mtime\"]:\n\t\t\t\t\tchanges[fn] = d2\n\t\t\t\t\tstatuses[fn] = \"update\"\n\t\t\t\t\tupdated += 1\n\t\t\t\t#endif\n\t\t\telse:\n\t\t\t\t# This is a new file.\n\t\t\t\tchanges[fn] = d2\n\t\t\t\tstatuses[fn] = \"create\"\n\t\t\t\tcreated += 1\n\t\t\t#endif\n\t\t#endfor\n\n\t\tfor fn in f1.keys():\n\t\t\tif fn not in f2:\n\t\t\t\t# This file was deleted.\n\t\t\t\tstatuses[fn] = \"delete\"\n\t\t\t\tdeleted += 1\n\t\t\t#endif\n\t\t#endfor\n\n\t\tlogging.info(\"Files/dirs affected between the specified manifests: {} to update, {} to create, {} to delete\".format(updated, created, deleted))\n\n\t\treturn changes, statuses\n\t#enddef\n\n\tdef diffManifestWithFileSystem(self, base, manifest=None):\n\t\t\"\"\" Check the manifest against the path for updating. \"\"\"\n\t\tmanifest = manifest or self.manifest\n\t\tfiles = manifest[\"files\"]\n\t\tchanges, statuses = {}, {}\n\t\tupdated, created = 0, 0\n\n\t\tfor fn, data in files.items():\n\t\t\tpath = os.path.join(base, fn)\n\t\t\ttry:\n\t\t\t\tfsize = os.path.getsize(path)\n\t\t\t\tmtime = int(os.path.getmtime(path))\n\t\t\t\tif mtime != data[\"mtime\"] or fsize != data[\"fsize\"]:\n\t\t\t\t\tchanges[fn] = data\n\t\t\t\t\tstatuses[fn] = \"update\"\n\t\t\t\t\tupdated += 1\n\t\t\t\t#endif\n\n\t\t\texcept (FileNotFoundError, NotADirectoryError):\n\t\t\t\tchanges[fn] = data\n\t\t\t\tstatuses[fn] = \"create\"\n\t\t\t\tcreated += 1\n\t\t\t#endtry\n\t\t#endfor\n\n\t\tlogging.info(\"Files/dirs affected in update: {} to update, {} to create\".format(updated, created))\n\n\t\treturn changes, statuses\n\t#enddef\n\n\tdef downloadFiles(self, path, files):\n\t\t\"\"\" The file list to download to path. \"\"\"\n\t\tfor fn, data in files.items():\n\t\t\t# Download parts.\n\t\t\tfsize = data[\"objects_fsize\"]\n\n\t\t\tfpath = os.path.join(path, fn)\n\n\t\t\t# Don't worry about creating new folders, whatever checks the statuses should do that.\n\t\t\ttry:\n\t\t\t\tif len(data[\"objects\"]) and data[\"objects\"][0] == \"__DIR__\":\n\t\t\t\t\tos.makedirs(fpath, exist_ok=True)\n\t\t\t\t\tcontinue\n\t\t\t\t#endif\n\n\t\t\t\twith open(fpath, \"wb\") as f:\n\t\t\t\t\tlogging.info(\"Downloading file \" + fn)\n\t\t\t\t\tfor i, obj in enumerate(data[\"objects\"]):\n\t\t\t\t\t\turl = self.BASE_URL + self.PART_URL.format(gameID = self.GAME_ID, part = obj)\n\t\t\t\t\t\tconn = self._getURL(url, obj, \"patch server\")\n\n\t\t\t\t\t\tcompressed = conn.read()\n\t\t\t\t\t\tclen = len(compressed)\n\n\t\t\t\t\t\tlogging.info(\"  Downloaded part \" + obj)\n\n\t\t\t\t\t\tdecompressed = zlib.decompress(compressed)\n\t\t\t\t\t\tlogging.debug(\"  Decompressed part \" + obj)\n\n\t\t\t\t\t\tdlen = len(decompressed)\n\n\t\t\t\t\t\t# I dunno man\n\t\t\t\t\t\tif clen != fsize[i] and dlen != fsize[i]:\n\t\t\t\t\t\t\tlogging.warn(\"  Unexpected filesize {} for part {}, expecting {}.\".format(dlen, obj, fsize[i]))\n\t\t\t\t\t\t#endif\n\n\t\t\t\t\t\tf.write(decompressed)\n\t\t\t\t\t\tdel decompressed\n\t\t\t\t\t#endfor\n\t\t\t\t#endwith\n\n\t\t\t\t# TODO: Check fsize\n\n\t\t\t\t# TODO: Don't change access time\n\t\t\t\tos.utime(fpath, times=(data[\"mtime\"], data[\"mtime\"]))\n\n\t\t\texcept PatchServerError as err:\n\t\t\t\tlogging.error(\"Failed to download file {}: {}\".format(fn, str(err)))\n\t\t\t\ttry: os.remove(path)\n\t\t\t\texcept OSError: pass\n\t\t\texcept IsADirectoryError:\n\t\t\t\tlogging.error(\"Tried to overwrite a folder with the file \" + fn)\n\t\t\t#endtry\n\t\t#endfor\n\t#enddef\n\n\tdef updateFileSystem(self, base, statuses):\n\t\t\"\"\" Create new directories and delete files. \"\"\"\n\t\tfor fn, action in statuses.items():\n\t\t\tif action in [\"create\", \"update\"]:\n\t\t\t\tpath = os.path.join(base, os.path.dirname(fn))\n\t\t\t\tos.makedirs(path, exist_ok=True)\n\t\t\telif action == \"delete\":\n\t\t\t\tpath = os.path.join(base, fn)\n\t\t\t\ttry: os.remove(path)\n\t\t\t\texcept OSError: pass\n\t\t\t#endif\n\t\t#endfor\n\t#enddef\n\n\t# Right now the dumb patcher system downloads from 183_full.pack and all the x_to_y.pack files after that\n\t# this is slow and gross so I hope they change it eventually. If they do, the code to handle it would\n\t# probably go in these functions, depending on how it's done.\n\n\tdef update(self, path):\n\t\t\"\"\" Update the installation. \"\"\"\n\t\tver = self.getLatestVersion()\n\n\t\tmanifest = self.getManifest(ver)\n\n\t\tchanges, statuses = self.diffManifestWithFileSystem(path, manifest)\n\t\tself.updateFileSystem(path, statuses)\n\n\t\t# FUTURE?: Select only local_to_latest.pack if available.\n\n\t\tself.downloadFiles(path, changes)\n\t#enddef\n\n\tdef _ver(self, path, f, t):\n\t\tif f and t is None:\n\t\t\tf, t = f - 1, f\n\t\telse:\n\t\t\ttry:\n\t\t\t\tf = f or self.local_version or self.getLocalVersion(path)\n\t\t\texcept PatchServerError:\n\t\t\t\tf = t - 1\n\t\t\t#endtry\n\t\t\t\n\t\t\tt = t or self.target_version or self.getLatestVersion()\n\t\t#endif\n\n\t\treturn (f - 1 if f == t else f), t\n\t#enddef\n\n\tdef download(self, path, f=None, t=None):\n\t\t\"\"\" Download patch f_to_t. \"\"\"\n\t\tf, t = self._ver(path, f, t)\n\n\t\tm1 = self.getManifest(f)\n\t\tm2 = self.getManifest(t)\n\n\t\tchanges, statuses = self.diffManifests(m1, m2)\n\t\tself.updateFileSystem(path, statuses)\n\n\t\t# FUTURE?: Select only f_to_t.pack if available.\n\n\t\tself.downloadFiles(path, changes)\n\t#enddef\n\n\tdef downloadFull(self, path, version=None):\n\t\t\"\"\" Download all the files for this version. \"\"\"\n\t\tversion = version or self.target_version or self.getLatestVersion()\n\n\t\tmanifest = self.getManifest(version)\n\n\t\tfiles = manifest[\"files\"]\n\n\t\tstatuses = {name: \"create\" for name in files.keys()}\n\t\tself.updateFileSystem(path, statuses)\n\n\t\t# FUTURE?: Select only version_full.pack if available.\n\n\t\tself.downloadFiles(path, files)\n\t#enddef\n\n\tdef continueDownload(self, path, f=None, t=None):\n\t\t\"\"\" Continue downloading an update. \"\"\"\n\t\tf, t = self._ver(path, f, t)\n\n\t\tm1 = self.getManifest(f)\n\t\tm2 = self.getManifest(t)\n\n\t\tchanges, statuses = self.diffManifests(m1, m2)\n\t\tchanges, statuses = self.diffManifestWithFileSystem(path, {\"files\": changes})\n\t\tself.updateFileSystem(path, statuses)\n\n\t\tself.downloadFiles(path, changes)\n\t#enddef\n\n\tdef continueDownloadFull(self, path, version=None):\n\t\t\"\"\" Continue downloading an update. \"\"\"\n\t\tversion = version or self.target_version or self.getLatestVersion()\n\n\t\tmanifest = self.getManifest(version)\n\n\t\tchanges, statuses = self.diffManifestWithFileSystem(path, manifest)\n\t\tself.updateFileSystem(path, statuses)\n\n\t\t# FUTURE?: Select only local_to_latest.pack if available.\n\n\t\tself.downloadFiles(path, changes)\n\t#enddef\n#endclass\n\n\ndef main(args):\n\tparser = argparse.ArgumentParser(description=\"Download Mabinogi NA patches.\")\n\tparser.add_argument(\"-u\", \"--update\", action=\"store_true\",\n\t\thelp=\"Update the mabi installation at the given location.\")\n\tparser.add_argument(\"-d\", \"--download\", default=0,\n\t\thelp=\"Download a specific version.\")\n\tparser.add_argument(\"-f\", \"--full\", action=\"store_true\",\n\t\thelp=\"Download all the files instead of just updating.\")\n\tparser.add_argument(\"-F\", \"--from\", dest=\"fromVer\", default=0,\n\t\thelp=\"Consider the installation to be this version.\")\n\tparser.add_argument(\"-m\", \"--manifest\", action=\"store_true\",\n\t\thelp=\"Download the manifest to manifest.json\")\n\tparser.add_argument(\"-v\", \"--verbose\", action=\"count\",\n\t\thelp=\"Print extra information.\")\n\tparser.add_argument(\"path\", nargs=\"?\", default=\"\",\n\t\thelp=\"Base Mabinogi installation directory.\")\n\tif NexonAPI:\n\t\tparser.add_argument(\"-u\", \"--username\", default=None,\n\t\t\thelp=\"Username to log in with.\")\n\t#endif\n\t\n\targs = parser.parse_args(args)\n\n\tlogging.basicConfig(\n\t\tlevel = {\n\t\t\t0: logging.WARNING,\n\t\t\t1: logging.INFO,\n\t\t}.get(args.verbose, logging.DEBUG),\n\t\tstyle = \"{\", format=\"{levelname}: {message}\",\n\t)\n\n\tif NexonAPI:\n\t\tusername = args.username\n\t\twhile not username: username = input(\"Enter username: \").strip()\n\n\t\tpassword = None\n\t\twhile not password: password = getpass.getpass(\"Enter password: \")\n\n\t\tNexonAPI.login(username, password)\n\t#endif\n\n\tpatcher = PatchServer()\n\n\tif not args.download and not patcher.getWebLaunchStatus():\n\t\tanswer = input(\n\t\t\t\"The web launcher indicates the game is down.\\n\"\n\t\t\t\"If the game is down for maintainence for the current patch,\\n\"\n\t\t\t\"there is a chance the patch could be changed before the game is back up.\\n\"\n\t\t\t\"Do you want to continue (Y/N)? \")\n\t\tif answer.upper()[:1] != \"Y\": return 0\n\t#endif\n\n\tpath = args.path or os.getcwd()\n\n\ttry:\n\t\ttarget = int(args.download)\n\t\tversion = (int(args.fromVer), target)\n\texcept ValueError:\n\t\tlogging.error(\"Please enter a number for the version.\")\n\t\treturn 1\n\t#endtry\n\n\tif args.manifest:\n\t\tpatcher.getManifest(target)\n\t\tpatcher.dumpManifest(os.path.join(path, \"manifest.json\"))\n\n\t\tprint(\"Dumpped manifest to manifest.json\")\n\t#endif\n\n\tif args.update:\n\t\tif args.full:\n\t\t\tpatcher.continueDownloadFull(path, target)\n\t\telif args.download:\n\t\t\tpatcher.continueDownload(path, *version)\n\t\telse:\n\t\t\tpatcher.update(path)\n\t\t#endif\n\n\t\tprint(\"Update complete.\")\n\telse:\n\t\tif args.full:\n\t\t\tpatcher.downloadFull(path, target)\n\t\telse:\n\t\t\tpatcher.download(path, *version)\n\t\t#endif\n\n\t\tprint(\"Download complete.\")\n\t#endif\n\n\treturn 0\n#enddef\n\nif __name__ == \"__main__\":\n\ttry: sys.exit(main(sys.argv[1:]))\n\texcept (KeyboardInterrupt, EOFError):\n\t\tprint(\"\\nProgram terminated by user.\")\n\texcept PatchServerError as err:\n\t\tlogging.error(str(err))\n\t#endtry\n#endif\n","repo_name":"logicplace/mabi-patch-thing","sub_path":"download.py","file_name":"download.py","file_ext":"py","file_size_in_byte":13751,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"30178127703","text":"# Prompt: https://www.hackerrank.com/challenges/ctci-ice-cream-parlor/problem?h_l=interview&playlist_slugs%5B%5D=interview-preparation-kit&playlist_slugs%5B%5D=search\n\n\ndef find_flavors(cost, money):\n\n    flavor_id ={}\n\n\n    for i in range(len(cost)):\n        change = money - cost[i]\n\n        if change in flavor_id:\n            print(flavor_id[change]+1, i+1)\n\n        flavor_id[cost[i]] = i\n\nprint(find_flavors([2,1,3,5,6],5))","repo_name":"mashikro/code-challenges","sub_path":"ice_cream_parlor.py","file_name":"ice_cream_parlor.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"18257134572","text":"from fpdf import FPDF\n\nclass MyPDF(FPDF):\n    def header(self):\n        self.set_font('Arial', 'B', 15)\n        self.cell(80)\n        self.cell(30, 10, 'CS50 Shirtificate', 0, 0, 'C')\n        self.ln(20)\n\npdf = MyPDF(orientation='P', unit='mm', format='A4')\npdf.add_page()\npdf.set_auto_page_break(False)\n\npdf.image('shirtificate.png', x=55, y=40, w=100)\n\npdf.set_font('Arial', 'B', 32)\npdf.set_text_color(255, 255, 255)\npdf.cell(0, -60, 'John Doe', 0, 0, 'C', fill=True)\n\npdf.output('shirtificate.pdf')\n","repo_name":"MariamBurjgi/cs50_tasks","sub_path":"shirtificate/shirtificate.py","file_name":"shirtificate.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"72388486044","text":"import sys\n\nimport tensorflow as tf\nimport numpy as np\n\nimport cv2\n\nfrom collections import deque\nimport timeit\nimport random\n\nfrom environment.RiverCrossingEnv import RiverCrossingEnv\nfrom agent import AgentModel as ag\n\nfrom planning.ValueIteration import ValueIteration as ValueIteration\n\nMIN_REPLAY_SIZE = 1000\n\nRANDOM_SEED = 5\ntf.random.set_seed(RANDOM_SEED)\nnp.random.seed(RANDOM_SEED)\n\nQUIET = True\n\ndef main():\n    # Building environment\n    shape = (10, 10)\n    env = RiverCrossingEnv(shape, state_as_img=True, state_img_width=500)\n\n    #print(env.reset())\n\n    #RiverCrossingEnv.draw_img_state(shape, policy=env.safe_policy, state_img_width=500)\n    data = ''\n    gamma = 0.99\n    for lamb in [-1.0, -0.5, -0.2, -0.1, 0.0, 0.1, 0.2, 0.5, 1.0]:\n        policy, v, steps, updates, diffs, v_history = ValueIteration.run(env, lamb, gamma)\n        safe_points = ValueIteration.find_safe_points(env, policy)\n        data+='{},'.format(safe_points)\n\n    print(data)\n\n    h, w = shape\n    for y in range(h):\n        for x in range(w):\n            s = x + (y * w)\n            #RiverCrossingEnv.draw_img_state(shape, s)\n    #process_state_image(0)\n\ndef process_state_image(state):\n    image = cv2.imread('environment/img/river_{}.png'.format(state))\n    image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    cv2.imwrite('environment/img/river_{}_2.png'.format(state), image_gray)\n    image_gray = image_gray.astype(float)\n    image_gray /= 255.0\n\n    image = image.astype(float)\n    image /= 255.0\n\n    for i in range(len(image)):\n        for j in range(len(image[i])):\n            #for c in range(len(image[i][j])):\n            print(image[i][j], image_gray[i][j])\n\n    return image_gray\n\n\nif __name__ == \"__main__\":\n    main()\n\n\n\n","repo_name":"dulpneto/RiverCrossing-DQN","sub_path":"TestImg.py","file_name":"TestImg.py","file_ext":"py","file_size_in_byte":1734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"16229076269","text":"# Given a list of integers and a target integer, write a function that expresses the target\n#  integer by inserting + and - operations between the list items. For example:\n\n\ndef findTotalWays(arr, i, k):\n\n    # If target is reached, return 1\n    if (k == 0 and i == len(arr)):\n        return 1\n\n    # If all elements are processed and\n    # target is not reached, return 0\n    if (i >= len(arr)):\n        return 0\n\n    # add it to target\n    return (findTotalWays(arr, i + 1, k) +\n            findTotalWays(arr, i + 1, k - arr[i]) +\n            findTotalWays(arr, i + 1, k + arr[i]))\n\n\n# In terms of time complexity, the function is in constant time O(1) but linear space because the\n# function is recursive in nature and needs more space in the call stack.\n# I had help from geek of geeks\n","repo_name":"Jeon316upzx/Others","sub_path":"nelititest/question4.py","file_name":"question4.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"8497308738","text":"from visualizers import surface_plot\nimport data.sample\nimport graph\nimport train\n\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pdb\nimport os\n\ndef get_prediction_surface(\n        network,\n        sess,\n        domain,\n        steps):\n    \n    meshgrid, grid_data = data.sample.sample_grid_data(\n        domain,\n        steps,\n        reshape=True)\n\n    inputs = {'data' : grid_data, 'is_training' : False}\n    feed_dict = network.create_feed_dict(inputs)\n    prob = sess.run(network.inference.get_prob(),feed_dict=feed_dict)\n\n    fig = surface_plot.pred_surf_plot(\n        meshgrid,\n        prob[:,0],\n        domain,\n        rstride=5,\n        cstride=5,\n        pred_format='array')\n\n    return fig\n\n\ndef get_gt_surface(\n        func,\n        domain,\n        steps):\n\n    meshgrid, grid_data = data.sample.sample_grid_data(\n        domain,\n        steps,\n        reshape=True)\n\n    \n    prob_1 = 0.5*(func(grid_data)+1.)\n\n    fig = surface_plot.pred_surf_plot(\n        meshgrid,\n        prob_1,\n        domain,\n        rstride=5,\n        cstride=5,\n        pred_format='array')\n\n    return fig\n\n    \ndef run(c):\n    train_data = data.sample.sample_training_data(\n        c.num_train_samples,\n        c.domain,\n        seed=0)\n\n    network = graph.Graph(\n        c.input_dims,\n        c.hidden_units,\n        c.residual,\n        c.activation,\n        c.keep_prob,\n        c.use_batchnorm,\n        c.learning_rate)\n\n    sess = tf.Session(graph=network.tf_graph)\n\n    labels = c.decision_func(train_data)\n\n    train.train_model(\n        network,\n        sess,\n        train_data,\n        labels,\n        c.batch_size,\n        c.num_epochs,\n        seed=1)\n\n    experiments_dir = c.get_experiments_dir()\n\n    pred_filepath = os.path.join(\n        experiments_dir,\n        c.pred_filename)\n\n    gt_filepath = os.path.join(\n        experiments_dir,\n        c.gt_filename)\n\n    fig = get_prediction_surface(\n        network,\n        sess,\n        c.domain,\n        c.steps)\n    \n    plt.savefig(\n        pred_filepath,\n        dpi=300,\n        bbox_inches='tight',\n        pad_inches = 0.3)\n\n    fig = get_gt_surface(\n        c.decision_func,\n        c.domain,\n        c.steps)\n\n    plt.savefig(\n        gt_filepath,\n        dpi=300,\n        bbox_inches='tight',\n        pad_inches = 0.3)\n    \n\n    sess.close()\n","repo_name":"BigRedT/nn_pred_surf","sub_path":"experiments/run_experiment.py","file_name":"run_experiment.py","file_ext":"py","file_size_in_byte":2344,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"40636011168","text":"\"\"\"Holds the Historical Feature Store writer class.\"\"\"\n\nimport os\nfrom typing import Any\n\nfrom pyspark.sql.dataframe import DataFrame\nfrom pyspark.sql.functions import dayofmonth, month, year\n\nfrom butterfree.clients import SparkClient\nfrom butterfree.configs import environment\nfrom butterfree.configs.db import AbstractWriteConfig, MetastoreConfig\nfrom butterfree.constants import columns\nfrom butterfree.constants.spark_constants import DEFAULT_NUM_PARTITIONS\nfrom butterfree.dataframe_service import repartition_df\nfrom butterfree.hooks import Hook\nfrom butterfree.hooks.schema_compatibility import SparkTableSchemaCompatibilityHook\nfrom butterfree.load.writers.writer import Writer\nfrom butterfree.transform import FeatureSet\n\n\nclass HistoricalFeatureStoreWriter(Writer):\n    \"\"\"Enable writing feature sets into the Historical Feature Store.\n\n    Attributes:\n        db_config: Datalake configuration for Spark, by default on AWS S3.\n            For more information check module 'butterfree.db.configs'.\n        database: database name to use in Spark metastore.\n            By default FEATURE_STORE_HISTORICAL_DATABASE environment variable.\n        num_partitions: value to use when applying repartition on the df before save.\n        validation_threshold: lower and upper tolerance to using in count validation.\n            The default value is defined in DEFAULT_VALIDATION_THRESHOLD property.\n            For example: with a validation_threshold = 0.01 and a given calculated\n            count on the dataframe equal to 100000 records, if the feature store\n            return a count equal to 995000 an error will not be thrown.\n            Use validation_threshold = 0 to not use tolerance in the validation.\n        debug_mode: \"dry run\" mode, write the result to a temporary view.\n\n    Example:\n        Simple example regarding HistoricalFeatureStoreWriter class instantiation.\n        We can instantiate this class without db configurations, so the class get the\n        S3Config() where it provides default configurations about AWS S3 service.\n\n    >>> spark_client = SparkClient()\n    >>> writer = HistoricalFeatureStoreWriter()\n    >>> writer.write(feature_set=feature_set,\n       ...           dataframe=dataframe,\n       ...           spark_client=spark_client)\n\n        However, we can define the db configurations,\n        like write mode, file format and S3 bucket,\n        and provide them to HistoricalFeatureStoreWriter.\n\n    >>> spark_client = SparkClient()\n    >>> config = MetastoreConfig(path=\"my_s3_bucket_name\",\n        ...               mode=\"overwrite\",\n        ...               format_=\"parquet\")\n    >>> writer = HistoricalFeatureStoreWriter(db_config=config)\n    >>> writer.write(feature_set=feature_set,\n       ...           dataframe=dataframe,\n       ...           spark_client=spark_client)\n\n        For what settings you can use on S3Config and default settings,\n        to read S3Config class.\n\n        We can write with interval mode, where HistoricalFeatureStoreWrite\n        will need to use Dynamic Partition Inserts,\n        the behaviour of OVERWRITE keyword is controlled by\n        spark.sql.sources.partitionOverwriteMode configuration property.\n        The dynamic overwrite mode is enabled Spark will only delete the\n        partitions for which it has data to be written to.\n        All the other partitions remain intact.\n\n    >>> spark_client = SparkClient()\n    >>> writer = HistoricalFeatureStoreWriter(interval_mode=True)\n    >>> writer.write(feature_set=feature_set,\n       ...           dataframe=dataframe,\n       ...           spark_client=spark_client)\n\n        We can instantiate HistoricalFeatureStoreWriter class to validate the df\n        to be written.\n\n    >>> spark_client = SparkClient()\n    >>> writer = HistoricalFeatureStoreWriter()\n    >>> writer.validate(feature_set=feature_set,\n       ...              dataframe=dataframe,\n       ...              spark_client=spark_client)\n\n        Both methods (write and validate) will need the Spark Client, Feature Set\n        and DataFrame, to write or to validate, according to the Writer's arguments.\n\n        P.S.: When writing, the HistoricalFeatureStoreWrite partitions the data to\n        improve queries performance. The data is stored in partition folders in AWS S3\n        based on time (per year, month and day).\n\n    \"\"\"\n\n    PARTITION_BY = [\n        columns.PARTITION_YEAR,\n        columns.PARTITION_MONTH,\n        columns.PARTITION_DAY,\n    ]\n\n    DEFAULT_VALIDATION_THRESHOLD = 0.01\n\n    __name__ = \"Historical Feature Store Writer\"\n\n    def __init__(\n        self,\n        db_config: AbstractWriteConfig = None,\n        database: str = None,\n        num_partitions: int = None,\n        validation_threshold: float = DEFAULT_VALIDATION_THRESHOLD,\n        debug_mode: bool = False,\n        interval_mode: bool = False,\n        check_schema_hook: Hook = None,\n        row_count_validation: bool = True,\n    ):\n        super(HistoricalFeatureStoreWriter, self).__init__(\n            db_config or MetastoreConfig(),\n            debug_mode,\n            interval_mode,\n            False,\n            row_count_validation,\n        )\n        self.database = database or environment.get_variable(\n            \"FEATURE_STORE_HISTORICAL_DATABASE\"\n        )\n        self.num_partitions = num_partitions or DEFAULT_NUM_PARTITIONS\n        self.validation_threshold = validation_threshold\n        self.check_schema_hook = check_schema_hook\n\n    def write(\n        self, feature_set: FeatureSet, dataframe: DataFrame, spark_client: SparkClient,\n    ) -> None:\n        \"\"\"Loads the data from a feature set into the Historical Feature Store.\n\n        Args:\n            feature_set: object processed with feature_set informations.\n            dataframe: spark dataframe containing data from a feature set.\n            spark_client: client for spark connections with external services.\n\n        If the debug_mode is set to True, a temporary table with a name in the format:\n        historical_feature_store__{feature_set.name} will be created instead of writing\n        to the real historical feature store.\n\n        \"\"\"\n        dataframe = self._create_partitions(dataframe)\n\n        dataframe = self._apply_transformations(dataframe)\n\n        if self.interval_mode:\n            partition_overwrite_mode = spark_client.conn.conf.get(\n                \"spark.sql.sources.partitionOverwriteMode\"\n            ).lower()\n\n            if partition_overwrite_mode != \"dynamic\":\n                raise RuntimeError(\n                    \"m=load_incremental_table, \"\n                    \"spark.sql.sources.partitionOverwriteMode={}, \"\n                    \"msg=partitionOverwriteMode have to \"\n                    \"be configured to 'dynamic'\".format(partition_overwrite_mode)\n                )\n\n        if self.debug_mode:\n            spark_client.create_temporary_view(\n                dataframe=dataframe,\n                name=f\"historical_feature_store__{feature_set.name}\",\n            )\n            return\n\n        s3_key = os.path.join(\"historical\", feature_set.entity, feature_set.name)\n\n        spark_client.write_table(\n            dataframe=dataframe,\n            database=self.database,\n            table_name=feature_set.name,\n            partition_by=self.PARTITION_BY,\n            **self.db_config.get_options(s3_key),\n        )\n\n    def _assert_validation_count(\n        self, table_name: str, written_count: int, dataframe_count: int\n    ) -> None:\n        lower_bound = (1 - self.validation_threshold) * written_count\n        upper_bound = (1 + self.validation_threshold) * written_count\n        validation = lower_bound <= dataframe_count <= upper_bound\n        assert validation, (\n            \"Data written to the Historical Feature Store and read back \"\n            f\"from {table_name} has a different count than the feature set dataframe. \"\n            f\"\\nNumber of rows in {table_name}: {written_count}.\"\n            f\"\\nNumber of rows in the dataframe: {dataframe_count}.\"\n        )\n\n    def validate(\n        self, feature_set: FeatureSet, dataframe: DataFrame, spark_client: SparkClient\n    ) -> None:\n        \"\"\"Calculate dataframe rows to validate data into Feature Store.\n\n        Args:\n            feature_set: object processed with feature_set informations.\n            dataframe: spark dataframe containing data from a feature set.\n            spark_client: client for spark connections with external services.\n\n        Raises:\n            AssertionError: if count of written data doesn't match count in current\n                feature set dataframe.\n        \"\"\"\n        table_name = (\n            os.path.join(\"historical\", feature_set.entity, feature_set.name)\n            if self.interval_mode and not self.debug_mode\n            else (\n                f\"{self.database}.{feature_set.name}\"\n                if not self.debug_mode\n                else f\"historical_feature_store__{feature_set.name}\"\n            )\n        )\n\n        written_count = (\n            spark_client.read(\n                self.db_config.format_,\n                path=self.db_config.get_path_with_partitions(\n                    table_name, self._create_partitions(dataframe)\n                ),\n            ).count()\n            if self.interval_mode and not self.debug_mode\n            else spark_client.read_table(table_name).count()\n        )\n\n        dataframe_count = dataframe.count()\n\n        self._assert_validation_count(table_name, written_count, dataframe_count)\n\n    def _create_partitions(self, dataframe: DataFrame) -> DataFrame:\n        # create year partition column\n        dataframe = dataframe.withColumn(\n            columns.PARTITION_YEAR, year(dataframe[columns.TIMESTAMP_COLUMN])\n        )\n        # create month partition column\n        dataframe = dataframe.withColumn(\n            columns.PARTITION_MONTH, month(dataframe[columns.TIMESTAMP_COLUMN])\n        )\n        # create day partition column\n        dataframe = dataframe.withColumn(\n            columns.PARTITION_DAY, dayofmonth(dataframe[columns.TIMESTAMP_COLUMN])\n        )\n        return repartition_df(dataframe, self.PARTITION_BY, self.num_partitions)\n\n    def check_schema(\n        self, client: Any, dataframe: DataFrame, table_name: str, database: str = None\n    ) -> DataFrame:\n        \"\"\"Instantiate the schema check hook to check schema between dataframe and database.\n\n        Args:\n            client: client for Spark or Cassandra connections with external services.\n            dataframe: Spark dataframe containing data from a feature set.\n            table_name: table name where the dataframe will be saved.\n            database: database name where the dataframe will be saved.\n        \"\"\"\n        if not self.check_schema_hook:\n            self.check_schema_hook = SparkTableSchemaCompatibilityHook(\n                client, table_name, database\n            )\n\n        return self.check_schema_hook.run(dataframe)\n","repo_name":"quintoandar/butterfree","sub_path":"butterfree/load/writers/historical_feature_store_writer.py","file_name":"historical_feature_store_writer.py","file_ext":"py","file_size_in_byte":10926,"program_lang":"python","lang":"en","doc_type":"code","stars":269,"dataset":"github-code","pt":"86"}
+{"seq_id":"5291834857","text":"\"\"\"fulfills Ynon Perek's nontrivial python, Decorator & Generators, par1\"\"\"\nclass after5(object):\n    \"\"\"\"\"\"\n    def __init__(self, func):\n        \"\"\"setc count and stores func as attribute\"\"\"\n        self.count = 0\n        self.func = func\n\n    def __call__(self):\n        \"\"\"checks count, calls func if count < 4, count++\"\"\"\n        count = self.count\n\n        if(count > 4):\n            self.func()\n\n        self.count = count + 1\n\n\n@after5\ndef doit():\n    \"\"\"prints 'Yo!'\"\"\"\n    print(\"Yo!\")\n\n\ndef main():\n    # ignore the first 5 calls\n    doit()\n    doit()\n    doit()\n    doit()\n    doit()\n\n    # so only print yo once\n    doit()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"manvillej/NonTrivialPython","sub_path":"decorator1.py","file_name":"decorator1.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"763464587","text":"# coding=utf-8\nfrom django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render_to_response, render\nfrom api.models import TaskList\nfrom instance.models import Instance\nfrom server.models import Server, SBasicStat\nfrom server.models import IosTemplate\nfrom server.models import VmFile\nfrom django.db.models import Q\nimport json\n\n\n# Create your views here.\n@login_required\ndef server_list(request):\n    errors = list([])\n    if request.method == 'GET':\n        order_by = request.GET.get('order_by', 'sn')\n        sort = request.GET.get('sort', 'up')\n        word = request.GET.get('word', '')\n\n        if sort == 'down':\n            new_sort = 'up'\n            order_str = '-' + order_by\n        else:\n            new_sort = 'down'\n            order_str = order_by\n\n        try:\n            word = json.loads(word)\n        except:\n            word = word\n\n        all_servers = Server.objects.all().count()\n        online_servers = Server.objects.filter(status=1).count()\n\n        if '' == word:\n            hosts_info = Server.objects.filter().order_by(order_str)\n        elif isinstance(word, int):\n            hosts_info = Server.objects.filter(status=word).order_by(order_str)\n        else:\n            hosts_info = Server.objects.filter(\n                Q(sn=word) | Q(name__contains=word) | Q(alias__contains=word)\n            ).order_by(order_str)\n\n        return render(request, 'server/server_list.html',\n                      {\n                          'sort': new_sort,\n                          'word': word,\n                          'all_servers': all_servers,\n                          'online_servers': online_servers,\n                          'hosts_info': hosts_info\n                      })\n    else:\n        errors.append('你咋发的不是GET, 搞笑啊!!!')\n        return render(request, '444.html', {'errors': errors})\n\n\n@login_required\ndef server_info(request, sn, tab):\n    data = list([])\n    iostemp_list = list([])\n    vm_files_list = list([])\n\n    errors = list([])\n    if request.method == 'GET':\n        if Server.objects.filter(sn=sn).count():\n            name = Server.objects.get(sn=sn).name\n            if tab != 'packages':\n                iostemp_list = IosTemplate.objects.filter(server__sn=sn)\n            else:\n                vm_files_list = VmFile.objects.filter(server__sn=sn)\n\n            if tab == 'logs':\n                data.append('logs')\n            elif tab == 'instances':\n                data = Instance.objects.filter(server__sn=sn)\n            elif tab == 'tasks_all':\n                data.append(TaskList.objects.order_by('-create_time').filter(server__sn=sn))\n                data.append(['创建未发布', '发布未领取', '领取未完成', '完成且成功', '完成却失败', '取消'])\n            elif tab == 'tasks':\n                # data[0] 创建未发布任务清单\n                data.append(TaskList.objects.order_by('-create_time').filter(server__sn=sn, status=0))\n\n                # data[1] 发布未领取任务清单\n                data.append(TaskList.objects.order_by('-create_time').filter(server__sn=sn, status=1))\n\n                # data[2] 领取处理中任务清单\n                data.append(TaskList.objects.order_by('-create_time').filter(server__sn=sn, status=2))\n\n                # data[3] 取消任务清单\n                data.append(TaskList.objects.order_by('-create_time').filter(server__sn=sn, status=5))\n\n                # data[4] 领取已完成任务清单\n                data.append(TaskList.objects.order_by('-create_time').filter(Q(server__sn=sn), Q(status=3) | Q(status=4)))\n\n            elif tab == 'setting':\n                data.append('setting')\n            elif tab == 'packages':\n                data = IosTemplate.objects.filter(server__sn=sn)\n            else:\n                tab = 'overview'\n                # ------ 顶部状态\n                # 服务器基本信息 data[0]\n                data.append(Server.objects.get(sn=sn))\n\n                # 服务器最近的状态信息 data[1]\n                basic_stat = dict({})\n                try:\n                    basic_stat['cpu'] = SBasicStat.objects.filter(server__sn=sn, stat_type='cpu_used').last().value\n                except:\n                    basic_stat['cpu'] = 0\n                try:\n                    basic_stat['disk'] = SBasicStat.objects.filter(server__sn=sn, stat_type='disk_used').last().value\n                except:\n                    basic_stat['disk'] = 0\n                try:\n                    basic_stat['mem'] = SBasicStat.objects.filter(server__sn=sn, stat_type='mem_used').last().value\n                except:\n                    basic_stat['mem'] = 0\n\n                # 在线虚拟机数量\n                basic_stat['runserver'] = Instance.objects.filter(status=1, server__sn=sn).count()\n                data.append(basic_stat)\n\n                # ------ 业务详情\n                # VM_files详情 data[2]\n                vm_files = VmFile.objects.filter(server__sn=sn)\n                data.append(vm_files)\n\n                # 任务详情 data[3]\n                task_info = dict({})\n                task_info[\"task0\"] = TaskList.objects.filter(server__sn=sn, status=0).count()\n                task_info[\"task1\"] = TaskList.objects.filter(server__sn=sn, status=1).count()\n                task_info[\"task2\"] = TaskList.objects.filter(server__sn=sn, status=2).count()\n                task_info[\"task3\"] = TaskList.objects.filter(server__sn=sn, status=3).count()\n                task_info[\"task4\"] = TaskList.objects.filter(server__sn=sn, status=4).count()\n                task_info[\"task5\"] = TaskList.objects.filter(server__sn=sn, status=5).count()\n                data.append(task_info)\n\n            return render(request, 'server/server_info.html',\n                          {'name': name,\n                           'sn': sn,\n                           'tab': tab,\n                           'data': data,\n                           'iostemp_list': iostemp_list,\n                           'vm_files_list': vm_files_list\n                           })\n        else:\n            return render_to_response('444.html', {'errors': ['SN不存在']})\n    else:\n        errors.append('你咋发的不是GET, 搞笑啊!!!')\n        return render(request, '444.html', {'errors': errors})","repo_name":"crazw/webkvmmgr","sub_path":"web/server/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6327,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"86"}
+{"seq_id":"73429963484","text":"#!/usr/bin/python3\n\nimport argparse\nimport codecs\nimport subprocess\nimport sys\nfrom collections import defaultdict\n\nimport argparse\n\nparser = argparse.ArgumentParser(description='Translate phrases (arg1)  if they are in phrase table (stdin)')\nparser.add_argument('phrasePath', type=str, help='path to file including phrases')\nparser.add_argument('--progress', '-p', action='store_true',\n                    help = 'show progress bar (pv command should be installed')\nargs = parser.parse_args()\n#if len(sys.argv) != 2:\n#    print(\"Usage: %s ngrams < phrasetable\" % sys.argv[0], file=sys.stderr)\n#    sys.exit(1)\n\nCMD='cat'\nif args.progress and subprocess.call('which pv > /dev/null', shell=True) == 0:\n    CMD='pv -Wl'\n\nsrctrg_list = []\nsrc_map = {}\n\n#sys.stderr.write(\"Loading: %s\\n\" % sys.argv[1])\nsys.stderr.write(\"Loading: %s\\n\" % args.phrasePath)\n#p = subprocess.Popen(\"%s %s\" % (CMD, sys.argv[1]), shell=True, stdout=subprocess.PIPE)\np = subprocess.Popen(\"%s %s\" % (CMD, args.phrasePath), shell=True, stdout=subprocess.PIPE)\n#with open(sys.argv[1]) as ngram_file:\nif p:\n    ngram_file = codecs.getreader('utf-8')(p.stdout)\n    for line in ngram_file:\n        tup = line.strip().split(\"\\t\")\n        if len(tup) != 2: continue\n        k, v = tup\n        src_map[k] = len(srctrg_list)\n        srctrg_list.append( (k, \"\", -1.0e99, float(v)) )\n    ngram_file.close()\n\nsys.stderr.write(\"Loading StdIn\\n\")\np = subprocess.Popen(\"%s\" % (CMD), shell=True, stdout=subprocess.PIPE)\nif p:\n    reader = codecs.getreader('utf-8')(p.stdout)\n#    for line in sys.stdin:\n    for line in reader:\n        src, trg, score = line.strip().split(\"\\t\")\n        score = float(score)\n        if src in src_map:\n            src_id = src_map[src]\n            if srctrg_list[src_id][2] < score:\n                srctrg_list[src_id] = (src, trg, score, srctrg_list[src_id][3])\n    reader.close()\n\np = subprocess.Popen(\"%s\" % (CMD), shell=True, stdin=subprocess.PIPE)\nif p:\n    writer = codecs.getwriter('utf-8')(p.stdin)\n    for src, trg, score, select in srctrg_list:\n        writer.write(\"%s\\t%s\\t%.4g\\t%.4g\\n\" % (src, trg, score, select))\n#        print(\"%s\\t%s\\t%.4g\\t%.4g\" % (src, trg, score, select))\n\n","repo_name":"akivajp/naacl2016","sub_path":"script/generate-translations.py","file_name":"generate-translations.py","file_ext":"py","file_size_in_byte":2181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"23082539962","text":"import turtle\nfrom turtle import*\nfrom random import randint\n#Configurações de entradas.\naldeia = turtle.Screen()                                 #Criação da tela para o jogo.\naldeia.tracer(0)\naldeia.title('Naruto Shippuden')                         #Título da janela.\naldeia.setup(height=1280, width=860, starty= 10)         #Definição da janela.\naldeia.addshape('Naruto1.gif')                           #Adicionando os formatos a serem utilizados.\naldeia.addshape('ramen.gif')\naldeia.addshape('fundo.gif')\naldeia.addshape('shuriken.gif')\naldeia.addshape('kunai.gif')\naldeia.bgcolor('black')\n\n#Configurações de objetos.\nestrada = turtle.Turtle()                                    #Criação do objeto que terá o formato da estrada.\nestrada.penup()\nestrada.shape('fundo.gif')                                   #Definição do formato da estrada.\nestrada2 = turtle.Turtle()\nestrada2.penup()\nestrada2.shape('fundo.gif')\nestrada2.rt(90)\nestrada2.hideturtle()\nestrada2.goto(0, 800)\n\n#Criação do jogador.\nnaruto = turtle.Turtle()                                     #Jogador adicionado.\nnaruto.up()                                                  #Jogador operando sem deixar rastros.\nnaruto.goto(0, -300)                                         #Posição inicial do jogador.\nnaruto.shape('Naruto1.gif')                                  #Adicionando o formato do personagem do jogador.\n\n#Obstáculos.\nobstaculo1 = turtle.Turtle()                                 #Primeiro obstáculo.\nobstaculo1.up()                                              #Não deixará rastros.\nobstaculo1.shape('shuriken.gif')                             #Adicionando o formato.\nobstaculo1.rt(90)\nobstaculo1.goto(randint(-200, 150), 600)\nobstaculo2 = turtle.Turtle()                                 #Segundo obstáculo.\nobstaculo2.up()                                              #Não deixará rastros.\nobstaculo2.shape('kunai.gif')                                #Adicionando o formato.\nobstaculo2.rt(90)\nobstaculo2.goto(randint(-200, 150), 600)\n\n#Combustível.\nramen = turtle.Turtle()                                      #Criando o combustível que gerará a pontuação.\nramen.shape('ramen.gif')                                     #Definindo o formato do combustível.\nramen.up()                                                   #Impedindo que o combustível deixe ratros.\nramen.speed(0)                                               #Definindo a velocidade do combustível.\nramen.rt(90)\nramen.goto(randint(-200, 150), 600)   #randint(min, max)\n\n\n#Pontuação & Tanque de Combustível.\nscore = turtle.Turtle()\nscore.up\nscore.goto\nscore.hideturtle()\n\n#Funções\n#def geracaoObs():\n\n\ndef direita():                                               #Definindo a primeira tecla de movimento e o espaço percorrido.\n    if naruto.xcor() >= 150:\n        naruto.setpos(naruto.xcor(), naruto.ycor())\n    else:\n        naruto.fd(50)                                            #Quantidade de pixels que irá movimentar para a direita.\n\ndef esquerda():                                              #Definindo a segunda tecla de movimento e o espaço percorrido.\n    if naruto.xcor() <= -200:\n        naruto.setpos(naruto.xcor(), naruto.ycor())\n    else:\n        naruto.bk(50)                                            #Quantidade de pixels percorridos para a esquerda.\n\n\ndef lacoPrincipal():                                         #Laço onde toda a \"mágica\" ocorre.\n    #estrada.setpos(estrada.xcor(), estrada.ycor()-10)\n    meiofio()                                                #Chamando a função que irá definir o limite da estrada.\n    #if estrada.ycor() == -700:\n        #estrada2.showturtle()\n        #estrada2.fd(10)\n    aldeia.update()\n    aldeia.ontimer(lacoPrincipal, 1000 // 60)\n\ndef meiofio():                                               #Limite da estrada para que ocorra a pausa.\n    if naruto.xcor() >= 200 or naruto.xcor() <= -250:        #Parâmetros dos limites.\n        naruto.setpos(naruto.xcor(), naruto.ycor())          #Caso o jogador atinja o limite, o jogador pausará.\n\n#Chamadas de teclas.\nonkeypress(esquerda, 'Left')                                  #Definindo a entrada da tecla de movimento para a esquerda.\nonkeypress(direita, 'Right')                                  #Definindo a entrada da tecla de movimento para a direita.\nlisten()                                                      #Dizendo ao programa para entender(\"escutar\") quando uma das duas teclas forem pressionadas.\n\nlacoPrincipal()\naldeia.mainloop()                                             #Girando o loop da tela.","repo_name":"VitorGabriel0501/Programa","sub_path":"AldeiaDaFolha.py","file_name":"AldeiaDaFolha.py","file_ext":"py","file_size_in_byte":4608,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"24768161219","text":"from flask import Flask, request, jsonify\nimport os\nimport subprocess\n\napp = Flask(__name__)\n\nUPLOAD_FOLDER = 'uploads'\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\n\n@app.route('/upload', methods=['POST'])\ndef upload_file():\n    if 'file' not in request.files:\n        return jsonify({'error': 'No file part'})\n\n    file = request.files['file']\n    if file.filename == '':\n        return jsonify({'error': 'No selected file'})\n\n    filename = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)\n    file.save(filename)\n\n    # Use the 'file' command to get information about the uploaded file\n    file_info = subprocess.check_output(['file', filename]).decode('utf-8')\n\n    return jsonify({'filename': file.filename, 'info': file_info})\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","repo_name":"noor02arora/Api_file","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":792,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"71998714843","text":"import os\nimport json\n\nfrom api import decimalencoder\nimport boto3\nfrom botocore.exceptions import ClientError\nfrom boto3.dynamodb.conditions import Key, Attr\nfrom lambda_decorators import cors_headers\nfrom itertools import islice\ndynamodb = boto3.resource('dynamodb')\n\n@cors_headers\ndef get_assets(event, context):\n    table = dynamodb.Table(\"Assets\")\n\n    page = int(event['pathParameters']['page'])\n    pagesize = 15\n    assets = []\n\n    try:\n        result = table.scan(\n            ProjectionExpression=\"scripthash, #name, symbol, decimals, firstseen\",\n            ExpressionAttributeNames={'#name': 'name'}\n        )\n\n        if \"Items\" in result:\n            assets = result['Items']\n\n        assets.sort(key=lambda x: x['firstseen'], reverse=True)\n        skip = (page - 1) * pagesize\n        items = list(islice(assets, skip, skip + pagesize))\n        result = { 'items': items, 'totalCount': len(assets) }\n\n        response = {\n                \"statusCode\": 200,\n                \"body\": json.dumps(result, cls=decimalencoder.DecimalEncoder)\n        }\n        return response\n\n    except ClientError as error:\n        return {\n                \"statusCode\": 500,\n                \"body\": json.dumps(error.response, cls=decimalencoder.DecimalEncoder)\n        }\n    except Exception as e:\n        return {\"statusCode\": 500, \"body\": e }\n\n\n","repo_name":"CityOfZion/neo3-explorer-api","sub_path":"api/get_assets.py","file_name":"get_assets.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"25746425391","text":"#created By: Sebastian L\nfrom flask import Flask, render_template, request\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\napp = Flask(__name__)\n\n# read the dataset\nhouse_sales = pd.read_csv('kc_house_data.csv')\n\n# define relevant filters\nbedrooms_filter = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 33]\nprice_range_filter = [0, 500000, 1000000, 1500000, 2000000, 2500000, 3000000, 3500000, 4000000, 4500000, 5000000]\n\n\n@app.route('/')\ndef home():\n    #return render_template(\"home.html\")\n    return render_template('home.html', bedrooms_filter=bedrooms_filter, price_range_filter=price_range_filter)\n    \n\n@app.route('/analysis', methods=['POST'])\ndef analysis():\n    # read the selected filters from the user\n    bedrooms = request.form['bedrooms']\n    price_range = request.form['price_range']\n\n    # filter the dataset based on the selected filters\n    filtered_sales = house_sales.loc[(house_sales['bedrooms'] == int(bedrooms)) &\n                                     (house_sales['price'] >= price_range_filter[int(price_range)]) &\n                                     (house_sales['price'] < price_range_filter[int(price_range) + 1])]\n\n    # compute the required statistics\n    volume_by_month = filtered_sales.groupby(pd.to_datetime(filtered_sales['date']).dt.to_period('M'))['price'].agg(['count', 'sum'])\n    avg_price_by_month = filtered_sales.groupby(pd.to_datetime(filtered_sales['date']).dt.to_period('M'))[['price', 'sqft_living', 'bedrooms']].mean()\n    \n    # plot the statistics\n    \n\n    # Plot the Volume of Deals vs Time\n    \n    # Create a new Matplotlib Figure object with two subplots\n    fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(8, 8))\n\n    # Plot the first subplot\n    volume_by_month.plot(ax=ax1, y='count', legend=False, marker='o', markerfacecolor='red', markersize=10)\n    ax1.set_title('Volume of Deals vs Time')\n    ax1.set_ylabel('Volume of Deals (Number)')\n    ax1.grid()\n\n    # Plot the second subplot\n    volume_by_month.plot(ax=ax2, y='sum', legend=False, marker='o', markerfacecolor='blue', markersize=10)\n    ax2.set_title('Volume of Deals vs Total Amount')\n    ax2.set_xlabel('Date')\n    ax2.set_ylabel('Volume of Deals (Total Amount )')\n    ax2.grid()\n\n    # Adjust the spacing between subplots and save the figure\n    fig.tight_layout()\n    fig.savefig('figure1.png')\n\n    # convert the plot to base64 string\n    import io\n    import base64\n    buf = io.BytesIO()\n    plt.savefig(buf, format='png')\n    plt.close(fig)\n    buf.seek(0)\n    plot_url = base64.b64encode(buf.read()).decode('utf-8')\n\n    \n    # Plot the average price per unit over time\n    \n    # Create a new Matplotlib Figure object with three subplots\n    fig2, (ax3, ax4, ax5) = plt.subplots(nrows=3, ncols=1, sharex=True, figsize=(8, 12))\n\n    # Plot the first subplot\n    avg_price_by_month.plot(ax=ax3, y='price', legend=False, marker='o', markerfacecolor='cyan', markersize=10)\n    ax3.set_title('Avg. Price per Deal vs Time')\n    ax3.set_xlabel('Date')\n    ax3.set_ylabel('Avg. Price per Deal ($)', color='r')\n    ax3.grid()\n\n    # Plot the second subplot\n    avg_price_by_month.plot(ax=ax4, y='sqft_living', legend=False, marker='o', markerfacecolor='magenta', markersize=10)\n    ax4.set_title('Avg. Price per Sqft vs Time')\n    ax4.set_xlabel('Date')\n    ax4.set_ylabel('Avg. Price per Sqft')\n    ax4.grid()\n\n    # Plot the third subplot\n    avg_price_by_month.plot(ax=ax5, y='bedrooms', legend=False, marker='o', markerfacecolor='green', markersize=10)\n    ax5.set_title('Avg. Price per Bedroom vs Time')\n    ax5.set_xlabel('Date')\n    ax5.set_ylabel('Avg. Price per Bedroom')\n    ax5.grid()\n\n    # Adjust the spacing between subplots and save the figure\n    fig2.tight_layout()\n    fig2.savefig('figure2.png')\n\n    # convert the plot to base64 string\n    \n    buf2 = io.BytesIO()\n    plt.savefig(buf2, format='png')\n    plt.close(fig2)\n    buf2.seek(0)\n    plot_url2 = base64.b64encode(buf2.read()).decode('utf-8')\n\n\n    # render the analysis template with the computed statistics and plot\n    return render_template('analysis.html', volume_by_month=volume_by_month.to_html(),\n                           avg_price_by_month=avg_price_by_month.to_html(), plot_url=plot_url, plot_url2=plot_url2)\n\n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n\n","repo_name":"JosephLSeb/webapp-kc-housing-ui","sub_path":"app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4288,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"14026730173","text":"from django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.shortcuts import render\n\n# Create your views here.\n\nfrom django.db.models import Count\nfrom django.views.generic import DetailView\n\nfrom App.models import Panel, Prof\n\n\ndef panel(request):\n    product = Panel.objects.all()\n    total = Panel.objects.aggregate(Count('nom'))\n    print(total)\n    total = total.get(\"nom__count\")\n    print(total)\n    total1 = Prof.objects.aggregate(Count('nom'))\n    print(total1)\n    total1 = total1.get(\"nom__count\")\n    print(total1)\n    context = {\n        \"produits\": product,\n        \"total\": total,\n        \"total1\": total1,\n\n    }\n\n    return render(request, \"app/panel.html\", context)\n\n\ndef eleve(request):\n    product = Panel.objects.all()\n    context = {\n        \"produits\": product,\n\n    }\n    return render(request, \"app/eleves.html\", context)\n\n\ndef prof(request):\n    product = Prof.objects.all()\n\n    context = {\n        \"produits\": product,\n\n    }\n    return render(request, \"app/prof.html\", context)\n\n\n\n","repo_name":"sibylassana95/DjangoPublicProject","sub_path":"Administration/src/App/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"42070996989","text":"import numpy as np\nimport math\nimport matplotlib.pyplot as plt\n\ndef heat_2d():\n    dx=0.05\n    dt=.5*dx*dx\n    x = []\n    for x_i in range(-20, 21):\n        x.append(dx*x_i)\n        \n    t = []\n    for t_i in range(0, 401):\n        t.append(t_i*dt)\n\n    f=np.zeros(shape=(len(x),len(t)))\n    for j in range(len(f)):\n        f[j,0] = math.exp(-5*abs(x[j]))\n\n\n    for i in range(len(f)):\n        f[0,i] = 0\n        f[-1,i] = 0\n\n    for i in range(len(t)-1):\n        for j in range(1,len(f)-1):\n            f[j,i+1] = f[j,i] + dt*((f[j+1,i] - 2*f[j,i] + f[j-1,i])/(dx*dx))\n\n    # Draw contour\n    plt.figure('2D heat equation')\n    plt.contourf(t,x,f,100)\n    plt.xlabel('t')\n    plt.ylabel('x')\n    plt.savefig('heat2d.png')\n    plt.show()\n\ndef heat_3d():\n    dx=0.05\n    dy=0.05\n    dt=.5*dx*dx\n    x = []\n    for x_i in range(-20, 21):\n        x.append(dx*x_i)\n    y = []\n    for y_i in range(-20, 21):\n        y.append(dy*y_i)\n        \n    t = []\n    for t_i in range(0, 401):\n        t.append(t_i*dt)\n\n    f=np.zeros(shape=(len(x),len(y),len(t)))\n    for i in range(len(f)):\n        for j in range(len(f[i])):\n            f[i,j,0] = math.exp(-5*abs(x[i])) * math.exp(-5*abs(y[j])) \n\n    for i in range(len(f)):\n        for j in range(len(t)):\n            f[i,0,j] = 0\n            f[i,-1,j] = 0\n            f[0,i,j] = 0\n            f[-1,i,j] = 0\n\n    for i in range(len(t)-1):\n        for j in range(1,len(f)-1):\n            for k in range(1,len(f[j])-1):\n                f[j,k,i+1] = f[j,k,i] + dt*((f[j+1,k,i] - 2*f[j,k,i] + f[j-1,k,i])/(dx*dx)) + dt*((f[j,k+1,i] - 2*f[j,k,i] + f[j,k-1,i])/(dy*dy)) \n\n    # Draw contour\n    #plt.figure()\n    #plt.contourf(t,x,y,f,100)\n    #plt.show()\n\nheat_2d()\nheat_3d()\n","repo_name":"rsullivan00/NumericalAnalysis","sub_path":"HeatEquation/heat.py","file_name":"heat.py","file_ext":"py","file_size_in_byte":1710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"127785373","text":"N, K = list(map(int, input().split()))\n\nm = {}\n\nfor n in range(N):\n    A, B = list(map(int, input().split()))\n    if A in m:\n        m[A] += B\n    else:\n        m[A] = B\n\nsortedm = sorted(m.items())\n#print(sortedm)\n\n#print(len(sortedm))\nfor e in range(len(sortedm)):\n    if int(sortedm[e][0]) <= K:\n        K += sortedm[e][1]\n\nif K < 10**100:\n    print(K)\nelse:\n    print(10**100)\n#print(mon)\n\n\n\n\n","repo_name":"tinaba96/coding","sub_path":"acode/abc203/c.py","file_name":"c.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"71308780765","text":"# author: fanchuangwater@gmail.com\n# date: 2020/5/3 上午11:47\n# 目的: \n\n\n\n# 注意是最长的长度\nnums = [8,2,4,7]\nlimit = 4\n\nleft = 0\nend = len(nums) -1\n\n# 排列组合　暴力啊暴力\n# while left <= end:\n\n\n# import itertools\n#\n# a = itertools.zip_longest(nums, fillvalue=)","repo_name":"buxuele/algo_snippet","sub_path":"junk/5402_longest.py","file_name":"5402_longest.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"26995131399","text":"import requests\nimport datetime\nimport store\nimport math\n\nstored_access_data = store.get_access_data()\nstrava_settings = store.get_strava_settings()\n\ndef refresh_access_token():\n    global stored_access_data\n\n    params = {\n        'client_id': strava_settings['client_id'], \n        'client_secret': strava_settings['client_secret'], \n        'grant_type': 'refresh_token', \n        'refresh_token': stored_access_data['refresh_token']\n    }\n    access_data = requests.post(strava_settings['api_url'] + 'oauth/token', params=params).json()\n    stored_access_data = access_data\n    store.save_access_data(access_data)\n\ndef has_access():\n    expires_at = stored_access_data['expires_at']\n    date = datetime.datetime.fromtimestamp(expires_at)\n    now = datetime.datetime.today()\n    \n    return date > now\n\ndef check_access():\n    if (not has_access()):\n        refresh_access_token()\n\ndef get(url):\n    check_access()\n\n    access_token = stored_access_data['access_token']\n    headers={'Authorization': f'Bearer {access_token}'}\n\n    return requests.get(strava_settings['api_url'] + url, headers=headers).json()\n\ndef get_stats():\n    athlete_id = strava_settings['athlete_id']\n    return get(f'athletes/{athlete_id}/stats')\n\ndef get_stats_text(stats):\n    time = get_time(stats['elapsed_time'])\n\n    distance = f\"🏃 {round(stats['distance'] / 1000, 2)} km\\n\"\n    elevation = f\"⛰ {math.floor(stats['elevation_gain'])} m\\n\"\n    elapsed = f\"🕒 {time}\\n\"\n    count = f\"🖐 {stats['count']} runs\"\n\n    return f'{distance}{elevation}{elapsed}{count}'\n\ndef get_avg_week_distance(stats):\n    distance = round(stats['distance'] / 1000 / 4, 2)\n    return f'🏃 {distance} km'\n\ndef get_time(minutes):\n    if (minutes < 60):\n        return f'{minutes}m'\n\n    if (minutes == 60):\n        return '1h'\n\n    h = minutes // 60\n    m = minutes % 60\n    return f'{h}h {m}m'\n\ndef get_stats_message():\n    stats = get_stats()\n    recent_stats = stats['recent_run_totals']\n    \n    recent_stats_text = get_stats_text(recent_stats)\n    ytd_stats_text = get_stats_text(stats['ytd_run_totals'])\n    avg_week_distance = get_avg_week_distance(recent_stats)\n\n    return f'Avg week:\\n{avg_week_distance}\\n\\nLast 4 weeks:\\n{recent_stats_text}\\n\\nThis year:\\n{ytd_stats_text}'\n","repo_name":"mklinovsky/strava-twitter-bot","sub_path":"strava.py","file_name":"strava.py","file_ext":"py","file_size_in_byte":2254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35317883862","text":"# function to find the once\r\n# appearing element in array\r\ndef findSingle( ar, n):\r\n\t\r\n\tres = ar[0]\r\n\t\r\n\t# Do XOR of all elements and return\r\n\tfor i in range(1,n):\r\n\t\tres = res ^ ar[i]\r\n\t\r\n\treturn res\r\n\r\n\r\nar = [2, 3, 5, 4, 5, 3, 4]\r\nprint (\"Element occurring once is\", findSingle(ar, len(ar)))","repo_name":"saurabh142001/LamprostechSaurabhkumar","sub_path":"singleinteger.py","file_name":"singleinteger.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3034214641","text":"import numpy as np\nimport os\n\nfrom sklearn.preprocessing import MinMaxScaler\nfrom numba import jit\n\n\ndef get_by_id_and_frame(M):\n\n    ids = list(set(M[:, 1]))\n    traj_id = {}\n\n    for id_p in ids:\n        traj_id[id_p] = np.delete(M[M[:, 1] == id_p], 1, 1)\n\n    frames = list(set(M[:, 0]))\n    traj_fr = {}\n\n    for frame in frames:\n        traj_fr[frame] = np.delete(M[M[:, 0] == frame] , 0, 1)\n\n    return traj_id, traj_fr\n\n\ndef get_trajs(config, trajs_id):\n\n    trajs = {\n\n        'id_p': [],\n        'frames': [],\n        'pos': []\n\n    }\n\n    for id_p in trajs_id:\n\n        data = trajs_id[id_p]\n        pos = len(data) - config['tot_l'] + 1\n\n        if pos < 1: continue\n\n        trajs['id_p'].extend([id_p for _ in range(pos)])\n        trajs['frames'].extend([np.expand_dims(data[i:i+config['tot_l'], 0], axis = 0) for i in range(pos)])\n        trajs['pos'].extend([np.expand_dims(data[i:i+config['tot_l'], 1:], axis = 0) for i in range(pos)])\n\n    trajs['id_p'] = np.array(trajs['id_p'])\n    trajs['frames'] = np.concatenate(trajs['frames'], axis = 0)\n    trajs['pos'] = np.concatenate(trajs['pos'], axis = 0)\n\n    return trajs\n\n@jit(nopython = True)\ndef get_features(max_d, pos, frames, id_p, trajs_fr, len_fr, c_reduce):\n\n    features = max_d + np.zeros((len(pos), len(pos[0]), 360))\n    features_g = np.zeros((len(pos), len(pos[0]), c_reduce))\n    window_s = 360//c_reduce\n\n    for i in range(len(pos)):\n\n        for j in range(len(pos[i])):\n\n            frame = int(frames[i][j])\n            all_p = trajs_fr[frame][:len_fr[frame]]\n            id_c = id_p[i]\n\n            point = all_p[all_p[:, 0] == id_c][0][1:]\n            all_p = all_p[all_p[:, 0] != id_c][:, 1:]\n            all_p = all_p - point\n            all_p_c = all_p[:, 0] + 1j*all_p[:, 1]\n            angles = np.angle(all_p_c, deg = True)\n            angles += (angles<0)*360\n\n            for k in range(len(angles)):\n\n                ang = int(angles[k])\n                features[i][j][ang] = min(features[i][j][ang], np.linalg.norm(all_p[k]))\n\n            for k in range(c_reduce):\n                features_g[i][j][k] = np.min(features[i][j][k*window_s:(k+1)*window_s])\n\n    return features_g\n\n\ndef create_matrix(trajs_fr):\n\n    a = int(np.max([x for x in trajs_fr]))+1\n    b = int(np.max([len(trajs_fr[x]) for x in trajs_fr]))\n    c = 3\n\n    mat = np.zeros((a, b, c))\n    len_m = np.zeros(len(mat))\n\n    for i in trajs_fr:\n\n        i = int(i)\n        a = len(trajs_fr[i])\n        len_m[i] = a\n        mat[i, :a, :] = trajs_fr[i]\n\n    for i in trajs_fr:\n        i = int(i)\n        assert len(trajs_fr[i]) == len_m[i], \"buuuu with the lengths!\"\n        n = np.linalg.norm(trajs_fr[i] - mat[i, :int(len_m[i]), :])\n        assert  n < 1e-1, \"buuuu with the copy: {0}\".format(n)\n\n    return mat, len_m\n\ndef process_4_nn(config, trajs, trajs_fr, dataset):\n\n    X = {\n        'encoder': None,\n        'decoder': None,\n        'dataset': None,\n        'pos': None,\n        'frames': None,\n        'id_p': None,\n    }\n\n    if config['use_features']:\n\n        X.update({\n            'obs_features': None,\n            'pre_features': None,\n        })\n\n    Y = None\n\n    if config['use_features']:\n\n        trajs_fr_m, trajs_fr_l = create_matrix(trajs_fr)\n        features = get_features(config['max_d'],\n                                trajs['pos'], trajs['frames'], trajs['id_p'],\n                                trajs_fr_m, trajs_fr_l, config['reduce'])\n        X['obs_features'] = features[:, :config['obs_l']-1, :]\n        X['pre_features'] = features[:, config['obs_l']-1:-1, :]\n\n    X['pos'] = np.array(trajs['pos'])\n    X['dataset'] = [dataset for _ in range(len(X['pos']))]\n    X['frames'] = np.array(trajs['frames'])\n    X['id_p'] = np.array(trajs['id_p'])\n\n    obs = trajs['pos'][:, :config['obs_l']]\n    pre = trajs['pos'][:, config['obs_l']:]\n\n    X['encoder'] = obs[:, 1:] - obs[:, :-1]\n    Y = pre - np.concatenate([obs[:, -1:], pre[:, :-1]], axis = 1)\n\n    X['decoder'] = np.zeros_like(Y)\n    X['decoder'][:, 0, :] = X['encoder'][:, -1, :]\n    X['decoder'][:, 1:, :] = Y[:, :-1, :]\n\n    return X, Y\n\n\ndef load_dataset(config, verbose):\n\n    dataset_path = os.path.join(config['data_path'], config['i_test'])\n    dictio = {}\n\n    for phase in os.listdir(dataset_path):\n\n        if verbose: print('   ', phase)\n\n        dictio[phase] = {\n            'X': {'encoder': [], 'decoder': [], 'dataset': [], 'pos': [], 'frames': [], 'id_p': []},\n            'Y': [],\n            'trajs_fr': {},\n        }\n\n        if config['use_features']:\n\n            dictio[phase]['X'].update({\n                'obs_features': [],\n                'pre_features': [],\n            })\n\n        phase_path = os.path.join(dataset_path, phase)\n\n        for file in os.listdir(phase_path):\n\n            file_path = os.path.join(phase_path, file)\n            if verbose: print('       ', file)\n\n            mat = np.loadtxt(file_path)\n            trajs_id, trajs_fr = get_by_id_and_frame(mat)\n\n            trajs_ds = get_trajs(config, trajs_id)\n            if verbose: print('       ', len(trajs_ds['pos']))\n\n            X, Y = process_4_nn(config, trajs_ds, trajs_fr, file)\n\n            dictio[phase]['X']['encoder'].append(X['encoder'])\n            dictio[phase]['X']['decoder'].append(X['decoder'])\n            dictio[phase]['X']['dataset'].append(X['dataset'])\n            dictio[phase]['X']['pos'].append(X['pos'])\n            dictio[phase]['X']['frames'].append(X['frames'])\n            dictio[phase]['X']['id_p'].append(X['id_p'])\n\n            if config['use_features']:\n\n                dictio[phase]['X']['obs_features'].append(X['obs_features'])\n                dictio[phase]['X']['pre_features'].append(X['pre_features'])\n\n            dictio[phase]['Y'].append(Y)\n            dictio[phase]['trajs_fr'][file] = trajs_fr\n\n        if len(dictio[phase]) > 0:\n\n            dictio[phase]['Y'] = np.concatenate(dictio[phase]['Y'], axis = 0)\n            dictio[phase]['X']['encoder'] = np.concatenate(dictio[phase]['X']['encoder'], axis = 0)\n            dictio[phase]['X']['decoder'] = np.concatenate(dictio[phase]['X']['decoder'], axis = 0)\n            dictio[phase]['X']['dataset'] = np.concatenate(dictio[phase]['X']['dataset'], axis = 0)\n            dictio[phase]['X']['pos'] = np.concatenate(dictio[phase]['X']['pos'], axis = 0)\n            dictio[phase]['X']['frames'] = np.concatenate(dictio[phase]['X']['frames'], axis = 0)\n            dictio[phase]['X']['id_p'] = np.concatenate(dictio[phase]['X']['id_p'], axis = 0)\n\n            if config['use_features']:\n\n                dictio[phase]['X']['features'] = np.concatenate(dictio[phase]['X']['obs_features'], axis = 0)\n                dictio[phase]['X']['pre_features'] = np.concatenate(dictio[phase]['X']['pre_features'], axis = 0)\n\n                dictio[phase]['X']['features'] = dictio[phase]['X']['features']/config['max_d']\n                dictio[phase]['X']['pre_features'] = dictio[phase]['X']['pre_features']/config['max_d']\n                \n                # print(dictio[phase]['X']['features'])\n                        \n        if verbose: print('   ', len(dictio[phase]['Y']))\n\n    return dictio\n    \n    \nclass LoadData:\n    \n    def __init__(self, config, verbose = 1):\n        \n        self.data = load_dataset(config, verbose = verbose)\n        \n        self.X_train = self.data['train']['X']\n        self.Y_train = self.data['train']['Y']\n\n        self.X_test = self.data['test']['X']\n        self.Y_test = self.data['test']['Y']\n\n        if config['formal_training']:\n            \n            self.X_vali = self.data['val']['X']\n            self.Y_vali = self.data['val']['Y']\n\n    ","repo_name":"jagmonroy/cvae_tp","sub_path":"ETH-UCY/dataset_processing.py","file_name":"dataset_processing.py","file_ext":"py","file_size_in_byte":7617,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"15510230259","text":"import socket\nimport threading\nimport sys\nimport os\n\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n# host = input(\"Enter host: \")\nhost = str(input(\"Enter host: \"))  # socket.gethostname()\nname = str(input(\"Enter name: \"))\nport = int(input(\"Enter port: \"))  # int(sys.argv[1])\n\ns.connect((host, port))\nos.system('clear')\nprint('Connected to', host)\nprint(\"Commands:\\n- \\\"clear()\\\"\\n- \\\"exit\\\"\")\ns.send(name.encode())\ns.send(str(\"User connected: \" + name).encode())\n\n\ndef unos():\n    try:\n        while True:\n            inp = str(input(name + \": \"))\n            z = name + \": \" + inp\n            if (inp == \"exit\"):\n                s.send(str(\"User disconnected: \" + name).encode())\n                s.close()\n                exit(0)\n            elif inp == \"clear()\":\n                os.system(\"clear\")\n            else:\n                s.send(z.encode())\n    except Exception as e:\n        s.send(str(\"User disconnected: \" + name).encode())\n        print(e)\n        exit(0)\n\n\ndef read():\n    while True:\n        msg = str(s.recv(1024).decode())\n        if len(msg):\n            print(\"\\n\" + msg)\n\n\nt1 = threading.Thread(target=unos)\nt2 = threading.Thread(target=read)\ntry:\n    t1.start()\n    t2.start()\nexcept Exception as e:\n    print(e, end=\"\\n\\n\\n\\n\")\n    print(\"Bye bye \")\n    exit(0)\n","repo_name":"ProCxxx/pychat","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"21027143194","text":"import numpy as np\nimport matplotlib.pyplot as plt\nplt.switch_backend('agg')\nfrom keras.models import Model\nfrom keras.layers import Input, Dense\n\nfrom keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D,BatchNormalization,Activation\n\nfrom keras import backend as K\n\nfrom keras.datasets import cifar10\n(data1, y_train), (data_test, y_test) = cifar10.load_data()\n\n\n\n\"------------------------------------------------------ CONVOLUTIONAL AUTOENCODER\"\n\n\ndata1 = data1.astype('float32') / 255\ndata_test = data_test.astype('float32') / 255\ndata1 = np.reshape(data1, (len(data1), 32, 32, 3))\ndata_test = np.reshape(data_test, (len(data_test), 32, 32, 3))\n\nnoise_factor = 0.5\nx_train_noisy = data1 + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=data1.shape) \nx_test_noisy = data_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=data_test.shape)\n\nx_train_noisy = np.clip(x_train_noisy, 0., 1.)\nx_test_noisy = np.clip(x_test_noisy, 0., 1.)\n\n\n\ninput_img = Input(shape=(32,32,3))  # adapt this if using `channels_first` image data format\n\nx = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)\nx = MaxPooling2D((2, 2), padding='same')(x)\nx = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\nencoded = MaxPooling2D((2, 2), padding='same')(x)\n\n# at this point the representation is (4, 4, 8) i.e. 128-dimensional\n\nx = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)\nx = UpSampling2D((2, 2))(x)\nx = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\nx = UpSampling2D((2, 2))(x)\ndecoded = Conv2D(3, (3, 3), activation='softmax', padding='same')(x)\n\nautoencoder = Model(input_img, decoded)\nautoencoder.compile(optimizer='adam', loss='mean_squared_error')\n\nhistory = autoencoder.fit(x_train_noisy, data1,\n                epochs=100,\n                batch_size=128,\n                shuffle=True,\n                validation_data=(x_test_noisy, data_test))\n\npredicted = autoencoder.predict(x_test_noisy)\n\n#n = 10  # how many digits we will display\n#plt.figure(figsize=(20, 4))\n#for i in range(n):\n    # display original\n    #ax = plt.subplot(2, n, i + 1)\n    #plt.imshow(x_test_noisy[i])\n    #plt.gray()\n    #ax.get_xaxis().set_visible(False)\n    #ax.get_yaxis().set_visible(False)\n\n    # display reconstruction\n    #ax = plt.subplot(2, n, i + 1 + n)\n    #plt.imshow(predicted[i])\n    #plt.gray()\n    #ax.get_xaxis().set_visible(False)\n    #ax.get_yaxis().set_visible(False)\n#plt.savefig('cifar_noisy.png')\n\n\nplt.figure(1)\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\n\nplt.savefig(\"loss_noisy.png\")\n\n","repo_name":"Kyziridis/Mnist-Machine-Learning-Basics","sub_path":"encoder_noisy_cifar.py","file_name":"encoder_noisy_cifar.py","file_ext":"py","file_size_in_byte":2593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"71150717404","text":"from typing import Any, Dict, Tuple\n\nimport torch\nfrom torch import optim\n\nfrom omnisafe.algorithms import registry\nfrom omnisafe.algorithms.offline.base import BaseOffline\nfrom omnisafe.models.actor.actor_builder import ActorBuilder\nfrom omnisafe.models.actor.vae_actor import VAE\n\n\n@registry.register\nclass VAEBC(BaseOffline):\n    \"\"\"Behavior Cloning with Variational Autoencoder.\n\n    References:\n        - Title: Off-Policy Deep Reinforcement Learning without Exploration\n        - Author: Fujimoto, ScottMeger, DavidPrecup, Doina.\n        - URL: `https://arxiv.org/abs/1812.02900`\n    \"\"\"\n\n    def _init_log(self) -> None:\n        \"\"\"Log the VAE-BC specific information.\n\n        +-------------------------+----------------------------------------------------+\n        | Things to log           | Description                                        |\n        +=========================+====================================================+\n        | Loss/Loss_vae           | Loss of VAE network                                |\n        +-------------------------+----------------------------------------------------+\n        | Loss/Loss_recon         | Reconstruction loss of VAE network                 |\n        +-------------------------+----------------------------------------------------+\n        | Loss/Loss_kl            | KL loss of VAE network                             |\n        +-------------------------+----------------------------------------------------+\n        \"\"\"\n        super()._init_log()\n        what_to_save: Dict[str, Any] = {\n            'vae': self._actor,\n        }\n        self._logger.setup_torch_saver(what_to_save)\n\n        self._logger.register_key('Loss/Loss_vae')\n        self._logger.register_key('Loss/Loss_recon')\n        self._logger.register_key('Loss/Loss_kl')\n\n    def _init_model(self) -> None:\n        self._actor: VAE = (\n            ActorBuilder(  # type: ignore\n                obs_space=self._env.observation_space,\n                act_space=self._env.action_space,\n                hidden_sizes=self._cfgs.model_cfgs.hidden_sizes,\n                activation=self._cfgs.model_cfgs.activation,\n                weight_initialization_mode=self._cfgs.model_cfgs.weight_initialization_mode,\n            )\n            .build_actor(actor_type='vae')\n            .to(self._device)\n        )\n\n        self._vae_optimizer = optim.Adam(\n            self._actor.parameters(),\n            lr=self._cfgs.model_cfgs.learning_rate,\n        )\n\n    def _train(\n        self,\n        batch: Tuple[torch.Tensor, ...],\n    ) -> None:\n        obs, act, _, _, _, _ = batch\n\n        recon_loss, kl_loss = self._actor.loss(obs, act)\n        loss = recon_loss + kl_loss\n        self._vae_optimizer.zero_grad()\n        loss.backward()\n        self._vae_optimizer.step()\n\n        self._logger.store(\n            **{\n                'Loss/Loss_vae': loss.item(),\n                'Loss/Loss_recon': recon_loss.item(),\n                'Loss/Loss_kl': kl_loss.item(),\n            },\n        )\n","repo_name":"PKU-Alignment/omnisafe","sub_path":"omnisafe/algorithms/offline/vae_bc.py","file_name":"vae_bc.py","file_ext":"py","file_size_in_byte":3015,"program_lang":"python","lang":"en","doc_type":"code","stars":734,"dataset":"github-code","pt":"86"}
+{"seq_id":"44442404267","text":"from airflow.hooks.postgres_hook import PostgresHook\nfrom airflow.models import BaseOperator\nfrom airflow.utils.decorators import apply_defaults\n\nclass LoadFactOperator(BaseOperator):\n\n    ui_color = '#F98866'\n    sql = \"\"\"\n        INSERT INTO {table} \n        {table_input};\n    \"\"\"\n\n    '''\n    An operator to load data into a fact table\n\n    Args:\n        redshift_conn_id (str): The id for the Redshift cluster to connect to\n        table (str): The fact table name\n        table_input (str): The input data in SQL\n        *args: Variable arguments\n        **kwargs: Keyword arguments\n\n    Attributes:\n        redshift_conn_id (str): The id for the Redshift cluster to connect to\n        table (str): The fact table name\n        table_input (str): The input data in SQL\n    '''\n    @apply_defaults\n    def __init__(self,\n                 redshift_conn_id,\n                 table,\n                 table_input,\n                 *args, **kwargs):\n\n        super(LoadFactOperator, self).__init__(*args, **kwargs)\n\n        self.redshift_conn_id = redshift_conn_id\n        self.table = table\n        self.table_input = table_input\n\n    def execute(self, context):\n        '''\n        Load the input data into the fact table\n\n        Args:\n            context (dict): The Airflow context object\n        '''\n        self.log.info('LoadFactOperator not implemented yet')\n       \n        redshift = PostgresHook(self.redshift_conn_id)\n        \n        redshift.run(LoadDimensionOperator.sql.format(\n            table=self.table,\n            table_input=self.table_input\n        ))\n","repo_name":"jackyho112/songplay-airflow","sub_path":"airflow/plugins/operators/load_fact.py","file_name":"load_fact.py","file_ext":"py","file_size_in_byte":1576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"23982439005","text":"import sys, math\n\nn = int(sys.stdin.readline())\nllist = list(map(int, sys.stdin.readline().split()))\navg = math.ceil(sum(llist)/n)\n\nmin = 2147000000\n\nfor index, value in enumerate(llist):\n    temp = abs(value - avg)\n    if temp < min:\n        min = temp\n        score = value\n        res = index+1\n    elif temp == min:\n        if value > score:\n            score = value\n            res = index + 1\nprint(avg, res)\n\n# my solution\n# 같다","repo_name":"WooosikS/python-algorithm","sub_path":"인프런/섹션 2/4. 대표값.py","file_name":"4. 대표값.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"71043680925","text":"import os\nfrom pathlib import Path\n\nBASE_DIR = Path(__file__).resolve().parent.parent\n\nSECRET_KEY = 'django-insecure-+wo&weufgyw5235x7_awwfy%xlep$tbn618joks*#)^&2)wyz_6ha6raw#-+='\n\nDEBUG = False\n\nALLOWED_HOSTS = [\"127.0.0.1\", \"194.58.121.241\", \"tehstat.ru\", \"www.tehstat.ru\"]\n\nALLOWED_ORIGINS = ['http://www.tehstat.ru', 'http://tehstat.ru', 'http://151.248.120.209', 'https://*']\nCSRF_TRUSTED_ORIGINS = ALLOWED_ORIGINS.copy()\n\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.postgresql_psycopg2',\n        'NAME': 'tehstat',\n        'USER': 'tehstat',\n        'PASSWORD': 'QUArschtaf32SIO4',\n        'HOST': 'localhost',\n        'PORT': '5432',\n    }\n}\n\nSTATIC_URL = '/static/'\n#STATIC_ROOT = os.path.join(BASE_DIR, 'static')\n\n#MEDIA_ROOT = os.path.join(BASE_DIR, 'media')\nMEDIA_ROOT = os.path.join(BASE_DIR, 'media')\nMEDIA_URL = '/media/'\n","repo_name":"spektres/tehstat","sub_path":"tehstat/prod_settings.py","file_name":"prod_settings.py","file_ext":"py","file_size_in_byte":861,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31326717468","text":"from api_client import ScraperAPI\n\n\nclass Task:\n    def __init__(self, **kwargs):\n        self.api: ScraperAPI = kwargs['api']\n        self.task_factory = kwargs['task_factory']\n\n    async def __call__(self, session, proxy_address):\n        raise NotImplementedError\n\n\nclass ScrapeTask(Task):\n    def __init__(self, service, country, region, city, **kwargs):\n        super().__init__(**kwargs)\n        self.service = service\n        self.country = country\n        self.region = region\n        self.city = city\n\n    async def __call__(self, session, proxy_address):\n        raise NotImplementedError\n\n\nclass ReviewTask(Task):\n    def __init__(self, country, region, city, service, facilities, **kwargs):\n        super().__init__(**kwargs)\n        self.country = country\n        self.region = region\n        self.city = city\n        self.service = service\n        self.facilities = facilities\n\n    async def __call__(self, session, proxy_address):\n        raise NotImplementedError\n","repo_name":"asbondarenko/tophealth","sub_path":"scrapers/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"71345775004","text":"'''\r\nCreated on 28 mai 2017\r\n\r\n@author: gabri\r\n'''\r\n\r\nimport vgg\r\nimport tensorflow as tf\r\nimport numpy as np\r\nfrom sys import stderr\r\nfrom PIL import Image\r\nfrom functools import reduce\r\n\r\nCONTENT_LAYERS = ('relu4_2', 'relu5_2')\r\nSTYLE_LAYERS = ('relu1_1', 'relu2_1', 'relu3_1', 'relu4_1', 'relu5_1')\r\n\r\n\r\n    \r\n#### Quelques fonctions utiles.... ######\r\n\r\ndef _tensor_size(tensor):\r\n    from operator import mul\r\n    return reduce(mul, (d.value for d in tensor.get_shape()), 1)\r\n\r\ndef rgb2gray(rgb):\r\n    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\r\n\r\ndef gray2rgb(gray):\r\n    w, h = gray.shape\r\n    rgb = np.empty((w,h,3), dtype='float32')\r\n    rgb[:,:,2] = rgb[:,:,1] = rgb[:,:,0] = gray\r\n    return rgb\r\n    \r\n################################################################\r\n\r\ndef stylize(network, initial, initial_noiseblend, content, styles, preserve_colors, iterations,\r\n        content_weight, content_weight_blend, style_weight, style_layer_weight_exp, style_blend_weights, tv_weight,\r\n        learning_rate, beta1, beta2, epsilon, pooling, print_iterations=None, checkpoint_iterations=None):\r\n    \r\n    \"\"\"Fonction de style de l'image basee sur le reseau VGG19 (https://arxiv.org/pdf/1409.1556.pdf) sans quoi rien n'aurait ete possible... \r\n    cette fonction renvoie un tuple (iterations, image) ; 'iterations' a la valeur None si c'est l'image finale. Des tuples sont renvoyes a chaque 'checkpoint_iterations' iterations\r\n    \r\n    \"\"\"\r\n    \r\n    shape = (1,) + content.shape\r\n    style_shapes = [(1,) + style.shape for style in styles]\r\n    content_features = {}\r\n    style_features = [{} for _ in styles]\r\n\r\n    vgg_weights, vgg_mean_pixel = vgg.load_net(network)\r\n    layer_weight = 1.0\r\n    style_layers_weights = {}\r\n    for style_layer in STYLE_LAYERS:\r\n        style_layers_weights[style_layer] = layer_weight\r\n        layer_weight *= style_layer_weight_exp\r\n        \r\n    \r\n    # normalisation des poids \r\n    \r\n    layer_weights_sum = 0\r\n    for style_layer in STYLE_LAYERS:\r\n        layer_weights_sum += style_layers_weights[style_layer]\r\n    for style_layer in STYLE_LAYERS:\r\n        style_layers_weights[style_layer] /= layer_weights_sum\r\n        \r\n    ######################################################\r\n    \r\n    # initialisation de Tensorflow pour l'image de base\r\n    g = tf.Graph()\r\n    with g.as_default(), g.device('/cpu:0'), tf.Session() as sess:\r\n        image = tf.placeholder('float', shape=shape)\r\n        net = vgg.net_preloaded(vgg_weights,image,pooling)\r\n        content_pre = np.array([vgg.preprocess(content, vgg_mean_pixel)])\r\n        for layer in CONTENT_LAYERS:\r\n            content_features[layer] = net[layer].eval(feed_dict={image:content_pre})\r\n     \r\n    ####################################################       \r\n             \r\n    \r\n    # calcul de 'l'image style' en 'feedforward'\r\n    for i in range(len(styles)):\r\n        g = tf.Graph()\r\n        with g.as_default(), g.device('/cpu:0'), tf.Session() as sess:\r\n            \r\n            image = tf.placeholder('float', shape=style_shapes[i])\r\n            net = vgg.net_preloaded(vgg_weights, image, pooling)\r\n            style_pre = np.array([vgg.preprocess(styles[i], vgg_mean_pixel)])\r\n            \r\n            for layer in STYLE_LAYERS:\r\n                features = net[layer].eval(feed_dict={image: style_pre})\r\n                features = np.reshape(features, (-1, features.shape[3]))\r\n                gram = np.matmul(features.T, features) / features.size\r\n                style_features[i][layer] = gram\r\n\r\n    ###################################################################\r\n    \r\n    initial_content_noise_coeff = 1.0 - initial_noiseblend\r\n    \r\n    # !!!!!!!!!!!!!! Creation de l'image de Sortie !!!!!!!!!!!!!!!!!!!!!!!! #\r\n    \r\n    with tf.Graph().as_default():\r\n        if initial is None:\r\n            noise = np.random.normal(size=shape, scale=np.std(content) * 0.1)\r\n            initial = tf.random_normal(shape) * 0.256 \r\n            \r\n        else:\r\n            initial = np.array([vgg.preprocess(initial, vgg_mean_pixel)])\r\n            initial = initial.astype('float32')\r\n            noise = np.random.normal(size=shape, scale=np.std(content) * 0.1)\r\n            initial = (initial) * initial_content_noise_coeff + (tf.random_normal(shape) * 0.256) * (1.0 - initial_content_noise_coeff)\r\n        \r\n        \r\n        image = tf.Variable(initial)\r\n        net = vgg.net_preloaded(vgg_weights, image, pooling)\r\n        \r\n        # loss\r\n        \r\n        content_layers_weights = {}\r\n        content_layers_weights['relu4_2'] = content_weight_blend\r\n        content_layers_weights['relu5_2'] = 1.0 - content_weight_blend\r\n        \r\n        content_loss = 0\r\n        content_losses = []\r\n        \r\n        for content_layer in CONTENT_LAYERS:\r\n            content_losses.append(content_layers_weights[content_layer] * content_weight * (2 * tf.nn.l2_loss(\r\n                    net[content_layer] - content_features[content_layer]) / content_features[content_layer].size))\r\n        \r\n        content_loss += reduce(tf.add, content_losses)\r\n        \r\n        \r\n        # style loss\r\n        \r\n        style_loss = 0\r\n        for i in range(len(styles)):\r\n            style_losses = []\r\n            for style_layer in STYLE_LAYERS:\r\n                layer = net[style_layer]\r\n                _, height, width, number = map(lambda i: i.value, layer.get_shape())\r\n                size = height * width * number\r\n                feats = tf.reshape(layer, (-1, number))\r\n                gram = tf.matmul(tf.transpose(feats), feats) / size\r\n                style_gram = style_features[i][style_layer]\r\n                style_losses.append(style_layers_weights[style_layer] * 2 * tf.nn.l2_loss(gram - style_gram) / style_gram.size)\r\n                \r\n            style_loss += style_weight * style_blend_weights[i] * reduce(tf.add, style_losses)\r\n            \r\n            tv_y_size = _tensor_size(image[:,1:,:,:])\r\n            tv_x_size = _tensor_size(image[:,:,1:,:])\r\n            tv_loss = tv_weight * 2 * ((tf.nn.l2_loss(image[:,1:,:,:] - image[:,:shape[1]-1,:,:]) / tv_y_size) + (tf.nn.l2_loss(image[:,:,1:,:] - image[:,:,:shape[2]-1,:]) / tv_x_size))\r\n            \r\n            # loss total\r\n            loss = content_loss + style_loss + tv_loss\r\n            \r\n            #Initialisation de l'optimisateur\r\n            train_step = tf.train.AdamOptimizer(learning_rate, beta1, beta2, epsilon).minimize(loss)\r\n            \r\n            def print_progress():\r\n                stderr.write('  content loss: %g\\n' % content_loss.eval())\r\n                stderr.write('    style loss: %g\\n' % style_loss.eval())\r\n                stderr.write('       tv loss: %g\\n' % tv_loss.eval())\r\n                stderr.write('    total loss: %g\\n' % loss.eval())\r\n                \r\n            \r\n            # Optimisation\r\n            \r\n            best_loss = float('inf')\r\n            best = None\r\n            \r\n            with tf.Session() as sess:\r\n                sess.run(tf.global_variables_initializer())\r\n                stderr.write('Optimization started...\\n')\r\n                \r\n                if (print_iterations and print_iterations != 0):\r\n                    print_progress()\r\n                    \r\n                \r\n                for i in range(iterations):\r\n                    stderr.write('Iteration %4d/%4d\\n' % (i + 1, iterations))\r\n                    train_step.run()\r\n                    last_step = (i == iterations - 1)\r\n                    \r\n                    if last_step or (print_iterations and i % print_iterations == 0):\r\n                        print_progress()\r\n\r\n                    \r\n                    if (checkpoint_iterations and i % checkpoint_iterations == 0) or last_step:\r\n                        this_loss = loss.eval()\r\n                        if this_loss < best_loss:\r\n                            best_loss = this_loss\r\n                            best = image.eval()\r\n                            \r\n                        img_out = vgg.unprocess(best.reshape(shape[1:]), vgg_mean_pixel)\r\n                        \r\n                        if preserve_colors and preserve_colors == True:\r\n                            original_image = np.clip(content, 0, 255)\r\n                            styled_image = np.clip(img_out, 0, 255)\r\n                            \r\n                            \r\n                            \r\n                            # Les differents etapes de transfert de luminosite et de couleurs...\r\n                            # 1) conversion de l'image stylisee rgb en grayscale\r\n                            # 2) conversion du grayscale stylisee en YUV ( voir https://fr.wikipedia.org/wiki/YUV) pour plus d'info\r\n                            # 3) conversion de l'image originale en YUV\r\n                            # 4) on reassemble stylizedYUV.Y , originalYUV.U, originalYUV.V\r\n                            # 5) Conversion de l'image reassemblee YUV en RGB\r\n                            \r\n                            #  1)\r\n                             \r\n                            styled_grayscale = rgb2gray(styled_image)\r\n                            styled_grayscale_rgb = gray2rgb(styled_grayscale)\r\n                            \r\n                            # 2)\r\n                            styled_grayscale_yuv = np.array(Image.fromarray(styled_grayscale_rgb.astype(np.uint8)).convert('YCbCr'))\r\n                            \r\n                            # 3)\r\n                            original_yuv = np.array(Image.fromarray(original_image.astype(np.uint8)).convert('YCbCr'))\r\n                            \r\n                            # 4)\r\n                            w, h, _ = original_image.shape\r\n                            combined_yuv = np.empty((w, h, 3), dtype=np.uint8)\r\n                            combined_yuv[..., 0] = styled_grayscale_yuv[..., 0]\r\n                            combined_yuv[..., 1] = original_yuv[..., 1]\r\n                            combined_yuv[..., 2] = original_yuv[..., 2]\r\n                            \r\n                            # 5)\r\n                            img_out = np.array(Image.fromarray(combined_yuv, 'YCbCr').convert('RGB'))\r\n                            \r\n                            \r\n                        yield(\r\n                            (None if last_step else i),\r\n                             img_out\r\n                             \r\n                        )\r\n                        \r\n                        ","repo_name":"gabrielmougard/neural-filter","sub_path":"stylize.py","file_name":"stylize.py","file_ext":"py","file_size_in_byte":10469,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"21837860711","text":"import apt_pkg\nimport datetime\nimport fcntl\nimport os\nimport select\n\nimport daklib.daksubprocess\n\ntry:\n    _MAXFD = os.sysconf(\"SC_OPEN_MAX\")\nexcept:\n    _MAXFD = 256\n\nclass GpgException(Exception):\n    pass\n\nclass _Pipe(object):\n    \"\"\"context manager for pipes\n\n    Note: When the pipe is closed by other means than the close_r and close_w\n    methods, you have to set self.r (self.w) to None.\n    \"\"\"\n    def __enter__(self):\n        (self.r, self.w) = os.pipe()\n        return self\n    def __exit__(self, type, value, traceback):\n        self.close_w()\n        self.close_r()\n        return False\n    def close_r(self):\n        \"\"\"close reading side of the pipe\"\"\"\n        if self.r:\n            os.close(self.r)\n            self.r = None\n    def close_w(self):\n        \"\"\"close writing part of the pipe\"\"\"\n        if self.w:\n            os.close(self.w)\n            self.w = None\n\nclass SignedFile(object):\n    \"\"\"handle files signed with PGP\n\n    The following attributes are available:\n      contents            - string with the content (after removing PGP armor)\n      valid               - Boolean indicating a valid signature was found\n      weak_signature      - signature uses a weak algorithm (e.g. SHA-1)\n      fingerprint         - fingerprint of the key used for signing\n      primary_fingerprint - fingerprint of the primary key associated to the key used for signing\n    \"\"\"\n    def __init__(self, data, keyrings, require_signature=True, gpg=\"/usr/bin/gpg\"):\n        \"\"\"\n        @param data: string containing the message\n        @param keyrings: sequence of keyrings\n        @param require_signature: if True (the default), will raise an exception if no valid signature was found\n        @param gpg: location of the gpg binary\n        \"\"\"\n        self.gpg = gpg\n        self.keyrings = keyrings\n\n        self.valid = False\n        self.expired = False\n        self.invalid = False\n        self.weak_signature = False\n        self.fingerprints = []\n        self.primary_fingerprints = []\n        self.signature_ids = []\n\n        self._verify(data, require_signature)\n\n    @property\n    def fingerprint(self):\n        assert len(self.fingerprints) == 1\n        return self.fingerprints[0]\n\n    @property\n    def primary_fingerprint(self):\n        assert len(self.primary_fingerprints) == 1\n        return self.primary_fingerprints[0]\n\n    @property\n    def signature_id(self):\n        assert len(self.signature_ids) == 1\n        return self.signature_ids[0]\n\n    def _verify(self, data, require_signature):\n        with _Pipe() as stdin:\n         with _Pipe() as contents:\n          with _Pipe() as status:\n           with _Pipe() as stderr:\n            pid = os.fork()\n            if pid == 0:\n                self._exec_gpg(stdin.r, contents.w, stderr.w, status.w)\n            else:\n                stdin.close_r()\n                contents.close_w()\n                stderr.close_w()\n                status.close_w()\n\n                read = self._do_io([contents.r, stderr.r, status.r], {stdin.w: data})\n                stdin.w = None # was closed by _do_io\n\n                (pid_, exit_code, usage_) = os.wait4(pid, 0)\n\n                self.contents = read[contents.r]\n                self.status   = read[status.r]\n                self.stderr   = read[stderr.r]\n\n                if self.status == \"\":\n                    raise GpgException(\"No status output from GPG. (GPG exited with status code %s)\\n%s\" % (exit_code, self.stderr))\n\n                for line in self.status.splitlines():\n                    self._parse_status(line)\n\n                if self.invalid:\n                    self.valid = False\n\n                if require_signature and not self.valid:\n                    raise GpgException(\"No valid signature found. (GPG exited with status code %s)\\n%s\" % (exit_code, self.stderr))\n\n        assert len(self.fingerprints) == len(self.primary_fingerprints)\n        assert len(self.fingerprints) == len(self.signature_ids)\n\n    def _do_io(self, read, write):\n        for fd in write.keys():\n            old = fcntl.fcntl(fd, fcntl.F_GETFL)\n            fcntl.fcntl(fd, fcntl.F_SETFL, old | os.O_NONBLOCK)\n\n        read_lines = dict( (fd, []) for fd in read )\n        write_pos = dict( (fd, 0) for fd in write )\n\n        read_set = list(read)\n        write_set = write.keys()\n        while len(read_set) > 0 or len(write_set) > 0:\n            r, w, x_ = select.select(read_set, write_set, ())\n            for fd in r:\n                data = os.read(fd, 4096)\n                if data == \"\":\n                    read_set.remove(fd)\n                read_lines[fd].append(data)\n            for fd in w:\n                data = write[fd][write_pos[fd]:]\n                if data == \"\":\n                    os.close(fd)\n                    write_set.remove(fd)\n                else:\n                    bytes_written = os.write(fd, data)\n                    write_pos[fd] += bytes_written\n\n        return dict( (fd, \"\".join(read_lines[fd])) for fd in read_lines.keys() )\n\n    def _parse_timestamp(self, timestamp, datestring=None):\n        \"\"\"parse timestamp in GnuPG's format\n\n        @rtype:   L{datetime.datetime}\n        @returns: datetime object for the given timestamp\n        \"\"\"\n        # The old implementation did only return the date. As we already\n        # used this for replay production, return the legacy value for\n        # old signatures.\n        if datestring is not None:\n            year, month, day = datestring.split('-')\n            date = datetime.date(int(year), int(month), int(day))\n            time = datetime.time(0, 0)\n            if date < datetime.date(2014, 8, 4):\n                return datetime.datetime.combine(date, time)\n\n        if 'T' in timestamp:\n            raise Exception('No support for ISO 8601 timestamps.')\n        return datetime.datetime.utcfromtimestamp(long(timestamp))\n\n    def _parse_status(self, line):\n        fields = line.split()\n        if fields[0] != \"[GNUPG:]\":\n            raise GpgException(\"Unexpected output on status-fd: %s\" % line)\n\n        # VALIDSIG    <fingerprint in hex> <sig_creation_date> <sig-timestamp>\n        #             <expire-timestamp> <sig-version> <reserved> <pubkey-algo>\n        #             <hash-algo> <sig-class> <primary-key-fpr>\n        if fields[1] == \"VALIDSIG\":\n            # GnuPG accepted MD5 as a hash algorithm until gnupg 1.4.20,\n            # which Debian 8 does not yet include.  We want to make sure\n            # to not accept uploads covered by a MD5-based signature.\n            # RFC 4880, table 9.4:\n            #   1 - MD5\n            #   2 - SHA-1\n            #   3 - RIPE-MD/160\n            if fields[9] == \"1\":\n                raise GpgException(\"Digest algorithm MD5 is not trusted.\")\n            if fields[9] in (\"2\", \"3\"):\n                self.weak_signature = True\n\n            self.valid = True\n            self.fingerprints.append(fields[2])\n            self.primary_fingerprints.append(fields[11])\n            self.signature_timestamp = self._parse_timestamp(fields[4], fields[3])\n\n        elif fields[1] == \"BADARMOR\":\n            raise GpgException(\"Bad armor.\")\n\n        elif fields[1] == \"NODATA\":\n            raise GpgException(\"No data.\")\n\n        elif fields[1] == \"DECRYPTION_FAILED\":\n            raise GpgException(\"Decryption failed.\")\n\n        elif fields[1] == \"ERROR\":\n            raise GpgException(\"Other error: %s %s\" % (fields[2], fields[3]))\n\n        elif fields[1] == \"SIG_ID\":\n            self.signature_ids.append(fields[2])\n\n        elif fields[1] in ('PLAINTEXT', 'GOODSIG', 'KEY_CONSIDERED',\n                           'NEWSIG', 'NOTATION_NAME', 'NOTATION_FLAGS',\n                           'NOTATION_DATA', 'SIGEXPIRED', 'KEYEXPIRED',\n                           'POLICY_URL', 'PROGRESS'):\n            pass\n\n        elif fields[1] in ('EXPSIG', 'EXPKEYSIG'):\n            self.expired = True\n            self.invalid = True\n\n        elif fields[1] in ('REVKEYSIG', 'BADSIG', 'ERRSIG', 'KEYREVOKED', 'NO_PUBKEY'):\n            self.invalid = True\n\n        else:\n            raise GpgException(\"Keyword '{0}' from GnuPG was not expected.\".format(fields[1]))\n\n    def _exec_gpg(self, stdin, stdout, stderr, statusfd):\n        try:\n            if stdin != 0:\n                os.dup2(stdin, 0)\n            if stdout != 1:\n                os.dup2(stdout, 1)\n            if stderr != 2:\n                os.dup2(stderr, 2)\n            if statusfd != 3:\n                os.dup2(statusfd, 3)\n            for fd in range(4):\n                old = fcntl.fcntl(fd, fcntl.F_GETFD)\n                fcntl.fcntl(fd, fcntl.F_SETFD, old & ~fcntl.FD_CLOEXEC)\n            os.closerange(4, _MAXFD)\n\n            args = [self.gpg,\n                    \"--status-fd=3\",\n                    \"--no-default-keyring\",\n                    \"--batch\",\n                    \"--no-tty\",\n                    \"--trust-model\", \"always\",\n                    \"--fixed-list-mode\"]\n            for k in self.keyrings:\n                args.extend([\"--keyring\", k])\n            args.extend([\"--decrypt\", \"-\"])\n\n            os.execvp(self.gpg, args)\n        finally:\n            os._exit(1)\n\n    def contents_sha1(self):\n        return apt_pkg.sha1sum(self.contents)\n\ndef sign(infile, outfile, keyids=[], inline=False, pubring=None, secring=None, homedir=None, passphrase_file=None):\n    args = [\n        '/usr/bin/gpg',\n        '--no-options', '--no-tty', '--batch', '--armour',\n        '--personal-digest-preferences', 'SHA256',\n    ]\n\n    for keyid in keyids:\n        args.extend(['--local-user', keyid])\n    if pubring is not None:\n        args.extend(['--keyring', pubring])\n    if secring is not None:\n        args.extend(['--secret-keyring', secring])\n    if homedir is not None:\n        args.extend(['--homedir', homedir])\n    if passphrase_file is not None:\n        args.extend(['--pinentry-mode', 'loopback',\n                     '--passphrase-file', passphrase_file])\n\n    args.append('--clearsign' if inline else '--detach-sign')\n\n    daklib.daksubprocess.check_call(args, stdin=infile, stdout=outfile)\n\n# vim: set sw=4 et:\n","repo_name":"purism/pdak","sub_path":"daklib/gpg.py","file_name":"gpg.py","file_ext":"py","file_size_in_byte":10059,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"36990237968","text":"import random\r\n\r\n\r\ntest_seed = int(input(\"Create a seed number: \"))\r\nrandom.seed(test_seed)\r\n\r\n# Obtain names here in a list\r\nnames_string = input(\"Give me everybody's names, separated by a comma. \")\r\nnames = names_string.split(\", \")\r\n\r\n\r\n# Logic to pick random person from the list \r\ncount = (len(names))\r\ntodays_pick = random.randint(0,count-1)\r\nbill_payer = (names[todays_pick])\r\nprint(f\"{bill_payer} is going to buy the meal today!\")","repo_name":"Divkat9/100_Days_Of_Python","sub_path":"whopaysthebill.py","file_name":"whopaysthebill.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"30936888049","text":"from aiogram.dispatcher import FSMContext\nfrom aiogram.dispatcher.filters.state import State, StatesGroup\nfrom aiogram import types, Dispatcher\nfrom create_bot import dp, bot\nfrom aiogram.dispatcher.filters import Text\nfrom database import sqlite_db\nfrom keyboards import admin_kb\nfrom aiogram.types import InlineKeyboardMarkup, InlineKeyboardButton\n\nID = None\n\nclass FSMadmin(StatesGroup):\n    photo = State()\n    name = State()\n    description = State()\n    price = State()\n    # edit_photo = State()\n    # edit_name = State()\n    # edit_description = State()\n    # edit_price = State()\n\n# @dp.message_handler(commands=['модератор'], is_chat_admin=True)\nasync def make_change_command(message:types.Message):\n    global ID\n    ID = message.from_user.id\n    await bot.send_message(message.from_user.id, 'Что хозяин надо?', reply_markup=admin_kb.button_case_admin)\n    await message.delete()\n\n# '''Начало диалога загрузки нового пункта меню'''\n# @dp.message_handler(commands='Загрузить', state=None)\nasync def cm_start(message: types.Message):\n    if message.from_user.id == ID:\n        await FSMadmin.photo.set()\n        await message.reply('Загрузи фото')\n\n# '''Выход из состояний'''\n# @dp.message_handler(state=\"*\", commands='отмена')\n# @dp.message_handler(Text(equals='отмена',ignore_case=True), state=\"*\")\nasync def cancel_handler(message:types.Message, state=FSMContext):\n    if message.from_user.id == ID:\n        currentState = await state.get_state()\n        if currentState is None:\n            return\n        await state.finish()\n        await message.reply('Ok')\n\n# '''Ловим первый ответ и пишем в словарь'''\n# @dp.message_handler(content_types=['photo'], state=FSMadmin.photo)\nasync def load_photo(message: types.Message, state: FSMContext):\n    if message.from_user.id == ID:\n        async with state.proxy() as data:\n            data['photo'] = message.photo[0].file_id\n        await FSMadmin.next()\n        await message.reply('Введите название ��ебаба')\n\nasync def load_name(message: types.Message, state: FSMContext):\n    if message.from_user.id == ID:\n        async with state.proxy() as data:\n            data['name'] = message.text\n        await FSMadmin.next()\n        await message.reply('Введите описание')\n\n# '''Ловим третий ответ'''\n# @dp.message_handler(state=FSMadmin.description)\nasync def load_description(message:types.Message, state:FSMContext):\n    if message.from_user.id == ID:\n        async with state.proxy() as data:\n            data['description'] = message.text\n        await FSMadmin.next()\n        await message.reply('Введите цену')\n\n# '''Ловим четвертый ответ'''\n# @dp.message_handler(state=FSMadmin.price)\nasync def load_price(message:types.Message, state:FSMContext):\n    if message.from_user.id == ID:\n        async with state.proxy() as data:\n            data['price'] = float(message.text)\n        await sqlite_db.sql_add_command(state)\n        await state.finish()\n\n# @dp.message_handler(commands='Поменять позицию')\n# async def change_position(message: types.Message):\n#     if message.from_user.id == ID:\n#         read = await sqlite_db.sql_read2()\n#         if len(read) > 0:\n#             for ret in read:\n#                 await bot.send_photo(message.from_user.id, ret[0], f'{ret[1]}\\nОписание: {ret[2]}\\nЦена {ret[-1]}')\n#                 await bot.send_message(message.from_user.id, text='^^^', reply_markup=InlineKeyboardMarkup().add(InlineKeyboardButton(f'Изменить {ret[1]}', callback_data=f'change {ret[1]}')))\n#         else:\n#             await bot.send_message(message.from_user.id, 'У вас пустое меню') \n\n# @dp.callback_query_handler(lambda x: x.data and x.data.startswith('change '))\n# async def change_data_item(callback: types.CallbackQuery, ):\n#     if message.from_user.id == ID:\n#         read = await sqlite_db.sql_read2()\n#         if len(read) > 0:\n#             for ret in read:\n#                 await bot.send_photo(message.from_user.id, ret[0], f'{ret[1]}\\nОписание: {ret[2]}\\nЦена {ret[-1]}')\n#                 await bot.send_message(message.from_user.id, text='^^^', reply_markup=InlineKeyboardMarkup().add(InlineKeyboardButton(f'Удалить {ret[1]}', callback_data=f'del {ret[1]}')))\n#         else:\n#             await bot.send_message(message.from_user.id, 'У вас пустое меню')\n\n\n# @dp.message_handler(commands='Удалить')\nasync def delete_item(message: types.Message):\n    if message.from_user.id == ID:\n        read = await sqlite_db.sql_read2()\n        if len(read) > 0:\n            for ret in read:\n                await bot.send_photo(message.from_user.id, ret[0], f'{ret[1]}\\nОписание: {ret[2]}\\nЦена {ret[-1]}')\n                await bot.send_message(message.from_user.id, text='^^^', reply_markup=InlineKeyboardMarkup().add(InlineKeyboardButton(f'Удалить {ret[1]}', callback_data=f'del {ret[1]}')))\n        else:\n            await bot.send_message(message.from_user.id, 'У вас пустое меню') \n\n@dp.callback_query_handler(lambda x: x.data and x.data.startswith('del '))\nasync def del_callback_run(callback_query: types.CallbackQuery):\n    await sqlite_db.sql_delete_command(callback_query.data.replace('del ', '')) #{ret[1]}\n    await callback_query.answer(text=f'{callback_query.data.replace(\"del \", \"\")} удалена.', show_alert=True)\n\n# async def edit_item(message: types.Message):\n#     if message.from_user.id == ID:\n#         read = await sqlite_db.sql_read2()\n#         if len(read) > 0:\n#             for ret in read:\n#                 await bot.send_photo(message.from_user.id, ret[0], f'{ret[1]}\\nОписание: {ret[2]}\\nЦена {ret[-1]}')\n#                 await bot.send_message(message.from_user.id, text='^^^', reply_markup=InlineKeyboardMarkup().add(InlineKeyboardButton(f'Изменить {ret[1]}', callback_data=f'edit {ret[1]}')))\n#         else:\n#             await bot.send_message(message.from_user.id, 'У вас пустое меню')                   \n\n# # @dp.callback_query_handler(lambda x: x.data and x.data.startswith('del '))\n\n# async def edit_callback_run(message:types.Message, callback_query: types.CallbackQuery, state=FSMContext):\n#     async with state.proxy() as data:\n#             data['name'] = message.text\n#     await sqlite_db.sql_edit_title(callback_query.data.replace('edit ', ''), callback_query.data)\n\n\nasync def empty(message:types.Message):\n    await message.answer('Нет такой команды')\n    await message.delete()\n\n#Регистрируем хэндлеры\ndef register_handlers_admin(dp : Dispatcher):\n    dp.register_message_handler(cm_start, commands=['Загрузить'])\n    dp.register_message_handler(cancel_handler, state='*', commands='отмена')\n    dp.register_message_handler(cancel_handler, Text(equals='отмена', ignore_case=True), state='*')\n    dp.register_message_handler(load_photo, content_types=['photo'], state=FSMadmin.photo)    \n    dp.register_message_handler(load_name, state=FSMadmin.name)\n    dp.register_message_handler(load_description, state=FSMadmin.description)\n    dp.register_message_handler(load_price, state=FSMadmin.price)\n    dp.register_message_handler(make_change_command, commands=['модератор'], is_chat_admin=True)\n    # dp.register_callback_query_handler(del_callback_run, lambda x: x.data and x.data.startswith('del '), is_chat_admin=True)\n    # dp.register_callback_query_handler(del_callback_run, lambda x: x.data and x.data.startswith('del '), is_chat_admin=True)\n    dp.register_callback_query_handler(del_callback_run, lambda x: x.data and x.data.startswith('del '))\n    dp.register_message_handler(delete_item, commands=['Удалить'])\n    # dp.register_callback_query_handler(edit_callback_run, lambda x: x.data and x.data.startswith('edit '))\n    # dp.register_message_handler(edit_item, commands=['Изменить_название'])\n    dp.register_message_handler(empty)","repo_name":"NortherNomad/stuff","sub_path":"handlers/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":8153,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3332664758","text":"\nfrom cgi import print_arguments\nimport numbers\nimport sqlite3\nimport random\nfrom matplotlib.pyplot import connect\n\ndef quest(query):\n    answers = []\n    cursor.execute(query)\n    stroka = cursor.fetchall()\n    question = (stroka[0])[0]\n    right_answer = (stroka[0])[-2]\n    answers = list((stroka[0])[1:-2])\n\n    return question ,answers,right_answer\n\nfor database_Title in range(1,5+1):\n    connection = sqlite3.connect(database=str(database_Title))\n    cursor = connection.cursor()\n    cursor.execute('select count (*)  from questions')\n    count = cursor.fetchall()\n    count = count[0] \n    number_of_questions  = int(count[0]) - 1 \n    print(f'на этом уровне уже будет {number_of_questions} вы точно готовы?')\n    print('{0:*^50}'.format('yes/no'))\n    exit = input()\n    if exit == 'no':\n        break\n    query = f'select * from questions where id={random.randint(1,number_of_questions)};'\n    q,ans,r_ans = quest(query)\n    print('{0:*^50}'.format(q))\n    print('{0:*<50}'.format('теперь вам нужно выбрать номер правильного ответа'))\n    print(ans)\n    b = int(input())\n    if b == r_ans:\n        if i == 5:\n            print('ну эт самое, мои поздравления')\n        else: \n            print('{0:*^50}'.format('красавчик'))\n            print('{0:*^50}'.format('идем на следующий уровень'))\n            #loading()\n    else:\n        print('{0:*^50}'.format('упсссс'))\n        break\n\n\ncursor.close()\nconnection.close()\n\n","repo_name":"bob4inski/python-with-albert","sub_path":"socccet/dd.py","file_name":"dd.py","file_ext":"py","file_size_in_byte":1565,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"40447116732","text":"#Import Library OpenCV\r\nimport cv2\r\n\r\n#Membuka file cascade.xml\r\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\r\n\r\n#Membaca input foto\r\nimg = cv2.imread('IMG4.jpg')\r\n\r\n#Konversi kedalam greyscale\r\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\n\r\n#Deteksi wajah\r\nfaces = face_cascade.detectMultiScale(gray, 1.25, 4)\r\n\r\n#Kotak Persegi\r\nfor (x, y, w, h) in faces:\r\n\tcv2.rectangle(img, (x,y), (x+w, y+h), (0,0,255), 2)\r\n\r\n#Output\r\ncv2.imshow('img', img)\r\ncv2.waitKey()","repo_name":"mannaufal/Face-Detection","sub_path":"face_detection/face_detect.py","file_name":"face_detect.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"26215050348","text":"import os\nfrom django.conf import settings\n\n\nBASE_DIR = os.path.dirname(os.path.dirname(\n    os.path.dirname(os.path.abspath(__file__))))\nSECRET_KEY = '-05sgp9!deq=q1nltm@^^2cc+v29i(tyybv3v2t77qi66czazj'\nDEBUG = True\nALLOWED_HOSTS = []\n\nINSTALLED_APPS = [\n    'django.contrib.admin',\n    'django.contrib.auth',\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.messages',\n    'django.contrib.staticfiles',\n    'core',\n    'corsheaders',\n    # auth\n    'django.contrib.sites',\n    'allauth',\n    'allauth.account',\n    'allauth.socialaccount',\n    'allauth.socialaccount.providers.facebook',\n    'crispy_forms',\n    'django_countries',\n\n    # aws\n    'storages',\n]\nSITE_ID = 1\n\n\n# CRISPY FORMS\n\nCRISPY_TEMPLATE_PACK = 'bootstrap4'\n\nMIDDLEWARE = [\n    'django.middleware.security.SecurityMiddleware',\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.common.CommonMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n    'django.middleware.clickjacking.XFrameOptionsMiddleware',\n]\n\nROOT_URLCONF = 'home.urls'\n\nTEMPLATES = [\n    {\n        'BACKEND': 'django.template.backends.django.DjangoTemplates',\n        'DIRS': [os.path.join(BASE_DIR, 'templates')],\n        'APP_DIRS': True,\n        'OPTIONS': {\n            'context_processors': [\n                'django.template.context_processors.debug',\n                'django.template.context_processors.request',\n                'django.contrib.auth.context_processors.auth',\n                'django.contrib.messages.context_processors.messages',\n            ],\n        },\n    },\n]\n\nLANGUAGE_CODE = 'en-us'\nTIME_ZONE = 'Asia/Dhaka'\nUSE_I18N = True\nUSE_L10N = True\nUSE_TZ = True\n\nSTATIC_URL = '/static/'\nSTATICFILES_DIRS = [os.path.join(BASE_DIR, 'static_files')]\nSTATIC_ROOT = os.path.join(BASE_DIR, 'static')\nMEDIA_URL = '/image/'\nMEDIA_ROOT = os.path.join(BASE_DIR, 'static_files/image')\nSITE_ID = 1\n\n\nAUTHENTICATION_BACKENDS = (\n\n    # Needed to login by username in Django admin, regardless of `allauth`\n    'django.contrib.auth.backends.ModelBackend',\n\n    # `allauth` specific authentication methods, such as login by e-mail\n    'allauth.account.auth_backends.AuthenticationBackend',\n\n)\n\nCSRF_COOKIE_NAME = \"csrftoken\"\n\nACCOUNT_UNIQUE_EMAIL = False\nACCOUNT_EMAIL_REQUIRED = False\nACCOUNT_AUTHENTICATION_METHOD = 'username'\nACCOUNT_EMAIL_VERIFICATION = 'none'\nLOGIN_REDIRECT_URL = '/'\n","repo_name":"rakib06/pochonder-shob","sub_path":"home/settings/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35982351515","text":"import math\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\ny = [0, 0.1, 0.2, 0.5, 1, 2.5, 3, 3.5, 4, 4.5, 5]\r\nx = [0, 20, 40, 100, 200, 500, 600, 700, 800, 900, 1000]\r\n\r\n\r\nplt.errorbar(x, y, xerr=0, yerr=0, marker='.', ls=\"\")\r\nk = np.polyfit(x, y, 1)\r\n\r\nprint(\"Угол наклона:\")\r\nprint(k)\r\n\r\ny_ = np.polyval(k, x)\r\nplt.plot(x, y_, 'c-')\r\nplt.tick_params(axis='both', which='major', labelsize=6)\r\nplt.title(\"График калибровки шага мотора\", fontsize = 14)\r\nplt.ylabel(\"Смещения трубки Пито, см\",  fontsize = 8) # ось абсцисс\r\nplt.xlabel(\"Количество шагов мотора\",  fontsize = 8) # ось ординат\r\nplt.grid()\r\nplt.savefig('график_1')\r\nplt.show()\r\n\r\n\r\n ","repo_name":"Danilov2003/test-repo","sub_path":"jet/scripts/len.py","file_name":"len.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74637017885","text":"\"\"\"Represent local and remote files and their metadata.\n\nSee module dcqc.target for the multi-file target class.\n\nClasses:\n\n    FileType: For collecting file type-specific information.\n    File: For bundling file location and metadata as well as\n    operations for retrieving file contents.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport glob\nimport os\nfrom collections.abc import Collection, Mapping\nfrom copy import deepcopy\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom tempfile import gettempdir, mkdtemp\nfrom typing import Any, ClassVar, Optional\nfrom warnings import warn\n\nfrom fs.base import FS\n\nfrom dcqc.mixins import SerializableMixin, SerializedObject\nfrom dcqc.utils import is_url_local, open_parent_fs\n\n\n@dataclass\nclass FileType:\n    \"\"\"Bundle information for a given file type.\"\"\"\n\n    _registry: ClassVar[dict[str, FileType]]\n    _registry = dict()\n\n    name: str\n    file_extensions: tuple[str, ...]\n    edam_iri: Optional[str]\n\n    def __init__(\n        self,\n        name: str,\n        file_extensions: Collection[str],\n        edam_iri: Optional[str] = None,\n    ):\n        \"\"\"Construct a FileType object.\n\n        Args:\n            name: File type name.\n            file_extensions: Valid file extensions.\n            edam_iri: EDAM format ontology identifier.\n        \"\"\"\n        self.name = name\n        self.file_extensions = tuple(file_extensions)\n        self.edam_iri = edam_iri\n        self.register_file_type()\n\n    def register_file_type(self) -> None:\n        \"\"\"Register instantiated file type for later retrieval.\n\n        Raises:\n            ValueError: If the file type's name has already\n                been registered previously.\n        \"\"\"\n        name = self.name.lower()\n        if name in self._registry:\n            message = f\"File type ({name}) is already registered ({self._registry}).\"\n            raise ValueError(message)\n        self._registry[name] = self\n\n    @classmethod\n    def list_file_types(cls) -> list[FileType]:\n        \"\"\"Retrieve all available file type objects.\n\n        Returns:\n            The full list of file type objects.\n        \"\"\"\n        return list(cls._registry.values())\n\n    @classmethod\n    def get_file_type(cls, file_type: str) -> FileType:\n        \"\"\"Retrieve file type object based on its name.\n\n        Args:\n            file_type: File type name.\n\n        Raises:\n            ValueError: If the given file type name has\n                not been registered previously.\n\n        Returns:\n            The file type object with the given name.\n        \"\"\"\n        file_type = file_type.lower()\n        if file_type not in cls._registry:\n            types = list(cls._registry)\n            message = f\"File type ({file_type}) not among available options ({types}).\"\n            raise ValueError(message)\n        return cls._registry[file_type]\n\n\n# TODO: These file types could be moved to an external file\n# Instantiated file types are automatically tracked by the FileType class\nFileType(\"*\", (), \"format_1915\")  # To represent all file types\nFileType(\"TXT\", (\".txt\",), \"format_1964\")\nFileType(\"JSON\", (\".json\",), \"format_3464\")\nFileType(\"JSON-LD\", (\".jsonld\",), \"format_3749\")\nFileType(\"TIFF\", (\".tif\", \".tiff\", \".svs\", \".scn\"), \"format_3591\")\nFileType(\"OME-TIFF\", (\".ome.tif\", \".ome.tiff\"), \"format_3727\")\nFileType(\"TSV\", (\".tsv\"), \"format_3475\")\nFileType(\"CSV\", (\".csv\"), \"format_3752\")\nFileType(\"BAM\", (\".bam\"), \"format_2572\")\nFileType(\"FASTQ\", (\".fastq\", \".fastq.gz\", \".fq\", \".fq.gz\"), \"format_1930\")\nFileType(\"HDF5\", (\".hdf\", \".hdf5\", \".h5\", \".he5\", \".h5ad\"), \"format_3590\")\n\n\n# TODO: Leverage post-init function in dataclasses\n@dataclass\nclass File(SerializableMixin):\n    \"\"\"Construct a File object.\n\n    Args:\n        url: Local or remote location of a file.\n        metadata: File metadata.\n        relative_to: Used to update any local URLs if they\n            are relative to a directory other than the\n            current work directory (default).\n    \"\"\"\n\n    tmp_dir: ClassVar[str] = \"dcqc-staged-\"\n\n    _serialized_properties = [\"name\", \"local_path\"]\n\n    url: str\n    metadata: dict[str, Any]\n    type: str\n\n    def __init__(\n        self,\n        url: str,\n        metadata: Optional[Mapping[str, Any]] = None,\n        relative_to: Optional[Path] = None,\n        local_path: Optional[Path] = None,\n    ):\n        self.url = self._relativize_url(url, relative_to)\n        metadata = metadata or dict()\n        self.metadata = dict(metadata)\n        self.type = self._pop_file_type()\n\n        self._fs: Optional[FS]\n        self._fs = None\n        self._fs_path: Optional[str]\n        self._fs_path = None\n        self._name: Optional[str]\n        self._name = None\n        self._local_path: Optional[Path]\n        self._local_path = local_path\n\n    def __hash__(self):\n        return hash((self.url, self.type, tuple(self.metadata.items())))\n\n    def __eq__(self, other):\n        return hash(self) == hash(other)\n\n    def _relativize_url(self, url: str, relative_to: Optional[Path]) -> str:\n        \"\"\"Update local URLs if relative to a directory other than CWD.\n\n        Args:\n            url: Local or remote location of a file.\n            relative_to: Used to update any local URLs if they\n                are relative to a directory other than the\n                current work directory (default).\n\n        Returns:\n            The relativized URL.\n        \"\"\"\n        if self.is_url_local(url):\n            relative_to = relative_to or Path.cwd()\n            scheme, separator, resource = url.rpartition(\"://\")\n            path = Path(resource)\n            if not path.is_absolute():\n                resource = os.path.relpath(relative_to / resource)\n            url = f\"{scheme}{separator}{resource}\"\n        elif not self.is_url_local(url) and relative_to is not None:\n            message = f\"URL ({url}) is remote. Ignoring relative_to ({relative_to}).\"\n            warn(message)\n        return url\n\n    def _pop_file_type(self) -> str:\n        \"\"\"Extract and remove file type from metadata.\n\n        This function defaults to the generic file type\n        (\"*\") if the key is absent from the metadata.\n\n        Returns:\n            The name of the file type in the metadata.\n        \"\"\"\n        file_type = self.metadata.pop(\"file_type\", \"*\")\n        return file_type\n\n    def _init_fs(self) -> tuple[FS, str]:\n        \"\"\"Initialize file system to access URL.\n\n        All queries with this file system should use\n        `self._fs_path` as the path, not `self.url`.\n\n        Returns:\n            A file system + basename pair.\n        \"\"\"\n        fs, fs_path = open_parent_fs(self.url)\n        self._fs_path = fs_path\n        self._fs = fs\n        return fs, fs_path\n\n    @property\n    def local_path(self) -> Path:\n        \"\"\"Retrieve the path to a local copy if available.\n\n        Raises:\n            FileNotFoundError: If a remote file has not been\n                staged yet and thus has no local copy.\n\n        Returns:\n            The path to the local copy or `None` if unavailable.\n        \"\"\"\n        if self._local_path is None and self.is_url_local():\n            _local_path = self.fs.getsyspath(self.fs_path)\n            self._local_path = Path(_local_path)\n        if self._local_path is None:\n            message = \"Local path is unavailable. Use stage() to create a local copy.\"\n            raise FileNotFoundError(message)\n        return self._local_path\n\n    @property\n    def fs(self) -> FS:\n        \"\"\"The file system that can access the URL.\"\"\"\n        fs = self._fs\n        if fs is None:\n            fs, _ = self._init_fs()\n        return fs\n\n    @property\n    def fs_path(self) -> str:\n        \"\"\"The path that can be used with the file system.\"\"\"\n        fs_path = self._fs_path\n        if fs_path is None:\n            _, fs_path = self._init_fs()\n        return fs_path\n\n    @property\n    def name(self) -> str:\n        \"\"\"The file name according to the file system.\"\"\"\n        if self._name is None:\n            info = self.fs.getinfo(self.fs_path)\n            self._name = info.name\n        return self._name\n\n    def get_file_type(self) -> FileType:\n        \"\"\"Retrieve the relevant file type object.\n\n        Returns:\n            FileType: File type object\n        \"\"\"\n        return FileType.get_file_type(self.type)\n\n    def get_metadata(self, key: str) -> Any:\n        \"\"\"Retrieve file metadata using a key.\n\n        Args:\n            key: Metadata key name.\n\n        Raises:\n            KeyError: If the metadata key doesn't exist.\n\n        Returns:\n            The metadata value associated with the given key.\n        \"\"\"\n        if key not in self.metadata:\n            url = self.url\n            md = self.metadata\n            message = f\"File ({url}) does not have '{key}' in its metadata ({md}).\"\n            raise KeyError(message)\n        return self.metadata[key]\n\n    def is_url_local(self, url: Optional[str] = None) -> bool:\n        \"\"\"Check whether a URL refers to a local location.\n\n        Args:\n            url: Local or remote location of a file.\n                Defaults to URL associated with file.\n\n        Returns:\n            Whether the URL refers to a local location.\n        \"\"\"\n        url = url or self.url\n        return is_url_local(url)\n\n    def is_file_local(self) -> bool:\n        \"\"\"Check if the file (or a copy) is available locally.\n\n        Unlike :func:`~dcqc.file.File.is_url_local`, this method\n        considers if a locally staged copy is available regardless\n        of whether the URL is local or remote.\n\n        To retrieve the location of the local copy, you can use\n        :attr:`~dcqc.file.File.local_path\n\n        Returns:\n            Whether the file has a copy available locally.\n        \"\"\"\n        return self._local_path is not None\n\n    def already_staged(self) -> list[Path]:\n        \"\"\"Check if the target file has already been staged to the remote directory.\n\n        Returns:\n            staged_file_paths (list): List of already staged file paths.\n            Empty list if file has not been staged.\n\n        Raises:\n            FileExistsError: If the file has already been staged more than once.\n            This would cause a name collision in Nextflow.\n        \"\"\"\n        path_str = os.path.join(gettempdir(), self.tmp_dir + \"*\", self.name)\n        staged_file_strs = glob.glob(path_str)\n        staged_file_paths = [Path(path) for path in staged_file_strs]\n        if len(staged_file_paths) > 1:\n            message = (\n                f\"File has already been staged multiple times: {staged_file_paths}\"\n            )\n            raise FileExistsError(message)\n        return staged_file_paths\n\n    def stage(\n        self,\n        destination: Optional[Path] = None,\n        overwrite: bool = False,\n    ) -> Path:\n        \"\"\"Create local copy of local or remote file.\n\n        A destination is not required for remote files; it\n        defaults to a temporary directory.\n        Local files aren't moved if a destination is omitted.\n\n        Args:\n            destination: File or folder where to store the file.\n                Defaults to None.\n            overwrite: Whether to ignore existing file at the\n                target destination. Defaults to False.\n\n        Raises:\n            ValueError: If the parent directory of the\n                destination does not exist.\n            FileExistsError: If the destination file already\n                exists and ``overwrite`` was not enabled.\n\n        Returns:\n            The path of the local copy.\n        \"\"\"\n        if not destination:\n            if self._local_path is not None:\n                return self._local_path\n            else:\n                # check if file has already been staged\n                staged_files = self.already_staged()\n                if not staged_files:\n                    destination_str = mkdtemp(prefix=self.tmp_dir)\n                    destination = Path(destination_str)\n                else:\n                    destination = staged_files[0]\n                    self._local_path = destination\n                    return destination\n\n        # By this point, destination is defined (not None)\n        if destination.is_dir():\n            destination = destination / self.name\n\n        if not destination.parent.exists():\n            dest = str(destination)\n            message = f\"Parent folder of destination ({dest}) does not exist.\"\n            raise ValueError(message)\n\n        if destination.exists() and not overwrite:\n            dest = str(destination)\n            message = f\"Destination ({dest}) already exists. Enable overwrite.\"\n            raise FileExistsError(message)\n\n        # By this point, the file either doesn't exist or overwrite is enabled\n        destination.unlink(missing_ok=True)\n\n        if self._local_path and self.is_url_local():\n            destination.symlink_to(self._local_path.resolve())\n        else:\n            with destination.open(\"wb\") as dest_file:\n                self.fs.download(self.fs_path, dest_file)\n\n        self._local_path = destination\n        return destination\n\n    @classmethod\n    def from_dict(cls, dictionary: SerializedObject) -> File:\n        \"\"\"Deserialize a dictionary into a file.\n\n        Args:\n            dictionary: A serialized file object.\n\n        Returns:\n            The reconstructed file object.\n        \"\"\"\n        dictionary = deepcopy(dictionary)\n\n        file_type = dictionary.pop(\"type\")\n        dictionary[\"metadata\"][\"file_type\"] = file_type\n\n        if dictionary[\"local_path\"] is not None:\n            dictionary[\"local_path\"] = Path(dictionary[\"local_path\"])\n\n        # Ignore serialized name since it's a dynamically-computed property\n        dictionary.pop(\"name\", None)\n\n        return cls(**dictionary)\n","repo_name":"Sage-Bionetworks-Workflows/py-dcqc","sub_path":"src/dcqc/file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":13727,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"26724778386","text":"#########\n# title: pyMCDSts.py\n#\n# language: python3\n# date: 2022-08-22\n# license: BSD-3-Clause\n# authors: Patrick Wall, Randy Heiland, Paul Macklin, Elmar Bucher\n#\n# description:\n#     pyMCDSts.py defines an object class, able to load and access\n#     within python a time series of mcds objects loaded from a single\n#     PhysiCell model output directory. pyMCDSts.py was first forked from\n#     PhysiCell-Tools python-loader, where it was implemented as\n#     pyMCDS_timeseries.py, then totally rewritten and further developed.\n#\n#     the make_image and make_movie functions are cloned from the PhysiCell\n#     Makefile. note on difference image magick convert and mogrify:\n#     + https://graphicsmagick-tools.narkive.com/9Sowc4HF/gm-tools-mogrify-vs-convert\n#########\n\n\n# load libraries\nimport os\nimport pathlib\nimport platform\nfrom .pyMCDS import pyMCDS\nimport xml.etree.ElementTree as ET\n\n# constants\nes_resize = {'*0.svg','*1.svg','*2.svg','*3.svg','*4.svg','*5.svg','*6.svg','*7.svg','*8.svg','*9.svg'} # only files matching this glob patterns will be resized\nls_glob = sorted(es_resize) + ['initial.svg','legend.svg']\n\n# classes\nclass pyMCDSts:\n    \"\"\"\n    input:\n        output_path: string, default '.'\n            relative or absolute path to the directory where\n            the PhysiCell output files are stored.\n\n        microenv: booler; default True\n            should the microenvironment be extracted?\n            setting microenv to False will use less memory and speed up\n            processing, similar to the original pyMCDS_cells.py script.\n\n        graph: boole; default True\n            should the graphs be extracted?\n            setting graph to False will use less memory and speed up processing.\n\n        verbose: boole; default True\n            setting verbose to False for less text output, while processing.\n\n    output:\n        mcdsts: pyMCDSts class instance\n            this instance offers functions to process all stored time steps\n            from a simulation. no data is fetched by initialization.\n\n    description:\n        pyMCDSts.__init__ generates a class instance and stores\n        the input parameters. no data is fetched at initialization.\n        the instance offers functions to process all time steps\n        in the output_path directory.\n    \"\"\"\n    def __init__(self, output_path='.', microenv=True, graph=True, verbose=True):\n        self.output_path = output_path\n        self.microenv = microenv\n        self.graph = graph\n        self.verbose = verbose\n\n\n    ## LOAD DATA\n    def get_xmlfile_list(self):\n        \"\"\"\n        input:\n            self: pyMCDSts class instance.\n\n        output:\n            xmlfile_list: list of strings\n                alphanumerical sorted list of /path/to/output*.xml strings.\n\n        description:\n            function returns an alphanumerical (and as such chronological)\n            ordered list of physicell xml path and output file names. the\n            list can be manipulated and used as input for the\n            mcdsts.read_mcds function.\n        \"\"\"\n        # bue 2022-10-22: is output*.xml always the correct pattern?\n        ls_pathfile = [o_pathfile.as_posix() for o_pathfile in sorted(pathlib.Path(self.output_path).glob('output*.xml'))]\n        return(ls_pathfile)\n\n\n    def read_mcds(self, xmlfile_list=None):\n        \"\"\"\n        input:\n            self: pyMCDSts class instance.\n\n            xmlfile_list: list of strings; default None\n                list of physicell output /path/to/output*.xml strings.\n\n        output:\n            l_mcds: list of mcds objects\n\n        description:\n            the function returns a list of mcds objects loaded by\n            pyMCDS calls.\n        \"\"\"\n        # handle input\n        if (xmlfile_list is None):\n            xmlfile_list = self.get_xmlfile_list()\n\n        # load mcds objects into list\n        l_mcds = []\n        for s_pathfile in xmlfile_list:\n            mcds = pyMCDS(\n                xmlfile = s_pathfile,\n                microenv = self.microenv,\n                graph = self.graph,\n                verbose = self.verbose\n            )\n            l_mcds.append(mcds)\n            if self.verbose:\n                print() # carriage return\n\n        # output\n        return(l_mcds)\n\n\n    ## TRANSFORM SVG\n    def _handle_magick(self):\n        \"\"\"\n        input:\n            self: pyMCDSts class instance.\n\n        output:\n            s_magick: string\n                image magick command line command call\n\n        description:\n            internal function manipulates the command line command call,\n            so that the call as well works on linux systems, which all\n            too often run image magick < 7.0\n        \"\"\"\n        s_magick = 'magick '\n        if (platform.system() in {'Linux'}) and (os.system('magick --version') != 0) and (os.system('convert --version') == 0):\n            s_magick = ''\n        return(s_magick)\n\n\n    def _handle_resize(self, resize_factor=1):\n        \"\"\"\n        input:\n            self: pyMCDSts class instance.\n\n            resize_factor: floating point number; default 1\n                to specify image magnification or scale down.\n                the resize parameter will in any case be adjusted,\n                so that the resulting image's height and width are\n                integer divisible by 2. this is because of a\n                ffmpeg constrain for generating a movie out of images.\n\n        output:\n            s_resize: string\n                image magick command resize parameter setting.\n\n        description:\n            internal function returns image magick command\n            resize parameter setting, which in any case, even when\n            resize_factor is 1, will generate ffmpeg compatible images.\n        \"\"\"\n        # extract information from svg and resize\n        tree = ET.parse(f'{self.output_path}/initial.svg')\n        root = tree.getroot()\n        r_width = float(root.get('width')) * resize_factor\n        r_height = float(root.get('height')) * resize_factor\n        # movie treat\n        r_width = int(round(r_width / 2)) * 2\n        r_height = int(round(r_height / 2)) * 2\n        # output\n        s_resize = f\"-resize '{r_width}!x{r_height}!'\"\n        return(s_resize)\n\n\n    def make_gif(self, resize_factor=1, giffile='timeseries.gif'):\n        \"\"\"\n        input:\n            self: pyMCDSts class instance.\n\n            giffile: string; default 'timeseries.gif'\n                gif image filename.\n\n            resize_factor: floating point number; default 1\n                to specify image magnification or scale down.\n\n        output:\n            gif file in output_path directory.\n`               additionally, the function will return the path and filename.\n\n        description:\n            this function generates a gif image from all snapshot svg files\n            found in the output_path directory.\n        \"\"\"\n        s_magick = self._handle_magick()\n        s_resize = self._handle_resize(resize_factor=resize_factor)\n\n        # generate gif\n        # bue: use convert, mogrify will cause troubles here!\n        s_opathfile = f'{self.output_path}/{giffile}'\n        os.system(f'{s_magick}convert {s_resize} {self.output_path}/snapshot*.svg {s_opathfile}')\n\n        # output\n        return(s_opathfile)\n\n\n    def make_jpeg(self, resize_factor=1):\n        \"\"\"\n        input:\n            self: pyMCDSts class instance.\n\n            glob: string\n                wildcard filename pattern.\n\n            resize_factor: floating point number; default 1\n                to specify image magnification or scale down.\n                the resize parameter will in any case be adjusted,\n                so that the resulting image's height and width are\n                integer divisible by 2. this is because of a\n                ffmpeg constrain for generating a movie out of images.\n\n        output:\n            jpeg files in output_path directory.\n\n        description:\n            this function generates jpeg image equivalents from all svg files\n            found in the output_path directory.\n            jpeg is by definition a lossy compressed image format.\n            https://en.wikipedia.org/wiki/JPEG\n        \"\"\"\n        # bue: use mogrify, convert might cause troubles here!\n        s_magick = self._handle_magick()\n        s_resize = self._handle_resize(resize_factor=resize_factor)\n        for s_glob in ls_glob:\n            if (len(set(pathlib.Path(self.output_path).glob(s_glob))) > 0):\n                if (s_glob in es_resize):\n                    os.system(f'{s_magick}mogrify {s_resize} -format jpeg {self.output_path}/{s_glob} &')\n                else:\n                    os.system(f'{s_magick}mogrify -format jpeg {self.output_path}/{s_glob} &')\n\n\n    def make_png(self, resize_factor=1, addargs='-transparent white'):\n        \"\"\"\n        input:\n            self: pyMCDSts class instance.\n\n            resize_factor: floating point number; default 1\n                to specify image magnification or scale down.\n                the resize parameter will in any case be adjusted,\n                so that the resulting image's height and width are\n                integer divisible by 2. this is because of a\n                ffmpeg constrain for generating a movie out of images.\n\n            addargs: string; default '-transparent white'\n                sting to additional image magick parameters.\n                by default, alpha channel transparency is set to white.\n\n        output:\n            png files in output_path directory.\n\n        description:\n            this function generates png image equivalents from all svg files\n            found in the output_path directory.\n            png is by definition a lossless compressed image format.\n            https://en.wikipedia.org/wiki/Portable_Network_Graphics\n        \"\"\"\n        # bue: use mogrify, convert might cause troubles here!\n        s_magick = self._handle_magick()\n        s_resize = self._handle_resize(resize_factor=resize_factor)\n        for s_glob in ls_glob:\n            if (len(set(pathlib.Path(self.output_path).glob(s_glob))) > 0):\n                if (s_glob in es_resize):\n                    os.system(f'{s_magick}mogrify {s_resize} {addargs} -format png {self.output_path}/{s_glob} &')\n                else:\n                    os.system(f'{s_magick}mogrify {addargs} -format png {self.output_path}/{s_glob} &')\n\n\n    def make_tiff(self, resize_factor=1):\n        \"\"\"\n        input:\n            self: pyMCDSts class instance.\n\n            resize_factor: floating point number; default 1\n                to specify image magnification or scale down.\n                the resize parameter will in any case be adjusted,\n                so that the resulting image's height and width are\n                integer divisible by 2. this is because of a\n                ffmpeg constrain for generating a movie out of images.\n\n        output:\n            tiff files in output_path directory.\n\n        decription:\n            this function generates tiff image equivalents from all svg files\n            found in the output_path directory.\n            https://en.wikipedia.org/wiki/TIFF\n        \"\"\"\n        # bue: use mogrify, convert might cause troubles here!\n        s_magick = self._handle_magick()\n        s_resize = self._handle_resize(resize_factor=resize_factor)\n        for s_glob in ls_glob:\n            if (len(set(pathlib.Path(self.output_path).glob(s_glob))) > 0):\n                if (s_glob in es_resize):\n                    os.system(f'{s_magick}mogrify {s_resize} -format tiff {self.output_path}/{s_glob} & ')\n                else:\n                    os.system(f'{s_magick}mogrify -format tiff {self.output_path}/{s_glob} & ')\n\n\n    def make_movie(self, interface='jpeg', moviefile='movie.mp4', frame_rate=24):\n        \"\"\"\n        input:\n            self: pyMCDSts class instance.\n\n            interface: string; default jpeg\n                ffmpeg cannot directly translate svg image into a move.\n                the interface image format will be used to bridge the gap.\n                this images, from which the movie will be generated, have to exist.\n                they can be generated with the make_jpeg, make_png, or make_tiff\n                function.\n\n            moviefile: sting; default 'movie.mp4'\n                mp4 movie file name.\n\n            frame_rate: integer; default 24\n                specifies how many images per second will be used.\n\n        output:\n            mp4 move file in output_path directory.\n                interface image files in output_path directory.\n`               additionally, the function will return the movie path and filename.\n\n        description:\n            this function generates a movie from all interface image files\n            found in the output_path directory.\n        \"\"\"\n        # generate movie\n        s_opathfile = f'{self.output_path}/{moviefile}'\n        os.system(f'ffmpeg -r {frame_rate} -f image2 -i {self.output_path}/snapshot%08d.{interface} -vcodec libx264 -pix_fmt yuv420p -strict -2 -tune animation -crf 15 -acodec none {s_opathfile}')\n\n        # output\n        return(s_opathfile)\n\n","repo_name":"furkankurtoglu/CRC-Organoids-Multiscale-Model","sub_path":"5_PhysiCell_Model_monolayer/py_analysis/pcDataLoader/pyMCDSts.py","file_name":"pyMCDSts.py","file_ext":"py","file_size_in_byte":13130,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"15270013314","text":"#https://programmers.co.kr/learn/courses/30/lessons/43238\n#범위와 기준을 뭐로 할 것인지\ndef solution(n, times):\n    answer = 0\n    left, right = 1, max(times) * n\n\n    while left <= right:\n        complete = 0\n        mid = (left + right) // 2\n\n        for time in times:\n            complete += mid // time\n\n            if complete >= n:\n                break\n\n        if complete >= n:\n            answer = mid\n            right = mid - 1\n\n        elif complete < n:\n            left = mid + 1\n\n\n\n    return answer\n\nprint(solution(6, [7, 10]))","repo_name":"Interesting-study/Algorithm","sub_path":"programmers/그 외/입국심사.py","file_name":"입국심사.py","file_ext":"py","file_size_in_byte":557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"27865322186","text":"from rayflare.transfer_matrix_method import tmm_structure\nfrom solcore import material, si\nimport matplotlib.pyplot as plt\nfrom solcore.structure import Layer\nimport numpy as np\nfrom rayflare.options import default_options\nfrom solcore.light_source import LightSource\nfrom ecopv.spectrum_functions import spec_to_XYZ, load_cmf\nfrom ecopv.optimization_functions import getPmax\nfrom ecopv.main_optimization import plot_outcome\nfrom solcore.constants import h, c\n\ninterval = 0.01\nwavelengths = np.arange(300, 1200, interval) * 1e-9\nopts = default_options()\n\nopts.wavelengths = wavelengths\nopts.pol = \"s\"\n\ncmf = load_cmf(wavelengths * 1e9)\n\n# Use AM1.5G spectrum:\nlight_source = LightSource(\n    source_type=\"black body\",\n    x=wavelengths * 1e9,\n    output_units=\"photon_flux_per_nm\",\n    entendue=\"Sun\",\n    T=5778,\n)\n\nphoton_flux_cell = np.array(light_source.spectrum(wavelengths * 1e9))\n\nphoton_flux_color = photon_flux_cell[\n    :, np.all((photon_flux_cell[0] >= 380, photon_flux_cell[0] <= 780), axis=0)\n]\n\nEgs = np.linspace(1, 1.8, 100)\nplt.figure()\nfor rad_eff in [0.01, 0.1, 1]:\n    eff = np.zeros_like(Egs)\n\n    for i1, eg in enumerate(Egs):\n\n        eff[i1] = getPmax(\n            [eg], photon_flux_cell[1], wavelengths * 1e9, interval, rad_eff\n        )\n\n    argm = np.argmax(eff)\n    print(Egs[argm])\n\n    plt.plot(Egs, eff, label=str(rad_eff))\nplt.legend()\nplt.show()\n\n# define the materials\nSiN = material(\"Si3N4\")()\nTiO2 = material(\"TiO2b\")()\nSi = material(\"Si\")()\nAg = material(\"Ag\")()\nAir = material(\"Air\")()\n\nplt.figure()\nplt.plot(wavelengths * 1e9, SiN.n(wavelengths), label=\"SiN\")\nplt.plot(wavelengths * 1e9, TiO2.n(wavelengths), label=\"TiO2\")\nplt.legend()\nplt.show()\n\ntarget_wavelength = 575 * 1e-9\ntarget_wavelength_2 = 450 * 1e-9\nn_DBR_reps = 2\n\nARC_layer = Layer(width=si(\"75nm\"), material=SiN)\n\ncell_layer = Layer(width=si(\"300um\"), material=Si)\n\nDBR_layers = [\n    Layer(3 * target_wavelength / (4 * TiO2.n(target_wavelength)), material=TiO2),\n    Layer(3 * target_wavelength / (4 * SiN.n(target_wavelength)), material=SiN),\n] * n_DBR_reps\n\nDBR_layers_2 = [\n    Layer(target_wavelength_2 / (4 * TiO2.n(target_wavelength_2)), material=TiO2),\n    Layer(target_wavelength_2 / (4 * SiN.n(target_wavelength_2)), material=SiN),\n] * n_DBR_reps\n\n\nstruct = tmm_structure(\n    [ARC_layer] + DBR_layers_2 + DBR_layers, incidence=Air, transmission=Si\n)\n\nRAT = struct.calculate(opts)\n\nplt.figure()\nplt.plot(wavelengths * 1e9, RAT[\"R\"], label=\"R\")\nplt.show()\n\nXYZ = spec_to_XYZ(RAT[\"R\"], h * c * photon_flux_cell[1] / wavelengths, cmf, interval)\n\nplot_outcome(RAT[\"R\"], photon_flux_cell, XYZ, \"test\")\nplt.show()\n","repo_name":"qpv-research-group/ECoPV","sub_path":"examples/DBR_example.py","file_name":"DBR_example.py","file_ext":"py","file_size_in_byte":2621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"44982123173","text":"import json\nimport os\n\nfrom bs4 import BeautifulSoup\n\nfrom eeclass_bot.EEBulletin import EEBulletin\nfrom eeclass_bot.EEConfig import EEConfig\nfrom eeclass_bot.EEHomework import EEHomework\nfrom eeclass_bot.EEMaterial import EEMaterial\nfrom eeclass_bot.models.BlockMaterial import BlockMaterial\n\n\nclass EECourse:\n    def __init__(self, bot, name: str, index: str):\n        self.bot = bot\n        self.name = name\n        self.index = index\n        self.url = EEConfig.get_index_url(EEConfig.COURSE_URL, index)\n        self.bulletin_page_url = EEConfig.get_index_url(EEConfig.BULLETIN_URL, index)\n        self.bulletins_url_list = [self.bulletin_page_url]\n        self.bulletins: list[EEBulletin] = []\n        self.homework_list_url = EEConfig.get_index_url(EEConfig.HOMEWORK_LIST, index)\n        self.homeworks_url = []\n        self.homeworks = []\n        self.material_url = EEConfig.get_index_url(EEConfig.COURSE_URL, index)\n        self.materials = []\n\n    def __repr__(self):\n        return f\"{self.name}\\n {self.url}\\n\"\n\n    @classmethod\n    async def retrieve_all(cls, bot, refresh=False, check=False):\n        if os.path.isfile(\"course_info.json\") and not refresh:\n            with open(\"course_info.json\", 'r') as f:\n                courses = json.load(f)\n        else:\n            url = \"https://ncueeclass.ncu.edu.tw/dashboard\"\n            resp = await bot.session.get(url, headers=EEConfig.HEADERS)\n            soup = BeautifulSoup(await resp.text(), 'lxml')\n            result = soup.select(\"div > ul > li > div > div > div> div > div.fs-label > a\")\n            courses = [dict(name=r.text.strip(), index=r['href'].split('/')[-1]) for r in result]\n            with open(\"course_info.json\", 'w') as f:\n                print(json.dumps(courses), file=f)\n\n        courses_list = []\n        for course in courses:\n            courses_list.append(EECourse(bot, course['name'], course['index']))\n\n        if check:\n            for c in courses_list:\n                print(c)\n\n        return courses_list\n\n    async def get_all_bulletin_page(self) -> list:\n        async with self.bot.session.get(self.bulletin_page_url, headers=EEConfig.HEADERS) as resp:\n            soup = BeautifulSoup(await resp.text(), 'lxml')\n            b_list = soup.select(\n                \"#bulletinMgrTable > tbody > tr > td > div > div.fs-singleLineText.afterText > div.text-overflow > a\"\n            )\n            # pagination = list(set(pagination))\n            # if pagination:\n            #     for p in pagination:\n            # self.bulletins.append(self.bulletin_page_url + p)\n            self.bulletins = [EEBulletin(bot=self.bot, link=p['data-url'], title=p['data-modal-title']) for p in b_list]\n            return self.bulletins\n\n    # async def get_all_bulletin_page(self):\n    #     task = [Bulletin.get_bulletin_page(url=url, bot=self.bot) for url in self.bulletins_url_list]\n    #     self.bulletins = await asyncio.gather(*task)\n    #     return self.bulletins\n\n    async def get_all_homework_page(self):\n        \"\"\" homework 暫且不會有選頁面的問題\"\"\"\n        async with self.bot.session.get(self.homework_list_url, headers=EEConfig.HEADERS) as resp:\n            # print(self)\n            soup = BeautifulSoup(await resp.text(), 'lxml')\n            soup_select = soup.select(\"#homeworkListTable > tbody > tr > td > div > div > div.text-overflow > a\")\n            for homework in soup_select:\n                url: str = homework['href']\n                title: str = homework['title']\n                self.homeworks.append(\n                    EEHomework(\n                        bot=self.bot,\n                        title=title,\n                        link=url,\n                        course=self\n                    )\n                )\n            return self.homeworks\n\n    async def get_all_material_page(self):\n        async with self.bot.session.get(self.material_url, headers=EEConfig.HEADERS) as resp:\n            soup = BeautifulSoup(await resp.text(), 'lxml')\n            material_block = soup.select(\".fs-block-body > div > ol.xtree-list > li\")\n            for block in material_block:\n                block_material_list = []\n                block_title = block.select_one(\"div.header.hover.hover > div > span > div.text > div\").text\n                material_in_block = block.select(\"div.body > ol.xtree-list > li\")\n                for material in material_in_block:\n                    if \" \".join(material['class']) == \"xtree-node type- clearfix\":\n                        type = EEConfig.CLASS_NAME_TO_MATERIAL_TYPE[' '.join(material.select_one(\n                            \"div.header.hover.hover > div.center-part > span.xtree-node-label > div.icon.pull-left > \"\n                            \"span\")['class'])]\n                        link = material.select(\n                            \"div.header.hover.hover > div.center-part > span.xtree-node-label > div.text > div.node-title > div.fs-singleLineText > div\")[\n                            1].select_one('a')['href']\n                        title = material.select(\n                            \"div.header.hover.hover > div.center-part > span.xtree-node-label > div.text > div.node-title > div.fs-singleLineText > div\")[\n                            1].select_one('a > span.text').text\n                        brief_condition = material.select_one(\n                            \"div.header.hover.hover > div.center-part > div.hidden-xs.pull-right\")\n                        deadline = brief_condition.select(\"div.ext-col.fs-text-nowrap.col-time.text-center\")[0].text\n                        complete_condition = brief_condition.select_one(\n                            \"div.ext-col.fs-text-nowrap.col-char7.text-center\").text\n                        read_time = brief_condition.select_one(\n                            \"div.ext-col.fs-text-nowrap.col-char4.text-center > span\").text if brief_condition.select_one(\n                            \"div.ext-col.fs-text-nowrap.col-char4.text-center > span\") != None else brief_condition.select_one(\n                            \"div.ext-col.fs-text-nowrap.col-char4.text-center\").text\n                        complete_check = True if brief_condition.select(\"div.ext-col.fs-text-nowrap.col-time.text-center\")[1].select_one(\"span.font-icon.fs-text-success.item-pass.fa.fa-check-circle\") != None else False\n                        block_material_list.append(\n                            EEMaterial(\n                                bot=self.bot,\n                                course=self,\n                                type=type,\n                                link=link,\n                                title=title,\n                                deadline=deadline,\n                                complete_condition=complete_condition,\n                                read_time=read_time,\n                                complete_check=complete_check\n                            )\n                        )\n                self.materials.append(\n                    BlockMaterial(\n                        block_title=block_title,\n                        materials=block_material_list\n                    )\n                )\n            return self.materials\n","repo_name":"quan0715/EECLASS_Notion_API","sub_path":"eeclass_bot/EECourse.py","file_name":"EECourse.py","file_ext":"py","file_size_in_byte":7175,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"32904698311","text":"'''\nDebug View Module\n\nThis module renders a debug view.\n'''\n\nimport json\nimport sys\nimport time\ntry:\n    import base64\n    import numpy as np\n    import cv2\nexcept:\n    pass # Well there cannot be any cv input, so there will not occur any error.\nfrom typing import Callable\nfrom gpm.pyGP.registry import register\nNODES = {}\n\n@register(NODES,\n    name=\"View\",\n    inputs=dict(val=\"Object\"),\n    outputs=dict(result=\"Object\"))\ndef init(node, global_state, max_fps: float = 2, width: int = 160, height: int = 120) -> Callable:\n    \"\"\"\n    Views and passes the object.\n    \"\"\"\n    node[\"last_transmission\"] = 0\n    def tick(val):\n        result = {\"result\": val}\n        if not \"debugger\" in global_state.shared_dict:\n            return result\n\n        now = time.time()\n        if now - node[\"last_transmission\"] < 1.0 / max_fps:\n            return result\n        \n        data_str = \"\"\n        if type(val) is list or type(val) is dict or type(val) is str or type(val) is int:\n            data_str = \"json:\" + json.dumps(val)\n        elif type(val) == np.ndarray and (len(val.shape) != 2 or len(val[1]) != 3) and not (len(val.shape) == 3 and val.shape[2] == 3):\n            data_str = \"json:\" + json.dumps(val.tolist())\n        elif type(val) == np.ndarray:\n            img = val\n            img = cv2.resize(img.copy(), (width, height), 0, 0, cv2.INTER_CUBIC)\n            cnt = cv2.imencode('.png', img)[1].tostring()\n            b64 = str(base64.encodestring(cnt))[2:-1]\n            data_str = (\"img:\" + b64).replace(\"\\n\", \"\").replace(\"\\\\n\", \"\")\n        else:\n            data_str = \"type:\" + str(type(val))\n        global_state.shared_dict[\"debugger\"].send(\"data_\" + node[\"node_uid\"] + \":\" + data_str)\n        node[\"last_transmission\"] = now\n        return result\n    return tick\n","repo_name":"GraphProgramming/pyGP","sub_path":"stdlib/debug/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":1782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"45588567417","text":"import json\nimport os\n\n\ndef get_data():\n    with open(\"../data/guestDB.json\") as f:\n        data = json.load(f)\n    return data\n\n\ndef get_database():\n    from pymongo import MongoClient\n\n    CONNECTION_STRING = os.environ[\"DATABASE_CONNECTION_STRING\"]\n    client = MongoClient(CONNECTION_STRING)\n\n    return client.weddingdb\n\n\ndef upload_data(collection, data):\n    collection.insert_many(data)\n\n\ndb_client = get_database()\ndata = get_data()\nupload_data(db_client.guests, data)\n","repo_name":"MahrRah/wedding-homepage","sub_path":"scripts/loadDataToDB.py","file_name":"loadDataToDB.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"18391440782","text":"resposta = input('[1] - Cadastrar novo usuário [2] - Fazer login: ')\n\nusuarios = ['Rafael', 'Metal Omega', 'Silveira']\nsenhas = ['12345', 'abcdef', '123abcd']\n\nif resposta == '1':\n    usuario_digitado = input('digite seu usuário: ')\n    if usuario_digitado in usuarios:\n        print('usuário existente')\n\n    else:\n        senha_digitada = input('digite sua senha: ')\n        usuarios.append(usuario_digitado)\n        senhas.append(senha_digitada)\nelif resposta == '2':\n    usuario_digitado = input('Digite seu usuário: ')\n    senha_digitada = input('Digite sua senha: ')\n    encontrado = False\n    for indice, usuario in enumerate(usuarios):\n        if usuario_digitado == usuario and senha_digitada == senhas[indice]:\n            print('Login realizado com sucesso')\n            encontrado = True\n        elif encontrado == False:\n            print('usuário ou senha incorreto!')\nelse:\n    print('digite apenas 1 ou 2')\n","repo_name":"MetalOmega/projeto-sistema-login-cadastro-user","sub_path":"teste.py","file_name":"teste.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"35302932783","text":"from django.conf.urls import url\n\nfrom atm import views\n\nurlpatterns = [\n    url(r'^define_atm$', views.define_atm, name='define_atm'),\n    url(r'^define_money$', views.define_money, name='define_money'),\n    url(r'^define_minimum$', views.define_minimum, name='define_minimum'),\n    url(r'^assign_money$', views.assign_money, name='assign_money'),\n    url(r'^create_card$', views.create_card, name='create_card'),\n    url(r'^atm_transaction$', views.atm_transaction, name='atm_transaction'),\n]\n","repo_name":"dani1373/Ibank","sub_path":"atm/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74448452123","text":"import os\n\nimport pandas as pd\n\n\n\n#ploting\n\nimport matplotlib.pyplot as plt\n\n\nimport seaborn as sns\n\nsns.set(style=\"darkgrid\")\n\n\n\n#plotly\n\nimport plotly.express as px\n\n\n\n#color\n\nfrom colorama import Fore, Style,Back\n\n\n\n#pydicom\n\nimport pydicom\n\n\n\nplt.style.use(\"seaborn-notebook\")\n\nplt.show()\n\n\n\n\n\n# Suppress warnings \n\nimport warnings\n\nwarnings.filterwarnings('ignore')\nroot_path = '/kaggle/input/osic-pulmonary-fibrosis-progression/'\ndf_train = pd.read_csv(os.path.join(root_path,\"train.csv\"))\n\ndf_test = pd.read_csv(os.path.join(root_path,\"test.csv\"))\n\nsubmission = pd.read_csv(os.path.join(root_path,\"sample_submission.csv\"))\n\ntrain_folder = root_path+'train/'\n\ntest_folder  = root_path+'train/'\ndf_train.head().style.bar(subset=[\"FVC\"],color=['#F7DC6F'])\ndf_train.info()\nplt.figure(figsize=(10,5))\n\na = sns.countplot(data=df_train,x=\"Sex\",hue=\"Sex\",color=\"blue\",palette=[\"#F5B041\",\"#58D68D\"])\n\nplt.title(\"Gender Distribution\")\n\nplt.legend(fontsize=10)\na = df_train[\"Age\"].plot.hist(colormap=\"jet\",legend=True,color=\"#BB8FCE\")\n\nplt.xlabel(\"Age\")\n\nplt.legend(fontsize=10)\na = df_train[\"Patient\"].value_counts().plot.hist(legend=True,color=\"#85C1E9\")\n\nplt.xlabel(\"no of visits\")\n\nplt.legend(fontsize=10)\na = df_train[\"FVC\"].plot.kde(legend=True,color=\"#F8C471\",linewidth=2.8)\n\nplt.legend(fontsize=10)\n\nplt.xlabel(\"FVC\")\nprint('The mean value of FCV is',Back.CYAN+Style.BRIGHT+Fore.BLACK, f'{df_train[\"FVC\"].mean()}')\na = df_train.plot.scatter(x=\"FVC\",y=\"Age\",color=\"#E74C3C\")\n\nplt.legend(fontsize=10)\nplt.figure(figsize=(45,8))\n\nsns.set(font_scale=1.1)\n\nax = sns.catplot(x=\"SmokingStatus\", hue=\"Sex\", col=\"Sex\",data=df_train, kind=\"count\",palette=[\"#F8C471\",\"#58D68D\"])\n\nplt.legend(fontsize=10)\nsns.pairplot(df_train, hue=\"Sex\", palette=\"Set2\", diag_kind=\"kde\", height=2.5)\n# sns.violinplot(x=\"SmokingStatus\", y=\"Age\", data=df_train, size=7,palette=[\"#F5B7B1\",\"#2ECC71\",\"#E74C3C\"])\n\nfig = px.violin(df_train, y=\"Age\", x=\"SmokingStatus\", color=\"Sex\", box=True, points=\"all\")\n\nfig.update_layout(\n\n    autosize=False,\n\n    width=1200,\n\n    height=700,)\n\nfig.show()\nfig = px.violin(df_train, y=\"Age\", x=\"Sex\", color=\"Sex\", box=True, points=\"all\")\n\nfig.update_layout(\n\n    autosize=False,\n\n    width=1000,\n\n    height=700,)\n\nfig.show()\ndf_train_corr = df_train.corr()\n\nsns.clustermap(df_train_corr, cmap=\"GnBu\",annot=True)\n\nplt.legend(fontsize=10)","repo_name":"aorursy/new-nb-6","sub_path":"santhoshgoku_a-statistical-analysis-new-approach.py","file_name":"santhoshgoku_a-statistical-analysis-new-approach.py","file_ext":"py","file_size_in_byte":2344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"5632189805","text":"import cv2\r\nimport numpy as np\r\n\r\nvideo_capture = cv2.VideoCapture(0)\r\n\r\nwhile True:\r\n    _, frame = video_capture.read()\r\n    hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\r\n\r\n\t# Blue color\r\n    low_blue = np.array([94, 80, 2])\r\n    high_blue = np.array([126, 255, 255])\r\n    blue_mask = cv2.inRange(hsv_frame, low_blue, high_blue)\r\n    blue = cv2.bitwise_and(frame, frame, mask=blue_mask)\r\n\r\n    # Show every color except white\r\n    low = np.array([0, 65, 0])\r\n    high = np.array([255, 255, 255])\r\n    mask = cv2.inRange(hsv_frame, low, high)\r\n    result = cv2.bitwise_and(frame, frame, mask=mask)\r\n\r\n    cv2.imshow(\"Frame\", frame)\r\n    cv2.imshow(\"Blue\", blue)\r\n    cv2.imshow(\"Result\", result)\r\n\r\n    #Sair do programa\r\n    if cv2.waitKey(1) & 0xFF == ord('q'):\r\n    \tbreak\r\n        \r\nvideo_capture.release()\r\ncv2.destroyAllWindows()\r\n","repo_name":"suzanasvm/ComputerVision","sub_path":"color_spaces.py","file_name":"color_spaces.py","file_ext":"py","file_size_in_byte":844,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"86"}
+{"seq_id":"14952300108","text":"from django.urls import path , include\nfrom .views import *\nfrom . import views\nfrom .views import CustomPasswordResetView,CustomPasswordResetDoneView,CustomPasswordResetConfirmView,CustomPasswordResetCompleteView\nurlpatterns = [\n    path('', index, name=\"index\"),\n    path('provider_reg/', provider_reg, name=\"provider_reg\"),\n    path('provider_reg/signin', signin, name=\"signin\"),\n    path('worker_reg/', worker_reg, name=\"worker_reg\"),\n    path('worker_reg/signin', signin, name=\"signin\"),\n    path('worker_reg/index', index, name=\"index\"),\n    path('provider_reg/index', index, name=\"index\"),\n    path('signin/', signin, name=\"signin\"),\n    path('signup/', register, name=\"signup\"),\n    path('providersignup/', providerregister, name=\"providersignup\"),\n    path('workersignup/', workerregister, name=\"workersignup\"),\n    path('signin/userpage/',userpage, name='userpage'),\n    path('signin/userpage/userpage',userpage, name='userpage'),\n    path('userpage/',userpage, name='userpage'),\n    path('signin/userpage/user_logout',user_logout),\n    path('search_providers/userpage.html',userpage, name='userpage'),\n    path('search_providers/userpage',userpage, name='userpage'),\n    path('userpage/providerlist',providerlist, name='providerlist'),\n    path('userpage/providerlist/book_service',book_service, name='book_service'),\n    path('workerpage/',workerpage, name='workerpage'),\n    path('worker_requests/workerpage',workerpage, name='workerpage'),\n    path('providerpage/',providerpage, name='providerpage'),\n    path('bookinghistory/providerpage/',providerpage, name='providerpage'),\n    path('bookinghistory/providerpage/providerpage',providerpage, name='providerpage'),\n    path('accounts/login/signin',signin),\n    path('accounts/login/',signin),\n    path('signin/signup', register ),\n    path('signin/index', index ),\n    path('userpage/userpage',userpage),\n    path('search/userpage',userpage),\n    path('service/Cleaning/userpage',userpage),\n    path('create_booking/userpage',userpage),\n    path('service/Cleaning/userpage.html',userpage),\n    path('service/Laundry/userpage',userpage),\n    path('service/Laundry/userpage.html',userpage),\n    path('service/Repair/userpage',userpage),\n    path('service/Plumbing/userpage',userpage),\n    path('service/Repair/userpage.html',userpage),\n    path('service/Plumbing/userpage.html',userpage),\n    path('service/Electrical/userpage.html',userpage),\n    path('service/Electrical/userpage',userpage),\n    path('service/Pestcontrol/userpage.html',userpage),\n    path('service/Plumbing/user_logout',user_logout),\n    path('service/Repair/user_logout',user_logout),\n    path('service/Cleaning/user_logout',user_logout),\n    path('service/Pestcontrol/user_logout',user_logout),\n    path('service/Laundry/user_logout',user_logout),\n    path('service/Electrical/user_logout',user_logout),\n    path('workerpage/workerpage',workerpage),\n    path('providerpage/providerpage',providerpage),\n    path('signup/signin', signin ),\n    path('accounts/login/signup', register ),\n    path('accounts/login/index', index ),      \n    path('signup/signup', register),\n    path('signup/index', index ),\n    path('signin/signin', signin ),   \n    path('signin/user_logout', views.user_logout, name='user_logout'),\n    path('user_logout', views.user_logout, name='user_logout'),\n    path('userpage/user_logout', views.user_logout, name='user_logout'),\n    path('userpage/providerlist/user_logout', views.user_logout, name='user_logout'),\n    path('worker_logout', views.worker_logout, name='worker_logout'),\n    path('admin_logout', views.admin_logout, name='admin_logout'),\n    path('provider_logout', views.provider_logout, name='provider_logout'),\n    path('resetpassword/', CustomPasswordResetView.as_view(), name='resetpassword'),\n    path('resetpassword/done/',CustomPasswordResetDoneView.as_view(),name='resetpassworddone'),\n    path('resetpassword/<uidb64>/<token>/',CustomPasswordResetConfirmView.as_view(),name='customresetpasswordconfirm'),\n    path('resetpassword/complete',CustomPasswordResetCompleteView.as_view(),name='passwordresetcomplete'),\n    path('custom_admin_page/', views.custom_admin_page, name='custom_admin_page'),\n    path('activate_user/<int:userid>/', views.activate_user, name='activate_user'),\n    path('deactivate_user/<int:userid>/', views.deactivate_user, name='deactivate_user'),\n    path('accounts/', include('allauth.urls')),\n    path('custom_admin_page/provider_registration/', views.provider_registration, name='provider_registration'),\n    path('providerpage/worker_registration/', views.worker_registration, name='worker_registration'),\n    #here google starts\n    path('auth/', include('social_django.urls', namespace='social')),\n    path(\"\", include(\"allauth.urls\")),\n    #here google ends\n    path('social/signup/', views.signup_redirect, name='signup_redirect'),\n    path('worker_registration/', views.worker_registration, name='worker_registration'),\n    path('providerpage/worker_registration/', views.worker_registration, name='worker_registration'),\n    path('worker_reg/register/', views.worker_registration, name='worker_registration'),\n     path('service/<str:category>/', views.service_providers_by_category, name='service_providers_by_category'),\n     path('update_profile/', views.update_profile, name='update_profile'),\n     path('update_profile/user_logout', views.user_logout, name='user_logout'),\n     path('view_profile/user_logout', views.user_logout, name='user_logout'),\n     path('view_profile/', views.profile_view, name='view_profile'),\n     path('update_profile/userpage.html',userpage, name='userpage'), \n     path('view_profile/userpage.html',userpage, name='userpage'), \n     path('view_profile/userpage.html',userpage, name='userpage'), \n     path('userpage/providerlist/userpage',userpage, name='userpage'), \n     path('userpage/providerlist/userpage.html',userpage, name='userpage'), \n     path('social-auth/', include('social_django.urls', namespace='social-auth')),\n     path('google-profile-update/<str:username>/<str:email>/', views.google_profile_update, name='google_profile_update'),\n     path('userpage/google-profile-update/<str:username>/<str:email>/', views.google_profile_update, name='google_profile_update'),\n     path('custom_admin_page/requests/', views.admin_requests, name='admin_requests'),\n     path('providerpage/requests/', views.worker_requests, name='worker_requests'),\n     path('custom_admin_page/requests/custom_admin_page', views.custom_admin_page, name='custom_admin_page'),\n     path('custom_admin_page/activate_provider/<int:user_id>/', views.activate_provider, name='activate_provider'),\n     path('providerpage/workerrequests/activate_worker/<int:user_id>/', views.activate_worker, name='activate_worker'),\n     path('providerpage/activate_worker/<int:userid>/', views.activate_worker, name='activate_worker'),\n     path('providerpage/providerrequests/providerpage', views.providerpage, name='providerpage'),\n     path('providerpage/providerrequests/workerpage', views.providerpage, name='providerpage'),\n     path('create_booking/', create_booking, name='create_booking'),\n     path('search/', views.search_providers, name='search_providers'),\n     path('search_providers/', views.search_providers, name='search_providers'),\n     path('book-service/<int:userid>/', views.render_booking_form, name='render_booking_form'),\n     path('approve_booking/<int:booking_id>/', views.approve_booking, name='approve_booking'),\n     path('reject_booking/<int:booking_id>/', views.reject_booking, name='reject_booking'),\n     path('provider_bookings/', views.provider_bookings, name='provider_bookings'),\n     path('provider_bookings/providerpage', views.providerpage, name='providerpage'),\n     path('bookinghistory/', views.bookinghistory, name='bookinghistory'),\n     path('approve_booking/providerpage',providerpage),\n     path('worker_requests/<int:user_id>', views.worker_requests, name='worker_requests'),\n     path('approve_worker/<int:user_id>/', views.activate_worker, name='approve_worker'),\n     path('providerpage/approve_worker/<int:user_id>/', views.activate_worker, name='approve_worker'),\n     path('approve_worker/<int:user_id>/', views.approve_worker, name='approve_worker'),\n     path('provider_bookings/userpage.html', views.providerpage, name='providerpage'),\n     path('bookinghistory/userpage.html', views.providerpage, name='providerpage'),\n     path('bookinghistory/userpage', views.userpage, name='userpage'),\n     path('bookinghistory/providerpage', views.providerpage, name='providerpage'),\n     path('worker_requests', views.worker_requests, name='worker_requests'),\n     path('worker_requests/<int:user_id>/', views.worker_requests, name='worker_requests'),\n     path('providerpage/worker_requests/<int:user_id>/', views.approve_worker, name='approve_worker'),\n     path('available_workers/<int:providerid_id>/<str:district>/', views.available_workers, name='available_workers'),\n     path('available_workers/<int:providerid_id>/<str:district>/<int:booking_id>/providerpage/', views.available_workers, name='available_workers'),\n     path('available_workers/<int:providerid_id>/<str:district>/<int:booking_id>/providerpage.html/', views.available_workers, name='available_workers_html'),\n     path('assign_worker/', views.assign_worker, name='assign_worker'),\n     path('workerjob/', views.worker_job, name='workerjob'),\n     path('assignedwork/', views.assignedwork, name='assignedwork'),\n     path('update_status/', views.update_status, name='update_status'),\n     path('generate_report/<int:bookingid_id>/', views.render_report_form, name='render_report_form'),\n     path('generate_report/', views.generate_report, name='generate_report'),\n     path('provider/<int:provider_id>/workers/', views.worker_list, name='worker_list'),\n     path('worker_report/<int:provider_id>/', views.worker_report, name='worker_report'),\n     path('client_bookings/<int:client_id>/', views.client_bookings, name='client_bookings'),\n     path('assign_workers/<int:reportid>/', views.assign_workers, name='assign_workers'),\n     path('assign_workers_service/', views.assign_workers_service, name='assign_workers_service'),\n     path('client_work_reports/<int:client_id>/', views.client_work_reports, name='client_work_reports'),\n     path('download_work_report/<int:report_id>/', views.download_worker_report, name='download_work_report'),\n     path('client_work_reports/<int:client_id>/userpage', views.userpage, name='userpage'),\n     path('assign_workers_service/assign_workers.html', views.providerpage, name='providerpage'),\n     path('bookinghistory/providerpage', views.providerpage, name='providerpage'),\n     path('bookinghistory/user_logout', views.user_logout, name='user_logout'),\n     path('approve_report/', views.approve_report, name='approve_report'),\n     path('cancel_service/', views.cancel_service, name='cancel_service'),\n     path('update_rating/', views.update_rating, name='update_rating'),\n     path('update_review/', views.update_review, name='update_review'),\n     path('update-service-and-booking/<int:booking_id>/', views.update_service_and_booking, name='update_service_and_booking'),\n     path('client_work_reports/<int:client_id>/payment_success', views.payment_success, name='payment_success'),\n     path('payment_success/', payment_success, name='payment_success'),\n\n\n\n\n\n\n]","repo_name":"Abhinandks06/Multi_service_provider","sub_path":"multiserviceprovider/app1/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":11349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35446490123","text":"import uuid\nimport random\nimport psycopg2\nimport psycopg2.extras\nfrom faker import Faker\nimport csv\n\n\n\nfake = Faker()\n\n# Connect to your Supabase instance\nconn = psycopg2.connect(\n    dbname=\"postgres\",\n    user=\"postgres\",\n    password=\"Phatiacy272\",\n    host=\"db.yqyheutdblzkrxymbhpw.supabase.co\",\n    port=\"5432\"\n)\npsycopg2.extras.register_uuid()\ncur = conn.cursor()\n\n# Read cities and countries from the CSV file\nwith open('cities.csv', newline='') as csvfile:\n    reader = csv.DictReader(csvfile)\n    cities = [row['city'] for row in reader]\n    csvfile.seek(0)  # Reset the CSV reader to the beginning\n    countries = [row['country'] for row in reader]\n\n# Generate and insert fake data for the users table\nuser_ids = []\nfor _ in range(30):  # Create 100 fake users\n    user_id = uuid.uuid4()\n    user_ids.append(user_id)\n    first_name = fake.first_name()\n    last_name = fake.last_name()\n    country = random.choice(countries)\n    city = random.choice(cities)\n    address = fake.street_address()\n    state = fake.state_abbr()\n    zip_code = fake.zipcode()\n    username = fake.user_name()\n    email = fake.email()\n    about = fake.text(max_nb_chars=200)\n    created_at = fake.date_between(start_date='-2y', end_date='today')\n    completed_jobs = fake.random_int(min=0, max=300)\n    \n    cur.execute(\n        \"\"\"\n        INSERT INTO users (user_id, first_name, last_name, country, address, city, state, zip, username, email, about, created_at, completed_jobs)\n        VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\n        \"\"\",\n        (user_id, first_name, last_name, country, address, city, state, zip_code, username, email, about, created_at, completed_jobs)\n    )\n    \n\n# Generate and insert fake data for the items table\nitem_ids = []\nfor _ in range(10):  # Create 5 fake items\n    item_id = uuid.uuid4()\n    item_ids.append(item_id)\n    title = fake.sentence(nb_words=4)\n    description = fake.text(max_nb_chars=200)\n    preferred_price = fake.random_int(min=1000, max=10000)\n    preferred_date = fake.date_between(start_date='-1y', end_date='today')\n    starting_bid = preferred_price - fake.random_int(min=100, max=500)\n    current_bid = starting_bid\n    bid_holder = fake.random_element(user_ids)\n    poster_id = fake.random_element(user_ids)\n    expiration_date = fake.date_between(start_date='today', end_date='+1y')\n    poster_first_name = fake.first_name()\n    poster_last_name = fake.last_name()\n    poster_country = random.choice(countries)\n    poster_address = fake.street_address()\n    poster_city = random.choice(cities)\n    poster_state = fake.state_abbr()\n    poster_zip = fake.zipcode()\n    created_at = fake.date_between(start_date='-2y', end_date='today')\n    \n    cur.execute(\n        \"\"\"\n        INSERT INTO items (item_id, title, description, preferred_price, preferred_date, starting_bid, current_bid, bid_holder, poster_id, expiration_date, poster_first_name, poster_last_name, poster_country, poster_address, poster_city, poster_state, poster_zip, created_at)\n        VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\n        \"\"\",\n        (item_id, title, description, preferred_price, preferred_date, starting_bid, current_bid, bid_holder, poster_id, expiration_date, poster_first_name, poster_last_name, poster_country, poster_address, poster_city, poster_state, poster_zip, created_at)\n    )\n\n# Generate and insert fake data for the bids table\nfor _ in range(200):  # Create 20 fake bids\n    bid_id = uuid.uuid4()\n    bidder_id = fake.random_element(user_ids)\n    item_id = fake.random_element(item_ids)\n    bid_amount = fake.random_int(min=1000, max=10000)\n    created_at = fake.date_between(start_date='-1y', end_date='today')\n    bid_completion_date = fake.date_between(start_date='today', end_date='+1y')\n    bid_completion_time = fake.random_int(min=1, max=100)\n    \n    cur.execute(\n        \"\"\"\n        INSERT INTO bids (bid_id, bidder_id, item_id, bid_amount, created_at, bid_completion_date, bid_completion_time)\n        VALUES (%s, %s, %s, %s, %s, %s, %s)\n        \"\"\",\n        (bid_id, bidder_id, item_id, bid_amount, created_at, bid_completion_date, bid_completion_time)\n    )\n\n# Generate and insert fake data for the reviews table\nfor _ in range(100):  # Create 30 fake reviews\n    review_id = uuid.uuid4()\n    reviewer_id = fake.random_element(user_ids)\n    reviewed_id = fake.random_element(user_ids)\n    text = fake.text(max_nb_chars=200)\n    rating = fake.random_int(min=1, max=5)\n    created_at = fake.date_between(start_date='-2y', end_date='today')\n    \n    cur.execute(\n        \"\"\"\n        INSERT INTO reviews (review_id, reviewer_id, reviewed_id, text, rating, created_at)\n        VALUES (%s, %s, %s, %s, %s, %s)\n        \"\"\",\n        (review_id, reviewer_id, reviewed_id, text, rating, created_at)\n    )\n\n# Commit the changes and close the connection\nconn.commit()\ncur.close()\nconn.close()\n","repo_name":"sergiulache/licentav2","sub_path":"python/database_data.py","file_name":"database_data.py","file_ext":"py","file_size_in_byte":4890,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"1313674615","text":"\"\"\"\nAuthor: Davy Neven\nLicensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)\n\"\"\"\nimport collections\nimport os\nimport threading\nfrom typing import List, Tuple\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom PIL import Image\nfrom sklearn.mixture import GaussianMixture\nimport torch\n\n\nclass AverageMeter(object):\n\n    def __init__(self, num_classes=1):\n        self.num_classes = num_classes\n        self.reset()\n        self.lock = threading.Lock()\n\n    def reset(self):\n        self.sum = [0] * self.num_classes\n        self.count = [0] * self.num_classes\n        self.avg_per_class = [0] * self.num_classes\n        self.avg = 0\n\n    def update(self, val, cl=0):\n        with self.lock:\n            self.sum[cl] += val\n            self.count[cl] += 1\n            self.avg_per_class = [\n                x / y if x > 0 else 0 for x, y in zip(self.sum, self.count)]\n            self.avg = sum(self.avg_per_class) / len(self.avg_per_class)\n\n\nclass Visualizer:\n\n    def __init__(self, keys):\n        self.wins = {k: None for k in keys}\n\n    def display(self, image, key):\n\n        n_images = len(image) if isinstance(image, (list, tuple)) else 1\n\n        if self.wins[key] is None:\n            self.wins[key] = plt.subplots(ncols=n_images)\n\n        fig, ax = self.wins[key]\n        n_axes = len(ax) if isinstance(ax, collections.Iterable) else 1\n\n        assert n_images == n_axes\n\n        if n_images == 1:\n            ax.cla()\n            ax.set_axis_off()\n            ax.imshow(self.prepare_img(image))\n        else:\n            for i in range(n_images):\n                ax[i].cla()\n                ax[i].set_axis_off()\n                ax[i].imshow(self.prepare_img(image[i]))\n\n        plt.draw()\n        self.mypause(0.001)\n\n    @staticmethod\n    def prepare_img(image):\n        if isinstance(image, Image.Image):\n            return image\n\n        if isinstance(image, torch.Tensor):\n            image.squeeze_()\n            image = image.numpy()\n\n        if isinstance(image, np.ndarray):\n            if image.ndim == 3 and image.shape[0] in {1, 3}:\n                image = image.transpose(1, 2, 0)\n            return image\n\n    @staticmethod\n    def mypause(interval):\n        backend = plt.rcParams['backend']\n        if backend in matplotlib.rcsetup.interactive_bk:\n            figManager = matplotlib._pylab_helpers.Gcf.get_active()\n            if figManager is not None:\n                canvas = figManager.canvas\n                if canvas.figure.stale:\n                    canvas.draw()\n                canvas.start_event_loop(interval)\n                return\n\n\nclass Cluster:\n\n    def __init__(self, ):\n\n        xm = torch.linspace(0, 2, 2048).view(1, 1, -1).expand(1, 1024, 2048)\n        ym = torch.linspace(0, 1, 1024).view(1, -1, 1).expand(1, 1024, 2048)\n        xym = torch.cat((xm, ym), 0)\n\n        self.xym = xym.cuda()\n\n    def cluster_with_gt(self, prediction, instance, n_sigma=1, ):\n\n        height, width = prediction.size(1), prediction.size(2)\n\n        xym_s = self.xym[:, 0:height, 0:width]  # 2 x h x w\n\n        spatial_emb = torch.tanh(prediction[0:2]) + xym_s  # 2 x h x w\n        sigma = prediction[2:2 + n_sigma]  # n_sigma x h x w\n\n        instance_map = torch.zeros(height, width).byte().cuda()\n\n        unique_instances = instance.unique()\n        unique_instances = unique_instances[unique_instances != 0]\n\n        for id in unique_instances:\n            mask = instance.eq(id).view(1, height, width)\n\n            center = spatial_emb[mask.expand_as(spatial_emb)].view(\n                2, -1).mean(1).view(2, 1, 1)  # 2 x 1 x 1\n\n            s = sigma[mask.expand_as(sigma)].view(n_sigma, -1).mean(1).view(n_sigma, 1, 1)\n            s = torch.exp(s * 10)  # n_sigma x 1 x 1\n\n            dist = torch.exp(-1 * torch.sum(torch.pow(spatial_emb - center, 2) * s, 0))\n\n            proposal = (dist > 0.5)\n            instance_map[proposal] = id\n\n        return instance_map\n\n    def cluster(self, prediction, n_sigma=1, threshold=0.5,\n                im_name=None, gt_instance=None, do_plot=False):\n        print(im_name)\n\n        height, width = prediction.size(1), prediction.size(2)\n        xym_s = self.xym[:, 0:height, 0:width]\n\n        spatial_emb = torch.tanh(prediction[0:2]) + xym_s  # 2 x h x w\n        sigma = prediction[2:2 + n_sigma]  # n_sigma x h x w\n        seed_map = torch.sigmoid(prediction[2 + n_sigma:2 + n_sigma + 1])  # 1 x h x w\n\n        instance_map = torch.zeros(height, width).byte()\n        instances = []\n\n        count = 1\n        mask = seed_map > 0.5\n        if mask.sum() > 128:\n            spatial_emb_masked = spatial_emb[mask.expand_as(spatial_emb)].view(2, -1)\n            sigma_masked = sigma[mask.expand_as(sigma)].view(n_sigma, -1)\n            seed_map_masked = seed_map[mask].view(1, -1)\n\n            unclustered = torch.ones(mask.sum()).byte().cuda()\n            instance_map_masked = torch.zeros(mask.sum()).byte().cuda()\n\n            if do_plot:\n                figure = plt.Figure(figsize=(16, 24))\n                ax0 = figure.add_subplot(3, 1, 1)\n                ax1 = figure.add_subplot(3, 1, 2)\n                ax2 = figure.add_subplot(3, 1, 3)\n\n                for ax in (ax0, ax1, ax2):\n                    ax.scatter(\n                        spatial_emb_masked[0].cpu().numpy(),\n                        spatial_emb_masked[1].cpu().numpy(),\n                        color='#dddddd',\n                        alpha=0.3,\n                        zorder=-1\n                    )\n\n            for inst_id in range(1, gt_instance.max().item() + 1):\n                gt_mask = gt_instance == inst_id\n                gt_mask = gt_mask[mask.squeeze()].view(-1)\n                if do_plot:\n                    ax1.scatter(\n                        spatial_emb_masked[0, gt_mask].cpu().numpy(),\n                        spatial_emb_masked[1, gt_mask].cpu().numpy(),\n                        # color=np.random.rand(3,),\n                        label='object_' + str(count),\n                        alpha=0.3,\n                    )\n\n            while (unclustered.sum() > 128):\n\n                seed = (seed_map_masked * unclustered.float()).argmax().item()\n                seed_score = (seed_map_masked * unclustered.float()).max().item()\n                if seed_score < threshold:\n                    break\n                center = spatial_emb_masked[:, seed:seed + 1]\n                unclustered[seed] = 0\n                s = torch.exp(sigma_masked[:, seed:seed + 1] * 10)\n                dist = torch.exp(-1 * torch.sum(torch.pow(spatial_emb_masked -\n                                                          center, 2) * s, 0, keepdim=True))\n\n                proposal = (dist > 0.5).squeeze()\n\n                if proposal.sum() > 128:\n                    if unclustered[proposal].sum().float() / proposal.sum().float() > 0.5:\n                        instance_map_masked[proposal.squeeze()] = count\n                        instance_mask = torch.zeros(height, width).bool()\n                        instance_mask[mask.squeeze().cpu()] = proposal.cpu()\n\n                        # tmp = instance_mask.squeeze() * 255\n                        # for inst in instances:\n                        #     tmp1 = inst['mask']\n                        #     cnt = ((tmp > 0) & (tmp1 > 0)).sum()\n\n                        instances.append(\n                            {'mask': instance_mask.squeeze() * 255, 'score': seed_score})\n\n                        count += 1\n\n                        if do_plot:\n                            ax0.text(\n                                center[0].item(), center[1].item(), str(count),\n                                fontsize=20\n                            )\n\n                            ax0.scatter(\n                                spatial_emb_masked[0, proposal].cpu().numpy(),\n                                spatial_emb_masked[1, proposal].cpu().numpy(),\n                                # color=np.random.rand(3,),\n                                label='object_' + str(count),\n                                alpha=0.3,\n                            )\n\n                unclustered[proposal] = 0\n\n            instance_map[mask.squeeze().cpu()] = instance_map_masked.cpu()\n\n            if len(instances) > 0:\n\n                centers, instance_map, instances = self.gmm_refine_clustering(\n                    instance_map,\n                    instances,\n                    spatial_emb,\n                    mask,\n                    sigma, n_sigma\n                )\n\n                if do_plot:\n                    n_instances = instance_map.max()\n                    for i in range(1, n_instances + 1):\n                        ax2.text(\n                            centers[i - 1, 0].item(),\n                            centers[i - 1, 1].item(), str(i),\n                            fontsize=20\n                        )\n                        spatial_emb_flatten = spatial_emb.permute(1, 2, 0).view(-1, 2)\n                        inst_mask = (instance_map == i).view(-1)\n                        ax2.scatter(\n                            spatial_emb_flatten[inst_mask, 0].cpu().numpy(),\n                            spatial_emb_flatten[inst_mask, 1].cpu().numpy(),\n                            # color=np.random.rand(3,),\n                            label='object_' + str(count),\n                            alpha=0.3,\n                        )\n\n            if do_plot:\n                figure.savefig(f'tmp/{im_name}')\n\n        return instance_map, instances\n\n    def get_instance_map(\n            self,\n            spatial_emb: torch.FloatTensor,\n            sigma: torch.FloatTensor,\n            n_sigma: int,\n            mask: torch.Tensor,\n            seed_emb: torch.FloatTensor,\n            seed_scores: List[float]\n    ):\n        _, height, width = spatial_emb.size()\n        instance_map = torch.zeros(height, width).byte()\n        instances = []\n\n        spatial_emb_masked = spatial_emb[mask.expand_as(spatial_emb)].view(2, -1)\n        sigma_masked = sigma[mask.expand_as(sigma)].view(n_sigma, -1)\n\n        instance_map_masked = torch.zeros(mask.sum()).byte().cuda()\n\n        for i, (center, score) in enumerate(zip(seed_emb, seed_scores)):\n            # print('i = ', i)\n            center = center[:, None]\n            seed = torch.argmin(((spatial_emb_masked - center) ** 2).sum(0))\n            s = torch.exp(sigma_masked[:, seed:seed + 1] * 10)\n            dist = torch.exp(-1 * torch.sum(torch.pow(spatial_emb_masked -\n                                                      center, 2) * s, 0, keepdim=True))\n\n            proposal = (dist > 0.5).squeeze()\n            # print('proposal', proposal.sum())\n            instance_map_masked[proposal.squeeze()] = i + 1\n            # print(instance_map_masked.max(), 'qqq')\n            instance_mask = torch.zeros(height, width).bool()\n            instance_mask[mask.squeeze().cpu()] = proposal.cpu()\n\n            instances.append(\n                {'mask': instance_mask.squeeze() * 255, 'score': score})\n\n            instance_map[mask.squeeze().cpu()] = instance_map_masked.cpu()\n        #     print(instance_map.max(), 'asfd')\n        # print(instance_map.max())\n        # print(len(instances))\n        return instance_map, instances\n\n    def gmm_refine_clustering(\n            self,\n            instance_map: torch.LongTensor,\n            instances: List[dict],\n            spatial_emb: torch.FloatTensor,\n            mask: torch.Tensor,\n            sigma: torch.FloatTensor,\n            n_sigma: int\n    ) -> Tuple[torch.Tensor, torch.Tensor, List[dict]]:\n        \"\"\"\n        instance_map: tensor of shape (h, w)\n        instances: List[dict]\n        spatial_emb: tensor of shape (2, h, w)\n        mask: tensor of shape (1, h, w)\n        sigma: tensor of shape (1, h, w)\n        n_sigma: int\n        \"\"\"\n        height, width = instance_map.size()\n        mask_flatten = mask.squeeze().view(-1).cpu().numpy()  # (h, w)\n        spatial_emb_flatten = spatial_emb.permute(1, 2, 0).view(-1, 2).cpu().numpy()\n        instance_map_flatten = instance_map.view(-1).cpu().numpy()\n        X = spatial_emb_flatten[mask_flatten]\n        max_id = instance_map_flatten.max()\n        centroids = [spatial_emb_flatten[instance_map_flatten == i].mean(0) for i in\n                     range(1, max_id + 1)]\n        centroids = np.stack(centroids, axis=0)\n        coovs = [np.cov(spatial_emb_flatten[instance_map_flatten == i].T) for i in range(1, max_id + 1)]\n        init_precision = [np.linalg.inv(x) for x in coovs]\n        n_iter = 15\n        gmm = GaussianMixture(\n            n_components=max_id,\n            means_init=centroids,\n            precisions_init=init_precision,\n            weights_init=np.full(max_id, 1 / max_id),\n            max_iter=n_iter\n        )\n        # prob = gmm.predict_proba(X)\n        # # print(gmm.covariances_)\n        # log_prob = np.log(prob)\n        # print(log_prob.max(1))\n        # # print(prob.max(1).mean())\n        if n_iter > 0:\n            y = gmm.fit_predict(X)\n            centers = torch.tensor(gmm.means_, device=spatial_emb.device)\n        else:\n            centers = torch.tensor(centroids, device=spatial_emb.device)\n        # instance_map = instance_map.clone()\n        # instance_map[:] = 0\n        # instance_map_masked = torch.zeros(mask.sum()).byte()\n        # new_instances = []\n        # for i in sorted(set(y.tolist())):\n        #     proposal = (y == i) & (log_prob[:, i] > -1e-6)\n        #     instance_map_masked[proposal] = i + 1\n        #     instance_map[mask.squeeze()] = instance_map_masked\n        #\n        #     instance_mask = torch.zeros(height, width).bool()\n        #     instance_mask[mask.squeeze().cpu()] = torch.tensor(proposal, dtype=bool)\n        #\n        #     new_instances.append(\n        #         {\n        #             'mask': instance_mask.squeeze() * 255,\n        #             'score': instances[i]['score']\n        #         }\n        #     )\n\n        instance_map, instances = self.get_instance_map(\n            spatial_emb, sigma, n_sigma, mask, centers,\n            [d['score'] for d in instances]\n        )\n        return centers, instance_map, instances\n\n\nclass Logger:\n\n    def __init__(self, keys, title=\"\"):\n\n        self.data = {k: [] for k in keys}\n        self.title = title\n        self.win = None\n\n        print('created logger with keys:  {}'.format(keys))\n\n    def plot(self, save=False, save_dir=\"\"):\n\n        if self.win is None:\n            self.win = plt.subplots()\n        fig, ax = self.win\n        ax.cla()\n\n        keys = []\n        for key in self.data:\n            keys.append(key)\n            data = self.data[key]\n            ax.plot(range(len(data)), data, marker='.')\n\n        ax.legend(keys, loc='upper right')\n        ax.set_title(self.title)\n\n        plt.draw()\n        Visualizer.mypause(0.001)\n\n        if save:\n            # save figure\n            fig.savefig(os.path.join(save_dir, self.title + '.png'))\n\n            # save data as csv\n            df = pd.DataFrame.from_dict(self.data)\n            df.to_csv(os.path.join(save_dir, self.title + '.csv'))\n\n    def add(self, key, value):\n        assert key in self.data, \"Key not in data\"\n        self.data[key].append(value)\n","repo_name":"yanlinf/SpatialEmbeddings","sub_path":"src/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":15205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"7109123594","text":"from math import ceil\n\nuseCount = {}\nrecipes = {}\nfor line in open(\"input.txt\"):\n    ingred, out = (line.strip().split(\" => \"))\n    count, out = out.split()\n    ingredients = []\n\n    for a in ingred.split(\", \"):\n        cnt, ing = a.split()\n        if ing not in useCount:\n            useCount[ing] = 0\n        useCount[ing] += 1\n        \n        ingredients.append((int(cnt),ing))\n\n    recipes[out] = (int(count),ingredients)\n\n\nuseCount[\"FUEL\"] = 0\ntotalNeeded = {\"FUEL\" : 1}\n\nwhile len(useCount) > 1:\n    for ingredient in useCount:\n        if useCount[ingredient] == 0:\n            n = totalNeeded[ingredient]\n            count, items = recipes[ingredient]\n            amt = ceil(n/count)\n            for itemAmt,item in items:\n                if item not in totalNeeded:\n                    totalNeeded[item] = 0\n                totalNeeded[item] += amt*itemAmt\n                useCount[item] -=1\n            del useCount[ingredient]\n            break\n\nprint(totalNeeded[\"ORE\"])","repo_name":"Xychic/AdventOfCode","sub_path":"2019/Day14/Python/MakeFuel.py","file_name":"MakeFuel.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"75057712603","text":"T = int(input())\n\nfor tc in range(1, T+1):\n    N, K = map(int, input().split())\n\n    arr = [list(map(int, input().split())) + [0] for _ in range(N)]\n    arr.append([0]*(N+1))  # 0을 더해주는 이유 - 끝값이 1이면 else문을 통해 cnt 검증을 하지 못해서\n\n    res = 0\n\n    for a in range(N):\n        cnt = 0\n\n        for b in range(N+1):  # 가로 순회\n            if arr[a][b]:\n                cnt += 1\n\n            else:\n                if cnt == K:\n                    res += 1\n                cnt = 0\n\n        for b in range(N+1):  # 세로 순회\n            if arr[b][a]:\n                cnt += 1\n            else:\n                if cnt == K:\n                    res += 1\n                cnt = 0\n\n    print(f\"#{tc} {res}\")\n\n# 예전에 푼 코드로 대체하겠습니다.\n# 모든 좌표를 순회하며 카운팅을 하고, 벽을 만나면 초기화하는 형태","repo_name":"algo-itzy/algo-itzy","sub_path":"SWEA/implementation/1979-어디에_단어가_들어갈_수_있을까/1979-어디에_단어가_들어갈_수_있을까-seokzin.py","file_name":"1979-어디에_단어가_들어갈_수_있을까-seokzin.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"ko","doc_type":"code","stars":10,"dataset":"github-code","pt":"86"}
+{"seq_id":"30899809030","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom fake_headers import Headers\nfrom pprint import pprint\nimport json\nfrom unicodedata import normalize\n\nKEYWORDS = [\"Django\", \"Flask\"]\n#Записать в json информацию о каждой вакансии - ссылка, вилка зп, название компании, город.\n\nHOST ='https://spb.hh.ru/search/vacancy?area=1&area=2&enable_snippets=true&excluded_text=&no_magic=' \\\n      'true&ored_clusters=true&search_field=name&search_field=description&search_period=' \\\n      '7&text=Python%2C+django%2C+Flask&order_by=publication_time'\n\ndef get_headers():\n    headers = Headers(browser='firefox', os='win')\n    return headers.generate()\n\nresponse = requests.get(HOST, headers=get_headers())\nhh_main = response.text\n#pprint(hh_main)\n\nsoup = BeautifulSoup(hh_main, features='lxml')\nvacancies = soup.find_all('div', class_ ='serp-item')\n#pprint(vacancies)\n\nparsed = []\n\nfor vacancy in vacancies:\n\n    h3_link = vacancy.find('h3')\n    a_link = h3_link.find('a')\n    href_link = a_link['href']\n    link_abs = f'https://spb.hh.ru{href_link}'\n    #print(link_abs)\n\n    salary = vacancy.find('span', \"bloko-header-section-3\")\n    if salary == None:\n        salary_num = ' '\n    else:\n        salary_num = salary.text\n    #print(salary_num)\n\n    company = vacancy.find('a', class_=\"bloko-link bloko-link_kind-tertiary\")\n    company_text = company.text\n    #print(company_text)\n\n    city = vacancy.find('div', {'data-qa': \"vacancy-serp__vacancy-address\"})\n    city_text = city.text\n    #print(city_text)\n\n    parsed.append(\n        {'link': link_abs,\n         'salary': normalize('NFKD', salary_num),\n         'company': normalize('NFKD', company_text),\n         'city': normalize('NFKD', city_text)\n\n        }\n    )\n\npprint(parsed)\n\nwith open('hh_vacs', 'w', encoding='utf-8') as file:\n    json.dump(parsed, file, indent=4, ensure_ascii=False, separators=(',', ': '))\n\n\n\n\n","repo_name":"Mikhail15011976/Web-scraping","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"16764784442","text":"def scale_ascii(string, scale):\n\n    ret_string = \"\"\n    \n    for line in string.splitlines(): # why is this not split_lines()?\n        line_to_scale = \"\"\n        \n        for char in line:   \n            for i in range(0, scale):\n                line_to_scale += char\n                \n        for i in range(0, scale):\n            ret_string += line_to_scale + \"\\n\" # not sure if this needs to differ between OSs   \n\n    return ret_string        \n\n\nstring = \"\"\n\nwith open(\"art.txt\", 'r') as file:\n    for line in file:\n        string += line\n\n\nprint(scale_ascii(string, 2))\n","repo_name":"nchlsb/fun_algs_in_python","sub_path":"scale_ascii.py","file_name":"scale_ascii.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"16112599266","text":"from flask import render_template, session, flash\nfrom flask.views import MethodView\nfrom models.event import Event\nfrom utils import helper\n\n\nclass HomeView(MethodView):\n    def get(self):\n        events = []\n        for event in Event.query.order_by(Event.time_start).all():\n            events.append({\n                'id': event.id,\n                'title': event.title,\n                'date': helper.get_breif_formatted_time(event.time_start),\n                'type': \"Seminar\" if event.event_type == Event.SEMINAR else \"Workshop\",\n                'is_ticketed': (f\"Rs. {event.ticket_price}\" if event.is_ticketed else \"Free\"),\n                'head': event.head\n            })\n\n        context = {\n            'title': \"Home\",\n            'user': session.get('user', None),\n            'events': events\n        }\n        return render_template(\"home.html\", **context)\n","repo_name":"ShaderOX/eventic-web-app","sub_path":"views/home.py","file_name":"home.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"19201754973","text":"from Person_message.Views.Add_View import Add_Views\nfrom Person_message.Views.Delete_View import Delete_Views\nfrom Person_message.Views.Find_View import Find_Views\nfrom Person_message.Views.Modify_View import Modify_Views\nfrom Person_message.Views.Show_View import Show_Views\n\n\nclass Message_Views:\n    \"\"\"\n        （2）需要实现的功能\n            1)\t新增信息产业发展数据条目。\n            2)\t查找数据（可按地区、年份、指标名称等查找）。\n            3)\t修改数据条目（先查找，再修改。若当前条件查找出多个记录，则提示用户增加查询条件继续查找，直到确定唯一记录后再修改）。\n            4)\t删除数据条目（请参考上面修改的处理）。\n            5)\t显示信息产业发展数据列表。\n    \"\"\"\n\n    def __init__(self):\n        self.function_list = Message_Views.__read_configuration_file()\n        self.length = len(self.function_list)\n\n    @staticmethod\n    def __read_configuration_file():\n        list_func = []\n        with open(\"config.txt\", \"r\", encoding=\"utf-8\") as f:\n            text = f.readline()\n            while text:\n                str_text = text.split(\"-\")\n                list_func.append((str_text[0], str_text[1], str_text[2]))\n                text = f.readline()\n        return list_func\n\n    def show_home_age(self):\n        print(\"\"\"\n*************************************\n*   欢迎光临&伟大信息产业发展统计系统  *\n        \"\"\")\n        for item in self.function_list:\n            print('\\t' + item[0], item[1])\n        print(\"\\t\" + str(self.length + 1) + \".\", \"退出程序！\")\n        print(\"\"\"\n*************************************\n        \"\"\")\n        while True:\n            n = self.__show_home_input(self.function_list)\n            if n == -1:\n                break\n\n    def __show_home_input(self, list_func):\n        n = input(\"请输入您要进入的功能:\")\n        try:\n            if int(n) == self.length + 1:\n                print(\"正在为您退出程序,欢迎下次光临 ^-^\")\n                return -1\n            elif int(n) <= self.length:\n                eval(list_func[int(n) - 1][2]).show_function()\n        except:\n            print(\"输入错误,请重新输入！\")\n","repo_name":"fsym-fs/Python_AID","sub_path":"MyProject/Person_message/Views/Message_Views.py","file_name":"Message_Views.py","file_ext":"py","file_size_in_byte":2248,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"38547780366","text":"import string\nfrom pprint import pprint\n\n\nclass Node:\n    def __init__(self, idx):\n        self.idx = idx\n        self.root = self\n        self.followers = set()  # Node\n\n    @property\n    def size(self):\n        return len(self.root.followers) + 1\n\n    def follow(self, node):  # node: Node\n        if self.is_connected(node):\n            return\n\n        if self != self.root:\n            self.root.follow(node)\n\n        for follower in self.followers:\n            node.root.followers.add(follower)\n            follower.root = node.root\n        self.followers = set()\n\n        node.root.followers.add(self)\n        self.root = node.root\n\n    def __repr__(self):\n        return f\"Idx: {self.idx} \" \\\n               f\"Root: {self.root.idx} ({', '.join([str(i.idx) for i in self.followers]) if self.followers else ''})\"\n\n\nclass UnionFind:\n    def __init__(self, length: int):\n        self.nodes = [Node(i) for i in range(length)]\n\n    def connect(self, idx1: int, idx2: int):\n        print()\n        print(f\"union_find.connect({idx1}, {idx2})\")\n        if self.nodes[idx1].is_connected(self.nodes[idx2]):\n            return\n\n        if self.nodes[idx1].size <= self.nodes[idx2].size:\n            self.nodes[idx1].follow(self.nodes[idx2])\n        else:\n            self.nodes[idx2].root.follow(self.nodes[idx1].root)\n\n        pprint([(idx, val) for idx, val in enumerate(self.nodes)])\n\n\nclass NonCompressedUnionFind:\n    def __init__(self, length: int):\n        self.parents = [i for i in range(length)]\n        print(self.parents)\n\n    def _find(self, i: int) -> int:\n        \"\"\"find for a non-compressed path\"\"\"\n        return i if self.parents[i] == i else self._find(self.parents[i])\n\n    # Naive implementation of union()\n    def connect(self, x: int, y: int):\n        \"\"\"union for a non-compressed path\"\"\"\n        xset = self._find(x)\n        self.parents[xset] = self._find(y)\n        print()\n        print([i for i in range(len(self.parents))])\n        print(self.parents)\n\n\ndef main():\n    lowercase_letters = \"abcdefghijk\" # list(string.ascii_lowercase)\n    union_find = UnionFind(len(lowercase_letters))\n    # union_find = NonCompressedUnionFind(len(lowercase_letters))\n    # union_find.connect(0, 2)\n    # union_find.connect(1, 2)\n    # union_find.connect(5, 6)\n    # union_find.connect(6, 7)\n    # union_find.connect(7, 8)\n    # union_find.connect(0, 7)\n\n    for i in range(len(lowercase_letters)-1):\n        union_find.connect(i, i+1)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"dosatos/algo-playground","sub_path":"dynamic/union_find.py","file_name":"union_find.py","file_ext":"py","file_size_in_byte":2486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"26168731119","text":"\"\"\"\"\n# 包含应用工厂\n# 告诉Python flaskr文件夹应当视作一个包\n\"\"\"\nimport os\n\nfrom flask import Flask\n\n\n# 应用工厂函数\ndef create_app(test_config=None):\n    # create and configure the app                                     # 创建flask实例（对象）__name__是当前Python模块的名称\n    app = Flask(__name__, instance_relative_config=True)               # instance_relative_config=True告诉应用配置文件是相对于instance folder的相对路径\n    app.config.from_mapping(                                           # 设置一个应用的缺省配置\n        SECRET_KEY='dev',                                              # 保证数据安全\n        DATABASE=os.path.join(app.instance_path, 'flaskr.sqlite'),     # 数据文件存放路径\n    )\n\n    if test_config is None:\n        # load the instance config, if it exists, when not testing\n        app.config.from_pyfile('config.py', silent=True)                # 使用config.py中的值来重载缺省配置\n    else:\n        # load the test config if passed in\n        app.config.from_mapping(test_config)\n\n    # ensure the instance folder exists\n    try:\n        os.makedirs(app.instance_path)                                   # 确保app.instance_path存在\n    except OSError:\n        pass\n\n    # a simple page that says hello\n    @app.route('/hello')\n    @app.route('/')\n    def hello():\n        return 'Hello, World!'\n\n    from . import db\n    db.init_app(app)\n\n    from . import auth\n    app.register_blueprint(auth.bp)                    # 导入并注册蓝图auth\n\n    from . import blog\n    app.register_blueprint(blog.bp)\n    app.add_url_rule('/', endpoint='index')             # 关联端点名称'index'和/ URL,是url_for()函数返回生成同样的/ URL\n\n    return app\n","repo_name":"shanjiankanhai/flask_control","sub_path":"build/lib/flaskr/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1783,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"14811091131","text":"import os\n\nfrom flask import render_template, redirect, url_for, request\nfrom werkzeug.urls import url_parse\nfrom werkzeug.utils import secure_filename\n\nfrom . import bp\nfrom .forms import UploadForm\nfrom .static.DCR_Validator import dcr_validator\n\n@bp.route('/')\ndef dcr_validator_upload_form():\n    form = UploadForm()\n    # static_folder = os.path.join(bp.static_folder, 'temp')\n    # for file in os.listdir(static_folder): \n    #     os.remove(static_folder + '/' + file)\n    return render_template('dcr_upload.html', form=form)\n\n@bp.route('/', methods=['GET', 'POST'])\ndef dcr_validator_results_view():\n    wo_file = request.files['wo_file']\n    dcr_file = request.files['dcr_file']\n    wo_filename = secure_filename(wo_file.filename)\n    dcr_filename = secure_filename(dcr_file.filename)\n    if wo_filename != '' and dcr_filename != '':\n        static_temp_dir = os.path.join(bp.static_folder, 'temp')\n        if not os.path.isdir(static_temp_dir):\n            os.mkdir(static_temp_dir)\n        wo_filename = os.path.join(static_temp_dir, wo_filename)\n        dcr_filename = os.path.join(static_temp_dir, dcr_filename)\n        wo_file.save(wo_filename)\n        dcr_file.save(dcr_filename)\n        output_file = dcr_validator(wo_filename, dcr_filename)\n        # os.remove(filename)\n    else:\n        return redirect(url_for('dcr_validator.dcr_validator_upload_form'))\n    return render_template('dcr_results_view.html', output_file='temp/' + output_file)\n","repo_name":"mike-lloyd03/de-toolsets","sub_path":"app/tools/dcr_validator/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"69884773084","text":"from .parser import *\nfrom lighthouse.util import *\nfrom lighthouse.util.qt import *\nfrom lighthouse.util.disassembler import disassembler\n\n#------------------------------------------------------------------------------\n# Composing Shell\n#------------------------------------------------------------------------------\n\nclass ComposingShell(QtWidgets.QWidget):\n    \"\"\"\n    The ComposingShell UI for interactive coverage composition.\n\n    This class ties together all the individual components that make up\n    the Composing Shell, wrapping it up in a nice portable widget. This\n    includes the label sitting at the head of the shell, the text box\n    (the shell, a.k.a ComposingLine), and the composition parser.\n\n    In theory, multiple ComposingShell objects could be instantiated and\n    placed in various dialogs, forms, views, etc. These shells are fairly\n    independent, but obviously must communicate with the director.\n    \"\"\"\n\n    def __init__(self, lctx, table_model, table_view=None):\n        super(ComposingShell, self).__init__()\n        self.setObjectName(self.__class__.__name__)\n\n        # external entities\n        self._director = lctx.director\n        self._palette = lctx.palette\n        self._table_model = table_model\n        self._table_view = table_view\n\n        # command / input\n        self._search_text = \"\"\n        self._command_timer = QtCore.QTimer()\n\n        # the last known user AST\n        self._last_ast = None\n\n        # composition parser related members\n        self._parser = CompositionParser()\n        self._parser_error = None\n        self._parsed_tokens = []\n        self._shorthand = []\n\n        # configure the widget for use\n        self._ui_init()\n        self.refresh_theme()\n\n    #--------------------------------------------------------------------------\n    # Properties\n    #--------------------------------------------------------------------------\n\n    @property\n    def text(self):\n        \"\"\"\n        The existing shell text.\n        \"\"\"\n        return str(self._line.toPlainText())\n\n    #--------------------------------------------------------------------------\n    # Initialization - UI\n    #--------------------------------------------------------------------------\n\n    def _ui_init(self):\n        \"\"\"\n        Initialize UI elements.\n        \"\"\"\n\n        # initialize a monospace font to use with our widget(s)\n        self._font = MonospaceFont()\n        self._font.setPointSizeF(normalize_to_dpi(10))\n        self._font_metrics = QtGui.QFontMetricsF(self._font)\n\n        # initialize our ui elements\n        self._ui_init_shell()\n        self._ui_init_completer()\n        self._ui_init_signals()\n        self._ui_layout()\n\n    def _ui_init_shell(self):\n        \"\"\"\n        Initialize the shell UI elements.\n        \"\"\"\n\n        # the composer label at the head of the shell\n        self._line_label = QtWidgets.QLabel(\"Composer\")\n        self._line_label.setStyleSheet(\"QLabel { margin: 0 1ex 0 1ex }\")\n        self._line_label.setAlignment(QtCore.Qt.AlignCenter)\n        self._line_label.setFont(self._font)\n        self._line_label.setFixedWidth(self._line_label.sizeHint().width())\n\n        # the text box / shell / ComposingLine\n        self._line = ComposingLine()\n\n    def _ui_init_completer(self):\n        \"\"\"\n        Initialize the coverage hint UI elements.\n        \"\"\"\n        self._completer_model = QtCore.QStringListModel([])\n\n        self._completer = QtWidgets.QCompleter(self)\n        self._completer.setCompletionMode(QtWidgets.QCompleter.PopupCompletion)\n        self._completer.setModelSorting(QtWidgets.QCompleter.CaseInsensitivelySortedModel)\n        self._completer.setCaseSensitivity(QtCore.Qt.CaseInsensitive)\n        self._completer.setModel(self._completer_model)\n        self._completer.setWrapAround(False)\n        self._completer.popup().setFont(self._font)\n        self._completer.setWidget(self._line)\n\n    def _ui_init_signals(self):\n        \"\"\"\n        Connect UI signals.\n        \"\"\"\n\n        # text changed in the shell\n        self._line.textChanged.connect(self._ui_shell_text_changed)\n\n        # cursor position changed in the shell\n        self._line.cursorPositionChanged.connect(self._ui_shell_cursor_changed)\n\n        # return key pressed in the shell\n        self._line.returnPressed.connect(self._ui_shell_return_pressed)\n\n        # register for cues from the director\n        self._director.coverage_created(self._internal_refresh)\n        self._director.coverage_deleted(self._internal_refresh)\n        self._director.coverage_modified(self._internal_refresh)\n        self._director.coverage_switched(self._coverage_switched)\n\n        # register for cues from the model\n        self._table_model.layoutChanged.connect(self._ui_shell_text_changed)\n\n    def _ui_layout(self):\n        \"\"\"\n        Layout the major UI elements of the widget.\n        \"\"\"\n\n        # create a qt layout for the 'composer' (the shell)\n        layout = QtWidgets.QHBoxLayout()\n        layout.setContentsMargins(0,0,0,0)\n\n        #\n        # Shell Layout:\n        #   [ [ 'Composer' ][ ComposingLine                  ... ] ]\n        #\n\n        layout.addWidget(self._line_label)\n        layout.addWidget(self._line)\n\n        # apply the widget layout\n        self.setLayout(layout)\n\n    #--------------------------------------------------------------------------\n    # Refresh\n    #--------------------------------------------------------------------------\n\n    def refresh(self):\n        \"\"\"\n        Public refresh of the shell.\n        \"\"\"\n        self._internal_refresh()\n\n    @disassembler.execute_ui\n    def refresh_theme(self):\n        \"\"\"\n        Refresh UI facing elements to reflect the current theme.\n        \"\"\"\n        assert (self._line and self._completer), \"UI not yet initialized...\"\n\n        # configure the shell background & default text color\n        qpal = self._line.palette()\n        qpal.setColor(QtGui.QPalette.Text, self._palette.shell_text)\n        qpal.setColor(QtGui.QPalette.WindowText, self._palette.shell_text)\n        self._line.setPalette(qpal)\n\n        # set other hard to access shell theme elements\n        self._line.setStyleSheet(\n            \"QPlainTextEdit {\"\n            \"    color: %s;\" % self._palette.shell_text.name() + # this line ensures the text cursor changes color, with the theme\n            \"    background-color: %s;\" % self._palette.shell_background.name() +\n            \"    border: 1px solid %s;\" % self._palette.shell_border.name() +\n            \"} \"\n            \"QPlainTextEdit:hover, QPlainTextEdit:focus {\"\n            \"    border: 1px solid %s;\" % self._palette.shell_border_focus.name() +\n            \"}\"\n        )\n\n        # refresh completer popup style...\n        self._completer.popup().setStyleSheet(\n            \"background: %s;\" % self._palette.shell_hint_background.name() +\n            \"color: %s;\" % self._palette.shell_hint_text.name()\n        )\n\n    @disassembler.execute_ui\n    def _internal_refresh(self):\n        \"\"\"\n        Internal refresh of the shell.\n        \"\"\"\n        self._refresh_hint_list()\n        self._ui_shell_text_changed()\n\n    def _refresh_hint_list(self):\n        \"\"\"\n        Refresh the shell coverage hint contents.\n        \"\"\"\n        hints = []\n        self._shorthand = []\n\n        # get the detailed coverage strings from the director\n        for x in self._director.coverage_names:\n            hints.append(self._director.get_coverage_string(x))\n            symbol = self._director.get_shorthand(x)\n            if symbol:\n                self._shorthand.append(symbol)\n\n        # install the fresh coverage strings to the hint completer dialog\n        self._completer_model.setStringList(hints)\n\n        # queue a UI coverage hint if necessary\n        self._ui_hint_coverage_refresh()\n\n    def _coverage_switched(self):\n        \"\"\"\n        Handle a coverage switched event.\n\n        specifically, we want cover the specical case where the hot shell is\n        being switched to. In these cases, we should forcefully clear the\n        'last' AST so that the full shell expression is re-evaluated and\n        sent forward to the director.\n\n        this will ensure that the director will evaluate and display the\n        results of the present expression as the 'Hot Shell' is now active.\n        \"\"\"\n        if self._director.coverage_name == \"Hot Shell\":\n            self._last_ast = None\n        self._internal_refresh()\n\n    #--------------------------------------------------------------------------\n    # Signal Handlers\n    #--------------------------------------------------------------------------\n\n    def _ui_hint_tooltip(self, text, index):\n        \"\"\"\n        Display a non-intrusive error tooltip to the user.\n        \"\"\"\n\n        #\n        # hide the coverage hint if it is visible. things can look cluttered\n        # down by the shell if we're trying to show both.\n        #\n\n        self._ui_hint_coverage_hide()\n\n        # create a cursor and move it to the parse error location on the shell\n        cursor_tip = QtGui.QTextCursor(self._line.document())\n        cursor_tip.setPosition(index)\n\n        #\n        # using our carefully positioned cursor, we can now extract the relative\n        # pixel position of the parse error on the shell and map its global\n        # (absolute) pixel position on the screen.\n        #\n\n        position = self._line.mapToGlobal(self._line.cursorRect(cursor_tip).topLeft())\n\n        # draw the tooltip at the computed parse error position\n        x = QtWidgets.QToolTip.showText(position, text)\n\n    def _ui_shell_cursor_changed(self):\n        \"\"\"\n        Cursor position changed in the shell.\n        \"\"\"\n        self._ui_hint_coverage_refresh()\n\n    def _ui_shell_text_changed(self):\n        \"\"\"\n        Text changed in the shell.\n        \"\"\"\n        text = self.text\n\n        #\n        # a Search, eg '/DnsParse_'\n        #\n\n        if self.is_search(text):\n            self._execute_search(text)\n            self._highlight_search()\n            return\n\n        # not a search query clear any lingering filters for it\n        else:\n            self._table_model.filter_string(\"\")\n\n        #\n        # a Jump, eg '0x804010a' or 'sub_1400016F0'\n        #\n\n        if self.is_jump(text) and self._table_view:\n            self._line_label.setText(\"Jump\")\n            self._highlight_jump()\n            return\n\n        #\n        # a Composition, eg '(A | B) - C'\n        #\n\n        self._execute_composition(text)\n        self._highlight_composition()\n        self._ui_hint_coverage_refresh()\n\n    def _ui_shell_return_pressed(self):\n        \"\"\"\n        Return / Enter pressed in the shell.\n\n        The user pressed 'enter' in the shell, this means we want to try\n        and save their composition as a new coverage set to the director.\n        \"\"\"\n        text = self.text\n\n        # a search query has no accept state, nothing to do\n        if self.is_search(text):\n            return\n\n        # jump to the function entry containing the requested address\n        if self.is_jump(text) and self._table_view:\n            self._execute_jump(text)\n            return\n\n        # attempt to save the user crafted composition\n        self._accept_composition()\n\n    #--------------------------------------------------------------------------\n    # Search\n    #--------------------------------------------------------------------------\n\n    @staticmethod\n    def is_search(text):\n        \"\"\"\n        Check if a string (text) looks like a search query.\n\n        A search query is used to filter functions listed in the coverage\n        overview table based on their name.\n\n        eg: text = '/DnsParse_'\n        \"\"\"\n        return (text and text[0] == \"/\")\n\n    def _execute_search(self, text):\n        \"\"\"\n        Execute the search semantics.\n        \"\"\"\n        self._search_text = text[1:]\n\n        #\n        # if the user input is only \"/\" (starting to type something), hint\n        # that they are entering the Search mode. nothing else to do!\n        #\n\n        if text == \"/\":\n            self._line_label.setText(\"Search\")\n            return\n\n        #\n        # stop an existing command timer if there is one running. we are about\n        # to schedule a new one or execute inline. so the old/deferred command\n        # is no longer needed.\n        #\n\n        self._command_timer.stop()\n\n        #\n        # if the functions list is HUGE, we want to defer the filtering until\n        # we think the user has stopped typing as each pass may take awhile\n        # to compute (while blocking the main thread...)\n        #\n\n        if self._director.metadata.is_big():\n            self._command_timer = singleshot(1000, self._execute_search_internal)\n            self._command_timer.start()\n\n        #\n        # the database is not *massive*, let's execute the search immediately\n        #\n\n        else:\n            self._execute_search_internal()\n\n        # done\n        return\n\n    def _execute_search_internal(self):\n        \"\"\"\n        Execute the actual search filtering & coverage metrics.\n        \"\"\"\n\n        # the given text is a real search query, apply it as a filter now\n        self._table_model.filter_string(self._search_text)\n\n        # compute coverage % of the visible (filtered) results\n        percent = self._table_model.get_modeled_coverage_percent()\n\n        # show the coverage % of the search results in the shell label\n        self._line_label.setText(\"%1.2f%%\" % percent)\n\n    def _highlight_search(self):\n        \"\"\"\n        Syntax highlight a search query.\n        \"\"\"\n\n        self._line.setUpdatesEnabled(False)\n        ################# UPDATES DISABLED #################\n\n        # clear any existing text colors\n        self._color_clear()\n\n        # color search based on if there are any matching results\n        if self._table_model.rowCount():\n            self._color_text(self._palette.shell_text_valid, start=1)\n        else:\n            self._color_text(self._palette.shell_text_invalid, start=1)\n\n        ################# UPDATES ENABLED #################\n        self._line.setUpdatesEnabled(True)\n\n        # done\n        return\n\n    #--------------------------------------------------------------------------\n    # Jump\n    #--------------------------------------------------------------------------\n\n    def is_jump(self, text):\n        \"\"\"\n        Check if a string (text) looks like a jump query.\n\n        A jump query is used to jump to a function in the coverage overview\n        table based on their address.\n\n        eg: text = '0x8040100', or 'sub_1400016F0'\n        \"\"\"\n        return self._compute_jump(text) != 0\n\n    def _compute_jump(self, text):\n        \"\"\"\n        Compute the function address destination of a jump target from a string.\n\n        eg: text = '0x8040100', or 'sub_8040100' --> jump to function 0x8040100\n        \"\"\"\n        text = text.strip()\n\n        #\n        # if the user input is less than two characters, we automatically\n        # dismiss it as a valid jump target. the primary reasons for this\n        # is to avoid possible shorthand parsing clashes.\n        #\n        # eg: imagine the user has a valid function named 'A' that they want to\n        # jump to - well we actually choose to ignore that request here.\n        #\n        # We favor the importance of shorthand symbols as used in compositions.\n        #\n\n        if len(text) < 2:\n            return 0\n\n        #\n        # attempt to convert the user input from a hex number eg '0x8040105'\n        # to its corresponding function address validated by the director\n        #\n\n        try:\n            address = int(text, 16)\n        except ValueError:\n            pass\n        else:\n            functions = self._director.metadata.get_functions_containing(address)\n            if functions:\n                return functions[0].address\n\n        #\n        # the user string did not translate to a parsable hex number (address)\n        # or the function it falls within could not be found in the director.\n        #\n        # attempt to convert the user input from a function name, eg 'main',\n        # or 'sub_1400016F0' to a function address validated by the director.\n        #\n\n        # special case to make 'sub_*' prefixed user inputs case insensitive\n        if text.lower().startswith(\"sub_\"):\n\n            # attempt uppercase hex (IDA...)\n            function_metadata = self._director.metadata.get_function_by_name(\"sub_\" + text[4:].upper())\n            if function_metadata:\n                return function_metadata.address\n\n            # attempt lowercase hex (Binja...)\n            function_metadata = self._director.metadata.get_function_by_name(\"sub_\" + text[4:].lower())\n            if function_metadata:\n                return function_metadata.address\n\n        #\n        # no luck yet, let's just throw the user's raw text at the lookup. this\n        # would probably be a function they renamed, such as 'foobar'\n        #\n\n        function_metadata = self._director.metadata.get_function_by_name(text)\n        if function_metadata:\n            return function_metadata.address\n\n        #\n        # the user string did not translate to a function name that could\n        # be found in the director. so I guess they're not trying to jump...\n        #\n\n        # failure, the user input (text) isn't a jump ...\n        return 0\n\n    def _execute_jump(self, text):\n        \"\"\"\n        Execute the jump semantics.\n        \"\"\"\n        assert self._table_view\n\n        # retrieve the jump target\n        function_address = self._compute_jump(text)\n        assert function_address\n\n        # select the function entry in the coverage overview table\n        self._table_view.selectRow(self._table_model.func2row[function_address])\n        self._table_view.scrollTo(\n            self._table_view.currentIndex(),\n            QtWidgets.QAbstractItemView.PositionAtCenter\n        )\n\n    def _highlight_jump(self):\n        \"\"\"\n        Syntax highlight a jump query.\n        \"\"\"\n\n        self._line.setUpdatesEnabled(False)\n        ################# UPDATES DISABLED #################\n\n        # clear any existing text colors\n        self._color_clear()\n\n        # color jump\n        self._color_text(self._palette.shell_text_valid)\n\n        ################# UPDATES ENABLED #################\n        self._line.setUpdatesEnabled(True)\n\n        # done\n        return\n\n    #--------------------------------------------------------------------------\n    # Composition\n    #--------------------------------------------------------------------------\n\n    def _execute_composition(self, text):\n        \"\"\"\n        Execute a composition query.\n        \"\"\"\n\n        # reset the shell head text\n        self._line_label.setText(\"Composer\")\n\n        # attempt to parse & execute a composition\n        try:\n\n            # clear any previous parse attempts/failures\n            self._parser_error = None\n\n            # attempt to parse the user input against the composition grammar\n            self._parsed_tokens, ast = self._parser.parse(text, self._shorthand)\n\n            # if the AST changed since the last parse, inform the director\n            if not ast_equal(self._last_ast, ast):\n                self._director.cache_composition(ast)\n\n            # save the newly parsed ast\n            self._last_ast = ast\n\n        # parse failure\n        except ParseError as e:\n            self._parser_error = e\n\n            #\n            # even though we failed to generate an AST that can be evaluated\n            # by the director, we still want to save the list of tokens parsed.\n            # these tokens will still be used for basic syntax highlighting.\n            #\n\n            self._parsed_tokens = e.parsed_tokens\n\n        # done\n        return True\n\n    def _highlight_composition(self):\n        \"\"\"\n        Syntax highlight a composition.\n        \"\"\"\n\n        self._line.setUpdatesEnabled(False)\n        ################# UPDATES DISABLED #################\n\n        # clear any existing text colors\n        self._color_clear()\n\n        # the parse failed, so there will be invalid text to highlight\n        if self._parser_error:\n            self._color_invalid()\n\n        # paint any valid tokens\n        self._color_tokens()\n\n        ################# UPDATES ENABLED #################\n        self._line.setUpdatesEnabled(True)\n\n        # done\n        return\n\n    def _accept_composition(self):\n        \"\"\"\n        Save the user crafted composition to the director.\n        \"\"\"\n\n        #\n        # if there's an existing parse error on the shell, there's nothing we\n        # can do but pop a hint for the user and have them try again\n        #\n\n        if self._parser_error:\n            self._ui_hint_tooltip(\"Invalid Composition\", self._parser_error.error_index)\n            return\n\n        #\n        # While the user is picking a name for the new composite, we might as well\n        # try and compute/cache it asynchronously :-). kick the caching off now.\n        #\n\n        self._director.cache_composition(self._last_ast, force=True)\n\n        #\n        # the user has entered a valid composition that we have parsed. we\n        # want to save this to the director, but first we need a name for the\n        # new composition. pop a simple dialog prompting the user for a\n        # composition name\n        #\n\n        ok, coverage_name = prompt_string(\n            \"Composition Name:\",\n            \"Please enter a name for this composition\",\n            \"COMP_%s\" % self.text\n        )\n\n        #\n        # once the naming prompt closes, the composing shell tries to pop\n        # the coverage hint again which can make it annoying and too\n        # aggressive.\n        #\n        # clearing focus on the text line will ensure the hint does not pop\n        #\n\n        self._line.clearFocus()\n\n        #\n        # returning back to the naming prompt, if the user did not enter a\n        # coverage name (or hit cancel), we will abort saving the composition\n        #\n\n        if not (ok and coverage_name):\n            return\n\n        #\n        # a name was given and all is good, ask the director to save the last\n        # composition under the user specified coverage name\n        #\n\n        self._director.add_composition(coverage_name, self._last_ast)\n\n        # switch to the newly created composition\n        self._director.select_coverage(coverage_name)\n\n    #--------------------------------------------------------------------------\n    # Coverage Hint\n    #--------------------------------------------------------------------------\n\n    def _ui_hint_coverage_refresh(self):\n        \"\"\"\n        Draw the coverage hint as applicable.\n        \"\"\"\n\n        #\n        # if the shell is not focused (or empty), don't bother to show a hint\n        # as it frequently gets in the way and is really annoying...\n        #\n\n        if not (self._line.hasFocus() and self.text):\n            self._ui_hint_coverage_hide()\n            return\n\n        #\n        # if the text cursor is moving and the user has their left mouse\n        # button held, then they are probably doing a click + drag text\n        # selection so we shouldn't be naggin them with hints and stuff\n        #\n        # without this condition, click+drag selection gets really choppy\n        #\n\n        if QtWidgets.QApplication.mouseButtons() & QtCore.Qt.LeftButton:\n            self._ui_hint_coverage_hide()\n            return\n\n        # scrape info from the current shell text state\n        cursor_index = self._line.textCursor().position()\n        text_token   = self._get_cursor_coverage_token(cursor_index)\n\n        #\n        # if the user's text cursor is touching the index that produced the\n        # parse error (assuming there was one) ...\n        #\n\n        if self._parser_error and self._parser_error.error_index == cursor_index:\n\n            #\n            # if the parse error indicates the parse failed because it expected\n            # a coverage token but didn't get one, show the complete coverage\n            # list. The user should know their list of options bro.\n            #\n\n            if self._parser_error.expected == TokenCoverageSingle:\n                self._ui_hint_coverage_show()\n\n        #\n        # if the user's text cursor is touching a valid coverage token, we want\n        # to pop a hint that shows the details for the coverage matching that\n        # explicit token / shorthand. It's a subtle convenience :-)\n        #\n\n        elif text_token and (text_token.type == \"COVERAGE_TOKEN\"):\n            self._ui_hint_coverage_show(text_token.value)\n\n        #\n        # if the user's text cursor is not touching any text index of interest,\n        # there's no reason for us to show any sort of hints. be sure any hints\n        # are hidden.\n        #\n\n        else:\n            self._ui_hint_coverage_hide()\n\n        # done\n        return\n\n    def _ui_hint_coverage_show(self, prefix=''):\n        \"\"\"\n        Show the coverage hint at the shell's cursor position.\n\n        Optionally, one can specify a prefix (eg, the shorthand 'A') to\n        limit the scope of coverage items hinted.\n        \"\"\"\n\n        #\n        # if the completer is already visible and showing the requested prefix,\n        # then we have nothing to do. this will help mitigate refresh flickers\n        #\n\n        if self._completer.popup().isVisible() and \\\n            self._completer.completionPrefix() == prefix:\n            return\n\n        # if there was anything previously selected in the popup, clear it now\n        self._completer.popup().clearSelection()\n\n        # show only hints matching the given prefix\n        #   eg: prefix = 'A' will show only entry 'A - 42.30% - drcov.8...'\n        self._completer.setCompletionPrefix(prefix)\n\n        # specify the position and size of the hint popup\n        cr = self._line.cursorRect()\n        cr.setWidth(self._completer.popup().sizeHintForColumn(0))\n\n        # show the coverage hint popup\n        self._completer.complete(cr)\n        self._completer.popup().repaint() # reduces hint flicker on the Hot Shell\n\n        # done\n        return\n\n    def _ui_hint_coverage_hide(self):\n        \"\"\"\n        Hide the coverage hint.\n        \"\"\"\n        self._completer.popup().hide()\n\n    def _get_cursor_coverage_token(self, index):\n        \"\"\"\n        Get the coverage token touching the cursor (if there is one).\n        \"\"\"\n\n        # iterate through the list of parsed tokens on the line edit / shell\n        for text_token in self._parsed_tokens:\n\n            # skip any non-coverage text tokens\n            if not text_token.type == \"COVERAGE_TOKEN\":\n                continue\n\n            # if this coverage text token touches our cursor, return it\n            if text_token.span[0] <= index <= text_token.span[1]:\n                return text_token\n\n        # no coverage token on either side of the cursor\n        return None\n\n    #--------------------------------------------------------------------------\n    # Composition Highlighting\n    #--------------------------------------------------------------------------\n\n    def _color_tokens(self):\n        \"\"\"\n        Syntax highlight the valid composition tokens.\n        \"\"\"\n\n        # more code-friendly, readable aliases\n        TOKEN_COLORS = self._palette.TOKEN_COLORS\n\n        #\n        # in order to syntax highlight text of interest, we must use a text\n        # cursor as the vehicle to move around the text box (shell) and\n        # manipulate its contents (eg, painting colors)\n        #\n        # this is simply the way Qt exposes this functionality\n        #\n\n        # alias the user cursor, and save its original (current) position\n        cursor = self._line.textCursor()\n        cursor_position = cursor.position()\n\n        # configure text formatting properties we want our cursor to apply\n        highlight = QtGui.QTextCharFormat()\n        highlight.setFontWeight(QtGui.QFont.Bold)   # bolds text we 'type'\n\n        #\n        # we are about to start painting our text, but we want to disable the\n        # shell from emitting any textChanged/cursorMoved kind of signals\n        # that originate from our painting code.\n        #\n        # we use the blockSignals gateways below to disable/enable the signals\n        # for the duration of our painting.\n        #\n\n        self._line.blockSignals(True)\n        ################# UPDATES DISABLED #################\n\n        # iterate through every parsed token, and paint it\n        for token in self._parsed_tokens:\n\n            # if the palette doesn't define a color for this token, ignore it\n            if token.type not in TOKEN_COLORS:\n                continue\n\n            # alias the start and end indexes of the text token to paint\n            token_start, token_end = token.span\n\n            # 'click' and 'drag' to select the token text\n            cursor.setPosition(token_start, QtGui.QTextCursor.MoveAnchor)\n            cursor.setPosition(token_end,   QtGui.QTextCursor.KeepAnchor)\n\n            # configure the colors/style for this explicit token\n            #highlight.setBackground(QtGui.QBrush(QtGui.QColor(TOKEN_COLORS[token.type])))\n            highlight.setForeground(QtGui.QBrush(TOKEN_COLORS[token.type]))\n            cursor.setCharFormat(highlight)\n\n        #\n        # we are done painting all the parsed tokens. let's restore the user\n        # cursor back to its original state so they are none-the-wiser\n        #\n\n        cursor.setPosition(cursor_position)\n        cursor.setCharFormat(QtGui.QTextCharFormat())\n        self._line.setTextCursor(cursor)\n\n        ################# UPDATES ENABLED #################\n        self._line.blockSignals(False)\n\n        # done\n        return\n\n    def _color_invalid(self):\n        \"\"\"\n        Highlight the invalid (un-parsable) text.\n\n        Please read through the _color_tokens() function for a more\n        complete walkthrough of the text painting process.\n        \"\"\"\n        assert self._parser_error\n\n        # the invalid text starts from the token that caused a parse error\n        invalid_start = self._parser_error.error_index\n        invalid_text  = self.text[invalid_start:]\n\n        # no invalid text? nothing to highlight I guess!\n        if not invalid_text:\n            return\n\n        # alias the user cursor, and save its original (current) position\n        cursor = self._line.textCursor()\n        cursor_position = cursor.position()\n\n        # setup the invalid text highlighter\n        invalid_color = self._palette.shell_highlight_invalid\n        highlight = QtGui.QTextCharFormat()\n        highlight.setFontWeight(QtGui.QFont.Bold)\n        highlight.setBackground(QtGui.QBrush(invalid_color))\n\n        self._line.blockSignals(True)\n        ################# UPDATES DISABLED #################\n\n        # select the invalid text\n        cursor.setPosition(invalid_start, QtGui.QTextCursor.MoveAnchor)\n        cursor.setPosition(len(self.text), QtGui.QTextCursor.KeepAnchor)\n\n        # insert a highlighted version of the invalid text\n        cursor.setCharFormat(highlight)\n\n        # reset the cursor position & style\n        cursor.setPosition(cursor_position)\n        cursor.setCharFormat(QtGui.QTextCharFormat())\n        self._line.setTextCursor(cursor)\n\n        ################# UPDATES ENABLED #################\n        self._line.blockSignals(False)\n\n        # done\n        return\n\n    #--------------------------------------------------------------------------\n    # General Highlighting\n    #--------------------------------------------------------------------------\n\n    def _color_clear(self):\n        \"\"\"\n        Clear any existing text colors.\n        \"\"\"\n        self._color_text()\n\n    def _color_text(self, color=None, start=0, end=0):\n        \"\"\"\n        Color shell text with the given color.\n        \"\"\"\n\n        # if no end was specified, apply the style till the end of input\n        if end == 0:\n            end = len(self.text)\n\n        # alias the user cursor, and save its original (current) position\n        cursor = self._line.textCursor()\n        cursor_position = cursor.position()\n\n        # setup a simple font coloring (or clearing) text format\n        simple = QtGui.QTextCharFormat()\n        if color:\n            simple.setForeground(QtGui.QBrush(color))\n\n        self._line.blockSignals(True)\n        ################# UPDATES DISABLED #################\n\n        # select the entire line\n        cursor.setPosition(start, QtGui.QTextCursor.MoveAnchor)\n        cursor.setPosition(end, QtGui.QTextCursor.KeepAnchor)\n\n        # set all the text to the simple format\n        cursor.setCharFormat(simple)\n\n        # reset the cursor position & style\n        cursor.setPosition(cursor_position)\n        self._line.setTextCursor(cursor)\n\n        ################# UPDATES ENABLED #################\n        self._line.blockSignals(False)\n\n        # done\n        return\n\n#------------------------------------------------------------------------------\n# Composing Line\n#------------------------------------------------------------------------------\n\nclass ComposingLine(QtWidgets.QPlainTextEdit):\n    \"\"\"\n    The textbox UI where user compositions are entered (typed).\n\n    While this a QLineEdit may appear to be more appropriate for our\n    'Composing Shell', its support for syntax highlighting like features\n    are completely absent.\n\n    QPlainTextEdit has much better support for coloring or highlighting\n    entered text, so we subclass from it and make a best effort attempt\n    to make it appear and act like a QLineEdit 'shell'\n    \"\"\"\n\n    #\n    # QLineEdit has a signal called 'returnPressed' which fires when the\n    # user hits 'return' or 'enter'. This is a convenient signal, but\n    # QPlainTextEdit does *not* have an equivalent.\n    #\n    # We define and fire this signal ourself for consistency and the same\n    # conveniences as the one QLineEdit offers.\n    #\n\n    returnPressed = QtCore.pyqtSignal()\n\n    def __init__(self, parent=None):\n        super(ComposingLine, self).__init__(parent)\n        self.setObjectName(self.__class__.__name__)\n\n        # configure the widget for use\n        self._ui_init()\n\n    #--------------------------------------------------------------------------\n    # Initialization - UI\n    #--------------------------------------------------------------------------\n\n    def _ui_init(self):\n        \"\"\"\n        Initialize UI elements.\n        \"\"\"\n\n        # initialize a monospace font to use with our widget(s)\n        self._font = MonospaceFont()\n        self._font.setPointSizeF(normalize_to_dpi(10))\n        self._font_metrics = QtGui.QFontMetricsF(self._font)\n        self.setFont(self._font)\n\n        # configure the QPlainTextEdit to appear and act as much like a\n        # QLineEdit as possible (a single line text box)\n        self.setWordWrapMode(QtGui.QTextOption.NoWrap)\n        self.setLineWrapMode(QtWidgets.QPlainTextEdit.NoWrap)\n        self.setVerticalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\n        self.setHorizontalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\n        self.setTabChangesFocus(True)\n        self.setMaximumBlockCount(1)\n\n        # set the height of the textbox based on some arbitrary math :D\n        LINE_PADDING = self.document().documentMargin()*2\n        line_height = self._font_metrics.height() + LINE_PADDING + 2\n        self.setFixedHeight(int(line_height))\n\n    #--------------------------------------------------------------------------\n    # QPlainTextEdit Overloads\n    #--------------------------------------------------------------------------\n\n    def keyPressEvent(self, e):\n        \"\"\"\n        Overload of the key press event.\n        \"\"\"\n\n        # trap the return/enter key event\n        if e.key() == QtCore.Qt.Key_Return or \\\n           e.key() == QtCore.Qt.Key_Enter:\n\n            #\n            # fire our convenience signal notifying listeners that the user\n            # pressed enter. this signal firing indicates the user is\n            # probably trying to complete their query / input.\n            #\n\n            self.returnPressed.emit()\n\n            #\n            # now we must consume the keypress so it doesn't get passed on\n            # to any other widgets/handlers/put in the text box\n            #\n\n            e.accept()\n\n        # business as usual\n        else:\n            super(ComposingLine, self).keyPressEvent(e)\n\n    def timerEvent(self, e):\n        \"\"\"\n        Stubbed out to prevent the QPlainTextEdit selection autoscroll.\n        \"\"\"\n        return\n","repo_name":"gaasedelen/lighthouse","sub_path":"plugins/lighthouse/composer/shell.py","file_name":"shell.py","file_ext":"py","file_size_in_byte":36468,"program_lang":"python","lang":"en","doc_type":"code","stars":2013,"dataset":"github-code","pt":"86"}
+{"seq_id":"13652306498","text":"# id успешной отправки: 78896004\nfrom exeptions import DataNotFound\n\n\nclass Stack:\n    \"\"\"\n    Класс - калькулятор. Произодит вычисления.\n    Поддерживает методы:\n    - calculate(data) - принимает на вход последовательность(data) операторов\n                         и операндов.\n                         Возвращает результат арифметического вычисления.\n                         Пример data: ['1', '2', '+', '3', '*']\n                         Пример результата выполнения функции: 9\n    \"\"\"\n    def __init__(self):\n        self.__stack = []\n        self.__math_operations = {\n            '-': lambda a, b: b - a,\n            '+': lambda a, b: b + a,\n            '/': lambda a, b: b // a,\n            '*': lambda a, b: b * a,\n        }\n\n    def calculate(self, data):\n        if not data:\n            raise DataNotFound\n        for i in data:\n            if i not in self.__math_operations.keys():\n                self.__stack.append(int(i))\n                continue\n            if len(self.__stack) >= 2:\n                result = self.__math_operations[i](\n                    self.__stack.pop(),\n                    self.__stack.pop()\n                )\n                self.__stack.append(result)\n        return self.__stack.pop()\n\n\ndef main():\n    \"\"\"\n    Читает арифметическое выражение из файла, записанное в форме обратной\n    польской нотации и показывает результат вычисления этого выражения.\n    \"\"\"\n    file = open('input.txt')\n    data = file.read().rstrip().split()\n\n    stack = Stack()\n\n    print(stack.calculate(data))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"VitaliiLuki/algorithms","sub_path":"calculator.py","file_name":"calculator.py","file_ext":"py","file_size_in_byte":1895,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"2785134089","text":"import shelve\nimport os\nclass Category:\n\tdef __init__(self,catName):\n\t\tname = catName\n\n\tdef addCategory(newCat):\n\t\twith shelve.open('categorydb','c') as db:\n\t\t\tif 'cats' not in db:\n\t\t\t\tdb['cats'] = []\n\t\t\tcategories = db['cats']\n\t\t\tif newCat not in categories:\n\t\t\t\tcategories.append(newCat)\n\t\t\t\tdb['cats'] = categories\n\t\t\t\treturn \"success\"\n\t\t\telse:\n\t\t\t\treturn \"duplicate\"\n\n\tdef getList():\n\t\twith shelve.open('categorydb','c') as db:\n\t\t\tif 'cats' not in db:\n\t\t\t\tdb['cats'] = []\n\t\t\tcategories = db['cats']\n\t\t\treturn categories\n\n\tdef deleteCategory(catDel):\n\t\twith shelve.open('categorydb','w') as db:\n\t\t\tcategories = db['cats']\n\t\t\tcategories.remove(catDel)\n\t\t\tdb['cats'] = categories\n\n\t\twith shelve.open('questiondb','w',writeback =True) as db:\n\t\t\tfor questionID in db.keys():\n\t\t\t\tif catDel in db[questionID].category:\n\n\t\t\t\t\ttStr = db[questionID].category\n\t\t\t\t\tprint(tStr)\n\t\t\t\t\ttStr = tStr.replace((catDel+\" \"),\"\")\n\t\t\t\t\tprint('inCategoryafterReplaceis' + tStr)\n\t\t\t\t\tdb[questionID].category = tStr\n\n\tdef questionCount(catCount):\n\t\tcount = 0\n\t\twith shelve.open('questiondb','r') as db:\n\t\t\tfor questionID in db.keys():\n\t\t\t\tif catCount in db[questionID].category:\n\t\t\t\t\tcount+=1\n\t\t\t\t\t\n\t\treturn count","repo_name":"joseffsmith/SoftwareEngineeringQuiz","sub_path":"Category.py","file_name":"Category.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70487676125","text":"import speech_recognition as sr\nfrom logger import Logger\nfrom general import clear_console\nfrom playsound import playsound\n\n\nclass Recognizer:\n    def __init__(self):\n        self.required = -1\n        for index, name in enumerate(sr.Microphone.list_microphone_names()):\n            if 'pulse' in name.lower():\n                self.required = index\n                break\n        self.language = 'en-US'\n        self.listener = sr.Recognizer()\n        self.logger = Logger()\n\n    def language_selector(self, language):\n        if language == 'ar':\n            self.language = 'ar-SA'\n        elif language == 'ch':\n            self.language = 'zh-CN'\n        elif language == 'de':\n            self.language = 'de-DE'\n        elif language == 'fa':\n            self.language = 'fa-IR'\n        elif language == 'fr':\n            self.language = 'fr-FR'\n        elif language == 'it':\n            self.language = 'it-IT'\n        elif language == 'ru':\n            self.language = 'ru-RU'\n        elif language == 'sp':\n            self.language = 'es-ES'\n        else:\n            self.language = 'en-US'\n\n    def start(self, _lang=None):\n        if _lang:\n            self.language_selector(_lang)\n        try:\n            with sr.Microphone(device_index=self.required) as source:\n                clear_console()\n                logger.log('anton-speech-recognition-is-starting',\n                           color='white', indent=2, _lang=self.language)\n                self.listener.adjust_for_ambient_noise(source, duration=0.5)\n                while True:\n                    voice = self.listener.listen(source)\n                    try:\n                        command = self.listener.recognize_google(\n                            voice, language=self.language).lower()\n                        logger.log(command, color='green',\n                                   prefix='You', _lang=self.language)\n                        if 'hey' in command or 'anton' in command:\n                            playsound('src/beep.wav')\n                            self.logger.log(\n                                'listening', color='yellow', indent=2, _lang=self.language)\n                            return True\n                    except Exception as e:\n                        logger.log('an-error-has-occurred',\n                                   color='red', indent=2, _lang=self.language)\n                        logger.log(str(e), color='red', indent=4)\n        except Exception:\n            return False\n\n    def listen(self, _lang=None):\n        if _lang:\n            self.language_selector(_lang)\n        try:\n            with sr.Microphone(device_index=self.required) as source:\n                self.listener.adjust_for_ambient_noise(source, duration=0.5)\n                voice = self.listener.listen(source)\n                command = self.listener.recognize_google(\n                    voice, language=self.language).lower()\n                self.logger.log(command, color='green',\n                                prefix='You', _lang=self.language)\n                return command\n        except KeyboardInterrupt:\n            self.logger.log('keyboard-interrupt', color='red',\n                            indent=2, _lang=self.language)\n            exit(0)\n        except sr.UnknownValueError:\n            self.logger.log('i-cant-understand-you', color='red',\n                            prefix='Anton', _lang=self.language)\n        except Exception:\n            return None\n","repo_name":"astrica1/anton","sub_path":"modules/recognizer.py","file_name":"recognizer.py","file_ext":"py","file_size_in_byte":3471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"37988806438","text":"\"\"\"\nModule providing convenience classes and functions aiming at\naccessing recorded ball trajectories.\nIt also provides method for generating ball trajectories.\n\"\"\"\n\n# for typing\nfrom __future__ import annotations\nimport typing\nimport nptyping as npt\n\nimport random\nimport math\nimport pathlib\nimport h5py\nimport numpy as np\n\nimport o80\nimport pam_configuration\nimport tennicam_client\n\n\nassert int(npt.__version__[0]) >= 2, \"Need nptyping >=2.\"\n\n# 3: 3d position , Any: nb of points in trajectory\nTrajectory = npt.NDArray[npt.Shape[\"*, 3\"], npt.Float32]\n\n# List of time stamps, in microseconds\nTimeStamps = npt.NDArray[\n    npt.Shape[\"*\"],\n    npt.UInt,\n]\n\n# List of time durations, in microseconds\nDurations = npt.NDArray[\n    npt.Shape[\"*\"],\n    npt.UInt,\n]\n\n# set of trajectories\nTrajectories = typing.Sequence[Trajectory]\n\n# trajectories and related time stamps\nStampedTrajectory = typing.Tuple[TimeStamps, Trajectory]\nStampedTrajectories = typing.Sequence[StampedTrajectory]\n\n# durations (microseconds), positions, velocities\nDurationTrajectory = typing.Tuple[Durations, Trajectory, Trajectory]\nDurationTrajectories = typing.Sequence[DurationTrajectory]\nDurationPoint = typing.Tuple[np.uint, o80.Item3dState]\n\n\ndef _list_files(\n    dir_path: pathlib.Path, extension: str = \"\", prefix: str = \"\"\n) -> typing.List[pathlib.Path]:\n    \"\"\"\n    Returns the list of file in dir_path of\n    the specified extension and/or the specified prefix.\n    \"\"\"\n    paths = sorted(\n        [\n            f\n            for f in dir_path.iterdir()\n            if f.is_file()\n            and str(f).endswith(extension)\n            and str(f.name).startswith(prefix)\n        ]\n    )\n    return paths\n\n\ndef to_stamped_trajectory(input: DurationTrajectory) -> StampedTrajectory:\n\n    \"\"\"\n    Converts a Duration trajectory to a stamped trajectory.\n    \"\"\"\n\n    durations = input[0]\n    positions = input[1]\n    size = len(durations)\n    stamps = np.zeros(size, np.uint)\n    stamps[1:] = np.cumsum(durations[:-1])\n    return stamps, positions\n\n\ndef to_duration_trajectory(input: StampedTrajectory) -> DurationTrajectory:\n    \"\"\"\n    Converts a StampedTrajectories to a DurationTrajectory.\n    The velocities are estimated by performing finite differences.\n    \"\"\"\n    dt = np.diff(input[0])\n    dp = np.diff(input[1], axis=0)\n    velocities = (dp.T / (dt * 1e-6)).T\n    positions = input[1][:-1, :]\n    return dt, positions, velocities\n\n\nclass RecordedBallTrajectories:\n\n    \"\"\"\n    Class for parsing an hdf5 file containing sets of\n    recorded ball trajectories.\n\n    The expected structure of the file is:\n    To get list of time stamps (micro seconds):\n    d[group name: str][index: int][\"time_stamps\"]\n    To get related list of 3d positions:\n    d[group name: str][index: int][\"trajectory\"]\n\n    To ensure the hdf5 file is properly closed, it is\n    adviced to use the context manager of this class\n    (i.e. ```with RecordedBallTrajectories() as rbt ...```)\n\n    Parameters\n    ----------\n    path: (optional)\n      path to the hdf5 file. If omitted, the file at the\n      default path will be used, i.e.\n      ~/.mpi-is/pam/context/ball_trajectories.hdf5 or\n      /opt/mpi-is/pam/context/ball_trajectories.hdf5\n      (as installed by the pam_configuration package)\n    file_mode: (optional)\n      mode in which the h5py file will be open. The default\n      is \"r\" (read only), which is sufficient for all the\n      method provided by the class. See the subclass\n      MutableRecordedBallTrajectories for methods that will\n      update the file.\n    \"\"\"\n\n    _TIME_STAMPS = \"time_stamps\"\n    _TRAJECTORY = \"trajectory\"\n\n    def __init__(self, path: pathlib.Path = None, file_mode: str = \"r\"):\n        if path is None:\n            path = self.get_default_path()\n        self._f = h5py.File(path, file_mode)\n\n    @staticmethod\n    def get_default_path(create: bool = False) -> pathlib.Path:\n        \"\"\"\n        Returns the default path to the file hosting all ball trajectories\n        (i.e. in ~/.mpi-is/pam/context/ball_trajectories.hdf5 or\n        /opt/mpi-is/pam/context/ball_trajectories.hdf5, as installed by\n        the pam_configuration package).\n\n        Parameters\n        ----------\n        create: optional\n          create an empty file if the file does not exists at the\n          default location. If create is False and the file does\n          not exists, a FileNotFoundError is raised.\n        \"\"\"\n        path = (\n            pathlib.Path(pam_configuration.get_path())\n            / \"context\"\n            / \"ball_trajectories.hdf5\"\n        )\n        if not path.exists():\n            if not create:\n                raise FileNotFoundError(\n                    \"context package: failed to find the file {}\".format(path)\n                )\n        return path\n\n    def get_groups(self) -> typing.Tuple[str, ...]:\n        \"\"\"\n        Returns all the group contained by the file\n        (i.e. all the sets)\n        \"\"\"\n        return tuple(self._f.keys())\n\n    def get_indexes(self, group: str) -> typing.Tuple[int, ...]:\n        \"\"\"\n        Returns all the indexes of the specified group, or\n        raise a ValueError if no such group.\n        \"\"\"\n        g = self._f[group]\n        return tuple([int(index) for index in g.keys()])\n\n    def get_stamped_trajectory(\n        self, group: str, index: int, direct: bool = False\n    ) -> StampedTrajectory:\n        \"\"\"\n        Returns a the stamped trajectory, or raise a ValueError\n        if no such group, or no such index in the group.\n        If not direct, a tuple of h5py data instances will be\n        returned (can not be accessed once the file is closed). Otherwise\n        a tuple of numpy arrays is returned.\n        \"\"\"\n        g = self._f[group][str(index)]\n        # returning directly the h5py datasets\n        if not direct:\n            return g[self._TIME_STAMPS], g[self._TRAJECTORY]\n        # converting the h5py datasets into numpy arrays\n        time_stamps_dset = g[self._TIME_STAMPS]\n        trajectory_dset = g[self._TRAJECTORY]\n        time_stamps = np.zeros(time_stamps_dset.shape, np.uint)\n        trajectory = np.zeros(trajectory_dset.shape, np.float32)\n        time_stamps_dset.read_direct(time_stamps)\n        trajectory_dset.read_direct(trajectory)\n        return time_stamps, trajectory\n\n    def get_stamped_trajectories(\n        self, group: str, direct: bool = False\n    ) -> typing.Dict[int, StampedTrajectory]:\n        \"\"\"\n        Returns all trajectories of the group, or raise a ValueError\n        if no such group.\n        If not direct, a tuple of h5py data instances will be\n        returned (can not be accessed once the file is closed). Otherwise\n        a tuple of numpy arrays is returned.\n        \"\"\"\n        indexes = self.get_indexes(group)\n        return {\n            int(index): self.get_stamped_trajectory(group, index, direct=direct)\n            for index in indexes\n        }\n\n    def close(self):\n        \"\"\"\n        Close the hdf5 file\n        \"\"\"\n        if self._f:\n            self._f.close()\n            self._f = None\n\n    def __enter__(self) -> RecordedBallTrajectories:\n        \"\"\"\n        For the use of this class as a context manager\n        which closes the hdf5.\n        \"\"\"\n        return self\n\n    def __exit__(self, type, value, traceback):\n        \"\"\"\n        For the use of this class as a context manager\n        which closes the hdf5.\n        \"\"\"\n        self._f.close()\n        self._f = None\n\n\nclass MutableRecordedBallTrajectories(RecordedBallTrajectories):\n    \"\"\"\n    Subclass of RecordedBallTrajectories that had some method that\n    will update the content of the HDF5 file. Open the file using the\n    \"r+\" mode.\n    \"\"\"\n\n    def __init__(self, path: pathlib.Path = None):\n        super().__init__(path, file_mode=\"r+\")\n\n    def rm_group(self, group: str) -> None:\n        \"\"\"\n        Remove the group of trajectories from the files\n        if such group exists, raise a KeyError otherwise\n        \"\"\"\n        if group not in self.get_groups():\n            raise KeyError(\"No such group: {}\".format(group))\n        del self._f[group]\n\n    def overwrite(\n        self, group: str, index: int, stamped_trajectory: StampedTrajectory\n    ) -> None:\n        \"\"\"\n        Overwrite the trajectory at the given group\n        and index.\n        \"\"\"\n        g = self._f[group]\n        del g[str(index)]\n        traj_group = g.create_group(str(index))\n        time_stamps = stamped_trajectory[0]\n        positions = stamped_trajectory[1]\n        traj_group.create_dataset(self._TIME_STAMPS, data=time_stamps)\n        traj_group.create_dataset(self._TRAJECTORY, data=positions)\n\n    def add_tennicam_trajectories(\n        self, group_name: str, tennicam_path: pathlib.Path\n    ) -> int:\n        \"\"\"\n        It is assumed that tennicam_path is a directory hosting (non recursively)\n        a collection of files named tennicam_* that have been generated by the\n        executable tennicam_client_logger (package tennicam_client). This function\n        will parse all these files and add them to the hdf5 under the specified\n        group name (or raise a FileNotFoundError if tennicam_path does not\n        exists).\n\n        Returns\n        -------\n        The number of trajectories added to the file.\n        \"\"\"\n\n        def _read_trajectory(tennicam_file: pathlib.Path) -> StampedTrajectory:\n            \"\"\"\n            Parse the file and returned the corresponding\n            stamped trajectory.\n            \"\"\"\n            time_stamps = []\n            trajectory = []\n            start_time = None\n            for ball_id, time_stamp, position, _ in tennicam_client.parse(\n                tennicam_file\n            ):\n                if ball_id >= 0:\n                    time_stamp = int(time_stamp * 1e-3)  # from nano to micro seconds\n                    if start_time is None:\n                        start_time = time_stamp\n                    time_stamp -= start_time\n                    time_stamps.append(time_stamp)\n                    trajectory.append(position)\n            return np.array(time_stamps, np.uint), np.array(trajectory, np.float32)\n\n        def _read_folder(tennicam_path: pathlib.Path) -> StampedTrajectories:\n            \"\"\"\n            List all the file in tennicam_path that have the tennicam_ prefix,\n            parse them and returns the corresponding list of stamped trajectories.\n            \"\"\"\n            files = _list_files(tennicam_path, prefix=\"tennicam_\")\n            stamped_trajectories = [_read_trajectory(tennicam_path / f) for f in files]\n            return stamped_trajectories\n\n        def _save_trajectory(\n            group: h5py._hl.group.Group,\n            index: int,\n            stamped_trajectory: StampedTrajectory,\n        ):\n            \"\"\"\n            Create in the group a new subgroup named according to the index\n            and add to it 2 datasets, \"time_stamps\" (list of microseconds\n            time stamps) and \"trajectory\" (list of corresponding 3d positions)\n            \"\"\"\n            # creating a new group for this trajectory\n            traj_group = group.create_group(str(index))\n            # adding 2 datasets: time_stamps and positions\n            time_stamps = stamped_trajectory[0]\n            positions = stamped_trajectory[1]\n            traj_group.create_dataset(self._TIME_STAMPS, data=time_stamps)\n            traj_group.create_dataset(self._TRAJECTORY, data=positions)\n\n        # reading all trajectories present in the directory\n        stamped_trajectories = _read_folder(tennicam_path)\n\n        # adding the new group to the hdf5 file\n        group = self._f.create_group(group_name)\n\n        # adding all trajectories as datasets to this group\n        for index, stamped_trajectory in enumerate(stamped_trajectories):\n            _save_trajectory(group, index, stamped_trajectory)\n\n        return len(stamped_trajectories)\n\n    def add_json_trajectories(\n        self, group_name: str, json_path: pathlib.Path, sampling_rate_us: int\n    ) -> int:\n        \"\"\"\n        It is assumed that json_path is a directory hosting (non recursively)\n        a collection of files named *.json. Each file host the (string) representation\n        of a dictionary with the key \"ob\" associated to an list of a 6d array (3d\n        position and 3d velocities).\n        This function will parse all these files and add them to the hdf5 under the\n        specified group name (or raise a FileNotFoundError if json_path does not\n        exists). (note: the velocities values are ignored, and the time stamp list\n        is created based on the sampling rate)\n\n        Returns\n        -------\n        The number of trajectories added to the file.\n        \"\"\"\n\n        def _read_trajectory(json_file: pathlib.Path) -> Trajectory:\n            \"\"\"\n            Parse the json file and return the trajectory it\n            hosts.\n            \"\"\"\n            with open(json_file, \"r\") as f:\n                content_str = f.read()\n            content_str = content_str.strip()\n            content = eval(content_str)\n            trajectory = np.array(content[\"ob\"], np.float32)[\n                :, :3\n            ]  # keeping only the position\n            return trajectory\n\n        def _read_folder(json_path: pathlib.Path) -> Trajectories:\n            \"\"\"\n            List the json files that are at the root of the path,\n            parse them and return the corresponding trajectories.\n            \"\"\"\n            trajectories = [\n                _read_trajectory(json_path / f) for f in _list_files(json_path, \".json\")\n            ]\n            return trajectories\n\n        def _save_trajectory(\n            group: h5py._hl.group.Group, index: int, trajectory: Trajectory\n        ):\n            \"\"\"\n            Create under the group a new subgroup named after the index,\n            and add 2 datasets, \"time_stamps\" (list of time stamps in\n            micro seconds inferred using the sample rate) and\n            \"trajectory\", the corresponding list of 3d positions.\n            \"\"\"\n            # creating a new group for this trajectory\n            traj_group = group.create_group(str(index))\n            # inferring time stamps\n            time_stamps = np.array(\n                [i * sampling_rate_us for i in range(trajectory.shape[0])], np.int32\n            )\n            # adding 2 datasets: time_stamps and positions\n            traj_group.create_dataset(self._TIME_STAMPS, data=time_stamps)\n            traj_group.create_dataset(self._TRAJECTORY, data=trajectory)\n\n        # reading all trajectories present in the directory\n        trajectories = _read_folder(json_path)\n\n        # adding the new group to the hdf5 file\n        group = self._f.create_group(group_name)\n\n        # adding all trajectories as datasets to this group\n        for index, trajectory in enumerate(trajectories):\n            _save_trajectory(group, index, trajectory)\n\n        return len(trajectories)\n\n\nclass BallTrajectories:\n    \"\"\"\n    Convenience wrapper over a hdf5 file which contains\n    sets (\"groups\") of ball trajectories.\n\n    The constructor loads a group of trajectories in the memory,\n    and methods provide convenience functions to access them.\n\n    A trajectory is tuple of two lists, one with time stamps\n    (in microseconds) and one with related 3d positions.\n\n    Parameters\n    ----------\n    group:\n      name of the group of trajectories to load\n    hdf5_path: optional\n      absolute path to the hdf5 file to load. If None,\n      the default file will be used (i.e. either\n      ~/.mpi-is/pam/context/ball_trajectories.hdf5 or\n      /opt/mpi-is/pam/context/ball_trajectories.hdf5\n    \"\"\"\n\n    def __init__(self, group: str, hdf5_path: pathlib.Path = None):\n        if hdf5_path is None:\n            hdf5_path = RecordedBallTrajectories.get_default_path()\n\n        self._path: pathlib.Path = hdf5_path\n\n        with RecordedBallTrajectories(hdf5_path) as rbt:\n            self._data: typing.Dict[\n                int, StampedTrajectory\n            ] = rbt.get_stamped_trajectories(group, direct=True)\n\n    def size(self) -> int:\n        \"\"\"\n        Returns the number of trajectories that have been loaded.\n        \"\"\"\n        return len(self._data)\n\n    def get_all_trajectories(self) -> typing.Dict[int, StampedTrajectory]:\n        \"\"\"\n        Returns a dictionary with key the index of the trajectory and\n        the trajectories as values.\n        \"\"\"\n        return self._data\n\n    def get_trajectory(self, index: int) -> StampedTrajectory:\n        \"\"\"\n        Returns the trajectory at the requested index.\n        \"\"\"\n        return self._data[index]\n\n    def random_trajectory(self) -> StampedTrajectory:\n        \"\"\"\n        Returns one of the trajectory, randomly selected.\n        \"\"\"\n        index = random.choice(list(range(len(self._data.keys()))))\n        return self._data[index]\n\n    def get_different_random_trajectories(\n        self, nb_trajectories: int\n    ) -> StampedTrajectories:\n        \"\"\"\n        Returns a list of trajectories, randomly\n        ordered and selected.\n        \"\"\"\n        if nb_trajectories > self.size():\n            raise ValueError(\n                \"BallTrajectories: only {} trajectories \"\n                \"available ({} requested)\".format(self.size(), nb_trajectories)\n            )\n        indexes = list(self._data.keys())\n        random.shuffle(indexes)\n        return [self._data[index] for index in indexes[:nb_trajectories]]\n\n    @staticmethod\n    def to_duration(input: StampedTrajectory) -> DurationTrajectory:\n        \"\"\"\n        Returns a corresponding duration trajectory\n        \"\"\"\n        return to_duration_trajectory(input)\n\n    @classmethod\n    def iterate(\n        cls, input: StampedTrajectory\n    ) -> typing.Generator[DurationPoint, None, None]:\n        \"\"\"\n        Generator over the trajectory.\n        Yields tuples (duration in microseconds, state), state having\n        a position and a velocity attribute.\n        \"\"\"\n        durations, positions, velocities = cls.to_duration(input)\n        for d, p, v in zip(durations, positions, velocities):\n            yield d, o80.Item3dState(p, v)\n        return\n\n\ndef velocity_line_trajectory(\n    start: typing.Sequence[float],\n    end: typing.Sequence[float],\n    velocity: float,\n    sampling_rate: float = 0.01,\n) -> DurationTrajectory:\n\n    \"\"\"\n    Start and end being n dimentional points, velocity\n    a float value (meter per seconds) and the sampling\n    rate between two points (in seconds), returns duration\n    trajectory corresponding to a point going from\n    start to end at the given velocity\n    \"\"\"\n\n    # vector between end and start\n    vector = [e - s for e, s in zip(end, start)]\n\n    # distance between end and start\n    distance = math.sqrt(sum([v**2 for v in vector]))\n\n    # duration of motion between start and end,\n    # at constant velocity\n    duration = distance / velocity\n\n    # the velocity vector\n    velnd = np.array([v / duration for v in vector], np.float32)\n\n    # discrete number of steps to go from start\n    # to end at given speed and sampling rate\n    nb_steps = int((duration / sampling_rate) + 0.5)\n\n    # displacement vector of one step\n    step = [v / nb_steps for v in vector]\n\n    # creating the trajectory, translating\n    # one displacement vector per step\n    point = start\n    positions_list = []\n    for cstep in range(nb_steps):\n        point = [p + s for p, s in zip(point, step)]\n        positions_list.append(np.array(point, np.float32))\n    positions = np.array(positions_list, np.float32)\n    velocities = np.array([velnd] * len(positions), np.float32)\n\n    # durations in microseconds\n    durations = np.array([int(sampling_rate * 1e6)] * nb_steps)\n\n    # returning the trajectory\n    return durations, positions, velocities\n\n\ndef duration_line_trajectory(\n    start: typing.Sequence[float],\n    end: typing.Sequence[float],\n    duration_ms: float,\n    sampling_rate: float = 0.01,\n) -> DurationTrajectory:\n\n    \"\"\"\n    Start and end being n dimentional points, duration\n    a float value (milliseconds) and the sampling\n    rate between two points (in seconds), returns duration\n    trajectory corresponding to a point going from\n    start to end over the provided duration\n    \"\"\"\n\n    # vector between end and start\n    vector = [e - s for e, s in zip(end, start)]\n\n    # duration of motion between start and end,\n    # at constant velocity\n    duration = duration_ms / 1000.0\n\n    # the velocity vector\n    velnd = np.array([v / duration for v in vector], np.float32)\n\n    # discrete number of steps to go from start\n    # to end at given speed and sampling rate\n    nb_steps = int((duration / sampling_rate) + 0.5)\n    if nb_steps == 0:\n        nb_steps = 1\n\n    # displacement vector of one step\n    step = [v / nb_steps for v in vector]\n\n    # creating the trajectory, translating\n    # one displacement vector per step\n    point = start\n    positions_list = []\n    for cstep in range(nb_steps):\n        point = [p + s for p, s in zip(point, step)]\n        positions_list.append(np.array(point, np.float32))\n    positions = np.array(positions_list, np.float32)\n    velocities = np.array([velnd] * len(positions), np.float32)\n\n    # durations in microseconds\n    durations = np.array([int(sampling_rate * 1e6)] * nb_steps)\n\n    # returning the trajectory\n    return durations, positions, velocities\n","repo_name":"intelligent-soft-robots/context","sub_path":"python/context/ball_trajectories.py","file_name":"ball_trajectories.py","file_ext":"py","file_size_in_byte":21282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"29268428992","text":"# Coca data source\n\nfrom constants import TOTAL, SUM\nfrom data_sources.general.data_from_yearly_shapefiles import DataFromYearlyShapefiles\n\n# Constants\nid = \"coca\"\nname = \"Coca\"\n\n\nclass Coca(DataFromYearlyShapefiles):\n    '''\n    Coca data source\n    '''\n\n    def __init__(self):\n        super().__init__(id=id,\n                         name=name,\n                         folder_name=\"coca_fields_shapes\",\n                         file_format=\"coca_fields_{year}.shp\",\n                         data_columns=['coca'],\n                         min_year=2000,\n                         max_year=2019,\n                         included_groupings=[TOTAL],\n                         time_resolution_aggregation_function = SUM,\n                         default_values=0)\n","repo_name":"Data-Lama/pathogen_study_regions_generator","sub_path":"src/data_sources/specific/coca.py","file_name":"coca.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"8804730010","text":"import abc\nimport json\nimport sys\n\n\nclass MessagePrinter(abc.ABC):\n\n    @abc.abstractmethod\n    def print_message(self, key, value):\n        pass\n\n\nclass SimpleMessagePrinter(MessagePrinter):\n\n    def __init__(self, show_key: bool, show_msg: bool, filter_empty: bool, output):\n        self.show_key = show_key\n        self.show_msg = show_msg\n        self.filter_empty = filter_empty\n        if output is None:\n            self.output = sys.stdout\n        else:\n            self.output = output\n\n\n    def print_message(self, key, value):\n        def print_output(str: str):\n            print(str, file=self.output)\n\n        if self.filter_empty:\n            if len(value.strip()) == 0:\n                return\n            try:\n                value_obj = json.loads(value)\n                if len(value_obj) == 0:\n                    return\n            except:\n                pass\n\n        if self.show_key:\n            print_output(key)\n            key_len = len(key)\n            key_ul = '=' * key_len\n            if self.show_msg:\n                print_output(key_ul)\n        if self.show_msg:\n            print_output(value)\n        if self.show_key or self.show_msg:\n            print_output('')\n\n\nclass JsonMessagePrinter(MessagePrinter):\n\n    def __init__(self, incl: list[str], excl: list[str], printer: MessagePrinter):\n        self.simple_printer = printer\n        self.incl = incl\n        self.excl = excl\n\n    @staticmethod\n    def __parse_selector(sel_str: str):\n        return Selector(list(sel_str.split('.')))\n\n    def print_message(self, key_str, value_str):\n        try:\n            value = json.loads(value_str)\n        except:\n            value = {}\n\n        sel_map = JsonMessagePrinter.__parse_selector\n\n        for excl_str in self.excl:\n            try:\n                excl_path = sel_map(excl_str)\n                excl_path.remove_from(value)\n            except:\n                pass\n\n        if len(self.incl) == 0:\n            self.simple_printer.print_message(key_str, json.dumps(value, indent=4))\n        else:\n            new_value = {}\n            for incl_str in self.incl:\n                try:\n                    incl_path = sel_map(incl_str)\n                    new_value[incl_str] = incl_path.retrieve_from(value)\n                except:\n                    pass\n            self.simple_printer.print_message(key_str, json.dumps(new_value, indent=4))\n\n\nclass IllegalSelectionError(Exception):\n    pass\n\n\nclass Selector:\n\n    def __init__(self, path: list[str]):\n        self.path = path\n\n    def __retrieve_sel(self, sel: str, obj):\n        try:\n            if sel in obj:\n                return obj[sel]\n        except TypeError:\n            # sel must be on iterable obj\n            raise IllegalSelectionError(f'cannot select from non-iterable object {obj}')\n\n        if isinstance(obj, list):\n            # for list, if sel is int, take it\n            try:\n                int_sel = int(sel)\n                if int_sel in obj:\n                    return obj[int_sel]\n            except ValueError:\n                # if sel is not int, sel nested elements\n                try:\n                    mapped_value = list(map(lambda ele : self.__retrieve_sel(sel, ele), obj))\n                    return mapped_value\n                except IllegalSelectionError as nested_e:\n                    raise IllegalSelectionError('unable to select from list elements ') from nested_e\n            except IndexError as i_err:\n                raise IllegalSelectionError('unable to select index {sel} from {obj}') from i_err\n\n        raise IllegalSelectionError(f'selection not found in object: {obj}')\n\n    def __remove_sel(self, sel: str, obj):\n        # test if selection exists in object\n        self.__retrieve_sel(sel, obj)\n\n        if sel in obj:\n            del obj[sel]\n            return\n\n        if isinstance(obj, list):\n            # for list, if sel is int, remove it as index\n            try:\n                int_sel = int(sel)\n                if int_sel in obj:\n                    obj.pop(int_sel)\n                    return\n            except ValueError:\n                # if sel is not int, remove sel from nested elements\n                for ele in obj:\n                    self.__remove_sel(sel, ele)\n\n    def __retrieve_path(self, path: list[str], obj):\n        if len(path) == 0:\n            return obj\n        this_sel = path[0]\n        new_obj = self.__retrieve_sel(this_sel, obj)\n        return self.__retrieve_path(path[1:], new_obj)\n\n    def __remove_path(self, path: list[str], obj):\n        if len(path) < 1:\n            raise IllegalSelectionError('cannot remove empty path')\n\n        if len(path) == 1:\n            self.__remove_sel(path[0], obj)\n            return\n        this_sel = path[0]\n        next_path = path[1:]\n        if this_sel in obj:\n            self.__remove_path(next_path, obj[this_sel])\n            return\n\n        if isinstance(obj, list):\n            # for list, if sel is int, remove remaining path from sel as index\n            try:\n                int_sel = int(this_sel)\n                if int_sel in obj:\n                    self.__remove_path(next_path, obj[int_sel])\n                    return\n            except ValueError:\n                # if sel is not int, remove path from nested elements\n                for ele in obj:\n                    self.__remove_path(path, ele)\n\n    def retrieve_from(self, obj):\n        return self.__retrieve_path(self.path, obj)\n\n    def remove_from(self, obj):\n        self.__remove_path(self.path, obj)\n","repo_name":"twosixlabs-dart/dart-cli","sub_path":"src/dart_cli/dart_kafka/message_printer.py","file_name":"message_printer.py","file_ext":"py","file_size_in_byte":5524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"39369588756","text":"#!/usr/bin/python3\nimport Adafruit_DHT\n\nimport time\nimport os\n\nimport RPi.GPIO as GPIO\n\n\nDHT_SENSOR = Adafruit_DHT.DHT11\n\nDHT_PIN = 4\n\nGruen = 25\n\nGelb = 24\n\nRot = 23\n\nGPIO.setwarnings(False)\n\nGPIO.setmode(GPIO.BCM)\n\nGPIO.setup(Rot, GPIO.OUT)\n\nGPIO.setup(Gelb, GPIO.OUT)\n\nGPIO.setup(Gruen, GPIO.OUT)\n\nmessen = True;\n\n\n\nwhile messen:\n       humidity, temperature = Adafruit_DHT.read(DHT_SENSOR, DHT_PIN)\n       if humidity is not None and temperature is not None:\n             print(\"Temp={0:0.1f}C Humidity={1:0.1f}%.\".format(temperature,humidity))\n\n\n             cmd = 'curl -X POST -d \"{\"temperature\": ' + str(temperature) + '}\"  https://demo.thingsboard.io/api/v1/PIobHRn5i2gqdKGMRUvr/telemetry  --header \"Content-Type:application/json\" '\n             os.system(cmd)\n\n             cmd = 'curl -X POST -d \"{\"humidity\": ' + str(humidity) + '}\"  https://demo.thingsboard.io/api/v1/PIobHRn5i2gqdKGMRUvr/telemetry  --header \"Content-Type:application/json\" '\n             os.system(cmd)\n\t\t\n             if humidity < 40:\n                    GPIO.output(Rot, GPIO.HIGH);\n                    GPIO.output(Gelb, GPIO.LOW);\n                    GPIO.output(Gruen, GPIO.LOW);\n             elif humidity > 40 and humidity < 50:\n                    GPIO.output(Gelb, GPIO.HIGH);\n                    GPIO.output(Rot, GPIO.LOW);\n                    GPIO.output(Gruen, GPIO.LOW);\n             else:\n                    GPIO.output(Gruen, GPIO.HIGH);\n                    GPIO.output(Rot, GPIO.LOW);\n                    GPIO.output(Gelb, GPIO.LOW);\n       else:\n             print(\"Sensor failure. Check wiring.\")\n       time.sleep(10);\nGPIO.cleanup();\n","repo_name":"antoniaprager/r1","sub_path":"girls-day2021/mydht11.py","file_name":"mydht11.py","file_ext":"py","file_size_in_byte":1636,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"41992384544","text":"import click\nimport os\nfrom os import path\nimport sys\nimport pkgutil\nimport yaml\nimport datetime\nfrom jinja2 import Environment, PackageLoader\nfrom bunch import Bunch, bunchify, unbunchify\nfrom traceback import print_tb\nfrom anytree import Node, RenderTree, Resolver, PreOrderIter\n\nfrom yadocgen.generator import generate_documentation\n\n# include for example purposes\nfrom yadocgen.example import foo\n\n\n@click.group()\ndef cli():\n    \"\"\"Yet Another Documentation Generator\"\"\"\n    print(\"yaDocGen v0.1.5\")\n\n\n@cli.command()\n@click.option(\n    \"--work-dir\",\n    envvar=\"YDG_WORK_DIR\",\n    default=\".\",\n    help=\"yadocgen working directory (default: current directory)\",\n)\n@click.option(\n    \"--name\",\n    envvar=\"YDG_PROJECT_NAME\",\n    prompt=\"Project name\",\n    help=\"project name used in the documentation\",\n)\n@click.option(\n    \"--author\",\n    envvar=\"YDG_AUTHOR\",\n    prompt=\"Author\",\n    help=\"Name(s) of the documentation's author\",\n)\n@click.option(\n    \"--version\",\n    envvar=\"YDG_VERSION\",\n    prompt=\"Version\",\n    help=\"version (taken automatically from VERSION file if it exists)\",\n)\n@click.option(\n    \"--theme\",\n    default=\"karma_sphinx_theme\",\n    envvar=\"YDG_THEME\",\n    prompt=\"Sphinx template\",\n    help=\"Sphinx theme to use (default: karma)\",\n)\n@click.option(\n    \"--welcome\",\n    default=\"README.md\",\n    envvar=\"YDG_WELCOME_PAGE\",\n    prompt=\"Welcome page\",\n    help=\"File to use a welcome page of the documentation (default: ./README.md)\",\n)\n@click.option(\n    \"--src-dir\",\n    envvar=\"YDG_SOURCE_DIR\",\n    default=\"src\",\n    prompt=\"Source code directory\",\n    help=\"directory that holds the source code of the project\",\n)\n@click.option(\n    \"--doc-dir\",\n    envvar=\"YDG_DOC_DIR\",\n    default=\"doc\",\n    prompt=\"Documentation directory\",\n    help=\"directory that holds arbitrary documentation pages\",\n)\n@click.option(\n    \"--output\",\n    envvar=\"YDG_OUTPUT_DIR\",\n    default=\"sphinx\",\n    prompt=\"Output directory\",\n    help=\"directory for generated documentation files\",\n)\ndef init(work_dir, src_dir, doc_dir, output, name, author, version, theme, welcome):\n    r\"\"\"Initialize yadocgen for a project.\n\n    This function takes the parameters, either from command line, prompt or\n    environment variables and creates the necessary directories and the\n    configuration file.\n\n    Parameters:\n    -----------\n    work_dir\n    src_dir\n    doc_dir\n    output\n    name\n    author\n    version\n    theme\n    welcome\n\n    \"\"\"\n\n    # build copyright string\n    date = datetime.date.today()\n    year = date.strftime(\"%Y\")\n    copyright = f\"{author} {year}\"\n\n    if doc_dir == \"\" or doc_dir.lower() == \"none\":\n        doc_dir = None\n\n    if src_dir == \"\" or src_dir.lower() == \"none\":\n        src_dir = None\n\n    # write yadocgen config file\n    CONFIG = Bunch(\n        src_dir=src_dir,\n        doc_dir=doc_dir,\n        output_dir=output,\n        auto_version=True,\n        sphinx_config=Bunch(\n            project_name=name,\n            copyright=copyright,\n            author=author,\n            version=version,\n            add_paths=[os.path.join(\"..\", \"..\", src_dir)],\n            theme=theme,\n            welcome=welcome,\n        ),\n    )\n\n    # write config file\n    with open(os.path.join(work_dir, \".yadocgen\"), \"w\") as f:\n        yaml.dump(CONFIG, f)\n\n    # create Sphinx directory structure\n    work_dir = path.abspath(work_dir)\n    os.makedirs(path.join(work_dir, CONFIG.output_dir, \"source\", \"_static\"))\n    os.makedirs(path.join(work_dir, CONFIG.output_dir, \"source\", \"_templates\"))\n    os.makedirs(path.join(work_dir, CONFIG.output_dir, \"build\"))\n\n    env = Environment(loader=PackageLoader(\"yadocgen\", \"templates\"))\n\n    # create Makefile\n    with open(path.join(work_dir, CONFIG.output_dir, \"Makefile\"), \"w\") as f:\n        template = env.get_template(\"Makefile.jinja\")\n        f.write(template.render(config=CONFIG.sphinx_config))\n\n    # create Sphinx config file\n    with open(path.join(work_dir, CONFIG.output_dir, \"source\", \"conf.py\"), \"w\") as f:\n        template = env.get_template(\"conf.py.jinja\")\n        f.write(template.render(config=CONFIG.sphinx_config))\n\n\n@cli.command()\n@click.option(\n    \"--work-dir\",\n    envvar=\"YDG_WORK_DIR\",\n    default=\".\",\n    help=\"yadocgen working directory (default: current directory)\",\n)\n@click.option(\n    \"--purge\",\n    default=True,\n    help=\"Clean Sphinx source (default: True)\",\n)\ndef generate(work_dir, purge):\n    \"\"\"Generates the documentation for a project.\"\"\"\n    work_dir = path.abspath(work_dir)\n\n    # load project config\n    config_file_path = os.path.join(work_dir, \".yadocgen\")\n    if not os.path.isfile(config_file_path):\n        print(f\"Config file not found in working directory ({config_file_path}).\")\n    with open(config_file_path, \"r\") as f:\n        CONFIG = yaml.full_load(f)\n\n    generate_documentation(work_dir, purge, CONFIG)\n\n\ndef configure_bibfiles():\n    ## TODO check if it is always valid to perform this on current work dir!\n    return [\n        f for f in os.listdir(\".\") if os.path.isfile(f) and f.lower().endswith(\".bib\")\n    ]\n","repo_name":"fraunhofer-iais/yadocgen","sub_path":"src/yadocgen/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":5032,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"32462879264","text":"import urllib.request as urlrequest\nimport json\n\nid_list = []\nwith open('firstdata.txt','w') as outputfile:   #w 重新写入，a 在文末添加\n    for id in id_list:\n        url = 'https://.../{}'.format(id)\n        url_content = urlrequest.urlopen(url)\n        json_content = json.loads(url_content.decode('utf8'))\n        rank = json_content['adsad']['adadad']\n        outputfile.write('{} {}\\n'.format(id,rank))","repo_name":"yoyojang/data_analysis","sub_path":"pachong_api.py","file_name":"pachong_api.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34232377455","text":"#!/usr/bin/python\n\nimport sys\n\ntracename = sys.argv[1]\nresulttrace = \"kv.ascii\"\n\nprint(\"converted tracename: %s\"%tracename)\nprint(\"result    tracename: %s\"%resulttrace)\n\nwith open(resulttrace, \"w\") as out:\n  with open(tracename, \"r\") as f:\n    lines = f.readlines()\n    for t in lines:\n      t = t.strip().split()\n      timestap = int(float(t[0])*1e9)\n      lsn = t[1]\n      size = int(t[2]) - int(t[1])\n      dev = 0\n      op = 0 # write:0, read:1\n      out.write(\"%s 0 %s %d %d\\n\" % (timestap,lsn,size,op))\n\nprint(\"done\")","repo_name":"virgilshi/special-functions-codes","sub_path":"3Dsim/convert_3Dsim_format.py","file_name":"convert_3Dsim_format.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"15628765167","text":"import datetime\n\nimport pytz\n\nfrom ..twilio import twilio\nfrom . import celery\nfrom .. import models\n\n\ndef int_time(t):\n    return (\n        t.microsecond + 1_000_000 * (t.second + 60 * (t.minute + 60 * t.hour)))\n\n\ndef time_in_interval(x, a, b):\n    n = 86_400_000_000\n    x = int_time(x)\n    a = int_time(a)\n    b = int_time(b)\n    return (x - a) % n <= (b - a) % n\n\n\ndef now_utc():\n    return datetime.datetime.now(pytz.utc)\n\n\ndef user_is_on_duty(now, user):\n    \"\"\"Determine if a user is on duty at a given time.\"\"\"\n    if user.alert_from is not None and user.alert_to is not None:\n        tz = pytz.timezone(user.timezone)\n        time = now.astimezone(tz).time()\n        if not time_in_interval(time, user.alert_from, user.alert_to):\n            return False\n    return True\n\n\n@celery.task(ignore_result=True, shared=False)\ndef call_for(endpoint, phone, **values):\n    twilio.call_for(endpoint, phone, **values)\n\n\n@celery.task(ignore_result=True, shared=False)\ndef text_for(body, phone, **values):\n    twilio.message(body, phone, **values)\n\n\n@celery.task(ignore_result=True, shared=False)\ndef text_everyone(body, **values):\n    now = now_utc()\n    for user in models.User.query.filter(models.User.phone.isnot(None)):\n        if user_is_on_duty(now, user):\n            text_for.s(body, user.phone.e164, **values).delay()\n\n\n@celery.task(ignore_result=True, shared=False)\ndef call_everyone(endpoint, **values):\n    now = now_utc()\n    for user in models.User.query.filter(models.User.phone.isnot(None)) \\\n            .filter(models.User.voice):\n        if user_is_on_duty(now, user):\n            call_for.s(endpoint, user.phone.e164, **values).delay()\n","repo_name":"growth-astro/growth-too-marshal","sub_path":"growth/too/tasks/twilio.py","file_name":"twilio.py","file_ext":"py","file_size_in_byte":1654,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"86"}
+{"seq_id":"33864745864","text":"\"\"\"An Azure RM Python Pulumi program\"\"\"\n\nimport pulumi\nfrom pulumi_azure_native import resources\nimport pulumi_azure_native as azure_native\n\n# Create an Azure Resource Group\nrg = resources.ResourceGroup('pulumi-py-rg',\n    location=\"australiaeast\",\n    resource_group_name=\"pulumi-py-rg\"\n)\nrgdbr = resources.ResourceGroup('pulumi-py-rgdbr',\n    location=\"australiaeast\",\n    resource_group_name=\"pulumi-py-rgdbr\"\n)\n\n# Create role assignment on managed identity\nra = azure_native.authorization.RoleAssignment(\"pulumi-py-ra\",\n    principal_id=\"64765f4d-06b5-4f56-8cc4-1c068f624992\",\n    principal_type=\"ServicePrincipal\",\n    role_assignment_name=\"5a53e7cc-3e62-4357-a85d-6ac4af0d6c18\",\n    role_definition_id=\"/subscriptions/5f3d7f2f-1189-427d-aaa3-5c220e2b3e9a/providers/Microsoft.Authorization/roleDefinitions/8e3af657-a8ff-443c-a75c-2fe8c4bcb635\",\n    scope=rgdbr.id\n)\n\n# create managed identity\nmidbr = azure_native.managedidentity.UserAssignedIdentity(\"pulumi-py-midbr\",\n    location=\"australiaeast\",\n    resource_group_name=rgdbr.name,\n    resource_name_=\"pulumi-py-midbr\",\n    tags={\n        \"applicaiton\": \"databricks\",\n        \"databricks-environment\": \"true\",\n    }\n)\n\n\n# create network security group\nnetwork_security_group = azure_native.network.NetworkSecurityGroup(\"pulumi-sgdbr\",\n    location=\"australiaeast\",\n    network_security_group_name=\"pulumi-sgdbr\",\n    resource_group_name=rgdbr.name,\n    security_rules=[azure_native.network.SecurityRuleArgs(\n        access=\"Allow\",\n        direction=\"Inbound\",\n        protocol=\"*\",\n        description=\"Required for Databricks control plane management of worker nodes.\",\n        destination_address_prefix=\"*\",\n        destination_port_range=\"22\",\n        name=\"databricks-control-plane-ssh\",\n        priority=100,\n        source_address_prefix=\"20.37.156.208/32,23.101.152.95/32\",\n        source_port_range=\"*\",\n    )]\n)","repo_name":"timotewb/developemnt","sub_path":"pulumi/python/python-test-01/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"20709567114","text":"import socket\r\nfrom threading import Thread\r\nimport traceback\r\nimport winsound\r\n\r\nHOST = \"192.168.2.5\"\r\nPORT = 65432\r\ndef recv_from_client(conn):\r\n    try:\r\n        content = conn.recv(1024)\r\n        return content\r\n    except Exception:\r\n        return None\r\n\r\nclass ServiceThread(Thread):\r\n    def __init__(self, conn, addr):\r\n        super().__init__()\r\n        self.conn = conn\r\n        self.addr = addr\r\n\r\n    def run(self):\r\n        try:\r\n            while True:\r\n                content = recv_from_client(self.conn)\r\n                if not content:\r\n                    break\r\n                print(f\"{self.addr}: {content.decode('utf-8')}\")\r\n                winsound.Beep(2222,111)\r\n                self.conn.sendall(content)\r\n            self.conn.close()\r\n            print(f\"{self.addr[0]}:{self.addr[1]} leave.\")\r\n        except Exception:\r\n            traceback.print_exc()\r\n\r\nif __name__ == \"__main__\":\r\n    s = None\r\n    try:\r\n        s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n        s.bind((HOST, PORT))\r\n        s.listen()\r\n        print(\"Repeater server started successfully.\")\r\n        while True:\r\n            conn, addr = s.accept()\r\n            print(f\"Connected from {addr}\")\r\n            #winsound.Beep(2222,111)\r\n            service_thread = ServiceThread(conn, addr)\r\n            service_thread.daemon = True\r\n            service_thread.start()\r\n    except Exception:\r\n        traceback.print_exc()\r\n    s.close()","repo_name":"BaiYouShiWo/Automatically-fill","sub_path":"server 1.0.py","file_name":"server 1.0.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"14737630039","text":"n = int(input(\"Insira um número inteiro: \"))\n\ndef primaridade(x):\n    i = 1\n    cont = 0\n    while i <= x:\n        if x % i == 0:\n            cont = cont + 1\n            i = i + 1\n        else:\n            i = i + 1\n    if cont == 2:\n        return True\n    else:\n        return False\n\nwhile n > 0:\n    if primaridade(n):\n        print(\"primo\")\n    else:\n        print(\"não primo\")\n    n = int(input(\"Insira um número inteiro: \"))","repo_name":"gustavocuore/IME-USP","sub_path":"Week7/primaridadevariasvezes.py","file_name":"primaridadevariasvezes.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"4294404319","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport fyt.utils.lat_lng\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('core', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='ExternalBus',\n            fields=[\n                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Route',\n            fields=[\n                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n                ('name', models.CharField(max_length=255)),\n                ('category', models.CharField(max_length=20, choices=[('INTERNAL', 'Internal'), ('EXTERNAL', 'External')])),\n            ],\n            options={\n                'ordering': ['category', 'vehicle', 'name'],\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='ScheduledTransport',\n            fields=[\n                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n                ('date', models.DateField()),\n                ('notes', models.TextField(help_text='for the bus driver')),\n            ],\n            options={\n                'ordering': ['date'],\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Stop',\n            fields=[\n                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n                ('name', models.CharField(max_length=255)),\n                ('address', models.CharField(max_length=255, help_text='Plain text address, eg. Hanover, NH 03755. This must take you to the location in Google maps.', blank=True, default='')),\n                ('lat_lng', models.CharField(max_length=255, default='', validators=[fyt.utils.lat_lng.validate_lat_lng], help_text='Latitude & longitude coordinates, eg. 43.7030,-72.2895', verbose_name='coordinates', blank=True)),\n                ('directions', models.TextField(blank=True)),\n                ('cost_round_trip', models.DecimalField(max_digits=5, decimal_places=2, null=True, blank=True, help_text='for external buses')),\n                ('cost_one_way', models.DecimalField(max_digits=5, decimal_places=2, null=True, blank=True, help_text='for external buses')),\n                ('dropoff_time', models.TimeField(null=True, blank=True)),\n                ('pickup_time', models.TimeField(null=True, blank=True)),\n                ('distance', models.IntegerField(help_text='this rough distance from Hanover is used for bus routing')),\n            ],\n            options={\n                'ordering': ['name'],\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='StopOrder',\n            fields=[\n                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n                ('order', models.PositiveSmallIntegerField()),\n                ('stop_type', models.CharField(max_length=10, choices=[('PICKUP', 'PICKUP'), ('DROPOFF', 'DROPOFF')])),\n                ('bus', models.ForeignKey(to='transport.ScheduledTransport', on_delete=models.CASCADE)),\n            ],\n            options={\n                'ordering': ['order'],\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Vehicle',\n            fields=[\n                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n                ('name', models.CharField(max_length=255)),\n                ('capacity', models.PositiveSmallIntegerField()),\n                ('trips_year', models.ForeignKey(to='core.TripsYear', on_delete=django.db.models.deletion.PROTECT, editable=False)),\n            ],\n            options={\n                'ordering': ['name'],\n            },\n            bases=(models.Model,),\n        ),\n    ]\n","repo_name":"rlmv/doc-trips","sub_path":"fyt/transport/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":4243,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"86"}
+{"seq_id":"16858875212","text":"from typing import Annotated, Any\n\nfrom fastapi import APIRouter, Depends\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom app import models, schemas, usecase\nfrom app.api.api_v0 import deps\nfrom app.utils import errors\n\nrouter = APIRouter()\n\nCurrentUser = Annotated[models.User, Depends(deps.get_current_active_user)]\nCurrentSuperUser = Annotated[models.User, Depends(deps.get_current_active_superuser)]\n\n\n@router.get(\"/\", response_model=schemas.SuccessfulResponse[list[schemas.Item]])\nasync def read_items(\n    *,\n    db: AsyncSession = Depends(deps.get_db),\n    skip: int = 0,\n    limit: int = 100,\n    current_user: CurrentUser,\n) -> Any:\n    \"\"\"\n    Retrieve items.\n    \"\"\"\n    return schemas.create_successful_response(\n        await usecase.item.get_multi(db, offset=skip, limit=limit)\n        if current_user.is_superuser\n        else await usecase.item.get_multi_by_owner(\n            db=db, owner_id=current_user.id, offset=skip, limit=limit\n        )\n    )\n\n\n@router.post(\"/\", response_model=schemas.SuccessfulResponse[schemas.Item])\nasync def create_item(\n    *,\n    db: AsyncSession = Depends(deps.get_db),\n    item_in: schemas.ItemCreate,\n    current_user: CurrentUser,\n) -> Any:\n    \"\"\"\n    Create new item.\n    \"\"\"\n    item = await usecase.item.create_with_owner(db=db, obj_in=item_in, owner_id=current_user.id)\n    return schemas.create_successful_response(item)\n\n\n@router.put(\"/{id}\", response_model=schemas.SuccessfulResponse[schemas.Item])\nasync def update_item(\n    *,\n    db: AsyncSession = Depends(deps.get_db),\n    id: int,  # pylint: disable=redefined-builtin\n    item_in: schemas.ItemUpdate,\n    current_user: CurrentUser,\n) -> Any:\n    \"\"\"\n    Update an item.\n    \"\"\"\n    item = await usecase.item.get(db=db, id=id)\n    if not item:\n        raise errors.ErrNotFound(\"item not found\")\n    if not current_user.is_superuser and (item.owner_id != current_user.id):\n        raise errors.ErrNotEnoughPrivileges(\"not enough permissions\")\n    item = await usecase.item.update(db=db, db_obj=item, obj_in=item_in)\n    return schemas.create_successful_response(item)\n\n\n@router.get(\"/{id}\", response_model=schemas.SuccessfulResponse[schemas.Item])\nasync def read_item(\n    *,\n    db: AsyncSession = Depends(deps.get_db),\n    id: int,  # pylint: disable=redefined-builtin\n    current_user: CurrentUser,\n) -> Any:\n    \"\"\"\n    Get item by ID.\n    \"\"\"\n    item = await usecase.item.get(db=db, id=id)\n    if not item:\n        raise errors.ErrNotFound(\"item not found\")\n    if not current_user.is_superuser and (item.owner_id != current_user.id):\n        raise errors.ErrNotEnoughPrivileges(\"not enough permissions\")\n    return schemas.create_successful_response(item)\n\n\n@router.delete(\"/{id}\", response_model=schemas.SuccessfulResponse[schemas.Item])\nasync def delete_item(\n    *,\n    db: AsyncSession = Depends(deps.get_db),\n    id: int,  # pylint: disable=redefined-builtin\n    current_user: CurrentUser,\n) -> Any:\n    \"\"\"\n    Delete an item.\n    \"\"\"\n    item = await usecase.item.get(db=db, id=id)\n\n    if not item:\n        raise errors.ErrNotFound(\"item not found\")\n\n    if not current_user.is_superuser and (item.owner_id != current_user.id):\n        raise errors.ErrNotEnoughPrivileges(\"not enough permissions\")\n\n    return schemas.create_successful_response(await usecase.item.delete(db=db, db_obj=item))\n","repo_name":"hiennguyen9874/async-fastapi-boilerplate-v2","sub_path":"backend/app/app/api/api_v0/endpoints/items.py","file_name":"items.py","file_ext":"py","file_size_in_byte":3327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"23329393081","text":"n = int(input())\narr = list(map(int,input().split()))\ncnt = 0\nstart = 0\nfor i in range(len(arr)):\n    if arr[i] == start:\n        cnt+=1\n        start+=1\n    if start==3:\n        start = 0\n\nprint(cnt)","repo_name":"juhyun-99/Baekjoon_algorithm","sub_path":"백준/Bronze/14720. 우유 축제/우유 축제.py","file_name":"우유 축제.py","file_ext":"py","file_size_in_byte":200,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35618284168","text":"#!/usr/bin/env python3\n# coding: utf-8\n\nfrom typing import List\n\n\nclass Solution:\n    def outerTrees(self, trees: List[List[int]]) -> List[List[int]]:\n        def cross(o, a, b):\n            return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0])\n        \n        if len(trees) <= 1:\n            return []\n\n        trees.sort(key=lambda x: (x[0], x[1]))\n\n        lower = []\n        for p in trees:\n            while len(lower) >= 2 and cross(lower[-2], lower[-1], p) < 0:\n                lower.pop()\n            lower.append(p)\n        \n        upper = []\n        for p in reversed(trees):\n            while len(upper) >= 2 and cross(upper[-2], upper[-1], p) < 0:\n                upper.pop()\n            upper.append(p)\n        \n        res = []\n        for tree in lower[:-1] + upper[:-1]:\n            if tree not in res:\n                res.append(tree)\n        return res\n","repo_name":"xuedong/leet-code","sub_path":"Problems/Algorithms/587. Erect the Fence/erect_fence.py","file_name":"erect_fence.py","file_ext":"py","file_size_in_byte":888,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"16247196054","text":"from collections import Counter, defaultdict \nimport math\nimport random\n\n\nclass AnagramChecker:\n    words_list = []\n    def __init__(self, file_name):\n        with open(file_name, \"r\") as english_words_file :\n            for line in english_words_file:\n                for word in line.split():\n                    self.words_list.append(word)\n\n    def is_valid_word(self, user_word):\n        return user_word in self.words_list\n    \n    def get_anagrams(self, word):\n        anagram_list =[]\n        created_anagrams_num = 0\n        combinations_num = math.factorial(len(word))\n        while created_anagrams_num < combinations_num:\n            cur_anagram = random.sample(word, len(word))\n            if (cur_anagram in anagram_list):\n                continue\n            created_anagrams_num += 1\n            anagram_list.append(cur_anagram)\n        return anagram_list\n\n\"\"\"\nanagram = AnagramChecker(\"english_words.txt\")\nuser_word = input(\"tell me a word:\")\nif (anagram.is_valid_word(user_word)):\n    created_anagrams = anagram.get_anagrams(user_word)\n\"\"\"\n","repo_name":"OxanaAntonova/DI-Bootcamp","sub_path":"Week3/Day5/Exercises/anagram_checkert.py","file_name":"anagram_checkert.py","file_ext":"py","file_size_in_byte":1059,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"42327905552","text":"box =[]\r\n\r\nwhile True:\r\n    num = int(input(\"ป้อนต้วเลข (พิมพ์ 0 เพื่อหยุด): \"))\r\n    if num == 0:\r\n        break\r\n    box.append(num)\r\n\r\ncheck = input(\"Min or Max: \")\r\ncheck = check.upper()\r\n\r\nif check == \"MIN\":\r\n    box.sort()\r\n    print(*box)\r\nelif check == \"MAX\":\r\n    box.sort(reverse=True)\r\n    print(*box)","repo_name":"tony007x/Python_Beginner","sub_path":"input_multiple_maxmin.py","file_name":"input_multiple_maxmin.py","file_ext":"py","file_size_in_byte":360,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"12303080454","text":"# -*- coding: utf-8 -*-\n\nimport RPi.GPIO as GPIO\nimport time\n\nclass Motor():\n        \"\"\"\n        Définit un moteur\n        \"\"\"\n        def __init__(self, a, b, freq):\n                \"\"\"\n                Instancier et configurer le moteur\n                \"\"\"\n                GPIO.setup(a, GPIO.OUT) #pin a configuré en sortie \n                GPIO.setup(b, GPIO.OUT) # pin b configuré en sortie\n                self.frontpin = GPIO.PWM(a, freq) # on démarre l'instance PWM\n                self.rearpin  = GPIO.PWM(b, freq) # idem\n                self.frontpin.start(0) # démarre le duty cycle à 0%\n                self.rearpin.start(0) # idem\n                \n        \n        def set_speed(self, speed):  # Méthode d'instance\n                \"\"\"\n                Prend les valeurs de speed entre -100 et 100 (AR / AV) , à 0 : on ne bouge pas \n                \"\"\"\n                print(\"[.] Motor.set_speed : got speed = \" + str(speed))\n                if speed > 0:\n                        self.frontpin.ChangeDutyCycle(speed) # Si speed>0 on souhaite avancer \n                        self.rearpin.ChangeDutyCycle(0)\n                elif speed < 0:\n                        self.frontpin.ChangeDutyCycle(0)    # on souhaite reculer\n                        self.rearpin.ChangeDutyCycle(-speed) # -speed car duty cycle doit etre positif\n                elif speed == 0:\n                        self.frontpin.ChangeDutyCycle(0) # si speed nul on ne souhaite pas bouger\n                        self.rearpin.ChangeDutyCycle(0)\n                        \n\nclass Robot():\n        \"\"\"\n        Classe définissant un robot\n        \"\"\"\n        def __init__ (self):\n                \"\"\"\n                But : Instancier un robot et le configurer\n                \"\"\"\n                self.angle = 0\n                self.vitesse = 0\n                \n                # Config GPIO\n                GPIO.setmode(GPIO.BCM) # Mapping des pins (en BCM pas en board)\n                self.PWM_FREQ = 50    \n                \"\"\" Fréquence de modulation PWM durant une période de 20ms  (fréquence : 50Hz)\n                (envoie des signaux numériques durant cette période suivant le rapport cyclique (dc) en %)\"\"\"\n                \n                # Instanciation des moteurs\n                self.moteur_gauche = Motor(14, 15 , self.PWM_FREQ) # moteur gauche va correspondre aux pins 14 et 15 (14 pour l'avant et 15 pour l'arrière) \n                self.moteur_droit = Motor(17, 18, self.PWM_FREQ) #même chose pour le moteur droit\n\n        def set_angle(self, angle):\n                \"\"\"\n                Définir la courbure à prendre\n                Valeur de angle :\n\n                Gauche                  Droite\n                <---------------------------->\n                -100    -50    0    50     100\n\n                \"\"\"\n                if angle > 100:\n                        print(\"[!] RobotControl : Valeur de angle supérieure à 100.\\n[.] Remplacé par 100.\")\n                        angle = 100\n                self.angle = angle\n\n        def set_speed(self, vitesse):\n                \"\"\"\n                Définir la vitesse du robot\n                Valeur de vitesse :\n\n                Arrière      Arrêt       Avant\n                <---------------------------->\n                -100    -50    0    50     100\n                FULL         NONE         FULL\n                \"\"\"\n                self.vitesse = vitesse\n        \n        def compute_wheelSpeeds(self):\n            \"\"\"\n            Robot.compute_wheelSpeeds()\n            Calculer la valeur de la vitesse théorique à appliquer à chaque moteur en fonction de la vitesse et de l'angle. \n            \"\"\"\n            left_motor = self.vitesse + self.angle\n            right_motor = self.vitesse - self.angle\n            \n            # Scale factor defaults to 1 (échelle, facteur de proportionnalité)\n            scale_factor = 1\n            \n            # Calculate scale factor\n            if abs(left_motor) > 100 or abs(right_motor) > 100:\n                # Find highest of the 2 values, since both could be above 100\n                x = max(abs(left_motor), abs(right_motor))\n            \n                # Calculate scale factor\n                scale_factor = 100.0 / x # revient entre 0 et 1\n            \n            # Use scale factor, and turn values back into integers\n            left_motor = int(left_motor * scale_factor)\n            right_motor = int(right_motor * scale_factor)\n            self.left = left_motor\n            self.right = right_motor\n            print(\"[.] -- GAUCHE : \" +str(self.left)+ \" DROITE : \" +str(self.right))\n            \n        def compute_and_go(self):\n                \"\"\"\n                Compute motor values, then apply them\n                \"\"\"\n                self.compute_wheelSpeeds()\n                self.go()\n        \n        def go(self):\n            \"\"\"\n            Appliquer les valeurs\n            \"\"\"\n            try :\n                self.moteur_gauche.set_speed(self.left)\n                self.moteur_droit.set_speed(self.right)\n                print(\"[.] -- GAUCHE : \" +str(self.left)+ \" DROITE : \" +str(self.right))\n            except :\n                print(\"[!] Robot.go() : Les valeurs des vitesses n'ont pas encore été configurées. Ignorez cette erreur si elle ne réapparait pas. \")\n        \n        \n        def stop(self):\n            \"\"\"\n            Un arrêt pur et simple. Pratique, n'efface pas les valeurs de vitesse et d'angle.\n            \"\"\"\n            self.moteur_gauche.set_speed(0)\n            self.moteur_droit.set_speed(0)\n                        ","repo_name":"pierre-isep/TIPE-2017","sub_path":"robotcontrol.py","file_name":"robotcontrol.py","file_ext":"py","file_size_in_byte":5599,"program_lang":"python","lang":"fr","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"7752326809","text":"import pya\nimport math\n\nclass Arrow(pya.PCellDeclarationHelper):\n\n  def __init__(self):\n    # Important: initialize the super class\n    super(Arrow, self).__init__()\n    # declare the parameters\n    self.param(\"l\", self.TypeLayer, \"Layer\", default = pya.LayerInfo(1, 0))\n    self.param(\"hb\", self.TypeShape, \"\", default = pya.DPoint(0.5, -6))\n    self.param(\"hh\", self.TypeShape, \"\", default = pya.DPoint(0, 4))\n    self.param(\"ha\", self.TypeShape, \"\", default = pya.DPoint(2.2314069574087765, 0))\n    self.param(\"b\", self.TypeDouble, \"Body length\", default = 6)\n    self.param(\"h\", self.TypeDouble, \"Head length\", default = 4)\n    self.param(\"a\", self.TypeDouble, \"Head angle\", default = 45)\n    self.param(\"w\", self.TypeDouble, \"Body width\", default = 1)\n    self.param(\"b_\", self.TypeDouble, \"Body length\", default = 6, hidden = True)\n    self.param(\"h_\", self.TypeDouble, \"Head length\", default = 4, hidden = True)\n    self.param(\"a_\", self.TypeDouble, \"Head angle\", default = 45, hidden = True)\n    self.param(\"w_\", self.TypeDouble, \"Body width\", default = 1, hidden = True)\n\n  def display_text_impl(self):\n    return \"Arrow(B=\" + str('%.1f' % self.b) + \", H=\" + ('%.1f' % self.h) + \", A=\" + ('%.1f' % self.a) + \")\"\n  \n  def coerce_parameters_impl(self):\n    if self.b < 0:\n      self.b *= -1\n    if self.h < 0:\n      self.h *= -1\n    if self.a < 0:\n      self.a *= -1\n    if self.w < 0:\n      self.w *= -1\n    if self.b_ != self.b or self.h_ != self.h or self.w_ != self.w or self.a_ != self.a:\n      # update handle\n      self.hh = pya.DPoint(0, self.h)\n      self.hb = pya.DPoint(self.w/2, -self.b)\n      self.ha = pya.DPoint(self.h*math.tan(math.radians(self.a/2)), 0)\n      # fix params\n      self.b_ = self.b\n      self.w_ = self.w\n      self.a_ = self.a\n      self.h_ = self.h\n    else:\n      # fix angle handle\n      self.ha.y = 0\n      self.hh.x = 0\n      # calc params from handle\n      self.b = self.b_ = abs(self.hb.y)\n      self.w = self.w_ = 2 * abs(self.hb.x)\n      self.h = self.h_ = abs(self.hh.y)\n      self.a = self.a_ = math.degrees(2 * math.atan(abs(self.ha.x)/self.h))\n  \n  def can_create_from_shape_impl(self):\n    return self.shape.is_box() or self.shape.is_polygon() or self.shape.is_path()\n  \n  def parameters_from_shape_impl(self):\n    w = self.shape.bbox().width()*self.layout.dbu\n    h = self.shape.bbox().height()*self.layout.dbu\n    if w > h:\n      w, h = h, w\n    self.b = 3*h/5\n    self.h = 2*h/5\n    self.a = math.degrees(2*math.atan(5/4*w/h))\n    self.w = w/5\n    self.l = self.layout.get_info(self.layer)\n  \n  def transformation_from_shape_impl(self):\n    w = self.shape.bbox().width()\n    h = self.shape.bbox().height()\n    if w > h:\n      return pya.Trans(3, False, pya.Point(self.shape.bbox().center().x+0.1*w, self.shape.bbox().center().y))\n    else:\n      return pya.Trans(pya.Point(self.shape.bbox().center().x, self.shape.bbox().center().y+0.1*h))\n  \n  def produce_impl(self):\n    dbu = self.layout.dbu\n    arrow = []\n    tg = math.tan(math.radians(self.a/2))\n    hw = self.h * tg\n    arrow.append(pya.Point.from_dpoint(pya.DPoint(self.w/(2*dbu), -self.b/dbu)))\n    arrow.append(pya.Point.from_dpoint(pya.DPoint(self.w/(2*dbu), 0)))\n    arrow.append(pya.Point.from_dpoint(pya.DPoint(hw/dbu, 0)))\n    arrow.append(pya.Point.from_dpoint(pya.DPoint(0, self.h/dbu)))\n    arrow.append(pya.Point.from_dpoint(pya.DPoint(-hw/dbu, 0)))\n    arrow.append(pya.Point.from_dpoint(pya.DPoint(-self.w/(2*dbu), 0)))\n    arrow.append(pya.Point.from_dpoint(pya.DPoint(-self.w/(2*dbu), -self.b/dbu)))\n    self.cell.shapes(self.l_layer).insert(pya.Polygon(arrow))","repo_name":"jurask/ShapeLib","sub_path":"python/arrow.py","file_name":"arrow.py","file_ext":"py","file_size_in_byte":3590,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"86"}
+{"seq_id":"25557377626","text":"import random\r\nfrom tkinter import *\r\nfrom tkinter import messagebox\r\nfrom tkinter import ttk\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nfrom random import *\r\n# Functions for Log In / SignUp / Admin\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef LogIn() :\r\n    WelcomeLabel.destroy()\r\n    WelcomeLabel.destroy()\r\n\r\n\r\n\r\n    WelcomeLabel1.destroy()\r\n    WelcomeLabel1.destroy()\r\n\r\n\r\n    WelcomeLabel2.destroy()\r\n    WelcomeLabel2.destroy()\r\n\r\n\r\n\r\n\r\n\r\n    Accounts = {}\r\n    # Variables\r\n    PriceRegular = DoubleVar()\r\n    PriceRegular.set(0.59)\r\n\r\n    PriceRegular2 = DoubleVar()\r\n    PriceRegular2.set(1.18)\r\n\r\n    PriceRegular3 = DoubleVar()\r\n    PriceRegular3.set(1.37)\r\n\r\n    PriceRegular4 = DoubleVar()\r\n    PriceRegular4.set(0.472)\r\n\r\n    PriceRegular5 = DoubleVar()\r\n    PriceRegular5.set(0.384)\r\n\r\n    #-----------------------------\r\n    with open(\"Accounts.txt\" ) as file:\r\n        lines = file.read().splitlines()\r\n    for i in lines :\r\n        key,value = i.split(', ')\r\n        Accounts.update({key:value})\r\n    print(Accounts)\r\n    def FindUser() :\r\n        AccountFile = open('Accounts.txt' , \"r\").read().splitlines()\r\n        #Functions for Calculation\r\n\r\n\r\n        if Label_EnterLog.get() in Accounts :\r\n            NewCheckDict = {Label_EnterLog.get() : Accounts[Label_EnterLog.get()]}\r\n            AcountPasses = NewCheckDict.values()\r\n            print(NewCheckDict)\r\n            if Label_EnterLogPass.get() in AcountPasses:\r\n                del NewCheckDict[Label_EnterLog.get()]\r\n                LITER = IntVar()\r\n                WindowMain.geometry('750x750')\r\n                frameMainWindow = Frame(WindowMain, bd='10', bg=\"#494c59\")\r\n                frameMainWindow.place(relwidth=1, relheight=1, relx=0.5, anchor='n')\r\n\r\n                def LiterOrMoney() :\r\n                    if LITER.get() == 1 :\r\n                        MoneyEntry.delete(0, END)\r\n                        LiterEntry.config(state=NORMAL)\r\n                        MoneyEntry.config(state=DISABLED )\r\n\r\n                    elif LITER.get() == 2 :\r\n                        LiterEntry.delete(0, END)\r\n                        LiterEntry.config(state=DISABLED )\r\n                        MoneyEntry.config(state=NORMAL )\r\n\r\n\r\n\r\n                frameGasoline = Frame(frameMainWindow, bd='10', bg=\"#4834d4\" , highlightthickness=3 , highlightbackground=\"#34ace0\")\r\n                frameGasoline.place(relheight=0.6, relwidth=0.4, relx=0.05, rely=0.06)\r\n\r\n                LabelWelcome = Label(frameMainWindow, text=f\"Welcome {Label_EnterLog.get()}\", bd='10', bg=\"#494c59\" , fg=\"white\" , font=(\"Arial\",13,\"bold\"))\r\n                LabelWelcome.place(relx=0.4,rely=0.001 ,relwidth=0.2 )\r\n\r\n                LabelGasoline = Label(frameGasoline, bd=\"10\", bg=\"#4834d4\", fg=\"white\", font=(\"Arial\", 20),\r\n                                      text=\"Gasoline Station\")\r\n                LabelGasoline.place(relwidth=1, rely=0.01)\r\n\r\n                Gasolines = {}\r\n\r\n                # Starts here\r\n                # Addint to Dict\r\n                with open(\"Gasolines.txt\") as file:\r\n                    lines = file.read().splitlines()\r\n                    print(lines)\r\n                for i in lines:\r\n                    key, value = i.split(', ')\r\n                    Gasolines.update({key: value})\r\n\r\n                print(Gasolines)\r\n\r\n\r\n\r\n                def Change(event):\r\n                    GasolinePriceEntry.config(state=NORMAL)\r\n                    GasolinePriceEntry.delete(0,END)\r\n                    GasolinePriceEntry.insert(0 , Gasolines[GasolineListBox.get()])\r\n                    GasolinePriceEntry.config(state=\"readonly\")\r\n                    print(Gasolines[GasolineListBox.get()])\r\n                GasolineListBox = ttk.Combobox(frameGasoline, values=list(Gasolines.keys()), width=13 , state=\"readonly\" )\r\n                GasolinesLabel = Label(frameMainWindow , text=\"Petrol : \", bg=\"#4834d4\", fg=\"white\", font=('Arial', 15))\r\n                GasolinesLabel.place(rely=0.15, relx=0.1)\r\n                GasolineListBox.place(rely=0.15, relx=0.4)\r\n                GasolineListBox.bind('<<ComboboxSelected>>', Change)\r\n\r\n\r\n\r\n                GasolinePrice = Label(frameMainWindow ,text=\"Price : \", bg=\"#4834d4\", fg=\"white\", font=('Arial', 15))\r\n                GasolinePrice.place(rely=0.2, relx=0.1)\r\n                DefaultString = StringVar()\r\n                DefaultString.set(\"0.59\")\r\n                GasolinePriceEntry = Entry(frameGasoline, fg=\"black\" , textvariable=DefaultString)\r\n                GasolinePriceEntry.place(rely=0.24, relx=0.4, relheight=0.065)\r\n                GasolinePriceEntry.config(state=DISABLED)\r\n\r\n\r\n\r\n                AznLabel = Label(frameGasoline, text=\"AZN\", font=('Arial', 13), bg=\"#4834d4\", fg=\"white\")\r\n                AznLabel.place(rely=0.24, relx=0.89)\r\n\r\n                Liter = Radiobutton(frameGasoline, text=\"Liter : \", activebackground=\"#4834d4\", bg=\"#4834d4\",\r\n                                    font=('Arial', 15), variable=LITER, value=1  , command= LiterOrMoney)\r\n                Money = Radiobutton(frameGasoline, text=\"Money : \", activebackground=\"#4834d4\", bg=\"#4834d4\",\r\n                                    font=(\"Arial\", 15), variable=LITER, value=2 , command= LiterOrMoney)\r\n\r\n                Liter.place(rely=0.4, relx=0.01)\r\n                Money.place(rely=0.5, relx=0.01)\r\n\r\n                DefaultLiterString = StringVar()\r\n                DefaultLiterString.set(\"0\")\r\n\r\n                DefaultMoneyString = StringVar()\r\n                DefaultMoneyString.set(\"0\")\r\n\r\n                LiterEntry = Entry(frameGasoline, textvariable=DefaultLiterString,  state= DISABLED)\r\n                LiterEntry.place(rely=0.41, relx=0.4, relheight=0.065)\r\n                MoneyEntry = Entry(frameGasoline,textvariable=DefaultMoneyString, state= DISABLED )\r\n                MoneyEntry.place(rely=0.51, relx=0.4, relheight=0.065 )\r\n\r\n                SubbFrameForGas = Frame(frameGasoline, bd=10, highlightbackground=\"#4bcffa\", highlightthickness=3,\r\n                                        bg=\"#4834d4\")\r\n                SubbFrameForGas.place(relwidth=0.85, relheight=0.3, rely=0.7, relx=0.1)\r\n                LabelAllPrice = Label(SubbFrameForGas, text=\"Total\", bg=\"#4834d4\", fg=\"white\",\r\n                                      font=('Arial', 20, \"bold\"))\r\n                LabelAllPrice.place(anchor=NW)\r\n\r\n                SummPriceValue = Label(SubbFrameForGas, text=\"0\", bg=\"#4834d4\", fg=\"white\",\r\n                                       font=(\"Arial\", 20, \"bold\"), highlightthickness=3,\r\n                                       highlightbackground=\"#4bcffa\")\r\n                SummPriceValue.place(rely=0.45, relx=0.1)\r\n\r\n                SummLabel = Label(SubbFrameForGas , text=\"AZN\" , bg=\"#4834d4\" , fg=\"white\" , font=('Arial' , 20 , \"bold\"))\r\n                SummLabel.place(rely=0.45 , relx=0.6)\r\n\r\n\r\n\r\n\r\n\r\n                # Cafe Frame\r\n                HOTDOG = IntVar()\r\n                FRIES = IntVar()\r\n                QAMBURGER = IntVar()\r\n                COLA = IntVar()\r\n\r\n                def CafeCheckBoxes() :\r\n                    if HOTDOG.get() == 1:\r\n                        Hot_Dog_Entry_Count.config(state=NORMAL )\r\n\r\n                    else :\r\n                        Hot_Dog_Entry_Count.delete(0, END)\r\n                        Hot_Dog_Entry_Count.config(state=DISABLED)\r\n\r\n                    if FRIES.get() == 1:\r\n\r\n                        Fries_Entry_Count.config(state=NORMAL)\r\n\r\n                    else:\r\n                        Fries_Entry_Count.delete(0, END)\r\n                        Fries_Entry_Count.config(state=DISABLED )\r\n\r\n                    if QAMBURGER.get() == 1:\r\n\r\n                        Qamburger_Entry_Count.config(state=NORMAL)\r\n                    else:\r\n                        Qamburger_Entry_Count.delete(0, END)\r\n                        Qamburger_Entry_Count.config(state=DISABLED)\r\n\r\n                    if COLA.get() == 1 :\r\n\r\n                        Cola_Entry_Count.config(state=NORMAL)\r\n\r\n                    else:\r\n                        Cola_Entry_Count.delete(0, END)\r\n                        Cola_Entry_Count.config(state=DISABLED)\r\n                    if Hot_Dog_Entry_Count.get() == \"\":\r\n                        Hot_Dog_Entry_Count.config(state=NORMAL)\r\n                        Hot_Dog_Entry_Count.insert(0, \"0\")\r\n                        Hot_Dog_Entry_Count.config(state=DISABLED)\r\n                    if Fries_Entry_Count.get() == \"\":\r\n                        Fries_Entry_Count.config(state=NORMAL)\r\n                        Fries_Entry_Count.insert(0, \"0\")\r\n                        Fries_Entry_Count.config(state=DISABLED)\r\n                    if Cola_Entry_Count.get() == \"\":\r\n                        Cola_Entry_Count.config(state=NORMAL)\r\n                        Cola_Entry_Count.insert(0, \"0\")\r\n                        Cola_Entry_Count.config(state=DISABLED)\r\n                    if Qamburger_Entry_Count.get() == \"\":\r\n                        Qamburger_Entry_Count.config(state=NORMAL)\r\n                        Qamburger_Entry_Count.insert(0, \"0\")\r\n                        Qamburger_Entry_Count.config(state=DISABLED)\r\n\r\n\r\n\r\n                Prices={}\r\n                with open(\"Products.txt\") as file:\r\n                    lines = file.read().splitlines()\r\n                for i in lines:\r\n                    key, value = i.split(', ')\r\n                    Prices.update({key: value})\r\n                print(Prices)\r\n\r\n                HotDogCountDefault = 1\r\n                FriesCountDefault = 1\r\n                ColaCountDefault = 1\r\n                QamburgerCountDefault = 1\r\n\r\n                frameCafe = Frame(frameMainWindow, bd=\"10\", bg=\"#4834d4\", highlightthickness=3 , highlightbackground=\"#34ace0\")\r\n                frameCafe.place(relwidth=0.4, relheight=0.6, rely=0.06, relx=0.55)\r\n\r\n                CafeLabel = Label(frameCafe, text=\"Mini Cafe\", anchor=N, bg=\"#4834d4\", fg=\"white\",\r\n                                  font=('Arial', 20))\r\n                CafeLabel.place(relx=0.25, rely=0.02)\r\n                # Hot Dog Main\r\n                Hot_Dog_CheckButton = Checkbutton(frameCafe, activebackground=\"#4834d4\", text=\"Hot-Dog\",\r\n                                                  bg=\"#4834d4\", font=('Arial', 15) , variable=HOTDOG , command=CafeCheckBoxes)\r\n                Hot_Dog_CheckButton.place(rely=0.2, relx=0.01)\r\n\r\n                Values = list(Prices.values())\r\n                print(Values[0])\r\n\r\n                Hot_Dog_Entry_Price = Entry(frameCafe, state= \"readonly\" )\r\n                Hot_Dog_Entry_Price.config(state = NORMAL)\r\n                Hot_Dog_Entry_Price.insert(0, Values[0])\r\n                Hot_Dog_Entry_Price.config(state=\"readonly\")\r\n                Hot_Dog_Entry_Price.place(rely=0.23, relx=0.45, relwidth=0.2, relheight=0.05)\r\n\r\n                Hot_Dog_Entry_Count = Entry(frameCafe, state=DISABLED)\r\n                Hot_Dog_Entry_Count.place(rely=0.23, relx=0.7, relwidth=0.2, relheight=0.05)\r\n                # Fries Main\r\n\r\n                Fries_CheckButton = Checkbutton(frameCafe, activebackground=\"#4834d4\", text=\"Fries\",\r\n                                                bg=\"#4834d4\", font=('Arial', 15) , variable=FRIES , command=CafeCheckBoxes)\r\n                Fries_CheckButton.place(rely=0.3, relx=0.01)\r\n\r\n\r\n                Fries_Price_Entry = Entry(frameCafe,  state=\"readonly\")\r\n                Fries_Price_Entry.place(rely=0.33, relx=0.45, relwidth=0.2, relheight=0.05)\r\n                Fries_Price_Entry.config(state = NORMAL)\r\n                Fries_Price_Entry.insert(0, Values[1])\r\n                Fries_Price_Entry.config(state=\"readonly\")\r\n                Fries_Entry_Count = Entry(frameCafe, state=DISABLED)\r\n                Fries_Entry_Count.place(rely=0.33, relx=0.7, relwidth=0.2, relheight=0.05)\r\n\r\n                # Cola Main\r\n\r\n                Cola_CheckButton = Checkbutton(frameCafe, activebackground=\"#4834d4\", text=\"Cola\", bg=\"#4834d4\",\r\n                                               font=('Arial', 15) , variable= COLA, command=CafeCheckBoxes)\r\n                Cola_CheckButton.place(rely=0.4, relx=0.01)\r\n\r\n\r\n                Cola_Price_Entry = Entry(frameCafe,  state=\"readonly\")\r\n                Cola_Price_Entry.place(rely=0.43, relx=0.45, relwidth=0.2, relheight=0.05)\r\n                Cola_Price_Entry.config(state=NORMAL)\r\n                Cola_Price_Entry.insert(0,Values[2])\r\n                Cola_Price_Entry.config(state=\"readonly\")\r\n                Cola_Entry_Count = Entry(frameCafe,  state=DISABLED)\r\n                Cola_Entry_Count.place(rely=0.43, relx=0.7, relwidth=0.2, relheight=0.05)\r\n\r\n                # Qamburger Main\r\n\r\n                Qamburger_CheckButton = Checkbutton(frameCafe, activebackground=\"#4834d4\", text=\"Qamburger\",\r\n                                                    bg=\"#4834d4\", font=('Arial', 15) , variable=QAMBURGER, command=CafeCheckBoxes)\r\n                Qamburger_CheckButton.place(rely=0.5, relx=0.01)\r\n\r\n\r\n                Qamburger_Price_Entry = Entry(frameCafe,  state=\"readonly\")\r\n                Qamburger_Price_Entry.place(rely=0.53, relx=0.5, relwidth=0.2, relheight=0.05)\r\n                Qamburger_Price_Entry.config(state=NORMAL)\r\n                Qamburger_Price_Entry.insert(0, Values[3])\r\n                Qamburger_Price_Entry.config(state=\"readonly\")\r\n                Qamburger_Entry_Count = Entry(frameCafe,  state=DISABLED)\r\n                Qamburger_Entry_Count.place(rely=0.53, relx=0.75, relwidth=0.2, relheight=0.05)\r\n\r\n                if Hot_Dog_Entry_Count.get() == \"\":\r\n                    Hot_Dog_Entry_Count.config(state=NORMAL)\r\n                    Hot_Dog_Entry_Count.insert(0, \"0\")\r\n                    Hot_Dog_Entry_Count.config(state=DISABLED)\r\n                if Fries_Entry_Count.get() == \"\":\r\n                    Fries_Entry_Count.config(state=NORMAL)\r\n                    Fries_Entry_Count.insert(0, \"0\")\r\n                    Fries_Entry_Count.config(state=DISABLED)\r\n                if Cola_Entry_Count.get() == \"\":\r\n                    Cola_Entry_Count.config(state=NORMAL)\r\n                    Cola_Entry_Count.insert(0, \"0\")\r\n                    Cola_Entry_Count.config(state=DISABLED)\r\n                if Qamburger_Entry_Count.get() == \"\":\r\n                    Qamburger_Entry_Count.config(state=NORMAL)\r\n                    Qamburger_Entry_Count.insert(0, \"0\")\r\n                    Qamburger_Entry_Count.config(state=DISABLED)\r\n\r\n\r\n\r\n                # All Money Main\r\n                def CalculateButton() :\r\n\r\n\r\n\r\n\r\n                    # Gasoline Calculation\r\n                    if LITER.get() == 1 :\r\n                        if GasolineListBox.get() == \"Regular 92\":\r\n                            SumOfGasoline = float(LiterEntry.get()) * PriceRegular.get()\r\n                            MoneyEntry.config(state=NORMAL)\r\n                            SummLabel.config(text = \"AZN\")\r\n                            SummPriceValue.config(text=f\"{SumOfGasoline:.1f}\")\r\n                        elif GasolineListBox.get() == \"Super 95\":\r\n                            SumOfGasoline = float(LiterEntry.get()) * PriceRegular2.get()\r\n                            SummPriceValue.config(text=f\"{SumOfGasoline:.1f}\")\r\n                            SummLabel.config(text=\"AZN\")\r\n                        elif GasolineListBox.get() == \"Premium 98\":\r\n                            SumOfGasoline = float(LiterEntry.get()) * PriceRegular3.get()\r\n                            SummPriceValue.config(text=f\"{SumOfGasoline:.1f}\")\r\n                            SummLabel.config(text=\"AZN\")\r\n                        elif GasolineListBox.get() == \"Diesel\":\r\n                            SumOfGasoline = float(LiterEntry.get()) * PriceRegular4.get()\r\n                            SummLabel.config(text=\"AZN\")\r\n                            SummPriceValue.config(text=f\"{SumOfGasoline:.1f}\")\r\n                        elif GasolineListBox.get() == \"LPG\":\r\n                            SumOfGasoline = float(LiterEntry.get()) * PriceRegular5.get()\r\n                            SummLabel.config(text=\"AZN\")\r\n                            SummPriceValue.config(text=f\"{SumOfGasoline:.1f}\")\r\n                    elif LITER.get() == 2 :\r\n                        if GasolineListBox.get() == \"Regular 92\":\r\n                            SumOfGass = int(MoneyEntry.get()) / PriceRegular.get()\r\n                            SummLabel.config(text=\"Liters\")\r\n                            SummPriceValue.config(text=f\"{SumOfGass:.1f}\")\r\n                        elif GasolineListBox.get() == \"Super 95\":\r\n                            SumOfGass = int(MoneyEntry.get()) / PriceRegular2.get()\r\n                            SummPriceValue.config(text=f\"{SumOfGass:.1f}\")\r\n                            SummLabel.config(text=\"Liters\")\r\n                        elif GasolineListBox.get() == \"Premium 98\":\r\n                            SumOfGass = int(MoneyEntry.get()) / PriceRegular3.get()\r\n                            SummLabel.config(text=\"Liters\")\r\n                            SummPriceValue.config(text=f\"{SumOfGass:.1f}\")\r\n                        elif GasolineListBox.get() == \"Diesel\":\r\n                            SumOfGass = int(MoneyEntry.get()) / PriceRegular4.get()\r\n                            SummLabel.config(text=\"Liters\")\r\n                            SummPriceValue.config(text=f\"{SumOfGass:.1f}\")\r\n                        elif GasolineListBox.get() == \"LPG\":\r\n                            SumOfGass = int(MoneyEntry.get()) / PriceRegular5.get()\r\n                            SummLabel.config(text=\"Liters\")\r\n                            SummPriceValue.config(text=f\"{SumOfGass:.1f}\")\r\n\r\n                    # Cafe Calculations\r\n\r\n                    SumCafeUp = (float(HotDogCountDefault * Hot_Dog_Entry_Count.get()) * float(Hot_Dog_Entry_Price.get())) + (\r\n                                float(FriesCountDefault * Fries_Entry_Count.get()) * float(Fries_Price_Entry.get())) + (\r\n                                    float(ColaCountDefault * Cola_Entry_Count.get()) * float(Cola_Price_Entry.get())) + (\r\n                                        float( QamburgerCountDefault * Qamburger_Entry_Count.get()) * float(Qamburger_Price_Entry.get()))\r\n                    SummPriceValueCafe.config(text = f\"{SumCafeUp:.1f}\")\r\n\r\n                    # Summ All UP\r\n\r\n                    SumAllUp = SumCafeUp + float(SummPriceValue.cget(\"text\"))\r\n                    CalculateLabel1.config(text=f\"{SumAllUp:.1f}\")\r\n\r\n\r\n                    ReceiptWindow = Tk()\r\n                    ReceiptWindow.geometry(\"500x700\")\r\n                    ReceiptWindow.resizable(False,False)\r\n                    ReceiptWindow.title(\"Receipt\")\r\n                    ReceiptWindow.config(bg=\"#c8d6e5\")\r\n\r\n\r\n\r\n                    LabelReceipt = Label(ReceiptWindow , text=\"Receipt\", bg=\"#c8d6e5\" , fg=\"black\" , font=(\"Arial\" , 20 , \"bold\"))\r\n                    LabelReceipt.place(rely=0.001,relx =0.4)\r\n\r\n\r\n\r\n\r\n                    FrameMainReceipt = Frame(ReceiptWindow,bd=\"10\",bg=\"#576574\")\r\n                    FrameMainReceipt.place(relheight=0.9, relwidth=0.9, relx=0.05, rely=0.05)\r\n\r\n\r\n\r\n\r\n\r\n                    CashierName = Label(FrameMainReceipt ,text=f\"Cashier Name : {Label_EnterLog.get()}\" , bg=\"#576574\" , fg=\"white\" , font=(\"Arial\" , 15 , \"bold\"))\r\n                    CashierName.place(relx=0.01 , rely=0.01)\r\n                    CashierName = Label(FrameMainReceipt ,text=\"Omar™\" , bg=\"#576574\" , fg=\"white\" , font=(\"Arial\" , 15 , \"bold\"))\r\n                    CashierName.place(relx=0.8 , rely=0.01)\r\n\r\n                    FoodFrame = Frame(FrameMainReceipt , bd=\"10\" , bg=\"#2d3436\")\r\n                    FoodFrame.place(relx=0.1,rely=0.1 , relheight=0.5 , relwidth=0.8)\r\n\r\n\r\n\r\n                    ProductLabel = Label(FoodFrame , bd=\"10\" , bg=\"#2d3436\" , text=\"Product\" , font=('Arial', 12 , \"bold\") , fg=\"white\")\r\n                    ProductLabel.place(relx=0.01 , rely=0.2)\r\n                    PriceLabel = Label(FoodFrame , bd=\"10\" , bg=\"#2d3436\" , text=\"Price\" , font=('Arial', 12 , \"bold\") , fg=\"white\")\r\n                    PriceLabel.place(relx=0.35 , rely=0.2 ,relwidth=0.25)\r\n                    CountLabel = Label(FoodFrame , bd=\"10\" , bg=\"#2d3436\" , text=\"Count\" , font=('Arial', 12 , \"bold\") , fg=\"white\")\r\n                    CountLabel.place(relx=0.7 , rely=0.2)\r\n                    # Hot Dog\r\n\r\n\r\n\r\n\r\n                    HotDogLabel = Label(FoodFrame , bd=\"10\" , bg=\"#2d3436\" , text=\"Hot Dog\" , font=('Arial', 12 , \"bold\") , fg=\"white\")\r\n                    HotDogLabel.place(relx=0.01 , rely=0.3)\r\n                    HotDogPriceLabel = Label(FoodFrame , bd=\"10\" , bg=\"#2d3436\" , text=Hot_Dog_Entry_Price.get() , font=('Arial', 12 , \"bold\") , fg=\"white\")\r\n                    HotDogPriceLabel.place(relx=0.35 , rely=0.3,relwidth=0.25)\r\n                    HotDogCountLabel = Label(FoodFrame , bd=\"10\" , bg=\"#2d3436\" , text=Hot_Dog_Entry_Count.get() , font=('Arial', 12 , \"bold\") , fg=\"white\")\r\n                    HotDogCountLabel.place(relx=0.75 , rely=0.3)\r\n\r\n                    # Fries\r\n\r\n\r\n                    FriesLabel = Label(FoodFrame, bd=\"10\", bg=\"#2d3436\", text=\"Fries\", font=('Arial', 12, \"bold\"),\r\n                                        fg=\"white\")\r\n                    FriesLabel.place(relx=0.01, rely=0.4)\r\n                    FriesPriceLabel = Label(FoodFrame, bd=\"10\", bg=\"#2d3436\", text=Fries_Price_Entry.get(),\r\n                                             font=('Arial', 12, \"bold\"), fg=\"white\")\r\n                    FriesPriceLabel.place(relx=0.35, rely=0.4, relwidth=0.25)\r\n                    FriesCountLabel = Label(FoodFrame, bd=\"10\", bg=\"#2d3436\", text=Fries_Entry_Count.get(),\r\n                                             font=('Arial', 12, \"bold\"), fg=\"white\")\r\n                    FriesCountLabel.place(relx=0.75, rely=0.4)\r\n\r\n                    # Cola\r\n\r\n\r\n                    ColaLabel = Label(FoodFrame, bd=\"10\", bg=\"#2d3436\", text=\"Cola\", font=('Arial', 12, \"bold\"),\r\n                                        fg=\"white\")\r\n                    ColaLabel.place(relx=0.01, rely=0.5)\r\n                    ColaPriceLabel = Label(FoodFrame, bd=\"10\", bg=\"#2d3436\", text=Cola_Price_Entry.get(),\r\n                                             font=('Arial', 12, \"bold\"), fg=\"white\")\r\n                    ColaPriceLabel.place(relx=0.35, rely=0.5, relwidth=0.25)\r\n                    ColaCountLabel = Label(FoodFrame, bd=\"10\", bg=\"#2d3436\", text=Cola_Entry_Count.get(),\r\n                                             font=('Arial', 12, \"bold\"), fg=\"white\")\r\n                    ColaCountLabel.place(relx=0.75, rely=0.5)\r\n\r\n                    # Qamburger\r\n\r\n                    QamburgerLabel = Label(FoodFrame, bd=\"10\", bg=\"#2d3436\", text=\"Qamburger\", font=('Arial', 12, \"bold\"),\r\n                                        fg=\"white\")\r\n                    QamburgerLabel.place(relx=0.01, rely=0.6)\r\n                    QamburgerPriceLabel = Label(FoodFrame, bd=\"10\", bg=\"#2d3436\", text=Qamburger_Price_Entry.get(),\r\n                                             font=('Arial', 12, \"bold\"), fg=\"white\")\r\n                    QamburgerPriceLabel.place(relx=0.35, rely=0.6, relwidth=0.25)\r\n                    QamburgerCountLabel = Label(FoodFrame, bd=\"10\", bg=\"#2d3436\", text=Qamburger_Entry_Count.get(),\r\n                                             font=('Arial', 12, \"bold\"), fg=\"white\")\r\n                    QamburgerCountLabel.place(relx=0.75, rely=0.6)\r\n\r\n                    # TotalLabel\r\n                    TotalLabel = Label(FoodFrame, bd=\"10\", bg=\"#2d3436\", text=\"Total : \",\r\n                                       font=('Arial', 12, \"bold\"), fg=\"white\")\r\n                    TotalLabel.place(relx=0.01, rely=0.8)\r\n\r\n                    TotalLabelPrice = Label(FoodFrame, bd=\"10\", bg=\"#2d3436\", text=SummPriceValueCafe.cget(\"text\"),\r\n                                             font=('Arial', 12, \"bold\"), fg=\"white\")\r\n                    TotalLabelPrice.place(relx=0.75, rely=0.8)\r\n                    MenuCafe = Label(FoodFrame, bd=\"10\", bg=\"#2d3436\", text=\"Cafe Menu\",\r\n                                             font=('Arial', 20, \"bold\"), fg=\"white\")\r\n                    MenuCafe.place(relx=0.1, rely=0.01 , relwidth=0.7)\r\n\r\n                    GasolineFrame = Frame(FrameMainReceipt, bd=\"10\", bg=\"#2d3436\")\r\n                    GasolineFrame.place(relx=0.1, rely=0.65, relheight=0.2, relwidth=0.8)\r\n\r\n                    GasolineTypeL = Label(GasolineFrame, bd=\"10\", bg=\"#2d3436\", text=\"Gasoline type\",\r\n                                              font=('Arial', 12, \"bold\"), fg=\"white\")\r\n                    GasolineTypeL.place(rely=0.01, relx=0.01)\r\n                    GasolineLitersL = Label(GasolineFrame, bd=\"10\", bg=\"#2d3436\", text=\"Liters\",\r\n                                              font=('Arial', 12, \"bold\"), fg=\"white\")\r\n                    GasolineLitersL.place(rely=0.01, relx=0.45)\r\n                    GasolineManatL = Label(GasolineFrame, bd=\"10\", bg=\"#2d3436\", text=\"Total\",\r\n                                              font=('Arial', 12, \"bold\"), fg=\"white\")\r\n                    GasolineManatL.place(rely=0.01, relx=0.75)\r\n\r\n                    GasolineTypeLabel = Label(GasolineFrame, bd=\"10\", bg=\"#2d3436\", text=GasolineListBox.get(),\r\n                                             font=('Arial', 12, \"bold\"), fg=\"white\")\r\n                    GasolineTypeLabel.place(rely=0.3,relx=0.01)\r\n\r\n                    GasolineLitersLabel = Label(GasolineFrame, bd=\"10\", bg=\"#2d3436\", text=LiterEntry.get(),\r\n                                             font=('Arial', 12, \"bold\"), fg=\"white\")\r\n                    GasolineLitersLabel.place(rely=0.3,relx=0.45)\r\n                    GasolineManatLabel = Label(GasolineFrame, bd=\"10\", bg=\"#2d3436\", text=SummPriceValue.cget(\"text\"),\r\n                                               font=('Arial', 12, \"bold\"), fg=\"white\")\r\n                    GasolineManatLabel.place(rely=0.3, relx=0.75)\r\n\r\n                    if LITER.get() == 1 :\r\n                        GasolineLitersL.config(text=\"Liters\")\r\n                        GasolineLitersLabel.config(text=LiterEntry.get())\r\n                    elif LITER.get()==2 :\r\n                        GasolineLitersL.config(text=\"Manat\")\r\n                        GasolineLitersLabel.config(text=MoneyEntry.get())\r\n\r\n\r\n\r\n\r\n\r\n                    TotalLabel = Label(FrameMainReceipt , text=f\"Total is : {CalculateLabel1.cget('text')} AZN\", bg=\"#576574\" , fg=\"white\", font=(\"Arial\" , 15 , \"bold\"))\r\n                    TotalLabel.place(relx=0.01 , rely=0.9)\r\n\r\n                    Receipt = {\"Name\":Label_EnterLog.get() , \"HotDog\" : [HotDogPriceLabel.cget(\"text\") ,HotDogCountLabel.cget(\"text\")]\r\n                        , \"Fries\" : [FriesPriceLabel.cget(\"text\") ,FriesCountLabel.cget(\"text\")] ,\r\n                               \"Cola\" : [ColaPriceLabel.cget(\"text\") ,ColaCountLabel.cget(\"text\")] ,\r\n                               \"Qamburger\" : [QamburgerPriceLabel.cget(\"text\") ,QamburgerCountLabel.cget(\"text\")] ,\r\n                               GasolineListBox.get() : [GasolineLitersL.cget(\"text\") , SummPriceValue.cget(\"text\")]}\r\n\r\n                    ReceiptFile = open('Receipt.txt', 'w')\r\n                    for key, value in Receipt.items():\r\n                        ReceiptFile.write(f'{key}, {value}\\n')\r\n                    print(ReceiptFile)\r\n\r\n\r\n                    def ExitReceipt() :\r\n                        ReceiptWindow.destroy()\r\n                    ExitButtonReceipt = Button(FrameMainReceipt , text=\"Exit\" ,activebackground=\"#686de0\",\r\n                                         bg=\"#686de0\", fg=\"white\", font=('Arial', 15, \"bold\")  , command=ExitReceipt)\r\n                    ExitButtonReceipt.place(relx=0.75 , rely=0.9 , relwidth=0.25 , relheight=0.09)\r\n\r\n\r\n                def DeleteAll():\r\n                    LITER.set(0)\r\n                    LiterEntry.delete(0 , END)\r\n                    MoneyEntry.delete(0,END)\r\n                    SummPriceValue.config(text = \"0\")\r\n\r\n                    #Cafe\r\n\r\n                    Hot_Dog_Entry_Count.delete(0, END)\r\n                    Fries_Entry_Count.delete(0,END)\r\n                    Qamburger_Entry_Count.delete(0,END)\r\n                    Cola_Entry_Count.delete(0,END)\r\n                    SummPriceValueCafe.config(text=\"0\")\r\n\r\n                    HOTDOG.set(0)\r\n                    COLA.set(0)\r\n                    QAMBURGER.set(0)\r\n                    FRIES.set(0)\r\n\r\n                    # All\r\n\r\n                    CalculateLabel1.config(text=\"0\")\r\n\r\n                SubbFrameForCafe = Frame(frameCafe, bd=10, highlightbackground=\"#0be881\", highlightthickness=3,\r\n                                         bg=\"#4834d4\")\r\n                SubbFrameForCafe.place(relwidth=0.85, relheight=0.3, rely=0.7, relx=0.1)\r\n                LabelAllPriceCafe = Label(SubbFrameForCafe, text=\"Total\", bg=\"#4834d4\", fg=\"white\",\r\n                                          font=('Arial', 20, \"bold\"))\r\n                LabelAllPriceCafe.place(anchor=NW)\r\n\r\n                SummPriceValueCafe = Label(SubbFrameForCafe, text=\"0\", bg=\"#4834d4\", fg=\"white\",\r\n                                           font=(\"Arial\", 20, \"bold\"), highlightthickness=3,\r\n                                           highlightbackground=\"#0be881\")\r\n                SummPriceValueCafe.place(rely=0.45, relx=0.1)\r\n                SummPriceValueCafeLabel = Label(SubbFrameForCafe, text=\"AZN\", font=('Arial', 20 , \"bold\"), bg='#4834d4',\r\n                                                fg='white')\r\n                SummPriceValueCafeLabel.place(rely=0.5, relx=0.7)\r\n\r\n                frameSumUpCafe = Frame(frameMainWindow, bd='10', bg=\"#4834d4\", highlightthickness=3 , highlightbackground=\"#34ace0\")\r\n                frameSumUpCafe.place(relwidth=0.9, relheight=0.25, rely=0.7, relx=0.05)\r\n\r\n                LabelSumUp = Label(frameSumUpCafe, text=\"All price\", bg=\"#4834d4\", fg=\"white\",\r\n                                   font=('Arial', 15, \"bold\"))\r\n                LabelSumUp.place(rely=0.01, relx=0.01)\r\n\r\n                CalculateButton = Button(frameSumUpCafe, text=\"Calculate\", activebackground=\"#686de0\",\r\n                                         bg=\"#686de0\", fg=\"white\", font=('Arial', 15, \"bold\") , command= CalculateButton)\r\n                CalculateButton.place(rely=0.4, relx=0.1)\r\n                CalculateButton = Button(frameSumUpCafe, text=\"Delete All\", activebackground=\"#686de0\",\r\n                                         bg=\"#686de0\", fg=\"white\", font=('Arial', 15, \"bold\") , command=DeleteAll)\r\n                CalculateButton.place(rely=0.4, relx=0.3)\r\n\r\n                CalculateLabel1 = Label(frameSumUpCafe, text=\"0\", bg=\"#686de0\", fg=\"white\",\r\n                                        font=('Arial', 15, \"bold\"))\r\n                CalculateLabel1.place(rely=0.35, relx=0.5, relheight=0.4, relwidth=0.2)\r\n                CalculateLabel2 = Label(frameSumUpCafe, text=\"AZN\", bg=\"#4834d4\", fg=\"white\",\r\n                                        font=('Arial', 15, \"bold\"))\r\n                CalculateLabel2.place(rely=0.35, relx=0.75, relheight=0.4, relwidth=0.09)\r\n\r\n                def ExitButton():\r\n                    frameMainWindow.destroy()\r\n                    Label_EnterLog.delete(0,END)\r\n                    Label_EnterLogPass.delete(0,END)\r\n\r\n                ExitButton = Button(frameMainWindow,text=\"Exit\" , activebackground=\"#686de0\",\r\n                                         bg=\"#686de0\", fg=\"white\", font=('Arial', 10, \"bold\"),command=ExitButton)\r\n                ExitButton.place(relx=0.8 , rely=0.97 , relwidth=0.2)\r\n        else:\r\n            Message = messagebox.showerror(title=\"Error\" , message=\"Login or Password incorrect! \")\r\n\r\n    frame = Frame(WindowMain, bd='10')\r\n    frame.place(relx=0.5, rely=0.2, relwidth=0.7, relheight=0.6, anchor='n')\r\n    Label_Title = Label(frame, text='Log In', font=16)\r\n    Label_Title.place(relwidth=1, relheight=0.1)\r\n\r\n    Label_Login = Label(frame, text='Login :')\r\n    Label_Login.place(rely=0.2, relwidth=0.35, relheight=0.1)\r\n\r\n    Label_PasswordLogin = Label(frame, text=\"Password : \")\r\n    Label_PasswordLogin.place(rely=0.4, relwidth=0.35, relheight=0.1)\r\n\r\n    Label_EnterLog = Entry(frame)\r\n    Label_EnterLog.place(relx=0.4, rely=0.2, relheight=0.1, relwidth=0.55)\r\n    Label_EnterLogPass = Entry(frame, show='*')\r\n    Label_EnterLogPass.place(relx=0.4, rely=0.4, relheight=0.1, relwidth=0.55)\r\n\r\n    ButtonSignUp = Button(frame, text=\"Log In\" , command=FindUser)\r\n    ButtonSignUp.place(relx=0.3, rely=0.6, relheight=0.15, relwidth=0.5)\r\ndef Admin() :\r\n    WelcomeLabel.destroy()\r\n    WelcomeLabel.destroy()\r\n    WelcomeLabel1.destroy()\r\n    WelcomeLabel1.destroy()\r\n    WelcomeLabel2.destroy()\r\n    WelcomeLabel2.destroy()\r\n    def AdminLogIn():\r\n\r\n        def AddGasoline() :\r\n            if ComboBoxForPick.get() == \"Gasoline\" :\r\n                NewGasolines = {}\r\n                with open(\"Gasolines.txt\") as file:\r\n                    lines = file.read().splitlines()\r\n                for i in lines:\r\n                    key, value = i.split(', ')\r\n                    NewGasolines.update({key: value})\r\n                print(NewGasolines)\r\n\r\n                if EntryForName.get() in NewGasolines :\r\n                    Message = messagebox.showerror(title=\"Error\" , message=\"Gasoline already exists\")\r\n                else:\r\n                    NewGasolines.update({EntryForName.get(): EntryForPassword.get()})\r\n                    GasolineFile = open('Gasolines.txt', 'w')\r\n                    for key, value in NewGasolines.items():\r\n                        GasolineFile.write(f'{key}, {value}\\n')\r\n                    Message = messagebox.showinfo(title=\"Success\", message=\"Gas has been added!\")\r\n                    print(NewGasolines)\r\n\r\n\r\n\r\n\r\n            elif ComboBoxForPick.get() == \"Cashier\" :\r\n                Accounts = {}\r\n                # reading files\r\n                with open(\"Accounts.txt\") as file:\r\n                    lines = file.read().splitlines()\r\n                for i in lines:\r\n                    key, value = i.split(', ')\r\n                    Accounts.update({key: value})\r\n                print(Accounts)\r\n                if  EntryForName.get() in Accounts :\r\n                    Message = messagebox.showerror(title=\"Eror\" , message=\"Account already exist\")\r\n                else:\r\n                    Accounts[EntryForName.get()] = EntryForPassword.get()\r\n                    # Adding to file\r\n                    AccountsFile = open('Accounts.txt', 'a')\r\n                    for key, value in Accounts.items():\r\n                        AccountsFile.write(f'{key}, {value}\\n')\r\n                    MessageBox = messagebox.showinfo(title=\"Account Created\", message=\"Account has been created\")\r\n            else:\r\n                MessageBox = messagebox.showinfo(title=\"Account Created\", message=\"Account has been created\")\r\n\r\n\r\n\r\n\r\n        def RemoveGasoline() :\r\n            if ComboBoxForPick.get() == \"Gasoline\" :\r\n                with open(\"Gasolines.txt\") as file:\r\n                    lines = file.read().splitlines()\r\n                for i in lines:\r\n                    key, value = i.split(', ')\r\n                    AllGas.update({key: value})\r\n\r\n                print(AllGas)\r\n                if EntryForName.get() not in AllGas :\r\n                    Message = messagebox.showerror(title=\"Error\" , message=\"Gasoline not found\")\r\n                else:\r\n                    AllGas.pop(EntryForName.get())\r\n                    print(AllGas)\r\n\r\n                    GasolineFile = open('Gasolines.txt', 'w')\r\n                    for key, value in AllGas.items():\r\n                        GasolineFile.write(f'{key}, {value}\\n')\r\n                    messagebox.showinfo(title=\"Success\", message=\"Gasoline has been removed\")\r\n\r\n            if ComboBoxForPick.get() == \"Cashier\" :\r\n                Accs= {}\r\n                with open(\"Accounts.txt\") as file:\r\n                    lines = file.read().splitlines()\r\n                for i in lines:\r\n                    key, value = i.split(', ')\r\n                    Accs.update({key: value})\r\n\r\n\r\n                print(Accs)\r\n                if EntryForName.get() not in Accs :\r\n                    Message = messagebox.showerror(title=\"Error\" , message=\"Account not found\")\r\n                else:\r\n                    Accs.pop(EntryForName.get())\r\n                    print(AllGas)\r\n                    AccsFiles = open('Accounts.txt', 'w')\r\n                    for key, value in Accs.items():\r\n                        AccsFiles.write(f'{key}, {value}\\n')\r\n                    messagebox.showinfo(title=\"Success\", message=\"Gas has been removed\")\r\n\r\n        def UpdateHotDogPrice():\r\n            with open(\"Products.txt\") as file:\r\n                lines = file.read().splitlines()\r\n            for i in lines:\r\n                key, value = i.split(', ')\r\n                Prices.update({key: value})\r\n\r\n            if HotDogsEntry.get() != \"\":\r\n                Prices[\"Hot Dog\"] = HotDogsEntry.get()\r\n                ProductFile = open('Products.txt', 'w')\r\n                for key, value in Prices.items():\r\n                    ProductFile.write(f'{key}, {value}\\n')\r\n                messagebox.showinfo(title=\"Success\", message=\"Successfully changed!\")\r\n            else:\r\n                Message = messagebox.showerror(title=\"Error\", message=\"No Value Given\")\r\n\r\n            print(Prices)\r\n\r\n\r\n        def UpdateFriesPrice():\r\n            with open(\"Products.txt\") as file:\r\n                lines = file.read().splitlines()\r\n            for i in lines:\r\n                key, value = i.split(', ')\r\n                Prices.update({key: value})\r\n\r\n            if FriesEntry.get() != \"\":\r\n                Prices[\"Fries\"] = FriesEntry.get()\r\n                ProductFile = open('Products.txt', 'w')\r\n                for key, value in Prices.items():\r\n                    ProductFile.write(f'{key}, {value}\\n')\r\n                messagebox.showinfo(title=\"Success\", message=\"Successfully changed!\")\r\n            else:\r\n                Message = messagebox.showerror(title=\"Error\", message=\"No Value Given\")\r\n\r\n            print(Prices)\r\n\r\n\r\n        def UpdateColaPrice():\r\n            with open(\"Products.txt\") as file:\r\n                lines = file.read().splitlines()\r\n            for i in lines:\r\n                key, value = i.split(', ')\r\n                Prices.update({key: value})\r\n\r\n            if ColaEntry.get() != \"\":\r\n                Prices[\"Cola\"] = ColaEntry.get()\r\n                ProductFile = open('Products.txt', 'w')\r\n                for key, value in Prices.items():\r\n                    ProductFile.write(f'{key}, {value}\\n')\r\n                messagebox.showinfo(title=\"Success\", message=\"Successfully changed!\")\r\n            else:\r\n                Message = messagebox.showerror(title=\"Error\", message=\"No Value Given\")\r\n\r\n            print(Prices)\r\n\r\n\r\n        def UpdateQamburgerPrice():\r\n            with open(\"Products.txt\") as file:\r\n                lines = file.read().splitlines()\r\n            for i in lines:\r\n                key, value = i.split(', ')\r\n                Prices.update({key: value})\r\n            if QamburgerEntry.get() != \"\" :\r\n                 Prices[\"Qamburger\"] = QamburgerEntry.get()\r\n                 ProductFile = open('Products.txt', 'w')\r\n                 for key, value in Prices.items():\r\n                     ProductFile.write(f'{key}, {value}\\n')\r\n                 messagebox.showinfo(title=\"Success\", message=\"Successfully changed!\")\r\n            else:\r\n                Message = messagebox.showerror(title=\"Error\" , message=\"No Value Given\")\r\n\r\n            print(Prices)\r\n\r\n\r\n\r\n        def ResetButton():\r\n            with open(\"Products.txt\") as file:\r\n                lines = file.read().splitlines()\r\n            for i in lines:\r\n                key, value = i.split(', ')\r\n                Prices.update({key: value})\r\n\r\n            Prices[\"Hot Dog\"] = 4\r\n            Prices[\"Qamburger\"] = 5.4\r\n            Prices[\"Fries\"] = 7.2\r\n            Prices[\"Cola\"] = 4.4\r\n\r\n            print(Prices)\r\n\r\n            ProductFile = open('Products.txt', 'w')\r\n            for key, value in Prices.items():\r\n                ProductFile.write(f'{key}, {value}\\n')\r\n            messagebox.showinfo(title=\"Success\", message=\"Successfully changed!\")\r\n\r\n        def BackButton():\r\n            frameMainWindow.destroy()\r\n            Label_EnterLog.delete(0 , END)\r\n            Label_EnterLogPass.delete(0,END)\r\n\r\n\r\n        if Label_EnterLog.get() == 'Omar' and Label_EnterLogPass.get() == '1' :\r\n\r\n            def ChangeLabels(event):\r\n                if ComboBoxForPick.get() == \"Cashier\" :\r\n                    LabelName.config(text= \"Login\")\r\n                    LabelPrice.config(text=\"Password\")\r\n                elif ComboBoxForPick.get() == \"Gasoline\" :\r\n                    LabelName.config(text=\"Gasoline\")\r\n                    LabelPrice.config(text=\"Price\")\r\n\r\n            AllGas = {}\r\n            Prices = {}\r\n            frameMainWindow = Frame(WindowMain, bd='10', bg=\"#494c59\")\r\n            frameMainWindow.place(relwidth=1, relheight=1, relx=0.5, anchor='n')\r\n\r\n            LabelWelcome = Label(frameMainWindow , bd='10' , bg=\"#494c59\" , text=f\"Welcome {Label_EnterLog.get()}\" , font=(\"Arial\" , 15 , \"bold\") , fg=\"white\")\r\n            LabelWelcome.place(anchor=\"n\", relx=0.5)\r\n\r\n\r\n            frameForAddGasoline = Frame(frameMainWindow , bd=\"10\" , bg=\"#4834d4\")\r\n            frameForAddGasoline.place(relx=0.01 , rely=0.069 , relheight=0.4 , relwidth=0.99)\r\n\r\n            LabelForAdd = Label(frameForAddGasoline, bg=\"#4834d4\" , text = \"Change Gasoline/Create User\" , font=('Arial', 15 , 'bold') , fg=\"white\" )\r\n            LabelForAdd.place(relx = 0.23 , rely=0.01 )\r\n\r\n            LabelName = Label(frameForAddGasoline , bg=\"#4834d4\" , text= \"Name\" , font=('Arial', 13 , 'bold') , fg=\"white\")\r\n            LabelName.place(relx=0.01 , rely=0.3)\r\n\r\n            EntryForName = Entry(frameForAddGasoline )\r\n            EntryForName.place(rely=0.3 , relx=0.2 , relheight=0.15 , relwidth=0.3)\r\n\r\n\r\n            LabelPrice = Label(frameForAddGasoline , bg=\"#4834d4\" , text= \"Password\" , font=('Arial', 13 , 'bold') , fg=\"white\")\r\n            LabelPrice.place(relx=0.01 , rely=0.6)\r\n\r\n            EntryForPassword = Entry(frameForAddGasoline)\r\n            EntryForPassword.place(rely=0.6 , relx=0.2 , relheight=0.15 , relwidth=0.3)\r\n\r\n\r\n            AddGasolineButton = Button(frameForAddGasoline ,text=\"Add\" , font=('Arial' ,10  , \"bold\") , command=AddGasoline)\r\n            AddGasolineButton.place(rely=0.3 , relx=0.7 , relheight=0.15 , relwidth=0.3)\r\n            AddGasolineButton = Button(frameForAddGasoline ,text=\"Remove\" , font=('Arial' ,10  , \"bold\") , command=RemoveGasoline)\r\n            AddGasolineButton.place(rely=0.5 , relx=0.7, relheight=0.15 , relwidth=0.3)\r\n            Pick = [\"Cashier\" , \"Gasoline\"]\r\n            ComboBoxForPick = ttk.Combobox(frameForAddGasoline, values=Pick , width=10)\r\n            ComboBoxForPick.current(0)\r\n            ComboBoxForPick.bind(\"<<ComboboxSelected>>\", ChangeLabels)\r\n            ComboBoxForPick.place(rely=0.7 , relx=0.7 , relheight=0.15 , relwidth=0.3)\r\n\r\n\r\n\r\n\r\n            frameforFoodPrice = Frame(frameMainWindow, bd=\"10\" , bg=\"#4834d4\")\r\n            frameforFoodPrice.place(relx=0.01 , rely=0.5 , relheight=0.4 , relwidth=0.99)\r\n\r\n            FoodPriceLabelMain = Label(frameforFoodPrice , text=\"Change Food Price\" , font=('Arial' , 16 , \"bold\") , bg=\"#4834d4\" ,fg=\"white\")\r\n            FoodPriceLabelMain.place(rely=0.01 , relx=0.28)\r\n\r\n            HotDogsLabel = Label(frameforFoodPrice , text=\"Hot Dog Price : \" , font=('Arial' , 13 , \"bold\") , bg=\"#4834d4\" , fg=\"white\")\r\n            HotDogsLabel.place(relx=0.01 , rely=0.2)\r\n\r\n            HotDogsEntry = Entry(frameforFoodPrice)\r\n            HotDogsEntry.place(relx=0.3 , rely=0.22 , relheight=0.1)\r\n\r\n            HotDogsButton = Button(frameforFoodPrice , text=\"Change\" , font=(\"Arial\" , 8 , \"bold\") , command=UpdateHotDogPrice)\r\n            HotDogsButton.place(relx=0.6 , rely=0.22 , relheight=0.1)\r\n\r\n            FriesLabel = Label(frameforFoodPrice , text=\"Fries Price : \" , font=('Arial' , 13 , \"bold\") , bg=\"#4834d4\" , fg=\"white\")\r\n            FriesLabel.place(relx=0.01 , rely=0.4)\r\n\r\n\r\n            FriesEntry = Entry(frameforFoodPrice)\r\n            FriesEntry.place(relx=0.3, rely=0.42, relheight=0.1)\r\n\r\n            FriesButton = Button(frameforFoodPrice , text=\"Change\" , font=(\"Arial\" , 8 , \"bold\") , command=UpdateFriesPrice)\r\n            FriesButton.place(relx=0.6 , rely=0.42 , relheight=0.1)\r\n\r\n            QamburgerLabel = Label(frameforFoodPrice , text=\"Qamburger Price : \" , font=('Arial' , 13 , \"bold\") , bg=\"#4834d4\" , fg=\"white\")\r\n            QamburgerLabel.place(relx=0.01 , rely=0.6)\r\n\r\n            QamburgerEntry = Entry(frameforFoodPrice)\r\n            QamburgerEntry.place(relx=0.35, rely=0.62, relheight=0.1)\r\n\r\n            QamburgerButton = Button(frameforFoodPrice , text=\"Change\" , font=(\"Arial\" , 8 , \"bold\") , command= UpdateQamburgerPrice)\r\n            QamburgerButton.place(relx=0.65 , rely=0.62 , relheight=0.1)\r\n\r\n            ColaLabel = Label(frameforFoodPrice , text=\"Cola Price : \" , font=('Arial' , 13 , \"bold\") , bg=\"#4834d4\" , fg=\"white\")\r\n            ColaLabel.place(relx=0.01 , rely=0.8)\r\n\r\n            ColaEntry = Entry(frameforFoodPrice)\r\n            ColaEntry.place(relx=0.3, rely=0.82, relheight=0.1)\r\n\r\n            ColaButton = Button(frameforFoodPrice , text=\"Change\" , font=(\"Arial\" , 8 , \"bold\") , command=UpdateColaPrice)\r\n            ColaButton.place(relx=0.6 , rely=0.82 , relheight=0.1)\r\n\r\n            ResetButton = Button(frameforFoodPrice , text=\"RESET\" , font=(\"Arial\" , 8 , \"bold\") , command=ResetButton)\r\n            ResetButton.place(relx=0.8 , rely=0.35 , relheight=0.5 , relwidth=0.2)\r\n\r\n            BackButton = Button(frameMainWindow , text=\"Back\" ,activebackground=\"#686de0\",\r\n                                         bg=\"#686de0\", fg=\"white\", font=('Arial', 15, \"bold\")  , command=BackButton)\r\n            BackButton.place(rely=0.95 , relx=0.7 , relwidth=0.3)\r\n        else:\r\n            Message =  messagebox.showerror(title=\"Error\" , message=\"Something went wrong!\")\r\n\r\n\r\n\r\n    frame = Frame(WindowMain, bd='10')\r\n    frame.place(relx=0.5, rely=0.2, relwidth=0.7, relheight=0.6, anchor='n')\r\n    Label_Title = Label(frame, text='Admin', font=16)\r\n    Label_Title.place(relwidth=1, relheight=0.1)\r\n\r\n    Label_Login = Label(frame, text='Login :')\r\n    Label_Login.place(rely=0.2, relwidth=0.35, relheight=0.1)\r\n\r\n    Label_PasswordLogin = Label(frame, text=\"Password : \")\r\n    Label_PasswordLogin.place(rely=0.4, relwidth=0.35, relheight=0.1)\r\n\r\n    Label_EnterLog = Entry(frame)\r\n    Label_EnterLog.place(relx=0.4, rely=0.2, relheight=0.1, relwidth=0.55)\r\n    Label_EnterLogPass = Entry(frame, show='*')\r\n    Label_EnterLogPass.place(relx=0.4, rely=0.4, relheight=0.1, relwidth=0.55)\r\n\r\n    ButtonSignUp = Button(frame, text=\"Log In\" , command=AdminLogIn)\r\n    ButtonSignUp.place(relx=0.3, rely=0.6, relheight=0.15, relwidth=0.5)\r\n\r\n\r\n\r\n\r\nWindowMain = Tk()\r\nWindowMain.resizable(False,False)\r\nWindowMain.title(\"Main\")\r\nWindowMain.geometry('750x750')\r\nWindowMain.config(bg=\"#494c59\")\r\n\r\nWelcomeLabel = Label(text=\"Welcome!\" , font=(\"Arial\" , 50 , \"bold\") , bg =\"#494c59\"  , fg = \"white\" )\r\nWelcomeLabel.place(rely=0.34 , relx=0.25 , relwidth=0.45)\r\nWelcomeLabel1 = Label(text=\"Press one of the buttons above\" , font=(\"Arial\" , 35 , \"bold\") , bg =\"#494c59\"  , fg = \"white\" )\r\nWelcomeLabel1.place(rely=0.54 , relx=0.04)\r\nWelcomeLabel2 = Label(text=\"to start!\" , font=(\"Arial\" , 35 , \"bold\") , bg =\"#494c59\"  , fg = \"white\" )\r\nWelcomeLabel2.place(rely=0.64 , relx=0.35)\r\n\r\n\r\nSignUp_Button = Button(text=\"Cashier\" , bg='gold' , command=LogIn  , font=(\"Arial\",15 , \"bold\"))\r\nSignUp_Button.place(relx = 0.1 , rely = 0.1 , relwidth=0.3 ,relheight=0.07 )\r\nSignUp_Button = Button(text=\"Admin\" , bg='gold' , command=Admin ,  font=(\"Arial\",15 , \"bold\"))\r\nSignUp_Button.place(relx = 0.6, rely = 0.1 , relwidth=0.3 , relheight=0.07)\r\n\r\nCreditsLabel = Label(text=\"Made by Omar™ \" , font=(\"Arial\" , 20 , \"bold\") , bg =\"#494c59\"  , fg = \"white\")\r\nCreditsLabel.place(rely=0.95 , relx=0.7)\r\nWindowMain.mainloop()","repo_name":"YoungDeveloper-Git/gasoline","sub_path":"ProjectWork.py","file_name":"ProjectWork.py","file_ext":"py","file_size_in_byte":48149,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"27031780071","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Jan 26 05:04:18 2020\r\n\r\n@author: Sanketh\r\n\"\"\"\r\n\r\ntrain_generator=train_datagen.flow_from_directory('D:\\\\train\\\\', # this is where you specify the path to the main data folder\r\n                                                 target_size=(224,224),\r\n                                                 color_mode='rgb',\r\n                                                 batch_size=32,\r\n                                                 class_mode='categorical',\r\n                                                 shuffle=True)\r\n\r\n\r\n# In[33]:\r\n\r\n\r\nmodel.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])\r\n# Adam optimizer\r\n# loss function will be categorical cross entropy\r\n# evaluation metric will be accuracy\r\n\r\nstep_size_train=train_generator.n//train_generator.batch_size\r\nmodel.fit_generator(generator=train_generator,\r\n                   steps_per_epoch=step_size_train,\r\n                   epochs=5)\r\n","repo_name":"Abhishek262/DiabolicalBaggage","sub_path":"Training.py","file_name":"Training.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"38823629107","text":"#!/usr/bin/python2.7\n# -*- coding:utf-8 -*-\n\n# Author: NetworkRanger\n# Date: 2018/11/2 下午9:23\n\n# 1.2 TensorFlow 如何工作\n\nimport tensorflow as tf\n\n# 1. 导入/生成样本数据集。\n\n# 2. 转换和归一化数据。\n# data = tf.nn.batch_norm_with_global_normalization(...)\n\n# 3. 划分样本数据集为训练样本集、测试样本集和验证样本集。\n\n# 4. 设置机器学习参数（超参数）。\nlearning_rate = 0.01\nbatch_size = 100\niterations = 1000\n\n# 5. 初始化变量和占位符。\na_var = tf.constant(42)\n# x_input = tf.placeholder(tf.float32, [None, input_size])\n# y_input = tf.placeholder(tf.float32, [None, num_classses])\n\n# 6. 定义模型结构。\n# y_pred = tf.add(tf.mul(x_input, weight_matrix), b_matrix)\n\n# 7. 声明损失函数。\n# loss = tf.reduce_mean(tf.square(y_actual - y_pred))\n\n# 8. 初始化模型和训练模型。\n# with tf.Session(graph=graph) as session:\n#     ...\n#     session.run(...)\n#     ...\n\n# 9. 评估机器学习模型。\n\n# 10. 调优超参数。\n\n# 11. 发布/预测结果。\n","repo_name":"NetworkRanger/tensorflow-ml-exercise","sub_path":"chapter01/demo_1.2.py","file_name":"demo_1.2.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"zh","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"74874829085","text":"# inference results archived module\nimport json\nimport os\nimport boto3\nfrom datetime import datetime, timedelta\nimport time\n\nBUCKET_NAME = os.environ.get('BUCKET_NAME')\ns3_client = boto3.client('s3') \nlog_client = boto3.client('logs')\ns3 = boto3.resource('s3') \n\ndef getMemoryUsed(info):\n    request_id = info['request_id']\n    log_group_name = info['log_group_name']\n\n    query = f\"fields @maxMemoryUsed | sort @timestamp desc | filter @requestId='{request_id}' | filter @maxMemoryUsed like ''\"\n    response = None\n    max_memory_used = 0\n\n    start_query_response = log_client.start_query(\n        logGroupName=log_group_name,\n        startTime=int((datetime.today() - timedelta(hours=24)).timestamp()),\n        endTime=int((datetime.now() + timedelta(hours=24)).timestamp()),\n        queryString=query,\n    )\n    query_id = start_query_response['queryId']\n    while response == None or response['status'] == 'Running':\n        time.sleep(1)\n        response = log_client.get_query_results(\n            queryId=query_id\n        )\n\n    res = response['results'][0]\n    for r in res:\n        if r['field'] == '@maxMemoryUsed':\n            max_memory_used = int(float(r['value']) / 1000000)\n\n    return max_memory_used\n\ndef getLatency(prefix, check , gettype):\n    obj_list = s3_client.list_objects(Bucket=BUCKET_NAME,Prefix=prefix)\n    contents_list = obj_list['Contents']\n\n    for content in contents_list:\n        # print(content)\n        if content['Key']== check : \n            # 파일 내용을 읽어오기\n            obj = s3.Object(BUCKET_NAME,f\"{check}\")\n            bytes_value = obj.get()['Body'].read()\n            filejson = bytes_value.decode('utf8')\n            fileobj = json.loads(filejson)\n            print(fileobj)\n            get_latency = fileobj[gettype]\n\n    return get_latency\n\ndef upload_data(info,max_memory_used):        \n    # get convert_time \n    try:\n        if info[\"optimizer\"] == \"onnx\":\n            convert_prefix = f'results/{info[\"optimizer\"]}/convert/'\n            convert_check = convert_prefix + f'{info[\"model_name\"]}_{info[\"model_size\"]}_convert.json'\n        else:\n            convert_prefix = f'results/{info[\"optimizer\"]}/{info[\"hardware\"]}/convert/'\n            convert_check = convert_prefix + f'{info[\"model_name\"]}_{info[\"model_size\"]}_{info[\"batchsize\"]}_convert.json'\n        convert_time = getLatency(convert_prefix, convert_check, \"convert_time\")\n    except:\n        # base 인 경우 convert time 0 \n        convert_time = 0\n    # get inference_time \n    prefix = f'results/{info[\"optimizer\"]}/{info[\"hardware\"]}/inference/'\n    inference_check = prefix + f'{info[\"model_name\"]}_{info[\"model_size\"]}_{info[\"batchsize\"]}_{info[\"lambda_memory\"]}_inference.json'\n    inference_time = getLatency(prefix,inference_check,\"inference_median\")\n\n\n    get_info = {\n            'model_name':info['model_name'],\n            'model_size':info['model_size'],\n            'hardware':info['hardware'],\n            'framework':info['framework'],\n            'optimizer':info['optimizer'],\n            'lambda_memory':info['lambda_memory'],\n            'batchsize':info['batchsize'],\n            'convert_time':convert_time,\n            'inference_time':inference_time,\n            'user_email':info['user_email'],\n            'request_id':info['request_id'],\n            'log_group_name':info['log_group_name'],\n            'max_memory_used':max_memory_used\n        }\n\n\n    with open(f'/tmp/{get_info[\"model_name\"]}_{get_info[\"model_size\"]}_{get_info[\"batchsize\"]}_{get_info[\"lambda_memory\"]}.json','w') as f:\n        json.dump(get_info, f, ensure_ascii=False, indent=4)  \n    s3_client.upload_file(f'/tmp/{get_info[\"model_name\"]}_{get_info[\"model_size\"]}_{get_info[\"batchsize\"]}_{get_info[\"lambda_memory\"]}.json',BUCKET_NAME,f'results/{get_info[\"optimizer\"]}/{get_info[\"hardware\"]}/{get_info[\"model_name\"]}_{get_info[\"model_size\"]}_{get_info[\"batchsize\"]}_{get_info[\"lambda_memory\"]}.json')\n\n    return get_info\n\ndef ses_send(info):\n    dst_format = {\"ToAddresses\":[f\"{info['user_email']}\"],\n    \"CcAddresses\":[],\n    \"BccAddresses\":[]}\n\n    dfile_path = \"/tmp/destination.json\"\n\n    with open(dfile_path, 'w', encoding='utf-8') as file:\n        json.dump(dst_format, file)\n\n    message_format = {\n                        \"Subject\": {\n                            \"Data\": \"AYCI : AllYouCanInference results mail\",\n                            \"Charset\": \"UTF-8\"\n                        },\n                        \"Body\": {\n                            \"Text\": {\n                                \"Data\": f\"AYCI convert time results\\n---------------------------------------\\n{info['model_name']} convert using {info['optimizer'].upper()} on {info['hardware'].upper()} \\n{info['model_name']} size : {info['model_size']} MB\\nConvert {info['model_name']} latency : {round(info['convert_time'],4)} s\\n\\nAYCI inference time results\\n---------------------------------------\\n{info['model_name']} inference Done!\\n{info['model_name']} size : {info['model_size']} MB\\nInference batchsize : {info['batchsize']}\\nInference {info['model_name']} latency on {info['hardware'].upper()}: {round(info['inference_time'],4)} s\\n-----------------------------------------------\\nLambda memory size : {info['lambda_memory']}\\nMax Memory Used : {info['max_memory_used']}\",\n                                \"Charset\": \"UTF-8\"\n                            },\n                        }\n                    }\n    mfile_path = \"/tmp/message.json\"\n\n    with open(mfile_path, 'w', encoding='utf-8') as mfile:\n        json.dump(message_format, mfile)\n\n    os.system(\"aws ses send-email --from allyoucaninference@gmail.com --destination=file:///tmp/destination.json --message=file:///tmp/message.json\")\n\n    \n    \ndef lambda_handler(event, context):\n\n    for i in range(len(event)):\n        if event[i]['execute'] :\n            info = {\n                'model_name':event[i]['model_name'],\n                'model_size':event[i]['model_size'],\n                'hardware':event[i]['hardware'],\n                'framework':event[i]['framework'],\n                'optimizer':event[i]['optimizer'],\n                'lambda_memory':event[i]['lambda_memory'],\n                'batchsize':event[i]['batchsize'],\n                'user_email':event[i]['user_email'],\n                'request_id':event[i]['request_id'],\n                'log_group_name':event[i]['log_group_name']\n            }\n            max_memory_used = getMemoryUsed(info)\n            print(max_memory_used)\n\n            get_info = upload_data(info,max_memory_used)\n            ses_send(get_info)\n\n        else:\n            pass\n\n\n    return { 'result':'upload done'}\n","repo_name":"ddps-lab/lambda-optimize-serving","sub_path":"lambda-archive/lambda_function.py","file_name":"lambda_function.py","file_ext":"py","file_size_in_byte":6630,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"25934620481","text":"#!/usr/bin/python3\n\nfrom pythonping import ping\nimport matplotlib.pyplot as plt\n\nx = []\ny = []\ni = 0\n  \ndef get_latency(host):\n   result = str(ping(host,count=1))\n   result = result.split(\" \")\n   result = result[6]\n   result = result[:-9]\n   x.append(str(result))\n   return result\n\n\n\nfor x in range(5):\n   get_latency(\"1.1.1.1\")\n   y.append(str(i))\n   i = i + 1\n\nplt.plot(x, y)\n  \n\nplt.xlabel('x - axis')\nplt.ylabel('y - axis')\n  \nplt.title('My first graph!')\nplt.show()","repo_name":"marvinnitz18/Projects","sub_path":"latency-monitor/latency-monitor.py","file_name":"latency-monitor.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"37432140409","text":"import requests\nimport sys\ndef recon():\n\n\tarq = open('wordlist.txt','r')\n\t\n\tfor domain in arq:\n\t\tdomain = domain.replace('\\n','')\n\t\tresult = f\"http://{site}/{domain}\"\n\t\t\n\t\ttry:\n\t\t\tresposta = requests.get(result)\n\t\t\tresposta = (resposta.status_code)\n\t\t\t\n\t\t\tif resposta == 200:\n\t\t\t\tprint('Diretorio Encontrado: '+domain)\n\n\t\texcept:\n\t\t\tcontinue\n\t\tarq.close()\n\ntry:\n\tsite = sys.argv[1]\n\trecon()\nexcept:\n\tprint(' Use: python domain.py www.site.com.br ')\n\texit()\n","repo_name":"valtercioj/recon_security","sub_path":"domain/domain.py","file_name":"domain.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"1065371418","text":"import asyncio\nimport random\nimport time\n\nimport aiosqlite\nimport discord\nimport discord.commands\nimport requests\nfrom discord import slash_command\nfrom discord.ext import commands\n\n\nclass FunCommands(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n\n    @commands.Cog.listener()\n    async def on_ready(self):\n        await asyncio.sleep(1.4)\n        print(\"\"\"\n            funcommands.py   ✅\n            ------------------------\"\"\")\n\n    @slash_command(description=\"Zeigt wie lange der Bot schon online ist!\")\n    async def ontime(self, ctx: discord.ApplicationContext):\n        print(f\"{ctx.author} hat /uptime gemacht\")\n        uptime_counter = time.time()\n        aktuell_zeit = time.time()\n        uptime_sek = aktuell_zeit - uptime_counter\n\n        uptime_timestamp = round(aktuell_zeit - uptime_sek)\n\n        await ctx.respond(f':green_circle: Der Bot ist seit <t:{uptime_timestamp}:R> online!')\n\n    @slash_command(description=\"Schlage jemanden\")\n    @commands.cooldown(1, 60, commands.BucketType.user)\n    async def slap(self, ctx, member: discord.Member):\n        guild: discord.Guild = self.bot.get_guild(724602228505313311)\n        muterolle: discord.Role = guild.get_role(1043532505887809577)\n        key = \"AIzaSyDHmg80hvYQrUvrTEee8ARuq9X-6hIE1EM\"\n        params = {\"q\": \"slap\",\n                  \"key\": key,\n                  \"limit\": 30,\n                  \"media_filter\": \"gif\"}\n\n        result = requests.get(f\"https://tenor.googleapis.com/v2/search\", params=params)\n        data = result.json()\n\n        number = random.randint(0, 30)\n\n        url = data[\"results\"][number][\"media_formats\"][\"gif\"][\"url\"]\n\n        if member == \"Cookie Manager#9104\":\n            print(member)\n            embot = discord.Embed(title=\"Ich bekomme alles mit!\", color=discord.Color.orange(),\n                                  description=\"Der Bot so krass, das du in nicht schlagen kannst!\")\n            embot.set_footer(text=\"Gif von Tenor\")\n            embot.set_image(\n                url=\"https://images-ext-2.discordapp.net/external/ZLjKGm6-I9EJNnCpHUMu-J1ChjOhbuRUuqVR_p7dYhY/https/\"\n                    \"media.tenor.com/FLGynS-9GqQAAAPo/will-smith-south-park.mp4\")\n\n        embed = discord.Embed(title=f\"{ctx.author.name} hat {member} geschlagen!\", color=discord.Color.red())\n        embed.set_image(url=url)\n        embed.set_footer(text=\"Gif von Tenor\")\n        print(f\"{ctx.author.name} hat den Befehl /slap genutzt\")\n        await ctx.respond(embed=embed)\n        geschlagen = discord.Embed(title=f\"{ctx.author} hat dich geschlagen!\", color=discord.Color.red(),\n                                   description=f\"RÄCHE DICH JETZT INDEM DU auf den **DER COOKIE CLAN** DC gehst und in \"\n                                               f\"https://discord.com/channels/724602228505313311/963740046995890176 \"\n                                               f\"/slap {ctx.author} machst 😉\")\n        if muterolle not in ctx.author.roles:\n            await member.send(embed=geschlagen)\n\n    @slash_command(description=\"Löse ein zufälliges Event aus. uiii\")\n    @commands.cooldown(1, 3600, commands.BucketType.user)\n    async def event(self, ctx):\n        async with aiosqlite.connect(\"level.db\") as db:\n            print(f\"{ctx.author} hat /event gemacht\")\n            guild: discord.Guild = self.bot.get_guild(724602228505313311)\n            hodenkrebsrole: discord.Role = guild.get_role(1037153279886503967)\n            hodenkrebs = random.randint(1, 1000)\n            goodordosent = random.randint(1, 2)\n            cookies = random.randint(1, 7)\n            cookiesmuell = cookies + random.randint(1, 5)\n            user = guild.members\n            eventgood = [f\"Du hast eine Packung Cookies auf der Straße gefunden du hast dich umgeschaut ob dich jemand \"\n                         f\"beobachtet... Als du festgestellt hat das dich niemand beobachtet hast du Lachend alle \"\n                         f\"Cookies mitgenommen es waren **{cookies}** Cookies.\", f\"Du hast im Aldilie eine Cookie \"\n                                                                                 f\"Packung geklaut allerdings hat dich \"\n                                                                                 f\"der Ladenbesitzer erwischt. Aber da \"\n                                                                                 f\"er mitleid hatte hast du \"\n                                                                                 f\"**{cookies}** Cookies bekommen.\",\n                         f\"Du hast auf Onlycookies ein neues Video hochgeladen du wurdest allerdings gehackt aber du \"\n                         f\"konntest trozdem **{cookies}** Cookies bekommen.\",\n                         f\"Du hast einer Alten Oma über die Straße geholfen. Aus ihrer Tasche sind wärendesen {cookies}\"\n                         f\" Cookies gefallen. Du hast alle vor ihren Augen eingesammelt und bist abgehauen.\",\n                         f\"Du bist nach Hause gegangen und hast im Müll etwas Cookie Artiges gesehen du hast geschaut \"\n                         f\"und es waren tatsächlich **{cookiesmuell}** Cookies im Müll du hollst alle raus und hast \"\n                         f\"jetzt **{cookies}** Cookies mehr, da {cookiesmuell - cookies} schlecht waren.\",\n                         f\"Du hast deine Cookies gezählt wie jeden morgen weil am Tag vorher {random.choice(user)} da \"\n                         f\"war. Dann hast du festgestellt das sie/er/es dir **{cookies}** dagelassen hat\",\n                         f\"Du bist in den Wald gegangen und hast dort eine Kiste gefunden. In der Kiste waren \"\n                         f\"**{cookies}** Cookies. Du hast sie genommen und bist abgehauen\",\n                         f\"Jemand hat dir **{cookies}** Cookies geschenkt. Du hast dich gefreut und hast sie genommen\",\n                         f\"Jemand hat dich gefragt ob du **{cookies}** Cookies haben willst oder ob er jemand anderen \"\n                         f\"doppelt geben soll. Du hast gesagt das du die Cookies haben willst und hast sie bekommen\",\n                         f\"Eine Person hat dir **{cookies}** Cookies geschenkt. Du hast dich gefreut und hast sie \"\n                         f\"genommen\",\n                         f\"Im Internet hast du eine Seite gefunden in der behauptet wurde das du **{cookies}** Cookies \"\n                         f\"bekommen kannst. Du hast dich angemeldet und hast die Cookies bekommen\",\n                         f\"In der Schule hast du einen Kuchen gebacken und hast ihn mitgenommen. Als du ihn essen \"\n                         f\"wolltest hast du festgestellt das es **{cookies}** Cookies waren\",\n                         f\"Die Oma die vorne an der Kasse war hat **{cookies}** Cookies verloren. Du hast sie \"\n                         f\"aufgelesen und hast sie genommen\"]\n\n            eventnotgood = [f\"Du hast Elon Musk nach Twitter+ gefragt, er hatte dich mit seinem Waschbeken beworfen \"\n                            f\"und dir sind **{cookies}** Cookies zerbrochen.\", f\"Du hast das neue Cyberpunk 2089 \"\n                                                                               f\"gekauft allerdings ist es voller Bugs \"\n                                                                               f\"und du ragest und zerbichst \"\n                                                                               f\"**{cookies}** Cookies dabei.\",\n                            f\"Du hast im Aldilie eine Cookie Packung geklaut allerdings hat dich der Ladenbesitzer \"\n                            f\"erwischt. Er hat dich verklagt und du musstest **{cookies}** Cookies strafe zahlen.\",\n                            f\"Du bist auf den Bürgersteig hingefallen da du noch Cookies in deiner Hosentasche hattest \"\n                            f\"sind **{cookies}** Cookies rausgerollt und wurden von einem Auto überfahren\",\n                            f\"Du hast deine Cookies gezählt wie jeden morgen weil am Tag vorher {random.choice(user)} \"\n                            f\"da war. Dann hast du festgestellt das sie/er/es dir **{cookies}** hinterhältig geklaut \"\n                            f\"hat!\", f\"Du bist zu McDonalds gegangen und hast dir einen McFlurry geholt. Als du \"\n                                     f\"zurückkamst war dein McFlurry weg und du hast **{cookies}** Cookies verloren.\",\n                            f\"{random.choice(user)} hat dir **{cookies}** Cookies geklaut. Allerdings ist er \"\n                            f\"gestollpert und alle sind zerbrochen\",\n                            f\"Etwas hat dir **{cookies}** Cookies geklaut. Du hast es nicht gesehen aber du hast \"\n                            f\"festgestellt das es ein Hund war. Du hast dich gefreut das es ein Hund war und hast \"\n                            f\"ihn weitergefüttert\",\n                            f\"Deine Mutter hat dir **{cookies}** Cookies geklaut. Du hast sie gefragt warum sie das \"\n                            f\"getan hat. Sie hat gesagt das sie es für dich getan hat weil sie dich liebt. Du hast \"\n                            f\"dir gedacht das sie es für sich selbst getan hat und hast sie verprügelt \"\n                            f\"(that escalated quickly)\",\n                            f\"Alle deine Cookies sind in der Waschmaschine gelandet. Du hast sie rausgeholt und alle \"\n                            f\"**{cookies}** Cookies waren kaputt\",\n                            f\"Im Internet hast du eine Seite gefunden in der behauptet wurde das du **{cookies}** \"\n                            f\"Cookies bekommen kannst. Du hast dich angemeldet und wurdest gescammt\",\n                            f\"Oben auf dem Dach hast du eine Kiste gefunden. In der Kiste waren **{cookies}** Cookies. \"\n                            f\"Du hast sie genommen und hast sie runtergeworfen. Sie sind alle kaputt gegangen\"]\n            hodenkrebsembed = discord.Embed(title=f\"{ctx.author.name} HAT HODENKREBS!\", description=\"Du hast Absofort \"\n                                                                                                    \"Hodenkrebs...\",\n                                            color=discord.Color.red())\n\n            eventgoodembed = discord.Embed(title=f\"{ctx.author.name} ist etwas **gutes** passiert...\",\n                                           description=random.choice(eventgood), color=discord.Color.green())\n\n            eventnotgoodembed = discord.Embed(title=f\"{ctx.author.name} ist etwas **schlechtes** passiert...\",\n                                              description=random.choice(eventnotgood), color=discord.Color.red())\n            if hodenkrebs == 1000:\n                await ctx.respond(embed=hodenkrebsembed)\n                await ctx.author.add_roles(hodenkrebsrole)\n                return\n            if goodordosent == 1:\n                await ctx.respond(embed=eventgoodembed)\n                await db.execute(\"UPDATE users SET cookies = cookies + ? WHERE user_id = ?\", (cookies, ctx.author.name))\n                await db.commit()\n                return\n            await ctx.respond(embed=eventnotgoodembed)\n            await db.execute(\"UPDATE users SET cookies = cookies - ? WHERE user_id = ?\", (cookies, ctx.author.name))\n            await db.commit()\n\n    @slash_command(description=\"Hacke andere User für Kekse hehe\")\n    @commands.cooldown(1, 43200, commands.BucketType.user)\n    async def hack(self, ctx, *, member: discord.Member):\n        async with aiosqlite.connect(\"level.db\") as db:\n            print(f\"{ctx.author} hat /hack gemacht\")\n            guild: discord.Guild = self.bot.get_guild(724602228505313311)\n            muterolle: discord.Role = guild.get_role(1043532505887809577)\n            embed2 = discord.Embed(title=\"Fehlgeschlagen!\",\n                                   description=f\"Der Hack auf **{member.name}** ist fehlgeschlagen. Du kannst es in 12h erneut probieren.\",\n                                   color=discord.Color.red())\n            async with db.execute(\"SELECT cookies FROM users WHERE user_id = ?\", (member.name,)) as cursor:\n                result = await cursor.fetchone()\n            if result == 0:\n                ehre = discord.Embed(title=\"Der Nutzer hat keine Erhackbaren Kekse!\",\n                                     description=f\"{member.name} hat keine Kekse dadurch hast du die Bank gehackt diese\"\n                                                 f\" hat dir die Polizei auf den Hals gejagt. Dadurch hast du **5** \"\n                                                 f\"Cookies Verloren!\")\n                await ctx.respond(embed=ehre)\n                await db.execute(\"UPDATE users SET cookies = cookies - 5 WHERE user_id = ?\", (ctx.author.name,))\n                await db.commit()\n                return\n            if member is ctx.author:\n                dumm = discord.Embed(title=\"Kann es sein das du dumm bist?\", description=\"Du hast auf Google nach den \"\n                                                                                         \"besten Hacker Tools gesucht \"\n                                                                                         \"und Virus.exe gefunden & \"\n                                                                                         \"heruntergeladen. Hast die \"\n                                                                                         \"datei allerdings selbst \"\n                                                                                         \"geöffnet. Dabei hast du \"\n                                                                                         \"**2** Cookies verloren.\",\n                                     color=discord.Color.red())\n                await ctx.respond(embed=dumm)\n                await db.execute(\"UPDATE users SET cookies = cookies - 2 WHERE user_id = ?\", (ctx.author.name,))\n                await db.commit()\n                return\n            opfer = member.name\n            emails = [\"2000\", \"2001\", \"2002\", \"2003\", \"2004\", \"2005\", \"2006\", \"2007\", \"2008\", \"2009\", \"2010\", \"_2000\",\n                      \"_2001\",\n                      \"_2002\", \"_2003\", \"_2004\", \"_2005\", \"_2006\", \"_2007\", \"_2008\", \"_2009\", \"_2010\", \"müller\",\n                      \"_müller\",\n                      \"Müller\", \"_Müller\", \"schmidt\", \"_schmidt\", \"Schmidt\", \"_Schmidt\", \"schneider\", \"_schneider\",\n                      \"Schneider\",\n                      \"_Schneider\", \"fischer\", \"Fischer\", \"_fischer\", \"_Fischer\", \"Weber\", \"weber\", \"_weber\", \"_Werber\",\n                      \"Meyer\", \"meyer\", \"_Meyer\", \"_meyer\", \"Wagner\", \"wagner\", \"_Wagner\", \"_wagner\", \"becker\",\n                      \"Becker\",\n                      \"_becker\", \"_Becker\", \"Thiel\", \"thiel\", \"_thiel\", \"_Thiel\"]\n            passwords = [\"hallo\", \"passwort\", \"hallo123\", \"schalke04\", \"passwort1\", \"qwertz\", \"arschl****\", \"schatz\",\n                         \"fi****\", \"password\", \"12345678\", \"123456789\", \"baseball\", \"footbal\", \"qwertzuiop\",\n                         \"1234567890\",\n                         \"superman\", \"1qwz2wsx\", \"trustno1\", \"jennifer\", \"sunshine\", \"iloveyou\", \"starwars\", \"computer\",\n                         \"michelle\", \"11111111\", \"princess\", \"987654321\", \"corvette\", \"1234qwer\", \"88888888\",\n                         \"q1w2e3r4t5\",\n                         \"internet\", \"samantha\", \"whatever\", \"maverick\", \"steelers\", \"mercedes\", \"123123123\",\n                         \"qwer1234\",\n                         \"hardcore\", \"midnight\", \"bigdaddy\", \"victoria\", \"cocacola\", \"marlboro\", \"asdfasdf\",\n                         \"jaordan32\",\n                         \"jonathan\"]\n            cookies = random.randint(0, 10)\n            fa = [\"an\", \"aus\"]\n            anbieter = [\"PornHub\", \"Microsoft\", \"Riotgames\", \"Ubisoft\", \"Discord\", \"Rewe\", \"Lidl\", \"Netto\", \"Steam\",\n                        \"Epic Games\", \"LeonMT1\"]\n            email = [\"@gmail.com\", \"@outlook.de\", \"@yahoo.com\", \"@gmx.de\", \"@t-online.de\", \"@web.de\"]\n\n            await ctx.respond(\"Hack wird gestartet\")\n            message = await ctx.send(f\"{member.name} wird jetzt gehackt...\")\n            await asyncio.sleep(2)\n            await message.edit(content=f\"[▘]Keks Konto login wurde gefunden... (2fa {random.choice(fa)})\")\n            await asyncio.sleep(3)\n            await message.edit(content=f\"\"\"[▖]\n        Email: {member.name}{random.choice(emails)}{random.choice(email)}\n        Password: {random.choice(passwords)}\"\"\")\n            await asyncio.sleep(3)\n            await message.edit(content=f\"[▝]Häufigste Überweisungs Anbieter: {random.choice(anbieter)}\")\n            await asyncio.sleep(3)\n            await message.edit(content=f\"[▘]Lädt...\")\n            await asyncio.sleep(3)\n            await message.edit(content=f\"[▖]Sucht IP...\")\n            await asyncio.sleep(3)\n            await message.edit(content=f\"[▝] IP: 49.124.134\")\n            await asyncio.sleep(3)\n            await message.edit(content=f\"Fertig mit dem Hack auf {member.mention}\")\n            await asyncio.sleep(2)\n            await db.execute(\"UPDATE users SET cookies = cookies + ? WHERE user_id = ?\", (cookies, ctx.author.name))\n            await db.execute(\"UPDATE users SET cookies = cookies - ? WHERE user_id = ?\", (cookies, opfer))\n            await db.commit()\n            if cookies == 0:\n                await message.edit(content=None, embed=embed2)\n                return\n            embed = discord.Embed(title=\"Erfolgreich abgeschloßen!\", description=f\"\"\"\nDu hast von **{member.mention}** **{cookies}** Cookies erhackt!\nEmail: {member.name}{random.choice(emails)}{random.choice(email)}\nPassword: {random.choice(passwords)}\n2FA Status: {random.choice(fa)}\nHäufigeste Überweisung an: {random.choice(anbieter)}\nKontostand: {result[0]} Cookies\"\"\", color=discord.Color.green())\n            embed.set_thumbnail(url=ctx.author.display_avatar.url)\n            await message.edit(embed=embed)\n            if muterolle not in member.roles:\n                await member.send(f\"\"\"Du wurdest von {ctx.author} gehackt, er hat dir {cookies} Cookies geklaut xD.\nDu kannst auch alle 12h jemand anderen Hacken und davon Cookies bekommen!\"\"\")\n\n\ndef setup(bot):\n    bot.add_cog(FunCommands(bot))\n","repo_name":"LeonMT1/CookieBot-old-","sub_path":"cogs/funcommands.py","file_name":"funcommands.py","file_ext":"py","file_size_in_byte":18150,"program_lang":"python","lang":"de","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"21677851441","text":"# -*- coding: utf-8 -*-\nimport sys\nfrom django.http import HttpResponse, Http404\nfrom django.views.generic.list import ListView\nfrom django.shortcuts import render\nfrom django.urls import reverse\nimport json\nimport urllib\nimport urllib2\nimport re\nimport requests\nfrom urllib2 import urlparse\nimport html\n\n\n# 首页\ndef index(request):\n    return render(request, 'index.html')\n\n\ndef index1(request):\n    if request.method == 'GET':\n        return render(request, 'index1.html')\n    else:\n        url_name = request.POST['url_name']\n        mydict = {\"url_name\": url_name}\n        return HttpResponse(\n            json.dumps(mydict),\n            content_type=\"application/json\"\n        )\n\n# 显示爬取页面\ndef page_item(request):\n    url_name = request.POST['url_name']\n    load_html = requests.get(url_name)\n    load_html.encoding = 'utf-8'\n    content = load_html.text\n    # 标题\n    title = re.findall(r'<title>(.*?)</title>', content)\n    title = title[0]\n    # 头部<head>\n    pattern_head = re.compile('<head>.*?</head>', re.S)\n    head_content = re.findall(pattern_head, load_html.text)\n    head = head_content[0].replace('<head>', '')   # 去掉head\n    head = head.replace('</head>', '')\n    pattern_head_href = re.compile('href=\\\"css.*?\\\"', re.S)   # 匹配没有http的css\n    head_href = re.findall(pattern_head_href, head)\n    head_href_arr = []\n    if head_href != '':\n        for h_href in head_href:\n            h_href = h_href.replace('href=\"', '')\n            h_href = h_href.replace('\"', '')\n            head = head .replace(h_href,'')\n            head_href_arr.append(h_href)\n    pattern_sc_head = re.compile('<script.*?</script>', re.S)\n    sc_head = re.findall(pattern_sc_head, head)   # 头部 除了js文件\n    src_head_arr1 = []  # 引入的js文件\n    src_head_arr2 = []  # js代码\n    for s_head in sc_head:\n        pattern_src = re.compile('[a-z]+:\\/\\/[a-zA-Z0-9_\\-\\/.%]+', re.L)  # 匹配js\n        src = re.findall(pattern_src, s_head)\n        if len(src) == 1:\n            src_head_arr1.append(src[0])  # 保存到一个数组 js文件\n        pattern_sc_ = re.compile('<script.*?>', re.S)  # 匹配script开头\n        s_ = re.findall(pattern_sc_, s_head)\n        s_head_ = s_head.replace(s_[0], '')\n        s_head_ = s_head_.replace('</script>', '')\n        s_head_ = s_head_.replace('\\r\\n', '')\n        if s_head_ != '':\n            src_head_arr2.append(s_head_)  # 保存到一个数组 js代码\n        head = head.replace(s_head, '')\n\n    # body\n    pattern_body = re.compile('<body.*?</body>', re.S)\n    pattern_body1 = re.compile('<body.*?>', re.S)\n    body_content = re.findall(pattern_body, load_html.text)\n    b = re.findall(pattern_body1, load_html.text)\n    body = body_content[0].replace(b[0], '')\n    body = body.replace('</body>', '')\n    pattern_script1 = re.compile('<script>.*?</script>', re.S)\n    script1 = re.findall(pattern_script1, body)\n    for sc1 in script1:\n        body = body.replace(sc1, '')\n    pattern_script2 = re.compile('<script.*?</script>', re.S)\n    script2 = re.findall(pattern_script2, body)\n    for sc2 in script2:\n        body = body.replace(sc2, '')\n    # 所有<body>里面的元素\n    element = re.findall(r'<([a-z]+)', body)\n    element_arr = []\n    for e in element:\n        element_arr.append(e)\n\n    # 根据元素获取里面所有所有的class及对应的链接\n    con = re.findall(r'<(.*?)>', body)\n    classes_arr = []\n    urls_arr = []\n    for co in con:\n        classes = re.findall(r'class=\"(.*?)\"', co)\n        if len(classes) != 0:\n            classes_arr.append(classes[0])\n        else:\n            classes_arr.append('')\n        url = re.findall(r'([a-z]+:\\/\\/[a-zA-Z0-9_\\-\\/.%]+)', co)\n        if len(url) != 0:\n            urls_arr.append(url[0])\n        else:\n            urls_arr.append('')\n\n    dic = {\"url\": url_name, \"title_name\": title, \"head\": head, \"head_href\": head_href_arr, \"src_head1\": src_head_arr1,\n           \"src_head2\": src_head_arr2, \"body\": body,\"element\":element_arr,\"classes\": classes_arr,\"urls\":urls_arr}\n    return render(request, 'page_item.html', {'dic':json.dumps(dic)})\n    # return render(request, 'page_item.html', dic)\n\n\n\n\ndef test(request):\n    url_name = request.POST['url_name']\n    # print url_name\n    # exit()\n    dic = {\"url_name\": url_name}\n    return render(request, 'test.html', dic)\n\n# 首页视图,继承自ListVIew\nclass IndexView(ListView):\n    # template_name属性用于指定使用哪个模板进行渲染\n    template_name = \"home/index1.html\"\n    # context_object_name属性用于给上下文变量取名（在模板中使用该名字）\n    context_object_name = \"home_index\"\n","repo_name":"littlemesie/spider","sub_path":"home/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"8681345094","text":"'''\nCreated on Dec 27, 2020\n\n@author: Q\n\nrotate the four elements in (left,up),(up,right),(right,bottom),(bottom,left) parts each time\nthe indices relationship:  m[i][j] -> m[j][N-1-i]\n'''\nclass Solution(object):\n    def rotate(self, matrix):\n        \"\"\"\n        :type matrix: List[List[int]]\n        :rtype: None Do not return anything, modify matrix in-place instead.\n        \"\"\"\n        N = len(matrix)\n        for i in range(0, N//2 + N%2):\n            for j in range(0, N//2):                \n                r, c = i, j\n                tmp = matrix[r][c]\n                for _ in range(4):                    \n                    tmp, matrix[c][N-1-r] = matrix[c][N-1-r], tmp                                        \n                    r, c = c, N-1-r\n                \nmsol = Solution()\nmatrix = [[1,2,3],[4,5,6],[7,8,9]]\nares = msol.rotate(matrix)\nprint(matrix)              ","repo_name":"chyylq/leetcode","sub_path":"0048_rotate_image/sol0048.py","file_name":"sol0048.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"23245750284","text":"\"\"\"Platform for text\"\"\"\nfrom __future__ import annotations\nfrom pathlib import Path\nfrom homeassistant.components.text import TextEntity\nfrom homeassistant.helpers.entity_platform import AddEntitiesCallback\nfrom homeassistant.core import HomeAssistant\nfrom homeassistant.const import ATTR_IDENTIFIERS, ATTR_MANUFACTURER, ATTR_NAME\nfrom homeassistant.helpers.update_coordinator import (\n    CoordinatorEntity,\n    DataUpdateCoordinator,\n)\nfrom .const import DOMAIN, DEVICE_NAME, CONF_FILE\nimport json\n\n\nasync def async_setup_entry(\n    hass: HomeAssistant, config_entry, async_add_entities: AddEntitiesCallback\n) -> None:\n    coordinator = hass.data[DOMAIN][config_entry.entry_id]\n    tunnor = coordinator.tunnor\n\n    # Skapar en dict på vilka tunnor som finns, men skippar last_update\n    entities = []\n    for tunna, _ in tunnor.items():\n        if tunna == \"last_update\":\n            continue\n        entities.append(Texter(hass, coordinator, tunna))\n\n    async_add_entities(entities)\n\n\nclass Texter(CoordinatorEntity, TextEntity):\n    \"\"\"Klass för text-fälten som skapar smeknamn för NextPickup\"\"\"\n\n    def __init__(\n        self, hass: HomeAssistant, coordinator: DataUpdateCoordinator, name\n    ) -> None:\n        super().__init__(coordinator)\n        self._name = name\n        self._attr_unique_id = name\n        self._hass = hass\n        self._coordinator = coordinator\n\n        self._attr_device_info = {\n            ATTR_IDENTIFIERS: {(DOMAIN, DEVICE_NAME)},\n            ATTR_NAME: DEVICE_NAME,\n            ATTR_MANUFACTURER: \"@nightcbis\",\n        }\n        self._attr_has_entity_name = True\n\n        smeknamn = self.readConfig()\n\n        self._coordinator.smeknamn[self._name] = smeknamn\n        self._attr_native_value = smeknamn\n\n    def writeConfig(self, smeknamn) -> None:\n        \"\"\"Sparar ner smeknamnet i config-filen samt i coordinatorn\"\"\"\n\n        # Skapar filen om den inte finns\n        path = self._hass.config.path(CONF_FILE)\n        configFile = Path(path)\n        configFile.touch(exist_ok=True)\n\n        # Öppnar och försöker läsa den som json. Om inte så skapar vi en tom data\n        with open(path, \"r\", encoding=\"utf-8\") as configFile:\n            try:\n                data = json.loads(configFile.read())\n            except:\n                data = {}\n\n        # Sparar ner i både data(till filen) och i coordinator för realtid.\n        data[self._name] = smeknamn\n        self._coordinator.smeknamn[self._name] = smeknamn\n\n        with open(path, \"w\", encoding=\"utf-8\") as configFile:\n            configFile.write(json.dumps(data, indent=4))\n\n    def readConfig(self, tunna=\"NAME\"):\n        \"\"\"Läser in smeknamn på en tunna och om ingen tunna defineras så används self._name\"\"\"\n        # Om ingen tunna defineras så kör vi mot self.\n        if tunna == \"NAME\":\n            tunna = self._name\n\n        # Skapar filen om den inte finns\n        path = self._hass.config.path(CONF_FILE)\n        configFile = Path(path)\n        configFile.touch(exist_ok=True)\n\n        # Laddar in filen och kollar så den är json. Om inte så skapar vi en tom data\n        with open(path, \"r\", encoding=\"utf-8\") as configFile:\n            try:\n                data = json.loads(configFile.read())\n            except:\n                data = {}\n\n        # Om inget smeknamn finns så sparar vi ner tunnans namn som smeknamn.\n        try:\n            smeknamn = data[tunna]\n        except:\n            self.writeConfig(tunna)\n            smeknamn = tunna\n\n        return smeknamn\n\n    def update(self) -> None:\n        \"\"\"Används ej\"\"\"\n\n    async def async_set_value(self, value: str) -> None:\n        \"\"\"Den här funktionen körs när man skriver i nytt smeknamn i ui't\"\"\"\n        if value == \"\":\n            value = self._name\n        self._attr_native_value = value\n        self.writeConfig(value)\n        self.async_schedule_update_ha_state(force_refresh=False)\n        await self._coordinator.async_request_refresh()\n\n    @property\n    def native_value(self) -> str | None:\n        return self._attr_native_value\n\n    @property\n    def name(self) -> str | None:\n        return self._name\n\n    @property\n    def unique_id(self) -> str | None:\n        return super().unique_id\n\n    @property\n    def state(self) -> str | None:\n        return self._attr_native_value\n\n    @property\n    def device_class(self) -> str | None:\n        return super().device_class\n","repo_name":"nightcbis/vmeab","sub_path":"custom_components/vmeab/text.py","file_name":"text.py","file_ext":"py","file_size_in_byte":4388,"program_lang":"python","lang":"sv","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"27709022434","text":"from pydantic.errors import DataclassTypeError\nfrom keep_alive import keep_alive\nfrom typing import Optional, List, Dict, Union\nfrom pydantic import BaseModel as DataClass\nimport discord\nimport datetime\nimport os\n\n# Gif libraries.\nimport random\nimport json\nimport requests\n\n\nclient = discord.Client()\ndiscord_key = os.getenv(\"DISCORD_TOKEN\")\n\n\nclass SpamUser(DataClass):\n    user_name: str\n    discriminator: str\n    search_terms: List[str]\n    last_mssg: Optional[datetime.datetime]\n\n\nclass UsersToSpam(DataClass):\n    users: List[SpamUser]\n\n    def get_user_by_discriminator(self, discriminator: str) -> Union[SpamUser, None]:\n        \"\"\"Retrieves the user whose discriminator matches the one given.\n\n        Args:\n            discriminator (str): Discriminator to be found.\n\n        Returns:\n            Union[SpamUser, None]: Associated user to the discriminator or None if not found.\n        \"\"\"\n        return next(\n            (\n                spam_user\n                for spam_user in users_to_spam.users\n                if spam_user.discriminator == discriminator\n            ),\n            None,\n        )\n\n\ndefault_starttime = datetime.datetime(2021, 1, 1)\ndefault_goodboy_until = datetime.timedelta(\n    seconds=300\n)  # 5 minutes of silence in this channel\nmuted_channels: Dict[str, datetime.datetime] = {}\n\n\ndef get_users_to_spam() -> List[SpamUser]:\n    \"\"\"Retrieves the list of users that will be spammed.\n\n    Returns:\n        List[SpamUser]: List of users that can be spammed.\n    \"\"\"\n\n    def get_spam_user(name: str, terms: List[str]) -> SpamUser:\n        return SpamUser(\n            user_name=name,\n            discriminator=os.getenv(f\"{name}_discriminator\"),\n            search_terms=terms,\n            last_mssg=default_starttime,\n        )\n\n    return [\n        get_spam_user(\"gabri\", [\"coffee\", \"spaghetti\", \"pasta\", \"pineapple pizza\"]),\n        get_spam_user(\"tim\", [\"god\", \"fresh-prince\", \"he-man\", \"all-mighty\"]),\n        get_spam_user(\"dennis\", [\"matrix\", \"unicorn\", \"dungeon\", \"spending-money\"]),\n        get_spam_user(\n            \"maarten\", [\"matrix\", \"frog\", \"breakingbad\", \"dexters laboratory\"]\n        ),\n        get_spam_user(\"prisca\", [\"cat\", \"matrix\"]),\n        get_spam_user(\"robin\", [\"baby\", \"fire\", \"spongebob\", \"matrix\"]),\n    ]\n\n\nusers_to_spam = UsersToSpam(users=get_users_to_spam())\n\n\ndef find_gif(search_term: str) -> str:\n    # set the apikey and limit\n    tenorgif_key = os.getenv(\"TENOR_TOKEN\")\n    lmt = 40\n\n    # get the top \"lmt\" GIFs for the search term\n    r = requests.get(\n        f\"https://api.tenor.com/v1/search?q={search_term}&key={tenorgif_key}&limit={lmt}\"\n    )\n\n    if r.status_code == 200:\n        # load the GIFs using the urls for the smaller GIF sizes\n        results = json.loads(r.content)[\"results\"]\n        selected_gif = results[random.randint(0, len(results) - 1)]\n        return selected_gif[\"url\"]\n    return \"\"\n\n\n@client.event\nasync def on_ready():\n    print(\"We have logged in as {0.user}\".format(client))\n\n\nasync def on_time_to_spam(message):\n    last_time = max(spam_user.last_mssg for spam_user in users_to_spam.users)\n    user_to_spam: SpamUser = users_to_spam.get_user_by_discriminator(\n        message.author.discriminator\n    )\n    if user_to_spam is None:\n        print(\n            f\"No user found to spam with discriminator {message.author.discriminator}\"\n        )\n        return\n\n    now_time = datetime.datetime.now()\n    if (now_time - last_time) > datetime.timedelta(minutes=60):\n        search_idx = random.randint(0, len(user_to_spam.search_terms) - 1)\n        gif = find_gif(user_to_spam.search_terms[search_idx])\n        default_mssg = f\"Ey {message.author.mention} bring me coffee.\"\n        # Update timestamp.\n        user_to_spam.last_mssg = now_time\n        await message.channel.send(default_mssg)\n        if gif:\n            await message.channel.send(gif)\n\n\nasync def on_reply_to_filter_mssg(message, filter_mssgs: List[str], find_str: str):\n    mssg_contnt = message.content.lower()\n    if any(mssg in mssg_contnt for mssg in filter_mssgs):\n        gif = find_gif(find_str)\n        if gif:\n            await message.channel.send(gif)\n        else:\n            await message.channel.send(\n                f\"{find_str.capitalize()}! (Couldn't find gifs soz).\"\n            )\n\n\n@client.event\nasync def on_message(message):\n\n    mute_keywords = [\"/badbot\", \"/badboy\", \"/mutebot\"]\n    unmute_keywords = [\"/goodbot\", \"/goodboy\", \"/unmutebot\"]\n\n    if message.author == client.user:\n        # Avoid infinite loop!\n        return\n    mssg_chn = message.channel\n    if isinstance(mssg_chn, discord.DMChannel):\n        # Don't talk to strangers :(\n        return\n    if (\n        mssg_chn.category.name.lower() == \"fpt internal\"\n        and mssg_chn.name.lower() == \"general\"\n    ):\n        await on_time_to_spam(message)\n\n    def mute_until() -> datetime.datetime:\n        return datetime.datetime.now() + default_goodboy_until\n\n    if message.content.lower() in mute_keywords:\n        muted_channels[mssg_chn] = mute_until()\n        await message.channel.send(\n            \"Sorry, I'll be a good bot for a while :cry: :innocent:\"\n        )\n        return\n    if message.content.lower() in unmute_keywords:\n        muted_channels.pop(message.channel, None)  # Remove from list if it was muted.\n        await message.channel.send(\"Thank you good lord.\")\n        return\n\n    mc = muted_channels.get(message.channel, None)\n    if mc is not None:\n        if mc > datetime.datetime.now():\n            # Channel is still muted.\n            return\n        elif mc <= datetime.datetime.now():\n            await message.channel.send(\"I was a good boy for a while now.\")\n            muted_channels.pop(message.channel)\n\n    await on_reply_to_filter_mssg(\n        message,\n        [\"hail hydra\", \"heil hydra\", \"dhydro\", \"dhydra\", \"d-hydro\", \"d-hydra\",],\n        \"hail-hydra\",\n    )\n    await on_reply_to_filter_mssg(\n        message,\n        [\"good morning\", \"goodmorning\", \"goedemorgen\", \"buon giorno\",],\n        \"good morning\",\n    )\n\n\nkeep_alive()\nclient.run(discord_key)\n","repo_name":"Carsopre/FPT_DiscordBot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6063,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32187089701","text":"import sys\nimport resource\n\nif sys.platform == 'darwin':\n\t# darwin returns bytes\n\tdivisor = 1024.0*1024.0\nelse:\n\t# other OS (AFAIK) return a number of pages\n\tdivisor = 1024.0*1024.0/resource.getpagesize()\n\ndef usage (label='usage'):\n\tusage=resource.getrusage(resource.RUSAGE_SELF)\n\treturn '%s: usertime=%s systime=%s mem=%s mb' % (label,usage.ru_utime,usage.ru_stime,(usage.ru_maxrss/divisor))\n","repo_name":"sdn-ixp/sdx-pyretic","sub_path":"exabgp/lib/exabgp/util/usage.py","file_name":"usage.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"86"}
+{"seq_id":"45588556657","text":"import json\n\nimport csv\n\n\nwith open(\"../data/guestDB.json\", newline=\"\") as jsonfile:\n    comosbd = json.load(jsonfile)\n\nwith open(\"../data/excelExportGuestsInvite.csv\", \"w\") as csvfile:\n    fieldnames = [\n        \"rsvpCode\",\n        \"firstname\",\n        \"lastname\",\n        \"plusOne:firstname\",\n        \"plusOne:lastname\",\n    ]\n    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n\n    writer.writeheader()\n    for line in comosbd:\n        tt = {\n            \"rsvpCode\": line[\"rsvpCode\"],\n            \"firstname\": line[\"firstname\"],\n            \"lastname\": line[\"lastname\"],\n            \"plusOne:firstname\": line[\"plusOne\"][0][\"firstname\"] if len(line[\"plusOne\"]) > 0  else \"\",\n            \"plusOne:lastname\": line[\"plusOne\"][0][\"lastname\"] if len(line[\"plusOne\"]) > 0  else \"\",\n            }\n        print(tt)\n        writer.writerow(tt)\n","repo_name":"MahrRah/wedding-homepage","sub_path":"scripts/excelExport-invites.py","file_name":"excelExport-invites.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35488916751","text":"import os\nimport tempfile\n\nimport dbtools\nimport drupaltools\nimport pantheon\nimport ygg\nfrom vars import *\n\nfrom fabric.api import *\n\n\nclass BuildTools(object):\n    \"\"\" Generic Pantheon project installation helper functions.\n\n    This is generally used as a base object, inherited by other project\n    building classes (install, import, and restore). The child classes\n    can use these methods directly or override/expand base processes.\n\n    \"\"\"\n    def __init__(self):\n        \"\"\" Initialize generic project installation object & helper functions.\n        project: the name of the project to be built.\n\n        \"\"\"\n        config = ygg.get_config()\n        self.server = pantheon.PantheonServer()\n\n        self.project = str(config.keys()[0])\n        self.config = config[self.project]\n        self.environments = set(self.config['environments'].keys())\n        self.project_path = os.path.join(self.server.webroot, self.project)\n        self.db_password = self.config\\\n                ['environments']['live']['mysql']['db_password']\n        self.version = None\n\n    def bcfg2_project(self):\n        local('bcfg2 -vqedb projects', capture=False)\n\n    def remove_project(self):\n        \"\"\" Remove a project and all related files/configs from the server.\n\n        \"\"\"\n        locations = list()\n\n        # Git repository\n        locations.append(os.path.join('/var/git/projects', self.project))\n        # Project webroot\n        locations.append(self.project_path)\n\n        # TODO: We also need to remove the following:\n        # Solr Index\n        # Apache vhost\n        # Jenkins cron\n        # Drush alias\n        # Databases\n\n        for location in locations:\n            if os.path.exists(location):\n                local('rm -rf %s' % location)\n\n    def setup_project_repo(self, upstream_repo=None):\n        \"\"\" Create a new project repo, and download pantheon/drupal core.\n\n        \"\"\"\n        project_repo = os.path.join('/var/git/projects', self.project)\n        dev_branch = None\n\n        # If this is a development server check MERCURY_BRANCH for source.\n        if MERCURY_BRANCH != 'master':\n            dev_branch = MERCURY_BRANCH\n\n        # For imports, no upstream is set. But self.version is known.\n        if upstream_repo is None:\n            # Is this a development branch?\n            if dev_branch:\n                upstream_repo = 'git://github.com/pantheon-systems/' + \\\n                                '%s-%s.git' % (self.version, dev_branch)\n            else:\n                upstream_repo = 'git://git.getpantheon.com/pantheon/%s.git' %(\n                                                                 self.version)\n        else:\n            # If this is a development server, make sure the upstream has\n            # not been changed to some other source before modifying. Mostly\n            # because we make hackish assumptions about determining version\n            # and destination.\n            if dev_branch and upstream_repo.startswith(\n                                 'git://git.getpantheon.com'):\n                self.version = upstream_repo[-5]\n                upstream_repo = 'git://github.com/pantheon-systems/' + \\\n                                '%s-%s.git' % (self.version, dev_branch)\n\n        # Get Pantheon core\n        local('git clone --mirror %s %s' % (upstream_repo, project_repo))\n\n        with cd(project_repo):\n            # Repo config\n            local('git config core.sharedRepository group')\n            # Group write.\n            local('chmod -R g+w .')\n\n        # post-receive-hook\n        post_receive_hook = os.path.join(project_repo,\n                                         'hooks/post-receive')\n        pantheon.copy_template('git.hook.post-receive', post_receive_hook)\n        local('chmod +x %s' % post_receive_hook)\n\n    def setup_project_branch(self):\n        \"\"\" Create a branch of the project.\n\n        \"\"\"\n        project_repo = os.path.join('/var/git/projects', self.project)\n        with cd(project_repo):\n            local('git branch %s' % self.project)\n\n    def setup_working_dir(self, working_dir):\n        \"\"\" Clone a project to a working directory for processing.\n        working_dir: temp directory for project processing (import/restore)\n\n        \"\"\"\n        local('git clone -l /var/git/projects/%s -b %s %s' % (self.project,\n                                                              self.project,\n                                                              working_dir))\n\n    def setup_database(self, environment, password, db_dump=None, onramp=False):\n        \"\"\" Create a new database based on project_environment, using password.\n        environment: the environment name (dev/test/live) in which to create db\n        password: password to identify user (username is same as project name).\n        db_dump (optional): full path to database dump to import into db.\n        onramp (optional): bool. perform additional prep during import process.\n\n        \"\"\"\n        username = self.config['environments'][environment]['mysql']['db_username']\n        database = self.config['environments'][environment]['mysql']['db_name']\n        password = self.config['environments'][environment]['mysql']['db_password']\n\n        dbtools.create_database(database)\n        dbtools.set_database_grants(database, username, password)\n        if db_dump:\n            dbtools.import_db_dump(db_dump, database)\n            if onramp:\n                dbtools.clear_cache_tables(database)\n                dbtools.convert_to_innodb(database)\n\n    def setup_settings_file(self, site_dir):\n        \"\"\" Setup pantheon.settings.php and settings.php.\n        site_dir: path to the site directory. E.g. /var/www/sites/default\n\n        \"\"\"\n        settings_file = os.path.join(site_dir, 'settings.php')\n        settings_default = os.path.join(site_dir, 'default.settings.php')\n        settings_pantheon = 'pantheon%s.settings.php' % self.version\n        os.path.join(self.project_path, settings_pantheon)\n\n        # Stomp on changes to default.settings.php - no need to conflict here.\n        local('git --git-dir=/var/git/projects/%s cat-file ' % self.project + \\\n           'blob refs/heads/master:sites/default/default.settings.php > %s' % (\n                                                             settings_default))\n        # Make sure settings.php exists.\n        if not os.path.isfile(settings_file):\n            local('cp %s %s' % (settings_default, settings_file))\n\n        # Comment out $base_url entries.\n        local(\"sed -i 's/^[^#|*]*\\$base_url/# $base_url/' %s\" % settings_file)\n\n        # Create pantheon.settings.php\n        if not os.path.isfile(os.path.join(self.project_path,\n                                           settings_pantheon)):\n            self.bcfg2_project()\n\n        # Import needs a valid settings file in the tmp directory\n        if hasattr(self, 'working_dir'):\n            tmp_file_dir = os.path.abspath(os.path.join(self.working_dir, '..'))\n            local(\"cp %s %s\" %\n                  (os.path.join(self.project_path, settings_pantheon),\n                   tmp_file_dir))\n            vhost_file = '/etc/apache2/sites-available/%s_dev' % self.project\n            local(\"sed -i -e 's|($vhost_file)|(\\\"%s\\\")|' %s/%s\" %\n                  (vhost_file, tmp_file_dir, settings_pantheon))\n\n        # Include pantheon.settings.php at the end of settings.php\n        with open(os.path.join(site_dir, 'settings.php'), 'a') as f:\n            f.write(\"\"\"\n/* Added by Pantheon */\nif (file_exists('../pantheon%s.settings.php')) {\n    include_once '../pantheon%s.settings.php';\n}\n\"\"\" % (self.version, self.version))\n\n    def setup_drush_alias(self):\n        \"\"\" Create drush aliases for each environment in a project.\n\n        \"\"\"\n        for env in self.environments:\n            root = os.path.join(self.server.webroot, self.project, env)\n            drush_dict = {'project': self.project,\n                          'environment': env,\n                          'root': root}\n            self.server.create_drush_alias(drush_dict)\n\n    def setup_solr_index(self):\n        \"\"\" Create solr index for each environment in a project.\n\n        \"\"\"\n        for env in self.environments:\n            self.server.create_solr_index(self.project, env, self.version)\n\n    def setup_drupal_cron(self):\n        \"\"\" Create drupal cron jobs in jenkins for each environment.\n\n        \"\"\"\n        for env in self.environments:\n            self.server.create_drupal_cron(self.project, env)\n\n    def setup_environments(self, handler=None, working_dir=None):\n        \"\"\" Send code/data/files from processing to destination (dev/test/live)\n        All import and restore processing is done in temp directories. Once\n        processing is complete, it is pushed out to the final destination.\n\n        handler: 'import' or None. If import, complete extra import processing.\n        working_dir: If handler is import, also needs full path to working_dir.\n\n        \"\"\"\n\n        # During import, only run updates/import processes a single database.\n        # Once complete, we import this 'final' database into each environment.\n        if handler == 'import':\n            tempdir = tempfile.mkdtemp()\n            dump_file = dbtools.export_data(self, 'dev', tempdir)\n\n        for env in self.environments:\n            # Code\n            destination = os.path.join(self.project_path, env)\n            local('git clone -l /var/git/projects/%s -b %s %s' % (self.project,\n                                                                 self.project,\n                                                                 destination))\n            # On import setup environment data and files.\n            if handler == 'import':\n                # Data (already exists in 'dev' - import into other envs)\n                if env != 'dev':\n                    dbtools.import_data(self, env, dump_file)\n\n                # Files\n                source = os.path.join(working_dir, 'sites/default/files')\n                file_dir = os.path.join(self.project_path, env,\n                                                'sites/default')\n                local('rsync -av %s %s' % (source, file_dir))\n\n        # Cleanup\n        if handler == 'import':\n            local('rm -rf %s' % tempdir)\n\n    def push_to_repo(self, tag):\n        \"\"\" Commit changes in working directory and push to central repo.\n\n        \"\"\"\n        with cd(self.working_dir):\n            local('git checkout %s' % self.project)\n            # Set up .gitignore\n            pantheon.copy_template('git.ignore', os.path.join(self.working_dir, '.gitignore'))\n            local('git add -A .')\n            local(\"git commit --author=\\\"%s\\\" -m 'Initialize Project: %s'\" % (\n                                                   self.author, self.project))\n            local('git tag %s.%s' % (self.project, tag))\n            local('git push')\n            local('git push --tags')\n\n    def setup_permissions(self, handler, environment=None):\n        \"\"\" Set permissions on project directory, settings.php, and files dir.\n\n        handler: one of: 'import','restore','update','install'. How the\n        permissions are set is determined by the handler.\n\n        environment: In most cases this is left to None, as we will be\n        processing all environments using self.environments. However,\n        if handler='update' we need to know the specific environment for which\n        the update is being run. We do this so we are not forcing permissions\n        updates on files that have not changed.\n\n        \"\"\"\n        # Get  owner\n        #TODO: Allow non-getpantheon users to set a default user.\n        if os.path.exists(\"/etc/pantheon/ldapgroup\"):\n            owner = self.server.get_ldap_group()\n        else:\n            owner = self.server.web_group\n\n        # During code updates, we only make changes in one environment.\n        # Otherwise, we are modifying all environments.\n        environments = list()\n        if handler == 'update':\n            #Single environment during update.\n            environments.append(environment)\n        else:\n            #All environments for install/import/restore.\n            environments = self.environments\n\n\n        \"\"\"\n        Project directory and sub files/directories\n\n        \"\"\"\n\n        # installs / imports / restores.\n        if handler in ['install', 'import', 'restore']:\n            # setup shared repo config and set gid\n            for env in environments:\n                with cd(os.path.join(self.server.webroot, self.project, env)):\n                    local('git config core.sharedRepository group')\n            with cd(self.server.webroot):\n                local('chown -R %s:%s %s' % (owner, owner, self.project))\n\n\n        \"\"\"\n        Files directory and sub files/directories\n\n        \"\"\"\n\n        # For installs, just set 770 on files dir.\n        if handler == 'install':\n            for env in environments:\n                site_dir = os.path.join(self.project_path,\n                                        env,\n                                        'sites/default')\n                with cd(site_dir):\n                    local('chmod 770 files')\n                    local('chown %s:%s files' % (self.server.web_group,\n                                                 self.server.web_group))\n\n        # For imports or restores: 770 on files dir (and subdirs). 660 on files\n        elif handler in ['import', 'restore']:\n            for env in environments:\n                file_dir = os.path.join(self.project_path, env,\n                                        'sites/default/files')\n                with cd(file_dir):\n                    local(\"chmod 770 .\")\n                    # All sub-files\n                    local(\"find . -type d -exec find '{}' -type f \\; | \\\n                           while read FILE; do chmod 660 \\\"$FILE\\\"; done\")\n                    # All sub-directories\n                    local(\"find . -type d -exec find '{}' -type d \\; | \\\n                          while read DIR; do chmod 770 \\\"$DIR\\\"; done\")\n                    # Apache should own files/*\n                    local(\"chown -R %s:%s .\" % (self.server.web_group,\n                                                self.server.web_group))\n\n        # For updates, set apache as owner of files dir.\n        elif handler == 'update':\n            site_dir = os.path.join(self.project_path,\n                                    environments[0],\n                                    'sites/default')\n            with cd(site_dir):\n                local('chown %s:%s files' % (self.server.web_group,\n                                             self.server.web_group))\n\n\n        \"\"\"\n        settings.php & pantheon.settings.php\n\n        \"\"\"\n\n        #TODO: We could split this up based on handler, but changing perms on\n        # two files is fast. Ignoring for now, and treating all the same.\n        for env in environments:\n            if pantheon.is_drupal_installed(self, env):\n                # Drupal installed, Apache does not need to own settings.php\n                settings_perms = '440'\n                settings_owner = owner\n                settings_group = self.server.web_group\n            else:\n                # Drupal is NOT installed. Apache must own settings.php\n                settings_perms = '660'\n                settings_owner = self.server.web_group\n                settings_group = self.server.web_group\n\n            site_dir = os.path.join(self.project_path, env, 'sites/default')\n            with cd(site_dir):\n                # settings.php\n                local('chmod %s settings.php' % settings_perms)\n                local('chown %s:%s settings.php' % (settings_owner,\n                                                    settings_group))\n                # TODO: New sites will not have a pantheon.settings.php in their\n                # repos. However, existing backups will, and if the settings\n                # file exists, we need it to have correct permissions.\n                if os.path.exists(os.path.join(site_dir,\n                                               'pantheon.settings.php')):\n                    local('chmod 440 pantheon.settings.php')\n                    local('chown %s:%s pantheon.settings.php' % (owner,\n                                                       settings_group))\n        if not self.version:\n            self.version = drupaltools.get_drupal_version('%s/dev' %\n                                                          self.project_path)[0]\n        with cd(self.project_path):\n            # pantheon.settings.php\n            local('chmod 440 pantheon%s.settings.php' % self.version)\n            local('chown %s:%s pantheon%s.settings.php' % (owner,\n                                                           settings_group,\n                                                           self.version))\n\n","repo_name":"pantheon-deprecated/mercury","sub_path":"fab/pantheon/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":16842,"program_lang":"python","lang":"en","doc_type":"code","stars":75,"dataset":"github-code","pt":"86"}
+{"seq_id":"29314489751","text":"from aplicacion.config.mysqlconnection import connectToMySQL\n\nclass Ninja:\n    def __init__(self,data):\n        self.id = data['id']\n        self.first_name = data['first_name']\n        self.last_name = data['last_name']\n        self.age = data['age']\n        self.dojo_id= data['dojo_id']\n        self.created_at = data['created_at']\n        self.updated_at = data['updated_at']\n\n    @classmethod\n    def crearninja(cls,data):\n        consulta = \"\"\"INSERT INTO ninjas (first_name, last_name, age, dojo_id) VALUES (%(first_name)s, %(last_name)s, %(age)s, %(dojo)s);\"\"\"\n        resultado = connectToMySQL(\"schema_dojos_y_ninjas\").query_db(consulta,data)\n        return resultado\n\n    @classmethod\n    def obtener_un_ninja(cls,data):\n        consulta = \"SELECT * FROM ninjas WHERE id= %(id)s\"\n        resultado = connectToMySQL(\"schema_dojos_y_ninjas\").query_db(consulta,data)\n        #esto va a dar una lista CON UN SOLO DICCIONARIO, por eso, el indice 0 se puede convertir facilmente así en objeto. Arriba como era una lista de VARIOS diccionarios se hace un for.\n        return cls(resultado[0])\n\n","repo_name":"carolinaorellana/dojos_y_ninjas","sub_path":"aplicacion/models/ninja.py","file_name":"ninja.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"5936006528","text":"#!/usr/bin/env python3\n'''Vocabulary: copied from StatisticVocabulary.java '''\nimport os\n''' input ontology'''\nNUMBER_OF_TBOX_AXIOMS = \"Statistic: Number_of_TBox_Axioms = \"\nNUMBER_OF_INPUT_CONCEPTASSERTIONS = \"Statistic: Number_of_input_concept_assertions = \"\nNUMBER_OF_INPUT_ROLEASSERTIONS = \"Statistic: Number_of_input_role_assertions = \"\nNUMBER_OF_INPUT_ASSERTIONS = \"Statistic: Number_of_input_concept_and_role_assertions = \"\n\n'''signature'''\nNUMBER_OF_INPUT_INDIVIDUALS = \"Statistic: Number_of_input_individuals = \"\nNUMBER_OF_INPUT_CONCEPTNAMES = \"Statistic: Number_of_input_concept_names = \"\nNUMBER_OF_INPUT_ROLENAMES = \"Statistic: Number_of_input_role_names = \"\n\n'''materialization'''\nNUMBER_OF_MATERIALIZED_CONCEPTASSERTIONS = \"Statistic: Number_of_materialized_concept_assertions = \"\nNUMBER_OF_MATERIALIZED_ROLEASSERTIONS = \"Statistic: Number_of_materialized_role_assertions = \"\nNUMBER_OF_MATERIALIZED_ASSERTIONS = \"Statistic: Number_of_materialized_concept_and_role_assertions = \"\n'''abstraction'''\nNUMBER_OF_TYPES = \"Statistic: Number_of_types = \"\nNUMBER_OF_X = \"Statistic: Number_of_x = \"\nNUMBER_OF_U = \"Statistic: Number_of_u = \"\nNUMBER_OF_YZ = \"Statistic: Number_of_yz = \"\nNUMBER_OF_ABSTRACT_INDIVIDUALS = \"Statistic: Number_of_abstract_individuals_xuyz = \"\nCURRENT_LOOP = \"Statistic: Current_loop = \"\nNUMBER_OF_ABSTRACT_CONCEPTASSERTIONS = \"Statistic: Number_of_abstract_concept_assertions = \"\nNUMBER_OF_ABSTRACT_ROLEASSERTIONS = \"Statistic: Number_of_abstract_role_assertions = \"\nNUMBER_OF_ABSTRACT_ASSERTIONS = \"Statistic: Number_of_abstract_concept_and_role_assertions = \"\n\n''' global abstractionDict, will store a dictioinary of the form:\n <LoopNumber, dict{<#Type, n1>, <#AbstractIndividuals,n2>, <#AbstractAssertions,n3>}}>'''\nabstractionDict = {}\n# global numberOfInputAssertions\nnumberOfInputAssertions = 1\nglobalLogFileName = \"\"\n''' given a log file, e.g. imdb.result.txt, this function will return a string of the following format:\nnameOfTheFile   &number_of_refinement_steps \n& #types_in_1st_abstraction  &#individual_1st_abstraction & #assertions_1st_abstraction & %size_1st_abstraction_wrt_originalAbox\n& #types_in_last_abstraction  &#individual_last_abstraction & #assertions_last_abstraction & %size_last_abstraction_wrt_originalAbox\n\\\\\n'''\ndef readAbstractionInfoForOneOntology(logFilePath):\n    global abstractionDict\n    abstractionDict.clear()\n    global globalLogFileName\n    fileNameInPath = os.path.basename(logFilePath)\n    splittedExentsion = os.path.splitext(fileNameInPath)\n    globalLogFileName = splittedExentsion[0]\n    \n    startAbox = False\n    # a dictionary for abstraction which map the first(1), second (2),... abstraction to its info,e.g. type, size,..., stored in a list\n\n    with open(logFilePath, encoding='utf-8') as logFile:\n        for aline in logFile:\n            aline = str(aline.strip())\n            '''print TBox information'''\n            if \"Ontology file:\" in aline:\n                print(aline)\n             \n            printInputInformation(aline, NUMBER_OF_INPUT_CONCEPTNAMES)\n            printInputInformation(aline, NUMBER_OF_INPUT_ROLENAMES)\n            printInputInformation(aline, NUMBER_OF_TBOX_AXIOMS)\n            numberOfInputAssertionsReadFromThisLine = getInputOntologyInformation(aline, NUMBER_OF_INPUT_ASSERTIONS) \n               \n            if not (numberOfInputAssertionsReadFromThisLine is None):\n                global numberOfInputAssertions\n                numberOfInputAssertions = numberOfInputAssertionsReadFromThisLine\n            \n            '''print ABox information'''\n            if \"ABoxList file\" in aline:\n                print(\"\\n\" + aline + \"\\n\")\n                startAbox = True\n            if startAbox:\n                printInputInformation(aline, NUMBER_OF_INPUT_INDIVIDUALS)\n                printInputInformation(aline, NUMBER_OF_INPUT_CONCEPTASSERTIONS)\n                printInputInformation(aline, NUMBER_OF_INPUT_ROLEASSERTIONS)\n                printInputInformation(aline, NUMBER_OF_INPUT_ASSERTIONS)\n                numberOfInputAssertionsReadFromThisLine = getInputOntologyInformation(aline, NUMBER_OF_INPUT_ASSERTIONS) \n               \n                if not (numberOfInputAssertionsReadFromThisLine is None):\n                    global numberOfInputAssertions\n                    numberOfInputAssertions = numberOfInputAssertionsReadFromThisLine\n                  \n            getAbstractionInfoForOneLoop(aline, NUMBER_OF_TYPES)\n            getAbstractionInfoForOneLoop(aline, NUMBER_OF_ABSTRACT_INDIVIDUALS)\n            getAbstractionInfoForOneLoop(aline, NUMBER_OF_ABSTRACT_ASSERTIONS)\n            \n    return getAbstractionInfoForAllLoops()\n            \n'''  '''\ndef getAbstractionInfoForAllLoops():\n    resultingString = \"\"\n    keys = list(abstractionDict.keys())\n    keys.sort()\n    for key in keys:\n        print(key + \"&    \" + \"\\t\", end=\"\")\n    print()\n    \n    '''print the ontology name'''\n    print(globalLogFileName + \"  \", end=\"\")\n    resultingString += globalLogFileName + \"  \"\n    \n    '''print number of refinements steps'''\n    print(\"&$%d$ \" % (len(keys) - 1), end=\"\")\n    resultingString += \"&$\" + str(len(keys) - 1) + \"$ \"\n    \n    '''print the first and the last abstracion information'''\n    firstAndLastKeys = []\n    firstAndLastKeys.append(keys[0])\n    firstAndLastKeys.append(keys[-1])\n    for key in firstAndLastKeys:\n        value = abstractionDict[key]\n#         print(\"&$\" + value[NUMBER_OF_TYPES] + \"$  \", end=\"\")\n        resultingString += \"&$\" + value[NUMBER_OF_TYPES] + \"$  \"\n        \n#         print(\"&$\" + value[NUMBER_OF_ABSTRACT_INDIVIDUALS] + \"$  \", end=\"\")\n        resultingString += \"&$\" + value[NUMBER_OF_ABSTRACT_INDIVIDUALS] + \"$  \"\n        \n#         print(\"&$\" + value[NUMBER_OF_ABSTRACT_ASSERTIONS] + \"$  \", end=\"\")\n        resultingString += \"&$\" + value[NUMBER_OF_ABSTRACT_ASSERTIONS] + \"$  \"\n        \n        abstractionSizeCompression = float(value[NUMBER_OF_ABSTRACT_ASSERTIONS]) / float(numberOfInputAssertions) * 100\n        abstractionSizeCompressionString = \"{0:.3f}\".format(abstractionSizeCompression)\n        resultingString += \"&$\" + abstractionSizeCompressionString + \"$ \"\n#         print(\"&$%.3f$ \" % (abstractionSizeCompression), end=\"\")\n           \n#     print(\"\\\\\\\\\")\n    resultingString += \"\\\\\\\\\"\n    print(resultingString)\n    return  resultingString\n\ndef printInputInformation(aString, informationToBePrinted):\n    listOfStrings = str(aString).split(informationToBePrinted)\n    sizeOfResultingList = len(listOfStrings)\n    if sizeOfResultingList > 1:\n        print(informationToBePrinted + listOfStrings[1])\n        return listOfStrings[1]\n\ndef getInputOntologyInformation(aString, informationToGet):\n    listOfStrings = str(aString).split(informationToGet)\n    sizeOfResultingList = len(listOfStrings)\n    if sizeOfResultingList > 1:\n        return listOfStrings[1]\n\n'''get a specific number in one loop, e.g. NUMBER_OF_ABSRTACT_INDIVIDUALS, and put it to the global dictionary'''\ndef getAbstractionInfoForOneLoop(aLine, informationToGet):\n    aString = str(aLine)\n    if CURRENT_LOOP in aString and informationToGet in aString:\n        twoSplittedParts = aString.split(\";\")\n#         print(aString)\n        abstractionLoop = getInputOntologyInformation(twoSplittedParts[0], CURRENT_LOOP)\n#         print(abstractionLoop)\n        informationValue = getInputOntologyInformation(aString, informationToGet)\n#         print(informationValue)\n        ''' put them to the dictionary'''\n        updateDictionary(abstractionLoop, informationToGet, informationValue)\n        return (abstractionLoop, informationToGet, informationValue)\n    \n        \n''' add a new value to the dictionary for abstraction'''        \ndef updateDictionary(key, typeOfTheValue, addedValue):\n    existingValues = abstractionDict.get(key)\n    if existingValues == None:\n        existingValues = {}\n         \n    existingValues[typeOfTheValue] = addedValue\n    abstractionDict[key] = existingValues\n# '''test'''\n# readAbstractionInfoForOneOntology(\"imdb.result.txt\")\n","repo_name":"kieen/OrarhshoifEValuation","sub_path":"OrarhshoifEvaluation/readAbstractionInfoForOneOntology.py","file_name":"readAbstractionInfoForOneOntology.py","file_ext":"py","file_size_in_byte":8018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"30030137469","text":"import numpy as nm\nimport matplotlib.pyplot as plt\nimport scipy.constants as cons \n\n\nn \t= 10\nbetarel = 1\nsigma \t= 5\n\nr = nm.arange(-10.,10,0.01)\n\nbbforce = - n * cons.e**2 / (2 * cons.pi * cons.epsilon_0 * r ) * (1 - nm.exp(-r**2/sigma))\n\nplt.plot(r,- n * cons.e**2 / (2 * cons.pi * cons.epsilon_0 * r )  * (1-nm.exp(-r**2/5)),'r',r,- n * cons.e**2 / (2 * cons.pi * cons.epsilon_0 * r )  * (1-nm.exp(-r**2/10)),'b',r,- n * cons.e**2 / (2 * cons.pi * cons.epsilon_0 * r )  * (1-nm.exp(-r**2/2)),'g')\nplt.show()\n","repo_name":"TMsangohan/pythonmath","sub_path":"beambeamplot.py","file_name":"beambeamplot.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"18334415472","text":"# -*- coding: utf-8 -*-\n#\n# PyTorch documentation build configuration file, created by\n# sphinx-quickstart on Fri Dec 23 13:31:47 2016.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\n# import sys\n\n# source code directory, relative to this file, for sphinx-autobuild\n# sys.path.insert(0, os.path.abspath('../..'))\n\nimport torch\n\ntry:\n    import torchvision  # noqa: F401\nexcept ImportError:\n    import warnings\n    warnings.warn('unable to load \"torchvision\" package')\n\nRELEASE = os.environ.get('RELEASE', False)\n\nimport pytorch_sphinx_theme\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#\nneeds_sphinx = '1.6'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n    'sphinx.ext.autodoc',\n    'sphinx.ext.autosummary',\n    'sphinx.ext.doctest',\n    'sphinx.ext.intersphinx',\n    'sphinx.ext.todo',\n    'sphinx.ext.coverage',\n    'sphinx.ext.napoleon',\n    'sphinx.ext.viewcode',\n    'sphinxcontrib.katex',\n    'sphinx.ext.autosectionlabel',\n]\n\n# build the templated autosummary files\nautosummary_generate = True\nnumpydoc_show_class_members = False\n\n# autosectionlabel throws warnings if section names are duplicated.\n# The following tells autosectionlabel to not throw a warning for\n# duplicated section names that are in different documents.\nautosectionlabel_prefix_document = True\n\n# katex options\n#\n#\n\nkatex_prerender = True\n\nnapoleon_use_ivar = True\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\nif RELEASE:\n    templates_path = ['_templates-stable'] + templates_path\n\n# TODO: document these and remove them from here.\n\ncoverage_ignore_modules = [\n    \"torch.autograd\",\n    \"torch.cuda\",\n    \"torch.distributed\",\n    \"torch.distributions\",\n    \"torch.hub\",\n    \"torch.jit.unsupported_tensor_ops\",\n    \"torch.onnx\",\n    \"torch.nn.quantized.functional\",\n    \"torchvision\",\n]\n\ncoverage_ignore_functions = [\n    # torch.jit\n    \"annotate\",\n    \"export_opnames\",\n    \"fuser\",\n    \"indent\",\n    \"interface\",\n    \"is_tracing\",\n    \"make_module\",\n    \"make_tuple\",\n    \"optimized_execution\",\n    \"script_method\",\n    \"validate_map_location\",\n    \"verify\",\n    \"whichmodule\",\n    \"wrap_check_inputs\",\n    # torch\n    # TODO: This should be documented eventually, but only after\n    # we build out more support for meta functions and actually\n    # do a release with it\n    \"empty_meta\",\n]\n\ncoverage_ignore_classes = [\n    # torch.jit\n    \"Attribute\",\n    \"CompilationUnit\",\n    \"ConstMap\",\n    \"Error\",\n    \"Future\",\n    \"ONNXTracedModule\",\n    \"OrderedDictWrapper\",\n    \"OrderedModuleDict\",\n    \"RecursiveScriptModule\",\n    \"ScriptFunction\",\n    \"ScriptMeta\",\n    \"ScriptModule\",\n    \"ScriptWarning\",\n    \"TopLevelTracedModule\",\n    \"TracedModule\",\n    \"TracerWarning\",\n    \"TracingCheckError\",\n]\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\n# source_suffix = ['.rst', '.md']\nsource_suffix = '.rst'\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# General information about the project.\nproject = 'PyTorch'\ncopyright = '2019, Torch Contributors'\nauthor = 'Torch Contributors'\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\n# TODO: change to [:2] at v1.0\nversion = 'master (' + torch.__version__ + ' )'\n# The full version, including alpha/beta/rc tags.\n# TODO: verify this works as expected\nrelease = 'master'\n\n# Customized html_title here. \n# Default is \" \".join(project, release, \"documentation\") if not set\nif RELEASE:\n    # remove hash (start with 'a') from version number if any\n    version_end = torch.__version__.find('a')\n    if version_end == -1:\n        html_title = \" \".join((project, torch.__version__, \"documentation\"))\n    else:\n        html_title = \" \".join((project, torch.__version__[:version_end], \"documentation\"))\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = None\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This patterns also effect to html_static_path and html_extra_path\nexclude_patterns = []\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = True\n\n# Disable docstring inheritance\nautodoc_inherit_docstrings = False\n\n\n# -- katex javascript in header\n#\n#    def setup(app):\n#    app.add_javascript(\"https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.js\")\n\n\n# -- Options for HTML output ----------------------------------------------\n#\n# The theme to use for HTML and HTML Help pages.  See the documentation for\n# a list of builtin themes.\n#\n#\n#\n\nhtml_theme = 'pytorch_sphinx_theme'\nhtml_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further.  For a list of options available for each theme, see the\n# documentation.\n\nhtml_theme_options = {\n    'pytorch_project': 'docs',\n    'canonical_url': 'https://pytorch.org/docs/stable/',\n    'collapse_navigation': False,\n    'display_version': True,\n    'logo_only': True,\n}\n\nhtml_logo = '_static/img/pytorch-logo-dark-unstable.png'\nif RELEASE:\n    html_logo = '_static/img/pytorch-logo-dark.svg'\n\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\nhtml_css_files = [\n    'css/jit.css',\n]\n\n\n# Called automatically by Sphinx, making this `conf.py` an \"extension\".\ndef setup(app):\n    # NOTE: in Sphinx 1.8+ `html_css_files` is an official configuration value\n    # and can be moved outside of this function (and the setup(app) function\n    # can be deleted).\n    html_css_files = [\n        'https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.css'\n    ]\n\n    # In Sphinx 1.8 it was renamed to `add_css_file`, 1.7 and prior it is\n    # `add_stylesheet` (deprecated in 1.8).\n    add_css = getattr(app, 'add_css_file', app.add_stylesheet)\n    for css_file in html_css_files:\n        add_css(css_file)\n\n# From PyTorch 1.5, we now use autogenerated files to document classes and\n# functions. This breaks older references since \n# https://docs.pytorch.org/torch.html#torch.flip\n# moved to \n# https://docs.pytorch.org/torch/generated/torchflip.html\n# which breaks older links from blog posts, stack overflow answers and more.\n# To mitigate that, we add an id=\"torch.flip\" in an appropriated place\n# in torch.html by overriding the visit_reference method of html writers.\n# Someday this can be removed, once the old links fade away\n\nfrom sphinx.writers import html, html5\n\ndef replace(Klass):\n    old_call = Klass.visit_reference\n\n    def visit_reference(self, node):\n        if 'refuri' in node and 'generated' in node.get('refuri'):\n            ref = node.get('refuri')\n            ref_anchor = ref.split('#')\n            if len(ref_anchor) > 1:\n                # Only add the id if the node href and the text match,\n                # i.e. the href is \"torch.flip#torch.flip\" and the content is\n                # \"torch.flip\" or \"flip\" since that is a signal the node refers\n                # to autogenerated content\n                anchor = ref_anchor[1]\n                txt = node.parent.astext()\n                if txt == anchor or txt == anchor.split('.')[-1]: \n                    self.body.append('<p id=\"{}\"/>'.format(ref_anchor[1]))\n        return old_call(self, node)\n    Klass.visit_reference = visit_reference\n\nreplace(html.HTMLTranslator)\nreplace(html5.HTML5Translator)\n\n# -- Options for HTMLHelp output ------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'PyTorchdoc'\n\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n    # The paper size ('letterpaper' or 'a4paper').\n    #\n    # 'papersize': 'letterpaper',\n\n    # The font size ('10pt', '11pt' or '12pt').\n    #\n    # 'pointsize': '10pt',\n\n    # Additional stuff for the LaTeX preamble.\n    #\n    # 'preamble': '',\n\n    # Latex figure (float) alignment\n    #\n    # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n#  author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n    (master_doc, 'pytorch.tex', 'PyTorch Documentation',\n     'Torch Contributors', 'manual'),\n]\n\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [\n    (master_doc, 'PyTorch', 'PyTorch Documentation',\n     [author], 1)\n]\n\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n#  dir menu entry, description, category)\ntexinfo_documents = [\n    (master_doc, 'PyTorch', 'PyTorch Documentation',\n     author, 'PyTorch', 'One line description of project.',\n     'Miscellaneous'),\n]\n\n\n# Example configuration for intersphinx: refer to the Python standard library.\nintersphinx_mapping = {\n    'python': ('https://docs.python.org/3', None),\n    'numpy': ('https://numpy.org/doc/stable', None),\n}\n\n# -- A patch that prevents Sphinx from cross-referencing ivar tags -------\n# See http://stackoverflow.com/a/41184353/3343043\n\nfrom docutils import nodes\nfrom sphinx.util.docfields import TypedField\nfrom sphinx import addnodes\nimport sphinx.ext.doctest\n\n# Without this, doctest adds any example with a `>>>` as a test\ndoctest_test_doctest_blocks = ''\ndoctest_default_flags = sphinx.ext.doctest.doctest.ELLIPSIS\ndoctest_global_setup = '''\ntry:\n    import torchvision\nexcept ImportError:\n    torchvision = None\n'''\n\n\ndef patched_make_field(self, types, domain, items, **kw):\n    # `kw` catches `env=None` needed for newer sphinx while maintaining\n    #  backwards compatibility when passed along further down!\n\n    # type: (List, unicode, Tuple) -> nodes.field\n    def handle_item(fieldarg, content):\n        par = nodes.paragraph()\n        par += addnodes.literal_strong('', fieldarg)  # Patch: this line added\n        # par.extend(self.make_xrefs(self.rolename, domain, fieldarg,\n        #                           addnodes.literal_strong))\n        if fieldarg in types:\n            par += nodes.Text(' (')\n            # NOTE: using .pop() here to prevent a single type node to be\n            # inserted twice into the doctree, which leads to\n            # inconsistencies later when references are resolved\n            fieldtype = types.pop(fieldarg)\n            if len(fieldtype) == 1 and isinstance(fieldtype[0], nodes.Text):\n                typename = u''.join(n.astext() for n in fieldtype)\n                typename = typename.replace('int', 'python:int')\n                typename = typename.replace('long', 'python:long')\n                typename = typename.replace('float', 'python:float')\n                typename = typename.replace('bool', 'python:bool')\n                typename = typename.replace('type', 'python:type')\n                par.extend(self.make_xrefs(self.typerolename, domain, typename,\n                                           addnodes.literal_emphasis, **kw))\n            else:\n                par += fieldtype\n            par += nodes.Text(')')\n        par += nodes.Text(' -- ')\n        par += content\n        return par\n\n    fieldname = nodes.field_name('', self.label)\n    if len(items) == 1 and self.can_collapse:\n        fieldarg, content = items[0]\n        bodynode = handle_item(fieldarg, content)\n    else:\n        bodynode = self.list_type()\n        for fieldarg, content in items:\n            bodynode += nodes.list_item('', handle_item(fieldarg, content))\n    fieldbody = nodes.field_body('', bodynode)\n    return nodes.field('', fieldname, fieldbody)\n\nTypedField.make_field = patched_make_field\n","repo_name":"snuspl/nimble","sub_path":"docs/source/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":13057,"program_lang":"python","lang":"en","doc_type":"code","stars":248,"dataset":"github-code","pt":"86"}
+{"seq_id":"34113963602","text":"import math as m\nimport timeit\nimport os\nimport csv\nfrom itertools import permutations \nstart = timeit.default_timer()\n\npath = 'c:\\\\Users\\Eric\\Projects\\python1\\Euler'\nos.chdir(path)\nfilename = \"Primes2to10000000.csv\"\n\nfile_to_read = open(filename)\n\nmatrix = []\n\nwith open(filename) as csvDataFile:\n    csvReader = csv.reader(csvDataFile)\n    i = 0\n    for row in csvReader:\n        clean_row = list(filter(None, row))\n        for item in clean_row:\n            matrix.append(int(item))\n        if (i % 1000) == 0:\n            print(\"Row \", str(i))\n        # j = 0\n        # while j < len(clean_row) :\n        #     clean_row[j] = int(clean_row[j])\n        #     j+=1\n        # matrix.insert(i,clean_row)\n        i += 1\n\nprint(\"matrix read\")\npan7 = []\n\nfor p in permutations(range(1, 8)):\n    new_num = 0\n    len_num = len(p)\n    i = 0\n    while i < len_num:\n        new_num += p[i]*(10**(len_num-1-i))\n        i += 1\n    pan7.append(new_num)\n\npan7.sort(reverse=True)\n\ncandidate = 0\nj = 0\n\nwhile candidate == 0 and j < len(pan7):\n    number = pan7[j]\n    if number in matrix:\n        print(\"We're done here.  The number is \", str(number))\n        candidate = number\n    j += 1\nif candidate == 0:\n    print(\"No seven digit pandigital primes.\")\n\n\nif candidate < 2:\n    pan4 = []\n    \n    for p in permutations(range(1, 5)):\n        new_num = 0\n        len_num = len(p)\n        i = 0\n        while i < len_num:\n            new_num += p[i]*(10**(len_num-1-i))\n            i += 1\n        pan4.append(new_num)\n    \n    pan4.sort(reverse=True)\n    \n    candidate = 0\n    j = 0\n    \n    while candidate == 0 and j < len(pan4):\n        number = pan4[j]\n        if number in matrix:\n            print(\"We're done here.  The number is \", str(number))\n            candidate = number\n        j += 1\n    if candidate == 0 :\n        print(\"I done messed up.\")\n\nstop = timeit.default_timer()\ntime = stop - start\nprint(\"Runtime =\",  time, \"s\")\n","repo_name":"egv78/Euler","sub_path":"Euler_041_Pandigital_Prime.py","file_name":"Euler_041_Pandigital_Prime.py","file_ext":"py","file_size_in_byte":1926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"6785371282","text":"# version code 542eddf1f327+\ncoursera = 1\n# Please fill out this stencil and submit using the provided submission script.\n\nfrom vec import Vec\n\nimport sys\nsys.setrecursionlimit(10000) \n\n## 1: (Problem 3.8.1) Vector Comprehension and Sum\ndef vec_select(veclist, k):  \n    '''\n    >>> D = {'a','b','c'}\n    >>> v1 = Vec(D, {'a': 1})\n    >>> v2 = Vec(D, {'a': 0, 'b': 1})\n    >>> v3 = Vec(D, {        'b': 2})\n    >>> v4 = Vec(D, {'a': 10, 'b': 10})\n    >>> vec_select([v1, v2, v3, v4], 'a') == [Vec(D,{'b': 1}), Vec(D,{'b': 2})]\n    True\n    '''\n    res = []\n    for vec in veclist:\n        if vec[k] == 0:     # this is a reload of  operator\n            res.append(vec)\n    return res\n\ndef vec_sum(veclist, D):\n    '''\n    >>> D = {'a','b','c'}\n    >>> v1 = Vec(D, {'a': 1})\n    >>> v2 = Vec(D, {'a': 0, 'b': 1})\n    >>> v3 = Vec(D, {        'b': 2})\n    >>> v4 = Vec(D, {'a': 10, 'b': 10})\n    >>> vec_sum([v1, v2, v3, v4], D) == Vec(D, {'b': 13, 'a': 11})\n    True\n    '''\n    res = Vec(D, {})\n    for v in veclist:\n        res = res + v\n    return res\n    \n\ndef vec_select_sum(veclist, k, D):\n    '''\n    >>> D = {'a','b','c'}\n    >>> v1 = Vec(D, {'a': 1})\n    >>> v2 = Vec(D, {'a': 0, 'b': 1})\n    >>> v3 = Vec(D, {        'b': 2})\n    >>> v4 = Vec(D, {'a': 10, 'b': 10})\n    >>> vec_select_sum([v1, v2, v3, v4], 'a', D) == Vec(D, {'b': 3})\n    True\n    '''\n    return vec_sum(vec_select(veclist, k), D)\n    \n\n\n## 2: (Problem 3.8.2) Vector Dictionary\ndef scale_vecs(vecdict):\n    '''\n    >>> v1 = Vec({1,2,4}, {2: 9})\n    >>> v2 = Vec({1,2,4}, {1: 1, 2: 2, 4: 8})\n    >>> result = scale_vecs({3: v1, 5: v2})\n    >>> len(result)\n    2\n    >>> [v in [Vec({1,2,4},{2: 3.0}), Vec({1,2,4},{1: 0.2, 2: 0.4, 4: 1.6})] for v in result]\n    [True, True]\n    '''\n    \n    res = [] \n    for (k,v) in vecdict.items():\n        res.append(1/k * v) \n    return res\n    \n    \n    \n\n\n\n## 3: (Problem 3.8.3) Constructing span of given vectors over GF(2)\ndef GF2_span(D, S):\n    '''\n    >>> from GF2 import one\n    >>> D = {'a', 'b', 'c'}\n    >>> GF2_span(D, {Vec(D, {'a':one, 'c':one}), Vec(D, {'c':one})}) == {Vec(D,{}), Vec(D,{'a':one, 'c':one}), Vec(D,{'c': one}), Vec(D,{'a':one})}\n    True\n    >>> GF2_span(D, {Vec(D, {'a':one, 'b':one}), Vec(D, {'a':one}), Vec(D, {'b':one})}) == {Vec(D,{'a':one, 'b':one}), Vec(D,{'b':one}), Vec(D,{'a':one}), Vec(D,{})}\n    True\n    >>> GF2_span(D, {Vec(D, {'a':one, 'b':one}), Vec(D, {'c':one})}) == {Vec(D,{}), Vec(D,{'a':one, 'b':one}), Vec(D,{'a':one, 'b':one, 'c':one}), Vec(D,{'c':one})}\n    True\n    >>> S={Vec({0,1},{0:one}), Vec({0,1},{1:one})}\n    >>> GF2_span({0,1}, S) == {Vec({0, 1},{0: one, 1: one}), Vec({0, 1},{1: one}), Vec({0, 1},{0: one}), Vec({0, 1},{})}\n    True\n    >>> S == {Vec({0, 1},{1: one}), Vec({0, 1},{0: one})}\n    True\n    '''\n    # using the thingking of recursion \n    \n    if len(S) == 0 :\n        return set()\n    \n    L = list(S)\n    maxind = 2 ** len(S) - 1 \n    res = [sum([L[j] for j in range(len(L)) if i//2**j%2]) for i in range(maxind+1)]\n    res.append(Vec(D, {}))\n    del res[0] \n    return set(res)\n    \n\n\n\n## 4: (Problem 3.8.7) Is it a vector space 1\n# Answer with a boolean, please.\nis_a_vector_space_1 = False\n\n\n\n## 5: (Problem 3.8.8) Is it a vector space 2\n# Answer with a boolean, please.\nis_a_vector_space_2 =  True\n\n\n\n## 6: (Problem 3.8.9) Is it a vector space 3\n# Answer with a boolean, please.\nis_a_vector_space_3 = False\n\n\n\n## 7: (Problem 3.8.10) Is it a vector space 4\n# Answer with a boolean, please.\nis_a_vector_space_4a = True\nis_a_vector_space_4b = False\n\n","repo_name":"taiyang-li/readings","sub_path":"coursera_Coding_the_Matrix_Linear_Algebra_through_Computer_Science_Applications/week2/The_Vector_Space_problems.py","file_name":"The_Vector_Space_problems.py","file_ext":"py","file_size_in_byte":3543,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"86"}
+{"seq_id":"17648328283","text":"import csv, utils, datetime, os, glob, re, itertools\n\nLINE_RE = re.compile(\"(\\d\\d?)\\.(\\d\\d?)\\.(\\d\\d\\d\\d)(.*?)([0-9.,-]+)\\s*(€|EUR|E)\")\n\ndef do_import(input_path, account):\n    print(\"\\t\", input_path)\n    entries = []\n    with open(input_path, \"r\", encoding=\"UTF-8\") as input_fp:\n        if input_path.endswith(\".csv\"):\n            reader = csv.DictReader(input_fp)\n            for row in reader:\n                if row[\"Datum\"]:\n                    d, m, y = row[\"Datum\"].split(\".\")\n                    date = datetime.date(int(y), int(m), int(d))\n                    description = (row[\"Beschreibung\"] + \" \" + row[\"Bemerkung\"]).strip()\n                    id = row[\"BuchungsID\"]\n                if not row[\"BuchungsID\"]:\n                    assert date\n                    assert description\n                    if row[\"Volle Kontobezeichnung\"] != \"Aktiva:Giro EasyBank\":\n                        entries.append(utils.Entry(\n                            input_path,\n                            account,\n                            date,\n                            description + \" (\" + id + \")\",\n                            -int(row[\"Wert numerisch\"].replace(\".\", \"\").replace(\",\", \"\")),\n                            \"EUR\"\n                        ))\n                    date = None\n                    description = None\n        else:\n            for line in input_fp.readlines():\n                line = line.strip()\n                if line:\n                    match = LINE_RE.match(line)\n                    entries.append(utils.Entry(\n                        input_path,\n                        account,\n                        datetime.date(int(match[3]), int(match[2]), int(match[1])),\n                        re.sub(r\"\\s+\", \" \", match[4]),\n                        -int(match[5].replace(\".\", \"\").replace(\",\", \"\")) * (1 if \",\" in match[5] else 100),\n                        \"EUR\"))\n    return entries\n\ndef main(pool, source, account, **kwargs):\n    files = list(glob.glob(os.path.join(source, \"*.txt\")))\n    files += list(glob.glob(os.path.join(source, \"*.csv\")))\n    return list(itertools.chain.from_iterable(pool.starmap(do_import, ((f, account) for f in files))))\n","repo_name":"kaini/money","sub_path":"src/parser/cash.py","file_name":"cash.py","file_ext":"py","file_size_in_byte":2170,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"11197146072","text":"import numpy as np\n\ndef compute_tlsq(X, Y, tlsq_rank):\n    \"\"\"\n    Compute Total Least Square.\n\n    :param numpy.ndarray X: the first matrix;\n    :param numpy.ndarray Y: the second matrix;\n    :param int tlsq_rank: the rank for the truncation; If 0, the method\n        does not compute any noise reduction; if positive number, the\n        method uses the argument for the SVD truncation used in the TLSQ\n        method.\n    :return: the denoised matrix X, the denoised matrix Y\n    :rtype: numpy.ndarray, numpy.ndarray\n\n    References:\n    https://arxiv.org/pdf/1703.11004.pdf\n    https://arxiv.org/pdf/1502.03854.pdf\n    \"\"\"\n    # Do not perform tlsq\n    if tlsq_rank == 0:\n        return X, Y\n\n    V = np.linalg.svd(np.append(X, Y, axis=0), full_matrices=False)[-1]\n    rank = min(tlsq_rank, V.shape[0])\n    VV = V[:rank, :].conj().T.dot(V[:rank, :])\n\n    return X.dot(VV), Y.dot(VV)\n","repo_name":"ZhichaoJin/DMD","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"39827523652","text":"import sys\nimport requests\nimport hashlib\nimport time\nfrom lxml import etree\nfrom elasticsearch import Elasticsearch\n\n\nclass Poetry:\n    def __init__(self, title=\"\", content=\"\", author=\"\", tag=None, dynasty=\"\"):\n        self.title = title\n        self.dynasty = dynasty\n        self.content = content\n        self.author = author\n        self.tag = tag\n\n\nclass PoetrySpider:\n    def __init__(self):\n        self.db = Elasticsearch([{\"host\": \"localhost\", \"port\": 9200}])\n        self.domain = ''\n        \n    def create_poetry_index(self):\n        self.db.indices.create('poetry_index', ignore=400, body={\n            \"mapping\": {\n                \"poetry\": {\n                    \"properties\": {\n                        \"title\": {\"type\": \"text\"},\n                        \"dynasty\": {\"type\": \"text\"},\n                        \"author\": {\"type\": \"text\"},\n                        \"content\": {\"type\": \"text\"},\n                        \"tag\": {\"type\": \"text\"},\n                    }\n                }\n            }\n        })\n    \n    def download(self, origin_url):\n        print('origin_url:{}'.format(origin_url))\n        self.domain = origin_url.split('/')[2]\n        data = requests.get(origin_url)\n        if data:\n            self.parse(data.text)\n\n    def parse(self, data):\n        response = etree.HTML(data)\n        for row in response.xpath('//div[@class=\"left\"]/div[@class=\"sons\"]'):\n            poetry = Poetry()\n            poetry.title = row.xpath('div[@class=\"cont\"]/p/a/b/text()')[0] \\\n                if row.xpath('div[@class=\"cont\"]/p/a/b/text()') else ''\n            poetry.dynasty = row.xpath('div[@class=\"cont\"]/p[@class=\"source\"]//text()')[0] \\\n                if row.xpath('div[@class=\"cont\"]/p[@class=\"source\"]//text()') else ''\n            poetry.author = row.xpath('div[@class=\"cont\"]/p[@class=\"source\"]//text()')[-1] \\\n                if row.xpath('div[@class=\"cont\"]/p[@class=\"source\"]//text()') else ''\n            poetry.content = ''.join(row.xpath('div[@class=\"cont\"]/div[@class=\"contson\"]//text()')).\\\n                replace('　　', '').replace('\\n', '') \\\n                if row.xpath('div[@class=\"cont\"]/div[@class=\"contson\"]//text()') else ''\n            poetry.tag = ','.join(row.xpath('div[@class=\"tag\"]/a/text()')) \\\n                if row.xpath('div[@class=\"tag\"]/a/text()') else ''\n            print('Title: {}'.format(poetry.title))\n            print('Dynasty: {}'.format(poetry.dynasty))\n            print('Author: {}'.format(poetry.author))\n            print('Content: {}'.format(poetry.content))\n            print('Tag: {}'.format(poetry.tag))\n            self.write_to_es(poetry)\n        if response.xpath('//div[@class=\"pagesright\"]/a[@class=\"amore\"]/@href'):\n            time.sleep(2)\n            self.download('http://' + self.domain +\n                          response.xpath('//div[@class=\"pagesright\"]/a[@class=\"amore\"]/@href')[-1])\n            \n    def write_to_es(self, poetry):\n        doc = {\n            \"title\": poetry.title,\n            \"dynasty\": poetry.dynasty,\n            \"author\": poetry.author,\n            \"content\": poetry.content,\n            \"tag\": poetry.tag,\n            \"timestamp\": int(time.time()*1000)\n        }\n        poetry_id = hashlib.sha1(poetry.content.encode(\"utf-8\")).hexdigest()[0:15]\n        if self.db.exists(\"poetry_index\", \"poetry\", poetry_id):\n            self.db.delete(\"poetry_index\", \"poetry\", poetry_id)\n        res = self.db.create(index=\"poetry_index\", doc_type=\"poetry\", id=poetry_id, body=doc)\n        print('Put Success: {}'.format(res))\n        \n    \nif __name__ == '__main__':\n    sys.setrecursionlimit(100000)\n    ori_url = 'http://so.gushiwen.org/type.aspx'\n    do = PoetrySpider()\n    #do.create_poetry_index()\n    do.download(ori_url)\n","repo_name":"minicaptain/python_spider","sub_path":"spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":3735,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"18278740659","text":"from django.shortcuts import render, redirect\r\nfrom .forms import BookForm\r\nfrom .models import Book\r\nfrom django.shortcuts import redirect, get_object_or_404\r\n# Create your views here.\r\n\r\ndef home(request):\r\n    return render(request, 'home.html')\r\n\r\ndef delete_book(request, book_id):\r\n    book = get_object_or_404(Book, id=book_id)\r\n    book.delete_book()\r\n    return redirect('book_list')\r\n\r\ndef book_list(request):\r\n    return render(request, 'book_list.html')\r\n\r\ndef book_list(request):\r\n    if request.method == 'POST':\r\n        form = BookForm(request.POST, request.FILES)\r\n        if form.is_valid():\r\n            form.save()\r\n            return redirect('book_list')\r\n    else:\r\n        form = BookForm()\r\n    books = Book.objects.all()\r\n    context = {\r\n        'form': form,\r\n        'books': books\r\n    }\r\n    return render(request, 'book_list.html', context)\r\n\r\ndef edit_book(request, book_id):\r\n    # Retrieve the book object using the book_id\r\n    book = Book.objects.get(id=book_id)\r\n\r\n    if request.method == 'POST':\r\n        form = BookForm(request.POST, instance=book)\r\n        if form.is_valid():\r\n            form.save()\r\n            # Redirect to the book list page\r\n            return redirect('book_list')\r\n    else:\r\n        form = BookForm(instance=book)\r\n\r\n    return render(request, 'edit_book.html', {'form': form, 'book': book})\r\n\r\n","repo_name":"Menmymissus/bookstoreusingxamppanddjango","sub_path":"bookstore using django and xampp/book_store/book_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"71443402523","text":"from apps.db import Base, engine\nfrom sqlalchemy import Column, CHAR, VARCHAR, ForeignKey\n\n\nclass Oanda(Base):\n    __tablename__ = \"oanda\"\n    id = Column(\n        'id',\n        CHAR(20),\n        ForeignKey('users.id'),\n        nullable=False,\n        primary_key=True,\n        autoincrement=False,\n        unique=True)\n    token = Column(\n        'token',\n        VARCHAR(255),\n        nullable=False)\n","repo_name":"shujishujishuji/fats","sub_path":"oanda/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"17484663296","text":"from nmigen.compat.sim import run_simulation\nfrom nmigen.cli import verilog, rtlil\nfrom nmigen import Module, Signal, Const, Elaboratable, Array\nfrom nmutil.latch import SRLatch\n\nfrom functools import reduce\nfrom operator import or_\n\n\nclass FUDependenceCell(Elaboratable):\n    \"\"\" implements 11.4.7 mitch alsup dependence cell, p27\n    \"\"\"\n    def __init__(self, dummy, n_fu, n_src, n_dest):\n        self.n_fu = n_fu\n        self.n_src = n_src\n        self.n_dest = n_dest\n        self.dummy = Const(~(1<<dummy), n_fu)\n        # inputs\n        self.rd_pend_i = Signal(n_fu, reset_less=True) # read pend in (left)\n        self.wr_pend_i = Signal(n_fu, reset_less=True) # write pend in (left)\n        self.issue_i = Signal(n_fu, reset_less=True)    # Issue in (top)\n\n        # set up go_wr and go_wr array\n        rd = []\n        for i in range(n_src):\n            j = i + 1 # name numbering to match src1/src2\n            rd.append(Signal(n_fu, name=\"gord%d_i\" % j, reset_less=True))\n        wr = []\n        for i in range(n_src):\n            j = i + 1 # name numbering to match src1/src2\n            wr.append(Signal(n_fu, name=\"gowr%d_i\" % j, reset_less=True))\n\n        self.go_wr_i = Array(wr)  # Go Write in (left)\n        self.go_rd_i = Array(rd)  # Go Read in (left)\n\n        self.go_die_i = Signal(n_fu, reset_less=True) # Go Die in (left)\n\n        # outputs (latched rd/wr wait)\n        self.rd_wait_o = Signal(n_fu, reset_less=True) # read wait out (right)\n        self.wr_wait_o = Signal(n_fu, reset_less=True) # write wait out (right)\n\n    def elaborate(self, platform):\n        m = Module()\n\n        # set up rd/wr latches\n        rd_c = []\n        for i in range(self.n_src):\n            rd_l = SRLatch(sync=False, name=\"rd%d_c\" % i, llen=self.n_fu)\n            setattr(m.submodules, \"src%d_c\" % (i+1), rd_l)\n            rd_c.append(rd_l)\n        wr_c = []\n        for i in range(self.n_dest):\n            wr_l = SRLatch(sync=False, name=\"wr%d_c\" % i, llen=self.n_fu)\n            setattr(m.submodules, \"dst%d_c\" % (i+1), wr_l)\n            wr_c.append(wr_l)\n\n        # reset on go HI, set on dest and issue\n\n        # connect go_wr / pend\n        for i in range(self.n_dest):\n            m.d.comb += wr_c[i].r.eq(self.go_wr_i[i] | self.go_die_i)\n            m.d.comb += wr_c[i].s.eq(self.issue_i & self.wr_pend_i & self.dummy)\n\n        # connect go_rd / pend_i\n        for i in range(self.n_src):\n            m.d.comb += rd_c[i].r.eq(self.go_rd_i[i] | self.go_die_i)\n            m.d.comb += rd_c[i].s.eq(self.issue_i & self.rd_pend_i & self.dummy)\n\n        # connect output with OR-reduce (DO NOT USE bool()!)\n        # read-wait (and write-wait) only go off when all GORD (and GOWR) fire\n        rd_q = []\n        for i in range(self.n_src):\n            rd_q.append(rd_c[i].qlq)\n        m.d.comb += self.rd_wait_o.eq(reduce(or_, rd_q) & ~self.issue_i)\n        wr_q = []\n        for i in range(self.n_dest):\n            wr_q.append(wr_c[i].qlq)\n        m.d.comb += self.wr_wait_o.eq(reduce(or_, wr_q) & ~self.issue_i)\n\n        return m\n\n    def __iter__(self):\n        yield self.rd_pend_i\n        yield self.wr_pend_i\n        yield self.issue_i\n        yield from self.go_wr_i\n        yield from self.go_rd_i\n        yield self.go_die_i\n        yield self.rd_wait_o\n        yield self.wr_wait_o\n                \n    def ports(self):\n        return list(self)\n\n\ndef dcell_sim(dut):\n    yield dut.dest_i.eq(1)\n    yield dut.issue_i.eq(1)\n    yield\n    yield dut.issue_i.eq(0)\n    yield\n    yield dut.src1_i.eq(1)\n    yield dut.issue_i.eq(1)\n    yield\n    yield dut.issue_i.eq(0)\n    yield\n    yield dut.go_rd_i.eq(1)\n    yield\n    yield dut.go_rd_i.eq(0)\n    yield\n    yield dut.go_wr_i.eq(1)\n    yield\n    yield dut.go_wr_i.eq(0)\n    yield\n\ndef test_dcell():\n    dut = FUDependenceCell(dummy=4, n_fu=4, n_src=2, n_dest=2)\n    vl = rtlil.convert(dut, ports=dut.ports())\n    with open(\"test_fu_dcell.il\", \"w\") as f:\n        f.write(vl)\n\n    run_simulation(dut, dcell_sim(dut), vcd_name='test_fu_dcell.vcd')\n\nif __name__ == '__main__':\n    test_dcell()\n","repo_name":"ngi-nix/libresoc-soc","sub_path":"src/soc/scoremulti/fu_dep_cell.py","file_name":"fu_dep_cell.py","file_ext":"py","file_size_in_byte":4061,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"34935059195","text":"import sys\nimport os\nimport shutil\n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\nfrom pyspark import SparkContext\nfrom pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating\n\ndef read_ratings_data(sc):\n    ''' Reads in the MovieLens rating data from the file\n    and returns it in a Spark RDD of (user, movie, rating). '''\n    data = sc.textFile('../../lab2/part2/ml-100k/u.data')\n    ratings = data.map(lambda l: l.split()).map(lambda l: Rating(int(l[0]), int(l[1]), float(l[2])))\n\n    return ratings\n\n\ndef load_personal_ratings(sc):\n    ''' Reads in personal rating data from the file and\n    returns it in a Spark RDD of (user (0), movie, rating). '''\n    if not os.path.exists('target/personal_ratings.txt'):\n        print('If you want personal recommendations, you must first run init_recommender.py')\n        sys.exit(1)\n\n    data = sc.textFile('target/personal_ratings.txt')\n    ratings = data.map(lambda l: l.split()).map(lambda l: Rating(0, int(l[0]), float(l[1])))\n\n    return ratings\n\n\ndef load_movies_dict():\n    ''' Creates a dictionary of movie ID to movie title\n    based on the MovieLens dataset. '''\n    all_movies = dict()\n\n    with open('../../lab2/part2/ml-100k/u.item') as f:\n        for line in f:\n            info = line.split('|')\n            movie_id = int(info[0])\n            movie_title = info[1]\n            \n            all_movies[movie_id] = movie_title\n\n    return all_movies\n\n\ndef titles_for_ids(ids):\n    ''' Returns the titles for a list of given movie ids. '''\n    movies = load_movies_dict()\n    titles = []\n\n    for i in ids:\n        titles.append(movies[i])\n\n    return titles\n\n\ndef makePlot(x, y, xlabel, ylabel):\n    ''' Plots x and y and displays it to the user. '''\n    plt.scatter(x, y, color='black')\n    plt.xlabel(xlabel)\n    plt.ylabel(ylabel)\n    plt.show()\n\n\ndef set_up():\n    ''' Sets up random initalization for the program. '''\n    if not os.path.exists('checkpoint/'):\n        os.makedirs('checkpoint/')\n\n    if not os.path.exists('target/'):\n        os.makedirs('target/')\n\n    reload(sys)\n    sys.setdefaultencoding('utf8')\n\n    ALS.checkpointInterval = 2\n\n\ndef use_personal_ratings():\n    ''' Returns true if the program was run with the -r option\n    and false if it was not. If it was run with any other options,\n    prints an error message and exits. '''\n    if len(sys.argv) >= 2:\n        if len(sys.argv) == 2 and sys.argv[1] == '-r':\n            return True\n        else:\n            print('Usage: .../spark-submit --master local[x] run_recommender.py <-r>')\n            sys.exit(1)\n\n    return False\n\n\ndef show_movie_titles(titles):\n    ''' Prints all movie titles in the given list. '''\n    print('Here are your top movie recommendations:')\n    for i, title in enumerate(titles):\n        num = i + 1\n        print('%s. %s' % (num, title))\n\n\ndef run_als(train, validation, rank=10, iterations=7, l=0.01, save_model=False, sc=None):\n    ''' Trains an ALS on train and reports the accuracy on validation. '''\n    model = ALS.train(train, rank, iterations, lambda_=l)\n    \n    if save_model and sc is not None:\n        if os.path.exists('target/recommender'):\n            shutil.rmtree('target/recommender')\n        \n        model.save(sc, 'target/recommender')\n\n    # Evaluate model\n    if validation is not None:\n        testData = validation.map(lambda d: (d[0], d[1]))\n        predictions = model.predictAll(testData).map(lambda r: ((r[0], r[1]), r[2]))\n        origAndPreds = validation.map(lambda r: ((r[0], r[1]), r[2])).join(predictions)\n        correct = origAndPreds.map(lambda r: (1 if (abs(r[1][0] - r[1][1]) <= 1.0) else 0))\n        accuracy = correct.mean()\n        return accuracy\n\n    return None\n\n\ndef als_vary_iterations(train, validation):\n    ''' Trains multiple ALS models, varying the numIterations parameter. '''\n    iterations = [2, 3, 4, 5, 6, 7]\n    accuracies = []\n    for i in iterations:\n        accuracy = run_als(train, validation, iterations=i)\n        accuracies.append(accuracy)\n\n    makePlot(iterations, accuracies, 'numIterations', 'accuracy')\n\n\ndef als_vary_rank(train, validation):\n    ''' Trains multiple ALS models, varying the rank parameter. '''\n    rank = range(5, 21)\n    accuracies = []\n    for r in rank:\n        accuracy = run_als(train, validation, rank=r)\n        accuracies.append(accuracy)\n\n    makePlot(rank, accuracies, 'rank', 'accuracy')\n\n\n### START HERE ###\nset_up()\n\n# Initalize Spark context\nsc = SparkContext(appName=\"Lab5\")\nsc.setLogLevel(\"ERROR\")\nsc.setCheckpointDir('checkpoint/')\n\n# Check how we are running\nif use_personal_ratings():\n    print('Recommending movies to you based on results from init_recommender.py')\n    personal_ratings = load_personal_ratings(sc)\n    ratings = read_ratings_data(sc).union(personal_ratings)\nelse:\n    ratings = read_ratings_data(sc)\n\n# Split into train and test\n(ratings_train, ratings_validation) = ratings.randomSplit([0.7, 0.3], seed=42)\n\n# Don't want to train all the various models if we're recommending, only the best one\nif use_personal_ratings():\n    run_als(ratings, None, rank=5, iterations=7, l=0.1, save_model=True, sc=sc)\n    model = MatrixFactorizationModel.load(sc, 'target/recommender')\n\n    # Get the 20 best recommended movies for user 0 (the user)\n    # Then filter out any they have already rated\n    # Convert the movie ids into titles\n    movies = model.recommendProducts(0, 20)\n    movie_ids = [m[1] for m in movies]\n    rated_movie_ids = [m[0] for m in personal_ratings.map(lambda m: (m[1], m[2])).collect() if m[1] != 0]\n    unwatched_movies = [m for m in movie_ids if m not in rated_movie_ids]\n    top_ten_movies = unwatched_movies[:10]\n    titles = titles_for_ids(top_ten_movies)\n\n    show_movie_titles(titles)\n\n    # Done with the personal recommendations\n    sys.exit(0)\n\n# Train recommenders\nprint('Training the model by varying the number of iterations.')\nals_vary_iterations(ratings_train, ratings_validation)\n\nprint('Training the model by varying the rank.')\nals_vary_rank(ratings_train, ratings_validation)\n\nprint('Training the model with the best parameters.')\naccuracy = run_als(ratings_train, ratings_validation, rank=5, iterations=7, l=0.1)\nprint('Validation set accuracy: %s' % (accuracy))\n\nprint('Training the model on all data and saving it.')\nrun_als(ratings, None, rank=5, iterations=7, l=0.1, save_model=True, sc=sc)\n\n","repo_name":"RohanNagar/big-data-utaustin","sub_path":"lab5/src/run_recommender.py","file_name":"run_recommender.py","file_ext":"py","file_size_in_byte":6353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"13205718019","text":"#!/usr/bin/env python\n\n# Author: Steven Miller\n\nimport requests\nimport urllib\nimport sys\nimport os\nimport lxml.html as html\nfrom random import randint\nfrom subprocess import check_output\nimport pydoc\n\n\ndef build_search_url(query):\n    \"\"\" Builds a Google search url\n\n    Args:\n        query (str): a query string to use when searching cloudformation docs\n\n    Returns:\n        str: the Google 'I'm feeling lucky' URL\n    \"\"\"\n    google_url = []\n    # Build URL to query Google\n    google_url.append('https://www.google.com/search?')\n    # I'm feeling lucky: go to first result\n    google_url.append('btnI=1')\n    # Limit results to only this specific website\n    google_url.append('&as_sitesearch=docs.aws.amazon.com')\n    # Build query\n    query = \"aws cloudformation \" + query\n    # This line escapes spaces and the like\n    query = urllib.quote_plus(query.strip())\n    # Attach query to URL\n    google_url.append(\"&q=\")\n    google_url.append(query)\n    return \"\".join(google_url)\n\n\ndef get_docs_html_content(url):\n    \"\"\" Get a webpage and extract relevant HTML for cloudformation documentation\n\n    Args:\n        url (str): url for cloudformation docs\n\n    Returns:\n        str: HTML of the page, stripped down to a minimum\n    \"\"\"\n    # Relevant tags\n    want_tags = ['p', 'h1', 'h2', 'h3', 'div']\n    # Relevant classes and ids for div elements\n    want_divs = ['variablelist', 'aws-note', 'YAML']\n    # Get request to amazon\n    response = requests.get(url, proxies=urllib.getproxies())\n    # Parse the raw HTML\n    parsed = html.fromstring(response.text)\n    # Print out the HTML elements we want\n    try:\n        main_content = parsed.get_element_by_id(\"main-col-body\")\n    except KeyError:\n        print(\"Sorry! Did not find a document.\")\n        print(url)\n        exit(1)\n    content = []\n    for el in main_content:\n        if (el.tag not in want_tags) or \\\n           (el.tag == 'div') and not ( \\\n             ('class' in el.attrib.keys() and el.attrib['class'] in want_divs) or \\\n             ('id' in el.attrib.keys() and el.attrib['id'] in want_divs)\n           ):\n            continue\n        content.append(html.tostring(el))\n    return \"\".join(content)\n\n\ndef format_html_content(content):\n    \"\"\" Given HTML, render for reading in a terminal\n\n    Args:\n        content (str): HTML as a string\n\n    Returns:\n        str: Rendered document, all HTML removed using 'links' command line utility\n    \"\"\"\n    # Use a random file name to avoid collision\n    # for writing temporary file\n    temp_file = str(randint(0, 10000000000)) + \"-tmp-cfn-man.html\"\n    try:\n        with open(temp_file, 'w') as f:\n            f.write(content)\n        return check_output(['links', '-dump', temp_file])\n    except OSError as e:\n        if 'No such file or directory' in str(e):\n            print(\n                \"Please make sure the command line utility 'links' is installed\"\n            )\n            exit(1)\n        raise e\n    # Use finally to ensure resource is cleaned up\n    finally:\n        os.remove(temp_file)\n\n\ndef main():\n    if len(sys.argv) < 2:\n        print(\"usage:\\ncfn_docs security group\\ncfn_docs ec2\")\n        exit(1)\n    query = []\n    for arg in sys.argv[1:]:\n        query.append(arg)\n        query.append(\" \")\n    url = build_search_url(\"\".join(query))\n    html_content = get_docs_html_content(url)\n    doc = format_html_content(html_content)\n    # this is the equivalent of piping into 'less'\n    pydoc.pager(doc)\n","repo_name":"stelligent/cfn-man","sub_path":"cfn_man/cfn_man.py","file_name":"cfn_man.py","file_ext":"py","file_size_in_byte":3443,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"74766111644","text":"import biostarPython as bio    #My Python package for dealing with GSDK\nimport threading\n\ngateway = bio.GatewayClient('127.0.0.1',4000,'C:\\GSDK\\Cert\\CA.crt') #Setup Gateway\nchannel = gateway.getChannel() # Setup Channel\nconnect = bio.ConnectSvc(channel) # Setup Connect\nevent = bio.EventSvc(channel) # Setup Event\n\nconnectEvents = connect.subscribe(300) # Connected events\n\nenabledDevices = []\n\ndef enableMonitoring(deviceID):\n  event.enableMonitoring(deviceID) # Enable monitoring on recieved Device ID\n  if deviceID not in enabledDevices: \n   enabledDevices.append(deviceID)\n   stream = event.subscribe(300) # Setup stream for events\n   for y in stream:\n    print(y) # print events\n\ndef connectedCheck():\n  for x in connectEvents: # check Connected events\n   if x.status == 1: #For TCP connected\n    print(x)\n    enableMonitorThread = threading.Thread(target=enableMonitoring,args=(x.deviceID,)) #callback to previous enable monitoring to setup a stream\n    enableMonitorThread.start()\n   \nconnectedCheckThread = threading.Thread(target=connectedCheck)\nconnectedCheckThread.start()\n\n\n\n\n","repo_name":"gcartlidge/biostarPython","sub_path":"biostarPython/Log Events.py","file_name":"Log Events.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"33563877481","text":"from django.urls import path\nfrom . import views\n\nurlpatterns=[\n            path('employee_home',views.employeehome,name=\"employeehome\"),\n            path('calculator',views.calculator,name=\"calculator\"),\n            path('logout',views.logout,name=\"logout\"),\n            path('employeesalary',views.employeesalary,name=\"employeesalary\"),\n            path('recordcheck',views.recordcheck,name=\"recordcheck\"),\n            path('employeeapplication',views.employeeapplication,name=\"employeeapplication\"),\n            path('applicationstatus',views.applicationstatus,name=\"applicationstatus\"),\n            path('updateother',views.updateother,name=\"updateother\"),\n]\n","repo_name":"rohitladhar/car-showroom","sub_path":"employee/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"21483340347","text":"import argparse\nimport logging\nfrom typing import Tuple\n\nimport torchvision\nimport torchvision.models as models\nfrom torch import nn\n\nfrom dynast.utils import log, measure_time, set_logger\nfrom dynast.utils.datasets import Dataset, ImageNet\nfrom dynast.utils.nn import get_macs, get_parameters, measure_latency, validate_classification\n\n\ndef get_torchvision_model(\n    model_name: str,\n    quantize: bool = True,\n    progress: bool = False,\n) -> nn.Module:\n    try:\n        if not quantize:\n            model = getattr(models, model_name)(pretrained=True, progress=progress)\n        else:\n            model = getattr(models.quantization, model_name)(pretrained=True, quantize=quantize, progress=progress)\n        model.eval()\n        return model\n    except AttributeError as ae:\n        log.error(\n            'Model {model_name} not available. This can be due to either a typo or the model is not '\n            'available in torchvision=={torchvision_version}. \\nAvailable models: {available_models}'.format(\n                model_name=model_name,\n                torchvision_version=torchvision.__version__,\n                available_models=', '.join([m for m in dir(models) if not m.startswith('_')]),\n            )\n        )\n        raise ae\n\n\nclass Reference(object):\n    @measure_time\n    def validate(\n        self,\n        device: str = 'cpu',\n        batch_size: int = 128,\n        input_size: int = 224,\n        test_size: int = None,\n    ):\n        raise NotImplementedError()\n\n    @measure_time\n    def benchmark(\n        self,\n        device: str = 'cpu',\n        batch_size: int = 128,\n        input_size: int = 224,\n        warmup_steps: int = 10,\n        measure_steps: int = 50,\n    ):\n        raise NotImplementedError()\n\n\nclass TorchVisionReference(Reference):\n    def __init__(\n        self,\n        model_name: str,\n        dataset: Dataset = ImageNet,\n        quantize: bool = False,\n    ) -> None:\n        self.model_name = model_name\n        self.dataset = dataset\n        self.quantize = quantize\n\n        log.info(\n            '{name} for \\'{model_name}\\' on \\'{dataset_name}\\' dataset'.format(\n                name=str(self),\n                model_name=self.model_name,\n                dataset_name=self.dataset.name(),\n            )\n        )\n        self.model = get_torchvision_model(model_name=self.model_name, quantize=self.quantize)\n\n    @measure_time\n    def validate(\n        self,\n        device: str = 'cpu',\n        batch_size: int = 128,\n        input_size: int = 224,\n        test_fraction: float = 1.0,\n    ) -> Tuple[float, float, float]:\n        model = self.model.to(device)\n        loss, top1, top5 = validate_classification(\n            model=model,\n            device=device,\n            data_loader=self.dataset.validation_dataloader(\n                batch_size=batch_size,\n                image_size=input_size,\n                fraction=test_fraction,\n            ),\n        )\n        log.info(\n            '\\'{model_name}\\' on \\'{dataset_name}\\' - top1 {top1} top5 {top5} loss {loss}'.format(\n                model_name=self.model_name,\n                dataset_name=self.dataset.name(),\n                top1=top1,\n                top5=top5,\n                loss=loss,\n            )\n        )\n        return loss, top1, top5\n\n    @measure_time\n    def benchmark(\n        self,\n        device: str = 'cpu',\n        batch_size: int = 128,\n        input_size: int = 224,\n        warmup_steps: int = 10,\n        measure_steps: int = 50,\n    ) -> Tuple[float, float]:\n        model = self.model.to(device)\n        latency_mean, latency_std = measure_latency(\n            model=model,\n            input_size=(batch_size, 3, input_size, input_size),\n            warmup_steps=warmup_steps,\n            measure_steps=measure_steps,\n            device=device,\n        )\n        log.info(\n            '\\'{model_name}\\' (BS={batch_size}) mean latency {latency_mean} +/- {latency_std}'.format(\n                model_name=self.model_name,\n                batch_size=batch_size,\n                latency_mean=latency_mean,\n                latency_std=latency_std,\n            )\n        )\n        return latency_mean, latency_std\n\n    @measure_time\n    def get_gflops(\n        self,\n        device: str = 'cpu',\n        input_size: int = 224,\n    ):\n        return get_macs(\n            model=self.model,\n            input_size=(1, 3, input_size, input_size),\n            device=device,\n        )\n\n    @measure_time\n    def get_params(self):\n        return get_parameters(model=self.model)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('-m', '--model', type=str, required=True)\n    parser.add_argument('-b', '--batch_size', type=int, default=128)\n    parser.add_argument(\n        '-t', '--test_size', type=int, default=None, help='How many batches should be used for validation.'\n    )\n    parser.add_argument(\n        '--warmup_steps',\n        type=int,\n        default=10,\n        help='How many batches should be used to warm up latency measurement when benchmarking.',\n    )\n    parser.add_argument(\n        '--measure_steps',\n        type=int,\n        default=50,\n        help='How many batches should be used for actual latency measurement when benchmarking.',\n    )\n    parser.add_argument('--device', type=str, choices=['cpu', 'cuda'], default='cpu')\n    parser.add_argument('--dataset', type=str, choices=['imagenet', 'imagenette', 'cifar10'], default='imagenet')\n    parser.add_argument('--input_size', type=int, default=224)\n    parser.add_argument('-d', '--debug', action='store_true')\n\n    args = parser.parse_args()\n\n    if args.debug:\n        set_logger(logging.DEBUG)\n\n    log.info('Settings: {}'.format(args))\n\n    ref = TorchVisionReference(\n        model_name=args.model,\n        dataset=Dataset.get(args.dataset),\n    )\n\n    ref.validate(\n        device=args.device,\n        batch_size=args.batch_size,\n        test_size=args.test_size,\n    )\n    ref.benchmark(\n        device=args.device,\n        batch_size=args.batch_size,\n        input_size=args.input_size,\n        warmup_steps=args.warmup_steps,\n        measure_steps=args.measure_steps,\n    )\n","repo_name":"IntelLabs/DyNAS-T","sub_path":"dynast/utils/reference.py","file_name":"reference.py","file_ext":"py","file_size_in_byte":6150,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"86"}
+{"seq_id":"43943982184","text":"import re\nfrom unidecode import unidecode\nfrom flask import request, redirect, url_for\nfrom urllib.parse import urlparse, urljoin\n\n\n_punct_re = re.compile(r'[\\t !\"#$&\\'()*\\-/<=>?@\\[\\\\\\]^_`{|},.]+')\n\n\ndef slugify(text, delim=u'-', max_len=None):\n    \"\"\"Generates and ASCII-only slug.\"\"\"\n    result = []\n    for word in _punct_re.split(text.lower()):\n        result.extend(unidecode(word).lower().split())\n\n    slug = str(delim.join(result))\n    return slug[:max_len] if max_len else slug\n\n\ndef is_safe_url(target):\n    \"\"\"保证域名相同\"\"\"\n    ref_url = urlparse(request.host_url)\n    test_url = urlparse(urljoin(request.host_url, target))\n    return test_url.scheme in ('http', 'https') and ref_url.netloc == test_url.netloc\n\n\ndef redirect_back(default='blog.index', **kwargs):\n    for target in request.args.get('next'), request.referrer:\n        if not target:\n            continue\n        if is_safe_url(target):\n            return redirect(target)\n    return redirect(url_for(default, **kwargs))\n\n","repo_name":"sky94520/Bluelog","sub_path":"bluelog/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"30789419500","text":"### NOTES: b_h5_in_elect_scale.py ###\n# finding elements in h5\nwanted =        ['cu', 'cd', 'te']\nh5_channels =   ['cd', 'fe', 'cu', 'zn', 'se', 'te']\nh5_channels = [x.encode('utf-8') for x in h5_channels]\n\ndef find_eles_in_channel_names(w, chan):\n    chan = [x.decode('utf-8') for x in chan]\n    index_list = [i for i,ele in enumerate(chan) for e in w if e == ele]\n    return index_list\n\nnew_list = find_eles_in_channel_names(wanted, h5_channels)\n\n# adding elements to sample dictionary\nlist_of_lists = NBL3_2['XBIC_eles']\nh5s = NBL3_2['XBIC_h5s']\n\ndef extract_maps(H5s, list_of_lists):\n    maps = []                                                       #initialize master list\n    for H5, channel_indices in zip(H5s, list_of_lists):\n        scan_maps = []                                              #initialize internal (single scan) list\n        XRF_fits = H5['/MAPS/XRF_fits']                             #navigate to structure containing all fitted XRF data\n        for element_index in channel_indices:\n            map_of_interest = XRF_fits[element_index,:,:]           #use element index to extract map of interest\n            scan_maps.append(map_of_interest)                       #build internal list\n    maps.append(scan_maps)                                      #add internal list (i.e. a scan) to master list\n    return \n\nlist_of_maps = extract_maps(h5s, list_of_lists)\n\nsample_dict = NBL3_2\nsample_maps = []\nfor h5, ch_inds in zip(sample_dict['XBIC_h5s'], sample_dict['XBIC_eles_i']):\n    maps_of_eles_in_scan = [h5['/MAPS/XRF_fits'][ind,:,:]  for ind in ch_inds]\n    sample_maps.append(maps_of_eles_in_scan)\nkey = 'test_ele_maps'\nsample_dict.setdefault(key, sample_maps)","repo_name":"tzwalker/xrays","sub_path":"python/z_dev_notes/notes_electrical.py","file_name":"notes_electrical.py","file_ext":"py","file_size_in_byte":1690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"30614833119","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar  9 14:01:55 2023\n\n@author: Kingsley\n\"\"\"\n#importing dependencies\nimport numpy as np\nimport pandas as pd\nfrom joblib import load\nimport streamlit as st\nfrom sklearn.preprocessing import StandardScaler\n\n# loading in the data(The dumped model)\nmodel = load('../model/log_model.joblib')\n\n\n# creating an object of standardscaler\nsc = StandardScaler()\n\n\n\n#Backend\ndef predictions(IsActiveMember,EstimatedSalary, HasCrCard, Balance, Age, CreditScore):\n    prediction = model.predict(np.array([[IsActiveMember,EstimatedSalary, HasCrCard, Balance, Age, CreditScore]]))\n\n    return prediction\n\n# fuction to create the UI\ndef main():\n    st.title('Bank customer churn model')\n    \n\n\n    Age = st.text_input('Enter your age: ')\n    IsActiveMember = st.selectbox('Are you an active member? :', ['yes', 'no'])   \n    EstimatedSalary = st.text_input('What is your expected salary :')\n    HasCrCard = st.text_input('Do you have a credit card? :')\n    Balance = st.number_input('What is your current balance :')\n    CreditScore = st.text_input('What is your credit score :')\n    \n    \n    \n    button = st.button('Predict')\n\n    \n    if IsActiveMember.lower() == 'yes':\n       IsActiveMember = 1\n    else:\n       IsActiveMember = 0\n       \n     \n    \n    \n    if HasCrCard .lower() == 'yes':\n        HasCrCard  = 1\n    else:\n        HasCrCard  = 0\n    \n    \n    \n \n    \n  \n    \n    \n    result = ''\n    \n    if (button):\n        result = predictions(IsActiveMember, EstimatedSalary, HasCrCard, Balance, Age, CreditScore)\n        if result == 0:\n            st.success('this user would not exit')\n        else:\n            st.success('this user would exit')\n    \n    \nif __name__ == '__main__':\n    main()","repo_name":"princekingsleysunday/Churn_model","sub_path":"UI/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74448178203","text":"# import dependencies\nimport os\nimport numpy as np\nimport pandas as pd\nimport sklearn\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\nimport plotly.express as px\nimport folium\nfrom folium import Marker,GeoJson,Choropleth, Circle\nfrom folium.plugins import HeatMap, MarkerCluster\nimport librosa.display\nfrom IPython.display import Audio\n\npd.set_option('display.max_columns', 50)\n# import data\ndf_train = pd.read_csv(\"../input/birdsong-recognition/train.csv\")\ndf_train.head\ndf_train.info()\ndf_train['ebird_code'].nunique()\ndf_train['species'].value_counts()\ndf_train['year'] = df_train['date'].apply(lambda x: x.split('-')[0])\ndf_train['month'] = df_train['date'].apply(lambda x: x.split('-')[1])\ngroup_year = df_train.groupby(['year']).size().reset_index(name='counts')\ngroup_year = group_year.iloc[3:]\ngroup_month = df_train.groupby(['month']).size().reset_index(name='counts')\n\n\n\nfig = make_subplots(rows=2, cols=1, subplot_titles = ('Number of recordings w.r.t year', 'Number of recordings w.r.t month'))\n\nfig.append_trace(go.Bar(\n    x=group_year['year'],\n    y=group_year['counts'],\n    #tickmode='linear'\n), row=1, col=1)\n\nfig.append_trace(go.Bar(\n    x=group_month['month'],\n    y=group_month['counts'],\n), row=2, col=1)\n\n\n\nfig.update_layout(height=1000, width=700, showlegend=False,  xaxis = dict(\n        tickmode = 'linear',\n    ), xaxis2 = dict(tickmode='linear'))\nfig.show()\nfig = make_subplots(rows=1, cols=2, specs=[[{\"type\": \"pie\"}, {\"type\": \"pie\"}]], subplot_titles = ('Distribution of Channels', 'Distribution of Sampling rate'))\n\ngroup_ch = df_train.groupby(['channels']).size().reset_index(name='counts')\nfig.append_trace(go.Pie(\n    labels=group_ch['channels'],\n    values=group_ch['counts'],\n), row=1, col=1)\n\ngroup_sr = df_train.groupby(['sampling_rate']).size().reset_index(name='counts')\nfig.append_trace(go.Pie(\n    labels=group_sr['sampling_rate'],\n    values=group_sr['counts'],\n), row=1, col=2)\n\n\nfig.show()\n\nmap = folium.Map(location=[54, 15], tiles='cartodbpositron', zoom_start=5)\ndf_train = df_train[df_train[\"latitude\"] != \"Not specified\"]\n\n#drop nan values and convert latitude and longitude to float\ndf_no_nan = df_train.dropna(subset=['latitude','longitude'], how='any')\ndf_no_nan.latitude.astype(float)\ndf_no_nan.longitude.astype(float)\n\nmap_cluster = MarkerCluster()\n\n# Add points to the map\nfor idx, row in df_no_nan.iterrows():\n    map_cluster.add_child(Marker([row['latitude'], row['longitude']]))\n\nmap.add_child(map_cluster)\n\n#Display map\nmap\n\n\n\naudio_path = '../input/birdsong-recognition/train_audio/aldfly/XC134874.mp3'\nx, sr = librosa.load(audio_path)\nAudio(x, rate=sr)\naudio_path = '../input/birdsong-recognition/train_audio/amepip/XC111040.mp3'\nx, sr = librosa.load(audio_path)\nAudio(x, rate=sr)\naudio_path = '../input/birdsong-recognition/train_audio/banswa/XC138517.mp3'\nx, sr = librosa.load(audio_path)\nAudio(x, rate=sr)\naudio_path = '../input/birdsong-recognition/train_audio/bkhgro/XC109305.mp3'\nx, sr = librosa.load(audio_path)\nAudio(x, rate=sr)\nfig, ax = plt.subplots(4, figsize = (20, 9))\nfig.suptitle('Waveplots', fontsize=16)\naudio_path1 = '../input/birdsong-recognition/train_audio/aldfly/XC134874.mp3'\naudio_path2 = '../input/birdsong-recognition/train_audio/amepip/XC111040.mp3'\naudio_path3 = '../input/birdsong-recognition/train_audio/banswa/XC138517.mp3'\naudio_path4 = '../input/birdsong-recognition/train_audio/bkhgro/XC109305.mp3'\n\ny1, sr1 = librosa.load(audio_path1)\ny2, sr2 = librosa.load(audio_path2)\ny3, sr3 = librosa.load(audio_path3)\ny4, sr4 = librosa.load(audio_path4)\n\nlibrosa.display.waveplot(y=y1, sr=sr1, color = \"#3371FF\", ax=ax[0])\nlibrosa.display.waveplot(y=y2 , sr=sr2, color = \"#F7A81E\", ax=ax[1])\nlibrosa.display.waveplot(y=y3 , sr=sr3, color = \"#2BF71E\", ax=ax[2])\nlibrosa.display.waveplot(y=y4 , sr=sr4, color = \"#F71E6D\", ax=ax[3])\n\n# Visualize an STFT power spectrum\n\naudio_path = '../input/birdsong-recognition/train_audio/aldfly/XC134874.mp3'\ny, sr = librosa.load(audio_path)\nplt.figure(figsize=(12, 8))\nD = librosa.amplitude_to_db(librosa.stft(y))\nplt.subplot(4, 2, 1)\nlibrosa.display.specshow(D, y_axis='linear')\nplt.colorbar(format='%+2.0f dB')\nplt.title('Linear-frequency power spectrogram')\n\n# logarithmic scale\n\nplt.subplot(4, 2, 2)\nlibrosa.display.specshow(D, y_axis='log')\nplt.colorbar(format='%+2.0f dB')\nplt.title('Log-frequency power spectrogram')\n\n#CQT scale\n\nCQT = librosa.amplitude_to_db(librosa.cqt(y, sr=sr), ref=np.max)\nplt.subplot(4, 2, 3)\nlibrosa.display.specshow(CQT, y_axis='cqt_hz')\nplt.colorbar(format='%+2.0f dB')\nplt.title('Constant-Q power spectrogram (Hz)')\n\nCQT = librosa.amplitude_to_db(librosa.cqt(y, sr=sr), ref=np.max)\nplt.subplot(4, 2, 4)\nlibrosa.display.specshow(CQT, y_axis='cqt_note')\nplt.colorbar(format='%+2.0f dB')\nplt.title('Constant-Q power spectrogram (note)')\n\n#Chromagram\nC = librosa.feature.chroma_cqt(y=y, sr=sr)\nplt.subplot(4, 2, 5)\nlibrosa.display.specshow(C, y_axis='chroma')\nplt.colorbar()\nplt.title('Chromagram')\n\n# Log power spectrogram\nplt.subplot(4, 2, 6)\nlibrosa.display.specshow(D, x_axis='time', y_axis='log')\nplt.colorbar(format='%+2.0f dB')\nplt.title('Log power spectrogram')\n\n# let's zoom in \nn0 = 7000\nn1 = 7100\nplt.figure(figsize=(14, 5))\nplt.plot(y[n0:n1])\nzero_crossings = librosa.zero_crossings(y[n0:n1], pad=False)\nzero_crossings.shape\nprint(sum(zero_crossings))\nzcrs = librosa.feature.zero_crossing_rate(y)\nprint(zcrs.shape)\nplt.figure(figsize=(14, 5))\nplt.plot(zcrs[0])\nspectral_centroid = librosa.feature.spectral_centroid(y, sr=sr)[0]\nspectral_centroid.shape\nplt.figure(figsize=(14, 5))\nplt.plot(spectral_centroid.T, label='Spectral centroid')\nplt.ylabel('Hz')\nplt.xticks([])\nplt.xlim([0, spectral_centroid.shape[-1]])\nplt.legend()\n#  time variable for visualization\nframes = range(len(spectral_centroid))\nt = librosa.frames_to_time(frames)\n\n# helper function to normalize the spectral centroid for visualization\n\ndef normalize(y, axis=0):\n    return sklearn.preprocessing.minmax_scale(y, axis=axis)\n\nspectral_rolloff = librosa.feature.spectral_rolloff(y+0.01, sr=sr)[0]\nlibrosa.display.waveplot(y, sr=sr, alpha=0.4)\nplt.plot(t, normalize(spectral_rolloff), color='r')\naudio_path = '../input/birdsong-recognition/train_audio/amecro/XC114552.mp3'\ny, sr = librosa.load(audio_path)\nAudio(y, rate=sr)\ndb = librosa.core.amplitude_to_db(y)\nmean_db = np.abs(db).mean()\nstd_db = db.std()\nx_split = librosa.effects.split(y=y, top_db = mean_db - std_db)\nsilence_removed = []\nfor i in x_split:\n    silence_removed.extend(y[i[0]:i[1]])\nsilence_removed = np.array(silence_removed)\nAudio(silence_removed, rate=sr)\n\n","repo_name":"aorursy/new-nb-6","sub_path":"rocky03_birdcall-eda-fe-and-silence-removal.py","file_name":"rocky03_birdcall-eda-fe-and-silence-removal.py","file_ext":"py","file_size_in_byte":6640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"28417189203","text":"def gnome_sort(a):\n    n = len(a)\n    i = 0\n\n    while i < n:\n        #go right if the current array element is greater than the previous one\n        #(or if we are at start )\n        if a[i] > a[i-1] or i == 0:\n            yield a, [i], [], []\n            i+=1\n        #else : make swap and go left\n        else:\n            yield a, [], [i], []\n            a[i], a[i-1] = a[i-1], a[i]\n            i-=1","repo_name":"sahidb/SORTING","sub_path":"gnome.py","file_name":"gnome.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70713188445","text":"from torchvision.datasets import ImageFolder\r\nfrom torch.utils.data import DataLoader\r\nimport torch\r\nfrom torch.utils.data import Dataset\r\nfrom torchvision import datasets\r\nfrom torchvision.transforms import ToTensor\r\nimport matplotlib.pyplot as plt\r\nfrom PIL import Image\r\n#from torchsummary import summary\r\nfrom torchvision.datasets import ImageFolder\r\nfrom torchvision.transforms import Compose, Resize, ToTensor, Grayscale\r\n\r\nimport os\r\nimport utils\r\nimport torch.optim as optim\r\nimport torch\r\nfrom torch import nn\r\nfrom torch.utils.data import DataLoader\r\nfrom torchvision import datasets, transforms\r\nimport torchvision.models as models\r\nfrom torch.utils.data import DataLoader, random_split\r\nfrom PIL import Image\r\nimport torchvision\r\nfrom torch import optim\r\nimport numpy as np\r\nfrom torch.utils.data.sampler import SubsetRandomSampler\r\nfrom imgaug import augmenters as iaa\r\nimport random\r\nimport matplotlib.image as mpimg\r\nimport cv2\r\n\r\n\r\n\r\n# Function to rename multiple files\r\ndef renamer(path):\r\n    iterater = 0     \r\n    for filename in os.listdir(path):\r\n        print(str(iterater))\r\n        dst = str(iterater) + \".jpg\"\r\n        src = os.path.join(path, filename)  # add trailing slash or backslash to path\r\n        dst = os.path.join(path, dst)  # add trailing slash or backslash to path\r\n        os.rename(src, dst) \r\n        iterater += 1\r\n\r\n#Images verification method\r\ndef verifier(path):    \r\n    for filename in os.listdir(path):\r\n        try:\r\n            img = Image.open(os.path.join(path, filename)) # add trailing slash or backslash to path\r\n        except (Exception, FileNotFoundError, AttributeError):\r\n            os.remove(os.path.join(path, filename)) # add trailing slash or backslash to path\r\n        try:\r\n            img.verify()\r\n        except (Exception, FileNotFoundError, AttributeError):\r\n            os.remove(os.path.join(path, filename)) # add trailing slash or backslash to path\r\n\r\n# Extra images generating loop\r\ndef img_generator(path, no_gen):\r\n  i = 0\r\n  for filename in os.listdir(path):\r\n    #try:\r\n    i +=1\r\n    img = load_image(path + '/' + filename)\r\n    img = img.astype('uint8')\r\n    img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\r\n    save_image(augment(img), path + '/Generated'+ str(i) +'.jpg')\r\n    #except (ValueError, OSError):\r\n    #  print(\"in exception\")\r\n    #  pass\r\n\r\ndef load_image(infilename):\r\n    img = Image.open( infilename )\r\n    img.load()\r\n    data = np.asarray( img, dtype=\"int32\" )\r\n    return data\r\n\r\ndef save_image(npdata, outfilename) :\r\n    img = Image.fromarray( np.asarray( np.clip(npdata,0,255), dtype=\"uint8\"), \"L\" )\r\n    img.save(outfilename, \"JPEG\")\r\n\r\n#Generating extra preprocessed images\r\n#This could be edited to choose the augmentation we want\r\ndef augment(img):\r\n  par_1 = random.uniform(0.1, 1.0)\r\n  par_2 = random.uniform(1.0, 15.0)\r\n  par_3 = random.uniform(2.0, 40.0)\r\n  par_4 = random.uniform(0.1, 1.0)\r\n  par_5 = random.uniform(0.1, 1.0)\r\n  par_6 = random.uniform(0.01, 0.2)\r\n  affine = iaa.Affine(rotate=(-10, 10), mode = 'edge')\r\n  img = affine.augment_image(img)\r\n  #blurer = iaa.GaussianBlur(iaa.Uniform(0.1,par_1)) \r\n  #img = blurer.augment_image(img)\r\n  elastic = iaa.ElasticTransformation(sigma=par_2, alpha=par_3)\r\n  img=elastic.augment_image(img)\r\n  flp=iaa.Flipud(p=par_4)\r\n  img=flp.augment_image(img)\r\n  salt = iaa.SaltAndPepper(p=par_6)\r\n  img=salt.augment_image(img)\r\n  flp2=iaa.Fliplr(p=par_5)\r\n  img=flp2.augment_image(img)\r\n  crop = iaa.CropToFixedSize(1500,1300,position=\"center-bottom\")\r\n  img = crop.augment_image(img)\r\n  return img\r\n\r\ndef augment2(img):\r\n    brightness = iaa.WithBrightnessChannels(iaa.Add((-50, 50)))\r\n    img = brightness.augment_image(img)\r\n    aug = iaa.WithHueAndSaturation([\r\n    iaa.WithChannels(0, iaa.Add((-30, 10))),\r\n    iaa.WithChannels(1, [\r\n        iaa.Multiply((0.5, 1.5)),\r\n        iaa.LinearContrast((0.75, 1.25))])])\r\n    img = aug.augment_image(img)\r\n    hueSaturation = iaa.MultiplyHueAndSaturation(mul_hue=(0.5, 1.5))\r\n    img = hueSaturation.augment_image(img)\r\n    return img\r\n\r\n\r\ndef augment_all_classes(PATH):\r\n    for class_folder in os.listdir(PATH):\r\n        class_path = PATH + '/' + class_folder\r\n        num_img = len(os.listdir(class_path))\r\n        img_generator(class_path, num_img-1)\r\n\r\ndef remove_augmented_img(PATH): \r\n    for class_folder in os.listdir(PATH):\r\n            class_path = PATH + '/' + class_folder\r\n            for img_name in os.listdir(class_path):\r\n                # Check if the file is an image file\r\n                if ('Generated' in img_name):\r\n                    # Delete the image file using os.remove()\r\n                    image_path = class_path + \"/\" + img_name\r\n                    os.remove(image_path)\r\n\r\ndef plot_images(PATH, class_name, top=25):\r\n            class_path = PATH + '/' + class_name\r\n            plt.figure(figsize = (12,12))\r\n            num_img = 0\r\n            for img_name in os.listdir(class_path):\r\n                if num_img >= top:\r\n                    break\r\n                num_img += 1\r\n                image_path = os.path.join(class_path, img_name)\r\n                plt.subplot(5, 5, num_img)\r\n                img = mpimg.imread(image_path)\r\n                plt.imshow(img)\r\n            plt.tight_layout()\r\n            plt.show()\r\n\r\ndef main():\r\n    augment_all_classes('C:/Users/ingvilrh/OneDrive - NTNU/Masteroppgave23/full_fishdata')\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()","repo_name":"ingvildrh/builtOnA3","sub_path":"data_augmentation.py","file_name":"data_augmentation.py","file_ext":"py","file_size_in_byte":5418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"30096991919","text":"import json\nimport logging\nimport hashlib\nfrom os.path import exists\n\nimport requests\nfrom requests.sessions import Session\nfrom M2Crypto import DSA\n\nimport active_document as ad\nfrom sugar_network.toolkit import sugar\nfrom sugar_network.toolkit.router import Redirect\nfrom sugar_network import client\nfrom active_toolkit import coroutine, util, enforce\n\n\nConnectionError = requests.ConnectionError\n\n_RECONNECTION_NUMBER = 1\n_RECONNECTION_TIMEOUT = 3\n\n_logger = logging.getLogger('http')\n\n\nclass Client(object):\n\n    def __init__(self, api_url='', sugar_auth=False, **kwargs):\n        self.api_url = api_url\n        self.params = kwargs\n        self._sugar_auth = sugar_auth\n\n        verify = True\n        if client.no_check_certificate.value:\n            verify = False\n        elif client.certfile.value:\n            verify = client.certfile.value\n\n        headers = {'Accept-Language': ad.default_lang()}\n        if self._sugar_auth:\n            privkey_path = sugar.privkey_path()\n            if not exists(privkey_path):\n                _logger.warning('Sugar session was never started, '\n                        'fallback to anonymous mode')\n                self._sugar_auth = False\n            else:\n                uid = sugar.uid()\n                headers['sugar_user'] = uid\n                headers['sugar_user_signature'] = _sign(privkey_path, uid)\n\n        self._session = Session(headers=headers, verify=verify, prefetch=False)\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, *args):\n        self.close()\n\n    def close(self):\n        self._session.close()\n\n    def exists(self, path):\n        response = self.request('GET', path, allowed=[404])\n        return response.status_code != 404\n\n    def get(self, path_=None, **kwargs):\n        response = self.request('GET', path_, params=kwargs)\n        return self._decode_reply(response)\n\n    def post(self, path_=None, data_=None, **kwargs):\n        response = self.request('POST', path_, json.dumps(data_),\n                headers={'Content-Type': 'application/json'}, params=kwargs)\n        return self._decode_reply(response)\n\n    def put(self, path_=None, data_=None, **kwargs):\n        response = self.request('PUT', path_, json.dumps(data_),\n                headers={'Content-Type': 'application/json'}, params=kwargs)\n        return self._decode_reply(response)\n\n    def delete(self, path_=None, **kwargs):\n        response = self.request('DELETE', path_, params=kwargs)\n        return self._decode_reply(response)\n\n    def request(self, method, path=None, data=None, headers=None, allowed=None,\n            params=None, **kwargs):\n        if not path:\n            path = ['']\n        if not isinstance(path, basestring):\n            path = '/'.join([i.strip('/') for i in [self.api_url] + path])\n\n        if params is None:\n            params = self.params\n        else:\n            params.update(self.params)\n\n        while True:\n            try:\n                response = requests.request(method, path, data=data,\n                        headers=headers, session=self._session, params=params,\n                        **kwargs)\n            except requests.exceptions.SSLError:\n                _logger.warning('Use --no-check-certificate to avoid checks')\n                raise\n\n            if response.status_code != 200:\n                if response.status_code == 401:\n                    enforce(self._sugar_auth,\n                            'Operation is not available in anonymous mode')\n                    _logger.info('User is not registered on the server, '\n                            'registering')\n                    self._register()\n                    continue\n                if allowed and response.status_code in allowed:\n                    return response\n                content = response.content\n                try:\n                    error = json.loads(content)\n                except Exception:\n                    _logger.debug('Got %s HTTP error for %r request:\\n%s',\n                            response.status_code, path, content)\n                    response.raise_for_status()\n                else:\n                    raise RuntimeError(error['error'])\n\n            return response\n\n    def call(self, request, response=None):\n        params = request.copy()\n        method = params.pop('method')\n        document = params.pop('document') if 'document' in params else None\n        guid = params.pop('guid') if 'guid' in params else None\n        prop = params.pop('prop') if 'prop' in params else None\n\n        path = []\n        if document:\n            path.append(document)\n        if guid:\n            path.append(guid)\n        if prop:\n            path.append(prop)\n\n        if request.content_type == 'application/json':\n            request.content = json.dumps(request.content)\n\n        headers = None\n        if request.content is not None:\n            headers = {}\n            headers['Content-Type'] = \\\n                    request.content_type or 'application/octet-stream'\n            headers['Content-Length'] = str(len(request.content))\n        elif request.content_stream is not None:\n            headers = {}\n            headers['Content-Type'] = \\\n                    request.content_type or 'application/octet-stream'\n            # TODO Avoid reading the full content at once\n            request.content = request.content_stream.read()\n            headers['Content-Length'] = str(len(request.content))\n\n        reply = self.request(method, path, data=request.content,\n                params=params, headers=headers, allowed=[303],\n                allow_redirects=request.allow_redirects)\n\n        if reply.status_code == 303:\n            raise Redirect(reply.headers['Location'])\n\n        if response is not None:\n            if 'Content-Disposition' in reply.headers:\n                response['Content-Disposition'] = \\\n                        reply.headers['Content-Disposition']\n            if 'Content-Type' in reply.headers:\n                response.content_type = reply.headers['Content-Type']\n\n        result = self._decode_reply(reply)\n        if result is None:\n            result = reply.raw\n        return result\n\n    def subscribe(self):\n        return _Subscription(self, _RECONNECTION_NUMBER)\n\n    def _register(self):\n        self.post(['user'], {\n            'name': sugar.nickname() or '',\n            'color': sugar.color() or '#000000,#000000',\n            'machine_sn': sugar.machine_sn() or '',\n            'machine_uuid': sugar.machine_uuid() or '',\n            'pubkey': sugar.pubkey(),\n            })\n\n    def _decode_reply(self, response):\n        if response.headers.get('Content-Type') == 'application/json':\n            return json.loads(response.content)\n        else:\n            return response.content\n\n\nclass _Subscription(object):\n\n    def __init__(self, aclient, tries):\n        self._tries = tries or 1\n        self._client = aclient\n        self._response = None\n\n    def __iter__(self):\n        while True:\n            event = self.pull()\n            if event is not None:\n                yield event\n\n    def fileno(self):\n        # pylint: disable-msg=W0212\n        return self._handshake()._fp.fp.fileno()\n\n    def pull(self):\n        for a_try in (1, 0):\n            stream = self._handshake()\n            try:\n                line = _readline(stream)\n                enforce(line is not None, 'Subscription aborted')\n                break\n            except Exception:\n                if a_try == 0:\n                    raise\n                util.exception('Failed to read from %r subscription, '\n                        'will resubscribe', self._client.api_url)\n                self._response = None\n\n        if line.startswith('data: '):\n            try:\n                return json.loads(line.split(' ', 1)[1])\n            except Exception:\n                util.exception('Failed to parse %r event from %r subscription',\n                        line, self._client.api_url)\n\n    def _handshake(self):\n        if self._response is not None:\n            return self._response.raw\n\n        _logger.debug('Subscribe to %r', self._client.api_url)\n\n        for a_try in reversed(xrange(self._tries)):\n            try:\n                self._response = self._client.request('GET',\n                        params={'cmd': 'subscribe'})\n                break\n            except Exception:\n                if a_try == 0:\n                    raise\n                util.exception(_logger,\n                        'Cannot subscribe to %r, retry in %s second(s)',\n                        self._client.api_url, _RECONNECTION_TIMEOUT)\n                coroutine.sleep(_RECONNECTION_TIMEOUT)\n\n        return self._response.raw\n\n\ndef _sign(privkey_path, data):\n    key = DSA.load_key(privkey_path)\n    return key.sign_asn1(hashlib.sha1(data).digest()).encode('hex')\n\n\ndef _readline(stream):\n    line = None\n    while True:\n        char = stream.read(1)\n        if not char:\n            break\n        if line is None:\n            line = char\n        else:\n            line += char\n        if char == '\\n':\n            break\n    return line\n","repo_name":"sugar-activities/4619-activity","sub_path":"site-packages/sugar_network/toolkit/http.py","file_name":"http.py","file_ext":"py","file_size_in_byte":9106,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"10761683494","text":"\ndef words():\n    holidayR = str(input('Holiday: '))\n    noun1R = str(input('noun: '))\n    placeR = str(input('A place please: ')),\n    personR = str(input('Person: ')),\n    adj1R = str(input('Adjective: ')),\n    body_partR = str(input('body part: ')),\n    verb1R = str(input('verb: ')),\n    adj2R = str(input('Adjective again: ')),\n    noun2R = str(input('noun: ')),\n    foodR = str(input('food: ')),\n    plural_nounR = str(input('plural noun!: '))\n    return holidayR, noun1R , placeR, personR, adj1R, body_partR, verb1R, adj2R, noun2R, foodR, plural_nounR\n\ndef main(holidayR, noun1R , placeR, personR, adj1R, body_partR, verb1R, adj2R, noun2R, foodR, plural_nounR):\n    madlibs = \"I can't believe it's already {holiday}! \\n\" \\\n              \"I can't wait to put on my {noun1} and visit every {place} in my neighborhood.\\n\" \\\n              \"This year, I am going to dress up as {person} with {adj1} {body_part}.\\n\" \\\n              \"Before I {verb1}, I make sure to grab my {adj2} {noun2} to hold all of my {food}.\\n\" \\\n              \"Finally, all of my {plural_noun} are ready to go!\".format(\n    holiday= holidayR,\n    noun1 = noun1R ,\n    place = placeR,\n    person = personR,\n    adj1 = adj1R,\n    body_part = body_partR,\n    verb1 = verb1R,\n    adj2 = adj2R,\n    noun2 = noun2R,\n    food = foodR,\n    plural_noun = plural_nounR)\n    print(madlibs)\n\n\nif __name__ == '__main__':\n    holidayR, noun1R , placeR, personR, adj1R, body_partR, verb1R, adj2R, noun2R, foodR, plural_nounR = words()\n    main(holidayR, noun1R , placeR, personR, adj1R, body_partR, verb1R, adj2R, noun2R, foodR, plural_nounR)\n\n\n\n\n","repo_name":"aaron-silicon-valley/madlibs","sub_path":"madlib.py","file_name":"madlib.py","file_ext":"py","file_size_in_byte":1607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"11636603301","text":"from panda3d.core import NodePath\nfrom panda3d.core import CollisionTraverser\nfrom panda3d.core import CollisionHandlerQueue\nfrom panda3d.core import BitMask32\n\ndef isCmCmCollided(objcm1, objcm2, toggleplot = False):\n    \"\"\"\n    detect the collision between collision models\n\n    :return: True or False\n\n    author: weiwei, Toyonaka\n    date: 20190312\n    \"\"\"\n\n    oocnp = NodePath(\"collision nodepath\")\n    obj1cnp = objcm1.copycdnpTo(oocnp)\n    obj2cnp = objcm2.copycdnpTo(oocnp)\n    if toggleplot:\n        oocnp.reparentTo(base.render)\n        obj1cnp.show()\n        obj2cnp.show()\n    ctrav = CollisionTraverser()\n    chan = CollisionHandlerQueue()\n    ctrav.addCollider(obj1cnp, chan)\n    ctrav.traverse(oocnp)\n    if chan.getNumEntries() > 0:\n        return True\n    else:\n        return False\n\ndef isCmCmListCollided(objcm, objcmlist, toggleplot = False):\n    \"\"\"\n    detect the collision between a collision model and a collision model list\n\n    :return: True or False\n\n    author: weiwei, Toyonaka\n    date: 20190312\n    \"\"\"\n\n    oocnp = NodePath(\"collision nodepath\")\n    objcnp = objcm.copycdnpTo(oocnp)\n    objcnplist = []\n    for objcm2 in objcmlist:\n        objcnplist.append(objcm2.copycdnpTo(oocnp))\n    if toggleplot:\n        oocnp.reparentTo(base.render)\n        objcnp.show()\n        for obj2cnp in objcnplist:\n            obj2cnp.show()\n    ctrav = CollisionTraverser()\n    chan = CollisionHandlerQueue()\n    ctrav.addCollider(objcnp, chan)\n    ctrav.traverse(oocnp)\n    if chan.getNumEntries() > 0:\n        return True\n    else:\n        return False\n\ndef isCmListCmListCollided(objcmlist0, objcmlist1, toggleplot = False):\n    \"\"\"\n    detect the collision between two collision model lists\n\n    :return: True or False\n\n    author: weiwei, Toyonaka\n    date: 20190422\n    \"\"\"\n\n    oocnp = NodePath(\"collision nodepath\")\n    obj0cnplist = []\n    for objcm0 in objcmlist0:\n        obj0cnplist.append(objcm0.copycdnpTo(oocnp))\n    obj1cnplist = []\n    for objcm1 in objcmlist1:\n        obj1cnplist.append(objcm1.copycdnpTo(oocnp))\n    if toggleplot:\n        oocnp.reparentTo(base.render)\n        for obj0cnp in obj0cnplist:\n            obj0cnp.show()\n        for obj1cnp in obj1cnplist:\n            obj1cnp.show()\n    ctrav = CollisionTraverser()\n    chan = CollisionHandlerQueue()\n    for obj0cnp in obj0cnplist:\n        obj0cnp.node().setFromCollideMask(BitMask32(0x1))\n        obj0cnp.setCollideMask(BitMask32(0x2))\n        ctrav.addCollider(obj0cnp, chan)\n    ctrav.traverse(oocnp)\n    if chan.getNumEntries() > 0:\n        return True\n    else:\n        return False\n\nif __name__ == '__main__':\n    import utiltools.robotmath as rm\n    import numpy as np\n    import environment.bunrisettingfree as bunrisettingfree\n    import pandaplotutils.pandactrl as pc\n    import robotsim.ur3dual.ur3dual as ur3dualsim\n    import robotsim.ur3dual.ur3dualmesh as ur3dualsimmesh\n    import manipulation.grip.robotiq85.robotiq85 as rtq85\n\n    base = pc.World(camp=[2700,300,2700], lookatp=[0,0,1000])\n    env = bunrisettingfree.Env()\n    env.reparentTo(base.render)\n    objcm = env.loadobj(\"bunnysim.stl\")\n\n    objcm.setColor(.2,.5,0,1)\n    objcm.setPos(400,-200,1200)\n    objcm.reparentTo(base.render)\n    objcm.showcn()\n    obscmlist = env.getstationaryobslist()\n    for obscm in obscmlist:\n        obscm.showcn()\n\n    objpos = np.array([400,-300,1200])\n    objrot = rm.rodrigues([0,1,0], 45)\n    objcm2 = env.loadobj(\"housing.stl\")\n    objcm2.setColor(1,.5,0,1)\n    env.addchangableobs(base.render, objcm2, objpos, objrot)\n\n    hndfa = rtq85.Robotiq85Factory()\n    rgthnd = hndfa.genHand()\n    lfthnd = hndfa.genHand()\n    robotsim = ur3dualsim.Ur3DualRobot(rgthnd = rgthnd, lfthnd = lfthnd)\n    robotmeshgen = ur3dualsimmesh.Ur3DualMesh()\n    robotmesh = robotmeshgen.genmnp(robotsim, toggleendcoord=False)\n    robotmesh.reparentTo(base.render)\n\n    print(isCmCmListCollided(objcm, obscmlist))\n\n    base.run()","repo_name":"wangyan-hlab/wrs-nxt-IL-RL","sub_path":"environment/pandacdhelper.py","file_name":"pandacdhelper.py","file_ext":"py","file_size_in_byte":3918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70343018203","text":"#PES Python Assignments SET 1\n#15 Python program Basics\n#Python program Basics\t-\n##Create a list of 5 names and check given name exist in the List.\n##        a) Use membership operator (IN) to check the presence of an element.\n##        b) Perform above task without using membership operator.\n##        c) Print the elements of the list in reverse direction.\n##\n\n\n#Manoj Dixit - 20141404\n#Python 3.9.0\n\na = ['Suraj','Manoj','Sai','Ranajit','Swati']\n\nprint('Below is the list\\n',a)\n\nb=input('Please enter a string to check whether it exists in list : ')\n\nif b in a:\n    print(b,'Exists in the list (via IN operator)')\nelse:\n    print(b,'Does not exist in the list')\n\nb=input('Please enter a string to check whether it exists in list : ')\n\nfor i in range(len(a)):\n    if a[i]==b:\n        print(b,'Exists in the list (without membership operator)')\n        break\n\na.reverse()\nprint('This is list reversed\\n',a)\n\n##Result:\n##    Below is the list\n##     ['Suraj', 'Manoj', 'Sai', 'Ranajit', 'Swati']\n##    Please enter a string to check whether it exists in list : Manoj\n##    Manoj Exists in the list (via IN operator)\n##    Please enter a string to check whether it exists in list : Suraj\n##    Suraj Exists in the list (without membership operator)\n##    This is list reversed\n##     ['Swati', 'Ranajit', 'Sai', 'Manoj', 'Suraj']\n\n\n    \n","repo_name":"mndxt007/PythonL1_Code","sub_path":"1_Programs/15.py","file_name":"15.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"13805523401","text":"from translation.base import Translator\nimport os\nimport requests\nimport uuid\nimport json\n\n\nclass AzureTranslator(Translator):\n    def __init__(self):\n        token = os.environ.get('AZURE_TOKEN')\n        if token is None:\n            raise Exception('Environment variable \"AZURE_TOKEN\" must be set')\n        self.__azure_token = token\n\n    def translate(self, src_text, src_lang, dest_lang):\n        endpoint = 'https://api-nam.cognitive.microsofttranslator.com/'\n        path = '/translate?api-version=3.0'\n        params = '&from=' + src_lang + '&to=' + dest_lang\n        constructed_url = endpoint + path + params\n\n        headers = {\n            'Ocp-Apim-Subscription-Key': self.__azure_token,\n            'Content-type': 'application/json',\n            'X-ClientTraceId': str(uuid.uuid4())\n        }\n\n        body = [{\n            'text': src_text\n        }]\n        request = requests.post(constructed_url, headers=headers, json=body)\n        response = request.json()\n\n        return response[0]['translations'][0]['text']\n","repo_name":"vincentlabonte/hercules-extraction","sub_path":"translation/azure.py","file_name":"azure.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"10661510580","text":"\nimport numpy as np\nimport math\nimport pandas as pd\n\nclass IG():\n    def __init__(self,X,y):\n\n        X = np.array(X)\n        n_feature = np.shape(X)[1]\n        n_y = len(y)\n\n        orig_H = 0\n        for i in set(y):\n            orig_H += -(y.count(i)/n_y)*math.log(y.count(i)/n_y)\n            print(\"y is \"+str(y.count(i)))\n\n        condi_H_list = []\n        for i in range(n_feature):\n            feature = X[:,i]\n            sourted_feature = sorted(feature)\n            threshold = [(sourted_feature[inde-1]+sourted_feature[inde])/2 for inde in range(len(feature)) if inde != 0 ]\n\n            if max(feature) in threshold:\n                threshold.remove(max(feature))\n            if min(feature) in threshold:\n              threshold.remove(min(feature))\n\n            pre_H = 0\n            for thre in set(threshold):\n                lower = [y[s] for s in range(len(feature)) if feature[s] < thre]\n                highter = [y[s] for s in range(len(feature)) if feature[s] > thre]\n                H_l = 0\n                for l in set(lower):\n                    H_l += -(lower.count(l) / len(lower))*math.log(lower.count(l) / len(lower))\n                H_h = 0\n                for h in set(highter):\n                    H_h += -(highter.count(h) / len(highter))*math.log(highter.count(h) / len(highter))\n                temp_condi_H = len(lower)/n_y *H_l+ len(highter)/n_y * H_h\n                condi_H = orig_H - temp_condi_H\n                pre_H = max(pre_H,condi_H)\n            condi_H_list.append(pre_H)\n\n        self.IG = condi_H_list\n\n\n    def getIG(self):\n        return self.IG\nif __name__ == \"__main__\":\n\n    metrics = pd.read_csv('dataset/' + 'commit_metrics' + '.csv')\n    metrics_fillnan=metrics.fillna(0)\n\n    metrics_drop=metrics_fillnan.drop(labels=['file','class','type'],axis=1)\n\n    # dataSet=metrics_fillnan.drop(labels=0)\n    dataSet = metrics_drop.values.tolist()\n    features=metrics_drop.columns.tolist()\n\n\n\n    # X =  [[1,0,0,1],\n    #       [0,1,1,1],\n    #       [0,0,1,0]]\n    # y = [0,0,1]\n    print(IG(dataSet,features).getIG())\n\n    # print(IG(X,y).getIG())\n\n\n\n\n\n","repo_name":"funing230/feature_select","sub_path":"next.py","file_name":"next.py","file_ext":"py","file_size_in_byte":2104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"44729975907","text":"def calculation_mean(row: list) -> object:\n    \"\"\"\n\n    :param row: every line for date\n    :return:alp_score for calculation of mean values\n    \"\"\"\n    row = row[:-1]\n\n    score_list = row.split(':')\n\n    student = score_list[0]\n    score = score_list[1].split(',')\n\n    first_score = int(score[0])\n    second_score = int(score[1])\n    third_score = int(score[2])\n\n    mean_value = (first_score + second_score + third_score) / 3\n    value = cal_alp(mean_value, student)\n    return value\n\n\ndef cal_alp(mean, stu):\n    \"\"\"\n\n    :param mean: mean score\n    :param stu: student name\n    :return: alp_score\n    \"\"\"\n    if mean > 90:\n        alp_score = \"AA\"\n    elif mean > 80:\n        alp_score = \"BA\"\n    elif mean > 70:\n        alp_score = \"BC\"\n    elif mean > 60:\n        alp_score = \"CA\"\n    elif mean > 50:\n        alp_score = \"CC\"\n    elif mean > 40:\n        alp_score = \"DC\"\n    else:\n        alp_score = \"FF\"\n    return stu + \":\" + alp_score + \"\\n\"\n\n\ndef mean_score():\n    \"\"\"\n\n    :return: mean score\n    \"\"\"\n    with open(\"Exam_score.txt\", \"r\") as file:\n        for i in file:\n            print(calculation_mean(i))\n\n\ndef enter_score():\n    \"\"\"\n\n    :return: write file\n    \"\"\"\n    name = input(\"Student's name : \")\n    surname = input(\"Student's surname : \")\n    first_score = input(\"Student's first score: \")\n    second_score = input(\"Student's second score : \")\n    third_score = input(\"Student's third score : \")\n\n    with open(\"Exam_score.txt\", \"a\") as file:\n        file.write(name + ' ' + surname + ':' + first_score + ',' + second_score + ',' + third_score + '\\n')\n\n\ndef save_score():\n    \"\"\"\n\n    :return: Exam_score.txt file and Result_score.txt file\n    \"\"\"\n    with open('Exam_score.txt', 'r') as file:\n        result_list = []\n        for i in file:\n            result_list.append(calculation_mean(i))\n\n        with open('Result_score.txt', \"w\") as file2:\n            for j in result_list:\n                file2.write(j)\n\n\ndef main():\n    \"\"\"\n    this function that can enter  student name, surname and grades.\n    its calculation the overage of grades,determine the letter grades and its saved all information\n    :return:\n    \"\"\"\n    while True:\n        process = input('1-Read Score\\n2-Enter Score\\n3-Save Score\\n4-Exit\\n')\n\n        if process == '1':\n            mean_score()\n        elif process == '2':\n            enter_score()\n        elif process == '3':\n            save_score()\n        else:\n            break\n\n\nmain()\n","repo_name":"sinaalparslan/Python","sub_path":"exercises/exercise4/exam_score/exam_score.py","file_name":"exam_score.py","file_ext":"py","file_size_in_byte":2450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"12727086373","text":"import os\nimport requests\n\nfrom scrapers.restaurant_scraper import RestaurantScraper, Dish\n\n\nclass ZomatoScraper(RestaurantScraper):\n\n    def __init__(self):\n        super().__init__()\n        self.api_key = os.environ['ZOMATO_API_KEY']\n        self.url = 'https://developers.zomato.com/api/v2.1/dailymenu?res_id=%d'\n        self.res_id = None\n        self.header = {\n            'user_key': self.api_key\n        }\n\n    def scrape(self):\n        url = self.url % self.res_id\n        response = requests.get(url, headers=self.header)\n\n        response_js = response.json()\n\n        if 'daily_menus' not in response_js or not response_js['daily_menus']:\n            return\n\n        dish_list = response_js['daily_menus'][0]['daily_menu']['dishes']\n        ids = [dish['dish']['dish_id'] for dish in dish_list]\n\n        prev_dish = ''\n        for i, dish in enumerate(dish_list):\n            if dish['dish']['dish_id'] in ids[i + 1:]:\n                continue\n\n            name = dish['dish']['name'].replace('  ', ' ').strip()\n            price = dish['dish']['price'].strip()\n\n            if not name or not price:\n                if name:\n                    prev_dish = name\n                continue\n\n            if prev_dish:\n                name = (prev_dish + ' ' + name).replace('  ', ' ').strip()\n                prev_dish = ''\n\n            self.dish_array.append(\n                Dish(name, price)\n            )\n","repo_name":"miguelamavel/slack-lunch","sub_path":"scrapers/zomato_scraper.py","file_name":"zomato_scraper.py","file_ext":"py","file_size_in_byte":1419,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"38592399398","text":"import argparse\nimport json\nimport pathlib\nimport shutil\nimport subprocess\nimport sys\nfrom logging import getLogger\nfrom time import sleep\nfrom typing import Callable, List\n\nimport appdirs\n\nimport onlinejudge_jordan.parse_download_history as parse_download_history\n\nlogger = getLogger(__name__)\n\n\n# APIリクエストの間隔の定数\nSHORT_DELAY = 0.1\nN_LONG_DELAY = 10\nLONG_DELAY = (1 - SHORT_DELAY) * N_LONG_DELAY\n\n\ndef add_subparser(subparsers: argparse.Action) -> None:\n    subparsers_add_parser: Callable[\n        ..., argparse.ArgumentParser\n    ] = subparsers.add_parser  # type: ignore\n    subparser = subparsers_add_parser(\n        \"prepare\",\n        aliases=[\"p\", \"pp\"],\n        help=\"oj-prepare を実行する 追加で問題のページをブラウザで開く 提出ファイルをVSCodeで開く\",\n    )\n\n    default_config_path = (\n        pathlib.Path(appdirs.user_config_dir(\"online-judge-tools\"))\n        / \"prepare.config.toml\"\n    )\n\n    help_url = \"コンテスト OR 問題のURL\"\n    help_n = \"ブラウザとVSCodeで開く問題の数の最大値 デフォルト=10\"\n    help_coding = '提出ファイルのパス デフォルト=[\"main.cpp\", \"main.py\"]'\n    help_stdin = \"標準入力リダイレクトファイルのパス\"\n\n    subparser.add_argument(\"url\", type=str, help=help_url)\n    subparser.add_argument(\"-n\", \"--number\", type=int, default=10, help=help_n)\n    subparser.add_argument(\n        \"-c\",\n        \"--coding_file\",\n        type=str,\n        nargs=\"*\",\n        default=[\"main.cpp\", \"main.py\"],\n        help=help_coding,\n    )\n    subparser.add_argument(\n        \"-s\", \"--stdin-file\", type=str, nargs=\"*\", default=[], help=help_stdin\n    )\n    subparser.add_argument(\n        \"--config-file\",\n        type=pathlib.Path,\n        help=f\"\"\"default: {str(default_config_path)}\"\"\",\n    )\n\n    subparser.set_defaults(handler=run)\n\n\ndef open_problems(\n    problem_urls: List[str],\n    coding_files: List[str],\n    stdin_files: List[str],\n    is_open_browser: bool = True,\n    is_open_vscode: bool = True,\n):\n    \"\"\"\n    問題をブラウザで開く\n    提出ファイルをVSCodeで開く\n    標準入力リダイレクトファイルを作成する\n    \"\"\"\n    history = parse_download_history.parse_oj_download_history()\n\n    for i, url in enumerate(problem_urls):\n        logger.info(\"#{} {} を処理します\".format(i + 1, url))\n        logger.info(\n            \"問題をブラウザで開く {} \"\n            \"提出ファイルをVSCodeで開く {} \"\n            \"標準入力リダイレクトファイルを作成する {}\".format(is_open_browser, is_open_vscode, True)\n        )\n        if url not in history:\n            logger.warning(\n                \"download_history.jsonl に {} のデータが見つかりません スキップします\".format(url)\n            )\n            continue\n\n        data = history[url]\n        path = pathlib.Path(data[\"directory\"])\n\n        if is_open_browser:\n            res = subprocess.run([\"open\", url])\n            if res.returncode != 0:\n                logger.warning(\"urlをopenできませんでした\")\n\n        if is_open_vscode:\n            for file in coding_files:\n                for generated_file in path.glob(file):\n                    subprocess.run([\"code\", generated_file])\n\n        for file in stdin_files:\n            tofile = pathlib.Path(path / file)\n            # testファイル内の拡張子.inで辞書順最小のファイルがコピー元\n            # 辞書順最小のファイルが最初のテストケースのことがおおいため\n            fromfile_path = pathlib.Path(path / \"test\")\n            fromfiles = list(sorted(fromfile_path.glob(\"*.in\")))\n            for fromfile in fromfiles:\n                shutil.copy(fromfile, tofile)\n                break\n\n        # APIリクエストの間隔を取る\n        sleep(SHORT_DELAY)\n        if (i + 1) % N_LONG_DELAY == 0:\n            sleep(LONG_DELAY)\n\n\ndef run(args: argparse.Namespace) -> bool:\n    # 引数をパース\n    arg_url: str = args.url\n    n_open: int = args.number\n    path_config_file: str = args.config_file\n    coding_files: List[str] = args.coding_file\n    stdin_files: List[str] = args.stdin_file\n\n    # oj-apiからコンテスト情報のJSONを取得\n    contest_raw = subprocess.run(\n        [\"oj-api\", \"get-contest\", arg_url], encoding=\"utf-8\", stdout=subprocess.PIPE\n    )\n    if contest_raw.returncode != 0:\n        logger.error(\"oj-api get-contestに失敗しました\")\n        sys.exit(1)\n    contest = json.loads(contest_raw.stdout)\n    logger.info(\"oj-api get-contestに成功しました\")\n\n    # コンテスト情報のJSONをパースして、各問題のURLリストを作成\n    problem_urls: List[str] = []\n    for problem in contest[\"result\"][\"problems\"]:\n        url = problem[\"url\"]\n        problem_urls.append(url)\n    # 引数のURLがコンテストではなく問題のURLのとき、その問題のURL以外を削除\n    if problem_urls.count(arg_url) > 0:\n        problem_urls = [arg_url]\n\n    logger.info(\"処理対象のコンテスト OR 問題のURLです\")\n    logger.info(problem_urls)\n\n    # 各問題を走査\n    has_opened_file = False\n    for i, url in enumerate(problem_urls):\n        # 問題URLをoj-prepareする\n        logger.info(\"#{} {} を処理します\".format(i + 1, url))\n        # 設定ファイルがあるならoj-prepareにわたす\n        set_config = (\n            [\"--config-file\", path_config_file] if path_config_file is not None else []\n        )\n        res = subprocess.run([\"oj-prepare\", url] + set_config)\n        if res.returncode != 0:\n            logger.warning(\"{} のoj-prepareに失敗しました\".format(url))\n\n        # 最大問題数に達する、または全問題を走査したら\n        # 問題をブラウザとVSCodeで開く\n        if not has_opened_file and ((i + 1) == n_open or (i + 1) == len(problem_urls)):\n            # 問題が1つのときは、問題をブラウザで開かない\n            # URLをコピーするため、すでに問題を開いている想定のため\n            is_open_browser = True if len(problem_urls) > 1 else False\n            open_problems(\n                problem_urls[: i + 1],\n                coding_files,\n                stdin_files,\n                is_open_browser,\n            )\n            has_opened_file = True\n\n        # APIリクエストの間隔を取る\n        sleep(SHORT_DELAY)\n        if (i + 1) % N_LONG_DELAY == 0:\n            sleep(LONG_DELAY)\n\n    # 空ファイル作成 ↓の順番にしたい\n    # 最大問題数の空ファイル作成→残りの問題のoj-prepare→残りの問題の空ファイル作成\n    open_problems(\n        problem_urls[n_open + 1 :],\n        coding_files,\n        stdin_files,\n        is_open_browser=False,\n        is_open_vscode=False,\n    )\n    return True\n","repo_name":"hotarunx/oj-jordan","sub_path":"onlinejudge_jordan/prepare.py","file_name":"prepare.py","file_ext":"py","file_size_in_byte":6857,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31753154983","text":"import pytest\nfrom ray.air import session\nfrom ray.air.checkpoint import Checkpoint\nimport torch\n\nimport ray\nfrom ray.air.examples.pytorch.torch_linear_example import (\n    train_func as linear_train_func,\n)\nfrom ray.train.torch import TorchPredictor, TorchTrainer\n\n\n@pytest.fixture\ndef ray_start_4_cpus():\n    address_info = ray.init(num_cpus=4)\n    yield address_info\n    # The code after the yield will run as teardown code.\n    ray.shutdown()\n\n\n@pytest.mark.parametrize(\"num_workers\", [1, 2])\ndef test_torch_linear(ray_start_4_cpus, num_workers):\n    def train_func(config):\n        result = linear_train_func(config)\n        assert len(result) == epochs\n        assert result[-1][\"loss\"] < result[0][\"loss\"]\n\n    num_workers = num_workers\n    epochs = 3\n    scaling_config = {\"num_workers\": num_workers}\n    config = {\"lr\": 1e-2, \"hidden_size\": 1, \"batch_size\": 4, \"epochs\": epochs}\n    trainer = TorchTrainer(\n        train_loop_per_worker=train_func,\n        train_loop_config=config,\n        scaling_config=scaling_config,\n    )\n    trainer.fit()\n\n\ndef test_torch_e2e(ray_start_4_cpus):\n    def train_func():\n        model = torch.nn.Linear(1, 1)\n        session.report({}, checkpoint=Checkpoint.from_dict(dict(model=model)))\n\n    scaling_config = {\"num_workers\": 2}\n    trainer = TorchTrainer(\n        train_loop_per_worker=train_func, scaling_config=scaling_config\n    )\n    result = trainer.fit()\n\n    predict_dataset = ray.data.range(3)\n\n    class TorchScorer:\n        def __init__(self):\n            self.pred = TorchPredictor.from_checkpoint(result.checkpoint)\n\n        def __call__(self, x):\n            return self.pred.predict(x, dtype=torch.float)\n\n    predictions = predict_dataset.map_batches(\n        TorchScorer, batch_format=\"pandas\", compute=\"actors\"\n    )\n    assert predictions.count() == 3\n\n\ndef test_torch_e2e_state_dict(ray_start_4_cpus):\n    def train_func():\n        model = torch.nn.Linear(1, 1).state_dict()\n        session.report({}, checkpoint=Checkpoint.from_dict(dict(model=model)))\n\n    scaling_config = {\"num_workers\": 2}\n    trainer = TorchTrainer(\n        train_loop_per_worker=train_func, scaling_config=scaling_config\n    )\n    result = trainer.fit()\n\n    # If loading from a state dict, a model definition must be passed in.\n    with pytest.raises(ValueError):\n        TorchPredictor.from_checkpoint(result.checkpoint)\n\n    class TorchScorer:\n        def __init__(self):\n            self.pred = TorchPredictor.from_checkpoint(\n                result.checkpoint, model=torch.nn.Linear(1, 1)\n            )\n\n        def __call__(self, x):\n            return self.pred.predict(x, dtype=torch.float)\n\n    predict_dataset = ray.data.range(3)\n    predictions = predict_dataset.map_batches(\n        TorchScorer, batch_format=\"pandas\", compute=\"actors\"\n    )\n    assert predictions.count() == 3\n\n\nif __name__ == \"__main__\":\n    import sys\n\n    import pytest\n\n    sys.exit(pytest.main([\"-v\", \"-x\", __file__]))\n","repo_name":"merlinepedra/RAY-1","sub_path":"python/ray/train/tests/test_torch_trainer.py","file_name":"test_torch_trainer.py","file_ext":"py","file_size_in_byte":2944,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"21904933229","text":"from utils import *\nimport sys\n\nimport os\nfiles = os.listdir(\"links/pediatrics_links/\")\n\n# each file\nfor file in files:\n    with open(\"links/pediatrics_links/\" + file) as fp:\n        # each url\n        url = fp.readline()\n        while url:\n            get_all_info(url.strip(), type=\"pediatrics\")\n\n            url = fp.readline()\n\n\nfiles = os.listdir(\"links/surgery_links/\")\n\nfor file in files:\n    with open(\"links/surgery_links/\" + file) as fp:\n        # each url\n        url = fp.readline()\n        while url:\n            \n            get_all_info(url.strip(), type=\"surgery\")\n\n            url = fp.readline()\n","repo_name":"ningkko/Medical_relation","sub_path":"medScape/s_p_txts.py","file_name":"s_p_txts.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"86"}
+{"seq_id":"37570804647","text":"'''Треугольник Паскаля\nБез функций/рекурсий - посмотрим на других занятиях другие способы реализации: через рекурсию - напишем факториал; через степени 11\n'''\n\nn = 5\n\nfor i in range(1, n+1):\n    for j in range(0, n - i + 1):\n        print(' ', end='')\n        \n    C = 1 \n    for j in range(1, i+1):\n        \n        print(' ', C, sep='', end='')\n        # Почему эта формула работает так и не разобрались\n        C = C * (i-j) // j\n        \n    print()\n","repo_name":"nibekasov/Algoritms_Ranepa_2022","sub_path":"Week01/Files from class/Треугольник Паскаля.py","file_name":"Треугольник Паскаля.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"15708835594","text":"\"\"\"\nCreated on Mon Jul 15 10:03:04 2019\nClassic Games Terminal\n\nThis application will allow you to play classic games in the command line\nAs new games are added, this main program will be used to interact with\nthe individual classes.\n@author: markjc\n\"\"\"\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n#         ----------------------          #\n#   Import Packages, Modules, and Games   #\n#         ----------------------          #\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#  \nimport sys, os\nfrom time import sleep\nimport rps, ttt, ngg\nimport TPrinter as printerObj\n\n\n\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n#         ----------------------          #\n#                Run Games                #\n#         ----------------------          #\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#  \ndef runGame(userInput):\n    ### Take user input and Map it to a runnable game - then engage that class\n    if userInput == 1:\n        #Run Game 1: Rock Paper Scissors\n        newGame = rps.Game()\n        newGame.intro()\n        newGame.gameLoop()\n        mainMenu()\n    elif userInput == 2:\n        #Run Game 2: Tic - Tac - Toe\n        newGame = ttt.Game()\n        newGame.intro()\n        newGame.gameLoop()\n        mainMenu()\n    elif userInput == 3:\n        #Run Game 3: Number Guessing Game\n        newGame = ngg.Game()\n        newGame.intro()\n        newGame.gameLoop()\n        mainMenu()\n    elif userInput == 4:\n        sys.exit()\n    else:\n        mainMenu()\n\n\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n#         ----------------------          #\n#                Main Menu                #\n#         ----------------------          #\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#  \ndef mainMenu():\n    ####___________________________________________________________________######\n    printer = printerObj.TPrinter()\n    userInput = 0\n    while True:\n        os.system('cls' if os.name == 'nt' else 'clear')\n        print('#################################################')\n        print('#                                               #')\n        print('#   Welcome to the Classic Games Command Line   #')\n        print('#               by: Mark Crabtree               #')\n        print('#                                               #')\n        print('#       Please choose a game by entering        #')\n        print('#                a number below.                #')\n        print('#                                               #')\n        print('#################################################')\n        print()\n        printer.tprint('            1. Rock, Paper, Scissors    \\n',0.02)\n        printer.tprint('            2. Tic - Tac - Toe          \\n',0.02)\n        printer.tprint('            3. Number Guessing Game          \\n', 0.02)\n        printer.tprint('            4. Exit      \\n',0.02)\n       \n        \n        \n        ### Input Validation ###\n        while True:\n            try:\n                \n                userInput = int(input('->'))\n            except ValueError:\n                print(\"Please enter a valid selection: \")\n                sleep(0.8)\n                continue\n            else:\n                break\n        \n        #### Launch the appropriate game based on user selection\n        runGame(userInput)\n\n\n###########################################\n#         ----------------------          #\n#-----||         Main Loop         ||-----#\n#         ----------------------          #\n########################################### \nwhile True:\n    os.system('cls' if os.name == 'nt' else 'clear')\n    mainMenu()\n    \nsys.exit()\n\n","repo_name":"markjc/cli-games","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3571,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"72764004124","text":"from twisted.test import proto_helpers\nfrom twisted.trial import unittest\nfrom time import strftime, gmtime\nimport json\nimport os\nimport sys\n\nsys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nfrom bot.yybot import YYBotFactory\nfrom logger.sqlite_handler import SQLiteHandler\nfrom server.request_handler import RequestHandlerFactory\n\n\nclass TestRequestHandler(unittest.TestCase):\n    def setUp(self):\n        self.db_manager = SQLiteHandler('test_db.sqlite3')\n\n        self._network = 'localhost'\n        self._channels = ['#test_channel']\n        self._username = 'cat'\n\n        self.bot_factory = YYBotFactory(\n            self._network,\n            self._channels,\n            self._username,\n        )\n\n        self.request_handler_factory = RequestHandlerFactory()\n\n        # IRC bot, SSL socket, SQLite handler should know each other\n        self.bot_factory.add_request_handler(self.request_handler_factory)\n        self.request_handler_factory.add_irc_bot(self.bot_factory)\n        self.bot_factory.add_db_manager(self.db_manager)\n\n        self.bot = self.bot_factory.buildProtocol(('127.0.0.1', 0))\n        self.fake_bot_transport = proto_helpers.StringTransport()\n        self.bot.makeConnection(self.fake_bot_transport)\n        self.bot.signedOn()\n        self.fake_bot_transport.clear()\n\n        self.fake_receiver_transport = proto_helpers.StringTransport()\n        self.request_handler = self.request_handler_factory.\\\n            buildProtocol(('127.0.0.1', 1))\n\n        self.request_handler.makeConnection(self.fake_receiver_transport)\n        self.fake_receiver_transport.clear()\n\n        # send some requests first\n        self.test_channel = '#test_channel2'\n        request = {\n            'type': 'send_message',\n            'message': 'supgaiz',\n            'target': self._channels[0],\n        }\n        request_str = json.dumps(request)\n        self.request_handler.lineReceived(request_str)\n\n        request = {\n            'type': 'join_channel',\n            'channel': self.test_channel,\n        }\n        request_str = json.dumps(request)\n        self.request_handler.lineReceived(request_str)\n\n        request = {\n            'type': 'leave_channel',\n            'channel': self.test_channel,\n        }\n        request_str = json.dumps(request)\n        self.request_handler.lineReceived(request_str)\n\n        request = {\n            'type': 'send_message',\n            'message': 'hi friends',\n            'target': self._channels[0],\n        }\n        request_str = json.dumps(request)\n        self.request_handler.lineReceived(request_str)\n        self.fake_receiver_transport.clear()\n\n    def tearDown(self):\n        '''Delete sqlite file'''\n        self.db_manager.remove()\n\n    def test_get_logs(self):\n        # client requests logs\n        request = {\n            'type': 'get_logs',\n            'network': self._network,\n            'target': self._channels[0],\n        }\n        request_str = json.dumps(request)\n        self.request_handler.lineReceived(request_str)\n\n        # check logs sent\n        response = {\n            'type': 'get_logs',\n            'content': [\n                {\n                    'type': 'status',\n                    'date': strftime(\"%Y-%m-%d %H:%M:%S\", gmtime()),\n                    'nick': self._username,\n                    'target': self._channels[0],\n                    'network': self._network,\n                    'message': \"has joined %s\" % self._channels[0],\n                },\n                {\n                    'type': 'msg',\n                    'date': strftime(\"%Y-%m-%d %H:%M:%S\", gmtime()),\n                    'nick': self._username,\n                    'target': self._channels[0],\n                    'network': self._network,\n                    'message': 'supgaiz',\n                },\n                {\n                    'type': 'msg',\n                    'date': strftime(\"%Y-%m-%d %H:%M:%S\", gmtime()),\n                    'nick': self._username,\n                    'target': self._channels[0],\n                    'network': self._network,\n                    'message': 'hi friends',\n                },\n            ]\n        }\n        expected = json.dumps(response) + '\\n'\n        self.assertEqual(self.fake_receiver_transport.value(), expected)\n\n    def test_get_join_leave_logs(self):\n        # client requests logs\n        request = {\n            'type': 'get_logs',\n            'network': self._network,\n            'target': self.test_channel,\n        }\n        request_str = json.dumps(request)\n        self.request_handler.lineReceived(request_str)\n\n        # check logs sent\n        response = {\n            'type': 'get_logs',\n            'content': [\n                {\n                    'type': 'status',\n                    'date': strftime(\"%Y-%m-%d %H:%M:%S\", gmtime()),\n                    'nick': self._username,\n                    'target': self.test_channel,\n                    'network': self._network,\n                    'message': \"has joined %s\" % self.test_channel,\n                },\n                {\n                    'type': 'status',\n                    'date': strftime(\"%Y-%m-%d %H:%M:%S\", gmtime()),\n                    'nick': self._username,\n                    'target': self.test_channel,\n                    'network': self._network,\n                    'message': \"has left %s\" % self.test_channel,\n                },\n            ]\n        }\n        expected = json.dumps(response) + '\\n'\n        self.assertEqual(self.fake_receiver_transport.value(), expected)\n","repo_name":"daeyun/yychat-server","sub_path":"tests/request_handler_get_logs_tests.py","file_name":"request_handler_get_logs_tests.py","file_ext":"py","file_size_in_byte":5541,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"33313610594","text":"import os\nimport io\nimport frappe\n\nfrom frappe import _\nfrom json import dumps\nfrom pyqrcode import create as qrcreate\nfrom erpnext_gst_compliance.utils import log_exception\nfrom frappe.integrations.utils import make_post_request, make_get_request, make_put_request\n\nclass CleartaxConnector:\n\tdef __init__(self, gstin):\n\n\t\tself.gstin = gstin\n\t\tself.settings = frappe.get_cached_doc(\"Cleartax Settings\")\n\t\tself.business = self.get_business_settings()\n\t\tself.auth_token = self.settings.auth_token\n\t\tself.host = self.get_host_url()\n\t\tself.endpoints = self.get_endpoints()\n\n\t\tself.validate()\n\n\tdef get_business_settings(self):\n\t\treturn next(\n\t\t\tfilter(lambda row: row.gstin == self.gstin, self.settings.credentials),\n\t\t\tfrappe._dict({}),\n\t\t)\n\n\tdef get_host_url(self):\n\t\tif self.settings.sandbox_mode:\n\t\t\treturn \"https://einvoicing.internal.cleartax.co\"\n\t\telse:\n\t\t\treturn \"https://api-einv.cleartax.in\"\n\n\tdef get_endpoints(self):\n\t\tbase_url = self.host + \"/v2/eInvoice\"\n\t\treturn frappe._dict({\n\t\t\t\"generate_irn\": base_url + \"/generate\",\n\t\t\t\"cancel_irn\": base_url + \"/cancel\",\n\t\t\t\"generate_ewaybill\": base_url + \"/ewaybill\",\n\t\t\t\"cancel_ewaybill\": base_url + \"/ewaybill/cancel\"\n\t\t})\n\n\tdef validate(self):\n\t\tif not self.settings.enabled:\n\t\t\tfrappe.throw(_(\"Cleartax Settings is not enabled. Please configure Cleartax Settings and try again.\"))\n\n\t\tif not self.business.owner_id:\n\t\t\tfrappe.throw(\n\t\t\t\t_(\"Cannot find Owner ID for GSTIN {}. Please add cleartax credentials for mentioned GSTIN in Cleartax Settings. \")\n\t\t\t\t\t.format(self.gstin))\n\n\t@log_exception\n\tdef get_headers(self):\n\t\treturn frappe._dict({\n\t\t\t\"x-cleartax-auth-token\": self.settings.get_password('auth_token'),\n\t\t\t\"x-cleartax-product\": \"EInvoice\",\n\t\t\t\"Content-Type\": \"application/json\",\n\t\t\t\"owner_id\": self.business.get_password('owner_id'),\n\t\t\t\"gstin\": self.gstin\n\t\t})\n\n\tdef log_einvoice_request(self, url, headers, payload, response):\n\t\theaders.update({\n\t\t\t\"x-cleartax-auth-token\": self.auth_token,\n\t\t\t\"owner_id\": self.business.owner_id\n\t\t})\n\t\trequest_log = frappe.get_doc({\n\t\t\t\"doctype\": \"E Invoice Request Log\",\n\t\t\t\"user\": frappe.session.user,\n\t\t\t\"reference_invoice\": self.einvoice.name,\n\t\t\t\"url\": url,\n\t\t\t\"headers\": dumps(headers, indent=4) if headers else None,\n\t\t\t\"data\": dumps(payload, indent=4) if isinstance(payload, dict) else payload,\n\t\t\t\"response\": dumps(response, indent=4) if response else None\n\t\t})\n\t\trequest_log.save(ignore_permissions=True)\n\t\tfrappe.db.commit()\n\n\t@log_exception\n\tdef make_request(self, req_type, url, headers, payload):\n\t\tif req_type == 'post':\n\t\t\tresponse = make_post_request(url, headers=headers, data=payload)\n\t\telif req_type == 'put':\n\t\t\tresponse = make_put_request(url, headers=headers, data=payload)\n\t\telse:\n\t\t\tresponse = make_get_request(url, headers=headers, data=payload)\n\t\t\t\n\t\tself.log_einvoice_request(url, headers, payload, response)\n\t\t\n\t\treturn response\n\n\t@log_exception\n\tdef make_irn_request(self):\n\t\theaders = self.get_headers()\n\t\turl = self.endpoints.generate_irn\n\n\t\teinvoice_json = self.einvoice.get_einvoice_json()\n\n\t\tpayload = [{\"transaction\": einvoice_json}]\n\t\tpayload = dumps(payload, indent=4)\n\n\t\tresponse = self.make_request('put', url, headers, payload)\n\t\t# Sample Response -> https://docs.cleartax.in/cleartax-for-developers/e-invoicing-api/e-invoicing-api-reference/cleartax-e-invoicing-apis-xml-schema#sample-response\n\n\t\tresponse = self.sanitize_response(response)\n\t\tif response.get('Success'):\n\t\t\tself.handle_successful_irn_generation(response)\n\n\t\treturn response\n\n\t@staticmethod\n\tdef generate_irn(einvoice):\n\t\tbusiness_gstin = einvoice.seller_gstin\n\t\tconnector = CleartaxConnector(business_gstin)\n\t\tconnector.einvoice = einvoice\n\t\tresponse = connector.make_irn_request()\n\t\tsuccess, errors = response.get('Success'), response.get('Errors')\n\n\t\treturn success, errors\n\n\tdef sanitize_response(self, response):\n\t\tsanitized_response = []\n\t\tfor entry in response:\n\t\t\tgovt_response = frappe._dict(entry.get('govt_response', {}))\n\t\t\tsuccess = govt_response.get('Success', \"N\")\n\n\t\t\tif success == \"Y\":\n\t\t\t\t# return irn & other info\n\t\t\t\tgovt_response.update({'Success': True})\n\t\t\t\tsanitized_response.append(govt_response)\n\t\t\telse:\n\t\t\t\t# return error message list\n\t\t\t\terror_details = govt_response.get('ErrorDetails', [])\n\t\t\t\terror_list = []\n\n\t\t\t\tfor d in error_details:\n\t\t\t\t\tif d.get('error_source') == 'CLEARTAX':\n\t\t\t\t\t\t# cleartax gives back the exact key that causes the error\n\t\t\t\t\t\t# error_message = \"sellerDetails.pinCode : <actual error message>\"\n\t\t\t\t\t\td['error_message'] = d['error_message'].split(' : ')[-1]\n\t\t\t\t\terror_list.append(d.get('error_message'))\n\n\t\t\t\tsanitized_response.append({\n\t\t\t\t\t'Success': False,\n\t\t\t\t\t'Errors': error_list\n\t\t\t\t})\n\n\t\treturn sanitized_response[0] if len(sanitized_response) == 1 else sanitized_response\n\n\tdef handle_successful_irn_generation(self, response):\n\t\tstatus = 'IRN Generated'\n\t\tirn = response.get('Irn')\n\t\tack_no = response.get('AckNo')\n\t\tack_date = response.get('AckDt')\n\t\tewaybill = response.get('EwbNo')\n\t\tewaybill_validity = response.get('EwbValidTill')\n\t\tqrcode = self.generate_qrcode(response.get('SignedQRCode'))\n\n\t\tself.einvoice.update({\n\t\t\t'irn': irn,\n\t\t\t'status': status,\n\t\t\t'ack_no': ack_no,\n\t\t\t'ack_date': ack_date,\n\t\t\t'ewaybill': ewaybill,\n\t\t\t'qrcode_path': qrcode,\n\t\t\t'ewaybill_validity': ewaybill_validity\n\t\t})\n\t\tself.einvoice.flags.ignore_permissions = 1\n\t\tself.einvoice.submit()\n\n\tdef generate_qrcode(self, signed_qrcode):\n\t\tdoctype = self.einvoice.doctype\n\t\tdocname = self.einvoice.name\n\t\tfilename = '{} - QRCode.png'.format(docname).replace(os.path.sep, \"__\")\n\n\t\tqr_image = io.BytesIO()\n\t\turl = qrcreate(signed_qrcode, error='L')\n\t\turl.png(qr_image, scale=2, quiet_zone=1)\n\t\t_file = frappe.get_doc({\n\t\t\t'doctype': 'File',\n\t\t\t'file_name': filename,\n\t\t\t'attached_to_doctype': doctype,\n\t\t\t'attached_to_name': docname,\n\t\t\t'attached_to_field': 'qrcode_path',\n\t\t\t'is_private': 1,\n\t\t\t'content': qr_image.getvalue()\n\t\t})\n\t\t_file.save()\n\t\treturn _file.file_url\n\n\t@log_exception\n\tdef make_cancel_irn_request(self, reason, remark):\n\t\theaders = self.get_headers()\n\t\turl = self.endpoints.cancel_irn\n\n\t\tirn = self.einvoice.irn\n\n\t\tpayload = [{'irn': irn, 'CnlRsn': reason, 'CnlRem': remark}]\n\t\tpayload = dumps(payload, indent=4)\n\n\t\tresponse = self.make_request('put', url, headers, payload)\n\t\t# Sample Response -> https://docs.cleartax.in/cleartax-for-developers/e-invoicing-api/e-invoicing-api-reference/cleartax-e-invoicing-apis-xml-schema#sample-response-1\n\n\t\tresponse = self.sanitize_response(response)\n\t\tif response.get('Success'):\n\t\t\tself.handle_successful_irn_cancellation(response)\n\n\t\treturn response\n\n\tdef handle_successful_irn_cancellation(self, response):\n\t\tself.einvoice.irn_cancelled = 1\n\t\tself.einvoice.irn_cancel_date = response.get('CancelDate')\n\t\tself.einvoice.status = 'IRN Cancelled'\n\t\tself.einvoice.flags.ignore_validate_update_after_submit = 1\n\t\tself.einvoice.flags.ignore_permissions = 1\n\t\tself.einvoice.save()\n\n\t@staticmethod\n\tdef cancel_irn(einvoice, reason, remark):\n\t\tbusiness_gstin = einvoice.seller_gstin\n\t\tconnector = CleartaxConnector(business_gstin)\n\t\tconnector.einvoice = einvoice\n\t\tresponse = connector.make_cancel_irn_request(reason, remark)\n\t\tsuccess, errors = response.get('Success'), response.get('Errors')\n\n\t\treturn success, errors\n\n\t@log_exception\n\tdef make_eway_bill_request(self):\n\t\theaders = self.get_headers()\n\t\turl = self.endpoints.generate_ewaybill\n\n\t\teway_bill_json = self.einvoice.get_eway_bill_json()\n\n\t\tpayload = [eway_bill_json]\n\t\tpayload = dumps(payload, indent=4)\n\n\t\tresponse = self.make_request('post', url, headers, payload)\n\t\t# Sample Response -> https://docs.cleartax.in/cleartax-for-developers/e-invoicing-api/e-invoicing-api-reference/cleartax-e-invoicing-apis-xml-schema#sample-response-3\n\n\t\tresponse = self.sanitize_response(response)\n\t\tif response.get('Success'):\n\t\t\tself.handle_successful_ewaybill_generation(response)\n\n\t\treturn response\n\n\tdef handle_successful_ewaybill_generation(self, response):\n\t\tself.einvoice.ewaybill = response.get('EwbNo')\n\t\tself.einvoice.ewaybill_validity = response.get('EwbValidTill')\n\t\tself.einvoice.status = 'E-Way Bill Generated'\n\t\tself.einvoice.flags.ignore_validate_update_after_submit = 1\n\t\tself.einvoice.flags.ignore_permissions = 1\n\t\tself.einvoice.save()\n\n\t@staticmethod\n\tdef generate_eway_bill(einvoice):\n\t\tbusiness_gstin = einvoice.seller_gstin\n\t\tconnector = CleartaxConnector(business_gstin)\n\t\tconnector.einvoice = einvoice\n\t\tresponse = connector.make_eway_bill_request()\n\t\tsuccess, errors = response.get('Success'), response.get('Errors')\n\n\t\treturn success, errors\n\n\t@log_exception\n\tdef make_cancel_ewaybill_request(self, reason, remark):\n\t\theaders = self.get_headers()\n\t\turl = self.endpoints.cancel_ewaybill\n\n\t\tewaybill = self.einvoice.ewaybill\n\n\t\tpayload = {'ewbNo': ewaybill, 'cancelRsnCode': reason, 'cancelRmrk': remark}\n\t\tpayload = dumps(payload, indent=4)\n\n\t\tresponse = self.make_request('post', url, headers, payload)\n\t\t# Sample Response -> https://docs.cleartax.in/cleartax-for-developers/e-invoicing-api/e-invoicing-api-reference/cleartax-e-invoicing-apis-xml-schema#sample-response-4\n\n\t\tresponse = self.sanitize_response(response)\n\t\tif response.get('Success'):\n\t\t\tself.handle_successful_ewaybill_cancellation()\n\n\t\treturn response\n\n\tdef handle_successful_ewaybill_cancellation(self):\n\t\tself.einvoice.ewaybill_cancelled = 1\n\t\tself.einvoice.status = 'E-Way Bill Cancelled'\n\t\tself.einvoice.flags.ignore_validate_update_after_submit = 1\n\t\tself.einvoice.flags.ignore_permissions = 1\n\t\tself.einvoice.save()\n\n\t@staticmethod\n\tdef cancel_ewaybill(einvoice, reason, remark):\n\t\tbusiness_gstin = einvoice.seller_gstin\n\t\tconnector = CleartaxConnector(business_gstin)\n\t\tconnector.einvoice = einvoice\n\t\tresponse = connector.make_cancel_ewaybill_request(reason, remark)\n\t\tsuccess, errors = response.get('Success'), response.get('Errors')\n\n\t\treturn success, errors","repo_name":"frappe/erpnext_gst_compliance","sub_path":"erpnext_gst_compliance/cleartax_integration/cleartax_connector.py","file_name":"cleartax_connector.py","file_ext":"py","file_size_in_byte":9817,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"86"}
+{"seq_id":"9634611206","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth import authenticate\nfrom django.contrib import auth\nfrom customer_portal.models import *\nfrom django.contrib.auth.decorators import login_required\nfrom car_dealer_portal.models import *\nfrom django.http import HttpResponseRedirect\n# Create your views here.\n\ndef index(request):\n    if not request.user.is_authenticated:\n        return render(request, 'customer/login.html')\n    else:\n        return render(request, 'customer/home_page.html')\n\ndef login(request):\n    return render(request, 'customer/login.html')\n\ndef auth_view(request):\n    if request.user.is_authenticated:\n        return render(request, 'customer/home_page.html')\n    else:\n        username = request.POST['username']\n        password = request.POST['password']\n        user = authenticate(request, username=username, password=password)\n        try:\n            customer = Customer.objects.get(user = user)\n        except:\n            customer = None\n        if customer is not None:\n            auth.login(request, user)\n            return render(request, 'customer/home_page.html')\n        else:\n            return render(request, 'customer/login_failed.html')\n\ndef logout_view(request):\n    auth.logout(request)\n    return render(request, 'customer/login.html')\n\ndef register(request):\n    return render(request, 'customer/register.html')\n\ndef registration(request):\n    username = request.POST['username']\n    password = request.POST['password']\n    mobile = request.POST['mobile']\n    firstname = request.POST['firstname']\n    lastname = request.POST['lastname']\n    email = request.POST['email']\n    city = request.POST['city']\n    city = city.lower()\n    pincode = request.POST['pincode']\n    try:\n        user = User.objects.create_user(username = username, password = password, email = email)\n        user.first_name = firstname\n        user.last_name = lastname\n        user.save()\n    except:\n        return render(request, 'customer/registration_error.html')\n    try:\n        area = Area.objects.get(city = city, pincode = pincode)\n    except:\n        area = None\n    if area is not None:\n        customer = Customer(user = user, mobile = mobile, area = area)\n    else:\n        area = Area(city = city, pincode = pincode)\n        area.save()\n        area = Area.objects.get(city = city, pincode = pincode)\n        customer = Customer(user = user, mobile = mobile, area = area)\n\n    customer.save()\n    return render(request, 'customer/registered.html')\n\n@login_required\ndef search(request):\n    return render(request, 'customer/search.html')\n\n@login_required\ndef search_results(request):\n    city = request.POST['city']\n    city = city.lower()\n    vehicles_list = []\n    area = Area.objects.filter(city = city)\n    for a in area:\n        vehicles = Vehicles.objects.filter(area = a)\n        for car in vehicles:\n            if car.is_available == True:\n                vehicle_dictionary = {'name':car.car_name, 'color':car.color, 'id':car.id, 'pincode':car.area.pincode, 'capacity':car.capacity, 'description':car.description}\n                vehicles_list.append(vehicle_dictionary)\n    request.session['vehicles_list'] = vehicles_list\n    return render(request, 'customer/search_results.html')\n\n\n@login_required\ndef rent_vehicle(request):\n    id = request.POST['id']\n    vehicle = Vehicles.objects.get(id = id)\n    cost_per_day = int(vehicle.capacity)*300\n    return render(request, 'customer/confirmation.html', {'vehicle':vehicle, 'cost_per_day':cost_per_day})\n\n@login_required\ndef confirm(request):\n    vehicle_id = request.POST['id']\n    username = request.user\n    user = User.objects.get(username = username)\n    days = request.POST['days']\n    vehicle = Vehicles.objects.get(id = vehicle_id)\n    if vehicle.is_available:\n        car_dealer = vehicle.dealer\n        rent = (int(vehicle.capacity))*300*(int(days))\n        car_dealer.wallet += rent\n        car_dealer.save()\n        try:\n            order = Orders(vehicle = vehicle, car_dealer = car_dealer, user = user, rent=rent, days=days)\n            order.save()\n        except:\n            order = Orders.objects.get(vehicle = vehicle, car_dealer = car_dealer, user = user, rent=rent, days=days)\n        vehicle.is_available = False\n        vehicle.save()\n        return render(request, 'customer/confirmed.html', {'order':order})\n    else:\n        return render(request, 'customer/order_failed.html')\n\n@login_required\ndef manage(request):\n    order_list = []\n    user = User.objects.get(username = request.user)\n    try:\n        orders = Orders.objects.filter(user = user)\n    except:\n        orders = None\n    if orders is not None:\n        for o in orders:\n            if o.is_complete == False:\n                order_dictionary = {'id':o.id,'rent':o.rent, 'vehicle':o.vehicle, 'days':o.days, 'car_dealer':o.car_dealer}\n                order_list.append(order_dictionary)\n    return render(request, 'customer/manage.html', {'od':order_list})\n\n@login_required\ndef update_order(request):\n    order_id = request.POST['id']\n    order = Orders.objects.get(id = order_id)\n    vehicle = order.vehicle\n    vehicle.is_available = True\n    vehicle.save()\n    car_dealer = order.car_dealer\n    car_dealer.wallet -= int(order.rent)\n    car_dealer.save()\n    order.delete()\n    cost_per_day = int(vehicle.capacity)*300\n    return render(request, 'customer/confirmation.html', {'vehicle':vehicle}, {'cost_per_day':cost_per_day})\n\n@login_required\ndef delete_order(request):\n    order_id = request.POST['id']\n    order = Orders.objects.get(id = order_id)\n    car_dealer = order.car_dealer\n    car_dealer.wallet -= int(order.rent)\n    car_dealer.save()\n    vehicle = order.vehicle\n    vehicle.is_available = True\n    vehicle.save()\n    order.delete()\n    return HttpResponseRedirect('/customer_portal/manage/')\n","repo_name":"hardikpnsp/ocrs","sub_path":"customer_portal/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5885,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"86"}
+{"seq_id":"19823997654","text":"import numpy as np\nimport pickle\nimport os\nfrom sklearn import manifold as mnfd\nfrom sklearn import decomposition as dcomp\nfrom sklearn import preprocessing as pre\nimport pandas as pd\nfrom analysis import TEST_REGEX, library_from_regex\nfrom sklearn.pipeline import Pipeline\nimport re\nfrom analysis import corpus_tag_generator\n\nTEST_REGEX = re.compile(TEST_REGEX.pattern)\nhere = os.path.dirname(__file__)\n\nmusicbee = 'C:\\\\Users\\\\Coen D. Needell\\\\Music\\\\MusicBee\\\\Playlists\\\\'  # Personal playlist location\n\n\ndef load_corpus(loc='cepstra\\\\', precompiled=False):\n    \"\"\"\n    Generates a corpus for machine learning from your preprocessed cepstra. Location should be the same folder you used\n    for the analysis.py run. Returns a dict with keys being the 'song code' as made by the analysis.corpus_tag_generator\n    function.\n\n    :param loc: str directory where the spectra are.\n\n    :param precompiled: bool triggers whether or not it should load the corpus from a pickle file or the cepstra folder\n    :return: dict\n    \"\"\"\n    if precompiled:\n        with open('../corpus.pkl', 'rb') as file:\n            return pickle.load(file)\n    corpus = {}\n    for song in os.listdir(loc):\n        with open(f'cepstra\\\\{song}', 'rb') as file:\n            corpus[song.replace('.pkl', '')] = pickle.load(file)\n    corpus = {title: song for title, song in corpus.items() if song is not None}\n    with open('../corpus.pkl', 'wb') as file:\n        pickle.dump(corpus, file)\n    return corpus\n\n\ndef create_tag_dict(lib, loc=here + '/' + 'locations.pkl'):\n    \"\"\"\n    Makes a dictionary that relates the tags to associated filename.\n\n    :param lib: list contains all of the filenames.\n\n    :param loc: str file to dump the tag dictionary in if you want to avoid doing this more than once.\n\n    :return:\n    \"\"\"\n    mdata_dict = {}\n    for song in lib:\n        mdata_dict[corpus_tag_generator(song)] = song\n    with open(loc, 'wb') as file:\n        pickle.dump(mdata_dict, file)\n    return mdata_dict\n\n\ndef load_tag_dict(loc=here + '/' + 'locations.pkl'):\n    \"\"\"\n    Loads the tag dictionary, and returns it.\n\n    :param loc: str location of pkl\n    :return:\n    \"\"\"\n    with open(loc, 'rb') as file:\n        mdata_dict = pickle.load(file)\n    return mdata_dict\n\n\ndef generate_m3u(tags, title, reference, locale='playlists\\\\'):\n    \"\"\"\n    Takes a list of corpus tags and turns it into a playlist (.m3u).\n\n    :param tags: list of tags\n    :param title: name of plist\n    :param reference: dict tag dictionary, default just runs load_tag_dict()\n    :param locale: str place to dump your playlist\n    :return:\n    \"\"\"\n    with open(f'{locale}{title}.m3u', 'w+', encoding='utf-8') as file:\n        for tag in tags:\n            file.write(reference[tag] + '\\n')\n\n\ndef padded_corpus(corp):\n    \"\"\"\n    Takes in a corpus and pads it out to the length of the longest song. -inf padding.\n\n    :param corp: dict corpus\n    :return: dict padded corpus\n    \"\"\"\n    lens = [song.shape[1] for _, song in corp.items()]\n    longest = np.max(lens)\n\n    new_corp = {}\n    for title, song in corp.items():\n        song_size = longest - song.shape[1]\n        if song_size == 0:\n            new_corp[title] = song\n        else:\n            new_corp[title] = np.pad(song, ((0, 0), (0, song_size)), constant_values=np.log(0))\n    return new_corp\n\n\ndef flattened_corpus(corp):\n    \"\"\"\n    Takes in a corpus and flattens it out so that the 2D gammatone cepstra is a single vector representation.\n\n    :param corp: dict\n    :return: dict\n    \"\"\"\n    new_corp = {}\n    for title, song in corp.items():\n        new_corp[title] = np.nan_to_num(song.flatten())\n    return new_corp\n\n\ndef cropped_corpus(corp, tar_len=90, pad_shorts=False):\n    \"\"\"\n    Takes in a corpus and crops out the middle tar_len seconds. Default is a minute and a half. If pad_shorts is True,\n    then it'll pad the shorter songs with -inf.\n\n    :param corp: dict\n    :param tar_len: int MUST BE EVEN\n    :param pad_shorts: bool\n    :return: dict\n    \"\"\"\n    new_corp = {}\n    for title, song in corp.items():\n        s_len = song.shape[1]\n        if s_len > tar_len:\n            st = (s_len // 2) - (tar_len // 2)\n            end = (s_len // 2) + (tar_len // 2)\n            assert end - st == tar_len\n            new_corp[title] = song[:, st:end]\n        else:\n            if pad_shorts:\n                new_corp[title] = np.pad(song, ((0, 0), (0, tar_len - s_len)), constant_values=np.log(0))\n\n    return new_corp\n\n\ndef make_manifold(processed_corp,\n                  pipeline=Pipeline([('reduce_dims', dcomp.PCA()), ('embedding', mnfd.Isomap(n_components=45))])):\n    \"\"\"\n    Uses sklearn to construct a manifold data frame. You can use whatever pipeline you like, but the default is PCA into\n    Isomap with 45 components, I've had good success with this value.\n    :param processed_corp: dict\n    :param pipeline: sklearn.pipeline.Pipeline\n    :return: pd.DataFrame\n    \"\"\"\n    flat_corp = flattened_corpus(processed_corp)\n    songs = list(flat_corp.values())\n    songs_scaled = np.nan_to_num(pre.RobustScaler().fit_transform(songs))\n    songs_scaled = np.clip(songs_scaled, -1000, 5)\n\n    songs_transformed = pipeline.fit_transform(songs_scaled)\n    manifold = {}\n    for title, song in zip(flat_corp, songs_transformed):\n        manifold[title] = song\n    manifold_df = pd.DataFrame(manifold)\n    return manifold_df\n\n\nif __name__ == '__main__':\n    libr = library_from_regex(re.compile(''))\n    cor = load_corpus()\n    nc = cropped_corpus(cor, tar_len=120, pad_shorts=True)\n    mandf = make_manifold(nc)\n    with open('manifold.pkl') as f:\n        pickle.dump(mandf, f)\n","repo_name":"SoyBison/ongaku","sub_path":"learning.py","file_name":"learning.py","file_ext":"py","file_size_in_byte":5606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"20194819470","text":"import numpy as np\nimport pandas as pd\nimport os\n\ninput_data_path = \"../input_data/\"\nweights_path = \"../weights/\"\noutput_data_path= \"../output_data/\"\n\n\n\ndef preprocessing( df ):\n    temp = np.expand_dims( df.pop(\"Temperatura\").values + 273., axis=1)**-(1)\n    y = np.expand_dims(  df.pop(\"Riv_Misurato\").values , axis=1)\n    data = np.log( df.values )\n    x = np.concatenate( [ temp, data ], axis=1 )\n    return x,y\n\n\ndef preprocessing_opt(df):\n    one_on_t = np.expand_dims(df[\"Temperatura\"].values + 273., axis=1) ** (-1)\n    log_h = np.log(np.expand_dims(df[\"Altezza\"].values, axis=1))\n    log_s = np.log(np.expand_dims(df[\"Velocità\"].values, axis=1))\n    log_p = np.log(np.expand_dims(df[\"Pressione_PID\"].values, axis=1))\n    log_t = np.log(np.expand_dims(df[\"Distanza_PID\"].values, axis=1))\n    x = np.concatenate([one_on_t, log_h, log_s, log_p, log_t], axis=1)\n    y = np.expand_dims( df[\"Rivestimento_PID\"].values, axis=1 )\n    return x, y\n\ndef load_join_shuffle_validation_set():\n    validation = pd.read_excel(input_data_path + \"prep_validation.xlsx\")\n    validation_opt = pd.read_excel(input_data_path + \"prep_validation_opt.xlsx\")\n\n    x_val_opt, y_val_opt = preprocessing_opt(validation_opt)\n    x_val, y_val = preprocessing(validation)\n    x_val_joined = np.concatenate([x_val, x_val_opt], axis=0)\n    y_val_joined = np.concatenate([y_val, y_val_opt], axis=0)\n\n    joined = np.concatenate([x_val_joined,y_val_joined], axis=1)\n\n    np.random.seed(42)\n    np.random.shuffle(joined)\n\n    x = joined[:,0:-1]\n    y = np.expand_dims( joined[:,-1],axis=1)\n\n    #to check the randomness, see the same data at the same shuffled position.\n    # print( x[1500] )\n    #print( y[1500] )\n\n    return x,y\n","repo_name":"Salvo000/Airknife_Model","sub_path":"airknife_model/code/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"36792595714","text":"__author__ = 'stephenosullivan'\n\n\nclass PyList:\n    def __init__(self, contents=[], size=10):\n        self.items = [None] * size\n        self.numItems = 0  # number of locations used\n        self.size = size\n\n        for e in contents:\n            self.append(e)\n\n    def __getitem__(self, index):\n        try:\n            return self.items[index]\n        except:\n            raise IndexError(\"PyList index out of range\")\n\n    def __setitem__(self, index, val):\n        try:\n            self.items[index] = val\n        except:\n            raise IndexError(\"PyList assignment index out of range\")\n\n    def __add__(self, other):\n        output = PyList(size=self.numItems + other.numItems)\n\n        for i in range(self.numItems):\n            output.append(self.items[i])\n\n        for i in range(other.numItems):\n            output.append(other.items[i])\n\n        return output\n\n    def __makeroom(self):\n        addsize = self.size // 4 + 1\n        self.size += addsize\n        self.items += [None] * addsize\n\n    def append(self, item):\n        if self.numItems == self.size:\n            self.__makeroom()\n\n        self.items[self.numItems] = item\n        self.numItems += 1\n\n    def insert(self, index, value):\n        if self.numItems == self.size:\n            self.__makeroom()\n\n        if index < self.numItems:\n            for i in range(self.numItems, self.numItems - index, -1):\n                self.items[i] = self.items[i - 1]\n            self.items[index] = value\n            self.numItems += 1\n\n        else:\n            self.append(value)\n\n    def __delitem__(self, index):\n        for i in range(index, self.numItems):\n            self.items[i] = self.items[i+1]\n        self.numItems -= 1\n\n    def __eq__(self, other):\n        # Type check\n        if type(self) != type(other):\n            return False\n\n        # size check\n        if self.numItems != other.numItems:\n            return False\n\n        # values check\n        for i in range(self.numItems):\n            if self.items[i] != other.items[i]:\n                return False\n\n        return True\n\n    def __iter__(self):\n        for i in range(self.numItems):\n            yield self.items[i]\n\n    def __len__(self):\n        return self.numItems\n\n    def __contains__(self, item):\n        for i in range(self.numItems):\n            if self.items[i] == item:\n                return True\n        return False\n\n    def __str__(self):\n        s = \"[\"\n        for i in range(self.numItems):\n            s += repr(self.items[i])\n            if i < self.numItems - 1:\n                s += \", \"\n        s += \"]\"\n        return s\n\n    def __repr__(self):\n        s = \"PyList([\"\n        for i in range(self.numItems):\n            s += repr(self.items[i])\n            if i < self.numItems - 1:\n                s += \", \"\n        s += \"])\"\n        return s\n\n\nif __name__ == '__main__':\n    a = PyList([5, 6, 7])\n    print(a)\n","repo_name":"stephenosullivan/Data_Structures_and_Algorithms","sub_path":"Python Algorithms/Lee and Hubbard/Chapter4/PyList.py","file_name":"PyList.py","file_ext":"py","file_size_in_byte":2883,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"20312700653","text":"from flask import Flask, request\nfrom textblob import TextBlob\nimport torch\nimport torchvision.models as models\nimport torchvision.transforms as transforms\nfrom PIL import Image\nimport json\n\napp = Flask(__name__)\nresnet = models.resnet101(pretrained=True)\nresnet.eval()  # 设置模型为评估模式\n\ndef analyze_sentiment(text):\n    blob = TextBlob(text)\n    sentiment = blob.sentiment.polarity\n    sentiment_type = 1\n    if sentiment < 0:\n        sentiment_type = 0\n    elif sentiment > 0:\n        sentiment_type = 4\n    return sentiment_type,sentiment\n\ndef extract_features(image_path):\n    # 加载图像\n    image = Image.open(image_path).convert('RGB')\n\n    # 定义预处理变换\n    preprocess = transforms.Compose([\n        transforms.Resize(256),\n        transforms.CenterCrop(224),\n        transforms.ToTensor(),\n        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[\n                             0.229, 0.224, 0.225])\n    ])\n\n    # 应用预处理变换\n    input_tensor = preprocess(image)\n\n    # 添加一个维度作为批处理维度\n    input_batch = input_tensor.unsqueeze(0)\n\n    # 将输入张量移动到所选设备上（如果可用）\n    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    input_batch = input_batch.to(device)\n\n    # 提取图像特征\n    with torch.no_grad():\n        input_batch = input_batch.to(device)\n        features = resnet(input_batch)\n\n    # 将特征向量转换为一维张量\n    features = torch.flatten(features, start_dim=1)\n\n    # 返回特征向量\n    return features\n\n\ndef compute_similarity_score(features1, features2):\n    # 计算余弦相似度\n    similarity_score = torch.cosine_similarity(features1, features2, dim=1)\n\n    return similarity_score.item()\n\n@app.route('/sentiment/predict', methods=['POST'])\ndef sentiment_predict():\n    # 获取请求数据\n    data = json.loads(request.get_data())\n    text = data.get(\"text\")\n    sentiment_type, sentiment_score = analyze_sentiment(text)\n    return {'sentiment_type': sentiment_type,'sentiment_score':sentiment_score}\n\n@app.route('/image/similarity', methods=['GET'])\ndef image_similarity():\n    image_dir = \"/Users/along/Documents/dataset/FaceDataset\"\n    origin = \"{}{}\".format(image_dir,request.args.get('origin'))\n    target = \"{}{}\".format(image_dir,request.args.get('target'))\n\n\n     # 提取图像特征\n    features1 = extract_features(origin)\n    features2 = extract_features(target)\n\n    # 计算相似度分数\n    similarity_score = compute_similarity_score(features1, features2)\n    return {'similarity':similarity_score}\n\n@app.route(\"/hello\",methods=['GET'])\ndef say_hello():\n    return \"hello\"\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"alongmao/FusionDataBench","sub_path":"src/main/java/script/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"26575082636","text":"from flask import Flask, render_template,url_for,redirect,request\r\nfrom flask_mysqldb import MySQL\r\n\r\napp = Flask(__name__)\r\n\r\n#mysql connection\r\napp.config['MYSQL_HOST']='localhost'\r\napp.config['MYSQL_USER']='root'\r\napp.config['MYSQL_PASSWORD']='root'\r\napp.config['MYSQL_DB']='karthi'\r\napp.config['MYSQL_CURSORCLASS']='DictCursor'\r\nmysql = MySQL(app)\r\n\r\n@app.route('/')\r\ndef home():\r\n        con = mysql.connection.cursor()\r\n        sql = \"select * from users\"\r\n        con.execute(sql)\r\n        res = con.fetchall()\r\n        return render_template(\"index.html\",datas = res)\r\n\r\n#add user\r\n@app.route('/addUser',methods=['POST','GET'])\r\ndef adduser():\r\n\tif request.method == \"POST\":\r\n\t\tfname = request.form['fname']\r\n\t\tmname = request.form['mname']\r\n\t\tlname = request.form['lname']\r\n\t\tcon = mysql.connection.cursor()\r\n\t\tsql = \"insert into users(NAME,MIDDLE_NAME,LAST_NAME) values(%s,%s,%s)\"\r\n\t\tcon.execute(sql,[fname,mname,lname])\r\n\t\tmysql.connection.commit()\r\n\t\tcon.close()\r\n\t\treturn redirect(url_for('home'))\r\n\r\n\treturn render_template(\"Adduser.html\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\tapp.run(debug=True)\r\n","repo_name":"karthi12503/name_swap","sub_path":"project/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"8278471865","text":"from linode_api4 import LinodeClient, Instance\nimport requests\nimport re\n\napi_token = '<YOUR API TOKEN>'  # replace it with your own API token\nclient = LinodeClient(api_token)\n\ndef get_invoices():\n    invoices = sorted(client.account.invoices(), key=lambda invoice: invoice.date, reverse=True)\n    return invoices\n\ndef get_latest_invoice_items(invoices):\n    latest_invoice = invoices[0]\n    items = latest_invoice.items\n    return items\n\ndef get_linode_ids(items):\n    bracket_id = [re.findall(r'\\((\\d{8})\\)', item.label) for item in items]\n    bracket_id = [detail for detail in bracket_id if detail]\n    linode_ids = [int(detail[0]) for detail in bracket_id if detail[0].isdigit()]\n    return linode_ids\n\ndef get_linode_result(linode_ids):\n    linode_result = []\n    for linode_id in linode_ids:\n        url = f'https://api.linode.com/v4/linode/instances/{linode_id}'\n        headers = {'Authorization':'Bearer ' + api_token}\n        response = requests.get(url, headers=headers)\n        if response.status_code == 200:\n            data = response.json()\n            id = int(data.get('id'))  \n            tags = data.get('tags', [])\n            linode_result.append([id, tags])  \n    return linode_result\n","repo_name":"guoyingyu1989/Linode-Billing-Customization","sub_path":"linode.py","file_name":"linode.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"2624855877","text":"import pandas as pd\nimport re\nimport os\n\ndef fileToDict(fileName, fileType):\n  file = pd.read_excel(fileName) if fileType == \"E\" else pd.read_csv(\n    fileName, encoding='latin1')\n  data = file.to_dict(\"records\")\n  return data\n\ndef dataToDict(header, data):\n  value = re.sub(\" +\", \" \", data).replace(\" \", \",\")\n  res = header + value\n  print(res[:-1], file=open(\"data.csv\", \"w\",encoding=\"utf-8\"))\n  result = fileToDict(\"data.csv\",\"C\")\n  os.remove(\"data.csv\")\n  return result\n","repo_name":"conext-noc/routerConflictResolver","sub_path":"parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34768908657","text":"import argparse\nimport random\nfrom concurrent.futures import ProcessPoolExecutor\nimport logging\nfrom trajdiff.utils import setup, set_seed, write_metadata, write_obj\nfrom trajdiff.static_obst.generator import gen_samples\nfrom trajdiff.static_obst import cfg\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"-n\",\n        \"--n_samples\",\n        type=int,\n        required=True,\n        help=\"Number of datapoints to generate\",\n    )\n    parser.add_argument(\n        \"-p\",\n        \"--processes\",\n        type=int,\n        help=\"Number of processes to generate data in parallel (defaults to all cores)\",\n        default=None,\n    )\n    parser.add_argument(\"-f\", \"--n_per_file\", type=int, default=1000)\n    parser.add_argument(\n        \"-o\",\n        \"--output_folder\",\n        required=True,\n        help=\"Output folder for .npy files from each process\",\n    )\n    parser.add_argument(\n        \"-l\",\n        \"--log_level\",\n        default=\"INFO\",\n        help=\"DEBUG, INFO, WARNING, ERROR\",\n    )\n    parser.add_argument(\"-s\", \"--seed\", default=42, type=int)\n    parser.add_argument(\"-c\", \"--constrain_obsts\", default=False, action=\"store_true\")\n    args = parser.parse_args()\n\n    output_folder = setup(args)\n\n    write_metadata(cfg, output_folder)\n\n    timeout = 25 * args.n_per_file\n    n_jobs = int(args.n_samples / args.n_per_file)\n\n    # starter seed to create seeds for each function\n    set_seed(args.seed)\n    seeds = [random.random() * (i + 1) for i in range(n_jobs)]\n\n    with ProcessPoolExecutor(max_workers=args.processes) as executor:\n\n        futures = [\n            executor.submit(\n                gen_samples, cfg, args.n_per_file, seed, args.constrain_obsts\n            )\n            for seed in seeds\n        ]\n\n        for i, future in enumerate(futures, 1):\n            try:\n                results = future.result(timeout=timeout)\n\n                write_obj(results, args.output_folder / f\"chunk{i}.pkl\")\n                logging.info(\"Generated chunk %d\", i)\n\n            except Exception as e:\n                logging.error(\"Error with generating a chunk of problems: \", e)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"vikrammeyer/trajectory-diffusion","sub_path":"scripts/static_obst/gen_data.py","file_name":"gen_data.py","file_ext":"py","file_size_in_byte":2171,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"73176062044","text":"# this code detects if a number is negative, positive, or zero\n\nimport time\n\nnum = float(input(\"Enter a number: \")) #get input\nif num > 0:\n   print(\"Positive number\")\nelif num == 0:\n   print(\"Zero\")\nelse:\n   print(\"Negative number\")\n\nprint(\"Job finished, Console will close in three seconds\")\ntime.sleep(3)#make sure to import time before callling this\nexit()\n# wait three seconds then close the console \n","repo_name":"devmanso/Beginner-python-code-samples","sub_path":"positiveOrNegativeNumDetector.py","file_name":"positiveOrNegativeNumDetector.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32466330588","text":"'''\nID: 11074501\nTASK: stamps\nLANG: PYTHON3\n'''\nimport math\nwith open('stamps.in', 'r') as f:\n\tir = f.readline().split()\n\tK = int(ir[0])\n\tN = int(ir[1])\n\n\tstamps = []\n\tfor i in range(N):\n\t\tfor i in f.readline().split():\n\t\t\tstamps.append(int(i))\n\tstamps.sort()\n\n\tpostages = [math.inf]*(200*10000 + 1)\n\tpostages[0] = 0\n\n\tfor i in range(N):\n\t\tfor j in range(0, 200*10000):\n\t\t\tif postages[j] < K:\n\t\t\t\tpostages[j + stamps[i]] = min(postages[j + stamps[i]], postages[j] + 1)\n\n\tans = 0\n\twhile(postages[ans] <= K):\n\t\tans += 1\n\n\twith open('stamps.out', 'w') as fo:\n\t\tfo.write(str(ans-1) + '\\n')","repo_name":"raymonddeng99/Competitive-Programming","sub_path":"USACO/3.1/stamps.py","file_name":"stamps.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"26105056242","text":"numeros = []\nfor x in range(3):\n    numeros.append(int(input('Digite um valor: ')))\nmaior = numeros[0]\nmenor = numeros[0]\nfor y in range(3):\n    if numeros[y] > maior:\n        maior = numeros[y]\n    if numeros[y] < menor:\n        menor = numeros[y]\nprint()\nprint(f'O maior valor é {maior} e o menor é {menor}')","repo_name":"RodrigoAnt93/Curso_CDD4.0","sub_path":"Python/Aula_9/Exerc_04.py","file_name":"Exerc_04.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"27898411942","text":"#[General_Constants]\nFIELD_WIDTH = 19\nFIELD_HEIGHT = 15\n\n#[Game_Constants]\nNO_ITERATIONS = 100\nMAX_CALC_TIME = 1\n\n#[Field_Constants]\nCELL_EMPTY = '.'\nCELL_SHEEP_1 = 'S'\nCELL_SHEEP_1_d = 'U'\nCELL_WOLF_1 = 'W'\nCELL_SHEEP_2 = 's'\nCELL_SHEEP_2_d = 'u'\nCELL_WOLF_2 = 'w'\nCELL_GRASS = 'g'\nCELL_RHUBARB = 'r'\nCELL_FENCE = '#'\n\n\n#[Movements]\nMOVE_NONE = 0\nMOVE_UP = -1\nMOVE_DOWN = 1\nMOVE_LEFT = -2\nMOVE_RIGHT = 2\n\n#[Awards]\nAWARD_RHUBARB = 5\nAWARD_GRASS = 1","repo_name":"alfunx/UZH-MINF4529","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"69796315164","text":"class Solution:\n\n    # Time O(n) and Space O(n)\n    def convertToTitle(self, columnNumber: int) -> str:\n        alpha = {\n            1: \"A\",\n            2: \"B\",\n            3: \"C\",\n            4: \"D\",\n            5: \"E\",\n            6: \"F\",\n            7: \"G\",\n            8: \"H\",\n            9: \"I\",\n            10: \"J\",\n            11: \"K\",\n            12: \"L\",\n            13: \"M\",\n            14: \"N\",\n            15: \"O\",\n            16: \"P\",\n            17: \"Q\",\n            18: \"R\",\n            19: \"S\",\n            20: \"T\",\n            21: \"U\",\n            22: \"V\",\n            23: \"W\",\n            24: \"X\",\n            25: \"Y\",\n            26: \"Z\"\n        }\n\n        ans = []\n        while columnNumber > 26:\n            r = columnNumber % 26\n            if r != 0:\n                ans.append(alpha[r])\n                tmp = columnNumber - r\n                columnNumber = tmp // 26\n            else:\n                ans.append(\"Z\")\n                columnNumber = (columnNumber // 26) - 1\n\n        ans.append(alpha[columnNumber])\n        return \"\".join(reversed(ans))\n\n\ns = Solution()\n\nassert \"AZ\" == s.convertToTitle(52), \"AZ\"\nassert \"AB\" == s.convertToTitle(28), \"AB\"\nassert \"FXSHRXW\" == s.convertToTitle(2147483647)\n\nprint(\"tests passed\")\n","repo_name":"haxul/algorithm_tasks_solving","sub_path":"python/tasks/ExcelSheetColumnTitle.py","file_name":"ExcelSheetColumnTitle.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"73751518365","text":"import hashlib\nimport operator\nimport os\nimport pathlib\nimport struct\nimport typing as tp\n\nfrom pyvcs.objects import hash_object\nfrom pyvcs.repo import repo_find\n\n\nclass GitIndexEntry(tp.NamedTuple):\n    # @see: https://github.com/git/git/blob/master/Documentation/technical/index-format.txt\n    ctime_s: int\n    ctime_n: int\n    mtime_s: int\n    mtime_n: int\n    dev: int\n    ino: int\n    mode: int\n    uid: int\n    gid: int\n    size: int\n    sha1: bytes\n    flags: int\n    name: str\n\n    def pack(self) -> bytes:\n        # PUT YOUR CODE HERE\n        fmt = \"IIIIIIIIII\" + \"20sh\" + str(len(self.name)) + \"s\"\n        packed = struct.pack(fmt, self.ctime_s, self.ctime_n, self.mtime_s, self.mtime_n,self.dev, self.ino, self.mode, self.uid, self.gid, self.size, self.sha1, self.flafs, self.name.encode())\n        return packed + b\"\\x00\\x00\\x00\"\n    \n    @staticmethod\n    def unpack(data: bytes) -> \"GitIndexEntry\":\n        # PUT YOUR CODE HERE\n        entry = {}\n        size = struct.calcsize(\"I\")\n        entry['ctime_s'] = struct.unpack(\"!I\", data[:size])[0]\n        data = data[size:]\n        entry['ctime_n'] = struct.unpack(\"!I\", data[:size])[0]\n        data = data[size:]\n        entry['mtime_s'] = struct.unpack(\"!I\", data[:size])[0]\n        data = data[size:]\n        entry['mtime_n'] = struct.unpack(\"!I\", data[:size])[0]\n        data = data[size:]\n        entry['dev'] = struct.unpack(\"!I\", data[:size])[0]\n        data = data[size:]\n        entry['ino'] = struct.unpack(\"!I\", data[:size])[0]\n        data = data[size:]\n        entry['mode'] = struct.unpack(\"!I\", data[:size])[0]\n        data = data[size:]\n        entry['uid'] = struct.unpack(\"!I\", data[:size])[0]\n        data = data[size:]\n        entry['gid'] = struct.unpack(\"!I\", data[:size])[0]\n        data = data[size:]\n        entry['size'] = struct.unpack(\"!I\", data[:size])[0]\n        data = data[size:]\n        entry['sha1'] = struct.unpack(\"!20s\", data[:struct.calcsize(\"20s\")])[0]\n        data = data[struct.calcsize(\"20s\"):]\n        entry['flags'] = struct.unpack(\"!H\", data[:struct.calcsize(\"H\")])[0]\n        name_len = entry['flags'] & 0xFFF\n        entry['name'] = struct.unpack(f\"!{name_len}s\", data[:struct.calcsize(f\"{name_len}s\")])[0].decode()\n        \n        gitindexentry = GitIndexEntry(**entry)\n        return gitindexentry\n\n\ndef read_index(gitdir: pathlib.Path) -> tp.List[GitIndexEntry]:\n    # PUT YOUR CODE HERE\n    repo = repo_find() / \"index\"\n    entries = []\n    if repo.exists():\n        with open(repo, \"rb\") as f:\n            _ = f.read(8)\n            num_entries = struct.unpack(\"!I\", f.read(4))[0]\n            for entry in range(num_entries):\n                indexEntry = {}\n\n                indexEntry['ctime_s'] = struct.unpack(\"!I\", f.read(struct.calcsize(\"I\")))[0]\n                indexEntry['ctime_n'] = struct.unpack(\"!I\", f.read(struct.calcsize(\"I\")))[0]\n                indexEntry['mtime_s'] = struct.unpack(\"!I\", f.read(struct.calcsize(\"I\")))[0]\n                indexEntry['mtime_n'] = struct.unpack(\"!I\", f.read(struct.calcsize(\"I\")))[0]\n                indexEntry['dev'] = struct.unpack(\"!I\", f.read(struct.calcsize(\"I\")))[0]\n                indexEntry['ino'] = struct.unpack(\"!I\", f.read(struct.calcsize(\"I\")))[0]\n                indexEntry['mode'] = struct.unpack(\"!I\", f.read(struct.calcsize(\"I\")))[0]\n                indexEntry['uid'] = struct.unpack(\"!I\", f.read(struct.calcsize(\"I\")))[0]\n                indexEntry['gid'] = struct.unpack(\"!I\", f.read(struct.calcsize(\"I\")))[0]\n                indexEntry['size'] = struct.unpack(\"!I\", f.read(struct.calcsize(\"I\")))[0]\n                indexEntry['sha1'] = struct.unpack(\"!20s\", f.read(struct.calcsize(\"20s\")))[0]\n                indexEntry['flags'] = struct.unpack(\"!H\", f.read(struct.calcsize(\"H\")))[0]\n                name_len = indexEntry['flags'] & 0xFFF\n                indexEntry['name'] = struct.unpack(\"!\" + str(name_len) + \"s\", f.read(struct.calcsize(\"!\" + str(name_len) + \"s\")))[0]\n                #entry_len = 62 + name_len\n                #pad_len = (8-(entry_len % 8)) or 8\n                nuls = 3\n                _ = f.read(nuls)\n    entries.sort(key=lambda x: x.name)\n    return entries\n                                            \ndef write_index(gitdir: pathlib.Path, entries: tp.List[GitIndexEntry]) -> None:\n    # PUT YOUR CODE HERE\n    git = gitdir / \"index\"\n    fmt = \"!ssssII\"\n    index = struct.pack(fmt, \"DIRC\", 2, len(entries))\n\n    for entry in entries:\n        packed = entry .pack()\n        index += packed\n\n    index += bytes.fromhex(hashlib.sha1(index).hedigest())\n    with open (git, \"bw\") as f:\n        f.write(index)\n\n\ndef ls_files(gitdir: pathlib.Path, details: bool = False) -> None:\n    # PUT YOUR CODE HERE\n    entries = read_index(gitdir)\n    if details:\n        info = []\n        for entry in entries:\n            s = f\"{oct(entry.mode)[2:]} {bytes.hex(entry.sha1)} 0\\t{entry.name}\"\n            info.append(s)\n        s = \"\\n\".join(info)\n        print (s)\n    else:\n        names = []\n        for entry in entries:\n            names.append(entry.name)\n        s = \"\\n\".join(names)\n        print(s)\n\n\ndef update_index(gitdir: pathlib.Path, paths: tp.List[pathlib.Path], write: bool = True) -> None:\n    # PUT YOUR CODE HERE\n    entries = []\n    for path in paths:\n        stat = path.stat()\n        with open(path, \"rb\") as f:\n            entry = GitIndexEntry(\n                ctime_s=stat.st_ctime,\n                ctime_n=0,\n                mtime_s=stat.st_mtime,\n                mtime_n=0,\n                dev=stat.st_dev,\n                ino=stat.st_ino,\n                mode=stat.st_mode,\n                uid=stat.st_uid,\n                gid=stat.st_gid,\n                size=stat.st_size,\n                sha1=bytes.fromhex(hash_object(f.read(), \"blob\", write=write)),\n                flags=len(str(path)),\n                name=str(path)\n            )\n        entries.append(entry)\n    write_index(gitdir, entries)\n","repo_name":"LenaSpevak/programming","sub_path":"homework04/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":5924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"23262560145","text":"# Copyright (c) Alibaba, Inc. and its affiliates.\r\n\r\nfrom typing import Dict\r\n\r\nimport numpy as np\r\nfrom sklearn.metrics import accuracy_score, f1_score\r\n\r\nfrom modelscope.metainfo import Metrics\r\nfrom modelscope.outputs import OutputKeys\r\nfrom modelscope.utils.registry import default_group\r\nfrom modelscope.utils.tensor_utils import (torch_nested_detach,\r\n                                           torch_nested_numpify)\r\nfrom .base import Metric\r\nfrom .builder import METRICS, MetricKeys\r\n\r\n\r\n@METRICS.register_module(\r\n    group_key=default_group, module_name=Metrics.seq_cls_metric)\r\nclass SequenceClassificationMetric(Metric):\r\n    \"\"\"The metric computation class for sequence classification tasks.\r\n\r\n    This metric class calculates accuracy of the whole input batches.\r\n    \"\"\"\r\n\r\n    def __init__(self, *args, **kwargs):\r\n        super().__init__(*args, **kwargs)\r\n        self.preds = []\r\n        self.labels = []\r\n\r\n    def add(self, outputs: Dict, inputs: Dict):\r\n        label_name = OutputKeys.LABEL if OutputKeys.LABEL in inputs else OutputKeys.LABELS\r\n        ground_truths = inputs[label_name]\r\n        eval_results = outputs[OutputKeys.LOGITS]\r\n        self.preds.append(\r\n            torch_nested_numpify(torch_nested_detach(eval_results)))\r\n        self.labels.append(\r\n            torch_nested_numpify(torch_nested_detach(ground_truths)))\r\n\r\n    def evaluate(self):\r\n        preds = np.concatenate(self.preds, axis=0)\r\n        labels = np.concatenate(self.labels, axis=0)\r\n        preds = np.argmax(preds, axis=1)\r\n        return {\r\n            MetricKeys.ACCURACY:\r\n            accuracy_score(labels, preds),\r\n            MetricKeys.F1:\r\n            f1_score(\r\n                labels,\r\n                preds,\r\n                average='micro' if any([label > 1\r\n                                        for label in labels]) else None),\r\n        }\r\n","repo_name":"sdjamesliu/alldata","sub_path":"ai/modelscope-versions/modelscope-master/modelscope/metrics/sequence_classification_metric.py","file_name":"sequence_classification_metric.py","file_ext":"py","file_size_in_byte":1870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"5668822160","text":"import random\nimport sys\n\ntry:\n    n = int(sys.argv[1])\nexcept IndexError:\n    print(\"Error! Enter N\")\n    n = int(input())\n    \nsum = 0;\nfor i in range(n):\n    r = random.uniform(-1, 1);\n    print (\"%.4f\"%r);\n    sum+=r;\nprint(\"average: %.4f\"%(sum/n))","repo_name":"restaver/summer_practice2018_Python","sub_path":"dz2.py","file_name":"dz2.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"39469290857","text":"#!/usr/bin/env python3\n# encoding: utf-8\n\n\"\"\"\n@Filename: removeDuplicates.py\n@Function: 删除有序数组中的重复项\n@Link: https://leetcode-cn.com/problems/remove-duplicates-from-sorted-array/\n@Python Version: 3.8\n@Author: Wei Li\n@Date：2021-08-04\n\"\"\"\n\n\nfrom typing import List\n\n\nclass Solution:\n    def removeDuplicates(self, nums: List[int]) -> int:\n        length_list = len(nums)\n        if not length_list:\n            return 0\n\n        # 双指针策略\n        fast = slow = 1\n        while fast < length_list:\n            if nums[fast] != nums[fast - 1]:\n                nums[slow] = nums[fast]\n                slow += 1\n            fast += 1\n\n        return slow\n\n\n# --------------------------\nif __name__ == \"__main__\":\n    # nums = [1, 1, 2]\n    nums = [0, 0, 1, 1, 1, 2, 2, 3, 3, 4]\n\n    solution = Solution()\n    num_duplicate_list = solution.removeDuplicates(nums)\n    print(f\"The length of original list is {len(nums)}\")\n    print(f\"The length of remove duplicate list is {num_duplicate_list}\")\n","repo_name":"2694048168/LeetCodeAlgorithm","sub_path":"Python/removeDuplicates.py","file_name":"removeDuplicates.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"86"}
+{"seq_id":"181093045","text":"from functools import cached_property\nimport re\nimport sys\n\nfrom bespokeasm.assembler.line_identifier import LineIdentifier\nfrom bespokeasm.assembler.bytecode.parts import NumericByteCodePart, ExpressionByteCodePartInMemoryZone\nfrom bespokeasm.assembler.model.operand import OperandWithArgument, OperandType, ParsedOperand\nfrom bespokeasm.assembler.label_scope import LabelScope\nfrom bespokeasm.expression import EXPRESSION_PARTS_PATTERN\nfrom bespokeasm.assembler.memory_zone.manager import MemoryZoneManager\nfrom bespokeasm.assembler.memory_zone import MemoryZone\n\n\nclass RelativeAddressByteCodePart(ExpressionByteCodePartInMemoryZone):\n    def __init__(\n                self,\n                value_expression: str,\n                value_size: int,\n                byte_align: bool,\n                endian: str,\n                line_id: LineIdentifier,\n                min_relative_value: int,\n                max_relative_value: int,\n                memzone: MemoryZone,\n                offset_from_instruction_end: bool,\n            ) -> None:\n        super().__init__(memzone, value_expression, value_size, byte_align, endian, line_id)\n        self._min_relative_value = min_relative_value\n        self._max_relative_value = max_relative_value\n        self._offset_from_instruction_end = offset_from_instruction_end\n\n    def get_value(self, label_scope: LabelScope, instruction_address: int, instruction_size: int) -> int:\n        if instruction_address is None:\n            raise ValueError('RelativeAddressByteCodePart.get_value had no instruction_address passed')\n        expression_value = super().get_value(label_scope, instruction_address, instruction_size)\n        relative_value = expression_value - instruction_address\n        if self._offset_from_instruction_end:\n            # minus one to account for the current address being 1 byte of instruction size\n            relative_value -= instruction_size - 1\n        if self._max_relative_value is not None and relative_value > self._max_relative_value:\n            sys.exit(\n                f'ERROR: {self.line_id} - Relative address offset is larger than configured '\n                f'maximum value of {self._max_relative_value}'\n            )\n        if self._min_relative_value is not None and relative_value < self._min_relative_value:\n            sys.exit(\n                f'ERROR: {self.line_id} - Relative address offset is smaller than configured '\n                f'minimum value of {self._min_relative_value}'\n            )\n        return relative_value\n\n\nclass RelativeAddressOperand(OperandWithArgument):\n    def __init__(self, operand_id: str, arg_config_dict: dict, default_endian: str, require_arg: bool = True) -> None:\n        super().__init__(operand_id, arg_config_dict, default_endian, require_arg)\n\n    def __str__(self):\n        return f'RelativeAddressOperand<{self.id}>'\n\n    @property\n    def type(self) -> OperandType:\n        return OperandType.RELATIVE_ADDRESS\n\n    @cached_property\n    def match_pattern(self) -> str:\n        base_match_str = r'((?:{0}|\\s)+)'.format(EXPRESSION_PARTS_PATTERN)\n        if self.uses_curly_braces:\n            return r'\\{{\\s*{0}\\s*\\}}'.format(base_match_str)\n        else:\n            return base_match_str\n\n    @property\n    def uses_curly_braces(self) -> bool:\n        return self.config.get('use_curly_braces', False)\n\n    @property\n    def max_offset(self) -> int:\n        return self.config['argument'].get('max', None)\n\n    @property\n    def min_offset(self) -> int:\n        return self.config['argument'].get('min', None)\n\n    @property\n    def offset_from_instruction_end(self) -> bool:\n        return self.config.get('offset_from_instruction_end', False)\n\n    def parse_operand(\n        self,\n        line_id: LineIdentifier,\n        operand: str,\n        register_labels: set[str],\n        memzone_manager: MemoryZoneManager,\n    ) -> ParsedOperand:\n        # find argument per the required pattern\n        match = re.match(self.match_pattern, operand.strip())\n        if match is None or len(match.groups()) != 1:\n            return None\n        # do not match if expression contains square bracks\n        if \"[\" in operand or \"]\" in operand:\n            return None\n        bytecode_part = NumericByteCodePart(\n            self.bytecode_value,\n            self.bytecode_size,\n            False,\n            'big',\n            line_id\n        ) if self.bytecode_value is not None else None\n        arg_part = RelativeAddressByteCodePart(\n            match.group(1).strip(),\n            self.argument_size,\n            self.argument_byte_align,\n            self.argument_endian,\n            line_id,\n            self.min_offset,\n            self.max_offset,\n            memzone_manager.global_zone,\n            self.offset_from_instruction_end,\n        )\n        if arg_part.contains_register_labels(register_labels):\n            return None\n        return ParsedOperand(self, bytecode_part, arg_part, operand)\n","repo_name":"michaelkamprath/bespokeasm","sub_path":"src/bespokeasm/assembler/model/operand/types/relative_address.py","file_name":"relative_address.py","file_ext":"py","file_size_in_byte":4944,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"86"}
+{"seq_id":"858193052","text":"# -*- coding: utf-8 -*-\n\n# Imports ---------------------------------------------------------------------\n\nimport csv\nimport datetime\nimport os\nimport pandas\nimport pathlib\nimport feedstream.data as data\nimport feedstream.exceptions as exceptions\nimport feedstream.fetch as fetch\nfrom feedstream.config import settings\n\n# Constants -------------------------------------------------------------------\n\nTIMESTAMP_FILE = os.path.join(settings.timestamp_file)\n\n# Download functions ----------------------------------------------------------\n\ndef download_entries(flatten=False):\n\n    \"\"\"\n    Download entries for each tag and return a dict of the parsed items. If the\n    API token is expired at any point, refresh the token and retry.\n\n    \"\"\"\n\n    try:\n        return _download_entries(flatten)\n    except exceptions.ApiError as e:\n        if e.status_code == 401 and e.api_msg.startswith(\"token expired\") :\n            if settings.enterprise:\n                fetch.fetch_access_token()\n                return _download_entries(flatten)\n            else:\n                raise\n        else:\n            raise\n\n\ndef _download_entries(flatten):\n\n    \"\"\"Download entries for each tag and return a dict of the parsed items.\"\"\"\n\n    items = []\n    since = get_last_downloaded() if settings.download_new else None\n    downloaded = data.get_timestamp_from_datetime(datetime.datetime.now())\n    tag_ids = fetch.fetch_tag_ids()\n\n    # Fetch the articles for each tag, including any continuations\n    for tag in tag_ids:\n\n        continuation = None\n\n        while True:\n\n            contents = fetch.fetch_tag_entries(tag['id'],\n                since=since, continuation=continuation)\n\n            for item in contents['items']:\n\n                # Check the tag data looks sane: accessing an enterprise\n                # account with settings.enterprise set to false causes problems\n                if not data.key_exists(tag, 'id') or \\\n                    not data.key_exists(tag, 'label'):\n\n                    raise exceptions.UnexpectedDataError(\n                        'missing fields in tag data: are you accessing an '\n                        'enterprise account without declaring it in your '\n                        'config file?')\n\n                item = data.parse_item(\n                    tag['id'],\n                    tag['label'],\n                    item,\n                    flatten)\n\n                items.append(item)\n\n            continuation = data.get_opt_key(contents, 'continuation')\n            if continuation is None:\n                break\n\n    entries = {\n        'timestamp': downloaded,\n        'fieldnames': data.FIELDNAMES,\n        'items': items}\n\n    return entries\n\n\ndef download_entries_df():\n\n    \"\"\"\n    Download entries for each tag and return a dataframe of the items. Nested\n    fields are flattened into a single field with items separated using the\n    separator string defined in the data module. The function returns a tuple\n    containing the timestamp of the download and the dataframe itself.\n\n    \"\"\"\n\n    entries = download_entries(flatten=True)\n    timestamp = entries['timestamp']\n    df = pandas.DataFrame(entries['items'])\n    return (timestamp, df)\n\n\ndef download_entries_csv():\n\n    \"\"\"Download entries to a csv\"\"\"\n\n    entries = download_entries(flatten=True)\n    downloaded = entries['timestamp']\n    fieldnames = entries['fieldnames']\n    items = entries['items']\n\n    try:\n\n        pathlib.Path(settings.data_dir).mkdir(exist_ok=True)\n\n        filename = '{0}-{1}-{2}.csv'.format(\n            settings.download_prefix,\n            data.get_date_from_timestamp(downloaded),\n            data.get_time_from_timestamp(downloaded).strftime('%H-%M-%S'))\n\n        filepath = os.path.join(settings.data_dir, filename)\n\n        with open(filepath, 'w', newline='', encoding='utf-8') as csvfile:\n            writer = csv.DictWriter(csvfile, fieldnames=fieldnames,\n                quoting=csv.QUOTE_NONNUMERIC)\n            writer.writeheader()\n            for item in items:\n                writer.writerow(item)\n\n        set_last_downloaded(downloaded)\n        return filename\n\n    except:\n        raise\n\n# Timestamp functions ---------------------------------------------------------\n\ndef get_last_downloaded():\n\n    \"\"\"Get the timestamp for the last time data was downloaded.\"\"\"\n\n    try:\n        with open(TIMESTAMP_FILE) as f:\n            timestamp_str = f.read()\n            return int(timestamp_str)\n    except FileNotFoundError:\n        return None\n\n\ndef set_last_downloaded(timestamp):\n\n    \"\"\"Set the timestamp for the last time data was downloaded.\"\"\"\n\n    pathlib.Path(settings.data_dir).mkdir(exist_ok=True)\n\n    with open(TIMESTAMP_FILE, 'w') as f:\n        f.write('{0}'.format(timestamp))\n","repo_name":"olihawkins/feedstream","sub_path":"feedstream/download.py","file_name":"download.py","file_ext":"py","file_size_in_byte":4744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70693236764","text":"'''\n가운데 글자 가져오기\nhttps://programmers.co.kr/learn/courses/30/lessons/12903\n문제 설명\n단어 s의 가운데 글자를 반환하는 함수, solution을 만들어 보세요. 단어의 길이가 짝수라면 가운데 두글자를 반환하면 됩니다.\n\n재한사항\ns는 길이가 1 이상, 100이하인 스트링입니다.\n입출력 예\ns\treturn\n\"abcde\"\t\"c\"\n\"qwer\"\t\"we\"\n\n'''\n'''\n# 첫시도 , 채점 시 오류가 계속 발생함. \n아무래도 round 때문인 것 같음.\ns = \"abcde\"\n# s = \"qwer\"\nprint(len(s))\ndef solution(s):\n    answer=''\n    # s의 글자 개수가 홀수 일 경우\n    ls = int(len(s))\n    a = round(ls / 2)\n    #print(a, 'a')\n    if ls % 2 == 1: answer = s[a]\n    # s의 글자 개수가 짝수 일 경우\n    elif ls % 2 == 0 : answer = s[a-1:a+1]\n    return answer\n\nprint(solution(\"abcde\"))\n# print(solution(\"qwer\"))\n'''\n# s = \"abcde\"\ns = \"qwer\"\n# b = (len(s)-1)//2\n# print(b)\ndef solution(s):\n    answer=''\n    # s의 글자 개수가 홀수 일 경우\n    if int(len(s)) % 2 == 1: answer = s[int(len(s)/2)]\n    # s의 글자 개수가 짝수 일 경우\n    elif int(len(s)) % 2 == 0 : answer = s[int(len(s)/2)-1:int(len(s)/2)+1]\n    return answer\n# print(solution(\"abcde\"))\nprint(solution(\"powerk\"))\n\n\n'''\n정리 : 몫을 구하면 되는 문제였는데, 굳이 if 문 없이도 몫만 구해서 아래와 같이 쉽게 구할 수 있었음. \nround에 집착하지 않았어도 됫었음\n\n다른사람 답\ndef string_middle(str):\n    # 함수를 완성하세요\n    return str[(len(str)-1)//2:len(str)//2+1]\n# 아래는 테스트로 출력해 보기 위한 코드입니다.\nprint(string_middle(\"power\"))\n\n****** '//' :몫을 구하면 정수화 시켜주지 않아도 나누기 연산 후 소수점 이하의 수를 버리고, 정수 부분의 수만 구함\ncf '%' : 몫이 아닌 나머지를 구함\n'/' : 나누\n================\n\ndef string_middle(str):\n    a = len(str)\n    if a % 2 == 0 :\n        a = (a-2) / 2\n    else :\n        a = (a-1) / 2\n    return str[int(a) : -int(a)]\n# 아래는 테스트로 출력해 보기 위한 코드입니다.\nprint(string_middle(\"power\"))\n\n\n'''","repo_name":"jiroh1/Python_algorithm","sub_path":"programmers/12903_string.py","file_name":"12903_string.py","file_ext":"py","file_size_in_byte":2133,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"1701451451","text":"# Author: Hye Yeon Park\n# Date: 03/09/2021\n# Description: [CS 162 Portfolio Project] This program is to simulate an abstract board game called Janggi.\n# It is played on a board nine lines wide by ten lines long with Blue and Red as the competing players and\n# Blue as the starting player. The game ends when one player checkmates the other's general.\n\nimport copy\n\n\nclass JanggiGame:\n    \"\"\"\n    Janggi Game class with a board nine lines wide by ten lines long with Blue and Red\n    as the competing players and Blue as the starting player.\n    The game ends when one player checkmates the other's general.\n    Includes methods called is_in_check, make_move, get_game_state and etc.\n    Current board can be printed using a method called get_board.\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"\n        Initializes all data members\n        \"\"\"\n        self._base_board = [\n            [\"a1\", \"b1\", \"c1\", \"d1\", \"e1\", \"f1\", \"g1\", \"h1\", \"i1\"],\n            [\"a2\", \"b2\", \"c2\", \"d2\", \"e2\", \"f2\", \"g2\", \"h2\", \"i2\"],\n            [\"a3\", \"b3\", \"c3\", \"d3\", \"e3\", \"f3\", \"g3\", \"h3\", \"i3\"],\n            [\"a4\", \"b4\", \"c4\", \"d4\", \"e4\", \"f4\", \"g4\", \"h4\", \"i4\"],\n            [\"a5\", \"b5\", \"c5\", \"d5\", \"e5\", \"f5\", \"g5\", \"h5\", \"i5\"],\n            [\"a6\", \"b6\", \"c6\", \"d6\", \"e6\", \"f6\", \"g6\", \"h6\", \"i6\"],\n            [\"a7\", \"b7\", \"c7\", \"d7\", \"e7\", \"f7\", \"g7\", \"h7\", \"i7\"],\n            [\"a8\", \"b8\", \"c8\", \"d8\", \"e8\", \"f8\", \"g8\", \"h8\", \"i8\"],\n            [\"a9\", \"b9\", \"c9\", \"d9\", \"e9\", \"f9\", \"g9\", \"h9\", \"i9\"],\n            [\"a10\", \"b10\", \"c10\", \"d10\", \"e10\", \"f10\", \"g10\", \"h10\", \"i10\"],\n        ]\n\n        # RC=RedChariot, RE=RedElephant, RH=RedHorse, RG=RedGuard, *RK=RedGeneral, *RN=RedCannon, RS=RedSoldier\n        # BC=BlueChariot, BE=BlueElephant, BH=BlueHorse, BG=BlueGuard, *BK=BlueGeneral, *BN=BlueCannon, BS=BlueSoldier\n        self._board = [\n            [\"RC\", \"RE\", \"RH\", \"RG\", \"OO\", \"RG\", \"RE\", \"RH\", \"RC\"],\n            [\"OO\", \"OO\", \"OO\", \"OO\", \"RK\", \"OO\", \"OO\", \"OO\", \"OO\"],\n            [\"OO\", \"RN\", \"OO\", \"OO\", \"OO\", \"OO\", \"OO\", \"RN\", \"OO\"],\n            [\"RS\", \"OO\", \"RS\", \"OO\", \"RS\", \"OO\", \"RS\", \"OO\", \"RS\"],\n            [\"OO\", \"OO\", \"OO\", \"OO\", \"OO\", \"OO\", \"OO\", \"OO\", \"OO\"],\n            [\"OO\", \"OO\", \"OO\", \"OO\", \"OO\", \"OO\", \"OO\", \"OO\", \"OO\"],\n            [\"BS\", \"OO\", \"BS\", \"OO\", \"BS\", \"OO\", \"BS\", \"OO\", \"BS\"],\n            [\"OO\", \"BN\", \"OO\", \"OO\", \"OO\", \"OO\", \"OO\", \"BN\", \"OO\"],\n            [\"OO\", \"OO\", \"OO\", \"OO\", \"BK\", \"OO\", \"OO\", \"OO\", \"OO\"],\n            [\"BC\", \"BE\", \"BH\", \"BG\", \"OO\", \"BG\", \"BE\", \"BH\", \"BC\"]\n        ]\n\n        self._turn_count = 0        # even count = blue's turn, odd count = red's turn\n        self._game_state = \"UNFINISHED\"  # 'UNFINISHED' or 'RED_WON' or 'BLUE_WON'\n        self._check = False\n        self._checkmate = False\n        self._move_from_idx = []    # index of the move_from position\n        self._move_to_idx = []      # index of the move_to position\n        self._moves = []            # all possible moves of the given piece as index of destination\n        self._captured = [\"OO\"]\n        self._threat = None         # threatening piece for the general\n        self._threat_idx = []       # threatening piece index\n\n    def get_board(self):\n        \"\"\" Print out the current board for testing purpose \"\"\"\n        print(\"-----This is the current board-----\")\n        for x in self._board:\n            for i in range(0, 9):\n                print(x[i], end=\"  \")\n            print()\n        print(\"-----------------------------------\")\n\n    def get_base_board(self):\n        \"\"\" Print out the base board for testing purpose \"\"\"\n        print(\"------This is the base board-------\")\n        for x in self._base_board[:-1]:\n            for i in range(0, 9):\n                print(x[i], end=\"  \")\n            print()\n        # print last row separate, for column alignment\n        for i in range(0, 9):\n            print(self._base_board[9][i], end=\" \")\n        print()\n        print(\"-----------------------------------\")\n\n    def get_game_state(self):\n        \"\"\" Returns game state \"\"\"\n        return self._game_state\n\n    def set_game_state(self):\n        \"\"\" Sets game state \"\"\"\n        if self.get_player() == \"B\":\n            self._game_state = \"RED_WON\"\n        else:\n            self._game_state = \"BLUE_WON\"\n\n    def get_player(self):\n        \"\"\" Returns whose turn it is \"\"\"\n        if self._turn_count % 2 == 0:\n            return \"B\"\n        else:\n            return \"R\"\n\n    def get_piece(self, row, col):\n        \"\"\" Returns a piece at a given location \"\"\"\n        return self._board[row][col]\n\n    def get_opponent(self):\n        \"\"\" Returns the opposing player \"\"\"\n        if self.get_player() == \"B\":\n            return \"R\"\n        else:\n            return \"B\"\n\n    def is_in_check(self, player):\n        \"\"\"\n        Takes as a parameter either 'red' or 'blue' and returns True if that player is in check,\n        but returns False otherwise.\n        \"\"\"\n        if self._check == \"B\" and player == \"blue\":\n            print(\"Blue general is in check!\")\n            return True\n        elif self._check == \"R\" and player == \"red\":\n            print(\"Red general is in check!\")\n            return True\n        return False\n\n    def is_check(self):\n        \"\"\"\n        Checks if the opposing general is in a direct threat on the player's next move.\n        Generate all possible moves of all the player's pieces.\n        If any pieces can capture the opponent's general, sets _check to the opponent.\n        \"\"\"\n        self._captured = [\"OO\"]     # reset captured list\n\n        # go through the board and check all possible moves for the player's piece\n        for x in range(0, 10):\n            for y in range(0, 9):\n                if self._board[x][y][0] == self.get_player():\n                    piece_initial = self._board[x][y][1]\n\n                    # save the current piece's index\n                    temp_idx = self._move_from_idx\n                    self._move_from_idx = [x, y]\n                    self.call_moves(piece_initial)\n                    # revert the index\n                    self._move_from_idx = temp_idx\n\n                    # if the opponent's general is captured, the opponent is in check\n                    for i in range(len(self._captured)):\n                        if self._captured[i][1] == \"K\" and self._captured[i][0] == self.get_opponent():\n                            self._check = self.get_opponent()\n\n    def is_checkmate(self):\n        \"\"\"\n        Checks if the general in check can escape in the next move.\n        First sets checkmate and then finds a way to escape.\n        Checks if the general can move away or capture the threatening piece.\n        If still checkmate, check if any pieces can capture the threatening piece.\n        If still checkmate, check if any pieces can block the threatening piece.\n        If there is no escape, it's checkmate.\n        Sets _checkmate to True, sets the game state and the game is over.\n        \"\"\"\n        # whose turn at this point = player in check\n        # set checkmate\n        self._checkmate = True\n\n        # get the general's current position\n        general = None\n        for i in range(1, 10):\n            for j in range(1, 9):\n                if self._board[i][j] == self.get_player() + \"K\":\n                    general = self._board[i][j]\n                    self._move_from_idx = [i, j]\n                    break\n                j += 1\n            i += 1\n\n        row = self._move_from_idx[0]\n        col = self._move_from_idx[1]\n\n        # get all possible moves of the general\n        self.call_moves(\"K\")\n\n        # save the current board\n        copied_board = copy.deepcopy(self._board)\n\n        for (x, y) in self._moves:\n            # make the general's move\n            self._board[x][y] = general\n            self._board[row][col] = \"OO\"\n\n            # check if still in check, if not, it's not checkmate\n            self._turn_count += 1       # switch player for is_check test\n\n            temp_check = self._check    # save current _check\n            self._check = False         # reset _check\n            self.is_check()             # rerun is_check to see if still in check\n            if self._check is False:\n                self._checkmate = False\n\n            self._check = temp_check\n            self._turn_count -= 1       # switch player back\n\n            # if player in check doesn't match the current player\n            if self._check != self.get_player():\n                self._checkmate = False\n\n        # revert the board\n        self._board = copied_board\n\n        # the general is still in checkmate\n        # check if other pieces can capture the threatening piece\n        # go through the board and check all possible moves for each piece\n        self._captured = [\"OO\"]    # reset captured list\n        for x in range(0, 10):\n            for y in range(0, 9):\n\n                if self.get_piece(x, y)[0] == self.get_player():\n                    piece_initial = self._board[x][y][1]\n\n                    self._move_from_idx = [x, y]    # set the current index with x, y\n                    self.call_moves(piece_initial)\n\n                    # if the threatening piece can be captured, it's not checkmate\n                    for i in range(len(self._captured)):\n                        if self._captured[i] == self._threat:\n                            self._checkmate = False\n                            break\n                        # if not, continue the loop\n\n        # if still checkmate, check if other pieces can block the threatening piece\n        if self._checkmate:\n            if self.check_block(row, col):   # push general's index\n                self._checkmate = False\n\n        # if still checkmate, set the game state and game is over\n        if self._checkmate:\n            self.set_game_state()\n\n    def check_block(self, row, col):\n        \"\"\"\n        Helper function for is_checkmate.\n        Receives general's index and checks if any non-general pieces can block the path\n        of the threatening piece and the player's general\n        \"\"\"\n        # whose turn? player in check\n        between_positions = []\n        gr = row                  # general's row index\n        gc = col                  # general's column index\n        tr = self._threat_idx[0]  # threatening piece's row index\n        tc = self._threat_idx[1]  # threatening piece's column index\n\n        # if the threatening piece is Cannon(N) or Chariot(C)\n        if self._threat[1] == \"N\" or self._threat[1] == \"C\":\n            # save the between positions between cannon and the general in check\n            # check if any piece can block the path\n\n            if tc == gc:    # the move is vertical\n                direction = (tr - gr) / abs(tr - gr)\n                for i in range(abs(tr - gr)):\n                    occupied_piece = self.get_piece(int(tr - direction * (i + 1)), gc)\n                    if occupied_piece == \"OO\":\n                        between_positions.append((int(tr - direction * (i + 1)), gc))\n\n            if tr == gr:    # the move is horizontal\n                direction = (tc - gc) / abs(tc - gc)\n                for i in range(abs(tc - gc)):\n                    occupied_piece = self.get_piece(gr, int(tc - direction * (i + 1)))\n                    if occupied_piece == \"OO\":\n                        between_positions.append((gr, int(tc - direction * (i + 1))))\n\n        # if the threatening piece is Horse(H)\n        if self._threat[1] == \"H\":\n            # calculate the between position based on the threatening piece and the general's positions\n\n            # check if the move is vertical\n            if abs(tr - gr) > abs(tc - gc):\n                between_positions.append((tr - int((tr - gr) / 2), tc))\n\n            else:   # move is horizontal\n                between_positions.append((tr, tc - int((tc - gc) / 2)))\n\n        # if the threatening piece is Elephant(E)\n        if self._threat[1] == \"E\":\n            # calculate the between positions based on the threatening piece and the general's positions\n\n            # check if the move is vertical\n            if abs(tr - gr) > abs(tc - gc):\n                between_positions.append((tr - int((tr - gr) / 3), tc))\n                between_positions.append((tr - int((tr - gr) * 2 / 3), tc - int((tc - gc) / 2)))\n\n            else:   # move is horizontal\n                between_positions.append((tr, tc - int((tc - gc) / 3)))\n                between_positions.append((tr - int((tr - gr) / 2), tc - int((tc - gc) * 2 / 3)))\n\n        # go through the board and check if any pieces can block the path\n        for x in range(0, 10):\n            for y in range(0, 9):\n                occupant = self.get_piece(x, y)\n\n                # check with all player's pieces except the general\n                if occupant[0] == self.get_player() and occupant[1] != \"K\":\n                    piece_initial = self._board[x][y][1]\n\n                    # save the current piece's index\n                    temp_idx = self._move_from_idx\n                    self._move_from_idx = [x, y]\n                    self.call_moves(piece_initial)\n                    self._move_from_idx = temp_idx    # revert the index\n\n                    for pos in between_positions:\n                        if pos in self._moves:\n                            return True\n\n    def is_selfcheck(self):\n        \"\"\"\n        Check if the valid move puts or leaves the player's general in check.\n        A player cannot make such moves.\n        \"\"\"\n        self._captured = [\"OO\"]     # reset captured list\n\n        # go through the board and check all possible moves for each piece\n        for x in range(0, 10):\n            for y in range(0, 9):\n                if self._board[x][y][0] == self.get_opponent():\n                    piece_initial = self._board[x][y][1]\n\n                    # switch player to check if opponent can catch player's general\n                    self._turn_count += 1\n                    # save the current piece's index\n                    temp_idx = self._move_from_idx\n                    self._move_from_idx = [x, y]\n                    self.call_moves(piece_initial)\n                    self._move_from_idx = temp_idx    # revert the index\n                    self._turn_count -= 1             # switch player back\n\n                    # if general is captured, the move puts or leaves the player's general in check.\n                    # is_selfcheck returns True meaning the move is invalid\n                    for i in range(len(self._captured)):\n                        if self._captured[i][1] == \"K\" and self._captured[i][0] == self.get_player():\n                            return True\n\n        return False\n\n    def make_move(self, move_from, move_to):\n        \"\"\"\n        Takes two string parameters that represent the square to move from and the square to move to.\n        If the square being moved from contains opponent's piece or if the indicated move is not legal,\n        or if the game has already been won, then return False.\n        Otherwise make the indicated move, remove any captured piece, update the game state if necessary\n        update whose turn it is, and return True.\n        Call is_selfcheck to check if a player makes a move that puts or leaves their general in check.\n        Call is_check to check if the opponent's general is in check.\n        Call is_checkmate to check if it's checkmate and the game is over.\n        \"\"\"\n        move_from_piece = None\n        move_to_piece = None\n        self._check = False     # reset _check\n\n        if self._game_state != \"UNFINISHED\":\n            print(\"Invalid move! The game is already over.\")\n            return False\n\n        # two passed strings are the same, the player passes its turn and return True\n        if move_from == move_to:\n            self._turn_count += 1\n            return True\n\n        else:\n            # get the piece at the move_from position and its index\n            i = int(move_from[1:]) - 1\n            j = 0\n            for j in range(0, 9):\n                if self._base_board[i][j] == move_from:\n                    move_from_piece = self._board[i][j]\n                    self._move_from_idx = [i, j]\n                    break\n\n            # get the piece at the move_to position and its index\n            x = int(move_to[1:]) - 1\n            y = 0\n            for y in range(0, 9):\n                if self._base_board[x][y] == move_to:\n                    move_to_piece = self._board[x][y]\n                    self._move_to_idx = [x, y]\n                    break\n\n            # the move_from position doesn't have the player's piece\n            if self.get_player() != move_from_piece[0]:\n                print(\"Invalid move! The starting position doesn't have the player's piece.\")\n                return False\n\n            # the move_to position has the player's own piece\n            if self.get_player() == move_to_piece[0]:\n                print(\"Invalid move! The destination position has the player's own piece.\")\n                return False\n\n            # get all possible moves for the current piece\n            self.call_moves(move_from_piece[1])\n\n            # if move_to index is one of the possible moves, the move is valid\n            if (self._move_to_idx[0], self._move_to_idx[1]) in self._moves:\n\n                # save the current board\n                copied_board = copy.deepcopy(self._board)\n\n                # make the indicated move\n                self._board[x][y] = move_from_piece\n                self._board[i][j] = \"OO\"\n\n                # check if the opponent's general is in check\n                temp_check = self._check    # save whose in check\n                self.is_check()\n\n                # check if the move puts or leaves the player's general in check\n                if self.is_selfcheck() is True:\n                    # if yes, revert the board and return False\n                    self._board = copied_board\n                    self._check = temp_check  # is_selfcheck() is True, invalid move, revert _check\n                    print(\"Invalid move! The move puts or leaves the player's general in check.\")\n                    return False\n\n                # update the turn\n                self._turn_count += 1\n\n                # if general in check, check if it's checkmate\n                if self._check:\n                    # save the threatening piece to use it in is_checkmate\n                    self._threat = self._board[x][y]\n                    self._threat_idx = [x, y]\n                    self.is_checkmate()\n\n                # the move is valid\n                return True\n\n            return False\n\n    def call_moves(self, piece_initial):\n        \"\"\"\n        Calls each piece's move and return all possible moves of the piece.\n        Called by make_move, is_selfcheck, is_check, is_checkmate.\n        \"\"\"\n        self._moves = []            # reset moves list\n        self._captured = [\"OO\"]     # reset captured list\n\n        if piece_initial == \"S\":\n            self.soldier_moves()\n            return self._moves\n        if piece_initial == \"H\":\n            self.horse_moves()\n            return self._moves\n        if piece_initial == \"E\":\n            self.elephant_moves()\n            return self._moves\n        if piece_initial == \"C\":\n            self.chariot_moves()\n            return self._moves\n        if piece_initial == \"N\":\n            self.cannon_moves()\n            return self._moves\n        if piece_initial == \"K\" or \"G\":\n            self.general_guard_moves()\n            return self._moves\n\n    def add_to_moves(self, directions):\n        \"\"\"\n        Called by each piece's move function to save the possible moves and any captured pieces\n        \"\"\"\n        row = self._move_from_idx[0]\n        col = self._move_from_idx[1]\n\n        for (x, y) in directions:\n            if self.check_range(row + x, col + y):\n                occupant = self.get_piece(row + x, col + y)\n\n                if occupant != \"OO\":\n                    # add captured piece and add the move\n                    if occupant[0] == self.get_opponent():\n                        self._captured.append(occupant)\n                        move = (row + x, col + y)\n                        self._moves.append(move)\n\n                else:\n                    # add the move\n                    move = (row + x, col + y)\n                    self._moves.append(move)\n\n    def check_range(self, row, col):\n        \"\"\" Checks if the given position is out of the board \"\"\"\n        if 0 <= row <= 9 and 0 <= col <= 8:\n            return True\n\n    def soldier_moves(self):\n        \"\"\" Returns all possible moves for Soldier piece \"\"\"\n        row = self._move_from_idx[0]\n        col = self._move_from_idx[1]\n\n        if self.get_player() == \"B\":\n            directions = [(0, -1), (0, 1), (-1, 0)]\n        else:\n            directions = [(0, -1), (0, 1), (1, 0)]\n\n        # if in palace, diagonal move may be possible\n        diag_pos = [(7, 3), (7, 5), (9, 3), (9, 5), (8, 4), (0, 3), (0, 5), (2, 3), (2, 5), (1, 4)]\n        diag_directions = []\n        if (row, col) in diag_pos:\n            if self.get_player() == \"B\":\n                diag_directions = [(-1, -1), (-1, 1)]\n            else:\n                diag_directions = [(1, 1), (1, -1)]\n\n        # add all the possible moves to self._moves\n        self.add_to_moves(directions + diag_directions)\n        return self._moves\n\n    def horse_moves(self):\n        \"\"\" Returns all possible moves for Horse piece \"\"\"\n        row = self._move_from_idx[0]\n        col = self._move_from_idx[1]\n        path = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n        directions = [(2, 1), (2, -1), (-2, 1), (-2, -1), (1, 2), (1, -2), (-1, 2), (-1, -2)]\n\n        # remove directions with intervening piece on the path\n        for (x, y) in path:\n            if self.check_range(row + x, col + y):\n                if self.get_piece(row + x, col + y) != \"OO\":\n                    if y == 0:\n                        directions.remove((x * 2, y + 1))\n                        directions.remove((x * 2, y - 1))\n                    if x == 0:\n                        directions.remove((x + 1, y * 2))\n                        directions.remove((x - 1, y * 2))\n\n        # add all the possible moves to self._moves\n        self.add_to_moves(directions)\n        return self._moves\n\n    def elephant_moves(self):\n        \"\"\" Returns all possible moves for Elephant piece \"\"\"\n        row = self._move_from_idx[0]\n        col = self._move_from_idx[1]\n        path1 = [(1, 0), (-1, 0), (0, 1), (0, -1)]  # 1st landing pos\n        path2 = [(2, 1), (2, -1), (-2, 1), (-2, -1), (1, 2), (1, -2), (-1, 2), (-1, -2)]  # 2nd landing pos\n        directions = [(3, 2), (3, -2), (-3, 2), (-3, -2), (2, 3), (2, -3), (-2, 3), (-2, -3)]\n\n        # remove directions with any intervening piece on the path\n        # first check path1 and remove invalid move directions\n        for (x, y) in path1:\n            if self.check_range(row + x, col + y):\n                if self.get_piece(row + x, col + y) != \"OO\":\n                    if y == 0:  # vertical move\n                        path2.remove((x * 2, y + 1))\n                        path2.remove((x * 2, y - 1))\n                        directions.remove((x * 3, y + 2))\n                        directions.remove((x * 3, y - 2))\n                    if x == 0:  # horizontal move\n                        path2.remove((x + 1, y * 2))\n                        path2.remove((x - 1, y * 2))\n                        directions.remove((x + 2, y * 3))\n                        directions.remove((x - 2, y * 3))\n\n        # then check path2 and remove invalid move directions\n        for (x, y) in path2:\n            if self.check_range(row + x, col + y):\n                if self.get_piece(row + x, col + y) != \"OO\":\n                    if abs(x) == 2:\n                        # remove one of (3, 2), (3, -2), (-3, 2), (-3, -2)\n                        directions.remove((int(x * 1.5), y * 2))\n                    if abs(y) == 2:\n                        # remove one of (2, 3), (2, -3), (-2, 3), (-2, -3)\n                        directions.remove((x * 2, int(y * 1.5)))\n\n        # add all the possible moves to self._moves\n        self.add_to_moves(directions)\n        return self._moves\n\n    def chariot_moves(self):\n        \"\"\" Returns all possible moves for Chariot piece \"\"\"\n        directions = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n        valid_direction = []\n        row = self._move_from_idx[0]\n        col = self._move_from_idx[1]\n\n        for (x, y) in directions:\n            i = 1  # a factor to multiply the directions\n\n            while self.check_range(row + (x * i), col + (y * i)):\n                move_to_piece = self.get_piece(row + (x * i), col + (y * i))\n\n                # if the position is empty, that direction is valid\n                if move_to_piece == \"OO\":\n                    valid_direction.append((x * i, y * i))\n                    i += 1\n\n                # if the position is occupied with opponent piece, valid direction and stop the loop\n                elif move_to_piece[0] == self.get_opponent():\n                    valid_direction.append((x * i, y * i))\n                    break\n\n                # if the occupant is player's own piece, no more valid direction, stop the loop\n                else:\n                    break\n\n        # if in palace, diagonal move may be possible\n        diag_position = [(7, 3), (7, 5), (9, 3), (9, 5), (8, 4), (0, 3), (0, 5), (2, 3), (2, 5), (1, 4)]\n        directions = [(1, 1), (1, -1), (-1, 1), (-1, -1)]\n\n        # if the chariot is in diag_position and the given move is diagonal\n        if (row, col) in diag_position:\n\n            for (x, y) in directions:\n                i = 1  # a factor to multiply the directions\n\n                # the move_to position must be within the palace\n                while (row + (x * i), col + (y * i)) in diag_position:\n                    move_to_piece = self.get_piece(row + (x * i), col + (y * i))\n\n                    # if the position is empty, that direction is valid\n                    if move_to_piece == \"OO\":\n                        valid_direction.append((x * i, y * i))\n                        i += 1\n\n                    # if the position is occupied with opponent piece, valid direction and stop the loop\n                    elif move_to_piece[0] == self.get_opponent():\n                        valid_direction.append((x * i, y * i))\n                        break\n\n                    # if the occupant is player's own piece, no more valid direction, stop the loop\n                    else:\n                        break\n\n        # add all the possible moves to self._moves\n        self.add_to_moves(valid_direction)\n        return self._moves\n\n    def cannon_moves(self):\n        \"\"\" Returns all possible moves for Cannon piece \"\"\"\n        directions = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n        valid_direction = []\n        row = self._move_from_idx[0]\n        col = self._move_from_idx[1]\n\n        for (x, y) in directions:\n            i = 1  # a factor to multiply the directions\n            jumpover_piece = None\n\n            while self.check_range(row + (x * i), col + (y * i)):\n                occupied_piece = self.get_piece(row + (x * i), col + (y * i))\n\n                # if the cannon has not jumped yet\n                if jumpover_piece is None:\n                    # if not occupied, continue the loop\n                    if occupied_piece == \"OO\":\n                        i += 1\n                    # if occupied with non-cannon piece, save the piece and continue\n                    elif occupied_piece[1] != \"N\":\n                        jumpover_piece = occupied_piece\n                        i += 1\n                    # if the occupied piece is cannon, stop the loop\n                    else:\n                        break\n\n                # if the cannon already jumped one piece\n                else:\n                    # if not occupied, it's valid direction and continue\n                    if occupied_piece == \"OO\":\n                        valid_direction.append((x * i, y * i))\n                        i += 1\n                    # if occupied by opponent, add the direction and stop\n                    elif occupied_piece[0] == self.get_opponent() and occupied_piece[1] != \"N\":\n                        valid_direction.append((x * i, y * i))\n                        break\n                    # if the occupied piece is cannon or self piece, stop the loop\n                    else:\n                        break\n\n        # if cannon is at the corner of palace, diagonal move is possible\n        diag_position = [(7, 3), (7, 5), (9, 3), (9, 5), (0, 3), (0, 5), (2, 3), (2, 5)]\n        directions = [(1, 1), (1, -1), (-1, 1), (-1, -1)]\n\n        # if the cannon is in diag_position and the given move is diagonal\n        if (row, col) in diag_position:\n\n            for (x, y) in directions:\n                # then the move_to position must be within the palace\n                if (row + (x * 2), col + (y * 2)) in diag_position:\n                    jumpover_piece = self.get_piece(row + x, col + y)\n                    move_to_piece = self.get_piece(row + (x * 2), col + (y * 2))\n\n                    # if jumpover piece exists and is not cannon\n                    if jumpover_piece != \"OO\" and jumpover_piece[1] != \"N\":\n\n                        # if move_to position is empty, valid move\n                        if move_to_piece == \"OO\":\n                            valid_direction.append((x * 2, y * 2))\n\n                        # if move_to is occupied with opponent's piece other than cannon, valid move\n                        elif move_to_piece[0] == self.get_opponent() and move_to_piece[1] != \"N\":\n                            valid_direction.append((x * 2, y * 2))\n\n        # add all the possible moves to self._moves\n        self.add_to_moves(valid_direction)\n        return self._moves\n\n    def general_guard_moves(self):\n        \"\"\" Returns all possible moves for General and Guard pieces \"\"\"\n        row = self._move_from_idx[0]\n        col = self._move_from_idx[1]\n\n        # palace index for each player\n        if self.get_player() == \"B\":\n            # positions with diagonal move allowed\n            diag_position = [(7, 3), (7, 5), (9, 3), (9, 5), (8, 4)]\n            # positions with only linear move\n            linear_position = [(8, 3), (8, 5), (7, 4), (9, 4)]\n        else:\n            diag_position = [(0, 3), (0, 5), (2, 3), (2, 5), (1, 4)]\n            linear_position = [(1, 3), (0, 4), (1, 5), (2, 4)]\n\n        if (row, col) in diag_position:\n            directions = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)]\n        else:\n            directions = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n\n        # generals and guards cannot leave the palace\n        palace = diag_position + linear_position\n        for (x, y) in directions:\n            if self.check_range(row + x, col + y):\n                if (row + x, col + y) not in palace:\n                    directions.remove((x, y))\n\n        # add all the possible moves to self._moves\n        self.add_to_moves(directions)\n        return self._moves\n\n\ndef main():\n    game = JanggiGame()\n    game.get_base_board()\n    game.get_board()\n    print('1', game.make_move('c7', 'c6'))\n    print('2', game.make_move('c1', 'd3'))\n    print('3', game.make_move('b10', 'd7'))\n    print('4', game.make_move('b3', 'e3'))\n    print('5', game.make_move('c10', 'd8'))\n    print('6', game.make_move('h1', 'g3'))\n    print('7', game.make_move('e7', 'e6'))\n    print('8', game.make_move('e3', 'e6'))\n    print('9', game.make_move('h8', 'c8'))\n    print('10', game.make_move('d3', 'e5'))\n    print('11', game.make_move('c8', 'c4'))\n    print('12', game.make_move('e5', 'c4'))\n    print('13', game.make_move('i10', 'i8'))\n    print('14', game.make_move('g4', 'f4'))\n    print('15', game.make_move('i8', 'f8'))\n    print('16', game.make_move('g3', 'h5'))\n    print('17', game.make_move('h10', 'g8'))\n    print('18', game.make_move('e6', 'e3'))\n    game.get_board()\n    print(\"blue in check?\", game.is_in_check(\"blue\"))\n    print(\"red in check?\", game.is_in_check(\"red\"))\n    # Game state should be UNFINISHED when a general is in check but not checkmated\n    print(\"Game state =\", game.get_game_state())\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"parkhyey/korean-chess-janggi","sub_path":"JanggiGame.py","file_name":"JanggiGame.py","file_ext":"py","file_size_in_byte":32318,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"7370567273","text":"from django.core.management.base import NoArgsCommand\n\nfrom ...utils import (\n    delete_empty_instances, remove_empty_directories, sync_cover_images,\n    sync_song_files\n)\n\n\nclass Command(NoArgsCommand):\n    help = \"\"\"Delete empty album and artist instances and synchronizes the\nfiles in the media folder with the models in the music library.\"\"\"\n\n    def handle_noargs(self, **options):\n        delete_empty_instances()\n        sync_song_files()\n        sync_cover_images()\n        remove_empty_directories()\n","repo_name":"rafikdraoui/vortex","sub_path":"library/management/commands/syncfiles.py","file_name":"syncfiles.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"30244264101","text":"# required if ~ else\r\nn_str = input().zfill(2)\r\nswap_str = n_str[1] + n_str[0]\r\nnumber = int(swap_str)\r\nresult = number * 2\r\n\r\nif result >= 100:\r\n  result %= 100\r\n\r\nprint(result)\r\nif result <= 50:\r\n  print('GOOD')\r\nelse:\r\n  print('OH MY GOD')\r\n","repo_name":"hkh876/coding_test","sub_path":"codeup/basic/1180.py","file_name":"1180.py","file_ext":"py","file_size_in_byte":244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"8506387550","text":"import cv2\nimport itertools as it\nimport kornia as K\nimport kornia.feature as KF\nimport numpy as np\nimport torch\n\nfrom pathlib import Path\nfrom typing import Any, Tuple\nfrom torch.utils.data.dataloader import default_collate\n\nfrom common.adapter.torch_adapter import TorchAdapter\nfrom common.dataset.collate import collate\nfrom common.device import Device\nfrom common.dataset.frame_pairs_dataset import FramePairsDataset\nfrom common.frames_pair import FramesPair\nfrom common.image_metadata import ImageMetadata\nfrom common.prediction import Prediction\n\n\nclass Adapter(TorchAdapter):\n    def __init__(\n        self,\n        images_path: Path,\n        lines_path: Path,\n        associations_dir: str,\n        output_path: Path,\n        pairs_file: Path,\n        frames_step: int,\n        device: Device,\n    ):\n        super().__init__(\n            images_path,\n            lines_path,\n            associations_dir,\n            output_path,\n            device,\n        )\n        self.frames_step = frames_step\n        self.pairs_file = pairs_file\n        self.model_resolution = (600, 600)\n\n    def _create_frame_pairs_loader(self):\n        return torch.utils.data.DataLoader(\n            FramePairsDataset(\n                self.images_path,\n                self.lines_path,\n                transform_frames_pair=self._transform_frames_pair,\n                frames_step=self.frames_step,\n                pairs_file=self.pairs_file,\n            ),\n            batch_size=1,\n            collate_fn=collate,\n            pin_memory=True,\n        )\n\n    def _transform_frames_pair(self, pair: FramesPair):\n        return FramesPair(\n            images_pair=tuple(map(self.__transform_image, pair.images_pair)),\n            images_metadata_pair=pair.images_metadata_pair,\n            lines_pair=tuple(\n                it.starmap(\n                    self.__transform_lines,\n                    zip(pair.lines_pair, pair.images_metadata_pair),\n                )\n            ),\n        )\n\n    def _build_model(self):\n        return KF.SOLD2().eval().to(self.device)\n\n    def _postprocess_prediction(\n        self, raw_predictions: Any, metadata: Tuple[ImageMetadata, ImageMetadata]\n    ) -> Prediction:\n        second_indices = raw_predictions.cpu().numpy()\n        first_indices = np.arange(len(second_indices))\n        matched = second_indices != -1\n        associations = np.column_stack(\n            (first_indices[matched], second_indices[matched])\n        )\n\n        return Prediction(associations=associations, pair_metadata=metadata)\n\n    def _predict(self, model, frames_pair: FramesPair):\n        outputs = model(frames_pair.images_pair)\n        first_image_descriptor, second_image_descriptor = outputs[\"dense_desc\"]\n        first_lines, second_lines = frames_pair.lines_pair\n        return model.match(\n            first_lines,\n            second_lines,\n            first_image_descriptor[None],\n            second_image_descriptor[None],\n        )\n\n    def __transform_image(self, image: np.ndarray):\n        transformed = cv2.resize(image, self.model_resolution)\n        transformed = K.image_to_tensor(transformed).float() / 255.0\n        transformed = K.color.rgb_to_grayscale(transformed)\n        return transformed.to(self.device)\n\n    def __transform_lines(self, lines: np.ndarray, image_metadata: ImageMetadata):\n        model_height, model_width = self.model_resolution\n        x_scale = model_width / image_metadata.width\n        y_scale = model_height / image_metadata.height\n        x_index = [0, 2]\n        y_index = [1, 3]\n        lines[:, x_index] *= x_scale\n        lines[:, y_index] *= y_scale\n        return torch.from_numpy(\n            np.flip(lines.reshape((-1, 2, 2)), axis=-1).astype(np.float32)\n        ).to(self.device)\n","repo_name":"prime-slam/line-detection-association-dockers","sub_path":"associators/SOLD2/adapter.py","file_name":"adapter.py","file_ext":"py","file_size_in_byte":3747,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"86"}
+{"seq_id":"39476244625","text":"from mimetypes import init\n\n\n#CheckOut = float(input('please enter your checkout code '))\n\n#houseNum = int(input('Please put your home num here '))\n\nlargeNum = int(input('enter larger num you can think '))\nminNum = int(input('enter your min num you can thing we will add up it for you'))\n\naddAll = largeNum + minNum\n\nprint(f'We made all imagination in one place for you {addAll}')","repo_name":"RaihanIIUC/Python-learning","sub_path":"ConfusingPyTypeInput.py","file_name":"ConfusingPyTypeInput.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"11129917709","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 29 22:55:00 2020\n\n@author: illcare\n\"\"\"\n\nclass item:\n    def __init__(self, item_name, price):\n        self.item_name = item_name\n        self.price = price\n        \nclass customer(item):\n    def __init__(self, item_name, price, customer_id, phone_number):\n        item.__init__(self, item_name, price)\n        self.customer_id = customer_id\n        self.phone_number = phone_number\n        \n    def purchase(self, amount):\n        total = self.price * amount\n        print(\"total price for\", amount, self.item_name, \"is\", total)\n        \nsold_item = \"chocolate bar\"\nsold_price = 2 # dollars\nsold_cust = \"Roger\"\nsold_phone = \"+90 569 565 0555\"\n\nsss = customer(sold_item, sold_price, sold_cust, sold_phone)\nsss.purchase(20)","repo_name":"Yllcare/bootrain","sub_path":"assignment_10.py","file_name":"assignment_10.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"2030731662","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport itertools\n\n\n# params\ntheta = .000001\nph = .75\ndiscount = 0.99\n\n# env info\nstates = range(1, 100)\nvalues = np.zeros([101])\nvalues[0] = 0\nvalues[100] = 0\nvalues[1:100] = np.random.uniform(0, 1, 99)\n\n\ndef transition_prob(s_new, r, s, a):\n    if s_new == s:\n        if a == 0:\n            if s_new >= 100:\n                if r == 1:\n                    return 1\n                else:\n                    return 0\n            else:\n                if r == 1:\n                    return 0\n                else:\n                    return 1\n        else:\n            return 0\n    elif s_new == s + a:\n        if s_new >= 100:\n            if r == 1:\n                return ph\n            else:\n                return 0\n        else:\n            if r == 1:\n                return 0\n            else:\n                return ph\n    elif s_new == s - a:\n        if r == 1:\n            return 0\n        else:\n            return 1 - ph\n\n# value func\ndef get_value_and_best_action(s, cur_values):\n    actions = range(0, np.min([s, 100 - s]) + 1)\n    max_val = 0\n    best_action = 0\n    for a in actions:\n        sum = 0\n        if a == 0:\n            s_primes = [s]\n        else:\n            s_primes = [s + a, s - a]\n        rews = [1, 0]\n        for s_p, r in itertools.product(s_primes, rews):\n            sum += transition_prob(s_p, r, s, a) * (r + discount * cur_values[s_p])\n\n        if sum > max_val:\n            max_val = sum\n            best_action = a\n\n    return max_val, best_action\n\n# value iteration and getting best action\ndelta = 1e10\npi_star = np.zeros([100])\nwhile delta >= theta:\n    delta = 0\n    for s in states:\n        old_v = values[s]\n        values[s], pi_star[s] = get_value_and_best_action(s, values)\n        delta = np.max([delta, np.abs(old_v - values[s])])\n\n# plot value estimates and policy\nplt.figure()\nplt.plot(range(0, 101), values)\n\nplt.figure()\nplt.plot(range(0, 100), pi_star)\n\nplt.show()","repo_name":"trevorablett/programming-exercises","sub_path":"sutton-barto-rl/ch4-dp/ex49-gamblers.py","file_name":"ex49-gamblers.py","file_ext":"py","file_size_in_byte":1969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"23329882661","text":"import sys\r\n\r\ninput = sys.stdin.readline\r\n\r\nn = int(input())\r\narr = [0] + list(map(int, input().split())) + [0]\r\nleft = [0] + [0] * (n + 1)\r\nright = [0] * (n + 1) + [0]\r\n\r\n\r\ndef gcd(a, b):\r\n    if b == 0:\r\n        return a\r\n\r\n    while a % b != 0:\r\n        a, b = b, a % b\r\n    return min(a, b)\r\n\r\n\r\nfor i in range(1, n + 1):\r\n    left[i] = gcd(max(arr[i - 1], left[i - 1]), min(arr[i - 1], left[i - 1]))\r\n\r\nfor i in range(n, 0, -1):\r\n    right[i] = gcd(max(arr[i + 1], right[i + 1]), min(arr[i + 1], right[i + 1]))\r\n\r\n# print(arr)\r\n# print(left)\r\n# print(right)\r\n\r\nans = 0\r\nidx = 0\r\n\r\nfor i in range(1, n + 1):\r\n    g = gcd(max(left[i], right[i]), min(left[i], right[i]))\r\n    # print(g)\r\n\r\n    if arr[i] % g != 0:\r\n        if ans < g:\r\n            ans = g\r\n            idx = arr[i]\r\n\r\nif ans == idx == 0:\r\n    print(-1)\r\n    exit()\r\n\r\nprint(ans, idx)\r\n","repo_name":"juhyun-99/Baekjoon_algorithm","sub_path":"백준/Gold/14476. 최대공약수 하나 빼기/최대공약수 하나 빼기.py","file_name":"최대공약수 하나 빼기.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"4552808525","text":"import multiprocessing as mp\nimport time\n\n\ndef sum_slice(data, start, stop, total: mp.Value):\n    partial_sum = sum(data[start:stop])\n    with total.get_lock():\n        total.value += partial_sum\n\n\ndef main():\n    N = 16_000_000\n    data = mp.Array(\"i\", list(range(N)), lock=False)\n    total = mp.Value(\"q\", 0)\n\n    tic = time.perf_counter()\n    classic = sum(data)\n    toc = time.perf_counter()\n\n    print(\"Classic : got\", classic, f\"in {(toc - tic) * 1000:4.2f} ms\")\n\n    n_workers = mp.cpu_count() // 2\n    slice_size = N // n_workers\n    processes = [mp.Process(target=sum_slice,\n                            args=(data, i * slice_size, (i + 1) * slice_size, total)\n                            ) for i in range(n_workers)]\n\n    tic = time.perf_counter()\n    for p in processes: p.start()\n    for p in processes: p.join()\n    toc = time.perf_counter()\n\n    print(\"Parallel: got\", total.value, f\"in {(toc - tic) * 1000:4.2f} ms\")\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"TheBlueChameleon/Py_ForScientists","sub_path":"projects/04-multiprocessing/ArraySummation.py","file_name":"ArraySummation.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70849158683","text":"import math\n\nimport os.path\nimport time\nfrom PyQt5.Qt import QObject\nfrom PyQt5.QtCore import pyqtSignal\nfrom scipy.ndimage import measurements\nimport numpy as np\n\nimport TigGUI.kitties.utils\nfrom TigGUI.Images.Colormaps import HistEqIntensityMap, LogIntensityMap, CubeHelixColormap\nfrom TigGUI.kitties.widgets import BusyIndicator\n\n_verbosity = TigGUI.kitties.utils.verbosity(name=\"rc\")\ndprint = _verbosity.dprint\ndprintf = _verbosity.dprintf\n\nfrom TigGUI.Images import Colormaps\n\nimport TigGUI.kitties.config\n\n\nImageConfigFile = TigGUI.kitties.config.DualConfigParser(\"tigger.images.conf\")\n\n\nclass RenderControl(QObject):\n    \"\"\"RenderControl represents all the options (slices, color and intensity policy data) associated with an image. This object is shared by various GUI elements\n    that control the rendering of images.\n    \"\"\"\n    intensityMapChanged = pyqtSignal(object, float)\n    colorMapChanged = pyqtSignal(object)\n    dataSubsetChanged = pyqtSignal(np.ndarray, tuple, str, str)\n    displayRangeChanged = pyqtSignal([float, float], [np.float32, np.float32], [HistEqIntensityMap, float])  # on file save np.float32's become float's on reload?\n    displayRangeLocked = pyqtSignal(bool)\n\n    SUBSET_FULL = \"full\"\n    SUBSET_SLICE = \"slice\"\n    SUBSET_RECT = \"rect\"\n\n    def __init__(self, image, parent):\n        QObject.__init__(self, parent)\n        self.image = image\n        self._config = TigGUI.kitties.config.SectionParser(ImageConfigFile, os.path.normpath(\n            os.path.abspath(image.filename))) if image.filename else None\n        # figure out the slicing -- find extra axes with size > 1\n        # self._current_slice contains all extra axis, including the size-1 ones\n        # self._sliced_axes is a list of (iextra,axisname,labels) tuples for size>1 axes\n        # where iextra is an index into self._current_slice.\n        self._current_slice = [0] * image.numExtraAxes()\n        self._slice_dims = [1] * image.numExtraAxes()\n        self._sliced_axes = []\n        for i in range(image.numExtraAxes()):\n            iaxis, axisname, labels = image.extraAxisNumberNameLabels(i)\n            self._slice_dims[i] = len(labels)\n            if len(labels) > 1:\n                self._sliced_axes.append((i, axisname, labels))\n        # set the full image range (i.e. mix/max) and current slice range\n        dprint(2, \"getting data min/max\")\n        self._fullrange = self._slicerange = image.dataMinMax()[:2]\n        dprint(2, \"done\")\n        # create dict of intensity maps\n        log_cycles = self._config.getfloat(\"intensity-log-cycles\", 6) if self._config else 6\n        self._imap_list = (\n            ('Linear', Colormaps.LinearIntensityMap()),\n            ('Histogram-equalized', Colormaps.HistEqIntensityMap()),\n            ('log(val-min)', Colormaps.LogIntensityMap(log_cycles))\n        )\n        # create list of color maps\n        self._cmap_list = Colormaps.getColormapList()\n        default_cmap = 0\n        for i, cmap in enumerate(self._cmap_list):\n            if isinstance(cmap, Colormaps.ColormapWithControls):\n                if self._config:\n                    cmap.loadConfig(self._config)\n                cmap.colormapChanged.connect(self.updateColorMapParameters)\n            if isinstance(cmap, Colormaps.CubeHelixColormap):\n                default_cmap = i\n        # set the initial intensity map\n        imap = self._config.getint(\"intensity-map-number\", 0) if self._config else 0\n        cmap = self._config.getint(\"colour-map-number\", default_cmap) if self._config else default_cmap\n        imap = max(min(len(self._imap_list) - 1, imap), 0)\n        cmap = max(min(len(self._cmap_list) - 1, cmap), 0)\n        self._current_imap_index = imap\n        self._current_cmap_index = cmap\n        self.image.setIntensityMap(self._imap_list[imap][1])\n        self.image.setColorMap(self._cmap_list[cmap])\n\n        # cache of min/max values for each slice, as these can be slowish to recompute when flipping slices\n        self._sliceranges = {}\n        # This is the data subset corresponding to the current display range. When the display range is set to\n        # _fullrange, this is the image cube. When it is set to _slicerange, this is the current image slice. When\n        # setLMRectDisplayRange() or setWindowDisplayRange() is used to set the range to the specified window,\n        # this is the a subset of the current slice. The data subset is passed to setDataSubset() of the intensity mapper object\n        self._displaydata = None\n        # This is a tuple of the extrema of the current data subset. This is not quite the same thing as self._displayrange below.\n        # When the display range is reset to cube/slice/window, _displayrange is set to _displaydata_minmax. But if\n        # setDisplayRange() is subsequently called (e.g. if the user manually enters new values into the Range boxes), then\n        # _displayrange will be set to something else until the next reset....() call.\n        self._displaydata_minmax = None\n        # This is a low,high tuple of the current display range -- will be initialized by resetFullDisplayRange()\n        self._displayrange = None\n        if self._config and self._config.has_option(\"range-min\") and self._config.has_option(\"range-max\"):\n            display_range = self._config.getfloat(\"range-min\"), self._config.getfloat(\"range-max\")\n        else:\n            display_range = None\n        self.setFullSubset(display_range, write_config=False)\n        # setup initial slice\n        if self.hasSlicing():\n            if self._config and self._config.has_option(\"slice\"):\n                try:\n                    curslice = list(map(int, self._config.get(\"slice\").split()))\n                except:\n                    curslice = []\n                if len(curslice) == len(self._current_slice):\n                    for iaxis, i in enumerate(curslice):\n                        naxis = len(self.image.extraAxisValues(iaxis))\n                        i = min(naxis - 1, max(0, i))\n                        self._current_slice[iaxis] = i\n            self.selectSlice(self._current_slice, write_config=False)\n        # lock display range if so configured\n        self._lock_display_range = self._config.getbool(\"lock-range\", 0) if self._config else False\n        if self._lock_display_range:\n            self.lockDisplayRange(True, write_config=False)\n\n    def startSavingConfig(self, image_filename):\n        \"\"\"Saves the current configuration under the specified image filename\"\"\"\n        self._config = TigGUI.kitties.config.SectionParser(ImageConfigFile, os.path.normpath(os.path.abspath(image_filename)))\n        if self._displayrange:\n            self._config.set(\"range-min\", self._displayrange[0], save=False)\n            self._config.set(\"range-max\", self._displayrange[1], save=False)\n        if self._current_slice:\n            self._config.set(\"slice\", \" \".join(map(str, self._current_slice)), save=False)\n        for cmap in self._cmap_list:\n            if isinstance(cmap, Colormaps.ColormapWithControls):\n                cmap.saveConfig(self._config, save=False)\n        self._config.set(\"intensity-map-number\", self._current_imap_index, save=False)\n        self._config.set(\"colour-map-number\", self._current_cmap_index, save=False)\n        self._config.set(\"lock-range\", self._lock_display_range, save=True)\n\n    def hasSlicing(self):\n        \"\"\"Returns True if image is a cube, and so has non-trivial slicing axes\"\"\"\n        return bool(self._sliced_axes)\n\n    def slicedAxes(self):\n        \"\"\"Returns list of (axis_num,name,label_list) tuples per each non-trivial slicing axis\"\"\"\n        return self._sliced_axes\n\n    def incrementSlice(self, iaxis, incr, write_config=True):\n        dprint(2, \"incrementing slice axis\", iaxis, \"by\", incr)\n        self._current_slice[iaxis] = (self._current_slice[iaxis] + incr) % self._slice_dims[iaxis]\n        self._updateSlice(write_config)\n\n    def changeSlice(self, iaxis, index, write_config=True):\n        dprint(2, \"changing slice axis\", iaxis, \"to\", index)\n        if self._current_slice[iaxis] != index:\n            self._current_slice[iaxis] = index\n            self._updateSlice(write_config)\n\n    def selectSlice(self, indices, write_config=True):\n        \"\"\"Selects slice given by indices\"\"\"\n        dprint(2, \"selecting slice\", indices)\n        self._current_slice = list(indices)\n        self._updateSlice(write_config)\n\n    def _updateSlice(self, write_config=True):\n        \"\"\"Common internal method called to finalize changes to _current_slice\"\"\"\n        busy = BusyIndicator()\n        dprint(2, \"_updateSlice\", self._current_slice, time.time() % 60)\n        indices = tuple(self._current_slice)\n        self.image.selectSlice(*indices)\n        dprint(2, \"image slice selected\", time.time() % 60)\n        img = self.image.image()\n        self._slicerange = self._sliceranges.get(indices)\n        if self._slicerange is None:\n            self._slicerange = self._sliceranges[indices] = self.image.imageMinMax()[:2]\n        dprint(2, \"min/max updated\", time.time() % 60)\n        self.setSliceSubset(set_display_range=False)\n        if write_config and self._config:\n            self._config.set(\"slice\", \" \".join(map(str, indices)))\n        busy.reset_cursor()\n\n    def displayRange(self):\n        return self._displayrange\n\n    def currentSlice(self):\n        return self._current_slice\n\n    def sliceDimensions(self):\n        return self._slice_dims\n\n    def getIntensityMapNames(self):\n        return [name for name, imap in self._imap_list]\n\n    def currentIntensityMapNumber(self):\n        return self._current_imap_index\n\n    def currentIntensityMap(self):\n        return self.image.intensityMap()\n\n    def setIntensityMapNumber(self, index, write_config=True):\n        busy = BusyIndicator()\n        self._current_imap_index = index\n        imap = self._imap_list[index][1]\n        imap.setDataSubset(self._displaydata, self._displaydata_minmax)\n        imap.setDataRange(*self._displayrange)\n        self.image.setIntensityMap(imap)\n        self.intensityMapChanged.emit(imap, index)\n        if self._config and write_config:\n            self._config.set(\"intensity-map-number\", index)\n        busy.reset_cursor()\n\n    def setIntensityMapLogCycles(self, cycles, notify_image=True, write_config=True):\n        busy = BusyIndicator()\n        imap = self.currentIntensityMap()\n        if isinstance(imap, Colormaps.LogIntensityMap):\n            imap.log_cycles = cycles\n            if notify_image:\n                self.image.setIntensityMap()\n            self.intensityMapChanged.emit(imap, self._current_imap_index)\n        if self._config and write_config:\n            self._config.set(\"intensity-log-cycles\", cycles)\n        busy.reset_cursor()\n\n    def lockDisplayRangeForAxis(self, iaxis, lock):\n        pass\n\n    def getColormapList(self):\n        return self._cmap_list\n\n    def updateColorMapParameters(self):\n        \"\"\"Call this when the colormap parameters have changed\"\"\"\n        busy = BusyIndicator()\n        self.image.updateCurrentColorMap()\n        if self._config:\n            self._cmap_list[self._current_cmap_index].saveConfig(self._config)\n        busy.reset_cursor()\n\n    def setColorMapNumber(self, index, write_config=True):\n        busy = BusyIndicator()\n        self._current_cmap_index = index\n        cmap = self._cmap_list[index]\n        self.image.setColorMap(cmap)\n        self.colorMapChanged.emit(cmap)\n        if self._config and write_config:\n            self._config.set(\"colour-map-number\", index)\n        busy.reset_cursor()\n\n    def currentSubset(self):\n        \"\"\"Returns tuple of subset,(dmin,dmax),description for current data subset\"\"\"\n        return self._displaydata, self._displaydata_minmax, self._displaydata_desc, self._displaydata_type\n\n    def _resetDisplaySubset(self, subset, desc, range=None, set_display_range=True, write_config=True,\n                            subset_type=None):\n        dprint(4, \"setting display subset\")\n        self._displaydata = subset\n        self._displaydata_desc = desc\n        self._displaydata_minmax = range = range or measurements.extrema(subset)[:2]\n        self._displaydata_type = subset_type\n        dprint(4, \"range set\")\n        self.image.intensityMap().setDataSubset(self._displaydata, minmax=range)\n        self.image.setIntensityMap(emit=False)\n        dprint(2, f\"dataSubsetChanged {type(subset)}, {type(range)}, {type(desc)}, {type(subset_type)}\")\n        self.dataSubsetChanged.emit(subset, range, desc, subset_type)\n        if set_display_range:\n            self.setDisplayRange(write_config=write_config, *range)\n\n    def setFullSubset(self, display_range=None, write_config=True):\n        shapedesc = \"\\u00D7\".join([\"%d\" % x for x in\n                                    list(self.image.imageDims()) + [len(labels) for iaxis, name, labels in\n                                                                    self._sliced_axes]])\n        desc = \"full cube\" if self._sliced_axes else \"full image\"\n        self._resetDisplaySubset(self.image.data(), desc, range=self._fullrange, subset_type=self.SUBSET_FULL,\n                                 write_config=write_config, set_display_range=False)\n        self.setDisplayRange(write_config=write_config, *(display_range or self._fullrange))\n\n    def _makeSliceDesc(self):\n        \"\"\"Makes a description of the current slice\"\"\"\n        if not self._sliced_axes:\n            return \"full image\"\n        descs = []\n        for iextra, name, labels in self._sliced_axes:\n            if name.upper() not in [\"STOKES\", \"COMPLEX\"]:\n                descs.append(\"%s=%s\" % (name, labels[self._current_slice[iextra]]))\n            else:\n                descs.append(labels[self._current_slice[iextra]])\n        return \"%s plane\" % (\" \".join(descs),)\n\n    def setSliceSubset(self, set_display_range=True, write_config=True): \\\n            return self._resetDisplaySubset(self.image.image(), self._makeSliceDesc(), self._slicerange,\n                                            subset_type=self.SUBSET_SLICE,\n                                            set_display_range=set_display_range, write_config=write_config)\n\n    def _setRectangularSubset(self, xx1, xx2, yy1, yy2):\n        descs = []\n        nx, ny = self.image.imageDims()\n        if xx1 or xx2 != nx:\n            descs.append(\"x=%d:%d\" % (xx1, xx2))\n        if yy1 or yy2 != ny:\n            descs.append(\"y=%d:%d\" % (yy1, yy2))\n        if descs:\n            descs.append(\"in\")\n        descs.append(self._makeSliceDesc())\n        return self._resetDisplaySubset(self.image.image()[xx1:xx2, yy1:yy2], \" \".join(descs),\n                                        subset_type=self.SUBSET_RECT)\n\n    def _lmRectToPix(self, rect):\n        \"\"\"helper function -- converts an LM rectangle to pixel coordinates\"\"\"\n        if rect.width() and rect.height():\n            # convert to pixel coordinates\n            x1, y1, x2, y2 = rect.getCoords()\n            x1, y1 = self.image.lmToPix(x1, y1)\n            x2, y2 = self.image.lmToPix(x2, y2)\n            dprint(2, x1, y1, x2, y2)\n            xx1, xx2 = int(math.floor(min(x1, x2))), int(math.ceil(max(x1, x2)))\n            yy1, yy2 = int(math.floor(min(y1, y2))), int(math.ceil(max(y1, y2)))\n            dprint(2, xx1, yy1, xx2, yy2)\n            # ensure limits\n            nx, ny = self.image.imageDims()\n            xx1, xx2 = max(xx1, 0), min(xx2, nx)\n            yy1, yy2 = max(yy1, 0), min(yy2, ny)\n            dprint(2, xx1, yy1, xx2, yy2)\n            # check that we actually selected some valid pixels\n            if xx1 < xx2 and yy1 < yy2:\n                return xx1, xx2, yy1, yy2\n        return None, None, None, None\n\n    def setLMRectSubset(self, rect):\n        xx1, xx2, yy1, yy2 = self._lmRectToPix(rect)\n        if xx1 is not None:\n            return self._setRectangularSubset(xx1, xx2, yy1, yy2)\n\n    def getLMRectStats(self, rect):\n        xx1, xx2, yy1, yy2 = self._lmRectToPix(rect)\n        if xx1 is not None:\n            subset = self.image.image()[xx1:xx2, yy1:yy2]\n            subset, mask = self.image.optimalRavel(subset)\n            mmin, mmax = measurements.extrema(subset, labels=mask, index=None if mask is None else False)[:2]\n            mean = measurements.mean(subset, labels=mask, index=None if mask is None else False)\n            std = measurements.standard_deviation(subset, labels=mask, index=None if mask is None else False)\n            ssum = measurements.sum(subset, labels=mask, index=None if mask is None else False)\n            return xx1, xx2, yy1, yy2, mmin, mmax, mean, std, ssum, subset.size\n        return None\n\n    def setWindowSubset(self, rect=None):\n        rect = rect or self.image.currentRectPix()\n        if rect.width() and rect.height():\n            tl = rect.topLeft()\n            return self._setRectangularSubset(tl.x(), tl.x() + rect.width(), tl.y(), tl.y() + rect.height())\n\n    def resetSubsetDisplayRange(self):\n        self.setDisplayRange(*self._displaydata_minmax)\n\n    def isSubsetDisplayRange(self):\n        return self._displayrange == self._displaydata_minmax\n\n    def setDisplayRange(self, dmin, dmax, notify_image=True, write_config=True):\n        if dmax < dmin:\n            dmin, dmax = dmax, dmin\n        if (dmin, dmax) != self._displayrange:\n            self._displayrange = dmin, dmax\n            self.image.intensityMap().setDataRange(dmin, dmax)\n            if notify_image:\n                busy = BusyIndicator()\n                self.image.setIntensityMap(emit=True)\n                busy.reset_cursor()\n            self.displayRangeChanged.emit(dmin, dmax)\n            if self._config and write_config:\n                self._config.set(\"range-min\", dmin, save=False)\n                self._config.set(\"range-max\", dmax)\n\n    def isDisplayRangeLocked(self):\n        return self._lock_display_range\n\n    def lockDisplayRange(self, lock=True, write_config=True):\n        self._lock_display_range = lock\n        self.displayRangeLocked.emit(lock)\n        if self._config and write_config:\n            self._config.set(\"lock-range\", bool(lock))\n","repo_name":"ratt-ru/tigger","sub_path":"TigGUI/Images/RenderControl.py","file_name":"RenderControl.py","file_ext":"py","file_size_in_byte":18034,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"86"}
+{"seq_id":"3794865940","text":"\"\"\"Configuration for imitation.scripts.train_preference_comparisons.\"\"\"\n\nimport sacred\n\nfrom imitation.scripts.common import common, reward, rl, train\n\ntrain_preference_comparisons_ex = sacred.Experiment(\n    \"train_preference_comparisons\",\n    ingredients=[\n        common.common_ingredient,\n        reward.reward_ingredient,\n        rl.rl_ingredient,\n        train.train_ingredient,\n    ],\n)\n\n\n@train_preference_comparisons_ex.config\ndef train_defaults():\n    fragment_length = 100  # timesteps per fragment used for comparisons\n    total_timesteps = int(1e6)  # total number of environment timesteps\n    total_comparisons = 1000  # total number of comparisons to elicit\n    # comparisons to gather before switching back to agent training\n    comparisons_per_iteration = 50\n    # factor by which to oversample transitions before creating fragments\n    transition_oversampling = 10\n\n    reward_trainer_kwargs = {\n        \"epochs\": 3,\n    }\n    save_preferences = False  # save preference dataset at the end?\n    agent_path = None  # path to a (partially) trained agent to load at the beginning\n    gatherer_kwargs = {}\n    # path to a pickled sequence of trajectories used instead of training an agent\n    trajectory_path = None\n    allow_variable_horizon = False\n\n    normalize = True  # Use VecNormalize\n    normalize_kwargs = {\"norm_reward\": False}  # kwargs for `VecNormalize`\n\n\n@train_preference_comparisons_ex.named_config\ndef cartpole():\n    common = dict(env_name=\"CartPole-v1\")\n    allow_variable_horizon = True\n\n\n@train_preference_comparisons_ex.named_config\ndef seals_cartpole():\n    common = dict(env_name=\"seals/CartPole-v0\")\n\n\n@train_preference_comparisons_ex.named_config\ndef pendulum():\n    common = dict(env_name=\"Pendulum-v0\")\n\n\n@train_preference_comparisons_ex.named_config\ndef mountain_car():\n    common = dict(env_name=\"MountainCar-v0\")\n    allow_variable_horizon = True\n\n\n@train_preference_comparisons_ex.named_config\ndef seals_mountain_car():\n    common = dict(env_name=\"seals/MountainCar-v0\")\n\n\n@train_preference_comparisons_ex.named_config\ndef fast():\n    # Minimize the amount of computation. Useful for test cases.\n    total_timesteps = 2\n    total_comparisons = 3\n    comparisons_per_iteration = 2\n    fragment_length = 2\n    reward_trainer_kwargs = {\n        \"epochs\": 1,\n    }\n","repo_name":"HumanCompatibleAI/eirli","sub_path":"tp/imitation/src/imitation/scripts/config/train_preference_comparisons.py","file_name":"train_preference_comparisons.py","file_ext":"py","file_size_in_byte":2308,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"86"}
+{"seq_id":"40311234024","text":"from unittest import TestCase, main\n\nfrom dsorm import Comparison, Where\nfrom dsorm.dsorm import columnify\n\n\nclass TestWhere(TestCase):\n    def test_comparison(self):\n        w = Where(\n            {\"column_name\": Comparison.get_comparison(target=\"thingy\", key=\"thingy\")}\n        )\n        self.assertEqual(w.sql(), \"WHERE [column_name] = :thingy\")\n\n    def test_comparison_no_target(self):\n        with self.assertRaises(TypeError):\n            w = Comparison.get_comparison()\n            w.sql()\n\n    def test_comparison_no_column(self):\n        with self.assertRaises(TypeError):\n            w = Comparison.get_comparison(target=\"thingy\")\n            w.sql()\n\n    def test_in(self):\n        w = Comparison.is_in(column=\"value\", target=[1, 2])\n        self.assertEqual(w.sql(), \"value  IN (1, 2)\")\n\n    def test_in_no_target(self):\n        with self.assertRaises(TypeError):\n            w = Comparison.is_in()\n            w.sql()\n\n    def test_where_construction(self):\n        w = Where()\n        w[1] = 2\n        w[\"this\"] = \"that\"\n        self.assertEqual(w[1], 2)\n        self.assertEqual(list(w.items()), [(1, 2), (\"this\", \"that\")])\n\n    def test_nested_where(self):\n\n        AUTHOR_NAME = \"JK Rowling\"\n        BOOK_NAME = \"Harry Potter\"\n        column_a = columnify(\"book.name\")\n        column_b = columnify(\"author.name\")\n        w = Where(\n            where={\n                column_a: Comparison.eq(target=BOOK_NAME, key=\"BookName\"),\n                \"or\": Where(\n                    {column_b: Comparison.eq(target=AUTHOR_NAME, key=\"AuthorName\")}\n                ),\n            }\n        )\n        self.assertEqual(\n            w.sql(),\n            \"WHERE [book].[name] = :BookName or ([author].[name] = :AuthorName)\",\n        )\n\n\nif __name__ == \"__main__\":\n    main()  # pragma: no cover\n","repo_name":"kajuberdut/dsorm","sub_path":"tests/test_where.py","file_name":"test_where.py","file_ext":"py","file_size_in_byte":1800,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"4113200656","text":"##Given n non-negative integers a1, a2, ..., an , where each represents a point at coordinate (i, ai). n vertical lines are drawn such that the two endpoints of line i is at (i, ai) and (i, 0). Find two lines, which together with x-axis forms a container, such that the container contains the most water.\n##\n##Note: You may not slant the container and n is at least 2.\n##Example:\n##\n##Input: [1,8,6,2,5,4,8,3,7]\n##Output: 49\n##The above vertical lines are represented by array [1,8,6,2,5,4,8,3,7]. In this case, the max area of water (blue section) the container can contain is 49.\n\n\ndef maxArea(li):\n    i = 0\n    j = len(li) - 1\n    if j < 0:\n        return 0\n    counter = 0\n    max_area = get_maxArea(i, j, li)\n    while i < j:\n        area = get_maxArea(i, j, li)\n        if area > max_area:\n            max_area = area\n        if counter == 0:\n            i += 1\n            counter += 1\n        else:\n            j -= 1\n            counter -= 1\n    return max_area\n\ndef get_maxArea(i, j, li):\n    return (j - i) * min(li[i], li[j])\n        \n","repo_name":"sophiajwchoi/daily-coding-challenges","sub_path":"Sept 2018/Container With Most Water.py","file_name":"Container With Most Water.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"4536964423","text":"import re\nline=input()\n\npattern_emojis=r\"(\\*{2}|:{2})([A-Z][a-z]{2,})\\1\"\npattern_digits=\"\\d\"\nmatches_emojis=re.finditer(pattern_emojis,line)\nmatches=re.findall(pattern_digits,line)\nthreshold = 1\nfor m in matches:\n    threshold*=int(m)\n\n\nall_emojis=[m for m in matches_emojis]\nprint(f\"Cool threshold: {threshold}\")\nprint(f\"{len(all_emojis)} emojis found in the text. The cool ones are:\")\n\nfor emoji in all_emojis:\n    sum=0\n    a=emoji[2]\n    for i in range(len(emoji[2])):\n        if emoji[2][i].isalpha():\n            sum+=ord(emoji[2][i])\n    if sum>threshold:\n        print(emoji[0])\n","repo_name":"RuzhaK/pythonProject","sub_path":"Fundamentals/FinalExamPreparation/Regex/EmojiDetector.py","file_name":"EmojiDetector.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"19378105997","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author:lipeijie\n\nfrom easyai.name_manager.block_name import NormalizationType\nfrom easyai.model_block.utility.base_block import *\n\n\nclass EmptyNormalization(BaseBlock):\n\n    def __init__(self):\n        super().__init__(NormalizationType.EmptyNormalization)\n\n    def forward(self, x):\n        return x\n\n\nclass FrozenBatchNorm2d(BaseBlock):\n    \"\"\"\n    BatchNorm2d where the batch statistics and the affine parameters\n    are fixed\n    \"\"\"\n\n    def __init__(self, input_channel):\n        super().__init__(NormalizationType.FrozenBatchNorm2d)\n        self.register_buffer(\"weight\", torch.ones(input_channel))\n        self.register_buffer(\"bias\", torch.zeros(input_channel))\n        self.register_buffer(\"running_mean\", torch.zeros(input_channel))\n        self.register_buffer(\"running_var\", torch.ones(input_channel))\n\n    def forward(self, x):\n        # Cast all fixed parameters to half() if necessary\n        if x.dtype == torch.float16:\n            self.weight = self.weight.half()\n            self.bias = self.bias.half()\n            self.running_mean = self.running_mean.half()\n            self.running_var = self.running_var.half()\n\n        scale = self.weight * self.running_var.rsqrt()\n        bias = self.bias - self.running_mean * scale\n        scale = scale.reshape(1, -1, 1, 1)\n        bias = bias.reshape(1, -1, 1, 1)\n        return x * scale + bias\n\n\n# class FrozenBatchNorm2d(nn.Module):\n#     \"\"\"\n#     BatchNorm2d where the batch statistics and the affine parameters are fixed.\n#     It contains non-trainable buffers called \"weight\" and \"bias\".\n#     The two buffers are computed from the original four parameters of BN:\n#     mean, variance, scale (gamma), offset (beta).\n#     The affine transform `x * weight + bias` will perform the equivalent\n#     computation of `(x - mean) / std * scale + offset`, but will be slightly cheaper.\n#     The pre-trained backbone models from Caffe2 are already in such a frozen format.\n#     \"\"\"\n#     def __init__(self, input_channel):\n#         super(FrozenBatchNorm2d, self).__init__()\n#         self.register_buffer(\"weight\", torch.ones(input_channel))\n#         self.register_buffer(\"bias\", torch.zeros(input_channel))\n#\n#     def forward(self, x):\n#         scale = self.weight.reshape(1, -1, 1, 1)\n#         bias = self.bias.reshape(1, -1, 1, 1)\n#         return x * scale + bias\n\n\nclass L2Norm(nn.Module):\n    def __init__(self, input_channel, scale):\n        super().__init__()\n        self.input_channel = input_channel\n        self.gamma = scale or None\n        self.eps = 1e-10\n        self.weight = nn.Parameter(torch.Tensor(self.input_channel))\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        nn.init.constant_(self.weight, self.gamma)\n\n    def forward(self, x):\n        norm = x.pow(2).sum(dim=1, keepdim=True).sqrt()+self.eps\n        x = x / norm\n        out = self.weight.unsqueeze(0).unsqueeze(2).unsqueeze(3).expand_as(x) * x\n        return out\n\n\nclass NormalizationFunction():\n\n    def __init__(self):\n        pass\n\n    @classmethod\n    def get_function(cls, name, input_channel, momentum=0.1):\n        if name == NormalizationType.BatchNormalize2d:\n            return nn.BatchNorm2d(input_channel, momentum=momentum)\n        elif name == NormalizationType.BatchNormalize1d:\n            return nn.BatchNorm1d(input_channel, momentum=momentum)\n        elif name == NormalizationType.InstanceNorm2d:\n            return nn.InstanceNorm2d(input_channel, momentum=0.1)\n        elif name == NormalizationType.BatchNormalize1d:\n            return nn.InstanceNorm1d(input_channel, momentum=0.1)\n        elif name == NormalizationType.EmptyNormalization:\n            return EmptyNormalization()\n        else:\n            print(\"%s Normalization function error!\" % name)\n","repo_name":"MiniBullLab/easy_ai","sub_path":"easyai/model_block/base_block/common/normalization_layer.py","file_name":"normalization_layer.py","file_ext":"py","file_size_in_byte":3806,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"24709739677","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport optimizers\n\nclass NeuralNetwork():\n    def __init__(self, n_inputs, n_hiddens_list, n_outputs):\n        self.n_inputs = n_inputs\n        self.n_hiddens_list = n_hiddens_list\n        self.n_outputs = n_outputs\n        self.n_layers = len(n_hiddens_list)\n        self.all_weights, self.Ws = self.make_weights()\n        self.all_gradients, self.Gs = self.make_weights()\n        self.initialize_weights()\n    def __repr__(self):\n        return f'NeuralNetwork({self.n_inputs}, {self.n_hiddens_list}, {self.n_outputs})'\n    \n    def make_weights(self):\n        n_Ws = []\n        n_Ws.append((1 + self.n_inputs) * self.n_hiddens_list[0])\n        if (len(self.n_hiddens_list) > 1):\n            for i in range(len(self.n_hiddens_list)-1):\n                n_Ws.append(n_Ws[i]+(1 + self.n_hiddens_list[i]) * self.n_hiddens_list[i+1])\n        n_Ws.append(n_Ws[len(n_Ws)-1]+(1 + self.n_hiddens_list[len(self.n_hiddens_list)-1]) * self.n_outputs)\n        n_weights = 0\n        for i in n_Ws:\n            n_weights = n_Ws[len(n_Ws)-1]\n        all_weights = np.zeros(n_weights)\n        Ws = []\n        Ws.append(all_weights[:n_Ws[0]].reshape(1 + self.n_inputs, self.n_hiddens_list[0]))\n        if (len(self.n_hiddens_list)!=1):\n            for i in range(len(self.n_hiddens_list)-1):\n                Ws.append(all_weights[n_Ws[i]:n_Ws[i+1]].reshape(1 + self.n_hiddens_list[i], self.n_hiddens_list[i+1])) \n        Ws.append(all_weights[n_Ws[len(n_Ws)-2]:].reshape(1 + self.n_hiddens_list[len(self.n_hiddens_list)-1], self.n_outputs))\n        return all_weights, Ws\n    \n    def initialize_weights(self):\n        self.Ws[0][:] = np.random.uniform(-1, 1, size = (1 + self.n_inputs, self.n_hiddens_list[0])) / np.sqrt(self.n_inputs + 1)\n        if (len(self.n_hiddens_list)!=1):\n            for i in range(len(self.n_hiddens_list)-1):\n                self.Ws[i+1][:] = np.random.uniform(-1, 1, size = (1 + self.n_hiddens_list[i], self.n_hiddens_list[i+1])) / np.sqrt(self.n_hiddens_list[i] + 1)\n        self.Ws[len(self.Ws)-1][:] = np.random.uniform(-1, 1, size=(1 + self.n_hiddens_list[len(self.n_hiddens_list)-1], self.n_outputs)) / np.sqrt(self.n_hiddens_list[len(self.n_hiddens_list)-1] + 1)\n\n  \n    def train(self, X, T, n_epochs, learning_rate=0, method = 'adam', verbose=True):\n        self.stand_params = calc_standardize_parameters(X, T)\n        Xst = standardize_X(X, self.stand_params)\n        Tst = standardize_T(T, self.stand_params)\n        optimizer = optimizers.Optimizers(self.all_weights)\n        def error_convert(mse_st):\n            if T.shape[1] == 1:\n                return np.sqrt(mse_st) * self.stand_params['Tstds'][0]\n            else:\n                return np.sqrt(mse_st)\n        if method == 'sgd':\n            self.error_trace = optimizer.sgd(self.mse, self.backward, [Xst, Tst], n_epochs, learning_rate, error_convert_f=error_convert)\n        elif method == 'adam':\n            self.error_trace = optimizer.adam(self.mse, self.backward, [Xst, Tst], n_epochs, learning_rate, error_convert_f=error_convert)\n        elif method == 'scg':\n            learning_rate = None\n            self.error_trace = optimizer.scg(self.mse, self.backward, [Xst, Tst], n_epochs, learning_rate)\n        else:\n            print('method must be ''sgd'', ''adam'', or ''scg''.')\n    \n    def use(self, X, return_hidden_layer_outputs=False):\n        Xst = standardize_X(X, self.stand_params)\n        Outs = self.forward(Xst) \n        if return_hidden_layer_outputs:\n            return unstandardize_T(Outs[len(Outs)-1], self.stand_params), Outs[:-1]\n        else:\n            return unstandardize_T(Outs[len(Outs)-1], self.stand_params)\n    \n    def get_error_trace(self):\n        return self.error_trace\n    \n    def forward(self, Xst):\n        Z = np.tanh(add_ones(Xst) @ self.Ws[0])\n        Outs = []\n        Outs.append(Z)\n        for i in range(1,len(self.Ws)-1):\n            Outs.append(np.tanh(add_ones(Outs[i-1]) @ self.Ws[i]))\n        Outs.append(add_ones(Outs[len(Outs)-1]) @ self.Ws[len(self.Ws)-1])\n        return Outs\n\n    def backward(self, Xst, Tst):\n        n_samples = Xst.shape[0]\n        n_outputs = Tst.shape[1]\n        Outs = self.forward(Xst)\n        gradient_ms = []\n        gradient_vs = []\n        delta = -2 * (Tst - Outs[len(Outs)-1]) /  (n_samples * n_outputs)\n        gradient_w = add_ones(Outs[len(Outs)-2]).T @ delta\n        lenH = len(Outs)\n        for i in range(2, lenH):\n            delta = (delta @ self.Ws[lenH-i+1][1:, :].T) * (1 - Outs[lenH-i] ** 2)\n            gradient_vs.append(add_ones(Outs[lenH-i-1]).T @ delta)\n        delta = (delta @ self.Ws[1][1:, :].T) * (1 - Outs[0] ** 2)\n        self.Gs[0][:] = add_ones(Xst).T @ delta\n        for i in reversed(range(len(gradient_vs))):\n            self.Gs[len(self.Gs)-i-2][:] = gradient_vs[i]\n        self.Gs[len(self.Gs)-1][:] = gradient_w\n        return self.all_gradients\n    \n    def mse(self, Xst, Tst):\n        Outs = self.forward(Xst)\n        return np.mean((Tst - Outs[len(Outs)-1])**2)\n\n\ndef add_ones(X):\n    return np.insert(X, 0, 1, axis=1)\n\ndef calc_standardize_parameters(X, T):\n    Xmeans = X.mean(axis=0)\n    Xstds = X.std(axis=0)\n    Tmeans = T.mean(axis=0)\n    Tstds = T.std(axis=0)\n    return {'Xmeans': Xmeans, 'Xstds': Xstds,\n            'Tmeans': Tmeans, 'Tstds': Tstds}\n\ndef standardize_X(X, stand_parms):\n    return (X - stand_parms['Xmeans']) / stand_parms['Xstds']\n\n\ndef unstandardize_X(Xst, stand_parms):\n    return Xst * stand_parms['Xstds'] + stand_parms['Xmeans']\n\n\ndef standardize_T(T, stand_parms):\n    return (T - stand_parms['Tmeans']) / stand_parms['Tstds']\n\n\ndef unstandardize_T(Tst, stand_parms):\n    return Tst * stand_parms['Tstds'] + stand_parms['Tmeans']\n\n\n\ndef run(Xtrain, Ttrain, Xtest, Ttest, method, n_epochs, learning_rate, hidden_unit_list=[50, 50, 50, 50, 50]):\n    \n    # n_samples = 30\n    # Xtrain = np.linspace(0., 20.0, n_samples).reshape((n_samples, 1))\n    # Ttrain = 0.2 + 0.05 * (Xtrain) + 0.4 * np.sin(Xtrain / 2) + 0.2 * np.random.normal(size=(n_samples, 1))\n\n    # Xtest = Xtrain + 0.1 * np.random.normal(size=(n_samples, 1))\n    # Ttest = 0.2 + 0.05 * (Xtest) + 0.4 * np.sin(Xtest / 2) + 0.2 * np.random.normal(size=(n_samples, 1))\n    # print(Xtrain)\n    n_inputs = Xtrain.shape[1]\n    n_hiddens_list = hidden_unit_list\n    n_outputs = Ttrain.shape[1]\n\n    nnet = NeuralNetwork(n_inputs, n_hiddens_list, n_outputs)\n    nnet.train(Xtrain, Ttrain, n_epochs, learning_rate, method=method, verbose=False)\n\n    def rmse(Y, T):\n        error = T - Y\n        return np.sqrt(np.mean(error ** 2))\n\n    Ytrain = nnet.use(Xtrain)\n    rmse_train = rmse(Ytrain, Ttrain)\n    Ytest = nnet.use(Xtest)\n    rmse_test = rmse(Ytest, Ttest)\n\n    print(f'Method: {method}, RMSE: Train {rmse_train:.2f} Test {rmse_test:.2f}')\n\n    # plt.figure(1, figsize=(10, 10))\n    # plt.clf()\n\n    # n_plot_rows = nnet.n_layers + 1\n    # ploti = 0\n\n    # ploti += 1\n    # plt.subplot(n_plot_rows, 1, ploti)\n    # plt.plot(nnet.get_error_trace())\n    # plt.xlabel('Epoch')\n    # plt.ylabel('RMSE')\n\n    # ploti += 1\n    # plt.subplot(n_plot_rows, 1, ploti)\n    # plt.plot(Xtrain, Ttrain, 'o', label='Training Data')\n    # plt.plot(Xtest, Ttest, 'o', label='Testing Data')\n    # X_for_plot = np.linspace(0, 20, 20).reshape(-1, 1)\n    # Y, Zs = nnet.use(X_for_plot, return_hidden_layer_outputs=True)\n    # # print(X_for_plot)\n    # # print(Y)\n    # plt.plot(X_for_plot, Y, label='Neural Net Output')\n    # plt.legend()\n    # plt.xlabel('X')\n    # plt.ylabel('Y')\n    # # Y= nnet.use(np.array([year]).reshape(-1, 1))\n    # # print(Y)\n    # for layeri in range(nnet.n_layers - 2, -1, -1):\n    #     ploti += 1\n    #     plt.subplot(n_plot_rows, 1, ploti)\n    #     plt.plot(X_for_plot, Zs[layeri])\n    #     plt.xlabel('X')\n    #     plt.ylabel(f'Outputs from Layer {layeri}')\n        \n    return nnet\n# n_samples=30\n# Xtrain = np.linspace(0., 20.0, n_samples).reshape((n_samples, 1))\n# print(Xtrain)\n# run('sgd', 4000, 0.1)\n","repo_name":"snyderg2/car_price_nnet","sub_path":"NN_torch.py","file_name":"NN_torch.py","file_ext":"py","file_size_in_byte":8002,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"12018424593","text":"'''\r\nAuthor: James Thomason\r\n'''\r\n\r\n# Line Graph, all Big West teams win loss percent from 2017-2022 (Big West conference data isnt listed before 2016)\r\n# 2022 Stats are NOT FINAL STATS.\r\nimport pandas as pd\r\nimport requests\r\nimport statsmodels.api as sm\r\nimport matplotlib.pyplot as plt\r\n\r\nlink_to_stats = \"http://stats.ncaa.org/rankings/change_sport_year_div\" \r\n\r\ndef pretend_browser(link):\r\n    years = [2017,2018,2019,2020,2021,2022]\r\n    win_loss_all_teams = []\r\n    header = {\r\n        \"User-Agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36\",\r\n        \"X-Requested-With\": \"XMLHttpRequest\"\r\n        }\r\n    for year in years:\r\n        r = requests.post(link, headers=header,\r\n                        data={\"sport_code\": \"MVB\",\r\n                                \"academic_year\": float(year),\r\n                                \"division\": 1.0,\r\n                                \"ranking_period\": 70.0,\r\n                                \"team_individual\":\"T\",\r\n                                \"game_high\": \"N\",\r\n                                \"stat_seq\": 530,\r\n                                \"org_id\": -1\r\n                                })\r\n        win_loss_all_teams.append(r.text)\r\n    return win_loss_all_teams\r\n\r\ndef scrape_request_text_for_big_west(text):\r\n    years = [2018,2019,2020,2021,2022]\r\n    scraped_dataframes_from_r_text_list = []\r\n    only_big_west_data = []\r\n    for data in text:\r\n        scraped_df = pd.read_html(data)\r\n        scraped_dataframes_from_r_text_list.append(scraped_df[1])\r\n    for df in scraped_dataframes_from_r_text_list:\r\n        filtered = df[df[\"Team\"].str.contains(\"Big West\") == True]\r\n        filtered_only_wl_PCT = filtered.loc[:,[\"Team\",\"Pct.\"]]\r\n        only_big_west_data.append(filtered_only_wl_PCT)\r\n\r\n    only_big_west_data = only_big_west_data[1:]\r\n\r\n    for i,df in enumerate(only_big_west_data):\r\n        df[\"Year\"] = years[i]\r\n    merged = pd.concat([only_big_west_data[0],only_big_west_data[1],only_big_west_data[2],only_big_west_data[3],only_big_west_data[4]],axis=0)\r\n    return merged\r\n\r\ndef get_each_year_by_team(merged_df):\r\n    team_one = merged_df[merged_df[\"Team\"].str.contains(\"Long Beach\") == True]\r\n    team_two = merged_df[merged_df[\"Team\"].str.contains(\"Hawaii\") == True]\r\n    team_three = merged_df[merged_df[\"Team\"].str.contains(\"UC Irvine\") == True]\r\n    team_four = merged_df[merged_df[\"Team\"].str.contains(\"CSUN\") == True]\r\n    team_five = merged_df[merged_df[\"Team\"].str.contains(\"UC Santa\") == True]\r\n    team_six = merged_df[merged_df[\"Team\"].str.contains(\"UC San Diego\") == True]\r\n\r\n    return [team_one,team_two,team_three,team_four,team_five,team_six]\r\n    \r\ndef make_plot(team_list_by_year):\r\n    plt.figure(figsize=(10,10))\r\n    for team in team_list_by_year:\r\n        plt.plot(team[\"Year\"],team[\"Pct.\"], label= team.iloc[0,0][:-10], linewidth=3)\r\n    plt.xticks([2018,2019,2020,2021,2022])\r\n    plt.title(\"Win pct of teams in Big West Conference by Year\",fontsize=14)\r\n    plt.xlabel(\"Year\",fontsize=14)\r\n    plt.ylabel(\"Win Percentage\",fontsize=14)\r\n    plt.legend(bbox_to_anchor=(1.01,1), loc='center', borderaxespad=0)\r\n    plt.grid(True)\r\n    \r\n\r\ndef main():\r\n    link = \"http://stats.ncaa.org/rankings/change_sport_year_div\"\r\n    \r\n    url_text = pretend_browser(link)\r\n    big_west_by_year = scrape_request_text_for_big_west(url_text)\r\n    teams_list_by_year = get_each_year_by_team(big_west_by_year)\r\n    make_plot(teams_list_by_year)\r\n    plt.show()\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()","repo_name":"Kazeazen/350FinalProject","sub_path":"final_proj_scrip_3.py","file_name":"final_proj_scrip_3.py","file_ext":"py","file_size_in_byte":3550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"2910442363","text":"import random\ndef rolldie():\n    print(\"Rolling the Dice\")\n    x = random.randrange(1,6)\n    print(x)\n\n\n\ndef main():\n    roll_again = \"yes\"\n    while roll_again == \"yes\" or roll_again== \"y\":\n        rolldie()\n        roll_again = input(\"Do you wanna roll the dice again and again\")\n\n\nif __name__ == \"__name__\":main()\n","repo_name":"arjunarunkumar/python-programs","sub_path":"dice.py","file_name":"dice.py","file_ext":"py","file_size_in_byte":317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"39704561061","text":"# 1D1P Day112 BOJ 14888번 연산자 끼워넣기 문제 - 2021.02.24\n\nimport sys\ninput = sys.stdin.readline\n\nN = int(input())\nnumbers = list(map(int, input().split()))\nopers = list(map(int, input().split()))\n\n\nMax = -1000000001\nMin = 1000000001\n\nvisited = [0] * (N-1)\n\ndef divide(a, b):\n    \n    if (a < 0 and b > 0) or (a > 0 and b < 0):\n        a, b = abs(a), abs(b)\n        q = a // b\n        return -q\n    \n    return a // b\n\ndef cal(element):\n    global Max, Min\n    \n    tmp = numbers[0]\n    for i in range(N-1):\n        if element[i] == 0:\n            tmp += numbers[i+1]\n        elif element[i] == 1:\n            tmp -= numbers[i+1]\n        elif element[i] == 2:\n            tmp *= numbers[i+1]\n        elif element[i] == 3:\n            tmp = divide(tmp, numbers[i+1])\n    if tmp > Max:\n        Max = tmp\n    if tmp < Min:\n        Min = tmp\n\n\ndef permutation(element, depth):\n    \n    if depth == N-1:\n        cal(element)\n        return\n    \n    for i in range(4):\n        if opers[i] != 0:\n            element.append(i)\n            opers[i] -= 1\n            permutation(element, depth + 1)\n            opers[i] += 1\n            element.pop()\n\npermutation([], 0)\n\n\nprint(Max)\nprint(Min)","repo_name":"WonHwang/1D1P","sub_path":"2021_02_24_Day112/14888.py","file_name":"14888.py","file_ext":"py","file_size_in_byte":1196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"38843435460","text":"# coding: utf-8\n\n\"\"\"\nThis module contains the implementation of :class:`can.Message`.\n\n.. note::\n    Could use `@dataclass <https://docs.python.org/3.7/library/dataclasses.html>`__\n    starting with Python 3.7.\n\"\"\"\n\nfrom __future__ import absolute_import, division\n\nimport warnings\nfrom copy import deepcopy\nfrom math import isinf, isnan\n\n\nclass Message(object):\n    \"\"\"\n    The :class:`~can.Message` object is used to represent CAN messages for\n    sending, receiving and other purposes like converting between different\n    logging formats.\n\n    Messages can use extended identifiers, be remote or error frames, contain\n    data and may be associated to a channel.\n\n    Messages are always compared by identity and never by value, because that\n    may introduce unexpected behaviour. See also :meth:`~can.Message.equals`.\n\n    :func:`~copy.copy`/:func:`~copy.deepcopy` is supported as well.\n\n    Messages do not support \"dynamic\" attributes, meaning any others than the\n    documented ones, since it uses :attr:`~object.__slots__`.\n    \"\"\"\n\n    __slots__ = (\n        \"timestamp\",\n        \"arbitration_id\",\n        \"is_extended_id\",\n        \"is_remote_frame\",\n        \"is_error_frame\",\n        \"channel\",\n        \"dlc\",\n        \"data\",\n        \"is_fd\",\n        \"bitrate_switch\",\n        \"error_state_indicator\",\n        \"__weakref__\",              # support weak references to messages\n        \"_dict\"                     # see __getattr__\n    )\n\n    def __getattr__(self, key):\n        # TODO keep this for a version, in order to not break old code\n        # this entire method (as well as the _dict attribute in __slots__ and the __setattr__ method)\n        # can be removed in 4.0\n        # this method is only called if the attribute was not found elsewhere, like in __slots__\n        try:\n            warnings.warn(\"Custom attributes of messages are deprecated and will be removed in 4.0\", DeprecationWarning)\n            return self._dict[key]\n        except KeyError:\n            raise AttributeError(\"'message' object has no attribute '{}'\".format(key))\n\n    def __setattr__(self, key, value):\n        # see __getattr__\n        try:\n            super(Message, self).__setattr__(key, value)\n        except AttributeError:\n            warnings.warn(\"Custom attributes of messages are deprecated and will be removed in 4.0\", DeprecationWarning)\n            self._dict[key] = value\n\n    @property\n    def id_type(self):\n        # TODO remove in 4.0\n        warnings.warn(\"Message.id_type is deprecated and will be removed in 4.0, use is_extended_id instead\", DeprecationWarning)\n        return self.is_extended_id\n\n    @id_type.setter\n    def id_type(self, value):\n        # TODO remove in 4.0\n        warnings.warn(\"Message.id_type is deprecated and will be removed in 4.0, use is_extended_id instead\", DeprecationWarning)\n        self.is_extended_id = value\n\n    def __init__(self, timestamp=0.0, arbitration_id=0, is_extended_id=None,\n                 is_remote_frame=False, is_error_frame=False, channel=None,\n                 dlc=None, data=None,\n                 is_fd=False, bitrate_switch=False, error_state_indicator=False,\n                 extended_id=None, # deprecated in 3.x, TODO remove in 4.x\n                 check=False):\n        \"\"\"\n        To create a message object, simply provide any of the below attributes\n        together with additional parameters as keyword arguments to the constructor.\n\n        :param bool check: By default, the constructor of this class does not strictly check the input.\n                           Thus, the caller must prevent the creation of invalid messages or\n                           set this parameter to `True`, to raise an Error on invalid inputs.\n                           Possible problems include the `dlc` field not matching the length of `data`\n                           or creating a message with both `is_remote_frame` and `is_error_frame` set to `True`.\n\n        :raises ValueError: iff `check` is set to `True` and one or more arguments were invalid\n        \"\"\"\n        self._dict = dict() # see __getattr__\n\n        self.timestamp = timestamp\n        self.arbitration_id = arbitration_id\n\n        if extended_id is not None:\n            # TODO remove in 4.0\n            warnings.warn(\"The extended_id parameter is deprecated and will be removed in 4.0, use is_extended_id instead\", DeprecationWarning)\n\n        if is_extended_id is not None:\n            self.is_extended_id = is_extended_id\n        else:\n            self.is_extended_id = True if extended_id is None else extended_id\n\n        self.is_remote_frame = is_remote_frame\n        self.is_error_frame = is_error_frame\n        self.channel = channel\n\n        self.is_fd = is_fd\n        self.bitrate_switch = bitrate_switch\n        self.error_state_indicator = error_state_indicator\n\n        if data is None or is_remote_frame:\n            self.data = bytearray()\n        elif isinstance(data, bytearray):\n            self.data = data\n        else:\n            try:\n                self.data = bytearray(data)\n            except TypeError:\n                err = \"Couldn't create message from {} ({})\".format(data, type(data))\n                raise TypeError(err)\n\n        if dlc is None:\n            self.dlc = len(self.data)\n        else:\n            self.dlc = dlc\n\n        if check:\n            self._check()\n\n    def __str__(self):\n        field_strings = [\"Timestamp: {0:>15.6f}\".format(self.timestamp)]\n        if self.is_extended_id:\n            arbitration_id_string = \"ID: {0:08x}\".format(self.arbitration_id)\n        else:\n            arbitration_id_string = \"ID: {0:04x}\".format(self.arbitration_id)\n        field_strings.append(arbitration_id_string.rjust(12, \" \"))\n\n        flag_string = \" \".join([\n            \"X\" if self.is_extended_id else \"S\",\n            \"E\" if self.is_error_frame else \" \",\n            \"R\" if self.is_remote_frame else \" \",\n            \"F\" if self.is_fd else \" \",\n            \"BS\" if self.bitrate_switch else \"  \",\n            \"EI\" if self.error_state_indicator else \"  \"\n        ])\n\n        field_strings.append(flag_string)\n\n        field_strings.append(\"DLC: {0:2d}\".format(self.dlc))\n        data_strings = []\n        if self.data is not None:\n            for index in range(0, min(self.dlc, len(self.data))):\n                data_strings.append(\"{0:02x}\".format(self.data[index]))\n        if data_strings:  # if not empty\n            field_strings.append(\" \".join(data_strings).ljust(24, \" \"))\n        else:\n            field_strings.append(\" \" * 24)\n\n        if (self.data is not None) and (self.data.isalnum()):\n            field_strings.append(\"'{}'\".format(self.data.decode('utf-8', 'replace')))\n\n        if self.channel is not None:\n            try:\n                field_strings.append(\"Channel: {}\".format(self.channel))\n            except UnicodeEncodeError:\n                pass\n\n        return \"    \".join(field_strings).strip()\n\n    def __len__(self):\n        # return the dlc such that it also works on remote frames\n        return self.dlc\n\n    def __bool__(self):\n        # For Python 3\n        return True\n\n    def __nonzero__(self):\n        # For Python 2\n        return self.__bool__()\n\n    def __repr__(self):\n        args = [\"timestamp={}\".format(self.timestamp),\n                \"arbitration_id={:#x}\".format(self.arbitration_id),\n                \"extended_id={}\".format(self.is_extended_id)]\n\n        if self.is_remote_frame:\n            args.append(\"is_remote_frame={}\".format(self.is_remote_frame))\n\n        if self.is_error_frame:\n            args.append(\"is_error_frame={}\".format(self.is_error_frame))\n\n        if self.channel is not None:\n            args.append(\"channel={!r}\".format(self.channel))                \n\n        data = [\"{:#02x}\".format(byte) for byte in self.data]\n        args += [\"dlc={}\".format(self.dlc),\n                 \"data=[{}]\".format(\", \".join(data))]\n\n        if self.is_fd:\n            args.append(\"is_fd=True\")\n            args.append(\"bitrate_switch={}\".format(self.bitrate_switch))\n            args.append(\"error_state_indicator={}\".format(self.error_state_indicator))\n\n        return \"can.Message({})\".format(\", \".join(args))\n\n    def __format__(self, format_spec):\n        if not format_spec:\n            return self.__str__()\n        else:\n            raise ValueError(\"non empty format_specs are not supported\")\n\n    def __bytes__(self):\n        return bytes(self.data)\n\n    def __copy__(self):\n        new = Message(\n            timestamp=self.timestamp,\n            arbitration_id=self.arbitration_id,\n            is_extended_id=self.is_extended_id,\n            is_remote_frame=self.is_remote_frame,\n            is_error_frame=self.is_error_frame,\n            channel=self.channel,\n            dlc=self.dlc,\n            data=self.data,\n            is_fd=self.is_fd,\n            bitrate_switch=self.bitrate_switch,\n            error_state_indicator=self.error_state_indicator\n        )\n        new._dict.update(self._dict)\n        return new\n\n    def __deepcopy__(self, memo):\n        new = Message(\n            timestamp=self.timestamp,\n            arbitration_id=self.arbitration_id,\n            is_extended_id=self.is_extended_id,\n            is_remote_frame=self.is_remote_frame,\n            is_error_frame=self.is_error_frame,\n            channel=deepcopy(self.channel, memo),\n            dlc=self.dlc,\n            data=deepcopy(self.data, memo),\n            is_fd=self.is_fd,\n            bitrate_switch=self.bitrate_switch,\n            error_state_indicator=self.error_state_indicator\n        )\n        new._dict.update(self._dict)\n        return new\n\n    def _check(self):\n        \"\"\"Checks if the message parameters are valid.\n        Assumes that the types are already correct.\n\n        :raises ValueError: iff one or more attributes are invalid\n        \"\"\"\n\n        if self.timestamp < 0.0:\n            raise ValueError(\"the timestamp may not be negative\")\n        if isinf(self.timestamp):\n            raise ValueError(\"the timestamp may not be infinite\")\n        if isnan(self.timestamp):\n            raise ValueError(\"the timestamp may not be NaN\")\n\n        if self.is_remote_frame and self.is_error_frame:\n            raise ValueError(\"a message cannot be a remote and an error frame at the sane time\")\n\n        if self.arbitration_id < 0:\n            raise ValueError(\"arbitration IDs may not be negative\")\n\n        if self.is_extended_id:\n            if 0x20000000 <= self.arbitration_id:\n                raise ValueError(\"Extended arbitration IDs must be less than 2^29\")\n        elif 0x800 <= self.arbitration_id:\n            raise ValueError(\"Normal arbitration IDs must be less than 2^11\")\n\n        if self.dlc < 0:\n            raise ValueError(\"DLC may not be negative\")\n        if self.is_fd:\n            if 64 < self.dlc:\n                raise ValueError(\"DLC was {} but it should be <= 64 for CAN FD frames\".format(self.dlc))\n        elif 8 < self.dlc:\n            raise ValueError(\"DLC was {} but it should be <= 8 for normal CAN frames\".format(self.dlc))\n\n        if self.is_remote_frame:\n            if self.data is not None and len(self.data) != 0:\n                raise ValueError(\"remote frames may not carry any data\")\n        elif self.dlc != len(self.data):\n            raise ValueError(\"the DLC and the length of the data must match up for non remote frames\")\n\n        if not self.is_fd:\n            if self.bitrate_switch:\n                raise ValueError(\"bitrate switch is only allowed for CAN FD frames\")\n            if self.error_state_indicator:\n                raise ValueError(\"error state indicator is only allowed for CAN FD frames\")\n\n    def equals(self, other, timestamp_delta=1.0e-6):\n        \"\"\"\n        Compares a given message with this one.\n\n        :param can.Message other: the message to compare with\n\n        :type timestamp_delta: float or int or None\n        :param timestamp_delta: the maximum difference at which two timestamps are\n                                still considered equal or None to not compare timestamps\n\n        :rtype: bool\n        :return: True iff the given message equals this one\n        \"\"\"\n        # see https://github.com/hardbyte/python-can/pull/413 for a discussion\n        # on why a delta of 1.0e-6 was chosen\n        return (\n            # check for identity first and finish fast\n            self is other or\n            # then check for equality by value\n            (\n                (\n                    timestamp_delta is None or\n                    abs(self.timestamp - other.timestamp) <= timestamp_delta\n                ) and\n                self.arbitration_id == other.arbitration_id and\n                self.is_extended_id == other.is_extended_id and\n                self.dlc == other.dlc and\n                self.data == other.data and\n                self.is_remote_frame == other.is_remote_frame and\n                self.is_error_frame == other.is_error_frame and\n                self.channel == other.channel and\n                self.is_fd == other.is_fd and\n                self.bitrate_switch == other.bitrate_switch and\n                self.error_state_indicator == other.error_state_indicator\n            )\n        )\n","repo_name":"CanBusHack/cmap","sub_path":"venv/Lib/site-packages/can/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":13163,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"86"}
+{"seq_id":"6776002064","text":"from inspect import signature\n\nimport uvicorn\nfrom fastapi import FastAPI\nfrom fastapi.openapi.utils import get_openapi\n\n# from api.routers import system\nfrom api.endpoints import system\n\nfrom config import (\n    API_HOST,\n    API_PORT,\n    UVICORN_LOG_LEVEL,\n)\nfrom logs import logger\n\nrouter = FastAPI(logger=logger)\n\n# router.include_router(system.router, prefix=\"/system\")\n\nrouter.add_api_route(\n    \"/heart\", system.heart, methods=[\"HEAD\", \"GET\"], response_model=signature(system.heart).return_annotation,\n)\n\nrouter.add_api_route(\n    \"/healthcheck\",\n    system.healthcheck,\n    methods=[\"HEAD\", \"GET\"],\n    response_model=signature(system.healthcheck).return_annotation,\n)\n\n\ndef app_openapi():\n    if router.openapi_schema:\n        return router.openapi_schema\n    else:\n        openapi_schema = get_openapi(\n            title=\"My API\", version=\"0.0.1\", description=\"My API for things\", routes=router.routes,\n        )\n        # openapi_schema[\"info\"][\"x-logo\"] = {\n        #     \"url\": \"https://fastapi.tiangolo.com/img/logo-margin/logo-teal.png\"\n        # }\n        router.openapi_schema = openapi_schema\n        return router.openapi_schema\n\n\nrouter.openapi = app_openapi\n\nif __name__ == \"__main__\":\n    print(f\"Starting server on host {API_HOST} at port {API_PORT}...\")\n    uvicorn.run(router, host=API_HOST, port=API_PORT, log_level=UVICORN_LOG_LEVEL)\n","repo_name":"christhekeele/python-celery-seed","sub_path":"app/api/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"42277524542","text":"\"\"\"Representation utilities.\"\"\"\nimport pathlib\nimport pprint\nfrom collections import defaultdict\n\nimport muspy\nimport numpy as np\n\nimport utils\n\n# Configuration\nRESOLUTION = 12\n# MAX_BEAT = 1024\nMAX_TIME_SHIFT = RESOLUTION * 4\nMAX_BAR = 8\nMAX_TRACK_NUM = 12\n\n# Instrument\nPROGRAM_INSTRUMENT_MAP = [\n    # Pianos\n    \"grand-piano\",\n    \"bright-piano\",\n    \"electric-grand-piano\",\n    \"honky-tony-piano\",\n    \"electric-piano-1\",\n    \"electric-piano-2\",\n    \"harpsichord\",\n    \"clavinet\",\n    # Chromatic Percussion\n    \"celesta\",\n    \"glockenspiel\",\n    \"music-box\",\n    \"vibraphone\",\n    \"marimba\",\n    \"xylophone\",\n    \"tubular-bells\",\n    \"dulcimer\",\n    # Organs\n    \"dwawbar-organ\",\n    \"percussive-organ\",\n    \"rock-organ\",\n    \"church-organ\",\n    \"reed-organ\",\n    \"accordion\",\n    \"harmonica\",\n    \"bandoneon\",\n    # Guitars\n    \"nylon-string-guitar\",\n    \"steel-string-guitar\",\n    \"jazz-electric-guitar\",\n    \"clean-electric-guitar\",\n    \"muted-electric-guitar\",\n    \"overdriven-electric-guitar\",\n    \"distort-electric-guitar\",\n    \"guitar-harmonic\",\n    # Basses\n    \"bass\",\n    \"finger-electric-bass\",\n    \"pick-electric-bass\",\n    \"fretless-electric-bass\",\n    \"slap-bass-1\",\n    \"slap-bass-2\",\n    \"synth-bass-1\",\n    \"synth-bass-2\",\n    # Strings\n    \"violin\",\n    \"viola\",\n    \"cello\",\n    \"contrabass\",\n    \"tremelo-strings\",\n    \"pizzicato-strings\",\n    \"harp\",\n    \"timpani\",\n    # Ensemble\n    \"strings\",\n    \"strings\",\n    \"synth-strings-1\",\n    \"synth-strings-2\",\n    \"voices-aah\",\n    \"voices-ooh\",\n    \"synth-voice\",\n    \"orchestra-hit\",\n    # Brass\n    \"trumpet\",\n    \"trombone\",\n    \"tuba\",\n    \"muted-trumpet\",\n    \"horn\",\n    \"brasses\",\n    \"synth-brasses-1\",\n    \"synth-brasses-2\",\n    # Reed\n    \"soprano-saxophone\",\n    \"alto-saxophone\",\n    \"tenor-saxophone\",\n    \"baritone-saxophone\",\n    \"oboe\",\n    \"english-horn\",\n    \"bassoon\",\n    \"clarinet\",\n    # Pipe\n    \"piccolo\",\n    \"flute\",\n    \"recorder\",\n    \"pan-flute\",\n    \"blown-bottle\",\n    \"Shakuhachi\",\n    \"Whistle\",\n    \"ocarina\",\n    # Synth Lead\n    \"lead-square\",\n    \"lead-sawtooth\",\n    \"lead-calliope\",\n    \"lead-chiff\",\n    \"lead-charang\",\n    \"lead-voice\",\n    \"lead-fifths\",\n    \"lead-bass+lead\",\n    # Synth Pad\n    \"pad-new-age\",\n    \"pad-warm\",\n    \"pad-polysynth\",\n    \"pad-choir\",\n    \"pad-bowed\",\n    \"pad-metallic\",\n    \"pad-halo\",\n    \"pad-sweep\",\n    # Synth Effects\n    \"fx-rain\",\n    \"fx-soundtrack\",\n    \"fx-crystal\",\n    \"fx-atmosphere\",\n    \"fx-brightness\",\n    \"fx-goblins\",\n    \"fx-echoes\",\n    \"fx-scifi\",\n    # Ethnic\n    \"sitar\",\n    \"banjo\",\n    \"shamisen\",\n    \"koto\",\n    \"kalimba\",\n    \"bag-pipe\",\n    \"violin\",\n    \"shehnai\",\n    # Percussive\n    \"tinkle-bell\",\n    \"agogo\",\n    \"steel-drum\",\n    \"woodblock\",\n    \"taiko\",\n    \"melodic-tom\",\n    \"synth-drums\",\n    \"reverse-cymbal\",\n    \"guitar-fret-noise\",\n    # Sound effects\n    \"breath-noise\",\n    \"seashore\",\n    \"bird-tweet\",\n    \"telephone-rang\",\n    \"helicopter\",\n    \"applause\",\n    \"gunshot\",\n    \"drumset\",\n]\nINSTRUMENT_PROGRAM_MAP = {\n    instrument: program\n    for program, instrument in enumerate(PROGRAM_INSTRUMENT_MAP)\n}\nPROGRAM_INSTRUMENT_MAP = {\n    program: instrument\n    for program, instrument in enumerate(PROGRAM_INSTRUMENT_MAP)\n}\nKNOWN_PROGRAMS = list(\n    k for k, v in INSTRUMENT_PROGRAM_MAP.items() if v is not None\n)\nKNOWN_INSTRUMENTS = list(dict.fromkeys(INSTRUMENT_PROGRAM_MAP.keys()))\n\nKNOWN_EVENTS = [\n    \"start-of-song\",\n    \"end-of-song\",\n    \"start-of-bar\",\n    \"end-of-bar\",\n    \"start-of-track\",\n    \"end-of-track\",\n]\nKNOWN_EVENTS.extend(\n    f\"instrument_{instrument}\" for instrument in KNOWN_INSTRUMENTS\n)\nKNOWN_EVENTS.extend(f\"note-on_{i}\" for i in range(128))\nKNOWN_EVENTS.extend(f\"note-off_{i}\" for i in range(128))\nKNOWN_EVENTS.extend(f\"time-shift_{i}\" for i in range(1, MAX_TIME_SHIFT + 1))\nEVENT_CODE_MAPS = {event: i for i, event in enumerate(KNOWN_EVENTS)}\nCODE_EVENT_MAPS = utils.inverse_dict(EVENT_CODE_MAPS)\n\n\nclass Indexer:\n    def __init__(self, data=None, is_training=False):\n        self._dict = dict() if data is None else data\n        self._is_training = is_training\n\n    def __getitem__(self, key):\n        if self._is_training and key not in self._dict:\n            self._dict[key] = len(self._dict)\n            return len(self._dict) - 1\n        return self._dict[key]\n\n    def __len__(self):\n        return len(self._dict)\n\n    def __contain__(self, item):\n        return item in self._dict\n\n    def get_dict(self):\n        \"\"\"Return the internal dictionary.\"\"\"\n        return self._dict\n\n    def train(self):\n        \"\"\"Set training mode.\"\"\"\n        self._is_training = True\n\n    def eval(self):\n        \"\"\"Set evaluation mode.\"\"\"\n        self._is_learning = False\n\n\ndef get_encoding():\n    \"\"\"Return the encoding configurations.\"\"\"\n    return {\n        \"resolution\": RESOLUTION,\n        \"max_bar\": MAX_BAR,\n        'max_track_num': MAX_TRACK_NUM,\n        \"max_time_shift\": MAX_TIME_SHIFT,\n        \"program_instrument_map\": PROGRAM_INSTRUMENT_MAP,\n        \"instrument_program_map\": INSTRUMENT_PROGRAM_MAP,\n        \"event_code_map\": EVENT_CODE_MAPS,\n        \"code_event_map\": CODE_EVENT_MAPS,\n    }\n\n\ndef load_encoding(filename):\n    \"\"\"Load encoding configurations from a JSON file.\"\"\"\n    encoding = utils.load_json(filename)\n    for key in (\"program_instrument_map\", \"code_event_map\"):\n        encoding[key] = {\n            int(k) if k != \"null\" else None: v\n            for k, v in encoding[key].items()\n        }\n    return encoding\n\n\ndef extract_notes(music, resolution):\n    \"\"\"Return a MusPy music object as a note sequence.\n\n    Each row of the output is a note specified as follows.\n\n        (beat, position, pitch, duration, program)\n\n    \"\"\"\n    # Check resolution\n    assert music.resolution == resolution\n\n    # Extract notes\n    notes = []\n    for track in music:\n        if track.program not in KNOWN_PROGRAMS:\n            continue\n        for note in track:\n            beat, position = divmod(note.time, resolution)\n            notes.append(\n                (beat, position, note.pitch, note.duration, track.program)\n            )\n\n    # Deduplicate and sort the notes\n    notes = sorted(set(notes))\n\n    return np.array(notes)\n\n\n# def encode_notes(notes, encoding, indexer):\n#     \"\"\"Encode the notes into a sequence of code tuples.\n\n#     Each row of the output is encoded as follows.\n\n#         (event_type, beat, position, pitch, duration, instrument)\n\n#     \"\"\"\n#     # Get variables\n#     resolution = encoding[\"resolution\"]\n#     max_beat = encoding[\"max_beat\"]\n#     max_time_shift = encoding[\"max_time_shift\"]\n\n#     # Get maps\n#     program_instrument_map = encoding[\"program_instrument_map\"]\n#     instrument_program_map = encoding[\"instrument_program_map\"]\n\n#     # Extract notes\n#     instruments = defaultdict(list)\n#     for note in notes:\n#         instrument = program_instrument_map[note[-1]]\n#         # Skip unknown instruments\n#         if instrument is None:\n#             continue\n#         instruments[instrument].append(note)\n\n#     # Sort the instruments\n#     instruments = dict(\n#         sorted(\n#             instruments.items(),\n#             key=lambda x: instrument_program_map[x[0]],\n#         )\n#     )\n\n#     # Collect events\n#     events = defaultdict(list)\n#     for instrument, instrument_notes in instruments.items():\n#         for beat, position, pitch, duration, _ in instrument_notes:\n#             if beat > max_beat:\n#                 continue\n#             time = beat * resolution + position\n#             events[instrument].append((time, f\"note-on_{pitch}\"))\n#             events[instrument].append((time + duration, f\"note-off_{pitch}\"))\n\n#     # Deduplicate and sort the events\n#     for instrument in events:\n#         events[instrument] = sorted(set(events[instrument]))\n\n#     # Start the codes with an SOS event\n#     codes = [indexer[\"start-of-song\"]]\n\n#     # Encode the instruments\n#     for instrument in events:\n#         codes.append(indexer[\"start-of-track\"])\n#         codes.append(indexer[f\"instrument_{instrument}\"])\n#         time = 0\n#         for event_time, event in events[instrument]:\n#             while time < event_time:\n#                 time_shift = min(event_time - time, max_time_shift)\n#                 codes.append(indexer[f\"time-shift_{time_shift}\"])\n#                 time += time_shift\n#             codes.append(indexer[event])\n#         codes.append(indexer[\"end-of-track\"])\n\n#     # End the codes with an EOS event\n#     codes.append(indexer[\"end-of-song\"])\n\n#     return np.array(codes)\n\n\ndef track_list_to_code(track_list, indexer):\n    np.random.shuffle(track_list)\n    codes = [indexer['start-of-song']]\n    for track in track_list:\n        codes.extend(track)\n    codes.append(indexer['end-of-song'])\n    return np.array(codes, dtype=np.int32)\n\n\ndef encode(music, encoding, indexer):\n    \"\"\"Encode a MusPy music object into a sequence of codes.\n\n    Each row of the input is encoded as follows.\n\n        (event_type, beat, position, pitch, duration, instrument)\n\n    \"\"\"\n    # Extract notes\n    # notes = extract_notes(music, encoding[\"resolution\"])\n\n    # # Encode the notes\n    # codes = encode_notes(notes, encoding, indexer)\n\n    assert music.resolution == encoding['resolution']\n\n    assert len(music.tracks) <= encoding['max_track_num']\n\n    bar_length = encoding['resolution'] * 4\n    max_onset = encoding['max_bar'] * bar_length\n    assert all([\n        ts.numerator == 4 and ts.denominator == 4\n        for ts in music.time_signatures\n        if ts.time < max_onset\n    ])\n\n    if len(music.time_signatures) != 0:\n        assert music.time_signatures[0].time == 0\n\n    sot_code = indexer['start-of-track']\n    eot_code = indexer['end-of-track']\n    sob_code = indexer['start-of-bar']\n    eob_code = indexer['end-of-bar']\n\n    track_list = []\n    for track in music:\n        program = 128 if track.is_drum else track.program\n        instrument = encoding[\"program_instrument_map\"][program]\n        cur_track = [sot_code, indexer[f'instrument_{instrument}'], sob_code]\n\n        note_event_list = []\n        for note in track:\n            if note.time < max_onset:\n                note_event_list.append((note.time, f'note-on_{note.pitch}'))\n                note_event_list.append((note.time+note.duration, f'note-off_{note.pitch}'))\n        # note_event_list = sorted(set(note_event_list))\n        note_event_list.sort()\n\n        next_bar_start_time = bar_length\n        note_cursor = 0\n        prev_time = 0\n        while note_cursor < len(note_event_list):\n            note_event = note_event_list[note_cursor]\n            if note_event[0] >= next_bar_start_time:\n                cur_track.append(eob_code)\n                cur_track.append(sob_code)\n                next_bar_start_time += bar_length\n                prev_time += bar_length\n            else:\n                if note_event[0] > prev_time:\n                    # if note_event[0] - prev_time > bar_length:\n                    #     print(note_event[0], prev_time, next_bar_start_time)\n                    #     raise ValueError\n                    cur_track.append(indexer[f'time-shift_{note_event[0] - prev_time}'])\n                    prev_time = note_event[0]\n                cur_track.append(indexer[note_event[1]])\n                note_cursor += 1\n\n        cur_track.extend([eob_code, eot_code])\n        track_list.append(cur_track)\n\n    return track_list\n\n\ndef decode_notes(data, encoding, vocabulary):\n    \"\"\"Decode codes into a note sequence.\"\"\"\n    # Get variables and maps\n    # resolution = encoding[\"resolution\"]\n    instrument_program_map = encoding[\"instrument_program_map\"]\n\n    # Initialize variables\n    program = 0\n    bar_start_time = -encoding['resolution'] * 4\n    time = 0\n    note_ons = {}\n\n    # Decode the codes into a sequence of notes\n    notes = []\n    for code in data:\n        event = vocabulary[code]\n        if event == \"start-of-song\":\n            continue\n        elif event == \"end-of-song\":\n            break\n        elif event in (\"start-of-track\", \"end-of-track\"):\n            # Reset variables\n            program = 0\n            time = 0\n            note_ons = {}\n        elif event == \"start-of-bar\":\n            bar_start_time = bar_start_time + encoding['resolution'] * 4\n            time = bar_start_time\n        elif event == \"end-of-bar\":\n            continue\n        elif event.startswith(\"instrument\"):\n            instrument = event.split(\"_\")[1]\n            program = instrument_program_map[instrument]\n        elif event.startswith(\"time-shift\"):\n            time += int(event.split(\"_\")[1])\n        elif event.startswith(\"note-on\"):\n            pitch = int(event.split(\"_\")[1])\n            note_ons[pitch] = time\n        elif event.startswith(\"note-off\"):\n            pitch = int(event.split(\"_\")[1])\n            # Skip a note-off event without a corresponding note-on event\n            if pitch not in note_ons:\n                continue\n            onset = note_ons[pitch]\n            notes.append(\n                (onset, pitch, time - note_ons[pitch], program)\n            )\n        else:\n            raise ValueError(f\"Unknown event type for: {event}\")\n\n    return notes\n\n\ndef reconstruct(notes, resolution):\n    \"\"\"Reconstruct a note sequence to a MusPy Music object.\"\"\"\n    # Construct the MusPy Music object\n    music = muspy.Music(resolution=resolution, tempos=[muspy.Tempo(0, 100)])\n\n    # Append the tracks\n    programs = sorted(set(note[-1] for note in notes))\n    for program in programs:\n        if program == 128:\n            music.tracks.append(muspy.Track(is_drum=True))\n        else:\n            music.tracks.append(muspy.Track(program))\n\n    # Append the notes\n    for onset, pitch, duration, program in notes:\n        track_idx = programs.index(program)\n        music[track_idx].notes.append(muspy.Note(onset, pitch, duration))\n\n    return music\n\n\ndef decode(codes, encoding, vocabulary):\n    \"\"\"Decode codes into a MusPy Music object.\n\n    Each row of the input is encoded as follows.\n\n        (event_type, beat, position, pitch, duration, instrument)\n\n    \"\"\"\n    # Get resolution\n    resolution = encoding[\"resolution\"]\n\n    # Decode codes into a note sequence\n    notes = decode_notes(codes, encoding, vocabulary)\n\n    # Reconstruct the music object\n    music = reconstruct(notes, resolution)\n\n    return music\n\n\ndef dump(data, vocabulary):\n    \"\"\"Decode the codes and dump as a string.\"\"\"\n    # Iterate over the rows\n    lines = []\n    for code in data:\n        event = vocabulary[code]\n        if (\n            event in (\"start-of-song\", \"start-of-track\", \"end-of-track\", \"start-of-bar\", \"end-of-bar\")\n            or event.startswith(\"instrument\")\n            or event.startswith(\"time-shift\")\n            or event.startswith(\"note-on\")\n            or event.startswith(\"note-off\")\n        ):\n            lines.append(event)\n        elif event == \"end-of-song\":\n            lines.append(event)\n            break\n        else:\n            raise ValueError(f\"Unknown event type for: {event}\")\n\n    return \"\\n\".join(lines)\n\n\ndef save_txt(filename, data, vocabulary):\n    \"\"\"Dump the codes into a TXT file.\"\"\"\n    with open(filename, \"w\") as f:\n        f.write(dump(data, vocabulary))\n\n\ndef save_csv_notes(filename, data):\n    \"\"\"Save the representation as a CSV file.\"\"\"\n    assert data.shape[1] == 5\n    np.savetxt(\n        filename,\n        data,\n        fmt=\"%d\",\n        delimiter=\",\",\n        header=\"beat,position,pitch,duration,program\",\n        comments=\"\",\n    )\n\n\ndef save_csv_codes(filename, data):\n    \"\"\"Save the representation as a CSV file.\"\"\"\n    assert data.ndim == 1\n    np.savetxt(\n        filename,\n        data,\n        fmt=\"%d\",\n        delimiter=\",\",\n        header=\"code\",\n        comments=\"\",\n    )\n\n\ndef main():\n    \"\"\"Main function.\"\"\"\n    # Get the encoding\n    encoding = get_encoding()\n\n    # Save the encoding\n    filename = pathlib.Path(__file__).parent / \"encoding_mmm.json\"\n    utils.save_json(filename, encoding)\n\n    # Load encoding\n    encoding = load_encoding(filename)\n\n    # Print the maps\n    print(f\"{' Maps ':=^40}\")\n    for key, value in encoding.items():\n        if key in (\"program_instrument_map\", \"instrument_program_map\"):\n            print(\"-\" * 40)\n            print(f\"{key}:\")\n            pprint.pprint(value, indent=2)\n\n    # Print the variables\n    print(f\"{' Variables ':=^40}\")\n    print(f\"resolution: {encoding['resolution']}\")\n    print(f\"max_bar: {encoding['max_bar']}\")\n    print(f\"max_time_shift: {encoding['max_time_shift']}\")\n\n    # Load the example\n    music = muspy.load(pathlib.Path(__file__).parent / \"example_mmm.json\")\n\n    # Get the indexer\n    indexer = Indexer(is_training=True)\n\n    # Encode the music\n    track_list = encode(music, encoding, indexer)\n    encoded = track_list_to_code(track_list, indexer)\n    print(f\"Codes:\\n{encoded}\")\n\n    # Get the learned vocabulary\n    vocabulary = utils.inverse_dict(indexer.get_dict())\n\n    print(\"-\" * 40)\n    print(f\"Decoded:\\n{dump(encoded, vocabulary)}\")\n\n    music = decode(encoded, encoding, vocabulary)\n    print(f\"Decoded musics:\\n{music}\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"leonw774/mmt","sub_path":"baseline/representation_mmm.py","file_name":"representation_mmm.py","file_ext":"py","file_size_in_byte":17092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"86"}
+{"seq_id":"73540560924","text":"# python\n\nfrom random import randint\nimport lx, lxifc, traceback\nimport json\nfrom TreeNode import TreeNode\nfrom TreeView import TreeView\n\nclass DropServer(lxifc.Drop):\n\n    def drop_ActionList(self, source, dest, addDropAction):\n        # Create a value array so we can access source.\n        vaSource = lx.object.ValueArray()\n        vaSource.set(source)\n\n        # Create a value array for dest\n        vaDest = lx.object.ValueArray()\n        vaDest.set(dest)\n\n        source_paths = [json.loads(vaSource.GetString(idx)) for idx in xrange(1, vaSource.Count())]\n        dest_path = json.loads(vaDest.GetString(1))\n\n        lumberjack = Lumberjack.final_class()\n        source_nodes = [lumberjack.node_for_path(path) for path in source_paths]\n        dest_node_parent = lumberjack.node_for_path(dest_path[:-1])\n\n        if not dest_node_parent.canAcceptDrop(source_nodes):\n            return\n\n        # Check unique key\n        if not self.check_key(vaSource) or not self.check_key(vaDest):\n            return\n\n        # Create AddDropAction interface to modify action list\n        obj = lx.object.AddDropAction()\n        obj.set(addDropAction)\n\n        # Add move action\n        obj.AddAction(1, \"Move item(s)\")\n\n    def drop_Drop(self, source, dest, action):\n        # Create a value array so we can access source.\n        vaSource = lx.object.ValueArray()\n        vaSource.set(source)\n\n        # Create a value array for dest\n        vaDest = lx.object.ValueArray()\n        vaDest.set(dest)\n\n        # Check unique key\n        if not self.check_key(vaSource) or not self.check_key(vaDest):\n            return\n\n        source_paths = [json.loads(vaSource.GetString(idx)) for idx in xrange(1, vaSource.Count())]\n        dest_path = json.loads(vaDest.GetString(1))\n\n        lumberjack = Lumberjack.final_class()\n\n        # Collect all selected children\n        source_nodes = [lumberjack.node_for_path(path) for path in source_paths]\n\n        # Move children\n        for source in source_nodes:\n            source.path = dest_path\n\n        lumberjack.on_drag_drop(source_nodes)\n\n        lumberjack.rebuild_view()\n\n    def drop_Preview(self, source, dest, action, draw):\n        lx.notimpl()\n\n    @classmethod\n    def check_key(cls, va):\n        lumberjack = Lumberjack.final_class()\n        if va.Count() > 1 and va.GetString(0) == lumberjack._drop_server_unique_key:\n            return True\n        return False\n\n    def drop_Recognize(self, source):\n        # Create a value array so we can access source.\n        va = lx.object.ValueArray()\n        va.set(source)\n\n        # Check unique key\n        if self.check_key(va):\n            return True\n        return True\n\nclass Lumberjack(object):\n    \"\"\"Metaclass containing everything necessary to create\n    and manage a working treeview in MODO.\n\n    COMMON OPERATIONS\n    -----------------\n\n    TreeView object must be blessed in order to be available in MODO.\n    Several parameters are required as a prerequisite of blessing, see\n    bless() method for more. TreeView can only be blessed\n    once per session.\n\n    `Lumberjack().bless({})`\n\n    The Lumberjack() root node is available with the `.root` property, but\n    all of its methods are also available on the Lumberjack() object itself\n    for convenience and readability.\n\n    `Lumberjack().root # gets root node`\n    `Lumberjack().add_child(**kwargs) # equiv of .root.add_child()`\n    `Lumberjack().tail_commands = [TreeNode()] # add UI commands to bottom of children`\n\n    Nodes have methods for various manipulations and properties for meta\n    properties like row color. Note that input mapping regions can be added\n    to rows or individual values (i.e. cells) as needed.\n\n    `Lumberjack().children[n].selectable = False`\n    `Lumberjack().children[n].selected = True`\n    `Lumberjack().children[n].setParent(node)`\n    `Lumberjack().children[n].clear_children(node)`\n    `Lumberjack().children[n].delete()`\n    `Lumberjack().children[n].delete_descendants()`\n    `Lumberjack().children[n].row_color = row_color_string`\n    `Lumberjack().children[n].input_region = region_name`\n    `Lumberjack().children[n].children`\n    `Lumberjack().children[n].descendants` # children, grandchildren, etc.\n    `Lumberjack().children[n].ancestors` # parents, grandparents, etc.\n    `Lumberjack().children[n].tier # returns number of ancestors`\n\n    Nodes have a `values` property containing keys for each column in the\n    TreeView. The value property has set/get built-in, but also contains\n    properties for metadata like color, font_weight, font_style, etc.\n    An optional display_value overrides the value parameter for display\n    in the TreeView UI, but the `value` is always used internally.\n\n    `Lumberjack().children[n].columns[col_name] = value`\n    `Lumberjack().children[n].columns[col_name].value = value # equiv of above`\n    `Lumberjack().children[n].columns[col_name].display_value = display_value`\n    `Lumberjack().children[n].columns[col_name].input_region = region_name`\n    `Lumberjack().children[n].columns[col_name].color.set_with_hex(\"#ffffff\")`\n    `Lumberjack().children[n].columns[col_name].font.set_bold()`\n    `Lumberjack().children[n].columns[col_name].font.set_italic()`\n\n    Attributes are TreeNodes that appear under the `+` sign in the MODO UI.\n    They have the same columns as other nodes, but are separate from the\n    node's children.\n\n    `Lumberjack().children[n].addAttribute(**kwargs)`\n    `Lumberjack().children[n].attribute[attribute_name] = attribute_value`\n\n    Various tree-wide properties and methods are available for the TreeView\n    from the Lumberjack object itself.\n\n    `Lumberjack().selected # list of selected nodes`\n    `Lumberjack().primary # most recently selected node (usually)`\n    `Lumberjack().all_nodes # all nodes in tree`\n    `Lumberjack().find(column_name, search_term) # list of matches`\n    'Lumberjack().clear_selection()'\n\n    Rebuild and Refresh methods are built into the various manipulation\n    methods in Lumberjack, so there is no need to manually Refresh or Rebuild\n    the treeview.\"\"\"\n\n    # A given MODO instance may create multiple TreeView class instances for display\n    # in the UI. As such, we use class variables within the TreeView to keep those\n    # various views in sync.\n\n    # This means, however, that if we use the Lumberjack wrapper to bless multiple\n    # different TreeViews, they will conflict with one another unless we create\n    # a subclass of TreeView that is unique to the Lumberjack() object in question.\n\n    # Life. It's complicated.\n    class _TreeViewSubclass(TreeView):\n        pass\n\n    class _DropServer(DropServer):\n        pass\n\n    class _RootNode(TreeNode):\n        def __init__(self, **kwargs):\n            super(self.__class__, self).__init__(**kwargs)\n\n        def canAcceptDrop(self, source_nodes):\n            return True\n\n\n    _root = None\n    _tree_view = None\n    _blessed = False\n    _internal_name = \"\"\n    _ident = \"\"\n    _nice_name = \"\"\n    _viewport_type = \"\"\n    _primary = None\n    _on_bless = None\n    final_class = None\n    _drop_server_unique_key = None\n    _dropserver_username = None\n    _dropsource_command = None\n\n    def __init__(self, **kwargs):\n        \"\"\"A lumberjack class is a self-contained model-view-controller system.\n\n        It maintains:\n        - a `TreeNode()` object\n        - a `TreeView()` object\n\n        The TreeNode object is the data model, the TreeView is the view model,\n        and the lumberjack object acts as controller.\"\"\"\n        if 'on_bless' in kwargs:\n            self.__class__._on_bless = kwargs['on_bless']\n\n    # In case you need to extend the TreeNode class, you can inherit TreeNode in\n    # your own class and then tell your Lumberjack Object to use it by overwriting this method\n    def create_child_node(self, **kwargs):\n        return TreeNode(**kwargs)\n\n    def on_drag_drop(self, source_nodes):\n        pass\n\n    @classmethod\n    def bless(cls, viewport_type, nice_name, internal_name, ident, column_definitions, input_regions, notifiers):\n        \"\"\"Blesses the TreeView into existence in the MODO GUI.\n\n        Requires seven arguments.\n\n        :param viewport_type:   category in the MODO UI popup\n                                vpapplication, vp3DEdit, vptoolbars, vpproperties, vpdataLists,\n                                vpinfo, vpeditors, vputility, or vpembedded\n\n        :param nice_name:       display name for the treeview in window title bars, etc\n                                should ideally be a message table lookup '@table@message@'\n\n        :param internal_name:   name of the treeview server (also used in config files)\n\n        :param ident:           arbitrary unique four-letter all-caps identifier (ID4)\n\n        :param column_definitions:\n                                A dictionary containing, at minimum, a key called 'list' containing a list\n                                of dictionaries corresponding to each column in the view. The 'name' strings\n                                for each column must correspond with the value entries for each node.\n\n                                Columns are semi-static in the Python API: they can be changed, but those\n                                changes only update when a new treeview is initiated. Don't expect to change\n                                columns on the fly.\n\n                                Example:\n\n                                ```\n                                column_definitions = {\n                                    'primary_position': 1,\n                                    'list': [\n                                            {\n                                                'name':'name',\n                                                # negative integers are summed and then divided for relative widths.\n                                                # in this example, -1 + -3 == -4, so -1/-4 is 25%.\n                                                'width':-1\n                                            }, {\n                                                'name':'enable',\n                                                # positive integers are pixel values (i.e. 20px)\n                                                'width':20\n                                            }, {\n                                                'name':'value',\n                                                'width':-3\n                                            }\n                                        ]\n                                }\n                                ```\n\n                                Somewhat confusingly, the \"primary\" column (i.e. the one with the carrot twirldown\n                                for revealing child nodes) is not necessarily the left-most column. The items\n                                list is a good example of this.\n\n                                The TreeView API wants us to provide the \"primary\" column as the first item\n                                in the list, but then _move_ it to a different slot using the `treeview_PrimaryColumnPosition()`\n                                method. Confusing. As. Hell. And apparently it works differently internally.\n                                Grumble grumble.\n\n                                So we hack it. In the above example, we might want to move the 'name' column\n                                to the right of the 'enable' column using the 'primary_position' key, which sets the\n                                value returned by `treeview_PrimaryColumnPosition()`.\n\n        :param input_regions:   list of regions for input remapping. These can be implemented from\n                                within the data object itself as described in TreeData(), and used\n                                in InputRemapping config files, like this:\n\n                                <atom type=\"InputRemapping\">\n                                    <hash type=\"Region\" key=\"treeViewConfigName+(contextless)/(stateless)+regionName@rmb\">render</hash>\n                                </atom>\n\n                                NOTE: slot zero [0] in the list is reserved for the .anywhere region.\n                                Don't use it.\n                                ```\n                                [\n                                    '(anywhere)',       # 0 reserved for .anywhere\n                                    'regionNameOne',    # 1\n                                    'regionNameTwo'     # 2\n                                ]\n                                ```\n\n        :param notifiers:       Returns a list of notifier tuples for auto-updating the tree. Optional.\n                                ```\n                                [\n                                    (\"select.event\", \"polygon +ldt\"),\n                                    (\"select.event\", \"item +ldt\")\n                                ]\n                                ```\n        \"\"\"\n\n        Lumberjack.final_class = cls\n\n        # Can only be blessed once per session.\n        if Lumberjack._blessed:\n            raise Exception('%s class has already been blessed.' % cls.__name__)\n\n        # The `TreeNode()` object is the root of the tree, and all other nodes\n        # will be children of this node. The root node is NOT visible in the GUI.\n        Lumberjack._root = Lumberjack._RootNode(\n            column_definitions = column_definitions.get('list', []),\n            controller = cls()\n        )\n        \n        cls._drop_server_unique_key = internal_name + str(randint(100000, 999999))\n        cls._dropserver_username = internal_name + \"_dropserver\"\n        cls._dropsource_command = internal_name + \"_dropCmd\"\n\n        # Our internal handle for the view itself.\n        Lumberjack._tree_view = Lumberjack._TreeViewSubclass(\n            root = Lumberjack._root,\n            primary_column_position = column_definitions.get('primary_position', 0),\n            input_regions = input_regions,\n            controller = cls()\n        )\n\n        # We store these as read-only properties of the class, just in case\n        # we ever need them.\n        cls._internal_name = internal_name\n        cls._ident = ident\n        cls._nice_name = nice_name\n        cls._viewport_type = viewport_type\n\n        # NOTE: MODO has three different strings for SERVERNAME, sSRV_USERNAME,\n        # and name to be used in config files. In practice, these should really\n        # be the same thing. So lumberjack expects only a single \"INTERNAL_NAME\"\n        # string for use in each of these fields.\n\n        config_name = internal_name\n        server_username = internal_name\n        server_name = internal_name\n\n        sTREEVIEW_TYPE = \" \".join((\n            viewport_type,\n            ident,\n            config_name,\n            nice_name\n        ))\n\n        sINMAP = \"name[{}] regions[{}]\".format(\n            server_username, \" \".join(\n                ['{}@{}'.format(n, i) for n, i in enumerate(input_regions) if n != 0]\n            )\n        )\n\n        tree_view_tags = {\n            lx.symbol.sSRV_USERNAME: server_username,\n            lx.symbol.sTREEVIEW_TYPE: sTREEVIEW_TYPE,\n            lx.symbol.sINMAP_DEFINE: sINMAP\n        }\n\n        drop_server_tags = {\n            lx.symbol.sDROP_SOURCETYPE: cls._dropsource_command,\n            lx.symbol.sDROP_ACTIONNAMES : \"1@moveAction\"\n        }\n\n        try:\n            # Remember: we've created a Lumberjack-specific subclass of our `TreeView()` class for\n            # the blessing, just in case more than one Lumberjack subclass exists.\n            lx.bless(Lumberjack._TreeViewSubclass, server_name, tree_view_tags)\n\n            # Make sure it doesn't happen again.\n            Lumberjack._blessed = True\n\n            lx.bless(Lumberjack._DropServer, cls._dropserver_username, drop_server_tags)\n\n        except:\n            traceback.print_exc()\n            raise Exception('Unable to bless %s.' % cls.__name__)\n\n        if cls._on_bless is not None:\n            cls._on_bless(cls())\n\n    @property\n    def root(self):\n        \"\"\"Returns the class `TreeNode()` object.\"\"\"\n        if Lumberjack._root:\n            return Lumberjack._root\n        else:\n            raise Exception('%s: Root cannot be accessed before `bless()`.' % self.__class__.__name__)\n\n    @property\n    def treeview(self):\n        \"\"\"Returns the class `TreeView()` object.\"\"\"\n        if Lumberjack._tree_view:\n            return Lumberjack._tree_view\n        else:\n            raise Exception('%s: Root cannot be accessed before `bless()`.' % self.__class__.__name__)\n\n    @property\n    def selected_descendants(self):\n        \"\"\"Returns the selected `TreeNode()` objects in the tree.\"\"\"\n        return self.root.selected_descendants\n\n    @property\n    def selected_children(self):\n        \"\"\"Returns the selected `TreeNode()` objects at the root of the tree.\"\"\"\n        return self.root.selected_children\n\n    def path_event(self):\n        \"\"\"Fired by `TreeNode` objects whenever the node's `path` property is changed.\n        Implement in Lumberjack subclass to fire custom notifiers, etc.\"\"\"\n        pass\n\n    def select_event(self):\n        \"\"\"Fired by `TreeNode` objects whenever the node's `selected` property is changed.\n        Implement in Lumberjack subclass to fire custom notifiers, etc.\"\"\"\n        pass\n\n    def clear_selection(self):\n        \"\"\"Returns the selected `TreeNode()` objects in the tree.\"\"\"\n        return self.root.deselect_descendants()\n\n    def primary():\n        doc = \"\"\"The primary node is typically the most recently selected.\"\"\"\n        def fget(self):\n            return self._primary\n        def fset(self, value):\n            self.__class__._primary = value\n        return locals()\n\n    primary = property(**primary())\n\n    def column_definitions():\n        doc = \"\"\"List of columns and their widths for the treeview in the\n        format `('name', width)`, where width can be a positive integer in pixels\n        or a negative integer representing a width relative to the total of all\n        netagive values. Set during bless. Cannot change during a session.\"\"\"\n        def fget(self):\n            return self._root.column_definitions\n        return locals()\n\n    column_definitions = property(**column_definitions())\n\n    def children():\n        doc = \"\"\"A list of `TreeNode()` objects that are children of the current\n        node. Note that children appear under the triangular twirl in the listview\n        GUI, while attributes appear under the + sign.\"\"\"\n        def fget(self):\n            return self.root.children\n        def fset(self, value):\n            self.root.children = value\n        return locals()\n\n    children = property(**children())\n\n    def all_nodes():\n        doc = \"\"\"Returns a list of all all_nodes in the tree.\"\"\"\n        def fget(self):\n            all_nodes = []\n            for child in self.root.children:\n                all_nodes.append(child)\n                all_nodes.extend(child.descendants)\n            return all_nodes\n        return locals()\n\n    all_nodes = property(**all_nodes())\n\n    def tail_commands():\n        doc = \"\"\"List of `TreeNode()` objects appended to the bottom of the node's list\n        of children, e.g. (new group), (new form), and (new command) in Form Editor.\n        Command must be mapped using normal input remapping to the node's input region.\"\"\"\n        def fget(self):\n            return self.root.tail_commands\n        def fset(self, value):\n            self.root.tail_commands = valuegg\n        return locals()\n\n    tail_commands = property(**tail_commands())\n\n    def add_child(self, **kwargs):\n        \"\"\"Adds a child `TreeNode()` to the current node and returns it.\"\"\"\n        if 'path' in kwargs:\n            kwargs['parent'] = self.node_for_path(kwargs['path'][:-1])\n            kwargs['index'] = kwargs['path'][-1]\n\n        if not 'parent' in kwargs:\n            kwargs['parent'] = self.root\n        newNode = self.create_child_node(**kwargs)\n        if 'index' not in kwargs:\n            kwargs['parent'].children.append(newNode)\n        else:\n            kwargs['parent'].children.insert(kwargs['index'], newNode)\n        return newNode\n\n    def clear(self):\n        \"\"\"Deletes all nodes from the tree.\"\"\"\n        self.primary = None\n        self.root.delete_descendants()\n\n    def find(self, column_name, search_term, regex=False):\n        \"\"\"Returns a list of `TreeNode()` objects with values matching search criteria.\n\n        Unless regex is enabled, the search_term requires an exact match.\n\n        :param column_name: (str) name of the column to search\n        :param search_term: (str, bool, int, or float) value to search for\n        :param regex: (bool) use regular expression\"\"\"\n\n        return self.root.find_in_descendants(column_name, search_term, regex)\n\n    def rebuild_view(self):\n        \"\"\"Rebuilds the `TreeView()` object from scratch. Must run every time any\n        structural change occurs in the node tree. Note: if cell values have changed\n        but the overal structure of the node tree has not changed, use `refresh()`\n        for performance.\"\"\"\n\n        # NOTE: We must _both_ notify attributes _and_ shape. (Facepalm.)\n        self.treeview.notify_NewAttributes()\n        self.treeview.notify_NewShape()\n\n    def refresh_view(self):\n        \"\"\"Refreshes `TreeView()` cell values, but not structure. Must run every\n        time a cell value changes in the node tree. Note: structural changes\n        (e.g. adding/removing nodes, reordering, reparenting) require the\n        `rebuild()`` method.\"\"\"\n\n        self.treeview.notify_NewAttributes()\n\n    class BadPath(Exception):\n        pass\n\n    @staticmethod\n    def depth_first_search_recursive(node):\n        for child in node.children:\n            for res in Lumberjack.depth_first_search_recursive(child):\n                yield res\n\n        yield node\n\n    def depth_first_search(self):\n        for node in Lumberjack.depth_first_search_recursive(self.root):\n            yield node\n\n    @staticmethod\n    def node_for_path_recursive(node, path):\n        # if leaf node\n        if len(node.children) == 0 and len(path) != 0:\n            raise Lumberjack.BadPath()\n\n        if len(path) == 0:\n            return node\n        else:\n            return Lumberjack.node_for_path_recursive(node.children[path[0]], path[1:])\n\n\n    def node_for_path(self, path):\n        try:\n            return Lumberjack.node_for_path_recursive(self.root, path)\n        except Lumberjack.BadPath:\n            raise Exception(\"Invalid path %s\" % str(path))\n","repo_name":"adamohern/lumberjack","sub_path":"BourbonTree/bourbon/lumberjack/Lumberjack.py","file_name":"Lumberjack.py","file_ext":"py","file_size_in_byte":22538,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"6144933390","text":"#!/usr/local/bin/python3\nimport numpy as np\n\nnum_bodies = 2\nduration = 365 * 24 * 60 * 60\ntimestep = 1\nsgp = np.array([1.327124400189e20, 3.9860044188e14])\ndynamics = np.zeros((num_bodies, 3, 3)) # position, velocity, acceleration\nposition = np.array([[-4.4910405450108775e5, 0, 0], [1.4952741801817593e11, 0, 0]])\nvelocity = np.array([[-4.4569063372411534e-10, -8.947881775228357e-2, 0], [1.4839093307604534e-4, 2.9791618338162836e4, 0]])\n# Update Acceleration\n#   Sum of gravitational force between all bodies\n# Update Velocity\n#   Velocity + timestep * acceleration\n# Update Position\n#   Position + timestep * velocity\n# distances[i,j,:] is the vector from j to i\nt = 0\nwhile t < duration:\n    distances = np.reshape(position, (num_bodies, 1, 3)) - np.reshape(position, (1, num_bodies, 3)) \n    magnitudes = np.sum(distances * distances, axis=2) + np.eye(num_bodies)\n    accelerations = (sgp[:,None] / (magnitudes ** (3/2)) * (1 - np.eye(num_bodies)))[:,:,None] * distances\n    accelerations = np.sum(accelerations, axis=0)\n\n    velocity += accelerations * timestep\n    position += velocity * timestep\n    # print(np.linalg.norm(position[1]), np.arctan2(position[1,1], position[1,0]))\n    if (t + timestep) % (24 * 60 * 60) < t % (24 * 60 * 60):\n        print(t)\n    t += timestep\nprint(position)\nprint(velocity)\n# acc_i += sgp_j*normalized_distance_vector\n#dynamics[:,1,:] = timestep * dynamics[:,2,:]\n#dynamics[:,0,:] = timestep * dynamics[:,1,:]","repo_name":"tobybell/mercura","sub_path":"python/simulator.py","file_name":"simulator.py","file_ext":"py","file_size_in_byte":1451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"38737000285","text":"class Reset:\n    @classmethod\n    def add_parser(cls, subparser):\n        parser = subparser.add_parser(\n            \"reset\",\n            description=\"Resets working state (by default, existing input files are kept)\",\n        )\n        parser.add_argument(\n            \"--hard\",\n            action=\"store_true\",\n            help=\"delete all files, including input files\",\n        )\n        parser.set_defaults(function=cls.run)\n\n    @classmethod\n    def run(cls, doc, hard=False, **kwargs):\n        from datetime import date\n        import glob\n        import os\n        from docman import Document\n\n        doc = Document.load({})\n        if not hard:\n            doc.input_files = sorted(glob.glob(os.path.join(doc.wd, \"*.jpg\")))\n        doc.save()\n        doc.cleanup()\n        return 0\n","repo_name":"jarvick257/docman","sub_path":"client/docman/cli/reset.py","file_name":"reset.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"16110627502","text":"from dateutil.relativedelta import relativedelta\n\nfrom odoo import fields\n\nfrom odoo.addons.rental_base.tests.stock_common import RentalStockCommon\n\n\nclass TestRentalProductPack(RentalStockCommon):\n    def setUp(self):\n        super().setUp()\n\n        # Product Created A, B, C\n        ProductObj = self.env[\"product.product\"]\n        self.productA = ProductObj.create({\"name\": \"Product A\", \"type\": \"product\"})\n        self.productB = ProductObj.create({\"name\": \"Product B\", \"type\": \"product\"})\n        self.productC = ProductObj.create({\"name\": \"Product C\", \"type\": \"product\"})\n        self.productA.write(\n            {\n                \"pack_ok\": True,\n                \"pack_type\": \"non_detailed\",\n                \"pack_line_ids\": [\n                    (0, 0, {\"product_id\": self.productB.id, \"quantity\": 1}),\n                    (0, 0, {\"product_id\": self.productC.id, \"quantity\": 2}),\n                ],\n            }\n        )\n        # Rental Service (Day) of Product A\n        self.rental_service_day = self._create_rental_service_day(self.productA)\n\n        self.date_start = fields.Date.from_string(fields.Date.today())\n        self.date_end = self.date_start + relativedelta(days=1)\n\n        self.rental_order = self.env[\"sale.order\"].create(\n            {\n                \"partner_id\": self.partnerA.id,\n                \"order_line\": [\n                    (\n                        0,\n                        0,\n                        {\n                            \"product_id\": self.rental_service_day.id,\n                            \"name\": self.rental_service_day.name,\n                            \"rental_type\": \"new_rental\",\n                            \"rental_qty\": 1.0,\n                            \"product_uom\": self.rental_service_day.uom_id.id,\n                            \"start_date\": self.date_start,\n                            \"end_date\": self.date_end,\n                            \"product_uom_qty\": 2.0,\n                        },\n                    )\n                ],\n            }\n        )\n\n    def test_00_rental_product_pack(self):\n        self.rental_order.action_confirm()\n        self.assertEqual(len(self.rental_order.picking_ids), 2)\n        for picking in self.rental_order.picking_ids:\n            if picking.picking_type_id == self.picking_type_out:\n                self.picking_out = picking\n                self.assertEqual(len(picking.move_lines), 3)\n            if picking.picking_type_id == self.picking_type_in:\n                self.picking_in = picking\n                self.assertEqual(len(picking.move_lines), 3)\n        for move in self.picking_out.move_lines:\n            if move.product_id == self.productA:\n                self.assertEqual(move.product_qty, 1)\n                self.moveDestId_A = move.move_dest_ids[0]\n            elif move.product_id == self.productB:\n                self.assertEqual(move.product_qty, 1)\n                self.moveDestId_B = move.move_dest_ids[0]\n            elif move.product_id == self.productC:\n                self.assertEqual(move.product_qty, 2)\n                self.moveDestId_C = move.move_dest_ids[0]\n        for move in self.picking_in.move_lines:\n            if move.product_id == self.productA:\n                self.assertEqual(move.product_qty, 1)\n                self.assertEqual(self.moveDestId_A, move)\n            elif move.product_id == self.productB:\n                self.assertEqual(move.product_qty, 1)\n                self.assertEqual(self.moveDestId_B, move)\n            elif move.product_id == self.productC:\n                self.assertEqual(move.product_qty, 2)\n                self.assertEqual(self.moveDestId_C, move)\n","repo_name":"OCA/vertical-rental","sub_path":"rental_product_pack/tests/test_rental_product_pack.py","file_name":"test_rental_product_pack.py","file_ext":"py","file_size_in_byte":3637,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"86"}
+{"seq_id":"25340950985","text":"import torch\n\nfrom mmdet.core import multi_apply, anchor_inside_flags\nfrom mmdet.core.bbox.iou_calculators import bbox_overlaps\n\nfrom mmdet.models import HEADS\nfrom mmdet.models.dense_heads import RPNHead\n\n\n@HEADS.register_module(force=True)\nclass RPNNHead(RPNHead):\n    \"\"\"RPN Noise v2 head.\n\n    Args:\n        in_channels (int): Number of channels in the input feature map.\n    \"\"\"  # noqa: W605\n\n    def __init__(self, in_channels, collect_cfg=None, **kwargs):\n        super(RPNNHead, self).__init__(in_channels, **kwargs)\n        self.collect_cfg = collect_cfg\n\n    def forward_train(self,\n                      x,\n                      img_metas,\n                      gt_bboxes,\n                      gt_labels=None,\n                      gt_bboxes_ignore=None,\n                      proposal_cfg=None,\n                      **kwargs):\n        \"\"\"\n        Args:\n            x (list[Tensor]): Features from FPN.\n            img_metas (list[dict]): Meta information of each image, e.g.,\n                image size, scaling factor, etc.\n            gt_bboxes (Tensor): Ground truth bboxes of the image,\n                shape (num_gts, 4).\n            gt_labels (Tensor): Ground truth labels of each box,\n                shape (num_gts,).\n            gt_bboxes_ignore (Tensor): Ground truth bboxes to be\n                ignored, shape (num_ignored_gts, 4).\n            proposal_cfg (mmcv.Config): Test / postprocessing configuration,\n                if None, test_cfg would be used\n\n        Returns:\n            tuple:\n                losses: (dict[str, Tensor]): A dictionary of loss components.\n                proposal_list (list[Tensor]): Proposals of each image.\n        \"\"\"\n        outs = self(x)\n        if gt_labels is None:\n            loss_inputs = outs + (gt_bboxes, img_metas)\n        else:\n            loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)\n        losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore, **kwargs)\n        if proposal_cfg is None:\n            return losses\n        else:\n            proposal_list = self.get_bboxes(*outs, img_metas, cfg=proposal_cfg, **kwargs)\n            return losses, proposal_list\n\n    def forward_collect_bboxes(self,\n                               x,\n                               img_metas,\n                               gt_bboxes,\n                               gt_labels=None,\n                               gt_bboxes_ignore=None,\n                               collect_cfg=None,\n                               **kwargs):\n        \"\"\"\n        forward and collect bboxes\n        Args:\n            x (list[Tensor]): Features from FPN.\n            img_metas (list[dict]): Meta information of each image, e.g.,\n                image size, scaling factor, etc.\n            gt_bboxes (Tensor): Ground truth bboxes of the image,\n                shape (num_gts, 4).\n            gt_labels (Tensor): Ground truth labels of each box,\n                shape (num_gts,).\n            gt_bboxes_ignore (Tensor): Ground truth bboxes to be\n                ignored, shape (num_ignored_gts, 4).\n            proposal_cfg (mmcv.Config): Test / postprocessing configuration,\n                if None, test_cfg would be used\n\n        Returns:\n            tuple:\n                losses: (dict[str, Tensor]): A dictionary of loss components.\n                proposal_list (list[Tensor]): Proposals of each image.\n        \"\"\"\n        cls_scores, bbox_preds = self(x)\n\n        proposal_list = self.collect_bboxes(\n            cls_scores,\n            bbox_preds,\n            img_metas,\n            gt_bboxes,\n            gt_labels,\n            gt_bboxes_ignore=gt_bboxes_ignore,\n            cfg=collect_cfg,\n            **kwargs\n        )\n\n        return proposal_list\n\n    def collect_bboxes(self,\n                       cls_scores,\n                       bbox_preds,\n                       img_metas,\n                       gt_bboxes_list,\n                       gt_labels_list=None,\n                       gt_bboxes_ignore_list=None,\n                       cfg=None,\n                       **kwargs):\n        assert len(cls_scores) == len(bbox_preds)\n\n        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]\n        assert len(featmap_sizes) == self.anchor_generator.num_levels\n\n        device = cls_scores[0].device\n\n        anchor_list, valid_flag_list = self.get_anchors(\n            featmap_sizes, img_metas, device=device)\n        \n        num_imgs = len(img_metas)\n        num_levels = len(cls_scores)\n        assert len(anchor_list) == len(valid_flag_list) == num_imgs\n\n        if gt_bboxes_ignore_list is None:\n            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]\n        if gt_labels_list is None:\n            gt_labels_list = [None for _ in range(num_imgs)]\n\n        bbox_preds_list = []\n        scores_list = []\n        for img_id in range(num_imgs):\n            assert len(anchor_list[img_id]) == len(valid_flag_list[img_id])\n            anchors_per_img = torch.cat(anchor_list[img_id])\n            anchors_valid_per_img = torch.cat(valid_flag_list[img_id])\n\n            bbox_preds_per_img = []\n            scores_per_img = []\n            for i in range(num_levels):\n                bbox_preds_per_level = bbox_preds[i][img_id].permute(1, 2, 0).reshape(-1, 4)\n                cls_scores_per_level = cls_scores[i][img_id].permute(1, 2, 0).reshape(-1, self.cls_out_channels)\n                if self.use_sigmoid_cls:\n                    cls_scores_per_level = cls_scores_per_level.sigmoid()\n                else:\n                    cls_scores_per_level = cls_scores_per_level.softmax(-1)[..., 0:1]\n                \n                bbox_preds_per_img.append(bbox_preds_per_level)\n                scores_per_img.append(cls_scores_per_level)\n\n            flatten_bbox_preds_per_img = torch.cat(bbox_preds_per_img)\n            decoded_bbox_preds = self.bbox_coder.decode(anchors_per_img, flatten_bbox_preds_per_img)\n            flatten_scores_per_img = torch.cat(scores_per_img)\n\n            inside_flags = anchor_inside_flags(anchors_per_img, anchors_valid_per_img,\n                                               img_metas[img_id]['img_shape'][:2],\n                                               self.train_cfg.allowed_border)\n            decoded_bbox_preds = decoded_bbox_preds[inside_flags, :]\n            flatten_scores_per_img = flatten_scores_per_img[inside_flags, :]\n            bbox_preds_list.append(decoded_bbox_preds)\n            scores_list.append(flatten_scores_per_img)\n        \n        cfg = cfg if cfg else self.collect_cfg\n        collected_bboxes, _ = multi_apply(\n            self._collect_bboxes_single,\n            bbox_preds_list,\n            scores_list,\n            gt_bboxes_list,\n            gt_labels_list,\n            cfg=cfg,\n        )\n\n        return collected_bboxes\n\n    @torch.no_grad()\n    def _collect_bboxes_single(self,\n                               bbox_preds,\n                               cls_scores,\n                               gt_bboxes,\n                               gt_labels,\n                               cfg):\n        assert bbox_preds.size(0) == cls_scores.size(0)\n        ious = bbox_overlaps(bbox_preds, gt_bboxes)\n        scores = cls_scores\n        iou_thr = cfg.get('iou_thr', 0.5)\n        weights = (ious > iou_thr) * scores\n        num_gts = gt_bboxes.size(0)\n        num_per_object = cfg.get('num_per_object', 100)\n        topk_weights, topk_inds = weights.topk(num_per_object, dim=0)\n        expand_bboxes = bbox_preds.unsqueeze(1).expand(-1, num_gts, -1)\n        bboxes_candidate = torch.gather(expand_bboxes, dim=0, index=topk_inds.unsqueeze(-1).expand(-1, -1, 4))\n        \n        flatten_bboxes_candidate = bboxes_candidate.reshape(-1, 4)\n        max_per_img = cfg.get('max_per_img', 1000)\n        if flatten_bboxes_candidate.size(0) > max_per_img:\n            flatten_weights = topk_weights.reshape(-1, 1)\n            _, inds = flatten_weights.topk(max_per_img, dim=0)\n            flatten_bboxes_candidate = flatten_bboxes_candidate[inds.squeeze(-1), :]\n        return flatten_bboxes_candidate, flatten_bboxes_candidate.size(0)\n\n\n","repo_name":"wangsr126/NDet","sub_path":"mmdet_noise/models/dense_heads/rpn_noise_head.py","file_name":"rpn_noise_head.py","file_ext":"py","file_size_in_byte":8096,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"86"}
+{"seq_id":"42751483717","text":"\nimport math\ndef power_two(n):\n    return int(math.log(n, 2))\ndef gre_power_two(n):\n\tp = 1\n\tif (n and not(n & (n - 1))):\n\t\treturn n\n\twhile (p < n) :\n\t\tp <<= 1\n\treturn p\n\n\nif __name__ == '__main__':\n\tt=int(input())\n\tfor _ in range(t):\n\t\tn=int(input())\n\t\ta=power_two(n)\n\t\tb=2**a\n\t\tif(b==n):\n\t\t\ta=a-1\n\t\t\tb=2**a\n\t\t#print(b)\n\t\tk=n-b\n\t\ta1=power_two(k)\n\t\tif(a1==a):\n\t\t\ta1=a1-1\n\t\tb1=2**a1\n\t\tif(b1==k):\n\t\t\tans=0\n\t\telse:\n\t\t\tans=k-b1\n\t\ta2=gre_power_two(k)\n\t\tif (a2==a):\n\t\t\ta2=a2+1\n\t\tb2=2**a2\n\t\taa=b2-k\n\t\th=gre_power_two(n)\n\t\tbb=2**h\n\t\tbb=bb+1-n\n\n\t\tif(ans>aa):\n\t\t\tans=aa\n\t\tif(ans>bb):\n\t\t\tans=bb\n\n\t\tprint(ans)\n\n","repo_name":"abhishek371/Data-Structure-And-Algorithms","sub_path":"CodeChef/SHKNUM.py","file_name":"SHKNUM.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74302073565","text":"import sys\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QListWidget, QListWidgetItem, QPushButton, QWidget\nfrom PyQt5.QtWidgets import QLabel, QInputDialog, QSpinBox, QMessageBox, QTextEdit\nimport PyQt5.QtWidgets as qtw\nfrom PyQt5.QtCore import Qt\nimport clingo, random, math, loadTracks, copy\nimport soundfile as sf\nfrom pysndfx import AudioEffectsChain\nimport numpy as np\n\nclingo_args = [ \"--warn=none\",\n                \"--sign-def=rnd\",\n                \"--sign-fix\",\n                \"--rand-freq=1\",\n                \"--seed=%s\"%random.randint(0,32767),\n                \"--restart-on-model\",\n                \"--enum-mode=record\"]\n\n\nclass ListboxWidget(QListWidget):\n\n    def __init__(self,  parent=None):\n        super().__init__(parent)\n        self.setAcceptDrops(True)\n\n    def dragEnterEvent(self, event):\n        if event.mimeData().hasUrls:\n            event.accept()\n        else:\n            event.ignore()\n\n    def dragMoveEvent(self, event):\n        if event.mimeData().hasUrls():\n            event.setDropAction(Qt.CopyAction)\n            event.accept()\n        else:\n            event.ignore()\n\n    def dropEvent(self, event):\n        if event.mimeData().hasUrls():\n            event.setDropAction(Qt.CopyAction)\n            event.accept()\n\n            links = []\n\n            for url in event.mimeData().urls():\n                if url.isLocalFile():\n                    links.append(str(url.toLocalFile()))\n                else:\n                    links.append(str(url.toString()))\n\n            self.addItems(links)\n        else:\n            event.ignore()\n\n    def setPos(self, posX, posY):\n        self.setGeometry(posX, posY, 200, 50)\n\n\nclass Main(QMainWindow, QWidget):\n\n    def __init__(self):\n        super().__init__()\n        self.resize(1200, 600)\n        self.todosWidgets = []\n        self.models = []\n        self.validNames = [\"kick\", \"snare\", \"hihat\", \"tomOne\", \"tomTwo\", \"tomThree\", \"over\", \"bass\", \"guitOne\",\n                           \"guitTwo\", \"piano\", \"vox\", \"clap\", \"cymbal\", \"shaker\", \"acouguit\", \"synth\", \"strings\",\n                           \"arp\", \"drums\", \"lead\", \"subbass\", \"fx\", \"violin\"]\n        self.tracks = [\"kick\", \"snare\", \"hihat\", \"tomOne\", \"tomTwo\", \"tomThree\", \"over\", \"bass\", \"piano\", \"vox\"]\n\n        self.initXBox = 240\n        self.initYBox = 15\n        self.initXLabel = 330\n        self.initYBoxLabel = 65\n        self.sizeTextX = 70\n\n        # LOAD AUDIOS #\n        self.btnMix = QPushButton('Mix', self)\n        self.btnMix.setGeometry(10, 10, 200, 50)\n        self.btnMix.clicked.connect(lambda: self.loadPathAudios())\n\n        # AÑADIR #\n        self.btnAdd = QPushButton('Añadir instrumento', self)\n        self.btnAdd.setGeometry(10, 70, 200, 50)\n        self.btnAdd.clicked.connect(lambda: self.showModalWindow())\n\n        # LIMPIAR #\n        self.btnAdd = QPushButton('Limpiar', self)\n        self.btnAdd.setGeometry(10, 130, 200, 50)\n        self.btnAdd.clicked.connect(lambda: self.clear())\n\n        self.numMixes = QLabel(self)\n        self.numMixes.setText(\"Numero de mezclas\")\n        self.numMixes.setGeometry(15, 200, 190, 30)\n\n        self.sp = QSpinBox(self)\n        self.sp.setGeometry(15, 230, 190, 30)\n        self.sp.setValue(1)\n        self.sp.show()\n\n        self.label = QLabel(self)\n        self.label.setText(\"Instrumentos validos:\")\n        self.label.setGeometry(15, 260, 190, 30)\n        self.label.show()\n\n        self.inicioX = 15\n        self.inicioY = 285\n\n        for name in self.validNames:\n            globals()['string%s' % name] = QLabel(self)\n            globals()['string%s' % name].setText(name)\n            globals()['string%s' % name].setGeometry(self.inicioX,  self.inicioY, 190, 30)\n            self.inicioY += 20\n\n            if self.inicioY >= 575:\n                self.inicioY = 285\n                self.inicioX = 100\n\n        for track in self.tracks:\n            self.checkDimensions()\n            globals()['string%s' % track] = ListboxWidget(self)\n            globals()['string%s' % track].setPos(self.initXBox, self.initYBox)\n            globals()['string%s' % track + 'label'] = qtw.QLabel(track, self)\n            globals()['string%s' % track + 'label'].setGeometry(self.initXLabel, self.initYBoxLabel, self.sizeTextX, 30)\n            self.todosWidgets.append(globals()['string%s' % track])\n            self.initYBox += 80\n            self.initYBoxLabel += 80\n\n        # TEXT BUTTON #\n        self.textEdit = QTextEdit(self)\n        self.textEdit.setGeometry(930, 15, 250, 575)\n\n    def clear(self):\n        for track in self.tracks:\n            globals()['string%s' % track].clear()\n\n    def loadPathAudios(self):\n        infoFinal = []\n        cont = 0\n        for item in self.todosWidgets:\n            path = QListWidgetItem(item.item(0))\n            if path.text():\n                path = path.text()\n                pista = self.tracks[cont]\n                infoFinal.append([pista, path])\n\n            cont += 1\n\n        longitudMax = loadTracks.checkStems(infoFinal)\n        self.loadedTracks = loadTracks.loadTrackswithPath(infoFinal, longitudMax)\n        self.solveWithClingo()\n\n    def solveWithClingo(self):\n        self.textEdit.clear()\n        self.printText(\"Starting...\")\n        self.printText(\"-------------\")\n        # **** CONFIGURAR Y CARGAR CLINGO ***** #\n        control = clingo.Control(clingo_args)\n        #print(self.sp.value())\n        control.configuration.solve.models = self.sp.value()\n        control.load(\"mixer.lp\")\n        models = []\n\n        # **** AÑADIR HECHOS A LP ***** #\n        for instrumento in self.loadedTracks:\n            fact = \"track(\" + instrumento[0] + \", on).\"\n            control.add(\"base\", [], str(fact))\n\n        # **** GROUNDING ***** #\n        print(\"Grounding...\")\n        self.printText(\"Grounding...\")\n        control.ground([(\"base\", [])])\n        print(\"------\")\n        self.printText(\"-------------\")\n\n        # **** SOLVE ***** #\n        print(\"Solving...\")\n        self.printText(\"Solving...\")\n        with control.solve(yield_=True) as solve_handle:\n            for model in solve_handle:\n                models.append(model.symbols(shown=True))\n        print(\"------\")\n        self.printText(\"-------------\")\n\n        cont = 0\n        resultados = []\n        for model in models:\n            resp = []\n            print(\"MIX \", cont + 1)\n            self.printText(\"MIX \" + str(cont + 1))\n            for atom in model:\n                instrumento = str(atom.arguments[0])\n                pan = int(str(atom.arguments[1]))\n                vol = int(str(atom.arguments[2]))\n                rev = int(str(atom.arguments[3]))\n\n                resul = []\n                resul.append(instrumento)\n                resul.append(pan)\n                resul.append(vol)\n                resul.append(rev)\n\n                resp.append(resul)\n\n                print(\" - Aplicar\", pan, \"de paneo a\", instrumento, \"con un volumen de\", vol, \"y reverb de\", rev * 10)\n                self.printText(\" - Aplicar \" + str(pan) + \" de paneo a \" + str(instrumento) + \" con un volumen de \" +\n                               str(vol) + \" y reverb de \" + str(rev * 10))\n\n            resultados.append(resp)\n            cont += 1\n            self.printText(\"-------------\")\n\n        # *** ORDENAR RESULTADOS Y AUDIOS **** #\n        self.loadedTracks = sorted(self.loadedTracks)\n        self.resultadosPre = sorted(resultados)\n        resultados = []\n        for result in self.resultadosPre:\n            resultados.append(sorted(result))\n\n        # *** MIXING *** #\n        print(\"---------\")\n        print(\"Mixing...\")\n        self.printText(\"Rendering...\")\n        for answer in range(self.sp.value()):\n            # ******** CHECAR SI HAY O NO MÁS ANSWERS DE LAS REQUERIDAS ******** #\n            if (answer + 1) <= len(resultados):\n                tracksModified = copy.deepcopy(self.loadedTracks)\n                trackFinal = 0\n                cont = 0\n\n                for track in resultados[answer]:\n\n                    # ****** CHECAR QUE PISTA SE VA A MODIFICAR ****** #\n                    numeroPista = 0\n                    for numPista in range(len(tracksModified)):\n\n                        nombre = track[0]\n\n                        if nombre == tracksModified[numPista][0]:\n                            numeroPista = numPista\n                            break\n\n                    # ********************* PANEO ****************** #\n                    factor = track[1] / 10\n                    left_factor = math.cos(3.141592 * (factor + 1) / 4)\n                    right_factor = math.sin(3.141592 * (factor + 1) / 4)\n\n                    # ******************** VOLUMEN ****************** #\n                    vol = track[2]\n                    vol = vol / 10\n\n                    # ********************* REVERB ****************** #\n                    rev = track[3] * 10\n                    reverb = AudioEffectsChain().reverb(reverberance=rev)\n\n                    withReverb = copy.deepcopy(tracksModified[numeroPista][1])\n                    left = []\n                    right = []\n\n                    for sample in withReverb:\n                        left.append(sample[0])\n                        right.append(sample[1])\n\n                    forEffect = []\n                    forEffect.append(left)\n                    forEffect.append(right)\n                    arr = np.array(forEffect)\n                    reverbAudio = reverb(arr)\n                    stereoSamples = []\n                    for sample in reverbAudio[0]:\n                        stereoSample = [sample, sample]\n                        stereoSamples.append(stereoSample)\n\n                    reverbSound = np.append([[0.0, 0.0]], stereoSamples, axis=0)\n                    reverbSound = np.delete(reverbSound, 0, 0)\n\n                    # ***************** OPERACIONES CON TRACKS **************** #\n                    tracksModified[numeroPista][1][:, 0] *= left_factor * vol\n                    tracksModified[numeroPista][1][:, 1] *= right_factor * vol\n                    reverbSound[:, 0] *= left_factor * vol * 0.5\n                    reverbSound[:, 1] *= right_factor * vol * 0.5\n                    # *********************** SUMAR TRACKS ******************** #\n                    #trackFinal += tracksModified[numeroPista][1]\n                    trackFinal += tracksModified[numeroPista][1] + reverbSound\n\n                    cont += 1\n\n                # ************************** RENDER MIX **************************** #\n                sf.write('mixes/mix_' + str(answer + 1) + '.wav', trackFinal, 44100, 'PCM_24')\n                print(\"Mezcla\", answer + 1, \"creada\")\n                self.printText(\"Mezcla \" + str(answer + 1) + \" creada\")\n            else:\n                print(\"Ya no hay más mezclas disponibles\")\n                self.printText(\"Ya no hay más mezclas disponibles\")\n                break\n\n        # *** END *** #\n        print(\"-------\")\n        self.printText(\"-------------\")\n        print(\"¡Ya puedes escuchar tus mezclas!\")\n        self.printText(\"¡Ya puedes escuchar tus mezclas!\")\n\n    def showModalWindow(self):\n        text, ok = QInputDialog.getText(self, 'Añadir Instrumento', 'Escribe el nombre de tu instrumento:')\n        if ok:\n            if text in self.validNames:\n                self.createNewBox(text)\n            else:\n                dialog = QMessageBox()\n                dialog.setWindowTitle(\"Error\")\n                dialog.setText(\"Nombre no valido\")\n                dialog.setIcon(QMessageBox.Critical)\n                dialog.exec_()\n\n    def checkDimensions(self):\n\n        if self.initYBox >= 575 and self.initXBox == 240:\n            self.initYBox = 15\n            self.initYBoxLabel = 65\n            self.initXBox = 470\n            self.initXLabel = 550\n\n        if self.initYBox >= 575 and self.initXBox == 470:\n            self.initYBox = 15\n            self.initYBoxLabel = 65\n            self.initXBox = 700\n            self.initXLabel = 780\n\n        if self.initYBox >= 575 and self.initXBox == 700:\n            self.initYBox = 15\n            self.initYBoxLabel = 65\n            self.initXBox = 920\n            self.initXLabel = 1020\n\n    def createNewBox(self, inName):\n\n        self.checkDimensions()\n\n        globals()['string%s' % inName] = ListboxWidget(self)\n        globals()['string%s' % inName].setPos(self.initXBox, self.initYBox)\n        globals()['string%s' % inName].show()\n        self.todosWidgets.append(globals()['string%s' % inName])\n\n        globals()['string%s' % inName + 'label'] = qtw.QLabel(inName, self)\n        globals()['string%s' % inName + 'label'].setGeometry(self.initXLabel, self.initYBoxLabel, self.sizeTextX, 30)\n        globals()['string%s' % inName + 'label'].show()\n        self.tracks.append(inName)\n\n        self.initYBox += 80\n        self.initYBoxLabel += 80\n\n        print(inName, \"añadido\")\n\n    def printText(self, inText):\n        cursor = self.textEdit.textCursor()\n        cursor.atEnd()\n        cursor.insertText(inText + \"\\n\")\n\n\napp = QApplication(sys.argv)\ndemo = Main()\ndemo.show()\nsys.exit(app.exec_())","repo_name":"jsvaldezv/SmartMixer","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":13099,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"39511225270","text":"\"\"\"\n-------------------------------------------------\n   File Name:db_utils02\n   Author:Lee\n   date: 2021/9/22-10:47\n-------------------------------------------------\n\"\"\"\n\nimport pymysql\nfrom configparser import ConfigParser\nfrom comms.constants import CONF_FILE\n\n\nclass DBUtils:\n    count = -1\n\n    # 封装连接对象和游标对象\n    def __init__(self):\n        try:\n            cf = ConfigParser()\n            cf.read(CONF_FILE, encoding='utf-8')\n            host = cf.get('mysql', 'host')\n            port = cf.getint('mysql', 'port')\n            user = cf.get('mysql', 'user')\n            passwd = cf.get('mysql', 'password')\n            db = cf.get('mysql', 'db')\n\n            self.conn = pymysql.connect(host=host, port=port, user=user, passwd=passwd, db=db)\n            self.cursor = self.conn.cursor()\n        except Exception as e:\n            print('工具类连接出现异常，请检查DBUtils中的__init__方法')\n            print(e)\n\n    # 封装关闭游标和连接对象\n    def close(self):\n        self.cursor.close()\n        self.conn.close()\n\n    # 封装查询结果集有多少条数据:条目数\n    # 如果execute()括号里只传一个参数,我们需要运行count = cursor.execute(sql)\n    # 如果execute()传2个参数,我们需要运行count = cursor.execute(sql,占位符数据(元组))\n    def find_count(self, sql, params=None):\n        self.conn.commit()\n        try:\n            if params is None:\n                self.count = self.cursor.execute(sql)\n                return self.count\n            elif params is not None:\n                self.count = self.cursor.execute(sql, params)\n                return self.count\n        except Exception as e:\n            print('查询数据库条目数失败:', e)\n\n    # 封装增删改\n    # 如果execute()括号里只传一个参数,我们需要运行count = cursor.execute(sql)\n    # 如果execute()传2个参数,我们需要运行count = cursor.execute(sql,占位符数据(元组))\n    def cud(self, sql, params=None):\n        self.conn.commit()\n        try:\n            if params is None:\n                self.count = self.cursor.execute(sql)\n            if isinstance(params, tuple):\n                self.count = self.cursor.execute(sql, params)\n            if isinstance(params, list):\n                self.count = self.cursor.executemany(sql, params)\n            self.conn.commit()\n            return self.count\n        except Exception as e:\n            print('增删改执行失败:', e)\n\n    # 封装查询一条数据:execute(sql)    execute(sql,params)\n    def find_one(self, sql, params=None):\n        self.conn.commit()\n        try:\n            if params is None:\n                self.cursor.execute(sql)  # 执行sql语句，并且把结果存在cursor里\n                return self.cursor.fetchone()  # 从结果集获取一条数据\n            elif params is not None:\n                self.cursor.execute(sql, params)\n                return self.cursor.fetchone()\n        except Exception as e:\n            print('查询单条数据失败:', e)\n\n    # 封装查询所有数据\n    def find_all(self, sql, params=None):\n        self.conn.commit()\n        try:\n            if params is None:\n                self.cursor.execute(sql)\n                return self.cursor.fetchall()\n            elif params is not None:\n                self.cursor.execute(sql, params)\n                return self.cursor.fetchall()\n        except Exception as e:\n            print('查询所有数据失败:', e)\n\n\nif __name__ == '__main__':\n    db = DBUtils()\n    one = db.find_one('select * from tb_user ORDER BY rand() limit 1;')\n    print(one[1], one[2])\n","repo_name":"1025magua/git_home","sub_path":"comms/db_utils.py","file_name":"db_utils.py","file_ext":"py","file_size_in_byte":3644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"40406478902","text":"def get_count_of_entry_symbol(current_str, ch):\r\n    entry_count = 0\r\n    for item in current_str:\r\n        if item == ch:\r\n            entry_count += 1\r\n\r\n    return entry_count\r\n\r\n\r\ndef reverse_str_word(current_str):\r\n    word_array = current_str.split(\" \")\r\n    return \" \".join(word for word in word_array[::-1])\r\n\r\n\r\nentryCount = get_count_of_entry_symbol(\"12311212111\", '2')\r\nprint(\"Кол-во вхождений\", \"Вхожденний не найденно\" if entryCount <= 0 else entryCount)\r\nprint(reverse_str_word(\"кашу ела Саша\"))\r\n","repo_name":"FedorovMisha/PUL_LAB_6","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"33563925901","text":"from django import forms\nfrom django.forms import ModelForm, Textarea\nfrom .models import Newservice,Accessories,NewShowroom\n\nclass NewServiceForm(forms.ModelForm):\n\n    class Meta:\n        model = Newservice\n        fields = ('sunique','sname','sphone','semail','sdate','sprice','sacc','sdiscount','stax')\n        PRICE_OPTIONS = (\n                ('', 'Select '),\n                ('10000', 'Petrol Variant'), \n                ('15000', 'Diesel Variant'),\n                )\n        TAX_OPTIONS = (\n                ('', 'Select '),\n                ('0.05', '5 percent'), \n                ('0.06', '6 percent'), \n                ('0.07', '7 percent'), \n                )\n        DISCOUNT_OPTIONS = (\n                ('', 'Select '),\n                ('1', 'No Discount'),\n                ('0.95', '5 Percent'), \n                ('0.90', '10 Percent'),\n                ('0.85', '15 Percent'),\n                ('0.80', '20 Percent'),\n                )\n        \n        labels = {\n            'sunique':'Bill Number:',\n            'sname':'Full Name',\n            'sphone':'Phone',\n            'semail':'Email',\n            'sdate':'Date',\n            'sprice':'Service Price',\n            'sacc':'Accesories',\n            'sdiscount':'Discount',\n            'stax':'Tax Rate'\n        }\n        \n        widgets = {\n            'sdate': forms.DateInput(attrs={'class':'datepicker'}),\n            'sprice': forms.Select(choices=PRICE_OPTIONS,attrs={'class': 'form-control'}),\n            'stax': forms.Select(choices=TAX_OPTIONS,attrs={'class': 'form-control'}),\n            'sdiscount': forms.Select(choices=DISCOUNT_OPTIONS,attrs={'class': 'form-control'}),\n        }\n\n    def __init__(self, *args, **kwargs):\n        super(NewServiceForm, self).__init__(*args, **kwargs)\n        self.fields['sacc'] = forms.MultipleChoiceField(\n            choices=[(c.price, c.name) for c in Accessories.objects.all()],\n            widget=forms.CheckboxSelectMultiple,\n            label=(\"Accessories\"),\n            initial=[0],\n            \n        )\n    \nclass AddShowroomForm(forms.ModelForm):\n\n    class Meta:\n        model = NewShowroom\n        fields = ('smonth','syear','srent','sunits','smain','sshow')\n        MONTH = (\n                ('', 'Select '),\n                ('January', 'January'), \n                ('Febuary', 'Febuary'),\n                ('March', 'March'), \n                ('April', 'April'),\n                ('May', 'May'), \n                ('June', 'June'),\n                ('July', 'July'), \n                ('August', 'August'),\n                ('September', 'September'), \n                ('October', 'October'),\n                ('November', 'November'), \n                ('December', 'December'),\n                )\n        YEAR = (\n                ('', 'Select '),\n                ('2020', '2020'), \n                ('2021', '2021'), \n                ('2022', '2022'), \n                ('2023', '2023'), \n                ('2024', '2024'), \n                )\n        SHOWROOM = (\n                ('', 'Select '),\n                ('Jalandhar', 'Jalandhar'),\n                ('Ludhiana', 'Ludhiana'), \n                ('Phagwara', 'Phagwara'),\n                )\n        \n        labels = {\n            'smonth':'Month:',\n            'syear':'Year',\n            'srent':'Monthly Rent',\n            'sunits':'Electricity Units',\n            'smain':'Maintenence Amount',\n            'sshow':'Showroom'\n            \n        }\n        \n        widgets = {\n            'smonth': forms.Select(choices=MONTH,attrs={'class': 'form-control'}),\n            'syear': forms.Select(choices=YEAR,attrs={'class': 'form-control'}),\n            'sshow': forms.Select(choices=SHOWROOM,attrs={'class': 'form-control'}),\n        }\n","repo_name":"rohitladhar/car-showroom","sub_path":"sales/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":3718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32934791335","text":"# Group members:\n# Andrew Dillon\n# Michael Lewson\n# Cole Ternes\n\n# References:\n# https://github.com/aimacode/aima-python/blob/master/search.ipynb\n\n# Node Class\nclass Node():\n    def __init__(self, state, edgeWeight=0):\n        self.state = state # Letter\n        self.edgeWeight = edgeWeight # Cost from parent to self\n        self.children = []\n\n    # Assigns the child's parent & edgeweight + appends childNode to self.children\n    def addChild(self, childNode, edgeWeight):\n        childNode.parent = self\n        childNode.edgeWeight = edgeWeight\n        self.children.append(childNode)\n\n# Tree Class\nclass Tree():\n    def __init__(self):\n        self.root = None\n        self.path = []\n        self.visited = []\n        self.destinationFound = False\n\n    # Adds the root to the tree\n    def addRoot(self, rootNode):\n        self.root = rootNode\n\n    # Adds the childNode to the tree under the parent node\n    def addNode(self, childNode, parentNode, edgeWeight):\n        parentNode.addChild(childNode, edgeWeight)\n\n    # Depth-first search\n    def dfs(self, currNode, destination):\n        # Skipping function if destination already found\n        if(self.destinationFound):\n            return\n\n        # Append the currNode to visited and path\n        self.visited.append(currNode.state)\n        self.path.append(currNode.state)\n\n        # Check if currNode is the destination\n        if(currNode.state == destination.state):\n            self.destinationFound = True\n            return\n\n        #adding children to frontier\n        for n in currNode.children:\n            # Recursively search tree\n            self.dfs(n, destination)\n            # Remove currNode if not in the path\n            if(not self.destinationFound):\n                self.path.pop()\n\n# Create the tree\ntree = Tree()\n\n# Define the nodes\nroot = Node(\"S\")\na = Node(\"A\")\nb = Node(\"B\")\nc = Node(\"C\")\nd = Node(\"D\")\ne = Node(\"E\")\ng = Node(\"G\")\n\n# Adding nodes to tree\ntree.addRoot(root)\ntree.addNode(a, tree.root, 3)\ntree.addNode(b, tree.root, 1)\ntree.addNode(c, tree.root, 8)\ntree.addNode(d, a, 3)\ntree.addNode(e, a, 7)\ntree.addNode(g, a, 15)\ntree.addNode(g, b, 20)\ntree.addNode(g, c, 5)\n\n# Depth-first search through tree\ntree.dfs(tree.root, g)\n\n# Print the path and all nodes visited\nprint(\"Expanded nodes:\\n\" + str(tree.visited))\nprint(\"Path:\\n\" + str(tree.path))\n","repo_name":"coleternes/CPSC390-Artificial-Intelligence","sub_path":"A01-DFS/dfs.py","file_name":"dfs.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"5885447916","text":"from django.contrib.auth import get_user_model\nfrom rest_framework.test import APITestCase\nimport os.path\nfrom uuid import UUID\nfrom pprint import pprint\n\nfrom kidsbook.models import Game, GameScene, GameAnswer, Group\nfrom kidsbook.user.views import generate_token\nfrom kidsbook.serializers import GameSceneSerializer,GameSuperuserSerializer\n\n\nUser = get_user_model()\nurl_prefix = '/api/v1'\n\nclass TestGameParser(APITestCase):\n    def setUp(self):\n        self.username = \"john\"\n        self.email = \"john@snow.com\"\n        self.password = \"you_know_nothing\"\n        self.superuser = User.objects.create_superuser(username=self.username, email_address=self.email, password=self.password)\n        self.superuser_token = self.get_token(self.superuser)\n\n        # User\n        self.username = \"hey\"\n        self.email = \"kid@s.sss\"\n        self.password = \"want_some_cookies?\"\n        self.user = User.objects.create_user(username=self.username, email_address=self.email, password=self.password)\n        self.user_token = self.get_token(self.user)\n\n        # Another User\n        self.username = \"123\"\n        self.email = \"dd@s.sss\"\n        self.password = \"want_some_cookies?\"\n        self.another_user = User.objects.create_user(username=self.username, email_address=self.email, password=self.password)\n        self.another_token = self.get_token(self.another_user)\n\n        # Create a group\n        response = self.client.post(url_prefix + '/group/', {\"name\": \"testing group\"}, HTTP_AUTHORIZATION=self.superuser_token)\n        self.group = Group.objects.get(id=response.data.get('data', {})['id'])\n        self.group.add_member(self.user)\n\n        # Url\n        self.url = \"{}/game/\".format(url_prefix)\n\n    def get_token(self, user):\n        token = generate_token(user)\n        return 'Bearer {0}'.format(token.decode('utf-8'))\n\n    def changes_no_difference_in_response(self, request_changes, current_state):\n        for key, val in iter(request_changes.items()):\n            if key not in current_state:\n                return False\n            if val != current_state[key]:\n                return False\n        return True\n\n    def compare_two_scenes(self, scenes, expected_scenes):\n        for scene, expected_scene in iter(zip(scenes, expected_scenes)):\n            for key, value in iter(expected_scene.items()):\n                if key not in scene or expected_scene[key] != scene[key]:\n                    return False\n\n        return True\n\n    def traverse_to_scene(self, scene_id, pathways, end_scenes):\n        # DFS\n        if scene_id in pathways:\n            for next_scene_id in pathways[scene_id]:\n                self.traverse_to_scene(next_scene_id, pathways, end_scenes)\n        else:\n            end_scenes.add(scene_id)\n\n    def build_scenes_as_a_tree_from_game(self, game_id):\n        tree = {}\n        mapping = {}\n        index = 1\n\n        def rename_dict(d):\n            new = {}\n            for k, v in d.items():\n                if isinstance(v, dict):\n                    v = rename_dict(v)\n                new[mapping[k]] = v\n            return new\n\n        def dfs_to_build_scenes(cur_scene_id, stack):\n            nonlocal index\n            scene = GameScene.objects.get(id=cur_scene_id)\n            if str(scene.id) not in mapping:\n                mapping[str(scene.id)] = index\n                index += 1\n\n            if scene.is_end:\n                return\n            pathways = [choice['pathway'] for choice in iter(scene.choices)]\n            if not stack:\n                tree[cur_scene_id] = {}\n                stack.append(cur_scene_id)\n\n            for pathway in iter(pathways):\n                # Treverse to current node\n                temp = tree\n                for id in stack:\n                    temp = temp[id]\n                temp[pathway] = {}\n                stack.append(pathway)\n                if str(pathway) not in mapping:\n                    mapping[str(pathway)] = index\n                    index += 1\n\n                dfs_to_build_scenes(pathway, stack)\n                stack.pop()\n\n        game = Game.objects.get(id=game_id)\n        dfs_to_build_scenes(game.first_scene, [])\n        return rename_dict(tree)\n\n    def test_create_a_game(self):\n        csv_file = \"Game_Module_Template.csv\"\n        with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), csv_file), 'rb') as upload_file:\n            response = self.client.post(\n                self.url, {\"group_id\": str(self.group.id), \"file\": upload_file},\n                HTTP_AUTHORIZATION=self.superuser_token\n            )\n        self.assertEqual(202, response.status_code)\n\n        # Matching number of game scenes\n        created_scenes = GameSceneSerializer(GameScene.objects.all(), many=True).data\n        created_scenes = [dict(scene) for scene in created_scenes]\n        self.assertEqual(6, len(created_scenes))\n\n        # Generate pathways\n        first_scene_id = response.data.get('data', {})['first_scene']\n        first_scene = GameScene.objects.get(id=first_scene_id)\n        pathways = {\n            str(scene['id']): tuple([choice['pathway'] for choice in scene['choices']]) for scene in created_scenes\n            if not scene['is_end']\n        }\n\n        # Check if 1 starting scene and 1 ending scene\n        end_scenes = set()\n        self.traverse_to_scene(first_scene_id, pathways, end_scenes)\n        self.assertEqual(1, len(end_scenes))\n\n        # Check if the logic tree is correct\n        expected_tree = {1: {2: {3: {4: {}}, 5: {4: {}}, 6: {4: {}}}}}\n        tree_from_game = self.build_scenes_as_a_tree_from_game(response.data['data']['id'])\n        self.assertEqual(expected_tree, tree_from_game)\n\n    def test_create_a_game_2(self):\n        csv_file = \"Game_Module_Template_2.csv\"\n        with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), csv_file), 'rb') as upload_file:\n            response = self.client.post(\n                self.url, {\"group_id\": str(self.group.id), \"file\": upload_file},\n                HTTP_AUTHORIZATION=self.superuser_token\n            )\n        self.assertEqual(202, response.status_code)\n\n        # Matching number of game scenes\n        created_scenes = GameSceneSerializer(GameScene.objects.all(), many=True).data\n        created_scenes = [dict(scene) for scene in created_scenes]\n        self.assertEqual(15, len(created_scenes))\n\n        # Generate pathways\n        first_scene_id = response.data.get('data', {})['first_scene']\n        first_scene = GameScene.objects.get(id=first_scene_id)\n        pathways = {\n            str(scene['id']): tuple([choice['pathway'] for choice in scene['choices']]) for scene in created_scenes\n            if not scene['is_end']\n        }\n\n        # Check if 1 starting scene and 1 ending scene\n        end_scenes = set()\n        self.traverse_to_scene(first_scene_id, pathways, end_scenes)\n        self.assertEqual(1, len(end_scenes))\n\n        # Check if the logic tree is correct\n        expected_tree = {1: {2: {3: {4: {5: {}}, 6: {5: {}}, 7: {5: {}}},\n         8: {9: {5: {}}, 10: {5: {}}, 11: {5: {}}},\n         12: {13: {5: {}}, 14: {5: {}}, 15: {5: {}}}}}}\n        tree_from_game = self.build_scenes_as_a_tree_from_game(response.data['data']['id'])\n        self.assertEqual(expected_tree, tree_from_game)\n\n    def test_create_many_games(self):\n        csv_file = \"Game_Module_Template.csv\"\n        for index in range(7):\n            with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), csv_file), 'rb') as upload_file:\n                response = self.client.post(\n                    self.url, {\"group_id\": str(self.group.id), \"file\": upload_file},\n                    HTTP_AUTHORIZATION=self.superuser_token\n                )\n            self.assertEqual(202, response.status_code)\n\n            # Matching number of game scenes\n            created_scenes = GameSceneSerializer(GameScene.objects.all(), many=True).data\n            created_scenes = [dict(scene) for scene in created_scenes]\n            self.assertEqual(6*(index+1), len(created_scenes))\n\n            # Generate pathways\n            first_scene_id = response.data.get('data', {})['first_scene']\n            first_scene = GameScene.objects.get(id=first_scene_id)\n            pathways = {\n                str(scene['id']): tuple([choice['pathway'] for choice in scene['choices']]) for scene in created_scenes\n                if not scene['is_end']\n            }\n\n            # Check if 1 starting scene and 1 ending scene\n            end_scenes = set()\n            self.traverse_to_scene(first_scene_id, pathways, end_scenes)\n            self.assertEqual(1, len(end_scenes))\n\n    def test_create_game_without_group_id(self):\n        csv_file = \"Game_Module_Template.csv\"\n        with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), csv_file), 'rb') as upload_file:\n            response = self.client.post(\n                self.url, {\"file\": upload_file},\n                HTTP_AUTHORIZATION=self.superuser_token\n            )\n        self.assertEqual(400, response.status_code)\n\n    def test_create_game_without_file(self):\n        response = self.client.post(\n            self.url, {\"group_id\": str(self.group.id)},\n            HTTP_AUTHORIZATION=self.superuser_token\n        )\n\n        self.assertEqual(400, response.status_code)\n\n    def test_create_game_with_other_info(self):\n        csv_file = \"Game_Module_Template.csv\"\n        with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), csv_file), 'rb') as upload_file:\n            params = {\n                \"group_id\": str(self.group.id),\n                \"file\": upload_file,\n                \"title\": \"hello 123\",\n                \"preface\": 'mkqwe;l'\n            }\n            response = self.client.post(\n                self.url,\n                params,\n                HTTP_AUTHORIZATION=self.superuser_token\n            )\n        self.assertEqual(202, response.status_code)\n        expected_response = {\n            \"group\": str(self.group.id),\n            \"title\": \"hello 123\",\n            \"preface\": 'mkqwe;l'\n        }\n\n        self.assertTrue(self.changes_no_difference_in_response(expected_response, response.data.get('data', {})))\n","repo_name":"lth08091998/classbuzzs","sub_path":"kidsbook-backend/kidsbook/game/tests_parsing.py","file_name":"tests_parsing.py","file_ext":"py","file_size_in_byte":10150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"8264013109","text":"import os\nimport pandas as pd\nimport controller as ctr\nimport sqlalchemy\nfrom sqlalchemy import create_engine\nfrom datetime import datetime\n\n# Configuration\ndata_dir = os.environ['DATA_DIR']\nsource_data_table = 'RawArticle'\ntarget_data_table = 'ProcessedArticle'\nsummary_data_table = 'ProcessedArticleSummary'\nengine = create_engine('sqlite:///{0}/the_reading_machine.db'.format(data_dir))\nsql_query = 'SELECT * FROM {}'.format(source_data_table)\n\n# Initialise processing parameters\nmodel_start_date = datetime(2010, 1, 1).date()\nmin_length = 30\nremove_captalisation = True\nremove_noun = True\nremove_numerical = True\nremove_punctuation = True\nstem = False\n\n\n# Reading data\narticles = pd.read_sql(sql_query, engine, parse_dates=['date'])\n\n# Post processing the data extraction\nprocessed_articles = ctr.scraper_post_processing(\n    articles, model_start_date=model_start_date)\n\n# Process the texts\npreprocessed_text, text_summary = (\n    ctr.text_preprocessing(article_df=processed_articles,\n                           article_col='article',\n                           min_length=min_length,\n                           remove_captalisation=remove_captalisation,\n                           remove_noun=remove_noun,\n                           remove_numerical=remove_numerical,\n                           remove_punctuation=remove_punctuation,\n                           stem=stem))\n\n# Save the data\ndata_field_type = {'id': sqlalchemy.types.Integer(),\n                   'date': sqlalchemy.types.Date(),\n                   'article': sqlalchemy.types.Text(),\n                   'title': sqlalchemy.types.NVARCHAR(300),\n                   'source': sqlalchemy.types.NVARCHAR(20),\n                   'link': sqlalchemy.types.NVARCHAR(255)\n                   }\n\nsummary_field_type = {'createTime': sqlalchemy.types.DateTime(),\n                      'article_count': sqlalchemy.types.Integer(),\n                      'average_article_length': sqlalchemy.types.Float(),\n                      'average_lexical_diversity': sqlalchemy.types.Float(),\n                      'vocab_size': sqlalchemy.types.Integer()\n                      }\n\npreprocessed_text.to_sql(con=engine,\n                         name=target_data_table,\n                         index=False,\n                         if_exists='replace',\n                         dtype=data_field_type)\n\ntext_summary.to_sql(con=engine,\n                    name=summary_data_table,\n                    index=False,\n                    if_exists='append',\n                    dtype=summary_field_type)\n","repo_name":"EST-Team-Adam/TheReadingMachine","sub_path":"pipeline/article_processing/processor.py","file_name":"processor.py","file_ext":"py","file_size_in_byte":2542,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"6848024008","text":"import requests\nimport pymysql\nfrom bs4 import BeautifulSoup\nimport numpy as py\nimport pandas as pd\n\ncon=pymysql.connect('localhost','root','root','pythonDB')\nurl =\"https://xiaoyuan.zhaopin.com/part/0/0_0_0_0_0_-1_人工智能_{}_0\"\n\nf= requests.get(url.format(1)).text\nsoup = BeautifulSoup(f, \"html.parser\")\npage= soup.find(\"span\",attrs={\"class\":\"searchResultPagePer fr\"}).get_text()\ndb=con.cursor()\n\n\ntime=page[2:4]\n#time=page.split(\"/\")[1]\nwith open(\"infomation.csv\",\"a+\",encoding=\"utf-8\") as q:\n    q.write('job#num#jobtype#city#company#pubtime')\n    for i in range(1,int(time)+1):\n        f=requests.get(url.format(i)).text\n        soup=BeautifulSoup(f,\"html.parser\")\n        divlist=soup.find_all(class_=\"searchResultItemSimple clearfix\")\n\n        for div in divlist:\n            job = div.find\\\n                (\"a\").get_text()\n            city=div.find(\"em\",attrs={\"class\":\"searchResultJobCityval\"}).get_text()\n            num=div.find(\"em\",attrs={\"class\":\"searchResultJobPeopnum\"}).get_text()\n            jobtype=div.find(\"p\",attrs={\"class\":\"searchResultCompanyIndustry\"}).get_text()\n            company=div.find(\"p\",attrs={\"class\":\"searchResultCompanyname\"}).get_text()\n            pubtimezzz=div.find(\"p\",attrs={\"class\":\"pt15 pb10\"})\n            pubtimezz=pubtimezzz.find(\"span\")\n            pubtimez=pubtimezz.find(\"span\").get_text()\n            pubtime=pubtimez[7:]\n            #note=div.find(\"span\",attrs={\"class\":\"oTips oTips1 fl\"}).get_text()\n            sql = \"insert into jobinfo(jobname,neednum,jobtype,city,company,pubtime) values ('{}','{}','{}','{}','{}','{}')\".format(job,num,jobtype,city,company,pubtime)\n            db.execute(sql)\n            con.commit()\n            q.write('{}#{}#{}#{}#{}#{}'.format(job,num,jobtype,city,company,pubtime)+'\\n')\n            print(\"sql结束\")\n\ndb.close()\ncon.close()\nprint(\"爬虫结束\")","repo_name":"intensifyfamily/spider","sub_path":"others/his.py","file_name":"his.py","file_ext":"py","file_size_in_byte":1849,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"25776421638","text":"import json\nimport server_objects\nimport client_objects\nimport ast\n\n__author__ = 'Alexandr'\n\n\nclient_objects_names = {\"ServerPlayer\": client_objects.Player,\n                        \"ServerFireball\": client_objects.Fireball}\n\n\ndef pack_patch(server_objects_dict):\n    res = {}\n    for uuid, server_object in server_objects_dict.items():\n        res[str(uuid)] = server_object.to_json()\n    return json.dumps(res)\n\n\ndef unpack_patch(json_data):\n    # print(json_data)\n    server_objects_dict = json.loads(json_data)\n    res = {}\n    for uuid, json_server_objects_dict in server_objects_dict.items():\n        object_name, json_server_object = json_server_objects_dict.split(\":\", maxsplit=1)\n        # print(json_server_object)\n        res[uuid] = [ast.literal_eval(json_server_object), object_name]\n    return res\n\n\ndef get_client_class_from_patch_class_name(class_name):\n    return client_objects_names[class_name]\n","repo_name":"alexandr2levin/MagicPewPew","sub_path":"MagicPewPew/client_server_tools.py","file_name":"client_server_tools.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"71874797403","text":"#!/usr/bin/env python\n\"\"\"\nNote: No SLURM commands are submitted within this script.\n\nExample usage command:\n  >> ./bin/run_steps/step2_modifymodel.py\n================================================================================\n\"\"\"\nimport sys\nimport datetime\nfrom argparse import ArgumentParser\n\nfrom bayota_util.spec_and_control_handler import notdry, read_expcon_file, read_control, write_progress_file\nfrom bayom_e.model_handling.utils import modify_model, save_model_pickle, load_model_pickle\n\nfrom bayota_settings.log_setup import set_up_detailedfilelogger\n\nfrom bayota_util.s3_operations import establish_s3_connection, pull_workspace_subdir_from_s3, \\\n    pull_model_instance_from_s3, move_controlfile_to_s3\n\nlogprefix = '** Modifying Model **: '\n\n\ndef main(control_file, dryrun=False, use_s3_ws=False, save_to_s3=False, log_level='INFO') -> int:\n    if save_to_s3 or use_s3_ws:\n        # Connection with S3 is established.\n        s3ops = establish_s3_connection(log_level, logger=None)\n\n    # If using s3, required workspace directories are pulled from buckets.\n    if use_s3_ws:\n        pull_workspace_subdir_from_s3(subdirname='control', s3ops=s3ops, log_level=log_level)\n        pull_workspace_subdir_from_s3(subdirname='data', s3ops=s3ops, log_level=log_level)\n        pull_workspace_subdir_from_s3(subdirname='specfiles', s3ops=s3ops, log_level=log_level)\n\n    # Control file is read.\n    control_dict, \\\n    actionlist, \\\n    compact_geo_entity_str, \\\n    expid, \\\n    expname, \\\n    list_of_trialdicts, \\\n    saved_model_file, \\\n    studyid = read_expcon_file(control_file)\n\n    # Logging formats are set up.\n    logger = set_up_detailedfilelogger(loggername=expname,  # same name as module, so logger is shared\n                                       filename=f\"step3_s{studyid}_e{expid}_{compact_geo_entity_str}.log\",\n                                       level=log_level,\n                                       also_logtoconsole=True,\n                                       add_filehandler_if_already_exists=True,\n                                       add_consolehandler_if_already_exists=False)\n\n    # If using s3, saved model instance is pulled from bucket.\n    if use_s3_ws:\n        pull_model_instance_from_s3(log_level=log_level, model_instance_name=saved_model_file, s3ops=s3ops)\n\n    # Progress report is updated.\n    progress_dict = read_control(control_file_name=control_dict['study']['uuid'])\n    progress_dict['run_timestamps']['step3b_expmodification_start'] = datetime.datetime.today().strftime('%Y-%m-%d-%H:%M:%S')\n\n    # The model is modified according to specified experiment set-up\n    logger.info(f\"{logprefix} {expname} - modification action list = {actionlist}\")\n    if notdry(dryrun, logger, '--Dryrun-- Would modify model with action <%s>' % actionlist):\n\n        # Check whether any model modifications are specified\n        if actionlist[0] == 'none':\n            logger.info(f\"{logprefix} {expname} - no model modifications made\")\n        else:\n            # Load the model object\n            my_model = load_model_pickle(savepath=saved_model_file, dryrun=dryrun)\n\n            for a in actionlist:\n                modify_model(my_model, actiondict=a)\n\n            save_model_pickle(model=my_model, savepath=saved_model_file, dryrun=dryrun, logprefix=logprefix)\n\n    # Progress report is finalized with timestamp and saved.\n    progress_dict['run_timestamps']['step3b_expmodification_done'] = datetime.datetime.today().strftime(\n        '%Y-%m-%d-%H:%M:%S')\n    progress_file_name = write_progress_file(progress_dict, control_name=control_dict['experiment']['uuid'])\n    if save_to_s3:\n        move_controlfile_to_s3(logger, s3ops, controlfile_name=progress_file_name, no_s3=False, )\n\n    return 0  # a clean, no-issue, exit\n\n\ndef parse_cli_arguments():\n    \"\"\" Input arguments are parsed. \"\"\"\n    parser = ArgumentParser(description=\"Modify a study's model\")\n\n    parser.add_argument(\"control_filename\", metavar='Control Filename', type=str,\n                        help=\"name for this model modification's control file\")\n\n    parser.add_argument(\"-d\", \"--dryrun\", action='store_true',\n                        help=\"run through the script without triggering any other scripts\")\n\n    parser.add_argument(\"--use_s3_ws\", dest=\"use_s3_ws\", action='store_true',\n                        help=\"Pull workspace files from s3 bucket to local workspace at start of running this step\")\n\n    parser.add_argument(\"--save_to_s3\", dest=\"save_to_s3\", action='store_true',\n                        help=\"Move model instance and progress files from local workspace to s3 buckets\")\n\n    parser.add_argument(\"--log_level\", nargs=None, default='INFO',\n                        choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],\n                        help=\"change logging level to {debug, info, warning, error, critical}\")\n\n    return parser.parse_args()\n\n\nif __name__ == '__main__':\n    opts = parse_cli_arguments()\n\n    # The main function is called.\n    sys.exit(main(opts.control_filename,\n                  dryrun=opts.dryrun,\n                  use_s3_ws=opts.use_s3_ws,\n                  save_to_s3=opts.save_to_s3,\n                  log_level=opts.log_level))\n","repo_name":"dkauf42/bayota","sub_path":"bin/run_steps/step2_modifymodel.py","file_name":"step2_modifymodel.py","file_ext":"py","file_size_in_byte":5200,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"23139925637","text":"from collections import deque\ndef solution(priorities, location):\n    answer = 0\n    ans=[]\n    queue=deque()\n    \n    for i in range(len(priorities)):\n        queue.append((i,priorities[i]))\n        \n    while queue:\n        a=queue.popleft()\n        flag=False\n        for i in queue:\n            if i[1]>a[1]:\n                flag=True\n        if flag:\n            queue.append(a)\n        else:\n            ans.append(a)\n            \n    for i in range(0,len(ans)):\n        if ans[i][0]==location:\n            answer=i+1\n        \n    return answer\n","repo_name":"jiwon199/Algorithms-Study","sub_path":"기타알고리즘/프린터.py","file_name":"프린터.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"45131627812","text":"class Solution:\n    def threeSumClosest(self, nums: List[int], target: int) -> int:\n        nums.sort()\n        res = nums[0] + nums[1] + nums[2]\n        for i in range(0, len(nums)):\n            begin, end = i + 1, len(nums) - 1\n            while begin < end:\n                sum = nums[i] + nums[begin] + nums[end]\n                res = res if abs(target - res) < abs(target - sum) else sum\n                if sum > target: end -= 1\n                else: begin += 1\n        return res\n","repo_name":"PengJiaqiao/LeetCode_Diary","sub_path":"src/0016_3Sum_Closest/3Sum_Closest.py","file_name":"3Sum_Closest.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"15406923365","text":"class Statistics:\n    def __init__(self):\n        self.five = 0\n        self.four = 0\n        self.three = 0\n        self.two = 0\n\n    def __add__(self, other):\n        result = Statistics()\n        result.five = self.five + other.five\n        result.four = self.four + other.four\n        result.three = self.three + other.three\n        result.two = self.two + other.two\n        return result\n\n    def __str__(self):\n        return \"Five: %s, four: %s, three: %s, two: %s\" % (self.five, self.four, self.three, self.two)\n\n# statistics1 = Statistics()\n# statistics2 = Statistics()\n# statistics3 = statistics1 + statistics2\n# statistics3 = statistics1.__add__(statistics2)\nclass Session:\n    def __init__(self, name):\n        self.name = name\n        self.exam_list = []\n\n    def add_exam(self, exam):\n        self.exam_list.append(exam)\n\n    def statistics(self):\n        result_statistics = Statistics()\n        for exam in self.exam_list:\n            result_statistics += exam.statistics()\n        return result_statistics\n\nclass Exam:\n    def __init__(self, subject, date, teacher):\n        self.subject = subject\n        self.date = date\n        self.teacher = teacher\n        self.marks = []\n\n    def add_mark(self, mark):\n        self.marks.append(mark)\n\n    def statistics(self):\n        result_statistics = Statistics()\n        for mark in self.marks:\n            if mark.mark == 2:\n                result_statistics.two += 1\n            if mark.mark == 3:\n                result_statistics.three += 1\n            if mark.mark == 4:\n                result_statistics.four +=1\n            if mark.mark == 5:\n                result_statistics.five += 1\n        return result_statistics\n\nclass Mark:\n    def __init__(self, student, mark):\n        self.student = student\n        self.mark = mark\n\nclass Student:\n    def __init__(self, name):\n        self.name = name\n\nclass Teacher:\n    def __init__(self, name):\n        self.name = name\n\nclass Subject:\n    def __init__(self, name):\n        self.name = name\n\nteacher1 = Teacher('teacher1')\nsubject1 = Subject('subject1')\nstudent1 = Student('student1')\nstudent2 = Student('student2')\nstudent3 = Student('student3')\nstudent4 = Student('student4')\nstudent5 = Student('student5')\nexam1 = Exam(subject1, 'today', teacher1)\nexam1.add_mark(Mark(student1, 3))\nexam1.add_mark(Mark(student2, 4))\nexam1.add_mark(Mark(student3, 5))\nexam1.add_mark(Mark(student4, 4))\nexam1.add_mark(Mark(student5, 2))\nsession = Session('Spring 2018')\nsession.add_exam(exam1)\nstatistics = session.statistics()\nprint(statistics)","repo_name":"SunPrime/Learn_Python","sub_path":"lesson4/less4_part1_sessia.py","file_name":"less4_part1_sessia.py","file_ext":"py","file_size_in_byte":2549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"14563605682","text":"from __future__ import division\nfrom math import *\n\ndef jd2utc(jd):\n    '''\n    Go from Julian date to UTC.\n    Source:    http://stackoverflow.com/questions/29627533/conversion-of-julian-date-number-to-normal-date-utc-in-javascript/29627963#29627963\n    Last edit: 04 April 2016\n    '''\n    X = jd + 0.5\n    Z = floor(X)\n    F = X-Z\n    Y = floor((Z-1867216.25)/36524.24)\n    A = Z+1+Y-floor(Y/4)\n    B = A+1524\n    C = floor((B-122.1)/365.25)\n    D = floor(365.25*C)\n    G = floor((B-D)/30.6001)\n    if G < 13.5:\n        month=G-1\n    else:\n        month=G-13\n    if month<2.5:\n        year = C-4715\n    else:\n        year = C-4716\n\n    UT = B-D-floor(30.6001*G)+F\n    day = floor(UT)\n    UT -= floor(UT)\n    UT *= 24\n    hour = floor(UT)\n    UT -= floor(UT)\n    UT *= 60\n    minute = floor(UT)\n    UT -= floor(UT)\n    UT *= 60\n    second = round(UT)\n    return '%04d-%02d-%02d %02d:%02d:%02d' % (year,month,day,hour,minute,second)\n","repo_name":"ovro-lwa/distributed-pipeline","sub_path":"orca/extra/coords/jd2utc.py","file_name":"jd2utc.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"80"}
+{"seq_id":"18879804921","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.contrib import admin\nfrom django.utils.dateparse import parse_datetime\nfrom django.db import models as dj_models\nfrom . import models\nfrom .forms import WatermeterForm\n\nfrom .widgets import StringDateTimeWidget,StringSplitDateTime\n\n# Register your models here.\n\nclass CommunityAdmin(admin.ModelAdmin):\n    list_display = ['name','districtid']\n\n    fields = ('name','districtid')\n\n\n@admin.register(models.Tblfminfo)\nclass TblfminfoAdmin(admin.ModelAdmin):\n    list_display = ['precinctname','filialename','usertype','userid','username','simid']\n\n\n    formfield_overrides = {\n        dj_models.DateTimeField: {'widget': StringSplitDateTime},\n    }\n\n\nclass FlowShareDayTaxAdmin(admin.ModelAdmin):\n\n    # date_hierarchy = 'readtime'\n    list_filter = ['warning','warningdesc']\n\n    list_display = ['readtime','simid','flux','plustotalflux','reversetotalflux','warning','warningdesc']\n\n    formfield_overrides = {\n        dj_models.DateTimeField: {'widget': StringSplitDateTime},\n    }\n\n    # def save_model(self, request, obj, form, change):\n    #     print 'FlowShareDayTaxAdmin::',obj.readtime\n    #     print 'form.cleaned_data',form.cleaned_data['readtime']\n    #     obj.readtime = obj.readtime[:20]\n    #     print 'FlowShareDayTaxAdmin::',obj.readtime\n    #     super(FlowShareDayTaxAdmin,self).save_model(request, obj, form, change)\n\n   \n@admin.register(models.Watermeter)\nclass WatermeterAdmin(admin.ModelAdmin):\n    # form = WatermeterForm\n    actions = ['change_meterstate','change_datetime']\n    list_display = ['id','communityid','buildingname','roomname','nodeaddr','wateraddr','rvalue','fvalue','metertype','meterstate','commstate','rtime','lastrvalue','lastrtime','dosage','islargecalibermeter']\n    search_fields = ['metertype','meterstate',]\n    list_filter = ['metertype','meterstate','commstate']\n    show_admin_actions = False\n\n    formfield_overrides = {\n        dj_models.DateTimeField: {'widget': StringSplitDateTime},\n    }\n\n    fieldsets = (\n            (None,{\n                'fields':(('nodeaddr','wateraddr'),)\n                }),\n            ('Community',{\n                'fields':('communityid',('buildingname','roomname'),)\n                }),\n            ('Values',{\n                'fields':(('rvalue','fvalue'),'metertype','meterstate','commstate','rtime')\n                }),\n            ('Last read',{\n                'fields':('lastrvalue','lastrtime')\n                }),\n            ('Others',{\n                'fields':('dosage','islargecalibermeter')\n                }),\n        )\n\n\n\n    # def change_view(self, request, object_id, form_url='', extra_context=None):\n    #     extra_context = extra_context or {}\n    #     # extra_context['osm_data'] = self.get_osm_info()\n    #     print 'here?',object_id,extra_context\n    #     print 'request:',request\n    #     return super(WatermeterAdmin,self).change_view(\n    #     request, object_id, form_url, extra_context=extra_context,\n    #     )\n\n    # def changelist_view(self, request, extra_context=None):\n    #     response = super(WatermeterAdmin,self).changelist_view(request, extra_context)\n\n    #     try:\n    #         qs = response.context_data['cl'].queryset\n\n    #     except (AttributeError, KeyError):\n    #         return response\n    #     # rtime_tmp = response.context['rtime']\n\n    #     metrics = {\n    #         'rtime': parse_datetime('rtime'),\n    #         'lastrtime': parse_datetime('lastrtime'),\n    #     }\n    #     # response.context_data['summary'] = list(\n    #     #     qs.values('product__name').annotate(**metrics)\n    #     # )\n    #     # response.context_data['summary_total'] = dict(\n    #     #     qs.aggregate(**metrics)\n    #     # )\n    #     return response\n\n        \n    def save_model(self, request, obj, form, change):\n        # print 'communityid:',obj.communityid\n        super().save_model(request, obj, form, change)\n\n    def save_formset(self, request, form, formset, change):\n        instances = formset.save(commit=False)\n        \n        for obj in formset.deleted_objects:\n            obj.delete()\n        for instance in instances:\n            \n            instance.save()\n            formset.save_m2m()\n\n    def change_meterstate(self,request,queryset):\n        rows_updated = queryset.update(meterstate='正常')\n        if rows_updated == 1:\n            message_bit = \"1 item was\"\n        else:\n            message_bit = \"%s items were\" % rows_updated\n        self.message_user(request, \"%s successfully updated as nomal.\" % message_bit)\n    change_meterstate.short_description = 'change meterstate' \n\n\n    def change_datetime(self,request,queryset):\n        \n        rows_updated = queryset.update(rtime=parse_datetime('rtime'),\n            lastrtime=parse_datetime('lastrtime'))\n        if rows_updated == 1:\n            message_bit = \"1 item was\"\n        else:\n            message_bit = \"%s items were\" % rows_updated\n        self.message_user(request, \"%s successfully updated .\" % message_bit)\n    change_datetime.short_description = 'alter timestr to datetime' \n\nadmin.site.register(models.District)\nadmin.site.register(models.Community,CommunityAdmin)\n# admin.site.register(models.Watermeter)\nadmin.site.register(models.FlowShareDayTax,FlowShareDayTaxAdmin)","repo_name":"apengok/DistrictMeter","sub_path":"water/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":5291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"39866802851","text":"from aiogram.types import ReplyKeyboardMarkup, KeyboardButton, InlineKeyboardButton, InlineKeyboardMarkup\nfrom typing import Tuple\n\nfrom registration import config\n\n\ndef start_kb(error_login: bool = False) -> Tuple[str, ReplyKeyboardMarkup]:\n    mess = \"Hello, are you ready to register! If yes, click the button 'Start Registration'\"\n    if error_login:\n        mess = \"Login already exists, please create another one. Fill out the form from the beginning!\"\n    start_keyboard = ReplyKeyboardMarkup(resize_keyboard=True)\n    button = KeyboardButton(\"Start Registration\")\n    start_keyboard.add(button)\n    return mess, start_keyboard\n\n\ndef go_to_site() -> Tuple[str, InlineKeyboardMarkup]:\n    mess = \"To go to the site, click 'Go to the site'\"\n    start_keyboard = InlineKeyboardMarkup(row_width=1)\n    button = InlineKeyboardButton(text=\"Go to the site\", url='https://registration-bot.herokuapp.com/login/')\n    start_keyboard.add(button)\n    return mess, start_keyboard\n\n\ndef get_contact_kb() -> Tuple[str, ReplyKeyboardMarkup]:\n    mess = \"To send a phone, click on the 'Send phone' button\"\n    contact_keyboard = ReplyKeyboardMarkup(resize_keyboard=True)\n    button = KeyboardButton('Send phone', request_contact=True)\n    contact_keyboard.add(button)\n    return mess, contact_keyboard\n\n\ndef get_photo_kb() -> Tuple[str, ReplyKeyboardMarkup]:\n    mess = \"You can add a photo to your profile, to do this, \" \\\n           \"send a photo to the chat bot, or click 'Finish'\"\n    photo_keyboard = ReplyKeyboardMarkup(resize_keyboard=True)\n    button_finish = KeyboardButton('Finish')\n    photo_keyboard.add(button_finish)\n    return mess, photo_keyboard\n\n\ndef get_done_to_save_data(message: object) -> Tuple[str, ReplyKeyboardMarkup]:\n    mess = f\"Check your details\\n\" \\\n           f\"login - {config.USERS_PROFILE[message.chat.id]['login']}\\n\" \\\n           f\"password - {config.USERS_PROFILE[message.chat.id]['password']}\\n\" \\\n           f\"first name - {config.USERS_PROFILE[message.chat.id]['first_name']}\\n\" \\\n           f\"last name - {config.USERS_PROFILE[message.chat.id]['last_name']}\\n\" \\\n           f\"user name - {config.USERS_PROFILE[message.chat.id]['user_name']}\\n\" \\\n           f\"phone - {config.USERS_PROFILE[message.chat.id]['phone']}\\n\" \\\n           f\"photo - {'Photo is done' if config.USERS_PROFILE[message.chat.id]['photo'] else 'No photo'}\\n\" \\\n           f\" if everything is correct, click 'Save', if you want to start filling from the beginning, click 'Restart'\"\n    go_to_site_keyboard = ReplyKeyboardMarkup(resize_keyboard=True)\n    button_save = KeyboardButton(\"Save\")\n    button_restart = KeyboardButton(\"Restart\")\n    go_to_site_keyboard.add(button_save, button_restart)\n    return mess, go_to_site_keyboard\n\n\ndef return_to_start_kb() -> Tuple[str, ReplyKeyboardMarkup]:\n    mess = \"An error occurred in the chat bot, try filling out the profile from the beginning.\"\n    start_keyboard = ReplyKeyboardMarkup(resize_keyboard=True)\n    button = KeyboardButton(\"Start Registration\")\n    start_keyboard.add(button)\n    return mess, start_keyboard\n","repo_name":"IhorVoskoboinikov/Registration_chat_bot","sub_path":"telegram_bot_registration/registration/keyboards.py","file_name":"keyboards.py","file_ext":"py","file_size_in_byte":3068,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"14219974223","text":"# v1222-1013\n\nimport sys\nimport threading\nimport modules\nfrom copy import deepcopy\n\nDEFAULT_IMAGE_PATH = 'C:\\\\TEMP\\\\QR.bmp'\nREAD_BINARY = 'rb'\n\nclass QR2LINES:\n    def __init__(self):\n        # readed BMP file\n        self.rd_file = 0\n\n        # BMP Headers\n        self.bfType = b''\n        self.bfSize = b''\n        self.bfReserved1 = b''\n        self.bfReserved2 = b''\n        self.bfOffBits = b''\n\n        # Informations\n        self.bcSize = b''\n        self.bcWidth = b''\n        self.bcHeight = b''\n        self.bcPlanes = b''\n        self.bcBitCount = b''\n        self.biCompression = b''\n        self.biSizeImage = b''\n        self.biXPixPerMeter = b''\n        self.biYPixPerMeter = b''\n        self.biClrUsed = b''\n        self.biCirImportant = b''\n\n        # Image Format\n        self.image_type = ''\n        self.image_offset = 0\n        self.image_width = 0\n        self.image_height = 0\n        self.image_bits = 0\n        self.image_comp = 0\n\n        # Image Array\n        self.image_array = []\n        self.image_start = 0\n\n        # Drawing Points\n        self.start_point_x = 0\n        self.end_point_x = 0\n        self.start_point_y = 0\n        self.end_point_y = 0\n        self.min_offset_x = 0\n        self.min_offset_y = 0\n\n        # Circle Drawing\n        self.circle_points = []\n\n    def openImageFile(self, filepath=''):\n        if filepath == '':\n            filepath = DEFAULT_IMAGE_PATH\n\n        return open(filepath, READ_BINARY)\n\n    def closeImageFile(self):\n        if self.rd_file:\n            self.rd_file.close()\n\n    def getImageHeaders(self, filepath=''):\n        self.closeImageFile()\n        self.rd_file = self.openImageFile(filepath)\n\n        # BMP Headers\n        self.bfType = self.rd_file.read(2)\n        self.bfSize = self.rd_file.read(4)\n        self.bfReserved1 = self.rd_file.read(2)\n        self.bfReserved2 = self.rd_file.read(2)\n        self.bfOffBits = self.rd_file.read(4)\n\n        # Informations\n        self.bcSize = self.rd_file.read(4)\n        self.bcWidth = self.rd_file.read(4)\n        self.bcHeight = self.rd_file.read(4)\n        self.bcPlanes = self.rd_file.read(2)\n        self.bcBitCount = self.rd_file.read(2)\n        self.biCompression = self.rd_file.read(4)\n        self.biSizeImage = self.rd_file.read(4)\n        self.biXPixPerMeter = self.rd_file.read(4)\n        self.biYPixPerMeter = self.rd_file.read(4)\n        self.biClrUsed = self.rd_file.read(4)\n        self.biCirImportant = self.rd_file.read(4)\n\n    def getImageFormat(self):\n        self.image_type = str(self.bfType.decode())\n        self.image_offset = int.from_bytes(self.bfOffBits, 'little')\n        self.image_width = int.from_bytes(self.bcWidth, 'little')\n        self.image_height = int.from_bytes(self.bcHeight, 'little')\n        self.image_bits = int.from_bytes(self.bcBitCount, 'little')\n        self.image_comp = int.from_bytes(self.biCompression, 'little')\n\n    def checkImageFormat(self):\n        if self.image_type != 'BM':\n            sys.exit(f'画像フォーマットが違います。BMPのみ対応: {self.image_type}')\n        if self.image_bits != 1:\n            sys.exit(f'ビット深度が違います。1bitのみ対応: {self.image_bits}')\n        if self.image_comp != 0:\n            sys.exit(f'圧縮画像は非対応です。: {self.image_comp}')\n\n    def imageToArray(self, filepath=''):\n        print(f'Image size is ({self.image_width}, {self.image_height})')\n        self.image_array = [[] for i in range(self.image_height)]\n\n        # jump to Image\n        self.closeImageFile()\n        self.rd_file = self.openImageFile(filepath)\n        print(self.rd_file.read(self.image_offset))\n\n        plus_one = 1 if self.image_width/8 != int(self.image_width/8) else 0\n        loop_x = int(self.image_width/8) + plus_one\n        plus_one = 1 if int(loop_x/4) != loop_x/4 else 0\n        loop_x4 = 4*(int(loop_x/4)+plus_one) # BMP width must be x4 byte\n        dummy_x = loop_x4 - loop_x\n\n        for axis_y in range(self.image_height):\n            for axis_x in range(loop_x4):\n                borw = 255 - int.from_bytes(self.rd_file.read(1), 'little')\n                #ここでbit単位に分解だ!!!\n                borw_binlist = reversed(modules.num2binList(borw, 8))\n                borw_binlist = borw_binlist\n                self.image_array[axis_y].extend(borw_binlist)\n            self.image_array[axis_y] = self.image_array[axis_y][: self.image_width]\n        print(f'Length of self.image_array = {len(self.image_array)}')\n        # debug print\n        for arr in self.image_array:\n            #print(arr)\n            pass\n\n    def searchDrawPoint(self, axis_x, axis_y, xory, start_or_end):\n        temp_x = axis_x\n        draw_x = axis_x\n        temp_y = axis_y\n        draw_y = axis_y\n        ending = 0\n\n        if xory == 'x':\n            if start_or_end == 'start':\n                # search true start_point_x\n                while not ending:\n                    temp_x = draw_x - 1\n                    temp_x = 0 if temp_x < 0 else temp_x\n                    if self.image_array[axis_y][temp_x]:\n                        draw_x = temp_x\n                        if draw_x == 0:\n                            ending = 1\n                    else:\n                        ending = 1\n                self.start_point_x = draw_x\n\n            elif start_or_end == 'end':\n                # search true end_point_x\n                while not ending:\n                    temp_x = draw_x + 1\n                    temp_x = self.image_width-1 if temp_x > self.image_width-1 else temp_x\n                    if self.image_array[axis_y][temp_x]:\n                        draw_x = temp_x\n                        if draw_x == self.image_width-1:\n                            ending = 1\n                    else:\n                        ending = 1\n                self.end_point_x = draw_x\n        elif xory == 'y':\n            if start_or_end == 'start':\n                # search true start_point_y\n                while not ending:\n                    temp_y = draw_y - 1\n                    temp_y = 0 if temp_y < 0 else temp_y\n                    if self.image_array[temp_y][axis_x]:\n                        draw_y = temp_y\n                        if draw_y == 0:\n                            ending = 1\n                    else:\n                        ending = 1\n                self.start_point_y = draw_y\n            elif start_or_end == 'end':\n                # search true end_point_y\n                while not ending:\n                    temp_y = draw_y + 1\n                    temp_y = self.image_height-1 if temp_y > self.image_height-1 else temp_y\n                    if self.image_array[temp_y][axis_x]:\n                        draw_y = temp_y\n                        if draw_y == self.image_height:\n                            ending = 1\n                    else:\n                        ending = 1\n                self.end_point_y = draw_y\n\n    def arrayToDrawing(self):\n        offset_x = 0\n        offset_y = 0\n        scaling = 1\n        argv = sys.argv\n        print(argv)\n        if len(argv) == 2:\n            offset_x = float(argv[1][: argv[1].find(' ')])\n            offset_y = float(argv[1][argv[1].find(' ')+1: ])\n        elif len(argv) >= 3:\n            offset_x = float(argv[1])\n            offset_y = float(argv[2])\n            scaling = float(argv[3])\n\n        scaled_line_list = []\n        trimmed_line_set = set()\n\n        self.min_offset_x = self.image_width\n        self.min_offset_y = self.image_height\n        for axis_y in range(self.image_height):\n            for axis_x in range(self.image_width):\n                if self.image_array[axis_y][axis_x]:\n                    # search drawinf points\n                    args1 = (axis_x, axis_y, 'x', 'start')\n                    args2 = (axis_x, axis_y, 'x', 'end')\n                    args3 = (axis_x, axis_y, 'y', 'start')\n                    args4 = (axis_x, axis_y, 'y', 'end')\n                    args_set = [args1, args2, args3, args4]\n                    th_list = []\n                    # never use multiprocessing!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n                    for args_exe in args_set:\n                        th = threading.Thread(target=self.searchDrawPoint, args=args_exe)\n                        th.start()\n                        th_list.append(th)\n                    # wait for threads finish\n                    for thz in th_list:\n                        thz.join()\n                    # make horizonal drawing\n                    if self.start_point_x - self.end_point_x:\n                        scaled_line_list.append([offset_x+self.start_point_x*scaling, offset_y+axis_y*scaling, offset_x+self.end_point_x*scaling, offset_y+axis_y*scaling])\n                    # make vertical drawing\n                    if self.start_point_y - self.end_point_y:\n                        scaled_line_list.append([offset_x+axis_x*scaling, offset_y+self.start_point_y*scaling, offset_x+axis_x*scaling, offset_y+self.end_point_y*scaling])\n                        if self.start_point_y*scaling < self.min_offset_y:\n                            self.min_offset_y = self.start_point_y*scaling\n        print(f'Length of scaled_line_list = {len(scaled_line_list)}')\n        self.min_offset_x = min(scaled_line_list, key=lambda x: x[0])[0]-offset_x\n        self.min_offset_y = min(scaled_line_list, key=lambda x: x[1])[1]-offset_y\n        # trimming white zone\n        for elem in scaled_line_list:\n            trimmed_line_set.add(str(f'{round(elem[0]-self.min_offset_x, 3)} {round(elem[1]-self.min_offset_y, 3)}_{round(elem[2]-self.min_offset_x, 3)} {round(elem[3]-self.min_offset_x, 3)}'))\n        print(f'Length of trimmed_line_set = {len(trimmed_line_set)*2}')\n\n        # debug print\n        #print(trimmed_line_set)\n\n        return trimmed_line_set\n\n    def searchCircleDrawPoint(self, axis_x_start, axis_y_start, axis_x_end, axis_y_end):\n        # search true start_point_x\n        for temp_y in range(int(axis_y_end - axis_y_start)):\n            for temp_x in range(int(axis_x_end - axis_x_start)):\n                if self.image_array[temp_y][temp_x]:\n                    self.circle_points.append([temp_x, temp_y])\n\n    def arrayToCircleDrawing(self):\n        offset_x = 0\n        offset_y = 0\n        scaling = 1\n        argv = sys.argv\n        print(argv)\n        if len(argv) == 2:\n            offset_x = float(argv[1][: argv[1].find(' ')])\n            offset_y = float(argv[1][argv[1].find(' ')+1: ])\n        elif len(argv) >= 3:\n            offset_x = float(argv[1])\n            offset_y = float(argv[2])\n            scaling = float(argv[3])\n\n        scaled_circle_list = []\n        trimmed_circle_set = set()\n\n        self.min_offset_x = self.image_width\n        self.min_offset_y = self.image_height\n\n        # search drawinf points\n        args0 = (0, 0, self.image_width, self.image_height)\n        #args1 = (0, 0, int(self.image_width/2)+10, int(self.image_height/2)+10)\n        #args2 = (int(self.image_width/2)-10, 0, self.image_width, int(self.image_height/2)+10)\n        #args3 = (0, int(self.image_height/2)-10, int(self.image_width/2)+10, self.image_height)\n        #args4 = (int(self.image_width/2)-10, int(self.image_height/2)-10, self.image_width, self.image_height)\n        #args_set = [args1, args2, args3, args4]\n        args_set = [args0]\n        th_list = []\n        # never use multiprocessing!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n        for args_exe in args_set:\n            th = threading.Thread(target=self.searchCircleDrawPoint, args=args_exe)\n            th.start()\n            th_list.append(th)\n        # wait for threads finish\n        for thz in th_list:\n            thz.join()\n        # scaling circle drawing\n        print(f'Length of self.circle_points = {len(self.circle_points)}')\n        for elem in self.circle_points:\n            scaled_circle_list.append([offset_x+elem[0]*scaling, offset_y+elem[1]*scaling])\n        print(f'Length of scaled_circle_list = {len(scaled_circle_list)}')\n        self.min_offset_x = min(scaled_circle_list, key=lambda x: x[0])[0]-offset_x\n        self.min_offset_y = min(scaled_circle_list, key=lambda x: x[1])[1]-offset_y\n        # trimming white zone\n        for elem in scaled_circle_list:\n            trimmed_circle_set.add(str(f'{round(elem[0]-self.min_offset_x, 3)} {round(elem[1]-self.min_offset_y, 3)}'))\n        print(f'Length of trimmed_circle_set = {len(trimmed_circle_set)}')\n\n        # debug print\n        #print(trimmed_circle_set)\n\n        return trimmed_circle_set\n\n    def writeOutTextFile(self, filename='', all_text='', line_or_circle=''):\n        with open(filename, mode='w', encoding='Shift-JIS') as op_file:\n            for lines in all_text:\n                if line_or_circle == 'circle':\n                    op_file.write(lines+'\\n')\n                elif line_or_circle == 'line':\n                    op_file.write(lines[: lines.find('_')]+'\\n')\n                    op_file.write(lines[lines.find('_')+1: ]+'\\n')\n                else:\n                    op_file.write(lines[: lines.find('_')]+'\\n')\n                    op_file.write(lines[lines.find('_')+1: ]+'\\n')\n\n\n    def sequenceImageToLines(self, filepath):\n        self.getImageHeaders(filepath)\n        self.getImageFormat()\n        self.checkImageFormat()\n        self.imageToArray(filepath)\n        if len(sys.argv) >= 3:\n            line_or_circle = sys.argv[4]\n        if line_or_circle == 'circle':\n            result = self.arrayToCircleDrawing()\n            self.writeOutTextFile('C:\\\\TEMP\\\\QR.txt', result, 'circle')\n        elif line_or_circle == 'line':\n            result = self.arrayToDrawing()\n            self.writeOutTextFile('C:\\\\TEMP\\\\QR.txt', result, 'line')\n        else:\n            result = self.arrayToDrawing()\n            self.writeOutTextFile('C:\\\\TEMP\\\\QR.txt', result, 'line')\n\ndef main():\n    qr_cls = QR2LINES()\n    qr_cls.sequenceImageToLines('C:\\\\TEMP\\\\QR.bmp')\n\nif __name__ == '__main__':\n    main()\n","repo_name":"KeigoMega/BMP2LINES","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":13928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"41732406616","text":"# coding=utf-8\n\nfrom entities.entity import Entity, RequiredField, Field, DateField\n\nclass Profile(Entity):\n    '''\n    Representa la entidad perfil\n    '''\n    def __init__(self, data):\n        super().__init__(data)\n\n\n    # Definición de los campos de la entidad Profile\n    friends_count = RequiredField()\n    listed_count = RequiredField()\n    favourites_count = RequiredField()\n    verified = RequiredField()\n    description = RequiredField()\n    photo = RequiredField()\n    url = RequiredField()\n    provider = RequiredField()\n    followers_count = RequiredField()\n    name = RequiredField()\n    location = RequiredField()\n    screenname = RequiredField()\n    lang = RequiredField()","repo_name":"Shokesu/shokesuapi","sub_path":"entities/profile.py","file_name":"profile.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"10139575095","text":"import tensorflow as tf\nfrom tensorflow.keras import Model, Input, regularizers\nfrom tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, UpSampling2D, Add, Dropout, Conv2DTranspose\nfrom tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n\n\nclass AutoEncoderNetBuilder(object):\n\n    @classmethod\n    def create_model(cls, inputShape=(200, 200, 3), nFeat=16, nLevel=2, nLayersPerLevel=2, kernel_size=(3, 3),\n                          activation='relu', kernel_regularizer_param=1e-7):\n\n        input = Input(shape=inputShape)\n        downLevels = []\n\n        filters = nFeat\n        downLevels.append(cls.createConvLevel(input,\n                                              1,\n                                              filters, kernel_size, activation, kernel_regularizer_param))\n        for i in range(nLevel + 1):\n            if i > 0:\n                downLevels.append(MaxPool2D(padding='same')(downLevels[i - 1]))\n            filters = 2 * filters\n            downLevels[i] = cls.createConvLevel(downLevels[i],\n                                                nLayersPerLevel,\n                                                filters, kernel_size, activation, kernel_regularizer_param)\n        upLevels = [None for i in range(nLevel + 1)]\n        upLevels[nLevel] = downLevels[nLevel]\n        for i in range(nLevel - 1, -1, -1):\n            upLevels[i] = UpSampling2D(size=(2, 2), data_format=cls.NetConfig.data_format, interpolation=cls.NetConfig.upsampling_interpolation)(upLevels[i + 1])\n            filters = filters / 2\n            upLevels[i] = cls.createConvLevel(upLevels[i],\n                                              1,\n                                              filters, kernel_size, activation, kernel_regularizer_param)\n            upLevels[i] = Add()([upLevels[i], downLevels[i]])\n            upLevels[i] = cls.createConvLevel(upLevels[i],\n                                              nLayersPerLevel,\n                                              filters, kernel_size, activation, kernel_regularizer_param)\n        output = cls.createConvLevel(upLevels[0],\n                                     inputShape[-1],\n                                     filters, kernel_size, activation, kernel_regularizer_param)\n        model = Model(input, output)\n        return model\n\n    @classmethod\n    def createConvLevel(cls, input,\n                        nLayersPerLevel,\n                        filters, kernel_size, activation, kernel_regularizer_param):\n        output = input\n\n        for i in range(nLayersPerLevel):\n            output = cls.createConvBlock(output,\n                                         filters, kernel_size, activation, kernel_regularizer_param)\n        return output\n\n    @classmethod\n    def createConvBlock(cls, input,\n                        filters, kernel_size, activation, kernel_regularizer_param):\n        convLayer = tf.keras.layers.Conv2D(\n            filters=filters, kernel_size=kernel_size, strides=cls.NetConfig.stride, padding='same',\n            data_format=cls.NetConfig.data_format, dilation_rate=cls.NetConfig.dilation_rate, groups=cls.NetConfig.groups,\n            activation=activation,\n            kernel_regularizer=regularizers.l1(kernel_regularizer_param)\n        )(input)\n        dropoutInput = convLayer\n        if cls.NetConfig.bNorm:\n            dropoutInput = tf.keras.layers.BatchNormalization()(convLayer)\n        output = tf.keras.layers.SpatialDropout2D(\n            rate=cls.NetConfig.dropout_rate, data_format=cls.NetConfig.data_format\n        )(dropoutInput)\n        return output\n\n    class NetConfig:\n        inputShape = (None, None, 3)\n        bNorm = True\n        dropout_rate = 0.1\n        groups = 1\n        data_format = \"channels_last\"\n        stride = (1, 1)\n        dilation_rate = (1, 1)\n        upsampling_interpolation = 'bilinear'\n\n","repo_name":"szinuccsi/neuralishf_denoise_upscale","sub_path":"net_modules/full_nets/AutoEncoderNetBuilder.py","file_name":"AutoEncoderNetBuilder.py","file_ext":"py","file_size_in_byte":3843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"38257281701","text":"import os\nimport csv\nimport argparse\nimport zipfile\n\nfrom nnabla.logger import logger\nfrom nnabla.utils.data_source_loader import download\n\n\ndef func(args):\n    path = args.output_dir\n    if not os.path.exists(path):\n        os.makedirs(path)\n\n    # Create original training set\n    logger.log(99, 'Downloading Character Extraction dataset...')\n\n    with zipfile.ZipFile(download('https://nnabla.org/sample/sample_dataset/character_extraction_dataset.zip')) as zip:\n        zip.extractall(path)\n\n    for csv_file in ['train40000\\\\train40000.csv', 'validation\\\\validation.csv', 'validation\\\\validation_small.csv']:\n        with open(os.path.join(path, csv_file)) as f:\n            reader = csv.reader(f)\n            csv_data = [row for row in reader]\n\n        csv_dir, file_name = os.path.split(csv_file)\n\n        for line in csv_data[1:]:\n            for col in range(2):\n                line[col] = os.path.join(csv_dir, line[col])\n\n        with open(os.path.join(path, file_name), 'w') as f:\n            writer = csv.writer(f, lineterminator='\\n')\n            writer.writerows(csv_data)\n\n    logger.log(99, 'Dataset creation completed successfully.')\n\n\ndef main():\n    parser = argparse.ArgumentParser(\n        description='CharacterExtraction\\n\\n' +\n        'Download Character Extraction dataset from https://support.dl.sony.com/blogs-ja/dataset/character-extraction-dataset/.\\n\\n',\n        formatter_class=argparse.RawTextHelpFormatter)\n    parser.add_argument(\n        '-o',\n        '--output-dir',\n        help='path to write NNC dataset CSV format (dir) default=synthetic_data\\\\character_extraction',\n        required=True)\n    parser.set_defaults(func=func)\n\n    args = parser.parse_args()\n    args.func(args)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"sony/nnc-plugin","sub_path":"plugins/_Pre_Process/_Download_or_Generate_Dataset/create_character_extraction_csv.py","file_name":"create_character_extraction_csv.py","file_ext":"py","file_size_in_byte":1759,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"80"}
+{"seq_id":"27021786630","text":"from .. import models, schemas\nfrom typing import List, Optional\nfrom fastapi import Response, status, HTTPException, Depends, APIRouter\nfrom ..database import get_db\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import func\nfrom ..utils import pwd_context as pass_hasher\nfrom .. import oauth2\n\nrouter = APIRouter(\n    prefix=\"/posts\",\n    tags=[\"Posts\"]\n)\n\n# @router.get(\"/\", response_model=List[schemas.PostResponse])\n# @router.get(\"/\")\n# async def get_posts(db: Session = Depends(get_db), limit: int = 10, skip: int = 0, search: Optional[str] = \"\"):\n#     # cursor.execute(\"\"\"SELECT * FROM post\"\"\")\n#     # posts = cursor.fetchall()\n#     # print(posts)  \n\n#     posts = db.query(models.Post).filter(models.Post.title.contains(search)).limit(limit).offset(skip).all()\n\n#     results = db.query(models.Post, func.count(models.Vote.post_id).label(\"votes\")).join(\n#         models.Vote, models.Vote.post_id == models.Post.id, isouter=True).group_by(models.Post.id).all()\n    \n#     print(posts)\n#     return results\n\n@router.get(\"/\")\nasync def get_posts(db: Session = Depends(get_db), limit: int = 10, skip: int = 0, search: Optional[str] = \"\"):\n    query = db.query(models.Post, func.count(models.Vote.post_id).label(\"votes\")).outerjoin(\n        models.Vote, models.Vote.post_id == models.Post.id).group_by(models.Post.id)\n\n    if search:\n        query = query.filter(models.Post.title.contains(search))\n\n    posts_with_vote_counts = query.limit(limit).offset(skip).all()\n\n    results = []\n    for post, vote_count in posts_with_vote_counts:\n        post_data = post.__dict__\n        post_data[\"votes\"] = vote_count\n        results.append(post_data)\n\n    return results\n\n@router.get(\"/myposts\", response_model=List[schemas.PostResponse])\nasync def get_posts(db: Session = Depends(get_db), current_user: int = Depends(oauth2.get_current_user)):\n    # cursor.execute(\"\"\"SELECT * FROM post\"\"\")\n    # posts = cursor.fetchall()\n    # print(posts)\n\n    posts = db.query(models.Post).filter(models.Post.owner_id == current_user.id).all()\n    return posts\n\n@router.get(\"/{id}\", response_model=schemas.PostResponse)\nasync def get_post(id: int, response: Response, db: Session = Depends(get_db), current_user: int = Depends(oauth2.get_current_user)):\n    # cursor.execute(\"\"\"SELECT * FROM post WHERE id = %s\"\"\", (str(id), ))\n    # post = cursor.fetchone()\n    post = db.query(models.Post).filter(models.Post.id == id).first()\n    if not post:\n        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=\"post not found\")\n\n    # Making user to get only his posts with an id\n    if post.owner_id != current_user.id:\n        raise HTTPException(status_code=status.HTTP_403_FORBIDDEN, detail=f\"Not Allowed to perform requested action\")    \n\n    return post\n\n@router.post(\"/\", status_code=status.HTTP_201_CREATED, response_model=schemas.PostResponse)\nasync def create_posts(post: schemas.Post, db: Session = Depends(get_db), current_user: int = Depends(oauth2.get_current_user)):\n    # cursor.execute(\"\"\"INSERT INTO post (title, content, published) VALUES (%s, %s, %s) RETURNING *\"\"\", \n    #                (post.title, post.content, post.published))    \n    # db_response = cursor.fetchall()\n\n    # conn.commit()  # commiting to database\n\n    db_response = models.Post(owner_id=current_user.id, **post.dict())\n    db.add(db_response)\n    db.commit()\n    db.refresh(db_response)\n    return db_response\n\n@router.delete(\"/{id}\", status_code=status.HTTP_204_NO_CONTENT)\nasync def delete_post(id: int, db: Session = Depends(get_db), current_user: int = Depends(oauth2.get_current_user)):\n    # cursor.execute(\"\"\"DELETE FROM post WHERE id = %s RETURNING *\"\"\", (str(id), ))\n    # deleted_post = cursor.fetchone()\n\n    # # committing deleted post\n    # conn.commit()\n\n    post_to_del = db.query(models.Post).filter(models.Post.id == id)\n    post = post_to_del.first()\n\n    if post_to_del.first() == None:\n        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f\"Post with id {id} does not exist\")\n\n    if post.owner_id != current_user.id:\n        raise HTTPException(status_code=status.HTTP_403_FORBIDDEN, detail=f\"Not Allowed to perform requested action\")\n\n    \n    post_to_del.delete(synchronize_session=False)\n\n    db.commit()\n\n    return Response(status_code=status.HTTP_204_NO_CONTENT)\n\n@router.put(\"/{id}\", response_model=schemas.PostResponse)\nasync def update_post(id: int, post: schemas.Post, db: Session = Depends(get_db), current_user: int = Depends(oauth2.get_current_user)):\n    \n    # cursor.execute(\"\"\"UPDATE post SET title = %s, content = %s, published = %s WHERE id = %s RETURNING *\"\"\",\n    #                (post.title, post.content, str(post.published), str(id), ))\n\n    # updated_post = cursor.fetchone()\n\n    # # Committing\n    # conn.commit()\n\n    post_update_query = db.query(models.Post).filter(models.Post.id == id)\n    updated_post = post_update_query.first()\n\n    if updated_post is None:\n        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f\"Post with id {id} does not exist\") \n    \n    if updated_post.owner_id != current_user.id:\n        raise HTTPException(status_code=status.HTTP_403_FORBIDDEN, detail=f\"Not Allowed to perform requested action\")\n    \n    post_update_query.update(post.dict(), synchronize_session=False)\n    db.commit()\n\n    return post_update_query.first()","repo_name":"dev-satyamthakur/FastAPI-CRUD-Application","sub_path":"app/routers/post.py","file_name":"post.py","file_ext":"py","file_size_in_byte":5343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"24294484311","text":"# coding: utf-8\n\n## Loading Relevant libraries\nimport pandas as pd\nimport os as os\nimport numpy as np\n\n## Mapping to data directory\ndata_dir = os.listdir('../../data/raw/tabula-RENALOC_Niger_733/')\n\n## Function to load csv files from tabula, and properly format them for aggregation\ndef get_data(adress) :\n    url = '../../data/raw/tabula-RENALOC_Niger_733/' + adress\n    try :\n        liste_depts = pd.read_csv( url , encoding = \"ISO-8859-1\" )\n        ## Table with too few columns are ignored\n        if len(liste_depts.columns) < 7:\n            out = float('nan')\n        if len(liste_depts.columns) >= 8 :\n            out = liste_depts.reset_index()\n            out = out.drop(0)\n            if len(out.columns) < 10 :\n                out = out.iloc[: , [1,2,3,4,5,6,7,8]]\n            if len(out.columns) >= 10 :\n                out = out.iloc[: , [0,1,3,4,5,6,7,8]]\n            out.columns = ['locality' , 'population' , 'hommes', 'femmes' , 'menages' , 'menages_agricoles' , 'geoloc','settlement_type']\n            #print(out.head())\n            ## Excluding tables that pass the column count test but are improper\n            if out.loc[1 , 'locality'].__class__.__name__ in ['int64' , 'float'] :\n                out = float('nan')\n        return out\n\n    except (ValueError , IndexError) :\n        print(out.head())\n        out = float('nan')\n\n\n## Ordering the tables in their original order, as we will be imputing geographical zones from line position in tables\norder = []\nfor n in range(len(data_dir)) :\n    u = data_dir[n].split('-')[2].split('.')[0]\n    order = order + [int(u)]\norder = sorted(order)\n\n## Importing all relevant Tabula csv files in one data frame\nrenaloc = []\nfor i in order :\n    addresse = 'tabula-RENALOC_Niger_733-' + str(i) + '.csv'\n    dat = get_data(addresse)\n    if (dat.__class__.__name__ == 'DataFrame') :\n        print(addresse)\n        if (len(renaloc) > 0) :\n            renaloc = renaloc.append(dat , ignore_index = True)\n        if (len(renaloc) == 0) :\n            renaloc = dat\n\n\n## Ad hoc corrections of problematic values\ndak_list = renaloc[renaloc.locality == 'DAKORO (Département)'].index.tolist()\nrenaloc.loc[dak_list[0],'locality'] = 'DAKORO : Urbain'\nrenaloc.loc[dak_list[1],'locality'] = 'DAKORO : Rural'\nrenaloc.loc[renaloc.locality == 'DEPARTEMENT : TESSAOUA'] = 'DEPARTEMENT DE : TESSAOUA'\n\nsarkin_haoussa = renaloc[renaloc.locality == 'SARKIN HAOUSSA : Rural'].index\nrenaloc.loc[(sarkin_haoussa[0] - 1),'locality'] = 'COMMUNE DE : SARKIN HAOUSSA'\n\nsarkin_yamma = renaloc[renaloc.locality == 'SARKIN YAMMA : Rural'].index\nrenaloc.loc[(sarkin_yamma[0] - 1),'locality'] = 'COMMUNE DE : SARKIN YAMMA'\n\nakoubounou = renaloc[renaloc.locality == 'AKOUBOUNOU: Rural'].index\nrenaloc.loc[(akoubounou[0] - 1),'locality'] = 'COMMUNE DE : AKOUBOUNOU'\n\n## Transforming document hierarchical structure into covariables for Geographical zones\nrenaloc['level']  = renaloc['region'] = renaloc['departement'] = renaloc['commune'] = renaloc['milieu'] =         region = departement = commune = level = ''\nfor i in range(1,len(renaloc)) :\n    u = renaloc.iloc[i]\n    name = u.locality\n    try :\n        if 'REGION DE' in name :\n            print(name)\n            level = 'Region'\n            renaloc.loc[i,'level'] = level\n            region = name.split('REGION DE')[1]\n            departement = ''\n            commune = ''\n            renaloc.loc[i,'region'] = region\n        elif 'DEPARTEMENT DE' in name :\n            level = 'Departement'\n            renaloc.loc[i,'level']=  'level'\n            departement = name.split('DEPARTEMENT DE')[1]\n            renaloc.loc[i,'region'] = region\n            renaloc.loc[i,'departement'] = departement\n            commune = ''\n        elif 'COMMUNE DE' in name :\n            level = 'Commune'\n            renaloc.loc[i,'level']= level\n            commune = name.split('COMMUNE DE')[1]\n            renaloc.loc[i,'region'] = region\n            renaloc.loc[i,'departement'] = departement\n            renaloc.loc[i,'commune'] = commune\n        elif 'VILLE DE' in name :\n            if level != 'Ville' :\n                level = 'Ville'\n                arr = ''\n                departement = name.split('VILLE DE')[1]\n\n            renaloc.loc[i,'level'] = level\n            renaloc.loc[i,'region'] = region\n            renaloc.loc[i,'departement'] = departement\n        elif 'ARRONDISSEMENT' in name :\n            if level != 'arrondissement' :\n                level = 'arrondissement'\n                commune = 'ARRONDISSEMENT' + name.split('ARRONDISSEMENT')[1]\n\n            renaloc.loc[i,'region'] = region\n            renaloc.loc[i,'departement'] = departement\n            renaloc.loc[i , 'commune'] = commune\n            renaloc.loc[i , 'level'] = level\n\n        else :\n            level = 'Localite'\n            renaloc.loc[i , 'level'] = 'Localite'\n            renaloc.loc[i , 'region'] = region\n            renaloc.loc[i , 'departement'] = departement\n            renaloc.loc[i , 'commune'] = commune\n\n        if (' Urbain' in name) or (' Rural' in name) :\n            if ' Urbain' in name :\n                renaloc.loc[i ,'milieu'] = 'Urbain'\n            if ' Rural' in name :\n                renaloc.loc[i, 'milieu'] = 'Rural'\n\n            if (level == 'Region'):\n                renaloc.loc[i , 'region'] = region\n                renaloc.loc[i , 'level'] = level\n            if (level == 'Departement') :\n                renaloc.loc[i , 'region'] = region\n                renaloc.loc[i , 'departement'] = departement\n\n                renaloc.loc[i , 'level'] = level\n            if (level == 'Commune') :\n                renaloc.loc[i , 'region'] = region\n                renaloc.loc[i , 'departement'] = departement\n                renaloc.loc[i , 'level'] = level\n            if (level == 'Ville') :\n                renaloc.loc[i , 'region'] = region\n                renaloc.loc[i , 'departement'] = departement\n                renaloc.loc[i , 'level'] = level\n\n\n\n    except (RuntimeError, TypeError, NameError , AttributeError):\n        pass\n\n\n## Taking out some special characters\nrenaloc['region'] = renaloc['region'].str.replace('\\r|\\n|:' , '').str.strip()\nrenaloc['departement'] = renaloc['departement'].str.replace('\\r|\\n|:' , '').str.strip()\nrenaloc['commune'] = renaloc['commune'].str.replace('\\r|\\n|:|Rural|' , '').str.strip()\nrenaloc['locality'] = renaloc['locality'].str.replace('\\r|\\n|:' , '').str.strip()\n\n## Function to convert GPS coordinates into Lat / long\n## Note : this is ad hoc for GPS coordinates in Niger (ie all GPS coordinates are North East)\ndef conversion(old):\n    new = old.replace(u'°',' ').replace('\\'',' ').replace('\"',' ')\n    new = new.split()\n    #new_dir = new.pop()\n    new.extend([0,0,0])\n    return (int(new[0])+int(new[1])/60.0+int(new[2])/3600.0)\n\nrenaloc.geoloc\n\n## Function to parse GPS coordinates as they appear in the Tabula extracted csv\ndef extract_gps(pdf_string):\n    long = pdf_string.split(';')[0]\n\n    coord1 = long.split(':')[1].split(\"°\")[0]\n    coord2 = long.split('Â')[1].split(',')[0]\n    coord3 = long.split(',')[1].split(';')[0]\n\n    long = coord1 + coord2 + coord3\n\n    long = long.replace('\\\\' , '').replace('\"','').replace(' ',\"\")\n\n    lat = pdf_string.split(';')[1]\n    coord4 = lat.split(\"°\")[0]\n    coord4 = coord4.replace(\"l\" , \"\").replace(':','')\n    coord5 = lat.split('Â')[1].split(\",\")[0]\n    coord6 = lat.split(',')[1].split(\")\")[0]\n\n    lat =  coord4 + coord5 + coord6\n\n    return [long , lat]\n\n# Function to force float all variables supposed to be numeric\ndef float_all(data):\n    if (data.__class__.__name__ != 'float') :\n        try :\n            data = data.split('\\r')[0]\n            data = float(data)\n        except (ValueError) :\n            data = float('nan')\n\n    return data\n\n## Now going through all loaded data and parsing coordinates and putting all variables into numeric\nrenaloc['longitude'] = renaloc['latitude'] = ''\nnum_variables = ['hommes' , 'femmes' , 'menages' , 'menages_agricoles' , 'population']\n\n\n\nfor i in range(len(renaloc)):\n\n    for var in range(len(num_variables)):\n        variable = num_variables[var]\n        renaloc.loc[i , variable] = float_all(renaloc.loc[i , variable])\n\n\n    gps = renaloc.loc[i , 'geoloc']\n    if pd.isnull(gps) == False :\n        try :\n            gps_list = extract_gps(gps)\n            if (conversion(gps_list[0]) > 0) & (conversion(gps_list[1]) > 10) :\n                renaloc.loc[i , 'longitude'] = conversion(gps_list[0])\n                renaloc.loc[i , 'latitude'] = conversion(gps_list[1])\n        except (IndexError) :\n            print('Index Error at ' + str(i))\n\n\nrenaloc.loc[(renaloc['commune'] == 'KORE') & (renaloc['region'] == 'DOSSO') , 'commune'] = \"KORE MAIROUA\"\nrenaloc.loc[(renaloc['commune'] == 'GUIDAN') & (renaloc['region'] == 'MARADI') , 'commune'] = \"GUIDAN AMOUMOUNE\"\nrenaloc.loc[(renaloc['commune'] == 'BIRNI') & (renaloc['region'] == 'TAHOUA') , 'commune'] = \"BIRNI N'KONNI\"\nrenaloc.loc[(renaloc['commune'] == 'GALMA') & (renaloc['region'] == 'TAHOUA') , 'commune'] = \"GALMA KOUDAWATCHE\"\nrenaloc.loc[(renaloc['commune'] == 'KOURFEYE') & (renaloc['region'] == 'TILLABERI') , 'commune'] = \"KOURFEYE CENTRE\"\nrenaloc.loc[(renaloc['commune'] == 'OURO') & (renaloc['region'] == 'TILLABERI') , 'commune'] = \"OURO GUELADJO\"\nrenaloc.loc[(renaloc['commune'] == 'ARRONDISSEMENT  3') , 'commune'] = \"ARRONDISSEMENT 3\"\nrenaloc.loc[(renaloc['commune'].isin(['KAO' , 'TCHINTABARADEN'])) , 'departement'] = \"TCHINTABARADEN\"\nrenaloc.loc[((renaloc['departement'] == 'BIRNI') & (renaloc['region'] == 'TAHOUA') ) , 'departement'] = \"BIRNI N'KONNI\"\n\n\n## Adding Unique IDs\ncommunes_listing = pd.read_csv('../../data/processed/org_units_listing.csv' , encoding = \"ISO-8859-1\")\n\nrenaloc_full = pd.merge(renaloc , communes_listing ,\n                            on = ['region' , 'departement' , 'commune'] ,\n                            how = 'right')\n\nrenaloc_full.GPS_ID.nunique()\n\nrenaloc_full[pd.isnull(renaloc_full.locality)]\n\nrenaloc[~(renaloc.commune.isin(renaloc_full.commune))]\n\ncommunes_listing.GPS_ID.nunique()\n\n## Outputting the full data\nrenaloc_full.to_csv('../../data/processed/renaloc_full.csv' , index = False)\n","repo_name":"grlurton/niger_election","sub_path":"src/data/load_format_renaloc.py","file_name":"load_format_renaloc.py","file_ext":"py","file_size_in_byte":10164,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"71243565699","text":"\"\"\"Base classes for all config file formats.\"\"\"\n\nfrom __future__ import division\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport os\n\nfrom .. import exc\nfrom ..core import compat\nfrom ..core import config\n\n\nclass ConfigurationFile(object):\n\n    \"\"\"Base class for configuration file parsers.\"\"\"\n\n    def __init__(self, path, strict=True):\n        self._path = path\n        self._content = None\n        self._strict = strict\n\n    @property\n    def path(self):\n        \"\"\"Get the file path given at initialization.\"\"\"\n        return self._path\n\n    @property\n    def abspath(self):\n        \"\"\"Get the absolute path to the file.\"\"\"\n        return os.path.abspath(self._path)\n\n    @property\n    def content(self):\n        \"\"\"Get the file contents.\n\n        This property is cached. The file is only read once.\n        \"\"\"\n        if not self._content:\n\n            self._content = self._read()\n\n        return self._content\n\n    @property\n    def config(self):\n        \"\"\"Get a Configuration object from the file contents.\"\"\"\n        conf = config.Configuration()\n        for namespace in self.namespaces:\n\n            if not hasattr(conf, namespace):\n\n                if not self._strict:\n\n                    continue\n\n                raise exc.NamespaceNotRegistered(\n                    \"The namespace {0} is not registered.\".format(namespace)\n                )\n\n            name = getattr(conf, namespace)\n\n            for item, value in compat.iteritems(self.items(namespace)):\n\n                if not hasattr(name, item):\n\n                    if not self._strict:\n\n                        continue\n\n                    raise exc.OptionNotRegistered(\n                        \"The option {0} is not registered.\".format(item)\n                    )\n\n                setattr(name, item, value)\n\n        return conf\n\n    @property\n    def namespaces(self):\n        \"\"\"Get an iterable of str representing namespaces within the config.\"\"\"\n        raise NotImplementedError()\n\n    def items(self, namespace):\n        \"\"\"Get a dictionary of entries under a given namespace.\"\"\"\n        raise NotImplementedError()\n\n    def _read(self):\n        \"\"\"Open the file and return its contents.\"\"\"\n        with open(self.path, \"r\") as file_handle:\n\n            content = file_handle.read()\n\n        # Py27 INI config parser chokes if the content provided is not unicode.\n        # All other versions seems to work appropriately. Forcing the value to\n        # unicode here in order to resolve this issue.\n        return compat.unicode(content)\n","repo_name":"kevinconway/confpy","sub_path":"confpy/loaders/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2602,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"80"}
+{"seq_id":"31587639121","text":"\"\"\"Removes total_cost column in favor of hybrid property\n\nRevision ID: 5e2bc6083503\nRevises: 748744207122\nCreate Date: 2022-05-26 17:18:29.231995\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n# revision identifiers, used by Alembic.\nrevision = \"5e2bc6083503\"\ndown_revision = \"748744207122\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column(\"incident\", \"total_cost\")\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column(\n        \"incident\", sa.Column(\"total_cost\", sa.NUMERIC(), autoincrement=False, nullable=True)\n    )\n    # ### end Alembic commands ###\n","repo_name":"Netflix/dispatch","sub_path":"src/dispatch/database/revisions/tenant/versions/2022-05-26_5e2bc6083503.py","file_name":"2022-05-26_5e2bc6083503.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","stars":4294,"dataset":"github-code","pt":"80"}
+{"seq_id":"791007336","text":"import numpy as np\nimport matplotlib.pyplot as plt  \nimport os\nimport pandas as pd\n\n#Initial constants \ng0=9.81 #Earth's Gravity\ngm=1.62 #Moon's Gravity\n\npath=os.getcwd()+\"\\\\Plots\\\\\"+\"Data for Range of 3.2.csv\"\nname=\"df32\"\ncommand=pd.read_csv(\"{}\".format(path))\nexec(name+\"=\"+\"command\")   #read the csv file (put 'r' before the path string to address any special characters in the path, such as '\\'). Don't forget to put the file name at the end of the path + \".csv\"\n\n\n#Parameters to be changed\nm_prop_m_total=(100-df32.iloc[-1,4])/100 #Percentage of the mass needed for each hop (0.0022 for normal method, 0.0033 for alternative)\nD_covered=200\nR_hop=3.2\n\ndef subsystem_size(pl_percentage):\n    \"\"\"\n    This function calculates the mass of the subsystems of the hopp\n    \n    Inputs:\n        pl_percentage: percentage of the total mass that is payload (8% to 13%)\n        \n    Outputs:\n        m_total_2-m_total_1:difference between initial mass and the mass after the iteration\n        m_total_1:initial guess of the total mass\n        m_total_2:Mass after the iteration        \n    \"\"\"    \n    \n    m_pl=3.3\n    m_total_1=m_pl/pl_percentage #The pl_percentage change from 8% to 12%\n    \n    m_propellant=m_total_1-m_total_1*((1-m_prop_m_total)**(np.ceil(D_covered/R_hop))) #Each hop uses 0.3% in the worst case, the descent into the pit uses 1.33% of the total mass\n    print(m_propellant)\n    m_dry=m_total_1-m_propellant-m_pl\n    m_inert= m_dry+m_pl\n\n    #Estimation of Thrust Subsystem -> Taken from MIT Talaris Propulsion sizing\n    m_e= 0.1*(1.3*gm*m_total_1)**(2/3) #Mass of the engine\n    m_tank=2/3*m_propellant**(2/3)\n    m_prop_system=(m_e+m_propellant+m_tank) * 1.1\n    m_structural=3/20*m_total_1 * 1.1\n    #1.1 is a safe margin\n    \n    m_power= ((23/0.22) * 2 )/100 * 1.1 #power from payload/ 0.22 (smad) /density + safe margin table 14.20\n    \n    m_mobility_system=5.3/30.9*m_dry *1.1 #Amalia rover -> change to percentage + 10%\n     \n    #SMAD Appendix A \n    m_cdh=0.04*m_dry *1.1\n    m_ttc=0.07*m_dry *1.1\n    m_thermal=0.06*m_dry *1.1\n    \n    # m_comms_avionics_power_thermal=32.03/91.91*m_total #Percentage of the MIT hopper\n    \n    m_other= 0.04*m_dry *1.1 #Paper -> A new sizing methodology\n\n    m_total_2=m_prop_system+m_structural+m_power+m_mobility_system+m_pl+m_cdh+m_ttc+m_thermal+m_other\n    # print(m_total_1)\n    # print(m_total_2)\n    return m_total_2-m_total_1, m_total_1,m_total_2\n\nprint(subsystem_size(.1))\ndiff=[]\npl_percentage=np.arange(0.10,0.17,0.01)\nfor i in pl_percentage:\n    i=round(i,2)\n    diff.append(subsystem_size(i)[0])\n    print(\"Difference between the masses of {} kg for a payload percentage of {}% and an initial mass of {} kg\".format(round(subsystem_size(i)[0],2),int(100*i),round(subsystem_size(i)[1],1)))\n    \n\nplt.plot([100*x for x in pl_percentage],[abs(subsystem_size(x)[0]) for x in pl_percentage],color=\"brown\")\nplt.title(\"Convergence of system subsizing\")\nplt.grid(True)\nplt.xlabel('Payload percentage (%)',fontsize=15)\nplt.ylabel(r'Mass Difference',fontsize=15)    \nplt.savefig(os.getcwd()+\"\\\\plots\\\\\" + \"system_subsizing_convergence.png\")\n\n\n# plt.plot([x for x in np.arange(0.08,0.13,0.001)],[abs(subsystem_size(x)[0]) for x in np.arange(0.08,0.13,0.001)],color=\"brown\")\n# plt.title(\"Convergence of system subsizing\")\n# plt.grid(True)\n# plt.xlabel('Payload percentage (decimal)',fontsize=15)\n# plt.ylabel(r'Mass Difference',fontsize=15)    \n\n","repo_name":"JoaoGamboa/Moon-Hopper","sub_path":"Mass_Estimation.py","file_name":"Mass_Estimation.py","file_ext":"py","file_size_in_byte":3410,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"80"}
+{"seq_id":"18691999881","text":"\"\"\"\n\n    get_aggregate_google_API_data.py\n\n    This data aggregates the Google API data stored in AWS. It assumes the existence of an existing aggregate datasets\n\n    It loads the most recent aggregate dataset, the most recent daily scrapes of data, and appends the most recent scrape\n    to the most recent aggregate dataset\n\n    Args:\n        - Date: a date, in the format of %Y-%m-%d (e.g., 2020-03-20)\n\n    Assumes that there is an existing aggregate dataset and that the most recent version has already been scraped\n    (via 'get_google_API_data.py')\n\n\"\"\"\nimport os\nimport sys\nimport re\nimport boto3\nimport pytrends\nfrom pytrends.request import TrendReq\nimport pandas as pd\nimport datetime\nimport re\n\nif __name__ == \"__main__\":\n\n    # get consts, creds, args\n\n    AWS_BUCKET = os.environ[\"AWS_BUCKET\"]\n    AWS_ACCESS = os.environ[\"AWS_ACCESS\"]\n    AWS_SECRET = os.environ[\"AWS_SECRET\"]\n\n    INTEREST_OVER_TIME_FILENAME = \"interest_over_time_\"\n    INTEREST_BY_REGION_FILENAME = \"interest_by_region_\"\n\n    LOCAL_SCRAPES_DIR = \"../../tweets/google_API_scrapes/\"\n    AGGREGATE_GOOGLE_DIR = \"aggregate_google_API_data/\"\n    AWS_SCRAPES_DIR = \"google_API_scrapes/\"\n\n    LOCAL_AGGREGATE_TIME_PATH = LOCAL_SCRAPES_DIR + \"aggregate_interest_over_time_\"\n    LOCAL_AGGREGATE_REGION_PATH = LOCAL_SCRAPES_DIR + \"aggregate_interest_by_region_\"\n\n    AWS_AGGREGATE_TIME_PATH = AGGREGATE_GOOGLE_DIR + \"aggregate_interest_over_time_\"\n    AWS_AGGREGATE_REGION_PATH = AGGREGATE_GOOGLE_DIR + \"aggregate_interest_by_region_\"\n\n    LOCAL_TIME_FILENAME = LOCAL_SCRAPES_DIR + INTEREST_OVER_TIME_FILENAME\n    LOCAL_REGION_FILENAME = LOCAL_SCRAPES_DIR + INTEREST_BY_REGION_FILENAME\n\n    AWS_TIME_FILENAME = AWS_SCRAPES_DIR + INTEREST_OVER_TIME_FILENAME\n    AWS_REGION_FILENAME = AWS_SCRAPES_DIR + INTEREST_BY_REGION_FILENAME\n\n    # get date provided, date of most recent aggregate file\n\n    START_DATE = \"2020-03-01\"\n    DATE = datetime.datetime.strptime(sys.argv[1], \"%Y-%m-%d\")\n    DATE_FORMATTED = DATE.strftime(\"%Y-%m-%d\")\n\n    PREV_DATE_FORMATTED = (\n        DATE - datetime.timedelta(days=1)).strftime(\"%Y-%m-%d\")\n\n    print(\n        f\"The provided date is: {DATE_FORMATTED} and the previous date is {PREV_DATE_FORMATTED}\")\n\n    # get filenames, of preexisting file, the most recent data scrape, and the next aggregate file\n    PREV_AGG_DATA_FILENAME = f\"{START_DATE}_{PREV_DATE_FORMATTED}.csv\"\n\n    NEW_FILE_SCRAPE_TIME = AWS_TIME_FILENAME + DATE_FORMATTED\n    NEW_FILE_SCRAPE_REGION = AWS_REGION_FILENAME + DATE_FORMATTED\n\n    NEW_AGG_DATA_FILENAME = f\"{START_DATE}_{DATE_FORMATTED}.csv\"\n\n    # connect with AWS S3\n    s3 = boto3.client('s3',\n                      aws_access_key_id=AWS_ACCESS,\n                      aws_secret_access_key=AWS_SECRET)\n\n    # load pre-existing data:\n\n    # load previous aggregate data\n    s3.download_file(Bucket=AWS_BUCKET,\n                     Key=AWS_AGGREGATE_TIME_PATH + PREV_AGG_DATA_FILENAME,\n                     Filename=LOCAL_AGGREGATE_TIME_PATH + PREV_AGG_DATA_FILENAME)\n\n    s3.download_file(Bucket=AWS_BUCKET,\n                     Key=AWS_AGGREGATE_REGION_PATH + PREV_AGG_DATA_FILENAME,\n                     Filename=AWS_AGGREGATE_REGION_PATH + PREV_AGG_DATA_FILENAME)\n\n    # load today's time and region data\n    s3.download_file(Bucket=AWS_BUCKET,\n                     Key=NEW_FILE_SCRAPE_TIME,\n                     Filename=LOCAL_TIME_FILENAME + DATE_FORMATTED + \".csv\")\n\n    s3.download_file(Bucket=AWS_BUCKET,\n                     Key=NEW_FILE_SCRAPE_REGION,\n                     Filename=LOCAL_REGION_FILENAME + DATE_FORMATTED + \".csv\")\n\n    # load as dfs\n    prev_agg_time_df = pd.read_csv(\n        LOCAL_AGGREGATE_TIME_PATH + PREV_AGG_DATA_FILENAME)\n    prev_agg_region_df = pd.read_csv(\n        AWS_AGGREGATE_REGION_PATH + PREV_AGG_DATA_FILENAME)\n\n    new_time_df = pd.read_csv(LOCAL_TIME_FILENAME + DATE_FORMATTED + \".csv\")\n    new_region_df = pd.read_csv(\n        LOCAL_REGION_FILENAME + DATE_FORMATTED + \".csv\")\n\n    # remove local copies\n    os.remove(LOCAL_AGGREGATE_TIME_PATH + PREV_AGG_DATA_FILENAME)\n    os.remove(AWS_AGGREGATE_REGION_PATH + PREV_AGG_DATA_FILENAME)\n    os.remove(LOCAL_TIME_FILENAME + DATE_FORMATTED + \".csv\")\n    os.remove(LOCAL_REGION_FILENAME + DATE_FORMATTED + \".csv\")\n\n    # add 'date' col to region df\n    new_region_df['date'] = DATE_FORMATTED\n\n    # append new dfs to running aggregate dfs\n    new_agg_time_df = prev_agg_time_df.append(new_time_df)\n    new_agg_region_df = prev_agg_region_df.append(new_region_df)\n\n    # save new dfs, locally\n    new_agg_time_df.to_csv(LOCAL_AGGREGATE_TIME_PATH +\n                           f\"{START_DATE}_{DATE_FORMATTED}.csv\")\n    new_agg_region_df.to_csv(\n        LOCAL_AGGREGATE_REGION_PATH + f\"{START_DATE}_{DATE_FORMATTED}.csv\")\n\n    # export to AWS\n    s3.upload_file(LOCAL_AGGREGATE_TIME_PATH +\n                   f\"{START_DATE}_{DATE_FORMATTED}.csv\",\n                   AWS_BUCKET, AWS_AGGREGATE_TIME_PATH + f\"{START_DATE}_{DATE_FORMATTED}.csv\")\n    s3.upload_file(LOCAL_AGGREGATE_REGION_PATH + f\"{START_DATE}_{DATE_FORMATTED}.csv\",\n                   AWS_BUCKET, AWS_AGGREGATE_REGION_PATH + f\"{START_DATE}_{DATE_FORMATTED}.csv\")\n\n    print(\n        f\"Finished running 'get_aggregate_google_API_data.py' at (in UTC time): {datetime.datetime.utcnow()}\")\n","repo_name":"mark-torres10/COVID_Twitter_dashboard","sub_path":"app/backend/get_aggregate_google_API_data.py","file_name":"get_aggregate_google_API_data.py","file_ext":"py","file_size_in_byte":5269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"44822646428","text":"import numpy as np\r\nimport cv2\r\nfrom matplotlib import pyplot as plt\r\n\r\n\r\ndef get_gray_his(img_file):\r\n    image = cv2.imread(img_file, cv2.IMREAD_GRAYSCALE)\r\n    count_gray = [0] * 256\r\n    total = float((image.shape[0] - 2) * (image.shape[1] - 2))\r\n\r\n    for i in range(image.shape[0]):\r\n        for j in range(image.shape[1]):\r\n            count_gray[image[i, j]] += 1 / total\r\n\r\n    ind = np.arange(256)\r\n    plt.title(\"Gray Histogram of \" + img_file.split('.')[0])\r\n    plt.bar(ind, count_gray, width=1)\r\n\r\n    plt.show()\r\n\r\n    return count_gray\r\n\r\n\r\ndef get_grad_his(img_file):\r\n    image = cv2.imread(img_file, cv2.IMREAD_GRAYSCALE)\r\n    count_grad = [0] * 361\r\n    total = float((image.shape[0] - 2) * (image.shape[1] - 2))\r\n\r\n    for i in range(1, image.shape[0] - 1):\r\n        for j in range(1, image.shape[1] - 1):\r\n            i_x = int(image[i + 1, j]) - int(image[i - 1, j])\r\n            i_y = int(image[i, j + 1]) - int(image[i, j - 1])\r\n\r\n            seth = int((i_x ** 2 + i_y ** 2) ** 0.5)\r\n            count_grad[seth] += 1 / total\r\n\r\n    ind = np.arange(361)\r\n    plt.title(\"Grad Histogram of \" + img_file.split('.')[0])\r\n    plt.bar(ind, count_grad, width=1)\r\n\r\n    plt.show()\r\n\r\n    return count_grad","repo_name":"fffffarmer/IEE-homework","sub_path":"Exp10/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"42707467063","text":"import pytest\n\nimport itertools\nimport tempfile\n\nimport matplotlib as mpl\nmpl.use('Agg') # backend plotting, i.e. to suppress window pops up\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom e15190.neutron_wall import geometry as nwgeom\nfrom e15190.utilities import geometry as geom\n\n@pytest.fixture()\ndef nw_wall():\n    return {AB: nwgeom.Wall(AB) for AB in 'AB'}\n\nclass TestWall:\n    def test_read_from_inventor_readings(self, nw_wall):\n        for AB in ('A', 'B'):\n            wall = nw_wall[AB]\n            bars = wall.read_from_inventor_readings(wall.path_inventor_readings)\n            assert len(bars) == 25\n            for bar in bars:\n                assert isinstance(bar, nwgeom.Bar)\n\n    def test_save_vertices_to_database(self, nw_wall):\n        for AB in ('A', 'B'):\n            wall = nw_wall[AB]\n            bars = wall.read_from_inventor_readings(wall.path_inventor_readings)\n            tmp_path = tempfile.NamedTemporaryFile(suffix='.dat').name\n            wall.save_vertices_to_database('B', tmp_path, bars)\n            df = pd.read_csv(tmp_path, delim_whitespace=True, comment='#')\n            assert tuple(df.columns) == ('nwb-bar', 'dir_x', 'dir_y', 'dir_z', 'x', 'y', 'z')\n            assert len(df) == 25 * 8 # n_bars * n_vertices\n    \n    def test_save_pca_to_database(self, nw_wall):\n        for AB in ('A', 'B'):\n            wall = nw_wall[AB]\n            bars = wall.read_from_inventor_readings(wall.path_inventor_readings)\n            tmp_path = tempfile.NamedTemporaryFile(suffix='.dat').name\n            wall.save_pca_to_database('B', tmp_path, bars)\n            df = pd.read_csv(tmp_path, delim_whitespace=True, comment='#')\n            assert tuple(df.columns) == ('nwb-bar', 'vector', 'lab-x', 'lab-y', 'lab-z')\n            assert len(df) == 25 * 4 # n_bars * (1 mean + 3 components)\n\n    def test___init__(self):\n        pyrex_wall = nwgeom.Wall('B', contain_pyrex=True, refresh_from_inventor_readings=False)\n        nopyrex_wall = nwgeom.Wall('B', contain_pyrex=False, refresh_from_inventor_readings=False)\n\n        for pyrex_bar, nopyrex_bar in zip(pyrex_wall.bars.values(), nopyrex_wall.bars.values()):\n            assert pyrex_bar.contain_pyrex == True\n            assert nopyrex_bar.contain_pyrex == False\n            assert pyrex_bar.length > nopyrex_bar.length\n    \n@pytest.fixture\ndef nw_bars():\n    return dict(\n        nwb_pyrex=nwgeom.Wall('B', contain_pyrex=True).bars,\n        nwb_nopyrex=nwgeom.Wall('B', contain_pyrex=False).bars,\n    )\n\nclass TestBar:\n    def test___init__(self):\n        # a hypothetical bar\n        vertices = np.array([\n            [5, 0, 0],\n            [5, 0, 2],\n            [5, 1, 0],\n            [5, 1, 2],\n            [-5, 0, 0],\n            [-5, 0, 2],\n            [-5, 1, 0],\n            [-5, 1, 2],\n        ])\n        bar = nwgeom.Bar(vertices)\n        expected_pca_components = np.array([\n            [-1, 0, 0],\n            [0, 0, 1],\n            [0, 1, 0],\n        ], dtype=float)\n        assert np.allclose(bar.pca.components_, expected_pca_components)\n    \n    def test_length(self, nw_bars):\n        for bar in nw_bars['nwb_pyrex'].values():\n            assert bar.length == pytest.approx(76 * 2.54 + 2 * bar.pyrex_thickness, abs=0.01)\n        for bar in nw_bars['nwb_nopyrex'].values():\n            assert bar.length == pytest.approx(76 * 2.54, abs=0.01)\n    \n    def test_height(self, nw_bars):\n        for bar in nw_bars['nwb_pyrex'].values():\n            assert bar.height == pytest.approx(3 * 2.54 + 2 * bar.pyrex_thickness, abs=0.01)\n        for bar in nw_bars['nwb_nopyrex'].values():\n            assert bar.height == pytest.approx(3 * 2.54, abs=0.01)\n    \n    def test_thickness(self, nw_bars):\n        for bar in nw_bars['nwb_pyrex'].values():\n            assert bar.thickness == pytest.approx(2.5 * 2.54 + 2 * bar.pyrex_thickness, abs=0.01)\n        for bar in nw_bars['nwb_nopyrex'].values():\n            assert bar.thickness == pytest.approx(2.5 * 2.54, abs=0.01)\n\n    def test_remove_pyrex(self, nw_bars):\n        for bar in nw_bars['nwb_pyrex'].values():\n            assert bar.contain_pyrex == True\n            bar.remove_pyrex()\n            assert bar.contain_pyrex == False\n            with pytest.raises(Exception) as err:\n                bar.remove_pyrex()\n\n        for bar in nw_bars['nwb_nopyrex'].values():\n            assert bar.contain_pyrex == False\n            with pytest.raises(Exception) as err:\n                bar.remove_pyrex()\n    \n    def test_add_pyrex(self, nw_bars):\n        for bar in nw_bars['nwb_pyrex'].values():\n            assert bar.contain_pyrex == True\n            with pytest.raises(Exception) as err:\n                bar.add_pyrex()\n        for bar in nw_bars['nwb_nopyrex'].values():\n            assert bar.contain_pyrex == False\n            bar.add_pyrex()\n            assert bar.contain_pyrex == True\n            with pytest.raises(Exception) as err:\n                bar.add_pyrex()\n\n    def test_to_local_coordinates(self, nw_bars):\n        for bar in nw_bars['nwb_pyrex'].values():\n            # test single 3-tuple\n            loc_coord = bar.to_local_coordinates([0, 0, 0])\n            # bar centers should be close to zero along the 1st principal axis of the bar\n            assert loc_coord[0] == pytest.approx(0, abs=10.0)\n            # bar centers should be around 445 cm from the target along the beam direction\n            assert loc_coord[2] == pytest.approx(445, abs=2.0)\n\n            # test 2d-array with one row of 3-tuple\n            assert np.allclose(bar.to_local_coordinates([[0, 0, 0]]), [loc_coord])\n\n            # test an array of 3-tuples\n            lab_coords = np.array(list(bar.vertices.values()))\n            loc_coords = bar.to_local_coordinates(lab_coords)\n            norms = np.linalg.norm(loc_coords, axis=1)\n            # bar vertices should all be ~100 cm away from the bar centers origin\n            assert np.all(np.isclose(norms, 100.0, atol=10.0))\n            break\n    \n    def test_to_lab_coordinates(self, nw_bars):\n        for bar in nw_bars['nwb_pyrex'].values():\n            # test single 3-tuple\n            lab_coords = bar.to_lab_coordinates([0, 0, 0])\n            xz_distance = np.sqrt(lab_coords[0]**2 + lab_coords[2]**2)\n            # bar centers should be around 445 cm from the target along the beam direction\n            assert xz_distance == pytest.approx(445, abs=2.0)\n\n            # test 2d-array with one row of 3-tuple\n            assert np.allclose(bar.to_lab_coordinates([[0, 0, 0]]), [lab_coords])\n\n            # test an array of 3-tuples\n            loc_coords = np.array(list(bar.loc_vertices.values()))\n            lab_coords = bar.to_lab_coordinates(loc_coords)\n            norms = np.linalg.norm(lab_coords, axis=1)\n            # bar vertices should all be at least 445 cm away from the target\n            assert np.all(norms > 445)\n            # all should have similar norms\n            assert np.all(np.isclose(norms, norms[0], atol=10.0))\n            # but not identical\n            assert ~np.all(np.isclose(norms, norms[0], atol=5.0))\n    \n    def test_consistent_frame_transformation(self, nw_bars):\n        rand_pos = np.random.uniform(-100, 100, size=(20, 3))\n        for bar in nw_bars['nwb_pyrex'].values():\n            loc_coords = bar.to_local_coordinates(rand_pos)\n            lab_coords = bar.to_lab_coordinates(loc_coords)\n            assert np.all(np.isclose(rand_pos, lab_coords))\n\n            lab_coords = bar.to_lab_coordinates(rand_pos)\n            loc_coords = bar.to_local_coordinates(lab_coords)\n            assert np.all(np.isclose(rand_pos, loc_coords))\n    \n    def test_is_inside(self, nw_bars):\n        for bar in nw_bars['nwb_pyrex'].values():\n            # test single 3-tuple\n            # bar center should be inside\n            assert bar.is_inside([0, 0, 0], frame='local')\n            # point far from bar center should not be inside\n            assert ~bar.is_inside([1e2, 1e2, 1e2], frame='local')\n            # point at the lab origin should not be inside\n            assert ~bar.is_inside([0, 0, 0], frame='lab')\n            # bar center in lab frame should be inside\n            assert bar.is_inside(bar.pca.mean_, frame='lab')\n\n            # test an array of 3-tuples\n            # points near the bar center should be inside\n            loc_coords = np.random.uniform(-0.1, 0.1, size=(20, 3))\n            assert np.all(bar.is_inside(loc_coords, frame='local'))\n            # points far from the bar center should not be inside\n            loc_coords = np.random.uniform(1e2, 1e4, size=(20, 3))\n            assert np.all(~bar.is_inside(loc_coords, frame='local'))\n            # points near the lab origin should not be inside\n            lab_coords = np.random.uniform(-0.1, 0.1, size=(20, 3))\n            assert np.all(~bar.is_inside(lab_coords, frame='lab'))\n            # points near the bar center in lab frame should be inside\n            lab_coords = bar.pca.mean_ + np.random.uniform(-0.1, 0.1, size=(20, 3))\n            assert np.all(bar.is_inside(lab_coords, frame='lab'))\n\n            # some edge cases: corners, edges and faces\n            bar_vertices = np.array(list(bar.vertices.values()))\n            vertex_pairs = np.array(list(itertools.combinations(bar_vertices, 2)))\n            bar_edges = 0.5 * np.sum(vertex_pairs, axis=1)\n            vertex_triplets = np.array(list(itertools.combinations(bar_vertices, 3)))\n            bar_faces = np.sum(vertex_triplets, axis=1) / 3\n\n            tol = 1e-3 # 10 micrometer should be included\n            assert np.all(bar.is_inside(bar_vertices, frame='lab', tol=tol))\n            assert np.all(bar.is_inside(bar_edges, frame='lab', tol=tol))\n            assert np.all(bar.is_inside(bar_faces, frame='lab', tol=tol))\n\n            tol = 1e-8 # 0.1 nanometer should not be able to include everything\n            coords = np.vstack([bar_vertices, bar_edges, bar_faces])\n            assert ~np.all(bar.is_inside(coords, frame='lab', tol=tol))\n    \n    def test_randomize_from_local_x(self, nw_bars):\n        for bar in nw_bars['nwb_pyrex'].values():\n            tol = 1e-1\n\n            # testing inside the bar\n            loc_x = np.random.uniform(-0.5 * bar.length + tol, 0.5 * bar.length - tol, size=(20))\n\n            lab_coords = bar.randomize_from_local_x(loc_x, return_frame='lab')\n            assert np.all(bar.is_inside(lab_coords, frame='lab'))\n\n            loc_coords = bar.randomize_from_local_x(loc_x, return_frame='local')\n            assert np.all(bar.is_inside(loc_coords, frame='local'))\n            assert np.allclose(loc_x, loc_coords[:, 0])\n\n            # testing outside the bar\n            loc_x = np.hstack([\n                np.random.uniform(bar.length + tol, 2 * bar.length, size=(10)),\n                np.random.uniform(-2 * bar.length, -bar.length - tol, size=(10)),\n            ])\n\n            lab_coords = bar.randomize_from_local_x(loc_x, return_frame='lab')\n            assert ~np.any(bar.is_inside(lab_coords, frame='lab'))\n\n            loc_coords = bar.randomize_from_local_x(loc_x, return_frame='local')\n            assert ~np.any(bar.is_inside(loc_coords, frame='local'))\n            assert np.allclose(loc_x, loc_coords[:, 0])\n\n            # testing local_ynorm\n            loc_coords = bar.randomize_from_local_x(loc_x, local_ynorm=0, return_frame='local')\n            assert np.allclose(loc_coords[:, 1], 0)\n            loc_coords = bar.randomize_from_local_x(loc_x, local_ynorm=[-0.2, 0.2], return_frame='local')\n            assert np.allclose(loc_coords[:, 1], 0, atol=0.2 * bar.height + 1e-6)\n            assert ~np.allclose(loc_coords[:, 1], 0, atol=1e-10)\n\n            # testing local_znorm\n            loc_coords = bar.randomize_from_local_x(loc_x, local_znorm=0, return_frame='local')\n            assert np.allclose(loc_coords[:, 2], 0)\n            loc_coords = bar.randomize_from_local_x(loc_x, local_znorm=[-0.2, 0.2], return_frame='local')\n            assert np.allclose(loc_coords[:, 2], 0, atol=0.2 * bar.thickness + 1e-6)\n            assert ~np.allclose(loc_coords[:, 2], 0, atol=1e-10)\n\n            # testing random_seed\n            rseed = np.random.randint(0, 100)\n            # without fixed random seed, the randomization should be different\n            coords0 = bar.randomize_from_local_x(loc_x)\n            coords1 = bar.randomize_from_local_x(loc_x)\n            assert ~np.allclose(coords0, coords1)\n            # with fixed random seed, the randomization should be the same\n            coords0 = bar.randomize_from_local_x(loc_x, random_seed=rseed)\n            coords1 = bar.randomize_from_local_x(loc_x, random_seed=rseed)\n            assert np.allclose(coords0, coords1)\n\n    def test_construct_plotly_mesh3d(self, nw_bars):\n        for bar in nw_bars['nwb_pyrex'].values():\n            bar.construct_plotly_mesh3d()\n            bar_vertices = [tuple(np.round(vertex, decimals=2)) for vertex in bar.vertices.values()]\n\n            triangles = bar.triangle_mesh.get_triangles()\n            tri_vertices = np.unique(np.round(triangles.reshape(-1, 3), decimals=2), axis=0)\n            tri_vertices = [tuple(vertex) for vertex in tri_vertices]\n            assert len(tri_vertices) == 8\n            assert set(tri_vertices) == set(bar_vertices)\n        \n    def test_simple_simulation(self, nw_bars):\n        n_rays = 20\n        for bar in nw_bars['nwb_nopyrex'].values():\n            result = bar.simple_simulation(n_rays=n_rays)\n            assert result['intersections'].shape == (12, n_rays, 3)\n            # rough check on coverage\n            assert 20 < np.degrees(result['polar_range'][0]) < 40\n            assert 40 < np.degrees(result['polar_range'][1]) < 60\n            assert -35 < np.degrees(result['azimuth_range'][0]) < 35\n            assert -35 < np.degrees(result['azimuth_range'][1]) < 35\n\n            # every n_rays should have either zero or two intersections\n            intersections = np.swapaxes(result['intersections'], 0, 1)\n            norms = np.linalg.norm(intersections, axis=2)\n            assert set(np.count_nonzero(norms, axis=1)).issubset({0, 2})\n\n            # check random_seed\n            rseeds = np.random.randint(0, 100)\n            result0 = bar.simple_simulation(n_rays=n_rays, random_seed=rseeds)\n            assert ~np.allclose(result['intersections'], result0['intersections'])\n            result1 = bar.simple_simulation(n_rays=n_rays, random_seed=rseeds)\n            assert np.allclose(result0['intersections'], result1['intersections'])\n    \n    def test_get_hit_positions(self, nw_bars):\n        n_rays = 20\n        for bar in nw_bars['nwb_nopyrex'].values():\n            bar.simple_simulation(n_rays=n_rays)\n\n            # default should be\n            # hit_t = 'uniform\n            # frame = 'local\n            # coordinate = 'cartesian'\n            rseed = np.random.randint(0, 100)\n            hits = bar.get_hit_positions(random_seed=rseed)\n            hits0 = bar.get_hit_positions(\n                hit_t='uniform', frame='local', coordinate='cartesian',\n                random_seed=rseed,\n            )\n            assert np.allclose(hits, hits0)\n\n            # all hits are inside the bar\n            n_hits = len(hits)\n            if n_hits > 0:\n                assert np.all(bar.is_inside(hits, frame='local'))\n            \n            # spherical lab frame\n            hits = bar.get_hit_positions(frame='lab', coordinate='spherical')\n            assert len(hits) == n_hits\n            if n_hits > 0:\n                hits = geom.spherical_to_cartesian(hits)\n                assert np.all(bar.is_inside(hits, frame='lab'))\n            \n            # cartesian lab frame\n            hits = bar.get_hit_positions(frame='lab', coordinate='cartesian')\n            assert len(hits) == n_hits\n            if n_hits > 0:\n                assert np.all(bar.is_inside(hits, frame='lab'))\n\n            # testing constant hit_t values\n            hits0 = bar.get_hit_positions(hit_t=0)\n            hits1 = bar.get_hit_positions(hit_t=1)\n            hits_out_pos = bar.get_hit_positions(hit_t=2)\n            hits_out_neg = bar.get_hit_positions(hit_t=-1)\n            if n_hits > 0:\n                assert np.all(bar.is_inside(hits0, frame='local'))\n                assert np.all(bar.is_inside(hits1, frame='local'))\n                assert np.all(bar.is_inside(0.5 * (hits0 + hits1), frame='local'))\n                assert np.all(~bar.is_inside(hits_out_pos, frame='local'))\n                assert np.all(~bar.is_inside(hits_out_neg, frame='local'))\n            \n            # testing a custom callable hit_t\n            hit_t = lambda size: np.clip(0, 1, np.random.exponential(0.2, size=size))\n            hits = bar.get_hit_positions(hit_t=hit_t)\n            n_hits = len(hits)\n            if n_hits > 0:\n                assert np.all(bar.is_inside(hits, frame='local'))\n    \n    def test_draw_hit_pattern2d(self, nw_bars):\n        fig, ax = plt.subplots()\n        for bar in nw_bars['nwb_pyrex'].values():\n            bar.simple_simulation(3)\n\n            # spherical lab frame\n            hits = bar.get_hit_positions(frame='lab', coordinate='spherical')\n            hist = bar.draw_hit_pattern2d(hits, ax=ax, frame='lab', coordinate='spherical')\n            # check if the histogram covers the entire bar\n            assert len(hits) == pytest.approx(np.sum(hist[0]))\n\n            # cartesian local frame\n            hits = bar.get_hit_positions(frame='local', coordinate='cartesian')\n            hist = bar.draw_hit_pattern2d(hits, ax=ax, frame='local', coordinate='cartesian')\n            # check if the histogram covers the entire bar\n            assert len(hits) == pytest.approx(np.sum(hist[0]))\n        plt.close()\n","repo_name":"fanurs/data-analysis-e15190-e14030","sub_path":"tests/neutron_wall/test_geometry.py","file_name":"test_geometry.py","file_ext":"py","file_size_in_byte":17659,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"80"}
+{"seq_id":"33328760540","text":"import sys\n\nitems = {\n    'A01': {'price': 100},\n    'A02': {'price': 200},\n    'B01': {'price': 1000}\n}\n\ntax_rate = 0\n\ndef main():\n    global tax_rate\n    if len(sys.argv) < 2:\n        print('税率を指定してください')\n        exit()\n    tax_rate = int(sys.argv[1])\n    sum = 0\n    while True:\n        code = input('商品コードを入力してください（終了q）：')\n        if code == 'q':\n            print(f'合計：{sum}円')\n            tax = calcTax(sum)\n            print(f'消費税：{tax}円　合計：{sum + tax}円')\n            break\n        if code in items:\n            print(f\"商品コード：{code} 単価：{items[code]['price']}\")\n            count = int(input('個数を入力してください：'))\n            sum += items[code]['price'] * count\n        else:\n            print(f'商品コード：{code}は、存在しません')\n\ndef calcTax(sum):\n    return int(sum * tax_rate / 100)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Learning-FIT/Python","sub_path":"calc/calc4.py","file_name":"calc4.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"22632580880","text":"#!/bin/python3\n\nimport os\nimport sys\n\n#\n# Complete the timeConversion function below.\n#\ndef timeConversion(s):\n    #\n    # The aim of this function is to use string manipulation and an if statement to \n    # change the format of the time\n    array = list(s)\n    hour = s[0]+s[1] \n    ampm = s[8]+s[9]\n    \n    if ampm == \"AM\":\n        if hour == \"12\": \n            hour = \"00\"\n        return hour+\"\".join(array[2:8]) \n    else:\n        hour = int(hour)+12\n        if hour == 24: \n            hour = 12\n        hour = str(hour)\n        if len(hour)==1:\n            hour = \"0\" + hour\n        return hour+\"\".join(array[2:8])\n\n\n\nif __name__ == '__main__':\n    f = open(os.environ['OUTPUT_PATH'], 'w')\n\n    s = input()\n\n    result = timeConversion(s)\n\n    f.write(result + '\\n')\n\n    f.close()\n","repo_name":"tauhir/HackerRank-Practice-Python-3","sub_path":"Solutions/Time Conversion.py","file_name":"Time Conversion.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"6193412443","text":"from pathlib import Path\nfrom typing import List\nfrom PIL import Image\nfrom datetime import datetime\n\n\nTOP_LEFT_X = 1437\nTOP_LEFT_Y = 1338\n\n\ndef load_image_from_file(file: str) -> Image:\n    image = Image.open(Path(file))\n    image.convert(\"RGBA\")\n    return image\n\n\ndef create_blank_canvas() -> Image:\n    blank_canvas = Image.new(\"RGBA\", (6000, 6000), (0, 0, 0, 0))\n    return blank_canvas\n\n\ndef convert_image_to_overlay(image: Image) -> Image:\n    new_image = Image.new(\"RGBA\", (image.size[0] * 3, image.size[1] * 3), (0, 0, 0, 0))\n    for i in range(image.size[0]):\n        for j in range(image.size[1]):\n            new_image.putpixel(((i * 3) + 1, (j * 3) + 1), image.getpixel((i, j)))\n    return new_image\n\n\ndef place_overlay_on_canvas(overlay: Image, canvas: Image, x: int, y: int) -> Image:\n    for i in range(overlay.size[0]):\n        for j in range(overlay.size[1]):\n            canvas.putpixel((x * 3 + i, y * 3 + j), overlay.getpixel((i, j)))\n    return canvas\n\n\ndef save_image(image: Image, name: str) -> None:\n    image.save(Path(\"output_images/archive\") / f\"{name}-{int(datetime.timestamp(datetime.now()))}.png\")\n    image.save(Path(\"output_images\") / f\"{name}.png\")\n\n\ndef main():\n    blank_canvas = create_blank_canvas()\n\n    start_CHAD = load_image_from_file(file=\"start_CHAD_with_link.png\")\n    overlay_CHAD = convert_image_to_overlay(image=start_CHAD)\n    canvas = place_overlay_on_canvas(overlay=overlay_CHAD, canvas=blank_canvas, x=TOP_LEFT_X, y=TOP_LEFT_Y)\n\n    # start_jrpg = load_image_from_file(file=\"start_JRPG.png\")\n    # overlay_jrpg = convert_image_to_overlay(image=start_jrpg)\n    # canvas = place_overlay_on_canvas(overlay=overlay_jrpg, canvas=canvas, x=1784, y=1640)\n\n    # start_duck = load_image_from_file(file=\"start_DUCK.png\")\n    # overlay_duck = convert_image_to_overlay(image=start_duck)\n    # canvas = place_overlay_on_canvas(overlay=overlay_duck, canvas=canvas, x=1887, y=1099)\n\n    save_image(image=canvas, name=\"final_CHAD\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"alexshore/suprCHAD","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"70347047938","text":"from flask import render_template, request, Blueprint\nfrom webapp.database import db, Grein\nfrom datetime import datetime, timedelta\n\ncontent_bp = Blueprint('content', __name__, url_prefix='', static_folder='content_static', template_folder='templates')\n\n@content_bp.route('/brøv/<string:article_id>')\ndef show_article(article_id):\n    art = artiklar.query.join(Verification).filter(artiklar.art_id == article_id, Verification.status == 'verified').first()\n    if art is None:\n        return render_template('error.html',error=\"Brævið finst ikki\")\n    art_dict = rowToDict(art)\n    date_string = art_dict[\"created_stamp\"].strftime('%Y-%m-%d')\n    art_dict[\"date_string\"] = date_string\n    return render_template('article.html',art_dict=art_dict)\n\n@content_bp.route('/index_loadMore', methods=['POST', 'GET'])\ndef index_loadMore():\n    ## fetch last article when scrolled to bottom of index page ##\n    last_floating_box_id = request.form.get('lastFloatingBoxId')\n    print(\"last floating box id\",last_floating_box_id)\n\n    ## find older articles than the last one on the page\n\n    entry = artiklar.query.filter_by(art_id=last_floating_box_id).first()\n    ## If the entry was found, retrieve the two articles written prior to it ##\n    if entry:\n        two_articles_prior = artiklar.query.filter(\n            artiklar.created_stamp < entry.created_stamp,\n            artiklar.verified == True\n        ).order_by(artiklar.created_stamp.desc()).limit(2).all()\n        seinastu_artiklar_dict = latest_articles_dict(two_articles_prior)\n\n\n        for article in seinastu_artiklar_dict:\n            time_delta = timeDelta(seinastu_artiklar_dict[article][\"created_stamp\"])\n            seinastu_artiklar_dict[article][\"time_delta\"] = time_delta\n\n            \n            preview_text = preview_article(seinastu_artiklar_dict[article][\"skriv\"])\n            seinastu_artiklar_dict[article][\"preview_text\"] = preview_text\n\n        \n        return render_template('base-prev.html', art=seinastu_artiklar_dict)","repo_name":"Simuns/perspektiv","sub_path":"src/webapp/content/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":2000,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"17515872609","text":"import FreeCAD\n\nimport ObjectsFem\n\nfrom . import manager\nfrom .boxanalysis_base import setup_boxanalysisbase\nfrom .manager import init_doc\n\n\ndef get_information():\n    return {\n        \"name\": \"Box Analysis Static\",\n        \"meshtype\": \"solid\",\n        \"meshelement\": \"Tet10\",\n        \"constraints\": [\"fixed\", \"force\", \"pressure\"],\n        \"solvers\": [\"calculix\", \"ccxtools\", \"elmer\"],\n        \"material\": \"solid\",\n        \"equations\": [\"mechanical\"]\n    }\n\n\ndef get_explanation(header=\"\"):\n    return header + \"\"\"\n\nTo run the example from Python console use:\nfrom femexamples.boxanalysis_static import setup\nsetup()\n\n\nSee forum topic post:\n...\n\n\"\"\"\n\n\ndef setup(doc=None, solvertype=\"ccxtools\"):\n\n    # init FreeCAD document\n    if doc is None:\n        doc = init_doc()\n\n    # explanation object\n    # just keep the following line and change text string in get_explanation method\n    manager.add_explanation_obj(doc, get_explanation(manager.get_header(get_information())))\n\n    # setup box static, add a fixed, force and a pressure constraint\n    doc = setup_boxanalysisbase(doc, solvertype)\n    geom_obj = doc.Box\n    analysis = doc.Analysis\n\n    # solver\n    if solvertype == \"calculix\":\n        solver_obj = ObjectsFem.makeSolverCalculix(doc, \"SolverCalculiX\")\n    elif solvertype == \"ccxtools\":\n        solver_obj = ObjectsFem.makeSolverCalculixCcxTools(doc, \"CalculiXccxTools\")\n        solver_obj.WorkingDir = u\"\"\n    elif solvertype == \"elmer\":\n        solver_obj = ObjectsFem.makeSolverElmer(doc, \"SolverElmer\")\n        ObjectsFem.makeEquationElasticity(doc, solver_obj)\n    else:\n        FreeCAD.Console.PrintWarning(\n            \"Unknown or unsupported solver type: {}. \"\n            \"No solver object was created.\\n\".format(solvertype)\n        )\n    if solvertype == \"calculix\" or solvertype == \"ccxtools\":\n        solver_obj.SplitInputWriter = False\n        solver_obj.AnalysisType = \"static\"\n        solver_obj.GeometricalNonlinearity = \"linear\"\n        solver_obj.ThermoMechSteadyState = False\n        solver_obj.MatrixSolverType = \"default\"\n        solver_obj.IterationsControlParameterTimeUse = False\n    analysis.addObject(solver_obj)\n\n    # constraint fixed\n    con_fixed = ObjectsFem.makeConstraintFixed(doc, \"FemConstraintFixed\")\n    con_fixed.References = [(geom_obj, \"Face1\")]\n    analysis.addObject(con_fixed)\n\n    # constraint force\n    con_force = ObjectsFem.makeConstraintForce(doc, \"FemConstraintForce\")\n    con_force.References = [(geom_obj, \"Face6\")]\n    con_force.Force = \"40000.0 N\"\n    con_force.Direction = (geom_obj, [\"Edge5\"])\n    con_force.Reversed = True\n    analysis.addObject(con_force)\n\n    # constraint pressure\n    con_pressure = ObjectsFem.makeConstraintPressure(doc, name=\"FemConstraintPressure\")\n    con_pressure.References = [(geom_obj, \"Face2\")]\n    con_pressure.Pressure = \"1000.0 MPa\"\n    con_pressure.Reversed = False\n    analysis.addObject(con_pressure)\n\n    doc.recompute()\n    return doc\n","repo_name":"FreeCAD/FreeCAD","sub_path":"src/Mod/Fem/femexamples/boxanalysis_static.py","file_name":"boxanalysis_static.py","file_ext":"py","file_size_in_byte":2940,"program_lang":"python","lang":"en","doc_type":"code","stars":15748,"dataset":"github-code","pt":"80"}
+{"seq_id":"5778070969","text":"from rest_framework import serializers, status\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.permissions import IsAuthenticated,AllowAny\nfrom apps.userprofile.models import Profile\nfrom .serializers import UserRegistrationSerializer, ProfileRegistrationSerializer, WebSerializer\nfrom .utilities import text_extractor, download_file, crawler, fill_web_form, extract_result_table   \n\n\n@api_view(['POST'])\n@permission_classes([AllowAny])\ndef registration_view(request):\n    usrSerializer= UserRegistrationSerializer(data= request.data)\n    proSerializer= ProfileRegistrationSerializer(data= request.data)\n    data, include= {}, ['username', 'email', 'phoneNumber', 'baseLocation', 'jobDescription', 'age']\n    u, p= usrSerializer.is_valid(), proSerializer.is_valid()\n    if u and p :\n        user= usrSerializer.create(usrSerializer.validated_data)\n        #validated_data= usrSerializer.validate()\n        user.set_password(usrSerializer.validated_data['password'])\n        user.save()\n        profile= proSerializer.save(user= user)\n        profile.save()\n        for key, value in user.__dict__.items():\n            if key in include:\n                data[key]= value\n        for key, value in profile.__dict__.items():\n            if key in include:\n                data[key]= value\n        resp= Response(data)\n        resp.status_code= 201\n    else:\n        data= usrSerializer.errors\n        data.update(proSerializer.errors)\n        resp= Response(data)\n        resp.status_code= 400\n    return resp\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated])\ndef getuser_view(request):\n    user = request.user\n    data, include= {}, ['username', 'email', 'phoneNumber', 'baseLocation', 'jobDescription', 'age']\n    for key, value in user.__dict__.items():\n            if key in include:\n                data[key]= value\n    try:\n        profile = Profile.objects.get(user= user)\n        for key, value in profile.__dict__.items():\n            if key in include:\n                data[key]= value\n    except Profile.DoesNotExist:\n        pass\n    resp= Response(data)\n    return resp\n\n@api_view(['GET'])\n@permission_classes([AllowAny])\ndef pdfcrawl_view(request, crawlLevel= '1'):\n    pdfurl= 'https://www.treasury.gov/ofac/downloads/mbs/mbslist.pdf'\n    pdfFile= download_file(pdfurl)\n    pdfData= text_extractor(pdfFile= pdfFile)\n    pdfText= pdfData[0]\n    urlList= pdfData[1]\n    crawledList= crawler(urlList, int(crawlLevel))\n    pdfText= pdfText.replace('\\n', '<br>')\n    data = {'crawledList':crawledList, 'pdfData':  pdfText}\n    resp= Response(data)\n    return resp\n\n@api_view(['POST'])\n@permission_classes([AllowAny])\ndef webcrawl_view(request):\n    weburl= 'https://sanctionssearch.ofac.treas.gov/'\n    serializer =  WebSerializer(request.data)\n    pageData= fill_web_form(weburl, serializer.data)\n    extracted_data= extract_result_table(pageData, weburl) \n    scraped_data= extracted_data[0]\n    urlList= extracted_data[1]\n    data = {'scrapedData':  scraped_data, 'crawledList':urlList}\n    resp= Response(data)\n    return resp\n\n    \n","repo_name":"arunavn/crawler","sub_path":"apps/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3137,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"29852312142","text":"# -*- coding: utf-8 -*-\n\nimport inspect\nimport os\nimport pytest\nimport re\nimport sys\nimport unittest.mock as mock\n\nimport cpboard\n\nfrom .utils import get_board\nfrom .fixtures import *  # noqa: F403,F401\n\n\ndef pytest_addoption(parser):\n    group = parser.getgroup('circuitpython')\n    group.addoption('--board', dest='boarddev', help='build_name, vid:pid or /dev/tty')\n    group.addoption('--file-overwrite', action='store_true', default=False, dest='file_overwrite',\n                    help=\"Force file upload, don't check\")\n\n\n# Import machinery\n# https://stackoverflow.com/questions/43571737/how-to-implement-an-import-hook-that-can-modify-the-source-code-on-the-fly-using\n\n_builtins_import = __import__\n\n\n# https://github.com/posener/mock-import/blob/master/mock_import.py\n# https://stackoverflow.com/questions/8658043/how-to-mock-an-import\ndef try_import(module_name, *args, **kwargs):\n    try:\n        return _builtins_import(module_name, *args, **kwargs)\n    except ImportError:\n        return mock.MagicMock()\n\n\n# Mock missing modules since they are assumed to be present on the board\n@pytest.hookimpl(tryfirst=True)\ndef pytest_pycollect_makemodule(path, parent):\n    config = parent.config\n    if not config.option.boarddev:\n        return\n\n    if 'test_board_' in str(path):\n        debug = config.option.verbose > 1\n        with mock.patch('builtins.__import__', try_import):\n            mod = path.pyimport(ensuresyspath='prepend')\n            if debug:\n                print('Import module:', mod.__name__)\n\n\ndef remote_path(session, f):\n    rel = os.path.relpath(str(f), str(session.fspath))\n    rpath = os.path.join('/tmp.pytest', session.name, rel)\n    return rpath\n\n\n# Mark tests, rewrite assert statements and upload files to the board\n@pytest.hookimpl(tryfirst=True)\ndef pytest_runtestloop(session):\n    config = session.config\n    if not config.option.boarddev:\n        return\n\n    if config.option.collectonly:\n        return\n\n    verbose = config.option.verbose\n\n    # print(\"Session\", session, session.fspath)\n    # print(dir(session))\n\n    for item in session.items:\n        # print('item', item, item.parent)\n        # print('  fixturenames', item.fixturenames)\n        # print(dir(item), '\\n')\n        if os.path.basename(item.name).startswith('test_board_') or \\\n           os.path.basename(item.parent.name).startswith('test_board_') or \\\n           (item.cls and item.cls.__name__.startswith('TestBoard')):\n            item.add_marker('board')\n\n    files = []\n    for item in session.items:\n        marker = item.get_marker('board')\n        if marker is None:\n            continue\n\n        item.rpath = remote_path(session, item.fspath)\n        path = str(item.fspath)\n        if path not in files:\n            files.append(path)\n\n    # Access pytest internals to cover all fixtures\n    fm = session._fixturemanager\n    for argname, fixturedefs in fm._arg2fixturedefs.items():\n        if not fixturedefs:\n            continue\n        for fixturedef in fixturedefs:\n            if not fixturedef.baseid:\n                continue\n            path = os.path.join(str(session.fspath), fixturedef.baseid)\n            # print('fixturedef', fixturedef, fixturedef.func, path)\n            # print(dir(fixturedef))\n            if os.path.basename(path).startswith('test_board_') or fixturedef.func.__name__.startswith('board_'):\n                fixturedef.rpath = remote_path(session, path)\n                if path not in files:\n                    files.append(path)\n\n    if not files:\n        return\n\n    board = get_board(session)\n\n    print('\\nCopy files to board: ', end='')\n    if verbose:\n        print()\n\n    disk = cpboard.ReplDisk(board)\n\n    overwrite = config.option.file_overwrite\n\n    def copyfile(src, dst):\n        if verbose:\n            print('  ', dst, end='')\n        else:\n            print('.', end='', flush=True)\n        disk.makedirs(os.path.dirname(dst), exist_ok=True)\n        copied = disk.copy(src, dst, force=overwrite)\n        if verbose:\n            print('' if copied else ' (unchanged)')\n\n    copyfile(str(os.path.join(os.path.dirname(__file__), 'boardlib', 'pytest.py')), '/lib/pytest.py')\n\n    for f in files:\n        src = str(f)\n        dst = remote_path(session, f)\n\n        if config.getvalue(\"assertmode\") == \"rewrite\":\n            src = assert_rewrite_module(session, src)\n\n        copyfile(src, dst)\n\n    if not verbose:\n        print()\n\n\ndef create_traceback(e, path):\n    if not e.exc:\n        return False\n    path = str(path)\n    fname = os.path.basename(path)\n    for tb in e.tb:\n        # print('tb', tb)\n        if fname in tb[0]:\n            tb = [(path, tb[1], tb[2])]\n            e.exc.__traceback__ = e.create_traceback(tb=tb)\n            return True\n    return False\n\n\ndef remote_import(session, path):\n    debug = session.config.option.verbose > 1\n    if debug:\n        print('remote_import', path)\n    board = session.board\n\n    fname = os.path.basename(path)\n    modname = os.path.splitext(fname)[0]\n\n    command = '%r in globals()' % modname\n    imported = board.eval(command, reset_repl=False, raise_remote=False)\n    if debug:\n        print('imported', imported)\n    if imported:\n        return\n\n    command = 'import os\\n'\n    command += 'os.chdir(%r)\\n' % os.path.dirname(path)\n    command += 'import gc; gc.collect()\\n'\n    if debug:\n        command += 'print(\"Free mem\", gc.mem_free())\\n'\n    command += 'import %s\\n' % modname\n    if debug:\n        command += 'print(globals())\\n'\n        print('command:\\n', command)\n    try:\n        board.exec(command, reset_repl=False, raise_remote=False, out=sys.stdout)\n    except cpboard.CPboardRemoteError as e:\n        if debug:\n            print('remote_import: e=', e)\n        msg = \"Failed to import '%s'\" % (modname,)\n        if e.exc_name:\n            msg += '(%s: %s)' % (e.exc_name, e.exc_val)\n        raise ImportError(msg) from e\n\n\n# Import test files on the board\ndef pytest_runtest_setup(item):\n    config = item.config\n    if not config.option.boarddev:\n        return\n\n    debug = config.option.verbose > 1\n    if debug:\n        print('pytest_runtest_setup', item)\n    marker = item.get_marker('board')\n    if marker is None:\n        return\n\n    remote_import(item.session, item.rpath)\n\n\n# Wrap fixture functions and execute them on the board\ndef pytest_fixture_setup(fixturedef, request):\n    if not request.session.config.option.boarddev:\n        return\n\n    def fixture_board_wrapper(request, **kwargs):\n        __tracebackhide__ = True\n        if debug:\n            print('fixture_board_wrapper:', request, request)\n            print()\n        # print(dir(request))\n        # print('fixture_board_wrapper: .func', fixture_board_wrapper.func)\n        # print('fixture_board_wrapper:', request, dir(request))\n        board = request.session.board\n\n        remote_import(request.session, fixturedef.rpath)\n\n        args = [repr('request')]  # dummy value for request argument\n        for key in kwargs.keys():\n            args.append('%s=fixture_%s_val' % (key, key))\n\n        fname = os.path.basename(fixturedef.rpath)\n        modname = os.path.splitext(fname)[0]\n        # modname = os.path.splitext(fixturedef.baseid)[0]\n        argname = fixturedef.argname\n        command = 'res = %s.%s(%s)\\n' % (modname, func.__name__, ', '.join(args))\n        command += 'fixture_%s = res\\n' % (argname,)\n        command += 'fixture_%s_val = res\\n' % (argname,)\n\n        if debug:\n            command += 'print(globals())\\n'\n            print('command:\\n', command)\n\n        try:\n            board.exec(command, reset_repl=False, raise_remote=False, out=sys.stdout)\n            res = board.eval('res', reset_repl=False, raise_remote=False, strict=False)\n        except cpboard.CPboardRemoteError as e:\n            if debug:\n                print('fixture_board_wrapper: e=', e)\n            if e.exc and create_traceback(e, fixturedef.rpath):\n                raise e.exc from None\n            raise\n\n        if debug:\n            print('res: %r' % (res,))\n\n        return res\n\n    def fixture_board_wrapper_yield(request, **kwargs):\n        __tracebackhide__ = True\n        print('fixture_board_wrapper_yield:', request.function)\n        board = request.session.board\n\n        remote_import(request.session, fixturedef.rpath)\n\n        args = [repr('request')]  # dummy value for request argument\n        for key in kwargs.keys():\n            args.append('%s=fixture_%s_val' % (key, key))\n\n        fname = os.path.basename(fixturedef.rpath)\n        modname = os.path.splitext(fname)[0]\n        # modname = os.path.splitext(fixturedef.baseid)[0]\n        argname = fixturedef.argname\n        command = 'fixture_%s = %s.%s(%r)\\n' % (argname, modname, func.__name__, ', '.join(args))\n        command += 'res = next(fixture_%s)\\n' % (argname,)\n        command += 'fixture_%s_val = res\\n' % (argname,)\n\n        if debug:\n            command += 'print(globals())\\n'\n            print('command:\\n', command)\n\n        try:\n            board.exec(command, reset_repl=False, raise_remote=False, out=sys.stdout)\n            res = board.eval('res', reset_repl=False, raise_remote=False, strict=False)\n        except cpboard.CPboardRemoteError as e:\n            if debug:\n                print('fixture_board_wrapper_yield: e=', e)\n            if e.exc and create_traceback(e, fixturedef.rpath):\n                raise e.exc from None\n            raise\n\n        yield res\n\n        board.exec('next(fixture_%s)' % func.__name__, out=sys.stdout, reset_repl=False, raise_remote=True)\n\n    if not getattr(request.session, 'board', None) or not getattr(fixturedef, 'rpath', ''):\n        return\n\n    debug = request.config.option.verbose > 1\n    if debug:\n        print('pytest_fixture_setup', fixturedef)\n        print('fixturedef.func', fixturedef.func)\n\n    # Only wrap the first time called\n    if getattr(fixturedef.func, '__wrapped_fixture__', None):\n        return\n\n    func = fixturedef.func\n    if inspect.isgeneratorfunction(fixturedef.func):\n        fixturedef.func = fixture_board_wrapper_yield\n    else:\n        fixturedef.func = fixture_board_wrapper\n    fixturedef.func.__wrapped_fixture__ = func\n\n\ndef delete_variables(board, variables, debug):\n    command = ''\n    for var in variables:\n        command += 'try: del %s\\nexcept (NameError, KeyError): pass\\n' % (var,)\n    command += '__import__(\"gc\").collect()\\n'\n\n    if debug:\n        command += 'print(globals())\\n'\n        print('command:\\n', command)\n\n    board.exec(command, out=sys.stdout, reset_repl=False, raise_remote=True)\n\n\n# Clean out fixture variables from the namespace\ndef pytest_fixture_post_finalizer(fixturedef, request):\n    session = request.session\n    config = session.config\n    if not config.option.boarddev:\n        return\n\n    debug = config.option.verbose > 1\n    if debug:\n        print('\\npytest_fixture_post_finalizer:', fixturedef, request)\n\n    func = getattr(fixturedef.func, '__wrapped_fixture__', None)\n    if not func:\n        return\n\n    argname = fixturedef.argname\n    variables = ['fixture_%s' % (argname,), 'fixture_%s_val' % (argname,), 'res']\n    delete_variables(session.board, variables, debug)\n\n\n# Run test functions marked with 'board' on the board\ndef pytest_pyfunc_call(pyfuncitem):\n    config = pyfuncitem.session.config\n    if not config.option.boarddev:\n        return\n\n    debug = config.option.verbose > 1\n\n    marker = pyfuncitem.get_marker('board')\n    if debug:\n        print('\\n\\npytest_pyfunc_call: item:', pyfuncitem, 'parent:', pyfuncitem.parent, 'marker:', marker)\n\n    if marker is None:\n        return\n\n    __tracebackhide__ = True\n    testfunction = pyfuncitem.obj\n    if pyfuncitem._isyieldedfunction():\n        # testfunction(*pyfuncitem._args)\n        raise NotImplementedError\n    else:\n        funcargs = pyfuncitem.funcargs\n        # testargs = {}\n        # for arg in pyfuncitem._fixtureinfo.argnames:\n        #     testargs[arg] = funcargs[arg]\n        # testfunction(**testargs)\n\n        # print('pytest_pyfunc_call: testargs =', testargs, 'testfunction = ', testfunction)\n        # print('pytest_pyfunc_call: pyfuncitem =', pyfuncitem, dir(pyfuncitem))\n        # print('pytest_pyfunc_call: pyfuncitem.fixturenames =', pyfuncitem.fixturenames)\n        # print('pytest_pyfunc_call: pyfuncitem.funcargs =', pyfuncitem.funcargs)\n        # print('pytest_pyfunc_call: pyfuncitem.param =', getattr(pyfuncitem, 'param', \"object has no attribute 'param'\"))\n        # print('pytest_pyfunc_call: board =', pyfuncitem.session.board)\n\n        board = pyfuncitem.session.board\n\n        command = ''\n\n        testargs = []\n        for arg in pyfuncitem._fixtureinfo.argnames:\n            fixturevar = 'fixture_%s_val' % (arg,)\n            argvar = 'funcarg_%s_val' % (arg,)\n            # If this is not a remote fixture argument, use the passed in argument value\n            command += 'try: %s = %s\\nexcept (NameError, KeyError): %s = %r\\n' % (argvar, fixturevar, argvar, funcargs[arg])\n            testargs.append('%s=%s' % (arg, argvar))\n            # testargs.append('%s=%r' % (arg, funcargs[arg]))\n\n        fname = os.path.basename(pyfuncitem.rpath)\n        modname = os.path.splitext(fname)[0]\n\n        if pyfuncitem.cls:\n            name = pyfuncitem.cls.__name__\n            varname = 'test_class_%s' % (name,)\n            # Instantiate the test class if it's not already done\n            command += 'try: %s\\nexcept (NameError, KeyError): %s = %s.%s()\\n' % (varname, varname, modname, name)\n            command += varname\n        else:\n            command += modname\n\n        command += '.%s(%s)\\n' % (testfunction.__name__, ', '.join(testargs))\n\n        # command  = '%s.%s(%s)\\n' % (modname, testfunction.__name__, ', '.join(testargs))\n\n        if debug:\n            command += 'print(globals())\\n'\n            print('command:\\n', command)\n\n        try:\n            board.exec(command, reset_repl=False, raise_remote=False, out=sys.stdout)\n        except cpboard.CPboardRemoteError as e:\n            if debug:\n                print('pytest_pyfunc_call: e=', e)\n            if e.exc:\n                for tb in e.tb:\n                    if fname in tb[0]:\n                        tb = [(str(pyfuncitem.fspath), tb[1], tb[2])]\n                        if debug:\n                            print('tb', tb)\n                        e.exc.__traceback__ = e.create_traceback(tb=tb)\n                        raise e.exc from None\n            raise\n\n    return True\n\n\n# Clean out funcarg variables from the namespace\ndef pytest_runtest_teardown(item, nextitem):\n    config = item.config\n    if not config.option.boarddev:\n        return\n\n    debug = config.option.verbose > 1\n    if debug:\n        print('\\n\\npytest_runtest_teardown: item =', item, item.parent)\n\n    marker = item.get_marker('board')\n    if marker is None:\n        return\n\n    try:\n        argnames = item._fixtureinfo.argnames\n    except Exception:\n        return\n\n    variables = ['funcarg_%s_val' % (arg,) for arg in argnames]\n    delete_variables(item.session.board, variables, debug)\n\n\ndef assert_rewrite_module(session, fname):\n    debug = session.config.option.verbose > 2\n    with open(fname) as f:\n        source = f.read()\n\n    s = []\n    changed = False\n    for line in source.splitlines():\n        if re.match(r'\\s+assert\\s+', line):\n            org = line\n            line = assert_rewrite(line, debug)\n            if line != org:\n                changed = True\n        s.append(line)\n\n    if not changed:\n        return fname\n\n    rewrite = '\\n'.join(s)\n    if debug:\n        print('\\nassert_rewrite_module(%r)' % fname)\n        print('--------------------------------------------------------------------------------')\n        print(rewrite)\n        print('--------------------------------------------------------------------------------')\n\n    cache_dir = os.path.join(str(session.fspath), \".pytest_board_cache\")\n    rel = os.path.relpath(fname, str(session.fspath))\n    dst = os.path.join(cache_dir, rel)\n    if debug:\n        print('dst', dst)\n\n    try:\n        os.makedirs(os.path.dirname(dst), exist_ok=True)\n    except OSError:\n        return fname\n\n    with open(dst, 'w') as f:\n        f.write(rewrite)\n\n    return dst\n\n\nimport token, symbol, parser\n\n\ndef assert_rewrite(line, debug):\n    org_line = line\n    map = dict(token.tok_name)\n    map.update(symbol.sym_name)\n\n    # https://stackoverflow.com/a/5454348\n    def shallow(ast):\n        if not isinstance(ast, list):\n            return ast\n        if len(ast) == 2:\n            return shallow(ast[1])\n        return [map[ast[0]]] + [shallow(a) for a in ast[1:]]\n\n    try:\n        ast = shallow(parser.st2list(parser.suite(line.strip())))\n    except SyntaxError as e:\n        if debug:\n            print('assert_rewrite: Parsing error', e)\n        return org_line\n\n    if debug:\n        print('XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX', line)\n        import pprint\n        pprint.pprint(ast)\n\n    try:\n        if ast[0] != 'file_input' or ast[1][0] != 'simple_stmt' or ast[1][1][0] != 'assert_stmt':\n            return org_line\n        simple_stmt = ast[1]\n        comment = simple_stmt[2]\n        assert_stmt = ast[1][1]\n        expression = assert_stmt[2]\n    except IndexError:\n        return org_line\n\n    if len(assert_stmt) > 3:  # Already has a message\n        return org_line\n\n    if debug:\n        print('comment', comment)\n    if comment:\n        line = line[:line.index(comment)].rstrip()\n        if debug:\n            print('new line: %r' % line)\n\n    if debug:\n        print('assert_stmt', len(assert_stmt), assert_stmt)\n        print('expression', expression)\n\n    ws, assrt, rest = line.partition('assert')\n\n    if not isinstance(expression, list):\n        rewrite = ws + '____l = ' + expression + '; assert ____l, \"%r\" % ____l'\n        return rewrite\n\n    # TODO: expression ['not_test', 'not', 'False']\n    #       expression ['not_test', 'not', ['comparison', '1', '==', '2']]\n    if len(expression) < 4 or expression[0] != 'comparison':\n        return org_line\n\n    # TODO: expression ['comparison', '1', '<=', '1', '<=', '1']\n    if len(expression) > 4:\n        return org_line\n\n    op = expression[2]\n    if debug:\n        print('left', expression[1])\n        print('op', op)\n        print('right', expression[3])\n\n    # TODO: op ['comp_op', 'is', 'not']\n    if isinstance(op, list):\n        return org_line\n\n    # In case of mutiple op's in the line, find the correct one to split on\n\n    def flatten(lst):\n        for e in lst:\n            if isinstance(e, list):\n                yield from flatten(e)\n            else:\n                yield e\n\n    num_ops = list(flatten([expression[1]])).count(op)\n    if debug:\n        print('num_ops', num_ops)\n        print('rest', rest)\n\n    index = -1\n    for num in range(num_ops + 1):\n        index = rest.find(op, index + 1)\n        if debug:\n            print('index', index)\n        if index == -1:\n            return org_line\n\n    left = rest[:index].strip()\n    right = rest[index + len(op):].strip()\n    if debug:\n        print('left: %r' % left)\n        print('right: %r' % right)\n\n    rewrite = ws + '____l = ' + left + '; ____r = ' + right + '; assert ____l ' + op + ' ____r, \"%r ' + op + ' %r\" % (____l, ____r)'\n    if debug:\n        print('rewrite', rewrite)\n\n    return rewrite\n","repo_name":"notro/pytest-circuitpython","sub_path":"pytest_circuitpython/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":19227,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"80"}
+{"seq_id":"26574017211","text":"from functools import partial\n\nfrom abstracts.graphics_abc import GraphicsABC\nfrom components.graphics import TurtleGraphics, PygGraphics\nfrom components.simulation import Simulation\nfrom components.camera import Camera\nfrom components.color import RGBA\nfrom components.frametime import FrameTimeHandler\nfrom components.draw_call import DrawCall\n\n\ndef main() -> None:\n    width = 1760\n    height = 960\n    background_color = RGBA(0.15, 0.15, 0.15, 1.0)\n\n    graphics = PygGraphics(width, height)\n    camera = Camera(width, height)\n    draw_call = DrawCall(graphics, camera)\n    frame_timing = FrameTimeHandler(10)\n\n    graphics.set_title(\"Physics System\")\n    graphics.set_background_color(background_color)\n\n    simulation = Simulation(draw_call)\n    simulation.setup_objects()\n    GraphicsHandler(graphics, simulation, camera, frame_timing)\n\n\nclass GraphicsHandler:\n    def __init__(\n        self,\n        graphics: GraphicsABC,\n        simulation: Simulation,\n        camera: Camera,\n        frame_timing: FrameTimeHandler,\n    ):\n        self.graphics = graphics\n        self.simulation = simulation\n        self.camera = camera\n        self.frame_timing = frame_timing\n        self.previous_pointer = graphics.get_pointer_xy()\n        self.register_keys()\n        self.draw_loop()\n\n    def handle_events(self) -> None:\n        self.on_mouse_move()\n        self.on_mouse_wheel_scroll()\n        self.on_window_resize()\n\n    def register_keys(self) -> None:\n        camera = self.camera\n        simulation = self.simulation\n\n        step_val = 60.0\n\n        increase_distance = partial(camera.increment_planes, step_val)\n        decrease_distance = partial(camera.increment_planes, -step_val)\n        increase_timestep = partial(simulation.increment_timestep, 100)\n        decrease_timestep = partial(simulation.increment_timestep, -100)\n\n        move_forward = partial(camera.increment_position_z, step_val)\n        move_backward = partial(camera.increment_position_z, -step_val)\n        move_right = partial(camera.increment_position_x, step_val)\n        move_left = partial(camera.increment_position_x, -step_val)\n        move_up = partial(camera.increment_position_y, -step_val)\n        move_down = partial(camera.increment_position_y, step_val)\n\n        toggle_frustum = partial(camera.toggle_frustum_clipping)\n\n        reset = partial(camera.reset)\n\n        self.graphics.register_onkeypress(move_forward, \"w\")\n        self.graphics.register_onkeypress(move_backward, \"s\")\n        self.graphics.register_onkeypress(move_left, \"a\")\n        self.graphics.register_onkeypress(move_right, \"d\")\n\n        self.graphics.register_onkeypress(move_up, \"Up\")\n        self.graphics.register_onkeypress(move_down, \"Down\")\n        self.graphics.register_onkeypress(toggle_frustum, \"o\", False)\n\n        self.graphics.register_onkeypress(reset, \"r\", False)\n        self.graphics.register_onkeypress(increase_distance, \"e\")\n        self.graphics.register_onkeypress(decrease_distance, \"q\")\n        self.graphics.register_onkeypress(increase_timestep, \".\")\n        self.graphics.register_onkeypress(decrease_timestep, \",\")\n\n    def on_window_resize(self):\n        g_width = self.graphics.width\n        g_height = self.graphics.height\n\n        width = self.graphics.get_width()\n        height = self.graphics.get_height()\n\n        # if g_width != width or g_height != height:\n        #     self.graphics.setup_coordinates(width, height)\n\n    def on_mouse_wheel_scroll(self) -> None:\n        pass\n\n    def on_mouse_move(self) -> None:\n        px, py = self.previous_pointer\n        nx, ny = self.graphics.get_pointer_xy()\n\n        if px != nx or py != ny:\n            dx, dy = self.graphics.get_pointer_xy()\n            camera = self.camera\n            camera.handle_mouse_movement(dx, dy)\n\n    def on_draw(self) -> None:\n        frametime = self.frame_timing.get_frametime_data()\n        self.graphics.clear_screen()\n        self.simulation.simulate(self.graphics, frametime)\n        self.frame_timing.tick()\n        self.graphics.update()\n\n    def draw_loop(self) -> None:\n        while True:\n            self.handle_events()\n            self.on_draw()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"syn-chromatic/python-g-engine","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"23658624704","text":"import os\nimport socket\nimport time\n\nimport requests\nfrom flask import current_app as app\n\nfrom . import celery\n\ndef search_songs(**args):\n\n    search_url = app.config['CLOUD_MUSIC_API_HOST'] + '/search'\n    resp = requests.get(search_url, params=args)\n\n    if resp.status_code != 200:\n        app.logger.error(resp.text)\n        return 'Something wrong with the api.', 500\n\n    result = resp.json()['result']\n    songs, song_count = result['songs'], result['songCount']\n\n    return {\n        'songs': [song_serializer(song) for song in songs],\n        'song_count': song_count\n    }\n\ndef append_song(song:dict, keywords=None):\n\n    query_url = app.config['CLOUD_MUSIC_API_HOST'] + '/song/url'\n    resp = requests.get(query_url, {'id': song['id']})\n    song_url = resp.json()['data'][0]['url']\n    download_song.delay(song_url, song['name'], song['artist'])\n\n    return {'result': 'success'}\n\n\n@celery.task\ndef download_song(url, name, artist):\n\n    from celery_worker import app\n    with app.app_context():\n        resp = requests.get(url)\n        with open(os.path.join(app.config['MUSIC_PATH'], f'{name}.mp3'), 'wb') as file:\n            file.write(resp.content)\n\n\ndef song_serializer(song:dict):\n    return {\n        'id': song['id'],\n        'name': song['name'],\n        'artists': [artist['name'] for artist in song['artists']]\n    }\n","repo_name":"Orenoid/NeteaseCloudMusicSongRequest","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"34129702570","text":"import os\nimport glob\n\ndef common_remove_allfiles(pathname, recursive=True):\n    if os.path.isdir(pathname):\n        pathname = pathname + '/*'\n        for p in glob.glob(pathname, recursive=recursive):\n            if os.path.isfile(p):\n                os.remove(p)\n    else:\n        logger.error(\"PATHにディレクトリが存在しません。\")","repo_name":"hajimekaneko/stk00100","sub_path":"Bin/common/delete_file.py","file_name":"delete_file.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"26558022473","text":"'''\r\nAuthor: Vihan Garg\r\nUniversity of Wyoming COSC 4555/5555 Machine Learning, Spring 2023\r\n-------\r\n\r\n'''\r\n\r\nimport numpy as np\r\n\r\n\r\ndef split_into_train_and_test(x_all_LF, frac_test=0.5, random_state=None):\r\n\r\n    \r\n    if random_state is None:\r\n        random_state = np.random\r\n    \r\n    \r\n    xl = x_all_LF.copy()\r\n    \r\n    L = xl[1].size * frac_test\r\n    \r\n    if L.is_integer() == False :\r\n        L = round(xl[1].size * frac_test + .5)\r\n    else :\r\n        L = int(L)\r\n    \r\n    random_state.shuffle(xl)\r\n    \r\n    n_train = xl[:L] \r\n    n_test = xl[L:xl[1].size] \r\n    \r\n    \r\n    return n_train, n_test\r\n    \r\n    \r\n    \r\nx_LF = np.eye(10)\r\nxcopy_LF = x_LF.copy() # preserve what input was before the call\r\ntrain_MF, test_NF = split_into_train_and_test(\r\nx_LF, frac_test=0.201, random_state=np.random.RandomState(0))\r\ntrain_MF.shape\r\ntest_NF.shape\r\nprint(train_MF)\r\nprint(test_NF)\r\nprint(np.allclose(x_LF, xcopy_LF))\r\n\r\nprint(x_LF[1].size)\r\n\r\nprint(x_LF[:x_LF[1].size])\r\n\r\n\r\n","repo_name":"vgarg1011/Projects","sub_path":"splitDataset.py","file_name":"splitDataset.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"35401792806","text":"from cmath import sqrt\nimport random\nfrom numpy import number\nimport matplotlib.pyplot as plt\n\n\ninputFile=\"Assignment 3 berlin52.tsp\"\ntau = 5\nnumberOfAnts = 20\nmax_iterations = 100\nalfa = 1\nbeta = 1\nevaporation  = 0.3\n\nclass City():\n    def __init__(self,id, x, y) -> None:\n        self.id=id\n        self.x=x\n        self.y=y\n\n\nclass Ant():\n    def __init__(self, visited) -> None:\n        self.visited=visited\n        self.visitedEdges = []\n        self.cost = 0\n\nclass Edge():\n    def __init__(self, start, end) -> None:\n        self.start=start\n        self.end = end\n        self.tau=tau\n        self.cost = distance(start, end)\n\n\ndef initialize():\n    ants = []\n    for i in range(numberOfAnts):\n        cit = []\n        cit.append(cities[0])\n        ant = Ant(cit)\n        ants.append(ant)\n    return ants\n\ndef distance(a, b):\n    return sqrt(pow(b.x-a.x, 2) + pow(b.y-a.y,2)).real\n\ndef isItLastEdge(e, ant):\n    return len(ant.visited)==len(cities)-1\n\ndef transitionRule(ant):\n    probs = []\n    selectedEdges = []\n    r = ant.visited[-1]\n    summ = 0\n    for e in edges:\n        s=e.end\n        if s not in ant.visited and e.start == r and (s.id!=1 or (s.id==1 and isItLastEdge(e, ant))):\n            eta = 1 / e.cost\n            summ += pow(e.tau, alfa)*pow(eta, beta)\n\n    for e in edges:\n        s=e.end\n        if s not in ant.visited and e.start == r and (s.id!=1 or (s.id==1 and isItLastEdge(e, ant))):\n            selectedEdges.append(e)\n            eta = 1 / e.cost\n            p = pow(e.tau, alfa)*pow(eta, beta)/summ\n            probs.append(p)\n    if len(selectedEdges)>0:\n\n        next = random.choices(selectedEdges, probs)[0]\n        ant.visitedEdges.append(next)\n        return next   \n    return 0\n\ndef pheromoneUpdate(e):\n    sumDeltas = 0\n    for ant in ants:\n        if e in ant.visitedEdges:\n            sumDeltas+=1/ant.cost\n    \n    res = (1-evaporation)*e.tau+sumDeltas\n    e.tau = res\n\nf=open(inputFile, mode=\"r\", encoding=\"utf-8\")\ncities=[]\nfor line in f.readlines():\n    sL=line.split(\" \")\n    n = City(int(sL[0]), float(sL[1]), float(sL[2]))\n    cities.append(n)\ncities.append(City(1, cities[0].x, cities[0].y))\nf.close()\n#initialize the edges\nedges = []\nfor i in range(len(cities)):\n    for j in range(len(cities)):\n        if cities[i].id!=cities[j].id:\n            e = Edge(cities[i], cities[j])\n            edges.append(e)\n            \n\nx=[]\ny=[]\nfor i in range(max_iterations):\n    print(\"iteration \", i)\n    ants = initialize()\n    print(\"transitioning\")\n    for j in range(0, len(cities)-1):\n        #print(\"transition at city : \", cities[j].id)\n        for k in range(numberOfAnts):\n            e = transitionRule(ants[k])\n            if e!=0:\n                ants[k].cost+=e.cost\n                ants[k].visited.append(e.end)\n            #print(\"transition done for ant \", k, \"/\", numberOfAnts, \"on city \", cities[j].id)\n    \n    actualBest = ants[0]\n    print(\"cost update\")\n    for ant in ants:\n        if ant.cost<actualBest.cost:\n            actualBest = ant\n    print(\"pheromone update\")\n    for edge in edges:\n        pheromoneUpdate(edge)\n\n    if i==0:\n        globalBest = actualBest\n    elif actualBest.cost < globalBest.cost:\n        globalBest = actualBest\n    y.append(globalBest.cost)\n    x.append(i)\n    print(globalBest.cost)\n    for c in globalBest.visited:\n        print(c.id, end=\"->\")\n    print(\"\\n\")\n\n\nfig=plt.figure(figsize=(24, 24), dpi=60)\nax = fig.add_subplot(111)\n\ns=\"Evolution of the cost of the global best over the generations\"\nax.plot(x,y)\nax.set_title(s)\n\nplt.savefig(\"ant.png\")\nplt.close()\n\n","repo_name":"nema-oss/Artificial-Intelligence-Assignments","sub_path":"assignment3/part2/assignment3-2.py","file_name":"assignment3-2.py","file_ext":"py","file_size_in_byte":3567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"73060532739","text":"from faker import Faker\nimport os,uuid,hashlib\nfrom apps import db\nfrom apps.model import Role,Article,Follow\nfrom alembic import op\nfrom functools import wraps\nfaker = Faker(locale='zh_CN')\ndef md5(data):\n    md = hashlib.md5()\n    md.update(data.encode('utf-8'))\n    data = md.hexdigest()\n    return data\nnum = 1\nwhile num <20:\n    name = faker.name()\n    username = faker.user_name()\n    pwd = md5(str(num))\n    num +=1\n    print('姓名：'+name)\n    print('用户名：'+username)\n    print('pwd：'+pwd)\n\"\"\"\nnum = 1\nwhile num < 10:\n    #role = Role.query.filter_by(id=2ecfa8fcf29344899654acaa9fa4c7f2).first()\n    u = Article()\n    u.uuid = '2ecfa8fcf29344899654acaa9fa4c7f2'\n    u.body = faker.name()\n    u.body_html = faker.name()\n    u.tittle = faker.company()\n    u.addtime = faker.date_time()\n    db.session.add(u)\n\n    u = Role()\n    uu_id = str(uuid.uuid4()).replace('-', '')\n    u.uuid = uu_id\n    u.username = faker.user_name()\n    u.pwd = md5(str(num))\n    u.email = faker.email()\n    db.session.add(u)\n   \n    try:\n        db.session.commit()\n        num += 1\n    except:\n        db.session.rollback()\n\n\n\ndef avatar(email):\n    size = 180\n    default = 'monsterid'\n    r = 'g'\n    m = hashlib.md5()\n    m.update(email.encode('utf-8'))\n    hash = m.hexdigest()\n    a_url = 'https://www.gravatar.com/avatar/{hash}?s={size}&d={default}&r={r}'.format(hash=hash,size=size,default=default,r=r)\n    return a_url\n    \nfollow = Role.query.filter_by(username='111').first()\nfollower = follow.followers\nfollowed = follow.followed\nlist1 = []\nlist2 = []\nfor i in follower:\n    list1.append(i.follower.uuid)\nfor j in followed:\n    list2.append(j.followed.uuid)\n\n\nlist = set(list1).intersection(set(list2))\nif '222' in list:\n    print('222in')\nprint(list)\ndef friends_circle(func):\n\n    @wraps(func)\n    def wrapper(*args, **kwargs):\n        follow = Role.query.filter_by(uuid= current_user.uuid).first()\n        follower = follow.followers\n        followed = follow.followed\n        list1 = []\n        list2 = []\n        for i in follower:\n            list1.append(i.follower.uuid)\n        for j in followed:\n            list2.append(j.followed.uuid)\n\n        list = set(list1).intersection(set(list2))\n        print(list)\n        return func(*args, **kwargs)\n    return wrapper\n@friends_circle\ndef u(username):\n    print('woshi u')\n    return u\n   \n\n\ndef seem_likes():\n    #f = Follow.query.filter(Follow.follower_id == '1f675baf550d4bf09bf4a2ea422c3572').all()\n\n    #p = Role.query.join(Follow, Follow.follower_id == '1f675baf550d4bf09bf4a2ea422c3572').filter(Follow.followed_id == \"1f675baf550d4bf09bf4a2ea422c3572\").all()\n    q = Article.query.join(Follow, Follow.followed_id == Article.uuid).filter(Follow.follower_id == '1f675baf550d4bf09bf4a2ea422c3572' ).order_by(Article.addtime.desc())\n    p = Article.query.join(Follow, Follow.follower_id ==  Article.uuid).filter(\n        Follow.followed_id == '1f675baf550d4bf09bf4a2ea422c3572').order_by(Article.addtime.desc())\n\n    o = [x for x in q if x in p].paginate\n\n    for i in o:\n        print(i.addtime)\n\n    return 'dd'\nseem_likes()\n\ndef kk():\n    a = Article.query.join(Follow, Follow.follower_id == Article.uuid).filter(\n        Follow.followed_id == '1f675baf550d4bf09bf4a2ea422c3572').order_by(Article.addtime.desc())\n    p = a.paginate(1,per_page=5,error_out=False)\n    s = p.items\n    print(s)\n    return 'd'\nkk()\n\n\ndef friends_circle():\n    follow = Role.query.filter_by(uuid='1f675baf550d4bf09bf4a2ea422c3572').first()\n    follower = follow.followers\n    followed = follow.followed\n    list1 = []\n    list2 = []\n    for i in follower:\n        list1.append(i.follower.uuid)\n    for j in followed:\n        list2.append(j.followed.uuid)\n\n    friend_list =[x for x in list1 if x in list2 ]\n    u = Article.query.filter(Article.uuid.in_(friend_list))\n    r = u.order_by(Article.addtime.desc()).paginate(1, per_page=10, error_out=False)\n    print(r.items)\n    return friend_list\nfriends_circle()\n\"\"\"","repo_name":"Jarry007/all-in","sub_path":"apps/mal.py","file_name":"mal.py","file_ext":"py","file_size_in_byte":3961,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"80"}
+{"seq_id":"39957835139","text":"nums=input().split(\",\")\nnums[0]=nums[0][1:len(nums[0])]\nnums[-1]=nums[-1][0:-1]\nnums_=[]\nfor i in nums:\n    nums_.append(int(i))\nnew=[]\nfor i in range(len(nums_)):\n    count=0\n    for j in range(i+1,len(nums_)):\n        if nums_[i]>nums_[j]:\n            count+=1\n    new.append(count)\nprint(new)\n","repo_name":"AdamZhouSE/pythonHomework","sub_path":"Code/CodeRecords/2456/60751/285461.py","file_name":"285461.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"80"}
+{"seq_id":"30377717645","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def pathSum(self, root: Optional[TreeNode], targetSum: int) -> List[List[int]]:\n#======== <Solution 1> ========#\n        ans = []\n        self.dfs(root, targetSum, [], ans)\n        return ans\n\n    def dfs(self, root, targetSum, path, ans):\n        if root:\n            path.append(root.val)\n            if not root.left and not root.right and root.val == targetSum:  # Found a valid root-to-leaf path\n                ans.append(path.copy())  # Need to append a copy of path in backtracking\n            self.dfs(root.left, targetSum - root.val, path, ans)\n            self.dfs(root.right, targetSum - root.val, path, ans)\n            path.pop()  # Backtracking\n\n#======== <Solution 2> ========#\n        if not root:\n            return []\n        if not root.left and not root.right and root.val == targetSum:\n            return [[root.val]]\n        child = self.pathSum(root.left, targetSum - root.val) + self.pathSum(root.right, targetSum - root.val)\n        return [[root.val] + value for value in child]\n\n#======== <Solution 3> ========#\n        import collections\n        ans = []\n        if root:\n            queue = collections.deque([(root, root.val, [root.val])])\n            while queue:\n                curr, value, path = queue.popleft()\n                if not curr.left and not curr.right and value == targetSum:\n                    ans.append(path)\n                if curr.left:\n                    queue.append((curr.left, value + curr.left.val, path + [curr.left.val]))\n                if curr.right:\n                    queue.append((curr.right, value + curr.right.val, path + [curr.right.val]))\n        return ans\n\n#======== <Solution 4> ========#\n        ans = []\n        if root:\n            stack = [(root, targetSum - root.val, [root.val])]\n            while stack:\n                curr, value, path = stack.pop()\n                if not curr.left and not curr.right and not value:\n                    ans.append(path)\n                if curr.right:\n                    stack.append((curr.right, value - curr.right.val, path + [curr.right.val]))\n                if curr.left:\n                    stack.append((curr.left, value - curr.left.val, path + [curr.left.val]))\n        return ans\n\n#======== <Solution 5> ========#\n        ans = []\n        if root:\n            stack = [(root, [root.val])]\n            while stack:\n                curr, path = stack.pop()\n                if not curr.left and not curr.right and sum(path) == targetSum:\n                    ans.append(path)\n                if curr.right:\n                    stack.append((curr.right, path + [curr.right.val]))\n                if curr.left:\n                    stack.append((curr.left, path + [curr.left.val]))\n        return ans\n","repo_name":"andy2167565/leetcode","sub_path":"Medium/0113_path-sum-ii/path-sum-ii.py","file_name":"path-sum-ii.py","file_ext":"py","file_size_in_byte":2933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"5877445125","text":"from django.urls import path\nfrom rest_framework.urlpatterns import format_suffix_patterns\n\nfrom . import views\n\napp_name='api'\n\nurlpatterns = [\n    path('cobrancas/emitir/', views.CobrancaEmitir.as_view(), name='cobrancas-emitir'),\n    path('cobrancas/consultar/', views.CobrancaConsulta.as_view(), name='cobrancas-consulta'),\n    path('token/redirect/', views.token_redirect, name='tokens-redirect-api'),\n    path('boletos/recebe-notificacao/', views.BoletoRecebeNotificacao.as_view(), name='boletos-recebe-notificacao'),\n\n    path('teste/', views.TesteList.as_view(), name='teste'),\n]\n\nurlpatterns = format_suffix_patterns(urlpatterns)","repo_name":"macdev14/btaxtest","sub_path":"api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"4302807807","text":"import logging\n\nimport numpy as np\nfrom scipy import constants\n\nfrom haloreader.type_guards import is_ndarray\nfrom haloreader.variable import Variable\n\nlog = logging.getLogger(__name__)\n\n\ndef compute_beta(\n    intensity: Variable, range_: Variable, focus: Variable, wavelength: Variable\n) -> Variable:\n    # pylint: disable=invalid-name\n    \"\"\"\n    Parameters\n    ----------\n    range_\n        distance from the instrument\n    focus\n        focal length og the telescope for the transmitter and receiver\n    lambda_\n        laser wavelength\n\n    Local variables\n    ---------------\n    eta\n        detector quantum efficiency\n    E\n        beam energy\n    nu\n        optical frequency\n    h\n        planc's constant\n    c\n        speed of light\n    B\n        reveiver bandwidth\n\n    References\n    ----------\n    Methodology for deriving the telescope focus function and\n    its uncertainty for a heterodyne pulsed Doppler lidar\n        authors:  Pyry Pentikäinen, Ewan James O'Connor,\n            Antti Juhani Manninen, and Pablo Ortiz-Amezcua\n        doi: https://doi.org/10.5194/amt-13-2849-2020\n    \"\"\"\n    if not is_ndarray(intensity.data):\n        raise TypeError\n    if not is_ndarray(range_.data):\n        raise TypeError\n    if not isinstance(wavelength.data, float):\n        raise TypeError\n    if wavelength.units != \"m\":\n        raise NotImplementedError(\n            f'Expected wavelength units \"m\", got \"{wavelength.units}\".'\n        )\n\n    r = range_.data\n    h = constants.Planck\n    eta = 1\n    c = constants.speed_of_light\n    E = 1e-5\n    lambda_ = wavelength.data\n    nu = c / lambda_\n    B = 5e7\n    A_e = compute_effective_receiver_energy(range_, focus, lambda_)\n    snr = intensity.data - 1\n    # ref: https://doi.org/10.5194/amt-13-2849-2020\n    beta = 2 * h * nu * B * r**2 * snr / (eta * c * E * A_e)\n    return Variable(\n        name=\"beta\",\n        long_name=\"attenuated backscatter coefficient\",\n        comment=(\n            \"Experimental variable. Computed using uncalibrated/placeholder values.\"\n        ),\n        units=\"m-1 sr-1\",\n        dimensions=intensity.dimensions,\n        data=beta,\n    )\n\n\ndef compute_effective_receiver_energy(\n    range_: Variable, focus: Variable, lambda_: float\n) -> np.ndarray:\n    # pylint: disable=invalid-name\n    \"\"\"\n    Parameters\n    ----------\n    range_\n        distance from the instrument\n    focus\n        effective focal length of the telescope for the transmitter and receiver\n    lambda_\n        laser wavelength\n    \"\"\"\n    if not is_ndarray(range_.data):\n        raise TypeError\n    log.warning(\n        \"Using placeholder values from https://doi.org/10.5194/amt-13-2849-2020\"\n    )\n    r = range_.data\n    D = 25e-3  # effective_diameter_of_gaussian_beam\n    f = focus.data  # effective_focal_length\n    return (\n        np.pi\n        * D**2\n        / (4 * (1 + (np.pi * D**2 / (4 * lambda_ * r)) ** 2 * (1 - r / f) ** 2))\n    )\n","repo_name":"actris-cloudnet/halo-reader","sub_path":"src/haloreader/attenuated_backscatter_coefficient.py","file_name":"attenuated_backscatter_coefficient.py","file_ext":"py","file_size_in_byte":2912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"40183230331","text":"import requests, uuid, json\nfrom fpdf import FPDF \nimport os\n\n\ndef translatorit(data,to,translate_path):\n    \n    \n    key = os.getenv('KEY_AZURE_TRANSLATE')\n    endpoint = \"https://api.cognitive.microsofttranslator.com/\"\n    location = \"westeurope\"\n    path = '/translate'\n    constructed_url = endpoint + path\n    params = {\n        'api-version': '3.0',\n        'to': to\n    }\n    headers = {\n        'Ocp-Apim-Subscription-Key': key,\n        'Ocp-Apim-Subscription-Region': location,\n        'Content-type': 'application/json',\n        'X-ClientTraceId': str(uuid.uuid4())\n    }\n    # body = [{\n    #     'text': 'I would really like to drive your car around the block a few times!'\n    # }]\n    body = [{'text': data}]\n    request = requests.post(constructed_url, params=params, headers=headers, json=body)\n    response = request.json()\n    # print(json.dumps(response, sort_keys=True, ensure_ascii=False, indent=4, separators=(',', ': ')))\n    # a = json.dumps(response, sort_keys=True, ensure_ascii=False, indent=4, separators=(',', ': '))\n    if len(to) <= 1:\n        _lang = f'-{to[0]}.text'\n        __translate_path = translate_path.replace('.text',_lang).replace('.txt',_lang).replace('.word',_lang)\n        _res = response[0]['translations'][0]['text']\n        with open(__translate_path, 'w') as f:\n                f.write(_res)\n    else:\n        for i in range(len(to)):\n            res = response[0]['translations'][i]['text']\n            lang = to[i]\n            lang = f'-{lang}.text'\n            _translate_path = translate_path.replace('.text',lang).replace('.txt',lang).replace('.word',lang)\n            with open(_translate_path, 'w') as f:\n                f.write(res)\n            \n\n        \n        \n        \n    \n","repo_name":"lola-pola/pdfer","sub_path":"app/tran.py","file_name":"tran.py","file_ext":"py","file_size_in_byte":1737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"26002741906","text":"from rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom .models import AssetTransportRequest, ShareTravelInfo\nfrom .serializers import AssetTransportRequestSerializer, ShareTravelInfoSerializer, MatchedRidesSerializer\nfrom rest_framework.pagination import LimitOffsetPagination\nfrom datetime import datetime\nfrom .helpers import get_pagination\n\n\nclass AssetTransportationRequestCreateView(APIView):\n    def post(self, request, *args, **kwargs):\n        data = request.data\n        serializer = AssetTransportRequestSerializer(data=data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data, status=201)\n        return Response(serializer.errors, status=400)\n\n\nclass TravelInfoShareView(APIView):\n    def post(self, request, *args, **kwargs):\n        serializer = ShareTravelInfoSerializer(data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data, status=201)\n        return Response(serializer.errors, status=400)\n\n\nclass AssetTransportationRequestListView(APIView):\n\n    def get(self, request, *args, **kwargs):\n        AssetTransportRequest.objects.filter(date_time__gt=datetime.now()).update(status='EXPIRED')\n        asset_requests = AssetTransportRequest.objects.all()\n\n        status = request.query_params.get('status', None)\n        page_index = request.query_params.get('page_index', None)\n        page_size = request.query_params.get('page_size', None)\n        sort = request.query_params.get('sort', None)\n        if status:\n            asset_requests = asset_requests.filter(status__icontains=status)\n\n        asset_type = request.query_params.get('asset_type', None)\n        if asset_type:\n            asset_requests = asset_requests.filter(asset_type__icontains=asset_type)\n        if sort == 'asc':\n            asset_requests = asset_requests.order_by('date_time')\n        elif sort == 'desc':\n            asset_requests = asset_requests.order_by('-date_time')\n        data = get_pagination(asset_requests, page_index, page_size)\n\n        serializer = AssetTransportRequestSerializer(data['queryset'], many=True)\n\n        return Response(serializer.data, status=200)\n\n\nclass AssetTransportationRidesListView(APIView):\n\n    def get(self, request, *args, **kwargs):\n        requester_requests = AssetTransportRequest.objects.filter(\n            # status='pending',\n            from_location=request.query_params.get('from_location', None),\n            to_location=request.query_params.get('to_location', None),\n            date_time=request.query_params.get('date_time', None),\n        )\n        page_index = request.query_params.get('page_index', None)\n        page_size = request.query_params.get('page_size', None)\n        matched_rides = []\n        # print(requester_requests)\n        for requester_request in requester_requests:\n            rides = ShareTravelInfo.objects.filter(\n                from_location=requester_request.from_location,\n                to_location=requester_request.to_location,\n                date_time=requester_request.date_time,\n            )\n            matched_rides.extend(rides)\n        # print(matched_rides)\n        data = get_pagination(matched_rides, page_index, page_size)\n        serializer = MatchedRidesSerializer(data['queryset'], many=True)\n\n        return Response(serializer.data, status=200)\n\n\nclass TravelInfoApplyView(APIView):\n    def post(self, request, *args, **kwargs):\n        try:\n            travel_info = ShareTravelInfo.objects.get(id=request.data.get('id'))\n        except:\n            return Response({'message':'Travel info not found'}, status=404)\n        travel_info.status = 'APPLIED'\n        travel_info.save()\n        return Response({'message':'Travel info Applied'}, status=200)\n","repo_name":"jaisipani/lets_ride","sub_path":"api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"74191574977","text":"import requests\nimport json\nimport pprint\nimport logging, sys\nfrom requests.packages.urllib3.exceptions import InsecureRequestWarning\n\n\nclass MSO:\n    def __init__(self, mso_url):\n        self.mso_url = mso_url\n        self.auth_token = None\n        self.hed = None\n        self.schemas = {}\n        # create logger\n        self.logger = logging.getLogger(__name__)\n        \n        \n        # create console handler and set level to debug\n        self.ch = logging.StreamHandler()\n        self.ch.setLevel(logging.DEBUG)\n        \n        # create formatter\n        self.formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n        \n        # add formatter to ch\n        self.ch.setFormatter(self.formatter)\n        \n        # add ch to logger\n        self.logger.addHandler(self.ch)\n        \n        #Disable URL Lib Warnings\n        requests.packages.urllib3.disable_warnings(InsecureRequestWarning)\n\n\n    def login(self, username, password):\n        data = {\n                \"username\": username,\n                \"password\": password,\n               }\n        # Login into MSO and get Authentication toke.  \n        self.logger.debug(\"Log In to MSO\")\n        r = requests.post(self.mso_url + \"/api/v1/auth/login\",json=data, verify=False)\n        login_data = json.loads(r.text) \n        self.auth_token = login_data['token']\n        self.hed = {'Authorization': 'Bearer ' + self.auth_token}\n    \n    def createSchema(self, name, templateName, tenant):\n\n        tenantId = self.getTenantId(name = tenant)\n\n        data = {\n                   \"displayName\": name,\n                   \"templates\": [\n                                   {\n                                       \"name\": templateName,\n                                       \"displayName\": templateName,\n                                       \"tenantId\": tenantId\n                                   }\n                               ]\n                }\n\n\n        r = requests.post(self.mso_url + \"/api/v1/schemas\",json=data,headers=self.hed, verify=False)\n        self.logger.debug(\"Schema creation status %s, reson %s\", r.status_code, r.reason)\n        if r.reason == \"Conflict\":\n             self.logger.info(\"Schema already exist! \")\n        \n        self.loadSchema(name)\n       \n\n    def loadSchema(self, name):\n        self.schemas[name] = Schema(name, False, self.logger, self.mso_url, self.hed)\n            \n    def createTenant(self,name, displayName = None, desc = \"\", sites = []):\n        \n        if not displayName:\n            displayName = name\n        # Tenant Data \n        data = {\n                \"displayName\": displayName,\n                \"name\": name,\n                \"description\": desc,\n                \"siteAssociations\": []\n                }\n        \n        #If I am mapping a Tenants to sites when I create it, then I add the site ID to the tenant siteAssociation.\n        if len(sites) > 0:\n            self.logger.debug(\"Create Tenant and map it to %d sites\", len(sites))\n            for site in sites:\n               data['siteAssociations'].append({'siteId':self.getSiteId(site),'securityDomains':[]})\n               print(data['siteAssociations'])\n        \n        else:\n            self.logger.debug(\"Create Tenant, not mapped to any site\")   \n\n        r = requests.post(self.mso_url + \"/api/v1/tenants\",json=data,headers=self.hed, verify=False)\n        self.logger.debug(\"Tenant creation status %s, reson %s\", r.status_code, r.reason)\n        if r.reason == \"Conflict\":\n             self.logger.info(\"Tenant already exist! \")\n\n    def getAllTenants(self):\n        self.logger.debug(\"Get all Tenants\")\n        r = requests.get(self.mso_url + \"/api/v1/tenants\", headers=self.hed, verify=False)\n        tenants = json.loads(r.text)\n        self.logger.debug(\"Found a total of %d Tenants\", len(tenants['tenants']))     \n        return tenants\n\n\n    def getTenantByName(self,name):\n        \n        #API does not support filtering so I need anyway to pull all the tenants and then find.\n        tenants = self.getAllTenants()\n        self.logger.debug(\"Looking for Tenant name %s\", name)\n        \n        for tenant in tenants['tenants']:\n            if tenant['name'] == name:\n                self.logger.debug(\"Found Tenant %s\",name)\n                return tenant\n        self.logger.debug(\"Site %s not found\",name)                \n        return None\n\n    def getTenantId(self, name):\n        tenant = self.getTenantByName(name)\n        self.logger.debug(\"Tenant ID %s\", tenant['id']) \n        return tenant['id']\n\n    def addTenantAssociations(self, name, sites = []):\n        if len(sites) > 0:\n            tenant = self.getTenantByName(name)\n            for site in sites:\n                siteId = self.getSiteId(site)\n                siteAssociation = {\n                                   'siteId':siteId,\n                                   'securityDomains':[]\n                                  }\n                if siteAssociation not in tenant['siteAssociations']:\n                     tenant['siteAssociations'].append(siteAssociation)\n                else:\n                    self.logger.info('Tenant %s to Site %s  association already existing', tenant['name'], site)\n                    \n        r = requests.put(self.mso_url + \"/api/v1/tenants/\" + tenant['id'] ,json=tenant, headers=self.hed, verify=False)\n        self.logger.debug('Tenant update status %s %s',r.status_code, r.reason)\n\n    def delTenantAssociations(self, name, sites = [], deleteAll = False):\n        if len(sites) > 0 and not deleteAll :\n            tenant = self.getTenantByName(name)\n            for site in sites:\n                siteId = self.getSiteId(site)\n                tenant['siteAssociations'][:] = [d for d in tenant['siteAssociations'] if d.get('siteId') != siteId]\n        elif deleteAll:\n             tenant = self.getTenantByName(name)\n             tenant['siteAssociations'] = []\n        else:\n             self.logger.error('You need to specify either a list of sites or deleteAll needs to be set to True')\n             exit()\n                \n        r = requests.put(self.mso_url + \"/api/v1/tenants/\" + tenant['id'] ,json=tenant, headers=self.hed, verify=False)\n        self.logger.debug('Tenant update status %s %s',r.status_code, r.reason)\n\n    def createSite(self, name, url, username, password, siteID):\n        data = {\n                  \"name\": name,\n                  \"urls\": url,\n                  \"username\": username,\n                  \"password\": password,\n                  \"apicSiteId\" : siteID\n               }\n\n        r = requests.post(self.mso_url + \"/api/v1/sites\",json=data,headers=self.hed, verify=False)\n        self.logger.debug(\"Site creation status %s, reson %s\", r.status_code, r.reason)\n        if r.reason == \"Conflict\":\n             self.logger.info(\"Tenant already exist! \")\n\n    def getAllSites(self):\n        self.logger.debug(\"Get all Sites\")\n        r = requests.get(self.mso_url + \"/api/v1/sites\", headers=self.hed, verify=False)\n        sites = json.loads(r.text)\n        self.logger.debug(\"Found a total of %d sites\", len(sites['sites']))   \n        if len(sites['sites'])==0:\n            self.logger.error(\"No sites found, please create a site first!\\n Execution Aborted\")\n            exit()\n\n        return sites\n    \n    def getSiteByName(self, name):\n\n        sites = self.getAllSites()\n        self.logger.debug(\"Looking for site name %s\", name)\n        \n        for site in sites['sites']:\n            if site['name'] == name:\n                self.logger.debug(\"Found site %s\",name)\n                return site\n        self.logger.debug(\"Site %s not found\",name)                \n        return None\n        \n    def getSiteId(self, name):\n        site = self.getSiteByName(name)\n        self.logger.debug(\"Site ID %s\", site['id']) \n        return site['id'] \n    \n    def getAudit(self):\n        limit = str(100)\n        offset = str(0)\n        audit = []\n        sort='-timestamp'\n        while(offset):\n            r = requests.get(self.mso_url + \"/api/v1/audit-records?limit=\"+limit+\"&offset=\"+offset+\"&sort=\"+sort, headers=self.hed, verify=False)\n            limit = r.headers['X-Page-Limit']\n            if 'X-Page-Next-Offset' in r.headers:\n                offset = r.headers['X-Page-Next-Offset']                \n            else:\n                offset = False\n                \n            audit= audit + (json.loads(r.text)['auditRecords'])\n        return audit\n    \nclass Schema:\n    def __init__(self, name, create, logger, mso_url,  hed):\n        self.logger = logger\n        self.mso_url = mso_url\n        self.hed = hed \n        if create:\n            self.schema = self.createSchema(name, tenant, templateName)\n                                             \n        else:\n            self.schema = self.getSchemaByName(name)\n\n        self.schemId =  self.schema['id']\n\n\n\n    \n    def getTempListID(self, templates, name):\n        index =  next((index for (index, d) in enumerate(templates) if d[\"name\"] == name), None)\n        if index != None :\n            return index\n        else:\n            self.logger.error(\"Template %s not found\", name)\n            exit()\n\n    def getAllSchema(self):\n        self.logger.debug(\"Get all Schemas\")\n        r = requests.get(self.mso_url + \"/api/v1/schemas\", headers=self.hed, verify=False)\n        schemas = json.loads(r.text)\n        return schemas\n\n    def getSchemaByName(self,name):\n        self.logger.debug(\"Looking for Schema name %s\", name)\n        schemas = self.getAllSchema()\n        for schema in schemas['schemas']:\n            if schema['displayName'] == name:\n                self.logger.debug(\"Found Schema %s\",name)                \n                return schema\n\n    def getSchemaId(self, name):\n        schema = self.getSchemaByName(name)\n        self.logger.debug(\"Schema ID %s\", schema['id']) \n        return schema['id'] \n    \n    def addBD(self,bd_template_name, name,vrf, vrf_template_name = None, intersiteBumTrafficAllowm = True, \n        l2Stretch = True, l2UnknownUnicast = 'proxy',optimizeWanBandwidth = True, \n        subnets = []):\n        # Here we need to pass a of parameters just keep in mind that the tempale for the VRF cab be different for the template\n        #of the BD so I give the ability to specify this. \n\n        if not vrf_template_name:\n            vrf_template_name = bd_template_name\n\n        bd = {\n                    \"bdRef\": \"/schemas/\" + self.schemId + \"/templates/\" + bd_template_name + \"/bds/\"+ name,\n                    'vrfRef':\"/schemas/\" + self.schemId + \"/templates/\" + vrf_template_name + '/vrfs/' + vrf,\n                    \"displayName\": name,\n                    \"intersiteBumTrafficAllow\": intersiteBumTrafficAllowm,\n                    \"l2Stretch\": l2Stretch,\n                    \"l2UnknownUnicast\": l2UnknownUnicast,\n                    \"name\": name,\n                    \"optimizeWanBandwidth\": optimizeWanBandwidth,\n                    \"subnets\": []\n\n                     } \n        if l2Stretch:\n            bd['subnets'] = subnets\n        else:\n            pass\n\n\n        index =  self.getTempListID(self.schema['templates'], bd_template_name)\n           \n        if bd not in self.schema['templates'][index]['bds']:\n           self.schema['templates'][index]['bds'].append(bd)\n           self.logger.debug(\"Adding BD %s\", name)\n        else:\n           self.logger.info(\"BD %s already exists, not addind\", name)\n    \n    def delBD(self, name, template_name):\n        self.logger.debug(\"Deleting BD %s\", name)\n        index =  self.getTempListID(self.schema['templates'], template_name)\n        self.schema['templates'][index]['bds'][:] = [d for d in  self.schema['templates'][index]['bds'] if d.get('name') != name]\n\n                    \n    def commit(self):\n        r = requests.put(self.mso_url + \"/api/v1/schemas/\" + self.schema['id'] ,json=self.schema, headers=self.hed, verify=False)\n        self.logger.debug('Schema update status %s %s',r.status_code, r.reason)\n\n\n","repo_name":"camrossi/multisite","sub_path":"multisite.py","file_name":"multisite.py","file_ext":"py","file_size_in_byte":11970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"40024065629","text":"s=list(input())\npairs=eval(input())\nmovs=[]\n\n\nwhile len(pairs)>0: #暴力将对换合成为各个轮换 即暴力实现并查集\n    mov=set()\n    pair=pairs.pop(0)\n    mov.add(pair[0])\n    mov.add(pair[1])\n    ps=pairs[:]\n    has=True\n    while has:\n        has=False\n        ps=pairs[:]\n        for p in ps:\n            if p[0] in mov or p[1] in mov:\n                has=True\n                mov.add(p[0])\n                mov.add(p[1])\n                pairs.remove(p)\n    mov=list(mov)\n    mov.sort()\n    movs.append(mov)\n\n\nfor m in movs:\n    arr=[]\n    for i in m:\n        arr.append(s[i])\n    arr.sort()\n    for i in m:\n        s[i]=arr[0]\n        arr.pop(0)\nprint(''.join(s))","repo_name":"AdamZhouSE/pythonHomework","sub_path":"Code/CodeRecords/2718/60589/254565.py","file_name":"254565.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"80"}
+{"seq_id":"40214452977","text":"\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n### GRADED\n### Code a function called `calc_posterior`\n\n### ACCEPT three inputs\n### Two floats: the likelihood and the prior\n### One list of tuples, where each tuple has two values corresponding to:\n### ### ( P(Bn) , P(A|Bn) )\n### ### ### Assume the list of tuples accounts for all potential values of B\n### ### ### And that those values of B are all mutually exclusive.\n### The list of tuples allows for the calculation of normalization constant.\n\n### RETURN a float corresponding to the posterior probability\n\n### YOUR ANSWER BELOW\n\ndef calc_posterior(likelihood, prior, norm_list):\n    \"\"\"\n    Calculate the posterior probability given likelihood,\n    prior, and normalization\n\n    Positional Arguments:\n        likelihood -- float, between 0 and 1\n        prior -- float, between 0 and 1\n        norm_list -- list of tuples, each tuple has two values\n            the first value corresponding to the probability of a value of \"b\"\n            the second value corresponding to the probability of\n                a value of \"a\" given that value of \"b\"\n    Example:\n        likelihood = .8\n        prior = .3\n        norm_list = [(.25 , .9), (.5, .5), (.25,.2)]\n        print(calc_posterior(likelihood, prior, norm_list))\n        # --> 0.45714285714285713\n    \"\"\"\n\n    joint = 0.0\n\n    for xx in norm_list:\n\n        joint += (xx[0] * xx[1])\n\n    print(joint)\n\n    post = (likelihood * prior) / joint\n\n    return post\n\n\n\nlikelihood = .8\nprior = .3\nnorm_list = [(.25 , .9), (.5, .5), (.25,.2)]\n\nprint(calc_posterior(likelihood, prior, norm_list))\n\n\n\n\n\n\n\n\n\n\n##\n","repo_name":"dariofl24/machineLearningPy","sub_path":"posterior.py","file_name":"posterior.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"6379576484","text":"# Dataset Documentation\n# https://www.kaggle.com/datasets/markmarkoh/near-earth-asteroids\n\n# Velocity Infinity          -> Pitch               -> Higher Speed         -> High Pitch\n# Minimum Distance           -> Note Length         -> Further Distance     -> Longer Note\n# ref                        -> Velocity            -> Higher Ref           -> Higher Velocity\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef convert_data(row):\n    converted = np.array([])\n    for i in row:\n        stripped = i.split('/')[0]\n        converted = np.append(converted, float(stripped))\n    return converted\n\n\ndef read_and_plot(file, r1, r2, r3):\n    d = pd.read_csv(file)\n    pitch = d[r1].values\n    length = d[r2].values\n    velocity = d[r3].values\n    if isinstance(pitch[0], str):\n        pitch = convert_data(pitch)\n    if isinstance(length[0], str):\n        length = convert_data(length)\n    if isinstance(velocity[0], str):\n        velocity = convert_data(velocity)\n    plt.scatter(pitch, length, s=pitch, c=velocity)\n    plt.xlabel(r1)\n    plt.ylabel(r2)\n    plt.show()\n\nread_and_plot('./00data/near_earth.csv', 'Vinfinity(km/s)', 'CA DistanceMinimum(LD/AU)', 'ref')\n\n\n","repo_name":"devoredevelops/python_ableton_resources","sub_path":"part_b/b14.py","file_name":"b14.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"38274559474","text":"import sys\nsys.stdin = open(\"sample_input.txt\")\n\nT = int(input())\n\nfor tc in range(1, T+1):\n    N = int(input())\n    score = list(map(int, input().split()))\n    visited = [1] + [0] * sum(score)  # 방문체크할 리스트\n\n    tmp = [0]\n    for ele in score:\n        for i in range(len(tmp)):\n            if not visited[ele+tmp[i]]:\n                visited[ele+tmp[i]] = 1  # 방문체크\n                tmp.append(ele+tmp[i])\n    print('#{} {}'.format(tc, len(tmp)))\n\n","repo_name":"FR0GM4N/Algorithm","sub_path":"src/swea/3752.py","file_name":"3752.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"36501666736","text":"#!/usr/bin/env python\nversion='SEDMATCHVERSION'\nscripts='SEDMATCHLIB'\nauthor='Julien Fouret'\ncontact='julien@fouret.me'\n\nimport argparse\n\nclass _HelpAction(argparse._HelpAction):\n\n\tdef __call__(self, parser, namespace, values, option_string=None):\n\t\tparser.print_help()\n\t\t# retrieve subparsers from parser\n\t\tsubparsers_actions = [\n\t\t\taction for action in parser._actions\n\t\t\tif isinstance(action, argparse._SubParsersAction)]\n\t\t\t# there will probably only be one subparser_action,\n\t\t\t# but better save than sorry\n\t\tfor subparsers_action in subparsers_actions:\n\t\t# get all subparsers and print help\n\t\t\tfor choice, subparser in subparsers_action.choices.items():\n\t\t\t\tprint(\"\\n\\n\\n\\n--------\"+(\"-\"*len(choice))+\"\\n\"+\"### {} ###\".format(choice))+\"\\n\"+\"--------\"+(\"-\"*len(choice)+\"\\n\")\n\t\t\t\tprint(subparser.format_help())\n\t\t\t\tprint(\"______________________________________________________________________________\")\n\t\tparser.exit()\n\nparser = argparse.ArgumentParser(description='Wraper for GIDEON analyses',epilog=\"Version : \"+str(version)+\"\\nAuthor : \"+author+\" for more informations or enquiries please contact \"+contact,add_help=False,formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\nparser.add_argument('-h','--help', action=_HelpAction, help='if you need some help')\n\nsubparsers=parser.add_subparsers(help='Sub-commands',dest=\"analysis\")\n\nvhelp=\"Print version and exit\"\nvparser=subparsers.add_parser('version',description=vhelp,help=vhelp)\n\ncheckhelp=\"Check if requirements are satisfied\"\ncheckparser=subparsers.add_parser('check',description=checkhelp,help=checkhelp)\n\ngenePythiaHelp=\"Knowledge-driven method to identify a set of genes linked with a thematic (defined by terms). PubMed data-mining\"\ngenePythiaParser=subparsers.add_parser('genePythia',description=genePythiaHelp,help=genePythiaHelp)\ngenePythiaParser.add_argument('-o','--outDir', metavar='Path',required=True, help=\"outPut directory\")\n#parse and gene name will be the prefix\ngenePythiaParser.add_argument('-geneList', metavar='file',required=True, help=\"2-columns tabular file without header separated by tabulation. 1st col: gene name | 2nd col: comma-separated alias list\")\ngenePythiaParser.add_argument('-term', metavar='syn1,syn2,syn3',required=True, help=\"commat-separated list of terms to search (synonyms). Please add\\\" if a term is composed with spaces\" )\ngenePythiaParser.add_argument('-b','--batch_number', metavar='N',default='50',required=False, help=\"\")\ngenePythiaParser.add_argument('-q','--queue', metavar='name',required=True, help=\"PBS queue to be used\")\ngenePythiaParser.add_argument('-maxPub', metavar='N',default='80',required=False, help=\"max number of publications to print\")\ngenePythiaParser.add_argument('-mail', metavar='user@domain.tld',required=True, help=\"mail for Entrez query\")\ngenePythiaParser.add_argument('-ab',action=\"store_true\",required=False, help=\"adding the column abstract\")\n\n\nconfHelp=\"Help you to create the conf file in YAML format\"\nconfParse=subparsers.add_parser('configure',description=confHelp,help=confHelp)\n\nanalysisHelp=\"run the GIDEON analysis with the yaml conf file for gene lists and databases\"\nanalysisParse=subparsers.add_parser('analyse',description=analysisHelp,help=analysisHelp)\nanalysisParse.add_argument('-o','--outDir', metavar='Path',required=True, help=\"outPut directory\")\nanalysisParse.add_argument('-Tmax', metavar='N',default='90',required=False, help=\"maximum percentage of target genes in a metagroup to be selected\")\nanalysisParse.add_argument('-Tmin', metavar='N',default='10',required=False, help=\"minimum percentage of target genes in a metagroup to be selected\")\nanalysisParse.add_argument('-onlyEnrich',action=\"store_true\",required=False, help=\"Perform only the enrichment test to evaluate enrichment qualities\")\nanalysisParse.add_argument('-multiplex',action=\"store_true\",required=False, help=\"Integrate several databases as a layer in a multiplex layer. Then the Louvain algorithm is used to build metagroup\")\n\nargs=parser.parse_args()\n\ndef check():\n\tfrom subprocess import Popen, PIPE\n\timport IPython\n\tres=\"### Informations related to Python ###\\n\"\n\tres+=IPython.sys_info().replace(\"': '\",\":\\t\").replace(\"': u'\",\":\\t\").replace(\"\",\"\").replace(\"',\",\"\").replace(\"{'\",\"\").replace(\" '\",\"\").replace(\"'}\",\"\")\n\tres+=\"\\n######\\n\"\n\tres+=\"\\n### Informations related to R ###\\n\"\n\tprocess = Popen(\"Rscript -e 'sessionInfo()'\", stdout=PIPE, stderr=PIPE,shell=True)\n\tstdout, stderr = process.communicate()\n\tres+=stdout\n\tres+=\"\\n######\\n\"\n\treturn(res)\n\nimport sys\n\nif args.analysis==\"version\":\n\tprint(version)\n\tsys.exit()\nelif args.analysis==\"check\":\n\tprint(check())\n\tsys.exit()\nelif args.analysis==\"genePythia\":\n\timport datetime\n\tnowTime=datetime.datetime.now()\n\tfrom jupype import *\n\trootedDir=RootDir(args.outDir,pbs=True)\n\trootedDir.logs.writeArgs(args)\n\twith open(rootedDir.logs.path+\"/check.txt\",'a')as checkFile:\n\t\tcheckFile.write(\"system check printed at time: \"+str(nowTime)+\"\\n\")\n\t\tcheckFile.write(check()+\"\\n\\n\\n\")\n\tif args.ab:\n\t\tab=\" -ab\"\n\telse:\n\t\tab=\"\"\n\tbatch_count=0\n\tjob_num=0\n\tcmdList=[]\n\tbatch_lim=int(args.batch_number)\n\twith open(args.geneList) as geneListFile:\n\t\tfor line in geneListFile.readlines():\n\t\t\tline=line.strip()\n\t\t\tbatch_count+=1\n\t\t\tif \"\\t\" in line:\n\t\t\t\tgene,alias=line.split(\"\\t\")\n\t\t\telse:\n\t\t\t\tgene=line\n\t\t\t\talias=\"None\"\n\t\t\tcmdList.append(\"mkdir -p \"+rootedDir.results+\"/\"+gene+\"\\ncd \"+rootedDir.results+\"/\"+gene+\"\\n\\n\"+scripts+\"/genePythia/genePythia.py -gene \"+gene+\" -alias \"+alias+\" -term '\"+args.term+\"' -maxPub \"+args.maxPub+\" -mail \"+args.mail+ab)\n\t\t\tif batch_count>batch_lim:\n\t\t\t\tbatch_count=0\n\t\t\t\tjob_num+=1\n\t\t\t\tsubmitQsubWithPBS(createPBS(cmdList,\"genePythia_\"+str(job_num),queue=args.queue,workdir=rootedDir.results))\n\t\t\t\tcmdList=[]\n\tif cmdList!=[]:\n\t\tjob_num+=1\n\t\tsubmitQsubWithPBS(createPBS(cmdList,\"genePythia_\"+str(job_num),queue=args.queue,workdir=rootedDir.results))\n\tsaveRoot(rootedDir)\n\tsys.exit(0)\n\n\n\n\n","repo_name":"jfouret/gideon","sub_path":"scripts/gideon.py","file_name":"gideon.py","file_ext":"py","file_size_in_byte":5887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"70741022339","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May 21 21:02:43 2022\n\n@author: iman\n\"\"\"\n\n#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May 21 15:24:47 2022\n\n@author: iman\n\"\"\"\n\nfrom tkinter import *\nfrom tkcalendar import *\nimport pygame\nimport time\nimport speech_recognition as sr \n\n# ----- Voon Tao ----- #\nfrom MinutesManager import MinutesManager\n\nws = Tk()\nws.title(\"Smart Meeting Minutes\")\nws.geometry(\"820x600\")\n\nhour_string=StringVar()\nmin_string=StringVar()\nlast_value_sec = \"\"\nlast_value = \"\"        \nf = ('Arial', 40)\n\n# ----- Voon Tao ----- #\ndef createTxtMinutes(start_date,start_time):\n    manager = MinutesManager()\n    manager.createMinutes(start_date,start_time)\n# ----- Voon Tao ----- #\n\ndef display_msg():\n    date = cal.get_date()\n    m = min_sb.get()\n    h = sec_hour.get()\n    s = sec.get()\n    t = f\"Your appointment is booked for {date} at {m}:{h}:{s}.\"\n    createTxtMinutes(date,f'{m}:{h}:{s}') # create txt file minutes\n    msg_display.config(text=t)\n    ws.destroy()\n    import options\n\nif last_value == \"59\" and min_string.get() == \"0\":\n    hour_string.set(int(hour_string.get())+1 if hour_string.get() !=\"23\" else 0)   \n    last_value = min_string.get()\n\nif last_value_sec == \"59\" and sec_hour.get() == \"0\":\n    min_string.set(int(min_string.get())+1 if min_string.get() !=\"59\" else 0)\n    \nif last_value == \"59\":\n    hour_string.set(int(hour_string.get())+1 if hour_string.get() !=\"23\" else 0)            \n    last_value_sec = sec_hour.get()\n\nfone = Frame(ws)\nftwo = Frame(ws)\n\nfone.pack(pady=50)\nftwo.pack(pady=10)\n\ncal = Calendar(\n    fone, \n    selectmode=\"day\", \n    year=2021, \n    month=2,\n    day=3\n    )\ncal.pack()\n\nmin_sb = Spinbox(\n    ftwo,\n    from_=0,\n    to=23,\n    wrap=True,\n    textvariable=hour_string,\n    width=2,\n    state=\"readonly\",\n    font=f,\n    justify=CENTER\n    )\n\nsec_hour = Spinbox(\n    ftwo,\n    from_=0,\n    to=59,\n    wrap=True,\n    textvariable=min_string,\n    font=f,\n    width=2,\n    justify=CENTER\n    )\n\nsec = Spinbox(\n    ftwo,\n    from_=0,\n    to=59,\n    wrap=True,\n    textvariable=sec_hour,\n    width=2,\n    font=f,\n    justify=CENTER\n    )\n\nmin_sb.pack(side=LEFT, fill=X, expand=True)\nsec_hour.pack(side=LEFT, fill=X, expand=True)\nsec.pack(side=LEFT, fill=X, expand=True)\n\nmsg = Label(\n    ws, \n    text=\"Hour            Minute           Seconds\",\n    font=(\"Arial\", 12)\n    )\n\nmsg.pack(side=TOP)\n\nactionBtn = Button(\n    ws,\n    text=\"Smart Meeting Minutes\",\n    padx=10,\n    pady=10,\n    command=display_msg\n)\n\nactionBtn.pack(pady=10)\n\nmsg_display = Label(\n    ws,\n    text=\"\"\n)\n\nmsg_display.pack(pady=10)\n\nws.mainloop()\n","repo_name":"Murtada169/IntelligentSystem","sub_path":"main_gui.py","file_name":"main_gui.py","file_ext":"py","file_size_in_byte":2634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"11999333494","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jan  5 13:15:36 2021\n\n@author: Crow108\n\"\"\"\n\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ninstrument_path = r\"C:\\Users\\Crow108\\Documents\\Python\\instr\\analyzer\"\nif instrument_path not in sys.path: sys.path.append(instrument_path )\nimport bnc\n\nanalyzer_path = r\"C:\\Users\\Crow108\\Documents\\Python\\instr\\python_interface\\python_without_WX2184C\"\nif analyzer_path not in sys.path: sys.path.append(analyzer_path )\nimport daq_programs\n\ntarget_bnc_address =  'USB0::0x03EB::0xAFFF::421-4385A0002-0784::INSTR'\n\ndef sweep_bnc_freq(start_freq, stop_freq, num_points):\n    freq_list = np.linspace(start_freq, stop_freq, num_points)\n    steps_in_seq = 51\n    num_averages =500# 10000\n    \n    out_I, out_Q = np.zeros( (2, num_points, steps_in_seq))\n    for i,freq in enumerate(freq_list):\n        bnc.set_bnc_output(freq, bnc_addr=target_bnc_address)\n    \n        rec_avg_all, rec_readout, rec_avg_vs_pats = daq_programs.run_daq2(steps_in_seq, num_averages, verbose=0)\n                \n        out_I[i] = rec_avg_vs_pats[0]\n        out_Q[i] = rec_avg_vs_pats[1]\n        #time.sleep(4)\n        \n    # make plots\n    plt.imshow(out_Q, extent=[0,steps_in_seq,stop_freq,start_freq],aspect='auto' )\n        \n    return out_Q\n##END sweep_bnc_freq","repo_name":"murchlab/analyzer","sub_path":"instruments/spectroscopy/chevron.py","file_name":"chevron.py","file_ext":"py","file_size_in_byte":1286,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"8095086416","text":"from rest_framework.authentication import BaseAuthentication, get_authorization_header\nfrom rest_framework import exceptions\nfrom user.models import User\nfrom django.conf import settings\nimport jwt\n\n\nclass JWTAuthentication(BaseAuthentication):\n    \"\"\"Used to decode a JSON Web Token and properly provide Oauth2 authorization in project\"\"\"\n\n    def authenticate(self, request):\n        \"\"\"Used to process the given request to extract the JWT and decode it\"\"\"\n        auth_header = get_authorization_header(request)\n        token = auth_header.decode(\"utf-8\").split(\" \")\n\n        # Token structure isn't 'Bearer $token'\n        if len(token) != 2:\n            raise exceptions.AuthenticationFailed(\"Invalid token provided\")\n\n        token = token[1]\n\n        try:\n            # Decoding the token with a secret key and extracting the User model info stored in it\n            payload = jwt.decode(token, settings.SECRET_KEY, algorithms=\"HS256\")\n            user = User.objects.get(username=payload[\"username\"])\n\n            return (user, token)\n\n        except jwt.ExpiredSignatureError as e:\n            raise exceptions.AuthenticationFailed(\"The provided token is expired\")\n\n        except jwt.DecodeError as e:\n            raise exceptions.AuthenticationFailed(\n                \"The provided token has invalid structure\"\n            )\n\n        except User.DoesNotExist as e:\n            raise exceptions.AuthenticationFailed(\"The token owner does not exists\")\n","repo_name":"AlejoM1908/alpha-genbank","sub_path":"apps/user/jwt.py","file_name":"jwt.py","file_ext":"py","file_size_in_byte":1461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"21846369196","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Oct  9 19:31:53 2019\n\n@author: junbaba\n\"\"\"\nn = int(input())\na = []\nb = []\nfor i in range(n):\n    list = input().split()\n    a.append((int(list[0]),int(list[1])))\nfor i in range(n):\n    list = input().split()\n    b.append((int(list[0]),int(list[1])))\ni = 0\nj = 0\ntotaltime = 0\nwhile i < n and j < n:\n    if a[i][1] <= b[j][0]:\n        i += 1\n        continue\n    if b[j][1] <= a[i][0]:\n        j += 1\n        continue\n    if a[i][0] >= b[j][0]:\n        if a[i][1] <= b[j][1]:\n            totaltime += a[i][1] - a[i][0]\n            i += 1\n        else:\n            totaltime += b[j][1] - a[i][0]\n            j += 1\n\n        continue\n    if a[i][0] < b[j][0]:\n        if b[j][1] <= a[i][1]:\n            totaltime += b[j][1] - b[j][0]\n            j += 1\n        else:\n            totaltime += a[i][1] - b[j][0]\n            i += 1\n        continue\nprint (totaltime)\n\n","repo_name":"RookieJunChen/CSP-examination-questions","sub_path":"CSP/bin/CSP2018_9/CSP2018.9-2.py","file_name":"CSP2018.9-2.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"15090571385","text":"#Others\n\nfrom tracemalloc import start\nfrom oreto_utils.terminal_utils import clearlines as out_clearlines\nfrom time import sleep as t_sleep, time as t_time, strftime as t_strftime\n\n__all__ = [\"countdown\", \"formatsize\", \"searchlist\"]\n\n#This is a countdown\ndef countdown(seconds:int, message:str=\"\", endmessage:str=\"\") -> None:\n    \"\"\"This is a countdown that will print a message every second until it reaches 0.\"\"\"\n    for _ in range(seconds):\n        print(f\"{message}{seconds}\")\n        t_sleep(1)\n        seconds -= 1\n        out_clearlines(1)\n    print(endmessage)            \n    \n#This will format a byte size in KB, MB, GB, TB or PB \ndef formatsize(bytesize:int) -> str:\n    \"\"\"The size in byte will be formated in KB, MB, GB, TB or PB.\"\"\"\n    if bytesize < 1024:\n        selected_unit = 0\n    elif bytesize < 1024**2:\n        selected_unit = 1\n    elif bytesize < 1024**3:\n        selected_unit = 2\n    elif bytesize < 1024**4:\n        selected_unit = 3\n    elif bytesize < 1024**5:\n        selected_unit = 4\n    else:\n        selected_unit = 5\n\n    if bytesize >= 1024:\n        formated_size = round(bytesize/1024**selected_unit, 2)\n    else:\n        formated_size = round(bytesize)\n\n    units = [\"Bytes\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\"]\n    return f\"{formated_size} {units[selected_unit]}\"\n\n#This searches for a specific value in a list, and returns the index of the value\n#It will return None if the value is not found\ndef searchlist(list:list, search:str, mode:str) -> (list | int):\n    \"\"\"\n    This searches for a specific value (STRING ONLY FOR NOW) in a list, and returns the index of the value.\n    It will return None if the value is not found.\\n\n    Modes:\n        - f: First\n            - f1: Returns the index of the value\n            - f2: Returns the value \n        - l: Last\n            - l1: Returns the index of the last occurrence of the value\n            - l2: Returns the value of the last occurrence of the value\n        - c: Contains\n            - c1: Returns index\n            - c2: Returns a list of exactly which ones contains the search term\n        - e: Exact\n    \"\"\"\n    filteredlist = [item for item in list if type(item) == str]\n    if search in filteredlist:\n        VALID = [\"f1\", \"f2\", \"l1\", \"l2\", \"c1\", \"c2\", \"e\"]\n        if mode not in VALID:\n            raise ValueError(f\"Mode {mode} is not valid. Valid modes are: {VALID}\")\n        \n        elif mode[0] == \"f\":\n            items = [item for item in filteredlist if item.startswith(search)]\n            if mode[1] == \"1\":\n                return list.index(items[0])\n            elif mode[1] == \"2\":\n                return items[0]\n        \n        elif mode[0] == \"l\":\n            items = [item for item in filteredlist if item.startswith(search)]\n            if mode[1] == \"1\":\n                return list.index(items[-1])\n            elif mode[1] == \"2\":\n                return items[-1]\n        \n        elif mode[0] == \"c\":\n            if mode[1] == \"1\":\n                return [list.index(items) for items in filteredlist if search in items]\n            elif mode[1] == \"2\":\n                return [items for items in filteredlist if search in items]\n        \n        elif mode == \"e\":\n            return list.index(search)\n        \n    return None","repo_name":"OhRetro/oreto-utils","sub_path":"oreto_utils/others_utils.py","file_name":"others_utils.py","file_ext":"py","file_size_in_byte":3248,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"598669643","text":"import qcircuits as qc\nfrom itertools import product\n\n\n# Creates each of the four Bell states\n\n\ndef bell_state(x, y):\n    H = qc.Hadamard()\n    CNOT = qc.CNOT()\n\n    phi = qc.bitstring(x, y)\n    phi = H(phi, qubit_indices=[0])\n\n    return CNOT(phi)\n\n\nif __name__ == '__main__':\n\n    for x, y in product([0, 1], repeat=2):\n\n        print('\\nInput: {} {}'.format(x, y))\n        print('Bell state:')\n        print(bell_state(x, y))\n","repo_name":"grey-area/qcircuits","sub_path":"examples/produce_bell_states.py","file_name":"produce_bell_states.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","stars":57,"dataset":"github-code","pt":"80"}
+{"seq_id":"69877088579","text":"# # 2.soru:\nimport random\nb = []\nwhile len(b) < 6:\n    e = random.randint(1,50)\n    if e not in b:\n        b.append(e)\nprint(b)\n\n# 3.soru:\nb=0\ne=input(\"en fazla iki basamakli bir sayi girin: \")\ny = open(\"1904107005.beyzatuzcu.py\",'w')\nif int(e)<100 and int(e)>1 :\n    for i in range(1,int(e)) :\n        z=i//10\n        a=i%10\n        b=z+a\n        if b % 2 == 1 :\n\n            y.write( str(i)+\"=\" +str(b) +\"  tek sayi oldugu icin dosyaya yazilir.\"+ \"\\n\")\n\n        else:\n            continue\n    y.close()\nelse:\n    print(\"hata oldu...\")\n\n# #SORU4:\nfrom functools import reduce\n\nb=['Aygun','Çiçek','Deniz','Atar','Dener','Yılmaz']\ne= [['Ayse', 3,6,7],['Ece', 5,2,4],['Arya', 6,5],['Ali', 3],['Can',5,7,9,11],['Aybar',2,3]]\ny=list(map(lambda z,:[z[0]+\" \"+b[0]] + [reduce(lambda a,b: a+b , z[1:])],  e))\nprint(\"Elde etmemiz gereken sonuc = \",y)\n","repo_name":"beyzatuzcu/deneme","sub_path":"beyzatuzcu/1904107005.beyzatuzcu.py","file_name":"1904107005.beyzatuzcu.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"28966928225","text":"from django.shortcuts import render, redirect\nimport json\nimport urllib.request\nfrom constants import api_key\n\n\n# Create your views here.\ndef index(req):\n    data = {}\n    if req.method == 'POST':\n        try:\n            city = req.POST['city']\n            res = urllib.request.urlopen(\n                f'http://api.openweathermap.org/data/2.5/weather?q={city}&appid={api_key}').read()\n            res_data = json.loads(res)\n            data = {\n                'country_code': str(res_data['sys']['country']),\n                'coordinate': f\"{res_data['coord']['lon']} {res_data['coord']['lat']}\",\n                'temp': f\"{res_data['main']['temp']}k\",\n                'pressure': str(res_data['main']['pressure']),\n                'humidity': str(res_data['main']['humidity']),\n            }\n        except:\n            return render(req, 'error.html')\n        return render(req, 'index.html', {'data': data, 'city': city})\n    return render(req, 'index.html')\n","repo_name":"prateekthakur272/weather_detector","sub_path":"weather/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"71857438020","text":"# Detect English module\n# http://inventwithpython.com/hacking (BSD Licensed)\n\n# To use, type this code:\n#   import detectEnglish\n#   detectEnglish.isEnglish(someString) # returns True or False\n\nimport socket\n\nfrom domdf_python_tools.utils import pyversion as version\n\n# Trys to connect to the internet\ns = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\ns.connect((\"google.com\", 80))\ns.close()  # Closes the connection to google\n# Above code from http://stackoverflow.com/questions/166506/finding-local-ip-addresses-using-pythons-stdlib\nif version == 2:\n\timport urllib\nelif version == 3:\n\timport urllib.request as urllib\nurllib.urlretrieve('http://invpy.com/dictionary.txt', 'dictionary.txt')\n\nUPPERLETTERS = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\nLETTERS_AND_SPACE = UPPERLETTERS + UPPERLETTERS.lower() + ' \\t\\n'\n\n\ndef loadDictionary():\n\tdictionaryFile = open('dictionary.txt')\n\tenglishWords = {}\n\tfor word in dictionaryFile.read().split('\\n'):\n\t\tenglishWords[word] = None\n\tdictionaryFile.close()\n\treturn englishWords\n\n\nENGLISH_WORDS = loadDictionary()\n\n\ndef getEnglishCount(message):\n\tmessage = message.upper()\n\tmessage = removeNonLetters(message)\n\tpossibleWords = message.split()\n\n\tif possibleWords == []:\n\t\treturn 0.0  # no words at all, so return 0.0\n\n\tmatches = 0\n\tfor word in possibleWords:\n\t\tif word in ENGLISH_WORDS:\n\t\t\tmatches += 1\n\treturn float(matches) / len(possibleWords)\n\n\ndef removeNonLetters(message):\n\tlettersOnly = []\n\tfor symbol in message:\n\t\tif symbol in LETTERS_AND_SPACE:\n\t\t\tlettersOnly.append(symbol)\n\treturn ''.join(lettersOnly)\n\n\ndef isEnglish(message, wordPercentage=20, letterPercentage=85):\n\t# By default, 20% of the words must exist in the dictionary file, and\n\t# 85% of all the characters in the message must be letters or spaces\n\t# (not punctuation or numbers).\n\twordsMatch = getEnglishCount(message) * 100 >= wordPercentage\n\tnumLetters = len(removeNonLetters(message))\n\tmessageLettersPercentage = float(numLetters) / len(message) * 100\n\tlettersMatch = messageLettersPercentage >= letterPercentage\n\treturn wordsMatch and lettersMatch\n","repo_name":"domdfcoding/Python_Modules","sub_path":"ciphers/detectEnglish.py","file_name":"detectEnglish.py","file_ext":"py","file_size_in_byte":2057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"70712852124","text":"from datetime import timedelta\nimport json\n\nfrom django.contrib.auth import get_user_model\nfrom django.http.request import QueryDict\nfrom django.shortcuts import reverse\nfrom django.utils import timezone\nfrom rest_framework import status\nfrom rest_framework.test import APITestCase\nfrom wagtail.wagtailcore.models import Page\n\nfrom users.models import RegUser, Profile\nfrom .models import (CoachSurvey, CoachFormField, CoachSurveySubmission, CoachSurveySubmissionDraft,\n                     EndlineSurveySelectUser)\nfrom .reports import survey_aggregates\n\nSINGLE_LINE = 'singleline'\nRADIO_FIELD = 'radio'\n\n\n# ================ #\n# Helper functions #\n# ================ #\n\n\ndef create_survey(title='Test Survey', intro='Take this challenge', outro='Thanks for taking the challenge',\n                  deliver_after=1, **kwargs):\n    parent_page = Page.get_root_nodes()[0]\n    survey = CoachSurvey(\n        title=title,\n        intro=intro,\n        outro=outro,\n        deliver_after=deliver_after,\n        **kwargs\n    )\n    parent_page.add_child(instance=survey)\n    survey.unpublish()\n    return survey\n\n\ndef publish(survey, user):\n    survey.save_revision(\n        user=user,\n        submitted_for_moderation=False\n    ).publish()\n\n\ndef create_user(username='Anon'):\n    user = RegUser.objects.create(\n        username=username,\n        email='a@b.c'\n    )\n    Profile.objects.create(\n        mobile='1234567890',\n        user=user\n    )\n    return user\n\n\nclass CoachSurveyAPITest(APITestCase):\n    def test_basic_submit(self):\n        \"\"\"Test that a submission can be received\"\"\"\n        user = create_user()\n        survey = create_survey()\n        survey.form_fields.create(\n            key='field-1',\n            label='First Form Field',\n            field_type=RADIO_FIELD,\n            choices='1,2,3,4,5'\n        )\n        publish(survey, user)\n\n        submission = {\n            'field-1': '3'\n        }\n\n        self.client.force_authenticate(user=user)\n        response = self.client.post(reverse('api:surveys-submission', kwargs={'pk': survey.pk}), submission,\n                                    format='json')\n\n        self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT, \"Survey submission request failed.\")\n\n        data = json.loads(CoachSurveySubmission.objects.get(user=user, page=survey).form_data)\n        self.assertEqual(data.get('field-1'), '3', \"Field not found in submission data\")\n\n    def test_current_after_registration_days_none_available(self):\n        \"\"\"Test that a survey is kept from the user before the specified number of days after registration has passed.\n        \"\"\"\n        now = timezone.now()\n\n        user = create_user()\n        user.date_joined = now - timedelta(days=2)\n        user.save()\n        survey = create_survey(deliver_after=3)  # Survey will only be available the next day\n        publish(survey, user)\n\n        self.client.force_authenticate(user=user)\n        response = self.client.get(reverse('api:surveys-current'))\n\n        self.assertEqual(response.status_code, status.HTTP_200_OK, \"Retrieve current survey failed.\")\n        self.assertFalse(response.data['available'], \"Available flag expected to be False.\")\n        self.assertIsNone(response.data['survey'], \"Survey field was unexpectedly populated.\")\n\n    def test_current_after_registration_days_available(self):\n        \"\"\"Test that a survey is made available to the user after the specified number of days after registration has\n        passed.\"\"\"\n        now = timezone.now()\n\n        user = create_user()\n        user.date_joined = now - timedelta(days=3)\n        user.save()\n        survey = create_survey(deliver_after=3)  # Survey is available today\n        publish(survey, user)\n\n        self.client.force_authenticate(user=user)\n        response = self.client.get(reverse('api:surveys-current'))\n\n        self.assertEqual(response.status_code, status.HTTP_200_OK, \"Retrieve current survey failed.\")\n        self.assertTrue(response.data['available'], \"Available flag expected to be True.\")\n        self.assertIsNotNone(response.data['survey'], \"Survey field was not populated.\")\n\n    def test_filter_by_bot_conversation(self):\n        \"\"\"Test that surveys can be filtered by the requested Bot conversation type.\n        \"\"\"\n        user = create_user('Anon')\n        baseline_survey = create_survey(title='Baseline survey', bot_conversation=CoachSurvey.BASELINE)\n        eatool_survey = create_survey(title='EA Tool', bot_conversation=CoachSurvey.EATOOL)\n\n        publish(baseline_survey, user)\n        publish(eatool_survey, user)\n\n        self.client.force_authenticate(user=user)\n\n        params = QueryDict(mutable=True)\n        params.update({\n            'bot-conversation': 'SURVEY_EATOOL'\n        })\n        response = self.client.get(u'%s?%s' % (reverse('api:surveys-list'), params.urlencode()))\n\n        self.assertEqual(len(response.data), 1, \"Unexpected number of surveys returned.\")\n        self.assertEqual(response.data[0]['id'], eatool_survey.id, \"Unexpected Survey returned.\")\n\n    def test_unavailable_after_submit(self):\n        \"\"\"Test that a survey is unavailable after a submission is successfully created.\n        \"\"\"\n        now = timezone.now()\n\n        user = create_user()\n        user.date_joined = now - timedelta(days=4)\n        user.save()\n        survey = create_survey(deliver_after=3)  # Survey is available today\n        survey.form_fields.create(\n            key='field-1',\n            label='First Form Field',\n            field_type=SINGLE_LINE,\n            required=False\n        )\n        publish(survey, create_user('Staff'))\n\n        submission = {\n            'field-1': 'one'\n        }\n\n        self.client.force_authenticate(user=user)\n        self.client.post(reverse('api:surveys-submission', kwargs={'pk': survey.pk}),\n                         submission, format='json')\n\n        response = self.client.get(reverse('api:surveys-current'), format='json')\n\n        self.assertFalse(response.data['available'], \"Survey still avaialble after submission.\")\n        self.assertEqual(response.data['inactivity_age'], 0,\n                         \"Inactivity age was returned when survey was not available.\")\n        self.assertIsNone(response.data['survey'], \"Survey object was returned.\")\n\n    def test_next_available(self):\n        \"\"\"If there are multiple surveys set up, and the user submits to the first, then the second must be available.\n        \"\"\"\n        now = timezone.now()\n\n        user = create_user()\n        user.date_joined = now - timedelta(days=8)  # Both surveys will be available\n        user.save()\n\n        survey1 = create_survey('Baseline', deliver_after=3, bot_conversation=CoachSurvey.BASELINE)\n        survey1.form_fields.create(\n            key='field-1',\n            label='First Form Field',\n            field_type=SINGLE_LINE,\n            required=False\n        )\n        publish(survey1, create_user('Staff1'))\n\n        survey2 = create_survey('EA Tool', deliver_after=7, bot_conversation=CoachSurvey.EATOOL)\n        survey2.form_fields.create(\n            key='field-1',\n            label='First Form Field',\n            field_type=SINGLE_LINE,\n            required=False\n        )\n        publish(survey2, create_user('Staff2'))\n\n        # User submits to first survey\n        self.client.force_authenticate(user=user)\n        self.client.post(reverse('api:surveys-submission', kwargs={'pk': survey1.pk}), data={\n            'field-1': 'one'\n        }, format='json')\n\n        # Second survey must now be available\n        response = self.client.get(reverse('api:surveys-current'), format='json')\n\n        self.assertTrue(response.data['available'], \"Survey is not available.\")\n        self.assertIsNotNone(response.data['survey'], \"Survey is not in response.\")\n        self.assertEqual(response.data['survey']['id'], survey2.id, \"Unexpected survey identity.\")\n        self.assertEqual(response.data['survey']['bot_conversation'], 'SURVEY_EATOOL',\n                         \"Unexpected Bot conversation type.\")\n\n\nclass SurveyNotificationAgeAPI(APITestCase):\n    \"\"\"\n    Tests to ensure that the days of inactivity is measured correctly. They are used by the frontend to determine\n    what notification to show the user.\n    \"\"\"\n\n    def test_notification_inactivity_days(self):\n        \"\"\"If the user has not completed the survey in 3 days, the first reminder will be triggered on the frontend.\"\"\"\n        now = timezone.now()\n\n        user = create_user()\n        user.date_joined = now - timedelta(days=7)\n        user.save()\n\n        survey = create_survey(deliver_after=3)\n        publish(survey, create_user('Staff'))\n\n        self.client.force_authenticate(user=user)\n        response = self.client.get(reverse('api:surveys-current'), format='json')\n\n        # Age is counted since the survey is available to the user. The user has been registered for 7 days, the survey\n        # was available after 3, so the age must be 4.\n        self.assertEqual(response.data['inactivity_age'], 4,\n                         \"Unexpected survey age. Will affect notification thresholds on frontend.\")\n\n\nclass DraftAPITest(APITestCase):\n    def test_basic_draft_submit(self):\n        \"\"\"Test that a draft can be submitted.\"\"\"\n        user = create_user('anon')\n        survey = create_survey()\n        survey.form_fields.create(\n            key='first',\n            label='First',\n            field_type=SINGLE_LINE\n        )\n        survey.form_fields.create(\n            key='second',\n            label='Second',\n            field_type=SINGLE_LINE\n        )\n        publish(survey, user)\n\n        self.client.force_authenticate(user=user)\n        response = self.client.patch(reverse('api:surveys-draft', kwargs={'pk': survey.pk}), {\n            'first': '1',\n            'second': '2'\n        }, format='json')\n\n        self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT, \"Draft submit request failed\")\n\n        updated_draft = CoachSurveySubmissionDraft.objects.get(user=user, survey=survey)\n        submission_data = json.loads(updated_draft.submission_data)\n        self.assertEqual(submission_data.get('first', None), '1', \"First submission field was not set\")\n        self.assertEqual(submission_data.get('second', None), '2', \"Second submission field was not set\")\n        self.assertFalse(updated_draft.complete, \"Draft was set to completed.\")\n        self.assertIsNone(updated_draft.submission, \"Draft has submission assigned.\")\n\n    def test_draft_partial_submit(self):\n        \"\"\"Test that a draft can be partially submitted.\"\"\"\n        user = create_user()\n        survey = create_survey()\n        survey.form_fields.create(\n            key='first',\n            label='First',\n            field_type=SINGLE_LINE\n        )\n        survey.form_fields.create(\n            key='second',\n            label='Second',\n            field_type=SINGLE_LINE\n        )\n        publish(survey, create_user('Staff'))\n\n        self.client.force_authenticate(user=user)\n        self.client.patch(reverse('api:surveys-draft', kwargs={'pk': survey.pk}), {\n            'second': '2'\n        }, format='json')\n\n        updated_draft = CoachSurveySubmissionDraft.objects.get(user=user, survey=survey)\n        data = json.loads(updated_draft.submission_data if updated_draft.has_submission else {})\n\n        self.assertEqual(data.get('second', None), '2', \"Second field was not set\")\n\n    def test_ensure_draft_on_submit(self):\n        \"\"\"\n        Test that a draft is created when a survey is submitted, if one hasn't been created. This is to simplify\n        reporting later, so data can exported using the drafts, and not inferred from existing submissions and missing\n        drafts.\n        \"\"\"\n        user = create_user()\n        survey = create_survey()\n        survey.form_fields.create(\n            key='first',\n            label='First',\n            field_type=SINGLE_LINE\n        )\n        publish(survey, create_user('Staff'))\n\n        self.client.force_authenticate(user=user)\n        self.client.post(reverse('api:surveys-submission', kwargs={'pk': survey.pk}), {\n            CoachSurvey.CONSENT_KEY: CoachSurvey.ANSWER_YES,\n            'first': '1'\n        }, format='json')\n\n        self.assertTrue(CoachSurveySubmissionDraft.objects.filter(user=user, survey=survey).exists(),\n                        \"Draft was not created.\")\n\n        submission = CoachSurveySubmission.objects.get(user=user, page=survey)\n        draft = CoachSurveySubmissionDraft.objects.get(user=user, survey=survey, complete=True)\n        self.assertEqual(draft.submission, submission, \"Submission was not assigned to draft.\")\n\n    def test_consent_set(self):\n        \"\"\"Test that submitting a draft answer containing the appropriate consent answer will store the draft correctly.\n        \"\"\"\n        user = create_user()\n        survey = create_survey()\n        survey.form_fields.create(\n            key='first',\n            label='First',\n            field_type=SINGLE_LINE\n        )\n        survey.form_fields.create(\n            key='second',\n            label='Second',\n            field_type=SINGLE_LINE\n        )\n        publish(survey, create_user('Staff'))\n\n        self.client.force_authenticate(user=user)\n        self.client.patch(reverse('api:surveys-draft', kwargs={'pk': survey.pk}), {\n            CoachSurvey.CONSENT_KEY: CoachSurvey.ANSWER_YES,\n            'first': '1',\n            'second': '2'\n        }, format='json')\n\n        updated_draft = CoachSurveySubmissionDraft.objects.get(user=user, survey=survey)\n        data = json.loads(updated_draft.submission_data) if updated_draft.has_submission else {}\n\n        self.assertTrue(updated_draft.consent, \"Consent was not stored\")\n        self.assertIsNone(data.get(CoachSurvey.CONSENT_KEY, None), \"Consent was stored in submission data\")\n\n    def test_consent_not_unset(self):\n        \"\"\"Test that doing a partial update without a consent value does not set an existing True consent value to\n        False.\n        \"\"\"\n        user = create_user()\n        survey = create_survey()\n        survey.form_fields.create(\n            key='first',\n            label='First',\n            field_type=SINGLE_LINE\n        )\n        survey.form_fields.create(\n            key='second',\n            label='Second',\n            field_type=SINGLE_LINE\n        )\n        publish(survey, create_user('Staff'))\n\n        self.client.force_authenticate(user=user)\n\n        # User provides consent and first answer\n        self.client.patch(reverse('api:surveys-draft', kwargs={'pk': survey.pk}), {\n            CoachSurvey.CONSENT_KEY: CoachSurvey.ANSWER_YES,\n            'first': '1'\n        }, format='json')\n\n        # User provides second answer\n        self.client.patch(reverse('api:surveys-draft', kwargs={'pk': survey.pk}), {\n            'second': '2'\n        }, format='json')\n\n        updated_draft = CoachSurveySubmissionDraft.objects.get(user=user, survey=survey)\n\n        self.assertTrue(updated_draft.consent, \"Consent was set to False on second draft update\")\n\n    def test_consent_partial(self):\n        \"\"\"Test that a draft can be submitted with only consent.\"\"\"\n        user = create_user()\n        survey = create_survey()\n        survey.form_fields.create(\n            key='first',\n            label='First',\n            field_type=SINGLE_LINE\n        )\n        publish(survey, create_user('Staff'))\n\n        self.client.force_authenticate(user=user)\n        self.client.patch(reverse('api:surveys-draft', kwargs={'pk': survey.pk}), {\n            CoachSurvey.CONSENT_KEY: CoachSurvey.ANSWER_YES\n        }, format='json')\n\n        updated_draft = CoachSurveySubmissionDraft.objects.get(user=user, survey=survey)\n        data = json.loads(updated_draft.submission_data) if updated_draft.has_submission else {}\n\n        self.assertTrue(updated_draft.consent, \"Consent was not set.\")\n        self.assertEqual(data, {}, \"Draft submission unexpectedly contains data.\")\n\n\nclass SurveyReportingRequirements(APITestCase):\n    def test_survey_report_aggregation(self):\n        \"\"\"Test that total data by survey aggregates correctly.\"\"\"\n        staff = create_user('Staff')\n        users = [create_user('anon' + str(i)) for i in range(10)]\n        surveys = []\n        for i in range(3):\n            survey = create_survey('Survey ' + str(i))\n            publish(survey, staff)\n            surveys.append(survey)\n\n        # Users who submitted drafts\n        for user in users[0:7]:\n            self.client.force_authenticate(user=user)\n\n            # Survey 1\n            self.client.put(reverse('api:surveys-draft', kwargs={'pk': surveys[0].pk}), {}, format='json')\n\n        survey_aggregates()\n        self.skipTest('TODO')\n\n\nclass SurveyDataPreservationTests(APITestCase):\n    \"\"\"Tests to check whether submission exports store historical data.\n    \"\"\"\n\n    def test_user_deletion(self):\n        \"\"\"Test whether deleting a user will preserve the data as it was at time of submission.\"\"\"\n        user = RegUser.objects.create(\n            username='anon',\n            first_name='Anonymous',\n            last_name='Rex',\n            email='a@b.c'\n        )\n        Profile.objects.create(\n            user=user,\n            age=30,\n            gender=Profile.GENDER_MALE,\n            mobile='12345667890'\n        )\n        survey = create_survey()\n        survey.form_fields.create(\n            key='first',\n            label='First',\n            field_type=SINGLE_LINE\n        )\n        publish(survey, create_user('Staff'))\n\n        self.client.force_authenticate(user=user)\n        self.client.post(reverse('api:surveys-submission', kwargs={'pk': survey.pk}), {\n            CoachSurvey.CONSENT_KEY: CoachSurvey.ANSWER_YES,\n            'first': '1'\n        }, format='json')\n\n        # Copy user info for testing\n        user_id = user.id\n        user_name = user.get_full_name()\n        user_username = user.username\n        user_mobile = user.profile.mobile\n        user_gender = user.profile.get_gender_display()\n        user_age = str(user.profile.age)\n        user_email = user.email\n\n        user.delete()\n\n        submission = CoachSurveySubmission.objects.filter(survey=survey).first()\n        self.assertIsNotNone(submission, \"Submission was deleted along with user\")\n\n        data = submission.get_data()\n        self.assertEqual(data['user_id'], user_id)\n        self.assertEqual(data['name'], user_name)\n        self.assertEqual(data['username'], user_username)\n        self.assertEqual(data['mobile'], user_mobile)\n        self.assertEqual(data['gender'], user_gender)\n        self.assertEqual(data['age'], user_age)\n        self.assertEqual(data['email'], user_email)\n\n\nclass TestEndlineSurvey(APITestCase):\n    def test_link_created_on_register(self):\n        \"\"\"Ensure that when a new user registers, they receive an endline survey link.\"\"\"\n\n        data = {\n            'username': 'anon',\n            'password': 'blargh',\n            'profile': {\n                'mobile': '1112223334',\n                'age': 18,\n                'gender': Profile.GENDER_FEMALE\n            }\n        }\n\n        response = self.client.post(reverse('api:users-list'), data, format='json')\n\n        self.assertEqual(response.status_code, status.HTTP_201_CREATED, \"User registration failed\")\n\n        link = EndlineSurveySelectUser.objects.get(user_id=response.data['id'])\n        self.assertIsNotNone(link, \"Link was not created\")\n","repo_name":"praekeltfoundation/gem-bbb-indo-server","sub_path":"survey/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":19399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"22089558687","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# (c) 2017, Joseph Benden <joe@benden.us>\n#\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\nDOCUMENTATION = '''\nmodule: xfconf\nauthor:\n    - \"Joseph Benden (@jbenden)\"\n    - \"Alexei Znamensky (@russoz)\"\nshort_description: Edit XFCE4 Configurations\ndescription:\n  - This module allows for the manipulation of Xfce 4 Configuration with the help of\n    xfconf-query.  Please see the xfconf-query(1) man pages for more details.\noptions:\n  channel:\n    description:\n    - A Xfconf preference channel is a top-level tree key, inside of the\n      Xfconf repository that corresponds to the location for which all\n      application properties/keys are stored. See man xfconf-query(1)\n    required: yes\n    type: str\n  property:\n    description:\n    - A Xfce preference key is an element in the Xfconf repository\n      that corresponds to an application preference. See man xfconf-query(1)\n    required: yes\n    type: str\n  value:\n    description:\n    - Preference properties typically have simple values such as strings,\n      integers, or lists of strings and integers. This is ignored if the state\n      is \"get\". For array mode, use a list of values. See man xfconf-query(1)\n    type: list\n    elements: raw\n  value_type:\n    description:\n    - The type of value being set. This is ignored if the state is \"get\".\n      For array mode, use a list of types.\n    type: list\n    elements: str\n    choices: [ int, uint, bool, float, double, string ]\n  state:\n    type: str\n    description:\n    - The action to take upon the property/value.\n    - State C(get) is deprecated and will be removed in community.general 5.0.0. Please use the module M(community.general.xfconf_info) instead.\n    choices: [ get, present, absent ]\n    default: \"present\"\n  force_array:\n    description:\n    - Force array even if only one element\n    type: bool\n    default: 'no'\n    aliases: ['array']\n    version_added: 1.0.0\n  disable_facts:\n    description:\n    - The value C(false) is no longer allowed since community.general 4.0.0.\n    - This option will be deprecated in a future version, and eventually be removed.\n    type: bool\n    default: true\n    version_added: 2.1.0\n'''\n\nEXAMPLES = \"\"\"\n- name: Change the DPI to \"192\"\n  xfconf:\n    channel: \"xsettings\"\n    property: \"/Xft/DPI\"\n    value_type: \"int\"\n    value: \"192\"\n\n- name: Set workspace names (4)\n  xfconf:\n    channel: xfwm4\n    property: /general/workspace_names\n    value_type: string\n    value: ['Main', 'Work1', 'Work2', 'Tmp']\n\n- name: Set workspace names (1)\n  xfconf:\n    channel: xfwm4\n    property: /general/workspace_names\n    value_type: string\n    value: ['Main']\n    force_array: yes\n\"\"\"\n\nRETURN = '''\n  channel:\n    description: The channel specified in the module parameters\n    returned: success\n    type: str\n    sample: \"xsettings\"\n  property:\n    description: The property specified in the module parameters\n    returned: success\n    type: str\n    sample: \"/Xft/DPI\"\n  value_type:\n    description:\n    - The type of the value that was changed (C(none) for C(get) and C(reset)\n      state). Either a single string value or a list of strings for array\n      types.\n    returned: success\n    type: string or list of strings\n    sample: '\"int\" or [\"str\", \"str\", \"str\"]'\n  value:\n    description:\n    - The value of the preference key after executing the module. Either a\n      single string value or a list of strings for array types.\n    returned: success\n    type: string or list of strings\n    sample: '\"192\" or [\"orange\", \"yellow\", \"violet\"]'\n  previous_value:\n    description:\n    - The value of the preference key before executing the module (C(none) for\n      C(get) state). Either a single string value or a list of strings for array\n      types.\n    returned: success\n    type: string or list of strings\n    sample: '\"96\" or [\"red\", \"blue\", \"green\"]'\n'''\n\nfrom ansible_collections.community.general.plugins.module_utils.module_helper import (\n    ModuleHelper, CmdMixin, StateMixin, ArgFormat, ModuleHelperException\n)\n\n\ndef fix_bool(value):\n    vl = value.lower()\n    return vl if vl in (\"true\", \"false\") else value\n\n\n@ArgFormat.stars_deco(1)\ndef values_fmt(values, value_types):\n    result = []\n    for value, value_type in zip(values, value_types):\n        if value_type == 'bool':\n            value = fix_bool(value)\n        result.extend(['--type', '{0}'.format(value_type), '--set', '{0}'.format(value)])\n    return result\n\n\nclass XFConfException(Exception):\n    pass\n\n\nclass XFConfProperty(CmdMixin, StateMixin, ModuleHelper):\n    change_params = 'value',\n    diff_params = 'value',\n    output_params = ('property', 'channel', 'value')\n    facts_params = ('property', 'channel', 'value')\n    module = dict(\n        argument_spec=dict(\n            state=dict(default=\"present\",\n                       choices=(\"present\", \"get\", \"absent\"),\n                       type='str'),\n            channel=dict(required=True, type='str'),\n            property=dict(required=True, type='str'),\n            value_type=dict(required=False, type='list',\n                            elements='str', choices=('int', 'uint', 'bool', 'float', 'double', 'string')),\n            value=dict(required=False, type='list', elements='raw'),\n            force_array=dict(default=False, type='bool', aliases=['array']),\n            disable_facts=dict(type='bool', default=True),\n        ),\n        required_if=[('state', 'present', ['value', 'value_type'])],\n        required_together=[('value', 'value_type')],\n        supports_check_mode=True,\n    )\n\n    default_state = 'present'\n    command = 'xfconf-query'\n    command_args_formats = dict(\n        channel=dict(fmt=('--channel', '{0}'),),\n        property=dict(fmt=('--property', '{0}'),),\n        is_array=dict(fmt=\"--force-array\", style=ArgFormat.BOOLEAN),\n        reset=dict(fmt=\"--reset\", style=ArgFormat.BOOLEAN),\n        create=dict(fmt=\"--create\", style=ArgFormat.BOOLEAN),\n        values_and_types=dict(fmt=values_fmt)\n    )\n\n    def update_xfconf_output(self, **kwargs):\n        self.update_vars(meta={\"output\": True, \"fact\": True}, **kwargs)\n\n    def __init_module__(self):\n        self.does_not = 'Property \"{0}\" does not exist on channel \"{1}\".'.format(self.module.params['property'],\n                                                                                 self.module.params['channel'])\n        self.vars.set('previous_value', self._get(), fact=True)\n        self.vars.set('type', self.vars.value_type, fact=True)\n        self.vars.meta('value').set(initial_value=self.vars.previous_value)\n\n        if self.module.params['disable_facts'] is False:\n            raise ModuleHelperException('Returning results as facts has been removed. Stop using disable_facts=false.')\n\n    def process_command_output(self, rc, out, err):\n        if err.rstrip() == self.does_not:\n            return None\n        if rc or len(err):\n            raise XFConfException('xfconf-query failed with error (rc={0}): {1}'.format(rc, err))\n\n        result = out.rstrip()\n        if \"Value is an array with\" in result:\n            result = result.split(\"\\n\")\n            result.pop(0)\n            result.pop(0)\n\n        return result\n\n    def _get(self):\n        return self.run_command(params=('channel', 'property'))\n\n    def state_get(self):\n        self.vars.value = self.vars.previous_value\n        self.vars.previous_value = None\n        self.module.deprecate(\n            msg=\"State 'get' is deprecated. Please use the module community.general.xfconf_info instead\",\n            version=\"5.0.0\", collection_name=\"community.general\"\n        )\n\n    def state_absent(self):\n        if not self.module.check_mode:\n            self.run_command(params=('channel', 'property', {'reset': True}))\n        self.vars.value = None\n\n    def state_present(self):\n        # stringify all values - in the CLI they will all be happy strings anyway\n        # and by doing this here the rest of the code can be agnostic to it\n        self.vars.value = [str(v) for v in self.vars.value]\n        value_type = self.vars.value_type\n\n        values_len = len(self.vars.value)\n        types_len = len(value_type)\n\n        if types_len == 1:\n            # use one single type for the entire list\n            value_type = value_type * values_len\n        elif types_len != values_len:\n            # or complain if lists' lengths are different\n            raise XFConfException('Number of elements in \"value\" and \"value_type\" must be the same')\n\n        # fix boolean values\n        self.vars.value = [fix_bool(v[0]) if v[1] == 'bool' else v[0] for v in zip(self.vars.value, value_type)]\n\n        # calculates if it is an array\n        self.vars.is_array = \\\n            bool(self.vars.force_array) or \\\n            isinstance(self.vars.previous_value, list) or \\\n            values_len > 1\n\n        params = ['channel', 'property', {'create': True}]\n        if self.vars.is_array:\n            params.append('is_array')\n        params.append({'values_and_types': (self.vars.value, value_type)})\n\n        if not self.module.check_mode:\n            self.run_command(params=params)\n\n        if not self.vars.is_array:\n            self.vars.value = self.vars.value[0]\n            self.vars.type = value_type[0]\n        else:\n            self.vars.type = value_type\n\n\ndef main():\n    XFConfProperty.execute()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"iamgini/ansible-real-life","sub_path":"Ansible-AWS-Provisioning/collections/ansible_collections/community/general/plugins/modules/system/xfconf.py","file_name":"xfconf.py","file_ext":"py","file_size_in_byte":9478,"program_lang":"python","lang":"en","doc_type":"code","stars":65,"dataset":"github-code","pt":"86"}
+{"seq_id":"17264344349","text":"import Vehicle\nimport argparse\nimport sys\n\nif sys.version_info[0] == 2:\n    input1 = input()\n\n\nclass ParkingLot:\n    def __init__(self):\n        self.capacity = 0\n        self.slotid = 0\n        self.numOfOccupiedSlots = 0\n        self.res_di = {}\n\n    def createParkingLot(self, capacity):\n        self.slots = [-1] * capacity\n        self.capacity = capacity\n        return self.capacity\n\n    def getEmptySlot(self):\n        for i in range(len(self.slots)):\n            if self.slots[i] == -1:\n                return i\n\n    def park(self, regno, color, age):\n        if self.numOfOccupiedSlots < self.capacity:\n            slotid = self.getEmptySlot()\n            self.slots[slotid] = Vehicle.Car(regno, color, age)\n            print(self.slots[slotid])\n            self.slotid = self.slotid + 1\n            self.numOfOccupiedSlots = self.numOfOccupiedSlots + 1\n            return slotid + 1\n        else:\n            return -1\n\n    def leave(self, slotid):\n        if self.numOfOccupiedSlots > 0 and self.slots[slotid - 1] != -1:\n            self.slots[slotid - 1] = -1\n            self.numOfOccupiedSlots = self.numOfOccupiedSlots - 1\n            return True\n        else:\n            return False\n\n    def status(self):\n        #res_di = dict()\n\n        print(\"Slot No.\\tRegistration No.\\tColour.\\t\\tAge\")\n        for i in range(len(self.slots)):\n            if self.slots[i] != -1:\n                li = []\n                print(str(i + 1) + \"\\t\\t\" + str(self.slots[i].regno) + \"\\t\\t\" + str(self.slots[i].color)+\"\\t\\t\" + str(self.slots[i].age))\n                li.append(str(self.slots[i].regno))\n                li.append(str(self.slots[i].age))\n            else:\n                continue\n            self.res_di[str(i+1)] = li\n        print(self.res_di)\n\n    def getRegNoFromColor(self, color):\n\n        regnos = []\n        for i in self.slots:\n\n            if i == -1:\n                continue\n            if i.color == color:\n                regnos.append(i.regno)\n        return regnos\n\n    def getSlotNoFromRegNo(self, regno):\n\n        for i in range(len(self.slots)):\n            if self.slots[i].regno == regno:\n                return i + 1\n            else:\n                continue\n        return -1\n\n    def getSlotNoFromColor(self, color):\n\n        slotnos = []\n        print(self.slots)\n        for i in range(len(self.slots)):\n\n            if self.slots[i] == -1:\n                continue\n            if self.slots[i].color == color:\n                slotnos.append(str(i + 1))\n        return slotnos\n\n# slot_no by age\n    def slot_no_of_age(self, age):\n        slotnos = []\n        for i in range(len(self.slots)):\n            if self.slots[i] == -1:\n                continue\n            if self.slots[i].age == age:\n                slotnos.append(str(i + 1))\n        return slotnos\n\n#vehicle registration by Age\n\n    def getRegNoFromAge(self, age):\n\n        regnos = []\n        for i in self.slots:\n\n            if i == -1:\n                continue\n            if i.age == age:\n                regnos.append(i.regno)\n        return regnos\n\n\n\n    def show(self, line):\n        if line.startswith('Create_parking_lot'):\n            n = int(line.split(' ')[1])\n            res = self.createParkingLot(n)\n            print('Created parking of ' + str(res) + ' slots')\n\n        elif line.startswith('Park'):\n            regno = line.split(' ')[1]\n            color = line.split(' ')[2]\n            age   = line.split(' ')[4]\n            res = self.park(regno, color, age)\n            if res == -1:\n                print(\"Sorry, parking lot is full\")\n            else:\n                print('Car with Vehicle Registration Number '+ '\"'+str(regno)+'\"' +' has been parked at slot number ' + str(res))\n\n        elif line.startswith('Leave'):\n            leave_slotid = int(line.split(' ')[1])\n            status = self.leave(leave_slotid)\n            if status:\n                print(\"Slot number {0} vacated, the car with vehicle registration number {1} left the space, the driver of the car was of age {2}\"\n                    .format(leave_slotid, self.res_di[str(leave_slotid)][0], self.res_di[str(leave_slotid)][1]))\n\n        elif line.startswith('Status'):\n            self.status()\n\n        elif line.startswith('registration_numbers_for_cars_with_colour'):\n            color = line.split(' ')[1]\n            regnos = self.getRegNoFromColor(color)\n            print(', '.join(regnos))\n\n        elif line.startswith('Slot_numbers_for_cars_with_colour'):\n            color = line.split(' ')[1]\n            slotnos = self.getSlotNoFromColor(color)\n            print(', '.join(slotnos))\n\n        elif line.startswith('Slot_number_for_car_with_number'):\n            regno = line.split(' ')[1]\n            slotno = self.getSlotNoFromRegNo(regno)\n            if slotno == -1:\n                print(\"Not found\")\n            else:\n                print(slotno)\n\n        elif line.startswith('Slot_numbers_for_driver_of_age'):\n            age = line.split(' ')[1]\n            slotno = self.slot_no_of_age(age)\n            if slotno == -1:\n                print(\"Not found\")\n            else:\n                print(slotno)\n\n        elif line.startswith('Vehicle_registration_number_for_driver_of_age 18'):\n            color = line.split(' ')[1]\n            regnos = self.getRegNoFromAge(color)\n            print(', '.join(regnos))\n\n        elif line.startswith('exit'):\n            exit(0)\n\n\ndef main():\n    parkinglot = ParkingLot()\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-f', action=\"store\", required=False, dest='src_file', help=\"Input File\")\n    args = parser.parse_args()\n\n    if args.src_file:\n        with open(args.src_file) as f:\n            for line in f:\n                line = line.rstrip('\\n')\n                parkinglot.show(line)\n    else:\n        while True:\n            line = input(\"$ \")\n            parkinglot.show(line)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"thakuranuj19/squadstack","sub_path":"Parkinglot.py","file_name":"Parkinglot.py","file_ext":"py","file_size_in_byte":5912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"17672610913","text":"\"\"\"install.py\n\nThis script facilitates the detection and configuration of Ghidra installation\npath.\n\nThe script provides functions to automatically find the Ghidra installation\ndirectory from a set of standard locations, retrieve the Ghidra path from a\nconfiguration file, or save a specified Ghidra path to a configuration file.\nThe main function orchestrates these tasks to ensure that the Ghidra path is\nproperly configured before proceeding with further analysis tasks.\n\nReturns:\n    None: This script is intended to be run as a standalone utility, and does\n    not return any values when executed.\n\"\"\"\n\nimport os\n\nGHIDRA_SCRIPT = \"support/analyzeHeadless\"\n\n# List of standard locations to check for Ghidra installation\nSTANDARD_LOCATIONS = [\n    \"/usr/bin/ghidra\",\n    \"/usr/local/bin/ghidra\",\n    \"/opt/ghidra\"\n]\n\ndef find_ghidra_installation():\n    \"\"\"\n    Try to find the Ghidra installation directory by checking standard\n    locations.\n\n    This function iterates through a predefined list of standard file paths\n    where Ghidra might be installed. It checks whether the `GHIDRA_SCRIPT`\n    exists in any of these locations, and returns the directory path if found.\n\n    Returns:\n        str or None: The file path of the Ghidra installation directory if\n        found, otherwise None.\n    \"\"\"\n    for location in STANDARD_LOCATIONS:\n        if os.path.exists(os.path.join(location, GHIDRA_SCRIPT)):\n            return location\n    return None\n\ndef get_ghidra_path_from_config():\n    \"\"\"\n    Try to get the Ghidra path from the configuration file.\n\n    This function checks for the existence of a configuration file and reads\n    the Ghidra path from it if present.\n\n    Returns:\n        str or None: The Ghidra path as a string if found in the configuration\n        file, otherwise None.\n    \"\"\"\n    if os.path.exists(\"./conf/main.conf\"):\n        with open(\"./conf/main.conf\", mode='r', encoding='utf-8') as f:\n            return f.readline().strip()\n    return None\n\ndef save_ghidra_path_to_config(path):\n    \"\"\"\n    Save the Ghidra path to the configuration file.\n\n    This function opens the configuration file for writing (or creates it if it\n    doesn't exist) and writes the specified Ghidra path to it.\n\n    Args:\n        path (str): The file path to the Ghidra installation to be saved to the\n        configuration file.\n    \"\"\"\n    with open(\"./conf/main.conf\", mode='w', encoding='utf-8') as f:\n        f.write(path)\n\n\ndef main():\n    \"\"\"\n    Main function to manage the Ghidra path configuration.\n\n    This function first attempts to retrieve the Ghidra path from the\n    configuration file. If not found, it tries to find the Ghidra installation\n    automatically from standard locations. If still not found, it prompts the\n    user to input the Ghidra path, which is then saved to the configuration\n    file for future use.\n    \"\"\"\n    ghidra_path = get_ghidra_path_from_config()\n\n    if not ghidra_path:\n        ghidra_path = find_ghidra_installation()\n\n    if not ghidra_path:\n        ghidra_path = input(\"Please enter the path to your Ghidra installation: \")\n        save_ghidra_path_to_config(ghidra_path)\n\n    print(f\"Using Ghidra installation at: {ghidra_path}\")\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"thomasthaddeus/ghidra_automation","sub_path":"src/install.py","file_name":"install.py","file_ext":"py","file_size_in_byte":3233,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"26466452306","text":"import os \nimport nrrd \nimport numpy as np\nimport pickle\nimport json  \nimport time \nimport collections\nimport datetime \nimport torch\nfrom lib.render import render_model_id #lib.render import render_model_id \nimport sys\nsys.path.append(\"..\")\nfrom config import cfg\n#from config import cfg\n\nimport pdb \n\n\n\ndef convert_idx_to_words(data_list):\n    \"\"\"Converts each sentence/caption in the data_list using the idx_to_word dict.\n    Args:\n        idx_to_word: A dictionary mapping word indices (keys) in string format (?) to words.\n        data_list: A list of dictionaries. Each dictionary contains a 'raw_embedding' field (among\n            other fields) that is a list of word indices.\n    Returns:\n        sentences: A list of sentences (strings).\n    \"\"\"\n    inputs_list = json.load(open(cfg.DIR.JSON_PATH, 'r'))\n    idx_to_word = inputs_list['idx_to_word']\n\n    sentences = []\n    #for idx, cur_dict in enumerate(data_list):\n    #    sentences.append(('%04d  ' % idx) + ' '.join([idx_to_word[str(word_idx)] for word_idx in cur_dict['raw_caption_embedding'] if word_idx != 0]))\n    sentences.append(' '.join([idx_to_word[str(word_idx)] for word_idx in data_list if word_idx != 0]))\n\n    return sentences\n\ndef create_embedding_tuples(trained_embeddings,embedd_type='text'): \n    #print(trained_embeddings.keys())\n    dim_emb = trained_embeddings['dataset_size']\n    embeddings_matrix = np.zeros((dim_emb, 128))\n    cat_mod_id = []\n    raw_caption = []\n    for idx,entry in enumerate(trained_embeddings['caption_embedding_tuples']):\n        \n        embeddings_matrix[idx] = entry[1]\n        cat_mod_id.append(entry[0])\n        if(embedd_type == 'text'):\n            raw_caption.append(entry[2])\n\n    if(embedd_type == 'text'):\n        return embeddings_matrix,cat_mod_id,raw_caption    \n    else:\n        return embeddings_matrix,cat_mod_id\n\ndef make_data_processes(data_process_class, queue, data_paths, opts, repeat): \n    \n    processes = [] \n    for i in range(opts.num_workers): \n        process = data_process_class(queue, data_paths, opts, repeat=repeat)\n        process.start() \n        processes.append(process) \n\n    return processes \n\ndef kill_processes(queue, processes): \n    print('signal processes to shutdown')\n\n    for p in processes: \n        p.shutdown() \n\n    \n    \n    while not queue.empty(): # If queue is not empty \n        time.sleep(0.5)\n        try: \n            queue.get(False) \n        except:\n            print('now queue size is {0}'.format(queue.qsize()))\n            pass \n\n    print('killing processes.') \n    for p in processes:\n        p.terminate() \n\ndef create_pickle_embedding(mat,embedd_type ='shape'):\n    print(\"start pickle !\")\n    dict_ = {}\n    seen_models = []\n    tuples = []\n    for ele in mat:\n        \n        if(ele[0] in seen_models):\n            continue\n        else:\n            seen_models.append(ele[0])\n            if(embedd_type == 'text_only'):\n                tuples.append((ele[0],ele[1],ele[2]))\n            else:\n                tuples.append((ele[0],ele[1]))\n            \n        \n    \n    dict_ = {\n    'caption_embedding_tuples': tuples,\n    'dataset_size':len(tuples)\n    }\n\n    output = open('{}.p'.format(embedd_type), 'wb')\n    pickle.dump(dict_, output)\n    output.close()\n\n\n    print(\"created pickle file for {}\".format(embedd_type))\n# read nrrd data \ndef read_nrrd(nrrd_filename):\n    \"\"\"\n    Reads an NRRD file and returns an RGBA tensor \n    Args: \n        nrrd_filename: filename of nrrd file \n    Returns: \n        voxel tensor: 4-dimensional voxel tensor with 4 channels (RGBA) where the alpha channel \n            is the last channel(aka vx[:, :, :, 3]).\n    \"\"\"\n    nrrd_tensor, options = nrrd.read(nrrd_filename)\n    assert nrrd_tensor.ndim == 4 \n\n    # convert to float [0,1]\n    voxel_tensor = nrrd_tensor.astype(np.float32) / 255 \n    # Move channel dimension to last dimensions \n    voxel_tensor = np.rollaxis(voxel_tensor, 0, 4) \n\n    # Make the model stand up straight by swapping axes\n    voxel_tensor = np.swapaxes(voxel_tensor, 0, 1) \n    voxel_tensor = np.swapaxes(voxel_tensor, 0, 2) \n\n    return voxel_tensor\n# write nrrd \ndef write_one_voxel2nrrd(voxel_tensor, filename):\n    \"\"\"\n    Converts binvox tensor to NRRD (RGBA) format and writes it if a filename is provided.\n    Example usage:\n        voxel_tensor = np.load('text2voxel/output/tmp/ckpt-10500/0000_voxel_tensor_output.npy')\n        _ = nrrd_rw.write_nrrd(voxel_tensor, filename='./models_checkpoint/test_nrrd.nrrd')\n    Args:\n        voxel_tensor: A tensor representing the binary voxels. Values can range from 0 to 1, and\n            they will be properly scaled. The format is [height, width, depth, channels].\n        filename: Filename that the NRRD will be written to.\n    Writes:\n        nrrd_tensor: An RGBA tensor where the channel dimension (RGBA) comes first\n            (channels, height, width, depth).\n    \"\"\"\n    if voxel_tensor.ndim == 3:  # Add a channel if there is no channel dimension\n        voxel_tensor = voxel_tensor[np.newaxis, :]\n    elif voxel_tensor.ndim == 4:  # Roll axes so order is (channel, height, width, depth) (not sure if (h, w, d))\n        voxel_tensor = np.rollaxis(voxel_tensor, 3)\n    else:\n        raise ValueError('Voxel tensor must have 3 or 4 dimensions.')\n\n    # Convert voxel_tensor to uint8\n    voxel_tensor = (voxel_tensor * 255).astype(np.uint8)\n\n    if voxel_tensor.shape[0] == 1:  # Add channels if voxel_tensor is a binvox tensor\n        nrrd_tensor_slice = voxel_tensor\n        nrrd_tensor = np.vstack([nrrd_tensor_slice] * 4)\n        nrrd_tensor[:3, :, :, :] = 128  # Make voxels gray\n        nrrd_tensor = nrrd_tensor.astype(np.uint8)\n    elif voxel_tensor.shape[0] == 4:\n        nrrd_tensor = voxel_tensor\n    elif voxel_tensor.shape[0] != 4:\n        raise ValueError('Voxel tensor must be single-channel or 4-channel')\n\n    nrrd.write(filename, nrrd_tensor)\n\n\ndef get_voxel_file(category, model_id, opts):\n    \"\"\"\n    get the voxel absolute filepath for the model specified by category and model_id \n    Args: \n        category: category of the model as a string , e.g., '03001627'\n        model_id: model id of the model as a string, e.g., '587ee5822bb56bd07b11ae648ea92233'\n    Returns: \n        voxel_filepath: Filepath of the binvox file corresponding to category and model_id \n    \"\"\"\n    #if opts.dataset == 'shapenet': # shapenet dataset \n    return opts.data_dir % (model_id, model_id) \n    #elif opts.dataset == 'primitives': # primitives dataset\n\n    #return opts.data_dir % (category, model_id) \n    #else: \n        #raise ValueError('please use a valid dataset (shapenet, primitives)')\n\ndef load_voxel(category, model_id, opts): \n    \"\"\"\n    Loads the voxel tensor given the model category and model ID \n    Args: \n        category: model category\n        model_id: model id \n    Returns: \n        voxel tensor of shape (height x width x depth x channels) \n    \"\"\"\n    \n    voxel_fn = get_voxel_file(category, model_id, opts)\n\n    \n    voxel_tensor = read_nrrd(voxel_fn) \n    \n    return voxel_tensor \n\n\n\ndef augment_voxel_tensor(voxel_tensor, max_noise=10):\n    \"\"\"\n    Arguments the RGB values of the voxel tensor. The RGB channelss are perturbed by the same single\n    noise value, and the noise is sampled from a uniform distribution.\n    Args: \n        voxel_tensor: a single voxel tensor \n        max_noise: Integer representing max noise range. We will perform voxel_value + max_noise to \n        augment the voxel tensor, where voxel_value and max_noise are [0, 255].\n    Returns: \n        augmented_voxel_tensor: voxel tensor after the data augmentation\n    \"\"\"\n    augmented_voxel_tensor = np.copy(voxel_tensor) # do nothing if binvox \n    if (voxel_tensor.ndim == 4) and (voxel_tensor.shape[3] != 1) and (max_noise > 0):\n        noise_val = float(np.random.randint(-max_noise, high=(max_noise + 1))) / 255\n        augmented_voxel_tensor[:, :, :, :3] += noise_val\n        augmented_voxel_tensor = np.clip(augmented_voxel_tensor, 0., 1.)\n    return augmented_voxel_tensor\n\ndef rescale_voxel_tensor(voxel_tensor):\n    \"\"\"Rescales all values (RGBA) in the voxel tensor from [0, 1] to [-1, 1].\n    Args:\n        voxel_tensor: A single voxel tensor.\n    Returns:\n        rescaled_voxel_tensor: A single voxel tensor after rescaling.\n    \"\"\"\n    rescaled_voxel_tensor = voxel_tensor * 2. - 1.\n    return rescaled_voxel_tensor\n\ndef open_pickle(pickle_file):\n    \"\"\"Open a pickle file and return its contents.\n    \"\"\"\n    with open(pickle_file, 'rb') as f:\n        pickle_data = pickle.load(f)\n    return pickle_data\n\n\n\ndef print_sentences(json_path, data_list):\n    # Opens the processed captions generated from tools/preprocess_captions.py\n    inputs_list = json.load(open(json_path, 'r'))\n    idx_to_word = inputs_list['idx_to_word']\n\n    if isinstance(data_list, list):\n        sentences = convert_idx_to_words(idx_to_word, data_list)\n    elif isinstance(data_list, np.ndarray):\n        sentences = []\n        for idx in range(data_list.shape[0]):\n            sentences.append(('%04d  ' % idx) + ' '.join([idx_to_word[str(word_idx)]\n                             for word_idx in data_list[idx, :] if word_idx != 0]))\n\n    for sentence in sentences:\n        print(sentence + '\\n')\n\n\n\n\n\ndef categorylist2labellist(category_list_batch, category2label, opts): \n    \"\"\"\n    for primitive datasets, a batch data with category list:\n        ['torus-cyan-h100-r20', 'torus-cyan-h100-r20', 'pyramid-orange-h50-r50', \n        'pyramid-orange-h50-r50', 'pyramid-yellow-h50-r100', 'pyramid-yellow-h50-r100', \n        'cone-purple-h50-r50', 'cone-purple-h50-r50']\n    We convert it to be: \n\n    \"\"\"\n    if opts.dataset == 'shapenet':\n        shape_labels = [category2label[cat] for cat in category_list_batch]\n        if len(shape_labels) > opts.batch_size: # TST, MM \n            shape_label_batch = np.asarray(shape_labels[::opts.LBA_n_captions_per_model])\n        else: # STS mode, validation \n            shape_label_batch = np.asarray(shape_labels) \n        return torch.from_numpy(shape_label_batch)\n    elif opts.dataset == 'primitives':\n        shape_labels = [category2label[cat] for cat in category_list_batch\n                            for _ in range(opts.LBA_n_primitive_shapes_per_category)]\n\n        if opts.LBA_model_type == 'TST' or opts.LBA_model_type == 'MM':\n            shape_label_batch = np.asarray(shape_labels[::opts.LBA_n_captions_per_model])\n        elif opts.LBA_model_type == 'STS': # STS mode, validation \n            # test_queue??? is false\n            if opts.test_or_val_phase: # test or val phase \n                shape_label_batch = np.asarray(shape_labels)[::opts.LBA_n_primitive_shapes_per_category]\n            else:  # we are in training phase \n                shape_label_batch = np.asarray(shape_labels)\n\n        return torch.from_numpy(shape_label_batch)\n    else: \n        raise ValueError('Please select a vlid dataset.')\n\n\n","repo_name":"sycz00/DL_Lab","sub_path":"lib/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":10886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"2721644712","text":"# Дано число обозначающее день недели. \n# Вывести его название и указать является ли он выходным.\n\nday = ['Понедельник', 'Вторник', 'Среда', 'Четверг', 'Пятница', 'Суббота', 'Воскресение']\nfrom random import randint\na = randint(1,7)\nprint ('Число ', a, ' - какой день недели?')\nif ((a-1) == 6 or (a-1) == 7):\n    print(day[a-1],' - выходной день!')\nelse:\n    print(day[a-1])","repo_name":"Skoroleva1203/Python","sub_path":"6.py","file_name":"6.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32109216893","text":"import os\nimport pwd\nimport sys\nimport argparse\nimport requests\n\nfrom ceda_mip_tools.pub_sys_intfc import config, dataset_drs\n\n\ndef get_user_name():\n    \"get a user name (to use as the requester)\"\n    name = pwd.getpwuid(os.getuid()).pw_gecos\n    return name[: config.max_requester_len]\n\n\ndef do_post_expecting_json(url, params, \n                           description=\"web service\",\n                           compulsory_fields=()):\n    \"\"\"\n    POST the params to the specified URL.\n    Return the parsed JSON.\n    Raise an exception if any required fields are missing.\n    \"\"\"\n    response = requests.post(url, data=params, timeout=config.timeout)\n\n    if response.status_code != 200:\n        raise Exception(\"Could not talk to {}\".format(description))\n        \n    try:\n        fields = response.json()\n        for key in compulsory_fields:\n            dummy = fields[key]\n    except (ValueError, KeyError):\n        raise Exception(\"Could not parse response from {}\".format(description))\n    return fields\n    \n\ndef paginate_list(lst, count):\n    for i in range(0, len(lst), count):\n        yield lst[i : i+count]\n\n\nclass ArgsFromCmdLineOrFileParser(object):\n    \"\"\"\n    A class which acts like (and encapsulates) an argparse parser.  It allows\n    the program to take a list of things that are specified either on the command\n    line or in a file which is referenced with --file-from / -f argument, with \n    the special filename \"-\" for standard input.  Lines from the file are stripped\n    of leading/trailing whitespace, and any blank lines are ignored.\n\n    Example:\n\n        parser = ArgsFromCmdLineOrFileParser('things', 'description of thing', \n                                              var_meta='thing',\n                                              var_help=help_about_thing,\n                                              description=description_of_command)\n\n        parser.add_argument('some_initial_positional_arg')\n        parser.add_standard_arguments()\n        parser.add_argument('some_other_keyword_arg')\n\n        args = parser.parse_args(sys.argv[1:])\n\n        print(args.things, args.other_arg)\n    \n    \"\"\"\n    def __init__(self, var_opt, var_desc, var_meta=None, var_help=None, \n                 allow_empty_list=False, **kwargs):\n        self._var_opt = var_opt\n        self._var_desc = var_desc\n        self._var_help = var_help\n        self._var_meta = var_meta\n        self._allow_empty_list = allow_empty_list\n        self._file_opt = '--from-file'\n        self._file_short_opt = '-f'\n        self._parser = argparse.ArgumentParser(**kwargs)\n\n\n    def add_standard_arguments(self):\n        self._add_positional_argument()\n        self._add_file_argument()\n\n\n    def __getattr__(self, k):\n        return getattr(self._parser, k)\n\n\n    def parse_args(self, *pa_args, **pa_kwargs):\n        args = self._parser.parse_args(*pa_args, **pa_kwargs)\n        self._add_values_from_file(args)\n        return args\n\n\n    def _add_positional_argument(self):\n        self._parser.add_argument(self._var_opt, nargs='*',\n                                 metavar=self._var_meta,\n                                 help=self._var_help)\n\n        \n    def _add_file_argument(self):\n        help = (\"filename to read {} from (or '-' for standard input) \"\n                \"in place of the command line\"\n                ).format(self._var_desc)\n        self._parser.add_argument(self._file_opt, self._file_short_opt,\n                                 metavar='filename',\n                                 help=help)\n\n\n    def _add_values_from_file(self, args, abort_on_error=True):\n        try:\n            self._add_values_from_file_core(args)\n        except (ValueError, IOError) as exc:\n            print(exc)\n            if abort_on_error:\n                sys.exit(1)\n            else:\n                raise\n\n\n    def _add_values_from_file_core(self, args):\n        \"\"\"\n        appends to list based on the --file-option option\n        \"\"\"\n        filename = getattr(args, self._file_opt[2:].replace('-', '_'))\n        values = getattr(args, self._var_opt)\n        if values and (filename != None):\n            raise ValueError(('{} cannot be given on command line if {} is used'\n                              ).format(self._var_desc, self._file_opt))\n        if filename:\n            if filename == '-':\n                fh = sys.stdin    \n            else:\n                fh = open(filename)\n            with fh:\n                for line in fh:\n                    val = line.strip()\n                    if val:\n                        values.append(val)\n\n        if not values and not self._allow_empty_list:\n            raise ValueError(('no {} specified - must specify one or more on command line '\n                              'or with {} option'\n                              ).format(self._var_desc, self._file_opt))\n\n\n\ndef add_api_root_arg(parser):\n    parser.add_argument('--api-url-root',\n                        default=config.api_url_root,\n                        help=argparse.SUPPRESS)\n\n\ndef add_project_arg(parser):\n    parser.add_argument('project', type=str, metavar='project',\n                        help='project',\n                        choices=config.projects.keys())\n\ndef parse_project_arg(args):\n\n    valid_projects = sorted(config.projects.keys())\n\n#    if not args.project or args.project not in valid_projects:\n#        raise ValueError(\"one of these projects: must be specified {}\"\n#                         .format(\", \".join(valid_projects)))\n#\n    project_config = config.projects[args.project]\n\n    drs_obj = dataset_drs.DatasetDRS(project_config[\"drs\"])\n    chain = project_config[\"chain\"]\n\n    return(drs_obj, chain)\n","repo_name":"cedadev/ceda-mip-tools","sub_path":"ceda_mip_tools/pub_sys_intfc/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":5663,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"70433832923","text":"from collections import OrderedDict\nnumber = int(input())\nd = OrderedDict()\nfor i in range(number):\n    string = input()\n    if string in d:\n        d[string] += 1\n    else:\n        d[string] = 1\nprint(len(d))\nfor i in d:\n    print(d[i], end = \" \")\n    ","repo_name":"Najamulhassan3383/DSA","sub_path":"HackerRank_python/wordOrder.py","file_name":"wordOrder.py","file_ext":"py","file_size_in_byte":253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"41980521592","text":"import pandas as pd\nimport glob\ndf = pd.DataFrame()\ndata_paths = glob.glob('./rec_sys_model/wanted/clear/*')\n\nfor path in data_paths:\n    df_temp = pd.read_csv(path)\n    df_temp.dropna(inplace=True)\n    df_temp.drop_duplicates(inplace=True)\n    df = pd.concat([df, df_temp], ignore_index=True)\ndf.drop_duplicates(inplace=True)\ndf.info()\ndf.to_csv('./rec_sys_model/wanted/clear_data/All_clear_data.csv', index=False)","repo_name":"silber60/job_posting_recommendation","sub_path":"rec_sys_model/job_02_Concat.py","file_name":"job_02_Concat.py","file_ext":"py","file_size_in_byte":415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"21643753058","text":"from django.conf import settings\nfrom userprofiles.models import UserProfile\n\nclass RegistrationPolicy(object):\n    def post_registration(self, user, registration_form_values):\n        if 'register_code' not in registration_form_values or not registration_form_values['register_code']:\n            return\n\n        register_code = registration_form_values['register_code']\n        register_code = register_code.upper()\n        if register_code not in settings.REGISTRATION_POLICIES['TEDRegistrationPolicy']['codes']:\n            return\n\n        user_profile = user.profile\n        if UserProfile.objects.filter(registration_code='TED - %s' % register_code).count():\n            user_profile.registration_status = 'DECLINED'\n            user_profile.save()\n            return\n\n        user_profile.registration_code = 'TED - %s' % register_code\n        user_profile.registration_status = 'APPROVED'\n        user_profile.save()","repo_name":"d8agroup/metaLayer-delv","sub_path":"userprofiles/registrationpolicies/tedregistrationpolicy.py","file_name":"tedregistrationpolicy.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"17672621673","text":"import subprocess\n\ndef extract_strings(binary_path):\n    \"\"\"\n    Extract strings from the given binary using the `strings` command.\n    Return the extracted strings as a list.\n    \"\"\"\n    try:\n        result = subprocess.run(['strings', binary_path], capture_output=True, check=True, text=True)\n        return result.stdout.splitlines()\n    except Exception as e:\n        print(f\"Error running strings on {binary_path}: {e}\")\n        return []\n","repo_name":"thomasthaddeus/ghidra_automation","sub_path":"src/utils/string_util.py","file_name":"string_util.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"39198988960","text":"# Online learning of a logistic\n# regression model using the Exponential-family\n# Extended Kalman Filter (EEKF) algorithm\n\n# Author: Gerardo Durán-Martín (@gerdm)\n\nimport superimport\n\nimport jax\nimport nlds_lib as ds\nimport jax.numpy as jnp\nimport matplotlib.pyplot as plt\nimport pyprobml_utils as pml\nfrom jax import random\nfrom jax.scipy.optimize import minimize\nfrom sklearn.datasets import make_biclusters\n\ndef sigmoid(x): return jnp.exp(x) / (1 + jnp.exp(x))\ndef log_sigmoid(z): return z - jnp.log(1 + jnp.exp(z))\ndef fz(x): return x\ndef fx(w, x): return sigmoid(w[None, :] @ x)\ndef Rt(w, x): return sigmoid(w @ x) * (1 - sigmoid(w @ x))\n\n## Data generating process\nn_datapoints = 50\nm = 2\nX, rows, cols = make_biclusters((n_datapoints, m), 2,\n                                noise=0.6, random_state=314,\n                                minval=-4, maxval=4)\n# whether datapoints belong to class 1\ny = rows[0] * 1.0\n\nPhi = jnp.c_[jnp.ones(n_datapoints)[:, None], X]\nN, M = Phi.shape\n\ncolors = [\"black\" if el else \"white\" for el in y]\n\n# Predictive domain\nxmin, ymin = X.min(axis=0) - 0.1\nxmax, ymax = X.max(axis=0) + 0.1\nstep = 0.1\nXspace = jnp.mgrid[xmin:xmax:step, ymin:ymax:step]\n_, nx, ny = Xspace.shape\nPhispace = jnp.concatenate([jnp.ones((1, nx, ny)), Xspace])\n\n### EEKF Approximation\nmu_t = jnp.zeros(M)\nPt = jnp.eye(M) * 0.0\nP0 = jnp.eye(M) * 2.0\n\nmodel = ds.ExtendedKalmanFilter(fz, fx, Pt, Rt)\nw_eekf_hist, P_eekf_hist = model.filter(mu_t, y, Phi, P0)\n\nw_eekf = w_eekf_hist[-1]\nP_eekf = P_eekf_hist[-1]\n\n### Laplace approximation\nkey = random.PRNGKey(314)\ninit_noise = 0.6\nw0 = random.multivariate_normal(key, jnp.zeros(M), jnp.eye(M) * init_noise)\nalpha = 1.0\ndef E(w):\n    an = Phi @ w\n    log_an = log_sigmoid(an)\n    log_likelihood_term = y * log_an + (1 - y) * jnp.log1p(-sigmoid(an))\n    prior_term = alpha * w @ w / 2\n\n    return prior_term - log_likelihood_term.sum()\n\nres = minimize(lambda x: E(x) / len(y), w0, method=\"BFGS\")\nw_laplace = res.x\nSN = jax.hessian(E)(w_laplace)\n\n### Ploting surface predictive distribution\nkey = random.PRNGKey(31415)\nnsamples = 5000\n\n# EEKF surface predictive distribution\neekf_samples = random.multivariate_normal(key, w_eekf, P_eekf, (nsamples,))\nZ_eekf = sigmoid(jnp.einsum(\"mij,sm->sij\", Phispace, eekf_samples))\nZ_eekf = Z_eekf.mean(axis=0)\n\nfig, ax = plt.subplots()\nax.contourf(*Xspace, Z_eekf, cmap=\"RdBu_r\", alpha=0.7, levels=20)\nax.scatter(*X.T, c=colors, edgecolors=\"black\", s=80)\nax.set_title(\"(EEKF) Predictive distribution\")\npml.savefig(\"logistic-regression-surface-eekf.pdf\")\n\n# Laplace surface predictive distribution\nlaplace_samples = random.multivariate_normal(key, w_laplace, SN, (nsamples,))\nZ_laplace = sigmoid(jnp.einsum(\"mij,sm->sij\", Phispace, laplace_samples))\nZ_laplace = Z_laplace.mean(axis=0)\n\nfig, ax = plt.subplots()\nax.contourf(*Xspace, Z_laplace, cmap=\"RdBu_r\", alpha=0.7, levels=20)\nax.scatter(*X.T, c=colors, edgecolors=\"black\", s=80)\nax.set_title(\"(Laplace) Predictive distribution\")\npml.savefig(\"logistic-regression-surface-laplace.pdf\")\n\n### Plot EEKF and Laplace training history\nP_eekf_hist_diag = jnp.diagonal(P_eekf_hist, axis1=1, axis2=2)\nP_laplace_diag = jnp.sqrt(jnp.diagonal(SN))\nlcolors = [\"black\", \"tab:blue\", \"tab:red\"]\nelements = w_eekf_hist.T, P_eekf_hist_diag.T, w_laplace, P_laplace_diag, lcolors\ntimesteps = jnp.arange(n_datapoints) + 1\n\nfor k, (wk, Pk, wk_laplace, Pk_laplace, c) in enumerate(zip(*elements)):\n    fig, ax = plt.subplots()\n    ax.errorbar(timesteps, wk, jnp.sqrt(Pk), c=c, label=f\"$w_{k}$ online (EEKF)\")\n    ax.axhline(y=wk_laplace, c=c, linestyle=\"dotted\", label=f\"$w_{k}$ batch (Laplace)\", linewidth=3)\n\n    ax.set_xlim(1, n_datapoints)\n    ax.legend(framealpha=0.7, loc=\"upper right\")\n    ax.set_xlabel(\"number samples\")\n    ax.set_ylabel(\"weights\")\n    plt.tight_layout()\n    pml.savefig(f\"eekf-laplace-hist-w{k}.pdf\")\n\nprint(\"EEKF weights\")\nprint(w_eekf, end=\"\\n\"*2)\n\nprint(\"Laplace weights\")\nprint(w_laplace, end=\"\\n\"*2)\nplt.show()\n\n","repo_name":"mileswang97/miles_ml_prob_book0","sub_path":"pyprobml-master/scripts/eekf_logistic_regression_demo.py","file_name":"eekf_logistic_regression_demo.py","file_ext":"py","file_size_in_byte":3967,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"31753136153","text":"import time\n\nimport pytest\n\nimport ray\nfrom ray.train._internal.accelerator import Accelerator\nfrom ray.train.constants import SESSION_MISUSE_LOG_ONCE_KEY\nfrom ray.train._internal.session import (\n    init_session,\n    shutdown_session,\n    get_session,\n    TrainingResultType,\n    get_accelerator,\n    set_accelerator,\n)\nfrom ray.train.train_loop_utils import (\n    world_rank,\n    local_rank,\n    report,\n    save_checkpoint,\n    load_checkpoint,\n    get_dataset_shard,\n    world_size,\n)\nfrom ray.train.error import SessionMisuseError\n\n\n@pytest.fixture(scope=\"function\")\ndef session():\n    def f():\n        return 1\n\n    init_session(training_func=f, world_rank=0, local_rank=0, world_size=1)\n    yield get_session()\n    shutdown_session()\n\n\ndef test_init_fail(session):\n    with pytest.raises(ValueError):\n        init_session(lambda: 1, 0)\n\n\ndef test_shutdown(session):\n    shutdown_session()\n    assert not get_session()\n\n\ndef test_world_rank(session):\n    assert world_rank() == 0\n    shutdown_session()\n    # Make sure default to 0.\n    assert world_rank() == 0\n\n\ndef test_local_rank(session):\n    assert local_rank() == 0\n    shutdown_session()\n    # Make sure default to 0.\n    assert local_rank() == 0\n\n\ndef test_world_size(session):\n    assert world_size() == 1\n    shutdown_session()\n    # Make sure default to 1.\n    assert world_size() == 1\n\n\ndef test_train(session):\n    session.start()\n    output = session.finish()\n    assert output == 1\n\n\ndef test_get_dataset_shard():\n    dataset = ray.data.from_items([1, 2, 3])\n    init_session(\n        training_func=lambda: 1,\n        world_rank=0,\n        local_rank=0,\n        world_size=1,\n        dataset_shard=dataset,\n    )\n    assert get_dataset_shard() == dataset\n    shutdown_session()\n\n\ndef test_report():\n    def train_func():\n        for i in range(2):\n            report(loss=i)\n\n    init_session(training_func=train_func, world_rank=0, local_rank=0, world_size=1)\n    session = get_session()\n    session.start()\n    assert session.get_next().data[\"loss\"] == 0\n    assert session.get_next().data[\"loss\"] == 1\n    shutdown_session()\n\n\ndef test_report_fail():\n    def train_func():\n        for i in range(2):\n            report(i)\n        return 1\n\n    init_session(training_func=train_func, world_rank=0, local_rank=0, world_size=1)\n    session = get_session()\n    session.start()\n    assert session.get_next() is None\n    with pytest.raises(TypeError):\n        session.finish()\n    shutdown_session()\n\n\ndef test_report_after_finish(session):\n    session.start()\n    session.pause_reporting()\n    session.finish()\n    for _ in range(2):\n        report(loss=1)\n    assert session.get_next() is None\n\n\ndef test_no_start(session):\n    with pytest.raises(RuntimeError):\n        session.get_next()\n\n\ndef test_checkpoint():\n    def train_func():\n        for i in range(2):\n            save_checkpoint(epoch=i)\n\n    def validate_zero(expected):\n        next = session.get_next()\n        assert next is not None\n        assert next.type == TrainingResultType.CHECKPOINT\n        assert next.data[\"epoch\"] == expected\n\n    init_session(training_func=train_func, world_rank=0, local_rank=0, world_size=1)\n    session = get_session()\n    session.start()\n    validate_zero(0)\n    validate_zero(1)\n    session.finish()\n    shutdown_session()\n\n    def validate_nonzero():\n        next = session.get_next()\n        assert next is not None\n        assert next.type == TrainingResultType.CHECKPOINT\n        assert next.data == {}\n\n    init_session(training_func=train_func, world_rank=1, local_rank=1, world_size=1)\n    session = get_session()\n    session.start()\n    validate_nonzero()\n    validate_nonzero()\n    session.finish()\n    shutdown_session()\n\n\ndef test_encode_data():\n    def train_func():\n        save_checkpoint(epoch=0)\n        report(epoch=1)\n\n    def encode_checkpoint(checkpoint):\n        checkpoint.update({\"encoded\": True})\n        return checkpoint\n\n    def validate_encoded(result_type: TrainingResultType):\n        next = session.get_next()\n        assert next.type is result_type\n        assert next.data[\"encoded\"] is True\n\n    init_session(\n        training_func=train_func,\n        world_rank=0,\n        local_rank=0,\n        world_size=1,\n        encode_data_fn=encode_checkpoint,\n    )\n\n    session = get_session()\n    session.start()\n    # Validate checkpoint is encoded.\n    validate_encoded(TrainingResultType.CHECKPOINT)\n    # Validate report is encoded.\n    validate_encoded(TrainingResultType.REPORT)\n    session.finish()\n    shutdown_session()\n\n\ndef test_load_checkpoint_after_save():\n    def train_func():\n        for i in range(2):\n            save_checkpoint(epoch=i)\n            checkpoint = load_checkpoint()\n            assert checkpoint[\"epoch\"] == i\n\n    init_session(training_func=train_func, world_rank=0, local_rank=0, world_size=1)\n    session = get_session()\n    session.start()\n    for i in range(2):\n        session.get_next()\n    session.finish()\n    shutdown_session()\n\n\ndef test_locking():\n    \"\"\"Tests that report pauses training until fetch_next or finish.\"\"\"\n\n    def train_1():\n        import _thread\n\n        _thread.interrupt_main()\n\n    init_session(training_func=train_1, world_rank=0, local_rank=0, world_size=1)\n    session = get_session()\n    with pytest.raises(KeyboardInterrupt):\n        session.start()\n    shutdown_session()\n\n    def train_2():\n        for i in range(2):\n            report(loss=i)\n        train_1()\n\n    init_session(training_func=train_2, world_rank=0, local_rank=0, world_size=1)\n    session = get_session()\n    session.start()\n    time.sleep(3)\n\n    session.pause_reporting()\n    # Releases session.continue_lock to resume the training thread.\n    session.get_next()\n\n    with pytest.raises(KeyboardInterrupt):\n        session.finish()\n    shutdown_session()\n\n\ndef reset_log_once_with_str(str_to_append=None):\n    key = SESSION_MISUSE_LOG_ONCE_KEY\n    if str_to_append:\n        key += f\"-{str_to_append}\"\n    ray.util.debug.reset_log_once(key)\n\n\n@pytest.mark.parametrize(\n    \"fn\", [load_checkpoint, save_checkpoint, report, get_dataset_shard]\n)\ndef test_warn(fn):\n    \"\"\"Checks if calling train functions outside of session raises warning.\"\"\"\n\n    with pytest.warns(UserWarning) as record:\n        fn()\n\n    assert fn.__name__ in record[0].message.args[0]\n\n    reset_log_once_with_str(fn.__name__)\n\n\ndef test_warn_once():\n    \"\"\"Checks if session misuse warning is only shown once per function.\"\"\"\n\n    with pytest.warns(UserWarning) as record:\n        assert not load_checkpoint()\n        assert not load_checkpoint()\n        assert not save_checkpoint(x=2)\n        assert not report(x=2)\n        assert not report(x=3)\n        assert not get_dataset_shard()\n\n    # Should only warn once.\n    assert len(record) == 4\n\n\nclass FakeAccelerator(Accelerator):\n    pass\n\n\ndef test_set_and_get_accelerator(session):\n    accelerator = FakeAccelerator()\n    set_accelerator(accelerator)\n    assert get_accelerator(FakeAccelerator) is accelerator\n\n\ndef test_get_accelerator_constructs_default_accelerator(session):\n    assert isinstance(get_accelerator(FakeAccelerator), FakeAccelerator)\n\n\ndef test_get_accelerator_raises_error_outside_session():\n    with pytest.raises(SessionMisuseError):\n        get_accelerator(FakeAccelerator)\n\n\ndef test_set_accelerator_raises_error_if_accelerator_already_set(session):\n    accelerator1, accelerator2 = FakeAccelerator(), FakeAccelerator()\n    set_accelerator(accelerator1)\n    with pytest.raises(RuntimeError):\n        set_accelerator(accelerator2)\n\n\ndef test_set_accelerator_raises_error_outside_session():\n    accelerator = FakeAccelerator()\n    with pytest.raises(SessionMisuseError):\n        set_accelerator(accelerator)\n\n\nif __name__ == \"__main__\":\n    import pytest\n    import sys\n\n    sys.exit(pytest.main([\"-v\", \"-x\", __file__]))\n","repo_name":"merlinepedra/RAY-1","sub_path":"python/ray/train/tests/test_session.py","file_name":"test_session.py","file_ext":"py","file_size_in_byte":7810,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"23573475614","text":"from typing import Optional\n\nfrom ray.rllib.utils.framework import try_import_tf\nfrom ray.rllib.utils.typing import TensorType\nfrom ray.rllib.utils.deprecation import deprecation_warning\nfrom ray.util import log_once\n\ntf1, tf, tfv = try_import_tf()\n\n\nclass RelativeMultiHeadAttention(tf.keras.layers.Layer if tf else object):\n    \"\"\"A RelativeMultiHeadAttention layer as described in [3].\n\n    Uses segment level recurrence with state reuse.\n    \"\"\"\n\n    def __init__(\n        self,\n        out_dim: int,\n        num_heads: int,\n        head_dim: int,\n        input_layernorm: bool = False,\n        output_activation: Optional[\"tf.nn.activation\"] = None,\n        **kwargs\n    ):\n        \"\"\"Initializes a RelativeMultiHeadAttention keras Layer object.\n\n        Args:\n            out_dim: The output dimensions of the multi-head attention\n                unit.\n            num_heads: The number of attention heads to use.\n                Denoted `H` in [2].\n            head_dim: The dimension of a single(!) attention head within\n                a multi-head attention unit. Denoted as `d` in [3].\n            input_layernorm: Whether to prepend a LayerNorm before\n                everything else. Should be True for building a GTrXL.\n            output_activation (Optional[tf.nn.activation]): Optional tf.nn\n                activation function. Should be relu for GTrXL.\n            **kwargs:\n        \"\"\"\n        if log_once(\"relative_multi_head_attention\"):\n            deprecation_warning(\n                old=\"rllib.models.tf.layers.RelativeMultiHeadAttention\",\n            )\n        super().__init__(**kwargs)\n\n        # No bias or non-linearity.\n        self._num_heads = num_heads\n        self._head_dim = head_dim\n        # 3=Query, key, and value inputs.\n        self._qkv_layer = tf.keras.layers.Dense(\n            3 * num_heads * head_dim, use_bias=False\n        )\n        self._linear_layer = tf.keras.layers.TimeDistributed(\n            tf.keras.layers.Dense(out_dim, use_bias=False, activation=output_activation)\n        )\n\n        self._uvar = self.add_weight(shape=(num_heads, head_dim))\n        self._vvar = self.add_weight(shape=(num_heads, head_dim))\n\n        # Constant (non-trainable) sinusoid rel pos encoding matrix, which\n        # depends on this incoming time dimension.\n        # For inference, we prepend the memory to the current timestep's\n        # input: Tau + 1. For training, we prepend the memory to the input\n        # sequence: Tau + T.\n        self._pos_embedding = PositionalEmbedding(out_dim)\n        self._pos_proj = tf.keras.layers.Dense(num_heads * head_dim, use_bias=False)\n\n        self._input_layernorm = None\n        if input_layernorm:\n            self._input_layernorm = tf.keras.layers.LayerNormalization(axis=-1)\n\n    def call(\n        self, inputs: TensorType, memory: Optional[TensorType] = None\n    ) -> TensorType:\n        T = tf.shape(inputs)[1]  # length of segment (time)\n        H = self._num_heads  # number of attention heads\n        d = self._head_dim  # attention head dimension\n\n        # Add previous memory chunk (as const, w/o gradient) to input.\n        # Tau (number of (prev) time slices in each memory chunk).\n        Tau = tf.shape(memory)[1]\n        inputs = tf.concat([tf.stop_gradient(memory), inputs], axis=1)\n\n        # Apply the Layer-Norm.\n        if self._input_layernorm is not None:\n            inputs = self._input_layernorm(inputs)\n\n        qkv = self._qkv_layer(inputs)\n\n        queries, keys, values = tf.split(qkv, 3, -1)\n        # Cut out memory timesteps from query.\n        queries = queries[:, -T:]\n\n        # Splitting up queries into per-head dims (d).\n        queries = tf.reshape(queries, [-1, T, H, d])\n        keys = tf.reshape(keys, [-1, Tau + T, H, d])\n        values = tf.reshape(values, [-1, Tau + T, H, d])\n\n        R = self._pos_embedding(Tau + T)\n        R = self._pos_proj(R)\n        R = tf.reshape(R, [Tau + T, H, d])\n\n        # b=batch\n        # i and j=time indices (i=max-timesteps (inputs); j=Tau memory space)\n        # h=head\n        # d=head-dim (over which we will reduce-sum)\n        score = tf.einsum(\"bihd,bjhd->bijh\", queries + self._uvar, keys)\n        pos_score = tf.einsum(\"bihd,jhd->bijh\", queries + self._vvar, R)\n        score = score + self.rel_shift(pos_score)\n        score = score / d**0.5\n\n        # Causal mask of the same length as the sequence.\n        mask = tf.sequence_mask(tf.range(Tau + 1, Tau + T + 1), dtype=score.dtype)\n        mask = mask[None, :, :, None]\n\n        masked_score = score * mask + 1e30 * (mask - 1.0)\n        wmat = tf.nn.softmax(masked_score, axis=2)\n\n        out = tf.einsum(\"bijh,bjhd->bihd\", wmat, values)\n        out = tf.reshape(out, tf.concat((tf.shape(out)[:2], [H * d]), axis=0))\n        return self._linear_layer(out)\n\n    @staticmethod\n    def rel_shift(x: TensorType) -> TensorType:\n        # Transposed version of the shift approach described in [3].\n        # https://github.com/kimiyoung/transformer-xl/blob/\n        # 44781ed21dbaec88b280f74d9ae2877f52b492a5/tf/model.py#L31\n        x_size = tf.shape(x)\n\n        x = tf.pad(x, [[0, 0], [0, 0], [1, 0], [0, 0]])\n        x = tf.reshape(x, [x_size[0], x_size[2] + 1, x_size[1], x_size[3]])\n        x = x[:, 1:, :, :]\n        x = tf.reshape(x, x_size)\n\n        return x\n\n\nclass PositionalEmbedding(tf.keras.layers.Layer if tf else object):\n    def __init__(self, out_dim, **kwargs):\n        super().__init__(**kwargs)\n        self.inverse_freq = 1 / (10000 ** (tf.range(0, out_dim, 2.0) / out_dim))\n\n    def call(self, seq_length):\n        pos_offsets = tf.cast(tf.range(seq_length - 1, -1, -1), tf.float32)\n        inputs = pos_offsets[:, None] * self.inverse_freq[None, :]\n        return tf.concat((tf.sin(inputs), tf.cos(inputs)), axis=-1)\n","repo_name":"ray-project/ray","sub_path":"rllib/models/tf/layers/relative_multi_head_attention.py","file_name":"relative_multi_head_attention.py","file_ext":"py","file_size_in_byte":5761,"program_lang":"python","lang":"en","doc_type":"code","stars":28715,"dataset":"github-code","pt":"86"}
+{"seq_id":"16375616651","text":"from django.shortcuts import render, redirect\nfrom django.urls import reverse\nfrom paypal.standard.forms import PayPalPaymentsForm\nfrom django.contrib import messages\nfrom django.conf import settings\nfrom django.db import connection\nfrom django.views.decorators.csrf import csrf_exempt\nfrom mainapp.views import get_timezone\nfrom mainapp.models import *\nfrom mainapp.views import get_location\nimport math\n\nimport uuid,sys,json\n# Create your views here.\n\n@csrf_exempt\ndef paypalbutton(request):\n    currentuser = request.user.id\n    infoid = request.GET.get('infoid')\n    bserviceid = request.GET.get('bserviceid')\n    bplanid = request.GET.get('bplanid')\n    othercharges = request.GET.get('othercharges')    \n    ccode = request.session.get('coup')\n    cursor = connection.cursor()\n   \n    # othercharges=othercharges[4:]\n\n    \n    print('infoid',infoid)\n    print('bservice',bserviceid)\n    \n\n    \n    order=None\n    \n    if bserviceid=='5' or bserviceid==5:\n        eventname = request.GET.get('eventname')\n        eventdate = request.GET.get('eventdate')\n        eventtype = request.GET.get('eventtype')\n        timefrom = request.GET.get('timefrom')\n        timeto = request.GET.get('timeto')\n        address = request.GET.get('address')\n        description = request.GET.get('description')\n        clienttimezone=get_timezone(request)\n\n        print(eventname,eventdate,eventtype,timefrom,timeto,address,description,clienttimezone)\n\n        if othercharges == '0' or othercharges == ' 0':\n            if ccode is not None and len(ccode) > 2:\n                cursor.execute('select * from placeinfluenceracquisitionorders(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, True, ccode,eventname,eventdate,eventtype,timefrom,timeto,address,description,clienttimezone])\n                print(\"1\",cursor.query)\n                request.session['coup'] = ''\n                order = cursor.fetchall()\n                amount = order[0][8]\n\n            else:\n                cursor.execute('select * from placeinfluenceracquisitionorders(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, False, ccode,eventname,eventdate,eventtype,timefrom,timeto,address,description,clienttimezone])\n                print(\"2\",cursor.query)\n                order = cursor.fetchall()\n                amount = order[0][3]\n                # print(\"template chagres.\")\n        else:\n            if ccode is not None and len(ccode) > 2:\n                cursor.execute('select * from placeinfluenceracquisitionorders(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, True, ccode,eventname,eventdate,eventtype,timefrom,timeto,address,description,clienttimezone])\n                print(\"3\",cursor.query)\n                request.session['coup'] = ''\n                order = cursor.fetchall()\n                amount = order[0][8]\n\n            else:\n                cursor.execute('select * from placeinfluenceracquisitionorders(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, False, ccode,eventname,eventdate,eventtype,timefrom,timeto,address,description,clienttimezone])\n                print(\"4\",cursor.query)\n                order = cursor.fetchall()\n                amount = order[0][3]\n                    \n    if request.GET.get('tempid') is not None and  request.GET.get('temptext') is not None:\n        \n        if othercharges == '0' or othercharges == ' 0':\n            if ccode is not None and len(ccode) > 2:\n                cursor.execute('select * from placegreetingsorders(%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, True, ccode,request.GET.get('tempid'),request.GET.get('temptext')])\n                print(\"5\",cursor.query)\n                request.session['coup'] = ''\n                order = cursor.fetchall()\n                amount = order[0][8]\n\n            else:\n                cursor.execute('select * from placegreetingsorders(%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, False, ccode,request.GET.get('tempid'),request.GET.get('temptext')])\n                print(\"6\",cursor.query)\n                order = cursor.fetchall()\n                amount = order[0][3]\n                # print(\"template chagres.\")\n        else:\n            if ccode is not None and len(ccode) > 2:\n                cursor.execute('select * from placegreetingsorders(%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, True, ccode,request.GET.get('tempid'),request.GET.get('temptext')])\n                print(\"7\",cursor.query)\n                request.session['coup'] = ''\n                order = cursor.fetchall()\n                amount = order[0][8]\n\n            else:\n                cursor.execute('select * from placegreetingsorders(%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, False, ccode,request.GET.get('tempid'),request.GET.get('temptext')])\n                print(\"8\",cursor.query)\n                order = cursor.fetchall()\n                amount = order[0][3]\n                # print(\"template chagres.\")\n    \n    if  request.GET.get('subslotid') is not None:\n\n        if othercharges == '0' or othercharges == ' 0' or othercharges == '  0' :\n            if ccode is not None and len(ccode) > 2:\n                cursor.execute('select * from placeorderss(%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, False, True, ccode,request.GET.get('subslotid')])\n                print(\"9\",cursor.query)\n                request.session['coup'] = ''\n                order = cursor.fetchall()\n                amount = order[0][8]\n            else:\n                cursor.execute('select * from placeorderss(%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, False, False, ccode,request.GET.get('subslotid')])\n                print(\"10\",cursor.query)\n                order = cursor.fetchall()\n                amount = order[0][3]\n        \n                           \n\n            print(\"no delivery chagres.\")\n        else:\n            if ccode is not None and len(ccode) > 2:\n                cursor.execute('select * from placeorderss(%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, True, True, ccode,request.GET.get('subslotid')])\n                request.session['coup'] = ''\n                print(\"11\",cursor.query)\n                order = cursor.fetchall()\n                amount = order[0][8]\n                \n            else:\n                cursor.execute('select * from placeorderss(%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, True, False, ccode,request.GET.get('subslotid')])\n                print(\"12\",cursor.query)\n                order = cursor.fetchall()\n                amount = order[0][3]\n            print('delivery',bserviceid )\n    \n    \n    sys.stdout = open(\"basic.txt\", \"a\")\n    print('rahulother',request.GET.get('othercharges'))\n    \n                \n    if bserviceid=='1' or bserviceid==1 or bserviceid=='3' or bserviceid==3 or bserviceid=='7' or bserviceid==7:\n\n        if othercharges == '0' or othercharges == ' 0' or othercharges == 0:\n            if ccode is not None and len(ccode) > 2:\n                cursor.execute('select * from placeorderss(%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, False, True, ccode,0])\n                request.session['coup'] = ''\n                print(\"13\",cursor.query)\n                order = cursor.fetchall()\n                amount = order[0][8]\n                ordes=order[0][10]\n                \n            else:\n                cursor.execute('select * from placeorderss(%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, False, False, ccode,0])\n                print(\"14\",cursor.query)\n                order = cursor.fetchall()\n                amount = order[0][3]\n                ordes=order[0][10]\n\n            # print(\"no delivery chagres.\")\n        else:\n            if ccode is not None and len(ccode) > 2:\n                cursor.execute('select * from placeorderss(%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, True, True, ccode,0])\n                request.session['coup'] = ''\n                print(\"15\",cursor.query)\n                order = cursor.fetchall()\n                amount = order[0][8]\n                ordes=order[0][10]\n                \n            else:\n                cursor.execute('select * from placeorderss(%s,%s,%s,%s,%s,%s,%s,%s)',\n                            [currentuser, infoid, bplanid, bserviceid, True, False, ccode,0])\n                print(\"16\",cursor.query)\n                order = cursor.fetchall()\n                amount = order[0][3]\n                ordes=order[0][10]\n                print('all3:', order,amount)\n    \n    # infoid = ''\n    # bserviceid = ''\n    # bplanid = ''\n    # othercharges = ''\n        # print('delivery charges.')\n    orderid = order[0][4]\n    currcode = order[0][5]\n    sername = order[0][6]\n \n    amout = amount\n    \n    infocurr=request.session.get('selectinfocurr')\n    \n    \n    print('orderid',orderid)\n    print('service',sername)\n    print('amount')\n    print(amout)\n    \n    \n    # infocurr='SLL'\n    if infocurr == 'INR':\n        rates = ExchangeRates.objects.filter(country='United States').values('rates')[:1]\n        rate_value = rates[0]['rates'] if rates else 1\n        amout=float(amout)*rate_value\n\n    # elif infocurr =='USD':\n    #     amout=amout\n    else:\n        \n        rates = ExchangeRates.objects.filter(country='United States').values('rates')[:1]\n        rate_value = rates[0]['rates'] if rates else 1\n        amout=float(amout)*rate_value\n    \n        # rates = ExchangeRates.objects.filter(countery_abbrevation=str(infocurr)).values('rates')[:1]\n        # rate_value = rates[0]['rates'] if rates else 1\n        # norrup=1/rate_value\n        # amout=amout*norrup\n        # rates = ExchangeRates.objects.filter(country='United States').values('rates')[:1]\n        # rate_value = rates[0]['rates'] if rates else 1\n        # amout=amout*rate_value\n        print('before change',amout)\n        \n    amout=math.ceil(amout)\n    \n    \n    print('aftre change',amout)\n    \n    \n    # country = get_location(request)\n  \n    # if country == 'India':\n    #     country = 'India'\n    # else:\n    #     country='United States'\n    # print(\"data\", country)\n    # curs = ExchangeRates.objects.get(country__icontains=country).rates\n    # amout=amout*curs\n  \n    \n    \n    \n    \n    \n    \n    \n    print('orderid',orderid)\n    print('service',sername)\n    print('amount')\n    print(amout)\n    \n    \n    \n    \n    \n    \n    host = request.get_host()\n    \n    paypal_dict = {\n        # 'PAYPAL_TEST':settings.PAYPAL_TEST,\n        'business': 'accounts@influencerhiring.com',#, #'ankit@bol7.com'\n        'amount': amout,\n        'currnecy_code': 'USD', \n        'item_name': 'sername',\n        # 'invoice': str(uuid.uuid4()),\n        'invoice': str(orderid),\n        \n        # 'notify_url': request.build_absolute_uri(reverse('paypal-ipn')),\n        'notify_url': f'http://{host}{reverse(\"paypal-ipn\")}',\n        'return': f'http://{host}{reverse(\"paypal_reverse\")}',#request.build_absolute_uri(reverse('paypal_reverse')),\n        'cancel_return': f'http://{host}{reverse(\"paypal_cancel\")}',#request.build_absolute_uri(reverse('paypal_cancel')),\n    }\n    form = PayPalPaymentsForm(initial=paypal_dict)\n    context = {'form':form}\n    print('execute')\n    return render(request, 'paypalbutton.html', context)\n    \n    \n    \ndef paypal_reverse(request):\n    # messages.success('payment successful')\n    return redirect('paypalbutton')\n\ndef paypal_cancel(request):\n    # messages.error('payment cancelled')\n    return redirect('paypalbutton')","repo_name":"rbarawal/Test-Project","sub_path":"paypalapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":12172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"20532696922","text":"from app.config import AppConfig\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\n\nfrom .routes import router\n\napp = FastAPI()\n\nconfig = AppConfig()\norigins = list(config.allowed_origins)\n\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=origins,\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"]\n)\n\napp.include_router(router)","repo_name":"FreakyEinstein/blog-api","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"36856340531","text":"import pyjsonrpc\n\n\nclass RequestHandler(pyjsonrpc.HttpRequestHandler):\n\n    @pyjsonrpc.rpcmethod\n    def add(self, a, b):\n        \"\"\"Test method\"\"\"\n        return a + b\n\n\n# Threading HTTP-Server\nhttp_server = pyjsonrpc.ThreadingHttpServer(\n    server_address = ('0.0.0.0', 8080),\n    RequestHandlerClass = RequestHandler\n)\nprint(\"Starting HTTP server ...\")\nprint(\"URL: http://0.0.0.0:8080\")\nhttp_server.serve_forever()","repo_name":"CoinBub/daemon-test-utils","sub_path":"src/test/resources/.docker/usr/bin/testrpc.py","file_name":"testrpc.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"11466501212","text":"import cv2, os\nimport numpy as np\nfrom keras.preprocessing.image import ImageDataGenerator\n\ndef opp_transform(image):\n    # split the image into its respective RGB components\n    (B, G, R) = cv2.split(image.astype(\"float\"))\n    # compute rg = R - G\n    O1 = ((R + G + B) -1.5)/ 1.5\n    O2 = ((R - G))\n    O3 = ((R + G) - (2 * B))/2\n    image = cv2.merge((O1,O2,O3))\n    return image\n\n\ndef hsv_transform(image):\n    image = np.array(image)\n    hsv_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV_FULL)\n    return hsv_image\n\ndef bgr_transform(image):\n    image = np.array(image)\n    bgr_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n    return bgr_image\n\ndef lab_transform(image):\n    image = np.array(image)\n    lab_image = cv2.cvtColor(image, cv2.COLOR_RGB2Lab)\n    return lab_image\n\ndef xyz_transform(image):\n    image = np.array(image)\n    xyz_image = cv2.cvtColor(image, cv2.COLOR_RGB2XYZ)\n    return xyz_image\n\ndef yCrCb_transform(image):\n    image = np.array(image)\n    yrb_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)\n    return yrb_image\n\ndef luv_transform(image):\n    image = np.array(image)\n    luv_image = cv2.cvtColor(image, cv2.COLOR_RGB2Luv)\n    return luv_image\n\ndef yuv_transform(image):\n    image = np.array(image)\n    yuv_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)\n    return yuv_image\n\ndef _get_preprocessing_function(transform):\n    if transform and transform.lower() == 'opp':\n        print('Using opp')\n        preprocessing_function = opp_transform\n    elif transform and transform.lower() == 'hsv':\n        preprocessing_function = hsv_transform\n    elif transform and transform.lower() == 'bgr':\n        print('Using bgr')\n        preprocessing_function = bgr_transform\n    elif transform and transform.lower() == 'lab':\n        preprocessing_function = lab_transform\n    elif transform and transform.lower() == 'xyz':\n        preprocessing_function = xyz_transform\n    elif transform and transform.lower() == 'ybr':\n        print('Using yCrCb')\n        preprocessing_function = yCrCb_transform\n    elif transform and transform.lower() == 'luv':\n        preprocessing_function = luv_transform\n    elif transform and transform.lower() == 'yuv':\n        print('Using yuv')\n        preprocessing_function = yuv_transform\n    else:\n        print('Using rgb')\n        preprocessing_function = None\n\n    return preprocessing_function\n\n\ndef _get_num_files(dr):\n    return sum([len(files) for r, d, files in os.walk(dr)])\n\n\ndef get_training_data(train_dir, colours, img_rows, img_cols, batch_size=16, transform='rgb', rescale=False):\n    preprocessing_function = _get_preprocessing_function(transform)\n\n    if rescale:\n        data_generator = ImageDataGenerator(\n            shear_range=0.2,\n            zoom_range=0.3,\n            horizontal_flip=True,\n            preprocessing_function=preprocessing_function,\n            rescale=1. / 255,\n        )\n    else:\n        data_generator = ImageDataGenerator(\n            shear_range=0.2,\n            zoom_range=0.3,\n            horizontal_flip=True,\n            preprocessing_function=preprocessing_function,\n        )\n\n    return data_generator.flow_from_directory(\n        train_dir,\n        shuffle=True,\n        classes=colours,\n        target_size=(img_rows, img_cols),\n        batch_size=batch_size,\n        class_mode='categorical')\n\n\ndef get_test_data(test_data, colours, img_rows, img_cols, transform='rgb', rescale=False):\n    preprocessing_function = _get_preprocessing_function(transform)\n\n    if rescale:\n        data_generator = ImageDataGenerator(\n            preprocessing_function=preprocessing_function,\n            rescale=1. / 255,\n        )\n    else:\n        data_generator = ImageDataGenerator(\n            preprocessing_function=preprocessing_function,\n        )\n\n    return data_generator.flow_from_directory(\n        test_data,\n        classes=colours,\n        target_size=(img_rows, img_cols),\n        batch_size=_get_num_files(test_data),\n        class_mode='categorical')\n\n\ndef dir_path(string):\n    if os.path.isdir(string):\n        return string\n    else:\n        os.mkdir(string)\n        return string\n","repo_name":"chrishickey/color_hierarchy_experiment","sub_path":"experiment1/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4106,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"3260548662","text":"# Uses python3\nimport sys\n\ndef get_optimal_value(capacity, weights, values):\n    arrayOfSelectedItems = [0] * (n + 1)\n    value = 0\n    for i in range(n):\n        if capacity == 0:\n            return(value, arrayOfSelectedItems )\n        max_index = select_max_index(values, weights)\n        if max_index >= 0:\n            available_Weight = min(weights[max_index],capacity)\n            value = value + available_Weight*(values[max_index]/weights[max_index])\n            weights[max_index] = weights[max_index] - available_Weight\n            arrayOfSelectedItems[max_index] = arrayOfSelectedItems[max_index] + available_Weight\n            capacity = capacity - available_Weight\n    #type(value) is float\n    #type(value) is int\n    return value, arrayOfSelectedItems\n\n\ndef select_max_index(values, weights):\n    index = -1\n    max = 0\n    for i in range(n):\n        if weights[i] > 0 and (values[i] / weights[i]) > max:\n            max = values[i] / weights[i]\n            index = i\n    return index\n\nif __name__ == \"__main__\":\n    data = list(map(int, sys.stdin.read().split()))\n    n, capacity = data[0:2]\n    values = data[2:(2 * n + 2):2]\n    weights = data[3:(2 * n + 2):2]\n    opt_value = get_optimal_value(capacity, weights, values)\n    #print(\"{!s:10}\".format(opt_value)) #works kinda but returns (180.0, [20, 0, 30, 0])\n    #print(\"{:.10f}\".format(opt_value)) #came with starter file\n    #print'{!s:20s}'.format(b\"Hi\")\"b'Hi' right. wrong --> \"{:20}\".format(b\"hi\")\n    #print(\"{0:.15f}\".format(opt_value)) TypeError: non-empty format string passed to object.__format__\n    #print(\"{!s:.10}\".format(opt_value)) wrong output format: could not convert string to float: '(180.0,' (180.0, [2\n    #print(\"{0:.2f}\".format(opt_value)) TypeError: non-empty format string passed to object.\n    #print(\"{!f:.2}\".format(opt_value)) wrong output format: list index out of range\n    #print(\"% .4f\".format(opt_value)) # wrong output format: could not convert string to float: '%'  % .4f\n    #print(\"{% .4f}\".format(opt_value)) wrong output format: list index out of range\n    #float(opt_value)\n    #opt_value = (float(opt_value[0])) TypeError: 'float' object is not subscriptable #print(\"{:.10f}\".format(opt_value))\n    opt_value = ((opt_value[0]))\n    print(\"{:.10f}\".format(float(opt_value)))\n\n    #print(opt_value) (180.0, [20, 0, 30, 0])","repo_name":"thiggimajig/algorithmsCourseraUCSanDiego","sub_path":"NEWNEWfractional_knapsack.py","file_name":"NEWNEWfractional_knapsack.py","file_ext":"py","file_size_in_byte":2334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"20881783600","text":"import math\n\nt=int(input())\ns=int(input())\nh=int(input())\n\nwidth=s*2+3\n\n#for tines\nfor j in range(0, t):\n    line=\"\"\n    for i in range(0,width):\n        if (i==0 or i==s+1 or i==width-1):\n            line+=\"*\"\n        else:\n            line+=\" \"\n    print(line)\n#middle part of trident\nline=\"\"\nfor i in range(0, width):\n    line+=\"*\"\nprint(line)\n\n#get pos of where handle should be\nmedian=math.ceil(width/2)\nfor i in range(0,h):\n    line=\"\"\n    for j in range(0, width):\n        if (j==median-1):\n            line+=\"*\"\n        else:\n            line+=\" \"\n    print(line)\n\n\n\n        ","repo_name":"Harushii18/CCC_ValentinesDay","sub_path":"Q7.py","file_name":"Q7.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"16427095299","text":"import json\n\nwith open('./Directories.json') as fp:\n    _param = json.load(fp)\n\nFDIR_HALOS = _param[\"Halo_dir\"]\nFDIR_CLUSTERS = _param[\"Cluster_dir\"]\nFDIR_SB_MAP_SAVE = _param[\"SB_Map_dir\"]\nFDIR_EVENT_MAP = _param[\"Event_Map_dir\"]\ndel _param\n","repo_name":"afarahi/XTRA","sub_path":"source/Objects/XTRA_Global_Var.py","file_name":"XTRA_Global_Var.py","file_ext":"py","file_size_in_byte":242,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"5657656031","text":"from typing import Union\nfrom math import inf\n\n\ndef LineCost(WORDS: list[str], LINE_WIDTH: int, i: int, j: int) -> Union[float, int]:\n    \"\"\"\n    i, j are valid indices, inclusive\n    \"\"\"\n    extras = LINE_WIDTH - len(\" \".join(WORDS[i : j + 1]))\n    if extras < 0:\n        return inf\n    if extras >= 0 and j == len(WORDS) - 1:\n        \"\"\"\n        Depends on the problem,\n        enable this if the last line is free\n        \"\"\"\n        return 0\n    else:\n        return extras**3\n\n\ndef PrettyPrint(\n    WORDS: list[str], LINE_WIDTH: int, last_word_idx: int\n) -> tuple[int, list[int]]:\n    if last_word_idx == -1:\n        return 0, []\n\n    best_cost = inf\n    best_breaks = []\n\n    for break_idx in range(0, last_word_idx + 1):\n        break_cost, breaks = PrettyPrint(WORDS, LINE_WIDTH, break_idx - 1)\n        break_cost += LineCost(WORDS, LINE_WIDTH, break_idx, last_word_idx)\n\n        if break_cost < best_cost:\n            best_cost = break_cost\n            best_breaks = breaks + [break_idx]\n\n    return best_cost, best_breaks\n\n\nif __name__ == \"__main__\":\n    text1 = \"Geeks for Geeks presents word wrap problem\"\n    text2 = \"aaa bb cc ddddd\"\n    # text3 = \"cat is an animal\"\n    text3 = \"The cat ran very slowly to the school\"\n    text4 = \"This is an example of text justification\"\n\n    words = text3.split(\" \")\n    line_width = 15\n\n    cost, breaks = PrettyPrint(words, line_width, len(words) - 1)\n    print(breaks)\n    print(f\"Cost = {cost}, printing justified words:\\n----\")\n\n    for i in breaks:\n        if i != 0:\n            words[i] = \"\\n\" + words[i]\n\n    print(\" \".join(words))\n","repo_name":"tomli380576/ECS122A-Algorithms-python-implementation","sub_path":"Implementations/backtracking-pretty-printing.py","file_name":"backtracking-pretty-printing.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"31769324634","text":"# k = 0\r\n# string = \"**A31\"\r\n# while k < len(string):\r\n\r\n\r\n#     valid_letters = [\"A\",\" \",\"X\",\"Y\",\"*\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"W\",\"F\"]\r\n#     if string[k] not in valid_letters:\r\n#         raise ValueError(\"Bad letter in configuration file: {}\".format(string[k]))\r\n    \r\n#     k += 1\r\n\r\n# lines = ['**X**', '*   *', '**Y**']\r\n# i = 0 \r\n# while i < len(lines):\r\n#     string = lines[i]\r\n#     k = 0\r\n#     while k < len(string):\r\n\r\n#         valid_letters = [\"A\",\" \",\"X\",\"Y\",\"*\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"W\",\"F\"]\r\n#         if string[k] not in valid_letters:\r\n#             raise  ValueError(\"Bad letter in configuration file: {}\".format(string[k]))\r\n#         # occurence_x = \"X\"\r\n#         # occurence_y = \"Y\"\r\n#         k += 1\r\n    \r\n#     i+=1\r\n\r\n# st = \"hi\"\r\n# x_count = 0\r\n# x_count += st.count(\"X\")\r\n# print(x_count)\r\nls = ['2', '3', '2', '4', '4','5']\r\ncounts = [[x,ls.count(x)] for x in set(ls)]\r\nprint(counts)\r\nprint(counts[0][1])\r\nx = 0\r\nwhile x < len(counts):\r\n    if counts[x][1] != 2:\r\n        print(\"this number does not have matching pair {}\".format(counts[x][0]))\r\n        break\r\n    x += 1","repo_name":"Rahultalla29/arcade-game","sub_path":"stringtester.py","file_name":"stringtester.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"22951141482","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jul 16 16:58:17 2017\n\n@author: melon\n\nperceptron learning example\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\nclass Perceptron():\n\n    def __init__(self, max_iter = 1000):\n        self.max_iter = max_iter\n        self.w = []\n        self.b = 0\n        self.num_data = 0\n        self.num_features = 0\n    \n    def train(self, X, Y):\n        self.num_data, self.num_features = np.shape(X)\n        self.w = np.zeros(self.num_features)\n        for i in range(self.max_iter):\n            update = False\n            for j in range(self.num_data):\n                y_pred = self.b + np.dot(self.w, X[j])\n                if np.sign(y_pred) != np.sign(Y[j]):\n                    update = True\n                    self.w += Y[j] * X[j]\n                    self.b += Y[j]\n            if not update:\n                print(\"Converged in {0} iterations\".format(i))\n                break\n \n    def classify_instance(self, x):\n        if len(self.w) == 0 :\n            self.w = np.zeros(len(x))\n        ans = self.b + np.dot(self.w, x)\n        return 1 if ans >= 0 else -1\n            \n    def classify(self, X):\n        Y_pred = []\n        for i in range(np.shape(X)[0]):\n            y_pred = self.classify_instance(X[i])\n            Y_pred.append(y_pred)\n        return Y_pred\n        \ndef GenerateData(num_points,seed=0):\n    random.seed(seed)\n    X = np.zeros((num_points, 2))\n    Y = np.zeros(num_points)\n    for i in range(num_points):\n        X[i][0] = random.randint(1,9)+0.1*random.randint(1,9)\n        X[i][1] = random.randint(1,9)+0.1*random.randint(1,9)\n        Y[i] = 1 if X[i][0]+X[i][1] >= 10 else -1\n    return X, Y\n            \ndef PlotData(X,Y,title):        \n    for i,v in enumerate(X):\n        if Y[i] == 1:\n            plt.plot(v[0],v[1],marker='o')\n        else:\n            plt.plot(v[0],v[1],marker='x')\n    plt.xlim(0,10)     \n    plt.title(title)\n    plt.show()              \n    \ndef ErrorRate(Y,Y_pred):\n    error = 0\n    for i in range(len(Y)):\n        if Y[i] != Y_pred[i]:\n            error += 1\n    return error/len(Y)\n        \nX_train,Y_train = GenerateData(100,seed=0)        \nPlotData(X_train,Y_train,\"training data\")        \n\nmodel = Perceptron()\nY_init = model.classify(X_train)\nprint(\"initial error rate = {0}\".format(ErrorRate(Y_train,Y_init)))\nmodel.train(X_train,Y_train)\nY_final = model.classify(X_train)\nprint(\"final error rate = {0}\".format(ErrorRate(Y_train,Y_final)))\nprint(\"final b = {0}, final w = {1},{2}\".format(model.b,model.w[0],model.w[1]))","repo_name":"jamie0618/ML_project","sub_path":"perceptron/perceptron.py","file_name":"perceptron.py","file_ext":"py","file_size_in_byte":2547,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32012576006","text":"from nltk.tag import StanfordNERTagger\nimport sys\n\nst = StanfordNERTagger('english.all.3class.distsim.crf.ser.gz')\n\ndef combine2dict():\n    f=open('combine2dist.csv','r')\n    combine2dist=dict()\n    for line in f:\n        line=eval(line)\n        link, artikelset=line[0],line[1]\n        combine2dist[link]=artikelset\n    return combine2dist\n\ncombine2dist=combine2dict()\n\ndef search(userinput):\n    try:\n        artikellst = []\n        append = artikellst.append\n        for k in combine2dist.keys():\n                try:\n                    if userinput in k:\n                        append(combine2dist[k])\n                except IndexError:\n                    print(' ')\n                    pass\n        l = ([(x, y) for (x, y) in artikellst if not len(x) == 0 if not x =='{{FULLPAGENAME}}'])\n        matchedarticle = (sorted(l, key=lambda x: x[1]))[-1][0]\n        return (\"[[%s|%s]]\"%(matchedarticle, userinput))\n\n    except IndexError:\n        return (userinput)\n        pass\n\ndef work_on_inqury_line(s):\n    lst = st.tag(s.split())\n    l = []\n    append = l.append\n    for (x, y) in lst:\n        if not y == 'O':\n            searched = search(x)\n            append(searched)\n        else:\n            append(x)\n    print(' '.join([word for word in l]))\n\n\ndef interactive_Mode():\n    s=input(\"Please input your Inqury: \")\n    if s=='':sys.exit()\n    else:\n        work_on_inqury_line(s)\n    interactive_Mode()\n\ndef batch_Mode(f,s):\n    f1=open(f,'r')\n    for line in f1:\n        f2=open(s,'a+')\n        sys.stdout = f2\n        work_on_inqury_line(line)\n\n\nif(len(sys.argv)==4):\n    f,s=sys.argv[2],sys.argv[3]\n    batch_Mode(f,s)\nelse: interactive_Mode()","repo_name":"siebeniris/Projekt-symbolischeProgrammierung","sub_path":"aufgabe_4.py","file_name":"aufgabe_4.py","file_ext":"py","file_size_in_byte":1658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"71052349723","text":"import pygame\nimport button\nimport random\nimport math\nfrom pygame import mixer\nimport time\n\npygame.init()\nmixer.init()\nclock = pygame.time.Clock()\n\nwinWidth = 800\nwinHeight = 600\nwin = pygame.display.set_mode((800,600))\npygame.display.set_caption(\"Car Crash\")\nbgImg = pygame.image.load(\"CarCrash/resources/background.png\")\nbg = pygame.transform.scale(bgImg, (800,600))\ni = 0 # background coordinate Y (which is going to change with a loop)\n\n# Setting the app icon\niconImg = pygame.image.load(\"CarCrash/resources/icon.png\")\npygame.display.set_icon(iconImg)\n\n# Button images\nstartImg = pygame.image.load(\"CarCrash/resources/Start.png\").convert_alpha()\nchangeColorImg = pygame.image.load(\"CarCrash/resources/ChangeColor.png\").convert_alpha()\nexitImg = pygame.image.load(\"CarCrash/resources/Exit.png\").convert_alpha()\nrestartImg = pygame.image.load(\"CarCrash/resources/restart.png\")\n\n# Car images\nblue = pygame.image.load(\"CarCrash/resources/myCarBlue.png\")\nblack = pygame.image.load(\"CarCrash/resources/myCarBlack.png\")\ncoffee = pygame.image.load(\"CarCrash/resources/myCarCoffee.png\")\ndarkBrown = pygame.image.load(\"CarCrash/resources/myCarDarkBrown.png\")\ndarkGrey = pygame.image.load(\"CarCrash/resources/myCarDarkGrey.png\")\ndarkPink = pygame.image.load(\"CarCrash/resources/myCarDarkPink.png\")\nlightBlue = pygame.image.load(\"CarCrash/resources/myCarLightBlue.png\")\nlightGrey = pygame.image.load(\"CarCrash/resources/myCarLightGrey.png\")\nopenBlue = pygame.image.load(\"CarCrash/resources/myCarOpenBlue.png\")\npurple = pygame.image.load(\"CarCrash/resources/myCarPurple.png\")\nred = pygame.image.load(\"CarCrash/resources/myCarRed.png\")\n\n# enemy car images\ncar1 = pygame.image.load(\"CarCrash/resources/car2.png\")\ncar2 = pygame.image.load(\"CarCrash/resources/car3.png\")\namb = pygame.image.load(\"CarCrash/resources/ambulance.png\")\ntaxi = pygame.image.load(\"CarCrash/resources/taxi.png\")\n\nCars = [blue, black, coffee, darkBrown, darkGrey, darkPink, lightBlue, lightGrey, openBlue, purple, red]\ncarsNumber = len(Cars)\n\n# Changing the sizes of the images of the cars \ncarWidth = 220\ncarHeight = 220\ncarsResized = [pygame.transform.scale(i, (carWidth,carHeight)) for i in Cars]\n\n# Car coordinates\nx = 230\ny = 380\n\n# Enemy\nenemyX_spawns = [105, 230, 355, 480]\nenemyImg = [car1,car2,amb,taxi]\nenemyX = []\nenemyY = -220\nenemyY_change = 0 #difficulty (it is zero before the start button is pressed)\n#numOfEnemies = 1\nenemyImgRes = [pygame.transform.scale(i,(carWidth,carHeight)) for i in enemyImg]\n\nenemyX.append(random.choice(enemyX_spawns))\n\n\n# Drawing Enemy         \ndef enemyDraw(image, x, y):\n        win.blit(image,(x,y))\n\n# game over text \noverFont = pygame.font.Font(\"CarCrash/resources/gameFont.ttf\", 64) \ndef gameOverText():\n        overText = overFont.render(\"GAME OVER\", True, (0,0,0))\n        win.blit(overText, (200,250))\n\n        \n       \n\n\n\n\n\n# Level text \nlevel = pygame.font.Font(\"CarCrash/resources/gameFont.ttf\", 25)\ndef levelDisplay(labelsGone):\n        levelText = level.render(\"Level: \"+str(levelNumber), True, (0,0,0))\n        \n        if labelsGone: \n                win.blit(levelText, (-100,-100))\n        else:\n                win.blit(levelText, (4,10))\n\n# score text \nscore = pygame.font.Font(\"CarCrash/resources/gameFont.ttf\", 25)\ndef scoreDisplay(labelsGone):\n        scoreText = score.render(\"Score: \"+str(scoreNumber), True, (0,0,0))\n        \n        if labelsGone:\n                win.blit(scoreText, (-100,-100))\n        else:\n                win.blit(scoreText, (4,70))\n\n# Collision \ndef isCollision(enemyX,enemyY,carX,carY):\n        distance = math.sqrt(math.pow(enemyX - carX, 2) + math.pow(enemyY - carY,2))\n        if distance < 125:\n                return True\n        else:\n                return False\n\n\n# Creating Buttons\nstartBtn = button.Button(50, 150, startImg, 0.3)\nexitBtn = button.Button(50, 250, exitImg, 0.3)\ncolorBtn = button.Button(50,200,changeColorImg, 0.3)\nrestartBtn = button.Button(-1000,-1000,restartImg, 4)\n\ncar = 0     \n\n# Choosing a random enemy to spawn first \nenemy = random.choice(enemyImgRes) # random enemy car\nenemy_x = random.choice(enemyX_spawns) # random spawn x coordinate\nenemy_y = enemyY # fixed y spawn \n\n# Ending picture\nendPicNormal = pygame.image.load(\"CarCrash/resources/flashyCar.png\")\nendPic = pygame.transform.scale(endPicNormal,(winWidth,winHeight))\n\n\nbgMove = 0 # how fast the background is running\n\nover = False\ngameWin = False\nshowEndPic = False\nlabelsGone = False\nbringRestart = False\nscoreNumber = 0\nlevelNumber = 1\n                                \nwhoosh = mixer.Sound(\"CarCrash/resources/whoosh.wav\")\nvictory = mixer.Sound(\"CarCrash/resources/victory.wav\")\n\n\nrunning = True \nwhile running:\n        \n        selectedCar = carsResized[car]\n        \n        for event in pygame.event.get():\n                # quiting window if red cross is pressed\n                if event.type == pygame.QUIT:\n                        pygame.quit()\n                        quit()\n                if event.type == pygame.KEYDOWN and not over:\n                        if event.key == pygame.K_LEFT:\n                                whoosh.play()\n                                x -= 125\n                        if event.key == pygame.K_RIGHT:\n                                whoosh.play()\n                                x += 125\n                        if event.key == pygame.K_SPACE:\n                                horn = mixer.Sound(\"CarCrash/resources/Horn.wav\")\n                                horn.play()\n                                \n\n        win.fill((0,0,0)) # filling window with black color behind the bg\n        win.blit(bg, (0, i)) # setting the background with a moving height parameter\n        win.blit(bg, (0, -winHeight +i)) # readding the picture a second time\n        \n        # changing the color of the car when the button is pressed\n        if colorBtn.draw(win):\n                car = (car + 1)%carsNumber\n\n        if i == winHeight:\n                win.blit(bg, (0, -winHeight + i)) # readding the picture in a loop so that the bg doesn't black out\n                i = 0\n\n        i += bgMove\n\n        # Starting the game when the button start is pressed\n        if startBtn.draw(win):\n                enemyY_change = 12\n                bgMove = 8\n                startBtn.rect.topleft = (-100,-100)\n                colorBtn.rect.topleft = (-100,-100)\n                exitBtn.rect.topleft = (-100,-100)\n                bgMusic = mixer.music.load(\"CarCrash/resources/bgMusic.wav\")\n                mixer.music.play(-1)\n                mixer.music.set_volume(0.4)\n                \n\n        \n        \n        \n        enemyDraw(enemy, enemy_x,enemy_y)\n        enemy_y += enemyY_change #difficulty \n\n        # restarting the enemy cars that go passed the player car\n        if enemy_y > winHeight:\n                enemy_y = 0 - carHeight\n                enemy_x = random.choice(enemyX_spawns)\n                enemy = random.choice(enemyImgRes)\n\n                                  \n        win.blit(selectedCar, (x,y))\n\n        # Border checking\n        if x <= 105:\n                x = 105\n        if x >= 480: \n                x = 480\n\n        # Enemy Colission\n        if isCollision(enemy_x,enemy_y,x,y):\n                gameOverText()\n                if not over:\n                        crash = mixer.Sound(\"CarCrash/resources/crash.wav\")\n                        crash.set_volume(0.6)\n                        crash.play()\n                \n                enemyY_change = 0\n                bgMove = 0\n                over = True     \n                mixer.music.fadeout(2000)\n                bringRestart = True\n                if restartBtn.draw(win):\n                        over = False\n                        enemy_x = random.choice(enemyX_spawns)\n                        enemy_y = 0 - carHeight\n                        x = 230\n                        scoreNumber = 0 \n                        levelNumber = 1\n                        mixer.music.play(-1)\n                        enemyY_change = 12\n                        bgMove = 8\n                        restartBtn.rect.topleft = (-1000,-1000)\n\n        # Bringing restart button\n        if bringRestart:\n                restartBtn.rect.topleft = (-100,350)\n                restartBtn.draw(win) \n                bringRestart = False\n        \n        # level change and difficulty increase\n        \n        if enemy_y > y + 208:\n                scoreNumber += 1\n                \n        if scoreNumber  == 15: # scoreNumber counts how many cars have been passed without crashing\n                levelNumber = 2 #2\n                enemyY_change = 13  \n                bgMove = 8\n        if scoreNumber == 30:\n                levelNumber = 3\n                enemyY_change = 14 \n        if scoreNumber == 45: \n                levelNumber = 4\n                enemyY_change = 15 \n        if scoreNumber == 60: \n                levelNumber = 5\n                enemyY_change = 16 \n        if scoreNumber == 75: \n                levelNumber = 6\n                enemyY_change = 17 \n        if scoreNumber == 90: \n                levelNumber = 7\n                enemyY_change = 18 \n        if scoreNumber == 105:\n                levelNumber = 8\n                enemyY_change = 19 \n        if scoreNumber == 120:\n                levelNumber = 9\n                enemyY_change = 20 \n        if scoreNumber ==  135:\n                gameWin = True\n                \n                \n        if gameWin:\n                victory.set_volume(0.5)\n                victory.play(0)\n                mixer.music.fadeout(1000)\n                levelNumber = 10\n                bgMove = 0\n                enemy_y = -1000\n                enemyY_change = 0\n                gameWin = False\n                showEndPic = True\n                labelsGone = True\n                scoreNumber += 1\n        \n        if showEndPic:\n                win.blit(endPic,(0,0))\n\n        \n        '''# CPU PLAY ---- uncomment this code to let the computer play perfectly -----\n        if enemy_x == 105 and x == 105:\n                x = 230\n        elif enemy_x == 230 and x == 230:\n                x = random.choice([355,105])\n        elif enemy_x == 355 and x == 355:\n                x = random.choice([480,230])\n        elif enemy_x == 480 and x == 480:\n                x = 355'''\n\n\n        levelDisplay(labelsGone)\n        scoreDisplay(labelsGone)\n\n        if exitBtn.draw(win):\n                running = False\n\n\n        pygame.display.update()\n\n\nclock.tick(60)\npygame.quit()\nquit()\n","repo_name":"Hendrix8/CarCrash","sub_path":"main2.py","file_name":"main2.py","file_ext":"py","file_size_in_byte":10468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"2341232982","text":"import os\n\nTOKEN = '6086808949:AAE9vyl1WKgQa_A2beWYqzf4Ct32bjkJSu0'\n\nDB_NAME = 'priest.db'\n\nVERSION = '0.1'\n\nAUTHOR = 'Axenov'\n\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\n\nDATABASE = os.path.join('sqlite:///' + BASE_DIR, DB_NAME)\n\nCOOLDOWN = 3 * 60 * 60\n\nMAXLEVEL = 10\n\nLEVELS = {\n    1: 'NOTHING',\n    2: 'CAVE',\n    3: 'WOODEN',\n    4: 'STONE',\n    5: 'RED_STONE',\n    6: 'CONCRETE'\n}\n\nCOMMANDS = {\n    'PRAY': 'pray',\n    'START': 'start',\n    'HELP': 'help',\n    'ME': 'me'\n}\n","repo_name":"Axenov9/PrayerBot","sub_path":"settings/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31503948063","text":"##################################################\n#          r o b o t F a c t o r y\n#\n# This class manage the various AI agent classes\n# and instantiates them with given parameters\n# upon request\n#\n##################################################\n\nfrom randomAI import randomAI\nimport subprocess, random, robotNames \n\nclass robotFactory:\n    def __init__(self):\n        self.taken = list()\n\n    def getAI(self, robotType, name, acquire_id):\n        return randomAI(name, acquire_id)\n\n    def startAI(self, robotType='Random', name = None, acquire_id = None):\n        arg_list = [\"python\"]\n        if robotType == 'Random':\n            arg_list.append(\"randomAI.py\")\n        arg_list.append(\"-n\")\n        if name == None:\n            arg_list.append(self.getName())\n        else:\n            arg_list.append(name)\n        if not acquire_id == None:\n            arg_list.append(\"-i\")\n            arg_list.append(acquire_id)\n        subprocess.Popen(arg_list)\n\n    def getName(self):\n        name = random.choice(robotNames.names)\n        while name in self.taken:\n            name = random.choice(robotNames.names)\n        return name\n\n","repo_name":"lelandwilliams/Acquire","sub_path":"robotFactory.py","file_name":"robotFactory.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"70298824606","text":"# -*- coding: utf-8 -*-\r\nimport os\r\nimport numpy as np\r\nfrom torch.utils import data\r\nimport torchvision.transforms as transforms\r\nfrom PIL import Image\r\nimport cv2\r\nfrom torch.utils.data import DataLoader\r\n\r\n__all__ = ['OCR_TEST_DataLoader', 'get_test_dataset']\r\n\r\n\r\nclass OCR_TEST_DataLoader(data.Dataset):\r\n    def __init__(self, cfg):\r\n        super(OCR_TEST_DataLoader, self).__init__()\r\n        self.cfg = cfg\r\n        self.long_size = self.cfg.DATASET.TEST.LONG_SIZE\r\n        self.img_paths = self._list_files(cfg.DATASET.TEST.ROOT_PATH)\r\n\r\n    def __len__(self):\r\n        return len(self.img_paths)\r\n\r\n    def __getitem__(self, idx):\r\n        img_path = self.img_paths[idx]\r\n        ori_name = os.path.split(img_path)[-1]\r\n        img_name = os.path.splitext(os.path.split(img_path)[-1])[0]\r\n\r\n        ori_image = np.array(Image.open(img_path).convert(\"RGB\"), dtype=np.uint8)\r\n        if ori_image.shape[-1] == 4:\r\n            ori_image = ori_image[:, :, :3].copy()\r\n\r\n        h, w = ori_image.shape[:2]\r\n        \r\n        if w > h:\r\n            target_w = int(self.long_size)\r\n            scale = self.long_size * 1.0 / w\r\n            target_h = int(h * scale)\r\n            target_h = int(target_h + (32 - target_h % 32))\r\n        else:\r\n            target_h = int(self.long_size)\r\n            scale = self.long_size * 1.0 / h\r\n            target_w = int(w * scale)\r\n            target_w = int(target_w + (32 - target_w % 32))\r\n        if len(ori_image) == 2:\r\n            print(img_name)\r\n        ori_image_bgr = ori_image[:, :, [2, 1, 0]].copy()\r\n        image = np.zeros(shape=(target_h, target_w, 3), dtype=np.uint8)\r\n        _image = cv2.resize(ori_image, dsize=None, fx=scale, fy=scale)\r\n        image[:_image.shape[0], :_image.shape[1], :] = _image\r\n\r\n        image = Image.fromarray(image)\r\n        image = transforms.ToTensor()(image)\r\n        image = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(image)\r\n\r\n        return ori_image_bgr, image, img_name, scale, ori_name\r\n\r\n    def _list_files(self, root_path):\r\n        print('*****TEST DATASET*****:')\r\n        img_paths = []\r\n\r\n        for (dirpath, dirnames, filenames) in os.walk(root_path):\r\n            for filename in filenames:\r\n                _filename, ext = os.path.splitext(filename)\r\n                if ext.lower() in self.cfg.DATASET.TEST.IMG_FORMATS:\r\n                    _img_path = os.path.join(dirpath, filename)\r\n                    img_paths.append(_img_path)\r\n\r\n        print('***sum_samples: {}'.format(len(img_paths)))\r\n        return img_paths\r\n\r\n\r\ndef get_test_dataset(cfg):\r\n    ocr_dataset = OCR_TEST_DataLoader(cfg)\r\n\r\n    dataset_loader = DataLoader(\r\n        ocr_dataset,\r\n        batch_size=1,\r\n        shuffle=False,\r\n        num_workers=16,)\r\n    return dataset_loader\r\n","repo_name":"luoda888/2021-DIGIX-BASELINE","sub_path":"baseline-game5/detector/dataset/test_dataset.py","file_name":"test_dataset.py","file_ext":"py","file_size_in_byte":2803,"program_lang":"python","lang":"en","doc_type":"code","stars":69,"dataset":"github-code","pt":"86"}
+{"seq_id":"74725054685","text":"from setuptools import setup, find_packages\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\nsetup(\n    name=\"Sta663-DCMMs\",\n    version=\"2.2.8\",\n    description=\"Dynamic Count Mixture Models\",\n    author=\"Daniel Deng\",\n    author_email=\"currurant@gmail.com\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    packages=find_packages(),\n    url=\"https://github.com/Currurant/DCMMs\",\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n    ], install_requires=['pandas', 'numpy', 'matplotlib', 'statsmodels', 'scipy', 'pybats'],\n    include_package_data=True,\n    python_requires='>=3.6'#'Examples_data/*.pickle', 'Examples_data/*.csv',\n)\n","repo_name":"Anluzi/Ziyuan-Daniel_DCMMs","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"13574183514","text":"\"\"\"\nhttps://leetcode.com/problems/decode-string/\n\nCategory - Medium\n\nGiven an encoded string, return its decoded string.\n\nThe encoding rule is: k[encoded_string], where the encoded_string inside the\nsquare brackets is being repeated exactly k times. Note that k is guaranteed\nto be a positive integer.\n\nYou may assume that the input string is always valid; there are no extra white\nspaces, square brackets are well-formed, etc. Furthermore, you may assume that\nthe original data does not contain any digits and that digits are only for\nthose repeat numbers, k. For example, there will not be input like 3a or 2[4].\n\nThe test cases are generated so that the length of the output will never\nexceed 105.\n\"\"\"\n\nclass Solution:\n    def decodeString(self, s: str) -> str:\n        stack, curr_str, curr_num = [], '', 0\n        for i in s:\n            if i.isdigit():\n                curr_num = curr_num * 10 + int(i)\n            elif i == \"[\":\n                stack.append(curr_num)\n                stack.append(curr_str)\n                curr_num, curr_str = 0, ''\n            elif i == \"]\":\n                prev_str = stack.pop()\n                prev_num = stack.pop()\n                curr_str = prev_str + curr_str * prev_num\n            else:\n                curr_str += i\n        while stack:\n            curr_str = stack.pop() + curr_str\n        return curr_str\n\n\"\"\"\nhint\nuse stack\n\"\"\"\n","repo_name":"vavilovnv/python_ex","sub_path":"Test tasks (unsorted)/Solutions/leetcode/394-Decode-string.py","file_name":"394-Decode-string.py","file_ext":"py","file_size_in_byte":1383,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"5267885844","text":"import argparse\nimport os\nfrom typing import List, Optional\n\nfrom chat_analyzer.models.app_data import AnalysisArgs, AppArgs, AnalysisType, OutputFormat\n\nallowed_formats = {\n    \"json\": OutputFormat.JSON,\n    \"png\": OutputFormat.PLOT_PNG,\n    \"plot\": OutputFormat.PLOT_UI,\n}\n\narg_parser = argparse.ArgumentParser(description='WhatsApp chat analyzer')\narg_parser.add_argument(\"path\",\n                        type=str,\n                        help=\"The path for the chat file(s) to analyze. Supports wildcards.\")\narg_parser.add_argument(\"--chat_stats\",\n                        default=argparse.SUPPRESS,\n                        action=\"store_true\",\n                        help=\"Simple stats on messages and words count\")\narg_parser.add_argument(\"--messages_day\",\n                        default=argparse.SUPPRESS,\n                        action=\"store_true\",\n                        help=\"Messages per day per person\")\narg_parser.add_argument(\"--initiation\",\n                        action=\"store\",\n                        type=int,\n                        default=argparse.SUPPRESS,\n                        metavar=\"interval\",\n                        help=\"Who initiates the conversation more often. Must specify [interval]\")\narg_parser.add_argument(\"--engagement\",\n                        action=\"store\",\n                        type=str,\n                        nargs=\"+\",\n                        default=argparse.SUPPRESS,\n                        metavar=\"subject\",\n                        help=\"Shows the engagement of one person (subject) with other partecipants over time. Must specify [subject: str]\")\narg_parser.add_argument(\"--word_rank\",\n                        nargs=2,\n                        type=int,\n                        default=argparse.SUPPRESS,\n                        metavar=(\"limit\", \"min_size\"),\n                        help=\"Most used words. May specify parameters [limit=10, min_size=4]\")\narg_parser.add_argument(\"--format\",\n                        action=\"store\",\n                        default=argparse.SUPPRESS,\n                        metavar=\"format\",\n                        choices=allowed_formats.keys(),\n                        type=str,\n                        help=f\"Output mode. accepted values are: [{','.join(allowed_formats.keys())}]\")\narg_parser.add_argument(\"--out\",\n                        action=\"store\",\n                        default=argparse.SUPPRESS,\n                        metavar=\"out_path\",\n                        type=str,\n                        help=\"By default is the same folder as input.\")\n\n\ndef get_args(manual_args: Optional[List[str]] = None) -> AppArgs:\n    analysis_list = []\n    parsed_args = vars(arg_parser.parse_args(manual_args))\n\n    path = parsed_args[\"path\"]\n    dir_path = os.path.dirname(path)\n\n    if not os.path.exists(dir_path):\n        raise Exception(f\"Input path {dir_path} does not exist.\")\n\n    initiation = parsed_args.get(\"initiation\")\n    engagement = parsed_args.get(\"engagement\")\n    word_rank = parsed_args.get(\"word_rank\")\n    out_format = allowed_formats.get(parsed_args.get(\"format\"), OutputFormat.JSON)\n    out_path = parsed_args.get(\"out\", dir_path)\n\n    if not os.path.exists(out_path):\n        raise Exception(f\"Output path {out_path} does not exist.\")\n\n    # Parameters for analysis\n    if \"chat_stats\" in parsed_args:\n        analysis_list.append(AnalysisArgs(AnalysisType.MESSAGES_COUNT))\n    if \"messages_day\" in parsed_args:\n        analysis_list.append(AnalysisArgs(AnalysisType.MESSAGES_PER_DAY))\n    if initiation:\n        analysis_list.append(\n            AnalysisArgs(\n                AnalysisType.INITIATION_SCORES,\n                {\"hour_interval\": initiation},\n            ))\n    if engagement:\n        analysis_list.append(\n            AnalysisArgs(\n                AnalysisType.ENGAGEMENT_SCORE,\n                {\"subject\": \" \".join(engagement)},\n            ))\n    if word_rank:\n        analysis_list.append(\n            AnalysisArgs(\n                AnalysisType.WORDS_MOST_USED,\n                {\"limit\": word_rank[0], \"min_length\": word_rank[1]}\n            ))\n\n    if not analysis_list:\n        raise Exception(\"Must select at least one analysis type\")\n\n    return AppArgs(\n        in_files_path=path,\n        out_path=out_path,\n        analyses=analysis_list,\n        out_format=out_format,\n    )\n","repo_name":"sechlol/whatsapp-chat-analyzer","sub_path":"chat_analyzer/args_helper.py","file_name":"args_helper.py","file_ext":"py","file_size_in_byte":4317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"69942364765","text":"import socket\n\nimport requests\n\nfrom clusterfuzz._internal.base import retry\nfrom clusterfuzz._internal.system import environment\n\n_METADATA_SERVER = 'metadata.google.internal'\n\n_RETRIES = 3\n_DELAY = 1\n\n\n@retry.wrap(\n    retries=_RETRIES,\n    delay=_DELAY,\n    function='python.google_cloud_utils.compute_metadata.get')\ndef get(path):\n  \"\"\"Get GCE metadata value.\"\"\"\n  attribute_url = (\n      'http://{}/computeMetadata/v1/'.format(_METADATA_SERVER) + path)\n  headers = {'Metadata-Flavor': 'Google'}\n  operations_timeout = environment.get_value('URL_BLOCKING_OPERATIONS_TIMEOUT')\n\n  response = requests.get(\n      attribute_url, headers=headers, timeout=operations_timeout)\n  response.raise_for_status()\n  return response.text\n\n\ndef is_gce():\n  \"\"\"Return whether or not we're on GCE.\"\"\"\n  try:\n    sock = socket.create_connection((_METADATA_SERVER, 80))\n    sock.close()\n  except Exception:\n    return False\n\n  return True\n","repo_name":"google/clusterfuzz","sub_path":"src/clusterfuzz/_internal/google_cloud_utils/compute_metadata.py","file_name":"compute_metadata.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","stars":5122,"dataset":"github-code","pt":"86"}
+{"seq_id":"41553112809","text":"from os import name\r\nimport string\r\nimport time\r\nfrom datetime import date, datetime\r\nfrom typing import Dict, List, NamedTuple, Optional, Tuple, cast\r\n\r\nfrom pathvalidate import ValidationError, validate_filename  # type: ignore\r\nimport tzlocal\r\nfrom geopy.geocoders import Nominatim\r\nfrom geopy.location import Location\r\n\r\nfrom . import __app_name__\r\nfrom .metadata import Metadata\r\nfrom .time import get_datetime_string\r\n\r\n\r\nclass FileNameField(NamedTuple):\r\n    name: str\r\n    required: bool\r\n    description: str  # html\r\n\r\n\r\n_file_name_fields: List[FileNameField] = [\r\n    FileNameField(\r\n        name=\"datetime\",\r\n        required=True,\r\n        description=\"Date and time string (e.g. '2022-01-01'). Exact format is governed by date format below.\",\r\n    ),\r\n    FileNameField(\r\n        name=\"geocode\",\r\n        required=False,\r\n        description=\"\"\"A human-readable description of the location the screenshot\r\nwas taken at (e.g. <i>USA-Texas-Austin</i>), as returned by <a href='https://wiki.openstreetmap.org/wiki/Nominatim'>\r\nOSM Nominatim</a>.\"\"\",\r\n    ),\r\n]\r\n\r\n\r\nclass FileNameComposer:\r\n\r\n    _user_agent = __app_name__.replace(\" \", \"_\")\r\n\r\n    def compose_name(\r\n        self, name_format: str, date_format: str, metadata: Optional[Metadata] = None\r\n    ):\r\n        is_valid_name_format, error = self.is_name_format_valid(name_format)\r\n        if not is_valid_name_format:\r\n            raise ValueError(f\"Invalid format string provided: {error}\")\r\n\r\n        is_valid_date_format, error = self.is_date_format_valid(date_format)\r\n        if not is_valid_date_format:\r\n            raise ValueError(f\"Invalid format string provided: {error}\")\r\n\r\n        capture_time = metadata.capture_time if metadata else time.time()\r\n\r\n        format_data: Dict[str, Optional[str]] = {\r\n            \"datetime\": get_datetime_string(\r\n                timestamp_utc=capture_time, date_format=date_format\r\n            ),\r\n            \"geocode\": (self._maybe_get_geocode_string(metadata) if metadata else None)\r\n            or \"no-geocode-found\",\r\n        }\r\n\r\n        return name_format.format(**format_data)\r\n\r\n    def is_name_format_valid(self, name_format: str) -> Tuple[bool, str]:\r\n        if not name_format:\r\n            return False, \"Name format must not be empty.\"\r\n\r\n        try:\r\n            mock_file_name = f\"{name_format}.extension\"\r\n            validate_filename(mock_file_name)\r\n        except ValidationError as e:\r\n            return False, str(e)\r\n\r\n        formatter = string.Formatter().parse(name_format)\r\n        try:\r\n            items = list(formatter)\r\n        except ValueError:\r\n            return False, \"Format string is invalid.\"\r\n\r\n        field_names = [name for text, name, spec, conv in items if name is not None]\r\n\r\n        known_names = []\r\n        for file_name_field in _file_name_fields:\r\n            known_names.append(file_name_field.name)\r\n            if file_name_field.required and file_name_field.name not in field_names:\r\n                return False, f\"Missing required field: {{{file_name_field.name}}}.\"\r\n\r\n        for field_name in field_names:\r\n            if field_name not in known_names:\r\n                return False, f\"Unrecognized field name: '{{{field_name}}}'\"\r\n\r\n        return True, \"\"\r\n\r\n    def is_date_format_valid(self, date_format: str) -> Tuple[bool, str]:\r\n        if not date_format:\r\n            return False, \"Date format must not be empty.\"\r\n\r\n        try:\r\n            mock_file_name = f\"{date_format}.extension\"\r\n            validate_filename(mock_file_name)\r\n        except ValidationError as e:\r\n            return False, str(e)\r\n\r\n        test_time = datetime.fromtimestamp(1631728655)\r\n        try:\r\n            formatted = test_time.strftime(date_format)\r\n        except Exception:\r\n            return False, \"Date format could not be parsed.\"\r\n\r\n        if formatted == \"\":\r\n            return False, \"Date format would result in empty string.\"\r\n\r\n        if formatted == date_format:\r\n            return False, \"Date format does not contain any placeholders.\"\r\n\r\n        return True, \"\"\r\n\r\n    def get_supported_fields(self) -> List[FileNameField]:\r\n        return _file_name_fields\r\n\r\n    def _maybe_get_geocode_string(self, metadata: Metadata) -> Optional[str]:\r\n        try:\r\n            geolocator = Nominatim(user_agent=self._user_agent)\r\n            location: Location = cast(\r\n                Location,\r\n                geolocator.reverse(\r\n                    (metadata.GPSLatitude, metadata.GPSLongitude),\r\n                    language=\"en-US,en\",\r\n                    exactly_one=True,\r\n                    zoom=10,  # limit to city region\r\n                ),\r\n            )\r\n        except Exception as e:\r\n            print(e)\r\n            return None\r\n\r\n        if not location or not getattr(location, \"address\", None):\r\n            return None\r\n\r\n        try:\r\n            location_str = \"-\".join(reversed(location.address.split(\", \"))).replace(\r\n                \" \", \"_\"\r\n            )\r\n        except Exception as e:\r\n            print(e)\r\n            return None\r\n\r\n        return location_str\r\n","repo_name":"pyviator/msfs-geoshot","sub_path":"msfs_geoshot/names.py","file_name":"names.py","file_ext":"py","file_size_in_byte":5091,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"86"}
+{"seq_id":"36036226777","text":"adjective1=input(\"Adjective: \")\nan_invention=input(\"An Invention: \")\na_food=input(\"A food: \")\nadjective2=input(\"Adjective: \")\npart_of_body_plural=input(\"Part of body(Plural): \")\nadjective3=input(\"Adjective: \")\nplural_noun1=input(\"Plural noun: \")\nplural_noun2=input(\"Plural noun: \")\n\nmadlib=f\"I would like to say a few {adjective1} words about the \\\n         most important invention of the twentieth century, I am not \\\n         referring to {an_invention} or even to the discovery of \\\n         {a_food}. The most {adjective2} invention, in my opinion, \\\n          is the sneaker. If it were not for sneakers, our {part_of_body_plural}\\\n          would be dirty, cold, and  {adjective3}. Sneakers keep me from skidding \\\n            if the {plural_noun1} are slippery, and when I run, they keep me from stubbing\\\n            my {plural_noun2}.\"\nprint(madlib)\n","repo_name":"bimoai857/Python","sub_path":"python projects/madlib.py","file_name":"madlib.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"24060513620","text":"import pyinputplus\nfrom time import sleep\nimport os\n\ndef check():\n    print(\"== Only odd numbers above 9 are allowed ==\")\n    a = pyinputplus.inputInt(\"Enter shape size$ \")\n\n    if a % 2 != 1 or a < 9:\n        check()\n    else:\n        os.system('cls')\n        space = 0\n        decrementor = a\n        for x in range(0, a, 2):\n            print(\" \"*space + \" *\"*decrementor)\n            sleep(0.5)\n            space += 2\n            decrementor -= 2\n\n        space = int((a-3))\n        incrementor = 3\n        for x in range(a-1, 0, -2):\n            print(\" \"*space + \" *\"*incrementor)\n            sleep(0.5)\n            space -= 2\n            incrementor += 2\n\n        big = \"\\n== THE BIG TEAM TREE ==\"\n\n        for x in range(len(big)):\n            sleep(0.3)\n            print(big[x], end=\"\")\n\n        print(\"\\n_________________________\")\ncheck()\n\n","repo_name":"EnGentech/My_python_codes","sub_path":"lamp_shape.py","file_name":"lamp_shape.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"20378304426","text":"import click\nimport shutil\nfrom tabulate import tabulate\nfrom pathlib import Path\nfrom where.utils import find_by_extension\nfrom where.utils import find_by_name\nfrom where.utils import find_by_modified\nfrom where.utils import get_files_details\nfrom where.utils import get_folders\nfrom datetime import datetime\nfrom where.exceptions import InvalidInputError, NoFileFound, FileFinderError\n\n\ndef copy_files(copy_to, files):\n    if copy_to:\n        copy_path = Path(copy_to)\n        if not copy_path.is_dir():\n            copy_path.mkdir(parents=True)\n        for file in files:\n            dst_file = copy_path / file.name\n\n            if dst_file.is_file():\n                dst_file = copy_path / f'{file.stem}{datetime.now().strftime(\"%d%m%Y%H%M%S%f\")}{file.suffix}'\n\n            shutil.copy(src=file.absolute(), dst=dst_file)\n\n\ndef save_report(save, report, root):\n    if save and report:\n        report_file_path = root / f'finder_report_{datetime.now().strftime(\"%d%m%Y%H%M%S%f\")}.txt'\n        with open(report_file_path.absolute(), mode='w') as report_file:\n            report_file.write(report)\n\n\ndef process_search(path, key, value, recursive):\n    search_dict = {\n        \"name\": find_by_name,\n        \"ext\": find_by_extension,\n        \"mod\": find_by_modified\n    }\n\n    files = search_dict[key](path, value)\n\n    if recursive:\n        subdirs = get_folders(path)\n        for subdir in subdirs:\n            files += process_search(subdir, key, value, recursive)\n\n    return files\n\n\ndef process_results(files, key, value):\n    if not files:\n        raise NoFileFound(f'Nenhum arquivo com {key} {value} foi encontrado.')\n\n    table_headers = [\"Nome\", \"Modificação\", \"Localização\"]\n    table_data = get_files_details(files)\n    tabulated_data = tabulate(tabular_data=table_data, headers=table_headers, tablefmt='tsv')\n    click.echo(tabulated_data)\n    return tabulated_data\n\n\n@click.command()\n@click.argument(\"path\", default=\"\")\n@click.option(\"-k\", \"--key\", required=True, type=click.Choice([\"name\", \"ext\", \"mod\"]), help=\"Define o tipo de chave utilizada para a busca, podendo ser: Nome, Extensão ou última data de modificação do arquivo\")\n@click.option(\"-v\", \"--value\", required=True, help=\"Define um valor para a chave.\")\n@click.option(\"-r\", \"--recursive\", is_flag=True, default=False, help=\"Se presente, faz busca recursiva em todos os sub-diretórios.\")\n@click.option(\"-c\", \"--copy-to\", help=\"Copia todos os arquivos para o caminho informado.\")\n@click.option(\"-s\", \"--save\", is_flag=True, default=False, help=\"Se presente salva um 'relatório' da busca realizada.\")\ndef finder(path, key, value, recursive, copy_to, save):\n    \"\"\"\n    Um programa que realiza busca de arquivos por meio de uma chave (-k | --key) a partir do diretório PATH.\n\n    PATH define o diretório onde a pesquisa inicia. Caso não informado, assume o diretório atual.\n    \"\"\"\n    root = Path(path)\n\n    if not root.is_dir():\n        raise InvalidInputError(f'O caminho \"{path}\" não representa um diretório existente.')\n    click.echo(f'O diretório selecionado foi: {root.absolute()}')\n\n    files = process_search(path=root, key=key, value=value, recursive=recursive)\n    report = process_results(files=files, key=key, value=value)\n\n    save_report(report=report, save=save, root=root)\n    copy_files(copy_to=copy_to, files=files)\n\n\nif __name__ == '__main__':\n    try:\n        finder()\n    except FileFinderError as err:\n        click.echo(click.style(f'❌ {err}', bg='black', fg='red', italic=True))\n","repo_name":"rdsgabriel/file_finder","sub_path":"where/finder.py","file_name":"finder.py","file_ext":"py","file_size_in_byte":3494,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"4074905","text":"import pytest\nfrom sales_per_customer import SalesPerCustomer\n\n\ndef test_merge_sales_and_customer_files():\n    customer_csv_file = 'customers.csv'\n    sales_csv_file = 'sales.csv'\n\n    files = SalesPerCustomer(customer_csv_file,sales_csv_file)\n    dst = files.merge_sales_and_customer_files(customer_csv_file,sales_csv_file)\n    print(dst)\n    expected = [['customer_id', 'name', 'email', 'address', 'sum(quantity)'],\n                ['1', 'Rike Weiss', 'rike.weiss@email.com', 'Berlinerstraße 1', 14],\n                ['2', 'Max Musterman', 'max.musterman@email.com', 'Berlinerstraße 1', 5],\n                ['3', 'Hildegard Hartmann', 'hildegard.hartmann@email.com', 'Torstraße 25', 7],\n                ['4', 'Kora Schegtel', 'kora.schegtel@email.com', 'Oranienstraße 10', 1]]\n\n    assert dst == expected\n\n\nclass test_some_stuff():\n    test_merge_sales_and_customer_files()\n","repo_name":"piyush9194/python","sub_path":"test_sales_per_customer.py","file_name":"test_sales_per_customer.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"12940545942","text":"from read_data import data_armies\nfrom strategy import Strategy\nfrom clock import Clock\nimport logging\n\n\ndef main():\n    logging.basicConfig(filename=\"battle_recap.log\", level=logging.INFO)\n    clock = Clock()\n    armies = data_armies(clock.time())\n    strategy = Strategy()\n    logging.info(\"Battle begin\")\n\n    while True:\n        clock.tick()\n        for army in armies:\n            for squad in army.squads:\n                print(squad.name, squad.health())\n\n        for army_a in armies:\n            for army_b in armies:\n                if army_a is not army_b:\n                    if army_a.alive() and army_b.alive():\n                        logging.info(\"{} health: {} --> {} health: {}\".format(\n                            army_a.name,\n                            army_a.health(),\n                            army_b.name,\n                            army_b.health()\n                        ))\n                        target_squad = army_a.select_strategy(strategy, army_b)\n                        for squad in army_a.squads:\n                            if squad.alive():\n                                for unit in squad.units:\n                                    if unit.clock <= clock.time():\n                                        squad.attack(target_squad, clock.time())\n                                        unit.up_exp()\n        army_alive = 0\n        army_name = \"\"\n        for army in armies:\n            if army.alive():\n                army_alive += 1\n                army_name = army.name\n        if army_alive == 1:\n            for army in armies:\n                for squad in army.squads:\n                    print(squad.name, squad.health())\n            logging.info(\"{} win!\".format(army_name))\n            print(army_name, \"win!\")\n            return\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"NedelkoA/light-it","sub_path":"battle_simulator/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3834344384","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set_style('dark')\nimport glob\nimport os\nimport PIL\n\n# Our image processing tools\nimport skimage.filters\nimport skimage.io\nimport skimage.measure\nimport skimage.morphology\nimport skimage.segmentation\n\n# Load in the series of images contained in the directory data/bacterial_growth/.\n# Be sure that however you store them (a list or tuple or other object) has the\n# frames in the proper order.\n# bac_list = [glob.glob('data/bacterial_growth/*.tif')]\n\n# Make an array to deposit areas later.\nfilelist = glob.glob(\"data/bacterial_growth/*.tif\")\ntot_area=np.zeros(len(filelist))\ni=0\nfor filename in filelist:\n    im_df = skimage.io.imread(filename)\n\n    # Perform the median filter.\n    # Make the structuring element\n    selem = skimage.morphology.square(3)\n\n    # Perform the median filter\n    im_filt = skimage.filters.median(im_df, selem)\n\n    # Apply a gaussian blur with a 50 pixel radius.\n    im_gauss = skimage.filters.gaussian(im_filt, 50.0)\n\n    # Convert the median-filtered phase image to a float64\n    im_float = skimage.img_as_float(im_filt)\n\n    # Subtract our gaussian blurred image from the original.\n    im_sub = im_float - im_gauss\n\n    # Segment the images to separate bacteria from background. You do not need\n    #to segment individual bacteria; this would likely require some more advanced\n    #techniques involving edge detection that we haven't covered in bootcamp.\n\n    # Compute Otsu thresholds\n    thresh_bac_otsu = skimage.filters.threshold_otsu(im_sub)\n\n    # # Threshold value, as obtained by eye\n    # thresh_bac = 588\n\n    # Generate thresholded image\n    im_bac_bw = im_sub > thresh_bac_otsu\n    #\n    # # Display filt and thresholded image\n    # with sns.axes_style('dark'):\n    #     fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n    #     ax[0].imshow(im_filt, cmap=plt.cm.gray)\n    #     ax[1].imshow(im_bac_bw, cmap=plt.cm.gray)\n\n    # Compute bacterial area\n    bacterial_area_pix = im_bac_bw.sum()\n\n    # Define interpixel distance\n    interpix_dist = 0.063 # microns\n\n    # Compute bacterial area\n    bacterial_area_micron = bacterial_area_pix * interpix_dist**2\n\n    # # Print total area\n    # print('bacterial area =', bacterial_area_pix, 'pixels')\n\n    # Define interpixel distance\n    interpix_dist = 0.063 # microns\n\n    # Compute bacterial area\n    bacterial_area_micron = bacterial_area_pix * interpix_dist**2\n\n    # # Print total area\n    #print('bacterial area =', bacterial_area_micron, 'square microns')\n\n    # Add areas to empty array\n    tot_area[i] = bacterial_area_micron\n    i += 1\n\n\n# Plot a growth curve for this growing colony. What values should be on the\n# yy -axis? (This is one of those times where I ask an open question for which\n# there is no \"right\" answer.)\ntime = np.linspace(0, 825, 55)\n# plt.semilogy(time, tot_area, marker='.', linestyle='none', alpha=0.5, markersize=15)\n# plt.xlabel('time (min)')\n# plt.ylabel('Change in Area of Bacterial Footprint')\n# plt.title('B. subtilis Growth Curve')\n# plt.show()\n\nfig, ax = plt.subplots(1, 2, figsize=(10, 5))\nax[0].semilogx(time, tot_area, marker='.', linestyle='none', alpha=0.5, markersize=15)\nax[0].set_title('B. subtilis Growth Curve semilog x')\nax[1].semilogy(time, tot_area, marker='.', linestyle='none', alpha=0.5, markersize=15)\nax[0].set_xlabel('time (min)')\nax[0].set_ylabel('Change in Area of Bacterial Footprint')\nax[1].set_xlabel('time (min)')\nax[1].set_ylabel('Change in Area of Bacterial Footprint')\nax[1].set_title('B. subtilis Growth Curve semilog y')\nplt.show()\n","repo_name":"hleighcurtis/bootcamp-1","sub_path":"growth_movie.py","file_name":"growth_movie.py","file_ext":"py","file_size_in_byte":3570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"86"}
+{"seq_id":"7852435043","text":"#!/usr/bin/python3\nimport functools\nimport time\n\n\"\"\"\ndef time_it(func): #    Stack udaydoneit\n    def wrapper(*args, **kwargs):\n        start = time.time()\n        res = func(*args, **kwargs)\n        end = time.time()\n        time_taken = end - start\n        print(f\"time taken = {time_taken:.6f}s \")\n        return res\n\n    return wrapper\n\"\"\"\n\n\ndef time_it(f):  # Stack overflow\n    is_evaluating = False\n\n    def inner(*args, **kwargs):\n        nonlocal is_evaluating\n        if is_evaluating:\n            return f(*args, **kwargs)\n        else:\n            start = time.time()\n            is_evaluating = True\n            try:\n                value = f(*args, **kwargs)\n            finally:\n                is_evaluating = False\n            end = time.time()\n            time_taken = end - start\n            print(f\"time taken = {time_taken:.6f}s \")\n            return value\n\n    return inner\n\n\n@functools.lru_cache(maxsize=1000)\n@time_it\ndef fact(num):\n    print(f\"Calculating factorial of {num}\")\n    if num < 2:\n        return 1\n    elif num >= 2:\n        return num * fact(num - 1)\n\n\n@functools.lru_cache(maxsize=1000)\ndef fibonacci(num):\n    print(f\"Calculating fibonacci of {num}\")\n    if num < 2:\n        return num\n    return fibonacci(num - 1) + fibonacci(num - 2)\n\n\nif __name__ == \"__main__\":\n    print(fact(100))\n    print(fibonacci(30))\n    print(fibonacci.cache_info())\n","repo_name":"UdayGarg/Practice_problems","sub_path":"decorators/decorator1.py","file_name":"decorator1.py","file_ext":"py","file_size_in_byte":1386,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"11635137251","text":"from __future__ import absolute_import, division, print_function\nimport pandas as pd\nimport seaborn as sns\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nimport os\nimport matplotlib.pyplot as plt\n\nthis_dir, this_filename = os.path.split(__file__)\ncolumn_names_low = ['sc_px','sc_py','sc_pz','sc_prx','sc_pry','sc_prz',\n                    'a_px','a_py','a_pz','a_prx','a_pry','a_prz','a_fx','a_fy','a_fz','a_frx','a_fry','a_frz',\n                    'sl_px','sl_py','sl_pz','sl_prx','sl_pry','sl_prz']\nraw_dataset_low = pd.read_csv(os.path.join(this_dir, \"document\", \"Data_low.csv\"), names=column_names_low, na_values=\"?\", comment='\\t', sep=\",\", skipinitialspace=True)\ndataset_low = raw_dataset_low.copy()\n\ndataset_low.isna().sum()\ndataset_low = dataset_low.dropna()\n\ntrain_dataset_low = dataset_low.sample(frac=0.8, random_state=0)\ntest_dataset_low = dataset_low.drop(train_dataset_low.index)\n#\n# sns.pairplot(train_dataset_low[['sc_px','sc_py','sc_pz']], diag_kind=\"kde\")\n#\ntrain_stats_low = train_dataset_low.describe()\nfor string in ['a_px','a_py','a_pz','a_prx','a_pry','a_prz','a_fx','a_fy','a_fz','a_frx','a_fry','a_frz']:\n    train_stats_low.pop(string)\ntrain_stats_low = train_stats_low.transpose()\n\ntrain_labels_low = train_dataset_low.copy()\ntest_labels_low = test_dataset_low.copy()\nfor string in ['sc_px','sc_py','sc_pz','sc_prx','sc_pry','sc_prz','sl_px','sl_py','sl_pz','sl_prx','sl_pry','sl_prz']:\n    train_labels_low.pop(string)\n    test_labels_low.pop(string)\nfor string in ['a_px', 'a_py', 'a_pz', 'a_prx', 'a_pry', 'a_prz', 'a_fx', 'a_fy', 'a_fz', 'a_frx', 'a_fry', 'a_frz']:\n    train_dataset_low.pop(string)\n    test_dataset_low.pop(string)\n\ndef norm(x):\n  return (x - train_stats_low['mean'])/train_stats_low['std']\n\nnormed_train_data_low = norm(train_dataset_low)\nnormed_test_data_low = norm(test_dataset_low)\n\ndef plot_history(history):\n  hist = pd.DataFrame(history.history)\n  hist['epoch'] = history.epoch\n  plt.figure()\n  plt.xlabel('Epoch')\n  plt.ylabel('Mean Abs Error [MPG]')\n  plt.plot(hist['epoch'],hist['mae'],label='Train Error')\n  plt.plot(hist['epoch'],hist['val_mae'],label='Val Error')\n  plt.legend()\n  plt.ylim([0,5])\n\n  plt.figure()\n  plt.xlabel('Epoch')\n  plt.ylabel('Mean Square Error [$MPG^2$]')\n  plt.plot(hist['epoch'],hist['mse'],label='Train Error')\n  plt.plot(hist['epoch'],hist['val_mse'],label='Val Error')\n  plt.legend()\n  plt.ylim([0,20])\n\nmodel = keras.Sequential([\n            layers.Dense(256,activation=tf.nn.relu,input_shape=[len(train_dataset_low.keys())]),\n            layers.Dense(256,activation=tf.nn.relu),\n            layers.Dense(12)\n])\n# optimizer = tf.keras.optimizers.RMSprop(0.005)\noptimizer = tf.keras.optimizers.Adam(0.005)\nmodel.compile(loss='mse',\n            optimizer=optimizer,\n            metrics=['mae','mse'])\n\nmodel.summary()\n\nearly_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\nimport time\nstart_time = time.time()\nhistory = model.fit(normed_train_data_low, train_labels_low, batch_size=128, epochs=200,\n                    validation_split=0.2, verbose=1, callbacks=[early_stop])\nprint(\"Training took {} seconds\".format(time.time()-start_time))\nhist = pd.DataFrame(history.history)\nhist['epoch'] = history.epoch\nprint(hist)\nplot_history(history)\n#\n# loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=0)\n# print('Testing set Mean Abs Error: {:5.2f} MPG'.format(mae))\n#\n# test_predictions = model.predict(normed_test_data).flatten()\n# plt.figure()\n# plt.scatter(test_labels, test_predictions)\n# plt.xlabel('True values [MPG]')\n# plt.ylabel('Predictions [MPG]')\n# plt.axis('equal')\n# plt.axis('square')\n# plt.xlim([0,plt.xlim()[1]])\n# plt.ylim([0,plt.ylim()[1]])\n# _ = plt.plot([-100, 100], [-100, 100])\n\n# error = test_predictions - test_labels\n# plt.figure()\n# plt.hist(error, bins=25)\n# plt.xlabel('Predicton Error [MPG]')\n# _ = plt.ylabel('Count')\nloss, mae, mse = model.evaluate(normed_test_data_low, test_labels_low, verbose=1)\nprint(\"loss = {}, mae = {}, mse = {}\".format(loss, mae, mse))\n# plt.show()\n\n# save the model\nmodel.save(os.path.join(this_dir, \"nnmodel\", \"low_model.h5\"))\nprint(\"Saved model to disk\")\n\n","repo_name":"wangyan-hlab/wrs-nxt-IL-RL","sub_path":"0001_yan/HR_NN_lowlevel.py","file_name":"HR_NN_lowlevel.py","file_ext":"py","file_size_in_byte":4186,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34936740446","text":"#!/usr/bin/env python\n\ndef quicksort(a, left, right):\n    if((right-left) <=1):\n        return\n    pivot_index = (left+right)//2\n    pivot = a[pivot_index]\n    a[pivot_index],a[right-1] = (a[right-1],a[pivot_index])\n    \n    i = left\n    for j in range(left,right-1):\n        if(a[j]<pivot):\n            a[i],a[j]=(a[j], a[i])\n            i+=1\n\n    a[i],a[right-1] = (a[right-1],a[i])\n    quicksort(a,left,i)\n    quicksort(a,i+1,right)\n    return a\n\nxs = [10,12,15,3,8,17,4,1]\nN = len(xs)\nprint(\"before:\",xs)\nxs=quicksort(xs,0,N)\nprint(\"after:\",xs)\n\n\n\n\n\n\n\n","repo_name":"shohei/algorithm-data-structure","sub_path":"sort/quick_sort.py","file_name":"quick_sort.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32556273426","text":"\r\nimport pickle\r\nimport os\r\nfrom klasyfikator_filmowy.vectorizer import vect\r\nimport numpy as np\r\n\r\nclf = pickle.load(open(os.path.join('klasyfikator_filmowy', 'pkl_objects', 'classifier2.pkl'), 'rb'))\r\n\r\nlabel = {0: 'Negative', 1: 'Positive'}\r\n\r\ndef print_result(text, processedText):\r\n    print('Text: %s\\nClass: %s\\nProbability: %.2f%%\\n' % (text, label[clf.predict(processedText)[0]], np.max(clf.predict_proba(processedText)) * 100))\r\n\r\n\r\nexample0 = ['I like this movie :)']\r\nexample1 = ['I hate this movie']\r\nexample2 = ['I do not like it']\r\nexample3 = ['This is my new favourite movie now']\r\n\r\n\r\nX1 = vect.transform(example0)\r\nX2 = vect.transform(example1)\r\nX3 = vect.transform(example2)\r\nX4 = vect.transform(example3)\r\n\r\nprint_result(example0, X1)\r\nprint_result(example1, X2)\r\nprint_result(example2, X3)\r\nprint_result(example3, X4)\r\n","repo_name":"EmilWalewski/System-diagnostics","sub_path":"review-classification-main.py","file_name":"review-classification-main.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"7785268493","text":"import pandas as pd\nfrom datashader.utils import lnglat_to_meters as webm\n\n\nclass OlistData(object):\n\n    # percentage_of_rows must be between 0 and 1.\n    percentage_of_rows = 0.1\n\n    def __init__(self):\n        if self.percentage_of_rows is None:\n            self.orders_items = pd.read_csv(\"data/olist_order_items_dataset.csv\")\n            self.orders = self.__read_orders()\n            self.products = pd.read_csv(\"data/olist_products_dataset.csv\")\n            self.orders_payments = pd.read_csv(\"data/olist_order_payments_dataset.csv\")\n            self.order_reviews = pd.read_csv('data/olist_order_reviews_dataset.csv')\n            self.sellers = self.__read_sellers()\n            self.costumer = self.__read_costumer()\n            self.geolocation = self.__read_geolocation()\n            self.costumer_geolocation = self.__costumer_geolocation()\n            self.seller_geolocation = self.__seller_geolocation()\n        else:\n            self.orders_items = pd.read_csv(\"data/olist_order_items_dataset.csv\").sample(frac=self.percentage_of_rows)\n            self.orders = self.__read_orders(frac=self.percentage_of_rows)\n            self.products = pd.read_csv(\"data/olist_products_dataset.csv\").sample(frac=self.percentage_of_rows)\n            self.orders_payments = pd.read_csv(\"data/olist_order_payments_dataset.csv\").sample(frac=self.percentage_of_rows)\n            self.order_reviews = pd.read_csv('data/olist_order_reviews_dataset.csv').sample(frac=self.percentage_of_rows)\n            self.sellers = self.__read_sellers(frac=self.percentage_of_rows)\n            self.costumer = self.__read_costumer(frac=self.percentage_of_rows)\n            self.geolocation = self.__read_geolocation(frac=self.percentage_of_rows)\n            self.costumer_geolocation = self.__costumer_geolocation()\n            self.seller_geolocation = self.__seller_geolocation()\n\n    @staticmethod\n    def __read_orders(frac=None):\n        if frac is None:\n            orders = pd.read_csv(\"data/olist_orders_dataset.csv\")\n        else:\n            orders = pd.read_csv(\"data/olist_orders_dataset.csv\").sample(frac=frac)\n        orders[\"order_purchase_timestamp\"] = pd.to_datetime(orders[\"order_purchase_timestamp\"])\n        orders[\"order_approved_at\"] = pd.to_datetime(orders[\"order_approved_at\"])\n        orders[\"order_delivered_carrier_date\"] = pd.to_datetime(orders[\"order_delivered_carrier_date\"])\n        orders[\"order_delivered_customer_date\"] = pd.to_datetime(orders[\"order_delivered_customer_date\"])\n        orders[\"order_estimated_delivery_date\"] = pd.to_datetime(orders[\"order_estimated_delivery_date\"])\n        return orders\n\n    @staticmethod\n    def __read_sellers(frac=None):\n        if frac is None:\n            sellers = pd.read_csv(\"data/olist_sellers_dataset.csv\")\n        else:\n            sellers = pd.read_csv(\"data/olist_sellers_dataset.csv\").sample(frac=frac)\n        sellers.rename(columns={\"seller_zip_code_prefix\": \"zip_code\",\n                                \"seller_city\": \"city\", \"seller_state\": \"state\"}, inplace=True)\n        return sellers\n\n    @staticmethod\n    def __read_costumer(frac=None):\n        if frac is None:\n            costumer = pd.read_csv(\"data/olist_customers_dataset.csv\")\n        else:\n            costumer = pd.read_csv(\"data/olist_customers_dataset.csv\").sample(frac=frac)\n        costumer.rename(columns={\"customer_zip_code_prefix\": \"zip_code\",\n                                 \"customer_city\": \"city\", \"customer_state\": \"state\"}, inplace=True)\n        return costumer\n\n    @staticmethod\n    def __read_geolocation(frac=None):\n        if frac is None:\n            geolocation = pd.read_csv(\"data/olist_geolocation_dataset.csv\")\n        else:\n            geolocation = pd.read_csv(\"data/olist_geolocation_dataset.csv\").sample(frac=frac)\n        geolocation.rename(columns={\"geolocation_zip_code_prefix\": \"zip_code\",\n                                    \"geolocation_city\": \"city\", \"geolocation_state\": \"state\"}, inplace=True)\n        # Removing some outliers\n\n        # Brazils most Northern spot is at 5 deg 16′ 27.8″ N latitude.;\n        geolocation = geolocation[geolocation.geolocation_lat <= 5.27438888]\n        # it’s most Western spot is at 73 deg, 58′ 58.19″W Long.\n        geolocation = geolocation[geolocation.geolocation_lng >= -73.98283055]\n        # It’s most southern spot is at 33 deg, 45′ 04.21″ S Latitude.\n        geolocation = geolocation[geolocation.geolocation_lat >= -33.75116944]\n        # It’s most Eastern spot is 34 deg, 47′ 35.33″ W Long.\n        geolocation = geolocation[geolocation.geolocation_lng <= -34.79314722]\n\n        x, y = webm(geolocation.geolocation_lng, geolocation.geolocation_lat)\n        geolocation['x'] = pd.Series(x)\n        geolocation['y'] = pd.Series(y)\n        return geolocation\n\n    def __costumer_geolocation(self):\n        costumer = self.costumer[[\"customer_id\", 'zip_code', 'city', 'state']]\n        return costumer.merge(self.geolocation, on=[\"zip_code\", \"city\", \"state\"], how=\"left\")\n\n    def __seller_geolocation(self):\n        return self.sellers.merge(self.geolocation, on=[\"zip_code\", \"city\", \"state\"], how=\"left\")\n\n    def get_costumer_distribution(self):\n        return self.costumer_geolocation.groupby([\"zip_code\", \"city\",\n                                                  \"state\", \"x\", \"y\"]).count()[\"customer_id\"].reset_index()\n\n    def get_seller_distribution(self):\n        return self.seller_geolocation.groupby([\"zip_code\", \"city\",\n                                                \"state\", \"x\", \"y\"]).count()[\"seller_id\"].reset_index()\n\n    def product_buy_by_costumer(self, year=None, month=None):\n        orders_delivered = self.orders[self.orders[\"order_status\"] == \"delivered\"]\n        products = self.products[[\"product_id\", \"product_category_name\"]]\n        orders_items = self.orders_items.merge(products, on=[\"product_id\"])\n        orders_delivered_by_costumer = orders_delivered[\n            ['order_id', 'customer_id', 'order_status', \"order_delivered_customer_date\"]]\n        orders_items = orders_items.merge(orders_delivered_by_costumer, on=['order_id'])\n        orders_items[\"order_delivered_customer_year\"] = orders_items['order_delivered_customer_date'].dt.year\n        orders_items[\"order_delivered_customer_month\"] = orders_items['order_delivered_customer_date'].dt.month\n        if year is None and month is None:\n            return orders_items\n        if year is not None and month is not None:\n            return orders_items[(orders_items[\"order_delivered_customer_year\"] == year) &\n                                (orders_items[\"order_delivered_customer_month\"] == month)]\n        if year is not None and month is None:\n            return orders_items[orders_items[\"order_delivered_customer_year\"] == year]\n\n        if year is None and month is not None:\n            return orders_items[orders_items[\"order_delivered_customer_month\"] == month]\n\n    @staticmethod\n    def product_best_sellers(orders_items_costumer, n_top=None):\n        top_products = orders_items_costumer.groupby([\"product_id\"]).sum()[\"order_item_id\"].sort_values(ascending=False)\n        top_products_category = orders_items_costumer.groupby([\"product_category_name\"]).sum()[\"order_item_id\"].sort_values(\n            ascending=False)\n        return top_products.reset_index().head(n_top), top_products_category.reset_index().head(n_top)\n\n    def top_products_by_costumer(self, orders_items_costumer):\n        orders_items_by_costumer = orders_items_costumer.groupby([\"product_id\", \"customer_id\"]).sum()[\n            \"order_item_id\"].sort_values(ascending=False).reset_index()\n        costumer_location = self.__costumer_geolocation()\n        top_products_by_costumer = orders_items_by_costumer.merge(costumer_location, on=[\"customer_id\"])\n        return top_products_by_costumer.groupby([\"product_id\", \"zip_code\", \"city\", \"state\", \"x\", \"y\"]).sum()[\n            \"order_item_id\"].sort_values(ascending=False).reset_index()\n\n    def get_incomes(self):\n        orders_df = self.orders\n        order_items = self.orders_items\n        order_reviews = self.order_reviews\n        customer = pd.read_csv('data/olist_customers_dataset.csv', dtype={'customer_zip_code_prefix': str})\n        # getting the first 3 digits of customer zipcode\n        customer['customer_zip_code_prefix_3_digits'] = customer['customer_zip_code_prefix'].str[0:3]\n        customer['customer_zip_code_prefix_3_digits'] = customer['customer_zip_code_prefix_3_digits'].astype(int)\n\n        geo = pd.read_csv(\"data/olist_geolocation_dataset.csv\", dtype={'geolocation_zip_code_prefix': str})\n        # Removing some outliers\n        #Brazils most Northern spot is at 5 deg 16′ 27.8″ N latitude.;\n        geo = geo[geo.geolocation_lat <= 5.27438888]\n        #it’s most Western spot is at 73 deg, 58′ 58.19″W Long.\n        geo = geo[geo.geolocation_lng >= -73.98283055]\n        #It’s most southern spot is at 33 deg, 45′ 04.21″ S Latitude.\n        geo = geo[geo.geolocation_lat >= -33.75116944]\n        #It’s most Eastern spot is 34 deg, 47′ 35.33″ W Long.\n        geo = geo[geo.geolocation_lng <= -34.79314722]\n\n        x, y = webm(geo.geolocation_lng, geo.geolocation_lat)\n        geo['x'] = pd.Series(x)\n        geo['y'] = pd.Series(y)\n\n        geo['geolocation_zip_code_prefix_1_digits'] = geo['geolocation_zip_code_prefix'].str[0:1]\n        geo['geolocation_zip_code_prefix_2_digits'] = geo['geolocation_zip_code_prefix'].str[0:2]\n        geo['geolocation_zip_code_prefix_3_digits'] = geo['geolocation_zip_code_prefix'].str[0:3]\n        geo['geolocation_zip_code_prefix_4_digits'] = geo['geolocation_zip_code_prefix'].str[0:4]\n\n        # transforming the prefixes to int for plotting purposes\n        geo['geolocation_zip_code_prefix'] = geo['geolocation_zip_code_prefix'].astype(int)\n        geo['geolocation_zip_code_prefix_1_digits'] = geo['geolocation_zip_code_prefix_1_digits'].astype(int)\n        geo['geolocation_zip_code_prefix_2_digits'] = geo['geolocation_zip_code_prefix_2_digits'].astype(int)\n        geo['geolocation_zip_code_prefix_3_digits'] = geo['geolocation_zip_code_prefix_3_digits'].astype(int)\n        geo['geolocation_zip_code_prefix_4_digits'] = geo['geolocation_zip_code_prefix_4_digits'].astype(int)\n\n        brazil_geo = geo.set_index('geolocation_zip_code_prefix_3_digits').copy()\n\n        orders = orders_df.merge(order_items, on='order_id')\n        orders = orders.merge(customer, on='customer_id')\n        orders = orders.merge(order_reviews, on='order_id')\n        gp = orders.groupby('customer_zip_code_prefix_3_digits')['price'].sum().to_frame()\n        revenue = brazil_geo.join(gp)\n\n        revenue[\"revenue\"] = revenue.price / 1000\n        return revenue\n\n\n\n\n\n\n\n","repo_name":"Alves663/proyect","sub_path":"app/olistdata.py","file_name":"olistdata.py","file_ext":"py","file_size_in_byte":10709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"21978580106","text":"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\nclass ITN3D(nn.Module):\r\n\r\n    def __init__(self, nf=16):\r\n        super(ITN3D, self).__init__()\r\n\r\n        self.conv0 = nn.Conv3d(1, nf, kernel_size=3, padding=1) #64-64\r\n        self.bn0 = nn.BatchNorm3d(nf)\r\n        self.conv1 = nn.Conv3d(nf, nf*2, kernel_size=3, padding=1, stride=2) #64-32\r\n        self.bn1 = nn.BatchNorm3d(nf*2)\r\n        self.conv2 = nn.Conv3d(nf*2, nf*4, kernel_size=3, padding=1, stride=2) #32-16\r\n        self.bn2 = nn.BatchNorm3d(nf*4)\r\n        self.conv3 = nn.Conv3d(nf * 4, nf * 8, kernel_size=3, padding=1, stride=2)  # 16-8\r\n        self.bn3 = nn.BatchNorm3d(nf * 8)\r\n\r\n        self.bottleneck0 = nn.Conv3d(nf*8, nf*8, kernel_size=3, padding=1) #8-8\r\n        self.bnb0 = nn.BatchNorm3d(nf * 8)\r\n        self.bottleneck1 = nn.Conv3d(nf*8, nf*8, kernel_size=3, padding=1) #8-8\r\n        self.bnb1 = nn.BatchNorm3d(nf * 8)\r\n\r\n        self.up31 = nn.Upsample(scale_factor=2, mode='trilinear', align_corners=False)  # 8-16\r\n        self.pad3 = nn.ConstantPad3d(1, 0)\r\n        self.up32 = nn.Conv3d(nf * 8, nf * 4, kernel_size=3, padding=0)\r\n        self.drop3 = nn.Dropout(0.5)\r\n\r\n        self.up21 = nn.Upsample(scale_factor=2, mode='trilinear', align_corners=False) #16-32\r\n        self.pad2 = nn.ConstantPad3d(1, 0)\r\n        self.up22 = nn.Conv3d(nf*4 + nf*4, nf*2, kernel_size=3, padding=0)\r\n        self.drop2 = nn.Dropout(0.5)\r\n\r\n        self.up11 = nn.Upsample(scale_factor=2, mode='trilinear', align_corners=False) #32-64\r\n        self.pad1 = nn.ConstantPad3d(1, 0)\r\n        self.up12 = nn.Conv3d(nf*2 + nf*2, nf, kernel_size=3, padding=0)\r\n        self.drop1 = nn.Dropout(0.5)\r\n\r\n        self.pad0 = nn.ConstantPad3d(1, 0)\r\n        self.output = nn.Conv3d(nf + nf, 1, kernel_size=3, padding=0)\r\n\r\n    def forward(self, x):\r\n\r\n        c0 = F.relu(self.bn0(self.conv0(x)))\r\n        c1 = F.relu(self.bn1(self.conv1(c0)))\r\n        c2 = F.relu(self.bn2(self.conv2(c1)))\r\n        c3 = F.relu(self.bn3(self.conv3(c2)))\r\n\r\n        b0 = F.relu(self.bnb0(self.bottleneck0(c3)))\r\n        b1 = F.relu(self.bnb1(self.bottleneck1(b0)))\r\n\r\n        u3 = F.relu(self.up32(self.pad3(self.up31(b1))))\r\n        u3cat = self.drop3(torch.cat([u3, c2], 1))\r\n        u2 = F.relu(self.up22(self.pad2(self.up21(u3cat))))\r\n        u2cat = self.drop2(torch.cat([u2, c1], 1))\r\n        u1 = F.relu(self.up12(self.pad1(self.up11(u2cat))))\r\n        u1cat = self.drop1(torch.cat([u1, c0], 1))\r\n        out = self.output(self.pad0(u1cat)) + x\r\n\r\n        return torch.tanh(out)\r\n\r\n\r\nclass BasicConv3d(nn.Module):\r\n    def __init__(self, in_channels, out_channels, **kwargs):\r\n        super(BasicConv3d, self).__init__()\r\n        self.conv = nn.Conv3d(in_channels, out_channels, bias=False, **kwargs)\r\n        self.norm = nn.InstanceNorm3d(out_channels, affine=True)\r\n        nn.BatchNorm1d\r\n\r\n    def forward(self, x):\r\n        x = self.conv(x)\r\n        x = self.norm(x)\r\n        x = F.relu(x, inplace=True)\r\n        return x\r\n\r\n\r\nclass FastSmoothSENorm(nn.Module):\r\n    class SEWeights(nn.Module):\r\n        def __init__(self, in_channels, reduction=2):\r\n            super().__init__()\r\n            self.conv1 = nn.Conv3d(in_channels, in_channels // reduction, kernel_size=1, stride=1, padding=0, bias=True)\r\n            self.conv2 = nn.Conv3d(in_channels // reduction, in_channels, kernel_size=1, stride=1, padding=0, bias=True)\r\n\r\n        def forward(self, x):\r\n            b, c, d, h, w = x.size()\r\n            out = torch.mean(x.view(b, c, -1), dim=-1).view(b, c, 1, 1, 1)  # output_shape: in_channels x (1, 1, 1)\r\n            out = F.relu(self.conv1(out))\r\n            out = self.conv2(out)\r\n            return out\r\n\r\n    def __init__(self, in_channels, reduction=2):\r\n        super(FastSmoothSENorm, self).__init__()\r\n        self.norm = nn.InstanceNorm3d(in_channels, affine=False)\r\n        self.gamma = self.SEWeights(in_channels, reduction)\r\n        self.beta = self.SEWeights(in_channels, reduction)\r\n\r\n    def forward(self, x):\r\n        gamma = torch.sigmoid(self.gamma(x))\r\n        beta = torch.tanh(self.beta(x))\r\n        x = self.norm(x)\r\n        return gamma * x + beta\r\n\r\n\r\nclass FastSmoothSeNormConv3d(nn.Module):\r\n    def __init__(self, in_channels, out_channels, reduction=2, **kwargs):\r\n        super(FastSmoothSeNormConv3d, self).__init__()\r\n        self.conv = nn.Conv3d(in_channels, out_channels, bias=True, **kwargs)\r\n        self.norm = FastSmoothSENorm(out_channels, reduction)\r\n\r\n    def forward(self, x):\r\n        x = self.conv(x)\r\n        x = F.relu(x, inplace=True)\r\n        x = self.norm(x)\r\n        return x\r\n\r\n\r\nclass RESseNormConv3d(nn.Module):\r\n    def __init__(self, in_channels, out_channels, reduction=2, **kwargs):\r\n        super().__init__()\r\n        self.conv1 = FastSmoothSeNormConv3d(in_channels, out_channels, reduction, **kwargs)\r\n\r\n        if in_channels != out_channels:\r\n            self.res_conv = FastSmoothSeNormConv3d(in_channels, out_channels, reduction, kernel_size=1, stride=1, padding=0)\r\n        else:\r\n            self.res_conv = None\r\n\r\n    def forward(self, x):\r\n        residual = self.res_conv(x) if self.res_conv else x\r\n        x = self.conv1(x)\r\n        x += residual\r\n        return x\r\n\r\n\r\nclass UpConv(nn.Module):\r\n    def __init__(self, in_channels, out_channels, reduction=2, scale=2):\r\n        super().__init__()\r\n        self.scale = scale\r\n        self.conv = FastSmoothSeNormConv3d(in_channels, out_channels, reduction, kernel_size=1, stride=1, padding=0)\r\n\r\n    def forward(self, x):\r\n        x = self.conv(x)\r\n        x = F.interpolate(x, scale_factor=self.scale, mode='trilinear', align_corners=False)\r\n        return x\r\n\r\n\r\nclass FN_FP_AM(nn.Module):\r\n    def __init__(self,in_channels):\r\n        super(FN_FP_AM, self).__init__()\r\n\r\n        self.conv1_1 = nn.Conv3d(in_channels,in_channels, kernel_size=3, stride=1, padding=1)\r\n        self.conv1_2 = nn.Conv3d(in_channels,in_channels, kernel_size=3, stride=1, padding=1)\r\n\r\n        self.conv1x1 = nn.Conv3d(in_channels,1,kernel_size=1)\r\n        self.sigmoid_1 = nn.Sigmoid()\r\n\r\n        self.avgpool = nn.AdaptiveAvgPool3d(1)\r\n        self.maxpool = nn.AdaptiveMaxPool3d(1)\r\n\r\n        self.conv1x1_2 = nn.Conv3d(in_channels*2, 1 ,kernel_size=1)\r\n        self.sigmoid_2 = nn.Sigmoid()\r\n\r\n    def forward(self,x):\r\n        res =x\r\n        x= self.conv1_2(self.conv1_1(x))\r\n\r\n        fn_fp = self.sigmoid_1(self.conv1x1(x))\r\n\r\n        avg_pool = self.avgpool(x)\r\n        max_pool = self.maxpool(x)\r\n        #atten = avg_pool + max_pool\r\n        x = avg_pool.expand_as(res)\r\n        x = torch.concat([x,res],dim=1)\r\n        x = self.sigmoid_2(self.conv1x1_2(x))\r\n        out = res * x\r\n        return out, fn_fp\r\n\r\nclass DAM(nn.Module):\r\n    def __init__(self,in_channels):\r\n        super(DAM, self).__init__()\r\n        self.fnam = FN_FP_AM(in_channels)\r\n        self.fpam = FN_FP_AM(in_channels)\r\n\r\n    def forward(self,x):\r\n        res = x\r\n\r\n\r\n\r\n        fne, fn = self.fnam(x)\r\n\r\n        fpe, fp = self.fpam(x)\r\n\r\n        ou = res + fne\r\n        out = ou - fpe\r\n        # print(out.shape, fn.shape, fp.shape)\r\n        return out, fn, fp","repo_name":"cho-ming/autoPET_algorithms","sub_path":"distraction/src/layers.py","file_name":"layers.py","file_ext":"py","file_size_in_byte":7134,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"23573612804","text":"import os\nfrom typing import Optional, TYPE_CHECKING\n\nfrom ray.rllib.utils.annotations import PublicAPI\n\nif TYPE_CHECKING:\n    from ray.rllib.algorithms.algorithm_config import AlgorithmConfig\n    from ray.rllib.evaluation.sampler import SamplerInput\n    from ray.rllib.evaluation.rollout_worker import RolloutWorker\n\n\n@PublicAPI\nclass IOContext:\n    \"\"\"Class containing attributes to pass to input/output class constructors.\n\n    RLlib auto-sets these attributes when constructing input/output classes,\n    such as InputReaders and OutputWriters.\n    \"\"\"\n\n    @PublicAPI\n    def __init__(\n        self,\n        log_dir: Optional[str] = None,\n        config: Optional[\"AlgorithmConfig\"] = None,\n        worker_index: int = 0,\n        worker: Optional[\"RolloutWorker\"] = None,\n    ):\n        \"\"\"Initializes a IOContext object.\n\n        Args:\n            log_dir: The logging directory to read from/write to.\n            config: The (main) AlgorithmConfig object.\n            worker_index: When there are multiple workers created, this\n                uniquely identifies the current worker. 0 for the local\n                worker, >0 for any of the remote workers.\n            worker: The RolloutWorker object reference.\n        \"\"\"\n        from ray.rllib.algorithms.algorithm_config import AlgorithmConfig\n\n        self.log_dir = log_dir or os.getcwd()\n        # In case no config is provided, use the default one, but set\n        # `actions_in_input_normalized=True` if we don't have a worker.\n        # Not having a worker and/or a config should only be the case in some test\n        # cases, though.\n        self.config = config or AlgorithmConfig().offline_data(\n            actions_in_input_normalized=worker is None\n        ).training(train_batch_size=1)\n        self.worker_index = worker_index\n        self.worker = worker\n\n    @PublicAPI\n    def default_sampler_input(self) -> Optional[\"SamplerInput\"]:\n        \"\"\"Returns the RolloutWorker's SamplerInput object, if any.\n\n        Returns None if the RolloutWorker has no SamplerInput. Note that local\n        workers in case there are also one or more remote workers by default\n        do not create a SamplerInput object.\n\n        Returns:\n            The RolloutWorkers' SamplerInput object or None if none exists.\n        \"\"\"\n        return self.worker.sampler\n\n    @property\n    @PublicAPI\n    def input_config(self):\n        return self.config.get(\"input_config\", {})\n\n    @property\n    @PublicAPI\n    def output_config(self):\n        return self.config.get(\"output_config\", {})\n","repo_name":"ray-project/ray","sub_path":"rllib/offline/io_context.py","file_name":"io_context.py","file_ext":"py","file_size_in_byte":2543,"program_lang":"python","lang":"en","doc_type":"code","stars":28715,"dataset":"github-code","pt":"86"}
+{"seq_id":"9665296042","text":"import sys\nimport Requests\nfrom PySide6.QtWidgets import (\n    QDialog, QApplication,\n    QLabel, QStatusBar\n)\nfrom PySide6.QtGui import QAction, QIcon\nfrom PySide6.QtCore import Qt\nfrom Ui.ui_CreateUser import Ui_CreateUser\nimport settings\nimport json\nimport keyring\n\nclass CreateUser(QDialog):\n    def __init__(self):\n        super().__init__()\n\n        self.ui = Ui_CreateUser()\n        self.ui.setupUi(self)\n\n        self.setWindowTitle(\"Create User\")\n\n        self.ui.cancel_button.clicked.connect(self.cancel)\n        self.ui.create_button.clicked.connect(self.create)\n\n    def cancel(self):\n        self.close()\n\n    def create(self):\n        url = settings.api_path + settings.moder_user_path\n        data = {\n            'email': self.ui.email_field.text(),\n            'username': self.ui.login_field.text(),\n            'password': self.ui.passworld_field.text()\n        }\n\n        response = Requests.post(url, json=data, needAuth=True)\n        if response.status_code == 200:\n            self.accept()\n        elif response.headers.get('content-type') == 'application/json':\n            response_json = response.json()\n            if 'status' in response_json and response_json['status'] == 'Error':\n                self.ui.error_label.setText(response_json['message'])","repo_name":"SharafeevRavil/GuitarClassification","sub_path":"Desktop/ModeratorApplication/CreateUser.py","file_name":"CreateUser.py","file_ext":"py","file_size_in_byte":1282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"30377211968","text":"from flask import Flask, render_template\napp=Flask(__name__)\n\n@app.route('/')\ndef hello_world():\n    return \"Hello world!\"\n\n@app.route('/about')\ndef about():\n    name='lili'\n    return render_template('about.html', user=name)\n\n\napp.run()","repo_name":"LilianaIL/Flask","sub_path":"Start.py","file_name":"Start.py","file_ext":"py","file_size_in_byte":237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"8232887122","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\n\n# points\np = np.array([[0.1,0.6], [0.15,0.71], [0.08,0.8], [0.16,0.85], [0.2,0.3], [0.25,0.5], [0.24, 0.1], [0.3,0.2]])\n\n# centroids\ncentroids = np.array([[0.1,0.6], [0.3,0.2]])\n\nKMClustering = KMeans(n_clusters = 2, init = centroids, n_init = 1 )\nKMClustering.fit(p)\n\nprint(\"Labels : \",  KMClustering.labels_)\n\n# a.)find p6 \nprint(\"P6 in cluster : \", KMClustering.labels_[5])\n\n# b.)population around cluster 2\nprint(\"population of cluster around cluster 2(m2) : \", np.count_nonzero(KMClustering.labels_ == 1))\n\n# c.)updated value of centroids\nprint(\"updated centroids (m1 and m2): \", KMClustering.cluster_centers_)\n\n#plt.plot(p, \"o\")\n#plt.show()\n\n# Plot the data\nplt.scatter(p[:,0],p[:,1])\n# Plot the clusters \nplt.scatter(KMClustering.cluster_centers_[:, 0], KMClustering.cluster_centers_[:, 1], \n            s=200,                             # Set centroid size\n            c='red')                           # Set centroid color\nplt.show()\n","repo_name":"viveksonar/sem8-sppu","sub_path":"ML/KM.py","file_name":"KM.py","file_ext":"py","file_size_in_byte":1052,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70607494045","text":"\nimport multiprocessing\nimport time\n\ndef func(msg):\n  for i in xrange(3):\n    print(msg)\n    time.sleep(1)\n\ndef test1():\n  p = multiprocessing.Process(target=func, args=(\"hello\", ))\n  p.start()\n  p.join()\n  print (\"Sub-process done.\")\n\ndef test2():\n    pool = multiprocessing.Pool(processes=4)\n    for i in xrange(10):\n        msg = \"hello %d\" %(i)\n        pool.apply_async(func, (msg, ))\n    pool.close()\n    pool.join()\n    print( \"Sub-process(es) done.\")\n\ndef test3():\n    pool = multiprocessing.Pool(processes=4)\n    result = []\n    for i in xrange(10):\n        msg = \"hello %d\" %(i)\n        result.append(pool.apply_async(func, (msg, )))\n    pool.close()\n    pool.join()\n    for res in result:\n        print (res.get())\n    print (\"Sub-process(es) done.\")","repo_name":"luofun/BigData_Portrait","sub_path":"bigdata25.py","file_name":"bigdata25.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"26052141145","text":"from datetime import datetime\nimport matplotlib.pyplot as plt\n\ndef read_daily_prices(filename):\n    prices = {}\n\n    date_str = filename.split('_')[-1].split('.')[0]\n    date = datetime.strptime(date_str, '%Y-%m-%d')\n\n    with open(filename, 'r') as file:\n        for line in file:\n            line = line.strip()\n            if not line:\n                continue\n\n            card_name, price = line.split(':')\n            card_name = card_name.strip()\n            price = price.strip()\n\n            if price not in (\"No prices found\", \"Could not find any prices\"):\n                price = float(price[1:])\n                prices[card_name] = (date, price)\n\n    return prices\n\n\ndef aggregate_historical_prices(daily_files):\n    historical_prices = {}\n\n    for daily_file in daily_files:\n        daily_prices = read_daily_prices(daily_file)\n\n        for card_name, (date, price) in daily_prices.items():\n            if card_name not in historical_prices:\n                historical_prices[card_name] = []\n\n            historical_prices[card_name].append((date, price))\n\n    return historical_prices\n\n\ndef write_historical_prices(historical_prices, output_file):\n    with open(output_file, 'w') as file:\n        for card_name, prices in historical_prices.items():\n            file.write(f'{card_name}:\\n')\n\n            for date, price in prices:\n                date_str = date.strftime('%Y-%m-%d')\n                file.write(f'  {date_str}: ${price:.2f}\\n')\n\n            file.write('\\n')\n\n\ndef read_historical_prices(filename):\n    historical_prices = {}\n\n    with open(filename, 'r') as file:\n        card_name = None\n        prices = []\n\n        for line in file:\n            line = line.strip()\n\n            if not line:\n                if card_name:\n                    historical_prices[card_name] = prices\n                    card_name = None\n                    prices = []\n                continue\n\n            if not card_name:\n                card_name = line[:-1]  # Remove the trailing colon\n            else:\n                date_str, price_str = line.split(':')\n                date = datetime.strptime(date_str.strip(), '%Y-%m-%d')\n                price = float(price_str.strip()[1:])  # Remove the dollar sign before converting to float\n                prices.append((date, price))\n\n    return historical_prices\n\n\ndef plot_historical_prices(historical_prices):\n    num_cards = len(historical_prices)\n    num_columns = 4\n    num_rows = (num_cards + num_columns - 1) // num_columns\n\n    fig, axes = plt.subplots(num_rows, num_columns, figsize=(20, num_rows * 5))\n    fig.tight_layout(pad=5.0)\n\n    for i, (card_name, prices) in enumerate(historical_prices.items()):\n        row, col = divmod(i, num_columns)\n        ax = axes[row, col]\n\n        dates, price_values = zip(*prices)\n        ax.plot(dates, price_values)\n        ax.set_title(card_name)\n        ax.set_xlabel('Date')\n        ax.set_ylabel('Price (USD)')\n        ax.tick_params(axis='x', rotation=45)\n\n    # Remove empty subplots if there are any\n    if num_cards % num_columns != 0:\n        for j in range(num_cards, num_rows * num_columns):\n            row, col = divmod(j, num_columns)\n            fig.delaxes(axes[row, col])\n\n    plt.savefig('charts/all_charts.png', bbox_inches='tight')\n    plt.close(fig)","repo_name":"montresoro/MTGPricing","sub_path":"Functions.py","file_name":"Functions.py","file_ext":"py","file_size_in_byte":3285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"71654035485","text":"\"\"\"\nAuthor: David Akre\nDescription: Helper python script to run benchmarks\n\"\"\"\n\nimport os\nimport time\nimport subprocess\nimport glob\nimport pandas as pd\n\ndef prog(results, bit_files, latency):\n  \"\"\"\n  Function: prog is a helper function that will program the FPGA iteratively\n  and capture timing results and relay them to a csv\n  \"\"\"\n  # Forward declaraions\n  files     = []\n  jitter    = []\n  treconfig = []\n  iters     = 10\n\n  \n  for f in bit_files:\n    files.append(f)\n    tmp = []\n    for i in range(iters):\n      time_start = time.time()\n      p = subprocess.Popen(\"xsdb -eval 'connect; fpga -f %s'\" % f, shell=True)\n      p.wait()\n      time_diff = time.time() - time_start\n      tmp.append(time_diff)\n\n    total = 0\n    for t in tmp:\n      total += t - latency\n\n    treconfig.append(total/len(tmp))\n    jitter.append(max(tmp) - min(tmp))\n\n\n  # Save results to csv\n  df = pd.DataFrame({\n      \"Bitstream File\" : files,\n      \"Reconfiguration Time (s)\" : treconfig,\n      \"Jitter (s)\" : jitter\n      })\n  df.to_csv(results)\n\n\ndef main():\n  # Forward declarations\n  nrt8      = \"nrt_8\"\n  rt8       = \"rt_8\"\n  rt16      = \"rt_16\"\n  time_start = time.time()\n  subprocess.call(\"xsdb -eval 'connect; dow -data test 0x1000000'\",shell=True)\n  latency = time.time() - time_start\n\n  files = []\n  for f in os.listdir(nrt8):\n    if f.endswith(\".bit\"):\n      files.append(os.path.join(nrt8, f)) \n\n\n  prog(\"results_%s.csv\" % nrt8, files, latency)\n\n  files = []\n  for f in os.listdir(rt8):\n    if f.endswith(\".bit\"):\n      files.append(os.path.join(rt8, f)) \n\n\n  prog(\"results_%s.csv\" % rt8, files, latency)\n\n  files = []\n  for f in os.listdir(rt16):\n    if f.endswith(\".bit\"):\n      files.append(os.path.join(rt16, f)) \n\n\n  prog(\"results_%s.csv\" % rt16, files, latency)\n\n  \nif __name__ == \"__main__\":\n  main()\n","repo_name":"dakre21/dpr_rt","sub_path":"boot/runbench.py","file_name":"runbench.py","file_ext":"py","file_size_in_byte":1802,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"44621977903","text":"standard_input = \"\"\"2\n5 5\n2 2 1 3 2\n2 4 9\n2 3 4\n1 5 5\n1 4 9\n2 4 3\n10 5\n1 1 1 1 1 1 1 1 1 1\n3 8 13\n2 5 10\n3 8 10\n1 10 2\n1 9 100\"\"\"\n# print(\"Hellow\")\n# ques 1\n# n=int(input())\n# ans=[]\n# for i in range(n):\n#     a,b,c=[int(x) for x in input().split()]\n#     if (a+b) == c:\n#         # print(a,b,a+b,c)\n#         ans.append(\"+\")\n#     else:\n#         ans.append(\"-\")\n# for a in ans:\n#     print(a)\n\n# ques 2\n# n=int(input())\n# ans=[]\n# for i in range(n):\n#     c=[]\n#     b=int(input())\n#     c=[int(x) for x in input().split()]\n#     fe=sum(list(filter(lambda x: x%2==0, c)))\n#     fo=sum(list(filter(lambda x: x%2, c)))\n#     if fe>fo:\n#         ans.append(\"YES\")\n#     else:\n#         ans.append(\"NO\")\n\n# for a in ans:\n#     print(a)\n\n# ques 3\n# ans = []\n# n = int(input())\n# for i in range(n):\n#     sl = int(input())\n#     ost = input()\n#     st=ost\n#     f = True\n#     for i in range(len(st)):\n#         # print(st[i], i % 2)\n#         # if i>1:\n#         if i>1 and st[i] == st[i-1]:\n#             # i+=1\n#             # ans.append(\"NO\")\n#             # print(\"in the false\")\n#             f = False\n#             break\n#         else:\n#             st = st.replace(st[i], str(i % 2))\n#         # print(st, f)\n#     if sl==2 and ost[0]==ost[1]:\n#         ans.append(\"NO\")\n#     elif f == True:\n#         ans.append(\"YES\")\n#     else:\n#         ans.append(\"NO\")\n# for a in ans:\n#     print(a)\n    # print(\"//////////////////////////////////////////////\")\n\n\n# ques 4\nn=int(input())\nans=[]\nfor i in range(n):\n    ans.append([0])\nfor i in range(n):\n    p,q=[int(x) for x in input().split()]\n    oa=[int(x) for x in input().split()]\n    for j in range(q):\n        l,r,k=[int(x) for x in input().split()]\n        if sum(oa[:l-1]+[k]*(r-l+1)+oa[r:])%2==0:\n            ans[i].append(\"NO\")\n        else:\n            ans[i].append(\"YES\")\n\nfor a in ans:\n    for i in a[1:]:\n        print(i)","repo_name":"realanupreet/glaringharmfulbinary","sub_path":"plusminux.py","file_name":"plusminux.py","file_ext":"py","file_size_in_byte":1884,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"43166192826","text":"# Harris Corner Detection\n\nimport sys\nimport argparse\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.ndimage import convolve1d\n\nrgbToYiqY = np.array([0.299, 0.587, 0.114])\n\n################################################################################\n#  perform RGB to grayscale conversion\n################################################################################\ndef rgb2gray(img_color) :\n    # input:\n    #    img_color - a h x w x 3 numpy ndarray (dtype = np.unit8) holding\n    #                the color image\n    # return:\n    #    img_gray - a h x w numpy ndarray (dtype = np.float64) holding\n    #               the grayscale image\n    img_gray = img_color @ rgbToYiqY\n    return img_gray\n\n################################################################################\n#  perform 1D smoothing using a 1D horizontal Gaussian filter\n################################################################################\ndef smooth1D(img, sigma) :\n    # input :\n    #    img - a h x w numpy ndarray holding the image to be smoothed\n    #    sigma - sigma value of the 1D Gaussian function\n    # return:\n    #    img_smoothed - a h x w numpy ndarry holding the 1D smoothing result\n\n    size = 2147483647\n    for i in range(2147483647):\n        if np.exp((i ** 2) / -2 / (sigma ** 2)) < 1 / 1000:\n            size = i-1\n            break\n    x = np.arange(-size, size+1)\n    filterSmooth = np.exp((x ** 2) / -2 / (sigma ** 2))\n    img_filtered = convolve1d(img, filterSmooth, mode='constant')\n    img_weighted = convolve1d(np.ones(img.shape), filterSmooth, mode='constant')\n    img_smoothed = img_filtered / img_weighted\n    return img_smoothed\n\n################################################################################\n#  perform 2D smoothing using 1D convolutions\n################################################################################\ndef smooth2D(img, sigma) :\n    # input:\n    #    img - a h x w numpy ndarray holding the image to be smoothed\n    #    sigma - sigma value of the Gaussian function\n    # return:\n    #    img_smoothed - a h x w numpy array holding the 2D smoothing result\n    img = smooth1D(img, sigma)\n    img = smooth1D(img.T, sigma)\n    img_smoothed = img.T\n    return img_smoothed\n\n################################################################################\n#   perform Harris corner detection\n################################################################################\ndef harris(img, sigma, threshold) :\n    # input:\n    #    img - a h x w numpy ndarry holding the input image\n    #    sigma - sigma value of the Gaussian function used in smoothing\n    #    threshold - threshold value used for identifying corners\n    # return:\n    #    corners - a list of tuples (x, y, r) specifying the coordinates\n    #              (up to sub-pixel accuracy) and cornerness value of each corner\n\n    ix, iy = np.gradient(img)\n    ix2 = ix * ix\n    iy2 = iy * iy\n    ixIy = ix * iy\n    ix2 = smooth2D(ix2, sigma)\n    iy2 = smooth2D(iy2, sigma)\n    ixIy = smooth2D(ixIy, sigma)\n    detA = ix2 * iy2 - ixIy * ixIy\n    traceA = ix2 + iy2\n    r = detA - 0.04 * (traceA ** 2)\n    cornerCandidates = []\n    corners = []\n    for i in range(1, r.shape[0] - 1):\n        for j in range(1, r.shape[1] - 1):\n            if r[i][j] >= max(r[i-1][j-1], r[i][j-1], r[i+1][j-1], r[i-1][j+1], r[i][j+1], r[i+1][j+1], r[i-1][j], r[i+1][j]):\n                cornerCandidates.append((i, j, r[i][j]))\n    for i in cornerCandidates:\n        a = (r[i[0]][i[1]-1] + r[i[0]][i[1]+1] - 2 * r[i[0]][i[1]]) / 2\n        b = (r[i[0]-1][i[1]] + r[i[0]+1][i[1]] - 2 * r[i[0]][i[1]]) / 2\n        c = (r[i[0]][i[1]+1] - r[i[0]][i[1]-1]) / 2\n        d = (r[i[0]+1][i[1]] - r[i[0]-1][i[1]]) / 2\n        e = r[i[0]][i[1]]\n        x = -c / 2 / a\n        y = -d / 2 / b\n        f = a * (x ** 2) + b * (y ** 2) + c * x + d * y + e\n        if i[2] >= threshold:\n            corners.append((i[1] + x, i[0] + y, f))\n    return sorted(corners, key = lambda corner : corner[2], reverse = True)\n\n################################################################################\n#   save corners to a file\n################################################################################\ndef save(outputfile, corners) :\n    try :\n        file = open(outputfile, 'w')\n        file.write('%d\\n' % len(corners))\n        for corner in corners :\n            file.write('%.4f %.4f %.4f\\n' % corner)\n        file.close()\n    except :\n        print('Error occurs in writting output to \\'%s\\''  % outputfile)\n        sys.exit(1)\n\n################################################################################\n#   load corners from a file\n################################################################################\ndef load(inputfile) :\n    try :\n        file = open(inputfile, 'r')\n        line = file.readline()\n        nc = int(line.strip())\n        print('loading %d corners' % nc)\n        corners = list()\n        for i in range(nc) :\n            line = file.readline()\n            (x, y, r) = line.split()\n            corners.append((float(x), float(y), float(r)))\n        file.close()\n        return corners\n    except :\n        print('Error occurs in writting output to \\'%s\\''  % outputfile)\n        sys.exit(1)\n\n################################################################################\n## main\n################################################################################\ndef main() :\n    parser = argparse.ArgumentParser(description = 'Harris Corner Detection')\n    parser.add_argument('-i', '--inputfile', type = str, default = 'table.jpg', help = 'filename of input image')\n    parser.add_argument('-s', '--sigma', type = float, default = 1.0, help = 'sigma value for Gaussain filter')\n    parser.add_argument('-t', '--threshold', type = float, default = 1e5, help = 'threshold value for corner detection')\n    parser.add_argument('-o', '--outputfile', type = str, help = 'filename for outputting corner detection result')\n    args = parser.parse_args()\n\n    print('------------------------------')\n    print('input file : %s' % args.inputfile)\n    print('sigma      : %.2f' % args.sigma)\n    print('threshold  : %.2e' % args.threshold)\n    print('output file: %s' % args.outputfile)\n    print('------------------------------')\n\n    # load the image\n    try :\n        #img_color = imageio.imread(args.inputfile)\n        img_color = plt.imread(args.inputfile)\n        print('%s loaded...' % args.inputfile)\n    except :\n        print('Cannot open \\'%s\\'.' % args.inputfile)\n        sys.exit(1)\n    # uncomment the following 2 lines to show the color image\n    # plt.imshow(np.uint8(img_color))\n    # plt.show()\n\n    print('perform RGB to grayscale conversion...')\n    img_gray = rgb2gray(img_color)\n    # uncomment the following 2 lines to show the grayscale image\n    # plt.imshow(np.float32(img_gray), cmap = 'gray')\n    # plt.show()\n\n    print('perform Harris corner detection...')\n    corners = harris(img_gray, args.sigma, args.threshold)\n\n    print('%d corners detected...' % len(corners))\n    x = [corner[0] for corner in corners]\n    y = [corner[1] for corner in corners]\n    fig = plt.figure()\n    plt.imshow(np.float32(img_gray), cmap = 'gray')\n    plt.plot(x, y,'r+',markersize = 5)\n    plt.show()\n\n    if args.outputfile :\n        save(args.outputfile, corners)\n        print('corners saved to \\'%s\\'...' % args.outputfile)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"martinkingtw/ComputerVision-HarrisCornerDetection","sub_path":"harrisCornerDetection.py","file_name":"harrisCornerDetection.py","file_ext":"py","file_size_in_byte":7408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"2265197501","text":"import random\n\n#Swappeo posicion a posicion asegurandome que en cada iteracion \n#ordeno un elemento, la lista se ordena desde el final hasta el \n#principio\ndef bubble(array): \n    #Este end es la ultima posicion que quedara bloqueada porque \n    # se habra organizado, lo empiezo en 1 porque hago la comparacion\n    # con j+1, por lo que se saldria del rango   \n    end = 1\n    #Uso esta variable para que no itere de mas si la lista ya esta ordenada\n    swapped = True\n    i = 0\n    while i <  len(array) and swapped:\n        swapped = False\n        for j in range(len(array) - end):\n            if array[j] > array[j+1]:\n                temp = array[j]\n                array[j] = array[j+1]\n                array[j+1] = temp\n                swapped = True\n        end += 1\n        i += 1\n    return array\n\nif __name__ == \"__main__\":\n    l = [random.randint(0, 100) for i in range(20)]\n    print(bubble(l))","repo_name":"davidcediel12/Cliente-Servidor","sub_path":"hilos/sort/bubbleSort.py","file_name":"bubbleSort.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"15488107754","text":"\"\"\"\nDesign a class to find the kth largest element in a stream.\nNote that it is the kth largest element in the sorted order, not the kth distinct element.\n\nImplement KthLargest class:\n\nKthLargest(int k, int[] nums) Initializes the object with the integer k and the stream of integers nums.\nint add(int val) Returns the element representing the kth largest element in the stream.\n\n\nExample 1:\n\nInput\n[\"KthLargest\", \"add\", \"add\", \"add\", \"add\", \"add\"]\n[[3, [4, 5, 8, 2]], [3], [5], [10], [9], [4]]\nOutput\n[null, 4, 5, 5, 8, 8]\n\nExplanation\nKthLargest kthLargest = new KthLargest(3, [4, 5, 8, 2]);\nkthLargest.add(3);   // return 4\nkthLargest.add(5);   // return 5\nkthLargest.add(10);  // return 5\nkthLargest.add(9);   // return 8\nkthLargest.add(4);   // return 8\n\n\nConstraints:\n\n1 <= k <= 10^4\n0 <= nums.length <= 10^4\n-10^4 <= nums[i] <= 10^4\n-10^4 <= val <= 10^4\nAt most 10^4 calls will be made to add.\nIt is guaranteed that there will be at least k elements in the array when you search for the kth element.\n\"\"\"\nfrom typing import List\n\n\nclass TreeNode:\n    def __init__(self, val=0, left=None, right=None, cnt=1):\n        self.val = val\n        self.left = left\n        self.right = right\n        self.cnt = cnt\n\n    def __str__(self):\n        d = {\n            'val': self.val,\n            'cnt': self.cnt\n        }\n        return str(d)\n\n\nclass KthLargest:\n\n    def __init__(self, k: int, nums: List[int]):\n        self.k = k\n        self.root = None\n        for num in nums:\n            self.root = self.insert(self.root, num)\n\n    def add(self, val: int) -> int:\n        self.root = self.insert(self.root, val)\n\n        def helper(node, k):\n            right_cnt = 0\n            if node.right is not None:\n                right_cnt = node.right.cnt\n\n            kth = right_cnt + 1\n\n            if kth == k:\n                return node.val\n            elif kth > k:\n                return helper(node.right, k)\n            else:\n                return helper(node.left, k - kth)\n\n        return helper(self.root, self.k)\n\n    def insert(self, root: TreeNode, val: int) -> TreeNode:\n        if root is None:\n            return TreeNode(val)\n        if val < root.val:\n            left_bst = self.insert(root.left, val)\n            root.left = left_bst\n        else:\n            right_bst = self.insert(root.right, val)\n            root.right = right_bst\n\n        root.cnt += 1\n        return root\n\n\nclass KthLargestIter:\n\n    def __init__(self, k: int, nums: List[int]):\n        self.k = k\n        self.root = None\n        for num in nums:\n            self.root = self.insert(self.root, num)\n\n    def add(self, val: int) -> int:\n        self.root = self.insert(self.root, val)\n\n        cur = self.root\n        cur_k = self.k\n        kth = 1 + (cur.right.cnt if cur.right else 0)\n\n        while True:\n            if kth > cur_k:\n                cur = cur.right\n                kth = 1 + (cur.right.cnt if cur.right else 0)\n            elif kth < cur_k:\n                cur_k = cur_k - kth\n                cur = cur.left\n                kth = 1 + (cur.right.cnt if cur.right else 0)\n            else:\n                return cur.val\n\n    def insert(self, root: TreeNode, val: int) -> TreeNode:\n        if root is None:\n            return TreeNode(val)\n\n        cur = root\n        while True:\n            if val < cur.val:\n                cur.cnt += 1\n                if cur.left:\n                    cur = cur.left\n                else:\n                    cur.left = TreeNode(val)\n                    return root\n            else:\n                cur.cnt += 1\n                if cur.right:\n                    cur = cur.right\n                else:\n                    cur.right = TreeNode(val)\n                    return root\n\n\n# Your KthLargest object will be instantiated and called as such:\n# obj = KthLargest(k, nums)\n# param_1 = obj.add(val)\n\nif __name__ == '__main__':\n    # solution = KthLargestIter(3, [4, 5, 8, 2])\n    # for i in [3, 5, 10, 9, 4]:\n    #     print(solution.add(i))\n\n    solution = KthLargestIter(1, [])\n    for i in [-3, -2, -4, 0, 4]:\n        print(solution.add(i))\n","repo_name":"mingmingli916/algorithms","sub_path":"binary_search_tree/Kth Largest Element in a Stream.py","file_name":"Kth Largest Element in a Stream.py","file_ext":"py","file_size_in_byte":4093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"37805568964","text":"import sys\nimport shutil\nimport os\n\nPROJ_PATH = os.path.abspath(os.path.dirname(os.getcwd()))\nFILE_PATH = PROJ_PATH + '/data/imgLevel1Label_20180504_01/'\n\nFILE_TYPE = '.txt'\n\ndef copy_file(src_idx, goal_start, goal_end):\n\told_file = FILE_PATH + str(src_idx) + FILE_TYPE\n\tfor i in range(goal_start, goal_end+1):\n\t\tnew_file = FILE_PATH + str(i) + FILE_TYPE\n\t\tshutil.copy(old_file, new_file)\n\n\nif __name__ == '__main__':\n\tsrc_idx = int(sys.argv[1])\n\tgoal_start = int(sys.argv[2])\n\tgoal_end = int(sys.argv[3])\n\tcopy_file(src_idx, goal_start, goal_end)\n","repo_name":"LogicRL/annotation_tool","sub_path":"src/copyLabel.py","file_name":"copyLabel.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"22951840876","text":"#!/usr/bin/python3 -u\n\n# This script will automatically delete your history message in all joined groups.\n# It will check the latest messages (until `n` seconds ago) of each joined group, and delete the message if:\n#    1. It was sent from you\n#    2. The group is not whitelisted in (WHITELIST_CHATS)\n#    3. The group was sent at least `t` seconds ago\n#\n# `n` is MSG_DOWNLOAD_LIMIT, `t` is MSG_ALIVE_TIME\n# It's recommended to auto-run this script daily.\n\n##################### Configuration Begin ######################\nTELEGRAM_API_ID = '11111111' # Get api_id and api_hash at my.telegram.org\nTELEGRAM_API_HASH = '67e72cc9e2b603e08d05446ad5ef8e6'\nTELEGRAM_PHONE = '+12223334444' # Phone number in International Format. Example: '+8617719890604'\nWHITELIST_CHATS = ['-692222222', '-100195111111111']\n\nMSG_DOWNLOAD_TIME_LIMIT = 3*24*60*60 # 3 days ago. Set to '0' for dry-run, set to a huge number for first-run.\nMSG_ALIVE_TIME = 24*60*60 # 1 day\n##################### Configuration End ########################\n\nfrom telegram.client import Telegram\nimport time\n\ntg = Telegram(\n    api_id=TELEGRAM_API_ID, \n    api_hash=TELEGRAM_API_HASH,\n    phone=TELEGRAM_PHONE,\n    database_encryption_key='any_password',\n    files_directory='tdlib_files/',\n)\n\ndef result_of(async_result):\n    async_result.wait()\n    return async_result.update\n\ndef delete_all_msg_from_me(telegram, group_id, pull_time_limit, my_userid):\n    receive = True\n    from_message_id = 0\n    stats_data = {}\n    processed_msg_count = 0\n    current_timestamp = time.time()\n\n    while receive:\n        response = telegram.get_chat_history(\n            chat_id=group_id,\n            limit=1000,\n            from_message_id=from_message_id,\n        )\n        response.wait()\n\n        msg_to_delete = []\n        for message in response.update['messages']:\n            if message['date'] < current_timestamp - pull_time_limit:\n                receive = False\n                break\n            if message['sender_id']['@type'] != 'messageSenderUser':\n                # Not sent from user. Ignore it.\n                from_message_id = message['id']\n                continue\n            if message['sender_id']['user_id'] == my_userid and message['date'] < current_timestamp - MSG_ALIVE_TIME:\n                msg_to_delete.append(message['id'])\n            else:\n                from_message_id = message['id']\n\n        if msg_to_delete != []:\n            print(\"DEBUG: delete msg count=\", len(msg_to_delete))\n            tg.delete_messages(group_id, msg_to_delete)\n\n        if not response.update['total_count']:\n            receive = False\n\n        processed_msg_count += len(response.update['messages'])\n        print(f'[{processed_msg_count}] processed')\n\n\nif __name__ == '__main__':\n    tg.login()\n\n    my_id = result_of(tg.get_me())['id']\n\n    for chatid in result_of(tg.get_chats())['chat_ids']:\n        if chatid >= 0:\n            print(f\"Ignore chat_id {chatid}, not a group\")\n            continue\n        if chatid in WHITELIST_CHATS or str(chatid) in WHITELIST_CHATS:\n            print(f\"Ignore chat_id {chatid}, whitelisted\")\n            continue\n        group_title = result_of(tg.get_chat(chatid))['title']\n        print(\"Will cleaning up chat_id \", chatid, group_title)\n        delete_all_msg_from_me(tg, str(chatid), MSG_DOWNLOAD_TIME_LIMIT, my_id)\n\n    tg.stop()\n\n","repo_name":"recolic/telegram-history-cleanup","sub_path":"tg-history-cleanup.py","file_name":"tg-history-cleanup.py","file_ext":"py","file_size_in_byte":3337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"26526896607","text":"import unittest\nimport arraymin\n\n\nclass TestTheArray(unittest.TestCase):\n    def test_my_array(self):\n        getVals = [5, 1, 6, 8, ]\n        arraymin.get_items_list(getVals)\n        self.assertNotEqual(0, len(getVals))\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"jisshub/python-development","sub_path":"pythonAdvanced/test_array.py","file_name":"test_array.py","file_ext":"py","file_size_in_byte":270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34506248994","text":"class Car(object):\n    def __init__(self, name=\"Corona\", model=\"T2016\", type=\"Toyota\"):\n        self.type = type\n        self.model = model\n        self.name = name\n        self.speed = 0\n        \n        if self.type == \"Toyota\" or self.type == \"mark11\":\n            self.num_of_doors = 2\n        else:\n            self.num_of_doors = 4\n        if self.type == \"Lorry\":\n            self.num_of_wheels = 8\n        else:\n            self.num_of_wheels = 4\n            \n    def is_saloon(self):\n        if self.type == \"lorry\":\n            return False\n        else:\n            return True\n        \n    def drive(self, speed):\n        self.speed += speed\n        return self.speed\n    \n#mycar = Car(\"Corona\", \"T2016\", \"Toyota\")\n#print (mycar.drive(400))\n","repo_name":"atuhe/SLC-labs-day2","sub_path":"oop_car.py","file_name":"oop_car.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"72189462365","text":"# Avaliação 02 individual\n# Autor: Gabriel Merigo\n\nrespostas_aceitas = ['1', '2', '3', '4']\nlogin = []\nsenha = []\nfuncionarios = ['Pedro' , 'Ana', 'Carlos', 'Maria Clara', 'João Antonio']\nsalarios = [3470.00, 2200.00, 3970.34, 7450.23, 5677.33]\n\n'''MÉTODOS'''\ndef verificarUsuario(usuarioDigitado):\n    if not usuarioDigitado in login: raise ValueError('O usuário que você digitou não existe na lista de usuarios') \n\ndef verificarSenha(senhaDigitada):\n    if not senhaDigitada in senha: raise ValueError('A senha que você digitou está incorreta')\n\ndef formatarMensagemErro(string):\n    string = string.replace('(', '')\n    string = string.replace(')', '')\n    string = string.replace(\"'\", '')\n    string = string.replace(',', '')\n    return string\n\ndef retornarMensagemErro(ex):\n    print(formatarMensagemErro(ex.args.__str__()))\n\n\ndef calcularMedia():\n    return round(sum(salarios) / salarios.__len__(), 2)\n\n\n'''ROTINAS DO USUÁRIO'''\ndef cadastroLoginSenha():\n    usuario = input('Por favor, digite um nome de usuário para ser cadastrado: ')\n\n    try:\n        if usuario in login: raise ValueError(f'O usuário {usuario} já existe na lista de login, por favor digite outro nome')\n        if not usuario in funcionarios: raise ValueError(f'O usuário {usuario} não existe na lista de funcionarios, por favor crie clicando [4]')\n\n        login.append(usuario)\n        senhaDigitada = input('Por favor, digite um senha para seu usuário: ')\n        senha.append(senhaDigitada) \n        print('Usuário cadastrado com sucesso!')\n    except ValueError as ex:\n        retornarMensagemErro(ex)\n\ndef aumentarSalario():\n    try:\n        usuarioDigitado = input('Digite o nome de seu usuário: ')\n        senhaDigitada = input(f'{usuarioDigitado}, digite a sua senha: ')\n        verificarUsuario(usuarioDigitado)\n        verificarSenha(senhaDigitada)\n        media = calcularMedia()\n\n        for i in range(salarios.__len__()):\n            if salarios[i] < media:\n                salarios[i] = round(salarios[i] + (salarios[i] * 0.10), 2)\n\n        print('O aumento de 10% nos salários dos funcionarios abaixo da media foi realizado com sucesso!')\n    except ValueError as ex:\n        retornarMensagemErro(ex)\n\ndef gerarRelatorio():\n    try:\n        usuarioDigitado = input('Digite o nome de seu usuário: ')\n        senhaDigitada = input(f'{usuarioDigitado}, digite a sua senha: ')\n        verificarUsuario(usuarioDigitado)\n        verificarSenha(senhaDigitada)\n        for i in range(funcionarios.__len__()):\n            print(f'{funcionarios[i]} - {salarios[i]}')\n    except ValueError as ex:\n        retornarMensagemErro(ex)\n\ndef criacaoNovoFuncionario():\n    nomeFuncionario = input('Digite o nome do novo funcionario: ')\n    try: \n        salarioFuncionario = float(input('Digite o salário do novo funcionario: '))\n        funcionarios.append(nomeFuncionario)\n        salarios.append(salarioFuncionario)\n        print('Funcionário cadastrado com sucesso!')\n    except:\n        print('Ops... algo aconteceu de errado! Verifique se você realmente está digitando número.')\n\nwhile True:\n    message = '''\\n MENU - Você pode escrever \"sair\" para sair do menu \\n 1 - Cadastrar Login e Senha \\n 2 - Aumento de 10% \\n 3 - Relatório \\n 4 - Cadastrar Funcionário \\n Escolha: '''\n    escolha = input(message)\n\n\n    if str(escolha).lower() == 'sair': break\n    \n    try: \n      if not escolha in respostas_aceitas: raise ValueError('Você só pode digitar 1, 2, 3 e 4')\n    except ValueError as ex:\n      retornarMensagemErro(ex)\n      continue\n    \n    if escolha == '1':\n        cadastroLoginSenha()\n    elif escolha == '2':\n        aumentarSalario()\n    elif escolha == '3':\n        gerarRelatorio()\n    elif escolha == '4':\n        criacaoNovoFuncionario()\n\nprint('Você saiu do loop')","repo_name":"GabrielMerigo/python-exercises","sub_path":"gabrielMerigo-tarefa-02.py","file_name":"gabrielMerigo-tarefa-02.py","file_ext":"py","file_size_in_byte":3796,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74761097243","text":"N = int(input())\r\narr = list(tuple(map(int, input().split())) for _ in range(N))\r\narr.sort()\r\nstart = arr[0][0]\r\nend = arr[0][1]\r\nans = 0\r\nfor i in range(1,N):\r\n    if arr[i][0] <= end:\r\n        end = max(end, arr[i][1])\r\n    else:\r\n        ans += (end-start)\r\n        start, end = arr[i][0], arr[i][1]\r\nans += (end-start)\r\nprint(ans)","repo_name":"junwson9/algorithm","sub_path":"백준/Gold/2170. 선 긋기/선 긋기.py","file_name":"선 긋기.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"9310229493","text":"def farm_tasks():\n    build_farm()\n    control_farm()\n    # Create patrols\n\ndef build_farm():\n    global farms\n    for cell in state['board'].cells.values():\n        if dist(cell.position,state['closestShipyard'][cell.position.x][cell.position.y]) == 1:\n            if cell.position in farms:\n                continue\n            farms.append(cell.position)\n\ndef control_farm():\n    global farms\n    for i,farm in enumerate(farms[:]):\n        if dist(farm,state['closestShipyard'][farm.x][farm.y]) > 1:\n            # Not worth it\n            farms.remove(farm)\n","repo_name":"lunw1024/halite20","sub_path":"mineBot/farm.py","file_name":"farm.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"129278323","text":"import math\nN = int(input())\n\nqmax = 10**6\n'''\ndef prime(N):\n  primes = []\n  for i in range(2, N + 1):\n    primes.append(i)\n    for p in range(2, i):\n      if i % p == 0:\n        primes.remove(i)\n        break\n  return primes\n'''\n\ndef sieve_of_eratosthenes(n):\n    prime = [True for i in range(n+1)]\n    prime[0] = False\n    prime[1] = False\n\n    sqrt_n = math.ceil(math.sqrt(n))\n    for i in range(2, sqrt_n):\n        if prime[i]:\n            for j in range(2*i, n+1, i):\n                prime[j] = False\n    return prime\n\npr = []\n\n#prime = sieve_of_eratosthenes(10**6)\n#for p in range(10**6+1):\nprime = sieve_of_eratosthenes(N)\nfor p in range(N+1):\n    if prime[p]:\n        pr.append(p)\n\n        #print(p, end=' ')\n#print(pr)\n#print(len(pr))\n#print(sum(prime(10**6)))\n#print(pr)\nanss = set()\n\n\n#print(pr[0])\n\n# this is O(n^2) which leads to TLE\nfor p in range(len(pr)):\n    for q in range(p+1, len(pr)): # binary search will make it faster\n        v = pr[q]**3\n        if pr[p]*v > N:\n            break\n        if pr[p]*v not in anss:\n            anss.add(pr[p]*v)\n\n#print(anss)\nprint(len(anss))        \n\n\n# pls refer to ans.py\n\n\n","repo_name":"tinaba96/coding","sub_path":"acode/abc250/d/try.py","file_name":"try.py","file_ext":"py","file_size_in_byte":1132,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"12679726238","text":"# -*- coding: utf-8 -*-\n__author__ = \"lundberg\"\n\nimport urllib.parse\nfrom contextlib import contextmanager\nfrom dataclasses import dataclass, field\nfrom datetime import datetime, timedelta, timezone\nfrom typing import Any, Dict, Iterator, List, Optional, Set\n\nfrom flask import abort, request\nfrom flask_limiter.util import get_remote_address\nfrom pymisp import PyMISPError\nfrom validators import domain, email, ipv4, ipv6, md5, sha1, sha256, sha512, url, validator\n\nfrom ioc_lookup.ioc_lookup_app import current_ioc_lookup_app\nfrom ioc_lookup.misp_api import Attr, AttrType, MISPApi\n\n\nclass ParseException(Exception):\n    pass\n\n\n@dataclass\nclass User:\n    identifier: str\n    is_trusted_user: bool\n    in_trusted_org: bool\n    org_domain: str\n\n\n@dataclass\nclass Votes:\n    positives: int = 0\n    positive_orgs: Set[str] = field(default_factory=set)\n    negatives: int = 0\n    negative_orgs: Set[str] = field(default_factory=set)\n\n\n@dataclass\nclass SightingsData:\n    can_add_sighting: bool\n    can_add_false_positive: bool\n    votes: Dict[str, Votes] = field(default_factory=dict)\n\n    @classmethod\n    def from_sightings(cls, data: List[Dict[str, Any]], votes: Dict[str, Votes]):\n        can_add_sighting = True\n        can_add_false_positive = True\n        now = datetime.utcnow()\n        for item in data:\n            # Check if a sighting has been reported in the latest 24 hours by this org\n            if can_add_sighting and item.get(\"type\", None) == \"0\":\n                date_sighting = datetime.utcfromtimestamp(int(item[\"date_sighting\"]))\n                min_vote_hours = current_ioc_lookup_app.config[\"SIGHTING_MIN_POSITIVE_VOTE_HOURS\"]\n                if date_sighting > (now - timedelta(hours=min_vote_hours)):\n                    can_add_sighting = False\n            # Check if there has been a false-positive report by this org\n            elif can_add_false_positive and item.get(\"type\", None) == \"1\":\n                can_add_false_positive = False\n        return cls(can_add_sighting=can_add_sighting, can_add_false_positive=can_add_false_positive, votes=votes)\n\n\n@validator\ndef defanged_url(value, public=False) -> bool:\n    \"\"\"\n    hxxps://defanged.url/path -> https://defanged.url/path\n    \"\"\"\n    if value.startswith(\"hxxp://\") or value.startswith(\"hxxps://\"):\n        value = value.replace(\"hxx\", \"htt\", 1)  # Replace only the first occurrence of hxx with htt\n        return url(value=value, public=public)\n    return False\n\n\ndef get_canonical_url(uri: str) -> str:\n    url_components = urllib.parse.urlsplit(uri)\n    # Always end url with /\n    path = url_components.path\n    if not path.endswith(\"/\"):\n        path = f\"{url_components.path}/\"\n    return urllib.parse.urlunsplit([url_components.scheme, url_components.netloc, path, None, None])\n\n\ndef parse_items(items: Optional[str]) -> List[Attr]:\n    parsed_items: List[Attr] = []\n    if not items:\n        return parsed_items\n    for item in items.split(\"\\n\"):\n        if item:\n            item = \"\".join(item.split())  # Normalize whitespace\n            item = urllib.parse.unquote_plus(item)\n            if domain(item):\n                typ = AttrType.DOMAIN\n                search_types = [AttrType.DOMAIN, AttrType.HOSTNAME, AttrType.DOMAIN_IP]\n                report_types = [AttrType.DOMAIN]\n            elif url(item):\n                typ = AttrType.URL\n                search_types = [AttrType.URL]\n                report_types = [AttrType.URL]\n                # Remove arguments from URLs\n                item = get_canonical_url(item)\n            elif defanged_url(item):\n                typ = AttrType.URL\n                search_types = [AttrType.URL]\n                report_types = [AttrType.URL]\n                # MISP wants a correct URL, so replace hxx with htt\n                item = item.replace(\"hxx\", \"htt\", 1)\n            elif ipv4(item) or ipv6(item):\n                typ = AttrType.IP_SRC\n                search_types = [\n                    AttrType.DOMAIN_IP,\n                    AttrType.IP_SRC,\n                    AttrType.IP_SRC_PORT,\n                    AttrType.IP_DST,\n                    AttrType.IP_DST_PORT,\n                ]\n                report_types = [AttrType.IP_SRC]\n            elif md5(item):\n                typ = AttrType.MD5\n                search_types = [AttrType.MD5, AttrType.FILENAME_MD5, AttrType.MALWARE_SAMPLE]\n                report_types = [AttrType.MD5]\n            elif sha1(item):\n                typ = AttrType.SHA1\n                search_types = [AttrType.SHA1, AttrType.FILENAME_SHA1, AttrType.MALWARE_SAMPLE]\n                report_types = [AttrType.SHA1]\n            elif sha256(item):\n                typ = AttrType.SHA256\n                search_types = [AttrType.SHA256, AttrType.FILENAME_SHA256, AttrType.MALWARE_SAMPLE]\n                report_types = [AttrType.SHA256]\n            elif sha512(item):\n                typ = AttrType.SHA512\n                search_types = [AttrType.SHA512, AttrType.FILENAME_SHA512, AttrType.MALWARE_SAMPLE]\n                report_types = [AttrType.SHA512]\n            elif email(item):\n                typ = AttrType.EMAIL\n                search_types = [\n                    AttrType.EMAIL,\n                    AttrType.EMAIL_SRC,\n                    AttrType.EMAIL_DST,\n                    AttrType.TARGET_EMAIL,\n                    AttrType.EPPN,\n                ]\n                report_types = [AttrType.EMAIL]\n            else:\n                raise ParseException(f\"Could not parse {item}\")\n            parsed_items.append(Attr(value=item, type=typ, search_types=search_types, report_types=report_types))\n    return parsed_items\n\n\ndef parse_item(item: Optional[str]) -> Optional[Attr]:\n    try:\n        items = parse_items(item)\n    except ParseException:\n        return None\n    if not items:\n        return None\n    return items[0]\n\n\ndef get_ipaddr_or_eppn() -> str:\n    \"\"\"\n    Uses eppn if supplied else remote address for rate limiting\n    \"\"\"\n    current_ioc_lookup_app.logger.debug(\"REQUEST ENVIRONMENT:\")\n    current_ioc_lookup_app.logger.debug(request.environ)\n    identifier = request.environ.get(\"HTTP_REMOTE_USER\", None)\n    current_ioc_lookup_app.logger.debug(f\"Identifier from request environment: {identifier}\")\n    if not identifier:\n        current_ioc_lookup_app.logger.warning(\"HTTP_REMOTE_USER is missing from request environment\")\n        identifier = get_remote_address()\n        current_ioc_lookup_app.logger.debug(f\"Identifier from get_ipaddr: {identifier}\")\n    return identifier\n\n\ndef get_user() -> User:\n    identifier = get_ipaddr_or_eppn()\n    return User(\n        identifier=identifier,\n        is_trusted_user=is_trusted_user(identifier),\n        in_trusted_org=in_trusted_orgs(identifier),\n        org_domain=get_org_domain(identifier),\n    )\n\n\ndef is_trusted_user(userid: str) -> bool:\n    \"\"\"\n    Checks the eppn against a whitelist\n    \"\"\"\n    if userid in current_ioc_lookup_app.trusted_users:\n        current_ioc_lookup_app.logger.debug(f\"User with id {userid} is a trusted user\")\n        return True\n    current_ioc_lookup_app.logger.debug(f\"User with id {userid} IS NOT a trusted user\")\n    return False\n\n\ndef get_org_domain(userid: str) -> str:\n    return userid.split(\"@\")[-1].lower()\n\n\ndef in_trusted_orgs(userid: str) -> bool:\n    org_domain = get_org_domain(userid)\n    return org_domain in current_ioc_lookup_app.trusted_orgs.get(\"org_domains\", [])\n\n\ndef get_sightings_data(user: User, search_result: List[Dict[str, Any]]) -> SightingsData:\n    attribute_votes = {}\n    org_sightings = []\n    with misp_api_for() as api:\n        for item in search_result:\n            votes = Votes()\n            for sighting in api.sighting_lookup(attribute_id=item[\"id\"]):\n                org_name = sighting[\"source\"].replace(current_ioc_lookup_app.config[\"SIGHTING_SOURCE_PREFIX\"], \"\")\n                if sighting[\"type\"] == \"0\":\n                    votes.positives += 1\n                    votes.positive_orgs.add(org_name)\n                elif sighting[\"type\"] == \"1\":\n                    votes.negatives += 1\n                    votes.negative_orgs.add(org_name)\n            attribute_votes[item[\"id\"]] = votes\n            with misp_api_for(user) as org_api:\n                org_sightings.extend(\n                    org_api.sighting_lookup(\n                        attribute_id=item[\"id\"],\n                        source=f'{current_ioc_lookup_app.config[\"SIGHTING_SOURCE_PREFIX\"]}{user.org_domain}',\n                    )\n                )\n    return SightingsData.from_sightings(data=org_sightings, votes=attribute_votes)\n\n\n@contextmanager\ndef misp_api_for(user: Optional[User] = None) -> Iterator[MISPApi]:\n    if current_ioc_lookup_app.misp_apis is None:\n        raise PyMISPError(\"No MISP session exists\")\n    if user is None:\n        # Use default api key as org specific api keys return org specific data\n        user = User(identifier=\"default\", is_trusted_user=False, in_trusted_org=False, org_domain=\"default\")\n        current_ioc_lookup_app.logger.debug(\"Default user used for api call\")\n    current_ioc_lookup_app.logger.debug(f\"User {user.identifier} mapped to domain {user.org_domain}\")\n\n    # Lazy load apis per org\n    if (\n        user.org_domain not in current_ioc_lookup_app.misp_apis\n        and user.org_domain in current_ioc_lookup_app.trusted_orgs.get(\"org_domains\", [])\n    ):\n        try:\n            current_ioc_lookup_app.misp_apis[user.org_domain] = MISPApi(\n                current_ioc_lookup_app.config[\"MISP_URL\"],\n                current_ioc_lookup_app.trusted_orgs[\"org_domains\"][user.org_domain],\n                current_ioc_lookup_app.config[\"MISP_VERIFYCERT\"],\n            )\n            current_ioc_lookup_app.logger.info(f\"Loaded api for {user.org_domain}\")\n        except PyMISPError:\n            abort(400, \"Authentication failed. Make sure your organizations api key is up to date.\")\n        except Exception as ex:\n            current_ioc_lookup_app.logger.exception(f\"Could not load domain mapping for {user.org_domain}: {ex}\")\n\n    api = current_ioc_lookup_app.misp_apis.get(user.org_domain)\n    if api is None:\n        current_ioc_lookup_app.logger.debug(\"Using default api\")\n        yield current_ioc_lookup_app.misp_apis[\"default\"]\n    else:\n        current_ioc_lookup_app.logger.debug(f\"Using {user.org_domain} api\")\n        yield api\n\n\ndef utc_now() -> datetime:\n    \"\"\"Return current time with tz=UTC\"\"\"\n    return datetime.now(tz=timezone.utc)\n","repo_name":"SUNET/flask-ioc-lookup","sub_path":"ioc_lookup/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":10414,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"19597626137","text":"import asyncio\nimport logging\n\nfrom nats.aio.client import Client as NATS\n\nclass Subscriber:\n    def __init__(self):\n        self.loop = asyncio.new_event_loop()\n    \n    def run(self):\n        self.loop.run_until_complete(self.subscribe())\n        self.loop.run_forever()\n\n    async def subscribe(self):\n        logging.info(f\"in NATS main\")\n        nc = NATS()\n\n        options = {\"servers\": \"nats://nats:4222\", \"loop\": self.loop, \"dont_randomize\": True}\n\n        async def disconnected_cb():\n            logging.warning(\"Got disconnected from nats!\")\n\n        async def reconnected_cb():\n            # See who we are connected to on reconnect.\n            logging.info(\"Got reconnected to {url}\".format(url=nc.connected_url.netloc))\n\n        async def error_cb(e):\n            logging.warning(\"There was an error: {}\".format(e))\n\n        async def closed_cb():\n            logging.info(\"NATS connection is closed\")\n\n        async def message_handler(msg):\n            logging.info(f\"In nats handler\")\n            subject = msg.subject\n            data = msg.data.decode()\n            logging.info(f\"Received a message on '{subject}': {data}\")\n    \n        options[\"disconnected_cb\"] = disconnected_cb\n        options[\"reconnected_cb\"] = reconnected_cb\n        options[\"max_reconnect_attempts\"] = -1\n        options[\"connect_timeout\"] = 5  # 5 sec\n        options[\"error_cb\"] = error_cb\n        options[\"closed_cb\"] = closed_cb\n\n    \n        logging.info(\"Harsh natssssssss\")\n        try:\n            await nc.connect(**options)\n        except Exception as e:\n            logging.error(\"Error occurred while connecting to nats server: {}\".format(e))\n        # await nc.connect(\"nats://nats:4222\", loop=self.loop)  # Connect to NATS server\n        await nc.subscribe(\"test\", cb=message_handler)  # Subscribe to subject\n        logging.info(f\"NATS connected\")","repo_name":"harsh4723/nats-demo","sub_path":"subscriber.py","file_name":"subscriber.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"41433655290","text":"def get_price(count: int) -> int:\n\t\"\"\"\n\t\tВозвращает цену за товар\n\n\t\t:param count: - количество товара\n\t\t:return int:\n\t\"\"\"\n\tresponse = 0\n\tfor i in range(count):\n\t\tresponse += 2.95 if response else 10.95\n\n\treturn round(response,2)\n\nif __name__ == \"__main__\":\n\tprint(f\"Цена за товары: ${get_price(count=int(input('Кол-во товаров: ')))}\")","repo_name":"kmplsmf/Practan","sub_path":"prog2.py","file_name":"prog2.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34898386433","text":"import time\n\nclass EmonHubCargo:\n    uri = 0\n\n    # The class \"constructor\" - It's actually an initializer\n    def __init__(self, timestamp, target, nodeid, nodename, names, realdata, rssi, rawdata):\n        EmonHubCargo.uri += 1\n        self.uri = EmonHubCargo.uri\n        self.timestamp = float(timestamp)\n        self.target = int(target)\n        self.nodeid = int(nodeid)\n        self.nodename = nodename\n        self.names = names\n        self.realdata = realdata\n        self.rssi = int(rssi)\n\n        # self.datacodes = []\n        # self.datacode = \"\"\n        # self.scale = 0\n        # self.scales = []\n        self.rawdata = rawdata\n        self.encoded = {}\n        # self.realdatacodes = []\n\ndef new_cargo(rawdata=\"\", nodename=False, names=[], realdata=[], nodeid=0, timestamp=0.0, target=0, rssi=0.0):\n    return EmonHubCargo(timestamp or time.time(), target, nodeid, nodename, names, realdata, rssi, rawdata)\n","repo_name":"openenergymonitor/emonhub","sub_path":"src/Cargo.py","file_name":"Cargo.py","file_ext":"py","file_size_in_byte":922,"program_lang":"python","lang":"en","doc_type":"code","stars":82,"dataset":"github-code","pt":"86"}
+{"seq_id":"14351698306","text":"from common import *\n\ndef parse(x):\n    return [int(y) for y in x]\n\ndata = filemap(parse, 'day09.txt')\n\ndef neighbors(x, y):\n    n = []\n    for tx, ty in ((x-1, y), (x, y-1), (x+1, y), (x, y+1)):\n        if tx >= 0 and tx < len(data) and ty >= 0 and ty < len(data[0]):\n            n.append((tx, ty))\n    return n\n\np1 = 0\nbasins = defaultdict(set)\nunassigned = set()\nflows = {}\nfor x in range(len(data)):\n    for y in range(len(data[x])):\n        d = data[x][y]\n        if all(d < data[tx][ty] for (tx, ty) in neighbors(x, y)):\n            p1 += 1 + d\n            basins[(x, y)].add((x, y))\n        elif d == 9:\n            continue\n        else:\n            unassigned.add((x, y))\n            flows[(x, y)] = min((data[tx][ty], (tx, ty)) for tx, ty in neighbors(x, y))[1]\nprint(p1)\n\nwhile unassigned:\n    cx, cy = unassigned.pop()\n    gathered = set([(cx, cy)])\n    while (cx, cy) in flows:\n        cx, cy = flows[(cx, cy)]\n        gathered.add((cx, cy))\n        unassigned.discard((cx, cy))\n    basins[(cx, cy)].update(gathered)\n\np2 = 1\nfor i in list(sorted(basins.values(), key=lambda x: len(x), reverse=True))[:3]:\n    p2 *= len(i)\nprint(p2)\n\n\n","repo_name":"mattr555/advent-of-code","sub_path":"2021/day09.py","file_name":"day09.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"45134360982","text":"\nfrom tkinter import *\n\n#tkinter is the Phyton's standard GUI, binding to the Tk GUI toolkit\n#GUI - Graphical User Interface\n# widgets = GUI elements: buttons, textboxes, labels, images\n# windows = serves as a cotainer to hold these widgets.\n\n\nwindow = Tk() #instantiate an instance ofa window.\nwindow.geometry(\"920x920\")\nwindow.title('Joao Window')\n#icon = PhotoImage(file=\"Hanasson Logo Transparente 1.png\")\n#window.iconphoto(True,icon)\n#google HEX colour picker, make sure you have hashtag\ncount = 0\ndef click():\n    global count\n    count += 1\n    print(\"Thanks for clicking {} times.\".format(count))\n\nwindow.config(background=\"#184022\")\n\n#INSERT IMAGE (Going to the label)\nphoto = PhotoImage(file='C:\\\\Users\\\\joaopaulo\\\\Google Drive\\\\'\n                        'HANASSON LIMITED\\\\Marketing\\\\Presentation\\\\Images\\\\Brazil.png')\n\nbutton_logo = PhotoImage(file='C:\\\\Users\\\\joaopaulo\\\\Google Drive\\\\'\n                        'HANASSON LIMITED\\\\Marketing\\\\Presentation\\\\Images\\\\HOUSE.png')\n\n#labels = is an area widget that holds text and/or an image within a window.\n#STEP 1: master of the lable to be in that window.\nlabel = Label(window,\n              text=\"Hello Joao Paulo\\n Good Morning\",\n              font=('arial',20,'bold'),\n              fg='#6a6e6b',bg='#184022',\n              relief=RAISED, bd=10,\n              padx=20, pady=10,\n              image=photo, compound=\"bottom\")\nlabel.pack() #STEP 2: Sends the label to the window on top position.\n#label.place(x=0, y=0)  #STEP 2: Sends the label to the window on coordinates in pixels.\n\nbutton = Button(window, text=\"Click me\", font=('Comic Sans', 10, 'bold'),\n                fg='#6a6e6b',bg='#184022',\n                padx=5, pady=5, activeforeground='#6a6e6b', activebackground='#60a672',\n                command=click,\n                state=ACTIVE, image=button_logo, compound=\"bottom\")\nbutton.place(x=200, y=320)\n\nwindow.mainloop() #place window on computer screen, listen for events\n\n#labels\n","repo_name":"Jornada217/Class.py","sub_path":"Course2/Window GUI.py","file_name":"Window GUI.py","file_ext":"py","file_size_in_byte":1958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"28399744494","text":"# -*- coding: utf-8 -*-\nimport time\nfrom VectorSpaceGenerator import VectorSpaceMaker\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport numpy\nimport random\nfrom sklearn.preprocessing import normalize\nfrom tmodels.som import SOM\nimport sys\nimport pdb\n#pdb.set_trace()\nfrom tmodels.somC import SomC\n\n\nclass Trainer():\n    def __init__(self):\n        self.vsm = VectorSpaceMaker()\n        self.tobetraineddata = self.vsm.getLinksandContents()\n        # It is a dictionary with links and content\n        self.trainingdata = self.tobetraineddata.values()\n        self.trainingdatakeys = self.tobetraineddata.keys()\n\n    '''Training Starts here '''\n\n    def initialize(self):\n        print(\"Starting intialization\")\n        self.vectorizer = CountVectorizer(min_df=3)\n        self.X = self.vectorizer.fit_transform(self.trainingdata)\n        self.n_number, self.n_dim = self.X.shape\n        self.n_todim = 300\n        print (len(self.vectorizer.vocabulary_))\n        '''transformer = random_projection.GaussianRandomProjection()\n        X_new = transformer.fit_transform(X)\n        print X_new.shape'''\n        ''' get new shape and array size here . 2/3 columns are zeros here.'''\n        rand = numpy.empty([self.n_dim, self.n_todim])\n        for i in range(0, self.n_number):\n            for j in range(0, self.n_todim):\n                a1 = random.random()\n                if a1 < 2.0 / 3:\n                    rand[i, j] = 0\n                elif a1 < 5.0 / 6:\n                    rand[i, j] = 1\n                elif a1 < 1.0:\n                    rand[i, j] = -1\n        # apply random projection\n        rand_normalized = normalize(rand, norm='l1', axis=0)\n        #print rand_normalized\n\n        # doing random projection here\n        self.final_matrix = self.X.dot(rand_normalized)\n        print(\"Initialization Done\")\n        #print final_matrix\n        #print final_matrix.shape\n\n\n    def startTraining(self):\n        ''' Here we are passing SOM mesh, dimensions of each node, Mesh dimensions '''\n        print(\"Starting Training\")\n        self.som = SomC(self.final_matrix, self.n_todim, (10, 10))\n        for j in range(self.som.num_iterations):\n            start = time.time()\n            for i in range(self.n_number):\n                self.som.train(self.final_matrix[i], j)\n            print (\" %d iteration on a %d-square grid took %f seconds\" % (j, self.som.grid_size[0], time.time() - start))\n            print(self.som.som_weights)\n\n        print(self.som.som_weights)\n        print(\"Training Done\")\n\n    def savetoFile(self):\n        numpy.save(\"../savedata/narray\", self.som.final_matrix)\n\n'''for i in range(1,1300):\n    if len(som.getSimilarArticles(final_matrix[i,:].tolist())) > 1:\n        print \"DFDF\" '''\n\n'''for i in range(20):\n    for j in range(20):\n        print (str(i) + \" \" + str(j))\n        try:\n            for k in som.nodes[i*10+j].content:\n                sys.stdout.write(trainingdatakeys[k])\n                sys.stdout.write(\" \")\n            print (\"\\n\")\n        except:\n            pass'''\n#print johnson_lindenstrauss_min_dim(n_samples=1330, eps=.5) #345\n\ntr = Trainer()\ntr.initialize()\ntr.startTraining()\ntr.savetoFile()\n\n","repo_name":"soorajambadi/pocket_SOM","sub_path":"trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":3174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"5495040150","text":"import csv\nfrom typing import Set\nfrom models.dish import Dish\nfrom models.ingredient import Ingredient\n\n\nclass MenuData:\n    def __init__(self, source_path: str) -> None:\n        self.source_path = source_path\n        self.dishes: Set[Dish] = set()\n        self._load_menu_data()\n\n    def _load_menu_data(self) -> None:\n        with open(self.source_path, newline='') as file:\n            reader = csv.reader(file)\n            next(reader)\n\n            for row in reader:\n                dish_na, dish_price, ingrent_name, ingrent_qutity = row\n\n                dish = self._get_or_create_dish(dish_na, float(dish_price))\n                ingredient = self._get_or_create_ingredient(dish, ingrent_name)\n\n                dish.add_ingredient_dependency(ingredient, int(ingrent_qutity))\n\n    def _get_or_create_dish(self, name: str, price: float) -> Dish:\n        for dish in self.dishes:\n            if dish.name == name and dish.price == price:\n                return dish\n\n        dish = Dish(name, price)\n        self.dishes.add(dish)\n        return dish\n\n    def _get_or_create_ingredient(self, dish: Dish, name: str) -> Ingredient:\n        for ingredient in dish.recipe.keys():\n            if ingredient.name == name:\n                return ingredient\n\n        ingredient = Ingredient(name)\n        dish.add_ingredient_dependency(ingredient, 0)\n        return ingredient\n","repo_name":"FranciscoVieir/Restaurant-orders","sub_path":"src/services/menu_data.py","file_name":"menu_data.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"25143642120","text":"import numpy as np\r\nimport cv2\r\nimport datetime\r\nimport os\r\n\r\ncap = cv2.VideoCapture(0)\r\ncap.set(3,640)\r\ncap.set(4,480)\r\n\r\npath_folder = 'C:\\\\Users\\\\Biancaa. R\\\\lumin_eye\\\\saved'\r\nos.chdir(path_folder)\r\nfourcc = cv2.VideoWriter_fourcc(*'MP4V') #MP4V codec,\r\n#ts=str(datetime.datetime.now())\r\n#path='C:\\\\Users\\\\Biancaa. R\\\\lumin_eye\\\\saved\\\\output_{}.mp4'.format(ts)\r\n#path='output_'+ts+'.mp4'\r\nts = (datetime.datetime.now()).strftime(\"%Y_%m_%d_%H_%M_%S\")\r\npath = 'C:\\\\Users\\\\Biancaa. R\\\\lumin_eye\\\\saved\\\\output_{}.mp4'.format(ts)\r\nprint(path)\r\nout = cv2.VideoWriter(path, fourcc, 10.0, (640,480))\r\nwhile(True):\r\n    ret, frame = cap.read()\r\n    out.write(frame)\r\n    cv2.imshow('frame', frame)\r\n    #c = cv2.waitKey(1)\r\n    #if c & 0xFF == ord('q'):\r\n    if cv2.waitKey(1)==ord('q'): \r\n        break\r\n\r\ncap.release()\r\nout.release() #closes the output video file\r\ncv2.destroyAllWindows()","repo_name":"Biancaa-R/Lumin_eye_vison_for-_the_impaired","sub_path":"lumin_eye/Pyqt_implementation/cap_2_ts.py","file_name":"cap_2_ts.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"4792094734","text":"from bs4 import BeautifulSoup\nfrom transformers import AutoTokenizer, EncoderDecoderModel\n\nfrom temporal_taggers.evaluation import clean_predictions\n\n\ndef find_timex_in_text(timex_preds, input_text, model_type):\n    if model_type == \"bert\":\n        original_paragraph = input_text.lower()\n    else:\n        original_paragraph = input_text\n    end_previous_timex = 0\n    previous_timex_cleaned_text = \"\"\n    new_text = \"\"\n    index = 0\n    for timex in timex_preds:\n        cleaned_text = timex.text.replace(\"<\", \"\").replace(\">\", \"\").replace(\"\\\"\", \"\").strip()\n        # sometimes the cleaned text has \"leftovers\"\n        if cleaned_text.startswith(\"- \"):\n            cleaned_text = cleaned_text[2:]\n\n        if len(cleaned_text) < 2:\n            continue\n\n        beginning_timex = original_paragraph[end_previous_timex:].find(cleaned_text)\n        if cleaned_text == \"day\" and beginning_timex != -1 and \\\n                original_paragraph[beginning_timex - 2:beginning_timex] == \"to\":\n            cleaned_text = \"today\"\n            beginning_timex = original_paragraph[end_previous_timex:].find(cleaned_text)\n\n        # if the model predicted a full year instead of the last two digits\n        if beginning_timex == -1 and len(cleaned_text) == 4 and cleaned_text.isdigit():\n            beginning_timex = original_paragraph[end_previous_timex:].find(cleaned_text[2:])\n            cleaned_text = cleaned_text[2:].strip()\n\n        # if the model predicted full year with an extra repetition\n        if beginning_timex == -1 and len(cleaned_text) == 6 and cleaned_text.isdigit():\n            beginning_timex = original_paragraph[end_previous_timex:].find(cleaned_text[:-2])\n            cleaned_text = cleaned_text[:-2].strip()\n\n        # if the first word is repeating\n        elif beginning_timex == -1 and len(cleaned_text.split(\" \")) > 1 and \\\n                cleaned_text.split(\" \")[0] == cleaned_text.split(\" \")[1]:\n            cleaned_text = ' '.join(cleaned_text.split(\" \")[:-1])\n            beginning_timex = original_paragraph[end_previous_timex:].find(cleaned_text)\n\n        # if the first and last word is repeating\n        elif beginning_timex == -1 and len(cleaned_text.split(\" \")) > 1 and \\\n                cleaned_text.split(\" \")[0] == cleaned_text.split(\" \")[-1]:\n            cleaned_text = ' '.join(cleaned_text.split(\" \")[1:])\n            beginning_timex = original_paragraph[end_previous_timex:].find(cleaned_text)\n        # if its single word separated by \"-\"\n        elif beginning_timex == -1 and len(cleaned_text.split(\" \")) < 2 and len(cleaned_text.split(\"-\")) > 1:\n            for word in cleaned_text.split(\"-\"):\n                if word in original_paragraph[end_previous_timex:]:\n                    cleaned_text = word\n                    beginning_timex = original_paragraph[end_previous_timex:].find(cleaned_text)\n                    break\n        # more than one words the first one is a digit\n        elif beginning_timex == -1 and len(cleaned_text.split(\" \")) < 2 and len(cleaned_text) > 2 and \\\n                not cleaned_text[:1].isdigit() and cleaned_text[-1].isdigit():\n            word = cleaned_text[:-1]\n            if word.lower() in original_paragraph[end_previous_timex:].lower():\n                cleaned_text = word\n                beginning_timex = original_paragraph[end_previous_timex:].lower().find(cleaned_text.lower())\n                break;\n        # if its just a single word\n        elif beginning_timex == -1 and len(cleaned_text.split(\" \")) < 2 and len(cleaned_text) > 2 and \\\n                not cleaned_text[0].isdigit() and cleaned_text[-1].isdigit():\n            for i in range(2, len(cleaned_text)):\n                word = cleaned_text[:i]\n                if \" \" + word + \" \" in original_paragraph[end_previous_timex:] or \\\n                        \" \" + word + \".\" in original_paragraph[end_previous_timex:] or \\\n                        \" \" + word + \",\" in original_paragraph[end_previous_timex:]:\n                    cleaned_text = word\n                    beginning_timex = original_paragraph[end_previous_timex:].find(cleaned_text)\n                    break;\n\n        # if its just a single word ending with digits\n        if beginning_timex == -1 and len(cleaned_text.split(\" \")) < 2:\n            for i in range(2, len(cleaned_text)):\n                word = cleaned_text[:i]\n                if \" \" + word + \" \" in original_paragraph[end_previous_timex:] or \\\n                        \" \" + word + \".\" in original_paragraph[end_previous_timex:] or \\\n                        \" \" + word + \",\" in original_paragraph[end_previous_timex:]:\n                    cleaned_text = word\n                    beginning_timex = original_paragraph[end_previous_timex:].find(cleaned_text)\n                    break;\n        # if you can not find it, see if you can match the first word in the multi word one\n        if beginning_timex == -1 and len(cleaned_text.split(\" \")) > 1:\n            for word in cleaned_text.split(\" \"):\n                if word in original_paragraph[end_previous_timex:] and word not in [\"a\", \"-\", \".\", \"the\",\n                                                                                    \"in\", \"then\", \"'s\",\n                                                                                    \"have\", \"at\", \"be\"]:\n                    cleaned_text = word\n                    beginning_timex = original_paragraph[end_previous_timex:].find(cleaned_text)\n                    break\n\n        if beginning_timex == -1 and cleaned_text.lower() in original_paragraph[\n                                                             end_previous_timex:].lower():\n            beginning_timex = original_paragraph[end_previous_timex:].lower().find(cleaned_text.lower())\n\n        # avoid tag repetition\n        if cleaned_text == previous_timex_cleaned_text:\n            continue\n\n        previous_timex_cleaned_text = cleaned_text\n\n        # if there is still no match, just forget it.\n        if beginning_timex == -1:\n            continue\n\n        index = index + 1\n        beginning_timex = beginning_timex + end_previous_timex\n        # if the word ended with one of these symbols do not put a space after timex tag\n        if original_paragraph[beginning_timex - 1:beginning_timex] in [\"\\n\", \"'\", \"-\", \",\", \"\\\"\", \"(\"] or \\\n                original_paragraph[beginning_timex - 1:beginning_timex].isdigit():\n            new_text += f'{input_text[end_previous_timex:beginning_timex]}<TIMEX3 tid=\"t{index + 1}\" ' \\\n                        f'type=\"{timex.attrs[\"type\"].upper()}\" ' \\\n                        f'value=\"{timex.attrs[\"value\"].strip().replace(\"</timex3>\", \"\").replace(\"<\", \"\").replace(\">\", \"\").replace(\" \", \"\").upper()}\">{input_text[beginning_timex:beginning_timex + len(cleaned_text)]}' \\\n                        f'</TIMEX3>'\n\n        else:  # otherwise put a space\n            new_text += f'{input_text[end_previous_timex:beginning_timex]} <TIMEX3 tid=\"t{index + 1}\" ' \\\n                        f'type=\"{timex.attrs[\"type\"].upper()}\" ' \\\n                        f'value=\"{timex.attrs[\"value\"].strip().replace(\"</timex3>\", \"\").replace(\"<\", \"\").replace(\">\", \"\").replace(\" \", \"\").upper()}\">{input_text[beginning_timex:beginning_timex + len(cleaned_text)]}' \\\n                        f'</TIMEX3>'\n\n        end_previous_timex = beginning_timex + len(cleaned_text)\n\n    new_text += input_text[end_previous_timex:]\n    return new_text\n\n\nif __name__ == \"__main__\":\n    model_type = \"roberta\"\n    tokenizer = AutoTokenizer.from_pretrained(\"satyaalmasian/temporal_tagger_roberta2roberta\")\n    model = EncoderDecoderModel.from_pretrained(\"satyaalmasian/temporal_tagger_roberta2roberta\")\n\n    # --- if you want to use the bert model, uncomment the following lines\n    # model_type=\"bert\"\n    # tokenizer = AutoTokenizer.from_pretrained(\"satyaalmasian/temporal_tagger_bert2bert\")\n    # model = EncoderDecoderModel.from_pretrained(\"satyaalmasian/temporal_tagger_bert2bert\")\n\n    input_texts = [\"I lived in New York for 10 years.\"]\n    input_texts += [\"Cumbre Vieja last erupted in 1971 and in 1949.\"]\n    input_texts += [\"The club's founding date, 15 January, was intentional.\"]\n    input_texts += [\"Police were first called to the scene just after 7.25am this morning, Sunday, September 19, \"\n                    \"and have confirmed they will continue to remain in the area for some time.\"]\n\n    for input_text in input_texts:\n        model_inputs = tokenizer(input_text, truncation=True, return_tensors=\"pt\")\n        out = model.generate(**model_inputs)\n        decoded_preds = tokenizer.batch_decode(out, skip_special_tokens=True)\n        pred_soup = BeautifulSoup(clean_predictions(decoded_preds[0]), \"lxml\")\n        timex_preds = pred_soup.findAll(\"timex3\")\n        new_text = find_timex_in_text(timex_preds, input_text, model_type)\n        print(new_text)\n","repo_name":"satya77/Transformer_Temporal_Tagger","sub_path":"examples/run_model_hub_seq2seq.py","file_name":"run_model_hub_seq2seq.py","file_ext":"py","file_size_in_byte":8818,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"86"}
+{"seq_id":"42487336360","text":"#!/usr/bin/env python\nimport math\nimport torch\nimport torch.nn as nn\nfrom torch.nn.parameter import Parameter\nfrom torch.nn.modules.module import Module\nimport torch.nn.functional as F\nimport numpy as np\nimport scipy.sparse as sp\nfrom scipy.sparse.linalg import norm as sparse_norm\n\nfrom utils.utils import sparse_mx_to_torch_sparse_tensor\n\n\nclass Sampler:\n    def __init__(self, features, adj, **kwargs):\n        allowed_kwargs = {'input_dim', 'layer_sizes', 'device'}\n        for kwarg in kwargs.keys():\n            assert kwarg in allowed_kwargs, \\\n                'Invalid keyword argument: ' + kwarg\n\n        self.input_dim = kwargs.get('input_dim', 1)\n        self.layer_sizes = kwargs.get('layer_sizes', [1])\n        self.scope = kwargs.get('scope', 'test_graph')\n        self.device = kwargs.get('device', torch.device(\"cpu\"))\n\n        self.num_layers = len(self.layer_sizes)\n\n        self.adj = adj\n        self.features = features\n\n        self.train_nodes_number = self.adj.shape[0]\n\n    def sampling(self, v_indices):\n        raise NotImplementedError(\"sampling is not implemented\")\n\n    def _change_sparse_to_tensor(self, adjs):\n        new_adjs = []\n        for adj in adjs:\n            new_adjs.append(\n                sparse_mx_to_torch_sparse_tensor(adj).to(self.device))\n        return new_adjs\n\n\nclass SamplerFastGCN(Sampler):\n    def __init__(self, pre_probs, features, adj, **kwargs):\n        super(SamplerFastGCN, self).__init__(features, adj, **kwargs)\n        col_norm = sparse_norm(adj, axis=0)\n        self.probs = col_norm / np.sum(col_norm)\n\n    def sampling(self, v):\n        all_support = [[]] * self.num_layers\n\n        cur_out_nodes = v\n        for layer_index in range(self.num_layers - 1, -1, -1):\n            cur_sampled, cur_support = self._one_layer_sampling(\n                cur_out_nodes, self.layer_sizes[layer_index])\n            all_support[layer_index] = cur_support\n            cur_out_nodes = cur_sampled\n\n        all_support = self._change_sparse_to_tensor(all_support)\n        sampled_X0 = self.features[cur_out_nodes]\n        return sampled_X0, all_support, 0\n\n    def _one_layer_sampling(self, v_indices, output_size):\n        support = self.adj[v_indices, :]\n        neis = np.nonzero(np.sum(support, axis=0))[1]\n        p1 = self.probs[neis]\n        p1 = p1 / np.sum(p1)\n        sampled = np.random.choice(np.array(np.arange(np.size(neis))),\n                                   output_size, True, p1)\n\n        u_sampled = neis[sampled]\n        support = support[:, u_sampled]\n        sampled_p1 = p1[sampled]\n\n        support = support.dot(sp.diags(1.0 / (sampled_p1 * output_size)))\n        return u_sampled, support\n\n\nclass GraphConvolution(Module):\n    def __init__(self, in_features, out_features, bias=False):\n        super(GraphConvolution, self).__init__()\n        self.in_features = in_features\n        self.out_features = out_features\n        self.weight = Parameter(torch.FloatTensor(in_features, out_features))\n        if bias:\n            self.bias = Parameter(torch.FloatTensor(out_features))\n        else:\n            self.register_parameter('bias', None)\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        stdv = 1.0 / math.sqrt(self.weight.size(1))\n        self.weight.data.uniform_(-stdv, stdv)\n        if self.bias is not None:\n            self.bias.data.uniform_(-stdv, stdv)\n\n    def forward(self, input, adj):\n        support = torch.mm(input, self.weight)\n        output = torch.spmm(adj, support)\n        self.embedding = output\n        if self.bias is not None:\n            return output + self.bias\n        else:\n            return output\n\n    def __repr__(self):\n        return self.__class__.__name__ + ' (' \\\n            + str(self.in_features) + ' -> ' \\\n            + str(self.out_features) + ')'\n\n\nclass FastGCN(nn.Module):\n    def __init__(self, nfeat, nhid, nclass, dropout, sampler):\n        super(FastGCN, self).__init__()\n\n        self.layer1 = GraphConvolution(nfeat, nhid)\n        self.layer2 = GraphConvolution(nhid, nclass)\n        self.dropout = dropout\n        self.sampler = sampler\n        self.out_softmax = nn.Softmax(dim=1)\n\n    def forward(self, x, adj):\n        outputs1 = F.relu(self.layer1(x, adj[0]))\n        outputs1 = F.dropout(outputs1, self.dropout, training=self.training)\n        outputs2 = self.layer2(outputs1, adj[1])\n        return F.log_softmax(outputs2, dim=1)\n\n    def sampling(self, *args, **kwargs):\n        return self.sampler.sampling(*args, **kwargs)\n","repo_name":"h36278284/AI6103_project_code_base","sub_path":"models/fgcn.py","file_name":"fgcn.py","file_ext":"py","file_size_in_byte":4493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"27286235131","text":"import collections, itertools, json, datetime, locale, re, os\nimport bs4, pystache\n\nimport utils\n\nfrom typing import List, Dict, Tuple, Optional, TypeVar, Set, Any\nfrom typing_extensions import TypedDict\n\nX = TypeVar(\"X\")\nEntry = TypedDict(\"Entry\", {\n  \"details\": str,\n  \"title\": str\n})\nCourse = TypedDict(\"Course\", {\n  \"title\": str,\n  \"details\": List[Entry],\n  \"dates\": List[str],\n  \"uedates\": List[str]\n})\nModule = TypedDict(\"Module\", {\n  \"content\": Dict[str, Course],\n  \"credits\": int,\n  \"details\": List[Entry],\n  \"module_id\": str,\n  \"regulations\": str\n})\nTermin = TypedDict(\"Termin\", {\n  \"count\": int,\n  \"day\": int,\n  \"start\": Tuple[int, int],\n  \"end\": Tuple[int, int],\n  \"room\": str,\n  \"firstdate\": str,\n})\nModule2 = TypedDict(\"Module2\", {\n  \"content\": Dict[str, Course],\n  \"weekly\": List[Termin],\n  \"id\": str,\n  \"title\": str,\n  \"title_short\": str,\n  \"owner\": str,\n  \"owner_short\": str,\n  \"category\": str,\n  \"credits\": int,\n  \"details\": List[Entry],\n  \"regulations\": str,\n  \"language\": str,\n})\n\npairs: dict[(str, int, float), int] = dict()\n\ndef stache(x: str, y: Dict[str, Any]) -> str:\n  return pystache.render(x, y) # type: ignore\n\ndef main() -> None:\n    prefix   = \"cache/\"\n    now      = datetime.datetime.today()\n    time_ym  = now.strftime(\"%Y-%m\")\n    time_dmy = now.strftime(\"%d. %b %Y\")\n    semester = utils.json_read(prefix + \"current_semester.json\", None)\n    semester = semester[0] +\" \"+ semester[1]\n    folder   = \"gh-pages/\"\n\n    pflicht: List[Tuple[str, str]] = []\n    fields: Dict[str, Dict[str, Tuple[str, str]]] = {}\n    pflicht = utils.json_read(prefix + \"pre-tucan-pflicht.json\", pflicht)\n    fields = utils.json_read(prefix + \"pre-inferno.json\", fields)\n\n    #nebenfach = utils.json_read(\"nebenfach.json\")\n#    back = utils.groupby(((course, major +\" · \"+ category)\n#            for major,v in nebenfach.items()\n#            for category,v in v.items()\n#            for module in v\n#            for course in module), key=lambda x:x[0])\n#    back = {k:[\"Y Nebenfach · \" + \" &<br> \".join(i[1] for i in v),\"\"] for k,v in back}\n#    fields = [back] + list(fields.values())\n#    print(json.dumps(fields, indent=2))\n\n    # dist/main.js with npm; code.orig.js without npm\n    if os.path.exists(\"dist/main.js\"):\n      CODE_FILE = \"dist/main.js\"\n    else:\n      CODE_FILE = \"code.orig.js\"\n\n    page_tmpl  = utils.file_read(\"page.html\")\n    index_tmpl = utils.file_read(\"index.html\")\n    code_tmpl  = utils.file_read(CODE_FILE) + utils.file_read(\"code.common.js\")\n    style_tmpl = utils.file_read(\"style.css\")\n\n    def filename(reg: str) -> str:\n      return \"\".join(c for c in reg if c.isalnum())\n\n    regulations = [\n      (k,\n       k.replace(\"B.Sc.\", \"Bachelor\")\n        .replace(\"M.Sc.\", \"Master\")\n        .replace(\" (2015)\", \"\"),\n       filename(k) + \".html\")\n      for k in fields.keys()\n      if k.endswith(\" (2015)\")\n     ] + [\n      # other FBs?\n      (\"BauUmwelt\", \"FB 13 Bau, Umwelt\", \"BauUmwelt.html\")\n    ]\n\n    listy = [\n      {'href': href, 'title': semester +\" \"+ display_regulation}\n      for regulation, display_regulation, href in regulations\n      if display_regulation.endswith(\" Informatik\")\n      if not display_regulation.startswith(\"FB \")\n    ]\n    experimentallist = [\n      {'href': href, 'title': semester +\" \"+ display_regulation}\n      for regulation, display_regulation, href in regulations\n      if not display_regulation.endswith(\" Informatik\")\n      if not display_regulation.startswith(\"FB \")\n    ]\n    speciallist = [\n      {'href': href, 'title': semester +\" \"+ display_regulation}\n      for regulation, display_regulation, href in regulations\n      if display_regulation.startswith(\"FB \")\n    ]\n    index_data = {\n      \"list\": listy,\n      \"experimentallist\": experimentallist,\n      \"speciallist\": speciallist,\n    }\n    utils.file_write(folder + \"/index.html\", stache(index_tmpl, index_data))\n    utils.file_write(folder + \"/main.js\", code_tmpl)\n    utils.file_write(folder + \"/style.css\", style_tmpl)\n\n    print(regulations)\n    for regulation, display_regulation, href in regulations:\n        print(prefix + \"-\" + filename(regulation) + \".json\")\n        modules: Dict[str, Module] = {}\n        modules = utils.json_read(prefix + \"-\" + filename(regulation) + \".json\", modules)\n        if modules == []: continue # if file exists\n\n        data = [clean(module_id, module, fields, regulation)\n                for module_id, module in modules.items()]\n\n        data.sort(key=lambda x: (x['category'], x['id'])) # -int(x['credits'])\n        js_data = json.dumps(data, indent=1)\n\n        page_data = {\n          \"today\":      time_dmy,\n          \"semester\":   semester,\n          \"regulation\": display_regulation,\n          \"js_data\":    js_data,\n          \"content\":    generate_page(data)\n        }\n        utils.file_write(folder + \"/\" + href, stache(page_tmpl, page_data))\n\n    import pprint; print(\"(FB, CP, SWS): count\"); pprint.pprint(pairs)\n    print(\"finished\")\n\n\ndef generate_page(data: List[Module2]) -> str:\n    def genmodule(x: Module2) -> str: return stache(\"\"\"\n<div class=flex>\n  <label><input id='checker-{{id}}' class=checker type=checkbox></label>\n  <details class=module-wrapper>\n    <summary id='module-{{id}}' class='module box-b box-b-{{id}}'>\n      <span>{{credits}}cp</span>\n      <span>{{title_short}}</span>\n      <span title='{{title}}'>{{title}}</span>\n      <span title='{{owner}}'>{{owner_short}}</span>\n      <!-- <span title='{{language}}'>{{{language}}}</span> -->\n      <div class=toggler-show></div>\n    </summary>\n    <div id='details-{{id}}' class=details>{{#details}}\n      <b>{{title}}</b><br>\n      {{#details}}{{{.}}}<br>{{/details}}\n    <!-- \"> --></a>{{/details}}</div>\n  </details>\n</div>\"\"\", x) #type: ignore\n\n    def gencategory(title: str, modules: str) -> str: return stache(\"\"\"\n<details class=category>\n  <summary>\n    <div class=toggler-show></div>\n    <b>{{title}}</b>\n  </summary>\n  <clear></clear>\n  {{{modules}}}\n</details>\n<br>\"\"\", {\"title\": title, \"modules\": modules}) # type: ignore\n\n    result = \"\"\n    for c, modules in utils.groupby(data, lambda x: x[\"category\"]):\n      str_modules = \"\\n\\n\".join(genmodule(m) for m in modules)\n      result += gencategory(c, str_modules)\n    return result\n\ndef clean(module_id: str, entry: Module,\n          fields: Dict[str, Dict[str, Tuple[str, str]]],\n          regulation: str) -> Module2:\n    def get_first(title: str, entry: List[Entry] = entry[\"details\"]) -> Optional[str]:\n        tmp = [detail for detail in entry if detail[\"title\"] == title]\n        return tmp[0].get('details') if len(tmp)>0 else None\n\n    def get_abbr(title: str) -> str:\n      # choose the best one of three abbreviations\n      abbr1 = \"\".join(i for i in title if i.isupper() or i.isnumeric())\n      abbr2 = \"\".join(i[0] if len(i)>0 else \"\" for i in title.strip().split(\" \"))\n      abbr3 = (get_first(\"Kürzel\") or \"\").strip().replace(\" \", \"\")\n      abbrs = ( [abbr3, abbr1, abbr2]\n                if 1 < len(abbr3) < 6 else\n                sorted((i for i in (abbr1, abbr2)), key=lambda x: abs(3.4 - len(x))) )\n      #print(abbrs)\n      return abbrs[0]\n\n    # module_id, title, abbr\n    courses = list(entry['content'].values())\n    first_entry = courses[0]\n    sort_title = first_entry['title'][10:]\n    _, title = sort_title.split(\" \", 1)\n    if len(courses) > 1:\n      title = get_first(\"Titel\") or title\n    orig_title = title\n    module_id = module_id or get_first(\"TUCaN-Nummer\") or \"\"\n    title = utils.remove_bracketed_part(title)\n    title = utils.remove_bracketed_part(title)\n    title = utils.roman_to_latin_numbers(title)\n    title = title.replace(\"Praktikum in der Lehre - \", \"Pidl - \")\n    title = title.replace(\"Praktikum in der Lehre zu \", \"Pidl - \")\n    abbr = get_abbr(title)\n\n    # language\n    mainlanguages = set(i.strip() for i in (get_first(\"Sprache\") or \"\").replace(\" und \", \"/\").replace(\"Deutsch\", \"🇩🇪\").replace(\"Englisch\", \"🇬🇧\").split(\"/\"))\n    sublanguages = {\n      j.strip() for i in courses for j in\n      ((get_first(\"Unterrichtssprache\", i[\"details\"])  or \"\").replace(\" und \", \"/\").replace(\"Deutsch\", \"🇩🇪\").replace(\"Englisch\", \"🇬🇧\").split(\"/\"))}\n    language = \" \".join(sorted((mainlanguages | sublanguages) - set([\"\"])))\n\n    # last name of owners\n    owner = \"; \".join(collections.OrderedDict(\n      (x,1) for course in courses\n            for x in (get_first(\"Lehrende\", course[\"details\"]) or\n                      get_first(\"Modulverantwortlicher\", course[\"details\"]) or \"???\").split(\"; \")\n    ).keys()) or \"???\"\n    owner = owner.replace(\"<br>\", \"\")\n    short_owner = \"; \".join(i.split()[-1] for i in owner.split(\"; \"))\n\n    # category\n    #isos = first_entry['title'].split(\" \")[0].endswith(\"-os\")\n    category = fields.get(regulation, {}).get(module_id, [\"\",\"\"])[0]\n    category = clean_category(category)\n    if category == \"C. Fachübergreifende Lehrveranstaltungen\": category = \"\"\n    category_based_on_module_id_ending = {\n      \"-se\": \"B. Seminare\",\n      \"-pr\": \"B. Praktika\",\n      \"-pl\": \"B. Praktika in der Lehre\",\n      \"-os\": \"B. Oberseminare\",\n      #\"-vl\": \"A. Vorlesungen und integrierte Veranstaltungen\",\n      #\"-iv\": \"A. Vorlesungen und integrierte Veranstaltungen\",\n    }.get(first_entry['title'].split(\" \")[0][-3:], None)\n    #print(first_entry['title'].split(\" \")[0][-3:], category_based_on_module_id_ending)\n    category = (\n      #\"B. Oberseminare\" if isos else # category == \"B. Seminare\" and entry[\"credits\"] == 0\n      category or {\n        \"01\": \"C. Nebenfach FB 01 (Wirtschaft & Recht; Entrepeneurship)\",\n        \"02\": \"C. Nebenfach FB 02 (Philosophie)\",\n        \"03\": \"C. Nebenfach FB 03 (Humanw.; Sportw.)\",\n        \"04\": \"C. Nebenfach FB 04 (Logik; Numerik; Optimierung; Stochastik)\",\n        \"05\": \"C. Nebenfach FB 05 (Elektrow.; Physik)\",\n        \"11\": \"C. Nebenfach FB 11 (Geow.)\",\n        \"13\": \"C. Nebenfach FB 13 (Bauinformatik; Verkehr)\",\n        \"16\": \"C. Nebenfach FB 16 (Fahrzeugtechnik)\",\n        \"18\": \"C. Nebenfach FB 18 (Elektrotechnik)\",\n        \"41\": \"C. Sprachkurse\",\n      }.get(module_id[:2], None)\n      or category_based_on_module_id_ending\n      or \"0. Nicht einsortierte Veranstaltungen\"\n    )\n    if \"B.Sc.\" in regulation:\n      category = category.replace(\"C. Nebenfach FB 04 (Logik; Numerik; Optimierung; Stochastik)\", \"0. Mathe und Pflichtveranstaltungen\")\n      category = category.replace(\"Nebenfach\", \"Fachübergreifend\")\n      #category = category.replace(\"Pflichtveranstaltungen\", \"Nicht einsortierte Veranstaltungen\")\n    #else:\n    #  category = category.replace(\"Pflichtveranstaltungen\", \"Nicht einsortierte Veranstaltungen\")\n\n    # dates\n    def pdt(day: str) -> datetime.datetime:\n      return datetime.datetime.strptime(day, \"%Y-%m-%d\")\n    def fmtdt(day: datetime.datetime) -> str:\n      return datetime.datetime.strftime(day, \"%Y-%m-%d\")\n    def shiftNweeks(n: int, day: str) -> str:\n      return fmtdt(pdt(day) + datetime.timedelta(weeks=n))\n\n    dates   = {i for course in courses for i in course.get('dates', [])}\n    uedates = {i for course in courses for i in course.get('uedates', [])}\n    uebung  = \"Übung \" if len(uedates) != 1 else \"Übungsstunde\"\n    uedates2 = {\"\\t\".join(\n                 [shiftNweeks(i, y.split(\"\\t\",1)[0])] +\n                 y.split(\"\\t\")[1:3] +\n                 [uebung]\n               ) #.replace(orig_title, \"\")\n               for y in uedates\n               for i in range( int((pdt(y.split(\"\\t\")[4])\n                               - pdt(y.split(\"\\t\")[0])).days/7+1) )}\n    alldates = {\"weekly\": clean_dates(dates | uedates2)}\n\n    # reorder details\n    later_titles = {\n        \"Unterrichtssprache\", \"Sprache\",\n        \"Min. | Max. Teilnehmerzahl\",\n\n        \"TUCaN-Nummer\", \"Kürzel\", \"Anzeige im Stundenplan\", # \"Titel\",\n        \"Lehrveranstaltungsart\", \"Veranstaltungsart\",\n        \"Turnus\", \"Startsemester\",\n        \"SWS\", \"Semesterwochenstunden\",\n        \"Diploma Supplement\",\n        \"Modulausschlüsse\", \"Modulvoraussetzungen\",\n        \"Studiengangsordnungen\", \"Verwendbarkeit\", \"Anrechenbar für\",\n        \"Orga-Einheit\", \"Gebiet\", \"Fach\",\n        \"Modulverantwortliche\", # \"Lehrende\",\n\n        \"Dauer\",\n        \"Anzahl Wahlkurse\",\n        \"Notenverbesserung nach §25 (2)\",\n        \"Wahlmöglichkeiten\",\n        \"Credits\",\n        \"Kurstermine\",\n        \"Titel\",\n    }\n    early = [i for i in entry[\"details\"] if i[\"title\"] not in later_titles]\n    late  = [i for i in entry[\"details\"] if i[\"title\"] in later_titles]\n    entry[\"details\"].clear()\n    modul_kurs_title = \"<br>\".join([\n      \"Modul: \" + module_id + \" \" + orig_title\n    ] + [\n      \"Kurs: \" + v['title'] for k,v in entry[\"content\"].items()\n    ])\n    date_detail: List[Entry] = []\n    if dates:\n      firstdate = min(dates).split(\"\\t\")[0]\n      lastdate  = max(dates).split(\"\\t\")[0]\n      def dt2str(x: Termin) -> str:\n        if x[\"count\"] == 1:\n          return \"{}x {} {} {:0>2}:{:0>2} - {:0>2}:{:0>2} ({})\".format(\n            x[\"count\"],\n            x[\"firstdate\"],\n            utils.num_to_day[x[\"day\"]],\n            x[\"start\"][0], x[\"start\"][1],\n            x[\"end\"][0], x[\"end\"][1],\n            x[\"room\"])\n        return \"{}x {} {:0>2}:{:0>2} - {:0>2}:{:0>2} ({})\".format(\n          x[\"count\"],\n          utils.num_to_day[x[\"day\"]],\n          x[\"start\"][0], x[\"start\"][1],\n          x[\"end\"][0], x[\"end\"][1],\n          x[\"room\"])\n      date_detail = [\n        {\"details\": \"<br>\".join(\"* \" + dt2str(i) for i in alldates[\"weekly\"]),\n         \"title\":\"Termine zwischen \" + firstdate + \" und \" + lastdate}]\n    modul_detail: List[Entry] = [{\"details\":modul_kurs_title, \"title\":\"Modul und Kurs\"}]\n    break_detail: List[Entry] = [{\"details\":\"<br><hr><b>Modul: \"+title+\"</b><br>\", \"title\":\"\"}]\n    entry[\"details\"].extend(\n        modul_detail\n      + date_detail\n      + early\n      + break_detail\n      + late\n    )\n    for k,course in entry['content'].items():\n      entry[\"details\"] += [{\"details\":\"<br><hr><b>Kurs: \"+k+\"</b><br>\", \"title\":\"\"}]\n      entry[\"details\"] += course['details']\n    for detail in entry[\"details\"]:\n        if detail[\"details\"].strip() != \"\":\n            detail[\"details\"] += \"<br>\"\n        if detail['title'] == \"Studiengangsordnungen\":\n            regs = [(x.split(\"(\", 1))\n              for x in sorted(detail['details'].replace(\"<br>\", \"<br/>\").split(\"<br/>\"))\n              if x.strip()]\n            regs2 = utils.groupby(regs, key=lambda x: x[0])\n            regs3 = [(k,list(v)) for k,v in regs2]\n#            print(detail['details'].replace(\"<br>\", \"<br/>\").split(\"<br/>\"))\n#            print([ k +\"(\"+ \", \".join(i[:-1] for _,i in v) + \")\" for k,v in regs2])\n            detail['details'] = \"<br/>\".join(\n              k+\"(\"+\", \".join(i[:-1] for _,i in sorted(v))+\")\" for k,v in regs3\n            ) + \"<br/>\"\n\n    # result\n    sws     = sum((float(i[\"details\"][:-4].replace(\",\",\".\")) for course in courses for i in course[\"details\"] if i[\"title\"] in [\"Semesterwochenstunden\", \"SWS\"]))\n    credits = entry[\"credits\"]\n    key = (module_id[0:2], credits, sws)\n    pairs[key] = pairs.get(key, 0) + 1\n    #print(module_id, sws, credits)\n\n    result = utils.merge_dict(entry, alldates) # type: ignore\n    assert result['module_id'] == module_id\n    del result['module_id']\n    result: Module2 = utils.merge_dict(result, { # type: ignore\n        \"id\": module_id,\n        \"title\": title, \"title_short\": abbr,\n        \"owner\": owner, \"owner_short\": short_owner,\n        \"credits\": str(credits).zfill(2) if credits != -1 else \"??\",\n        \"sws\": str(sws),\n        'category': category,\n        \"language\": language,\n    })\n    return result\n\ndef clean_category(path: str) -> str:\n    replacements = [\n        # PO 2009\n        (\"Grundstudium\", \"Pflicht\"),\n        (\"Kanonikfächer \\| Kanonische Einführungsveranstaltungen\", \"Pflicht\"),\n        (\"Wahlpflichtbereich \\| Wahlpflichtbereich A\", \"Wahl-A\"),\n        (\"Wahlpflichtbereich \\| Wahlpflichtbereich B\", \"Wahl-B\"),\n        (\"Projekte, Projektpraktika und ähnliche Veranstaltungen\", \"B. Praktika\"),\n        (\" \\| [^ ]* Prüfungsleistungen\", \"\"),\n        (\" \\| [^|]* \\| ([A-Z]*) Studienleistungen \\| \\\\1 (.*)$\", \" | \\\\2 /// \\\\1 \"),\n\n        # PO 2015\n        (\"Pflichtbereich\", \"BSc Pflicht\"),\n        (\"Wahlbereich \\| Studienleistungen\", \"BSc Wahl\"),\n        (\"Vorgezogene Masterleistungen \\| Vorgezogene Masterleistungen der Informatik \\|\", \"MSc\"),\n        (\"Wahlbereich Fachprüfungen\", \"Wahl-A\"),\n        (\"Wahlbereich Studienleistungen\", \"Wahl-B\"),\n        (\" \\(sp-FB20\\)\", \"\"),\n        (\"Praktika, Projektpraktika, ähnliche LV\", \"B. Praktika\"),\n        (\"Praktika, Projektpraktika und ähnliche Veranstaltungen\", \"B. Praktika\"),\n        (\"Fachübergreifende Lehrveranstaltungen\", \"C. Fachübergreifende Lehrveranstaltungen\"),\n        (\"Wahlbereiche \\| \", \"\"),\n\n        # common\n        (\"Praktika in der Lehre\", \"B. Praktika in der Lehre\"),\n        (\"Praktikum in der Lehre\", \"B. Praktika in der Lehre\"),\n        (\"Module der \", \"\"),\n        (\"Fachübergreifend \\| Gesamtkatalog aller Module des Sprachenzentrums\", \"Sprachzentrum\"),\n        (\" \\| ([^|]*) \\| \\\\1\", \" | \\\\1 \"),\n        (\"Projektpraktika\", \"X Praktika\"),\n        (\"Projekte\", \"B. Praktika\"),\n        (\"Seminare\", \"B. Seminare\")\n    ]\n    for match, result in replacements:\n        path = re.sub(match, result, path)\n    if path and not path[:3] in [\"A. \", \"B. \", \"C. \", \"0. \"]:\n        path = \"A. \" + path\n    return path\n\ndef clean_dates(item: Set[str]) -> List[Termin]:\n    def parse_date(string: str) -> Tuple[\n        datetime.datetime, Tuple[int, int], Tuple[int, int], str]:\n      day, start, end, room = string.split(\"\\t\", 3)\n      room  = room.split(\"\\t\")[0]\n      day   = datetime.datetime.strptime(day, \"%Y-%m-%d\")\n      start = utils.parse_hm(start)\n      end   = utils.parse_hm(end)\n      return day, start, end, room\n\n    dates = list(sorted(parse_date(i) for i in item))\n\n#    # first, last event\n#    first = last = first_to_last = \"\"\n#    if len(dates) > 0:\n#        first = dates[ 0][0].strftime(\"%Y-%m-%d\")\n#        last  = dates[-1][0].strftime(\"%Y-%m-%d\")\n#        first_to_last = \"Termine liegen von %s bis %s:<br>\" % (\n#            dates[ 0][0].strftime(\"%d. %b\"),\n#            dates[-1][0].strftime(\"%d. %b\"),\n#        )\n\n    # how many weeks does the event repeat?\n    uniqdates = {i[:4] for i in dates}\n    counted = [(i[0].weekday(), *i[1:]) for i in uniqdates]\n    counted = collections.Counter(counted)\n    counted: List[Termin] = [\n      {\"count\": count, \"day\": v[0], \"start\": v[1], \"end\": v[2], \"room\": v[3],\n       \"firstdate\": \"\"}\n       for v, count in counted.items()]\n\n    # add rooms of weekly events together\n    for d in counted:\n#        roomlst = [room for i in dates\n#                        if (i[0].weekday(), i[1], i[2]) ==\n#                           (d['day'], d['start'], d['end'])\n#                        for room in i[3].split(\",\")]\n#        d['room']  = \", \".join(sorted(set(roomlst)))\n        d['firstdate'] = min(i[0] for i in dates\n                                  if (i[0].weekday(), i[1], i[2]) ==\n                                     (d['day'], d['start'], d['end'])).strftime(\"%Y-%m-%d\")\n\n    counted.sort(key=lambda a: (a[\"firstdate\"], a[\"start\"]))\n    return counted\n\nif __name__ == \"__main__\": main()\n","repo_name":"drcicero/beautiful-tucan","sub_path":"step2.py","file_name":"step2.py","file_ext":"py","file_size_in_byte":19194,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"86"}
+{"seq_id":"40309480080","text":"from jerex.entities import Document\nfrom jerex.sampling.sampling_common import create_mention_candidates, create_context_tensors, create_mention_tensors, \\\n    create_coref_tensors, create_entity_tensors, create_rel_global_tensors, create_rel_mi_tensors, \\\n    create_pos_relations, create_neg_relations, create_rel_mention_pairs, \\\n    create_pos_coref_pairs, create_neg_coref_pairs, create_entities, create_positive_mentions, \\\n    create_negative_mentions, create_mention_candidate_tensors\n\n\ndef create_joint_train_sample(doc: Document, neg_mention_count: int, neg_rel_count: int, neg_coref_count: int,\n                              max_span_size: int, neg_mention_overlap_ratio: float, rel_type_count: int):\n    encodings = doc.encodings  # document sub-word encoding\n    context_size = len(encodings)\n\n    # positive entity mentions\n    pos_mention_spans, pos_mention_masks, pos_mention_sizes = create_positive_mentions(doc, context_size)\n\n    # negative entity mentions\n    neg_mention_spans, neg_mention_sizes, neg_mention_masks = create_negative_mentions(doc, pos_mention_spans,\n                                                                                       neg_mention_count,\n                                                                                       max_span_size,\n                                                                                       context_size,\n                                                                                       overlap_ratio=neg_mention_overlap_ratio)\n\n    # entities\n    entities, entity_types = create_entities(doc, pos_mention_spans)\n\n    # positive coreference pairs\n    pos_coref_mention_pairs, pos_coref_spans, pos_eds = create_pos_coref_pairs(doc, pos_mention_spans)\n\n    # negative coreference pairs\n    neg_coref_mention_pairs, neg_coref_spans, neg_eds = create_neg_coref_pairs(doc, pos_mention_spans,\n                                                                               neg_coref_count)\n\n    # positive relations\n    pos_rel_entity_pairs, pos_rel_types, rels_between_entities = create_pos_relations(doc, rel_type_count)\n\n    (pos_rel_entity_pair_mp, pos_rel_mention_pair_ep, pos_rel_mention_pairs, pos_rel_ctx_masks,\n     pos_rel_token_distances, pos_rel_sentence_distances) = create_rel_mention_pairs(doc, pos_rel_entity_pairs,\n                                                                                     pos_mention_spans, context_size)\n\n    # negative relations\n    # use only strong negative relations, i.e. pairs of actual (labeled) entities\n    neg_rel_entity_pairs, neg_rel_types = create_neg_relations(entities, rels_between_entities,\n                                                               rel_type_count, neg_rel_count)\n\n    (neg_rel_entity_pair_mp, neg_rel_mention_pair_ep, neg_rel_mention_pairs, neg_rel_ctx_masks,\n     neg_rel_token_distances, neg_rel_sentence_distances) = create_rel_mention_pairs(doc, neg_rel_entity_pairs,\n                                                                                     pos_mention_spans, context_size,\n                                                                                     offset_mp=len(\n                                                                                         pos_rel_mention_pairs),\n                                                                                     offset_ep=len(\n                                                                                         pos_rel_entity_pairs))\n\n    encodings, context_masks = create_context_tensors(encodings)\n\n    mention_types, mention_masks, mention_sizes, mention_spans, mention_sample_masks = create_mention_tensors(\n        context_size,\n        pos_mention_spans,\n        pos_mention_masks,\n        pos_mention_sizes,\n        neg_mention_spans,\n        neg_mention_masks,\n        neg_mention_sizes)\n\n    (coref_mention_pairs, coref_types, coref_eds, coref_sample_masks) = create_coref_tensors(\n        pos_coref_mention_pairs, pos_eds, neg_coref_mention_pairs, neg_eds)\n\n    entities, entity_masks, entity_types, entity_sample_masks = create_entity_tensors(entities, entity_types)\n\n    rel_entity_pairs, rel_types, rel_sample_masks = create_rel_global_tensors(pos_rel_entity_pairs, pos_rel_types,\n                                                                              neg_rel_entity_pairs, neg_rel_types)\n\n    (rel_entity_pair_mp, rel_mention_pair_ep, rel_mention_pairs, rel_ctx_masks, rel_pair_masks,\n     rel_token_distances,\n     rel_sentence_distances) = create_rel_mi_tensors(\n        context_size,\n        pos_rel_entity_pair_mp, pos_rel_mention_pair_ep,\n        pos_rel_mention_pairs,\n        pos_rel_ctx_masks,\n        pos_rel_token_distances,\n        pos_rel_sentence_distances,\n        neg_rel_entity_pair_mp, neg_rel_mention_pair_ep,\n        neg_rel_mention_pairs,\n        neg_rel_ctx_masks,\n        neg_rel_token_distances,\n        neg_rel_sentence_distances)\n\n    assert len(mention_masks) == len(mention_sizes) == len(mention_sample_masks) == len(mention_types)\n    assert len(coref_mention_pairs) == len(coref_sample_masks) == len(coref_types) == len(coref_eds)\n    assert len(entities) == len(entity_types)\n    assert len(rel_entity_pairs) == len(rel_types)\n\n    return dict(encodings=encodings, context_masks=context_masks, mention_masks=mention_masks,\n                mention_sizes=mention_sizes, mention_types=mention_types, mention_sample_masks=mention_sample_masks,\n                entities=entities, entity_masks=entity_masks, entity_types=entity_types,\n                entity_sample_masks=entity_sample_masks,\n                coref_mention_pairs=coref_mention_pairs, coref_types=coref_types,\n                coref_eds=coref_eds, coref_sample_masks=coref_sample_masks,\n                rel_entity_pairs=rel_entity_pairs, rel_types=rel_types, rel_types_evidence=rel_types,\n                rel_sample_masks=rel_sample_masks,\n                rel_entity_pair_mp=rel_entity_pair_mp, rel_mention_pair_ep=rel_mention_pair_ep,\n                rel_mention_pairs=rel_mention_pairs, rel_ctx_masks=rel_ctx_masks, rel_pair_masks=rel_pair_masks,\n                rel_token_distances=rel_token_distances, rel_sentence_distances=rel_sentence_distances)\n\n\ndef create_joint_inference_sample(doc, max_span_size: int):\n    encodings = doc.encodings\n    context_size = len(encodings)\n\n    # create mention candidates\n    (mention_masks, mention_sizes, mention_spans,\n     mention_orig_spans, mention_sent_indices) = create_mention_candidates(doc, max_span_size, context_size)\n\n    (mention_masks, mention_sizes, mention_spans,\n     mention_orig_spans, mention_sent_indices, mention_sample_masks) = create_mention_candidate_tensors(context_size,\n                                                                                                        mention_masks,\n                                                                                                        mention_sizes,\n                                                                                                        mention_spans,\n                                                                                                        mention_orig_spans,\n                                                                                                        mention_sent_indices)\n\n    encodings, context_masks = create_context_tensors(encodings)\n\n    return dict(encodings=encodings, context_masks=context_masks, mention_masks=mention_masks,\n                mention_sizes=mention_sizes, mention_spans=mention_spans, mention_sample_masks=mention_sample_masks,\n                mention_orig_spans=mention_orig_spans, mention_sent_indices=mention_sent_indices)\n","repo_name":"lavis-nlp/jerex","sub_path":"jerex/sampling/sampling_joint.py","file_name":"sampling_joint.py","file_ext":"py","file_size_in_byte":7687,"program_lang":"python","lang":"en","doc_type":"code","stars":57,"dataset":"github-code","pt":"86"}
+{"seq_id":"8238487765","text":"from pathlib import Path\n\nimport pytest\n\nfrom hatch_modulefile.inputs import ModulefileInputs\n\nINPUTS_WITH_EXTRAS = {\n    \"requires\": [\"test\"],\n    \"extra-paths\": [{\"type\": \"setenv\", \"variable\": \"QT_XCB_GL_INTEGRATION\", \"value\": \"none\"}],\n}\n\nINPUTS_WITH_MODULEFILE = {\n    \"modulefile_path\": \"custom_modulefile\",\n}\nINPUTS_WITH_EXTRAS_AND_MODULEFILE = {\n    \"requires\": [\"test\"],\n    \"extra-paths\": [{\"type\": \"setenv\", \"variable\": \"QT_XCB_GL_INTEGRATION\", \"value\": \"none\"}],\n    \"modulefile_path\": \"custom_modulefile\",\n}\n\nROOT_DIRECTORY = Path(__file__).parent\n\n\ndef test_read_inputs_extras():\n    inputs = ModulefileInputs(INPUTS_WITH_EXTRAS, ROOT_DIRECTORY)\n    assert inputs.requires == INPUTS_WITH_EXTRAS[\"requires\"]\n    assert inputs.extra_paths == INPUTS_WITH_EXTRAS[\"extra-paths\"]\n    assert inputs.modulefile_path is None\n\n\ndef test_read_inputs_specified_modulefile():\n    inputs = ModulefileInputs(INPUTS_WITH_MODULEFILE, ROOT_DIRECTORY)\n    assert inputs.requires == []\n    assert inputs.extra_paths == []\n    assert inputs.modulefile_path == ROOT_DIRECTORY.joinpath(INPUTS_WITH_MODULEFILE[\"modulefile_path\"])\n\n\ndef test_read_inputs_specified_modulefile_and_extras():\n    with pytest.raises(ValueError, match=\"Cannot combine requires/extra_paths with modulefile_path\"):\n        ModulefileInputs(INPUTS_WITH_EXTRAS_AND_MODULEFILE, ROOT_DIRECTORY)\n","repo_name":"ReubenVandezande/hatch-modulefile","sub_path":"tests/test_inputs.py","file_name":"test_inputs.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"38822192647","text":"import textwrap\nfrom .bltButton import *\nfrom bearlibterminal import terminal\nfrom bltGui import bltSkins as Skins\nfrom .bltControl import bltControl as Control\n\n\nclass bltListbox(Control):\n    def __init__(self, owner, x, y, items, collapse=False, lock_focus = False, header = None, w = 15):\n        Control.__init__(self, ['hover', 'changed'])\n        self.owner = owner\n        self.x = x\n        self.y = y\n        self.w = w\n        self.header_h = 0\n        self.items = items\n        self.header = header\n        self.frame_element = False\n        self.dirty = True\n        self.length = len(max(items, key=len))\n        self.selected_index = None\n        self.hover_index = -1\n        self.hover = False\n        self.lock_focus = lock_focus\n\n        self.colors = Skins.COLOR_SKINS['GRAY']\n\n        self.collapse = collapse\n        if self.collapse:\n            self.expanded = False\n        else:\n            self.expanded = True\n\n    def apply_header(self):\n        if self.header is not None:\n            self.header = textwrap.wrap(self.header, self.w-2)\n            self.header_h = len(self.header)\n\n    def update(self):\n        mouse = Input.mouse\n        key = Input.key\n        if self.owner:\n            layer = self.owner.layer\n            x = self.owner.pos.x\n            y = self.owner.pos.y\n        else:\n            layer = terminal.state(terminal.TK_LAYER)\n            x = 0\n            y = 0\n        \n        if self.expanded:\n            if self.hover or self.lock_focus:\n                if key is not None and key not in range(128,141):\n                    if key in [terminal.TK_UP, terminal.TK_DOWN, terminal.TK_ENTER, terminal.TK_KP_ENTER]:\n                        if self.hover_index is not None:\n                            if key == terminal.TK_UP:\n                                if self.hover_index == 0:\n                                    self.hover_index = len(self.items) - 1\n                                else:\n                                    self.hover_index -= 1\n                            elif key in [terminal.TK_ENTER, terminal.TK_KP_ENTER]:\n                                self.selected_index = self.hover_index\n                                self.dispatch('changed', self.selected_index)\n                            else:\n                                if self.hover_index == len(self.items) - 1:\n                                    self.hover_index = 0\n                                else:\n                                    self.hover_index += 1\n                            self.dispatch('changed', self.hover_index)\n                            self.dirty = True\n                        else:\n                            if key == terminal.TK_UP:\n                                self.hover_index = 0\n                            else:\n                                self.hover_index = len(self.items) - 1\n                            self.dispatch('changed', self.hover_index)\n                            self.dirty = True\n                    else:\n                        if isinstance(self.items,list):\n                            for item in self.items:\n                                item_index = self.items.index(item)\n                                cp = 4 + item_index\n                                if cp == key:\n                                    self.hover_index = item_index\n                                    self.selected_index = item_index\n                                    self.dispatch('changed', self.hover_index)\n                                    self.dispatch('changed', self.selected_index)\n                                    self.dirty = True\n                                    break\n                        else:\n                            for item in self.items:\n                                item_index = list(self.items).index(item)\n                                cp = 4 + item_index\n                                if cp == key:\n                                    self.hover_index = item_index\n                                    self.selected_index = item_index\n                                    self.dispatch('changed', self.hover_index)\n                                    self.dispatch('changed', self.selected_index)\n                                    self.dirty = True\n                                    break\n\n\n            if mouse.hover_rect(self.x + x, self.y + y, self.length + 1, len(self.items)):\n                if mouse.hover_rect(self.x + x + self.length, self.y + y, 1, 1) and self.collapse:\n\n                    self.hover = True\n                    if mouse.lbutton_pressed:\n                        self.expanded = False\n                        self.dirty = True\n                else:\n                    self.hover = True\n                    self.hover_index = mouse.cy - (self.y + self.owner.pos.y)\n                    #DEBUG\n                    # print(\"Mouse pos: \" + str(mouse.cy))\n                    # print(\"Control pos: \" + str(self.y))\n                    # print(\"Owner pos: \" + str(self.owner.pos.y))\n                    # print(\"Item index: \" + str(self.hover_index))\n                    self.dispatch('changed', self.hover_index)\n                    if mouse.lbutton_pressed:\n                        self.selected_index = mouse.cy - (self.y + self.owner.pos.y)\n                        self.dispatch('changed', self.selected_index)\n                        if self.collapse:\n                            self.expanded = False\n                    self.dirty = True\n            else:\n                if self.hover:\n                    self.dirty = True\n                self.hover = False\n                self.pressed = False\n                #self.hover_index = -1\n        elif mouse.hover_rect(self.x + x + self.length, self.y + y, 1, 1) and self.collapse:\n            self.hover = True\n            if mouse.lbutton_pressed:\n                self.expanded = True\n\n        else:\n            self.hover = False\n            if self.collapse:\n                self.expanded = False\n\n    def draw(self):\n        if self.expanded:\n            if self.header is not None:\n                color = self.colors['COLOR']\n                bkcolor = self.colors['BKCOLOR']\n                terminal.puts(self.x + self.owner.pos.x, self.y + self.owner.pos.y, self.owner.font + \"[c={0}] {2}\".format(color, bkcolor, self.header))\n            for i, item in enumerate(self.items):\n                letter_index = ord('a') + i\n                color = self.colors['COLOR']\n                bkcolor = self.colors['BKCOLOR']\n                if i == self.hover_index:\n                    color = self.colors['HOVER']\n                    bkcolor = self.colors['BKHOVER']\n                if i == self.selected_index:\n                    color = self.colors['SELECTED']\n                    bkcolor = self.colors['BKSELECTED']\n\n                if bkcolor is not None:\n                    terminal.puts(self.x + self.owner.pos.x, self.y + self.owner.pos.y + i + self.header_h, self.owner.font + \"[c={0}]\".format(bkcolor) + str(\"[U+2588]\" * (self.length+5)))\n                terminal.puts(self.x + self.owner.pos.x, self.y + self.owner.pos.y + i + self.header_h, self.owner.font + \"[c={0}] {3}) {2}\".format(color, bkcolor, item, chr(letter_index)))\n\n            if self.collapse:\n                bkcolor = self.colors['SELECTED']\n                terminal.puts(self.x + self.owner.pos.x + self.length, self.y + self.owner.pos.y,\n                              \"[c={0}]\".format(bkcolor) + str(\"[U+2588]\"))\n                terminal.puts(self.x + self.owner.pos.x + self.length, self.y + self.owner.pos.y,\n                              \"[c={0}]\".format(color) + str(\"[U+25BC]\"))\n            self.dirty = False\n        if not self.expanded:\n\n            color = self.colors['COLOR']\n            bkcolor = self.colors['BKCOLOR']\n\n\n            i = self.selected_index\n\n\n            if bkcolor is not None:\n                terminal.puts(self.x + self.owner.pos.x, self.y + self.owner.pos.y , self.owner.font + \"[c={0}]\".format(bkcolor) + str(\"[U+2588]\" * (self.length+5)))\n            if self.selected_index is not None:\n                item = self.items[i]\n                terminal.puts(self.x + self.owner.pos.x, self.y + self.owner.pos.y, self.owner.font + \"[c={0}] {3}) {2}\".format(color, bkcolor, item, chr(letter_index)))\n\n            if self.collapse:\n                bkcolor = self.colors['BKCOLOR']\n                terminal.puts(self.x + self.owner.pos.x + self.length, self.y + self.owner.pos.y,\n                              \"[c={0}]\".format(bkcolor) + str(\"[U+2588]\"))\n                terminal.puts(self.x + self.owner.pos.x + self.length, self.y + self.owner.pos.y,\n                              \"[c={0}]\".format(color) + str(\"[U+25B2]\"))\n\n            self.dirty = False\n\n    def return_item(self):\n        if isinstance(self.items,dict):\n            item = list(self.items.keys())[self.selected_index]\n        else:\n            item = self.items[self.selected_index]\n        return item\n\n\n\n\n\n\n","repo_name":"networkingguru/CrimsonSand","sub_path":"bltGui/bltListbox.py","file_name":"bltListbox.py","file_ext":"py","file_size_in_byte":8995,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"86"}
+{"seq_id":"37934075637","text":"\"\"\"\nHome of GstPlayer\n\"\"\"\nimport logging\nfrom queue import Empty\nfrom typing import Dict, List\n\nfrom aioprocessing import AioManager, AioQueue\nimport gi                                                                                           # pylint: disable=import-error\ngi.require_version('Gst', '1.0')\nfrom gi.repository import GLib, Gst                                                                 # pylint: disable=import-error,wrong-import-position\n\nfrom utils.constants.events import TYPE_ATF, TYPE_STATE, TYPE_REPEAT                                      # pylint: disable=wrong-import-position\n\n# GstPlayer states\nSTATE_READY         : str = 'ready'\nSTATE_PLAYING       : str = 'playing'\nSTATE_PAUSED        : str = 'paused'\nSTATE_ATF           : str = 'atf'\nSTATE_ERR           : str = 'err'\n\n# GstPlayer commands\nCMD_SHUTDOWN        : str = 'shutdown'\nCMD_PLAY            : str = 'play'\nCMD_PAUSE           : str = 'pause'\nCMD_STOP            : str = 'stop'\nCMD_AGAIN           : str = 'play_again'\nCMD_REPEAT          : str = 'toggle_repeat'\nCMD_SKIP_NEXT       : str = 'skip_next'\nCMD_SKIP_FW         : str = 'skip_forward'\nCMD_SKIP_BW         : str = 'skip_back'\nCMD_SET_POSITION    : str = 'set_position'\nCMD_SET_VOLUME      : str = 'set_volume'\n\n# GstPlayer dashboard attributes\nDASH_DURATION       : str = 'duration'\nDASH_ERROR          : str = 'error'\nDASH_POSITION       : str = 'position'\nDASH_REPEAT         : str = 'repeat'\nDASH_STATE          : str = 'state'\nDASH_URI            : str = 'uri'\nDASH_VOLUME         : str = 'volume'\n\nDASHBOARD           : Dict[str, str] = {\n    DASH_DURATION: None,\n    DASH_ERROR: None,\n    DASH_POSITION: None,\n    DASH_REPEAT: False,\n    DASH_STATE: None,\n    DASH_URI: None,\n    DASH_VOLUME: None,\n}\n\n# Playbin properties and constants\n_FORMAT_TIME        : Gst.Format = Gst.Format(Gst.Format.TIME)\n_NANOSEC_MULT       : int = 10 ** 9\n_ATF_THRESHOLD      : float = 0.95\n_GST_STATE_TIMEOUT  : int = 200 * Gst.MSECOND\n_PROP_VOLUME        : str = 'volume'\n_PROP_URI           : str = 'uri'\n_PROP_VIS           : str = 'vis-plugin'\n_PROP_FLAGS         : str = 'flags'\n_PERIODIC_DELAY     : int = 500\n_VIS_CLASS          : str = 'Visualization'\n_VIS_FLAGS          : int = 0x01+0x02+0x08+0x10+0x200+0x400\n\n\n_LOGGER: logging.Logger = logging.getLogger(__name__)\n\nclass _GstPlayerError(Exception):\n    \"\"\"General GstPlayer error\"\"\"\n\n\nclass GstPlayer:\n    \"\"\"\n    Wrapper around Gstreamer process executing playbin.\n    All communications with Python are handled by AioQueues and AioManager.\n    \"\"\"\n    def __init__(\n        self,\n        dashboard: AioManager,\n        command_queue: AioQueue,\n        media_queue: AioQueue,\n        ui_event_queue: AioQueue,\n        visualizer: str = None,\n    ):\n        self._ui_event_queue: AioQueue = ui_event_queue\n        self._command_queue: AioQueue = command_queue\n        self._media_queue: AioQueue = media_queue\n        self._dashboard: AioManager = dashboard\n\n        self._atf_sent: bool = False\n        self._repeat: bool = False\n\n        # Create gst playbin and set event callbacks\n        Gst.init(None)\n        self._playbin: Gst.Element = Gst.ElementFactory.make('playbin', 'player')\n        self._dashboard[DASH_VOLUME] = self._playbin.get_property(_PROP_VOLUME)\n        self._playbin.connect(\"about-to-finish\", self._on_atf)\n        bus: Gst.Bus = self._playbin.get_bus()\n        bus.add_signal_watch()\n        bus.connect('message::error', self._on_error)\n        bus.connect('message::eos', self._on_eos)\n        bus.connect('message::state-changed', self._on_state_changed)\n        if visualizer:\n            try:\n                self._set_visualizer(visualizer)\n            except _GstPlayerError as exc:\n                _LOGGER.warning('Cannot set visualizer: %s', exc)\n        self._loop: GLib.MainLoop = GLib.MainLoop()\n        _LOGGER.debug('Created Gstreamer playbin.')\n\n    def run(self) -> None:\n        \"\"\"\n        Gstreamer's main loop.\n        Must be executed as a separate OS process to avoid GIL locks of Python's threads.\n        \"\"\"\n        self._set_playbin_state(Gst.State.READY)\n\n        _LOGGER.debug('Gstreamer playbin is starting.')\n        GLib.timeout_add(_PERIODIC_DELAY, self._periodic_task)\n        self._loop.run()\n\n        self._set_playbin_state(Gst.State.NULL)\n        self._playbin = None\n        _LOGGER.debug('Gstreamer playbin is shut down.')\n\n    def _periodic_task(self) -> bool:\n        \"\"\"\n        Is called periodically by the GLib's main loop.\n        Dequeues pending commands and executes them.\n        Updates own state, queues new track URIs to the playbin.\n        \"\"\"\n        try:\n            result = self._command_queue.get(False)\n        except Empty:\n            result = None\n\n        try:\n            if result:\n                method, args = result\n                if not method.startswith('_') and hasattr(self, method):\n                    getattr(self, method)(**args)\n                else:\n                    _LOGGER.warning('Skipping invalid command: \"%s\"', method)\n            if self._state == Gst.State.PLAYING:\n                position: float = self._get_media_position()\n                self._dashboard[DASH_POSITION] = position\n                if self._dashboard[DASH_DURATION] == 0:\n                    duration: float = self._get_media_duration()\n                    self._dashboard[DASH_DURATION] = duration\n            elif self._state == Gst.State.READY:\n                self._dequeue_next_media()\n        except _GstPlayerError as exc:\n            self._error_handler(exc)\n            return False\n\n        return True\n\n    # Pipeline commands that can be called via _command_queue\n    def shutdown(self) -> None:\n        \"\"\"Shutdown process.\"\"\"\n        if self._state != Gst.State.NULL:\n            self.stop()\n        if self._loop.is_running():\n            self._loop.quit()\n\n    def play(self) -> None:\n        \"\"\"Change state to playing.\"\"\"\n        if self._state == Gst.State.PAUSED:\n            self._set_playbin_state(Gst.State.PLAYING)\n\n    def pause(self) -> None:\n        \"\"\"Change state to paused.\"\"\"\n        if self._state == Gst.State.PLAYING:\n            self._set_playbin_state(Gst.State.PAUSED)\n\n    def stop(self) -> None:\n        \"\"\"Stop pipeline.\"\"\"\n        self._set_playbin_state(Gst.State.READY)\n\n    def play_again(self) -> None:\n        \"\"\"Start from the beginning.\"\"\"\n        self.set_position(0)\n\n    def toggle_repeat(self) -> None:\n        \"\"\"Toggle repeat of current track.\"\"\"\n        self._set_repeat(not self._repeat)\n\n    def skip_next(self) -> None:\n        \"\"\"Skip to the next media.\"\"\"\n        if self._repeat:\n            self._set_repeat(False)\n            self._on_atf(None)\n        self._set_playbin_state(Gst.State.READY)\n\n    def skip_forward(self) -> None:\n        \"\"\"Skip 5% of media.\"\"\"\n        inc = self._get_media_duration() / 20\n        current = self._get_media_position()\n        self.set_position(current + inc)\n\n    def skip_back(self) -> None:\n        \"\"\"Back 5% of media.\"\"\"\n        inc = self._get_media_duration() / 20\n        current = self._get_media_position()\n        self.set_position(current - inc)\n\n    def set_position(self, position: float) -> None:\n        \"\"\"Set media position.\"\"\"\n        duration: float = self._get_media_duration()\n        if position > duration - (duration * 0.01):\n            # Emission of ATF during seek is unreliable,\n            # leave some time for track to complete and fire ATF\n            return\n        position = max(position, 0)\n        self._playbin.seek_simple(\n            _FORMAT_TIME, Gst.SeekFlags.FLUSH,\n            position * _NANOSEC_MULT\n        )\n        self._dashboard[DASH_POSITION] = position\n        _LOGGER.debug('Set position to %d s.', position)\n\n    def set_volume(self, volume: float) -> None:\n        \"\"\"Set volume.\"\"\"\n        self._playbin.set_property(_PROP_VOLUME, volume)\n        self._dashboard[DASH_VOLUME] = volume\n        _LOGGER.debug('volume set to %.2f', volume)\n\n    # Private pipeline properties and methods\n    @property\n    def _state(self) -> Gst.State:\n        \"\"\"Return only final state, wait if state change is currently transient\"\"\"\n        result, current, pending = self._playbin.get_state(_GST_STATE_TIMEOUT)\n        while result != Gst.StateChangeReturn.SUCCESS and pending != Gst.State.VOID_PENDING:\n            if result == Gst.StateChangeReturn.FAILURE:\n                raise _GstPlayerError('Unable to get current playbin state.')\n            _LOGGER.debug('---TRANSIENT STATE---')\n            result, current, pending = self._playbin.get_state(_GST_STATE_TIMEOUT)\n\n        return current\n\n    def _set_repeat(self, val: bool) -> None:\n        self._repeat = val\n        self._dashboard[DASH_REPEAT] = self._repeat\n        self._emit_repeat_event()\n        _LOGGER.debug('Repeat: %s.', 'enabled' if self._repeat else 'disabled')\n\n    def _get_media_duration(self) -> float:\n        \"\"\"Get media duration.\"\"\"\n        duration = 0.0\n        if self._state in [Gst.State.PAUSED, Gst.State.PLAYING]:\n            ok, dur = self._playbin.query_duration(_FORMAT_TIME)\n            if ok:\n                duration = dur // _NANOSEC_MULT\n        return duration\n\n    def _get_media_position(self) -> float:\n        \"\"\"Get media position.\"\"\"\n        position: float = 0.0\n        if self._state in [Gst.State.PAUSED, Gst.State.PLAYING]:\n            position = self._dashboard[DASH_POSITION]\n            ok, pos = self._playbin.query_position(_FORMAT_TIME)\n            if ok:\n                position = pos // _NANOSEC_MULT\n        return position\n\n    def _dequeue_next_media(self) -> None:\n        \"\"\"Get next uri from media queue and set it as next uri in the playbin\"\"\"\n        if not self._repeat:\n            try:\n                uri: str = self._media_queue.get(False)\n            except Empty:\n                return\n            self._dashboard[DASH_URI] = uri\n            self._dashboard[DASH_POSITION] = 0\n            self._dashboard[DASH_DURATION] = 0\n            self._playbin.set_property(_PROP_URI, uri)\n            _LOGGER.debug('Dequeued %s.', self._playbin.get_property(_PROP_URI))\n        else:\n            self.play_again()\n        self._atf_sent = False\n        if self._state == Gst.State.READY:\n            self._set_playbin_state(Gst.State.PLAYING)\n\n    def _set_own_state(self, state: str, emit: bool = True) -> None:\n        self._dashboard[DASH_STATE] = state\n        if emit:\n            self._emit_state_event()\n\n    def _set_playbin_state(self, state: Gst.State):\n        if self._playbin.set_state(state) == Gst.StateChangeReturn.FAILURE:\n            raise _GstPlayerError(f'Unable to set the pipeline to the {state.name} state.')\n\n    def _emit_state_event(self) -> None:\n        self._ui_event_queue.put({'type': TYPE_STATE})\n\n    def _emit_repeat_event(self) -> None:\n        self._ui_event_queue.put({'type': TYPE_REPEAT})\n\n    def _emit_atf_event(self) -> None:\n        if not self._atf_sent:\n            self._ui_event_queue.put({'type': TYPE_ATF})\n            self._atf_sent = True\n\n    def _eos_handler(self):\n        self._set_playbin_state(Gst.State.READY)\n        _LOGGER.debug('Finished %s.', self._dashboard[DASH_URI])\n\n    def _error_handler(self, error: str):\n        # Stop and shutdown if something goes wrong.\n        _LOGGER.error('Gstreamer fired error: %s. Shutting down.', error)\n        self._dashboard[DASH_ERROR] = error\n        self._set_own_state(STATE_ERR)\n        self.shutdown()\n\n    def _set_visualizer(self, name: str):\n        vis_list: List[Gst.ElementFactory] = Gst.Registry.feature_filter(\n            Gst.Registry.get(),\n            lambda e, _: isinstance(e, Gst.ElementFactory) and e.get_klass() == _VIS_CLASS,\n            False,\n            None)\n        for item in vis_list:\n            if item.name == name:\n                self._playbin.set_property(_PROP_VIS, Gst.ElementFactory.create(item))\n                self._playbin.set_property(_PROP_FLAGS, _VIS_FLAGS)\n                return\n        raise _GstPlayerError(f'No such visualizer: {name}!')\n\n    # Pipeline events handlers\n    def _on_atf(self, stream: Gst.Stream) -> None:                                                  # pylint: disable=unused-argument\n        _LOGGER.debug('Track %s about to finish.', self._playbin.get_property(_PROP_URI))\n        if not self._repeat:\n            self._emit_atf_event()\n            return\n        _LOGGER.debug('Repeating.')\n\n    def _on_error(self, bus: Gst.Bus, message: Gst.Message) -> None:                                # pylint: disable=unused-argument\n        error, debug = message.parse_error()\n        _LOGGER.warning('Gstreamer error details: %s.', debug)\n        self._error_handler(error)\n\n    def _on_eos(self, bus: Gst.Bus, message: Gst.Message) -> None:                                  # pylint: disable=unused-argument\n        # Just change internal state, next media URI will be requested and\n        # queued after ATF event and will be dequeued in the run loop.\n        self._eos_handler()\n\n    def _on_state_changed(self, bus: Gst.Bus, message: Gst.Message) -> None:                        # pylint: disable=unused-argument\n        if not message.src == self._playbin:\n            return\n        old, new, pending = message.parse_state_changed()\n        _LOGGER.debug('GST state %s -> %s -> (%s).',\n            Gst.Element.state_get_name(old),\n            Gst.Element.state_get_name(new),\n            Gst.Element.state_get_name(pending))\n        if new == Gst.State.PLAYING:\n            self._set_own_state(STATE_PLAYING)\n        elif new == Gst.State.READY:\n            self._set_own_state(STATE_READY)\n        elif new == Gst.State.PAUSED:\n            self._set_own_state(STATE_PAUSED)\n","repo_name":"dfokin/yaMusic","sub_path":"yamusic/gstreamer/gst.py","file_name":"gst.py","file_ext":"py","file_size_in_byte":13710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"25785497696","text":"from django.db import models\nfrom DjangoUeditor.models import UEditorField\nimport django.utils.timezone as timezone\n\n\nclass MyNew(models.Model):\n    NEWS_CHOICES = (\n        ('行业新闻', '行业新闻'),\n        ('公司新闻', '公司新闻'),\n    )\n    title = models.CharField(max_length=50, verbose_name='新闻标题')\n    newType = models.CharField(choices=NEWS_CHOICES,\n                               max_length=50,\n                               verbose_name='新闻类型')\n    img = models.ImageField(verbose_name='缩略图',\n                            upload_to='Award/',\n                            blank=True)  # 新闻图片\n    content = UEditorField(verbose_name='内容',\n                           width=700,\n                           height=400,\n                           toolbars='full',\n                           imagePath='ueditor/images/',\n                           filePath='ueditor/files/',\n                           upload_settings={'iamgesMaxSizing': 1024000},\n                           default='')\n    publishDate = models.DateTimeField(max_length=20,\n                                       default=timezone.now,\n                                       verbose_name='发布时间')\n\n    def __str__(self):\n        return self.title\n\n    class Meta:\n        ordering = ['-publishDate']\n        verbose_name = '新闻'\n        verbose_name_plural = verbose_name\n","repo_name":"sxp2015/my-app2","sub_path":"newsApp/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"16255634697","text":"'''\n#############################################################################################################\n**题目121：（数组）\n给定一个数组 prices ，它的第 i 个元素 prices[i] 表示一支给定股票第 i 天的价格。\n你只能选择 某一天 买入这只股票，并选择在 未来的某一个不同的日子 卖出该股票。设计一个算法来计算你所能获取的最大利润。\n返回你可以从这笔交易中获取的最大利润。如果你不能获取任何利润，返回 0 。\n**示例：\n输入：[7,1,5,3,6,4]\n输出：5\n解释：在第 2 天（股票价格 = 1）的时候买入，在第 5 天（股票价格 = 6）的时候卖出，最大利润 = 6-1 = 5 。\n     注意利润不能是 7-1 = 6, 因为卖出价格需要大于买入价格；同时，你不能在买入前卖出股票。\n**条件：\n1 <= prices.length <= 105\n0 <= prices[i] <= 104\n#############################################################################################################\n'''\n\n\n\n\n'''\n动规方法：\ndp定义：dp[i][k][j]代表第i天，还剩买卖机会k次，目前��股状态为j。j可选0可选1,0代表目前是无股的状态，1代表目前是有股的状态\n转移方程为：dp[i][k][0]=(dp[i-1][k][0],dp[i-1][k][1]+price[i])；\ndp[i][k][1]=dp[i-1][k][1],dp[i-1][k-1][0]-price[i]\nbase case为：dp[i][0][0]=0,dp[i][0][1]=-float('inf') \n由于k固定为仅允许交易一次，因此三维dp可简化为二维dp\n复杂度分析：\n时间复杂度：O(N)\n空间复杂度：O(N)\n'''\nclass Solution1(object):\n    def maxProfit(self, prices):\n        m=len(prices)\n        dp=[[0]*2 for _ in range(m+1)]\n        dp[0][1]=-float('inf')\n        for i in range(m):\n            dp[i+1][0]=max(dp[i][0],dp[i][1]+prices[i])\n            dp[i+1][1]=max(dp[i][1],-prices[i])\n        return dp[m][0]\n\n'''\n动规优化空间：\ndp[i+1][0]只与dp[i][0]及dp[i][1]有关\ndp[i+1][1]与dp[i][1]有关\n复杂度分析：\n时间复杂度：O(N)\n空间复杂度：O(1)\n'''\nclass Solution1(object):\n    def maxProfit(self, prices):\n        m=len(prices)\n        pre0=0\n        pre1=-float('inf')\n        for i in range(m):\n            pre0=max(pre0,pre1+prices[i])\n            pre1=max(pre1,-prices[i])\n        return pre0\n\n\n'''\n贪心方法：\n如果第i天股票的价格大于之前的买入的价格，那么就用在用之前的价格买入，并找后面价格最高时卖掉。\n如果第i天股票的价格小于之前的买入的价格，则在第i天买入\n复杂度分析：\n时间复杂度：O(N)\n空间复杂度：O(1)\n'''\nclass Solution2(object):\n    def maxProfit(self, prices):\n        max_val=0\n        min_buy=float('inf')\n        for i in range(len(prices)):\n            if min_buy<prices[i]:\n                max_val=max(max_val,prices[i]-min_buy)\n            else:\n                min_buy=prices[i]\n        return max_val\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ndef maxProfit(prices):\n    m = len(prices)\n    dp = [[0] * 2 for _ in range(m + 1)]\n    dp[0][1] = -float('inf')\n    for i in range(1, m + 1):\n        dp[i][0] = max(dp[i - 1][0], dp[i - 1][1] + prices[i - 1])\n        dp[i][1] = max(dp[i - 1][1], -prices[i - 1])\n    return max(dp[m][0], dp[m][1])\n\n\n\n\n\n\ndef maxProfit1(prices):\n    m = len(prices)\n    dp = [0, -float('inf')]\n    for i in range(m):\n        dp[0] = max(dp[0], dp[1] + prices[i])\n        dp[1] = max(dp[1], -prices[i])\n    return dp[0]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"zhangchen-fzu/my_leetcode","sub_path":"121lc买卖股票的最佳时机.py","file_name":"121lc买卖股票的最佳时机.py","file_ext":"py","file_size_in_byte":3406,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"13800334507","text":"import csv\r\nfrom collections import defaultdict\r\n\r\ngraph = defaultdict(list)\r\nvisited = []\r\nqueue = []\r\nd = []\r\n\r\ndef addEdge(u, v):\r\n    graph[u].append(v)\r\n\r\n\r\ndef bfs(visited, graph, node):\r\n  count = 0\r\n  visited.append(node)\r\n  queue.append(node)\r\n\r\n  while queue:\r\n    s = queue.pop(0)\r\n    count += 1\r\n    if count <= 9:\r\n        str1 = \"C000\" + str(count)\r\n    elif count <= 99:\r\n        str1 = \"C00\" + str(count)\r\n    elif count <= 999:\r\n        str1 = \"C0\" + str(count)\r\n    else:\r\n        str1 = \"C\" + str(count)\r\n    d.append((s,str1))\r\n\r\n\r\n    for neighbour in sorted(graph[s]):\r\n      if neighbour not in visited:\r\n        visited.append(neighbour)\r\n        queue.append(neighbour)\r\n\r\n\r\ntsv_file = open(\"categories.tsv\")\r\nread_tsv = csv.reader(tsv_file, delimiter=\"\\t\")\r\ncounter = 0\r\nfor row in read_tsv:\r\n  if row:\r\n    if not row[0].startswith(\"#\"):\r\n        str0 = row[1]\r\n        str1 = str0.split(\".\")\r\n        second = str1[0] + \".\" + str1[1]\r\n        addEdge(str1[0],second)\r\n        try:\r\n            third = second + \".\" + str1[2]\r\n            addEdge(second, third)\r\n        except:\r\n            pass\r\n        try:\r\n            fourth = third + \".\" + str1[3]\r\n            addEdge(third, fourth)\r\n        except:\r\n            pass\r\nbfs(visited, graph, 'subject')\r\n\r\nfields = ['Category_Name', 'Category_ID']\r\nfilename = \"category-ids.csv\"\r\nwith open(filename, 'w', newline='') as csvfile:\r\n    csvwriter = csv.writer(csvfile)\r\n    csvwriter.writerow(fields)\r\n    csvwriter.writerows(sorted(d))\r\n\r\n\r\n","repo_name":"rohitjha20iitk/Wikispeedia-Navigation-Paths","sub_path":"Wikispeedia-Navigation-Paths/category.py","file_name":"category.py","file_ext":"py","file_size_in_byte":1522,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"69796183644","text":"from typing import List\n\n\nclass Solution:\n    #time O(n), space O(n)\n    def findOcurrences(self, text: str, first: str, second: str) -> List[str]:\n        arr = text.split(\" \")\n        ans = []\n\n        for i in range(2, len(arr)):\n            fs = arr[i - 2]\n            ss = arr[i - 1]\n            ts = arr[i]\n            if fs == first and ss == second:\n                ans.append(ts)\n\n        return ans\n\n\nassert Solution().findOcurrences(text=\"alice is a good girl she is a good student\", first=\"a\", second=\"good\") == [\n    \"girl\", \"student\"]\n","repo_name":"haxul/algorithm_tasks_solving","sub_path":"python/leetcode/1078. Occurrences After Bigram.py","file_name":"1078. Occurrences After Bigram.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"13058324904","text":"from fpdf import FPDF\nimport jsonlines\nfrom typing import Dict, List\n\ndef get_policies(data):\n    polices = set()\n    for sample in data:\n        for completion in sample[\"completions\"]:\n            polices.add(completion[\"policy_id\"])\n    return list(polices)\n\ndef prompt_and_completions(data: List[Dict], num_samples: int, output_path: str):\n    font_family = 'Arial'\n    font_size = 8\n    cell_height = 4\n            \n    pdf = FPDF()\n\n    pdf.add_page()\n    pdf.set_font(font_family, '', font_size) \n\n    cnt = 0\n    for i, sample in enumerate(data):\n        if cnt >= num_samples:\n            break\n        \n        prompt = sample[\"prompt\"]\n        pdf.add_page()\n        \n        pdf.set_font(font_family, 'B', font_size)\n        pdf.cell(0, cell_height, f'Prompt {i}', ln=1)\n\n        pdf.set_font(font_family, '', font_size)\n        prompt = prompt.encode('latin-1', 'replace').decode('latin-1')\n        pdf.multi_cell(0, cell_height, prompt, border=1)\n\n        continuations = {}\n        for completion in sample[\"completions\"]:\n            continuations[completion[\"policy_id\"]] = completion[\"completion_str\"]\n\n        for policy, completion_str in continuations.items():\n            if completion_str is None:\n                continue\n            pdf.ln()\n\n            pdf.set_font(font_family, 'B', font_size)\n            pdf.cell(0, cell_height, f'{policy}', ln=1)\n\n            pdf.set_font(font_family, '', font_size)\n            completion_str = completion_str.encode('latin-1', 'replace').decode('latin-1')\n            pdf.multi_cell(0, cell_height, completion_str, border=1) \n\n        cnt+=1 \n    pdf.output(output_path, \"F\")\n        \n\ndef main(input_path, output_path, reward_model):\n    with jsonlines.open(input_path, \"r\") as reader:\n        data = list(reader)\n\n    font_family = 'Arial'\n    font_size = 8\n    cell_height = 4\n            \n    pdf = FPDF()\n\n    pdf.add_page()\n    pdf.set_font(font_family, '', font_size)\n    policies = get_policies(data)\n\n    for i, policy in enumerate(policies):\n        pdf.cell(0, cell_height, f'{policy}', ln=1)\n\n    pdf.cell(0, cell_height, f'Reward model: {reward_model}', ln=1)\n\n    for i, sample in enumerate(data):\n        prompt = sample[\"prompt\"]\n        pdf.add_page()\n        \n        pdf.set_font(font_family, 'B', font_size)\n        pdf.cell(0, cell_height, f'Prompt {i}', ln=1)\n\n        pdf.set_font(font_family, '', font_size)\n        prompt = prompt.encode('latin-1', 'replace').decode('latin-1')\n        pdf.multi_cell(0, cell_height, prompt, border=1)\n\n        continuations = {}\n        for completion in sample[\"completions\"]:\n            continuations[completion[\"policy_id\"]] = (completion[\"completion_str\"], completion[\"rank\"], completion[\"score\"], completion[\"normalized_score\"])\n\n        for policy, (continuation, rank, score, normalized_score) in continuations.items():\n            pdf.ln()\n\n            pdf.set_font(font_family, 'B', font_size)\n            pdf.cell(0, cell_height, f'{policy}', ln=1)\n\n            pdf.set_font(font_family, '', font_size)\n\n            pdf.cell(0, cell_height, f'Rank: {rank} | Score: {score:.3f} | Normalized score: {normalized_score:.3f}', ln=1)\n\n            continuation = continuation.encode('latin-1', 'replace').decode('latin-1')\n            pdf.multi_cell(0, cell_height, continuation, border=1)\n        \n    pdf.output(output_path, \"F\")\n\n\n\nif __name__ == \"__main__\":\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--input_path\", type=str, required=True)\n    parser.add_argument(\"--output_path\", type=str, required=True)\n    parser.add_argument(\"--reward_model\", type=str, required=True)\n    args = parser.parse_args()\n    \n    main(args.input_path, args.output_path, args.reward_model)","repo_name":"hsl89/mstar","sub_path":"scripts/rlhf/reward_model/data_analysis/generate_report.py","file_name":"generate_report.py","file_ext":"py","file_size_in_byte":3747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"33550419413","text":"# -*- coding: utf-8 -*-\r\nimport sys, os\r\nimport torch\r\nimport numpy as np\r\nimport torch.nn.functional as F\r\nfrom torch.utils.data import DataLoader\r\nfrom torchvision import datasets, transforms\r\nfrom torch.utils.data.sampler import SubsetRandomSampler\r\nimport matplotlib.pyplot as plt\r\nfrom tqdm import tqdm\r\nMODEL_PATH = \"MLPmodel.pth.tar\"\r\nMODEL_PATH_SUB = \"MLPmodel2.pth.tar\"\r\n\r\ndef load_cifar10(batch=100):\r\n    num_workers = 0\r\n    valid_size = 0.2\r\n    train_data = datasets.CIFAR10('./data', train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]))\r\n    test_data = datasets.CIFAR10('./data', train=False, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]))\r\n    \"\"\"train_loader = DataLoader(\r\n        datasets.CIFAR10('./data',\r\n                         train=True,\r\n                         download=True,\r\n                         transform=transforms.Compose([\r\n                             transforms.ToTensor(),\r\n                             transforms.Normalize(\r\n                                [0.5, 0.5, 0.5],  # RGB 平均\r\n                                [0.5, 0.5, 0.5]   # RGB 標準偏差\r\n                                )\r\n                         ])),\r\n        batch_size=batch,\r\n        shuffle=True\r\n    )\r\n\r\n    test_loader = DataLoader(\r\n        datasets.CIFAR10('./data',\r\n                         train=False,\r\n                         download=True,\r\n                         transform=transforms.Compose([\r\n                             transforms.ToTensor(),\r\n                             transforms.Normalize(\r\n                                 [0.5, 0.5, 0.5],  # RGB 平均\r\n                                 [0.5, 0.5, 0.5]  # RGB 標準偏差\r\n                             )\r\n                         ])),\r\n        batch_size=batch,\r\n        shuffle=True\r\n    )\"\"\"\r\n    num_train = len(train_data)\r\n    indices = list(range(num_train))\r\n    np.random.shuffle(indices)\r\n    # trainとvalidの境目(split)を指定\r\n    split = int(np.floor(valid_size * num_train))\r\n    train_index, valid_index = indices[split:], indices[:split]\r\n\r\n    # samplerの準備\r\n    train_sampler = SubsetRandomSampler(train_index)\r\n    valid_sampler = SubsetRandomSampler(valid_index)\r\n    # data loaderの準備\r\n    train_loader = torch.utils.data.DataLoader(train_data, batch_size = batch,\r\n                                               sampler = train_sampler, num_workers = num_workers)\r\n    valid_loader = torch.utils.data.DataLoader(train_data, batch_size = batch,\r\n                                              sampler = valid_sampler, num_workers = num_workers)\r\n    test_loader = torch.utils.data.DataLoader(test_data, batch_size = batch,\r\n                                             num_workers = num_workers)\r\n\r\n    return {'train_loader': train_loader, 'valid_loader': valid_loader, 'test_loader': test_loader}\r\n\r\nclass MyMLP(torch.nn.Module):\r\n    def __init__(self):\r\n        super(MyMLP, self).__init__()\r\n        # 隠れ層 (512)\r\n        hidden_1 = 1000\r\n        hidden_2 = 1000\r\n        hidden_3 = 1000\r\n        hidden_4 = 200\r\n        self.fc1 = torch.nn.Linear(16 * 3 * 8 * 8, hidden_1)\r\n        torch.nn.init.kaiming_normal_(self.fc1.weight)\r\n        self.fc2 = torch.nn.Linear(hidden_1,hidden_2)\r\n        torch.nn.init.kaiming_normal_(self.fc2.weight)\r\n        self.fc3 = torch.nn.Linear(hidden_2,hidden_3)\r\n        torch.nn.init.kaiming_normal_(self.fc3.weight)\r\n        self.fc4 = torch.nn.Linear(hidden_3,hidden_4)\r\n        torch.nn.init.kaiming_normal_(self.fc4.weight)\r\n        self.fc5 = torch.nn.Linear(hidden_4,10)\r\n        torch.nn.init.kaiming_normal_(self.fc5.weight)\r\n        self.droput = torch.nn.Dropout(0.2)\r\n\r\n    def forward(self, x):\r\n        # flatten image input\r\n        x = x.view(-1,16 * 3 * 8 * 8)\r\n        # activation functionとしてrelu\r\n        x = F.relu(self.fc1(x))\r\n        # add dropout layer\r\n        x = self.droput(x)\r\n        # activation functionとしてrelu\r\n        x = F.relu(self.fc2(x))\r\n        x = self.droput(x)\r\n        x = F.relu(self.fc3(x))\r\n        x = self.droput(x)\r\n        x = F.relu(self.fc4(x))\r\n        x = self.droput(x)\r\n        x = self.fc5(x)\r\n        return F.log_softmax(x,dim=1)\r\n\r\n#def save_checkpoint(state, filename = MODEL_PATH):\r\n#    torch.save(state, filename)\r\n\r\ndef train():\r\n    print(\"will begin training\")\r\n    for ep in range(epoch):\r\n        train_loss_total = 0\r\n        train_acc_total = 0\r\n        valid_loss_total = 0\r\n        valid_acc_total = 0\r\n        net.train()\r\n        loss = None\r\n        for i, (images, labels) in enumerate(loader['train_loader']):\r\n            optimizer.zero_grad()\r\n            output = net(images)\r\n            loss = criterion(output, labels)\r\n            # 出力と結果が一致している個数を計算\r\n            _,pred = torch.max(output,1)\r\n            acc = np.squeeze(pred.eq(labels.data.view_as(pred)).sum())\r\n            train_acc_total += acc\r\n            loss.backward()\r\n            optimizer.step()\r\n            # training lossの計算\r\n            train_loss_total += loss.item() * images.size(0)\r\n            \"\"\"save_checkpoint({ # save parameters\r\n                'epoch': ep + 1,\r\n                'state_dict': net.state_dict(),\r\n                'optimizer' : optimizer.state_dict()\r\n            })\"\"\"\r\n            if i % 10 == 0:\r\n                print('Training log: {} epoch ({} / 50000 train. data). Loss: {}, Acc: {}'.format(ep + 1,\r\n                                                                                         (i + 1) * 128,\r\n                                                                                         loss.item(),\r\n                                                                                         acc)\r\n                      )\r\n\r\n        torch.save(net.state_dict(), MODEL_PATH)\r\n        torch.save(net.state_dict(), MODEL_PATH_SUB)\r\n        train_loss = train_loss_total / len(loader['train_loader'].sampler)\r\n        train_acc = train_acc_total.item() / len(loader['train_loader'].sampler)\r\n\r\n        history['train_loss'].append(train_loss)\r\n        history['train_acc'].append(train_acc)\r\n\r\n        net.eval()\r\n        correct = 0\r\n        with torch.no_grad():\r\n            for i, (images, labels) in enumerate(tqdm(loader['valid_loader'])):\r\n                outputs = net(images)\r\n                loss = criterion(outputs,labels) # 損失を計算\r\n                # 出力と結果が一致している個数を計算\r\n                _,pred = torch.max(outputs,1)\r\n                acc = np.squeeze(pred.eq(labels.data.view_as(pred)).sum())\r\n                valid_acc_total += acc\r\n                # validation lossの計算\r\n                valid_loss_total += loss.item() * images.size(0)\r\n\r\n        valid_loss = valid_loss_total / len(loader['valid_loader'].sampler)\r\n        valid_acc = valid_acc_total.item() / len(loader['valid_loader'].sampler)\r\n        #acc = float(correct / 50000)\r\n        history['valid_loss'].append(valid_loss)\r\n        history['valid_acc'].append(valid_acc)\r\n\r\ndef test():\r\n    #correct = 0\r\n    test_loss_total = 0\r\n    test_acc_total = 0\r\n    total = 0\r\n    class_correct = list(0. for i in range(10))\r\n    class_total = list(0. for i in range(10))\r\n    net.eval() # ネットワークを推論モードへ\r\n    with torch.no_grad():\r\n        for i, (images, labels) in enumerate(tqdm(loader['test_loader'])):\r\n            outputs = net(images)\r\n            loss = criterion(outputs,labels) # 損失を計算\r\n            # 出力と結果が一致している個数を計算\r\n            _,pred = torch.max(outputs,1)\r\n            test_acc_total += np.squeeze(pred.eq(labels.data.view_as(pred)).sum())\r\n            total += labels.size(0)\r\n            test_loss_total += loss.item()*images.size(0)\r\n            #correct += (predicted == labels).sum()\r\n            c = (pred == labels).squeeze()\r\n            for i in range(4):\r\n                label = labels[i]\r\n                class_correct[label] += c[i]\r\n                class_total[label] += 1\r\n\r\n    #acc = float(correct / 10000)\r\n    test_loss = test_loss_total / len(loader['test_loader'].sampler)\r\n    test_acc = test_acc_total.item() / len(loader['test_loader'].sampler)\r\n    history['test_loss'].append(test_loss)\r\n    history['test_acc'].append(test_acc)\r\n\r\n    print('Accuracy of the network on the 10000 test images: %d %%' % (\r\n        100 * test_acc_total.item() / total))\r\n    for i in range(10):\r\n        print('Accuracy of %5s : %2d %%' % (\r\n            classes[i], 100 * class_correct[i] / class_total[i]))\r\n\r\ndef plot():\r\n    # 結果をプロット\r\n    plt.figure()\r\n    plt.plot(range(1, epoch+1), history['train_loss'], label='train_loss', color='red')\r\n    plt.plot(range(1, epoch+1), history['valid_loss'], label='val_loss', color='blue')\r\n    plt.title('MLP Training Loss [CIFAR10]')\r\n    plt.xlabel('epoch')\r\n    plt.ylabel('loss')\r\n    plt.legend()\r\n    plt.savefig('img/MLP_cifar10_loss.png')\r\n\r\n    plt.figure()\r\n    plt.plot(range(1, epoch+1), history['train_acc'], label='train_acc', color='red')\r\n    plt.plot(range(1, epoch+1), history['valid_acc'], label='val_acc', color='blue')\r\n    plt.title('MLP Accuracies [CIFAR10]')\r\n    plt.xlabel('epoch')\r\n    plt.ylabel('accuracy')\r\n    plt.legend()\r\n    plt.savefig('img/MLP_cifar10_acc.png')\r\n    plt.close()\r\n\r\nif __name__ == '__main__':\r\n    epoch = 50\r\n    loader = load_cifar10()\r\n    classes = ('plane', 'car', 'bird', 'cat', 'deer',\r\n               'dog', 'frog', 'horse', 'ship', 'truck')  # CIFAR10のクラス\r\n\r\n    net: MyMLP = MyMLP()\r\n    criterion = torch.nn.CrossEntropyLoss()  # ロスの計算\r\n    optimizer = torch.optim.SGD(params=net.parameters(), lr=0.001, momentum=0.9,weight_decay=0.00005)\r\n    flag = os.path.exists(MODEL_PATH)\r\n    if flag: #前回の続きから学習\r\n        print('loading parameters...')\r\n        #net.load_state_dict(torch.load(MODEL_PATH))\r\n        source = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage)\r\n        net.load_state_dict(source)\r\n        print('parameters loaded')\r\n\r\n    history = {\r\n        'train_loss': [],\r\n        'train_acc': [],\r\n        'valid_loss': [],\r\n        'valid_acc': [],\r\n        'test_loss': [],\r\n        'test_acc': []\r\n    }\r\n    train()\r\n    test()\r\n    if flag == False:\r\n        plot()\r\n","repo_name":"itakumi/B4-","sub_path":"スタートアップ/CIFAR10/MLP/MLP.py","file_name":"MLP.py","file_ext":"py","file_size_in_byte":10449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"27834190824","text":"from DAO.TermometroDAO  import TermometroDAO\nfrom DAO.PersonaDAO     import PersonaDAO\nfrom Modelos.Persona    import Persona\nfrom Modelos.Termometro import Termometro\nfrom os                 import system\nfrom tabulate           import tabulate\n\nimport time\nimport Vista.Menu as Menu\n\ndao_persona = PersonaDAO()\ndao_termometro = TermometroDAO()\n\ndef start():\n    op = 1\n    while op in [1, 2, 3]:\n        op = Menu.menu()\n        if op == 1:\n            system('cls')\n            print('******************CARGA AL SISTEMA******************')\n            name = input('| Ingrese nombre.................: ')\n            temp = input('| Ingrese temperatura corporal...: ')\n            temp = temp + '°'\n            \n            persona = Persona(name)\n            if not dao_persona.exists(persona.nombre):\n                dao_persona.add(persona)\n\n            persona.id = dao_persona.get_id_by_name(persona.nombre)\n\n            termometro = Termometro(temp, time.strftime(\"%x\") + \" | \" + time.strftime(\"%X\"), persona.id)\n            dao_termometro.add(persona, termometro.temperatura, termometro.fecha)\n\n            system('cls')\n\n        elif op == 2:\n            system('cls')\n            print('******************CARGA AL SISTEMA******************')\n            name = input('| Ingrese nombre.................: ')\n            temp = input('| Ingrese temperatura corporal...: ')\n            temp = temp + '°'\n            hora = input('| Ingrese la hora (HH-MM-SS).....: ')\n\n            persona = Persona(name)\n            if not dao_persona.exists(persona.nombre):\n                dao_persona.add(persona)\n\n            persona.id = dao_persona.get_id_by_name(persona.nombre)\n\n            termometro = Termometro(temp, time.strftime(\"%x\") + \" | \" + hora, persona.id)\n            dao_termometro.add(persona, termometro.temperatura, termometro.fecha)\n\n            system('cls')\n\n        elif op == 3:\n            system('cls')\n            name = input('| Ingrese nombre.................: ')\n            if not dao_persona.exists(name):\n                print('|| Ese no existe! cargue en el sistema sus temperaturas ||')\n                system('pause')\n                system('cls')\n            else:\n                system('cls')\n                id_persona = dao_persona.get_id_by_name(name)\n                lista = dao_termometro.get_temperatura_persona(id_persona)\n                print(tabulate(lista, headers=['Temperatura', 'Fecha'], showindex=True), end='\\n')\n                system('pause')\n                system('cls')\n","repo_name":"JuanGilSosa/Temperatura-Paciente","sub_path":"Controllers/MainController.py","file_name":"MainController.py","file_ext":"py","file_size_in_byte":2529,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34050307491","text":"import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom keras.models import Sequential\nfrom keras.optimizers import Adam\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.layers import Lambda, Conv2D, MaxPooling2D, Dropout, Dense, Flatten\nfrom utils import INPUT_SHAPE, batch_generator\nimport argparse\nimport cv2, os\nimport matplotlib.pyplot as plt\n\nnp.random.seed(0)\n\n\ndef load_data(args):\n    \"\"\"\n    Load training data and split it into training and validation set\n    \"\"\"\n    data_df = pd.read_csv(os.path.join(os.getcwd(), args.data_dir, 'driver_log_02.csv'), names=['center', 'aceleracao', 'rotacao']) #carrega o arquivo CSV para o DataFrame e atribuir os valores de imagens para center, aceleração para aceleração e rotação para rotação\n\n    X = data_df['center'].values #colocar o valor do dataframe center pro X\n    y = data_df[['rotacao']].values #colocar o valor do dataframe center pro Y (rotação)\n\n    X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=args.test_size, random_state=0) #random_state = pegar as imagens em ordem aleatória\n\t#test_size = percentual de treino e teste\n    print('--------- ESSE E O X TRAIN -------------')\n    print(X_train)\n\n    print('--------- ESSE E O X VALID -------------')\n    print(X_valid)\n    print('--------- ESSE E O Y TRAIN -------------')\n    print(y_train)\n    print('--------- ESSE E O Y VALID -------------')\n    print(y_valid)\n    print(args.data_dir)\n\n    return X_train, X_valid, y_train, y_valid\n\n\ndef build_model(args): #criar a rede neural\n\n    model = Sequential() #cria um espaço em branco na memoria para o keras trabalhar (modelo sequencial)\n    model.add(Lambda(lambda x: x/127.5-1.0, input_shape=INPUT_SHAPE)) #camada de normalização de imagem (ele vai escapar da saturação e o gradiente vai funcionar melhor) os números acima foram escolhidos depois de treinar e escolher diferentes valores. Esses foram os melhores. eles normalizam as imagens assim que são colocadas e evitam a saturação e fazem com que o gradiente funcione melhor.\n#ou seja, as imagens podem vim com sombras, de má qualidade. Essa função pode formatar e remodelar a imagem para trazer boas predições.\n    model.add(Conv2D(24, 5, 5, activation='elu', subsample=(2, 2))) #ELU = exponetial linear units ( usa ele porque ele cuida do problema gradiente de fuga)\n    model.add(Conv2D(36, 5, 5, activation='elu', subsample=(2, 2)))\n    model.add(Conv2D(48, 5, 5, activation='elu', subsample=(2, 2)))\n    model.add(Conv2D(64, 3, 3, activation='elu'))\n    model.add(Conv2D(64, 3, 3, activation='elu'))\n    model.add(Dropout(args.keep_prob))\n    model.add(Flatten())\n    model.add(Dense(100, activation='elu'))\n    model.add(Dense(50, activation='elu'))\n    model.add(Dense(10, activation='elu'))\n    model.add(Dense(1))\n    model.summary() #imprime os valores dos layers\n\n    return model\n\n\ndef train_model(model, args, X_train, X_valid, y_train, y_valid): #treinar o modelo\n    \"\"\"\n    Train the model\n    \"\"\"\n    checkpoint = ModelCheckpoint('model-{epoch:03d}.h5', #procurar ModelCheckpoint no keras\n                                 monitor='val_loss',     \n                                 verbose=0, \n                                 save_best_only=args.save_best_only, \n                                 mode='auto')\n\n    model.compile(loss='mean_squared_error', optimizer=Adam(lr=args.learning_rate)) #mean_squared_error no youtube\n\n    model.fit_generator(batch_generator(args.data_dir, X_train, y_train, args.batch_size, True),    #treinamento do modelo \n                        args.samples_per_epoch, \n                        args.nb_epoch,\n                        max_q_size=1,\n                        validation_data=batch_generator(args.data_dir, X_valid, y_valid, args.batch_size, False), #compara o treinamento com os dados validos\n                        nb_val_samples=len(X_valid),\n                        callbacks=[checkpoint], \n                        verbose=1)\n\n\ndef s2b(s): #converter uma string para um valor booleano\n    \"\"\"\n    Converts a string to boolean value\n    \"\"\"\n    s = s.lower()\n    return s == 'true' or s == 'yes' or s == 'y' or s == '1'\n\n\ndef main(): #Parametros do modelo\n    \"\"\"\n    Load train/validation data set and train the model\n    \"\"\"\n    parser = argparse.ArgumentParser(description='Behavioral Cloning Training Program')\n    parser.add_argument('-d', help='data directory',        dest='data_dir',          type=str,   default='Data')\n    parser.add_argument('-t', help='test size fraction',    dest='test_size',         type=float, default=0.2)\n    parser.add_argument('-k', help='drop out probability',  dest='keep_prob',         type=float, default=0.5)\n    parser.add_argument('-n', help='number of epochs',      dest='nb_epoch',          type=int,   default=20)\n    parser.add_argument('-s', help='samples per epoch',     dest='samples_per_epoch', type=int,   default=50000)\n    parser.add_argument('-b', help='batch size',            dest='batch_size',        type=int,   default=300)\n    parser.add_argument('-o', help='save best models only', dest='save_best_only',    type=s2b,   default='True')\n    parser.add_argument('-l', help='learning rate',         dest='learning_rate',     type=float, default=1.0e-3)\n    args = parser.parse_args() #cria uma váriavel args com com todos os paramentos acima. \"dest\" será o nome da variável\n    print('-' * 30)\n    print('Parameters')\n    print('-' * 30)\n    for key, value in vars(args).items():\n        print('{:<20} := {}'.format(key, value))\n    print('-' * 30)\n\n    data = load_data(args) #atribui os valores da função load_data para a variavel data\n    #print(data)\n    model = build_model(args)  #atribui os valores do modelo criado do build_model para a variavel model\n    #print(model)\n    train_model(model, args, *data)\n\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"pedrobcavalcante/behavioral-cloning-one-camera","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":5899,"program_lang":"python","lang":"pt","doc_type":"code","dataset":"github-code","pt":"86"}
+{"seq_id":"26482901658","text":"from scraper import Scraper\nimport time\nimport pandas as pd\nif __name__ == '__main__':\n    # the periods options that we could choose from\n    # for example: 1wk means to search for the houses that were sold in last week\n    periods = ['1wk', '1mo', '3mo', '6mo', '1yr', '2yr', '3yr']\n\n    # Getting input from the console.\n    periodIdx = input(\n        'How far back in time do you want to search? (Hit enter if you want default option: 3 month) \\n 1: 1 week \\n 2: 1 month \\n 3: 3 months \\n 4: 6 months \\n 5: 1 year \\n 6: 2 years \\n 7: 3 years \\n')\n    if periodIdx == '':\n        periodIdx = 3\n\n   # create the scraper\n    scraper = Scraper()\n\n    # scrape the information from the page\n    scraper.search_houses('sold-{}'.format(periods[int(periodIdx) - 1]))\n    scraper.houses.to_csv(\n        'LA-sold-{}.csv'.format(periods[int(periodIdx) - 1]), index=False)\n","repo_name":"chuanxiuXiong/python-webscraper-redfin","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":865,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"10586357358","text":"import os\nimport platform\nimport socket\n\n# ------------------------------------------------------------------------------\n\n# Module version\n__version_info__ = (1, 0, 1)\n__version__ = \".\".join(str(x) for x in __version_info__)\n\n# Documentation strings format\n__docformat__ = \"restructuredtext en\"\n\n# ------------------------------------------------------------------------------\n\n\ndef ipproto_ipv6():\n    \"\"\"\n    Returns the value of socket.IPPROTO_IPV6\n\n    :return: The value of socket.IPPROTO_IPV6\n    :raise AttributeError: Python or system doesn't support IPv6\n    \"\"\"\n    try:\n        # pylint: disable=E1101\n        return socket.IPPROTO_IPV6\n    except AttributeError:\n        if os.name == \"nt\":\n            # Known bug: http://bugs.python.org/issue6926\n            return 41\n        else:\n            # Unknown value\n            raise\n\n\ndef set_double_stack(socket_obj, double_stack=True):\n    # type: (socket.socket, bool) -> None\n    \"\"\"\n    Sets up the IPv6 double stack according to the operating system\n\n    :param socket_obj: A socket object\n    :param double_stack: If True, use the double stack, else only support IPv6\n    :raise AttributeError: Python or system doesn't support V6\n    :raise socket.error: Error setting up the double stack value\n    \"\"\"\n    try:\n        # Use existing value\n        opt_ipv6_only = socket.IPV6_V6ONLY\n    except AttributeError:\n        # Use \"known\" value\n        if os.name == \"nt\":\n            # Windows: see ws2ipdef.h\n            opt_ipv6_only = 27\n        elif platform.system() == \"Linux\":\n            # Linux: see linux/in6.h (in recent kernels)\n            opt_ipv6_only = 26\n        else:\n            # Unknown value: do nothing\n            raise\n\n    # Setup the socket (can raise a socket.error)\n    socket_obj.setsockopt(ipproto_ipv6(), opt_ipv6_only, int(not double_stack))\n","repo_name":"tcalmant/ipopo","sub_path":"pelix/ipv6utils.py","file_name":"ipv6utils.py","file_ext":"py","file_size_in_byte":1839,"program_lang":"python","lang":"en","doc_type":"code","stars":69,"dataset":"github-code","pt":"86"}
+{"seq_id":"23378071323","text":"\n\nclass Solution:\n    def reverseWords(self, s: str) -> str:\n        l=s.split()\n        l=[i[::-1] for i in l]\n        return ' '.join(l)\n\nsl=Solution()\nprint(sl.reverseWords(\"Let's take LeetCode contest\"))\n","repo_name":"zzz136454872/leetcode","sub_path":"reverseWords3.py","file_name":"reverseWords3.py","file_ext":"py","file_size_in_byte":208,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3713477412","text":"from tkinter import Frame, Canvas, Label, ALL, Tk\n\n\nclass main:\n\n    def __init__(self, master):\n        self.frame = Frame(master)\n        self.frame.pack(fill=\"both\", expand=True)\n        self.canvas = Canvas(self.frame, width=300, height=300)\n        self.canvas.pack(fill=\"both\", expand=True)\n        self.label = Label(self.frame, text='Tic Tac Toe Game', height=6, bg='black', fg='red')\n        self.label.pack(fill=\"both\", expand=True)\n\n        self._draw_board()\n        self.setup()\n\n    def setup(self):\n        self.canvas.delete(ALL)\n        self.label['text'] = ('Tic Tac Toe Game')\n        self.canvas.bind(\"<ButtonPress-1>\", self.check_click_event)\n        self._draw_board()\n        self.TTT = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]\n        self.i = 0\n        self.j = False\n\n    def end(self):\n        self.canvas.unbind(\"<ButtonPress-1>\")\n        self.j = True\n\n    def _draw_board(self):\n        self.canvas.create_rectangle(0, 0, 300, 300, outline=\"black\")\n        self.canvas.create_rectangle(100, 300, 200, 0, outline=\"black\")\n        self.canvas.create_rectangle(0, 100, 300, 200, outline=\"black\")\n\n    def _draw_circle(self, X, Y):\n        self.canvas.create_oval(X + 25, Y + 25, X - 25, Y - 25, width=4, outline=\"black\")\n\n    def _draw_x(self, X, Y):\n        self.canvas.create_line(X + 20, Y + 20, X - 20, Y - 20, width=4, fill=\"black\")\n        self.canvas.create_line(X - 20, Y + 20, X + 20, Y - 20, width=4, fill=\"black\")\n\n    def check_click_event(self, event):\n        print(event)\n        for k in range(0, 300, 100):\n            for j in range(0, 300, 100):\n                if event.x in range(k, k + 100) and event.y in range(j, j + 100):\n                    if self.canvas.find_enclosed(k, j, k + 100, j + 100) == ():\n                        if self.i % 2 == 0:\n                            X = (2 * k + 100) / 2\n                            Y = (2 * j + 100) / 2\n                            X1 = int(k / 100)\n                            Y1 = int(j / 100)\n                            self._draw_circle(X, Y)\n                            self.TTT[Y1][X1] += 1\n                            self.i += 1\n                        else:\n                            X = (2 * k + 100) / 2\n                            Y = (2 * j + 100) / 2\n                            X1 = int(k / 100)\n                            Y1 = int(j / 100)\n                            self._draw_x(X, Y)\n                            self.TTT[Y1][X1] += 9\n                            self.i += 1\n        self.check()\n\n    def check(self):\n        # horizontal check\n        # vertical check\n        # check for diagonal wins\n        # check for draws\n        return\n\n\nroot = Tk()\napp = main(root)\nroot.mainloop()\n\nif __name__ == '__main__':\n    main.__init__()\n","repo_name":"golubot/python_tutorial","sub_path":"tutorial/solutions/TicTacToe.py","file_name":"TicTacToe.py","file_ext":"py","file_size_in_byte":2749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"35200712779","text":"# -*- coding: utf-8 -*-\nfrom django.contrib import admin\nadmin.autodiscover()\nfrom app.models import Subscription\nfrom django.views.generic.base import RedirectView, TemplateView\nfrom django.conf.urls.defaults import patterns, include, url\nfrom app.views import SubscriptionListView, EntryListView, EntryListResponseView\n\nurlpatterns = patterns('',\n\turl(r'^admin/', include(admin.site.urls)),\n\t# Users\n\turl(r'^login/?', 'django.contrib.auth.views.login', {'template_name': 'users/login.html'}),\n\turl(r'^logout/?', 'django.contrib.auth.views.logout', {'template_name': 'users/logout.html'}),\n\turl(r'^register/?',\n\t\tTemplateView.as_view(\n\t\t\ttemplate_name='users/register.html')),\n\t\n\t# Subscriptions\n\turl(r'^$', \n\t\tSubscriptionListView.as_view(),\n\t\tname='home'),\n\turl(r'subscription/(?P<slug>[\\w-]+)/$',\n\t\tEntryListView.as_view(),\n\t\tname='subscription'),\n\n\t# Ajax\n\turl(r'_ajax/load_rss/', \n\t\tEntryListResponseView.as_view(), \n\t\tname='load_rss'),\n)\n","repo_name":"jgasteiz/nicereeder","sub_path":"urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"18961177517","text":"import re\n\nif __name__ == '__main__':\n    lines = open(\"timeTest/cpp_time.txt\").read().split(\"\\n\")\n    file = open('yourCode/main.cpp')\n    old_code = file.read()\n    code_text = old_code\n    bak = open('timeTest/main_bak.cpp', 'w')\n    bak.write(old_code)\n    bak.close()\n    file.close()\n    code_text = lines[0] + '\\n' + code_text\n    reg = re.compile(r'[ ]*// \\*{14}')\n    m = re.findall(reg, code_text)\n    l = (len(m[0]) - len(m[0].lstrip()))\n    code_text = re.sub(r'// \\*{14}', lines[1], code_text, count=1)\n    code_text = re.sub(r'// \\*{14}', lines[2] + '\\n' + ' ' * l + lines[3] + '\\n' + ' ' * l + lines[4] + '\\n',\n                       code_text, count=1)\n    file = open('yourCode/main.cpp', 'w')\n    file.write(code_text)\n","repo_name":"naser-kazemi/Pattern-Matching-Engine","sub_path":"Tester/timeTest/test_time_cpp.py","file_name":"test_time_cpp.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"36498064928","text":"import sys\nfrom PyQt5 import QtWidgets, QtGui\n\n\ndef window():\n    app = QtWidgets.QApplication(sys.argv)\n    pencere = QtWidgets.QWidget()\n\n    pencere.setWindowTitle('PyQt Ders 2')\n    button = QtWidgets.QPushButton(pencere)\n    button.setText('Bir Buton')\n    etiket = QtWidgets.QLabel(pencere)\n    etiket.setText('Merhaba PyQt5')\n\n    etiket.move(200, 30)\n    button.move(200, 60)\n    pencere.setGeometry(100, 100, 500, 500)\n\n    pencere.show()\n    sys.exit(app.exec_())\n\n\nwindow()\n","repo_name":"ilteriskeskin/Python-ornekler","sub_path":"PyQT5/buton_olusturma.py","file_name":"buton_olusturma.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"86"}
+{"seq_id":"29382740544","text":"# -*- coding: utf-8 -*-\n#------------------------------------------------------------------------------------------#\n# This file is part of Pyccel which is released under MIT License. See the LICENSE file or #\n# go to https://github.com/pyccel/pyccel/blob/master/LICENSE for full license details.     #\n#------------------------------------------------------------------------------------------#\n\"\"\" Module containing helper functions for managing strings\n\"\"\"\n\ndef create_incremented_string(forbidden_exprs, prefix = 'Dummy', counter = 1, name_clash_checker = None):\n    \"\"\"\n    Create a new unique string by incrementing a prefix.\n\n    This function takes a prefix and a counter and uses them to construct\n    a new name of the form:\n            prefix_counter\n    Where counter is formatted to fill 4 characters\n    The new name is checked against a list of forbidden expressions. If the\n    constructed name is forbidden then the counter is incremented until a valid\n    name is found.\n\n    Parameters\n    ----------\n    forbidden_exprs : set\n        A set of all the values which are not valid solutions to this problem.\n    prefix : str\n        The prefix used to begin the string.\n    counter : int\n        The expected value of the next name.\n    name_clash_checker : pyccel.naming.languagenameclashchecker.LanguageNameClashChecker\n        A class instance providing access to a `has_clash` function which determines\n        if names clash in a given language.\n\n    Returns\n    -------\n    name : str\n        The incremented string name.\n    counter : int\n        The expected value of the next name.\n    \"\"\"\n    nDigits = 4\n\n    if prefix is None:\n        prefix = 'Dummy'\n\n    name_format = \"{prefix}_{counter:0=\"+str(nDigits)+\"d}\"\n    name = name_format.format(prefix=prefix, counter = counter)\n    counter += 1\n    if name_clash_checker:\n        while name_clash_checker.has_clash(name, forbidden_exprs):\n            name = name_format.format(prefix=prefix, counter = counter)\n            counter += 1\n    else:\n        while name in forbidden_exprs:\n            name = name_format.format(prefix=prefix, counter = counter)\n            counter += 1\n\n    return name, counter\n","repo_name":"pyccel/pyccel","sub_path":"pyccel/utilities/strings.py","file_name":"strings.py","file_ext":"py","file_size_in_byte":2185,"program_lang":"python","lang":"en","doc_type":"code","stars":297,"dataset":"github-code","pt":"86"}
+{"seq_id":"12889228759","text":"import os\nimport openai\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\")\n\nmessages = [\n    {\"role\": \"system\", \"content\" : \"Hi Barbie!\"}\n]\n\nwhile True :\n    content = input(\"User: \")\n    messages.append({\"role\": \"user\", \"content\": content})\n\n    completion = openai.ChatCompletion.create(\n        model=\"gpt-3.5-turbo\",\n        messages=messages\n    )\n\n    chat_response = completion.choices[0].message.content\n    print(f'ChatGPT: {chat_response}')\n    messages.append({\"role\": \"assistant\", \"content\": chat_response})","repo_name":"betty-godier/chatgpt-gpt3.5-turbo","sub_path":"chatgpt-gpt3.5-turbo/chatGPT.py","file_name":"chatGPT.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"2626273514","text":"# See this article on creation of GMail oAuth credentials...\n# https://developers.google.com/gmail/api/quickstart/python#authorize_credentials_for_a_desktop_application\n# Ensure that the Google App that you are publishing has the GMail API enabled.\n# Make sure to include the correct scope (/auth/gmail.modify).\n\nfrom __future__ import print_function\nimport os.path\nimport base64\nimport logging\nfrom email.message import EmailMessage\nfrom configparser import ConfigParser\n\nfrom googleapiclient.discovery import build\nfrom googleapiclient.errors import HttpError\nfrom google.auth.transport.requests import Request\nfrom google.oauth2.credentials import Credentials\nfrom google_auth_oauthlib.flow import InstalledAppFlow\n\n# If modifying these scopes, delete the file token.json.\nSCOPES = ['https://www.googleapis.com/auth/gmail.modify']\n\ndef main():\n    logging.basicConfig(level=logging.DEBUG)\n    logging.debug('Authenticating with Google.')\n    creds = auth()\n\n    logging.debug('Loading and parsing \"mail.ini\" file.')\n    config = ConfigParser()\n    configFile = r'./mail.ini'\n    config.read(configFile)\n\n    to_address = config.get('GMail', 'to_smtp')\n    from_address = config.get('GMail', 'from_smtp')\n\n    logging.info('Sending test message from ' + from_address + ' going to ' + to_address + \".\")\n    send_mail(creds, to_address, from_address, 'Test Message Subject', 'Test Message Body')\n\ndef auth():\n    creds = None\n    # The file token.json stores the user's access and refresh tokens, and is\n    # created automatically when the authorization flow completes for the first\n    # time.\n    if os.path.exists('token.json'):\n        logging.debug('Loading Google credentials from \"token.json\".')\n        creds = Credentials.from_authorized_user_file('token.json', SCOPES)\n    \n    # If there are no (valid) credentials available, let the user log in.\n    if not creds or not creds.valid:\n        if creds and creds.expired and creds.refresh_token:\n            logging.debug('Attempting to refresh Google Token.')\n            creds.refresh(Request())\n        else:\n            logging.debug('No token found, starting login process.')\n            flow = InstalledAppFlow.from_client_secrets_file(\n                'credentials.json', SCOPES)\n            creds = flow.run_local_server(port=0)\n        # Save the credentials for the next run\n        with open('token.json', 'w') as token:\n            token.write(creds.to_json())\n            logging.debug('Token cached in \"token.json\"')\n\n    return creds\n\ndef send_mail(creds, to_smtp, from_smtp, subject_text, message_text):\n    try:\n        # create gmail api client\n        logging.debug('Building GMail message.')\n        service = build('gmail', 'v1', credentials=creds)\n        message = EmailMessage()\n\n        message.set_content(str(message_text))\n\n        message['To'] = str(to_smtp)\n        message['From'] = str(from_smtp)\n        message['Subject'] = str(subject_text)\n\n        # encoded message\n        encoded_message = base64.urlsafe_b64encode(message.as_bytes()).decode()\n\n        create_message = {\n                'raw': encoded_message\n        }\n        logging.debug('Sending mail from ' + from_smtp + ' to ' + to_smtp + '.')\n        # pylint: disable=E1101\n        send_message = (service.users().messages().send\n                        (userId=\"me\", body=create_message).execute())\n\n        logging.info(F'Message Id: {send_message[\"id\"]}')\n\n    except HttpError as error:\n        logging.error(F'An error occurred: {error}')\n        send_message = None\n\n    return send_message\n\n\nif __name__ == '__main__':\n    main()","repo_name":"JonDeeming/hive-availability-checker","sub_path":"gmailSender.py","file_name":"gmailSender.py","file_ext":"py","file_size_in_byte":3596,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"12648583988","text":"import tkinter as tk\nfrom tkinter import ttk\nfrom tkinter import font as tkfont\nfrom item import AmazonItem\nfrom multiprocessing.dummy import Pool as ThreadPool\nimport threading\nimport smtplib\nimport pickle\n\n\ndef doNothing():\n    print('Do Nothing')\n\n\ndef calculateParallel(urls, threads=4):\n    pool = ThreadPool(threads)\n    results = pool.map(AmazonItem, urls)\n    pool.close()\n    pool.join()\n    return results\n\n\ndef parseItem(item):\n    arr = [None]*9\n    arr[0] = item.title\n    arr[1] = item.current_price\n    arr[2] = item.highest_price\n    arr[3] = item.highest_price_date\n    arr[4] = item.lowest_price\n    arr[5] = item.lowest_price_date\n    arr[6] = item.avg_price\n    arr[7] = item.availability\n    arr[8] = item.url\n    return arr\n\n\ndef popupmsg(msg, controller):\n    popup = tk.Tk()\n    popup.wm_minsize(150, 85)\n    popup.wm_title(\"!\")\n    popup.wm_maxsize(150, 85)\n    label = ttk.Label(popup, text=msg, font=controller.NORM_FONT)\n    label.pack(side=\"top\", fill=\"x\", pady=10, padx=12)\n    B1 = ttk.Button(popup, text=\"Okay\", command=popup.destroy)\n    B1.pack()\n    popup.mainloop()\n\n\ndef send_email(controller, content, recipient):\n    server = smtplib.SMTP('smtp.gmail.com', 587)\n    server.ehlo()\n    server.starttls()  # Encrypts the information.\n    server.ehlo()\n    server.login('sebascaicedo25@gmail.com', 'vhhrbzhfcvoyffkz')\n\n    subject = 'PriceTracker reminder!'\n    body = content\n\n    msg = f\"Subject: {subject}\\n\\n{body}\"\n    server.sendmail(\n        'sebascaicedo25@gmail.com',\n        recipient,\n        msg\n    )\n    popupmsg(\"EMAIL HAS BEEN SENT!\")\n    server.quit()\n\n\nclass Driver(tk.Tk):\n    def __init__(self, *args, **kwargs):\n        tk.Tk.__init__(self, *args, **kwargs)\n        tk.Tk.wm_title(self, \"Price Tracker\")\n        tk.Tk.wm_minsize(self, 600, 230)\n        tk.Tk.iconbitmap(self, default='icon.ico')\n        self.title_font = tkfont.Font(family='Helvetica', size=18, weight='bold', slant='italic')\n        self.NORM_FONT = tkfont.Font(family=\"Helvetica\", size=10)\n        self.container = tk.Frame(self, relief='raised', borderwidth=5)\n        self.container.pack(side=\"top\", fill=\"both\", expand=True)\n        self.container.grid_rowconfigure(1, weight=1)\n        self.container.grid_columnconfigure(0, weight=1)\n\n        self.frames = {}\n\n        self.frames[\"WelcomePage\"] = WelcomePage(parent=self.container, controller=self)\n        self.frames[\"LoadingScreen\"] = LoadingScreen(parent=self.container, controller=self)\n        self.frames[\"EmailPage\"] = EmailPage(parent=self.container, controller=self)\n\n        self.frames[\"WelcomePage\"].grid(row=1, column=0, sticky=\"nsew\")\n        self.frames[\"LoadingScreen\"].grid(row=1, column=0, sticky=\"nsew\")\n        self.frames[\"EmailPage\"].grid(row=1, column=0, sticky=\"nsew\")\n\n        self.show_frame(\"WelcomePage\")\n\n    def show_frame(self, page_name):\n        frame = self.frames[page_name]\n        frame.tkraise()\n\n    def init_new_session(self):\n        if 'NewSession' not in self.frames:\n            NewSession(parent=self.container, controller=self)\n            # self.frames[\"NewSession\"] = NewSession(parent=self.container, controller=self)\n            # self.frames[\"NewSession\"].grid(row=1, column=0, sticky=\"nsew\")\n        else:\n            self.show_frame(\"NewSession\")\n\n    def init_old_session(self):\n        if \"OldSession\" not in self.frames:\n            # TODO If there is time, make the loading screen work\n            # temp_process = multiprocessing.Process(target=OldSession, args=(self.container,\n            #                                                  self))\n            # temp_process.start()\n            # self.show_frame()\n            OldSession(parent=self.container, controller=self)\n        else:\n            self.show_frame(\"OldSession\")\n\n\nclass LoadingScreen(tk.Frame):\n    def __init__(self, parent, controller):\n        tk.Frame.__init__(self, parent)\n        self.controller = controller\n        label = tk.Label(self, text=\"Loading...\", font=controller.title_font)\n        label.pack(side=\"top\", fill=\"x\", pady=10)\n        self.progress_bar = ttk.Progressbar(self, orient=\"horizontal\", mode=\"indeterminate\")\n        self.progress_bar.pack(expand=\"True\", fill=\"x\", side=tk.TOP)\n\n    def start_bar(self):\n        self.progress_bar.start(50)\n\n    def stop_bar(self):\n        self.progress_bar.stop()\n\n\nclass EmailPage(tk.Frame):\n    def __init__(self, parent, controller):\n        tk.Frame.__init__(self, parent)\n        self.prev_frame = \"WelcomePage\"\n        self.controller = controller\n        label1 = tk.Label(self, text=\"Edit Email \", font=controller.title_font)\n        label2 = tk.Label(self, text=\"Email content \")\n        label3 = tk.Label(self, text=\"Recipient \")\n        email_text = tk.Text(self, width=40, height=1)\n        email_text.insert(tk.END, \"example@example.com\")\n        button1 = ttk.Button(self, text=\"Back\",\n                             command=lambda: controller.show_frame(self.prev_frame))\n        button2 = ttk.Button(self, text=\"Send Email\", command=lambda:\n                             send_email(controller, self.text.get(\"1.0\",\n                                        tk.END).strip(), email_text.get(\"1.0\", tk.END).strip()))\n        body = \"Check out this link: \"\n        self.text = tk.Text(self, width=40, height=10, wrap=\"word\")\n        self.text.insert(tk.END, body)\n\n        label1.grid(row=0, column=0, padx=20)\n        label2.grid(row=1, column=0)\n        label3.grid(row=2, column=0)\n        email_text.grid(row=2, column=1)\n        self.text.grid(row=1, column=1)\n        button1.grid(row=3, column=2, pady=4)\n        button2.grid(row=3, column=1, pady=5)\n\n\n\n        controller.frames[\"EmailPage\"] = self\n        controller.frames[\"EmailPage\"].grid(row=1, column=0, sticky=\"nsew\")\n\n    def update_body(self, url):\n        self.text.insert(tk.END, url)\n\n\nclass WelcomePage(tk.Frame):\n\n    def __init__(self, parent, controller):\n        tk.Frame.__init__(self, parent)\n        self.controller = controller\n        label = tk.Label(self, text=\"This is Welcome Page\", font=controller.title_font)\n        label.pack(side=\"top\", fill=\"x\", pady=10)\n\n        button1 = ttk.Button(self, text=\"New Session\",\n                             command=controller.init_new_session)\n        button2 = ttk.Button(self, text='Old Session',\n                             command=controller.init_old_session)\n        button3 = ttk.Button(self, text=\"Show loading screen\",\n                             command=lambda: controller.show_frame(\"LoadingScreen\"))\n        button1.pack()\n        button2.pack()\n        button3.pack()\n\n\nclass NewSession(tk.Frame):\n    def __init__(self, parent, controller):\n        tk.Frame.__init__(self, parent, height=100, width=100)\n        self.name = \"NewSession\"\n        self.controller = controller\n        self.table = TableOfItems(self, controller)\n        self.table.grid(row=0, column=0, rowspan=2, columnspan=2)\n\n        table = self.table.get_table()\n\n        buttons = ActionButtons(self, controller, tree=table.tree, table=table, items_dict=self.table.items)\n        buttons.grid(row=0, column=3, rowspan=10, columnspan=3)\n\n        controller.frames[\"NewSession\"] = self\n        controller.frames[\"NewSession\"].grid(row=1, column=0, sticky=\"nsew\")\n\n    def get_name(self):\n        return self.name\n\n\nclass OldSession(tk.Frame):\n    def __init__(self, parent, controller):\n        tk.Frame.__init__(self, parent)\n        self.controller = controller\n        self.table = TableOfItems(self, controller=controller, file=\"cache.txt\")\n        self.table.grid(row=0, column=0, rowspan=2, columnspan=2)\n        table = self.table.get_table()\n\n        buttons = ActionButtons(self, controller, tree=table.tree, table=table, items_dict=self.table.items)\n        buttons.grid(row=0, column=3, rowspan=10, columnspan=3)\n\n        controller.frames[\"OldSession\"] = self\n        controller.frames[\"OldSession\"].grid(row=1, column=0, sticky=\"nsew\")\n\n    def get_name(self):\n        return self.name\n\n\nclass TableOfItems(tk.Frame):\n    def __init__(self, parent, controller, file=None):\n        tk.Frame.__init__(self, parent, background=\"black\")\n        self.tree = ttk.Treeview(self)\n        self.tree[\"columns\"]=(\"Price\", \"two\")\n        self.tree.column(\"Price\", width=100)\n        self.tree.column(\"two\", width=100)\n        self.tree.heading(\"Price\", text=\"Price ($)\")\n        self.tree.pack(side=\"left\")\n        vsb = ttk.Scrollbar(self, orient=\"vertical\", command=self.tree.yview)\n        vsb.pack(side=\"right\", fill=\"y\")\n        self.items = {}\n        if file is not None:\n            parse_file = open(file, 'rb')\n            prev_sess_dict = pickle.load(parse_file)\n            parse_file.close()\n            for item in prev_sess_dict:\n                temp = AmazonItem(arr=prev_sess_dict[item])\n                self.items[temp.title] = temp\n\n            for key in self.items:\n                temp_thread = threading.Thread(target=self.populate_tree,\n                                               args=(self.items[key],))\n                temp_thread.start()\n        else:\n            pass\n\n    def populate_tree(self, item):\n        temp = self.tree.insert(\"\", \"end\", item.title, text=item.title,\n                                values=(\"${:.2f}\".format(item.current_price)))\n        self.tree.insert(temp, \"end\", text=\"Highest Price\",\n                         values=(\"${:.2f}\".format(item.highest_price),\n                                 item.highest_price_date))\n        self.tree.insert(temp, \"end\", text=\"Lowest Price\",\n                         values=(\"${:.2f}\".format(item.lowest_price),\n                                 item.lowest_price_date))\n        self.tree.insert(temp, \"end\", text=\"Average Price\",\n                         values=(\"${:.2f}\".format(item.avg_price)))\n        self.tree.insert(temp, \"end\", text=\"Availability\",\n                         values=item.availability)\n\n    def get_table(self):\n        return self\n\n\nclass ActionButtons(tk.Frame):\n    def __init__(self, parent, controller, tree, table, items_dict):\n        tk.Frame.__init__(self, parent)\n        self.table = table\n        self.parent_name = type(parent).__name__\n        self.controller = controller\n        self.tree = tree\n        self.items_dict = items_dict\n        button1 = ttk.Button(self, text=\"Add Item\", command=self.add_item)\n        button2 = ttk.Button(self, text=\"Delete Item\", command=self.delete_tree_item)\n        button3 = ttk.Button(self, text=\"Send Email\",\n                             command=self.show_email_page)\n        button4 = ttk.Button(self, text=\"Update Selection\", command=self.update_selection)\n        button5 = ttk.Button(self, text=\"Update All\", command=self.update_all)\n        button6 = ttk.Button(self, text=\"Back\", command=lambda: controller.show_frame(\"WelcomePage\"))\n        button7 = ttk.Button(self, text=\"Quit\", command=self.quit_window)\n        self.v = tk.StringVar()\n\n        self.e = ttk.Entry(self)\n        self.v.set(\"Enter url here\")\n        self.e.config(textvariable=self.v)\n\n        button1.grid(row=0, column=0, padx=5, pady=5)\n        button2.grid(row=1, column=0, padx=5, pady=5)\n        button3.grid(row=2, column=0, padx=5, pady=5)\n        button4.grid(row=3, column=0, padx=5, pady=5)\n        button5.grid(row=4, column=0, padx=5, pady=5)\n        button6.grid(row=5, column=0, padx=5, pady=5)\n        button7.grid(row=6, column=0, padx=5, pady=5)\n        self.e.grid(row=0, column=1, padx=5, pady=5)\n\n    def show_email_page(self):\n        try:\n            self.controller.show_frame(\"EmailPage\")\n            url = self.table.items[self.table.tree.selection()[0]].url\n            self.controller.frames[\"EmailPage\"].update_body(url)\n            self.controller.frames[\"EmailPage\"].prev_frame = self.parent_name\n            self.controller.show_frame(\"EmailPage\")\n        except IndexError:\n            popupmsg(\"Select an item!\")\n\n    def delete_tree_item(self, item=None):\n        if item is not None:\n            self.tree.delete(item)\n        else:\n            try:\n                del self.tree.items[self.tree.selection()[0]]  # Delete from dictionary\n                self.tree.delete(self.tree.selection()[0])  # delete from tree\n            except IndexError:\n                popupmsg(\"Select an item!\")\n\n    def add_item(self, url=None, index=None, spec_item=None):\n        if spec_item is not None:\n            item = spec_item\n            self.items_dict[item.title] = item\n            temp = self.tree.insert(\"\", index, item.title, text=item.title,\n                                    values=(\"${:.2f}\".format(item.current_price)))\n            self.tree.insert(temp, \"end\", text=\"Highest Price\",\n                             values=(\"${:.2f}\".format(item.highest_price),\n                                     item.highest_price_date))\n            self.tree.insert(temp, \"end\", text=\"Lowest Price\",\n                             values=(\"${:.2f}\".format(item.lowest_price),\n                                     item.lowest_price_date))\n            self.tree.insert(temp, \"end\", text=\"Average Price\",\n                             values=(\"${:.2f}\".format(item.avg_price)))\n            self.tree.insert(temp, \"end\", text=\"Availability\",\n                             values=item.availability)\n            self.tree.selection_add(item.title)  # Highlights in the treeview\n        elif url is not None:\n            item = AmazonItem(url=url)\n            self.items_dict[item.title] = item\n            temp = self.tree.insert(\"\", index, item.title, text=item.title,\n                                    values=(\"${:.2f}\".format(item.current_price)))\n            self.tree.insert(temp, \"end\", text=\"Highest Price\",\n                             values=(\"${:.2f}\".format(item.highest_price),\n                                     item.highest_price_date))\n            self.tree.insert(temp, \"end\", text=\"Lowest Price\",\n                             values=(\"${:.2f}\".format(item.lowest_price),\n                                     item.lowest_price_date))\n            self.tree.insert(temp, \"end\", text=\"Average Price\",\n                             values=(\"${:.2f}\".format(item.avg_price)))\n            self.tree.insert(temp, \"end\", text=\"Availability\",\n                             values=item.availability)\n            self.tree.selection_add(item.title)  # Highlights in the treeview\n\n        elif self.e.get().find(\"amazon.com\") != -1:  # Just making sure it's an amazon link\n            item = AmazonItem(self.e.get().strip())\n            self.items_dict[item.title] = item\n            self.e.delete(0, tk.END)\n            temp = self.tree.insert(\"\", \"end\", item.title, text=item.title,\n                                    values=(\"${:.2f}\".format(item.current_price)))\n            self.tree.insert(temp, \"end\", text=\"Highest Price\",\n                             values=(\"${:.2f}\".format(item.highest_price),\n                                     item.highest_price_date))\n            self.tree.insert(temp, \"end\", text=\"Lowest Price\",\n                             values=(\"${:.2f}\".format(item.lowest_price),\n                                     item.lowest_price_date))\n            self.tree.insert(temp, \"end\", text=\"Average Price\",\n                             values=(\"${:.2f}\".format(item.avg_price)))\n            self.tree.insert(temp, \"end\", text=\"Availability\",\n                             values=item.availability)\n            self.v.set(\"Enter url here\")\n        else:\n            popupmsg(\"Enter valid URL\")\n            self.v.set(\"Enter url here\")\n            pass\n\n    def update_all(self):\n        index = 0\n        urls = [None]*len(self.items_dict)\n        for item in self.items_dict:  # This loop is just to get the urls\n            urls[index] = self.items_dict[item].url\n            index += 1\n\n        arr_amazon_items = calculateParallel(urls)  # array with updated values items\n\n        for am_item in arr_amazon_items:\n            self.items_dict[am_item.title] = am_item  # updating values in the dictionary\n            temp_index = self.tree.index(am_item.title)\n            self.delete_tree_item(item=am_item.title)  # delete old item from tree\n            self.add_item(index=temp_index, spec_item=am_item)  # Add updated item\n\n    def update_selection(self):\n        url = self.items_dict[self.tree.selection()[0]].url\n        _index = self.tree.index(self.tree.selection()[0])\n        del self.items_dict[self.tree.selection()[0]].url\n        self.delete_tree_item(item=self.tree.selection()[0])\n        self.add_item(url=url, index=_index)\n\n    def quit_window(self):\n        if len(self.items_dict) > 0:\n            save_dict = {}\n            for item in self.items_dict:\n                save_dict[item.title] = parseItem(self.items_dict[item])\n\n            print(\"saving to cache...\")\n            file_name = \"cache.txt\"\n            file = open(file_name, 'wb')\n            pickle.dump(save_dict, file)\n            file.close()\n            self.controller.quit()\n            pass\n        else:\n            print(\"quitting...\")\n            self.controller.quit()\n\n\nclass Menus:\n    def __init__(self, parent):\n        self.menu = tk.Menu(parent)\n        parent.config(menu=self.menu)\n        self.sub_menu = tk.Menu(self.menu)\n        self.menu.add_cascade(label=\"File\", menu=self.sub_menu)\n        self.sub_menu.add_command(label=\"Add item\", command=doNothing)\n        self.sub_menu.add_command(label=\"Remove item\", command=doNothing)\n        self.sub_menu.add_separator()\n        self.sub_menu.add_command(label=\"Exit\", command=doNothing)\n\n        self.edit_menu = tk.Menu(self.menu)\n        self.menu.add_cascade(label=\"Edit\", menu=self.edit_menu)\n\n\nclass Toolbar:\n    def __init__(self, parent):\n        self.toolbar = tk.Frame(parent, bg=\"blue\")\n        self.add_button = tk.Button(self.toolbar, text=\"Add Item\", command=doNothing)\n        self.add_button.pack(side=tk.LEFT, padx=2, pady=2)\n        self.remove_item = tk.Button(self.toolbar, text=\"Remove Item\", command=doNothing)\n        self.toolbar.pack(side=tk.TOP, fill=tk.X)\n\n\nclass StatusBar:\n    def __init__(self, parent):\n        self.status = tk.Label(parent, text=\"Doing nothing(for now)\", bd=1, relief=tk.SUNKEN, anchor=tk.W)\n        self.status.pack(side=tk.BOTTOM, fill=tk.X)\n\n\nif __name__ == \"__main__\":\n    app = Driver()\n    app.mainloop()\n","repo_name":"SCaicedo99/PriceTracker","sub_path":"GUI_draft.py","file_name":"GUI_draft.py","file_ext":"py","file_size_in_byte":18301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3817556150","text":"import copy\nfrom textwrap import dedent\n\nimport pytest\nfrom ruamel.yaml import YAML\nfrom traitlets import TraitError\n\nfrom pangeo_forge_runner.meta_yaml import MetaYaml\n\nyaml = YAML()\n\n\n@pytest.fixture\ndef with_recipes_list() -> str:\n    return dedent(\n        \"\"\"\\\n    title: 'AWS NOAA WHOI SST'\n    description: 'Analysis-ready datasets derived from AWS NOAA WHOI NetCDF'\n    recipes:\n      - id: aws-noaa-sea-surface-temp-whoi\n        object: 'recipe:recipe'\n    provenance:\n      providers:\n        - name: 'AWS NOAA Oceanic CDR'\n          description: 'Registry of Open Data on AWS National Oceanographic & Atmospheric Administration National Centers for Environmental Information'\n          roles:\n            - producer\n            - licensor\n          url: s3://noaa-cdr-sea-surface-temp-whoi-pds/\n      license: 'Open Data'\n    maintainers:\n      - name: 'Jo Contributor'\n        orcid: '0000-0000-0000-0000'\n        github: jocontributor123\n    \"\"\"  # noqa: E501\n    )\n\n\n@pytest.fixture\ndef valid_meta_yaml(with_recipes_list: str) -> dict:\n    return yaml.load(with_recipes_list)\n\n\n@pytest.fixture\ndef valid_meta_yaml_dict_object(with_recipes_list: str) -> dict:\n    with_dict_object = with_recipes_list.replace(\n        dedent(\n            \"\"\"\\\n        recipes:\n          - id: aws-noaa-sea-surface-temp-whoi\n            object: 'recipe:recipe'\n        \"\"\"\n        ),\n        dedent(\n            \"\"\"\\\n        recipes:\n          - dict_object: 'recipe:recipes'\n        \"\"\"\n        ),\n    )\n    return yaml.load(with_dict_object)\n\n\ndef test_schema_valid(valid_meta_yaml):\n    _ = MetaYaml(**valid_meta_yaml)\n\n\ndef test_schema_valid_dict_object(valid_meta_yaml_dict_object):\n    _ = MetaYaml(**valid_meta_yaml_dict_object)\n\n\n@pytest.mark.parametrize(\n    \"field\",\n    [\n        \"title\",\n        \"description\",\n        \"recipes\",\n        \"provenance\",\n        \"maintainers\",\n    ],\n)\ndef test_missing_toplevel_field(valid_meta_yaml, field):\n    meta_yaml_copy = copy.deepcopy(valid_meta_yaml)\n    del meta_yaml_copy[field]\n    if field == \"recipes\":\n        # ``recipes`` is the only required field\n        with pytest.raises(TraitError):\n            _ = MetaYaml(**meta_yaml_copy)\n    else:\n        # all others fields can be left empty without raising an error\n        _ = MetaYaml(**meta_yaml_copy)\n\n\n@pytest.mark.parametrize(\n    \"subfield\",\n    [\n        \"id\",\n        \"object\",\n    ],\n)\ndef test_missing_recipes_subfield(valid_meta_yaml, subfield):\n    invalid_meta_yaml = copy.deepcopy(valid_meta_yaml)\n    del invalid_meta_yaml[\"recipes\"][0][subfield]\n\n    with pytest.raises(TraitError):\n        _ = MetaYaml(**invalid_meta_yaml)\n\n\ndef test_recipes_field_cannot_be_empty_container():\n    with pytest.raises(TraitError):\n        _ = MetaYaml(recipes=[])\n\n\ndef test_recipes_field_invalid_keys():\n    with pytest.raises(TraitError):\n        # \"dict_object\" key can't be used in combination with other keys\n        _ = MetaYaml(recipes={\"id\": \"abcdefg\", \"dict_object\": \"abc:def\"})\n    with pytest.raises(TraitError):\n        # the only valid keys are {\"id\", \"object\"} together,\n        # or \"dict_object\" alone. other keys are not allowed.\n        _ = MetaYaml(recipes={\"some_other_key\": \"abc:def\"})\n\n\n# TODO: In a future \"strict\" mode, ensure provenance fields are all provided.\n# --------------------------------------------------------------------------\n# @pytest.mark.parametrize(\n#     \"subfield\",\n#     [\n#         \"providers\",\n#         \"license\",\n#     ],\n# )\n# def test_missing_provenance_subfield(valid_meta_yaml, subfield, schema):\n#     invalid_meta_yaml = copy.deepcopy(valid_meta_yaml)\n#     del invalid_meta_yaml[\"provenance\"][subfield]\n#\n#     with pytest.raises(TraitError):\n#         _ = MetaYaml(**meta_yaml_copy)\n\n\n# TODO: In a future \"strict\" mode, ensure providers fields are all provided.\n# --------------------------------------------------------------------------\n# @pytest.mark.parametrize(\n#     \"subfield\",\n#     [\n#         \"name\",\n#         \"description\",\n#         \"roles\",\n#         \"url\",\n#     ],\n# )\n# def test_missing_providers_subfield(valid_meta_yaml, subfield, schema):\n#     invalid_meta_yaml = copy.deepcopy(valid_meta_yaml)\n#     del invalid_meta_yaml[\"provenance\"][\"providers\"][0][subfield]\n#\n#     with pytest.raises(ValidationError, match=f\"'{subfield}' is a required property\"):\n#         _ = MetaYaml(**meta_yaml_copy)\n\n\n# TODO: In a future \"strict\" mode, ensure maintainers fields are all provided.\n# ----------------------------------------------------------------------------\n# @pytest.mark.parametrize(\n#     \"subfield\",\n#     [\n#         \"name\",\n#         \"orcid\",\n#         \"github\",\n#     ],\n# )\n# def test_missing_maintainers_subfield(valid_meta_yaml, subfield, schema):\n#     invalid_meta_yaml = copy.deepcopy(valid_meta_yaml)\n#     del invalid_meta_yaml[\"maintainers\"][0][subfield]\n#\n#     with pytest.raises(TraitError):\n#         _ = MetaYaml(**meta_yaml_copy)\n","repo_name":"pangeo-forge/pangeo-forge-runner","sub_path":"tests/unit/test_meta_yaml.py","file_name":"test_meta_yaml.py","file_ext":"py","file_size_in_byte":4932,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"86"}
+{"seq_id":"5529819213","text":"# TODO: if script complains, update this dict and rerun\nects = {\n  0.0: 1,\n  0.5: 1,\n  1.0: 2,\n  1.5: 3,\n  2.0: 3,\n  3.0: 4,\n  4.0: 5,\n  5.0: 7,\n  6.0: 8,\n  7.0: 9,\n  9.0: 10,\n}\n\nfrom bs4 import BeautifulSoup\nimport sys\nimport re\nimport os\nimport os.path\nimport course_functions\n\ndef logger(message):\n  print(message)\n\nargs = sys.argv\nsoup = None\n\nwith open(args[1]) as fp:\n    soup = BeautifulSoup(fp, 'html.parser')\n\ncourses = soup.find_all('h3')\nlogger('Found {} courses.'.format(len(courses)))\n\ndef process_achievement(course):\n  achievement = dict()\n  header = course.string.strip().replace('\\r', '').replace('\\n', '')\n  header_regex = r\"^([0-9\\-]+) - ([\\w-]+) (.+) \\(([\\d.]+)SWS .+ ([WS])S 20(\\d+)/(\\d+)\\)$\"\n  header_match = re.search(header_regex, header)\n  if header_match == None:\n    raise Exception(header)\n  header_number = header_match.group(2)\n  header_number_regex = r\"^([A-Z]+).+$\"\n  header_dep = re.search(header_number_regex, header_number).group(1)\n  header_school = None\n  if header_dep in course_functions.schools:\n    header_school = course_functions.schools[header_dep]\n  else:\n    raise Exception('Unknown course dep: {}'.format(header_dep))\n  header_sem = header_match.group(6) if header_match.group(5) == 'W' else header_match.group(7)\n  header_hours = float(header_match.group(4))\n  header_ects = 0\n  if header_hours not in ects:\n    raise Exception('Unknown SWS: {}'.format(header_hours))\n\n  achievement = {\n    'id': 0,\n    'href': '',\n    'mode': 'written',\n    'date': '{} 12:00:00 +0100'.format(header_match.group(1)),\n    'number': header_number,\n    'name': header_match.group(3),\n    'type': 'endterm',\n    'semester': '20{}S'.format(header_sem + header_match.group(5)),\n    'hours': header_hours,\n    'ects': ects[header_hours],\n    'school': header_school,\n  }\n\n  grade_elem = next(filter(lambda x: x.name == 'div', course.next_siblings), None)\n  grade_list = grade_elem.find_all('div', class_='kandcountbox')\n  \n  achievement = process_grades(achievement, grade_list)\n  return achievement\n  \ndef process_grades(achievement, grade_list):\n  grades = dict()\n  for grade_elem in grade_list:\n    grade_people_elem = next(filter(lambda x: x.name == 'div', grade_elem.next_siblings), None)\n    grade_people_text = grade_people_elem.contents[-1].strip()\n    grade_people_regex = r\"^(\\d+) K.$\"\n    grade_people = int(re.search(grade_people_regex, grade_people_text).group(1))\n    if grade_people == 0:\n      continue\n\n    grade_value_elem = next(filter(lambda x: x.name == 'div', grade_people_elem.next_siblings), None)\n    grade_value_text = grade_value_elem.div.contents[-1].strip()\n    grade_value = 0.0\n    # X didn't show up\n    # U cheated\n    # Q withdrew\n    # Z rejected\n    # B passed without grade\n    # N didn't pass without grade\n    if 'X' in grade_value_text:\n      grade_value = 6.0\n    elif 'U' in grade_value_text:\n      grade_value = 5.0\n      achievement['cheated'] = grade_people\n    elif 'Z' in grade_value_text:\n      grade_value = 5.0\n      achievement['rejected'] = grade_people\n    elif 'Q' in grade_value_text:\n      achievement['withdrew'] = grade_people\n      continue\n    elif 'B' in grade_value_text:\n      continue\n    else:\n      try:\n        grade_value = float(grade_value_text.replace(',', '.'))\n      except:\n        logger('Failed to parse {}. Contact mcmikecreations.'.format(grade_value_text))\n        achievement['grades'] = None\n        return achievement\n    \n    grades[grade_value] = {\n        'value': grade_value,\n        'count': grade_people,\n      }\n\n  achievement['grades'] = list(grades.values())\n  achievement['grades'].sort(key=lambda x: x['value'])\n  return achievement\n\nfor course in courses:\n  achievement = process_achievement(course)\n  if achievement['grades'] == None or len(achievement['grades']) == 0 or course_functions.achievement_exists(achievement, logger, True):\n    continue\n  course_functions.achievement_save(achievement, logger, True)\n","repo_name":"mcmikecreations/tum_info","sub_path":"scripts/course_html_parse.py","file_name":"course_html_parse.py","file_ext":"py","file_size_in_byte":3935,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"86"}
+{"seq_id":"39814143597","text":"  \n\n\nfrom bgi_h import *;\n\ndef draw(window):\n\tglobal TIME,CLK;\n\t#HUB\n\tbox(window,(sx+1,0),(nx,sy+1),TABBG);\n\tbox(window,(0,sy+1),(nx,ny),TABBG);\n\t#text = FONT.render(str(TIME), True, color(7));\n\t#text_rect = text.get_rect(center = window.get_rect().center);\n\t#window.blit(text, text_rect);\n\trectangle(window,(10,sy+2),(sx-10,ny-50),color(9));\n\tRR=CLK.RR();\n\tIX=CLK.IX();\n\tHIGH=abs(ny-51-(sy+3));\n\tlx=10;\n\tdata=(RR[0]+128)/256*HIGH;\n\tlast=(10,ny-51-data);\n\tscx=4;\n\tbox(window,(lx,sy+3),(sx-10,ny-51),color(7));\n\t\n\tcircle(window,(128,128),64,color(4));\n\tcfa(window,(128,128),64,color(3));\n\t\n\tfor f in range(1,len(RR)):\n\t\tnwx=10+f*scx;\n\t\tline(window,(lx,sy+3+HIGH/2),(nwx,sy+3+HIGH/2),color(0));\n\t\tlx=nwx;\n\t\tdata=((64 if RR[f]>0 else -64)+128)/256*HIGH;\n\t\tNEW=(nwx,ny-51-data);\n\t\tline(window,last,NEW,color(1));\n\t\tlast=NEW;\n\t\tif(f==IX):\n\t\t\tline(window,last,[last[0]+scx,last[1]],color(1));\n\t\t\tplot(window,last,color(2));\n\t\telse:\n\t\t\tline(window,last,[last[0]+scx,last[1]],color(6));\n\t\t\n\t\t#line(window,last,(lx,sy+3+HIGH/2),color(4));\n\t\t\n\tgprintf(window,[sx/2,ny-49],\"Pulses:%5.1f\",TIME/HZ);\n\tgprintf(window,[sx/2,ny-20],\"Noise:%5i\",NOISE_RR[NOISE_IX]);\n\t\n\tgprintf(window,[sx/2,ny-10],\"CLK:%5s\",CLK.text_status());\n\nclass Mem:\n\tdef __init__(self,length=NULL):\n\t\tif(length==NULL):\n\t\t\tself._LENGTH=M64K;\n\t\telse:\n\t\t\tself._LENGTH=length;\n\t\t\t\n\t\tself._FULLMEM=[0]*self._LENGTH;\n\t\tself._MAR=0;\n\t\tself._LINE=16;\n\t\tself._START=1;\n\t\tself._BLOCK=2;#2==WORD,1==BYTE\n\t\tself._FLAG=FlagReg();\n\t\n\tdef flag(self):\n\t\treturn(self._FLAG);\n\t\n\tdef __repr__(self):\n\t\tout=\"\";\n\t\tfor row in range(self._START,self._LENGTH,16):\n\t\t\tout+=\"%04x: \"%(row-row%self._LINE);\n\t\t\tfor col in range(0,self._LINE):\n\t\t\t\tout+=\"%02x \"%(self._FULLMEM[row+col]);\n\t\t\t\t\n\t\t\tout+=\"\\n\";\n\t\treturn(out);\n\tdef addr(self,pos=NULL):\n\t\tif (pos==NULL):\n\t\t\tpos=self._MAR;\n\t\telse:\n\t\t\tself._MAR=pos % self._LENGTH;\n\t\tif(self._MAR<self._START):\n\t\t\tself._MAR=self._START;\n\t\tself._MAR=pos;\n\t\treturn(pos);\n\tdef setAddr(self,pos=NULL):\n\t\tself.addr(pos);\n\t\t\n\tdef addrNext(self):\n\t\tself.addr(self._MAR+1);\n\t\treturn(self._MAR);\n\t\n\tdef addrPrev(self):\n\t\tself.addr(self._MAR-1);\n\t\treturn(self._MAR);\n\t\n\tdef get(self):\n\t\tout=0;\n\t\ttry:\n\t\t\tout=self._FULLMEM[self._MAR];\n\t\t\tself._FLAG.resetEF();\n\t\texcept:\n\t\t\tself._FLAG.setEF();\n\t\treturn(out);\n\t\n\tdef set(self,value=NULL):\n\t\tout=0;\n\t\tif(value==NULL):\n\t\t\tvalue=0;\n\t\telif(type(value)==type(\"a\")):\n\t\t\tvalue=ord(value[0]);\n\t\telif(type(value)==type(0)):\n\t\t\tvalue=value % (2**(8*self._BLOCK));\n\t\t\ts=0 if value>=0 else 1;\n\t\t\ti=int(value);\n\t\t\t\n\t\telif(type(value)==type(0.0)):#fld\n\t\t\ts=0 if value>=0 else 1;\n\t\t\tvalue=abs(value);\n\t\t\ti=int(value);\n\t\t\td=(value-i);\n\t\t\te=i/abs(value);\n\t\ttry:\n\t\t\tself._FULLMEM[self._MAR]=value;\n\t\t\tself._FLAG.resetEF();\n\t\texcept:\n\t\t\tself._FLAG.setEF();\n\t\treturn(out);\n\t\n\t\n\tdef reprRange(self,start=NULL,end=NULL):\n\t\tout=\"\";\n\t\tif (end==NULL):\n\t\t\tend=self._LENGTH;\n\t\tend=end % self._LENGTH;\n\t\t\n\t\tif(start==NULL):\n\t\t\tstart=self._START;\n\t\t\n\t\tend=\tend   % self._LENGTH;\n\t\tstart=\tstart % self._LENGTH;\n\t\t\n\t\tfor row in range(start,end,16):\n\t\t\tout+=\"%04x: \"%(row-row%self._LINE);\n\t\t\tfor col in range(0,self._LINE):\n\t\t\t\tout+=\"%02x \"%(self._FULLMEM[row+col]);\n\t\t\t\t\n\t\t\tout+=\"\\n\";\n\t\treturn(out);\n\n\n\ndef main(args):\n\tglobal window,BG,tick,TIME,HZ,NOISE_IX,NOISE_RR,INK;\n\tcol=0;\n\tMEM=Mem();\n\tNUM_KEYS=[\n\t\t\tK_0, \tK_1, \tK_2, \tK_3,\n\t\t\tK_4, \tK_5, \tK_6, \tK_7,\n\t\t\tK_8, \tK_9, \tK_KP0, \tK_KP1,\n\t\t\tK_KP2,\tK_KP3,\tK_KP4,\tK_KP5,\n\t\t\tK_KP6,\tK_KP7,\tK_KP8,\tK_KP9,\n\t\t\tK_PERIOD, K_MINUS,K_PLUS, K_SLASH, \n\t\t\tK_ASTERISK, K_LEFTPAREN,K_RIGHTPAREN, K_CARET];\n\t\n\tCOLKEYS=[\n\t\t\tK_0, \tK_1, \tK_2, \tK_3,\n\t\t\tK_4, \tK_5, \tK_6, \tK_7,\n\t\t\tK_KP0,\tK_KP1,\tK_KP2,\tK_KP3,\n\t\t\tK_KP4,\tK_KP5,\tK_KP6,\tK_KP7 ];\n\tSHIFTKEYS=[\n\t\t\tK_LSHIFT,\tK_RSHIFT,\tK_LCTRL,\tK_RCTRL,\n\t\t\tK_LALT,\t\tK_RALT,\t\tK_LMETA,\tK_RMETA,\n\t\t\tK_LSUPER,\tK_RSUPER,\tK_MODE\t\t\t];\n\t\n\tprint(MEM.reprRange(0,100));\n\tset_title(\"Ensamble\");\n\tset_icon(\"icon.png\");\n\tclrscr(window);\n\trun=true;\n\tx,y=0,0;\n\twhile run:\n\t\tclock.tick(HZ);\n\t\tfor event in pygame.event.get():\n\t\t\tif event.type == timer_event:\n\t\t\t\tTIME+=1;\n\t\t\t\tNOISE_IX=(NOISE_IX+1)%RR_LENGTH;\n\t\t\t\tNOISE_RR[NOISE_IX]=rnd(-7,7);\n\t\t\t\tCLK.tick();\n\t\t\tif event.type == pygame.QUIT or (event.type==pygame.KEYDOWN and event.key == pygame.K_ESCAPE):\n\t\t\t\trun = False;\n\t\t\tif event.type==pygame.KEYDOWN:\n\t\t\t\tKEYPRESSED.add(event.key);\n\t\t\telif event.type==pygame.KEYUP:\n\t\t\t\tKEYPRESSED.discard(event.key);\n\t\tif x<sx and (pygame.K_RIGHT in KEYPRESSED or pygame.K_d in KEYPRESSED):\n\t\t\tx+=1;\n\t\tif x>0  and (pygame.K_LEFT in KEYPRESSED or pygame.K_a in KEYPRESSED):\n\t\t\tx-=1;\n\t\tif y<sy and (pygame.K_DOWN in KEYPRESSED or pygame.K_s in KEYPRESSED or pygame.K_x in KEYPRESSED):\n\t\t\ty+=1;\n\t\tif y>0  and (pygame.K_UP in KEYPRESSED or pygame.K_w in KEYPRESSED):\n\t\t\ty-=1;\n\t\tif pygame.K_SPACE in KEYPRESSED:\n\t\t\tcol+=1;\n\t\t\tcol=col % len(COLOURS);\n\t\t\tBG=color(col);\n\t\t\tINK=invert(BG,189);\n\t\t\t#color(len(COLOURS)-col)\n\t\t\t#print(BG,\"&&\",INK);\n\t\tfor f in range(0,len(COLKEYS)):\n\t\t\tif (COLKEYS[f] in KEYPRESSED):\n\t\t\t\tcol=f%8;\n\t\t\t\tif((K_LSHIFT in KEYPRESSED) or (K_RSHIFT in KEYPRESSED)):\n\t\t\t\t\tcol+=8;\n\t\t\t\t\n\t\t\t\t#col=col % len(COLOURS);\n\t\t\t\tBG=color(col);\n\t\t\t\tINK=invert(BG,189);\n\t\t\t\t\n\t\tif pygame.K_t in KEYPRESSED:\n\t\t\tprint([abs(f)>10 for f in CLK.RR()]);\n\t\t\t\n\t\tclrscr(window,BG);\n\t\tdraw(window);\n\t\tsz=8;\n\t\tfor f in range(sz):\n\t\t\tfor n in range(sz*2):\n\t\t\t\tplot(window,(x+f,y+n),invert(BG));\n\t\tsomethingChanged=False;\n\t\ttick=0 if tick==7 else 7;\n\t\tplot(window,(sx,sy),color(tick));\n\t\tpygame.display.flip();\n\t\t\n\tpygame.quit();\n\treturn(0);\n\nif __name__ == '__main__':\n\tsys.exit(main(sys.argv));\n","repo_name":"metfar/interTerms","sub_path":"base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":5501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"22686286829","text":"import sys, glob,subprocess,difflib\n \n\ndef run(fname):\n\n    res = subprocess.Popen([\"..\\glox.exe\",\"%s\" % fname],stdout=subprocess.PIPE)\n    return res\n\ndef basename(path):\n \n    if \"\\\\\" in path:\n        return path.split(\"\\\\\")[-1]\n    return path\n\ndef process(fname,write,verbose):\n\n    pipe = run(fname)\n    testdatafile=\"output/%s.testoutput\" % basename(fname)\n \n    if write:\n        with open(testdatafile,\"wb\") as outfile:\n            res=pipe.communicate()\n            outfile.write(res[0])\n    else:\n        with open(testdatafile,\"rb\") as infile:\n            res=pipe.communicate()\n            data=infile.read()\n            match=data==res[0]\n            if match:\n                print (\"Test %-30s : PASS\" % fname)\n            else:\n                print (\"Test %-30s : FAIL\" % fname)\n                if verbose:\n                    a=res[0].decode('ascii').splitlines()\n                    b=data.decode('ascii').splitlines()\n                    d=difflib.context_diff(a,b)\n                    print ('\\n'.join(d))\n\n######################################################################################################################\n\nwrite=False\nverbose=False\n\nif len(sys.argv) > 1 :\n    if sys.argv[1] in (\"--read\",\"--write\"):\n        write=True if sys.argv[1]==\"--write\" else False\n        del(sys.argv[1])\n    if sys.argv[1] == \"--verbose\":\n        verbose=True\n        del(sys.argv[1])\n\nif len(sys.argv) > 1 :\n    process(sys.argv[1],write,verbose)\nelse:\n    for f in glob.glob(\"lox/*lox\"):\n        process(f,write,verbose)\n    ","repo_name":"nickharrismcr/glox","sub_path":"tests/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"70019822046","text":"# whole team\n\n# gather inputs from user\nprint('We will ask you for three inputs.')\npath = input(\"Copy/ paste file path: \")\nfile_name = input(\"What would you like the file called? \")\ndestination = input('Where would you like the file to go? ')\n\n# input validation\nwhile path == None:\n    path = input(\"File path missing value, please copy/paste the file path for the document to process: \")\n\nwhile destination == None:\n    destination = input(\"Destination path missing value, please copy/paste the file path for the destination folder: \")\n\nwhile file_name == None:\n    file_name = input(\"File name missing value, please input what you would like the processed file to be called: \")\n\npath = str(path)\ndestination = str(destination)\nfile_name = str(file_name)\n\n# process excel spreadsheet\nimport pandas as pd\n\nheader_index = 2\n\ntry:\n    df = pd.read_excel(f\"{path}\", header = header_index)\nexcept:\n    path = input(\"Error with file path, please double check path and copy/paste file path for document to process: \")\n\ndf = pd.read_excel(path, header = header_index)\n\ndf = df.iloc[0:len(df) - header_index]\n\ndef overlap(p):\n    return {df['Incident Number'][i] for i in set(df.index) - {p} if (df['Dispatched Date'][i] <= df['Dispatched Date'][p] <= df['Clear Date'][i])\n                                                                     or (df['Dispatched Date'][p] <= df['Dispatched Date'][i] <= df['Clear Date'][p])}\n\ndf['overlap'] = df.index.map(overlap)\n\ndf['num_overlaps'] = df.overlap.map(len)\n\n# output validation\nif destination[len(destination)] != \"/\":\n    destination = destination + \"/\"\n\nif \".xls\" not in file_name:\n    file_name = file_name + \".xls\"\n\ntry:\n    df.to_excel(f\"{destination + file_name}\")\nexcept:\n    print(\"An error has occurred. Please re-enter the folder destination and file name.\")\n    file_name = input(\"File name: \")\n    destination = input(\"Path to folder: \")\n\ndf.to_excel(f\"{destination + file_name}\")\n","repo_name":"ChrisSCorliss/Project-4","sub_path":"combined.py","file_name":"combined.py","file_ext":"py","file_size_in_byte":1934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74886192605","text":"import datetime\nimport logging\nimport os\nimport numpy as np\nimport pandas as pd\nimport subprocess\nimport sys\nimport torch\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\n\nfrom argparse import Namespace\nfrom math import ceil\nfrom typing import List, Type\nfrom torch import nn\n\n\ndef _get_git_revision_hash() -> str:\n    try:\n        return subprocess.check_output(['git', 'rev-parse', 'HEAD'])\\\n            .decode('ascii').strip()\n    except:\n        return 'no_git'\n\n\ndef _get_git_revision_short_hash() -> str:\n    try:\n        return subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'])\\\n            .decode('ascii').strip()\n    except:\n        return 'no_git'\n\n\ndef _generate_output_dirname(prefix: str) -> str:\n    git_hash = _get_git_revision_short_hash()\n    now = datetime.datetime.now().strftime(\"%Y-%m-%d_%H:%M:%S\")\n    dirname = git_hash + '_' + now\n    if prefix is not None:\n        dirname = prefix + '_' + dirname\n    return dirname\n\n\ndef generate_output_dirpath(module_path: str, output_rootname: str='output', prefix: str=None):\n    module_dirpath = os.path.dirname(os.path.abspath(module_path))\n    output_dirname = _generate_output_dirname(prefix)\n    output_dirpath = f'{module_dirpath}/{output_rootname}/{output_dirname}'\n    if not os.path.exists(output_dirname):\n        os.makedirs(output_dirpath)\n    return output_dirpath\n\n\ndef generate_model_experiment_name(args: Namespace, is_test_mode: bool=False) -> str:\n    return \"_\".join([\n        args.model,\n        args.pretrained if args.pretrained else 'scratch',\n        f\"ub{args.unfreeze_blocks}\",\n        'discretized' if args.discretized_image else 'original',\n        args.optimizer,\n        f\"lr{args.lr}\",\n        f\"wd{args.weight_decay}\",\n        f\"ep{args.num_train_epochs}\",\n        f\"bs{args.train_batch_size}\",\n        'test' if is_test_mode else 'train'\n    ])\n\n\ndef generate_classifier_experiment_name(args: Namespace, is_test_mode: bool=False) -> str:\n    return \"_\".join([\n        args.model,\n        args.pretrained,\n        'discretized' if args.discretized_image else 'original',\n        'test' if is_test_mode else 'train'\n    ])\n\n\ndef get_logger(filename: str=None) -> logging.Logger:\n    log_format = '%(asctime)s [%(levelname)-5.5s] %(message)s'\n    log_formatter = logging.Formatter(log_format)\n\n    logger = logging.getLogger()\n    logger.setLevel(logging.DEBUG)\n\n    if filename is not None and filename != '':\n        file_handler = logging.FileHandler(filename)\n        file_handler.setFormatter(log_formatter)\n        logger.addHandler(file_handler)\n\n    console_handler = logging.StreamHandler(sys.stdout)\n    console_handler.setFormatter(log_formatter)\n    logger.addHandler(console_handler)\n\n    return logger\n\n\ndef matplotlib_imshow(img, one_channel=False):\n    if one_channel:\n        img = img.mean(dim=0)\n    img = img / 2 + 0.5     # unnormalize\n    npimg = img.numpy()\n    if one_channel:\n        plt.imshow(npimg, cmap=\"Greys\")\n    else:\n        plt.imshow(np.transpose(npimg, (1, 2, 0)))\n\n\ndef images_to_probs(model: Type[nn.Module], inputs):\n    '''\n    Generates predictions and corresponding probabilities from a trained\n    network and a list of images\n    '''\n    output = model(inputs).cpu()\n    # convert output probabilities to predicted class\n    _, preds_tensor = torch.max(output, 1)\n    preds = np.squeeze(preds_tensor.numpy())\n    return preds, [F.softmax(el, dim=0)[i].item() for i, el in zip(preds, output)]\n\n\ndef plot_classes_preds(model: Type[nn.Module], images: Type[torch.tensor], \n                       labels: Type[torch.tensor], classes: List[str]):\n    '''\n    Generates matplotlib Figure using a trained network, along with images\n    and labels from a batch, that shows the network's top prediction along\n    with its probability, alongside the actual label, coloring this\n    information based on whether the prediction was correct or not.\n    Uses the \"images_to_probs\" function.\n    '''\n    n_images = min(40, len(images))\n    n_cols = min(4, n_images)\n    n_rows = int(ceil(float(n_images) / n_cols))\n\n    preds, probs = images_to_probs(model, images)\n    images = images.cpu()\n    # plot the images in the batch, along with predicted and true labels\n    fig = plt.figure(figsize=(12, 36))\n    for idx in np.arange(n_images):\n        ax = fig.add_subplot(n_rows, n_cols, idx+1, xticks=[], yticks=[])\n        matplotlib_imshow(images[idx], one_channel=True)\n        ax.set_title(\"{0}, {1:.1f}%\\n(label: {2})\".format(\n            classes[preds[idx]],\n            probs[idx] * 100.0,\n            classes[labels[idx]]),\n            color=(\"green\" if preds[idx]==labels[idx].item() else \"red\")\n        )\n    return fig\n\n\ndef combine_dfs(dfs: List[pd.DataFrame]) -> pd.DataFrame:\n    df_concat = pd.concat(dfs)\n    group_df = df_concat.groupby(level=0)\n\n    df_means = group_df.mean().applymap('{:.4f}'.format)\n    df_stds = group_df.std().applymap('{:.4f}'.format)\n    df = df_means.combine(df_stds, lambda x1, x2: x1 + ' ± ' + x2)\n    return df\n","repo_name":"matwerner/resume_parser","sub_path":"resume_parser/layout/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"21520290067","text":"import re\n\"\"\"\n处理纠错log\n\"\"\"\nfile = open(\"/Users/ff/Desktop/word_output_huawei_ur_correct_17_0831_1598943561.txt\", 'r', encoding='utf-8')\nfile_out = open(\"/Users/ff/Desktop/word_output_huawei_ur_correct_17.txt\", 'w', encoding='utf-8')\nregex = re.compile('input=(.*),\\s+desire=(.*)\\t(.*)screen=\\[(.*)\\]\\):')\nfor l in file:\n    # l=\"TASK: input=a, desire=b \tWORD(input=[آفریدی]|desire=[آفریدی]|screen=[آفریدی]): (autoCorrectHappen=0, correctPositiveCount=0, needToCorrectWordCount=0, autoCorrectHappenIncandidateTop3=0) \"\n    if l.startswith(\"TASK\") and '\\t' in l:\n        # print(l)\n        # input= l.split('\\t')[0]\n        input = regex.search(l)\n        if input.group(1).strip() != input.group(2).strip():\n            result=\"input=\"+input.group(1)+'\\t'+\"desire=\"+input.group(2)+'\\t'+\"screen=\"+input.group(4)\n            file_out.write(result.strip() + '\\n')\n    else:\n        file_out.write(l.strip() + '\\n')\n\n# if regex.match(input):\n#     input1 = lambda x: x.group(1)\n#     print(input1)\n","repo_name":"Messiff10/dict_maker_crubadan","sub_path":"log_deal/correctAnaly.py","file_name":"correctAnaly.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"16555475548","text":"# Developed by Redjumpman for Redbot.\n# Inspired by the snail race mini game\n\n# STD Library\nimport asyncio\nimport random\nimport os\n\n# Discord and Red Utils\nimport discord\nfrom discord.ext import commands\nfrom __main__ import send_cmd_help\nfrom .utils import checks\nfrom .utils.dataIO import dataIO\nfrom pprint import pprint\n\nanimals = ((':rabbit2:', 'fast'), (':monkey:', 'fast'), (':cat2:', 'fast'), (':mouse2:', 'slow'),\n           (':chipmunk:', 'fast'), (':rat:',\n                                    'fast'), (':dove:', 'fast'), (':bird:', 'fast'),\n           (':dromedary_camel:', 'steady'), (':camel:',\n                                             'steady'), (':dog2:', 'steady'),\n           (':poodle:', 'steady'), (':racehorse:', 'steady'), (':ox:', 'abberant'),\n           (':cow2:', 'abberant'), (':elephant:',\n                                    'abberant'), (':water_buffalo:', 'abberant'),\n           (':ram:', 'abberant'), (':goat:', 'abberant'), (':sheep:', 'abberant'),\n           (':leopard:', 'predator'), (':tiger2:',\n                                       'predator'), (':dragon:', 'special'),\n           (':unicorn:', 'special'), (':turtle:',\n                                      'slow'), (':bug:', 'slow'), (':rooster:', 'slow'),\n           (':snail:', 'slow'), (':scorpion:',\n                                 'slow'), (':crocodile:', 'slow'), (':pig2:', 'slow'),\n           (':turkey:', 'slow'), (':duck:', 'slow'), (':baby_chick:', 'slow'))\n\n\nclass Racer:\n    track = '•   ' * 20\n\n    def __init__(self, animal, mode, user):\n        self.animal = animal\n        self.mode = mode\n        self.user = user\n        self.turn = 0\n        self.position = 80\n        self.placed = False\n        self.current = Racer.track + self.animal\n        self.bet = 0\n\n    def get_user(self):\n        return self.user\n\n    def field(self):\n        field = \":carrot: **{}** :flag_black:  [{}]\".format(\n            self.current, self.user)\n        return field\n\n    def get_position(self):\n        return self.current.find(self.animal)\n\n    def update_track(self):\n        distance = self.move()\n        self.current = (Racer.track[:max(0, self.position - distance)] + self.animal +\n                        Racer.track[max(0, self.position - distance):])\n\n    '''self.get_position()'''\n\n    def update_position(self):\n        self.turn += 1\n        self.update_track()\n        self.position = self.get_position()\n\n    def move(self):\n        if self.mode == 'slow':\n            return random.randint(1, 3) * 3\n\n        elif self.mode == 'fast':\n            return random.randint(0, 4) * 3\n\n        elif self.mode == 'steady':\n            return 2 * 3\n\n        elif self.mode == 'abberant':\n            if random.randint(1, 100) >= 90:\n                return 5 * 3\n            else:\n                return random.randint(0, 2) * 3\n\n        elif self.mode == 'predator':\n            if self.turn % 2 == 0:\n                return 0\n            else:\n                return random.randint(2, 5) * 3\n\n        elif self.animal == ':unicorn:':\n            if self.turn % 3:\n                return random.choice([len('blue'), len('red'), len('green')]) * 3\n            else:\n                return 0\n        else:\n            if self.turn == 1:\n                return 14 * 3\n            elif self.turn == 2:\n                return 0\n            else:\n                return random.randint(0, 2) * 3\n\n\nclass Race:\n    \"\"\"Cog for racing animals\"\"\"\n\n    def __init__(self, bot):\n        self.bot = bot\n        self.bets = {}\n        self.system = {}\n        self.config = dataIO.load_json('data/race/race.json')\n        self.version = \"1.1.04\"\n\n    @commands.group(pass_context=True, no_pm=True)\n    async def race(self, ctx):\n        \"\"\"Race cog's group command\"\"\"\n\n        if ctx.invoked_subcommand is None:\n            await send_cmd_help(ctx)\n\n    @commands.group(pass_context=True, no_pm=True)\n    async def setrace(self, ctx):\n        \"\"\"Race cog's settings group command\"\"\"\n\n        if ctx.invoked_subcommand is None:\n            await send_cmd_help(ctx)\n\n    @setrace.command(name=\"prize\", pass_context=True)\n    @checks.admin_or_permissions(manage_server=True)\n    async def _prize_setrace(self, ctx, minimum: int, maximum: int):\n        \"\"\"Set the prize range\n\n        A number of credits will be randomly picked from the set\n        miminum to the set maximum.\n\n        Parameters:\n            minimum: integer\n                Must be lower than maximum\n            maximum: integer\n                Must be higher than minimum\n\n        Returns:\n            Bot replies with invalid mode\n            Bot replies with valid mode and saves choice\n        \"\"\"\n\n        if minimum > maximum:\n            return await self.bot.say(\"https://simple.wikipedia.org/wiki/Maximum_and_minimum\")\n        server = ctx.message.server\n        settings = self.check_config(server)\n        settings['Prize'] = (minimum, maximum)\n        self.save_settings()\n        await self.bot.say(\"Prize range set to {}-{}\".format(minimum, maximum))\n\n    @setrace.command(name=\"time\", pass_context=True)\n    @checks.admin_or_permissions(manage_server=True)\n    async def _time_setrace(self, ctx, time: int):\n        \"\"\"Set the time players have to enter a race\n\n        Amount of time for the bot to wait for entrants until the race\n        is ready to begin.\n\n        Parameters:\n            time: integer\n                Unit is expressed in seconds\n                Default is set to 60 seconds\n\n        Returns:\n            Bo\n        \"\"\"\n        author = ctx.message.author\n        if time < 0:\n            return await self.bot.say(\"{}. You are a dumbass. I can't turn back\"\n                                      \"time.\".format(author.name))\n\n        settings = self.check_config(author.server)\n        settings['Time'] = time\n        self.save_settings()\n        await self.bot.say(\"Wait time set to {}s\".format(time))\n\n    @setrace.command(name=\"mode\", pass_context=True)\n    @checks.admin_or_permissions(manage_server=True)\n    async def _mode_setrace(self, ctx, mode: str):\n        \"\"\"Set the race mode\n\n        Standard Mode assigns everyone a turtle. Everyone has the same\n        random movement formula.\n\n        Zoo Mode assigns every entrant a random animal. Animals are grouped into\n        classes that meet a special formula for movement. 8 different animal classes!\n\n        Parameters:\n            mode: string\n                Must be standard or zoo\n        Returns:\n            Bot replies with invalid mode\n            Bot replies with valid mode and saves choice\n        \"\"\"\n        server = ctx.message.server\n        settings = self.check_config(server)\n        mode = mode.lower()\n        modes = ['standard', 'zoo']\n        if mode not in modes:\n            return await self.bot.say(\"Invalid mode. Acceptable responses \"\n                                      \"include: {}.\".format(', '.join(modes)))\n        settings['Mode'] = mode\n        self.save_settings()\n        await self.bot.say(\"Mode now set to {}.\".format(mode))\n\n    @race.command(name=\"version\")\n    async def _version_race(self):\n        \"\"\"Displays the version of race\"\"\"\n        await self.bot.say(\"You are running race version {}\".format(self.version))\n\n    @race.command(name=\"reset\", pass_context=True, hidden=True)\n    @checks.admin_or_permissions(manage_server=True)\n    async def _reset_race(self, ctx):\n        \"\"\"Reset race parameters DEBUG USE ONLY\"\"\"\n        server = ctx.message.server\n        data = self.check_server(server)\n        self.game_teardown(data, force=True)\n        await self.bot.say(\"Parameters reset.\")\n\n    @race.command(name=\"start\", pass_context=True)\n    @commands.cooldown(1, 30, commands.BucketType.server)\n    async def _start_race(self, ctx):\n        \"\"\"Start an animal race and enter yourself as participant\n\n            Returns:\n                Two text outputs. One to start the race,\n                and the second to represent the race. The second\n                msg will be edited multiple times to represent the race.\n\n            Notes:\n                Must wait 2 minutes after every race to start a new one.\n                You cannot start a race if a race is already active.\n                A race is considered active once this command is used.\n                A race is considered started once the track is displayed.\n                The user who starts a race, will be automatically entered.\n                The bot will always join a race.\n                There are no cheaters and it isn't rigged.\n        \"\"\"\n        author = ctx.message.author\n        data = self.check_server(author.server)\n        settings = self.check_config(author.server)\n\n        if data['Race Active']:\n            return\n\n        self.game_teardown(data, force=True)\n\n        data['Race Active'] = True\n        data['Players'][author.id] = {}\n        wait = settings['Time']\n        await self.bot.say(\":triangular_flag_on_post: A race has begun! Type {}race enter \"\n                           \"to join the race! :triangular_flag_on_post:\\n{}The race will \"\n                           \"begin in {} seconds!\\n\\n**{}** entered the \"\n                           \"race!\".format(ctx.prefix, ' ' * 25, wait, author.mention))\n        await asyncio.sleep(wait)\n        if len(data['Players']) == 1:\n            if self.bets != {}:\n                await self.npc_make_bet(ctx)\n        await self.bot.say(\":checkered_flag: The race is now in progress :checkered_flag:\")\n\n        data['Race Start'] = True\n\n        racers = self.game_setup(author, data, settings['Mode'])\n        race_msg = await self.bot.say('\\u200b' + '\\n' + '\\n'.join([player.field() for player in racers]))\n        await self.run_game(racers, race_msg, data)\n\n        footer = \"Type {}race claim to receive prize money. You must claim it before the next race!\"\n        first = ':first_place:  {0}'.format(*data['First'])\n        fv = '{1}\\n{2:.2f}s'.format(*data['First'])\n        second = ':second_place: {0}'.format(*data['Second'])\n        sv = '{1}\\n{2:.2f}s'.format(*data['Second'])\n        if data['Third']:\n            third = ':third_place:  {0}'.format(*data['Third'])\n            tv = '{1}\\n{2:.2f}s'.format(*data['Third'])\n        else:\n            third = ':third_place:'\n            tv = '--\\n--'\n\n        embed = discord.Embed(colour=0x00CC33)\n        embed.add_field(name=first, value=fv)\n        embed.add_field(name=second, value=sv)\n        embed.add_field(name=third, value=tv)\n        embed.add_field(\n            name='-' * 99, value='{} is the winner!'.format(data['Winner']))\n        embed.title = \"Race Results\"\n        embed.set_footer(text=footer.format(ctx.prefix))\n        await self.bot.say(content=data['Winner'].mention, embed=embed)\n        if self.bets != {}:\n            await self.payout_betters(data, ctx)\n        self.game_teardown(data)\n\n    @race.command(name=\"totalBets\", pass_context=True)\n    @commands.cooldown(1, 5, commands.BucketType.server)\n    async def _get_total_bets(self):\n        \"\"\"View total bets.\n\n        Returns:\n            Text informing the user that of the current Pot size\n\n        Notes:\n            Users can only place 1 bet,\n            They are only entitled to the amount the place (if other enter at higher value)\n            If Player does not bet, they are not entitled to anything.\n        \"\"\"\n        bet_total = 0\n        for key, value in self.bets.items():\n            bet_total += int(value)\n        await self.bot.say(\"Total Pot: {}\".format(bet_total))\n\n    @race.command(name=\"bet\", pass_context=True)\n    async def _enter_bet(self, ctx, betAmount: int):\n        \"\"\"Place a bet on the current race - 1 bet per player.\n\n        Returns:\n            Text informing the user that they have placed a bet on themselves.\n\n        Notes:\n            Users can only place 1 bet,\n            They are only entitled to the amount the place (if other enter at higher value)\n            If Player does not bet, they are not entitled to anything.\n        \"\"\"\n        author = ctx.message.author\n        data = self.check_server(author.server)\n        bets = self.bets\n        bot = self.bot\n\n        if data['Race Start']:\n            return\n        elif not data['Race Active']:\n            return\n        elif author.id in bets:\n            return await bot.say(\"{0} has already placed a bet of **{1}**\".format(author.name, bets[author.id]))\n        elif len(bets) == 8:\n            return\n        else:\n            if author.id in data['Players']:\n                try:\n                    bank = bot.get_cog('Economy').bank\n                    bank.withdraw_credits(author, betAmount)\n                    bets[author.id] = int(betAmount)\n                    await bot.say(\"{0} placed a **{1}** credit bet!\".format(author.name, betAmount))\n                except AttributeError:\n                    return await bot.say(\"Economy is not loaded.\")\n                except Exception as e:\n                    return await bot.say(\"Insufficient Funds, you looking for handouts? {}\".format(e))\n            else:\n                return await bot.say(\"NO {} !!! YOU ARE NOT IN THE RACE! SIDDOWN!\".format(author.name))\n\n    @race.command(name=\"enter\", pass_context=True)\n    async def _enter_race(self, ctx):\n        \"\"\"Enter an animal race\n\n        Returns:\n            Text informing the user they have entered the race.\n            If they cannot join for any reason (look at notes) then\n            it will return silently with no response.\n\n        Notes:\n            Users cannot join if a race is not active, has 5 (exluding the bot)\n            or more players, or is already in the race.\n        \"\"\"\n        author = ctx.message.author\n        data = self.check_server(author.server)\n\n        if data['Race Start']:\n            return\n        elif not data['Race Active']:\n            return\n        elif author.id in data['Players']:\n            return\n        elif len(data['Players']) == 8:\n            return\n        else:\n            data['Players'][author.id] = {}\n            await self.bot.say(\"**{}** entered the race!\".format(author.name))\n\n    @race.command(name=\"claim\", pass_context=True)\n    async def _claim_race(self, ctx):\n        \"\"\"Claim your prize from the animal race\n\n        Returns:\n                One of three outcomes based on result\n            :Text output giving random credits from 10-100\n            :Text output telling you are not the winner\n            :Text output telling you to get a bank account\n\n        Raises:\n            cogs.economy.NoAccount Error when bank account not found.\n\n        Notes:\n            If you do not have a bank account with economy, the bot will take your money\n            and spend it on cheap booze and potatoes.\n        \"\"\"\n        author = ctx.message.author\n        data = self.check_server(author.server)\n        settings = self.check_config(author.server)\n\n        if data['Race Active']:\n            return\n\n        if data['Winner'] != author:\n            return await self.bot.say(\"Scram kid. You didn't win nothing yet.\")\n        try:\n            bank = self.bot.get_cog('Economy').bank\n        except AttributeError:\n            return await self.bot.say(\"Economy is not loaded.\")\n\n        prize_range = settings['Prize']\n        prize = random.randint(*prize_range)\n\n        try:  # Because people will play games for money without a fucking account smh\n            bank.deposit_credits(author, prize)\n        except Exception as e:\n            print('{} raised {} because they are stupid.'.format(author.name, type(e)))\n            await self.bot.say(\"We wanted to give you a prize, but you didn't have a bank \"\n                               \"account.\\nTo teach you a lesson, your winnings are mine this \"\n                               \"time. Now go register!\")\n        else:\n            await self.bot.say(\"After paying for animal feed, entrance fees, track fees, \"\n                               \"you get {} credits.\".format(prize))\n        finally:\n            data['Winner'] = None\n\n    def check_server(self, server):\n        if server.id in self.system:\n            return self.system[server.id]\n        else:\n            self.system[server.id] = {'Race Start': False, 'Race Active': False, 'Players': {},\n                                      'Winner': None, 'First': None, 'Second': None, 'Third': None\n                                      }\n            return self.system[server.id]\n\n    def check_config(self, server):\n        if server.id in self.config['Servers']:\n            return self.config['Servers'][server.id]\n        else:\n            self.config['Servers'][server.id] = {'Prize': (1, 100), 'Mode': 'standard', 'Time': 60}\n            self.save_settings()\n            return self.config['Servers'][server.id]\n\n    def game_teardown(self, data, force=False):\n        if data['Winner'] == self.bot.user or force:\n            data['Winner'] = None\n        data['Race Active'] = False\n        data['Race Start'] = False\n        data['First'] = None\n        data['Second'] = None\n        data['Third'] = None\n        data['Players'].clear()\n        self.bets = {}\n\n    def save_settings(self):\n        dataIO.save_json('data/race/race.json', self.config)\n\n    async def npc_make_bet(self, ctx):\n        total_bets = 0\n        bot = self.bot\n        botuser = ctx.message.server.me\n        bets = self.bets\n        pprint(botuser)\n        for key, value in bets.items():\n            total_bets += int(value)\n        try:\n            try:\n                economy_cog = bot.get_cog('Economy')\n                bank = economy_cog.bank\n\n                if bank.get_balance(botuser) < total_bets:\n                    bank.deposit_credits(botuser, total_bets)\n                bank.withdraw_credits(botuser, total_bets)\n                self.bets[bot.user.id] = total_bets\n                await bot.say(\"Bot {0} bets ***{1}*** credits.\".format(bot.user.name, total_bets))\n            except AttributeError:\n                return await bot.say(\"Economy is not loaded.\")\n            except Exception as e:\n                return await bot.say(\"Insufficient Funds, you looking for handouts?\")\n        except Exception as e:\n            print('{} raised {} because they are stupid.'.format(bot.user, type(e)))\n            econ = self.bot.get_cog('Economy')\n            bank = econ.bank\n            bank.create_account(botuser)\n            await self.npc_make_bet(ctx)\n\n    async def payout_betters(self, data, ctx):\n        totalpayout = 0\n        bets = self.bets\n        bot = self.bot\n        if bot.user == data['Winner']:\n            winner = ctx.message.server.me\n        else:\n            winner = data['Winner']\n\n        for key, value in bets.items():\n            totalpayout += int(value)\n\n        if data['Winner'].id in bets:\n            try:  # Because people will play games for money without a fucking account smh\n                try:\n                    bank = bot.get_cog('Economy').bank\n                except AttributeError:\n                    return await bot.say(\"Economy is not loaded.\")\n                bank.deposit_credits(winner, totalpayout)\n                await bot.say(\"Congrats {0}, you get {1} credits.\".format(data['Winner'].name, totalpayout))\n            except Exception as e:\n                print('{} raised {} because they are stupid.'.format(data['Winner'], type(e)))\n                return await bot.say(\"We wanted to give you a prize, but you didn't have a bank \"\n                                     \"account.\\nGo register a bank account ya hippie!\"\n                                     \"\\nYou missed out on {} credits\".format(totalpayout))\n        else:\n            await bot.say(\"You didn't bet, siddown, one day you'll all be refunded.\")\n\n    def game_setup(self, author, data, mode):\n\n        racers = []\n\n        if mode == 'zoo':\n            if len(data['Players']) == 1:\n                bot_set = random.choice(animals)\n                racers = [Racer(bot_set[0], bot_set[1], self.bot.user)]\n\n            for user in data['Players']:\n                mobj = author.server.get_member(user)\n                animal_set = random.choice(animals)\n                racers.append(Racer(animal_set[0], animal_set[1], mobj))\n        else:\n            animal_set = (\":turtle:\", \"slow\")\n            if len(data['Players']) == 1:\n                racers = [Racer(animal_set[0], animal_set[1], self.bot.user)]\n\n            for user in data['Players']:\n                mobj = author.server.get_member(user)\n                racers.append(Racer(animal_set[0], animal_set[1], mobj))\n\n        return racers\n\n    async def run_game(self, racers, game, data):\n        while True:\n            await asyncio.sleep(2.0)\n            for player in racers:\n                player.update_position()\n                position = player.get_position()\n                if position == 0:\n                    if not data['Winner']:\n                        speed = player.turn + random.uniform(0.1, 0.88)\n                        data['Winner'] = player.user\n                        data['First'] = (player.user, player.animal, speed)\n                        player.placed = True\n                    elif not data['Second'] and not player.placed:\n                        if data['First'][2] > player.turn:\n                            speed = player.turn + random.uniform(0.89, 0.99)\n                        else:\n                            speed = player.turn + random.uniform(0.1, 0.88)\n                        data['Second'] = (player.user, player.animal, speed)\n                        player.placed = True\n                    elif not data['Third'] and not player.placed:\n                        if data['Second'][2] > player.turn:\n                            speed = player.turn + random.uniform(0.89, 0.99)\n                        else:\n                            speed = player.turn + random.uniform(0.1, 0.88)\n                        data['Third'] = (player.user, player.animal, speed)\n                        player.placed = True\n            field = [player.field() for player in racers]\n            await self.bot.edit_message(game, '\\u200b' + '\\n' + '\\n'.join(field))\n\n            if [player.get_position() for player in racers].count(0) == len(racers):\n                break\n\n        prize = random.randint(10, 100)\n        data['Prize'] = prize\n\n\ndef check_folders():\n    if not os.path.exists('data/race'):\n        print(\"Creating data/race folder...\")\n        os.makedirs('data/race')\n\n\ndef check_files():\n    system = {\"Servers\": {}}\n\n    f = 'data/race/race.json'\n    if not dataIO.is_valid_json(f):\n        print('data/race/race.json')\n        dataIO.save_json(f, system)\n\n\ndef setup(bot):\n    check_folders()\n    check_files()\n    bot.add_cog(Race(bot))\n","repo_name":"NBKChat/nbk-cogs","sub_path":"race/race.py","file_name":"race.py","file_ext":"py","file_size_in_byte":22702,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"3519084451","text":"from video_streaming.grpc import GrpcServer\nfrom video_streaming.celery import celery_app\n\n# To found main.celery_app module and load celery application.\n__all__ = ['celery_app']\n\n\ndef main():\n    \"\"\"Main entry point\"\"\"\n\n    GrpcServer().serve()  # start gRPC server\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"mojtaba-arvin/video-service","sub_path":"video-streaming/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"86"}
+{"seq_id":"70692724124","text":"import time\nimport serial\n\nDEBUG=True\n\nSAMSUNG_REQUEST_PREFIX='\\x08\\x22'\nSAMSUNG_RESPONSE_SUCCESS='\\x03\\x0C\\xF1'\nSAMSUNG_RESPONSE_FAILURE='\\x03\\x0C\\xFF'\n\nST_INIT=0\nST_CC1=1\nST_CC2=2\n\nclass TVRemote(object):\n    def __init__(self, device=None):\n        if device is None:\n            device = '/dev/ttyS0'\n\n        self.port = serial.Serial(device,\n                baudrate=9600,\n                bytesize=8,\n                parity='N',\n                stopbits=1,\n                timeout=3,\n                writeTimeout=1)\n\n    def close(self):\n        self.port.close()\n\n    def _checksum(self, cmd):\n        chk = 0\n        for ch in cmd:\n            chk = (chk + ord(ch)) % 256\n        return (~chk + 1) % 256\n\n    def _analyze_response(self):\n        status = ST_INIT\n        while True:\n            c = self.port.read(size=1)\n\n            # timeout\n            if not c:\n                return False\n\n            if DEBUG:\n                print(\"%02X\" % ord(c), sep=' ')\n\n            if status == ST_INIT:\n                if c == SAMSUNG_RESPONSE_SUCCESS[0]:\n                    status = ST_CC1\n                else:\n                    status = ST_INIT\n            elif status == ST_CC1:\n                if c == SAMSUNG_RESPONSE_SUCCESS[1]:\n                    status = ST_CC2\n                else:\n                    status = ST_INIT\n            elif status == ST_CC2:\n                if c == SAMSUNG_RESPONSE_SUCCESS[2]:\n                    return True\n                elif c == SAMSUNG_RESPONSE_FAILURE[2]:\n                    return False\n                else:\n                    status = ST_INIT\n            else:\n                status = ST_INIT\n\n    def _send_cmd(self, cmd1=0, cmd2=0, cmd3=0, value=0, timeout=0.1):\n        cmd = \"%s%c%c%c%c\" % (SAMSUNG_REQUEST_PREFIX, cmd1, cmd2, cmd3, value)\n        cmd += chr(self._checksum(cmd))\n\n        self.port.write(cmd.encode('latin1'))\n\n        time.sleep(timeout)\n\n        response = self._analyze_response()\n\n        return response == SAMSUNG_RESPONSE_SUCCESS\n\n    def cmd_volume_set(self, volume):\n        \"\"\"Set the volume to a specific level (0-255)\"\"\"\n        if volume > 255:\n            volume = 255\n        elif volume < 0:\n            volume = 0\n        return self._send_cmd(0x01, 0x00, 0x00, volume)\n    cmd_volume_set.nargs = 1\n\n    def cmd_volume_up(self):\n        \"\"\"Pump up the volume\"\"\"\n        return self._send_cmd(0x01, 0x00, 0x01, 0x00)\n    cmd_volume_up.nargs = 0\n\n    def cmd_volume_down(self):\n        \"\"\"Pump down the volume\"\"\"\n        return self._send_cmd(0x01, 0x00, 0x02, 0x00)\n    cmd_volume_down.nargs = 0\n\n    def cmd_volume_mute(self):\n        \"\"\"Mute\"\"\"\n        return self._send_cmd(0x02, 0x00, 0x00, 0x00)\n    cmd_volume_mute.nargs = 0\n\n    def cmd_source_tv(self):\n        \"\"\"Change image source to TV\"\"\"\n        return self._send_cmd(0x0a, 0x00, 0x00, 0x00)\n    cmd_source_tv.nargs = 0\n\n    def cmd_source_av(self, av=0):\n        \"\"\"Change image source to AV\"\"\"\n        return self._send_cmd(0x0a, 0x00, 0x01, av)\n    cmd_source_av.nargs = 0\n\n    def cmd_source_svideo(self, svideo=0):\n        \"\"\"Change image source to SVideo\"\"\"\n        return self._send_cmd(0x0a, 0x00, 0x02, svideo)\n    cmd_source_svideo.nargs = 0\n\n    def cmd_source_component(self, component=0):\n        \"\"\"Change image source to Component\"\"\"\n        return self._send_cmd(0x0a, 0x00, 0x03, component)\n    cmd_source_component.nargs = 0\n\n    def cmd_source_pc(self, pc=0):\n        \"\"\"Change image source to PC\"\"\"\n        return self._send_cmd(0x0a, 0x00, 0x04, pc)\n    cmd_source_pc.nargs = 0\n\n    def cmd_source_hdmi(self, hdmi=0):\n        \"\"\"Change image source to HDMI\"\"\"\n        return self._send_cmd(0x0a, 0x00, 0x05, hdmi)\n    cmd_source_hdmi.nargs = 0\n\n    def cmd_source_dvi(self, dvi=0):\n        \"\"\"Change image source to DVI\"\"\"\n        return self._send_cmd(0x0a, 0x00, 0x06, dvi)\n    cmd_source_dvi.nargs = 0\n\n    def cmd_tv_channel_set(self, channel):\n        \"\"\"Change TV channel (0-255)\"\"\"\n        if channel > 255:\n            chanel = 255\n        elif channel < 0:\n            channel = 0\n\n        return self._send_cmd(0x04, 0, 0, channel)\n    cmd_tv_channel_set.nargs = 1\n\n    def cmd_tv_channel_up(self):\n        \"\"\"Change to next TV channel\"\"\"\n        return self._send_cmd(0x03, 0x00, 0x01, 0x00)\n    cmd_tv_channel_up.nargs = 0\n\n    def cmd_tv_channel_down(self):\n        \"\"\"Change to previous TV channel\"\"\"\n        return self._send_cmd(0x03, 0x00, 0x02, 0x00)\n    cmd_tv_channel_down.nargs = 0\n\n    def cmd_power_off(self):\n        \"\"\"Power off TV. Warning: cannot be powered up again with ex-link cable\"\"\"\n        return self._send_cmd(0x00, 0x00, 0x00, 0x01)\n    cmd_power_off.nargs = 0\n\n    def cmd_power_on(self):\n        \"\"\"Power on TV. Actually not working\"\"\"\n        return self._send_cmd(0x00, 0x00, 0x00, 0x02)\n    cmd_power_on.nargs = 0\n\n\n    @classmethod\n    def method_list(cls):\n        command_methods = [cls.__dict__[m] for m in cls.__dict__.keys() if m.startswith(\"cmd_\")]\n        return command_methods\n\n    @classmethod\n    def command_list(cls):\n        commands = [method.__name__[4:] for method in cls.method_list()]\n        commands.sort()\n        return commands\n\nif __name__ == '__main__':\n    try:\n        tv = TVRemote(\"/dev/ttyS0\")\n        tv.cmd_source_pc(pc=0)\n        tv.cmd_volume_mute()\n        time.sleep(0.5)\n        tv.cmd_set_volume(15)\n    finally:\n        tv.close()\n\n\n","repo_name":"enlavin/exlink","sub_path":"exlink.py","file_name":"exlink.py","file_ext":"py","file_size_in_byte":5403,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"86"}
+{"seq_id":"27526317746","text":"from bj.player import Player, Dealer\nfrom bj.deck import Deck\nclass Game:\n    \n    def __init__(self):\n        self.player = Player()\n        self.dealer = Dealer()\n        self.deck = Deck()\n        self.deck.add_player(player=self.player, dealer=self.dealer)\n\n    def start(self):\n        print(\"---------ゲーム開始---------\")\n        # 初期はまず2枚ずつ引く\n        for i in range(2):\n            self.player.draw_card(self.deck)\n            self.dealer.draw_card(self.deck)\n        print(self.deck.display_deck())\n        self.play()\n\n    def play(self):\n        while True:\n            draw = input(\"追加でカードを引きますか? yes/no: \")\n            if \"n\" in draw:\n                break\n            self.player.draw_card(self.deck)\n            if self.deck.is_bust(self.player):\n                print(\"\\n\")\n                print(\"player bust!!\")\n                break\n            else:\n                print(\"\\n\")\n                print(\"---------デッキ状況---------\")\n                print(self.deck.display_deck())\n        self.final_judge()\n\n    def final_judge(self):\n        self.dealer.draw_card_by_17(self.deck)\n        msg = self.deck.final_judge()\n        self.end(msg)\n \n    def end(self, msg):\n        print(\"=========ゲーム結果=========\")\n        print(\"###\", msg, \"###\")\n        print(self.deck.display_deck(final_judge=True))\n\n\ndef main():\n    game = Game()\n    game.start()","repo_name":"anaaki/bj","sub_path":"bj/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":1429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"20888890699","text":"\"\"\"\nGiven the root of a binary tree, invert the tree, and return its root.\n\"\"\"\n\nclass TreeNode(object):\n     def __init__(self, val=0, left=None, right=None):\n         self.val = val\n         self.left = left\n         self.right = right\n\nclass InvertBinaryTree(object):\n    def invertTree(self, root):\n        if root is None:\n            return None\n        elif root.left is None and root.right is None:\n            return root\n        elif root.left is None and root.right is not None:\n            root.left = self.invertTree(root.right)\n            root.right = None\n            return root\n        elif root.left is not None and root.right is None:\n            root.right = self.invertTree(root.left)\n            root.left = None\n            return root\n        else:\n            newLeft = self.invertTree(root.right)\n            newRight = self.invertTree(root.left)\n            root.left = newLeft\n            root.right = newRight\n            return root","repo_name":"jasonwang7517/Interview-Prep","sub_path":"InvertBinaryTree.py","file_name":"InvertBinaryTree.py","file_ext":"py","file_size_in_byte":962,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"19132299178","text":"def findLow(num):\n    l = 0\n    h = len(LIS)-1\n    ret = 1000000\n\n    while l <= h:\n        mid = (l + h)//2\n        if LIS[mid] >= num:\n            if ret > mid:\n                ret = mid\n            h = mid - 1\n        else:\n            l = mid + 1\n\n    return ret\n\n\nN = int(input())\nnumber = [int(x) for x in input().split()]\nLIS = [number[0]]\n\nfor i in range(1, N):\n    if LIS[len(LIS)-1] < number[i]:\n        LIS.append(number[i])\n    else:\n        LIS[findLow(number[i])] = number[i]\n\nprint(LIS)\nprint(len(LIS))","repo_name":"JungDayoon/AlgorithmStudy","sub_path":"BOJ/[12015] 가장 긴 증가하는 부분 수열 2/ekdbsl/LIS_bs.py","file_name":"LIS_bs.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"11108238883","text":"\nfrom pathlib import Path\n\nimport pickle\nimport logging\nimport os\nimport math\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader, Dataset, BatchSampler\nimport pytorch_lightning as pl\n\nimport torchtext\nfrom datasets import load_dataset, DatasetDict\n\nfrom .utils.collators import collate_batch_pad\nfrom .utils.bucket_sampler import BucketSampler\n\n# LRA tokenizer renames ']' to 'X' and delete parentheses as their tokenizer removes\n# non-alphanumeric characters.\n# https://github.com/google-research/long-range-arena/blob/264227cbf9591e39dd596d2dc935297a2070bdfe/lra_benchmarks/listops/input_pipeline.py#L46\ndef listops_tokenizer(s):\n    return s.translate({ord(\"]\"): ord(\"X\"), ord(\"(\"): None, ord(\")\"): None}).split()\n\n\nclass ListOpsDataModuleVar(pl.LightningDataModule):\n    def __init__(\n        self,\n        data_dir,\n        batch_size,\n        num_workers,\n        max_length=2048,\n        **kwargs,\n    ):\n        super().__init__()\n        self.data_dir = Path(data_dir)\n        self.batch_size = batch_size\n        self.num_workers = num_workers\n        self.max_length = max_length\n        self.cache_dir = self.get_cache_dir()\n\n    def prepare_data(self):\n        self.process_dataset()\n\n    def setup(self, stage=None):\n        if stage == \"test\" and hasattr(self, \"dataset_test\"):\n            return\n        dataset, self.tokenizer, self.vocab = self.process_dataset()\n        self.vocab_size = len(self.vocab)\n        dataset.set_format(type=\"torch\", columns=[\"sequence\", \"label\", \"len\"])\n\n        # Create all splits\n        self.train_dataset, self.val_dataset, self.test_dataset = (\n            dataset[\"train\"],\n            dataset[\"val\"],\n            dataset[\"test\"],\n        )\n        self.collate_fn = collate_batch_pad\n        \n    def process_dataset(self):\n        if self.cache_dir is not None:\n            return self._load_from_cache()\n\n        dataset = load_dataset(\n            \"csv\",\n            data_files={\n                \"train\": str(self.data_dir / \"basic_train.tsv\"),\n                \"val\": str(self.data_dir / \"basic_val.tsv\"),\n                \"test\": str(self.data_dir / \"basic_test.tsv\"),\n            },\n            delimiter=\"\\t\",\n            keep_in_memory=True,\n        )\n        dataset = dataset.rename_column(\"Target\", \"label\")\n        \n        # Remove unnecessary tokens\n        tokenizer = listops_tokenizer\n        tokenize = lambda x: {\"tokens\": tokenizer(x[\"Source\"])}\n        dataset = dataset.map(\n            tokenize,\n            remove_columns=[\"Source\"],\n            keep_in_memory=True,\n            load_from_cache_file=False\n        )\n        \n        # Calculate lengths\n        lengths_calc = lambda x: {\"len\": len(x[\"tokens\"])}\n        dataset = dataset.map(\n            lengths_calc,\n            keep_in_memory=True,\n            load_from_cache_file=False\n        )\n        \n        # Biuld vocab\n        vocab = torchtext.vocab.build_vocab_from_iterator(\n            dataset[\"train\"][\"tokens\"],\n            specials=(\n                [\"<pad>\", \"<unk>\"]\n            ),\n        )\n        vocab.set_default_index(vocab[\"<unk>\"])\n        \n        # Map vocab values\n        numericalize = lambda x: {\"sequence\": vocab(x[\"tokens\"])}\n        dataset = dataset.map(\n            numericalize,\n            remove_columns=[\"tokens\"],\n            keep_in_memory=True,\n            load_from_cache_file=False\n        )\n\n        self._save_to_cache(dataset, tokenizer, vocab)\n        return dataset, tokenizer, vocab\n\n    def _save_to_cache(self, dataset, tokenizer, vocab):\n        cache_dir = self.data_dir / self._cache_dir_name\n        os.makedirs(str(cache_dir), exist_ok=True)\n        logger = logging.getLogger(__name__)\n        logger.info(f\"Saving to cache at {str(cache_dir)}\")\n        dataset.save_to_disk(str(cache_dir))\n        with open(cache_dir / \"tokenizer.pkl\", \"wb\") as f:\n            pickle.dump(tokenizer, f)\n        with open(cache_dir / \"vocab.pkl\", \"wb\") as f:\n            pickle.dump(vocab, f)\n\n    def _load_from_cache(self):\n        assert self.cache_dir.is_dir()\n        logger = logging.getLogger(__name__)\n        logger.info(f\"Load from cache at {str(self.cache_dir)}\")\n        dataset = DatasetDict.load_from_disk(str(self.cache_dir))\n        with open(self.cache_dir / \"tokenizer.pkl\", \"rb\") as f:\n            tokenizer = pickle.load(f)\n        with open(self.cache_dir / \"vocab.pkl\", \"rb\") as f:\n            vocab = pickle.load(f)\n        return dataset, tokenizer, vocab\n\n    @property\n    def _cache_dir_name(self):\n        return f\"listops1_var_{self.batch_size}\"\n\n    def get_cache_dir(self):\n        cache_dir = self.data_dir / self._cache_dir_name\n        if cache_dir.is_dir():\n            return cache_dir\n        else:\n            return None\n\n    def get_bucket_boundaries(self, df):\n        max_log2_bin = math.ceil(np.log2(max(df['len'])))\n        min_log2_bin = math.floor(np.log2(min(df['len'])))\n        return [2**i for i in range(min_log2_bin, max_log2_bin + 1)]\n    \n       # Defining DataLoaders\n    def train_dataloader(self):\n        boundaries = self.get_bucket_boundaries(self.train_dataset)\n        sampler_train = BucketSampler(\n            lengths=self.train_dataset['len'], \n            bucket_boundaries=boundaries,\n            batch_size=self.batch_size\n        )\n        train_dataloader = DataLoader(\n            self.train_dataset,\n            batch_size=None,\n            shuffle=False,\n            sampler=sampler_train,\n            num_workers=self.num_workers,\n            drop_last=False,\n            collate_fn=self.collate_fn\n        )\n        return train_dataloader\n\n    def val_dataloader(self):\n        boundaries = self.get_bucket_boundaries(self.val_dataset)\n        sampler_val = BucketSampler(\n            lengths=self.val_dataset['len'], \n            bucket_boundaries=boundaries,\n            batch_size=self.batch_size\n        )\n        val_dataloader = DataLoader(\n            self.val_dataset,\n            batch_size=None,\n            shuffle=False,\n            sampler=sampler_val,\n            num_workers=self.num_workers,\n            drop_last=False,\n            collate_fn=self.collate_fn\n        )\n        return val_dataloader\n    \n    def test_dataloader(self):\n        sampler_test = BucketSampler(\n            lengths=self.test_dataset['len'], \n            bucket_boundaries=self.get_bucket_boundaries(self.test_dataset),\n            batch_size=self.batch_size\n        )\n        test_dataloader = DataLoader(\n            self.test_dataset,\n            batch_size=None,\n            shuffle=False,\n            sampler=sampler_test,\n            num_workers=self.num_workers,\n            drop_last=False,\n            collate_fn=self.collate_fn\n        )\n        return test_dataloader","repo_name":"RuslanKhalitov/ChordMixer","sub_path":"dataloaders/lra_listops_var.py","file_name":"lra_listops_var.py","file_ext":"py","file_size_in_byte":6767,"program_lang":"python","lang":"en","doc_type":"code","stars":52,"dataset":"github-code","pt":"86"}
+{"seq_id":"43133824059","text":"'''\nThis module stores all of the data loading tools needed for GeneratorNet training and testing.\nThe tools available include a midi file information parser and a sound file parser.\n'''\n\n# dependencies\nimport random\nimport numpy as np\nimport h5py\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\nimport torch\n\n# user defined modules\nfrom DataParameters import PARAMETERS as params\n\n\nclass RoseEtudes(Dataset):\n    '''Data loader class for reading the Rose Etude data from the .h5 file stored in path.\n\n    Args: path\n        path (string): The location of the Rose Etudes .h5 files.\n        data_name (string): Name of the Rose Etudes data .h5 file.\n        labels_name (string): Name of the Rose Etudes label .h5 file.\n    '''\n    def __init__(self, path, data_name, labels_name):\n        self.rose_data_frame = h5py.File(path + data_name, 'r')\n        self.rose_data_keys = list(self.rose_data_frame.keys())\n        self.rose_labels_frame = h5py.File(path + labels_name, 'r')\n        self.rose_labels_keys = list(self.rose_labels_frame.keys())\n        # the number of frames to include from the file\n        self.num_frames = int(params['sound_duration'] * 44100)\n\n    def __len__(self):\n        return len(self.rose_data_keys)\n\n    def __getitem__(self, idx):\n        rose_data = torch.from_numpy(\n            self.rose_data_frame[self.rose_data_keys[idx]][:self.num_frames])\n        rose_labels = self.rose_labels_frame[self.rose_labels_keys[idx]][:, 3:5]\n        rose_labels = torch.tensor([self.name_to_midi(note, octave) for note, octave in\n                                    zip(rose_labels[:, 0], rose_labels[:, 1])])\n        return rose_data, rose_labels\n    def name_to_midi(self, note, octave):\n        '''Method for converting between note names and midi labels\n\n        Input: note, octave\n            note (string): The name of the note to be converted to midi.\n            octave (int): The octave of the note to be converted to midi.\n\n        Output: midi\n            midi (int): The midi note corresponding to the input.\n        '''\n        name = {b'rest': 0,\n                b'C-': -1, b'C': 0, b'C#': 1, b'C##': 2,\n                b'D-': 1, b'D': 2, b'D#': 3,\n                b'E-':3, b'E': 4, b'E#': 5,\n                b'F-': 4, b'F': 5, b'F#': 6, b'F##': 7,\n                b'G-': 6, b'G': 7, b'G#': 8, b'G##': 9,\n                b'A-': 8, b'A': 9, b'A#': 10,\n                b'B--': 9, b'B-': 10, b'B': 11, b'B#': 12}\n        midi = name[note] + (int(octave) + 1) * 12\n        return midi\n\n\nclass Philharmonia(Dataset):\n    '''Data loader class for reading the Philharmonia data from the .h5 file stored in path.\n\n    Args: path\n        path (string): The location of the Philharmonia .h5 file.\n        name (string): Name of the Philharmonia .h5 file.\n    '''\n    def __init__(self, path, name):\n        self.phil_frame = h5py.File(path + name, 'r')\n        phil_keys = np.array(list(self.phil_frame.keys()))\n        # shuffle the keys so as to not bias the input data\n        random.Random(4).shuffle(phil_keys)\n        '''\n        Information from the key names separated by the '_' delimiter:.\n        Index 0: instrument (banjo, bass-clarinet, bassoon, ..., violin).\n        Index 1: midi note (22, 23,24, ..., 108).\n        Index 2: duration (025, 05, 1, ..., very long).\n        Index 3: dynamics (pianissimo, piano, mezzo-piano, ... fortissimo).\n        Index 4: style (normal, fluttertonguing, nonlegato, ..., glissando).\n        '''\n        information = np.array([key.split('_') for key in phil_keys])\n        # only include samples with monophonic, dynamically stable sounds\n        # played normally on the clarinet,\n        useful_samples = [(inst == 'clarinet' and 'phrase' not in dur\n                           and 'long' not in dur and 'cresc' not in dyn\n                           and 'normal' in style)\n                          for inst, dur, dyn, style in zip(information[:, 0],\n                                                           information[:, 2],\n                                                           information[:, 3],\n                                                           information[:, 4])]\n        self.phil_keys = phil_keys[useful_samples]\n        self.information = information[useful_samples]\n        # the labels are the note names\n        self.labels = torch.tensor([\n            self.name_to_midi(info) for info in self.information[:, 1]]).long()\n\n    def __len__(self):\n        return len(self.phil_keys)\n\n    def __getitem__(self, idx):\n        phil_data = torch.from_numpy(\n            self.phil_frame[self.phil_keys[idx]][:]).float()\n        phil_labels = self.labels[idx].long()\n        return phil_data, phil_labels\n    def name_to_midi(self, note):\n        '''Method for converting note name labels to midi labels\n        Input: note\n            note (string): Note name to convert to midi\n\n        Output: midi\n            midi (int): output midi note\n        '''\n        note_names = 'C Cs D Ds E F Fs G Gs A As B'.split(' ')\n        midi = (note_names.index(note[:-1]))+(int(note[-1])+1)*12\n        return midi\n\nDATASETS = {'Rose Etudes': RoseEtudes('../data/audio_data/', 'Rose_Data.h5', 'Rose_Labels.h5'),\n            'Philharmonia': Philharmonia('../data/audio_data/', 'Phil.h5')}\n\n\ndef get_loader(dataset, batch_size=1, shuffle=False,\n               sampler=None, batch_sampler=None, num_workers=0,\n               pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None):\n    '''Method for loading datasets with batching, samplers, and collate functions'''\n\n    loader = DataLoader(dataset=DATASETS[dataset],\n                        batch_size=batch_size,\n                        shuffle=shuffle,\n                        sampler=sampler,\n                        batch_sampler=batch_sampler,\n                        num_workers=num_workers,\n                        pin_memory=pin_memory,\n                        drop_last=drop_last,\n                        timeout=timeout,\n                        worker_init_fn=worker_init_fn)\n    return loader\n","repo_name":"orlandomelchor/BeyondMIDI","sub_path":"loader/DataLoader.py","file_name":"DataLoader.py","file_ext":"py","file_size_in_byte":6075,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"26181963227","text":"from collections import Counter\n\nclass Codigo:\n\n    def __init__(self):\n        pass\n\n    def maoListada(self,board,hole_cards): \n        mao_listada = {}\n        listar_repeticao = self.listarRepeticao(board,hole_cards)\n        listar_flush = self.listarFlush(board,hole_cards)\n        listar_street = self.listarStreet(board,hole_cards)\n        listar_street_flush = self.listarStreet(board,hole_cards,True)\n        mao_listada['repeticao'] = listar_repeticao\n        mao_listada['flush'] = listar_flush\n        mao_listada['street'] = listar_street\n        mao_listada['street_flush'] = listar_street_flush\n        return mao_listada\n\n    #============================= Metodos Privados =================================\n\n    def listarFlush(self,board,hole_cards):\n        valores = self.separarNumerosDosNapes(board,hole_cards)\n        nape = self.napeMaisRecorrente(valores)\n        numeros = self.numerosComNape(valores,nape)\n        numeros = numeros if valores['napes'].count(nape) >= 5 else None\n        return numeros\n\n    def napeMaisRecorrente(self,valores):\n        repeticao_napes = dict(Counter(valores['napes']).items())\n        maior_repeticao = 0\n        nape_mais_repeticao = ''\n        for chave,valor in repeticao_napes.items():\n            if valor > maior_repeticao:\n                maior_repeticao = valor\n                nape_mais_repeticao = chave\n        return nape_mais_repeticao\n\n    def numerosComNape(self,valores,nape):\n        numeros = []\n        for idx in range(len(valores['napes'])):\n            if valores['napes'][idx] == nape:\n                numeros.append(valores['numeros'][idx])\n        numeros.sort(reverse = True)\n        return numeros\n\n    def listarStreet(self,board,hole_cards,flush=False):\n        numero_anterior = 0\n        street = []\n        valores = self.separarNumerosDosNapes(board,hole_cards)\n        numeros = self.numerosComNaipeMaisRecorrente(valores,flush)\n        for numero in numeros:\n            if (numero_anterior - numero) == 1:\n                street.append(numero)\n                numero_anterior = numero\n                if len(street) == 5:\n                    street.sort(reverse = True)\n                    return street\n            else:\n                street = [numero]\n                numero_anterior = numero\n        return None\n\n    def numerosComNaipeMaisRecorrente(self,valores,flush=False):\n        numeros = []\n        if flush == True:\n            naipe = self.napeMaisRecorrente(valores)\n            numeros = self.numerosComNape(valores,naipe)\n        else:\n            numeros = valores['numeros']\n        return self.numerosSemRepeticao(numeros)\n\n    def listarRepeticao(self,board=[],hole_cards=None):\n        valores = self.separarNumerosDosNapes(board,hole_cards)\n        grupos_repeticao = self.agruparPorRepeticao(valores['numeros'])\n        numeros = []\n        for i in reversed(range(1,5)):\n            numeros += grupos_repeticao[i]\n        return numeros[:5]\n\n    def agruparPorRepeticao(self,numeros=[]):\n        repeticao_numeros = dict(Counter(numeros).items())\n        grupos_repeticao = {1:[],2:[],3:[],4:[]}\n        for chave,valor in repeticao_numeros.items():\n            for i in range(valor):\n                grupos_repeticao[valor].append(chave)\n            grupos_repeticao[valor].sort(reverse = True) \n        return grupos_repeticao\n\n    def numerosSemRepeticao(self,numeros):\n        numeros_sem_repeticao = []\n        repeticao_numeros = dict(Counter(numeros).items())\n        for chave,valor in repeticao_numeros.items():\n            numeros_sem_repeticao.append(chave)\n        if 14 in numeros_sem_repeticao:\n            numeros_sem_repeticao.append(1)\n        numeros_sem_repeticao.sort(reverse = True)\n        return numeros_sem_repeticao\n\n\n    def separarNumerosDosNapes(self,board,holecads):\n        cartas = board + holecads\n        valores = {'numeros':[],'napes':[]}\n        for carta in cartas:\n            valores['numeros'].append(self.tranformarFiguraEmNumero(carta[0]))\n            valores['napes'].append(carta[1])\n        return valores\n\n    def tranformarFiguraEmNumero(self,figura):\n        figuras = { '2':2,'3':3,'4':4,'5':5,'6':6,'7':7,\n                    '8':8,'9':9,'T':10,'J':11,'Q':12,'K':13,'A':14}\n        return figuras[figura]\n\n","repo_name":"robsonlopesunifor/Booker_api","sub_path":"MaosPossiveis/viewsets/Possibilidades/Codigo.py","file_name":"Codigo.py","file_ext":"py","file_size_in_byte":4279,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"1416615485","text":"from unittest.mock import MagicMock\n\nimport pytest\n\nfrom dao.genre import GenreDAO\nfrom dao.model.genre import Genre\n\n\n@pytest.fixture\ndef genre_dao():\n    '''\n    We will create a fixture with a mock,\n    and here we have 3 instances of the class.\n    :return: values for each method in GenreDAO\n    '''\n    genre_dao = GenreDAO(None)\n\n    thriller = Genre(id=1, name='thriller')\n    drama = Genre(id=2, name='drama')\n    horror = Genre(id=3, name='horror')\n\n    genre_dao.get_one = MagicMock(return_value=thriller)\n    genre_dao.get_all = MagicMock(return_value=[thriller, drama, horror])\n    genre_dao.create = MagicMock(return_value=Genre(id=3))\n    genre_dao.update = MagicMock()\n    genre_dao.delete = MagicMock()\n\n    return genre_dao\n","repo_name":"TheFimerHub/SKYPRO_homework20_problem_1","sub_path":"tests/service/fixtures/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"17662873173","text":"import base64\nimport requests\nfrom urllib.parse import urlencode\n\nfrom django.shortcuts import render\nfrom django.http import HttpResponse, HttpResponseRedirect\n\n# Create your views here.\n\nclient_id = ''\nclient_secret = ''\n\ndef go_to_spotify_for_auth(req):\n\treturn HttpResponseRedirect('https://accounts.spotify.com/en/authorize?{}'.format(\n\t\turlencode(\n\t\t\t{\n\t\t\t\t'client_id': client_id,\n\t\t\t\t'redirect_uri': 'http://localhost:8000/auth/receive-redirect',\n\t\t\t\t'response_type': 'code',\n\t\t\t\t'scopes': 'playlist-read-private playlist-modify-private playlist-modify-public playlist-read-collaborative',\n\t\t\t\t'state': 'somestate'\n\t\t\t}\n\t\t)\n\t))\n\ndef receive_redirect_refresh(req):\n\treturn HttpResponse(\"Hello world\")\n\ndef receive_redirect(req):\n\tauth_code = req.GET['code']\n\tstate_value = req.GET['state']\n\n\tclient_stuff = '{}:{}'.format(client_id, client_secret)\n\tclientb64 = base64.b64encode(client_stuff.encode('utf-8')).decode('utf-8')\n\n\tprint(clientb64)\n\tprint('Basic ' + clientb64)\n\n\tresp = requests.post(\n\t\t\"https://accounts.spotify.com/api/token\",\n\t\t{\n\t\t\t'grant_type': 'authorization_code',\n\t\t\t'code': auth_code,\n\t\t\t'redirect_uri': 'http://localhost:8000/auth/receive-redirect'\n\t\t},\n\t\theaders={\n\t\t\t'Authorization': 'Basic ' + clientb64,\n\t\t},\n\t)\n\n\tprint(resp)\n\n\treturn HttpResponse(resp.content)\n","repo_name":"mrhwick/splitlist","sub_path":"discoverwith/spotify/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"6333026201","text":"import json\nimport unittest\n\nfrom unittest import mock\n\nfrom ops.testing import Harness\nfrom ops.charm import CharmBase\n\nfrom charmhelpers.contrib.openstack.ha.utils import VIP_GROUP_NAME\n\nimport interface_hacluster\nimport interface_mssql_cluster\n\n\nclass TestInterfaceHaCluster(unittest.TestCase):\n\n    def setUp(self):\n        self.harness = Harness(CharmBase, meta='''\n            name: mssql\n            peers:\n              cluster:\n                interface: mssql-cluster\n            requires:\n              ha:\n                interface: hacluster\n                scope: container\n        ''')\n        self.addCleanup(self.harness.cleanup)\n\n    @mock.patch.object(interface_hacluster,\n                       'update_hacluster_vip')\n    @mock.patch.object(interface_hacluster.HaCluster,\n                       'setup_pacemaker_mssql_login')\n    @mock.patch.object(interface_hacluster,\n                       'apt_install')\n    @mock.patch.object(interface_mssql_cluster.MssqlCluster,\n                       'is_ag_ready',\n                       new_callable=mock.PropertyMock)\n    def test_on_joined(self, _is_ag_ready, _apt_install,\n                       _setup_pacemaker_mssql_login, _update_hacluster_vip):\n        _is_ag_ready.return_value = True\n        self.harness.begin()\n        self.harness.charm.cluster = interface_mssql_cluster.MssqlCluster(\n            self.harness.charm, 'cluster')\n        self.harness.charm.ha = interface_hacluster.HaCluster(\n            self.harness.charm, 'ha')\n        rel_id = self.harness.add_relation('ha', 'hacluster')\n        self.harness.add_relation_unit(rel_id, 'hacluster/0')\n\n        _apt_install.assert_called_once_with(\n            packages=self.harness.charm.ha.APT_PACKAGES, fatal=True)\n        _setup_pacemaker_mssql_login.assert_called_once_with()\n        _update_hacluster_vip.assert_called_once()\n\n        rel_data = self.harness.get_relation_data(rel_id, 'mssql/0')\n        expected_rel_data = {}\n        keys = ['resources', 'resource_params', 'ms', 'colocations', 'orders']\n        for key in keys:\n            json_value = rel_data.get('json_{}'.format(key))\n            self.assertIsNotNone(json_value)\n            expected_rel_data.update({key: json.loads(json_value)})\n        group_name = VIP_GROUP_NAME.format(service='mssql')\n        self.assertDictEqual(\n            expected_rel_data,\n            {\n                'resources': {\n                    'ag_cluster': 'ocf:mssql:ag'\n                },\n                'resource_params': {\n                    'ag_cluster':\n                    'params ag_name=\"{}\" '\n                    'meta failure-timeout=60s '\n                    'op start timeout=60s '\n                    'op stop timeout=60s '\n                    'op promote timeout=60s '\n                    'op demote timeout=10s '\n                    'op monitor timeout=60s interval=10s '\n                    'op monitor timeout=60s interval=11s role=\"Master\" '\n                    'op monitor timeout=60s interval=12s role=\"Slave\" '\n                    'op notify timeout=60s'.format(\n                        self.harness.charm.cluster.AG_NAME)\n                },\n                'ms': {\n                    'ms-ag_cluster':\n                    'ag_cluster meta '\n                    'master-max=\"1\" master-node-max=\"1\" '\n                    'clone-max=\"3\" clone-node-max=\"1\" notify=\"true\"'\n                },\n                'colocations': {\n                    'vip_on_master':\n                    'inf: {} ms-ag_cluster:Master'.format(group_name)\n                },\n                'orders': {\n                    'ag_first':\n                    'inf: ms-ag_cluster:promote {}:start'.format(group_name)\n                }\n            })\n\n    def test_on_changed(self):\n        self.harness.begin()\n        self.harness.charm.cluster = interface_mssql_cluster.MssqlCluster(\n            self.harness.charm, 'cluster')\n        self.harness.charm.ha = interface_hacluster.HaCluster(\n            self.harness.charm, 'ha')\n        rel_id = self.harness.add_relation('ha', 'hacluster')\n        self.harness.add_relation_unit(rel_id, 'hacluster/0')\n        self.harness.update_relation_data(\n            rel_id, 'hacluster/0', {'clustered': 'yes'})\n\n        self.assertEqual(self.harness.charm.unit.status,\n                         self.harness.charm.ha.UNIT_ACTIVE_STATUS)\n        self.assertTrue(self.harness.charm.ha.state.ha_cluster_ready)\n\n    @mock.patch.object(interface_mssql_cluster.MssqlCluster,\n                       'mssql_db_client',\n                       new_callable=mock.PropertyMock)\n    @mock.patch.object(interface_hacluster.HaCluster,\n                       'setup_pacemaker_mssql_login')\n    def test_on_created_ag(self, _setup_pacemaker_mssql_login,\n                           _mssql_db_client):\n        self.harness.begin()\n        self.harness.charm.cluster = interface_mssql_cluster.MssqlCluster(\n            self.harness.charm, 'cluster')\n        self.harness.charm.ha = interface_hacluster.HaCluster(\n            self.harness.charm, 'ha')\n        self.harness.charm.cluster.on.created_ag.emit()\n\n        _setup_pacemaker_mssql_login.assert_called_once_with()\n        _mssql_db_client.assert_called_once_with()\n        db_client_mock = _mssql_db_client.return_value\n        db_client_mock.return_value.exec_t_sql.assert_called_once_with(\"\"\"\n        GRANT ALTER, CONTROL, VIEW DEFINITION\n            ON AVAILABILITY GROUP::[{ag_name}] TO [{login_name}]\n        GRANT VIEW SERVER STATE TO [{login_name}]\n        \"\"\".format(ag_name=self.harness.charm.cluster.AG_NAME,\n                   login_name=self.harness.charm.ha.PACEMAKER_LOGIN_NAME))\n\n    @mock.patch.object(interface_mssql_cluster.MssqlCluster,\n                       'mssql_db_client')\n    @mock.patch('charmhelpers.core.host.pwgen')\n    @mock.patch('os.chown')\n    @mock.patch('os.chmod')\n    @mock.patch('builtins.open', new_callable=mock.mock_open)\n    def test_setup_pacemaker_mssql_login(\n            self, _open, _chmod, _chown, _pwgen, _mssql_db_client):\n\n        _pwgen.return_value = 'test-password'\n        self.harness.begin()\n        self.harness.charm.cluster = interface_mssql_cluster.MssqlCluster(\n            self.harness.charm, 'cluster')\n        self.harness.charm.ha = interface_hacluster.HaCluster(\n            self.harness.charm, 'ha')\n        self.harness.charm.ha.setup_pacemaker_mssql_login()\n\n        _pwgen.assert_called_once_with(32)\n        _mssql_db_client.assert_called_once_with()\n        db_client_mock = _mssql_db_client.return_value\n        db_client_mock.create_login.assert_called_once_with(\n            name=self.harness.charm.ha.PACEMAKER_LOGIN_NAME,\n            password='test-password',\n            server_roles=['sysadmin'])\n        _open.assert_called_once_with(\n            self.harness.charm.ha.PACEMAKER_LOGIN_CREDS_FILE, 'w')\n        _open.return_value.write.assert_called_once_with(\n            '{}\\n{}\\n'.format(self.harness.charm.ha.PACEMAKER_LOGIN_NAME,\n                              'test-password'))\n        _chmod.assert_called_once_with(\n            self.harness.charm.ha.PACEMAKER_LOGIN_CREDS_FILE, 0o400)\n        _chown.assert_called_once_with(\n            self.harness.charm.ha.PACEMAKER_LOGIN_CREDS_FILE, 0, 0)\n        self.assertTrue(self.harness.charm.ha.state.pacemaker_login_ready)\n","repo_name":"ionutbalutoiu/charm-infra-mssql","sub_path":"unit_tests/test_interface_hacluster.py","file_name":"test_interface_hacluster.py","file_ext":"py","file_size_in_byte":7308,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"20650405936","text":"def count_cs(list):\n    count = 0\n    for item in list:\n        if item == 'c':\n            count += 1\n    return count\n\nmy_list = ['a', 'b', 'c', 'c', 10]\n# print(count_cs(my_list))    # prints: 2\n# can pass in my_list as list because it is being defined\n# can also do:\n# print(my_list.count('c'))   # prints: 2\n\n\n\ndef sum_up_items(list_of_nums):\n    result = 0\n    for num in list_of_nums:\n        result += num\n    return result\n\n# print(sum_up_items([1, 2, 3, 4, 5, 6, 7, 8]))   # prints: 36\n# print(sum_up_items(['a', 2, 3]))    # can NOT pass in string(s) because it it isn't possible to add a string with an integer\n\n\n\ndef print_elements(collection):\n    for item in collection:\n        print('item is:', item)\n\n# print(print_elements([1, 2, 3, 4, 5, 6, 7, 8]))   # prints:\n# item is: 1\n# item is: 2\n# item is: 3\n# item is: 4\n# item is: 5\n# item is: 6\n# item is: 7\n# item is: 8\n\n\n\n# In Python, it is possible to reassign values of variables by swapping them unlike other programming languages\na, b = 10, 20\n# print(f'a = {a}, b = {b}')      # prints: a = 10, b = 20\n\n# Swap variable values by doing so:\na, b = b, a\n# print(f'a = {a}, b = {b}')      # prints: a = 20, b = 10","repo_name":"amyawong/baruch-cis2300","sub_path":"notes/nov29.py","file_name":"nov29.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"12761970243","text":"import CharmCord.CharmErrorHandling as ErrorHandling\nEH = ErrorHandling.CharmErrorHandling()\n\nasync def channelCreated(args: str, Context, timezones, format_datetime):\n    if len(args) < 1:\n        raise EH.Errors(4, \"No parameter provided for '$channelCreated'\")\n    est, utc, pst = timezones\n    try:\n        ID, TIME, FORM = tuple(args.split(\",\"))\n        TIME = locals()[TIME.strip()]\n        FORM = FORM.lower()\n    except ValueError:\n        FORM=\"full\"\n        try:\n            ID, TIME = tuple(args.split(\",\"))\n            TIME = locals()[TIME.strip()]\n        except:\n            ID   = args\n            TIME = utc\n    \n\n\n    \n    from CharmCord.Classes.CharmCord import bots\n    try:\n        int(ID)\n        channel = await bots.fetch_channel(ID)\n\n        desiredDateForm = format_datetime(channel.created_at, FORM, TIME)\n        if desiredDateForm != \"ERROR\":\n            return desiredDateForm\n        else:\n            raise SyntaxError(\"Invalid Format option in $channelCreated!\")\n\n    except ValueError:\n        EH.Errors(2, ID)","repo_name":"1lgrand/CharmCord","sub_path":"CharmCord/Functions/Channels/channelCreated.py","file_name":"channelCreated.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"86"}
+{"seq_id":"17579892104","text":"#!/usr/bin/python3\n# Advent of code 2017 day 24\n# See https://adventofcode.com/2017/day/24\n\n\nwith open(\"input.txt\") as f:\n    lines = f.readlines()\n\ncomponents = []\nfor line in lines:\n    components.append(tuple([int(port) for port in line.split(\"/\")]))\n\n\ndef search(bridge: list, remaining, open_link):\n    leaf = True\n    for component in remaining:\n        if component[0] == open_link:\n            new_bridge = bridge[:]\n            new_bridge.append(component)\n            new_remaining = remaining[:]\n            new_remaining.remove(component)\n            leaf = False\n            yield from search(new_bridge, new_remaining, component[1])\n        if component[1] == open_link:\n            new_bridge = bridge[:]\n            new_bridge.append(component)\n            new_remaining = remaining[:]\n            new_remaining.remove(component)\n            leaf = False\n            yield from search(new_bridge, new_remaining, component[0])\n    if leaf:\n        yield (bridge)\n\n\ndef score(bridge):\n    score = 0\n    for component in bridge:\n        score = score + component[0] + component[1]\n    return score\n\n\nbridges = search([], components, 0)\n# Part 1\n# print(max([score(bridge) for bridge in bridges]))\n# Part 2\nprint(max([(len(bridge), score(bridge)) for bridge in bridges]))\n","repo_name":"chrisglencross/advent-of-code","sub_path":"aoc2017/day24/day24.py","file_name":"day24.py","file_ext":"py","file_size_in_byte":1284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"12837279421","text":"from django.urls import path\nfrom commons import views\n\nurlpatterns = [\n    path(\"country/list/\", views.ListCountry.as_view(), name=\"country-list\"),\n    path(\"country/region/list/\", views.ListRegionByCountryName.as_view(), name=\"region-list-by-country-name\"),\n    path(\"region/list/\", views.ListRegionByCountry.as_view(), name=\"region-list\"),\n    path(\"city/list/\", views.ListCityByRegion.as_view(), name=\"city-list\"),\n    path(\"popular/city/list/\", views.PopularCityListView.as_view(), name=\"city-list\"),\n    path(\"address/<int:pk>/update/\", views.AddressRetrieveUpdateDestroyView.as_view(), name=\"address-update\"),\n\n    path(\"periodicity/list/\", views.PeriodicityListView.as_view(), name=\"periodicity-list\"),\n]\n","repo_name":"zekariass/grinmove_backend","sub_path":"commons/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"40288290949","text":"import shutil\nimport os\n\nimport numpy as np\nimport tensorflow as tf\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout\nfrom keras.optimizers import Adam\nfrom keras.callbacks import EarlyStopping\nfrom keras_tuner.tuners import BayesianOptimization, Hyperband\nfrom sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\n\nclass ANNs_model():     \n    def __init__(self, X_list, y_list, model=None, model_name='Untitled_model'):\n        self.model_name = model_name\n        self.X_list = X_list\n        self.y_list = y_list\n        # Define the input and output variables\n        self.num_input_features = X_list.shape[1] # x2, y2\n        self.num_output_features = y_list.shape[1] # q1, q2\n        self.patience = 10 # if the validation loss does not improve for 20 epochs, the training will be stopped\n        self.epochs = 500\n        self.batch_size = 1024\n        self.my_model = model if model is not None else self.get_neural_network()\n        self.X_train, self.X_val, self.y_train, self.y_val = train_test_split(self.X_list, self.y_list, test_size = 0.2, random_state = 101)\n        self.history = self.train_neural_network()\n    \n    def get_neural_network(self):\n        # Define the ANN architecture\n        model = Sequential()\n        model.add(Dense(256, input_dim=self.num_input_features, activation='relu'))\n        model.add(Dropout(0.2))\n        model.add(Dense(128, input_dim=self.num_input_features, activation='relu'))\n        model.add(Dropout(0.2))\n        model.add(Dense(64, input_dim=self.num_input_features, activation='relu'))\n        model.add(Dense(64, input_dim=self.num_input_features, activation='relu'))        \n        model.add(Dropout(0.2))\n        model.add(Dense(self.num_output_features, activation='linear')) #we want the model to output continuous values representing the x and y coordinates of the corner of the SCARA arm\n        model.compile(loss='mse', optimizer=Adam(learning_rate=0.001))\n        return model\n    \n    def train_neural_network(self):\n        history = self.my_model.fit(self.X_train, self.y_train, epochs=self.epochs, batch_size=self.batch_size, validation_split=0.2, callbacks=[EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=self.patience)]) # early stopping callback to prevent overfitting\n        self.valid_neural_network()\n        self.my_model.save(f'{self.model_name}_{self.loss}.h5')\n        return history\n        \n    def valid_neural_network(self):\n        # Validate the ANN\n        self.loss = self.my_model.evaluate(self.X_val, self.y_val)\n        return self.loss\n        \n    def plot_loss(self):\n        plt.plot(self.history.history['loss'])\n        plt.plot(self.history.history['val_loss'])\n        plt.title('Model loss')\n        plt.ylabel('Loss')\n        plt.xlabel('Epoch')\n        plt.legend(['Train', 'Validation'], loc='upper right')\n        plt.savefig(f'{self.model_name}_{self.loss}.png')\n        plt.show()\n\n\nclass Tuner():\n    def __init__(self, inputs, outputs, num_trials=20, tuner_dir='my_dir', algorithm=None):\n        self.X_list = inputs\n        self.y_list = outputs\n        self.num_trials = num_trials\n        self.tuner_dir = tuner_dir\n        self.algorithm = algorithm\n        self.best_model, self.best_hyperparameters = self.tune_model()\n        \n    def build_model(self, hp): # Define the Keras Tuner search space\n        model = Sequential()\n        #input layer\n        model.add(Dense(units=hp.Int('units_1', min_value=32, max_value=2048, step=32),\n                        activation=hp.Choice('activation_1', values=['relu', 'leaky_relu', 'elu', 'selu','tanh', 'sigmoid']),\n                        input_shape=(self.X_list.shape[1],)))\n        # hidden layer\n        model.add(Dropout(rate=hp.Float('dropout_1', min_value=0.0, max_value=0.5, step=0.1)))\n        for i in range(hp.Int('num_layers', min_value=1, max_value=10)):\n            model.add(Dense(units=hp.Int(f'units_{i+2}', min_value=32, max_value=1024, step=32),\n                            activation=hp.Choice(f'activation_{i+2}', values=['relu', 'leaky_relu', 'elu', 'selu','tanh', 'sigmoid'])))\n            model.add(Dropout(rate=hp.Float(f'dropout_{i+2}', min_value=0.0, max_value=0.5, step=0.1)))\n        #output layer\n        model.add(Dense(self.y_list.shape[1], activation='linear'))\n        optimizer = Adam(learning_rate=hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4, 1e-5, 1e-6, 1e-7]),)\n        model.compile(optimizer=optimizer, loss='mean_squared_error')\n        return model\n    \n    def tune_model(self): # Define the Keras Tuner search strategy and perform hyperparameter tuning\n        if \"B\" in str(self.algorithm):\n            tuner = BayesianOptimization(self.build_model, objective='val_loss', max_trials=self.num_trials, directory= self.tuner_dir, project_name='scara_hyperparameter_tuning')\n        else:\n            tuner = Hyperband(self.build_model, objective='val_loss', max_epochs = 100, factor=3, directory= self.tuner_dir, project_name='scara_hyperparameter_tuning')\n        early_stopping = EarlyStopping(patience=3)\n        tuner.search(self.X_list, self.y_list, epochs=100, validation_split=0.2, callbacks=[early_stopping])\n        best_model = tuner.get_best_models(num_models=1)[0]\n        best_hyperparameters = tuner.get_best_hyperparameters(num_trials=1)[0] #we retrieve the best hyperparameters from the top-performing trial.\n        return best_model, best_hyperparameters\n\n    def write_best_model(self, file_name=\"best_model\"):\n        with open(str(file_name)+\".txt\", \"w\") as file:\n            file.write(\"Best Hyperparameters:\\n\")\n            file.write(str(self.best_hyperparameters.get_config()))\n            print(\"Best Model Summary:\", file=file)\n            self.best_model.summary(print_fn=lambda x: file.write(x + '\\n'))\n            file.write(\"\\n\\nLearning Rate:\\n\")\n            file.write(str(self.best_hyperparameters.get('learning_rate')))\n            file.write(\"\\n\\nActivation:\\n\")\n            activation_hyperparams = {key: value for key, value in self.best_hyperparameters.values.items() if 'activation' in key}\n            for key, value in activation_hyperparams.items():\n                file.write(f\"{key}: {value}\\n\")\n            file.write(\"\\nDropout:\\n\")\n            dropout_hyperparams = {key: value for key, value in self.best_hyperparameters.values.items() if 'dropout' in key}\n            for key, value in dropout_hyperparams.items():\n                file.write(f\"{key}: {value}\\n\")\n\n\ndef main(tuner_dir='my_dir', model_name='my_best_model', num_tuner_samples=100000, num_train_samples=10000000):\n    print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))\n    os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n    gpus = tf.config.experimental.list_physical_devices('GPU')\n    if gpus:\n        try:\n            tf.config.experimental.set_memory_growth(gpus[0], True)\n        except RuntimeError as e:\n            print(e)\n    try:\n        shutil.rmtree(tuner_dir) # Delete the folder\n        shutil.rmtree(f'{tuner_dir}_BO')\n    except:\n        pass\n    # Generate training data\n    inputs = np.load(f\"data\\\\positions_orientations_{num_tuner_samples}.npy\")\n    outputs = np.load(f\"data\\\\angles_{num_tuner_samples}.npy\")\n    # Perform hyperparameter tuning\n    tuner_BO = Tuner(inputs, outputs, num_trials=20, tuner_dir=f'{tuner_dir}_BO', algorithm=\"BayesianOptimization\")\n    tuner = Tuner(inputs, outputs, num_trials=20, tuner_dir=tuner_dir)\n    # Save the best model and hyperparameters\n    tuner.write_best_model(file_name=model_name)\n    tuner_BO.write_best_model(file_name=f'{model_name}_BO')\n    # train the best model\n    X_list = np.load(f\"data\\\\positions_orientations_{num_train_samples}.npy\")\n    print(f\"X_list: {X_list.shape}\")\n    y_list = np.load(f\"data\\\\angles_{num_train_samples}.npy\")\n    print(f\"y_list: {y_list.shape}\")\n    scara_model = ANNs_model(X_list, y_list, tuner.best_model, model_name)\n    scara_model_BO = ANNs_model(X_list, y_list, tuner_BO.best_model, f'{model_name}_BO')\n    scara_model.plot_loss()\n    scara_model_BO.plot_loss()\n    print(model_name if scara_model.loss <= scara_model_BO.loss else f'{model_name}_BO')\n\n\nif __name__ == \"__main__\":\n    main(tuner_dir='my_dir', model_name='my_best_model', num_tuner_samples=int(1e6), num_train_samples=int(1e7))","repo_name":"nguyenngocvy1/Thesis","sub_path":"build_model.py","file_name":"build_model.py","file_ext":"py","file_size_in_byte":8324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"26236629521","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('secretsantaapp', '0005_assignees'),\n    ]\n\n    operations = [\n        migrations.RenameField(\n            model_name='secretsantagroups',\n            old_name='users',\n            new_name='members',\n        ),\n    ]\n","repo_name":"FadiAlnabolsi/Secret-Santa","sub_path":"secretsanta/secretsantaapp/migrations/0006_auto_20151128_1903.py","file_name":"0006_auto_20151128_1903.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"18756212846","text":"import xlwings as xw\nimport os #os package which implements Environment variables\nimport numpy as np\n\n#wb = xw.Book()\n#wb.save(\"test.xlsx\")\n\"\"\" wbTest = xw.Book(\"test2.xlsx\")\n\nws1 = wbTest.sheets['Tab1']\nws2 = wbTest.sheets[\"Tab2\"]\nws3 = wbTest.sheets[\"Tab3\"]\n\nws1.range(\"A1:E100\").value = 100\nTmp = []\nTmp = ws1.range(\"A1:E100\").value \"\"\"\n#print(os.environ) #This prints out all the environment variables\n\ndict_a = {'key_1':'value_1',\n          'key_2':'value_2'}\ndict_b = {'key_3':'value_3',\n          'key_4':'value_4'}\nlist_a = [dict_a, dict_b, dict_a]\nprint(list_a)\ntype(list_a)\n\nsquares_dict = {x:x**2 for x in range(10)}\nprint(squares_dict)\ntype(squares_dict)\n\nsquares = [x**2 for x in range(10)]\nprint(squares)\ntype(squares)\n\na = np.ones((3,))\nb = np.ones((2,))\nc = np.append(a, b)\nprint(c)\n\n\na = np.ones((3,))\nb = np.ones((2,))\nc = np.append(a, b)\nprint(c)\n\n#print(Tmp)\n# the end","repo_name":"NnamdiOdozi/Python","sub_path":"xlwings basic.py","file_name":"xlwings basic.py","file_ext":"py","file_size_in_byte":888,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"23275157412","text":"from init import *\n\ndef properties(data, mode = 0):\n    \"\"\"Extracts the different properties from the [data] dictionnary of performances. Either outputs the raw arrays of performances (mode = 0)\n       or outputs the performances at the optimum (mode = 1).\n       pre: data (dict {string:np.ndarray}): Contains the evolution of the validation set performances through time and through the different experiments.\n                                             The four keys are : \"loss\", \"val_loss\", \"val_acc\", \"val_AUC\".\n            mode (int): Changes the output to either be the raw arrays of performances (at mode = 0), or the best performances across trials (mode = 1)\n       post: data_loss (np.ndarray): the training loss of the model through the epochs (axis 0) and through the experiments (axis 1)\n             data_val_loss (np.ndarray): the validation loss of the model through the epochs (axis 0) and through the experiments (axis 1)\n             data_val_acc (np.ndarray): the validation accuracy of the model through the epochs (axis 0) and through the experiments (axis 1)\n             data_val_AUC (np.ndarray): the validation AUC of the model through the epochs (axis 0) and through the experiments (axis 1)\n             \n             min_data_val_loss (np.1darray): the minimum validation loss through the experiments\n             epoch_min_data_val_loss (np.1darray): epoch at which the minimum validation loss occurs through the experiments\n             acc_at_data_val_loss (np.1darray): accuracy at the minimum validation loss through all the experiments\n             AUC_at_data_val_loss (np.1darray): AUC at the minimum validation loss through all the experiments\n\n    \"\"\"\n\n    data_loss = data[\"loss\"]\n    data_val_loss = data[\"val_loss\"]\n    data_val_acc = data[\"val_acc\"]\n    data_val_AUC = data[\"val_AUC\"]\n\n    min_data_val_loss = np.min(data_val_loss, axis=1)\n    epoch_min_data_val_loss = np.argmin(data_val_loss, axis=1)\n    acc_at_min_val_loss = np.array([data_val_acc[i,j] for i,j in zip(np.arange(data_val_acc.shape[0]),epoch_min_data_val_loss)])\n    AUC_at_min_val_loss = np.array([data_val_AUC[i,j] for i,j in zip(np.arange(data_val_AUC.shape[0]),epoch_min_data_val_loss)])\n\n    if mode==0:\n        return data_loss, data_val_loss, data_val_acc, data_val_AUC\n    if mode==1:\n        return min_data_val_loss, epoch_min_data_val_loss + 1, acc_at_min_val_loss, AUC_at_min_val_loss\n\ndef stats_at_min_val_loss(properties):\n    \"\"\"Extracts the mean and std from the best performances through the different experiments.\n       pre : properties (np.ndarray): Contains the best performances metrics (i.e. validation loss, epoch of convergence, accuracy, AUC) through\n                                      the different experiments.\n       post: statistics (np.ndarray): The mean and std of the best performance metrics over the experiments.\n    \"\"\"\n    statistics = np.empty((len(properties),2))\n    i = 0\n    for a_property in properties:\n        statistics[i,0] = np.mean(a_property)\n        statistics[i,1] = np.std(a_property)\n        i+=1\n    return statistics\n\ndef multiple_ttests(all_data):\n    \"\"\"Performs statistical significance testing of all the arrays present in [all_data] under the form of a half double entry table. Test used\n       is a Welch's t.\n       pre : all_data (list of np.1darray): Contains the data to be compared with one another using a Welch's t test. Each np.array the values of\n                                             a performance metrics through the experiments.\n       post: p_vals (2d array): table filled with -1 on the lower triangle and diagonal. Upper diagonal contains the p_values of the comparison of\n                                the different sets by pairs.\n\n       \"\"\"\n    p_vals = - np.ones((len(all_data),len(all_data)))\n    for i in range(len(all_data)):\n        j = i+1\n        while j < len(all_data):\n            _, p_vals[i,j] = stats.ttest_ind(all_data[i], all_data[j], equal_var = False)\n            j+=1\n    return np.round(p_vals,5)\n\n# Load the performances saved earlier\nwith open('../results/pre_trainings_performances.pickle', 'rb') as file:\n    raw_results = pickle.load(file)\n\nepochs = np.arange(40)+1\n\n\n# Extract the performances values (raw and minimum) from the different pre-trainings\nno_pt_loss, no_pt_val_loss, no_pt_val_acc, no_pt_val_AUC = properties(raw_results[\"no_pre\"], mode=0)\nswap_pt_loss, swap_pt_val_loss, swap_pt_val_acc, swap_pt_val_AUC = properties(raw_results[\"swap\"], mode=0)\n\nmin_no_pt_val_loss, epoch_min_no_pt_val_loss, _, _ = properties(raw_results[\"no_pre\"], mode=1)\nmin_swap_pt_val_loss, epoch_min_swap_pt_val_loss, _, _ = properties(raw_results[\"swap\"], mode=1)\n\ngauss_pt_loss, gauss_pt_val_loss, gauss_pt_val_acc, gauss_pt_val_AUC = properties(raw_results[\"gauss\"], mode=0)\nshuffle_pt_loss, shuffle_pt_val_loss, shuffle_pt_val_acc, shuffle_pt_val_AUC = properties(raw_results[\"shuffle\"], mode=0)\n\nmin_gauss_pt_val_loss, epoch_min_gauss_pt_val_loss, _, _ = properties(raw_results[\"gauss\"], mode=1)\nmin_shuffle_pt_val_loss, epoch_min_shuffle_pt_val_loss, _, _ = properties(raw_results[\"shuffle\"], mode=1)\n\nhybrid_pt_loss, hybrid_pt_val_loss, hybrid_pt_val_acc, hybrid_pt_val_AUC = properties(raw_results[\"hybrid\"], mode=0)\nmin_hybrid_pt_val_loss, epoch_min_hybrid_pt_val_loss, _, _ = properties(raw_results[\"hybrid\"], mode=1)\n\nprint(\"# samples: \\n\", hybrid_pt_loss.shape[0])\n\n# Show the mean and std of each performance metrics for each pre-training\nprint(\"Stats no pre-training:\\n\", stats_at_min_val_loss(properties(raw_results[\"no_pre\"],1)))\nprint(\"\\nStats swap pre-training:\\n\", stats_at_min_val_loss(properties(raw_results[\"swap\"],1)))\nprint(\"\\nStats noise pre-training:\\n\", stats_at_min_val_loss(properties(raw_results[\"gauss\"],1)))\nprint(\"\\nStats shuffle pre-training:\\n\", stats_at_min_val_loss(properties(raw_results[\"shuffle\"],1)))\nprint(\"\\nStats hybrid pre-training:\\n\", stats_at_min_val_loss(properties(raw_results[\"hybrid\"],1)))\n\nnames = [\"min_data_val_loss\", \"epoch_min_data_val_loss\", \"acc_at_min_val_loss\", \"AUC_at_min_val_loss\"]\ndata_swap = properties(raw_results[\"swap\"],1)\ndata_no = properties(raw_results[\"no_pre\"],1)\ndata_gauss = properties(raw_results[\"gauss\"],1)\ndata_shuffle = properties(raw_results[\"shuffle\"],1)\ndata_hybrid = properties(raw_results[\"hybrid\"],1)\n\n# Show significance cross testing per metric for all pre-trainings\nprint(\"\\n\\n\\n\")\nfor i in range(len(names)):\n    print(names[i])\n    print(multiple_ttests([data_no[i],data_swap[i],data_gauss[i], data_shuffle[i], data_hybrid[i]]),\"\\n\")\n\n# Show significance cross testing per metric for pooled pre-trainings vs no pre-training\nall_data = np.concatenate((data_swap,data_shuffle,data_hybrid,data_gauss),axis=1)\nprint(\"\\n\\n\\nComparison between pre-training and no pre-training:\")\nprint(stats_at_min_val_loss(all_data))\nfor i in range(len(names)):\n    print(names[i])\n    print(multiple_ttests([data_no[i],all_data[i]]),\"\\n\")\n\n# Check if there is a linear relation between epoch and val_loss\nreg_shuffle = LinearRegression().fit(epoch_min_shuffle_pt_val_loss.reshape(-1, 1), min_shuffle_pt_val_loss)\nreg_hybrid = LinearRegression().fit(epoch_min_hybrid_pt_val_loss.reshape(-1, 1), min_hybrid_pt_val_loss)\nreg_gauss = LinearRegression().fit(epoch_min_gauss_pt_val_loss.reshape(-1, 1), min_gauss_pt_val_loss)\nreg_swap = LinearRegression().fit(epoch_min_swap_pt_val_loss.reshape(-1, 1), min_swap_pt_val_loss)\nreg_no = LinearRegression().fit(epoch_min_no_pt_val_loss.reshape(-1, 1), min_no_pt_val_loss)\n\nR2_shuffle = reg_shuffle.score(epoch_min_shuffle_pt_val_loss.reshape(-1, 1), min_shuffle_pt_val_loss)\nR2_hybrid = reg_hybrid.score(epoch_min_hybrid_pt_val_loss.reshape(-1, 1), min_hybrid_pt_val_loss)\nR2_gauss = reg_gauss.score(epoch_min_gauss_pt_val_loss.reshape(-1, 1), min_gauss_pt_val_loss)\nR2_swap = reg_swap.score(epoch_min_swap_pt_val_loss.reshape(-1, 1), min_swap_pt_val_loss)\nR2_no = reg_no.score(epoch_min_no_pt_val_loss.reshape(-1, 1), min_no_pt_val_loss)\n\nprint(\"R2 score of shuffle regression:\", R2_shuffle)\nprint(\"Shuffle regression slope:\", reg_shuffle.coef_[0],'\\n')\nprint(\"R2 score of hybrid regression:\",R2_hybrid)\nprint(\"Hybrid regression slope:\", reg_hybrid.coef_[0],'\\n')\nprint(\"R2 score of white noise regression:\", R2_gauss)\nprint(\"White noise regression slope:\", reg_gauss.coef_[0],'\\n')\nprint(\"R2 score of mixing regression:\",R2_swap)\nprint(\"Mixing regression slope:\", reg_swap.coef_[0],'\\n')\nprint(\"R2 score of no pre-training regression:\",R2_no)\nprint(\"No pre-training regression slope:\", reg_no.coef_[0])\n\ndef ci_95(data):\n    \"\"\"Computes the 95% confidence interval of a given [data] through the experiment axis.\n       pre : data (2d array): An array of data points (axis 0: epochs, axis 1: experiments).\n       post: The 95% CI of the input datathrough the experiment axis.\n    \"\"\"\n    return 1.96 * np.std(data, axis = 0)/np.sqrt(data.shape[0])\n\ndef ci_99(data):\n    \"\"\"Computes the 99% confidence interval of a given [data] through the experiment axis.\n       pre : data (2d array): An array of data points (axis 0: epochs, axis 1: experiments).\n       post: The 99% CI of the input datathrough the experiment axis.\n    \"\"\"\n    return 2.81 * np.std(data, axis = 0)/np.sqrt(data.shape[0])\n\ndef scatter_hist(x, y, label, ax, ax_histx, ax_histy):\n#https://matplotlib.org/stable/gallery/lines_bars_and_markers/scatter_hist.html\n    n_plot = len(x)\n    # no labels\n    ax_histx.tick_params(axis=\"x\", labelbottom=False)\n    ax_histy.tick_params(axis=\"y\", labelleft=False)\n\n    for i in range(n_plot):\n        ax.scatter(x[i], y[i], label = label[i], marker='.')\n        mu = np.mean(x[i])\n        sigma = np.std(x[i])\n        base = np.linspace(max(1,mu - 3*sigma), min(45,mu + 3*sigma), 100)\n        ax_histx.plot(base, stats.norm.pdf(base, mu, sigma))\n        mu = np.mean(y[i])\n        sigma = np.std(y[i])\n        base = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)\n        ax_histy.plot(stats.norm.pdf(base, mu, sigma)/100,base)\n\n\n#https://matplotlib.org/stable/gallery/lines_bars_and_markers/scatter_hist.html (lines 172--189)\nfig = plt.figure(figsize=(6, 6))\ngs = fig.add_gridspec(2, 2,  width_ratios=(4, 1), height_ratios=(1, 4),\n                      left=0.1, right=0.9, bottom=0.1, top=0.9,\n                      wspace=0.05, hspace=0.05)\n# Create the Axes.\nax = fig.add_subplot(gs[1, 0])\nax_histx = fig.add_subplot(gs[0, 0], sharex=ax)\nax_histx.set_ylabel(\"Density [-]\")\nax_histy = fig.add_subplot(gs[1, 1], sharey=ax)\nax_histy.set_xlabel(\"Density [-]\")\n# Draw the scatter plot and marginals.\nscatter_hist([epoch_min_shuffle_pt_val_loss, epoch_min_swap_pt_val_loss, epoch_min_gauss_pt_val_loss, epoch_min_hybrid_pt_val_loss, epoch_min_no_pt_val_loss],\n    [min_shuffle_pt_val_loss, min_swap_pt_val_loss, min_gauss_pt_val_loss, min_hybrid_pt_val_loss,min_no_pt_val_loss],\n    [\"Shuffling\", \"Mixing\", \"White noise\", \"Hybrid\", \"No pre-training\"],ax, ax_histx, ax_histy)\nax.legend()\nax.set_xlabel(\"Epoch of convergence [-]\")\nax.set_ylabel(\"Minimum validation loss [-]\")\nplt.show()\n\n\n\"\"\" Supplementary graphs\n\nplt.scatter(epoch_min_shuffle_pt_val_loss, min_shuffle_pt_val_loss,marker='.')\nplt.plot(np.arange(0,42), reg_shuffle.predict(np.arange(0,42).reshape(-1, 1)), label=\"Shuffling (R2 = {:.4f})\".format(R2_shuffle))\nplt.scatter(epoch_min_swap_pt_val_loss, min_swap_pt_val_loss,marker='.')\nplt.plot(np.arange(0,42), reg_swap.predict(np.arange(0,42).reshape(-1, 1)), label=\"Mixing (R2 = {:.4f})\".format(R2_swap))\n\nplt.scatter(epoch_min_gauss_pt_val_loss, min_gauss_pt_val_loss,marker='.')\nplt.plot(np.arange(0,42), reg_gauss.predict(np.arange(0,42).reshape(-1, 1)), label=\"White noise (R2 = {:.4f})\".format(R2_gauss))\n\nplt.scatter(epoch_min_hybrid_pt_val_loss, min_hybrid_pt_val_loss, marker='.')\nplt.plot(np.arange(0,42), reg_hybrid.predict(np.arange(0,42).reshape(-1, 1)), label=\"Hybrid (R2 = {:.4f})\".format(R2_hybrid))\n\nplt.scatter(epoch_min_no_pt_val_loss, min_no_pt_val_loss, marker='.')\nplt.plot(np.arange(0,42), reg_no.predict(np.arange(0,42).reshape(-1, 1)), label=\"No pre-training (R2 = {:.4f})\".format(R2_no))\n\nplt.xlabel(\"Epoch of convergence [-]\")\nplt.ylabel(\"Minimum validation loss [-]\")\nplt.legend()\nplt.show()\n\n\nplt.subplot(211)\nplt.title(\"Training set loss function\")\nplt.plot(epochs,np.mean(no_pt_loss,axis=0), label = \"No pre-training\")\nplt.fill_between(epochs, (np.mean(no_pt_loss,axis=0)-ci_95(no_pt_loss)), (np.mean(no_pt_loss,axis=0)+ci_95(no_pt_loss)), alpha=.1)\nplt.plot(epochs,np.mean(swap_pt_loss, axis=0), label = \"Swapping\")\nplt.fill_between(epochs, (np.mean(swap_pt_loss, axis=0)-ci_95(swap_pt_loss)), (np.mean(swap_pt_loss, axis=0)+ci_95(swap_pt_loss)), alpha=.1)\nplt.plot(epochs,np.mean(gauss_pt_loss,axis=0), label = \"Noise\")\nplt.fill_between(epochs, (np.mean(gauss_pt_loss,axis=0)-ci_95(gauss_pt_loss)), (np.mean(gauss_pt_loss,axis=0)+ci_95(gauss_pt_loss)), alpha=.1)\nplt.plot(epochs,np.mean(shuffle_pt_loss, axis=0), label = \"Shuffle\")\nplt.fill_between(epochs, (np.mean(shuffle_pt_loss, axis=0)-ci_95(shuffle_pt_loss)), (np.mean(shuffle_pt_loss, axis=0)+ci_95(shuffle_pt_loss)), alpha=.1)\nplt.plot(epochs,np.mean(hybrid_pt_loss, axis=0), label = \"Hybrid\")\nplt.fill_between(epochs, (np.mean(hybrid_pt_loss, axis=0)-ci_95(hybrid_pt_loss)), (np.mean(hybrid_pt_loss, axis=0)+ci_95(hybrid_pt_loss)), alpha=.1)\nplt.ylabel(\"Loss [-]\")\nplt.legend()\n\nplt.subplot(212)\nplt.title(\"Validation set loss function\")\nplt.plot(epochs,np.mean(no_pt_val_loss,axis=0), label = \"No pre-training\")\nplt.fill_between(epochs, (np.mean(no_pt_val_loss,axis=0)-ci_95(no_pt_val_loss)), (np.mean(no_pt_val_loss,axis=0)+ci_95(no_pt_val_loss)), alpha=.1)\nplt.plot(epochs,np.mean(swap_pt_val_loss, axis=0), label = \"Swapping\")\nplt.fill_between(epochs, (np.mean(swap_pt_val_loss, axis=0)-ci_95(swap_pt_val_loss)), (np.mean(swap_pt_val_loss, axis=0)+ci_95(swap_pt_val_loss)), alpha=.1)\nplt.plot(epochs,np.mean(gauss_pt_val_loss,axis=0), label = \"Noise\")\nplt.fill_between(epochs, (np.mean(gauss_pt_val_loss,axis=0)-ci_95(gauss_pt_val_loss)), (np.mean(gauss_pt_val_loss,axis=0)+ci_95(gauss_pt_val_loss)), alpha=.1)\nplt.plot(epochs,np.mean(shuffle_pt_val_loss, axis=0), label = \"Shuffle\")\nplt.fill_between(epochs, (np.mean(shuffle_pt_val_loss, axis=0)-ci_95(shuffle_pt_val_loss)), (np.mean(shuffle_pt_val_loss, axis=0)+ci_95(shuffle_pt_val_loss)), alpha=.1)\nplt.plot(epochs,np.mean(hybrid_pt_val_loss, axis=0), label = \"Hybrid\")\nplt.fill_between(epochs, (np.mean(hybrid_pt_val_loss, axis=0)-ci_95(hybrid_pt_val_loss)), (np.mean(hybrid_pt_val_loss, axis=0)+ci_95(hybrid_pt_val_loss)), alpha=.1)\nplt.ylabel(\"Loss [-]\")\nplt.xlabel(\"Epoch [-]\")\nplt.legend()\n\nplt.show()\n\n\nplt.subplot(211)\nplt.title(\"Validation set accuracy\")\nplt.plot(epochs,np.mean(no_pt_val_acc,axis=0), label = \"No pre-training\")\nplt.fill_between(epochs, (np.mean(no_pt_val_acc,axis=0)-ci_95(no_pt_val_acc)), (np.mean(no_pt_val_acc,axis=0)+ci_95(no_pt_val_acc)), alpha=.1)\nplt.plot(epochs,np.mean(swap_pt_val_acc, axis=0), label = \"Swapping\")\nplt.fill_between(epochs, (np.mean(swap_pt_val_acc, axis=0)-ci_95(swap_pt_val_acc)), (np.mean(swap_pt_val_acc, axis=0)+ci_95(swap_pt_val_acc)), alpha=.1)\nplt.plot(epochs,np.mean(gauss_pt_val_acc,axis=0), label = \"Noise\")\nplt.fill_between(epochs, (np.mean(gauss_pt_val_acc,axis=0)-ci_95(gauss_pt_val_acc)), (np.mean(gauss_pt_val_acc,axis=0)+ci_95(gauss_pt_val_acc)), alpha=.1)\nplt.plot(epochs,np.mean(shuffle_pt_val_acc, axis=0), label = \"Shuffle\")\nplt.fill_between(epochs, (np.mean(shuffle_pt_val_acc, axis=0)-ci_95(shuffle_pt_val_acc)), (np.mean(shuffle_pt_val_acc, axis=0)+ci_95(shuffle_pt_val_acc)), alpha=.1)\nplt.plot(epochs,np.mean(hybrid_pt_val_acc, axis=0), label = \"Hybrid\")\nplt.fill_between(epochs, (np.mean(hybrid_pt_val_acc, axis=0)-ci_95(hybrid_pt_val_acc)), (np.mean(hybrid_pt_val_acc, axis=0)+ci_95(hybrid_pt_val_acc)), alpha=.1)\nplt.ylabel(\"Accuracy [%]\")\nplt.legend()\n\nplt.subplot(212)\nplt.title(\"Validation set AUC\")\nplt.plot(epochs,np.mean(no_pt_val_AUC,axis=0), label = \"No pre-training\")\nplt.fill_between(epochs, (np.mean(no_pt_val_AUC,axis=0)-ci_95(no_pt_val_AUC)), (np.mean(no_pt_val_AUC,axis=0)+ci_95(no_pt_val_AUC)), alpha=.1)\nplt.plot(epochs,np.mean(swap_pt_val_AUC, axis=0), label = \"Swapping\")\nplt.fill_between(epochs, (np.mean(swap_pt_val_AUC, axis=0)-ci_95(swap_pt_val_AUC)), (np.mean(swap_pt_val_AUC, axis=0)+ci_95(swap_pt_val_AUC)), alpha=.1)\nplt.plot(epochs,np.mean(gauss_pt_val_AUC,axis=0), label = \"Noise\")\nplt.fill_between(epochs, (np.mean(gauss_pt_val_AUC,axis=0)-ci_95(gauss_pt_val_AUC)), (np.mean(gauss_pt_val_AUC,axis=0)+ci_95(gauss_pt_val_AUC)), alpha=.1)\nplt.plot(epochs,np.mean(shuffle_pt_val_AUC, axis=0), label = \"Shuffle\")\nplt.fill_between(epochs, (np.mean(shuffle_pt_val_AUC, axis=0)-ci_95(shuffle_pt_val_AUC)), (np.mean(shuffle_pt_val_AUC, axis=0)+ci_95(shuffle_pt_val_AUC)), alpha=.1)\nplt.plot(epochs,np.mean(hybrid_pt_val_AUC, axis=0), label = \"Hybrid\")\nplt.fill_between(epochs, (np.mean(hybrid_pt_val_AUC, axis=0)-ci_95(hybrid_pt_val_AUC)), (np.mean(hybrid_pt_val_AUC, axis=0)+ci_95(hybrid_pt_val_AUC)), alpha=.1)\nplt.ylabel(\"AUC [-]\")\nplt.xlabel(\"Epoch [-]\")\nplt.legend()\n\nplt.show()\n\"\"\"","repo_name":"tbary/MasterThesis","sub_path":"src/3_plot_pre_training_selection_results.py","file_name":"3_plot_pre_training_selection_results.py","file_ext":"py","file_size_in_byte":17084,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"33892404384","text":"# -*- coding: utf-8 -*-\n\nimport os\nfrom datetime import datetime\nfrom reportlab.pdfgen import canvas\nfrom reportlab.lib.pagesizes import A4\nfrom reportlab.lib.units import cm\nfrom reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet\nfrom reportlab.lib.colors import Color\nfrom reportlab.platypus import (\n    SimpleDocTemplate, Paragraph, PageBreak, Image, Spacer, Table, TableStyle, Flowable)\nfrom reportlab.lib.enums import TA_CENTER\nfrom reportlab.graphics.shapes import  Image as sImage\nfrom reportlab.pdfbase import pdfmetrics\nfrom reportlab.pdfbase.ttfonts import TTFont\n\n\ndef getWeekdayName(time: datetime, language: str = 'vie') -> str:\n    if language == 'vie':\n        names = [\"Thứ 2\", \"Thứ 3\", \"Thứ 4\", \"Thứ 5\", \"Thứ 6\", \"Thứ 7\", \"Chủ Nhật\"]\n    else:\n        names = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\n    return names[time.weekday()]\n\n\nclass ReportConfig:\n    def __init__(self) -> None:\n        self.marginTop = 2.5*cm\n        self.marginBottom = 2.5*cm\n        self.marginLeft = 2.0*cm\n        self.marginRight = 2.0*cm\n        self.pageWidth, self.pageHeight = A4\n        self.bodyWidth = self.pageWidth - self.marginLeft - self.marginRight\n        self.bodyHeight = self.pageHeight - self.marginTop - self.marginBottom\n\n    def registerFont(self):\n        pdfmetrics.registerFont(\n            TTFont('NotoSans', './fonts/NotoSans-Regular.ttf'))\n        pdfmetrics.registerFont(\n            TTFont('NotoSansB', './fonts/NotoSans-Bold.ttf'))\n        pdfmetrics.registerFont(\n            TTFont('NotoSansBI', './fonts/NotoSans-BoldItalic.ttf'))\n        pdfmetrics.registerFont(\n            TTFont('NotoSansI', './fonts/NotoSans-Italic.ttf'))\n        pdfmetrics.registerFontFamily('NotoSans', normal='NotoSans',\n                                      bold='NotoSansB', italic='NotoSansI', boldItalic='NotoSansBI')\n\n\nclass FooterCanvas(canvas.Canvas):\n    def __init__(self, filename, language='vie', config: ReportConfig = ReportConfig(), *args, **kwargs):\n        canvas.Canvas.__init__(self, filename, *args, **kwargs)\n        self.pages = []\n        self.conf = config\n        self.conf.registerFont()\n        self.language = language\n\n    def showPage(self):\n        self.pages.append(dict(self.__dict__))\n        self._startPage()\n\n    def save(self):\n        page_count = len(self.pages)\n        for page in self.pages:\n            self.__dict__.update(page)\n            self.draw_canvas(page_count)\n            canvas.Canvas.showPage(self)\n        canvas.Canvas.save(self)\n\n    def draw_canvas(self, page_count):\n\n        if self.language == 'vie':\n            page = \"Trang %s/%s\" % (self._pageNumber, page_count)\n        else:\n            page = \"Page %s/%s\" % (self._pageNumber, page_count)\n\n        now = datetime.now()\n        dateTime = getWeekdayName(now, language=self.language) + now.strftime(\", %d/%m/%Y %I:%M %p\")\n        self.saveState()\n        self.setStrokeColorRGB(0, 0, 0)\n        self.setLineWidth(1)\n        self.drawImage(\"icons/logo.png\", self.conf.marginLeft, self.conf.pageHeight - self.conf.marginBottom,\n                       width=150, height=50, preserveAspectRatio=True, mask='auto')\n        self.line(self.conf.marginLeft, self.conf.pageHeight-self.conf.marginBottom,\n                  self.conf.pageWidth - self.conf.marginRight, self.conf.pageHeight-self.conf.marginBottom)\n        self.line(self.conf.marginLeft, self.conf.marginTop,\n                  self.conf.pageWidth - self.conf.marginRight, self.conf.marginTop)\n        self.setFont('NotoSans', 10)\n        self.drawString(self.conf.marginLeft,\n                        self.conf.marginBottom-0.5*cm, dateTime)\n        self.drawRightString(self.conf.pageWidth - self.conf.marginLeft,\n                             self.conf.marginBottom-0.5*cm, page)\n        self.restoreState()\n\n\nclass Energy(Flowable):\n    def __init__(self, energy_kwh: float, rect_size: float = 100.0):\n        super().__init__()\n        self.energy_kwh = energy_kwh\n        self.width = rect_size\n        self.height = rect_size\n        self.hAlign = \"CENTER\"\n\n    def draw(self):\n        self.canv.saveState()\n        self.canv.setLineWidth(2)\n        self.canv.setStrokeColor(Color(50.0/255, 115.0/255, 50.0/255, 1))\n        self.canv.roundRect(0, 0, self.width, self.height, self.width/6)\n        self.canv.drawImage(\"icons/energy.png\", 35, 60,\n                            width=30, height=30, preserveAspectRatio=True, mask='auto')\n        self.canv.restoreState()\n        self.canv.setFont('NotoSansB', 16)\n        self.canv.setFillColor(Color(50.0/255, 115.0/255, 50.0/255, 1))\n        self.canv.drawCentredString(50, 25, \"%.3f kWh\" % self.energy_kwh)\n\n\nclass ACActivity:\n    def __init__(self, type: str, power_status: str, op_mode: str, op_time: str, configured_temp: str, fan_speed: str) -> None:\n        self.type = type\n        self.power_status = power_status\n        self.op_mode = op_mode\n        self.op_time = op_time\n        self.configured_temp = configured_temp\n        self.fan_speed = fan_speed\n\n\nclass BenKonReportData:\n    def __init__(self, user: str, device: str, report_date: datetime, energy_kwh: float, chart_url: str, activities: \"list[ACActivity]\") -> None:\n        self.user = user\n        self.device = device\n        self.report_date = report_date\n        self.energy_kwh = energy_kwh\n        self.chart_url = chart_url\n        self.activities = activities\n\n\nclass BenKonReport:\n    def __init__(\n            self,\n            path: str,\n            isGenSummaryPage: bool,\n            url_pie_chart: str,\n            url_bar_chart: str,\n            data: \"list[BenKonReportData]\",\n            config: ReportConfig = ReportConfig(),\n            language: str = 'vie'\n    ):\n        # Create path if not exists\n        if not os.path.exists(os.path.dirname(path)):\n            os.makedirs(os.path.dirname(path), exist_ok=True)\n\n        # Initialization\n        self.conf = config\n        self.conf.registerFont()\n        self.path = path\n        self.data = data\n        self.language = language\n        self.url_pie_chart = url_pie_chart\n        self.url_bar_chart = url_bar_chart\n        self.styleSheet = getSampleStyleSheet()\n        self.elements = []\n\n        self.colorBlue = Color((54.0/255), (122.0/255), (179.0/255), 1)\n        self.colorWhite = Color(1, 1, 1, 1)\n        self.colorGreen = Color(50.0/255, 115.0/255, 50.0/255, 1)\n        self.colorGrey = Color(192.0/255, 192.0/255, 192.0/255, 1)\n        self.colorBKLight = Color((246.0/255), (246.0/255), (247.0/255), 1)\n        self.colorBKLightGray = Color((84.0/255), (181.0/255), (236.0/255), 1)\n        self.colorBKNormal = Color((50.0/255), (123.0/255), (198.0/255), 1)\n        self.colorBKDarkGray = Color((183.0/255), (143.0/255), (109.0/255), 1)\n        self.colorBKDark = Color((31.0/255), (74.0/255), (154.0/255), 1)\n\n        # Create page content\n        if isGenSummaryPage:\n            self.summaryPage()\n        for idx in range(len(self.data)):\n            self.firstPage(idx)\n            self.activityPage(idx)\n\n        # Build\n        self.doc = SimpleDocTemplate(\n            path, pagesize=A4, leftMargin=2*cm, rightMargin=2*cm,\n            topMargin=1.5*cm, bottomMargin=1.5*cm)\n        self.doc.multiBuild(self.elements, canvasmaker=FooterCanvas)\n\n    def summaryPage(self):\n        # Pie chart title\n        self.elements.append(Spacer(10, 1 * cm))\n\n        if self.language == 'vie':\n            chartTitleText = \"Tổng điện năng tiêu thụ và thời gian hoạt động (3 ngày gần nhất)\"\n        else:\n            chartTitleText = \"Total energy consumption and working hours (last 3 days)\"\n\n        chartTitleStyle = ParagraphStyle(\n            name=\"chartTitleStyle\", fontName=\"NotoSans\", fontSize=14, alignment=TA_CENTER)\n        chartTitle = Paragraph(\"<b>%s</b>\" % chartTitleText, chartTitleStyle)\n        self.elements.append(chartTitle)\n\n        # Pie chart image\n        self.elements.append(Spacer(10, 0.25 * cm))\n        if self.url_pie_chart == '':\n            pass\n        else:\n            imgChart = Image(self.url_pie_chart)\n            chartWidth = self.conf.pageWidth\n            chartHeight = self.conf.pageHeight\n            imgChart.drawHeight = chartHeight * 0.25\n            imgChart.drawWidth = chartWidth * 0.6\n            imgChart.hAlign = 'CENTER'\n            self.elements.append(imgChart)\n\n        # Bar chart image\n        if self.url_bar_chart == '':\n            pass\n        else:\n            imgChart = Image(self.url_bar_chart)\n            chartWidth = self.conf.pageWidth\n            chartHeight = self.conf.pageHeight\n            imgChart.drawHeight = chartHeight * 0.55\n            imgChart.drawWidth = chartWidth\n            imgChart.hAlign = 'CENTER'\n            self.elements.append(imgChart)\n\n        self.elements.append(PageBreak())\n\n    def firstPage(self, dataIndex: int):\n\n        # report title\n        self.elements.append(Spacer(10, 1.25 * cm))\n        reportTitleStyle = ParagraphStyle(\n            name=\"reportTitleStyle\", fontName=\"NotoSans\", fontSize=20, alignment=TA_CENTER)\n\n        if self.language == 'vie':\n            title = \"BÁO CÁO HOẠT ĐỘNG MÁY LẠNH\"\n        else:\n            title = \"AIR CONDITIONER's ACTIVITIES DAILY REPORT\"\n\n        reportTitle = Paragraph(\"<b>%s</b>\" % title, reportTitleStyle)\n        self.elements.append(reportTitle)\n\n        # report date\n        self.elements.append(Spacer(10, 0.5 * cm))\n        dateTime = getWeekdayName(\n            self.data[dataIndex].report_date, language=self.language) + self.data[dataIndex].report_date.strftime(\", %d/%m/%Y\")\n\n        reportDateStyle = ParagraphStyle(\n            name=\"reportDateStyle\", fontName=\"NotoSans\", fontSize=12, alignment=TA_CENTER)\n        reportDate = Paragraph(\"<b>%s</b>\" % dateTime, reportDateStyle)\n        self.elements.append(reportDate)\n\n        # Header info\n        iconSize = 0.7*cm\n        spacer = Spacer(10, 0.5*cm)\n        self.elements.append(spacer)\n\n        imgUser = Image('icons/username.png')\n        imgUser.drawHeight = iconSize\n        imgUser.drawWidth = iconSize\n        imgUser.hAlign = 'LEFT'\n\n        imgAC = Image('icons/ac.png')\n        imgAC.drawHeight = iconSize\n        imgAC.drawWidth = iconSize\n        imgAC.hAlign = 'LEFT'\n\n        labelStyle = ParagraphStyle(\n            name=\"Label\", fontName=\"NotoSans\")\n        valueStyle = ParagraphStyle(\n            name=\"Value\", borderWidth=3, fontName=\"NotoSans\")\n\n        if self.language == 'vie':\n            rowUser = [imgUser, Paragraph(\"Khách hàng:\", labelStyle), Paragraph(\n                \"<b>%s</b>\" % self.data[dataIndex].user, valueStyle)]\n            rowAC = [imgAC, Paragraph(\"Tên thiết bị:\", labelStyle), Paragraph(\n                \"<b>%s</b>\" % self.data[dataIndex].device, valueStyle)]\n        else:\n            rowUser = [imgUser, Paragraph(\"Username:\", labelStyle), Paragraph(\n                \"<b>%s</b>\" % self.data[dataIndex].user, valueStyle)]\n            rowAC = [imgAC, Paragraph(\"Device name:\", labelStyle), Paragraph(\n                \"<b>%s</b>\" % self.data[dataIndex].device, valueStyle)]\n\n        tableData = [rowUser, rowAC]\n        colWidths = [iconSize+0.3*cm, 2.5*cm, 1*cm]\n        colWidths[2] = self.conf.bodyWidth - colWidths[0] - colWidths[1]\n        titleTable = Table(tableData, colWidths=colWidths)\n        titleTableStyle = TableStyle([\n            ('VALIGN', (0, 0), (-1, -1), 'CENTER'),\n        ])\n        titleTable.setStyle(titleTableStyle)\n        self.elements.append(titleTable)\n\n        # chart title\n        self.elements.append(Spacer(10, 1 * cm))\n\n        if self.language == 'vie':\n            chartTitleText = \"Biểu đồ trạng thái máy lạnh trong ngày\"\n        else:\n            chartTitleText = \"Air conditioner's status chart\"\n\n        chartTitleStyle = ParagraphStyle(\n            name=\"chartTitleStyle\", fontName=\"NotoSans\", fontSize=16, alignment=TA_CENTER)\n        chartTitle = Paragraph(\"<b>%s</b>\" % chartTitleText, chartTitleStyle)\n        self.elements.append(chartTitle)\n\n        # chart image\n\n        if self.data[dataIndex].chart_url == '':\n            pass\n        else:\n            self.elements.append(Spacer(10, 0.25 * cm))\n            imgChart = Image(self.data[dataIndex].chart_url)\n            chartSize = self.conf.pageWidth\n            imgChart.drawHeight = chartSize * 0.8\n            imgChart.drawWidth = chartSize\n            imgChart.hAlign = 'CENTER'\n            self.elements.append(imgChart)\n\n        # page break\n        self.elements.append(PageBreak())\n\n    def _getTableColumnWidth(self, percentageWidth: \"list[float]\") -> \"list[float]\":\n        tableMaxWidth = self.conf.pageWidth - \\\n            self.conf.marginLeft - self.conf.marginRight\n        totalWidth = 0\n        for colWidth in percentageWidth:\n            totalWidth += colWidth\n        result = []\n        for colWidth in percentageWidth:\n            result.append(colWidth*tableMaxWidth/totalWidth)\n        return result\n\n    def activityPage(self, dataIndex: int):\n\n        # Energy title\n        self.elements.append(Spacer(10, 1.5*cm))\n\n        if self.language == 'vie':\n            chartTitleText = \"Tổng điện năng tiêu thụ\"\n        else:\n            chartTitleText = \"Total energy consumption\"\n\n        chartTitleStyle = ParagraphStyle(\n            name=\"chartTitleStyle\", fontName=\"NotoSans\", fontSize=16, alignment=TA_CENTER)\n        chartTitle = Paragraph(\"<b>%s</b>\" % chartTitleText, chartTitleStyle)\n        self.elements.append(chartTitle)\n\n        # Energy rect\n        self.elements.append(Spacer(10, 1*cm))\n        self.elements.append(\n            Energy(energy_kwh=self.data[dataIndex].energy_kwh))\n\n        # Activities table\n        spacer = Spacer(10, 1.5*cm)\n        self.elements.append(spacer)\n        psHeaderText = ParagraphStyle(\n            'Hed0', fontSize=14, alignment=TA_CENTER, borderWidth=3, textColor=self.colorBlue, fontName=\"NotoSans\")\n\n        if self.language == 'vie':\n            paragraphReportHeader = Paragraph(\n                'Bảng các hoạt động trong ngày của máy lạnh', psHeaderText)\n        else:\n            paragraphReportHeader = Paragraph(\n                \"Air conditioner's activities table of a day\", psHeaderText)\n\n        self.elements.append(paragraphReportHeader)\n\n        spacer = Spacer(10, 22)\n        self.elements.append(spacer)\n\n        \"\"\"\n        Create the line items\n        \"\"\"\n        d = []\n\n        if self.language == 'vie':\n            textData = [\"STT\", \"Thời gian\", \"Hoạt động\", \"Trạng thái\", \"Chế độ\",\n                        \"Nhiệt độ thiết lập\", \"Tốc độ quạt\"]\n        else:\n            textData = [\"No.\", \"Timestamp\", \"Activity\", \"Status\", \"Mode\",\n                        \"Set up temperature\", \"Fan speed\"]\n\n        fontSize = 8\n        centered = ParagraphStyle(\n            name=\"centered\", alignment=TA_CENTER, fontName=\"NotoSans\")\n        for text in textData:\n            ptext = \"<font size='%s'><b>%s</b></font>\" % (fontSize, text)\n            titlesTable = Paragraph(ptext, centered)\n            d.append(titlesTable)\n\n        data = [d]\n        formattedLineData = []\n\n        alignStyle = [ParagraphStyle(name=\"01\", alignment=TA_CENTER),\n                      ParagraphStyle(name=\"02\", alignment=TA_CENTER),\n                      ParagraphStyle(name=\"03\", alignment=TA_CENTER),\n                      ParagraphStyle(name=\"04\", alignment=TA_CENTER),\n                      ParagraphStyle(name=\"05\", alignment=TA_CENTER),\n                      ParagraphStyle(name=\"06\", alignment=TA_CENTER),\n                      ParagraphStyle(name=\"07\", alignment=TA_CENTER)]\n\n        for id, activity in enumerate(self.data[dataIndex].activities):\n            lineData = [str(id+1), activity.op_time, activity.type, activity.power_status,\n                        activity.op_mode, activity.configured_temp, activity.fan_speed]\n            columnNumber = 0\n            for item in lineData:\n                ptext = \"<font size='%s'>%s</font>\" % (fontSize-1, item)\n                p = Paragraph(ptext, alignStyle[columnNumber])\n                formattedLineData.append(p)\n                columnNumber = columnNumber + 1\n            data.append(formattedLineData)\n            formattedLineData = []\n\n        ''' Set table Columns width '''\n        table = Table(data, colWidths=self._getTableColumnWidth(\n            [7, 15, 18, 15, 15, 16, 15]))\n        tStyle = TableStyle([  # ('GRID',(0, 0), (-1, -1), 0.5, grey),\n            ('ALIGN', (0, 0), (0, -1), 'CENTER'),\n            ('VALIGN', (0, 0), (-1, -1), 'CENTER'),\n            (\"ALIGN\", (1, 0), (1, -1), 'RIGHT'),\n            ('LINEBELOW', (0, 0), (-1, -1), 0.5, self.colorGrey),\n            ('BACKGROUND', (0, 0), (-1, 0), self.colorBKLightGray),\n            ('ROWBACKGROUNDS', (0, 1), (-1, -1),\n             [self.colorBKLight, self.colorWhite]),\n            # ('SPAN', (0, -1), (-2, -1))\n        ])\n        table.setStyle(tStyle)\n        self.elements.append(table)\n\n        # page break\n        self.elements.append(PageBreak())\n","repo_name":"nhat-thai/test_new_api","sub_path":"bkreport/benkon_report.py","file_name":"benkon_report.py","file_ext":"py","file_size_in_byte":17097,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"12329507013","text":"#!/bin/env python\n\n\nimport xml.dom.minidom\n\n\nfrom GeneralScriptSupport import *\n\n\n#\n# Default file locations\n#\n\ndefaultTrilinosDepsXmlInFile = getScriptBaseDir()+\"/data/TrilinosPackageDependencies.xml\"\n\ndefaultTrilinosDepsHtmlOutFile = getScriptBaseDir()+\"/data/TrilinosPackageDependenciesTable.html\"\n\ndefaultCDashDepsXmlFile = getScriptBaseDir()+\"/data/CDashSubprojectDependencies.xml\"\n\n\n#\n# Store and manipulate the dependencies\n#\n\n\nclass PackageEmailAddresses:\n\n  def __init__(self, checkin_in, regression_in):\n    self.checkin = checkin_in\n    self.regression = regression_in\n\n  def __str__(self):\n    return \"{checkin=\"+self.checkin+\", regression=\"+self.regression+\"}\"\n  \n\nclass PackageDependencies:\n\n  def __init__(self, packageName_in, packageDir_in,\n    libRequiredDepPackages_in, libOptionalDepPackages_in,\n    testRequiredDepPackages_in, testOptionalDepPackages_in,\n    emailAddresses_in\n    ):\n    self.packageName = packageName_in\n    self.packageDir = packageDir_in\n    self.packageID = -1\n    self.libRequiredDepPackages = libRequiredDepPackages_in\n    self.libOptionalDepPackages = libOptionalDepPackages_in\n    self.testRequiredDepPackages = testRequiredDepPackages_in\n    self.testOptionalDepPackages = testOptionalDepPackages_in\n    self.emailAddresses = emailAddresses_in\n\n  def __str__(self):\n    return \"{\\n\"+\\\n      \"  packageName=\"+self.packageName+\",\\n\"+\\\n      \"  packageID=\"+str(self.packageID)+\",\\n\"+\\\n      \"  packageDir=\"+str(self.packageDir)+\",\\n\"+\\\n      \"  libRequiredDepPackages=\"+str(self.libRequiredDepPackages)+\",\\n\"+\\\n      \"  libOptionalDepPackages=\"+str(self.libOptionalDepPackages)+\",\\n\"+\\\n      \"  testRequiredDepPackages=\"+str(self.testRequiredDepPackages)+\",\\n\"+\\\n      \"  testOptionalDepPackages=\"+str(self.testOptionalDepPackages)+\" \\n\"+\\\n      \"  emailAddresses=\"+str(self.emailAddresses)+\"\\n\"+\\\n      \"}\\n\"\n\n\ndef isRequiredDep(dep):\n  return (dep[-1] == 'R')\n\n\ndef isDirectDep(dep):\n  return (dep[0] != 'I')\n\n\ndef isLibDep(dep):\n  return (dep[0] == 'L' or dep[1] == 'L')\n\n\n#\n# (dep1, dep2) => newDep\n#\n# (*) Required dependencies trump optional dependencies\n# (*) Direct dependencies trump indirect dependencies\n# (*) Library dependicnes trump test dependencies\n#\ndef updatePackageDep(dep1, dep2):\n\n  #print \"\\n    updatePackageDep(\"+dep1+\", \"+dep2+\") ...\"\n\n  dep1_required = isRequiredDep(dep1)\n  dep1_direct = isDirectDep(dep1)\n  dep1_lib = isLibDep(dep1)\n\n  dep2_required = isRequiredDep(dep2)\n  dep2_direct = isDirectDep(dep2)\n  dep2_lib = isLibDep(dep2)\n\n  selectedDep = False\n\n  if dep1 == dep2:\n    newDep = dep1\n    selectedDep = True\n\n  # Required trumps optional\n  if not selectedDep:\n    if dep1_required and not dep2_required:\n      newDep = dep1\n      selectedDep = True\n    elif not dep1_required and dep2_required:\n      newDep = dep2\n      selectedDep = True\n\n  # Direct trumps indirect\n  if not selectedDep:\n    if dep1_direct and not dep2_direct:\n      newDep = dep1\n      selectedDep = True\n    elif not dep1_direct and dep2_direct:\n      newDep = dep2\n      selectedDep = True\n\n  # Library trumps test\n  if not selectedDep:\n    if dep1_lib and not dep2_lib:\n      newDep = dep1\n      selectedDep = True\n    elif not dep1_lib and dep2_lib:\n      newDep = dep2\n      selectedDep = True\n\n  assert(selectedDep)\n\n  #print \"\\n      newDep =\", newDep\n\n  return newDep\n\n\nclass DepStats:\n  isDirect = None\n  isRequired = None\n  isTestDepChain = None\n  def __init__(self, isDirect, isRequired, isTestDepChain):\n    self.isDirect = isDirect\n    self.isRequired = isRequired\n    self.isTestDepChain = isTestDepChain\n\n\nclass TrilinosDependencies:\n\n\n  def __init__(self):\n    self.__packagesList = []\n    self.__packagesNameToID = {}\n    self.__packagesDirToID = {}\n\n\n  def addPackageDependencies(self, packageDeps):\n    packageName = packageDeps.packageName\n    packageDir = packageDeps.packageDir\n    self.__packagesList.append(packageDeps)\n    packageDeps.packageID = len(self.__packagesList)-1 \n    self.__packagesNameToID.update( { packageName : packageDeps.packageID } )\n    self.__packagesDirToID.update( { packageDir : packageDeps.packageID } )\n\n\n  def numPackages(self):\n    return len(self.__packagesList)\n\n\n  def packageNameToID(self, packageName):\n    return self.__packagesNameToID.get(packageName, -1)\n\n\n  def getPackageByID(self, packageID):\n    return self.__packagesList[packageID]\n\n\n  def getPackageByName(self, packageName):\n    return self.getPackageByID(self.__packagesNameToID[packageName])\n\n\n  def getPackageByDir(self, packageDir):\n    packageID = self.__packagesDirToID.get(packageDir, -1)\n    #print \"\\ngetPackageByDir: packageDir=\"+packageDir+\", packageID=\"+str(packageID)\n    if packageID >= 0:\n      return self.__packagesList[packageID]\n    return None\n\n\n  def getPackageNameFromPath(self, fullPath, prefixPath):\n    #print \"\\nfullPath=\"+fullPath\n    fullPathArray = getFilePathArray(fullPath)\n    if fullPathArray[0] == \"packages\":\n      regexPathPrefix = \"packages/\"\n      pathPrefix = \"\"\n    else:\n      regexPathPrefix = \"\"\n      pathPrefix = \"../\"\n    #print \"regexPathPrefix = '\"+regexPathPrefix+\"'\"\n    #print \"pathPrefix = '\"+pathPrefix+\"'\"\n    for packageDep in self.__packagesList:\n      regexFilePath = regexPathPrefix+packageDep.packageDir+\"/\"\n      ammendedFullPath = pathPrefix+fullPath \n      #print \"regexFilePath=\"+regexFilePath\n      #print \"ammendedFullPath=\"+ammendedFullPath\n      if re.match(regexFilePath, ammendedFullPath):\n        #print \"MATCH!\"\n        return packageDep.packageName\n    return u\"\"\n\n\n  def __str__(self):\n    strRep = \"\"\n    for packageDep in self.__packagesList:\n      strRep += str(packageDep)\n    return strRep\n\n\n  def updateDepCell(self, packageRow, packageID, depStats, depCategoryName):\n\n    currentDepName = packageRow[packageID+1]\n\n    newDepName = depCategoryName\n\n    # If we are in a test dependency chain, we must change library\n    # dependencies to test dependencies.\n    if depStats.isTestDepChain:\n      newDepName = 'T'+newDepName[1:]\n\n    if depStats.isDirect:\n      newDepName = newDepName\n    else:\n      newDepName = \"I\"+newDepName\n\n    if not depStats.isRequired:\n      newDepName = newDepName[0:-1]+\"O\"\n\n    if currentDepName:\n      #print \"\\n    updateDepCell: depStats.isDirect=\"+str(depStats.isDirect)+\", depStats.isRequired=\"+str(depStats.isRequired)+\", depCategoryName=\"+depCategoryName\n      newDepName = updatePackageDep(currentDepName, newDepName)\n\n    packageRow[packageID+1] = newDepName\n\n\n  def updatePackageDepsCategory(self, libsOnly, packageRowID, packageID, depCategory,\n    depCategoryName, depStats, trilinosDepsTable\n    ):\n\n    packageRow = trilinosDepsTable[packageRowID+1]\n    #print \"\\npackageRow =\", packageRow\n\n    depList = getattr(self.__packagesList[packageID], depCategory)\n    #print \"\\ndepList =\", depList\n\n    for dep in depList:\n\n      depPackage = self.getPackageByName(dep)\n      #print \"\\n  depPackageName =\", depPackage.packageName\n\n      dep_i = depPackage.packageID\n\n      self.updateDepCell(packageRow, dep_i, depStats, depCategoryName)\n      \n      if not depStats.isRequired:\n        isRequiredDep = False\n      elif depCategoryName[-1]==\"R\":\n        isRequiredDep = True\n      else:\n        isRequiredDep = False\n\n      childDepStats = DepStats(False, isRequiredDep, depStats.isTestDepChain)\n\n      self.updatePackageDeps(libsOnly, packageRowID, dep_i, childDepStats,\n         trilinosDepsTable)\n\n\n  def updatePackageDeps(self, libsOnly, packageRowID, packageID, depStats,\n    trilinosDepsTable\n    ):\n\n    self.updatePackageDepsCategory(libsOnly, packageRowID, packageID,\n      \"libRequiredDepPackages\", \"LR\", depStats, trilinosDepsTable)\n    self.updatePackageDepsCategory(libsOnly, packageRowID, packageID,\n      \"libOptionalDepPackages\", \"LO\", depStats, trilinosDepsTable)\n\n    # Only process the test dependencies if we are asked to do so\n    # (i.e. libsOnly=True) or if this is the top-level package.  The tests for\n    # dependent packages are not any kind of dependency for tests for the\n    # top-level package.  However, we need to record that these are test\n    # dependencies so that any package libraries that get recursed are\n    # recorded as 'ITR' or 'ITO' and not as library dependencies.\n    if not libsOnly and depStats.isDirect:\n      libDepStats = DepStats(True, depStats.isRequired, True)\n      self.updatePackageDepsCategory(False, packageRowID, packageID,\n        \"testRequiredDepPackages\", \"TR\", libDepStats, trilinosDepsTable)\n      self.updatePackageDepsCategory(False, packageRowID, packageID,\n        \"testOptionalDepPackages\", \"TO\", libDepStats, trilinosDepsTable)\n\n  \n  def createRawTable(self, libsOnly):\n\n    numPackages = self.numPackages()\n    #print \"\\nnumPackages =\", numPackages\n\n    trilinosDepsTable = []\n\n    topRow = [ \"Packages\" ]\n    topRow.extend([\"P%02d\"%(i+1) for i in range(numPackages)] )\n    trilinosDepsTable.append(topRow)\n\n    for packageDeps in self.__packagesList:\n      i = packageDeps.packageID\n      row = [\"P%02d\"%(i+1)+\") \"+packageDeps.packageName]\n      row.extend([\"\" for i in range(numPackages)])\n      trilinosDepsTable.append(row)\n\n    for packageDeps in self.__packagesList:\n      #print \"\\npackageName =\", packageDeps.packageName\n      i = packageDeps.packageID\n      trilinosDepsTable[i+1][i+1] = \"X\"\n      self.updatePackageDeps(libsOnly, i, i, DepStats(True, True, False), trilinosDepsTable)\n\n    return trilinosDepsTable\n\n  def createTrilinosPackagesNumberedList(self):\n    numPackages = self.numPackages()\n    htmlText = \"<p><b>Packages:</b> \" + \\\n      \", \".join( \\\n        [ \"P%02d\"%(i+1)+\") \"+self.__packagesList[i].packageName \\\n           for i in range(self.numPackages())] \\\n        ) + \\\n        \"</p>\"\n    return htmlText\n\n  def createHtmlFromTable(self, rawTable):\n\n    numPackages = self.numPackages()\n\n    htmlText = \\\n      \"<TABLE BORDER=4>\\n\"+\\\n      \"\\n\"\n\n    for i in range(numPackages+2):\n      htmlText += \"<COL ALIGN=LEFT>\\n\"\n\n    topRow = rawTable[0]\n    htmlText += \"\\n<TR>\\n\"\n    for j in range(numPackages+1):\n      htmlText += \" <TD><b>\"+topRow[j]+\"</b></TD>\\n\"\n    htmlText += \" <TD><b>Packages</b></TD>\\n\"\n    htmlText += \"</TR>\\n\"\n      \n    for package_i in range(numPackages):\n      row = rawTable[package_i+1]\n      htmlText += \"\\n<TR>\\n\"\n      htmlText += \" <TD><b>\"+row[0]+\"</b></TD>\\n\"\n      for j in range(numPackages):\n        entry = row[j+1]\n        if not entry: entry = \".\"\n        htmlText += \" <TD>\"+entry+\"</TD>\\n\"\n      htmlText += \" <TD><b>\"+row[0]+\"</b></TD>\\n\"\n      htmlText += \"</TR>\\n\"\n\n    htmlText += \"\\n<TR>\\n\"\n    htmlText += \" <TD><b>Packages</b></TD>\\n\"\n    for j in range(numPackages):\n      htmlText += \" <TD><b>P%02d\"%(j+1)+\"</b></TD>\\n\"\n    htmlText += \" <TD><b>Packages</b></TD>\\n\"\n    htmlText += \"</TR>\\n\"\n\n    htmlText += \"</TABLE>\\n\"\n    \n    return htmlText\n\n\n  def createHtmlTableLegend(self, libsOnly):\n\n    htmlText =\\\n      \"\\n\"+\\\n      \"<ul>\\n\"+\\\n      \"<li> <b>X</b>: Diagonal entry for the package itself\\n\"+\\\n      \"<li> <b>LR</b>: Direct library required dependency\\n\"+\\\n      \"<li> <b>ILR</b>: Indirect library required dependency\\n\"+\\\n      \"<li> <b>LO</b>: Direct library optional dependency\\n\"+\\\n      \"<li> <b>ILO</b>: Indirect library optional dependency\\n\"\n\n    if not libsOnly:\n      htmlText +=\\\n        \"<li> <b>TR</b>: Direct test/example required dependency\\n\"+\\\n        \"<li> <b>ITR</b>: Indirect test/example required dependency\\n\"+\\\n        \"<li> <b>TO</b>: Direct test/example optional dependency\\n\"+\\\n        \"<li> <b>ITO</b>: Indirect test/example optional dependency\\n\"\n\n    htmlText +=\\\n      \"</ul>\\n\"+\\\n      \"\\n\"+\\\n      \"NOTE: When more than one type of dependency is present for any cell\"+\\\n      \" the final selection is determined in the following order:\\n\"+\\\n      \"<ul>\\n\"+\\\n      \"<li> A required dependency trumps an optional dependency\\n\"+\\\n      \"<li> A direct dependency trumps an indirect dependency\\n\"+\\\n      \"<li> A library dependency trumps a test/example dependency\\n\"+\\\n      \"</ul>\\n\"\n\n    return htmlText\n\n\n  def createFullHtmlForTables(self):\n\n    packagesListHtml = self.createTrilinosPackagesNumberedList()\n\n    htmlText = \\\n      \"<p><huge><b>Trilinos Test/Example and Library Package Dependencies</b></huge></p>\\n\"+\\\n      \"\\n\"+\\\n      self.createHtmlFromTable(self.createRawTable(False))+\\\n      \"\\n\"+\\\n      packagesListHtml+\"\\n\"+\\\n      \"\\n\"+\\\n      \"<p><b>Legend</b></p>\\n\"+\\\n      \"\\n\"+\\\n      self.createHtmlTableLegend(False)+\\\n      \"\\n\"+\\\n      \"<p><b><huge>Trilinos Libary-Only Package Dependencies</huge></b></p>\\n\"+\\\n      \"\\n\"+\\\n      self.createHtmlFromTable(self.createRawTable(True))+\\\n      \"\\n\"+\\\n      packagesListHtml+\"\\n\"+\\\n      \"\\n\"+\\\n      \"<p><b>Legend</b></p>\\n\"+\\\n      \"\\n\"+\\\n      self.createHtmlTableLegend(True)\n\n    return htmlText\n\n  def createFullHtmlPage(self):\n\n    htmlText = \\\n      \"<html>\\n\"+\\\n      \"<head>\\n\"+\\\n      \"<title>Trilinos Package Dependencies</title>\\n\"+\\\n      \"</head>\\n\"+\\\n      \"\\n\"+\\\n      \"<body>\\n\"+\\\n      \"\\n\"+\\\n      self.createFullHtmlForTables()+\\\n      \"\\n\"+\\\n      \"</body>\\n\"+\\\n      \"\\n\"+\\\n      \"</html>\\n\"\n\n    return htmlText\n\n\n  def writeFullHtmlPage(self, htmlFileName=defaultTrilinosDepsHtmlOutFile):\n    htmlString = self.createFullHtmlPage()\n    htmlFile = open(htmlFileName, 'w')\n    htmlFile.write(htmlString)\n    htmlFile.close()\n\n\n  #\n  # CDash stuff\n  #\n\n\n  def createCDashDepsXMLFromRawDepsTable(self, rawTable):\n    \n    xmlText = \"\"\n\n    xmlText += \"<Project name=\\\"Trilinos\\\">\\n\"\n\n    numPackages = self.numPackages()\n\n    for package_i in range(numPackages):\n\n      packageDeps = self.__packagesList[package_i]\n\n      packageName = packageDeps.packageName\n      xmlText += (\"  <SubProject name=\\\"\"+packageName+\"\\\">\\n\")\n\n      row = rawTable[package_i+1]\n\n      for dep_j in range(numPackages):\n        entry = row[dep_j+1]\n        if entry and entry != \"X\":\n          depPackageName = self.__packagesList[dep_j].packageName\n          xmlText += (\"    <Dependency name=\\\"\"+depPackageName+\"\\\"\" + \\\n            \" type=\\\"\"+entry+\"\\\"/>\\n\" )\n\n      xmlText += \\\n        \"    <EmailAddresses>\\n\"+\\\n        \"      <Email address=\\\"\"+packageDeps.emailAddresses.regression+\"\\\"/>\\n\"+\\\n        \"    </EmailAddresses>\\n\"\n\n      xmlText += (\"  </SubProject>\\n\")\n\n    xmlText += \"</Project>\\n\"\n\n    return xmlText\n  \n\n  def createCDashDepsXML(self):\n    return self.createCDashDepsXMLFromRawDepsTable(self.createRawTable(False))\n\n\n  def writeCDashXmlDepsFile(self, xmlDepsFile=defaultCDashDepsXmlFile):\n    xmlString = self.createCDashDepsXML()\n    xmlFile = open(xmlDepsFile, 'w')\n    xmlFile.write(xmlString)\n    xmlFile.close()\n\n\n#\n# Read in the dependencies from XML\n#\n\n\ndef getDependenciesByType(packageEle, typeName):\n  packageDepsStr = packageEle.getElementsByTagName(typeName)[0].getAttribute('value');\n  if len(packageDepsStr) == 0:\n    return []\n  return packageDepsStr.split(',')\n\n\ndef getSingleEmailAddress(emailEle, emailType):\n  singleEmailEle = emailEle.getElementsByTagName(emailType)[0]\n  singleEmailAddress = singleEmailEle.getAttribute('address');\n  return singleEmailAddress\n\n\ndef getPackageEmailAddresses(packageEle):\n  emailEle = packageEle.getElementsByTagName(\"EmailAddresses\")[0]\n  checkinEmail = getSingleEmailAddress(emailEle, \"Checkin\")\n  regressionEmail = getSingleEmailAddress(emailEle, \"Regression\")\n  return PackageEmailAddresses(checkinEmail, regressionEmail)\n\n\ndef getTrilinosDependenciesFromXmlFile(xmlFile=defaultTrilinosDepsXmlInFile):\n  #print \"xmlFile =\", xmlFile\n  packageDepXmlDom = xml.dom.minidom.parse(xmlFile)\n  trilinosDependencies = TrilinosDependencies()\n  for ele in packageDepXmlDom.childNodes[0].childNodes:\n    if ele.nodeType == ele.ELEMENT_NODE:\n      packageName = ele.getAttribute('name')\n      packageDir = ele.getAttribute('dir')\n      #print \"\\npackageName =\", packageName\n      packageDeps = PackageDependencies(packageName, packageDir,\n        getDependenciesByType(ele, \"LIB_REQUIRED_DEP_PACKAGES\"),\n        getDependenciesByType(ele, \"LIB_OPTIONAL_DEP_PACKAGES\"),\n        getDependenciesByType(ele, \"TEST_REQUIRED_DEP_PACKAGES\"),\n        getDependenciesByType(ele, \"TEST_OPTIONAL_DEP_PACKAGES\"),\n        getPackageEmailAddresses(ele)\n        )\n      #print \"\\npackageDeps =\", str(packageDeps)\n      trilinosDependencies.addPackageDependencies(packageDeps)\n  return trilinosDependencies\n","repo_name":"qsnake/trilinos","sub_path":"cmake/python/TrilinosDependencies.py","file_name":"TrilinosDependencies.py","file_ext":"py","file_size_in_byte":16309,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"19068645646","text":"import pickle\nimport sys\n# local module\nfrom text_processing import *\n\n# get top k book summaries for a particluar query\ndef getSummaries(q, k):\n    # pre processing query: tokenization, removing stopwords, stemming\n    words = token_list(q)\n    if len(words) == 0:\n        return []\n    else:\n        # getting index created for all summaries in database with weight values for a summary and token\n        index_file = open(\"search_data/index.pkl\",\"rb\")\n        index = pickle.load(index_file)\n\n        # get all matches with score,ids in sorted in desc order of scores\n        docScores = get_all_matches(index, words)\n\n        top_matches = []\n        # getting summaries data from top summary ids found\n        summaries_file = open(\"search_data/summaries.pkl\",\"rb\")\n        summaries = pickle.load(summaries_file)\n        count = 0\n        for el in docScores:\n            if count == k:\n                break\n            top_matches.append(summaries[el[1]])\n            count += 1\n        index_file.close()\n        summaries_file.close()\n    return top_matches\n\ndef get_all_matches(index, words):\n    search_tokens = []\n    # getting all summary ids (from index data) containing any of the query tokens\n    result = set()\n    for word in words:\n        if word in index:\n            search_tokens += [word]\n            entries = [x[0] for x in index[word]]\n            result=result|set(entries)\n\n    # scoring for each summary document found\n    # adding up wieghts of each token if present in summary document\n    docscores = {}\n    for query_token in search_tokens:\n        for doc_weights in index[query_token]:\n            if doc_weights[0] in result:\n                if doc_weights[0] in docscores:\n                    docscores[doc_weights[0]] += doc_weights[1]\n                else:\n                    docscores[doc_weights[0]] = doc_weights[1]\n\n    # sorting summary document ids accoring to scores\n    docScores=[ [score,doc] for doc,score in docscores.items()]\n    docScores.sort(reverse=True)\n\n    return docScores\n\nif __name__ == \"__main__\":\n    print(getSummaries(sys.argv[1], int(sys.argv[2])))\n","repo_name":"monaligupta13/prototype-search-engine","sub_path":"search_summaries.py","file_name":"search_summaries.py","file_ext":"py","file_size_in_byte":2118,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3777795265","text":"import pandas as pd\nimport plotly\nimport plotly.graph_objs as go\n\nimport dashboards.prepare_feature_data as pfd\nfrom dashboards.dashboard import AbstractDashboard\n\nPLAN_PREFIX = '<b>Plan: </b>'\nFACT_PREFIX = '<b>Fact: </b>'\n\n\nclass FeatureProgressDashboard(AbstractDashboard):\n    '''Plotly Bar Stacked Chart'''\n\n    plan = None\n    fact = None\n    details = None\n\n    def prepare(self, data):\n\n        columns_size = 0\n        epic_df = data[(data.issuetype == \"Epic\") | (data.issuetype == \"Documentation\")]\n        epic_df = data[data[\"labels\"].str.contains(pat=\"num\")]\n\n        plan_df, fact_df = pfd.prepare(epic_data=epic_df, issue_data=data, or_filter_list=self.filter_list,\n                                       and_filter_list=None,\n                                       plan_prefix=PLAN_PREFIX, fact_prefix=FACT_PREFIX, with_total=False,\n                                       details=self.details)\n        columns_size = plan_df.columns.size\n\n        self.data = pd.DataFrame()\n\n        for idx in range(0, columns_size):\n            if self.fact:\n                self.data[fact_df.columns[idx]] = fact_df[fact_df.columns[idx]]\n            if self.plan:\n                self.data[plan_df.columns[idx]] = plan_df[plan_df.columns[idx]]\n\n        sorted_data = self.data.reindex(sorted(self.data.columns, key=lambda x: x[len(PLAN_PREFIX):], reverse=False),\n                                      axis=1)\n        self.data = sorted_data\n\n        #self.data = self.data.assign(tmp=self.data.sum(axis=1)).sort_values('tmp', ascending=False)\n        #self.data = self.data.sort_values(by=, ascending=False)\n\n    def export_to_plotly(self):\n\n        if len(self.data) == 0:\n            return\n\n        ind = 1\n\n        feature_name_max_length = max(len(column) for column in self.data.columns)\n\n        first_feature = 0\n        last_feature = self.data.columns.size\n\n        loop_exit = False\n\n        # screen loop\n        for current_feature in range(first_feature, last_feature, self.items_on_chart):\n\n            final_feature = current_feature + self.items_on_chart\n            if last_feature - final_feature <= self.min_item_tail:\n                final_feature = last_feature\n                loop_exit = True\n\n            data_part = self.data.iloc[:, current_feature:final_feature]\n            # reverse sorting due to go from top to down\n            data_part = data_part.reindex(\n                sorted(data_part.columns, key=lambda x: x[len(PLAN_PREFIX):], reverse=True), axis=1)\n\n            traces = list()\n            shapes = list()\n\n            # now the colors\n            # clrred = 'rgb(222,0,0)'\n            # clrgrn = 'rgb(0,222,0)'\n\n            for component_idx in range(0, len(data_part)):\n                # clrs = [clrred if x%2 else clrgrn for x in range(data_part.iloc[component_idx].size)]\n                total = str(data_part.iloc[component_idx].values.sum())\n\n                bar_plan = go.Bar(\n                    y=data_part.columns,\n                    x=data_part.iloc[component_idx],\n                    name=data_part.index[component_idx],\n                    textposition='auto',\n                    orientation='h',\n                    legendgroup=data_part.index[component_idx],\n                    hoverinfo='name + text',\n                    text=data_part.iloc[component_idx],\n\n                    # marker=[x for x in dict(color=clrred)]\n                )\n                traces.append(bar_plan)\n            plan_fact_str = ''\n            if self.plan:\n                plan_fact_str = 'plan '\n            if self.fact:\n                plan_fact_str = plan_fact_str + 'fact'\n\n            if last_feature > self.items_on_chart:\n                title = \"{0} \\nPart #{1} <br>{2}\".format(self.dashboard_name.replace('num', ''), str(ind),\n                                                         plan_fact_str)\n            else:\n                title = \"{0} <br>{1}\".format(self.dashboard_name, plan_fact_str)\n\n            layout = go.Layout(\n                annotations=[\n                    dict(\n                        x=1.09,\n                        y=1.03,\n                        xref='paper',\n                        yref='paper',\n                        text='Components',\n                        showarrow=False,\n                        font=dict(\n                            family='sans-serif',\n                            size=12,\n                            color='#000'\n                        )\n                    )\n                ],\n                legend=dict(\n                    x=1,\n                    y=1,\n                    traceorder='normal',\n                    font=dict(\n                        family='sans-serif',\n                        size=10,\n                        color='#000'\n                    )\n                ),\n                showlegend=True,\n                margin=dict(t=50, b=50, r=100, l=feature_name_max_length * 6),\n                autosize=True,\n                font=dict(size=9, color='black'),\n                barmode='stack',\n                shapes=shapes,\n                title=title,\n                plot_bgcolor='white',\n                yaxis=dict(\n                    rangemode=\"tozero\",\n                    autorange=True,\n                    showgrid=True,\n                    zeroline=True,\n                    showline=True,\n                    ticks='',\n                    showticklabels=True,\n                    tickangle=0,\n                    tickfont=dict(\n                        size=10,\n                        color='black'\n\n                    ),\n                ),\n                xaxis=dict(\n                    rangemode=\"tozero\",\n                    autorange=True,\n                    showgrid=True,\n                    zeroline=True,\n                    showline=True,\n                    ticks='',\n                    showticklabels=True,\n                    tickfont=dict(\n                        size=10,\n                        color='black'\n\n                    ),\n                    title='Estimates (man-days)',\n                    titlefont=dict(\n                        size=16,\n                        color='black'\n                    )\n                )\n            )\n            file_name = self.dashboard_name.replace('num', '') + ' ' + plan_fact_str\n            html_file = self.png_dir + \"{0}_{1}.html\".format(file_name, str(ind))\n            fig = go.Figure(data=traces, layout=layout)\n            plotly.offline.plot(fig, filename=html_file, auto_open=True)\n\n            ind = ind + 1\n            if loop_exit:\n                break\n\n    def export_to_plot(self):\n        self.export_to_plotly()\n\n    def export_to_json(self):\n        raise NotImplementedError('export_to_json')\n","repo_name":"jazav/ProjectDashboard","sub_path":"dashboards/feature_progress_dashboard.py","file_name":"feature_progress_dashboard.py","file_ext":"py","file_size_in_byte":6724,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74627534044","text":"#################### Tetris ByHiroki ####################\r\n\r\nimport tkinter as tk\r\nimport random\r\nfrom tkinter import messagebox\r\n\r\nSIZE = 30       #ブロックのサイズ\r\nmoveX = 4       #テトロミノ表示位置（横）\r\nmoveY = 0       #テトロミノ表示位置（縦）\r\ntype = random.randint(0, 6)        #テトロミノのタイプ\r\n\r\ntimer = 800     #ゲームスピードコントロール\r\nscore = 0       #スコア\r\n\r\ncolor = [\"magenta\", \"blue\", \"cyan\", \"yellow\", \"orange\", \"red\", \"green\", \"black\", \"white\"]\r\n\r\n#テトロミノデータ\r\ntetroT = [-1, 0, 0, 0, 1, 0, 0, 1]\r\ntetroJ = [-1, 0, 0, 0, 1, 0, 1, 1]\r\ntetroI = [-1, 0, 0, 0, 1, 0, 2, 0]\r\ntetroO = [ 0, 0, 1, 0, 0, 1, 1, 1]\r\ntetroL = [-1, 0, 0, 0, 1, 0,-1, 1]\r\ntetroZ = [-1,-1, 0,-1, 0, 0, 1, 0]\r\ntetroS = [ 0, 0, 1, 0, 0, 1,-1, 1]\r\ntetro = [tetroT, tetroJ, tetroI, tetroO, tetroL, tetroZ, tetroS]\r\n\r\n#フィールドデータ\r\nfield = []\r\nfor y in range(22):\r\n    sub = []\r\n    for x in range(12):\r\n        if x==0 or x==11 or y==21 :\r\n            sub.append(8)\r\n        else :\r\n            sub.append(7)\r\n    field.append(sub)\r\n\r\n#テトロミノを表示する関数\r\ndef drawTetris():\r\n    for i in range(4):\r\n        x = (tetro[type][i*2]+moveX)*SIZE\r\n        y = (tetro[type][i*2+1]+moveY)*SIZE\r\n        can. create_rectangle(x, y, x+SIZE, y+SIZE, fill=color[type])\r\n\r\n#フィールドを表示する関数\r\ndef drawField():\r\n    for i in range(21):\r\n        for j in range(12):\r\n            outLine=0 if color[field[i+1][j]]==\"white\" else 1   #白いブロックは枠無しで表示\r\n            can.create_rectangle(j*SIZE, i*SIZE, (j+1)*SIZE, (i+1)*SIZE, fill=color[field[i+1][j]], width=outLine)\r\n\r\n#テトロミノを動かす関数\r\ndef keyPress(event):   \r\n    global moveX, moveY\r\n    afterX = moveX\r\n    afterY = moveY\r\n    afterTetro = []\r\n    afterTetro.extend(tetro[type])\r\n    if event.keysym==\"Right\" :      #右移動\r\n        afterX += 1\r\n    elif event.keysym==\"Left\" :     #左移動\r\n        afterX -= 1\r\n    elif event.keysym==\"Down\" :     #下移動\r\n        afterY += 1\r\n    elif event.keysym==\"space\" :    #右回転\r\n        afterTetro.clear()\r\n        for i in range(4):\r\n            afterTetro.append(tetro[type][i*2+1]*(-1))\r\n            afterTetro.append(tetro[type][i*2])\r\n    judge(afterX, afterY, afterTetro)   #アタリ判定関数呼び出し\r\n\r\ndef judge(afterX, afterY, afterTetro):  #アタリ判定をする関数\r\n    global moveX, moveY\r\n    result = True\r\n    for i in range(4):\r\n        x = afterTetro[i*2]+afterX\r\n        y = afterTetro[i*2+1]+afterY\r\n        if field[y+1][x]!=7 :\r\n            result = False\r\n    if result==True :\r\n        moveX = afterX\r\n        moveY = afterY\r\n        tetro[type].clear()\r\n        tetro[type].extend(afterTetro)\r\n    return result\r\n\r\ndef dropTetris():\r\n    global moveX, moveY, type, timer\r\n    afterTetro = []\r\n    afterTetro.extend(tetro[type])\r\n    result = judge(moveX, moveY+1, afterTetro)\r\n    if result==False :\r\n        for i in range(4):\r\n            x = tetro[type][i*2]+moveX\r\n            y = tetro[type][i*2+1]+moveY\r\n            field[y+1][x] = type\r\n        deleteLine()\r\n        type = random.randint(0, 6)\r\n        moveX = 4\r\n        moveY = 0\r\n    can.after(timer, dropTetris)\r\n    timer -= 2                          #落下速度コントロール\r\n    if timer<140 :\r\n        timer = 180\r\n\r\ndef deleteLine():\r\n    global score\r\n    for i in range(1, 21):\r\n        if 7 not in field[i]:\r\n            for j in range(i):\r\n                for k in range(12):\r\n                    field[i-j][k] = field[i-j-1][k]\r\n            score += 800-timer\r\n    for i in range(1, 11):\r\n        if 7 != field[1][i]:\r\n            messagebox.showinfo(\"information\", \"GAME OVER !\")\r\n            exit()\r\n\r\n####################  ゲームループ  ####################    \r\nwin = tk.Tk()\r\nwin.geometry(\"340x630\")\r\nwin.title(\"Tetris ByHiroki\")\r\ncan = tk.Canvas(win, width=12*SIZE, height=21*SIZE)\r\ncan.place(x=-10, y=0)\r\nvar = tk.StringVar()\r\nlab = tk.Label(win, textvariable=var, fg=\"blue\", bg=\"white\", font=(\"\", \"20\"))   #得点表示\r\nlab.place(x=50, y=600)\r\n\r\nwin.bind(\"<Any-KeyPress>\", keyPress)    #キープレスをバインド\r\n\r\ndef gameLoop():\r\n    can.delete(\"all\")\r\n    var.set(score)\r\n    drawField()\r\n    drawTetris()\r\n    can.after(50, gameLoop)\r\n\r\ngameLoop()\r\ndropTetris()\r\n\r\nwin.mainloop()\r\n","repo_name":"XxPandaHirokixX/TetrisSample","sub_path":"Tetris.py","file_name":"Tetris.py","file_ext":"py","file_size_in_byte":4373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"43172804961","text":"# A keras script to train a conditional variational auto-encoder (CVAE)\n# with a conditioning variable optimised via an auxillary neural network.\n# The conditioning network is further used for dimensionality reduction \n# to perform Artificial bandwidth extension using GMM regression. \n#\n# Written by Pramod Bachhav, Aug 2018\n# Contact : bachhav[at]eurecom[dot]fr, bachhavpramod[at]gmail[dot]com\n#\n#   References:\n# \n#    P.Bachhav, M. Todisco and N. Evans, \"Latent Representation Learning for Artificial \n#    Bandwidth Extension using a Conditional Variational Auto-encoder\",\n#    accepted in ICASSP 2019.\n# \n#   Acknowledgements : \n#\n#     https://github.com/twolffpiggott/autoencoders/blob/master/autoencoders.py#L34\n#     https://tiao.io/post/tutorial-on-variational-autoencoders-with-a-concise-keras-implementation/\n#\n#     https://blog.keras.io/building-autoencoders-in-keras.html\n#        - Here, note that KL loss definition has a minor mistake - \n#        - It should be KL-loss = -0.5 K.sum() .... instead of KL-loss = -0.5 K.mean()\n#     Better version can be found at => \n#     https://github.com/keras-team/keras/blob/master/examples/variational_autoencoder.py\n#\n#    A very nice understanding about Kingma's papers on VAE at \n#     http://bjlkeng.github.io/posts/variational-autoencoders/\n    \n##############################################################################################\n\nimport numpy as np\nnp_seed = 1337\nimport os\nos.environ['PYTHONHASHSEED'] = '0'\nos.sys.path.append('./../../ABE_SSAE_IS18/2_SSAE_training') # to include files HTK.p, HTKFeat.py and my_functions.py\nimport random as rn\nrn.seed(12345)\nos.environ['KERAS_BACKEND'] = 'tensorflow'\nimport keras\t\nnp.random.seed(np_seed) \nfrom keras.callbacks import ModelCheckpoint\nimport my_functions\nfrom keras.models import Model\nimport cvae\n\n\nl1 = 2 \nl2 = 2\nmodelpath='./your_models_CVAE/'\nif not os.path.exists(modelpath):\n    os.makedirs(modelpath)\n     \nfeature='LPS'\nprint('Feature used is {}'.format(feature))\n\nprint( 'Loading data...')\ndata = my_functions.load_data(l1,l2, feature)        \ninp_train, inp_dev, inp_test, op_reg_train, op_reg_dev, op_reg_test, feat_dim_X, feat_dim_Y = data\nprint('Data loaded') \nfeat_dim_X = (l1+l2+1)*feat_dim_X\n\n# =============================================================================\n# CVAE configuration\n# =============================================================================\n\nzy_dim = 10 # size of latent variable of CVAE (zy)\nzx_dim = 10 # size of conditioning variable of CVAE (zx)\n\nalpha_vae = 10\nalpha_cvae = 10\n  \nhidden_layers_enc=[512, 256]; \nhidden_layers_dec=[256, 512]; \n\nactiv = 'tanh'; act = 'tanh'\nactivations_enc =[activ,activ]; \nactivations_dec =[activ,activ,'linear'] \n\nL_enc_X = np.append(feat_dim_X,hidden_layers_enc) \nL_dec_X = np.append( zx_dim, hidden_layers_dec); L_dec_X = np.append( L_dec_X , feat_dim_X)\n\nL_enc_Y = np.append( feat_dim_Y, hidden_layers_enc); \nL_dec_Y = np.append( zy_dim + zx_dim , hidden_layers_dec); L_dec_Y = np.append( L_dec_Y , feat_dim_Y)\n\n# =============================================================================\n# training parameters\n# =============================================================================\n\npDrop = 0; BN = 'b'\nreduce_lr_factor = 0.5; min_lr = 0.00001  # parameters for callback ReduceLROnPlateau\nbs = 512 # batch_size\nshuff = True\nloss='mse'\n\nepochs = 50; epochs_cvae = 50; patience = 5; patience_cvae = 5\nLR = 0.001; optimizer = 'adam'; \nbatch_size = 512\nshuff = True; loss = 'mse'\n\noptim1 = keras.optimizers.Adam(lr=LR, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0)  # 0.87, 0.88, 0.90 decay - 0.0, bs=128\noptim2 = keras.optimizers.Adam(lr=LR, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0)  # 0.87, 0.88, 0.90 decay - 0.0, bs=128\nopt_params = 'LR='+str(LR)\n\nkernel_initializer = keras.initializers.he_normal(seed=7); init='he_n'\nkr = 0; kernel_regularizer = keras.regularizers.l2(kr)\n\n################################################################\n\nsheet_name1 = ''\nfor i in range(len(hidden_layers_enc)):\n    sheet_name1 = sheet_name1 + str(hidden_layers_enc[i])\n    if i is not len(hidden_layers_enc)-1:\n        sheet_name1 = sheet_name1+'_' \nsheet_name1 = 'ENC_' + sheet_name1 \nsheet_name2 = ''\nfor i in range(len(hidden_layers_dec)):\n    sheet_name2 = sheet_name2 + str(hidden_layers_dec[i])\n    if i is not len(hidden_layers_dec)-1:\n        sheet_name2 = sheet_name2+'_'        \nsheet_name2 = 'DEC_' + sheet_name2 \n        \n################################################################\narch = 'CVAE'     \nexpName=sheet_name1+'_'+sheet_name2+'_'+arch+'_'+feature+'_NB_LPC_'+str(int(feat_dim_X/(l1+l2+1)))+'.'+str(feat_dim_Y)+'_mem_'+str(l1)+'.'+str(l2)+'_act_'+act+'_dr='+str(pDrop)+'_BN='+str(BN)# HL+1-OL\nmodel_name = expName+'_'+optimizer+'_'+opt_params+'_bs='+str(batch_size)+'_ep='+str(epochs_cvae)+'_'+str(patience_cvae)+'_'+init+'_alpha='+str(alpha_cvae)\n\n################################################################\n\ndata_vae = inp_train, inp_dev, inp_train, inp_dev\ndata_cvae = op_reg_train, op_reg_dev, inp_train, inp_dev \n\n################################################################\nif not os.path.exists(modelpath):\n    os.makedirs(modelpath)  \npath_to_save = modelpath+model_name\nprint('Experiment setup is : '+path_to_save)\n    \ndef sampling(z_mean, z_log_var, latent_dim):\n    np.random.seed(np_seed) # for reproducibility\n    epsilon = np.random.normal( 0., 1.0, (z_mean.shape[0], latent_dim))\n    return z_mean + np.exp( z_log_var / 2) * epsilon \n\nprint('*******************************************************************')\nprint('             TRAINING A VAE for X           ')\nprint('*******************************************************************')\n\nmonitor = 'val_loss'\nreduce_lr = keras.callbacks.ReduceLROnPlateau(monitor = monitor, factor=0.5, \n                                              verbose = 1, patience = patience, min_lr = min_lr)\ncheckpointer = ModelCheckpoint(filepath = path_to_save+'_VAEx_init', verbose = 0, \n                               save_best_only = True, monitor= monitor)\ncb  = [reduce_lr, checkpointer]\n\nparameters = {'zx_dim': zx_dim, 'optimizer':optim1, 'loss':loss,\n              'epochs' : epochs, 'batch_size' : batch_size, 'shuff' : shuff,\n              'kernel_initializer':kernel_initializer, 'kernel_regularizer' :kernel_regularizer,\n              'alpha' : alpha_vae}\n\nvae_instance = cvae.VAE (parameters)\n\nprint('--------------- Initializing encoder network for X --------------- ')\ninit_encoder_ff_X = vae_instance.init_feedforward( L_enc_X , activations_enc, pDrop, BN, 'encX_last_layer' )\nprint('--------------- Initializing decoder network for X ---------------')\ninit_decoder_ff_X = vae_instance.init_feedforward( L_dec_X, activations_dec, pDrop, BN, 'reconstructed_x')\nprint('--------------- Initializing VAE for X --------------- ')\nw_encX_init = init_encoder_ff_X.get_weights()\nw_decX_init = init_decoder_ff_X.get_weights()\nvae_instance.init(init_encoder_ff_X, init_decoder_ff_X, feat_dim_X)\nprint('--------------- Training VAE for X---------------')\nencX, encX_mean, encX_var, decX, vaeX, vae_arch, encX_arch, decX_arch, encX_check = vae_instance.train (data_vae, cb) \n\n\n# Weights of best model with best validation loss are saven in (path_to_save+'_VAEx_initial') \n# load best weights in model vaeX\nw_vaeX = vaeX.get_weights()\nvaeX.load_weights(path_to_save+'_VAEx_init')\nw_vaeX_best = vaeX.get_weights()\n\n# MATLAB, yet (Oct 2018), does not support loading keras models with lambda layer. \n# Therefore, it is not possible to use the model enc_x\nencX =  Model(inputs=vaeX.input, outputs=vaeX.get_layer('VAE_lambda_z').output)\n# Save models for mean and variances of stochastic layer 'zx', separately.\nencX_mean =  Model(inputs = vaeX.input, outputs=vaeX.get_layer('VAE_z_mean').output)\nencX_var =  Model(inputs = vaeX.input, outputs=vaeX.get_layer('VAE_z_var').output)\n\n# this workaround helpful to read these models in MATLAB during ABE estimation, using importkeras add-on. \nencX_mean.save(path_to_save+'_encX_mean_init.hdf5')\nencX_var.save(path_to_save+'_encX_var_init.hdf5')\n\n\nprint('*******************************************************************')\nprint('             TRAINING A CVAE           ')\nprint('*******************************************************************')\n\nreduce_lr = keras.callbacks.ReduceLROnPlateau(monitor = monitor, factor=0.5, \n                                              verbose = 1, patience = patience_cvae, min_lr = min_lr)\ncheckpointer = ModelCheckpoint(filepath = path_to_save+'_CVAE', verbose = 0, \n                               save_best_only = True, monitor= monitor)\ncb  = [reduce_lr, checkpointer]\nparameters['optimizer'] = optim2\nparameters['alpha'] = alpha_cvae\nparameters['zy_dim'] = zy_dim\n\ncvae_instance = cvae.CVAE (parameters)\n\nprint('             Initializing encoder network          ')\ninit_encoder_ff_Y = cvae_instance.init_feedforward( L_enc_Y , activations_enc, pDrop, BN, 'encY_last_layer' )\nprint('             Initializing decoder network          ')\ninit_decoder_ff_Y = cvae_instance.init_feedforward( L_dec_Y, activations_dec, pDrop, BN, 'reconstructed_y' )\nw_decY_init = init_decoder_ff_Y.get_weights()\nw_encY_init = init_encoder_ff_Y.get_weights()\nprint('             Initializing CVAE              ')\ncvae_instance.init(init_encoder_ff_Y, init_decoder_ff_Y, encX_mean, encX_var, feat_dim_X, feat_dim_Y)\nprint('             Training CVAE              ')\nencY, decY, encX_mean_new, encX_var_new, encX_new, cvae, cvae_arch, encY_arch, decY_arch, encX_mean_arch, encX_var_arch, encX_arch = cvae_instance.train(data_cvae, cb) \ndecY = []\n\n\n# load best weights in 'cvae'\ncvae.load_weights(path_to_save+'_CVAE')\n\n#encY_mean_new =  Model(inputs = cvae.input, outputs = cvae.get_layer('CVAE_z_mean').output)\n#encY_var_new =  Model(inputs = cvae.input, outputs = cvae.get_layer('CVAE_z_var').output)\n\n# Dec is last 'sequential model of' cvae\ndecY = cvae.get_layer(index = len(cvae.layers)-1)\n\n# zx_mean and zx_var should be 6th and 7th sequential layers\nencX_mean_best = cvae.get_layer(index = 5)\nencX_mean_best.summary()\nencX_var_best = cvae.get_layer(index = 6)\nencX_var_best.summary()\nencX_best =  Model(inputs = cvae.input[1], outputs = cvae.get_layer('CVAE_zx').output)\nencX_best.summary()\n\nencY_mean_best =  Model(inputs = cvae.input[0], outputs = cvae.get_layer('CVAE_z_mean').output)\nencY_mean_best.summary()\nencY_mean_best.input\n\nencY_var_best =  Model(inputs = cvae.input[0], outputs = cvae.get_layer('CVAE_z_var').output)\nencY_var_best.summary()\nencY_var_best.input\n\n# Save models\nencX_best.save(path_to_save+'_encX.hdf5')\nencX_mean_best.save(path_to_save+'_encX_mean.hdf5')\nencX_var_best.save(path_to_save+'_encX_var.hdf5')\ndecY.save(path_to_save+'_decY.hdf5')\n\n\n# =============================================================================\n# Evaluation of the model (testing phase - where zy is sampled from prior distribution) \n# on train, validation/developement and test  dataset\n# =============================================================================\n\n# Sample 'zy' from Normal distribution  during estimation phase\nnp.random.seed(11); zy_train = np.random.normal( 0, 1.0, (inp_train.shape[0], zy_dim))     \nnp.random.seed(12); zy_dev = np.random.normal( 0, 1.0, (inp_dev.shape[0], zy_dim))     \nnp.random.seed(13); zy_test = np.random.normal( 0, 1.0, (inp_test.shape[0], zy_dim))     \n \n\nprint('*******************************************************************')\nprint('             EVALUATION - with best weights for encX and decY  ')\nprint('*******************************************************************')\n\nmeans_train = encX_mean_best.predict(inp_train) \nlog_vars_train = encX_var_best.predict(inp_train) \nzx_train = sampling(means_train, log_vars_train, zx_dim)\n\nop_reg_train_est0 = decY.predict( np.concatenate((zy_train, zx_train), axis=-1) )  \nscore_pred_model0 = np.append(np.mean(np.square(op_reg_train - op_reg_train_est0)), np.mean(np.square(op_reg_train[:,0]-op_reg_train_est0[:,0])))\nprint (\"Train score : {0:.3f},,{1:.7f}\".format(score_pred_model0[0],score_pred_model0[1]))\n\n# ---------------------------------------------------------------------------\n\nmeans_dev = encX_mean_new.predict(inp_dev) \nlog_vars_dev = encX_var_new.predict(inp_dev) \nzx_dev = sampling(means_dev, log_vars_dev, zx_dim)\n\nop_reg_dev_est1 = decY.predict( np.concatenate((zy_dev, zx_dev), axis=-1) )  \nscore_pred_model1 = np.append(np.mean(np.square(op_reg_dev - op_reg_dev_est1)), np.mean(np.square(op_reg_dev[:,0]-op_reg_dev_est1[:,0])))\nprint (\"Dev score : {0:.3f},,{1:.7f}\".format(score_pred_model1[0],score_pred_model1[1]))\n\n# ---------------------------------------------------------------------------\n\nmeans_test = encX_mean_new.predict(inp_test) \nlog_vars_test = encX_var_new.predict(inp_test) \nzx_test = sampling(means_test, log_vars_test, zx_dim)\n\nop_reg_est2 = decY.predict( np.concatenate((zy_test, zx_test), axis=-1) )  \nscore_pred_model2 = np.append(np.mean(np.square(op_reg_test - op_reg_est2)), np.mean(np.square(op_reg_test[:,0]-op_reg_est2[:,0])))\nprint (\"Test score : {0:.3f},,{1:.7f}\".format(score_pred_model2[0],score_pred_model2[1]))\n\n  \n##################################################################\n#\nprint('----------- Evaluation finished ----------')  \nprint('-----------Experiment setup is : ' +path_to_save + '-----------')\n\n#################################################################\n\n\n","repo_name":"bachhavpramod/bandwidth_extension","sub_path":"ABE_CVAE_ICASSP19/2_CVAE_training/Train_CVAE.py","file_name":"Train_CVAE.py","file_ext":"py","file_size_in_byte":13429,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"86"}
+{"seq_id":"42000406010","text":"import sys\nimport os\nfrom subprocess import Popen, PIPE\nimport psutil\nfrom builtins import super\nimport datetime\nfrom PyQt5 import QtWidgets, QtCore\nimport pygui\n\n\nclass ExampleApp(QtWidgets.QMainWindow, pygui.Ui_MainWindow):\n    def __init__(self):\n        super().__init__()\n        self.setupUi(self)\n        self.file_types = {\n            \".js\": \"node\"\n        }\n        self.processes = []\n        self.default_dir = \"C:/Users/ilya1/OneDrive/Desktop/Automation Selenium Project/Tests\"\n        self.current_dir = self.default_dir\n        self.logger_output_file_full_path = \"\"\n\n        # Event Handlers\n        self.select_folder_btn.clicked.connect(self.browse_folder)\n        self.run_btn.clicked.connect(self.run_checked_tests)\n        self.stop_btn.clicked.connect(self.stop_all_tests)\n        self.clear_btn.clicked.connect(self.clear)\n        self.log_btn.clicked.connect(self.print_logs)\n        self.select_all_btn.clicked.connect(self.select_all)\n        self.unselect_all_btn.clicked.connect(self.unselect_all)\n\n        # Styles\n        self.treeWidget.setHeaderLabels(['Files'])\n\n    def select_all(self):\n        for file in self.get_tree_children():\n            file.setCheckState(0, QtCore.Qt.Checked)\n\n    def unselect_all(self):\n        for file in self.get_tree_children():\n            file.setCheckState(0, QtCore.Qt.Unchecked)\n\n    def browse_folder(self):\n        self.current_dir = QtWidgets.QFileDialog.getExistingDirectory(self, \"Select a folder\",\n                                                                      directory=self.default_dir)\n        if not self.current_dir:\n            return\n\n        self.treeWidget.clear()\n        allowed_files = filter(lambda file_name: self.is_allowed_file(file_name), os.listdir(self.current_dir))\n        for allowed_file in allowed_files:\n            QtWidgets.QTreeWidgetItem(self.treeWidget, [allowed_file]).setCheckState(0, QtCore.Qt.Unchecked)\n\n    def is_allowed_file(self, file_name):\n        return not self.file_types or len(list(filter(lambda eof: file_name.endswith(eof), self.file_types))) > 0\n\n    def get_tree_children(self):\n        root = self.treeWidget.invisibleRootItem()\n        child_count = root.childCount()\n        return list(map(lambda i: root.child(i), range(child_count)))\n\n    def run_file_with(self, file_name):\n        if not self.file_types:\n            return \"\"\n\n        for eof in self.file_types:\n            if file_name.endswith(eof):\n                return self.file_types[eof]\n        return \"\"\n\n    def kill(self, pid):\n        try:\n            process = psutil.Process(pid)\n            for process_child in process.children(recursive=True):\n                process_child.kill()\n            process.kill()\n        except Exception as e:\n            print(f'Got exception: {e}')\n\n    def run_checked_tests(self):\n        checked_files = list(map(lambda file: file.text(0),\n                                 filter(lambda file: file.checkState(0) == QtCore.Qt.Checked,\n                                        self.get_tree_children())))\n        if not checked_files:\n            return\n\n        run_command = \" && \".join(\n            list(map(lambda file_name: f\"{self.run_file_with(file_name)} {file_name}\", checked_files)))\n        self.logger_output_file_full_path = f\"{self.current_dir}/log/{datetime.datetime.now().strftime('%H-%M-%S %Y-%m-%d')}.log \"\n\n        with open(self.logger_output_file_full_path, \"w\") as logger_output_file:\n            process = Popen(run_command, stdout=logger_output_file, stderr=logger_output_file, stdin=PIPE,\n                            shell=True, cwd=self.current_dir, universal_newlines=True, start_new_session=True)\n\n            self.processes.append({\"id\": process.pid, \"process\": process})\n            self.run_btn.setEnabled(False)\n\n    def print_logs(self):\n        if not self.logger_output_file_full_path:\n            return\n        with open(self.logger_output_file_full_path, \"r\") as logger_output_file:\n            log_content = \" \".join(logger_output_file.read().split(\"\\n\"))\n            self.logBrowser.setText(\"\")  # log to screen\n            self.logBrowser.setText(log_content)  # log to screen\n\n    def stop_all_tests(self):\n        for p in self.processes:\n            self.kill(p[\"id\"])\n        self.processes = []\n        self.run_btn.setEnabled(True)\n\n    def clear(self):\n        self.logBrowser.clear()\n\n\ndef main():\n    app = QtWidgets.QApplication(sys.argv)\n    window = ExampleApp()\n    window.show()\n    app.exec_()\n\n\nif __name__ == '__main__':  # Если мы запускаем файл напрямую, а не импортируем\n    main()  # то запускаем функцию main()\n","repo_name":"ilya1200/gui-project","sub_path":"start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":4677,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"14314861507","text":"from collections import defaultdict\nimport pprint\nfrom typing import List\n\n\nclass Solution:\n    def gardenNoAdj(self, n: int, paths: List[List[int]]) -> List[int]:\n        m = defaultdict(list[int])\n        for u, v in paths:\n            m[u].append(v)\n            m[v].append(u)\n        ans = [0]*n\n        pprint.pprint(m)\n        for u in range(1, n + 1):\n            colors = set(range(1, 5)) - set(ans[v - 1] for v in m[u])\n            ans[u - 1] = colors.pop()\n            # pprint.pprint(ans)\n        return ans\n\nif __name__ == '__main__':\n    print(Solution().gardenNoAdj(n = 3, paths = [[1,2],[2,3],[3,1]]))","repo_name":"mole828/leetcode","sub_path":"problems/p1042.py","file_name":"p1042.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"9908609171","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jan 15 17:32:43 2021\n\n@author: vicente\n\nEste algoritmo sirve para aplicar isolation forest con un rango de numero de arboles\nse obtiene una grafica de el numero ed anomlias encontradas vs el numero de arboles\nel fin es poder hacer un analisis para escoger el numero de arboles ideal para el proyecto\n\"\"\"\nimport pandas as pd # data processing\nimport warnings\n#import os\nfrom sklearn.ensemble import IsolationForest\nimport matplotlib.pyplot as plt\n\nwarnings.filterwarnings('ignore')\n#print(os.listdir(\"../Tesis\"))\n\n#leer dataset de huellas digitales\ndf=pd.read_csv(\"../Tesis/fingerprints.csv\")\ndf.head()\nmetrics_df=df\n\nmetrics_df.columns\nto_model_columns=metrics_df.columns[3:18]\n\n#clf=IsolationForest(n_estimators=100, max_samples='auto', contamination=float(.12),\n                    #max_features=1.0, bootstrap=False, n_jobs=-1, random_state=42, \n                    #verbose=0)\nanomalias=[]\nestimador=[]   \n#estimar modelo para un rango de 50 a 400 arboles \nfor i in range(5,40): \n    n_estimator=i*10\n    clf=IsolationForest(n_estimators=n_estimator, max_samples='auto', contamination='auto',\n                        max_features=1.0, bootstrap=False, n_jobs=-1, random_state=42, \n                        verbose=0)\n    clf.fit(metrics_df[to_model_columns])\n    pred= clf.predict(metrics_df[to_model_columns])\n    metrics_df['anomaly']=pred\n    outliers=metrics_df.loc[metrics_df['anomaly']==-1]\n    outlier_index=list(outliers.index)\n    #print(outlier_index)\n    #Find the number of anomalies and normal points here points classified -1 are anomalous\n    a=metrics_df['anomaly'].value_counts()\n    estimador.append(n_estimator)\n    anomalias.append(a.values[1])\n    print(n_estimator)\n    print(metrics_df['anomaly'].value_counts())\n    \n#graficar numero de anomlias vs numero de arboles\nplt.figure()\nplt.title(\"Número de Anomalías Encontradas\")\nplt.xlabel(\"Numero de Árboles\")\nplt.ylabel(\"Cantidad de Anomalías\")\nplt.plot(estimador,anomalias)\nplt.show()\n","repo_name":"ViQuezada/Bootnet_Detection_Modules","sub_path":"Insolation Forest.py","file_name":"Insolation Forest.py","file_ext":"py","file_size_in_byte":2031,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"36438774635","text":"import tensorflow as tf\nimport os\n\n# 降低输出log等级\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'\n# tf1.X\ntf.compat.v1.disable_eager_execution()\n\na = tf.constant(2, name='a')\nb = tf.constant(3, name='b')\nx = tf.add(a, b, name='add')\n\nwriter = tf.compat.v1.summary.FileWriter('./graphs', tf.compat.v1.get_default_graph())\nwith tf.compat.v1.Session() as sess:\n    print(sess.run(x))\nwriter.close()\n\n# Constant\n# tf.constant(value,dtype=None,shape=None,name='Const',verify_shape=False)\na = tf.constant([2, 2], name='a')\nb = tf.constant([[2, 2], [3, 2]], name='b')\nx = tf.multiply(a, b, name='mul')\n\nwith tf.compat.v1.Session() as sess:\n    print(sess.run(x))\n\n# 0填充张量\n# tf.zeros(shape, dtype=tf.float32, name=None)\n# 将输入的张量中的所有元素全部置为0\n# tf.zeros_like(input_tensor, dtype=None, name=None, optimize=True)\n# 张量每行大小要一样\ninput_tensor = tf.constant([[1, 2], [2, 3], [4, 6]])\na = tf.zeros([2, 3], tf.int32)\nc = tf.zeros_like(input_tensor)\n\nwith tf.compat.v1.Session() as sess:\n    print(\"将元素置为0\")\n    print(sess.run(c))\n\n# 将其中的元素置为1,意义同上\n# tf.ones(shape, dtype=tf.float32, name=None)\n# tf.ones_like(input_tensor, dtype=None, name=None, optimize=True)\na = tf.ones([2, 3], dtype=tf.int32)\nb = tf.ones_like(input_tensor)\nwith tf.compat.v1.Session() as sess:\n    print(\"将元素置为1\")\n    print(sess.run(a))\n    print(sess.run(b))\n\n# 用指定值填充新建张量\n# tf.fill(dims, value, name=None)\na = tf.fill([2, 3], 8)\nwith tf.compat.v1.Session() as sess:\n    print(\"填充指定值\")\n    print(sess.run(a))\n","repo_name":"zyxeeker/CS-20","sub_path":"02_TF_Op.py","file_name":"02_TF_Op.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"44242287039","text":"class Depend:\n    def process(self, commandArr, graph, edges):\n        commandArr = self.sanitize(commandArr)\n        parent = commandArr[0]\n        for i in range(1, len(commandArr)):\n            package = commandArr[i]\n            if package != \" \":\n                graph[parent].append(package)\n                edges[package] += 1\n\n        print(\"\".join(commandArr))\n        return [graph, edges, \"DEPEND \"+\" \".join(commandArr)+\"\\n\"]\n\n    def sanitize(self, arr):\n        i = 0\n        while arr[i] == \" \":\n            i += 1\n\n        return arr[i:]","repo_name":"tarun29061990/system_dependencies","sub_path":"src/commands/depend.py","file_name":"depend.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"12653794583","text":"\"\"\"\nModule: read JSON file\nAuthor: Max\nDate: 30/05/2022\n\nDescription: reads JSON file.\n\"\"\"\n\nimport pandas as pd\nfrom utils.read_data import ReadData\nfrom pathlib import Path\n\nroot = Path('/')\n\n\nclass JSONReadData(ReadData):\n    \"\"\"\n    Class to read JSON file.\n\n    Attribute:\n    ----------\n\n    Methods:\n    --------\n\n\n\n    \"\"\"\n    def __init__(self, path, file_name):\n        super().__init__(path, file_name)\n\n    def read_json_data(self):\n        print(\"Start reading source data: read_json_data()\")\n        self.data = pd.read_json(root / self.path / self.file_name)\n","repo_name":"maxkad/code-20220530-maxk","sub_path":"utils/read_json_data.py","file_name":"read_json_data.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74246877725","text":"import json\nimport os\nfrom pprint import pprint\nimport time\nimport requests\nfrom tqdm import tqdm\n\nDAY_1 = 86400\nVERBOSE = False\nINTERACTIVE = False\nDATA_DIR = \"data\"\n\n\ndef read_constituents():\n    with open(\"constituents.csv\", \"r\") as f:\n        lines = f.readlines()\n    lines = [line.strip().split(\",\") for line in lines]\n    return lines\n\n\ndef fetch_data(start, end, candle_size):\n    print(\"Reading list of symbols...\")\n    stocks = read_constituents()\n\n    if candle_size == \"D\":\n        URL = \"https://query1.finance.yahoo.com/v7/finance/download/{symbol}?period1={start}&period2={end}&interval=1d&events=history&includeAdjustedClose=true\"\n        fetch_daily_data(start, end, stocks, URL)\n    elif candle_size == \"H\":\n        # get access key from secerts.json\n        try:\n            with open(\"secrets.json\", \"r\") as f:\n                secrets = f.read()\n            secrets = json.loads(secrets)\n            api_key = secrets[\"api_key\"]\n        except FileNotFoundError:\n            print(\"Could not find secrets.json file\")\n            return\n        except KeyError:\n            print(\"Could not find access_key in secrets.json\")\n            return\n\n        URL = \"https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=30min&outputsize=full&apikey={api_key}\"\n        print(\"Fetching data...\")\n        num = 1\n        for stock in tqdm(stocks[1:]):\n            symbol = stock[0]\n            formattedURL = URL.format(symbol=symbol, api_key=api_key)\n            headers = {\n                \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36\"\n            }\n            r = requests.get(formattedURL, headers=headers)\n            # Create data folder if it doesn't exist\n            if not os.path.exists(DATA_DIR):\n                os.makedirs(DATA_DIR)\n\n            if not os.path.exists(DATA_DIR + \"/intraday\"):\n                os.makedirs(DATA_DIR + \"/intraday\")\n\n            with open(DATA_DIR + \"/\" + \"intraday\" + \"/\" + symbol + \".csv\", \"w\") as f:\n                f.write(r.text)\n\n            num += 1\n            if num % 75 == 0:\n                time.sleep(60)\n\n\ndef fetch_daily_data(start, end, stocks, URL):\n    print(\"Fetching daily data for {} stocks...\".format(len(stocks)))\n    for stock in tqdm(stocks[1:]):\n        symbol = stock[0]\n        formattedURL = URL.format(symbol=symbol, start=start, end=end)\n        headers = {\n            \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36\"\n        }\n        r = requests.get(formattedURL, headers=headers)\n        # Create data folder if it doesn't exist\n        if not os.path.exists(DATA_DIR):\n            os.makedirs(DATA_DIR)\n            os.makedirs(DATA_DIR + \"/daily\")\n        with open(DATA_DIR + \"/\" + \"daily\" + \"/\" + symbol + \".csv\", \"w\") as f:\n            f.write(r.text)\n\n        # Rate limiting\n        time.sleep(2)\n\n\ndef backtest(win_percent, loss_percent, threshold):\n    # starting from the second day\n    # list all of the stocks that opened 1% or more above the previous close\n    # for each stock, calculate the daily return\n\n    print(\"Getting list of symbols...\")\n    stocks = read_constituents()\n    # read data from stocks\n    daily_data = {}\n    hourly_data = {}\n    for stock in tqdm(stocks[1:]):\n        symbol = stock[0]\n        daily_data[symbol] = {}\n        with open(\"data/daily/\" + symbol + \".csv\", \"r\") as f:\n            lines = f.readlines()\n        stock_data = [line.strip().split(\",\") for line in lines]\n\n        for line in stock_data[1:]:\n            if \"null\" in line:\n                continue\n            daily_data[symbol][line[0]] = {\n                \"open\": float(line[1]),\n                \"high\": float(line[2]),\n                \"low\": float(line[3]),\n                \"close\": float(line[4]),\n                \"adj_close\": float(line[5]),\n                \"volume\": float(line[6]),\n            }\n\n        if not daily_data.get(symbol):\n            continue\n\n        # calculate the total volume of the stock\n        total_volume = 0\n        for value in daily_data[symbol].values():\n            total_volume += value[\"volume\"]\n\n        # calculate the average daily volume\n        average_volume = total_volume / len(daily_data[symbol])\n\n        # if average volume is below 5 million then skip\n        if average_volume < 7500000:\n            daily_data.pop(symbol)\n            continue\n\n        with open(\"data/intraday/\" + symbol + \".csv\", \"r\") as f:\n            data = f.read()\n        data = json.loads(data)\n        data = data.get(\"Time Series (30min)\")\n        if not data:\n            continue\n\n        hourly_data[symbol] = {}\n        for timestamp in data:\n            if hourly_data[symbol].get(timestamp[:10]) is None:\n                hourly_data[symbol][timestamp[:10]] = {}\n            hourly_data[symbol][timestamp[:10]][timestamp[11:]] = {\n                \"open\": float(data[timestamp][\"1. open\"]),\n                \"high\": float(data[timestamp][\"2. high\"]),\n                \"low\": float(data[timestamp][\"3. low\"]),\n                \"close\": float(data[timestamp][\"4. close\"]),\n                \"volume\": float(data[timestamp][\"5. volume\"]),\n            }\n\n    stocks = [[key] for key in daily_data.keys()]\n    print(stocks, len(stocks))\n\n    # get first start date available\n    symbol = stocks[0][0]\n    start_date = min(hourly_data[symbol].keys())\n    end_date = max(hourly_data[symbol].keys())\n\n    start = convert_to_timestamp(start_date)\n    end = convert_to_timestamp(end_date)\n\n    results = []\n\n    print(\"Running backtest...\")\n    pbar = tqdm(total=int((start - end) / DAY_1))\n    num_of_days = 0\n    while start < end:\n        num_of_days += 1\n        #  add a day\n        start += DAY_1\n        start_date = convert_to_date(start)\n        # list each stock that opened 1% or more above the previous close\n        horses = get_horses(stocks, daily_data, start_date)\n        # for each horse, calclate current current days return\n        calculate_return(daily_data, hourly_data, start_date, horses)\n        # for each horse, calculate the total drawdown\n        calculate_drawdown(daily_data, start_date, horses)\n        # for each horse, calculate the first hour's return\n        calculate_first_hour(start_date, hourly_data, horses)\n\n        potential_horses = get_potential_horses(\n            start_date, hourly_data, horses, threshold\n        )\n        winning_horses = get_winning_horses(\n            win_percent,\n            loss_percent,\n            daily_data,\n            hourly_data,\n            start_date,\n            potential_horses,\n        )\n\n        # calculate total gain of winning horses\n        total_return = 0\n        for horse in winning_horses:\n            total_return += daily_data[horse][start_date][\"return\"]\n        # calculate average return\n        average_return = total_return / len(winning_horses) if winning_horses else 0\n        losing_horses = list(set(potential_horses) - set(winning_horses))\n\n        # calculate total loss of losing horses\n        total_loss = 0\n        for horse in losing_horses:\n            total_loss += daily_data[horse][start_date][\"return\"]\n        # calculate average loss\n        average_loss = total_loss / len(losing_horses) if losing_horses else 0\n\n        # print the winning horses\n        if VERBOSE:\n            print_details(\n                win_percent,\n                daily_data,\n                hourly_data,\n                start_date,\n                horses,\n                potential_horses,\n                winning_horses,\n                average_return,\n                threshold,\n            )\n            print(\"-\" * 20)\n\n        # if there were horses and some won, add to results\n        if potential_horses and winning_horses:\n            result = \"win\"\n        elif potential_horses and not winning_horses:\n            result = \"loss\"\n        else:\n            result = \"no_horses\"\n\n        results.append(\n            {\n                \"result\": result,\n                \"num_of_winning_horses\": len(winning_horses),\n                \"percent_of_winners\": len(winning_horses) / len(potential_horses)\n                if potential_horses\n                else 100,\n                \"average_return_of_winners\": average_return,\n                \"average_return_of_losers\": average_loss,\n                \"num_of_losing_horses\": len(losing_horses),\n            }\n        )\n\n        if INTERACTIVE:\n            # wait for user to continue\n            input(\"Press Enter to continue...\")\n        else:\n            pbar.update(1)\n    pbar.close()\n\n    # for each day that there were horses, how many days had winning horses?\n    print(\"-\" * 20)\n    print(\"Results:\")\n    print(\"From: {}\".format(min(hourly_data[symbol].keys())))\n    print(\"To: {}\".format(end_date))\n\n    potential_days = sum([1 for result in results if result[\"result\"] != \"no_horses\"])\n    percent_of_days_with_potential_horses = round(\n        (potential_days / len(results)) * 100, 2\n    )\n    print(\n        \"Percent of days with potential horses: {}%\".format(\n            percent_of_days_with_potential_horses\n        )\n    )\n    winning_days = sum([1 for result in results if result[\"result\"] == \"win\"])\n    percent_of_days_with_winning_horses = round(\n        (winning_days / potential_days) * 100, 2\n    )\n    print(\n        \"Percent of potential days with winning horses: {}%\".format(\n            percent_of_days_with_winning_horses\n        )\n    )\n\n    average_daily_percent_of_winning_horses = round(\n        (\n            sum(\n                [\n                    results[\"percent_of_winners\"]\n                    for results in results\n                    if results[\"result\"] == \"win\"\n                ]\n            )\n            / potential_days\n        ),\n        2,\n    )\n    print(\n        \"Average daily percent of winning horses: {}%\".format(\n            average_daily_percent_of_winning_horses * 100\n        )\n    )\n\n    print(\"-\" * 20)\n\n    average_return_of_winning_horses = sum(\n        [\n            result[\"average_return_of_winners\"]\n            for result in results\n            if result[\"result\"] == \"win\"\n        ]\n    ) / sum(\n        [\n            result[\"num_of_winning_horses\"]\n            for result in results\n            if result[\"result\"] == \"win\"\n        ]\n    )\n    average_return_of_losing_horses = sum(\n        [\n            result[\"average_return_of_losers\"]\n            for result in results\n            if result[\"result\"] != \"no_horses\"\n        ]\n    ) / sum(\n        [\n            result[\"num_of_losing_horses\"]\n            for result in results\n            if result[\"result\"] != \"no_horses\"\n        ]\n    )\n    print(\n        \"Average return of winning horses: {}%\".format(average_return_of_winning_horses)\n    )\n    print(\n        \"Average return of losing horses: {}%\".format(average_return_of_losing_horses)\n    )\n\n    # TODO: calculate win rate of how many times a winning horse actually won ( stocks that go up 1% in the first hour and then go up 4% by the rest of the day)\n    print(\"-\" * 20)\n\n\ndef print_details(\n    win_percent,\n    data,\n    hourly_data,\n    start_date,\n    horses,\n    potential_horses,\n    winning_horses,\n    average_return,\n    threshold,\n):\n    print(start_date)\n    print(\"-\" * 20)\n    if horses:\n        print(\"Stocks up by 1% in premarket: ({}/{})\".format(len(horses), len(horses)))\n        for horse in horses:\n            if start_date not in data[horse]:\n                continue\n            print(horse + \": \" + str(data[horse][start_date][\"preMarket\"]) + \" %\")\n    else:\n        print(\"No stocks up by 1% in premarket\")\n\n    if potential_horses:\n        print(\n            \"Stocks up by {}% in the first hour: ({}/{})\".format(\n                threshold, len(potential_horses), len(horses)\n            )\n        )\n        for horse in potential_horses:\n            if start_date not in data[horse]:\n                continue\n            print(\n                horse\n                + \": \"\n                + str(hourly_data[horse][start_date][\"first_hour_return\"])\n                + \" %\"\n            )\n    else:\n        print(\"No stocks up by {}% in the first hour\".format(threshold))\n    if winning_horses:\n        print(\n            f\"Stocks went up {win_percent}% after opening: ({len(winning_horses)}/{len(horses)})\"\n        )\n        for horse in winning_horses:\n            if start_date not in data[horse]:\n                continue\n            print(horse + \": \" + str(data[horse][start_date].get(\"return\")) + \" %\")\n        print(\"Average return of winning horses: {}%\".format(average_return))\n    else:\n        print(f\"No stocks went up {win_percent}% after opening\")\n\n\ndef get_potential_horses(start_date, hourly_data, horses, threshold):\n    potential_horses = []\n    for horse in horses:\n        if horse not in hourly_data:\n            continue\n        if start_date not in hourly_data[horse]:\n            continue\n        if hourly_data[horse][start_date][\"first_hour_return\"] > threshold:\n            potential_horses.append(horse)\n    return potential_horses\n\n\ndef get_winning_horses(\n    win_percent, loss_percent, daily_data, hourly_data, start_date, horses\n):\n    winning_horses = []\n    for horse in horses:\n        if horse not in hourly_data:\n            continue\n        if start_date not in daily_data[horse]:\n            continue\n        if (\n            daily_data[horse][start_date][\"return\"] > win_percent\n            and daily_data[horse][start_date][\"drawdown\"] < loss_percent\n        ):\n            winning_horses.append(horse)\n    return winning_horses\n\n\ndef calculate_drawdown(data, start_date, horses):\n    for horse in horses:\n        if start_date not in data[horse]:\n            continue\n        current_open = data[horse][start_date][\"open\"]\n        current_low = data[horse][start_date][\"low\"]\n        current_drawdown = ((current_open - current_low) / current_open) * 100\n        data[horse][start_date][\"drawdown\"] = round(current_drawdown, 2)\n\n\ndef calculate_return(data, hourly_data, start_date, horses):\n    for horse in horses:\n        if start_date not in data[horse]:\n            continue\n        if horse not in hourly_data:\n            continue\n        initial_price = hourly_data[horse][start_date][\"10:30:00\"][\"open\"]\n        close = hourly_data[horse][start_date][\"16:00:00\"][\"close\"]\n        current_return = ((close - initial_price) / initial_price) * 100\n        data[horse][start_date][\"return\"] = round(current_return, 2)\n\n\ndef calculate_first_hour(date, hourly_data, horses):\n    for horse in horses:\n        if horse not in hourly_data:\n            continue\n        if date not in hourly_data[horse]:\n            continue\n        open = hourly_data[horse][date][\"10:00:00\"][\"open\"]\n        close = hourly_data[horse][date][\"10:00:00\"][\"close\"]\n        first_hour_return = ((close - open) / open) * 100\n        hourly_data[horse][date][\"first_hour_return\"] = round(first_hour_return, 2)\n\n\ndef get_horses(stocks, data, start_date):\n    horses = []\n    for stock in stocks[1:]:\n        symbol = stock[0]\n        if start_date not in data[symbol]:\n            continue\n            # calculate the daily return\n        start_timestamp = convert_to_timestamp(start_date)\n        yesterday_timestamp = start_timestamp - DAY_1\n        yesterday_date = convert_to_date(yesterday_timestamp)\n        if yesterday_date in data[symbol]:\n            previous_close = data[symbol][yesterday_date][\"close\"]\n            current_open = data[symbol][start_date][\"open\"]\n            if (\n                current_open > previous_close * 1.01\n                and current_open < previous_close * 1.04\n            ):\n                preMarket = ((current_open - previous_close) / previous_close) * 100\n                data[symbol][start_date][\"preMarket\"] = round(preMarket, 2)\n                horses.append(symbol)\n\n    return horses\n\n\ndef get_dates():\n    start_date = input(f\"Please enter the start date (YYYY-MM-DD): ({default_start})\")\n    if not start_date:\n        start_date = default_start\n    # Ask for end date\n    end_date = input(f\"Please enter the end date (YYYY-MM-DD): ({default_end})\")\n    if not end_date:\n        end_date = default_end\n    return start_date, end_date\n\n\ndef convert_to_timestamp(date):\n    return int(time.mktime(time.strptime(date, \"%Y-%m-%d\")))\n\n\ndef convert_to_date(timestamp):\n    return time.strftime(\"%Y-%m-%d\", time.localtime(timestamp))\n\n\nwhile True:\n    default_start = \"2017-03-24\"\n    default_end = \"2022-03-25\"\n    # Ask user if they want to backtest or get data\n    print(\"What would you like to do?\")\n    print(\"1. Get data\")\n    print(\"2. Backtest\")\n    print(\"3. Exit\")\n\n    # get user input\n    user_input = input(\"Please enter your choice: \")\n\n    if user_input == \"1\":\n        # Ask for dates\n        start_date, end_date = get_dates()\n        # convert to timestamp\n        start_timestamp = convert_to_timestamp(start_date)\n        end_timestamp = convert_to_timestamp(end_date)\n\n        # ask for candle size\n        candle_size = input(\"Please enter the candle size (D, H): \")\n        if not candle_size:\n            candle_size = \"D\"\n\n        fetch_data(start_timestamp, end_timestamp, candle_size)\n        print(\"Done!\")\n\n    elif user_input == \"2\":\n        # check if data directory has at least one file\n        if not os.listdir(DATA_DIR + \"/\" + \"daily\"):\n            print(\"No data found! Please fetch data first\")\n        # Get win percent and loss percent\n        win_percent = input(\"Please enter the win percent: (5) \")\n        if not win_percent:\n            win_percent = 5\n        else:\n            win_percent = int(win_percent)\n        loss_percent = input(\"Please enter the loss percent: (2) \")\n        if not loss_percent:\n            loss_percent = 2\n        else:\n            loss_percent = int(loss_percent)\n        threshold = input(\"Please enter the first hour threshold: (2) \")\n        if not threshold:\n            threshold = 2\n        else:\n            threshold = int(threshold)\n\n        # Ask for verbosity level\n        verbose = input(\"Display daily results? (y/n): (n) \")\n        VERBOSE = verbose == \"y\"\n\n        # Ask for interactive mode\n        interactive = input(\"Interactive mode? (y/n): (n) \")\n        INTERACTIVE = interactive == \"y\"\n\n        backtest(win_percent, loss_percent, threshold)\n\n    elif user_input == \"3\":\n        print(\"Goodbye!\")\n        break\n\n# Current analysis:\n\n# if there are stocks up by 1% in premarket, there's a 33% chance that one of them will go up 5% on the day, without going down by 2%\n\n# TODO:\n\"\"\"\n1. Of the pre-market stocks, which ones go up by 1% in the first hour?\n    Needs:\n        - algo to find horses that go up 1% in the first hour\n\"\"\"\n","repo_name":"jdriscoll98/trading","sub_path":"HorseRacing.py","file_name":"HorseRacing.py","file_ext":"py","file_size_in_byte":18786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34075571974","text":"#!/usr/bin/env python3\n\nimport asyncio\nimport aiohttp.web\nimport typing\nimport json\nimport logging\nimport collections\n\nModel = collections.namedtuple('Model', 'queues entries')\nModels: typing.Dict[str, Model] = {}\nlogger = logging.getLogger('sse')\n\nclass ServerSentEventResponse (aiohttp.web.StreamResponse):\n    def __init__(self, name):\n        super().__init__()\n        self.name = name\n        self.headers['Cache-Control'] = 'no-store'\n        self.headers['Content-Type'] = 'text/event-stream'\n        self.headers['Access-Control-Allow-Origin'] = '*'\n\n    async def send(self, data):\n        logger.info('[Sending.|%s] %s', self.name, data)\n        return await self.write(b'data: ' + data + b'\\n\\n')\n\nasync def handle_send(request):\n    name = request.match_info['name']\n    checkModel(name)\n\n    logger.info('[Listener|%s] %s', name, request.remote)\n    sse = ServerSentEventResponse(name)\n    await sse.prepare(request)\n\n    for entry in Models[name].entries:\n        await sse.send(entry)\n    queue = asyncio.Queue()\n    Models[name].queues.append(queue)\n    while True:\n        entry = await queue.get()\n        await sse.send(entry)\n        queue.task_done()\n\nasync def handle_receive(request):\n    name = request.match_info['name']\n    checkModel(name)\n\n    entry = await request.content.read()\n    if entry == b'$flush$':\n        Models[name].entries.clear()\n        logger.info('[Flushing|%s]', name)\n        resp = aiohttp.web.Response(text=json.dumps({\"result\": \"flush\"}))\n    else:\n        logger.info('[Received|%s] %s', name, entry)\n        new_entry(name, entry)\n        resp = aiohttp.web.Response(text=json.dumps({\"result\": \"success\", \"length\": len(entry)}))\n    resp.headers['Access-Control-Allow-Origin'] = '*'\n    return resp\n\ndef new_entry(name, entry):\n    for queue in Models[name].queues:\n        queue.put_nowait(entry)\n    Models[name].entries.append(entry)\n\ndef checkModel(name):\n    global Models\n    if name not in Models:\n        Models[name] = Model([], [])\n\n\ndef main():\n    logHandler = logging.StreamHandler()\n    logHandler.setFormatter(logging.Formatter('%(asctime)s %(message)s'))\n    logHandler.setLevel(logging.INFO)\n    logger.setLevel(logging.INFO)\n    logger.addHandler(logHandler)\n    app = aiohttp.web.Application()\n    app.router.add_get('/{name}', handle_send)\n    app.router.add_post('/{name}', handle_receive)\n    aiohttp.web.run_app(app, port=5001)\n\n\nif __name__ == '__main__':\n    main()","repo_name":"HoffmannP/Stabsarbeit","sub_path":"backend/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"69832445085","text":"from __future__ import unicode_literals\nfrom logging import getLogger\n\nimport docker\nimport docker.errors\n\nfrom ds import context\nfrom . import naming\n\n\nlogger = getLogger()\n\n\nclass BaseDockerContext(naming.ContainerNaming, context.Context):\n    def __init__(self):\n        super(BaseDockerContext, self).__init__()\n        self._client = None\n\n    @property\n    def client(self):\n        if self._client is None:\n            self._client = docker.from_env()\n        return self._client\n\n    @property\n    def container(self):\n        if not self.container_name:\n            return\n        try:\n            return self.client.containers.get(self.container_name)\n        except docker.errors.NotFound:\n            pass\n\n    def get_run_options(self, **options):\n        \"\"\"\n        https://docker-py.readthedocs.io/en/stable/containers.html#docker.models.containers.ContainerCollection.run\n        \"\"\"\n        result = dict(\n            detach=False,\n            auto_remove=True,\n            stdin_open=True,\n            tty=True,\n        )\n        result.update(options)\n        return result\n\n    def filter_commands(self, commands):\n        result = []\n        for command in commands:\n            if command.container_name_required and not self.has_container_name:\n                logger.debug('Filter command %s', command)\n                continue\n            if command.image_name_required and not self.has_image_name:\n                logger.debug('Filter command %s', command)\n                continue\n            result.append(command)\n        return super(BaseDockerContext, self).filter_commands(result)\n\n    def calc_cpu_to_options(self, cpu=1.0):\n        period = int(cpu * 100000)\n        quota = int(cpu * 50000)\n        return {\n            'cpu_period': period,\n            'cpu_quota': quota,\n        }\n","repo_name":"hell10w/ds","sub_path":"ds-docker/dsjk_docker/presets/base/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"9876011868","text":"import unittest\n\nfrom bigflow import transforms\nfrom bigflow import serde\nfrom bigflow.test import test_base\n\n\nclass FlattenValuesTestCase(test_base.PipelineBasedTest):\n    def serde_equal(self, expect, real):\n        self.assertEqual(str(serde.of(expect)), str(serde.of(real)))\n\n    def test_flatten(self):\n        data = self._pipeline.parallelize([(\"A\", 4), (\"A\", 3), (\"B\", 2), (\"A\", 1)])\n        grouped = data.group_by_key()\n        self.assertEqual(0, grouped.nested_level())\n\n        flatten = grouped.flatten()\n\n        self.assertItemsEqual([(\"B\", 2), (\"A\", 4), (\"A\", 3), (\"A\", 1)], self._pipeline.get(flatten))\n\n    def test_nested_flatten(self):\n        def to_tuple_list(elem):\n            tuple2 = (chr(elem + ord('a') - 1), elem)\n            return [tuple2, tuple2]\n\n        data = self._pipeline.parallelize([(\"A\", 1), (\"B\", 2), (\"C\", 3), (\"D\", 4)])\n        data = data.map(lambda x: x, serde = serde.of((str, int)))\n\n        self.serde_equal(data.serde(), serde.of((str, int)))\n\n        grouped = data.group_by_key().apply_values(transforms.flat_map, to_tuple_list,\n            serde = serde.of((str, int)))\n\n        self.serde_equal(str, grouped.key_serdes()[0])\n        self.serde_equal((str, int), grouped.serde())\n\n        self.assertEqual(0, grouped.nested_level())\n\n        nested = grouped.apply_values(transforms.group_by_key)\n\n        self.serde_equal(str, nested.key_serdes()[0])\n        self.serde_equal(str, nested.key_serdes()[1])\n        self.serde_equal(int, nested.serde())\n\n        self.assertEqual(1, nested.nested_level())\n\n        flatten = nested.flatten()\n        self.serde_equal((str, (str, int)), flatten.serde())\n\n        expected = [(\"D\", (\"d\", 4)),\n                    (\"D\", (\"d\", 4)),\n                    (\"C\", (\"c\", 3)),\n                    (\"C\", (\"c\", 3)),\n                    (\"B\", (\"b\", 2)),\n                    (\"B\", (\"b\", 2)),\n                    (\"A\", (\"a\", 1)),\n                    (\"A\", (\"a\", 1))]\n\n        self.assertItemsEqual(expected, self._pipeline.get(flatten))\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"baidu/bigflow","sub_path":"bigflow_python/python/bigflow/transform_impls/test/flatten_test.py","file_name":"flatten_test.py","file_ext":"py","file_size_in_byte":2070,"program_lang":"python","lang":"en","doc_type":"code","stars":1140,"dataset":"github-code","pt":"86"}
+{"seq_id":"28801633291","text":"import pygame\n\nclass Game:\n    screen = None\n    aliens = []\n    shots = []\n    lost = False\n\n\n    def __init__(self, width, height):\n        pygame.init()\n        self.width = width\n        self.height = height\n        self.screen = pygame.display.set_mode((width, height))\n        self.clock = pygame.time.Clock()\n        done = False\n\n        helt = Helt(self, width / 2, height - 20)\n        generator = Generator(self)\n        shot = None\n\n        while not done:\n            if len(self.aliens) == 0:\n                self.displayText(\"VICTORY ACHIEVED\")\n\n            pressed = pygame.key.get_pressed()\n            if pressed[pygame.K_LEFT]:\n                helt.x -= 2 if helt.x > 20 else 0\n            elif pressed[pygame.K_RIGHT]:\n                helt.x += 2 if helt.x < width - 20 else 0\n\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    done = True\n                if event.type == pygame.KEYDOWN and event.key == pygame.K_SPACE and not self.lost:\n                    self.shot.append(Shot(self, helt.x, helt.y))\n\n\nclass Generator:\n    def __init__(self, game):\n        margin = 30\n        width = 50\n        for x in range(margin, game.width - margin, width):\n            for y in range(margin, int(game.height / 2), width):\n                game.aliens.append(Alien(game, x, y))\n\nclass Shot:\n    def __init__(self, game, x, y):\n        self.x = x\n        self.y = y\n        self.game = game\n\n    def draw(self):\n        pygame.draw.rect(self.game.screen,\n                         (254, 52, 110),\n                         pygame.Rect(self.x, self.y, 2, 4))\n        self.y -= 2\n\nclass Alien:\n    def __init__(self, game, x, y):\n        self.x = x\n        self.game = game\n        self.y = y\n        self.size = 30\n\n    def draw(self):\n        pygame.draw.rect(self.game.screen,  # renderovací plocha\n                         (81, 43, 88),  # barva objektu\n                         pygame.Rect(self.x, self.y, self.size, self.size))\n        self.y += 0.05\n\n    def checkCollision(self, game):\n        for shot in game.shots:\n            if (shot.x < self.x + self.size and\n                    shot.x > self.x - self.size and\n                    shot.y < self.y + self.size and\n                    shot.y > self.y - self.size):\n                game.shot.remove(shot)\n                game.aliens.remove(self)\n\npygame.init()\n\nscreen = pygame.display.set_mode((800, 800))\n\npygame.display.set_caption(\"SpaceInvader\")\nicon = pygame.image.load('whatthef.PNG')\npygame.display.set_icon(icon)\n\n\nplayerImg = pygame.image.load('pirat64.png')\nplayerX = 370\nplayerY = 480\n\nclass Helt:\n\n    def __init__(self, game, x, y):\n        self.x = x\n        self.game = game\n        self.y = y\n\n\ndef player():\n    screen.blit(playerImg,(playerX, playerY))\n\n\nrunning = True\nwhile running:\n\n    #Background\n    screen.fill((0, 90, 55))\n\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            running = False\n\n\n    player()\n    pygame.display.update()\n\n\nclass RocketClass:\n    def __init__(self, game, x, y):\n        self.x = x\n        self.y = y\n        self.game = game\n\n    def draw(self):\n        pygame.draw.rect(self.game.screen,\n                         (254, 52, 110),\n                         pygame.Rect(self.x, self.y, 2, 4))\n        self.y -= 2\n","repo_name":"MightGit/SpaceInvader","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"29070662830","text":"from django.shortcuts import render\nimport requests\n\n# Create your views here.\ndef weatherIndex(request):\n    urln = 'http://api.openweathermap.org/data/2.5/weather?q=gaza&units=imperial&appid=2398c9d11bf92c38bac9f03d1054d924'\n\n    r=requests.get(urln).json()\n\n    city_weather = {\n        'city': 'gaza',\n       'temperature': r['main']['temp'],\n       'description': r['weather'][0]['description'],\n       'icon': r['weather'][0]['icon']\n    }\n\n\n    context = {'city_weather':city_weather}\n    a=render(request,'apisection/weather.html',context)\n    return a\n\n","repo_name":"asiaetewi/asiasite","sub_path":"apisection/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"5845939833","text":"import pandas as pd\n\nfrom . import _base_class\n\n\ndef sqlStmt():\n    return \"\"\"\nwith USR as (\nselect\n  username             name\n ,account_status       acc_stat\n ,profile\n ,default_tablespace   ts\n ,temporary_tablespace tempts\n ,to_char(created, 'yyyy-mm-dd hh24:mi:ss') created\n ,to_char(last_login, 'yyyy-mm-dd hh24:mi:ss') last_login\n ,oracle_maintained    ora\n ,password_versions    pwd_versions\nfrom\n  dba_users\n)\nselect * from USR\nwhere 1=1\n  {}\norder by {}\n\"\"\"\n\n\nclass usr(_base_class.OraCommand):\n\n    def __init__(self, ctx):\n        super().__init__(ctx)\n        self.cols = ['NAME', 'ACC_STAT', 'PROFILE', 'TS',\n                     'TEMPTS', 'CREATED', 'LAST_LOGIN', 'ORA', 'PWD_VERSIONS']\n\n    def execute(self):\n        super().checkColNames(self.ctx.filterExpr)\n\n        predicateString = super().predicateExpr(\n            super().adjustCase_forColumnValues(self.ctx.filterExpr, []))\n        SQL = sqlStmt().format(predicateString, super().sortExpr(self.ctx.sortExpr))\n        super().printSQL(SQL)\n\n        self.ctx.session.openConnection()\n        df = pd.read_sql(SQL, con=self.ctx.session.connection)\n\n        return df\n","repo_name":"solicon-it/o","sub_path":"cmd/usr.py","file_name":"usr.py","file_ext":"py","file_size_in_byte":1139,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"31627225434","text":"import asyncio\nimport random\nimport time\n\n\n@asyncio.coroutine\ndef newProducer(myque):\n    while True:\n        yield from myque.put(random.randint(2, 10))\n        yield from asyncio.sleep(1)\n\n\n@asyncio.coroutine\ndef newConsumer(myque):\n    while True:\n        articleId = yield from myque.get()\n        print(f\"New reader consumed the article {articleId}\")\n\n\nmyQueue = asyncio.Queue()\n\nloop = asyncio.get_event_loop()\n\nloop.create_task(newProducer(myQueue))\nloop.create_task(newConsumer(myQueue))\ntry:\n    loop.run_forever()\nfinally:\n    loop.close()\n\n\n\n\n","repo_name":"sreekanthreddyv/CodeFiles","sub_path":"Hacker/2_Async_q.py","file_name":"2_Async_q.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"14351475036","text":"from common import *\n\ndef digits(n):\n    n = abs(n)\n    ret = []\n    while n != 0:\n        ret.append(n % 10)\n        n //= 10\n    ret.reverse()\n    return ret\n\ndef check(n):\n    s = digits(n)\n    if len(s) != 6:\n        return False, False\n\n    last = -1\n    good1 = False\n    good2 = False\n    repeats = 1\n    for i in s:\n        if i < last:\n            return False, False\n        elif i == last:\n            repeats += 1\n            good1 = True\n        else:\n            if repeats == 2:\n                good2 = True\n            repeats = 1\n        last = i\n    if repeats == 2:\n        good2 = True\n    return good1, good2\n\n\ncount1 = 0\ncount2 = 0\nfor i in range(123257, 647016):\n    good1, good2 = check(i)\n    if good1:\n        count1 += 1\n    if good2:\n        count2 += 1\nprint(count1)\nprint(count2)\n","repo_name":"mattr555/advent-of-code","sub_path":"2019/day4.py","file_name":"day4.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"18267214003","text":"import keras.backend as K\nfrom keras.layers.core import Layer\n\nclass AttentionLayer(Layer):\n    def __init__(self, **kwargs):\n        super(AttentionLayer, self).__init__(**kwargs)\n\n    def build(self, input_shape):\n        assert len(input_shape) == 3\n        # W.shape = (time_steps, time_steps)\n        self.W = self.add_weight(name='att_weight',\n                                 shape=(input_shape[1], input_shape[1]),\n                                 initializer='uniform',\n                                 trainable=True)\n        self.b = self.add_weight(name='att_bias',\n                                 shape=(input_shape[1],),\n                                 initializer='uniform',\n                                 trainable=True)\n        super(AttentionLayer, self).build(input_shape)\n\n    def call(self, inputs):\n        # inputs.shape = (batch_size, time_steps, seq_len)\n        x = K.permute_dimensions(inputs, (0, 2, 1))\n        ##################################################################\n        # x.shape = (batch_size, seq_len, time_steps)\n        # W.shape = (time_steps,time_steps) b.shape(time_steps)\n        a = K.softmax(K.tanh(K.dot(x, self.W) + self.b))\n        # a.shape = x.shape = (batch_size, seq_len, time_steps)\n        outputs = K.permute_dimensions(a * x, (0, 2, 1))\n        # outputs.shape = inputs.shape = (batch_size, seq_len, time_steps)\n        outputs = K.sum(outputs, axis=1)\n        # outputs.shape=(batch_size,time_steps)\n        ###################################################################\n        return outputs\n\n    def compute_output_shape(self, input_shape):\n        return input_shape[0], input_shape[2]\n","repo_name":"wp931120/Algorithm_Learning","sub_path":"deep_learing/sentiment/attention.py","file_name":"attention.py","file_ext":"py","file_size_in_byte":1666,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"11971710050","text":"def gradingStudents(grades):\n    grades_table = [(grade - (grade % 5) + 5, grade) for grade in grades]\n    ans = []\n    for multiple, grade in grades_table:\n        diff = multiple - grade\n        if grade < 38 or diff >= 3:\n            ans.append(grade)\n        else:\n            ans.append(multiple)\n    return ans","repo_name":"tjdud0123/daily_algorithm","sub_path":"파이썬/gradingStudents.py","file_name":"gradingStudents.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"86"}
+{"seq_id":"25490175931","text":"from flipkart_page import addEntry\r\nfrom bs4 import BeautifulSoup\r\nimport requests\r\n#import time\r\n\r\ndef populateDB(url):\r\n    headers = {\"User-Agent\" : \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.114 Safari/537.36 Edg/91.0.864.59\"}\r\n\r\n# U L     R C S I G\r\n#  R    P  O E S N\r\n\r\n    r = requests.get(url, headers)\r\n    htmlContent = r.content\r\n    soup = BeautifulSoup(htmlContent, 'html.parser')\r\n            \r\n    data = soup.find_all('div', class_=\"_13oc-S\")\r\n    \r\n    product_class = data[0].contents[0].div['class'][0]\r\n        \r\n    links = soup.find_all('div', class_=product_class)\r\n\r\n    #i = 1\r\n    for link in links:\r\n        product_link = 'https://flipkart.com' + link.find('a')['href']\r\n        # print(i)\r\n        # i+=1\r\n        addEntry(product_link)\r\n        #time.sleep(1)","repo_name":"Jinchuriki09/PyCK_Proj","sub_path":"flipkart_sectionB.py","file_name":"flipkart_sectionB.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34185092517","text":"from know_me.profile import serializers\n\n\ndef test_serialize(api_rf, profile_item_factory, profile_topic_factory):\n    \"\"\"\n    Test serializing a profile topic.\n    \"\"\"\n    topic = profile_topic_factory()\n    api_rf.user = topic.profile.km_user.user\n    request = api_rf.get(topic.get_absolute_url())\n\n    profile_item_factory(topic=topic)\n    profile_item_factory(topic=topic)\n\n    serializer = serializers.ProfileTopicDetailSerializer(\n        topic, context={\"request\": request}\n    )\n\n    item_serializer = serializers.ProfileItemListSerializer(\n        topic.items.all(), context={\"request\": request}, many=True\n    )\n    list_serializer = serializers.ProfileTopicListSerializer(\n        topic, context={\"request\": request}\n    )\n\n    additional = {\"items\": item_serializer.data}\n\n    expected = dict(list_serializer.data.items())\n    expected.update(additional)\n\n    assert serializer.data == expected\n\n\ndef test_validate():\n    \"\"\"\n    Test validating the attributes required to create a new profile\n    topic.\n    \"\"\"\n    data = {\"is_detailed\": True, \"name\": \"Test Topic\"}\n    serializer = serializers.ProfileTopicDetailSerializer(data=data)\n\n    assert serializer.is_valid()\n","repo_name":"knowmetools/km-api","sub_path":"km_api/know_me/profile/tests/serializers/test_profile_topic_detail_serializer.py","file_name":"test_profile_topic_detail_serializer.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"22072759697","text":"import os\nimport pandas as pd\nfrom glob import glob\n\n\nfrom Parser import Parser\n\ndataPath = '/Users/marcel/workspace/Equities/data/'\nkeyStatsPath = dataPath + 'Yahoo/forward/data/*'\nfileGlob = '*html'\n\np = Parser(dataPath)\nfullpaths = (e for e in glob(keyStatsPath))\n\noutput_df = pd.DataFrame()\n\nfor fullpath in fullpaths:\n    output = {}\n    keyStats = {}\n    regex = 'market_time.....,\\s(...)\\s(..),\\s(....)'\n    ticker = p.getTickerFromFullPath(fullpath, 'forward/data/', '.html')\n    forwardDate, _ = p.getDateFromMarketTime(fullpath)\n    fwdUnixTime, date = p.getDateFromMarketTime(fullpath)\n\n    # pull key stats from Yahoo screen\n    for feature in p.features:\n        value = p.searchSourceForFeature(fullpath, feature)\n        value = p.cleanup(value)\n        keyStats[feature] = value\n\n    # Pull stock price and s&p adjusted close on date and forward date and clean missing values\n    price = p.getValueFromDf(p.stock_df, ticker.upper(), p.oneYearAgo(fwdUnixTime))\n    priceFwd = p.getValueFromDf(p.stock_df, ticker.upper(),forwardDate)\n\n    sp500 = p.getValueFromDf(p.sp500_df, 'Adjusted Close', p.oneYearAgo(fwdUnixTime))\n    sp500Fwd = p.getValueFromDf(p.sp500_df, 'Adjusted Close', forwardDate)\n\n    # clean up\n    p.setDefaultIfNone(price, fwdUnixTime)\n    p.setDefaultIfNone(priceFwd, fwdUnixTime)\n    p.setDefaultIfNone(sp500, fwdUnixTime)\n    p.setDefaultIfNone(sp500Fwd, fwdUnixTime)\n\n    # calculate returns and alphas\n    stockReturn = p.getReturn(price, priceFwd)\n    sp500Return = p.getReturn(sp500, sp500Fwd)\n    if all([stockReturn, sp500Return]):\n        difference = stockReturn - sp500Return\n    else:\n        difference = 0.0\n\n    # concatenate key stats from screens at forward date, stock price from Quandl at forward date\n    # and index value from Yahoo index at forward date\n    output['difference'] = difference\n    output['stock_p_change'] = stockReturn\n    output['sp500_p_change'] = sp500Return\n    output['stock_price'] = price\n    output['sp500_value'] = sp500\n    output['ticker'] = ticker\n    output['fullpath'] = fullpath\n\n    output = dict({k: keyStats[v] for (k, v) in p.featuresDict.items()}, **output)\n    output_df = output_df.append(output, ignore_index=True)\n\n# write output dataframe to file\noutput_df.to_csv(\"forward_sample_ALL.csv\")","repo_name":"mstampfer/Equities","sub_path":"YahooForward.py","file_name":"YahooForward.py","file_ext":"py","file_size_in_byte":2286,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"7386034882","text":"#!/usr/bin/python3\r\n# -*- coding: UTF-8 -*-\r\n__author__ = \"A.L.Kun\"\r\n__file__ = \"exts.py\"\r\n__time__ = \"2022/9/11 23:39\"\r\n\r\nfrom flask_sqlalchemy import SQLAlchemy\r\nfrom flask_migrate import Migrate\r\n\r\ndb = SQLAlchemy()  # 操���数据库\r\n\r\n\r\ndef init_exts(app):\r\n    db.init_app(app)\r\n    Migrate().init_app(app, db)  # 使用app初始化Migrate\r\n    app.config[\"db\"] = db\r\n","repo_name":"liuzhongkun1/flask_","sub_path":"bot/App/exts.py","file_name":"exts.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"1546526344","text":"import os\nimport sys\n\nfrom flask import Flask\nfrom flask import redirect, url_for, abort, render_template, flash\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_wtf import FlaskForm\nfrom wtforms import SubmitField, TextAreaField\nfrom wtforms.validators import DataRequired\n\n# 兼容的sqlite url\nWIN = sys.platform.startswith('win')\nif WIN:\n    prefix = 'sqlite:///'\nelse:\n    prefix = 'sqlite:////'\n\napp = Flask(__name__)\n\n\napp.config['SECRET_KEY'] ='sdjsldj4323sdsdfssfdf43434'\n\napp.config['SQLALCHEMY_DATABASE_URI'] = os.getenv('DATABASE_URL', prefix + os.path.join(app.root_path, 'data.db'))\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False  # 禁止警告\n\ndb = SQLAlchemy(app)\n\n\nclass NewNoteForm(FlaskForm):\n    body = TextAreaField('内容', validators=[DataRequired()])\n    submit = SubmitField('保存')\n\n\nclass EditNoteForm(FlaskForm):\n    body = TextAreaField('内容', validators=[DataRequired()])\n    submit = SubmitField('更新')\n\n\nclass DeleteNoteForm(FlaskForm):\n    submit = SubmitField('删除')\n\n\n# Models\nclass Note(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    body = db.Column(db.Text)\n\n    # optional\n    def __repr__(self):\n        return '<Note %r>' % self.body\n\n\n@app.route('/')\ndef index():\n    form = DeleteNoteForm()\n    notes = Note.query.all()\n\n    return render_template('index.html', notes=notes, form=form)\n\n\n@app.route('/new', methods=['GET', 'POST'])\ndef new_note():\n    form = NewNoteForm()\n    if form.validate_on_submit():\n        body = form.body.data\n        note = Note(body=body)\n        db.session.add(note)\n        db.session.commit()\n        flash('笔记已经被保存.')\n        return redirect(url_for('index'))\n    return render_template('new_note.html', form=form)\n\n\n@app.route('/edit/<int:note_id>', methods=['GET', 'POST'])\ndef edit_note(note_id):\n    form = EditNoteForm()\n    note = Note.query.get(note_id)\n    if form.validate_on_submit():\n        note.body = form.body.data\n        db.session.commit()\n        flash('笔记已经被更新.')\n        return redirect(url_for('index'))\n    form.body.data = note.body\n    return render_template('edit_note.html', form=form)\n\n\n@app.route('/delete/<int:note_id>', methods=['POST'])\ndef delete_note(note_id):\n    form = DeleteNoteForm()\n    if form.validate_on_submit():\n        note = Note.query.get(note_id)\n        db.session.delete(note)\n        db.session.commit()\n        flash('笔记已经被删除.')\n    else:\n        abort(400)\n    return redirect(url_for('index'))\n\n\nif __name__ == '__main__':\n    app.run(host = '0.0.0.0', port='1234')","repo_name":"geekori/flask","sub_path":"src/database/webnote/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2577,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"1691245587","text":"from selenium import webdriver\nfrom time import sleep\nimport sys\nimport settings\nfrom datetime import datetime\nimport random\nimport os\n\n\nclass WebDriverControl(object):\n    def __init__(self):\n        super(WebDriverControl, self).__init__()\n        self.username = ''\n        self.password = ''\n\n    @staticmethod\n    def create_driver(option):\n        if sys.platform.startswith('win'):\n            driver = webdriver.Chrome(settings.WEB_DRIVER_PATH_WIN, options=option)\n        else:\n            driver = webdriver.Chrome(settings.WEB_DRIVER_PATH_LINUX, chrome_options=option)\n\n        # driver.implicitly_wait(10)\n        return driver\n\n    def init_driver(self):\n        option = webdriver.ChromeOptions()\n        # option.add_argument('headless')\n        # option.add_argument(\"--start-maximized\")\n        return self.create_driver(option)\n\n    def init_driver_headless(self):\n        option = webdriver.ChromeOptions()\n        option.add_argument('headless')\n        option.add_argument(\"window-size=1920,1080\")\n        # option.add_argument(\"--start-maximized\")\n        return self.create_driver(option)\n\n\nclass AutoPoint(WebDriverControl):\n    def __init__(self):\n        super(AutoPoint, self).__init__()\n\n        self.isoweek = datetime.today().isoweekday()\n\n        driver_control = WebDriverControl()\n\n        if settings.DEBUG:\n            self.driver = driver_control.init_driver()\n        else:\n            self.driver = driver_control.init_driver_headless()\n\n    def start(self):\n        if self.isoweek > 5:\n            # do not run when week 6 and 7\n            self.log_print(\"no need to run, return!!\")\n            self.stop()\n            return\n\n        self.log_print(\"start!\")\n\n        if settings.RANDOM_DELAY:\n            delay_time = random.randint(0, settings.RANDOM_DELAY_SECOND)\n            print_str = \"random delay : \" + str(delay_time) + \" second\"\n            print(print_str)\n            self.log_print(print_str)\n\n            sleep(delay_time)\n\n        try:\n            self.driver.get(settings.POINT_URL)\n            element = self.driver.find_element_by_class_name(\"checkHealth\")\n            element.click()\n            sleep(2)\n            element = self.driver.find_element_by_name(\"send\")\n            element.click()\n            sleep(2)\n\n            self.log_print(\"success!\")\n        except Exception as e:\n            self.log_print(\"error!, log: \" + str(e))\n        finally:\n            self.stop()\n\n    def stop(self):\n        self.driver.quit()\n        self.log_print(\"*************\", data_time=False)\n\n    def log_print(self, log_str, data_time=True):\n        today_str = str(datetime.today())\n        isoweek_str = str(self.isoweek)\n        if data_time:\n            log_str = \"echo \" + today_str + \" week: \" + isoweek_str + \" , \" + log_str + \" >> \" + settings.LOG_PATH\n        else:\n            log_str = \"echo \" + log_str + \" >> \" + settings.LOG_PATH\n        os.system(log_str)\n\n\nauto_control = AutoPoint()\nauto_control.start()\n","repo_name":"Llona/AJ-auto_point","sub_path":"auto_point.py","file_name":"auto_point.py","file_ext":"py","file_size_in_byte":2978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"36621772644","text":"from . import rules\nfrom .grammar import ps_program\n\nfrom hidc.utils.lazylist import lazy_list\nfrom hidc.errors import ParserError\nfrom hidc.lexer import lex\n\n\ndef parse(source, rule=ps_program(), partial=False):\n    if isinstance(rule, rules.Parser):\n        rule = rules.Parser(rule.consume, backtrack=False)\n    elif not isinstance(rule, rules.Rule):\n        # coroutine passed directly, eg expect\n        rule = rules.Parser((lambda r: lambda: r)(rule), backtrack=False)\n\n    result, remaining = rule.process(lazy_list(lex(source)))\n\n    if remaining and not partial:\n        raise ParserError(\n            f'Unprocessed token: {remaining.head.token}',\n            remaining.head.span\n        )\n\n    return result\n","repo_name":"benburrill/halt_is_defeat","sub_path":"hidc/parser/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"23572128038","text":"import random\nimport colorama\nfrom colorama import Fore, Back, Style\ncolorama.init(autoreset=True)\n\nclass UIModule:\n    def __init__(self):\n        pass\n\n    def rand_or_not(self,time,w_e,randam_key,previous_num,remain_words):\n        if randam_key == None:\n            num = time % len(w_e)\n            if num == previous_num and len(remain_words):\n                self.rand_or_not(time,w_e,randam_key,previous_num,remain_words)\n            else:\n                return num\n        else:\n            num = random.randint(0, len(w_e)-1)\n            return num\n\n    def add_del(self, add_list, del_list, word):\n        if word not in add_list:\n            add_list.append(word)\n        if word in del_list:\n            del_list.remove(word)\n        return add_list, del_list\n\n    def right(self, except_words, remain_words, num ,w_e):\n        print(Fore.BLUE + str(w_e[num]))\n        except_words, remain_words =\\\n            self.add_del(except_words, remain_words, num)\n        print(\"残り: \" + str(len(remain_words))+ \"/\" + str(len(w_e)))\n\n    def wrong(self, w_e, num):\n        print(Fore.BLUE + str(w_e[num]))\n        for i in range(100):\n            trash = str(input(\"練習して：\"))\n            if(trash == w_e[num]):\n                break\n","repo_name":"KoyoJimbo/WordCards","sub_path":"gui/elec/sampleapp/pyword/ui_modules.py","file_name":"ui_modules.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"25477500711","text":"# -*- coding: utf-8 -*-\n\"\"\" nns/models/unet/models/unet3d \"\"\"\n\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom nns.models.unet.network_others import init_weights\nfrom nns.models.unet.utils import UnetConv3, UnetUp_CT\n\n\n__all__ = ['UNet3D']\n\n\nclass UNet3D(nn.Module):\n    \"\"\"\n    Original 3D UNet from Attention Gated Networks\n    source: https://github.com/ozan-oktay/Attention-Gated-Networks/blob/master/models/networks/unet_3D.py\n    \"\"\"\n\n    def __init__(self, feature_scale=4, n_classes=21, n_channels=3, is_batchnorm=True):\n        super().__init__()\n        self.feature_scale = feature_scale\n        self.n_classes = n_classes\n        self.n_channels = n_channels\n        self.is_batchnorm = is_batchnorm\n\n        filters = [64, 128, 256, 512, 1024]\n        filters = [int(x / self.feature_scale) for x in filters]\n        maxpool_kernel_size = (2, 2, 2)\n\n        # downsampling\n        self.conv1 = UnetConv3(self.n_channels, filters[0], self.is_batchnorm)\n        self.maxpool1 = nn.MaxPool3d(kernel_size=maxpool_kernel_size)\n\n        self.conv2 = UnetConv3(filters[0], filters[1], self.is_batchnorm)\n        self.maxpool2 = nn.MaxPool3d(kernel_size=maxpool_kernel_size)\n\n        self.conv3 = UnetConv3(filters[1], filters[2], self.is_batchnorm)\n        self.maxpool3 = nn.MaxPool3d(kernel_size=maxpool_kernel_size)\n\n        self.conv4 = UnetConv3(filters[2], filters[3], self.is_batchnorm)\n        self.maxpool4 = nn.MaxPool3d(kernel_size=maxpool_kernel_size)\n\n        self.center = UnetConv3(filters[3], filters[4], self.is_batchnorm)\n\n        # upsampling\n        self.up_concat4 = UnetUp_CT(filters[4], filters[3], is_batchnorm, data_dimensions=3,\n                                    scale_factor=maxpool_kernel_size)\n        self.up_concat3 = UnetUp_CT(filters[3], filters[2], is_batchnorm, data_dimensions=3,\n                                    scale_factor=maxpool_kernel_size)\n        self.up_concat2 = UnetUp_CT(filters[2], filters[1], is_batchnorm, data_dimensions=3,\n                                    scale_factor=maxpool_kernel_size)\n        self.up_concat1 = UnetUp_CT(filters[1], filters[0], is_batchnorm, data_dimensions=3,\n                                    scale_factor=maxpool_kernel_size)\n\n        # final conv (without any concat)\n        self.final = nn.Conv3d(filters[0], self.n_classes, 1)\n\n        # initialise weights\n        for m in self.modules():\n            if isinstance(m, nn.Conv3d):\n                init_weights(m, init_type='kaiming')\n            elif isinstance(m, nn.BatchNorm3d):\n                init_weights(m, init_type='kaiming')\n\n    def forward(self, inputs):\n        conv1 = self.conv1(inputs)\n        maxpool1 = self.maxpool1(conv1)\n\n        conv2 = self.conv2(maxpool1)\n        maxpool2 = self.maxpool2(conv2)\n\n        conv3 = self.conv3(maxpool2)\n        maxpool3 = self.maxpool3(conv3)\n\n        conv4 = self.conv4(maxpool3)\n        maxpool4 = self.maxpool4(conv4)\n\n        center = self.center(maxpool4)\n        up4 = self.up_concat4(conv4, center)\n        up3 = self.up_concat3(conv3, up4)\n        up2 = self.up_concat2(conv2, up3)\n        up1 = self.up_concat1(conv1, up2)\n\n        final = self.final(up1)\n\n        return final\n\n    @staticmethod\n    def apply_argmax_softmax(pred):\n        log_p = F.softmax(pred, dim=1)\n\n        return log_p\n","repo_name":"giussepi/disagreement-attention","sub_path":"nns/models/unet/models/unet3d.py","file_name":"unet3d.py","file_ext":"py","file_size_in_byte":3317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"42543568118","text":"import sys\nsys.path.append('/home/noah/Desktop/proj/chainlink/LinkTunes')\n\nfrom scripts.helper_functions import get_account\nfrom flask import Flask, jsonify, render_template, request\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\n\n##CREATE DB\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///temp.db'\ndb = SQLAlchemy()\ndb.init_app(app)\n\n##CREATE TABLE\n# User account info\nclass User(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(250), unique=True, nullable=False)\n    wallet_id = db.Column(db.String(500), nullable=False)\n    img_url = db.Column(db.String(500), nullable=False)\n    location = db.Column(db.String(250), nullable=False)\n    \n    def to_dict(self):\n        return {column.name: getattr(self, column.name) for column in self.__table__.columns}\n\n# Artist account\nclass Artist(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(250), unique=True, nullable=False)\n    #artist type can be musician, visual artist, clothing etc\n    artist_type = db.Column(db.String(30), nullable=False)\n    wallet_id = db.Column(db.String(500), nullable=False)\n    img_url = db.Column(db.String(500), nullable=False)\n    location = db.Column(db.String(250), nullable=False)\n    #preferred type of crypto to be paid in\n    payment_favorite = db.Column(db.String(250), nullable=False)\n\n    def to_dict(self):\n        return {column.name: getattr(self, column.name) for column in self.__table__.columns}\n\n# Merch \nclass Merch(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(250), unique=True, nullable=False)\n    #merch type can be music, art, clothing etc\n    merch_type = db.Column(db.String(30), nullable=False)\n    img_url = db.Column(db.String(500), nullable=False)\n    \n    def to_dict(self):\n        return {column.name: getattr(self, column.name) for column in self.__table__.columns}\n\nwith app.app_context():\n    db.create_all()\n\n#get all users\n@app.route('/users', methods=['GET'])\ndef get_all_users():\n    result = db.session.execute(db.select(User).order_by(User.name))\n    all_users = result.scalars().all()\n    return jsonify(users=[user.to_dict() for user in all_users]) \n\n#get user by id\n@app.route('/users/<int:id>', methods=['GET'])\ndef get_user_by_id(id: int):\n    user_selected = db.get_or_404(User,id)\n    return jsonify(user_selected.to_dict()) \n\n#get user by wallet id\n@app.route(\"/search\")\ndef get_user_by_wallet():\n    query_wallet = request.args.get(\"wallet\")\n    result = db.session.execute(db.select(User).where(User.wallet_id == query_wallet))\n    \n    all_users = result.scalars().all()\n    if all_users:\n        return jsonify(users=[user.to_dict() for user in all_users])\n    else:\n        return jsonify(error={\"Not Found\": \"Sorry, no user with that wallet.\"}), 404\n\n# Create a new user\n# Test this inside Postman. Request type: Post ->  Body ->  x-www-form-urlencoded\n@app.route(\"/add_user\", methods=[\"POST\"])\ndef post_new_user():\n    new_user = User(\n        name=request.form.get(\"name\"),\n        img_url=request.form.get(\"img_url\"),\n        location=request.form.get(\"loc\"),\n        wallet_id=request.form.get(\"wallet_id\"),\n    )\n    db.session.add(new_user)\n    db.session.commit()\n    return jsonify(response={\"success\": \"Successfully added the new user.\"})\n\n# Create a new Artist\n# Test this inside Postman. Request type: Post ->  Body ->  x-www-form-urlencoded\n@app.route(\"/add_artist\", methods=[\"POST\"])\ndef post_new_artist():\n    new_artist = Artist(\n        name=request.form.get(\"name\"),\n        img_url=request.form.get(\"img_url\"),\n        location=request.form.get(\"loc\"),\n        artist_type=request.form.get(\"artist_type\"),\n        payment_favorite=request.form.get(\"payment_favorite\"),\n        wallet_id=request.form.get(\"wallet_id\"),\n    )\n    db.session.add(new_artist)\n    db.session.commit()\n    return jsonify(response={\"success\": \"Successfully added the new artist.\"})\n\n# Create a new merch item\n# Test this inside Postman. Request type: Post ->  Body ->  x-www-form-urlencoded\n@app.route(\"/add_merch\", methods=[\"POST\"])\ndef post_new_merch():\n    new_merch = Merch(\n        name=request.form.get(\"name\"),\n        img_url=request.form.get(\"img_url\"),\n        merch_type=request.form.get(\"merch_type\"),\n   \n    )\n    db.session.add(new_merch)\n    db.session.commit()\n    return jsonify(response={\"success\": \"Successfully added the new merch.\"})\n\n@app.route(\"/account\", methods=[\"GET\"])\ndef get_current_account():\n    get_account(index=None, id=None)\n\n\n\n\nif __name__ == '__main__':\n    app.run(debug=True)","repo_name":"jonnytrex/LinkTunes","sub_path":"api/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"25126638575","text":"import cv2\nimport numpy as np\n\nclass yolo_mosaic():\n    # Initialize parameters\n    def __init__(self, net_filename, conf_threshold=0.5, nms_threshold=0.5, obj_threshold=0.5):\n        anchors = [[4, 5,  8, 10,  13, 16], [23, 29,  43,\n                                             55,  73, 105], [146, 217,  231, 300,  335, 433]]\n        num_classes = 1\n        self.nl = len(anchors)\n        self.na = len(anchors[0]) // 2\n        self.no = num_classes + 5 + 10\n        self.grid = [np.zeros(1)] * self.nl\n        self.stride = np.array([8., 16., 32.])\n        self.anchor_grid = np.asarray(\n            anchors, dtype=np.float32).reshape(self.nl, -1, 2)\n        self.inp_width = 640\n        self.inp_height = 640\n        self.net = cv2.dnn.readNet(net_filename)\n        self.conf_threshold = conf_threshold\n        self.nms_threshold = nms_threshold\n        self.obj_threshold = obj_threshold\n\n    def _make_grid(self, nx=20, ny=20):\n        xv, yv = np.meshgrid(np.arange(ny), np.arange(nx))\n        return np.stack((xv, yv), 2).reshape((-1, 2)).astype(np.float32)\n\n    def post_process(self, frame, outs):\n        frame_height = frame.shape[0]\n        frame_width = frame.shape[1]\n        ratioh, ratiow = frame_height / self.inp_height, frame_width / self.inp_width\n\n        # Scan through all the bounding boxes output from the network and keep only the ones with\n        # high confidence scores. Assign the box's class label as the class with the highest score.\n        confidences = []\n        boxes = []\n        landmarks = []\n        for detection in outs:\n            confidence = detection[15]\n            if detection[4] > self.obj_threshold:\n                center_x = int(detection[0] * ratiow)\n                center_y = int(detection[1] * ratioh)\n                width = int(detection[2] * ratiow)\n                height = int(detection[3] * ratioh)\n                left = int(center_x - width / 2)\n                top = int(center_y - height / 2)\n\n                confidences.append(float(confidence))\n                boxes.append([left, top, width, height])\n                landmark = detection[5:15] * \\\n                    np.tile(np.float32([ratiow, ratioh]), 5)\n                landmarks.append(landmark.astype(np.int32))\n\n        # Perform non maximum suppression to eliminate redundant overlapping boxes with lower\n        # confidences.\n        indices = cv2.dnn.NMSBoxes(\n            boxes, confidences, self.conf_threshold, self.nms_threshold)\n        for i in indices:\n            box = boxes[i]\n            left = box[0]\n            top = box[1]\n            width = box[2]\n            height = box[3]\n            landmark = landmarks[i]\n            frame = self.draw_pred(\n                frame, left, top, left + width, top + height, landmark)\n        return frame\n\n    # Add mosaic to the image crop\n    def crop_mosaic(self, img, alpha):\n        crop_width = img.shape[1]\n        crop_height = img.shape[0]\n\n        # Add mosaic by resizing the picture (INTER_LINEAR or INTER_NEAREST)\n        img = cv2.resize(img, (int(crop_width*alpha + 1), int(crop_height*alpha + 1)))\n        img = cv2.resize(img, (crop_width, crop_height),\n                         interpolation=cv2.INTER_LINEAR)\n\n        return img\n\n    # Draw mosaic on predicted face(s)\n    def draw_pred(self, frame, left, top, right, bottom, landmark):\n        frame_height = frame.shape[0]\n        frame_width = frame.shape[1]\n\n        # Pre-process, to handle unexpected prediction\n        top = 0 if top < 0 else top\n        top = frame_height if top > frame_height else top\n        bottom = 0 if bottom < 0 else bottom\n        bottom = frame_height if bottom > frame_height else bottom\n        left = 0 if left < 0 else left\n        left = frame_width if left > frame_width else left\n        right = 0 if right < 0 else right\n        right = frame_width if right > frame_width else right\n\n        # Draw mosaic on the predicted face\n        frame[top:bottom, left:right] = self.crop_mosaic(\n            frame[top:bottom, left:right], 0.02)\n\n        return frame\n\n    def detect(self, srcimg):\n        blob = cv2.dnn.blobFromImage(\n            srcimg, 1 / 255.0, (self.inp_width, self.inp_height), [0, 0, 0], swapRB=True, crop=False)\n        # Sets the input to the network\n        self.net.setInput(blob)\n\n        # Runs the forward pass to get output of the output layers\n        outs = self.net.forward(self.net.getUnconnectedOutLayersNames())[0]\n\n        # Inference output\n        outs[..., [0, 1, 2, 3, 4, 15]] = 1 / \\\n            (1 + np.exp(-outs[..., [0, 1, 2, 3, 4, 15]]))   # sigmoid function\n        row_ind = 0\n        for i in range(self.nl):\n            h, w = int(self.inp_height /\n                       self.stride[i]), int(self.inp_width/self.stride[i])\n            length = int(self.na * h * w)\n            if self.grid[i].shape[2:4] != (h, w):\n                self.grid[i] = self._make_grid(w, h)\n\n            g_i = np.tile(self.grid[i], (self.na, 1))\n            a_g_i = np.repeat(self.anchor_grid[i], h * w, axis=0)\n            outs[row_ind:row_ind + length, 0:2] = (\n                outs[row_ind:row_ind + length, 0:2] * 2. - 0.5 + g_i) * int(self.stride[i])\n            outs[row_ind:row_ind + length,\n                 2:4] = (outs[row_ind:row_ind + length, 2:4] * 2) ** 2 * a_g_i\n\n            outs[row_ind:row_ind + length, 5:7] = outs[row_ind:row_ind + length,\n                                                       5:7] * a_g_i + g_i * int(self.stride[i])   # landmark x1 y1\n            outs[row_ind:row_ind + length, 7:9] = outs[row_ind:row_ind +\n                                                       length, 7:9] * a_g_i + g_i * int(self.stride[i])  # landmark x2 y2\n            outs[row_ind:row_ind + length, 9:11] = outs[row_ind:row_ind +\n                                                        length, 9:11] * a_g_i + g_i * int(self.stride[i])  # landmark x3 y3\n            outs[row_ind:row_ind + length, 11:13] = outs[row_ind:row_ind +\n                                                         length, 11:13] * a_g_i + g_i * int(self.stride[i])  # landmark x4 y4\n            outs[row_ind:row_ind + length, 13:15] = outs[row_ind:row_ind +\n                                                         length, 13:15] * a_g_i + g_i * int(self.stride[i])  # landmark x5 y5\n            row_ind += length\n\n        return outs","repo_name":"adamwuqwq/yolo_facemosaic","sub_path":"yolo_mosaic.py","file_name":"yolo_mosaic.py","file_ext":"py","file_size_in_byte":6343,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"27139275109","text":"#!/usr/bin/python3\n\nimport os\nimport json\n\n\nkLabelIfFirstPlayerLoss = -1\nkLabelIfFirstPlayerWin = 1\nkLabelFirstPlayerWinIfGreaterThan = 0\n\nkHeroFeatures = 1\nkMinionFeatures = 7  # exclude card id\nkMinions = 7\n\nkCurrentHandCards = 10\nkCurrentHandCardFeatures = 1  # exclude card id\nkCurrentHandFeatures = 1\n\nkMaxCardId = 1860\n\nclass DynamicMapper:\n  def __init__(self, start_index = 0):\n    self._dict_v_to_index = {}\n    self._dict_index_to_v = {}\n    self._next_index = start_index\n\n  def _add_map(self, v):\n    idx = self._next_index\n    self._next_index = idx + 1\n\n    self._dict_v_to_index[v] = idx\n    self._dict_index_to_v[idx] = v\n\n  def get_index(self, v):\n    if v not in self._dict_v_to_index:\n      self._add_map(v)\n    return self._dict_v_to_index[v]\n\n  def get_index_to_v_dict(self):\n    return self._dict_index_to_v\n\nclass DataReader:\n  def __init__(self, dirname):\n    self._dirname = dirname\n    self._data = []\n    self._hand_card_mapper = DynamicMapper(1)\n\n  @classmethod\n  def from_bool(cls, v):\n    if v == True:\n      return 1.0\n    else:\n      return -1.0\n\n  def _read_hero_data(self, data):\n    hp = data['hero']['hp']\n    armor = data['hero']['armor']\n\n    self._data.extend([\n      hp + armor])\n\n  def _add_minion_data_placeholder(self):\n    self._data.extend([\n      0,\n      0,\n      0,\n      0,\n      -1.0,\n      -1.0,\n      -1.0,\n      -1.0])\n\n  def _add_minion_data(self, minion):\n    attackable = False\n    try:\n      attackable = minion['attackable']\n    except Exception as _:\n      attackable = False\n\n    self._data.extend([\n      minion['card_id'],\n      minion['hp'],\n      minion['max_hp'],\n      minion['attack'],\n      self.from_bool(attackable),\n      self.from_bool(minion['taunt']),\n      self.from_bool(minion['shield']),\n      self.from_bool(minion['stealth'])])\n\n  def _read_minions_data(self, minions):\n    if minions is None:\n      minions = []\n\n    count = len(minions)\n\n    for idx in range(0, 7):\n      if idx < count:\n        self._add_minion_data(minions[idx])\n      else:\n        self._add_minion_data_placeholder()\n\n  def _add_resource(self, resource):\n    self._data.extend([\n        resource['current'],\n        resource['total'],\n        resource['overload_next']])\n\n  def _add_current_hand(self, hand):\n    if hand is None:\n      hand = {}\n\n    playable = 0\n    for card in hand:\n      if card['playable']:\n        playable = playable + 1\n    self._data.append(playable)\n\n    def add_data(card_id, cost):\n      mapped_card_id = self._hand_card_mapper.get_index(card_id)\n      self._data.append(mapped_card_id)\n      self._data.append(cost)\n\n    for card in hand:\n      card_id = card['card_id']\n      cost = card['cost']\n      add_data(card_id, cost)\n    for _ in range(10 - len(hand)):\n      card_id = 0\n      cost = -1\n      add_data(card_id, cost)\n\n  def _add_opponent_hand(self, hand):\n    if hand is None:\n      hand = {}\n\n    self._data.extend([\n        len(hand)])\n\n  def _add_heropower(self, hero_power):\n    self._data.extend([\n        self.from_bool(hero_power['playable'])])\n\n  def _read_board(self, board):\n    self._data.clear()\n\n    self._read_hero_data(board['current_player'])\n    self._read_hero_data(board['opponent_player'])\n    self._read_minions_data(board['current_player']['minions'])\n    self._read_minions_data(board['opponent_player']['minions'])\n    self._add_current_hand(board['current_player']['hand'])\n    self._add_opponent_hand(board['opponent_player']['hand'])\n\n    self._add_resource(board['current_player']['resource'])\n    self._add_heropower(board['current_player']['hero_power'])\n\n    return list(self._data)\n\n  def _get_label(self, current_player, first_player_win):\n    kFirstPlayerString = 'kFirstPlayer'\n    kSecondPlayerString = 'kSecondPlayer'\n\n    if kFirstPlayerString == current_player:\n      current_player_is_first = True\n    elif current_player == kSecondPlayerString:\n      current_player_is_first = False\n    else:\n      assert False\n\n    assert first_player_win is not None\n    if current_player_is_first == first_player_win:\n      return kLabelIfFirstPlayerWin\n    else:\n      return kLabelIfFirstPlayerLoss\n\n  def _win_or_loss(self, result):\n    kWinString = 'kResultFirstPlayerWin'  # kResultWin for newer datasets\n    kLossString = 'kResultSecondPlayerWin'  # kResultLoss for newer datasets\n\n    if result == kWinString:\n      return True\n\n    assert result == kLossString\n    return False\n\n  def _read_one_json(self, all_data, all_label, json_data):\n    first_player_win = None\n    for block_data in json_data:\n      action_type = block_data['type']\n      if action_type == 'kEnd':\n        first_player_win = self._win_or_loss(block_data['result'])\n    assert first_player_win is not None\n\n    for block_data in json_data:\n      action_type = block_data['type']\n      if action_type == 'kMainAction':\n        board = block_data['board']\n        data = self._read_board(board)\n        label = self._get_label(board['current_player_id'], first_player_win)\n\n        all_data.append(data)\n        all_label.append(label)\n\n  def parse(self):\n    data = []\n    label = []\n\n    for (dirpath, _, filenames) in os.walk(self._dirname):\n      for idx, filename in enumerate(filenames):\n        fullpath = os.path.join(dirpath, filename)\n        print(\"Reading file (%d / %d): %s \" % (idx+1, len(filenames), fullpath))\n        with open(fullpath) as data_file:\n          try:\n            json_data = json.load(data_file)\n          except Exception:\n            print(\"Skipped file %s: Failed to read json\" % fullpath)\n            continue\n          self._read_one_json(data, label, json_data)\n      break\n\n    return data, label, self._hand_card_mapper.get_index_to_v_dict()\n","repo_name":"peter1591/hearthstone-ai","sub_path":"agents/train/tensorflow/data_reader.py","file_name":"data_reader.py","file_ext":"py","file_size_in_byte":5681,"program_lang":"python","lang":"en","doc_type":"code","stars":289,"dataset":"github-code","pt":"86"}
+{"seq_id":"70712705245","text":"from collections import deque\nimport warnings\n\nimport moderngl\n\nfrom ...basic_shapes import ShapeDrawer\nfrom ....fonts.font_render import DirectFontRender, FormattedText\nfrom ....math import Matrix4, Vec2\n\n\nV0_PARAMS_V3 = {\n    \"text_limit\": 256,\n    \"max_text_size\": 32,\n    \"pb_spacing_side\": 4,\n    \"pb_spacing_main\": 2,\n    \"node_bg_color\": (0.1, 0.1, 0.1, 1),\n    \"text_node_bg_color\": (0.05, 0.05, 0.05, 1),\n    \"node_border_color\": (0.2, 0.2, 0.2, 1),\n    \"button_border_inactive_color\": (0.5, 0.5, 0.5, 1),\n    \"button_border_hover_color\": (0.5, 0.7, 0.7, 0.8),\n    \"button_border_active_color\": (0.5, 1, 1, 0.8),\n    \"selectable_inactive_color\": (0.2, 0.2, 0.2, 1),\n    \"selectable_active_color\": (0.7, 1, 1, 0.8),\n    \"text_color\": (1, 1, 1),\n    \"scrollbar_color\": (1, 1, 1, 0.8),\n    \"progressbar_color\": (1, 1, 1, 0.8),\n    \"img_flip\": True,\n}\n\nV0_PARAMS_V1 = {\n    \"text_limit\": 256,\n    \"max_text_size\": 32,\n    \"pb_spacing_side\": 4,\n    \"pb_spacing_main\": 2,\n    \"node_bg_color\": (1, 1, 1, 0.1),\n    \"node_border_color\": (1, 1, 1, 0.2),\n    \"button_border_inactive_color\": (1, 1, 0.5, 0.4),\n    \"button_border_hover_color\": (0.5, 1, 1, 0.6),\n    \"button_border_active_color\": (0.5, 1, 1, 0.7),\n    \"selectable_inactive_color\": (1, 1, 0.6, 0.3),\n    \"selectable_active_color\": (0.7, 1, 1, 0.7),\n    \"text_color\": (1, 1, 1),\n    \"scrollbar_color\": (1, 1, 1, 0.8),\n    \"progressbar_color\": (1, 1, 1, 0.8),\n}\n\nclass V0Renderer:\n    def __init__(self, window, node=None, font=None, font_path=None, font_render=None, is_selected_cb=None):\n        if font is not None or font_path is not None:\n            warnings.warn(\"font and font_path are not supported anymore\", category=DeprecationWarning)\n        self.ctx = window.ctx\n        self.font_render = font_render or DirectFontRender(self.ctx, font, font_path=font_path)\n        self.font_render.flip_y = True\n        self.drawer = ShapeDrawer(self.ctx)\n        self.node = node\n        self.window = window\n        self.text_queue = []\n        self.excludes = [\"centerer\", \"scrollablelist\"]\n        self.buttons = [\"button\", \"togglebutton\", \"radiobutton\"]\n        self.border_excludes = [\"node\", \"textnode\", \"textinput\"]\n        self.drawer.default_z = -1\n        self.params = V0_PARAMS_V3.copy()\n        self.is_selected_cb = is_selected_cb\n        self.matrix = None\n        self.sprite_master = None\n        self.image_ident = 0\n    \n    def __getattr__(self, key):\n        if key.startswith(\"param_\"): #TODO - node-specific params\n            return self.params[key[6:]]\n        raise AttributeError(\"Unknown key %s\" % key)\n\n    def queue_text(self, text, x, y, scale=1, multiline=False, scissor_params=None):\n        self.text_queue.append((text, x, y, scale, scissor_params, multiline))\n\n    def render_image(self, node):\n        self.render_drawer()\n        #print(\"Rendering\", node.image_id, \"at\", node.pos)\n        if node.image_mode is None:\n            self.sprite_master.add_sprite_rect(node.image_id, *node.position, *node.size)\n        else:\n            \n            dx = 1\n            ratio = node.size.y / node.size.x\n            dy = dx*node.image_ratio*ratio\n            \n            r = 1    \n            if node.image_mode.startswith(\"fill\"):\n                r = min(dx, dy)\n            elif node.image_mode.startswith(\"fit\"):\n                r = max(dx, dy)\n            else:\n                warnings.warn(\"Node %s has invalid image mode %s\" % (node, node.image_mode))\n                \n            dx /= r\n            dy /= r\n\n            cpos = node.position + node.size/2\n\n            sizey = node.size.y*dx\n            if self.param_img_flip:\n                sizey *= -1\n\n            if node.image_mode.endswith(\"cleft\"):\n                x = node.position.x + node.size.x*dy * 0.5\n                y = node.position.y + node.size.y*dx * 0.5\n                self.image_ident = node.size.x*dy + 1\n                self.sprite_master.add_sprite_centered(node.image_id, x, y, node.size.x*dy, sizey)\n            else:\n                self.sprite_master.add_sprite_centered(node.image_id, *cpos, node.size.x*dy, node.size.y*dx)# tpoints=tpoints)\n        self.sprite_master.render(mvp=self.matrix)\n\n    def render_node(self, node):\n        self.image_ident = 0\n        if node.hidden:\n            return\n        if node.type in self.excludes and node.image_id is None:\n            return\n        if node.type == \"customrender\":\n            self.render_drawer()\n            #mvp = Matrix4.orthogonal_projection(\n            #    node.position.x,\n            #    node.position.x+node.size.x,\n            #    node.position.y+node.size.y,\n            #    node.position.y,\n            #)\n            bak = self.ctx.viewport\n            try:\n                self.ctx.viewport = (node.position.x, node.position.y, node.size.x, node.size.y)\n                node.ctx = self.ctx\n                node.render()\n            finally:\n                self.ctx.viewport = bak\n            return\n\n        is_selected = node.selectable\n        if self.is_selected_cb is not None:\n            is_selected = self.is_selected_cb(node)\n\n        if node.image_id is not None:\n            self.render_image(node)\n        else:\n            if node.type == \"textinput\":\n                color = self.param_text_node_bg_color\n            else:\n                color = self.param_node_bg_color\n            self.drawer.add_rectangle(*node.position, *node.size, color=color)\n\n        if node.type in self.buttons:\n            active = getattr(node, \"state\", getattr(node, \"pressed\", False))\n            hovered = getattr(node, \"hovered\", False)\n            if active:\n                color = self.param_button_border_active_color\n            elif hovered:\n                color = self.param_button_border_hover_color\n            else:\n                color = self.param_button_border_inactive_color\n            self.drawer.add_line_rectangle(*node.position, node.size.x, node.size.y, color=color)\n        elif node.selectable:\n            if is_selected:\n                color = self.param_selectable_active_color\n            else:\n                color = self.param_selectable_inactive_color\n            self.drawer.add_line_rectangle(*node.position, node.size.x, node.size.y, color=color)\n        else:\n            if node.type not in self.border_excludes:\n                self.drawer.add_line_rectangle(*node.position, node.size.x, node.size.y, color=self.param_node_border_color)\n\n        if node.type == \"progressbar\":\n            if node.size.x > node.size.y:\n                self.drawer.add_rectangle(node.position.x+self.param_pb_spacing_main, node.position.y+self.param_pb_spacing_side, (node.size.x-self.param_pb_spacing_main*2)*node.fraction, node.size.y-self.param_pb_spacing_side*2, color=self.param_progressbar_color)\n            else:\n                self.drawer.add_rectangle(node.position.x+self.param_pb_spacing_side, node.position.y+self.param_pb_spacing_main, node.size.x-self.param_pb_spacing_side*2, (node.size.y-self.param_pb_spacing_side*2)*node.fraction, color=self.param_progressbar_color)\n\n        if node.type == \"scrollbar\":\n            if node.direction == 1:\n                pos_x = node.position.x\n                size_x = node.size.x\n                pos_y = node.position.y + node.size.y * node.pos * 0.9\n                size_y = node.size.y * 0.1\n            else:\n                pos_y = node.position.y\n                size_y = node.size.y\n                pos_x = node.position.x + node.size.x * node.pos * 0.9\n                size_x = node.size.x * 0.1\n            self.drawer.add_rectangle(pos_x, pos_y, size_x, size_y, color=self.param_scrollbar_color)\n        \n        if hasattr(node, \"text\"):\n            if node.type == \"text\" or isinstance(node.text, FormattedText):\n                pos = Vec2(*node.position)\n                pos.y = self.window.height - pos.y - node.size.y\n                scissor = (*pos, *node.size)\n                self.queue_text(node.text, node.position.x+4, node.position.y+node.scale, node.scale, multiline=node.size.x-8, scissor_params=scissor)\n            else:\n                \n                if len(node.text) > self.param_text_limit: #TODO later\n                    text = node.text[:self.param_text_limit//2] + \"...\" + node.text[-self.param_text_limit//2:]\n                else:\n                    text = node.text\n\n                used_scale = min(node.size.y, self.param_max_text_size)\n                ident = 8\n                size = self.font_render.calc_size(text, scale=used_scale)\n                if size > 0 and size > node.size.x - self.image_ident - ident:\n                    used_scale *= (node.size.x-self.image_ident-ident) / size\n                    size = self.font_render.calc_size(text, scale=used_scale)\n\n                align_ajust = node.size.x // 2 - size // 2\n                if hasattr(node, \"textalign\"):\n                    if node.textalign == \"left\":\n                        align_ajust = ident / 2\n\n                pos = node.position + node.size // 2\n                pos.x += self.image_ident + align_ajust - node.size.x // 2\n                text_height = self.font_render.calc_height(text, scale=used_scale)\n                if text_height == 0:\n                    text_height = self.param_max_text_size\n                pos.y += text_height // 2\n                self.queue_text(text, *pos, scale=used_scale)\n\n                if node.type == \"textinput\":\n                    if is_selected: #Draw cursor\n                        cpos = node.position.x + self.font_render.calc_size(text[:node.cursor], scale=used_scale) + align_ajust\n                        cy = node.position.y+node.size.y/2\n                        txh = text_height / 2\n                        self.drawer.add_line((cpos, cy-txh), (cpos, cy+txh))\n\n    def render_drawer(self):\n        self.drawer.render(mvp=self.matrix, change_context_state=False)\n\n    def render(self, node):\n        self.node = node\n        try:\n            self.ctx.enable_only(moderngl.BLEND)\n            self.text_queue.clear()\n            self.matrix = Matrix4.orthogonal_projection(0, self.window.width, self.window.height, 0, -1, 1)\n            queue = deque()\n            queue.append(self.node)\n            while queue:\n                current = queue.pop()\n                self.render_node(current)\n                for child in current.children:\n                    queue.append(child)\n            self.render_drawer()\n            \n            for text_data in self.text_queue:\n                *text, scissor, multiline = text_data\n                if not multiline:\n                    self.font_render.render_string(*text, mvp=self.matrix, color=self.param_text_color)\n                else:\n                    self.ctx.scissor = scissor\n                    self.font_render.render_multiline(*text[:3], multiline, mvp=self.matrix)\n                    self.ctx.scissor = None\n        finally:\n            self.ctx.scissor = None\n            ","repo_name":"IntQuant/qlibs","sub_path":"qlibs/gui/widgets/renderers/v0.py","file_name":"v0.py","file_ext":"py","file_size_in_byte":10944,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"35923583088","text":"\"\"\"\nThis module defines classes for computing halfspace intersections\n\"\"\"\n\nfrom __future__ import division\n\n__author__ = \"Will Richards\"\n__version__ = \"2.0\"\n__maintainer__ = \"Will Richards\"\n__email__ = \"wrichard@mit.edu\"\n__date__ = \"August 2, 2013\"\n\nfrom pyhull import qhalf\n\nimport numpy as np\n\nclass Halfspace(object):\n    \"\"\"\n    A halfspace defined by dot(normal, coords) + offset <= 0\n    \"\"\"\n    def __init__(self, normal, offset):\n        \"\"\"\n        Initializes a Halfspace.\n\n        Args:\n            normal: vector normal to hyperplane\n            offset: offset of hyperplane from origin\n        \"\"\"\n        self.normal = normal\n        self.offset = offset\n\n    def __str__(self):\n        return \"Halfspace, normal: {}, offset: {}\".format(self.normal, self.offset)\n\n    @staticmethod\n    def from_hyperplane(basis, origin, point, internal = True):\n        \"\"\"\n        Returns a Halfspace defined by a list of vectors parallel to the\n        bounding hyperplane.\n\n        Args:\n            basis: basis for the hyperplane (array with vector rows)\n            origin: point on the hyperplane\n            point: point not on the hyperplane\n            internal: whether point is inside the halfspace\n        \"\"\"\n        basis = np.array(basis)\n        assert basis.shape[0] + 1 == basis.shape[1]\n\n        big_basis = np.zeros((basis.shape[1], basis.shape[1]))\n        big_basis[:basis.shape[0],:basis.shape[1]] = basis\n\n        u, s, vh = np.linalg.svd(big_basis)\n        null_mask = (s <= 1e-8)\n        normal = np.compress(null_mask, vh, axis=0)[0]\n\n        if np.inner(np.array(point)-np.array(origin), normal) > 0:\n            if internal:\n                normal *= -1\n        else:\n            if not internal:\n                normal *= -1\n        offset = -np.dot(origin, normal)\n        return Halfspace(normal, offset)\n\n\nclass HalfspaceIntersection(object):\n    \"\"\"\n    Uses qhalf to calculate the vertex representation of the intersection\n    of a set of halfspaces\n    \"\"\"\n    def __init__(self, halfspaces, interior_point):\n        self.halfspaces = halfspaces\n        self.interior_point = interior_point\n        self._v_out = None\n        self._fbv_out = None\n        self._fbh_out = None\n\n    @property\n    def vertices(self):\n        \"\"\"\n        Returns the vertices of the halfspace intersection\n        \"\"\"\n        if self._v_out is None:\n            output = qhalf('Fp', self.halfspaces, self.interior_point)\n            pts = []\n            for l in output[2:]:\n                pt = []\n                for c in l.split():\n                    c = float(c)\n                    if c != 10.101 and c != -10.101:\n                        pt.append(c)\n                    else:\n                        pt.append(np.inf)\n                pts.append(pt)\n            self._v_out = np.array(pts)\n        return self._v_out\n\n    @property\n    def facets_by_vertex(self):\n        \"\"\"\n        Returns a list of non-redundant halfspace indices for each vertex\n        e.g: facets_by_vertex[0] is the list of indices of halfspaces\n        incident to vertex 0\n        \"\"\"\n        if self._fbv_out is None:\n            output = qhalf('Fv', self.halfspaces, self.interior_point)\n            facets = []\n            for l in output[1:]:\n                facets.append([int(i) for i in l.split()[1:]])\n            self._fbv_out = facets\n        return self._fbv_out\n\n    @property\n    def facets_by_halfspace(self):\n        \"\"\"\n        Returns a list of vertex indices for each halfspace\n        e.g: facets_by_halfspace[0] is the list of indices ov vertices\n        incident to halfspace 0\n        \"\"\"\n        if self._fbh_out is None:\n            output = qhalf('FN', self.halfspaces, self.interior_point)\n            facets = []\n            for l in output[1:]:\n                facets.append([int(i) for i in l.split()[1:]])\n            self._fbh_out = facets\n        return self._fbh_out\n\n\n","repo_name":"materialsvirtuallab/pyhull","sub_path":"pyhull/halfspace.py","file_name":"halfspace.py","file_ext":"py","file_size_in_byte":3900,"program_lang":"python","lang":"en","doc_type":"code","stars":96,"dataset":"github-code","pt":"86"}
+{"seq_id":"41592171672","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\"\"\"\nCreated on Wed Dec  2 17:35:40 2015\n\n@author: craigmoodie\n\"\"\"\nfrom __future__ import print_function, division, absolute_import, unicode_literals\n\nimport os\nfrom copy import deepcopy\n\nfrom niworkflows.nipype.pipeline import engine as pe\nfrom niworkflows.nipype.interfaces import utility as niu\n\nfrom fmriprep.interfaces import BIDSDataGrabber, BIDSFreeSurferDir\nfrom fmriprep.utils.misc import collect_bids_data\n\nfrom fmriprep.workflows.anatomical import init_anat_preproc_wf\n\nfrom fmriprep.workflows.epi import init_func_preproc_wf\n\nfrom bids.grabbids import BIDSLayout\n\n\ndef init_fmriprep_wf(subject_list, task_id, run_uuid,\n                     ignore, debug, anat_only, omp_nthreads,\n                     skull_strip_ants, reportlets_dir, output_dir, bids_dir,\n                     freesurfer, output_spaces, template, hires,\n                     bold2t1w_dof, fmap_bspline, fmap_demean, use_syn, force_syn,\n                     use_aroma, ignore_aroma_err, output_grid_ref,):\n    fmriprep_wf = pe.Workflow(name='fmriprep_wf')\n\n    if freesurfer:\n        fsdir = pe.Node(\n            BIDSFreeSurferDir(\n                derivatives=output_dir,\n                freesurfer_home=os.getenv('FREESURFER_HOME'),\n                spaces=output_spaces),\n            name='fsdir')\n\n    for subject_id in subject_list:\n        single_subject_wf = init_single_subject_wf(subject_id=subject_id,\n                                                   task_id=task_id,\n                                                   name=\"single_subject_\" + subject_id + \"_wf\",\n                                                   ignore=ignore,\n                                                   debug=debug,\n                                                   anat_only=anat_only,\n                                                   omp_nthreads=omp_nthreads,\n                                                   skull_strip_ants=skull_strip_ants,\n                                                   reportlets_dir=reportlets_dir,\n                                                   output_dir=output_dir,\n                                                   bids_dir=bids_dir,\n                                                   freesurfer=freesurfer,\n                                                   output_spaces=output_spaces,\n                                                   template=template,\n                                                   hires=hires,\n                                                   bold2t1w_dof=bold2t1w_dof,\n                                                   fmap_bspline=fmap_bspline,\n                                                   fmap_demean=fmap_demean,\n                                                   use_syn=use_syn,\n                                                   force_syn=force_syn,\n                                                   output_grid_ref=output_grid_ref,\n                                                   use_aroma=use_aroma,\n                                                   ignore_aroma_err=ignore_aroma_err)\n\n        single_subject_wf.config['execution']['crashdump_dir'] = (\n            os.path.join(output_dir, \"fmriprep\", \"sub-\" + subject_id, 'log', run_uuid)\n        )\n        for node in single_subject_wf._get_all_nodes():\n            node.config = deepcopy(single_subject_wf.config)\n        if freesurfer:\n            fmriprep_wf.connect(fsdir, 'subjects_dir',\n                                single_subject_wf, 'inputnode.subjects_dir')\n        else:\n            fmriprep_wf.add_nodes([single_subject_wf])\n\n    return fmriprep_wf\n\n\ndef init_single_subject_wf(subject_id, task_id, name,\n                           ignore, debug, anat_only, omp_nthreads,\n                           skull_strip_ants, reportlets_dir, output_dir, bids_dir,\n                           freesurfer, output_spaces, template, hires,\n                           bold2t1w_dof, fmap_bspline, fmap_demean, use_syn, force_syn,\n                           output_grid_ref, use_aroma, ignore_aroma_err):\n    \"\"\"\n    The adaptable fMRI preprocessing workflow\n    \"\"\"\n\n    if name == 'single_subject_wf':\n        # for documentation purposes\n        subject_data = {'func': ['/completely/made/up/path/sub-01_task-nback_bold.nii.gz']}\n        layout = None\n    else:\n        layout = BIDSLayout(bids_dir)\n\n        subject_data = collect_bids_data(bids_dir, subject_id, task_id)\n\n        if not anat_only and subject_data['func'] == []:\n            raise Exception(\"No BOLD images found for participant {} and task {}. \"\n                            \"All workflows require BOLD images.\".format(\n                                subject_id, task_id if task_id else '<all>'))\n\n        if not subject_data['t1w']:\n            raise Exception(\"No T1w images found for participant {}. \"\n                            \"All workflows require T1w images.\".format(subject_id))\n\n    workflow = pe.Workflow(name=name)\n\n    inputnode = pe.Node(niu.IdentityInterface(fields=['subjects_dir']),\n                        name='inputnode')\n\n    bidssrc = pe.Node(BIDSDataGrabber(subject_data=subject_data, anat_only=anat_only),\n                      name='bidssrc')\n\n    # Preprocessing of T1w (includes registration to MNI)\n    anat_preproc_wf = init_anat_preproc_wf(name=\"anat_preproc_wf\",\n                                           skull_strip_ants=skull_strip_ants,\n                                           output_spaces=output_spaces,\n                                           template=template,\n                                           debug=debug,\n                                           omp_nthreads=omp_nthreads,\n                                           freesurfer=freesurfer,\n                                           hires=hires,\n                                           reportlets_dir=reportlets_dir,\n                                           output_dir=output_dir)\n\n    workflow.connect([\n        (inputnode, anat_preproc_wf, [('subjects_dir', 'inputnode.subjects_dir')]),\n        (bidssrc, anat_preproc_wf, [('t1w', 'inputnode.t1w'),\n                                    ('t2w', 'inputnode.t2w')]),\n    ])\n\n    if anat_only:\n        return workflow\n\n    for bold_file in subject_data['func']:\n        func_preproc_wf = init_func_preproc_wf(bold_file=bold_file,\n                                               layout=layout,\n                                               ignore=ignore,\n                                               freesurfer=freesurfer,\n                                               bold2t1w_dof=bold2t1w_dof,\n                                               reportlets_dir=reportlets_dir,\n                                               output_spaces=output_spaces,\n                                               template=template,\n                                               output_dir=output_dir,\n                                               omp_nthreads=omp_nthreads,\n                                               fmap_bspline=fmap_bspline,\n                                               fmap_demean=fmap_demean,\n                                               use_syn=use_syn,\n                                               force_syn=force_syn,\n                                               debug=debug,\n                                               output_grid_ref=output_grid_ref,\n                                               use_aroma=use_aroma,\n                                               ignore_aroma_err=ignore_aroma_err)\n\n        workflow.connect([\n            (anat_preproc_wf, func_preproc_wf,\n             [('outputnode.t1_preproc', 'inputnode.t1_preproc'),\n              ('outputnode.t1_brain', 'inputnode.t1_brain'),\n              ('outputnode.t1_mask', 'inputnode.t1_mask'),\n              ('outputnode.t1_seg', 'inputnode.t1_seg'),\n              ('outputnode.t1_tpms', 'inputnode.t1_tpms'),\n              ('outputnode.t1_2_mni_forward_transform', 'inputnode.t1_2_mni_forward_transform'),\n              ('outputnode.t1_2_mni_reverse_transform', 'inputnode.t1_2_mni_reverse_transform')])\n        ])\n\n        if freesurfer:\n            workflow.connect([\n                (anat_preproc_wf, func_preproc_wf,\n                 [('outputnode.subjects_dir', 'inputnode.subjects_dir'),\n                  ('outputnode.subject_id', 'inputnode.subject_id'),\n                  ('outputnode.fs_2_t1_transform', 'inputnode.fs_2_t1_transform')]),\n            ])\n\n    return workflow\n","repo_name":"vsoch/fmriprep-debug","sub_path":"fmriprep/workflows/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":8584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"24888821054","text":"import pandas as pd\nfrom gensim.corpora import Dictionary\nfrom gensim.models.ldamodel import LdaModel\nfrom sys import path as syspath\nfrom os import path as osPath, getcwd\nfrom Classifier import get_KNN_Model, get_lin_SVM_Model, get_NaiveBayes_Model\nimport numpy as np\nimport torch.nn as nn\nimport warnings\nimport torch\n\nfrom tensorflow.keras.models import load_model\n\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.utils import to_categorical\nfrom filters import train_test_splitter\nfrom tensorflow.keras.layers import Input, Embedding, Bidirectional, LSTM, BatchNormalization, Dense, Dropout, Flatten\nfrom tensorflow.keras.callbacks import EarlyStopping\nfrom tensorflow.keras.models import Sequential\nfrom os import listdir\n\nwarnings.simplefilter(action='ignore')\n\nsyspath.insert(1, (osPath.dirname(getcwd()).replace('\\\\', '\\\\')) + '\\\\2_Cleaning_Visualization')\n\n# noinspection PyUnresolvedReferences\nfrom text_cleaning import text_clean\n\nnum_topics = 10\n\nx_train, x_test, y_train, y_test = None, None, None, None\ndf, dict_genuine, dict_fake, lda_genuine, lda_fake = None, None, None, None, None\n\nis_LDA_trained = False\ndata_updated = False\n\nLDA = None\ncorpus = None\nDict = None\n\n\nclass LstmClassifierModel(nn.Module):\n    def __init__(self, input_size, hidden_size, num_layers, output_size):\n        super(LstmClassifierModel, self).__init__()\n\n        self.num_layers = num_layers\n        self.hidden_size = hidden_size\n\n        self.lstm = nn.LSTM(input_size, hidden_size, num_layers)\n\n        self.fc = nn.Linear(hidden_size, output_size)\n\n    def forward(self, inputs):\n        h0 = torch.zeros(self.num_layers, inputs.size(1), self.hidden_size).to(device)\n        c0 = torch.zeros(self.num_layers, inputs.size(1), self.hidden_size).to(device)\n\n        out, _ = self.lstm(inputs, (h0, c0))\n        out = out[:, -1, :]\n\n        out = self.fc(out)\n\n        return out\n\n\nclass LDAClassifierModel(nn.Module):\n    def __init__(self, input_size, output_size):\n        super(LDAClassifierModel, self).__init__()\n        self.layer1 = nn.Linear(input_size, 64)\n        self.layer2 = nn.Linear(64, 128)\n        self.layer3 = nn.Linear(128, 256)\n        self.layer4 = nn.Linear(256, 512)\n        self.layer5 = nn.Linear(512, 256)\n\n        self.layer6 = nn.Linear(256, 128)\n        self.layer7 = nn.Linear(128, 64)\n        self.layer8 = nn.Linear(64, 32)\n        self.layer9 = nn.Linear(32, 16)\n        self.layer10 = nn.Linear(16, output_size)\n        self.relu = nn.ReLU()\n\n    def forward(self, inputs):\n        out = self.layer1(inputs)\n        out = self.relu(out)\n\n        out = self.layer2(out)\n        out = self.relu(out)\n\n        out = self.layer3(out)\n        out = self.relu(out)\n\n        out = self.layer4(out)\n        out = self.relu(out)\n\n        out = self.layer5(out)\n        out = self.relu(out)\n\n        out = self.layer6(out)\n        out = self.relu(out)\n\n        out = self.layer7(out)\n        out = self.relu(out)\n\n        out = self.layer8(out)\n        out = self.relu(out)\n\n        out = self.layer9(out)\n        out = self.relu(out)\n\n        out = self.layer10(out)\n\n        return out\n\n\ndef get_Device():\n    return torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n\ndevice = get_Device()\n\n\ndef get_topic_vector(tweet_text, num_topics, Dict, LDA):\n    d2b = Dict.doc2bow(tweet_text)\n\n    topic_vector_sparse = LDA.get_document_topics(d2b)\n\n    topic_vector = np.zeros(num_topics)\n\n    for pair in topic_vector_sparse:\n        topic_vector[pair[0]] = pair[1]\n\n    return topic_vector\n\n\ndef get_LDA_trained_Models():\n    dataset_path = \"C:/Users/Sampad/Desktop/Projects/Capstone/Implimentation/Code/0_DataSet/\"\n\n    df = pd.read_csv(dataset_path + \"CompleteAnnotated.csv\")\n\n    df.tweet_text = df.tweet_text.apply(text_clean)\n\n    all_docs_genuine = []\n    all_docs_fake = []\n    labels = []\n    complete_docs = []\n\n    for i in range(df.shape[0]):\n        labels.append(df.iloc[i]['Annotation'])\n        complete_docs.append(df.iloc[i]['tweet_text'].split())\n        if df.iloc[i]['Annotation'] == 0:\n            all_docs_genuine.append(df.iloc[i]['tweet_text'].split())\n        else:\n            all_docs_fake.append(df.iloc[i]['tweet_text'].split())\n    complete_dict = Dictionary(complete_docs)\n\n    complete_corpus = [complete_dict.doc2bow(text) for text in complete_docs]\n\n    lda = LdaModel(complete_corpus, num_topics=10)\n\n    X = np.array([get_topic_vector(tweet.split(), 10, complete_dict, lda) for tweet in df.tweet_text])\n    Y = np.array(labels)\n    x_train, x_test, y_train, y_test = train_test_splitter(X, Y)\n    return x_train, x_test, y_train, y_test, complete_dict, complete_corpus, lda\n\n\ndef get_BOW_trainable_data():\n    dataset_path = \"C:/Users/Sampad/Desktop/Projects/Capstone/Implimentation/Code/0_DataSet/\"\n\n    df = pd.read_csv(dataset_path + \"CompleteAnnotated.csv\")\n\n    df.tweet_text = df.tweet_text.apply(text_clean)\n    tokenizer = Tokenizer()\n    tokenizer.fit_on_texts(df.tweet_text)\n    counts = tokenizer.word_counts\n\n    word_size = 7000\n    vocab_size = word_size\n    tokenizer = Tokenizer(num_words=word_size)\n\n    tokenizer.fit_on_texts(df.tweet_text)\n    tokenized = tokenizer.texts_to_sequences(df.tweet_text)\n\n    sequence_size = 18\n    padded = pad_sequences(tokenized, maxlen=sequence_size, padding='post', truncating='post')\n    x_train, x_test, y_train, y_test = train_test_split(padded, df.Annotation.values, test_size=0.20)\n    return x_train, x_test, y_train, y_test, tokenizer\n\n\ndef get_trainable_data():\n    global x_train, x_test, y_train, y_test, data_updated, LDA, corpus, Dict\n    x_train, x_test, y_train, y_test, Dict, corpus, LDA = get_LDA_trained_Models()\n    data_updated = True\n    return x_train, x_test, y_train, y_test\n\n\ndef get_text_knn_model():\n    global x_train, x_test, y_train, y_test, data_updated\n    if data_updated == False:\n        x_train, x_test, y_train, y_test = get_trainable_data()\n    return get_KNN_Model(x_train, y_train)\n\n\ndef get_text_svm_model():\n    global x_train, x_test, y_train, y_test, data_updated\n    if data_updated == False:\n        x_train, x_test, y_train, y_test = get_trainable_data()\n    return get_lin_SVM_Model(x_train, y_train)\n\n\ndef get_text_NB_model():\n    global x_train, x_test, y_train, y_test, data_updated\n    if data_updated == False:\n        x_train, x_test, y_train, y_test = get_trainable_data()\n    return get_NaiveBayes_Model(x_train, y_train)\n\n\ndef Validator(model, x_test, y_test):\n    predicted = model(x_test).to(device)\n    pred = torch.max(predicted.data, 1)[1]\n    total_test = len(y_test)\n    correct_pred = 0\n\n    for i in range(total_test):\n        if y_test[i] == pred[i]:\n            correct_pred += 1\n\n    return correct_pred / total_test\n\n\ndef get_text_nnLDA_model():\n    global x_train, x_test, y_train, y_test, data_updated\n\n    if \"textual_simple_nn.pt\" in listdir(\n            \"C:\\\\Users\\\\Sampad\\\\Desktop\\\\Projects\\\\Capstone\\\\Implimentation\\\\Code\\\\4_Model\\\\saved_model\"):\n        return torch.load(\n            \"C:\\\\Users\\\\Sampad\\\\Desktop\\\\Projects\\\\Capstone\\\\Implimentation\\\\Code\\\\4_Model\\\\saved_model\\\\textual_simple_nn.pt\").to(\n            device)\n\n    if data_updated == False:\n        x_train, x_test, y_train, y_test = get_trainable_data()\n\n    x_tr = torch.Tensor(x_train).to(device)\n    y_tr = torch.Tensor(y_train).to(device)\n    x_ts = torch.Tensor(x_test).to(device)\n    y_ts = torch.Tensor(y_test).to(device)\n    y_tr = y_tr.to(torch.long)\n\n    input_size = 10\n    output_size = 2\n    learning_rate = 0.0001\n    n_epochs = 250\n\n    model = LDAClassifierModel(input_size=input_size,\n                               output_size=output_size)\n\n    model.to(device)\n\n    lossfn = torch.nn.CrossEntropyLoss()\n    lossfn.to(device)\n    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n\n    models = []\n    acc = []\n\n    for epoch in range(n_epochs):\n        predicted = model(x_tr).to(device)\n\n        loss = lossfn(predicted, y_tr)\n\n        optimizer.zero_grad()\n        loss.backward()\n\n        optimizer.step()\n\n        val_acc = Validator(model, x_ts, y_ts.to(torch.int))\n\n        acc.append(val_acc)\n        models.append(model)\n\n        # print(f'Epoch [ {epoch + 1} / {n_epochs} ] Training-Loss = {loss.item():.4f} Training-Accuracy = {1 - loss.item()} Validation-Accuracy = {val_acc}')\n    model = models[acc.index(max(acc))]\n    torch.save(model,\n               \"C:\\\\Users\\\\Sampad\\\\Desktop\\\\Projects\\\\Capstone\\\\Implimentation\\\\Code\\\\4_Model\\\\saved_model\\\\textual_simple_nn.pt\")\n    return model\n\n\ndef get_text_nnBOW_model():\n    x_train, x_test, y_train, y_test, tokenizer = get_BOW_trainable_data()\n\n    if \"textual_lstm_nn.pt\" in listdir(\n            \"C:\\\\Users\\\\Sampad\\\\Desktop\\\\Projects\\\\Capstone\\\\Implimentation\\\\Code\\\\4_Model\\\\saved_model\"):\n        return torch.load(\n            \"C:\\\\Users\\\\Sampad\\\\Desktop\\\\Projects\\\\Capstone\\\\Implimentation\\\\Code\\\\4_Model\\\\saved_model\\\\textual_lstm_nn.pt\").to(\n            device), tokenizer\n\n    x_train = x_train.reshape(x_train.shape[0], 1, x_train.shape[1])\n    x_test = x_test.reshape(x_test.shape[0], 1, x_test.shape[1])\n\n    x_train = torch.Tensor(x_train).to(device)\n    y_train = torch.Tensor(y_train).to(device)\n    x_test = torch.Tensor(x_test).to(device)\n    y_test = torch.Tensor(y_test).to(device)\n    y_train = y_train.to(torch.long)\n\n    input_size = 18\n    output_size = 2\n    hidden_size = 256\n    num_layers = 16\n    learning_rate = 0.0001\n    n_epochs = 250\n\n    model = LstmClassifierModel(input_size=input_size,\n                                hidden_size=hidden_size,\n                                num_layers=num_layers,\n                                output_size=output_size)\n\n    model.to(device)\n\n    lossfn = torch.nn.CrossEntropyLoss()\n    lossfn.to(device)\n    optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n\n    models = []\n    acc = []\n\n    for epoch in range(n_epochs):\n        predicted = model(x_train).to(device)\n\n        loss = lossfn(predicted, y_train)\n\n        optimizer.zero_grad()\n        loss.backward()\n\n        optimizer.step()\n        val_acc = Validator(model, x_test, y_test.to(torch.int))\n        acc.append(val_acc)\n        models.append(model)\n\n        # print(f'Epoch [{epoch + 1}/{n_epochs}] Training-Loss = {loss.item():.4f} Train-Accuracy = {1 - loss.item():.4f} Valid-Accuracy = {val_acc}')\n\n    torch.save(models[acc.index(max(acc))],\n               \"C:\\\\Users\\\\Sampad\\\\Desktop\\\\Projects\\\\Capstone\\\\Implimentation\\\\Code\\\\4_Model\\\\saved_model\\\\textual_lstm_nn.pt\")\n    return models[acc.index(max(acc))], tokenizer\n\n\ndef get_text_bilstm_model():\n    x_train, x_test, y_train, y_test, tokenizer = get_BOW_trainable_data()\n    y_train = to_categorical(y_train, num_classes=2)\n\n    word_vec_size = 20\n    hidden_size = 128\n    sequence_size = 18\n    vocab_size = 7000\n\n    if \"textual_bilstm_nn.h5\" in listdir(\n            \"C:\\\\Users\\\\Sampad\\\\Desktop\\\\Projects\\\\Capstone\\\\Implimentation\\\\Code\\\\4_Model\\\\saved_model\"):\n        return load_model(\n            \"C:\\\\Users\\\\Sampad\\\\Desktop\\\\Projects\\\\Capstone\\\\Implimentation\\\\Code\\\\4_Model\\\\saved_model\\\\textual_bilstm_nn.h5\"), tokenizer\n\n    model = Sequential()\n    model.add(Input(shape=[sequence_size]))\n    model.add(Embedding(vocab_size, word_vec_size, input_length=sequence_size))\n\n    model.add(Bidirectional(LSTM(hidden_size, return_sequences=True)))\n    model.add(BatchNormalization())\n    model.add(Bidirectional(LSTM(int(hidden_size / 2), return_sequences=True)))\n    model.add(BatchNormalization())\n    model.add(Bidirectional(LSTM(int(hidden_size / 2), return_sequences=True)))\n\n    model.add(Flatten())\n    model.output_shape\n    model.add(Dense(256, activation='relu'))\n    model.add(Dense(128, activation='relu'))\n    model.add(Dropout(0.25))\n    model.add(Dense(2, activation='softmax'))\n\n    # model = keras.models.Model(X,Y)\n    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n    # model.summary()\n\n    es = EarlyStopping(monitor='val_accuracy', mode='min', patience=6, verbose=1)\n\n    hist = model.fit(x_train, y_train, epochs=100, batch_size=256, validation_split=0.2, callbacks=[es])\n\n    model.save(\n        \"C:\\\\Users\\\\Sampad\\\\Desktop\\\\Projects\\\\Capstone\\\\Implimentation\\\\Code\\\\4_Model\\\\saved_model\\\\textual_bilstm_nn.h5\")\n    return model, tokenizer\n","repo_name":"Sampad-Hegde/Detecting-Fake-Re_Tweeters-and-Hashtag-misuse-using-Machine-Learning-and-Topic-Modelling","sub_path":"4_Model/LDA.py","file_name":"LDA.py","file_ext":"py","file_size_in_byte":12425,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"14500455427","text":"import numpy as np\nimport pygame\nimport sys\nimport time\n#module I wrote to keep maze wall definitions in, also in the github portfolio:\nimport rw\n\npygame.init()\n\nepsilon = 0.9            \ndiscount_factor = 0.9\nlearning_rate = 0.9\n\nwidth = 1000\nheight = 500\nsteps = 0\ntotal = 0\nenv_x = 25\nenv_y = 50\n\n#drawing coordinates:\nx = 0\ny = 0\nagent_x = 0\nagent_y = 100\ncolor = (66, 135, 245)\n\niterations = 1200\nmax_steps = 10000\ndelay = 5\n\npygame.display.set_caption(\"optimum path\")\nscreen = pygame.display.set_mode((width, height))\nclock = pygame.time.Clock()\nrunning = True\n\n#3d tensor of q values based on location and 4 possible actions\nq_values = np.random.random((env_x, env_y, 4))\n\n#to avoid redrawing squares:\ndrawn = np.zeros((25, 50)) \n\n#did agent reach the goal?\ndef episode_complete(current_row_index, current_col_index):\n    if rw.rewards[current_row_index][current_col_index] == 75:\n        return True\n    \n#what to do next? act on experience or explore?   \ndef get_next_action(current_row_index, current_col_index, epsilon):\n    if np.random.random() < epsilon:\n        return np.argmax(q_values[current_row_index, current_col_index])\n    else:\n        return np.random.randint(4)\n\n#update location\ndef get_next_location(current_row_index, current_col_index, action_index):\n    new_row_index = current_row_index\n    new_col_index = current_col_index\n    if action_index == 0 and current_row_index > 0:\n        new_row_index -= 1\n    elif action_index == 1 and current_col_index < 49:\n        new_col_index += 1\n    elif action_index == 2 and current_row_index < 24:\n        new_row_index += 1\n    elif action_index == 3 and current_col_index > 0:\n        new_col_index -= 1\n    return new_row_index, new_col_index\n\n#the final iteration will be with epsilon = 1, and hence reveal optimum path (no randomness). \nfor episode in range((iterations + 1)): \n\n    drawn.fill(0)\n    screen.fill((0, 0, 0))\n    \n    #how experience vs explore is decided. explore a lot at the beginning and act on experience later\n    if 1000 < episode:\n        epsilon = 0.95\n    elif 100 < episode:\n        epsilon = 0.9\n    else:\n        epsilon = 0.2\n        \n    #drawing loop\n    for i in range(len(rw.rewards)):\n        for j in range(len(rw.rewards[i])):\n            if (rw.rewards[i][j] == -100) and (episode % 100 == 0):\n                pygame.draw.rect(screen, (42, 163, 127), (x, y, 20, 20), 5)\n            x += 20\n        x = 0\n        if y == 480:\n            y = 0\n        else:\n            y += 20\n\n    total += steps\n    steps = 0\n    \n    #return to starting point\n    row_index = 5\n    col_index = 0\n    \n    if episode == iterations:\n        color = (180, 202, 237)\n        avg = round(total / episode, 2)\n        epsilon = 1\n        \n    while not episode_complete(row_index, col_index):\n        \n        action_index = get_next_action(row_index, col_index, epsilon)\n        old_row_index, old_col_index = row_index, col_index\n        row_index, col_index = get_next_location(row_index, col_index, action_index)\n        #receive reward for current loction\n        reward = rw.rewards[row_index, col_index]\n        #save old q value before updating\n        old_q_value = q_values[old_row_index, old_col_index, action_index]\n        #how much should the q value change?\n        temporal_difference = reward + (discount_factor * np.max(q_values[row_index, col_index])) - old_q_value            \n        #update q value for current location\n        new_q_value = old_q_value + (learning_rate * temporal_difference)\n        q_values[old_row_index, old_col_index, action_index] = new_q_value\n        \n        if steps == max_steps:\n            break\n        \n        steps += 1\n        \n        #drawing coordinates\n        agent_x = (col_index * 20)\n        agent_y = (row_index * 20)\n        \n        #update best path drawing every 100 iterations, make sure not to redraw squares\n        if drawn[row_index][col_index] == 0 and episode % 100 == 0:\n\n            pygame.draw.rect(screen, color, (agent_x, agent_y, 20, 20), 2)\n            pygame.draw.rect(screen, (117, 240, 117), (780, 440, 20, 20))\n            pygame.draw.rect(screen, (255, 255, 255), (0, 100, 20, 20))\n            pygame.display.flip()\n            \n        drawn[row_index][col_index] = 1\n        \n    if episode % 100 == 0:\n        print(episode, steps)\n\n    if episode == (iterations):\n        print(\"Average steps =\", avg)\n        print(\"Optimum path is\", steps, \"steps.\")\n        \n    episode += 1\n        \n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            running = False\n            \ntime.sleep(delay)\npygame.quit()\nsys.exit()\n\n        \n    \n","repo_name":"AntonHufford/Portfolio","sub_path":"q_learning_optimum_path.py","file_name":"q_learning_optimum_path.py","file_ext":"py","file_size_in_byte":4651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"5258277106","text":"from unittest import TestCase\n\nfrom nose_parameterized import parameterized\n\nfrom triangle import Solution\n\n\nclass TestTriangle(TestCase):\n    @parameterized.expand([\n        [\n            {\n                'triangle': [\n                    [2],\n                    [3, 4],\n                    [6, 5, 7],\n                    [4, 1, 8, 3],\n                ],\n            },\n            11,\n        ],\n    ])\n    def test_minimum_total(self, kwargs, expected_ans):\n        # Setup\n        sol = Solution()\n\n        # Exercise\n        ans = sol.minimum_total(**kwargs)\n\n        # Verify\n        self.assertEqual(ans, expected_ans)\n","repo_name":"nkukarl/leetcode","sub_path":"triangle_test.py","file_name":"triangle_test.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"6017109974","text":"from django.contrib import admin\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom relativedeltafield import RelativeDeltaField\n\nfrom ztc.datamodel.models.besluittype import BesluitType\n\nfrom ..models import ResultaatType, ZaakInformatieobjectTypeArchiefregime\nfrom .forms import RelativeDeltaField as RelativeDeltaFormField, ResultaatTypeForm\n\n\nclass ZaakInformatieobjectTypeArchiefregimeInline(admin.TabularInline):\n    model = ZaakInformatieobjectTypeArchiefregime\n    extra = 1\n\n\nclass BesluitTypeInline(admin.TabularInline):\n    model = BesluitType.resultaattypen.through\n    extra = 1\n\n\n@admin.register(ResultaatType)\nclass ResultaatTypeAdmin(admin.ModelAdmin):\n    model = ResultaatType\n    form = ResultaatTypeForm\n\n    # List\n    list_display = (\n        \"omschrijving\",\n        \"omschrijving_generiek\",\n        \"selectielijstklasse\",\n        \"uuid\",\n    )\n    ordering = (\"zaaktype\", \"omschrijving\")\n    search_fields = (\n        \"omschrijving\",\n        \"omschrijving_generiek\",\n        \"selectielijstklasse\",\n        \"toelichting\",\n        \"uuid\",\n    )\n\n    # Details\n    fieldsets = (\n        (\n            _(\"Algemeen\"),\n            {\n                \"fields\": (\n                    \"zaaktype\",\n                    \"omschrijving\",\n                    \"toelichting\",\n                    \"procesobjectaard\",\n                    \"indicatie_specifiek\",\n                    \"procestermijn\",\n                    \"datum_begin_geldigheid\",\n                    \"datum_einde_geldigheid\",\n                )\n            },\n        ),\n        (\n            _(\"Gemeentelijke selectielijst\"),\n            {\"fields\": (\"resultaattypeomschrijving\", \"selectielijstklasse\")},\n        ),\n        (\n            _(\"Bepaling brondatum archiefprocedure\"),\n            {\n                \"fields\": (\n                    \"brondatum_archiefprocedure_afleidingswijze\",\n                    \"brondatum_archiefprocedure_datumkenmerk\",\n                    \"brondatum_archiefprocedure_einddatum_bekend\",\n                    \"brondatum_archiefprocedure_objecttype\",\n                    \"brondatum_archiefprocedure_registratie\",\n                    \"brondatum_archiefprocedure_procestermijn\",\n                )\n            },\n        ),\n        (\n            _(\"Relaties\"),\n            {\n                \"fields\": (\n                    \"catalogus\",\n                    \"zaakobjecttypen\",\n                    \"informatieobjecttypen\",\n                )\n            },\n        ),\n    )\n    raw_id_fields = (\"zaaktype\",)\n    filter_horizontal = (\n        \"zaakobjecttypen\",\n        \"informatieobjecttypen\",\n    )\n    inlines = (BesluitTypeInline,)\n\n    def formfield_for_dbfield(self, db_field, request, **kwargs):\n        if isinstance(db_field, RelativeDeltaField):\n            kwargs[\"form_class\"] = RelativeDeltaFormField\n        return super().formfield_for_dbfield(db_field, request, **kwargs)\n","repo_name":"VNG-Realisatie/catalogi-api","sub_path":"src/ztc/datamodel/admin/resultaattype.py","file_name":"resultaattype.py","file_ext":"py","file_size_in_byte":2885,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"86"}
+{"seq_id":"32811532792","text":"import math\n\ndef issosu(num):\n    if num == 1:\n        return False\n\n    for i in range(2,int(math.sqrt(num))+1):\n        if num%i==0:\n            return False\n    return True\n\narr = [0]*(123456*2+1)\nsosusum = 0\nfor i in range(2,123456*2+1):\n if issosu(i):\n    sosusum+=1\n    arr[i]=sosusum\n else:\n    arr[i]=sosusum\n\n\nwhile(True):\n    result=0\n    n = int(input())\n    if n==0:\n        break\n    \n    print(arr[2*n]-arr[n])","repo_name":"soongsari/Algorithm","sub_path":"백준알고리즘/손지혜/소수_4948.py","file_name":"소수_4948.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"5671743497","text":"import torch\n\nfrom ..trainer import Trainer\nfrom ...evaluation.evaluators.evaluator import VoidEvaluator\n\n\nclass MultitaskSupervisedTrainer(Trainer):\n    \"\"\"\n    Trainer for multitask supervised tasks learning of predicting multiple outputs given x (classification or regression).\n    \"\"\"\n\n    def __init__(self, model, optimizer, by_task_loss_functions, by_task_loss_weights=None,\n                 train_evaluator=VoidEvaluator(), val_evaluator=VoidEvaluator(),\n                 callback=None, device=torch.device(\"cpu\")):\n        \"\"\"\n        :param model: model that outputs a dictionary with name and output for each task.\n        :param optimizer: optimizer.\n        :param by_task_loss_functions: dictionary of loss functions, names should match outputs of model.\n        :param by_task_loss_weights: dictionary of weights for the corresponding loss functions.\n        :param train_evaluator: train phase evaluator.\n        :param val_evaluator: validation phase evaluator.\n        :param callback: callback for the training process.\n        :param device: device to run on.\n        \"\"\"\n        super().__init__(model, optimizer, train_evaluator, val_evaluator, callback, device)\n        self.by_task_loss_functions = by_task_loss_functions\n        self.by_task_loss_weights = by_task_loss_weights if by_task_loss_weights is not None else {}\n\n    def batch_update(self, batch_num, batch, total_num_batches):\n        self.optimizer.zero_grad()\n\n        x, by_task_y = batch\n        x = x.to(self.device)\n        by_task_y = {task_name: y.to(self.device) for task_name, y in by_task_y.items()}\n\n        by_task_y_pred = self.model(x)\n        by_task_loss = self.__calculate_by_task_losses(by_task_y_pred, by_task_y)\n\n        total_loss = self.__calculate_total_loss(by_task_loss)\n        total_loss.backward()\n        self.optimizer.step()\n\n        return {\n            \"loss\": total_loss.item(),\n            \"by_task_loss\": {name: loss.item() for name, loss in by_task_loss.items()},\n            \"by_task_y_pred\": {task_name: task_y_pred.detach() for task_name, task_y_pred in by_task_y_pred.items()},\n            \"by_task_y\": by_task_y\n        }\n\n    def __calculate_by_task_losses(self, by_task_y_preds, by_task_y):\n        by_task_losses = {}\n        for name, loss_fn in self.by_task_loss_functions.items():\n            y_pred = by_task_y_preds[name]\n            y = by_task_y[name]\n\n            loss = loss_fn(y_pred, y)\n            by_task_losses[name] = loss\n\n        return by_task_losses\n\n    def __calculate_total_loss(self, by_task_losses):\n        losses = [self.by_task_loss_weights.get(name, 1) * loss for name, loss in by_task_losses.items()]\n        return sum(losses)\n","repo_name":"noamrazin/gnn_interactions","sub_path":"common/train/trainers/multitask_supervised_trainer.py","file_name":"multitask_supervised_trainer.py","file_ext":"py","file_size_in_byte":2689,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"86"}
+{"seq_id":"21138019669","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom operator_pool import *\nfrom torch.autograd import Variable\nfrom genotypes import PRIMITIVES\n# from utils.darts_utils import drop_path, compute_speed, compute_speed_tensorrt\nfrom pdb import set_trace as bp\nfrom seg_oprs import Head\nimport numpy as np\nimport networks\nfrom layers import *\nfrom collections import OrderedDict\nfrom loss import MonodepthLoss\n###############depth#################################\n\ndef rot_from_axisangle(vec):\n    \"\"\"Convert an axisangle rotation into a 4x4 transformation matrix\n    (adapted from https://github.com/Wallacoloo/printipi)\n    Input 'vec' has to be Bx1x3\n    \"\"\"\n    angle = torch.norm(vec, 2, 2, True)\n    axis = vec / (angle + 1e-7)\n\n    ca = torch.cos(angle)\n    sa = torch.sin(angle)\n    C = 1 - ca\n\n    x = axis[..., 0].unsqueeze(1)\n    y = axis[..., 1].unsqueeze(1)\n    z = axis[..., 2].unsqueeze(1)\n\n    xs = x * sa\n    ys = y * sa\n    zs = z * sa\n    xC = x * C\n    yC = y * C\n    zC = z * C\n    xyC = x * yC\n    yzC = y * zC\n    zxC = z * xC\n\n    rot = torch.zeros((vec.shape[0], 4, 4)).to(device=vec.device)\n\n    rot[:, 0, 0] = torch.squeeze(x * xC + ca)\n    rot[:, 0, 1] = torch.squeeze(xyC - zs)\n    rot[:, 0, 2] = torch.squeeze(zxC + ys)\n    rot[:, 1, 0] = torch.squeeze(xyC + zs)\n    rot[:, 1, 1] = torch.squeeze(y * yC + ca)\n    rot[:, 1, 2] = torch.squeeze(yzC - xs)\n    rot[:, 2, 0] = torch.squeeze(zxC - ys)\n    rot[:, 2, 1] = torch.squeeze(yzC + xs)\n    rot[:, 2, 2] = torch.squeeze(z * zC + ca)\n    rot[:, 3, 3] = 1\n\n    return rot\n\n\ndef get_translation_matrix(translation_vector):\n    \"\"\"Convert a translation vector into a 4x4 transformation matrix\n    \"\"\"\n    T = torch.zeros(translation_vector.shape[0], 4, 4).to(device=translation_vector.device)\n\n    t = translation_vector.contiguous().view(-1, 3, 1)\n\n    T[:, 0, 0] = 1\n    T[:, 1, 1] = 1\n    T[:, 2, 2] = 1\n    T[:, 3, 3] = 1\n    T[:, :3, 3, None] = t\n\n    return T\n\ndef transformation_from_parameters(axisangle, translation, invert=False):\n    \"\"\"Convert the network's (axisangle, translation) output into a 4x4 matrix\n    \"\"\"\n    R = rot_from_axisangle(axisangle)\n    t = translation.clone()\n\n    if invert:\n        R = R.transpose(1, 2)\n        t *= -1\n\n    T = get_translation_matrix(t)\n\n    if invert:\n        M = torch.matmul(R, T)\n    else:\n        M = torch.matmul(T, R)\n\n    return M\n\n\n\ndef predict_poses(self, inputs, features):\n    \"\"\"Predict poses between input frames for monocular sequences.\n    \"\"\"\n    a = []\n    outputs = {}\n    model_pose_encoder = networks.ResnetEncoder(self.num_layers, self.weights_init == \"pretrained\",num_input_images=2)\n\n    model_pose = networks.PoseDecoder(model_pose_encoder.num_ch_enc,num_input_features=1,num_frames_to_predict_for=2)\n\n\n    # In this setting, we compute the pose to each source frame via a\n    # separate forward pass through the pose network.\n\n    # select what features the pose network takes as input\n\n    pose_feats = {f_i: inputs[\"color_aug\", f_i, 0] for f_i in self.frame_ids}\n\n    for f_i in self.frame_ids[1:]:\n        if f_i != \"s\":\n            # To maintain ordering we always pass frames in temporal order\n            if f_i < 0:\n                pose_inputs = [pose_feats[f_i], pose_feats[0]]\n            else:\n                pose_inputs = [pose_feats[0], pose_feats[f_i]]\n\n            pose_inputs[0] = torch.randn((1,3,192,640))\n            pose_inputs[1] = torch.randn((1,3,192,640))\n            pose_inputs = model_pose_encoder(torch.cat(pose_inputs, 1))\n            a.append(pose_inputs)\n            axisangle, translation = model_pose(a[f_i])\n            outputs[(\"axisangle\", 0, f_i)] = axisangle\n            outputs[(\"translation\", 0, f_i)] = translation\n\n            # Invert the matrix if the frame id is negative\n            outputs[(\"cam_T_cam\", 0, f_i)] = transformation_from_parameters(\n                axisangle[:, 0], translation[:, 0], invert=(f_i < 0))\n    return outputs\n\nclass conv(nn.Module):\n    def __init__(self, num_in_layers=128, num_out_layers=256, kernel_size=3, stride=1):\n        super(conv, self).__init__()\n        self.kernel_size = kernel_size\n        self.conv_base = nn.Conv2d(num_in_layers, num_out_layers, kernel_size=kernel_size, stride=stride)\n        self.normalize = nn.BatchNorm2d(num_out_layers)\n\n    def forward(self, x):\n        p = int(np.floor((self.kernel_size-1)/2))\n        p2d = (p, p, p, p)\n        x = self.conv_base(F.pad(x, p2d))\n        x = self.normalize(x)\n        return F.elu(x, inplace=True)\n\n\nclass convblock(nn.Module):\n    def __init__(self, num_in_layers, num_out_layers, kernel_size):\n        super(convblock, self).__init__()\n        self.conv1 = conv(num_in_layers, num_out_layers, kernel_size, 1)\n        self.conv2 = conv(num_out_layers, num_out_layers, kernel_size, 2)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        return self.conv2(x)\n\n\nclass maxpool(nn.Module):\n    def __init__(self, kernel_size):\n        super(maxpool, self).__init__()\n        self.kernel_size = kernel_size\n\n    def forward(self, x):\n        p = int(np.floor((self.kernel_size-1) / 2))\n        p2d = (p, p, p, p)\n        return F.max_pool2d(F.pad(x, p2d), self.kernel_size, stride=2)\n\n\nclass resconv(nn.Module):\n    def __init__(self, num_in_layers, num_out_layers, stride):\n        super(resconv, self).__init__()\n        self.num_out_layers = num_out_layers\n        self.stride = stride\n        self.conv1 = conv(num_in_layers, num_out_layers, 1, 1)\n        self.conv2 = conv(num_out_layers, num_out_layers, 3, stride)\n        self.conv3 = nn.Conv2d(num_out_layers, 4*num_out_layers, kernel_size=1, stride=1)\n        self.conv4 = nn.Conv2d(num_in_layers, 4*num_out_layers, kernel_size=1, stride=stride)\n        self.normalize = nn.BatchNorm2d(4*num_out_layers)\n\n    def forward(self, x):\n        # do_proj = x.size()[1] != self.num_out_layers or self.stride == 2\n        do_proj = True\n        shortcut = []\n        x_out = self.conv1(x)\n        x_out = self.conv2(x_out)\n        x_out = self.conv3(x_out)\n        if do_proj:\n            shortcut = self.conv4(x)\n        else:\n            shortcut = x\n        return F.elu(self.normalize(x_out + shortcut), inplace=True)\n\n\nclass resconv_basic(nn.Module):\n    # for resnet18\n    def __init__(self, num_in_layers, num_out_layers, stride):\n        super(resconv_basic, self).__init__()\n        self.num_out_layers = num_out_layers\n        self.stride = stride\n        self.conv1 = conv(num_in_layers, num_out_layers, 3, stride)\n        self.conv2 = conv(num_out_layers, num_out_layers, 3, 1)\n        self.conv3 = nn.Conv2d(num_in_layers, num_out_layers, kernel_size=1, stride=stride)\n        self.normalize = nn.BatchNorm2d(num_out_layers)\n\n    def forward(self, x):\n        #         do_proj = x.size()[1] != self.num_out_layers or self.stride == 2\n        do_proj = True\n        shortcut = []\n        x_out = self.conv1(x)\n        x_out = self.conv2(x_out)\n        if do_proj:\n            shortcut = self.conv3(x)\n        else:\n            shortcut = x\n        return F.elu(self.normalize(x_out + shortcut), inplace=True)\n\n\ndef resblock(num_in_layers, num_out_layers, num_blocks, stride):\n    layers = []\n    layers.append(resconv(num_in_layers, num_out_layers, stride))\n    for i in range(1, num_blocks - 1):\n        layers.append(resconv(4 * num_out_layers, num_out_layers, 1))\n    layers.append(resconv(4 * num_out_layers, num_out_layers, 1))\n    return nn.Sequential(*layers)\n\n\ndef resblock_basic(num_in_layers, num_out_layers, num_blocks, stride):\n    layers = []\n    layers.append(resconv_basic(num_in_layers, num_out_layers, stride))\n    for i in range(1, num_blocks):\n        layers.append(resconv_basic(num_out_layers, num_out_layers, 1))\n    return nn.Sequential(*layers)\n\n\nclass upconv(nn.Module):\n    def __init__(self, num_in_layers, num_out_layers, kernel_size, scale):\n        super(upconv, self).__init__()\n        self.scale = scale\n        self.conv1 = conv(num_in_layers, num_out_layers, kernel_size, 1)\n\n    def forward(self, x):\n        x = nn.functional.interpolate(x, scale_factor=self.scale, mode='bilinear', align_corners=True)\n        return self.conv1(x)\n\n\nclass get_disp(nn.Module):\n    def __init__(self, num_in_layers):\n        super(get_disp, self).__init__()\n        self.conv1 = nn.Conv2d(num_in_layers, 2, kernel_size=3, stride=1)\n        self.normalize = nn.BatchNorm2d(2)\n        self.sigmoid = torch.nn.Sigmoid()\n\n    def forward(self, x):\n        p = 1\n        p2d = (p, p, p, p)\n        x = self.conv1(F.pad(x, p2d))\n        x = self.normalize(x)\n        return 0.3 * self.sigmoid(x)\n\n\n\n\n# https://github.com/YongfeiYan/Gumbel_Softmax_VAE/blob/master/gumbel_softmax_vae.py\ndef sample_gumbel(shape, eps=1e-20):\n    U = torch.rand(shape)\n    U = U.cuda()\n    return -torch.log(-torch.log(U + eps) + eps)\n\n\ndef gumbel_softmax_sample(logits, temperature=1):\n    y = logits + sample_gumbel(logits.size())\n    return F.softmax(y / temperature, dim=-1)\n\n\ndef gumbel_softmax(logits, temperature=1, hard=False):\n    \"\"\"\n    ST-gumple-softmax\n    input: [*, n_class]\n    return: flatten --> [*, n_class] an one-hot vector\n    \"\"\"\n    y = gumbel_softmax_sample(logits, temperature)\n\n    if not hard:\n        return y\n\n    shape = y.size()\n    _, ind = y.max(dim=-1)\n    y_hard = torch.zeros_like(y).view(-1, shape[-1])\n    y_hard.scatter_(1, ind.view(-1, 1), 1)\n    y_hard = y_hard.view(*shape)\n    # Set gradients w.r.t. y_hard gradients w.r.t. y\n    y_hard = (y_hard - y).detach() + y\n    return y_hard\n\n\nclass MixedOp(nn.Module):\n\n    def __init__(self, C_in, C_out, stride=1, width_mult_list=[1.]):\n        super(MixedOp, self).__init__()\n        self._ops = nn.ModuleList()\n        self._width_mult_list = width_mult_list\n        for primitive in PRIMITIVES:\n            op = OPS[primitive](C_in, C_out, stride, True, width_mult_list=width_mult_list)\n            self._ops.append(op)\n\n    def set_prun_ratio(self, ratio):\n        for op in self._ops:\n            op.set_ratio(ratio)\n\n    def forward(self, x, weights, thetas):\n        # int: force #channel; tensor: arch_ratio; float(<=1): force width\n        result = 0\n        if isinstance(thetas[0], torch.Tensor):\n            ratio0 = self._width_mult_list[thetas[0].argmax()]\n            r_score0 = thetas[0][thetas[0].argmax()]\n        else:\n            ratio0 = thetas[0]\n            r_score0 = 1.\n        if isinstance(thetas[1], torch.Tensor):\n            ratio1 = self._width_mult_list[thetas[1].argmax()]\n            r_score1 = thetas[1][thetas[1].argmax()]\n        else:\n            ratio1 = thetas[1]\n            r_score1 = 1.\n        self.set_prun_ratio((ratio0, ratio1))\n        for w, op in zip(weights, self._ops):\n            op(x).cuda()\n            result = result + op(x) * w * r_score0 * r_score1 #  每一次的结果相加\n        return result\n\n    def forward_latency(self, size, weights, thetas):\n        # int: force #channel; tensor: arch_ratio; float(<=1): force width\n        result = 0\n        if isinstance(thetas[0], torch.Tensor):\n            ratio0 = self._width_mult_list[thetas[0].argmax()]\n            r_score0 = thetas[0][thetas[0].argmax()]\n        else:\n            ratio0 = thetas[0]\n            r_score0 = 1.\n        if isinstance(thetas[1], torch.Tensor):\n            ratio1 = self._width_mult_list[thetas[1].argmax()]\n            r_score1 = thetas[1][thetas[1].argmax()]\n        else:\n            ratio1 = thetas[1]\n            r_score1 = 1.\n        self.set_prun_ratio((ratio0, ratio1))\n        for w, op in zip(weights, self._ops):\n            latency, size_out = op.forward_latency(size)\n            result = result + latency * w * r_score0 * r_score1\n        return result, size_out\n\n    def forward_flops(self, size, fai, ratio):\n        # int: force #channel; tensor: arch_ratio; float(<=1): force width\n        result = 0\n        if isinstance(ratio[0], torch.Tensor):\n            ratio0 = self._width_mult_list[ratio[0].argmax()]\n            r_score0 = ratio[0][ratio[0].argmax()]\n        else:\n            ratio0 = ratio[0]\n            r_score0 = 1.\n        if isinstance(ratio[1], torch.Tensor):\n            ratio1 = self._width_mult_list[ratio[1].argmax()]\n            r_score1 = ratio[1][ratio[1].argmax()]\n        else:\n            ratio1 = ratio[1]\n            r_score1 = 1.\n\n\n        self.set_prun_ratio((ratio0, ratio1))\n\n        for w, op in zip(fai, self._ops):\n            flops, size_out = op.forward_flops(size)\n            result = result + flops * w * r_score0 * r_score1\n        return result, size_out\n\nclass Cell(nn.Module):\n    def __init__(self, C_in, C_out=None, down=True, width_mult_list=[1.]):\n        super(Cell, self).__init__()\n        self._C_in = C_in\n        if C_out is None: C_out = C_in\n        self._C_out = C_out\n        self._down = down\n        self._width_mult_list = width_mult_list\n\n        self._op = MixedOp(C_in, C_out, width_mult_list=width_mult_list)\n\n        if self._down:\n            self.downsample = MixedOp(C_in, C_in*2, stride=2, width_mult_list=width_mult_list)\n\n    def forward(self, input, fais, thetas):\n        # thetas: (in, out, down)\n        out = self._op(input, fais, (thetas[0], thetas[1]))\n        assert (self._down and (thetas[2] is not None)) or ((not self._down) and (thetas[2] is None))\n        down = self.downsample(input, fais, (thetas[0], thetas[2])) if self._down else None\n        return out, down\n\n    def forward_latency(self, size, fais, thetas):\n        # thetas: (in, out, down)\n        out = self._op.forward_latency(size, fais, (thetas[0], thetas[1]))\n        assert (self._down and (thetas[2] is not None)) or ((not self._down) and (thetas[2] is None))\n        down = self.downsample.forward_latency(size, fais, (thetas[0], thetas[2])) if self._down else None\n        return out, down\n\n    def forward_flops(self, size, fais, thetas):\n        # thetas: (in, out, down)\n        out = self._op.forward_flops(size, fais, (thetas[0], thetas[1]))\n        assert (self._down and (thetas[2] is not None)) or ((not self._down) and (thetas[2] is None))\n        down = self.downsample.forward_latency(size, fais, (thetas[0], thetas[2])) if self._down else None\n        return out, down\n\nclass get_disp(nn.Module):\n    def __init__(self, num_in_layers):\n        super(get_disp, self).__init__()\n        self.conv1 = nn.Conv2d(num_in_layers, 2, kernel_size=3, stride=1)\n        self.normalize = nn.BatchNorm2d(2)\n        self.sigmoid = torch.nn.Sigmoid()\n\n    def forward(self, x):\n        p = 1\n        p2d = (p, p, p, p)\n        x = self.conv1(F.pad(x, p2d))\n        x = self.normalize(x)\n        return 0.3 * self.sigmoid(x)\n\ndef get_smooth_loss(disp, img):\n    \"\"\"Computes the smoothness loss for a disparity image\n    The color image is used for edge-aware smoothness\n    \"\"\"\n    if(img.size()==3):\n       img = torch.randn(1,img.size(0),img.size(1),img.size(2))\n    img = img.cuda()\n    grad_disp_x = torch.abs(disp[:, :, :, :-1] - disp[:, :, :, 1:])\n    grad_disp_y = torch.abs(disp[:, :, :-1, :] - disp[:, :, 1:, :])\n\n    grad_img_x = torch.mean(torch.abs(img[:, :, :, :-1] - img[:, :, :, 1:]), 1, keepdim=True)\n    grad_img_y = torch.mean(torch.abs(img[:, :, :-1, :] - img[:, :, 1:, :]), 1, keepdim=True)\n\n    grad_disp_x *= torch.exp(-grad_img_x)\n    grad_disp_y *= torch.exp(-grad_img_y)\n\n    return grad_disp_x.mean() + grad_disp_y.mean()\n\n\nclass SSIM(nn.Module):\n    \"\"\"Layer to compute the SSIM loss between a pair of images\n    \"\"\"\n\n    def __init__(self):\n        super(SSIM, self).__init__()\n        self.mu_x_pool = nn.AvgPool2d(3, 1)\n        self.mu_y_pool = nn.AvgPool2d(3, 1)\n        self.sig_x_pool = nn.AvgPool2d(3, 1)\n        self.sig_y_pool = nn.AvgPool2d(3, 1)\n        self.sig_xy_pool = nn.AvgPool2d(3, 1)\n\n        self.refl = nn.ReflectionPad2d(1)\n\n        self.C1 = 0.01 ** 2\n        self.C2 = 0.03 ** 2\n\n    def forward(self, x, y):\n        x = self.refl(x)\n        y = self.refl(y)\n\n        mu_x = self.mu_x_pool(x)\n        mu_y = self.mu_y_pool(y)\n\n        sigma_x = self.sig_x_pool(x ** 2) - mu_x ** 2\n        sigma_y = self.sig_y_pool(y ** 2) - mu_y ** 2\n        sigma_xy = self.sig_xy_pool(x * y) - mu_x * mu_y\n\n        SSIM_n = (2 * mu_x * mu_y + self.C1) * (2 * sigma_xy + self.C2)\n        SSIM_d = (mu_x ** 2 + mu_y ** 2 + self.C1) * (sigma_x + sigma_y + self.C2)\n\n        return torch.clamp((1 - SSIM_n / SSIM_d) / 2, 0, 1)\n\n\ndef compute_reprojection_loss(self, pred, target):\n        \"\"\"Computes reprojection loss between a batch of predicted and target images\n        \"\"\"\n        self.ssim = SSIM()\n\n        target = torch.randn(1, target.size(0), target.size(1), target.size(2))\n        target = target.cuda()\n        abs_diff = torch.abs(target - pred)\n        l1_loss = abs_diff.mean(1, True)\n        if(pred.dim() == 3):\n            pred = torch.randn(1,pred.size(0),pred.size(1),pred.size(2))\n            pred = pred.cuda()\n        ssim_loss = self.ssim(pred, target).mean(1, True)\n        reprojection_loss = 0.85 * ssim_loss + 0.15 * l1_loss\n\n        return reprojection_loss\n\n\ndef compute_losses(self, inputs, outputs):\n    \"\"\"Compute the reprojection and smoothness losses for a minibatch\n    \"\"\"\n    losses = {}\n    total_loss = 0\n\n    for scale in self.scales:\n        loss = 0\n        reprojection_losses = []\n\n        if self.v1_multiscale:\n            source_scale = scale\n        else:\n            source_scale = 0\n\n        disp = outputs[(\"disp\", scale)]\n        color = inputs[(\"color\", 0, scale)]\n        target = inputs[(\"color\", 0, source_scale)]\n\n        for frame_id in self.frame_ids[1:]:\n            pred = outputs[(\"color\", frame_id, scale)]\n            reprojection_losses.append(compute_reprojection_loss(self,pred, target))\n\n        reprojection_losses = torch.cat(reprojection_losses, 1)\n\n        if not self.disable_automasking:\n            identity_reprojection_losses = []\n            for frame_id in self.frame_ids[1:]:\n                pred = inputs[(\"color\", frame_id, source_scale)]\n                identity_reprojection_losses.append(\n                    compute_reprojection_loss(self,pred, target))\n\n            identity_reprojection_losses = torch.cat(identity_reprojection_losses, 1)\n\n            if self.avg_reprojection:\n                identity_reprojection_loss = identity_reprojection_losses.mean(1, keepdim=True)\n            else:\n                # save both images, and do min all at once below\n                identity_reprojection_loss = identity_reprojection_losses\n\n        elif self.predictive_mask:\n            # use the predicted mask\n            mask = outputs[\"predictive_mask\"][\"disp\", scale]\n            if not self.v1_multiscale:\n                mask = F.interpolate(\n                    mask, [self.height, self.width],\n                    mode=\"bilinear\", align_corners=False)\n\n            reprojection_losses *= mask\n\n            # add a loss pushing mask to 1 (using nn.BCELoss for stability)\n            weighting_loss = 0.2 * nn.BCELoss()(mask, torch.ones(mask.shape).cuda())\n            loss += weighting_loss.mean()\n\n        if self.avg_reprojection:\n            reprojection_loss = reprojection_losses.mean(1, keepdim=True)\n        else:\n            reprojection_loss = reprojection_losses\n\n        if not self.disable_automasking:\n            # add random numbers to break ties\n            identity_reprojection_loss += torch.randn(\n                identity_reprojection_loss.shape, device=0) * 0.00001\n\n            combined = torch.cat((identity_reprojection_loss, reprojection_loss), dim=1)\n        else:\n            combined = reprojection_loss\n\n        if combined.shape[1] == 1:\n            to_optimise = combined\n        else:\n            to_optimise, idxs = torch.min(combined, dim=1)\n\n        if not self.disable_automasking:\n            outputs[\"identity_selection/{}\".format(scale)] = (\n                idxs > identity_reprojection_loss.shape[1] - 1).float()\n\n        loss += to_optimise.mean()\n\n        mean_disp = disp.mean(2, True).mean(3, True)\n        norm_disp = disp / (mean_disp + 1e-7)\n        smooth_loss = get_smooth_loss(norm_disp, color)\n\n        loss += self.disparity_smoothness * smooth_loss / (2 ** scale)\n        total_loss += loss\n        losses[\"loss/{}\".format(scale)] = loss\n\n    total_loss /= self.nums_scales\n    losses[\"loss\"] = total_loss\n    return losses\n\ndef disp_to_depth(disp, min_depth, max_depth):\n    \"\"\"Convert network's sigmoid output into depth prediction\n    The formula for this conversion is given in the 'additional considerations'\n    section of the paper.\n    \"\"\"\n    min_disp = 1 / max_depth\n    max_disp = 1 / min_depth\n    scaled_disp = min_disp + (max_disp - min_disp) * disp\n    depth = 1 / scaled_disp\n    return scaled_disp, depth\n\ndef generate_images_pred(self, inputs, outputs):\n    \"\"\"Generate the warped (reprojected) color images for a minibatch.\n    Generated images are saved into the `outputs` dictionary.\n    \"\"\"\n    self.backproject_depth = {}\n    self.project_3d = {}\n    for scale in self.scales:\n        h = self.height // (2 ** scale)\n        w = self.width // (2 ** scale)\n\n        self.backproject_depth[scale] = BackprojectDepth(self.batch_size, h, w)\n        self.backproject_depth[scale] = self.backproject_depth[scale].cuda()\n\n        self.project_3d[scale] = Project3D(self.batch_size, h, w)\n        self.project_3d[scale] = self.project_3d[scale].cuda()\n\n    for scale in self.scales:\n        disp = outputs[(\"disp\", scale)]\n        if self.v1_multiscale:\n            source_scale = scale\n        else:\n            disp = F.interpolate(disp, [self.height, self.width], mode=\"bilinear\", align_corners=False)\n            source_scale = 0\n\n        _, depth = disp_to_depth(disp, self.min_depth, self.max_depth)\n\n        outputs[(\"depth\", 0, scale)] = depth\n\n        for i, frame_id in enumerate(self.frame_ids[1:]):\n\n            if frame_id == \"s\":\n                T = inputs[\"stereo_T\"]\n            else:\n                T = outputs[(\"cam_T_cam\", 0, frame_id)]\n\n            # from the authors of https://arxiv.org/abs/1712.00175\n            if self.pose_model_type == \"posecnn\":\n\n                axisangle = outputs[(\"axisangle\", 0, frame_id)]\n                translation = outputs[(\"translation\", 0, frame_id)]\n\n                inv_depth = 1 / depth\n                mean_inv_depth = inv_depth.mean(3, True).mean(2, True)\n\n                T = transformation_from_parameters(\n                    axisangle[:, 0], translation[:, 0] * mean_inv_depth[:, 0], frame_id < 0)\n\n            cam_points = self.backproject_depth[source_scale](\n                 depth, inputs[(\"inv_K\", source_scale)])\n            pix_coords = self.project_3d[source_scale](\n                cam_points, inputs[(\"K\", source_scale)], T)\n\n            outputs[(\"sample\", frame_id, scale)] = pix_coords\n            if(inputs[(\"color\", frame_id, source_scale)].dim()==3):\n              inputs[(\"color\", frame_id, source_scale)] = torch.randn(1,inputs[(\"color\", frame_id, source_scale)].size(0),inputs[(\"color\", frame_id, source_scale)].size(1),inputs[(\"color\", frame_id, source_scale)].size(2))\n            inputs[(\"color\", frame_id, source_scale)] = inputs[(\"color\", frame_id, source_scale)].cuda()\n            outputs[(\"color\", frame_id, scale)] = F.grid_sample(\n                inputs[(\"color\", frame_id, source_scale)],\n                outputs[(\"sample\", frame_id, scale)],\n                padding_mode=\"border\")\n            inputs[(\"color\", frame_id, source_scale)] = torch.randn(inputs[(\"color\", frame_id, source_scale)].size(1),inputs[(\"color\", frame_id, source_scale)].size(2),inputs[(\"color\", frame_id, source_scale)].size(3))\n            inputs[(\"color\", frame_id, source_scale)] = inputs[(\"color\", frame_id, source_scale)].cuda()\n\n            if not self.disable_automasking:\n                outputs[(\"color_identity\", frame_id, scale)] = \\\n                    inputs[(\"color\", frame_id, source_scale)]\nfrom collections import OrderedDict\nfrom layers import *\n\n\nclass DepthDecoder(nn.Module):\n    def __init__(self, num_ch_enc, scales=range(4), num_output_channels=1, use_skips=True):\n        super(DepthDecoder, self).__init__()\n\n        self.num_output_channels = num_output_channels\n        self.use_skips = use_skips\n        self.upsample_mode = 'nearest'\n        self.scales = scales\n\n        self.num_ch_enc = num_ch_enc\n        self.num_ch_dec = np.array([16, 32, 64, 128, 256])\n\n        # decoder\n        self.convs = OrderedDict()\n        for i in range(4, -1, -1):\n            # upconv_0\n            num_ch_in = self.num_ch_enc[-1] if i == 4 else self.num_ch_dec[i + 1]\n            num_ch_out = self.num_ch_dec[i]\n            self.convs[(\"upconv\", i, 0)] = ConvBlock(num_ch_in, num_ch_out)\n\n            # upconv_1\n            num_ch_in = self.num_ch_dec[i]\n            if self.use_skips and i > 0:\n                num_ch_in += self.num_ch_enc[i - 1]\n            num_ch_out = self.num_ch_dec[i]\n            self.convs[(\"upconv\", i, 1)] = ConvBlock(num_ch_in, num_ch_out)\n\n        for s in self.scales:\n            self.convs[(\"dispconv\", s)] = Conv3x3(self.num_ch_dec[s], self.num_output_channels)\n\n        self.decoder = nn.ModuleList(list(self.convs.values()))\n        self.sigmoid = nn.Sigmoid()\n\n    def forward(self, input_features):\n        self.outputs = {}\n\n        # decoder\n        x = input_features[-1]\n        x = x.cuda()\n        for i in range(4, -1, -1):\n            x = self.convs[(\"upconv\", i, 0)](x)\n            x = [upsample(x)]\n            if self.use_skips and i > 0:\n                x += [input_features[i - 1]]\n            x = torch.cat(x, 1)\n            x = self.convs[(\"upconv\", i, 1)](x)\n            if i in self.scales:\n                self.outputs[(\"disp\", i)] = self.sigmoid(self.convs[(\"dispconv\", i)](x))\n\n        return self.outputs\n\nclass Network_Multi_Path(nn.Module):\n    def __init__(self, num_classes=19, layers=16, criterion=nn.CrossEntropyLoss(ignore_index=-1), Fch=12, width_mult_list=[1.,], prun_modes=['arch_ratio',], stem_head_width=[(1., 1.),]):\n        super(Network_Multi_Path, self).__init__()\n        self._num_classes = num_classes\n        assert layers >= 3\n        self._layers = layers\n        self._criterion = criterion\n        self._Fch = Fch\n        self._width_mult_list = width_mult_list\n        self._prun_modes = prun_modes\n        self.prun_mode = None # prun_mode is higher priority than _prun_modes\n        self._stem_head_width = stem_head_width\n        self._flops = 0\n        self._params = 0\n        #self.lossdepth =\n        \"\"\"\n            stem由5个3*3的卷积组成\n            \"\"\"\n        self.stem = nn.ModuleList([\n            nn.Sequential(\n                ConvNorm(3, self.num_filters(2, stem_ratio)*2, kernel_size=3, stride=2, padding=1, bias=False, groups=1, slimmable=False),\n                BasicResidual2x(self.num_filters(2, stem_ratio)*2, self.num_filters(4, stem_ratio)*2, kernel_size=3, stride=2, groups=1, slimmable=False),\n                BasicResidual2x(self.num_filters(4, stem_ratio)*2, self.num_filters(8, stem_ratio), kernel_size=3, stride=2, groups=1, slimmable=False)\n            ) for stem_ratio, _ in self._stem_head_width ])\n        #构建基础Cell\n        #########depth###############################\n        self.convdepth1 = resblock_basic(3,self.num_filters(2, 1)*2,2,2)\n        self.convdepth2 = resblock_basic(self.num_filters(2, 1)*2, self.num_filters(4, 1)*2,2,2)\n        self.convd = conv()\n        self.cells = nn.ModuleList()\n        for l in range(layers):# 网络层数\n            cells = nn.ModuleList()\n            if l == 0:\n                # first node has only one input (prev cell's output)\n                cells.append(Cell(self.num_filters(8), width_mult_list=width_mult_list))\n            elif l == 1:#第二层\n                cells.append(Cell(self.num_filters(8), width_mult_list=width_mult_list))\n                cells.append(Cell(self.num_filters(16), width_mult_list=width_mult_list))\n            elif l < layers - 1:#中间层\n                cells.append(Cell(self.num_filters(8), width_mult_list=width_mult_list))\n                cells.append(Cell(self.num_filters(16), width_mult_list=width_mult_list))\n                cells.append(Cell(self.num_filters(32), down=False, width_mult_list=width_mult_list))\n            else:#最后一层\n                cells.append(Cell(self.num_filters(8), down=False, width_mult_list=width_mult_list))\n                cells.append(Cell(self.num_filters(16), down=False, width_mult_list=width_mult_list))\n                cells.append(Cell(self.num_filters(32), down=False, width_mult_list=width_mult_list))\n            self.cells.append(cells)\n\n        self.refine32 = nn.ModuleList([\n            nn.ModuleList([\n                ConvNorm(self.num_filters(32, head_ratio), self.num_filters(16, head_ratio), kernel_size=1, bias=False, groups=1, slimmable=False),\n                ConvNorm(self.num_filters(32, head_ratio), self.num_filters(16, head_ratio), kernel_size=3, padding=1, bias=False, groups=1, slimmable=False),\n                ConvNorm(self.num_filters(16, head_ratio), self.num_filters(8, head_ratio), kernel_size=1, bias=False, groups=1, slimmable=False),\n                ConvNorm(self.num_filters(16, head_ratio), self.num_filters(8, head_ratio), kernel_size=3, padding=1, bias=False, groups=1, slimmable=False)]) for _, head_ratio in self._stem_head_width ])\n        self.refine16 = nn.ModuleList([\n            nn.ModuleList([\n                ConvNorm(self.num_filters(16, head_ratio), self.num_filters(8, head_ratio), kernel_size=1, bias=False, groups=1, slimmable=False),\n                ConvNorm(self.num_filters(16, head_ratio), self.num_filters(8, head_ratio), kernel_size=3, padding=1, bias=False, groups=1, slimmable=False)]) for _, head_ratio in self._stem_head_width ])\n\n        self.head0 = nn.ModuleList([ Head(self.num_filters(8, head_ratio), num_classes, False) for _, head_ratio in self._stem_head_width ])\n        self.head1 = nn.ModuleList([ Head(self.num_filters(8, head_ratio), num_classes, False) for _, head_ratio in self._stem_head_width ])\n        self.head2 = nn.ModuleList([ Head(self.num_filters(8, head_ratio), num_classes, False) for _, head_ratio in self._stem_head_width ])\n        self.head02 = nn.ModuleList([ Head(self.num_filters(8, head_ratio)*2, num_classes, False) for _, head_ratio in self._stem_head_width ])\n        self.head12 = nn.ModuleList([ Head(self.num_filters(8, head_ratio)*2, num_classes, False) for _, head_ratio in self._stem_head_width ])\n\n        # contains arch_param names: {\"fais\": fais, \"mjus\": mjus, \"thetas\": thetas}\n        self._arch_names = []\n        self._arch_parameters = []\n        for i in range(len(self._prun_modes)):\n            arch_name, arch_param = self._build_arch_parameters(i)\n            self._arch_names.append(arch_name)\n            self._arch_parameters.append(arch_param)\n            self._reset_arch_parameters(i)\n        # switch set of arch if we have more than 1 arch\n        self.arch_idx = 0\n\n\n    def num_filters(self, scale, width=1.0):\n        return int(np.round(scale * self._Fch * width))\n\n    def new(self):\n        model_new = Network(self._num_classes, self._layers, self._criterion, self._Fch).cuda()\n        for x, y in zip(model_new.arch_parameters(), self.arch_parameters()):\n                x.data.copy_(y.data)\n        return model_new\n\n    def sample_prun_ratio(self, mode=\"arch_ratio\"):\n        '''\n        mode: \"min\"|\"max\"|\"random\"|\"arch_ratio\"(default)\n        '''\n        assert mode in [\"min\", \"max\", \"random\", \"arch_ratio\"]\n        if mode == \"arch_ratio\":\n            thetas = self._arch_names[0][\"thetas\"]\n            thetas0 = getattr(self, thetas[0])\n            thetas0_sampled = []\n            for layer in range(self._layers - 1):\n                thetas0_sampled.append(gumbel_softmax(F.log_softmax(thetas0[layer], dim=-1), hard=True))\n            thetas1 = getattr(self, thetas[1])\n            thetas1_sampled = []\n            for layer in range(self._layers - 1):\n                thetas1_sampled.append(gumbel_softmax(F.log_softmax(thetas1[layer], dim=-1), hard=True))\n            thetas2 = getattr(self, thetas[2])\n            thetas2_sampled = []\n            for layer in range(self._layers - 2):\n                thetas2_sampled.append(gumbel_softmax(F.log_softmax(thetas2[layer], dim=-1), hard=True))\n            return [thetas0_sampled, thetas1_sampled, thetas2_sampled]\n        elif mode == \"min\":\n            thetas0_sampled = []\n            for layer in range(self._layers - 1):\n                thetas0_sampled.append(self._width_mult_list[0])\n            thetas1_sampled = []\n            for layer in range(self._layers - 1):\n                thetas1_sampled.append(self._width_mult_list[0])\n            thetas2_sampled = []\n            for layer in range(self._layers - 2):\n                thetas2_sampled.append(self._width_mult_list[0])\n            return [thetas0_sampled, thetas1_sampled, thetas2_sampled]\n        elif mode == \"max\":\n            thetas0_sampled = []\n            for layer in range(self._layers - 1):\n                thetas0_sampled.append(self._width_mult_list[-1])\n            thetas1_sampled = []\n            for layer in range(self._layers - 1):\n                thetas1_sampled.append(self._width_mult_list[-1])\n            thetas2_sampled = []\n            for layer in range(self._layers - 2):\n                thetas2_sampled.append(self._width_mult_list[-1])\n            return [thetas0_sampled, thetas1_sampled, thetas2_sampled]\n        elif mode == \"random\":\n            thetas0_sampled = []\n            for layer in range(self._layers - 1):\n                thetas0_sampled.append(np.random.choice(self._width_mult_list))\n            thetas1_sampled = []\n            for layer in range(self._layers - 1):\n                thetas1_sampled.append(np.random.choice(self._width_mult_list))\n            thetas2_sampled = []\n            for layer in range(self._layers - 2):\n                thetas2_sampled.append(np.random.choice(self._width_mult_list))\n            return [thetas0_sampled, thetas1_sampled, thetas2_sampled]\n\n\n    def forward(self, arg, input,inputs):\n        # out_prev: cell-state\n        # index 0: keep; index 1: down\n        input = torch.randn((1,3,192,640))\n        input = input.cuda(non_blocking=True)\n        x1 = self.convdepth1(input)\n        x2 = self.convdepth2(x1)\n\n        stem = self.stem[0]\n        refine16 = self.refine16[0]\n        refine32 = self.refine32[0]\n        head0 = self.head0[0]\n        head1 = self.head1[0]\n        head2 = self.head2[0]\n        head02 = self.head02[0]\n        head12 = self.head12[0]\n\n        fais0 = F.softmax(getattr(self, self._arch_names[0][\"fais\"][0]), dim=-1).cuda()\n        fais1 = F.softmax(getattr(self, self._arch_names[0][\"fais\"][1]), dim=-1).cuda()\n        fais2 = F.softmax(getattr(self, self._arch_names[0][\"fais\"][2]), dim=-1).cuda()\n        fais = [fais0, fais1, fais2]\n        mjus1 = F.softmax(getattr(self, self._arch_names[0][\"mjus\"][0]), dim=-1).cuda()\n        mjus2 = F.softmax(getattr(self, self._arch_names[0][\"mjus\"][1]), dim=-1).cuda()\n        mjus = [None, mjus1, mjus2]\n        if self.prun_mode is not None:\n            thetas = self.sample_prun_ratio(mode=self.prun_mode)\n        else:\n            thetas = self.sample_prun_ratio(mode=self._prun_modes[0])\n\n        out_prev = [[stem(input), None]]  # stem: one cell\n\n\n        #out_prev = [[stem(input), None]] # stem: one cell\n        # i: layer | j: scale\n        for i, cells in enumerate(self.cells):\n            # layers\n            out = []\n            for j, cell in enumerate(cells):\n                # scales\n                # out,down -- 0: from down; 1: from keep\n                out0 = None; out1 = None\n                down0 = None; down1 = None\n                fai = fais[j][i-j]\n                # ratio: (in, out, down)\n                # int: force #channel; tensor: arch_ratio; float(<=1): force width\n                if i == 0 and j == 0:\n                    # first cell\n                    ratio = (self._stem_head_width[0][0], thetas[j][i-j], thetas[j+1][i-j])\n                elif i == self._layers - 1:\n                    # cell in last layer\n                    if j == 0:\n                        ratio = (thetas[j][i-j-1], self._stem_head_width[0][1], None)\n                    else:\n                        ratio = (thetas[j][i-j], self._stem_head_width[0][1], None)\n                elif j == 2:\n                    # cell in last scale: no down ratio \"None\"\n                    ratio = (thetas[j][i-j], thetas[j][i-j+1], None)\n                else:\n                    if j == 0:\n                        ratio = (thetas[j][i-j-1], thetas[j][i-j], thetas[j+1][i-j])\n                    else:\n                        ratio = (thetas[j][i-j], thetas[j][i-j+1], thetas[j+1][i-j])\n                # out,down -- 0: from down; 1: from keep\n                if j == 0:\n                    out1, down1 = cell(out_prev[0][0], fai, ratio)\n                    out.append((out1, down1))\n                else:\n                    if i == j:\n                        out0, down0 = cell(out_prev[j-1][1], fai, ratio)\n                        out.append((out0, down0))\n                    else:\n                        if mjus[j][i-j-1][0] > 0:\n                            out0, down0 = cell(out_prev[j-1][1], fai, ratio)\n                        if mjus[j][i-j-1][1] > 0:\n                            out1, down1 = cell(out_prev[j][0], fai, ratio)\n                        out.append((\n                            sum(w * out for w, out in zip(mjus[j][i-j-1], [out0, out1])),\n                            sum(w * down if down is not None else 0 for w, down in zip(mjus[j][i-j-1], [down0, down1])),\n                            ))\n            out_prev = out\n        ###################################\n        out0 = None; out1 = None; out2 = None\n        #pose\n        outputs = {}\n        out_pose = predict_poses(arg,inputs,out)\n        features = []\n        x3 = out[0][0]#64*128\n        x4 = out[1][0]#32*64\n        x5 = out[2][0]#16*32\n        features.append(x1)\n        features.append(x2)\n        features.append(x3)\n        features.append(x4)\n        features.append(x5)\n        num_ch_enc = np.array([x1.size(1), x2.size(1), x3.size(1), x4.size(1), x5.size(1)])\n        scales = [0,1,2,3]\n        decoder = DepthDecoder(num_ch_enc, scales).cuda()\n        outputs = decoder(features)\n        outputs.update(out_pose)\n        generate_images_pred(arg,inputs, outputs)\n\n        return outputs\n        ###################################\n\n    def forward_latency(self, size, fai=True, beta=True, ratio=True):\n        # out_prev: cell-state\n        # index 0: keep; index 1: down\n        stem = self.stem[0]\n\n        if fai:\n            fais0 = F.softmax(getattr(self, self._arch_names[0][\"fais\"][0]), dim=-1)\n            fais1 = F.softmax(getattr(self, self._arch_names[0][\"fais\"][1]), dim=-1)\n            fais2 = F.softmax(getattr(self, self._arch_names[0][\"fais\"][2]), dim=-1)\n            fais = [fais0, fais1, fais2]\n        else:\n            fais = [\n                torch.ones_like(getattr(self, self._arch_names[0][\"fais\"][0])).cuda() * 1./len(PRIMITIVES),\n                torch.ones_like(getattr(self, self._arch_names[0][\"fais\"][1])).cuda() * 1./len(PRIMITIVES),\n                torch.ones_like(getattr(self, self._arch_names[0][\"fais\"][2])).cuda() * 1./len(PRIMITIVES)]\n        if beta:\n            mjus1 = F.softmax(getattr(self, self._arch_names[0][\"mjus\"][0]), dim=-1)\n            mjus2 = F.softmax(getattr(self, self._arch_names[0][\"mjus\"][1]), dim=-1)\n            mjus = [None, mjus1, mjus2]\n        else:\n            mjus = [\n                None,\n                torch.ones_like(getattr(self, self._arch_names[0][\"mjus\"][0])).cuda() * 1./2,\n                torch.ones_like(getattr(self, self._arch_names[0][\"mjus\"][1])).cuda() * 1./2]\n        if ratio:\n            # thetas = self.sample_prun_ratio(mode='arch_ratio')\n            if self.prun_mode is not None:\n                thetas = self.sample_prun_ratio(mode=self.prun_mode)\n            else:\n                thetas = self.sample_prun_ratio(mode=self._prun_modes[0])\n        else:\n            thetas = self.sample_prun_ratio(mode='max')\n\n        stem_latency = 0\n        latency, size = stem[0].forward_latency(size); stem_latency = stem_latency + latency\n        latency, size = stem[1].forward_latency(size); stem_latency = stem_latency + latency\n        latency, size = stem[2].forward_latency(size); stem_latency = stem_latency + latency\n        out_prev = [[size, None]] # stem: one cell\n        latency_total = [[stem_latency, 0], [0, 0], [0, 0]] # (out, down)\n\n        # i: layer | j: scale\n        for i, cells in enumerate(self.cells):\n            # layers\n            out = []\n            latency = []\n            for j, cell in enumerate(cells):\n                # scales\n                # out,down -- 0: from down; 1: from keep\n                out0 = None; out1 = None\n                down0 = None; down1 = None\n                fai = fais[j][i-j]\n                # ratio: (in, out, down)\n                # int: force #channel; tensor: arch_ratio; float(<=1): force width\n                if i == 0 and j == 0:\n                    # first cell\n                    ratio = (self._stem_head_width[0][0], thetas[j][i-j], thetas[j+1][i-j])\n                elif i == self._layers - 1:\n                    # cell in last layer\n                    if j == 0:\n                        ratio = (thetas[j][i-j-1], self._stem_head_width[0][1], None)\n                    else:\n                        ratio = (thetas[j][i-j], self._stem_head_width[0][1], None)\n                elif j == 2:\n                    # cell in last scale\n                    ratio = (thetas[j][i-j], thetas[j][i-j+1], None)\n                else:\n                    if j == 0:\n                        ratio = (thetas[j][i-j-1], thetas[j][i-j], thetas[j+1][i-j])\n                    else:\n                        ratio = (thetas[j][i-j], thetas[j][i-j+1], thetas[j+1][i-j])\n                # out,down -- 0: from down; 1: from keep\n                if j == 0:\n                    out1, down1 = cell.forward_latency(out_prev[0][0], fai, ratio)\n                    out.append((out1[1], down1[1] if down1 is not None else None))\n                    latency.append([out1[0], down1[0] if down1 is not None else None])\n                else:\n                    if i == j:\n                        out0, down0 = cell.forward_latency(out_prev[j-1][1], fai, ratio)\n                        out.append((out0[1], down0[1] if down0 is not None else None))\n                        latency.append([out0[0], down0[0] if down0 is not None else None])\n                    else:\n                        if mjus[j][i-j-1][0] > 0:\n                            # from down\n                            out0, down0 = cell.forward_latency(out_prev[j-1][1], fai, ratio)\n                        if mjus[j][i-j-1][1] > 0:\n                            # from keep\n                            out1, down1 = cell.forward_latency(out_prev[j][0], fai, ratio)\n                        assert (out0 is None and out1 is None) or out0[1] == out1[1]\n                        assert (down0 is None and down1 is None) or down0[1] == down1[1]\n                        out.append((out0[1], down0[1] if down0 is not None else None))\n                        latency.append([\n                            sum(w * out for w, out in zip(mjus[j][i-j-1], [out0[0], out1[0]])),\n                            sum(w * down if down is not None else 0 for w, down in zip(mjus[j][i-j-1], [down0[0] if down0 is not None else None, down1[0] if down1 is not None else None])),\n                        ])\n            out_prev = out\n            for ii, lat in enumerate(latency):\n                # layer: i | scale: ii\n                if ii == 0:\n                    # only from keep\n                    if lat[0] is not None: latency_total[ii][0] = latency_total[ii][0] + lat[0]\n                    if lat[1] is not None: latency_total[ii][1] = latency_total[ii][0] + lat[1]\n                else:\n                    if i == ii:\n                        # only from down\n                        if lat[0] is not None: latency_total[ii][0] = latency_total[ii-1][1] + lat[0]\n                        if lat[1] is not None: latency_total[ii][1] = latency_total[ii-1][1] + lat[1]\n                    else:\n                        if lat[0] is not None: latency_total[ii][0] = mjus[j][i-j-1][1] * latency_total[ii][0] + mjus[j][i-j-1][0] * latency_total[ii-1][1] + lat[0]\n                        if lat[1] is not None: latency_total[ii][1] = mjus[j][i-j-1][1] * latency_total[ii][0] + mjus[j][i-j-1][0] * latency_total[ii-1][1] + lat[1]\n        ###################################\n        latency0 = latency_total[0][0]\n        latency1 = latency_total[1][0]\n        latency2 = latency_total[2][0]\n        latency = sum([latency0, latency1, latency2])\n        return latency\n        ###################################\n\n    def forward_flops(self, size, fai=True, beta=True, ratio=True):\n        # out_prev: cell-state\n        # index 0: keep; index 1: down\n        stem = self.stem[0]\n\n        if fai:\n            fais0 = F.softmax(getattr(self, self._arch_names[0][\"fais\"][0]), dim=-1)\n            fais1 = F.softmax(getattr(self, self._arch_names[0][\"fais\"][1]), dim=-1)\n            fais2 = F.softmax(getattr(self, self._arch_names[0][\"fais\"][2]), dim=-1)\n            fais = [fais0, fais1, fais2]\n        else:\n            fais = [\n                torch.ones_like(getattr(self, self._arch_names[0][\"fais\"][0])).cuda() * 1./len(PRIMITIVES),\n                torch.ones_like(getattr(self, self._arch_names[0][\"fais\"][1])).cuda() * 1./len(PRIMITIVES),\n                torch.ones_like(getattr(self, self._arch_names[0][\"fais\"][2])).cuda() * 1./len(PRIMITIVES)]\n        if beta:\n            mjus1 = F.softmax(getattr(self, self._arch_names[0][\"mjus\"][0]), dim=-1)\n            mjus2 = F.softmax(getattr(self, self._arch_names[0][\"mjus\"][1]), dim=-1)\n            mjus = [None, mjus1, mjus2]\n        else:\n            mjus = [\n                None,\n                torch.ones_like(getattr(self, self._arch_names[0][\"mjus\"][0])).cuda() * 1./2,\n                torch.ones_like(getattr(self, self._arch_names[0][\"mjus\"][1])).cuda() * 1./2]\n        if ratio:\n            # thetas = self.sample_prun_ratio(mode='arch_ratio')\n            if self.prun_mode is not None:\n                thetas = self.sample_prun_ratio(mode=self.prun_mode)\n            else:\n                thetas = self.sample_prun_ratio(mode=self._prun_modes[0])\n        else:\n            thetas = self.sample_prun_ratio(mode='max')\n\n        stem_flops = 0\n        flops, size = stem[0].forward_flops(size); stem_flops = stem_flops + flops\n        flops, size = stem[1].forward_flops(size); stem_flops = stem_flops + flops\n        flops, size = stem[2].forward_flops(size); stem_flops = stem_flops + flops\n        out_prev = [[size, None]] # stem: one cell\n        flops_total = [[stem_flops, 0], [0, 0], [0, 0]] # (out, down)\n\n        # i: layer | j: scale\n        for i, cells in enumerate(self.cells):\n            # layers\n            out = []\n            flops = []\n            for j, cell in enumerate(cells):\n                # scales\n                # out,down -- 0: from down; 1: from keep\n                out0 = None; out1 = None\n                down0 = None; down1 = None\n                fai = fais[j][i-j]\n                # ratio: (in, out, down)\n                # int: force #channel; tensor: arch_ratio; float(<=1): force width\n                if i == 0 and j == 0:\n                    # first cell\n                    ratio = (self._stem_head_width[0][0], thetas[j][i-j], thetas[j+1][i-j])\n                elif i == self._layers - 1:\n                    # cell in last layer\n                    if j == 0:\n                        ratio = (thetas[j][i-j-1], self._stem_head_width[0][1], None)\n                    else:\n                        ratio = (thetas[j][i-j], self._stem_head_width[0][1], None)\n                elif j == 2:\n                    # cell in last scale\n                    ratio = (thetas[j][i-j], thetas[j][i-j+1], None)\n                else:\n                    if j == 0:\n                        ratio = (thetas[j][i-j-1], thetas[j][i-j], thetas[j+1][i-j])\n                    else:\n                        ratio = (thetas[j][i-j], thetas[j][i-j+1], thetas[j+1][i-j])\n                # out,down -- 0: from down; 1: from keep\n                if j == 0:\n                    out1, down1 = cell.forward_flops(out_prev[0][0], fai, ratio)\n                    out.append((out1[1], down1[1] if down1 is not None else None))\n                    flops.append([out1[0], down1[0] if down1 is not None else None])\n                else:\n                    if i == j:\n                        out0, down0 = cell.forward_flops(out_prev[j-1][1], fai, ratio)\n                        out.append((out0[1], down0[1] if down0 is not None else None))\n                        flops.append([out0[0], down0[0] if down0 is not None else None])\n                    else:\n                        if mjus[j][i-j-1][0] > 0:\n                            # from down\n                            out0, down0 = cell.forward_flops(out_prev[j-1][1], fai, ratio)\n                        if mjus[j][i-j-1][1] > 0:\n                            # from keep\n                            out1, down1 = cell.forward_flops(out_prev[j][0], fai, ratio)\n                        assert (out0 is None and out1 is None) or out0[1] == out1[1]\n                        assert (down0 is None and down1 is None) or down0[1] == down1[1]\n                        out.append((out0[1], down0[1] if down0 is not None else None))\n                        flops.append([\n                            sum(w * out for w, out in zip(mjus[j][i-j-1], [out0[0], out1[0]])),\n                            sum(w * down if down is not None else 0 for w, down in zip(mjus[j][i-j-1], [down0[0] if down0 is not None else None, down1[0] if down1 is not None else None])),\n                        ])\n            out_prev = out\n            for ii, lat in enumerate(flops):\n                # layer: i | scale: ii\n                if ii == 0:\n                    # only from keep\n                    if lat[0] is not None: flops_total[ii][0] = flops_total[ii][0] + lat[0]\n                    if lat[1] is not None: flops_total[ii][1] = flops_total[ii][0] + lat[1]\n                else:\n                    if i == ii:\n                        # only from down\n                        if lat[0] is not None: flops_total[ii][0] = flops_total[ii-1][1] + lat[0]\n                        if lat[1] is not None: flops_total[ii][1] = flops_total[ii-1][1] + lat[1]\n                    else:\n                        if lat[0] is not None: flops_total[ii][0] = mjus[j][i-j-1][1] * flops_total[ii][0] + mjus[j][i-j-1][0] * flops_total[ii-1][1] + lat[0]\n                        if lat[1] is not None: flops_total[ii][1] = mjus[j][i-j-1][1] * flops_total[ii][0] + mjus[j][i-j-1][0] * flops_total[ii-1][1] + lat[1]\n        ###################################\n        flops0 = flops_total[0][0]\n        flops1 = flops_total[1][0]\n        flops2 = flops_total[2][0]\n        flops = sum([flops0, flops1, flops2])\n        return flops\n        ###################################\n    def _loss(self, arg, input, target, pretrain=False):\n        losses = []\n        val_losses = []\n        running_val_loss = 0.0\n        if pretrain is not True:\n            # \"random width\": sampled by gambel softmax\n            self.prun_mode = None\n            for idx in range(len(self._arch_names)):\n                #self.arch_idx = idx\n                logits = self(arg,input,target)\n                losses = compute_losses(arg,target,logits)\n                losses[\"loss\"].backward()\n                #loss = loss + sum(self._criterion(logit, target) for logit in logits)\n        if len(self._width_mult_list) > 1:\n            self.prun_mode = \"max\"\n            logits = self(arg, input, target)\n            losses = compute_losses(arg, target, logits)\n            losses[\"loss\"].backward()\n            self.prun_mode = \"min\"\n            logits = self(arg, input, target)\n            losses = compute_losses(arg, target, logits)\n            losses[\"loss\"].backward()\n        return losses\n\n    def _build_arch_parameters(self, idx):\n        num_ops = len(PRIMITIVES)\n\n        # define names\n        fais = [ \"fai_\"+str(idx)+\"_\"+str(scale) for scale in [0, 1, 2] ]\n        mjus = [ \"beta_\"+str(idx)+\"_\"+str(scale) for scale in [1, 2] ]\n\n        setattr(self, fais[0], nn.Parameter(Variable(1e-3*torch.ones(self._layers, num_ops), requires_grad=True)))\n        setattr(self, fais[1], nn.Parameter(Variable(1e-3*torch.ones(self._layers-1, num_ops), requires_grad=True)))\n        setattr(self, fais[2], nn.Parameter(Variable(1e-3*torch.ones(self._layers-2, num_ops), requires_grad=True)))\n        # mjus are now in-degree probs\n        # 0: from down; 1: from keep\n        setattr(self, mjus[0], nn.Parameter(Variable(1e-3*torch.ones(self._layers-2, 2), requires_grad=True)))\n        setattr(self, mjus[1], nn.Parameter(Variable(1e-3*torch.ones(self._layers-3, 2), requires_grad=True)))\n\n        thetas = [ \"ratio_\"+str(idx)+\"_\"+str(scale) for scale in [0, 1, 2] ]\n        if self._prun_modes[idx] == 'arch_ratio':\n            # prunning ratio\n            num_widths = len(self._width_mult_list)\n        else:\n            num_widths = 1\n        setattr(self, thetas[0], nn.Parameter(Variable(1e-3*torch.ones(self._layers-1, num_widths), requires_grad=True)))\n        setattr(self, thetas[1], nn.Parameter(Variable(1e-3*torch.ones(self._layers-1, num_widths), requires_grad=True)))\n        setattr(self, thetas[2], nn.Parameter(Variable(1e-3*torch.ones(self._layers-2, num_widths), requires_grad=True)))\n\n\n\n\n\n        return {\"fais\": fais, \"mjus\": mjus, \"thetas\": thetas}, [getattr(self, name) for name in fais] + [getattr(self, name) for name in mjus] + [getattr(self, name) for name in thetas]\n\n    def _reset_arch_parameters(self, idx):\n        num_ops = len(PRIMITIVES)\n        if self._prun_modes[idx] == 'arch_ratio':\n            # prunning ratio\n            num_widths = len(self._width_mult_list)\n        else:\n            num_widths = 1\n\n        getattr(self, self._arch_names[idx][\"fais\"][0]).data = Variable(1e-3*torch.ones(self._layers, num_ops), requires_grad=True)\n        getattr(self, self._arch_names[idx][\"fais\"][1]).data = Variable(1e-3*torch.ones(self._layers-1, num_ops), requires_grad=True)\n        getattr(self, self._arch_names[idx][\"fais\"][2]).data = Variable(1e-3*torch.ones(self._layers-2, num_ops), requires_grad=True)\n        getattr(self, self._arch_names[idx][\"mjus\"][0]).data = Variable(1e-3*torch.ones(self._layers-2, 2), requires_grad=True)\n        getattr(self, self._arch_names[idx][\"mjus\"][1]).data = Variable(1e-3*torch.ones(self._layers-3, 2), requires_grad=True)\n        getattr(self, self._arch_names[idx][\"thetas\"][0]).data = Variable(1e-3*torch.ones(self._layers-1, num_widths), requires_grad=True)\n        getattr(self, self._arch_names[idx][\"thetas\"][1]).data = Variable(1e-3*torch.ones(self._layers-1, num_widths), requires_grad=True)\n        getattr(self, self._arch_names[idx][\"thetas\"][2]).data = Variable(1e-3*torch.ones(self._layers-2, num_widths), requires_grad=True)\n        #getattr(self, self._arch_names[idx][\"log_latency\"][0]).data = Variable(torch.zeros((1,), requires_grad=True))\n        #getattr(self, self._arch_names[idx][\"log_flops\"][0]).data = Variable(torch.zeros((1,), requires_grad=True))\n","repo_name":"douziwenhit/FasterMDE","sub_path":"train_new/model_search.py","file_name":"model_search.py","file_ext":"py","file_size_in_byte":55533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"39551914208","text":"# -*- coding: utf-8 -*-\nimport datetime\nimport json\nimport socketserver\n\n\"\"\"\nVariables and functions that must be used by all the ClientHandler objects\nmust be written here (e.g. a dictionary for connected clients)\n\"\"\"\n\n# Dictionary\nclients = {}\nhistory_list = {}\n\nclass ClientHandler(socketserver.BaseRequestHandler):\n    \"\"\"\n    This is the ClientHandler class. Everytime a new client connects to the\n    server, a new ClientHandler object will be created. This class represents\n    only connected clients, and not the server itself. If you want to write\n    logic for the server, you must write it outside this class\n    \"\"\"\n\n    username = \"\"\n    timestamp = \"\"\n    is_logged_in = False\n\n    def handle(self):\n        \"\"\"\n        This method handles the connection between a client and the server.\n        \"\"\"\n        self.ip = self.client_address[0]\n        self.port = self.client_address[1]\n        self.connection = self.request\n\n        # Loop that listens for messages from the client\n        while True:\n            print(\"Listening!\")\n            self.timestamp = datetime.datetime.now().strftime('%H.%M %d %b')\n            received_string = self.connection.recv(4096)\n            if not received_string:\n                print('error!')\n                break\n            decoded_object = json.loads(received_string.decode(\"utf-8\"))\n            request = decoded_object['request']\n            content = decoded_object['content']\n            print(\"Found something! request:\",request,\", content:\",content)\n            if request == 'login':\n                self.login(content)\n            elif request == 'logout':\n                self.logout()\n            elif request == 'names':\n                self.names()\n            elif request == 'help':\n                self.help()\n            elif request == 'msg':\n                self.msg(content)\n            elif request == 'history':\n                self.history()\n\n    def login(self, username):\n        global clients\n        self.username = username\n        self.is_logged_in = True\n        clients[username] = self\n        self.send_response('server', 'info', 'Login successful!', False, False)\n\n    def logout(self):\n        if self.username:\n            global clients\n            del clients[self.username]\n        self.send_response('server', 'info', 'Logout successful', False)\n\n    def names(self):\n        names = []\n        for username in clients:\n            names.append(username)\n        strigToReturn = 'All users in this channel: ' + str(names)\n        self.send_response('server', 'info', strigToReturn, False)\n\n    def help(self):\n        help_string = '\\nThese are the available commands:'\n        help_string += '\\nlogin <username> - Logs in to the server'\n        help_string += '\\nlogout- Logs out'\n        help_string += '\\nnames - Returns a list of all the connected clients\\' names'\n        help_string += '\\nmsg <message> - Sends the enclosed message to all connected clients'\n        help_string += '\\nhistory - lists all the messages posted to this server'\n        self.send_response('server', 'info', help_string, False, False)\n\n    def msg(self, message):\n        if self.username:\n            global history_list\n            if not self.username in history_list:\n                history_list[self.username] = []\n            history_list[self.username].append(message)\n            self.send_response(self.username, 'message', message, True)\n        else:\n            self.send_response('server', 'info', 'error inc', False)\n\n    def history(self):\n        history_string = ''\n        if history_list:\n            for username, user_history in history_list.items():\n                history_string += 'User ' + username + ' has posted the following messages:\\n'\n                for post in user_history:\n                    history_string += '\\t' + post + '\\n'\n        else:\n            history_string = 'No messages in the server\\'s history.'\n        self.send_response('server', 'history', history_string, False)\n\n    def send_response(self, sender, response, message, send_to_all, must_be_logged_in = True):\n        if must_be_logged_in and not self.is_logged_in:\n            raw_respone = {\n                'timestamp': self.timestamp,\n                'sender': 'server',\n                'response': 'error',\n                'content': 'You need to log in!'\n            }\n        else:\n            raw_respone = {\n                'timestamp': self.timestamp,\n                'sender': sender,\n                'response': response,\n                'content': message\n            }\n        JSON_response = json.dumps(raw_respone).encode(\"utf-8\")\n\n        if send_to_all:\n            for username, client in clients.items():\n                client.connection.send(JSON_response)\n        else:\n            self.connection.send(JSON_response)\n\nclass ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer):\n    \"\"\"\n    This class is present so that each client connected will be ran as a own\n    thread. In that way, all clients will be served by the server.\n\n    No alterations are necessary\n    \"\"\"\n    allow_reuse_address = True\n\nif __name__ == \"__main__\":\n    \"\"\"\n    This is the main method and is executed when you type \"python Server.py\"\n    in your terminal.\n\n    No alterations are necessary\n    \"\"\"\n    HOST, PORT = '78.91.69.136', 30000\n    print('Server running...')\n\n    # Set up and initiate the TCP server\n    server = ThreadedTCPServer((HOST, PORT), ClientHandler)\n    server.serve_forever()\n","repo_name":"finnss/KTN-Project","sub_path":"Server/Server.py","file_name":"Server.py","file_ext":"py","file_size_in_byte":5511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"27787608091","text":"import seaborn as sns\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom src.cardpool import builder\nfrom src.apimanager.apifields import APICollectibleFields\n\n\n# TODO refactor duplicate code (mostly matplotlib stuff)\n\n\nclass CardPoolVisualizer(builder.CardPoolBuilder):\n    def __init__(self, card_pool, path_to_plots, save=True):\n        super().__init__(card_pool)\n        self.path_to_plots = path_to_plots\n        self.save = save\n\n    def _distplot(self, data, xlabel, ylabel, plot_title, kde=False, save_fig=True, save_title=None):\n        sns.set()\n        plt.figure()\n        sns.distplot(data[APICollectibleFields.COST], kde=kde,\n                     bins=max(data[APICollectibleFields.COST].unique().tolist()),\n                     color='Red')\n        plt.xlabel(xlabel)\n        plt.ylabel(ylabel)\n        plt.title(plot_title)\n        if save_fig:\n            self.save_plot(save_title)\n        plt.tight_layout()\n        plt.show()\n\n    @staticmethod\n    def annotation():\n        pass\n\n    @staticmethod\n    def plt_customization():\n        pass\n\n    def save_plot(self,\n                  plot_title,\n                  extension='.png'):\n        plt.savefig('{path}{title}{ext}'.format(path=self.path_to_plots,\n                                                title=plot_title,\n                                                ext=extension), bbox_inches='tight')\n\n    def cost_distribution(self, complete=True, minions=False, spells=False, kde=False):\n        if complete:\n            self._distplot(self.card_pool,\n                           'Mana Cost',\n                           'Total Cards',\n                           'Cost Distribution: All cards',\n                           kde=kde,\n                           save_title='cost_distribution_kde_{}'.format(kde))\n        if minions:\n            self._distplot(self.minions,\n                           'Mana Cost',\n                           'Total Minions',\n                           'Cost Distribution: Minions',\n                           kde=kde,\n                           save_title='minion_distribution_kde_{}'.format(kde))\n        if spells:\n            self._distplot(self.spells,\n                           'Mana Cost',\n                           'Total Spells',\n                           'Cost Distribution: Spells',\n                           kde=kde,\n                           save_title='spell_distribution_kde_{}'.format(kde))\n\n    def generate_countplots(self, adventure=False):\n        sns.set(palette='bright')\n        xpos = 'center'\n        xpos = xpos.lower()\n        ha = {'center': 'center',\n              'right': 'left',\n              'left': 'right'}\n        offset = {'center': 0.5,\n                  'right': 0.60,  # 0.57\n                  'left': 0.45}  # 0.43\n        countable_fields = APICollectibleFields.COUNTABLE_FIELDS\n        if adventure:\n            countable_fields.remove(APICollectibleFields.FACTION)\n        for countable in countable_fields:\n            plt.figure()\n            ax = sns.countplot(x=countable, data=self.card_pool)\n            if not np.issubdtype(self.card_pool[countable], np.integer):\n                ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n            # Annotate the top of the bars with their corresponding values\n            for rect in ax.patches:\n                height = rect.get_height()\n                ax.text(rect.get_x() + rect.get_width() * offset[xpos], 1.002 * height,\n                        '{}'.format(height), ha=ha[xpos], va='bottom', rotation=90, fontsize=11)\n            plt.ylim(0, max(self.card_pool[countable].value_counts()) * 1.20)\n            plt.xlabel(countable.upper())\n            plt.ylabel('counts'.upper())\n            plt.title('{} counts'.format(countable).upper())\n            if self.save:\n                self.save_plot('{}_counts'.format(countable))\n        plt.tight_layout()\n        plt.show()\n\n    def generate_avg_stats_plot(self):\n        ax = sns.barplot(x='MANA_COST', y='STATS', data=self.avg_stats(), hue='TYPE_OF_STATS', palette='seismic')\n        xpos = 'center'\n        xpos = xpos.lower()  # normalize the case of the parameter\n        ha = {'center': 'center',\n              'right': 'left',\n              'left': 'right'}\n        offset = {'center': 0.5,\n                  'right': 0.60,\n                  'left': 0.45}\n        # Annotate the top of the bars with their corresponding values\n        for rect in ax.patches:\n            height = rect.get_height()\n            ax.text(rect.get_x() + rect.get_width() * offset[xpos], 1.008 * height,\n                    '{}'.format(height), ha=ha[xpos], va='bottom', rotation=90, fontsize=11)\n            # Customize plot's Legend\n        leg = ax.legend(loc=2)\n        leg.set_title('Type of Stats')\n        for t, l in zip(leg.texts, ['Average Attack', 'Average Health']):\n            t.set_text(l)\n        plt.ylim(0, 12)\n        plt.xlabel('Mana Cost')\n        plt.ylabel('Average Stats')\n        plt.title('Average Stats Per Mana Cost')\n        sns.despine()\n        if self.save:\n            self.save_plot('average_stats')\n        plt.show()\n\n    def stats_per_race(self):\n        sns.set()\n        avg_stats = self.avg_stats_by_race()\n        races = avg_stats['RACE'].unique().tolist()\n        xpos = 'center'\n        xpos = xpos.lower()  # normalize the case of the parameter\n        ha = {'center': 'center',\n              'right': 'left',\n              'left': 'right'}\n        offset = {'center': 0.5,\n                  'right': 0.60,\n                  'left': 0.45}\n        for race in races:\n            plt.figure()\n            ax = sns.barplot(x='COST', y='STATS', data=avg_stats[avg_stats['RACE'] == race],\n                             hue='STATS TYPE', palette='seismic')\n            # Annotate the top of the bars with their corresponding values\n            for rect in ax.patches:\n                height = rect.get_height()\n                if height == 0:\n                    continue\n                ax.text(rect.get_x() + rect.get_width() * offset[xpos], 1.008 * height,\n                        '{}'.format(height), ha=ha[xpos], va='bottom', rotation=90, fontsize=11)\n\n            leg = ax.legend(loc=2)\n            leg.set_title('Type of Stats')\n            for t, l in zip(leg.texts, ['Average Attack', 'Average Health']):\n                t.set_text(l)\n            plt.ylim(0, avg_stats[avg_stats['RACE'] == race]['STATS'].max() + 2)\n            plt.xlabel('Mana Cost')\n            plt.ylabel('Average Stats')\n            plt.title('Average Stats Per Mana Cost: ' + race)\n            sns.despine()\n            if self.save:\n                self.save_plot('avg_stats_{}'.format(race.lower()))\n            plt.show()\n\n    def probability_plots(self):\n        sns.set(palette='muted')\n        ax = sns.barplot(x='cost', y='probability', data=self.probabilities(), hue='mechanics', palette='Set1')\n        xpos = 'center'\n        xpos = xpos.lower()  # normalize the case of the parameter\n        ha = {'center': 'center',\n              'right': 'left',\n              'left': 'right'}\n        offset = {'center': 0.5,\n                  'right': 0.60,\n                  'left': 0.45}\n        # Annotate the top of the bars with their corresponding values\n        for rect in ax.patches:\n            height = rect.get_height()\n            if height == 0:\n                continue\n            ax.text(rect.get_x() + rect.get_width() * offset[xpos], 1.008 * height,\n                    '{}%'.format(int(height)), ha=ha[xpos], va='bottom', rotation=90, fontsize=7)\n            # Customize plot's Legend\n        leg = ax.legend(loc=0)\n        leg.set_title('Type of Mechanic')\n        plt.ylim(0, self.probabilities()['probability'].max() + 5)\n        plt.xlabel('Mana Cost')\n        plt.ylabel('Probability')\n        plt.title('Taunt, Rush, Charge Probabilities per Cost')\n        sns.despine()\n        if self.save:\n            self.save_plot('probabilities')\n        plt.show()\n","repo_name":"RottenCrab/HearthVizualizer","sub_path":"src/cardpool/visualizer.py","file_name":"visualizer.py","file_ext":"py","file_size_in_byte":7968,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"41927679285","text":"import pygame, sys\nimport time\nfrom pygame.locals import *\n\npygame.init()\n\n#constants\nDOWNLEFT = 'downleft'\nDOWNRIGHT = 'downright'\nUPLEFT = 'upleft'\nUPRIGHT = 'upright'\nMOVESPEED = 4\nWHITE = (255, 255, 255)\nRED = (255, 0, 0)\nGREEN = (0, 255, 0)\nBLUE = (0, 0, 255)\nWINDOW_V = 400\nWINDOW_H = 400\n\n#main window\nwindow_surface = pygame.display.set_mode((WINDOW_H, WINDOW_V), 0, 32)\npygame.display.set_caption(\"Basic animation\")\n\n#boxes\nb1 = {'rect': pygame.Rect(300, 80, 50, 100), 'color': RED, 'dir': UPRIGHT}\nb2 = {'rect':pygame.Rect(200, 200, 20, 20), 'color':GREEN, 'dir':UPLEFT}\nb3 = {'rect':pygame.Rect(100, 150, 60, 60), 'color':BLUE, 'dir':DOWNLEFT}\nboxes = [b1, b2, b3]\n\n#main loop\nwhile True:\n    for event in pygame.event.get():\n        if event.type == QUIT:\n            pygame.quit()\n            sys.exit()\n    \n    window_surface.fill(WHITE)\n    \n    for box in boxes:\n        if box['dir'] == DOWNRIGHT:\n            box['rect'].left += MOVESPEED\n            box['rect'].top += MOVESPEED\n        if box['dir'] == DOWNLEFT:\n            box['rect'].left -= MOVESPEED\n            box['rect'].top += MOVESPEED\n        if box['dir'] == UPLEFT:\n            box['rect'].left -= MOVESPEED\n            box['rect'].top -= MOVESPEED\n        if box['dir'] == UPRIGHT:\n            box['rect'].left += MOVESPEED\n            box['rect'].top -= MOVESPEED\n        \n        if box['rect'].top < 0:\n            if box['dir'] == UPRIGHT:\n                box['dir'] = DOWNRIGHT\n            if box['dir'] == UPLEFT:\n                box['dir'] = DOWNLEFT\n        \n        if box['rect'].right > WINDOW_H:\n            if box['dir'] == UPRIGHT:\n                box['dir'] = UPLEFT\n            if box['dir'] == DOWNRIGHT:\n                box['dir'] = DOWNLEFT\n        \n        if box['rect'].bottom > WINDOW_V:\n            if box['dir'] == DOWNLEFT:\n                box['dir'] = UPLEFT\n            if box['dir'] == DOWNRIGHT:\n                box['dir'] = UPRIGHT \n        \n        if box['rect'].left < 0:\n            if box['dir'] == UPLEFT:\n                box['dir'] = UPRIGHT \n            if box['dir'] == DOWNLEFT:\n                box['dir'] = DOWNRIGHT\n    \n        pygame.draw.rect(window_surface, box['color'], box['rect'])\n    \n    pygame.display.update()\n    time.sleep(0.02)\n","repo_name":"HardEnoughy/games_on_python","sub_path":"Pygame/basic_animation.py","file_name":"basic_animation.py","file_ext":"py","file_size_in_byte":2271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"41245417164","text":"import pymongo\nimport re\n\n# hidden DB information\nclient = pymongo.MongoClient(\"\")\ndb = client[ \"\" ]\ncol = db[ \"\" ] \n\n\n    \ndef handler(event, context):\n  \n  maskFlag = 0\n  socialDistancingFlag = 0\n  sickFlag = 0\n  dirtyFlag = 0\n  \n  maskWords = ['not wearing', 'not covering', 'no mask', 'under chin', 'under his chin', 'under her chin', 'under their chin', 'without mask', 'took off mask', 'without a mask', 'without his mask', 'without her mask', 'took off his mask', 'took off her mask', 'took off their mask', 'under nose', 'under his nose', 'under her nose', 'under their nose']\n  socialDistancing = ['too many people', 'social distancing', 'close', 'group', 'groups', 'crowd',  'crowds', 'crowded', 'no space', '6 feet', 'close', 'touching', 'shaking hands', 'sharing food', 'packed', 'gathering', 'not socially distanc']\n  sick = ['cough', 'coughing', 'sick', 'ill', 'sneeze', 'sneezing', 'sneezed', 'puke', 'debilitated', 'infected', 'green', 'ailing', 'frail', 'fever', 'feverish', 'vomit', ]\n  dirty = ['dirty', 'nasty', 'gross', 'not clean', 'unsanitary', 'unclean', 'not sanitary', 'grubby', 'filthy', 'unwashed', 'not washed', 'stains', 'stain', 'smeared', 'kams']\n  \n  cmpl = event['Complaints'].lower()\n  addr = event['Address']\n\n  for word in maskWords:\n      if word in cmpl:\n          maskFlag = 1\n          break\n  for word in socialDistancing:\n      if word in cmpl:\n          socialDistancingFlag = 1\n          break\n  for word in sick:\n      if word in cmpl:\n          sickFlag = 1\n          break\n  for word in dirty:\n      if word in cmpl:\n          dirtyFlag = 1\n          break\n  firstHalf = addr.split(' ')[0]\n  firstHalf = re.sub(\"[^0-9]\", \"\", firstHalf)\n  street_address = addr.split(',')[0].split(' ')\n  processed_address = [street_address[0] + ' ' + ' '.join(filter(lambda x: ('#' not in x and 'suite' not in x.lower() and 'ste' not in x.lower()), street_address[1:]))] + addr.split(',')[1:]\n  processed_address = ','.join(processed_address)\n  personDocument = {\n    \"Address\": processed_address,\n    \"violations\": [cmpl],\n    \"mask\": maskFlag,\n    \"socialDistancing\": socialDistancingFlag,\n    \"sick\": sickFlag,\n    \"dirty\": dirtyFlag\n  }\n  if(col.find({'Address': processed_address}).count() > 0):\n    myquery = { \"Address\": processed_address }\n    curViol = col.find({'Address': processed_address})\n    violList = [cmpl]\n    numMask = 0\n    numSocialDist = 0\n    numSick = 0\n    numDirty = 0\n    for x in curViol:\n      numMask = x[\"mask\"]\n      numSocialDist = x[\"socialDistancing\"]\n      numSick = x[\"sick\"]\n      numDirty = x[\"dirty\"]\n      if(isinstance(x[\"violations\"], str)):\n        violList.append(x[\"violations\"])\n      else:\n        for violation in x[\"violations\"]:\n          violList.append(violation)\n    newvalues = { \"$set\": { \"violations\": violList, \"mask\": numMask + maskFlag, \"socialDistancing\": numSocialDist + socialDistancingFlag, \"sick\": numSick + sickFlag, \"dirty\": numDirty + dirtyFlag} }\n    col.update_one(myquery, newvalues)\n  else:\n    col.insert_one(personDocument)\n\n  client.close()\n  return\n","repo_name":"balooop/covid-tracker","sub_path":"mysql/customerComplaints/customerComplaints.py","file_name":"customerComplaints.py","file_ext":"py","file_size_in_byte":3057,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"5012416544","text":"import numpy as np\n\nfrom duplicates_model import DuplicatesModels\nfrom learn_engine import LearnEngine\n\nimport app_env\nimport helpers\nfrom data_loader import DataLoader\n\nimport os\n\nclass DuplicatesEngine:\n    def __init__(self):\n        self.md = DuplicatesModels()\n        self.md_loaded = False\n\n    def get_idea_duplicates(self, idea_id, with_own_project=True, project_ids=None,\n                            idea_batch_size=app_env.ml_params_dataset_scoring_limit()):\n        if project_ids is None:\n            project_ids = []\n\n        dl = DataLoader()\n        df = dl.ideas(idea_id, with_own_project=with_own_project, project_ids=project_ids)\n        df['answer'] = np.nan\n\n        result = self.idea_duplicates_by_batch(idea_id, df, idea_batch_size=idea_batch_size)\n\n        return result\n\n    def idea_duplicates_by_batch(self, idea_id, df,\n                                 idea_batch_size=app_env.ml_params_dataset_scoring_limit()):\n        result = {}\n\n        if idea_batch_size is not None:\n            index_start = 0\n            index_stop = idea_batch_size\n            while True:\n                df1 = df.iloc[index_start:index_stop]\n                if len(df1) == 0:\n                    break\n                result_batch = self.__idea_duplicates(idea_id, df1)\n                for k in result_batch:\n                    v = result.setdefault(k, [])\n                    v += result_batch[k]\n                index_start = index_stop\n                index_stop += idea_batch_size\n        else:\n            result = self.__idea_duplicates(idea_id, df)\n\n        return result\n\n    def __idea_duplicates(self, idea_id, df):\n        self.load_md()\n\n        results_proba = self.md.predict(df)\n        results = helpers.expand(df).copy()\n        results['proba'] = list(results_proba) + list(results_proba)\n        results['is_duplicate'] = (results['proba'] > self.md.th).astype(int)\n        results['scoring_search_filter'] = df['scoring_search_filter'].values[0]\n\n        def project_apply(row):\n            if row.is_own_project is True:\n                return 'own_project'\n            else:\n                return row.project_id2\n\n        results['project'] = results[['project_id2', 'is_own_project']].apply(project_apply, axis=1)\n\n        results_dups = results[results['is_duplicate'] == 1]\n        results_dups.sort_values(by='proba', ascending=False, inplace=True)\n\n        result_ideas = results_dups[results['id2'] != idea_id]\n        result_ideas = result_ideas[['id2', 'proba', 'project']].copy()\n        result_ideas = result_ideas.rename(columns={'id2': 'id', 'proba': 'score'})\n\n        result_array = result_ideas.to_dict('records')\n        result_dict = {}\n        for row in result_array:\n            v = result_dict.setdefault(row['project'], [])\n            row_copy = row.copy()\n            del row_copy['project']\n            v.append(row_copy)\n\n        return result_dict\n\n    def ideas_for_project(self, project_id):\n        return DataLoader().ideas_in_project(project_id)\n\n    def learn(self):\n        learn_engine = LearnEngine(self.md)\n\n        self.md = learn_engine.learn()\n        self.md_loaded = True\n\n    def load_md(self):\n        if self.md_loaded is False:\n            data_dir = app_env.data_model_runtime_path('v3')\n            if(os.path.isdir(data_dir)):\n                path_for_load = data_dir\n            data_file = app_env.data_model_runtime_path('v3.pkl')\n            if(os.path.isfile(data_file)):\n                path_for_load = data_file\n            self.md.load(path_for_load)\n            self.md_loaded = True\n","repo_name":"alik1993/duplicates","sub_path":"python/duplicates_engine.py","file_name":"duplicates_engine.py","file_ext":"py","file_size_in_byte":3574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"43828086157","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport json\nimport yaml\nimport logging\nimport logging.config\nimport time\nimport traceback\nimport pathlib\n\nimport aiohttp\nfrom aiohttp import web\nimport numpy\n\nfrom word2vec.w2v import Word2Vec\nfrom word2vec.svc_config import SvcConfig\n\n\ndef _get_logger():\n    logger = logging.getLogger('word2vec.server')\n    return logger\n\n\nclass JsonEncoder(json.JSONEncoder):\n    def default(self, obj):\n        if isinstance(obj, numpy.integer):\n            return int(obj)\n        elif isinstance(obj, numpy.floating):\n            return float(obj)\n        elif isinstance(obj, numpy.ndarray):\n            return obj.tolist()\n        else:\n            return super(JsonEncoder, self).default(obj)\n\n\nclass Word2VecServer:\n    def __init__(self):\n        self.__w2v = None\n        self.__mean = None\n        self.__dim = None\n        self.__loading = True\n        self.logger = _get_logger()\n\n    def load(self, path):\n        wv = Word2Vec(path=path)\n        self.logger.info(\"Loading vectors...\")\n        time1 = time.time()\n        self.__w2v = wv.load_embeddings()\n        self.__loading = False\n        self.__dim = len(list(self.__w2v.values())[-1])\n        self.__mean = wv.get_mean_norm(self.__w2v)\n        time2 = time.time()\n        self.logger.info(\n            \"Done loading vectors - took {}\".format(time2 - time1))\n\n    def gen_random_mean_norm_vector(self):\n        tmp = numpy.random.normal(size=self.__dim).astype(numpy.float64)\n        tmp /= numpy.linalg.norm(tmp) / self.__mean\n        return tmp\n\n    async def handle_reload(self, request):\n        data = await request.json()\n        if 'path' not in data:\n            raise web.HTTPBadRequest()\n        path = data['path']\n        self.load(path)\n        return web.Response()\n\n    async def handle_request_multiple_words(self, request):\n        \"\"\"\n        This endpoint handles a request that takes a JSON array of words, and returns\n        a dictionary containing the vectorization of those words.\n        Example:\n        Request: {\"words\" : [\"word1\", \"word2\"]}\n        Assuming we have the vectorisation for word1 but not for word2\n        Response: {\"vectors\":{\"word1\":[...], \"word2\":null}}\n        \"\"\"\n\n        data = await request.json()\n        if 'words' not in data:\n            raise web.HTTPBadRequest()\n        words = data['words']\n        self.logger.info(\"Request for {} words\".format(len(words)))\n        wordvec_dict = {}\n        try:\n            for word in words:\n                vecs = self.__w2v.get(word)\n                if vecs is not None:\n                    wordvec_dict[word] = vecs\n                else:\n                    self.logger.info(\"unknown word {}\".format(word))\n            json_response = json.dumps({'vectors': wordvec_dict},\n                                       cls=JsonEncoder)\n            return web.json_response(body=json_response)\n        except Exception:\n            self.logger.exception(\"Error obtaining the vectors\")\n            raise\n\n    async def handle_request_health(self, request):\n        return web.Response(status=200)\n\n    async def handle_request_unknown_words(self, request):\n        data = await request.json()\n        if 'words' not in data:\n            raise web.HTTPBadRequest()\n        words = data['words']\n        self.logger.info(\"checking for unknown words from {} words\".format(\n            len(words)))\n        try:\n            unk_words = [w for w in words if w not in self.__w2v.keys()]\n            json_response = json.dumps({'unk_words': unk_words},\n                                       cls=JsonEncoder)\n            return web.json_response(body=json_response)\n        except Exception:\n            self.logger.exception(\"Error obtaining unknown words\")\n            raise\n\n\nLOGGING_CONFIG_TEXT = \"\"\"\nversion: 1\nroot:\n  level: DEBUG\n  handlers: ['console']\nformatters:\n  json:\n    class: pythonjsonlogger.jsonlogger.JsonFormatter\n    format: \"(asctime) (levelname) (name) (message)\"\nfilters:\n    w2vlogfilter:\n        (): word2vec.server.W2vLogFilter\nhandlers:\n  console:\n    class: logging.StreamHandler\n    level: INFO\n    stream: ext://sys.stdout\n    formatter: json\n    filters: [w2vlogfilter]\n\"\"\"\n\n\n@web.middleware\nasync def log_error_middleware(request, handler):\n    try:\n        response = await handler(request)\n    except aiohttp.web_exceptions.HTTPException:\n        # assume if we're throwing this that it's already logged\n        raise\n    except Exception:\n        _get_logger().exception(\"Unexpected exception in call\")\n\n        error_string = \"Internal Server Error\\n\" + traceback.format_exc()\n        raise aiohttp.web_exceptions.HTTPInternalServerError(text=error_string)\n    return response\n\n\ndef initialize_web_app(app, w2v_server):\n    app.middlewares.append(log_error_middleware)\n    app.router.add_post('/words', w2v_server.handle_request_multiple_words)\n    app.router.add_get('/health', w2v_server.handle_request_health)\n    app.router.add_post('/unk_words', w2v_server.handle_request_unknown_words)\n    app.router.add_post('/reload', w2v_server.handle_reload)\n\n\nclass W2vLogFilter(logging.Filter):\n    def __init__(self):\n        self.language = os.environ.get(\"W2V_LANGUAGE\", \"en\")\n        self.version = os.environ.get(\"W2V_VERSION\", None)\n\n    def filter(self, record):\n        \"\"\"Add language, and if available, the version\"\"\"\n        record.w2v_language = self.language\n        if self.version:\n            record.w2v_version = self.version\n        return True\n\n\ndef main():\n    \"\"\"Main function\"\"\"\n    logging_config_file = os.environ.get(\"LOGGING_CONFIG_FILE\", None)\n    if logging_config_file:\n        logging_config_path = pathlib.Path(logging_config_file)\n        with logging_config_path.open() as file_handle:\n            logging_config = yaml.safe_load(file_handle)\n    else:\n        logging_config = yaml.safe_load(LOGGING_CONFIG_TEXT)\n    print(\"*** LOGGING CONFIG ***\")\n    print(logging_config)\n    print(\"*** LOGGING CONFIG ***\")\n    logging.config.dictConfig(logging_config)\n\n    config = SvcConfig.get_instance()\n    server = Word2VecServer()\n    server.load(config.vectors_file)\n\n    app = web.Application()\n    initialize_web_app(app, server)\n    web.run_app(app, port=config.server_port)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"hutomadotAI/word2vec","sub_path":"src/word2vec/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":6257,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"21241076143","text":"import json\n\nfrom django.contrib import auth\nfrom django.contrib.auth.decorators import login_required\nfrom django.forms import model_to_dict\nfrom django.http import HttpResponseNotFound, HttpResponseRedirect, HttpResponse, JsonResponse\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.views.decorators.http import require_http_methods, require_POST\n\nfrom app.forms import *\nfrom app.models import *\nfrom app.utilits import *\n\n\ndef paginate(content, request, number):\n    paginator = Paginator(content, number)\n    page_num = request\n    return paginator.get_page(page_num)\n\ndef search(query, request, search_field):\n    search_query = request.GET.get('search','')\n    if search_query:\n        if search_field == 'title':\n            return query.filter(title__icontains=search_query).all()\n        elif search_field == 'content':\n            return query.filter(content__icontains=search_query).all()\n    else:\n        return query.all()\n\ndef index(request):\n    new_questions = search(Question.new_questions, request, 'title')\n    questions = paginate(new_questions, request.GET.get('page'), 20)\n    return render(request, \"index.html\", {\"questions\":questions})\n\ndef hot(request):\n    hot_questions = search(Question.hot_questions, request, 'title')\n    questions = paginate(hot_questions, request.GET.get('page'), 20)\n    return render(request, \"hot.html\", {\"questions\":questions})\n\ndef question(request, id):\n\n    if request.method == 'POST':\n        form = AnswerForm(request.user, id, data=request.POST)\n        if form.is_valid():\n            quest = form.save().question\n            url = quest\n            return redirect(url)\n    else:\n        form = AnswerForm(request.user, id)\n\n    question = get_object_or_404(Question, pk = id)\n\n    answers = Answer.answers.filter(question = question)\n    answers = search(answers, request, 'content')\n\n    answers = paginate(answers, request.GET.get('page'), 10)\n    return render(request, \"question.html\", {\"question\":question, \"answers\":answers, \"form\":form,})\n\n\n\n@login_required\ndef ask(request):\n    if request.method == 'POST':\n        form = QuestionForm(request.user, data=request.POST)\n        if form.is_valid():\n            question = form.save()  #?\n            url = question.get_absolute_url()\n            return redirect(url)\n    else:\n        form = QuestionForm(request.user, data=request.POST)\n    return render(request, \"ask.html\", {'form':form, })\n\ndef login(request):\n    if request.method == 'POST':\n        form = LoginForm(request.POST)\n        if form.is_valid():\n            user = auth.authenticate(request, **form.cleaned_data)\n            if user:\n                print(user)\n                auth.login(request, user)\n\n                return redirect(reverse('home'))\n            else:\n                form.add_error(None, 'Incorrect login or password')\n    else:\n        form = LoginForm()\n\n    return render(request, \"login.html\", { \"form\":form })\n\n@login_required()\ndef logout(request):\n    auth.logout(request)\n    return redirect(reverse('login'))\n\ndef signup(request):\n    if request.method == 'POST':\n        user_form = UserForm(request.POST, request.FILES)\n        if user_form.is_valid():\n            new_user = user_form.save(commit=False)\n            new_user.set_password(user_form.cleaned_data['password'])\n            new_user.save()\n\n            new_profile = Profile.users.create(user=new_user)\n            new_profile.avatar = user_form.cleaned_data['avatar']\n            new_profile.save()\n            auth.login(request, new_user)\n            return redirect(reverse('home'))\n        else:\n\n            print(\"bad\")\n\n    else:\n        user_form = UserForm()\n    return render(request, 'signup.html', {'user_form':user_form})\n\n\n\ndef tag(request, slug):\n    tag_questions = get_object_or_404(Tag.tags.filter(tag=slug)).questions.all()\n\n    questions = paginate(tag_questions, request.GET.get('page'), 20)\n    return render(request, \"tag.html\", {\"questions\":questions, \"slug\":slug})\n\n@login_required\n@require_http_methods(['GET','POST'])\ndef settings(request):\n    if request.method == 'POST':\n        initial_data = request.POST\n        instance = request.user\n        user_form = SettingsForm(initial=initial_data, instance = instance, files = request.FILES)\n        if user_form.is_valid():\n            user_form.save()\n\n            return redirect(reverse('home'))\n    else:\n        initial_data = model_to_dict(request.user)\n        initial_data['avatar'] = request.user.profile.avatar\n        user_form = SettingsForm(initial=initial_data)\n\n\n    return render(request, \"settings.html\", {\"user_form\":user_form, })\n\n@login_required\n@require_POST\ndef vote(request):\n    type_vote = request.POST['vote']\n    type_object = request.POST['type_object']\n    object_id = request.POST['object_id']\n    user = request.user\n    if type_object == 'question':\n        print(\"tyt\")\n        object = Question.new_questions.get(id=object_id)\n        vote = Question.hot_questions.is_liked(user, object_id)\n    else:\n        object = Answer.answers.get(id=object_id)\n        vote = Answer.answers.is_liked(user, object_id)\n\n    print(vote)\n    if vote:\n        if vote.type_vote == int(type_vote):\n            vote.delete()\n        elif vote.type_vote == -1:\n            vote.delete()\n            vote = Vote.objects.create(user=user, content_object=object, type_vote=1)\n            vote.save()\n        else:\n            vote.delete()\n            vote = Vote.objects.create(user=user, content_object=object, type_vote=-1)\n            vote.save()\n    else:\n        print('net')\n        vote = Vote.objects.create(user=user, content_object=object, type_vote=type_vote)\n        vote.save()\n\n    likes = object.votes.likes().count()\n    print(likes)\n    # likes=0\n    # dislikes=0\n    dislikes = object.votes.dislikes().count()\n    print(dislikes)\n    response_data = {}\n    response_data['likes'] = likes\n    response_data['dislikes'] = dislikes\n    return HttpResponse(json.dumps(response_data),content_type=\"application/json\")\n\n\n@login_required\n@require_POST\ndef correct_answer(request):\n    print(request.GET)\n    answer_id = request.POST['answer_id']\n    question_id = request.POST['question_id']\n    print((answer_id))\n    answer = Answer.answers.get(id = answer_id)\n    question = Question.new_questions.get(id=question_id)\n    if question.author == request.user:\n        if answer.is_correct:\n            answer.is_correct = False\n        else:\n            answer.is_correct = True\n    answer.save()\n    print(answer.is_correct)\n    return JsonResponse({'is_correct':answer.is_correct})\n\n\ndef pageNotFound(request, exception):\n    return HttpResponseNotFound('<h1>Not found!</h1>')\n\n\n\n","repo_name":"Julia1505/WEB","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6773,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"72112777243","text":"#!/usr/bin/python\n\nLIMIT = 1000000\nsteps = [0] * LIMIT\nmax_step = 0\nmax_step_pos = 0\n\nfor i in range(2, LIMIT):\n    test = i\n    n_steps = 0\n\n    while test > 1:\n        if test % 2 == 1:\n            test = (3 * test) + 1\n        else:\n            test //= 2\n        n_steps += 1\n        if test < i:\n            n_steps += steps[test]\n            break\n\n    if n_steps > max_step:\n        max_step = n_steps\n        max_step_pos = i\n    steps[i] = n_steps\n\nprint(max_step_pos)\n","repo_name":"cifvts/PyEuler","sub_path":"euler014.py","file_name":"euler014.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"69796293084","text":"from typing import List\n\n\nclass Solution:\n\n    # time (n * 2 ^n)\n    def subsetsWithDup(self, nums: List[int]) -> List[List[int]]:\n        ans = []\n        nums.sort()\n        def decision_tree(cur: List[int], idx):\n            if idx >= len(nums):\n                ans.append(cur)\n                return\n\n            rarr = cur[:]\n            larr = cur[:]\n            larr.append(nums[idx])\n\n            decision_tree(larr, idx + 1)\n\n            idx = idx + 1\n            while idx != 0 and idx < len(nums) and nums[idx - 1] == nums[idx]:\n                idx += 1\n            decision_tree(rarr, idx)\n\n        decision_tree([], 0)\n\n        return ans\n\n\nprint(Solution().subsetsWithDup([1, 1]))\nassert [[1, 2, 2], [1, 2], [1], [2, 2], [2], []] == Solution().subsetsWithDup([1, 2, 2])\n","repo_name":"haxul/algorithm_tasks_solving","sub_path":"python/leetcode/medium/90. Subsets II.py","file_name":"90. Subsets II.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"18340354992","text":"import pickle\nimport torch\nimport warnings\nfrom torch._six import string_classes\nfrom datetime import timedelta\n\n# This module is wildcard imported from torch.distributed.\n# TODO: specify __all__\n\nfrom .constants import default_pg_timeout\nfrom .rendezvous import rendezvous, register_rendezvous_handler  # noqa: F401\nfrom . import (\n    AllreduceOptions,\n    AllreduceCoalescedOptions,\n    AllToAllOptions,\n    BroadcastOptions,\n    GatherOptions,\n    ReduceOptions,\n    ReduceScatterOptions,\n    ScatterOptions,\n)\nfrom . import ReduceOp\nfrom . import PrefixStore\n\n\n_MPI_AVAILABLE = True\n_NCCL_AVAILABLE = True\n_GLOO_AVAILABLE = True\n\n\ntry:\n    from. import ProcessGroupMPI\nexcept ImportError:\n    _MPI_AVAILABLE = False\n\ntry:\n    from. import ProcessGroupNCCL\nexcept ImportError:\n    _NCCL_AVAILABLE = False\n\ntry:\n    from. import ProcessGroupGloo\nexcept ImportError:\n    _GLOO_AVAILABLE = False\n\n\nclass Backend(object):\n    \"\"\"\n    An enum-like class of available backends: GLOO, NCCL, MPI, and other registered\n    backends.\n\n    The values of this class are lowercase strings, e.g., ``\"gloo\"``. They can\n    be accessed as attributes, e.g., ``Backend.NCCL``.\n\n    This class can be directly called to parse the string, e.g.,\n    ``Backend(backend_str)`` will check if ``backend_str`` is valid, and\n    return the parsed lowercase string if so. It also accepts uppercase strings,\n    e.g., ``Backend(\"GLOO\")`` returns ``\"gloo\"``.\n\n    .. note:: The entry ``Backend.UNDEFINED`` is present but only used as\n              initial value of some fields. Users should neither use it directly\n              nor assume its existence.\n    \"\"\"\n    UNDEFINED = \"undefined\"\n    GLOO = \"gloo\"\n    NCCL = \"nccl\"\n    MPI = \"mpi\"\n    TCP = \"tcp\"\n\n    def __new__(cls, name):\n        if not isinstance(name, string_classes):\n            raise ValueError(\"Backend name must be a string, but got: {}\".format(name))\n        value = getattr(Backend, name.upper(), Backend.UNDEFINED)\n\n        if value == Backend.TCP:\n            raise ValueError(\"TCP backend has been deprecated. Please use \"\n                             \"Gloo or MPI backend for collective operations \"\n                             \"on CPU tensors.\")\n        elif value == Backend.UNDEFINED:\n            raise ValueError(\"Invalid backend: '{}'\".format(name))\n        elif value != Backend.GLOO and value != Backend.NCCL and value != Backend.MPI:\n            value = name\n        return value\n\n    @classmethod\n    def register_backend(cls, name, func):\n        \"\"\"\n        Registers a new backend.\n\n        This class method is used by 3rd party cpp extension to register new backend.\n\n        Arguments:\n            name (str): Backend name matching with the one in `init_process_group()`.\n            func (function): Function handler that instantiates the backend.\n                             The function should be implemented in the backend cpp extension\n                             and takes four arguments, including prefix_store, rank,\n                             world_size, and timeout.\n\n        .. note:: This support of 3rd party backend is experimental and subject to change.\n\n        \"\"\"\n        setattr(Backend, name.upper(), func)\n\n# `_backend`, `dist_backend`, and `reduce_op` are here to maintain backward\n# compatibility with pre-c10d distributed package.\n# TODO: remove them when users are ready to take a hard dependency on PyTorch 1.\n_backend = Backend.UNDEFINED\ndist_backend = Backend\n\n\nclass reduce_op(object):\n    r\"\"\"\n    Deprecated enum-like class for reduction operations: ``SUM``, ``PRODUCT``,\n    ``MIN``, and ``MAX``.\n\n    :class:`~torch.distributed.ReduceOp` is recommended to use instead.\n    \"\"\"\n\n    def __init__(self):\n        # __members__ is a dict storing key-value pairs for enum classes\n        for k, v in ReduceOp.__members__.items():\n            setattr(self, k, v)\n        self.__members__ = ReduceOp.__members__\n\n    def __getattribute__(self, key):\n        warnings.warn(\"torch.distributed.reduce_op is deprecated, please use \"\n                      \"torch.distributed.ReduceOp instead\")\n        return object.__getattribute__(self, key)\n\nreduce_op = reduce_op()\n\n\nclass group(object):\n    WORLD = object()\n\n\nclass GroupMember(object):\n    # Alias to group.WORLD for backward compatibility\n    WORLD = group.WORLD\n    NON_GROUP_MEMBER = object()\n\n\n# Cached process groups\n# For NCCL and GLOO pg, it is a map from ProcessGroup to (Backend, Store)\n# For MPI pg, it is a map from ProcessGroup to (Backend, None)\n_pg_map = {}\n# Process group's names, map from ProcessGroup to str\n_pg_names = {}\n# Process group's global rank to local rank mapping\n_pg_group_ranks = {}\n\n# Default process group state\n_default_pg = None\n_default_pg_init_method = None\n\n# Process group count for default naming\n_group_count = 0\n\n\ndef _rank_not_in_group(group):\n    \"\"\"\n    Helper that checks if the current process's rank is not in a given group\n\n    \"\"\"\n    if group == GroupMember.WORLD:\n        return False\n    return group == GroupMember.NON_GROUP_MEMBER\n\n\ndef _get_group_rank(group, rank):\n    \"\"\"\n    Helper that gets a given group's local rank in the group from a given global\n    rank\n\n    \"\"\"\n    if group is GroupMember.WORLD:\n        raise RuntimeError(\"group.WORLD does not have local rank to global \"\n                           \"rank mapping\")\n    if group not in _pg_group_ranks:\n        raise RuntimeError(\"The given group does not exist\")\n    try:\n        group_rank = _pg_group_ranks[group][rank]\n    except KeyError:\n        raise RuntimeError(f\"The global rank {rank} is not part of the group {group}\") from None\n    return group_rank\n\n\ndef _get_global_rank(group, group_rank):\n    \"\"\"\n    Helper that gets a given group's global rank from a given local rank in the\n    group\n\n    \"\"\"\n    if group is GroupMember.WORLD:\n        raise RuntimeError(\"group.WORLD does not have local rank to global \"\n                           \"rank mapping\")\n    group_rank_map = _pg_group_ranks[group]\n    for rank, grp_rank in group_rank_map.items():\n        if grp_rank == group_rank:\n            return rank\n    raise RuntimeError(\"The group rank is not part of the group\")\n\n\ndef _check_default_pg():\n    \"\"\"\n    Helper that checks if the default ProcessGroup has been initialized, with\n    assertion\n\n    \"\"\"\n    assert _default_pg is not None, \\\n        \"Default process group is not initialized\"\n\n\ndef _get_group_size(group):\n    \"\"\"\n    Helper that gets a given group's world size\n\n    \"\"\"\n    if group is GroupMember.WORLD:\n        _check_default_pg()\n        return _default_pg.size()\n    if group not in _pg_group_ranks:\n        raise RuntimeError(\"The given group does not exist\")\n    return len(_pg_group_ranks[group])\n\n\ndef _check_single_tensor(param, param_name):\n    \"\"\"\n    Helper to check that the parameter ``param_name`` is a single tensor.\n\n    \"\"\"\n    if not isinstance(param, torch.Tensor):\n        raise RuntimeError(\"Invalid function argument. Expected parameter `{}` \"\n                           \"to be of type torch.Tensor.\".format(param_name))\n\n\ndef _check_tensor_list(param, param_name):\n    \"\"\"\n    Helper to check that the parameter ``param_name`` is a list of tensors.\n\n    \"\"\"\n    if not isinstance(param, list) or \\\n       not all(isinstance(p, torch.Tensor) for p in param):\n        raise RuntimeError(\"Invalid function argument. Expected parameter `{}` \"\n                           \"to be of type List[torch.Tensor].\".format(param_name))\n\n\ndef is_mpi_available():\n    \"\"\"\n    Checks if the MPI backend is available.\n\n    \"\"\"\n    return _MPI_AVAILABLE\n\n\ndef is_nccl_available():\n    \"\"\"\n    Checks if the NCCL backend is available.\n\n    \"\"\"\n    return _NCCL_AVAILABLE\n\n\ndef is_gloo_available():\n    \"\"\"\n    Checks if the Gloo backend is available.\n\n    \"\"\"\n    return _GLOO_AVAILABLE\n\n\ndef is_initialized():\n    \"\"\"\n    Checking if the default process group has been initialized\n\n    \"\"\"\n    return _default_pg is not None\n\n\ndef _get_default_group():\n    \"\"\"\n    Getting the default process group created by init_process_group\n\n    \"\"\"\n    if not is_initialized():\n        raise RuntimeError(\"Default process group has not been initialized, \"\n                           \"please make sure to call init_process_group.\")\n    return _default_pg\n\n\ndef _get_default_store():\n    \"\"\"\n    Getting the default store created by init_process_group\n\n    \"\"\"\n    if not is_initialized():\n        raise RuntimeError(\"Default process group has not been initialized, \"\n                           \"please make sure to call init_process_group.\")\n    _, default_store = _pg_map[_default_pg]\n    return default_store\n\n\ndef get_backend(group=group.WORLD):\n    \"\"\"\n    Returns the backend of the given process group.\n\n    Arguments:\n        group (ProcessGroup, optional): The process group to work on. The\n            default is the general main process group. If another specific group\n            is specified, the calling process must be part of :attr:`group`.\n\n    Returns:\n        The backend of the given process group as a lower case string.\n\n    \"\"\"\n    _check_default_pg()\n\n    if group == GroupMember.WORLD:\n        pg = _default_pg\n    else:\n        pg = group\n    if _rank_not_in_group(pg):\n        raise RuntimeError(\"Invalid process group specified\")\n    return _pg_map.get(pg, None)[0]\n\n\ndef init_process_group(backend,\n                       init_method=None,\n                       timeout=default_pg_timeout,\n                       world_size=-1,\n                       rank=-1,\n                       store=None,\n                       group_name=''):\n    \"\"\"\n    Initializes the default distributed process group, and this will also\n    initialize the distributed package.\n\n    There are 2 main ways to initialize a process group:\n        1. Specify ``store``, ``rank``, and ``world_size`` explicitly.\n        2. Specify ``init_method`` (a URL string) which indicates where/how\n           to discover peers. Optionally specify ``rank`` and ``world_size``,\n           or encode all required parameters in the URL and omit them.\n\n    If neither is specified, ``init_method`` is assumed to be \"env://\".\n\n\n    Arguments:\n        backend (str or Backend): The backend to use. Depending on\n            build-time configurations, valid values include ``mpi``, ``gloo``,\n            and ``nccl``. This field should be given as a lowercase string\n            (e.g., ``\"gloo\"``), which can also be accessed via\n            :class:`Backend` attributes (e.g., ``Backend.GLOO``). If using\n            multiple processes per machine with ``nccl`` backend, each process\n            must have exclusive access to every GPU it uses, as sharing GPUs\n            between processes can result in deadlocks.\n        init_method (str, optional): URL specifying how to initialize the\n                                     process group. Default is \"env://\" if no\n                                     ``init_method`` or ``store`` is specified.\n                                     Mutually exclusive with ``store``.\n        world_size (int, optional): Number of processes participating in\n                                    the job. Required if ``store`` is specified.\n        rank (int, optional): Rank of the current process.\n                              Required if ``store`` is specified.\n        store(Store, optional): Key/value store accessible to all workers, used\n                                to exchange connection/address information.\n                                Mutually exclusive with ``init_method``.\n        timeout (timedelta, optional): Timeout for operations executed against\n            the process group. Default value equals 30 minutes.\n            This is applicable for the ``gloo`` backend. For ``nccl``, this is\n            applicable only if the environment variable ``NCCL_BLOCKING_WAIT``\n            or ``NCCL_ASYNC_ERROR_HANDLING`` is set to 1. When\n            ``NCCL_BLOCKING_WAIT`` is set, this is the duration for which the\n            process will block and wait for collectives to complete before\n            throwing an exception. When ``NCCL_ASYNC_ERROR_HANDLING`` is set,\n            this is the duration after which collectives will be aborted\n            asynchronously and the process will crash. ``NCCL_BLOCKING_WAIT``\n            will provide errors to the user which can be caught and handled,\n            but due to its blocking nature, it has a performance overhead. On\n            the other hand, ``NCCL_ASYNC_ERROR_HANDLING`` has little\n            performance overhead, but crashes the process on errors. This is\n            done since CUDA execution is async and it is no longer safe to\n            continue executing user code since failed async NCCL operations\n            might result in subsequent CUDA operations to run on corrupted\n            data. Only one of these two environment variables should be set.\n        group_name (str, optional, deprecated): Group name.\n\n    To enable ``backend == Backend.MPI``, PyTorch needs to be built from source\n    on a system that supports MPI.\n\n    \"\"\"\n    global _pg_group_ranks\n    global _backend\n    global _default_pg\n    global _default_pg_init_method\n\n    if not isinstance(timeout, timedelta):\n        raise RuntimeError(\"Expected timeout argument to be of type\"\n                           \"datetime.timedelta\")\n\n    if _default_pg is not None:\n        raise RuntimeError(\"trying to initialize the default process group \"\n                           \"twice!\")\n\n    assert (store is None) or (init_method is None), \\\n        \"Cannot specify both init_method and store.\"\n\n    if store is not None:\n        assert world_size > 0, 'world_size must be positive if using store'\n        assert rank >= 0, 'rank must be non-negative if using store'\n    elif init_method is None:\n        init_method = \"env://\"\n\n    backend = Backend(backend)\n\n    if backend == Backend.MPI:\n        if world_size != -1 or rank != -1:\n            warnings.warn(\n                \"For MPI backend, world_size ({}) and rank ({}) \"\n                \"are ignored since they are assigned by the \"\n                \"MPI runtime.\".format(world_size, rank))\n\n        _default_pg = _new_process_group_helper(\n            -1,\n            -1,\n            [],\n            Backend.MPI,\n            None,\n            group_name=group_name,\n            timeout=timeout)\n    else:\n        # backward compatible API\n        if store is None:\n            rendezvous_iterator = rendezvous(\n                init_method, rank, world_size, timeout=timeout\n            )\n            store, rank, world_size = next(rendezvous_iterator)\n            store.set_timeout(timeout)\n\n        _default_pg = _new_process_group_helper(\n            world_size,\n            rank,\n            [],\n            backend,\n            store,\n            group_name=group_name,\n            timeout=timeout)\n\n    _pg_group_ranks[_default_pg] = {i: i for i in range(_default_pg.size())}\n    _backend = _pg_map[_default_pg][0]\n    _default_pg_init_method = init_method\n\n    # barrier at the end to ensure that once we return from this method, all\n    # process groups including global variables are updated correctly on all\n    # ranks.\n    barrier()\n\ndef _new_process_group_helper(world_size,\n                              rank,\n                              group_ranks,\n                              backend,\n                              store,\n                              group_name=None,\n                              timeout=default_pg_timeout):\n    \"\"\"\n    Create a new distributed process group.\n\n    This function must be called by ALL processes in the global group, even if\n    the calling process is not part of the newly created group. In that case,\n    this function returns GroupMember.NON_GROUP_MEMBER.\n\n    This function is called with ``group_ranks == []`` for the default group.\n    \"\"\"\n    global _pg_map\n    global _group_count\n    global _pg_names\n\n    if not group_name:\n        group_name = str(_group_count)\n        _group_count += 1\n\n    if group_name in _pg_names.values():\n        raise RuntimeError(\"The specified group name has already been \"\n                           \"created, please use a different group name\")\n\n    if not isinstance(timeout, timedelta):\n        raise RuntimeError(\"Expected timeout argument to be of type\"\n                           \"datetime.timedelta\")\n\n    # The list of group ranks is empty if we're creating the default group.\n    is_default_group = (len(group_ranks) == 0)\n\n    backend = Backend(backend)\n    if backend == Backend.MPI:\n        if not is_mpi_available():\n            raise RuntimeError(\n                \"Distributed package doesn't have MPI built in.\"\n                \" MPI is only included if you build PyTorch from\"\n                \" source on a host that has MPI installed.\")\n        pg = ProcessGroupMPI.create(group_ranks)\n        if not pg:\n            return GroupMember.NON_GROUP_MEMBER\n        _pg_map[pg] = (Backend.MPI, None)\n        _pg_names[pg] = group_name\n    else:\n        # If this is a subgroup (which means group_ranks is specified),\n        # we check if the current process is a member of the new group.\n        if not is_default_group:\n            global_rank = _default_pg.rank()\n            if global_rank not in group_ranks:\n                return GroupMember.NON_GROUP_MEMBER\n\n        # Use the group name as prefix in the default store, such that\n        # a single store can be reused by multiple groups.\n        prefix_store = PrefixStore(group_name, store)\n\n        if backend == Backend.GLOO:\n            pg = ProcessGroupGloo(\n                prefix_store,\n                rank,\n                world_size,\n                timeout=timeout)\n            _pg_map[pg] = (Backend.GLOO, store)\n            _pg_names[pg] = group_name\n        elif backend == Backend.NCCL:\n            if not is_nccl_available():\n                raise RuntimeError(\"Distributed package doesn't have NCCL \"\n                                   \"built in\")\n            pg = ProcessGroupNCCL(\n                prefix_store,\n                rank,\n                world_size,\n                timeout)\n            _pg_map[pg] = (Backend.NCCL, store)\n            _pg_names[pg] = group_name\n        else:\n            pg = getattr(Backend, backend.upper())(\n                prefix_store,\n                rank,\n                world_size,\n                timeout)\n            _pg_map[pg] = (backend, store)\n            _pg_names[pg] = group_name\n\n    return pg\n\n\ndef destroy_process_group(group=group.WORLD):\n    \"\"\"\n    Destroy a given process group, and deinitialize the distributed package\n\n    Arguments:\n        group (ProcessGroup, optional): The process group to be destroyed, if\n                                        group.WORLD is given, all process\n                                        groups including the default one will\n                                        be destroyed.\n    \"\"\"\n    global _pg_map\n    global _pg_names\n    global _pg_group_ranks\n    global _default_pg\n    global _default_pg_init_method\n    global _group_count\n\n    if group == GroupMember.NON_GROUP_MEMBER:\n        return\n\n    if group == GroupMember.WORLD:\n        pg = _default_pg\n    else:\n        pg = group\n\n    if _pg_map.get(pg, None) is None:\n        raise RuntimeError(\"Invalid process group specified\")\n\n    if group == GroupMember.WORLD:\n        _default_pg = None\n        _default_pg_init_method = None\n        _pg_map.clear()\n        _pg_names.clear()\n        _pg_group_ranks.clear()\n\n        # when process group doesn't have an explicit name (only WORLD (default)\n        # process group can have an explicit name), we use global _group_counter\n        # to generate the name. We need to reset the counter on destruction to\n        # allow consistent value to be generated when we re-create process\n        # groups after some trainers recover from failure\n        #\n        # We only reset this when WORLD is being destroyed because if this\n        # process group is in good state, we aren't dealing with failures.\n        _group_count = 0\n    else:\n        del _pg_map[pg]\n        del _pg_names[pg]\n        del _pg_group_ranks[pg]\n\n\ndef get_rank(group=group.WORLD):\n    \"\"\"\n    Returns the rank of current process group\n\n    Rank is a unique identifier assigned to each process within a distributed\n    process group. They are always consecutive integers ranging from 0 to\n    ``world_size``.\n\n    Arguments:\n        group (ProcessGroup, optional): The process group to work on\n\n    Returns:\n        The rank of the process group\n        -1, if not part of the group\n\n    \"\"\"\n    if _rank_not_in_group(group):\n        return -1\n\n    _check_default_pg()\n    if group == GroupMember.WORLD:\n        return _default_pg.rank()\n\n    return _get_group_rank(group, _default_pg.rank())\n\n\ndef get_world_size(group=group.WORLD):\n    \"\"\"\n    Returns the number of processes in the current process group\n\n    Arguments:\n        group (ProcessGroup, optional): The process group to work on\n\n    Returns:\n        The world size of the process group\n        -1, if not part of the group\n\n    \"\"\"\n    if _rank_not_in_group(group):\n        return -1\n\n    return _get_group_size(group)\n\n\ndef isend(tensor,\n          dst,\n          group=group.WORLD,\n          tag=0):\n    \"\"\"\n    Sends a tensor asynchronously.\n\n    Arguments:\n        tensor (Tensor): Tensor to send.\n        dst (int): Destination rank.\n        group (ProcessGroup, optional): The process group to work on\n        tag (int, optional): Tag to match send with remote recv\n\n    Returns:\n        A distributed request object.\n        None, if not part of the group\n\n    \"\"\"\n    _check_single_tensor(tensor, \"tensor\")\n    if _rank_not_in_group(group):\n        return\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        return _default_pg.send([tensor], dst, tag)\n    else:\n        group_dst_rank = _get_group_rank(group, dst)\n        return group.send([tensor], group_dst_rank, tag)\n\n\ndef irecv(tensor,\n          src,\n          group=group.WORLD,\n          tag=0):\n    \"\"\"\n    Receives a tensor asynchronously.\n\n    Arguments:\n        tensor (Tensor): Tensor to fill with received data.\n        src (int): Source rank.\n        group (ProcessGroup, optional): The process group to work on\n        tag (int, optional): Tag to match recv with remote send\n\n    Returns:\n        A distributed request object.\n        None, if not part of the group\n\n    \"\"\"\n    _check_single_tensor(tensor, \"tensor\")\n    if _rank_not_in_group(group):\n        return\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        return _default_pg.recv([tensor], src, tag)\n    else:\n        group_src_rank = _get_group_rank(group, src)\n        return group.recv([tensor], group_src_rank, tag)\n\n\ndef send(tensor,\n         dst,\n         group=group.WORLD,\n         tag=0):\n    \"\"\"\n    Sends a tensor synchronously.\n\n    Arguments:\n        tensor (Tensor): Tensor to send.\n        dst (int): Destination rank.\n        group (ProcessGroup, optional): The process group to work on\n        tag (int, optional): Tag to match send with remote recv\n\n    \"\"\"\n    _check_single_tensor(tensor, \"tensor\")\n    if _rank_not_in_group(group):\n        return\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        _default_pg.send([tensor], dst, tag).wait()\n    else:\n        group_dst_rank = _get_group_rank(group, dst)\n        group.send([tensor], group_dst_rank, tag).wait()\n\n\ndef recv(tensor,\n         src=None,\n         group=group.WORLD,\n         tag=0):\n    \"\"\"\n    Receives a tensor synchronously.\n\n    Arguments:\n        tensor (Tensor): Tensor to fill with received data.\n        src (int, optional): Source rank. Will receive from any\n            process if unspecified.\n        group (ProcessGroup, optional): The process group to work on\n        tag (int, optional): Tag to match recv with remote send\n\n    Returns:\n        Sender rank\n        -1, if not part of the group\n\n    \"\"\"\n    _check_single_tensor(tensor, \"tensor\")\n    if _rank_not_in_group(group):\n        return -1\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        pg = _default_pg\n    else:\n        pg = group\n\n    if src is None:\n        work = pg.recv_anysource([tensor], tag)\n        work.wait()\n        src_rank = work._source_rank()\n        if group == GroupMember.WORLD:\n            return src_rank\n        else:\n            return _get_global_rank(pg, src_rank)\n    else:\n        if group == GroupMember.WORLD:\n            pg.recv([tensor], src, tag).wait()\n        else:\n            group_src_rank = _get_group_rank(pg, src)\n            pg.recv([tensor], group_src_rank, tag).wait()\n        return src\n\n\ndef broadcast_multigpu(tensor_list,\n                       src,\n                       group=group.WORLD,\n                       async_op=False,\n                       src_tensor=0):\n    \"\"\"\n    Broadcasts the tensor to the whole group with multiple GPU tensors\n    per node.\n\n    ``tensor`` must have the same number of elements in all the GPUs from\n    all processes participating in the collective. each tensor in the list must\n    be on a different GPU\n\n    Only nccl and gloo backend are currently supported\n    tensors should only be GPU tensors\n\n    Arguments:\n        tensor_list (List[Tensor]): Tensors that participate in the collective\n            operation. If ``src`` is the rank, then the specified ``src_tensor``\n            element of ``tensor_list`` (``tensor_list[src_tensor]``) will be\n            broadcast to all other tensors (on different GPUs) in the src process\n            and all tensors in ``tensor_list`` of other non-src processes.\n            You also need to make sure that ``len(tensor_list)`` is the same\n            for all the distributed processes calling this function.\n\n        src (int): Source rank.\n        group (ProcessGroup, optional): The process group to work on\n        async_op (bool, optional): Whether this op should be an async op\n        src_tensor (int, optional): Source tensor rank within ``tensor_list``\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group\n\n    \"\"\"\n    if _rank_not_in_group(group):\n        return\n\n    opts = BroadcastOptions()\n    opts.rootRank = src\n    opts.rootTensor = src_tensor\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.broadcast(tensor_list, opts)\n    else:\n        group_src_rank = _get_group_rank(group, src)\n        opts.rootRank = group_src_rank\n        work = group.broadcast(tensor_list, opts)\n    if async_op:\n        return work\n    else:\n        work.wait()\n\n\ndef broadcast(tensor,\n              src,\n              group=group.WORLD,\n              async_op=False):\n    \"\"\"\n    Broadcasts the tensor to the whole group.\n\n    ``tensor`` must have the same number of elements in all processes\n    participating in the collective.\n\n    Arguments:\n        tensor (Tensor): Data to be sent if ``src`` is the rank of current\n            process, and tensor to be used to save received data otherwise.\n        src (int): Source rank.\n        group (ProcessGroup, optional): The process group to work on\n        async_op (bool, optional): Whether this op should be an async op\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group\n\n    \"\"\"\n    _check_single_tensor(tensor, \"tensor\")\n    if _rank_not_in_group(group):\n        return\n\n    opts = BroadcastOptions()\n    opts.rootRank = src\n    opts.rootTensor = 0\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.broadcast([tensor], opts)\n    else:\n        group_src_rank = _get_group_rank(group, src)\n        opts.rootRank = group_src_rank\n        work = group.broadcast([tensor], opts)\n    if async_op:\n        return work\n    else:\n        work.wait()\n\n\ndef all_reduce_multigpu(tensor_list,\n                        op=ReduceOp.SUM,\n                        group=group.WORLD,\n                        async_op=False):\n    r\"\"\"\n    Reduces the tensor data across all machines in such a way that all get\n    the final result. This function reduces a number of tensors on every node,\n    while each tensor resides on different GPUs.\n    Therefore, the input tensor in the tensor list needs to be GPU tensors.\n    Also, each tensor in the tensor list needs to reside on a different GPU.\n\n    After the call, all ``tensor`` in ``tensor_list`` is going to be bitwise\n    identical in all processes.\n\n    Only nccl and gloo backend is currently supported\n    tensors should only be GPU tensors\n\n    Arguments:\n        tensor list (List[Tensor]): List of input and output tensors of\n            the collective. The function operates in-place and requires that\n            each tensor to be a GPU tensor on different GPUs.\n            You also need to make sure that ``len(tensor_list)`` is the same for\n            all the distributed processes calling this function.\n        op (optional): One of the values from\n            ``torch.distributed.ReduceOp``\n            enum.  Specifies an operation used for element-wise reductions.\n        group (ProcessGroup, optional): The process group to work on\n        async_op (bool, optional): Whether this op should be an async op\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group\n\n    \"\"\"\n    if _rank_not_in_group(group):\n        return\n\n    opts = AllreduceOptions()\n    opts.reduceOp = op\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.allreduce(tensor_list, opts)\n    else:\n        work = group.allreduce(tensor_list, opts)\n\n    if async_op:\n        return work\n    else:\n        work.wait()\n\n\ndef all_reduce(tensor,\n               op=ReduceOp.SUM,\n               group=group.WORLD,\n               async_op=False):\n    \"\"\"\n    Reduces the tensor data across all machines in such a way that all get\n    the final result.\n\n    After the call ``tensor`` is going to be bitwise identical in all processes.\n\n    Arguments:\n        tensor (Tensor): Input and output of the collective. The function\n            operates in-place.\n        op (optional): One of the values from\n            ``torch.distributed.ReduceOp``\n            enum.  Specifies an operation used for element-wise reductions.\n        group (ProcessGroup, optional): The process group to work on\n        async_op (bool, optional): Whether this op should be an async op\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group\n\n    \"\"\"\n    _check_single_tensor(tensor, \"tensor\")\n    if _rank_not_in_group(group):\n        return\n\n    opts = AllreduceOptions()\n    opts.reduceOp = op\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.allreduce([tensor], opts)\n    else:\n        work = group.allreduce([tensor], opts)\n\n    if async_op:\n        return work\n    else:\n        work.wait()\n\n\ndef all_reduce_coalesced(tensors,\n                         op=ReduceOp.SUM,\n                         group=group.WORLD,\n                         async_op=False):\n    \"\"\"\n    WARNING: at this time individual shape checking is not implemented across nodes.\n    For example, if the rank 0 node passes [torch.rand(4), torch.rand(2)] and the\n    rank 1 node passes [torch.rand(2), torch.rand(2), torch.rand(2)], the allreduce\n    operation will proceed without complaint and return erroneous outputs. This lack\n    of shape checking results in significant performance improvements but users of this\n    function should take extra care to ensure that each node passes in tensors whose\n    shapes match across nodes.\n\n    Reduces each tensor in tensors (residing on the same device) across all machines\n    in such a way that all get the final result.\n\n    After the call each tensor in tensors is going to bitwise identical\n    in all processes.\n\n    Arguments:\n        tensors (List[Tensor]): Input and output of the collective. The function\n            operates in-place.\n        op (Optional[ReduceOp]): One of the values from\n            ``torch.distributed.ReduceOp`` enum. Specifies an operation used for\n            element-wise reductions.\n        group (Optional[ProcessGroup]): The process group to work on.\n        async_op (Optional[bool]): Whether this op should be an async op.\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group.\n\n    \"\"\"\n    _check_tensor_list(tensors, \"tensor\")\n    if _rank_not_in_group(group):\n        return\n\n    opts = AllreduceCoalescedOptions()\n    opts.reduceOp = op\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.allreduce_coalesced(tensors, opts)\n    else:\n        work = group.allreduce_coalesced(tensors, opts)\n\n    if async_op:\n        return work\n    else:\n        work.wait()\n\n\ndef reduce_multigpu(tensor_list,\n                    dst,\n                    op=ReduceOp.SUM,\n                    group=group.WORLD,\n                    async_op=False,\n                    dst_tensor=0):\n    \"\"\"\n    Reduces the tensor data on multiple GPUs across all machines. Each tensor\n    in ``tensor_list`` should reside on a separate GPU\n\n    Only the GPU of ``tensor_list[dst_tensor]`` on the process with rank ``dst``\n    is going to receive the final result.\n\n    Only nccl backend is currently supported\n    tensors should only be GPU tensors\n\n    Arguments:\n        tensor_list (List[Tensor]): Input and output GPU tensors of the\n            collective. The function operates in-place.\n            You also need to make sure that ``len(tensor_list)`` is the same for\n            all the distributed processes calling this function.\n        dst (int): Destination rank\n        op (optional): One of the values from\n            ``torch.distributed.ReduceOp``\n            enum.  Specifies an operation used for element-wise reductions.\n        group (ProcessGroup, optional): The process group to work on\n        async_op (bool, optional): Whether this op should be an async op\n        dst_tensor (int, optional): Destination tensor rank within\n                                    ``tensor_list``\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, otherwise\n\n    \"\"\"\n    if _rank_not_in_group(group):\n        return\n\n    opts = ReduceOptions()\n    opts.reduceOp = op\n    opts.rootRank = dst\n    opts.rootTensor = dst_tensor\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.reduce(tensor_list, opts)\n    else:\n        group_dst_rank = _get_group_rank(group, dst)\n        opts.rootRank = group_dst_rank\n        work = group.reduce(tensor_list, opts)\n\n    if async_op:\n        return work\n    else:\n        work.wait()\n\n\ndef reduce(tensor,\n           dst,\n           op=ReduceOp.SUM,\n           group=group.WORLD,\n           async_op=False):\n    \"\"\"\n    Reduces the tensor data across all machines.\n\n    Only the process with rank ``dst`` is going to receive the final result.\n\n    Arguments:\n        tensor (Tensor): Input and output of the collective. The function\n            operates in-place.\n        dst (int): Destination rank\n        op (optional): One of the values from\n            ``torch.distributed.ReduceOp``\n            enum.  Specifies an operation used for element-wise reductions.\n        group (ProcessGroup, optional): The process group to work on\n        async_op (bool, optional): Whether this op should be an async op\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group\n\n    \"\"\"\n    _check_single_tensor(tensor, \"tensor\")\n    if _rank_not_in_group(group):\n        return\n\n    opts = ReduceOptions()\n    opts.reduceOp = op\n    opts.rootRank = dst\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.reduce([tensor], opts)\n    else:\n        group_dst_rank = _get_group_rank(group, dst)\n        opts.rootRank = group_dst_rank\n        work = group.reduce([tensor], opts)\n\n    if async_op:\n        return work\n    else:\n        work.wait()\n\n\ndef all_gather_multigpu(output_tensor_lists,\n                        input_tensor_list,\n                        group=group.WORLD,\n                        async_op=False):\n    \"\"\"\n    Gathers tensors from the whole group in a list.\n    Each tensor in ``tensor_list`` should reside on a separate GPU\n\n    Only nccl backend is currently supported\n    tensors should only be GPU tensors\n\n    Arguments:\n        output_tensor_lists (List[List[Tensor]]): Output lists. It should\n            contain correctly-sized tensors on each GPU to be used for output\n            of the collective, e.g. ``output_tensor_lists[i]`` contains the\n            all_gather result that resides on the GPU of\n            ``input_tensor_list[i]``.\n\n            Note that each element of ``output_tensor_lists`` has the size of\n            ``world_size * len(input_tensor_list)``, since the function all\n            gathers the result from every single GPU in the group. To interpret\n            each element of ``output_tensor_lists[i]``, note that\n            ``input_tensor_list[j]`` of rank k will be appear in\n            ``output_tensor_lists[i][k * world_size + j]``\n\n            Also note that ``len(output_tensor_lists)``, and the size of each\n            element in ``output_tensor_lists`` (each element is a list,\n            therefore ``len(output_tensor_lists[i])``) need to be the same\n            for all the distributed processes calling this function.\n\n        input_tensor_list (List[Tensor]): List of tensors(on different GPUs) to\n            be broadcast from current process.\n            Note that ``len(input_tensor_list)`` needs to be the same for\n            all the distributed processes calling this function.\n\n        group (ProcessGroup, optional): The process group to work on\n        async_op (bool, optional): Whether this op should be an async op\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group\n\n    \"\"\"\n    if _rank_not_in_group(group):\n        return\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.allgather(output_tensor_lists, input_tensor_list)\n    else:\n        work = group.allgather(output_tensor_lists, input_tensor_list)\n\n    if async_op:\n        return work\n    else:\n        work.wait()\n\n\ndef _object_to_tensor(obj):\n    buffer = pickle.dumps(obj)\n    byte_storage = torch.ByteStorage.from_buffer(buffer)\n    byte_tensor = torch.ByteTensor(byte_storage)\n    local_size = torch.LongTensor([byte_tensor.numel()])\n    return byte_tensor, local_size\n\n\ndef _tensor_to_object(tensor, tensor_size):\n    buf = tensor.numpy().tobytes()[:tensor_size]\n    out = pickle.loads(buf)\n    return out\n\n\ndef all_gather_object(object_list, obj, group=group.WORLD):\n    \"\"\"\n    Gathers picklable objects from the whole group into a list. Similar to\n    :func:`all_gather`, but Python objects can be passed in. Note that the object\n    must be picklable in order to be gathered.\n\n    Arguments:\n        object_list (list[Any]): Output list. It should be correctly sized as the\n            size of the group for this collective and will contain the output.\n        object (Any): Pickable Python object to be broadcast from current process.\n        group (ProcessGroup, optional): The process group to work on\n\n    Returns:\n        None. If the calling rank is part of this group, the output of the\n        collective will be populated into the input ``object_list``. If the\n        calling rank is not part of the group, the passed in ``object_list`` will\n        be unmodified.\n\n    .. note:: Note that this API differs slightly from the :func:`all_gather`\n        collective since it does not provide an ``async_op`` handle and thus\n        will be a blocking call.\n\n    .. warning::\n        :func:`all_gather_object` uses ``pickle`` module implicitly, which is\n        known to be insecure. It is possible to construct malicious pickle data\n        which will execute arbitrary code during unpickling. Only call this\n        function with data you trust.\n    \"\"\"\n    if _rank_not_in_group(group):\n        return\n\n    input_tensor, local_size = _object_to_tensor(obj)\n    group_backend = get_backend(group)\n    my_rank = get_rank()\n    is_nccl_backend = group_backend == Backend.NCCL\n    if is_nccl_backend:\n        input_tensor, local_size = input_tensor.to(my_rank), local_size.to(my_rank)\n    # Gather all local sizes. This is so that we can find the max size, and index\n    # until the correct size when deserializing the tensors.\n    group_size = get_world_size(group=group)\n    object_sizes_tensor = torch.zeros(group_size, dtype=int).to(\n        my_rank if is_nccl_backend else \"cpu\"\n    )\n    object_size_list = [\n        object_sizes_tensor[i].unsqueeze(dim=0) for i in range(group_size)\n    ]\n    # Allgather tensor sizes\n    all_gather(object_size_list, local_size, group=group)\n    max_object_size = max(object_size_list)\n    # Resize tensor to max size across all ranks.\n    input_tensor.resize_(max_object_size)\n    coalesced_output_tensor = torch.empty(\n        max_object_size * group_size, dtype=torch.uint8\n    ).to(my_rank if is_nccl_backend else \"cpu\")\n    # Output tensors are nonoverlapping views of coalesced_output_tensor\n    output_tensors = [\n        coalesced_output_tensor[max_object_size * i : max_object_size * (i + 1)]\n        for i in range(group_size)\n    ]\n    all_gather(output_tensors, input_tensor, group=group)\n    # Deserialize outputs back to object.\n    for i, tensor in enumerate(output_tensors):\n        tensor = tensor.type(torch.ByteTensor)\n        tensor_size = object_size_list[i]\n        object_list[i] = _tensor_to_object(tensor, tensor_size)\n\n\ndef gather_object(obj, object_gather_list=None, dst=0, group=group.WORLD):\n    \"\"\"\n    Gathers picklable objects from the whole group in a single process.\n    Similar to :func:`gather`, but Python objects can be passed in. Note that the\n    object must be picklable in order to be gathered.\n\n    Arguments:\n        obj (Any): Input object. Must be picklable.\n        object_gather_list (list[Any]): Output list. On the ``dst`` rank, it\n            should be correctly sized as the size of the group for this\n            collective and will contain the output. Must be ``None`` on non-dst\n            ranks. (default is ``None``)\n        dst (int, optional): Destination rank. (default is 0)\n        group: (ProcessGroup, optional): The process group to work on.\n\n    Returns:\n        None. On the ``dst`` rank, ``object_gather_list`` will contain the\n        output of the collective.\n\n    .. note:: Note that this API differs slightly from the gather collective\n        since it does not provide an async_op handle and thus will be a blocking\n        call.\n\n    .. note:: Note that this API is not supported when using the NCCL backend.\n\n    .. warning::\n        :func:`gather_object` uses ``pickle`` module implicitly, which is\n        known to be insecure. It is possible to construct malicious pickle data\n        which will execute arbitrary code during unpickling. Only call this\n        function with data you trust.\n    \"\"\"\n    if _rank_not_in_group(group):\n        return\n\n    # Ensure object_gather_list is specified appopriately.\n    my_rank = get_rank()\n    _validate_output_list_for_rank(my_rank, dst, object_gather_list)\n    input_tensor, local_size = _object_to_tensor(obj)\n    group_backend = get_backend(group)\n    is_nccl_backend = group_backend == Backend.NCCL\n    if is_nccl_backend:\n        input_tensor, local_size = input_tensor.to(my_rank), local_size.to(my_rank)\n    # Gather all local sizes. This is so that we can find the max size, and index\n    # until the correct size when deserializing the tensors.\n    group_size = get_world_size(group=group)\n    object_sizes_tensor = torch.zeros(group_size, dtype=int).to(\n        my_rank if is_nccl_backend else \"cpu\"\n    )\n    object_size_list = [\n        object_sizes_tensor[i].unsqueeze(dim=0) for i in range(group_size)\n    ]\n    # Allgather tensor sizes. An all-gather is needed here despite this being a gather,\n    # since each rank needs to broadcast a tensor of the same (maximal) size.\n    all_gather(object_size_list, local_size, group=group)\n    max_object_size = max(object_size_list)\n    # Resize tensor to max size across all ranks.\n    input_tensor.resize_(max_object_size)\n    # Avoid populating output tensors if the result won't be gathered on this rank.\n    if my_rank == dst:\n        coalesced_output_tensor = torch.empty(\n            max_object_size * group_size, dtype=torch.uint8\n        ).to(my_rank if is_nccl_backend else \"cpu\")\n        # Output tensors are nonoverlapping views of coalesced_output_tensor\n        output_tensors = [\n            coalesced_output_tensor[max_object_size * i : max_object_size * (i + 1)]\n            for i in range(group_size)\n        ]\n    # All ranks call gather with equal-sized tensors.\n    gather(\n        input_tensor,\n        gather_list=output_tensors if my_rank == dst else None,\n        dst=dst,\n        group=group,\n    )\n    if my_rank != dst:\n        return\n    for i, tensor in enumerate(output_tensors):\n        tensor = tensor.type(torch.ByteTensor)\n        tensor_size = object_size_list[i]\n        object_gather_list[i] = _tensor_to_object(tensor, tensor_size)\n\n\ndef broadcast_object_list(object_list, src, group=group.WORLD):\n    \"\"\"\n    Broadcasts picklable objects in ``object_list`` to the whole group. Similar\n    to :func:`broadcast`, but Python objects can be passed in.\n    Note that all objects in ``object_list`` must be picklable in order to be\n    broadcasted.\n\n    Arguments:\n        object_list (List[Any]): List of input objects to broadcast.\n            Each object must be picklable. Only objects on the ``src`` rank will\n            be broadcast, but each rank must provide lists of equal sizes.\n        src (int): Source rank from which to broadcast ``object_list``.\n        group: (ProcessGroup, optional): The process group to work on.\n\n    Returns:\n        ``None``. If rank is part of the group, ``object_list`` will contain the\n        broadcasted objects from ``src`` rank.\n\n    .. note:: Note that this API differs slightly from the broadcast collective\n        since it does not provide an ``async_op`` handle and thus will be a\n        blocking call.\n\n    .. warning::\n        :func:`broadcast_object_list` uses ``pickle`` module implicitly, which\n        is known to be insecure. It is possible to construct malicious pickle\n        data which will execute arbitrary code during unpickling. Only call this\n        function with data you trust.\n    \"\"\"\n    if _rank_not_in_group(group):\n        return\n\n    my_rank = get_rank()\n    # Serialize object_list elements to tensors on src rank.\n    if my_rank == src:\n        tensor_list, size_list = zip(*[_object_to_tensor(obj) for obj in object_list])\n        object_sizes_tensor = torch.cat(size_list)\n    else:\n        object_sizes_tensor = torch.LongTensor(len(object_list))\n\n    group_backend = get_backend(group)\n    is_nccl_backend = group_backend == Backend.NCCL\n    if is_nccl_backend:\n        object_sizes_tensor = object_sizes_tensor.to(my_rank)\n\n    # Broadcast object sizes\n    broadcast(object_sizes_tensor, src=src, group=group)\n\n    # Concatenate and broadcast serialized object tensors\n    if my_rank == src:\n        object_tensor = torch.cat(tensor_list)\n    else:\n        object_tensor = torch.ByteTensor(torch.sum(object_sizes_tensor).item())\n\n    if is_nccl_backend:\n        object_tensor = object_tensor.to(my_rank)\n    broadcast(object_tensor, src=src, group=group)\n    # Deserialize objects using their stored sizes.\n    offset = 0\n    if my_rank != src:\n        for i, obj_size in enumerate(object_sizes_tensor):\n            obj_view = object_tensor[offset : offset + obj_size]\n            obj_view = obj_view.type(torch.ByteTensor)\n            offset += obj_size\n            object_list[i] = _tensor_to_object(obj_view, obj_size)\n\n\ndef all_gather(tensor_list,\n               tensor,\n               group=group.WORLD,\n               async_op=False):\n    \"\"\"\n    Gathers tensors from the whole group in a list.\n\n    Arguments:\n        tensor_list (list[Tensor]): Output list. It should contain\n            correctly-sized tensors to be used for output of the collective.\n        tensor (Tensor): Tensor to be broadcast from current process.\n        group (ProcessGroup, optional): The process group to work on\n        async_op (bool, optional): Whether this op should be an async op\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group\n\n    \"\"\"\n    _check_tensor_list(tensor_list, \"tensor_list\")\n    _check_single_tensor(tensor, \"tensor\")\n    if _rank_not_in_group(group):\n        return\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.allgather([tensor_list], [tensor])\n    else:\n        work = group.allgather([tensor_list], [tensor])\n\n    if async_op:\n        return work\n    else:\n        work.wait()\n\ndef all_gather_coalesced(output_tensor_lists,\n                         input_tensor_list,\n                         group=group.WORLD,\n                         async_op=False):\n    \"\"\"\n    Gathers input tensors from the whole group in a list in a coalesced manner.\n\n    Arguments:\n        output_tensor_lists (list[list[Tensor]]): Output list. It should contain\n            correctly-sized tensors to be used for output of the collective.\n        input_tensor_list (list[Tensor]): Tensors to be broadcast from\n            current process. At least one tensor has to be non empty.\n        group (ProcessGroup, optional): The process group to work on\n        async_op (bool, optional): Whether this op should be an async op.\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group\n\n    Example:\n        we have 2 process groups, 2 ranks.\n        rank 0 passes:\n            input_tensor_list = [[[1, 1], [1, 1]], [2], [3, 3]]\n            output_tensor_lists =\n               [[[[-1, -1], [-1, -1]], [-1], [-1, -1]],\n                [[[-1, -1], [-1, -1]], [-1], [-1, -1]]]\n        rank 1 passes:\n            input_tensor_list = [[[3, 3], [3, 3]], [5], [1, 1]]\n            output_tensor_lists =\n               [[[[-1, -1], [-1, -1]], [-1], [-1, -1]],\n                [[[-1, -1], [-1, -1]], [-1], [-1, -1]]]\n        both rank 0 and 1 get:\n            output_tensor_lists =\n               [[[1, 1], [1, 1]], [2], [3, 3]],\n                [[3, 3], [3, 3]], [5], [1, 1]]].\n\n    WARNING: at this time individual shape checking is not implemented across nodes.\n    For example, if the rank 0 node passes [torch.rand(4), torch.rand(2)] and the\n    rank 1 node passes [torch.rand(2), torch.rand(2), torch.rand(2)], the\n    all_gather_coalesced operation will proceed without complaint and return\n    erroneous outputs. This lack of shape checking results in significant\n    performance improvements but users of this function should take extra care\n    to ensure that each node passes in tensors whose shapes match across nodes.\n    \"\"\"\n    # We only check basic compatibility with C++ params here, C++ code will\n    # do shape and type checking.\n    if _rank_not_in_group(group):\n        return\n    _check_tensor_list(input_tensor_list, \"tensor_list\")\n    if not isinstance(output_tensor_lists, list):\n        raise RuntimeError(\"Invalid function argument: \"\n                           \"output_tensor_lists should be a list\")\n    for output_tensor_list in output_tensor_lists:\n        _check_tensor_list(output_tensor_list, \"output_tensor_lists\")\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.allgather_coalesced(\n            output_tensor_lists, input_tensor_list)\n    else:\n        work = group.allgather_coalesced(output_tensor_lists, input_tensor_list)\n\n    if async_op:\n        return work\n    else:\n        work.wait()\n\ndef _validate_output_list_for_rank(my_rank, dst, gather_list):\n    if dst == my_rank:\n        if not gather_list:\n            raise ValueError(\n                \"Argument ``gather_list`` must be specified on destination rank.\"\n            )\n    elif gather_list:\n        raise ValueError(\n            \"Argument ``gather_list`` must NOT be specified \"\n            \"on non-destination ranks.\"\n        )\n\n\ndef gather(tensor,\n           gather_list=None,\n           dst=0,\n           group=group.WORLD,\n           async_op=False):\n    \"\"\"\n    Gathers a list of tensors in a single process.\n\n    Arguments:\n        tensor (Tensor): Input tensor.\n        gather_list (list[Tensor], optional): List of appropriately-sized\n            tensors to use for gathered data (default is None, must be specified\n            on the destination rank)\n        dst (int, optional): Destination rank (default is 0)\n        group (ProcessGroup, optional): The process group to work on\n        async_op (bool, optional): Whether this op should be an async op\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group\n\n    \"\"\"\n    _check_single_tensor(tensor, \"tensor\")\n\n    # Parameter ``gather_list`` may be left unspecified on non-dst ranks.\n    if gather_list:\n        _check_tensor_list(gather_list, \"gather_list\")\n    else:\n        gather_list = []\n\n    if _rank_not_in_group(group):\n        return\n\n    my_rank = get_rank()\n    _validate_output_list_for_rank(my_rank, dst, gather_list)\n    output_tensors = [gather_list] if dst == my_rank else []\n    input_tensors = [tensor]\n\n    opts = GatherOptions()\n    opts.rootRank = dst\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.gather(output_tensors, input_tensors, opts)\n    else:\n        group_dst_rank = _get_group_rank(group, dst)\n        opts.rootRank = group_dst_rank\n        work = group.gather(output_tensors, input_tensors, opts)\n\n    if async_op:\n        return work\n    else:\n        work.wait()\n\n\ndef scatter(tensor,\n            scatter_list=None,\n            src=0,\n            group=group.WORLD,\n            async_op=False):\n    \"\"\"\n    Scatters a list of tensors to all processes in a group.\n\n    Each process will receive exactly one tensor and store its data in the\n    ``tensor`` argument.\n\n    Arguments:\n        tensor (Tensor): Output tensor.\n        scatter_list (list[Tensor]): List of tensors to scatter (default is\n            None, must be specified on the source rank)\n        src (int): Source rank (default is 0)\n        group (ProcessGroup, optional): The process group to work on\n        async_op (bool, optional): Whether this op should be an async op\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group\n\n    \"\"\"\n    _check_single_tensor(tensor, \"tensor\")\n\n    # Parameter ``scatter_list`` may be left unspecified on non-src ranks.\n    if scatter_list:\n        _check_tensor_list(scatter_list, \"scatter_list\")\n    else:\n        scatter_list = []\n\n    if _rank_not_in_group(group):\n        return\n\n    my_rank = get_rank()\n    if src == my_rank:\n        if not scatter_list:\n            raise ValueError(\"Argument ``scatter_list`` must be specified \"\n                             \"on source rank.\")\n        input_tensors = [scatter_list]\n        output_tensors = [tensor]\n    else:\n        if scatter_list:\n            raise ValueError(\"Argument ``scatter_list`` must NOT be specified \"\n                             \"on non-source ranks.\")\n        input_tensors = []\n        output_tensors = [tensor]\n\n    opts = ScatterOptions()\n    opts.rootRank = src\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.scatter(output_tensors, input_tensors, opts)\n    else:\n        group_src_rank = _get_group_rank(group, src)\n        opts.rootRank = group_src_rank\n        work = group.scatter(output_tensors, input_tensors, opts)\n\n    if async_op:\n        return work\n    else:\n        work.wait()\n\n\ndef reduce_scatter_multigpu(output_tensor_list,\n                            input_tensor_lists,\n                            op=ReduceOp.SUM,\n                            group=group.WORLD,\n                            async_op=False):\n    \"\"\"\n    Reduce and scatter a list of tensors to the whole group.  Only nccl backend\n    is currently supported.\n\n    Each tensor in ``output_tensor_list`` should reside on a separate GPU, as\n    should each list of tensors in ``input_tensor_lists``.\n\n    Arguments:\n        output_tensor_list (List[Tensor]): Output tensors (on different GPUs)\n            to receive the result of the operation.\n\n            Note that ``len(output_tensor_list)`` needs to be the same for all\n            the distributed processes calling this function.\n\n        input_tensor_lists (List[List[Tensor]]): Input lists.  It should\n            contain correctly-sized tensors on each GPU to be used for input of\n            the collective, e.g. ``input_tensor_lists[i]`` contains the\n            reduce_scatter input that resides on the GPU of\n            ``output_tensor_list[i]``.\n\n            Note that each element of ``input_tensor_lists`` has the size of\n            ``world_size * len(output_tensor_list)``, since the function\n            scatters the result from every single GPU in the group.  To\n            interpret each element of ``input_tensor_lists[i]``, note that\n            ``output_tensor_list[j]`` of rank k receives the reduce-scattered\n            result from ``input_tensor_lists[i][k * world_size + j]``\n\n            Also note that ``len(input_tensor_lists)``, and the size of each\n            element in ``input_tensor_lists`` (each element is a list,\n            therefore ``len(input_tensor_lists[i])``) need to be the same for\n            all the distributed processes calling this function.\n\n        group (ProcessGroup, optional): The process group to work on.\n        async_op (bool, optional): Whether this op should be an async op.\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group.\n\n    \"\"\"\n    if _rank_not_in_group(group):\n        return\n\n    opts = ReduceScatterOptions()\n    opts.reduceOp = op\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.reduce_scatter(\n            output_tensor_list,\n            input_tensor_lists,\n            opts\n        )\n    else:\n        work = group.reduce_scatter(\n            output_tensor_list,\n            input_tensor_lists,\n            opts\n        )\n\n    if async_op:\n        return work\n    else:\n        work.wait()\n\n\ndef reduce_scatter(output,\n                   input_list,\n                   op=ReduceOp.SUM,\n                   group=group.WORLD,\n                   async_op=False):\n    \"\"\"\n    Reduces, then scatters a list of tensors to all processes in a group.\n\n    Arguments:\n        output (Tensor): Output tensor.\n        input_list (list[Tensor]): List of tensors to reduce and scatter.\n        group (ProcessGroup, optional): The process group to work on.\n        async_op (bool, optional): Whether this op should be an async op.\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group.\n\n    \"\"\"\n    _check_single_tensor(output, \"output\")\n    _check_tensor_list(input_list, \"input_list\")\n    if _rank_not_in_group(group):\n        return\n\n    opts = ReduceScatterOptions()\n    opts.reduceOp = op\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.reduce_scatter([output], [input_list], opts)\n    else:\n        work = group.reduce_scatter([output], [input_list], opts)\n\n    if async_op:\n        return work\n    else:\n        work.wait()\n\n\ndef all_to_all_single(output,\n                      input,\n                      output_split_sizes=None,\n                      input_split_sizes=None,\n                      group=group.WORLD,\n                      async_op=False):\n    \"\"\"\n    Each process splits input tensor and then scatters the split list\n    to all processes in a group. Then concatenate the received tensors from all\n    the processes in the group and return single output tensor.\n\n    Arguments:\n        output (Tensor): Gathered cancatenated output tensor.\n        input (Tensor): Input tensor to scatter.\n        output_split_sizes: (list[Int], optional): Output split sizes for dim 0\n            if specified None or empty, dim 0 of ``output`` tensor must divide\n            equally by ``world_size``.\n        input_split_sizes: (list[Int], optional): Input split sizes for dim 0\n            if specified None or empty, dim 0 of ``input`` tensor must divide\n            equally by ``world_size``.\n        group (ProcessGroup, optional): The process group to work on.\n        async_op (bool, optional): Whether this op should be an async op.\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group.\n\n    .. warning::\n        `all_to_all_single` is experimental and subject to change.\n\n    Examples:\n        >>> input = torch.arange(4) + rank * 4\n        >>> input\n        tensor([0, 1, 2, 3])     # Rank 0\n        tensor([4, 5, 6, 7])     # Rank 1\n        tensor([8, 9, 10, 11])   # Rank 2\n        tensor([12, 13, 14, 15]) # Rank 3\n        >>> output = torch.empty([4], dtype=torch.int64)\n        >>> dist.all_to_all_single(output, input)\n        >>> output\n        tensor([0, 4, 8, 12])    # Rank 0\n        tensor([1, 5, 9, 13])    # Rank 1\n        tensor([2, 6, 10, 14])   # Rank 2\n        tensor([3, 7, 11, 15])   # Rank 3\n\n        >>> # Essentially, it is similar to following operation:\n        >>> scatter_list = list(input.chunk(world_size))\n        >>> gather_list  = list(output.chunk(world_size))\n        >>> for i in range(world_size):\n        >>>   dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)\n\n        >>> # Another example with uneven split\n        >>> input\n        tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0\n        tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1\n        tensor([20, 21, 22, 23, 24])                                     # Rank 2\n        tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3\n        >>> input_splits\n        [2, 2, 1, 1]                                                     # Rank 0\n        [3, 2, 2, 2]                                                     # Rank 1\n        [2, 1, 1, 1]                                                     # Rank 2\n        [2, 2, 2, 1]                                                     # Rank 3\n        >>> output_splits\n        [2, 3, 2, 2]                                                     # Rank 0\n        [2, 2, 1, 2]                                                     # Rank 1\n        [1, 2, 1, 2]                                                     # Rank 2\n        [1, 2, 1, 1]                                                     # Rank 3\n        >>> output = ...\n        >>> dist.all_to_all_single(output, input, output_splits, input_splits)\n        >>> output\n        tensor([ 0,  1, 10, 11, 12, 20, 21, 30, 31])                     # Rank 0\n        tensor([ 2,  3, 13, 14, 22, 32, 33])                             # Rank 1\n        tensor([ 4, 15, 16, 23, 34, 35])                                 # Rank 2\n        tensor([ 5, 17, 18, 24, 36])                                     # Rank 3\n    \"\"\"\n    if _rank_not_in_group(group):\n        return\n\n    opts = AllToAllOptions()\n    _check_single_tensor(output, \"output\")\n    _check_single_tensor(input, \"input\")\n    output_split_sizes = [] if output_split_sizes is None else output_split_sizes\n    input_split_sizes = [] if input_split_sizes is None else input_split_sizes\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.alltoall_base(output, input, output_split_sizes, input_split_sizes, opts)\n    else:\n        work = group.alltoall_base(output, input, output_split_sizes, input_split_sizes, opts)\n\n    if async_op:\n        return work\n    else:\n        work.wait()\n\ndef all_to_all(output_tensor_list,\n               input_tensor_list,\n               group=group.WORLD,\n               async_op=False):\n    \"\"\"\n    Each process scatters list of input tensors to all processes in a group and\n    return gathered list of tensors in output list.\n\n    Arguments:\n        output_tensor_list (list[Tensor]): List of tensors to be gathered one\n            per rank.\n        input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.\n        group (ProcessGroup, optional): The process group to work on.\n        async_op (bool, optional): Whether this op should be an async op.\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group.\n\n    .. warning::\n        `all_to_all` is experimental and subject to change.\n\n    Examples:\n        >>> input = torch.arange(4) + rank * 4\n        >>> input = list(input.chunk(4))\n        >>> input\n        [tensor([0]), tensor([1]), tensor([2]), tensor([3])]     # Rank 0\n        [tensor([4]), tensor([5]), tensor([6]), tensor([7])]     # Rank 1\n        [tensor([8]), tensor([9]), tensor([10]), tensor([11])]   # Rank 2\n        [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3\n        >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))\n        >>> dist.all_to_all(output, input)\n        >>> output\n        [tensor([0]), tensor([4]), tensor([8]), tensor([12])]    # Rank 0\n        [tensor([1]), tensor([5]), tensor([9]), tensor([13])]    # Rank 1\n        [tensor([2]), tensor([6]), tensor([10]), tensor([14])]   # Rank 2\n        [tensor([3]), tensor([7]), tensor([11]), tensor([15])]   # Rank 3\n\n        >>> # Essentially, it is similar to following operation:\n        >>> scatter_list = input\n        >>> gather_list  = output\n        >>> for i in range(world_size):\n        >>>   dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)\n\n        >>> input\n        tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0\n        tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1\n        tensor([20, 21, 22, 23, 24])                                     # Rank 2\n        tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3\n        >>> input_splits\n        [2, 2, 1, 1]                                                     # Rank 0\n        [3, 2, 2, 2]                                                     # Rank 1\n        [2, 1, 1, 1]                                                     # Rank 2\n        [2, 2, 2, 1]                                                     # Rank 3\n        >>> output_splits\n        [2, 3, 2, 2]                                                     # Rank 0\n        [2, 2, 1, 2]                                                     # Rank 1\n        [1, 2, 1, 2]                                                     # Rank 2\n        [1, 2, 1, 1]                                                     # Rank 3\n        >>> input = list(input.split(input_splits))\n        >>> input\n        [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])]                   # Rank 0\n        [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1\n        [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])]                 # Rank 2\n        [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])]         # Rank 3\n        >>> output = ...\n        >>> dist.all_to_all(output, input)\n        >>> output\n        [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])]   # Rank 0\n        [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])]           # Rank 1\n        [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])]              # Rank 2\n        [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])]                  # Rank 3\n    \"\"\"\n    if _rank_not_in_group(group):\n        return\n\n    opts = AllToAllOptions()\n    _check_tensor_list(output_tensor_list, \"output_tensor_list\")\n    _check_tensor_list(input_tensor_list, \"input_tensor_list\")\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.alltoall(output_tensor_list, input_tensor_list, opts)\n    else:\n        work = group.alltoall(output_tensor_list, input_tensor_list, opts)\n\n    if async_op:\n        return work\n    else:\n        work.wait()\n\n\ndef barrier(group=group.WORLD,\n            async_op=False):\n    \"\"\"\n    Synchronizes all processes.\n\n    This collective blocks processes until the whole group enters this function,\n    if async_op is False, or if async work handle is called on wait().\n\n    Arguments:\n        group (ProcessGroup, optional): The process group to work on\n        async_op (bool, optional): Whether this op should be an async op\n\n    Returns:\n        Async work handle, if async_op is set to True.\n        None, if not async_op or if not part of the group\n    \"\"\"\n    if _rank_not_in_group(group):\n        return\n\n    if group == GroupMember.WORLD:\n        _check_default_pg()\n        work = _default_pg.barrier()\n    else:\n        work = group.barrier()\n\n    if async_op:\n        return work\n    else:\n        work.wait()\n\n\ndef new_group(ranks=None, timeout=default_pg_timeout, backend=None):\n    \"\"\"\n    Creates a new distributed group.\n\n    This function requires that all processes in the main group (i.e. all\n    processes that are part of the distributed job) enter this function, even\n    if they are not going to be members of the group. Additionally, groups\n    should be created in the same order in all processes.\n\n    Arguments:\n        ranks (list[int]): List of ranks of group members. If ``None``, will be\n            set to all ranks. Default is ``None``.\n        timeout (timedelta, optional): Timeout for operations executed against\n            the process group. Default value equals 30 minutes.\n            This is only applicable for the ``gloo`` backend.\n        backend (str or Backend, optional): The backend to use. Depending on\n            build-time configurations, valid values are ``gloo`` and ``nccl``.\n            By default uses the same backend as the global group. This field\n            should be given as a lowercase string (e.g., ``\"gloo\"``), which can\n            also be accessed via :class:`Backend` attributes (e.g.,\n            ``Backend.GLOO``).\n\n    Returns:\n        A handle of distributed group that can be given to collective calls.\n    \"\"\"\n\n    _check_default_pg()\n\n    global _pg_group_ranks\n\n    default_backend, default_store = _pg_map[_default_pg]\n    global_rank = _default_pg.rank()\n    global_world_size = _default_pg.size()\n\n    # Default to the same backend as the global process group\n    # if the backend is not specified.\n    if not backend:\n        backend = default_backend\n\n    # checks the input ranks\n    if ranks is not None:\n        ranks = sorted(ranks)\n        group_world_size = len(ranks)\n        if group_world_size > global_world_size:\n            raise RuntimeError(\"the new group's world size should be less or \"\n                               \"equal to the world size set by \"\n                               \"init_process_group\")\n        # check ranks' sanity\n        for rank in ranks:\n            if rank < 0 or rank >= global_world_size:\n                raise RuntimeError(\"The new group's rank should be within the \"\n                                   \"the world_size set by init_process_group\")\n        if global_rank in ranks:\n            group_rank = ranks.index(global_rank)\n        else:\n            group_rank = None\n    else:\n        ranks = list(range(global_world_size))\n        group_world_size = global_world_size\n        group_rank = global_rank\n\n    backend = Backend(backend)\n    pg = _new_process_group_helper(group_world_size,\n                                   group_rank,\n                                   ranks,\n                                   backend,\n                                   default_store,\n                                   timeout=timeout)\n\n    # Create the global rank to group rank mapping\n    _pg_group_ranks[pg] = {\n        global_rank: group_rank\n        for group_rank, global_rank in enumerate(ranks)\n    }\n\n    # barrier at the end to ensure that once we return from this method, all\n    # process groups including global variables are updated correctly on all\n    # ranks.\n    barrier()\n\n    return pg\n","repo_name":"snuspl/nimble","sub_path":"torch/distributed/distributed_c10d.py","file_name":"distributed_c10d.py","file_ext":"py","file_size_in_byte":72925,"program_lang":"python","lang":"en","doc_type":"code","stars":248,"dataset":"github-code","pt":"86"}
+{"seq_id":"71229707164","text":"import yahooquery as yq\n\nfrom src.empresas.outils.data_management.information_sources.y_finance import YFinanceInfo\n\n\ndef simple_stock_analysis(empresa):\n    inf = YFinanceInfo(empresa).request_info_yfinance\n    current_price = inf.get(\"currentPrice\")\n    result = {\"result\": 4}\n    if not current_price:\n        yahooquery_info = yq.Ticker(empresa.ticker).price\n        key = yahooquery_info.keys()[0]  # type: ignore\n        if yahooquery_info[key] != \"Quote not found for ticker symbol: LB\":\n            current_price = yahooquery_info[key][\"regularMarketPrice\"]  # type: ignore\n\n    result_buy = {\"result\": 1}\n\n    result_sell = {\"result\": 2}\n\n    result_hold = {\"result\": 3}\n\n    if \"recommendationKey\" in inf:\n        recommendationKey = inf[\"recommendationKey\"]\n        if recommendationKey == \"buy\":\n            result = result_buy\n        elif recommendationKey == \"hold\":\n            result = result_hold\n        elif recommendationKey == \"sell\":\n            result = result_sell\n\n    else:\n        if \"targetMeanPrice\" in inf:\n            targetMeanPrice = inf[\"targetMeanPrice\"]\n            if targetMeanPrice < current_price:\n                result = result_sell\n            elif targetMeanPrice > current_price:\n                result = result_buy\n            elif targetMeanPrice == current_price:\n                result = result_hold\n\n        else:\n            current_price = inf[\"currentPrice\"]\n            try:\n                per = empresa.per_share_values.latest().eps / current_price\n                if per < 10:\n                    result = result_buy\n                elif per > 20:\n                    result = result_sell\n                elif per > 10 and per < 20:\n                    result = result_hold\n            except Exception:\n                pass\n\n    return result\n","repo_name":"InvFin/InvFin-Backend","sub_path":"src/empresas/brain/analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":1802,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"7752351019","text":"import pya\nimport math\n\nclass TText(pya.PCellDeclarationHelper):\n\n  def __init__(self):\n    # Important: initialize the super class\n    super(TText, self).__init__()\n    db = pya.QFontDatabase()\n    fonts = []\n    for font in db.families():\n      fonts.append([font, font])\n    # declare the parameters\n    self.param(\"l\", self.TypeLayer, \"Layer\", default = pya.LayerInfo(1, 0))\n    self.param(\"text\", self.TypeString, \"Text\", default = \"\")\n    self.param(\"f\", self.TypeList, \"Font\", choices = fonts)\n    self.param(\"w\", self.TypeList, \"Weight\", default = 50, choices = [[\"Thin\", 0], [\"Extra light\", 12], [\"Light\", 25], [\"Normal\", 50], [\"Medium\", 57], [\"Demi Bold\", 63], [\"Bold\", 75], [\"Extra Bold\", 81], [\"Black\", 87]])\n    self.param(\"s\", self.TypeList, \"Style\", default = 0, choices = [[\"Normal\", 0], [\"Italic\", 1], [\"Oblique\", 2]])\n    self.param(\"q\", self.TypeInt, \"Resolution\", default = 20)\n\n  def display_text_impl(self):\n    return \"Text(\" + self.text + \")\"\n  \n  def coerce_parameters_impl(self):\n    pass  \n    \n  def can_create_from_shape_impl(self):\n    return self.shape.is_text()\n  \n  def parameters_from_shape_impl(self):\n    self.text = self.shape.text.string\n    \n  def transformation_from_shape_impl(self):\n    return pya.Trans(self.shape.bbox().center())\n  \n  def produce_impl(self):\n    dbu = self.layout.dbu\n    path = pya.QPainterPath()\n    font =  pya.QFont(self.f, self.q, self.w, False)\n    if self.s == 0:\n      font.setStyle(pya.QFont.StyleNormal)\n    elif self.s == 1:\n      font.setStyle(pya.QFont.StyleItalic)\n    else:\n      font.setStyle(pya.QFont.StyleOblique)\n    path.addText(0, 0, font, self.text)\n    polygons = path.toSubpathPolygons()\n    # gen polygon data\n    source = []\n    for polygon in polygons:\n      points = []\n      for point in polygon:\n        points.append(pya.Point.from_dpoint(pya.DPoint(point.x/dbu/self.q, -point.y/dbu/self.q)))\n      source.append([points, []])\n    # generate parent tree\n    for polygon in source:\n      for suspectedParent in source:\n        if polygon != suspectedParent:\n          inside = True\n          for point in polygon[0]:\n            if not pya.Polygon(suspectedParent[0]).inside(point):\n              inside = False\n          if inside:\n            polygon[1].append(suspectedParent)\n    # generate KLayout polygons\n    outpoly = []\n    i = 0\n    while len(source):\n      # find top\n      for poly in source:\n        if len(poly[1]) == 0:\n          source.remove(poly)\n          top = pya.Polygon(poly[0])\n          break\n      remove = []\n      # add corresponding holes\n      for polygon in source:\n        if poly in polygon[1]:\n          if len(polygon[1]) == 1:\n            remove.append(polygon)\n            top.insert_hole(polygon[0])\n          polygon[1].remove(poly)\n      for polygon in remove:\n        source.remove(polygon)\n      self.cell.shapes(self.l_layer).insert(top)","repo_name":"jurask/ShapeLib","sub_path":"python/text.py","file_name":"text.py","file_ext":"py","file_size_in_byte":2872,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"86"}
+{"seq_id":"22992752960","text":"import sys\nfrom typing import List\n\n\"\"\"\nName: Amos Choo Jia Shern\nID: 31164498\n\n\"\"\"\n\n\ndef read_file(txt_file,pat_file):\n    \"\"\"\n    Read file\n    \n    \"\"\"\n    with open (txt_file,\"r\",encoding=\"utf-8\") as f:\n        txt=f.read()\n    with open(pat_file,\"r\",encoding=\"utf-8\") as f:\n        pat=f.read()\n    return txt,pat\n\ndef write_tofile(occurences:List[int]):\n    \"\"\"\n    Write to file\n    \"\"\"\n    with open(\"output_modkmp.txt\",\"w\",encoding=\"utf-8\") as f:\n        if not occurences:\n            f.close()\n            return\n\n        #This line of code is referenced from \n        # https://monash.au.panopto.com/Panopto/Pages/Viewer.aspx?id=c11e7654-565b-415b-a7d9-ad7f0090bc75&start=0    \n        f.write(str(occurences[0]+1))\n        for i in range(1,len(occurences)):\n            f.write(\"\\n\")\n            f.write(str(occurences[i]+1))\n\n\ndef zalgo(word:str):\n    \"\"\"\n    Z-algorithm for pattern matching\n    Generate and return zarray\n\n    Time Complexity: O(len(word))\n\n    \n    \"\"\"\n    zarray=[0]*len(word)\n\n    zarray[0]=len(word) #first position holds no info\n    left,right=0,0\n    for i in range(1,len(word)):\n\n        #Case 1: explicit comparison\n        if i>right:\n            count=explicit_comparison(word,i,0) #start from i\n            if count>0:\n                #update count\n                zarray[i]=count\n                left=i\n                right=i+count-1\n        else: #Case 2, inside z box\n            k=i-left\n            remaining=right-i+1\n            #Case 2a, value of previous zbox lesseer than remaining\n            if zarray[k]<remaining:\n                zarray[i]=zarray[k] #use the previous zbox value\n\n            #Case 2b: previous zbox exceeds remaining\n            elif zarray[k]>remaining:\n                zarray[i]=remaining\n\n            else: #zarray[k]==remaining: Case 2c, need to explicit compare\n                count=explicit_comparison(word,right+1,remaining) #start from right+1\n                zarray[i]=zarray[k]+count\n                if count>0:\n                    right=remaining-1\n                    left=i\n    return zarray\n\ndef explicit_comparison(pattern,start1,start2):\n    \"\"\"\n    Explicit comparison\n    given two indexes\n    \n    \"\"\"\n    count=0\n    for i in range(start1,len(pattern)):\n        if pattern[i]!=pattern[start2]:\n            break\n        start2+=1\n        count+=1\n    return count\n\ndef shared_prefix(pattern,zarray):\n    \"\"\"\n    Shared prefix array\n    Algorithm referenced from Lecture notes\n\n    \"\"\"\n    m=len(pattern)\n    sp=[0]*m\n    for j in range(m-1,0,-1):\n        i=j+zarray[j]-1\n        sp[i]=zarray[j]\n    return sp\n\ndef spix(pattern,zarray):\n    \"\"\"\n    spi(x) matrix\n\n    Same thing as SPi array but fill up mismatches according to the mismatch\n    character index\n    \n    \"\"\"\n\n    m=len(pattern)\n    matrix=[[0 for i in range(m)] for j in range(126-32)] #126-32 == printable ascii characters\n\n    for j in range(m-1,0,-1):\n        i=j+zarray[j]-1\n        x=zarray[j] #mismatch position at pattern from prefix\n        matrix[ord(pattern[x])-32][i]=zarray[j]\n    return matrix\n\n    \n\ndef kmp_mod(pattern,text):\n    \"\"\"\n    \n    Modded kmp to use SPi(x) when a mismatch occurs\n    Time complexity: O(m+n)\n    \"\"\"\n\n    #Preprocess\n    zarray=zalgo(pattern)\n    sp=shared_prefix(pattern,zarray)\n    spix_array=spix(pattern,zarray)\n\n\n    i=0\n    n=len(text)\n    m=len(pattern)\n    matches=[]\n    resume=0 #galil variable\n\n\n    while i+m<=n:\n        j=0\n        mismatch=False\n\n        while j<m:\n            if pattern[j]!=text[i+j]: #explicit comparison from position i+j and i\n                mismatch=True\n                break\n            elif j<resume: #galil optimsiation, skip comparison till resume\n                j=resume-1\n            j+=1\n\n\n        if not mismatch: #if full match happens\n            matches.append(i)\n            shift=m-sp[m-1] #shift according to the formula\n\n\n        else: #mismatch happens\n            if j==0: #at first position, move by one\n                shift=1\n            else:\n                shift=j-spix_array[ord(text[i+j])-32][j-1] #use reference from spix array at mismatch pos\n                resume=spix_array[ord(text[i+j])-32][j-1] #move resume\n        i+=shift\n    return matches\n\n\nif __name__==\"__main__\":\n\n    txt_file=sys.argv[1]\n    pat_file=sys.argv[2]\n    text,pattern=read_file(txt_file,pat_file)\n    occurences=kmp_mod(pattern,text)\n    write_tofile(occurences)\n\n    pass\n\n","repo_name":"amoschoomy/fit3155","sub_path":"Assignment1/31164498/q1/modkmp.py","file_name":"modkmp.py","file_ext":"py","file_size_in_byte":4422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"38739488318","text":"from sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\ndb_url = \"{dialect}://{username}:{password}@{host}:{port}/{database}\".format(\n    dialect=\"postgresql+psycopg2\",\n    username=\"root\",\n    password=\"root\",\n    host=\"db\",\n    port=5432,\n    database=\"root\"\n)\n\nengine = create_engine(db_url)\n\nfrom sqlalchemy.ext.declarative import declarative_base\nBase = declarative_base()\nimport traceback\nfrom sqlalchemy.schema import Column\nfrom sqlalchemy.types import Integer, String\n\nfrom sqlalchemy import MetaData, event, util\nNAMING_CONVENTION = util.immutabledict({\n    'ix': 'ix_%(column_0_label)s',\n    'uq': 'uq_%(table_name)s_%(column_0_name)s',\n    'ck': 'ck_%(table_name)s_%(constraint_name)s',\n    'fk': 'fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s',\n    'pk': 'pk_%(table_name)s',\n})\nmetadata = MetaData(naming_convention=NAMING_CONVENTION)\nsession = sessionmaker(bind=engine)()\nclass User(Base):\n    __tablename__ = \"user_table\"\n    user_id = Column(Integer,primary_key=True)\n    first_name = Column(String(255))\n    last_name = Column(String(255))\n    \n    def full_name(self):\n        return \"{} {}\".format(self.first_name,self.last_name)\n    @classmethod\n    def create1(cls, first_name, last_name):\n        with session.begin_nested():\n            obj = cls(first_name=first_name,last_name=last_name)\n            session.add(obj)\n        return obj\n\n    @classmethod\n    def create2(cls, first_name, last_name):\n        obj = User(first_name=first_name,last_name=last_name)\n        session.add(obj)\n        return obj\nBase.metadata.create_all(engine)\n\n#print()\n#if len(session.query(User).all()) < 1:\n#    try:\n#        session.add(User(first_name=\"xxx\",last_name=\"yyy\"))\n#        raise Exception(\"test\")\n#        session.commit()\n#    except Exception as e:\n#        print(traceback.format_exc())\n#        print(\"raise error\")\n#        session.rollback()\n#    \n#print(session.query(User).first().__dict__)\n#session.close()\n#try:\n#    user1=User.create(first_name=\"test111\",last_name=\"tar111\")\n#    raise Exception(\"test_error\")\n#    session.commit()#\n#except Exception as e:\n#    session.rollback()\n#print(user1)\n#print(user1.full_name())\n#\n#print([f.full_name() for f in session.query(User).all()])\n\n\n#try:\n#    new_user = User.create2(\"tests\",\"ssss\")\n#    raise Exception(\"t\")\n#    session.commit()\n#except Exception as e:\n#    session.rollback()\n#    print(\"raise error\")\n#print(new_user.first_name)\n#\n#def test():\n#    try:\n#        new_user = User(first_name=\"ssss\",last_name=\"ssss\")\n#        session.add(new_user)\n#        session.commit()\n#    except Exception as e:\n#        session.rollback()\n#    return new_user\n\n#uuu = test()\n#print(uuu.last_name)\n#print(session.query(User).first().last_name)\n\ntry:\n    new_user = User(first_name=\"test1\",last_name=\"testtest\")\n    session.add(new_user)\n    raise Exception(\"test\")\n    session.commit()\n    \nexcept Exception as e:\n    session.rollback()\n    print(\"raise error\")\n\n\n    ","repo_name":"komepi/sample_code","sub_path":"postgresql/src/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"72284881219","text":"from random import randint\nfrom time import sleep\n\n\ndef guessing_game(GUESS_RANGE, GUESS_LIMIT):\n    # Set the initial values.\n    RANDOM = randint(1, GUESS_RANGE)\n    GUESS = int(input(\"What is your guess? \"))\n    ATTEMPTS_ALLOWED = GUESS_LIMIT\n    done = False\n\n    # Validate the inputted guess.\n    GUESS = InputValidation(GUESS, GUESS_RANGE)\n\n    # Now we have a valid guess.\n    while GUESS_LIMIT > 0 and not done:\n        GUESS_LIMIT -= 1  # Take one guess = lose one chance\n        if GUESS_LIMIT > 0:\n            if GUESS < RANDOM:\n                print(f\"It should be higher than {GUESS}.\")\n            elif GUESS > RANDOM:\n                print(f\"It should be lower than {GUESS}.\")\n            else:\n                ATTEMPTS_TOOK = ATTEMPTS_ALLOWED - GUESS_LIMIT\n                print(f\"You nailed it! And it only took you {ATTEMPTS_TOOK} attempts.\")\n                done = True\n            if GUESS_LIMIT > 0 and not done:\n                print(f\"You still have {GUESS_LIMIT} chances left.\\n\")\n                GUESS = int(input(\"Try a new guess: \"))\n                # Another input validation loop.\n                GUESS = InputValidation(GUESS, GUESS_RANGE)\n        elif GUESS_LIMIT == 0 and not done:  # Last chance to guess\n            if GUESS == RANDOM:\n                print(\n                    f\"You nailed it! However, it took you all the {ATTEMPTS_ALLOWED} attempts.\"\n                )\n            else:\n                print(\n                    f\"GAME OVER! It took you more than {ATTEMPTS_ALLOWED} attempts. \"\n                    f\"The correct number is {RANDOM}.\"\n                )\n\n\ndef InputValidation(GUESS, GUESS_RANGE):\n    while not 1 <= GUESS <= GUESS_RANGE:\n        print(\"TRY AGAIN! Your guess is out of range!\\n\")\n        GUESS = int(input(\"What is your guess? \"))\n    return GUESS\n\n\ndef easy():\n    print(\"You are to guess a number between 1 and 10 in no more than 6 attempts.\")\n    guessing_game(10, 6)\n\n\ndef medium():\n    print(\"You are to guess a number between 1 and 20 in no more than 4 attempts.\")\n    guessing_game(20, 4)\n\n\ndef hard():\n    print(\"You are to guess a number between 1 and 50 in no more than 3 attempts.\")\n    guessing_game(50, 3)\n\n\ndef try_again():\n    print()\n    again = input(\"Do you want to play again? (yes/no) \")\n    if again.lower() in [\"y\", \"yes\"]:\n        welcome()\n    elif again.lower() in [\"n\", \"no\"]:\n        print(\"Thanks for playing the game\")\n    else:\n        print(\"INVALID VALUE\")\n        try_again()\n\n\ndef welcome():\n    print(\"Hello, Welcome to the Guessing Game!\")\n    name = input(\"I'm Geek! What's Your Name? \")\n    sleep(1)\n\n    print(f\"Okay, {name}. Let's Begin The Guessing Game!\")\n    print(\n        \"Choose a level:\",\n        \"1. Easy\",\n        \"2. Medium\",\n        \"3. Hard\",\n        sep=\"\\n\",\n    )\n    sleep(1)\n    level = int(input(\"Pick a number: \"))\n    print()\n    sleep(1)\n    if level == 1:\n        easy()\n        try_again()\n    elif level == 2:\n        medium()\n        try_again()\n    elif level == 3:\n        hard()\n        try_again()\n    else:\n        print(\"INVALID VALUE! Please try again.\\n\")\n        welcome()\n\n\nwelcome()\n","repo_name":"geekcomputers/Python","sub_path":"Guessing_Game.py","file_name":"Guessing_Game.py","file_ext":"py","file_size_in_byte":3129,"program_lang":"python","lang":"en","doc_type":"code","stars":28675,"dataset":"github-code","pt":"80"}
+{"seq_id":"40706507867","text":"# functions/relu.py\nimport torch\nfrom torch.autograd import Function\nfrom _ext import ext_lib\n\n\nclass ReLUF(Function):\n    def forward(self, input):\n        self.save_for_backward(input)\n\n        output = input.new()\n        if not input.is_cuda:\n            ext_lib.relu_forward(input, output)\n        else:\n            raise Exception(\"No CUDA Implementation\")\n        return output\n\n    def backward(self, grad_output):\n        input, = self.saved_tensors\n\n        grad_input = grad_output.new()\n        if not grad_output.is_cuda:\n            ext_lib.relu_backward(grad_output, input, grad_input)\n        else:\n            raise Exception(\"No CUDA Implementation\")\n        return grad_input\n","repo_name":"DingKe/pytorch_workplace","sub_path":"cffi/functions/relu.py","file_name":"relu.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","stars":174,"dataset":"github-code","pt":"80"}
+{"seq_id":"11954641931","text":"import csv\nimport xml.etree.cElementTree as ET\n\nroot = ET.Element(\"root\") # set up root\ndoc = ET.SubElement(root, \"input\") # set up input\n\nwith open('OfficialDatatoConvert.csv') as csv_file: # import csv file\n    csv_reader = csv.reader(csv_file, delimiter=';') # split the file using the | as a delimiter\n    for row in csv_reader: # iterate through each row in the file\n        blanks_removed_row = ' '.join(row).split() # remove any row in the file that is empty (I've assumed you needed this as the output didn't have blank data)\n        input = ET.SubElement(doc, \"item\") # create an item \n        for i, item in enumerate(blanks_removed_row, start=1): # iterate through each row item and enumerate (to start counting from 1)\n            ET.SubElement(input, \"data{0}\".format(i)).text = item # insert a new data element with the item appending the count number to the data\n\ntree = ET.ElementTree(root) \ntree.write(\"filename.xml\", encoding='utf-8', xml_declaration=True) # save tree","repo_name":"LiamKaist/PostBuildingData","sub_path":"converttoxml.py","file_name":"converttoxml.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"21894433940","text":"#!/usr/bin/env python3\nimport json\n\ndef parse_bytes(n):\n    xs = [n&0xFF]\n    n >>= 8\n    while n != 0:\n        xs.append(n&0xFF)\n        n >>= 8\n    return xs\n\ndef val2str(n):\n    s = ''\n    for b in parse_bytes(n):\n        s += chr(b) if b > 31 and b < 128 else '' # '\\\\x'+str(hex(b))\n    return s\n\ndef main(path):\n    with open(path, 'r') as f:\n        data = json.load(f)\n    for cmp in data['cmps']:\n        print(cmp['header'])\n        for i, log in enumerate(cmp['log']):\n            v0 = val2str(log['v0'])\n            v1 = val2str(log['v1'])\n            if len(v0) or len(v1):\n                print(f\"{i:02} - /{v0}/ - /{v1}/\")\n            v0_128 = val2str(log['v0_128'])\n            v1_128 = val2str(log['v1_128'])\n            if len(v0_128) or len(v1_128):\n                print(f\"{i:02} - /{v0_128}/ - /{v1_128}/   (128)\")\n\nif __name__ == '__main__':\n    import sys\n    if len(sys.argv) < 2:\n        print(f\"usage: {sys.argv[0]} <path-to-json>\")\n        sys.exit(1)\n    main(sys.argv[1])\n","repo_name":"acidghost/cmplog-runner","sub_path":"inspect.py","file_name":"inspect.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"38361575596","text":"#travelling salesman problem\r\nimport doctest\r\nfrom itertools import permutations\r\n\r\ndef distance(point1, point2):\r\n    return ((point1[0] - point2[0])**2 + (point1[1] - point2[1])**2) ** 0.5\r\n\r\n\r\ndef total_distance(points):\r\n    return sum([distance(point, points[index + 1]) for index, point in enumerate(points[:-1])])\r\n\r\n\r\ndef travelling_salesman(points, start=None):\r\n    if start is None:\r\n        start = points[0]\r\n    return min([perm for perm in permutations(points) if perm[0] == start], key=total_distance)\r\n\r\n\r\ndef optimized_travelling_salesman(points, start=None):\r\n    if start is None:\r\n        start = points[0]\r\n    must_visit = points\r\n    path = [start]\r\n    must_visit.remove(start)\r\n    while must_visit:\r\n        nearest = min(must_visit, key=lambda x: distance(path[-1], x))\r\n        path.append(nearest)\r\n        must_visit.remove(nearest)\r\n    return path\r\n\r\n\r\ndoctest.testmod()\r\npoints = [[0, 0], [1, 5.7], [2, 3], [3, 7],\r\n               [0.5, 9], [3, 5], [9, 1], [10, 5]]\r\nprint(\"\"\"The minimum distance to visit all the following points: {}\r\nstarting at {} is {}.\r\n\r\nThe optimized algorithm yields a path long {}.\"\"\".format(\r\ntuple(points),\r\npoints[0],\r\ntotal_distance(travelling_salesman(points)),\r\ntotal_distance(optimized_travelling_salesman(points))))\r\n","repo_name":"coderanony/ezpz","sub_path":"TravellingSalesman.py","file_name":"TravellingSalesman.py","file_ext":"py","file_size_in_byte":1285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"36908553266","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution(object):\n    def inorderTraversal(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: List[int]\n        \"\"\"\n        # def helper(root,stack):\n        #     if root is None: return None\n        #     if root.left:\n        #         helper(root.left,stack)\n        #     stack.append(root.val)\n        #     if root.right:\n        #         helper(root.right,stack)\n        # res = []\n        # helper(root,res)\n        # return res\n        if root is None: return []\n        lis = []\n        stack = []\n        cur = root\n        while cur is not None or stack:\n            while cur is not None:\n                stack.append(cur)\n                cur = cur.left\n            tmp = stack.pop()\n            lis.append(tmp.val)\n            cur = tmp.right\n\n\n\n\n","repo_name":"yiz202/leetcode","sub_path":"94.py","file_name":"94.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"35167241131","text":"import glob\nimport yaml\n\n\ndef get_platform(input_file: str) -> str:\n    if \"/android/\" in input_file:\n        return \"android\"\n    elif \"/ios/\" in input_file:\n        return \"ios\"\n\ndef get_mastg_tests_dict():\n\n    mastg_tests = {}\n\n    for file in glob.glob(\"tests/**/*.md\", recursive=True):\n        with open(file, 'r') as f:\n            id = \"\"\n            content = f.read()\n            platform = get_platform(file)\n\n            frontmatter = next(yaml.load_all(content, Loader=yaml.FullLoader))\n            masvs_v2_id = frontmatter['masvs_v2_id']\n            frontmatter['path'] = file\n            if masvs_v2_id:\n                id = masvs_v2_id[0] \n                if id not in mastg_tests:\n                    mastg_tests[id] = {}\n                if platform not in mastg_tests[id]:\n                    mastg_tests[id][platform] = []\n                mastg_tests[id][platform].append(frontmatter)\n            else:\n                print(f\"No MASVS v2 coverage for: {frontmatter['title']} (was {frontmatter['masvs_v1_id']})\")\n    return mastg_tests\n\n# with open('mastg_tests.yaml', 'w') as f:\n#     f.write(yaml.dump(get_mastg_tests_dict(), indent=4, sort_keys=False))\n","repo_name":"OWASP/owasp-mastg","sub_path":"src/scripts/get_tests_dict.py","file_name":"get_tests_dict.py","file_ext":"py","file_size_in_byte":1176,"program_lang":"python","lang":"en","doc_type":"code","stars":10847,"dataset":"github-code","pt":"80"}
+{"seq_id":"43070114029","text":"\"\"\"\nMADE BY RDUAN, FOR RESEARCH ONLY\n\nThis programe is for displace all the \n\nWARNING:\n1. THIS PROGRAME CAN BE USED IN GET HORTZONTAL PROJECTILE MOTION ONLY\n2. PLEASE PLACE YOUR ORIGINAL DATA FILE ON ./csv_data FOLDER\n3. THE OUTPUT DATA WILL BE PLACED IN ./csv_displacement\n\"\"\"\n\nimport csv\nimport os\n\n# VARIABLE DEFINE AREA\n\ncsv_dir='./csv_data' # ORIGINAL CSV DATA PATH\noutput_csv_dir='./csv_displacement' # OUTPUT CSV DATA PATH\nratio_csv_dir='./csv_data/ratio.csv' # ratio_csv_data\nobject_height=60 #object height #物件高度(cm)\nratio_switch=True\n# 設定True會轉換為公分，False不會\n\n\n\ndef read_csv_to_array(file_path):\n    data_array = []\n\n    with open(file_path, 'r') as file:\n        csv_reader = csv.reader(file)\n        for row in csv_reader:\n            try:\n                row_int = [int(element) for element in row]\n                data_array.append(row_int)\n            except ValueError:\n                continue\n\n    return data_array\n\ndef write_array_to_csv(data_array, file_path):\n    file_path = os.path.join(output_csv_dir, file_path)\n    title = [['frame', 'x', 'y']] + [row for row in data_array]\n    with open(file_path, 'w', newline='') as file:\n        csv_writer = csv.writer(file)\n        csv_writer.writerows(title)\n\n\nratio_data=read_csv_to_array(ratio_csv_dir)\nr_b = ratio_data[0][2] #bottom\nr_h= ratio_data[1][2] #height\nratio=(abs(r_h-r_b)/object_height)\n\n\ncsv_datas=[] # DO NOT CHANGE THIS VARIABLE\nprint(csv_datas)\nfor root,dir,files in os.walk(csv_dir):\n    for file in files:\n        print(type(file))\n        if os.path.basename(file) == \"ratio.csv\":\n            continue\n        else:\n            csv_datas.append(os.path.join(root,file))\nprint(csv_datas)\n\nfor csv_name in csv_datas:\n    data = read_csv_to_array(csv_name)\n    try:\n        x_f = data[0][1]\n        y_f = data[0][2]\n        print(\"FILE {} BEFORE DISPLACEMENT\".format(csv_name))\n        print(data)\n        \n        for i in range(len(data)):\n            data[i][1] -= x_f\n            data[i][2] -= y_f\n            if ratio_switch==True:\n                data[i][1] /= ratio\n                data[i][2] /= ratio\n        \n        print(\"FILE {} AFTER DISPLACEMENT\".format(csv_name))\n        # 使用 os.path.basename() 取得檔案名稱\n        filename = os.path.basename(csv_name)\n\n        # 使用 os.path.splitext() 分割檔案名稱和副檔名\n        write_array_to_csv(data,filename)\n\n    \n    except IndexError:\n        print(\"FILE {} Index out of range error occurred.\".format(csv_name))\n","repo_name":"Rduanchen/ScientNet","sub_path":"data_process.py","file_name":"data_process.py","file_ext":"py","file_size_in_byte":2510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"6227303654","text":"\"\"\"Abstract Syntax Tree Nodes\n\nThis module defines classes for the AST nodes. Only the initializers are\ndefined here. Semantic analysis methods, optimization methods, and code\ngeneration are handled by other modules. This keeps the compiler organized\nby phase.\n\"\"\"\n\nfrom io import StringIO\n\n\nclass Program:\n    def __init__(self, statements):\n        self.statements = statements\n\n    def __str__(self):\n        return text(self)\n\n\nclass Declaration:\n    def __init__(self, name, initializer):\n        self.name = name\n        self.initializer = initializer\n\n\nclass Assignment:\n    def __init__(self, target, source):\n        self.target = target\n        self.source = source\n\n\nclass PrintStatement:\n    def __init__(self, expression):\n        self.expression = expression\n\n\nclass BinaryExpression:\n    def __init__(self, op, left, right):\n        self.op = op\n        self.left = left\n        self.right = right\n\n\nclass UnaryExpression:\n    def __init__(self, op, operand):\n        self.op = op\n        self.operand = operand\n\n\nclass IdentifierExpression:\n    def __init__(self, name):\n        self.name = name\n\n\nclass LiteralExpression:\n    def __init__(self, value):\n        self.value = value\n\n\ndef text(node):\n    # Return a compact but pretty string representation of the node graph,\n    # taking care of cycles. Written here from scracth because the built-in\n    # Python pprint library has a function that works fine on dictionaries\n    # but not on custom classes (unless you were to write your own str\n    # function for each class, which would be very tedious and not necessary\n    # in this case).\n    buffer = StringIO()\n    seen = {}\n    node_id = 0\n\n    def subtree_text(node, prefix, indent):\n        nonlocal node_id\n        node_id += 1\n        seen[node] = node_id\n        descriptor = f\"{' ' * indent}{prefix}: {type(node).__name__}\"\n        simple_attributes, complex_attributes = \"\", []\n        for attribute, child in node.__dict__.items():\n            if '__dict__' in dir(child) and child in seen:\n                simple_attributes += f\" {attribute}=${seen[child]}\"\n            elif isinstance(child, list) or '__dict__' in dir(child):\n                complex_attributes.append((attribute, child))\n            else:\n                simple_attributes += f\" {attribute}={repr(child)}\"\n        print(f\"{node_id:4} | {descriptor}{simple_attributes}\", file=buffer)\n        for attribute, child in complex_attributes:\n            if isinstance(child, list):\n                for index, node in enumerate(child):\n                    subtree_text(node, f'{attribute}[{index}]', indent + 2)\n            else:\n                subtree_text(child, attribute, indent + 2)\n\n    subtree_text(node, prefix='program', indent=0)\n    return buffer.getvalue()\n","repo_name":"rtoal/ael","sub_path":"ael/ast.py","file_name":"ast.py","file_ext":"py","file_size_in_byte":2764,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"80"}
+{"seq_id":"3452151887","text":"# -*- coding: utf-8 -*-\n\n# Resource object code\n#\n# Created by: The Resource Compiler for PyQt5 (Qt v5.13.2)\n#\n# WARNING! All changes made in this file will be lost!\n\nfrom PyQt5 import QtCore\n\nqt_resource_data = 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= [int(v) for v in QtCore.qVersion().split('.')]\nif qt_version < [5, 8, 0]:\n    rcc_version = 1\n    qt_resource_struct = qt_resource_struct_v1\nelse:\n    rcc_version = 2\n    qt_resource_struct = qt_resource_struct_v2\n\ndef qInitResources():\n    QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)\n\ndef qCleanupResources():\n    QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)\n\nqInitResources()\n","repo_name":"teyying/LearningForChildren","sub_path":"main/resource/images_rc.py","file_name":"images_rc.py","file_ext":"py","file_size_in_byte":793589,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"74812023299","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport sys\nimport io\nfrom xml.etree.ElementTree import parse\nfrom os import urandom\nimport struct\n\n\ndef xml2pcap(input_file, output_file):\n    tree = parse(input_file)\n    root = tree.getroot()\n    header = root[0]\n    snaplen = int(header[4].text)\n    with io.open(output_file, mode='wb') as f:\n        f.write(struct.pack('=IhhiIII', *([0xa1b2c3d4] + [int(t.text) for t in header])))\n        for c in root[1:]:\n            incl_len = min(int(c[2].text), snaplen)\n            f.write(struct.pack('=IIII', int(c[0].text), int(c[1].text), incl_len, incl_len))\n            f.write(bytearray(urandom(incl_len)))\n\n\nif __name__ == '__main__':\n    xml2pcap(sys.argv[1], sys.argv[2])","repo_name":"fasia/mxmlmate","sub_path":"subjects/pcap/converters/xml2pcap.py","file_name":"xml2pcap.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"30543392055","text":"from django.urls import path\n\nfrom . import views\n\napp_name = 'salingbantu'\n\nurlpatterns = [\n    path('', views.SalingBantu.as_view(), name='salingbantu'),\n    path('details/<int:pk>', views.DetailsPost.as_view(), name='details_post'),\n    path('details/<int:pk>/comment', views.AddComment.as_view(), name='add_comment'),\n    path('add-post/', views.AddPost.as_view(), name='add_post'),\n]","repo_name":"rafiatha09/berlapan-website","sub_path":"salingbantu/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"27035470657","text":"import pandas as pd\nimport numpy as np\nfrom sklearn import preprocessing\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.naive_bayes import GaussianNB\nfrom matplotlib import pyplot as plt\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.naive_bayes import ComplementNB\nfrom sklearn.naive_bayes import BernoulliNB\nfrom sklearn import tree\nfrom random import randint\n\ndata = pd.read_csv('iris.data', header=None)\nfeatures = data.iloc[:, :4].to_numpy()\nlabels = data.iloc[:, 4].to_numpy()\n\nencoder = preprocessing.LabelEncoder()\nencoded_labels = encoder.fit_transform(labels)\nX_train, X_test, y_train, y_test = train_test_split(features, encoded_labels, test_size=0.5)\n\n# 1. calculate number of misclassified observations\ngnb = GaussianNB()\ny_pred = gnb.fit(X_train, y_train).predict(X_test)\nmisclassified = np.count_nonzero(y_test != y_pred)\nprint(\"Number of misclassified observations:\", misclassified)\n\n# 2. calculate classification accuracy\naccuracy = gnb.fit(X_train, y_train).score(X_test, y_test) * 100\nprint(\"Classification accuracy:\", accuracy)\n\n# 3. plot classification accuracy and percentage of misclassified observations against test set size\ntest_sizes = []\nmisclassification_percentages = []\naccuracies = []\n\nsize = 0\nwhile size <= 0.95:\n    size += 0.05\n    X_train, X_test, y_train, y_test = train_test_split(features, encoded_labels, test_size=size)\n    gnb = GaussianNB()\n    y_pred = gnb.fit(X_train, y_train).predict(X_test)\n    test_sizes.append(size)\n    misclassification_percentages.append(np.count_nonzero(y_test != y_pred) / len(y_pred))\n    accuracies.append(gnb.fit(X_train, y_train).score(X_test, y_test))\n\n# create bar plot\nfig, ax = plt.subplots()\nax.bar(test_sizes, accuracies, width=0.03, color='dodgerblue', label='Accuracy')\nax.bar(test_sizes, misclassification_percentages, width=0.03, color='salmon', label='% Misclassified')\nax.set_facecolor('seashell')\nfig.set_figwidth(17)\nfig.set_figheight(8)\nfig.set_facecolor('floralwhite')\nax.legend()\nax.set_xlabel('Test Set Size')\nax.set_ylabel('Accuracy/Misclassification %')\nax.set_title('Classification Accuracy and Misclassification % vs Test Set Size')\nplt.show()\n\n# 1. calculate number of misclassified observations for MultinomialNB\nclf = MultinomialNB(alpha=1.0, fit_prior=True, class_prior=None)\ny_pred = clf.fit(X_train, y_train).predict(X_test)\nmisclassified = (y_test != y_pred).sum()\nprint(\"Number of misclassified observations for MultinomialNB:\", misclassified)\n\nprint(np.count_nonzero(y_test != y_train))\nprint(clf.fit(X_train, y_train).score(X_test, y_test) * 100)\nclf = ComplementNB(force_alpha=True)\nY_pred = clf.fit(X_train, y_train).predict(X_test)\nprint(f'Number of observations that were misclassified: {np.count_nonzero(y_test != Y_pred)}')\nprint(f'Classification accuracy: {clf.fit(X_train, y_train).score(X_test, y_test) * 100}%')\nclf = BernoulliNB(force_alpha=True)\nY_pred = clf.fit(X_train, y_train).predict(X_test)\nprint(f'Number of observations that were misclassified: {np.count_nonzero(y_test != Y_pred)}')\nprint(f'Classification accuracy: {clf.fit(X_train, y_train).score(X_test, y_test) * 100}%')\nclf = tree.DecisionTreeClassifier()\nY_pred = clf.fit(X_train, y_train).predict(X_test)\nprint(np.count_nonzero(y_test != Y_pred))\nprint(f'Classification accuracy: {clf.fit(X_train, y_train).score(X_test, y_test) * 100}%')\n#3\nprint(f'Number of leaves: {clf.get_n_leaves()}')\nprint(f'Depth: {clf.get_depth()}')\n#4\nplt.subplots(1, 1, figsize=(10, 10))\ntree.plot_tree(clf, filled=True)\nplt.show()\n#5\nsize = 0\nlist_test_size = []\npercentage_misclassified_observations = []\nclassification_accuracy = []\nwhile size <= 0.95:\n    size += 0.05\n    X_train, X_test, Y_train, Y_test = train_test_split(features, labels, test_size=size)\n    gnb = tree.DecisionTreeClassifier()\n    Y_pred = gnb.fit(X_train, Y_train).predict(X_test)\n    list_test_size.append(size)\n    percentage_misclassified_observations.append(np.count_nonzero(Y_test != Y_pred) / len(Y_pred))\n    classification_accuracy.append(gnb.fit(X_train, Y_train).score(X_test, Y_test))\nfig, ax = plt.subplots()\nax.bar(list_test_size, classification_accuracy, width=0.03, color='mediumaquamarine')\nax.bar(list_test_size, percentage_misclassified_observations, width=0.03, color='coral')\nax.set_facecolor('lightsteelblue')\nfig.set_figwidth(17)\nfig.set_figheight(8)\nfig.set_facecolor('lightgray')\nplt.xlabel('Test size')\nplt.ylabel('Accuracy and percentage of misclassified observations')\nplt.title('Accuracy and percentage of misclassified observations vs. test size')\nplt.show()\n#6\ncriterion_parameters = ('gini', 'entropy', 'log_loss')\nsplitter_parameter = ('best', 'random')\nfor parameter in criterion_parameters:\n    sp_par_random = splitter_parameter[randint(0, 1)]\n    max_dp_random = randint(5, 40)\n    min_samples_split_random = randint(5, 40)\n    min_samples_leaf_random = randint(5, 40)\n    gnb = tree.DecisionTreeClassifier(criterion=parameter, splitter=sp_par_random, max_depth=max_dp_random,\n                                      min_samples_split=min_samples_split_random,\n                                      min_samples_leaf=min_samples_leaf_random)\n    Y_pred = gnb.fit(X_train, Y_train).predict(X_test)\n    print(f'With criterion: {parameter}, splitter: {sp_par_random}, max_depth: {max_dp_random}, min_samples_split: {min_samples_split_random}, min_samples_leaf: {min_samples_leaf_random} \\n classification accuracy: {gnb.fit(X_train, Y_train).score(X_test, Y_test) * 100}%, number of leaves: {gnb.get_n_leaves()}, depth: {gnb.get_depth()}\\n')","repo_name":"louisesesese/pythonProject_Lab7_MachineLearning","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"28789891259","text":"import requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\n\nr = requests.get('http://chart.capital.com.tw/Chart/TWII/TAIEX11.aspx')\nif r.status_code == requests.codes.ok:\n    soup = BeautifulSoup(r.text, 'lxml')\n    tables = soup.find_all('table', attrs={'cellpadding': '2'})\n    content = []\n    title = ''\n    for table in tables:\n        details = table.find_all('tr')\n        title = list(details.pop(0).stripped_strings)\n        for detail in details:\n            c = list(detail.stripped_strings)\n            content.append(c)\n\n\ndf = pd.DataFrame(content, columns=title)\nprint(df.head())\n# df.to_csv('big888.csv', index=False)\n\n\n\n# data = []\n# r = requests.get('http://chart.capital.com.tw/Chart/TWII/TAIEX11.aspx')\n\n# if r.status_code == requests.codes.ok:\n#     r.encoding = 'utf-8'\n#     soup = BeautifulSoup(r.text, 'lxml')\n#     tables = soup.find_all('table', attrs={'cellpadding': '2'})\n\n#     for table in tables:\n#         details = table.find_all('tr')\n#         for detail in details:\n#             date, value, price = [trs.text for trs in detail.find_all('td')]\n#             if date == '日期':\n#                 continue\n\n#             data.append([date, value, price])\n\n\n# df = pd.DataFrame(data, columns=['日期', '買賣超金額', '台指期'])\n# df.to_csv('big8.csv', index=False)\n# df.to_excel('big8.xlsx', index=False)\n# df.to_html('big8.html', index=False)\n","repo_name":"aws753951/ptt-crawler","sub_path":"bank8.py","file_name":"bank8.py","file_ext":"py","file_size_in_byte":1395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"14788330972","text":"from django.shortcuts import render,HttpResponse,redirect\nfrom .forms import PersonDataForm,FaceImageForm\nimport face_recognition\nfrom .models import PersonData\nfrom django.contrib import messages\n\n\n# Create your views here.\ndef home(request):\n    return render(request,\"face/index.html\",context={\n\n    })\n\n\ndef user_data(request):\n    if request.method==\"POST\":\n        form=PersonDataForm(request.POST)\n        if form.is_valid():\n            form.save()\n            return redirect(\"success\")\n    \n    else:\n        form =PersonDataForm()\n\n    return render(request,\"face/person_data.html\",{\n        \"form\":form,\n    })\n\n\n\ndef face_search(request):\n    if request.method==\"POST\":\n        form=FaceImageForm(request.POST,request.FILES)\n        if form.is_valid():\n            image=form.cleaned_data['image']\n            print(image)\n            unknown_image = face_recognition.load_image_file(f\"/home/rtr/Pictures/{image}\",)\n\n\n            # unknown_encoding = face_recognition.face_encodings(unknown_image)[0]\n            try:\n                unknown_encoding = face_recognition.face_encodings(unknown_image)[0]\n\n            except IndexError:\n                messages.error(request,\"Please give a person image not any fictional object\")\n                return redirect(face_search)\n\n            print(unknown_encoding)\n\n            user_data=PersonData.objects.all()\n\n            for data in user_data:\n                print(data.person_face_image.url)\n                known_image = face_recognition.load_image_file(f\"/home/rtr/biometric_data_finder/{data.person_face_image.url}\")\n                known_encoding = face_recognition.face_encodings(known_image)[0]\n                print(f\"known_encoding{data.id}: {known_encoding}\")\n                results = face_recognition.compare_faces([known_encoding], unknown_encoding)\n                print(f\"result{data.id}:{results}\")\n\n                if results[0]==True:\n                    match_data=data\n                    messages.success(request,f\"The {data.last_name} face matched with your image face\")\n                    print(\"Matched\")\n                    return render(request,\"face/person_details.html\",{\n                                        \"match_data\":match_data,\n                                    })\n                    \n                \n                else:\n                    print(\"not matched\")\n            \n            messages.error(request,\"Sorry The person you want is not available in our database\")\n\n\n            return redirect(\"face_search\")\n        \n    else:\n        form=FaceImageForm()\n        \n    return render(request,\"face/face_search.html\",{\n        'form':form,\n    })\n\n\n\ndef person_details(request):\n    return render(request,\"face/person_details.html\")","repo_name":"AbdullahAlmizan644/biometric_data_finder","sub_path":"face/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"9314210651","text":"import model\r\n\r\ndef napisi_tabelo_naprej(datoteka, morje, velikost, st):\r\n    vsebina = ''\r\n    with open(datoteka, 'w', encoding='utf-8') as dat:\r\n        dat.write(\"%rebase('base_za_sestavljanje_ladjic\" + str(velikost) + \".tpl')\\n\")\r\n        dat.write('Izberi si nadaljne kvadratke tvoje ladjice.\\n')\r\n        if st == 0:\r\n            dat.write('<form action=\"/oblikovanje_mape/ladja/\" method=\"post\">\\n')\r\n        if st == 1:\r\n            dat.write('<form action=\"/oblikovanje_mape/ladja_prvi/\" method=\"post\">\\n')\r\n        if st == 2:\r\n            dat.write('<form action=\"/oblikovanje_mape/ladja_drugi/\" method=\"post\">\\n')\r\n        dat.write('<div style=\"margin:20px\">\\n<table>\\n')\r\n        for x in range(velikost ** 2):\r\n            stanje = morje.mapa[x]\r\n            ime = str(x)\r\n            if x in morje.predlagaj_okolico_ladje(morje.trenutna_ladja):\r\n                vsebina = '<button class=\"velikost_gumba\" style=\"background-color:#3394b5;\" name=\"ladja\" type=\"submit\" value=\"' + ime + '\"></button>'\r\n            else:\r\n                if stanje == model.MORJE:\r\n                    vsebina = '<button class=\"velikost_gumba\" style=\"background-color:##4bafd1;\" type=\"button\" disabled></button>'\r\n                if stanje == model.LADJA:\r\n                    vsebina = '<button class=\"velikost_gumba\" style=\"background-color:#e1e3e1\" type=\"button\" disabled><i class=\"material-icons\" style=\"color:black;\">directions_boat</i></button>'                   \r\n            if x % velikost == 0:\r\n                dat.write('<tr>\\n')\r\n            dat.write('<td>')\r\n            dat.write(vsebina)\r\n            dat.write('</td>\\n')\r\n            if (x + 1) % velikost == 0:\r\n                dat.write('</tr>\\n')\r\n        dat.write('</div>\\n</table>\\n</div>\\n</form>\\n')","repo_name":"klarakresnik/Potapljanje-ladjic","sub_path":"napisi_tabelo_naprej.py","file_name":"napisi_tabelo_naprej.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"sl","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"45842159025","text":"from gtk import PrintOperation, PageSetup\nimport gtksourceview2\nimport gobject\n\n########################\n###\n###   SEARCH\n###\n########################\n\ndef search(act):\n    act.vibase.view.emit(\"start_interactive_search\")\n   \ndef search_cursor(act, forward=True):\n    \"\"\"search for word under cursor\"\"\"\n    doc = act.vibase.doc\n    view = act.vibase.view\n    buf = view.get_buffer()\n\n    word = act.pos.get_word_under_cursor(act)\n    doc.set_search_text(word, 0)\n\n    #get search boundaries\n    start = buf.get_iter_at_mark(buf.get_insert())\n    if start.inside_word:\n        if start.inside_word: start.backward_word_start()\n\n    end = buf.get_iter_at_mark(buf.get_insert())\n    if end.inside_word:\n        if end.inside_word: end.forward_word_end()\n\n    if forward:\n        ret = end.forward_search(word, 0)\n\n        #if nothing was found, search again from start\n        if not ret:\n            ret = buf.get_bounds()[0].forward_search(word, 0)\n    else:\n        ret = start.backward_search(word, 0)\n\n        if not ret:\n            ret = buf.get_bounds()[1].backward_search(word, 0)\n    act.pos.moveInsert(act, ret[0])\n\ndef search_cursor_backward(act):\n    search_cursor(act, forward=False)\n\n########################\n###\n###   UNDO/REDO\n###\n########################\n\ndef undo(act):\n    act.vibase.view.emit(\"undo\")\n    \ndef redo(act):\n    act.vibase.view.emit(\"redo\")\n\ndef redoLastOperation(act):\n    act.vibase.lastOperation(act)\n\ndef getTerminal(act):\n    # Get the terminal\n    # TODO Probably needs a more sophisticated lookup, e.g., python terminal not installed, etc.\n    window = act.vigtk.window\n    bottom_panel = window.get_bottom_panel()\n    notebook = bottom_panel.get_children()[0].get_children()[0]\n    if len(notebook.get_children()) != 0:\n        terminal = notebook.get_children()[1]\n        return terminal\n    return None\n\n########################\n###\n###   PRINTING\n###\n########################\n\ndef draw_page(operation, context, page_nr, compositor):\n    compositor.draw_page(context, page_nr)\n    \ndef begin_print(operation, context, compositor):\n    n_pages = 1\n    while not compositor.paginate(context):\n        pass\n    \n    n_pages = compositor.get_n_pages()\n    operation.set_n_pages(n_pages)\n    \ndef printall(act, location):\n    views = [view for view in act.vigtk.window.get_views()]\n\n    count = 1\n    for view in views:\n            \n        po = PrintOperation()\n        setup = PageSetup()\n        po.set_default_page_setup(setup)\n        \n        po.set_export_filename(\"%s/%d.pdf\" % (location, count))\n        count += 1\n        \n        pc = gtksourceview2.print_compositor_new_from_view(view)\n        \n        po.connect(\"begin_print\", begin_print, pc)\n        po.connect(\"draw_page\", draw_page, pc)\n    \n        res = po.run(act.gtk.PRINT_OPERATION_ACTION_EXPORT)\n","repo_name":"icebreaker/dotfiles","sub_path":"gnome/gnome2/gedit/plugins.symlink/ViGedit/actions/others.py","file_name":"others.py","file_ext":"py","file_size_in_byte":2807,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"80"}
+{"seq_id":"37637309239","text":"# Definition for singly-linked list.\n# class ListNode(object):\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution(object):\n    def reverseList(self, head):\n        \"\"\"\n        :type head: ListNode\n        :rtype: ListNode\n        \"\"\"\n        stack = []\n        temp = head\n        while temp:\n            stack.append(temp.val)\n            temp = temp.next\n        temp = head\n        while temp:\n            temp.val = stack.pop()\n            temp = temp.next\n        return head","repo_name":"concealedtea/Coding-Interview-Prep","sub_path":"Easy/0206_Reverse_LL.py","file_name":"0206_Reverse_LL.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"18531097345","text":"import pandas as pd\nimport xml.etree.ElementTree as et\n\n\ndef extract_from_csv(file):\n    dataframe = pd.read_csv(file)\n    return dataframe\n\n\ndef extract_from_json(file):\n    dataframe = pd.read_json(file, lines=True)\n    return dataframe\n\n\ndef extract_from_xml(file):\n\n    dataframe = pd.DataFrame(\n        columns=['car_model', 'year_of_manufacture', 'price', 'fuel'])\n\n    tree = et.parse(file)\n    root = tree.getroot()\n\n    for car in root:\n        car_model = car.find(\"car_model\").text\n        year_of_manufacture = int(car.find(\"year_of_manufacture\").text)\n        price = float(car.find(\"price\").text)\n        fuel = car.find(\"fuel\").text\n        dataframe = dataframe.append({\"car_model\": car_model,\n                                      \"year_of_manufacture\": year_of_manufacture,\n                                      \"price\": price,\n                                      \"fuel\": fuel\n                                      }, ignore_index=True)\n\n    return dataframe\n","repo_name":"seniorpe/python-etl","sub_path":"extract/extract.py","file_name":"extract.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"71427207619","text":"from page.main_page import MainPage\nfrom business.init import instance\nfrom page.project_page import ProjectPage\nimport time\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom page.login_page import LoginPage\n\n\ndef page():\n    return MainPage(instance.driver)\n\n\ndef log_out():\n    hover = ActionChains(page().driver).move_to_element(page().username_text())\n    hover.perform()\n    time.sleep(2)\n    # page().wait_for_text(page().LOG_OUT_BUTTON_XPATH, '退出登录')\n    page().log_out_button().click()\n    LoginPage(instance.driver).wait_for_text(LoginPage(instance.driver).LOGIN_BUTTON_XPATH, '登录')\n    time.sleep(2)\n\n\ndef verify_username(user_name):\n    assert page().username_text().text == user_name\n    time.sleep(5)\n\n\ndef verify_sidebar():\n    assert page().xpath_is_visible(page().PROJECT_LIST_BUTTON_XPATH)\n    assert page().xpath_is_visible(page().MY_LIBRARY_BUTTON_XPATH)\n    assert page().xpath_is_visible(page().CUBE_LIBRARY_BUTTON_XPATH)\n\n\ndef verify_click_side_bar():\n    page().my_library_button().click()\n    time.sleep(3)\n    page().wait_for_text(page().MY_LIBRARY_TEXT_XPATH, '我的构件库')\n\n    page().cube_library_button().click()\n    time.sleep(3)\n    page().wait_for_text(page().CUBE_LIBRARY_TEXT_XPATH, 'CUBE构件库')\n\n    page().project_list_button().click()\n    time.sleep(3)\n    page().wait_for_text(page().PROJECT_LIST_TEXT_XPATH, '项目列表')\n\n\ndef open_project():\n    page().project_button().click()\n    ProjectPage(instance.driver).xpath_is_visible(ProjectPage(instance.driver).SIDE_BAR_BUTTON_XPATH)\n    ProjectPage(instance.driver).loading_text_disappear()\n    time.sleep(5)\n","repo_name":"harrisson-li/cube","sub_path":"business/main_page.py","file_name":"main_page.py","file_ext":"py","file_size_in_byte":1640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"5659783779","text":"from dbutils.to_string import auto_str\nfrom schedule.models import ExtraActivity\n\n\n@auto_str\nclass ExtraFacActivity:\n\n    def __init__(self, activity: ExtraActivity):\n        self.id = activity.id\n        self.title = activity.title\n        self.priority = activity.priority\n        self.day = activity.day\n        self.frequency = activity.frequency\n        self.duration = activity.duration\n        self.location = activity.location\n        self.start_time = activity.start_time\n        self.description = activity.description\n","repo_name":"Team-Guy/timetable","sub_path":"dbutils/extra_activity.py","file_name":"extra_activity.py","file_ext":"py","file_size_in_byte":529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"29996894261","text":"#-*- coding: utf-8 -*-\nfrom collections import defaultdict\nimport networkx as nx\n#-*- coding: utf-8 -*-\n#    Copyright (C) 2011 by \n#    Jordi Torrents <jtorrents@milnou.net>\n#    Aric Hagberg <hagberg@lanl.gov>\n#    All rights reserved.\n#    BSD license.\n__author__ = \"\"\"\\n\"\"\".join(['Jordi Torrents <jtorrents@milnou.net>',\n                            'Aric Hagberg (hagberg@lanl.gov)'])\n__all__ = [\"average_neighbor_degree\",\n           \"average_neighbor_in_degree\",\n           \"average_neighbor_out_degree\",\n           'average_degree_connectivity',\n           'average_in_degree_connectivity',\n           'average_out_degree_connectivity',\n           'k_nearest_neighbors']\n\ndef _average_nbr_deg(G, degree_method, nodes=None, weighted=False):\n    avg_nbr_deg = {}\n    for n in G.nbunch_iter(nodes):\n        nbrdeg = degree_method(G[n])\n        if weighted:\n            nbrv = [G[n][nbr].get('weight',1)*d for nbr,d in nbrdeg.items()]\n        else:\n            nbrv = nbrdeg.values()\n        norm = degree_method(n,weighted=weighted)\n        avg_nbr_deg[n] = float(sum(nbrv))\n        if norm > 0:\n            avg_nbr_deg[n] /= norm\n    return avg_nbr_deg\n\ndef average_neighbor_degree(G, nodes=None, weighted=False):\n    r\"\"\"Returns the average degree of the neighborhood of each node.\n\n    The average degree of a node `i` is\n\n    .. math::\n\n        k_{nn,i} = \\frac{1}{|N(i)|} \\sum_{j \\in N(i)} k_j\n\n    where `N(i)` are the neighbors of node `i` and `k_j` is\n    the degree of node `j` which belongs to `N(i)`. For weighted \n    graphs, an analogous measure can be defined [1]_,\n\n    .. math::\n\n        k_{nn,i}^{w} = \\\\frac{1}{s_i} \\sum_{j \\in N(i)} w_{ij} k_j\n\n    where `s_i` is the weighted degree of node `i`, `w_{ij}`\n    is the weight of the edge that links `i` and `j` and\n    `N(i)` are the neighbors of node `i`.\n\n\n    Parameters\n    ----------\n    G : NetworkX graph\n\n    nodes: list or iterable (optional)\n        Compute neighbor connectivity for these nodes. The default is all nodes.\n\n    weighted: bool (default=False)\n        Compute weighted average nearest neighbors degree.\n\n    Returns\n    -------\n    d: dict\n       A dictionary keyed by node with average neighbors degree value.\n\n    Examples\n    --------\n    >>> G=nx.path_graph(4)\n    >>> G.edge[0][1]['weight'] = 5\n    >>> G.edge[2][3]['weight'] = 3\n    >>> nx.average_neighbor_degree(G)\n    {0: 2.0, 1: 1.5, 2: 1.5, 3: 2.0}\n    >>> nx.average_neighbor_degree(G, weighted=True)\n    {0: 2.0, 1: 1.1666666666666667, 2: 1.25, 3: 2.0}\n\n    >>> G=nx.DiGraph()\n    >>> G.add_path([0,1,2,3])\n    >>> nx.average_neighbor_in_degree(G)\n    {0: 1.0, 1: 1.0, 2: 1.0, 3: 0.0}\n    >>> nx.average_neighbor_out_degree(G)\n    {0: 1.0, 1: 1.0, 2: 0.0, 3: 0.0}\n \n    Notes\n    -----\n    For directed graphs you can also specify in-degree or out-degree \n    by calling the relevant functions.  \n\n    See Also\n    --------\n    average_neighbor_out_degree, average_neighbor_in_degree, \n    average_degree_connectivity \n    \n    References\n    ----------    \n    .. [1] A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A. Vespignani, \n       \"The architecture of complex weighted networks\". \n       PNAS 101 (11): 3747–3752 (2004).\n    \"\"\"\n    degree_method = G.degree\n    return _average_nbr_deg(G, degree_method, nodes=nodes, weighted=weighted)\n\ndef average_neighbor_in_degree(G, nodes=None, weighted=False):\n    if not G.is_directed():\n        raise nx.NetworkXError(\"Not defined for undirected graphs.\")\n    degree_method = G.in_degree\n    return _average_nbr_deg(G, degree_method, nodes=nodes, weighted=weighted)\naverage_neighbor_in_degree.__doc__=average_neighbor_degree.__doc__\n\ndef average_neighbor_out_degree(G, nodes=None, weighted=False):\n    if not G.is_directed():\n        raise nx.NetworkXError(\"Not defined for undirected graphs.\")\n    degree_method = G.out_degree\n    return _average_nbr_deg(G, degree_method, nodes=nodes, weighted=weighted)\naverage_neighbor_out_degree.__doc__=average_neighbor_degree.__doc__\n\ndef _avg_deg_conn(G, degree_method, nodes=None, weighted=False):\n    # \"k nearest neighbors, or neighbor_connectivity\n    if nodes is None:\n        node_iter = G\n    else:\n        node_iter = G.nbunch_iter(nodes)\n    dsum = defaultdict(float)\n    dnorm = defaultdict(float)\n    for n,k in degree_method(node_iter).items():\n        nbrdeg = degree_method(G[n])\n        if weighted:\n            nbrv = [G[n][nbr].get('weight',1)*d for nbr,d in nbrdeg.items()]\n            dnorm[k] += degree_method(n, weighted=weighted)\n        else:\n            nbrv = nbrdeg.values()\n            dnorm[k] += k\n        dsum[k] += float(sum(nbrv))\n\n    dc = {}\n    for k,avg in dsum.items():\n        dc[k]=avg\n        if avg > 0:\n            dc[k]/=dnorm[k]\n    return dc\n\ndef average_degree_connectivity(G, nodes=None, weighted=False):\n    r\"\"\"Compute the average degree connectivity of graph.\n\n    The average degree connectivity is the average nearest neighbor degree of\n    nodes with degree k. For weighted graphs, an analogous measure can \n    be computed using the weighted average neighbors degree defined in \n    [1]_, for a node `i`, as:\n\n    .. math::\n\n        k_{nn,i}^{w} = \\frac{1}{s_i} \\sum_{j \\in N(i)} w_{ij} k_j\n\n    where `s_i` is the weighted degree of node `i`, \n    `w_{ij}`is the weight of the edge that links `i` and `j`,\n    and `N(i)` are the neighbors of node `i`.\n\n    Parameters\n    ----------\n    G : NetworkX graph\n\n    nodes: list or iterable (optional)\n        Compute neighbor connectivity for these nodes. The default is all nodes.\n\n    weighted: bool (default=False)\n        Compute weighted average nearest neighbors degree.\n\n    Returns\n    -------\n    d: dict\n       A dictionary keyed by degree k with the value of average neighbor degree.\n    \n    Examples\n    --------\n    >>> G=nx.path_graph(4)\n    >>> G.edge[1][2]['weight'] = 3\n    >>> nx.k_nearest_neighbors(G)\n    {1: 2.0, 2: 1.5}\n    >>> nx.k_nearest_neighbors(G, weighted=True)\n    {1: 2.0, 2: 1.75}\n\n    See also\n    --------\n    neighbors_average_degree\n\n    Notes\n    -----\n    This algorithm is sometimes called \"k nearest neighbors'.\n\n    References\n    ----------    \n    .. [1] A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A. Vespignani, \n       \"The architecture of complex weighted networks\". \n       PNAS 101 (11): 3747–3752 (2004).\n    \"\"\"\n    degree_method = G.degree\n    return _avg_deg_conn(G, degree_method, nodes=nodes, weighted=weighted)\n\ndef average_in_degree_connectivity(G, nodes=None, weighted=False):\n    if not G.is_directed():\n        raise nx.NetworkXError(\"Not defined for undirected graphs.\")\n    degree_method = G.in_degree\n    return _avg_deg_conn(G, degree_method, nodes=nodes, weighted=weighted)\naverage_in_degree_connectivity.__doc__=average_degree_connectivity.__doc__\n\ndef average_out_degree_connectivity(G, nodes=None, weighted=False):\n    if not G.is_directed():\n        raise nx.NetworkXError(\"Not defined for undirected graphs.\")\n    degree_method = G.out_degree\n    return _avg_deg_conn(G, degree_method, nodes=nodes, weighted=weighted)\naverage_out_degree_connectivity.__doc__=average_degree_connectivity.__doc__\n\n\nk_nearest_neighbors=average_degree_connectivity\nneighbor_connectivity=average_degree_connectivity\n","repo_name":"fstwn/cockatoo","sub_path":"modules/networkx/algorithms/neighbor_degree.py","file_name":"neighbor_degree.py","file_ext":"py","file_size_in_byte":7228,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"80"}
+{"seq_id":"3657132399","text":"import random\n\n# two people take turns counting up by 1 or 2. The goal is to say 21.\n# different variations can exist based on the values that can be counted up by or the goal number.\n# This is the 1 player version, playing against a bot that will always play the best moves.\n\n# play function: first argument is max_interval, the most that can be counted up to by a player in a single turn\n# second argument: goal, the number that each player is trying to say and wins upon saying\n# third argument: whether the player goes first\ndef play(max_interval:int, goal:int, human_turn:int, difficulty:int):\n    current = 0\n    player_turn = 0\n\n    while current < goal:\n        if human_turn == player_turn % 2:\n            print(\"player turn\")\n            move = 0\n            while move < 1 or move > max_interval:\n                try:\n                    move = int(input('Type a valid number between 1 and ' + str(max_interval) + \" \"))\n                    if move < 1 or move > max_interval:\n                        print(\"Invalid input. Choose a number between 1 and \" + str(max_interval) + \" \")\n                except (TypeError, ValueError):\n                    print(\"Invalid input. Choose a number between 1 and \" + str(max_interval) + \" \")\n            player_turn += 1\n            current += move\n            print(str(current))\n        else:\n            print(\"computer turn\")\n            move = 0\n            if (goal - current) % (max_interval + 1) == 0 or difficulty == 0:\n                move = random.randint(1,max_interval)\n            else:\n                move = (goal - current) % (max_interval + 1)\n            current += move\n            player_turn += 1\n            print(str(current))\n\n    player_turn -= 1\n    print(\"Game Over: Player \" + str(player_turn % 2 + 1) + \" wins\")\n    if player_turn % 2 == human_turn:\n        print(\"Human victory\")\n    else:\n        print(\"Bot victory\")\n\nwhile True:\n    print(\"Counting Game\")\n    if (input('Type \"1\" to play: ')) == \"1\":\n        max_interval = 0\n        goal = 0\n        human_turn = -1\n        difficulty = -1\n        while human_turn == -1:\n            try:\n                human_turn = int(input('Do you want to go first? Type \"0\" if you want to go first and \"1\" if you want to go second: '))\n                if human_turn < 0 or human_turn >= 2:\n                    print(\"Invalid input\")\n                    human_turn = -1\n            except (TypeError, ValueError):\n                print(\"Invalid input\")\n        while max_interval < 2:\n            try:\n                max_interval = int(input('What is the maximum interval? '))\n                if max_interval < 2:\n                    print(\"Invalid input\")\n            except (TypeError, ValueError):\n                print(\"Invalid input. Choose a number greater than 1\")\n        while goal < 2:\n            try:\n                goal = int(input('What is the goal number(the number you need to say to win)? '))\n                if goal < 2:\n                    print(\"Invalid Input\")\n            except (TypeError, ValueError):\n                print(\"Invalid input. Choose a number greater than 2\")\n        while difficulty < 0 or difficulty > 1:\n            try:\n                difficulty = int(input('How difficult do you want the bot to be? Type \"1\" for hard mode and type \"0\" for easy mode: '))\n                if difficulty < 0 or difficulty > 1:\n                    print(\"Invalid Input\")\n            except (TypeError, ValueError):\n                print(\"Invalid Input\")\n        play(max_interval, goal, human_turn, difficulty)\n    else:\n        break","repo_name":"NathanN70/Simple-Games","sub_path":"counting_vs_bot.py","file_name":"counting_vs_bot.py","file_ext":"py","file_size_in_byte":3582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"5619947383","text":"#!/usr/bin/env python3\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.spatial import ConvexHull\nfrom scipy.spatial.distance import cdist\nimport os\nimport shutil\nimport fnmatch\nimport sys\n\n#subsamples = [10,20,30,40,50,60,80,100,200,300]\nsubsamples = [50]\n\n#scenes = ['001_standing_coated', '002_lying_coated', '003_standing_liquid', '004_lying_liquid', '005_standing_empty', '006_lying_empty']\nscenes = ['001_standing_coated']\n\ntest_img_indices = [3, 12, 13, 17, 18, 22, 23, 27, 30, 33, 34, 36, 41, 43, 46, 47, 49, 56, 64, 79, 87, 93, 97, 99, 103, 105, 106, 107, 108, 111, 112, 113, 114, 120, 121, 123, 124, 125, 127, 131, 137, 140, 156, 157, 166, 168, 171, 174, 179, 182, 183, 186, 187, 189, 195, 196, 197, 201, 202, 203, 212, 213, 214,\n                    217, 224, 225, 230, 234, 235, 239, 242, 245, 250, 252, 253, 254, 255, 257, 258, 261, 268, 276, 301, 302, 305, 307, 308, 309, 311, 317, 319, 320, 324, 325, 327, 328, 329, 333, 335, 336, 339, 341, 349, 353, 368, 376, 380, 383, 386, 387, 391, 392, 395, 396, 402, 403, 408, 409, 410, 413, 414, 419, 420, 424]\nsubsample_image_names = [(str(item).zfill(6)) +\n                            '.png' for item in test_img_indices]\n\n#/home/julius/Documents/Julius_03_x_auswertung/Julius_03_10/scenes/001_standing_coated/depth_nerf_v2_centered_nerfacto\n#/home/julius/Documents/Julius_03_x_auswertung/Julius_03_10/scenes/001_standing_coated/depth_nerf_124\n\n#path_to_master = '/home/julius/Documents/Julius_03_x_auswertung/Julius_03_' \npath_to_master = '/home/julius/Videos/Julius_03_x/Julius_03_' \n#folders = [\"rgb_nerf\", \"depth_nerf\", \"depth_norm_nerf\"]\nfolders = [\"depth_nerf\"]\n\nextension = \"_v2_centered_nerfacto_only_subsamples_v2\"\n\nfor folder in folders:\n    for subsample in subsamples:\n        for scene in scenes:\n            path = path_to_master + f\"{subsample}/scenes/{scene}/{folder}\"\n            #print(path)\n            dest = os.path.dirname(path.replace(\"Videos/Julius_03_x\", \"Documents/Julius_03_x_auswertung\")) + f\"/{folder}{extension}\"\n            #print(dest)\n            os.makedirs(dest, exist_ok=True)\n            dest_names_to_read = dest.replace(f\"{folder}{extension}\", \"rgb\")\n            files = sorted([os.path.join(path, f) for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))])\n            file_names_to_read = sorted([os.path.basename(os.path.join(dest_names_to_read, f)) for f in os.listdir(dest_names_to_read) if os.path.isfile(os.path.join(dest_names_to_read, f))])\n            \n            for idx, file in enumerate(files):\n                dest2 = os.path.join(dest, file_names_to_read[idx])\n                shutil.copy2(file, dest2)\n              \n                # if os.path.basename(file) in subsample_image_names:\n                    \n                #     shutil.copy2(file, dest)\n                    \n                \n","repo_name":"juliusroschmann/viewpoint_sampling","sub_path":"misc/test_img_extraction.py","file_name":"test_img_extraction.py","file_ext":"py","file_size_in_byte":2820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"22144211195","text":"from mitsui.Mitsui import *\n\nfrom controllers.parseArgs import *\n\nclass MitsuiScraping:\n\n    def switch(self, scraping_type, args, states):\n        default = getattr(self, '_default')\n        request = parseArgs(args)\n        return getattr(self, scraping_type, lambda: default)(request, states)\n\n    def _default(self, request, states):\n      return {\n        'status' : 500,\n        'error' : 'Server Error.'\n      }\n\n    def login_search(self, request, states):\n\n\n      return {\n        'data' : {},\n        'states' : states\n      }\n\n    def get_ptable(self, request, states):\n      \n      helper = Mitsui()\n      browser = helper.new_browser(debug=False)\n      try:\n        helper.login(browser, request)\n      except:\n        browser.quit()\n        return{\n          'data' : {\n            'status' : 500,\n            'message' : 'Login Failed'\n          },\n          'states' : states\n        }\n      browser_id = request['browser_id']\n      states[browser_id] = browser\n      response = helper.ptable_search(browser, request, browser_id)\n\n      if(response['browser_table']['has_next'] == None or response['browser_table']['has_next'] == -1):\n        # Clear Browser\n        browser.quit()\n        states[browser_id] = None\n        del states[browser_id]\n        browser_id = 0\n\n      return {\n        'data' : response,\n        'states' : states\n      }\n\n    def get_batch(self, request, states):\n      helper = Mitsui()\n      browser_id = request['browser_id']\n      browser = states[browser_id]\n\n      # Traverse PTABLE\n      result = helper.getSmallBatchResult(browser)\n\n      if (result['has_next'] == -1):\n        # Clear Browser\n        browser.quit()\n        states[browser_id] = None\n        del states[browser_id]\n        browser_id = 0\n\n      return {\n        'data' : {\n          'status' : 200,\n          'browser_table' : {\n            'browser_id' : browser_id,\n            'has_next' : result['has_next']\n          },\n          'payload' : result['payload'],\n        },\n        'states' : states\n      }\n    \n    def get_detail(self, request, states):\n      helper = Mitsui()\n      # Check if Browser is initialized\n      if 'mitsui_detail' in states:\n        browser = states['mitsui_detail']\n        if(helper.isBrowserActive(browser) is False):\n          # Clear Browser\n          browser.quit()\n          states['mitsui_detail'] = None\n          del states['mitsui_detail']\n          return self.get_detail(request, states)\n      # Login if not\n      else:\n        # New Browser\n        browser = helper.new_browser(debug=False)\n        # Login\n        helper.login(browser, request)\n        # Store on States\n        states['mitsui_detail'] = browser\n\n      # Visit Url\n      browser.visit(request['detail_link'])\n      # Get Detail\n      response = helper.getDetailPage(browser)\n      # Prep for Next\n      browser.back()\n      browser.reload()\n\n      return {\n        'data' : response,\n        'states' : states,\n        'detail_browser' : True\n      }","repo_name":"kenta-crv/perfect","sub_path":"python/controllers/MitsuiScraping.py","file_name":"MitsuiScraping.py","file_ext":"py","file_size_in_byte":2984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"1468895406","text":"from sympy.testing.pytest import raises, XFAIL\nfrom sympy.external import import_module\n\nfrom sympy import (\n    Symbol, Mul, Add, Abs, sin, asin, cos, Pow, csc, sec,\n    Limit, oo, Derivative, Integral, factorial, sqrt, root,\n    conjugate, StrictLessThan, LessThan, StrictGreaterThan,\n    GreaterThan, Sum, Product, E, log, tan, Function, binomial,\n    exp, floor, ceiling, Unequality\n)\nfrom sympy.core.relational import Eq, Ne, Lt, Le, Gt, Ge\nfrom sympy.physics.quantum.state import Bra, Ket\nfrom sympy.abc import x, y, z, a, b, c, t, k, n\nantlr4 = import_module(\"antlr4\")\n\n# disable tests if antlr4-python*-runtime is not present\nif not antlr4:\n    disabled = True\n\ntheta = Symbol('theta')\nf = Function('f')\n\n\n# shorthand definitions\ndef _Add(a, b):\n    return Add(a, b, evaluate=False)\n\n\ndef _Mul(a, b):\n    return Mul(a, b, evaluate=False)\n\n\ndef _Pow(a, b):\n    return Pow(a, b, evaluate=False)\n\n\ndef _Sqrt(a):\n    return sqrt(a, evaluate=False)\n\n\ndef _Conjugate(a):\n    return conjugate(a, evaluate=False)\n\n\ndef _Abs(a):\n    return Abs(a, evaluate=False)\n\n\ndef _factorial(a):\n    return factorial(a, evaluate=False)\n\n\ndef _exp(a):\n    return exp(a, evaluate=False)\n\n\ndef _log(a, b):\n    return log(a, b, evaluate=False)\n\n\ndef _binomial(n, k):\n    return binomial(n, k, evaluate=False)\n\n\ndef test_import():\n    from sympy.parsing.latex._build_latex_antlr import (\n        build_parser,\n        check_antlr_version,\n        dir_latex_antlr\n    )\n    # XXX: It would be better to come up with a test for these...\n    del build_parser, check_antlr_version, dir_latex_antlr\n\n\n# These LaTeX strings should parse to the corresponding SymPy expression\nGOOD_PAIRS = [\n    (r\"0\", 0),\n    (r\"1\", 1),\n    (r\"-3.14\", _Mul(-1, 3.14)),\n    (r\"(-7.13)(1.5)\", _Mul(_Mul(-1, 7.13), 1.5)),\n    (r\"x\", x),\n    (r\"2x\", 2*x),\n    (r\"x^2\", x**2),\n    (r\"x^{3 + 1}\", x**_Add(3, 1)),\n    (r\"-c\", -c),\n    (r\"a \\cdot b\", a * b),\n    (r\"a / b\", a / b),\n    (r\"a \\div b\", a / b),\n    (r\"a + b\", a + b),\n    (r\"a + b - a\", _Add(a+b, -a)),\n    (r\"a^2 + b^2 = c^2\", Eq(a**2 + b**2, c**2)),\n    (r\"(x + y) z\", _Mul(_Add(x, y), z)),\n    (r\"\\left(x + y\\right) z\", _Mul(_Add(x, y), z)),\n    (r\"\\left( x + y\\right ) z\", _Mul(_Add(x, y), z)),\n    (r\"\\left(  x + y\\right ) z\", _Mul(_Add(x, y), z)),\n    (r\"\\left[x + y\\right] z\", _Mul(_Add(x, y), z)),\n    (r\"\\left\\{x + y\\right\\} z\", _Mul(_Add(x, y), z)),\n    (r\"1+1\", _Add(1, 1)),\n    (r\"0+1\", _Add(0, 1)),\n    (r\"1*2\", _Mul(1, 2)),\n    (r\"0*1\", _Mul(0, 1)),\n    (r\"x = y\", Eq(x, y)),\n    (r\"x \\neq y\", Ne(x, y)),\n    (r\"x < y\", Lt(x, y)),\n    (r\"x > y\", Gt(x, y)),\n    (r\"x \\leq y\", Le(x, y)),\n    (r\"x \\geq y\", Ge(x, y)),\n    (r\"x \\le y\", Le(x, y)),\n    (r\"x \\ge y\", Ge(x, y)),\n    (r\"\\lfloor x \\rfloor\", floor(x)),\n    (r\"\\lceil x \\rceil\", ceiling(x)),\n    (r\"\\langle x |\", Bra('x')),\n    (r\"| x \\rangle\", Ket('x')),\n    (r\"\\sin \\theta\", sin(theta)),\n    (r\"\\sin(\\theta)\", sin(theta)),\n    (r\"\\sin^{-1} a\", asin(a)),\n    (r\"\\sin a \\cos b\", _Mul(sin(a), cos(b))),\n    (r\"\\sin \\cos \\theta\", sin(cos(theta))),\n    (r\"\\sin(\\cos \\theta)\", sin(cos(theta))),\n    (r\"\\frac{a}{b}\", a / b),\n    (r\"\\frac{a + b}{c}\", _Mul(a + b, _Pow(c, -1))),\n    (r\"\\frac{7}{3}\", _Mul(7, _Pow(3, -1))),\n    (r\"(\\csc x)(\\sec y)\", csc(x)*sec(y)),\n    (r\"\\lim_{x \\to 3} a\", Limit(a, x, 3)),\n    (r\"\\lim_{x \\rightarrow 3} a\", Limit(a, x, 3)),\n    (r\"\\lim_{x \\Rightarrow 3} a\", Limit(a, x, 3)),\n    (r\"\\lim_{x \\longrightarrow 3} a\", Limit(a, x, 3)),\n    (r\"\\lim_{x \\Longrightarrow 3} a\", Limit(a, x, 3)),\n    (r\"\\lim_{x \\to 3^{+}} a\", Limit(a, x, 3, dir='+')),\n    (r\"\\lim_{x \\to 3^{-}} a\", Limit(a, x, 3, dir='-')),\n    (r\"\\infty\", oo),\n    (r\"\\lim_{x \\to \\infty} \\frac{1}{x}\", Limit(_Pow(x, -1), x, oo)),\n    (r\"\\frac{d}{dx} x\", Derivative(x, x)),\n    (r\"\\frac{d}{dt} x\", Derivative(x, t)),\n    (r\"f(x)\", f(x)),\n    (r\"f(x, y)\", f(x, y)),\n    (r\"f(x, y, z)\", f(x, y, z)),\n    (r\"\\frac{d f(x)}{dx}\", Derivative(f(x), x)),\n    (r\"\\frac{d\\theta(x)}{dx}\", Derivative(Function('theta')(x), x)),\n    (r\"x \\neq y\", Unequality(x, y)),\n    (r\"|x|\", _Abs(x)),\n    (r\"||x||\", _Abs(Abs(x))),\n    (r\"|x||y|\", _Abs(x)*_Abs(y)),\n    (r\"||x||y||\", _Abs(_Abs(x)*_Abs(y))),\n    (r\"\\pi^{|xy|}\", Symbol('pi')**_Abs(x*y)),\n    (r\"\\int x dx\", Integral(x, x)),\n    (r\"\\int x d\\theta\", Integral(x, theta)),\n    (r\"\\int (x^2 - y)dx\", Integral(x**2 - y, x)),\n    (r\"\\int x + a dx\", Integral(_Add(x, a), x)),\n    (r\"\\int da\", Integral(1, a)),\n    (r\"\\int_0^7 dx\", Integral(1, (x, 0, 7))),\n    (r\"\\int_a^b x dx\", Integral(x, (x, a, b))),\n    (r\"\\int^b_a x dx\", Integral(x, (x, a, b))),\n    (r\"\\int_{a}^b x dx\", Integral(x, (x, a, b))),\n    (r\"\\int^{b}_a x dx\", Integral(x, (x, a, b))),\n    (r\"\\int_{a}^{b} x dx\", Integral(x, (x, a, b))),\n    (r\"\\int^{b}_{a} x dx\", Integral(x, (x, a, b))),\n    (r\"\\int_{f(a)}^{f(b)} f(z) dz\", Integral(f(z), (z, f(a), f(b)))),\n    (r\"\\int (x+a)\", Integral(_Add(x, a), x)),\n    (r\"\\int a + b + c dx\", Integral(_Add(_Add(a, b), c), x)),\n    (r\"\\int \\frac{dz}{z}\", Integral(Pow(z, -1), z)),\n    (r\"\\int \\frac{3 dz}{z}\", Integral(3*Pow(z, -1), z)),\n    (r\"\\int \\frac{1}{x} dx\", Integral(Pow(x, -1), x)),\n    (r\"\\int \\frac{1}{a} + \\frac{1}{b} dx\",\n     Integral(_Add(_Pow(a, -1), Pow(b, -1)), x)),\n    (r\"\\int \\frac{3 \\cdot d\\theta}{\\theta}\",\n     Integral(3*_Pow(theta, -1), theta)),\n    (r\"\\int \\frac{1}{x} + 1 dx\", Integral(_Add(_Pow(x, -1), 1), x)),\n    (r\"x_0\", Symbol('x_{0}')),\n    (r\"x_{1}\", Symbol('x_{1}')),\n    (r\"x_a\", Symbol('x_{a}')),\n    (r\"x_{b}\", Symbol('x_{b}')),\n    (r\"h_\\theta\", Symbol('h_{theta}')),\n    (r\"h_{\\theta}\", Symbol('h_{theta}')),\n    (r\"h_{\\theta}(x_0, x_1)\",\n     Function('h_{theta}')(Symbol('x_{0}'), Symbol('x_{1}'))),\n    (r\"x!\", _factorial(x)),\n    (r\"100!\", _factorial(100)),\n    (r\"\\theta!\", _factorial(theta)),\n    (r\"(x + 1)!\", _factorial(_Add(x, 1))),\n    (r\"(x!)!\", _factorial(_factorial(x))),\n    (r\"x!!!\", _factorial(_factorial(_factorial(x)))),\n    (r\"5!7!\", _Mul(_factorial(5), _factorial(7))),\n    (r\"\\sqrt{x}\", sqrt(x)),\n    (r\"\\sqrt{x + b}\", sqrt(_Add(x, b))),\n    (r\"\\sqrt[3]{\\sin x}\", root(sin(x), 3)),\n    (r\"\\sqrt[y]{\\sin x}\", root(sin(x), y)),\n    (r\"\\sqrt[\\theta]{\\sin x}\", root(sin(x), theta)),\n    (r\"\\sqrt{\\frac{12}{6}}\", _Sqrt(_Mul(12, _Pow(6, -1)))),\n    (r\"\\overline{z}\", _Conjugate(z)),\n    (r\"\\overline{\\overline{z}}\", _Conjugate(_Conjugate(z))),\n    (r\"\\overline{x + y}\", _Conjugate(_Add(x, y))),\n    (r\"\\overline{x} + \\overline{y}\", _Conjugate(x) + _Conjugate(y)),\n    (r\"x < y\", StrictLessThan(x, y)),\n    (r\"x \\leq y\", LessThan(x, y)),\n    (r\"x > y\", StrictGreaterThan(x, y)),\n    (r\"x \\geq y\", GreaterThan(x, y)),\n    (r\"\\mathit{x}\", Symbol('x')),\n    (r\"\\mathit{test}\", Symbol('test')),\n    (r\"\\mathit{TEST}\", Symbol('TEST')),\n    (r\"\\mathit{HELLO world}\", Symbol('HELLO world')),\n    (r\"\\sum_{k = 1}^{3} c\", Sum(c, (k, 1, 3))),\n    (r\"\\sum_{k = 1}^3 c\", Sum(c, (k, 1, 3))),\n    (r\"\\sum^{3}_{k = 1} c\", Sum(c, (k, 1, 3))),\n    (r\"\\sum^3_{k = 1} c\", Sum(c, (k, 1, 3))),\n    (r\"\\sum_{k = 1}^{10} k^2\", Sum(k**2, (k, 1, 10))),\n    (r\"\\sum_{n = 0}^{\\infty} \\frac{1}{n!}\",\n     Sum(_Pow(_factorial(n), -1), (n, 0, oo))),\n    (r\"\\prod_{a = b}^{c} x\", Product(x, (a, b, c))),\n    (r\"\\prod_{a = b}^c x\", Product(x, (a, b, c))),\n    (r\"\\prod^{c}_{a = b} x\", Product(x, (a, b, c))),\n    (r\"\\prod^c_{a = b} x\", Product(x, (a, b, c))),\n    (r\"\\exp x\", _exp(x)),\n    (r\"\\exp(x)\", _exp(x)),\n    (r\"\\ln x\", _log(x, E)),\n    (r\"\\ln xy\", _log(x*y, E)),\n    (r\"\\log x\", _log(x, 10)),\n    (r\"\\log xy\", _log(x*y, 10)),\n    (r\"\\log_{2} x\", _log(x, 2)),\n    (r\"\\log_{a} x\", _log(x, a)),\n    (r\"\\log_{11} x\", _log(x, 11)),\n    (r\"\\log_{a^2} x\", _log(x, _Pow(a, 2))),\n    (r\"[x]\", x),\n    (r\"[a + b]\", _Add(a, b)),\n    (r\"\\frac{d}{dx} [ \\tan x ]\", Derivative(tan(x), x)),\n    (r\"\\binom{n}{k}\", _binomial(n, k)),\n    (r\"\\tbinom{n}{k}\", _binomial(n, k)),\n    (r\"\\dbinom{n}{k}\", _binomial(n, k)),\n    (r\"\\binom{n}{0}\", _binomial(n, 0)),\n    (r\"a \\, b\", _Mul(a, b)),\n    (r\"a \\thinspace b\", _Mul(a, b)),\n    (r\"a \\: b\", _Mul(a, b)),\n    (r\"a \\medspace b\", _Mul(a, b)),\n    (r\"a \\; b\", _Mul(a, b)),\n    (r\"a \\thickspace b\", _Mul(a, b)),\n    (r\"a \\quad b\", _Mul(a, b)),\n    (r\"a \\qquad b\", _Mul(a, b)),\n    (r\"a \\! b\", _Mul(a, b)),\n    (r\"a \\negthinspace b\", _Mul(a, b)),\n    (r\"a \\negmedspace b\", _Mul(a, b)),\n    (r\"a \\negthickspace b\", _Mul(a, b)),\n    (r\"\\int x \\, dx\", Integral(x, x)),\n    (r\"\\log_2 x\", _log(x, 2)),\n    (r\"\\log_a x\", _log(x, a)),\n]\n\ndef test_parseable():\n    from sympy.parsing.latex import parse_latex\n    for latex_str, sympy_expr in GOOD_PAIRS:\n        assert parse_latex(latex_str) == sympy_expr\n\n# These bad LaTeX strings should raise a LaTeXParsingError when parsed\nBAD_STRINGS = [\n    r\"(\",\n    r\")\",\n    r\"\\frac{d}{dx}\",\n    r\"(\\frac{d}{dx})\",\n    r\"\\sqrt{}\",\n    r\"\\sqrt\",\n    r\"\\overline{}\",\n    r\"\\overline\",\n    r\"{\",\n    r\"}\",\n    r\"\\mathit{x + y}\",\n    r\"\\mathit{21}\",\n    r\"\\frac{2}{}\",\n    r\"\\frac{}{2}\",\n    r\"\\int\",\n    r\"!\",\n    r\"!0\",\n    r\"_\",\n    r\"^\",\n    r\"|\",\n    r\"||x|\",\n    r\"()\",\n    r\"((((((((((((((((()))))))))))))))))\",\n    r\"-\",\n    r\"\\frac{d}{dx} + \\frac{d}{dt}\",\n    r\"f(x,,y)\",\n    r\"f(x,y,\",\n    r\"\\sin^x\",\n    r\"\\cos^2\",\n    r\"@\",\n    r\"#\",\n    r\"$\",\n    r\"%\",\n    r\"&\",\n    r\"*\",\n    r\"\" \"\\\\\",\n    r\"~\",\n    r\"\\frac{(2 + x}{1 - x)}\",\n]\n\ndef test_not_parseable():\n    from sympy.parsing.latex import parse_latex, LaTeXParsingError\n    for latex_str in BAD_STRINGS:\n        with raises(LaTeXParsingError):\n            parse_latex(latex_str)\n\n# At time of migration from latex2sympy, should fail but doesn't\nFAILING_BAD_STRINGS = [\n    r\"\\cos 1 \\cos\",\n    r\"f(,\",\n    r\"f()\",\n    r\"a \\div \\div b\",\n    r\"a \\cdot \\cdot b\",\n    r\"a // b\",\n    r\"a +\",\n    r\"1.1.1\",\n    r\"1 +\",\n    r\"a / b /\",\n]\n\n@XFAIL\ndef test_failing_not_parseable():\n    from sympy.parsing.latex import parse_latex, LaTeXParsingError\n    for latex_str in FAILING_BAD_STRINGS:\n        with raises(LaTeXParsingError):\n            parse_latex(latex_str)\n","repo_name":"thebaselab/codeapp","sub_path":"LanguageResources/Library/lib/python3.9/site-packages/sympy/parsing/tests/test_latex.py","file_name":"test_latex.py","file_ext":"py","file_size_in_byte":9914,"program_lang":"python","lang":"en","doc_type":"code","stars":2422,"dataset":"github-code","pt":"80"}
+{"seq_id":"14746283490","text":"import sys\r\nimport os\r\nimport random\r\n\r\nif os.path.dirname(os.path.abspath(__file__)) in sys.path:\r\n    sys.path.remove(os.path.dirname(os.path.abspath(__file__)))\r\n\r\ndef main(num_small, num_large):\r\n    for i in range(num_small):\r\n        capacity_w = random.randrange(100, 1000, 17)\r\n        num_classes_m = random.randint(1,6)\r\n        n = random.randint(10,40)\r\n        weights = [None] * n\r\n        values = [None] * n\r\n        classes = [None] * n\r\n        for j in range(n):\r\n            weights[j] = random.randrange(1, capacity_w - 15, 3)\r\n            values[j] =  random.randrange(1, capacity_w - 15, 3)\r\n            if num_classes_m == 1:\r\n                classes[j] = 1\r\n            else:\r\n                classes[j] = random.randint(1, num_classes_m)\r\n        fileName = \"INPUT_\" + str(i+1) + \".txt\"\r\n        f =  open(fileName, 'w')\r\n        f.write(str(capacity_w) + '\\n')\r\n        f.write(str(num_classes_m) + '\\n')\r\n        f.write(str(weights).replace('[','').replace(']','') + '\\n')\r\n        f.write(str(values).replace('[','').replace(']','') + '\\n')\r\n        f.write(str(classes).replace('[','').replace(']','') + '\\n')\r\n    \r\n    for i in range(num_large):\r\n        capacity_w = random.randrange(1000, 10000, 29)\r\n        num_classes_m = random.randint(5, 10)\r\n        n = random.randint(50, 1000)\r\n        weights = [None] * n\r\n        values = [None] * n\r\n        classes = [None] * n\r\n        for j in range(n):\r\n            weights[j] = random.randrange(10, capacity_w - 53, 23)\r\n            values[j] =  random.randrange(10, capacity_w - 53, 23)\r\n            if num_classes_m == 1:\r\n                classes[j] = 1\r\n            else:\r\n                classes[j] = random.randint(1, num_classes_m)\r\n        fileName = \"INPUT_\" + str(num_small + i + 1) + \".txt\"\r\n        f =  open(fileName, 'w')\r\n        f.write(str(capacity_w) + '\\n')\r\n        f.write(str(num_classes_m) + '\\n')\r\n        f.write(str(weights).replace('[','').replace(']','') + '\\n')\r\n        f.write(str(values).replace('[','').replace(']','') + '\\n')\r\n        f.write(str(classes).replace('[','').replace(']','') + '\\n')\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    if len(sys.argv) != 3:\r\n        print(\"Usage: create_input.py <num_small_dataset> <num_large_dataset>\")\r\n        sys.exit(0)\r\n    main(max(5,int(sys.argv[1])), max(5,int(sys.argv[2])))","repo_name":"khangkontum/knapsack","sub_path":"input/create_input.py","file_name":"create_input.py","file_ext":"py","file_size_in_byte":2335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"9230629571","text":"#!/usr/bin/env python3\n\nimport os\nimport numpy as np\nimport jax.numpy as jnp\nfrom multiprocessing import Pool, Manager, Condition, Value, Process,TimeoutError\nfrom multiprocessing.dummy import Pool as ThreadPool\nfrom functools import partial\nimport sys\nimport io\nimport time\nimport re\nimport traceback\nimport subprocess\nfrom multiprocessing.managers import BaseManager\nfrom multiprocessing import Queue as mpQueue\nfrom queue import Queue\nfrom .isa import *\nfrom .utils import *\n\nfrom rich.progress import (\n    Progress,\n    TextColumn,\n    BarColumn,\n    TimeElapsedColumn,\n    TimeRemainingColumn\n)\n\nfrom .common import import_from_directory\nimport_from_directory('isa', globals())\nimport_from_directory('utils', globals())\n\n\ndef sync_variable():\n    # to synchronize the runner processes with the main process\n    globals()[\"manager\"] = Manager()\n    globals()[\"result_dict\"] = manager.dict()\n    globals()[\"result_condition\"] = Condition()\n    globals()[\"result_detail_dict\"] = manager.dict()\n    globals()[\"tests\"] = Value('L', 0)\n    globals()[\"fails\"] = Value('L', 0)\n\n# run failed cases last time\ndef get_retry_cases():\n    print(\"retry last failed cases...\")\n    if os.access('log/runner_report.log', os.R_OK):\n        with open('log/runner_report.log') as fp:\n            cases = []\n            lines = fp.read().splitlines()\n            for line in lines:\n                if line.startswith('PASS '):\n                    continue\n                line = line.replace('FAIL ', '')\n                line = line.split( ' ', 1 )[0]\n                cases.append( line )\n            \n            if len(cases) == 0:\n                print('all pass, retry abort.')\n                sys.exit(0)\n\n            return cases               \n    else:\n        print('could not retry without last run log.')\n        sys.exit(-1)\n\n# get cases from arguments\ndef get_arg_cases(args_cases):\n    s = lambda l: l.strip()\n    f = lambda l: l != '' and not l.startswith('#')\n    if os.access(args_cases, os.R_OK):\n        with open(args_cases) as fp:\n            cases = list(filter(f, map(s, fp.read().splitlines())))\n    elif args_cases != '':\n        cases = list(filter(f, map(s, args_cases.split(','))))\n    else:\n        cases = []   \n    \n    return cases\n\ndef get_generator_case():\n    with open(\"log/generator_case.log\") as fp:\n        s = lambda l: l.strip()\n        f = lambda l: l != '' and not l.startswith('#')\n        generator_info_list = list(filter(f, map(s, fp.read().splitlines())))\n        generator_case_list = []\n        generator_num_list = []\n        for no in range(len(generator_info_list)):\n            [case_name, case_num] = re.split(r'\\s*,\\s*', generator_info_list[no])\n            generator_case_list.append(case_name)\n            generator_num_list.append(int(case_num))  \n\n    return [generator_case_list, generator_num_list]  \n\ndef select_run_case( generator_case_list, generator_num_list, cases ):\n    total_num = 0\n    run_case_list = []\n\n    if len(cases) > 0:\n        for no in range(len(generator_case_list)):\n            case_name = generator_case_list[no]\n            for case in cases:\n                if not case in case_name:\n                    continue\n\n                run_case_list.append(case_name)\n                total_num += generator_num_list[no]\n                break\n    else:\n        run_case_list = generator_case_list\n        total_num = sum(generator_num_list)\n\n    return [run_case_list, total_num]\n\ndef process_bar_setup( total_num ):\n    # progress bar configurations\n    progress = Progress(\n        TextColumn(\"[bold blue]{task.fields[name]}\"),\n        BarColumn(bar_width=None),\n        \"[progress.percentage]{task.percentage:>3.1f}%\",\n        \"case_sum:\",\n        TextColumn(\"[bold red]{task.total}\"),\n        \"elapsed:\",\n        TimeElapsedColumn(),\n        \"remaining:\",\n        TimeRemainingColumn()\n    )\n\n    progress.start()\n    task_id = progress.add_task(\"runner\", name = \"runner\", total=total_num, start=True) \n\n    return [progress, task_id]\n\ndef runner_error(case):\n    with result_condition:\n        result_dict[case] = \"python run failed.\"\n        result_detail_dict[case] = ''\n        with open(f'build/{case}/runner.log', 'w') as f:\n            f.write( result_dict[case] + '\\n' + result_detail_dict[case] + '\\n' )       \n\ndef runner_callback(progress, task_id, completed, total):\n    progress.update( task_id, completed = completed )\n\ndef abortable_worker(func, *args, **kwargs):\n    timeout = kwargs.get('timeout', None)\n    p = ThreadPool(1)\n    res = p.apply_async(func, args=args)\n    try:\n        out = res.get(timeout)  # Wait timeout seconds for func to complete.\n        return out\n    except TimeoutError:\n        case = args[0]\n        result_dict[case] = \"TimeoutError\"\n        return io.StringIO()\n\ndef gen_runner_report( ps, args, generator_case_list, generator_num_list ):\n\n    failed_num = 0\n\n    # save the runner result into the log file\n    if args.append:\n        write_mode = 'a+'\n    else:\n        write_mode = 'w'\n    report_path = f'log/runner_report{args.worker_job_name}.log'\n    report = open(report_path, write_mode)\n    for case, p in ps:\n        ok = True\n\n        p_str = p.get().getvalue()            \n        # find case result in result_dict\n        if result_dict[case] != \"ok\":\n            reason = result_dict[case]\n            ok = False\n        if p_str != '':    \n            with open(f'build/{case}/runner.log', 'w') as f:\n                f.write(p_str)  \n\n        if not ok:\n            failed_num += 1\n            if args.failing_info:                    \n                time.sleep(0.5)\n                print(f'FAIL {case} - {reason}')\n            \n            report.write(f'FAIL {case} - {reason}\\n')\n        else:\n            report.write(f'PASS {case}\\n')\n\n    report.close()\n\n    return failed_num    \n\n# the main entrance of the runner process, including run in simulators and check the data\ndef run_test(case, args):\n    try:\n        stdout = sys.stdout\n        stderr = sys.stderr\n        output = io.StringIO()\n        sys.stdout = output\n        sys.stderr = output\n\n        check_golden = f'build/{case}/check_golden.npy'                  \n\n        # get the cases list in the case, including test_num, name, check string, golden\n        case_list = np.load( check_golden, allow_pickle=True )\n\n        case_info_list = []\n        for test_case in case_list:\n\n            test_case_dict = dict()\n\n            if args.riscv_dv != True:\n                for key, value in test_case[\"params\"].items():\n                    if key.endswith('_data') and key.replace('data', 'dtype') in test_case[\"params\"].keys():\n                        test_case_dict[key.replace('_data','')] = copy_to_dtype( value, eval(f\"jnp.{test_case['params'][key.replace('data', 'dtype')]}\") )\n                    elif key.endswith('_dtype') and key.replace('dtype', 'data') in test_case[\"params\"].keys():\n                        continue\n                    else:\n                        if value == 'bfloat16':\n                            test_case_dict[ key ] = jnp.bfloat16\n                        else:\n                            test_case_dict[ key ] = value\n\n            param_str = ','.join( f'{key}=test_case_dict[\"{key}\"]' for key in test_case_dict.keys() )\n            test_case_dict['inst'] = test_case[\"inst\"]\n\n            if args.riscv_dv != True:\n                inst = eval( f'{test_case_dict[\"inst\"]}({param_str})' )\n                test_case_dict['golden'] = inst.golden()\n\n            test_case_dict['no'] = test_case[\"no\"]\n            test_case_dict['name'] = test_case[\"name\"]\n            test_case_dict['rule'] = test_case[\"rule\"]\n            test_case_dict['rule_params'] = test_case[\"rule_params\"]\n\n            case_info_list.append( test_case_dict )\n\n        param_str = '( args=args, case=case, case_info_list=case_info_list )'\n        [ test_result, test_detail, failed_num ] = eval( case_info_list[0]['rule'] + param_str, globals(), locals() )           \n\n\n        with result_condition:           \n            result_dict[case] = test_result\n            result_detail_dict[case] = test_detail\n            fails.value += failed_num\n            tests.value += len(case_list)\n            with open(f'build/{case}/runner.log', 'w') as f:\n                f.write( result_dict[case] + '\\n' + result_detail_dict[case] + '\\n' )     \n\n        sys.stdout = stdout\n        sys.stderr = stderr\n\n        return output               \n    \n    except:\n        if output in locals().keys():\n            sys.stdout = stdout\n            sys.stderr = stderr\n        else:\n            output = io.StringIO()\n\n        result_dict[case] = 'python failed'\n\n        error_output = io.StringIO()\n        traceback.print_tb(sys.exc_info()[2], file=error_output)\n        error_str = error_output.getvalue()\n        error_str += \"\\nUnexpected error: \" + str(sys.exc_info()[0]) + \" \" + str(sys.exc_info()[1])\n        result_detail_dict[case] = error_str\n        with open(f'build/{case}/runner.log', 'w') as f:\n            f.write( result_dict[case] + '\\n' + result_detail_dict[case] + '\\n' )\n\n        return output\n\ndef bsub_run(cmd,port,job_name,queue_hosts):\n    os.system( f'bsub -Ip -J {job_name} -m \"{queue_hosts}\" -q normal {cmd} --worker true --worker_port {port} -wjn {job_name}' )\n\ndef hosts_bqueues(queue):\n    while True:\n        try: \n            result = subprocess.check_output( 'bqueues -l', shell=True, stderr=subprocess.STDOUT, encoding='utf-8' )\n        except subprocess.CalledProcessError:\n            continue\n\n        if f\"QUEUE: {queue}\" in result:\n            break\n\n    result_list = result.split('\\n')\n    no = 0\n    queue_flag = False\n    while True:\n        if result_list[no].startswith( f\"QUEUE: {queue}\" ):\n            queue_flag = True\n        if queue_flag and result_list[no].startswith( \"HOSTS\" ):            \n            hosts_str = result_list[no]\n            hosts_str = hosts_str.replace('HOSTS:  ', '')\n            break\n        no += 1\n        if no == len(result_list):\n            hosts_str = ''\n            break\n    return hosts_str\n\ndef host_bjobs(job_name):\n    while True:\n        try:\n            result = subprocess.check_output( f'bjobs -a -w | grep {job_name}', shell=True, stderr=subprocess.STDOUT, encoding='utf-8' )\n        except subprocess.CalledProcessError as e:\n            result = e.output       # Output generated before error          \n            code      = e.returncode   # Return code             \n            continue\n\n        result_list = result.split(' ')\n        if result_list[4] == 'RUN':\n            host = result_list[13]\n            break\n    \n    return host\n\ndef merge_runner_report():\n    files = os.listdir('log')\n    with open('log/runner_report.log', 'w') as report:\n        for file in files:\n            if file.startswith( 'runner_report' ):\n                with open(f'log/{file}', 'r') as f_read:\n                    report.write(f_read.read())\n                os.remove( f'log/{file}' )\n\ndef worker_runner_callback( tests, tests_done_q ):\n    # transfer completed tests number to worker process\n    tests_done_q.put( tests )\n\n# create workers in different hosts\ndef create_workers( cmd, batch, port ):\n    queue_hosts = hosts_bqueues('normal').split(' ')\n    for i in range(batch):\n        job_name = str(port) + '_' + str(i)\n        p = Process( target = bsub_run, args=(cmd,port,job_name, ' '.join(queue_hosts)) )\n        p.daemon = True # when its parent process terminates, this process terminates, too.\n        p.start()\n        # to know which host this job use\n        host =  host_bjobs( job_name ) \n        # remove the host to make sure next job will use another host       \n        queue_hosts.remove(host)\n\ndef main(args, tests_done_q=None):\n\n    try:\n        if not args.worker:\n            if args.retry:\n                cases = get_retry_cases()\n            else:\n                cases = get_arg_cases(args.cases)\n            \n            if not args.append: # runner process of worker needn't print this information.\n                print(\"looking for the cases...\")\n\n            [generator_case_list, generator_num_list] = get_generator_case()\n\n            [run_case_list, total_num] = select_run_case( generator_case_list, generator_num_list, cases )\n\n            # server process, if args.batch == 0, don't use lsf cluster\n            if args.batch == 0:\n\n                # define some global sync variables to synchronize the runner processes with the main process\n                sync_variable()\n\n                if not args.append: # runner process of worker needn't process bar.\n                    [progress, task_id] = process_bar_setup( total_num )\n                \n                ps = []\n                with Pool(processes=args.nproc, maxtasksperchild=1) as pool:\n                    # FIXME It's better to hidden --worker/append/worker_port/worker_job_name from users, but now we haven't found methods to do that.\n                    if args.append:\n                        # runner process of worker needn't process bar.\n                        for case in run_case_list:\n                            abortable_func = partial(abortable_worker, run_test, timeout=args.timeout)\n                            res = pool.apply_async(abortable_func, [ case, args ], \n                            callback=lambda _: worker_runner_callback( tests.value, tests_done_q ), \n                            error_callback=lambda _: runner_error(case)  )\n                            ps.append((case, res))                        \n\n                    else:\n                        global tests\n                        for case in run_case_list:\n                            abortable_func = partial(abortable_worker, run_test, timeout=args.timeout)\n                            res = pool.apply_async(abortable_func, [ case, args ], \n                            callback=lambda _: runner_callback( progress, task_id, tests.value, total_num ), \n                            error_callback=lambda _: runner_error(case)  )\n                            ps.append((case, res))              \n\n                    \n                    failed_num = gen_runner_report( ps, args, generator_case_list, generator_num_list )\n\n                    if not args.append: # runner process of worker needn't process bar.\n                        progress.stop()\n\n                    # spike may make that user can't input in command line, use stty sane to fix that.\n                    os.system(\"stty sane\")\n\n                    if args.append:\n                        global fails\n                        # transfer test results to worker process\n                        tests_done_q.put( [ len(ps), failed_num, tests.value, fails.value ] )\n                        return\n\n                    else:\n                        if failed_num == 0:\n                            print(f'{len(ps)} files running finish, all pass.( {tests.value} tests )')\n                            sys.exit(0)\n                        else:\n                            if args.failing_info:\n                                print(f'{len(ps)} files running finish, {failed_num} failed.( {tests.value} tests, {fails.value} failed.)')                     \n                            else:\n                                if args.timeout == None:\n                                    print(f'{len(ps)} files running finish, {failed_num} failed.( {tests.value} tests, {fails.value} failed, please look at the log/runner_report.log for the failing information. )')             \n                                else:\n                                    print(f'{len(ps)} files running finish, {failed_num} failed.( please look at the log/runner_report.log for the failing information. )')             \n                            \n                            sys.exit(-1) \n            else:\n                # dispatcher\n                if total_num != 0:\n                    if os.path.exists('log/runner_report.log'):                           \n                        os.remove( 'log/runner_report.log' )\n\n                [progress, task_id] = process_bar_setup( total_num )\n\n                # lsf server\n                class QueueManager(BaseManager):\n                    pass \n                case_q = Queue()\n                done_q = Queue() \n\n                QueueManager.register( 'get_case_queue', callable=lambda:case_q )\n                QueueManager.register( 'get_done_queue', callable=lambda:done_q )\n\n                port = 5000\n                while True:\n                    try:\n                        m = QueueManager( address=('',port), authkey=b'123456' )\n                        m.start()\n                        break\n                    except OSError:\n                        # sometimes port has been used, that will raise OSError. So just pick another port.\n                        port += 1\n                        continue\n\n                # a queue to dispatch cases and a queue to know workers done\n                case_q = m.get_case_queue()\n                done_q = m.get_done_queue()  \n\n                print(f\"{total_num} cases need to dispatch...\")\n\n                for i in range(len(run_case_list)):\n                    case_q.put( run_case_list[i] )\n                \n                # create workers process in different host\n                cmd = ' '.join(sys.argv)                \n                p = Process( target=create_workers, args=(cmd, args.batch, port)  )\n                p.start()\n\n                # get result info from workers\n                files_dict = dict()\n                failed_files_dict = dict()\n                tests_dict = dict()\n                fails_dict = dict()\n                done_workers = 0\n                while True:\n\n                    res_str = done_q.get()\n                    if res_str == 'done':\n                        done_workers += 1\n                        if done_workers == args.batch:\n                            progress.stop()\n                            break\n                        else:\n                            continue\n\n                    res_strs = res_str.split('--')\n\n                    if len( res_strs ) == 2:\n                        # use tests_done info to update progress bar\n                        tests_dict[ res_strs[1] ] = int( res_strs[0].replace('tests_done', '') )\n                        tests_sum = sum( tests_dict.values() )\n                        progress.update( task_id, completed = tests_sum )\n\n                    elif len( res_strs ) == 5:\n                        # update more detail info when a runner process finished\n                        files_dict[ res_strs[4] ] = int( res_strs[0].replace('files', '') )\n                        failed_files_dict[ res_strs[4] ] = int( res_strs[1].replace('failed_files', '') )\n                        tests_dict[ res_strs[4] ] = int( res_strs[2].replace('tests', '') )\n                        fails_dict[ res_strs[4] ] = int( res_strs[3].replace('fails', '') )\n                        if sum(files_dict.values()) == len(run_case_list):\n                            progress.stop()\n                            break\n                        \n\n\n                if p.is_alive():\n                    # terminate worker create process\n                    p.terminate()\n\n\n                files_sum = sum( files_dict.values() )\n                failed_files_sum = sum( failed_files_dict.values() )\n                tests_sum = sum( tests_dict.values() )\n                fails_sum = sum( fails_dict.values() )\n\n                # merge all runner reports by workers to log/runner_report.log \n                merge_runner_report()\n\n                m.shutdown()\n\n                os.system(\"sleep 1\")\n                os.system(\"stty sane\")\n                \n                print('', end='\\r') # the stop function of progress bar make there are some null characters in command line, so take the cursor back to start.\n                if fails_sum == 0:\n                    print(f'{files_sum} files running finish, all pass.( {tests_sum} tests )')\n                    sys.exit(0)\n                else:\n                    if args.failing_info:\n                        print(f'{files_sum} files running finish, {failed_files_sum} failed.( {tests_sum} tests, {fails_sum} failed.)')                    \n                    else:\n                        print(f'{files_sum} files running finish, {failed_files_sum} failed.( {tests_sum} tests, {fails_sum} failed, please look at the log/runner_report.log for the failing information. )')             \n                    \n                    sys.exit(-1)                 \n\n        else:\n            #worker\n            class QueueManager(BaseManager):\n                pass\n            QueueManager.register('get_case_queue')\n            QueueManager.register('get_done_queue')    \n            m = QueueManager( address=('bjsw-expsrv01', args.worker_port), authkey=b'123456' )\n            try:\n                m.connect()\n            except ConnectionRefusedError:\n                return\n            case_q = m.get_case_queue()\n            done_q = m.get_done_queue()\n\n            my_name = os.popen(\"hostname\").read()\n            nproc = subprocess.check_output( 'nproc', encoding='utf-8' )\n            cmd = ' '.join(sys.argv)\n            cases_str = ''\n            cases_num = 0\n\n            # update args values in runner process\n            orig_args = args\n            orig_args.retry = False\n            orig_args.nproc = int( nproc )\n            orig_args.batch = 0\n            orig_args.worker = False\n            orig_args.append = True \n\n            # queue and variables to get and keep runner info\n            tests_done_q = mpQueue()\n            tests_done_last = 0\n            files_last = 0\n            failed_files_last = 0\n            tests_last = 0\n            fails_last = 0\n\n            while True:\n                if case_q.empty():\n                    break\n\n                case_str = case_q.get()\n\n                if cases_num == 0:\n                    cases_str += case_str\n                else:\n                    cases_str += ',' + case_str\n\n                cases_num += 1\n\n                if cases_num == int(nproc):\n                    orig_args.cases = cases_str\n                    # call main function as runner process\n                    p = Process( target=main, args=(orig_args, tests_done_q) )\n                    p.start()\n                                        \n                    while True:\n                        q_content = tests_done_q.get()\n                        if isinstance( q_content, list ):\n                            break\n                        tests_done = tests_done_last + q_content\n                        done_q.put( f'tests_done{tests_done}--{my_name}' )\n\n                    [files, failed_files, tests, fails] = q_content\n                    files_last += files\n                    failed_files_last += failed_files\n                    tests_last += tests\n                    tests_done_last += tests\n                    fails_last += fails                  \n                    done_q.put( f'files{files_last}--failed_files{failed_files_last}--tests{tests_last}--fails{fails_last}--{my_name}' )\n\n                    cases_str = ''\n                    cases_num = 0\n\n            if cases_num != 0:\n                orig_args.cases = cases_str\n                p = Process( target=main, args=(orig_args, tests_done_q) )\n                p.start()\n                                    \n                while True:\n                    q_content = tests_done_q.get()\n                    if isinstance( q_content, list ):\n                        break\n                    tests_done = tests_done_last + q_content\n                    done_q.put( f'tests_done{tests_done}--{my_name}' )\n\n                [files, failed_files, tests, fails] = q_content\n                files_last += files\n                failed_files_last += failed_files\n                tests_last += tests\n                fails_last += fails                  \n                done_q.put( f'files{files_last}--failed_files{failed_files_last}--tests{tests_last}--fails{fails_last}--{my_name}' )\n\n            done_q.put(\"done\")\n                       \n    \n    except KeyboardInterrupt:\n        \n        if 'pool' in locals():\n            pool.close()\n            pool.join()\n        \n        if 'progress' in locals():\n            progress.stop()\n        \n        print(\"Catch KeyboardInterrupt!\")\n        os.system(\"stty sane\")\n        sys.exit(-1)    \n\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"riscv-stc/riscv-pvp","sub_path":"rvpvp/runner.py","file_name":"runner.py","file_ext":"py","file_size_in_byte":24554,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"37928278556","text":"MAX = 100000000\r\ndef solution(arr):\r\n    n = len(arr)\r\n    maxdp = [[0 for j in range(n)] for i in range(n)]\r\n    mindp = [[0 for j in range(n)] for i in range(n)]\r\n    for j in range(0, n, 2):\r\n        for i in range(0, n, 2):\r\n            if i+j<n:\r\n                if j==0:\r\n                    maxdp[i][i]=int(arr[i])\r\n                    mindp[i][i]=int(arr[i])\r\n                else:\r\n                    maxdp[i][i+j]=-MAX\r\n                    mindp[i][i+j]=MAX\r\n                    for k in range(1, j, 2):\r\n                        if arr[i+k]==\"+\":\r\n                            maxdp[i][i+j] = max(maxdp[i][i+j], maxdp[i][i+k-1]+maxdp[i+k+1][i+j])\r\n                            mindp[i][i+j] = min(mindp[i][i+j], mindp[i][i+k-1]+mindp[i+k+1][i+j])\r\n                        else:\r\n                            maxdp[i][i+j] = max(maxdp[i][i+j], maxdp[i][i+k-1]-mindp[i+k+1][i+j])\r\n                            mindp[i][i+j] = min(mindp[i][i+j], mindp[i][i+k-1]-maxdp[i+k+1][i+j])\r\n    return maxdp[0][n-1]","repo_name":"ggyuchive/SWMaestro-Practice","sub_path":"4회차/programmers_algorithm/4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"80"}
+{"seq_id":"18593400530","text":"import requests, csv\n\nurl = \"https://rnbi.rouen.fr/fr/notice/ovide-moralis%C3%A9\"\n\nurls = [url + '-{}'.format(x) for x in range(1312)]\nurls = [url] + urls\n\nurlmap = []\n\n# save all page info locally\nfor i, u in enumerate(urls):\n    page = requests.get(u)\n    fname = 'url{}.html'.format(i)\n    with open('htmlpages/'+fname, 'w') as f:\n        print('writing ' + fname + '...')\n        f.write(page.text)\n    urlmap.append([fname, u])\n\nwith open('urlmap.csv', 'w', newline='') as csvfile:\n    csvwriter = csv.writer(csvfile, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n    print('writing csv file...')\n    for row in urlmap:\n        csvwriter.writerow(row) \n\nprint('done')\n","repo_name":"omnivert/web_scraping","sub_path":"ovide_scrape/ovide_download.py","file_name":"ovide_download.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"11711828990","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom django.contrib.auth.decorators import login_required\nfrom .forms import ProfileUpdateForm, UserUpdateForm, BecomeSeller\nfrom django.contrib import messages\nfrom .models import User, Profile\nfrom core.models import Order, Item\n# Create your views here.\n\n\n@login_required\ndef profile(request):\n    \"\"\"This view provides form for users to update their profile.\"\"\"\n    if request.method == 'POST':\n        p_form = ProfileUpdateForm(\n            request.POST, \n            request.FILES, \n            instance=request.user.profile)\n        u_form = UserUpdateForm(request.POST, instance=request.user)\n\n        if p_form.is_valid() and u_form.is_valid():\n            p_form.save()\n            u_form.save()\n            messages.success(\n                request, 'Your profile has been updated!'\n                )  \n            return redirect('users:dashboard')\n    else:\n        p_form = ProfileUpdateForm(instance=request.user.profile)\n        u_form = UserUpdateForm(instance=request.user)\n    return render(request, 'users/profile.html', {\n        'p_form':p_form, \n        'u_form':u_form\n        })\n\n@login_required\ndef dashboard(request):\n    user = request.user\n    if user.seller:\n        if 'payout' in request.POST:\n            pay = request.POST['payout']\n            \n            if float(pay) >= 100:\n                messages.success(request, f'Your request for the payment of ${pay} was successful!\\\n                    Please update your bank account details in your profile.')\n            else:\n                messages.warning(request, f'Your request for the payment of ${pay} was unsuccessful.\\\n                    Your earning is less than the payout threshold of $100.')\n        items = Item.objects.filter(user=user)\n        context = {\n            'items':items\n        }\n        return render(request, 'users/admin.html', context)\n    else:\n        orders = Order.objects.filter(user=user).order_by('-id')\n        form = BecomeSeller()\n        context = {\n            'orders': orders,\n            'form':form\n        }\n        return render(request, 'users/dashboard.html', context)\n\n@login_required\ndef become_a_seller(request):\n    if request.method == 'POST':\n        form = BecomeSeller(request.POST)\n        if form.is_valid():\n            business_name = form.cleaned_data.get('business_name')\n            experience = form.cleaned_data.get('experience')\n\n            request.user.profile.business_name = business_name\n            request.user.profile.experience = experience\n            request.user.profile.save()\n            request.user.seller = True\n            request.user.customer = False\n            request.user.save()\n            messages.success(request, 'You have successflu signed up a seller.')\n            return redirect('/dashboard')\n    else:\n        form = BecomeSeller()\n    return render(request, 'form.html', {'form':form})\n\ndef earnings(request):\n    user = request.user\n    if user.seller:\n        items = Item.objects.filter(user=user)\n        total_earning = 0\n        earning = 0\n        for item in items:\n            if item.discount_price:\n                earning = item.downloads * item.discount_price\n                total_earning += earning\n            else:\n                earning = item.downloads * item.price\n                total_earning += earning\n        context = {\n            'items':items,\n            'total_earning':total_earning\n        }\n        return render(request, 'users/earnings.html', context)\n    else:\n        return redirect('/dashboard')","repo_name":"yemiemy/imgrepo","sub_path":"users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"9373382488","text":"import re\nimport random\nfrom playwright.sync_api import sync_playwright, TimeoutError as PlaywrightTimeoutError\n\n# Haga falso si desea ver la automatización en vivo.\nhead = True\n\n\n\ndef agenteUsuario():\n    \"\"\"\n    agenteUsuario Funcion para generar aleatoriamente un navegador en la nueva consulta a realizar\n\n    Returns:\n        string: User Agent\n    \"\"\"\n    with open('comparer/user-agents.txt') as f:\n    # with open('user-agents.txt') as f:\n        agente = f.read().split(\"\\n\")\n        return random.choice(agente)\n\n\nclass TryExcept:\n    \"\"\"\n     Creamos un try general para cuando no existe un texto o atributo que estemos buscando con los XPATH\n    \"\"\"\n    def text(self, element):\n        try:\n            return element.inner_text().strip()\n        except AttributeError:\n            return \"-\"\n\n    def attributes(self, element, attr):\n        try:\n            return element.get_attribute(attr)\n        except AttributeError:\n            return \" Valor No disponible\"\n\n\ndef scraping(prompt, head=True):\n    \"\"\"\n    scraping Función principal del código, se ingresa el producto y realiza las búsquedas y extracción de información\n    \"\"\"\n    # main Data\n    data = []\n    # Instanciamos la clase\n    catchClause = TryExcept()\n\n    ml_product1 = prompt.replace(\" \", \"-\")\n    ml_product2 = prompt.replace(\" \", \"%20\")\n\n    # Definir la URL del sitio web que se desea consultar\n    url = f\"https://listado.mercadolibre.com.mx/{ml_product1}#D[A:{ml_product2}]\"\n\n    # Inicializar Playwright y abrir un navegador\n    with sync_playwright() as play:\n        # browser = play.chromium.launch(headless=head, slow_mo=3*1000)\n        browser = play.chromium.launch(headless=head)\n        page = browser.new_page(user_agent=agenteUsuario())\n\n        # Acceder a la página web\n        page.goto(url)\n\n        # Obtenemos todos los resultados de la pagina\n        totalResults = \"//li[contains(@class,'ui-search-layout__item')]\"\n\n        # Creamos los XPATH para cada elemento que vamos a obtener\n        name = \"//h2[contains(@class,'ui-search-item__title')]\"\n        id = \"//input[@name='itemId']\"\n        price = \"//div[@class='ui-search-price ui-search-price--size-medium shops__price']//div[@class='ui-search-price__second-line shops__price-second-line']//span[@class='price-tag-amount']//span\"\n        original_price = \"//s[@class='price-tag ui-search-price__part ui-search-price__original-value shops__price-part price-tag__disabled']//span[@class='price-tag-amount']//span\"\n        flash_sale = \"//label[@class='ui-search-styled-label ui-search-item__highlight-label__text' and contains(text(), 'OFERTA')]\"\n        rating = \"//span[@class='ui-search-reviews__ratings']//*[contains(@class, 'star-full')]\"\n        rating_half = \"//span[@class='ui-search-reviews__ratings']//*[contains(@class, 'star-half')]\"\n        rating_number = \"//span[@class='ui-search-reviews__amount']\"\n        link = \"//div[@class='ui-search-result__image shops__picturesStyles']//a\"\n        img = \"//div[@class='ui-search-result__image shops__picturesStyles']//img\"\n        label = \"//label[@class='ui-search-styled-label ui-search-item__highlight-label__text']\"\n        free_shipping = \"//p[@class='ui-search-item__shipping ui-search-item__shipping--free shops__item-shipping-free']\"\n        is_full = \"//*[@href='#full']\"\n\n        # Esperamos la carga total de la pagina, de lo contrario controlamos el error y mostramos un error\n        try:\n            page.wait_for_selector(totalResults, timeout=10*1000)\n        except PlaywrightTimeoutError:\n            print(f\"Error al cargar contenido. Vuelva a intentarlo en unos minutos.. URL: {url}\")\n\n        # Comenzamos con la extraccion de datos\n        for content in page.query_selector_all(totalResults):\n            # Inicializamos las variables que requieren tratamiento especial\n            real_price = \"\"\n            real_original_price = \"\"\n            real_rating = 0.0\n            real_rating_number = 0\n            real_flash_sale = False\n            real_link = \"-\"\n            real_img = \"-\"\n            real_free_shipping = False\n            real_is_full = False\n\n            # Seccion de tratamiento especial de datos\n            for single_element in content.query_selector_all(price):\n                real_price += single_element.inner_text().strip()\n\n            for single_element in content.query_selector_all(original_price):\n                real_original_price += single_element.inner_text().strip()\n\n            real_rating += len(content.query_selector_all(rating))\n\n            if len(content.query_selector_all(rating_half)):\n                real_rating += .5\n\n            if \"-\" not in catchClause.text(content.query_selector(rating_number)):\n                real_rating_number = int(catchClause.text(content.query_selector(rating_number)))\n\n            if \"#\" in catchClause.attributes(content.query_selector(link), 'href'):\n                real_link = catchClause.attributes(content.query_selector(link), 'href').split(\"#\")[0]\n\n            if len(content.query_selector_all(flash_sale)):\n                real_flash_sale = True\n\n            if \"webp\" in catchClause.attributes(content.query_selector(img), 'src'):\n                real_img = catchClause.attributes(content.query_selector(img), 'src').replace(\"webp\", \"jpg\")\n\n            if len(content.query_selector_all(free_shipping)):\n                real_free_shipping = True\n\n            if len(content.query_selector_all(is_full)):\n                real_is_full = True\n\n            single_data = {\n                \"source\": \"mercado_libre\",\n                \"name\": catchClause.text(content.query_selector(name)),\n                \"id\": catchClause.attributes(content.query_selector(id), 'value'),\n                \"price\": real_price,\n                \"price_float\": float(real_price.replace(\"$\", \"\").replace(\",\", \"\")),\n                \"original_price\": real_original_price,\n                \"flash_sale\": real_flash_sale,\n                \"rating\": real_rating,\n                \"rating_number\": real_rating_number,\n                \"link\": real_link,\n                \"img\": real_img,\n                \"label\": catchClause.text(content.query_selector(label)).lower().capitalize(),\n                \"free_shipping\": real_free_shipping,\n                \"is_full_or_prime\": real_is_full,\n            }\n\n            # Agregando información recolectada\n            data.append(single_data)\n\n        # Cerrar el navegador\n        # browser.close()\n\n    ####################################################################################################\n\n    amnz_product = prompt.replace(\" \", \"=\")\n\n    # Definir la URL del sitio web que se desea consultar\n    url = f\"https://www.amazon.com.mx/s?k={amnz_product}\"\n\n    # Inicializar Playwright y abrir un navegador\n    with sync_playwright() as play:\n        # browser = play.chromium.launch(headless=head, slow_mo=3*1000)\n        browser = play.chromium.launch(headless=head)\n        page = browser.new_page(user_agent=agenteUsuario())\n\n        # Acceder a la página web\n        page.goto(url)\n\n        # Obtenemos todos los resultados de la pagina\n        totalResults = \"//div[@data-component-type='s-search-result']\"\n\n        # Creamos los XPATH para cada elemento que vamos a obtener\n        name = \"//a[@class='a-link-normal s-underline-text s-underline-link-text s-link-style a-text-normal']\"\n        id = \"data-asin\"\n        price = \"//span[@data-a-color='base']/span[@class='a-offscreen']\"\n        original_price = \"//span[@data-a-color='secondary']/span[@class='a-offscreen']\"\n        flash_sale = \"//span[@data-a-badge-color='sx-lightning-deal-red']//span[@class='a-badge-text'][@data-a-badge-color='sx-cloud']\"\n        rating = \"//span[@class='a-declarative']/a/i/span[@class='a-icon-alt']\"\n        # rating_half = \"//span[@class='ui-search-reviews__ratings']//*[contains(@class, 'star-half')]\"\n        rating_number = \"//a[@class='a-link-normal s-underline-text s-underline-link-text s-link-style']/span[@class='a-size-base s-underline-text']\"\n        link = \"//div[@class='ui-search-result__image shops__picturesStyles']//a\"\n        img = \"//img[@class='s-image']\"\n        label = \"//label[@class='ui-search-styled-label ui-search-item__highlight-label__text']\"\n        free_shipping = \"//span[contains(@aria-label, 'GRATIS')]\"\n        is_prime = \"//span[contains(@class, 's-prime')]\"\n\n        # Esperamos la carga total de la pagina, de lo contrario controlamos el error y mostramos un error\n        try:\n            page.wait_for_selector(totalResults, timeout=10*1000)\n        except PlaywrightTimeoutError:\n            print(f\"Error al cargar contenido. Vuelva a intentarlo en unos minutos. URL: {url}\")\n\n        # Comenzamos con la extraccion de datos\n        for content in page.query_selector_all(totalResults):\n            real_price = catchClause.text(content.query_selector(price))\n            real_price_float = 0.0\n            real_flash_sale = False\n            real_rating = 0.0\n            real_rating_number = 0\n            real_label = \"-\"\n            real_link = \"-\"\n            real_free_shipping = False\n            real_is_prime = False\n\n            if \"-\" not in real_price:\n                real_price_float = float(catchClause.text(content.query_selector(price)).replace(\"$\", \"\").replace(\",\", \"\"))\n\n            if len(content.query_selector_all(flash_sale)):\n                real_flash_sale = True\n\n            if \"-\" not in catchClause.text(content.query_selector(rating)):\n                real_rating = float(catchClause.text(content.query_selector(rating)).split(\" \")[0])\n\n            if \"-\" not in catchClause.text(content.query_selector(rating_number)):\n                real_rating_number = int(re.sub(r\"[()]\", \"\", catchClause.text(content.query_selector(rating_number))).replace(\",\", \"\"))\n\n            for single_label in content.query_selector_all(label):\n                real_label += \" \" + single_label.strip()\n\n            # if \"-\" not in catchClause.attributes(content.query_selector(name), 'href'):\n            real_link = f\"\"\"http://www.amazon.com.mx{catchClause.attributes(content.query_selector(name), 'href')}\"\"\"\n            real_link = '/'.join(real_link.split('/')[:6])\n\n            if len(content.query_selector_all(free_shipping)):\n                real_free_shipping = True\n\n            if len(content.query_selector_all(is_prime)):\n                real_is_prime = True\n\n            single_data = {\n                \"source\": \"amazon\",\n                \"name\": catchClause.text(content.query_selector(name)),\n                \"id\": catchClause.attributes(content, id),\n                \"price\": real_price,\n                \"price_float\": real_price_float,\n                \"original_price\": catchClause.text(content.query_selector(original_price)),\n                \"flash_sale\": real_flash_sale,\n                \"rating\": real_rating,\n                \"rating_number\": real_rating_number,\n                \"link\": real_link,\n                \"img\": f\"\"\"{catchClause.attributes(content.query_selector(img), 'src')}\"\"\",\n                \"label\": real_label,\n                \"free_shipping\": real_free_shipping,\n                \"is_full_or_prime\": real_is_prime,\n            }\n\n            # Agregando información recolectada\n            data.append(single_data)\n\n        # Cerrar el navegador\n        browser.close()\n    return sorted(data, key=lambda x: x[\"price_float\"], reverse=True)\n\n# print(scraping(\"triangulo pikler\", False))\n","repo_name":"developer-edwin/ml","sub_path":"price_compare/comparer/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":11441,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"80"}
+{"seq_id":"18226476370","text":"\"\"\"TwitterClone URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/3.1/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\nfrom core.views import splash, signup_view, login_view, home_view, tweet, profile_view, logout_action, hashtag_view, like_tweet, delete_tweet, hashtag_all_view\n\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    path('', splash, name = \"splash\"),\n    path('signup', signup_view, name = \"signup\"),\n    path('login', login_view, name = \"login\"),\n    path('home', home_view, name = \"home\"),\n    path('tweet', tweet, name = \"tweet\"),\n    path('profile/<author>', profile_view, name = \"profile\"),\n    path('logout', logout_action, name = 'logout'),\n    path('hashtag/<hashtag>', hashtag_view, name = \"hashtag\"),\n    path('like/<id>', like_tweet, name = \"like_tweet\"),\n    path('delete/<id>', delete_tweet, name = \"delete_tweet\"),\n    path('hashtag', hashtag_all_view, name = \"hashtag_all_view\")\n]\n","repo_name":"JerryWuuuuuu/TwitterClone","sub_path":"TwitterClone/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"70466024260","text":"#\n# @lc app=leetcode id=560 lang=python3\n#\n# [560] Subarray Sum Equals K\n#\n\n# @lc code=start\nclass Solution:\n    def subarraySum(self, nums: List[int], k: int) -> int:\n        if len(nums) == 0:\n            return 0\n        counts =  {0: 1}\n        sumValue, ans = 0, 0\n        for num in nums:\n            sumValue += num\n            ans += counts[sumValue - k]\n            counts[sumValue] += 1\n        return ans\n# @lc code=end\n","repo_name":"DarkAlexWang/leetcode","sub_path":"Solutions/560.subarray-sum-equals-k.py","file_name":"560.subarray-sum-equals-k.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"80"}
+{"seq_id":"2160943635","text":"\"\"\"\n    Help functions\n\"\"\"\n\nimport os\nfrom typing import Optional\nimport functools\nimport torch\nfrom torch import nn\nfrom torch.nn import init\nimport matplotlib.pyplot as plt\n\n\ndef data_reg(images):\n    \"\"\"Regularization\"\"\"\n    images_mean = images.mean()\n    images_std = images.std()\n    images = (images - images_mean) / images_std\n    images_min = images.min()\n    images = images - images_min\n    return images\n\n\ndef init_weights(net, init_type=\"normal\", init_gain=0.02):\n    \"\"\"Initialization methods provided by CycleGAN.\"\"\"\n\n    def init_func(m):  # define the initialization function\n        classname = m.__class__.__name__\n        if hasattr(m, \"weight\") and (\n            classname.find(\"Conv\") != -1 or classname.find(\"Linear\") != -1\n        ):\n            if init_type == \"normal\":\n                init.normal_(m.weight.data, 0.0, init_gain)\n            elif init_type == \"xavier\":\n                init.xavier_normal_(m.weight.data, gain=init_gain)\n            elif init_type == \"kaiming\":\n                init.kaiming_normal_(m.weight.data, a=0, mode=\"fan_in\")\n            elif init_type == \"orthogonal\":\n                init.orthogonal_(m.weight.data, gain=init_gain)\n            else:\n                raise NotImplementedError(\n                    f\"initialization method {init_type} is not implemented\"\n                )\n            if hasattr(m, \"bias\") and m.bias is not None:\n                init.constant_(m.bias.data, 0.0)\n        elif classname.find(\"BatchNorm2d\") != -1:\n            init.normal_(m.weight.data, 1.0, init_gain)\n            init.constant_(m.bias.data, 0.0)\n\n    print(f\"initialize network with {init_type}\")\n    net.apply(init_func)\n\n\ndef get_embedding_function(\n    num_encoding_functions=6, include_input=True, log_sampling=True\n):\n    r\"\"\"Returns a lambda function that internally calls positional_encoding.\"\"\"\n    return lambda x: positional_encoding(\n        x, num_encoding_functions, include_input, log_sampling\n    )\n\n\ndef positional_encoding(\n    tensor, num_encoding_functions=6, include_input=True, log_sampling=True\n):\n    r\"\"\"Apply positional encoding to the input.\n\n    Args:\n      tensor (torch.Tensor): Input tensor to be positionally encoded.\n      num_encoding_functions (optional, int): Number of encoding functions used to\n          compute a positional encoding (default: 6).\n      include_input (optional, bool): Whether or not to include the input in the\n          computed positional encoding (default: True).\n      log_sampling (optional, bool): Sample logarithmically in frequency space, as\n          opposed to linearly (default: True).\n\n    Returns:\n      (torch.Tensor): Positional encoding of the input tensor.\n    \"\"\"\n    # Trivially, the input tensor is added to the positional encoding.\n    encoding = [tensor] if include_input else []\n    # Now, encode the input using a set of high-frequency functions and append the\n    # resulting values to the encoding.\n    frequency_bands = None\n    if log_sampling:\n        frequency_bands = 2.0 ** torch.linspace(\n            0.0,\n            num_encoding_functions - 1,\n            num_encoding_functions,\n            dtype=tensor.dtype,\n            device=tensor.device,\n        )\n    else:\n        frequency_bands = torch.linspace(\n            2.0 ** 0.0,\n            2.0 ** (num_encoding_functions - 1),\n            num_encoding_functions,\n            dtype=tensor.dtype,\n            device=tensor.device,\n        )\n\n    for freq in frequency_bands:\n        for func in [torch.sin, torch.cos]:\n            encoding.append(func(tensor * freq))\n\n    # Special case, for no positional encoding\n    if len(encoding) == 1:\n        return encoding[0]\n    else:\n        return torch.cat(encoding, dim=-1)\n\n\ndef get_minibatches(inputs: torch.Tensor, chunksize: Optional[int] = 1024 * 8):\n    r\"\"\"Takes a huge tensor (ray \"bundle\") and splits it into a list of minibatches.\n    Each element of the list (except possibly the last) has dimension `0` of length\n    `chunksize`.\n    \"\"\"\n    return [inputs[:, i : i + chunksize] for i in range(0, inputs.shape[1], chunksize)]\n\n\ndef meshgrid_xy(\n    tensor1: torch.Tensor, tensor2: torch.Tensor\n) -> (torch.Tensor, torch.Tensor):\n    \"\"\"Mimick np.meshgrid(..., indexing=\"xy\") in pytorch. torch.meshgrid only allows \"ij\" indexing.\n    Args:\n      tensor1 (torch.Tensor): Tensor whose elements define the first dimension of the returned meshgrid.\n      tensor2 (torch.Tensor): Tensor whose elements define the second dimension of the returned meshgrid.\n    \"\"\"\n    ii, jj = torch.meshgrid(tensor1, tensor2)\n    return ii.transpose(-1, -2), jj.transpose(-1, -2)\n\n\ndef get_ray_bundle(height, width, tform_cam2world):\n    # Generate camera rays\n    ii, jj = meshgrid_xy(\n        torch.arange(width).to(tform_cam2world),\n        torch.arange(height).to(tform_cam2world),\n    )\n    # return B,H,W,3\n    scale_factor = height / width\n    grid = torch.stack(\n        [\n            (ii - width * 0.5) / width,\n            -((jj - height * 0.5) / height) * scale_factor,\n            torch.zeros_like(ii),\n        ],\n        dim=0,\n    )\n    grid = grid.reshape([3, -1])\n    ray_directions = torch.matmul(\n        tform_cam2world[:, :3, :3], torch.tensor([0, 0, -1]).to(tform_cam2world)\n    )\n    ray_origins = (\n        torch.matmul(tform_cam2world[:, :3, :3], grid) + tform_cam2world[:, :3, 3:]\n    )\n    ray_origins = ray_origins.reshape(*ray_origins.shape[:2], width, height).permute(\n        0, 2, 3, 1\n    )\n    ray_directions = ray_directions[:, None, None, :].expand(ray_origins.shape)\n    return ray_origins, ray_directions\n\n\ndef repeat_interleave(data, repeats):\n    \"\"\"\n    Repeat interleave along axis 0\n    torch.repeat_interleave is currently very slow\n    https://github.com/pytorch/pytorch/issues/31980\n    \"\"\"\n    output = data.unsqueeze(1).expand(-1, repeats, *data.shape[1:])\n    return output.reshape(-1, *data.shape[1:])\n\n\ndef combine_interleaved(t, inner_dims=(1,), agg_type=\"average\"):\n    if len(inner_dims) == 1 and inner_dims[0] == 1:\n        return t\n    t = t.reshape(-1, *inner_dims, *t.shape[1:])\n    if agg_type == \"average\":\n        t = torch.mean(t, dim=1)\n    elif agg_type == \"max\":\n        t = torch.max(t, dim=1)[0]\n    else:\n        raise NotImplementedError(\"Unsupported combine type \" + agg_type)\n    return t\n\n\ndef get_norm_layer(norm_type=\"instance\", group_norm_groups=32):\n    \"\"\"Return a normalization layer\n    Parameters:\n        norm_type (str) -- the name of the normalization layer: batch | instance | none\n    For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).\n    For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.\n    \"\"\"\n    if norm_type == \"batch\":\n        norm_layer = functools.partial(\n            nn.BatchNorm2d, affine=True, track_running_stats=True\n        )\n    elif norm_type == \"instance\":\n        norm_layer = functools.partial(\n            nn.InstanceNorm2d, affine=False, track_running_stats=False\n        )\n    elif norm_type == \"group\":\n        norm_layer = functools.partial(nn.GroupNorm, group_norm_groups)\n    elif norm_type == \"none\":\n        norm_layer = None\n    else:\n        raise NotImplementedError(f\"Normalization layer {norm_type} is not found.\")\n    return norm_layer\n\n\ndef gather_cdf_util(cdf, inds):\n    # A very contrived way of mimicking a version of the tf.gather()\n    orig_inds_shape = inds.shape\n    inds_flat = [inds[i].view(-1) for i in range(inds.shape[0])]\n    valid_mask = [\n        torch.where(ind >= cdf.shape[1], torch.zeros_like(ind), torch.ones_like(ind))\n        for ind in inds_flat\n    ]\n    inds_flat = [\n        torch.where(ind >= cdf.shape[1], (cdf.shape[1] - 1) * torch.ones_like(ind), ind)\n        for ind in inds_flat\n    ]\n    cdf_flat = [cdf[i][ind] for i, ind in enumerate(inds_flat)]\n    cdf_flat = [cdf_flat[i] * valid_mask[i] for i in range(len(cdf_flat))]\n    cdf_flat = [\n        cdf_chunk.reshape([1] + list(orig_inds_shape[1:])) for cdf_chunk in cdf_flat\n    ]\n    return torch.cat(cdf_flat, dim=0)\n\n\ndef sample_pdf(bins, weights, num_samples, det=False):\n    # Get pdf\n    weights = weights + 1e-5  # prevent nans\n    pdf = weights / weights.sum(-1).unsqueeze(-1)\n    cdf = torch.cumsum(pdf, -1)\n    cdf = torch.cat((torch.zeros_like(cdf[..., :1]), cdf), -1)\n\n    # Take uniform samples\n    if det:\n        u = torch.linspace(0.0, 1.0, num_samples).to(weights)\n        u = u.expand(list(cdf.shape[:-1]) + [num_samples])\n    else:\n        u = torch.rand(list(cdf.shape[:-1]) + [num_samples]).to(weights)\n    # use pytorch 1.8 searchsorted\n    inds = torch.searchsorted(cdf.contiguous(), u.contiguous(), right=True)\n    below = torch.max(torch.zeros_like(inds), inds - 1)\n    above = torch.min((cdf.shape[-1] - 1) * torch.ones_like(inds), inds)\n    inds_g = torch.stack((below, above), -1)\n\n    cdf_g = gather_cdf_util(cdf, inds_g)\n    bins_g = gather_cdf_util(bins, inds_g)\n\n    denom = cdf_g[..., 1] - cdf_g[..., 0]\n    denom = torch.where(denom < 1e-5, torch.ones_like(denom), denom)\n    t = (u - cdf_g[..., 0]) / denom\n    samples = bins_g[..., 0] + t * (bins_g[..., 1] - bins_g[..., 0])\n\n    return samples\n\n\ndef cumprod_exclusive(tensor: torch.Tensor) -> torch.Tensor:\n    r\"\"\"Mimick functionality of tf.math.cumprod(..., exclusive=True), as it isn't available in PyTorch.\n\n    Args:\n      tensor (torch.Tensor): Tensor whose cumprod (cumulative product, see `torch.cumprod`) along dim=-1\n        is to be computed.\n\n    Returns:\n      cumprod (torch.Tensor): cumprod of Tensor along dim=-1, mimiciking the functionality of\n        tf.math.cumprod(..., exclusive=True) (see `tf.math.cumprod` for details).\n    \"\"\"\n    # Only works for the last dimension (dim=-1)\n    dim = -1\n    # Compute regular cumprod first (this is equivalent to `tf.math.cumprod(..., exclusive=False)`).\n    cumprod = torch.cumprod(tensor, dim)\n    # \"Roll\" the elements along dimension 'dim' by 1 element.\n    cumprod = torch.roll(cumprod, 1, dim)\n    # Replace the first element by \"1\" as this is what tf.cumprod(..., exclusive=True) does.\n    cumprod[..., 0] = 1.0\n\n    return cumprod\n\n\ndef save_tensor_plot(data, save_folder=0, save_name=0):\n    plt.rcParams.update({\"font.size\": 22})\n    plt.figure(figsize=(20, 20))\n    plt.imshow(data.squeeze().cpu().numpy())\n    plt.axis(\"off\")\n\n    if save_folder != 0 and save_name != 0:\n        if not os.path.exists(save_folder):\n            os.makedirs(save_folder)\n        plt.savefig(f\"{save_folder}/{save_name}.png\")\n        plt.cla()\n        plt.close()\n","repo_name":"pvilla/ONIX","sub_path":"models/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":10532,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"1082213289","text":"\nimport sys\nimport glob, os\nimport numpy as np\nimport nltk\nimport csv\nfrom collections import Counter\nfrom nltk.corpus import stopwords\nfrom sklearn import svm\nfrom sklearn.feature_selection import VarianceThreshold\nfrom sklearn.metrics import accuracy_score\nimport matplotlib.pyplot as mp\nfrom arrays import *  \n\nimdbpath = \"./trainingdata/train_file.txt\"\ntestpath = \"./trainingdata/test_file.txt\"\n\nprediction = []\nactual = []\npunct = ['.',',', '-']\nstops = punct + stopwords.words('english')\n\n#Percent of variance allowed: Ignore all features that are the same (whether 1 or 0) in p*100% of the samples.\np = .9\n\ndef read_data(path):\n    data_file = open(path,'r')\n    sentences = []\n    scores = []\n    for line in data_file:\n        sentence, score = line.split(\"      \",1)\n        sentences.append(sentence)\n        scores.append(score.strip())\n    return sentences, scores\n\ndef create_vocab(sentences):\n    text = ' '.join(sentences)\n    tokens = nltk.word_tokenize(text)\n\n    tokens_filtered = [x.lower() for x in tokens if not x in stops]\n    \n    freq_dist = nltk.FreqDist(tokens_filtered)\n    return freq_dist.keys()\n    \ndef transform_sentence(sentence, vocab):\n    tokens = [x.lower() for x in nltk.word_tokenize(sentence) if not x in stops]\n    fdist = nltk.FreqDist(tokens)\n    features = [fdist[x] for x in vocab]\n    return features\n\ndef predict(sentence, vocab, clf):\n    answer = clf.predict([transform_sentence(sentence, vocab)])\n    answer = answer.astype(int)\n    prediction.append(answer[0])\n    return answer[0]\n\n    \n\nsentences, scores = read_data(imdbpath)\nvocab = create_vocab(sentences)\nX = [transform_sentence(x, vocab) for x in sentences]\nclf = svm.SVC(kernel='linear', C = 1.0)\nclf.fit(X, scores)\n\n\ntest_data = open(testpath,'r')\n \nfor line in test_data:\n        sentence, score = line.split(\"       \",1)\n        predict(sentence,vocab,clf)\n        actual.append(int(score))\n\n\nprint(accuracy_score(actual,prediction))\n\nprediction = []\n\nprint(\" SVM \")\n\ni=0\nn=0\nxaxis = [\"BUSS\",\"ENT\",\"LIFE\",\"SPORTS\",\"TECH\"]\nyaxis = []\nwidth = 1/1.5\nind = [0, 1, 2, 3, 4]\n\nprint(\"DNA \")\nos.chdir(\"../news_output/DNA\")\nfor file in glob.glob(\"*.txt\"):\n    print(file)\n    actual = []\n    with open(file,'r') as txtinput:\n        with open(file.replace('.txt','')+\"DnaSvm.csv\",'w') as txtoutput:\n          writer = csv.writer(txtoutput, delimiter=',')\n          j=0\n          for line in txtinput:\n             actual.append(DNA[n][j])   \n             x = line+\",\"+str(predict(line,vocab,clf))+\",\"+str(DNA[n][j])\n             print(x)\n             print(str(n)+str(j))\n             writer.writerow(x.split(\",\"))\n             j+=1\n          txtoutput.close()\n    print(actual)\n    print(prediction)\n    n+=1\n    print(accuracy_score(actual,prediction))       \n    final= dict(Counter(prediction))\n    ans = (final[1])/(final[1]+final[-1])*100\n    print ('Objective Percentage\\t'+str(ans))\n    yaxis.append(ans)\n    prediction = []\n    i=i+1\n\nmp.bar(ind, yaxis, width, color=\"white\",align ='center')\nmp.xticks(ind,xaxis)\nmp.ylabel('Objective Percentage')\nmp.title('DNA India')\nfor a,b in zip(ind, yaxis):\n    mp.text(a-0.25, b-2.4, str(round(b,2)),size=14)\nfont = {'family' : 'normal',\n        'size'   : 15}\nmp.rc('font', **font)     \nmp.show()  \n    \n \nprint(\"THE TIMES OF INDIA\")\n\nyaxis = []\ni=0\nn=0\nos.chdir(\"../TOI\")\nfor file in glob.glob(\"*.txt\"):\n    print(file)\n    actual = []\n    with open(file,'r') as txtinput:\n        with open(file.replace('.txt','')+\"ToiSvm.csv\",'w') as txtoutput:\n            writer = csv.writer(txtoutput, delimiter=',')\n            j=0\n            for line in txtinput:\n               actual.append(TOI[n][j])  \n               x = line+\",\"+str(predict(line,vocab,clf))+\",\"+str(TOI[n][j])\n               writer.writerow(x.split(\",\"))\n               j+=1\n            txtoutput.close()\n    #print(actual)\n    #print(prediction)     \n    print(accuracy_score(actual,prediction))        \n    final= dict(Counter(prediction))\n    print(final)\n    ans = (final[1])/(final[1]+final[-1])*100\n    print ('Objective Percentage\\t'+str(ans))\n    yaxis.append(ans)     \n    prediction = []\n    i=i+1\n    n+=1\nmp.bar(ind, yaxis, width, color=\"white\",align ='center')\nmp.xticks(ind,xaxis)\nmp.ylabel('Objective Percentage')\nmp.title('THE TIMES OF INDIA')\nfor a,b in zip(ind, yaxis):\n    mp.text(a-0.25, b-2.4, str(round(b,2)),size =14)\nfont = {'family' : 'normal',\n        'size'   : 15}\n\nmp.rc('font', **font)    \nmp.show()\n\n\n","repo_name":"rheyavlan/SentimentAnalysis","sub_path":"sentiment_analysis_svm.py","file_name":"sentiment_analysis_svm.py","file_ext":"py","file_size_in_byte":4450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"5839160959","text":"\nclass constants(object):\n  HTTP_METHODS = ['GET', 'HEAD', 'POST', 'PUT', 'DELETE', 'CONNECT', 'OPTIONS', 'TRACE', 'PATCH']\n  \nclass constantEndpoints(object):\n  \n  ENDPOINT_CUSTOMER     = \"customer\"\n  ENDPOINT_TRANSPORTER  = \"transporter\"\n  ENDPOINT_PUBLIC       = \"public\"\n\n  class ENDPOINT_EMPLOYEE(object):  \n    ENDPOINT    = \"employee\"\n    CUSTOMER_REPRESENTATIVE = \"customer_rep\"\n    STOREKEEPER = \"storekeeper\"\n    PRODUCTION_PLANNER = \"production_planner\"\n                  \n  \nclass http(object):\n# The server encountered an unexpected condition that prevented it from fulfilling the request.\n  StatusInternalServerError = 500\n# The request could not be understood by the server due to incorrect syntax. \n# The client SHOULD NOT repeat the request without modifications.\n  StatusBadRequest = 400\n# Indicates that the request requires user authentication information. \n# The client MAY repeat the request with a suitable Authorization header field\n  StatusUnauthorized = 401\n# Unauthorized request. The client does not have access rights to the content. \n# Unlike 401, the client’s identity is known to the server.\n  StatusForbidden = 403\n# The request HTTP method is known by the server but has been disabled and cannot be used for that resource.\n  StatusMethodNotAllowed = 405\n# The server doesn’t find any content.\n# That conforms, to the criteria given by the user agent in the Accept header sent in the request.\n  StatusNotAcceptable = 406\n# Allows a client to tell the server that the same resource (with the same binding) was mentioned earlier. \n# It never appears as a true HTTP response code in the status line, and only appears in bodies.\n  StatusAlreadyReported = 208\n# Indicates that the request has succeeded and a new resource has been created as a result.\n  StatusCreated = 201\n# Indicates that the request has succeeded.\n  StatusOk = 200\n  \n  \nclass error(object):\n  Login_InvalidMethod = \"Invalid method, Use \"\n  \n  Endpoint = \": \"\n\n  Login_NotLoggedIn = (\"LOGIN: Login as correct user first\")\n  \n  Body_Invalid = \"BODY: Invalid input\"\n  \n  Query_Duplicate = \"QUERY: Input already exists within database\"\n  \n  No_DataBase_Found = \"FATAL ERROR: Database connection not found\"\n  DataBase_Found = \" * SERVER: Database connection found!\"\n","repo_name":"Mystodan/database_prosjekt_33","sub_path":"iteration-2/code/constants/REST.py","file_name":"REST.py","file_ext":"py","file_size_in_byte":2264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"13606081353","text":"u=0\nwhile(u<11):\n    print(\"Shirsha\")\n    u=u+1\nfor t in range(0,11):\n    print(\"Avinaba\",end=\"+Shirsha,\")\nprint()\no=int(input(\"enter a number\"))\ncount=0\nwhile o!=0:\n    count=count+1\n    o=o//10\nprint(\"your number is carrying\",count,\"digits\")\nh=int(input(\"enter a number\"))\nsum=0\nwhile h!=0:\n    sum+=h%10\n    h//=10\nprint(sum)          \n","repo_name":"avinabadey6/python","sub_path":"name2.py","file_name":"name2.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"41406383996","text":"import os.path\nfrom datetime import datetime\nfrom enum import Enum\nfrom typing import *\n\nfrom pros.common import logger\nimport pros.common.ui as ui\nfrom pros.config import Config\nfrom pros.config.cli_config import cli_config\nfrom .manifests import *\nfrom .instructions import UpgradeResult\n\n\nclass ReleaseChannel(Enum):\n    Stable = 'stable'\n    Beta = 'beta'\n\n\nclass UpgradeManager(Config):\n    def __init__(self, file=None):\n        if file is None:\n            file = os.path.join(cli_config().directory, 'upgrade.pros.json')\n        self._last_check: datetime = datetime.min\n        self._manifest: Optional[UpgradeManifestV1] = None\n        self.release_channel: ReleaseChannel = ReleaseChannel.Stable\n\n        super().__init__(file)\n\n    @property\n    def has_stale_manifest(self):\n        if self._manifest is None:\n            logger(__name__).debug('Upgrade manager\\'s manifest is nonexistent')\n        if datetime.now() - self._last_check > cli_config().update_frequency:\n            logger(__name__).debug(f'Upgrade manager\\'s last check occured at {self._last_check}.')\n            logger(__name__).debug(f'Was longer ago than update frequency ({cli_config().update_frequency}) allows.')\n        return (self._manifest is None) or (datetime.now() - self._last_check > cli_config().update_frequency)\n\n    def get_manifest(self, force: bool = False) -> UpgradeManifestV1:\n        if not force and not self.has_stale_manifest:\n            return self._manifest\n\n        ui.echo('Fetching upgrade manifest...')\n        import requests\n        import jsonpickle\n        import json\n\n        channel_url = f'https://purduesigbots.github.io/pros-mainline/{self.release_channel.value}'\n        self._manifest = None\n\n        manifest_urls = [f\"{channel_url}/{manifest.__name__}.json\" for manifest in manifests]\n        for manifest_url in manifest_urls:\n            resp = requests.get(manifest_url)\n            if resp.status_code == 200:\n                try:\n                    self._manifest = jsonpickle.decode(resp.text, keys=True)\n                    logger(__name__).debug(self._manifest)\n                    self._last_check = datetime.now()\n                    self.save()\n                    break\n                except json.decoder.JSONDecodeError as e:\n                    logger(__name__).warning(f'Failed to decode {manifest_url}')\n                    logger(__name__).debug(e)\n            else:\n                logger(__name__).debug(f'Failed to get {manifest_url} ({resp.status_code})')\n        if not self._manifest:\n            manifest_list = \"\\n\".join(manifest_urls)\n            logger(__name__).warning(f'Could not access any upgrade manifests from any of:\\n{manifest_list}')\n        return self._manifest\n\n    @property\n    def needs_upgrade(self) -> bool:\n        manifest = self.get_manifest()\n        if manifest is None:\n            return False\n        return manifest.needs_upgrade\n\n    def describe_update(self) -> str:\n        manifest = self.get_manifest()\n        assert manifest is not None\n        return manifest.describe_update()\n\n    @property\n    def can_perform_upgrade(self):\n        manifest = self.get_manifest()\n        assert manifest is not None\n        return manifest.can_perform_upgrade\n\n    def perform_upgrade(self) -> UpgradeResult:\n        manifest = self.get_manifest()\n        assert manifest is not None\n        return manifest.perform_upgrade()\n\n    def describe_post_upgrade(self) -> str:\n        manifest = self.get_manifest()\n        assert manifest is not None\n        return manifest.describe_post_install()\n","repo_name":"purduesigbots/pros-cli","sub_path":"pros/upgrade/upgrade_manager.py","file_name":"upgrade_manager.py","file_ext":"py","file_size_in_byte":3575,"program_lang":"python","lang":"en","doc_type":"code","stars":101,"dataset":"github-code","pt":"80"}
+{"seq_id":"72951456899","text":"import pandas as pd\n\npd.set_option('display.max_columns', None)\npd.set_option('display.max_rows', 20)\npd.set_option('display.width', 50)\npd.set_option('display.expand_frame_repr', False)\n\ndf_ = pd.read_excel('online_retail_II.xlsx',sheet_name=\"Year 2010-2011\")\ndf = df_.copy()\ndf.head()\n\ndef outlier_thresholds(dataframe, variable):\n    quartile1 = dataframe[variable].quantile(0.01)\n    quartile3 = dataframe[variable].quantile(0.99)\n    interquantile_range = quartile3 - quartile1\n    up_limit = quartile3 + 1.5 * interquantile_range\n    low_limit = quartile1 - 1.5 * interquantile_range\n    return low_limit, up_limit\n\ndef replace_with_thresholds(dataframe, variable):\n    low_limit, up_limit = outlier_thresholds(dataframe, variable)\n    dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit\n    dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit\n\ndef retail_data_prep(dataframe):\n    dataframe.drop(dataframe[dataframe[\"StockCode\"] == \"POST\"].index, inplace=True)\n    dataframe.dropna(inplace=True)\n    dataframe = dataframe[~dataframe[\"Invoice\"].str.contains(\"C\", na=False)]\n    dataframe = dataframe[dataframe[\"Quantity\"] > 0]\n    dataframe = dataframe[dataframe[\"Price\"] > 0]\n    replace_with_thresholds(dataframe, \"Quantity\")\n    replace_with_thresholds(dataframe, \"Price\")\n    return dataframe\n\ndf = retail_data_prep(df)\n\ndf_grm = df[df['Country']=='Germany']\ndf_grm.head()\n\ndef create_invoice_product_df(dataframe, id=False):\n    if id:\n        return dataframe.groupby(['Invoice', \"StockCode\"])['Quantity'].sum(). \\\n            unstack(). \\\n            fillna(0). \\\n            applymap(lambda x: 1 if x > 0 else 0)\n    else:\n        return dataframe.groupby(['Invoice', 'Description'])['Quantity'].sum(). \\\n            unstack(). \\\n            fillna(0). \\\n            applymap(lambda x: 1 if x > 0 else 0)\n\ngrm_inv_sto_df = create_invoice_product_df(df_grm, id=True)\n\ndef check_id(dataframe, stock_code):\n    product_name = dataframe[dataframe[\"StockCode\"] == stock_code][[\"Description\"]].values[0].tolist()\n    print(product_name)\n\nfrom mlxtend.frequent_patterns import apriori, association_rules\nfrequent_itemsets = apriori(grm_inv_sto_df, min_support=0.01, use_colnames=True)\nrules = association_rules(frequent_itemsets, metric=\"support\", min_threshold=0.01)\n\nsorted_rules = rules.sort_values(\"lift\", ascending=False)\n\ndef arl_recommender(rules_df, product_id, rec_count=1):\n    sorted_rules = rules_df.sort_values(\"lift\", ascending=False)\n    recommendation_list = []\n    for i, product in sorted_rules[\"antecedents\"].items():\n        for j in list(product):\n            if j == product_id:\n                recommendation_list.append(list(sorted_rules.iloc[i][\"consequents\"]))\n    recommendation_list = list({item for item_list in recommendation_list for item in item_list})\n    return recommendation_list[:rec_count]\n\n# Example: Reccommendation 5 products for 21987\narl_recommender(rules,21987,rec_count=5)\n\ncheck_id(df, 21244)\ncheck_id(df, 23307)\ncheck_id(df, 22029)\ncheck_id(df, 20750)\ncheck_id(df, 22423)\n","repo_name":"cansinkutlucan/Association-Rule-Learning","sub_path":"ARL.py","file_name":"ARL.py","file_ext":"py","file_size_in_byte":3062,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"80"}
+{"seq_id":"71049677698","text":"import collections\nimport functools\n\nimport oslo_messaging as messaging\nfrom oslo_serialization import jsonutils\n\n\nNOTIFICATIONS = []\nVERSIONED_NOTIFICATIONS = []\n\n\ndef reset():\n    del NOTIFICATIONS[:]\n    del VERSIONED_NOTIFICATIONS[:]\n\n\nFakeMessage = collections.namedtuple('Message',\n                                     ['publisher_id', 'priority',\n                                      'event_type', 'payload', 'context'])\n\n\nclass FakeNotifier(object):\n\n    def __init__(self, transport, publisher_id, serializer=None):\n        self.transport = transport\n        self.publisher_id = publisher_id\n        self._serializer = serializer or messaging.serializer.NoOpSerializer()\n\n        for priority in ['debug', 'info', 'warn', 'error', 'critical']:\n            setattr(self, priority,\n                    functools.partial(self._notify, priority.upper()))\n\n    def prepare(self, publisher_id=None):\n        if publisher_id is None:\n            publisher_id = self.publisher_id\n        return self.__class__(self.transport, publisher_id,\n                              serializer=self._serializer)\n\n    def _notify(self, priority, ctxt, event_type, payload):\n        payload = self._serializer.serialize_entity(ctxt, payload)\n        # NOTE(Dinesh_Bhor): simulate the kombu serializer\n        # this permit to raise an exception if something have not\n        # been serialized correctly\n        jsonutils.to_primitive(payload)\n        # NOTE(Dinesh_Bhor): Try to serialize the context, as the rpc would.\n        #                An exception will be raised if something is wrong\n        #                with the context.\n        self._serializer.serialize_context(ctxt)\n        msg = FakeMessage(self.publisher_id, priority, event_type,\n                          payload, ctxt)\n        NOTIFICATIONS.append(msg)\n\n\nclass FakeVersionedNotifier(FakeNotifier):\n    def _notify(self, priority, ctxt, event_type, payload):\n        payload = self._serializer.serialize_entity(ctxt, payload)\n        VERSIONED_NOTIFICATIONS.append({'publisher_id': self.publisher_id,\n                                        'priority': priority,\n                                        'event_type': event_type,\n                                        'payload': payload})\n","repo_name":"openstack/masakari","sub_path":"masakari/tests/unit/fake_notifier.py","file_name":"fake_notifier.py","file_ext":"py","file_size_in_byte":2252,"program_lang":"python","lang":"en","doc_type":"code","stars":56,"dataset":"github-code","pt":"80"}
+{"seq_id":"44538345705","text":"#! /usr/bin/env python3\n\n# https://projecteuler.net/problem=46\n\nfrom math import sqrt\nfrom itertools import count\n\ndef is_prime(n):\n    if n <= 1:\n        return False\n    if n > 2 and n % 2 == 0:\n        return False\n    for elem in range(3, int(sqrt(n)) + 1, 2):\n        if not n % elem:\n            return False\n    return True\n\ndef theorem_matches(n):\n    for power in count(start=1):\n        if 2 * power ** 2 >= n:\n            return False\n        if is_prime(n - (2 * power ** 2)):\n            return True\n\n# All odd numbers\nfor n in count(start=3, step=2):\n    if not is_prime(n):\n        if not theorem_matches(n):\n            print(n)\n            break\n","repo_name":"AlbertoPeon/project-euler","sub_path":"46/46.py","file_name":"46.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"30913624825","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Jun 16 22:13:15 2020\r\n\r\n@author: Sardor Mirzaev\r\n\"\"\"\r\nimport os\r\nimport pyodbc\r\nimport pandas as pd\r\n\r\nusername1 = '006'\r\npassword1 = 'Vrl'\r\n                     \r\n#  Databank Connection  \r\n#s = 'Driver={SQL Server};DSN=ZDA;Description=ZDA;UID='+username1+';PWD='+password1\r\ns = 'DSN=ZDA;UID='+username1+';PWD='+password1\r\n\r\ncnxn2 = pyodbc.connect(s)#+';DATABASE=ZDA_006000;SCHEMA=dbo')\r\ncursor = cnxn2.cursor()\r\n\r\n\r\n#%%  \r\n\r\nt2 = pd.read_sql(\"select CAST(RAT_CREATION_DATE as DATE) xxNEU, * from dbo.RATING where RATING_ID = '7990007800'\", cnxn2)   \r\nt2.to_clipboard(decimal =',')\r\n# %%\r\nt2 = pd.read_sql(\"select CAST(RAT_CREATION_DATE as DATE) RAT_CREATION_DATE from dbo.RATING where RATING_ID = '7990007827'\", cnxn2)   \r\nt2.to_clipboard(decimal =',')\r\n\r\n# %%\r\nsZDA = \"\"\"SELECT \r\n'6000'            as X01_Mandant_ID ,\r\nCUST_ID           as X02_CUST_ID,\r\nMODUL_ID          as X03a_MODUL_ID,\r\nMODUL_ID_FUNC     as X03b_MODUL_ID_FUNC,\r\nSUBMODUL          as X03c_SUBMODUL,\r\nRAT_BUS_ID        as X04_RAT_BUS_ID,\r\n'2017_12'         as X05_Stichtag,\r\nRAT_SCORE_INT     as X07_FCR_Stufe_ST1,\t\r\nRAT_APPROVE_DATE  as X08_ST1_RAT_APPROVE_DATE, \r\nOVERRIDE_FLAG     as X14_Overrides, \r\nRAT_APPROVE_DATE  as Y01_RAT_APPROVE_DATE,\r\nRAT_CLEAR_DATE    as Y02_RAT_CLEAR_DATE,\r\nTECH_RATING_ID    as Y03_TECH_RATING_ID,\r\nRAT_MAN_INAKTIV   as Y04_RAT_MAN_INAKTIV\r\nFROM dbo.RATING \r\nwhere MODUL_ID in (10298, 10299, 10303, 10304, 10305, 10306, 10307, 17596, 48342, 58703, 65911, 66170, 70596, 72216, 76249, 79042)\r\n\"\"\" + \\\r\n\"order by RAT_APPROVE_DATE\"  \r\n\r\n\r\nt3 = pd.read_sql(sZDA, cnxn2)\r\n\r\n# %%\r\n\r\ndiff1 = set( t2.Y03_TECH_RATING_ID ).difference( t3.Y03_TECH_RATING_ID  )\r\n\r\nt2.Y03_TECH_RATING_ID.min()\r\nt3.Y03_TECH_RATING_ID.min()\r\n\r\n\r\nt2.Y03_TECH_RATING_ID.max()\r\nt3.Y03_TECH_RATING_ID.max()\r\n\r\n\r\n# %%\r\nmerged1 = pd.merge(t2[['Y03_TECH_RATING_ID', 'X02_CUST_ID', 'Y01_RAT_APPROVE_DATE']], \r\n                   t3[['Y03_TECH_RATING_ID', 'X02_CUST_ID', 'Y01_RAT_APPROVE_DATE']], how = 'outer', \r\n                      left_on = 'Y03_TECH_RATING_ID', right_on = 'Y03_TECH_RATING_ID')\r\nmerged1.to_clipboard(decimal =',')\r\n\r\n\r\n# => The not-approved Ratings left out in ZDA","repo_name":"sardormirzaev/access_SQL_ZDA-database-","sub_path":"Databank_access.py","file_name":"Databank_access.py","file_ext":"py","file_size_in_byte":2196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"4285172174","text":"from tensorflow.keras import models\nfrom tensorflow.keras import layers\nfrom tensorflow.keras import utils\n\nfrom tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D\n\nimport tensorflow.keras\n\nimport os\nimport cv2\nimport numpy as np\n\n#path = \"cards_imgs/\"\npath = \"result_photos/\"\ncards = os.listdir(path=path) \n\nx_train_cards = []\ny_train1 = []\n\nh = 200 # 355\nw = 200\n\nfor card in cards:    \n    image = cv2.imread(path + card, cv2.IMREAD_COLOR)\n    x_train_cards.append(image)\n    y_train1.append(card.split('[')[1].split(']')[0])\n\nx_train1 = []\n\nfor x in x_train_cards:    \n    img_g = cv2.cvtColor(x, cv2.COLOR_BGR2GRAY) # BGR to GRAY\n    dim = (h,w)    \n    img_rs = cv2.resize(img_g, dim, interpolation=cv2.INTER_AREA) # Изменение размера\n    \n    x_train1.append(img_rs)\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn import preprocessing\nfrom tensorflow.keras.utils import to_categorical\nimport pandas as pd\n\nle = preprocessing.LabelEncoder()\ny_train = np.asarray(y_train1)\nid_numbers = pd.Series(y_train)\n\nd = {\"abbr\": y_train, \"value\": y_train}\ndf = pd.DataFrame(d[\"abbr\"])\n\ntrain_lbl = df.apply(le.fit_transform) # Преобразовали слова (действия) в цифры\n\ny_train = to_categorical(train_lbl)\n\nvalues = y_train1\nnumbers = np.asarray(train_lbl)\ndic = {}\ndic_r = {}\ni = -1\nfor n in numbers:\n    i += 1\n    dic[y_train1[i]] = numbers[i][0]\n    dic_r[numbers[i][0]] = y_train1[i]\n    \"\"\"dic.update({\n        \"value\": y_train1[i],\n        \"number\": numbers[i][0]\n    })\"\"\"\n    \nprint(dic_r)\n\nx_train = x_train1\nx_train = np.asarray(x_train)\nx_train = x_train.astype('float32')\n\nprint(x_train[0].shape)\nprint(x_train.shape)\nx_train = x_train.reshape(x_train.shape[0], h, w, 1)\nprint(x_train.shape)\n\n\nimport tensorflow as tf\n\nnum_classes = id_numbers.nunique()\nbatch_size = 24\nepochs = 8\n\nmodel = models.Sequential()\nmodel.add(Conv2D(100, (3, 3), input_shape=(200, 200, 1), activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Conv2D(100, (3, 3), activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Flatten())\nmodel.add(Dense(units=200, activation='relu'))\n#model.add(Dense(units=num_classes, activation='sigmoid'))\nmodel.add(Dense(units=num_classes, activation='softmax'))\nmodel.compile(optimizer=\"adam\",\n            loss='categorical_crossentropy',\n            metrics=['accuracy'])\n\nmodel.fit(x_train, y_train,\n              batch_size=batch_size,\n              epochs=epochs,\n              validation_data=(x_train, y_train),\n              shuffle=True)\n\nmodel.save('model.h5')\nprint(\"model saved\")","repo_name":"kerassun/Recognition_playing_cards_IRT","sub_path":"create_model.py","file_name":"create_model.py","file_ext":"py","file_size_in_byte":2660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"8871770209","text":"# combination으로 방해물 위치 정할 수 있긴 하다.\r\n# 그래도 dfs로 구현해보는게 좋을듯?\r\n# bfs로는 설치 불가능 ㅇㅈ?\r\nn = int(input())\r\ndata =[]\r\nfor _ in range(n):\r\n    data.append(list(map(str,input().split())))\r\n# 위 왼 아 오\r\ndx = [-1,0,1,0]\r\ndy = [0,-1,0,1]\r\n# 학생이 한명이라도 걸리면 False 모두 안걸리면 True\r\ndef canHide():\r\n    global n\r\n    for i in range(n):\r\n        for j in range(n):\r\n            if data[i][j]=='T':\r\n                for d in range(4):\r\n                    x,y = i,j\r\n                    while  0<=x<=n-1 and 0<=y<=n-1:\r\n                        if data[x][y] == 'O':\r\n                            break\r\n                        elif data[x][y] =='S':\r\n                            return False\r\n                        x+=dx[d]\r\n                        y+=dy[d]\r\n    return True\r\ncomb = []\r\nfor i in range(n):\r\n    for j in range(n):\r\n        comb.append([i,j])\r\n\r\nhide = False\r\ndef dfs(cnt,idx):\r\n    global hide\r\n    # 처음에 이렇게 했다가 계속 시간초과가 떴다.\r\n    # dfs의 탈출 조건(return 조건이 없었기 때문이다.)\r\n    # dfs에선 dfs 탈출조건 잘 구현하자.\r\n    # if cnt == 3:\r\n    #     if canHide():\r\n    #         hide = True\r\n    if cnt == 3:\r\n        hide =hide or canHide()\r\n        return \r\n\r\n    for i in range(idx,len(comb)):\r\n        # if i == len(comb)-(3-cnt):\r\n        #     return\r\n        x,y = comb[i]\r\n        if data[x][y] == 'X':\r\n            data[x][y] = 'O'\r\n            dfs(cnt+1,i)\r\n            data[x][y]='X'\r\ndfs(0,0)\r\nif hide:\r\n    print(\"YES\")\r\nelse:\r\n    print(\"NO\")","repo_name":"AlgorithmGosu/2022-05-challenge","sub_path":"chapter13_DFS_BFS_Problem/20_yuje.py","file_name":"20_yuje.py","file_ext":"py","file_size_in_byte":1625,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"43144527402","text":"from pyramid.response import Response\nfrom pyramid.view import view_config\nfrom pyramid.httpexceptions import HTTPFound\n\nfrom sqlalchemy import desc\nfrom sqlalchemy.exc import DBAPIError\nfrom sqlalchemy.orm import aliased\n\nfrom webhelpers.paginate import PageURL, Page\n\nfrom ..models import (\n    DBSession,\n    IPBan,\n    User,\n    )\nfrom ..lib import cache\n\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\nclass IPBanExistsException(Exception):\n    pass\n\n\n@view_config(request_method=\"POST\", route_name='ipbans', permission=\"ipban-create\")\ndef ipban_create(request):\n    existing = DBSession.query(IPBan).filter(IPBan.ip.ilike(request.POST[\"ip\"])).first()\n    if existing:\n        raise IPBanExistsException(\"%s is already banned (until %s for %s)\" % (existing.ip, existing.until, existing.reason))\n\n    end = request.POST[\"until\"].strip()\n    ipban = IPBan(banner=request.user, ip=request.POST[\"ip\"].strip(),\n        reason=request.POST[\"reason\"].strip(), until=end)\n    logger.info(\"Create ipban for %s because %s\", ipban.ip, ipban.reason)\n    DBSession.add(ipban)\n    DBSession.flush()\n    return HTTPFound(request.route_url('ipbans'), headers=[(\"X-VTB-Ban-ID\", ipban.id)])\n\n\n@view_config(request_method=\"GET\", route_name='ipbans', renderer='ipban/list.mako', permission=\"ipban-list\")\ndef ipban_list(request):\n    ipbans_per_page = int(request.registry.settings.get(\"votabo.ipbans_per_page\", 200))\n    page = int(request.GET.get(\"page\", \"1\"))\n    url_for_page = PageURL(request.path, request.params)\n\n    sql = DBSession.query(IPBan).order_by(desc(IPBan.id))\n    if request.GET.get(\"ip\"):\n        if \"/\" not in request.GET[\"ip\"]:\n            sql = sql.filter(IPBan.ip == request.GET[\"ip\"])\n        else:  # pragma: no cover -- requires postgres\n            sql = sql.filter(IPBan.ip.op(\">>=\")(request.GET[\"ip\"]))\n    if request.GET.get(\"reason\"):\n        sql = sql.filter(IPBan.reason.ilike(\"%\" + request.GET[\"reason\"] + \"%\"))\n    if request.GET.get(\"banner\"):\n        sql = sql.join(User).filter(User.username.ilike(request.GET[\"banner\"]))\n    ipbans = Page(sql, page=page, items_per_page=ipbans_per_page, url=url_for_page)\n    return {\"ipbans\": ipbans, \"pager\": ipbans}\n\n\n@view_config(request_method=\"DELETE\", route_name='ipban', permission=\"ipban-delete\")\ndef ipban_delete(request):\n    bid = request.matchdict[\"id\"]\n    ipban = DBSession.query(IPBan).filter(IPBan.id == bid).first()\n    if ipban:\n        logger.info(\"Deleting ban for %s\", ipban.ip)\n        DBSession.delete(ipban)\n    return HTTPFound(request.referrer or request.route_url('ipbans'))\n","repo_name":"shish/votabo","sub_path":"votabo/views/ipban.py","file_name":"ipban.py","file_ext":"py","file_size_in_byte":2568,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"41110266112","text":"# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this file,\n# You can obtain one at http://mozilla.org/MPL/2.0/.\n\nimport dateutil.parser\nfrom distutils import dir_util\nimport json\nimport logging\nimport os\nimport shutil\n\nfrom tlscanary.tools import cert\n\n\nlogger = logging.getLogger(__name__)\nmodule_dir = os.path.split(__file__)[0]\n\n\ndef generate(mode, logs, output_dir):\n    global logger\n\n    logger.debug(\"Generating `%s` report for %d logs in `%s`\" % (mode, len(logs), output_dir))\n\n    if mode == \"web\":\n        for log_name in sorted(logs.keys()):\n            log = logs[log_name]\n            meta = log.get_meta()\n            if meta[\"mode\"] != \"regression\":\n                logger.warning(\"Skipping report generation for non-regression log `%s`\" % log_name)\n                continue\n            if not log.has_finished():\n                logger.warning(\"Skipping report generation for incomplete log `%s`\" % log_name)\n                continue\n            if not log.is_compatible():\n                logger.warning(\"Skipping report generation for incompatible log `%s`\" % log_name)\n                continue\n            web_report(log, output_dir)\n    else:\n        logger.critical(\"Report generator mode `%s` not implemented\" % mode)\n\n\ndef web_report(log, report_dir):\n    global logger\n\n    # Create report directory if necessary.\n    if not os.path.exists(report_dir):\n        logger.debug('Creating report directory %s' % report_dir)\n        os.makedirs(report_dir)\n\n    # Fetch log metadata\n    meta = log.get_meta()\n    run_start_time = dateutil.parser.parse(meta[\"run_start_time\"])\n    timestamp = run_start_time.strftime(\"%Y-%m-%d-%H-%M-%S\")\n\n    # Read the complete runs log to see if this log was already reported\n    runs_log_file = os.path.join(report_dir, \"runs\", \"runs.json\")\n\n    if os.path.exists(runs_log_file):\n        with open(runs_log_file) as f:\n            runs_log = json.load(f)\n            for line in runs_log[0][\"data\"]:\n                logger.debug(\"Line read from runs.json: `%s`\" % line)\n    else:\n        # File does not exist, create an empty log\n        runs_log = json.loads('[{\"data\":[]}]')\n\n    if timestamp in json.dumps(runs_log):\n        logger.warning(\"Skipping log `%s` which was already reported before\" % log.handle)\n        return\n\n    # Write log file\n    run_dir = os.path.join(report_dir, \"runs\", timestamp)\n    logger.info(\"Writing HTML report to `%s`\" % run_dir)\n\n    uri_data = []\n    for line in log:\n        if meta[\"args\"][\"filter\"] == 1:\n            # Filter out stray timeout errors\n            connection_speed = line[\"response\"][\"response_time\"]-line[\"response\"][\"command_time\"]\n            timeout = line[\"response\"][\"original_cmd\"][\"args\"][\"timeout\"] * 1000\n            try:\n                error_message = line[\"response\"][\"result\"][\"info\"][\"short_error_message\"]\n            except KeyError:\n                error_message = \"unknown\"\n            if error_message == \"NS_BINDING_ABORTED\" and connection_speed > timeout:\n                continue\n        uri_data.append(line)\n\n    log_data = [{\"meta\": log.get_meta(), \"data\": uri_data}]\n\n    # Install static template files in report directory\n    template_dir = os.path.join(module_dir, \"template\")\n    dir_util.copy_tree(os.path.join(template_dir, \"js\"),\n                       os.path.join(report_dir, \"js\"))\n    dir_util.copy_tree(os.path.join(template_dir, \"css\"),\n                       os.path.join(report_dir, \"css\"))\n    dir_util.copy_tree(os.path.join(template_dir, \"img\"),\n                       os.path.join(report_dir, \"img\"))\n    shutil.copyfile(os.path.join(template_dir, \"index.htm\"),\n                    os.path.join(report_dir, \"index.htm\"))\n\n    # Create per-run directory for report output\n    if not os.path.isdir(run_dir):\n        os.makedirs(run_dir)\n\n    # Copy profiles\n    if \"profiles\" in meta:\n        for profile in meta[\"profiles\"]:\n            log_zip = log.part(profile[\"log_part\"])\n            run_dir_zip = os.path.join(run_dir, profile[\"log_part\"])\n            logger.debug(\"Copying `%s` profile archive from `%s` to `%s`\" % (profile[\"name\"], log_zip, run_dir_zip))\n            shutil.copyfile(log_zip, run_dir_zip)\n\n    cert_dir = os.path.join(run_dir, \"certs\")\n    __extract_certificates(log, cert_dir)\n\n    shutil.copyfile(os.path.join(template_dir, \"report_template.htm\"),\n                    os.path.join(run_dir, \"index.htm\"))\n\n    # Write the final log file\n    with open(os.path.join(run_dir, \"log.json\"), \"w\") as log_file:\n        log_file.write(json.dumps(log_data, indent=4, sort_keys=True))\n\n    # Append to runs log\n    new_run_log = {\n            \"run\": timestamp,\n            \"branch\": meta[\"test_metadata\"][\"branch\"].capitalize(),\n            \"errors\": len(log),\n            \"description\": \"Fx%s %s vs Fx%s %s\" % (meta[\"test_metadata\"][\"app_version\"],\n                                                   meta[\"test_metadata\"][\"branch\"],\n                                                   meta[\"base_metadata\"][\"app_version\"],\n                                                   meta[\"base_metadata\"][\"branch\"])\n        }\n    runs_log[0][\"data\"].append(new_run_log)\n    logger.debug(\"Writing back runs log to `%s`\" % runs_log_file)\n    with open(runs_log_file, \"w\") as f:\n        f.write(json.dumps(runs_log, indent=4, sort_keys=True))\n\n\ndef __extract_certificates(log, cert_dir):\n    global logger\n\n    if not os.path.exists(cert_dir):\n        os.makedirs(cert_dir)\n\n    for log_line in log:\n        result = {\n            \"host\": log_line[\"host\"],\n            \"rank\": log_line[\"rank\"],\n            \"response\": log_line[\"response\"]\n        }\n        cert_file = os.path.join(cert_dir, \"%s.der\" % result[\"host\"])\n        if \"certificate_chain\" in result[\"response\"][\"result\"][\"info\"] \\\n                and result[\"response\"][\"result\"][\"info\"][\"certificate_chain\"] is not None:\n            server_cert_string = \"\".join(map(chr, result[\"response\"][\"result\"][\"info\"][\"certificate_chain\"][0]))\n            logger.debug(\"Writing certificate data for `%s` to `%s`\" % (result[\"host\"], cert_file))\n            with open(cert_file, \"w\") as f:\n                f.write(server_cert_string)\n        else:\n            logger.debug(\"No certificate data available for `%s`\" % result[\"host\"])\n\n\nNSErrorMap = {\n    # For network error messages that are not obtainable otherwise\n    # https://developer.mozilla.org/en-US/docs/Mozilla/Errors\n    0X00000000: \"NS_OK\",\n    0X80004004: \"NS_ERROR_ABORT\",\n    0X8000FFFF: \"UNEXPECTED_ERROR\",\n    0X804B0002: \"NS_BINDING_ABORTED\",\n    0X804B000A: \"ERROR_MALFORMED_URI\",\n    0X804B000D: \"CONNECTION_REFUSED_ERROR\",\n    0X804B0014: \"NET_RESET_ERROR\",\n    0X804B001E: \"DOMAIN_NOT_FOUND_ERROR\",\n}\n\n\ndef decode_ns_status(scan_result):\n    status = scan_result[\"response\"][\"result\"][\"info\"][\"status\"]\n    try:\n        return NSErrorMap[status]\n    except KeyError:\n        return \"UNKNOWN_STATUS\"\n\n\ndef decode_error_type(scan_result):\n    status = scan_result[\"response\"][\"result\"][\"info\"][\"status\"]\n    if status & 0xff0000 == 0x5a0000:  # security module\n        error_class = scan_result[\"response\"][\"result\"][\"info\"][\"error_class\"]\n        if error_class == 2:  # nsINSSErrorsService::ERROR_CLASS_BAD_CERT\n            return \"certificate\"\n        else:\n            return \"protocol\"\n    else:\n        return \"network\"\n\n\ndef decode_raw_error(scan_result):\n    if \"raw_error\" in scan_result[\"response\"][\"result\"][\"info\"]:\n        raw_error = scan_result[\"response\"][\"result\"][\"info\"][\"raw_error\"]\n        if \"Error code:\" in raw_error:\n            return raw_error.split(\"Error code:\")[1].split(\">\")[1].split(\"<\")[0]\n    return decode_ns_status(scan_result)\n\n\ndef collect_error_info(scan_result):\n    error_info = {\n        \"message\": decode_raw_error(scan_result),\n        \"code\": \"%s\" % hex(scan_result[\"response\"][\"result\"][\"info\"][\"status\"]),\n        \"type\": decode_error_type(scan_result)\n    }\n    return error_info\n\n\ndef collect_site_info(scan_result):\n    site_info = {\n        \"timestamp\": scan_result[\"response\"][\"response_time\"],\n        \"connectionSpeed\": scan_result[\"response\"][\"response_time\"] - scan_result[\"response\"][\"command_time\"],\n        \"uri\": scan_result[\"host\"],\n        \"rank\": scan_result[\"rank\"]\n    }\n    return site_info\n\n\ndef collect_certificate_info(scan_result):\n\n    result = scan_result[\"response\"][\"result\"]\n\n    if not result[\"info\"][\"ssl_status_status\"]:\n        return {}\n\n    status = result[\"info\"][\"ssl_status\"]\n\n    server_cert = status[\"serverCert\"]\n    parsed_server_cert = cert.Cert(result[\"info\"][\"certificate_chain\"][0])\n\n    root_cert = server_cert\n    chain_length = 1\n    while root_cert[\"issuer\"] is not None:\n        root_cert = root_cert[\"issuer\"]\n        chain_length += 1\n\n    cert_info = {\n        \"nickname\": server_cert[\"nickname\"] if \"nickname\" in server_cert else \"(no nickname)\",\n        \"emailAddress\": server_cert[\"emailAddress\"],\n        \"subjectName\": server_cert[\"subjectName\"],\n        \"commonName\": server_cert[\"commonName\"],\n        \"organization\": server_cert[\"organization\"],\n        \"organizationalUnit\": server_cert[\"organizationalUnit\"],\n        \"issuerCommonName\": server_cert[\"issuerCommonName\"],\n        \"issuerOrganization\": server_cert[\"issuerOrganization\"],\n        \"sha1Fingerprint\": server_cert[\"sha1Fingerprint\"],\n        \"sha256Fingerprint\": server_cert[\"sha256Fingerprint\"],\n        \"chainLength\": chain_length,\n        \"certifiedUsages\": result[\"info\"][\"certified_usages\"],\n        \"validityNotBefore\": server_cert[\"validity\"][\"notBeforeGMT\"],\n        \"validityNotAfter\": server_cert[\"validity\"][\"notAfterGMT\"],\n        \"isEV\": str(status[\"isExtendedValidation\"]),\n        \"subjectAltName\": parsed_server_cert.subject_alt_name(),\n        \"signatureAlgorithm\": parsed_server_cert.signature_hash_algorithm(),\n        \"keyUsage\": server_cert[\"keyUsages\"],\n        \"extKeyUsage\": parsed_server_cert.ext_key_usage(),\n        \"rootCertificateSubjectName\": root_cert[\"subjectName\"],\n        \"rootCertificateOrganization\": root_cert[\"organization\"],\n        \"rootCertificateOrganizationalUnit\": root_cert[\"organizationalUnit\"],\n        \"rootCertificateSHA1Fingerprint\": root_cert[\"sha1Fingerprint\"],\n    }\n\n    return cert_info\n\n\ndef collect_scan_info(scan_result):\n    return {\n        \"site_info\": collect_site_info(scan_result),\n        \"error\": collect_error_info(scan_result),\n        \"cert_info\": collect_certificate_info(scan_result)\n    }\n\n\ndef add_performance_info(log_data, scan_result):\n    log_data[\"site_info\"][\"connectionSpeedChange\"] = scan_result[\"response\"][\"connection_speed_change\"]\n    log_data[\"site_info\"][\"connectionSpeedSamples\"] = scan_result[\"response\"][\"connection_speed_samples\"]\n","repo_name":"arroway/tls-canary","sub_path":"tlscanary/report.py","file_name":"report.py","file_ext":"py","file_size_in_byte":10757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"80"}
+{"seq_id":"33004838807","text":"from torch.utils.data import DataLoader\nimport torch\nfrom hetu.gpu_ops.Node import Op\nfrom hetu import ndarray\n\nimport numpy as np\nimport os.path as osp\nimport h5py\n\nfrom more_itertools import peekable\n\ndef tensor2ndarray(x):\n    return ndarray.array(x.numpy(), ctx=ndarray.cpu())\n\nclass WikiCorpusDataset():\n    def __init__(self, data_name, rank, nrank):\n        self.data_name = data_name\n        self.rank = rank\n        self.nrank = nrank\n        self.data_id = rank\n        self.reload_data()\n\n    def reload_data(self):\n        directory = osp.expanduser(\"~/.cache/hetu/datasets/wikicorpus_en/\")\n        fname = directory + \"wikicorpus_en_training_{}.hdf5\".format(self.data_id)\n        if not osp.exists(fname):\n            self.data_id = self.rank\n            fname = directory + \"wikicorpus_en_training_{}.hdf5\".format(self.data_id)\n        assert osp.exists(fname)\n        self.data_id += self.nrank\n        f = h5py.File(fname, mode='r')\n        if self.data_name == \"input_ids\":\n            self.data = f[\"input_ids\"][:]\n        elif self.data_name == \"token_type_ids\":\n            self.data = f[\"segment_ids\"][:]\n        elif self.data_name == \"masked_lm_labels\":\n            masked_lm_positions = f[\"masked_lm_positions\"][:]\n            masked_lm_ids = f[\"masked_lm_ids\"][:]\n            self.data = np.ones(f[\"input_ids\"].shape, dtype=np.int64) * -1\n            # store number of masked tokens in index\n            n, max_pred_len = masked_lm_positions.shape\n            x = np.arange(n).repeat(max_pred_len).reshape(n, max_pred_len)\n            self.data[(x, masked_lm_positions)] = masked_lm_ids\n            self.data[:, 0] = -1\n        elif self.data_name == \"next_sentence_label\":\n            self.data = f[\"next_sentence_labels\"][:]\n        elif self.data_name == \"attention_mask\":\n            self.data = f[\"input_mask\"][:]\n        else:\n            raise NameError(\"Data name not correct.\")\n        f.close()\n        self.data = torch.Tensor(self.data)\n\n    def __getitem__(self, index):\n        return self.data[index]\n\n    def __len__(self):\n        return self.data.shape[0]\n\n    def shape(self):\n        return self.data[0].shape\n\nclass WikiCorpusDataLoader(Op):\n    def __init__(self, batch_size, data_name, transform=None, tondarry=True):\n        super().__init__(WikiCorpusDataLoader, [], ndarray.cpu(0))\n        self.on_gpu = True\n        self.on_cpu = False\n        self.name = \"WikiCorpusDataLoader\"\n        self.desc = self.name\n        self.batch_size = batch_size\n        self.tondarry=tondarry\n        self.transform = transform\n        self.data_name = data_name\n\n    def get_batch_num(self, name):\n        if name==\"train\":\n            return 100\n        else:\n            assert False\n\n    def get_arr(self, name):\n        if name==\"train\":\n            return next(self.dl_train_gen)\n        else:\n            assert False\n\n    def get_cur_shape(self, name):\n        return self.dl_train_gen.peek().shape\n\n    def infer_shape(self, input_shapes):\n        raise NotImplementedError\n\n    def gradient(self, output_grad):\n        return None\n\n    def backward_hook(self, config):\n        if config.pipeline:\n            rank, nrank = config.pipeline_dp_rank, config.nrank // config.pipeline_nrank\n        elif config.context_launch:\n            rank, nrank = config.rank, config.nrank\n        else:\n            rank, nrank = 0, 1\n        self.train_data = WikiCorpusDataset(self.data_name, rank, nrank)\n        gen = torch.Generator()\n        gen.manual_seed(rank)\n        self.dl_train = DataLoader(\n            self.train_data, shuffle=False, drop_last=True,\n            num_workers=1, batch_size=self.batch_size, generator=gen)\n        self.dl_train_gen = peekable(self.get_generator(self.dl_train))\n\n    def get_generator(self, dataloader):\n        while True:\n            for _, x in enumerate(dataloader):\n                if self.transform:\n                    x = self.transform(x)\n                if self.tondarry:\n                    yield tensor2ndarray(x)\n                else:\n                    yield x\n            self.train_data.reload_data()\n\n\n","repo_name":"Hsword/VLDB2023_SDPipe","sub_path":"artifacts/pipeline/models/wiki.py","file_name":"wiki.py","file_ext":"py","file_size_in_byte":4096,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"80"}
+{"seq_id":"43022726988","text":"import cv2 as cv\r\nimport numpy as np\r\ncap = cv.VideoCapture(\"bolt_test_pothole.mp4\")  #opening the video\r\n#cap = cv.VideoCapture(\"virat_test_pothole.mp4\")\r\nwhile cap.isOpened():\r\n    #reading each frame individually\r\n    ret, frame = cap.read()  \r\n    # if frame is read correctly ret is True\r\n    if not ret:\r\n        #breaking when the stream ends\r\n        print(\"Can't receive frame (stream end!). Exiting ...\")  \r\n        break\r\n    #convert to grayscale\r\n    gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)\r\n    #run a binary thresholding process to detect only white pixels above the threshold\r\n    ret, thresh = cv.threshold(gray, 220, 255, cv.THRESH_BINARY)\r\n    kernel1 = cv.getStructuringElement(cv.MORPH_RECT, (3,3))\r\n    kernel2 = np.ones((5,5),np.uint8)\r\n    #erosion and dilation\r\n    erosion = cv.erode(thresh,kernel2,iterations = 1)\r\n    dilate = cv.dilate(erosion, kernel1, iterations=4)\r\n    #detecting contours\r\n    contours, _ = cv.findContours(dilate, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)\r\n    color = (0,255,0)\r\n    for c in contours:\r\n        poly=[None]*1\r\n        perim = cv.arcLength(c, True)\r\n        boundRect=cv.boundingRect(c)\r\n        epsilon = 0.02 * cv.arcLength(c, True)\r\n        poly[0] = cv.approxPolyDP(c, epsilon, True)\r\n        #filtering the contours and draw their apprroximated polygons and bouding boxes\r\n        if perim <600 and perim>30 and boundRect[2]<250 and boundRect[3]<100  and boundRect[2]>30 and len(poly[0])>3 :\r\n            cv.drawContours(frame, poly, -1, color)\r\n            cv.rectangle(frame, (int(boundRect[0]), int(boundRect[1])), \r\n            (int(boundRect[0]+boundRect[2]), int(boundRect[1]+boundRect[3])), color, 2)\r\n            #draw everything on the original frame itself\r\n    cv.imshow('frame', frame)\r\n    # wait after each frame to adjust speed\r\n    if cv.waitKey(2) == ord('q'):\r\n        break\r\ncap.release()\r\ncv.destroyAllWindows()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"RamziDevil/Abhiyaan_App","sub_path":"opencv.py","file_name":"opencv.py","file_ext":"py","file_size_in_byte":1924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"22702797862","text":"import json\n\n\ndef add_q_and_a():\n\tq = input('Enter a question:')\n\ta = input('Enter answer:')\n\n\tq_num=len(q_and_a)\n\tq = f'{q_num+1}.{q}'\n\tq_and_a[q]=a\n\ndef to_json(data):\n\tf = open(json_file, 'w')\n\tjson.dump(data, f)\n\n\njson_file = 'data.json'\n\nq_and_a = json.load(open(json_file,'r'))\n\nadd_q_and_a()\n\nfor q, a in q_and_a.items():\n\tprint(f'{q} - {a}')\n\nto_json(q_and_a)\n\n\n\n\n","repo_name":"WWWCourses/PythonCourse_27.02.2023-Labs","sub_path":"lab26/data_interchange_demos/QuizGame/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"80"}
+{"seq_id":"40662529146","text":"import os\n\nimport views\nfrom framework.core import Application\n\nroot = os.getcwd() + '\\\\html'\n\nfolders_tree = {\n    'root': root,\n    'static': root + '\\\\static\\\\',\n    'pictures': root + '\\\\pictures\\\\',\n    'templates': root + '\\\\templates\\\\'\n}\n\nurlpatterns = {\n    '/': views.main_view,\n    '/about': views.about_view,\n    '/about/': views.about_view,\n    '/contacts': views.contacts_view,\n    '/contacts/': views.contacts_view,\n    '/register': views.register_view,\n    '/register/': views.register_view,\n    '/info': views.info_view,\n    '/info/': views.info_view,\n    '/new_category': views.n_cat_view,\n    '/new_category/': views.n_cat_view,\n    '/new_course': views.n_course_view,\n    '/new_course/': views.n_course_view,\n    '*': views.page_404_view\n}\n\n\ndef secret_controller(request):\n    request['secret_key'] = 'SECRET'\n\n\nfront_controllers = [\n    secret_controller\n]\n\napplication = Application(urlpatterns, front_controllers)\n\n\n# waitress-serve --listen=127.0.0.1:8000 main:application\n","repo_name":"RomanovYS/Lesson-Arch-and-Design-Patterns-in-Python","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"16542800079","text":"# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\"\"\"\nProxies for shared freeswitch objects\n\"\"\"\nimport functools\nimport signal\nfrom collections import OrderedDict\nimport multiprocessing as mp\nfrom multiprocessing import managers, util\n# from multiprocessing.managers import State, DictProxy\nfrom utils import get_logger\nfrom . import utils\n\n\n# mp debugging\n_log_level = None\n\n\ndef set_debug(toggle=True):\n    global _log_level\n    logger = mp.log_to_stderr()\n    if toggle:\n        # _log_level = logger.getLevel()\n        logger.setLevel('DEBUG')\n    # else:\n    #     logger.setLevel(_log_level)\n\n\n# \"transparent\" proxy methods\ndef _repr(self):\n    return self._callmethod('__repr__')\n\n\ndef _proxy_dir(self):\n    try:  # to get the proxy listing\n        attrs = self._callmethod('__dir__')\n    except IOError:\n        assert not self._manager._process.is_alive(),\\\n            \"Mng is alive but proxy received IOerror!?\"\n        raise RuntimeError(\"the proxy mng has died?!\")\n    except managers.RemoteError:\n        attrs = dir(type(self))\n    # attrs.extend(self.exposed)\n    attrs.extend(utils._dir(self))\n    return attrs\n\n\ndef make_inst_proxy(cls, exposed=None, method_to_typeid=None):\n    '''\n    Return a custom proxy for wrapping access to a shared instance\n\n    Parameters\n    ----------\n    cls : type\n        Class for which a proxy object should be created\n    exposed : list\n        Sequence of methods which should be made public via the proxy\n        object.  If not provided public methods are automatically\n        retrieved from the class' declared interface\n\n    Returns\n    -------\n    proxy : a subclass of mp.managers.BaseProxy with a getattr/setattr\n        interface (see mp.managers.py for details)\n    '''\n    if exposed is None:\n        try:\n            exposed = cls._exposed\n        except AttributeError:\n            exposed = managers.public_methods(cls)\n\n    # auto-attach listed methods\n    ProxyBase = managers.MakeProxyType('ProxyBase', exposed)\n\n    # make mutable to extend\n    exposed = list(ProxyBase._exposed_)\n\n    class InstProxy(ProxyBase):\n        _exposed_ = tuple(exposed + ['__getattribute__', '__setattr__',\n                                     '__dir__'])\n        _attr_redirect = {}\n\n        __repr__ = _repr\n\n        __dir__ = _proxy_dir\n\n        def __getattr__(self, key):\n            try:\n                return object.__getattribute__(self, key)\n            except AttributeError:\n                callmethod = object.__getattribute__(self, '_callmethod')\n                # handle attr redirects declared by this proxy\n                if key in self._attr_redirect:\n                    method = self._attr_redirect[key]\n                    return callmethod(method)\n                else:\n                    method = '__getattribute__'\n                    return callmethod(method, (key,))\n\n        def __setattr__(self, key, value):\n            if key[0] == '_':  # this is critical do not change\n                return object.__setattr__(self, key, value)\n            else:\n                callmethod = object.__getattribute__(self, '_callmethod')\n                return callmethod('__setattr__', (key, value))\n\n    # mark shared 'sub-proxy' attributes\n    if method_to_typeid:\n        InstProxy._method_to_typeid_.update(method_to_typeid)\n\n    return InstProxy\n\n\n# override the default manager to catch a weird OSError and\n# add some functionality\nclass CustomSyncMng(managers.SyncManager):\n\n    @staticmethod\n    def _finalize_manager(process, address, authkey, state, _Client):\n        '''\n        Shutdown the manager process; will be registered as a finalizer\n        '''\n        if process.is_alive():\n            util.info('sending shutdown message to manager')\n            try:\n                conn = _Client(address, authkey=authkey)\n                try:\n                    managers.dispatch(conn, None, 'shutdown')\n                finally:\n                    conn.close()\n            except Exception:\n                pass\n\n            process.join(timeout=0.2)\n            if process.is_alive():\n                util.info('manager still alive')\n                if hasattr(process, 'terminate'):\n                    util.info('trying to `terminate()` manager process')\n\n                    try:\n                        process.terminate()\n                        process.join(timeout=0.1)\n            # XXX: catch the OS error ... something weird is going on here..\n                    except OSError:\n                        pass\n                    if process.is_alive():\n                        util.info('manager still alive after terminate')\n\n        state.value = managers.State.SHUTDOWN\n        try:\n            del managers.BaseProxy._address_to_local[address]\n        except KeyError:\n            pass\n\n    @functools.wraps(managers.BaseManager.start)\n    def start(self, *args, **kwargs):\n        try:\n            # disable SIGINT while we spawn\n            signal.signal(signal.SIGINT, signal.SIG_IGN)\n            super(self.__class__, self).start(*args, **kwargs)\n        finally:\n            # re-enable SIGINT\n            signal.signal(signal.SIGINT, signal.default_int_handler)\n\n    @classmethod\n    def auto_register(mng_cls, cls, proxytype=None, init_args=(),\n                      init_kwargs={}, **kwargs):\n        '''\n        Register shared object classes with a default proxytype.\n\n        Parameters\n        ----------\n        cls : type\n            class which is to be registered with the manager for use\n            as a shared object\n        proxytype : subclass of multiprocessing.managers.BaseProxy\n            Proxy object used to communicate with a shared instance of cls.\n            If None, then the following steps are attempted:\n            1) an attempt is made to call the class' build_proxy method which\n               is expected to provision and return a proxy object as well as\n               register with the manager any sub-proxies which it expects to\n               utilize.\n            2) failing that, a default -> make_inst_proxy(cls) will be used.\n        '''\n        assert type(cls) == type\n        typeid = cls.__name__\n        if proxytype is None:\n            try:  # to use cls defined proxy\n                proxytype = cls.build_proxy(mng_cls)\n            except AttributeError:\n                proxytype = make_inst_proxy(cls)\n                get_logger().debug(\"no proxy was provided for '{}' using \"\n                                   \"default '{}'\".format(cls, proxytype))\n\n        cls = functools.partial(cls, *init_args, **init_kwargs)\n        mng_cls.register(typeid, cls, proxytype=proxytype, **kwargs)\n\n\n# Register some more useful shared types\nCustomSyncMng.register('MpLock', mp.Lock, managers.AcquirerProxy)\n\n\nclass OrderedDictProxy(managers.DictProxy):\n    __dir__ = _proxy_dir\n    __repr__ = _repr\n\nCustomSyncMng.register('OrderedDict', OrderedDict, OrderedDictProxy)\n\n\ndef dict_of_proxies(value_type, mng, dict_typeid='OrderedDict'):\n    assert type(value_type) == type\n    name = value_type.__name__\n    assert name in mng._registry\n    dicttype, exp, meth_to_type, dictproxytype = mng._registry[dict_typeid]\n    proxy_name = '{}sDictProxy'.format(name)\n    # make a new subclass of the specified dict proxy type\n    # and make it contain sub-proxies of value_type\n    proxytype = type(proxy_name, (dictproxytype,), {})\n    proxytype._method_to_typeid_ = {'__getitem__': name}\n    mng.register(proxy_name, dicttype, proxytype)\n    return proxy_name, proxytype\n\n\ndef get_mng(address=None, authkey=None, proxy_map={},\n            _mng_type=CustomSyncMng,\n            _mng_cache={}, **kwargs):\n    '''\n    Return a custom multiprocessing.mangers proxy manager which has\n    some extra features.\n\n    Parameters\n    ----------\n    proxy_map : map\n        An optional map of python objects to proxy objects which will\n        immediately be 'auto registered' with the requested manager.\n        Proxies must inherit from multiprocessing.managers.BaseProxy\n    kwargs : same as for mp.BaseManager\n\n    Returns\n    -------\n    mng : instance of {} by default\n    '''.format(_mng_type)\n    try:\n        addr = kwargs.get('address', None)\n        mng = _mng_cache[addr]\n    except KeyError:\n        # TODO: calls to rypc if address is not found on this host\n        # eg. if kwargs['address'] not on localhost: rpyc.connect()\n        mng = _mng_type(**kwargs)\n        _mng_cache[addr] = mng\n\n    # register shared objects with mng cls\n    for cls, proxy in proxy_map.items():\n        mng.auto_register(cls, proxytype=proxy, **kwargs)\n    return mng\n","repo_name":"wwezhuimeng/switch","sub_path":"switchy/multiproc.py","file_name":"multiproc.py","file_ext":"py","file_size_in_byte":8732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"80"}
+{"seq_id":"73536452444","text":"import json\nimport xml.etree.ElementTree as ET\nimport sys\n\nclass Human:\n    def __init__(self, name, age, gender, birth_year):\n        self.name = name\n        self.age = age\n        self.gender = gender\n        self.birth_year = birth_year\n\n    def convert_to_json(self):\n        data = {\n            'name': self.name,\n            'age': self.age,\n            'gender': self.gender,\n            'birth_year': self.birth_year\n        }\n        return json.dumps(data, indent=2)\n\n    def convert_to_xml(self):\n        human_element = ET.Element('Human')\n        name_element = ET.SubElement(human_element, 'Name')\n        name_element.text = self.name\n\n        age_element = ET.SubElement(human_element, 'Age')\n        age_element.text = str(self.age)\n\n        gender_element = ET.SubElement(human_element, 'Gender')\n        gender_element.text = self.gender\n\n        birth_year_element = ET.SubElement(human_element, 'BirthYear')\n        birth_year_element.text = str(self.birth_year)\n\n        xml_str = ET.tostring(human_element, encoding='utf-8').decode('utf-8')\n        return xml_str\n\nif __name__ == \"__main__\":\n    if len(sys.argv) != 2:\n        print(\"Usage: python script.py [json/xml]\")\n        sys.exit(1)\n\n    output_format = sys.argv[1].lower()\n\n    human_instance = Human(\"Pears Brosnan\", 25, \"Male\", 1998)\n\n    if output_format == 'json':\n        json_data = human_instance.convert_to_json()\n        print(json_data)\n        with open('output.json', 'w') as json_file:\n            json_file.write(json_data)\n    elif output_format == 'xml':\n        xml_data = human_instance.convert_to_xml()\n        print(xml_data)\n        with open('output.xml', 'w') as xml_file:\n            xml_file.write(xml_data)\n    else:\n        print(\"Invalid format. Please use 'json' or 'xml'.\")\n\n","repo_name":"YevheniyaSilenko/pytestFramework","sub_path":"HumanProject/Human_converter.py","file_name":"Human_converter.py","file_ext":"py","file_size_in_byte":1789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"38859040209","text":"import json\r\nimport psycopg2\r\n\r\n# Define the filename\r\nfilename = \"C:/Users/ekodh/OneDrive/Desktop/Heart/db.json\"\r\n\r\nprint(\"Reading configuration from:\", filename)\r\n\r\ndef connectDB():\r\n    try:\r\n        print(\"Reading database configuration...\")  \r\n        with open(filename) as config_file:\r\n            config = json.load(config_file)\r\n        print(\"Connecting to the database...\")\r\n        db_connection = psycopg2.connect(\r\n            host=config['host'],\r\n            dbname=config['dbname'],\r\n            user=config['user'],\r\n            password=config['password'],\r\n            port=config['port']\r\n        )\r\n        print(\"Database connection established.\")\r\n        return db_connection\r\n    except Exception as error:\r\n        print('Error while connecting to the database:', error)\r\n        return None\r\n\r\nif __name__ == \"__main__\":\r\n    connection = connectDB()\r\n    if connection is not None:\r\n        try:\r\n            cursor = connection.cursor()\r\n\r\n            # Create users table\r\n            create_users_table = \"\"\"\r\n            CREATE TABLE IF NOT EXISTS users (\r\n                id SERIAL PRIMARY KEY,\r\n                username VARCHAR(100) NOT NULL,\r\n                password VARCHAR(100) NOT NULL,\r\n                gender VARCHAR(10),\r\n                email VARCHAR(100) NOT NULL,\r\n                phone VARCHAR(20)\r\n            )\r\n            \"\"\"\r\n            cursor.execute(create_users_table)\r\n\r\n            # Create contact_form table\r\n            create_contact_form_table = \"\"\"\r\n            CREATE TABLE IF NOT EXISTS contact_form (\r\n                id SERIAL PRIMARY KEY,\r\n                name VARCHAR(100) NOT NULL,\r\n                email VARCHAR(100) NOT NULL,\r\n                message TEXT\r\n            )\r\n            \"\"\"\r\n            cursor.execute(create_contact_form_table)\r\n\r\n            # Commit the changes\r\n            connection.commit()\r\n            print(\"Tables created successfully.\")\r\n\r\n        except psycopg2.Error as e:\r\n            print(\"Error creating tables:\", e)\r\n            connection.rollback()\r\n        finally:\r\n            if cursor:\r\n                cursor.close()\r\n            if connection:\r\n                connection.close()\r\n    else:\r\n        print('Database connection could not be established.')\r\n","repo_name":"PrasannaNarisetti/Heart-Attack-Prediction","sub_path":"connect2DB.py","file_name":"connect2DB.py","file_ext":"py","file_size_in_byte":2273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3987722918","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.chrome.options import Options\nimport time\nimport pyautogui\n\n\nopt = Options()\nopt.add_experimental_option(\"debuggerAddress\", \"localhost:8982\")\ndriver = webdriver.Chrome(\n    executable_path=\"C:/Users/musta/Desktop/click/chromedriver.exe\", chrome_options=opt)\n# driver.get(\"https://...........\")\n\nfor i in range(350):\n    pyautogui.dragTo(100, 150)\n    print('-------------------------------Click Deneme Sayısı :',i)\n    driver.execute_script(\"window.scrollTo(0,document.body.scrollHeight)\")\n    time.sleep(2)\n    ileri = driver.find_element(\n        By.XPATH, '/html/body/in-root/in-users/in-dashboard-layout/main/in-lecture/div/div[1]/div/div[2]/span[2]/in-loading-button/dx-button/div/span')\n\n    ileri.click()\n    pyautogui.dragTo(150, 100)\n    for x in range(60):\n        time.sleep(1)\n        print(x)\n\n\n","repo_name":"marcussteel/python-auto-click","sub_path":"click.py","file_name":"click.py","file_ext":"py","file_size_in_byte":909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74677475805","text":"# This module is meant to accompany the work in \"Unitary Entanglement Generation\n# in Hierarchical Quantum Networks\" Bapat et al 2018\n# IPython notebooks are provided which demonstrate the functions included by\n# reproducing the results printed in the text\n# Contact Zachary Eldredge at eldredge@umd.edu with questions\n\nimport matplotlib.pyplot as plt\nimport itertools as it\nimport random\nimport copy\nimport numpy as np\nimport networkx as nx\nfrom functools import reduce\nfrom operator import mul, add\nfrom math import log, sqrt, ceil\nimport os\nimport pandas as pd\n\n########################################\n## PART 1: Functions for Probabilistic Unitary Simulation\n########################################\n\n\n#1A: Weighted Graph construction\ndef build_weighted_hierarchical(H : nx.Graph,levels : int, wfun): \n    \"\"\" Function to build a hierarchical weighted graph out of a small, simple\n    graph\n\n    Parameters\n    ----------\n    H, networkx.Graph:\n        A graph which will be hierarchically expanded to a larger one\n    levels, int:\n        Number of times to repeat the nesting process\n    wfun, function from int -> int or float:\n        A function that defines what weight to use for connections at the lth\n        level of the hierarchy.\n\n    Returns\n    -------\n    hierarchy, Graph:\n        The iterated hierarchical product of H with itself and the appropriate\n        weight function\n    \"\"\"\n    # Build a hierarchical product of one graph ('H') with 'levels' levels,\n    # where the l-th level has weight wfun(l) Note that this identical to the\n    # build_hierarchical function, and I refer you to that function for the\n    # algorithm that constructs the graph. In this function, we use the same\n    # logic except that we add the weight according to the passed function\n    label_list = list(it.product(list(H.nodes()),repeat=levels))\n    hierarchy = nx.Graph()\n    for i in label_list: # Add all those nodes\n        hierarchy.add_node(i)\n    \n    for node in label_list:\n        node_num = hierarchyToNumber(node, len(H.nodes()))\n        for l in range(levels):\n            base = node[:-(1+l)] # get the first part of the node\n            for h in H.edges(node[-(l+1)]): # for every edge in base graph\n                connected_node = base + (h[-1],) + tuple(0 for i in range(l))\n                connected_node_num = hierarchyToNumber(connected_node, \n                        len(H.nodes()))\n                hierarchy.add_edge(node, connected_node, weight = wfun(l)) \n                # add the corresponding edge in this hierarchy graph\n            if node[-(l+1)] != 0:\n                break\n    \n    return hierarchy\n\ndef hierarchyToNumber(digits, b):\n    \"\"\" Function to convert an address in the hierarchy to a number.\n\n    Parameters\n    ----------\n    digits, tuple of ints:\n        The address in the hierarchy.\n    b, int:\n       The order of the base graph for the hierarcical product.\n\n    Returns\n    -------\n    count, int:\n        A number which can be used to index the hierarchy.\n    \"\"\"\n\n    count = 0\n    for pos, d in enumerate(digits):\n        count += d*(b**pos)\n    return count\n\n#1B: GHZ Creation (\"Color Spread\") Simulation\ndef nx_color_spread(init_pt, graph,maxtsteps):\n    \"\"\" Function to perform the random simulation of spreading the GHZ state on\n    a weighted graph\n\n    Parameters\n    ----------\n    init_pt, any type:\n        Label for the initial point we are spreading from. Should be present in\n        the graph.\n    graph, Graph:\n        Weighted graph describing connections between nodes. Weight of each node\n        will be the probability of success\n    maxtsteps, int:\n        Maximum number of timesteps to run simulation for\n\n    Returns\n    -------\n    color_count, list of ints:\n        List whose ith element is the number of converted nodes at the ith\n        timestep\n    boundary_count, list of ints:\n        List whose ith element is the size of the boundary at the ith timestep\n    graph, Graph:\n        The graph (complete with converted nodes recolored blue) after the final\n        timestep\n    \"\"\"\n    # Try to probabilistically color the graph and see how long it takes\n    # Function will return a list of how many points were colored at each time\n    # step\n    color_count = [1]\n    color_dict = {}\n    for x in graph.nodes():\n        color_dict[x] = 'white'\n    color_dict[init_pt] = 'blue' # I like blue.\n    nodes = [init_pt]\n    boundary = list(graph[init_pt].keys())\n    boundary_count = [len(boundary)]\n    for t in range(maxtsteps):\n        new_boundary = copy.deepcopy(boundary) # Make a copy of the current\n                                               # boundary to update as we go\n        for b in boundary: # Look at each node b on the boundary\n            for n in graph[b]: \n                # Look at each node connected to those nodes\n                if color_dict[n] == 'blue': \n                # If they are blue, there is chance they spread the entanglement\n                    if random.uniform(0,1) <= graph[n][b]['weight']:\n                        # weight provides the probability of spread\n                        color_dict[b] = 'blue' # color that node\n                        nodes.append(b)\n                        # Now that we have added the node b, we need to remove\n                        # it from the list of the boundary\n                        new_boundary.remove(b)\n                        for new_b in graph[b]:\n                            # Don't add already-added nodes to boundary!\n                            if color_dict[new_b] != 'blue':\n                                # Don't double up in the boundary\n                                if new_b not in new_boundary: \n                                    new_boundary.append(new_b)\n                if color_dict[b] == 'blue':\n                    break # Break out of this loop\n        boundary = new_boundary\n        color_count.append(len(nodes))\n        boundary_count.append(len(boundary))\n    return color_count\n\n\ndef run_trials(graph, ntrials, init_pt, maxtsteps = 10000):\n    \"\"\" Function to run many trials of the function nx_color_spread and report\n    the average.\n\n    Parameters\n    ----------\n    graph, nx.Graph:\n        The graph which the GHZ state creation is being tried on.\n    ntrials, int:\n        Number of trials to run.\n    init_pt:\n        A node label in graph which is identified as the inital qubit in state\n        |+> from which the GHZ state will be created.\n\n    Returns\n    -------\n    mean_time, float:\n        The average number of steps required to complete the state creation.\n    \"\"\"\n    trials = []\n    for trial in range(ntrials):\n        trials.append(nx_color_spread(init_pt, graph, maxtsteps).index(len(graph)))\n\n    mean_time = np.mean(trials)\n    return mean_time\n\n#1C Analytic functions for weighted diameters\ndef nn_fit_fn(x):\n    \"\"\" Function to guess from analytics the time requried to complete the GHZ\n    state creation on the nearest-neighbor 2D grid when starting from one\n    corner.\n\n    Parameters\n    ----------\n    x, int:\n        Size of the grid, in total number of qubits.\n\n    Returns\n    -------\n    The total distance that needs to be traversed by two-qubit gates to create\n    the GHZ state.\n    \"\"\"\n    return 2*(sqrt(x) - 1)\n\ndef hier_fit_fn(x, alpha,unit_size):\n    \"\"\" Function to guess from analytics the time requried to complete the GHZ\n    state creation on the hierarchy when starting from a bottom-level node.\n\n    Parameters\n    ----------\n    x, int:\n        Size of the graph, in total number of qubits.\n    alpha, float:\n        Scaling constant of the graph.\n    unit_size, int:\n        Order of the base graph in the hierarchy.\n\n    Returns\n    -------\n    The total distance that needs to be traversed by two-qubit gates to create\n    the GHZ state.\n    \"\"\"\n    #Note that, as dicussed in the paper, we convert alpha (the weight that\n    # scales the probabilities) to 1/alpha (to get estimated times)\n    levels = log(x, unit_size)\n    beta = 1/alpha\n    hier_fit = (beta**levels + beta**(levels - 1)  - 2)/(beta - 1)\n    return hier_fit\n\n########################################\n## PART 2: Functions for Circuit Placement \n########################################\n\n# note the circuit placement notebook will also call some Part 1 functions\n\n\n#2A: Functions that aren't (directly) related to parition and rotate algorithm\ndef get_random_comp_graph(nqubits, ngates):\n    \"\"\" Function to build a random computational graph.\n\n    Parameters\n    ----------\n    nqubits, int:\n        Number of qubits which will be included in the graph\n    ngates, int: \n        The total number of gates in the circuit the computational graph\n        represents\n\n    Returns\n    -------\n    comp_graph, nx.Graph:\n        A computational graph for a random circuit with ngates nodes and total\n        edge weigth ngates\n    \"\"\"\n    comp_graph = nx.Graph() # Create an empty graph\n    for q in range(nqubits): # Add all the desired nodes\n        comp_graph.add_node(q)\n    for g in range(ngates): # Now we will add all the edges\n        # Pick two random points\n        choose = np.random.choice(np.arange(nqubits),(2,),replace=False) \n         # If we've already got that edge, (there is already a gate)\n        if comp_graph.has_edge(*choose):\n             # increase its weight by one (add another gate)\n            comp_graph[choose[0]][choose[1]]['weight'] += 1\n        else:\n            comp_graph.add_edge(*choose, weight = 1) # Otherwise make a new edge\n    return comp_graph\n\n\ndef length_cost(c, metric, mapping):\n    \"\"\" Function to evaluate the cost of a computational graph C being placed on\n    physical architecture.\n\n    Parameters\n    ----------\n    c, nx.Graph:\n        The computational graph. c[i][j]['weight'] yields the total number of\n        gates between qubit i and qubit j in the algorithm to be placed.\n\n    metric, nx.Graph:\n        The graph representing the physical architecture (the metric graph).\n        metric[i][j] exists if and only if the nodes i and j can perform a\n        two-qubit gate between them.\n\n    mapping, dictionary:\n        A dictionary where every key is a node label from the comptuational\n        graph and every value is a node label from the metric graph,\n        representing the proposed circuit placement. Should be a one-to-one map. \n\n    Returns\n    -------\n    cost, int or float:\n        The total cost of the mapping, that is, the total distance traversed by\n        all gates.\n    \"\"\"\n    # First we reverse the mapping\n    rev_mapping = {v:k for k,v in mapping.items()}  \n    # A note from the authors: why use rev_mapping like this, why not make the\n    # map in that order to start with?\n    # The reason is that in other code we have developed, we have used\n    # rev_mapping to relabel the nodes, which networkx allows you to do easily\n    # when the dictionary is in this order. \n    cost = 0 # Initialize the cost\n    for i, j in c.edges(): \n        # For every edge, accumulate the cost and multiply by the weight in c\n        cost += nx.shortest_path_length(metric, source = rev_mapping[i], \n                target = rev_mapping[j])*c[i][j]['weight']\n    return cost\n\n\n#2B: Partition-and-Rotate Algorithm Functions\ndef pr_split_nodes(cgraph, node_list, k):\n    \"\"\" Function which uses partition-and-rotate to produce a grouping of the\n    nodes that can be used to place the circuit on a hierarchical graph. \n\n    Parameters\n    ----------\n    cgraph, nx.Graph:\n        Computational graph to cluster\n\n    node_list, list of labels from cgraph:\n        Nodes in node_list which will be clustered (used because this function\n        is recursive)\n\n    k, int:\n        Number of partitions to create in the \"partition\" part of the algorithm \n\n    Returns\n    -------\n    cluster_list, a list of lists of lists, etc:\n        A list which can be fed to convert_split_list_to_dict() to produce the\n        circuit mapping. In this list, nodes in the same sub-hierarchies are in\n        the same sub-lists, and the first node or cluster in a list is at the\n        root of that hierarchy.\n    \"\"\"\n    \n    # PARTITION\n\n    if len(node_list) <=  k: # Everything in one cluster? no need to split!\n        cluster_list = node_list # Return the list\n        \n    else:\n        # Perform clustering on this set of nodes\n        if len(nx.subgraph(cgraph,node_list).edges()) > 0: \n            # Assuming there are edges, hand it off to paritioning subroutine\n            cluster_list = metis_partition(nx.subgraph(cgraph,node_list), k)\n        else:\n            # If there are no edges, just chop it into three, it doesn't matter\n            cluster_list = list(np.array_split(node_list,3)) \n        # Now for each sublist, call pr_split_nodes (this function!) again\n        for l in enumerate(cluster_list):\n            # Now, we replace every list element with an element which is split.\n            # this recursion continues until we only have k nodes in the\n            # subgraph in question\n\n            cluster_list[l[0]] = pr_split_nodes(cgraph,l[1], k)     \n\n    # ROTATE\n\n    # Next, we sort each cluster in terms of the number of connections it has\n    # leading outside the cluster. So for instance, at this point cluster_list\n    # should be:\n    # [ [cluster 1 ], [cluster 2], [cluster 3]] \n    # We now look at each of them in turn and ask which one has the most\n    # connections (in Cgraph) that lead to none of the others. That one gets the\n    # 0-coordinate. See the function count_out_from_set for more info.\n\n    # Each cluster starts out assuming it has no outward connection\n    out_scores = [0]*k\n    for l in range(k):\n        # Then for each one we count how many outward connections it has\n        out_scores[l] = count_out_from_set(cgraph, cluster_list[l], node_list) \n\n    # Now sort by those scores\n    cluster_list = sorted(cluster_list, key = lambda x: \n            count_out_from_set(cgraph, x, node_list), reverse = True) \n\n    return cluster_list\n\n\ndef metis_partition(graph, nparts):\n    \"\"\" Function which uses the Metis software package to partition a graph into\n    several parts which are minimally connected and perfectly balanced. \n\n    Parameters\n    ----------\n    graph, nx.Graph:\n        Graph to partition\n    nparts, int:\n        Number of parts to partition the graph into \n\n    Returns\n    -------\n    A list of nparts lists, where the elements which share a sublist belong to\n    the same partition\n    \"\"\"\n\n    # This function works through what I will confess is an ugly hack: it writes\n    # the graph to file using my function write_metis and then calls the Metis\n    # command-line tool\n\n    # Write the file\n    write_metis(graph, './temp')\n    # Execute Metis\n    os.system('gpmetis -ptype=rb ' + './temp ' + str(nparts) + ' > /dev/null')\n    # Remove the file we wrote\n    os.system('rm ./temp')\n    \n    # Now, we're going to read in the file and put it into a numpy array\n    part_data = np.empty(len(graph), int)\n    with open('./temp.part.' + str(nparts)) as f: # Open the file\n        # The kth line has information on the kth node\n        for k,line in enumerate(f): \n            part_data[k] = int(line[0]) \n            # Node k will now be in partition labeled by part_data[k]\n    os.system('rm ./temp.part.' + str(nparts)) # Delete that file\n\n    # On semi-rare occasions, METIS does not yield a balanced partition, so\n    # here's some code to fix that. I think this tends to happen if the graph is\n    # disconnected or something, I'm not sure.\n\n    # First, count all the different values in part_data \n    vals, inds, counts =  np.unique(part_data, \n            return_index = True, return_counts = True)\n    \n    # IF all the partitions were the same size, every element of counts would be\n    # the same, there'd be no standard deviation, so we use that as a diagnostic\n    while np.std(counts) > 0: \n        # find indices with too many and too few\n        k_to_reduce = np.where(counts == max(counts)) \n        k_to_increase = np.where(counts == min(counts)) \n        # flip the first in k_to_reduce to be in k_to_increase\n        part_data[inds[k_to_reduce]] = k_to_increase \n        # redo standard dev calculation\n        vals, inds, counts = np.unique(part_data, return_index = True, \n                return_counts = True)\n    \n    # Alright, now we want to turn our partition data into a list of lists (how\n    # we handle the data elsewhere). partition will be that list of lists;\n    # initialize it empty\n    partition = np.empty((nparts, len(graph)//nparts), int)\n\n    for i in range(nparts): \n        # For every partition, find every node whose corresponding part_data\n        # indicates it belongs there and put it in that array\n        partition[i,:] = np.array(graph.nodes())[np.where(\n            np.array(part_data) == i)[0]]\n\n    # Then return that array as a list-of-lists\n    return [list(i) for i in partition] \n\ndef write_metis(graph, filename):\n    \"\"\" Function to write a NetworkX Graph object to file in a form that can\n    then be acted on by the Metis command-line program.\n\n    Parameters\n    ----------\n    graph, nx.Graph:\n        Graph to write to file\n\n    filename, string:\n        filename to use\n\n    Returns\n    -------\n    None\n    \"\"\"\n    metis_file = [str(len(graph)) + ' ' + str(len(graph.edges())) + ' 001\\n']\n    \n    # Metis doesn't want a label above number of nodes\n    metis_node_label_dict = {j:i for i,j in enumerate(graph.nodes())} \n    \n    for node in graph:\n        node_string = ''\n        for adj_node in graph[node]:\n            node_string += ' ' + str(metis_node_label_dict[adj_node] + \n                    1) + ' ' + str(graph[node][adj_node]['weight'])\n        node_string += '\\n'\n        metis_file.append(node_string)\n        \n    with open(filename,'w') as f:\n        for n in metis_file:\n            f.write(n)\n            \n    return\n\ndef count_out_from_set(cgraph, set_to_eval, set_to_leave):\n    \"\"\" Function which counts how many connections lead out of a set. Used in\n    rotation, since we want to ensure this set ends up being at the root of a\n    hierarchy.  \n\n    Parameters\n    ----------\n    cgraph, nx.Graph:\n        Computational graph partition-and-rotate is being performed on.\n\n    set_to_eval:\n        Set we are interested in seeing the total number of outward connections\n        from.\n\n    set_to_leave:\n        Set of nodes whose edges we want to discount\n     \n\n    Returns\n    -------\n    count, float:\n        Total weight of all edges that originate in set_to_eval and end\n        somewhere besides set_to_leave\n    \"\"\"\n    count = 0 # initiate the count\n   \n    # we flatten so we have an easy to work with list of nodes of interest\n    for node in flatten(set_to_eval): \n        for edge in cgraph[node]: # look at every one of those edges\n            # make sure they don't go to where we're ignoring\n            if edge not in set_to_leave:\n                # add that weight to the count\n                count+=cgraph[node][edge]['weight'] \n    return count\n\ndef flatten(l):\n    \"\"\" Function for flattening lists-of-lists-of-lists in Python. \n\n    Parameters\n    ----------\n    l, lists of lists of lists\n\n    Returns\n    -------\n    A flattened version, a list of the individual items\n    \"\"\"\n\n    # We work with lists-of-lists-of-lists-etc rather than numpy arrays, because\n    # we need to be able to replace the list elements with further lists for our\n    # recursive scheme to work. BUT python lists can't be easily flattened\n    # unlike numpy arrays. Since sometimes we just need the list without all the\n    # smaller-scale detail, we need this function\n\n    return list(np.array(l).flatten())\n\n\ndef convert_split_list_to_dict(in_list):\n    \"\"\" Function to convert a \"splitting list\" of the type returned by our\n    clustering algorithm into a dictionary with node mappings.\n\n    Parameters\n    ----------\n    in_list, list of lists (of lists, etc):\n        The output of a function like pr_split_nodes. This is a nested list of\n        numbers in which qubits in the same sub-hierarchy are in the same\n        sub-list, and the first entry in every element is the node/cluster which\n        is at the root of that sub-hierarchy.\n\n    Returns\n    -------\n    dictionary, a dictionary of tuple:int pairings:\n        This tells us which node in the computational graph each node in the\n        hierarchy corresponds to, with each node in the hierarchy represented by\n        a tuple (equivalently, a base-k number where k is the order of the base\n                graph).\n    \"\"\"\n    splitting_array = np.array(in_list)\n    nlevels = len(splitting_array.shape) # we can deduce the number of levels\n    k = splitting_array.shape[0] # as well as the order of the base graph k\n    # Now just use the fact that every node in hierarchy is a base-k number\n    dictionary = {i:splitting_array[i] for i in \n            it.product(tuple(range(k)),  repeat = nlevels)}\n    return dictionary\n","repo_name":"zeldredge/unitary-modular","sub_path":"modular_entanglement.py","file_name":"modular_entanglement.py","file_ext":"py","file_size_in_byte":20952,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"13808479711","text":"#\n# Perceptron\n#\nfrom random import uniform\n\nLEARNING_RATE = 0.001\nCONST_A = uniform(0, 10)\nCONST_B = uniform(-0.5, 0.5)\n\ndef f(x):\n    return CONST_A * x + CONST_B\n\ndef translateX(value):\n    return int(-value * 200 + 200)\n\ndef translateY(value):\n    return int(value * 200 + 200)\n\n#\n# Perceptron\n#\nclass Perceptron:\n    ''' Perceptron '''\n    def __init__(self, array_size, canvas=None):\n        self.canvas = canvas\n        self.neuron = Neuron()\n        self.points = []\n        if self.canvas is not None:\n            self.errorLabel = self.canvas.create_text(5, 5, text='Current error : -', anchor='nw')\n        for i in range(array_size):\n            self.points.append(Point(canvas=self.canvas))\n\n    def draw(self):\n        for point in self.points:\n            point.draw()\n\n    def learn(self):\n        errors = self.checkPoints()\n        # FIXME: self.neuron.learn(error) for error in errors\n        for error in errors:\n            self.neuron.learn(error)\n        # TODO: valider la gestion des erreurs\n        error = sum(abs(x.error) for x in errors)\n        if self.canvas is not None:\n            self.canvas.itemconfig(self.errorLabel, text='Current error : %s' % error)\n        else:\n            print('Total error : %s' % error)\n        return error\n    \n    def guessY(self, x):\n        w0 = self.neuron.weights[0]\n        w1 = self.neuron.weights[1]\n        w2 = self.neuron.bias\n        return -(w0/w1) * x - (w2/w1)\n\n    def checkPoints(self):\n        errors = []\n        for point in self.points:\n            guess = self.neuron.guess([point.x, point.y])\n            point.cheked = (point.label ==  guess)\n            errors.append(Error([point.x, point.y], point.label-guess))\n        return errors\n\n#\n# Neuron\n#\nclass Neuron:\n    def __init__(self):\n        self.weights = [uniform(-1, 1), uniform(-1, 1)]\n        self.bias = uniform(-1, 1)\n    \n    def guess(self, inputs):\n        sum = self.bias\n        for i in range(len(self.weights)):\n            sum += inputs[i] * self.weights[i]\n        if(sum >= 0):\n            return 1\n        return -1\n\n    def learn(self, error):\n        self.bias += error.error * LEARNING_RATE\n        for i in range(len(self.weights)):\n            self.weights[i] += error.inputs[i] * error.error * LEARNING_RATE\n    \n#\n# Error\n#\nclass Error:\n    def __init__(self, inputs, error):\n        self.inputs = inputs\n        self.error = error\n\n#\n# Point\n#\nclass Point:\n    def __init__(self, x=None, y=None, canvas=None):\n        self.canvas = canvas\n        self.r = 5\n        self.cheked = False\n\n        if x is None:\n            self.x = uniform(-1, 1)\n        else:\n            self.x = x\n\n        if y is None:\n            self.y = uniform(-1, 1)\n        else:\n            self.y = y\n\n        if self.y >= f(self.x):\n            self.label = 1\n        else:\n            self.label = -1\n\n        if self.canvas is not None:\n            fill_color = '#0000FF'\n            if self.label == 1: #self.cheked:\n                fill_color = '#00FF00'\n            self.gx_proxy = self.canvas.create_oval(translateX(self.x)-self.r, translateY(self.y)-self.r, translateX(self.x)+self.r, translateY(self.y)+self.r, fill=fill_color)\n    \n    def draw(self):\n        if self.canvas is not None:\n            fill_color = '#0000FF'\n            if self.label == 1: #self.cheked:\n                fill_color = '#00FF00'\n            self.canvas.coords(self.gx_proxy, translateX(self.x)-self.r, translateY(self.y)-self.r, translateX(self.x)+self.r, translateY(self.y)+self.r)\n            self.canvas.itemconfig(self.gx_proxy, fill=fill_color)\n        else:\n            print('Point[%s, %s, %s] : %s' % (self.x, self.y, self.label, self.cheked))\n","repo_name":"vincentlambert/python-ml","sub_path":"src/perceptron/perceptron.py","file_name":"perceptron.py","file_ext":"py","file_size_in_byte":3688,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"72242101083","text":"# coding=utf-8\nfrom __future__ import absolute_import, print_function\n\nfrom suanpan.app.arguments import Folder, String, Float, Int\nfrom suanpan.app import app\nfrom tools import cli\nfrom lib.cli import FullHelpArgumentParser\n\n\n@app.input(Folder(key=\"inputData\"))\n@app.param(\n    String(\n        key=\"sortBy\",\n        default=\"face\",\n        help=\"blur, face, face-cnn, face-cnn-dissim, face-yaw, hist, hist-dissim\",\n    )\n)\n@app.param(\n    Float(\n        key=\"refThreshold\",\n        default=-1.0,\n        help=\"(-1.0, 10.0) Defaults: face-cnn 7.2, hist 0.3\",\n    )\n)\n@app.param(String(key=\"finalProcess\", default=\"rename\", help=\"folders, rename\"))\n@app.param(String(key=\"groupBy\", default=\"hist\", help=\"blur, face-cnn, face-yaw, hist\"))\n@app.param(Int(key=\"bins\", default=5, help=\"(1, 100)\"))\n@app.output(Folder(key=\"outputData\"))\ndef SPSort(context):\n    args = context.args\n\n    PARSER = FullHelpArgumentParser()\n    SORT = cli.SortArgs(\n        PARSER, \"sort\", \"This command lets you sort images using various methods.\"\n    )\n\n    ARGUMENTS = PARSER.parse_args(\n        [\n            \"--input\",\n            args.inputData,\n            \"--output\",\n            args.outputData,\n            \"--sort-by\",\n            args.sortBy,\n            \"--ref_threshold\",\n            str(args.refThreshold),\n            \"--final-process\",\n            args.finalProcess,\n            \"--group-by\",\n            args.groupBy,\n            \"--bins\",\n            str(args.bins),\n        ]\n    )\n    ARGUMENTS.func(ARGUMENTS)\n\n    return args.outputData\n\n\nif __name__ == \"__main__\":\n    SPSort()\n","repo_name":"yanqinghao/AiLab-Faceswap","sub_path":"components/docker/SPSortSwap.py","file_name":"SPSortSwap.py","file_ext":"py","file_size_in_byte":1576,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"23013116386","text":"import requests\n\nr = requests.get('http://api.icndb.com/jokes/random')\n\n\ndata = r.json()\n\nprint('Joke #{}: '.format(data['value']['id']))\n\nprint('{}'.format(data['value']['joke']))\n\"\"\"don't put imports in functions. You generally won't have to do that ever\"\"\"\n#we will be using this script A LOT. We should know how to drill down in to a JS\n#object and figure out its path/tree. Need to be able to look at a JSON piece and\n#create this path\n","repo_name":"tabdansby/newRepo","sub_path":"nightclass/playing_api.py","file_name":"playing_api.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34781102372","text":"def dewp_temp_to_ah(D, T):\n    \"\"\"Determine absolute humidity from the Dewpoint and the temperature\"\"\"\n    # (Invalid name) pylint: disable=C0103\n\n    k = 0.21668\n    d = 273\n    a = -4.9283\n    c = 23.5518\n    b = -2937.4\n    return k / (T + d) * (D + d) ** a * 10 ** (c + (b / (D + d)))\n","repo_name":"SandervanNoort/mconvert","sub_path":"mconvert/newtools/dewp_temp_to_ah.py","file_name":"dewp_temp_to_ah.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"25463736038","text":"import torch\r\nfrom torch import nn\r\n\r\n\r\nclass LTSFLinear(nn.Module):\r\n    def __init__(self, latent_dim, lookback):\r\n        super(LTSFLinear, self).__init__()\r\n        self.latent_dim = latent_dim\r\n        self.lookback = lookback\r\n\r\n        self.ll = nn.Linear(latent_dim * lookback, latent_dim)\r\n\r\n    def forward(self, x):\r\n        x = x.view(x.shape[0], -1)\r\n        return self.ll(x[:, -self.lookback * self.latent_dim :])\r\n\r\n\r\nclass LTSFNLinear(nn.Module):\r\n    def __init__(self, latent_dim, lookback):\r\n        super(LTSFNLinear, self).__init__()\r\n        self.latent_dim = latent_dim\r\n        self.lookback = lookback\r\n\r\n        self.ll = nn.Linear(latent_dim * lookback, latent_dim)\r\n\r\n    def forward(self, x):\r\n        last_l = x[:, 0].unsqueeze(1).repeat(1, x.shape[1], 1)\r\n        x = x - last_l\r\n        x = x.view(x.shape[0], -1)\r\n        return self.ll(x[:, -self.lookback * self.latent_dim :]) + last_l[:, 0]\r\n\r\n\r\nclass moving_avg(nn.Module):\r\n    \"\"\"\r\n    Moving average block to highlight the trend of time series\r\n    \"\"\"\r\n\r\n    def __init__(self, kernel_size, stride):\r\n        super(moving_avg, self).__init__()\r\n        self.kernel_size = kernel_size\r\n        self.avg = nn.AvgPool1d(\r\n            kernel_size=kernel_size, stride=stride, padding=0\r\n        )\r\n\r\n    def forward(self, x):\r\n        # padding on the both ends of time series\r\n        front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1)\r\n        end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1)\r\n        x = torch.cat([front, x, end], dim=1)\r\n        x = self.avg(x.permute(0, 2, 1))\r\n        x = x.permute(0, 2, 1)\r\n        return x\r\n\r\n\r\nclass series_decomp(nn.Module):\r\n    \"\"\"\r\n    Series decomposition block\r\n    \"\"\"\r\n\r\n    def __init__(self, kernel_size):\r\n        super(series_decomp, self).__init__()\r\n        self.moving_avg = moving_avg(kernel_size, stride=1)\r\n\r\n    def forward(self, x):\r\n        moving_mean = self.moving_avg(x)\r\n        res = x - moving_mean\r\n        return res, moving_mean\r\n\r\n\r\nclass LTSFDLinear(nn.Module):\r\n    \"\"\"\r\n    Decomposition-Linear\r\n    \"\"\"\r\n\r\n    def __init__(self, latent_dim, lookback):\r\n        super(LTSFDLinear, self).__init__()\r\n\r\n        kernel_size = 25\r\n        self.decompsition = series_decomp(kernel_size)\r\n\r\n        self.latent_dim = latent_dim\r\n        self.lookback = lookback\r\n\r\n        self.llt = nn.Linear(latent_dim * lookback, latent_dim)\r\n        self.lls = nn.Linear(latent_dim * lookback, latent_dim)\r\n\r\n    def forward(self, x):\r\n        # x: [Batch, Input length, Channel]\r\n        seasonal_init, trend_init = self.decompsition(x)\r\n        trend_init = trend_init.view(trend_init.shape[0], -1)\r\n        seasonal_init = seasonal_init.view(seasonal_init.shape[0], -1)\r\n        trend_output = self.llt(\r\n            trend_init[:, -self.latent_dim * self.lookback :]\r\n        )\r\n        seasonal_output = self.lls(\r\n            seasonal_init[:, -self.latent_dim * self.lookback :]\r\n        )\r\n\r\n        x = seasonal_output + trend_output\r\n        return x\r\n\r\n\r\nclass MLP(nn.Module):\r\n    def __init__(self, layers, activation, use_batchnorm, add_last=False):\r\n        super(MLP, self).__init__()\r\n\r\n        if use_batchnorm:\r\n            raise NotImplementedError\r\n\r\n        if activation == \"ELU\":\r\n            self.activation = nn.ELU()\r\n        elif activation == \"Tanh\":\r\n            self.activation = nn.Tanh()\r\n        elif activation == \"sigmoid\":\r\n            self.activation = nn.Sigmoid()\r\n        else:\r\n            raise NotImplementedError(\r\n                \"unknown activation: {}\".format(activation)\r\n            )\r\n\r\n        self.activation = nn.Tanh()\r\n\r\n        self.fcs = nn.ModuleList(\r\n            [\r\n                nn.Linear(\r\n                    in_dim,\r\n                    out_dim,\r\n                )\r\n                for (\r\n                    in_dim,\r\n                    out_dim,\r\n                ) in layers\r\n            ]\r\n        )\r\n        self.fcs_bn = nn.ModuleList(\r\n            [\r\n                nn.BatchNorm1d(in_dim)\r\n                for (\r\n                    in_dim,\r\n                    _,\r\n                ) in layers\r\n            ]\r\n        )\r\n\r\n        self.add_last = add_last\r\n        print(add_last)\r\n\r\n        self.bias = nn.Parameter(torch.Tensor(layers[-1][1]))\r\n\r\n    def set_freeze(self, freeze):\r\n        for param in self.parameters():\r\n            param.requires_grad = not freeze\r\n\r\n    def forward(self, x):\r\n        orig_dim = len(x.shape)\r\n        orig_shape = x.shape\r\n\r\n        dx = x\r\n\r\n        if orig_dim == 3:\r\n            dx = dx.view(-1, orig_shape[2])\r\n\r\n        # dx = self.fcs_bn[0](dx)\r\n        for fc, bn in zip(self.fcs[:-1], self.fcs_bn[1:]):\r\n            dx = fc(dx)\r\n            # dx = bn(dx)\r\n            if dx.size(-1) == fc.out_features:\r\n                ddx = self.activation(dx)\r\n                dx = dx + ddx\r\n            else:\r\n                dx = self.activation(dx)\r\n\r\n        dx = self.fcs[-1](dx)\r\n\r\n        if orig_dim == 3:\r\n            dx = dx.view(orig_shape[0], orig_shape[1], -1)\r\n\r\n        if self.add_last:\r\n            return x + dx / 100\r\n        else:\r\n            return dx\r\n","repo_name":"MIMUW-RL/Unified-Long-Horizon-Time-Series-Benchmark","sub_path":"src/model/components/mlp.py","file_name":"mlp.py","file_ext":"py","file_size_in_byte":5152,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"86"}
+{"seq_id":"9963743473","text":"import unittest\nimport random\nimport os\nfrom definitions import EDRM_DIR, TEMP_DIR\nfrom test.context import documents, directories, trec, db\n\n\nclass TC(unittest.TestCase):\n    def test_attachment_types(self):\n        d1 = directories.general.compose_dir(EDRM_DIR, \"Allen\", \"P\")\n        d2 = directories.general.compose_dir(EDRM_DIR, \"Arnold\", \"J\")\n        res = set()\n\n        def build_set(_, file_dir):\n            with open(file_dir, encoding=\"utf-8\") as file:\n                types = documents.attachment.parse_attachment_types(file)\n                for type_id in types:\n                    res.add(type_id)\n\n        for d in [d1, d2]:\n            directories.general.for_each_file(d, build_set)\n\n        self.assertEqual(len(res), 9)\n        self.assertCountEqual(res, {\n            \"application/msexcell\", \"application/msword\", \"application/pdf\", \"application/mspowerpoint\",\n            \"application/octet-stream\", \"application/rtf\", \"image/gif\", \"image/jpeg\", \"image/bmp\"\n        })\n\n    def test_process(self):\n        export_dir = os.path.join(TEMP_DIR, \"test_doc_attachments\")\n        if not os.path.exists(export_dir):\n            os.makedirs(export_dir)\n        cached = trec.docids.Cached()\n        doc_ids = trec.docids.doc_ids()\n        with db.doc_to_dir.Reader() as reader_dir:\n            with db.attachment_type.Reader() as reader_attach:\n                for n in range(20):\n                    doc_id = random.choice(doc_ids)\n                    doc_id = cached.find(doc_id)\n                    if not documents.attachment.is_attachment(doc_id):\n                        continue\n                    with reader_dir.open(doc_id) as file:\n                        lst = documents.attachment.process(file)\n                    self.assertIsNotNone(lst)\n                    attachment_type = reader_attach.find(doc_id)\n                    if attachment_type == \"application/msexcell\":\n                        seen = set()\n                        lst = [w for w in lst if not (w in seen or seen.add(w))]\n                    file_dir = os.path.join(export_dir, str(n))\n                    with open(file_dir, mode=\"w\", encoding=\"utf-8\") as file:\n                        file.write(doc_id + \"\\n\")\n                        for token in lst:\n                            file.write(token + \"\\n\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"lsylove/tstutorial","sub_path":"test/documents/test_attachment.py","file_name":"test_attachment.py","file_ext":"py","file_size_in_byte":2353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"5256692696","text":"class Solution:\n    def maxProfit(self, prices):\n        profits = [a - b for a, b in zip(prices[1:], prices[:-1])]\n        l2r = self.helper(profits)\n        r2l = self.helper(profits[::-1])[::-1]\n\n        l2r.insert(0, 0)\n        r2l.append(0)\n\n        ans = 0\n\n        for i in range(len(l2r)):\n            ans = max(ans, l2r[i] + r2l[i])\n\n        return ans\n\n    def helper(self, profits):\n        curP = []\n        cur = 0\n        maxP = 0\n        for p in profits:\n            cur += p\n            if cur <= 0:\n                cur = 0\n            maxP = max(maxP, cur)\n            curP.append(maxP)\n        return curP\n\n\nprices = [1, 2, 1, 3, 7, 1]\nprices = [2, 1, 2, 0, 1]\n\ninst = Solution()\nprint(inst.maxProfit(prices))\n","repo_name":"nkukarl/leetcode","sub_path":"13.5 Best Time to Buy and Sell Stock III.py","file_name":"13.5 Best Time to Buy and Sell Stock III.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"341917437","text":"# day05\n# s=\"ABC DE\"\n# for ch in s:\n#     print(\"ch------>\",ch)\n#     print(ord(ch))\n# else:\n#     print(\"结束\")\n\n#练习：\n#  1.任意输入一段字符串,写程序做如下两件事:　　\n#l=input(\"请随意输入:\")\n# i=0\n# print(l[1])\n# for n in l:\n#    if n ==' ':\n#        i+=1\n# else:\n#     print(\"空格个数\",i)\n#-----while实现????\n# while i<len(l):\n# b=-1\n# s=input(\"输入:\")\n# while len(s)>=0:\n#  for a in s[b]:\n#    print(a)\n#    b-=1\n# index = len(s)-1\n# for ch in l[::-1]:　利用切片来实现，-1代表从右向左开始获取索引\n#     print (ch)\n# for x in range(1,21):\n#     print(x,end=' ')\n#     print()\n#公式x*(x+1)% 11== 8:的０－１００里的数\n# for x in range(1,101):\n#   if x*(x+1)% 11== 8:\n#     print(x)\n# y=0\n# for x in range(1,100,2):\n#     y+=x\n# print(y)\n#-----------练习－－－－－－－－－\n# 1234\n# 2345\n# 3456\n# 4567\n# y=int(input(\"请输入正方形长度:\"))\n# for x in range(y):\n#     j=x+1\n#     z = y+j\n#     for z in range(j,z):\n#        print(z,end=' ',flush=True)  \n#     print()\n\n#-----------continue\n# for x in range(5):\n#     if x==2:\n#         continue  #跳过２这个数\n#     print(x)    \n# for num in range(10):#跳过基数，打印偶数\n#     if num %2 == 1:　　#取余\n#         continue:\n#     print(num)\n# y=0    \n# for x in range(1,101):\n#     if x%2==0 or x%3==0 or x%5==0 or x%7==0: #条件或同时判断\n#         continue\n#     else:\n#         y+=x\n#     print(y)\n# if 2<3 and 2>3:\n#     print(\"dd\")\n# else:\n#     print(\"yy\")\n\n#------练习--------\n# L1=input(\"请输入文字\")\n# L2=input(\"请输入文字\")\n# L3=input(\"请输入文字\")\n# L =[]\n# L+=[L1]\n# L+=[L2]\n# L+=[L3]\n# print(L)\n#------------------\n# i=0\n# L=[]\n# while True:\n#  x=int(input(\"请输入正整数\"))\n#  if x>0:\n#        L+=[x]\n#        i+=x\n#  else:\n#        print(\"输入的数字为\" + str(L))\n#        print(\"输入的总和为\" + str(i))\n#        break\n\n#------------练习1-------\n\n#------------99乘法表------------   \n# i=0\n# for x in range(1,10):\n#     for y in range(1,11):\n#         if  y<10:\n#             print(x,\"*\",i+1,\"=\",x*(i+1),sep='',end=' ',flush=True)\n#             i+=1\n#         else :\n#             i=0\n#             print()\n#             continue    \n\n#-------------练习2------\n# 写一个程序,任意输入一个整数,判断这个数是否为素数prime\n#     素数(也叫质数),是只能被1和自身整数的正整数\n#     如:  2 3 5 7 11 13 17 19 ...\n#     提示:\n#       用排除法: 当判断x是否为素数时,只要让x分别除以\n#        2, 3, 4, 5, 6 ... x-1,只要有一次被整除,则x不是\n#        素数,否则x是素数\n#方法１---------------------------------------\nx = int(input('请输入一个整数: '))\nif x < 2:\n    print(x, '不是素数')\nelse:\n    # 用一个变量Flag作为标志,很假设x是素数,Flag=True\n    # 当不是素数时,把变量值改变False,最后由变量Flag的真假值\n    # 来判断x是否为素数\n    flag = True  # 先假设x为素数\n    for i in range(2, x):  # i为2,3,4,.... x-1\n        if x % i == 0:\n            # print(x, '不是素数')\n            flag = False\n            break\n    if flag:\n        print(x, '是素数')\n    else:\n        print(x, '不是素数')\n#方法２----------------------------------------\nx = int(input('请输入一个整数: '))\nif x < 2:\n    print(x, '不是素数')\nelse:\n    for i in range(2, x):  # i为2,3,4,.... x-1\n        if x % i == 0:\n            print(x, '不是素数')\n            break\n    else:\n        print(x, '是素数')\n\n\n#-------------练习3------\n# 输入一个整数,此整数代表树干的高度,打印一棵如下形状的圣\n#     诞树\n# i=int(input(\"请输入一个整数:\"))\n# h=0\n# for x in range(1,i+1):\n#     print (\" \"*(i-x)+\"*\"*(h+1),flush=True)\n#     h+=2\n# for y in range(1,i+1):\n#     print(\" \"*(i-1)+\"*\")\n\n#------------练习4--------\n# 算出 100 ~ 999 范围内的水仙花数(Narcissistic Number)\n#     水仙花数是指百位的3次方 + 十位的3次方 + 个位的3次方 等于原\n#     数的整数\n#     如:\n#       153 = 1**3 + 5**3 + 3**3\n#     答案:\n#       153, 370, ....\n\n# for x in range(100,1000):\n# w=567\n# w1=w/100\n# w2=w/10%10\n# w3=w%10\n# print(int(w1),int(w2),int(w3))    #5,6,7\nfor bai in range(1,10):\n    for shi in range(0,10):\n        for ge in range(0,10):\n            #print(bai,shi,ge)\n            x= bai *100 +shi *10 +ge\n            if x == bai **3+ shi **3+ge **3:\n                print(x)\n\n# L=[0,1.2,2,3,4,5,6,7,8]\n# #L2=L[1:8:2] #L2 = [1,3,5,7]\n# L[:]=[]\n# print(L)\n\n# L=[1,2,3,4]\n# L2=L\n# L=[]\n# print(L2)\n\n# L=[1,2,3,4,]\n# L2= L\n# L[:]=[] #此处与上面不同\n# print(L2)","repo_name":"timzk/AID1811","sub_path":"aid1811/pbase/Python/day05/day05.py","file_name":"day05.py","file_ext":"py","file_size_in_byte":4660,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"15184309801","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom .models import Question, Answer\nfrom .forms import QuestionForm, AnswerForm\nfrom .serializers import QuestionSerializer, AnswerSerializer\nfrom rest_framework import generics\nfrom django.contrib.auth.decorators import login_required\n\n\ndef question_list(request):\n    questions = Question.objects.all()\n    return render(request, 'question_list.html', {'questions': questions})\n\n\ndef question_detail(request, question_id):\n    question = get_object_or_404(Question, id=question_id)\n    answers = question.answer_set.all()\n    context = {'question': question, 'answers': answers}\n    return render(request, 'question_detail.html', context)\n\n\n@login_required\ndef create_question(request):\n    if request.method == 'POST':\n        form = QuestionForm(request.POST)\n        if form.is_valid():\n            question = form.save(commit=False)\n            question.author = request.user\n            question.save()\n            return redirect('QuestionAnswer:question_list')\n    else:\n        form = QuestionForm()\n\n    return render(request, 'create_question.html', {'form': form})\n\n\n@login_required\ndef answer_question(request, question_id):\n    question = get_object_or_404(Question, id=question_id)\n\n    if request.method == 'POST':\n        form = AnswerForm(request.POST)\n        if form.is_valid():\n            answer = form.save(commit=False)\n            answer.question = question\n            answer.author = request.user\n            answer.save()\n            return redirect('QuestionAnswer:question_detail', question_id=question_id)\n    else:\n        form = AnswerForm()\n\n    answers = question.answer_set.all()\n    context = {'form': form, 'question': question, 'answers': answers}\n    return render(request, 'answer_question.html', context)\n\n\n@login_required\ndef like_answer(request, answer_id):\n    answer = get_object_or_404(Answer, id=answer_id)\n    if request.user not in answer.likes.all():\n        answer.likes.add(request.user)\n    return redirect('QuestionAnswer:question_detail', question_id=answer.question.id)\n\n\n@login_required\ndef unlike_answer(request, answer_id):\n    answer = get_object_or_404(Answer, id=answer_id)\n    if request.user not in answer.unlikes.all():\n        answer.unlikes.add(request.user)\n    return redirect('QuestionAnswer:question_detail', question_id=answer.question.id)\n\n\nclass QuestionListAPIView(generics.ListAPIView):\n    queryset = Question.objects.all()\n    serializer_class = QuestionSerializer\n\n\nclass QuestionDetailAPIView(generics.RetrieveAPIView):\n    queryset = Question.objects.all()\n    serializer_class = QuestionSerializer\n\n\nclass AnswerListAPIView(generics.ListAPIView):\n    queryset = Answer.objects.all()\n    serializer_class = AnswerSerializer\n\n\nclass AnswerDetailAPIView(generics.RetrieveAPIView):\n    queryset = Answer.objects.all()\n    serializer_class = AnswerSerializer\n","repo_name":"JosephAntony123/Quora","sub_path":"quora/QuestionAnswer/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2904,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"11916516744","text":"\"\"\"Django models utilities.\"\"\"\n\n# Django\nfrom django.db import models\nfrom django.conf import settings\n\n\nclass TemplateModel(models.Model):\n    \"\"\" Manager base model.\"\"\"\n\n    created = models.DateTimeField(\n        \"created at\",\n        auto_now_add=True,\n        help_text=\"Date time on which the object was created.\",\n    )\n    modified = models.DateTimeField(\n        \"modified at\",\n        auto_now=True,\n        help_text=\"Date time on which the object was last modified.\",\n    )\n\n    class Meta:\n        \"\"\"Meta option.\"\"\"\n\n        abstract = True\n        get_latest_by = \"created\"\n        ordering = [\"-created\", \"-modified\"]\n\n\nclass GeoPoint(TemplateModel):\n    latitude = models.DecimalField(\n        max_digits=10,\n        decimal_places=settings.GEOPOINT_DECIMAL_PLACES,\n        blank=True,\n        null=True,\n    )\n    longitude = models.DecimalField(\n        max_digits=11,\n        decimal_places=settings.GEOPOINT_DECIMAL_PLACES,\n        blank=True,\n        null=True,\n    )\n    street_number = models.CharField(blank=True, null=True, max_length=50)\n    route = models.CharField(blank=True, null=True, max_length=50)\n    neighborhood = models.CharField(blank=True, null=True, max_length=50)\n    political = models.CharField(blank=True, null=True, max_length=50)\n    administrative_area_level_1 = models.CharField(blank=True, null=True, max_length=50)\n    administrative_area_level_2 = models.CharField(blank=True, null=True, max_length=50)\n    country = models.CharField(blank=True, null=True, max_length=50)\n    postal_code = models.CharField(blank=True, null=True, max_length=10)\n\n    @property\n    def formatted_address(self):\n        return \"{street_number} {route} {neighborhood} {political}, {administrative_area_level_1} {administrative_area_level_2} {postal_code}, {country}\".format(\n            street_number=self.street_number or \"\",\n            route=self.route or \"\",\n            neighborhood=self.neighborhood or \"\",\n            political=self.political or \"\",\n            administrative_area_level_1=self.administrative_area_level_1 or \"\",\n            administrative_area_level_2=self.administrative_area_level_2 or \"\",\n            country=self.country or \"\",\n            postal_code=self.postal_code or \"\",\n        )\n\n    def __str__(self):\n        \"\"\"Return details.\"\"\"\n        return \"created at {created} postiion  {latitude} | {longitude} \".format(\n            created=self.created, latitude=self.latitude, longitude=self.longitude,\n        )\n","repo_name":"rielsu/hitman-target-detector","sub_path":"api/geocodes/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"11931591505","text":"from rest_framework import generics\n\nfrom rest_framework import status, views, viewsets\nfrom rest_framework.response import Response\nfrom django.http import JsonResponse\n\nfrom rest_framework.decorators import  permission_classes, action\nfrom rest_framework.permissions import IsAuthenticated, IsAdminUser, AllowAny\nfrom commons.permissions import IsUserOrReadOnly\n\nfrom django.db.models import Q\nfrom django.contrib.auth import get_user_model\nfrom django.shortcuts import get_object_or_404\n\nfrom knox.auth import TokenAuthentication\nfrom knox.views import LoginView as KnoxLoginView\nfrom django.contrib.auth import authenticate\nfrom django.contrib.auth import logout, login\n\nfrom .models import PhoneOTP\nfrom .serializers import (AllUserSerializer, \n                        LoginSerializer, \n                        ChangePasswordSerializer )\n\nfrom concurrent.futures import ThreadPoolExecutor\nfrom utils.utils import send_otp\n\nUser = get_user_model()\nexecutor = ThreadPoolExecutor()\n\n\nclass LoginView(KnoxLoginView):\n    permission_classes = (AllowAny,)\n    serializer_class = LoginSerializer\n\n    def post(self, format=None):\n        serializer = LoginSerializer(data = self.request.data)\n        serializer.is_valid(raise_exception=True)\n\n        user = serializer.validated_data['user']\n        login(self.request, user)\n        return super().post(self.request, format=None)\n\n\n\n\nclass UserView(viewsets.ModelViewSet):\n    authentication_classes = (TokenAuthentication, IsUserOrReadOnly)\n    serializer_class = AllUserSerializer\n    permission_classes = [IsAuthenticated,]\n    http_method_names = ['get','delete','put','post']\n\n    def get_object(self):\n        return self.request.user\n\n    def get_queryset(self):\n        return User.objects.filter(phone = self.request.user)\n\n    @action(methods=['post'], detail=True) #url :http://127.0.0.1:8000/users-api/user/3/changephoneotp/\n    def changephoneotp(self, *args, **kwargs):\n        new_phone = self.request.data.get('new_phone')\n        if new_phone:\n            user = User.objects.filter(phone = new_phone)\n\n            if user.exists():\n                return JsonResponse({\n                    'error':'New phone number is already taken'\n                    })\n    \n            thread = executor.submit(send_otp, new_phone)\n            otp = thread.result()\n            print(otp)\n            if otp:\n                otp = str(otp)\n                old = PhoneOTP.objects.filter(phone__iexact=new_phone)\n                if old.exists():\n                    old = old.first()\n                    if old.count > 7:\n                        return JsonResponse({\n                            'error':'Maximum otp limits reached. Kindly support our customer care or try with different number'\n                            })\n                    old.count = old.count + 1\n                    old.otp = otp\n                    old.save()\n                    return Response(otp, status=status.HTTP_200_OK)\n\n                count = 0\n                count = count+1\n                PhoneOTP.objects.create(\n                    phone = new_phone,\n                    otp = otp,\n                    changephoneOTP = True,\n                    count = count\n                    )\n                return Response('OTP sent successfully.', status=status.HTTP_200_OK)\n\n            return JsonResponse({\n                'error':'OTP sending error. Please try after sometime.'\n                })\n\n        return JsonResponse({\n            'error':'No phone number has been received. Kindly do the POST request.'\n            })\n\n\n    def update(self, request, pk=None):\n        otp = self.request.data.get('otp')\n        new_phone = self.request.data.get('new_phone')\n\n        if otp and new_phone:\n            old = PhoneOTP.objects.filter(Q(phone__iexact = new_phone) & Q(otp__iexact = otp))\n            if old.exists():\n                old = old.first()\n\n                if str(otp) == str(old.otp):\n                    if old.changephoneOTP:\n                        user = get_object_or_404(User,id=pk)\n                        user.phone = new_phone\n                        user.save()\n                        old.delete()\n                        return Response(new_phone, status=status.HTTP_200_OK)\n\n                    return JsonResponse({\n                        'error':'OTP Verification failed. Please verify OTP'\n                        }) \n\n                return JsonResponse({\n                    'error':'OTP incorrect, please try again'\n                    })\n\n            return JsonResponse({\n                'error':'Phone and otp are not matching or a new phone has entered. Request a new otp in forgot password'\n                })\n\n        return JsonResponse({'error':'Either phone or otp was not recieved in Put request'})\n\n\n\nclass ChangePasswordView(generics.UpdateAPIView):\n    serializer_class = ChangePasswordSerializer\n    permission_classes = (IsAuthenticated, IsUserOrReadOnly)\n\n    def get_object(self):\n        return self.request.user\n\n    \n\n\n\n\n","repo_name":"satyarth12/Food-Delivery-API","sub_path":"account/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5022,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"86"}
+{"seq_id":"22724013958","text":"\nimport os\n\nimport re\nimport tensorflow as tf\nimport numpy as np\nimport argparse\n\nfrom layers.bert_basic_model import *\n\n\n# Load tf checkpoints in a pytorch model.\ndef load_tf_weights_in_bert(model, tf_checkpoint_path):\n    tf_path = os.path.abspath(tf_checkpoint_path)\n    logger.info(\"Converting TensorFlow checkpoint from {}\".format(tf_path))\n    # Load weights from TF model\n    init_vars = tf.train.list_variables(tf_path)\n    names = []\n    arrays = []\n    for name, shape in init_vars:\n        logger.info(\"Loading TF weight {} with shape {}\".format(name, shape))\n        array = tf.train.load_variable(tf_path, name)\n        names.append(name)\n        arrays.append(array)\n\n    for name, array in zip(names, arrays):\n        name = name.split('/')\n        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v\n        # which are not required for using pretrained model\n        if any(n in [\"adam_v\", \"adam_m\", \"global_step\", \"cls\"] for n in name):\n            logger.info(\"Skipping {}\".format(\"/\".join(name)))\n            continue\n        pointer = model\n        for m_name in name:\n            if re.fullmatch(r'[A-Za-z]+_\\d+', m_name):\n                l = re.split(r'_(\\d+)', m_name)\n            else:\n                l = [m_name]\n            if l[0] == 'kernel' or l[0] == 'gamma':\n                pointer = getattr(pointer, 'weight')\n            elif l[0] == 'output_bias' or l[0] == 'beta':\n                pointer = getattr(pointer, 'bias')\n            elif l[0] == 'output_weights':\n                pointer = getattr(pointer, 'weight')\n            elif l[0] == 'squad':\n                pointer = getattr(pointer, 'classifier')\n            else:\n                try:\n                    pointer = getattr(pointer, l[0])\n                except AttributeError:\n                    logger.info(\"Skipping {}\".format(\"/\".join(name)))\n                    continue\n            if len(l) >= 2:\n                num = int(l[1])\n                pointer = pointer[num]\n        if m_name[-11:] == '_embeddings':\n            pointer = getattr(pointer, 'weight')\n        elif m_name == 'kernel':\n            array = np.transpose(array)\n        try:\n            assert pointer.shape == array.shape\n        except AssertionError as e:\n            e.args += (pointer.shape, array.shape)\n            raise\n        logger.info(\"Initialize PyTorch weight {}\".format(name))\n        pointer.data = torch.from_numpy(array)\n    return model\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description=\"Converts tf bert ckpt weights to pytorch bin\")\n    parser.add_argument(\"--infile\", type=str, help=\"Path to the ckpt.\")\n    parser.add_argument(\"--outfile\", type=str, help=\"Path to the pytorch dump.\")\n    args = parser.parse_args()\n    bert_config = BertConfig.from_json_file(\"chinese_L-12_H-768_A-12/bert_config.json\")\n    model = BertModel(bert_config)\n    load_tf_weights_in_bert(model, args.infile)\n    torch.save(model.state_dict(), args.outfile)\n","repo_name":"EuphoriaYan/ICCRE","sub_path":"convert_tf_ckpt_to_pytorch.py","file_name":"convert_tf_ckpt_to_pytorch.py","file_ext":"py","file_size_in_byte":2996,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"10758358713","text":"import nltk\nfrom nltk.stem import *\nfrom nltk.stem.porter import *\nfrom textblob import TextBlob\nfrom nltk.corpus import stopwords\n\nstemmer = SnowballStemmer(\"english\")\nf = open('sentiment/negative_words.txt', 'r')\nnegative = set(f.read().split('\\n'))\nstop = set(stopwords.words('english'))\n\ndef tokenize(text):\n    words = TextBlob(text).words.lower()\n    words = filter(lambda w: not w in stop, words)\n    tokens = [0]*len(words)\n    i = 0\n    while i < len(words):\n        if words[i] in negative:\n            try:\n                tokens[i+1] = '!' + stemmer.stem(words[i+1])\n                tokens[i] = stemmer.stem(words[i])\n                tokens[i-1] = '!' + stemmer.stem(words[i-1])\n                i += 2\n                continue\n            except IndexError:\n                pass\n        tokens[i] = stemmer.stem(words[i])\n        #tokens[i] = words[i]\n        i += 1\n    return tokens\n\n","repo_name":"Radahika/Persimmon","sub_path":"sentiment/tokenizer.py","file_name":"tokenizer.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"24928111190","text":"\"\"\"\nUse map and lambda expression together to\nmake the codes clean and beautiful.\n\"\"\"\n\n\ndef square(x): return x * x\n\n\ndef multiply(x, y): return list(map(lambda s, t: s * t, x, y))\n\n\ndef main():\n    # Ordinary style.\n    squares = list(map(square, range(10)))\n    print(squares)\n\n    # A more compact way.\n    squares = list(map(lambda x: x * x, range(10)))\n    print(squares)\n\n    # Another alternative way.\n    xes = [x for x in range(10)]\n    print(multiply(xes, xes))\n\n    # Filter numbers that are greater than 10 in the list 'squares'.\n    result = [x for x in squares if x >= 10]\n    print(result)\n\n    # Filter odd numbers.\n    odds = list(filter(lambda x: x % 2 == 1, range(10)))\n    print(odds)\n\n    # List induction alternative.\n    odds = [x for x in range(10) if x % 2 == 1]\n    print(odds)\n\n    # Sort an array of dicts with lambda expression.\n    test_array = [\n        {'name': 'Anna', 'age': 10},\n        {'name': 'Catherine', 'age': 8}\n    ]\n    print(sorted(test_array, key=lambda dict_item: dict_item['name']))\n    print(sorted(test_array, key=lambda dict_item: dict_item['age']))\n    test_array.sort(key=lambda dict_item: dict_item['name'])\n    print(test_array)\n    test_array.sort(key=lambda dict_item: dict_item['age'])\n    print(test_array)\n\n    # Sort a list with lambda expression, small to big.\n    test_array = [-5, 8, 0, 4, 9, -4, -20, -2, 8, 2, -4]\n    test_array = [[i, test_array[i]] for i in range(len(test_array))]\n    test_array.sort(key=lambda array_item: array_item[1])\n    print([test_array[i][1] for i in range(len(test_array))])\n\n    # Sort a list with lambda expression, with positives\n    # from small to big, and negatives from big to small.\n    test_array = [-5, 8, 0, 4, 9, -4, -20, -2, 8, 2, -4]\n    positives = [t for t in test_array if t >= 0]\n    negatives = [t for t in test_array if t < 0]\n    positives = [[i, positives[i]] for i in range(len(positives))]\n    negatives = [[i, negatives[i]] for i in range(len(negatives))]\n    positives.sort(key=lambda array_item: array_item[1])\n    negatives.sort(key=lambda array_item: array_item[1])\n    positives = [positives[i][1] for i in range(len(positives))]\n    negatives = [negatives[i][1] for i in range(len(negatives))]\n    test_array = positives + negatives\n    print(test_array)\n\n    # Sort a list of tuples with lambda expression.\n    test_array = [(0, 'c'), (1, 'b'), (2, 'a')]\n    print(sorted(test_array, key=lambda array_item: array_item[0]))\n    print(sorted(test_array, key=lambda array_item: array_item[1]))\n    test_array.sort(key=lambda array_item: array_item[0])\n    print(test_array)\n    test_array.sort(key=lambda array_item: array_item[1])\n    print(test_array)\n\n    # Sort a list of strings by their lengths in reverse order.\n    test_array = [\n        'a', 'ab', 'cd', 'c',\n        'that', 'ssf', 'sds',\n        'Challenge', 'great'\n    ]\n    test_array = [[len(t), t] for t in test_array]\n    test_array.sort(key=lambda array_item: array_item[0], reverse=True)\n    test_array = [test_array[i][1] for i in range(len(test_array))]\n    print(test_array)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"volksvagen/mypy","sub_path":"src/map_lambda.py","file_name":"map_lambda.py","file_ext":"py","file_size_in_byte":3110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"799855022","text":"\nimport logging\nfrom decorators import run_daemon\n\nCHECK_CONNECTORS_JOB_RESTART_DELAY = 10.0\n\n\nclass CheckConnectorJob:\n\n    # __mapper_args__ = {'polymorphic_identity': 'ConnectorCheckJob'}\n\n    @run_daemon\n    def execute(self):\n        logging.info('Сервис проверки коннекторов был запущен')\n        print('Сервис проверки к��ннекторов был запущен')\n\n        from connector import check_connectors_status\n        # timers = EventScheduler()\n        check_connectors_status()\n        # timers.call_regular_interval(\n        #     CHECK_CONNECTORS_JOB_RESTART_DELAY,\n        #     check_connectors_status,\n        # )\n        # for _ in itertools.count(start=1):\n        #     self.heartbeat()\n        #     timers.run(blocking=False)\n","repo_name":"rundect/learn_python_oop","sub_path":"decorator/decorator_scheduler_daemon/jobs.py","file_name":"jobs.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32396861282","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport click\nimport pickle\nimport torch.backends.cudnn as cudnn\n\nfrom content_model import QA_RNN\nfrom buzz_model import DDQNQuizBowlPlayer\n\nfrom util.helper_functions import load_checkpoint_policy, load_checkpoint\nfrom util.helper_classes import MBLoader\n\nnp.random.seed(0)\ntorch.manual_seed(0)\n\n@click.command()\n@click.option('--model_name', default=\"buzz_RL\", help='Name of model.',show_default=True)\n@click.option('--data_dir', default=\"data/\", help='Path to dataset file containing questions.')\n@click.option('--content_model_path', default=\"checkpoints/content/best_model.pth\", help='Path of checkpoint_file of best content model')\n@click.option('--buzz_model_path', default=\"checkpoints/buzz/policy.pth\", help='Path of checkpoint_file of best buzz model')\n@click.option('--checkpoint_file', default=\"checkpoints/dolly/checkpoint.pth\", help='Path of checkpoint_file of dolly')\n@click.option('--batch_size', default=64, help=\"Batch size.\",show_default=True)\n@click.option('--num_layers', default=1, help=\"Number of RNN layers.\",show_default=True)\n@click.option('--learning_rate', default=0.001, help=\"LR\",show_default=True)\n@click.option('--state_size', default=128, help=\"RNN state size.\",show_default=True)\n@click.option('--dropout', default=0.0, help=\"keep_prob for droupout.\",show_default=True)\n@click.option('--val_interval', default=1, help='validation interval for early stopping. ',show_default=True)\n@click.option('--num_epochs', default=50, help='Number of iteration to train.',show_default=True)\n@click.option('--train_embeddings', default=False, is_flag=True, help='train word embeddings.',show_default=True)\n@click.option('--disable_cuda', default=False, is_flag=True, help='run on gpu or not',show_default=True)\n@click.option('--restore', default=False, is_flag=True, help='restore previous model',show_default=True)\n@click.option('--early_stopping', default=True, is_flag=True, help='early stopping on validation error.',show_default=True)\n@click.option('--early_stopping_interval', default=15, help='early stopping on validation error.',show_default=True)\ndef main(model_name,data_dir,batch_size,num_layers,learning_rate, state_size,dropout,val_interval,early_stopping_interval,num_epochs,train_embeddings,early_stopping,disable_cuda,content_model_path,buzz_model_path,checkpoint_file,restore):\n    preprocessed_file = os.path.join(data_dir,\"preprocessed_data.npz\")\n    nf = np.load(preprocessed_file)\n    train_X,train_y,train_seq_len,\\\n    train_buzzes,\\\n    test_X,test_y,test_seq_len,\\\n    test_buzzes,\\\n    val_X,val_y,val_seq_len,\\\n    val_buzzes,\\\n    embd_mat = nf[\"train_X\"],nf[\"train_y\"],nf[\"train_seq_len\"],\\\n        nf[\"train_buzzes\"],\\\n        nf[\"test_X\"],nf[\"test_y\"],nf[\"test_seq_len\"],\\\n        nf[\"test_buzzes\"],\\\n        nf[\"val_X\"],nf[\"val_y\"],nf[\"val_seq_len\"],\\\n        nf[\"val_buzzes\"],\\\n        nf[\"embd_mat\"]\n\n    print(list(map(lambda x:x.shape  ,[train_X,train_y,train_seq_len,train_buzzes])))\n    print(list(map(lambda x:x.shape  ,[test_X,test_y,test_seq_len,test_buzzes])))\n    print(list(map(lambda x:x.shape  ,[val_X,val_y,val_seq_len,val_buzzes])))\n\n    in_file = os.path.join(data_dir,\"mapping_opp.pkl\")\n    with open(in_file,\"rb\") as handle:\n        user_features = pickle.load(handle)\n        user_features = user_features[0]\n   \n    num_ans = len(set(train_y)|set(test_y)|set(val_y))\n    print(\"#Answers :\",num_ans)\n\n    model_name = model_name+\"_\"+str(train_X.shape[0])+\"_\"+str(val_X.shape[0])+\"_\"+str(test_X.shape[0])+\"_\"+str(batch_size)+\"_\"+str(dropout)\n\n    train_X = torch.from_numpy(train_X)\n    train_y = torch.from_numpy(train_y)\n    train_seq_len = torch.from_numpy(train_seq_len)\n    val_X = torch.from_numpy(val_X)\n    val_y = torch.from_numpy(val_y)\n    val_seq_len = torch.from_numpy(val_seq_len)\n    test_X = torch.from_numpy(test_X)\n    test_y = torch.from_numpy(test_y)\n    test_seq_len = torch.from_numpy(test_seq_len)\n    embd_mat = torch.from_numpy(embd_mat)#.cuda()\n\n\n    content_model = QA_RNN(batch_size, train_X.size(1), num_layers, state_size, num_ans + 1, embd_mat, non_trainable = True, disable_cuda = disable_cuda)\n    buzz_model = DDQNQuizBowlPlayer(inp_state_dim, opp_state_dim, n_actions)\n\n    inputs = [(train_X,train_y,train_seq_len), \n                (val_X,val_y,val_seq_len), \n                (test_X,test_y,test_seq_len)]\n\n    loader = MBLoader(inputs, batch_size)\n    optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, content_model.parameters()), lr=learning_rate)\n\n    content_model = load_best_model(content_model, content_model_path)\n    buzz_model = load_checkpoint_policy(buzz_model, buzz_model_path)\n\n    if not disable_cuda:\n        torch.backends.cudnn.enabled = True\n        cudnn.benchmark = True\n        model.cuda()\n        criterion = criterion.cuda()\n        train_X = train_X.cuda()\n        # train_seq_len = train_seq_len.cpu()\n        train_y = train_y.cuda()\n        test_X = test_X.cuda()\n        test_y = test_y.cuda()\n        # test_seq_len = test_seq_len.cpu()\n        val_X = val_X.cuda()\n        val_y = val_y.cuda()\n        # val_seq_len = val_seq_len.cpu()\n        \n\n\n    print(optimizer)\n    print(criterion)\n    # print(next(model.parameters()).is_cuda)\n\n    logger = run(loader, content_model, buzz_model, criterion, optimizer, early_stopping, early_stopping_interval, checkpoint_file = checkpoint_file, num_epochs = num_epochs, restore = restore)\n\n    plot_from_logger(logger)\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"ranjithkumar007/Dolly","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"27142839300","text":"\n# programa para um usuario jogar p\\r ou imp\\r com o computador\n\nfrom random import randint\nctdor=0\n\nwhile True:\n    cpdor=randint(1,10)\n    jdor=int(input('Digite um numero entre 1 e 10_'))\n    dsao=str(input('Escolha par ou ímpar [P/I]?_')).strip().upper()[0]\n    print(f'Jogador tirou {jdor} e decidiu {dsao}. Computador tirou {cpdor}')\n    soma=jdor+cpdor\n\n    if soma%2==0: # linha para marcar o resultado par ou impar\n        res='P'\n    else:\n        res=\"I\"\n\n    if dsao==res: # linha para saber quem ganhou\n        ctdor+=1\n        print('jogador ganhou')\n        \n    else:\n        print('Computador ganhou.')\n        break\nprint(f'Fim. Vc ganhou {ctdor} vezes')","repo_name":"GuglielmoTargino/PY_m2","sub_path":"exer068.py","file_name":"exer068.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"27804686960","text":"# coding: utf-8\n\"\"\"Visualize samples from dataset.\"\"\"\n\nimport argparse\nimport time\n\nimport cv2\nimport numpy as np\nfrom torch.utils.data import DataLoader\n\nfrom dataloader import TLessDataset, TransparentDataset\n\n\ndef read_dataset(path, b, dataset='notex'):\n    dataset = TLessDataset(path) if dataset == 'notex' else TransparentDataset(path)\n    print(len(dataset), \"samples in dataset.\")\n\n    return DataLoader(dataset, batch_size=b, shuffle=True)\n\n\ndef create_view(frame):\n    \"\"\"Show current frame of the RGB-D dataset as images.\n\n    :param frame:\n    :return:\n    \"\"\"\n    image, depth, norms, mask = frame\n\n    # convert normals from [-1, 1] to [0, 255]\n    norms = ((norms + 1) / 2) * 255\n\n    # apply a colormap on grayscale depth map, makes easier to see depth changes\n    depth = cv2.applyColorMap((depth * 255.0).astype(np.uint8), cv2.COLORMAP_JET)\n\n    masked_image = image.copy()\n\n    bg_color = 128  # gray window background\n    masked_image[mask, :] = bg_color\n    depth[mask, :] = bg_color\n    norms[mask, :] = bg_color\n\n    dst = np.hstack((image, masked_image, depth, norms))\n    return dst.astype(np.uint8)\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--dataset_dir', '-d', type=str, default='../out')\n    parser.add_argument('--batch_size', '-b', type=int, default=4)\n    parser.add_argument('--dataset', '-ds', type=str, default='notex', choices=['notex', 'trans'])\n    return parser.parse_args()\n\n\ndef main(args):\n    data = read_dataset(args.dataset_dir, args.batch_size, args.dataset)\n    for it in data:\n        image, label = it\n        dmap, nmap, mask = label\n\n        rows = []\n        for i in range(image.shape[0]):\n            im = image[i].numpy()\n            dm = dmap[i].numpy()\n            nm = nmap[i].numpy()\n            ma = mask[i].numpy()\n\n            rows.append(create_view(frame=(im, dm, nm, ma)))\n\n        cv2.imshow('Dataset', np.vstack(rows))\n        if cv2.waitKey(delay=1) == ord('q'):\n            raise KeyboardInterrupt\n\n        time.sleep(3)\n\n\nif __name__ == '__main__':\n    try:\n        main(parse_args())\n    except KeyboardInterrupt:\n        print(\"Visualization interrupted.\")\n","repo_name":"tahirashehzadi/blender_texless_data","sub_path":"src/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":2171,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"72422085085","text":"import os\nimport pickle\n\nfrom loguru import logger\nfrom tensorflow import keras\n\n\nclass AiTokenizer:\n    def __init__(self, max_words=1000, max_len=1000):\n        self.max_words = max_words\n        self.max_len = max_len\n        self.tokenizer = None\n        logger.info(\"Tokenizer initialized\")\n\n        self.tokenizer_path = os.path.abspath(\n            os.path.join(os.path.dirname(__file__), '..', '..', 'dataset', 'tokenizer.pickle'))\n\n    def tokenize(self, data):\n        tokenizer = keras.preprocessing.text.Tokenizer(num_words=1000)\n        tokenizer.fit_on_texts(data)\n        with open(self.tokenizer_path, 'wb') as handle:\n            pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)\n        self.tokenizer = tokenizer\n        return self.tokenizer\n","repo_name":"TheQuiu/Eternal-Algorithm-AI","sub_path":"network/tokenizer/tokenizer.py","file_name":"tokenizer.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32020296189","text":"# coding: utf-8\n\nfrom __future__ import absolute_import\nfrom datetime import date, datetime  # noqa: F401\n\nfrom typing import List, Dict  # noqa: F401\n\nfrom legion.sdk.models.base_model_ import Model\nfrom legion.sdk.models import util\n\n\nclass Parameter(Model):\n    \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n    Do not edit the class manually.\n    \"\"\"\n\n    def __init__(self, name: str=None, value: object=None):  # noqa: E501\n        \"\"\"Parameter - a model defined in Swagger\n\n        :param name: The name of this Parameter.  # noqa: E501\n        :type name: str\n        :param value: The value of this Parameter.  # noqa: E501\n        :type value: object\n        \"\"\"\n        self.swagger_types = {\n            'name': str,\n            'value': object\n        }\n\n        self.attribute_map = {\n            'name': 'name',\n            'value': 'value'\n        }\n\n        self._name = name\n        self._value = value\n\n    @classmethod\n    def from_dict(cls, dikt) -> 'Parameter':\n        \"\"\"Returns the dict as a model\n\n        :param dikt: A dict.\n        :type: dict\n        :return: The Parameter of this Parameter.  # noqa: E501\n        :rtype: Parameter\n        \"\"\"\n        return util.deserialize_model(dikt, cls)\n\n    @property\n    def name(self) -> str:\n        \"\"\"Gets the name of this Parameter.\n\n        Parameter name  # noqa: E501\n\n        :return: The name of this Parameter.\n        :rtype: str\n        \"\"\"\n        return self._name\n\n    @name.setter\n    def name(self, name: str):\n        \"\"\"Sets the name of this Parameter.\n\n        Parameter name  # noqa: E501\n\n        :param name: The name of this Parameter.\n        :type name: str\n        \"\"\"\n\n        self._name = name\n\n    @property\n    def value(self) -> object:\n        \"\"\"Gets the value of this Parameter.\n\n        Parameter value  # noqa: E501\n\n        :return: The value of this Parameter.\n        :rtype: object\n        \"\"\"\n        return self._value\n\n    @value.setter\n    def value(self, value: object):\n        \"\"\"Sets the value of this Parameter.\n\n        Parameter value  # noqa: E501\n\n        :param value: The value of this Parameter.\n        :type value: object\n        \"\"\"\n\n        self._value = value\n","repo_name":"legion-platform/legion","sub_path":"legion/sdk/legion/sdk/models/parameter.py","file_name":"parameter.py","file_ext":"py","file_size_in_byte":2227,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"86"}
+{"seq_id":"2632831783","text":"import io\nimport logging\nimport logging.handlers\nimport threading\nimport time\nfrom abc import ABC, abstractmethod\nfrom collections import defaultdict\nfrom datetime import datetime, timedelta\n\n_4h = timedelta(hours=4)\n_1min = timedelta(minutes=1)\n\n\nclass BaseBufferedHandler(ABC, logging.Handler):\n    def __init__(\n        self,\n        level: int | str = logging.NOTSET,\n        *,\n        capacity: int | None = None,\n        flush_interval: timedelta | int = _4h,\n        starting_times: int | None = 10,\n        starting_interval: timedelta | int | None = _1min,\n    ):\n        super().__init__(level)\n\n        self.capacity = capacity or None\n        self.buffer: list[logging.LogRecord] = []\n\n        if isinstance(flush_interval, timedelta):\n            self.flush_interval = int(flush_interval.total_seconds())\n        elif isinstance(flush_interval, int):\n            self.flush_interval = flush_interval\n\n        if starting_times:\n            self.starting_times = starting_times\n            if isinstance(starting_interval, timedelta):\n                self.starting_interval = int(starting_interval.total_seconds())\n            elif isinstance(starting_interval, int):\n                self.starting_interval = starting_interval\n        else:\n            self.starting_times = 0\n            self.starting_interval = None\n\n        self.closed = threading.Event()\n        self._start_flushing_thread()\n\n    def emit(self, record: logging.LogRecord):\n        if self.closed.is_set():\n            return\n        self.buffer.append(record)\n        if self._should_flush(record):\n            self.flush()\n\n    def _should_flush(self, record: logging.LogRecord) -> bool:\n        if self.capacity is None:\n            # using timed flush only\n            return False\n        return len(self.buffer) >= self.capacity\n\n    @abstractmethod\n    def flush(self):\n        raise NotImplementedError\n\n    def close(self):\n        try:\n            self.closed.set()\n            self.flush()\n        finally:\n            super().close()\n\n    def _start_flushing_thread(self):\n        self.thread = threading.Thread(\n            target=self._flush_intervals,\n            daemon=True,\n        )\n        self.thread.start()\n\n    def _sleep_time_generator(self):\n        for _ in range(self.starting_times):\n            yield self.starting_interval\n        while True:\n            yield self.flush_interval\n\n    def _flush_intervals(self):\n        for sleep_time in self._sleep_time_generator():\n            if self.closed.is_set():\n                break\n            time.sleep(sleep_time)\n            self.flush()\n\n    def build_message(self) -> str:\n        sb = io.StringIO()\n        count = defaultdict(lambda: 0)\n        min_time = None\n        max_time = None\n        for r in self.buffer:\n            key = f\"{r.filename}:{r.lineno}::{r.funcName}()\"\n            count[key] += 1\n\n            if min_time is None or r.created < min_time:\n                min_time = r.created\n            if max_time is None or r.created > max_time:\n                max_time = r.created\n        min_date_str = datetime.fromtimestamp(min_time).isoformat(sep=\" \", timespec=\"milliseconds\")\n        max_date_str = datetime.fromtimestamp(max_time).isoformat(sep=\" \", timespec=\"milliseconds\")\n\n        if len(self.buffer) > 1:\n            sb.write(f\"Collected {len(self.buffer)} logs created between {min_date_str} and {max_date_str}\\n\")\n        else:\n            sb.write(f\"Collected 1 log created at {max_date_str}\\n\")\n        sb.write(\"\\n\")\n\n        for k, v in count.items():\n            sb.write(f\"{v} - {k}\\n\")\n        sb.write(\"\\n\")\n\n        for r in self.buffer:\n            sb.write(f\"{self.format(r)}\\n\\n\")\n\n        sb.write(\"-- End of message --\\n\")\n        return sb.getvalue()\n","repo_name":"a-was/logdog.py","sub_path":"src/logdog/handler/base_buffered_handler.py","file_name":"base_buffered_handler.py","file_ext":"py","file_size_in_byte":3761,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"8333772998","text":"import os\nimport pprint\nimport json\nimport tweepy\n\n\nclient = tweepy.Client(os.environ['BEARER_TOKEN'], wait_on_rate_limit=True)\n\ncnt = 0\nall = 0\n\nfor users_tweets in tweepy.Paginator(\n    client.get_users_tweets,\n    id=1511906485349265415,\n    exclude=['retweets', 'replies'],\n    max_results=100,\n):\n    for tweet in users_tweets.data:\n        all += 1\n        if '彼女' in tweet.text:\n            cnt += 1\n\nprint('{}/{}'.format(cnt, all))\n","repo_name":"mio256/airrep","sub_path":"cnt_kanojo_hanya.py","file_name":"cnt_kanojo_hanya.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32531436652","text":"\n# Specialized script for preprocesing the compilation\n\n\nimport sys\nsrc = sys.argv[1:-1]\ndst = sys.argv[-1]\n\n# collect class names\nclass_names = []\nfor s in src:\n    class_names += [line.strip().split()[1].split(':')[0] for line in open(s, 'r') \n        if line.strip().startswith('class ')]\nfor line in open(dst, 'r'):\n    if line.strip().startswith('preprocfac'):\n        cname = line.split()[1].split(';')[0]\n        for c in class_names:\n            print('    %s (strcmp(%s, %s::get_name()) == 0){return new %s();}' % \n            ('if' if c == class_names[0] else 'else if', cname, c, c))\n        print('    else {throw std::runtime_error(\"Potential not found\\\\n\");}')\n    else:\n        print(line.strip('\\n'))\n\n","repo_name":"subotnikgroup/scatter2","sub_path":"preprocfac.py","file_name":"preprocfac.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"7703793715","text":"from datetime import datetime\nfrom io import TextIOBase\nimport logging\nfrom logging import FileHandler, Formatter, Handler, StreamHandler\nfrom pathlib import Path\nimport sys\nimport time\nfrom typing import Optional\n\nimport colorama\n\nfrom .env_vars import dispatcher_env_vars, trial_env_vars\n\n\ndef init_logger() -> None:\n    \"\"\"\n    This function will (and should only) get invoked on the first time of importing nni (no matter which submodule).\n    It will try to detect the running environment and setup logger accordingly.\n\n    The detection should work in most cases but for `nnictl` and `nni.experiment`.\n    They will be identified as \"standalone\" mode and must configure the logger by themselves.\n    \"\"\"\n    colorama.init()\n\n    if dispatcher_env_vars.SDK_PROCESS == 'dispatcher':\n        _init_logger_dispatcher()\n        return\n\n    trial_platform = trial_env_vars.NNI_PLATFORM\n\n    if trial_platform == 'unittest':\n        return\n\n    if trial_platform and not trial_env_vars.REUSE_MODE:\n        _init_logger_trial()\n        return\n\n    _init_logger_standalone()\n\n\ndef init_logger_experiment() -> None:\n    \"\"\"\n    Initialize logger for `nni.experiment.Experiment`.\n\n    This function will get invoked after `init_logger()`.\n    \"\"\"\n    formatter.format = _colorful_format\n\n\ntime_format = '%Y-%m-%d %H:%M:%S'\n\nformatter = Formatter(\n    '[%(asctime)s] %(levelname)s (%(name)s/%(threadName)s) %(message)s',\n    time_format\n)\n\ndef _init_logger_dispatcher() -> None:\n    log_level_map = {\n        'fatal': logging.CRITICAL,\n        'error': logging.ERROR,\n        'warning': logging.WARNING,\n        'info': logging.INFO,\n        'debug': logging.DEBUG,\n        'trace': 0\n    }\n\n    log_path = _prepare_log_dir(dispatcher_env_vars.NNI_LOG_DIRECTORY) / 'dispatcher.log'\n    log_level = log_level_map.get(dispatcher_env_vars.NNI_LOG_LEVEL, logging.INFO)\n    _setup_root_logger(FileHandler(log_path), log_level)\n\n\ndef _init_logger_trial() -> None:\n    log_path = _prepare_log_dir(trial_env_vars.NNI_OUTPUT_DIR) / 'trial.log'\n    log_file = open(log_path, 'w')\n    _setup_root_logger(StreamHandler(log_file), logging.INFO)\n\n    if trial_env_vars.NNI_PLATFORM == 'local':\n        sys.stdout = _LogFileWrapper(log_file)\n\n\ndef _init_logger_standalone() -> None:\n    _setup_nni_logger(StreamHandler(sys.stdout), logging.INFO)\n\n    # Following line does not affect NNI loggers, but without this user's logger won't\n    # print log even it's level is set to INFO, so we do it for user's convenience.\n    # If this causes any issue in future, remove it and use `logging.info()` instead of\n    # `logging.getLogger('xxx').info()` in all examples.\n    logging.basicConfig()\n\n\ndef _prepare_log_dir(path: Optional[str]) -> Path:\n    if path is None:\n        return Path()\n    ret = Path(path)\n    ret.mkdir(parents=True, exist_ok=True)\n    return ret\n\ndef _setup_root_logger(handler: Handler, level: int) -> None:\n    _setup_logger('', handler, level)\n\ndef _setup_nni_logger(handler: Handler, level: int) -> None:\n    _setup_logger('nni', handler, level)\n\ndef _setup_logger(name: str, handler: Handler, level: int) -> None:\n    handler.setFormatter(formatter)\n    logger = logging.getLogger(name)\n    logger.addHandler(handler)\n    logger.setLevel(level)\n    logger.propagate = False\n\ndef _colorful_format(record):\n    if record.levelno >= logging.ERROR:\n        color = colorama.Fore.RED\n    elif record.levelno >= logging.WARNING:\n        color = colorama.Fore.YELLOW\n    elif record.levelno >= logging.INFO:\n        color = colorama.Fore.GREEN\n    else:\n        color = colorama.Fore.BLUE\n    msg = color + (record.msg % record.args) + colorama.Style.RESET_ALL\n    time = formatter.formatTime(record, time_format)\n    if record.levelno < logging.INFO:\n        return '[{}] {}:{} {}'.format(time, record.threadName, record.name, msg)\n    else:\n        return '[{}] {}'.format(time, msg)\n\nclass _LogFileWrapper(TextIOBase):\n    # wrap the logger file so that anything written to it will automatically get formatted\n\n    def __init__(self, log_file: TextIOBase):\n        self.file: TextIOBase = log_file\n        self.line_buffer: Optional[str] = None\n        self.line_start_time: Optional[datetime] = None\n\n    def write(self, s: str) -> int:\n        cur_time = datetime.now()\n        if self.line_buffer and (cur_time - self.line_start_time).total_seconds() > 0.1:\n            self.flush()\n\n        if self.line_buffer:\n            self.line_buffer += s\n        else:\n            self.line_buffer = s\n            self.line_start_time = cur_time\n\n        if '\\n' not in s:\n            return len(s)\n\n        time_str = cur_time.strftime(time_format)\n        lines = self.line_buffer.split('\\n')\n        for line in lines[:-1]:\n            self.file.write(f'[{time_str}] PRINT {line}\\n')\n        self.file.flush()\n\n        self.line_buffer = lines[-1]\n        self.line_start_time = cur_time\n        return len(s)\n\n    def flush(self) -> None:\n        if self.line_buffer:\n            time_str = self.line_start_time.strftime(time_format)\n            self.file.write(f'[{time_str}] PRINT {self.line_buffer}\\n')\n            self.file.flush()\n            self.line_buffer = None\n","repo_name":"ShireFolk/nni","sub_path":"nni/runtime/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":5169,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"12361539529","text":"import asyncio\nimport datetime\n\nimport parsedatetime as pdt\nfrom discord.ext import commands\n\n\ndef get_date(text):\n    cal = pdt.Calendar()\n    time, res = cal.parseDT(text, datetime.datetime.utcnow())\n    return time if res else None\n\nclass Reminders:\n    \"\"\"Tools for reminding me\"\"\"\n    def __init__(self, bot):\n        self.bot = bot\n        self.timers = bot.get_cog('Timers')\n\n    async def on_message(self, msg):\n        if msg.content.lower().startswith('remind '):\n            reminder = msg.content[7:]\n            if reminder.lower().startswith('me '):\n                reminder = reminder[3:]\n            if reminder.lower().startswith('to '):\n                reminder = reminder[3:]\n\n            time = get_date(reminder)\n            if not time:\n                await msg.channel.send(\"When?\")\n                msg = await self.bot.wait_for('message',\n                                              check=lambda m: m.author == msg.author and m.channel == msg.channel)\n                time = get_date(msg.content)\n            if time:\n                await self.timers.create_timer('reminder', time, [msg.author.id, msg.channel.id, reminder])\n                await msg.channel.send(f'I\\'ll remind you then!')\n            else:\n                await msg.channel.send(f\"Idk when you want me to remind you\")\n\n    async def on_reminder_event(self, author_id, destination_id, msg):\n        author = self.bot.get_user(author_id)\n        if author is None:\n            return\n        channel = self.bot.get_channel(destination_id)\n        if channel is None:\n            # Check if it's a DM channel\n            author = self.bot.get_user(author_id)\n            try:\n                channel = await author.dm_channel()\n            except:\n                return\n\n        await channel.send(f'{author.mention}\\n{msg}')\n\ndef setup(bot):\n    bot.add_cog(Reminders(bot))\n","repo_name":"Rooni/rub","sub_path":"cogs/reminders.py","file_name":"reminders.py","file_ext":"py","file_size_in_byte":1870,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"70604062046","text":"\"\"\"\nSerenity Force Field.\n\"\"\"\n\nfrom setuptools import find_packages, setup\nimport sys\nimport versioneer\n\nneeds_pytest = {\"pytest\", \"test\", \"ptr\"}.intersection(sys.argv)\npytest_runner = [\"pytest-runner\"] if needs_pytest else []\n\nshort_description = __doc__.split(\"\\n\")\n\ntry:\n    with open(\"README.md\", \"r\") as handle:\n        long_description = handle.read()\nexcept IOError:\n    long_description = \"\\n\".join(short_description[2:])\n\nsetup(\n    name=\"serenityff\",\n    author=\"rinikerlab\",\n    author_email=\"mlehner@ethz.ch\",\n    description=short_description[0],\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"http://github.com/rinikerlab/serenityff\",\n    setup_requires=[] + pytest_runner,\n    version=versioneer.get_version(),\n    cmdclass=versioneer.get_cmdclass(),\n    license=\"MIT\",\n    packages=find_packages(),\n    # packages=find_namespace_packages(include=[\"serenityff/charge/*\"]),\n    include_package_data=True,\n    keywords=\"molecular dynamics, force field, parametrization, nonbonded parameters, explainable ml\",\n    python_requires=\">=3.7\",\n    entry_points={\n        \"openff.toolkit.plugins.handlers\": [\n            \"SerenityFFCharge = serenityff.charge.utils.serenityff_charge_handler:SerenityFFChargeHandler\"\n        ]\n    },\n)\nprint(\"test setup.py done\")\n","repo_name":"rinikerlab/DASH-tree","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"86"}
+{"seq_id":"21323656448","text":"import sys\nfrom setuptools import setup, find_packages\n\n\n__version__ = open(\"triform2/version.py\").readline().split(\" = \")[1].replace(\n    '\"', '').strip()\n\ninstall_requires = [\"pyranges\"]\n\nsetup(\n    name=\"triform2\",\n    packages=find_packages(),\n    # package_dir=find_packages(),\n    # package_data={\"triform2\": [\"scripts/chromsizes/*.chromsizes\"]},\n    scripts=[\"bin/triform2\"],\n    version=__version__,\n    description=\n    \"Improved sensitivity, specificity and control of false discovery rates in ChIP-Seq peak finding.\",\n    author=\"Endre Bakken Stovner\",\n    author_email=\"endrebak85@gmail.com\",\n    url=\"http://github.com/endrebak/triform2\",\n    keywords=[\"ChIP-Seq\"],\n    license=[\"MIT\"],\n    install_requires=install_requires,\n    classifiers=[\n        \"Programming Language :: Python :: 2.7\",\n        \"Programming Language :: Python :: 3\",\n        \"Development Status :: 2 - Pre-Alpha\",\n        \"Environment :: Other Environment\", \"Intended Audience :: Developers\",\n        \"Intended Audience :: Science/Research\",\n        \"License :: OSI Approved :: GNU General Public License v3 (GPLv3)\",\n        \"Operating System :: POSIX :: Linux\",\n        \"Operating System :: MacOS :: MacOS X\",\n        \"Topic :: Scientific/Engineering\"\n    ],\n    long_description=\n    \"Improved sensitivity, specificity and control of false discovery rates in ChIP-Seq peak finding.\")\n","repo_name":"endrebak/triform","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"36621791473","text":"#!/usr/bin/python\n# -*- coding:utf-8 -*- \nimport unittest\nfrom devicewrapper.android import device as d\nimport util as u\n\nclass MessageTest(unittest.TestCase):\n    def setUp(self):\n        super(MessageTest, self).setUp()\n        d.wakeup()\n        #d.start_activity(action='android.intent.action.DIAL', data='tel:13581739891', flags=0x04000000)\n        u.backHome(d)\n\n    def tearDown(self):\n        super(MessageTest, self).tearDown()\n        u.backHome(d)\n\n    def testMO_MTSms(self):\n        str_receiver = '10010'\n        str_content = 'Message Test Content'\n        #assert d.exists(text='Messaging') , 'message app not appear on the home screen'\n        #assert d.exists(text='Apps')  , 'apps not appear on the home screen'\n        #d(text='Messaging').click.wait()\n\n        d.start_activity(component='com.android.mms/.ui.ConversationList')\n        assert d(text='Messaging').wait.exists(timeout=3000), 'can not launch message in 3s'\n\n        #Delete messages\n        if not d(text=\"No conversations.\").wait.exists(timeout=2000):\n            d.press('menu')\n            d(text='Delete all threads').click.wait()\n            d(text='Delete', className='android.widget.Button').click.wait()\n            assert d(text=\"No conversations.\").wait.exists(timeout=3000), 'Delete message failed'\n\n        d(description='New message').click.wait()\n        d(text='To').set_text(str_receiver)\n        assert d(text=str_receiver).wait.exists(timeout=10000), 'receiver number input error'            \n        d(text='Type message').set_text(str_content)\n        assert d(text=str_content).wait.exists(timeout=10000), 'content input error'            \n        d(description='Send', className='android.widget.ImageButton').click.wait()\n\n        assert d(text='SENDING…').wait.exists(timeout=10000), 'Sending not start in 10s'\n        assert d(text='SENDING…').wait.gone(timeout=20000), 'sms sending failed in 20s'\n        d.sleep(15)\n        assert d(textStartsWith='尊敬的').wait.exists(timeout=20000), 'No feedback in 35s'\n\n    def testMoMMS(self):\n        str_receiver = '13501101339'\n        str_content = 'Message Test Content'\n        d.start_activity(component='com.android.mms/.ui.ConversationList')\n\n        if not d(text=\"No conversations.\").wait.exists(timeout=2000):\n            d.press('menu')\n            d(text='Delete all threads').click.wait()\n            d(text='Delete', className='android.widget.Button').click.wait()\n            assert d(text=\"No conversations.\").wait.exists(timeout=3000), 'Delete message failed'\n\n        d(description='New message').click.wait()\n        d(text='To').set_text(str_receiver)\n        #assert d(text=str_receiver).wait.exists(timeout=10000), 'receiver number input error'            \n        d(text='Type message').set_text(str_content)\n        #assert d(text=str_content).wait.exists(timeout=10000), 'content input error'            \n        #d(description='Send', className='android.widget.ImageButton').click.wait()\n\n        d(description='Attach').click.wait()\n        assert d(text='Capture picture').wait.exists(timeout=3000), 'no adding attachment panel' \n        d(text='Capture picture').click.wait()\n        assert d(description='Shutter button').wait.exists(timeout=3000), 'no camera' \n        d(description='Shutter button').click.wait()\n        d.sleep(1)\n        assert d(description='Review done').wait.exists(timeout=3000), 'Take picture failed.'\n        d(description='Review done').click.wait()\n        assert d(text='MMS', description='Send MMS').wait.exists(timeout=3000), 'add attachment failed'\n        d(text='MMS', description='Send MMS').click.wait()\n        assert d(text='SENDING…').wait.exists(timeout=10000), 'No sending status'\n        d.sleep(30)\n        assert d(text='SENDING…').wait.gone(timeout=20000), 'MMS sending failed in 50s'\n\n\n\n            \n\n","repo_name":"shaofang/falcon","sub_path":"scripts/testcases/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":3841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"33607183678","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom random import randint\n\nclass Card(object):\n    def __init__(self, suit_idx = 0,number_idx = 0, deck_name=None):\n        suits = ['Club', 'Heart', 'Spade', 'Diamond']\n        numbers = [i for i in range(1,14)] # indica o valor da carta\n        names = ['ace','two','three','four','five','six','seven','eigth','nine','ten','jack','queen','king']\n        self.suit = suits[suit_idx]\n        self.number = numbers[number_idx]\n        self.name = names[number_idx]\n        self.face_on = False # indica se esta virada (True) ou nao (False)\n        self.deck_name = deck_name\n        self.shows_deck_name = False # for debug, change it to True\n\n    def turn(self):\n        self.face_on = not self.face_on\n\n    def is_turned_on(self):\n        return self.face_on\n\n    def __str__(self):\n        if self.deck_name and self.shows_deck_name:\n            return '%s: %s of %s' % (self.deck_name, self.name, self.suit)\n        else:\n            return '%s of %s' % (self.name, self.suit)\n\nclass Deck(Card):\n    def __init__(self, deck_name=None):\n        self.deck_name = deck_name\n        self.cards=[]\n        for i in range(0,4):\n            for j in range(0,13):\n                card=Card(i, j, self.deck_name)\n                self.cards.append(card)\n\n    def shuffle(self):\n        '''shuffle deck'''\n        buffer=[]\n        while(len(self.cards)):\n            i = randint(0, len(self.cards)-1)\n            card = self.cards.pop(i)\n            buffer.append(card)\n        print('I like to shuffle it, shuffle it')\n        self.cards = buffer\n\n    def get_number_of_cards_with_faces_on(self):\n        sum = 0\n        for card in self.cards:\n            sum = sum + 1 if card.is_turned_on() else sum\n        return sum\n\n    def __str__(self):\n        # for card in self.cards:\n        #     print(card)\n        return 'deck: %s' % (self.deck_name)","repo_name":"FredericoTakayama/blackjack","sub_path":"deck.py","file_name":"deck.py","file_ext":"py","file_size_in_byte":1893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"41919240755","text":"from operator import add,sub,mul,truediv\n\n# a solution to https://adventofcode.com/2020/day/18\n\nclass Statement:\n    def __init__(self):\n        self.operator = None\n        self.operand1 = None\n        self.operand2 = None\n\n    def add_operand(self, op):\n        if self.operand1 is None:\n            self.operand1 = op\n        else:\n            if self.operand2 is None:\n                self.operand2 = op\n            else:  # only relevant for part 2\n                self.operand2.add_operand(op)\n\n    def ready(self):\n        return self.operand1 is not None and self.operand2 is not None and self.operator is not None\n\n    def value(self):\n        return self.operator(self.operand1.value(),self.operand2.value())\n\nclass Num:\n    def __init__(self, val):\n        self.val = float(val)\n\n    def ready(self):\n        return True\n\n    def value(self):\n        return self.val\n\nclass ParenGroup:\n    def __init__(self, content):\n        self.content = content[1:-1]  # strip off enclosing parentheses\n        self.val = parse(self.content)\n\n    def value(self):\n        return self.val\n\nknown_operators = {\n    '+':add,\n    '-':sub,\n    '*':mul,\n    '/':truediv\n}\n\ndef parse(line):\n    chunks = line.strip().split(' ')\n    token_index = 0\n    statement = Statement()\n\n    while token_index < len(chunks):\n        chunk = chunks[token_index]\n        if chunk.isnumeric():\n            n = Num(chunk)\n            statement.add_operand(n)\n        elif chunk in known_operators:\n            if statement.ready():\n                s2 = Statement()\n                s2.operator = known_operators[chunk]\n\n                if part == 1 or chunk != '+':\n                    s2.operand1 = statement\n                    statement = s2\n                else:\n                    s2.operand1 = statement.operand2\n                    statement.operand2 = s2\n            else:\n                statement.operator = known_operators[chunk]                    \n        elif chunk.startswith('('):\n            num_parens = 0\n            parenthetical = []\n            while token_index < len(chunks):\n                num_parens += chunks[token_index].count('(')\n                num_parens -= chunks[token_index].count(')')\n                parenthetical.append(chunks[token_index])\n                if num_parens == 0:\n                    break\n                token_index += 1\n            pg = ParenGroup(' '.join(parenthetical))\n            statement.add_operand(pg)\n        else:\n            print(f\"Uh oh: unexpected: {chunk}\")\n            exit()\n\n        token_index += 1\n    \n    return statement.value()\n\n\ndef process():\n    total = 0\n    with open(\"dec18in.txt\") as in_file:\n\n        for line in in_file:\n            result = parse(line.strip())\n            total += result\n            print(result)\n\n        print(total)\n\npart = 1\n#print(parse('1 + 2 * 3 + 4 * 5 + 6'))\nprocess()\npart = 2\n# print(parse('1 + (2 * 3) + (4 * (5 + 6))'))\n# print(parse('2 * 3 + (4 * 5)'))\n# print(parse('5 + (8 * 3 + 9 + 3 * 4 * 3)'))\n# print(parse('5 * 9 * (7 * 3 * 3 + 9 * 3 + (8 + 6 * 4))'))\n# print(parse('((2 + 4 * 9) * (6 + 9 * 8 + 6) + 6) + 2 + 4 * 2'))\nprocess()","repo_name":"mjsambol/advent-of-code","sub_path":"2020/dec18.py","file_name":"dec18.py","file_ext":"py","file_size_in_byte":3133,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34438587471","text":"import errno\nimport fnmatch\nimport io\nimport os\nimport pygame\nimport stat\nimport sys\nimport threading\nimport time\nfrom pygame.locals import *\nfrom subprocess import call  \n\n# fb/ts setup\nos.putenv('SDL_VIDEODRIVER', 'fbcon')\nos.putenv('SDL_FBDEV'      , '/dev/fb1')\nos.putenv('SDL_MOUSEDRV'   , 'TSLIB')\nos.putenv('SDL_MOUSEDEV'   , '/dev/input/touchscreen')\n\n# init\npygame.init()\npygame.mouse.set_visible(False)\nscreen = pygame.display.set_mode((0,0), pygame.FULLSCREEN)\nscreen.fill((255,255,255))\n\n# fonts\ntemp_font = pygame.font.SysFont('Monospace', 16)\nwelcome_font = pygame.font.SysFont('Monospace', 12)\nsize_font = pygame.font.SysFont('Monospace', 36)\n\n# classes\nclass Icon:\n    def __init__(self, name):\n      self.name = name\n      try:\n        self.bitmap = pygame.image.load(iconPath + '/' + name + '.png')\n      except:\n        pass\n\n# Text Class\n# __init__:\n#  size    (W,H)\n#  textpos (X,Y), X=-1 for centered\n#  font    pygame.[Sys]Font\n#  text    string\n#  color   (R,G,B); default (0,0,0)\n# getRenderedSurface:\n#  returns: pygame.Surface\n\nclass Text:\n    def __init__(self, size, textpos, font, text, color=(0,0,0)):\n        self.size    = size\n        self.textposx= textpos[0]\n        self.textposy= textpos[1]\n        self.font    = font\n        self.text    = text\n        self.color   = color\n        self.surface = pygame.Surface(size, pygame.SRCALPHA, 32)\n        self.textsz  = self.font.size(text)\n        if (self.textposx == -1):\n            self.textposx = (size[0]-self.textsz[0])/2\n        rtext = self.font.render(self.text, 1, self.color)\n        self.surface.blit(rtext, (self.textposx, self.textposy))\n        self.font = None\n\n    def getRenderedSurface(self):\n        return self.surface\n\nclass Button:\n    def __init__(self, rect, **kwargs):\n        self.rect       = rect # Bounds\n        self.color      = None # Background fill color, if any\n        self.iconBg     = None # Background Icon (atop color fill)\n        self.iconFg     = None # Foreground Icon (atop background)\n        self.bg         = None # Background Icon name\n        self.fg         = None # Foreground Icon name\n        self.callback   = None # Callback function\n        self.value      = None # Value passed to callback\n        self.selectable = True\n        for key, value in kwargs.iteritems():\n            if   key == 'color'     : self.color      = value\n            elif key == 'bg'        : self.bg         = value\n            elif key == 'fg'        : self.fg         = value\n            elif key == 'cb'        : self.callback   = value\n            elif key == 'value'     : self.value      = value\n            elif key == 'selectable': self.selectable = value\n\n    def selected(self, pos):\n        if(self.selectable == False):\n            return False\n        x1 = self.rect[0]\n        y1 = self.rect[1]\n        x2 = self.rect[2]\n        y2 = self.rect[3]\n        if ((pos[0] >= x1) and (pos[0] <= x2) and\n            (pos[1] >= y1) and (pos[1] <= y2)):\n            if self.callback:\n                if self.value is None: self.callback()\n                else:                  self.callback(self.value)\n            return True\n        return False\n\n    def draw(self, screen):\n        if self.color:\n            screen.fill(self.color, self.rect)\n        if self.iconBg:\n            screen.blit(self.iconBg.bitmap, (self.rect[0], self.rect[1]))\n        if self.iconFg:\n            screen.blit(self.iconFg.bitmap,\n                (self.rect[0]+(self.rect[2]-self.iconFg.bitmap.get_width())/2,\n                self.rect[1]+(self.rect[3]-self.iconFg.bitmap.get_height())/2))\n        elif isinstance(self.fg, Text):\n            screen.blit(self.fg.getRenderedSurface(), (self.rect[0], self.rect[1]))\n\n    def setBg(self, name):\n        if name is None:\n            self.iconBg = None\n        else:\n            for i in icons:\n                if name == i.name:\n                    self.iconBg = i\n                    break\n\n\n# globals\niconPath = 'icons'\ntemp = 192\nuser = None\nicons = []\n\n# callbacks\n\ndef changeTemp(updn):\n    global temp\n    if(updn == 0):\n        temp -= 1\n    elif(updn == 1):\n        temp += 1\n\nbuttons = [\n    # screen mode 0 - login page\n    [],\n\n    # screen mode 1 - main menu\n    [\n     Button((295,  0,315, 30), bg='check', selectable=False ),\n     Button((  0, 80, 18,160), bg='slide-left-disabled'     ),\n     Button((302, 80,320,160), bg='slide-right'             ),\n     Button(( 10,200,110,220), fg=Text((100,20), (0,0), temp_font, \"Boil Temp:\"), selectable=False),\n     Button(( 16,220, 30,234), bg='btn-minus', cb=changeTemp, value=0  ),\n     Button(( 86,220, 100,234), bg='btn-plus',  cb=changeTemp, value=1 ),\n     Button(( 24, 80, 89,160), bg='size-frame',          fg=Text((65,80), (-1,6), size_font, \"3\")  ),\n     Button(( 93, 80,158,160), bg='size-frame-selected', fg=Text((65,80), (-1,6), size_font, \"7\")  ),\n     Button((162, 80,227,160), bg='size-frame',          fg=Text((65,80), (-1,6), size_font, \"12\") ),\n     Button((231, 80,296,160), bg='size-frame',          fg=Text((65,80), (-1,6), size_font, \"16\") ),\n     Button((198,198,318,238), bg='brew-ok', cb=exit        ),\n    ],\n\n    # screen mode 2 - working\n    [],\n\n    # screen mode 3 - standby mode\n    [],\n]\n\nfor file in os.listdir(iconPath):\n  if fnmatch.fnmatch(file, '*.png'):\n    icons.append(Icon(file.split('.')[0]))\n\n# Assign Icons to Buttons, now that they're loaded\nfor s in buttons:        # For each screenful of buttons...\n  for b in s:            #  For each button on screen...\n    for i in icons:      #   For each icon...\n      if b.bg == i.name: #    Compare names; match?\n        b.iconBg = i     #     Assign Icon to Button\n#        b.bg     = None  #     Name no longer used; allow garbage collection\n      if b.fg == i.name:\n        b.iconFg = i\n        b.fg     = None\n\nscreenMode=1\nwhile(True):\n    for event in pygame.event.get():\n        if(event.type is MOUSEBUTTONDOWN):\n            pos = pygame.mouse.get_pos()\n            for b in buttons[screenMode]:\n                if b.selected(pos): print(\"selected button: \"+b.bg)\n\n    screen.fill((255,255,255))\n    for i,b in enumerate(buttons[screenMode]):\n       b.draw(screen)\n    welcome = welcome_font.render('Welcome, Katelyn!', 0, (0,0,0))\n    screen.blit(welcome, (5,5))\n    temp_txt = temp_font.render(str(temp)+'F', 0, (0,0,0))\n    screen.blit(temp_txt, (38,218))\n    pygame.display.flip()\n    if ( len(sys.argv) > 1 and sys.argv[1] == \"screen\" ):\n        print(\"taking screen capture\")\n        pygame.image.save(screen, \"keurig-screen.jpg\")\n        exit()\n","repo_name":"slowbro/keurig-control","sub_path":"keurig.py","file_name":"keurig.py","file_ext":"py","file_size_in_byte":6559,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"35162434195","text":"list = [(\"student1\", \"A\"),(\"student2\", \"B\"),(\"student3\",\"A\"),(\"student4\",\"F\"),(\"student5\",\"D\"),(\"student6\",\"B\")]\r\ndict={}\r\ndict['A'] = []\r\ndict['B'] = []\r\ndict['C'] = []\r\ndict['D'] = []\r\ndict['F'] = []\r\n\r\nfor i in list:\r\n\tdict[i[1]].append(i[0])\r\n\r\n\r\nprint(dict)","repo_name":"McSherryBP/pythonCodeSnippets","sub_path":"dictionaryFromListGradesAlternative.py","file_name":"dictionaryFromListGradesAlternative.py","file_ext":"py","file_size_in_byte":262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"41368947344","text":"annual_salary = int(input(\"Enter the starting salary: \"))\n\ncurrent_savings = 0\nsemi_annual_raise = 0.07\nr = 0.04\ndown_payment_portion = 0.25\ntotal_cost = 1000000\nmonths = 36\ndiff = 5000\nlow = 0.00\nhigh = 1.00\nportion_guess = (low+high)/2\nnum_guesses = 0\n\nwhile abs(current_savings - total_cost * down_payment_portion) >= diff and low < high:\n    current_savings = 0\n    temp_annual_salary = annual_salary\n\n    for n in range(months+1):\n        monthly_salary = temp_annual_salary/12\n        if n % 6 == 0 and n != 0 :\n            temp_annual_salary += temp_annual_salary * semi_annual_raise\n        current_savings = current_savings + portion_guess * monthly_salary\n        current_savings = current_savings + current_savings * r/12\n\n    if current_savings < total_cost * down_payment_portion:\n        low = portion_guess\n    else:\n        high = portion_guess\n\n    portion_guess = (low+high)/2\n    num_guesses += 1\n\nif(low < high):\n    print(\"Best saving rate: \" + str(portion_guess))\n    print(\"Steps in bisection search: \" + str(num_guesses))\nelse:\n    print(\"It is not possible to pay the down payment in three years.\")\n","repo_name":"PanPapag/MIT-OCW-6.0001","sub_path":"PS1/ps1c.py","file_name":"ps1c.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"8509066727","text":"# First take command from user\n# todos = [] no need to create a list its done by readlines method.\nimport functions\nimport time\n\n# To show current time and date. \nnow = time.strftime(\"%b %d %H:%M:%S\")\n\nprint(\"Welcome, Its\", now)\nwhile True:\n\n    userAction = input(\"Type add, show, complete, edit or exit: \")\n    userAction = userAction.lower().strip()\n    # Using match case for all given commands.\n\n    if userAction.startswith(\"add\"):\n        newTodo = userAction[4:]\n        # This is a line to read it back help to store all todos.\n        # file = open(\"Files/todos.txt\", \"r\")\n        # # store it into todos variable to iterate it.\n        # todos = file.readlines()\n        # file.close()\n        # Using with context manager to do the same thing\n\n        todos = functions.read_todos()\n\n        todos.append(newTodo + \"\\n\")\n\n        # file = open(\"Files/todos.txt\", \"w\")\n        # file.writelines(todos)\n        # file.close()\n\n        functions.write_todos(todos)\n\n    elif userAction.startswith(\"show\"):\n        # we have to open todos here to show it\n        # file = open(\"Files/todos.txt\", \"r\")\n        # todos = file.readlines()\n        # file.close()\n        # todos = [t.title().strip() for t in todos]  # use of list comprehension\n        todos = functions.read_todos()\n\n        for index, t in enumerate(todos):\n            t = t.title().strip()\n            # print(index,t)\n\n            # using f strings to print variables same as $ in js\n            toPrint = f\"{index + 1}- {t}\"\n            print(toPrint)\n\n    elif userAction.startswith(\"edit\"):\n        try:\n\n            # In this by entering index we are editing but user counts from 1..\n            index = int(userAction[5:])\n            todos = functions.read_todos()\n\n            todos[index - 1] = input(\"Enter edited todo: \") + \"\\n\"\n\n            # This part is writing the edited todos so first edit then write.\n            # file = open(\"Files/todos.txt\", \"w\")\n            # file.writelines(todos)\n            # file.close()\n\n            functions.write_todos(todos)\n\n        except ValueError:\n            print(\"Please enter number of particular todo\")\n            continue\n        except IndexError:\n            print(\"There is no todo at that number\")\n            continue\n\n    # This case is for completed todos, removing it from the list.\n    elif userAction.startswith(\"complete\"):\n        try:\n            index = int(userAction[9:])\n            index = index - 1\n\n            todos = functions.read_todos()\n\n            todo_to_remove = todos[index].strip(\"\\n\")\n            todos.pop(index)\n\n            # This part is re writing the todos which is available.\n            # file = open(\"Files/todos.txt\", \"w\")\n            # file.writelines(todos)\n            # file.close()\n\n            functions.write_todos(todos)\n\n            # its good to display a message which todo is removed.\n            message = f\"Todo '{todo_to_remove}' is removed from list.\"\n            print(message.title())\n        except IndexError:\n            print(\"There is no todo at that number\")\n            continue\n        except ValueError:\n            print(\"Please enter number of particular todo\")\n            continue\n\n    elif userAction.startswith(\"exit\"):\n        break\n\n    # This case is executed if no any case matches.\n    else:\n        print(\"Command is not valid..\")\n","repo_name":"Navyam-Raushan/To_do_app","sub_path":"cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":3349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"29408685599","text":"from urllib.request import urlopen\nfrom bs4 import BeautifulSoup\nimport json\nimport requests\nimport ssl\nssl._create_default_https_context = ssl._create_unverified_context\n\ndef text(raw):\n    if 'span class=\"desc\"' in str(raw):\n        t = raw.getText().strip().replace(\"\\n\", \"\")\n        while \"  \" in t:\n            t = t.replace(\"  \", \" \")\n        return t + \"<i>\"\n    else:\n        t = raw.getText().strip().replace(\"\\n\", \"\")\n        while \"  \" in t:\n            t = t.replace(\"  \", \" \")\n        return t\n\ndef get_player_stats(name, num):\n    l = name.lower().split(\" \")\n    player_string = f\"{l[1][0:5] + l[0][0:2]}0{str(num)}\"\n    url = f\"https://www.basketball-reference.com/players/{player_string[0]}/{player_string}.html\"\n\n    r = requests.get(url)\n    try:\n        html = urlopen(url)\n    except:\n        print('player not found')\n        return False\n    soup = BeautifulSoup(html, 'html.parser')\n\n    with open('file.html', 'w+') as file:\n        file.write(r.text)\n\n    stats = {}\n\n    ps = soup.findAll('p')\n    for p in range(len(ps)):\n\n        t = text(ps[p])\n        if p == 0 and \"pronunciation\" in t.lower():\n            t = text(ps[p + 1])\n        if p == 0 and \"▪\" not in t.lower():\n            stats['name'] = t\n        elif p == 0:\n            ats = t.split(\" ▪ \")\n            stats['name'] = ats[0].split(\": \")\n            if 'twitter' in t.lower() and 'instagram' in t.lower():\n                stats['twitter'] = ats[1].split(\": \")[1]\n                stats['instagram'] = ats[2].split(\": \")[1]\n            if 'twitter' in t.lower() and 'instagram' not in t.lower():\n                stats['twitter'] = ats[1].split(\": \")[1]\n            if 'instagram' in t.lower() and 'twitter' not in t.lower():\n                stats['instagram'] = ats[1].split(\": \")[1]\n        if (p == 1 or p == 2) and '<i>' in t.lower():\n            stats['former_name'] = t.split(\"<i>\")[0].replace(\")\", \"\").replace(\"(\", \"\")\n        if (p in list(range(0, 5))) and t.lower()[0] == '(' and '<i>' not in t.lower() and t[1] != '-':\n            stats['nicknames'] = t.replace(\"(\", \"\").replace(\")\", \"\").split(\", \")\n        if p in list(range(0, 6)) and 'position: ' in t.lower():\n            stats['position'] = t.lower().split('position: ')[1].split(' ▪')[0]\n            stats['shooting_hand'] = t.lower().split('shoots: ')[1]\n        if p in list(range(1, 10)) and len(t) > 0 and t[1] == '-':\n            stats['height'] = t.split(',')[0]\n            stats['weight'] = t.split(',')[1].split('lb')[0].strip()\n        if p in list(range(2, 11)) and len(t) > 0 and t.lower()[0:6] == 'born: ':\n            stats['birthday'] = (t.lower().split('born')[1].split(' in')[0])[2:]\n            if \"in\" in t.lower():\n                stats['birthplace'] = t.lower().split('in ')[1].replace(', ', ', ')[0:-2] + \" \" + t.lower()[len(t)-2:len(t)].upper()\n        if p in list(range(2, 12)) and len(t) > 0 and t.lower()[0:6] == 'died: ':\n            stats['died'] = (t.lower().split('died')[1].split('(')[0])[2:].replace(' ', ' ')\n        if p in list(range(2, 14)) and len(t) > 0 and (t.lower()[0:9] == 'college: ' or t.lower()[0:10] == 'colleges: '):\n            if 'college: ' in t.lower():\n                stats['college'] = t.lower().split('college:')[1][1:]\n            if 'colleges: ' in t.lower():\n                stats['colleges'] = t.lower().split('colleges:')[1][1:].split(', ')\n        if p in list(range(2, 16)) and len(t) > 0 and t.lower().startswith(\"high school\"):\n            stats['high school'] = t.lower()[12:]\n        if p in list(range(2, 18)) and len(t) > 0 and t.lower().startswith('draft: '):\n            stats['draft'] = t.lower().split('draft: ')[1]\n            # stats['drafted_to'] = t.lower().split('draft: ')[1].split(',')[0] + \", \" + t.lower().split('), ')[1].replace(' nba draft', '')\n            # stats['draft_pick'] = t.lower().split('draft: ')[1].split(',')[1][1:] + t.lower().split('draft: ')[1].split(',')[2]  \n        if p in list(range(2, 20)) and len(t) > 0 and t.lower().startswith('nba debut: '):\n            stats['debut'] = t.lower().split(': ')[1]\n            break\n\n    \n    try:\n        awards = []\n        blings = soup.findAll('ul', {'id': 'bling'})[0].findAll('li')\n        for b in blings:\n            awards.append(b.getText().lower())\n\n        stats['accomplishments'] = awards\n    except IndexError:\n        pass\n\n\n    stats['career_summary'] = {}\n\n    career_div = soup.findAll('div', {'class': 'stats_pullout'})[0]\n    # print(r.text.split('<span><strong>SUMMARY</strong></span>')[1].split('<strong>WS</strong>')[0])\n    stat_fields = career_div.findAll('span', {'class': 'poptip'})\n    stat_names = []\n    for stat_line in stat_fields:\n        stat = str(stat_line)\n        stat_name = stat.split('<strong>')[1].split('</strong>')[0]\n        stat_names.append(stat_name)\n\n    p_tags = career_div.findAll('p')[2:]\n    \n    counter = 0\n    for p in range(1, len(p_tags), 2):\n        stats['career_summary'][stat_names[counter]] = p_tags[p].getText()\n        counter += 1\n\n    return stats\n","repo_name":"asboyer/goat_grade_all_time","sub_path":"all_time.py","file_name":"all_time.py","file_ext":"py","file_size_in_byte":5027,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"42057697629","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import Select\nfrom selenium.webdriver.common.action_chains import ActionChains\nimport urllib.request\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as ec\nimport os\nimport errno\nfrom selenium.webdriver.firefox.options import Options\n\n\nclass WgaspiderSpider(scrapy.Spider):\n    \"\"\"\n    This class scrapes the wga website and download all images based on category and type.\n\n    The path to geckodriver needs to be added as path environment variable.\n\n    Should be executed with scrapy like:\n    'scrapy crawl wgaSpider -a category=painting -a typeof=landscape -a folder=./mydata/' from the commandline\n    in the root of the project.\n\n    :param category -- the category of the artworks (default is any)\n    :param typeof -- the type of the artworks (default is any)\n    :param folder -- the folder, where the images will be saved (default is actual folder)\n    :param max_site_load_delay -- after every click on the website the next operation must be delayed. Change this value\n                                    depending on computer and internet speed (default is 2.5)\n\n    all categories: any, painting, sculpture, graphics, illumination, architecture, ceramics, furniture, glassware,\n                    metalwork, mosaic, stained-glass, tapestry, others\n    all types: any, religious, historical, mythological, landscape, portrait, still-life, interior, genre, study, others\n    \"\"\"\n    name = 'wgaSpider'\n    allowed_domains = ['www.wga.hu']\n    start_urls = ['https://www.wga.hu/']\n\n    def __init__(self, category='any', typeof='any', folder='', max_site_load_delay=2.5, restart_after_pages=5, *args,\n                 **kwargs):\n        super(WgaspiderSpider, self).__init__(*args, **kwargs)\n\n        # set parameters\n        self.category = category\n        self.typeof = typeof\n        self.folder = folder\n        self.max_site_load_delay = max_site_load_delay\n        self.restart_after_pages = int(restart_after_pages)\n\n        # init folder\n        try:\n            os.makedirs(self.folder)\n        except OSError as e:\n            if e.errno != errno.EEXIST:\n                raise\n\n        # init web driver\n        options = Options()\n        options.headless = True\n        profile = webdriver.FirefoxProfile()\n        # profile.set_preference(\"browser.cache.disk.enable\", True)\n        # profile.set_preference(\"browser.cache.memory.enable\", True)\n        # profile.set_preference(\"browser.cache.offline.enable\", True)\n        # profile.set_preference(\"network.http.use-cache\", True)\n        self.driver = webdriver.Firefox(profile, options=options)\n        self.driver.minimize_window()\n\n    def restart_browser(self, response):\n        \"\"\"\n        Restarts the browser instance to free ram.\n\n        :param response: The http response of the actual scraped website\n        :return: nothing\n        \"\"\"\n        # close driver\n        self.driver.close()\n        self.driver.quit()\n\n        # init new driver\n        options = Options()\n        options.headless = True\n        profile = webdriver.FirefoxProfile()\n        # profile.set_preference(\"browser.cache.disk.enable\", True)\n        # profile.set_preference(\"browser.cache.memory.enable\", True)\n        # profile.set_preference(\"browser.cache.offline.enable\", True)\n        # profile.set_preference(\"network.http.use-cache\", True)\n        self.driver = webdriver.Firefox(profile, options=options)\n        self.driver.minimize_window()\n\n        # open website\n        self.driver.get(response.url)\n\n    def parse(self, response):\n        \"\"\"\n        The parse method get automatically called by scrapy and should no be used manuel.\n        :param response: The http response of the actual scraped website\n        :return: yields for scrapy\n        \"\"\"\n\n        # open the next website\n        self.driver.get(response.url)\n\n        # image id will be used as filename\n        actual_img_id = 0\n        # save actual page\n        actual_page_number = 0\n\n        search_done = self.get_through_search()\n\n        while search_done:\n            # get all image boxes from actual page\n            image_boxes = self.driver.find_elements_by_xpath('/html/body/center[3]/table/tbody/tr')\n\n            # for loop over image boxes on the current page\n            i = 2\n            while i < len(image_boxes):\n                # save main window\n                window_before = self.driver.window_handles[0]\n\n                # open popup window with image\n                preview_img = self.driver.find_element_by_xpath('/html/body/center[3]/table/tbody/tr['\n                                                                + str(i) + ']/td[1]/a/img')\n                preview_img.click()\n\n                # switch to popup window\n                self.driver.switch_to.window(self.driver.window_handles[1])\n                WebDriverWait(self.driver, self.max_site_load_delay).until(\n                    ec.visibility_of_element_located((By.XPATH, '/html/frameset/frame[2]')))\n\n                # switch to new frame in popup window\n                frame = self.driver.find_element_by_xpath('/html/frameset/frame[2]')\n                self.driver.switch_to.frame(frame)\n\n                # wait\n                WebDriverWait(self.driver, self.max_site_load_delay).until(\n                    ec.visibility_of_element_located((By.XPATH, '/html/body/center/img')))\n\n                # get and save the image\n                image = self.driver.find_element_by_xpath('/html/body/center/img')\n                src = image.get_attribute('src')\n                urllib.request.urlretrieve(src, self.folder + str(actual_img_id) + '.jpg')\n\n                # increment image id\n                actual_img_id += 1\n\n                # close popup window\n                self.driver.close()\n\n                # switch to main window\n                self.driver.switch_to.window(window_before)\n\n                # increment counter for images on the current page\n                i += 1\n\n            # increment page counter\n            actual_page_number += 1\n\n            # restart browser every few pages to hold ram footprint small\n            if actual_page_number % self.restart_after_pages == 0:\n                self.restart_browser(response)\n                search_done = self.get_through_search()\n                self.iterate_pages(actual_page_number)\n\n        # close main window to end scraping\n        self.driver.close()\n        self.driver.quit()\n\n    def iterate_pages(self, number_pages):\n        \"\"\"\n        Iterates through search result pages.\n\n        :param number_pages: The number of pages which should be skipped\n        :return: nothing\n        \"\"\"\n        for i in range(number_pages):\n            # get next page button\n            next_page_button = self.driver.find_elements_by_xpath(\n                '/html/body/center[4]/table/tbody/tr/td/p/a')[-1]\n\n            # switch to next page\n            next_page_button.click()\n\n            # wait for page to load\n            self.driver.implicitly_wait(self.max_site_load_delay)\n\n    def get_through_search(self):\n        \"\"\"\n        Navigates the scraper from home screen to search engine, enters fill boxes and starts search to get results.\n\n        :return: True if successful\n        \"\"\"\n        # hit enter button, which is 'hidden' from bots, by manuel click\n        img_over_enter_button = self.driver.find_element_by_xpath('/html/body/table/tbody/tr[5]/td[1]/img')\n        ActionChains(self.driver).move_to_element_with_offset(img_over_enter_button, 35, 33).click().perform()\n\n        # Wait for javscript to load in Selenium\n        WebDriverWait(self.driver, self.max_site_load_delay).until(\n            ec.visibility_of_element_located((By.XPATH, '/html/frameset/frame[2]')))\n\n        # switch to new frame\n        frame = self.driver.find_element_by_xpath('/html/frameset/frame[2]')\n        self.driver.switch_to.frame(frame)\n\n        # click link to get to search engine\n        WebDriverWait(self.driver, self.max_site_load_delay).until(\n            ec.visibility_of_element_located((By.LINK_TEXT, 'Search Engine')))\n        self.driver.find_element_by_link_text('Search Engine').click()\n\n        # Wait for javscript to load in Selenium\n        WebDriverWait(self.driver, self.max_site_load_delay).until(\n            ec.visibility_of_element_located((By.XPATH, '/html/body/form/center/p[4]/font/input[1]')))\n\n        # fill search engine forms\n        select = Select(self.driver.find_element_by_name('form'))\n        select.select_by_visible_text(self.category)\n        select = Select(self.driver.find_element_by_name('type'))\n        select.select_by_visible_text(self.typeof)\n\n        # get search button and click it\n        search_engine_button = self.driver.find_element_by_xpath(\n            '/html/body/form/center/p[4]/font/input[1]')\n        search_engine_button.click()\n\n        # Wait for javscript to load in Selenium\n        WebDriverWait(self.driver, self.max_site_load_delay).until(\n            ec.visibility_of_element_located((By.XPATH, '/html/body/center[3]/table/tbody/tr')))\n\n        return True\n","repo_name":"steveklute/neuronart","sub_path":"src/scripts/scrapers/scrapers/spiders/wgaSpider.py","file_name":"wgaSpider.py","file_ext":"py","file_size_in_byte":9261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"47234193812","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jul 19 11:01:47 2019\n\n@author: fm897\n\"\"\"\n\nimport numpy as np\n\n\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.feature_selection import SelectKBest, f_classif\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\n\nimport mne\nfrom mne.minimum_norm import apply_inverse_epochs, read_inverse_operator\nfrom mne.decoding import (cross_val_multiscore, LinearModel, SlidingEstimator,\n                          get_coef)\n\nprint(__doc__)\n\n#from joblib import Parallel, delayed\n\nimport csv\n\nwith open('/autofs/space/taito_005/users/fahimeh/doc/txt/list_1.txt') as csv_file:\n    csv_reader = csv.reader(csv_file, delimiter=' ')\n    subjects = [row[0] for row in csv_reader]\n\ntmin = -.2\ntmax = 1.0\n\n#event_id = {'1': 1002, '2': 1005, '3': 1008, '4': 1011, '5': 1014, '6': 1017}  # just use two\nevent_id = {'1': 2102, '2': 2105, '3': 2108, '4': 2111, '5': 2114, '6': 2117}  # just use two\n\n\ndata_path = '/autofs/space/voima_001/users/awmrc/'\nsubjects_dir = '/autofs/space/voima_001/users/awmrc/subjects_mri/'\n\n#def compute_scores(X_split):\n#    scores = list()\n#    for idx in range(X_split.shape[1]):\n#        X_vert = X_split[:, idx, :]\n#        scores.append(cross_val_multiscore(clf, X_vert, y, cv=5, n_jobs=1))\n#        print('Score for vert %d of %d = %f' % (idx, X_split.shape[1], scores[-1].mean()))\n#        print('scores %d' % (scores))\n#        \n#    return scores\n\nfrom sklearn import decomposition\n\n\ndef compute_ml(X,y):\n    \n    clf = make_pipeline(StandardScaler(),  # z-score normalization\n          #          SelectKBest(f_classif, k=300),  # select features for speed\n                    SVC(gamma='auto_deprecated'))\n    pca = decomposition.PCA(n_components=100)\n    \n    scores = list()\n    vr = list()\n    for idx in range(X.shape[1]):\n        \n        X_vert = X[:, idx, :]\n        pca.fit(X_vert)\n        X_vert = pca.transform(X_vert)\n        v = pca.explained_variance_ratio_\n        vr.append(np.sum(v[:100]))\n\n        scores.append(cross_val_multiscore(clf, X_vert, y, cv=10, n_jobs=1))\n        print('Score for vert %d of %d = %f' % (idx, X.shape[1], \n                                                scores[-1].mean()))\n#    n_jobs = 5\n#    parallel = Parallel(n_jobs=n_jobs)\n#    delayed_scores = delayed(compute_scores)\n#    scores = parallel(delayed_scores(X_split) for X_split in\n#                  np.array_split(X, n_jobs, axis=1))\n    \n    scores_mean = np.array(scores).mean(axis=1)\n    variance_explained =np.array(vr).mean()\n    return scores_mean, variance_explained\n\n\nsubj_score = list()\nsnr = 3.0\n\n\nfor subj in subjects[0:2]:\n    \n    raws = list()\n    events = list()\n    if subj == 'awmrc_001':\n        runs=['2','3','4','5']\n    else:\n        runs=['1','2','3','4']\n    \n    for run in runs:\n    \n        raw_fname = data_path + subj + '/megdata/' + subj + '_aw_' + run + \\\n        '_0.5_140fil_raw.fif'\n\n        eventn = data_path + subj + '/megdata/' + subj + '_aw_' + run + \\\n        '_decim_recode_sss_mergestim-eve.fif'\n        \n        raws.append(mne.io.read_raw_fif(raw_fname, preload=True))\n        events.append(mne.read_events(eventn))\n    \n    first_samps = list()\n    last_samps = list()\n    \n    for index in range(len(raws)):\n        print(index)\n        raws[index].info['projs'] = []\n        raws[index].pick_types(meg=True, eog=True, stim=True, eeg=False)\n        first_samps.append(raws[index].first_samp)\n        last_samps.append(raws[index].last_samp)\n        \n    raw = mne.concatenate_raws(raws, preload=True)\n    raw.filter(0.5, 12., fir_design='firwin')\n    event = mne.concatenate_events(events,first_samps,last_samps)\n\n    epochs = mne.Epochs(raw, event, event_id, tmin, tmax, proj=True,\n                    baseline=(-0.2, -0.05), preload=True,\n                    reject=dict(grad=4000e-13, mag=4e-12),\n                    decim=1)  # decimate to save memory and increase speed\n\n    \n    epochs.pick_types(meg=True)  # remove stim and EOG\n\n    fname_inv = data_path + subj + '/megdata/' + subj + \\\n    '_aw_0.5_140_calc-inverse_loose_ico4_weight_new_erm_megreg_0_new_MNE_proj-inv.fif'\n\n\n    inverse_operator = read_inverse_operator(fname_inv)\n\n    stcs = apply_inverse_epochs(epochs, inverse_operator,\n                            lambda2=1.0 / snr ** 2, verbose=False,\n                            method=\"dSPM\", pick_ori=\"normal\")\n    \n    \"left hemisphere\"\n    Xl = np.array([stc.lh_data for stc in stcs])  # only keep left hemisphere\n    y = epochs.events[:, 2]\n    \n    Xr = np.array([stc.rh_data for stc in stcs])  # only keep right hemisphere\n        \n    \n\n    \n    ind_time = np.where(epochs.times>0)[0]\n    \n    scores_meanl, varl = compute_ml(Xl[:,:,ind_time],y)\n    \n    scores_meanr, varr = compute_ml(Xr[:,:,ind_time],y)\n\n    save_file = data_path + subj + '/megdata/impulseActual_' + subj + \\\n    '_aw_source_timeseries_12Hz'\n    \n    np.savez(save_file, Xl = Xl, Xr = Xr, y = y, varr=varr, varl= varl)\n    \n#    scores_meanl = compute_ml(Xl,y)\n#    \n#    scores_meanr = compute_ml(Xr,y)\n    \n    \n    stc = stcs[0]  # for convenience, lookup parameters from first stc\n\n#    \n    vertices = [stc.lh_vertno, stc.rh_vertno]\n    tt = np.concatenate((scores_meanl,scores_meanr),axis=0)\n    tt = tt.reshape(len(tt),1)\n    a=np.repeat(tt,len(stc.times),axis=1)\n    \n    stc_feat = mne.SourceEstimate(a, vertices=vertices,\n                              tmin=stc.tmin, tstep=stc.tstep, subject=subj) \n    \n    fname = data_path + subj + '/megdata/impulseActual_' + subj + '_0to1s_' + \\\n    'pca100_aw_decoding_scores'\n    \n\n    stc_feat.save(fname)\n\n#%%\n\nfor subj in subjects[2:21]:\n\n    save_file = data_path + subj + '/megdata/impulseActual_' + subj + \\\n    '_aw_source_timeseries_12Hz'\n    \n    npzfile = np.load(save_file + '.npz')\n    \n    Xl = npzfile['Xl']\n    Xr = npzfile['Xr']\n    y = npzfile['y']\n     \n    ind_time = np.where(epochs.times>0)[0]\n    \n    scores_meanl = compute_ml(Xl[:,:,ind_time],y)\n    \n    scores_meanr = compute_ml(Xr[:,:,ind_time],y)\n\n    \n#    scores_meanl = compute_ml(Xl,y)\n#    \n#    scores_meanr = compute_ml(Xr,y)\n    \n    \n    stc = stcs[0]  # for convenience, lookup parameters from first stc\n\n#    \n    vertices = [stc.lh_vertno, stc.rh_vertno]\n    tt = np.concatenate((scores_meanl,scores_meanr),axis=0)\n    tt = tt.reshape(len(tt),1)\n    a=np.repeat(tt,len(stc.times),axis=1)\n    \n    stc_feat = mne.SourceEstimate(a, vertices=vertices,\n                              tmin=stc.tmin, tstep=stc.tstep, subject=subj) \n    \n    fname = data_path + subj + '/megdata/impulseActual_' + subj + '_0to1s_' + \\\n    'pca180_aw_decoding_scores'\n    \n\n    stc_feat.save(fname)\n\n","repo_name":"fmamashli/CellReports","sub_path":"mvpa_source_2_impulse.py","file_name":"mvpa_source_2_impulse.py","file_ext":"py","file_size_in_byte":6723,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31094392807","text":"import os\nimport urllib.error\nimport urllib.request\n\n\ndef download_github_repo(owner, repo, ref, cache_dir=None):\n    filename = os.path.join(cache_dir, f'{owner}-{repo}-{ref}.zip')\n    if not os.path.isfile(filename):\n        url = f'https://github.com/{owner}/{repo}/archive/{ref}.zip'\n        print(f'downloading: {url}')\n        try:\n            with urllib.request.urlopen(url) as response:\n                with open(filename, 'wb') as f:\n                    f.write(response.read())\n        except urllib.error.HTTPError:\n            print(f'failed to download: {url}')\n            return None\n    else:\n        print(f'cached: {filename:64}')\n    return filename\n","repo_name":"scientific-python/python-api-inspect","sub_path":"inspect_api/download.py","file_name":"download.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"86"}
+{"seq_id":"14015249313","text":"class TextStats:\n  def __init__(self,text):\n    self.text = text\n    \n \n  def stat(self):\n    self.Dic = {}\n    f = open(self.text,encoding = 'utf-8')\n    for i in f:\n      word = i.split()\n      for i in word:\n        if i in self.Dic:\n          self.Dic[i] += 1\n        else:\n          self.Dic[i] = 1\n    print(self.Dic)\n    f.close()\n\n  def top(self,k):\n    self.Sort = sorted(self.Dic.items(), key=lambda x: x[1], reverse=True)\n    print(self.Sort)\n    for i in range(k):\n      print(self.Sort[i])\n\n  def save(self):\n    Save = dict(self.Sort)\n    with open(\"OUTPUT.txt\",'w',encoding = 'utf-8') as f:\n      for x,y in Save.items():\n          f.write(str(x))\n          f.write(\" \")\n          f.write(str(y))\n          f.write(\"\\n\")\n\n\n\n       \ns = \"INPUT.txt\"\nf  =TextStats(s)\nf.stat()\nf.top(3)\nf.save()\n","repo_name":"tedhwang007/Natural-Language-Processing","sub_path":"Week_2/TextStats.py","file_name":"TextStats.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"21677275781","text":"import websocket, json, pprint, talib, numpy\r\nimport config\r\nimport datetime as dt\r\nimport requests\r\nimport datetime\r\nimport time\r\nimport sys\r\nfrom binance.client import Client\r\nfrom binance.enums import *\r\nfrom binance.client import Client\r\n\r\n\r\n#Only Change TRADE_SYMBOL [Buy/Sell]\r\nTRADE_SYMBOL = config.symbol\r\nTRADE_SYMBOL_lowercase = TRADE_SYMBOL.lower()\r\nSOCKET = \"wss://stream.binance.com:9443/ws/{0}@kline_1m\".format(TRADE_SYMBOL_lowercase)\r\nclient = Client(config.API_KEY, config.API_SECRET)\r\nRSI_PERIOD = config.RSI_PERIOD\r\nRSI_OVERBOUGHT = config.RSI_OVERBOUGHT\r\nRSI_OVERSOLD = config.RSI_OVERSOLD\r\n#################################### Logging #################################################\r\nimport logging\r\nlogging.basicConfig(filename='null',format='%(message)s', level=logging.INFO)\r\nlogger = logging.getLogger(__name__)\r\n#create a file handler\r\nhandler = logging.FileHandler('logfile.log')\r\nhandler.setLevel(logging.INFO)\r\n#create a logging format\r\ntitle = logging.Formatter('%(message)s')\r\nhandler.setFormatter(title)\r\n# add the handlers to the logger\r\nlogger.addHandler(handler)\r\nlogger.info(\"Date                Close             %K                 %D                 Open Profit                 Total Profit                 Pos Status                 Profit (Previous Trade)        \")\r\ninfo = logging.Formatter('%(asctime)s %(message)s ','%Y-%m-%d %H:%M:%S')\r\nhandler.setFormatter(info)\r\nlogger.addHandler(handler)\r\n\r\n################################### Telegram Bot #############################################\r\ndef telegram_bot_sendtext(bot_message):\r\n    if config.telegram_token:\r\n        bot_token = config.telegram_token\r\n        bot_chatID = config.telegram_chatid\r\n        send_text = 'https://api.telegram.org/bot' + bot_token + \\\r\n            '/sendMessage?chat_id=' + bot_chatID + '&parse_mode=Markdown&text=' + bot_message\r\n        response = requests.get(send_text)\r\n        return response.json()\r\n\r\n\r\n\r\n\r\n\r\n############################################Order Functions################################################\r\n#get balance of futur account wallet (usdt , bnb , busd)\r\ndef balance():\r\n    try:\r\n        blnc = client.balance()\r\n        for counter in blnc:\r\n            for value in counter:\r\n                if value == 'asset':\r\n                    print(value , ' >> ' , counter[value])\r\n                if value == 'balance' :\r\n                    print(value , ' >> ' , counter[value])\r\n                    #Telegram Notif\r\n                    msg = f\"{value}: ${counter[value]}\"\r\n                    telegram_bot_sendtext(msg)\r\n                if value == 'withdrawAvailable':\r\n                    print(value , ' >> ' , counter[value])\r\n    except Exception as Error:\r\n        print(Error)\r\n#create new order\r\ndef new_order(symbol , type , quantity , side , priceProtect = False ,\r\n              closePosition = False , price = None, stopPrice = None , positionSide = None ,\r\n              reduceonly = False , timeInForce = None):\r\n    try:\r\n        result = client.new_order(side=side,\r\n                                  quantity=quantity,\r\n                                  symbol=symbol,\r\n                                  reduceOnly=reduceonly,\r\n                                  positionSide=positionSide,\r\n                                  stopPrice = stopPrice,\r\n                                  timeInForce = timeInForce,\r\n                                  price= price,\r\n                                  closePosition= closePosition,\r\n                                  priceProtect= priceProtect ,\r\n                                orderType=type)\r\n        print(result)\r\n        return result\r\n    except Exception as Error:\r\n        print(Error)\r\n\r\n\r\n\r\n\r\n#print history of last close position\r\ndef HPrint(history):\r\n    for value in history[0]:\r\n        if value == 'time':\r\n            print('time >> ', datetime.fromtimestamp(history[0]['time'] / 1e3))\r\n        elif value == 'realizedPnl':\r\n            print(value, f' >> {float(history[0][value])} ')\r\n        else:\r\n            print(value , f' >> {history[0][value]} ')\r\n#get history from 5 days ago until now\r\ndef history():\r\n    dt2 = datetime.now()\r\n    dt = datetime(dt2.year, dt2.month, dt2.day-5)\r\n    milliseconds = int(round(dt.timestamp() * 1000))\r\n    now = int(round(dt2.timestamp())*1000)\r\n    his2 = client.trade_list(limit=1000 , startTime=milliseconds , endTime=now)\r\n    HPrint(his2)\r\n#get information of open position\r\ndef position_info():\r\n    posInf = client.position_info()\r\n    for counter in posInf:\r\n        if counter['symbol'] == TRADE_SYMBOL:\r\n            print(counter)\r\n            return counter\r\n#close orders function\r\ndef close_orders():\r\n    client.cancel_all_open_orders(symbol=TRADE_SYMBOL)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n####################################Variables######################################\r\ncloses = []\r\nin_position = False\r\ntype_position = False\r\nopenLong = 0.0000\r\nopenShort = 0.0000\r\ncloseLong = 0.0000\r\ncloseShort = 0.0000\r\nprofit = 0.0000\r\ntotalProfit = 0.0000\r\nopenPositionProfit = 0.0000 \r\npositionStatus = 'NULL'\r\nmaximumProfitLoss = -10.0000\r\n##################################Main#############################################\r\ndef on_open(ws):\r\n    print('opened connection')\r\n    #Telegram Notif\r\n    msg = f\"Opened Connection\"\r\n    telegram_bot_sendtext(msg)\r\ndef on_close(ws):\r\n    print('closed connection')\r\ndef on_message(ws, message):\r\n    global closes, in_position, type_position\r\n    global openLong, openShort, closeLong, closeShort, profit, totalProfit, openPositionProfit\r\n    global positionStatus\r\n    global maximumProfitLoss\r\n    #Force shutdown if Total Profit below -10%\r\n    if (totalProfit < maximumProfitLoss):\r\n        #CLOSE ALL OPEN POSITIONS FIRST\r\n        if config.backtesting == False:\r\n            close_orders()\r\n        print(\"Total Profit -10% so STOP the bot!\")\r\n        print(\"All open trades are force closed\")\r\n        #Telegram Notif\r\n        msg = f\"*Total Profit -10% so STOP the bot!\\nAll open trades are force closed*\"\r\n        telegram_bot_sendtext(msg)\r\n        sys.exit(\"BOT SHUTDOWN\")\r\n    try:\r\n        \r\n\r\n        # fig, axes = plt.subplots(2, 1, sharex=True)\r\n        # #ax1, ax2 = axes[0], axes[1]\r\n        #print('received message')\r\n        json_message = json.loads(message)\r\n        #pprint.pprint(json_message)\r\n        messageTime = json_message['E']\r\n        timestamp = dt.datetime.fromtimestamp(int(messageTime)/1000)\r\n        messageSentTime = timestamp.strftime(\"%Y-%m-%d %H:%M:%S.%f\")\r\n        #print(\"Message Sent Time : \")\r\n        #print(messageSentTime)\r\n        candle = json_message['k']\r\n        is_candle_closed = candle['x']\r\n        close = candle['c']\r\n        # timeOfClose = candle['T']\r\n        # print(\"Original Time of Close from candle: \")\r\n        # print(timeOfClose)\r\n        # timestamp = dt.datetime.fromtimestamp(int(timeOfClose)/1000)\r\n        # closingTime = timestamp.strftime(\"%Y-%m-%d %H:%M:%S.%f\")\r\n        # print(\"Closing Time : \")\r\n        # print(closingTime)\r\n\r\n        #Add close price to list only if candle is closed\r\n        if is_candle_closed:\r\n            print(\"candle closed at {}\".format(close))\r\n            closes.append(float(close))\r\n            #print(\"closes\")\r\n            #print(closes)\r\n        \r\n\r\n\r\n\r\n            #Number of closes is greater than 14, the first 14 values cannot be used to calculate RSI or Stoch RSI\r\n            if len(closes) > RSI_PERIOD:\r\n                np_closes = numpy.array(closes)\r\n                rsi = talib.RSI(np_closes, RSI_PERIOD)\r\n                #print(\"all rsis calculated so far\")\r\n                #print(rsi)\r\n                last_rsi = rsi[-1]\r\n                print(\"the current rsi is {}\".format(last_rsi))\r\n\r\n\r\n\r\n                #Calculate StochRSI\r\n                fastk, fastd = talib.STOCH(rsi, rsi, rsi, fastk_period=14,slowk_period=3,slowk_matype=0,slowd_period=3, slowd_matype=0)\r\n                #Matplot lib (plt.show() idk where to put it)\r\n                # axes[1].plot(fastk, 'r-')\r\n                # axes[1].plot(fastd, 'r-')\r\n                last_stochrsi_fastk = fastk[-1]\r\n                print(\"the current STOCH rsi K is {}\".format(last_stochrsi_fastk))\r\n                last_stochrsi_fastd = fastd[-1]\r\n                print(\"the current STOCH rsi D is {}\".format(last_stochrsi_fastd))\r\n\r\n                \r\n                #Long/Short Decision\r\n                #Go Long\r\n                if (last_stochrsi_fastk > last_stochrsi_fastd): #The may bug occur when candle K reaches 100, if it does, don't let it exit the trade\r\n                    #Greater than and less than are bugged, idk why they will be flipped\r\n                    print(\"Fast K > Fast D\")\r\n                    if (in_position == True and type_position == False): #opened short position, so exit short position and open long position\r\n                        #Close all orders\r\n                        print(\"Close SHORT position at : $\" + close)\r\n                        closeShort = float(close)\r\n                        positionStatus = 'Close SHORT Pos'\r\n                        \r\n                        profit = (openShort - closeShort)/openShort*100\r\n                        totalProfit += profit\r\n                        print(\"Current trade profit: \", format(profit,'2f'),\"%\")\r\n                        print(\"Total trade profit: \", format(totalProfit,'2f'),\"%\")\r\n                        print(\"\")\r\n                        in_position = False\r\n                        #Backtesting settings\r\n                        if (config.backtesting == False):\r\n                            close_orders()\r\n                            #Telegram Notif\r\n                            msg = f\"{positionStatus} : {history()}%\\nCurrent Trade Profit: *{profit}%*\\nTotal Profit: *{totalProfit}%*\"\r\n                            telegram_bot_sendtext(msg)\r\n                        else:\r\n                            #Telegram Notif\r\n                            msg = f\"{positionStatus}\\nCurrent Trade Profit: *{profit}%*\\nTotal Profit: *{totalProfit}%*\"\r\n                            telegram_bot_sendtext(msg)\r\n                        \r\n                    if in_position == False :\r\n                        #Place Long Order\r\n                        \r\n                        type_position = True\r\n                        positionStatus = 'LONG'\r\n                        print(\"Open LONG position at : $\" + close)\r\n                        openLong = float(close)          \r\n                        \r\n                        #Backtesting Settings\r\n                        if (config.backtesting == False):\r\n                            #Place SHORT Order\r\n                            order = new_order(symbol=TRADE_SYMBOL, type='MARKET', quantity=config.buy_quantity, side='BUY')\r\n                            in_position = True\r\n                            #Telegram Notif\r\n                            msg = f\"{positionStatus} : {config.buy_quantity} of {TRADE_SYMBOL} at {order} {config.alt}\"\r\n                            telegram_bot_sendtext(msg)\r\n                        else:\r\n                            in_position = True\r\n                            #Telegram Notif\r\n                            msg = f\"{positionStatus} : 0 of {TRADE_SYMBOL} at {close}{config.alt}\"\r\n                            telegram_bot_sendtext(msg)\r\n                        \r\n                    \r\n                #Go Short\r\n                if (last_stochrsi_fastk < last_stochrsi_fastd): \r\n                    print(\"Fast K < Fast D\")\r\n                    if (in_position == True and type_position == True): #opened long position, so exit long position and open short position\r\n                        \r\n                        \r\n                        #Close all orders\r\n                        \r\n                        print(\"Close LONG position at : $\" + close)\r\n                        positionStatus = 'Close Long Pos'\r\n                        closeLong = float(close)\r\n                        profit = (closeLong - openLong)/openLong*100\r\n                        totalProfit += profit\r\n                        print(\"Current trade profit: \", format(profit,'2f'),\"%\")\r\n                        print(\"Total trade profit: \", format(totalProfit,'2f'),\"%\")\r\n                        print(\"\")\r\n                        in_position = False\r\n                        #Backtesting settings\r\n                        if (config.backtesting == False):\r\n                            close_orders()\r\n                            #Telegram Notif\r\n                            msg = f\"{positionStatus} : {history()}%\\nCurrent Trade Profit: *{profit}%*\\nTotal Profit: *{totalProfit}%*\"\r\n                            telegram_bot_sendtext(msg)\r\n                        else:\r\n                            #Telegram Notif\r\n                            msg = f\"{positionStatus}\\nCurrent Trade Profit: *{profit}%*\\nTotal Profit: *{totalProfit}%*\"\r\n                            telegram_bot_sendtext(msg)\r\n                        \r\n                    if in_position == False :\r\n                        \r\n                        type_position = False\r\n                        positionStatus = 'SHORT'\r\n                        print(\"Open SHORT position at : $\" + close)  \r\n                        openShort = float(close)         \r\n                        \r\n                        #Backtesting Settings\r\n                        if (config.backtesting == False):\r\n                            #Place SHORT Order\r\n                            order = new_order(symbol=TRADE_SYMBOL, type='MARKET', quantity=config.buy_quantity, side='SELL')\r\n                            in_position = True\r\n                            #Telegram Notif\r\n                            msg = f\"{positionStatus} : {config.buy_quantity} of {TRADE_SYMBOL} at {order} {config.alt}\"\r\n                            telegram_bot_sendtext(msg)\r\n                        else:\r\n                            in_position = True\r\n                            #Telegram Notif\r\n                            msg = f\"{positionStatus} : 0 of {TRADE_SYMBOL} at {close}{config.alt}\"\r\n                            telegram_bot_sendtext(msg)\r\n                        \r\n                #Track profitability of open position\r\n                if (in_position == True and type_position == True): #Long position is open\r\n                    currentPrice = float(close)\r\n                    openPositionProfit = (currentPrice - openLong)/openLong*100\r\n                    print(\"Open trade profit: \", format(openPositionProfit,'2f'),\"%\")\r\n                if (in_position == True and type_position == False): #Short position is open\r\n                    currentPrice = float(close)\r\n                    openPositionProfit = (openLong - currentPrice)/openLong*100\r\n                    print(\"Open trade profit: \", format(openPositionProfit,'2f'),\"%\")\r\n        logger.info(\"$\" + str(close) + \"     \"\r\n            + str(last_stochrsi_fastk) + \"     \"\r\n            + str(last_stochrsi_fastd) + \"     \"\r\n            + str(openPositionProfit) + \"%     \"\r\n            + str(totalProfit) + \"%    \"\r\n            + positionStatus + \"       \"\r\n            + str(profit) + \"%\")\r\n        \r\n        profit = 0.0000\r\n    \r\n    except:\r\n        # ping client to avoid timeout\r\n        client = Client(config.API_KEY, config.API_SECRET)\r\n\r\n\r\n\r\n\r\n            \r\n\r\n                \r\nws = websocket.WebSocketApp(SOCKET, on_open=on_open, on_close=on_close, on_message=on_message)\r\nws.run_forever()\r\n","repo_name":"limwechern/HFT-Bot-Binance","sub_path":"FuturesBot/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":15470,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"86"}
+{"seq_id":"7153901224","text":"A = [5,2,3,4,6,89]\n#A = A + 1 It won't work out\n#this works\nA = A + [1]\n\n#A = A + \"BCD\" It won't work out\nA = A + [\"BCD\"]\n\nA = A + list(\"BCD\") #strings are iterable\n\n#A = A + list(123) it won't work out because integers are not iterable\n\nA = A + [1,2,3]\n\nA = A + list(str(123))\n\nA = [5,2,3,4,6,89]\nprint(A)\n\nA= A + [5,6,7,8]\nprint(A)\n\nA = A + [[5,6,7,8]]\nprint(A)\nprint(A[-1])\nA.append([10,11,12,13])\nprint(A)\n\nA = [5,6,7,8]\nprint(A)\n\nA = A.append(10) #append returns 0 at the end\nprint(A)\n\nA = [5,6,7,8]\nA.insert(2,100)\nprint(A)\nA.insert(2,[10,20,30])\nprint(A)\n\n#lists are mutable data types, not immutable, means they can be changed after you declare them\nA=A.insert(2,60)\nprint(A)\n \ns=\"123\"\ns[0]=5 #Strings are immutable but lists are mutable\nA=[1,2,3]\nA[0]=5\nprint(A)\n","repo_name":"sudhirbelagali/PythonCourseMaterials","sub_path":"list_more_operations.py","file_name":"list_more_operations.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"32250751236","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom django.views import generic\nfrom django.contrib.auth.decorators import login_required\nfrom django.db.models import Q\nfrom django.contrib import messages\n\nfrom projects.models import Project\nfrom .forms import ProjectForm, ReviewForm\n\n\nclass ProjectIndexView(generic.ListView):\n    model = Project\n    paginate_by = 6\n    template_name = 'projects/index.html'\n\n    def get_queryset(self, *args, **kwargs):\n        search_query = self.request.GET.get('search_query')\n        if search_query:\n            projects = Project.objects.filter(\n                Q(title__icontains=search_query) |\n                Q(description__icontains=search_query) |\n                Q(owner__name__icontains=search_query) |\n                Q(tags__name__icontains=search_query)\n            ).distinct()\n            return projects\n        else:\n            projects = super().get_queryset(*args, **kwargs)\n            return projects\n\n    def get_context_data(self, *args, **kwargs):\n        context = super().get_context_data(*args, **kwargs)\n        context['search_query'] = self.request.GET.get('search_query') or ''\n        return context\n\n\n\ndef project_detail(request, pk):\n    project = get_object_or_404(Project, id=pk)\n    form = ReviewForm()\n\n    if request.method == 'POST':\n        form = ReviewForm(request.POST)\n        if form.is_valid():\n            review_obj = form.save(commit=False)\n            review_obj.owner = request.user.profile\n            review_obj.project = project\n            review_obj.save()\n            project.refresh_votes\n            messages.success(request, 'Your review successfully added!')\n            return redirect('projects:detail', pk=pk)\n\n\n    context = {\n        'project': project,\n        'form': form\n    }\n    return render(request, 'projects/project_detail.html', context)\n\n\n\n@login_required(login_url='users:login_user')\ndef createproject(requeset):\n    form = ProjectForm()\n\n    if requeset.method == \"POST\":\n        form = ProjectForm(requeset.POST, files=requeset.FILES)\n        if form.is_valid():\n            profile = requeset.user.profile\n            project = form.save(commit=False)\n            project.owner = profile\n            project.save()\n            return redirect('users:user_account')\n\n    context = {\n        'form': form\n    }\n\n    return render(requeset, 'projects/project_form.html', context)\n\n\n@login_required(login_url='users:login_user')\ndef updateproject(request, p_uuid):\n    profile = request.user.profile\n    project = get_object_or_404(profile.project_set, id=p_uuid)\n    form = ProjectForm(instance=project)\n\n    if request.method == \"POST\":\n        form = ProjectForm(request.POST, files=request.FILES, instance=project)\n        if form.is_valid():\n            form.save()\n            return redirect('users:user_account')\n\n    context = {\n        'form': form\n    }\n    return render(request, 'projects/project_form.html', context)\n\n\n@login_required(login_url='users:login_user')\ndef deleteproject(request, p_uuid):\n    profile = request.user.profile\n    project = get_object_or_404(profile.project_set, id=p_uuid)\n    if request.method == \"POST\":\n        project.delete()\n        return redirect('users:user_account')\n\n    context = {\n        'project': project\n    }\n    return render(request, 'projects/delete_project.html', context)","repo_name":"laxgic/DevSearch","sub_path":"projects/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"44904164168","text":"import re\nfrom collections import defaultdict\n\n\nclass QuantMaterial():\n    quantity = None\n    material = None\n\n    def __init__(self, quantity, material):\n        self.quantity = quantity\n        self.material = material\n\n    def __repr__(self):\n        return f'[{self.quantity} {self.material}]'\n\n    def parse(rawMaterial):\n        result = re.search('([0-9]+) ([A-Z]+)', rawMaterial)\n\n        return QuantMaterial(int(result.groups()[0]), result.groups()[1])\n\n\nclass Reaction():\n    requirements = None\n    produces = None\n\n    def __init__(self, requirements, produces):\n        self.requirements = requirements\n        self.produces = produces\n\n    def __repr__(self):\n        return f'[{self.requirements}] => [{self.produces}]\\n'\n\n    def parse(rawReaction):\n        rawMaterials = re.findall('([0-9]+ [A-Z]+)', rawReaction)\n        num_materials = len(rawMaterials)\n\n        # Assuming that each reaction produces ONLY ONE element. So, last rawmaterial should be what is produced.\n\n        rawrequirements = rawMaterials[0:num_materials-1]\n        rawproduces = rawMaterials[num_materials-1]\n\n        requirements = [QuantMaterial.parse(rr) for rr in rawrequirements]\n        produces = QuantMaterial.parse(rawproduces)\n\n        return Reaction(requirements, produces)\n\n\nclass Grimoire():\n    reactions = None\n\n    def __init__(self):\n        self.reactions = {}\n\n    def loadReactions(self, rawReactions):\n        reactions = [Reaction.parse(rawReaction) for rawReaction in rawReactions.split('\\n')]\n\n        for r in reactions:\n            assert r.produces.material not in self.reactions, \\\n                    f'There is already one reaction for material {r.produces.material}!!'\n            self.reactions[r.produces.material] = r\n\n        if 'ORE' not in self.reactions:\n            self.reactions['ORE'] = Reaction([], QuantMaterial(1, 'ORE'))\n\n    def getReaction(self, formaterial):\n        reaction = self.reactions.get(formaterial)\n        if reaction is None:\n            raise Exception(f'No reaction for {formaterial} found!!!')\n        return reaction\n\n    def calculateOre(self, formaterial=QuantMaterial(1, 'FUEL'), verbose=False, surplus=None):\n        elements_needed = defaultdict(int)\n\n        if surplus is None:\n            elements_surplus = {}\n            for reaction in self.reactions:\n                elements_surplus[reaction] = 0\n        else:\n            elements_surplus = surplus\n\n        elements_needed[formaterial.material] = formaterial.quantity\n        elements_surplus[formaterial.material] = 0\n\n        while not (len(elements_needed) == 1 and [k for k in elements_needed.keys()][0] == 'ORE'):\n            if verbose:\n                print(50*'-')\n            tmp_elements = defaultdict(int)\n            for element in elements_needed:\n                if elements_needed[element] != 0:\n                    reaction = self.getReaction(element)\n                    if reaction.produces.material == 'ORE':\n                        if verbose:\n                            print('ORE is not produced.')\n                        tmp_elements['ORE'] += elements_needed['ORE']\n                        continue\n\n                    if verbose:\n                        print(f'I need to make {elements_needed[element]} units of {element}. I already had {elements_surplus[element]} units.')\n\n                    if elements_surplus[element] >= elements_needed[element]:\n                        if verbose:\n                            print(f'I have enough surplus to meet the needs. Surplus left: {elements_surplus[element] - elements_needed[element]}')\n                        elements_surplus[element] -= elements_needed[element]\n                    else:\n                        if verbose:\n                            print(f'With {elements_surplus[element]} units of surplus, I still need {elements_needed[element] - elements_surplus[element]} units of {element}.')\n                        needed = elements_needed[element] - elements_surplus[element]\n                        elements_surplus[element] = 0\n\n                        q = reaction.produces.quantity\n                        number_of_needed_reactions = (needed // q) + 1\n                        if needed % q == 0:\n                            number_of_needed_reactions -= 1\n                        if verbose:\n                            print(f'The reaction is {reaction}. As I need {needed} units of {element}, I need to produce it {number_of_needed_reactions} times.')\n                        for r in reaction.requirements:\n                            req_mat = r.material\n                            req_qty = r.quantity\n                            tmp_elements[req_mat] += req_qty * number_of_needed_reactions\n\n                        produced = q * number_of_needed_reactions\n                        if verbose:\n                            print(f'So, {produced} units of {element} has been produced.. as I needed {needed} units it has been generated a surplus of {produced - needed} units.')\n                        elements_surplus[element] += produced - needed\n\n            if verbose:\n                print('elements_needed', elements_needed)\n                print('tmp_elements', tmp_elements)\n                print('elements_surplus', elements_surplus)\n            elements_needed = tmp_elements\n\n        coste = elements_needed['ORE']\n\n        return coste, elements_surplus\n\n\n############\n# PART 1\n############\n# TESTS\ndef execute_test_part1(idtest, reactions, expected):\n    testlabel = idtest.upper()\n    print(f'\\n{testlabel}')\n\n    grim = Grimoire()\n    grim.loadReactions(reactions)\n\n    print(grim.reactions)\n\n    actual, _ = grim.calculateOre()\n    assert expected == actual, f'{testlabel} Failed! expected {expected}, actual {actual}'\n    print(f'{testlabel} passed! expected {expected}, actual {actual}')\n\n\n# TEST 1\nreactionsT1 = '10 ORE => 10 A\\n\\\n1 ORE => 1 B\\n\\\n7 A, 1 B => 1 C\\n\\\n7 A, 1 C => 1 D\\n\\\n7 A, 1 D => 1 E\\n\\\n7 A, 1 E => 1 FUEL'\n\nexecute_test_part1('TEST 1', reactionsT1, 31)\n\n# TEST 2\nreactionsT2 = '9 ORE => 2 A\\n\\\n8 ORE => 3 B\\n\\\n7 ORE => 5 C\\n\\\n3 A, 4 B => 1 AB\\n\\\n5 B, 7 C => 1 BC\\n\\\n4 C, 1 A => 1 CA\\n\\\n2 AB, 3 BC, 4 CA => 1 FUEL'\n\nexecute_test_part1('TEST 2', reactionsT2, 165)\n\n# TEST 3\nreactionsT3 = '157 ORE => 5 NZVS\\n\\\n165 ORE => 6 DCFZ\\n\\\n44 XJWVT, 5 KHKGT, 1 QDVJ, 29 NZVS, 9 GPVTF, 48 HKGWZ => 1 FUEL\\n\\\n12 HKGWZ, 1 GPVTF, 8 PSHF => 9 QDVJ\\n\\\n179 ORE => 7 PSHF\\n\\\n177 ORE => 5 HKGWZ\\n\\\n7 DCFZ, 7 PSHF => 2 XJWVT\\n\\\n165 ORE => 2 GPVTF\\n\\\n3 DCFZ, 7 NZVS, 5 HKGWZ, 10 PSHF => 8 KHKGT'\n\nexecute_test_part1('TEST 3', reactionsT3, 13312)\n\n\n# TEST 4\nreactionsT4 = '2 VPVL, 7 FWMGM, 2 CXFTF, 11 MNCFX => 1 STKFG\\n\\\n17 NVRVD, 3 JNWZP => 8 VPVL\\n\\\n53 STKFG, 6 MNCFX, 46 VJHF, 81 HVMC, 68 CXFTF, 25 GNMV => 1 FUEL\\n\\\n22 VJHF, 37 MNCFX => 5 FWMGM\\n\\\n139 ORE => 4 NVRVD\\n\\\n144 ORE => 7 JNWZP\\n\\\n5 MNCFX, 7 RFSQX, 2 FWMGM, 2 VPVL, 19 CXFTF => 3 HVMC\\n\\\n5 VJHF, 7 MNCFX, 9 VPVL, 37 CXFTF => 6 GNMV\\n\\\n145 ORE => 6 MNCFX\\n\\\n1 NVRVD => 8 CXFTF\\n\\\n1 VJHF, 6 MNCFX => 4 RFSQX\\n\\\n176 ORE => 6 VJHF'\n\nexecute_test_part1('TEST 4', reactionsT4, 180697)\n\n# TEST 5\nreactionsT5 = '171 ORE => 8 CNZTR\\n\\\n7 ZLQW, 3 BMBT, 9 XCVML, 26 XMNCP, 1 WPTQ, 2 MZWV, 1 RJRHP => 4 PLWSL\\n\\\n114 ORE => 4 BHXH\\n\\\n14 VRPVC => 6 BMBT\\n\\\n6 BHXH, 18 KTJDG, 12 WPTQ, 7 PLWSL, 31 FHTLT, 37 ZDVW => 1 FUEL\\n\\\n6 WPTQ, 2 BMBT, 8 ZLQW, 18 KTJDG, 1 XMNCP, 6 MZWV, 1 RJRHP => 6 FHTLT\\n\\\n15 XDBXC, 2 LTCX, 1 VRPVC => 6 ZLQW\\n\\\n13 WPTQ, 10 LTCX, 3 RJRHP, 14 XMNCP, 2 MZWV, 1 ZLQW => 1 ZDVW\\n\\\n5 BMBT => 4 WPTQ\\n\\\n189 ORE => 9 KTJDG\\n\\\n1 MZWV, 17 XDBXC, 3 XCVML => 2 XMNCP\\n\\\n12 VRPVC, 27 CNZTR => 2 XDBXC\\n\\\n15 KTJDG, 12 BHXH => 5 XCVML\\n\\\n3 BHXH, 2 VRPVC => 7 MZWV\\n\\\n121 ORE => 7 VRPVC\\n\\\n7 XCVML => 6 RJRHP\\n\\\n5 BHXH, 4 VRPVC => 5 LTCX'\n\nexecute_test_part1('TEST 5', reactionsT5, 2210736)\n\n# SOLUTION\nprint('SOLUTION PART 1')\ninput_14 = r'data\\aoc2019-input-day14.txt'\nwith open(input_14) as f:\n    data14 = f.read()\n\ngrim = Grimoire()\ngrim.loadReactions(data14)\n\nprint(grim.reactions)\n\nactual = grim.calculateOre()\n\nprint(actual)\n\n#>>>SOLUTION: 365768\n\n############\n# PART 2\n############\n# TESTS\ndef perform_test_p2(reactions):\n    grim = Grimoire()\n\n    grim.loadReactions(reactions)\n    print(grim.reactions)\n\n    totalORE = 1000000000000\n    ore_per_fuel, _ = grim.calculateOre(QuantMaterial(1, 'FUEL'), verbose=False)\n    print('ore_per_fuel', ore_per_fuel)\n    theoric_fuel = totalORE // ore_per_fuel\n\n    total_used_ore, surplus = grim.calculateOre(QuantMaterial(theoric_fuel, 'FUEL'), verbose=False)\n    maxFUEL = theoric_fuel\n    while True:\n        # print(f'Calculating ore to produce {maxFUEL} FUEL')\n        used_ore, surplus = grim.calculateOre(QuantMaterial(1, 'FUEL'), surplus=surplus, verbose=False)\n        # print(f'{used_ore} required to produce {maxFUEL} FUEL')\n        if total_used_ore + used_ore <= totalORE:\n            maxFUEL += 1\n            total_used_ore += used_ore\n        else:\n            break\n        if maxFUEL % 10000 == 0:\n            print(f'Used {total_used_ore} ORE ({(total_used_ore / totalORE) * 100}%) to produce {maxFUEL} FUEL')\n    return maxFUEL, total_used_ore\n\ndef execute_test_part2(idtest, reactions, expected):\n    testlabel = idtest.upper()\n    print(f'\\n{testlabel}')\n\n    maxFUEL, total_used_ore = perform_test_p2(reactions)\n\n    print(maxFUEL)\n    print(total_used_ore)\n\n    assert expected == maxFUEL, f'{testlabel} Failed! expected {expected}, actual {actual}'\n    print(f'{testlabel} passed! expected {expected}, actual {actual}')\n\n\n# TEST 1\nexecute_test_part2('TEST 1', reactionsT3, 82892753)\n\n# TEST 2\nexecute_test_part2('TEST 2', reactionsT4, 5586022)\n\n# TEST 3\nexecute_test_part2('TEST 3', reactionsT5, 460664)\n\n# SOLUTION\nmaxFUEL, _ = perform_test_p2(data14)\nprint(maxFUEL)\n\n#>>>SOLUTION: 3756877","repo_name":"rodalgar/aoc","sub_path":"AOC2019/aoc2019-day14/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":9765,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"18340586332","text":"import torch\nimport sys\nimport ast\nimport inspect\nimport string\nfrom textwrap import dedent\nfrom torch._C._jit_tree_views import (\n    ClassDef, Ident, Stmt, Decl, Def, Var,\n    EmptyTypeAnnotation, Param, ExprStmt, Assign,\n    Delete, Return, Raise, Assert, AugAssign, While,\n    For, If, Pass, Break, Continue, Apply, Dots, Select,\n    TrueLiteral, FalseLiteral, NoneLiteral, Starred,\n    ListLiteral, TupleLiteral, DictLiteral, Const,\n    StringLiteral, ListComp, Attribute, BinOp, UnaryOp,\n    SliceExpr, Subscript, TernaryIf, With, WithItem, Property,\n)\nfrom torch._utils_internal import get_source_lines_and_file\n\nfrom torch._jit_internal import SourceContext, should_drop, is_static_fn\nimport torch.jit.annotations\n\n# Borrowed from cPython implementation\n# https://github.com/python/cpython/blob/561612d8456cfab5672c9b445521113b847bd6b3/Lib/textwrap.py#L411#\n\n_reserved_prefix = '__jit'\n_reserved_names = {'print'}\n_identifier_chars = set(string.ascii_lowercase + string.ascii_uppercase + string.digits)\n\n\ndef is_reserved_name(name):\n    return name.startswith(_reserved_prefix) or name in _reserved_names\n\n\npretty_node_names = {\n    ast.FunctionDef: \"function definitions\",\n    ast.For: \"for loops\",\n    ast.Delete: \"del statements\",\n    ast.ClassDef: \"class definitions\",\n    ast.With: \"with statements\",\n    ast.Raise: \"raise statements\",\n    ast.Assert: \"assertions\",\n    ast.Import: \"import statements\",\n    ast.ImportFrom: \"import statements\",\n    ast.Global: \"global variables\",\n    ast.Break: \"break statements\",\n    ast.Continue: \"continue statements\",\n}\n\nnode_start_tokens = {\n    ast.FunctionDef: \"def\",\n    ast.For: \"for\",\n    ast.Delete: \"del\",\n    ast.ClassDef: \"class\",\n    ast.With: \"with\",\n    ast.Raise: \"raise\",\n    ast.Assert: \"assert\",\n    ast.Import: \"import\",\n    ast.ImportFrom: \"from\",\n    ast.Global: \"global\",\n    ast.Break: \"break\",\n    ast.Continue: \"continue\",\n}\n\npretty_node_names.update({\n    ast.AsyncFunctionDef: \"async function definitions\",\n    ast.AsyncFor: \"async for loops\",\n    ast.AsyncWith: \"async with statements\",\n    ast.Try: \"try blocks\",\n    ast.Nonlocal: \"nonlocal variables\",\n})\n\nnode_start_tokens.update({\n    ast.AsyncFunctionDef: \"async def\",\n    ast.AsyncFor: \"async for\",\n    ast.AsyncWith: \"async with\",\n    ast.Try: \"try\",\n    ast.Nonlocal: \"nonlocal\",\n})\n\nif sys.version_info >= (3, 6):\n    pretty_node_names.update({\n        ast.AnnAssign: \"annotated assignments\",\n    })\n    # NB: no specific token for AnnAssign\n\n\nclass FrontendError(Exception):\n    def __init__(self, source_range, msg):\n        self.source_range = source_range\n        self.msg = msg\n\n        # This has to be instantiated here so the ErrorReport is accurate to the\n        # call stack when the FrontendError was raised\n        self.error_report = torch._C.ErrorReport(self.source_range)\n\n    def __str__(self):\n        return self.msg + self.error_report.what().lstrip()\n\n\nclass NotSupportedError(FrontendError):\n    pass\n\n\nclass UnsupportedNodeError(NotSupportedError):\n    def __init__(self, ctx, offending_node, reason=''):\n        # If we don't have a specific token, we default to length of 1\n        node_type = type(offending_node)\n        range_len = len(node_start_tokens.get(node_type, ' '))\n        source_range = ctx.make_range(offending_node.lineno,\n                                      offending_node.col_offset,\n                                      offending_node.col_offset + range_len)\n        feature_name = pretty_node_names.get(node_type, node_type.__name__)\n        msg = \"{} {}aren't supported\".format(feature_name, reason + ' ' if reason else '')\n        super(UnsupportedNodeError, self).__init__(source_range, msg)\n\n\nclass FrontendTypeError(FrontendError):\n    pass\n\n\ndef build_withitems(ctx, items):\n    items = [build_withitem(ctx, i) for i in items]\n    return list(items)\n\n\ndef build_stmts(ctx, stmts):\n    stmts = [build_stmt(ctx, s) for s in stmts]\n    return list(filter(None, stmts))\n\n\ndef get_class_properties(cls, self_name):\n    \"\"\"\n    Get a list of Property objects representing the properties of a class.\n\n    Arguments:\n        cls:  The class to get properties of.\n        self_name: The name of the class that the properties should belong to.\n    Returns:\n        A list of Property objects corresponding to the properties of cls. Property\n        here refers to the subclass of TreeView.\n    \"\"\"\n    props = inspect.getmembers(\n        cls, predicate=lambda m: isinstance(m, property))\n    # Any property that should not compiled must be in this list on the Module.\n    unused_properties = getattr(cls, \"__jit_unused_properties__\", [])\n\n    # Create Property TreeView objects from inspected property objects.\n    properties = []\n    for prop in props:\n        if prop[0] not in unused_properties and not should_drop(prop[1].fget):\n            getter = get_jit_def(prop[1].fget, f\"__{prop[0]}_getter\", self_name=self_name)\n            setter = get_jit_def(prop[1].fset, f\"__{prop[0]}_setter\", self_name=self_name) if prop[1].fset else None\n            properties.append(Property(getter.range(), Ident(getter.range(), prop[0]), getter, setter))\n\n    return properties\n\n\ndef get_jit_class_def(cls, self_name):\n    # Get defs for each method within the current class independently\n    # TODO: proper overriding analysis when implementing class inheritance\n    methods = inspect.getmembers(\n        cls,\n        predicate=lambda m: (inspect.ismethod(m) or inspect.isfunction(m))\n        and not is_static_fn(cls, m.__name__)\n        and m.__name__ in cls.__dict__\n    )\n    methods = [get_jit_def(method[1],\n                           method[0],\n                           self_name=self_name) for method in methods]\n\n    properties = get_class_properties(cls, self_name)\n\n    sourcelines, file_lineno, filename = get_source_lines_and_file(cls, torch._C.ErrorReport.call_stack())\n    source = ''.join(sourcelines)\n    dedent_src = dedent(source)\n    py_ast = ast.parse(dedent_src)\n    leading_whitespace_len = len(source.split('\\n', 1)[0]) - len(dedent_src.split('\\n', 1)[0])\n    ctx = SourceContext(source, filename, file_lineno, leading_whitespace_len, False)\n    return build_class_def(ctx, py_ast.body[0], methods, properties, self_name)\n\n\ndef get_jit_def(fn, def_name, self_name=None):\n    \"\"\"\n    Build a JIT AST (TreeView) from the given function.\n\n    Arguments:\n        fn: A function object to compile\n        def_name: The name to give to the resulting AST object. This is not\n            always the same as `fn.__name__`, for example:\n                def _forward(self):\n                    ...\n                forward = _forward\n            In this case, the `__name__` attribute of the function object is \"_forward\",\n            but we want the result AST to have the name \"forward\".\n        self_name: If this function is a method, what the type name of `self` is.\n    \"\"\"\n    sourcelines, file_lineno, filename = get_source_lines_and_file(fn, torch._C.ErrorReport.call_stack())\n    source = ''.join(sourcelines)\n    dedent_src = dedent(source)\n    py_ast = ast.parse(dedent_src)\n    if len(py_ast.body) != 1 or not isinstance(py_ast.body[0], ast.FunctionDef):\n        raise RuntimeError(\"Expected a single top-level function\")\n    leading_whitespace_len = len(source.split('\\n', 1)[0]) - len(dedent_src.split('\\n', 1)[0])\n    type_line = torch.jit.annotations.get_type_line(source)\n    ctx = SourceContext(source, filename, file_lineno, leading_whitespace_len, True)\n    fn_def = py_ast.body[0]\n\n    # Swap out the function signature and body if it is unused\n    if should_drop(fn):\n        unused_fn_def = ast.parse(\"def unused_fn(self: Any):\\n\\traise RuntimeError(\\\"Cannot call @unused methods\\\")\")\n        if len(unused_fn_def.body) != 1 or not isinstance(unused_fn_def.body[0], ast.FunctionDef):\n            raise RuntimeError(\"Expected a single top-level function\")\n        unused_def = unused_fn_def.body[0]\n        fn_def.body = unused_def.body\n        # kwarg/vararg not supported by `build_def`\n        fn_def.args.kwarg = fn_def.args.vararg = None\n        for arg in fn_def.args.args + fn_def.args.kwonlyargs:\n            # Replace potentially unsupported type annotations by \"Any\"\n            arg.annotation = unused_def.args.args[0].annotation\n\n    return build_def(ctx, fn_def, type_line, def_name, self_name=self_name)\n\n\nclass Builder(object):\n    def __call__(self, ctx, node):\n        method = getattr(self, 'build_' + node.__class__.__name__, None)\n        if method is None:\n            raise UnsupportedNodeError(ctx, node)\n        return method(ctx, node)\n\n\ndef build_class_def(ctx, py_def, methods, properties, self_name):\n    r = ctx.make_range(py_def.lineno, py_def.col_offset,\n                       py_def.col_offset + len(\"class\"))\n    return ClassDef(Ident(r, self_name), [Stmt(method) for method in methods], properties)\n\n\ndef build_def(ctx, py_def, type_line, def_name, self_name=None):\n    body = py_def.body\n    r = ctx.make_range(py_def.lineno + len(py_def.decorator_list),\n                       py_def.col_offset,\n                       py_def.col_offset + len(\"def\"))\n    param_list = build_param_list(ctx, py_def.args, self_name)\n    return_type = None\n    if getattr(py_def, 'returns', None) is not None:\n        return_type = build_expr(ctx, py_def.returns)\n    decl = Decl(r, param_list, return_type)\n    is_method = self_name is not None\n    if type_line is not None:\n        type_comment_decl = torch._C.parse_type_comment(type_line)\n        decl = torch._C.merge_type_from_type_comment(decl, type_comment_decl, is_method)\n\n    return Def(Ident(r, def_name),\n               decl,\n               build_stmts(ctx, body))\n\n\n_vararg_kwarg_err = (\"Compiled functions can't take variable number of arguments \"\n                     \"or use keyword-only arguments with defaults\")\n\n\ndef build_param_list(ctx, py_args, self_name):\n    if py_args.kwarg is not None:\n        expr = py_args.kwarg\n        ctx_range = ctx.make_range(expr.lineno, expr.col_offset - 1, expr.col_offset + len(expr.arg))\n        raise NotSupportedError(ctx_range, _vararg_kwarg_err)\n    if py_args.vararg is not None:\n        expr = py_args.vararg\n        ctx_range = ctx.make_range(expr.lineno, expr.col_offset - 1, expr.col_offset + len(expr.arg))\n        raise NotSupportedError(ctx_range, _vararg_kwarg_err)\n    if len(py_args.kw_defaults) > 0:\n        # kw_defaults is a list of the values for the kwargs (which default to None),\n        # so they don't actually have line numbers.\n        for arg in py_args.kw_defaults:\n            if arg is not None:\n                ctx_range = build_expr(ctx, arg).range()\n                raise NotSupportedError(ctx_range, _vararg_kwarg_err)\n    result = [build_param(ctx, arg, self_name, False) for arg in py_args.args]\n    result += [build_param(ctx, arg, self_name, True) for arg in py_args.kwonlyargs]\n    return result\n\n\ndef build_param(ctx, py_arg, self_name, kwarg_only):\n    # NB: In Python3 py_arg is a pair of (str arg, expr? annotation)\n    name = py_arg.arg\n    r = ctx.make_range(py_arg.lineno, py_arg.col_offset, py_arg.col_offset + len(name))\n    if getattr(py_arg, 'annotation', None) is not None:\n        annotation_expr = build_expr(ctx, py_arg.annotation)\n    elif self_name is not None and name == 'self':\n        annotation_expr = Var(Ident(r, self_name))\n    else:\n        annotation_expr = EmptyTypeAnnotation(r)\n    return Param(annotation_expr, Ident(r, name), kwarg_only)\n\n\ndef get_default_args(fn):\n    if fn is None:\n        return {}\n\n    signature = inspect.signature(fn)\n    return {\n        k: v.default\n        for k, v in signature.parameters.items()\n        if v.default is not inspect.Parameter.empty\n    }\n\n\ndef get_default_args_for_class(cls):\n    \"\"\"\n    Get default arguments for all methods in a class (except for static methods).\n\n    Args:\n        cls: type - The class type to inspect for default arguments.\n    Returns:\n        A Dict[str, Dict[str, Any]] which maps each method name to a Dict[str, Any]\n        that maps each argument name to its default value.\n    \"\"\"\n    # Get methods (except static methods because those are compiled separately as\n    # if they were independent script functions).\n    methods = inspect.getmembers(\n        cls,\n        predicate=lambda m: (inspect.ismethod(m) or inspect.isfunction(m))\n        and not is_static_fn(cls, m.__name__)\n        and m.__name__ in cls.__dict__\n    )\n\n    # Get method defaults. Property defaults do not need to be considered\n    # because setters cannot be invoked without a value.\n    defaults = {method_name: get_default_args(method_impl) for method_name, method_impl in methods}\n\n    return defaults\n\n\nclass WithItemBuilder(Builder):\n    @staticmethod\n    def build_withitem(ctx, item):\n        lineno = item.context_expr.lineno\n        start = item.context_expr.col_offset\n        end = start + len(pretty_node_names[ast.With])\n        op_vars = item.optional_vars\n        r = ctx.make_range(lineno, start, end)\n\n        return WithItem(r, build_expr(ctx, item.context_expr), build_expr(ctx, op_vars) if op_vars else None)\n\n\nclass StmtBuilder(Builder):\n    augassign_map = {\n        ast.Add: '+',\n        ast.Sub: '-',\n        ast.Mult: '*',\n        ast.Div: '/',\n        ast.Mod: '%',\n    }\n\n    @staticmethod\n    def build_Expr(ctx, stmt):\n        value = stmt.value\n        if value.__class__.__name__ == 'Str':\n            # If a statement is a string literal expression,\n            # then it is a docstring. Just ignore it.\n            return None\n        else:\n            return ExprStmt(build_expr(ctx, value))\n\n    @staticmethod\n    def build_Assign(ctx, stmt):\n        rhs = build_expr(ctx, stmt.value)\n        lhs = list(map(lambda x: build_expr(ctx, x), stmt.targets))\n        return Assign(lhs, rhs)\n\n    @staticmethod\n    def build_AnnAssign(ctx, stmt):\n        if stmt.value is None:\n            raise UnsupportedNodeError(ctx, stmt, reason='without assigned value')\n        rhs = build_expr(ctx, stmt.value)\n        lhs = build_expr(ctx, stmt.target)\n        the_type = build_expr(ctx, stmt.annotation)\n        return Assign([lhs], rhs, the_type)\n\n    @staticmethod\n    def build_Delete(ctx, stmt):\n        if len(stmt.targets) > 1:\n            source_range = ctx.make_range(stmt.lineno, stmt.col_offset,\n                                          stmt.col_offset + len(\"del\"))\n            raise NotSupportedError(\n                source_range, 'del with more than one operand is not supported')\n        return Delete(build_expr(ctx, stmt.targets[0]))\n\n    @staticmethod\n    def build_Return(ctx, stmt):\n        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len(\"return\"))\n        return Return(r, None if stmt.value is None else build_expr(ctx, stmt.value))\n\n    @staticmethod\n    def build_Raise(ctx, stmt):\n        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len(\"raise\"))\n        expr = build_expr(ctx, stmt.exc)\n        return Raise(r, expr)\n\n    @staticmethod\n    def build_Assert(ctx, stmt):\n        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len(\"assert\"))\n        test = build_expr(ctx, stmt.test)\n        msg = build_expr(ctx, stmt.msg) if stmt.msg is not None else None\n        return Assert(r, test, msg)\n\n    @staticmethod\n    def build_AugAssign(ctx, stmt):\n        lhs = build_expr(ctx, stmt.target)\n        rhs = build_expr(ctx, stmt.value)\n        op = type(stmt.op)\n        if op in StmtBuilder.augassign_map:\n            op_token = StmtBuilder.augassign_map[op]\n        else:\n            raise NotSupportedError(\n                find_before(ctx, rhs.range().start, '=', offsets=(-1, 0)),\n                \"unsupported kind of augumented assignment: \" + op.__name__)\n        return AugAssign(lhs, op_token, rhs)\n\n    @staticmethod\n    def build_While(ctx, stmt):\n        if stmt.orelse:\n            # TODO: try to recover the location of else:? Python doesn't give us useful\n            # annotations in this case\n            raise NotSupportedError(None, \"else branches of while loops aren't supported\")\n        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len(\"while\"))\n        return While(r, build_expr(ctx, stmt.test),\n                     build_stmts(ctx, stmt.body))\n\n    @staticmethod\n    def build_For(ctx, stmt):\n        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len(\"for\"))\n        return For(\n            r, [build_expr(ctx, stmt.target)],\n            [build_expr(ctx, stmt.iter)], build_stmts(ctx, stmt.body))\n\n    @staticmethod\n    def build_If(ctx, stmt):\n        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len(\"if\"))\n        return If(r, build_expr(ctx, stmt.test),\n                  build_stmts(ctx, stmt.body),\n                  build_stmts(ctx, stmt.orelse))\n\n    @staticmethod\n    def build_Print(ctx, stmt):\n        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len(\"print\"))\n        if stmt.dest:\n            raise NotSupportedError(r, \"print statements with non-default destinations aren't supported\")\n        args = [build_expr(ctx, val) for val in stmt.values]\n        return ExprStmt(Apply(Var(Ident(r, \"print\")), args, []))\n\n    @staticmethod\n    def build_Pass(ctx, stmt):\n        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len(\"pass\"))\n        return Pass(r)\n\n    @staticmethod\n    def build_Break(ctx, stmt):\n        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len(\"break\"))\n        return Break(r)\n\n    @staticmethod\n    def build_Continue(ctx, stmt):\n        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len(\"continue\"))\n        return Continue(r)\n\n    @staticmethod\n    def build_With(ctx, stmt):\n        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len(\"with\"))\n        return With(r, build_withitems(ctx, stmt.items), build_stmts(ctx, stmt.body))\n\nclass ExprBuilder(Builder):\n    binop_map = {\n        ast.Add: '+',\n        ast.Sub: '-',\n        ast.Mult: '*',\n        ast.Div: '/',\n        ast.Pow: '**',\n        ast.Mod: '%',\n        ast.FloorDiv: '//',\n        ast.BitAnd: '&',\n        ast.BitXor: '^',\n        ast.BitOr: '|',\n        ast.LShift: '<<',\n        ast.RShift: '>>',\n    }\n\n    binop_map[ast.MatMult] = '@'\n\n    unop_map = {\n        ast.Not: 'not',\n        ast.USub: '-',\n        ast.Invert: '~',\n    }\n\n    boolop_map = {\n        ast.And: 'and',\n        ast.Or: 'or',\n    }\n\n    cmpop_map = {\n        ast.Eq: '==',\n        ast.NotEq: '!=',\n        ast.LtE: '<=',\n        ast.Lt: '<',\n        ast.GtE: '>=',\n        ast.Gt: '>',\n        ast.Is: 'is',\n        ast.IsNot: 'is not',\n        ast.In: 'in',\n        ast.NotIn: 'not in',\n    }\n\n    @staticmethod\n    def build_Attribute(ctx, expr):\n        base = build_expr(ctx, expr.value)\n        # expr.attr is just a string, so it's not annotated in any way, so we have\n        # to build the range manually\n        source = ctx.source.encode('utf-8')\n\n        def get_char(index):\n            return chr(source[index])\n\n        start_pos = base.range().end + 1\n        while get_char(start_pos) in string.whitespace:  # Skip whitespace\n            start_pos += 1\n        end_pos = start_pos + len(expr.attr)\n        name_range = ctx.make_raw_range(start_pos, end_pos)\n        return Select(base, Ident(name_range, expr.attr))\n\n    @staticmethod\n    def build_Call(ctx, expr):\n        func = build_expr(ctx, expr.func)\n        args = [build_expr(ctx, py_arg) for py_arg in expr.args]\n        if hasattr(expr, 'starargs') and expr.starargs:\n            stararg_expr = build_expr(ctx, expr.starargs)\n            args += [Starred(stararg_expr.range(), stararg_expr)]\n        kwargs = []\n        for kw in expr.keywords:\n            kw_expr = build_expr(ctx, kw.value)\n            # XXX: we could do a better job at figuring out the range for the name here\n            if not kw.arg:\n                raise NotSupportedError(kw_expr.range(), 'keyword-arg expansion is not supported')\n            kwargs.append(Attribute(Ident(kw_expr.range(), kw.arg), kw_expr))\n        return Apply(func, args, kwargs)\n\n    @staticmethod\n    def build_Ellipsis(ctx, expr):\n        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 3)  # len(\"...\") == 3\n        return Dots(r)\n\n    @staticmethod\n    def build_Name(ctx, expr):\n        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + len(expr.id))\n        if expr.id.startswith(_reserved_prefix):\n            raise NotSupportedError(r, \"names of variables used in JIT-ed functions \"\n                                       \"can't start with \" + _reserved_prefix)\n        if expr.id == \"True\":\n            return TrueLiteral(r)\n        elif expr.id == \"False\":\n            return FalseLiteral(r)\n        elif expr.id == \"None\":\n            return NoneLiteral(r)\n        return Var(Ident(r, expr.id))\n\n    @staticmethod\n    def build_NameConstant(ctx, expr):\n        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + len(str(expr.value)))\n        if expr.value is True:\n            return TrueLiteral(r)\n        elif expr.value is False:\n            return FalseLiteral(r)\n        elif expr.value is None:\n            return NoneLiteral(r)\n        else:\n            raise ValueError(\"Name constant value unsupported: \" + str(expr.value))\n\n    @staticmethod\n    def build_BinOp(ctx, expr):\n        lhs = build_expr(ctx, expr.left)\n        rhs = build_expr(ctx, expr.right)\n        op = type(expr.op)\n\n        if op == ast.Div and not ctx.uses_true_division:\n            err_range = ctx.make_raw_range(lhs.range().end, rhs.range().start)\n            raise FrontendError(err_range, 'Division of ints in TorchScript uses Python 3 true '\n                                'division semantics. Please put `from __future__ '\n                                'import division` at the top of your file')\n        op_token = ExprBuilder.binop_map.get(op)\n        if op_token is None:\n            err_range = ctx.make_raw_range(lhs.range().end, rhs.range().start)\n            raise NotSupportedError(err_range, \"unsupported binary operator: \" + op.__name__)\n        return BinOp(op_token, lhs, rhs)\n\n    @staticmethod\n    def build_UnaryOp(ctx, expr):\n        sub_expr = build_expr(ctx, expr.operand)\n        op = type(expr.op)\n        op_token = ExprBuilder.unop_map.get(op)\n        if op_token is None:\n            raise NotSupportedError(expr.range(), \"unsupported unary operator: \" + op.__name__)\n        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + len(op_token))\n        return UnaryOp(r, op_token, sub_expr)\n\n    @staticmethod\n    def build_BoolOp(ctx, expr):\n        if len(expr.values) < 2:\n            raise AssertionError(\"expected at least 2 values in BoolOp, but got \" + str(len(expr.values)))\n        sub_exprs = [build_expr(ctx, sub_expr) for sub_expr in expr.values]\n        op = type(expr.op)\n        op_token = ExprBuilder.boolop_map.get(op)\n        if op_token is None:\n            err_range = ctx.make_raw_range(sub_exprs[0].range().end, sub_exprs[1].range().start)\n            raise NotSupportedError(err_range, \"unsupported boolean operator: \" + op.__name__)\n        lhs = sub_exprs[0]\n        for rhs in sub_exprs[1:]:\n            lhs = BinOp(op_token, lhs, rhs)\n        return lhs\n\n    @staticmethod\n    def build_IfExp(ctx, expr):\n        return TernaryIf(build_expr(ctx, expr.test),\n                         build_expr(ctx, expr.body),\n                         build_expr(ctx, expr.orelse))\n\n    @staticmethod\n    def build_Compare(ctx, expr):\n        operands = [build_expr(ctx, e) for e in [expr.left] + list(expr.comparators)]\n        result = None\n        for lhs, op_, rhs in zip(operands, expr.ops, operands[1:]):\n            op = type(op_)\n            op_token = ExprBuilder.cmpop_map.get(op)\n            r = ctx.make_raw_range(lhs.range().end, rhs.range().start)\n            if op_token is None:\n                raise NotSupportedError(r, \"unsupported comparison operator: \" + op.__name__)\n\n            if op == ast.NotIn:\n                # NB: `not in` is just `not( in )`, so we don't introduce new tree view\n                # but just make it a nested call in our tree view structure\n                in_expr = BinOp('in', lhs, rhs)\n                cmp_expr = UnaryOp(r, 'not', in_expr)\n            else:\n                cmp_expr = BinOp(op_token, lhs, rhs)\n\n            if result is None:\n                result = cmp_expr\n            else:\n                result = BinOp('and', result, cmp_expr)\n        return result\n\n    @staticmethod\n    def build_Subscript(ctx, expr):\n        def build_SliceExpr(ctx, base, slice_expr):\n            lower = build_expr(ctx, slice_expr.lower) if slice_expr.lower is not None else None\n            upper = build_expr(ctx, slice_expr.upper) if slice_expr.upper is not None else None\n            step = build_expr(ctx, slice_expr.step) if slice_expr.step is not None else None\n            return SliceExpr(base.range(), lower, upper, step)\n\n        def build_Index(ctx, base, index_expr):\n            if isinstance(index_expr.value, ast.Tuple) or \\\n                    isinstance(index_expr.value, ast.List):\n                raise NotSupportedError(base.range(),\n                                        \"slicing multiple dimensions with \"\n                                        \"sequences not supported yet\")\n            return build_expr(ctx, index_expr.value)\n\n        def build_ExtSlice(ctx, base, extslice):\n            sub_exprs = []\n            for expr in extslice.dims:\n                sub_type = type(expr)\n                if sub_type is ast.Index:\n                    sub_exprs.append(build_Index(ctx, base, expr))\n                elif sub_type is ast.Slice:\n                    sub_exprs.append(build_SliceExpr(ctx, base, expr))\n                elif sub_type is ast.Ellipsis:\n                    sub_exprs.append(Dots(base.range()))\n                else:\n                    raise NotSupportedError(base.range(),\n                                            \"slicing multiple dimensions with \"\n                                            \"{} not supported\".format(sub_type))\n            return sub_exprs\n\n        base = build_expr(ctx, expr.value)\n        sub_type = type(expr.slice)\n        if sub_type is ast.Index:\n            if isinstance(expr.slice.value, ast.Tuple):\n                # N-dimensional indexing using Tuple: x[(i, j, k)] is equivalent to x[i, j, k]\n                # XXX: Indexing using a list is **different**! It triggers advanced indexing.\n                indices = [build_expr(ctx, index_expr) for index_expr in expr.slice.value.elts]\n                return Subscript(base, indices)\n            else:\n                return Subscript(base, [build_expr(ctx, expr.slice.value)])\n        elif sub_type is ast.Slice:\n            return Subscript(base, [build_SliceExpr(ctx, base, expr.slice)])\n        elif sub_type is ast.ExtSlice:\n            return Subscript(base, build_ExtSlice(ctx, base, expr.slice))\n        elif sys.version_info >= (3, 9):  # In Python3.9 array indicies are not wrapped in ast.Index\n            if sub_type is ast.Tuple:\n                # N-dimensional indexing using Tuple: x[(i, j, k)] is equivalent to x[i, j, k]\n                indices = []\n                for index_expr in expr.slice.elts:\n                    if isinstance(index_expr, ast.Slice):\n                        indices.append(build_SliceExpr(ctx, base, index_expr))\n                    else:\n                        indices.append(build_expr(ctx, index_expr))\n                return Subscript(base, indices)\n            return Subscript(base, [build_expr(ctx, expr.slice)])\n        else:  # Ellipsis (can only happen in Python 2)\n            raise NotSupportedError(base.range(), \"ellipsis is not supported\")\n\n    @staticmethod\n    def build_List(ctx, expr):\n        return ListLiteral(ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1),\n                           [build_expr(ctx, e) for e in expr.elts])\n\n    @staticmethod\n    def build_Tuple(ctx, expr):\n        return TupleLiteral(ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1),\n                            [build_expr(ctx, e) for e in expr.elts])\n\n    @staticmethod\n    def build_Dict(ctx, expr):\n        return DictLiteral(ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1),\n                           [build_expr(ctx, e) for e in expr.keys], [build_expr(ctx, e) for e in expr.values])\n\n    @staticmethod\n    def build_Num(ctx, expr):\n        value = str(expr.n)\n        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + len(value))\n        return Const(r, value)\n\n    @staticmethod\n    def build_Constant(ctx, expr):\n        value = expr.value\n        if value is None or isinstance(value, bool):\n            # NB: this check has to happen before the int check because bool is\n            # a subclass of int\n            return ExprBuilder.build_NameConstant(ctx, expr)\n        if isinstance(value, (int, float)):\n            return ExprBuilder.build_Num(ctx, expr)\n        elif isinstance(value, str):\n            return ExprBuilder.build_Str(ctx, expr)\n        elif isinstance(value, type(Ellipsis)):\n            return ExprBuilder.build_Ellipsis(ctx, expr)\n        else:\n            error_range = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + len(str(value)))\n            raise FrontendError(error_range, \"Unknown Constant expression type\")\n\n    @staticmethod\n    def build_Str(ctx, expr):\n        value = str(expr.s)\n        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1)\n        return StringLiteral(r, value)\n\n    @staticmethod\n    def build_JoinedStr(ctx, expr):\n        s = ''\n        args = []\n        for value in expr.values:\n            r = ctx.make_range(value.lineno, value.col_offset, value.col_offset + 1)\n            if isinstance(value, ast.FormattedValue):\n                if value.conversion != -1:\n                    raise NotSupportedError(r, 'Don\\'t support conversion in JoinedStr')\n                if value.format_spec is not None:\n                    raise NotSupportedError(r, 'Don\\'t support formatting in JoinedStr')\n                s += '{}'\n                args.append(build_expr(ctx, value.value))\n            elif isinstance(value, ast.Str):\n                s += value.s\n            else:\n                raise NotSupportedError(r, 'Unsupported value in JoinedStr')\n\n        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1)\n        return Apply(Select(StringLiteral(r, s), Ident(r, 'format')), args, [])\n\n    @staticmethod\n    def build_ListComp(ctx, stmt):\n        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset)\n        if (len(stmt.generators) > 1):\n            raise NotSupportedError(r, \"multiple comprehension generators not supported yet\")\n\n        if (len(stmt.generators[0].ifs) != 0):\n            raise NotSupportedError(r, \"comprehension ifs not supported yet\")\n\n        elt_expr = build_expr(ctx, stmt.elt)\n        target_expr = build_expr(ctx, stmt.generators[0].target)\n\n        iter_expr = build_expr(ctx, stmt.generators[0].iter)\n        return ListComp(r, elt_expr, target_expr, iter_expr)\n\n    @staticmethod\n    def build_Starred(ctx, expr):\n        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1)\n        return Starred(r, build_expr(ctx, expr.value))\n\nbuild_expr = ExprBuilder()\nbuild_stmt = StmtBuilder()\nbuild_withitem = WithItemBuilder()\n\ndef find_before(ctx, pos, substr, offsets=(0, 0)):\n    new_pos = ctx.source[:pos].rindex(substr)\n    return ctx.make_raw_range(new_pos + offsets[0], new_pos + len(substr) + offsets[1])\n","repo_name":"snuspl/nimble","sub_path":"torch/jit/frontend.py","file_name":"frontend.py","file_ext":"py","file_size_in_byte":31724,"program_lang":"python","lang":"en","doc_type":"code","stars":248,"dataset":"github-code","pt":"86"}
+{"seq_id":"71228998683","text":"\n\nimport math\n\ndef steps(n):\n    values=[math.inf]\n    i=1\n    \n    while i<=n:\n        minSteps=i-1\n        if i%3==0:\n            if i/3 < len(values):\n                minSteps=min(minSteps, values[int(i/3)]+1)\n        if i%2==0:\n            if i/2 < len(values):\n                minSteps=min(minSteps, values[int(i/2)]+1)\n        if i-1 < len(values):\n            minSteps=min(minSteps,values[i-1]+1)\n        \n        values.append(minSteps)\n        i+=1\n        \n    return values[n]\n\n\ndef greedy_steps(n):\n    if n==1: return 0\n    elif n%3==0: return steps(n//3) +1\n    elif n%2==0: return steps(n//2)+1\n    else: return steps(n-1)+1\n    \n    \ndef main():\n    num=10000\n    dif=0\n    for i in range(num):\n        dynamic=steps(i)\n        greedy=greedy_steps(i)\n        #print(i,\": \",dynamic,\" \",greedy)\n        if greedy!=dynamic: dif+=1\n        \n    print(100*dif/num,\"% different values\")\n        \nmain()","repo_name":"m3gan3/upgraded-memory","sub_path":"stepsToOne-2.py","file_name":"stepsToOne-2.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"43156576076","text":"import datetime\nimport logging\n\nimport pytz\nfrom tzlocal import get_localzone\nimport numpy as np\nimport copy\n\n\n# This helper function extracts the meta-data from the filename\n# Ex: RawLog_Sub999_Trial1_19_22_02_10-04-2017.csv\ndef get_filename_meta_data(fn, path):\n    parts = fn.split('_')\n    file_type = parts[0]\n    sub_id = parts[1].replace('Sub', '').replace('sub', '')\n    trial_num = int(parts[2].replace('Trial', '').replace('trial', ''))\n    date_time_string = '_'.join(parts[3:]).replace('.csv', '')\n    dt = datetime.datetime.strptime(date_time_string, '%H_%M_%S_%d-%m-%Y')\n    phase = 'unknown'\n    if 'Practice' in path:\n        phase = 'practice'\n    elif 'Study' in path:\n        phase = 'study'\n    elif 'Test' in path:\n        phase = 'test'\n    return {\"fileType\": file_type, \"subID\": sub_id, \"trial\": trial_num, \"phase\": phase, \"datetime\": dt}\n\n\n# From http://stackoverflow.com/questions/15919598/serialize-datetime-as-binary\n# This function is used in reading the binary files to parse the binary .NET DateTime into a Python datetime\ndef datetime_from_dot_net_binary(data):\n    kind = (data % 2 ** 64) >> 62  # This says about UTC and stuff...\n    ticks = data & 0x3FFFFFFFFFFFFFFF\n    seconds = ticks / 10000000\n    tz = pytz.utc\n    if kind == 0:\n        tz = get_localzone()\n    return datetime.datetime(1, 1, 1, tzinfo=tz) + datetime.timedelta(seconds=seconds)\n\n\ndef get_object_info_from_string(info_string):\n    vals = info_string.split(':')[1].split(',')\n    pos = (float(vals[0].strip()), float(vals[1].strip()), float(vals[2].strip()))\n    rot = (float(vals[3].strip()), float(vals[4].strip()), float(vals[5].strip()), float(vals[6].strip()))\n    sca = (float(vals[7].strip()), float(vals[8].strip()), float(vals[9].strip()))\n    return pos, rot, sca\n\n\ndef get_object_info_from_summary_string(summary_info_string):\n    split_line = summary_info_string.split(':')\n    name = split_line[0].split(',')[1]\n    pos_list = split_line[1].replace('(', '').replace(')', '').split(',')\n    pos = (float(pos_list[0]), float(pos_list[1]), float(pos_list[2]))\n    return name, pos\n\n\ndef read_summary_file(path):\n    events = []\n    with open(path, 'rb') as f:\n        f.readline()  # Remove header\n        file_string = f.readlines()\n    current_dt = None\n    for line in file_string:\n        if line[0] == '-':\n            current_dt = datetime_from_dot_net_binary(int(line.replace(',', '').strip()))\n        if line.startswith(\"ChangeTextureEvent_ObjectClicked\"):\n            events.append({'time': current_dt, 'eventType': 'clicked', 'objectName': line.split(',')[1].strip()})\n        if line.startswith(\"Object_Placed\"):\n            name, pos = get_object_info_from_summary_string(line)\n            events.append({'time': current_dt, 'eventType': 'placed', 'objectName': name, \"location\": pos})\n        if line.startswith(\"Object_Picked_Up\"):\n            name, pos = get_object_info_from_summary_string(line)\n            events.append({'time': current_dt, 'eventType': 'picked', 'objectName': name, \"location\": pos})\n        if line.startswith(\"Object_Identity_Set\"):\n            name, pos = get_object_info_from_summary_string(line)\n            events.append({'time': current_dt, 'eventType': 'identified', 'objectName': name, \"location\": pos})\n        if line.startswith(\"Object_Identity_Removed\"):\n            name, pos = get_object_info_from_summary_string(line)\n            events.append({'time': current_dt, 'eventType': 'deidentified', 'objectName': name, \"location\": pos})\n    return events\n\n\ndef read_raw_file(path):\n    iterations = []\n    with open(path, 'rb') as f:\n        file_string = f.readlines()\n    current_dt = None\n    current_state = {\"Main Camera\": None, \"First Person Controller\": None}\n    events = []\n    for line in file_string:\n        if line[0] == '-':\n            if current_dt is not None:\n                iterations.append({\"time\": current_dt, \"state\": current_state})\n                current_state = copy.deepcopy(current_state)\n            current_dt = datetime_from_dot_net_binary(int(line.strip()))\n        if line.startswith('Main Camera'):\n            pos, rot, sca = get_object_info_from_string(line.strip())\n            current_state[\"Main Camera\"] = {\"position\": pos, \"rotation\": rot, \"scale\": sca}\n        if line.startswith('First Person Controller'):\n            pos, rot, sca = get_object_info_from_string(line.strip())\n            current_state[\"First Person Controller\"] = {\"position\": pos, \"rotation\": rot, \"scale\": sca}\n        if line.startswith(\"ChangeTextureEvent_ObjectClicked\"):\n            events.append({'time': current_dt, 'eventType': 'clicked', 'objectName': line.split(',')[1].strip()})\n        if line.strip() == 'End of File':\n            logging.debug('End of File')\n    return iterations, events\n\n\ndef get_simple_path_from_raw_iterations(raw_iterations, make_2d=True):\n    points = []\n    for i in raw_iterations:\n        p = i['state']['First Person Controller']['position']\n        if make_2d:\n            points.append((p[0], p[2]))\n        else:\n            points.append(p)\n    return np.array(points)\n\n\ndef quat2euler(q):\n    roll = np.arctan2(2*(q[1]*q[3] + q[0]*q[2]), 1-2*(q[1]*q[1]+q[2]*q[2]))\n    yaw = np.arcsin(2*(q[0]*q[1]-q[2]*q[3]))\n    pitch = np.arctan2(2*(q[0]*q[3]+q[1]*q[2]), 1-2*(q[0]*q[0]+q[2]*q[2]))\n    return roll, pitch, yaw\n\n\ndef get_simple_orientation_path_from_raw_iterations(raw_iterations):\n    angles = []\n    for i in raw_iterations:\n        p = i['state']['First Person Controller']['rotation']\n        x, y, z = quat2euler(p)\n        angles.append(np.pi - x - np.pi/2.)\n    return angles\n\n\ndef compress(pos, orient):\n    new_pos = [pos[0]]\n    new_orient = [orient[0]]\n    for p, o in zip(pos, orient)[1:]:\n        # noinspection PyTypeChecker\n        if all(new_pos[-1] == p) and new_orient[-1] == o:\n            continue\n        else:\n            new_pos.append(p)\n            new_orient.append(o)\n    return np.array(new_pos), new_orient\n\n\ndef get_final_state_from_summary_events():  # summary_events):\n    raise NotImplemented\n\n\ndef validate_summary_events_are_complete():  # summary_events):\n    raise NotImplemented\n\n\ndef compare_summary_and_raw_events():  # raw_events, summary_events):\n    raise NotImplemented\n","repo_name":"kevroy314/Holodeck-Navigation-Task","sub_path":"Holodeck-Spatial-Navigation-Task-Analytics/log_parser.py","file_name":"log_parser.py","file_ext":"py","file_size_in_byte":6239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"38576848445","text":"#!/usr/bin/env python\nfrom __future__ import print_function\nimport rospy\nfrom std_msgs.msg import String\nimport tty, select, sys, termios\nimport time\n\n\ndef getKey(key_timeout, settings):\n    tty.setraw(sys.stdin.fileno())\n    rlist, _, _ = select.select([sys.stdin], [], [], key_timeout)\n    if rlist:\n        key = sys.stdin.read(1)\n    else:\n        key = \"\"\n    termios.tcsetattr(sys.stdin, termios.TCSADRAIN, settings)\n    return key\n\n\nlisten_for = \"abcdefghijklmnopqrstuvwxyz .,;':\\\"[]\\{\\}+\\\\=/?><-1234567890\"\nenter_key = \"\\x0d\"\nctrl_c = \"\\x03\"\n\n\ndef talker():\n    rospy.init_node(\"lift_trigger\", anonymous=True)\n    pub = rospy.Publisher(\"lift_arm\", String, queue_size=10)\n    msg = String()\n\n    settings = termios.tcgetattr(sys.stdin)\n\n    rospy.loginfo(\"Starting loop. Each keypress will send a trigger. Ctrl-C to quit.\")\n\n    while not rospy.is_shutdown():\n        key = getKey(None, settings)\n        if key in listen_for:\n            print(key, end=\"\")\n            sys.stdout.flush()\n            pub.publish(msg)\n        elif key == enter_key:\n            print(\"\")\n            sys.stdout.flush()\n            pub.publish(msg)\n        elif key == ctrl_c:\n            print(\"\\nKeyboard interrupt\")\n            msg.data = \"q\"\n            pub.publish(msg)\n            break\n        time.sleep(0.05)\n\n\nif __name__ == \"__main__\":\n    try:\n        talker()\n    except rospy.ROSInterruptException:\n        pass\n","repo_name":"ethz-asl/typing_aid","sub_path":"anydrive_typing_aid/scripts/trigger.py","file_name":"trigger.py","file_ext":"py","file_size_in_byte":1415,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"28443163842","text":"from django.conf.urls import url\nfrom django.urls import path\nfrom . import views\n\nurlpatterns= [\n    path('', views.index, name='index'),\n    path('create', views.create, name='create'),\n    path('edit/<int:id>', views.edit, name='edit'),\n    path('edit/update/<int:id>', views.update, name='update'),\n    path('delete/<int:id>', views.delete, name='delete'),\n]\n\n# urlpatterns= [\n#     url(r'^$', views.index, name='index'),\n#     url(r'^create$', views.create, name='create'),\n#     url(r'^edit/(?P<id>\\d+)$', views.edit, name='edit'),\n#     url(r'^edit/update/(?P<id>\\d+)$', views.update, name='update'),\n#     url(r'^delete/(?P<id>\\d+)$', views.delete, name='delete'),\n# ]","repo_name":"Ajul-kushwah/Todo-App","sub_path":"crud/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"30095052547","text":"# Faça um programa que leia um número qualquer e mostre o seu fatorial.\n# Ex: 5! = 5 x 4 x 3 x 2 x 1 = 120\n\nclass CalculadoraFatorial():\n\n    def __init__(self):\n        self.numeros = []\n        self.resultado = ''\n\n    def iniciar(self):\n        print('*** Cálculo de fatorial ***\\nIndique o número: ')\n        n = int(input())\n        self.calcular_fatorial(n)\n        self.exibir_resultado()\n\n    def calcular_fatorial(self, n):\n        '''Cálculo de fatorial em \"n\" vezes.'''\n        fatorial = 1  # Deve começar em \"1\", pois se for \"0\" o resultado será \"0\".\n        for i in range(n, 0, -1):\n            fatorial *= i\n            self.numeros.append(i)\n        self.resultado = fatorial\n        return self.resultado\n\n    def exibir_resultado(self):\n        '''Exibir os resultados calculados.'''\n        print(str(self.numeros[0]) + '! = ', end='')\n        for c in self.numeros:\n            print(c, 'x ' if c != 1 else '= ', end='')\n        print(self.resultado)\n        \n\ncalculadora = CalculadoraFatorial()\ncalculadora.iniciar()\n","repo_name":"alexander-colaneri/python","sub_path":"studies/curso_em_video/ex060-calculo-de-fatorial.py","file_name":"ex060-calculo-de-fatorial.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"39291831619","text":"from django.shortcuts import render\n\n\n\nfrom django.http import Http404\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom rest_framework_jwt.serializers import jwt_encode_handler,jwt_payload_handler\n\n\nfrom rest_framework import mixins,generics\nfrom rest_framework.pagination import PageNumberPagination\nfrom rest_framework import viewsets\nfrom django_filters.rest_framework import  DjangoFilterBackend\nfrom  rest_framework import filters\nfrom  rest_framework.authentication import TokenAuthentication\nfrom  django.contrib.auth import get_user_model\nfrom random  import choice\nfrom users.models import VerifyCode\n\nfrom MxShop.settings import API_KEY\nfrom  utils.yunpian import YunPian\n\n\nfrom  .serializer import SmsSerializer,UserRegSerializer\n# Create your views here.\n\n\nUser = get_user_model()\n\nclass SmsCodeViewset(mixins.CreateModelMixin,viewsets.GenericViewSet):\n    \"\"\"\n    发送短信验证\n    \"\"\"\n    serializer_class = SmsSerializer\n\n    def generate_code(self):\n        \"\"\"\n\n        :return:\n        \"\"\"\n        seeds = '1234567890'\n        random_str =[]\n        for i in range(4):\n            random_str.append(choice(seeds))\n        return  \"\".join(random_str)\n\n    def create(self, request, *args, **kwargs):\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n\n\n        mobile = serializer.validated_data['mobile']\n        code = self.generate_code()\n\n        yun_pian = YunPian(api_key=API_KEY)\n        sms_status= yun_pian.send_ms(code=code,mobile = mobile)\n\n        if sms_status['code']!=0:\n            return  Response({\n                \"mobile\":sms_status['msg']\n            },status = status.HTTP_400_BAD_REQUEST)\n        else:\n            code_record = VerifyCode(code=code,mobile=mobile)\n            code_record.save()\n            return  Response({\n                \"mobile\":mobile\n            },status = status.HTTP_201_CREATED)\n\n        self.perform_create(serializer)\n        headers = self.get_success_headers(serializer.data)\n        return Response(serializer.data, status=status.HTTP_201_CREATED, headers=headers)\n\nclass UserViewset(mixins.CreateModelMixin,viewsets.GenericViewSet):\n    serializer_class = UserRegSerializer\n    queryset = User.objects.all()\n\n    def perform_create(self, serializer):\n        return serializer.save()\n\n    def create(self, request, *args, **kwargs):\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        user= self.perform_create(serializer)\n        re_dict = serializer.data\n        payload = jwt_payload_handler(user)\n        re_dict[\"token\"]  =jwt_encode_handler(payload)\n\n        headers = self.get_success_headers(serializer.data)\n        return Response(re_dict, status=status.HTTP_201_CREATED, headers=headers)\n","repo_name":"ShoutangYang/django_vue_shop","sub_path":"MxShop/apps/users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"11820766137","text":"from typing import Any\nimport seaborn as sns\nfrom matplotlib import pyplot as plt\nfrom pandas import DataFrame\nimport pandas as pd\n\nclass MakerPlots:\n    def __init__(self, data : DataFrame) -> None:\n        self.data = data\n        sns.set_theme()\n        self.all_plots_funs = [\n            self.make_corners_hist_plot,\n            self.make_boxplots_plot,\n            self.make_hists_plot,\n            self.make_heatmap_plot,\n            self.make_map_plot\n        ]\n\n    def make_corners_hist_plot(self):\n        bins = 4\n        cols = ['rb_corners', 'gt_corners']\n        sns.set_theme()\n        fig, ax = plt.subplots()\n        ax.hist(self.data[cols], bins=bins, label=cols, color=['r', 'b'])\n        fig.legend()\n        return fig\n\n    def make_boxplots_plot(self):\n        fig, axs = plt.subplots(ncols=3)\n        fig.set_size_inches(12, 9)\n        for data, ax in zip([self.data.iloc[:, 3:6], self.data.iloc[:, 6:9], self.data.iloc[:, 9:]], axs):\n            sns.boxplot(data, ax=ax)\n        return fig\n    \n    def make_hists_plot(self):\n        bins=range(1, 200, 10)\n        fig, axs = plt.subplots(ncols=3)\n        fig.set_size_inches(12, 8)\n        cols = ['mean', 'floor_mean', 'ceiling_mean']\n        for i, cols in enumerate(list(zip(self.data.columns[3:], \n                                          self.data.columns[6:], \n                                          self.data.columns[9:]))):\n            for col in cols:\n                sns.distplot(self.data[col], bins=bins, ax=axs[i], kde=False)\n            axs[i].legend(labels = cols)\n        return fig\n    \n    def make_heatmap_plot(self):\n        fig, ax = plt.subplots()\n        fig.set_size_inches(10, 10)\n        sns.heatmap(self.data.iloc[:, 1:].corr().round(3), annot=True, ax=ax)\n        return fig\n\n    def make_map_plot(self):\n        g = sns.PairGrid(self.data.iloc[:, 3:])\n        g.map_upper(sns.scatterplot)\n        g.map_lower(sns.kdeplot, fill=True)\n        g.map_diag(sns.histplot, kde=True)\n        return g.figure\n\n    def get_plots(self):\n        return {\n            f'{\"_\".join(func.__name__.split(\"_\")[1:])}' : func() \n                        for func in self.all_plots_funs\n        }\n\nclass DrawerPlots:\n    def __init__(self, plots : dict) -> None:\n        self.plots = plots\n\n    def draw(self):\n        plt.show()\n\nclass JSONImporter:\n    def __init__(self, path : str) -> None:\n        self.path = path\n\n    def import_(self, *args: Any, **kwds: Any) -> dict:\n        return pd.read_json(self.path)\n\nclass PlotsSaver:\n    def __init__(self, plots : dict) -> None:\n        self.plots = plots\n\n    def save(self, path='/', *args: Any, **kwds: Any) -> Any:\n        for plot_name, plot in self.plots.items():\n            plot.savefig(f'{path}/{plot_name}.png')\n\n\n\n","repo_name":"Argen7umCode/docusketch","sub_path":"tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":2764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"40787095017","text":"#!/usr/bin/env python\n# Standard imports\nimport cProfile\nimport sys\n#sys.path.insert( 0, '..')\n#sys.path.insert( 0, '.')\nimport time\nimport pickle\nimport copy\nimport itertools\nimport numpy as np\nimport operator\nimport functools\n\nimport MultiNode\n\ndefault_cfg = {\n    \"n_trees\" : 100,\n    \"learning_rate\" : 0.2, \n    \"loss\" : \"MSE\", # or \"CrossEntropy\" \n#    \"bagging_fraction\": 1.,\n    \"learn_global_score\": False,\n}\n\nclass MultiBoostedInformationTree:\n\n    def __init__( self, training_features, training_weights, \n                    **kwargs ):\n\n        # make cfg and node_cfg from the kwargs keys known by the Node\n        self.cfg = default_cfg\n        self.cfg.update( kwargs )\n        self.node_cfg = {}\n        for (key, val) in kwargs.items():\n            if key in MultiNode.default_cfg.keys():\n                self.node_cfg[key] = val \n            elif key in default_cfg.keys():\n                self.cfg[key]      = val\n            else:\n                raise RuntimeError( \"Got unexpected keyword arg: %s:%r\" %( key, val ) )\n        self.node_cfg['loss'] = self.cfg['loss'] \n\n        for (key, val) in self.cfg.items():\n                setattr( self, key, val )\n\n        # Attempt to learn 98%. (1-learning_rate)^n_trees = 0.02 -> After the fit, the score is at least down to 2% \n        if self.learning_rate == \"auto\":\n            self.learning_rate = 1-0.02**(1./self.n_trees)\n\n        self.training_weights   = copy.deepcopy(training_weights)\n        if training_weights is not None:\n            self.training_weights = {tuple(sorted(key)):val for key,val in self.training_weights.items()}\n\n        self.training_features  = training_features\n\n        # Will hold the trees\n        self.trees              = []\n\n    @classmethod\n    def load(cls, filename):\n        with open(filename,'rb') as file_:\n            old_instance = pickle.load(file_)\n            new_instance = cls( None, None, \n                    n_trees             = old_instance.n_trees, \n                    learning_rate       = old_instance.learning_rate,\n                    learn_global_score  = old_instance.learn_global_score,\n                    )\n            new_instance.trees = old_instance.trees\n\n            new_instance.derivatives = old_instance.trees[0].derivatives[1:]\n\n            return new_instance  \n\n    def __setstate__(self, state):\n        self.__dict__ = state\n\n    def save(self, filename):\n        with open(filename,'wb') as file_:\n            pickle.dump( self, file_ )\n\n    def boost( self ):\n\n        toolbar_width = min(20, self.n_trees)\n\n        # setup toolbar\n        sys.stdout.write(\"[%s]\" % (\" \" * toolbar_width))\n        sys.stdout.flush()\n        sys.stdout.write(\"\\b\" * (toolbar_width+1)) # return to start of line, after '['\n\n        weak_learner_time = 0.0\n        update_time = 0.0\n        for n_tree in range(self.n_trees):\n\n            training_time = 0\n\n            # store the score vector in the first tree:\n            _get_only_score = ( (n_tree==0) and self.cfg[\"learn_global_score\"] )\n            self.node_cfg[\"_get_only_score\"] = _get_only_score \n\n            # fit to data\n            time1 = time.process_time()\n            root = MultiNode.MultiNode(   \n                            self.training_features, \n                            training_weights = self.training_weights,\n                            **self.node_cfg \n                        )\n\n            if n_tree==0:\n                self.derivatives = root.derivatives[1:]\n\n            time2 = time.process_time()\n            weak_learner_time += time2 - time1\n            training_time      = time2 - time1\n\n            self.trees.append( root )\n\n            # Recall current tree\n            time1 = time.process_time()\n\n            prediction   = root.vectorized_predict(self.training_features)\n            len_         = len(prediction)\n            delta_weight = self.training_weights[tuple()].reshape(len_,-1)*prediction[:,1:]/prediction[:,0].reshape(len_,-1)\n            learning_rate = 1. if _get_only_score else self.learning_rate \n            for i_der, der in enumerate(root.derivatives[1:]):\n                self.training_weights[der] += -learning_rate*delta_weight[:,i_der]\n\n            time2 = time.process_time()\n            update_time   += time2 - time1\n            training_time += time2 - time1\n\n            self.trees[-1].training_time = training_time \n\n            # update the bar\n            if self.n_trees>=toolbar_width:\n                if n_tree % (self.n_trees/toolbar_width)==0:   sys.stdout.write(\"-\")\n            sys.stdout.flush()\n\n        sys.stdout.write(\"]\\n\") # this ends the progress bar\n        print (\"weak learner time: %.2f\" % weak_learner_time)\n        print (\"update time: %.2f\" % update_time)\n       \n        # purge training data\n        del self.training_weights       \n        del self.training_features      \n\n    def predict( self, feature_array, max_n_tree = None, summed = True, last_tree_counts_full = False):\n        # list learning rates\n        learning_rates = self.learning_rate*np.ones(max_n_tree if max_n_tree is not None else self.n_trees)\n        # keep the last tree?\n        if last_tree_counts_full and (max_n_tree is None or max_n_tree==self.n_trees):\n            learning_rates[-1] = 1\n        # Does the first tree hold the global score?\n        if self.cfg[\"learn_global_score\"]:\n             learning_rates[0] = 1\n            \n        predictions = np.array([ tree.predict( feature_array ) for tree in self.trees[:max_n_tree] ])\n        predictions = predictions[:,1:]/predictions[:,0].reshape(-1,1)\n        if summed:\n            return np.dot(learning_rates, predictions)\n        else:\n            return learning_rates.reshape(-1, 1)*predictions\n    \n    def vectorized_predict( self, feature_array, max_n_tree = None, summed = True, last_tree_counts_full = False):\n        # list learning rates\n        learning_rates = self.learning_rate*np.ones(max_n_tree if max_n_tree is not None else self.n_trees)\n        # keep the last tree?\n        if last_tree_counts_full and (max_n_tree is None or max_n_tree==self.n_trees):\n            learning_rates[-1] = 1\n        # Does the first tree hold the global score?\n        if self.cfg[\"learn_global_score\"]:\n             learning_rates[0] = 1\n            \n        predictions = np.array([ tree.vectorized_predict( feature_array ) for tree in self.trees[:max_n_tree] ])\n        predictions = predictions[:,:,1:]/np.expand_dims(predictions[:,:,0], -1)\n        if summed:\n            return np.sum(learning_rates.reshape(-1,1,1)*predictions, axis=0)\n        else:\n            return learning_rates.reshape(-1,1,1)*predictions \n\n    def losses( self, feature_array, weight_dict, max_n_tree = None, last_tree_counts_full = False):\n        ## list learning rates\n        #learning_rates = self.learning_rate*np.ones(max_n_tree if max_n_tree is not None else self.n_trees)\n        ## keep the last tree?\n        #if last_tree_counts_full and (max_n_tree is None or max_n_tree==self.n_trees):\n        #    learning_rates[-1] = 1\n        ## Does the first tree hold the global score?\n        #if self.cfg[\"learn_global_score\"]:\n        #     learning_rates[0] = 1\n\n        # recover base points from tree\n        base_points      = self.trees[0].base_points\n        base_point_const = np.array([[ functools.reduce(operator.mul, [point[coeff] if (coeff in point) else 0 for coeff in der ], 1) for der in self.derivatives] for point in base_points]).astype('float')\n        for i_der, der in enumerate(self.derivatives):\n            if not (len(der)==2 and der[0]==der[1]): continue\n            for i_point in range(len(base_points)):\n                base_point_const[i_point][i_der]/=2.\n        \n        predictions = np.array([ tree.vectorized_predict( feature_array ) for tree in self.trees[:max_n_tree] ])\n        predictions = predictions[:,:,1:]/np.expand_dims(predictions[:,:,0], -1)\n\n        weight_ratio = np.array( [ (weight_dict[der]/weight_dict[()] if der in weight_dict else weight_dict[tuple(reversed(der))]/weight_dict[()]) for der in self.derivatives]).transpose().astype('float')\n        # losses\n        return -( weight_dict[()][np.newaxis,...,np.newaxis]*np.dot( (predictions - (weight_ratio[np.newaxis,...])), base_point_const )**2).sum(axis=(1,2))\n        \n#max_n_tree      = None\n#derivatives     = bit.derivatives\n## recover base points from tree\n#base_points      = bit.trees[0].base_points\n#base_point_const = np.array([[ functools.reduce(operator.mul, [point[coeff] if (coeff in point) else 0 for coeff in der ], 1) for der in bit.derivatives] for point in base_points]).astype('float')\n#for i_der, der in enumerate(bit.derivatives):\n#    if not (len(der)==2 and der[0]==der[1]): continue\n#    for i_point in range(len(base_points)):\n#        base_point_const[i_point][i_der]/=2.\n#\n#learning_rates  = bit.learning_rate*np.ones(max_n_tree if max_n_tree is not None else bit.n_trees)\n#predictions     = np.array([ tree.vectorized_predict( training_features ) for tree in bit.trees[:max_n_tree] ])\n#predictions     = predictions[:,:,1:]/np.expand_dims(predictions[:,:,0], -1)\n#\n#weight_ratio = np.array([training_weights[der]/training_weights[()] for der in derivatives]).transpose().astype('float')\n#\n#losses = -( training_weights[()][np.newaxis,...,np.newaxis]*(np.dot( (predictions - (weight_ratio[np.newaxis,...])), base_point_const ))**2).sum(axis=(1,2))\n\nif __name__=='__main__':\n\n    #import toy_models.ZH_Nakamura as model\n    #coefficients = sorted(['cHW', 'cHWtil'])\n\n    import toy_models.analytic as model\n    coefficients = sorted(['theta1'])\n\n    base_points = []\n    for comb in list(itertools.combinations_with_replacement(coefficients,1))+list(itertools.combinations_with_replacement(coefficients,2)):\n        base_points.append( {c:comb.count(c) for c in coefficients} )\n\n    nTraining    = 50000\n\n    features          = model.getEvents(nTraining)\n    training_weights  = model.getWeights(features, eft=model.default_eft_parameters)\n    print (\"Created training data set of size %i\" % len(features) )\n\n    for key in training_weights.keys():\n        if key==tuple(): continue\n        if not all( [ k in coefficients for k in key] ):\n            del training_weights[key]\n\n    print (\"nEvents: %i Weights: %s\" %( len(features), [ k for k in training_weights.keys() if k!=tuple()] ))\n\n    # cfg & preparation for node split\n    min_size    = 50\n    max_n_split = -1\n\n    bit = MultiBoostedInformationTree( \n                      features,\n                      training_weights,\n                      min_size    = min_size,\n                      max_n_split = max_n_split,\n                      base_points = base_points,\n                      feature_names = model.feature_names,\n                    )\n    bit.boost()\n","repo_name":"HephyAnalysisSW/ML-pytorch","sub_path":"BIT/MultiBoostedInformationTree.py","file_name":"MultiBoostedInformationTree.py","file_ext":"py","file_size_in_byte":10769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"73692926364","text":"from time import time\nfrom collections import deque\n\nl = deque()\nbegin = time()\nfor i in range(1, 10**5):\n    l.append(i)\n\nmiddle = time()\nprint(middle-begin)\n\nfor i in range(1, 10**5):\n    l.popleft()\n\nend = time()\nprint(end-middle)","repo_name":"maeeri/tira","sub_path":"Wk4/list2.py","file_name":"list2.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"38542871112","text":"from ..models.db import Base, get_session, get_engine, drop_sessions\nfrom ..models.secrets import Secrets\nfrom ..models.session import User\nfrom .scope import global_scope as gs\n\n__all__ = ['create_db', 'validation_key_new', 'validation_key_rekey',\n           'validation_key_validate']\n\n\ndef create_db():\n    \"\"\"\n    Create database\n    \"\"\"\n\n    session = get_session()\n    Base.metadata.create_all(get_engine())\n    session.commit()\n\n\ndef validation_key_new():\n    \"\"\"\n        Create a validation key\n    \"\"\"\n\n    key_salt = gs['encrypt'].key + \\\n        gs['conf'].salt.encode()\n\n    # Save user\n    user = User(key='key_validation',\n                     value=gs['encrypt'].encrypt(key_salt))\n    get_session().add(user)\n    get_session().commit()\n\n\ndef validation_key_validate(key):\n    \"\"\"\n        Verify if a validation key is valid\n    \"\"\"\n\n    # validation key from database\n    try:\n        user = get_session().query(User).filter(\n            User.key == 'key_validation').order_by(User.id.desc()).first()\n    except exc.DatabaseError:  # In case of encrypted db, if the encryption key is invalid\n        # Drop db sessions to force a re-connection with the new key\n        drop_sessions()\n\n        return False\n\n    # Concatenate user given key and config's salt\n    key_salt = key + gs['conf'].salt.encode()\n\n    # Key is valid\n    try:\n        if gs['encrypt'].decrypt(user.value) == key_salt:\n            return True\n    except ValueError:  # Decryption error\n        return False\n\n    return False\n\n\ndef validation_key_rekey(newenc):\n    \"\"\"\n        Replace a validation key with a new master key\n    \"\"\"\n\n    # Get validation key\n    user = get_session().query(User).filter(\n        User.key == 'key_validation').order_by(User.id.desc()).first()\n\n    if user:\n        key_salt = newenc.key + \\\n            gs['conf'].salt.encode()\n\n        # Update validation key\n        user.value = newenc.encrypt(key_salt)\n\n        get_session().add(user)\n        get_session().commit()\n\n        return True\n\n    return False","repo_name":"c-demone/catena-py","sub_path":"src/catena/lockbox/utils/session.py","file_name":"session.py","file_ext":"py","file_size_in_byte":2029,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"33304448394","text":"import numpy as np\nfrom lightenn import types\nfrom lightenn import utils\nfrom lightenn.layers.vec import base_layer\n\nclass FullyConnectedLayer(base_layer.BaseLayer):\n\n    def __init__(self, nn_config, size, idx,\n                 activation_type=types.ActivationType.SIGMOID,\n                 wgts=None, biases=None, dropout_p=0.0):\n\n        super().__init__(nn_config, size, idx)\n        self.activations = None\n        self.size = size\n        self.idx = idx\n        self.activation_type = activation_type\n        self.wgts = wgts\n        self.biases = biases\n        self.z_vals = None\n        self.cost_wrt_activations = []  # partial derivatives of cost w.r.t. my activations\n        self.cost_wrt_wgts = []  # partial derivatives of cost w.r.t. weights\n        self.cost_wrt_biases = []  # partial derivatives of cost w.r.t. biases\n        self.dropout_p = dropout_p\n        self.dropout_mask = np.ones((size,), dtype=np.float)\n\n    def initialize(self):\n\n        assert (self.size > 0), 'Error: cannot add a layer of size 0.'\n        assert (self.dropout_p >= 0.0 and self.dropout_p < 1.0), 'Error: dropout_p must be in the range [0.0, 1.0).'\n\n        if self.wgts is None:\n            self.wgts = np.random.normal(loc=self.nn_config['mu'],\n                                         scale=self.nn_config['stddev'],\n                                         size=(self.prev.size, self.size))\n        else:\n            assert (len(self.wgts.shape) == 2), 'Error: wgts ndarray must be 2-D.'\n            assert (self.wgts.dtype == np.float), 'Error: wgts must have dtype np.float.'\n            assert (self.wgts.shape[0] == self.prev.size), 'Error: wgts.shape[0] must match previous layer size.'\n            assert (self.wgts.shape[1] == self.size), 'Error: wgts.shape[1] must match layer size.'\n\n        if self.biases is None:\n            self.biases = np.random.normal(loc=self.nn_config['mu'],\n                                           scale=self.nn_config['stddev'],\n                                           size=(self.size,))\n        else:\n            assert (len(self.biases.shape) == 1), 'Error: biases ndarray must be 1-D.'\n            assert (self.biases.dtype == np.float), 'Error: biases must have dtype np.float.'\n            assert (self.biases.shape[0] == self.size), 'Error: size of biases ndarray must match layer size.'\n\n    def activate(self, z_vals):\n        if self.activation_type == types.ActivationType.SIGMOID:\n            return utils.sigmoid(z_vals)\n        elif self.activation_type == types.ActivationType.RELU:\n            return utils.relu(z_vals)\n        elif self.activation_type == types.ActivationType.NONE:\n            return z_vals\n\n    def activation_prime(self, z_vals, activation_type):\n        if activation_type == types.ActivationType.SIGMOID:\n            return utils.sigmoid_prime(z_vals)\n        elif activation_type == types.ActivationType.RELU:\n            return utils.relu_prime(z_vals)\n        elif activation_type == types.ActivationType.NONE:\n            if isinstance(z_vals, np.float):\n                return 1.0\n            else:\n                return np.ones_like(z_vals)\n\n    # Computes partial regularization terms for all incoming weights in one\n    # operation\n    def compute_partial_reg_term(self):\n\n        batch_size = self.activations.shape[0]\n        reg = self.nn_config['regularizer']\n\n        if reg == None:\n            return np.zeros_like(self.wgts)\n\n        reg_type = reg[0]\n        lambd = reg[1]\n\n        if reg_type == types.RegType.L1:\n            # reg_terms[wgts == 0] are already 0.0 since init'd as zero-array.\n            reg_terms = np.zeros_like(self.wgts)\n            reg_terms[self.wgts > 0.0] = lambd / batch_size\n            reg_terms[self.wgts < 0.0] = -lambd / batch_size\n            return reg_terms\n\n        elif reg_type == types.RegType.L2:\n            return np.multiply(lambd / batch_size, self.wgts)\n\n    def compute_cost_wrt_activations(self, batch_size, y, y_hat):\n\n        next_zs_prime = self.activation_prime(self.next.z_vals, self.next.activation_type)\n        a = next_zs_prime\n        b = self.next.wgts.reshape(self.next.wgts.shape[0], 1, self.next.wgts.shape[1])\n        next_act_wrt_curr_act = np.multiply(a, b)\n        cost_wrt_next_acts = self.next.cost_wrt_activations\n        cost_wrt_acts = np.sum(np.multiply(cost_wrt_next_acts, next_act_wrt_curr_act), axis=2).transpose()\n\n        # Dropout: gradient of cost w.r.t. my activations must\n        # also now include the dropout mask and inverse terms that are\n        # associated with my layer\n        return np.divide(np.multiply(cost_wrt_acts, self.dropout_mask), 1.0 - self.dropout_p)\n\n    def forward(self):\n\n        self.z_vals = np.add(np.dot(self.prev.activations, self.wgts), self.biases)\n        self.activations = self.activate(self.z_vals)\n\n        # Dropout: in the forward pass, set to 0 any activations that are masked\n        # by the dropout_mask. Apply the invert as well with divide.\n        self.activations = np.divide(np.multiply(self.activations, self.dropout_mask), 1.0 - self.dropout_p)\n\n    def backward_compute_grads(self, y, y_hat):\n\n        # Batch size that was fed in at the input layer. Because the dot product\n        # is used for every forward, this will always be the value of the first\n        # dimension of the activation layer\n        batch_size = self.activations.shape[0]\n\n        # Compute gradient of my activations w.r.t. wgts and biases\n        activations_prime = self.activation_prime(self.z_vals, self.activation_type)\n        in_acts = self.prev.activations\n        a = in_acts.reshape(batch_size, in_acts.shape[1], 1)\n        b = activations_prime.reshape(batch_size, 1, activations_prime.shape[1])\n        acts_wrt_wgts = np.multiply(a, b)\n        acts_wrt_bias = activations_prime\n\n        # compute grad of cost w.r.t. my activations\n        self.cost_wrt_activations = self.compute_cost_wrt_activations(batch_size, y, y_hat)\n\n        # compute cost w.r.t. weights and biases\n        a = acts_wrt_wgts\n        b = self.cost_wrt_activations.reshape(\n            (self.cost_wrt_activations.shape[0], 1, self.cost_wrt_activations.shape[1]))\n        self.cost_wrt_wgts = np.sum(np.multiply(a, b), axis=0)\n        self.cost_wrt_biases = np.sum(np.multiply(self.cost_wrt_activations, acts_wrt_bias), axis=0)\n\n        # Add partial regularization term, as applicable\n        partial_reg_term = self.compute_partial_reg_term()\n        self.cost_wrt_wgts = np.add(self.cost_wrt_wgts, partial_reg_term)\n\n    def backward_adjust(self):\n\n        learning_rate = self.nn_config['learning_rate']\n        self.wgts = np.add(self.wgts, np.multiply(-1.0 * learning_rate, self.cost_wrt_wgts))\n        self.biases = np.add(self.biases, np.multiply(-1.0 * learning_rate, self.cost_wrt_biases))\n","repo_name":"antonskourides/lightenn","sub_path":"lightenn/layers/vec/fully_connected_layer.py","file_name":"fully_connected_layer.py","file_ext":"py","file_size_in_byte":6796,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"32865354306","text":"import os.path\nimport numpy as np\nimport re\n\n#local imports\nfrom rmgpy.chemkin import getSpeciesIdentifier\nfrom rmgpy.scoop_framework.util import broadcast, get, map_\nfrom rmgpy.scoop_framework.util import logger as logging\nfrom rmgpy.rmg.settings import ModelSettings, SimulatorSettings\n\nfrom model import ReductionReaction\nfrom rates import isImportant\n\n\n#global variables\nreactions = None\n\n\ndef simulateOne(reactionModel, atol, rtol, reactionSystem):\n    \"\"\"\n\n    Simulates one reaction system, listener registers results, \n    which are returned at the end.\n\n\n    The returned data consists of a array of the species names, \n    and the concentration data.\n\n    The concentration data consists of a number of elements for each timestep \n    the solver took to reach the end time of the batch reactor simulation.\n\n    Each element consists of the time and the concentration data of the species at that \n    particular timestep in the order of the species names.\n\n    \"\"\"\n\n    #register as a listener\n    listener = ConcentrationListener()\n\n    coreSpecies = reactionModel.core.species\n    regex = r'\\([0-9]+\\)'#cut of '(one or more digits)'\n    speciesNames = []\n    for spc in coreSpecies:\n        name = getSpeciesIdentifier(spc)\n        name_cutoff = re.split(regex, name)[0]\n        speciesNames.append(name_cutoff)\n\n    listener.speciesNames = speciesNames\n\n    reactionSystem.attach(listener)\n\n    pdepNetworks = []\n    for source, networks in reactionModel.networkDict.items():\n        pdepNetworks.extend(networks)\n        \n    simulatorSettings = SimulatorSettings(atol,rtol)\n    modelSettings = ModelSettings(toleranceKeepInEdge=0,toleranceMoveToCore=1,toleranceInterruptSimulation=1)\n    \n    terminated,resurrected,obj,sspcs,srxns = reactionSystem.simulate(\n        coreSpecies = reactionModel.core.species,\n        coreReactions = reactionModel.core.reactions,\n        edgeSpecies = reactionModel.edge.species,\n        edgeReactions = reactionModel.edge.reactions,\n        surfaceSpecies = [],\n        surfaceReactions = [],\n        pdepNetworks = pdepNetworks,\n        modelSettings = modelSettings,\n        simulatorSettings=simulatorSettings,\n    ) \n\n    assert terminated\n\n    #unregister as a listener\n    reactionSystem.detach(listener) \n\n    return listener.speciesNames, listener.data\n\ndef simulateAll(rmg):\n    \"\"\"\n    Simulate the RMG job, \n    for each of the simulated reaction systems.\n\n    Each element i of the data corresponds to a reaction system.\n    \"\"\"\n    reactionModel = rmg.reactionModel\n\n    data = []\n\n    atol, rtol = rmg.simulatorSettingsList[-1].atol, rmg.simulatorSettingsList[-1].rtol\n    for reactionSystem in rmg.reactionSystems:\n        data.append(simulateOne(reactionModel, atol, rtol, reactionSystem))\n\n    return data\n        \n\ndef initialize(wd, rxns):\n    global working_dir, reactions\n    working_dir = wd\n    assert os.path.isdir(working_dir)\n    \n    #set global variable here such that functions executed in the root worker have access to it.\n    \n    reactions = [ReductionReaction(rxn) for rxn in rxns]\n    broadcast(reactions, 'reactions')\n    \n\ndef retrieveReactions():\n    \"\"\"\n    Reactions can be retrieved either through the global variable 'reactions' if parallel computing\n    is not used.\n\n    With the use of multiple workers, the reactions are retrieved from the previously broadcasted \n    constant.\n\n    In any case, the references to the original reactions of the reaction model are assumed to be \n    broken.\n\n    \"\"\"\n    global reactions    \n\n    broadcastedReactions = get('reactions')\n    if broadcastedReactions:\n        reactions = broadcastedReactions\n    return reactions\n\ndef findImportantReactions(rmg, tolerance):\n    \"\"\"\n    This function:\n\n    - loops over all the species involved in a specific reaction\n    - decides whether the specific reaction is important for the species.\n\n    Whenever it is found that a reaction is important for a species, we break\n    the species loop, and keep the reaction in the model.\n\n\n    Returns:\n        a list of rxns that can be removed.\n    \"\"\"\n    \n    # run the simulation, creating concentration profiles for each reaction system defined in input.\n    simdata = simulateAll(rmg)\n\n\n    reduceReactions = retrieveReactions()\n\n    def chunks(l, n):\n        \"\"\"Yield successive n-sized chunks from l.\"\"\"\n        for i in xrange(0, len(l), n):\n            yield l[i:i+n]\n\n    CHUNKSIZE = 40\n    boolean_array = []\n    for chunk in chunks(reduceReactions,CHUNKSIZE):\n        N = len(chunk)\n        partial_results = list(\n            map_(\n                assessReaction, chunk, [rmg.reactionSystems] * N, [tolerance] * N, [simdata] * N\n                )\n            )\n        boolean_array.extend(partial_results)\n\n    \"\"\"\n    Assuming that the order of the reduced reactions array and the core reactions of the reaction model\n    are identical, iterate over the boolean array and retain those reactions of the reaction model\n    that are deemed 'important'.\n    \"\"\"\n    importantRxns = []\n    for isImport, rxn in zip(boolean_array, rmg.reactionModel.core.reactions):\n        logging.debug('Is rxn {rxn} important? {isImport}'.format(**locals()))\n        if isImport:\n            importantRxns.append(rxn)\n\n\n    return importantRxns\n\ndef assessReaction(rxn, reactionSystems, tolerance, data):\n    \"\"\"\n    Returns whether the reaction is important or not in the reactions.\n\n    It iterates over the reaction systems, and loads the concentration profile \n    of each reaction system.\n\n    It iterates over a number of samples in profile and \n    evaluates the importance of the reaction at every sample.\n\n    \"\"\"\n\n\n    logging.debug('Assessing reaction {}'.format(rxn))\n\n    reactions = retrieveReactions()    \n\n    # read in the intermediate state variables\n\n    for datum, reactionSystem in zip(data, reactionSystems):    \n        T, P = reactionSystem.T.value_si, reactionSystem.P.value_si\n        \n        speciesNames, profile = datum\n\n        # take N evenly spaced indices from the table with simulation results:\n\n        \"\"\"\n\n        Number of time steps between start and end time of the batch reactor simulation at which the importance of \n        reactions should be evaluated.\n\n\n\n        The more timesteps, the less chance we have to remove an important reactions, but the more simulations\n        need to be carried out.\n        \"\"\"\n        \n        timesteps = len(profile) / 2\n        logging.debug('Evaluating the importance of a reaction at {} time samples.'.format(timesteps))\n\n        assert timesteps <= len(profile)\n        indices = map(int, np.linspace(0, len(profile)-1, num = timesteps))\n        for index in indices:\n            assert profile[index] is not None\n            timepoint, coreSpeciesConcentrations = profile[index]\n\n            coreSpeciesConcentrations = {key: float(value) for (key, value) in zip(speciesNames, coreSpeciesConcentrations)}\n            \n            for species_i in rxn.reactants:\n                if isImportant(rxn, species_i, reactions, 'reactant', tolerance, T, P, coreSpeciesConcentrations):\n                    return True\n\n            #only continue if the reaction is not important yet.\n            for species_i in rxn.products:\n                if isImportant(rxn, species_i, reactions, 'product', tolerance, T, P, coreSpeciesConcentrations):\n                    return True\n\n    return False\n\n    \ndef searchTargetIndex(targetLabel, reactionModel):\n    \"\"\"\n    Searches for the Species object in the core species\n    of the reaction that has the same label as the parameter string.\n    reactionModel must be of class CoreEdgeReactionModel\n\n    Has known issues dealing with duplicate labels. See reductionTest.py\n    for a unittest of this issue.\n    \"\"\"\n    for i, spc in enumerate(reactionModel.core.species):\n        if spc.label == targetLabel:\n            return i\n\n    raise Exception('{} could not be found...'.format(targetLabel))\n\n\ndef computeObservables(targets, reactionModel, reactionSystem, atol, rtol):\n    \"\"\"\n    Computes the observables of the targets, provided in the function signature.\n\n    Currently, the species mole fractions at the end time of the\n    batch reactor simulation are the only observables that can be computed.\n\n    - resetting the reaction system, initialing with empty variables\n    - running the simulation at the conditions stored in the reaction system\n    \"\"\"\n    simulatorSettings = SimulatorSettings(atol,rtol)\n    reactionSystem.initializeModel(\\\n        reactionModel.core.species, reactionModel.core.reactions,\\\n        reactionModel.edge.species, reactionModel.edge.reactions, \\\n        [],[],[],atol=simulatorSettings.atol,rtol=simulatorSettings.rtol,\n        sens_atol=simulatorSettings.sens_atol, sens_rtol=simulatorSettings.sens_rtol)\n\n    #run the simulation:\n    simulateOne(reactionModel, atol, rtol, reactionSystem)\n\n    observables = computeMoleFractions(targets, reactionModel, reactionSystem)\n\n    return observables\n\ndef computeMoleFractions(targets, reactionModel, reactionSystem):\n    \"\"\"\n    Computes the mole fractions of the targets, identified by the list \n    of species names in the function signature.\n\n    Returns a numpy array with the mole fractions at the end time of the reactor\n    simulation.\n\n    - searching the index of the target species in the core species\n    of the global reduction variable\n    - fetching the computed moles variable y\n\n    \"\"\"\n    moleFractions = np.zeros(len(targets), np.float64)\n\n    for i, label in enumerate(targets):\n        targetIndex = searchTargetIndex(label, reactionModel)\n\n        moleFractions[i] = reactionSystem.y[targetIndex]\n\n    return moleFractions\n\ndef computeConversion(targetLabel, reactionModel, reactionSystem, atol, rtol):\n    \"\"\"\n    Computes the conversion of a target molecule by\n\n    - searching the index of the target species in the core species\n    of the global reduction variable\n    - resetting the reaction system, initialing with empty variables\n    - fetching the initial moles variable y0\n    - running the simulation at the conditions stored in the reaction system\n    - fetching the computed moles variable y\n    - computing conversion\n    \"\"\"\n\n    targetIndex = searchTargetIndex(targetLabel, reactionModel)\n\n    #reset reaction system variables:\n    logging.info('No. of rxns in core reactions: {}'.format(len(reactionModel.core.reactions)))\n    \n    simulatorSettings = SimulatorSettings(atol,rtol)\n    \n    reactionSystem.initializeModel(\\\n        reactionModel.core.species, reactionModel.core.reactions,\\\n        reactionModel.edge.species, reactionModel.edge.reactions, \\\n        [],[],[],atol=simulatorSettings.atol,rtol=simulatorSettings.rtol,\n        sens_atol=simulatorSettings.sens_atol,sens_rtol=simulatorSettings.sens_rtol)\n\n    #get the initial moles:\n    y0 = reactionSystem.y.copy()\n\n    #run the simulation:\n    simulateOne(reactionModel, atol, rtol, reactionSystem)\n\n    #compute conversion:\n    conv = 1 - (reactionSystem.y[targetIndex] / y0[targetIndex])\n    return conv\n\ndef reduceModel(tolerance, targets, reactionModel, rmg, reactionSystemIndex):\n    \"\"\"\n    Reduces the model for the given tolerance and evaluates the \n    target observables.\n    \"\"\"\n\n    # reduce model with the tolerance specified earlier:\n    importantReactions = findImportantReactions(rmg, tolerance)\n\n    no_importantReactions = len(importantReactions)\n    logging.info('No. of reactions in tested reduced model: {}'.format(no_importantReactions))\n\n    #set the core reactions to the reduced reaction set:\n    originalReactions = reactionModel.core.reactions\n    rmg.reactionModel.core.reactions = importantReactions\n\n    #re-compute observables: \n    observables = computeObservables(targets, rmg.reactionModel,\\\n     rmg.reactionSystems[reactionSystemIndex],\\\n     rmg.simulatorSettingsList[-1].atol, rmg.simulatorSettingsList[-1].rtol)\n\n    #reset the reaction model to its original state:\n    rmg.reactionModel.core.reactions = originalReactions\n\n    logging.info('Observables of reduced model ({} rxns):'.format(no_importantReactions))\n    for target, observable in zip(targets, observables):\n        logging.info('Observable in reduced model: {}: {:.2f}%'.format(target, observable * 100))\n\n    return observables, importantReactions\n\nclass ConcentrationListener(object):\n    \"\"\"Returns the species concentration profiles at each time step.\"\"\"\n\n    def __init__(self):\n        self.speciesNames = []\n        self.data = []\n\n    def update(self, subject):\n        \"\"\"\n        Register the time (t) and the species mole fractions at the\n        given time.\n\n        The snapshots variable stores time and Volume as the first two \n        elements in the array.\n        \"\"\"\n        data = subject.snapshots\n        self.data = process(data)\n\ndef process(data):\n    \"\"\"\n    The data is structured as a list of lists.\n\n    Each list contains [time, Volume, [species mole fractions]]\n\n    The volume is cut out of each list, the remaining part is stored as a tuple.\n    \"\"\"\n    processed = []\n\n    for d in data:\n        processed.append((d[0], d[2:]))\n\n    return processed\n","repo_name":"Molecular-Image-Recognition/Molecular-Image-Recognition","sub_path":"code/rmgpy/reduction/reduction.py","file_name":"reduction.py","file_ext":"py","file_size_in_byte":13107,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"86"}
+{"seq_id":"21286426967","text":"from collections import OrderedDict\nfrom numbers import Number\n\nimport funsor\n\nfrom pyro.contrib.funsor.handlers.named_messenger import (\n    DimRequest,\n    DimType,\n    GlobalNamedMessenger,\n    NamedMessenger,\n)\nfrom pyro.contrib.funsor.handlers.primitives import to_data, to_funsor\nfrom pyro.distributions.util import copy_docs_from\nfrom pyro.poutine.broadcast_messenger import BroadcastMessenger\nfrom pyro.poutine.indep_messenger import CondIndepStackFrame\nfrom pyro.poutine.messenger import Messenger\nfrom pyro.poutine.runtime import effectful\nfrom pyro.poutine.subsample_messenger import (\n    SubsampleMessenger as OrigSubsampleMessenger,\n)\nfrom pyro.util import ignore_jit_warnings\n\nfunsor.set_backend(\"torch\")\n\n\nclass IndepMessenger(GlobalNamedMessenger):\n    \"\"\"\n    Vectorized plate implementation using :func:`~pyro.contrib.funsor.to_data` instead of\n    :class:`~pyro.poutine.runtime._DimAllocator`.\n    \"\"\"\n\n    def __init__(self, name=None, size=None, dim=None, indices=None):\n        assert dim is None or dim < 0\n        super().__init__()\n        # without a name or dim, treat as a \"vectorize\" effect and allocate a non-visible dim\n        self.dim_type = (\n            DimType.GLOBAL if name is None and dim is None else DimType.VISIBLE\n        )\n        self.name = name if name is not None else funsor.interpreter.gensym(\"PLATE\")\n        self.size = size\n        self.dim = dim\n        if not hasattr(self, \"_full_size\"):\n            self._full_size = size\n        if indices is None:\n            indices = funsor.ops.new_arange(\n                funsor.tensor.get_default_prototype(), self.size\n            )\n        assert len(indices) == size\n\n        self._indices = funsor.Tensor(\n            indices, OrderedDict([(self.name, funsor.Bint[self.size])]), self._full_size\n        )\n\n    def __enter__(self):\n        super().__enter__()  # do this first to take care of globals recycling\n        name_to_dim = OrderedDict([(self.name, DimRequest(self.dim, self.dim_type))])\n        indices = to_data(self._indices, name_to_dim=name_to_dim)\n        # extract the dimension allocated by to_data to match plate's current behavior\n        self.dim, self.indices = -indices.dim(), indices.reshape(-1)\n        return self\n\n    def _pyro_sample(self, msg):\n        frame = CondIndepStackFrame(self.name, self.dim, self.size, 0)\n        msg[\"cond_indep_stack\"] = (frame,) + msg[\"cond_indep_stack\"]\n\n    def _pyro_param(self, msg):\n        frame = CondIndepStackFrame(self.name, self.dim, self.size, 0)\n        msg[\"cond_indep_stack\"] = (frame,) + msg[\"cond_indep_stack\"]\n\n\n@copy_docs_from(OrigSubsampleMessenger)\nclass SubsampleMessenger(IndepMessenger):\n    def __init__(\n        self,\n        name=None,\n        size=None,\n        subsample_size=None,\n        subsample=None,\n        dim=None,\n        use_cuda=None,\n        device=None,\n    ):\n        size, subsample_size, indices = OrigSubsampleMessenger._subsample(\n            name, size, subsample_size, subsample, use_cuda, device\n        )\n        self.subsample_size = subsample_size\n        self._full_size = size\n        self._scale = float(size) / subsample_size\n        # initialize other things last\n        super().__init__(name, subsample_size, dim, indices)\n\n    def _pyro_sample(self, msg):\n        super()._pyro_sample(msg)\n        msg[\"scale\"] = msg[\"scale\"] * self._scale\n\n    def _pyro_param(self, msg):\n        super()._pyro_param(msg)\n        msg[\"scale\"] = msg[\"scale\"] * self._scale\n\n    def _subsample_site_value(self, value, event_dim=None):\n        if (\n            self.dim is not None\n            and event_dim is not None\n            and self.subsample_size < self._full_size\n        ):\n            event_shape = value.shape[len(value.shape) - event_dim :]\n            funsor_value = to_funsor(value, output=funsor.Reals[event_shape])\n            if self.name in funsor_value.inputs:\n                return to_data(funsor_value(**{self.name: self._indices}))\n        return value\n\n    def _pyro_post_param(self, msg):\n        event_dim = msg[\"kwargs\"].get(\"event_dim\")\n        new_value = self._subsample_site_value(msg[\"value\"], event_dim)\n        if new_value is not msg[\"value\"]:\n            if hasattr(msg[\"value\"], \"_pyro_unconstrained_param\"):\n                param = msg[\"value\"]._pyro_unconstrained_param\n            else:\n                param = msg[\"value\"].unconstrained()\n\n            if not hasattr(param, \"_pyro_subsample\"):\n                param._pyro_subsample = {}  # TODO is this going to persist correctly?\n\n            param._pyro_subsample[self.dim - event_dim] = self.indices\n            new_value._pyro_unconstrained_param = param\n            msg[\"value\"] = new_value\n\n    def _pyro_post_subsample(self, msg):\n        event_dim = msg[\"kwargs\"].get(\"event_dim\")\n        msg[\"value\"] = self._subsample_site_value(msg[\"value\"], event_dim)\n\n\nclass PlateMessenger(SubsampleMessenger):\n    \"\"\"\n    Combines new :class:`~IndepMessenger` implementation with existing\n    :class:`pyro.poutine.BroadcastMessenger`. Should eventually be a drop-in\n    replacement for :class:`pyro.plate`.\n    \"\"\"\n\n    def __enter__(self):\n        super().__enter__()\n        return self.indices  # match pyro.plate behavior\n\n    def _pyro_sample(self, msg):\n        super()._pyro_sample(msg)\n        BroadcastMessenger._pyro_sample(msg)\n\n    def __iter__(self):\n        return iter(\n            _SequentialPlateMessenger(\n                self.name, self.size, self._indices.data.squeeze(), self._scale\n            )\n        )\n\n\nclass _SequentialPlateMessenger(Messenger):\n    \"\"\"\n    Implementation of sequential plate. Should not be used directly.\n    \"\"\"\n\n    def __init__(self, name, size, indices, scale):\n        self.name = name\n        self.size = size\n        self.indices = indices\n        self._scale = scale\n        self._counter = 0\n        super().__init__()\n\n    def __iter__(self):\n        with ignore_jit_warnings([(\"Iterating over a tensor\", RuntimeWarning)]), self:\n            self._counter = 0\n            for i in self.indices:\n                self._counter += 1\n                yield i if isinstance(i, Number) else i.item()\n\n    def _pyro_sample(self, msg):\n        frame = CondIndepStackFrame(self.name, None, self.size, self._counter)\n        msg[\"cond_indep_stack\"] = (frame,) + msg[\"cond_indep_stack\"]\n        msg[\"scale\"] = msg[\"scale\"] * self._scale\n\n    def _pyro_param(self, msg):\n        frame = CondIndepStackFrame(self.name, None, self.size, self._counter)\n        msg[\"cond_indep_stack\"] = (frame,) + msg[\"cond_indep_stack\"]\n        msg[\"scale\"] = msg[\"scale\"] * self._scale\n\n\nclass VectorizedMarkovMessenger(NamedMessenger):\n    \"\"\"\n    Construct for Markov chain of variables designed for efficient elimination of Markov\n    dimensions using the parallel-scan algorithm. Whenever permissible,\n    :class:`~pyro.contrib.funsor.vectorized_markov` is interchangeable with\n    :class:`~pyro.contrib.funsor.markov`.\n\n    The for loop generates both :class:`int` and 1-dimensional :class:`torch.Tensor` indices:\n    :code:`(0, ..., history-1, torch.arange(0, size-history), ..., torch.arange(history, size))`.\n    :class:`int` indices are used to initiate the Markov chain and :class:`torch.Tensor` indices\n    are used to construct vectorized transition probabilities for efficient elimination by\n    the parallel-scan algorithm.\n\n    When ``history==0`` :class:`~pyro.contrib.funsor.vectorized_markov` behaves\n    similar to :class:`~pyro.contrib.funsor.plate`.\n\n    After the for loop is run, Markov variables are identified and then the ``step``\n    information is constructed and added to the trace. ``step`` informs inference algorithms\n    which variables belong to a Markov chain.\n\n    .. code-block:: py\n\n        data = torch.ones(3, dtype=torch.float)\n\n        def model(data, vectorized=True):\n\n            init = pyro.param(\"init\", lambda: torch.rand(3), constraint=constraints.simplex)\n            trans = pyro.param(\"trans\", lambda: torch.rand((3, 3)), constraint=constraints.simplex)\n            locs = pyro.param(\"locs\", lambda: torch.rand(3,))\n\n            markov_chain = \\\\\n                pyro.vectorized_markov(name=\"time\", size=len(data), dim=-1) if vectorized \\\\\n                else pyro.markov(range(len(data)))\n            for i in markov_chain:\n                x_curr = pyro.sample(\"x_{}\".format(i), dist.Categorical(\n                    init if isinstance(i, int) and i < 1 else trans[x_prev]),\n\n                pyro.sample(\"y_{}\".format(i),\n                            dist.Normal(Vindex(locs)[..., x_curr], 1.),\n                            obs=data[i])\n                x_prev = x_curr\n\n        #  trace.nodes[\"time\"][\"value\"]\n        #  frozenset({('x_0', 'x_slice(0, 2, None)', 'x_slice(1, 3, None)')})\n        #\n        #  pyro.vectorized_markov trace\n        #  ...\n        #  Sample Sites:\n        #      locs dist               | 3\n        #          value               | 3\n        #       log_prob               |\n        #       x_0 dist               |\n        #          value     3 1 1 1 1 |\n        #       log_prob     3 1 1 1 1 |\n        #       y_0 dist     3 1 1 1 1 |\n        #          value               |\n        #       log_prob     3 1 1 1 1 |\n        #  x_slice(1, 3, None) dist   3 1 1 1 1 2 |\n        #          value 3 1 1 1 1 1 1 |\n        #       log_prob 3 3 1 1 1 1 2 |\n        #  y_slice(1, 3, None) dist 3 1 1 1 1 1 2 |\n        #          value             2 |\n        #       log_prob 3 1 1 1 1 1 2 |\n        #\n        #  pyro.markov trace\n        #  ...\n        #  Sample Sites:\n        #      locs dist             | 3\n        #          value             | 3\n        #       log_prob             |\n        #       x_0 dist             |\n        #          value   3 1 1 1 1 |\n        #       log_prob   3 1 1 1 1 |\n        #       y_0 dist   3 1 1 1 1 |\n        #          value             |\n        #       log_prob   3 1 1 1 1 |\n        #       x_1 dist   3 1 1 1 1 |\n        #          value 3 1 1 1 1 1 |\n        #       log_prob 3 3 1 1 1 1 |\n        #       y_1 dist 3 1 1 1 1 1 |\n        #          value             |\n        #       log_prob 3 1 1 1 1 1 |\n        #       x_2 dist 3 1 1 1 1 1 |\n        #          value   3 1 1 1 1 |\n        #       log_prob 3 3 1 1 1 1 |\n        #       y_2 dist   3 1 1 1 1 |\n        #          value             |\n        #       log_prob   3 1 1 1 1 |\n\n    .. warning::  This is only valid if there is only one Markov\n        dimension per branch.\n\n    :param str name: A unique name of a Markov dimension to help inference algorithm\n        eliminate variables in the Markov chain.\n    :param int size: Length (size) of the Markov chain.\n    :param int dim: An optional dimension to use for this Markov dimension.\n        If specified, ``dim`` should be negative, i.e. should index from the\n        right. If not specified, ``dim`` is set to the rightmost dim that is\n        left of all enclosing :class:`~pyro.contrib.funsor.plate` contexts.\n    :param int history: Memory (order) of the Markov chain. Also the number\n        of previous contexts visible from the current context. Defaults to 1.\n        If zero, this is similar to :class:`~pyro.contrib.funsor.plate`.\n    :return: Returns both :class:`int` and 1-dimensional :class:`torch.Tensor` indices:\n        ``(0, ..., history-1, torch.arange(size-history), ..., torch.arange(history, size))``.\n    \"\"\"\n\n    def __init__(self, name=None, size=None, dim=None, history=1):\n        self.name = name\n        self.size = size\n        self.dim = dim\n        self.history = history\n        super().__init__()\n\n    @staticmethod\n    @effectful(type=\"markov_chain\")\n    def _markov_chain(name=None, markov_vars=set(), suffixes=list()):\n        \"\"\"\n        Constructs names of markov variables in the `chain`\n        from markov_vars prefixes and suffixes.\n\n        :param str name: The name of the markov dimension.\n        :param set markov_vars: Markov variable name markov_vars.\n        :param list suffixes: Markov variable name suffixes.\n            (`0, ..., history-1, torch.arange(0, size-history), ..., torch.arange(history, size)`)\n        :return: step information\n        :rtype: frozenset\n        \"\"\"\n        chain = frozenset(\n            {\n                tuple(\"{}{}\".format(var, suffix) for suffix in suffixes)\n                for var in markov_vars\n            }\n        )\n        return chain\n\n    def __iter__(self):\n        self._auxiliary_to_markov = {}\n        self._markov_vars = set()\n        self._suffixes = []\n        for self._suffix in range(self.history):\n            self._suffixes.append(self._suffix)\n            yield self._suffix\n        with self:\n            with IndepMessenger(\n                name=self.name, size=self.size - self.history, dim=self.dim\n            ) as time:\n                time_indices = [time.indices + i for i in range(self.history + 1)]\n                time_slices = [\n                    slice(i, self.size - self.history + i)\n                    for i in range(self.history + 1)\n                ]\n                self._suffixes.extend(time_slices)\n                for self._suffix, self._indices in zip(time_slices, time_indices):\n                    yield self._indices\n        self._markov_chain(\n            name=self.name, markov_vars=self._markov_vars, suffixes=self._suffixes\n        )\n\n    def _pyro_sample(self, msg):\n        if type(msg[\"fn\"]).__name__ == \"_Subsample\":\n            return\n        BroadcastMessenger._pyro_sample(msg)\n        # replace tensor suffix with a nice slice suffix\n        if isinstance(self._suffix, slice):\n            assert msg[\"name\"].endswith(str(self._indices))\n            msg[\"name\"] = msg[\"name\"][: -len(str(self._indices))] + str(self._suffix)\n        if str(self._suffix) != str(self._suffixes[-1]):\n            # _do_not_score: record these sites when tracing for use with replay,\n            # but do not include them in ELBO computation.\n            msg[\"infer\"][\"_do_not_score\"] = True\n            # map auxiliary var to markov var name prefix\n            # assuming that site name has a format: \"markov_var{}\".format(_suffix)\n            # is there a better way?\n            markov_var = msg[\"name\"][: -len(str(self._suffix))]\n            self._auxiliary_to_markov[msg[\"name\"]] = markov_var\n\n    def _pyro_post_sample(self, msg):\n        \"\"\"\n        At the last step of the for loop identify markov variables.\n        \"\"\"\n        if type(msg[\"fn\"]).__name__ == \"_Subsample\":\n            return\n        # if last step in the for loop\n        if str(self._suffix) == str(self._suffixes[-1]):\n            funsor_log_prob = (\n                msg[\"funsor\"][\"log_prob\"]\n                if \"log_prob\" in msg.get(\"funsor\", {})\n                else to_funsor(msg[\"fn\"].log_prob(msg[\"value\"]), output=funsor.Real)\n            )\n            # for auxiliary sites in the log_prob\n            for name in set(funsor_log_prob.inputs) & set(self._auxiliary_to_markov):\n                # add markov var name prefix to self._markov_vars\n                markov_var = self._auxiliary_to_markov[name]\n                self._markov_vars.add(markov_var)\n","repo_name":"pyro-ppl/pyro","sub_path":"pyro/contrib/funsor/handlers/plate_messenger.py","file_name":"plate_messenger.py","file_ext":"py","file_size_in_byte":15139,"program_lang":"python","lang":"en","doc_type":"code","stars":8201,"dataset":"github-code","pt":"86"}
+{"seq_id":"5906400385","text":"import pytest\n\nfrom pytest_factoryboy import register\n\nfrom src.trades.tests.factories import (\n    StockFactory,\n    StockTradeFactory,\n)\n\nregister(StockFactory)\nregister(StockTradeFactory)\n\n\n@pytest.fixture\ndef stock_in_db(db, stock):\n    db.session.add(stock)\n    db.session.commit()\n    return stock\n","repo_name":"Marek-Chalabis/Stock-buyer-microservices","sub_path":"reporter/backend/src/trades/tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"39056542643","text":"\"\"\"Scrapes Data from PVOUTPUT\"\"\"\nimport configparser\nimport os\nfrom pathlib import Path\nimport requests\nfrom bs4 import BeautifulSoup as bs\nimport pandas as pd\n\nfrom definitions import CONFIG_PATH\n\n\ndef main():\n    save_table_as_csv()\n\ncfg = configparser.ConfigParser()\ncfg.read(CONFIG_PATH)\nOUTPUT_DIR = cfg.get(\"PROJECT\", \"output_dir\")\n\n#creating a set of webpages\nw = [\n    'https://pvoutput.org/aggregate.jsp?id=74611&sid=71729&v=0&t=m', \n    'https://pvoutput.org/list.jsp?id=51500&sid=46834', \n    'https://pvoutput.org/list.jsp?id=53465&sid=48697', \n    'https://pvoutput.org/list.jsp?id=53465&sid=48699', \n    'https://pvoutput.org/list.jsp?id=84471&sid=74894', \n    'https://pvoutput.org/list.jsp?id=84471&sid=77747', \n    'https://pvoutput.org/list.jsp?id=84471&sid=77748', \n    'https://pvoutput.org/list.jsp?id=53465&sid=48698', \n    'https://pvoutput.org/list.jsp?id=53465&sid=48702', \n    'https://pvoutput.org/list.jsp?id=53465&sid=48701'\n]\n\n #create new folder: PVs\nif not os.path.exists('PVs'):\n    os.makedirs('PVs')\n\n# downloading the contents of the webpage\nfor x in w:\n    r = requests.get(x)\n\n    # creating a beautiful soup object\n    soup = bs(r.content, 'html5lib')\n    \n    # getting table\n    table = soup.find('table', id = 'tb')\n\n    # creating dataframe\n    df_list = pd.read_html(str(table))\n\n    # getting name\n    name = soup.find('title')\n    filename = (name.string + \".csv\").replace(\" \", \"_\")\n    filename = filename.replace(\"|\", \"\")\n    for df in df_list:\n        if not os.path.exists(os.path.join(OUTPUT_DIR, \"PVs\")):\n            os.mkdir(os.path.join(OUTPUT_DIR, \"PVs\"))\n        df.to_csv(os.path.join(OUTPUT_DIR, \"PVs\", filename))\n\ndef get_filename_from_title(soup):\n    \"\"\"Returns the name of the pv system,\"\"\"\n    name = soup.find(\"title\")\n    filename = (name.string + \".csv\").replace(\" \", \"_\")\n    filename = filename.replace(\"|\", \"\")\n    return filename\n\ndef df_to_csv(df, filename):\n    \"\"\"\n    Saves the df as the specified filename in the output directory.\n    \n    Parameters\n    ----------\n    df: pd.DataFrame\n    \n    filename: str\n        name with which the csv file is to be saved,\n    \"\"\"\n    if not os.path.exists(os.path.join(OUTPUT_DIR, \"PVs\")):\n        os.mkdir(os.path.join(OUTPUT_DIR, \"PVs\"))\n    df.to_csv(os.path.join(OUTPUT_DIR, \"PVs\", filename))\n\ndef save_table_as_csv():\n    \"\"\"\n    Reads the table in the html file and saves it as csv file.\n    \"\"\"\n    counter = 0\n    soup = bs(request.content, \"html5lib\")\n    # maybe I should use a try except block instead\n    # url should have a way of passing the page number using counter\n    filename = get_filename_from_title(soup)\n\n    while True:\n        response = request.get(\"https://example.com\") # add url variable\n        if response.status_code == 200:\n            table = soup.find(\"table\", id=\"tb\")\n            if df:\n                df.append(pd.read_html(str(table)))\n            else:\n                df = pd.read_html(str(table))\n        else:\n            break\n        counter += 1\n    df_to_csv(df, filename)","repo_name":"KNnaobiV/PV_Data","sub_path":"pvoutput_scrapper.py","file_name":"pvoutput_scrapper.py","file_ext":"py","file_size_in_byte":3036,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"7103305068","text":"from search_method import Search\nimport time\nimport os\nimport configparser\nimport shutil\nimport sys\n\ncurPath = os.path.abspath(os.path.dirname(__file__))\nrootPath = os.path.split(curPath)[0]\nsys.path.append(os.path.split(rootPath)[0])\n\nif sys.platform.startswith('win32'):\n    ROOT='\\\\'\nelif sys.platform.startswith('linux'):\n    ROOT='/'\nelif sys.platform.startswith('darwin'):\n    ROOT='/'\ndef user_input():\n    print('\\n')\n    text1 = \"1. Check the template of the configuration file.(.ini file)\"\n    print(text1)\n    print(\"\\n\")\n    text2 = \"2. Check the example of the configuration file.(.ini file)\"\n    print(text2)\n    print('\\n')\n    text3 = \"3. Upload your configuration.(.ini file)\"\n    print(text3)\n    print('\\n')\n    text4 = \"4. Exit.\"\n    print(text4)\n    print('\\n')\n    print(\"Your choice?[1/2/3/4]\",end=' ')\n    user_choice = input()\n    print('\\n')\n    return user_choice\n\ndef template_file():\n    print(\"The template is shown below:\")\n    program = '''\n[program]\nroot= \n;(Required)\n;Description: The absolute root of your quantum program file.\nnum_qubit= \n;(Required)\n;Description: The total number of qubit of your quantum program.\ninputID= \n;(Required)\n;Description: The IDs of input qubits.\n;Format: A non-repeating sequence separated by commas.\noutputID= \n;(Required)\n;Description: The IDs of output qubits which are the qubits to be measured.\n;Format: A non-repeating sequence separated by commas.\n    '''\n    qusbt_configuration = '''\n[qusbt_configuration]\nbeta=\n;(Optional)\n;Description: The percentage of possible inputs as the number of test cases in a test suite.\nM=\n;(Optional)\n;Description: The number of test cases in a test suite.\n;Attention: You should use either 'beta' or 'M'. We use 'beta' as 0.05 by default.\n'''\n    GA_parameter = '''\n[GA_parameter]\npopulation_size= \n;(Optional) \n;Description: The population size in GA, population_size=10 by default.\noffspring_population_size= \n;(Optional)\n;Description: The offspring population size in GA, offspring_population_size=10 by default.\nmax_evaluations=\n;(Optional)\n;Description: The maximum evaluations in GA, max_evaluations=500 by default.\nmutation_probability=\n;(Optional)\n;Description: mutation probability in GA, mutation_probability=1.0/M, 'M' is the size of a test suite by default.\nmutation_distribution_index=\n;(Optional)\n;Description: mutation distribution in GA, mutation_distribution_index=20 by default.\ncrossover_probability=\n;(Optional)\n;Description: crossover probability in GA, crossover_probability=0.9 by default.\ncrossover_distribution_index=\n;(Optional)\n;Description: crossover probability in GA, crossover_distribution_index=20 by default.\n'''\n    program_specification = '''\n[program_specification]\n;Description: The program specification.\n;Format:input string (binary),output string (binary)=probability\n;Example:\n;00,1=0.5\n;00,0=0.5\n;01,1=0.5\n;01,0=0.5\n;or\n;0-,-=0.5\n;Attention: '-' can refer to both '0' and '1'.\n'''\n    print(program)\n    print(qusbt_configuration)\n    print(GA_parameter)\n    print(program_specification)\n    print('\\n')\n\ndef example_file():\n    print(\"The example is shown below:\")\n    example = '''\n[program]\nroot=C:\\IQ.py\nnum_qubit=10\ninputID=0,1,2,3,4,5,6,7,8,9\noutputID=0,1,2,3,4,5,6,7,8,9\n\n[qusbt_configuration]\nbeta=0.05\nconfidence_level=0.01\n\n[GA_parameter]\npopulation_size=10\noffspring_population_size=10\nmax_evaluations=500  \n\n[program_specification]\n----------,----------=0.0009765625\n'''\n    print(example)\n\ndef dec2bin(value, n):\n    a = 1\n    list = []\n    while a > 0:\n        a, b = divmod(value, 2)\n        list.append(str(b))\n        value = a\n    s = \"\"\n    for i in range(len(list) - 1, -1, -1):\n        s += str(list[i])\n    s = s.zfill(n)\n    return s\n\ndef expand_ps(ps,inputID,outputID):#check and expand\n    flag = True\n    for i in range(len(ps)):\n        pair = ps[i][0].split(',')\n        if len(pair[0]) != len(inputID) or len(pair[1]) != len(outputID):\n\n            flag = False\n            break\n        for j in range(len(inputID)):\n            if ps[i][0][j] != '0' and ps[i][0][j] != '1' and ps[i][0][j] != '-':\n                flag = False\n        for j in range(len(inputID)+1, len(inputID) + len(outputID) + 1):\n            if ps[i][0][j] != '0' and ps[i][0][j] != '1' and ps[i][0][j] != '-':\n                flag = False\n    if flag == False:\n        print(\"Error: The format of the program specification is wrong.\")\n        print(\"Example: 00-1,01=0.5\")\n        end_running()\n    else:\n        l = len(inputID) + len(outputID) + 1\n        length_origin = len(ps)\n        items = []\n        ps_new = []\n\n        for i in range(length_origin):\n            if ps[i][0].find('-',0,len(ps[i][0])) != -1:\n                mark = []\n                item = ps[i]\n                items.append(item)\n                for j in range(l):\n                    if ps[i][0][j] == '-':\n                        mark.append(j)\n                for j in range(pow(2,len(mark))):\n                    t=dec2bin(j,len(mark))\n                    temp = list(item[0])\n                    for k in range(len(mark)):\n                        temp[mark[k]] = t[k]\n                    temp = ''.join(temp)\n                    temp_item = (temp,item[1])\n                    ps_new.append(temp_item)\n        if len(items) == 0:\n            return ps\n        else:\n            for i in range(len(items)):\n                ps.remove(items[i])\n            ps += ps_new\n            return ps\n\ndef check_configuration_file(config):\n    if config.has_section('program') == False:\n        print(\"Error: QuSBT cannot find section 'program' in this configuration file.\")\n        end_running()\n    else:\n        if config.has_option('program', 'root') == False:\n            print(\"Error: QuSBT cannot find the root of the quantum program file.\")\n            end_running()\n        if config.has_option('program', 'num_qubit') == False:\n            print(\"Error: QuSBT cannot find the number of qubits of the program.\")\n            end_running()\n        if config.has_option('program', 'inputID') == False:\n            print(\"Error: QuSBT cannot find the input IDs of the program.\")\n            end_running()\n        if config.has_option('program', 'outputID') == False:\n            print(\"Error: QuSBT cannot find the output IDs of the program.\")\n            end_running()\n\n    if config.has_section('program_specification') == False:\n        print(\"Error: QuSBT cannot find section 'program_spacification' in this configuration file.\")\n        end_running()\n\n\n\ndef check_unique(l):\n    return len(l) == len(set(l))\n\ndef check_unique_pair(ps):\n    pair = []\n    for i in range(len(ps)):\n        pair.append(ps[i][0])\n    return len(pair) == len(set(pair))\n\ndef check_inputID_outputID(num_qubit, inputID, outputID):\n    if check_unique(inputID) == False:\n        print(\"Wrong format of 'inputID'.\")\n        end_running()\n    if check_unique(outputID) == False:\n        print(\"Wrong format of 'outputID'.\")\n        end_running()\n    try:\n        inputID = [int(i) for i in inputID]\n    except:\n        print(\"Wrong format of 'inputID'.\")\n        end_running()\n    try:\n        outputID = [int(i) for i in outputID]\n    except:\n        print(\"Wrong format of 'outputID'.\")\n        end_running()\n    inputID.sort()\n    outputID.sort()\n\n    if int(inputID[-1]) > num_qubit - 1:#the last one\n        print(\"Wrong input IDs\")\n        end_running()\n    if int(inputID[-1]) > num_qubit - 1:\n        print(\"Wrong output IDs\")\n        end_running()\n\n    return inputID, outputID\n\ndef check_bin(bin_str, n):\n    if len(bin_str) != n:\n        print(\"Error: The format of the program specification is wrong.\")\n        end_running()\n   # print(\"check bin: \"+str(bin_str))\n    for i in range(len(bin_str)):\n        if bin_str[i] != '0' and bin_str[i] != '1':\n            print(\"Error: The format of the program specification is wrong.\")\n            end_running()\n\ndef input_group(valid_input):\n    index = [] #unique input index\n    index_flag = valid_input[0]\n    index.append(0)\n    for i in range(1,len(valid_input)):\n        if valid_input[i] != index_flag:\n            index.append(i)\n            index_flag = valid_input[i]\n    return index\n\ndef check_full_ps(valid_input, p):\n    index = input_group(valid_input)\n    p_sum = 0\n    for i in range(len(index)):\n        start = index[i]\n        if i == len(index) - 1:\n            end = len(valid_input)\n        else:\n            end = index[i+1]\n        for j in range(start,end):\n            p_sum += p[j]\n        if p_sum != 1:\n            print(\"Error: This is not a complete program specification.\")\n            end_running()\n        else:\n            p_sum = 0\n\ndef end_running():\n    exit()\n\nif __name__ == '__main__':\n    # warnings.filterwarnings('ignore')\n    print(\"\".center(60,\"=\"))\n    print('\\n')\n    print(\"Welcome to QuSBT\".center(60))\n    print('\\n')\n    print(\"\".center(60,\"=\"))\n    user_choice = user_input()\n    while user_choice != '3' and user_choice != '4':\n        if user_choice != '1' and user_choice != '2' and user_choice != '4':\n            print(\"Error: Please type in '1', '2', '3' or '4'.\")\n            user_choice = user_input()\n        elif user_choice == '1':\n            template_file()\n            user_choice = user_input()\n        elif user_choice == '2':\n            example_file()\n            user_choice = user_input()\n    if user_choice == '3':\n        print(\"please enter the root of your configuration file.(.ini file)\")\n\n        #get configuration file\n        root_con_string = input()\n        root_con = root_con_string\n        START = time.time()\n\n        if os.path.isfile(root_con) == True:\n            config = configparser.ConfigParser(allow_no_value=True)\n            config.read(root_con, encoding='utf-8')\n        else:\n            print(\"Error: QuSBT cannot find the configuration file.\")\n            end_running()\n\n        check_configuration_file(config)\n\n        # get quantum program\n        root = config.get('program', 'root')\n        if os.path.isfile(root) != True:\n            print(\"Error: QuSBT cannot find the quantum program file.\")\n            end_running()\n\n        root_list = root.split(ROOT)\n        program_file = root_list[len(root_list) - 1]\n        program_folder = root_list[:len(root_list) - 1]\n        program_folder = ROOT.join(str(i) for i in program_folder)\n        sys.path.append(program_folder)\n        # print(program_file.split('.')[0])\n        module_name = program_file.split('.')[0]\n\n        # get inputID, outputID and numner of qubits\n        try:\n            num_qubit = int(config.get('program', 'num_qubit'))\n        except:\n            print(\"Error: The data type of 'num_qubit' should be an Integer\")\n            end_running()\n        inputID_o = config.get('program', 'inputID').split(',')\n        outputID_o = config.get('program', 'outputID').split(',')\n        inputID, outputID = check_inputID_outputID(num_qubit, inputID_o, outputID_o)\n\n        population_size = 10\n        offspring_population_size = 10\n        max_evaluations = 500\n        if config.get('GA_parameter', 'population_size') != None:\n            try:\n                population_size = int(config.get('GA_parameter', 'population_size'))\n            except:\n                print(\"Error: The data type of 'population_size' should be an Integer\")\n                end_running()\n        if config.get('GA_parameter', 'offspring_population_size') != None:\n            try:\n                offspring_population_size = int(config.get('GA_parameter', 'offspring_population_size'))\n            except:\n                print(\"Error: The data type of 'off_population_size' should be an Integer\")\n                end_running()\n\n        if config.get('GA_parameter', 'max_evaluations') != None:\n            try:\n                max_evaluations = int(config.get('GA_parameter', 'max_evaluations'))\n            except:\n                print(\"Error: The data type of 'max_evaluations' should be an Integer\")\n                end_running()\n\n        if os.path.isfile(root) != True:\n            print(\"Error: QuSBT cannot find the quantum program file.\")\n            end_running()\n\n        M_flag = False\n        beta = 0\n        if config.has_option('qusbt_configuration','M') == True:\n            M_flag == True\n            try:\n                M = int(config.get('qusbt_configuration','M'))\n            except:\n                print(\"Error: The data type of 'M' should be an Integer.\")\n                end_running()\n            if config.has_option('qusbt_configuration', 'beta') == True:\n                print(\"Error: You should use either 'M' or 'beta'.\")\n                end_running()\n        elif config.has_option('qusbt_configuration','beta') == True:\n            try:\n                beta = float(config.get('qusbt_configuration','beta'))\n            except:\n                print(\"Error: The data type of 'beta' should be a Float.\")\n                end_running()\n        else:\n            beta = 0.05\n\n        confidence_level = 0.01\n        if config.has_option('qusbt_configuration', 'confidence_level') == True:\n            try:\n                confidence_level = float(config.get('qusbt_configuration', 'confidence_level'))\n            except:\n                print(\"Error: The data type of 'confidence_level' should be a Float.\")\n                end_running()\n\n        mutation_probability_flag = False\n        if config.has_option('GA_configuration', 'mutation_probability') == True:\n            mutation_probability_flag = True\n            try:\n                mutation_probability = float(config.get('GA_configuration', 'crossover_probability'))\n            except:\n                print(\"Error: The data type of 'mutation_probability' should be a Float.\")\n                end_running()\n            if mutation_probability >= 1:\n                print(\"Error: The value of 'mutation_probability' should be a Float smaller than 1.0.\")\n                end_running()\n\n        mutation_distribution_index = 20\n        if config.has_option('GA_configuration', 'mutation_distribution_index') == True:\n            try:\n                mutation_distribution_index = int(config.get('GA_configuration', 'mutation_distribution_index'))\n            except:\n                print(\"Error: The data type of 'mutation_distribution_index' should be an Integer.\")\n                end_running()\n\n        crossover_distribution_index = 20\n        if config.has_option('GA_configuration', 'crossover_distribution_index') == True:\n            try:\n                crossover_distribution_index = int(config.get('GA_configuration', 'crossover_distribution_index'))\n            except:\n                print(\"Error: The data type of 'crossover_distribution_index' should be an Integer.\")\n                end_running()\n\n        crossover_probability = 0.9\n        if config.has_option('GA_configuration', 'crossover_probability') == True:\n            try:\n                crossover_probability = float(config.get('GA_configuration', 'crossover_probability'))\n            except:\n                print(\"Error: The data type of 'crossover_probability' should be a Float.\")\n                end_running()\n            if crossover_probability >= 1:\n                print(\"Error: The value of 'crossover_probability' should be a Float smaller than 1.0.\")\n                end_running()\n\n\n        #PS\n        valid_input = []\n        valid_output = []\n        p = []\n        ps = config.items('program_specification')\n        ps = expand_ps(ps, inputID, outputID)\n\n        # print(ps)\n        #print(\"origin:\"+str(ps))\n        if check_unique_pair(ps) == False:\n            print(\"There are duplicate input-output pairs in the program specification.\")\n            end_running()\n        #sort PS according to input and output\n        ps.sort(key=lambda x:x[0])\n        #print(\"new:\"+str(ps))\n        for i in range(len(ps)):\n            valid_input_item = ps[i][0][:len(inputID)]\n            valid_output_item = ps[i][0][len(inputID)+1:]\n            check_bin(valid_input_item,len(inputID))\n            check_bin(valid_output_item,len(outputID))\n            valid_input.append(valid_input_item)\n            valid_output.append(valid_output_item)\n            p.append(float(ps[i][1]))\n        check_full_ps(valid_input, p)\n\n        config_dic = {}\n        config_dic['program_folder'] = program_folder\n        config_dic['root'] = root_con_string\n        config_dic['module_name'] = module_name\n        config_dic['num_qubit'] = num_qubit\n        config_dic['inputID'] = inputID\n        config_dic['outputID'] = outputID\n        if M_flag == True:\n            config_dic['M'] = M\n        else:\n            config_dic['beta'] = beta\n        config_dic['confidence_level'] = confidence_level\n        config_dic['population_size'] = population_size\n        config_dic['offspring_population_size'] = offspring_population_size\n        config_dic['max_evaluations'] = max_evaluations\n        config_dic['valid_input'] = valid_input\n        config_dic['valid_output'] = valid_output\n        config_dic['p'] = p\n        config_dic['crossover_probability'] = crossover_probability\n        config_dic['crossover_distribution_index'] = crossover_distribution_index\n        if mutation_probability_flag == True:\n            config_dic['mutation_probability'] = mutation_probability\n        config_dic['mutation_distribution_index'] = mutation_distribution_index\n        Search.search(config_dic)\n        # Search.search(root, module_name, alg, len(inputID), beta)\n        END = time.time()\n        T1 = END-START\n\n    elif user_choice == '4':\n        print(\"Exit :D\")\n","repo_name":"Simula-COMPLEX/qusbt-tool","sub_path":"QuSBT/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":17554,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"86"}
+{"seq_id":"38668397273","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\nsetuptools.setup(\n    name='automate_finance',\n    version='0.01',\n    author=\"Anna Koretchko\",\n    author_email='annakoretchko@gmail.com',\n    description='Automating finance tasks and strategy',\n    packages=setuptools.find_packages(),\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"Operating System :: OS Independent\",\n    ],\n    python_requires='>=3.6',\n)","repo_name":"annakoretchko/algo_trading","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"35764493856","text":"import time\nimport os\nimport sys\nimport io\nimport sip\nimport six\nimport logging\nimport signal\nfrom urllib import parse\nfrom uuid import uuid4\nfrom PyQt4.QtCore import *\nfrom loggers import loggers\nfrom PyQt4.QtGui import *\n# from PyQt4.QtWidgets import *\n# from PyQt4.QtWebKitWidgets import *\nfrom PyQt4.QtWebKit import *\n# from PyQt4.QtPrintSupport import *\nfrom PyQt4.QtWebKit import QWebSettings\n# from PyQt5.QtWebPage import *\nfrom PyQt4.QtNetwork import *\nfrom reportlab.pdfgen import canvas\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.units import *\nfrom multiprocessing import Queue, Process\nfrom subprocess import call\n\nrenderHints={\n    \"news.mail.ru\":{\n        \"classes\":\"kkmr-close\"\n    },\n    \"adme.ru\":{\n        \"cookies\":{\n            \"social.overlay.fb.status\": \"disabled\",\n            \"social\": \"1\"\n        }\n    }\n\n}\n\nSignal = pyqtSignal\nSlot = pyqtSlot\n# LOG_FILENAME = 'webkit2x.log'\n# logger = logging.getLogger('webkit2x')\n\nclass WebkitRenderer(QObject):\n    def __init__(self,**kwargs):\n        if not QApplication.instance():\n            raise RuntimeError(self.__class__.__name__ + \" requires a running QApplication instance\")\n        QObject.__init__(self)\n\n        # Initialize default properties\n        self.width = kwargs.get('width', 0)\n        self.kind = kwargs.get('kind', 'desktop')\n        self.height = kwargs.get('height', 0)\n        self.dpi = kwargs.get('dpi', 96)\n        self.timeout = kwargs.get('timeout', 0)\n        self.wait = kwargs.get('wait', 0)\n        self.scaleToWidth = kwargs.get('scaleToWidth', 0)\n        self.scaleToHeight = kwargs.get('scaleToHeight', 0)\n        self.scaleRatio = kwargs.get('scaleRatio', 'keep')\n        self.format = kwargs.get('format', 'png')\n        self.logger = kwargs.get('logger', None)\n        self.grabWholeWindow = kwargs.get('grabWholeWindow', False)\n        self.renderTransparentBackground = kwargs.get('renderTransparentBackground', False)\n        self.ignoreAlert = kwargs.get('ignoreAlert', True)\n        self.ignoreConfirm = kwargs.get('ignoreConfirm', True)\n        self.ignorePrompt = kwargs.get('ignorePrompt', True)\n        self.interruptJavaScript = kwargs.get('interruptJavaScript', True)\n        self.encodedUrl = kwargs.get('encodedUrl', False)\n        self.cookies = kwargs.get('cookies', {})\n        self.afterRender=kwargs.get('afterRender',{})\n        self.qWebSettings = {\n            QWebSettings.JavascriptEnabled : True,\n            QWebSettings.PluginsEnabled : True,\n            QWebSettings.PrivateBrowsingEnabled : False,\n            QWebSettings.JavascriptCanOpenWindows : False\n        }\n\n\n    def render(self,res,fileName=\"\"):\n        self.logger.debug(\"create _WebkitRendererHelper\")\n        helper = _WebkitRendererHelper(self)\n        self.logger.debug(\"createed _WebkitRendererHelper\")\n        helper._window.resize(self.width, self.height )\n        result = helper.render(res,fileName)\n        # helper._window.close()\n        return result\n\n## @brief The CookieJar class inherits QNetworkCookieJar to make a couple of functions public.\nclass CookieJar(QNetworkCookieJar):\n    def __init__(self, cookies, qtUrl, parent=None):\n        QNetworkCookieJar.__init__(self, parent)\n        encodedCookies=[]\n        cookies =None\n        if cookies:\n            for cookie in cookies:\n                encodedCookies.append(QNetworkCookie(QByteArray(cookie),QByteArray(cookies[cookie])))\n        QNetworkCookieJar.setCookiesFromUrl(self, encodedCookies, qtUrl)\n\n    def allCookies(self):\n        return QNetworkCookieJar.allCookies(self)\n\n    def setAllCookies(self, cookieList):\n        QNetworkCookieJar.setAllCookies(self, cookieList)\n\nclass _WebkitRendererHelper(QObject):\n\n    def __init__(self, parent):\n        QObject.__init__(self)\n\n        for key,value in parent.__dict__.items():\n            setattr(self,key,value)\n\n        proxy = QNetworkProxy(QNetworkProxy.NoProxy)\n        if 'http_proxy' in os.environ:\n            proxy_url = QUrl(os.environ['http_proxy'])\n            if proxy_url.scheme().startswith('http'):\n                protocol = QNetworkProxy.HttpProxy\n            else:\n                protocol = QNetworkProxy.Socks5Proxy\n\n            proxy = QNetworkProxy(\n                protocol,\n                proxy_url.host(),\n                proxy_url.port(),\n                proxy_url.userName(),\n                proxy_url.password()\n            )\n\n        self._page = CustomWebPage(logger=self.logger, ignore_alert=self.ignoreAlert,\n            ignore_confirm=self.ignoreConfirm, ignore_prompt=self.ignorePrompt,\n            interrupt_js=self.interruptJavaScript,kind=self.kind)\n        self._page.networkAccessManager().setProxy(proxy)\n        self._page.settings().setAttribute(QWebSettings.DeveloperExtrasEnabled, True)\n        self._page.mainFrame().setScrollBarPolicy(Qt.Horizontal, Qt.ScrollBarAlwaysOff)\n        self._page.mainFrame().setScrollBarPolicy(Qt.Vertical, Qt.ScrollBarAlwaysOff)\n        # self._page.kind=self.kind\n        self._view = QWebView()\n        self._view.setPage(self._page)\n        self._window = QMainWindow()\n        self._window.setCentralWidget(self._view)\n        for key, value in self.qWebSettings.items():\n            self._page.settings().setAttribute(key, value)\n\n        self._view.loadFinished.connect(self._on_load_finished)\n        self._view.loadStarted.connect(self._on_load_started)\n        # self._page.networkAccessManager().finished.connect(self._on_each_reply)\n        self._page.networkAccessManager().sslErrors.connect(self._on_ssl_errors)\n\n        self._page.settings().setUserStyleSheetUrl(QUrl(\"data:text/css,html,body{overflow-y:hidden !important;}\"))\n\n        self._window.show()\n\n    def __del__(self):\n        \"\"\"\n        Clean up Qt4 objects.\n        \"\"\"\n        if hasattr(self,\"_window\"):\n            self._window.close()\n            del self._window\n            del self._view\n            del self._page\n\n    def render(self, res, fileName):\n        print(self.width)\n        pdfOnly=True\n        pngOnly=False\n        # if fileName[-3:]==\"pdf\":\n        #     pdfOnly=True\n        # if fileName[-3:]==\"png\":\n        #     pngOnly=True\n        time1=time.time()\n        self._page.setViewportSize(QSize(self.width, self.height))\n        self._load_page(res, self.width, self.height, self.timeout)\n        self.logger.debug(\"загрузка %s\",str(time.time()-time1))\n        time1=time.time()\n        if self.wait > 0:\n            if self.logger: self.logger.debug(\"Waiting %d seconds \" % self.wait)\n            waitToTime = time.time() + self.wait\n            while time.time() < waitToTime:\n                time.sleep(1)\n                if QApplication.hasPendingEvents():\n                    QApplication.processEvents()\n        self.logger.debug(\"подождали %s\",str(time.time()-time1))\n        time1=time.time()\n        # self._page.setFixedWidth(self.width)\n        size = self._page.mainFrame().contentsSize()\n        self.logger.debug(size)\n        newsize=QSize(self.width, size.height())\n        self._page.setViewportSize(size)\n        self._view.resize(size)\n        if fileName and not pdfOnly:\n            image = QPixmap.grabWidget(self._view)\n            qBuffer = QBuffer()\n            image.save(qBuffer, 'png')\n            if os.path.exists(fileName+(\"\" if pngOnly else \".png\")):\n                os.remove(fileName+(\"\" if pngOnly else \".png\"))\n            open(fileName+(\"\" if pngOnly else \".png\"), \"wb\").write(qBuffer.buffer().data())\n\n        # self._window.resize(newsize)\n        if pngOnly:\n            return fileName\n\n        painter = QPainter()\n        # painter.setDevicePixelRatio(3)\n        self._printer = QPrinter(QPrinter.ScreenResolution)\n        self._printer.setPaperSize(QSizeF(size),QPrinter.DevicePixel)\n        self._printer.setOutputFormat(QPrinter.PdfFormat)\n        # self._printer.printEngine.PPK_ImageQuality=80\n        self._printer.setOrientation(QPrinter.Portrait)\n        # self._printer.setResolution(self.winDpi)\n        self._printer.setFullPage(True)\n\n        self._printer.setFontEmbeddingEnabled(True)\n        self._printer.setPageMargins(0,0,0,0,QPrinter.Millimeter)\n        tmpFileName=str(uuid4())\n        self._printer.setOutputFileName(tmpFileName)\n        self._printer.setCreator(\"MagicFeed\")\n        r=False\n        x=0\n        while not r:\n            if x>10:\n                QApplication.exit()\n                return None\n            print(x)\n            x+=1\n            r=painter.begin(self._printer)\n            time.sleep(1)\n        self.logger.debug(\"рендер начат  %s\",str(time.time()-time1))\n        time1=time.time()\n        self._page.mainFrame().render(painter)\n        self.logger.debug(\"рендер окончен  %s\",str(time.time()-time1))\n        time1=time.time()\n        # if self.wait > 0:\n        #     if self.logger: self.logger.debug(\"Waiting %d seconds \" % self.wait)\n        #     waitToTime = time.time() + self.wait\n        #     while time.time() < waitToTime:\n        #         time.sleep(1)\n        #         if QApplication.hasPendingEvents():\n        #             QApplication.processEvents()\n        painter.save()\n        painter.end()\n\n        links=[]\n        location=self._page.mainFrame().baseUrl()\n        self.logger.debug(\"поиск ссылок  %s\",str(time.time()-time1))\n        time1=time.time()\n        linkElements = self._page.mainFrame().findAllElements(\"a\")\n        for link in linkElements:\n            if(link.geometry().width() and link.geometry().height()):\n                rect=(link.geometry().bottomLeft().x()*inch/96,link.geometry().bottomLeft().y()*inch/96,\n                      link.geometry().width()*inch/96,link.geometry().height()*inch/96)\n                url=link.attribute(\"href\")\n                if url and url[:10]!=\"javascript\":\n                    if len(url)>1 and url[0]==\"/\" and url[1]==\"/\":\n                        url=\"http:\"+url\n                    elif url[0]==\"/\":\n                        # print(location.host()+url)\n                        url=location.scheme()+\"://\"+location.host()+url\n                    links.append((url,rect,\"link\"))\n\n        linkElements = self._page.mainFrame().findAllElements(\"iframe\")\n        for link in linkElements:\n            if(link.geometry().width() and link.geometry().height()):\n                rect=(link.geometry().bottomLeft().x()*inch/96,link.geometry().bottomLeft().y()*inch/96,\n                      link.geometry().width()*inch/96,link.geometry().height()*inch/96)\n                url=link.attribute(\"src\")\n                if url:\n                    if len(url)>1 and url[0]==\"/\" and url[1]==\"/\":\n                        url=\"http:\"+url\n                    elif url[0]==\"/\":\n                        # print(location.host()+url)\n                        url=location.scheme()+\"://\"+location.host()+url\n                    links.append((url,rect,\"youtube\"))\n        if self._window:\n            self._window.close()\n        del self._window\n        del self._view\n        del self._page\n\n        self.logger.debug(\"добавление ссылок  %s\",str(time.time()-time1))\n        time1=time.time()\n        result = self.add_links((size.width()*inch/96,size.height()*inch/96),links,tmpFileName,fileName)\n        # print(tmpFileName)\n        try:\n            os.remove(tmpFileName)\n        except Exception as err:\n            self.logger.debug(err)\n        self.logger.debug(\"готово  %s\",str(time.time()-time1))\n        return result\n\n    def add_links(self, size, links,inputFileName, fileName=None):\n        stream = open(inputFileName+\".links\",\"wb\")\n        newCanvas = canvas.Canvas(stream, pagesize=(size[0],size[1]),bottomup=1,pageCompression=1,verbosity=0)\n        time1=time.time()\n        for url,rect,kind in links:\n            if rect[0]>=0 and rect[1]>=0 and rect[2]>0 and rect[3]>0:\n                newCanvas.linkURL(url=url, rect=(rect[0],size[1]-rect[1],rect[0]+rect[2],size[1]-rect[1]+rect[3]), relative=0)\n        self.logger.debug(\"Ссылки  %s\",str(time.time()-time1))\n        time1=time.time()\n        newCanvas.rect(0,0,1,1)\n        newCanvas.save()\n        stream.close()\n        # stream.seek(0)\n        # new_pdf = pyPdf.PdfFileReader(stream,overwriteWarnings=True)\n        # existing_pdf = pyPdf.PdfFileReader(open(inputFileName, \"rb\"),overwriteWarnings=True)\n        # output = pyPdf.PdfFileWriter()\n        # page = existing_pdf.getPage(0)\n        # page.mergePage(new_pdf.getPage(0))\n        # page.compressContentStreams()\n        # output.addPage(page)\n        # if fileName:\n        fileExt=\"\" #\".pdf\" if fileName[-3:]!=\"pdf\" else \"\"\n        # outputStream = open(fileName+fileExt+\".tmp\", \"wb\")\n        # output.compressContentStreams()\n        # output.write(outputStream)\n        # outputStream.close()\n        try:\n            retcode = call(['gs',\"-sDEVICE=pdfwrite\",\"-dNOPAUSE\",\"-dQUIET\", \"-dBATCH\", \"-sOutputFile=\"+fileName+fileExt+'.tmp',inputFileName])\n            if retcode < 0:\n                print (\"Child was terminated by signal\", -retcode)\n        except OSError as e:\n            print (sys.stderr, \"Execution failed:\", e)\n        else:\n            try:\n                try:\n                    os.remove(fileName+fileExt)\n                except Exception as err:\n                    pass\n\n                retcode = call(['pdftk',inputFileName+\".links\",\"multistamp\",fileName+fileExt+'.tmp', \"output\",fileName+fileExt])\n\n                if retcode < 0:\n                    print (\"Child was terminated by signal\", -retcode)\n            except OSError as e:\n                print (sys.stderr, \"Execution failed:\", e)\n            finally:\n                try:\n                    os.remove(fileName+fileExt+'.tmp')\n                except Exception as err:\n                    self.logger.error(err)\n        finally:\n            try:\n                os.remove(inputFileName+\".links\")\n            except Exception as err:\n                self.logger.error(err)\n\n\n        self.logger.debug(\"Файлы  %s\",str(time.time()-time1))\n        return fileName+fileExt\n\n\n    def _load_page(self, res, width, height, timeout):\n        cancelAt = time.time() + timeout\n        self.__loading = True\n        self.__loadingResult = False # Default\n        if type(res) == tuple:\n            url = res[1]\n        else:\n            url = res\n\n        # if self.encodedUrl:\n        qtUrl = QUrl.fromEncoded(url)\n        # else:\n        #     qtUrl = QUrl(url)\n\n        # Set the required cookies, if any\n        if not self.cookies:\n            self.cookies=renderHints.get(qtUrl.host(),{}).get(\"cookies\")\n\n        self.cookieJar = CookieJar(self.cookies, qtUrl)\n        self._page.networkAccessManager().setCookieJar(self.cookieJar)\n\n        # Load the page\n        if type(res) == tuple:\n            self._page.mainFrame().setHtml(res[0], qtUrl) # HTML, baseUrl\n        else:\n            self._page.mainFrame().load(qtUrl)\n\n        while self.__loading:\n            time.sleep(1)\n            if timeout > 0 and time.time() >= cancelAt:\n                # self.view.stop()\n                # time.sleep(1)\n                break\n                # raise RuntimeError(\"Request timed out on %s\" % res)\n            while QApplication.hasPendingEvents():\n                QCoreApplication.processEvents()\n\n        if self.logger: self.logger.debug(\"Processing result\")\n        js_scroll = \"window.scrollBy(0, 5000);setTimeout(3,function(){window.scrollBy(0, 0)})\"\n        self._page.mainFrame().documentElement().evaluateJavaScript(js_scroll)\n\n        if not self.afterRender:\n            self.afterRender=renderHints.get(qtUrl.host())\n        if self.afterRender:\n            js = \"\"\"\n                alert(\"start\")\n                function eventFire(el, etype){\n                  if (el.fireEvent) {\n                    el.fireEvent('on' + etype);\n                  } else {\n                    var evObj = document.createEvent('Events');\n                    evObj.initEvent(etype, true, false);\n                    el.dispatchEvent(evObj);\n                  }\n                }\n            \"\"\"\n\n            if self.afterRender.get(\"classes\"):\n                js = js+\"var elements = document.getElementsByClassName('\"+self.afterRender[\"classes\"]+\"');for (var i=0; i<elements.length; i++)eventFire(elements[i],'click');\"\n            if self.afterRender.get(\"id\"):\n                js = js+\"eventFire(document.getElementById('\"+self.afterRender[\"id\"]+\"'),'click')\"\n            if self.afterRender.get(\"js\"):\n                js = js + self.afterRender.get(\"js\")\n\n\n            self._page.mainFrame().documentElement().evaluateJavaScript(js)\n\n\n        # if self.__loading_result == False:\n        #     if self.logger: self.logger.warning(\"Failed to load %s\" % res)\n\n\n        # Set initial viewport (the size of the \"window\")\n        size = self._page.mainFrame().contentsSize()\n        if self.logger: self.logger.debug(\"contentsSize: %s\", size)\n        # if width > 0:\n        #     size.setWidth(width)\n        # if height > 0:\n        #     size.setHeight(height)\n        # self._window.resize(size)\n\n    # @pyqtSlot()\n    def _on_each_reply(self,reply):\n      \"\"\"\n      Logs each requested uri\n      \"\"\"\n      # print(\"Received %s\" % (reply.url().toString()))\n      # self.logger.debug(\"Received %s\" % (reply.url().toString()))\n\n    # Eventhandler for \"loadStarted()\" signal\n    # @pyqtSlot()\n    def _on_load_started(self):\n        \"\"\"\n        Slot that sets the '__loading' property to true\n        \"\"\"\n        if self.logger: self.logger.debug(\"loading started\")\n        self.__loading = True\n\n    # Eventhandler for \"loadFinished(bool)\" signal\n    # @pyqtSlot()\n    def _on_load_finished(self, result):\n        \"\"\"Slot that sets the '__loading' property to false and stores\n        the result code in '__loading_result'.\n        \"\"\"\n        if self.logger: self.logger.debug(\"loading finished with result %s\", result)\n        self.__loading = False\n        self.__loading_result = result\n\n    # Eventhandler for \"sslErrors(QNetworkReply *,const QList<QSslError>&)\" signal\n    # @pyqtSlot()\n    def _on_ssl_errors(self, reply, errors):\n        \"\"\"\n        Slot that writes SSL warnings into the log but ignores them.\n        \"\"\"\n        # for e in errors:\n        #     if self.logger: self.logger.warn(\"SSL: \" + e.errorString())\n        reply.ignoreSslErrors()\n\n\nclass CustomWebPage(QWebPage):\n    def __init__(self, **kwargs):\n        super(CustomWebPage, self).__init__()\n        self.logger = kwargs.get('logger', None)\n        self.ignore_alert = kwargs.get('ignore_alert', True)\n        self.ignore_confirm = kwargs.get('ignore_confirm', True)\n        self.ignore_prompt = kwargs.get('ignore_prompt', True)\n        self.interrupt_js = kwargs.get('interrupt_js', True)\n        self.kind=kwargs.get('kind', \"mobile\")\n\n    def javaScriptAlert(self, frame, message):\n        if self.logger: self.logger.debug('Alert: %s', message)\n        if not self.ignore_alert:\n            return super(CustomWebPage, self).javaScriptAlert(frame, message)\n\n    def javaScriptConfirm(self, frame, message):\n        if self.logger: self.logger.debug('Confirm: %s', message)\n        if not self.ignore_confirm:\n            return super(CustomWebPage, self).javaScriptConfirm(frame, message)\n        else:\n            return False\n\n    def javaScriptPrompt(self, frame, message, default, result):\n        if self.logger: self.logger.debug('Prompt: %s (%s)' % (message, default))\n        if not self.ignore_prompt:\n            return super(CustomWebPage, self).javaScriptPrompt(frame, message, default, result)\n        else:\n            return False\n\n    def shouldInterruptJavaScript(self):\n        if self.logger: self.logger.debug(\"WebKit ask to interrupt JavaScript\")\n        return self.interrupt_js\n\n\n    def userAgentForUrl(self, url):\n        if self.kind==\"mobile\":\n            return \"Mozilla/5.0 (iPhone; CPU iPhone OS 9_3 like Mac OS X) AppleWebKit/537.21 (KHTML, like Gecko) Version/9.0 Mobile/13E230 Safari/537.21\"\n        else:\n            return \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_3) AppleWebKit/601.4.4 (KHTML, like Gecko) Version/9.0.3 Safari/601.4.4\"\n\n    # loadFinished signal handler receives ok=False at least these cases:\n    # 1. when there is an error with the page (e.g. the page is not available);\n    # 2. when a redirect happened before all related resource are loaded;\n    # 3. when page sends headers that are not parsed correctly\n    #    (e.g. a bad Content-Type).\n    # By implementing ErrorPageExtension we can catch (1) and\n    # distinguish it from (2) and (3).\n    # def extension(self, extension, info=None, errorPage=None):\n    #     if extension == QWebPage.ErrorPageExtension:\n    #         # catch the error, populate self.errorInfo and return an error page\n    #         print(1)\n    #         info = sip.cast(info, QWebPage.ErrorPageExtensionOption)\n    #\n    #         domain = 'Unknown'\n    #         if info.domain == QWebPage.QtNetwork:\n    #             domain = 'Network'\n    #         elif info.domain == QWebPage.Http:\n    #             domain = 'HTTP'\n    #         elif info.domain == QWebPage.WebKit:\n    #             domain = 'WebKit'\n    #\n    #         self.error_info = RenderErrorInfo(\n    #             domain,\n    #             int(info.error),\n    #             six.text_type(info.errorString),\n    #             six.text_type(info.url.toString())\n    #         )\n    #\n    #         # XXX: this page currently goes nowhere\n    #         content = u\"\"\"\n    #             <html><head><title>Failed loading page</title></head>\n    #             <body>\n    #                 <h1>Failed loading page ({0.text})</h1>\n    #                 <h2>{0.url}</h2>\n    #                 <p>{0.type} error #{0.code}</p>\n    #             </body></html>\"\"\".format(self.error_info)\n    #\n    #         errorPage = sip.cast(errorPage, QWebPage.ErrorPageExtensionReturn)\n    #         errorPage.content = QByteArray(content.encode('utf-8'))\n    #         return True\n    #\n    #     # XXX: this method always returns True, even if we haven't\n    #     # handled the extension. Is it correct? When can this method be\n    #     # called with extension which is not ErrorPageExtension if we\n    #     # are returning False in ``supportsExtension`` for such extensions?\n    #     return True\n\n    # def supportsExtension(self, extension):\n    #     if extension == QWebPage.ErrorPageExtension:\n    #         return True\n    #     return False\n\n#\n# class Screener():\n#     def __init__(self,kind):\n#         # self.queue = Queue(1)\n#         super(Screener, self).__init__()\n#         # logging.basicConfig(filename=LOG_FILENAME,level=logging.WARN,)\n#         self.app=self.init_qtgui(\":100\") if kind == \"mobile\" else self.init_qtgui(\":101\")\n#         signal.signal(signal.SIGINT, signal.SIG_DFL)\n#         self.renderer = self.createRenderer(kind)\n#     def render(self,url,fileName):\n#         result = None\n#         try:\n#             result = self.renderer.render(res=url, fileName=fileName)\n#             # self.queue.put(result)\n#         except Exception as err:\n#             logger.debug(\"err\")\n#             print(err)\n#         return result\n#     def __del__(self):\n#         QApplication.exit(0)\n#\n#         # QTimer.singleShot(0, __main_qt)\n#         # return app.exec_()\n#\n#     def init_qtgui(self,display=None, style=None, qtargs=None):\n#         if QApplication.instance():\n#             logger.debug(\"QApplication has already been instantiated. \\\n#                             Ignoring given arguments and returning existing QApplication.\")\n#             return QApplication.instance()\n#\n#         qtargs2 = [sys.argv[0]]\n#\n#         if display:\n#             qtargs2.append('-display')\n#             qtargs2.append(display)\n#             # Also export DISPLAY var as this may be used\n#             # by flash plugin\n#             os.environ[\"DISPLAY\"] = display\n#         # os.environ[\"QTWEBKIT_DEVICE_WIDTH\"]=\"360\"\n#         # os.environ[\"QTWEBKIT_DEVICE_HEIGHT\"]=\"640\"\n#         # os.environ['QT_DEVICE_PIXEL_RATIO'] = str( int(2) )\n#         # os.environ['QT_HIGHDPI_SCALE_FACTOR'] = str(int(2))\n#         # os.environ['QT_AUTO_SCREEN_SCALE_FACTOR'] = str(int(0))\n#         # os.environ['QT_SCALE_FACTOR'] = str(int(2))\n#         qtargs2.extend(qtargs or [])\n#\n#         return QApplication(qtargs2)\n#\n#     def createRenderer(self,kind):\n#         # Render the page.\n#         # If this method times out or loading failed, a\n#         # RuntimeException is thrown\n#         try:\n#             cookies=\"\"\n#             renderer = WebkitRenderer(kind=kind)\n#             # url=\"https://habrahabr.ru/post/302694/\"\n#             # url=\"http://arzamas.academy/materials/530\"\n#             # url= \"http://sergeydolya.livejournal.com/1227195.html\"\n#             # fileName=\"test1.pdf\"\n#             renderer.logger = logger\n#             renderer.kind=kind\n#             renderer.width = 375 if kind == \"mobile\" else 1136\n#             renderer.height = 559 if kind == \"mobile\" else 800\n#             renderer.dpi = 96\n#             renderer.timeout = 60\n#             renderer.wait = 2\n#             renderer.format = \"pdf\"\n#             # renderer.encodedUrl = options.encoded_url\n#             if cookies:\n#                 renderer.cookies = cookies\n#             renderer.qWebSettings[QWebSettings.JavascriptEnabled] = True\n#             renderer.qWebSettings[QWebSettings.PluginsEnabled] = True\n#             renderer.qWebSettings[QWebSettings.PrivateBrowsingEnabled] = True\n#             renderer.qWebSettings[QWebSettings.JavascriptCanOpenWindows] = False\n#             return renderer\n#\n#         except RuntimeError as e:\n#             logger.error(\"main: %s\" % e)\n#             print (e)\n#             # QApplication.exit(1)\n#             # QApplication.exit(1)\n#\n\ndef createScreenshot(url,fileName,mode,logger,cookies=\"\",timeout=50):\n    qtargs2 = []\n    qtargs2.append('-display')\n    display=\":100\" if mode == \"mobile\" else \":101\"\n    qtargs2.append(display)\n    os.environ[\"DISPLAY\"] = display\n    app=QApplication(qtargs2)\n    # logging.basicConfig(filename=LOG_FILENAME,level=logging.DEBUG,)\n    renderer = WebkitRenderer(kind=mode,logger=loggers[logger][\"info\"][\"logger\"])\n    renderer.logger = loggers[logger][\"info\"][\"logger\"]\n    renderer.kind=mode\n    renderer.width = 375 if mode == \"mobile\" else 1136\n    renderer.height = 559 if mode == \"mobile\" else 800\n    renderer.dpi = 96\n    renderer.timeout = timeout\n    renderer.wait = 2\n    renderer.format = \"pdf\"\n    # renderer.encodedUrl = options.encoded_url\n    if cookies:\n        renderer.cookies = cookies\n    renderer.qWebSettings[QWebSettings.JavascriptEnabled] = True\n    renderer.qWebSettings[QWebSettings.PluginsEnabled] = True\n    renderer.qWebSettings[QWebSettings.PrivateBrowsingEnabled] = False\n    renderer.qWebSettings[QWebSettings.JavascriptCanOpenWindows] = False\n\n    def renderToFile():\n        print(\"render1\")\n        result=renderer.render(res=url, fileName=fileName)\n        if result:\n            print(\"rendered\")\n            QApplication.exit(0)\n        else:\n            QApplication.exit(1)\n    print(\"render\")\n    QTimer.singleShot(0, renderToFile)\n    return app.exec_()\n\n","repo_name":"tiulkin/magicfeed","sub_path":"webkit2pdf.py","file_name":"webkit2pdf.py","file_ext":"py","file_size_in_byte":27204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"74897835485","text":"# from PIL import Image                                                                                \r\n# img1 = Image.open('C:/Users/sushr/Dropbox/My PC (LAPTOP-AC0PDSKE)/Desktop/Sushree/wallpapers/orange.jpg')\r\n# img2 = Image.open('C:/Users/sushr/Dropbox/My PC (LAPTOP-AC0PDSKE)/Desktop/Sushree/wallpapers/orange.jpg')\r\n\r\n# img1.show()\r\n\r\n# import cv2\r\n# import numpy as np\r\n# a = cv2.imread(\"C:/Users/sushr/Dropbox/My PC (LAPTOP-AC0PDSKE)/Desktop/Sushree/wallpapers/pic1.jpg\")\r\n# b = cv2.imread(\"C:/Users/sushr/Dropbox/My PC (LAPTOP-AC0PDSKE)/Desktop/Sushree/wallpapers/pic2.jpg\")\r\n# difference = cv2.subtract(a, b)    \r\n# # cv2.imshow('Subtracted Image', difference)\r\n\r\n# print(difference)\r\n# result = not np.any(difference)\r\n# if result is True:\r\n#     print(\"Pictures are the same\")\r\n# else:\r\n#     print(\"Pictures are different\")\r\n\r\nfrom skimage.metrics import structural_similarity as compare_ssim\r\nimport argparse\r\n# import imutils\r\nimport cv2\r\n\r\na = cv2.imread(\"C:/Users/sushr/Dropbox/My PC (LAPTOP-AC0PDSKE)/Desktop/Sushree/wallpapers/img1.jpg\")\r\nb = cv2.imread(\"C:/Users/sushr/Dropbox/My PC (LAPTOP-AC0PDSKE)/Desktop/Sushree/wallpapers/img2.jpg\")\r\n\r\n# A = cv2.resize(a, (W, H))\r\n(H, W) = a.shape[:-1]\r\nb = cv2.resize(b, (W, H))\r\n\r\ngrayA = cv2.cvtColor(a, cv2.COLOR_BGR2GRAY)\r\ngrayB = cv2.cvtColor(b, cv2.COLOR_BGR2GRAY)\r\n\r\n(score, diff) = compare_ssim(grayA, grayB, full=True)\r\ndiff = (diff * 255).astype(\"uint8\")\r\nprint(\"SSIM: {}\".format(score))\r\n\r\nif(score == 1):\r\n    print(\"Images are Same\")\r\nelse:\r\n    print(\"Images are Different\")","repo_name":"sushree-20/my-gitassn","sub_path":"CompareImage.py","file_name":"CompareImage.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"137813627","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Dec 13 11:28:49 2019\n\nGeneralized ECG viewer that can also plot dots/markers\n\n@author: Simon\n\"\"\"\nimport os\nimport misc\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nimport argparse\nimport logging\nimport datetime\nfrom cycler import cycler\nfrom pyedflib import highlevel\n\nmpl_logger = logging.getLogger('matplotlib')\nmpl_logger.setLevel(logging.INFO)\n\n\n\nclass ECGPlotter():\n\n    def _handle_close(self,evt):\n        plt.close('all')\n        return\n\n    def __init__(self, data, fs, markers={}, interval=30, nrows=3, ncols=3,\n                  startpage=0, title='', verbose=True):\n        \"\"\"\n        :param data: ECG data or any other continuous data\n        :param fs: sample rate of the signal\n        :markers: a dictionary, where each item contains the name and the\n                  occurence of the marker in seconds\n        :param interval: \n        \"\"\"\n        if len(markers)>4:\n            print('More than 4 markers currently not supported')\n\n        self.flipped = False\n        self.markers = markers\n        self.page = startpage\n        self.interval = interval\n        self.nrows = nrows\n        self.ncols = ncols\n        self.gridsize = nrows*ncols\n        self.title = title\n        self.verbose = verbose\n\n        # load \n        self.data = data\n        self.fs = fs\n        self.max_page = len(data)//fs//self.interval//self.gridsize\n\n        # set up the plot, connect the button presses\n        self.axs = []\n        self.fig, axs = plt.subplots(nrows, ncols)\n        self.axs = np.array(axs).flatten()\n\n        _ = self.fig.canvas.mpl_connect(\"button_press_event\", self.mouse_toggle_select)\n        _ = self.fig.canvas.mpl_connect(\"key_press_event\", self.key_press)\n        _ = self.fig.canvas.mpl_connect('close_event', self._handle_close)\n        self.background = self.fig.canvas.copy_from_bbox(self.axs[0].bbox)\n\n        self.colors = ['b', 'g', 'r', 'k']\n        self.styles = ['x', '*', '+', 'o']\n        self.update()\n\n\n\n    def draw(self):\n        self.fig.canvas.draw()\n\n    def get_marker(self, plot_nr, marker_name, plotdata):\n        \"\"\" get the marker height (yy) given for certain seconds\"\"\"\n        marker_sec = self.markers.get(marker_name)\n        plotstart_sec = (self.page*self.gridsize+plot_nr)*self.interval\n        idx_start = np.searchsorted(marker_sec, plotstart_sec)\n        idx_stop  = np.searchsorted(marker_sec, plotstart_sec+self.interval)\n        marker_samples = marker_sec[idx_start:idx_stop]*self.fs\n        yy = self.data[marker_samples.round().astype(int)]\n        xx = marker_samples-plotstart_sec*self.fs\n        return xx, yy\n\n\n    #%% update\n    def update(self):\n        gridsize = self.gridsize\n        page = self.page\n        data = self.data\n        fs = self.fs\n        interval = self.interval\n        markers = self.markers\n\n        formatter = ticker.FuncFormatter(lambda x, pos: f'{int(x//self.fs)}')\n\n        for i in range(self.gridsize):\n\n            if page*gridsize+i>=len(self.data):\n                ax  = self.axs[i]\n                ax.clear()\n                continue\n            plotdata = data[(page*gridsize+i)*interval*fs:\n                            (page*gridsize+i+1)*interval*fs]\n            ax  = self.axs[i]\n            ax.clear()\n\n            for j, marker_name in enumerate(markers):\n                xx, yy = self.get_marker(i, marker_name, plotdata)\n                scatter = 0 #markerpoints.max()*0.005*j\n                m = self.styles[j]\n                c = self.colors[j]\n                ax.scatter(xx+scatter, yy, marker=m, color=c,\n                           linewidth=1, alpha=0.65)\n\n            if i==0:\n                self.fig.legend(list(markers))\n            ax.plot(plotdata, linewidth=0.5)\n            # ax.set_xlim([0, self.interval*self.fs])\n            ax.set_xlabel('seconds')\n            ax.xaxis.set_major_formatter(formatter)\n            seconds = (page*gridsize+i)*interval\n            timestr1 = (datetime.timedelta(seconds=seconds))\n            timestr2 = (datetime.timedelta(seconds=seconds+interval))\n            ax.set_title(f'{timestr1} - {timestr2}', fontsize=11)\n\n\n        title = f'ECGPlotter: {self.title}\\n{page}/{self.max_page}'\n        title += ' - flipped'*self.flipped\n        plt.suptitle(title)\n        if self.verbose:\n            print('printing batch {}'.format(title))\n\n        self.draw()\n        # plt.tight_layout()\n\n\n    def _get_xlims(self, ax):\n        \"\"\"\n        a function to calculate the middle of an axis.\n        as the axis can be negative as well we can't just\n        take the half of xlim[1]\n\n        :param ax: an axis element\n        :returns:\n        \"\"\"\n        xmin, xmax = ax.get_xlim()\n        middle = ((xmax-xmin)//2)+xmin\n        return xmin, middle, xmax\n\n    #%% key press\n    def key_press(self, event):\n\n\n        helpstr = 'right\\tnext page\\n'\\\n                  'left\\tprevious page\\n'\\\n                  'enter\\tjump to page X\\n'\\\n                  'escape\\tquit\\n'\\\n                  '\\nmouse button\\tmark as artefact\\n\\n'\\\n\n        if event.key=='escape':\n            plt.close('all')\n            return\n\n        elif event.key =='enter':\n            page = misc.input_box('Please select new page position', dtype=int,\n                                 initialvalue=self.page, minvalue=0,\n                                 maxvalue=self.max_page)\n            if page and self.verbose:\n                print('jumping to {}'.format(page))\n                self.page = page\n        elif event.key in ('right'):\n            self.page += 1\n        elif event.key=='left':\n                self.page -= 1\n        elif event.key=='u':\n            self.data = -self.data\n            if self.verbose: print('flipping u/d')\n            self.flipped = not self.flipped\n        else:\n            if self.verbose:\n                print(helpstr)\n                print('unknown key {}'.format(event.key))\n        if self.page<0:\n            self.page=self.max_page\n        elif self.page>self.max_page:\n            self.page=0\n        self.update()\n\n\n    def mouse_toggle_select(self, event):\n        if event.inaxes is None:\n            'Please click inside a plot'\n            return\n        idx = np.where(self.axs==event.inaxes)[0]\n        ax = self.axs[idx]\n        if(str(event.button)=='MouseButton.LEFT'):\n            self.toggle_artefact('both', idx)\n        elif (str(event.button)=='MouseButton.RIGHT'):\n            xin, middle, xmax = self._get_xlims(ax)\n            if event.xdata>xmax//2:\n                self.toggle_artefact('right', idx)\n            else:\n                self.toggle_artefact('left', idx)\n        elif (str(event.button)=='MouseButton.MIDDLE'):\n            ax.show()\n        elif (str(event.button)=='MouseButton.BACK'):\n            self.fig.canvas.restore_region(self.background)\n            self.fig.canvas.blit(ax.bbox)\n        else:\n            if self.verbose:\n                print('unknown button', event.button)\n        plt.pause(0.001)\n\n#%% main\nif __name__=='__main__':\n    parser = argparse.ArgumentParser(description='Load the visualizer for artefacts')\n    parser.add_argument('-edf', '--edf_file', type=str,\n                         help='A link to an edf-file. The channel ECG I  needs to be present.')\n    parser.add_argument('-nrows', type=int, default=2,\n                         help='Number of rows to display in the viewer')\n    parser.add_argument('-ncols', type=int, default=2,\n                         help='Number of columns to display in the viewer')\n    parser.add_argument('-page', type=int, default=0,\n                         help='At which page (epoch*gridsize) to start the viewer')\n    args = parser.parse_args()\n    edf_file = args.edf_file\n    nrows = args.nrows\n    ncols = args.ncols\n    page = args.page\n\n    if edf_file is None:\n        edf_file = misc.choose_file(exts=['edf', 'npy'],\n                                    title='Choose a EDF to display')\n    print('loading {}'.format(edf_file))\n\n\n    channels = highlevel.read_edf_header(edf_file)['channels']\n    ch_nr = channels.index([ch for ch in channels if 'ECG' in ch.upper()][0])\n    data, sheader, header = highlevel.read_edf(edf_file, ch_nrs=ch_nr)\n    data = data[0]\n    fs = sheader[0]['sample_rate']\n    title = os.path.basename(edf_file)\n    self = ECGPlotter(data=data, fs=fs, startpage=page,\n                      nrows=nrows, ncols=ncols, title=title)\n    plt.show(block=True)\n\n\n","repo_name":"skjerns/NT1-HRV","sub_path":"viewer/viewer.py","file_name":"viewer.py","file_ext":"py","file_size_in_byte":8455,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"680332948","text":"'''\nLSTM main module\n'''\n\nimport numpy as np\n\nfrom wrapper import Method\n\nfrom keras.models import Model\nfrom keras.callbacks import EarlyStopping, History, ModelCheckpoint\nfrom keras.layers import LSTM, Dense, Input\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.wrappers.scikit_learn import KerasClassifier\nfrom keras.optimizers import adam\n\nfrom sklearn.model_selection import GridSearchCV\nfrom keras.layers.core import Masking\n\n\ndef create_model(num_states=1, \n                 max_seq_len=50, \n                 lrate=0.01, \n                 num_units=50, \n                 dropout_rate=0.):\n    '''\n    Auxiliary function for GridSearchCV\n    '''\n    in_layer = Input(shape=(max_seq_len, num_states + 1))\n#     mask_layer = Masking(mask_value=0)(in_layer)\n    lstm_layer = LSTM(units=num_units, dropout=dropout_rate, recurrent_dropout=dropout_rate)(in_layer)\n    out_layer = Dense(num_states + 1, activation='softmax', name='output')(lstm_layer)\n    \n    model = Model(in_layer, out_layer)\n    \n    opt = adam(lr=lrate)\n    model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n    \n    return model\n\n\nclass MethodLSTM(Method):\n    \n    def __init__(self, \n                 num_states, \n                 adjacency_list, \n                 max_seq_len, \n                 strat='sequential', \n                 epochs=20, \n                 fname='lstm_model'):\n        super(MethodLSTM, self).__init__(num_states, adjacency_list)\n        self._max_seq_len = max_seq_len\n        self._strat = strat\n        self._epochs = epochs\n        self._fname = fname\n        \n    \n    def name(self):\n        return 'lstm'\n    \n    def tostr(self):\n        return 'lstm_msl{:d}_strat{:s}_epoch{:d}'.format(\n            self._max_seq_len,\n            'S' if self._strat == 'sequential' else 'G',\n            self._epochs)\n    \n    def validate(self, data):\n        # create model\n        model = KerasClassifier(build_fn=create_model, \n                                num_states=self._num_states,\n                                epochs=self._epochs)\n        \n        # define the grid search parameters\n        batch_size = [32, 64, 96, 128]\n        num_units = [50, 75, 100, 125]\n        lrate = [0.001, 0.002, 0.005, 0.01]\n        dropout_rate = [0.0, 0.1, 0.2]\n        \n        # full grid-search\n        if self._strat == 'grid':\n             \n            param_grid = dict(max_seq_len=[self._max_seq_len], \n                              lrate=lrate, \n                              num_units=num_units, \n                              dropout_rate=dropout_rate, \n                              batch_size=batch_size)\n             \n            grid = GridSearchCV(estimator=model, \n                                param_grid=param_grid,\n                                cv=10,\n                                refit=False, \n                                n_jobs=1, \n                                verbose=0)\n            grid_result = grid.fit(data[0], data[1])\n              \n            grid_result = grid_result.best_params_\n         \n        # sequential search\n        else:         \n            order = ['batch_size', 'num_units', 'lrate', 'dropout_rate']\n            param_grid = dict(max_seq_len=[self._max_seq_len], \n                              lrate=[lrate[0]], \n                              num_units=[num_units[0]], \n                              dropout_rate=[dropout_rate[0]], \n                              batch_size=[batch_size[0]])\n             \n            for param in order:\n                param_grid[param] = eval(param)\n                grid = GridSearchCV(estimator=model, \n                                    param_grid=param_grid,\n                                    cv=10,\n                                    refit=False, \n                                    n_jobs=1,\n                                    verbose=0)\n                grid_result = grid.fit(data[0], data[1])\n                param_grid[param] = [grid_result.best_params_[param]]\n                \n            grid_result = {p:v[0]  for p, v in param_grid.items()}\n        \n        self._best_params = grid_result\n    \n    \n    \n    def train(self, data):\n        # split data for validation purpose\n        idx = np.random.permutation(np.arange(data[0].shape[0]))\n        x = data[0][idx]\n        y = data[1][idx]\n        val_size = int(0.1 * data[0].shape[0])\n        x_train, y_train = x[:-val_size], y[:-val_size]\n        x_val, y_val = x[-val_size:], y[-val_size:]\n        \n        self._model = create_model(num_states=self._num_states, \n                                   max_seq_len=self._max_seq_len, \n                                   lrate=self._best_params['lrate'], \n                                   num_units=self._best_params['num_units'], \n                                   dropout_rate=self._best_params['dropout_rate'])\n        \n        earlystop_cb = EarlyStopping(monitor='val_loss', patience=5, verbose=0)\n        modelcheck_cb = ModelCheckpoint(self._fname, monitor='val_loss', save_best_only=True, verbose=0)\n        \n        hist = self._model.fit(x_train, y_train, \n                               batch_size=self._best_params['batch_size'], \n                               epochs=self._epochs,\n                               callbacks=[earlystop_cb, modelcheck_cb], \n                               validation_data=(x_val, y_val),\n                               verbose=0)\n        \n    \n    \n    def predict(self, sequence, *args):\n        # take only the last sample since preparation can make this sequence into multiple samples\n        return self._model.predict(sequence[0])[-1][1:], None    \n    \n    \n    def prepare_data(self, data):\n        # add additional samples by taking portions of sequences\n        ndata = []\n        next_state = []\n        for i in range(len(data)):\n            if len(data[i]) > self._max_seq_len:\n                for j in range(0, len(data[i]) - self._max_seq_len):\n                    ndata.append(data[i][j: j+self._max_seq_len])\n                    next_state.append(data[i][j+self._max_seq_len])\n            else:\n                ndata.append(data[i][:-1])\n                next_state.append(data[i][-1])\n            \n        ndata = pad_sequences(ndata, self._max_seq_len)  # pad to max length\n        \n        x = np.zeros((len(ndata), self._max_seq_len, self._num_states + 1), dtype=np.bool)\n        y = np.zeros((len(ndata), self._num_states + 1), dtype=np.bool)\n        for i, seq in enumerate(ndata):\n            for j, state in enumerate(seq):\n                x[i, j, state + 1] = 1\n            y[i, next_state[i] + 1] = 1\n            \n        return (x, y)\n\n","repo_name":"dusansovilj/umap18_bbcf","sub_path":"algs/lstm.py","file_name":"lstm.py","file_ext":"py","file_size_in_byte":6663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"9073374990","text":"import requests\nfrom bs4 import BeautifulSoup\nimport lxml\nimport re\nfrom system_handler import writeJSON, openDir\nfrom alternate_agent import getRamdomUserAgent\nfrom start_private_proxy import startPrivateProxy\n\n\n\n\ndef getLexico(word, index, proxies, headers):\n\tDATA_STATUS_OK = 200\n\n\t#result = requests.get(\"https://www.lexico.com/en/definition/\" + word)\n\t\n\n\tif (index):\n\t\turl =  \"https://www.lexico.com/en/list/\" + word + '/' + index\n\telse:\n\t\turl =  \"https://www.lexico.com/en/list/\" + word\n\t\n\tresponse = requests.get(url, proxies=proxies, headers=headers)\n\t#response = session.get(url, headers=headers)\n\n\tif (response.status_code == DATA_STATUS_OK):\n\t\tif (response.content):\n\t\t\ttry:\n\t\t\t\tsoup = BeautifulSoup(response.content, 'lxml')\n\t\t\t\t#statusMessage = \"Successfully get the word: \" + word\n\t\t\t\t\t\t\t\n\t\t\t\t#print(data)\n\t\t\t\treturn (str(soup)) \n\t\t\texcept:\t\t\t\t\n\t\t\t\t#statusMessage = \"An exception occurred while getting \" + word\n\t\t\t\treturn (None)\n\t\n\n\n\nif __name__ == \"__main__\":\n\n\tWORD = \"0\"\n\tINDEX = \"3\"\n\tdirOut = \"E:/FULLTEXT/LEXICO/LIST/HTML\" \n\tpathOut = dirOut + '/list' +  WORD + INDEX + \".html\"\n\n\tproxies = startPrivateProxy()\n\n\tuser_agent = getRamdomUserAgent()\t\t\n\theaders = {'User-Agent': user_agent}\n\n\n\t\n\t\n\thtmlContent = getLexico(WORD, INDEX, proxies, headers)\n\tif(htmlContent):\n\t\twith open(pathOut, \"w\", encoding='utf-8') as file:\n\t\t\tfile.write(htmlContent)\n\topenDir(dirOut)\n\t","repo_name":"andytrandaoanh/web-scraper","sub_path":"main_list.py","file_name":"main_list.py","file_ext":"py","file_size_in_byte":1384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"33862351813","text":"\n# import yaml\n# def read_configs():\n#     with open('config.yml') as f:\n#         config = yaml.load(f, Loader=yaml.FullLoader)\n\n# # Access hyperparameters\n#     learning_rate = config['learning_rate']\n#     batch_size = config['batch_size']\n#     num_epochs = config['num_epochs']\n#     gamma = config['gamma']\n\n# # Access dataset parameters\n#     dataset_path = config['dataset']['path']\n#     train_split = config['dataset']['train_split']\n    \n#     return learning_rate, batch_size, num_epochs, dataset_path, train_split, gamma\n\n\ndef read_configs():\n# Access hyperparameters\n    learning_rate = 0.000001\n    batch_size = 64\n    num_epochs = 25\n    gamma = 0.75\n\n# Access dataset parameters\n    dataset_path = '/content/landscapes'\n    train_split = 0.2\n    \n    return learning_rate, batch_size, num_epochs, dataset_path, train_split, gamma\n\n\n\n\n","repo_name":"yasamanhbn/Image-Colorization","sub_path":"utils/yamlRead.py","file_name":"yamlRead.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"7487786714","text":"def splitData(posDocs, negDocs, nfold, iteration):\r\n\r\n\tposEnd = (len(posDocs)*(iteration)/nfold)-1;\r\n\tif (posEnd<0):\r\n\t\tposEnd = 0\r\n\tnegEnd = (len(negDocs)*(iteration)/nfold)-1;\r\n\tif (negEnd<0):\r\n\t\tnegEnd = 0\r\n\ttrainPosDocs1 = dict(posDocs.items()[:posEnd])\r\n\ttrainNegDocs1 = dict(negDocs.items()[:negEnd])\r\n\r\n\r\n\r\n\ttestPosDocs = dict(posDocs.items()[(len(posDocs)*(iteration)/nfold):(len(posDocs)*(iteration+1)/nfold)-1])\r\n\ttestNegDocs = dict(negDocs.items()[(len(negDocs)*(iteration)/nfold):(len(negDocs)*(iteration+1)/nfold)-1])\r\n\r\n\r\n\tposStart = len(posDocs)*(iteration+1)/nfold\r\n\tif (posStart > len(posDocs)):\r\n\t\tposStart = len(posDocs)\r\n\tnegStart = len(negDocs)*(iteration+1)/nfold\r\n\tif (negStart > len(negDocs)):\r\n\t\tnegStart = len(negDocs)\r\n\r\n\ttrainPosDocs2 = dict(posDocs.items()[posStart:])\r\n\ttrainNegDocs2 = dict(negDocs.items()[negStart:])\r\n\r\n\ttrainPosDocs = dict(trainPosDocs1.items() + trainPosDocs2.items())\r\n\ttrainNegDocs = dict(trainNegDocs1.items() + trainNegDocs2.items())\r\n\r\n\treturn trainPosDocs, trainNegDocs, testPosDocs, testNegDocs\r\n\r\n\r\n","repo_name":"christegho/sentimentanalysis","sub_path":"splitData.py","file_name":"splitData.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"15330791117","text":"class Solution:\n    def simplifyPath(self, path: str) -> str:\n        path = path.split('/')\n        stack = []\n        for letter in path:\n            if letter != '.' and letter != '':\n                if letter == '..' and stack and stack[-1] != '/':\n                    stack.pop()\n                elif letter != '..':\n                    stack.append('/' + letter)\n        \n        if not stack:\n            stack.append('/')\n            \n        return \"\".join(stack)","repo_name":"shtanriverdi/Full-Time-Interview-Track","sub_path":"0071-simplify-path/0071-simplify-path.py","file_name":"0071-simplify-path.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"29505485243","text":"import math\nimport json\nimport pymysql\nfrom flask import *\nfrom flask_sqlalchemy import SQLAlchemy\n\nimport config\napp = Flask(__name__)\napp.config.from_object(__name__)\n\n\ndef db_execute(keyword):\n    conn = pymysql.connect('localhost',\n                           'zwb',\n                           '123456',\n                           'bqb',\n                           cursorclass=pymysql.cursors.DictCursor)\n\n    query = 'select image_url,image_des from bqb_scrapy where image_des like \"%{}%\" limit 2;'.format(\n        keyword)\n    cur = conn.cursor()\n    cur.execute(query)\n    res = cur.fetchall()\n    cur.close()\n    conn.close()\n    return res\n\n\ndef get_page(total, p):\n    show_page  = 5  # 显示的页码数\n    pageoffset = 2  # 偏移量\n    start      = 1  # 分页条开始\n    end        = total  # 分页条结束\n\n    if total > show_page:\n        if p > pageoffset:\n            start = p - pageoffset\n            if total > p + pageoffset:\n                end = p + pageoffset\n            else:\n                end = total\n        else:\n            start = 1\n            if total > show_page:\n                end = show_page\n            else:\n                end = total\n        if p + pageoffset > total:\n            start = start - (p + pageoffset - end)\n    # 用于模版中循环\n    dic = range(start, end + 1)\n    return dic\n\n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n\n@app.route('/gets/', methods=['POST', 'GET'])\ndef search():\n    global db\n    #conn = pymysql.connect('localhost','zwb','123456','bqb',cursorclass=pymysql.cursors.DictCursor)\n    # datas = db_execute(s)\n\n\n    page = int(request.args.get('page', 1))\n    show_shouye_status = 0\n    limit_start = (int(page) - 1) * 10  # 起始\n    db = config.SQLManager()\n    word = str(request.args.get('word', \"我\"))\n    s = str(request.values.get('question',\"我\"))\n\n    if page > 1:\n        show_shouye_status = 1\n    if page > 1:\n        data = db.get_list('select image_url,image_des from bqb_scrapy where image_des like \"%{}%\" limit {},2'.format(word,limit_start))\n    else:\n        data = db.get_list('select image_url,image_des from bqb_scrapy where image_des like \"%{}%\" limit {},2'.format(s,limit_start))\n    # sql   = 'select image_url,image_des from bqb_scrapy where image_des like \"%{}%\" limit {},2;'.format(s,limit_start)\n    if page > 1:\n        sql = \" select count(image_des) as total from bqb_scrapy where image_des like '%\" + word + \"%'\"\n    else:\n        sql = \" select count(image_des) as total from bqb_scrapy where image_des like '%\" + s + \"%'\"\n    count = db.get_one(sql)['total']  # 总记录\n    total = int(math.ceil(count / 10.0))  # 总页数\n    dic = get_page(total, page)\n    page = int(page)\n    db.close()\n\n    return render_template('search.html', data=data, count=count,show_shouye_status=show_shouye_status, total=total, dic_list=dic, page=page,s = s,word=word)\n\n\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"zhangwenbo1229/demo","sub_path":"demo-search/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2948,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"1758076008","text":"import random\r\n\r\nmap = {\r\n    0:complex(-3,-3), 1:complex(-3,-1), 2:complex(-3,3), 3:complex(-3,1),\r\n    4:complex(-1,-3), 5:complex(-1,-1), 6:complex(-1,3), 7:complex(-1,1),\r\n    8:complex(3,-3), 9:complex(3,-1), 10:complex(3,3), 11:complex(3,1),\r\n    12:complex(1,-3), 13:complex(1,-1), 14:complex(1,3), 15:complex(1,1),\r\n}\r\nmapper1 = 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15#gray\r\nmapper2 = 13,11,15,9,6,0,4,2,12,10,14,8,7,1,5,3#samra\r\nmapper3 = 15,1,2,12,4,10,9,7,8,6,5,11,3,13,14,0#xu\r\nmapper4 = 7,4,5,6,11,8,9,10,15,12,13,14,3,0,1,2#seddik\r\nmapper5 = 0,10,7,13,9,3,14,4,12,5,11,2,6,15,1,8#huang\r\nmapper6 = 6,10,0,5,3,15,12,9,13,1,11,14,8,4,7,2#krasicki 1\r\nmapper7 = 4,7,2,8,14,13,1,11,9,10,15,5,3,0,12,6#krasicki 2\r\n\r\n\r\n\r\ndef mappoints(map, mapper):\r\n    mapped = {}\r\n    for i in range(len(mapper)):\r\n        for j in map.keys():\r\n            if j == mapper[i]:\r\n                #print(mapper[i], map.get(i))\r\n                mapped[j] = map.get(i)\r\n    return mapped\r\n\r\ndef fitness(list1, list2, map):\r\n    list3 = []\r\n    list1, list2 = mappoints(map, list1), mappoints(map, list2)\r\n    for i in range(len(list1)):\r\n        for j in range(len(list2)):\r\n            if i != j:\r\n                fit = abs(list1[i]-list1[j]) * abs(list2[i]-list2[j])\r\n                list3.append(fit)\r\n    return round(min(list3),4)\r\n\r\ndef selection(gray, candidates):\r\n        fit = []\r\n        score = 0\r\n        ind = 0\r\n        for i in range(len(candidates)):\r\n            fit.append(fitness(gray, candidates[i], map))\r\n        S = sum(fit)\r\n        rand1 = random.randint(0, int(S))\r\n        for j in range(len(candidates)):\r\n            score = score + fitness(gray, candidates[j], map)\r\n            if score > rand1:\r\n                ind = j\r\n                break\r\n        return ind\r\n\r\n\r\ndef tournament(candidates):\r\n    result = 0\r\n    ind1, ind2, ind3 = random.randint(0, len(candidates)-1), random.randint(0, len(candidates)-1), random.randint(0, len(candidates)-1)\r\n    x = fitness(mapper1, candidates[ind1], map)\r\n    y = fitness(mapper1, candidates[ind2], map)\r\n    z = fitness(mapper1, candidates[ind3], map)\r\n    if x>y and x>z: result = ind1\r\n    elif y>x and y>z: result = ind2\r\n    elif z>x and z>y: result = ind3\r\n\r\n    return result\r\n\r\n\r\nmaps = [mapper2, mapper3, mapper4, mapper5, mapper6, mapper7]\r\n\r\nprint(tournament(maps))\r\n","repo_name":"ShaheenSolwa/Uncoded-Space-Time-Labeling-Diversity---Genetic-Algorithm","sub_path":"ParentSelection.py","file_name":"ParentSelection.py","file_ext":"py","file_size_in_byte":2338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"33912092603","text":"lst=list()\nlst.append(50)\nprint(lst)\n#1 to 50\nlst=list()\nfor i in range(1,51):\n    lst.append(i)\nprint(lst)\n#total sum\ntotal=sum(lst)\nprint(total)\n#max\nhigh=max(lst)\nprint(high)","repo_name":"sreeragkm77/pythondjangoluminar","sub_path":"pythoncollections/listprograms/listmethods.py","file_name":"listmethods.py","file_ext":"py","file_size_in_byte":177,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"37378364616","text":"from server_request import Server_Request\nimport select\n\nclass Server_Client:\n\n\tdef __init__(self, socket, addr, server):\n\t\tself.socket = socket\n\t\tself.addr = addr\n\t\tself.server  = server\n\t\t#user properties\n\t\tself.logged_in = False\n\t\tself.username = ''\n\t\tself.people_map = {\n\t\t\t'John' : 'joe',\n\t\t\t'Jill' : 'jane'\n\t\t}\n\t\tself.channel = None\n\n\tdef start(self):\n\t\tself.socket.send(b'AUTHENTICATE;')\n\t\twhile True:\n\t\t\tdata = self.socket.recv(1024).decode('ascii')\n\t\t\trequest = Server_Request(data, self)\n\t\t\tresponse = request.handle()\n\t\t\tif response == 'CLIENT_CLOSE;':\n\t\t\t\tself.socket.send(response.encode('acsii'))\n\t\t\t\tbreak\n\t\t\tself.socket.send(response.encode('ascii'))\n\t\tself.socket.close()\n\n\tdef broadcast(self, text):\n\t\tif self.channel != None:\n\t\t\tself.server.broadcast(text, self.channel, self.username)\n\t\telse:\n\t\t\tresponse = 'NOCHANNEL;'\n\t\t\treturn self.socket.send(response.encode('ascii'))\n\n\tdef msg(self, text):\n\t\ttry:\n\t\t\tname, message = text.split(' ', 1)\n\t\texcept:\n\t\t\tname = text\n\t\t\tmessage = ' '\n\t\tresponse = self.server.message(message, self.username, name)\n\t\treturn self.socket.send(response.encode('ascii'))\n\n\tdef join(self, text):\n\t\tself.channel = text\n\n\tdef list(self, text):\n\t\tchannels = self.server.list()\n\t\tresponse = 'LIST;' + (', '.join(channels))\n\t\tprint('channel list ' +response)\n\t\treturn self.socket.send(response.encode('ascii'))\n\n\tdef disconnect(self, text):\n\t\tself.logged_in == False\n\t\tself.exit == True\n\t\tresponse = 'CLIENT_CLOSE;'\n\t\treturn response\n\n\tdef online(self, text):\n\t\tusers = self.server.users()\n\t\tresponse = 'USER_LIST;' + (', '.join(users))\n\t\tprint('user list ' + response)\n\t\treturn self.socket.send(response.encode('ascii'))\n","repo_name":"mysterious64/TCPPythonChat","sub_path":"server_client.py","file_name":"server_client.py","file_ext":"py","file_size_in_byte":1663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"6547669435","text":"from django.urls import path\nfrom . import views\n\n\napp_name = 'Post_App'\n\nurlpatterns = [\n     path('',views.home,name='home_page'),\n     path('like/<pk>/',views.liked ,name='like'),\n     path('unlike/<pk>/',views.unliked,name='unlike'),\n     path('comment/<pk>/',views.comment,name='comment'),\n     path('delete-post/<pk>/',views.delete_post, name='delete_post')\n\n]","repo_name":"raihanalam/SocialMediaApp","sub_path":"Post_App/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"4714197647","text":"T = int(input())\nfor _ in range(T):\n    chk = dict()\n    words = input().replace(' ', '')\n    for c in words:\n        chk[c] = chk.get(c, 0) + 1\n    chk_ = sorted(chk.items(), key=lambda x: x[1], reverse=True)\n    if len(chk_) > 1 and chk_[0][1] == chk_[1][1]:\n        print('?')\n    else:\n        print(chk_[0][0])","repo_name":"alweiis/Algorithm","sub_path":"BOJ/implementation/BOJ_9046.py","file_name":"BOJ_9046.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"22208876271","text":"import pandas as pd\n\na = pd.read_csv(\"data.csv\")\nb = pd.read_csv(\"data2.csv\")\n\n\nimport csv\nwith open('data.csv') as csvfile:\n\treader = csv.DictReader(csvfile)\n\tfor row in reader:\n\t\tprint(row['image'])\n\n\n\n# b = b.dropna(axis=1)\n# merged = a.merge(b, on='title')\n# merged.to_csv(\"output.csv\", index=False)\n\n","repo_name":"TamPhilip/Smart-Photoshop-Detector","sub_path":"Resized_data/merge.py","file_name":"merge.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"86"}
+{"seq_id":"36448326709","text":"import datetime\n\nimport pandapower\nfrom wattson.powergrid.profiles.profile_provider_interface import PowerProfileProviderInterface\n\n\nclass ProfileApplicator:\n    def __init__(self, power_grid: pandapower.pandapowerNet, provider: PowerProfileProviderInterface):\n        self.net = power_grid\n        self.provider = provider\n\n    def apply(self, date_time: datetime.datetime):\n        for element_type in [\"load\", \"sgen\"]:\n            for element_id in self.net[element_type].index:\n                for dimension in [\"p\", \"q\"]:\n                    value = self.provider.get_value(element_type, element_id, date_time, dimension)\n                    if value is None:\n                        continue\n                    col = {\n                        \"p\": \"p_mw\",\n                        \"q\": \"q_mvar\"\n                    }.get(dimension)\n                    self.net[element_type].at[element_id, col] = value\n        return self.net\n\n","repo_name":"fkie-cad/wattson","sub_path":"wattson/powergrid/profiles/profile_applicator.py","file_name":"profile_applicator.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"86"}
+{"seq_id":"38647135461","text":"\r\n\r\n# 내가 푼 문제 아니다. 진짜 참고용\r\n\r\n'''\r\n게임 화면은 1 x 1 크기의 칸들로 이루어진 N x N 크기의 정사각 격자이며 위쪽에는 크레인이 있고 오른쪽에는 바구니가 있습니다.\r\n(위 그림은 5 x 5 크기의 예시입니다). 각 격자 칸에는 다양한 인형이 들어 있으며 인형이 없는 칸은 빈칸입니다.\r\n모든 인형은 1 x 1 크기의 격자 한 칸을 차지하며 격자의 가장 아래 칸부터 차곡차곡 쌓여 있습니다.\r\n게임 사용자는 크레인을 좌우로 움직여서 멈춘 위치에서 가장 위에 있는 인형을 집어 올릴 수 있습니다.\r\n집어 올린 인형은 바구니에 쌓이게 되는 데, 이때 바구니의 가장 아래 칸부터 인형이 순서대로 쌓이게 됩니다.\r\n다음 그림은 [1번, 5번, 3번] 위치에서 순서대로 인형을 집어 올려 바구니에 담은 모습입니다.\r\n\r\n\r\n만약 같은 모양의 인형 두 개가 바구니에 연속해서 쌓이게 되면 두 인형은 터뜨려지면서 바구니에서 사라지게 됩니다.\r\n위 상태에서 이어서 [5번] 위치에서 인형을 집어 바구니에 쌓으면 같은 모양 인형 두 개가 없어집니다.\r\n\r\n크레인 작동 시 인형이 집어지지 않는 경우는 없으나 만약 인형이 없는 곳에서 크레인을 작동시키는 경우에는 아무런 일도 일어나지 않습니다.\r\n 또한 바구니는 모든 인형이 들어갈 수 있을 만큼 충분히 크다고 가정합니다. (그림에서는 화면표시 제약으로 5칸만으로 표현하���음)\r\n\r\n게임 화면의 격자의 상태가 담긴 2차원 배열 board와 인형을 집기 위해 크레인을 작동시킨 위치가 담긴 배열 moves가 매개변수로 주어질 때,\r\n크레인을 모두 작동시킨 후 터트려져 사라진 인형의 개수를 return 하도록 solution 함수를 완성해주세요.\r\n\r\n[제한사항]\r\nboard 배열은 2차원 배열로 크기는 5 x 5 이상 30 x 30 이하입니다.\r\nboard의 각 칸에는 0 이상 100 이하인 정수가 담겨있습니다.\r\n0은 빈 칸을 나타냅니다.\r\n1 ~ 100의 각 숫자는 각기 다른 인형의 모양을 의미하며 같은 숫자는 같은 모양의 인형을 나타냅니다.\r\nmoves 배열의 크기는 1 이상 1,000 이하입니다.\r\nmoves 배열 각 원소들의 값은 1 이상이며 board 배열의 가로 크기 이하인 자연수입니다.\r\n\r\n입출력 예\r\nboard\t                                                        moves\t            result\r\n[[0,0,0,0,0],[0,0,1,0,3],[0,2,5,0,1],[4,2,4,4,2],[3,5,1,3,1]]\t[1,5,3,5,1,2,1,4]\t4\r\n\r\n입출력 예 #1\r\n\r\n인형의 처음 상태는 문제에 주어진 예시와 같습니다.\r\n크레인이 [1, 5, 3, 5, 1, 2, 1, 4] 번 위치에서 차례대로 인형을 집어서 바구니에 옮겨 담은 후,\r\n상태는 아래 그림과 같으며 바구니에 담는 과정에서 터트려져 사라진 인형은 4개 입니다.\r\n\r\n'''\r\n\r\n\r\ndef solution(board,moves):\r\n\r\n    answer=[]\r\n    basket_list=[]\r\n    for i in moves:\r\n        for j in range(len(board)):\r\n            if board[j][i-1]>0:\r\n                basket_list.append(board[j][i-1])\r\n                board[j][i-1]=0\r\n                if basket_list[-1:]==basket_list[-2:-1]:\r\n                    answer+=basket_list[-1:]\r\n                    basket_list=basket_list[:-2]\r\n                break\r\n    return len(answer)*2\r\n\r\n\r\n\r\n\r\nprint(solution([[0,0,0,0,0],[0,0,1,0,3],[0,2,5,0,1],[4,2,4,4,2],[3,5,1,3,1]],[1,5,3,5,1,2,1,4]))\r\n\r\n\r\n\r\n\r\n\r\n'''\r\n문제 설명\r\n문자열 s에는 공백으로 구분된 숫자들이 저장되어 있습니다. \r\nstr에 나타나는 숫자 중 최소값과 최대값을 찾아 \r\n이를 (최소값) (최대값)형태의 문자열을 반환하는 함수, solution을 완성하세요.\r\n예를들어 s가 1 2 3 4라면 1 4를 리턴하고, -1 -2 -3 -4라면 -4 -1을 리턴하면 됩니다.\r\n\r\n제한 조건\r\ns에는 둘 이상의 정수가 공백으로 구분되어 있습니다.\r\n\r\n입출력 예\r\ns\t                return\r\n'1 2 3 4'\t        '1 4'\r\n'-1 -2 -3 -4'\t    '-4 -1'\r\n'-1 -1'\t            '-1 -1'\r\n'''\r\n\r\n\r\ndef solution(s):\r\n    answer=''\r\n    s=s.split()            # 문자열을 공백을 기준으로 리스트로 변환하는 함수\r\n    value=[int(e) for i,e in enumerate(s)]   # 인덱스값이 아니라 실제 값을 축출하고 정수형으로 캐스팅한 리스트 생성\r\n    small_value=min(value)                   # 그 리스트 중에 제일 작은 값\r\n    big_value=max(value)                     # 그 리스트 중에 제일 큰 값\r\n\r\n    sort_add=str(small_value)+' '+str(big_value)   # 최솟값 - 공백 - 최댓값 순으로 나열한 뒤,  각각 문자열로 캐스팅\r\n    answer+=sort_add                               # 결과 값 반환\r\n    return answer\r\n\r\n\r\nprint(solution('1 2 3 4'))\r\n\r\n\r\n# enumerate 함수를 사용해보았다. 다른 풀이를 볼 때, list 사용도 있지만, enumerate를 한번 활용해보고 싶었다.\r\n# 변수 사용이 좀 많은 것이 흠이나, 변수 사용은 코딩에 안전성을 제고해준다.\r\n# 단, 안전성을 담보하는 만큼 속도 면에서는 느리다는 측면은 감안해야 한다.\r\n\r\n\r\n# 리스트 반복문 작성 시 변수 입력시 i+1==i와 같은 조건무은 먹히지 않는다.\r\n\r\n\r\n'''\r\n문제 설명\r\n어떤 문장의 각 알파벳을 일정한 거리만큼 밀어서 다른 알파벳으로 바꾸는 암호화 방식을 시저 암호라고 합니다. 예를 들어 AB는 1만큼 밀면 BC가 되고, 3만큼 밀면 DE가 됩니다. z는 1만큼 밀면 a가 됩니다. 문자열 s와 거리 n을 입력받아 s를 n만큼 민 암호문을 만드는 함수, solution을 완성해 보세요.\r\n\r\n제한 조건\r\n공백은 아무리 밀어도 공백입니다.\r\ns는 알파벳 소문자, 대문자, 공백으로만 이루어져 있습니다.\r\ns의 길이는 8000이하입니다.\r\nn은 1 이상, 25이하인 자연수입니다.\r\n입출력 예\r\ns\t    n\tresult\r\n'AB'\t1\t'BC'\r\n'z'\t    1\t'a'\r\n'a B z'\t4\t'e F d'\r\n'''\r\n\r\n\r\n\r\ndef solution(s,n):\r\n\r\n\r\n    daeMun='ABCDEFGHIJKLMNOPQRSTUVWXYZ'\r\n    soMun='abcdefghijklmnopqrstuvwxyz'\r\n    answer=\"\"\r\n\r\n\r\n    for idx in s:                       # s의 인덱스를 축출\r\n        if idx in soMun:                # 동일변수를 소문자열에서 속해 있는지 확인한다.\r\n            com_idx=soMun.find(idx)+n   # 변수를 만들어서 find 함수가 거르는 인덱스 번호�� 밀어낸 n의 값만큼을 더한다\r\n            answer+=soMun[com_idx%26]   # 문자열 길이를 넘지 않기 위해 [변수%열 총길이]\r\n        elif idx in daeMun:             # 동일변수를 대문자열에 속해 있는지 확인한다.\r\n            com_idx2=daeMun.find(idx)+n #위 과정과 동일\r\n            answer+=daeMun[com_idx2%26]\r\n        else:\r\n            answer += \" \"               # 공백일 경우 결과값에 공백 하나씩 추가\r\n    return answer\r\n\r\nprint(solution('a  B',1))\r\n\r\n\r\n\r\n# 원 문제는 아스키 코드값을 응용한 함수가 쓰였으나, 아직 쓰임이 익숙치 않아. 좀 더 익숙한 방법을 참고했다.\r\n# 인덱스의 길이문제에 걸리지 않기 위해서\r\n# [변수(매개변수)% 열(컨테이너)의 총 길이]를 많이 쓴다. 이 부분도 잘 기억해두자.\r\n# [%] 이 부분은 따로 뭐라고 설명을 해야할까...\r\n\r\n\r\n\r\n'''\r\nLeo는 카펫을 사러 갔다가 아래 그림과 같이 중앙에는 노란색으로 칠해져 있고 테두리 1줄은 갈색으로 칠해져 있는 격자 모양 카펫을 봤습니다.\r\n\r\n\r\n\r\nLeo는 집으로 돌아와서 아까 본 카펫의 노란색과 갈색으로 색칠된 격자의 개수는 기억했지만, 전체 카펫의 크기는 기억하지 못했습니다.\r\n\r\nLeo가 본 카펫에서 갈색 격자의 수 brown, 노란색 격자의 수 yellow가 매개변수로 주어질 때\r\n카펫의 가로, 세로 크기를 순서대로 배열에 담아 return 하도록 solution 함수를 작성해주세요.\r\n\r\n제한사항\r\n갈색 격자의 수 brown은 8 이상 5,000 이하인 자연수입니다.\r\n노란색 격자의 수 yellow는 1 이상 2,000,000 이하인 자연수입니다.\r\n카펫의 가로 길이는 세로 길이와 같거나, 세로 길이보다 깁니다.\r\n\r\n입출력 예\r\nbrown\tyellow\treturn\r\n10\t    2\t    [4, 3]\r\n8\t    1\t    [3, 3]\r\n24\t    24\t    [8, 6]\r\n'''\r\n\r\n\r\ndef solution(brown, yellow):\r\n    answer=[]\r\n    for i in range(1,yellow+1):\r\n        if yellow%i==0:\r\n            answer.append(i)\r\n    for j in range(len(answer)//2+1):\r\n        if (answer[j]+2)*(answer[-j-1]+2)-yellow==brown:\r\n           return [answer[-j - 1] + 2, answer[j] + 2]\r\n\r\n\r\n\r\n\r\n\r\nprint(solution(10,2))\r\n\r\n# 완전 탐색이라고 다 길게 하는 건 아닌가봄\r\n# 수학 능지가 매우 처참하다.\r\n\r\n\r\n\r\n'''\r\n2016년 1월 1일은 금요일입니다. 2016년 a월 b일은 무슨 요일일까요?\r\n두 수 a ,b를 입력받아 2016년 a월 b일이 무슨 요일인지 리턴하는 함수, solution을 완성하세요.\r\n요일의 이름은 일요일부터 토요일까지 각각 SUN,MON,TUE,WED,THU,FRI,SAT\r\n\r\n입니다. 예를 들어 a=5, b=24라면 5월 24일은 화요일이므로 문자열 TUE를 반환하세요.\r\n\r\n제한 조건\r\n2016년은 윤년입니다.\r\n2016년 a월 b일은 실제로 있는 날입니다. (13월 26일이나 2월 45일같은 날짜는 주어지지 않습니다)\r\n\r\n입출력 예\r\na\tb\tresult\r\n5\t24\t'TUE'\r\n'''\r\n\r\nimport datetime\r\n\r\ndef solution(a,b):\r\n    days=['MON','TUE','WED','THU','FRI','SAT','SUN']   # 날짜 리스트 작성\r\n    dt=datetime.datetime(2016,a,b)                     # datetime 은 날짜를 지정하는 함수\r\n    dt2=dt.weekday()                                   # 그 날짜의 요일을 숫자로 나타내는 weekday() 함수, 화요일은 1\r\n    result=days[dt2]                                   # 숫자에 맞춰 슬라이싱한 날짜를 결과값에 반환.\r\n    return result\r\n\r\nprint(solution(2,25))\r\n\r\n\r\n# weekday() 함수는 요일을 숫자로 나타내주는 함수이다.\r\n# 0 - 월요일\r\n# 1 - 화요일\r\n# 2 - 수요일\r\n# 3 - 목요일\r\n# 4 - 금요일\r\n# 5 - 토요일\r\n# 6 - 일요일\r\n# 리스트의 슬라이싱 위치와 weekday() 함수의 숫자값에 해당하는 요일이 같다.\r\n# datetime 함수는 특정한 인수가 나오지 않은 이상, 잘 안쓰지 않을까 싶다.\r\n","repo_name":"ajikang1235/pythonProblemSolve","sub_path":"푼 문제들 7.py","file_name":"푼 문제들 7.py","file_ext":"py","file_size_in_byte":10352,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"23803683118","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun  7 20:09:12 2017\n\n@author: dhan\n\"\"\"\n\ndef polynomial_dataframe(feature, degree): # feature is pandas.Series type\n    # assume that degree >= 1\n    # initialize the dataframe:\n    poly_dataframe = pandas.DataFrame()\n    # and set poly_dataframe['power_1'] equal to the passed feature\n    # first check if degree > 1\n    if degree > 1:\n        # then loop over the remaining degrees:\n        for power in range(2, degree+1):\n            # first we'll give the column a name:\n            name = 'power_' + str(power)\n            # assign poly_dataframe[name] to be feature^power; use apply(*)\n    return poly_dataframe\n","repo_name":"dhan78/testrepo","sub_path":"ml.py","file_name":"ml.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"13244128528","text":"# write a function that takes in a non empty araay of distinct integers\n# and an integer representing a target sum. If any two numbers in the\n# input array sum up to the target sum, the function should return them\n# in an array, in any order. If no two numbers sum up to the target sum,\n# the function should return an empty array\n\ndef two_number_sum(arr, target_sum):\n    # [1, 2, 3, 4] = 5\n    arr.sort()\n    left_pointer = 0\n    right_pointer = len(arr) - 1\n    while left_pointer < right_pointer:\n        current_sum = arr[left_pointer] + arr[right_pointer]\n        if current_sum == target_sum:\n            return [arr[left_pointer], arr[right_pointer]]\n        elif current_sum < target_sum:\n            left_pointer += 1\n        elif current_sum > target_sum:\n            right_pointer -= 1\n    return []\n","repo_name":"ghostspida/InterviewPrepWork","sub_path":"AlgoExpert/Python/Arrays/two_number_sum.py","file_name":"two_number_sum.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"70358639005","text":"import logging\nimport itertools\nimport traceback\nfrom multiprocessing import Pool\n\nimport numpy as np\n\nfrom .settings import get_settings, CPUS\n\n\nlogger = logging.getLogger(__name__)\n\nsimilarity_threshold = get_settings()[\"similarity_threshold\"]\n\n\ndef count_pairs(pair: tuple):\n    try:\n        mean = round(np.array([z1 @ z2 for z1, z2 in zip(pair[0][0], pair[1][0])]).mean(), 3)\n        # if pairs amount more than threshold - create duplicate\n        if mean >= similarity_threshold:\n            return pair[0][1], pair[1][1], mean\n    except Exception:\n        logger.error(traceback.format_exc())\n        return None\n\n\ndef feature_description(images_list: tuple) -> list:\n    \"\"\"\n    Return the Hamming distance between equal-length sequences\n\n    :param images_list: Tuple of lists contains image ORB descriptor and ID:\n                            0 - image descriptor\n                            1 - image ID\n                        Example:\n                        (\n                            (image_descriptor, image_id),\n                        )\n    :return: List of similar images pairs:\n            0 - first similar image ID\n            1 - second similar image ID\n            2 - images similarity\n            Example:\n            [\n                (first_image_id, second_image_id, similarity),\n            ]\n    \"\"\"\n    similar_pairs = []\n    try:\n        logger.info(\"Feature description run\")\n        images_pairs = itertools.combinations(images_list, 2)\n\n        with Pool(processes=CPUS - 1) as pool:\n            # run tasks in separate process\n            pairs = pool.map(count_pairs, images_pairs)\n\n        # filter only indexed files\n        similar_pairs = [pair for pair in pairs if pair is not None]\n    except Exception:\n        logger.critical(traceback.format_exc())\n    finally:\n        return similar_pairs\n","repo_name":"AndreiDrang/SimilarMemes","sub_path":"image_processing/feature_description.py","file_name":"feature_description.py","file_ext":"py","file_size_in_byte":1842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"23075771922","text":"import unittest\nimport numpy as np\nimport sys\nsys.path.append('numanalysis')\nimport root, interpolate, quad, least_squares\n\nclass TestRootFinding(unittest.TestCase):\n    def test_bisection(self):\n        # Test bisection method with a simple function and known root\n        polynomial = np.polynomial.Polynomial([-4, 0, 1])\n        result = root.Bisection(polynomial).fit(0, 3)\n        self.assertAlmostEqual(result, 2.0, delta = 1e-3)\n        \n\n    def test_fixed_point(self):\n        # Test cases for the fixed-point iteration method\n        func = lambda x: np.sqrt(10/(4 + x))\n        result = root.FixedPoint(expr = func, expr_type = \"function\").fit(1.5)\n        self.assertAlmostEqual(result, 1.365230013, delta = 1e-3)\n\n    def test_newton(self):\n        # Test cases for Newton's method\n        polynomial = np.polynomial.Polynomial([-5, -2, 0, 2])\n        result = root.Newton(polynomial).fit(3)\n        self.assertAlmostEqual(result, 1.6006, delta = 1e-3)\n\n    def test_secant(self):\n        # Test cases for the secant method\n        polynomial = np.polynomial.Polynomial([-5, -2, 0, 2])\n        result = root.Secant(polynomial).fit(3, 4)\n        self.assertAlmostEqual(result, 1.6006, delta = 1e-3)\n\nclass TestInterpolation(unittest.TestCase):\n    \n    def test_lagrange_interpolation(self):\n        # Test cases for Lagrange interpolation\n        f = lambda x: 8 * x + (2 * x ** 3) + (4 * x ** 4)\n        X = np.array([1,2,3,4,5])\n        Y = f(X)\n        result = interpolate.Lagrange(X, Y).fit(3.5)\n        self.assertAlmostEqual(result, f(3.5), delta = 1e-3)\n        \n\n    def test_hermite_interpolation(self):\n        # Test cases for Hermite interpolation\n        f = lambda x: 8 * x + (2 * x ** 3) + (4 * x ** 4)\n        f_prime = lambda x: 8 + (6 * x ** 2) + (16 * x ** 3)\n        X = np.array([1,2,3,4,5])\n        Y = f(X)\n        Y_prime = f_prime(X)\n        result = interpolate.Hermite(X, Y, Y_prime).fit(3.5)\n        self.assertAlmostEqual(result, f(3.5), delta = 1e-3)\n\n    def test_naville_interpolation(self):\n        # Test cases for Naville interpolation\n        f = lambda x: 8 * x + (2 * x ** 3) + (4 * x ** 4)\n        X = np.array([1,2,3,4,5])\n        Y = f(X)\n        result = interpolate.Naville(X, Y).fit(3.5)\n        self.assertAlmostEqual(result, f(3.5), delta = 1e-3)\n\n    def test_newton_divided_difference(self):\n        # Test cases for Newton divided difference interpolation\n        f = lambda x: 8 * x + (2 * x ** 3) + (4 * x ** 4)\n        X = np.array([1,2,3,4,5])\n        Y = f(X)\n        result = interpolate.NewtonDivDiff(X, Y).fit(4.5)\n        self.assertAlmostEqual(result, f(4.5), delta = 1e-3)\n\nclass TestQuad(unittest.TestCase):\n    def test_simpson_rule(self):\n        # Test cases for Simpson's rule\n        f = lambda x: 2 * x + 4 * x ** 2\n        result = quad.Simpson(f, [0, 5], 100).fit()\n        self.assertAlmostEqual(result, 191.6666666667, delta = 1e-2)        \n        \n\n    def test_trapezoid_rule(self):\n        # Test cases for the trapezoidal rule\n        f = lambda x: 2 * x + 4 * x ** 2\n        result = quad.Trapezoid(f, [0, 5], 100).fit()\n        self.assertAlmostEqual(result, 191.6666666667, delta = 1e-2)  \n\n    def test_gaussian_quadrature(self):\n        # Test cases for Gaussian quadrature\n        f = lambda x: 2 * x + 4 * x ** 2\n        result = quad.Gaussian(f, [0, 5], 2).fit()\n        self.assertAlmostEqual(result, 191.6666666667, delta=1e-2)  \n\nclass TestLeastSquares(unittest.TestCase):\n    def test_ols(self):\n        # Test cases for Ordinary Least Squares (OLS)\n        n = 100\n        X = np.linspace(0, 100, n)\n        f = lambda x: 3 * x + 8 + np.random.randn(n)\n        result_coef = least_squares.OLS(X, f(X)).fit(formula = 'linear', show_coef = True)[2]\n        self.assertAlmostEqual(result_coef[1], 3, delta = 0.1)\n\n    def test_polynomial_least_squares(self):\n        # Test cases for Polynomial Least Squares\n        n = 100\n        X = np.linspace(0, 100, n)\n        f = lambda x: 3 * x ** 2 + 2 * x + 8 + np.random.randn(n)\n        result_coef = least_squares.PLS(X, f(X)).fit(degree = 2, show_coef = True)[2]\n        self.assertAlmostEqual(result_coef[1], 2, delta = 0.1)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"viraj-550/NumAnalysis","sub_path":"tests/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":4224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"27430007615","text":"import os\n\nfrom flask import flash, jsonify, Response\nfrom flask import request, redirect\nfrom flask import url_for\nfrom time import sleep\n\nfrom werkzeug.utils import secure_filename\n\nfrom blogon_events import checkLogTime, logTail\nfrom main import app\nfrom blogon_views import blogon_login_required\nfrom actions import create_post, update_post, publish_post, delete_post, unpublish_post\n\n\n@app.route(\"/blogon/tasks/post/create\", methods=['POST'])\n@blogon_login_required\ndef create_post_task():\n    l = request.values.getlist('categories')\n    l.pop(0)\n    a = create_post(title=request.values['title'],\n                    content=request.values['content'],\n                    description=request.values['description'],\n                    categories=l,\n                    tags=request.values['tags'])\n\n    flash(\"Post Saved\")\n    return jsonify(result=a)\n\n\n@app.route(\"/blogon/tasks/post/update\", methods=['POST'])\n@blogon_login_required\ndef update_post_task():\n    l = request.values.getlist('categories')\n    l.pop(0)\n    a = update_post(request.values['postid'],\n                    title=request.values['title'],\n                    content=request.values['content'],\n                    decription=request.values['description'],\n                    categories=l,\n                    tags=request.values['tags'])\n    return jsonify(result=a)\n\n\n@app.route(\"/blogon/tasks/post/publish\", methods=['POST'])\n@blogon_login_required\ndef publish_post_task():\n    a = publish_post(request.form['postid'])\n    flash(\"Your post has been published.\")\n    return jsonify(result=a)\n\n\n@app.route(\"/blogon/tasks/post/unpublish\", methods=['POST'])\n@blogon_login_required\ndef unpublish_post_task():\n    a = unpublish_post(request.form['postid'])\n    flash(\"Your post has been unpublished.\")\n    return jsonify(result=a)\n\n\n@app.route(\"/blogon/tasks/post/delete\", methods=['POST'])\n@blogon_login_required\ndef delete_post_task():\n    a = delete_post(request.values['postid'])\n    if a == -1:\n        flash(\"You are not the author of this post\")\n    else:\n        flash(\"Post has been deleted\")\n    return redirect(url_for('posts_page'))\n\n\n@app.route(\"/blogon/task/logstream\")\n@blogon_login_required\ndef log_stream():\n    def log():\n        oldLog = 0\n        for i in range(10):\n            newLog = checkLogTime()\n            if newLog > oldLog:\n                oldLog = newLog\n                data = logTail()\n                res = \"event: logUpdate\\n\"\n                res += \"retry: 2000\\n\"\n                res += \"data: { 'logs' :\" + str(data) + \"}\\n\\n\"\n                yield res.encode()\n            sleep(2)\n\n    return Response(log(), mimetype=\"text/event-stream\")\n\n\nALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif'])\n\n\ndef allowed_file(filename):\n    return '.' in filename and \\\n           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n\n@app.route(\"/blogon/task/upload_img\",methods=['POST'])\n@blogon_login_required\ndef upload_img_task():\n    path = \"\"\n    if request.method == 'POST':\n        # check if the post request has the file part\n        if 'file' not in request.files:\n            flash('No file part')\n            return redirect(request.url)\n        file = request.files['file']\n        # if user does not select file, browser also\n        # submit a empty part without filename\n        if file.filename == '':\n            flash('No selected file')\n            return redirect(request.url)\n        if file and allowed_file(file.filename):\n            filename = secure_filename(file.filename)\n            path = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n            file.save(path)\n    return jsonify({\"location\": url_for('static',filename=(\"uploads/\"+filename))})\n","repo_name":"xSooDx/BlogOn","sub_path":"blogon_tasks.py","file_name":"blogon_tasks.py","file_ext":"py","file_size_in_byte":3700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"2495905292","text":"from .portfolio import PortfolioManager\n\n\nclass Algorithms:\n    \"\"\"\n    General class for trading strategies\n    \"\"\"\n    def __init__(self):\n        self.cash = 10000.0 # set via config \n        self.initial_margin = 1.0 # set via config\n        self.quantity = 100 # set via config\n        self.market_price = 0.0\n        self.portfolio = PortfolioManager(self.cash, self.quantity, self.initial_margin, self.trader_id)\n        \n        self.order = 0\n\n    def set_position(self, position_):\n        \"\"\"\n        Accessing portfolio to set a new position\n        :param product_: string name of product\n        :param position_: float of position\n        :return:\n        \"\"\"\n        self.portfolio.push(self.market_price, position_)\n\n        self.order = self.portfolio.create_order(self.value)\n","repo_name":"kgeoffrey/StockMarketSimulator","sub_path":"market_sim/algorithm.py","file_name":"algorithm.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"86"}
+{"seq_id":"3869395083","text":"import cv2\nimport numpy as np\nfrom scipy.optimize import curve_fit\ndef distance(vec1,vec2):\n    dist = np.sqrt(np.sum(np.square(vec1 - vec2)))\n    return dist\ndef f_1(x,A,B):\n    return A*x+B\ndef fit_line(line_points):\n    x0 = line_points[:,0]\n    y0 = line_points[:,1]\n    A1, B1 = curve_fit(f_1, x0, y0)[0]\n    return A1,B1\ndef corss_point(line1,line2):\n    k1 = line1[0]\n    b1 = line1[1]\n    k2 = line2[0]\n    b2 = line2[1]\n    x = (b2-b1)/(k1-k2)\n    y = k1*x+b1\n    return x,y\ndef point_3_index(cross_point,points):\n    \"\"\"\n    通过给定交点和标定板上一行线上的点，来确定最最近的三个点进行交比不变，来计算其三维坐标位置\n    返回值是按顺序的点的索引\n    :param cross_point:\n    :param points:\n    :return:\n    \"\"\"\n    cross_point = np.array(cross_point)\n    #print(cross_point,\"#\")\n    #print(points)\n    t = np.square(points-cross_point)\n    print(t.shape)\n    print(t)\n    distxy = np.square(points-cross_point)\n    #print(distxy)\n    dist = np.sum(distxy,axis=1)\n    #print(dist)\n    index = np.argsort(dist)[:3]\n    A = line_points[index[2]]\n    AB = distance(A,line_points[index[0]])\n    AC = distance(A,line_points[index[1]])\n    if AB>AC:\n        tmp = index[0]\n        index[0] = index[1]\n        index[1] = tmp\n    return index\n#求取激光线和每一行的方块的交点，然后提取最近的三个点的坐标\ncriteria = (cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER,30,0.001)\nobjp = np.zeros((6*7,3),np.float32)\nobjp[:,:2] = np.mgrid[0:7,0:6].T.reshape(-1,2)\nobjp = objp.reshape((6,7,3))\nprint(\"3D point\",objp)\nfname = \"image/left01.jpg\"\nimg = cv2.imread(fname)\ngray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\nret,corners  =cv2.findChessboardCorners(gray,(7,6),None)\nif ret:\n    print(corners.shape)\n    for i in corners:\n        print(i)\n    corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)\n    #print(\"corners2\", corners2)\n    # 绘制和展示角点,按照颜色来进行划分（7,6），6种颜色\n    corners3= corners2.reshape((6,7,2))\n    test_point = np.array([[300, 0], [400, 800]])\n    line_laser = fit_line(test_point)\n    for j,line_points in enumerate(corners3):\n        line_i = fit_line(line_points)\n        A1, B1 = line_i\n        #-----------------------------\n        x1= np.arange(0,1500,500)\n        y1 = A1*x1+B1\n        #--------------------------------\n        cross_point = corss_point(line_i,line_laser)\n        #print(cross_point)\n        x,y = int(cross_point[0]),int(cross_point[1])\n        index_i = point_3_index(cross_point,line_points)\n        #print(index_i)\n        A = line_points[index_i[2]]\n        B = line_points[index_i[0]]\n        C = line_points[index_i[1]]\n        AD = distance(A,cross_point)\n        BD = distance(B,cross_point)\n        AC = distance(A,C)\n        BC = distance(B,C)\n        K = (AD/BD)/(AC/BC)\n        #print(K)\n        l = 50\n        solution_x  =  l/(2*K-1)\n\n        B_3d = objp[j][index_i[0]]*50\n        if A[0]>C[0]:\n            point_3d = [B_3d[0]+solution_x,B_3d[1],B_3d[2]]\n        else:\n            point_3d = [B_3d[0]-solution_x,B_3d[1],B_3d[2]]\n        print(\"---\")\n        print(B_3d)\n        print(point_3d)\n        print(\"---\")\n\n        #print(\"distance\",AD,BD,AC,BC)\n        cv2.circle(img,(x,y),3,[0,255,0],3)\n        for i in index_i:\n            x = int(line_points[i][0])\n            y = int(line_points[i][1])\n            cv2.circle(img, (x,y), 3, [255, 0, 0], 2)\n        #cross_point = cross_point\n        cv2.line(img,(x1[0],int(y1[0])),(x1[-1],int(y1[-1])),[0,0,255],1)\n\n    cv2.line(img, (300, 0), (400, 800), [0, 0, 255], 2)\n    #img = cv2.drawChessboardCorners(img, (7, 6), corners2, ret)\n    cv2.namedWindow(fname,0)\n    cv2.imshow(fname, img)\ncv2.waitKey(0)\n","repo_name":"HotView/PycharmProjects","sub_path":"OpenCV相机标定/线激光与棋盘线的交点和最近的三个点.py","file_name":"线激光与棋盘线的交点和最近的三个点.py","file_ext":"py","file_size_in_byte":3751,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"16701808852","text":"import torch\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"  # 장치 선택\n\n# 입력\nX = torch.Tensor([[0, 0],\n                  [0, 1],\n                  [1, 0],\n                  [1, 1]]).to(device)\n\n# 정답\nY = torch.Tensor([[0],\n                  [1],\n                  [1],\n                  [0]]).to(device)\n\n\n# XOR 모델\nclass XOR(torch.nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.layer1 = torch.nn.Linear(2, 2)\n        self.layer2 = torch.nn.Linear(2, 1)\n\n    def forward(self, x):\n        x = torch.relu(self.layer1(x))\n        x = torch.sigmoid(self.layer2(x))\n        return x\n\n\nmodel = XOR().to(device)\n\ncriterion = torch.nn.BCELoss().to(device)  # Binary Cross Entropy\noptimizer = torch.optim.SGD(model.parameters(), lr=0.02)  # SGD 사용, 학습률 0.02\nfor t in range(10000):\n    y_pred = model(X)\n\n    loss = criterion(y_pred, Y)  # loss 계산\n    if t % 1000 == 999:\n        print(f'epoch: {t + 1}/{10000}, loss: {loss.item()}')\n\n    optimizer.zero_grad()  # 초기화\n    loss.backward()  # 역전파\n    optimizer.step()  # 가중치 갱신\n\nwith torch.no_grad():\n    print(f'출력: {model(X)}')\n","repo_name":"JEO-96/Pytorch_XOR","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"25335651628","text":"from sklearn.cross_validation import cross_val_score\nfrom sklearn import svm\nimport read_file as rf\n\ndef main():\n\t\n\tdata, targets = rf.read_letters()\n\tclf = svm.SVC()\n\tscores = cross_val_score(clf, data, targets)\n\tprint(\"SVM_Letters: \", end=\"\")\n\tprint(scores.mean() * 100)\n\n\tdata, targets = rf.read_abalone()\n\tclf = svm.SVC()\n\tscores = cross_val_score(clf, data, targets)\n\tprint(\"SVM_Abalone: \", end=\"\")\n\tprint(scores.mean() * 100)\n\n\tdata, targets = rf.read_lungs()\n\tclf = svm.SVC()\n\tscores = cross_val_score(clf, data, targets)\n\tprint(\"SVM_Lungs: \", end=\"\")\n\tprint(scores.mean() * 100)\n\n\nmain()","repo_name":"wxhucong/Ensemble_Learning","sub_path":"svm.py","file_name":"svm.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"86"}
+{"seq_id":"35998781977","text":"from chatterbot import ChatBot\nfrom chatterbot.trainers import ListTrainer\n\nbot = ChatBot(\n    'Mirka',\n    logic_adapters=[\n        'chatterbot.logic.BestMatch']\n)\n\ntrainer = ListTrainer(bot)\ntrainer.train([\n    'The prediction is.....',\n])\n\nprint(\"Welcome to the Bot Service!\")\nname = input(\"Mirka: What's your name? \")\nan = input('Mirka: Please enter your age, gender, weight and height\\n' + name + ': ').split(' ')\n\nuser = {}\nfeatures = ['age', 'gender', 'weight', 'height']\nuser['name'] = name\n\nfor i in range(len(an)):\n    user[features[i]] = an[i]\n\n\nwhile True:\n    request = input('Mirka: Do you want a prediction?\\n '+ name + ': ');\n    if request == 'No' or request == 'no':\n        print('Mirka: Ok, bye')\n        break\n\n    else:\n        response = bot.get_response(request)\n        print('Mirka: ',response)\n","repo_name":"mpatsiou/Altrouist_Bot","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"45000985399","text":"# ##### BEGIN GPL LICENSE BLOCK #####\n#\n#  This program is free software; you can redistribute it and/or\n#  modify it under the terms of the GNU General Public License\n#  as published by the Free Software Foundation; either version 3\n#  of the License, or (at your option) any later version.\n#\n#  This program is distributed in the hope that it will be useful,\n#  but WITHOUT ANY WARRANTY; without even the implied warranty of\n#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#  GNU General Public License for more details.\n#\n#  You should have received a copy of the GNU General Public License\n#  along with this program; if not, write to the Free Software Foundation,\n#  Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n#\n# ##### END GPL LICENSE BLOCK #####\n\nimport bpy\n\nfrom .func import (POSE_FUNC_LOAD_ACTION, POSE_FUNC_SAVE_ACTION, POSE_FUNC_APPLY_ACTION_FRAME,\n    POSE_FUNC_APPLY_ACTION_SCRIPT)\nfrom .operator import (AMH2B_OT_ActionFrameSaveText, AMH2B_OT_ActionFrameLoadText, AMH2B_OT_ActionFrameLoadPreset,\n    AMH2B_OT_ActionFrameSavePreset, AMH2B_OT_RefreshPosePresets, AMH2B_OT_ApplyActionFrame,\n    AMH2B_OT_LoadActionScriptMOHO)\n\ndef draw_panel_anim_pose(self, context, func_grp_box):\n    layout = self.layout\n    a = context.scene.amh2b\n    func_grp_box.prop(a, \"pose_function\", text=\"\")\n    layout.separator()\n    if a.pose_function == POSE_FUNC_LOAD_ACTION:\n        layout.prop_search(a, \"pose_load_action_frame_text\", bpy.data, \"texts\", text=\"\")\n        layout.operator(AMH2B_OT_ActionFrameLoadText.bl_idname)\n        row = layout.row()\n        row.prop(a, \"pose_preset\", text=\"\")\n        row.operator(AMH2B_OT_RefreshPosePresets.bl_idname, text=\"\", icon=\"FILE_REFRESH\")\n        layout.operator(AMH2B_OT_ActionFrameLoadPreset.bl_idname)\n        layout.separator()\n        layout.prop(a, \"pose_action_name_prepend\", text=\"Prepend\")\n        layout.prop(a, \"pose_load_mark_asset\")\n    elif a.pose_function == POSE_FUNC_SAVE_ACTION:\n        layout.label(text=\"Size Reference Bones\")\n        layout.prop_search(a, \"pose_ref_bones_action\", bpy.data, \"actions\", text=\"Action\")\n        layout.label(text=\"Choose Actions\")\n        layout.template_list(\"AMH2B_UL_SelectAction\", \"\", bpy.data, \"actions\", a, \"pose_select_action_index\", rows=5)\n        layout.label(text=\"Pose Label\")\n        layout.prop(a, \"pose_action_frame_label\", text=\"\")\n        layout.label(text=\"Text\")\n        layout.prop(a, \"pose_save_action_frame_text\", text=\"\")\n        layout.operator(AMH2B_OT_ActionFrameSaveText.bl_idname)\n        layout.operator(AMH2B_OT_ActionFrameSavePreset.bl_idname)\n    elif a.pose_function == POSE_FUNC_APPLY_ACTION_FRAME:\n        layout.operator(AMH2B_OT_ApplyActionFrame.bl_idname)\n        layout.prop_search(a, \"pose_apply_action\", bpy.data, \"actions\", text=\"\")\n    elif a.pose_function == POSE_FUNC_APPLY_ACTION_SCRIPT:\n        layout.operator(AMH2B_OT_LoadActionScriptMOHO.bl_idname)\n        layout.label(text=\"Action Names\")\n        layout.prop(a, \"pose_action_name_prepend\", text=\"Prepend\")\n        layout.label(text=\"Timeline\")\n        layout.prop(a, \"pose_script_frame_scale\")\n        layout.prop(a, \"pose_script_frame_offset\")\n        layout.label(text=\"Replace Unknown\")\n        layout.prop_search(a, \"pose_script_replace_unknown_action\", bpy.data, \"actions\", text=\"Action\")\n","repo_name":"DreamSpoon/AMH2B","sub_path":"amh2b/anim_pose/panel.py","file_name":"panel.py","file_ext":"py","file_size_in_byte":3333,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"86"}
+{"seq_id":"32105795213","text":"import os\nimport stat\n\nfrom cmip6_object_store.cmip6_zarr.lotus import Lotus\n\n\ndef test_Lotus():\n    host_script = os.path.expanduser(\"~/get_host.sh\")\n\n    with open(host_script, \"w\") as writer:\n        writer.writelines([\"#!/bin/bash\\n\", \"/bin/hostname > ~/host.txt\\n\"])\n\n    os.chmod(host_script, stat.S_IREAD | stat.S_IWRITE | stat.S_IEXEC)\n\n    lotus = Lotus()\n    lotus.run(host_script)\n","repo_name":"cedadev/cmip6-object-store","sub_path":"tests/cmip6_zarr/test_lotus.py","file_name":"test_lotus.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"74726730205","text":"import logging\nimport socket\nimport ssl\n\nimport prove.remote\n\nLOG = logging.getLogger(__name__)\n\n\ndef get_client(target, env, callback):\n\tsocket = RemoteSocket(\n\t\ttarget.host,\n\t\ttarget.options.get('port', prove.remote.DEFAULT_PORT),\n\t\tcafile=env.options['ssl']['ca_path'],\n\t\tcertfile=env.options['ssl']['master_cert'],\n\t\tkeyfile=env.options['ssl']['master_key'],\n\t)\n\treturn RemoteClient(socket, callback, target, env)\n\n\nclass RemoteClientException(Exception):\n\tpass\n\n\nclass RemoteClient:\n\tdef __init__(self, sock, callback, target, env):\n\t\tself.socket = sock\n\t\tself.callback = callback\n\t\tself.target = target\n\t\tself.env = env\n\n\tdef connect(self):\n\t\tself.socket.connect()\n\n\tdef run_action(self, action):\n\t\tdata = {\n\t\t\t'action': action.name,\n\t\t\t'args': action.args,\n\t\t\t'env': prove.remote.serialize(self.env),\n\t\t\t'target': prove.remote.serialize(self.target),\n\t\t}\n\t\tself.socket.send(prove.remote.encode(data))\n\t\tresponse = {'status': 'initiating'}\n\n\t\ttry:\n\t\t\twhile response['status'] != 'finished':\n\t\t\t\tLOG.debug('status %r != finished, waiting for more data',\n\t\t\t\t\tresponse['status'])\n\t\t\t\tresponses = self._receive()\n\t\t\t\tfor response in responses:\n\t\t\t\t\tself.callback(response)\n\t\tfinally:\n\t\t\tself.disconnect()\n\n\tdef _receive(self):\n\t\tdata = prove.remote.read_socket(self.socket)\n\t\tif data == b'':\n\t\t\traise ValueError('received empty binary response')\n\n\t\tresponses = []\n\n\t\tfor line in data.split(prove.remote.LINE_DELIMITER):\n\t\t\tif line:\n\t\t\t\tresponses.append(prove.remote.decode(line))\n\n\t\treturn responses\n\n\tdef disconnect(self):\n\t\tself.socket.close()\n\n\nclass RemoteSocket:\n\tdef __init__(self, host, port, cafile=None, certfile=None, keyfile=None, keypass=None):\n\t\tself.host = host\n\t\tself.port = port\n\t\tself.socket = None\n\n\t\tif cafile or certfile:\n\t\t\tself.ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLSv1_2)\n\t\t\t# alternatively: https://docs.python.org/3/library/ssl.html#protocol-versions\n\t\t\t# self.ssl_context = ssl.SSLContext(ssl.PROTOCOL_SSL_v23)\n\t\t\t# self.ssl_context.options |= ssl.OP_NO_SSLv2 # pylint: disable=no-member\n\t\t\t# self.ssl_context.options |= ssl.OP_NO_SSLv3 # pylint: disable=no-member\n\n\t\t\tself.ssl_context.verify_mode = ssl.CERT_REQUIRED\n\t\t\tself.ssl_context.check_hostname = True\n\t\t\tif cafile:\n\t\t\t\tLOG.info('loading ssl ca: %r', cafile)\n\t\t\t\tself.ssl_context.load_verify_locations(cafile=cafile)\n\t\t\tif certfile:\n\t\t\t\tLOG.info('loading ssl cert: %r', certfile)\n\t\t\t\tLOG.info('loading ssl key: %r - password: %s',\n\t\t\t\t\tkeyfile, 'yes' if keypass else 'no')\n\t\t\t\tself.ssl_context.load_cert_chain(certfile, keyfile, keypass)\n\t\telse:\n\t\t\tself.ssl_context = None\n\n\tdef connect(self):\n\t\tLOG.debug('Looking up address info for %s:%s', self.host, self.port)\n\t\taddrinfo = socket.getaddrinfo(\n\t\t\tself.host, self.port,\n\t\t\tsocket.AF_UNSPEC, socket.SOCK_STREAM\n\t\t)\n\n\t\tfor res in addrinfo:\n\t\t\taf, socktype, proto, canonname, address = res\n\n\t\t\ttry:\n\t\t\t\tself.socket = socket.socket(af, socktype, proto)\n\t\t\t\tif self.ssl_context:\n\t\t\t\t\tself.socket = self.ssl_context.wrap_socket(\n\t\t\t\t\t\tself.socket, server_hostname=self.host\n\t\t\t\t\t)\n\t\t\texcept ConnectionRefusedError:\n\t\t\t\tLOG.debug('connection refused: %s:%s', address[0], address[1], exc_info=True)\n\t\t\t\tself.close()\n\t\t\t\tcontinue\n\n\t\t\ttry:\n\t\t\t\tself.socket.settimeout(10)\n\t\t\t\tLOG.debug('Trying to connect to %s:%s', address[0], address[1])\n\t\t\t\tself.socket.connect(address)\n\t\t\texcept ConnectionRefusedError:\n\t\t\t\tLOG.debug('connection refused: %s:%s', address[0], address[1], exc_info=True)\n\t\t\t\tself.close()\n\t\t\t\tcontinue\n\n\t\t\t# if we reach this point, the socket has been successfully created,\n\t\t\t# so break out of the loop\n\t\t\tbreak\n\n\t\tif self.socket is None:\n\t\t\traise RemoteClientException('Could not connect to {}:{}'.format(\n\t\t\t\tself.host, self.port))\n\n\t\tself.socket.settimeout(None)\n\n\tdef recv(self, bufsize=4096):\n\t\treturn self.socket.recv(bufsize)\n\n\tdef send(self, data):\n\t\tif isinstance(data, str):\n\t\t\tdata = data.encode('utf-8')\n\t\tLOG.debug('sending data: %r', data)\n\t\treturn self.socket.send(data)\n\n\tdef close(self):\n\t\t# socket may already have been closed\n\t\tif not self.socket:\n\t\t\treturn\n\n\t\ttry:\n\t\t\tself.socket.shutdown(socket.SHUT_RDWR)\n\t\texcept OSError:\n\t\t\t# shutdown will fail if the socket has already been closed by the\n\t\t\t# server, which will happen if we get throttled for example\n\t\t\tLOG.debug(\"OSError on socket.shutdown, but we think it's ok\",\n\t\t\t\texc_info=True)\n\t\t\tpass\n\n\t\tself.socket.close()\n\t\tself.socket = None\n","repo_name":"anlutro/prove-cm","sub_path":"prove/remote/transport/tcp/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":4366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"295407399","text":"import os\nimport time\nimport json\nfrom django.shortcuts import render, HttpResponse\nfrom crm.utils import get_paginator_query_sets\nfrom crm import models\nfrom PerfectCRM import settings\n\n\n# Create your views here.\ndef index(request):\n    \"\"\"教师首页\"\"\"\n    return render(request, \"teacher/index.html\")\n\n\ndef class_taken(request):\n    \"\"\"所带班级\"\"\"\n    contact_list = request.user.classlist_set.all()\n    class_taken_list = get_paginator_query_sets(request, contact_list, 10)  # 获取带分页对象的query_sets\n    return render(request, \"teacher/class_taken.html\", {\"class_taken_list\": class_taken_list})\n\n\ndef homework_list(request):\n    \"\"\"\n    下载某节学习记录下的所有作业文件\n    :param request:\n    :return:\n    \"\"\"\n    if request.method == \"POST\":\n        ret = {\"status\": False, \"error\": None, \"data\": None}  # 要返回的内容\n        if request.is_ajax():  # 确保是ajax请求\n            study_record_id = request.POST.get(\"study_record_id\")\n            study_record_obj = models.StudyRecord.objects.filter(id=study_record_id).first()  # 获取学习记录对象\n            if study_record_obj:  # 学习记录存在\n                stu_info_id = study_record_obj.student.customer.id  # 学员信息对象ID\n                course_record_id = study_record_obj.course_record.id  # 上课记录对象ID\n                student_homework_abspath = os.path.join(\n                    settings.STUDENT_HOMEWORK_DIR, str(stu_info_id),\n                    str(course_record_id), str(study_record_id)\n                )  # 获取学员作业存放路径\n                os.makedirs(student_homework_abspath, exist_ok=True)  # 可以确保目录存在\n                exist_homwork_files_name_list = os.listdir(student_homework_abspath)  # 已经存在的作业\n                ret[\"data\"] = {}  # 存放所有作业信息，返回给前端\n                for homework_file_name in exist_homwork_files_name_list:\n                    homework_file_path = os.path.join(student_homework_abspath, homework_file_name)  # 作业文件绝对路径\n                    homework_file_stat = os.stat(homework_file_path)  # 获取作业文件的基本信息\n                    ret[\"data\"][homework_file_name] = {\n                        \"file_size\": homework_file_stat.st_size,\n                        \"st_mtime\": time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(homework_file_stat.st_mtime)),\n                        \"download_url\": \"/student/download_homework/?stu_info_id={stu_info_id}&course_record_id={course_record_id}&study_record_id={study_record_id}&homework_file_name={homework_file_name}\".format(\n                            stu_info_id=stu_info_id,\n                            course_record_id=course_record_id,\n                            study_record_id=study_record_id,\n                            homework_file_name=homework_file_name\n                        )\n                    }\n                ret[\"status\"] = True\n            else:\n                ret[\"error\"] = \"学习记录不存在\"\n        else:\n            ret[\"error\"] = \"非Ajax请求\"\n        return HttpResponse(json.dumps(ret))\n","repo_name":"lordhamster66/PythonHomeWork","sub_path":"Project2_PerfectCRM/PerfectCRM/teacher/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3134,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"71296943966","text":"# -*- coding: utf-8 -*-\n\nimport logging\nimport configparser\n\nLOGGER = logging.getLogger('funani')\n\ndef parse_config(args):\n    config = configparser.ConfigParser()\n    cfg_file = args.config\n    config.read(cfg_file)\n    LOGGER.debug(\"Read configuration file '%s'\", cfg_file)\n    return config\n","repo_name":"Color-Of-Code/Funani","sub_path":"docker/cas/src/funanicfg.py","file_name":"funanicfg.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"23375561553","text":"from functools import cache\n\n\nclass Solution:\n    def countVowelStrings(self, n: int) -> int:\n        @cache\n        def find(a, k):\n            if k == 1:\n                return 1\n\n            if a == 0:\n                return 0\n            res = 1\n\n            for i in range(a):\n                res += find(a - i, k - 1)\n\n            return res\n\n        return find(n, 5)\n\n\nn = 1\nn = 2\nn = 33\nprint(Solution().countVowelStrings(n))\n","repo_name":"zzz136454872/leetcode","sub_path":"countVowelStrings.py","file_name":"countVowelStrings.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"20789301514","text":"'''\nAuthor: Mohd Khizir Siddiqui\nGithub: khizirsiddiqui\nUsing Tutorial: https://inventwithpython.com/chapter10.html\nExtended with: https://mblogscode.wordpress.com/2016/06/03/python-naughts-cross\n                       estic-tac-toe-coding-unbeatable-ai/\n'''\n\nimport time\nimport random\nimport os\n\nplayer = 'Player (User)'\ncomputer = 'Computer (AI)'\nleft_margin = '                 '\n\n\ndef draw_board(board):\n    for i in range(1, 10):\n        print()\n    print(left_margin + '     |     |')\n    print(left_margin + '  ' + board[7] +\n          '  |  ' + board[8] + '  |  ' + board[9])\n    print(left_margin + '     |     |')\n    print(left_margin + '----------------')\n    print(left_margin + '     |     |')\n    print(left_margin + '  ' + board[4] +\n          '  |  ' + board[5] + '  |  ' + board[6])\n    print(left_margin + '     |     |')\n    print(left_margin + '----------------')\n    print(left_margin + '     |     |')\n    print(left_margin + '  ' + board[1] +\n          '  |  ' + board[2] + '  |  ' + board[3])\n    print(left_margin + '     |     |')\n\n\ndef inputPlayerLetter():\n    letter = ''\n    while not (letter == 'X' or letter == 'O'):\n        print(left_margin + \"Select X or O:\")\n        letter = input(left_margin).upper()\n    if letter is 'X':\n        return ['X', 'O']\n    else:\n        return ['O', 'X']\n\n\ndef whoGoesFirst():\n    if random.randint(0, 1):\n        return computer\n    else:\n        return player\n\n\ndef playAgain():\n    print(left_margin + \"Do you want to play again?\")\n    return input(left_margin).upper().startswith('Y')\n\n\ndef makeMove(board, letter, move):\n    board[move] = letter\n\n\ndef isWinner(bo, le):\n    return ((bo[7] == le and bo[8] == le and bo[9] == le) or\n            (bo[4] == le and bo[5] == le and bo[6] == le) or\n            (bo[1] == le and bo[2] == le and bo[3] == le) or\n            (bo[7] == le and bo[4] == le and bo[1] == le) or\n            (bo[8] == le and bo[5] == le and bo[2] == le) or\n            (bo[9] == le and bo[6] == le and bo[3] == le) or\n            (bo[7] == le and bo[5] == le and bo[3] == le) or\n            (bo[9] == le and bo[5] == le and bo[1] == le))\n\n\ndef getBoardCopy(board):\n    mockBoard = []\n    for i in board:\n        mockBoard.append(i)\n    return mockBoard\n\n\ndef isSpaceFree(board, move):\n    return board[move] == ' '\n\n\ndef getPlayerMove(board):\n    move = ' '\n    while True:\n        print(left_margin + 'Play your move? (1-9)')\n        move = input(left_margin)\n        if move not in '1 2 3 4 5 6 7 8 9'.split() or not isSpaceFree(board, int(move)):\n            print(left_margin + 'Select from a free space.')\n        else:\n            break\n    return int(move)\n\n\ndef chooseRandomMoveFromList(board, movesList):\n    possibleMoves = []\n    for i in movesList:\n        if isSpaceFree(board, i):\n            possibleMoves.append(i)\n\n    if len(possibleMoves) != 0:\n        return random.choice(possibleMoves)\n    else:\n        return None\n\n\ndef testWinMove(board, letter, move):\n    bCopy = getBoardCopy(board)\n    makeMove(bCopy, letter, move)\n    return isWinner(bCopy, letter)\n\n\ndef testForkMove(board, letter, move):\n    bCopy = getBoardCopy(board)\n    makeMove(bCopy, letter, move)\n    winningMoves = 0\n    for j in range(1, 10):\n        if testWinMove(bCopy, letter, j) and isSpaceFree(bCopy, j):\n            winningMoves += 1\n    return winningMoves > 1\n\n\ndef getComputerMove(board, computerLetter, difficulty):\n    if computerLetter is 'X':\n        playerLetter = 'O'\n    else:\n        playerLetter = 'X'\n\n    for i in range(1, 10):\n        if isSpaceFree(board, i) and testWinMove(board, computerLetter, i):\n            return i\n\n    for i in range(1, 10):\n        if isSpaceFree(board, i) and testWinMove(board, playerLetter, i):\n            return i\n\n    if difficulty == 2 or difficulty == 3:\n        for i in range(1, 10):\n            if isSpaceFree(board, i) and testForkMove(board, computerLetter, i):\n                return i\n\n        for i in range(1, 10):\n            if isSpaceFree(board, i) and testForkMove(board, playerLetter, i):\n                return i\n\n    if difficulty == 3:\n        playerForks = 0\n        for i in range(1, 10):\n            if isSpaceFree(board, i) and testForkMove(board, playerLetter, i):\n                playerForks += 1\n                tempMove = i\n        if playerForks == 1:\n            return tempMove\n        elif playerForks == 2:\n            return chooseRandomMoveFromList(board, [1, 3, 5, 7])\n\n    move = chooseRandomMoveFromList(board, [1, 3, 7, 9])\n    if move is not None:\n        return move\n\n    if isSpaceFree(board, 5):\n        return 5\n\n    return chooseRandomMoveFromList(board, [2, 4, 6, 8])\n\n\ndef isBoardFull(board):\n    for i in range(1, 10):\n        if isSpaceFree(board, i):\n            return False\n    return True\n\n\nprint(left_margin + 'Welcome to Tic-Tac-Toe')\nwhile True:\n    theBoard = [' '] * 10\n\n    playerLetter, computerLetter = inputPlayerLetter()\n\n    print(left_margin + 'Select Difficulty Level:')\n    print(left_margin + '1. Easy Beginner (Default)')\n    print(left_margin + '2. Warmed Up Cooker')\n    print(left_margin + '3. Defeat in Hell')\n    difficulty = int(input(left_margin))\n    if difficulty not in range(1, 3):\n        print(left_margin + 'Try Again')\n        continue\n\n    turn = whoGoesFirst()\n    move = ' '\n    print(left_margin + turn + ' goes first')\n    gameIsPlaying = True\n\n    while gameIsPlaying:\n        print()\n        if turn == player:\n            move = getPlayerMove(theBoard)\n            makeMove(theBoard, playerLetter, move)\n\n            if isWinner(theBoard, playerLetter):\n                gameIsPlaying = False\n            else:\n                if isBoardFull(theBoard):\n                    print(left_margin + 'A TIE')\n                    break\n                else:\n                    turn = computer\n        else:\n            move = getComputerMove(theBoard, computerLetter, difficulty)\n            makeMove(theBoard, computerLetter, move)\n            if isWinner(theBoard, computerLetter):\n                gameIsPlaying = False\n            else:\n                if isBoardFull(theBoard):\n                    print(left_margin + 'The game is a tie')\n                    break\n                else:\n                    turn = player\n        os.system('cls' if os.name == 'nt' else 'clear')\n        if isWinner(theBoard, playerLetter):\n            print(left_margin + 'Hooray! ' + player + ' won.')\n        elif isWinner(theBoard, computerLetter):\n            print(left_margin + computer + ' won. You Lost')\n        print()\n        draw_board(theBoard)\n\n    if not playAgain():\n        break\n","repo_name":"khizirsiddiqui/tic-tac-toe","sub_path":"tic_tac_toe.py","file_name":"tic_tac_toe.py","file_ext":"py","file_size_in_byte":6605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"7296716128","text":"import argparse\r\nimport os\r\nimport os.path as osp\r\nimport socket\r\nfrom PIL import Image\r\n\r\nimport yaml\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport wandb\r\nfrom torch.utils.data import DataLoader\r\nfrom torch.optim.lr_scheduler import CosineAnnealingLR\r\nfrom torchvision import transforms\r\nfrom tqdm import tqdm\r\n\r\nimport datasets\r\nimport models\r\nimport utils\r\n\r\n\r\ndef train_step(linear, data, label, optimizer):\r\n    logits = linear(data)\r\n    loss = F.cross_entropy(logits, label)\r\n    acc = (torch.argmax(logits, dim=1) == label).float().mean().item()\r\n\r\n    optimizer.zero_grad()\r\n    loss.backward()\r\n    optimizer.step()\r\n\r\n    return {\r\n        'loss': loss.item(),\r\n        'acc': acc,\r\n    }\r\n\r\n\r\ndef eval_step(linear, data, label):\r\n    with torch.no_grad():\r\n        logits = linear(data)\r\n    loss = F.cross_entropy(logits, label)\r\n    acc = (torch.argmax(logits, dim=1) == label).float().mean().item()\r\n\r\n    return {\r\n        'loss': loss.item(),\r\n        'acc': acc,\r\n    }\r\n\r\n\r\ndef log_temp_scalar(k, v, t):\r\n    writer.add_scalar(k, v, global_step=t)\r\n    wandb.log({k: v}, step=t)\r\n\r\n\r\ndef main(config):\r\n    # ---- Environment setup ---- #\r\n    resume = config.get('resume')\r\n    n_gpus = len(os.environ['CUDA_VISIBLE_DEVICES'].split(','))\r\n\r\n    save_dir = config['save_dir']\r\n    global logger, writer\r\n    logger, writer = utils.set_save_dir(save_dir, remove=(resume is None))\r\n    with open(osp.join(save_dir, 'config.yaml'), 'w') as f:\r\n        yaml.dump(config, f, sort_keys=False)\r\n\r\n    os.environ['WANDB_NAME'] = config['exp_name']\r\n    os.environ['WANDB_DIR'] = config['save_dir']\r\n    if not config.get('wandb_upload', False):\r\n        os.environ['WANDB_MODE'] = 'dryrun'\r\n    _ = config['wandb']\r\n    os.environ['WANDB_API_KEY'] = _['api_key']\r\n    wandb.init(project=_['project'], entity=_['entity'], config=config)\r\n\r\n    logger.info(f'Hostname: {socket.gethostname()}')\r\n    # -------- #\r\n\r\n    # ---- Dataset, model and optimizer ---- #\r\n    train_dataset = datasets.make(config['train_dataset'])\r\n    test_dataset = datasets.make(config['test_dataset'])\r\n\r\n    img_size = train_dataset[0][0].shape[1]\r\n    train_dataset.transform = transforms.Compose([\r\n        transforms.RandomResizedCrop(img_size, scale=(0.8, 1.0),\r\n                                     ratio=(3.0 / 4.0, 4.0 / 3.0),\r\n                                     interpolation=Image.BICUBIC),\r\n        transforms.RandomHorizontalFlip(),\r\n        transforms.ToTensor(),\r\n        transforms.Normalize(train_dataset.data_mean,\r\n                             train_dataset.data_std),\r\n    ])\r\n    test_dataset.transform = transforms.Compose([\r\n        transforms.Resize(int(img_size * (8 / 7)), interpolation=Image.BICUBIC),\r\n        transforms.CenterCrop(img_size),\r\n        transforms.ToTensor(),\r\n        transforms.Normalize(test_dataset.data_mean,\r\n                             test_dataset.data_std)\r\n    ])\r\n\r\n    n_classes = train_dataset.n_classes\r\n    logger.info('Train dataset: {}, shape={}'.format(len(train_dataset),\r\n        tuple(train_dataset[0][0].shape)))\r\n    logger.info('Test dataset: {}, shape={}'.format(len(test_dataset),\r\n        tuple(test_dataset[0][0].shape)))\r\n    logger.info(f'Num classes: {n_classes}')\r\n\r\n    pth_file = torch.load(config['resume'])\r\n    model = models.make(pth_file['model'], load_sd=True).cuda()\r\n    model = model.encoder[0]\r\n    model.eval()\r\n    logger.info('Model (Encoder) #params={}, out_dim={}'.format(\r\n        utils.compute_num_params(model),\r\n        model.out_dim))\r\n    if n_gpus > 1:\r\n        model = nn.DataParallel(model)\r\n\r\n    # ## hack\r\n    # train_dataset.transform = test_dataset.transform\r\n    # train_loader = DataLoader(train_dataset,\r\n    #                           batch_size=config['batch_size'],\r\n    #                           num_workers=8,\r\n    #                           pin_memory=True)\r\n    # val_loader = DataLoader(test_dataset,\r\n    #                         batch_size=config['batch_size'],\r\n    #                         num_workers=8,\r\n    #                         pin_memory=True)\r\n    # proto = [[] for _ in range(10)]\r\n    # for i, (images, target) in tqdm(enumerate(train_loader)):\r\n    #     # measure data loading time\r\n    #     images = images.cuda()\r\n    #     target = target.cuda()\r\n    #     # compute output\r\n    #     output = model(images)\r\n    #     for a, b in zip(output, target):\r\n    #         proto[int(b)].append(a.detach())\r\n    # for i in range(10):\r\n    #     proto[i] = torch.nn.functional.normalize(torch.stack(proto[i]).mean(dim=0), dim=-1)\r\n    # proto = torch.stack(proto)\r\n    # acc = []\r\n    # for i, (images, target) in tqdm(enumerate(val_loader)):\r\n    #     # measure data loading time\r\n    #     images = images.cuda()\r\n    #     target = target.cuda()\r\n    #     # compute output\r\n    #     output = model(images)\r\n    #     logits = torch.mm(output, proto.t())\r\n    #     acc.append((torch.argmax(logits, dim=1) == target).float().mean().item())\r\n    # print('proto acc:', sum(acc) / len(acc))\r\n    # exit()\r\n\r\n    linear = nn.Linear(model.out_dim, n_classes).cuda()\r\n    linear.weight.data.normal_(mean=0.0, std=0.01)\r\n    linear.bias.data.zero_()\r\n\r\n    optimizer = utils.make_optimizer(linear.parameters(), config['optimizer'])\r\n    # -------- #\r\n\r\n    # ---- Ready to train ---- #\r\n    max_epoch = config['max_epoch']\r\n    n_milestones = config.get('n_milestones', 1)\r\n    milestone_epoch = max_epoch // n_milestones\r\n    min_test_loss = 1e18\r\n    start_epoch = 1\r\n\r\n    num_workers = 8\r\n    train_loader = DataLoader(train_dataset, config['batch_size'],\r\n                              shuffle=True, drop_last=True,\r\n                              num_workers=num_workers, pin_memory=True)\r\n    test_loader = DataLoader(test_dataset, config['batch_size'],\r\n                             num_workers=num_workers, pin_memory=True)\r\n\r\n    lr_scheduler = CosineAnnealingLR(optimizer, T_max=max_epoch)\r\n\r\n    epoch_timer = utils.EpochTimer(max_epoch)\r\n    # -------- #\r\n\r\n    # def adjust_learning_rate(optimizer, epoch):\r\n    #     \"\"\"Decay the learning rate based on schedule\"\"\"\r\n    #     lr = 30\r\n    #     for milestone in [60, 80]:\r\n    #         lr *= 0.1 if epoch >= milestone else 1.\r\n    #     for param_group in optimizer.param_groups:\r\n    #         param_group['lr'] = lr\r\n\r\n    for epoch in range(start_epoch, max_epoch + 1):\r\n        log_text = f'Epoch {epoch}'\r\n\r\n        # ---- Train ---- #\r\n        linear.train()\r\n        #adjust_learning_rate(optimizer, epoch)\r\n\r\n        log_temp_scalar('lr', optimizer.param_groups[0]['lr'], epoch)\r\n\r\n        _ = ['loss', 'acc']\r\n        ave_scalars = {k: utils.Averager() for k in _}\r\n\r\n        pbar = tqdm(train_loader, desc='train', leave=False)\r\n        #_cnt = 0\r\n        for data, label in pbar:\r\n            data, label = data.cuda(), label.cuda()\r\n            with torch.no_grad():\r\n                data = model(data)\r\n            _ = train_step(linear, data, label, optimizer)\r\n            for k, v in _.items():\r\n                ave_scalars[k].add(v, len(data))\r\n            #_cnt += 1; print(_cnt, _['loss'], ave_scalars['acc'].item()*100, _['acc']*100)\r\n            pbar.set_description(desc=f\"train loss:{_['loss']:.4f}\")\r\n\r\n        log_text += ', train:'\r\n        for k, v in ave_scalars.items():\r\n            v = v.item()\r\n            log_text += f' {k}={v:.4f}'\r\n            log_temp_scalar('train/' + k, v, epoch)\r\n\r\n        lr_scheduler.step()\r\n        # -------- #\r\n\r\n        if epoch % milestone_epoch == 0:\r\n            # ---- Test ---- #\r\n            linear.eval()\r\n            _ = ['loss', 'acc']\r\n            ave_scalars = {k: utils.Averager() for k in _}\r\n\r\n            pbar = tqdm(test_loader, desc='test', leave=False)\r\n            for data, label in pbar:\r\n                data, label = data.cuda(), label.cuda()\r\n                with torch.no_grad():\r\n                    data = model(data)\r\n                _ = eval_step(linear, data, label)\r\n                for k, v in _.items():\r\n                    ave_scalars[k].add(v, len(data))\r\n                pbar.set_description(desc=f\"test loss:{_['loss']:.4f}\")\r\n\r\n            log_text += ', test:'\r\n            for k, v in ave_scalars.items():\r\n                v = v.item()\r\n                log_text += f' {k}={v:.4f}'\r\n                log_temp_scalar('test/' + k, v, epoch)\r\n\r\n            test_loss = ave_scalars['loss'].item()\r\n            # -------- #\r\n        else:\r\n            test_loss = 1e18\r\n\r\n        # ---- Summary and save ---- #\r\n        log_text += ', {} {}/{}'.format(*epoch_timer.step())\r\n        logger.info(log_text)\r\n\r\n        model_ = linear.module if n_gpus > 1 else linear\r\n        model_spec = dict()\r\n        model_spec['sd'] = model_.state_dict()\r\n        optimizer_spec = config['optimizer']\r\n        optimizer_spec['sd'] = optimizer.state_dict()\r\n        pth_file = {\r\n            'model': model_spec,\r\n            'optimizer': optimizer_spec,\r\n            'epoch': epoch,\r\n        }\r\n\r\n        if test_loss < min_test_loss:\r\n            min_test_loss = test_loss\r\n            torch.save(pth_file, osp.join(save_dir, 'min-test-loss.pth'))\r\n\r\n        torch.save(pth_file, osp.join(save_dir, 'epoch-last.pth'))\r\n\r\n        writer.flush()\r\n        # -------- #\r\n\r\n\r\nif __name__ == '__main__':\r\n    parser = argparse.ArgumentParser()\r\n    parser.add_argument('--config', default='configs/linear_eval_cifar10.yaml')\r\n    parser.add_argument('--load-root', default='../../data')\r\n    parser.add_argument('--save-root', default='save')\r\n    parser.add_argument('--name', '-n', default=None)\r\n    parser.add_argument('--tag', default=None)\r\n    parser.add_argument('--gpu', '-g', default='0')\r\n    parser.add_argument('--wandb-upload', action='store_true')\r\n    parser.add_argument('--resume')\r\n    args = parser.parse_args()\r\n\r\n    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu\r\n\r\n    with open(args.config, 'r') as f:\r\n        config = yaml.load(f, Loader=yaml.FullLoader)\r\n\r\n    def translate_load_root_(d):\r\n        for k, v in d.items():\r\n            if isinstance(v, dict):\r\n                translate_load_root_(v)\r\n            elif isinstance(v, str):\r\n                d[k] = v.replace('${load_root}', args.load_root)\r\n\r\n    translate_load_root_(config)\r\n\r\n    if args.name is None:\r\n        exp_name = '_' + osp.basename(args.config).split('.')[0]\r\n    else:\r\n        exp_name = args.name\r\n    if args.tag is not None:\r\n        exp_name += '_' + args.tag\r\n    config['exp_name'] = exp_name\r\n    save_dir = osp.join(args.save_root, exp_name)\r\n    config['save_dir'] = save_dir\r\n\r\n    config['wandb_upload'] = args.wandb_upload\r\n\r\n    config['resume'] = args.resume\r\n\r\n    main(config)\r\n","repo_name":"cwksp/cotr","sub_path":"linear_eval.py","file_name":"linear_eval.py","file_ext":"py","file_size_in_byte":10699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"18419129009","text":"import random\nclass Time:\n    \"\"\"A class that represents time in the format HH:MM\"\"\"\n    def __init__(self, hour, minute):\n        self.hour = int(hour)\n        self.minute = int(minute)\n\n    def __lt__(self, other):\n        \"\"\"Compare two times based on their hour and minute\"\"\"\n        \"\"\" return True if self < other, and False otherwise\"\"\"\n        if self.hour < other.hour:\n            return True\n        elif self.hour == other.hour and self.minute < other.minute:\n            return True\n        else:\n            return False\n    \n    def __eq__(self, other):\n        \"\"\"Compare two times based on their hour and minute\"\"\"\n        \"\"\" return True if self == other, and False otherwise\"\"\"\n        if self.hour ==  other.hour and self.minute == other.minute:\n            return True\n        else:\n            return False\n\n    def __repr__(self):\n        \"\"\"Return the string representation of the time\"\"\"\n        return f\"{self.hour:02d}:{self.minute:02d}\"\n\nclass Entry:\n    \"\"\"A class that represents a customer in the waitlist\"\"\"\n    def __init__(self, name, time):\n        self.name = name\n        self.time = time\n\n    def __lt__(self, other):\n        \"\"\"Compare two customers based on their time, if equal then compare based on the customer name\"\"\"\n        if self.time == other.time:\n            return self.name < other.name\n        return self.time < other.time\n    \n\nclass Waitlist:\n    def __init__(self):\n        self._entries = []\n\n    def add_customer(self, item, time):\n        #TODO add customers to the waiting list.\n\n        # validate time\n        if self.validate_time(time) is True:\n            # splits time string to allow for initialization of hour and minutes\n            priority = Time(*time.split(\":\"))\n            # creates new entry with name and time\n            customer = Entry(item, priority)\n            # appends new entry to self._entries\n            self._entries.append(customer)\n\n            # upheap until all balanced\n            self._upheap(len(self)-1)\n            return True\n        else:\n            return False\n        \n\n    def peek(self):\n        #TODO peek and see the first customer in the waitlist (i.e., the customer with the highest priority).\n        # Return a tuple of the extracted item (customer, time). Return None if the heap is empty\n        \n        # checks to see if list is empty\n        if len(self) == 0:\n            return None\n        else:\n            # returns tuple of name and time\n            return self._entries[0].name, self._entries[0].time\n        \n\n    def seat_customer(self):\n        #TODO The program should extract the customer with the highest priority \n        # (i.e., the earliest reservation time) from the priority queue.\n        # Return a tuple of the extracted item (customer, time)\n        \n        # save the item to return later on as tuple\n        temp = self._entries[0].name, self._entries[0].time\n\n        # move last item in list to front (top of heap)\n        # edge case - one item left\n        if len(self) == 1: \n            self._entries.pop()\n        else:        \n            self._entries[0] = self._entries.pop()\n\n            # downheap until all good\n            self._downheap(idx=0)\n\n        # return the temporary variable\n        return temp\n\n\n    def print_reservation_list(self):\n        #TODO Prints all customers in order of their priority (reservation time)\n\n        # use insertionsort to sort list based on time priority\n        for i in range(len(self._entries)):\n            for j in range(len(self._entries)-i, len(self._entries)): #Look at the last i items of the list\n                if self._entries[j-1] > self._entries[j]: # if the items are out of order\n                    self._entries[j], self._entries[j-1] = self._entries[j-1], self._entries[j] # switch them\n\n        reservation_list = []\n        # for loop used to append name and time to list\n        for entry in self._entries:\n            # append to list\n            reservation_list.append((entry.name, entry.time))\n\n        # returns list of tuples\n        return reservation_list\n\n\n    def change_reservation(self, name, new_priority):\n        #TODO Change the reservation time (priority) for the customer with the given name\n        #Maintain the heap property\n\n        # checks through the tuples in the self._entries list\n        for entry in self._entries:\n            # if the name is found\n            if entry.name == name:\n                # new time is passed through the class Time and assigned to new_priority\n                new_priority = Time(*new_priority.split(\":\"))\n                # new time is assigned to the entry.time\n                entry.time = new_priority\n                # downheap to ensure balance\n                self._downheap(idx=0)\n                return True\n            \n        return False\n\n\n    #Add other methods you may need\n    def _i_parent(self, idx):\n        \"returns index of parent of idx\"\n        return (idx-1) // 2 if (idx-1) // 2 >= 0 else None\n    \n    def _i_left(self, idx):\n        \"left child\"\n        il = idx*2+1\n        return il if il<len(self) else None\n    \n    def _i_right(self, idx):\n        \"right child\"\n        ir = idx*2+2\n        return ir if ir<len(self) else None\n\n        \n    def _i_min_child(self, idx):\n        \"returns idx of minimum child if it exists, otherwise None\"\n        il = self._i_left(idx)\n        ir = self._i_right(idx)\n\n        # 0 or 1 children\n        if ir is None: return il\n\n        return il if self._entries[il] < self._entries[ir] else ir\n\n\n    def _upheap(self, idx):\n        \"upheaps item at idx\"\n        # find parent index\n        i_p = self._i_parent(idx)\n\n        # while parent exists and parent is bigger: swap\n        while i_p is not None and self._entries[i_p] > self._entries[idx]:\n            # swap them\n            self._entries[i_p], self._entries[idx] = self._entries[idx], self._entries[i_p]\n            # update vars for next loop\n            idx = i_p\n            i_p = self._i_parent(idx)\n\n\n    def _downheap(self, idx):\n        \"downheaps item at idx\"\n        i_min = self._i_min_child(idx)\n\n        # while loop used to check that i_min is not Noen and the value associated with it is less than the value associated with idx\n        while i_min is not None and self._entries[i_min] < self._entries[idx]:\n            self._entries[i_min], self._entries[idx] = self._entries[idx], self._entries[i_min]\n\n            # update vars for next loop\n            idx = i_min\n            i_min = self._i_min_child(idx)\n\n\n    def __len__(self):\n        return len(self._entries)\n    \n    \n    def validate_time(self, time):\n        \"\"\"Validates the time is the correct format as well as not allowing for times that are not possible\"\"\"\n        try:\n            # splits the time into appropriate format\n            time = time.split(':')\n            # assigns to hour and minute\n            hour, minute = time[0], time[1]\n            # if the hours and minutes fit inside the criteria of hours in a day and minutes in an hour\n            if int(hour) < 24 and int(hour) >= 0 and int(minute) < 60 and int(minute) >= 0:\n                return True\n            else:\n                return False\n        except:\n            return False\n\n\n\n        \n    \n\n\n\n","repo_name":"BryanM2204/CSE-2050","sub_path":"HW/HW_10/waitlist.py","file_name":"waitlist.py","file_ext":"py","file_size_in_byte":7268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"40894407243","text":"from PyQt5.QtWidgets import QProgressDialog, QPushButton\nfrom PyQt5.QtCore import Qt\nfrom .base_dialogue import BaseDialogue\nfrom random import random\n\nclass ShakeDialogue(BaseDialogue):\n    STATE_MAX = 1000\n\n    def __init__(self, input_handler):\n        super().__init__(input_handler)\n\n        self.widget = QProgressDialog(\n            \"The ghost is on you! Shake the controller to get rid of it\", \"Die\", 0, 1000)\n        self.widget.setWindowModality(Qt.WindowModality.WindowModal)\n        self.widget.setAutoReset(False)\n        self.state = 100.0\n\n    def run(self):\n        super().run()\n        self.evs.append(self.input_handler.attach(\n            \"FRAME\", self.handle_increment))\n        self.widget.canceled.connect(self.handle_select)\n\n    def handle_increment(self):\n        shake = self.input_handler.get_shake()\n        self.state += shake * 30.0\n        self.state -= (random() + 0.3) * 12\n        print(self.state)\n        self.widget.setValue(int(self.state))\n        if self.state >= self.STATE_MAX:\n            self.handle_select()\n\n    def evaluate(self, button: QPushButton | None ):\n        return self.state >= self.STATE_MAX \n\n# Add the shake dialogue to random dialogues\nBaseDialogue.RANDOM_DIALOGUES.append(ShakeDialogue)\n","repo_name":"Wilkuu-2/AIP_Final_2022","sub_path":"dialogues/shake_dialogue.py","file_name":"shake_dialogue.py","file_ext":"py","file_size_in_byte":1251,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"71351087645","text":"\"\"\"\nhttps://leetcode.com/problems/largest-positive-integer-that-exists-with-its-negative/description/\n\nTime complexity: O(n), n = len(nums)\nSpace complexity: O(n)\n\"\"\"\nclass Solution:\n    def findMaxK(self, nums: List[int]) -> int:\n        s = set(nums)\n        res = -1\n\n        for n in s:\n            if n > 0 and -n in s:\n                res = max(res, n)\n        return res","repo_name":"dextermallo/Dev-Notes","sub_path":"leetcode/2441.Largest-Positive-Integer-That-Exists-With-Its-Negative.py","file_name":"2441.Largest-Positive-Integer-That-Exists-With-Its-Negative.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"28681188435","text":"import gzip, glob\nfrom lxml import etree\nfrom collections import namedtuple\n\n# Namedtuple to represent tokens.\nToken = namedtuple('Token', ['token', 'POS', 'lemma', 'depid', 'dephead', 'deprel'])\n\ndef cowfiles(path='./'):\n    \"Wrapper for the glob module.\"\n    return glob.glob(path+'*.xml.gz')\n\ndef split_element_text(element):\n    \"\"\"Helper function to make things readable. etree.tostring() returns byte representations,\n    but you cannot use .split() on them. For that, you need to decode to utf-8.\n    This function does just that, and returns the text as a list of lines.\"\"\"\n    return etree.tostring(element).decode('utf-8').split('\\n')\n\ndef sentence_data_generator(element):\n    \"Generator that takes a sentence element and yields tokens.\"\n    for line in split_element_text(element):\n        if not line.startswith('<') and not line == '':\n            data = line.split('\\t')\n            # Ugly hack because there are some elements with messed up data:\n            if len(data) == 6:\n                yield Token(*data)\n\ndef get_sentence_data(element):\n    \"Takes a sentence element and returns a list of tokens.\"\n    return list(sentence_data_generator(element))\n\ndef get_full_sentence_data(element):\n    structure = []\n    tokens    = []\n    for line in split_element_text(element):\n        if line.startswith('<s') or line.startswith('</s'):\n            continue\n        elif line.startswith('</'):\n            structure.append(line[2:-1] + '_close')\n        elif line.startswith('<'):\n            structure.append(line[1:-1] + '_open')\n        elif not line == '':\n            data = line.split('\\t')\n            # Ugly hack because there are some elements with messed up data:\n            if len(data) == 6:\n                structure.append(data[3])\n                tokens.append(Token(*data))\n    return (structure, tokens)\n\ndef clear_references(element):\n    \"\"\"\n    Lower memory by clearing references to elements we won't need\n    anymore. See also the answer at: http://stackoverflow.com/a/12161078/2899924\n    \"\"\"\n    element.clear()\n    for ancestor in element.xpath('ancestor-or-self::*'):\n        while ancestor.getprevious() is not None:\n            del ancestor.getparent()[0]\n\ndef sentence_generator(filename, gzipped=True, structure=False):\n    \"\"\"Returns metadata, optionally the sentence structure, and the sentence itself.\n    Each sentence is represented as a list of Token objects. Tokens are named tuples,\n    with the following values: ['token', 'POS', 'lemma', 'depid', 'dephead', 'deprel']\n    \n    Arguments\n    ---------\n    filename:  filename\n    gzipped:   assumes the file is gzipped. Change to False for unpacked files\n    structure: assumes we don't need information about sentence structure.\n               change to True to get this info.\n    \"\"\"\n    source = gzip.GzipFile(filename) if gzipped else filename\n    parser = etree.iterparse(source, html=True, events=('start','end',), tag='s')\n    # get_full_sentence_data() returns the structure and a list of tokens\n    # get_sentence_data() returns a list of tokens\n    data_func = get_full_sentence_data if structure else get_sentence_data\n    for event, element in parser:\n        if event == 'start':\n            # element.attrib() returns a dictionary with metadata for the sentence.\n            yield (element.attrib, data_func(element))\n            opening_element = element\n        elif event == 'end':\n            clear_references(opening_element)\n            clear_references(element)\n    # Aggressively keep memory load down\n    del parser\n\n\ndef separate(list_of_tokens):\n    return list(zip(*list_of_tokens))\n\ndef sentences_for_dir(path='./', gzipped=True, structure=False):\n    \"\"\"Sentence generator for an entire corpus directory.\n    \n    Arguments\n    ---------\n    path    :  path to the COW files\n    gzipped :  assumes the file is gzipped. Change to False for unpacked files\n    structure: assumes we don't need information about sentence structure.\n               change to True to get this info.\n    \"\"\"\n    for filename in cowfiles(path):\n        for metadata, data in sentence_generator(filename, gzipped, structure):\n            yield metadata, data\n","repo_name":"evanmiltenburg/cowparser_english","sub_path":"encowparser.py","file_name":"encowparser.py","file_ext":"py","file_size_in_byte":4169,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"32910000357","text":"import glob\nimport pandas\nimport numpy as np\nimport cv2\nimport os\nfrom utils import data_io\nfrom skimage.morphology import binary_erosion, disk\nfrom utils.progress_bar import progress_bar\nimport xml.etree.ElementTree as ET\nimport scipy\n\nGT_FILE = None\nPATH = None\n\ndef set_path(path):\n    global PATH\n    PATH = path\n\ndef load_all_patients(v=True):\n    files = glob.glob(os.path.join(PATH, \"AllDICOMs\", \"*\"))\n    pats = list(set([os.path.basename(file.split(\"_\")[1]) for file in files]))\n    return pats\n\ndef get_sides(p):\n    files = glob.glob(os.path.join(PATH, \"AllDICOMs\", f\"*_{p}_*\"))\n    sides = list(set([os.path.basename(file.split(\"_\")[3]) for file in files]))\n    return sides\n\ndef get_views(p, b):\n    files = glob.glob(os.path.join(PATH, \"AllDICOMs\", f\"*_{p}_*_{b}_*\"))\n    views = list(set([os.path.basename(file.split(\"_\")[4]) for file in files]))\n    return views\n\ndef get_unprocessed_mask_points(path):\n    if(not os.path.isfile(path)):\n        return []\n    tree = ET.parse(path)\n    root = tree.getroot()\n\n    numberOfROIs = root[0][1][0][3].text\n\n    lesions = []\n    inner = 0\n    for lesion in root[0][1][0][5]:\n        name = lesion[15].text\n        Npoints = int(lesion[17].text)\n        points = np.zeros((Npoints,2))\n\n        for i in range(Npoints):\n            text = lesion[21][i].text\n            numT = text.strip(\"()\").split(\",\")\n            points[i,0] = float(numT[0])\n            points[i,1] = float(numT[1])\n\n        if name == \"Calcification\":\n            lesions.append([points,\"C\"])\n        elif name == \"Mass\":\n            lesions.append([points,\"M\"])\n        elif name == \"Cluster\":\n            lesions.append([points,\"R\"])\n        elif name == \"Point1\":\n            print(\"ONE POINT ONE IGNORED\")\n        elif name == \"Distortion\":\n            lesions.append(([points,\"R\"]))\n    return lesions\n\ndef get_masses(img_code):\n    files = glob.glob(os.path.join(PATH, \"AllDICOMs\", f\"{img_code}_*\"))\n    assert len(files) == 1\n    image_code = os.path.basename(files[0]).split(\"_\")[0]\n    path = os.path.join(PATH, \"AllXML\", f\"{image_code}.xml\")\n    if not os.path.isfile(path):\n        return []\n    else:\n        masses = []\n        count = 1\n        lesions = get_unprocessed_mask_points(path)\n        for lesion in lesions:\n            if lesion[1] == \"M\":\n                masses.append((lesion[0], count))\n                count+=1\n        return masses\n\ndef get_img_codes(p):\n    candidates = glob.glob(os.path.join(PATH, \"AllDICOMs\", f\"*_{p}_*.dcm\"))\n    return [os.path.basename(x).split(\"_\")[0] for x in candidates]\n\ndef load_view(img_code):\n    return data_io.read_dicom_img(glob.glob(os.path.join(PATH, \"AllDICOMs\", f\"{img_code}_*.dcm\"))[0])\n\ndef load_mass_mask(points, shape):\n    points = np.array(points)\n    if points.shape[0] > 2:\n        points = scipy.signal.resample(points, 100000)\n\n    mask = np.zeros([*shape])\n    xx = np.round(points[:,1]).astype(int).clip(0, mask.shape[0]-1)\n    yy = np.round(points[:,0]).astype(int).clip(0, mask.shape[1]-1)\n    mask[xx, yy] = 1\n    mask = scipy.ndimage.morphology.binary_fill_holes(mask)\n    assert mask.max() == True\n    return mask * 255\n\ndef proc_lesion_mask(mask, shape):\n    return cv2.resize(mask, dsize=shape[::-1], interpolation=cv2.INTER_NEAREST)\n\ndef load_gt_files():\n    global GT_FILE\n    if GT_FILE is None:\n        GT_FILE = pandas.read_csv(PATH+\"INbreast.csv\", delimiter=\";\")\n\ndef get_file_id(p, b, v):\n    print(p, b, v)\n    candidates = glob.glob(os.path.join(PATH, \"AllDICOMs\", f\"*_{p}_*_{b}_{v}_*.dcm\"))\n    assert len(candidates) == 1\n    return os.path.basename(candidates[0]).split(\"_\")[0]\n\n\nbirads_to_malignancy = {\"1\":\"BENIGN\",\n                        \"2\":\"BENIGN\",\n                        \"3\":\"MALIGNANT\",\n                        \"4\":\"MALIGNANT\",\n                        \"5\":\"MALIGNANT\",\n                        \"6\":\"MALIGNANT\"}\n\ndef get_lesion_label(file_name):\n    global GT_FILE\n    if GT_FILE is None:\n        load_gt_files()\n\n    def loc(file_name):\n        cell = GT_FILE.loc[(GT_FILE[\"File Name\"] == int(file_name))][\"Bi-Rads\"]\n        return cell\n\n    cell = loc(file_name)\n    #print(cell)\n    assert len(cell) == 1\n    return birads_to_malignancy[str(cell.iloc[0])[0]]\n\ndef get_sampling_neg(breast_mask, lesions):\n    mask = breast_mask.copy()\n\n    for num, _, lesion_mask in lesions:\n        mask = np.logical_and(mask, np.logical_not(lesion_mask))\n\n    mask = np.pad(mask, (1, 1), \"constant\")\n    selem = disk(8)\n    for _ in range(18):\n        mask = binary_erosion(mask, selem)\n    mask = mask[1:-1, 1:-1]\n    return mask\n\n\ndef overlap_patch_roi(xy, patch_size, lesion_mask):\n    x,y=xy\n    lesion_area = lesion_mask.sum()\n    patch_covered_area = lesion_mask[y - patch_size//2:y + patch_size//2,\n                                     x - patch_size//2:x + patch_size//2].sum()\n    return patch_covered_area/min(lesion_area, patch_size**2)\n\ndef overlap_breast_patch(xy, patch_size, breast_mask):\n    x,y=xy\n    patch_area = patch_size**2\n    breast_covered_area = breast_mask[y - patch_size//2:y + patch_size//2,\n                                      x - patch_size//2:x + patch_size//2].sum()\n    return breast_covered_area/patch_area\n","repo_name":"edux300/symmetry-based-regularization-in-deep-breast-cancer-screening","sub_path":"datasets/inbreast/auxiliary.py","file_name":"auxiliary.py","file_ext":"py","file_size_in_byte":5169,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"86"}
+{"seq_id":"744556692","text":"import unittest\nimport pysam\nimport tempfile\n\nfrom svim.SVIM_COLLECT import bam_iterator, analyze_alignment_file_querysorted, retrieve_other_alignments\nfrom svim.SVIM_input_parsing import parse_arguments\nfrom random import choice, triangular, uniform\n\nclass TestCollect(unittest.TestCase):\n\n    def generate_random_sequence(self, length):\n        sequence=\"\"\n        for i in range(length):\n            sequence+=choice(\"ACGT\")\n        return sequence\n\n    def generate_random_cigar_string(self, readlength):\n        \"\"\"Generate random cigar string for a read of a given length. Simulate small mismatches and indels but nothing larger than 10bp.\"\"\"\n        softclip_left = round(triangular(0, readlength, min(1000, readlength * 0.5)))\n        non_clipped = readlength - softclip_left\n        softclip_right = round(triangular(0, non_clipped, min(1000, non_clipped * 0.5)))\n        non_clipped = readlength - softclip_left - softclip_right\n        sequence = \"\"\n        read_bases_consumed = 0\n        while read_bases_consumed < non_clipped:\n            #choose next operation\n            if len(sequence) == 0 or sequence[-1] == \"I\" or sequence[-1] == \"D\":\n                next_operation = \"M\"\n                next_length = round(triangular(1, non_clipped - read_bases_consumed, min(30, non_clipped - read_bases_consumed)))\n                read_bases_consumed += next_length\n            else:\n                next_operation = choice(\"ID\")\n                if next_operation == \"I\":\n                    next_length = round(triangular(1, min(10, non_clipped - read_bases_consumed), 1))\n                    read_bases_consumed += next_length\n                else:\n                    next_length = round(triangular(1, 10, 1))\n            sequence += str(next_length) + next_operation\n        return \"{0}S{1}{2}S\".format(softclip_left, sequence, softclip_right)\n\n\n    def generate_random_cigar_string_hardclipped(self, readlength):\n        \"\"\"Generate random cigar string for a read of a given length.\n        Simulate small mismatches and indels but nothing larger than 10bp. Simulate hard-clipping and return tuple (left-clipped, right-clipped, cigar)\"\"\"\n        hardclip_left = round(triangular(0, readlength, min(1000, readlength * 0.5)))\n        non_clipped = readlength - hardclip_left\n        hardclip_right = round(triangular(0, non_clipped, min(1000, non_clipped * 0.5)))\n        non_clipped = readlength - hardclip_left - hardclip_right\n        sequence = \"\"\n        read_bases_consumed = 0\n        while read_bases_consumed < non_clipped:\n            #choose next operation\n            if len(sequence) == 0 or sequence[-1] == \"I\" or sequence[-1] == \"D\":\n                next_operation = \"M\"\n                next_length = round(triangular(1, non_clipped - read_bases_consumed, min(30, non_clipped - read_bases_consumed)))\n                read_bases_consumed += next_length\n            else:\n                next_operation = choice(\"ID\")\n                if next_operation == \"I\":\n                    next_length = round(triangular(1, min(10, non_clipped - read_bases_consumed), 1))\n                    read_bases_consumed += next_length\n                else:\n                    next_length = round(triangular(1, 10, 1))\n            sequence += str(next_length) + next_operation\n        return (hardclip_left, hardclip_right, \"{0}H{1}{2}H\".format(hardclip_left, sequence, hardclip_right))\n\n    def generate_read(self, qname, flag):\n        rname = \"chr1\"\n        pos = int(uniform(1,249250620))\n        mapq = int(triangular(0, 60, 50))\n        length = int(triangular(100, 20000, 15000))\n        cigar = self.generate_random_cigar_string(length)\n        seq = self.generate_random_sequence(length)\n\n        read_info = (qname, flag, rname, pos, mapq, cigar, \"*\", 0, 0, seq, \"*\", \"\")\n\n        return read_info\n\n\n    def generate_split_read_with_sa_tags(self, qname, flag):\n        length = int(triangular(100, 20000, 15000))\n        seq = self.generate_random_sequence(length)\n\n        suppl_rname = \"chr1\"\n        suppl_pos = int(uniform(1,249250620))\n        suppl_mapq = int(triangular(0, 60, 50))\n        suppl_hardclipped_left, suppl_hardclipped_right, suppl_cigar = self.generate_random_cigar_string_hardclipped(length)\n\n        prim_rname = \"chr1\"\n        prim_pos = int(uniform(1,249250620))\n        prim_mapq = int(triangular(0, 60, 50))\n        prim_cigar = self.generate_random_cigar_string(length)\n\n        supplementary_read_info = ( qname,\n                                    flag + 2048,\n                                    suppl_rname,\n                                    suppl_pos,\n                                    suppl_mapq,\n                                    suppl_cigar,\n                                    \"*\",\n                                    0,\n                                    0,\n                                    seq[suppl_hardclipped_left:-suppl_hardclipped_right],\n                                    \"*\",\n                                    \"SA:Z:{rname},{pos},{strand},{cigar},{mapq},{nm};\".format(rname=prim_rname,\n                                                                                              pos=prim_pos,\n                                                                                              strand=(\"-\" if flag & 16 else \"+\"),\n                                                                                              cigar=prim_cigar,\n                                                                                              mapq=prim_mapq,\n                                                                                              nm=0))\n        primary_read_info = (   qname,\n                                flag,\n                                prim_rname,\n                                prim_pos,\n                                prim_mapq,\n                                prim_cigar,\n                                \"*\",\n                                0,\n                                0,\n                                seq,\n                                \"*\",\n                                \"SA:Z:{rname},{pos},{strand},{cigar},{mapq},{nm};\".format(rname=suppl_rname,\n                                                                                          pos=suppl_pos,\n                                                                                          strand=(\"-\" if flag & 16 else \"+\"),\n                                                                                          cigar=suppl_cigar.replace(\"H\", \"S\"),\n                                                                                          mapq=suppl_mapq,\n                                                                                          nm=0))\n        return (primary_read_info, supplementary_read_info)\n\n    def setUp(self):\n        self.bam_file = tempfile.NamedTemporaryFile()\n        header = \"\"\"@HD\tVN:1.0\tSO:queryname\n@SQ\tSN:chr1\tLN:249250621\n@SQ\tSN:chr2\tLN:243199373\n@SQ\tSN:chr3\tLN:198022430\n@SQ\tSN:chr4\tLN:191154276\n@SQ\tSN:chr5\tLN:180915260\n@SQ\tSN:chr6\tLN:171115067\n@SQ\tSN:chr7\tLN:159138663\n@SQ\tSN:chr8\tLN:146364022\n@SQ\tSN:chr9\tLN:141213431\n@SQ\tSN:chr10\tLN:135534747\n@SQ\tSN:chr11\tLN:135006516\n@SQ\tSN:chr12\tLN:133851895\n@SQ\tSN:chr13\tLN:115169878\n@SQ\tSN:chr14\tLN:107349540\n@SQ\tSN:chr15\tLN:102531392\n@SQ\tSN:chr16\tLN:90354753\n@SQ\tSN:chr17\tLN:81195210\n@SQ\tSN:chr18\tLN:78077248\n@SQ\tSN:chr19\tLN:59128983\n@SQ\tSN:chr20\tLN:63025520\n@SQ\tSN:chr21\tLN:48129895\n@SQ\tSN:chr22\tLN:51304566\n@SQ\tSN:chrX\tLN:155270560\n@SQ\tSN:chrY\tLN:59373566\n@SQ\tSN:chr6_ssto_hap7\tLN:4928567\n@SQ\tSN:chr6_mcf_hap5\tLN:4833398\n@SQ\tSN:chr6_cox_hap2\tLN:4795371\n@SQ\tSN:chr6_mann_hap4\tLN:4683263\n@SQ\tSN:chr6_apd_hap1\tLN:4622290\n@SQ\tSN:chr6_qbl_hap6\tLN:4611984\n@SQ\tSN:chr6_dbb_hap3\tLN:4610396\n@SQ\tSN:chr17_ctg5_hap1\tLN:1680828\n@SQ\tSN:chr4_ctg9_hap1\tLN:590426\n@SQ\tSN:chr1_gl000192_random\tLN:547496\n@SQ\tSN:chrUn_gl000225\tLN:211173\n@SQ\tSN:chr4_gl000194_random\tLN:191469\n@SQ\tSN:chr4_gl000193_random\tLN:189789\n@SQ\tSN:chr9_gl000200_random\tLN:187035\n@SQ\tSN:chrUn_gl000222\tLN:186861\n@SQ\tSN:chrUn_gl000212\tLN:186858\n@SQ\tSN:chr7_gl000195_random\tLN:182896\n@SQ\tSN:chrUn_gl000223\tLN:180455\n@SQ\tSN:chrUn_gl000224\tLN:179693\n@SQ\tSN:chrUn_gl000219\tLN:179198\n@SQ\tSN:chr17_gl000205_random\tLN:174588\n@SQ\tSN:chrUn_gl000215\tLN:172545\n@SQ\tSN:chrUn_gl000216\tLN:172294\n@SQ\tSN:chrUn_gl000217\tLN:172149\n@SQ\tSN:chr9_gl000199_random\tLN:169874\n@SQ\tSN:chrUn_gl000211\tLN:166566\n@SQ\tSN:chrUn_gl000213\tLN:164239\n@SQ\tSN:chrUn_gl000220\tLN:161802\n@SQ\tSN:chrUn_gl000218\tLN:161147\n@SQ\tSN:chr19_gl000209_random\tLN:159169\n@SQ\tSN:chrUn_gl000221\tLN:155397\n@SQ\tSN:chrUn_gl000214\tLN:137718\n@SQ\tSN:chrUn_gl000228\tLN:129120\n@SQ\tSN:chrUn_gl000227\tLN:128374\n@SQ\tSN:chr1_gl000191_random\tLN:106433\n@SQ\tSN:chr19_gl000208_random\tLN:92689\n@SQ\tSN:chr9_gl000198_random\tLN:90085\n@SQ\tSN:chr17_gl000204_random\tLN:81310\n@SQ\tSN:chrUn_gl000233\tLN:45941\n@SQ\tSN:chrUn_gl000237\tLN:45867\n@SQ\tSN:chrUn_gl000230\tLN:43691\n@SQ\tSN:chrUn_gl000242\tLN:43523\n@SQ\tSN:chrUn_gl000243\tLN:43341\n@SQ\tSN:chrUn_gl000241\tLN:42152\n@SQ\tSN:chrUn_gl000236\tLN:41934\n@SQ\tSN:chrUn_gl000240\tLN:41933\n@SQ\tSN:chr17_gl000206_random\tLN:41001\n@SQ\tSN:chrUn_gl000232\tLN:40652\n@SQ\tSN:chrUn_gl000234\tLN:40531\n@SQ\tSN:chr11_gl000202_random\tLN:40103\n@SQ\tSN:chrUn_gl000238\tLN:39939\n@SQ\tSN:chrUn_gl000244\tLN:39929\n@SQ\tSN:chrUn_gl000248\tLN:39786\n@SQ\tSN:chr8_gl000196_random\tLN:38914\n@SQ\tSN:chrUn_gl000249\tLN:38502\n@SQ\tSN:chrUn_gl000246\tLN:38154\n@SQ\tSN:chr17_gl000203_random\tLN:37498\n@SQ\tSN:chr8_gl000197_random\tLN:37175\n@SQ\tSN:chrUn_gl000245\tLN:36651\n@SQ\tSN:chrUn_gl000247\tLN:36422\n@SQ\tSN:chr9_gl000201_random\tLN:36148\n@SQ\tSN:chrUn_gl000235\tLN:34474\n@SQ\tSN:chrUn_gl000239\tLN:33824\n@SQ\tSN:chr21_gl000210_random\tLN:27682\n@SQ\tSN:chrUn_gl000231\tLN:27386\n@SQ\tSN:chrUn_gl000229\tLN:19913\n@SQ\tSN:chrM\tLN:16571\n@SQ\tSN:chrUn_gl000226\tLN:15008\n@SQ\tSN:chr18_gl000207_random\tLN:4262\n@PG\tID:ngmlr\tPN:nextgenmap-lr\tVN:0.2.7\tCL:ngmlr -t 10 -r hg19.fa -q reads.fa -o reads.ngmlr.hg19.bam\"\"\"\n        self.bam_file.write(header.encode('utf-8'))\n\n        self.read_infos = []\n        #10 reads with only primary alignment\n        for index in range(10):\n            read_info = self.generate_read(\"read{}\".format(index+1), 0)\n            self.read_infos.append(read_info)\n            sam_entry = \"\\n\" + \"\\t\".join([str(el) for el in read_info])\n            self.bam_file.write(sam_entry.encode('utf-8'))\n\n        #10 reads with primary and supplementary alignment\n        for index in range(10, 20):\n            primary_read_info, supplementary_read_info = self.generate_split_read_with_sa_tags(\"read{}\".format(index+1), 0)\n            #primary with SA tag\n            self.read_infos.append(primary_read_info)\n            sam_entry = \"\\n\" + \"\\t\".join([str(el) for el in primary_read_info])\n            self.bam_file.write(sam_entry.encode('utf-8'))\n            #supplementary\n            self.read_infos.append(supplementary_read_info)\n            sam_entry = \"\\n\" + \"\\t\".join([str(el) for el in supplementary_read_info])\n            self.bam_file.write(sam_entry.encode('utf-8'))            \n\n        self.bam_file.seek(0)\n        self.alignment_file = pysam.AlignmentFile(self.bam_file.name, \"rb\")\n\n    def test_bam_iterator(self):\n        bam_it = bam_iterator(self.alignment_file)\n\n        num_primary_only = 0\n        num_primary_supplementary = 0\n        num_total = 0\n        for prim, suppl, sec in bam_it:\n            if len(prim) == 1 and len(suppl) == 0 and len(sec) == 0:\n                num_primary_only += 1                \n            if len(prim) == 1 and len(suppl) == 1 and len(sec) == 0:\n                num_primary_supplementary += 1\n            num_total += 1\n        \n        self.assertEqual(num_total, 20)\n        self.assertEqual(num_primary_only, 10)\n        self.assertEqual(num_primary_supplementary, 10)\n\n    def test_analyze_alignment_file_querysorted(self):\n        arguments = ['alignment', 'myworkdir', 'mybamfile', 'mygenome']\n        options = parse_arguments('1.2.0', arguments)\n        signatures, translocation_signatures_all_bnds = analyze_alignment_file_querysorted(self.alignment_file, options)\n        self.assertEqual(len([sig for sig in signatures if sig.signature == \"cigar\"]), 0)\n    \n    def test_retrieve_supplementary_alignment_from_primary(self):\n        alignment_it = self.alignment_file.fetch(until_eof=True)\n        alignments = list(alignment_it)\n        for i in range(10,30,2):\n            primary = alignments[i]\n            supplementary = alignments[i+1]\n            retrieved_supplementary_alns = retrieve_other_alignments(primary, self.alignment_file)\n            self.assertEqual(len(retrieved_supplementary_alns), 1)\n            self.assertEqual(retrieved_supplementary_alns[0].cigarstring, supplementary.cigarstring.replace(\"H\", \"S\"))\n            self.assertEqual(retrieved_supplementary_alns[0].reference_id, supplementary.reference_id)\n            self.assertEqual(retrieved_supplementary_alns[0].reference_start, supplementary.reference_start)\n            self.assertEqual(retrieved_supplementary_alns[0].reference_end, supplementary.reference_end)\n            self.assertEqual(retrieved_supplementary_alns[0].flag, supplementary.flag)\n            self.assertEqual(retrieved_supplementary_alns[0].mapping_quality, supplementary.mapping_quality)\n            self.assertEqual(retrieved_supplementary_alns[0].query_sequence[retrieved_supplementary_alns[0].query_alignment_start:retrieved_supplementary_alns[0].query_alignment_end], supplementary.query_sequence)\n            self.assertEqual(retrieved_supplementary_alns[0].query_name, supplementary.query_name)\n\n    def test_retrieve_primary_alignment_from_supplementary(self):\n        alignment_it = self.alignment_file.fetch(until_eof=True)\n        alignments = list(alignment_it)\n        for i in range(10,30,2):\n            primary = alignments[i]\n            supplementary = alignments[i+1]\n            retrieved_primary_alns = retrieve_other_alignments(supplementary, self.alignment_file)\n            self.assertEqual(len(retrieved_primary_alns), 0)","repo_name":"eldariont/svim","sub_path":"src/tests/test_Collect.py","file_name":"test_Collect.py","file_ext":"py","file_size_in_byte":13988,"program_lang":"python","lang":"en","doc_type":"code","stars":134,"dataset":"github-code","pt":"86"}
+{"seq_id":"74448141083","text":"import numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nfrom pathlib import Path\n\nimport os\n\nimport IPython\n\nimport IPython.display\n\nimport matplotlib.pyplot as plt\n\nfrom tqdm import tqdm_notebook\n\nfrom PIL import Image               # to load images\n\nfrom IPython.display import display\n\nimport time\n\nprint(os.listdir(\"../input\"))\n# Special thanks to https://github.com/makinacorpus/easydict/blob/master/easydict/__init__.py\n\nclass EasyDict(dict):\n\n    def __init__(self, d=None, **kwargs):\n\n        if d is None:\n\n            d = {}\n\n        if kwargs:\n\n            d.update(**kwargs)\n\n        for k, v in d.items():\n\n            setattr(self, k, v)\n\n        # Class attributes\n\n        for k in self.__class__.__dict__.keys():\n\n            if not (k.startswith('__') and k.endswith('__')) and not k in ('update', 'pop'):\n\n                setattr(self, k, getattr(self, k))\n\n\n\n    def __setattr__(self, name, value):\n\n        if isinstance(value, (list, tuple)):\n\n            value = [self.__class__(x)\n\n                     if isinstance(x, dict) else x for x in value]\n\n        elif isinstance(value, dict) and not isinstance(value, self.__class__):\n\n            value = self.__class__(value)\n\n        super(EasyDict, self).__setattr__(name, value)\n\n        super(EasyDict, self).__setitem__(name, value)\n\n\n\n    __setitem__ = __setattr__\n\n\n\n    def update(self, e=None, **f):\n\n        d = e or dict()\n\n        d.update(f)\n\n        for k in d:\n\n            setattr(self, k, d[k])\n\n\n\n    def pop(self, k, d=None):\n\n        delattr(self, k)\n\n        return super(EasyDict, self).pop(k, d)\nconf = EasyDict()\n\nconf.sampling_rate = 44100\n\nconf.duration = 2\n\nconf.hop_length = 347 # to make time steps 128\n\nconf.fmin = 20\n\nconf.fmax = conf.sampling_rate // 2\n\nconf.n_mels = 128\n\nconf.n_fft = conf.n_mels * 20\n\nconf.samples = conf.sampling_rate * conf.duration\nimport librosa\n\nimport librosa.display\n\ndef read_audio(conf, pathname, trim_long_data):\n\n    y, sr = librosa.load(pathname, sr=conf.sampling_rate)\n\n    # trim silence\n\n    if 0 < len(y): # workaround: 0 length causes error\n\n        y, _ = librosa.effects.trim(y) # trim, top_db=default(60)\n\n    # make it unified length to conf.samples\n\n    if len(y) > conf.samples: # long enough\n\n        if trim_long_data:\n\n            y = y[0:0+conf.samples]\n\n    else: # pad blank\n\n        padding = conf.samples - len(y)    # add padding at both ends\n\n        offset = padding // 2\n\n        y = np.pad(y, (offset, conf.samples - len(y) - offset), 'constant')\n\n    return y\nstart_time_data_processing = time.time()\n\n\n\nDATA = Path('../input')\n\nCSV_TRN_CURATED = DATA/'train_curated.csv'\n\nCSV_TRN_NOISY = DATA/'train_noisy.csv'\n\nCSV_SUBMISSION = DATA/'sample_submission.csv'\n\nTRN_CURATED = DATA/'train_curated'\n\nTRN_NOISY = DATA/'train_noisy'\n\nTEST = DATA/'test'\n\n\n\nWORK = Path('work')\n\nIMG_TRN_CURATED = WORK/'image/trn_curated'\n\nIMG_TRN_NOISY = WORK/'image/train_noisy'\n\nIMG_TEST = WORK/'image/test'\n\n\n\ndf_train_curated = pd.read_csv(CSV_TRN_CURATED)\n\nprint(df_train_curated.head(10))\n\n# Collecting various data frames for further processing.\n\ndf_bark = df_train_curated.loc[df_train_curated['labels'] == 'Bark'][1:5]\n\ndf_run = df_train_curated.loc[df_train_curated['labels'] == 'Run'][1:5]\nbuzz_1_file =  DATA/'train_curated'/'02f54ef1.wav'\n\ny_2_secs = read_audio(conf, buzz_1_file, trim_long_data = True)\n\ny_full = read_audio(conf, buzz_1_file, trim_long_data = False)\n\nprint(len(y_full))\n\nprint(len(y_2_secs))\n\nprint(y_full.shape[0]/44100)\nbark_file = DATA/'train_curated'/'0006ae4e.wav'\n\ny_bark = read_audio(conf, buzz_1_file, trim_long_data = False)\n\nprint(y_bark.shape[0]/ 44100)\ndef audio_to_melspectrogram(conf, audio):\n\n    spectrogram = librosa.feature.melspectrogram(audio, \n\n                                                 sr=conf.sampling_rate,\n\n                                                 n_mels=conf.n_mels,\n\n                                                 hop_length=conf.hop_length,\n\n                                                 n_fft=conf.n_fft,\n\n                                                 fmin=conf.fmin,\n\n                                                 fmax=conf.fmax)\n\n    spectrogram = librosa.power_to_db(spectrogram)\n\n    spectrogram = spectrogram.astype(np.float32)\n\n    return spectrogram\n\n\n\ndef show_melspectrogram(conf, mels, title='Log-frequency power spectrogram'):\n\n    librosa.display.specshow(mels, x_axis='time', y_axis='mel', \n\n                             sr=conf.sampling_rate, hop_length=conf.hop_length,\n\n                            fmin=conf.fmin, fmax=conf.fmax)\n\n    plt.colorbar(format='%+2.0f dB')\n\n    plt.title(title)\n\n    plt.show()\n\n\n\ndef read_as_melspectrogram(conf, pathname, trim_long_data, debug_display=False):\n\n    x = read_audio(conf, pathname, trim_long_data)\n\n    mels = audio_to_melspectrogram(conf, x)\n\n    if debug_display:\n\n        IPython.display.display(IPython.display.Audio(x, rate=conf.sampling_rate))\n\n        show_melspectrogram(conf, mels)\n\n    return mels\n\nTRN_CURATED = DATA/'train_curated'\n\nx = read_audio(conf, TRN_CURATED/'0006ae4e.wav', trim_long_data=False)\n\nprint(x.shape)\n\nmels = audio_to_melspectrogram(conf, x)\n\nprint(mels.dtype)\n\nprint(mels.shape)\n\nprint(mels)\nbark = read_as_melspectrogram(conf, TRN_CURATED/'0006ae4e.wav', trim_long_data=False, debug_display=True)\n\nbuzz = read_as_melspectrogram(conf, TRN_CURATED/'02f54ef1.wav', trim_long_data=False, debug_display=True)\nprint(bark.shape)\n\nprint(buzz.shape)\ndef mono_to_color(X, mean=None, std=None, norm_max=None, norm_min=None, eps=1e-6):\n\n    # Stack X as [X,X,X]\n\n    X = np.stack([X, X, X], axis=-1)\n\n\n\n    # Standardize\n\n    mean = mean or X.mean()\n\n    std = std or X.std()\n\n    Xstd = (X - mean) / (std + eps)\n\n    _min, _max = Xstd.min(), Xstd.max()\n\n    norm_max = norm_max or _max\n\n    norm_min = norm_min or _min\n\n    if (_max - _min) > eps:\n\n        # Scale to [0, 255]\n\n        V = Xstd\n\n        V[V < norm_min] = norm_min\n\n        V[V > norm_max] = norm_max\n\n        V = 255 * (V - norm_min) / (norm_max - norm_min)\n\n        V = V.astype(np.uint8)\n\n    else:\n\n        # Just zero\n\n        V = np.zeros_like(Xstd, dtype=np.uint8)\n\n    return V\n\n\n\ndef convert_wav_to_image(df, source, img_dest):\n\n    X = []\n\n    for i, row in tqdm_notebook(df.iterrows()):\n\n        x = read_as_melspectrogram(conf, source/str(row.fname), trim_long_data=False)\n\n        x_color = mono_to_color(x)\n\n        X.append(x_color)\n\n    return df, X\n\n\n\ndef convert_wav_to_cropped_image(df, source, img_dest):\n\n    X = []\n\n    for i, row in tqdm_notebook(df.iterrows()):\n\n        x = read_as_melspectrogram(conf, source/str(row.fname), trim_long_data=False)\n\n        x_color = mono_to_color(x)\n\n        # - - - - - - - - - - #\n\n        x_color = Image.fromarray(x_color)\n\n        time_dim, base_dim = x_color.size\n\n        crop_x = random.randint(0, time_dim - base_dim)\n\n        x_cropped = x_color.crop([crop_x, 0, crop_x+base_dim, base_dim]) \n\n        # - - - - - - - - - - #\n\n        X.append(x_cropped)\n\n    return df, X\nprint(bark.shape)\n\nbark_image = mono_to_color(bark)\n\nprint(bark_image.shape)\nimport random\n\nx = Image.fromarray(bark_image)\n\ndisplay(x)\n\ntime_dim, base_dim = x.size\n\ncrop_x = random.randint(0, time_dim - base_dim)\n\nx_cropped = x.crop([crop_x, 0, crop_x+base_dim, base_dim]) \n\ndisplay(x_cropped)\ndef get_cropped_image(conf, path_of_image, display_image =True):\n\n    mel_spec_gram = read_as_melspectrogram(conf, path_of_image, trim_long_data=False, debug_display=False)\n\n    img_array = mono_to_color(mel_spec_gram)\n\n    img = Image.fromarray(img_array)\n\n    time_dim, base_dim = img.size\n\n    cropped = random.randint(0, time_dim - base_dim)\n\n    cropped_image = img.crop([cropped, 0, cropped+base_dim, base_dim]) \n\n    if display:\n\n        display(cropped_image)\n\n    return(cropped_image)\nx1 = get_cropped_image(conf, TRN_CURATED/df_bark.iloc[0,0])\n\nx2 = get_cropped_image(conf, TRN_CURATED/df_bark.iloc[1,0])\n\nx3 = get_cropped_image(conf, TRN_CURATED/df_bark.iloc[2,0])\nget_cropped_image(conf, TRN_CURATED/df_run.iloc[0,0])\n\nget_cropped_image(conf, TRN_CURATED/df_run.iloc[1,0])\n\nget_cropped_image(conf, TRN_CURATED/df_run.iloc[2,0])\ndf_test_bark, X_train_bark = convert_wav_to_cropped_image(df_bark, source=TRN_CURATED, img_dest=IMG_TRN_CURATED)\n\ndf_test_run, X_train_run = convert_wav_to_cropped_image(df_run, source=TRN_CURATED, img_dest=IMG_TRN_CURATED)\n# Just a quick dimension check here.\n\ntest_np_bark = np.vstack(X_train_bark)\n\ntest_np_run = np.vstack(X_train_run)\n\nprint(test_np_bark.shape)\n\nprint(test_np_run.shape)\n\nend_time_data_processing = time.time()\ndf_train_curated = df_train_curated.sample(100) # taking a sample data set of 100 examples.\n\ndf_train_curated, X_train_curated = convert_wav_to_cropped_image(df_train_curated, source=TRN_CURATED, img_dest=IMG_TRN_CURATED)\n\nnp_train_curated = np.vstack(X_train_curated)\n\nX_train_curated = np_train_curated.reshape(-1, 128, 128, 3) # Reshaping the training dataset.\n\nprint(X_train_curated.shape)\nX_train = X_train_curated\n\ndf_train = df_train_curated\n# Getting the labels required for submission, there are eighty of them.\n\ndf_test = pd.read_csv('../input/sample_submission.csv')\n\nlabel_columns = list( df_test.columns[1:] )\n\nlabel_mapping = dict((label, index) for index, label in enumerate(label_columns))\n\n#label_mapping\n\ndef split_and_label(rows_labels):\n\n    row_labels_list = []\n\n    for row in rows_labels:\n\n        row_labels = row.split(',')\n\n        labels_array = np.zeros((80))\n\n        \n\n        for label in row_labels:\n\n            index = label_mapping[label]\n\n            labels_array[index] = 1\n\n        \n\n        row_labels_list.append(labels_array)\n\n    \n\n    return row_labels_list\ntrain_curated_labels = split_and_label(df_train['labels'])\n\nfor f in label_columns:\n\n    df_train[f] = 0.0 # This adds all the labels as column names.\n\n\n\ndf_train[label_columns] = train_curated_labels\nY_train = np.vstack(train_curated_labels)\n\nprint(Y_train.shape)\n\nprint(X_train.shape)\ndf_test, X_test = convert_wav_to_cropped_image(df_test, source=TEST, img_dest=IMG_TEST)\n\nnp_test_small = np.vstack(X_test)\n\nprint(np_test_small.shape)\n\nX_test = np_test_small.reshape(-1, 128, 128, 3)\n\nprint(X_test.shape)\nprint (\"number of training examples = \" + str(X_train.shape[0]))\n\nprint (\"number of test examples = \" + str(X_test.shape[0]))\n\nprint (\"X_train shape: \" + str(X_train.shape))\n\nprint (\"Y_train shape: \" + str(Y_train.shape))\n\nprint (\"X_test shape: \" + str(X_test.shape))\nimport numpy as np\n\nfrom keras import layers\n\nfrom keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D\n\nfrom keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D\n\nfrom keras.models import Model\n\nfrom keras.preprocessing import image\n\nfrom keras.utils import layer_utils\n\nfrom keras.utils.data_utils import get_file\n\nfrom keras.applications.imagenet_utils import preprocess_input\n\nimport pydot\n\nfrom IPython.display import SVG\n\nfrom keras.utils.vis_utils import model_to_dot\n\nfrom keras.utils import plot_model\n\nfrom keras.initializers import glorot_uniform\n\n#from kt_utils import *\n\n\n\nimport keras.backend as K\n\nK.set_image_data_format('channels_last')\n\nimport matplotlib.pyplot as plt\n\nfrom matplotlib.pyplot import imshow\n\n\n\ndef Audio2DConvModel(input_shape, classes):\n\n    \"\"\"\n\n    Implementation of the Basic Model.\n\n    Arguments:\n\n    input_shape -- shape of the images of the dataset\n\n    Returns:\n\n    model -- a Model() instance in Keras\n\n    \"\"\"\n\n    X_input = Input(input_shape)\n\n    X = ZeroPadding2D((3, 3))(X_input)\n\n    X = Conv2D(64, (16, 16), strides = (1, 1), name = 'conv0')(X)\n\n    X = BatchNormalization(axis = 3, name = 'bn0')(X)\n\n    X = Activation('relu')(X)\n\n    #X = MaxPooling2D((2, 2), name='max_pool')(X)\n\n    X = MaxPooling2D(pool_size=(2, 2), name = 'maxpool_0')(X)\n\n    X = Dropout(rate=0.1)(X)\n\n# -------------------------------------------------------------------------------------\n\n    X = ZeroPadding2D((3, 3))(X)\n\n    X = Conv2D(32, (8, 8), strides = (1, 1), name = 'conv1')(X)\n\n    X = BatchNormalization(axis = 3, name = 'bn1')(X)\n\n    X = Activation('relu')(X)\n\n    X = MaxPooling2D(pool_size=(2, 2), name = 'maxpool_1')(X)\n\n    # ------------------------------------------------------------------------------------\n\n    X = ZeroPadding2D((3, 3))(X)\n\n    X = Conv2D(16, (4, 4), strides = (1, 1), name = 'conv2')(X)\n\n    X = BatchNormalization(axis = 3, name = 'bn2')(X)\n\n    X = Activation('relu')(X)\n\n    X = MaxPooling2D((2, 2), name='maxpool_2')(X)\n\n    #------------------------------------------------------------\n\n    X = ZeroPadding2D((3, 3))(X)\n\n    X = Conv2D(16, (2, 2), strides = (1, 1), name = 'conv3')(X)\n\n    X = BatchNormalization(axis = 3, name = 'bn32')(X)\n\n    X = Activation('relu')(X)\n\n    X = MaxPooling2D((2, 2), name='maxpool_3')(X)\n\n    #X = AveragePooling2D(pool_size=(2, 2), name = 'avg_pool_1')(X)\n\n    # -------------------------------------------------------------------------------------    \n\n    X = Flatten()(X)\n\n    X = Dense(classes, activation='softmax', name='fc_softmax' + str(classes), kernel_initializer=glorot_uniform(seed=0))(X)\n\n    model = Model(inputs = X_input, outputs = X, name='Audio2DConvModel')\n\n    return model\nbasic_conv_model = Audio2DConvModel(X_train.shape[1:], 80)\n\nbasic_conv_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n\nbasic_conv_model.fit(X_train/255, Y_train, epochs=1, batch_size=64)\n\nbasic_conv_model.summary()\n\ny_hat = basic_conv_model.predict(X_test/255)\n\ndf_test[label_columns] = y_hat\n\nprint(df_test.head())","repo_name":"aorursy/new-nb-6","sub_path":"riteshsinha_audio-data-analysis-and-convolutional-models.py","file_name":"riteshsinha_audio-data-analysis-and-convolutional-models.py","file_ext":"py","file_size_in_byte":13662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"72455381724","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d.art3d import Poly3DCollection\nimport os\nimport math\nimport sys\n\n#%%i_beam_perpendicular_task_2\ndirectory_2_1 = r\"D:\\GIT STUFF\\task-clustering\\results\\PERFORMANCE\\i_beam\\perpendicular_2\\test_1\\performance.csv\"\ndirectory_2_2 = r\"D:\\GIT STUFF\\task-clustering\\results\\PERFORMANCE\\i_beam\\perpendicular_2\\test_2\\performance_2.csv\"\n#fileName     = \"\\i_beam_perpendicular.txt\"\n#fname = directory + fileName\n#%%\ndf_2_1 = pd.read_csv (directory_2_1, header = None)\ndf_2_2 = pd.read_csv (directory_2_2, header = None)\n#print(df)\n#%%\nresolution_2_1   = np.array(df_2_1[1])[:31]\ntime_elapsed_2_1 = np.array(df_2_1[2])[:31]\nreached_2_1      = np.array(df_2_1[3])[:31]\nnum_bases_2_1    = np.array(df_2_1[4])[:31]\n\nresolution_2_2   = np.array(df_2_2[1])[:31]\ntime_elapsed_2_2 = np.array(df_2_2[2])[:31]\nreached_2_2      = np.array(df_2_2[3])[:31]\nnum_bases_2_2    = np.array(df_2_2[4])[:31]\n\np_2_1= np.polyfit(resolution_2_1, time_elapsed_2_1, 4)\nfit_2_1 = np.polyval(p_2_1, resolution_2_1)\n\np_2_2= np.polyfit(resolution_2_2, time_elapsed_2_2, 4)\nfit_2_2 = np.polyval(p_2_2, resolution_2_2)\n#%%\n\nplt.scatter(resolution_2_1, time_elapsed_2_1, color = 'blue', alpha = 0.5)\nplt.plot(resolution_2_1, fit_2_1, color = 'red', label = 'test #1', linewidth = 1)\n\n#plt.scatter(resolution_2_2, time_elapsed_2_2, color = 'red', alpha = 0.5)\n#plt.plot(resolution_2_2, fit_2_2, color = 'red', label = 'test #2', linewidth = 1)\n\nplt.title(\"Resolution vs Time for task: i_beam_perpendicular_task_2\")\nplt.ylabel(\"Time Elapsed [sec]\")\nplt.xlabel(\"Search Space Resolution\")\n\n#%%\n\n\n\n\n\n\n#%%\ndirectory_1_1 = r\"D:\\GIT STUFF\\task-clustering\\results\\PERFORMANCE\\i_beam\\perpendicular\\test_1\\performance.csv\"\n#directory_1_2 = r\"D:\\GIT STUFF\\task-clustering\\results\\PERFORMANCE\\i_beam\\perpendicular_2\\test_2\\performance_2.csv\"\n#fileName     = \"\\i_beam_perpendicular.txt\"\n#fname = directory + fileName\n#%%\ndf_1_1 = pd.read_csv (directory_1_1, header = None)\n#df_1_2 = pd.read_csv (directory_1_2, header = None)\n#print(df)\n#%%\nresolution_1_1   = np.array(df_1_1[1])\ntime_elapsed_1_1 = np.array(df_1_1[2])\nreached_1_1      = np.array(df_1_1[3])\nnum_bases_1_1    = np.array(df_1_1[4])\n\n#resolution_2   = np.array(df_2[1])[:31]\n#time_elapsed_2 = np.array(df_2[2])[:31]\n#reached_2      = np.array(df_2[3])[:31]\n#num_bases_2    = np.array(df_2[4])[:31]\n\np_1_1= np.polyfit(resolution_1_1, time_elapsed_1_1, 4)\nfit_1_1 = np.polyval(p_1_1, resolution_1_1)\n\n#p_2= np.polyfit(resolution_2, time_elapsed_2, 4)\n#fit_2 = np.polyval(p_2, resolution_2)\n#%%\n\nplt.scatter(resolution_1_1, time_elapsed_1_1, color = 'blue', alpha = 0.5)\nplt.plot(resolution_1_1, fit_1_1, color = 'blue', label = 'test #1', linewidth = 1)\n\n#plt.scatter(resolution_1_2, time_elapsed_1_2, color = 'red', alpha = 0.5)\n#plt.plot(resolution_1_2, fit_1_2, color = 'red', label = 'test #2', linewidth = 1)\n\nplt.title(\"Resolution vs Time for task: i_beam_perpendicular_task\")\nplt.ylabel(\"Time Elapsed [sec]\")\nplt.xlabel(\"Search Space Resolution\")\n\n#%%\nplt.plot(resolution_1_1, fit_1_1, color = 'blue', label = '223 points', linewidth = 1)\nplt.plot(resolution_2_1[:14], fit_2_1[:14], color = 'red', label = '119 points', linewidth = 1)\n\nplt.title(\"Comparison based on number of task points\")\nplt.ylabel(\"Time Elapsed [sec]\")\nplt.xlabel(\"Search Space Resolution\")\n\n#%%\ndirectory = r\"D:\\GIT STUFF\\task-clustering\\results\\PERFORMANCE\\i_beam\\perpendicular_2\\test_19\\performance_2.csv\"\ndf = pd.read_csv(directory,header = None)\nres = list(df[1])\ntime = list(df[2])\nscore = list(df[7])\n\nroots = []\n\nfor i in res:\n    roots.append(np.sqrt(i))\n#%%\n\npoly = np.polyfit(roots,score,5)\nfit =np.polyval(poly, roots)\n\npoly2 = np.polyfit(roots,time, 3)\nfit2 = np.polyval(poly2,roots)\n\n#plt.scatter(res,score)\n#plt.scatter(roots,time)\nplt.plot(roots,fit,color = 'blue', linewidth = 1.5)\nplt.plot(roots,fit2,linestyle = 'dotted', color = \"red\",linewidth = 2)\nplt.xlabel(\"Seach space reoslution\")\nplt.ylabel(\"Time [s]\")\nplt.title(\"Time vs Resolution\")\n\n#%%\n\npercent_change = []\n\nfor i in range(len(score)):\n    if score[i] == 0:\n        continue\n    else:\n        diff = 100*(score[i]-score[i-1])/score[i]\n        percent_change.append( float('%.4g' % diff))  \nfor i in percent_change:\n    if i > 0 and i< 0.5:\n        print(i)\n        \n\n#%%\ndirectory = r\"D:\\GIT STUFF\\task-clustering\\results\\PERFORMANCE\\i_beam\\perpendicular_2\\test_21\\performance_2.csv\"\n        \ndf = pd.read_csv(directory,header = None)[6:17]\nres = list(df[1])\ntime = list(df[2])\nscore = list(df[7])       \n\ntorq_dir = r\"D:\\GIT STUFF\\task-clustering\\results\\PERFORMANCE\\i_beam\\perpendicular_2\\test_torque_2\\performance.csv\"\n\n        \ndf2 = pd.read_csv(torq_dir,header = None)\nres2 = list(df2[1])\ntime2 = list(df2[2])\nscore2 = list(df2[7])    \n        ","repo_name":"egebalkan/ATB","sub_path":"scripts/analyze_results.py","file_name":"analyze_results.py","file_ext":"py","file_size_in_byte":4808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"71998705883","text":"'''Test helper functions and classes.'''\nimport datetime\n\nimport ckan.config.middleware\nimport pylons.config as config\nimport webtest\n\nimport ckanext.deadoralive.model.results as results\nimport ckanext.deadoralive.logic.action.update as update\n\n\ndef make_broken(resources, user):\n    \"\"\"Make the given resources be reported as having broken links.\n\n    By default a resource needs to have >= 3 consecutive failed link checks over\n    a period of >= 3 days to be considered broken.\n\n    \"\"\"\n    for resource in resources:\n        one_day_ago = datetime.datetime.now() - datetime.timedelta(days=1)\n        four_days_ago = datetime.datetime.now() - datetime.timedelta(days=4)\n        seven_days_ago = datetime.datetime.now() - datetime.timedelta(days=7)\n        data_dict = dict(\n            resource_id=resource[\"id\"],\n            alive=False,\n        )\n        context = {\"user\": user[\"name\"]}\n        update.upsert(context=context, data_dict=data_dict,\n                      last_checked=one_day_ago)\n        update.upsert(context=context, data_dict=data_dict,\n                      last_checked=four_days_ago)\n        update.upsert(context=context, data_dict=data_dict,\n                      last_checked=seven_days_ago)\n\n\ndef make_working(resources, user):\n    \"\"\"Make the given resources have successful link checker results.\"\"\"\n    for resource in resources:\n        data_dict = dict(\n            resource_id=resource[\"id\"],\n            alive=True,\n        )\n        update.upsert(context={\"user\": user[\"name\"]}, data_dict=data_dict)\n\n\n# This is a copy of CKAN core's call_auth() test helper, but modified to go\n# through check_access() instead of calling the auth functions directly,\n# this means it supports auth functions defined in plugins.\ndef call_auth(auth_name, context, **kwargs):\n    '''Call the named ``ckan.logic.auth`` function and return the result.\n\n    This is just a convenience function for tests in\n    :py:mod:`ckan.new_tests.logic.auth` to use.\n\n    Usage::\n\n        result = helpers.call_auth('user_update', context=context,\n                                   id='some_user_id',\n                                   name='updated_user_name')\n\n    :param auth_name: the name of the auth function to call, e.g.\n        ``'user_update'``\n    :type auth_name: string\n\n    :param context: the context dict to pass to the auth function, must\n        contain ``'user'`` and ``'model'`` keys,\n        e.g. ``{'user': 'fred', 'model': my_mock_model_object}``\n    :type context: dict\n\n    :returns: the dict that the auth function returns, e.g.\n        ``{'success': True}`` or ``{'success': False, msg: '...'}``\n        or just ``{'success': False}``\n    :rtype: dict\n\n    '''\n    import ckan.logic.auth.update\n\n    assert 'user' in context, ('Test methods must put a user name in the '\n                               'context dict')\n    assert 'model' in context, ('Test methods must put a model in the '\n                                'context dict')\n\n    return ckan.logic.check_access(auth_name, context, data_dict=kwargs)\n\n\ndef _get_test_app():\n    '''Return a webtest.TestApp for CKAN, with legacy templates disabled.\n\n    For functional tests that need to request CKAN pages or post to the API.\n    Unit tests shouldn't need this.\n\n    '''\n    config['ckan.legacy_templates'] = False\n    app = ckan.config.middleware.make_app(config['global_conf'], **config)\n    app = webtest.TestApp(app)\n    return app\n\n\ndef _load_plugin(plugin):\n    '''Add the given plugin to the ckan.plugins config setting.\n\n    This is for functional tests that need the plugin to be loaded.\n    Unit tests shouldn't need this.\n\n    If the given plugin is already in the ckan.plugins setting, it won't be\n    added a second time.\n\n    :param plugin: the plugin to add, e.g. ``datastore``\n    :type plugin: string\n\n    '''\n    plugins = set(config['ckan.plugins'].strip().split())\n    plugins.add(plugin.strip())\n    config['ckan.plugins'] = ' '.join(plugins)\n\n\nclass FunctionalTestBaseClass():\n    '''A base class for functional test classes to inherit from.\n\n    This handles loading the deadoralive plugin and resetting the CKAN config\n    after your test class has run. It creates a webtest.TestApp at self.app for\n    your class to use to make HTTP requests to the CKAN web UI or API.\n\n    If you're overriding methods that this class provides, like setup_class()\n    and teardown_class(), make sure to use super() to call this class's methods\n    at the top of yours!\n\n    '''\n    @classmethod\n    def setup_class(cls):\n        # Make a copy of the Pylons config, so we can restore it in teardown.\n        cls.original_config = config.copy()\n        _load_plugin('deadoralive')\n        cls.app = _get_test_app()\n\n    def setup(self):\n        import ckan.model as model\n        model.Session.close_all()\n        model.repo.rebuild_db()\n        results.create_database_table()\n\n    @classmethod\n    def teardown_class(cls):\n        # Restore the Pylons config to its original values, in case any tests\n        # changed any config settings.\n        config.clear()\n        config.update(cls.original_config)\n","repo_name":"ckan/ckanext-deadoralive","sub_path":"ckanext/deadoralive/tests/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":5107,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"86"}
+{"seq_id":"798184422","text":"January_de = []\nFebruary_de = []\nMarch_de = []\nApril_de = []\nMay_de = [\n    \"der Arbeit\", #1\n    \"der Kartoffel\", #2\n    \"des rechteckigen Fensters\", #3\n    \"der Vier\", #4\n    \"des Datums\", #5\n    \"des Backofens\", #6\n    \"der Katze\", #7\n    \"des Käses\", #8\n    \"des Zettel auf der Toilette wechseln\", #9\n    \"des Muffins\", #10\n    \"des Wörterausdenkens\", #11\n    \"des Icons\", #12\n    \"der Nussmandelerdnusswalnusskreme\", #13\n    \"des Spülmittelkonzentrates\", #14\n    \"des Handtuches\", #15\n    \"der Induktion\", #16\n    \"der Feuerwehr\", #17\n    \"des Tages\", #18\n    \"des Messfehlers\", #19\n    \"des Käfers\", #20\n    \"der Mikrowelle\", #21\n    \"der Milch\", #22\n    \"der Unterhose\", #23\n    \"der Uhr\", #24\n    \"des Hundes\", #25\n    \"der duftenden Socken\", #26\n    \"des Spülens\", #27\n    \"der Tür\", #28\n    \"des Schicksals\", #29\n    \"der Frisur\", #30\n    \"des Bachelors\", #31\n]\nJune_de = [\n    \"des Johannisbeersaftes\", #1\n    \"der Europäischen Komission für sinnvolle Jahrestage\", #2\n    \"der Europäischen Komission für sinnlose Jahrestage\", #3\n    \"des Logoüberdenkens\", #4\n    \"des Betätigen von Lichtschaltern\", #5\n    \"des Lachens\", #6\n    \"des übermäßigen Alkoholkonsumes\", #7\n    \"des Pinguins\", #8\n    \"des Kuchens\", #9\n    \"des Klaufens\", #10\n    \"der Couch\", #11\n    \"der Weltmeisterschaft\", #12\n    \"der schlechten Schullektüre\", #13\n    \"der Musik\", #14\n    \"der Kissenschlacht\", #15\n    \"der Banane\", #16\n    \"der Wasserbombenschlacht\", #17\n    \"des Patiententacklings\", #18\n    \"der Bestätigung\", #19\n    \"des Hamsters\", #20\n    \"des Hackfleisches\", #21\n    \"der Prokrastination\", #22\n    \"der Gürtelschnalle\", #23\n    \"des Salzes & Pfeffers\", #24\n    \"der Pizza\", #25\n    \"des Fleckens\", #26\n    \"des Speiseeises in der Mikrowelle\", #27\n    \"der Tortilla\", #28\n    \"der Paella\", #29\n    \"des Streichens\", #30\n]\nJuly_de=[\n    \"des Vorbereitens\", #1\n    \"der Torte\", #2\n    \"der Nachbereitung\", #3\n    \"der Zwiebel\", #4\n    \"des Blutspendens\", #5\n    \"des Kullenhofes\", #6\n    \"der deutschen Sprache\", #7\n    \"des Patentes\", #8\n    \"des Füße auf den Tisch legen\", #9\n    \"der Gliedermaßstabes\", #10\n    \"des fliegenden Walfisches\", #11\n    \"des Mandelkuchens\", #12\n    \"der netten Mitbewohner\", #13\n    \"der Fahrradtaschen\", #14\n    \"der Butter\", #15\n    \"des Essens\", #16\n    \"des Flyers\", #17\n    \"des Wasserverschüttens\", #18\n    \"des Quartetts\", #19\n    \"der Klobrille\", #20\n    \"des Jahres\", #21\n    \"des Frühstückes\", #22\n    \"der Wasserspritze\", #23\n    \"des Küheschubsens\", #24\n    \"des Was\", #25\n    \"der Kalaschnikow\", #26\n    \"der Nacht\", #27\n    \"der Brezel\", #28\n    \"des Fernsehers\", #29\n    \"des Kniffels\", #30\n    \"der Insekten\", #31\n]\nAugust_de = []\nSeptember_de = []\nOctober_de = []\nNovember_de = []\nDecember_de = []\n\ndata_de = [January_de, February_de, March_de, April_de, May_de, June_de, July_de, August_de, September_de, October_de, November_de, December_de]\n","repo_name":"Faerbit/daily-twitter-poster","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":2912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"2896085833","text":"#-----------------------------------------------------------------------------\n# Name:        AdvancedInterfaceFrame.py\n# Purpose:     An advanced frame design that includes control and interface areas\n# and all of the standard menu,tool and status bars.\n#\n# Author:      Aric Sanders\n#\n# Created:     2010/04/20\n# RCS-ID:      $Id: AdvancedInterfaceFrame.py $\n#-----------------------------------------------------------------------------\n#Boa:Frame:AdvancedInterfaceFrame\n\"\"\" Advanced wx.Frame with standard containers for interface and control this\nis meant as a template for GUI design and a test bed for pyMez\n\nHelp\n---------------\n<a href=\"./index.html\">`pyMez.Code.FrontEnds`</a>\n<div>\n<a href=\"../../../pyMez_Documentation.html\">Documentation Home</a> |\n<a href=\"../../index.html\">API Documentation Home</a> |\n<a href=\"../../../Examples/html/Examples_Home.html\">Examples Home</a> |\n<a href=\"../../../Reference_Index.html\">Index</a>\n</div>\"\"\"\nimport sys\nimport os\nimport wx\nfrom types import *\nimport re\nimport copy\n# Add pyMez to sys.path (this allows imports that don't go through pyMez/__init__.py\nsys.path.append(os.path.join(os.path.dirname( __file__ ), '..','..'))\ntry:\n    #import pyMez\n    from Code.FrontEnds.IEPanel import *\n    from Code.FrontEnds.EndOfDayDialog import *\n    #from Code.FrontEnds.ShellPanel import *\n    from Code.FrontEnds.SimpleLogLowerInterfacePanel import *\n    from Code.FrontEnds.SimpleArbDBLowerInterfacePanel import *\n    from Code.FrontEnds.VisaDialog import *\n    from Code.FrontEnds.MatplotlibWxPanel import *\n    from Code.FrontEnds.KeithleyIVPanel import *\nexcept:\n    print(\"\"\"Cannot load Shell Panel or IEPanel add The folder above pyMeaure to sys.path\n            Also check that the Boa Constructor Source is on sys.path \"\"\")\n    raise\n\n#-------------------------------------------------------------------------------\n#Constants\nPYMEASURE_ROOT=os.path.join(os.path.dirname( __file__ ), '..','..')\nFRONTENDS_DIRECTORY=os.path.join(PYMEASURE_ROOT,'Code','FrontEnds')\nIMAGE_DIRECTORY=os.path.join(PYMEASURE_ROOT,'Code','FrontEnds','img')\nJAVASCRIPT_STRING=\"\"\"<html>\n<head>\n<script type=\"text/javascript\">\nfunction disp_prompt()\n{\nvar fname=prompt(\"Please enter your name:\",\"Your name\")\ndocument.getElementById(\"msg\").innerHTML=\"Greetings \" + fname\n}\n</script>\n</head>\n<body>\n\n<input type=\"button\" onclick=\"disp_prompt()\" value=\"Display a prompt box\" />\n<br /><br />\n\n<div id=\"msg\"></div>\n\n</body>\n</html> \n\n\n\"\"\"\n\nPANELS=['ShellPanel','IEPanel',\n    'SimpleLogLowerInterfacePanel','SimpleArbDBLowerInterfacePanel','MatplotlibWxPanel','KeithleyIVPanel']\n\n#-------------------------------------------------------------------------------\n#Functions\n\ndef get_top_parent(window):\n    \"\"\"Returns the topmost parent window\"\"\"\n    try:\n        parent=window.Parent\n        print(parent)\n        if parent in [None,''] or not isinstance(parent, InstanceType):\n            raise\n        get_top_parent(parent)\n    except:\n        return window\ndef convert_datetime(ISO_datetime_string,format_string='%m/%d/%Y at %H:%M:%S GMT') :\n    \"Converts from long ISO format 2010-05-13T21:54:25.755000 to something reasonable\"\n    #strip any thing smaller than a second\n    time_seconds=ISO_datetime_string.split('.')[0]\n    \n    #then get it into a datetime format\n    time_datetime=datetime.datetime.strptime(time_seconds,\"%Y-%m-%dT%H:%M:%S\")\n    return time_datetime.strftime(format_string)\n\n\n\n        \n        \n#-------------------------------------------------------------------------------\n# Boa Code\n\ndef create(parent):\n    return AdvancedInterfaceFrame(parent)\n\n[wxID_BASICINTERFACEFRAME, wxID_BASICINTERFACEFRAMEARBDBPANEL, \n wxID_BASICINTERFACEFRAMEBUTTON1, wxID_BASICINTERFACEFRAMEBUTTON2, \n wxID_BASICINTERFACEFRAMEBUTTON3, wxID_BASICINTERFACEFRAMEDISPLAY, \n wxID_BASICINTERFACEFRAMEENDOFTHEDAYBUTTON, \n wxID_BASICINTERFACEFRAMEEXECUTEBUTTON, \n wxID_BASICINTERFACEFRAMEINSTRUMENTSBUTTON, \n wxID_BASICINTERFACEFRAMEINTERFACESTATUSBAR, \n wxID_BASICINTERFACEFRAMEINTERFACETOOLBAR, \n wxID_BASICINTERFACEFRAMELEFTINTERFACEPANEL, \n wxID_BASICINTERFACEFRAMELOGSPANEL, wxID_BASICINTERFACEFRAMELOWERCONTROLPANEL, \n wxID_BASICINTERFACEFRAMELOWERINTERFACE, \n wxID_BASICINTERFACEFRAMELOWERINTERFACEPANEL, \n wxID_BASICINTERFACEFRAMEMAINPANEL, wxID_BASICINTERFACEFRAMEPANEL1, \n wxID_BASICINTERFACEFRAMEPLOTPANEL, \n wxID_BASICINTERFACEFRAMEREPLACERIGHTCONTROLBUTTTON, \n wxID_BASICINTERFACEFRAMERIGHTCONTROLPANEL, wxID_BASICINTERFACEFRAMESHELL, \n wxID_BASICINTERFACEFRAMESTATESBUTTON, wxID_BASICINTERFACEFRAMEUPPERINTERFACE, \n wxID_BASICINTERFACEFRAMEUPPERINTERFACEPANEL, \n wxID_BASICINTERFACEFRAMEVISADIALOGBUTTON, \n] = [wx.NewId() for _init_ctrls in range(26)]\n\n[wxID_BASICINTERFACEFRAMEINTERFACETOOLBARLAUNCHBOA, \n wxID_BASICINTERFACEFRAMEINTERFACETOOLBARSAGE, \n] = [wx.NewId() for _init_coll_InterfaceToolBar_Tools in range(2)]\n\n[wxID_BASICINTERFACEFRAMETOOLMENULOADSCRIPTS, \n wxID_BASICINTERFACEFRAMETOOLMENURUN_PYTHON, \n] = [wx.NewId() for _init_coll_ToolMenu_Items in range(2)]\n\n[wxID_BASICINTERFACEFRAMEFILEMENUNEW, wxID_BASICINTERFACEFRAMEFILEMENUOPEN, \n] = [wx.NewId() for _init_coll_FileMenu_Items in range(2)]\n\nclass AdvancedInterfaceFrame(wx.Frame):\n    # Needs to have an explicit list for the designer to work \n    _custom_classes = {'wx.Panel':['ShellPanel','IEPanel',\n    'SimpleLogLowerInterfacePanel','SimpleArbDBLowerInterfacePanel','MatplotlibWxPanel','KeithleyIVPanel'],'wx.Dialog':'EndOfDayDialog'}\n    def _init_coll_boxSizer7_Items(self, parent):\n        # generated method, don't edit\n\n        parent.AddWindow(self.StatesButton, 0, border=0, flag=0)\n        parent.AddWindow(self.InstrumentsButton, 0, border=0, flag=0)\n\n    def _init_coll_boxSizer6_Items(self, parent):\n        # generated method, don't edit\n\n        parent.AddWindow(self.button1, 0, border=0, flag=0)\n        parent.AddWindow(self.button2, 0, border=0, flag=0)\n        parent.AddWindow(self.button3, 0, border=0, flag=0)\n        parent.AddWindow(self.ExecuteButton, 0, border=0, flag=0)\n        parent.AddWindow(self.ReplaceRightControlButtton, 0, border=0, flag=0)\n        parent.AddWindow(self.EndOfTheDayButton, 0, border=0, flag=0)\n        parent.AddWindow(self.VisaDialogButton, 0, border=0, flag=0)\n\n    def _init_coll_boxSizer4_Items(self, parent):\n        # generated method, don't edit\n\n        parent.AddWindow(self.UpperInterface, 1, border=2,\n              flag=wx.EXPAND | wx.ALL)\n\n    def _init_coll_boxSizer5_Items(self, parent):\n        # generated method, don't edit\n\n        parent.AddWindow(self.LowerInterface, 1, border=2,\n              flag=wx.ALL | wx.EXPAND)\n\n    def _init_coll_boxSizer3_Items(self, parent):\n        # generated method, don't edit\n\n        parent.AddWindow(self.UpperInterfacePanel, 4, border=2,\n              flag=wx.ALL | wx.EXPAND)\n        parent.AddWindow(self.LowerInterfacePanel, 0, border=2,\n              flag=wx.ALL | wx.EXPAND)\n        parent.AddWindow(self.LowerControlPanel,0, border=2,\n              flag=wx.ALL | wx.EXPAND)\n\n    def _init_coll_boxSizer1_Items(self, parent):\n        # generated method, don't edit\n\n        parent.AddWindow(self.MainPanel, 1, border=1, flag=wx.ALL | wx.EXPAND)\n\n    def _init_coll_boxSizer2_Items(self, parent):\n        # generated method, don't edit\n\n        parent.AddWindow(self.LeftInterfacePanel, 1, border=2,\n              flag=wx.EXPAND | wx.ALL)\n        parent.AddWindow(self.RightControlPanel, 0, border=2,\n              flag=wx.ALIGN_RIGHT | wx.ALL | wx.EXPAND)\n\n    def _init_coll_ToolMenu_Items(self, parent):\n        # generated method, don't edit\n\n        parent.Append(help='Runs a python program as a script',\n              id=wxID_BASICINTERFACEFRAMETOOLMENURUN_PYTHON,\n              kind=wx.ITEM_NORMAL, text='Run Python Module As a Script')\n        parent.Append(help='Load Scripts from a python Module',\n              id=wxID_BASICINTERFACEFRAMETOOLMENULOADSCRIPTS,\n              kind=wx.ITEM_NORMAL, text='Load Scripts from Module')\n        self.Bind(wx.EVT_MENU, self.OnToolMenuRun_pythonMenu,\n              id=wxID_BASICINTERFACEFRAMETOOLMENURUN_PYTHON)\n        self.Bind(wx.EVT_MENU, self.OnToolMenuLoadscriptsMenu,\n              id=wxID_BASICINTERFACEFRAMETOOLMENULOADSCRIPTS)\n\n    def _init_coll_FileMenu_Items(self, parent):\n        # generated method, don't edit\n\n        parent.Append(help='Open a file',\n              id=wxID_BASICINTERFACEFRAMEFILEMENUOPEN, kind=wx.ITEM_NORMAL,\n              text='Open')\n        parent.Append(help='Adds a new panel to main notebook',\n              id=wxID_BASICINTERFACEFRAMEFILEMENUNEW, kind=wx.ITEM_NORMAL,\n              text='New Panel')\n        self.Bind(wx.EVT_MENU, self.OnFileMenuOpenMenu,\n              id=wxID_BASICINTERFACEFRAMEFILEMENUOPEN)\n        self.Bind(wx.EVT_MENU, self.OnFileMenuNewMenu,\n              id=wxID_BASICINTERFACEFRAMEFILEMENUNEW)\n\n    def _init_coll_InterfaceMenuBar_Menus(self, parent):\n        # generated method, don't edit\n\n        parent.Append(menu=self.FileMenu, title='File')\n        parent.Append(menu=self.HelpMenu, title='Help')\n        parent.Append(menu=self.ToolMenu, title='Tools')\n\n    def _init_coll_UpperInterface_Pages(self, parent):\n        # generated method, don't edit\n\n        parent.AddPage(imageId=-1, page=self.Display, select=True,\n              text='Display')\n        parent.AddPage(imageId=-1, page=self.PlotPanel, select=False,\n              text='Plot')\n        parent.AddPage(imageId=-1, page=self.panel1, select=False,\n              text='Keithley IV')\n\n    def _init_coll_LowerInterface_Pages(self, parent):\n        # generated method, don't edit\n\n        parent.AddPage(imageId=-1, page=self.Shell, select=True, text='Shell')\n        parent.AddPage(imageId=-1, page=self.LogsPanel, select=False,\n              text='Logs')\n        parent.AddPage(imageId=-1, page=self.ArbDBPanel, select=False,\n              text='Files')\n\n    def _init_coll_InterfaceStatusBar_Fields(self, parent):\n        # generated method, don't edit\n        parent.SetFieldsCount(1)\n\n        parent.SetStatusText(number=0, text='Status')\n\n        parent.SetStatusWidths([-1])\n\n    def _init_coll_InterfaceToolBar_Tools(self, parent):\n        # generated method, don't edit\n\n        parent.DoAddTool(bitmap=wx.Bitmap(str(os.path.join(IMAGE_DIRECTORY,'Component.png')),\n              wx.BITMAP_TYPE_PNG), bmpDisabled=wx.NullBitmap,\n              id=wxID_BASICINTERFACEFRAMEINTERFACETOOLBARLAUNCHBOA,\n              kind=wx.ITEM_NORMAL, label='Boa',\n              longHelp='Launch Boa Constructor',\n              shortHelp='Launch Boa Constructor')\n        parent.DoAddTool(bitmap=wx.Bitmap(str(os.path.join(IMAGE_DIRECTORY,'jupyter-logo.png')),\n              wx.BITMAP_TYPE_PNG), bmpDisabled=wx.NullBitmap,\n              id=wxID_BASICINTERFACEFRAMEINTERFACETOOLBARSAGE,\n              kind=wx.ITEM_NORMAL, label='',\n              longHelp='Launch SAGE and display ', shortHelp='Launch SAGE')\n        self.Bind(wx.EVT_TOOL, self.OnInterfaceToolBarLaunchboaTool,\n              id=wxID_BASICINTERFACEFRAMEINTERFACETOOLBARLAUNCHBOA)\n        self.Bind(wx.EVT_TOOL, self.OnInterfaceToolBarTools1Tool,\n              id=wxID_BASICINTERFACEFRAMEINTERFACETOOLBARSAGE)\n\n        parent.Realize()\n\n    def _init_sizers(self):\n        # generated method, don't edit\n        self.boxSizer1 = wx.BoxSizer(orient=wx.VERTICAL)\n\n        self.boxSizer2 = wx.BoxSizer(orient=wx.HORIZONTAL)\n\n        self.boxSizer3 = wx.BoxSizer(orient=wx.VERTICAL)\n\n        self.boxSizer4 = wx.BoxSizer(orient=wx.VERTICAL)\n\n        self.boxSizer5 = wx.BoxSizer(orient=wx.VERTICAL)\n\n        self.boxSizer6 = wx.BoxSizer(orient=wx.HORIZONTAL)\n\n        self.boxSizer7 = wx.BoxSizer(orient=wx.VERTICAL)\n\n        self._init_coll_boxSizer1_Items(self.boxSizer1)\n        self._init_coll_boxSizer2_Items(self.boxSizer2)\n        self._init_coll_boxSizer3_Items(self.boxSizer3)\n        self._init_coll_boxSizer4_Items(self.boxSizer4)\n        self._init_coll_boxSizer5_Items(self.boxSizer5)\n        self._init_coll_boxSizer6_Items(self.boxSizer6)\n        self._init_coll_boxSizer7_Items(self.boxSizer7)\n\n        self.SetSizer(self.boxSizer1)\n        self.RightControlPanel.SetSizer(self.boxSizer7)\n        self.LowerInterfacePanel.SetSizer(self.boxSizer5)\n        self.LeftInterfacePanel.SetSizer(self.boxSizer3)\n        self.UpperInterfacePanel.SetSizer(self.boxSizer4)\n        self.LowerControlPanel.SetSizer(self.boxSizer6)\n        self.MainPanel.SetSizer(self.boxSizer2)\n\n    def _init_utils(self):\n        # generated method, don't edit\n        self.FileMenu = wx.Menu(title='')\n\n        self.HelpMenu = wx.Menu(title='')\n\n        self.InterfaceMenuBar = wx.MenuBar()\n\n        self.ToolMenu = wx.Menu(title='Tools')\n\n        self._init_coll_FileMenu_Items(self.FileMenu)\n        self._init_coll_InterfaceMenuBar_Menus(self.InterfaceMenuBar)\n        self._init_coll_ToolMenu_Items(self.ToolMenu)\n\n    def _init_ctrls(self, prnt):\n        # generated method, don't edit\n        wx.Frame.__init__(self, id=wxID_BASICINTERFACEFRAME,\n              name='AdvancedInterfaceFrame', parent=prnt, pos=wx.Point(-5, 106),\n              size=wx.Size(1448, 874), style=wx.DEFAULT_FRAME_STYLE,\n              title='Advanced Interface')\n        self._init_utils()\n        self.SetClientSize(wx.Size(1440, 840))\n        self.SetMenuBar(self.InterfaceMenuBar)\n        self.Bind(wx.EVT_CLOSE, self.OnBasicInterfaceFrameClose)\n\n        self.InterfaceStatusBar = wx.StatusBar(id=wxID_BASICINTERFACEFRAMEINTERFACESTATUSBAR,\n              name='InterfaceStatusBar', parent=self, style=0)\n        self.InterfaceStatusBar.SetHelpText('Status')\n        self.InterfaceStatusBar.SetLabel('')\n        self._init_coll_InterfaceStatusBar_Fields(self.InterfaceStatusBar)\n        self.SetStatusBar(self.InterfaceStatusBar)\n\n        self.InterfaceToolBar = wx.ToolBar(id=wxID_BASICINTERFACEFRAMEINTERFACETOOLBAR,\n              name='InterfaceToolBar', parent=self, pos=wx.Point(0, 0),\n              size=wx.Size(1440, 40),\n              style=wx.TAB_TRAVERSAL | wx.TB_3DBUTTONS | wx.TB_HORIZONTAL | wx.MAXIMIZE_BOX | wx.NO_BORDER)\n        self.InterfaceToolBar.SetHelpText('Launch Boa Contstructor')\n        self.InterfaceToolBar.SetToolTipString('InterfaceToolBar')\n        self.InterfaceToolBar.SetToolBitmapSize(wx.Size(30, 30))\n        self.InterfaceToolBar.SetToolPacking(0)\n        self.InterfaceToolBar.SetToolSeparation(1)\n        self.SetToolBar(self.InterfaceToolBar)\n\n        self.MainPanel = wx.Panel(id=wxID_BASICINTERFACEFRAMEMAINPANEL,\n              name='MainPanel', parent=self, pos=wx.Point(1, 1),\n              size=wx.Size(1438, 838), style=wx.TAB_TRAVERSAL)\n\n        self.LeftInterfacePanel = wx.Panel(id=wxID_BASICINTERFACEFRAMELEFTINTERFACEPANEL,\n              name='LeftInterfacePanel', parent=self.MainPanel, pos=wx.Point(2,\n              2), size=wx.Size(1355, 834), style=wx.TAB_TRAVERSAL)\n\n        self.RightControlPanel = wx.Panel(id=wxID_BASICINTERFACEFRAMERIGHTCONTROLPANEL,\n              name='RightControlPanel', parent=self.MainPanel,\n              pos=wx.Point(1361, 2), size=wx.Size(75, 834),\n              style=wx.TAB_TRAVERSAL)\n        self.RightControlPanel.SetBackgroundColour(wx.Colour(192, 192, 192))\n\n        self.LowerControlPanel = wx.Panel(id=wxID_BASICINTERFACEFRAMELOWERCONTROLPANEL,\n              name='LowerControlPanel', parent=self.LeftInterfacePanel,\n              pos=wx.Point(2, 808), size=wx.Size(1351, 24),\n              style=wx.TAB_TRAVERSAL)\n        self.LowerControlPanel.SetBackgroundColour(wx.Colour(0, 255, 128))\n\n        self.UpperInterfacePanel = wx.Panel(id=wxID_BASICINTERFACEFRAMEUPPERINTERFACEPANEL,\n              name='UpperInterfacePanel', parent=self.LeftInterfacePanel,\n              pos=wx.Point(2, 2), size=wx.Size(1351, 640),\n              style=wx.TAB_TRAVERSAL)\n        self.UpperInterfacePanel.SetBackgroundColour(wx.Colour(128, 128, 128))\n        self.UpperInterfacePanel.SetHelpText('UpperInterfacePanel')\n\n        self.LowerInterfacePanel = wx.Panel(id=wxID_BASICINTERFACEFRAMELOWERINTERFACEPANEL,\n              name='LowerInterfacePanel', parent=self.LeftInterfacePanel,\n              pos=wx.Point(2, 646), size=wx.Size(1351, 158),\n              style=wx.TAB_TRAVERSAL)\n        self.LowerInterfacePanel.SetBackgroundColour(wx.Colour(192, 192, 192))\n\n        self.UpperInterface = wx.Notebook(id=wxID_BASICINTERFACEFRAMEUPPERINTERFACE,\n              name='UpperInterface', parent=self.UpperInterfacePanel,\n              pos=wx.Point(2, 2), size=wx.Size(1347, 636), style=0)\n\n        self.LowerInterface = wx.Treebook(id=wxID_BASICINTERFACEFRAMELOWERINTERFACE,\n              name='LowerInterface', parent=self.LowerInterfacePanel,\n              pos=wx.Point(2, 2), size=wx.Size(1347, 154), style=0)\n        self.LowerInterface.Bind(wx.EVT_TREEBOOK_PAGE_CHANGED,\n              self.OnLowerInterfaceTreebookPageChanged,\n              id=wxID_BASICINTERFACEFRAMELOWERINTERFACE)\n\n        self.Display = IEPanel(id=wxID_BASICINTERFACEFRAMEDISPLAY,\n              name='Display', parent=self.UpperInterface, pos=wx.Point(0, 0),\n              size=wx.Size(1339, 610), style=wx.TAB_TRAVERSAL)\n\n        self.Shell = ShellPanel(id=wxID_BASICINTERFACEFRAMESHELL, name='Shell',\n              parent=self.LowerInterface, pos=wx.Point(0, 0), size=wx.Size(1281,\n              154), style=wx.TAB_TRAVERSAL)\n\n        self.button1 = wx.Button(id=wxID_BASICINTERFACEFRAMEBUTTON1,\n              label='Java Script Example', name='button1',\n              parent=self.LowerControlPanel, pos=wx.Point(0, 0),\n              size=wx.Size(136, 23), style=0)\n        self.button1.Bind(wx.EVT_BUTTON, self.OnButton1Button,\n              id=wxID_BASICINTERFACEFRAMEBUTTON1)\n\n        self.button2 = wx.Button(id=wxID_BASICINTERFACEFRAMEBUTTON2,\n              label='button2', name='button2', parent=self.LowerControlPanel,\n              pos=wx.Point(136, 0), size=wx.Size(75, 23), style=0)\n        self.button2.Bind(wx.EVT_BUTTON, self.OnButton2Button,\n              id=wxID_BASICINTERFACEFRAMEBUTTON2)\n\n        self.button3 = wx.Button(id=wxID_BASICINTERFACEFRAMEBUTTON3,\n              label='button3', name='button3', parent=self.LowerControlPanel,\n              pos=wx.Point(211, 0), size=wx.Size(75, 23), style=0)\n\n        self.ExecuteButton = wx.Button(id=wxID_BASICINTERFACEFRAMEEXECUTEBUTTON,\n              label='Execute IEPanel.py', name='ExecuteButton',\n              parent=self.LowerControlPanel, pos=wx.Point(286, 0),\n              size=wx.Size(111, 23), style=0)\n        self.ExecuteButton.Bind(wx.EVT_BUTTON, self.OnExecuteButtonButton,\n              id=wxID_BASICINTERFACEFRAMEEXECUTEBUTTON)\n\n        self.ReplaceRightControlButtton = wx.Button(id=wxID_BASICINTERFACEFRAMEREPLACERIGHTCONTROLBUTTTON,\n              label='Replace Right Control Panel',\n              name='ReplaceRightControlButtton', parent=self.LowerControlPanel,\n              pos=wx.Point(397, 0), size=wx.Size(180, 24), style=0)\n        self.ReplaceRightControlButtton.Bind(wx.EVT_BUTTON,\n              self.OnReplaceRightControlButttonButton,\n              id=wxID_BASICINTERFACEFRAMEREPLACERIGHTCONTROLBUTTTON)\n\n        self.StatesButton = wx.Button(id=wxID_BASICINTERFACEFRAMESTATESBUTTON,\n              label='States', name='StatesButton',\n              parent=self.RightControlPanel, pos=wx.Point(0, 0),\n              size=wx.Size(75, 23), style=0)\n        self.StatesButton.Bind(wx.EVT_BUTTON, self.OnStatesButtonButton,\n              id=wxID_BASICINTERFACEFRAMESTATESBUTTON)\n\n        self.InstrumentsButton = wx.Button(id=wxID_BASICINTERFACEFRAMEINSTRUMENTSBUTTON,\n              label='Instruments', name='InstrumentsButton',\n              parent=self.RightControlPanel, pos=wx.Point(0, 23),\n              size=wx.Size(75, 23), style=0)\n        self.InstrumentsButton.Bind(wx.EVT_BUTTON,\n              self.OnInstrumentsButtonButton,\n              id=wxID_BASICINTERFACEFRAMEINSTRUMENTSBUTTON)\n\n        self.LogsPanel = SimpleLogLowerInterfacePanel(id=wxID_BASICINTERFACEFRAMELOGSPANEL,\n              name='LogsPanel', parent=self.LowerInterface, pos=wx.Point(0, 0),\n              size=wx.Size(1281, 154), style=wx.TAB_TRAVERSAL)\n\n        self.ArbDBPanel = SimpleArbDBLowerInterfacePanel(id=wxID_BASICINTERFACEFRAMEARBDBPANEL,\n              name='ArbDBPanel', parent=self.LowerInterface, pos=wx.Point(0,\n              0), size=wx.Size(1281, 154), style=wx.TAB_TRAVERSAL)\n\n        self.EndOfTheDayButton = wx.Button(id=wxID_BASICINTERFACEFRAMEENDOFTHEDAYBUTTON,\n              label='End of The Day Log', name='EndOfTheDayButton',\n              parent=self.LowerControlPanel, pos=wx.Point(577, 0),\n              size=wx.Size(103, 23), style=0)\n        self.EndOfTheDayButton.Bind(wx.EVT_BUTTON,\n              self.OnEndOfTheDayButtonButton,\n              id=wxID_BASICINTERFACEFRAMEENDOFTHEDAYBUTTON)\n\n        self.VisaDialogButton = wx.Button(id=wxID_BASICINTERFACEFRAMEVISADIALOGBUTTON,\n              label='VISA Communicator', name='VisaDialogButton',\n              parent=self.LowerControlPanel, pos=wx.Point(680, 0),\n              size=wx.Size(184, 23), style=0)\n        self.VisaDialogButton.Bind(wx.EVT_BUTTON, self.OnVisaDialogButtonButton,\n              id=wxID_BASICINTERFACEFRAMEVISADIALOGBUTTON)\n\n        self.PlotPanel = MatplotlibWxPanel(id=wxID_BASICINTERFACEFRAMEPLOTPANEL,\n              name='PlotPanel', parent=self.UpperInterface, pos=wx.Point(0, 0),\n              size=wx.Size(1339, 610), style=wx.TAB_TRAVERSAL)\n\n        self.panel1 = KeithleyIVPanel(id=wxID_BASICINTERFACEFRAMEPANEL1,\n              name='panel1', parent=self.UpperInterface, pos=wx.Point(0, 0),\n              size=wx.Size(1339, 610), style=wx.TAB_TRAVERSAL)\n\n        self._init_coll_InterfaceToolBar_Tools(self.InterfaceToolBar)\n        self._init_coll_UpperInterface_Pages(self.UpperInterface)\n        self._init_coll_LowerInterface_Pages(self.LowerInterface)\n\n        self._init_sizers()\n\n    def __init__(self, parent):\n        self._init_ctrls(parent)\n        #make the shell self aware--requires that ShellEditor.interp.locals=locals()\n        self.Shell.ShellEditor.pushLine(\"shell=locals()['self']\")\n        #This assumes the main frame is exactly 6 levels above\n        self.Shell.ShellEditor.pushLine(\"frame=shell.Parent.Parent.Parent.Parent.Parent.Parent\")\n        self.Shell.ShellEditor.pushLine(\"print('\\# The object corresponding to the main frame is called frame')\")\n        #self.Shell.ShellEditor.pushLine(\"shell=locals()['self']\",'\\n')\n        self.right_control_panels=[self.RightControlPanel]\n        self.current_right_control_panel=self.right_control_panels[0]\n        \n        # TODO Delete this out it is a test\n        self.NewPanel=wx.Panel(self.MainPanel,name='Green')\n        self.NewPanel.SetBackgroundColour(wx.Colour(0,255,0))\n        self.right_control_panels.append(self.NewPanel)\n        self.NewPanel2=wx.Panel(self.MainPanel,name='Blue')\n        self.NewPanel2.SetBackgroundColour(wx.Colour(0,0,255))\n        self.right_control_panels.append(self.NewPanel2)\n        self.NewPanel3=wx.Panel(self.MainPanel,name='Red')\n        self.NewPanel3.SetBackgroundColour(wx.Colour(255,0,0))\n        self.right_control_panels.append(self.NewPanel3)\n        \n        self.NewPanel.Show(False)\n        self.NewPanel2.Show(False)\n        self.NewPanel3.Show(False)\n        \n        \n        # This initializes the locals dictionary, needed to get output from \n        # execfile command. This attribute needs to be asigned to locals() at \n        # the time that the frame is created. See Module Runner\n        self.locals={}\n        \n        \n        # This passes the button event from the log panel to the main frame\n        self.LogsPanel.Bind(wx.EVT_BUTTON,self.update_display)\n        self.ScriptsMenu=wx.Menu()\n        self.ToolMenu.AppendMenu(-1,'Scripts',self.ScriptsMenu)\n        \n        #Added these for script handling\n        self.loaded_script_menu_items=[]\n        self.event_handlers={}\n        \n        self.tool_menu_number=0\n        \n        #This adds panels to the FileMenu->Add Panel item\n        self.panels_menu()\n    \n    def panels_menu(self):\n        \"\"\" Creates the Add Panels Menu\"\"\"\n        # First we select only files of the for *Panel.py\n        FrontEnds_files=os.listdir(FRONTENDS_DIRECTORY)\n        self.panel_files=[]\n        for file in FrontEnds_files:\n            if re.search('Panel.py',file) and not re.search('.pyc|pyo',file):\n                self.panel_files.append(file)\n        \n         \n        #This adds panels to the FileMenu->Add Panel item\n        self.add_panel_menu=wx.Menu()\n        self.panel_dictionary={}\n        for panel in self.panel_files:\n            new_id=wx.NewId()\n            self.panel_dictionary[new_id]=panel\n            self.add_panel_menu.Append(new_id,panel,'Add the Panel %s'%panel)\n            self.Bind(wx.EVT_MENU, lambda evt: self.OnAddPanel(evt,new_id),id=new_id)\n            #print(new_id,panel)\n        #print(self.panel_dictionary)\n        self.FileMenu.AppendMenu(0,'Add a Panel',self.add_panel_menu)   \n    def OnAddPanel(self,event,id):\n        \"\"\" Adds a panel\"\"\"\n        sys.path.append(FRONTENDS_DIRECTORY)\n        #print(dir(event),event.Id)\n        panel_name=self.panel_dictionary[event.Id].replace('.py','')\n        exec('from %s import *'%panel_name)\n        exec('self.%s_%s=%s(id=%s,name=\"%s_%s\", parent=self.UpperInterface, pos=wx.Point(0, 0),size=wx.Size(1339, 610), style=wx.TAB_TRAVERSAL)'%(panel_name,id,panel_name,id,panel_name,id))\n        exec(\"self.UpperInterface.AddPage(imageId=-1, page=self.%s_%s, select=True,text='%s')\"%(panel_name,id,panel_name))\n                  \n\n    def OnFileMenuOpenMenu(self, event):\n        event.Skip()\n\n    def OnReplaceRightControlButttonButton(self, event):\n        try:\n            new=self.right_control_panels.index(\n            self.current_right_control_panel)%len(self.right_control_panels)+1\n            new_panel=self.right_control_panels[new]\n        except:\n            new=0\n            new_panel=self.right_control_panels[new]\n            \n        self.swap_panel(self.current_right_control_panel,new_panel,self.boxSizer2)\n\n    def swap_panel(self,old_panel,new_panel,sizer):\n        \"Swaps a panel and places the new one in the sizer\"\n        new_panel.SetPosition(old_panel.Position)\n        new_panel.SetSize(old_panel.Size)\n        old_panel.Show(False)\n        sizer.Replace(old_panel,new_panel)\n        new_panel.Show(True)\n        self.current_right_control_panel=new_panel\n        self.Update()\n\n    def OnInterfaceToolBarLaunchboaTool(self, event):\n        os.system(r'start python C:\\Anaconda2\\Lib\\site-packages\\boa\\Boa.py')\n        event.Skip()\n\n    def OnExecuteButtonButton(self, event):\n        path=str(os.path.join(PYMEASURE_ROOT,'Code/FrontEnds/IEPanel.py'))\n        exec(compile(open(path).read(), path, 'exec'))\n\n    def OnLowerInterfaceTreebookPageChanged(self, event):\n        self.update_display()\n        event.Skip()\n\n    def update_display(self,event=None):\n        \"Updates the display\"\n        if self.LowerInterface.GetCurrentPage() is self.LogsPanel:\n            self.Display.ie.Navigate(self.LogsPanel.current_log.path)\n        else: \n            self.Display.Refresh()       \n\n    def OnInterfaceToolBarTools1Tool(self, event):\n        #sage_script=r'C:\\\"Program Files\"\\AutoIt3\\Examples\\SAGE_NOIE2.exe'\n        #os.system('jupyter notebook')\n        self.Display.ie.Navigate('http://localhost:8888/tree')\n        pass\n    \n    def OnBasicInterfaceFrameClose(self, event):\n        \"\"\" What to do when the frame is closed, needed to avoid memory leaks\"\"\"\n        try:\n            app=wx.GetApp()\n            app.stdioWin.close()\n        except:pass\n        \n        self.Destroy()\n        \n        event.Skip()\n\n    def OnButton1Button(self, event):\n        self.Display.write(JAVASCRIPT_STRING)\n        event.Skip()\n\n    def OnToolMenuRun_pythonMenu(self, event):\n        dlg = wx.FileDialog(self, 'Choose a file', '.', '', '*.py', wx.OPEN)\n        try:\n            if dlg.ShowModal() == wx.ID_OK:\n                filename = dlg.GetPath()\n                \n\n                try:\n                    exec(compile(open(filename).read(), filename, 'exec'),self.locals)\n                except:\n                    pass\n\n               \n        finally:\n            dlg.Destroy()\n        event.Skip()\n\n    def OnToolMenuLoadscriptsMenu(self, event):\n        \" Loads Scripts to run\"\n        dlg = wx.FileDialog(self, 'Choose a Python Module', '.', '', '*.py', wx.OPEN)\n        try:\n            if dlg.ShowModal() == wx.ID_OK:\n                filename = dlg.GetPath()\n                module_scripts=self.import_scripts(filename)\n                module=module_scripts['module']\n                \n                try:\n\n                    for index,script in enumerate(module_scripts['scripts']):\n                        new_id=wx.NewId()\n                        menu_item=wx.MenuItem(self.ToolMenu,id=new_id,\n                        kind=wx.ITEM_NORMAL, text='Run %s '%script,\n                        help='Run %s  from the %s module'%(script,module))\n                        self.ScriptsMenu.AppendItem(menu_item)\n                        self.loaded_script_menu_items.append(menu_item)\n                        \n                        \n                        #event_handler=(lambda event: self.execute_script(event))\n                        #eval('def event\\_handler%s(event):return self.execute_script(event,module\\_scripts[\"%s\"],\"%s\")'%(index,script,script),locals())\n                        \n                        \n                        #event_handler.func_name='%s'%index\n                        self.event_handlers[menu_item.GetId()]=module_scripts[script]\n                        #print(self.event_handlers)\n                        self.Bind(wx.EVT_MENU,self.execute_script,id=menu_item.GetId())\n                    #print(self.event_handlers)\n                    #self.bind_menu()        \n              \n                except:\n                    print(('Could Not Import {0}'.format(module)))\n                    raise\n        finally:\n            dlg.Destroy()\n            event.Skip()\n            #print(self.event_handlers)\n\n##    def bind_menu(self): \n##        for index,menu_item in enumerate(self.loaded_script_menu_items):\n##            print(self.event_handlers[menu_item.GetId()],menu_item,menu_item.GetId())\n##            self.Bind(wx.EVT_MENU,self.event_handlers[menu_item.GetId()],id=menu_item.GetId())       \n   \n   \n   \n   \n   \n    def import_scripts(self,file_name):\n        \"Loads a module given a path and returns a dictionary of things recognized as scripts\"\"\"\n        #added file path to sys.path\n        output={}\n        scripts=[]\n        module_directory=os.path.split(file_name)[0]\n        module_name=os.path.split(file_name)[1].replace('.py','')\n        sys.path.append(module_directory)\n        exec('import {0}'.format(module_name))\n        attributes=eval('dir({0})'.format(module_name))\n        for item in attributes:\n            if re.search('test_|_robot|_script',item,re.IGNORECASE):\n                scripts.append(item)\n                output[item]=eval('{0}.{1}'.format(module_name,item))\n            \n        output['module']=module_name\n        output['scripts']=scripts\n        \n        return output\n\n    def execute_script(self,event):\n\n        #print(self.event_handlers)\n        #print(event.Id)\n        return self.event_handlers[event.Id]()\n\n    def add_notebook_page(self):\n        \"\"\" Adds a page to the InterfaceNotebook\"\"\"\n        pass    \n        \n        \n\n        \n    def OnButton2Button(self, event):\n        self.tool_menu_number=self.tool_menu_number+1\n        if self.tool_menu_number is 1:\n            self.ScriptsMenu=wx.Menu()\n            self.ToolMenu.AppendMenu(-1,'Scripts',self.ScriptsMenu)\n        self.ScriptsMenu.Append(help='Runs the {0} script'.format(self.tool_menu_number),\n                            id=-1,kind=wx.ITEM_NORMAL, text='Run the {0} script'.format(self.tool_menu_number))\n        event.Skip()\n    def OnStatesButtonButton(self, event):\n        states_directory=os.path.join(PYMEASURE_ROOT,'Data/States')\n        \n        self.Display.ie.Navigate(states_directory)\n        event.Skip()\n\n    def OnInstrumentsButtonButton(self, event):\n        instruments_directory=os.path.join(PYMEASURE_ROOT,'Instruments')\n        \n        self.Display.ie.Navigate(instruments_directory)\n        event.Skip()\n\n    def OnEndOfTheDayButtonButton(self, event):\n        dlg = EndOfDayDialog(self)\n        try:\n            result = dlg.ShowModal()\n        finally:\n            dlg.Destroy()\n        event.Skip\n        \n    def OnVisaDialogButtonButton(self, event):\n        dlg = VisaDialog(self)\n        try:\n            result = dlg.Show()\n        finally:\n            pass\n        event.Skip\n\n    def OnFileMenuNewMenu(self, event):\n        \n##        dlg = wx.FileDialog(self, 'Choose a file', '.', '', '*.*', wx.OPEN)\n##        try:\n##            if dlg.ShowModal() == wx.ID_OK:\n##                filename = dlg.GetPath()\n##                # Your code\n##        finally:\n##            dlg.Destroy()\n##        self.upper_Interface.AddPage(\n##        event.Skip()\n        pass\n##    def OnAddPanel(self, event):\n##        exec('self.%s'+'_%s'+'=%s(self.UpperInterface)'%(panel_name,panel_number,panel_name))\n        \ndef test_AdvancedInterfaceFrame():\n    \"\"\" Tests the AdvancedInterfaceFrame Class\"\"\"\n    app = wx.App(False)\n    app.RedirectStdio()\n\n    frame = create(None)\n    frame.Show()\n    # This is needed for the execfile command to output properly--can't redirect\n    frame.locals=locals()\n    \n    \n    app.MainLoop()\n\n\n\nif __name__ == '__main__':\n    app = wx.App(False)\n    app.RedirectStdio()\n    # The import here stops the Error wx Debug Alert\n    from Code.FrontEnds.ShellPanel import *\n    frame = create(None)\n    frame.Show()\n    # This is needed for the execfile command to output properly--can't redirect\n    frame.locals=locals()\n    \n    \n    app.MainLoop()\n","repo_name":"aricsanders/pyMez","sub_path":"Code/FrontEnds/Wx/Interfaces/AdvancedInterfaceFrame.py","file_name":"AdvancedInterfaceFrame.py","file_ext":"py","file_size_in_byte":33925,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"29053839568","text":"import logging\nfrom azure.data.tables import TableServiceClient, TableClient\nimport azure.functions as func\n\n\ndef main(req: func.HttpRequest) -> func.HttpResponse:\n    conn_str=\"DefaultEndpointsProtocol=https;AccountName=storageforys;AccountKey=sTNZfBUGl7EbNxi7duoUuzXnBuWPdfVIcgn4HzDu2y8q6BVz9oJNxr0XkJD2lFmZBsNHVpSbz8rbEzLFLZHfbQ==;EndpointSuffix=core.windows.net\"\n    table_service_client = TableServiceClient.from_connection_string(conn_str=conn_str)\n    table_client = table_service_client.get_table_client(table_name=\"myTable\")\n    table_list={}\n    cnt=1\n    for entity in table_client.list_entities():\n        tmp_list={}\n        tmp_list[\"PartitionKey\"]=entity[\"PartitionKey\"]\n        tmp_list[\"title\"]=entity[\"title\"]\n        table_list[entity[\"RowKey\"]]=tmp_list\n        #tmp=entity[\"title\"]\n        #title[entity[\"RowKey\"]]=tmp\n        #cnt+=1 \n        #print(title)\n    str_list=str(table_list)\n    print(str_list)\n    return func.HttpResponse(str_list)\n   \n","repo_name":"SH0909/youtube-scheduler","sub_path":"Frontend/azure-static-web-app/api/getlist/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"86"}
+{"seq_id":"72225357405","text":"import time\n\n\ndef caching(timeout):\n    _caches = {}\n\n    def func_wrapper(func):\n\n        def inner():\n            func_name = func.__name__\n\n            if func_name in _caches:\n                cache, func_timeout, time_of_adding = _caches[func_name]\n                if time.time() - time_of_adding < func_timeout:\n                    return cache\n                else:\n                    _caches.pop(func_name)\n\n            cache = func()\n            _caches[func_name] = cache, timeout, time.time()\n            return cache\n\n        return inner\n\n    return func_wrapper\n","repo_name":"alexdyagel-zz/PythonQAAutomationTraining","sub_path":"task9/functional.py","file_name":"functional.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"13029708574","text":"from django.shortcuts import render,redirect\nfrom django.http import HttpResponse, Http404, HttpResponseRedirect\nfrom .models import *\nfrom .forms import *\nfrom django.urls import reverse\nfrom django.utils import timezone\nfrom django.views import View\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib import messages\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.db import transaction\n\n# Create your views here.\ndef Index(request):\n    return render(request,'book/index.html')\n\ndef RegisterPage(request):\n    form = CreateUserForm()\n    if request.method == 'POST':\n        form = CreateUserForm(request.POST)\n        if form.is_valid():\n            form.save()\n            user=form.cleaned_data.get('username')\n            messages.success(request, 'account created successfully: '+ user)\n            return redirect('book:login')\n    context={'form': form}\n    return render(request,'accounts/register.html',context)\n\ndef LoginPage(request):\n    context={}\n    if request.method == 'POST':\n        username= request.POST.get('username')\n        pw = request.POST.get('password')\n        user = authenticate(request,username=username,password=pw)\n        if user is not None:\n            login(request,user)\n            return redirect('book:my_booking')\n        else:\n            messages.info(request, 'username or password not correct')\n    return render(request,'accounts/login.html',context)\n\ndef Logout(request):\n    context={}\n    logout(request)\n    return redirect('book:login')\n\ndef CourtsView(request):\n    context={}\n    all_court = court.objects.all()\n    context[\"courts\"] = all_court\n    return render(request, \"book/courts.html\", context)\n\n#show user the available courts\nclass BookView(LoginRequiredMixin,View):\n    login_url = 'book:login'\n    \n    def get(self,request):\n        context={}\n        form= SearchForm()\n        context[\"searchform\"] = form\n        return render(request, \"book/book_courts.html\",context)\n    \n    def post(self,request):\n        context={}\n        if request.POST['select1']==\"1\":\n            if_indoor=[True,False]\n        else: \n            if_indoor = [True] if request.POST['select1'] == \"2\" else [False]\n\n        if request.POST['select2']==\"1\":\n            if_std=[True,False]\n        else: \n            if_std = [True] if request.POST['select2'] == \"2\" else [False]\n        ids=court.objects.filter(indoor__in = if_indoor, std__in = if_std)\n        #filter today's slots\n        slots = slot.objects.select_related().filter(courtid__in = ids)\n        context['slots'] = slots[:]\n        return render(request, \"book/book_courts.html\",context)\n\n# user try to book a slot\n@transaction.atomic\ndef DoBook(request):\n    context={}\n    #if post\n    if request.method == 'POST':\n        slot_int=int(request.POST.get('slot_num','0'))\n        # if slot_int not inside the 3 value\n        if slot_int not in [1,2,3]:\n            messages.info(request, 'you did not select any slots')\n            return redirect('book:book_courts')\n\n        slot_id=int(request.POST.get('slot_id',0))\n        the_slot=slot.objects.get(pk=slot_id)\n        #construct new order record and commit\n        new_order = order(username= request.user,slotid=the_slot, slot_num=slot_int)\n        new_order.save()\n        update_slot = slot.objects.get(pk=slot_id)\n        err=False\n        if slot_int == 1:\n            if update_slot.slot1==True: err= True\n            else: update_slot.slot1=True\n        elif slot_int ==2:\n            if update_slot.slot2==True: err= True\n            else: update_slot.slot2=True\n        else:\n            if update_slot.slot3==True: err= True\n            else: update_slot.slot3=True\n        #if error happens, rollback\n        if err: \n            transaction.set_rollback(True)\n            messages.info(request, 'Sorry, your slot already taken.')\n            return redirect('book:book_courts')\n        # commit update message\n        update_slot.save()\n        return redirect('book:my_booking')\n    else:\n        return redirect('book:book_courts')\n\n#list users booking records\ndef MyBookingView(request):\n    context={}\n    update_slot = slot.objects.get(pk=1)\n    update_slot\n    if request.user.is_authenticated:\n        orders= order.objects.filter(username=request.user)\n        context['orders']=orders[:]\n        return render(request,\"book/my_booking.html\",context)\n    else:\n        return redirect('book:login')\n\ndef AboutView(request):\n    return render(request,'book/about_us.html')","repo_name":"Nnnate423/Web_Dev","sub_path":"court/book/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"73551369884","text":"from skimage.transform import rotate\nfrom skimage.feature import local_binary_pattern\nfrom skimage import data, io\nfrom skimage.color import label2rgb\nimport skimage\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nimport cv2\nimport matplotlib.image as mpimg\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.svm import SVR\nfrom skimage import feature as skft\nfrom sklearn.svm import SVC\nimport os\n\ndef lbp(imagename):\n    image = cv2.imread(imagename)\n    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    block=5\n    wideth=image.shape[1]\n    heigh=image.shape[0]\n    column = wideth // block\n    row=heigh//block\n    lst = []\n    for i in range(block*block):\n        lbp1 = local_binary_pattern(image[row*(i//block):row*((i//block)+1),column*(i % block):column*((i % block)+1)], 8, 1,  'default')\n        hist, _ = np.histogram(lbp1, density=True, bins=256, range=(0, 256))\n        lst.append(hist)\n    return np.concatenate(lst)\n\ndef train_and_test_and_score(train_label,train_histogram,test_label,test_histogram):\n    global rptFile\n    print(\"\\n\\n-----merge finished-----\\n\\n\")\n    svc = SVC(kernel='linear', degree=2, gamma=1, coef0=0, C = 2)\n    svc.fit(train_histogram,train_label.ravel())\n    predict_result=svc.predict(test_histogram)\n    TP = 0\n    FP = 0\n    FN = 0\n    TN = 0\n    for i in range(len(predict_result)):\n        if test_label[i]==1:\n            if predict_result[i]==1:\n                TP+=1\n            else:\n                FN+=1\n        else:\n            if predict_result[i]==1:\n                FP+=1\n            else:\n                TN+=1\n    print(\"F1: %.6lf\"%(2*TP/(2*TP+FP+FN)))\n    print(\"TP: %d ; TN: %d ; FP: %d ; FN: %d ;\"%(TP, TN, FP, FN))\n    _rpt = open(rptFile, \"a\")\n    _rpt.write(\"F1: %.6lf\\n\"%(2*TP/(2*TP+FP+FN)))\n    _rpt.write(\"TP: %d ; TN: %d ; FP: %d ; FN: %d ;\\n\"%(TP, TN, FP, FN))\n    _rpt.close()\n\ndef merge(lst):\n    Len = len(lst)\n    if Len == 1:\n        return lst[0]\n    M = Len >> 1\n    return np.vstack((merge(lst[:M]), merge(lst[M:])))\n\nif __name__ == '__main__':\n    rptFile = \"report.txt\"\n    incatalog = \"train_and_test\"\n    _rpt = open(rptFile, \"w\")\n    _rpt.close()\n    for time in range(10):\n        trainLabel = []\n        trainHist = []\n        testLabel = []\n        testHist = []\n        for number in range(0, 10):\n            if number == time:\n                continue\n            print(\"Testtime: %d  Traingroup: %d  DATA COLLECTION START\" % (time, number))\n            trainFaces = open(\"%s/%d/faces.txt\"%(incatalog, number), \"r\")\n            while True:\n                line = trainFaces.readline()\n                if len(line) == 0 or len(line) == 1:\n                    break\n                content = line[:-1].split()[0]\n                label = int(line[:-1].split()[1][0])\n                hist = lbp(\"%s/%d/%s\"%(incatalog, number, content))\n                trainLabel.append(label)\n                trainHist.append(hist)\n            trainFaces.close()\n            print(\"Testtime: %d  Traingroup: %d  DATA COLLECTION FINISHED\" % (time, number))\n        testFaces = open(\"%s/%d/faces.txt\"%(incatalog, time), \"r\")\n        print(\"Testgroup %d : TEST DATA COLLECTION START\"%(time))\n        while True:\n            line = testFaces.readline()\n            if len(line) == 0 or len(line) == 1:\n                break\n            content = line[:-1].split()[0]\n            label = int(line[:-1].split()[1][0])\n            hist = lbp(\"%s/%d/%s\"%(incatalog, time, content))\n            testLabel.append(label)\n            testHist.append(hist)\n        testFaces.close()\n        print(\"Testgroup %d : TEST DATA COLLECTION FINISHED\"%(time))\n        _rpt = open(rptFile, \"a\")\n        _rpt.write(\"Test group %d:\\n\"%time)\n        _rpt.close()\n        train_and_test_and_score(merge(trainLabel), merge(trainHist), merge(testLabel), merge(testHist))","repo_name":"lym01803/2018-final_project","sub_path":"大作业/project_2018/project_2018/Smile_Detection/smile_detection.py","file_name":"smile_detection.py","file_ext":"py","file_size_in_byte":3854,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"14617473114","text":"class Solution(object):\n    def letterCombinations(self, digits):\n        if len(digits) == 0:\n            return []\n        phones = {\"1\":\"\" , \"2\":\"abc\" , \"3\":\"def\" , \"4\":\"ghi\" , \"5\":\"jkl\" , \"6\":\"mno\" , \"7\":\"pqrs\" , \"8\":\"tuv\" , \"9\":\"wxyz\"}\n        solution  = []\n        solution.append(\"\")\n        \n        for digit in digits:\n            if digit == \"1\":\n                continue\n            word = phones[digit]\n            temp = []\n            for alphabet in word:\n                for s in solution:\n                    temp.append(s+alphabet)\n            solution = temp\n        \n        return solution\n        \"\"\"\n        :type digits: str\n        :rtype: List[str]\n        \"\"\"\n        ","repo_name":"shahrush/LeetCode","sub_path":"Letter Combinations of a Phone Number.py","file_name":"Letter Combinations of a Phone Number.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"41164149633","text":"from bottle import run, get, post, view, request, redirect\nimport requests\nimport bottle\nimport json\nimport threading\nimport time\nimport sys\nimport hashlib\n\n# BUGLIST\n# - insercao da mesma chave 2 vezes, possibilita inserir o mesmo par de chave/valer em posicoes diferentes da DHT\n# - necessario cria uma maneira para inicializar a DHT, a partir de uma chave inicial\n# - necessario implementar comunicacao em grupo, e propagar os inserts e lookups\n\nmyPort \t\t= sys.argv[1]\n\nh = hashlib.sha256(myPort.encode())\nh.hexdigest()\nn = int(h.hexdigest(), base = 16) % 15\nn = \"{0:b}\".format(n)\np = str(n)\np = p.zfill(4)\n\ndef subkeys(k):\n    for i in range(len(k), 0, -1):\n        yield k[:i]\n    yield \"\"\n\n\nclass DHT:\n    def __init__(self, k):\n        self.k = k\n        self.h = {}\n        print(\"CHAVE \"+p)\n        print (type(self.h))\n\n        for sk in subkeys(self.k):\n            self.h[sk+'0'] = None\n\n        for sk in subkeys(self.k):\n            self.h[sk+'1'] = None\n        \n        sorted(self.h.keys())\n\n    def insert(self, k, v):\n        \n        for sk in subkeys(k):\n            print(sk)\n            if sk in self.h:\n                if not self.h[sk]:\n                    self.h[sk] = (k, v)\n                    return sk + p\n        return None\n\n    def lookup(self, k):\n        print(list(subkeys(k)))\n        for sk in subkeys(k):\n            print(sk)\n            print(self.h)\n            if sk in self.h:\n                if self.h[sk]:\n                    (ki, vi) = self.h[sk]\n                    if ki == k:\n                        return vi\n        return None\n\n    def __repr__(self):\n        return \"<<DHT:\"+ repr(self.h) +\">>\"\n\ndht = DHT(p)\n\n\n\n\n@get('/dht/<key>')\ndef dht_lookup(key):\n    global dht\n    print(p)\n    return json.dumps(dht.lookup(key))\n\n@bottle.route('/dht/<key>/<value>')\ndef dht_insert(key, value):\n    global dht\n    return json.dumps(dht.insert(key, value))\n\n\nrun(host='localhost', port=myPort)\n","repo_name":"laurivansareta/ComputacaoDistribuida","sub_path":"servidor_dht/dht.py","file_name":"dht.py","file_ext":"py","file_size_in_byte":1937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"22282746472","text":"#!/usr/bin/python3\n# fuzzer.py v1\n\nimport socket\nfrom time import sleep\nimport argparse\n\n\ndef parse_arguments():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"host\")\n    parser.add_argument(\"-p\", \"--port\", type=int, default=110,\n                        help=\"Port to establish connection. Default port is 110\")\n    parser.add_argument(\"-u\", \"--username\", type=str, default=\"admin\")\n    parser.add_argument(\"-f\", \"--fuzz_pattern\", type=str, default=\"A\",\n                        help=\"Fuzzing pattern to fill buffer. Default is 'A'\")\n    parser.add_argument(\"-o\", \"--offset\", type=int, default=50,\n                        help=\"Offset used to build buffer. Default is 50\")\n    parser.add_argument(\"-l\", \"--length\", type=int, default=20,\n                        help=\"Size of the buffer (i.e. amount of attempts based on how many words are stored). Default is 20\")\n    parser.add_argument(\"-t\", \"--time_sleep\", type=int, default=2,\n                        help=\"Sleep time (in seconds) to use between each request. Default is 2\")\n    args = parser.parse_args()\n    return args\n\n\ndef create_buffer(ch: str, length: int, offset: int) -> list[str]:\n    buffer = [ch]\n    while len(buffer) <= length:\n        buffer.append(ch * offset)\n        offset += offset\n    return buffer\n\n\ndef fuzz(buffer: list[str], host: str, username: str, port: int = 110, sleep_time: int = 1) -> None:\n    for word in buffer:\n        print(f\"Fuzzing PASSWORD with {len(word)} byte(s)\")\n        tcp_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        tcp_socket.connect((host, port))\n        tcp_socket.recv(1024)\n        tcp_socket.send(f\"USER {username}\\r\\n\")\n        sleep(sleep_time)\n        tcp_socket.recv(1024)\n        tcp_socket.send(f\"PASS {word}\\r\\n\")\n        sleep(sleep_time)\n        tcp_socket.send(\"QUIT\\r\\n\")\n        tcp_socket.close()\n\n\ndef main():\n    args = parse_arguments()\n    buffer = create_buffer(args.fuzz_pattern, args.length, args.offset)\n    fuzz(buffer, args.host, args.username, args.time_sleep)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Aleffelisberto/pop3-fuzzer","sub_path":"fuzzer.py","file_name":"fuzzer.py","file_ext":"py","file_size_in_byte":2071,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"38141159962","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n\nif __name__ == '__main__':\n    dataset = 2*np.random.rand(100, 3)-1\n    x = dataset[:, :2]\n    y = dataset[:, 2]\n    layers = [2, 16, 16, 1]\n    weights = [2 * np.random.randint(low=0, high=2, size=(layers[i], layers[i + 1])) -1 for i in range(len(layers) - 1)]\n    # biases = [2 * np.random.randint(low=0, high=2, size=(1, layers[i + 1])) - 1 for i in range(len(layers) - 1)]\n    dw = [np.zeros(shape=(layers[i], layers[i + 1])) for i in range(len(layers) - 1)]\n    for i in range(len(weights)):\n        new_x = np.tanh(np.dot(x, weights[i]))\n        dw[i] = np.dot(x.T, new_x)\n        x = new_x\n    y - x.reshape(-1)\n\n\n\n","repo_name":"omrijsharon/FpyV","sub_path":"tests/nn_1bit_weights.py","file_name":"nn_1bit_weights.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"12905995532","text":"import sys\nimport socket\nimport os\nimport time\nimport select\nimport traceback\n\nfrom gi.repository import Gtk, GObject, GLib\n\nsys.path.insert(0,\"..\")\n\nfrom core import SubottoCore\nfrom data import Session, Team, Player, Match, PlayerMatch, Event, Base, AdvantagePhase\n\n\n\n\n# This class contains the functions to communicate with the arduino in the Subotto\nclass ArdCon():\n\n    BUFFER_RCV_LENGTH = 2\n    TIMEOUT = 0.001\n    ARD_TEAM = [\"RED\", \"BLUE\"]\n    EVENT = [\"VOID\", \"GOAL\", \"SUPERGOAL\", \"PLUS_ONE\", \"MINUS_ONE\"]\n    CORE_EVENT = {\n        \"RED\":{\n            \"VOID\":None,\n            \"GOAL\":Event.EV_SOURCE_CELL_BLUE_PLAIN,\n            \"SUPERGOAL\":Event.EV_SOURCE_CELL_BLUE_SUPER,\n            \"PLUS_ONE\":Event.EV_SOURCE_BUTTON_RED_GOAL,\n            \"MINUS_ONE\":Event.EV_SOURCE_BUTTON_RED_UNDO\n        },\n        \"BLUE\":{\n            \"VOID\":None,\n            \"GOAL\":Event.EV_SOURCE_CELL_RED_PLAIN,\n            \"SUPERGOAL\":Event.EV_SOURCE_CELL_RED_SUPER,\n            \"PLUS_ONE\":Event.EV_SOURCE_BUTTON_BLUE_GOAL,\n            \"MINUS_ONE\":Event.EV_SOURCE_BUTTON_BLUE_UNDO\n        }\n    }\n    UNDO_EVENT = [CORE_EVENT[team][\"MINUS_ONE\"] for team in ARD_TEAM]\n\n\n    # Initialize with socket\n    def __init__(self,sock,debugLog):\n        self.s = sock\n        self.debugLog = debugLog\n        self.ATC_SIGNAL = {\n            \"score\": lambda msg: ((msg[0] & 0xF)<<8) + msg[1],\n            \"team\": lambda msg: \"BLUE\" if msg[0] & 0x80 else \"RED\",\n            \"event\": lambda msg: self.EVENT[(msg[0] & 0x70) >> 4]\n        }\n        self.CTA_SIGNAL = {\n            \"askData\": lambda team: self.sendNumber(64) if team == \"RED\" else self.sendNumber(192)\n        }\n\n\n    # Send an int to the Arduino\n    def sendNumber(self,num):\n        self.s.send(chr(num))\n    \n\n    # Elaborate the data received from arduino\n    def dataFromBuff(self,rcv):\n        rcv = map(ord, rcv)\n        return {\n            \"score\": self.ATC_SIGNAL[\"score\"](rcv),\n            \"team\": self.ATC_SIGNAL[\"team\"](rcv),\n            \"event\": self.ATC_SIGNAL[\"event\"](rcv)\n        }\n\n    # Receive data from Arduino; return false as the second element if the socket is closed\n    def receiveData(self):\n        rlist, _, _ = select.select([self.s], [], [], 0)\n        if len(rlist):\n            rcv = \"\"\n            while len(rcv) < self.BUFFER_RCV_LENGTH:\n                rcv += self.s.recv(self.BUFFER_RCV_LENGTH - len(rcv))\n            return self.dataFromBuff(rcv)\n        return None\n\n    # Send a score change command to the Arduino\n    def sendScoreCommand (self, team, score_change ):\n        if team == \"RED\":\n            baseMsg = 0\n        else:\n            baseMsg = 128\n        if score_change < 0:\n            baseMsg += 32\n            score_change = - score_change\n        i = 0\n        while score_change != 0:\n            if score_change & 1:\n                self.sendNumber(baseMsg + i)\n            score_change = score_change >> 1\n            i += 1\n    \n    # Send a sensor activation/deactivation command to the arduino\n    def sendSensorCommand (self, team, event, toActivate):\n        if team == \"RED\":\n            baseMsg = 72\n        else:\n            baseMsg = 200\n        baseMsg += (self.EVENT.index(event)-1) << 1\n        if not toActivate:\n            baseMsg += 1\n        self.sendNumber(baseMsg)\n    \n    # Asks Arduino the score\n    def askData(self,team):\n        self.CTA_SIGNAL[\"askData\"](team)\n\n\n\n# This class contains the implementation of the interface\nclass Interface:\n\n    _numDebugebugLines = 0\n\n    DEVICE = [\"arduino\",\"core\"]\n    MAX_NUM_CONSOLE_ROWS = 100\n\n    connected = False\n    toDisconnect = False\n    score = {dev:[0,0] for dev in DEVICE}\n    lastToScore = {dev:None for dev in DEVICE}\n\n\n    # === init function ===\n\n    def __init__(self):\n        filename = \"main.glade\"\n        self.builder = Gtk.Builder()\n        self.builder.add_from_file(filename)\n        self.builder.connect_signals(self)\n\n        self.mainWindow = self.builder.get_object(\"mainWindow\")\n        self.connectionWindow = self.builder.get_object(\"connectionWindow\")\n        self.debugConsole = Gtk.ListStore(str)\n        self.consoleView = self.builder.get_object(\"console\")\n        self.consoleView.set_model(self.debugConsole)\n        self.consoleView.insert_column(Gtk.TreeViewColumn(\"Debug Log\", Gtk.CellRendererText(), text=0),0)\n\n        self.scoreTextView = {\n            \"arduino\":{\"RED\":self.builder.get_object(\"redArduinoScore\"),\n                       \"BLUE\":self.builder.get_object(\"blueArduinoScore\")},\n            \"core\":{\"RED\":self.builder.get_object(\"redCoreScore\"),\n                    \"BLUE\":self.builder.get_object(\"blueCoreScore\")}\n        }\n        self.lastToScoreBar = {\n            \"arduino\":{\"RED\":self.builder.get_object(\"redArduinoLastToScore\"),\n                       \"BLUE\":self.builder.get_object(\"blueArduinoLastToScore\")},\n            \"core\":{\"RED\":self.builder.get_object(\"redCoreLastToScore\"),\n                    \"BLUE\":self.builder.get_object(\"blueCoreLastToScore\")},\n        }\n        self.sensorSwitch = {\n            \"RED\":{\n                \"VOID\":None,\n                \"GOAL\":self.builder.get_object(\"redGoalSwitch\"),\n                \"SUPERGOAL\":self.builder.get_object(\"redSupergoalSwitch\"),\n                \"PLUS_ONE\":self.builder.get_object(\"redAddButtonSwitch\"),\n                \"MINUS_ONE\":self.builder.get_object(\"redUndoButtonSwitch\")\n            },\n            \"BLUE\":{\n                \"VOID\":None,\n                \"GOAL\":self.builder.get_object(\"blueGoalSwitch\"),\n                \"SUPERGOAL\":self.builder.get_object(\"blueSupergoalSwitch\"),\n                \"PLUS_ONE\":self.builder.get_object(\"blueAddButtonSwitch\"),\n                \"MINUS_ONE\":self.builder.get_object(\"blueUndoButtonSwitch\")\n            }\n        }\n\n        self.core = SubottoCore(int(sys.argv[1]))\n        for i in [0,1]:\n            self.score[\"core\"][i] = self.getCoreScore(i)\n       \n        GObject.timeout_add(300, self.loopFunction)\n        self.core = SubottoCore(int(sys.argv[1]))\n\n\n    # === control functions ===\n\n    # write on the console\n    def debugLog(self,string):\n        self._numDebugebugLines += 1\n        self.debugConsole.append([string])\n        #if len(self.debugConsole) > self.MAX_NUM_CONSOLE_ROWS:\n        #    self.debugConsole.remove(self.debugConsole.get_iter_first())\n\n\n    # === main loop ===\n\n    def loopFunction(self,*args):\n        if self.connected:\n            self.updateScore()\n        elif self.toDisconnect:\n            self.disconnect()\n        else:\n            self.debugLog(\"Not connected\")\n        self.core.update()\n        return True\n\n\n    # === connection/disconnection functions ===\n\n    # connect to socket\n    def connect(self):\n        if not self.connected:    \n            try:\n                self.debugLog(\"Getting data..\")\n                TCP_IP = self.builder.get_object(\"tcpipText\").get_text()\n                TCP_PORT = int(self.builder.get_object(\"tcpportText\").get_text())\n                PWD = self.builder.get_object(\"pwdText\").get_text()\n                self.debugLog(\"Connecting to socket...\")\n                self.debugLog(\"TCP_IP: \" + TCP_IP)\n                self.debugLog(\"TCP_PORT: \" + str(TCP_PORT))\n                self.s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n                self.s.connect((TCP_IP, TCP_PORT))\n                self.debugLog(\"Connected!\")\n                self.s.send(PWD)\n                self.connected = True\n                self.debugLog(\"Creating arduino controller object...\")\n                self.ac = ArdCon(self.s,self.debugLog)\n                self.debugLog(\"Arduino controller object Created!\")\n                self.debugLog(\"Asking Arduino data...\")\n                for i in [0,1]:\n                    self.getArduinoScore(i)\n                self.connectionWindow.hide()\n            except:\n                traceback.print_exc()\n                self.debugLog(\"Connection failed\\n\")\n            else:\n                self.connected = True\n                self.debugLog(\"Connection successful\\n\")\n\n    # disconnect from socket\n    def disconnect(self):\n        self.toDisconnect = False\n        #if not self.connected:\n        try:\n            self.s.close()\n        except:\n            pass\n        self.connectionWindow.show()\n        self.connected = False\n        self.debugLog(\"Disconnected\\n\")\n\n\n    def readEvents(self):\n        try:\n            rcv = self.ac.receiveData()\n            while rcv is not None:\n                self.debugLog(str(rcv))\n                self.sendEventToCore(rcv)\n                rcv = self.ac.receiveData()\n        except:\n            traceback.print_exc()\n            self.connected = False\n            self.toDisconnect = True\n            self.debugLog(\"Disconnected\")\n\n\n    # === update functions ===\n\n    # asks the score to arduino and the core; updates arduino score\n    def updateScore(self):\n        self.readEvents()\n        for i in [0,1]:\n            self.score[\"core\"][i] = self.getCoreScore(i)\n            if self.score[\"arduino\"][i] == None:\n                self.debugLog(\"Arduino score unknown\")\n                self.lastToScore[\"arduino\"] = None\n            else:\n                if self.score[\"core\"][i] != self.score[\"arduino\"][i]:\n                    # server takes priority over arduino\n                    self.setArduinoScore(i,self.score[\"core\"][i])\n                    self.lastToScore[\"arduino\"] = None\n            self.updateView(i)\n\n    # update scores on the main window\n    def updateView(self,team):\n        for dev in self.DEVICE:\n            score = self.score[dev][team]\n            if score == None:\n                self.scoreTextView[dev][self.ac.ARD_TEAM[team]].set_text(\"Unknown\")\n            else:\n                self.scoreTextView[dev][self.ac.ARD_TEAM[team]].set_text(str(score))\n            if self.lastToScore[dev] == team:\n                self.lastToScoreBar[dev][self.ac.ARD_TEAM[team]].set_fraction(1)\n            else:\n                self.lastToScoreBar[dev][self.ac.ARD_TEAM[team]].set_fraction(0)\n\n\n    # === core communication ===\n\n    # get score from the core\n    def getCoreScore(self,i):\n        return self.core.score[self.core.detect_team(self.core.order[i])]\n\n    # send an event received from arduino\n    def sendEventToCore(self,data):\n        team = self.ac.ARD_TEAM.index(data[\"team\"])\n        self.score[\"arduino\"][team] = data[\"score\"]\n        event = self.ac.CORE_EVENT[data[\"team\"]][data[\"event\"]]\n        if event is None:\n            pass\n        elif event in self.ac.UNDO_EVENT:\n            self.core.act_goal_undo(self.core.order[team], event)\n        else:\n            self.core.act_goal(self.core.order[team], event)\n            self.lastToScore[\"core\"] = team\n            self.lastToScore[\"arduino\"] = team\n\n\n    # === arduino communication ===\n\n    # get arduino score\n    def getArduinoScore(self,i):\n        self.ac.askData(self.ac.ARD_TEAM[i])\n\n    # set arduino score\n    def setArduinoScore(self,team,score):\n        scoreChange = score - self.score[\"arduino\"][team]\n        self.ac.sendScoreCommand(self.ac.ARD_TEAM[team],scoreChange)\n        self.getArduinoScore(team)\n\n    \n    # === event handlers ===\n\n    # called when the console shows\n    def onConsoleShow(self,*args):\n        adj = self.consoleView.get_vadjustment()\n        adj.set_value( adj.get_page_size() )\n\n    # called when a new line is added to the console\n    def onSizeAllocate(self,*args):\n        numDebugLines = self._numDebugebugLines\n        self._numDebugebugLines = 0\n        adj = self.consoleView.get_vadjustment()\n        if adj.get_value() >= adj.get_upper() - adj.get_page_size() - numDebugLines * adj.get_step_increment():\n            adj.set_value( adj.get_upper() - adj.get_page_size() )\n\n    # called when a switch is switched\n    def onSwitchNotify(self,*args):\n        if self.connected:\n            for team in self.ac.ARD_TEAM:\n                for event in self.ac.EVENT:\n                    if args[0] == self.sensorSwitch[team][event]:\n                        toActivate = self.sensorSwitch[team][event].get_active()\n                        self.ac.sendSensorCommand(team,event,toActivate)\n                        self.debugLog(\"switch \" + str(team) + \" \" + str(event) + \" was turned \" + str(toActivate))\n\n    # called when the connection button is hit\n    def onConnection(self,*args):\n        self.debugLog(\"Connecting...\")\n        self.connect()\n\n    # called when the main window is closed\n    def onDestroyWindow(self,*args):\n        try:\n            del(self.ac)\n            self.disconnect()\n        except:\n            traceback.print_exc()\n            pass\n        sys.exit(0)\n    \n\n\n\n\ndef main():\n    app = Interface()\n\n    mainWindow = app.builder.get_object(\"mainWindow\")\n    mainWindow.show_all()\n    connectionWindow = app.builder.get_object(\"connectionWindow\")\n    connectionWindow.show_all()\n\n    Gtk.main()\n    \n    return 0\n\nif __name__ == '__main__':\n    main()\n","repo_name":"subotto/subotto","sub_path":"arduino_ethernet_interface/guy.py","file_name":"guy.py","file_ext":"py","file_size_in_byte":12902,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"72127625883","text":"\"\"\"creating simulated dataset for our code.\n\"\"\"\nimport random\n\nlist_range = 360\ndef create_ran_df():\n\twith open('data/random.csv','w') as f:\n\t\tfor i in range(list_range):\n\t\t\targ = [i,random.randint(0,10),random.randint(0,2)]\n\t\t\tf.write(\"{},{},{}\\n\".format(arg[0],arg[1],arg[2]))\ncreate_ran_df()","repo_name":"ashfarhangi/Real-Time-Schedule-Prediciton","sub_path":"prediction-for-mixed-criticality-real-time/data_gen.py","file_name":"data_gen.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"7926722143","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport cv2\nfrom PIL import Image\nfrom torchvision import datasets, models, transforms\nfrom . import model_vgg\n\ndef shibie(image):\n\n    model = model_vgg.VGG()\n\n    model.load_state_dict(torch.load('../model_/params.pkl'))\n\n    data_transform = transforms.Compose([\n        transforms.Resize((48, 48)),\n        transforms.ToTensor(),\n        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n    ])\n\n    img = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n    img = data_transform(img)\n    img = img.view(1, 3, 48, 48)\n\n    output = model(img)\n    _, preds = torch.max(output, 1)\n    preds = preds[0].item()\n    expression_dict = {0: 'anger', 1: 'contempt', 2: 'disgust', 3: 'fear', \n                        4: 'happy', 5: 'sadness', 6: 'surprise'}\n\n    expression_state = \"expression:\" + expression_dict[preds]\n\n    return expression_state\n\n# if __name__ == \"__main__\":\n#     file_path = '/home/wang/b.jpg'\n#     img = cv2.imread(file_path)\n#     text = shibie(img)\n#     print(text)","repo_name":"178581399/12131","sub_path":"model_/test_.py","file_name":"test_.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"15116511036","text":"# CODE INTENDED TO RUN ON WINDOWS OS\r\n# Install required libraries\r\n# This code downloads the required files from BSEE website and checks for any updates. If there are any updates,\r\n# it refreshes the existing data\r\n\r\n# ----------------------------------------------------------------------------------------------------------------------\r\n\r\n# Imports\r\nfrom winreg import *\r\nfrom bs4 import BeautifulSoup\r\nimport requests\r\nimport re  # regular expression\r\nimport webbrowser\r\nimport os\r\nimport shutil\r\nimport time\r\nimport pandas as pd\r\nfrom datetime import datetime\r\n\r\n# Get the default Downloads directory of the computer\r\nwith OpenKey(HKEY_CURRENT_USER, 'SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Shell Folders') as key:\r\n    pc_downloads_directory = QueryValueEx(key, '{374DE290-123F-4565-9164-39C4925E467B}')[0]\r\n\r\npc_downloads_directory = pc_downloads_directory.replace('\\\\', '/')  # Replace to make the path consistent with the code\r\n\r\n# Get the current working directory\r\ncwd = os.getcwd()\r\n\r\n# Years the data is required for\r\nyears = [2019, 2020]  # <========================= MODIFY THIS TO GET DATA FOR OTHER YEARS /////////////////////////////\r\n\r\n# Create an empty dataframe to be used later for appending each year's data\r\ndf = pd.DataFrame()\r\n\r\n# URL of the website from where the files have to be scraped\r\nurl = \"https://www.data.bsee.gov/Main/OGOR-A.aspx\"\r\n\r\n# URL of the home page of BSEE website\r\nurl_homepage = \"https://www.data.bsee.gov\"\r\n\r\n# List of column names of the production file\r\ncol_names = ['Lease Number', 'Completion Name', 'Production Date', 'Days On Prod', 'Product Code',\r\n                 'Monthly Oil Volume', 'Monthly Gas Volume', 'Monthly Water Volume', 'Api Well Number',\r\n                 'Well Status Code', 'Area/Block/Bottom Area/Bottom Block', 'Operator Num', 'Operator Name',\r\n                 'Field Name Code', 'Injection Volume', 'Production Interval Code', 'First Production Date',\r\n                 'Unit Agreement Number', 'Unit Aloc Suffix']\r\n\r\n# Get HTML code of the URL\r\npage = requests.get(url).text\r\ndoc = BeautifulSoup(page, \"html.parser\")\r\n\r\n# -------------------------------------------- SCRAPING PART BEGINS ----------------------------------------------------\r\n\r\nif 'Download' not in os.listdir(cwd):\r\n\r\n    # Loop through the years to scrape the required data\r\n    for year in years:\r\n\r\n        # Find parts of the HTML where instances of the required year are present\r\n        # Find the parent's parent's parent block which contains the required info of 'Last Updated' and the 'Download Link'\r\n        # The 2nd TD tag contains the 'Last Updated' value. Split twice the final string to get the date\r\n        # Replace \"/\" by \"-\" just for convenience\r\n        last_updated = doc.find_all(text=re.compile(f\"{year}\"))[0].parent.parent.parent.find_all(\"td\")[1].text.split(' ')[0].replace(\"/\", \"-\")\r\n\r\n        # The 4th TD tag contains the 'Download Link'. The link is embedded in the HREF part\r\n        link = doc.find_all(text=re.compile(f\"{year}\"))[0].parent.parent.parent.find_all(\"td\")[3].a['href']\r\n\r\n        # The download link is the combination of home page URL and the link scraped\r\n        link_to_file = url_homepage + link\r\n\r\n        # Opening the link downloads the required .zip file\r\n        webbrowser.open(link_to_file)\r\n\r\n        # Time delay to make sure the file is downloaded before unzipping\r\n        time.sleep(6)\r\n\r\n        # Unzip the .zip file to the 'Download' sub-folder of the working directory where the python script is executed\r\n        shutil.unpack_archive(f\"{pc_downloads_directory}/ogora{year}delimit.zip\", \"Download\")\r\n\r\n        # Time delay to make sure the unzipping process is done before deleting the .zip file\r\n        time.sleep(2)\r\n\r\n        # Delete the .zip file now that the contents are unzipped\r\n        os.remove(f\"{pc_downloads_directory}/ogora{year}delimit.zip\")\r\n\r\n        # Rename the unzipped file to contain the year for which the data is scrapped and the 'Last Updated' date\r\n        os.rename(f\"{cwd}\\\\Download\\\\ogora{year}delimit.txt\", f\"{cwd}\\\\Download\\\\ogora{year}_{last_updated}.txt\")\r\n\r\n        # Read the unzipped .txt file and convert it into .csv\r\n        read_file = pd.read_csv(fr'{cwd}\\\\Download\\\\ogora{year}_{last_updated}.txt')\r\n        read_file.to_csv(fr'{cwd}\\\\Download\\\\ogora{year}_{last_updated}.csv', index=None)\r\n\r\n        # Convert the .csv file to a temporary dataframe\r\n        df_temp = pd.read_csv(fr'{cwd}\\\\Download\\\\ogora{year}_{last_updated}.csv', names=col_names, low_memory=False)\r\n\r\n        # Keep the required columns only in the dataframe\r\n        df_temp = df_temp[\r\n            ['Lease Number', 'Production Date', 'Product Code', 'Monthly Oil Volume', 'Monthly Gas Volume', 'Operator Num']]\r\n\r\n        # Concat the temporary dataframe to the dataframe df (initialized before the loop)\r\n        # Hence, the df will contain data for all the required years\r\n        df = pd.concat([df, df_temp])\r\n\r\n        # Remove the temporary .csv file\r\n        os.remove(fr'{cwd}\\\\Download\\\\ogora{year}_{last_updated}.csv')\r\n\r\n    # --------------------------------------------- LOOP ENDS HERE -----------------------------------------------------\r\n\r\n    # Data cleaning\r\n    # Let's drop the entries with Null values.\r\n    # The entries will Null values are negligible compared to the total number of rows, hence it is safe to drop these\r\n    # Used Jupyter notebook to get the number of rows with Null entries\r\n    df = df.dropna()\r\n\r\n    # No more cleaning is required. Convert the df to the final Production.csv file\r\n    df.to_csv(fr'{cwd}\\\\Download\\\\production.csv', index=None)\r\n\r\n# ------------------------ CHECK IF UPDATED DATA IS PRESENT AND REFRESH THE Production.csv data ------------------------\r\n\r\nelse:\r\n\r\n    # Loop through the required years to check if the data in the website was updated\r\n    for year in years:\r\n\r\n        # Get the last updated date\r\n        last_updated = doc.find_all(text=re.compile(f\"{year}\"))[0].parent.parent.parent.find_all(\"td\")[1].text.split(' ')[0].replace(\"/\", \"-\")\r\n\r\n        # Get a list of available .txt files, the data for which we have currently\r\n        list_files = os.listdir(f\"{cwd}\\\\Download\")\r\n\r\n        # Compare the last updated date (from file name) and last_updated date scraped from website for the required year's production file\r\n        for file in list_files:\r\n\r\n            # Skip the Production.csv file\r\n            if file != \"production.csv\":\r\n\r\n                # Compare to make sure we are checking the correct year's production file\r\n                if int(file[5:9]) == year:\r\n\r\n                    # Compare both the dates to check if any new updated data is present\r\n                    if datetime.strptime(last_updated, '%m-%d-%Y') > datetime.strptime(file.split('_')[-1].split('.')[0], '%m-%d-%Y'):\r\n\r\n                        # Initialize an empty dataframe so that the data does not concat with the existing data\r\n                        df = pd.DataFrame()\r\n\r\n                        # Get the date of last updated fetched from the website\r\n                        last_updated = str(last_updated)[0:10]\r\n\r\n                        # Remove the old file\r\n                        os.remove(f\"{cwd}\\\\Download\\\\ogora{year}_{file.split('_')[-1]}\")\r\n\r\n                        # Get the link to the .txt production file for the required year\r\n                        link = doc.find_all(text=re.compile(f\"{year}\"))[0].parent.parent.parent.find_all(\"td\")[3].a['href']\r\n                        link_to_file = url_homepage + link\r\n\r\n                        # Open the link and download the file\r\n                        webbrowser.open(link_to_file)\r\n\r\n                        # Time delay to make sure the file is downloaded before unzipping\r\n                        time.sleep(6)\r\n\r\n                        # Unzip the .zip file to the 'Download' sub-folder of the working directory where the python script is executed\r\n                        shutil.unpack_archive(f\"{pc_downloads_directory}/ogora{year}delimit.zip\", \"Download\")\r\n\r\n                        # Time delay to make sure the unzipping process is done before deleting the .zip file\r\n                        time.sleep(2)\r\n\r\n                        # Delete the .zip file now that the contents are unzipped\r\n                        os.remove(f\"{pc_downloads_directory}/ogora{year}delimit.zip\")\r\n\r\n                        # Time delay to make sure the unzipping process is done before deleting the .zip file\r\n                        time.sleep(2)\r\n\r\n                        # Rename the unzipped file to contain the year for which the data is scrapped and the 'Last Updated' date\r\n                        os.rename(f\"{cwd}\\\\Download\\\\ogora{year}delimit.txt\",\r\n                                  f\"{cwd}\\\\Download\\\\ogora{year}_{last_updated}.txt\")\r\n\r\n                        # Delete the already existing Production.csv, if it exists\r\n                        if 'production.csv' in list_files:\r\n                            os.remove(f\"{cwd}\\\\Download\\\\production.csv\")\r\n\r\n                        # Create a list of files present in the 'Download' sub-folder currently after the update\r\n                        list_files = os.listdir(f\"{cwd}\\\\Download\")\r\n\r\n                        # For each file in the 'Download' sub-folder currently\r\n                        for new_file in list_files:\r\n\r\n                            # Read the unzipped .txt file and convert it into .csv\r\n                            read_file = pd.read_csv(fr'{cwd}\\Download\\{new_file}')\r\n                            read_file.to_csv(fr'{cwd}\\\\Download\\\\ogora{year}_{last_updated}.csv', index=None)\r\n\r\n                            df_temp = pd.read_csv(fr'{cwd}\\Download\\ogora{year}_{last_updated}.csv', names=col_names,\r\n                                                  low_memory=False)\r\n\r\n                            # Keep the required columns only in the dataframe\r\n                            df_temp = df_temp[\r\n                                ['Lease Number', 'Production Date', 'Product Code', 'Monthly Oil Volume',\r\n                                 'Monthly Gas Volume', 'Operator Num']]\r\n\r\n                            # Concat the temporary dataframe to the dataframe df (initialized above as an empty df)\r\n                            # Hence, the df will contain data for all the required years\r\n                            df = pd.concat([df, df_temp])\r\n\r\n                            # Remove the temporary .csv file\r\n                            os.remove(fr'{cwd}\\Download\\ogora{year}_{last_updated}.csv')\r\n\r\n    # # Data cleaning\r\n    # # Let's drop the entries with Null values.\r\n    # # The entries will Null values are negligible compared to the total number of rows, hence it is safe to drop these\r\n    # # Used Jupyter notebook to get the number of rows with Null entries\r\n    df = df.dropna()\r\n\r\n    # No more cleaning is required. Convert the df to the final Production.csv file\r\n    df.to_csv(fr'{cwd}\\\\Download\\\\production.csv', index=None)\r\n\r\n# ------------------------------------------ UPDATE PROCESS ENDS HERE --------------------------------------------------\r\n","repo_name":"Aayush7Kumar/web_scraping","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11142,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"41254264632","text":"from core.base import Base\r\nfrom core.openGLUtils import OpenGLUtils\r\nfrom core.attribute import Attribute\r\nfrom OpenGL.GL import *\r\nimport numpy as np\r\n#La función circle_vertex_shader genera una lista de coordenadas de vértices para dibujar un círculo en un plano XY.\r\ndef circle_vertex_shader(radius, cx, cy, cz, num_segments):#cordenadas del circulo el radio y el numero de segmentos\r\n    vertices = []\r\n    kernel = [cx, cy, cz] #se crea lista\r\n    for i in range(num_segments):# se crea bucle que recorre los segmentos\r\n        theta = 2.0 * np.pi * float(i) / float(num_segments)\r\n        x = radius * np.cos(theta)+kernel[0]# calculo de la cordena x\r\n        y = radius * np.sin(theta)+kernel[1]# calculo de la cordena y\r\n        z = 0.0+kernel[2]# se establece cordenada\r\n        vertices.append([x, y, z]) #Se agrega una lista de tres elementos que representan las coordenadas\r\n    vertices.append(vertices[0])\r\n    return np.array(vertices, dtype=np.float32)#se devuelve resultado\r\n\r\nclass Test(Base):\r\n    def initialize(self):\r\n        print(\"Initializing program...\")\r\n        vsCode = \"\"\"\r\n        in vec3 position;\r\n        void main()\r\n        {\r\n            gl_Position = vec4(position.x, position.y, position.z, 1.0);\r\n        }\r\n        \"\"\"\r\n        fsCode = \"\"\"\r\n        out vec4 fragColor;\r\n        void main()\r\n        {\r\n            fragColor = vec4(1.0, 1.0, 0.0, 1.0);\r\n        }\r\n        \"\"\"\r\n        self.programRef = OpenGLUtils.initializeProgram(vsCode,fsCode)\r\n        ### render settings ###\r\n        glLineWidth(4)\r\n        ### set up vertex array object - triangle ###\r\n        self.vaoTri = glGenVertexArrays(1)\r\n        glBindVertexArray(self.vaoTri)\r\n        positionDataTri = [[-0.2, 0.8, 0.8], [-0.9, 0.8, -0.8], [-0.2, -0.2, 0.9]]\r\n        self.vertexCountTri = len(positionDataTri)\r\n        positionAttributeTri = Attribute(\"vec3\", positionDataTri)\r\n        positionAttributeTri.associateVariable(self.programRef, \"position\")\r\n\r\n        self.vaoSquare = glGenVertexArrays(1)\r\n        glBindVertexArray(self.vaoSquare)\r\n        positionDataSquare = [[0.8, 0.8, 0.0], [0.8, -0.2, 0.0], [0.2, -0.2, 0.0], [0.2,0.8, 0.0]]\r\n        self.vertexCountSquare = len(positionDataSquare)\r\n        positionAttributeSquare = Attribute(\"vec3\", positionDataSquare)\r\n        positionAttributeSquare.associateVariable(self.programRef, \"position\")\r\n\r\n        self.vaoCircle = glGenVertexArrays(1)\r\n        glBindVertexArray(self.vaoCircle)\r\n        positionDataCircle = circle_vertex_shader(0.3, 0.0, -0.5, 0.8, 40)\r\n        self.vertexCountCircle = len(positionDataCircle)\r\n        positionAttributeCircle = Attribute(\"vec3\", positionDataCircle)\r\n        positionAttributeCircle.associateVariable(self.programRef, \"position\")\r\n\r\n\r\n    def update(self):\r\n        # using same program to render both shapes\r\n        glUseProgram(self.programRef)\r\n        # draw the triangle\r\n        glBindVertexArray(self.vaoTri)\r\n        glDrawArrays(GL_LINE_LOOP, 0, self.vertexCountTri)\r\n        glBindVertexArray(self.vaoSquare)\r\n        glDrawArrays(GL_LINE_LOOP, 0, self.vertexCountSquare)\r\n        glBindVertexArray(self.vaoCircle)\r\n        glDrawArrays(GL_LINE_LOOP, 0, self.vertexCountCircle)\r\nTest().run()\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Ivantji/Clases-Atibutes-y-Uniforms","sub_path":"test-2-4.py","file_name":"test-2-4.py","file_ext":"py","file_size_in_byte":3238,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"30127862477","text":"# -*- coding: utf-8 -*-\nclass visual_():\n    \n    def __init__(self, file_path):\n        self.dir= file_path\n        \n    def show_error(self, iteration, error, dim, name):\n        # 画 L_2 relative error vs. iteration 图像的函数\n        # This function designed for drawing L_2 relative error vs. iteration\n        plt.figure()\n        plt.semilogy(iteration, error, color='b')\n        plt.xlabel(\"Iteration\", size=20)\n        plt.ylabel(\"Relative error\", size=20)        \n        plt.tight_layout()\n        plt.savefig(self.dir+'figure_err/error_iter_%s_%dd.png'%(name, dim))\n        plt.close()\n        \n    def show_error_abs(self, mesh, x_y, z, name, dim):\n        # 画pointwise absolute error 图像的函数\n        # This function designed for drawing point-wise absolute error\n        x= np.ravel(x_y[:,0])\n        y= np.ravel(x_y[:,1])\n        #\n        xi,yi = mesh\n        zi = griddata((x, y), np.ravel(z), (xi, yi), method='linear')\n        plt.figure() \n        plt.contourf(xi, yi, zi, 15, cmap=plt.cm.jet)\n        plt.colorbar()\n        plt.xlim(np.min(xi), np.max(xi))\n        plt.xlabel('x', fontsize=20)\n        plt.ylim(np.min(yi), np.max(yi))\n        plt.ylabel('y', fontsize=20)\n        plt.tight_layout()\n        plt.savefig(self.dir+'figure_err/error_abs_%s_%dd.png'%(name, dim))\n        plt.close()\n\n\n    def show_u_val(self, mesh, z1, z2, name, i):\n        # 画u(x)的函数\n        x1, x2 = mesh\n        z1= np.reshape(z1, [self.mesh_size, self.mesh_size])\n        z2= np.reshape(z2, [self.mesh_size, self.mesh_size])\n        #*******************\n        fig= plt.figure(figsize=(12,5))\n        ax1= fig.add_subplot(1,2,1)\n        graph1= ax1.contourf(x1, x2, z1, 10,  cmap= cm.jet)\n        fig.colorbar(graph1, ax= ax1)\n        #\n        ax2= fig.add_subplot(1,2,2)\n        graph2= ax2.contourf(x1, x2, z2, 10,  cmap= cm.jet)\n        fig.colorbar(graph2, ax= ax2)\n        #*******************\n        plt.tight_layout()\n        plt.savefig(self.dir+'figure_%s/%s_val_%d.png'%(name, name, i))\n        plt.close()\n        \n    def show_v_val(self, mesh, x_y, z, name, i):\n        # 画v(x)的函数\n        # This function designed for drawing the figure of test function v(x)\n        x= np.ravel(x_y[:,0])\n        y= np.ravel(x_y[:,1])\n        #\n        xi,yi = mesh\n        zi = griddata((x, y), np.ravel(z), (xi, yi), method='linear')\n        plt.figure() \n        plt.contourf(xi, yi, zi, 15, cmap=plt.cm.jet)\n        plt.colorbar()\n        plt.xlim(np.min(xi), np.max(xi))\n        plt.xlabel('x', fontsize=20)\n        plt.ylim(np.min(yi), np.max(yi))\n        plt.ylabel('y', fontsize=20)\n        plt.tight_layout()\n        plt.savefig(self.dir+'figure_%s/%s_%d.png'%(name, name, i))\n        plt.close()\n    \nclass wan_inv():\n    \n    def __init__(self, file_name, dim, beta, N_dm, N_bd):\n        import numpy as np\n        global np\n        #\n        import time\n        global time\n        #\n        import tensorflow as tf\n        global tf\n        #\n        import matplotlib.pyplot as plt\n        global plt\n        #\n        from scipy.interpolate import griddata\n        global griddata\n        #\n        from scipy.stats import truncnorm\n        global truncnorm\n        # \n        from matplotlib import cm\n        global cm\n        #\n        self.dim= dim                           #问题的维度\n        self.low, self.up=  0.0, 1.0           #矩形区域[-1,1]^d\n        self.la= np.pi\n        #\n        self.mesh_size= 50                      #用来生成testing data\n        self.beta= beta\n        #\n        self.v_layer= 6                          #test function v  的hidden layers 层数\n        self.v_h_size= 20                        #test function v  每层的neuron 数目\n        self.v_step= 1                      \n        self.v_rate= 0.008     \n        #\n        self.a_layer= 6                         \n        self.a_h_size= 20\n        self.u_layer= 6                          \n        self.u_h_size= 20      \n        #        \n        self.ua_step= 2                          \n        self.ua_rate= 0.01                    \n        #\n        self.dm_size= N_dm                       #内部采样点数目                   \n        self.bd_size= N_bd                       #边界采样点数目\n        self.iteration= 20001\n        #\n        self.dir= file_name              #运行的时候需要建一个文件夹，以此名字命名，然后在该文件夹下面\n                                                 #新建文件夹figure_err, figure_u, figure_a, figure_v，分别用来保存中间过程输出的图像\n        \n    def sample_train(self, dm_size, bd_size, dim):\n        # 生成训练数据\n        low, up= self.low, self.up\n        #********************************************************\n        # collocation points in domain\n        x_dm= np.random.uniform(low, up, [dm_size, dim])\n        # collocation points on boundary\n        x_bd_list=[]\n        n_vector_list=[]\n        for i in range(dim):\n            x_bound= np.random.uniform(low, up, [bd_size, dim])\n            x_bound[:,i]= up\n            x_bd_list.append(x_bound)\n            n_vector= np.zeros_like(x_bound)\n            n_vector[:,i]=1\n            n_vector_list.append(n_vector)\n            x_bound= np.random.uniform(low, up, [bd_size, dim])\n            x_bound[:,i]= low\n            x_bd_list.append(x_bound)\n            n_vector= np.zeros_like(x_bound)\n            n_vector[:,i]=-1\n            n_vector_list.append(n_vector)\n        x_bd= np.concatenate(x_bd_list, axis=0)\n        n_vector= np.concatenate(n_vector_list, 0)\n        #***********************************************************\n        # observation of u(x) on boundary\n        param= (dim-1)*self.la**2/2\n        u_bd= np.exp(param*x_bd[:,0]*(x_bd[:,0]-1))\n        for i in range(dim-1):\n            u_bd= np.multiply(u_bd, np.sin(self.la*x_bd[:,i+1]))\n        u_bd= np.reshape(u_bd, [-1, 1])\n        #*********************************************************\n        # observation of a(x) on boundary\n        a_bd= np.exp(param*x_bd[:,0]*(1-x_bd[:,0]))\n        a_bd= np.reshape(a_bd/self.la, [-1,1])\n        #*********************************************************\n        int_dm= (up-low)**dim\n        #\n        x_dm= np.float32(x_dm)\n        x_bd= np.float32(x_bd)\n        int_dm= np.float32(int_dm)\n        u_bd= np.float32(u_bd)\n        a_bd= np.float32(a_bd)\n        n_vector= np.float32(n_vector)\n        return(x_dm, x_bd, int_dm, u_bd, a_bd, n_vector)\n        \n    def sample_test(self, mesh_size, dim):\n        # 生成测试数据\n        low, up= self.low, self.up\n        #**********************************************************\n        # generate meshgrid in the domain\n        x_mesh= np.linspace(low, up, mesh_size)\n        mesh= np.meshgrid(x_mesh, x_mesh)\n        x1_dm= np.reshape(mesh[0], [-1,1])\n        x2_dm= np.reshape(mesh[1], [-1,1])\n        #\n        x3_dm= np.random.uniform(low, up, [self.mesh_size*self.mesh_size, dim-2])\n        x_dm= np.concatenate([x1_dm, x2_dm, x3_dm], axis=1)\n        x4_dm= np.zeros([self.mesh_size*self.mesh_size, dim-2])\n        x_draw_dm= np.concatenate([x1_dm, x2_dm, x4_dm], axis=1)\n        #***********************************************************\n        # The exact u(x)\n        param= (dim-1)*self.la**2/2\n        u_dm= np.exp(param*x_dm[:,0]*(x_dm[:,0]-1))\n        u_draw_dm= np.exp(param*x_draw_dm[:,0]*(x_draw_dm[:,0]-1))\n        for i in range(dim-1):\n            u_dm= np.multiply(u_dm, np.sin(self.la*x_dm[:,i+1]))\n            u_draw_dm= np.multiply(u_draw_dm, np.sin(self.la*x_draw_dm[:,i+1]))\n        u_dm= np.reshape(u_dm, [-1, 1])\n        u_draw_dm= np.reshape(u_draw_dm, [-1, 1])\n        #***********************************************************\n        # The exact a(x)\n        a_dm= np.exp(param*x_dm[:,0]*(1-x_dm[:,0]))\n        a_dm= np.reshape(a_dm/self.la, [-1,1])  \n        a_draw_dm= np.exp(param*x_draw_dm[:,0]*(1-x_draw_dm[:,0]))\n        a_draw_dm= np.reshape(a_draw_dm/self.la, [-1,1])\n        #***********************************************************\n        x_dm= np.float32(x_dm)\n        x_draw_dm= np.float32(x_draw_dm)\n        u_dm= np.float32(u_dm)\n        u_draw_dm= np.float32(u_draw_dm)\n        a_dm= np.float32(a_dm)\n        a_draw_dm= np.float32(a_draw_dm)\n        return(mesh, x_dm, u_dm, a_dm, x_draw_dm, u_draw_dm, a_draw_dm)\n \n    def net_a(self, x_in, out_size, name, reuse):\n        # 逼近 a(x) 的神经网络\n        #*****************************************************\n        # Neural Net for a(x)\n        h_size= self.a_h_size\n        with tf.variable_scope(name, reuse=reuse):\n            hi= tf.layers.dense(x_in, h_size, activation= tf.nn.tanh, name='input_layer')\n            hi= tf.layers.dense(hi, h_size, activation= tf.nn.tanh, name='input_layer1')\n            for i in range(self.a_layer):\n                if i%2==0:\n                    hi= tf.layers.dense(hi, h_size, activation= tf.nn.elu, name='h_layer'+str(i))\n                else:\n                    hi= tf.layers.dense(hi, h_size, activation= tf.nn.tanh, name='h_layer'+str(i))\n            out= tf.layers.dense(hi, out_size, activation= tf.nn.elu, name='output_layer')\n        return(out)\n    \n    def net_u(self, x_in, out_size, name, reuse):\n        # 逼近 u(x) 的神经网络\n        #*******************************************************\n        # Neural Net for u(x)\n        h_size= self.u_h_size\n        with tf.variable_scope(name, reuse=reuse):\n            hi= tf.layers.dense(x_in, h_size, activation= tf.nn.tanh, name='input_layer')\n            hi= tf.layers.dense(hi, h_size, activation= tf.nn.tanh, name='input_layer1')\n            for i in range(self.u_layer):\n                if i%2==0:\n                    hi= tf.layers.dense(hi, h_size, activation= tf.nn.softplus, name= 'h_layer'+str(i))\n                else:\n                    hi= tf.sin(tf.layers.dense(hi, h_size), name='h_layer'+str(i))\n            out= tf.layers.dense(hi, out_size, name='output_layer')\n        return(out)\n        \n    def net_v(self, x_in, out_size, name, reuse):\n        # 逼近 v(x) 的神经网络\n        #*********************************************************\n        # Neural Net for v(x)\n        h_size= self.v_h_size\n        with tf.variable_scope(name, reuse=reuse):\n            hi= tf.layers.dense(x_in, h_size, activation= tf.nn.tanh, name='input_layer')\n            hi= tf.layers.dense(hi, h_size, activation= tf.nn.tanh, name='input_layer1')\n            for i in range(self.v_layer):\n                if i%2==0:\n                    hi= tf.sin(tf.layers.dense(hi, h_size), name='h_layer'+str(i))\n                else:\n                    hi= tf.sin(tf.layers.dense(hi, h_size), name='h_layer'+str(i))\n            out= tf.layers.dense(hi, out_size, name='output_layer')\n        return(out)\n   \n    def fun_w(self, x, low, up):\n        I1= 0.110987\n        x_list= tf.split(x, self.dim, 1)\n        #\n        x_scale_list=[]\n        h_len= (up-low)/2.0\n        for i in range(self.dim):\n            x_scale= (x_list[i]-low-h_len)/h_len\n            x_scale_list.append(x_scale)\n        #\n        z_x_list=[];\n        for i in range(self.dim):\n            supp_x= tf.greater(1-tf.abs(x_scale_list[i]), 0)\n            z_x= tf.where(supp_x, tf.exp(1/(tf.pow(x_scale_list[i], 2)-1))/I1, \n                          tf.zeros_like(x_scale_list[i]))\n            z_x_list.append(z_x)\n        #\n        w_val= tf.constant(1.0)\n        for i in range(self.dim):\n            w_val= tf.multiply(w_val, z_x_list[i])\n        dw= tf.gradients(w_val, x, unconnected_gradients='zero')[0]\n        dw= tf.where(tf.is_nan(dw), tf.zeros_like(dw), dw)\n        return(w_val, dw)\n    \n    def grad_u(self, x_in, name, out_size=1):\n        # 计算神经网络u(x)的数值和导数\n        u_val= self.net_u(x_in, out_size, name, tf.AUTO_REUSE)\n        #\n        grad_u= tf.gradients(u_val, x_in, unconnected_gradients='zero')[0]\n        return(u_val, grad_u)\n        \n    def grad_v(self, x_in, name, out_size=1):\n        # 计算神经网络v(x)的数值和导数\n        v_val= self.net_v(x_in, out_size, name, tf.AUTO_REUSE)\n        #\n        grad_v= tf.gradients(v_val, x_in, unconnected_gradients='zero')[0]\n        return(v_val, grad_v)\n\n    def fun_g(self, x, n_vec):\n        x_list= tf.split(x, self.dim, 1)\n        #**************************************\n        param= (self.dim-1)*self.la**2/2\n        u_val= tf.exp(tf.multiply(param, tf.multiply(x_list[0], x_list[0]-1)))\n        for i in range(self.dim-1):\n            u_val= tf.multiply(u_val, tf.sin(tf.multiply(self.la, x_list[i+1])))\n        u_val= tf.reshape(u_val, [-1,1])\n        #\n        du= tf.gradients(u_val, x, unconnected_gradients='zero')[0]\n        du= tf.where(tf.is_nan(du), tf.zeros_like(du), du)\n        g_obv= tf.reduce_sum(tf.multiply(du, n_vec), axis=1)\n        g_obv= tf.reshape(g_obv, [-1,1])\n        return(u_val, du, g_obv)\n    \n    def build(self):\n        #*********************************************************************\n        with tf.name_scope('placeholder'):\n            self.x_dm= tf.placeholder(tf.float32, shape=[None, self.dim], name='x_dm')\n            self.x_bd= tf.placeholder(tf.float32, shape=[None, self.dim], name='x_bd')\n            self.int_dm= tf.placeholder(tf.float32, shape=(), name='int_dm')\n            self.u_bd= tf.placeholder(tf.float32, shape=[None, 1], name='u_bd')\n            self.a_bd= tf.placeholder(tf.float32, shape=[None, 1], name='a_bd')\n            self.n_vec= tf.placeholder(tf.float32, shape=[None, self.dim], name='n_vec')\n        #*********************************************************************\n        name_a='net_a'; name_u='net_u'; name_v='net_v';\n        self.a_val= self.net_a(self.x_dm, 1, name_a, tf.AUTO_REUSE) \n        self.u_val, grad_u= self.grad_u(self.x_dm, name_u)\n        self.v_val, grad_v= self.grad_v(self.x_dm, name_v)\n        w_val, grad_w= self.fun_w(self.x_dm, self.low, self.up)\n        u_bd_pred, grad_u_bd= self.grad_u(self.x_bd, name_u)\n        a_bd_pred= self.net_a(self.x_bd, 1, name_a, tf.AUTO_REUSE)\n        #**********************************************************************\n        wv_val= tf.multiply(w_val, self.v_val)\n        #\n        dudw_val= tf.reduce_sum(tf.multiply(grad_u, grad_w), axis=1)\n        dudw_val= tf.reshape(dudw_val, [-1,1])\n        #\n        dudv_val= tf.reduce_sum(tf.multiply(grad_u, grad_v), axis=1)\n        dudv_val= tf.reshape(dudv_val, [-1,1])\n        #\n        dudwv_val= tf.add(tf.multiply(self.v_val, dudw_val),\n                          tf.multiply(w_val, dudv_val))\n        #\n        _, _, g_obv= self.fun_g(self.x_bd, self.n_vec)\n        g_val= tf.reduce_sum(tf.multiply(grad_u_bd, self.n_vec), axis=1)\n        g_val= tf.reshape(g_val, [-1,1]) \n        #**********************************************************************\n        with tf.variable_scope('loss'):\n            with tf.name_scope('loss_u'):\n                test_norm = tf.multiply(tf.reduce_mean(wv_val**2), self.int_dm)  # w*v_u 的l_2范数(v_u表示关于u的test function)\n                #******************************************************************\n                # operator-norm (a(x)固定，学习u(x))\n                int_r1= tf.multiply(tf.reduce_mean(tf.multiply(self.a_val, dudwv_val)), self.int_dm)\n                #int_l1= tf.multiply(tf.reduce_mean(tf.multiply(self.f_val, wv_val_u)), self.int_dm)\n                self.loss_int= 10*tf.square(int_r1) / test_norm\n                #*******************************************************************\n                #\n                self.loss_u_bd= tf.reduce_mean(tf.abs(u_bd_pred-self.u_bd))  # loss on boundary for u(x)\n                self.loss_g_bd= tf.reduce_mean(tf.abs(g_val - g_obv))\n                #\n                self.loss_a_bd= tf.reduce_mean(tf.abs(a_bd_pred-self.a_bd))  # loss on boundary for a(x)\n                #\n                self.loss_total= (self.beta)*(self.loss_u_bd+self.loss_g_bd+self.loss_a_bd)+self.loss_int\n            with tf.name_scope('loss_v'):\n                # \n                self.loss_v=  - tf.log(self.loss_int)                      # loss for v\n        #**************************************************************\n        # \n        u_vars= tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=name_u)\n        v_vars= tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=name_v)\n        a_vars= tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=name_a)\n        #***************************************************************\n        # \n        with tf.name_scope('optimizer'):\n            self.ua_opt= tf.train.AdagradOptimizer(self.ua_rate).minimize(\n                    self.loss_total, var_list= u_vars+a_vars)\n            self.v_opt= tf.train.AdagradOptimizer(self.v_rate).minimize(\n                    self.loss_v, var_list= v_vars)\n    \n    def train(self):\n        #*********************************************************************\n        tf.reset_default_graph(); self.build()\n        #*********************************************************************\n        # generate points for testing usage\n        mesh, test_x, test_u, test_a, draw_x, draw_u, draw_a= self.sample_test(self.mesh_size, self.dim)\n        #\n        #saver= tf.train.Saver()\n        step=[]; error_u=[]; error_a=[]\n        time_begin=time.time(); time_list=[]; iter_time_list=[]\n        visual=visual_(self.dir)\n        with tf.Session() as sess:\n            sess.run(tf.global_variables_initializer())\n            for i in range(self.iteration):\n                train_data= self.sample_train(self.dm_size, self.bd_size, self.dim)\n                feed_train= {self.x_dm: train_data[0],\n                            self.x_bd: train_data[1],\n                            self.int_dm: train_data[2],\n                            self.u_bd: train_data[3],\n                            self.a_bd: train_data[4],\n                            self.n_vec: train_data[5]}\n                if i%5==0:\n                    #\n                    pred_u, pred_a= sess.run([self.u_val, self.a_val],feed_dict={self.x_dm: test_x})                 \n                    err_u= np.sqrt(np.mean(np.square(test_u-pred_u)))\n                    total_u= np.sqrt(np.mean(np.square(test_u)))\n                    err_a= np.sqrt(np.mean(np.square(test_a-pred_a)))\n                    total_a= np.sqrt(np.mean(np.square(test_a)))\n                    step.append(i+1)\n                    error_u.append(err_u/total_u)\n                    error_a.append(err_a/total_a)\n                    time_step= time.time(); time_list.append(time_step-time_begin)\n                if i%500==0:\n                    loss_total, loss_v, loss_int, loss_a_bd= sess.run(\n                        [self.loss_total, self.loss_v, self.loss_int, self.loss_a_bd], \n                        feed_dict= feed_train)\n                    print('Iterations:{}'.format(i))\n                    print('loss_total:{} loss_v:{} loss_int:{} loss_a_bd:{} l2r_a:{} l2r_u:{}'.format(\n                        loss_total, loss_v, loss_int, loss_a_bd, error_a[-1], error_u[-1]))\n                    #\n                    pred_u_draw, pred_a_draw, pred_v_draw= sess.run(\n                            [self.u_val, self.a_val, self.v_val], \n                            feed_dict={self.x_dm: draw_x})\n                    #visual.show_error(step, error_u, self.dim, 'l2r_u')\n                    #visual.show_error(step, error_a, self.dim, 'l2r_a')\n                    #visual.show_u_val(mesh, draw_a, pred_a_draw, 'a',  i)\n                    #visual.show_u_val(mesh, draw_u, pred_u_draw, 'u',  i)\n                #\n                iter_time0= time.time()\n                for _ in range(self.v_step):\n                    _ = sess.run(self.v_opt, feed_dict=feed_train)                    \n                for _ in range(self.ua_step):\n                    _ = sess.run(self.ua_opt, feed_dict=feed_train)\n                iter_time_list.append(time.time()-iter_time0)\n                #\n            #*******************************************\n            #visual.show_error_abs(mesh, draw_x, np.abs(draw_a-pred_a_draw), 'a', self.dim)\n            #visual.show_error_abs(mesh, draw_x, np.abs(draw_u-pred_u_draw), 'u', self.dim)\n            print('L2r_a is {}, L2r_u is {}'.format(np.min(error_a), np.min(error_u)))\n        return(mesh, test_x, draw_x, test_u, draw_u, test_a, draw_a, pred_u, pred_u_draw, pred_a, pred_a_draw, \n               step, error_a, error_u, time_list, iter_time_list, self.dim)\n\nif __name__=='__main__':\n    dim, beta, N_dm, N_bd= 5, 10000, 100000, 50\n    file_name= './problem_EIT/'\n    demo= wan_inv(file_name, beta, N_dm, N_bd)\n    mesh, test_x, draw_x, test_u, draw_u, test_a, draw_a, pred_u, pred_u_draw, pred_a, pred_a_draw, step, error_a, error_u, time_list, iter_time_list, dim= demo.train()\n    #***************************\n    # save data as .mat form\n    import scipy.io\n    data_save= {}\n    data_save['mesh']= mesh\n    data_save['test_x']= test_x\n    data_save['test_u']= test_u\n    data_save['test_a']= test_a\n    data_save['pred_u']= pred_u\n    data_save['pred_a']= pred_a\n    data_save['draw_x']= draw_x\n    data_save['draw_u']= draw_u\n    data_save['draw_a']= draw_a\n    data_save['pred_u_draw']= pred_u_draw\n    data_save['pred_a_draw']= pred_a_draw\n    data_save['step']= step\n    data_save['error_a']= error_a\n    data_save['error_u']= error_u\n    data_save['time_list']= time_list\n    data_save['iter_time_list']= iter_time_list\n    scipy.io.savemat(file_name+'iwan_%dd'%(dim), data_save)\n","repo_name":"yaohua32/iwan","sub_path":"experiment_EIT/iwan_EIT.py","file_name":"iwan_EIT.py","file_ext":"py","file_size_in_byte":21552,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"34554999859","text":"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom quantization.quant_functions import *\nfrom mapper.conv_mapper import BaseConvMapper\nimport numpy as np\nfrom tqdm import tqdm\nfrom quantization.sublayer_quantizer import SubLayerQuantizer\nimport random\nimport copy\n\n\nclass QuantizeConv2d(nn.Conv2d):\n    def __init__(self,in_channels: int,\n        out_channels: int,\n        kernel_size,\n        stride = 1,\n        padding = 0,\n        dilation = 1,\n        groups: int = 1,\n        bias: bool = True,\n        padding_mode: str = 'zeros'):\n        super().__init__(in_channels,out_channels,kernel_size,stride,padding,dilation,groups,bias,padding_mode)\n        \n        self.mode=None\n        self.quantizer=None\n        self.bn_fused=False\n    \n    def forward(self, x):\n        if self.mode=='raw':\n            out=super().forward(x)\n        elif self.mode==\"quant_forward\":\n            out=self.quant_forward(x)\n        elif self.mode==\"calibration_forward\":\n            out=self.calibrate_forward(x)\n        elif self.mode=='statistic_forward':\n            out=self.statistic_forward(x)\n        elif self.mode=='mapped_forward':\n            out=self.mapped_forward(x)\n        else:\n            raise NotImplementedError\n        return out\n    \n    def quant_forward(self,x):\n        assert self.quantizer.calibrated is not None,f\"You should run calibrate_forward before run quant_forward for {self}\"\n        weight_sim,bias_sim=self.quantizer.quant_weight_bias(self.weight,self.bias)\n        x_sim=self.quantizer.quant_activation(x)\n        out_sim=F.conv2d(x_sim, weight_sim, bias_sim, self.stride, self.padding, self.dilation, self.groups)\n        out_sim=self.quantizer.quant_output(out_sim)\n        return out_sim\n    \n    def get_quant_weight_bias(self):\n        return self.quantizer.quant_weight_bias(self.weight,self.bias)\n\n    def calibrate_forward(self,x):\n        # assert self.weight_bits is not None and self.act_bits is not None, f\"You should set the weight_bits and bias_bits for {self}\"\n        op=lambda input,weight,bias:F.conv2d(input,weight,bias,self.stride,self.padding, self.dilation, self.groups)\n        out_sim=self.quantizer.calibration(x,self.weight,self.bias,op)\n        return out_sim\n\n    def mapped_forward(self,x):\n        assert self.crossbars is not None,f\"You should map the conv to the crossbar before using mapped_forward\"\n        outputs=[]\n        for crossbar in self.crossbars:\n            output=crossbar(x)\n            outputs.append(output)\n        rst=self.merger(x,outputs)\n        # bias\n        if self.bias:\n            rst+=self.bias.view(1,-1,1,1)\n        return rst\n\nclass BitwiseStatisticConv2d(QuantizeConv2d):\n    def __init__(self,in_channels: int,\n        out_channels: int,\n        kernel_size,\n        stride = 1,\n        padding = 0,\n        dilation = 1,\n        groups: int = 1,\n        bias: bool = True,\n        padding_mode: str = 'zeros'):\n        super().__init__(in_channels,out_channels,kernel_size,stride,padding,dilation,groups,bias,padding_mode)\n        self.slice_size=None\n        self.statistic={}\n        \n    def statistic_forward(self,x):\n        print(f\"Run statistic_forward of {self} with input {x.size()}\")\n        weight_sim,bias_sim=self.quantizer.quant_weight_bias(self.weight,self.bias)\n        w_integer=weight_sim.integer\n        x_sim=self.quantizer.quant_activation(x)\n        in_integer=x_sim.integer\n        out_sim=F.conv2d(x_sim, weight_sim, bias_sim, self.stride, self.padding, self.dilation, self.groups)\n        out_sim=self.quantizer.quant_output(out_sim)\n        \n        \n        # if 'in_quant' in self.activation_quant_mode:\n        #     if self.activation_quant_mode=='in_quant_unsigned':\n        #         in_max_int=2**(self.act_bits)-1\n        #     else:\n        #         in_max_int=2**(self.act_bits-1)-1\n        #     in_integer=torch.round_(x/self.x_scale).clamp_(-in_max_int,in_max_int)\n        #     x=in_integer*self.x_scale\n        # w_max_int=2**(self.weight_bits)-1\n        # w_integer=torch.round_(self.weight.data/self.weight_scale).clamp_(-w_max_int,w_max_int)\n        # w_q=w_integer*self.weight_scale\n        # raw_out=F.conv2d(x,w_q,self.bias,self.stride,self.padding,self.dilation,self.groups)\n        \n        # b,oc,oh,ow=raw_out.size()\n        b,oc,oh,ow=out_sim.size()\n        \n        kernel_size=self.weight.size()[2:]\n        x_unfolded=F.unfold(in_integer,kernel_size,self.dilation,self.padding,self.stride) # shape N,C×∏(kernel_size),L\n        W=w_integer.view(oc,-1) # shape oc,C*∏(kernel_size)\n        b,win_size,n_window=x_unfolded.size()\n        n_slice=win_size//self.slice_size\n        # ignore the un-aligned datas\n        x_unfolded=x_unfolded[:,:n_slice*self.slice_size]\n        W=W[:,:n_slice*self.slice_size]\n        \n        all_x_slice=x_unfolded.view(b,n_slice,self.slice_size,n_window)\n        \n        W_slice=W.view(oc,n_slice,self.slice_size)\n        # print(f\"x_slice {x_slice.size()} W_slice {W_slice.size()}\")\n        S=0 # shape N,oc,L\n        all_x_slice=all_x_slice.long()\n        W_slice=W_slice.long()\n        if f'in_num' not in self.statistic:\n            self.statistic[f'in_num']=0\n        self.statistic['in_num']+=b*n_slice*n_window\n        if f'out_num' not in self.statistic:\n            self.statistic[f'out_num']=0\n        self.statistic['out_num']+=b*n_slice*oc*n_window*self.act_bits\n        with torch.no_grad():\n            for act_bit_i in range(self.act_bits):\n                x_bit=((all_x_slice>>act_bit_i)&1).float()\n                zero_in_num=torch.sum((torch.sum(x_bit,2)==0).long()).item()\n                if f'zero_in_{act_bit_i}' not in self.statistic:\n                    self.statistic[f'zero_in_{act_bit_i}']=0\n                self.statistic[f'zero_in_{act_bit_i}']+=zero_in_num\n                \n                for w_bit_i in range(self.weight_bits):\n                    w_bit=((W_slice>>w_bit_i)&1).float()\n                    zero_out_num=0\n                    for i in range(n_slice):\n                        psum=torch.matmul(w_bit[:,i],x_bit[:,i])\n                        # if i not in self.statistic:\n                        #     self.statistic[i]=[]\n                        # psum_sorted=torch.sort(psum.view(-1))[0][::int(psum.view(-1).size(0)/1000)]\n                        # self.statistic[i].append(psum_sorted.detach().cpu().numpy())\n                        zero_out_num+=torch.sum((psum==0).long()).item()\n                    if f'zero_out_{w_bit_i}' not in self.statistic:\n                        self.statistic[f'zero_out_{w_bit_i}']=0\n                    self.statistic[f'zero_out_{w_bit_i}']+=zero_out_num\n                    if f'zero_out_{w_bit_i}_exclude_in_zero' not in self.statistic:\n                        self.statistic[f'zero_out_{w_bit_i}_exclude_in_zero']=0\n                    # shape of zero out: n_slice*(b*oc*L); shape of zero in: b*n_slice*L\n                    self.statistic[f'zero_out_{w_bit_i}_exclude_in_zero']+=zero_out_num-oc*zero_in_num\n                    if f'tot_out_{w_bit_i}_exclude_in_zero' not in self.statistic:\n                        self.statistic[f'tot_out_{w_bit_i}_exclude_in_zero']=0\n                    # shape of zero out: n_slice*(b*oc*L); shape of zero in: b*n_slice*L\n                    self.statistic[f'tot_out_{w_bit_i}_exclude_in_zero']+=psum.numel()*n_slice-oc*zero_in_num\n                \n        return out_sim\n\nclass BaseMappedConv2d(nn.Conv2d):\n    \"\"\"\n    Map the nn.Conv2d to different size of crossbar\n    \"\"\"\n    def __init__(self,in_channels: int,\n        out_channels: int,\n        kernel_size,\n        stride = 1,\n        padding = 0,\n        dilation = 1,\n        groups: int = 1,\n        bias: bool = True,\n        padding_mode: str = 'zeros'):\n        super().__init__(in_channels,out_channels,kernel_size,stride,padding,dilation,groups,bias,padding_mode)\n        \n        self.mode='raw'\n        # self.crossbar_cols=None\n        # self.crossbar_rows=None\n        # self.n_cell_per_weight=None\n        # self.n_input_steps=None\n        self.crossbars=None\n        self.merger=None\n    \n    def map_to_crossbars(self,rows,cols,n_cell_per_weight=1,n_input_steps=1):\n        mapper=BaseConvMapper(rows,cols,n_cell_per_weight)\n        self.crossbars,self.merger=mapper(self)\n        self.rows=rows\n        self.cols=cols\n\n    def forward(self, x):\n        if self.mode=='raw':\n            out=super().forward(x)\n        elif self.mode==\"mapped_forward\":\n            out=self.mapped_forward(x)\n        else:\n            raise NotImplementedError\n        return out\n    \n    def mapped_forward(self,x):\n        assert self.crossbars is not None,f\"You should map the conv to the crossbar before using mapped_forward\"\n        outputs=[]\n        for crossbar in self.crossbars:\n            output=crossbar(x)\n            outputs.append(output)\n        rst=self.merger(x,outputs)\n        # bias\n        if self.bias:\n            rst+=self.bias.view(1,-1,1,1)\n        return rst\n\nclass LayerWiseQuantMappedConv2d(QuantizeConv2d,BaseMappedConv2d):\n    def __init__(self,in_channels: int,\n        out_channels: int,\n        kernel_size,\n        stride = 1,\n        padding = 0,\n        dilation = 1,\n        groups: int = 1,\n        bias: bool = True,\n        padding_mode: str = 'zeros'):\n        super().__init__(in_channels,out_channels,kernel_size,stride,padding,dilation,groups,bias,padding_mode)\n    \n    def quant_forward(self,x):\n        assert self.crossbars is not None\n        assert self.quantizer.calibrated is not None,f\"You should run calibrate_forward before run quant_forward for {self}\"\n        \n        x_sim=self.quantizer.quant_activation(x)\n        out_sims=[]\n        for crossbar in self.crossbars:\n            weight_sim,_=self.quantizer.quant_weight_bias(crossbar.weight,None)\n            out_sim=crossbar(x_sim,weight_sim)\n            out_sims.append(out_sim)\n        out_sim=self.merger(x,out_sims)\n        out_sim=self.quantizer.quant_output(out_sim)\n        if self.bias is not None:\n            out_sim+=self.bias.view(1,-1,1,1)\n        return out_sim\n    \n    def calibrate_forward(self,x):\n        assert self.weight_bits is not None and self.act_bits is not None, f\"You should set the weight_bits and bias_bits for {self}\"\n        out_sims=[]\n        bias=None\n        weights=[]\n        def op(input,weights,bias):\n            for i,crossbar in enumerate(self.crossbars):\n                out_sim=crossbar(input,weights[i*self.cols:(i+1)*self.cols])\n                out_sims.append(out_sim)\n            out_sim=self.merger(x,out_sims)\n            return out_sim\n        for crossbar in self.crossbars:\n            weights.append(crossbar.weight)\n        weights=torch.cat(weights,0)\n        out_sim=self.quantizer.calibration(x,weights,bias,op)\n        if self.bias is not None:\n            out_sim+=self.bias.view(1,-1,1,1)\n        return out_sim\n\nclass CrossbarWiseQuantMappedConv2d(LayerWiseQuantMappedConv2d):\n    def __init__(self,in_channels: int,\n        out_channels: int,\n        kernel_size,\n        stride = 1,\n        padding = 0,\n        dilation = 1,\n        groups: int = 1,\n        bias: bool = True,\n        padding_mode: str = 'zeros'):\n        super().__init__(in_channels,out_channels,kernel_size,stride,padding,dilation,groups,bias,padding_mode)\n    \n    def quant_forward(self,x):\n        assert self.crossbars is not None\n        assert self.quantizer.calibrated is not None,f\"You should run calibrate_forward before run quant_forward for {self}\"\n        out_sims=[]\n        for i,crossbar in enumerate(self.crossbars):\n            weight_sim,_=self.quantizer[i].quant_weight_bias(crossbar.weight,None)\n            x_i=self.quantizer[i].quant_activation(x)\n            out_sim=crossbar(x_i,weight_sim)\n            out_sim=self.quantizer[i].quant_output(out_sim)\n            out_sims.append(out_sim)\n        out_sim=self.merger(x,out_sims)\n        if self.bias is not None:\n            out_sim+=self.bias.view(1,-1,1,1)\n        return out_sim\n    \n    def calibrate_forward(self,x):\n        if not isinstance(self.quantizer,SubLayerQuantizer):\n            self.quantizer=SubLayerQuantizer(len(self.crossbars),self.quantizer)\n        out_sims=[]\n        bias=None\n        for i,crossbar in enumerate(self.crossbars):\n            op=lambda input,weight,bias: crossbar(input,weight)\n            out_sim=self.quantizer[i].calibration(x,crossbar.weight.data,bias,op)\n            # print(f\"CF for {i} self.quantizer[i]={self.quantizer[i].weight_interval}\")\n            out_sims.append(out_sim)\n        out_sim=self.merger(x,out_sims)\n        if self.bias is not None:\n            out_sim+=self.bias.view(1,-1,1,1)\n        return out_sim\n\n\nclass ReorderingCrossbarWiseQuantMappedConv2d(CrossbarWiseQuantMappedConv2d):\n    def __init__(self, in_channels: int,\n        out_channels: int,\n        kernel_size,\n        stride = 1,\n        padding = 0,\n        dilation = 1,\n        groups: int = 1,\n        bias: bool = True,\n        padding_mode: str = 'zeros'):\n        super().__init__(in_channels,out_channels,kernel_size,stride,padding,dilation,groups,bias,padding_mode)\n        self.now_layer = self\n        self.next_layer = None\n        self.best_similarity = -1e9\n        self.best_reordering_weight = None\n        self.next_layer_best_reordering_weight = None\n        self.outputfile = None\n\n    def calibrate_forward(self,x):\n        if not isinstance(self.quantizer,SubLayerQuantizer):\n            self.quantizer=SubLayerQuantizer(len(self.crossbars),self.quantizer)\n        out_sims=[]\n        bias=None\n\n        for i,crossbar in enumerate(self.crossbars):\n            op=lambda input,weight,bias: crossbar(input,weight)\n            out_sim=self.quantizer[i].calibration(x,crossbar.weight.data,bias,op)\n            # print(f\"CF for {i} self.quantizer[i]={self.quantizer[i].weight_interval}\")\n            out_sims.append(out_sim)\n        out_sim=self.merger(x,out_sims)\n        if self.bias is not None:\n            out_sim+=self.bias.view(1,-1,1,1)\n\n        #when quantizing weight\n        print(self.quantizer[0].calibration_step)\n        print(self.next_layer == None)\n        if self.quantizer[0].calibration_step==2 and (not self.next_layer == None):\n            self.mode = 'raw'\n            out = self.forward(x)\n            self.mode = 'calibration_forward'\n            similarity=F.cosine_similarity(out.reshape(-1), out_sim.reshape(-1), 0)\n            print(\"similarity:\", similarity)\n            print(\"best_similarity:\", self.best_similarity)\n            if self.outputfile != None:\n                self.outputfile.write(\"similarity:\", similarity, '\\n')\n                self.outputfile.write(\"best_similarity:\", self.best_similarity, '\\n')\n\n            if similarity > self.best_similarity:\n                #TODO:to speedup, I shall reserve it then\n                #self.best_reordering_weight = copy.deepcopy(self.weight)\n                #self.next_layer_best_reordering_weight = copy.deepcopy(self.next_layer.weight)\n                self.best_similarity = similarity\n        \n        return out_sim\n\n    def change_row(self, a, b):\n        self.weight.data[[a, b]] = self.weight.data[[b, a]].contiguous()\n        self.next_layer.weight.data = self.next_layer.weight.data.transpose(0, 1)\n        self.next_layer.weight.data[[a, b]] = self.next_layer.weight.data[[b, a]].contiguous()\n        self.next_layer.weight.data = self.next_layer.weight.data.transpose(0, 1).contiguous()\n\n    def shuffle_row(self, times, channels):\n        for i in range(times):\n            a = random.randint(0, channels-1)\n            b = random.randint(0, channels-1)\n            if not (a == b or self.next_layer == None):\n                self.change_row(a, b) \n            \n\n    def reorder(self, command, channels, random_times=30, your_next_layer=None):\n        if self.next_layer == None and (not your_next_layer == None):\n            self.next_layer = your_next_layer\n        if command == 'random_reorder':\n            self.shuffle_row(random_times, channels)\n        if command == 'mc_reorder':\n            pass\n        self.remap()\n\n    def remap(self):\n        self.map_to_crossbars(self.rows, self.cols)\n        if isinstance(self.next_layer, ReorderingCrossbarWiseQuantMappedConv2d):\n            self.next_layer.map_to_crossbars(self.rows, self.cols)\n\n    \n","repo_name":"memory-of-star/cyq_try_one","sub_path":"my_cyq/pimanalyzer/nn_layers/conv.py","file_name":"conv.py","file_ext":"py","file_size_in_byte":16307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"27711400448","text":"from __future__ import division, unicode_literals\n\nfrom collections import namedtuple\nfrom monty.json import MSONable\n\n\"\"\"\nA module to perform NMR data analysis/processing.\n\"\"\"\n\n\n__author__ = \"Xiaohui Qu\"\n__copyright__ = \"Copyright 2016, The Materials Project\"\n__version__ = \"0.1\"\n__maintainer__ = \"Xiaohui Qu\"\n__email__ = \"xhqu1981@gmail.com\"\n__date__ = \"Apr 17, 2016\"\n\n\nclass NMRChemicalShiftNotation(MSONable):\n    \"\"\"\n    Helper class to convert between different chemical shift conventions\n    internally using the Mehring notation. Note that this is different than the\n    default notion adopted by VASP which is the Maryland notation.\n\n    Three notations to describe chemical shift tensor (RK Harris; Magn. Reson.\n    Chem. 2008, 46, 582–598; DOI: 10.1002/mrc.2225) are supported.\n\n    Args:\n        sigma_1 (float): chemical shift tensor principle component 1\n        sigma_2 (float): chemical shift tensor principle component 2\n        sigma_3 (float): chemical shift tensor principle component 3\n\n    .. attribute:: sigma_11, simga_22, sigma33\n        principle components in Mehring notation\n\n    Authors: Xiaohui Qu\n    \"\"\"\n\n    HaeberlenNotation = namedtuple(typename=\"HaeberlenNotion\",\n                                   field_names=\"sigma_iso, delta_sigma, zeta, eta\")\n    MehringNotation = namedtuple(typename=\"MehringNotation\",\n                                 field_names=\"sigma_iso, sigma_11, sigma_22, sigma_33\")\n    MarylandNotation = namedtuple(typename=\"MarylandNotation\",\n                                  field_names=\"sigma_iso, omega, kappa\")\n\n    def __init__(self, sigma_1, sigma_2, sigma_3):\n        sigmas = sorted([sigma_1, sigma_2, sigma_3])\n        self.sigma_11, self.sigma_22, self.sigma_33 = sigmas\n\n    @property\n    def haeberlen_values(self):\n        \"\"\"\n        Returns: the Chemical shift tensor in Haeberlen Notation\n        \"\"\"\n        sigma_iso = (self.sigma_11 + self.sigma_22 + self.sigma_33) / 3.0\n        h_order_sigmas = sorted([self.sigma_11, self.sigma_22, self.sigma_33],\n                                key=lambda x: abs(x - sigma_iso),\n                                reverse=True)\n        sigma_zz, sigma_xx, sigma_yy = h_order_sigmas\n        delta_sigma = sigma_zz - 0.5 * (sigma_xx + sigma_yy)\n        zeta = sigma_zz - sigma_iso\n        assert abs(delta_sigma - 1.5 * zeta) < 1.0E-5\n        eta = (sigma_yy - sigma_xx) / zeta\n        return self.HaeberlenNotation(sigma_iso, delta_sigma, zeta, eta)\n\n    @property\n    def mehring_values(self):\n        \"\"\"\n        Returns: the Chemical shift tensor in Mehring Notation\n        \"\"\"\n        sigma_iso = (self.sigma_11 + self.sigma_22 + self.sigma_33) / 3.0\n        return self.MehringNotation(sigma_iso, self.sigma_11,\n                                    self.sigma_22, self.sigma_33)\n\n    @property\n    def maryland_values(self):\n        \"\"\"\n        Returns: the Chemical shift tensor in Maryland Notation\n        \"\"\"\n        sigma_iso = (self.sigma_11 + self.sigma_22 + self.sigma_33) / 3.0\n        omega = self.sigma_33 - self.sigma_11\n        # There is a typo in equation 20 from Magn. Reson. Chem. 2008, 46, 582–598, the sign is wrong.\n        # There correct order is presented in Solid State Nucl. Magn. Reson. 1993, 2, 285-288.\n        kappa = 3.0 * (self.sigma_22 - sigma_iso) / omega\n        return self.MarylandNotation(sigma_iso, omega, kappa)\n\n    @classmethod\n    def from_maryland_notation(cls, sigma_iso, omega, kappa):\n        sigma_22 = sigma_iso + kappa * omega / 3.0\n        sigma_11 = (3.0 * sigma_iso - omega - sigma_22) / 2.0\n        sigma_33 = 3.0 * sigma_iso - sigma_22 - sigma_11\n        return cls(sigma_11, sigma_22, sigma_33)\n\n    def as_dict(self):\n        d = {\"sigma_11\": self.sigma_11,\n             \"sigma_22\": self.sigma_22,\n             \"sigma_33\": self.sigma_33}\n        return d\n\n    @classmethod\n    def from_dict(cls, d):\n        return cls(d[\"sigma_11\"], d[\"sigma_22\"], d[\"sigma_33\"])\n","repo_name":"comscope/ComDMFT","sub_path":"ComRISB/pyextern/pymatgen/pymatgen/analysis/nmr.py","file_name":"nmr.py","file_ext":"py","file_size_in_byte":3937,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"86"}
+{"seq_id":"16341340786","text":"thisdict = {\n    \"brand\": \"Ford\",\n    \"model\": \"Mustang\",\n    \"year\": 1964\n}\nprint(thisdict)\nprint(thisdict[\"brand\"])\n\n\n# ! duplicate NOT ALLOWED\nthisdict = {\n    \"brand\": \"Ford\",\n    \"model\": \"Mustang\",\n    \"year\": 1964,\n    \"year\": 2020\n}\nprint(thisdict)\nprint(len(thisdict))\n\nthisdict = {\n    \"brand\": \"Ford\",\n    \"electric\": False,\n    \"year\": 1964,\n    \"colors\": [\"red\", \"white\", \"blue\"]\n}\n\n\n# create dict with constructor\nthisdict = dict(name=\"John\", age=36, country=\"Norway\")\nprint(thisdict)\n\nx = thisdict[\"model\"]  # * Get value of model\nx = thisdict.get(\"model\")  # * Get value of model\nx = thisdict.keys()\nx = thisdict.values()\nx = thisdict.items()  # * Get a list of the key:value pairs\n\n\n# add item to dict\ncar = {\n    \"brand\": \"Ford\",\n    \"model\": \"Mustang\",\n    \"year\": 1964\n}\nx = car.keys()\nprint(x)  # before the change\ncar[\"color\"] = \"white\"  # add new item\nthisdict.pop(\"model\")  # remove item from dict\ndel thisdict[\"model\"]  # remove item from dict\nthisdict.popitem()  # removes the last inserted item\ncar[\"year\"] = 2020  # update existing item\nthisdict.update({\"year\": 2020})  # update multi existing item\nprint(x)  # after the change\n\n\n# check if key is existing in dict\nif \"model\" in thisdict:\n    print(\"Yes, 'model' is one of the keys in the thisdict dictionary\")\n\n\n# delete dict\nthisdict = {\n    \"brand\": \"Ford\",\n    \"model\": \"Mustang\",\n    \"year\": 1964\n}\ndel thisdict\n# print(thisdict) # ! this will cause an error because \"thisdict\" no longer exists.\n\nthisdict = {\n    \"brand\": \"Ford\",\n    \"model\": \"Mustang\",\n    \"year\": 1964\n}\nthisdict.clear()\nprint(thisdict)\n\n# loop dict & get key & val\nthisdict = {\n    \"brand\": \"Ford\",\n    \"model\": \"Mustang\",\n    \"year\": 1964\n}\nfor x, y in thisdict.items():\n    print(x, y)\n\n\n# copy dict\nthisdict = {\n    \"brand\": \"Ford\",\n    \"model\": \"Mustang\",\n    \"year\": 1964\n}\nmydict = thisdict.copy()\nprint(mydict)\n'''--------------'''\nmydict = dict(thisdict)\nprint(mydict)\n","repo_name":"mhmdnzr/UniProjects","sub_path":"Python_learn/src/builtInDt/dict/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1931,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"42502247093","text":"\r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nimport os\r\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\r\nimport tensorflow as tf\r\nimport matplotlib.pyplot as plt\r\nfrom tqdm import tqdm\r\nimport os\r\nfrom datetime import datetime\r\nfrom VAE import VAE\r\nfrom Decoder import Decoder\r\nfrom Encoder import Encoder\r\nimport numpy as np\r\n\r\ntf.debugging.enable_check_numerics()\r\n\r\nlatent_dim = 12\r\ndatagen = ImageDataGenerator(rescale=1/255.0,\r\n                             featurewise_std_normalization=True,\r\n                             samplewise_std_normalization=True,\r\n                             zca_whitening=True,\r\n                             zca_epsilon=1e-06,\r\n                             rotation_range=30,\r\n                             width_shift_range=20.0,\r\n                             height_shift_range=30.0,\r\n                             shear_range=20.0,\r\n                             zoom_range=20.0,\r\n\r\n                             )\r\ntest_datagen = ImageDataGenerator(rescale=1 / 255.0,\r\n                                  featurewise_std_normalization=True,\r\n                                  samplewise_std_normalization=True,\r\n                                  zca_whitening=True,\r\n                                  zca_epsilon=1e-06,\r\n                                  rotation_range=30,\r\n                                  width_shift_range=20.0,\r\n                                  height_shift_range=30.0,\r\n                                  shear_range=20.0,\r\n                                  zoom_range=20.0,\r\n                                  )\r\n\r\n# importing non label data\r\n\r\n\r\n\r\n#train_ds = tf.keras.preprocessing.image_dataset_from_directory('C:/Users/farideh/Desktop/Rnadom field/Random_Var/Dataset/TF_loader/train',\r\n#                                                       image_size=(100, 100),\r\n#                                                       color_mode='grayscale',\r\n#                                                       shuffle=True,\r\n#                                                       batch_size=1000,\r\n#                                                       label_mode=None)\r\n\r\n\r\ntrain_ds = tf.keras.preprocessing.image_dataset_from_directory('C:/Users/farideh/Desktop/Rnadom field/VAE and RF/DATA/',\r\n                                                       image_size=(100, 100),\r\n                                                       color_mode='grayscale',\r\n                                                       shuffle=True,\r\n                                                       batch_size=1000,\r\n                                                       label_mode=None)\r\n\r\nval_ds = tf.keras.preprocessing.image_dataset_from_directory('C:/Users/farideh/Desktop/Rnadom field/VAE and RF/DATA/',\r\n                                                     image_size=(100, 100),\r\n                                                     color_mode='grayscale',\r\n                                                     shuffle=True,\r\n                                                     batch_size=1,\r\n                                                     label_mode=None)\r\n\r\ndef process(image):\r\n    image = tf.cast(image/255., tf.float32)\r\n    return image\r\n\r\nAUTOTUNE = tf.data.AUTOTUNE\r\n# keep the images in memory (performant on-disk cache)\r\ntrain_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)\r\nval_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)\r\n\r\n\r\nlogdir = \"logs/fit/\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\")\r\ntensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)\r\n\r\nvae = VAE(Encoder, Decoder)\r\n\r\n\r\n\r\n\r\n\r\nvae.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001))\r\ncheckpoint_path = \"model.ckpt\"\r\ncheckpoint_dir = os.path.dirname(checkpoint_path)\r\n# Create a callback that saves the model's weights\r\nvae.fit(train_ds, epochs=10000, batch_size=2000, shuffle=True)\r\ntf.debugging.disable_check_numerics()\r\n\r\n#plot_model(decoder, to_file='model_plot.png', show_shapes=True,show_layer_names=True)\r\nfig, axs = plt.subplots(nrows=2, ncols=6)\r\n# apply the model\r\n\r\nfor i, image in tqdm(enumerate(val_ds)):\r\n\r\n    z_mean, z_log_var, z = vae.encoder.predict(image)  # get the parameter of the hidden space\r\n    decoded_info = vae.decoder.predict(z)\r\n    img = (np.array(image[0, :, :, 0])).reshape(100, 100)\r\n    recons_img = decoded_info[0].reshape(100, 100)\r\n    axs[0, i].imshow(img)\r\n    axs[0, i].set_title(\"real\")\r\n    axs[1, i].imshow(recons_img)\r\n    axs[1, i].set_title(\"reconstructed\")\r\n    if i == 5:\r\n        break\r\nplt.show()\r\n\r\n\r\ndef plot_latent_space(vae, n=5, figsize=30):\r\n    # display a n*n 2D manifold of digits\r\n    digit_size = 100\r\n    scale = 1.0\r\n    figure = np.zeros((digit_size * n, digit_size * n))\r\n    # linearly spaced coordinates corresponding to the 2D plot\r\n    # of digit classes in the latent space\r\n    grid_x = np.linspace(-scale, scale, n)\r\n    grid_y = np.linspace(-scale, scale, n)[::-1]\r\n    print(grid_y)\r\n    print(grid_x)\r\n\r\n    for i, yi in enumerate(grid_y):\r\n        for j, xi in enumerate(grid_x):\r\n            z_sample = np.array([[xi, yi]])\r\n            x_decoded = vae.decoder.predict(z_sample)\r\n            digit = x_decoded[0].reshape(digit_size, digit_size)\r\n            figure[\r\n                i * digit_size: (i + 1) * digit_size,\r\n                j * digit_size: (j + 1) * digit_size,\r\n            ] = digit\r\n\r\n    plt.figure(figsize=(figsize, figsize))\r\n    start_range = digit_size // 2\r\n    end_range = n * digit_size + start_range\r\n    pixel_range = np.arange(start_range, end_range, digit_size)\r\n    sample_range_x = np.round(grid_x, 1)\r\n    sample_range_y = np.round(grid_y, 1)\r\n    plt.xticks(pixel_range, sample_range_x)\r\n    plt.yticks(pixel_range, sample_range_y)\r\n    plt.xlabel(\"z[0]\")\r\n    plt.ylabel(\"z[1]\")\r\n    plt.imshow(figure, cmap=\"hot\")\r\n    plt.show()\r\n\r\n\r\nplot_latent_space(vae)\r\n\r\n\r\n# the output of the decoder is nan value therefore the loss is nan as well and the weights are probably bad too\r\n# check the initioalization of the weights and anlso th outpu of the network","repo_name":"Bazangani/DensityEst","sub_path":"Random_field/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6066,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"34314048432","text":"import board\nimport neopixel\nimport time\nimport pulseio\nimport random\nimport touchio\nfrom adafruit_motor import servo\nfrom sauce import Sauce\nfrom item_selection import ItemSelection\nimport busio\nfrom digitalio import DigitalInOut, Direction, Pull\n\npwm = pulseio.PWMOut(board.A2, duty_cycle= 2**15, frequency=50)\npwm1 = pulseio.PWMOut(board.A3, duty_cycle= 2**15, frequency=50)\npwm2 = pulseio.PWMOut(board.A4, duty_cycle= 2**15, frequency=50)\npwm3 = pulseio.PWMOut(board.D12, duty_cycle= 2**15, frequency=50)\n\npi = DigitalInOut(board.D6)\npi.direction = Direction.INPUT\npi.pull = Pull.UP\n\nsauce_pi = DigitalInOut(board.D7)\nsauce_pi.direction = Direction.INPUT\nsauce_pi.pull = Pull.UP\n\nsauce_servo = servo.Servo(pwm)\nplate_servo = servo.Servo(pwm2)\nspat_servo = servo.Servo(pwm3)\n\nsauce_unit = Sauce(sauce_servo, sauce_pi)\nuart = busio.UART(board.TX, board.RX, baudrate=9600)\nx = 0\nplate_unit = ItemSelection(plate_servo, spat_servo, pi)\n\ntoggle = True\n\nwhile True:\n    plate_servo.angle = 90\n    spat_servo.angle = 180\n\n    try:\n        data = uart.read(1)\n        data = data.decode()\n        print(data)\n\n        if data == \"a\":\n            plate_unit.select_spec(1)\n\n        elif data == \"b\":\n            plate_unit.select_spec(2)\n\n        elif data == \"c\":\n            plate_unit.select_spec(3)\n\n        elif data == \"d\":\n            plate_unit.select_spec(4)\n\n        elif data == \"e\":\n            plate_unit.select_spec(5)\n\n        elif data == \"f\":\n            plate_unit.select_spec(6)\n\n        elif data == \"g\":\n            for i in range(6):\n                plate_unit.flip()\n                time.sleep(1)\n                plate_unit.change_one()\n\n        elif data == \"h\":\n            sauce_unit.squirt()\n\n    except AttributeError:\n        print(\"no data\")\n    time.sleep(1/60/4)","repo_name":"clyman88/Robot-Arm","sub_path":"Code/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"8945484261","text":"\"\"\"\nScript for storing authentication credentials into the database. Client ID\nand Client Secret. Using that, the script will add the appropriate tokens\nto the database.\n\"\"\"\nimport argparse\nimport asyncio\nimport os\nimport psycopg2\nimport urllib.parse as urlparse\nimport webbrowser\n\nfrom aiohttp import ClientSession, web\nfrom xbox.webapi.authentication.manager import AuthenticationManager\nfrom xbox.webapi.authentication.models import OAuth2TokenResponse\nfrom xbox.webapi.scripts import REDIRECT_URI\n\n\nqueue = asyncio.Queue(1)\nurl = urlparse.urlparse(os.environ['DATABASE_URL'])\n\nasync def auth_callback(request):\n    error = request.query.get(\"error\")\n    if error:\n        description = request.query.get(\"error_description\")\n        print(f\"Error in auth_callback: {description}\")\n        return\n    # Run in task to not make unsuccessful parsing the HTTP response fail\n    asyncio.create_task(queue.put(request.query[\"code\"]))\n    return web.Response(\n        headers={\"content-type\": \"text/html\"},\n        text=\"You can close this tab now.\",\n    )\n\n\nasync def async_main(client_id: str, client_secret: str, redirect_uri: str):\n    async with ClientSession() as session:\n        auth_mgr = AuthenticationManager(\n            session, client_id, client_secret, redirect_uri\n        )\n        # Here we need to access the database, \n        # if there are existing tokens AND the secrets match, then we can just refresh the tokens\n        # otherwise, we need to create new tokens.\n        with psycopg2.connect(dbname=url.path[1:],user=url.username,password=url.password,host=url.hostname,port=url.port) as conn:\n            with conn.cursor() as cursor:\n                cursor.execute(\"SELECT * FROM xbox_credential\")\n                existing_credentials = cursor.fetchone()\n                if existing_credentials and existing_credentials[2] == client_secret:\n                    tokens = {\n                        \"token_type\": existing_credentials[3],\n                        \"expires_in\": existing_credentials[4],\n                        \"scope\": existing_credentials[5],\n                        \"access_token\": existing_credentials[6],\n                        \"refresh_token\": existing_credentials[7], \n                        \"user_id\": existing_credentials[8], \n                        \"issued\": existing_credentials[9]\n                    }\n                    auth_mgr.oauth = OAuth2TokenResponse.parse_raw(tokens)\n                    try:\n                        await auth_mgr.refresh_tokens()\n                    except:\n                        print(\"Error refreshing tokens\")\n\n                    tokens = auth_mgr.oauth.dict()\n                    cursor.execute(f\"UPDATE xbox_credential SET token_type = '{tokens['token_type']}', expires_in = {tokens['expires_in']}, scope = '{tokens['scope']}', access_token = '{tokens['access_token']}, refresh_token = '{tokens['refresh_token']}', user_id = '{tokens['user_id']}', issued = '{tokens['issued']}' WHERE client_secret = '{existing_credentials[2]}'\")\n                else:\n                    # here we need to create new token\n                    auth_url = auth_mgr.generate_authorization_url()\n                    webbrowser.open(auth_url)\n                    code = await queue.get()\n                    await auth_mgr.request_tokens(code)\n                    tokens = auth_mgr.oauth.dict()\n                    if existing_credentials:\n                        cursor.execute(f\"DELETE FROM xbox_credential WHERE client_id='{existing_credentials[1]}'\")\n                    cursor.execute(f\"INSERT INTO xbox_credential (client_id, client_secret, token_type, expires_in, scope, access_token, refresh_token, user_id, issued) VALUES('{client_id}', '{client_secret}', '{tokens['token_type']}', {tokens['expires_in']}, '{tokens['scope']}', '{tokens['access_token']}', '{tokens['refresh_token']}', '{tokens['user_id']}', '{tokens['issued']}')\")\n\n                print(\"Success! The Xbox API is now ready to be used.\")\n\ndef main():\n    parser = argparse.ArgumentParser(description=\"Authenticate with XBL\")\n    parser.add_argument(\n        \"--client-id\",\n        \"-cid\",\n        help=\"OAuth2 Client ID\",\n        required=True\n    )\n    parser.add_argument(\n        \"--client-secret\",\n        \"-cs\",\n        help=\"OAuth2 Client Secret\",\n        required=True\n    )\n\n    args = parser.parse_args()\n\n    app = web.Application()\n    app.add_routes([web.get(\"/auth/callback\", auth_callback)])\n    runner = web.AppRunner(app)\n\n    loop = asyncio.get_event_loop()\n    loop.run_until_complete(runner.setup())\n    site = web.TCPSite(runner, \"localhost\", 8080)\n    loop.run_until_complete(site.start())\n    loop.run_until_complete(\n        async_main(args.client_id, args.client_secret, REDIRECT_URI)\n    )\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"AhmedNSidd/chaddicts-tg-bot","sub_path":"scripts/authentication/xbox_authentication.py","file_name":"xbox_authentication.py","file_ext":"py","file_size_in_byte":4796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"39767326724","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport wandb\n\nfrom pytorch_lightning import LightningModule\n\nfrom cliport.utils import utils\nfrom cliport.models.core.clip import tokenize\nfrom nat_policies.models.roboclip import RoboCLIP\nfrom nat_policies.utils.eval_utils import (\n    ground_truth_L2, cross_batch_L2, knn_classification, start_pred_goal_ratio,\n    ground_truth_cossim, cross_batch_cossim\n)\nfrom nat_policies.utils.common import count_parameters\n\n\nclass FinetuneCLIPAgent(LightningModule):\n    def __init__(self, name, cfg, train_dataloader, val_dataloader):\n        super().__init__()\n        utils.set_seed(0)\n\n        self.device_type = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # this is bad for PL :(\n        self.name = name\n        self.cfg = cfg\n        self.train_dl = train_dataloader\n        self.val_dl = val_dataloader\n\n        self.name = name\n        self.total_steps = 0\n\n        self.roboclip = RoboCLIP(\n            clip_variant=cfg['train']['clip_variant'],\n            device=self.device_type\n        )\n        n_trainable = count_parameters(self.roboclip, count_trainable_only=True)\n\n        self.ce_loss_vis = nn.CrossEntropyLoss()\n        self.ce_loss_fusion = nn.CrossEntropyLoss()\n        print(f'Num trainable parameters: {n_trainable}')\n\n    '''Loss computation methods'''\n\n    def contrastive_loss(self, start_imgs, lang_goals, goal_imgs, return_emb=False):\n        # Compute the predicted goal image embedding\n        pred_goal_embeddings, start_embeddings, lang_embeddings = self.roboclip(start_imgs, lang_goals)\n        \n        # Compute the actual goal image embedding\n        goal_embeddings = self.roboclip.encode_image(goal_imgs)\n        \n        start_embeddings_normalized = start_embeddings / start_embeddings.norm(dim=1, keepdim=True)\n        lang_embeddings_normalized = lang_embeddings / lang_embeddings.norm(dim=1, keepdim=True)\n        pred_goal_embeddings_normalized = pred_goal_embeddings / pred_goal_embeddings.norm(dim=1, keepdim=True)\n        goal_embeddings_normalized = goal_embeddings / goal_embeddings.norm(dim=1, keepdim=True)\n\n        # Compute similarity matrix for (start_img+lang_goal, goal_img) pairs\n        logit_scale = self.roboclip.logit_scale.exp()\n        logits_per_pred_goal = logit_scale * pred_goal_embeddings_normalized @ goal_embeddings_normalized.t()\n        logits_per_goal = logits_per_pred_goal.t()\n\n        ground_truth = torch.arange(len(logits_per_goal), dtype=torch.long, device=logits_per_goal.device)\n        goal_similarity_loss = (\n            self.ce_loss_vis(logits_per_pred_goal, ground_truth) + \n            self.ce_loss_vis(logits_per_goal, ground_truth)\n        ) / 2\n        loss = goal_similarity_loss\n\n        emb_data = None\n        if return_emb:\n            emb_data = {\n                'start_embeddings_normalized': start_embeddings_normalized,\n                'lang_embeddings_normalized': lang_embeddings_normalized,\n                'pred_goal_embeddings_normalized': pred_goal_embeddings_normalized,\n                'goal_embeddings_normalized': goal_embeddings_normalized,\n                'start_embeddings_unnormalized': start_embeddings,\n                'goal_embeddings_unnormalized': goal_embeddings,\n            }\n\n        return loss, emb_data\n\n    '''Utility methods'''\n\n    def preprocess_batch(self, batch):\n        start_imgs, lang_goals, goal_imgs = batch['start_img'], batch['lang_goal'], batch['goal_img']\n        lang_goals = tokenize(lang_goals).to(self.device_type)\n        return start_imgs, lang_goals, goal_imgs\n\n    def compute_stats_and_log(self, loss, emb_data, is_train):\n        tag = 'train' if is_train else 'val'\n        with torch.no_grad():\n            start_embeddings, lang_embeddings, pred_goal_embeddings, goal_embeddings = (\n                emb_data['start_embeddings_normalized'],\n                emb_data['lang_embeddings_normalized'],\n                emb_data['pred_goal_embeddings_normalized'],\n                emb_data['goal_embeddings_normalized']\n            )\n\n            pred_goal_start_img_dist = ground_truth_L2(pred_goal_embeddings, start_embeddings)\n            pred_goal_real_goal_dist = ground_truth_L2(pred_goal_embeddings, goal_embeddings)\n            pred_goal_start_img_sim = ground_truth_cossim(pred_goal_embeddings, start_embeddings)\n            pred_goal_real_goal_sim = ground_truth_cossim(pred_goal_embeddings, goal_embeddings)\n            \n            start_img_goal_img_dist = ground_truth_L2(start_embeddings, goal_embeddings)\n            cross_batch_start_img_dist = cross_batch_L2(start_embeddings, start_embeddings)\n            cross_batch_goal_img_dist = cross_batch_L2(goal_embeddings, goal_embeddings)\n            cross_batch_lang_dist = cross_batch_L2(lang_embeddings, lang_embeddings)\n            start_img_goal_img_sim = ground_truth_cossim(start_embeddings, goal_embeddings)\n            cross_batch_start_img_sim = cross_batch_cossim(start_embeddings, start_embeddings)\n            cross_batch_goal_img_sim = cross_batch_cossim(goal_embeddings, goal_embeddings)\n            cross_batch_lang_sim = cross_batch_cossim(lang_embeddings, lang_embeddings)\n\n            top_1_l2, top_5_l2, top_1_cosine, top_5_cosine = knn_classification(\n                pred_goal_embeddings, goal_embeddings, K=5\n            )\n        \n        self.log(f'{tag}/loss', loss.detach().item())\n\n        self.log(f'{tag}/pred_goal_2_start_img_dist', pred_goal_start_img_dist)\n        self.log(f'{tag}/pred_goal_2_goal_img_dist', pred_goal_real_goal_dist)\n        self.log(f'{tag}/pred_goal_2_start_img_sim', pred_goal_start_img_sim)\n        self.log(f'{tag}/pred_goal_2_goal_img_sim', pred_goal_real_goal_sim)\n\n        self.log(f'{tag}/start_img_2_goal_img_dist', start_img_goal_img_dist)\n        self.log(f'{tag}/cross_batch_start_img_dist', cross_batch_start_img_dist)\n        self.log(f'{tag}/cross_batch_goal_img_dist', cross_batch_goal_img_dist)\n        self.log(f'{tag}/cross_batch_lang_dist', cross_batch_lang_dist)\n        self.log(f'{tag}/start_img_2_goal_img_sim', start_img_goal_img_sim)\n        self.log(f'{tag}/cross_batch_start_img_sim', cross_batch_start_img_sim)\n        self.log(f'{tag}/cross_batch_goal_img_sim', cross_batch_goal_img_sim)\n        self.log(f'{tag}/cross_batch_lang_sim', cross_batch_lang_sim)\n\n        self.log(f'{tag}/top_1_acc_L2', top_1_l2)\n        self.log(f'{tag}/top_5_acc_L2', top_5_l2)\n        self.log(f'{tag}/top_1_acc_cosine', top_1_cosine)\n        self.log(f'{tag}/top_5_acc_cosine', top_5_cosine)\n\n        print(f'{tag} loss: {loss}')\n\n    '''Overridden methods for PyTorch Lightning'''\n    \n    def configure_optimizers(self):\n        optimizer = torch.optim.Adam(\n            self.roboclip.parameters(),\n            lr=1e-5, betas=(0.9, 0.98), eps=1e-6\n        )\n\n        return optimizer\n\n    def train_dataloader(self):\n        return self.train_dl\n\n    def val_dataloader(self):\n        return self.val_dl\n    \n    def training_step(self, batch, batch_idx):\n        self.roboclip.train()\n\n        start_imgs, lang_goals, goal_imgs = self.preprocess_batch(batch)\n        loss, emb_data = self.contrastive_loss(start_imgs, lang_goals, goal_imgs, return_emb=True)\n        self.compute_stats_and_log(loss, emb_data, is_train=True)\n\n        return loss\n    \n    def validation_step(self, batch, batch_idx):\n        self.roboclip.eval()\n\n        start_imgs, lang_goals, goal_imgs = self.preprocess_batch(batch)\n        with torch.no_grad():\n            loss, emb_data = self.contrastive_loss(start_imgs, lang_goals, goal_imgs, return_emb=True)\n            self.compute_stats_and_log(loss, emb_data, is_train=False)\n\n        return dict(\n            val_loss=loss\n        )","repo_name":"mpiseno/nat_policies","sub_path":"nat_policies/agents/finetune_clip_agent.py","file_name":"finetune_clip_agent.py","file_ext":"py","file_size_in_byte":7738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"3306840823","text":"\"\"\"Tools for huubum.\n\nThis package is NOT allowed to import anything from internally in hubuum.\n\"\"\"\n\n\nfrom django.apps import apps\nfrom rest_framework.exceptions import NotFound\n\n\ndef get_model(model):\n    \"\"\"Return the model from a string. Returns None if it fails..\"\"\"\n    try:\n        return apps.get_model(\"hubuum\", model)\n    except LookupError:\n        return None\n\n\ndef get_object(cls, lookup_value, lookup_fields=None, raise_exception=True):\n    \"\"\"Get a object from a class.\n\n    A generic way to find objects in a model.\n    By default the list of fields searched are in order of precedence:\n      - the list passed to the lookup_fields keyword argument\n      - the models class attribute 'lookup_fields'\n      - the list [\"id\"]\n\n    param: cls (the model to look into)\n    param: lookup_value (value to search for)\n    param: lookup_fields=[] (explicitly declare fields to look into)\n\n    return object or None\n    \"\"\"\n    obj = None\n    fields = [\"id\"]\n    if lookup_fields:\n        fields = lookup_fields\n    elif hasattr(cls, \"lookup_fields\"):\n        fields = cls.lookup_fields\n\n    for field in fields:\n        try:\n            obj = cls.objects.get(**{field: lookup_value})\n            if obj:\n                return obj\n\n        except Exception:  # nosec pylint: disable=broad-except\n            pass\n\n    if raise_exception:\n        raise NotFound()\n\n    return None\n","repo_name":"hubuum/hubuum","sub_path":"hubuum/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"12707548850","text":"import pygame\nfrom constants import *\nfrom utility import *\n\n\nclass Effect(pygame.sprite.Sprite):\n    path = '../assets/player/dust_effect'\n\n    def __init__(self, type, pos):\n        super().__init__()\n        if type == JUMP_IDX:\n            self.animation_list = load_images(f'{self.path}/jump/*.png')\n        if type == LAND_IDX:\n            self.animation_list = load_images(f'{self.path}/land/*.png')\n\n        self.current_time = pygame.time.get_ticks()\n        self.frame_index = 0\n        self.image = self.animation_list[self.frame_index]\n        self.rect = self.image.get_rect(midbottom=pos)\n\n    def update(self, x_shift):\n        self.animate()\n        self.rect.x += x_shift\n\n    def animate(self):\n        # increment frame index based on action's cooldown\n        self.image = self.animation_list[self.frame_index]\n        if (pygame.time.get_ticks() - self.current_time) > DUST_ANI:\n            self.current_time = pygame.time.get_ticks()\n            self.frame_index += 1\n\n        if self.frame_index >= len(self.animation_list):\n            self.kill()\n","repo_name":"high-Cy/Platformer","sub_path":"src/effect.py","file_name":"effect.py","file_ext":"py","file_size_in_byte":1072,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"24779760385","text":"import pandas as pd\npd.set_option('display.max_columns', 100)\npd.set_option('display.max_rows', 100)\npd.set_option('expand_frame_repr', False)\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Reading Training Data Set:\ndf_train = pd.read_csv(\"C:/Users/sivac/Documents/Python Projects/Introduction to Data Science Course/Regression Project/input/train.csv\")\ndf_test = pd.read_csv(\"C:/Users/sivac/Documents/Python Projects/Introduction to Data Science Course/Regression Project/input/test.csv\")\ndf_test['Item_Outlet_Sales'] = np.nan\ndf = pd.concat([df_train, df_test], axis=0, sort=True) # 5045 Test data Set rows\n\n# EXPLORATORY ANALYSIS\n\n# EXAMINING THE NULL VALUES\ndf.isnull().sum() * 100 / len(df)\n### 17% of Item_Weight is missing, and # 29% of Outlet Size is missing\n\n# EXAMINING OUTLIERS\nplt.subplot(421)\ndf['Item_MRP'].plot.box()\nplt.subplot(422)\ndf['Item_MRP'].plot.hist()\nplt.subplot(423)\ndf['Item_Visibility'].plot.box()\nplt.subplot(424)\ndf['Item_Visibility'].plot.hist()\nplt.subplot(425)\ndf['Item_Weight'].plot.box()\nplt.subplot(426)\ndf['Item_Weight'].plot.hist()\nplt.subplot(427)\ndf['Outlet_Establishment_Year'].plot.box()\nplt.subplot(428)\ndf['Outlet_Establishment_Year'].plot.hist()\nplt.show()\n\n# The Item_Visibility has lot of outliers, and the distribution is also right skewed. We can apply some form of transformation to Item_Visibility\n# FE1: Also, the minimum visibility cannot be zero. So, let us impute all zeros to min of the Item Visibility (ignoring zeros)\n\n# ITEM VISIBILITY\n\nplt.subplot(221)\ndf.Item_Visibility.plot.hist()\nplt.xlabel('Item_Visibility')\nplt.subplot(222)\nnp.log10(df.Item_Visibility).plot.hist()\nplt.xlabel('Log10')\nplt.subplot(223)\nnp.sqrt(df.Item_Visibility).plot.hist()\nplt.xlabel('SQRT')\nplt.subplot(224)\nnp.cbrt(df.Item_Visibility).plot.hist()\nplt.xlabel('CBRT')\nplt.show()\n# FE2 - Either the Square Root Transformation or the Cube Root Transformation should work fine\n\n# ANALYZING ITEM WEIGHT TO FIX MISSING VALUES\ndf[(~df['Item_Weight'].isnull()) & (df['Outlet_Establishment_Year'] == 1985)].head(10)\ndf[(df['Item_Weight'].isnull()) & (df['Outlet_Establishment_Year'] == 1985)].head(10)\ndf.groupby(['Item_Type', 'Outlet_Establishment_Year']).agg({'Item_Weight': 'mean'})\n# From the above analysis, all outlets with Establishment Year 1985 have their Item_Weight missing\n# FE3 The mean or median Item Weight by Item Type can be used to impute missing values\n\n# ANALYZING OUTLET SIZE TO FIX MISSING VALUES\ndf[(df['Outlet_Size'].isnull())].head(10)\npd.crosstab(df['Outlet_Type'], df['Outlet_Size'])\n# FE4 From the above analysis, all grocery store are small, and all supermarket type 2 and 3 are Medium. Let us fix those values first\n##\nsupermarket1 = df[df['Outlet_Type'] == 'Supermarket Type1']\npd.crosstab(supermarket1['Outlet_Size'], supermarket1['Outlet_Location_Type'])\n##\nsupermarket1 = df[df.Outlet_Size.isnull()]\nsupermarket1.Outlet_Location_Type.value_counts()\n# FE5 The above analysis suggests that all Supermarket1 Tier 3 are high, and Supermarket1 Tier 2 are Small. Also, in our data set all the missing values are from Tier 2\n\n# ITEM FAT CONTENT\ndf.Item_Fat_Content.value_counts()\n\n# FE6 The duplicate values of Item Fat Content should be fixed\n\ndf.groupby('Item_Fat_Content').Item_Outlet_Sales.mean()\ndf.groupby('Item_Fat_Content').Item_Outlet_Sales.sum() / df.Item_Outlet_Sales.sum()  # Though the average cost of Low and Regular Fat items are almost comparable, there is a greater chance that more of Low Fat items might be sold\ndf.groupby('Item_Type').Item_Outlet_Sales.sum() / df.Item_Outlet_Sales.sum()\ndf.groupby('Outlet_Type').Item_Outlet_Sales.sum() / df.Item_Outlet_Sales.sum()\ndf.groupby('Outlet_Location_Type').Item_Outlet_Sales.sum() / df.Item_Outlet_Sales.sum()\ndf.groupby('Outlet_Size').Item_Outlet_Sales.sum() / df.Item_Outlet_Sales.sum()\ndf.groupby('Item_Type').Item_Outlet_Sales.mean()\ndf.groupby(['Item_Fat_Content', 'Item_Type']).Item_Outlet_Sales.mean()\nplt.scatter(df.Item_Weight, df.Item_Outlet_Sales) # Looks like there is not much information with Weight and Sales\n\n\n# FE1 - IMPUTING THE ZERO VALUES OF ITEM VISIBILITY\nminItemVisibility = (df['Item_Visibility'][df.Item_Visibility != 0]).min()\ndf.loc[df['Item_Visibility'] == 0, 'Item_Visibility'] = minItemVisibility\n\n# FE2 - CUBE ROOT TRANSFORMATION TO FIX THE RIGHT SKEWNESS\ndf.loc[:, 'Item_Visibility'] = np.cbrt(df['Item_Visibility'])\n# df.Item_Visibility.plot.box()\n# The cube root transformation has fixed the distribution as well as outliers\n\n# FE3 - IMPUTING MISSING ITEM WEIGHT BY THE MEAN OF ITEM TYPE\ndf['Item_Weight'] = df.groupby('Item_Type').Item_Weight.transform(lambda x: x.fillna(x.median()))\n\n# FE4 - FIXING MISSING VALUES FOR OUTLET SIZE - ALL GROCERY STORE ARE SMALL IN SIZE, AND ALL SUPERMARKET TYPE 2 AND 3 ARE MEDIUM IN SIZE\ndf['Outlet_Size'] = np.where(df['Outlet_Size'].isnull(), np.where(df['Outlet_Type'] == 'Grocery Store', \"Small\", np.where(df['Outlet_Type'].isin([\"Supermarket Type2\", \"Supermarket Type3\"]), \"Medium\", df['Outlet_Size'])), df['Outlet_Size'])\n\n# FE5 - WITHIN SUPERMARKET1, ALL TIER 3 ARE HIGH IN SIZE AND ALL TIER 2 ARE SMALL\ndf['Outlet_Size'] = np.where(df['Outlet_Size'].isnull(), np.where((df['Outlet_Type'] == 'Supermarket Type1') & (df['Outlet_Location_Type'] == 'Tier 3'), 'High', np.where((df['Outlet_Type'] == 'Supermarket Type1') & (df['Outlet_Location_Type'] == 'Tier 2'), 'Small', df['Outlet_Size'])), df['Outlet_Size'])\n\n\n# FE6 - FIXING THE DUPLICATE VALUES OF ITEM FAT CONTENT\n\ndf.loc[df['Item_Fat_Content'].isin(['LF', 'low fat']), 'Item_Fat_Content'] = 'Low Fat'\ndf.loc[df['Item_Fat_Content'] == 'reg', 'Item_Fat_Content'] = 'Regular'\n\n# FE7 - CONVERTING ALL CATEGORICAL VARIABLES TO PROPORTION\ndf['Fat_Content_Prop'] = df.groupby('Item_Fat_Content').Item_Outlet_Sales.transform(lambda x: x.sum()) / df.Item_Outlet_Sales.sum()\ndf['Item_Type_Prop'] = df.groupby('Item_Type').Item_Outlet_Sales.transform(lambda x: x.sum()) / df.Item_Outlet_Sales.sum()\ndf['Outlet_Location_Type_Prop'] = df.groupby('Outlet_Location_Type').Item_Outlet_Sales.transform(lambda x: x.sum()) / df.Item_Outlet_Sales.sum()\ndf['Outlet_Type_Prop'] = df.groupby('Outlet_Type').Item_Outlet_Sales.transform(lambda x: x.sum()) / df.Item_Outlet_Sales.sum()\ndf['Outlet_Size_Prop'] = df.groupby('Outlet_Size').Item_Outlet_Sales.transform(lambda x: x.sum()) / df.Item_Outlet_Sales.sum()\ndf['Total_Prop'] = df.groupby(['Item_Fat_Content', 'Item_Type', 'Outlet_Location_Type', 'Outlet_Type', 'Outlet_Size']).Item_Outlet_Sales.transform(lambda x: x.sum()) / df.Item_Outlet_Sales.sum()\n\n# FE8 - CREATING A VARIABLE THAT HAS INFORMATION ON HOW MANY YEARS THE OUTLET HAS BEEN IN BUSINESS\nfrom datetime import datetime as dt\ncurr_dt = dt.now()\ndf['Years_In_Business'] = curr_dt.year - df['Outlet_Establishment_Year']\n\n# FEATURE SELECTION FOR MODEL\ndf_final = df[['ID', 'Item_Outlet_Sales', 'Total_Prop', 'Item_MRP', 'Item_Weight',  'Fat_Content_Prop', 'Item_Type_Prop',  'Outlet_Location_Type_Prop', 'Outlet_Type_Prop',  'Outlet_Size_Prop', 'Years_In_Business', 'Item_Fat_Content', 'Item_Type', 'Outlet_Location_Type', 'Outlet_Size', 'Outlet_Type']]\n\n# ENCODING CATEGORICAL VARIABLES\n\none_hot_encoding = ['Item_Fat_Content', 'Item_Type', 'Outlet_Location_Type', 'Outlet_Size', 'Outlet_Type']\ndf_one_hot = df_final.loc[:, one_hot_encoding]\ndf_final.drop(one_hot_encoding, axis=1, inplace=True)\n\n# PERFORMING ONE HOT ENCODING\ndf_one_hot = pd.get_dummies(df_one_hot, drop_first=True)\n\n# CONCATENATING ALL COLUMNS\ndf_final = pd.concat([df_final, df_one_hot], axis=1)\n\n\n# SEGREGATING THE TRAIN AND TEST\ntrain = df_final[0:len(df_train)]\ntest = df_final[len(df_train):len(df_final)]\n\ny_train = train['Item_Outlet_Sales']\nx_train = train.drop(['ID', 'Item_Outlet_Sales'], axis=1)\n\ntest_ID = test['ID']\ntest = test.drop(['ID', 'Item_Outlet_Sales'], axis=1)\n\n# SPLITTING TRAIN DATA SET IN TO TRAIN AND VALIDATION\nfrom sklearn.model_selection import train_test_split\n\nx_train, x_validation, y_train, y_validation = train_test_split(x_train, y_train, test_size= 0.2)\n\n# FITTING A LINEAR REGRESSION MODEL\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import r2_score\n\nmodel = LinearRegression()\nmodel.fit(x_train, y_train)\nmodel.score(x_train, y_train)\n\npredictions = model.predict(x_validation)\nr2_score(y_validation, predictions)\n\n# The model is only able to explain 55% of the variance\n\n# RUNNING A LASSO REGRESSION TO OVERCOME MULTICOLLINEARITY IF PRESENT\n\nfrom sklearn.linear_model import Lasso\n\nmodel = Lasso(alpha=1.5, max_iter=700)\nmodel.fit(x_train, y_train)\nmodel.score(x_train, y_train)\n\npredictions = model.predict(x_validation)\nmodel.score(x_validation, y_validation)\n\n# RUNNING A RIDGE REGRESSION TO OVERCOME MULTICOLLINEARITY IF PRESENT\n\nfrom sklearn.linear_model import Ridge\n\nmodel = Ridge(alpha=0.79)\nmodel.fit(x_train, y_train)\nmodel.score(x_train, y_train)\n\npredictions = model.predict(x_validation)\nmodel.score(x_validation, y_validation)\n\n# RUNNING A RANDOM FOREST REGRESSOR\n\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_squared_error\n\nmodel = RandomForestRegressor(n_estimators=200, random_state=1, max_depth=6, bootstrap=True, max_features=20, oob_score=True)\n# model = RandomForestRegressor(n_estimators=300, random_state=1, max_depth=6, bootstrap=True, max_features=20, min_samples_leaf=20, min_samples_split=30, n_jobs=2, oob_score=True, criterion='mse')\n#model = RandomForestRegressor(n_estimators=300, random_state=1, max_depth=6, bootstrap=True, max_features='auto', min_samples_leaf=20, min_samples_split=30, n_jobs=2, oob_score=True, criterion='mse')\n#model = RandomForestRegressor(n_estimators=1600, random_state=1, max_depth=90, bootstrap=True, max_features='sqrt', min_samples_leaf=4, min_samples_split=2, n_jobs=6, oob_score=True, criterion='mse')\nmodel.fit(x_train, y_train)\nmodel.score(x_train, y_train)\n\npredictions = model.predict(x_validation)\nmodel.score(x_validation, y_validation)\nnp.sqrt(mean_squared_error(y_validation, predictions))\n\n# PREDICTING THE TEST DATA SET\n\npredictions = model.predict(test)\noutput = pd.DataFrame({'ID': test_ID, 'Item_Outlet_Sales': predictions})\noutput.to_csv(\"C:/Users/sivac/Documents/Python Projects/Introduction to Data Science Course/Regression Project/output/Submission 10 - RF EST200 DEPTH6 BOOTTRUE MAXFEAT20 OOBTrue.csv\", index=False, header=True)","repo_name":"sivacharansrc/Introduction-to-Data-Science-Course","sub_path":"Regression Project/pyScript.py","file_name":"pyScript.py","file_ext":"py","file_size_in_byte":10334,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"25139865418","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import api, fields, models\nfrom odoo.exceptions import ValidationError\n\n\nclass ProductTemplate(models.Model):\n    \"\"\"Add fields related to event sets. It will allow to trigger event registrations upon the validation of a sale\n    order.\n\n    \"\"\"\n    _inherit = \"product.template\"\n\n    event_set_ok = fields.Boolean(string='Is an Event Set',\n                                  help=\"If checked this product automatically creates an event registration at the \"\n                                       \"sales order confirmation.\")\n    event_availability = fields.Selection([\n        ('always', 'Show availability on website'),\n        ('threshold', 'Show availability below a threshold'),\n    ], string='Event Availability', help='Adds an event availability status on the web product page.')\n    event_available_threshold = fields.Integer(string='Availability Threshold', default=5)\n    event_ids = fields.Many2many('event.event', string='Events', help='Buying the product will automatically register '\n                                                                      'the user to the events.',\n                                 compute='_compute_event_ids', inverse='_set_event_ids', store=True)\n    event_seats_availability = fields.Selection([('limited', 'Limited'), ('unlimited', 'Unlimited')],\n                                                string='Maximum Attendees', store=True, readonly=True,\n                                                compute='_compute_event_seats')\n    event_seats_available = fields.Integer('Available Seats', store=True, readonly=True,\n                                           compute='_compute_event_seats')\n\n    @api.constrains('event_set_ok', 'type')\n    def _check_event_set_type(self):\n        \"\"\"Check if the type of an Event Set is a Service.\n\n        \"\"\"\n        if self.event_set_ok and self.type != 'service':\n            raise ValidationError(\"The type of an Event Set must be a Service\")\n\n    @api.constrains('event_set_ok', 'product_variant_ids')\n    def _check_event_set_variants(self):\n        \"\"\"Check if the there is not any existing relationships between the product variants of an Event Set and Event\n        records before modifying the field.\n\n        \"\"\"\n        if not self.event_set_ok and self.product_variant_ids and self.product_variant_ids.event_ids:\n            raise ValidationError(\"All existing relationships between the product variants of an Event Set and Event \"\n                                  \"records have to be removed before modifying Event Set field\")\n\n    @api.onchange('event_set_ok')\n    def _onchange_event_set_ok(self):\n        if self.event_set_ok:\n            self.type = 'service'\n\n    @api.depends('product_variant_ids', 'product_variant_ids.event_ids')\n    def _compute_event_ids(self):\n        \"\"\"Get the events associated to its unique variant. Allows to edit event sets from the product template form\n        view.\n\n        \"\"\"\n        unique_variants = self.filtered(lambda template: len(template.product_variant_ids) == 1)\n        for template in unique_variants:\n            # replaces all existing records in the set\n            template.event_ids = [(6, 0, [event.id for event in template.product_variant_ids.event_ids])]\n        for template in (self - unique_variants):\n            # removes all records from the set\n            template.event_ids = [(5,)]\n\n    @api.depends('product_variant_ids', 'product_variant_ids.event_ids')\n    def _set_event_ids(self):\n        \"\"\"Set events to its unique variant. Allows to edit event sets from the product template form view.\n\n        \"\"\"\n        unique_variants = self.filtered(lambda template: len(template.product_variant_ids) == 1)\n        for template in unique_variants:\n            # replaces all existing records in the set\n            template.product_variant_ids.event_ids = [(6, 0, [event.id for event in template.event_ids])]\n        for template in (self - unique_variants):\n            # removes all records from the set\n            template.event_ids = [(5,)]\n\n    @api.depends('product_variant_ids', 'product_variant_ids.event_seats_availability',\n                 'product_variant_ids.event_seats_available')\n    def _compute_event_seats(self):\n        \"\"\"Get event information from its unique variant.\n\n        \"\"\"\n        unique_variants = self.filtered(lambda template: len(template.product_variant_ids) == 1)\n        for template in unique_variants:\n            template.event_seats_availability = template.product_variant_ids.event_seats_availability\n            template.event_seats_available = template.product_variant_ids.event_seats_available\n        for template in (self - unique_variants):\n            template.event_seats_availability = None\n            template.event_seats_available = None\n\n    def _get_combination_info(self, combination=False, product_id=False, add_qty=1, pricelist=False,\n                              parent_combination=False, only_template=False):\n        \"\"\"Override function in order to add information about event sets.\n\n        \"\"\"\n        combination_info = super(ProductTemplate, self)._get_combination_info(\n            combination=combination, product_id=product_id, add_qty=add_qty, pricelist=pricelist,\n            parent_combination=parent_combination, only_template=only_template\n        )\n\n        if not self.env.context.get('website_sale_event_set_get_quantity'):\n            return combination_info\n\n        if combination_info['product_id']:\n            product = self.env['product.product'].sudo().browse(combination_info['product_id'])\n            combination_info.update({\n                'event_seats_availability': product.event_seats_availability,\n                'event_seats_available': product.event_seats_available,\n                'event_set_ok': product.event_set_ok,\n                'event_availability': product.event_availability,\n                'event_available_threshold': product.event_available_threshold,\n                'event_is_expired': product.event_is_expired,\n                'product_template': product.product_tmpl_id.id,\n                'cart_qty': product.cart_qty,\n                'uom_name': product.uom_id.name,\n            })\n        else:\n            product_template = self.sudo()\n            combination_info.update({\n                # 'event_seats_availability': 'unlimited',\n                # 'event_seats_available': 0,\n                'event_set_ok': product_template.event_set_ok,\n                'event_availability': product_template.event_availability,\n                'event_available_threshold': product_template.event_available_threshold,\n                'product_template': product_template.id,\n                'cart_qty': 0\n            })\n\n        return combination_info\n","repo_name":"kadogams/event_set","sub_path":"models/product_template.py","file_name":"product_template.py","file_ext":"py","file_size_in_byte":6772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"19805277996","text":"import tensorflow as tf  # TF 2.0\nfrom sn import SpectralNormalization\n\nfrom tensorflow.keras import datasets, layers, models\n\n\nbatch_size = 200\nbuffer_size = 2000\nnum_epochs = 200\nmethod = 'func'\n\n(train_images, train_labels), (_, _) = datasets.mnist.load_data()\n\ntrain_images = train_images.reshape((60000, 28, 28, 1)).astype('float32')\n\ntrain_images = train_images / 255.0\n\ntrain_dataset = tf.data.Dataset.from_tensor_slices((train_images, train_labels)).shuffle(buffer_size).batch(batch_size)\n\nif method == 'func':\n    inputs = layers.Input(shape=(28,28,1))\n    x = SpectralNormalization(layers.Conv2D(32, (3, 3), activation='relu'))(inputs)\n    x = layers.MaxPooling2D((2, 2))(x)\n    x = SpectralNormalization(layers.Conv2D(64, (3, 3), activation='relu'))(x)\n    x = layers.MaxPooling2D((2, 2))(x)\n    x = layers.Flatten()(x)\n    x = layers.Dense(64, activation='relu')(x)\n    output = layers.Dense(10, activation='softmax')(x)\n    model = models.Model(inputs=inputs, outputs=output)\nelse:\n    model = models.Sequential()\n    model.add(SpectralNormalization(layers.Conv2D(32, (3, 3), activation='relu')))\n    model.add(layers.MaxPooling2D((2, 2)))\n    model.add(SpectralNormalization(layers.Conv2D(64, (3, 3), activation='relu')))\n    model.add(layers.MaxPooling2D((2, 2)))\n    model.add(layers.Flatten())\n    model.add(layers.Dense(64, activation='relu'))\n    model.add(layers.Dense(10, activation='softmax'))\n\n\ndef loss(model, x, y):\n    y_ = model(x)\n\n    return loss_object(y_true=y, y_pred=y_)\n\n\ndef grad(model, inputs, targets):\n    with tf.GradientTape() as tape:\n      loss_value = loss(model, inputs, targets)\n    return loss_value, tape.gradient(loss_value, model.trainable_variables)\n\n\nloss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n\noptimizer = tf.keras.optimizers.Adam(learning_rate=0.01)\n\ntrain_loss_results = []\ntrain_accuracy_results = []\n\nfor epoch in range(num_epochs):\n    epoch_loss_avg = tf.keras.metrics.Mean()\n    epoch_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()\n\n    for x, y in train_dataset:\n        loss_value, grads = grad(model, x, y)\n        optimizer.apply_gradients(zip(grads, model.trainable_variables))\n\n        epoch_loss_avg(loss_value)\n        epoch_accuracy(y, model(x))\n\n    train_loss_results.append(epoch_loss_avg.result())\n    train_accuracy_results.append(epoch_accuracy.result())\n\n    print(\"Epoch {:03d}: Loss: {:.3f}, Acc: {:.3%}\".format(epoch,\n                                                           epoch_loss_avg.result(),\n                                                           epoch_accuracy.result()))\n","repo_name":"thisisiron/spectral_normalization-tf2","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2609,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"86"}
+{"seq_id":"22168636673","text":"import psycopg2\nimport pandas as pd\nfrom flask import Flask,request\nfrom flask_cors import CORS\napp = Flask(__name__)\nCORS(app)\n #==========  this file save data to postgresql after taking resolution from frontend =============\n@app.route(\"/\")\ndef hello_world():\n    return 'Hello, World!'\nconnection = psycopg2.connect(\n    host=\"postgres\",\n    database=\"postgres\",\n    user=\"postgres\",\n    password=\"postgres\"\n)\n#password- postgres\nconnection.autocommit = True\ncur = connection.cursor()\n\n#========= alarm-resolution =======================\n@app.route('/alarm-resolution',methods=['GET','POST'])\ndef update():\n    res =  request.get_json()\n    array = []\n    items = {}\n    for key in res:\n        value = res[key]\n        cur.execute( '''UPDATE public.alarmdata SET \"Resolution\" = (%s) WHERE \"id\" = (%s) ''',(value,key))\n        items= {}\n        items[\"id\"] = key\n        items[\"resolution\"] = value\n        array.append(items)\n        print(array)\n    return{\"return\" : array}\nif(__name__ == \"__main__\"):\n    app.run(port=5050)\nconnection.commit()\ncur.close()\nconnection.close()\n","repo_name":"AbhishekJamhoriya/daksh","sub_path":"daksh_backend/Alarm/Alarm_resolution.py","file_name":"Alarm_resolution.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"31535232703","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution:\n    def bstToGst(self, root: TreeNode) -> TreeNode:\n        if root == None: return None\n        \n        self.helper(root, 0)\n        \n        return root\n    \n    def helper(self, root, val):\n        if root == None: return None\n        if root.right != None:\n            right = self.helper(root.right, val)\n            root.val += right.val\n        else:\n            root.val += val\n            \n        if root.left != None:\n            return self.helper(root.left, root.val)\n        return root\n        \n        \n","repo_name":"rum2mojito/Leetcode","sub_path":"1038_Binary_Search_Tree_to_Greater_Sum_Tree.py","file_name":"1038_Binary_Search_Tree_to_Greater_Sum_Tree.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"41739285022","text":"from datetime import date, datetime\nfrom django.db import models\nfrom django.db.models.deletion import SET_NULL\nfrom django.db.models.signals import post_save, pre_save\nfrom django.utils.translation import ugettext_lazy as _\n\n\nfrom Inventory.models import Inventory\nfrom Products.models import Product\nfrom Revenue.models import Invoice, Sales\n\ndef Sold(sender, instance, created, *args, **kwargs):\n    if created:\n        # check last sale of today if exists update it\n        today       = Sales.objects.filter(timestamp=date.today(), business=instance.saler)\n        inventory   = Inventory.objects.filter(business=instance.saler)\n        product     = Product.objects.filter(inventory=inventory)\n\n        # Generate revenue \n        _revenue    = int(instance.total) - int(instance.product.price.wholesale)\n        _earned     = int(_revenue) - int(instance.tax)\n\n\n        print(_earned)\n\n        if today:\n\n            # update today's total sales amount and revenue\n            for object in today:\n                sales       = object.sales\n                revenue     = object.revenue\n                \n                # get product price \n                sold        = int(sales) + int(instance.total)\n                earned      = int(revenue) + int(_earned)\n                today.update(\n                    sales   = sold,\n                    revenue = earned,\n                )\n\n        else:\n            # get product price \n            Sales.objects.create(\n                sales   = instance.total,\n                revenue = _earned,\n                business   = instance.saler\n            )\n\npost_save.connect(Sold, sender=Invoice)","repo_name":"thegreatestbeing/Production","sub_path":"Revenue/signals.py","file_name":"signals.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"22016449812","text":"\"\"\"\nCreated on 01.12.2015\n\n:author: Rene Hollander\n\"\"\"\n\nimport glob\nimport json\n\nimport os\nfrom os.path import basename, splitext\nfrom solarsystem.body import OrbitingBody, StationaryBody\nfrom solarsystem.orbit import CircularOrbit, EllipticOrbit\nfrom solarsystem.renderer import OrbitingBodyWithRingRenderer, setup_ring_renderer\nfrom util import dts\n\n\ndef load_bodies(directory):\n    \"\"\"\n    Loads all bodies that are defined in the JSON files from the given directory\n\n    :param directory: directory to load the bodies from\n    :type directory: str\n    :return: list of the loaded bodies\n    :rtype: list\n    \"\"\"\n\n    files = glob.glob(os.path.join(directory, \"*.json\"))\n    bodies = {}\n    for file in files:\n        print(\"Loading body \" + file)\n        with open(file) as data_file:\n            internal_name = splitext(basename(file))[0]\n            bodies[internal_name] = load_body(json.load(data_file))\n    for key in bodies:\n        body = bodies[key]\n        if body.parent_internal_name is not None:\n            body.parent = bodies[body.parent_internal_name]\n            del body.parent_internal_name\n\n    for body in bodies.values():\n        print(\"Executing post_init for \" + body.name)\n        body.post_init()\n\n    return bodies.values()\n\n\ndef load_body(data):\n    \"\"\"\n    Load the body from the specified JSON data. Parent is not set here!\n\n    :param data: JSON data to load the body from\n    :return: Body from the supplied data\n    :rtype: :class:`solarsystem.body.Body`\n    \"\"\"\n\n    name = data[\"name\"]\n    parent = None\n    if \"parent\" in data:\n        parent = data[\"parent\"]\n    texture = data[\"texture\"]\n    basecolor = data[\"basecolor\"]\n    radius = data[\"radius\"]\n    axial_tilt = data[\"axial_tilt\"]\n    sidereal_rotation_period = data[\"sidereal_rotation_period\"] * dts\n    mass = data[\"mass\"]\n    has_orbit = False\n    orbit = None\n    has_ring = False\n    ring_texture = None\n    ring_inner_radius = None\n    ring_outer_radius = None\n\n    if \"orbit\" in data:\n        has_orbit = True\n        orbit = load_orbit(data[\"orbit\"])\n    if \"ring\" in data:\n        ring_data = data[\"ring\"]\n        has_ring = True\n        ring_texture = ring_data[\"texture\"]\n        ring_inner_radius = ring_data[\"radius\"][\"inner\"]\n        ring_outer_radius = ring_data[\"radius\"][\"outer\"]\n\n    body = None\n\n    if has_orbit:\n        body = OrbitingBody(None, name, texture, basecolor, radius, orbit, axial_tilt, sidereal_rotation_period, mass)\n        if has_ring:\n            body.renderer = OrbitingBodyWithRingRenderer()\n            body = setup_ring_renderer(ring_texture, ring_inner_radius, ring_outer_radius, body)\n    else:\n        body = StationaryBody(None, name, texture, basecolor, radius, axial_tilt, sidereal_rotation_period, mass)\n\n    body.parent_internal_name = parent\n    return body\n\n\ndef load_orbit(data):\n    \"\"\"\n    Load the orbit from the given data\n\n    :param data: JSON data\n    :return: Orbit from the JSON data\n    :rtype: :class:`solarsystem.orbit.Orbit`\n    \"\"\"\n\n    type = data[\"type\"]\n    if type == \"circular\":\n        radius = data[\"radius\"]\n        orbital_period = data[\"orbital_period\"] * dts\n        inclination = data[\"inclination\"]\n        return CircularOrbit(radius, orbital_period, inclination)\n    elif type == \"elliptic\":\n        apoapsis = data[\"apoapsis\"]\n        periapsis = data[\"periapsis\"]\n        longtitude_ascending_node = data[\"longtitude_ascending_node\"]\n        argument_of_periapsis = data[\"argument_of_periapsis\"]\n        inclination = data[\"inclination\"]\n        initial_mean_anomaly = data[\"initial_mean_anomaly\"]\n        multiplier = data[\"multiplier\"]\n        return EllipticOrbit(apoapsis, periapsis, longtitude_ascending_node, argument_of_periapsis, inclination, initial_mean_anomaly=initial_mean_anomaly, multiplier=multiplier)\n    else:\n        raise TypeError(\"type \" + type + \" is invalid\")\n","repo_name":"ReneHollander/solarsystem","sub_path":"solarsystem/loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":3865,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"30189425","text":"'''\nThis file contains the some global functions for our economy.\nThe first one is taken from the code supplied by Prof. Hendrik Rommeswinkel and\nis concerned with the calculation of the cross product of two vectors.\nThe second one deals with the calculation of externalities.\n'''\nimport numpy as np\n\ndef FlexibleCrossProduct(a, b):\n    '''\n    This function corrects for different lengths of the c and the weight vector. \n    It treats missing values as zero.\n    See https://stackoverflow.com/questions/27096966/multiply-array-of-different-size\n    In the future, this function should be extended to allow for multiplication\n    of goods vectors that skip goods, etc.\n    '''\n    return np.append(a,np.zeros(0 if (len(b) - len(a))<0 else (len(b) - len(a))))*np.append(b,np.zeros(0 if (len(a) - len(b))<0 else (len(a) - len(b))))\n\ndef externalities(X, H):\n    '''\n    This is the externalities function. Its input is the number of consumers\n    and a array X\n    First we create to empty list of lists that are multiplied with the number \n    of consumers to determine the appropriate length.\n    Then we iterate over the two newly created elements for the number of \n    consumers to fille the externalites matrix, which the function returns in \n    the end.\n    '''\n    L = [[]] * H\n    EXT = [[]] * H\n    for i in range(H):\n        L[i] = [j for j in range(H) if j != i]\n        EXT[i] = np.sum([X[j] for j in L[i]])\n    return EXT\n","repo_name":"ntuecon/2018groupCE","sub_path":"economy/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"86"}
+{"seq_id":"74824859164","text":"import matplotlib.pyplot as plt\nfrom os import path\nimport networkx as nx\nfrom staking.agents import Tx_fee, HBar\nfrom staking.env import HederaSystem\n\nfrom staking.constants import FIGURES_PATH\n\nT = HederaSystem()\n\nStakers_Balance_t = {}\nNodes_Balance_t = {}\nReward_Account_t = []\nTreasury_Account_t = []\n\nfor t in range(1, 50):\n    T.iterate()\n    Stakers_Balance_t[t] = T.stakers.balance_stakers_account.copy()\n    Nodes_Balance_t[t] = T.nodes.balance_nodes_account.copy()\n    Reward_Account_t.append(T.hbar.reward)\n    Treasury_Account_t.append(T.hbar.treasury)\n\n\nplt.plot(Reward_Account_t)\n\nplt.plot(Treasury_Account_t)\n\nReward_node = {}\nfor node in Nodes_Balance_t[1].keys():\n    for t in Nodes_Balance_t.keys():\n        Reward_node[node] = Reward_node.get(node, []) + [Nodes_Balance_t[t][node]]\n    plt.plot(Reward_node[node], label=node)\nplt.legend()\n\nReward_staker = {}\nfor staker in Stakers_Balance_t[1].keys():\n    for t in Stakers_Balance_t.keys():\n        Reward_staker[staker] = Reward_staker.get(staker, []) + [\n            Stakers_Balance_t[t][staker]\n        ]\n    plt.plot(Reward_staker[staker], label=staker)\n# plt.legend()\n\n### Test amount of Tx_fee\n\nfee_t = []\nfor t in range(50):\n    fee_t.append(Tx_fee(t).calculate_sum_fee())\n\nplt.plot(fee_t)\n\n# %%\n###Test Totoal_reward at each time t\n\nT_r = HBar(\n    pra=0,\n    prb=0,\n    prc=5,\n    prm=10,\n    ta0=100,\n    ra0=10,\n    alpha=0.1,\n    beta=0.5,\n    epsilon=0.05,\n    parameter_l=0.1,\n)\n\nreward_t = []\nfor t in range(1, 50):\n    reward_t.append(T_r.reward_schedule(t))\n\nplt.plot(reward_t)\n\n###Test staking network\n\nsn_network = T.S_N_network\nplt.figure(figsize=(5, 6), dpi=200)\n\ncolor_map = []\nfor node in sn_network.nodes:\n    if node < 10000:\n        color_map.append(\"green\")\n    else:\n        color_map.append(\"tab:blue\")\n\npos = {node: [0, i] for i, node in enumerate(T.stakers.agents_staker)}\npos.update({node: [1, i] for i, node in enumerate(T.stakers.agents_node)})\n# print(pos)\nnx.draw(\n    sn_network,\n    pos=pos,\n    node_color=color_map,\n    with_labels=True,\n    node_size=100,\n    font_size=8,\n)\nfig_path = path.join(FIGURES_PATH, \"network_graph.pdf\")\nplt.savefig(fig_path)\n","repo_name":"xujiahuayz/hedera-staking","sub_path":"script/simulate.py","file_name":"simulate.py","file_ext":"py","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"71687342683","text":"# improtanodo libreria\nimport sqlite3\nimport random\n\n\n# creando la conexion, o el archivo nuevo si no existe\nconexion = sqlite3.connect('test_bd.db')\n\ncursor = conexion.cursor()\n\n#sql_create_table = 'CREATE TABLE usuarios (nombre VARCHAR(100), edad INTEGER, email VARCHAR(100))';\n#sql_insert_register = \"INSERT INTO usuarios VALUES ('Gustavo', 24, 'gus@s.cm')\"\n\n# insertando varios registros\nusuarios_insert = [\n    ('Laura', random.randint(1, 5000) , 'laur'),\n    ('Martha', random.randint(1, 5000) , 'mart'),\n]\n\n# Ejecutando varios registors al tiempo\ncursor.executemany(\"INSERT INTO usuarios VALUES (?,?,?)\", usuarios_insert)\n\nconexion.commit()\n\nsql_query = \"SELECT * FROM usuarios\"\n\n#Ejecutando script\ncursor.execute(sql_query)\n\n# recuperando registros\nusuarios = cursor.fetchall()\n\nprint(\"usuarios\", usuarios)\n\n\n\n\nconexion.close()","repo_name":"gustavo9601/python_2","sub_path":"sqlite/conection_test.py","file_name":"conection_test.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}
+{"seq_id":"23316475764","text":"\"\"\"empty message\n\nRevision ID: e2e25fae0225\nRevises: cd94ae07eb15\nCreate Date: 2020-04-07 05:32:35.141976\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\n# revision identifiers, used by Alembic.\nrevision = 'e2e25fae0225'\ndown_revision = 'cd94ae07eb15'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('people', sa.Column('name', sa.String(length=255), nullable=False))\n    op.drop_column('people', 'lastName')\n    op.drop_column('people', 'firstName')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('people', sa.Column('firstName', mysql.VARCHAR(length=255), nullable=False))\n    op.add_column('people', sa.Column('lastName', mysql.VARCHAR(length=225), nullable=False))\n    op.drop_column('people', 'name')\n    # ### end Alembic commands ###\n","repo_name":"jackieo5023/cs411-exercise-tracker","sub_path":"api/migrations/versions/e2e25fae0225_.py","file_name":"e2e25fae0225_.py","file_ext":"py","file_size_in_byte":967,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"86"}
+{"seq_id":"72158619805","text":"\"\"\"\n16年的训练集就是由15年的训练集和测试集加在一起组成的，所以不考虑15年的数据\n\"\"\"\n\nimport json\n\nfrom nlp_tasks.absa.conf import data_path\nfrom nlp_tasks.absa.utils import file_utils\nfrom nlp_tasks.absa.gcae import getsemeval\n\n\ndef generate_examples(data: list):\n    \"\"\"\n\n    :param data:\n    :return:\n    \"\"\"\n    result = []\n    i = 0\n    sentiment_count = {'positive': 0,\n                       'negative': 0,\n                       'neutral': 0}\n    category_count = {}\n    for example in data:\n        sentence = example['sentence']\n        for aspect, sentiment in example['aspect_sentiment']:\n            sentiment_count[sentiment] += 1\n            if aspect in category_count:\n                category_count[aspect] += 1\n            else:\n                category_count[aspect] = 1\n            result.append('\\t'.join([str(i), sentence, aspect, sentiment]))\n            i += 1\n    print(sentiment_count)\n    print(category_count)\n    return result\n\nyears = [14, 15, 16]\nsemeval_train, semeval_test = getsemeval.get_semeval(years, None, 'r', False)\n\nhead = ['content_id\\tcontent\\tsubject\\tsentiment_value']\n\ntrain_example = generate_examples(semeval_train)\nfile_utils.write_lines(head + train_example, data_path.data_base_dir + '/train.csv')\n\ntest_examples = generate_examples(semeval_test)\nfile_utils.write_lines(head + test_examples, data_path.data_base_dir + '/test_public.csv')\nfile_utils.write_lines(head + test_examples, data_path.data_base_dir + '/test_public_gold.csv')\n\n","repo_name":"l294265421/ACSA-SSPL","sub_path":"nlp_tasks/absa/data_adapter/semeval-141516-large_rest_adapter_v3.py","file_name":"semeval-141516-large_rest_adapter_v3.py","file_ext":"py","file_size_in_byte":1519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"86"}